Datasets:

alexshengzhili
/

0612_output_processed_final_human_written

Dataset card Viewer Files Files and versions Community

Split (1)

train · 9.27k rows

Unnamed: 0 int64 0 10k	input stringlengths 9.18k 112k	output stringlengths 136 194k	instruction stringclasses 1 value
428	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>To solve the problems of poor stability and low modularity (Q) of community division results caused by the randomness of node selection and label update in the traditional label propagation algorithm, an improved two-stage label propagation algorithm based on LeaderRank was proposed in this study. In the first stage, the order of node updating is determined by the participation coefficient (PC). Then, a new similarity measure is defined to improve the label selection mechanism so as to solve the problem of label oscillation caused by multiple labels of the node with the most similarity to the node.</ns0:p><ns0:p>Moreover, the influence of the nodes is comprehensively used to find the initial community structure. In the second stage, the rough communities obtained in the first stage are regarded as nodes, and their merging sequence is determined by the PC. Next, the nonweak community and the community with the largest number of connected edges are combined. Finally, the community structure is further optimized to improve the modularity so as to obtain the final partition result. Experiments were performed on 9 classic realistic networks and 19 artificial datasets with different scales, complexities, and densities. The modularity and normalized mutual information (NMI) were used as evaluation indexes for comparing the improved algorithm with dozens of relevant classic algorithms. The results showed that the proposed algorithm yields superior performance, and the results of community partitioning obtained using the improved algorithm were stable and more accurate than those obtained using other algorithms. In addition, the proposed algorithm always performs well in nine large-scale artificial data sets with 6000 to 50000 nodes and three large realistic network datasets, which verifies its computational performance and utility in community detection for large-scale networks.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>With the rapid development of the Internet and big data technology, research on complex networks has gradually penetrated into many fields, such as information science and biological science, and has thus become a very challenging research topic <ns0:ref type='bibr' target='#b7'>(6)</ns0:ref>. In social networks, such as scientific research cooperation and transportation networks, objects are usually represented as nodes, and relationships between objects are represented as edges <ns0:ref type='bibr' target='#b47'>(42)</ns0:ref>. Real-world networks have one important feature, community structure, that is, a network is usually composed of several communities, with relatively close node connections within the community and relatively sparse node connections between the communities. The discovery of community structure is an important basis for exploring the formation principle 0 and function of complex network structures <ns0:ref type='bibr' target='#b29'>(25)</ns0:ref> and plays a vital role in many fields. For instance, in the field of biology <ns0:ref type='bibr' target='#b25'>(21)</ns0:ref>, community detection is of great significance for understanding the specific organizational structure, functional analysis, and behavior prediction of biological systems. In the field of ecommerce, consumers with similar purchasing habits can be mined through community detection, thus creating greater business value through the establishment of efficient recommendation systems <ns0:ref type='bibr' target='#b14'>(12)</ns0:ref>. In the field of infectious diseases, community detection can be used to analyze and identify the key population of infectious diseases so as to effectively control the spread of diseases <ns0:ref type='bibr' target='#b4'>(3)</ns0:ref>. Therefore, the quick and effective discovery of the community structure of networks has become the primary task and an important branch of social network research.</ns0:p><ns0:p>With the extensive research on social network analysis, many community detection algorithms have emerged, but most of them suffer from limitations such as high complexity, low accuracy of community division, and unstable results. Label propagation algorithm (LPA) has attracted attention due to its advantages of low time complexity, no prior conditions, and suitability for community detection in large-scale networks <ns0:ref type='bibr' target='#b13'>(11)</ns0:ref>. However, the traditional LPA has the following disadvantages: 1) LPA adopts a random strategy in the updating sequence of nodes, resulting in randomness in the community partition results; 2) LPA treats every node as equally important and does not distinguish the importance of each node; 3) LPA assigns a unique label to each node and fails to identify overlapping communities <ns0:ref type='bibr' target='#b21'>(18)</ns0:ref>.</ns0:p><ns0:p>In view of the abovementioned shortcomings, numerous improved algorithms have been proposed. <ns0:ref type='bibr' target='#b6'>(5)</ns0:ref> proposed an improved LPA algorithm based on label propagation ability, developed a calculation method based on a k-shell decomposition algorithm for determining the importance of individual nodes <ns0:ref type='bibr' target='#b28'>(24)</ns0:ref>, and formulated a label update strategy through the importance ranking of nodes and label propagation ability. <ns0:ref type='bibr' target='#b37'>(33)</ns0:ref> proposed a community detection algorithm based on node influence and similarity (NIS-LPA), wherein the selected seed nodes are used to expand into seed regions, and then the similarity between nodes is calculated based on the network topology and real attributes of nodes, thus improving the stability and accuracy of the algorithm. <ns0:ref type='bibr' target='#b41'>(37)</ns0:ref> proposed a community detection algorithm integrating LeaderRank and label propagation (LLPA) wherein the three aspects of node label initialization, node update sequence, and label propagation selection process are improved. The LeaderRank algorithm is adopted to select key nodes, and labels are assigned to them by calculating the influence of the nodes. Thereafter, the nodes are updated according to the influence of the nodes, and the propagation ability between nodes is considered in the process of label propagation. <ns0:ref type='bibr' target='#b9'>(8)</ns0:ref> proposed a community detection algorithm based on boundary nodes and label propagation (LBN), which determines core nodes and boundary nodes, respectively, and then determines the community to which they belonged according to the weight of the boundary nodes, thus improving the stability of the algorithm. However, the values of Q <ns0:ref type='bibr' target='#b40'>(36)</ns0:ref> and NMI are still unsatisfactory. <ns0:ref type='bibr' target='#b45'>(40)</ns0:ref> proposed label importance-based label propagation algorithm (LILPA) for community detection for application in core drug detection. In LILPA, when labels are transmitted to other nodes, the label updating process based on node importance, node attractiveness, and label importance is used to improve the label instability and the accuracy and efficiency of community division. For overlapping communities, <ns0:ref type='bibr' target='#b17'>(15)</ns0:ref> proposed an efficient community detection algorithm based on label propagation with community kernel (CK-LPA), which assigns a corresponding weight to each node according to the importance of the node in the network and updates node labels according to the weight order. They also discussed the composition of weights, label updating, propagation strategies, and convergence conditions. ( <ns0:ref type='formula'>20</ns0:ref>) improved the label update order and propagation threshold, and proposed an overlapping community detection algorithm by using the PageRank and node clustering coefficients algorithms (COPRAPC), wherein nodes with low influence are selected for label propagation, and the node clustering coefficient is used to control the maximum number of communities that nodes belong to. <ns0:ref type='bibr' target='#b32'>(28)</ns0:ref> proposed an overlapping community detection algorithm integrating label preprocessing and node influence (FLPNI), thereby greatly reducing the randomness of label propagation. <ns0:ref type='bibr' target='#b36'>(32)</ns0:ref> proposed an improved LPA for community detection based on two-level neighborhood similarity (TNS-LPA); defined a new two-level neighborhood similarity, which selected the initial community center by considering the minimum distance and local centrality index; and optimized the algorithm by adopting the asynchronous updating label strategy according to the importance of nodes, thereby further improving the accuracy of community division. <ns0:ref type='bibr' target='#b15'>(13)</ns0:ref> proposed an improved label propagation algorithm based on modularity and node importance (LPA-MNI) wherein the initial community is identified based on the value of modularity, and then the remaining nodes that have not been assigned to the initial community are clustered through label propagation. Node importance is used to improve the label update sequence, and the label selection mechanism is used when the majority of nodes contain multiple labels. <ns0:ref type='bibr' target='#b10'>(9)</ns0:ref> proposed the node importance-based label propagation algorithm (NI-LPA) to identify overlapping communities to address the problem of instability in the LPA algorithm caused by random updating. NI-LPA uses information derived from node attributes to simulate special propagation and filtering processes, and experiments revealed its high efficiency for overlapping community detection. <ns0:ref type='bibr' target='#b31'>(27)</ns0:ref> proposed another LPA algorithm based on node importance (NI-LPA) wherein the importance of nodes is defined by combining the signal propagation of nodes, the value of K-shell nodes themselves, and the Jaccard distance between adjacent nodes, which better avoids the instability caused by random selection of nodes in traditional LPA algorithm. ( <ns0:ref type='formula'>16</ns0:ref>) encoded both semantic and geometric information of the environment in a weighted colored graph, in which the edges were partitioned into a finite set of ordered semantic classes (e.g., colors), and then incrementally searched for the shortest path among the set of paths with minimal inclusion of inferior classes. <ns0:ref type='bibr' target='#b1'>(1)</ns0:ref> In the first and second iterations (t <= 2) of the propagation, if the number of maximum label frequencies in neighbor nodes was equal, the Adamic/Adar index was used to select the appropriate label. For the other iterations (t > 2), a new criterion, known as label strength, was applied to select the label with the highest strength of a node. <ns0:ref type='bibr' target='#b42'>(38)</ns0:ref> proposed a new node similarity metric, and the label was updated according to the similarity between the current node and neighbor nodes.</ns0:p><ns0:p>The abovementioned algorithms focus on the calculation of the node importance and seed node selection and consider the randomness of node update order but ignore the importance of label update strategy, resulting in the unstable and less accurate community division. Therefore, this study focused on the updating strategy of nodes and labels to achieve efficient and accurate community division. The two-stage community detection algorithm based on the label propagation algorithm (41) (LPA-TS) has the following problems. 1) In the first stage, the algorithm determines the node update sequence from the descending participation coefficient (PC) and then updates the node label to that with the largest similarity so as to obtain the initial partition result. However, only the number and degree of common neighbors are considered in the definition of similarity. There may be multiple nodes with maximum similarity with the same number and degree of common neighbors. If one node is randomly selected for label update, the result of community division will be unstable. 2) In the second stage, the algorithm first regards the initial community as nodes and then determines the order of community mergers from the PC. Then, the algorithm performs merging according to the conditions of a weak community and finally obtains the community structure. However, in some classical networks, the community division results are not ideal, and the modularity is low because LPA-TS has some shortcomings in the updating strategy of nodes and labels and the definition of initial community merge conditions. To solve these problems, an improved two-stage label propagation algorithm (LPA-ITSLR) was proposed in this paper. The contributions and innovations of this paper are as follows.</ns0:p><ns0:p>(1) To solve the problem of unstable and inaccurate community division results yielded by the LPA-TS algorithm, a new similarity measurement between nodes was proposed to optimize the node label updating strategy. In the initial stage, the number and degree of common neighbors of nodes and the similarity of structural information between common neighbors are considered comprehensively. In view of the situation that multiple nodes may have the maximum similarity, the importance of nodes is sorted by calculating the LeaderRank so as to avoid the randomness of node label update order and ensure the stability of the initial community division result.</ns0:p><ns0:p>(2) To address the problem of low modularity in LPA-TS, the optimal parameter value was determined by improving the definition of weak community in the original algorithm, and the evaluation function based on complementary entropy was changed to the objective function based on modularity optimization in the community merging stage so as to further improve the quality of community division and the accuracy of the final division result.</ns0:p><ns0:p>(3) Experiments were conducted on 9 realistic networks and 19 artificial datasets with different scales (1000 nodes to 50000 nodes). The Q and NMI were used as evaluation indexes to compare the proposed algorithm with several classic algorithms. The time complexity of the algorithm was also analyzed. Experimental results showed that the improved algorithm has higher quality and stability in community division than the comparative algorithms. For largescale data sets, the proposed algorithm can still achieve high quality of community division. </ns0:p></ns0:div> <ns0:div><ns0:head>Theoretical Basis</ns0:head><ns0:formula xml:id='formula_0'>𝑑 𝑖 (𝛺 𝑟 ) = \|{𝑣 𝑗 │(𝑣 𝑖 ,𝑣 𝑗 ) ∈ 𝐸 ∧ 𝑣 𝑗 ∈ 𝛺 𝑟 }\|</ns0:formula><ns0:p>indicates that the node is connected with more communities and that the node has a low degree of belonging to each community. In contrast, a low PC value indicates that the node is connected to a fewer number of communities and that the node has a high degree of belonging to each community. When community detection is performed, nodes with low PC and obvious community affiliation are selected to start traversal, which is more conducive for finding the correct community structure.</ns0:p><ns0:p>(1)</ns0:p><ns0:formula xml:id='formula_1'>𝑃𝐶 𝑖 = 1 -∑ 𝑘 𝑟 = 1 ( 𝑑 𝑖 (𝛺 𝑟 ) 𝑑 𝑖 ) 2</ns0:formula></ns0:div> <ns0:div><ns0:head>Strong and weak communities</ns0:head><ns0:p>Community structures can be strong or weak <ns0:ref type='bibr' target='#b46'>(41)</ns0:ref>. A strong community means that the number of links between any node in the community and the inside of the community is greater than the number of links between the node and the outside of the community. It can be defined as Eq. ( <ns0:ref type='formula' target='#formula_2'>2</ns0:ref>).</ns0:p><ns0:p>A weak community means that the sum of the edges of all nodes in the community and the nodes inside the community is greater than the sum of the edges of all nodes outside the community. It can be defined as Eq. ( <ns0:ref type='formula'>3</ns0:ref>). In general, a community should satisfy at least the character of weak community.</ns0:p><ns0:p>( </ns0:p><ns0:formula xml:id='formula_2'>) 𝛼 * 𝑑 𝑖𝑛 𝑖 (𝛺 𝑟 ) > 𝑑 𝑜𝑢𝑡 𝑖 (𝛺 𝑟 ) , ∀ 𝑖 ϵ 𝛺 𝑟 (3) 𝛼 * ∑ 𝑖ϵ𝛺 𝑟 𝑑 𝑖𝑛 𝑖 (𝛺 𝑟 ) > ∑ 𝑖ϵ𝛺<ns0:label>2</ns0:label></ns0:formula></ns0:div> <ns0:div><ns0:head>LeaderRank algorithm</ns0:head><ns0:p>The LeaderRank algorithm( <ns0:ref type='formula'>37</ns0:ref>) is used to calculate the influence of nodes in the network. A background node is added to the network and connected with all the nodes in the network to 𝑣 𝑔 form a new network. The algorithm assigns 1 unit of LeaderRank (LR) value to all nodes except the background node in advance, assigns 0 unit of LR value to node , uses Eq. ( <ns0:ref type='formula' target='#formula_3'>4</ns0:ref>) to calculate 𝑣 𝑔 the LR value of node in each iteration, and iterates repeatedly until reaches a steady state 𝑣 𝑖 𝑣 𝑖 and then uses Eq. ( <ns0:ref type='formula'>5</ns0:ref>) to divide the background nodes evenly among all the nodes.</ns0:p><ns0:p>( </ns0:p><ns0:formula xml:id='formula_3'>)<ns0:label>4</ns0:label></ns0:formula><ns0:formula xml:id='formula_4'>𝐿𝑅 𝑖 (𝑡) = ∑ 𝑁 𝑗 = 0 𝑎 𝑖𝑗 𝑑 𝑗 𝐿𝑅 𝑗 (𝑡 -</ns0:formula></ns0:div> <ns0:div><ns0:head>Evaluation Indicators</ns0:head><ns0:p>Modularity. Modularity (Q) is commonly used for measuring the strength of community structures. The closer its value is to 1, the better the quality of community division <ns0:ref type='bibr' target='#b40'>(36)</ns0:ref>. Q can be calculated as follows in Eq.( <ns0:ref type='formula'>6</ns0:ref>). Where m is the total number of edges in the network, is an 𝑎 𝑖𝑗 element in the adjacency matrix A of network G, and is the degree of node . When and 𝑑 𝑖 𝑣 𝑖 𝑣 𝑖 𝑣 𝑗 belong to the same community, 1; otherwise 0.</ns0:p><ns0:formula xml:id='formula_5'>𝛿 𝑖,𝑗 = 𝛿 𝑖,𝑗 = (6) 𝑄 = 1 2𝑚 ∑ 𝑖,𝑗 (𝑎 𝑖𝑗 - 𝑑 𝑖 𝑑 𝑗 2𝑚</ns0:formula><ns0:p>)𝛿 𝑖,𝑗 Normalized mutual information. For networks with a known community structure, NMI is generally used to evaluate the community division effect. The higher the NMI, the more similar the result is to the realistic community structure. A value of 1 indicates that the partition result is completely consistent with the actual community structure. Assuming that 𝐴 = {𝐴 1 , 𝐴 2 , …, 𝐴 𝑘 } and are the realistic community structure and the division result of the</ns0:p><ns0:formula xml:id='formula_6'>𝐵 = {𝐵 1 , 𝐵 2 , …, 𝐵 𝑘 ' }</ns0:formula><ns0:p>network by an algorithm, respectively, and are the number of communities under the two 𝑘 𝑘'</ns0:p><ns0:p>divisions, NMI can be defined as follows in Eq.( <ns0:ref type='formula'>7</ns0:ref>). Where n is the total number of nodes, is 𝑇 the confusion matrix, is the number of common nodes included in the realistic communities 𝑇 𝑖𝑗 and , and is the sum of the elements in the i-th row of the confusion matrix. Manuscript to be reviewed</ns0:p><ns0:formula xml:id='formula_7'>𝐴 𝑖 𝐵 𝑗 𝑇 𝑖 (7) NMI = 2∑ 𝑘 𝑖 = 1 ∑ 𝑘' 𝑗 = 1 𝑇 𝑖𝑗 𝑙𝑜𝑔 𝑛𝑇 𝑖𝑗 𝑇 𝑖 𝑇 𝑗 -∑ 𝑘 𝑖 = 1 𝑇 𝑖 𝑙𝑜𝑔 𝑇 𝑖 𝑛 -∑ 𝑘' 𝑗 = 1</ns0:formula></ns0:div> <ns0:div><ns0:head>Computer Science</ns0:head><ns0:p>The Proposed Algorithm</ns0:p></ns0:div> <ns0:div><ns0:head>Question-posing</ns0:head><ns0:p>In the first stage of the LPA-TS algorithm, when there are two or more nodes with the largest similarity with the current node, the algorithm randomly selects one node for label update; this may lead to unstable partition results. LPA-TS algorithm expresses the similarity of nodes as CN, which can be expressed as Eq. ( <ns0:ref type='formula'>8</ns0:ref> community. As shown in Figure <ns0:ref type='figure' target='#fig_6'>1</ns0:ref>, .</ns0:p><ns0:formula xml:id='formula_8'>𝑁 0 = {𝑣 2 ,𝑣 8 , 𝑣 1 , 𝑣 5 } ∧ (𝑣 2 ,𝑣 8 ∈ 𝛺 1 ) ∧ (𝑣 1 ,𝑣 5 ∈ 𝛺 2 )</ns0:formula><ns0:p>According to the similarity calculation formula of the LPA-TS algorithm, the node has a large 𝑣 0 similarity with in the community , and has a large similarity with in the community 𝑣 9 𝛺 1 𝑣 0 𝑣 3 . Moreover, both and have the same neighbor attributes as node , that is, 𝛺 2 𝑣 9 𝑣 3 𝑣 0 𝐶𝑁(𝑣 9 ,𝑣 0 ) . At this time, the LPA-TS algorithm randomly selects a community to merge = 𝐶𝑁(𝑣 3 ,𝑣 0 ) 𝑣 0 into it and finally yields two community division results, 𝛺 = {{𝑣 7 ,𝑣 8 , 𝑣 9 , 𝑣 2 }, {𝑣 0 ,𝑣 1 ,𝑣 3 , 𝑣 4 , 𝑣 5 , and , resulting in instability. 𝑣 6 }} 𝛺' = {{𝑣 7 ,𝑣 8 ,𝑣 9 , 𝑣 2 , 𝑣 0 }, {𝑣 1 ,𝑣 3 , 𝑣 4 , 𝑣 5 , 𝑣 6 }}</ns0:p></ns0:div> <ns0:div><ns0:head>Figure 1 A network instance with two communities</ns0:head><ns0:p>The traditional LPA algorithm and the LPA-TS algorithm were used to conduct 100 experiments on classic Karate and Football networks. The corresponding module degree Q (36) of the community division results is shown in Figure <ns0:ref type='figure' target='#fig_1'>2</ns0:ref>. Both algorithms exhibited obvious oscillations, indicating that the community division results of the algorithms are unstable. The LPA-TS algorithm only yielded an initial community division result in the first stage, and there were still many small communities. In the Karate network shown in Figure <ns0:ref type='figure' target='#fig_2'>3</ns0:ref>, some nodes with higher degrees have greater similarities with many nodes. For example, node can easily 𝑣 34 pass its label to neighboring nodes, while those at the edge of the network have low similarity to central nodes higher degrees. For example, nodes and can easily form small-scale 𝑣 <ns0:ref type='bibr' target='#b29'>25</ns0:ref> 𝑣 26 communities, such as triangle nodes and diamond nodes in Figure <ns0:ref type='figure' target='#fig_2'>3</ns0:ref>. To merge these small communities, LPA-TS uses the definition of weak communities and the evaluation function based on complementary entropy in the second stage. However, in the definition of weak communities α is set as 2, which leads to unstable division results in some networks, that is, the final community division results are not ideal, and the degree of modularity is low. Therefore, in this study, the parameters and the objective function in the second-stage community merger strategy were improved and a new community division method, LPA-ITSLR, was developed to achieve stable and more accurate community division results. </ns0:p></ns0:div> <ns0:div><ns0:head>Node similarity definition</ns0:head><ns0:p>To solve the abovementioned problems, a new similarity index was proposed, which considers the common neighbors, degrees of nodes, and the structural relationship between common neighbors. The more common neighbors two nodes have, the more similar they are. The higher the degree of a node, the more the number of nodes it shares its edges with, that is, the similarity of two nodes is inversely proportional to the degree of the node itself. The number of connected edges between neighboring nodes is combined to avoid multiple nodes with the same similarity as the original node. The improved similarity index can be expressed as follows:</ns0:p><ns0:formula xml:id='formula_9'>(9) 𝑆 𝐼𝐶𝑁 (𝑖, 𝑗) = \|𝑁 𝑖 ∩ 𝑁 𝑗 \| + 1 \|𝑁 𝑖 ∪ 𝑁 𝑗 \| + 1 𝑑 𝑖 𝑑 𝑗 + 𝐶(𝑣 𝑖 , 𝑣 𝑗 ) \|𝑁 𝑖 ∩ 𝑁 𝑗 \|</ns0:formula><ns0:p>Where represents the number of edges between the common neighbor nodes of nodes 𝐶(𝑣 𝑖 , 𝑣 𝑗 ) and . The numerator of the first term of the equation is increased by 1 so that the improved 𝑣 𝑖 𝑣 𝑗 similarity index is not 0 when there is no public neighbor. According to the definition of similarity in Eq. ( <ns0:ref type='formula'>9</ns0:ref>), the similarity between nodes and in Figure <ns0:ref type='figure' target='#fig_6'>1</ns0:ref> <ns0:ref type='table' target='#tab_5'>1</ns0:ref>. First, each node is assigned a unique label, and the similarity between nodes is calculated according to Eq. ( <ns0:ref type='formula'>9</ns0:ref>). Then, the PC value of each node is calculated and sorted in ascending order. Next, the labels are updated according to the sorted nodes. In the label updating strategy, the similarity between the current node and other nodes is compared. If the node with the largest similarity is not unique, the LR is further compared; if not, one is randomly selected to obtain the rough initial community structure in the first stage. <ns0:ref type='table' target='#tab_7'>2</ns0:ref>. In view of the problem that small communities may cause low modularity in the first stage, whether the initial community meets the weak community condition is judged first. If the condition is not met, the community with the largest number of connected edges is selected for merging; this process is repeated until the entire network meets the weak community condition. The research of LPA-TS <ns0:ref type='bibr' target='#b46'>(41)</ns0:ref> shows that 𝛼 is generally set as 2 in Eq. (3). In order to achieve more accurate results, 𝛼 is set as 0.5, 1, 1.2, 1.5 and 2, respectively for the 8 data sets used in this paper. Through experiments, it can be known that when 𝛼 is set as 1.5, good division results are achieved. Moreover, experiments are also carried out on 15 artificial data sets to verify the rationality of the value of 𝛼. Therefore, in our study, 𝛼 is determined to be 1.5 to achieve better performance. Each community is regarded as a node, and its PC value is calculated using Eq. ( <ns0:ref type='formula'>1</ns0:ref>); then, the community with the most links is determined for merging. If the modularity increases Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Computer Science</ns0:head><ns0:p>after the merge, the merge will be selected; otherwise, it will not be merged, thus ensuring that the community structure after the second stage merge will have a higher modularity and be closer to the realistic community structure. Community division results. The proposed LPA-ITSLR algorithm was used to divide communities in the six abovementioned real datasets. The results are illustrated in Figure <ns0:ref type='figure'>4</ns0:ref>, where nodes in different communities are represented by different color.</ns0:p></ns0:div> <ns0:div><ns0:head>Figure 4 Community detection results of real networks</ns0:head><ns0:p>Stability analysis of LPA-ITSLR. The proposed LPA-ITSLR algorithm and the LPA and LPA-TS algorithms were compared and analyzed in the six abovementioned real datasets. Each dataset was run independently for 10 times, and the average value of the three algorithms on the six datasets was obtained (denoted as <Q>), as shown in Table <ns0:ref type='table' target='#tab_12'>4</ns0:ref>. The independent experimental results for each time are shown in Figure <ns0:ref type='figure' target='#fig_3'>5</ns0:ref>. As can be seen from the experimental results presented in Table <ns0:ref type='table' target='#tab_12'>4</ns0:ref> and Figure <ns0:ref type='figure' target='#fig_3'>5</ns0:ref>, LPA-ITSLR performed well in all datasets, with the exception that the average module degree on the NetScience was slightly lower than that obtained using the other two algorithms. Moreover, LPA-ITSLR yielded more stable community partitioning results and a higher modularity than the other two algorithms. NetScience is a weighted network; however, in the experiment, the weight was ignored, and it was transformed into a powerless network for community division. Therefore, the quality of community division on this network obtained using LPA-ITSLR was slightly lower than that obtained using the other two algorithms. However, in the 10 independent experiments, the results of the LPA and LPA-TS algorithms exhibited fluctuations, indicating that the two algorithms are unstable due to the randomness of node and label update. The module-degree value of the proposed LPA-ITSLR algorithm always remained stable for every network, indicating that LPA-ITSLR effectively solves the oscillation problem in the process of label propagation and has higher accuracy and stability. Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Table 4 Average modularity values of 10 experiments for the three algorithms on real datasets</ns0:head></ns0:div> <ns0:div><ns0:head>Computer Science</ns0:head><ns0:p>To further verify the robustness of LPA-ITSLR, 100 independent experiments were conducted on the Karate, Dolphin, and Football networks; the results are presented in Figure <ns0:ref type='figure' target='#fig_4'>6</ns0:ref>. The community division results obtained using the LPA algorithm exhibited the most serious fluctuations in the modularity value, followed by the LPA-TS algorithm. In contrast, LPA-ITSLR maintained the same community division results in 100 experiments, and the modularity was higher than that of LPA and LPA-TS. To further evaluate the performance of the LPA-ITSLR algorithm, it was compared with four recent community detection algorithms based on label propagation. Among them, the COPRA algorithm <ns0:ref type='bibr' target='#b8'>(7)</ns0:ref> realizes community division by assigning multiple labels with attribution coefficients to a node. The WLPA algorithm <ns0:ref type='bibr' target='#b30'>(26)</ns0:ref> first selects the label with a larger weight for propagation during the label propagation process. The LINSIA (29) algorithm is based on node importance and employs label importance to complete the community division. The LILPA <ns0:ref type='bibr' target='#b45'>(40)</ns0:ref> algorithm uses a fixed label update sequence based on the ascending order of node importance. The modularity of the results obtained using the five algorithms on the four real datasets is presented in Table <ns0:ref type='table' target='#tab_14'>5</ns0:ref>. From Table <ns0:ref type='table' target='#tab_14'>5</ns0:ref>, it can be seen LPA-ITSLR yielded the highest modularity and most stable in community division results. Thus, instability caused by label oscillation is avoided effectively by using LPA-ITSLR.</ns0:p></ns0:div> <ns0:div><ns0:head>Table 5 Modularity comparison of five algorithms</ns0:head><ns0:p>Performance comparison of LPA-ITSLR with other algorithms. For further analysis of the effectiveness of the proposed algorithm for community partition and correctness, three classic datasets of Karate, Dolphins, and Football were used, and the LPA-ITSLR algorithm and seven classic community detection algorithms were employed for obtaining the division results for correlation analysis in terms of the number of communities and module Q as evaluation \|𝛺\| indicators, The results are presented in Table <ns0:ref type='table' target='#tab_15'>6</ns0:ref>.</ns0:p><ns0:p>Table <ns0:ref type='table' target='#tab_15'>6</ns0:ref> Results of eight algorithms on classical networks From Table <ns0:ref type='table' target='#tab_15'>6</ns0:ref>, it can be seen that the number of communities and modularity of the partition results of the eight algorithms on the three classical networks were different, but LPA-ITSLR exhibited good performance on these datasets; moreover, the partition results and the number of communities were consistent with the realistic network structure, and the modularity was higher than that obtained using other algorithms.</ns0:p></ns0:div> <ns0:div><ns0:head>Analysis of experimental results of artificial datasets</ns0:head><ns0:p>Artificial datasets. Ten artificial networks were generated using the LFR benchmark (34) ; the basic information is presented in Table <ns0:ref type='table' target='#tab_9'>7</ns0:ref>. The number of nodes \|V\| in the top eight artificial networks is 1000, and the community size \| \| is 10-50, that is, min = 10, max = 50. The Manuscript to be reviewed</ns0:p><ns0:p>Computer Science Comparative analysis of algorithm performance. For the first eight artificial datasets, the proposed LPA-ITSLR algorithm was compared with the LPA and LPA-TS algorithms in terms of the community division results. The average modularity <Q> and NMI were used as evaluation indicators. The experimental results are shown in Figure <ns0:ref type='figure'>7</ns0:ref>. As the value of µ increased, the network became more complex. The modularity of the community division results of the three algorithms on the corresponding network decreased by varying degrees, but LPA-ITSLR yielded higher modularity than the other algorithms. Moreover, the NMI value of LPA-ITSLR on the first seven networks was 1, and the NMI value of the network with a µ value of 0.45 was 0.9943, showing extremely strong stability and higher quality of community division.</ns0:p><ns0:p>Figure <ns0:ref type='figure'>7</ns0:ref> Comparison of modularity and NMI on eight synthetic datasets For large-scale artificial networks LFR-9 and LFR-10 with high complexity, the proposed algorithm was compared with seven recent LPA algorithms. Q and NMI were considered as evaluation parameters. The results are presented in Table <ns0:ref type='table' target='#tab_18'>8</ns0:ref>. It can be seen that the community division results obtained using the seven algorithms were not stable, and the algorithm proposed in this paper maintains stable community division results on the two complex artificial data sets. Although the NMI value was slightly lower than that for other algorithms, the modularity was far higher. In the community merge phase optimization strategy based on modularity, LPA-ITSLR is superior as it can yield stable and high-quality community division results.</ns0:p></ns0:div> <ns0:div><ns0:head>Table 8 Results for LFR9 and LFR10</ns0:head><ns0:p>For the above 10 artificial networks, the experimental results show that the proposed algorithm is superior to other algorithms in both Q and NMI. In order to further verify the superiority of the proposed algorithm, we compare the number of communities detected by LPA-ITSLR algorithm with the actual number of communities of the ten networks, and the results are shown in Table <ns0:ref type='table'>9</ns0:ref>. It can be seen from table <ns0:ref type='table'>9</ns0:ref> that the number of communities detected by the algorithm proposed in this paper is basically consistent with the actual number of communities. In general, good results are obtained except for small deviations in some networks.</ns0:p></ns0:div> <ns0:div><ns0:head>Table 9 Actual number of communities and the number of communities detected by LPA-ITSLR</ns0:head></ns0:div> <ns0:div><ns0:head>Performance analysis of the algorithm for large data sets</ns0:head><ns0:p>Experiments on large scale artificial datasets. In order to further verify the effectiveness, the computational performance and utility of the proposed algorithm for large-scale networks, nine artificial data sets were used and the number of nodes of these networks was from 6000 to 50000, and these networks were denoted as LFR-11 to LFR-19, respectively. Table <ns0:ref type='table' target='#tab_10'>10</ns0:ref> shows the experimental results on these 9 large-scale networks, including the actual number of communities, the number of communities detected by the LPA-ITSLR algorithm, Q and NMI. It can be seen from table 10 that, the algorithm performs well on these large data sets. With the increase of the number of nodes, the network scale and complexity continues to expand, and there is a discrepancy between the actual number of communities and the number of communities obtained by the algorithm. But the Q is always above 0.86, and the NMI is more than 0.96 by and large. Specially, on the dataset containing 20000 to 50000 nodes, the NMI basically reaches more than 0.98, which shows the utility of the proposed algorithm in community division for large-scale networks. In addition, the number of communities detected by the algorithm proposed is basically consistent with the actual number of communities, which further verifies the effectiveness and superiority of the LPA-ITSLR algorithm. Experiments on large scale realistic datasets. Similarly, experiments were also carried out on large-scale realistic data sets to further verify the performance of the proposed algorithm. There are a variety of community discovery algorithms, mainly divided into split-based methods, such as GN (Girvan-Newman) algorithm <ns0:ref type='bibr' target='#b27'>(23)</ns0:ref>; methods based on Modularity, such as CNM (Clauset-Newman Modularity) algorithm( <ns0:ref type='formula' target='#formula_3'>4</ns0:ref>); methods based on spectral analysis, such as SC (Spectral Clustering) <ns0:ref type='bibr' target='#b11'>(10)</ns0:ref>, and methods based on label propagation, such as LPA. So, in addition to the six classic small realistic data sets mentioned above, three representative large-scale realistic networks were obtained, and comparative experiments were conducted with the four classic community discovery algorithms mentioned above. The three data sets are Email, Political Blogs (PB) and Power Grid (PG), and the topological properties are shown in Table <ns0:ref type='table' target='#tab_5'>11</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Table11 Properties of large-scale social network topology</ns0:head><ns0:p>The community discovery in realistic networks is more challenging than that in the simulation network, and the community structure cannot be predicted in advance, so the modularity can only be used for comparison. Table <ns0:ref type='table' target='#tab_7'>12</ns0:ref> shows the community division results of the proposed algorithm and the four classic algorithms on the above three large-scale realistic networks. In Table <ns0:ref type='table' target='#tab_7'>12</ns0:ref>, the first column is a list of realistic networks, and the second to sixth columns are five classic community discovery algorithms. For each algorithm, the maximum modularity and the number of communities are computed. For example, in the Email network, the maximum modularity obtained by GN algorithm is 0.532, and the number of communities founded is 61, which is recorded as 0.532/61. The data with the largest modularity value in the table is displayed in bold font, and the data with the second largest value is underlined.</ns0:p></ns0:div> <ns0:div><ns0:head>Table12 Comparison of community division results of five classic algorithms</ns0:head><ns0:p>It can be seen from Table <ns0:ref type='table' target='#tab_7'>12</ns0:ref> that the algorithm proposed in this paper has achieved better community division results on the three data sets in terms of modularity. On the Email network, the modularity is slightly lower than GN algorithm. On the PB network, the performance is better than the other four algorithms. On the PG network, the modularity of the proposed algorithm is only 0.003 lower than that of CNM algorithm. In general, the GN algorithm based on global, the CNM algorithm considering modularity increment and the LPA-ITSLR algorithm proposed in this paper are not very different from each other in community division results for the three datasets. However, GN and CNM algorithms have higher computational complexity than other algorithms, while SC algorithm and LPA algorithm perform relatively poor. From the perspective of the number of communities, the GN algorithm tends to get more communities. For example, the GN algorithm divides the PB network into 205 communities, which is significantly higher than other algorithms. The LPA algorithm divides the PB network into 3 communities, which is closest to the realistic number of communities. For PB network, the algorithm proposed in this paper achieves a result that is completely consistent with the number of realistic communities, while the number of communities given by other methods is more than 10. Obviously, the corresponding methods tend to over fit. Combining the two indicators of modularity and the number of communities, experimental results on 6 small-scale and 3 largescale realistic data sets show that the LPA-ITSLR algorithm proposed can effectively realize good community division with a higher modularity, and the number of communities discovered is basic consistent with the realistic community structure.</ns0:p></ns0:div> <ns0:div><ns0:head>Comparison of time complexity of algorithms.</ns0:head><ns0:p>LPA is a fast and nearly linear time-complexity algorithm for community discovery. However, the traditional LPA algorithm has poor stability due to the randomness of node selection and label update. Therefore, this paper improved the LPA algorithm and proposed the LPA-ITSLR algorithm. For the algorithm proposed in this paper, in the first stage, the similarity between nodes is firstly calculated, and the time complexity is O(nk), where n is the number of nodes in the network and k is the average degree of nodes. The time complexity corresponding to the computing of PC values is O(n 2 ). Quicksort is used to determine the node update sequence, and the corresponding time complexity is O(nlogn). The time complexity of the label propagation is O(n). In the second stage, the algorithm first judges whether the communities generated in the first stage meet the conditions of the weak community, which takes O(c) time complexity, where c is the number of communities formed in the first stage, so c is far less than n. The time complexity of the later stage of community merging is O(c 2 ). So, the computational complexity of the proposed algorithm in this paper is O(nk)+O(n 2 )+O(nlogn)+O(n)+O(c)+O(c 2 ), which is approximately equal to O(n 2 +nlogn). Table <ns0:ref type='table' target='#tab_11'>13</ns0:ref> lists the time complexity analysis results of several classic community discovery algorithms.</ns0:p></ns0:div> <ns0:div><ns0:head>Table 13 Comparisons of time complexity</ns0:head><ns0:p>In the table <ns0:ref type='table' target='#tab_11'>13</ns0:ref>, n represents the number of nodes in the network, m represents the number of edges, K is the number of eigenvectors, t represents the number of iterations of the algorithm, and d represents the depth of the tree. The first seven algorithms in Table <ns0:ref type='table' target='#tab_11'>13</ns0:ref> are classic community discovery algorithms, while the last four are community discovery methods based on label propagation proposed in recent years. Among them, the GN algorithm and Edge-Betweenness algorithm are community discovery algorithms based on hierarchical clustering and splitting, respectively. Their ideas are very intuitive and the effect is good. However, the Edgebetweenness algorithm needs to repeatedly compute the shortest path, so the time complexity is high. The Fastgreedy algorithm is a community discovery algorithm based on the idea of modularity. The CNM algorithm is a new greedy algorithm based on Newman FastGN algorithm, using the data structure of the heap to calculate and update the network modularity, which has improved the time complexity. The clustering effect of the SC algorithm depends on the similarity matrix, and the final clustering effect obtained by different similarity matrix may be very different, and the calculation complexity of the algorithm is high. The Walktrap is a community discovery method based on random walk. Due to the complexity of the loss function, the time complexity of the Walktrap algorithm is also high <ns0:ref type='bibr' target='#b22'>(19)</ns0:ref>. By comparison, the algorithm proposed in this paper also carries out community merging based on modularity in the last stage, and its computational complexity is almost the same as that of the Fastgreedy algorithm. By using sparse adjacency matrix, large networks containing millions of nodes and edges can be analyzed, and better community division results can be obtained. Meanwhile, compared with LPA, the LPA-ITSLR algorithm has significantly improved the stability of community discovery results. In addition, although the time complexity of the NIBLPA algorithm is linear, and the time complexity of the LPA-ITSLR algorithm is slightly higher than that of the LPA-NMI algorithm, the proposed algorithm solves the problems of the traditional LPA algorithm and gets higher quality community division results and has better stability. On the whole, though the performance of the proposed algorithm on a certain data set is slightly inferior to other algorithms, the experimental results on 9 realistic data sets and 19 simulation data sets show that the modularity and NMI of the proposed algorithm are higher than other comparison algorithms, which shows good quality and stability of community division.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusion</ns0:head><ns0:p>To solve the problem of unstable results and low modularity of the LPA-TS algorithm in community detection, an improved LeaderRank-based two-stage label propagation algorithm named LPA-ITSLR was proposed in this study. In the first stage, the order of node updating is determined by descending order of the PC values. In the label propagation strategy, the improved similarity index is used, and then the influence of the nodes is compared so as to obtain the initial community division. In the second stage, the community is regarded as a node, and the PC is calculated again and sorted in ascending order. For determining the optimal parameter value in the weak community condition, the community is merged. Finally, the community structure is further improved based on the modularity optimization, and the final community division result is obtained. The proposed LPA-ITSLR algorithm solves the problem that the randomness of LPA-TS algorithm may yield unstable community partition results. Moreover, LPA-ITSLR yielded higher modularity than other algorithms on 9 realistic networks and 19 artificial datasets and achieved a more stable community division. However, it has a higher time complexity in the case of certain large-scale networks with special structures such as when the network community structure is complex, when there are many small communities and less contact between communities, and for nonequilibrium size distribution networks. So a community detection method based on label propagation integrated deep learning and optimization could be employed to determine the node similarities and label influence. In the future, community detection in large-scale networks will be further studied to reduce the time complexity of the algorithm, and to achieve more accurate and efficient community detection results. Manuscript to be reviewed</ns0:p><ns0:p>Computer Science Manuscript to be reviewed</ns0:p><ns0:p>Computer Science Manuscript to be reviewed</ns0:p><ns0:p>Computer Science Manuscript to be reviewed Manuscript to be reviewed</ns0:p><ns0:note type='other'>Computer Science 1 2</ns0:note><ns0:p>Computer Science Results for LFR9 and LFR10</ns0:p><ns0:p>For large-scale artificial networks LFR-9 and LFR-10 with high complexity, the proposed LPA-ITSLR algorithm was compared with seven recent label propagation algorithms for community division. Q and NMI were considered as evaluation parameters.</ns0:p><ns0:p>PeerJ Comput. Sci. reviewing PDF \| (CS-2021:11:67478:3:2:NEW 30 Mar 2022)</ns0:p><ns0:p>Manuscript to be reviewed Manuscript to be reviewed</ns0:p><ns0:note type='other'>Computer Science</ns0:note><ns0:p>Computer Science Manuscript to be reviewed</ns0:p><ns0:p>Computer Science Manuscript to be reviewed</ns0:p><ns0:p>Computer Science</ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>𝑇 𝑗 𝑙𝑜𝑔 𝑇 𝑗 𝑛 PeerJ Comput. Sci. reviewing PDF \| (CS-2021:11:67478:3:2:NEW 30 Mar 2022)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head>Figure 2</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2 Results of 100 experiments of the two algorithms on two networks</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>Figure 3</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Figure 3 LPA-TS algorithm partitioning results for Karate network in the first stage</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head>Figure 5</ns0:head><ns0:label>5</ns0:label><ns0:figDesc>Figure 5 Comparison of algorithm stability (a) (b) (c)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head>Figure 6</ns0:head><ns0:label>6</ns0:label><ns0:figDesc>Figure 6 Comparison of modularity of LPA, LPA-TS, and LPA-ITSLR</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head /><ns0:label /><ns0:figDesc>nodes <k> is 20, and the maximum degree max(k) is 50. The values of 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, and 0.45 were employed as the mixing parameter µ, and the eight networks were denoted as LFR-1-LFR-8. The latter two artificial networks are more complicated. The number of nodes is 5000, the community size is 50, the average degree of nodes is 10, and the maximum degree is 50. The mixing parameter µ was 0.1 and 0.3, respectively, and these two networks were denoted as LFR-9 and LFR-10.PeerJ Comput. Sci. reviewing PDF \| (CS-2021:11:67478:3:2:NEW 30 Mar 2022)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head>1 Table2</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>9853 土 0.00466 0.9859 土 0.00413 0.4259 土 0.00786 0.3353 土 0.00286 SLPA 0.9994 土 0.00081 0.9931 土 0.00352 0.4467 土 0.00122 0.3437 土 0.00193 LINSIA 0.8813 土 0.00000 0.8267 土 0.00007 0.3221 土 0.00007 0.3107 土 0.00007 DLPA+ 0.9887 土 0.00135 0.9414 土 0.00156 0.4423 土 0.00164 0.3381 土 0.00074 WLPA 0.9980 土 0.00113 0.9979 土 0.00111 0.4443 土 0.00174 0.3366 土 0.00145 LPA_NI 0.9987 土 0.00082 0.9847 土 0.00124 0.4467 土 0.00024 0.3437 土 0.00112 LILPA 0.9955 土 0.00084 0.9692 土 0.00115 0.4472 土 0.00011 0.3453 土 0PeerJ Comput. Sci. reviewing PDF \| (CS-2021:11:67478:3:2:NEW 30 Mar 2022)</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='19,42.52,229.87,480.75,291.00' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='22,42.52,204.37,419.25,231.00' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='25,42.52,204.37,525.00,187.50' type='bitmap' /></ns0:figure> <ns0:figure type='table' xml:id='tab_1'><ns0:head /><ns0:label /><ns0:figDesc>𝑣 𝑖 𝛺 𝑟 𝛺 𝑟</ns0:figDesc><ns0:table /><ns0:note>𝑟 𝑑 𝑜𝑢𝑡 𝑖 (𝛺 𝑟 ) PeerJ Comput. Sci. reviewing PDF \| (CS-2021:11:67478:3:2:NEW 30 Mar 2022) Manuscript to be reviewed Computer Science In Eqs. (2) and (3), represents the number of connected edges between the node in 𝑑 𝑖𝑛 𝑖 (𝛺 𝑟 ) 𝑣 𝑖 the community and the internal nodes of , represents the number of connected 𝛺 𝑟 𝛺 𝑟 𝑑 𝑜𝑢𝑡 𝑖 (𝛺 𝑟 )edges between node in and other nodes except . In general, 𝛼 = 2.</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_2'><ns0:head /><ns0:label /><ns0:figDesc>1)Where t is the number of iterations and N is the number of nodes in the network. If there is an edge between nodes and , then</ns0:figDesc><ns0:table><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>𝑣 𝑖</ns0:cell><ns0:cell>𝑣 𝑗</ns0:cell><ns0:cell>, otherwise, 𝑎 𝑖𝑗 = 1</ns0:cell><ns0:cell>; represents the degree of node 𝑎 𝑖𝑗 = 0 𝑑 𝑖</ns0:cell></ns0:row><ns0:row><ns0:cell>, and 𝑣 𝑖</ns0:cell><ns0:cell>𝑎 𝑖𝑗 /𝑑 𝑗</ns0:cell><ns0:cell cols='3'>represents the probability of node walking to node randomly. 𝑣 𝑖 𝑣 𝑗</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>𝐿𝑅</ns0:cell><ns0:cell>(5)</ns0:cell></ns0:row></ns0:table><ns0:note>𝑖 = 𝐿𝑅 𝑖 (𝑡 𝑐 ) + 𝐿𝑅 𝑔 (𝑡 𝑐 ) 𝑁 Where is the number of iterations when it reaches stability and is the LR value 𝑡 𝑐 𝐿𝑅 𝑖 (𝑡 𝑐 ) when node reaches stability. Similarly, is the LR when node reaches stability. 𝑣 𝑖 𝐿𝑅 𝑔 (𝑡 𝑐 ) 𝑣 𝑔</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_3'><ns0:head /><ns0:label /><ns0:figDesc>), where represents the neighbor nodes of node and 𝐶𝑁(𝑣 𝑖 , 𝑣 𝑗 ) = \|𝑁 𝑖 ∩ 𝑁 𝑗 \| + 1 𝑑 𝑖 𝑑 𝑗 In the network diagram shown in Figure 1, when the LPA-TS algorithm is used for the first stage of community division, two initial rough communities are obtained: 𝛺 1 = {𝑣 7 ,𝑣 8 , 𝑣 9 , 𝑣 2 } and . The nodes in different communities are represented by different 𝛺 2 = {𝑣 1 ,𝑣 3 , 𝑣 4 , 𝑣 5 , 𝑣 6 } shapes and colors in Figure 1. At this time, node has not yet been merged into any 𝑣 0</ns0:figDesc><ns0:table><ns0:row><ns0:cell>𝑁 𝑖</ns0:cell><ns0:cell>𝑣 𝑖</ns0:cell><ns0:cell>𝑑 𝑖</ns0:cell></ns0:row><ns0:row><ns0:cell>represents the degree of node . 𝑣 𝑖</ns0:cell><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>(8)</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_5'><ns0:head>Table 1 First stage of the LPA-ITSLR algorithm Stage 2: Community merge. The</ns0:head><ns0:label>1</ns0:label><ns0:figDesc /><ns0:table /><ns0:note>second stage of the LPA-ITSLR algorithm is shown in Table</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_7'><ns0:head>Table 2 Second stage of the LPA-ITSLR algorithm Experiment and Analysis In</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>this study, numerous experiments are conducted on representative realistic networks and artificial datasets with different structural parameters. The traditional LPA algorithm, LPA-TS algorithm, and several classic community detection algorithms were compared; moreover, the effectiveness, correctness, stability, and accuracy of the proposed algorithm were verified. Analysis of experimental results on realistic networks Realistic dataset. Six classic realistic datasets were used in the experiment; their attributes are presented in Table3. Here, \|V\| represents the total number of nodes in the network, \|E\| represents the total number of edges, \| \| represents the number of communities included in the network, 𝛺 max(k) represents the maximum node degree, <k> represents the average node degree, <L> represents the average path length, and <c> represents the clustering coefficient.</ns0:figDesc><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_8'><ns0:head>Table 3 Basic structural parameters of real datasets</ns0:head><ns0:label>3</ns0:label><ns0:figDesc /><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_9'><ns0:head>Table 7 Description of synthetic networks</ns0:head><ns0:label>7</ns0:label><ns0:figDesc /><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_10'><ns0:head>Table 10 Community detection results of 9 large-scale artificial networks.</ns0:head><ns0:label>10</ns0:label><ns0:figDesc /><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_11'><ns0:head>Table 3 Basic structural parameters of real datasets</ns0:head><ns0:label>3</ns0:label><ns0:figDesc /><ns0:table><ns0:row><ns0:cell>Dataset</ns0:cell><ns0:cell>\|𝑉\|</ns0:cell><ns0:cell>\|𝐸\|</ns0:cell><ns0:cell>\|𝛺\|</ns0:cell><ns0:cell>max(k)</ns0:cell><ns0:cell><k></ns0:cell><ns0:cell><d></ns0:cell><ns0:cell><c></ns0:cell></ns0:row><ns0:row><ns0:cell>Karate</ns0:cell><ns0:cell>34</ns0:cell><ns0:cell>78</ns0:cell><ns0:cell>2</ns0:cell><ns0:cell>17</ns0:cell><ns0:cell>4.588</ns0:cell><ns0:cell>2.408</ns0:cell><ns0:cell>0.588</ns0:cell></ns0:row><ns0:row><ns0:cell>Dolphin</ns0:cell><ns0:cell>62</ns0:cell><ns0:cell>159</ns0:cell><ns0:cell>2</ns0:cell><ns0:cell>12</ns0:cell><ns0:cell>5.129</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>0.309</ns0:cell></ns0:row><ns0:row><ns0:cell>Polbooks</ns0:cell><ns0:cell>105</ns0:cell><ns0:cell>441</ns0:cell><ns0:cell>3</ns0:cell><ns0:cell>25</ns0:cell><ns0:cell>8.4</ns0:cell><ns0:cell>3.079</ns0:cell><ns0:cell>0.448</ns0:cell></ns0:row><ns0:row><ns0:cell>Football</ns0:cell><ns0:cell>115</ns0:cell><ns0:cell>613</ns0:cell><ns0:cell>12</ns0:cell><ns0:cell>12</ns0:cell><ns0:cell>10.661</ns0:cell><ns0:cell>2.508</ns0:cell><ns0:cell>0.403</ns0:cell></ns0:row><ns0:row><ns0:cell>Les_Miserable</ns0:cell><ns0:cell>77</ns0:cell><ns0:cell>254</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>36</ns0:cell><ns0:cell>6.597</ns0:cell><ns0:cell>2.641</ns0:cell><ns0:cell>0.736</ns0:cell></ns0:row><ns0:row><ns0:cell>NetScience</ns0:cell><ns0:cell>379</ns0:cell><ns0:cell>914</ns0:cell><ns0:cell>16</ns0:cell><ns0:cell>34</ns0:cell><ns0:cell>3.451</ns0:cell><ns0:cell>6.042</ns0:cell><ns0:cell>0.798</ns0:cell></ns0:row></ns0:table><ns0:note>PeerJ Comput. Sci. reviewing PDF \| (CS-2021:11:67478:3:2:NEW 30 Mar 2022)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_12'><ns0:head>Table 4 (on next page)</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Average modularity values of 10 experiments for the three algorithms on real datasets</ns0:figDesc><ns0:table /><ns0:note>PeerJ Comput. Sci. reviewing PDF \| (CS-2021:11:67478:3:2:NEW 30 Mar 2022)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_13'><ns0:head>Table 4 Average modularity values of 10 experiments for the three algorithms on real datasets</ns0:head><ns0:label>4</ns0:label><ns0:figDesc /><ns0:table><ns0:row><ns0:cell>Dataset/<Q></ns0:cell><ns0:cell>LPA</ns0:cell><ns0:cell>LPA-TS</ns0:cell><ns0:cell>LPA-ITSLR</ns0:cell></ns0:row><ns0:row><ns0:cell>Karate</ns0:cell><ns0:cell>0.3174</ns0:cell><ns0:cell>0.3716</ns0:cell><ns0:cell>0.4242</ns0:cell></ns0:row><ns0:row><ns0:cell>Dolphin</ns0:cell><ns0:cell>0.4920</ns0:cell><ns0:cell>0.3759</ns0:cell><ns0:cell>0.5418</ns0:cell></ns0:row><ns0:row><ns0:cell>Polbooks</ns0:cell><ns0:cell>0.3801</ns0:cell><ns0:cell>0.4569</ns0:cell><ns0:cell>0.5207</ns0:cell></ns0:row><ns0:row><ns0:cell>Football</ns0:cell><ns0:cell>0.5819</ns0:cell><ns0:cell>0.6010</ns0:cell><ns0:cell>0.6068</ns0:cell></ns0:row><ns0:row><ns0:cell>Les_Miserable</ns0:cell><ns0:cell>0.2719</ns0:cell><ns0:cell>0.5007</ns0:cell><ns0:cell>0.5102</ns0:cell></ns0:row><ns0:row><ns0:cell>NetScience</ns0:cell><ns0:cell>0.7769</ns0:cell><ns0:cell>0.7573</ns0:cell><ns0:cell>0.7567</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_14'><ns0:head>Table 5 Modularity comparison of five algorithms</ns0:head><ns0:label>5</ns0:label><ns0:figDesc>土 0.10187 0.3741 土 0.03946 0.4884 土 0.03215 0.5972 土 0.02115 WLPA 0.3682 土 0.08176 0.3695 土 0.02517 0.5070 土 0.00622 0.5981 土 0.01374 LINSIA 0.3989 土 0.00004 0.3878 土 0.00005 0.4521 土 0.00007 0.5853 土 0.00007 LILPA 0.4213 土 0.0029 0.4003 土 0.00214 0.4635 土 0.00646 0.6061 土 0.00151</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Network</ns0:cell><ns0:cell>Karate</ns0:cell><ns0:cell>Dolphin</ns0:cell><ns0:cell>Polbooks</ns0:cell><ns0:cell>Football</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>COPRA 0.2348 LPA-ITSLR 0.4242</ns0:cell><ns0:cell>0.5418</ns0:cell><ns0:cell>0.5207</ns0:cell><ns0:cell>0.6068</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_15'><ns0:head>Table 6 Results of eight algorithms on classical networks</ns0:head><ns0:label>6</ns0:label><ns0:figDesc /><ns0:table><ns0:row><ns0:cell>Network Criteria</ns0:cell><ns0:cell>\|𝛺\|</ns0:cell><ns0:cell>Karate</ns0:cell><ns0:cell>Q</ns0:cell><ns0:cell>\|𝛺\|</ns0:cell><ns0:cell>Dolphin</ns0:cell><ns0:cell>Q</ns0:cell><ns0:cell>\|𝛺\|</ns0:cell><ns0:cell>Football</ns0:cell><ns0:cell>Q</ns0:cell></ns0:row><ns0:row><ns0:cell>Fastgreedy</ns0:cell><ns0:cell>3</ns0:cell><ns0:cell /><ns0:cell>0.38</ns0:cell><ns0:cell>4</ns0:cell><ns0:cell cols='2'>0.495</ns0:cell><ns0:cell>6</ns0:cell><ns0:cell cols='2'>0.549</ns0:cell></ns0:row><ns0:row><ns0:cell>LPA</ns0:cell><ns0:cell>2</ns0:cell><ns0:cell cols='2'>0.292</ns0:cell><ns0:cell>3</ns0:cell><ns0:cell cols='2'>0.492</ns0:cell><ns0:cell>9</ns0:cell><ns0:cell cols='2'>0.576</ns0:cell></ns0:row><ns0:row><ns0:cell>Leading Eigenvector</ns0:cell><ns0:cell>4</ns0:cell><ns0:cell cols='2'>0.393</ns0:cell><ns0:cell>5</ns0:cell><ns0:cell cols='2'>0.491</ns0:cell><ns0:cell>8</ns0:cell><ns0:cell cols='2'>0.492</ns0:cell></ns0:row><ns0:row><ns0:cell>Walktrap</ns0:cell><ns0:cell>5</ns0:cell><ns0:cell cols='2'>0.353</ns0:cell><ns0:cell>4</ns0:cell><ns0:cell cols='2'>0.489</ns0:cell><ns0:cell>10</ns0:cell><ns0:cell cols='2'>0.602</ns0:cell></ns0:row><ns0:row><ns0:cell>NIBLPA</ns0:cell><ns0:cell>3</ns0:cell><ns0:cell cols='2'>0.352</ns0:cell><ns0:cell>5</ns0:cell><ns0:cell cols='2'>0.452</ns0:cell><ns0:cell>9</ns0:cell><ns0:cell cols='2'>0.542</ns0:cell></ns0:row><ns0:row><ns0:cell>EdMot</ns0:cell><ns0:cell>3</ns0:cell><ns0:cell cols='2'>0.412</ns0:cell><ns0:cell>4</ns0:cell><ns0:cell cols='2'>0.518</ns0:cell><ns0:cell>9</ns0:cell><ns0:cell cols='2'>0.604</ns0:cell></ns0:row><ns0:row><ns0:cell>LPA-MNI</ns0:cell><ns0:cell>2</ns0:cell><ns0:cell cols='2'>0.372</ns0:cell><ns0:cell>4</ns0:cell><ns0:cell cols='2'>0.527</ns0:cell><ns0:cell>11</ns0:cell><ns0:cell cols='2'>0.582</ns0:cell></ns0:row><ns0:row><ns0:cell>LPA-ITSLR</ns0:cell><ns0:cell>2</ns0:cell><ns0:cell cols='2'>0.4242</ns0:cell><ns0:cell>2</ns0:cell><ns0:cell cols='2'>0.5418</ns0:cell><ns0:cell>10</ns0:cell><ns0:cell cols='2'>0.6068</ns0:cell></ns0:row></ns0:table><ns0:note>2 PeerJ Comput. Sci. reviewing PDF \| (CS-2021:11:67478:3:2:NEW 30 Mar 2022)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_16'><ns0:head>Table 7 (on next page)</ns0:head><ns0:label>7</ns0:label><ns0:figDesc /><ns0:table><ns0:row><ns0:cell>Description of synthetic networks</ns0:cell></ns0:row></ns0:table><ns0:note>PeerJ Comput. Sci. reviewing PDF \| (CS-2021:11:67478:3:2:NEW 30 Mar 2022)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_17'><ns0:head>Table 7 Description of synthetic networks</ns0:head><ns0:label>7</ns0:label><ns0:figDesc /><ns0:table><ns0:row><ns0:cell>Network</ns0:cell><ns0:cell>\|V\|</ns0:cell><ns0:cell><k></ns0:cell><ns0:cell>max (k)</ns0:cell><ns0:cell>𝑚𝑖𝑛\|𝛺\|</ns0:cell><ns0:cell>𝑚𝑎𝑥\|𝛺\|</ns0:cell><ns0:cell>𝜇</ns0:cell></ns0:row><ns0:row><ns0:cell>LFR-1 ~ LFR-8</ns0:cell><ns0:cell>1000</ns0:cell><ns0:cell>20</ns0:cell><ns0:cell>50</ns0:cell><ns0:cell>10</ns0:cell><ns0:cell>50</ns0:cell><ns0:cell>0.1~0.45</ns0:cell></ns0:row><ns0:row><ns0:cell>LFR-9</ns0:cell><ns0:cell>5000</ns0:cell><ns0:cell>10</ns0:cell><ns0:cell>50</ns0:cell><ns0:cell>50</ns0:cell><ns0:cell>50</ns0:cell><ns0:cell>0.1</ns0:cell></ns0:row><ns0:row><ns0:cell>LFR-10</ns0:cell><ns0:cell>5000</ns0:cell><ns0:cell>10</ns0:cell><ns0:cell>50</ns0:cell><ns0:cell>50</ns0:cell><ns0:cell>50</ns0:cell><ns0:cell>0.3</ns0:cell></ns0:row><ns0:row><ns0:cell>3</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row></ns0:table><ns0:note>PeerJ Comput. Sci. reviewing PDF \| (CS-2021:11:67478:3:2:NEW 30 Mar 2022)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_18'><ns0:head>Table 8 (on next page)</ns0:head><ns0:label>8</ns0:label><ns0:figDesc /><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_19'><ns0:head>Table 9 (on next page) 1 Table 9 Actual number of communities and the number of communities detected by LPA-ITSLR</ns0:head><ns0:label>919</ns0:label><ns0:figDesc>Actual number of communities and the number of communities detected by LPA-ITSLRThe number of real communities and algorithm division</ns0:figDesc><ns0:table><ns0:row><ns0:cell>2</ns0:cell><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>LFR networks</ns0:cell><ns0:cell>Actual number of communities</ns0:cell><ns0:cell>Number of communities divided by LPA-ITSLR</ns0:cell></ns0:row><ns0:row><ns0:cell>LFR-1</ns0:cell><ns0:cell>35</ns0:cell><ns0:cell>40</ns0:cell></ns0:row><ns0:row><ns0:cell>LFR-2</ns0:cell><ns0:cell>35</ns0:cell><ns0:cell>35</ns0:cell></ns0:row><ns0:row><ns0:cell>LFR-3</ns0:cell><ns0:cell>38</ns0:cell><ns0:cell>38</ns0:cell></ns0:row><ns0:row><ns0:cell>LFR-4</ns0:cell><ns0:cell>45</ns0:cell><ns0:cell>45</ns0:cell></ns0:row><ns0:row><ns0:cell>LFR-5</ns0:cell><ns0:cell>39</ns0:cell><ns0:cell>39</ns0:cell></ns0:row><ns0:row><ns0:cell>LFR-6</ns0:cell><ns0:cell>42</ns0:cell><ns0:cell>42</ns0:cell></ns0:row><ns0:row><ns0:cell>LFR-7</ns0:cell><ns0:cell>42</ns0:cell><ns0:cell>42</ns0:cell></ns0:row><ns0:row><ns0:cell>LFR-8</ns0:cell><ns0:cell>42</ns0:cell><ns0:cell>40</ns0:cell></ns0:row><ns0:row><ns0:cell>LFR-9</ns0:cell><ns0:cell>85</ns0:cell><ns0:cell>81</ns0:cell></ns0:row><ns0:row><ns0:cell>LFR-10</ns0:cell><ns0:cell>98</ns0:cell><ns0:cell>69</ns0:cell></ns0:row></ns0:table><ns0:note>PeerJ Comput. Sci. reviewing PDF \| (CS-2021:11:67478:3:2:NEW 30 Mar 2022) Manuscript to be reviewed Computer Science PeerJ Comput. Sci. reviewing PDF \| (CS-2021:11:67478:3:2:NEW 30 Mar 2022)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_20'><ns0:head>Table 10 (on next page)</ns0:head><ns0:label>10</ns0:label><ns0:figDesc /><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_21'><ns0:head>Table 10</ns0:head><ns0:label>10</ns0:label><ns0:figDesc>Community detection results of 9 large-scale artificial networksLarge scale data results presentation</ns0:figDesc><ns0:table /><ns0:note>PeerJ Comput. Sci. reviewing PDF \| (CS-2021:11:67478:3:2:NEW 30 Mar 2022)Manuscript to be reviewedComputer Science1</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_22'><ns0:head>Table 10 Community detection results of 9 large-scale artificial networks.</ns0:head><ns0:label>10</ns0:label><ns0:figDesc>¥¦ §¨© g¦ of community d results of f© classic algorithms</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Dataset</ns0:cell><ns0:cell cols='5'>\|V\| <k> max(k) 𝑚𝑖𝑛\|𝛺\| 𝑚𝑎𝑥\|𝛺\| 𝜇</ns0:cell><ns0:cell>actual number of communities</ns0:cell><ns0:cell>number of communities found</ns0:cell><ns0:cell><Q></ns0:cell><ns0:cell>NMI</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>LFR-11 6000 10</ns0:cell><ns0:cell>50</ns0:cell><ns0:cell>30</ns0:cell><ns0:cell>60</ns0:cell><ns0:cell>0.1</ns0:cell><ns0:cell>125</ns0:cell><ns0:cell>128</ns0:cell><ns0:cell>0.8730 0.9762</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>LFR-12 7000 10</ns0:cell><ns0:cell>50</ns0:cell><ns0:cell>30</ns0:cell><ns0:cell>60</ns0:cell><ns0:cell>0.1</ns0:cell><ns0:cell>130</ns0:cell><ns0:cell>133</ns0:cell><ns0:cell>0.8686 0.9510</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>LFR-13 8000 10</ns0:cell><ns0:cell>50</ns0:cell><ns0:cell>30</ns0:cell><ns0:cell>60</ns0:cell><ns0:cell>0.1</ns0:cell><ns0:cell>176</ns0:cell><ns0:cell>176</ns0:cell><ns0:cell>0.8828 0.9805</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>LFR-14 9000 10</ns0:cell><ns0:cell>50</ns0:cell><ns0:cell>30</ns0:cell><ns0:cell>60</ns0:cell><ns0:cell>0.1</ns0:cell><ns0:cell>175</ns0:cell><ns0:cell>178</ns0:cell><ns0:cell>0.8722 0.9629</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>LFR-15 10</ns0:cell><ns0:cell>50</ns0:cell><ns0:cell>30</ns0:cell><ns0:cell>60</ns0:cell><ns0:cell>0.1</ns0:cell><ns0:cell>175</ns0:cell><ns0:cell>180</ns0:cell><ns0:cell>0.8775 0.9678</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>LFR-16 10</ns0:cell><ns0:cell>50</ns0:cell><ns0:cell>30</ns0:cell><ns0:cell>60</ns0:cell><ns0:cell>0.1</ns0:cell><ns0:cell>436</ns0:cell><ns0:cell>464</ns0:cell><ns0:cell>0.8842 0.9844</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>LFR-17 10</ns0:cell><ns0:cell>50</ns0:cell><ns0:cell>30</ns0:cell><ns0:cell>60</ns0:cell><ns0:cell>0.1</ns0:cell><ns0:cell>668</ns0:cell><ns0:cell>683</ns0:cell><ns0:cell>0.8846 0.9851</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>LFR-18 10</ns0:cell><ns0:cell>50</ns0:cell><ns0:cell>30</ns0:cell><ns0:cell>60</ns0:cell><ns0:cell>0.1</ns0:cell><ns0:cell>1058</ns0:cell><ns0:cell>1049</ns0:cell><ns0:cell>0.8837 0.9793</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>LFR-19 10</ns0:cell><ns0:cell>50</ns0:cell><ns0:cell>30</ns0:cell><ns0:cell>60</ns0:cell><ns0:cell>0.1</ns0:cell><ns0:cell>1382</ns0:cell><ns0:cell>1341</ns0:cell><ns0:cell>0.8643 0.9637</ns0:cell></ns0:row><ns0:row><ns0:cell>2</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row></ns0:table><ns0:note>¡¢£¤¤ Properties of large-scale social network topology $%&'( 13 Comparisons of time complexity 2 PeerJ Comput. Sci. reviewing PDF \| (CS-2021:11:67478:3:2:NEW 30 Mar 2022)</ns0:note></ns0:figure> <ns0:note place='foot'>PeerJ Comput. Sci. reviewing PDF \| (CS-2021:11:67478:3:2:NEW 30 Mar 2022)Manuscript to be reviewed</ns0:note> </ns0:body> "	"Dear Editor， We are truly grateful to you and the reviewers for the critical evaluation and thoughtful suggestions on our manuscript (“An improved two-stage label propagation algorithm based on LeaderRank”) during the previous rounds of review. According to the opinions of the reviewers, we have carefully revised the paper. At the same time, we are very happy that after the first two rounds of revision, the research results of our study have been greatly affirmed by experts, and the existing problems have been gradually solved one by one. In this round of review, the two reviewers also have a concern about the performance of the algorithm on large-scale realistic data sets. So, based on the previous experiments on several large-scale simulation data sets, we obtained three relatively large-scale realistic datasets and carried out the comparative experiment again. Meanwhile, we also analyzed and compared the performance and time complexity of the proposed algorithm in this paper with several classic community discovery algorithms, such as GN, CNM, Spectral Clustering, Fastgreedy algorithm, Walktrap algorithm, Edge-betweenness algorithm, and several LPA algorithms so as to verify the effectiveness of the proposed algorithm. Our point-by-point responses to this review’s comments are as given below. We hope that these revisions are satisfactory and the revised version will be acceptable for publication in “PeerJ Computer Science”. Thank you very much for your attention to our paper. Wish you all the best! If you have any question, please feel free to contact me. Thank you very much! Response to the reviewer 2: Dear reviewer, thank you very much for all your valuable comments on the first two rounds of review. According to your suggestion, we conducted further comparative experiments and revised the manuscript. Based on your previous suggestions, we verified the effectiveness of the algorithm proposed in our paper through six classic and widely used realistic data sets. In addition, experiments have also been conducted on 19 artificial datasets of varying sizes and complexity, involving large datasets with 50,000 nodes. At the same time, taking the modularity and NMI as evaluation indexes, the performance of the algorithm proposed in this paper has been verified and analyzed by comparing it with the classical algorithms and some new algorithms. The corresponding experimental results are shown in tables such as Table 6. Aiming at the performance of the algorithm you concerned about on large-scale realistic data sets, we obtained three relatively large-scale realistic datasets and carried out the comparative experiment again. Meanwhile, we also analyzed and compared the performance and time complexity of the proposed algorithm in this paper with several classic community discovery algorithms. Firstly, we obtained three relative large-scale realistic datasets, namely, Email network, Political Blogs (PB) and Power Grid (PG). These three data sets are representative data sets and their topological properties are listed in Table 11. Table11 Properties of large-scale social network topology Network max(k) <k> <d> <c> Email 1133 5451 71 9.6220 3.6060 0.2540 PB 1224 33430 702 54.6242 3 0.2259 PG 4941 6594 19 2.6691 20.0941 0.1031 Then we conducted a comparative experiment and analysis on these datasets with four classic community discovery algorithms, namely, GN algorithm, CNM (Clauset-Newman Modularity) algorithm， spectral clustering algorithm and LPA. The experimental results are shown in Table 12. Table12 Comparison of community division results of five classic algorithms Network GN CNM SC LPA LPA-ITSLR Email 0.532/61 0.446/10 0.412/45 0.014/4 0.504/7 PB 0.418/205 0.426/77 0.328/62 0.410/3 0.428/2 PG 0.857/39 0.934/42 0.830/42 0.871/38 0.931/42 Finally, we also analyze the complexity, advantages and disadvantages of these classical community discovery algorithms described in this paper, such as GN, Fastgreedy, Edge-Betweenness, and LPA algorithms, as shown in Table 13. Table 13 Comparisons of time complexity Algorithm Time Complexity GN O(nm2) Newman Fastgreedy O (n(m+n)) Edge-Betweenness O(m2n) CNM O(n(logn)2) SC O(mKt+nK2t+K3t+n3) Walktrap O (n2 m) LPA O (m) NIBLPA O (m) LPA-MNI O(m + nlogn) LPA-TS O(n2+c2+t(n+c)) LPA-ITSLR O(n2+nlogn) Relevant descriptions have also been supplemented in this round of revision. Please check the revised text for details. However, different kinds of community discovery algorithms have their own advantages and disadvantages. It is the common goal of all methods to design an algorithm with low computational complexity and high quality of community division. It should be emphasized that we focus on the community discovery method based on label propagation, and the algorithm proposed in this paper is an improvement of the LPA-TS algorithm based on the existing problems of the traditional LPA algorithm. The improved algorithm solves the problem that the randomness of the LPA-TS algorithm in label propagation may bring about the instability of community partition results, and the comparative analysis of experimental results also shows the superiority of the algorithm in the quality of community detection. For classical algorithms such as Fastgreedy and Edge-Betweenness, as well as NIBLPA and LPA-TS, the computational complexity of the improved algorithm in this paper is either equal to that of the classical algorithms, or slightly higher or lower than that of relevant algorithms. However, for algorithms with the same order of magnitude of computational complexity, the proposed algorithm achieves good community division results on both classical real data sets and artificial data sets, which can further improve the quality of community partitioning results while ensuring computational efficiency, showing a good overall superiority. The research of this paper focuses on the LPA algorithm, though some problems of the existing LPA algorithms have been solved, the algorithm proposed in this paper still has some problems. So, in the future, we will further study the community discovery algorithm based on modularity and hierarchical clustering so as to reduce the computational complexity, improve the quality of community division results, and design and implement an algorithm with better performance. We hope that these revisions are satisfactory and thank you very much for your attention to our paper. Wish you all the best! Response to the review 3: Dear reviewer, thank you very much for all your valuable comments on the first two rounds of review. Given the computational complexity problem of our proposed algorithm you concerned about, we analyzed the advantages and disadvantages of several classic community discovery algorithms, such as GN, Fastgreedy, Edge-Betweenness and LPA algorithms. We also do a comparison of the computational complexity of these algorithms, as shown in Table 13. In addition, aiming at the performance of the algorithm on large-scale realistic data sets, we obtained three relatively large-scale realistic datasets and carried out the comparative experiment again. Meanwhile, we also analyzed and compared the performance of the proposed algorithm in this paper with several classic community discovery algorithms, as shown in Table 11 and Table 12. Table11 Properties of large-scale social network topology Network max(k) <k> <d> <c> Email 1133 5451 71 9.6220 3.6060 0.2540 PB 1224 33430 702 54.6242 3 0.2259 PG 4941 6594 19 2.6691 20.0941 0.1031 Table12 Comparison of community division results of five classic algorithms Network GN CNM SC LPA LPA-ITSLR Email 0.532/61 0.446/10 0.412/45 0.014/4 0.504/7 PB 0.418/205 0.426/77 0.328/62 0.410/3 0.428/2 PG 0.857/39 0.934/42 0.830/42 0.871/38 0.931/42 Table 13 Comparisons of computational complexity Algorithm Time Complexity GN O(nm2) Newman Fastgreedy O (n(m+n)) Edge-Betweenness O(m2n) CNM O(n(logn)2) SC O(mKt+nK2t+K3t+n3) Walktrap O (n2 m) LPA O (m) NIBLPA O (m) LPA-MNI O(m + nlogn) LPA-TS O(n2+c2+t(n+c)) LPA-ITSLR O(n2+nlogn) Relevant descriptions have also been supplemented in this round of revision. Please check the revised text for details. It is hoped that through these analysis and comparison, the effectiveness and correctness of the algorithm proposed in this paper for community discovery on large-scale data sets can be more clearly demonstrated. We hope that these revisions are satisfactory and thank you very much for your attention to our paper. Wish you all the best! "	Here is a paper. Please give your review comments after reading it.
429	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>The Industrial Wireless Sensor Network (IWSN) is a surface-type of Wireless Sensor Network (WSN) that suffers from high levels of security breaches and energy consumption.</ns0:p><ns0:p>In modern complex industrial plants, it is essential to maintain the security, energy efficiency, and green sustainability of the network. In an IWSN, sensors are connected to the Internet in a non-monitored environment. Hence, non-authorized sensors can retrieve information from the IWSN. Therefore, to ensure that data access between sensors remains sustainable and secure, energy-efficient authentication and authorization are required. In this paper, a novel Quantum Readout Gradient Secured Deep Learning (QR-GSDL) model is proposed to ensure that only trustworthy sensors can access IWSN data.</ns0:p><ns0:p>The major objective of this QR-GSDL model is to create secure, energy-efficient IWSN to attain green sustainability and reduce the industrial impact on the environment. First, using the quantum readout and hash function, a registration method is designed to efficiently perform the registration process. Next, a gradient secured deep learning method is adopted to implement the authentication and authorization process in order to ensure energy-saving and secure data access. Simulations are conducted to evaluate the QR-GSDL model and compare its performance with that of three well-known models: online threshold anomaly detection, machine learning-based anomaly detection, and dynamic CNN. The simulation outcomes show that the proposed model is secure and energy efficient for use in the IWSN. Moreover, the experimental results prove that the QR-SGDL model outperforms the existing models in terms of energy consumption, authentication rate, authentication time, and false acceptance rate.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>INTRODUCTION</ns0:head><ns0:p>The underlying rationale for the recent conceptualization of the Industrial Internet of Things (IIoT) has been to leverage the Internet of Things (IoT) and apply its advantages to the industrial wireless sensor networks (IWSNs) in order to create interconnected industrial environments. IWSNs play an essential part in the management and operation of industrial machinery across a wide range of sectors. The main task of an IWSNs is to monitor the performance of different devices through the collection, storage, and retrieval of data in real time in an industrial environment. The application of the IWSNs framework in such systems is intended to increase optimization and improve industrial automation processes. One of the most well-known models that has been utilized in the Industrial Internet of Things (IIoT) domain is the online threshold anomaly detection model, which employs a learning method based on statistical formulations to distinguish the characteristics of devices and flag up any differences in those characteristics as anomalies <ns0:ref type='bibr' target='#b14'>Li et al. (2019)</ns0:ref>. The model is independent in terms of device operations because statistical data about the system are acquired by using the IoT application program interface. For this model, multiple machine learning techniques have been introduced in the training process and performance on normal system were designed in similar manner. The model is able to detect anomalous activities in an efficient manner by summing cumulative operations and using localized outliers, thereby improving accuracy and simultaneously reducing false alarms. However, despite improvements in the accuracy and PeerJ Comput. Sci. reviewing PDF \| (CS-2021:08:64590:1:0:NEW 27 Jan 2022)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p><ns0:p>Computer Science false alarm rates, this model does not address the issue of the security of data communication in the context of the industrial sector.</ns0:p><ns0:p>The other well-recognized model that has been employed in the IIoT is the machine learning-based anomaly detection model <ns0:ref type='bibr' target='#b32'>Zolanvari et al. (2019)</ns0:ref>. This model was developed to address the most prevalent susceptibility in the IIoT, namely, the injection attack. There are three main forms of injection attack that can be mitigated by applying machine learning techniques: command injection, structured query language, and backdoor attacks. Through the adoption of a machine learning-based approach, not only was it demonstrated that the attack detection rate was improved, there was also a steep reduction in the value of the mean absolute error. However, despite the improvement in the attack detection rate and the minimization of the mean absolute error, the false negative rate was not minimized by machine learning-based anomaly detection. In other words, anomaly intruders were still incorrectly detected, thus leading to a lack of overall efficacy.</ns0:p><ns0:p>In <ns0:ref type='bibr' target='#b30'>Yuan et al. (2020)</ns0:ref>, a dynamic CNN (DCNN) technique is planned to learn the hierarchical local nonlinear dynamic features of soft sensor modeling. Every 1D process sample in DCNN is dynamically increased into a 2D data sample with lagged unlabeled process variables, comprising both spatial crossrelationships and temporal auto-correlations. Then, to derive the local nonlinear spatial-temporal function from the 2D sample data matrix, the convolutional and pooling layers are alternately used. In addition, the concept of how the local nonlinear spatial-temporal function can be taught from the network is studied for DCNN. In an industrial hydrocracking process, the efficacy of the proposed DCNN is tested. However, the authors did not provide any prove of energy efficiency of their approach <ns0:ref type='bibr' target='#b8'>Alzubi et al. (2020b</ns0:ref><ns0:ref type='bibr' target='#b3'>) Alzubi et al. (2020a)</ns0:ref>.</ns0:p><ns0:p>In this paper a deep learning-based solution is presented to overcome the security issues that currently exist in the authentication and authorization protocol for the industrial wireless sensor network (IWSN).</ns0:p><ns0:p>The proposed solution employs a novel model named the Quantum Readout Gradient secured Deep Learning (QR-GSDL) model. This model first verifies the authenticity of a given sensor seeking access to data in the IWSN by using a quantum readout and hash (QRH) function. This registration process facilitates effective validation and therefore reduces the false acceptance rate. Next, the security issues inherent in the authentication and authorization procedure are addressed by using a gradient sparse auto deep learning algorithm. This type of algorithm was adopted because it was envisaged that its usage would lead to an improvement in the authentication rate (AR) with minimum time consumption and delay.</ns0:p><ns0:p>Accordingly, the designed model substantially minimizes the false acceptance rate (FAR), leading to an improvement in both the authentication rate and authentication time (AT ).</ns0:p><ns0:p>We believe that the best-suited real-world environment to implement our proposed QR-GSDL model is in industrial applications such as machine health, automated metering, remote monitoring, and staff management. The only requirement is that the pre-defined setting of IWSN be stationary.</ns0:p></ns0:div> <ns0:div><ns0:head>RELATED WORK</ns0:head><ns0:p>With the advancement of technologies, because of their benefits over conventional wired networks, wireless sensor networks (WSNs) have fantastic deployment opportunities for industrial scenarios. However, fully integrated mechanized processes and wireless networking conditions allow the high security and low energy consumption requirements of industrial wireless sensor networks (IWSNs) more stringent. We will discuss the relevant work in this section from the point of view of security and energy consumption.</ns0:p><ns0:p>Many researchers presume industrial wireless sensor networks and present different authentication and authorization schemes. However, these schemes were not ideal for IWSN. This is due to the fact that in terms of energy efficiency and computing overhead, node authentication by cluster head on a regular basis results in considerable overhead.</ns0:p><ns0:p>Several studies have been conducted in the area of deep learning to make the IoT-enabled WSN more efficient , robust and secure <ns0:ref type='bibr' target='#b6'>Alzubi et al. (2019b)</ns0:ref>. The works that are most relevant to this paper include the deep learning model that was proposed in <ns0:ref type='bibr' target='#b16'>Liang et al. (2020)</ns0:ref>. This model is based on edge computing and aimed at minimizing the traffic (data transmissions) in the network to reduce network congestion while maintaining classification accuracy. However, the method in <ns0:ref type='bibr' target='#b16'>Liang et al. (2020)</ns0:ref> did not provide each user with data privacy. Therefore, to address this privacy issue, two privacy-preserving deep learning models named DeepPAR and DeepDPA were presented in <ns0:ref type='bibr' target='#b31'>Zhang et al. (2020)</ns0:ref>. The DeepPAR model offered a mechanism that prevented a user's information from being leaked to others, while keeping the secrecy level dynamically updated. To address this issue, the DeepDPA model applied a set of key Despite the above achievements in the area of deep learning, a survey of the application of deep learning tools in the smart industry presented in <ns0:ref type='bibr' target='#b19'>Ma et al. (2019)</ns0:ref> concluded that while deep learning provides an opportunity to solve many classical issues, authentication and authorization problems are not tackled. Therefore, as a first step in addressing these problems, the QR-GSDL model is designed in such a way so as to ensure that the gateway node in the IWSN checks the authenticity of the sensor node that is seeking access to information in the IWSN to thereby guarantee correct and appropriate authorization.</ns0:p><ns0:p>Other works have also explored methods to improve authentication. For instance, in <ns0:ref type='bibr' target='#b11'>Chen et al. (2020)</ns0:ref>, a secure authentication scheme was introduced that depended on credential and dynamic IDs for WSNs in IoT environments. For the scheme, an authentication key agreement protocol based on three parties was designed using the Burrows-Abhadi-Needham logic method. It was reported that the scheme was able to ensure low computational and communication cost, but it was admitted that the false acceptance rate was not improved. Consequently, the gradient secured deep learning method is integrated into the proposed QR-GSDL model in order to achieve a reduction in the false acceptance rate.</ns0:p><ns0:p>Jiang et al. proposes a re-authentication scheme for the Voronoi graph-based network model. The scheme maintains anonymity while using less resources than the previous schemes. The system, however, suggests neighbor wandering, which might not be ideal for a realistic situation. Also, they did prove the efficiency of their model in terms of energy consumption <ns0:ref type='bibr' target='#b2'>Alrabea et al. (2020)</ns0:ref> <ns0:ref type='bibr' target='#b5'>Alzubi et al. (2019a)</ns0:ref>.</ns0:p><ns0:p>In an alternative attempt to improve the security aspect of the WSN, a convolutional technique (CT) was developed in <ns0:ref type='bibr' target='#b0'>Alghamdi (2019)</ns0:ref>, which involved generating security bits using convolutional codes. The aim of the CT was to protect the WSN from attacks caused by malicious nodes. The designed technique improved network security and minimized computational complexity because no key distribution was needed. However, authentication time was not minimized by CT. Thus, a gradient secured deep learning method is included in the proposed QR-GSDL model in order to attempt to reduce the time consumed in the authentication procedure. Industrial wireless sensor networks, which are an evolved category of WSN in which sensors are combined to monitor the status of equipment and to control systems in real time, also have limitations related to security, privacy, and energy. To address these drawbacks in the IWSN context, a lightweight decision-making framework based on trust value identification was designed in <ns0:ref type='bibr' target='#b22'>Ramesh and Yaashuwanth (2019)</ns0:ref>. The lightweight trust framework was used for quality of service clustering in order to perform the secure routing process. A quantifiable trust value was determined through the cluster head within the cluster. It was claimed that flawed, untrusted, counteract, and malicious nodes could be predicted using this framework. However, the communication cost was not minimized. Therefore, in the QR-GSDL model, quantum sparse auto encoding and decoding is employed to reduce the communication cost in the IWSN system.</ns0:p><ns0:p>A different protocol named secure directed diffusion was suggested in <ns0:ref type='bibr' target='#b23'>Sengupta et al. (2018)</ns0:ref>. This protocol depended on binding the node's geographic location and ID in order to induce a cryptographic key based on the location. The produced key then formed the foundation of a neighborhood authentication</ns0:p></ns0:div> <ns0:div><ns0:head>3/18</ns0:head><ns0:p>PeerJ Comput. Sci. reviewing PDF \| (CS- <ns0:ref type='table' target='#tab_5'>2021:08:64590:1:0:NEW 27 Jan 2022)</ns0:ref> Manuscript to be reviewed Computer Science process for the IWSN. However, only theoretical statements were provided regarding the effectiveness of this authentication process and the computational overhead.</ns0:p><ns0:p>On the other hand, in <ns0:ref type='bibr' target='#b21'>Qureshi et al. (2020)</ns0:ref>, a centroid position analysis was performed in an attempt to decrease data transmission failure and delay. In addition, a gateway clustering routing protocol was used for cluster head selection from the centroid position. Then the gateway node minimized the load from the cluster head nodes and transmitted the data to the base station. However, security issues were not taken into consideration. Therefore, in our proposed approach, an authentication process is carried out to establish secure communication.</ns0:p><ns0:p>Cooperation between the sensors that are communicating with a central base station is one of the factors that contribute to security. In light of this, cryptographic algorithms based on secret keys were designed in <ns0:ref type='bibr' target='#b29'>Tahir et al. (2018)</ns0:ref> in which the ICMetric method employed the device features to generate secret keys for use in cryptographic services. However, the proposed method failed to offer an effective authentication process that at the same time did not increase resource overheads. Hence, in the proposed QR-GSDL model, a gradient secured deep learning algorithm performs the authentication to allow secure data communication.</ns0:p><ns0:p>An energy-efficient data transmission mechanism was proposed in that sought to improve emergency data transmission by increasing accuracy and decreasing packet delay <ns0:ref type='bibr' target='#b26'>Sheikh et al. (2012</ns0:ref><ns0:ref type='bibr' target='#b28'>)Singanamalla et al. (2019</ns0:ref><ns0:ref type='bibr' target='#b20'>) Nazir et al. (2020)</ns0:ref>. It was claimed that the scheme was reliable, but a mechanism for ensuring the security of data transmission was not presented. In contrast, an authentication process is carried out in the QR-GSDL model to enable secure data transmission.</ns0:p><ns0:p>A cooperative mechanism was presented in <ns0:ref type='bibr' target='#b12'>Iqbal et al. (2017)</ns0:ref> to reduce both the false alarm rate and energy consumption. The designed mechanism improved the probability of accurate decisions being made at specified signal level. Also, the suggested mechanism in <ns0:ref type='bibr' target='#b12'>Iqbal et al. (2017)</ns0:ref> was reported to be able to achieve a reduction in the false alarm rate, but only for indoor IWSNs. However, it was not particularly efficient in terms of energy consumption in relation to the time needed to perform its computations.</ns0:p><ns0:p>Therefore, quantum sparse encoding and decoding is used in our proposed QR-GSDL model in order to The three issues of security, network lifetime and coverage were handled in <ns0:ref type='bibr' target='#b9'>Cao et al. (2020)</ns0:ref> by converting the disjoint routing paths to address the flow issues. However, despite an improvement being observed in security and coverage, optimization and operation time were not focused. Therefore, again the quantum sparse encoding and decoding approach is used in the proposed model in order to minimize time consumption.</ns0:p><ns0:p>To address the issue of security, in <ns0:ref type='bibr' target='#b10'>Cao et al. (2019)</ns0:ref>, multi-objective evolutionary algorithms were designed for a heterogeneous WSN. Moreover, a 3D signal propagation model used the line-of-sight idea to determine the signal path loss. However, the security level was not improved by the designed model. Hence, the gradient secured deep learning model is employed in the current study to perform authentication for secure data communication.</ns0:p><ns0:p>Another password-based authentication scheme was proposed in <ns0:ref type='bibr' target='#b13'>Lee et al. (2018)</ns0:ref> to verify security with minimal communication and computation cost. However, the communication and computation overheads were not minimized by the developed password-based authentication scheme. Therefore, in the proposed model, quantum sparse encoding and decoding is used with the expectation that this approach can reduce the computation overhead.</ns0:p><ns0:p>Lastly, a mutual authentication system integrating temporal credential and multiple passwords was proposed in <ns0:ref type='bibr' target='#b18'>Liu et al. (2017)</ns0:ref> in order to minimize the overheads. However, while the authentication time was reduced, the false alarm rate was not minimized. Therefore, in the proposed QR-SGDL model, the QRH is used to offload the false alarm rate. Motivated by the above issues encountered in related studies, in this work, a novel model for IWSN, named the Quantum Readout Gradient Secured Deep Learning (QR-GSDL) model, is developed in order to improve not only the authentication rate, but also the authentication time and false acceptance rate. In the following, an elaboration of the QR-GSDL model is presented.</ns0:p></ns0:div> <ns0:div><ns0:head>QUANTUM READOUT GRADIENT SECURED DEEP LEARNING IN IWSN</ns0:head><ns0:p>In this section, we present our proposed model, QR-SGDL, which consists of three phases: i) secure and energy efficient localization, ii) sensor node registration and iii) authentication. The system model and the three phases of QR-SGDL are elaborated in the following subsections. The gateway node gathered two types of packets-data packets and control packets-from the sensor nodes. Before collecting the data packets, the gateway node ensures that these packets are originated from an authenticated sensor nodes and it verifies whether the sensor node has been tampered with or not. This will be accomplished by applying the Quantum Readout Hash registration which is explained thoroughly in algorithm 1. After attaining the data, the QR-GSDL method analyzes the data about industrial plants in order to maintain the green sustainability.</ns0:p></ns0:div> <ns0:div><ns0:head>System architecture of the QR-SGDP model</ns0:head><ns0:p>The proposed QR-SGDL model uses the deep learning concepts to perform multiple processes in several layers. The deep learning network uses one input layer, two hidden layers, and one output layer for improving security during the data access and to improve the green sustainability of the network. The feed-forward fashion deep learning network collects the nodes in input layer, learns in the hidden layers and transforms the results into an output layer.</ns0:p><ns0:p>The system architecture of the QR-SGDL model, which is designed to address energy and green sustainability issues through analyzing the industrial plants data. It also aims to handle security issues such as authentication and authorization in the IWSN by achieving a minimum false acceptance rate.</ns0:p><ns0:p>In the IWSN, the upper layer of the architecture is a transmission layer comprising base stations, sensor nodes, gateway node and supervisory control unit. All of these elements can be found in any <ns0:ref type='formula' target='#formula_0'>5</ns0:ref>.</ns0:p><ns0:formula xml:id='formula_0'>β = HASH(α s \|\|spwd s )<ns0:label>(5)</ns0:label></ns0:formula><ns0:p>By applying the above integrated functions, it is considered to be possible to achieve authentic sensor registration with a minimum false acceptance rate.</ns0:p></ns0:div> <ns0:div><ns0:head>GRADIENT SECURED DEEP LEARNING MODEL</ns0:head><ns0:p>Let us assume that a sensor S wants to acquire industrial plant data from a specific sensor in the IWSN.</ns0:p><ns0:p>Then, mutual authentication between the two sensors S i and S j has to be established, where the identity Let us consider sensors S i and S j at different locations A 1 , B 1 , A 2 and B 2 , mathematically formulated as given below.</ns0:p><ns0:formula xml:id='formula_1'>S i0 = A 1 + B 1 (6) S j0 = A 2 + B 2 (7)</ns0:formula><ns0:p>Based on equations 6 and 7, the distance between the sensors are mathematically calculated by applying equation 8.</ns0:p><ns0:formula xml:id='formula_2'>S i j = (A 1 + A 2 ) 2 + (B 1 + B 2 ) 2 (8)</ns0:formula><ns0:p>However, in order to analyze the relation between gradient associating sensors, it is computed by equations 9 and 10. </ns0:p><ns0:formula xml:id='formula_3'>cos θ = S i0 + S j0 − (S i S j ) 2 2S i0 Q 0 (9)</ns0:formula><ns0:formula xml:id='formula_4'>cos θ = A 1 A 2 + B 1 B 2 A 2 1 + B 2 1 A 2 2 + B 2 2 (10)</ns0:formula><ns0:p>By introducing the Gradient Secure Localization (GSL) method, security, energy efficiency, and green sustainability can be ensured even in the presence of attacks. In this manner, with the location verification by the GSL method, the precision or exactness of the disclosed locations of the sensors is made in an effective fashion, therefore ensuring authentication. Note that the comparison here is made based on the disclosed locations. If they are not equal, the session is terminated. Upon successful comparison the base station perceives S as a normal sensor.</ns0:p><ns0:p>Next, the base station generates a nonce N S and selects IID S as the interim identity of the sensor and sends the signing message IID S , N S via a secure channel. After obtaining the signing on message, the gateway node GN initially locates the IID S in its database. The gateway node then chooses the QRCRP(C S , R S ), where n denotes the total number of the input sensors. In addition, vector N :</ns0:p><ns0:formula xml:id='formula_5'>generates</ns0:formula><ns0:formula xml:id='formula_6'>R = [R 1 , R 2 , ..., R m ],</ns0:formula><ns0:formula xml:id='formula_7'>(C S , N′ GN , Z o , R)) → S (12)</ns0:formula><ns0:p>Upon reception of the message as given 12, the base station BS of the corresponding sensor S extracts</ns0:p><ns0:formula xml:id='formula_8'>R S = Q D (C S )</ns0:formula><ns0:p>and verifies the criterion Z o . Upon successful verification, the base station asks S to input its identity S i ID and the sensor identity S j ID that it needs to access. The decoding process obtains the reconstructed vector R of the output layer from the hidden layer value R, and this is mathematically formulated as in equation 13. n) ] and W T represents the weight. Upon reception of the decoding output Q, the gateway node establishes the output. If the verification is successful, then the gateway node authorizes the sensor and successful energy efficient communication is thus established between the sensors S i and S j ; otherwise, the process is terminated.</ns0:p><ns0:formula xml:id='formula_9'>Q = g(R) = δ (W T R + b) (13) In equation 13, Q = [Q (1) , Q (2) , ..., Q<ns0:label>(</ns0:label></ns0:formula></ns0:div> <ns0:div><ns0:head>SIMULATION SETUP</ns0:head><ns0:p>In order to examine the performance of the proposed QR-GSDL model in an IIoT system operation, a simulation environment is set up using Network Simulator 2 (NS2) with plants data collected by IoT devices downloaded from the Kaggle Website 1 . The dataset composed of 7 attributes and 16382 instances.</ns0:p><ns0:p>The attributes are demand response, area, season, energy, cost, pair no and distance. The dataset comprises the common information to facilitate the development of demand response (DR) energy management system for industrial customers. IoT platform improves the inter connectivity of entities in industrial energy management systems and minimizes the energy costs of industrial facilities. In our simulations, networks with a designated number of sensors are distributed in a random pattern within an area of 1500m times 1500m. The number of sensors is varied from 50 to 500. The positioning of nodes is made in a random fashion. Finally, the chosen simulation runs were 10 due to the fact that after the 10 th run, there was a very small gain in the criteria values. We believe this is an advantage of our proposed model where it converges after a low number of simulation runs compared with the existing models in the literature where they converge after 45 simulation runs. Table <ns0:ref type='table' target='#tab_1'>1</ns0:ref> provides the simulation parameters used in our work. </ns0:p></ns0:div> <ns0:div><ns0:head>RESULTS AND DISCUSSION</ns0:head><ns0:p>The performance of the proposed QR-GSDL is compared with three well-known models: online threshold Manuscript to be reviewed Computer Science and dynamic <ns0:ref type='bibr'>CNN Yuan et al. (2020)</ns0:ref>. The performance analysis is based on three measures: the energy consumption, false acceptance rate, authentication rate, and authentication time. The experimental results are presented in the form of tables and graphs.</ns0:p></ns0:div> <ns0:div><ns0:head>Performance analysis of energy consumption</ns0:head><ns0:p>Energy consumption is defined as the product of number of samples and energy consumed by one sensor node for performing authorization to achieve secured communication green sustainability. It is computed as:</ns0:p><ns0:p>Energy consumption is defined as the product of number of samples and energy consumed by one sensor node for performing authorization to achieve secured communication green sustainability. It is computed as:</ns0:p><ns0:formula xml:id='formula_10'>EC = n ∑ i=1 Samples i × Energyconsumedbyonesensornode<ns0:label>(14)</ns0:label></ns0:formula><ns0:p>In Equation <ns0:ref type='formula' target='#formula_10'>14</ns0:ref>, the energy consumption EC is computed based on the samples considered for experimentation (Samples i ) and energy consumed for authorization to achieve secured communication and green sustainability. It is measured in terms of joules (J). The energy consumed for four different methods is given in Table <ns0:ref type='table' target='#tab_2'>2</ns0:ref>. Manuscript to be reviewed</ns0:p><ns0:p>Computer Science </ns0:p></ns0:div> <ns0:div><ns0:head>Performance analysis of false acceptance rate</ns0:head><ns0:p>The false acceptance rate (FAR) is a measure of the likelihood that the IWSN will incorrectly accept an access attempt made by an unauthorized sensor. The false acceptance rate is formulated as the percentage ratio of the number of false acceptances (FA) to the number of sensors (Samples) as input, as in equation <ns0:ref type='formula' target='#formula_11'>15</ns0:ref>:</ns0:p><ns0:formula xml:id='formula_11'>FAR = FA Samples 100<ns0:label>(15)</ns0:label></ns0:formula><ns0:p>In equation 15, FA denotes the false acceptance made and number of samples (sensors) Samples provided as input. It is computed in terms of percentage (%). The results of false acceptance rate in modeling green sustainability issues in IWSN is summarized in Table <ns0:ref type='table' target='#tab_3'>3</ns0:ref>.</ns0:p><ns0:p>The false acceptance rates generated by the IWSN when using the proposed method and the two compared methods are presented in Table <ns0:ref type='table' target='#tab_3'>3</ns0:ref> and graphically illustrated in Figure <ns0:ref type='figure' target='#fig_10'>5</ns0:ref>.</ns0:p><ns0:p>It can be seen from Table <ns0:ref type='table' target='#tab_3'>3</ns0:ref> and Figure <ns0:ref type='figure' target='#fig_10'>5</ns0:ref> that when the number of sensors increases, the number of sensors that are checked for authenticity by the supervisory control unit via the gateway node also increased. Correspondingly, in all four models, the false acceptance rate increases. The false rate cannot be optimized by the QR-GSDL model.</ns0:p><ns0:p>However, the proposed model is able to reduce the rate when compared to the three models. As an example, when there are 150 sensors in the simulation, the number of sensors whose information is incorrectly accepted for transmission is 15 using QR-GSDL compared to 28, 45, and 48 using the online threshold anomaly detection <ns0:ref type='bibr' target='#b14'>Li et al. (2019)</ns0:ref>, machine learning-based anomaly detection <ns0:ref type='bibr' target='#b32'>Zolanvari et al. (2019)</ns0:ref>, and dynamic <ns0:ref type='bibr'>CNN Yuan et al. (2020)</ns0:ref>, respectively. Therefore, it can be inferred that the false acceptance rate is improved by QR-GSDL. It is considered that this is due to the application of an integration function, namely, QRH, which verifies and validates the authentication of the corresponding sensor in an effective manner. Also, the application of interim identity and quasi congruence results in the generation of distinct and unique identities that are not stored in the gateway node, but held by the supervisory control unit. Hence the level of complexity and the false acceptance rate is reduced using QR-GSDL by 39%, 56%, 58% compared to <ns0:ref type='bibr' target='#b14'>Li et al. (2019)</ns0:ref>, <ns0:ref type='bibr' target='#b32'>Zolanvari et al. (2019)</ns0:ref>, and Yuan et al.</ns0:p><ns0:p>(2020), respectively.</ns0:p></ns0:div> <ns0:div><ns0:head>12/18</ns0:head><ns0:p>PeerJ Comput. Sci. reviewing PDF \| (CS-2021:08:64590:1:0:NEW 27 Jan 2022)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p><ns0:p>Computer Science The authentication rates produced by the IWSN for different numbers of sensors (ranging from 50 to 406 500) when using the three tested methods are presented in Table <ns0:ref type='table' target='#tab_4'>4</ns0:ref> and Figure <ns0:ref type='figure'>6</ns0:ref>. Manuscript to be reviewed</ns0:p><ns0:p>Computer Science As the number of the sensor increases, the authentication rate for all four models also increased. This is because as the number of sensor increases, the frequency of sensors in the gateway node via the base station increases, so there is a higher probability of a longer amount of time being consumed for encryption and decryption to deal with the request made by each sensor.</ns0:p><ns0:p>However, a significant improvement and gain increasing trend can be observed with the QR-GSDL approach. For instance, in the case of the simulation using 50 sensors, a total of 46 sensors are correctly authenticated as authentic sensors by the gateway node when applying QR-GSDL, whereas only 44, 41, </ns0:p></ns0:div> <ns0:div><ns0:head>Performance analysis of authentication time</ns0:head><ns0:p>Authentication time refers to the time consumed in authenticating the sensors as either normal or abnormal (malicious). The mathematical formula used to compute authentication time is shown in equation 17:</ns0:p><ns0:formula xml:id='formula_12'>AT = n ∑ i=1 Samples i × Time[R + Q] (<ns0:label>17</ns0:label></ns0:formula><ns0:formula xml:id='formula_13'>)</ns0:formula><ns0:p>Where AT is authentication time that is measured based on the samples considered for experimentation (Samples i ) and the time consumed in encryption (R) and decryption (Q). The value is given in milliseconds (ms). Table <ns0:ref type='table' target='#tab_5'>5</ns0:ref> and Figure <ns0:ref type='figure' target='#fig_13'>7</ns0:ref> present authentication times of the different models for different numbers of sensors. Manuscript to be reviewed</ns0:p><ns0:p>Computer Science It can be summarized that the proposed QR-GSDL outperforms the existing methods for the following three reasons: i) Authenticity of the sensors is performed by the supervisory control via the gateway node by using gradients. ii) Authorization of the authenticated sensors to access the data transmitted between them is carried out by using quantum sparse auto encoding. iii) Intermediate calculations are made at the supervisory control level and not at the gateway node.</ns0:p></ns0:div> <ns0:div><ns0:head>CONCLUSION</ns0:head><ns0:p>In this paper, we formalized the problem space for IWSN in computational and structured manner, where we concentrated on the security aspects between sensors in order to provide authenticity and authorization for the massive amount of data interchanged between them. Based on our problem formalization, we </ns0:p></ns0:div> <ns0:div><ns0:head>FUTURE WORK</ns0:head><ns0:p>Applicability of the proposed model in the field of Internet of Things (IoT) is left as a future work. It will be interesting to apply some adaptions on the QR-GSDL model and perform experiments to evaluate its performance in IoT. In addition, it could be interesting to deeply analyze the performance of QR-GSDL model on different data set.</ns0:p></ns0:div> <ns0:div><ns0:head>16/18</ns0:head><ns0:p>PeerJ Comput. Sci. reviewing PDF \| (CS-2021:08:64590:1:0:NEW 27 Jan 2022)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p><ns0:p>Computer Science</ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>Sci. reviewing PDF \| (CS-2021:08:64590:1:0:NEW 27 Jan 2022) Manuscript to be reviewed Computer Science management techniques to guarantee the backward secrecy of group participants. However, DeepDPA and DeepPAR were not able to minimize the false acceptance rate. Therefore, the design of the proposed QR-GSDL model is aimed at addressing the data privacy problem while at the same time minimizing the false acceptance rate. Another deep learning model was proposed in Liao et al. (2019) in order to improve the authentication performance of the IWSN. The model employed three methods to authenticate sensor nodes. Each of these methods was based on a machine learning algorithm. The first one applied an improved algorithm based on a convolution preprocessing neural network (CPNN), the second utilized a deep neural network, and the third one used a convolutional neural network. Although the model required minimal computing resources to reduce the latency in performing multi-node authentication, it failed to reduce the authentication time, even when using a so-called improved CPNN-based algorithm. Hence, it is anticipated that the proposed QR-GSDL model will overcome the time consumption-related shortcoming encountered in Liao et al. (2019) through the use of a quantum readout and hash (QRH) function to verify and validate the authentication of the sensor in a minimal amount of time. It is also envisaged that the use of a QRH function in the proposed QR-GSDL model will also be able to minimize the volume of network traffic and consequently reduce communication cost. Thus, overcoming the communication cost limitation of the deployment-based optimization model that was introduced in Li et al. (2017) to ensure network security and simultaneously improve network lifetime.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>reduce computation time Sheikh et al. (2016) Alrabea et al. (2019) Alzubi et al. (2014).</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>Figure 1 .</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure1provides the system model of IWSN where the QR-SGDL model will be implemented. The IWSN system model comprises four entities: a number of sensor nodes, a number of base stations, a gateway node and a supervisory control unit connected to a network<ns0:ref type='bibr' target='#b32'>Zolanvari et al. (2019)</ns0:ref>.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head>Figure 2 .</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head>Figure 3 .</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Figure 3. Energy efficient authentication and authorization activity diagram.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head /><ns0:label /><ns0:figDesc>the nonce N GN and determines N′ GN = N GN ⊕ R S and criterion Z o = H(N′ GN \|\|R S \|\|N S ). With the obtained criterion, in order to ensure that only authorized sensors communicate with each other, encryption and decryption are conducted using the QSAE. Moreover, the presence of three different layers in the system architecture (i.e., input layer, hidden layer and output layer) reduces the false acceptance rate and ensures more accurate energy efficient authorization. Here, R represents the feature expression of the output layer and encoding is performed as shown in equation 11. R = δ (W * DP + b) (11) From equation 11, the data packets required by the sensor is represented as vector DP = [DP 1 , DP 2 , ..., DP n ],</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head /><ns0:label /><ns0:figDesc>represent the feature expression of the hidden layer, where m representing the sensors of the hidden layers. Finally, 9/18 PeerJ Comput. Sci. reviewing PDF \| (CS-2021:08:64590:1:0:NEW 27 Jan 2022) Manuscript to be reviewed Computer Science b represents the bias vectors and W the weight matrix from input to hidden layer, respectively, while δ denotes the activation function. Finally, the gateway node formulates a message and sends it to the respective sensor as presented in equation 12.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_7'><ns0:head /><ns0:label /><ns0:figDesc>anomaly detection<ns0:ref type='bibr' target='#b14'>Li et al. (2019)</ns0:ref>, machine learning-based anomaly detection Zolanvari et al. (2019), 1 kaggle. INDUSTRIAL INTERNET OF THINGS DATA [Online].Website: https://www.kaggle.com/pitasr/ industrialiot [accessed 27-12-2020]. 10/18 PeerJ Comput. Sci. reviewing PDF \| (CS-2021:08:64590:1:0:NEW 27 Jan 2022)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_8'><ns0:head /><ns0:label /><ns0:figDesc>Figure 4 describes the energy consumption of proposed method compared with the three existing methods for different number of sensors. The attained results illustrate that When the number of sensor increases, the energy consumption by the sensor during the authorization also gets increased linearly. However the the energy consumption of QR-GSDL model is lesser when compared to Li et al. (2019), Zolanvari et al. (2019), and Yuan et al. (2020). The sample simulations carried out with 50 sensors shows that the amount of energy consumed by one sensor for authorization using QR-GSDL is 0.26J while energy consumed by one sensor using Li et al. (2019) is 0.36J, using Zolanvari et al. (2019) is 0.46J, and using Yuan et al. (2020) is 0.50J. The energy consumption saving is due to the application of Gradient Sparse Auto Deep Learning algorithm where the gateway node checks data access by the sensors for ensuring the energy efficient and green sustainability. It is clear from the obtained results that QR-GSDL reduces the energy consumption by 13%, 20%, and 28% when compared with Li et al. (2019), Zolanvari et al. (2019), and Yuan et al. (2020), respectively. 11/18 PeerJ Comput. Sci. reviewing PDF \| (CS-2021:08:64590:1:0:NEW 27 Jan 2022)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_9'><ns0:head>Figure 4 .</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Figure 4. Comparisons of energy consumption.</ns0:figDesc><ns0:graphic coords='14,128.37,58.92,434.23,217.25' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_10'><ns0:head>Figure 5 .</ns0:head><ns0:label>5</ns0:label><ns0:figDesc>Figure 5. Comparisons of false acceptance rate.</ns0:figDesc><ns0:graphic coords='15,129.09,347.97,430.44,215.36' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_11'><ns0:head /><ns0:label /><ns0:figDesc>Sci. reviewing PDF \| (CS-2021:08:64590:1:0:NEW 27 Jan 2022)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_12'><ns0:head /><ns0:label /><ns0:figDesc>and 40 sensors are correctly identified by applying<ns0:ref type='bibr' target='#b14'>Li et al. (2019)</ns0:ref>,<ns0:ref type='bibr' target='#b32'>Zolanvari et al. (2019), and</ns0:ref><ns0:ref type='bibr' target='#b30'>Yuan et al. (2020)</ns0:ref>, respectively. Thus the authentication rate is higher with QR-GSDL compared to<ns0:ref type='bibr' target='#b14'>Li et al. (2019)</ns0:ref>,<ns0:ref type='bibr' target='#b32'>Zolanvari et al. (2019), and</ns0:ref><ns0:ref type='bibr' target='#b30'>Yuan et al. (2020)</ns0:ref>. It is considered that this improvement is due to the application of a gradient sparse auto deep learning algorithm. By applying this algorithm, localization is first achieved using gradients for each sensor in a secure manner, rather than just by identifying the14/18 PeerJ Comput. Sci. reviewing PDF \| (CS-2021:08:64590:1:0:NEW 27 Jan 2022)Manuscript to be reviewedComputer Sciencedistance between the sensors based on their neighbors<ns0:ref type='bibr' target='#b14'>Li et al. (2019)</ns0:ref>. Hence the proposed method leads to a higher rate of correct authentications being made by the supervisory control via the gateway node of 5%, 8%, and 9% compared to<ns0:ref type='bibr' target='#b14'>Li et al. (2019)</ns0:ref>,<ns0:ref type='bibr' target='#b32'>Zolanvari et al. (2019), and</ns0:ref><ns0:ref type='bibr' target='#b30'>Yuan et al. (2020)</ns0:ref>, respectively.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_13'><ns0:head>Figure 7 .</ns0:head><ns0:label>7</ns0:label><ns0:figDesc>Figure 7. Comparisons of authentication time.</ns0:figDesc><ns0:graphic coords='18,130.86,60.76,431.69,215.99' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_14'><ns0:head /><ns0:label /><ns0:figDesc>proposed a Quantum Readout Gradient Secured Deep Learning method to improve authentication rate and time with minimum false acceptance rate. To deploy the proposed QR-GSDL method, location information of the sensors is extracted to reduce the false acceptance rate of the method. This was performed by integrating Quantum Readout and Hash functions. Next, based on the system model, a Gradient Secured Deep Learning model was designed for ensuring authenticity and authorization by means of Quantum Sparse Auto Encoding and Decoding model. To evaluate the proposed QR-GSDL method, a comprehensive simulation was designed using network simulator. Extensive experimental results indicate that our proposed deep learning-based Sparse Auto Encoding and Decoding model is not only ensuring authentication but also ensuring authorization comparing to state-of-the-art methods.</ns0:figDesc></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head /><ns0:label /><ns0:figDesc>Gradient sparse auto deep learning Input: Sensor S = S 1 , S 2 , ..., S n , Gateway Node GN, Base Station BS = BS 1 , BS 2 , ..., BS n Output: Energy efficient and secure communication Begin: for each Sensor S with Gateway Node GN and Base Station BS do Obtain energy efficient secure localization based on gradients associating sensors using equations 9 and 10</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Authentication:</ns0:cell></ns0:row><ns0:row><ns0:cell>if disclosed locations equal then</ns0:cell></ns0:row><ns0:row><ns0:cell>Authentication successful</ns0:cell></ns0:row><ns0:row><ns0:cell>Go to Authorization</ns0:cell></ns0:row><ns0:row><ns0:cell>else</ns0:cell></ns0:row><ns0:row><ns0:cell>Authentication is not successful</ns0:cell></ns0:row><ns0:row><ns0:cell>Session is terminated</ns0:cell></ns0:row><ns0:row><ns0:cell>end if</ns0:cell></ns0:row><ns0:row><ns0:cell>Authorization:</ns0:cell></ns0:row><ns0:row><ns0:cell>Perform Quantum Sparse Auto Encoding using equation 11</ns0:cell></ns0:row><ns0:row><ns0:cell>Perform Quantum Sparse Auto Decoding using equation 13</ns0:cell></ns0:row><ns0:row><ns0:cell>if R=Q then</ns0:cell></ns0:row><ns0:row><ns0:cell>Energy efficient authorization is successful</ns0:cell></ns0:row><ns0:row><ns0:cell>Energy efficient secure communication</ns0:cell></ns0:row><ns0:row><ns0:cell>else</ns0:cell></ns0:row><ns0:row><ns0:cell>Energy efficient authorization not successful</ns0:cell></ns0:row><ns0:row><ns0:cell>Session is terminated</ns0:cell></ns0:row><ns0:row><ns0:cell>end if</ns0:cell></ns0:row><ns0:row><ns0:cell>end for</ns0:cell></ns0:row><ns0:row><ns0:cell>End</ns0:cell></ns0:row></ns0:table><ns0:note>8/18PeerJ Comput. Sci. reviewing PDF \| (CS-2021:08:64590:1:0:NEW 27 Jan 2022)Manuscript to be reviewedComputer ScienceAlgorithm 2</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_1'><ns0:head>Table 1 .</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Simulation parameters</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Parameters</ns0:cell><ns0:cell>Description</ns0:cell></ns0:row><ns0:row><ns0:cell>Simulation time</ns0:cell><ns0:cell>50s</ns0:cell></ns0:row><ns0:row><ns0:cell>Area size</ns0:cell><ns0:cell>1500m × 1500m</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>Number of sensors 50, 100,150, 200, 250, 300, 350, 400, 450, 500</ns0:cell></ns0:row><ns0:row><ns0:cell>Sensor placement</ns0:cell><ns0:cell>Random distribution</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>Transmission range 400m</ns0:cell></ns0:row><ns0:row><ns0:cell>Simulation runs</ns0:cell><ns0:cell>10</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_2'><ns0:head>Table 2 .</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Energy consumption for different models and sensors</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Number</ns0:cell><ns0:cell /><ns0:cell cols='2'>False acceptance rate</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>of sensors</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>QR-GSDL</ns0:cell><ns0:cell>Online threshold</ns0:cell><ns0:cell cols='2'>Machine learning-based Dynamic</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>anomaly detection</ns0:cell><ns0:cell>anomaly detection</ns0:cell><ns0:cell>CNN</ns0:cell></ns0:row><ns0:row><ns0:cell>50</ns0:cell><ns0:cell>13</ns0:cell><ns0:cell>18</ns0:cell><ns0:cell>23</ns0:cell><ns0:cell>25</ns0:cell></ns0:row><ns0:row><ns0:cell>100</ns0:cell><ns0:cell>21</ns0:cell><ns0:cell>28</ns0:cell><ns0:cell>35</ns0:cell><ns0:cell>39</ns0:cell></ns0:row><ns0:row><ns0:cell>150</ns0:cell><ns0:cell>34</ns0:cell><ns0:cell>40</ns0:cell><ns0:cell>46</ns0:cell><ns0:cell>50</ns0:cell></ns0:row><ns0:row><ns0:cell>200</ns0:cell><ns0:cell>49</ns0:cell><ns0:cell>59</ns0:cell><ns0:cell>64</ns0:cell><ns0:cell>67</ns0:cell></ns0:row><ns0:row><ns0:cell>250</ns0:cell><ns0:cell>63</ns0:cell><ns0:cell>70</ns0:cell><ns0:cell>73</ns0:cell><ns0:cell>76</ns0:cell></ns0:row><ns0:row><ns0:cell>300</ns0:cell><ns0:cell>76</ns0:cell><ns0:cell>85</ns0:cell><ns0:cell>89</ns0:cell><ns0:cell>90</ns0:cell></ns0:row><ns0:row><ns0:cell>350</ns0:cell><ns0:cell>94</ns0:cell><ns0:cell>101</ns0:cell><ns0:cell>105</ns0:cell><ns0:cell>108</ns0:cell></ns0:row><ns0:row><ns0:cell>400</ns0:cell><ns0:cell>107</ns0:cell><ns0:cell>115</ns0:cell><ns0:cell>118</ns0:cell><ns0:cell>120</ns0:cell></ns0:row><ns0:row><ns0:cell>450</ns0:cell><ns0:cell>118</ns0:cell><ns0:cell>126</ns0:cell><ns0:cell>129</ns0:cell><ns0:cell>132</ns0:cell></ns0:row><ns0:row><ns0:cell>500</ns0:cell><ns0:cell>134</ns0:cell><ns0:cell>142</ns0:cell><ns0:cell>145</ns0:cell><ns0:cell>149</ns0:cell></ns0:row><ns0:row><ns0:cell>Table 2 and</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_3'><ns0:head>Table 3 .</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>False acceptance rate for different models and sensors</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Number</ns0:cell><ns0:cell /><ns0:cell cols='2'>False acceptance rate</ns0:cell></ns0:row><ns0:row><ns0:cell>of sensors</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>QR-GSDL</ns0:cell><ns0:cell>Online threshold</ns0:cell><ns0:cell cols='2'>Machine learning-based Dynamic</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>anomaly detection</ns0:cell><ns0:cell>anomaly detection</ns0:cell><ns0:cell>CNN</ns0:cell></ns0:row><ns0:row><ns0:cell>50</ns0:cell><ns0:cell>8</ns0:cell><ns0:cell>10</ns0:cell><ns0:cell>12</ns0:cell></ns0:row><ns0:row><ns0:cell>100</ns0:cell><ns0:cell>12</ns0:cell><ns0:cell>15</ns0:cell><ns0:cell>25</ns0:cell></ns0:row><ns0:row><ns0:cell>150</ns0:cell><ns0:cell>15</ns0:cell><ns0:cell>28</ns0:cell><ns0:cell>45</ns0:cell></ns0:row><ns0:row><ns0:cell>200</ns0:cell><ns0:cell>25</ns0:cell><ns0:cell>45</ns0:cell><ns0:cell>60</ns0:cell></ns0:row><ns0:row><ns0:cell>250</ns0:cell><ns0:cell>30</ns0:cell><ns0:cell>50</ns0:cell><ns0:cell>75</ns0:cell></ns0:row><ns0:row><ns0:cell>300</ns0:cell><ns0:cell>30</ns0:cell><ns0:cell>58</ns0:cell><ns0:cell>80</ns0:cell></ns0:row><ns0:row><ns0:cell>350</ns0:cell><ns0:cell>25</ns0:cell><ns0:cell>40</ns0:cell><ns0:cell>85</ns0:cell></ns0:row><ns0:row><ns0:cell>400</ns0:cell><ns0:cell>40</ns0:cell><ns0:cell>65</ns0:cell><ns0:cell>70</ns0:cell></ns0:row><ns0:row><ns0:cell>450</ns0:cell><ns0:cell>45</ns0:cell><ns0:cell>80</ns0:cell><ns0:cell>95</ns0:cell></ns0:row><ns0:row><ns0:cell>500</ns0:cell><ns0:cell>40</ns0:cell><ns0:cell>75</ns0:cell><ns0:cell>100</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_4'><ns0:head>Table 4 .</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Authentication rate for different models and sensors</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Number</ns0:cell><ns0:cell /><ns0:cell cols='2'>False acceptance rate</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>of sensors</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>QR-GSDL</ns0:cell><ns0:cell>Online threshold</ns0:cell><ns0:cell cols='2'>Machine learning-based Dynamic</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>anomaly detection</ns0:cell><ns0:cell>anomaly detection</ns0:cell><ns0:cell>CNN</ns0:cell></ns0:row><ns0:row><ns0:cell>50</ns0:cell><ns0:cell>92</ns0:cell><ns0:cell>88</ns0:cell><ns0:cell>82</ns0:cell><ns0:cell>80</ns0:cell></ns0:row><ns0:row><ns0:cell>100</ns0:cell><ns0:cell>90</ns0:cell><ns0:cell>86</ns0:cell><ns0:cell>80</ns0:cell><ns0:cell>78</ns0:cell></ns0:row><ns0:row><ns0:cell>150</ns0:cell><ns0:cell>85</ns0:cell><ns0:cell>81</ns0:cell><ns0:cell>78</ns0:cell><ns0:cell>74</ns0:cell></ns0:row><ns0:row><ns0:cell>200</ns0:cell><ns0:cell>88</ns0:cell><ns0:cell>83</ns0:cell><ns0:cell>75</ns0:cell><ns0:cell>70</ns0:cell></ns0:row><ns0:row><ns0:cell>250</ns0:cell><ns0:cell>86</ns0:cell><ns0:cell>82</ns0:cell><ns0:cell>74</ns0:cell><ns0:cell>68</ns0:cell></ns0:row><ns0:row><ns0:cell>300</ns0:cell><ns0:cell>88</ns0:cell><ns0:cell>84</ns0:cell><ns0:cell>75</ns0:cell><ns0:cell>70</ns0:cell></ns0:row><ns0:row><ns0:cell>350</ns0:cell><ns0:cell>90</ns0:cell><ns0:cell>85</ns0:cell><ns0:cell>78</ns0:cell><ns0:cell>74</ns0:cell></ns0:row><ns0:row><ns0:cell>400</ns0:cell><ns0:cell>85</ns0:cell><ns0:cell>81</ns0:cell><ns0:cell>75</ns0:cell><ns0:cell>71</ns0:cell></ns0:row><ns0:row><ns0:cell>450</ns0:cell><ns0:cell>87</ns0:cell><ns0:cell>82</ns0:cell><ns0:cell>77</ns0:cell><ns0:cell>73</ns0:cell></ns0:row><ns0:row><ns0:cell>500</ns0:cell><ns0:cell>89</ns0:cell><ns0:cell>85</ns0:cell><ns0:cell>79</ns0:cell><ns0:cell>75</ns0:cell></ns0:row></ns0:table><ns0:note>Figure 6. Comparisons of authentication rate.</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_5'><ns0:head>Table 5 .</ns0:head><ns0:label>5</ns0:label><ns0:figDesc>Authentication time for different models and sensors</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Number</ns0:cell><ns0:cell /><ns0:cell cols='2'>False acceptance rate</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>of sensors</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>QR-GSDL</ns0:cell><ns0:cell>Online threshold</ns0:cell><ns0:cell cols='2'>Machine learning-based Dynamic</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>anomaly detection</ns0:cell><ns0:cell>anomaly detection</ns0:cell><ns0:cell>CNN</ns0:cell></ns0:row><ns0:row><ns0:cell>50</ns0:cell><ns0:cell>28.50</ns0:cell><ns0:cell>35.50</ns0:cell><ns0:cell>43</ns0:cell><ns0:cell>47</ns0:cell></ns0:row><ns0:row><ns0:cell>100</ns0:cell><ns0:cell>42.40</ns0:cell><ns0:cell>55.25</ns0:cell><ns0:cell>65.35</ns0:cell><ns0:cell>69.75</ns0:cell></ns0:row><ns0:row><ns0:cell>150</ns0:cell><ns0:cell>65.25</ns0:cell><ns0:cell>70.35</ns0:cell><ns0:cell>80.25</ns0:cell><ns0:cell>85.35</ns0:cell></ns0:row><ns0:row><ns0:cell>200</ns0:cell><ns0:cell>83.50</ns0:cell><ns0:cell>100.25</ns0:cell><ns0:cell>125.55</ns0:cell><ns0:cell>130</ns0:cell></ns0:row><ns0:row><ns0:cell>250</ns0:cell><ns0:cell>105.25</ns0:cell><ns0:cell>125.35</ns0:cell><ns0:cell>135.35</ns0:cell><ns0:cell>138.45</ns0:cell></ns0:row><ns0:row><ns0:cell>300</ns0:cell><ns0:cell>125.35</ns0:cell><ns0:cell>140.55</ns0:cell><ns0:cell>145.55</ns0:cell><ns0:cell>150.25</ns0:cell></ns0:row><ns0:row><ns0:cell>350</ns0:cell><ns0:cell>145.55</ns0:cell><ns0:cell>175.35</ns0:cell><ns0:cell>180.25</ns0:cell><ns0:cell>186.75</ns0:cell></ns0:row><ns0:row><ns0:cell>400</ns0:cell><ns0:cell>170</ns0:cell><ns0:cell>190</ns0:cell><ns0:cell>200.35</ns0:cell><ns0:cell>220.15</ns0:cell></ns0:row><ns0:row><ns0:cell>450</ns0:cell><ns0:cell>182.35</ns0:cell><ns0:cell>195.25</ns0:cell><ns0:cell>225.55</ns0:cell><ns0:cell>238.15</ns0:cell></ns0:row><ns0:row><ns0:cell>500</ns0:cell><ns0:cell>190</ns0:cell><ns0:cell>200.35</ns0:cell><ns0:cell>245.55</ns0:cell><ns0:cell>250.55</ns0:cell></ns0:row></ns0:table><ns0:note>15/18PeerJ Comput. Sci. reviewing PDF \| (CS-2021:08:64590:1:0:NEW 27 Jan 2022)</ns0:note></ns0:figure> <ns0:note place='foot' n='18'>/18 PeerJ Comput. Sci. reviewing PDF \| (CS-2021:08:64590:1:0:NEW 27 Jan 2022) Manuscript to be reviewed Computer Science</ns0:note> </ns0:body> "	"Manuscript Title: 'Quantum readout and gradient deep learning model for secure and sustainable data access in IWSN' To: Academic Editor, PeerJ Computer Science (Professor Shadi Aljawarneh) Re: Response to reviewers’ comments Dear Editor, I am thankful to the reviewers for their constructive comments, which help to improve this manuscript. Also, thank you for giving me the opportunity to address the reviewers’ comments. Based on the review comments, I have made the required modifications to the manuscript. Please find below answers to the reviewers’ comments as well. ▪ gradient based algorithm is not the best approach in security mechanisms as , things need to be as deterministic as possible . Sometimes the position of a sensor or its configuration could be constantly changing in the IWSN. In such a case it's not clear how this system works. - The pre-determined setting or configuration of the IWSN covered in this manuscript is stationary, which means that all sensor nodes and the base station are fixed in position. ▪ There is a need to explain the authentication problem in more details, some of the IIoT networks are designed in a closed network where they have their own cables and devices and no one outside the network has any access to it, in this case the authentication problem needs to be clarified at what circumstances it is considered an issue. I suggest the authors provide an example to explain when/how the authentication step should be included in the design. - The necessity of the authentication step in industrial wireless sensor networks has been addressed in many research articles. There are many IWSN settings or configurations required authentication such as the one mentioned in lines 78 – 80. In these unprotected configurations the sensor nodes are susceptible for different security attacks. ▪ Line 67: “in this paper” is repeated. - The repeated words have been removed. ▪ Authors need to identify the terminologies earlier in the paper such as authentication rate, authentication acceptance false rate, and authentication time. - The terminologies are identified earlier in the paper (as they appear for first time). ▪ Figure 1 in the paper should be improved to tell more details about the system model; not much information can be extracted from the figure in its current design. - Figure 1 now is replaced with more illustrative figure. More details about the system model are given in lines 218 - 223. ▪ Line 213: “the gateway node has to approve the authenticity of the sensors. The gateway node verifies whether the sensor has been tampered with or not in order to guarantee the authenticity of the data packets”. Authors are required to identify what the gateway node is, what type of data is gathered, and how the authentication is being verified? - The gateway node gathered two types of packets- data packets and control packets- from the sensor nodes. The gateway node ensures that these packets are originated from an authenticated sensor nodes. This will be accomplished by applying the Quantum Readout Hash registration which is explained thoroughly in algorithm 1. ▪ Line 220: Extra word “input”. - The extra word removed. ▪ Figure 2 is meaningless and not clear, E.g., What is the input look like? Etc. …, - Yes, I do agree with the reviewer that Figure 2 is not necessary, therefore it has been deleted from the manuscript. ▪ The paper has no example of where their design is applicable. - A paragraph is added to show the applicability of the proposed model. Please see lines 78 - 80. ▪ Line 237, “The QSAE checks for energy availability, authenticity and authorization of the sensor by employing the quantum readout function to ensure smooth communication between the sensor and the supervisory control unit with a minimum false acceptance rate”. How are the energy availability, authenticity, and authorization verified by the QSAE? Authors should mention that it will be explained in coming sections. - A sentence has been added, please see line 243 – 245. ▪ QRH should be used in full words for the first time then used as a short cut. - Now, the term GRH has been identified for the first time is used (in the related work section) ▪ Equation #7 should be Sj0 instead of Si0. - In equation #7, the variable Si0 is corrected to Sj0. ▪ Line 296: The gradient-associating sensors is not identified anywhere earlier in the paper. - The correction has been made so the term is the same throughout the manuscript. ▪ Why the number of the simulation runs is 10? No explanation of why this value is chosen, in the literature, values converged after 45 runs. - An explanation for selecting10 simulation runs is given in lines 340 – 345. ▪ In all tables, the increase in values gradually with the increase in the number of sensors; this is intuitively expected and should not be mentioned as a result. However results of optimizations are valid. - The intuitively expected results is removed throughout the manuscripts. "	Here is a paper. Please give your review comments after reading it.
430	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>The Industrial Wireless Sensor Network (IWSN) is a surface-type of Wireless Sensor Network (WSN) that suffers from high levels of security breaches and energy consumption.</ns0:p><ns0:p>In modern complex industrial plants, it is essential to maintain the security, energy efficiency, and green sustainability of the network. In an IWSN, sensors are connected to the Internet in a non-monitored environment. Hence, non-authorized sensors can retrieve information from the IWSN. Therefore, to ensure that data access between sensors remains sustainable and secure, energy-efficient authentication and authorization are required. In this paper, a novel Quantum Readout Gradient Secured Deep Learning (QR-GSDL) model is proposed to ensure that only trustworthy sensors can access IWSN data.</ns0:p><ns0:p>The major objective of this QR-GSDL model is to create secure, energy-efficient IWSN to attain green sustainability and reduce the industrial impact on the environment. First, using the quantum readout and hash function, a registration method is designed to efficiently perform the registration process. Next, a gradient secured deep learning method is adopted to implement the authentication and authorization process in order to ensure energy-saving and secure data access. Simulations are conducted to evaluate the QR-GSDL model and compare its performance with that of three well-known models: online threshold anomaly detection, machine learning-based anomaly detection, and dynamic CNN. The simulation outcomes show that the proposed model is secure and energyefficient for use in the IWSN. Moreover, the experimental results prove that the QR-SGDL model outperforms the existing models in terms of energy consumption, authentication rate, authentication time, and false acceptance rate.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>INTRODUCTION</ns0:head><ns0:p>The underlying rationale for the recent conceptualization of the Industrial Internet of Things (IIoT) has been to leverage the Internet of Things (IoT) and apply its advantages to the industrial wireless sensor networks (IWSNs) in order to create interconnected industrial environments. IWSNs play an essential part in the management and operation of industrial machinery across a wide range of sectors. The main task of an IWSNs is to monitor the performance of different devices through the collection, storage, and retrieval of data in real-time in an industrial environment. The application of the IWSNs framework in such systems is intended to increase optimization and improve industrial automation processes. Regardless of its advantages, the IWSN suffers from many security and privacy issues like data leaking, node compromise, authentication and authorization problems, data loss, and many more. The authentication problem is the most widespread concern among all the issues because all the sensing devices are located PeerJ Comput. Sci. reviewing PDF \| (CS-2021:08:64590:2:2:NEW 8 Apr 2022)</ns0:p><ns0:p>Manuscript to be reviewed Computer Science and accessed remotely. A false authentication may hamper the complete security of the system. The first security step must be strong so that no unauthorized user can access the system. Several authenticated key agreement schemes are proposed, but they are limited to only one device at a time. In some scenarios, the IIoT networks are closed so no unauthorized user can access the internal networks of the system. But, still, the authentication problem for insider users is present. An insider attack can be possible and creates an issue if the authentication mechanism is not much strong. Thus, these issues motivated us to design a dynamic deep learning-based solution named the Quantum Readout Gradient secured Deep Learning (QR-GSDL) for the authenticity of the sensor-based IIoT networks. The proposed model will work on a different layer for insider user authentication and authorization purpose. The proposed authentication steps included an improvement in the authentication rate with minimum time consumption and delay.</ns0:p><ns0:p>One of the most well-known models that has been utilized in the Industrial Internet of Things (IIoT) domain is the online threshold anomaly detection model. It employs a learning method based on statistical formulations to distinguish the characteristics of devices and flag any differences in those characteristics as anomalies <ns0:ref type='bibr' target='#b14'>Li et al. (2019)</ns0:ref>. The model is independent in terms of device operations because statistical data about the system is acquired by using the IoT application program interface. For this model, multiple machine learning techniques have been introduced in the training process, and the performance on normal systems was designed in a similar manner. The model is able to detect anomalous activities in an efficient manner by summing cumulative operations and using localized outliers, thereby improving accuracy and simultaneously reducing false alarms. However, despite improvements in the accuracy and false alarm rates, this model does not address the issue of the security of data communication in the context of the industrial sector.</ns0:p><ns0:p>The other well-recognized model that has been employed in the IIoT is the machine learning-based anomaly detection model <ns0:ref type='bibr' target='#b31'>Zolanvari et al. (2019)</ns0:ref>. This model was developed to address the most prevalent susceptibility in the IIoT, namely, the injection attack. There are three main forms of injection attacks that can be mitigated by applying machine learning techniques: command injection, structured query language, and backdoor attacks. Through the adoption of a machine learning-based approach, not only was it demonstrated that the attack detection rate was improved, but there was also a steep reduction in the value of the mean absolute error. However, despite the improvement in the attack detection rate and the minimization of the mean absolute error, the false-negative rate was not minimized by machine learning-based anomaly detection. In other words, anomaly intruders were still incorrectly detected, thus leading to a lack of overall efficacy.</ns0:p><ns0:p>In <ns0:ref type='bibr' target='#b29'>Yuan et al. (2020)</ns0:ref>, a dynamic CNN (DCNN) technique is planned to learn the hierarchical local nonlinear dynamic features of soft sensor modeling. Every 1D process sample in DCNN is dynamically increased into a 2D data sample with lagged unlabeled process variables, comprising both spatial crossrelationships and temporal auto-correlations. Then, to derive the local nonlinear spatial-temporal function from the 2D sample data matrix, the convolutional and pooling layers are alternately used. In addition, the concept of how the local nonlinear spatial-temporal function can be taught from the network is studied for DCNN. In an industrial hydrocracking process, the efficacy of the proposed DCNN is tested. However, the authors did not provide any proof of the energy efficiency of their approach Alzubi et al. (2020b) <ns0:ref type='bibr' target='#b3'>Alzubi et al. (2020a)</ns0:ref>.</ns0:p><ns0:p>In this paper, a deep learning-based solution is presented to overcome the security issues that currently exist in the authentication and authorization protocol for the industrial wireless sensor network (IWSN).</ns0:p><ns0:p>The proposed solution employs a novel model named the Quantum Readout Gradient secured Deep Learning (QR-GSDL) model. This model first verifies the authenticity of a given sensor seeking access to data in the IWSN by using a quantum readout and hash (QRH) function. This registration process facilitates effective validation and therefore reduces the false acceptance rate. Next, the security issues inherent in the authentication and authorization procedure are addressed by using a gradient sparse auto deep learning algorithm. This type of algorithm was adopted because it was envisaged that its usage would lead to an improvement in the authentication rate (AR) with minimum time consumption and delay.</ns0:p><ns0:p>Accordingly, the designed model substantially minimizes the false acceptance rate (FAR), leading to an improvement in both the authentication rate and authentication time (AT ).</ns0:p><ns0:p>We believe that the best-suited real-world environment to implement our proposed QR-GSDL model is in industrial applications such as machine health, automated metering, remote monitoring, and staff management. The only requirement is that the pre-defined setting of IWSN be stationary.</ns0:p></ns0:div> <ns0:div><ns0:head>2/19</ns0:head><ns0:p>PeerJ Comput. Sci. reviewing PDF \| (CS-2021:08:64590:2:2:NEW 8 Apr 2022)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Computer Science</ns0:head></ns0:div> <ns0:div><ns0:head>RELATED WORK</ns0:head><ns0:p>With the advancement of technologies, because of their benefits over conventional wired networks, wireless sensor networks (WSNs) have fantastic deployment opportunities for industrial scenarios. However, fully integrated mechanized processes and wireless networking conditions allow the high security and low energy consumption requirements of industrial wireless sensor networks (IWSNs) more stringent. We will discuss the relevant work in this section from the point of view of security and energy consumption.</ns0:p><ns0:p>Many researchers presume industrial wireless sensor networks and present different authentication and authorization schemes. However, these schemes were not ideal for IWSN. This is due to the fact that in terms of energy efficiency and computing overhead, node authentication by cluster head on a regular basis results in considerable overhead.</ns0:p><ns0:p>Several studies have been conducted in the area of deep learning to make the IoT-enabled WSN more efficient, robust, and secure <ns0:ref type='bibr' target='#b6'>Alzubi et al. (2019b)</ns0:ref>. The works that are most relevant to this paper include the deep learning model that was proposed in <ns0:ref type='bibr' target='#b16'>Liang et al. (2020)</ns0:ref>. This model is based on edge computing and aimed at minimizing the traffic (data transmissions) in the network to reduce network congestion while maintaining classification accuracy. However, the method in <ns0:ref type='bibr' target='#b16'>Liang et al. (2020)</ns0:ref> did not provide each user with data privacy. Therefore, to address this privacy issue, two privacy-preserving deep learning models named DeepPAR and DeepDPA were presented in <ns0:ref type='bibr' target='#b30'>Zhang et al. (2020)</ns0:ref>. The DeepPAR model offered a mechanism that prevented a user's information from being leaked to others while keeping the secrecy level dynamically updated. To address this issue, the DeepDPA model applied a set of key management techniques to guarantee the backward secrecy of group participants. However, DeepDPA and DeepPAR were not able to minimize the false acceptance rate. Therefore, the design of the proposed QR-GSDL model is aimed at addressing the data privacy problem while at the same time minimizing the false acceptance rate.</ns0:p><ns0:p>Another deep learning model was proposed in <ns0:ref type='bibr' target='#b17'>Liao et al. (2019)</ns0:ref> in order to improve the authentication performance of the IWSN. The model employed three methods to authenticate sensor nodes. Each of these methods was based on a machine learning algorithm. The first one applied an improved algorithm based on a convolution preprocessing neural network (CPNN), the second utilized a deep neural network, and the third one used a convolutional neural network. Although the model required minimal computing resources to reduce the latency in performing multi-node authentication, it failed to reduce the authentication time, even when using a so-called improved CPNN-based algorithm. Hence, it is anticipated that the proposed QR-GSDL model will overcome the time consumption-related shortcoming encountered in <ns0:ref type='bibr' target='#b17'>Liao et al. (2019)</ns0:ref> through the use of a quantum readout and hash (QRH) function to verify and validate the authentication of the sensor in a minimal amount of time. It is also envisaged that the use of a QRH function in the proposed QR-GSDL model will also be able to minimize the volume of network traffic and consequently reduce communication costs. Thus, overcoming the communication cost limitation of the deployment-based optimization model that was introduced in <ns0:ref type='bibr' target='#b15'>Li et al. (2017)</ns0:ref> to ensure network security and simultaneously improve network lifetime.</ns0:p><ns0:p>Despite the above achievements in the area of deep learning, a survey of the application of deep learning tools in the smart industry presented in <ns0:ref type='bibr' target='#b19'>Ma et al. (2019)</ns0:ref> concluded that while deep learning provides an opportunity to solve many classical issues, authentication and authorization problems are not tackled. Therefore, as a first step in addressing these problems, the QR-GSDL model is designed in such a way as to ensure that the gateway node in the IWSN checks the authenticity of the sensor node that is seeking access to information in the IWSN, thereby guaranteeing correct and appropriate authorization.</ns0:p><ns0:p>Other works have also explored methods to improve authentication. For instance, in <ns0:ref type='bibr' target='#b11'>Chen et al. (2020)</ns0:ref>, a secure authentication scheme was introduced that depended on credential and dynamic IDs for WSNs in IoT environments. For the scheme, an authentication key agreement protocol based on three parties was designed using the Burrows-Abhadi-Needham logic method. It was reported that the scheme was able to ensure low computational and communication costs, but it was admitted that the false acceptance rate was not improved. Consequently, the gradient secured deep learning method is integrated into the proposed QR-GSDL model in order to achieve a reduction in the false acceptance rate.</ns0:p><ns0:p>Jiang et al. propose a re-authentication scheme for the Voronoi graph-based network model. The scheme maintains anonymity while using fewer resources than the previous schemes. The system, however, suggests neighbor wandering, which might not be ideal for a realistic situation. Also, they did prove the efficiency of their model in terms of energy consumption <ns0:ref type='bibr' target='#b2'>Alrabea et al. (2020)</ns0:ref> <ns0:ref type='bibr' target='#b5'>Alzubi et al. (2019a)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>3/19</ns0:head><ns0:p>PeerJ Comput. Sci. reviewing PDF \| (CS-2021:08:64590:2:2:NEW 8 Apr 2022)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Computer Science</ns0:head><ns0:p>In an alternative attempt to improve the security aspect of the WSN, a convolutional technique (CT) was developed in <ns0:ref type='bibr' target='#b0'>Alghamdi (2019)</ns0:ref>, which involved generating security bits using convolutional codes. The aim of the CT was to protect the WSN from attacks caused by malicious nodes. The designed technique improved network security and minimized computational complexity because no key distribution was needed. However, authentication time was not minimized by CT. Thus, a gradient secured deep learning method is included in the proposed QR-GSDL model in order to attempt to reduce the time consumed in the authentication procedure. Industrial wireless sensor networks, which are an evolved category of WSN in which sensors are combined to monitor the status of equipment and to control systems in real-time, also have limitations related to security, privacy, and energy. To address these drawbacks in the IWSN context, a lightweight decision-making framework based on trust value identification was designed in <ns0:ref type='bibr' target='#b23'>Ramesh and Yaashuwanth (2019)</ns0:ref>. The lightweight trust framework was used for quality of service clustering in order to perform the secure routing process. A quantifiable trust value was determined through the cluster head within the cluster. It was claimed that flawed, untrusted, counteract, and malicious nodes could be predicted using this framework. However, the communication cost was not minimized. Therefore, in the QR-GSDL model, quantum sparse auto-encoding and decoding are employed to reduce the communication cost in the IWSN system.</ns0:p><ns0:p>A different protocol named secure directed diffusion was suggested in <ns0:ref type='bibr' target='#b24'>Sengupta et al. (2018)</ns0:ref>. This protocol depended on binding the node's geographic location and ID in order to induce a cryptographic key based on the location. The produced key then formed the foundation of a neighborhood authentication process for the IWSN. However, only theoretical statements were provided regarding the effectiveness of this authentication process and the computational overhead.</ns0:p><ns0:p>On the other hand, in <ns0:ref type='bibr' target='#b22'>Qureshi et al. (2020)</ns0:ref>, a centroid position analysis was performed in an attempt to decrease data transmission failure and delay. In addition, a gateway clustering routing protocol was used for cluster head selection from the centroid position. Then the gateway node minimized the load from the cluster head nodes and transmitted the data to the base station. However, security issues were not taken into consideration. Therefore, in our proposed approach, an authentication process is carried out to establish secure communication.</ns0:p><ns0:p>Cooperation between the sensors that are communicating with a central base station is one of the factors that contribute to security. In light of this, cryptographic algorithms based on secret keys were designed in <ns0:ref type='bibr' target='#b28'>Tahir et al. (2018)</ns0:ref> in which the ICMetric method employed the device features to generate secret keys for use in cryptographic services. However, the proposed method failed to offer an effective authentication process that at the same time did not increase resource overheads. Hence, in the proposed QR-GSDL model, a gradient secured deep learning algorithm performs the authentication to allow secure data communication.</ns0:p><ns0:p>An energy-efficient data transmission mechanism is proposed to improve emergency data transmission by increasing accuracy and decreasing packet delay <ns0:ref type='bibr' target='#b25'>Sheikh et al. (2012</ns0:ref><ns0:ref type='bibr' target='#b27'>)Singanamalla et al. (2019</ns0:ref><ns0:ref type='bibr' target='#b20'>) Nazir et al. (2020)</ns0:ref>. It was claimed that the scheme was reliable, but a mechanism for ensuring the security of data transmission was not presented. In contrast, an authentication process is carried out in the QR-GSDL model to enable secure data transmission.</ns0:p><ns0:p>A cooperative mechanism was presented in <ns0:ref type='bibr' target='#b12'>Iqbal et al. (2017)</ns0:ref> to reduce both the false alarm rate and energy consumption. The designed mechanism improved the probability of accurate decisions being made at a specified signal level. Also, the suggested mechanism in <ns0:ref type='bibr' target='#b12'>Iqbal et al. (2017)</ns0:ref> was reported to be able to achieve a reduction in the false alarm rate, but only for indoor IWSNs. However, it was not particularly efficient in terms of energy consumption in relation to the time needed to perform its computations. Therefore, quantum sparse encoding and decoding are used in our proposed QR-GSDL model in order to reduce computation time <ns0:ref type='bibr' target='#b26'>Sheikh et al. (2016</ns0:ref><ns0:ref type='bibr' target='#b1'>) Alrabea et al. (2019</ns0:ref><ns0:ref type='bibr' target='#b7'>) Alzubi et al. (2014)</ns0:ref>.</ns0:p><ns0:p>The three issues of security, network lifetime, and coverage were handled in <ns0:ref type='bibr' target='#b9'>Cao et al. (2020)</ns0:ref> by converting the disjoint routing paths to address the flow issues. However, despite an improvement being observed in security and coverage, optimization and operation time were not focused on. Therefore, again the quantum sparse encoding and decoding approach is used in the proposed model in order to minimize time consumption.</ns0:p><ns0:p>To address the issue of security, in <ns0:ref type='bibr' target='#b10'>Cao et al. (2019)</ns0:ref>, multi-objective evolutionary algorithms were designed for a heterogeneous WSN. Moreover, a 3D signal propagation model used the line-of-sight idea to determine the signal path loss. However, the security level was not improved by the designed Another password-based authentication scheme was proposed in <ns0:ref type='bibr' target='#b13'>Lee et al. (2018)</ns0:ref> to verify security with minimal communication and computation cost. However, the communication and computation overheads were not minimized by the developed password-based authentication scheme. Therefore, in the proposed model, quantum sparse encoding and decoding are used with the expectation that this approach can reduce the computation overhead.</ns0:p><ns0:p>Lastly, a mutual authentication system integrating temporal credentials and multiple passwords was proposed in <ns0:ref type='bibr' target='#b18'>Liu et al. (2017)</ns0:ref> in order to minimize the overheads. However, while the authentication time was reduced, the false alarm rate was not minimized. Therefore, in the proposed QR-SGDL model, the QRH is used to offload the false alarm rate. Motivated by the above issues encountered in related studies, in this work, a novel model for IWSN, named the Quantum Readout Gradient Secured Deep Learning (QR-GSDL) model, is developed in order to improve not only the authentication rate but also the authentication time and false acceptance rate. In the following, an elaboration of the QR-GSDL model is presented.</ns0:p></ns0:div> <ns0:div><ns0:head>QUANTUM READOUT GRADIENT SECURED DEEP LEARNING IN IWSN</ns0:head><ns0:p>In this section, we present our proposed model, QR-SGDL, which consists of three phases: i) secure and energy-efficient localization, ii) sensor node registration, and iii) authentication. The system model and the three phases of QR-SGDL are elaborated on in the following subsections.</ns0:p></ns0:div> <ns0:div><ns0:head>System model</ns0:head><ns0:p>Figure <ns0:ref type='figure' target='#fig_1'>1</ns0:ref> provides the system model of IWSN where the QR-SGDL model will be implemented. The Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Computer Science</ns0:head><ns0:p>The gateway node gathered two types of packets-data packets and control packets-from the sensor nodes. Before collecting the data packets, the gateway node ensures that these packets are originated from authenticated sensor nodes and verifies whether the sensor node has been tampered with or not. This will be accomplished by applying the Quantum Readout Hash registration which is explained thoroughly in algorithm 1. After attaining the data, the QR-GSDL method analyzes the data about industrial plants in order to maintain green sustainability.</ns0:p></ns0:div> <ns0:div><ns0:head>System architecture of the QR-SGDP model</ns0:head><ns0:p>The proposed QR-SGDL model uses deep learning concepts to perform multiple processes in several layers. The deep learning network uses one input layer, two hidden layers, and one output layer for improving security during data access and to improve the green sustainability of the network. The feed-forward fashion deep learning network collects the nodes in the input layer, learns in the hidden layers, and transforms the results into an output layer.</ns0:p><ns0:p>The system architecture of the QR-SGDL model is designed to address energy and green sustainability issues by analyzing the industrial plant's data. It also aims to handle security issues such as authentication and authorization in the IWSN by achieving a minimum false acceptance rate.</ns0:p><ns0:p>In the IWSN, the upper layer of the architecture is a transmission layer comprising base stations, sensor nodes, gateway node, and supervisory control unit. All of these elements can be found in any Manuscript to be reviewed</ns0:p><ns0:p>Computer Science β = HASH(α s \|\|spwd s )</ns0:p><ns0:p>(5) By applying the above-integrated functions, it is considered to be possible to achieve authentic sensor registration with a minimum false acceptance rate.</ns0:p></ns0:div> <ns0:div><ns0:head>GRADIENT SECURED DEEP LEARNING MODEL</ns0:head><ns0:p>Let us assume that a sensor S wants to acquire industrial plant data from a specific sensor in the IWSN.</ns0:p><ns0:p>Then, mutual authentication between the two sensors S i and S j has to be established, where the identity of the sensor has to be checked prior to providing access. With authenticated nodes, the gateway node checks </ns0:p><ns0:formula xml:id='formula_0'>S i0 = A 1 + B 1 (6) S j0 = A 2 + B 2 (7)</ns0:formula><ns0:p>Based on equations 6 and 7, the distance between the sensors is mathematically calculated by applying equation 8.</ns0:p><ns0:formula xml:id='formula_1'>S i j = (A 1 + A 2 ) 2 + (B 1 + B 2 ) 2 (8)</ns0:formula><ns0:p>However, in order to analyze the relation between gradient associating sensors, it is computed by equations 9 and 10. By introducing the Gradient Secure Localization (GSL) method, security, energy efficiency, and green sustainability can be ensured even in the presence of attacks. In this manner, with the location verification by the GSL method, the precision or exactness of the disclosed locations of the sensors is made in an effective fashion, therefore ensuring authentication. Note that the comparison here is made based on the disclosed locations. If they are not equal, the session is terminated. Upon successful comparison, the base station perceives S as a normal sensor.</ns0:p><ns0:formula xml:id='formula_2'>cos θ = S i0 + S j0 − (S i S j ) 2 2S i0 Q 0 (9) cos θ = A 1 A 2 + B 1 B 2 A 2 1 + B 2 1 A 2 2 + B 2 2 (10)</ns0:formula><ns0:p>Next, the base station generates a nonce N S and selects IID S as the interim identity of the sensor, and sends the signing message IID S , N S via a secure channel. After obtaining the signing on message, the gateway node GN initially locates the IID S in its database. The gateway node then chooses the QRCRP(C S , R S ), generates the nonce N GN and determines N′ GN = N GN ⊕ R S and criterion Z o = H(N′ GN \|\|R S \|\|N S ). With the obtained criterion, in order to ensure that only authorized sensors communicate with each other, encryption and decryption are conducted using the QSAE. Moreover, the presence of three different layers in the system architecture (i.e., input layer, hidden layer, and output layer) reduces the false acceptance rate and ensures more accurate energy-efficient authorization. Here, R represents the feature expression of the output layer and encoding is performed as shown in equation 11.</ns0:p><ns0:formula xml:id='formula_3'>R = δ (W * DP + b) (11)</ns0:formula><ns0:p>From equation 11, the data packets required by the sensor are represented as vector DP = [DP 1 , DP 2 , ..., DP n ],</ns0:p><ns0:p>where n denotes the total number of the input sensors. In addition, vector Manuscript to be reviewed</ns0:p><ns0:formula xml:id='formula_4'>R = [R 1 , R 2 , ..., R m ],</ns0:formula></ns0:div> <ns0:div><ns0:head>Computer Science</ns0:head><ns0:p>Upon reception of the message as given 12, the base station BS of the corresponding sensor S extracts R S = Q D (C S ) and verifies the criterion Z o . Upon successful verification, the base station asks S to input its identity S i ID and the sensor identity S j ID that it needs to access. The decoding process obtains the reconstructed vector R of the output layer from the hidden layer value R, and this is mathematically formulated as in equation 13.</ns0:p><ns0:formula xml:id='formula_5'>Q = g(R) = δ (W T R + b)<ns0:label>(13)</ns0:label></ns0:formula><ns0:p>In equation 13, n) ] and W T represents the weight. Upon reception of the decoding output Q, the gateway node establishes the output. If the verification is successful, then the gateway node authorizes the sensor, and successful energy-efficient communication is thus established between the sensors S i and S j ; otherwise, the process is terminated.</ns0:p><ns0:formula xml:id='formula_6'>Q = [Q (1) , Q (2) , ..., Q<ns0:label>(</ns0:label></ns0:formula></ns0:div> <ns0:div><ns0:head>SIMULATION SETUP</ns0:head><ns0:p>In order to examine the performance of the proposed QR-GSDL model in an IIoT system operation, a simulation environment is set up using Network Simulator 2 (NS2) with plants data collected by IoT devices downloaded from the Kaggle Website 1 . The dataset is composed of 7 attributes and 16382 instances. The attributes are demand response, area, season, energy, cost, pair no, and distance. The dataset comprises the common information to facilitate the development of a demand response (DR)</ns0:p><ns0:p>energy management system for industrial customers. IoT platform improves the inter connectivity of entities in industrial energy management systems and minimizes the energy costs of industrial facilities.</ns0:p><ns0:p>In our simulations, networks with a designated number of sensors are distributed in a random pattern within an area of 1500m times 1500m. The number of sensors is varied from 50 to 500. The positioning of nodes is made in a random fashion. Finally, the chosen simulation runs were 10 due to the fact that after the 10 th run, there was a very small gain in the criteria values. We believe this is an advantage of our <ns0:ref type='bibr'>50,</ns0:ref><ns0:ref type='bibr'>100,</ns0:ref><ns0:ref type='bibr'>150,</ns0:ref><ns0:ref type='bibr'>200,</ns0:ref><ns0:ref type='bibr'>250,</ns0:ref><ns0:ref type='bibr'>300,</ns0:ref><ns0:ref type='bibr'>350,</ns0:ref><ns0:ref type='bibr'>400,</ns0:ref><ns0:ref type='bibr'>450,</ns0:ref><ns0:ref type='bibr'>500</ns0:ref> Sensor placement Random distribution Transmission range 400m Simulation runs 10</ns0:p></ns0:div> <ns0:div><ns0:head>RESULTS AND DISCUSSION</ns0:head><ns0:p>The performance of the proposed QR-GSDL is compared with three well-known models: online threshold anomaly detection <ns0:ref type='bibr' target='#b14'>Li et al. (2019)</ns0:ref>, machine learning-based anomaly detection <ns0:ref type='bibr' target='#b31'>Zolanvari et al. (2019)</ns0:ref>, and dynamic <ns0:ref type='bibr'>CNN Yuan et al. (2020)</ns0:ref>. The performance analysis is based on three measures: energy consumption, false acceptance rate, authentication rate, and authentication time. The experimental results are presented in the form of tables and graphs.</ns0:p></ns0:div> <ns0:div><ns0:head>Performance analysis of energy consumption</ns0:head><ns0:p>Energy consumption is defined as the product of a number of samples and energy consumed by one sensor node for performing authorization to achieve secured communication green sustainability. It is computed as: Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Computer Science</ns0:head><ns0:p>Energy consumption is defined as the product of a number of samples and energy consumed by one sensor node for performing authorization to achieve secured communication green sustainability. It is computed as:</ns0:p><ns0:formula xml:id='formula_7'>EC = n ∑ i=1 Samples i × Energyconsumedbyonesensornode<ns0:label>(14)</ns0:label></ns0:formula><ns0:p>In Equation <ns0:ref type='formula' target='#formula_7'>14</ns0:ref>, the energy consumption EC is computed based on the samples considered for experimentation (Samples i ) and energy consumed for authorization to achieve secured communication and green sustainability. It is measured in terms of joules (J). The energy consumed for four different methods is given in Table <ns0:ref type='table' target='#tab_2'>2</ns0:ref>. Manuscript to be reviewed</ns0:p><ns0:p>Computer Science </ns0:p></ns0:div> <ns0:div><ns0:head>Performance analysis of false acceptance rate</ns0:head><ns0:p>The false acceptance rate (FAR) is a measure of the likelihood that the IWSN will incorrectly accept an access attempt made by an unauthorized sensor. The false acceptance rate is formulated as the percentage ratio of the number of false acceptances (FA) to the number of sensors (Samples) as input, as in equation <ns0:ref type='formula' target='#formula_8'>15</ns0:ref>:</ns0:p><ns0:formula xml:id='formula_8'>FAR = FA Samples 100<ns0:label>(15)</ns0:label></ns0:formula><ns0:p>In equation 15, FA denotes the false acceptance made and a number of samples (sensors) Samples provided as input. It is computed in terms of percentage (%). The results of the false acceptance rate in modeling green sustainability issues in IWSN are summarized in Table <ns0:ref type='table' target='#tab_3'>3</ns0:ref>.</ns0:p><ns0:p>The false acceptance rates generated by the IWSN when using the proposed method and the two compared methods are presented in Table <ns0:ref type='table' target='#tab_3'>3</ns0:ref> and graphically illustrated in Figure <ns0:ref type='figure' target='#fig_9'>6</ns0:ref>.</ns0:p><ns0:p>It can be seen from Table <ns0:ref type='table' target='#tab_3'>3</ns0:ref> and Figure <ns0:ref type='figure' target='#fig_9'>6</ns0:ref> that when the number of sensors increases, the number of sensors that are checked for authenticity by the supervisory control unit via the gateway node also increased. Correspondingly, in all four models, the false acceptance rate increases. The false rate cannot be optimized by the QR-GSDL model.</ns0:p><ns0:p>However, the proposed model is able to reduce the rate when compared to the three models. As an example, when there are 150 sensors in the simulation, the number of sensors whose information is incorrectly accepted for transmission is 15 using QR-GSDL compared to 28, 45, and 48 using the online threshold anomaly detection <ns0:ref type='bibr' target='#b14'>Li et al. (2019)</ns0:ref>, machine learning-based anomaly detection <ns0:ref type='bibr' target='#b31'>Zolanvari et al. (2019)</ns0:ref>, and dynamic <ns0:ref type='bibr'>CNN Yuan et al. (2020)</ns0:ref>, respectively. Therefore, it can be inferred that the false acceptance rate is improved by QR-GSDL. It is considered that this is due to the application of an integration function, namely, QRH, which verifies and validates the authentication of the corresponding sensor in an effective manner. Also, the application of interim identity and quasi congruence results in the generation of distinct and unique identities that are not stored in the gateway node but held by the supervisory control unit. Hence the level of complexity and the false acceptance rate is reduced using QR-GSDL by 39%, 56%, 58% compared to <ns0:ref type='bibr' target='#b14'>Li et al. (2019)</ns0:ref>, <ns0:ref type='bibr' target='#b31'>Zolanvari et al. (2019)</ns0:ref>, and Yuan et al.</ns0:p><ns0:p>(2020), respectively.</ns0:p></ns0:div> <ns0:div><ns0:head>13/19</ns0:head><ns0:p>PeerJ Comput. Sci. reviewing PDF \| (CS-2021:08:64590:2:2:NEW 8 Apr 2022)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p><ns0:p>Computer Science The authentication rates produced by the IWSN for different numbers of sensors (ranging from 50 to 429 500) when using the three tested methods are presented in Table <ns0:ref type='table' target='#tab_4'>4</ns0:ref> and Figure <ns0:ref type='figure'>7</ns0:ref>. Manuscript to be reviewed</ns0:p><ns0:p>Computer Science As the number of sensors increased, the authentication rate for all four models also increased. This is because as the number of sensors increases, the frequency of sensors in the gateway node via the base station increases, so there is a higher probability of a longer amount of time being consumed for encryption and decryption to deal with the request made by each sensor.</ns0:p><ns0:p>However, significant improvement and gain increasing are trends that can be observed with the QR-GSDL approach. For instance, in the case of the simulation using 50 sensors, a total of 46 sensors are correctly authenticated as authentic sensors by the gateway node when applying QR-GSDL, whereas only Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Computer Science</ns0:head><ns0:p>distance between the sensors based on their neighbors <ns0:ref type='bibr' target='#b14'>Li et al. (2019)</ns0:ref>. Hence the proposed method leads to a higher rate of correct authentications being made by the supervisory control via the gateway node of 5%, 8%, and 9% compared to <ns0:ref type='bibr' target='#b14'>Li et al. (2019)</ns0:ref>, <ns0:ref type='bibr' target='#b31'>Zolanvari et al. (2019), and</ns0:ref><ns0:ref type='bibr' target='#b29'>Yuan et al. (2020)</ns0:ref>, respectively.</ns0:p></ns0:div> <ns0:div><ns0:head>Performance analysis of authentication time</ns0:head><ns0:p>Authentication time refers to the time consumed in authenticating the sensors as either normal or abnormal (malicious). The mathematical formula used to compute authentication time is shown in equation 17:</ns0:p><ns0:formula xml:id='formula_9'>AT = n ∑ i=1 Samples i × Time[R + Q] (<ns0:label>17</ns0:label></ns0:formula><ns0:formula xml:id='formula_10'>)</ns0:formula><ns0:p>Where AT is authentication time that is measured based on the samples considered for experimentation (Samples i ) and the time consumed in encryption (R) and decryption (Q). The value is given in milliseconds (ms). Table <ns0:ref type='table' target='#tab_6'>5</ns0:ref> and Figure <ns0:ref type='figure' target='#fig_11'>8</ns0:ref> present authentication times of the different models for different numbers of sensors. Manuscript to be reviewed </ns0:p><ns0:note type='other'>Computer Science</ns0:note></ns0:div> <ns0:div><ns0:head>FUTURE WORK</ns0:head><ns0:p>Application of the proposed model in the field of the Internet of Things (IoT) is left as future work. It will be interesting to apply some adaptions to the QR-GSDL model and perform experiments to evaluate its performance in the IoT. In addition, it could be interesting to deeply analyze the performance of the QR-GSDL model on different datasets.</ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>Sci. reviewing PDF \| (CS-2021:08:64590:2:2:NEW 8 Apr 2022) Manuscript to be reviewed Computer Science model. Hence, the gradient secured deep learning model is employed in the current study to perform authentication for secure data communication.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head>Figure 1 .</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1. System model for an industrial wireless sensor network.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>Figure 2 .</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2. Registration activity diagram.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head>Figure 3 .Figure 4 .</ns0:head><ns0:label>34</ns0:label><ns0:figDesc>Figure 3. energy-efficient authentication and authorization activity diagram.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head /><ns0:label /><ns0:figDesc>represents the feature expression of the hidden layer, where m represents the sensors of the hidden layers. Finally, b represents the bias vectors and W the weight matrix from input to hidden layer, respectively, while δ denotes the activation function. Finally, the gateway node formulates a message and sends it to the respective sensor as presented in equation 12. N : (C S , N′ GN , Z o , R)) → S (12) 10/19 PeerJ Comput. Sci. reviewing PDF \| (CS-2021:08:64590:2:2:NEW 8 Apr 2022)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head /><ns0:label /><ns0:figDesc>proposed model where it converges after a low number of simulation runs compared with the existing models in the literature<ns0:ref type='bibr' target='#b14'>Li et al. (2019)</ns0:ref>;<ns0:ref type='bibr' target='#b31'>Zolanvari et al. (2019)</ns0:ref>;<ns0:ref type='bibr' target='#b29'>Yuan et al. (2020)</ns0:ref> where they converge after 45 simulation runs.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head /><ns0:label /><ns0:figDesc>1 kaggle. INDUSTRIAL INTERNET OF THINGS DATA [Online].Website: https://www.kaggle.com/pitasr/ industrialiot [accessed 27-12-2020]. 11/19 PeerJ Comput. Sci. reviewing PDF \| (CS-2021:08:64590:2:2:NEW 8 Apr 2022)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_7'><ns0:head /><ns0:label /><ns0:figDesc>Figure 5 describe the energy consumption of the proposed method compared with the three existing methods for a different number of sensors. The attained results illustrate that When the number of sensors increases, the energy consumption by the sensor during the authorization also gets increased linearly. However the the energy consumption of QR-GSDL model is lesser when compared to Li et al. (2019), Zolanvari et al. (2019), and Yuan et al. (2020). The sample simulations carried out with 50 sensors show that the amount of energy consumed by one sensor for authorization using QR-GSDL is 0.26J while energy consumed by one sensor using Li et al. (2019) is 0.36J, using Zolanvari et al. (2019) is 0.46J, and using Yuan et al. (2020) is 0.50J. The energy consumption saving is due to the application of the Gradient Sparse Auto Deep Learning algorithm where the gateway node checks data access by the sensors for ensuring energy-efficient and green sustainability. It is clear from the obtained results that QR-GSDL reduces the energy consumption by 13%, 20%, and 28% when compared with Li et al. (2019), Zolanvari et al. (2019), and Yuan et al. (2020), respectively. 12/19 PeerJ Comput. Sci. reviewing PDF \| (CS-2021:08:64590:2:2:NEW 8 Apr 2022)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_8'><ns0:head>Figure 5 .</ns0:head><ns0:label>5</ns0:label><ns0:figDesc>Figure 5. Comparisons of energy consumption.</ns0:figDesc><ns0:graphic coords='14,128.37,58.92,434.23,217.25' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_9'><ns0:head>Figure 6 .</ns0:head><ns0:label>6</ns0:label><ns0:figDesc>Figure 6. Comparisons of false acceptance rate.</ns0:figDesc><ns0:graphic coords='15,129.09,347.97,430.44,215.36' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_10'><ns0:head /><ns0:label /><ns0:figDesc>Sci. reviewing PDF \| (CS-2021:08:64590:2:2:NEW 8 Apr 2022)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_11'><ns0:head>Figure 8 .</ns0:head><ns0:label>8</ns0:label><ns0:figDesc>Figure 8. Comparisons of authentication time.</ns0:figDesc><ns0:graphic coords='18,130.86,60.76,431.69,215.99' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='16,130.26,341.60,431.69,215.99' type='bitmap' /></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head /><ns0:label /><ns0:figDesc>Gradient sparse auto deep learning Input: Sensor S = S 1 , S 2 , ..., S n , Gateway Node GN, Base Station BS = BS 1 , BS 2 , ..., BS n Output: energy-efficient and secure communication Begin: for each Sensor S with Gateway Node GN and Base Station BS do Obtain energy-efficient secure localization based on gradients associating sensors using equations 9 and 10</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Authentication:</ns0:cell></ns0:row><ns0:row><ns0:cell>if disclosed locations equal then</ns0:cell></ns0:row><ns0:row><ns0:cell>Authentication successful</ns0:cell></ns0:row><ns0:row><ns0:cell>Go to Authorization</ns0:cell></ns0:row><ns0:row><ns0:cell>else</ns0:cell></ns0:row><ns0:row><ns0:cell>Authentication is not successful</ns0:cell></ns0:row><ns0:row><ns0:cell>Session is terminated</ns0:cell></ns0:row><ns0:row><ns0:cell>end if</ns0:cell></ns0:row><ns0:row><ns0:cell>Authorization:</ns0:cell></ns0:row><ns0:row><ns0:cell>Perform Quantum Sparse Auto-Encoding using equation 11</ns0:cell></ns0:row><ns0:row><ns0:cell>Perform Quantum Sparse Auto Decoding using equation 13</ns0:cell></ns0:row><ns0:row><ns0:cell>if R=Q then</ns0:cell></ns0:row><ns0:row><ns0:cell>energy-efficient authorization is successful</ns0:cell></ns0:row><ns0:row><ns0:cell>energy-efficient secure communication</ns0:cell></ns0:row><ns0:row><ns0:cell>else</ns0:cell></ns0:row><ns0:row><ns0:cell>energy-efficient authorization not successful</ns0:cell></ns0:row><ns0:row><ns0:cell>Session is terminated</ns0:cell></ns0:row><ns0:row><ns0:cell>end if</ns0:cell></ns0:row><ns0:row><ns0:cell>end for</ns0:cell></ns0:row><ns0:row><ns0:cell>End</ns0:cell></ns0:row></ns0:table><ns0:note>9/19PeerJ Comput. Sci. reviewing PDF \| (CS-2021:08:64590:2:2:NEW 8 Apr 2022)Manuscript to be reviewedComputer ScienceAlgorithm 2</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_1'><ns0:head /><ns0:label /><ns0:figDesc>Table 1 provides the simulation parameters used in our work.</ns0:figDesc><ns0:table><ns0:row><ns0:cell cols='2'>Table 1. Simulation parameters</ns0:cell></ns0:row><ns0:row><ns0:cell>Parameters</ns0:cell><ns0:cell>Description</ns0:cell></ns0:row><ns0:row><ns0:cell>Simulation time</ns0:cell><ns0:cell>50s</ns0:cell></ns0:row><ns0:row><ns0:cell>Area size</ns0:cell><ns0:cell>1500m × 1500m</ns0:cell></ns0:row><ns0:row><ns0:cell>Number of sensors</ns0:cell><ns0:cell /></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_2'><ns0:head>Table 2 .</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Energy consumption for different models and sensors</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Number</ns0:cell><ns0:cell /><ns0:cell cols='2'>False acceptance rate</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>of sensors</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>QR-GSDL</ns0:cell><ns0:cell>Online threshold</ns0:cell><ns0:cell cols='2'>Machine learning-based Dynamic</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>anomaly detection</ns0:cell><ns0:cell>anomaly detection</ns0:cell><ns0:cell>CNN</ns0:cell></ns0:row><ns0:row><ns0:cell>50</ns0:cell><ns0:cell>13</ns0:cell><ns0:cell>18</ns0:cell><ns0:cell>23</ns0:cell><ns0:cell>25</ns0:cell></ns0:row><ns0:row><ns0:cell>100</ns0:cell><ns0:cell>21</ns0:cell><ns0:cell>28</ns0:cell><ns0:cell>35</ns0:cell><ns0:cell>39</ns0:cell></ns0:row><ns0:row><ns0:cell>150</ns0:cell><ns0:cell>34</ns0:cell><ns0:cell>40</ns0:cell><ns0:cell>46</ns0:cell><ns0:cell>50</ns0:cell></ns0:row><ns0:row><ns0:cell>200</ns0:cell><ns0:cell>49</ns0:cell><ns0:cell>59</ns0:cell><ns0:cell>64</ns0:cell><ns0:cell>67</ns0:cell></ns0:row><ns0:row><ns0:cell>250</ns0:cell><ns0:cell>63</ns0:cell><ns0:cell>70</ns0:cell><ns0:cell>73</ns0:cell><ns0:cell>76</ns0:cell></ns0:row><ns0:row><ns0:cell>300</ns0:cell><ns0:cell>76</ns0:cell><ns0:cell>85</ns0:cell><ns0:cell>89</ns0:cell><ns0:cell>90</ns0:cell></ns0:row><ns0:row><ns0:cell>350</ns0:cell><ns0:cell>94</ns0:cell><ns0:cell>101</ns0:cell><ns0:cell>105</ns0:cell><ns0:cell>108</ns0:cell></ns0:row><ns0:row><ns0:cell>400</ns0:cell><ns0:cell>107</ns0:cell><ns0:cell>115</ns0:cell><ns0:cell>118</ns0:cell><ns0:cell>120</ns0:cell></ns0:row><ns0:row><ns0:cell>450</ns0:cell><ns0:cell>118</ns0:cell><ns0:cell>126</ns0:cell><ns0:cell>129</ns0:cell><ns0:cell>132</ns0:cell></ns0:row><ns0:row><ns0:cell>500</ns0:cell><ns0:cell>134</ns0:cell><ns0:cell>142</ns0:cell><ns0:cell>145</ns0:cell><ns0:cell>149</ns0:cell></ns0:row><ns0:row><ns0:cell>Table 2 and</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_3'><ns0:head>Table 3 .</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>False acceptance rate for different models and sensors</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Number</ns0:cell><ns0:cell /><ns0:cell cols='2'>False acceptance rate</ns0:cell></ns0:row><ns0:row><ns0:cell>of sensors</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>QR-GSDL</ns0:cell><ns0:cell>Online threshold</ns0:cell><ns0:cell cols='2'>Machine learning-based Dynamic</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>anomaly detection</ns0:cell><ns0:cell>anomaly detection</ns0:cell><ns0:cell>CNN</ns0:cell></ns0:row><ns0:row><ns0:cell>50</ns0:cell><ns0:cell>8</ns0:cell><ns0:cell>10</ns0:cell><ns0:cell>12</ns0:cell></ns0:row><ns0:row><ns0:cell>100</ns0:cell><ns0:cell>12</ns0:cell><ns0:cell>15</ns0:cell><ns0:cell>25</ns0:cell></ns0:row><ns0:row><ns0:cell>150</ns0:cell><ns0:cell>15</ns0:cell><ns0:cell>28</ns0:cell><ns0:cell>45</ns0:cell></ns0:row><ns0:row><ns0:cell>200</ns0:cell><ns0:cell>25</ns0:cell><ns0:cell>45</ns0:cell><ns0:cell>60</ns0:cell></ns0:row><ns0:row><ns0:cell>250</ns0:cell><ns0:cell>30</ns0:cell><ns0:cell>50</ns0:cell><ns0:cell>75</ns0:cell></ns0:row><ns0:row><ns0:cell>300</ns0:cell><ns0:cell>30</ns0:cell><ns0:cell>58</ns0:cell><ns0:cell>80</ns0:cell></ns0:row><ns0:row><ns0:cell>350</ns0:cell><ns0:cell>25</ns0:cell><ns0:cell>40</ns0:cell><ns0:cell>85</ns0:cell></ns0:row><ns0:row><ns0:cell>400</ns0:cell><ns0:cell>40</ns0:cell><ns0:cell>65</ns0:cell><ns0:cell>70</ns0:cell></ns0:row><ns0:row><ns0:cell>450</ns0:cell><ns0:cell>45</ns0:cell><ns0:cell>80</ns0:cell><ns0:cell>95</ns0:cell></ns0:row><ns0:row><ns0:cell>500</ns0:cell><ns0:cell>40</ns0:cell><ns0:cell>75</ns0:cell><ns0:cell>100</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_4'><ns0:head>Table 4 .</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Authentication rate for different models and sensors</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Number</ns0:cell><ns0:cell /><ns0:cell cols='2'>False acceptance rate</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>of sensors</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>QR-GSDL</ns0:cell><ns0:cell>Online threshold</ns0:cell><ns0:cell cols='2'>Machine learning-based Dynamic</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>anomaly detection</ns0:cell><ns0:cell>anomaly detection</ns0:cell><ns0:cell>CNN</ns0:cell></ns0:row><ns0:row><ns0:cell>50</ns0:cell><ns0:cell>92</ns0:cell><ns0:cell>88</ns0:cell><ns0:cell>82</ns0:cell><ns0:cell>80</ns0:cell></ns0:row><ns0:row><ns0:cell>100</ns0:cell><ns0:cell>90</ns0:cell><ns0:cell>86</ns0:cell><ns0:cell>80</ns0:cell><ns0:cell>78</ns0:cell></ns0:row><ns0:row><ns0:cell>150</ns0:cell><ns0:cell>85</ns0:cell><ns0:cell>81</ns0:cell><ns0:cell>78</ns0:cell><ns0:cell>74</ns0:cell></ns0:row><ns0:row><ns0:cell>200</ns0:cell><ns0:cell>88</ns0:cell><ns0:cell>83</ns0:cell><ns0:cell>75</ns0:cell><ns0:cell>70</ns0:cell></ns0:row><ns0:row><ns0:cell>250</ns0:cell><ns0:cell>86</ns0:cell><ns0:cell>82</ns0:cell><ns0:cell>74</ns0:cell><ns0:cell>68</ns0:cell></ns0:row><ns0:row><ns0:cell>300</ns0:cell><ns0:cell>88</ns0:cell><ns0:cell>84</ns0:cell><ns0:cell>75</ns0:cell><ns0:cell>70</ns0:cell></ns0:row><ns0:row><ns0:cell>350</ns0:cell><ns0:cell>90</ns0:cell><ns0:cell>85</ns0:cell><ns0:cell>78</ns0:cell><ns0:cell>74</ns0:cell></ns0:row><ns0:row><ns0:cell>400</ns0:cell><ns0:cell>85</ns0:cell><ns0:cell>81</ns0:cell><ns0:cell>75</ns0:cell><ns0:cell>71</ns0:cell></ns0:row><ns0:row><ns0:cell>450</ns0:cell><ns0:cell>87</ns0:cell><ns0:cell>82</ns0:cell><ns0:cell>77</ns0:cell><ns0:cell>73</ns0:cell></ns0:row><ns0:row><ns0:cell>500</ns0:cell><ns0:cell>89</ns0:cell><ns0:cell>85</ns0:cell><ns0:cell>79</ns0:cell><ns0:cell>75</ns0:cell></ns0:row></ns0:table><ns0:note>Figure 7. Comparisons of authentication rate.</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_6'><ns0:head>Table 5 .</ns0:head><ns0:label>5</ns0:label><ns0:figDesc>Authentication time for different models and sensors</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Number</ns0:cell><ns0:cell /><ns0:cell cols='2'>False acceptance rate</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>of sensors</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>QR-GSDL</ns0:cell><ns0:cell>Online threshold</ns0:cell><ns0:cell cols='2'>Machine learning-based Dynamic</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>anomaly detection</ns0:cell><ns0:cell>anomaly detection</ns0:cell><ns0:cell>CNN</ns0:cell></ns0:row><ns0:row><ns0:cell>50</ns0:cell><ns0:cell>28.50</ns0:cell><ns0:cell>35.50</ns0:cell><ns0:cell>43</ns0:cell><ns0:cell>47</ns0:cell></ns0:row><ns0:row><ns0:cell>100</ns0:cell><ns0:cell>42.40</ns0:cell><ns0:cell>55.25</ns0:cell><ns0:cell>65.35</ns0:cell><ns0:cell>69.75</ns0:cell></ns0:row><ns0:row><ns0:cell>150</ns0:cell><ns0:cell>65.25</ns0:cell><ns0:cell>70.35</ns0:cell><ns0:cell>80.25</ns0:cell><ns0:cell>85.35</ns0:cell></ns0:row><ns0:row><ns0:cell>200</ns0:cell><ns0:cell>83.50</ns0:cell><ns0:cell>100.25</ns0:cell><ns0:cell>125.55</ns0:cell><ns0:cell>130</ns0:cell></ns0:row><ns0:row><ns0:cell>250</ns0:cell><ns0:cell>105.25</ns0:cell><ns0:cell>125.35</ns0:cell><ns0:cell>135.35</ns0:cell><ns0:cell>138.45</ns0:cell></ns0:row><ns0:row><ns0:cell>300</ns0:cell><ns0:cell>125.35</ns0:cell><ns0:cell>140.55</ns0:cell><ns0:cell>145.55</ns0:cell><ns0:cell>150.25</ns0:cell></ns0:row><ns0:row><ns0:cell>350</ns0:cell><ns0:cell>145.55</ns0:cell><ns0:cell>175.35</ns0:cell><ns0:cell>180.25</ns0:cell><ns0:cell>186.75</ns0:cell></ns0:row><ns0:row><ns0:cell>400</ns0:cell><ns0:cell>170</ns0:cell><ns0:cell>190</ns0:cell><ns0:cell>200.35</ns0:cell><ns0:cell>220.15</ns0:cell></ns0:row><ns0:row><ns0:cell>450</ns0:cell><ns0:cell>182.35</ns0:cell><ns0:cell>195.25</ns0:cell><ns0:cell>225.55</ns0:cell><ns0:cell>238.15</ns0:cell></ns0:row><ns0:row><ns0:cell>500</ns0:cell><ns0:cell>190</ns0:cell><ns0:cell>200.35</ns0:cell><ns0:cell>245.55</ns0:cell><ns0:cell>250.55</ns0:cell></ns0:row></ns0:table><ns0:note>16/19PeerJ Comput. Sci. reviewing PDF \| (CS-2021:08:64590:2:2:NEW 8 Apr 2022)</ns0:note></ns0:figure> <ns0:note place='foot' n='19'>/19 PeerJ Comput. Sci. reviewing PDF \| (CS-2021:08:64590:2:2:NEW 8 Apr 2022)Manuscript to be reviewed Computer Science</ns0:note> </ns0:body> "	" Manuscript Title: 'Quantum readout and gradient deep learning model for secure and sustainable data access in IWSN' To: Academic Editor, PeerJ Computer Science (Professor Shadi Aljawarneh) Re: Response to reviewers’ comments Dear Editor, I am thankful to the reviewers for their constructive comments, which help to improve this manuscript. Also, thank you for giving me the opportunity to address the reviewers’ comments. Based on the review comments, I have made the required modifications to the manuscript. Please find below answers to the reviewers’ comments as well. 1. The paper needs further improvements - the paper methodology should be clarified more. The authentication process should be explained as a flowchart figure. Response: We have improve the methodology section by including a flow diagram of the proposed authentication process. 2. There is a need to explain the authentication problem in more details, some of the IIoT networks are designed in a closed network where they have their own cables and devices and no one outside the network has any access to it, in this case the authentication problem needs to be clarified at what circumstances it is considered an issue. I suggest the authors provide an example to explain when/how the authentication step should be included in the design. Response: In the introduction section, the present authentication problem is discussed in details. The circumstances is also discussed in which authentication is really an issue in the closed IIoT networks. The discussion also included how the authentication steps are included in the system design. Regardless of its advantages, the IWSN suffers from many security and privacy issues like data leaking issue, node compromise issue, authentication and authorization problems, data loss, and many more. Among all the issues the authentication problem is most widely concerned problem because all the sensing devices are located and access through remotely. A false authentication may hamper the complete security of the system. It is the first security steps which must be strong so that no unauthorized user can access the system. There are several authenticated key agreement schemes are proposed but they are limited to only one devices at a time. In some scenario the IIoT networks are closed so no unauthorized user can access the internal networks of the system. But still the authentication problem for insider user is present. The insider attack can be possible and creates an issue if the authentication mechanism is not much strong. Thus, these issues motivated us to design a dynamic deep learning-based solution named the Quantum Readout Gradient secured Deep Learning (QR-GSDL) for authenticity of the sensor based IIoT networks. The proposed model will work on different layer for insider user authentication and authorization purpose. The proposed authentication steps included an improvement in the authentication rate with minimum time consumption and delay. 3. Authors need to identify the terminologies earlier in the paper such as authentication rate, authentication acceptance false rate, and authentication time. Response: Terminologies are defined at the beginning of the manuscript, just after the abstract. (See lines 32 and 33). 4. Lines 343-347 references and citations are required. Response: Lines 343-347 references and citations are added. 5. Conclusion is not sufficient; more details should be illustrated in this section. Response: Now the conclusion has been revised by including more details. "	Here is a paper. Please give your review comments after reading it.
431	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Facial images are used for kinship verification. Traditional convolutional neural networks and transfer learning-based approaches are presently used for kinship identification.</ns0:p><ns0:p>Transfer-learning approach is useful in many fields. However, it lacks for identification of humans' kinship accurately because transfer-learning models are trained on a different type of data that is significantly different as compared to human face image data, a technique which may be able for kinship identification by comparing images of parents and their children with transformed age instead of comparing their actual images is required. In this paper, a technique for kinship identification by using a Siamese neural network and age transformation algorithm is proposed. The results are satisfactory as an overall accuracy of 76.38% has been achieved. Further work can be carried out to improve the accuracy by improving the Life Span Age Transformation (LAT) algorithm for kinship identification using facial images.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>The pictorial data generated by the business, social media, public industry, non-profit sectors, and scientific research have increased tremendously <ns0:ref type='bibr' target='#b0'>[1]</ns0:ref>. This graphical data contains many useful and worthwhile information that could be used for various purposes <ns0:ref type='bibr' target='#b1'>[2,</ns0:ref><ns0:ref type='bibr' target='#b2'>3]</ns0:ref>.</ns0:p><ns0:p>In the last few years, researchers became interested in extracting kinship information from pictorial data having human faces, which can be used for different purposes. As face image data provides different unique features of humans and contains a wealth of information that can be used for various purposes <ns0:ref type='bibr' target='#b3'>[4]</ns0:ref>. The purpose of extracting genetic relationships between human images is to verify human kinship, which is useful information for medical sciences, psychologists, security agencies and family album organizations. Furthermore, it can be utilized Instead of comparing direct images of parents and children, this research work suggests an approach of age transformation and converts images of parents and children into the same age of 15-19-year age and then compares them to get better accuracy of similarity. In this model, we have used a pre-processing stage of age transformation before image comparison for kinship identification and verification. At first, our model uses an age transformation algorithm to transform facial images by increasing or decreasing the age of face images and making them at the same age stage. After making images at the same stage of age, makes it easy and will make it easy to compare images and finding similarities between them to exploit kinship identification between them. Furthermore, the robustness of our technique is validated through extensive experiments and analysis on a huge dataset. Figure <ns0:ref type='figure' target='#fig_0'>1</ns0:ref> shows a high level overview of the proposed methodology.</ns0:p><ns0:p>The contributions of this paper include 1) proposing improved pre-processing of dataset images through employing the use of the Life Span Age Transformation (LAT) algorithm for transforming the images onto the same scale of age, 2) using the Siamese network for performing the feature extraction from the transformed images, 3) introduced technique is validated by using the state-of-the-art benchmarked dataset namely RFIW (Recognizing Family in the Wild), <ns0:ref type='bibr' target='#b3'>4)</ns0:ref> finally, extensive experiments conducted on the dataset using the proposed technique to identify the improved effectiveness. Moreover, the comparative analysis indicates that the proposed technique outperformed the existing methods. introduced the problem and used simple features for kinship identification like eyes and skin color and distances between facial parts for kinship verification <ns0:ref type='bibr' target='#b12'>[11]</ns0:ref>. Subsequently. <ns0:ref type='bibr'>Xia et al.</ns0:ref> claimed the similarity between parents and their children is quite large and proposed an approach of kinship learning by removing the gap between two facial images of a parent, one image of young age and another image of old age and children's images <ns0:ref type='bibr' target='#b13'>[12]</ns0:ref>. Lu et al. used a metric learning approach for kinship verification and found effective features, which provided the most discriminative results <ns0:ref type='bibr' target='#b14'>[13]</ns0:ref>. <ns0:ref type='bibr'>Levi</ns0:ref> approach deals with resemblance by using the father and mother's facial shapes and extracting a similar face with a combination of facial feathers of the father and mother <ns0:ref type='bibr' target='#b8'>[8]</ns0:ref>. Yan H, Hu J. revealed that Euclidian similarity metric is not a powerful way to measure the similarity of facial images, especially when captured in wild conditions. They clarified that the similarity metric can handle the problem better to deal with face variations compared to Euclidian similarity. They used a mid-level feature vector with discriminative metric learning and proposed a prototypebased feature learning approach for kinship verification <ns0:ref type='bibr' target='#b14'>[13,</ns0:ref><ns0:ref type='bibr' target='#b15'>14]</ns0:ref>. Yan H, Hu J. proposed a methodology of video-based kinship verification by using data set of video faces called Kinship Face Videos in the Wild (KFVW). Dataset was built by capturing facial images from videos for kinship verification. This methodology analyzes the human faces in the video by getting training set from video poses and then applying distance metric learning approaches to get a positive semi definite matrix (PSD) for face recognition and kinship identification <ns0:ref type='bibr' target='#b9'>[9]</ns0:ref>. Joseph P.R. et al.</ns0:p><ns0:p>introduced the first large-scale image database for kinship recognition called Families In the Wild (FIW) and exploits the challenges in kinship recognition. The FIW database consists of thousands of images of faces for kinship recognition <ns0:ref type='bibr' target='#b10'>[10]</ns0:ref>. Yong L. et al. presented a framework in which knowledge of face recognition from large-scale data-driven transferred and then finetuned metric space to get discrimination of kin related people. They also proposed an augmented strategy to balance family members' images and used triplet and ResNet to extract face encoding for kinship identification <ns0:ref type='bibr' target='#b17'>[15]</ns0:ref>. In early techniques, kinship verification uses handcrafted descriptors from facial images to perform classification for learning. Fang et al. used facial features like eye and skin colors and distance of eye-to-nose for kinship verification <ns0:ref type='bibr' target='#b12'>[11]</ns0:ref>. Zhou et al. proposed an approach based on spatial pyramid features for kinship verification. This approach used Gabor-based facial image gradient orientation features <ns0:ref type='bibr' target='#b18'>[16]</ns0:ref>. Liu et al. applied a transferrable approach of fisher vectors derived from each facial image to extract similarity for kinship verification <ns0:ref type='bibr' target='#b19'>[17]</ns0:ref>. <ns0:ref type='bibr'>Kohli et al.</ns0:ref> proposed an approach to achieving kinship similarity using a self-similarity descriptor. They introduced that kinship verification is a two-factor classification problem. They revealed that low-level features could not be used as an underlying source of Manuscript to be reviewed Computer Science visual resemblance between people with kinship relations <ns0:ref type='bibr' target='#b20'>[18]</ns0:ref>. In Shallow metric-based approaches, metric learning methods are used to learn discriminative features for kinship verification. These approaches learn a Mahalanobis distance using handcrafted features identification and get a better score of similarity between kinship-related pairs with non-kinshiprelated pairs <ns0:ref type='bibr' target='#b4'>[5]</ns0:ref>. In the Deep learning-based approach, Kaiming He <ns0:ref type='bibr' target='#b21'>[19]</ns0:ref>, Kohli et al <ns0:ref type='bibr' target='#b23'>[20]</ns0:ref> motivated kinship identification and verification after getting impressive success by applying deep learning approaches to classify different facial images. Many techniques have been adopted for deep metric learning to get discriminant features for kinship verification. Dehghan et al.</ns0:p><ns0:p>introduced an approach of fusing the features using gated auto-encoders. They extracted optimal features by reflecting parent-offspring resemblance <ns0:ref type='bibr' target='#b8'>[8]</ns0:ref>. Wang S et al. proposed the Kinship Verification on Families in the Wild with Marginalized Designing Metric Learning (DML). That technique used the largest kinship verification using Auto-encoder and Discriminative Low-rank Metric Learning (DLML) algorithm for feature discrimination <ns0:ref type='bibr' target='#b24'>[21]</ns0:ref>. After using matric learning, researchers found a better way to find similarity for kinship identification by using a Convolutional neural network. Zhang et al. adopted an approach of kinship verification using a convolution neural network (CNN) to train the algorithm with concentrated image pairs <ns0:ref type='bibr' target='#b25'>[22]</ns0:ref>. Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Computer Science</ns0:head><ns0:p>discriminative constraints on a hypersphere to get a better face recognition (FR) problem under open-set protocol <ns0:ref type='bibr' target='#b30'>[26]</ns0:ref>. Savas and Akin introduced an approach of synthesizing child faces with a pre-trained model by analyzing facial images of parents <ns0:ref type='bibr' target='#b31'>[27]</ns0:ref>. Habin Y. & Chaohui S worked on Multi-scale Deep Relational Reasoning for Facial Kinship Verification and used two convolutional neural networks, which shared network parameters and extracted different scales of features for kinship identification <ns0:ref type='bibr' target='#b32'>[28]</ns0:ref>. After using a convolutional neural network, researchers moved to find kinship using the Generative Adversarial Network (GAN), introduced by Iain Good fellow <ns0:ref type='bibr' target='#b33'>[29]</ns0:ref>. Fady S. et al. proposed GANKIN: generating Kin faces using disentangled GAN and image synthesis approach from parents to children, they also used pertained FaceNet and GAN network <ns0:ref type='bibr' target='#b35'>[30]</ns0:ref>. Tuan H et al. proposed an approach of recognizing families through images with pertained encoders. They used pre-trained networks FceNet, Siamese and FGG network to get face image encoding and find kinship between facial images <ns0:ref type='bibr' target='#b36'>[31]</ns0:ref>. Keeping in view the efficiency factor of GAN based approaches, we also used GAN based age transformation algorithm and Siamese network to build and train our model.</ns0:p><ns0:p>Although some encouraging results have been obtained from proposed methodologies for kinship identification and verification in the last few years, automatic kinship verification is being performed poorly in the real-world applications used in daily life. Due to the nonavailability of large-scale datasets, results are not too accurate to handle the kinship identification problems. Existing datasets like Family101, UB KinFace, Cornell KinFace, KinFaceW-I, and KinFaceW-II provide a few examples, but they fail to achieve accurate distributions of genetic or kinship relationships. Moreover, they have a limited pair of images for parents and children;</ns0:p><ns0:p>Classifier trained on a limited scale dataset fails while recognizing real-world images. To handle these issues, we proposed an approach to find the kinship relationship between parents and children. Our methodology uses Age Transformation and converts images of parents and children to the age of 15-20 because images of this age have maximum facial features, which can be a good source for the discrimination of features between facial images. After the process of age transformation and converting facial images to a young age for both parents and children, these faces get closer to each other in facial look and expression and then it makes it easy to find the similarity between them. With these images, there is much probability of getting parent's faces and images close to each other. Ultimately, it will make it Manuscript to be reviewed Computer Science easy for the face encoder to generate close face encoding. As a result, we get a low distance value while finding cosine similarity. Figure <ns0:ref type='figure' target='#fig_5'>2</ns0:ref> shows the effect of age transformation</ns0:p></ns0:div> <ns0:div><ns0:head>Proposed Work</ns0:head><ns0:p>This section outlines the proposed methodology for performing the kinship identification. In the proposed method, we presented a model of a deep relational network that uses a preprocessing stage of age transformation of two facial images before comparing them to exploit kinship relationships from facial images. This scheme first transforms facial images by increasing or decreasing the age factor and making two images into the same age stage and then compares them to find and verify kinship. After transforming facial images, we proposed the use of a Siamese network with two convolutional neural networks by sharing parameters between them.</ns0:p><ns0:p>Afterward, it extracts different scales of features to find similarities between images by using triplet loss. We also aimed to conduct experiments on a widely used facial kinship dataset, namely RFIW. In this methodology, the proposed model uses age transformation and converts facial images at the same stage of age, between 15 and 19 years. However, we considered this age because, in this age period, a person's face looks strong and can provide clear facial features and better encode facial images. Furthermore, after encoding transformed faces, we applied triplet loss on three faces of parents and images and extracted the kinship relationship between parents and images. In addition, we have employed parent's images as anchor and negative part of the triplet while children's images as a positive part of the triplet. We fixed the father and mother position of being positive or negative to each other while training in the Siamese network. Likewise, we used an age transformation algorithm that provided close pair of facial images of parents and children for processing to exploit kinship identification between them. This age transformation algorithm will provide images for processing to consider for kinship identification. More graphical representation and the working flow of our proposed methodology is depicted in Figure <ns0:ref type='figure' target='#fig_5'>2</ns0:ref>. The pairs with higher loss might have more impact on the model training. The training set can be defined as: Let X a , X p and X n are finite set of images for Father, Children and Mother having 'm' number of images for each set.</ns0:p><ns0:formula xml:id='formula_0'>X a = {x a 1, x a 2 , ….. x a m }<ns0:label>(1)</ns0:label></ns0:formula><ns0:p>Where X a is set of anchor images for father images.</ns0:p><ns0:formula xml:id='formula_1'>X p = {x p 1, x p 2 , ….. x p m } (2)</ns0:formula><ns0:p>Where X p is set of positive images which are taken from children's images.</ns0:p><ns0:formula xml:id='formula_2'>X n = {x n 1, x n 2 , ….. x n m }<ns0:label>(3)</ns0:label></ns0:formula><ns0:p>X n is a set of negative images taken from the set of mother images.</ns0:p><ns0:p>Then input sample taken from these three sets will be a powerful set of three sets to make a set of triplets let X is the power of X a, X p and X n, set then we get set X as a set of the triplet. X = {(x a 1 , x p 1 , x n 1) , (x a 2 , x p 3 , x n 4) … (x a n , x p n , x n n )}</ns0:p><ns0:p>X is a power set of images having three members as triplet of anchor as x a , positive as x p and negative image as x n respectively where sequence of triplet members are anchor, positive and negative members with images of father, child, and mother respectively. After getting feature extracted from pertained Siamese network, we get a set of features: X s = {(x a 1 , x p 2 , x r 1) , (x a 2 , x p 3 , x r 2) … (x a n , x p m , x r n )}</ns0:p><ns0:formula xml:id='formula_4'>F(X) = {[f(x a 1 ), f(x p 1 ), f(x n 1 )], {[f(x a 2 ), f(x p 2 ), f(x n 2 )] … {[f(x a n ), f(x p n ), f(x n n )]}<ns0:label>(5)</ns0:label></ns0:formula><ns0:p>Where X s is a power set of images having three members as triplet of anchor as x a , positive as x p and negative image as x r respectively.</ns0:p></ns0:div> <ns0:div><ns0:head>Loss Function</ns0:head><ns0:p>The loss function for the triplet loss on the extracted feature, For three cases 1.</ns0:p><ns0:p>While comparing the father's image with the child's image, if D f is distance of child's image with father's image and D m is distance of child's image with mother's image then we define the loss function as:</ns0:p><ns0:formula xml:id='formula_6'>D f = \|\|f(P) -f(A)\|\|², D m = \|\|f(P) -f(N)\|\|² (7)</ns0:formula><ns0:p>and some margin 'm' as hyperparameter, whereas A, P, N are anchor, positive and negative images, and f(A), f(P) and f(N) are features of father, child, and mother respectively. If father's image is closer to child's image then we increase the distance of child's image with mother's image and decrease the distance of child's image with father's image, so loss function to get similarity between father and child will be:</ns0:p><ns0:formula xml:id='formula_7'>£ f (A, P, N) = max (D f -D m + m, 0) if D f <D m (8) 2.</ns0:formula><ns0:p>While checking the similarity of children with mothers then, we revert the loss function.</ns0:p><ns0:p>To find the similarity of the child image with the mother image, we increase the distance of child image with father image and decrease the distance of child image with mother image then loss function will be: Manuscript to be reviewed</ns0:p><ns0:p>Computer Science</ns0:p><ns0:formula xml:id='formula_8'>£ m (A, P, N) = max (D m -D f + m, 0) if D f >D m (9)</ns0:formula><ns0:p>3.</ns0:p><ns0:p>While comparing images of siblings, we use distance measures of images of two siblings, S1, S2. We find the distance between siblings and random images as:</ns0:p><ns0:formula xml:id='formula_9'>Ds 1= \|\|f(S1) -f(S2)\|\|²,D s2 = \|\|f(S1) -f(N r )\|\|² (<ns0:label>10</ns0:label></ns0:formula><ns0:formula xml:id='formula_10'>)</ns0:formula><ns0:p>where f(S1), f(S2) and f(Nr) features of siblings and a random image, respectively. After calculating distance and using margin 'm' as hyper parameter, we can define the loss function as:</ns0:p><ns0:formula xml:id='formula_11'>£s (A,P,N) = Max ( D s1 -Ds 2 + m ,0). (<ns0:label>11</ns0:label></ns0:formula><ns0:formula xml:id='formula_12'>)</ns0:formula><ns0:p>D s1 is the distance between one sibling with other siblings. Similarly, D s2 is the distance of siblings with a random image to find triplet loss and minimize the distance between the first and second siblings.</ns0:p></ns0:div> <ns0:div><ns0:head>Network Structure</ns0:head><ns0:p>To select information from different scales of features for input to the relational network, we use the pre-trained Siamese network and get a feature map of size R 512×1 . Network contains three dense layers to down sample the features map and get a features vector of size 128x1. Each features vector of size 128x1 will provide information of the faces as face encoding. This face encoding is then used to find the cosine similarity between face images respectively. After that relational network analyzes these selected features with multi-layer perceptrons which consists of some fully connected layers and relu activation functions.</ns0:p><ns0:p>Following are steps of model training:</ns0:p><ns0:p>1. Pictorial data is fetched from the data set and all images are converted to the same stage of age by LATS. After age transformation, an intermediate dataset is prepared for training from original images. 2. Transformed data is fetched into three vectors: father, mother and children, to prepare a triplet for the Siamese network 3. One vector is used as positive, one for negative and one as anchor 4. The triplet is used by the Siamese network to extract face features 5. Define the Triplet loss function. It decreases the distance between positive and anchor images and increases the distance between positive and negative images. Manuscript to be reviewed Computer Science 6. Setting up for training and evaluation 7. This multi-layer perceptron will extract the relation of features and output feature of size R 128×1 . Then we compare these features of size R 128×1 at the element level to represent the distance between features of faces. 8. Lastly, we use another multi-layer perceptron to find the similarity of faces for kinship identification from the relation of different face images. It also consists of some fully connected layers and relu activation functions.</ns0:p><ns0:p>A flow of model traing is represented in figure <ns0:ref type='figure' target='#fig_9'>4</ns0:ref>. The CNN structure uses Saimese network; its input size is 3256256 and final output features vector size is 1281. This network has three dense layers subsequently with batch normalization and relu activation function to minizise the size of feature vector.</ns0:p><ns0:p>The relational network has three convolutional layers, each layer uses 128 feature vector of 10 images with batch normalization and relu activation function. The input feature size of each layer is R 10x128x128x128 and last dense layer has output feature size of 1x128. It applies segmoid function to establish kin relationship between images, detailed relational network with input parameters is depicted in Table-1.</ns0:p></ns0:div> <ns0:div><ns0:head>Table 1: Parameters of Deep Relational Network</ns0:head><ns0:p>To optimize the network, contrastive loss function is used with below specifications:</ns0:p><ns0:formula xml:id='formula_13'>L(d,Y) =∑N i [1/2 Y i d i 2 + (1 -y i ) 1/2*max(0,m -d i )]<ns0:label>(12)</ns0:label></ns0:formula><ns0:p>where L denotes the loss, N represents the number of samples, y i is the ground truth of ith sample, and d i is the distance between the output of the encoder, m is margin parameter.</ns0:p><ns0:p>Similar face images are pushed close and dissimilar images pushed away to get maximum similarity between similar images.</ns0:p></ns0:div> <ns0:div><ns0:head>Data Set</ns0:head><ns0:p>We </ns0:p></ns0:div> <ns0:div><ns0:head>Results and Discussion</ns0:head><ns0:p>The CNN-based deep relational network is utilized for extracting the features from the facial images of the dataset. Table <ns0:ref type='table'>1</ns0:ref>. outlines the details of the included parameters for the CNN-based deep relational network. Unlike the previously existing models, it represents that our model explicitly establishes relations between three feature maps rather than making relations within one another. Additionally, it depicts that our model takes ten images of each member and finds the triplet loss on 128 features maps of each ten images for one member. In total used, 30</ns0:p><ns0:p>features map for one comparison to find the similarity between them. The proposed model delivers the optimal performance by utilizing this methodology.</ns0:p><ns0:p>In this section of the study, we have listed the experiments and achieved results by employing the use of the proposed technique of utilizing a deep relational network along with the LAT age transformation algorithm <ns0:ref type='bibr' target='#b37'>[32]</ns0:ref>. We have used the large dataset of Recognizing family in the wild Similarly, Table <ns0:ref type='table' target='#tab_4'>3</ns0:ref>. represents the observed results on the baseline dataset. While comparing accuracy with a model trained on dataset RFIW, the results from Table <ns0:ref type='table' target='#tab_4'>3</ns0:ref>. indicate that our proposed model has delivered better performance than the existing state-of-the-art models by improving the overall accuracy. Meanwhile, the previous models have failed to deliver improved performance for up to 73.21% accuracy. On the other hand, the proposed model has outperformed existing state-of-the-art models by delivering an accuracy of 76.38%. Furthermore, we plan to improve the model and accuracy in the future by improving the underlying relational network and applying it to transformed images with the same stage of age.</ns0:p><ns0:p>The major contribution of our research is to introduce a robust way of kinship identification by comparing images of parents and their children with transformed ages instead of comparing their actual images. Improved accuracy of methodology proved that we could get better results for kinship identification if we compare images after age transformation instead of comparing direct actual images. From the results obtained after training indicates that similarity between the same genders is greater than opposite gender because the similarity score between father-son and mother-daughter is greater than father-daughter and mother-son, respectively. The obtained results show that due to the same gender factor, daughter looks more similar to the mother compared to the father. Similarly, the son seems more similar to father rather than the mother.</ns0:p><ns0:p>PeerJ Comput. Sci. reviewing PDF \| (CS-2022:01:70263:1:2:NEW 23 Apr 2022)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Computer Science</ns0:head></ns0:div> <ns0:div><ns0:head>Conclusion</ns0:head><ns0:p>Kinship identification is used for kinship verification by using facial images. Meanwhile, the previous studies have explored this area by employing transfer learning-based solutions. This study, however, presents a different approach to perform kinship verification.</ns0:p><ns0:p>In this study, we have introduced a technique that uses a pre-trained LAT model along with a Siamese network for performing kinship identification. Additionally, we have employed the age transformation approach to find similarities between parents with children. The extensive experimental results were used to validate the performance of our proposed model. Furthermore, the comparative analysis with previously carried out studies reflects that our model outperformed the existing state-of-the-art models using a similar approach, thereby delivering an overall accuracy of 76.38%. In the future, we aim to improve the model performance by improving the underlying relational network and applying it on transformed images with the same age stage. </ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head>Figure 1 :</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1: High Level Methodology of Proposed Study</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>G and Hassner T proposed a classification methodology using PeerJ Comput. Sci. reviewing PDF \| (CS-2022:01:70263:1:2:NEW 23 Apr 2022) Manuscript to be reviewed Computer Science age and gender by applying convolutional neural networks and got better results [7]. Dehghan A. et al. proposed the genetic identification technique by determining resemblance between parent and offspring via gated autoencoders. They used deep learning techniques to learn the most discriminative features between parents and children to find out their resemblance. That</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ Comput. Sci. reviewing PDF \| (CS-2022:01:70263:1:2:NEW 23 Apr 2022)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head>Kaming</ns0:head><ns0:label /><ns0:figDesc>He et al. introduced a deep residual learning approach for image recognition. Their approach used residual training with neural networks and multiple layers as learning residual functions [19]. Duan Q, Zahng L., Zuo W proposed a deep kinship verification technique named Coarse to Fine Transfer(CFT) using Convolutional Neural Network (CNN) from face recognition to kinship recognition and used Deep Transfer Learning [23]. Yana H and Hu J proposed a kinship verification technique, which works on videos. This technique uses distance metric learning on Kinship Face Videos in the Wild (KFVW) dataset for kinship verification [24]. Lu J. et al. developed a discriminative deep multi-metric learning (DDMML) methodology. They used multiple neural networks jointly to maximize the association of different features of each sample and reduce the distance of positive pair and increase the distance of negative pair [25]. Yong Li et al. introduced the kinship verification technique using KinNet: Fine-to-Coarse Deep Metric Learning and Pre-training the network and minimizing a soft triplet loss. They used four CNN networks to boost the performance [15]. Liu W. et al. introduced SphereFace, a deep hyper sphere embedding for face recognition. They addressed angular SoftMax loss and angular margins problem. Their technique uses a 64-layer CNN neural network for training and PeerJ Comput. Sci. reviewing PDF \| (CS-2022:01:70263:1:2:NEW 23 Apr 2022)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ Comput. Sci. reviewing PDF \| (CS-2022:01:70263:1:2:NEW 23 Apr 2022)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head>Figure 2 :</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2: Effect of Age Transformation</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head>Figure 3 :</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Figure 3: Working of Proposed Methodology It uses ResNet 50 with two fully connected layers and one Dense output Layer to extract features. It extracts a feature vector of 128x128 for all input facial images and uses triplet loss to discriminate features for kinship identification. It maximizes the distance of the anchor image with a negative image and minimizes the distance with a positive image. The size of input images is 224x224x3 and the feature vector returned by the Siamese network is 128. During the training process, hard sample selection for positive or negative pairs are not equally important.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_7'><ns0:head>PeerJ</ns0:head><ns0:label /><ns0:figDesc>Comput. Sci. reviewing PDF \| (CS-2022:01:70263:1:2:NEW 23 Apr 2022)Manuscript to be reviewedComputer ScienceThis sequence of the set is used for extracting similarities of children with father and mother to get kinship relation of Father-Son (F-S), Father-Daughter (F-D), Mother-Son (M-S) and Mother-Daughter. For sibling relationships, we changed some sequence of power set. We took one sibling image as an anchor, one as positive, and one as negative if the third image of the sibling did not exist. For negative position, we took any random image from the set of mother or father. So, for negative position random set of images: Xr = P {Xa \| Xn}. Then set of triplets for sibling relationship Brother-Brother (B-B), Sister-Sister (S-S) and Brother-Sister (B-S) is as follows:</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_8'><ns0:head>PeerJ</ns0:head><ns0:label /><ns0:figDesc>Comput. Sci. reviewing PDF \| (CS-2022:01:70263:1:2:NEW 23 Apr 2022)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_9'><ns0:head>Figure 4 :</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Figure 4: Flow of Model Training with Age Transformation and Face Encoding</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_10'><ns0:head>Figure 1 High</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='20,42.52,229.87,525.00,260.25' type='bitmap' /></ns0:figure> <ns0:figure type='table' xml:id='tab_1'><ns0:head /><ns0:label /><ns0:figDesc>used a dataset of Recognizing Family in the Wild (RFIW) and took images of 200 families having good resolution images as a constraint of Life Span Age Transformation (LATS) that requires images having good resolution. LATS generates ten age clusters and each age cluster Manuscript to be reviewed Computer Science has ten images. We picked one age cluster between 15 and 19, so we used 10 images to train our model. We used images of ages between 15 and 19 years because in this age period person's face looks strong and can provide clear facial features and we can get the better encoding of facial images. For model training, we used images of 200 families; each family has average four members. For each member we used 10 transformed images and our model is trained on approximate 200x4x10=8000, from this pool of data, we used 30% data for validation. As we used the LATS model for preprocessing and Siamese Network for training our model, which are CNN based network architecture, therefore we also adopted CNN network for feature extraction and training model to get compatibility with the existing model and achieve efficient results. Moreover, for high dimensional data CNN provides automatic feature extraction and forwards extracted features to the classifier to get classification results.</ns0:figDesc><ns0:table /><ns0:note>PeerJ Comput. Sci. reviewing PDF \| (CS-2022:01:70263:1:2:NEW 23 Apr 2022)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_2'><ns0:head /><ns0:label /><ns0:figDesc>(RFIW) for the training and validation of our proposed technique. In the first phase, we converted images of datasets RFIW to different life stages for age transformation. After the age transformation of facial images, we converted images at the same stage of ages by adjusting the age factor. In the first stage, we transformed facial images by increasing or decreasing the age factor and making two images into the same stage of age. In the second phase, we trained our algorithm by comparing two images and evaluating metrics and parameter settings to extract kinship relation accuracy.For Age Transformation, we employed the Lifespan Age Transformation Synthesis algorithm, proposed by Roy Or-El et al.<ns0:ref type='bibr' target='#b37'>[32]</ns0:ref>. Using this algorithm, we prepared our data set images for comparison that converts images at different stages of life. Afterward, we picked the images of ages between 15-19 and used them for feature extraction. Table2. outlines the training and validation accuracies observed in different relationships by utilizing the proposed model.</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Age Transformation</ns0:cell></ns0:row></ns0:table><ns0:note>PeerJ Comput. Sci. reviewing PDF \| (CS-2022:01:70263:1:2:NEW 23 Apr 2022) Manuscript to be reviewed Computer Science</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_3'><ns0:head>Table 2 :</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Achieved Model Performance for different Relationships</ns0:figDesc><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_4'><ns0:head>Table 3 :</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Comparison of Results on Baseline Dataset</ns0:figDesc><ns0:table /></ns0:figure> </ns0:body> "	"Dear Editors, We would like to say thanks to the reviewers for their generous comments on the manuscript. As per the reviewers guidance we have edited the manuscript to address their recommendations and concerns. We believe that manuscript is now suitable for publication. Arshad Abbas On behalf of co-author Reviewer 1 (Anonymous) Basic reporting - Problem statement, solution provided and outcome should be clearly explained in abstract. The flow of information should be in order and connected. - Description of big data in the beginning of introduction is not relevant and should be removed. Start from pictorial discussion. - Clearly explain the problem statement in the introduction section. - Contribution of paper mainly highlights the methodology not the contribution. Explain how the results and outcome of this research contributes to the body of knowledge. - Remove paper organization section. - 'Joseph P.R. et al Introduced.....' use standard style of reference citation in the text. - 'resemblance [8]. Zhang et al. (2015)' - follow only one style of referencing throughout the paper. - Literature should be written in discussion form not simple descriptive form. - 'we present a model.....' don't use I, we, our in research. use passive voice sentences. We have revised the paper to clearly describe the problem statement in abstract and introduction and made better flow of information and connection. Description of big data and paper organizations are removed from introduction. We have also added the dataset section to describe the dataset in detail. Manuscript is also revised as per the other guidance and recommendation for corrections. Experimental design In proposed work, use modeling form (graphical/mathematical etc) to explain the methodology and explain all the steps in sequence. Give research model. - Support the methodology with references. - Provide details of data set, its source and how much data is used for training and implementation? - Equations are inline with text. Should be on separate lines, with standard equation format and numbering, and explain them. - Properly define the design of methodology and explain it step by step. Proposed work is revised as per the directions, we have written steps of methodology working and inline equations are numbered. Validity of the findings - How the validity of the results checked? - Explain results and show them with the proper technical format rather than simply mentioning them. - Use tables, data, numbers and figures to explain the results. - Data given in tables should be well explained and justified. - How results are significant? - Conclusion should explain how results support the objectives. what is the significance of the study. Summarize the outcome of the research, its impact and applicability. We have checked the validity by validation of classification results. We have made comparisons with state of the Art methodologies on dataset RFIW Additional comments - Make grammatical corrections. Grammar is checked and corrected Reviewer 2 (Anonymous) Basic reporting no comment Experimental design a)-What was the specific reason for picking images of an age between 15-20 only? b)-What was the dataset size used in the experimental validation. c)-Why CNN-based deep relational network was used for features extraction from the facial images of the dataset? We have answered these questions in dataset section of the manuscript. Validity of the findings a)- Why the proposed model is taking 10 images of each member? why not 5 or 15 or more? b)-Any specific reason why Father-Daughter and Mother-Son accuracy in both Training and Validation Accuracy is lower as compared to Father-Son and Mother-Daughter? c)-Have we also compared any other performance factor other than accuracy from the other proposed models mentioned in Table1. For Age Transformation, We have used Life Span Age Transformation (LATS) which generates 10 age clusters and each age cluster has 10 images. As we picked one age cluster having age between 15 and 19, so we used 10 images for training our model. This reason is have also mentioned in dataset section. The obtained results show that due to same gender factor, daughter looks more similar with mother as compare to father. Similarly, son looks more similar with father rather than mother. Additional comments We can only say that the proposed model's performance is better than the previously proposed model (Table1). We cannot say that the previously proposed models failed to deliver improved performance. Agreed, we have proposed a technique which may be able for kinship identification by comparing images of parents and their children with transformed age instead of comparing their actual images. Reviewer 3 (Anonymous) Basic reporting English language corrections are needed at some places in the article. Proofreading is recommended. E.g. line 194: Spelling mistake ‘pre-processing’, Line 284: use figure 4 instead of diagram 4 All mistakes are corrected as per the given direction. Experimental design no comments Validity of the findings no comment Additional comments Repetition of the basic working is observed in multiple sections. It is suggested that more details be added instead of repetitions of the basic working. Theme of Figure 3 and figure 4 is the same. Care must be taken when using abbreviations. It is suggested that the full form and its abbreviation be provided at the first appearance. Afterward only abbreviated form be used. E.g. RFIW –Full form and its abbreviation should be provided at first appearance only. Afterward only abbreviated form should be used. Lines 62, 82, 315. Similarly, at line 324 only the abbreviation LAT should have been used. Many other such examples are present. All equations must be numbered and cited in the text. Corrections have been incorporated and manuscript is revised as per the guidance and recommendation. "	Here is a paper. Please give your review comments after reading it.
432	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Malware harms the confidentiality and integrity of the information that causes material and moral damages to institutions or individuals. This study proposed a malware detection model based on API-call graphs and used Graph Variational Autoencoder (GVAE) to reduce the size of graph node features extracted from Android apk files. GVAE-reduced embeddings were fed to linear-based (SVM) and ensemble-based (LightGBM) models to finalize the malware detection process. To validate the effectiveness of the GVAE-reduced features, Recursive Feature Elimination (RFE) and Fisher Score (FS) were applied to select informative feature sets with the same sizes as GVAE-reduced embeddings. The results with RFE and FS selections revealed that LightGBM and RFE-selected 50 features achieved the highest accuracy (0.907) and F-measure (0.852) rates. When we used GVAE-reduced embeddings in the classification, there was an approximate increase of %4 in both models' accuracy rates. The same performance increase occurred in F-Measure rates which directly indicated the improvement in the discrimination powers of the models. The last conducted experiment that combined the strengths of RFE selection and GVAE led to a performance increase compared to only GVAE-reduced embeddings. RFE selection achieved an accuracy rate of 0.967 in LightGBM with the help of selected 30 relevant features from the combination of all GVAE-embeddings.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>INTRODUCTION</ns0:head><ns0:p>In our daily life, the use of mobile devices increases gradually as they facilitate many human needs.</ns0:p><ns0:p>The increasing interest of the end-users on mobile devices gives rise to service providers transferring their activities and services to mobile platforms. Due to this transformation, the number of mobile applications increases day by day. Moreover, end-users store personal multimedia data such as photos and videos, as well as confidential data such as card information, user names, and passwords on mobile devices. Attackers can generate malware on the mobile operating systems (OS) to gain financial benefits from users. Android OS is frequently targeted by malware because of both having a higher market share and more number of developed applications compared to its competitors. According to the report published by Kaspersky in 2021, the number of malware increased from approximately 1.150M in Q1 of 2020 to approximately 1.250M in the Q2 of 2020. The rapid increase in the number of malware shows the importance of analysis methods on Android OS (https://go.kaspersky.com/rs/ 802-IJN-240/images/KSB_statistics_2020_en.pdf).</ns0:p><ns0:p>Malware defects the confidential information of institutions or individuals that causes material and moral damages. Therefore, intensive studies are carried out at both academic and industry levels to detect malware components effectively and continuously in Android-based devices. Literature studies on malware analysis present us with two types of analysis techniques named static and dynamic. Static analysis performs malware examinations based on source files without running them on any virtual/real devices. On the other hand, attackers can bypass the detection mechanisms of static analysis with code obfuscation. Dynamic analysis is more effective in detecting malicious activity consisting of code obfuscations. One of the cons of dynamic analysis is that malicious activity in the applications can be PeerJ Comput. Sci. reviewing PDF \| (CS-2022:03:71513:1:2:NEW 22 Apr 2022)</ns0:p><ns0:p>Manuscript to be reviewed Computer Science triggered during the conduction of analysis. In addition, running a malicious application within a sufficient time interval will have a great impact on the dynamic analysis results.</ns0:p><ns0:p>In the last decade, the analysis of Android applications was conducted automatically with the help of Machine Learning (ML) and Deep Learning (DL) models. The performances of such models were directly related to the quality of handcrafted features. To boost the predictive power and increase the generalization ability of ML models, dimensionality reduction methods reduce the size of the feature space. Feature selection is a type of dimensionality reduction method that aims to find the subset of informative features from the entire feature space. It is a process that needs human labor and domain expertise. Since DL can directly extract high-level features from instances and automatize the feature engineering process, it has gained popularity in malware detection tasks. The ability of DL models on solving complex problems is another aspect that attracts many researchers to use such models in the detection of malware.</ns0:p><ns0:p>ML and DL models perform malware detection in 3 steps:</ns0:p><ns0:p>1. The analysis of Android apk files with appropriate tools.</ns0:p><ns0:p>2. The extraction of static and dynamic features from the analyzed files.</ns0:p></ns0:div> <ns0:div><ns0:head n='3.'>The use of extracted features in model training to discriminate malware from benign</ns0:head><ns0:p>While ML models such as K-Nearest Neighbors, Support Vectors Machines, Naive <ns0:ref type='bibr'>Bayes, Random Forest, and Decision Trees (Al-Kasassbeh et al., 2020;</ns0:ref><ns0:ref type='bibr' target='#b9'>Chumachenko, 2017;</ns0:ref><ns0:ref type='bibr' target='#b30'>Mahajan et al., 2019)</ns0:ref> were the most preferred algorithms in malware detection, several DL models such as Convolutional Neural Networks (CNN), Artificial Neural Networks (ANN), and Recurrent Neural Networks (RNN) were employed in recent detection studies <ns0:ref type='bibr' target='#b2'>(Alzaylaee et al., 2020;</ns0:ref><ns0:ref type='bibr' target='#b17'>Hemalatha et al., 2021;</ns0:ref><ns0:ref type='bibr' target='#b24'>Kim et al., 2018)</ns0:ref>.</ns0:p><ns0:p>Features mainly obtained from apk files of the applications such as permissions, op-code sequences, Function Call Graphs (FCG), and Application Programming Interface (API) calls were used as inputs to these models to detect the malicious applications <ns0:ref type='bibr' target='#b27'>(Liu et al., 2020)</ns0:ref>.</ns0:p><ns0:p>Graph-based models recently have been adopted in many tasks since they can model the latent properties between nodes and edges successfully. For instance, Graph Convolutional Neural Networks (GCNs), and Graph Attention Networks (GANs) can extract rich representations that result in performance increases compared to the traditional ML and DL models <ns0:ref type='bibr' target='#b25'>(Kipf and Welling, 2016;</ns0:ref><ns0:ref type='bibr' target='#b39'>Veličković et al., 2017)</ns0:ref>.</ns0:p><ns0:p>The advantage of graph-based models is that they can capture the behavioral features and information accurately which lack in other methods.</ns0:p><ns0:p>Our study proposed a malware detection framework based on the Graph Variational Autoencoder (GVAE). To train and validate the framework performance, we acquired malware and benign Android applications from two public datasets. Since GVAE considered the edges between pair nodes that may contain specific information about its end-points in generating node representations, we applied it to reduce the size of API-call graph nodes extracted from Android .apk files. Thus, GVAE produced low-dimensional node representation vectors from API-call graphs to generate graph embeddings with varying sizes. We then provided GVAE-reduced embeddings to linear (SVM) and ensemble-based (LightGBM) models to realize the malware detection process and finally assessed the performances of these models with both accuracy and F-Measure metrics. To validate the effectiveness of the GVAE model on dimensionality reduction, we chose Recursive Feature Elimination (RFE) and Fisher Score (FS) as alternative selection methods. For making a fair comparison, we determined the same number of features as GVAE-reduced feature sets from node features by RFE and FS selections. In the last step, we pipelined GVAE with RFE and FS selections to create a hybrid reduction method.</ns0:p><ns0:p>The main contributions of our work can be summarized as follows:</ns0:p><ns0:p>• First of all, our malware detection framework contributes to the generation of robust node feature embeddings from the API-call graphs with the help of GVAE. GVAE handles irregularities in latent space by embedding input data to distribution rather than a point and generates the less noisy and compact node embeddings from raw node features. To the best of our knowledge, this is the first malware detection study that employs GVAE in generating node embeddings from API-call graphs.</ns0:p><ns0:p>• Our second contribution is the use of a hybrid reduction pipeline that combined GVAE with two different feature selection methods. The proposed model selected relevant feature representations from GVAE-reduced embeddings to improve the malware detection performance. The notable Manuscript to be reviewed Computer Science performance of RFE and Fisher Score in handling noisy data during the selection process is the main factor in using these methods hierarchically. To the best of our knowledge, it is the first study that combines different types of selection methods with GVAE-reduced embeddings to use in the malware detection process.</ns0:p><ns0:p>• Our last contribution is that the model used in the dimensionality reduction steps has a flexible structure. Networks (such as biological, citation, etc.), contain nodes with high dimensional features that can result in memory and computation issues in the training and inference phase of ML models.</ns0:p><ns0:p>Our hybrid feature reduction model has a generic structure that can be applied to the aforementioned tasks to address the complexity and memory issues.</ns0:p><ns0:p>The remainder of the paper consists of 4 sections. 'Related Work' briefly summarizes recent malware studies. 'Methods' section explains the used dimensionality reduction methods, classification models, and evaluation metrics in detail. 'Experimental Results' section presents the results of all conducted experiments in order, and 'Conclusion and Discussion' section concludes the paper and compares the experimental findings with recent studies.</ns0:p></ns0:div> <ns0:div><ns0:head>RELATED WORK</ns0:head><ns0:p>As mentioned before, static malware analysis is performed without running applications on virtual or real mobile devices. There are many studies in which a static analysis approach is used to detect Android malware. This approach is based on the intuition that the static attributes of applications belonging to the same malware family should be similar. The use of static analysis for the detection of malware is quite common because malware can be examined quickly without infecting any device. In addition, low analysis cost and low resource consumption can be mentioned as positive aspects of static analysis.</ns0:p><ns0:p>Many malware static analysis studies used different types of feature sets given as inputs to the ML and DL models. These features can be listed as source codes of the applications, application permissions, Application Programming Interface (API)-call graphs, and Component Dependency Graphs (CDG).</ns0:p><ns0:p>For instance; Malgenome was a pioneer study in which the static analysis approach was employed to inspect Android malware families within 1260 collected malware applications. Methods in the application source codes and application permissions in the AndroidManifest.xml were two data sources during the examination process. Malgenome study revealed that malicious applications requested SMS-related permissions such as READ SMS, WRITE SMS, RECEIVE SMS, and SEND SMS more frequently than non-malicious applications <ns0:ref type='bibr' target='#b45'>(Zhou and Jiang, 2012)</ns0:ref>.</ns0:p><ns0:p>Drebin was another study supporting evidence in the Malgenome and used API-calls to evaluate for services in sending/receiving SMS messages <ns0:ref type='bibr' target='#b5'>(Arp et al., 2014)</ns0:ref>. It was also one of the early studies that used machine learning models for the detection of Android malware. Permissions, activities, services, content providers, and broadcast receivers were the types of features extracted from apps in the Drebin dataset. Linear Support Vector Machines were used as an ML model to determine the families of 5560 malware applications. Drebin was been an inspiration to future malware detection studies with the specification of using different features and performing feature selection to find effective features in malware detection. <ns0:ref type='bibr' target='#b36'>Suarez-Tangil et al. (2014)</ns0:ref> proposed a model named 'Dendroid' that utilized control flow graphs (CFG) as model inputs. CFGs were used to extract the code structures/blocks in malware applications.</ns0:p><ns0:p>The K-Nearest Neighbor model was formed with the extracted frequencies of code blocks based on each malware family. A single linkage hierarchical clustering algorithm was used to extract hierarchical similarities between malware families and the results were represented with dendrogram trees.</ns0:p><ns0:p>DroidSIFT <ns0:ref type='bibr' target='#b43'>(Zhang et al., 2014)</ns0:ref> was the first study that employed a graph-based method for malware family classification. This study extracted methods and API-calls from the source code of applications to express apps with weighted contextual API-based graphs. The classification process considered API-calls that match the permissions requested from the user as well as security-related API-calls. To perform the classification process, the similarity value between each graph obtained from a new app and the graphs of different malware apps was computed.</ns0:p><ns0:p>Image processing-based features were also employed in static analysis. In <ns0:ref type='bibr' target='#b19'>Iadarola et al. (2020)</ns0:ref>, malware applications were converted to grayscale and binary images before passing them through 4 different image filters. An accuracy rate of 96.9% has been demonstrated with the combination of the feature representations obtained from filters and the Random Forest model.</ns0:p></ns0:div> <ns0:div><ns0:head>3/13</ns0:head><ns0:p>PeerJ Comput. Sci. reviewing PDF \| (CS- <ns0:ref type='table' target='#tab_9'>2022:03:71513:1:2:NEW 22 Apr 2022)</ns0:ref> Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Computer Science</ns0:head><ns0:p>Deep learning architectures were also used together with the static analysis approach in recent malware detection studies. <ns0:ref type='bibr' target='#b34'>Sewak et al. (2018)</ns0:ref> rate accuracy over a dataset that contained nearly 70,000 instances. In <ns0:ref type='bibr' target='#b21'>Kang et al. (2020)</ns0:ref>, two different dataset samples were derived from the .dex files of applications using image processing techniques.</ns0:p><ns0:p>The first dataset created feature sets from the entire .dex files, while the second dataset created features considering only the data part of the files. CNN model was trained with the extracted datasets and malware families were predicted with an accuracy rate of 91%. Autoencoder is the other deep learning architecture actively studied in cyber-security domain for anomaly detection <ns0:ref type='bibr' target='#b41'>(Xu et al., 2021b)</ns0:ref>, data generation <ns0:ref type='bibr' target='#b20'>(Kabore et al., 2021)</ns0:ref>, and dimensionality reduction <ns0:ref type='bibr' target='#b16'>(Haseeb et al., 2022)</ns0:ref>. For example, several autoencoder models have been utilized in intelligent Network Intrusion Detection Systems (NIDS) to handle zero-day attacks with high accuracy <ns0:ref type='bibr' target='#b35'>(Song et al., 2021)</ns0:ref>.</ns0:p><ns0:p>Variational Autoencoder (VAE) has been used to generate intrusion data in a generative manner to cover the imbalanced data problem generally seen in many intrusion detection systems <ns0:ref type='bibr' target='#b28'>(Lopez-Martin et al., 2019;</ns0:ref><ns0:ref type='bibr' target='#b38'>Vaiyapuri and Binbusayyis, 2020)</ns0:ref>. <ns0:ref type='bibr' target='#b42'>(Yousefi-Azar et al., 2017)</ns0:ref> Our recent study on malware detection extracted API-call graphs from Android apk files and detected malicious applications over Android-based devices placed at intelligent transportation systems. Our work constructed two types of node features as Node2Vec embedding and network properties for each node in API-call graphs. Graph Attention Networks (GAN) were trained with extracted feature sets and the combination of GAN and Node2Vec features showed the best performances over the entire feature set and GNN combinations <ns0:ref type='bibr' target='#b8'>(Catal et al., 2021)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>METHODS</ns0:head><ns0:p>This section provides information on the proposed framework and explains the details of each component of such framework. The proposed malware detection framework is designed as an end-to-end model that takes the Android .apk files as model input and classifies these files as benign or malware in the output.</ns0:p><ns0:p>Figure <ns0:ref type='figure'>1</ns0:ref> presents the graphical representation of the framework. The proposed framework consists of four sequential steps. In the first step, training and test apk files were collected from two public datasets.</ns0:p><ns0:p>The details about the used datasets were explained in the next subsection. In the second step, API-call graphs, which represented caller-callee relationships between the methods in a source code, were created from .apk files with the help of the Androguard tool. After the call graph generation, the Node2Vec model produced 100-dimensional features for each node in the graphs. The third step of the framework realized the dimensionality reduction process. Graph Variational Autoencoder (GVAE) first was applied to obtain reduced node embeddings with varying sizes. Following the production of node embeddings, the graph embedding vector was constructed by averaging all the node embeddings in the graph. At last, Recursive Feature Elimination (RFE) and Fisher Score (FS) were used to reduce the dimensions of the graph embedding vector. As stated in the introduction, the RFE and FS methods were used to demonstrate the effectiveness of the GVAE method in size reduction. RFE was determined as an alternative model to GVAE due to its success in handling the dependencies and collinearity between the attributes that are also present in the graph-structured data. On the other hand, FS was chosen as another comparison method since it considered both positive and negative class samples during the computation of feature relevances The details of each component in the framework were explained in the subsections below.</ns0:p></ns0:div> <ns0:div><ns0:head>Dataset</ns0:head><ns0:p>To assess the performance of our proposed framework, we used two open-access datasets from Canadian Institute for Cybersecurity website (https://www.unb.ca/cic/datasets, accessed on 10 December 2021). The first dataset is ISCX-AndroidBot-2015 which comprises 14 botnet families with 1929 instances (https://www.unb.ca/cic/datasets/android-botnet.html, accessed on 10 December 2021). Since this dataset does not include any benign instances, we provide benign apps from another dataset named CICMalDroid (https://www.unb.ca/cic/datasets/maldroid-2020.</ns0:p><ns0:p>html, accessed on 10 December 2021). We acquired 1795 benign instances from CICMalDroid and finally devised a dataset consisting of 3724 instances (Table <ns0:ref type='table' target='#tab_1'>1</ns0:ref>). After generating the dataset, we extracted the API-call graphs from application .apk files. We used the Androguard tool for the API-call extraction process. We first determined the sensitive Android APIs from all API sets. Selected sensitive APIs are 'Landroid.accounts','Landroid.app', 'Landroid.bluetooth', 'Landroid.content', 'Landroid.location', 'Landroid.net', 'Landroid.nfc', 'Landroid.provider', 'Landroid.telecom', and 'Landroid.telephony'. We then created the nodes of call graphs by representing caller-callee relationships between the methods of the sensitive APIs. Following the creation of API-call graphs, we used the Node2Vec for generating 100-dimensional features for each node in the graphs.</ns0:p></ns0:div> <ns0:div><ns0:head>5/13</ns0:head><ns0:p>PeerJ Comput. Sci. reviewing PDF \| (CS-2022:03:71513:1:2:NEW 22 Apr 2022)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p><ns0:p>Computer Science</ns0:p></ns0:div> <ns0:div><ns0:head>Graph Variational Autoencoder (GVAE)</ns0:head><ns0:p>An autoencoder is a neural network architecture that reconstructs the samples given to the input layer at the output layer. Variational AutoEncoder (VAE), on the other hand, is a generative autoencoder model that forces the distribution of samples in the hidden space to a normal distribution <ns0:ref type='bibr' target='#b4'>(An and Cho, 2015)</ns0:ref>. VAE composes of two separate components as encoder and decoder. The encoder creates a hidden representation vector h from the input vector x in the hidden space, while the decode makes use of h vector to reconstruct the r output with the decoder network. VAE expresses the vector x in the input layer in terms of 2 parameters in the hidden space. These parameters are the mean and standard deviation (SD), </ns0:p><ns0:formula xml:id='formula_0'>which</ns0:formula></ns0:div> <ns0:div><ns0:head>Recursive Feature Elimination</ns0:head><ns0:p>Recursive Feature Elimination (RFE), which is a wrapper feature selection method, uses ML methods such as SVM and GBM to assign the feature relevance scores <ns0:ref type='bibr' target='#b13'>(Granitto et al., 2006)</ns0:ref>. RFE initially builds a model from whole features and calculates a feature importance score for each feature. After that, the feature with the least importance score is removed from the feature space and the model is reconstructed with the remaining features for computing new importance scores. This procedure is maintained up to the predefined number of features retains in the dataset. Hence, the desired feature count is a hyper-parameter for RFE. Another parameter to be specified in the RFE is the ML model, which is employed in calculating the feature importance scores. SVM is a favored algorithm for RFE to its high accuracy and robust generalization ability. At each iteration of the RFE, the Linear SVM model is trained to assign a weight coefficient to each feature. Since the feature with the lowest weight could have the least effect on the classification, this feature can be ignored in the next iteration. In the case of high dimensional feature space, more than one feature may be omitted per each iteration of RFE <ns0:ref type='bibr' target='#b14'>(Gunduz, 2021a)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Fisher Score</ns0:head><ns0:p>Fisher Score is a filter-based method that aims to measure the relevance between each feature and the class label to select the informative features. Fisher Score utilizes the mean and standard deviation values of the features for each class in computing feature relevances. The equation of Fisher Score is shown below:</ns0:p><ns0:formula xml:id='formula_1'>f (k) = ∑ C j=1 n j (µ k j − µ k ) 2 ∑ C j=1 n j (σ k j ) 2 (1)</ns0:formula><ns0:p>In Equation <ns0:ref type='formula'>1</ns0:ref>, µ k j denotes to the mean of the k-th feature in the j-th class while σ k j indicates the variance of the k-th feature in the j-th class. n j refers to the total instance counts in the j-th class. µ k shows the mean of the k-th feature. In the Fisher Score selection, the Fisher scores of all the features are sorted in descending order, and the desired number of features are selected starting with the high-scoring features <ns0:ref type='bibr' target='#b37'>(Sun et al., 2021;</ns0:ref><ns0:ref type='bibr' target='#b15'>Gunduz, 2021b)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>6/13</ns0:head><ns0:p>PeerJ Comput. Sci. reviewing PDF \| (CS-2022:03:71513:1:2:NEW 22 Apr 2022)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Computer Science</ns0:head><ns0:p>Support Vector Machines (SVM)</ns0:p><ns0:p>Support vector machines (SVM) is a machine learning algorithm used to solve both classification and regression problems. SVM aims to find an optimal hyperplane that separates the instances of two classes in a binary classification task. SVM creates this hyperplane using different kernel functions in datasets in which the number of dimensions is more than the number of instances. However, some problems consist of data points that cannot be separated linearly. Therefore, SVM projects the patterns in non-separable data into a new space and looks for a hyperplane in the new space. SVM uses the kernel functions to project the linearly non-separable dataset to larger dimensional spaces that can be linearly separated.</ns0:p><ns0:p>The linear separation of the dataset is realized with a certain error because of the noisy and complex structure of the data. In the case of linear separation with a certain error, a slack-bound approach is used to separate the two-class dataset. To reduce the probability of misclassification, the problem turns into an optimization problem by performing transformations in the linear separation case with the help of the C coefficient. Lower C values can cause under-fitted models that may have more misclassified samples, while higher C values tend to rise the variance of the model and lead to overfitting <ns0:ref type='bibr' target='#b18'>(Huda et al., 2016)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>LightGBM</ns0:head><ns0:p>Boosting is an ensemble model that constructs a single strong learner from a predefined number of base learners. Boosting trains a group of learners with the same dataset instances, but adjusts the weights of the instances according to the errors of the final prediction. The intuition behind the boosting is to empower models to focus on instances that are hard to predict.</ns0:p><ns0:p>LightGBM is a fast, distributed, high-performance boosting model built on decision trees. It is a typical gradient boosting strategy that utilizes many weak decision trees. Opposite to the bagging strategy, LightGBM iteratively combines models. Boosting models have two formation approaches, level-oriented and leaf-oriented during the iterative training of each decision tree. The level-oriented approach maintains the balance property during tree expansion, whereas the leaf-oriented approach continues to split the biggest loss decreasing leaf. LightGBM makes use of a leaf-oriented approach that considers both losses in a particular tree split and the contribution of this splitting to the entire loss. Therefore, it forms the trees with lower errors rather than a level-oriented tree growing <ns0:ref type='bibr' target='#b22'>(Ke et al., 2017)</ns0:ref>. The training time of a simple decision tree is directly related to the number of possible node splits. Small variations in splitting often do not make a big distinction in model performance. LightGBM utilizes this case by grouping the features into several bins and splitting them into the bins instead of the features. This property can decrease the computational complexity and result in reductions in model training time.</ns0:p><ns0:p>The basic parameters to be determined in the LightGBM are the number of learners, learning rate, and max-depth. The number of learners is the number of iterations used in setting up the ensemble model. A high number of iterations can lead to overfitting while a low number prevents us from learning patterns.</ns0:p><ns0:p>Learning rate (lr) is a value between 0 and 1 for scaling generated trees. A smaller lr can help better predictive power. However, it can increase model training time and result in possible overfitting. Max-dept is used to limit the depth of the tree to be built. It should be optimized to avoid overfitting. Too much branching will cause overfitting, and too little branching will cause underfitting.</ns0:p></ns0:div> <ns0:div><ns0:head>Evaluation Metrics</ns0:head><ns0:p>Although accuracy is a common measure in performance evaluation, it has a lack of ability in the assessment of class discrimination. F-measure is an alternative metric to use in the validation of the class-based model performance. The computation of the accuracy and F-Measure is directly related to Confusion Matrix (CM) in which basically presents the number of correct and incorrect predicted instances per class (Table <ns0:ref type='table'>2</ns0:ref>). True positive (t p), false positive ( f p), false negative ( f n), and true negative (tn) are the values used to compute aforementioned metrics <ns0:ref type='bibr' target='#b15'>(Gunduz, 2021b)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Table 2. Confusion matrix for two-class classification.</ns0:head><ns0:p>Actual/Predicted as Positive Negative</ns0:p></ns0:div> <ns0:div><ns0:head>Positive t p f n</ns0:head></ns0:div> <ns0:div><ns0:head>Negative f p tn</ns0:head><ns0:p>Accuracy is defined as the ratio of the number of accurate predictions to the total number of instances.</ns0:p></ns0:div> <ns0:div><ns0:head>7/13</ns0:head><ns0:p>PeerJ Comput. Sci. reviewing PDF \| (CS-2022:03:71513:1:2:NEW 22 Apr 2022)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Computer Science</ns0:head><ns0:p>However, when the ratio between the f p and f n becomes very large, F-measure needs to handle the job in performance evaluation.</ns0:p><ns0:p>F-measure considers precision and recall by taking the harmonic mean of both metrics. Therefore, false positive and false negative samples are involved in the assessment of class discrimination. Based on the confusion matrix, F-Measure is computed as follows:</ns0:p><ns0:formula xml:id='formula_2'>precision = t p t p + f p (2) recall = t p t p + f n (3) F-Measure = 2 × precision × recall precision + recall . (<ns0:label>4</ns0:label></ns0:formula><ns0:formula xml:id='formula_3'>)</ns0:formula></ns0:div> <ns0:div><ns0:head>EXPERIMENTAL RESULTS</ns0:head><ns0:p>As aforementioned before, experiments were conducted with a dataset formed from the combination of two public datasets. Seeing that graph-structured data needed a high computational resource, experiments were realized on a PC with GTX 1070 Graphics Processing Unit (GPU) support. Pytorch-Geometric framework <ns0:ref type='bibr' target='#b10'>(Fey and Lenssen, 2019)</ns0:ref> of the Pytorch was employed to build Graph Variational Autoencoder (GVAE) models. Compared to Keras and Tensorflow, Pytorch presents great and diverse opportunities for building graph neural networks with the help of the Pytorch-Geometric package.</ns0:p><ns0:p>In the first experiments, the GVAE model reduced the size of the node features and generated low dimensional node embeddings by considering the adjacency relations between the nodes as well as neighbors node features. The proposed GVAE architecture used the GCN network in its encoder component. Table <ns0:ref type='table' target='#tab_3'>3</ns0:ref> presented the hyperparameters of the generated network architecture. The training of the GVAE model was realized in 100 epochs with 64 batch sizes. While there were diverse options for the selection of optimizer functions including Adagrad, AdaBelief, and Rmsprop, Adam was chosen as the optimizer due to its ability to achieve convergence quickly with high accuracy <ns0:ref type='bibr' target='#b6'>(Bock and Weiß, 2019)</ns0:ref>. Both dropout and regularizer layers were also attached subsequently to GCN layers for avoiding overfitting during model generation. and the performances of the models have evaluated with the 10-Fold Cross-Validation (CV) method.</ns0:p><ns0:p>Despite the most preferred approach in performance assessment being a hold-out method, this method cannot consider all instances in the dataset and cause biases in performance evaluation. In addition, cross-validation is simple to comprehend and is less susceptible to biased prediction in the evaluation of the model success. A grid search was conducted on the parameters specified in Tables <ns0:ref type='table' target='#tab_6'>4 and 5</ns0:ref> in company with the CV to find out the optimal parameter set. To assess the statistical properties of the obtained results, the Wilcoxon Signed Rank Test was utilized with a 0.05 significance level.</ns0:p><ns0:p>The results in Table <ns0:ref type='table' target='#tab_7'>6</ns0:ref> showed that LightGBM had more successful performance than SVM in terms of accuracy and F-Measure metrics. LightGBM reached 0.943 accuracy with 0.912 F-Measure rates using Manuscript to be reviewed</ns0:p><ns0:p>Computer Science To make a fair comparison with the results obtained in the first experiments, the size of graph vectors was reduced from 100 to 20, 30, 40, and 50, respectively. SVM and LightGBM models were trained with the obtained selected relevant features and their classification performances were evaluated with 10-Fold CV.</ns0:p><ns0:p>Table <ns0:ref type='table' target='#tab_9'>7</ns0:ref> showed that the highest classification accuracy was achieved with 50 features selected by RFE. With this feature set, LightGBM and SVM had accuracy rates of 0.907 and 0.886, respectively.</ns0:p><ns0:p>The combination of FS with LightGBM underperformed slightly than RFE selection with an accuracy of 0.895 (Table <ns0:ref type='table' target='#tab_10'>8</ns0:ref>). The performance of SVM stayed behind LightGBM and obtained 0.874 accuracy with a subset of 50 FS-selected features.</ns0:p><ns0:p>In the last experiment, two-dimensionality reduction methods used in the previous experiments were blended. In order to achieve this, all GVAE-reduced embedding sets <ns0:ref type='bibr'>(20,30,40, and 50)</ns0:ref> were concatenated.</ns0:p><ns0:p>The combination of all embedding features resulted in a 140-dimensional vector for each graph. After the expansion of feature space with all embedding sets, the dimensions of feature vectors was reduced via RFE selection. The main reason for using RFE is that it outperformed FS in the previous experiments.</ns0:p><ns0:p>Classification results obtained with the combination of the GVAE-reduced embeddings and RFE selection were shown in Table <ns0:ref type='table' target='#tab_12'>9</ns0:ref>.</ns0:p><ns0:p>The results obtained with the combination of GVAE-reduced embeddings were higher than those obtained with the individual GVAE-reduced features sets. When RFE selection was made on the combined GVAE-reduced embeddings, the most successful classification result was again obtained with LightGBM. Manuscript to be reviewed</ns0:p><ns0:p>Computer Science </ns0:p></ns0:div> <ns0:div><ns0:head>CONCLUSION AND DISCUSSION</ns0:head><ns0:p>Even though numerous studies have been realized on malware detection using ML and DL models, detecting malware effectively using Graph Variational Autoencoders remains an unexplored topic area in the cyber-security domain. Our study used API-call graphs for malware detection and performed different dimensionality reduction methods on node features to find malicious code patterns. The first experiments utilized GVAE to extract low-dimensional node embeddings of several sizes from API-call graphs. The next experiments applied RFE and FS selections to select informative feature sets with the same sizes as GVAE-reduced embeddings. The results with RFE and FS selections revealed that LightGBM achieved the highest accuracy (0.907) and F-measure (0.852) rates using 50 features. SVM again showed sufficient performance with an accuracy rate of 0.886. When both models used GVAE embeddings as model inputs, there was an approximate increase of 4 percent in their accuracy rates. Same performance increases could also be seen in F-Measure rates that directly indicated the improvement in the discrimination power of the models. LightGBM and SVM reached the best accuracy rates with 40 and 30 reduced features, respectively. The last conducted experiment combined the strengths of RFE selection and GVAE that led to the performance rise compared to only GVAE-reduced embeddings. RFE selected 30 relevant features from the combination of all GVAE-reduced features and boosted prediction accuracy to 0.967 in LightGBM. SVM also reached an accuracy of 0.955 with 0.921 F-Measure scores with 40 features that resulted in a nearly %2 increase on both performance metrics compared to only GVAE-reduced features.</ns0:p><ns0:p>All conducted experiments revealed that the proposed hybrid size-reduction framework has two prominent properties that helped to achieve the best results compared to all individual models. The first property is that GVAE uses the GCN model in its encoder component. GCN considers adjacent nodes as well as node features during the generation of node embeddings. The second property of the framework is that RFE employs LightGBM in computing feature importance scores. LightGBM is an efficient model for reducing variance and preventing overfitting during the computation of feature relevances. Obtained test results confirmed that the proposed framework can effectively detect malware with high accuracy and F-Measure scores.</ns0:p><ns0:p>The experimental results we obtained were also compared with the results of the recent malware studies that had deployed DL and ML models in the detection process. Recent survey articles presented the dominance of static analysis in the detection/classification process due to the ease of finding malware code structures without running on real devices. Moreover, most of these studies employed the features extracted from source code files such as Android permissions, Op-code sequences, API-call sequences, and API-call graphs in malware detections. Classification performances of recent studies are presented in Table <ns0:ref type='table' target='#tab_13'>10</ns0:ref>. When the results were examined, it was concluded that the performances of the proposed models in these studies vary between 0.90 and 0.99 in terms of accuracy and F-Measure rates. In addition, these studies benefited from DL models in feature extraction and classification steps.</ns0:p><ns0:p>Considering the studies using the same feature set as in our study, it was seen that the highest success Manuscript to be reviewed</ns0:p><ns0:p>Computer Science <ns0:ref type='bibr' target='#b31'>(Narayanan et al., 2018)</ns0:ref> APIs' permissions NA 0.985 DBN <ns0:ref type='bibr' target='#b26'>(Li et al., 2018)</ns0:ref> API-calls 0.900 NA CNN <ns0:ref type='bibr' target='#b3'>(Amin et al., 2019)</ns0:ref> Risky permissions 0.974 0.974 BiLSTM <ns0:ref type='bibr' target='#b29'>(Ma et al., 2020)</ns0:ref> API-call sequences 0.972 0.982 GE+CNN (Pektas ¸and Acarman, 2020) API-call graphs 0.988 0.986 LightGBM <ns0:ref type='bibr' target='#b1'>(Al Sarah et al., 2021)</ns0:ref> APIs' permissions 0.990 0.980 GAT <ns0:ref type='bibr' target='#b8'>(Catal et al., 2021)</ns0:ref> API-call graphs 0.961 0.948 GVAE+LightGBM (Proposed study) API-call graphs 0.967 0.934 rate was achieved by Pektas and Acarman (Pektas ¸and Acarman, 2020) that compared the performances of different graph embeddings on CNN models. Since this study trains a shallow CNN model with a relatively small number of instances (5560 samples), it is not feasible to build such a model due to the chance of increasing overfitting. Another difficulty faced in this study is that the CNN model has many trainable parameters and the determination of the best parameter setting is a time-consuming process.</ns0:p><ns0:p>Unlike the aforementioned study, our study used a deep learning model, GVAE, during the extraction of low dimensional embeddings. After dimensionality reduction with GVAE, each application was represented by vectors with a maximum of 50 dimensions. Reducing the feature space of the data has also enabled the optimum parameters of the models to be found in a short time. Our study also benefited from the LightGBM model, which reduces overfitting by adjusting the model variance at each step during training. Our previous study <ns0:ref type='bibr' target='#b8'>(Catal et al., 2021)</ns0:ref> trained the Graph Attention Network model with the dataset used in this study and reached an accuracy rate of 0.96 using 100-dimensional node features.</ns0:p><ns0:p>Although the classification performance of the previous study was close to this study, our proposed model achieved this performance with only 30 features.</ns0:p><ns0:p>Experimental studies have some limitations and threats to validity. In this study, experimental setups were trained with two open-source datasets. The performance of the proposed model on other datasets might be slightly different; however, we do not expect too many variations in the performance. Different researchers also might develop new malware detection frameworks models using novel deep learning architectures and achieve better performance results than the one reported in this study.</ns0:p><ns0:p>This study focused to enhance the performance of malware detection models using a novel dimensionality reduction method. The proposed framework fused the GVAE and RFE. Experimental results presented that the execution of RFE selection on GVAE embeddings provided remarkable results. In addition, the proposed framework has a generic form that can be widened to diverse domains including graph-structured data types. Tasks in bioinformatics and recommendation systems are some examples of these domains where our framework can be adopted. Future work will conduct research on the utilization of DL and ML models in malware detection systems from the point of view of Explainable Artificial Intelligence (XAI).</ns0:p></ns0:div> <ns0:div><ns0:head>11/13</ns0:head><ns0:p>PeerJ Comput. Sci. reviewing PDF \| (CS-2022:03:71513:1:2:NEW 22 Apr 2022)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p><ns0:p>Computer Science</ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>blended different types of deep learning architectures and extracted feature representations from deep layers automatically. Their model achieved accuracy and false-positive rates of 99.21% and 0.19% respectively. Another study presented a shallow malware detection model to handle the overfitting of DL models. This model used the combination of a Convolutional Neural Network (CNN) with given a sequence of op-code instructions as input data. The proposed model achieved a 95%</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>employed autoencoders to generate code vectors that captured latent representations of different feature sets. Trained autoencoders generated distinguished features from original features in an unsupervised fashion for malware classification and decreased the computational complexity of the proposed model significantly. Recently, Graph Neural Network (GNN) has gained popularity in the cyber-security domain, especially in malware detection tasks. For instance, Xu et al. (2021a) proposed a GNN-based malware family classification model that transformed function call graphs into dense embedding vectors to maintain the relationships between functions in the applications. The accuracy rates of the models increased up to 99.6% in malware detection and 98.7% in malware classification tasks. Gao et al. (2021)'s study presented a model named 'Gdriod' for malware classification. This study made use of a GNN model on a heterogeneous graph to model edge-based relationships between applications mapped APIs. The success rate of the proposed model measured 98.99% in terms of accuracy in the malware detection task.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head /><ns0:label /><ns0:figDesc>Figure 1. Graphical representation of the proposed framework.</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='6,141.73,63.78,413.56,235.93' type='bitmap' /></ns0:figure> <ns0:figure type='table' xml:id='tab_1'><ns0:head>Table 1 .</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Information about datasets.</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Dataset</ns0:cell><ns0:cell>#instances</ns0:cell><ns0:cell>Type</ns0:cell></ns0:row><ns0:row><ns0:cell>CICMalDroid</ns0:cell><ns0:cell>1929</ns0:cell><ns0:cell>benign</ns0:cell></ns0:row><ns0:row><ns0:cell>ISCX-AndroidBot-2015</ns0:cell><ns0:cell>1843</ns0:cell><ns0:cell>malware</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_2'><ns0:head /><ns0:label /><ns0:figDesc>are the descriptive statistics of the learnt normal distribution. VAE generates new low-dimensional samples with the learnt mean and sd vectors after model training. Although mean and sd values are deterministic, samples generated from these values are probabilistic.Graph Variational Autoencoder (GVAE) is proposed by<ns0:ref type='bibr' target='#b25'>Kipf and Welling (2016)</ns0:ref> for representation learning that operates the VAE over the graph data. GVAE basically generates new graphs from original input graphs. Due to having irregularities in graph-structured data, VAE is not directly applied to form feature representations for each node in the graphs. GVAE uses adjacency and feature matrices in generating node embeddings. While adjacency matrix A represents the neighborhood relationships between each node, feature matrix X extracts the feature information of each node from the input graph.The encoder component of GVAE consists of two consecutive GCN layers to generate the latent variable Z as output. The first GCN layer takes A and X matrices as inputs and creates Ã and X matrices to be inputted to the second GCN layer. The output of the second GCN is µ and logσ vectors. Low dimensional Z matrix is calculated with generated µ and logσ matrices using parameterization trick. The decoder component of GVAE is defined by an inner product between latent variable Z and the output of decoder component is a reconstructed adjacency matrix.</ns0:figDesc><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_3'><ns0:head>Table 3 .</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Parameters of proposed GVAE model.</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Parameter</ns0:cell><ns0:cell>Value</ns0:cell></ns0:row><ns0:row><ns0:cell>#epoch</ns0:cell><ns0:cell>100</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>#hidden units in GCN {20, 30, 40, 50}</ns0:cell></ns0:row><ns0:row><ns0:cell>#GCN layers</ns0:cell><ns0:cell>2</ns0:cell></ns0:row><ns0:row><ns0:cell>Dropout rate</ns0:cell><ns0:cell>0.2</ns0:cell></ns0:row><ns0:row><ns0:cell>L2-regularization rate</ns0:cell><ns0:cell>0.01</ns0:cell></ns0:row><ns0:row><ns0:cell>Optimizer</ns0:cell><ns0:cell>Adam</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>GVAE reduced the sizes of node features from 100 to 20, 30, 40, and 50, respectively. After producing</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>low-dimensional node embeddings, each graph was represented by a graph embedding vector by averaging</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>the node embedding vectors. SVM and LightGBM models were trained with graph embedding vectors,</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_5'><ns0:head>Table 4 .</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Parameter space of LightGBM.</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Parameters</ns0:cell><ns0:cell>Value</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>number of learners {100,200,500,1000}</ns0:cell></ns0:row><ns0:row><ns0:cell>learning rate</ns0:cell><ns0:cell>{0.1,0.01}</ns0:cell></ns0:row><ns0:row><ns0:cell>L2-regularizer</ns0:cell><ns0:cell>{0.001,0.0001}</ns0:cell></ns0:row><ns0:row><ns0:cell>max depth</ns0:cell><ns0:cell>{7,9,11}</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_6'><ns0:head>Table 5 .</ns0:head><ns0:label>5</ns0:label><ns0:figDesc>Parameter space of SVM.</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Parameters</ns0:cell><ns0:cell>Value</ns0:cell></ns0:row><ns0:row><ns0:cell>Kernel Type</ns0:cell><ns0:cell>{rbf,poly}</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>Regularization (C) {0.5,0.1,1,2,4,8}</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>only 40 features. SVM also showed satisfactory performance with 40 features that resulted in an accuracy</ns0:cell></ns0:row><ns0:row><ns0:cell>rate of 0,935 with an F-measure rate of 0.912.</ns0:cell><ns0:cell /></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_7'><ns0:head>Table 6 .</ns0:head><ns0:label>6</ns0:label><ns0:figDesc>Classification results with GVAE-reduced node embeddings.</ns0:figDesc><ns0:table><ns0:row><ns0:cell>#Features</ns0:cell><ns0:cell cols='4'>LightGBM Accuracy F-Mea Accuracy F-Mea SVM</ns0:cell></ns0:row><ns0:row><ns0:cell>20</ns0:cell><ns0:cell>0.917</ns0:cell><ns0:cell>0.875</ns0:cell><ns0:cell>0.909</ns0:cell><ns0:cell>0.865</ns0:cell></ns0:row><ns0:row><ns0:cell>30</ns0:cell><ns0:cell>0.943</ns0:cell><ns0:cell>0.909</ns0:cell><ns0:cell>0.927</ns0:cell><ns0:cell>0.892</ns0:cell></ns0:row><ns0:row><ns0:cell>40</ns0:cell><ns0:cell>0.943</ns0:cell><ns0:cell>0.912</ns0:cell><ns0:cell>0.935</ns0:cell><ns0:cell>0.902</ns0:cell></ns0:row><ns0:row><ns0:cell>50</ns0:cell><ns0:cell>0.937</ns0:cell><ns0:cell>0.901</ns0:cell><ns0:cell>0.927</ns0:cell><ns0:cell>0.889</ns0:cell></ns0:row><ns0:row><ns0:cell cols='5'>After the classification process with GVAE-reduced embeddings, the second experiments used raw</ns0:cell></ns0:row><ns0:row><ns0:cell cols='5'>node features directly in the classification process. Since each node in the graph included 100 features,</ns0:cell></ns0:row><ns0:row><ns0:cell cols='5'>each graph was represented by a 100-dimensional vector by averaging such node features. Following</ns0:cell></ns0:row><ns0:row><ns0:cell cols='5'>the formation of the graph vectors, feature subsets with varying sizes were created with RFE and FS</ns0:cell></ns0:row><ns0:row><ns0:cell cols='5'>selections. RFE benefited from SVM and LightGBM models for the computation of feature-relevance</ns0:cell></ns0:row><ns0:row><ns0:cell>scores.</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_8'><ns0:head /><ns0:label /><ns0:figDesc>Classification accuracy was up to 0.967 with LightGBM, while the accuracy rate was realized in 0.955 with SVM. LightGBM achieved this result with 40 informative features. On the other hand, SVM reached the highest success rate with selected 30 features.</ns0:figDesc><ns0:table /><ns0:note>9/13PeerJ Comput. Sci. reviewing PDF \| (CS-2022:03:71513:1:2:NEW 22 Apr 2022)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_9'><ns0:head>Table 7 .</ns0:head><ns0:label>7</ns0:label><ns0:figDesc>Classification results with raw node features (RFE-selected).</ns0:figDesc><ns0:table><ns0:row><ns0:cell>#Features</ns0:cell><ns0:cell cols='4'>LightGBM Accuracy F-Measure Accuracy F-Measure SVM</ns0:cell></ns0:row><ns0:row><ns0:cell>20</ns0:cell><ns0:cell>0.873</ns0:cell><ns0:cell>0.802</ns0:cell><ns0:cell>0.851</ns0:cell><ns0:cell>0.779</ns0:cell></ns0:row><ns0:row><ns0:cell>30</ns0:cell><ns0:cell>0.892</ns0:cell><ns0:cell>0.831</ns0:cell><ns0:cell>0.870</ns0:cell><ns0:cell>0.808</ns0:cell></ns0:row><ns0:row><ns0:cell>40</ns0:cell><ns0:cell>0.901</ns0:cell><ns0:cell>0.846</ns0:cell><ns0:cell>0.883</ns0:cell><ns0:cell>0.823</ns0:cell></ns0:row><ns0:row><ns0:cell>50</ns0:cell><ns0:cell>0.907</ns0:cell><ns0:cell>0.852</ns0:cell><ns0:cell>0.886</ns0:cell><ns0:cell>0.835</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_10'><ns0:head>Table 8 .</ns0:head><ns0:label>8</ns0:label><ns0:figDesc>Classification results with raw node features (FS-selected).</ns0:figDesc><ns0:table><ns0:row><ns0:cell>#Features</ns0:cell><ns0:cell cols='4'>LightGBM Accuracy F-Measure Accuracy F-Measure SVM</ns0:cell></ns0:row><ns0:row><ns0:cell>20</ns0:cell><ns0:cell>0.861</ns0:cell><ns0:cell>0.791</ns0:cell><ns0:cell>0.841</ns0:cell><ns0:cell>0.767</ns0:cell></ns0:row><ns0:row><ns0:cell>30</ns0:cell><ns0:cell>0.883</ns0:cell><ns0:cell>0.820</ns0:cell><ns0:cell>0.862</ns0:cell><ns0:cell>0.798</ns0:cell></ns0:row><ns0:row><ns0:cell>40</ns0:cell><ns0:cell>0.889</ns0:cell><ns0:cell>0.834</ns0:cell><ns0:cell>0.872</ns0:cell><ns0:cell>0.811</ns0:cell></ns0:row><ns0:row><ns0:cell>50</ns0:cell><ns0:cell>0.895</ns0:cell><ns0:cell>0.841</ns0:cell><ns0:cell>0.874</ns0:cell><ns0:cell>0.825</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_12'><ns0:head>Table 9 .</ns0:head><ns0:label>9</ns0:label><ns0:figDesc>Classification results with the combination of GVAE-reduced embedding and RFE selection.</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Model</ns0:cell><ns0:cell cols='3'>#Features Accuracy F-Measure</ns0:cell></ns0:row><ns0:row><ns0:cell>LightGBM</ns0:cell><ns0:cell>30</ns0:cell><ns0:cell>0.967</ns0:cell><ns0:cell>0.934</ns0:cell></ns0:row><ns0:row><ns0:cell>SVM</ns0:cell><ns0:cell>40</ns0:cell><ns0:cell>0.955</ns0:cell><ns0:cell>0.924</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_13'><ns0:head>Table 10 .</ns0:head><ns0:label>10</ns0:label><ns0:figDesc>Classification results of recent malware studies.</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Models</ns0:cell><ns0:cell>Feature Set</ns0:cell><ns0:cell cols='2'>Accuracy F-Measure</ns0:cell></ns0:row><ns0:row><ns0:cell>SVM, kNN (Zhao et al., 2015)</ns0:cell><ns0:cell>Permissions, API-calls</ns0:cell><ns0:cell>0.975</ns0:cell><ns0:cell>NA</ns0:cell></ns0:row><ns0:row><ns0:cell>RF, SVM (Canfora et al., 2015)</ns0:cell><ns0:cell>n-opcode of classes.dex</ns0:cell><ns0:cell>0.965</ns0:cell><ns0:cell>NA</ns0:cell></ns0:row><ns0:row><ns0:cell>CNN (Ganesh et al., 2017)</ns0:cell><ns0:cell>Permissions</ns0:cell><ns0:cell>0.930</ns0:cell><ns0:cell>NA</ns0:cell></ns0:row><ns0:row><ns0:cell>MKL</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row></ns0:table></ns0:figure> </ns0:body> "	"14.04.2022 Dear Editor-in-Chief and Reviewers, We have carefully considered each comment and addressed them in the best possible manner. We put so much effort into addressing all these useful comments, which helped us to improve the paper. We thank you again for your valuable comments and efforts to help improve the paper. Sincerely, Assoc. Prof. Hakan Gunduz Reviewer-1 P1: “Why to choose linear SVM and LightGBM and not others.” R1: In this revised version, we have added the following paragraph under “Methods” section. “ As stated in review studies \ref{liu2020review,pan2020systematic}, the vast majority of malware detection studies employed Support Vector Machines (SVM) and ensemble learning models in the classification process. To assess the effectiveness of the reduced graph embeddings in the detection process and compare their performances with recent studies, we selected SVM and LightGBM models in our framework.” P2: “Highlight all assumptions and limitations of your work.” R2: We totally agreed on this point and therefore, added the following paragraph to the “Conclusion and Discussion” section. “Experimental studies have some limitations and threats to validity. In this study, experimental setups were trained with two open source datasets. The performance of the proposed model on other datasets might be slightly different; however, we do not expect too much variations in the performance. Different researchers also might develop new malware detection frameworks models using novel deep learning architectures and achieve better performance results than the one reported in this study. P3: “Conclusions should provide some lessons learnt.” R3: We added the following sentence to the “Conclusion and Discussion” section. “This study focused to enhance the performance of malware detection models using a novel dimensionality reduction method. The proposed framework fused the GVAE and RFE. Experimental results presented that the execution of RFE selection on GVAE embeddings provided remarkable results. In addition, the proposed framework has a generic form that can be widened to diverse domains including graph-structured data types. Tasks in bioinformatics and recommendation systems are some examples of these domains where our framework can be adopted.” P4: “Related works section does not mention recent research effors in feature characterization in NIDS using also clustering and contrastive learning, as well as, review works on VAEs. Authors are advised to refer to the following related articles to add some discussions: [1] Variational data generative model for intrusion detection, Knowledge and Information Systems, 2018 [2] Analysis of Autoencoders for Network Intrusion Detection, Sensors, 2021. [3] Network Intrusion Detection Based on Supervised Adversarial Variational Auto-Encoder With Regularization, IEEE Access, 2020 [4] Supervised contrastive learning over prototype-label embeddings for network intrusion detection, Information Fusion, 2022.” R4: We agreed on this point and added the following paragraph to the “Related Work” section. This paragraph briefly mentioned about suggested [1] and [2] studies. “Autoencoder is the other deep learning architecture actively studied in cyber-security domain for anomaly detection\citep{xu2021improving}, data generation\citep{kabore2021review}, and dimensionality reduction \citep{haseeb2022autoencoder}. For example, several autoencoder models have been utilized in intelligent Network Intrusion Detection Systems (NIDS) to handle zero-day attacks with high accuracy \citep{song2021analysis}. Variational Autoencoder (VAE) has been used to generate intrusion data in a generative manner to cover the imbalanced data problem generally seen in many intrusion detection systems \citep{lopez2019variational,vaiyapuri2020application}. \citep{yousefi2017autoencoder} employed autoencoders to generate code vectors that captured latent representations of different feature sets. Trained autoencoders generated distinguished features from original features in an unsupervised fashion for malware classification and decreased the computational complexity of the proposed model significantly. Reviewer-2 P1: ”Basic reporting is good. However, literature work should be added more from the year 2020-2021.” R1: We agreed on this point and added the following paragraph to the “Related Work” section. This paragraph summarizes the studies published in the years between 2020 and 2022. “Autoencoder is the other deep learning architecture actively studied in cyber-security domain for anomaly detection\citep{xu2021improving}, data generation\citep{kabore2021review}, and dimensionality reduction \citep{haseeb2022autoencoder}. For example, several autoencoder models have been utilized in intelligent Network Intrusion Detection Systems (NIDS) to handle zero-day attacks with high accuracy \citep{song2021analysis}. Variational Autoencoder (VAE) has been used to generate intrusion data in a generative manner to cover the imbalanced data problem generally seen in many intrusion detection systems \citep{lopez2019variational,vaiyapuri2020application}. \citep{yousefi2017autoencoder} employed autoencoders to generate code vectors that captured latent representations of different feature sets. Trained autoencoders generated distinguished features from original features in an unsupervised fashion for malware classification and decreased the computational complexity of the proposed model significantly. P2: “Authors did not mentioned why they use Recursive Feature Elimination (RFE) and Fisher Score (FS) to conduct feature selection in comparison to other studies. Please explain why you used these methods. Furthermore, Is the analysis is sufficient enough by using SVM as a model., why not considering other well-known classifiers that classifies better than SVM?” R2: Thanks for your valuable suggestion. We added to following paragraphs under Methods section. “RFE was determined as an alternative model to GVAE due to its success in handling the dependencies and collinearity between the attributes that are also present in the graph-structured data. On the other hand, FS was chosen as another comparison method since it considers both positive and negative class samples during the computation of feature relevances and directly assesses the relevance between each feature and class labels. “ “ As stated in review studies \ref{liu2020review,pan2020systematic}, the vast majority of malware detection studies employed Support Vector Machines (SVM) and ensemble learning models in the classification process. To assess the effectiveness of the reduced graph embeddings in the detection process and compare their performances with recent studies, we selected SVM and LightGBM models in our framework.” P3: “In table 6 it seems that increasing number of features will increase accuracy, which is normally not true. So, what will be the accuracy at 60 features?” R3: We can explain this case as follows: “Autoencoder (AE) is a deep learning model used for mainly dimensionality reduction aim. Specifically, the AE accepts an original feature vector to extract a code vector that captures the semantic similarity between all feature vectors. The number of neurons in the encoder component of the AE directly determines the size of the code vector. This size of the code vector should be selected as smaller than the dimension of the original feature. For this reason, expanding the dimension of the code vectors generally results in smaller reconstruction error values in the output of the autoencoder. The intuition behind the autoencoder is to generate code vectors with smaller dimensions that cause a minimum reconstruction error. Since the expanding size of code vectors results in smaller reconstruction errors, it will also tend to increase model accuracy up to a point. This situation is seen in our GAVE-reduced embedding vectors. The performance of the model trained with GVAE-embeddings increased until the number of used feature sizes come to 40. Contrary to this, when the feature size was 50, the model accuracy began to decrease.” P4: “Please explain the parameter space of LightGBM in detail.” R4: We totally agreed on this point and therefore, add the following paragraph under LighGBM subsection. “The basic parameters to be determined in the LightGBM are the number of learners, learning rate, and max_depth. The number of learners is the number of iterations used in setting up the ensemble model. A high number of iterations can lead to overfitting while a low number prevents us from learning patterns. Learning rate (lr) is a value between 0 and 1 for scaling generated trees. A smaller lr can help better predictive power. However, it can increase model training time and result in possible overfitting. Max_dept is used to limit the depth of the tree to be built. It should be optimized to avoid overfitting. Too much branching will cause overfitting, and too little branching will cause underfitting.” Reviewer-3 P1: ”The abstract of the paper is lengthy. Please reduce the content of the abstract and omitting detailed methodology and only report the primary results.” R1: We totally agreed on this point and therefore, reduced the content of the abstract and omit detailed methodology. P2: “The Figure methodology diagram should be re-drawn as a block diagram. The diagram should be divided in to different connected components.” R2: Thanks for your valuable suggestion. We added a 5-step block diagram bottom of the Figure and aligned the steps with the block diagram. P3: “The contributions of the paper should be written in bullet points or with numbering.” R3: We have addressed this comment and written the contributions of the paper with bullet points. P4: “The paragraph after the contributions should be re-written.” R4: We totally agreed on this point and rewrote this paragraph. P5: “The API extraction from the data set is not explained. How did you extracted the API calls? Which tools did you used? This should be explained.” R5: Thanks for your valuable suggestion. We added the explanation of the API-Call graph generation steps under “Dataset” subsection. P6: “Include a section which explains the dataset. I do not see the number of benign apps being used. Is the dataset balanced or not? Include a table to present/explain the dataset.” R6: Thanks for your valuable suggestion. We added “Dataset” subsection under “Methods” section and presented brief information about used datasets in Table 1. "	Here is a paper. Please give your review comments after reading it.
433	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>This paper focuses on the mining dilemma of block withholding attack between the mining pools in the bitcoin system. In order to obtain the higher revenue, the rational mining pool usually chooses infiltration attack, that is, the pool will falls into the mining dilemma of the PoW consensus algorithm. Thus the paper proposes to apply Zero-determinant strategies for optimizing the behavior selection of the mining pool under PoW consensus mechanism to increase the total revenues of the system, so as to solve the mining dilemma. After theoretically studying the set and extortionate strategy of Zero-determinant, the paper devises an adaptive Zero-determinant strategy that the pool can change the corporation probability of the next round based on its previous revenues. To verify the effectiveness of Zero-determinant strategies, based on the actual revenue of the mining pool defined and deduced in the paper, it simulates 30 sets of game strategies to illustrate the revenue variation of the mining pools. The simulation results show that the three Zero-determinant strategies can effectively improve the convergence rate of cooperation, mitigate block withholding attack and maximize the total revenues of the system. Compared with the set and extortionate strategy, the adaptive strategy can ensure more stability and more revenue.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>INTRODUCTION</ns0:head><ns0:p>As a decentralized shared ledger, the blockchain ensures the non-tampering property and unforgeability of transactions with an asymmetric encryption algorithm, realizes decentralization through the peer-to-peer (P2P) technology of point-to-point self-organizing network, and guarantees the consistency of block data between nodes, using a consensus algorithm <ns0:ref type='bibr' target='#b16'>(Nakamoto, 2009)</ns0:ref>. Due to its special properties, the blockchain has been widely used in many fields <ns0:ref type='bibr' target='#b19'>(Ren et al., 2021</ns0:ref><ns0:ref type='bibr' target='#b21'>(Ren et al., , 2022))</ns0:ref>. Bitcoin is one of the most successful applications of the blockchain. It introduces the proof of work (PoW) mechanism to the block generation process. In the bitcoin system, every node participates in the production of blocks, and provides the PoW. The node that produces a block faster than others will receive a bitcoin reward. Here, the block generation is called mining, and the mining nodes are known as miners <ns0:ref type='bibr' target='#b22'>(Rosenfeld, 2011)</ns0:ref>.</ns0:p><ns0:p>Currently, each miner can receive a reward of 12.5 bitcoins (BTC) for unearthing a block, and the reward is halved every four years. On average, it takes about 10 mins to produce a block. The difficulty in mining is adjusted automatically by the system every two weeks. The growing difficulty of mining means a miner needs to spend a long time before receiving a revenue. To obtain stable, higher income, miners choose to work cooperatively in open mining pools. Each mining pool consists of an administrator and several miners. The miners continue to send partial or complete PoWs to the administrator, who will distribute the revenue to the miners according to their shares of the workload. Most mining pools are open to the public. Any miner can join such a mining pool by providing a public network interface. As a result, open mining pools are highly susceptible to attacks.</ns0:p><ns0:p>To gain more revenue, some mining pools send their own miners to infiltrate other pools. These miners PeerJ Comput. Sci. reviewing PDF \| (CS-2021:12:68933:1:2:NEW 5 May 2022)</ns0:p><ns0:p>Manuscript to be reviewed Computer Science only send partial PoWs to the administrator, and discard the complete PoW being acquired. In other words, the miners receive the partial revenue from the infiltrated pool, without contributing effective computing power. This behavior is called a block withholding (BWH) attack <ns0:ref type='bibr' target='#b4'>(Courtois and Bahack, 2014;</ns0:ref><ns0:ref type='bibr' target='#b0'>Bag et al., 2017)</ns0:ref>. Rather than provide effective revenue to the pool being attacked, the BWH attacker shares the revenue of the pool, such that the attacked pool receives less revenue and the attacking pool losses computing power. When all pools attack each other, their overall revenue will be lower than that when no attack takes place. To gain more revenue, all mining pools with rational thinking will choose to infiltrate others, i.e., fall into the mining dilemma <ns0:ref type='bibr' target='#b6'>(Eyal, 2015)</ns0:ref> of PoW consensus algorithm. This is equivalent to the prisoner's dilemma in the game theory <ns0:ref type='bibr' target='#b11'>(Kenter and Meigs, 2016;</ns0:ref><ns0:ref type='bibr' target='#b12'>Kostyuk, 2013)</ns0:ref>. The state (attack, attack) is the only Nash equilibrium of the miners' dilemma <ns0:ref type='bibr' target='#b2'>(Barlow, 2014;</ns0:ref><ns0:ref type='bibr' target='#b3'>Carbonell-Nicolau et al., 2018)</ns0:ref>. The attack is the optimal strategy for individuals, but not optimal for the system. At present, the mining pools in China account for 81% of the computing power of the bitcoin network, and joining mining pools is the most important way for miners to obtain revenue. Hence, it is an urgent task to solve the miners' dilemma.</ns0:p><ns0:p>Zero-determinant (ZD) strategy is an emerging approach in the game theory. As a hybrid strategy set, the ZD strategy controls the players' strategy selection by probability. This strategy breaks through the traditional Nash equilibrium theory, and optimizes the prisoner's dilemma model <ns0:ref type='bibr' target='#b18'>(Press and Dyson, 2012)</ns0:ref>. On the one hand, the strategy presents a solution to the low system revenue. On the other hand, a player following this strategy ensures that his/her revenue is linearly correlated with the opponent's revenue, regardless of the opponent's strategy <ns0:ref type='bibr' target='#b10'>(Hilbe et al., 2015b)</ns0:ref>. The core of this paper is to utilize the ZD strategies to optimize the selection of mining pool behaviors under the PoW consensus mechanism, aiming to increase the per-capita revenue, and thus solve the mining disaster induced by the BWH attack.</ns0:p><ns0:p>The main contributions of this paper are as follows. 1. Assuming that the entire network has only two mining pools and the honest miners, the paper derived the calculation formulas for the actual revenue of each mining pool, when the BWH attack is launched by one or both sides. 2. The paper creatively used the ZD strategies to mitigate the BWH attacks. Thus the set strategy and extortionate strategy of the ZD were investigated firstly, and then an adaptive ZD strategy was proposed, under which the mining pools will change the cooperation probability in the next round based on the revenues in the previous rounds. The proposed adaptive strategy effectively eases the BWH attack between the pools, promotes the cooperation between the pools, and increases the overall revenues of the mining pools. 3. The revenue variation of the pools was simulated under 30 sets of game strategies to verify that the ZD strategies especially the proposed adaptive Strategy can effectively mitigate the BWH attack between mining pools.</ns0:p></ns0:div> <ns0:div><ns0:head>RELATED WORKS</ns0:head><ns0:p>Research of BWH and mitigation strategy <ns0:ref type='bibr' target='#b16'>Nakamoto (2009)</ns0:ref> proposed the concept of 51% attack. The ledger of the blockchain needs to be maintained by all the nodes in the network; an attacker must master 51% of the computing power of the whole network in order to tamper with the data in the ledger, which is recognized as the first attack on bitcoin consensus mechanism. Traditionally, it is believed that the safety of bitcoin can be guaranteed, as long as the miners possessing most of the computing power remain honest. With the development of bitcoin, Finney suggested that an attacker can realize double spending by maliciously withholding blocks. In 2011, <ns0:ref type='bibr' target='#b22'>Rosenfeld (2011)</ns0:ref> formally put forward the concept of BWH attack, which indicated after joining a mining pool, the attacker only provided the partial PoW <ns0:ref type='bibr' target='#b13'>(Kwon et al., 2017)</ns0:ref>, maliciously withheld blocks, and simultaneously harmed the revenue of him/her and that of the pool. <ns0:ref type='bibr' target='#b4'>Courtois and Bahack (2014)</ns0:ref> extended the BWH attack, held that an attacker can freely distribute his/her computing power between mining independently and attacking the target pool, and demonstrated that the attacker can gain relatively more reward in this scenario. <ns0:ref type='bibr' target='#b0'>Bag et al. (2017)</ns0:ref> presented sponsored BWH attack to account for the probability that a miner might be employed by a pool to attack other pools. In 2014, the mining pool Eligius was hit by a massive BWH attack <ns0:ref type='bibr' target='#b4'>(Courtois and Bahack, 2014)</ns0:ref>, which brought a loss of 300 BTC.</ns0:p><ns0:p>The BWH attack both harms the interests of the pools, and threatens the stability of the bitcoin network.</ns0:p><ns0:p>Therefore, an effective strategy should be designed to mitigate and resist such an attack.</ns0:p><ns0:p>One mitigation approach is to improve the PoW algorithm in terms of task assignment and reward mechanism. <ns0:ref type='bibr' target='#b22'>Rosenfeld (2011)</ns0:ref> presented the defense mechanism of task assignment. Under the mechanism, the administrator of a pool redistributes the effective PoW to the miners for calculation; any miner failing to submit the block is deemed as an attacker. However, the miners are forced by the administrator to Manuscript to be reviewed Computer Science complete additional computing tasks, resulting in a waste of computing power. Since the miners are rewarded by the administrator, who evaluates their contributions according to partial PoWs, <ns0:ref type='bibr' target='#b23'>Schrijvers et al. (2017)</ns0:ref> proposed an incentive-compatible reward mechanism, which encourages miners to submit blocks immediately in exchange for reward, thereby ensuring the revenue of the pool. <ns0:ref type='bibr' target='#b1'>Bag and Sakurai (2016)</ns0:ref> created the incentive mechanism with extra reward, which showed a miner submitting the block received an extra reward in addition to the reward proportional to his/her contribution, while an attacker never received any extra reward. Later, <ns0:ref type='bibr' target='#b0'>Bag et al. (2017)</ns0:ref> developed a mitigation scheme for the BWH attack between mining pools based on hash function encryption. Under the scheme, the attack is withstood as the miners cannot differentiate between partial and complete PoWs. Nevertheless, the attacking pool increases its revenue with the partial PoWs submitted by the infiltration miners, without needing to spend extra computing power to calculate the complete PoW. Hence, incentives do not work on the administrator of the attacking pool. Besides, the task assignment mechanism has an inherent defect, namely, the miners often carry out useless computations, resulting in a waste of computing power.</ns0:p><ns0:p>In 2015, <ns0:ref type='bibr' target='#b6'>Eyal (2015)</ns0:ref> explored the mining disaster induced by the BWH attack. Specifically, the game between mining pools was qualitatively analyzed under the mutual attacks between two pools and multiple pools, and treated as an iterated prisoner's dilemma (IPD). The Nash equilibrium theory was adopted to prove that the mutual attacks reduced the revenues of all pools, forcing them to converge to the closed and stable state. Hence, another mitigation strategy for the BWH attack is grounded on the prisoner's dilemma model. <ns0:ref type='bibr' target='#b24'>Tang et al. (2017)</ns0:ref> further investigated the pure strategy and mixed strategy problems in game dilemmas, and optimized the system revenue of single pool mining dilemma with the ZD strategy. Their strategy ensures that the revenue of attacking miners is linearly correlated with that of honest miners in the pool, increases the revenue of the entire pool, and mitigates the loss of the pool brought by the BWH attack.</ns0:p></ns0:div> <ns0:div><ns0:head>Research of ZD Strategy</ns0:head><ns0:p>The ZD strategy, initially proposed by Press and Dyson, has attracted widespread attention. <ns0:ref type='bibr' target='#b9'>Hilbe et al. (2015a)</ns0:ref> considerd 3 different strategy classes, including ZD, for the Iterated Prisoner's Dilemma and characterized these 3 classes within the space of memory-one strategies. <ns0:ref type='bibr' target='#b20'>Ren et al. (2014)</ns0:ref> extend the theory of ZD strategies to multiplayer games to describe which strategies maintain cooperation and proposed two simple models of alliances in multiplayer dilemmas to show how individuals could further enhance their strategic options by coordinating their play with others. Later, <ns0:ref type='bibr' target='#b10'>Hilbe et al. (2015b)</ns0:ref> pointed out that the ZD strategy was not evolutionarily stable in some cases, and stable in some other conditions. <ns0:ref type='bibr' target='#b8'>He et al. (2016)</ns0:ref> applied the ZD strategy to multi-person multi-strategy iterative game model, and proved that every player could act as the leader to control the expected revenue of his/her opponents. <ns0:ref type='bibr' target='#b7'>Hao et al. (2015)</ns0:ref> proposed a general model of the ZD strategies for noisy repeated games and derived the pinning strategy under noise, by which the ZD strategy player coercively sets the opponent's expected payoff to his desired level, although his payoff control ability declines with the increase of noise strength. <ns0:ref type='bibr' target='#b15'>Mcavoy and Hauert (2017)</ns0:ref> applied the ZD strategy theory from traditional synchronous games to alternate games, and discussed the autocratic strategy both in a strictly-alternating game and in a randomly-alternating game. Ueda and T. ( <ns0:ref type='formula'>2020</ns0:ref>) provided a general framework for investigating situations where more than one players employ ZD strategies in terms of linear algebra and theoretically proved general mathematical properties of the ZD strategy. <ns0:ref type='bibr' target='#b26'>Ueda (2021)</ns0:ref> In reality, the ZD strategy has been applied in various fields, for its good properties and unlimited research potential. <ns0:ref type='bibr' target='#b5'>Daoud et al. (2014)</ns0:ref> introduced the ZD strategy to the secondary sharing of licensed spectrum, likened the licensed spectrum problem to the non-cooperative iterative game model of power control, and determined the long-term mean rate by changing the power level for the maximal value, which could be realized through the ZD strategy. <ns0:ref type='bibr'>Zhang et al. applied</ns0:ref> the ZD strategy to manage the deceptions in wireless network cooperation <ns0:ref type='bibr' target='#b28'>(Zhang et al., 2014a)</ns0:ref> and to share wireless network resources <ns0:ref type='bibr' target='#b30'>(Zhang et al., 2016)</ns0:ref>, and described the resource sharing between players with an IPD game model. Regardless of the opponent's strategy, a player could guarantee the high and stable system revenue with the ZD strategy. <ns0:ref type='bibr' target='#b29'>Zhang et al. (2014b)</ns0:ref> also implemented the ZD strategy to small cell networks to maximize the system revenue. <ns0:ref type='bibr' target='#b17'>Pan et al. (2014)</ns0:ref> <ns0:ref type='table' target='#tab_3'>2021:12:68933:1:2:NEW 5 May 2022)</ns0:ref> Manuscript to be reviewed Computer Science the following conclusions. When the number of players or the multiplication factor was small, a player could unilaterally control the expected revenue of all the other players through the ZD strategy, and set the proportion of his/her own revenue to the overall revenue of all the other players. In the IPD game model, regardless of the opponent's strategy, the ZD strategy can control the expected revenue of the opponent, and keep it linearly correlated with the expected revenue of the player adopting the strategy. All the above studies provide a reference for our research, which tries to migrate the BWH attack with the ZD strategy.</ns0:p></ns0:div> <ns0:div><ns0:head>MINING DILEMMA ANALYSIS</ns0:head></ns0:div> <ns0:div><ns0:head>Calculation of actual revenue of mining pool</ns0:head><ns0:p>The BWH attack can be traced back to the nascency of pool mining. The attacker could be an administrator of a mining pool. He/she might arrange the computing power under his/her control to mine honestly or infiltrate another pool. The attacking behavior is either honest mining or withholding blocks. Such an attack will harm the mining revenue of the attacked pool and other participants. The attacking miners will only send partial PoWs to the attacked pool, and discard any complete PoW being acquired. The pool will continue to distribute mining revenue to the attacker, but cannot benefit from the computing power of the attacker. In this way, both the revenue of every participant of the attacked pool and that of the attacker will be lowered. However, the pool that launches the attack eyes the maximization of its own revenue.</ns0:p><ns0:p>Then, whether the revenue of the attacking pool will increase or decrease through the BWH attack? To answer this question, it is necessary to define the calculation formulas for the pool revenues.</ns0:p></ns0:div> <ns0:div><ns0:head n='1.'>Pool revenue</ns0:head><ns0:p>For simplicity, the computing power is regarded as equivalent to the revenue. The greater the computing power, the stronger the competitiveness of a pool, and the more revenue acquired by the pool through mining. Obviously, the total revenue of the bitcoin system is the revenue obtained by the effective computing power of the system. The system computing power H is defined as the total computing power of all miners in the bitcoin network. The effective computing power of the system is equal to the system computing power minus the computing power for the BWH attack H attack . Similarly, the total computing power of a pool h pool minus the computing power for the BWH attack h attack is the effective computing power of a pool. Then, the revenue of a pool can be defined as</ns0:p><ns0:formula xml:id='formula_0'>R pool = h pool − h attack H − H attack .</ns0:formula><ns0:p>(1)</ns0:p></ns0:div> <ns0:div><ns0:head n='2.'>Average revenue of miners</ns0:head><ns0:p>The average revenue of the miners in a pool can be defined as</ns0:p><ns0:formula xml:id='formula_1'>R miner = R pool h pool .</ns0:formula><ns0:p>(2)</ns0:p></ns0:div> <ns0:div><ns0:head n='3.'>Honest mining revenue</ns0:head><ns0:p>The honest mining revenue of a miner is the average revenue of the miners multiplied by the computing power of honest miners. That is, the honest mining revenue is distributed according to the computing power of honest miners h honest as a proportion of the total computing power of the pool h pool . The formula is</ns0:p><ns0:formula xml:id='formula_2'>R honest = h honest • R pool h pool = h honest • R miner .</ns0:formula><ns0:p>(3)</ns0:p></ns0:div> <ns0:div><ns0:head n='4.'>Attack revenue</ns0:head><ns0:p>The attack revenue is the product of the average revenue of the miners in the attacked pool and the computing power of the attack h attack .</ns0:p><ns0:formula xml:id='formula_3'>R attack = h attack • R pool h pool = h attack • R miner (4)</ns0:formula></ns0:div> <ns0:div><ns0:head n='5.'>Actual revenue</ns0:head><ns0:p>The actual revenue of a pool is the sum of the revenue of honest mining and the attack revenue. Manuscript to be reviewed</ns0:p><ns0:formula xml:id='formula_4'>R real = R honest + R attack<ns0:label>(</ns0:label></ns0:formula></ns0:div> <ns0:div><ns0:head>Computer Science</ns0:head><ns0:p>The actual bitcoin network is rather complicated. There are many mining pools in the whole network.</ns0:p><ns0:p>Each pool has complex mining behaviors. To facilitate the analysis of the BWH attack, this paper considers the simplest situation, i.e., there are only two pools named Pool 1 and Pool 2 in the network, and the other miners conduct mining honestly and independently. Suppose the computing power of the entire network is 1, and the computing power of Pool 1 , Pool 2 and the other miners are h 1 , h 2 and h 3 , respectively.</ns0:p><ns0:p>It is obvious that h 1 + h 2 + h 3 = 1. The administrator of each pool distributes block reward fairly to each miner according to his/her proportion of computing power. If no attack takes place, the actual revenues of Pool 1 and Pool 2 are h 1 and h 2 , respectively. The following is an analysis on the revenue variation of each pool under two difference scenarios, namely, the BWH attack launched by only one pool and the attack launched by both pools.</ns0:p></ns0:div> <ns0:div><ns0:head>Unilateral attack</ns0:head><ns0:p>Figure <ns0:ref type='figure' target='#fig_3'>1</ns0:ref> shows the scenario of unilateral attack. It is assumed that Pool 1 attacks Pool 2 with r 1 h 1 (0 < r 1 < 1) of its own computing power, and conducts honest mining with the remaining computing power (1 − r 1 ) h 1 , while Pool 2 does not attack Pool 1 . Note that r 1 is the infiltration rate, i.e., the percentage of infiltration miners in all miners of the pool (Normally, r 1 = 0.1). Then, the effective computing power of the entire network is 1 − r 1 h 1 . The pool revenues of Pool 1 and Pool 2 are</ns0:p><ns0:formula xml:id='formula_5'>R pool 1 = (1 − r 1 ) h 1 1 − r 1 h 1 ,<ns0:label>(6)</ns0:label></ns0:formula><ns0:formula xml:id='formula_6'>R pool 2 = h 2 1 − r 1 h 1 .<ns0:label>(7)</ns0:label></ns0:formula><ns0:p>The average revenue of the miners in Pool 1 and Pool 2 are</ns0:p><ns0:formula xml:id='formula_7'>R miner 1 = 1 1 − r 1 h 1 ,<ns0:label>(8)</ns0:label></ns0:formula><ns0:formula xml:id='formula_8'>R miner 2 = h 2 (1 − r 1 h 1 ) (r 1 h 1 + h 2 ) . (<ns0:label>9</ns0:label></ns0:formula><ns0:formula xml:id='formula_9'>)</ns0:formula><ns0:p>The honest mining revenue of a miner in Pool 1 and Pool 2 are</ns0:p><ns0:formula xml:id='formula_10'>R honest 1 = (1 − r 1 ) h 1 1 − r 1 h 1 ,<ns0:label>(10)</ns0:label></ns0:formula><ns0:formula xml:id='formula_11'>R honest 2 = h 2 2 (1 − r 1 h 1 ) (r 1 h 1 + h 2 ) . (<ns0:label>11</ns0:label></ns0:formula><ns0:formula xml:id='formula_12'>)</ns0:formula><ns0:p>The attack revenue of Pool 1 is Manuscript to be reviewed</ns0:p><ns0:formula xml:id='formula_13'>R attack 1 = r 1 h 1 h 2 (1 − r 1 h 1 ) (r 1 h 1 + h 2 ) . (<ns0:label>12</ns0:label></ns0:formula></ns0:div> <ns0:div><ns0:head>Computer Science</ns0:head><ns0:p>The actual revenue of Pool 1 and Pool 2 are</ns0:p><ns0:formula xml:id='formula_14'>R real 1 = h 1 h 2 + r 1 h 2 1 − r 1 2 h 2 1 (1 − r 1 h 1 ) (r 1 h 1 + h 2 ) ,<ns0:label>(13)</ns0:label></ns0:formula><ns0:formula xml:id='formula_15'>R real 2 = h 2 2 (1 − r 1 h 1 ) (r 1 h 1 + h 2 ) .<ns0:label>(14)</ns0:label></ns0:formula><ns0:p>If Pool 1 does not launch an attack, the original revenue of Pool</ns0:p><ns0:formula xml:id='formula_16'>1 is R ′ real 1 = h 1 . Let ∆R 1 = R real 1 − R ′ real 1 = r 1 h 2 1 (r 1 h 1 −r 1 +h 2 ) (1−r 1 h 1 )(r 1 h 1 +h 2 )</ns0:formula><ns0:p>, where </ns0:p><ns0:formula xml:id='formula_17'>r 1 h 2 1 (1−r 1 h 1 )(r 1 h 1 +h 2 ) > 0. When r 1 < h 2 1−h 1 , r 1 h 1 − r 1 + h 2 is</ns0:formula><ns0:formula xml:id='formula_18'>is R ′ real 2 = h 2 . Then, ∆R 2 = R real 2 − R ′ real 2 = r 1 h 1 h 2 (r 1 h 1 +r 1 h 2 −1) (1−r 1 h 1 )(r 1 h 1 +h 2 )</ns0:formula><ns0:p>. Since r 1 h 1 + r 1 h 2 − 1 < 0 and any other term in the formula is greater than zero, ∆R 2 < 0. Hence, as shown in Figure <ns0:ref type='figure' target='#fig_5'>2</ns0:ref>, an attack by Pool 1 will definitely lower the revenue of Pool 2 . Therefore, a rational pool will choose to attack in order to control its own loss, that is, both pools will choose to attack. The total revenue of miners engaged in independent mining is R 3 = h 3 1−r 1 h 1 . Since 1 − r 1 h 1 < 1, the actual revenue of these miners is greater than the original revenue h 3 . </ns0:p></ns0:div> <ns0:div><ns0:head>Mutual attacks</ns0:head><ns0:p>Under the premise that Pool 1 launches an attack, it is assumed that Pool 2 attacks Pool 1 with r 2 h 2 (0 < r 2 < 1) of its computing power, as is shown in Figure <ns0:ref type='figure' target='#fig_6'>3</ns0:ref>. In this case, the effective computing power of the entire network is 1 − r 1 h 1 − r 2 h 2 . The pool revenues of Pool 1 and Pool 2 are Manuscript to be reviewed</ns0:p><ns0:formula xml:id='formula_19'>R pool 1 = (1 − r 1 ) h 1 1 − r 1 h 1 − r 2 h 2 , (<ns0:label>15</ns0:label></ns0:formula><ns0:p>Computer Science</ns0:p><ns0:formula xml:id='formula_20'>R pool 2 = (1 − r 2 ) h 2 1 − r 1 h 1 − r 2 h 2 . (<ns0:label>16</ns0:label></ns0:formula><ns0:formula xml:id='formula_21'>)</ns0:formula><ns0:p>The average revenue of the miners in Pool 1 and Pool 2 are</ns0:p><ns0:formula xml:id='formula_22'>R miner 1 = (1 − r 1 ) h 1 (1 − r 1 h 1 − r 2 h 2 ) ((1 − r 1 ) h 1 + r 2 h 2 ) ,<ns0:label>(17)</ns0:label></ns0:formula><ns0:formula xml:id='formula_23'>R miner 2 = (1 − r 2 ) h 2 (1 − r 1 h 1 − r 2 h 2 ) (r 1 h 1 + (1 − r 2 ) h 2 ) . (<ns0:label>18</ns0:label></ns0:formula><ns0:formula xml:id='formula_24'>)</ns0:formula><ns0:p>The honest mining revenue of a miner in Pool 1 and Pool 2 are</ns0:p><ns0:formula xml:id='formula_25'>R honest 1 = (1 − r 1 ) 2 h 2 1 (1 − r 1 h 1 − r 2 h 2 ) ((1 − r 1 ) h 1 + r 2 h 2 ) ,<ns0:label>(19)</ns0:label></ns0:formula><ns0:formula xml:id='formula_26'>R honest 2 = (1 − r 2 ) 2 h 2 2 (1 − r 1 h 1 − r 2 h 2 ) (r 1 h 1 + (1 − r 2 ) h 2 ) . (<ns0:label>20</ns0:label></ns0:formula><ns0:formula xml:id='formula_27'>)</ns0:formula><ns0:p>The attack revenue of Pool 1 and Pool 2 are</ns0:p><ns0:formula xml:id='formula_28'>R attack 1 = r 1 (1 − r 2 ) h 1 h 2 (1 − r 1 h 1 − r 2 h 2 ) (r 1 h 1 + (1 − r 2 ) h 2 ) ,<ns0:label>(21)</ns0:label></ns0:formula><ns0:formula xml:id='formula_29'>R attack 2 = (1 − r 1 ) r 2 h 1 h 2 (1 − r 1 h 1 − r 2 h 2 ) ((1 − r 1 ) h 1 + r 2 h 2 ) . (<ns0:label>22</ns0:label></ns0:formula><ns0:formula xml:id='formula_30'>)</ns0:formula><ns0:p>The actual revenue of Pool 1 and Pool 2 are</ns0:p><ns0:formula xml:id='formula_31'>R real 1 = (1 − r 1 ) 2 h 2 1 (1 − r 1 h 1 − r 2 h 2 ) ((1 − r 1 ) h 1 + r 2 h 2 ) + r 1 (1 − r 2 ) h 1 h 2 (1 − r 1 h 1 − r 2 h 2 ) (r 1 h 1 + (1 − r 2 ) h 2 ) ,<ns0:label>(23)</ns0:label></ns0:formula><ns0:formula xml:id='formula_32'>R real 2 = (1 − r 2 ) 2 h 2 2 (1 − r 1 h 1 − r 2 h 2 ) (r 1 h 1 + (1 − r 2 ) h 2 ) + (1 − r 1 ) r 2 h 1 h 2 (1 − r 1 h 1 − r 2 h 2 ) ((1 − r 1 ) h 1 + r 2 h 2 ) . (<ns0:label>24</ns0:label></ns0:formula><ns0:formula xml:id='formula_33'>)</ns0:formula><ns0:p>Figure <ns0:ref type='figure' target='#fig_8'>4</ns0:ref> shows the revenues of the two pools and honest miners at different infiltration rates, with h 1 = 0.3 and h 2 = 0.5. When the two pools attack each other, in most cases, the revenue of each side is lower than that of honest mining, yet higher than that under the scenario of being attacked but not attacking the other side. This phenomenon can be explained by the revenue variation of the miners engaged in independent mining. The actual total revenue of independent miners R 3 = h 3 1−r 1 h 1 −r 2 h 2 is always above the original revenue h 3 . Whereas the total revenue of the bitcoin system is fixed, the total revenue of Pool 1 and Pool 2 will definitely drop. In each round of mining, the best strategy for each pool is to attack the other side. When the system reaches the equilibrium, the revenue of each pool will be lower than that of honest mining. This is the mining dilemma, which is comparable to the classic prisoner's dilemma in the game theory. In the entire bitcoin system, a pool administrator can select the strategy for each round of mining, i.e., how much computing power should be reserved for mining in the pool, and how many miners should Manuscript to be reviewed Computer Science be sent to launch the BWH attack. From the perspective of the IPD, the continuous mining competition between pools is an iterative game. In each round, each pool, as a game party, can choose between launching an BWH attack (i.e., the defection strategy of the prisoner's dilemma) and not to attack (i.e., the cooperation strategy of the prisoner's dilemma).</ns0:p></ns0:div> <ns0:div><ns0:head>Prisoner's dilemma and IPD</ns0:head><ns0:p>The prisoner's dilemma, proposed by A.W.Tucker in 1950, is a classic problem in the game theory. The model involves two members X and Y of a gang of robbers, who have been arrested and interrogated in separate rooms. If both plead guilty, i.e., choose defection, each will be sentenced to 3 years; if one pleads guilty and the other does not, the former will be released immediately, while the latter will be sentenced to 5 years; if both do not plead guilty, i.e., choose cooperation, each will be sentenced to 1 year.</ns0:p><ns0:p>The revenues of the two prisoners can be described by Table <ns0:ref type='table'>1</ns0:ref>, where R is the reward for mutual cooperation; T is the temptation to defect for the defector; S is the sucker's payoff when one party chooses defection and the other chooses cooperation; P is the punishment for mutual defection. These parameters satisfy T > R > P > S and 2R > T + S, and normally (T, R, P, S) = (5, 3, 1, 0). For X, if Y chooses cooperation, his/her best choice is defection; if Y chooses defection, he/she will also choose defection, because defection reduces his/her loss. Hence, regardless of the other party's choice, the best choice is always defection. Rational prisoners will always betray each other. That is, (defection, defection) is the Nash equilibrium of the prisoner's dilemma. However, the Nash equilibrium point is not necessarily the optimal strategy combination for system revenue. The revenue of the state is below that under (cooperation, cooperation). Therefore, the prisoners face the dilemma of selecting between cooperation and defection <ns0:ref type='bibr' target='#b18'>(Press and Dyson, 2012)</ns0:ref>.</ns0:p><ns0:formula xml:id='formula_34'>Y Corporation, C Defection, D X Corporation, C (R, R) (S, T ) Defection, D (T, S) (P, P)</ns0:formula><ns0:p>Table <ns0:ref type='table'>1</ns0:ref>. Revenue matrix of prisoner's dilemma.</ns0:p><ns0:p>An iterative game is multiple (greater than 2) repetitions of a game. If the prisoner's dilemma only lasts 1 round, (defection, defection) is the inevitable outcome. In the Iterated Prisoner's Dilemma (IPD) model, however, the same gamers encounter each other repeatedly. If a player chooses to defect, he/she must consider the fact that the other side also prefers defection to reduce loss. Hence, the revenues of both sides will remain low. Then, each side of the game faces the pressure that long-term revenue is better than short-term revenue. Since the game is iterative, a rational prisoner will not stick to defection, but cautiously choose between defection and cooperation according to the selection of the opponent in the previous round. Defection might evoke punishment from the opponent, and cooperation might invite a return favor. If the iterative game lasts indefinitely, the equilibrium of (cooperation, cooperation) might appear.</ns0:p></ns0:div> <ns0:div><ns0:head>ZD STRATEGY</ns0:head><ns0:p>There is a Zero-determinant Strategy in IPD game, enforcing linear relationships on the payoffs. The ZD strategy is very surprising that a player can exert unilateral control over iterated interactions, regardless of his/her opponent's strategy <ns0:ref type='bibr' target='#b14'>(McAvoy and Hauert, 2016)</ns0:ref>.</ns0:p><ns0:p>During the IPD game, a player can deduce the opponent's strategy from the game results of the previous rounds, and choose his/her strategy for the next round. It is assumed the players can only memorize a limited history. It has been proved that long-term memory is not superior to short-term memory, if the game repeats itself indefinitely, i.e., the players, revenue matrix, and game strategy set are the same in each round. Therefore, the following analysis assumes that each player can only remember the game outcome of the previous round, i.e., have only one-step memory.</ns0:p><ns0:p>For player X, the game result can be represented as XY ∈ (CC,CD, DC, DD), where C and D are cooperation and defection, respectively. For player Y , the game result can be represented as Y X ∈ (CC, DC,CD, DD). The revenues of X and Y can be vectorized as U X = (R, S, T, P) and U Y = (R, T, S, P), respectively. Let p = (p 1 , p 2 , p 3 , p 4 ) and q = (q 1 , q 2 , q 3 , q 4 ) be the probability for X and Y to choose Manuscript to be reviewed</ns0:p><ns0:p>Computer Science cooperation according to the four game results in the previous round, respectively. Then, the strategy selection probabilities of the players in the current round can be summarized as Table <ns0:ref type='table'>2</ns0:ref>.</ns0:p><ns0:formula xml:id='formula_35'>Y Corporation, C Defection, D X Corporation, C p i q j p i (1 − q j ) Defection, D (1 − p i ) q j (1 − p i ) (1 − q j )</ns0:formula><ns0:p>Table <ns0:ref type='table'>2</ns0:ref>. Strategy selection probabilities of the players in the current round.</ns0:p><ns0:p>According to the game sequence of X, the transfer of X and Y 's strategy selection can be described as a Markov state transfer matrix.</ns0:p><ns0:formula xml:id='formula_36'>M =     p 1 q 1 p 1 (1 − q 1 ) (1 − p 1 ) q 1 (1 − p 1 ) (1 − q 1 ) p 2 q 3 p 2 (1 − q 3 ) (1 − p 2 ) q 3 (1 − p 2 ) (1 − q 3 ) p 3 q 2 p 3 (1 − q 2 ) (1 − p 3 ) q 2 (1 − p 3 ) (1 − q 2 ) p 4 q 4 p 4 (1 − q 4 ) (1 − p 4 ) q 4 (1 − p 4 ) (1 − q 4 )     (25)</ns0:formula><ns0:p>There are four possible results of each round. Thus, each row of M adds up to 1, i.e., M has a unit eigenvalue. Suppose M ′ ≡ M − I. Then, det (M ′ ) = 0. The steady-state vector of M can be represented as v = (v 1 , v 2 , v 3 , v 4 ) T , and v 1 + v 2 + v 3 + v 4 = 1. Any vector proportional to the steady-state vector satisfies</ns0:p><ns0:formula xml:id='formula_37'>v T M = v T , or v T M ′ = 0.</ns0:formula><ns0:p>According to Cramer's rule, Ad j (M ′ ) M ′ = det (M ′ ) I = 0. Hence, each row of Ad j (M ′ ) is proportional to v.</ns0:p><ns0:p>The dot product between v and any four-dimensional vector f can be represented as</ns0:p><ns0:formula xml:id='formula_38'>v • f ≡ D (p, q, f ) = det     −1 + p 1 q 1 −1 + p 1 −1 + q 1 f 1 p 2 q 3 −1 + p 2 q 3 f 2 p 3 q 2 p 3 −1 + q 2 f 3 p 4 q 4 p 4 q 4 f 4     .<ns0:label>(26)</ns0:label></ns0:formula><ns0:p>The second column of the determinant is controlled by X separately.</ns0:p><ns0:formula xml:id='formula_39'>p ≡ (−1 + p 1 , −1 + p 2 , p 3 , p 4 )<ns0:label>(27)</ns0:label></ns0:formula><ns0:p>The third column is controlled by Y separately.</ns0:p><ns0:p>q ≡ (−1 + q 1 , q 3 , −1 + q 2 , q 4 ) (28)</ns0:p><ns0:p>In the stable state, the expected revenues of X and Y are</ns0:p><ns0:formula xml:id='formula_40'>u X = v •U X v • 1 = D (p, q,U X ) D (p, q, 1) , (<ns0:label>29</ns0:label></ns0:formula><ns0:formula xml:id='formula_41'>)</ns0:formula><ns0:formula xml:id='formula_42'>u Y = v •U Y v • 1 = D (p, q,U Y ) D (p, q, 1) , (<ns0:label>30</ns0:label></ns0:formula><ns0:formula xml:id='formula_43'>)</ns0:formula><ns0:p>where 1 is the all-one vector, and the denominator normalizes the sum of the elements in v to 1.</ns0:p><ns0:p>Since u is linearly dependent on U, we have</ns0:p><ns0:formula xml:id='formula_44'>αu X +β u Y + γ = D (p, q, αU X +βU Y + γ1) D (p, q, 1) . (<ns0:label>31</ns0:label></ns0:formula><ns0:formula xml:id='formula_45'>)</ns0:formula><ns0:p>If the strategy selected by X satisfies p = αU X +βU Y + γ1, or the strategy selected by Y satisfies q = αU X +βU Y + γ1, then αu X +β u Y + γ = 0. This is the ZD strategy. Different sub-strategies can be obtained if parameters α and β have different values. There are usually two types of sub-strategies of the ZD strategy which are the set strategy and the extortionate strategy. Under the set strategy, a player can unilaterally set the other's revenue to a fixed value. Under the extortionate strategy, the player choosing the extortionate strategy will receive a higher revenue than the other party, regardless of the other's strategy. </ns0:p><ns0:formula xml:id='formula_46'>   −1 + p 1 −1 + p 2 p 3 p 4     =     β R + γ β T + γ β S + γ β P + γ     .<ns0:label>(32)</ns0:label></ns0:formula><ns0:p>Eliminate β and γ, and represent p 2 and p 3 as p 1 and p 4 .</ns0:p><ns0:formula xml:id='formula_47'>u Y = (1 − p 1 ) P + p 4 R (1 − p 1 ) + p 4 (<ns0:label>33</ns0:label></ns0:formula><ns0:formula xml:id='formula_48'>)</ns0:formula><ns0:p>In the mining dilemma, T > R > P > S. Thus, the revenue range of</ns0:p><ns0:formula xml:id='formula_49'>Y is P ≤ u Y ≤ R. When p 1 → 1, u Y → R; when p 4 → 0, u Y → P.</ns0:formula><ns0:p>Regardless of Y 's strategy, X can set the long-term revenue of Y to a fixed value.</ns0:p></ns0:div> <ns0:div><ns0:head n='2.'>Extortionate strategy</ns0:head><ns0:p>If X plays a game with Y , following the ZD strategy with −β α = χ and γ α= − (1−χ) P, his/her revenue minus Y 's defection revenue P will be times Y 's revenue minus P.</ns0:p><ns0:formula xml:id='formula_50'>U X − P1 = χ (U Y − P1)<ns0:label>(34)</ns0:label></ns0:formula><ns0:p>χ is the extortion factor, χ ≥ 1. If the extortion factor is fixed as a constant, X will continuously receive a high revenue, but Y will not be encouraged to choose cooperation. Therefore, this paper sets the extortion factor as a dynamic factor χ=10 P C , where P C is the cooperation probability. It can be observed that a small cooperation probability corresponds to a large extortion factor. In this case, each pool aims to receive a high revenue.</ns0:p></ns0:div> <ns0:div><ns0:head>Suppose</ns0:head><ns0:formula xml:id='formula_51'>p = φ [(U X − P1) − χ (U Y − P1)] =     φ [(R − P) − χ (R − P)] φ [(S − P) − χ (T − P)] φ [(T − P) − χ (S − P)] φ [(P − P) − χ (P − P)]     =     −1 + p 1 −1 + p 2 p 3 p 4     . (<ns0:label>35</ns0:label></ns0:formula><ns0:formula xml:id='formula_52'>) Since p 1 , p 2 , p 3 , p 4 ∈ [0, 1], we have 0 < φ ≤ P − S (P − S) + χ (T − P) .<ns0:label>(36)</ns0:label></ns0:formula><ns0:p>If X adopts the extortionate ZD strategy, his/her revenue will depend on the strategy q of Y . If Y chooses the AllC strategy, i.e., q = (1, 1, 1, 1), then both X and Y will receive the maximum revenue. In this case, the revenues of X and Y can be respectively calculated by</ns0:p><ns0:formula xml:id='formula_53'>u X = P (T − R) + χ [R (T − S) − P (T − R)] (T − R) + χ (R − S) , u Y = R (T − S) + P (χ − 1) (R − S) (T − R) + χ (R − S) .<ns0:label>(37)</ns0:label></ns0:formula></ns0:div> <ns0:div><ns0:head n='3.'>Adaptive ZD strategy</ns0:head><ns0:p>Replace R in the extortionate strategy formula.</ns0:p><ns0:formula xml:id='formula_54'>U X −V 1 = χ (U Y −V 1) , (<ns0:label>38</ns0:label></ns0:formula><ns0:formula xml:id='formula_55'>)</ns0:formula><ns0:p>where V is the reference revenue variable that adjusts the surplus, P ≤ V ≤ R. The adaptive zerodeterminant strategy continuously adjust the value of V through the game according to the environment.</ns0:p><ns0:p>During the donation game model, a player choosing cooperation provide a revenue b to his/her opponent at the cost c, b > c > 0. Hence, the reference revenue variable V can be represented as </ns0:p><ns0:formula xml:id='formula_56'>V = σ (R − P) + P = σ (b − c) ,<ns0:label>(39)</ns0:label></ns0:formula></ns0:div> <ns0:div><ns0:head>Analysis of convergence of average revenue</ns0:head><ns0:p>Next, the initial cooperation probability of X was set to 0.9, and 500 rounds of game were simulated under the set strategy, extortionate strategy, and adaptive strategy of the ZD, respectively. The average revenue variation of each party is recorded in Figure <ns0:ref type='figure' target='#fig_12'>7</ns0:ref>. Obviously, every set of game strategies could converge very quickly. After less than 10 rounds at least or no more than 40 rounds at most, the average revenue of both X and Y has been basically stable, indicating that the mining dilemma can be effectively mitigated.</ns0:p></ns0:div> <ns0:div><ns0:head>CONCLUSIONS</ns0:head><ns0:p>This paper mainly studied how to mitigate the mining dilemma of block withholding attack between the mining pools by means of Zero-determinant strategies. It deduced the calculation formula for the actual revenue of the mining pool at first when the block withholding attack is launched. And then, the ZD strategies such as the set strategy and the extortionate strategy are theoretically studied to solve the Nash equilibrium problem of the mining dilemma. Based on these theories, the adaptive ZD strategy was put forward, changing the corporation probability of the next round based on the previous revenues. Finally, 30 sets of game strategies were selected and simulated to show the actual revenue variation of the pools.</ns0:p><ns0:p>The experimental simulation indicated that these ZD strategies, especially the proposed adaptive strategy, can promote the cooperation between the pools and increase both the overall revenue of the pool and the revenue of each miner. However, the paper only considered the two-player applying discrete strategy.</ns0:p><ns0:p>Therefore, the authors will continue to study the multi-player applying discrete strategy iterative game , the two-player and multi-player applying continuous strategy games. </ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>Sci. reviewing PDF \| (CS-2021:12:68933:1:2:NEW 5 May 2022)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>defined and provided examples of memory-two ZD strategy in the repeated Prisoner's Dilemma game. McAvoy and Hauert (2016) introduced a broader class of autocratic strategies by extending ZD strategies to iterated games with the continuous action space. These studies have enriched the theory of ZD strategy.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head /><ns0:label /><ns0:figDesc>Sci. reviewing PDF \| (CS-2021:12:68933:1:2:NEW 5 May 2022)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head>Figure 1 .</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1. Pool 1 Attacking Pool 2 .</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head /><ns0:label /><ns0:figDesc>Sci. reviewing PDF \| (CS-2021:12:68933:1:2:NEW 5 May 2022)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head>Figure 2 .</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2. Revenue at different infiltration rates.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head>Figure 3 .</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Figure 3. Mutual attacks between the two pools.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_7'><ns0:head /><ns0:label /><ns0:figDesc>Sci. reviewing PDF \| (CS-2021:12:68933:1:2:NEW 5 May 2022)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_8'><ns0:head>Figure 4 .</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Figure 4. Revenue at different infiltration rates.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_9'><ns0:head /><ns0:label /><ns0:figDesc>Sci. reviewing PDF \| (CS-2021:12:68933:1:2:NEW 5 May 2022)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_10'><ns0:head /><ns0:label /><ns0:figDesc>Sci. reviewing PDF \| (CS-2021:12:68933:1:2:NEW 5 May 2022)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_11'><ns0:head /><ns0:label /><ns0:figDesc>strategy, X plays a game with Y , following the ZD strategy with α = 0. By adjusting the cooperation probability, X can unilaterally control the revenue of Y . In this case, p = βU Y + γ1, i.e., p = </ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_12'><ns0:head>Figure 7 .</ns0:head><ns0:label>7</ns0:label><ns0:figDesc>Figure 7. Convergence of the average revenue.</ns0:figDesc></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head /><ns0:label /><ns0:figDesc>incorporated the ZD strategy to public good game model, and drew</ns0:figDesc><ns0:table /><ns0:note>3/17PeerJ Comput. Sci. reviewing PDF \| (CS-</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_1'><ns0:head /><ns0:label /><ns0:figDesc>also greater than zero, i.e., ∆R 1 > 0. In this case, Pool 1 can obtain more revenue than what it can obtain without launching any attack. Apparently, there is a suitable infiltration rate for Pool 1 to maximize its revenue, showed in Figure2.If Pool 1 does not launch an attack, the original revenue of Pool 2</ns0:figDesc><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_2'><ns0:head>Table 4 .</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Total revenues of X and Y . can be seen, all the variances are very small, which means that the revenues of X and Y in all games are</ns0:figDesc><ns0:table><ns0:row><ns0:cell cols='2'>very stable.</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell cols='2'>AllC</ns0:cell><ns0:cell /><ns0:cell>AllD</ns0:cell><ns0:cell cols='2'>TFT</ns0:cell><ns0:cell cols='2'>WSLS</ns0:cell><ns0:cell>Random</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>X's</ns0:cell><ns0:cell>Y 's</ns0:cell><ns0:cell>X's</ns0:cell><ns0:cell>Y 's</ns0:cell><ns0:cell>X's</ns0:cell><ns0:cell>Y 's</ns0:cell><ns0:cell>X's</ns0:cell><ns0:cell>Y 's</ns0:cell><ns0:cell>X's</ns0:cell><ns0:cell>Y 's</ns0:cell></ns0:row><ns0:row><ns0:cell>X</ns0:cell><ns0:cell>Set Strategy Extortionate Strategy</ns0:cell><ns0:cell cols='9'>4.56 5.57 3.34 16.62 3.38 4.12 18.17 5.35 22.28 21.43 3.24 3.95 2.81 13.98 2.23 2.72 17.37 5.77 21.64 21.33</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>Adaptive Strategy</ns0:cell><ns0:cell cols='9'>4.98 6.07 3.64 18.12 4.08 4.97 18.40 5.11 21.68 21.17</ns0:cell></ns0:row></ns0:table><ns0:note>* The units of data are 10 −5 .</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_3'><ns0:head>Table 5 .</ns0:head><ns0:label>5</ns0:label><ns0:figDesc>Average Variance of X and Y .</ns0:figDesc><ns0:table /></ns0:figure> <ns0:note place='foot' n='10'>/17 PeerJ Comput. Sci. reviewing PDF \| (CS-2021:12:68933:1:2:NEW 5 May 2022)Manuscript to be reviewed</ns0:note> </ns0:body> "	"REBUTTAL LETTER We sincerely thank the editor and all reviewers for their valuable feedback that we have used to improve the quality of our manuscript. The reviewers’ comments were laid out below in italics and had been numbered accordingly, our response was given in normal font following the corresponding comment. In addition, we found there were some minor problems with the format of the paper, so we also revised it. Reviewer 1 Thank you very much for reading our manuscript and reviewing it, which will help us improve it to a better scientific level. As suggested by the reviewer, we revised our manuscript carefully, and the detailed modifications are as follows. Comment 1: I recommend to (also) display the revenue of the independent miners in an additional Figure 4(c). Response: Thank for this comment. We are sorry we weren't thoughtful. We added the revenue of the independent honest miners in Figure 4 (page 7). But according to the requirements of the journal, 3-D image can not be used. So, we format Figure 4 as 2-D image. In addition, we also added a description of the revenue of the independent honest miners in Figure 2 (page 6). Comment 2: Could mention, at least as outlook / room for further consideration: Would mining difficulty adjustment (after 14 days) adjust pool revenue facing a block withholding attack? Response: Thank for this comment. In order to better illustrate whether the ZD strategies could mitigate the BWH attacks, We deleted Figure 8 and replaced it with Tables 3 and 4. Table 3 shows all revenues of X and Y in the games (page 13), and Table 4 shows the total revenues of X and Y (page 14). In addition, we analyzed the data in both tables in detail (page 12, line 331-339). We also added some more detailed result analysis to indicate whether the mining dilemma was mitigated in section 'Analysis of convergence of average revenue' (page 14, line 345-349). Comment 3: Section 'ZD strategy' (from 7/17, line 225) actually lacks a precise and compact definition of a 'ZD strategy': Lines 245/246 on page 8/17 do provide some conditions ('If the strategy selected by X satisfies ... This is the ZD strategy'), yet unclear what set of candidate 'strategies' (which then do or do not fulfil the conditions) X can select from. Response: Thank for this comment. We have added a a precise and compact definition of a 'ZD strategy' on page 8, line 253-255. We can give an explanation for the second comment. A player can select all policies that are related to p= p1, p2 , p3 , p4  , which means the probability to choose cooperation according to the four game results (CC,CD,DC,DD) in the previous round (page 8, line 265-266).And the value range of pi is [0, 1]. When the strategy of X satisfifies p  U X +U Y   1 (Fomular (31) on page 9), where p   1  p1 , 1  p 2 , p3 , p 4  (Fomular (27) on page 9), it is the ZD strategy. Comment 4: Conclusions sections very short. Should also summarize restrictions (e.g.: for the case of two pools) and outline further work. Response: Thank for this comment. Sorry, the conclusion section is a little sketchy. So we rewrote the'Conclusion' section according to this comment (page 14, line 351-362). Comment 5: Should define all abbreviations on first mention (TFT = Tit for tat? WSLS=?) Response: Thank for this comment. We have defined all abbreviations and you can see the modifications on page 11, line 302-313. Comment 6: Should use a small space (typically '\,' in LaTeX) between number and unit (10mins ->10 mins, 300BTC -> 300 BTC). Response: Thank for this comment. We are very sorry for our carelessness. We have corrected the errors you mentioned (page 1, line 38 and page 2, line 94) and checked the entire manuscript to make sure there are no similar errors. Comment 7: 'Pool1'/'Pool2' should use subscript für index values ('Pool$_1$'). Response: Thank for this comment. According to this comment, we changed 'Pool1' and 'Pool2' into corresponding 'Pool1' and 'Pool2' in the body of the manuscript, and also made corresponding modifications to these names involved in Figure 1-4. Comment 8: Usual not to find experiments replicated. Would expect multiple runs of 100 (or 500) steps each and variance reviewed. If not, should (at least) argue why - according to the authors - this is not necessary. Response: Thank for this comment. We used the same random seed to initialize the cooperation probability, because we wanted to compare the change of both revenues under the same condition when a player chose a strategy and the opponent chose a different one. However, this comment really makes sense. So we added Table 5 (page 14) and the analysis of the average variance (page 12, line 340-343) if the games repeated 50 times that X chose the three ZD strategies and Y chose other five strategies. Reviewer 2 We appreciate very much for the comments and take it very seriously. We have revised the manuscript carefully. Our modifications of the manuscript are as follows. Comment 1: In the abstract section, the author should make clear which works are his own, rather than linearly describe the prose structure of the work. Response: Thank for this comment. We are sorry the abstract isn't good enough. So we rewrote the abstract to the best of our ability. Comment 2: Some formulas are not in the same format as (11) (15). Authors should proofread carefully to ensure that all formulas are in the same format. Response: Thank for this comment. We are sorry we weren't thoughtful. We checked all the formulas and changed them into the same format. Comment 3: In some of the figures, it is difficult to see the specific situation corresponding to Y as shown in Figure 6 and Figure 7. The author should make corresponding changes to make the figure clearer. Response: Thank for this comment. In order to show the overlap of the two lines corresponding to X and Y, we used the solid and dashed line respectively. As a result, the specific situation of Y is not clear. According to the requirements of the journal, the figures should accessible to those with color blindness. So we changed the dashed line to a thin solid line and chose the different thickness to well demonstrate the coincidence of the two lines (page 12, Figure 5 and page 13, Figure 6). Comment 4: In the conclusion part, the author should make a concise summary of each chapter in the conclusion, clarifying his own contribution. Response: Thank for this comment. Sorry, the conclusion section is a little sketchy. So we rewrote the'Conclusion' section according to this comment (page 14, line 351-362). Comment 5: The following major workings are missing in the references: Multiple cloud storage mechanism based on blockchain in smart homes; Novel vote scheme for decision-making feedback based on blockchain in internet of vehicles. Response: Thank for this comment. We have added these two references to the manuscript (page 1, line 32). Reviewer 3 Thank you very much for your excellent comments on the manuscript. We have carefully revised the manuscript according to the comments and resubmitted the revised version. Our detailed modifications and explanations are as follows. Comment 1: In this manuscript, the authors investigated block withholding attack in blockchain based on game theory. They consider the situation that only two mining pools exist and one or both pools attack each other. By calculating revenue of each mining pool, they pointed out that choosing whether it attacks the other pool or not is similar to choice of action in the prisoner's dilemma game. Then, they investigated zero-determinant (ZD) strategies in the iterated prisoner's dilemma (IPD) game, and showed that an adaptive ZD strategy promotes cooperation between pools. Although their idea about applying ZD strategies to blockchain mining is interesting, I think that the current version should not be published. The reason is that, whereas the first part 'Mining dilemma analysis' is significant as an application of game theory, the second part 'ZD strategy' does not contain any novel results. They just investigated ZD strategies in IPD, which have already been studied by many researchers, and the paper does not advance our understanding at all. I think that the problem comes from the fact that the authors reduced their model to IPD, which is too simple, although action in their original model is the continuous parameter $r_i$. I recommend that the authors investigate ZD strategies in their mining game without reducing it to IPD. In research on ZD strategies, McAvoy and Hauert extended ZD strategies to continuous action space [PNAS 113(13), 3573 (2016)]. I think that this paper is useful for improving their manuscript. Response: We sincerely appreciate for this comment and take it very seriously. And hank you for recommending an excellent reference for us. It fully demonstrates that you have a high attainments in game theory, which is worth learning from you. We have carefully studied the autocratic strategies with arbitrary action spaces proposed in this manuscript, which will give us a good inspiration in the future research. We have all shown a keen interest in it. However, we want to explain why we regard mining dilemma as an iterative game with the discrete strategy of two players. Because we think there are three innovations in this manuscript. The first one is to fully define the calculating formulas of the revenue when the BWH attack occurs. If the network situation is too complicated, it is not easy to understand the derivation of the revenue formulas and analyze how the revenue changes under the attack. So we use a simplified model with only two mining pools and other honest miners. The second innovation is creatively using the ZD strategy to mitigate the BWH attacks. Though there is few contributions to the ZD strategy, we consider it an application innovation. As the comment said that the idea about applying ZD strategies to blockchain mining was interesting. The third innovative work is the proposed adaptive ZD strategy. Unfortunately, the original adaptive ZD strategy may not be innovative enough. So we further improved it, and discussed it in detail in the manuscript. According to this comment, ZD strategies to continuous action space can solve the problems raised in this manuscript from another perspective. Therefore, Therefore, we will continue to study the multi-player applying discrete strategy iterative game , the two-player and multi-player applying continuous strategy games. Obviously, the references provided by this comment will be of great help to us. Comment 2: In addition, as a minor comment, their survey on ZD strategies is not sufficient. There are many papers which should be mentioned in 'Related works'. For example, ZD alliance [Hilbe, Wu, Traulsen, Nowak, PNAS 111(46), 16425 (2014)] ZD strategies in noisy games [Hao, Rong, Zhou, Phys. Rev. E 91, 052803 (2015)] ZD strategies in games with a discount factor [Hilbe, Traulsen, Sigmund, Games and Economic Behavior 92, 41 (2015)] Extension to alternating games [McAvoy, Hauert, Theoretical Population Biology 113, 13 (2017)] Consistency and independence of ZD strategies [Ueda, Tanaka, PLoS ONE 15(4), e0230973 (2020)] Extension to memory-$n$ strategies [Ueda, Royal Society Open Science 8, 202186 (2021)] I hope that the authors resubmit a revised version. Response: Thank for this comment. We have added the discussions of these references in the 'ZD Strategy' section of the manuscript (page 3-4, line 125-160). These recommended papers have indeed enriched our 'Related works' and improved the quality of our manuscript. "	Here is a paper. Please give your review comments after reading it.
434	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>With numerous countermeasures, the number of deaths in the construction industry is still higher compared to other industries. Personal Protective Equipment (PPE) is being improved day by day to avoid these accidents though workers intentionally or unintentionally forget to use such safety measures. It is challenging to run a safety check manually as the number of co-workers on a site can be large; however, it is a prime duty of the authority to give maximum protection to the workers on the working site. From these motivations, we have created a Computer Vision (CV) based automatic PPE detection system that detects various types of PPE. This study also created a novel dataset named CHVG (four Colored hardhats, Vest, Safety glass) containing eight different classes, including four colored hardhats, vest, safety glass, person-body, and person-head. The dataset contains 1699 images and corresponding annotations of these eight classes. For the detection algorithm, this study has used the You Only Look Once (YOLO) family's anchor-free architecture, YOLOX, which yields better performance than the other object detection models within a satisfactory time interval. Moreover, this study found that the YOLOX-m model yields the highest mean Average Precision (mAP) than the other three versions of the YOLOX.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>INTRODUCTION</ns0:head><ns0:p>Due to urbanization, a significant number of workers are currently employed at construction sites to implement large projects in different parts of the world. Constructions site is always a place of terrible danger. A small number of inspectors can not take care of the health and safety of a large number of workers. According to the International Labour Organization (ILO), every year approximately 2.3 million people die due to work-related accidents or diseases <ns0:ref type='bibr' target='#b18'>(Neale, 2013)</ns0:ref>. Every year at least 60 million terrible accidents happen to construction sites all over the world <ns0:ref type='bibr' target='#b9'>(ILO, 2005)</ns0:ref>. It is estimated that an accident occurs every 10 minutes which is actually a thundering signal. One out of every 6 fatal calamities at the workplace happened at a construction site. In industrialized countries, as many as 25% to 40% of work-related deaths befall in construction sites <ns0:ref type='bibr' target='#b9'>(ILO, 2005)</ns0:ref>. Most of the workers are under forty years of age. One study found that almost 25% of the workers had a mishap within a year of starting the work. In many countries, on an average 30% of the workers get into trouble from back pains or any other musculoskeletal disorders <ns0:ref type='bibr' target='#b9'>(ILO, 2005)</ns0:ref>.</ns0:p><ns0:p>In construction site there are more than 71% injury appears compared to all other industry <ns0:ref type='bibr' target='#b25'>(Waehrer et al., 2007)</ns0:ref>. However, workers can be protected from these types of terrible dangers by wearing Personal Protective Equipment (PPE). Hardhat, safety glasses, gloves, safety vest, safety goggles, and so on are included as PPE. Workers can use hardhat to protect against minor head injuries. The chance of skull fracture, neck sprain, and concussion when falling from height can be diminished by wearing a hardhat <ns0:ref type='bibr' target='#b8'>(Hume et al., 1995)</ns0:ref>. It also reduces the possibility of severe brain injury. Hence, hardhat is an exigent part of the PPE in a construction site. Eye injuries are a very common phenomenon in the workplace, especially in construction sites. According to the National Institute for Occupational Safety and Health (NIOSH), approximately 2000 workers in the U.S are suffering from a work-related eye injury. A study by the Bureau of Labor Statistics (BLS) shows that almost three out of five eye injured workers did not wear any protective shield at the time of the accident. A safety vest is another kind of PPE that helps a worker be more visible to other co-workers. Reflective strip lines of the vest may be helpful to extrapolate the location of the workers and reduce the chance of accidents in low lighting conditions and also in bad weather <ns0:ref type='bibr' target='#b28'>(Wang et al., 2021b)</ns0:ref>. Hardhat colors may play a vital role to differentiate among the workers in different countries. In the United Kingdom (UK) black-colored hardhats are worn by the site supervisors, PeerJ Comput. Sci. reviewing PDF \| (CS-2021:11:68217:1:2:NEW 22 Apr 2022)</ns0:p><ns0:p>Manuscript to be reviewed Computer Science slinger/signaller wear an orange-colored hardhat, site manager wears a white-colored hardhat and the rest of them wear a blue-colored hardhat <ns0:ref type='bibr' target='#b28'>(Wang et al., 2021b)</ns0:ref>. In the construction site, workers are willingly or unwillingly forget to wear any element of PPE which would be dangerous for them or the whole construction site. Proper steps may diminish the risk of impending danger. The authority of the site should ensure that every worker wears PPE while they work in the construction site. However, manual checking would not be time and cost-effective. The background study found that correct detection of PPE is inevitable because misdetection or under-detection can cause a rigorous problem. From this motivation, the authors felt that precious detection of PPE can be helpful for workers' safety in an industrial manner.</ns0:p><ns0:p>Moreover, additional PPE detection, i.e., increasing the class number, increases the detection challenge in computer vision. That is why this study tries to recognize different types of PPE.</ns0:p><ns0:p>In this case, Computer Vision (CV) may be helpful. A system that can detect PPE in the construction site from the workers reduces the time and cost of the authority and improves the safety argument. For this purpose, this study created a new dataset named after CHVG that contains real-time construction site images. The primary objective of this study is to detect personal protective gear more accurately within a reasonable time interval. Moreover, misdetection or false detection would be harmful to both the workers and the authority of the construction site. The YOLO architecture is popular for the fast and accurate detection of objects from the image. An anchor-free manner architecture is published by a company named Megvii 1 which is chosen as the architecture to conduct this study. Satisfactory performance yields by the YOLOX are better than the previous study on the safety of the workers on the construction site.</ns0:p><ns0:p>A recent publication by <ns0:ref type='bibr' target='#b28'>(Wang et al., 2021b)</ns0:ref> </ns0:p></ns0:div> <ns0:div><ns0:head n='1'>RELATED WORKS</ns0:head><ns0:p>In recent years several works have been done for the safety of the workers in construction sites based on CV and Deep Learning (DL). DL methods have a strong ability to self-learning from useful features.</ns0:p><ns0:p>Region-based CNNs (R-CNNs) are used by <ns0:ref type='bibr' target='#b5'>(Fang et al., 2018)</ns0:ref> to identify whether a worker wears a hardhat or not on the construction site. Their precision and recall rate was approximately 95.7% and 94.9% for the non-hardhat users (NHU). The authors did not consider hardhat detection rather they only detect the non-hardhat users. Histograms of Oriented Gradient (HOG) is used by <ns0:ref type='bibr' target='#b34'>(Zhu et al., 2015)</ns0:ref> to extract head features from images. Feeding the extracted features into the Support Vector Machine (SVM) the authors try to classify whether one worker wears a helmet or not. Single Shot Detector (SSD) based algorithm is proposed by <ns0:ref type='bibr' target='#b30'>(Wu et al., 2019)</ns0:ref> to detect hardhat. The authors found that Reverse Progressive Attention (RPA) network into the SSD can enhance the performance. Retinanet architecture is proposed by <ns0:ref type='bibr' target='#b6'>(Ferdous and Ahsan, 2021)</ns0:ref> to detect both hardhat and head of the workers in the construction site. The authors found the Average Precision (AP) for hardhat is 95.8% and the head is 93.8% for the publicly available dataset <ns0:ref type='bibr' target='#b32'>(Xie, 2019)</ns0:ref>. SSD-Mobilenet is used by <ns0:ref type='bibr' target='#b11'>(Li et al., 2020)</ns0:ref> to detect hardhat. The authors did not consider head detection as a safety issue. To identify protective helmets in the construction site modified SSD is presented by <ns0:ref type='bibr' target='#b14'>(Long et al., 2019)</ns0:ref>. The AP of their model is 78.3% with 21.6 Frame Per Seconds (FPS). A model is proposed by <ns0:ref type='bibr' target='#b27'>(Wang et al., 2020)</ns0:ref> where the authors use MobileNet architecture as the backbone and residual block-based module for object prediction. The AP for hardhat is 87.4% and the head is 89.4% with a 62 FPS rate. <ns0:ref type='bibr' target='#b16'>(Mneymneh et al., 2019)</ns0:ref> try to isolate a moving worker using the motion from videos then try to find any helmet on the top region. The color-based detection system is used by <ns0:ref type='bibr' target='#b4'>(Du et al., 2011)</ns0:ref>. They utilize color threshold to separate face, helmet, and other objects from an image. K-Nearest Neighbors (KNN) is used to capture moving objects from the videos then classification is done using CNN by <ns0:ref type='bibr' target='#b29'>(Wu and Zhao, 2018)</ns0:ref>. <ns0:ref type='bibr' target='#b2'>(Chen and Demachi, 2020)</ns0:ref> try to detect hardhats and full-face masks using YOLOv3 in Decommissioning of Fukushima Daiichi Nuclear Power Station. Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Computer Science</ns0:head><ns0:p>The YOLOv3 architecture is used by <ns0:ref type='bibr' target='#b3'>(Delhi et al., 2020)</ns0:ref> to predict four classes such as NOT SAFE, SAFE, NoHardHat, and NoJacket. The authors train their model using 2509 images that were collected from video recordings from the construction sites and internet-based collections. The average precision and recall rate is 96% on the test data. Authors try to make the CV more trustable in the real world by doing an alarm system by integrating it and also a reporting system with a certain time period. <ns0:ref type='bibr' target='#b26'>(Wang et al., 2021a)</ns0:ref> try to human identity recognition and helmet detection using YOLOv3 in a construction site. <ns0:ref type='bibr' target='#b17'>(Nath et al., 2020)</ns0:ref> present a PPE detector using YOLOv3 algorithm. The authors proposed a dataset named Pictor-v3, which contains 1500 images and corresponding annotations. They reported the highest performance is 72.3% mean Average Precision (mAP) with 11 FPS. The AP for vest and helmet is 84.96% and 79.81% respectively. <ns0:ref type='bibr' target='#b33'>(Zhang et al., 2021)</ns0:ref> proposed an improved weighted bi-directional feature pyramid network (BiFPN) for hardhat wearing detection, they also try to detect the color of hardhat into the construction site. The author shows 87.04% mAP yields by their proposed method for five classes. <ns0:ref type='bibr' target='#b28'>(Wang et al., 2021b)</ns0:ref> present a dataset named CHV and use YOLO family architecture (YOLOv3, YOLOv4, and YOLOv5) to detect the PPE of the workers. The authors found that YOLOv5x outperforms the others method and the mAP is 86.55% for six classes. They proclaimed that 52 FPS for one single image is processed by the YOLOv5s model. This study is relevant and complementary to the aforementioned research. An attempt has been made to further enhancement of the above studies through this study. A new anchor-free training architecture is proposed for PPE detection into the construction site. This study also tries to explore the latest dataset for PPE detection both increasing data size and the number of classes.</ns0:p></ns0:div> <ns0:div><ns0:head n='2'>MATERIALS AND METHODS</ns0:head></ns0:div> <ns0:div><ns0:head n='2.1'>Dataset preparation (CHVG dataset)</ns0:head><ns0:p>Several sensor-based PPE detections have also been performed in the past years. In this study, a CV-based system is preferred since it is low costing, less complex, and easily usable at the field level than the sensor-based system. <ns0:ref type='bibr' target='#b28'>(Wang et al., 2021b)</ns0:ref> present a dataset named CHV to detect the PPE of the workers.</ns0:p><ns0:p>Into the CHV dataset, there are six classes including four different helmet colors, vests, and person. In a construction site, not only hardhat detection is important but also head detection is exigent. To make the CV more practical in an alarm system for non-hardhat users a major part is head detection. Every year more than 10,600 eye injuries disable the workers <ns0:ref type='bibr' target='#b23'>(Thompson and Mollan, 2009)</ns0:ref>. Hence, we try to detect person head and safety glass also as a part of PPE detection. To conduct this work, a new dataset is created named CHVG which consists of eight classes including four different colored hardhats (white, blue, red, and yellow), person head, vest, person body, and safety glass as an extension of the dataset named CHV.</ns0:p><ns0:p>The name CHVG; CH for Color Hardhat, V for Vest, and G for Glass. Several images of the CHVG dataset are internet mined and most of the images are taken from <ns0:ref type='bibr' target='#b32'>(Xie, 2019)</ns0:ref> and <ns0:ref type='bibr' target='#b28'>(Wang et al., 2021b)</ns0:ref>. Moreover, hardhat is the major safety gear for workers to protect themselves from a minor accident.</ns0:p><ns0:p>Hardhat color may play a different role in the construction site. The vest strip helps the authorities to observe workers who are located at a distance. In a construction area, it is a matter of tension that whether the personnel are without a hardhat. Hence, person head detection would be a solution to get rid of this worry. Safety glass protect workers from harmful light rays, dust, air, gas, and other injuries. Several images from the CHVG dataset are shown in Figure <ns0:ref type='figure' target='#fig_2'>1</ns0:ref>. The image size of the dataset is 640 × 640. The dataset can be found https://doi.org/10.6084/m9.figshare.19625166.v1.</ns0:p><ns0:p>The CHVG and CHV datasets are partially similar, but CHVG contains more 369 images than the CHV dataset. However, the CHVG dataset also contains extra two classes, such as safety glass and a head without a hard hat. In machine learning, more challenges for more classes. For this reason, we try to increase the class number. Besides, safety glass and head without hardhat detection on construction sites is essential, like other objects. After collecting the images, handcrafted annotations are done using the LabelImg <ns0:ref type='bibr' target='#b24'>(Tzutalin, 2018)</ns0:ref> annotation tool. The CHVG dataset contains 1699 images and corresponding annotations. The proposed dataset consists of 11,604 objects in total including 40.28% persons, 18.42% vests, 4.58% glass, 6.29% heads, 4.27% red, 10.34% yellow, 4.68%, and 11.13% white instances. Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref> illustrates about the dataset. The CHVG dataset is divided into three subcategories i.e. train, test, and validation set. Moreover, the test dataset for the model evaluation and the validation set is provided at the time of model training to see that whether the training is on the right path.</ns0:p></ns0:div> <ns0:div><ns0:head>3/18</ns0:head><ns0:p>PeerJ Comput. Sci. reviewing PDF \| (CS- <ns0:ref type='table' target='#tab_8'>2021:11:68217:1:2:NEW 22 Apr 2022)</ns0:ref> Manuscript to be reviewed </ns0:p><ns0:note type='other'>Computer Science</ns0:note></ns0:div> <ns0:div><ns0:head n='2.2'>The proposed framework</ns0:head><ns0:p>In Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Computer Science</ns0:head><ns0:p>Moreover, it can do more floating points operations than other models within less time <ns0:ref type='bibr' target='#b7'>(Ge et al., 2021)</ns0:ref>. However, the dataset contains complex objects we need to do feature extraction with less time and better accuracy. For this reason, this study uses Darknet53 as the backbone of the experimented network. A feature pyramid is a pyramidal hierarchy of feature maps from low to high levels <ns0:ref type='bibr' target='#b6'>(Ferdous and Ahsan, 2021)</ns0:ref>. It builds a pyramid of features. Then transfer the feature pyramid to the head of the YOLOX architecture. Thereafter, the head network regresses the bounding boxes and classifies objects utilizing the features that come from the backbone. An imaginal is shown in Figure <ns0:ref type='figure' target='#fig_4'>2</ns0:ref> Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Computer Science</ns0:head><ns0:p>Backbone (e.g. Path Aggregation Network (PAN) <ns0:ref type='bibr' target='#b13'>(Liu et al., 2018)</ns0:ref> and Feature Pyramid Network (FPN) <ns0:ref type='bibr' target='#b10'>(Kim et al., 2018)</ns0:ref> YOLOv3, YOLOv4 and YOLOv5 head network emits anchor boxes however, YOLOX <ns0:ref type='bibr' target='#b7'>(Ge et al., 2021)</ns0:ref> head network does not emit anchor boxes, hence it is said to be anchor-free manner architecture.</ns0:p></ns0:div> <ns0:div><ns0:head n='3'>TRAINING PROCESS</ns0:head><ns0:p>The Manuscript to be reviewed Manuscript to be reviewed</ns0:p><ns0:note type='other'>Computer Science</ns0:note><ns0:p>Computer Science </ns0:p></ns0:div> <ns0:div><ns0:head n='3.1'>Data Preprocessing</ns0:head><ns0:p>Several geometric changes of the images are performed as the mosaic augmentation, the random affine transformation is performed where rotation is accomplished on both axis using a value between (-10 deg to +10 deg). Both X and Y-axis translation is performed within a value of (0.4, 0.6). Scaling is done on both axis using a value between (0.1, 2), the same amount of shear is performed on both axis. The quantity of shear is accomplished within a value of (-2 to +2). All aforesaid values are uniform random values between the range. Photometric changes are executed by changing the brightness, contrast, hue, and saturation. Moreover, RandomHorizontalFlip and RandomResizedCrop strategies were also performed as the data preprocessing.</ns0:p></ns0:div> <ns0:div><ns0:head n='4'>HAZE, RAIN AND LOW-LIGHT EFFECT</ns0:head><ns0:p>Different types of challenging conditions are revealed in construction sites that creates problem for a machine to recognize an object properly from the scene. In such situations, human also make small mistakes, so keep in mind the conditions that may be created in real-time, this study tries to generate artificial images by doing several photometric changes into the image. These artificial images are not used at the model's training time. They are used only for the testing purpose. The HAZE, RAIN, and LOW-LIGHT images are created from the test dataset to see the model's robustness. The effected images are not included in our dataset. According to the background study, this study found a variety of challenging conditions may appear in real-time construction site e.x. low-light, rain, and haze effect are some of them.</ns0:p><ns0:p>Algorithm 1 shows how to add artificial haze to make a hazy image. First of all, we take a 2d array of the same size as the original image to create a hazy image. The real hazy image contains some noise, hence to make the artificially created image more realistic we add some noise to it. Noise amplitude controls the severity of the noise and noise offset controls the brightness of the noise. This study adds pixel by pixel values of noisy images and original images to make the hazy images. The same noise is added in all color channels so that it does not change the color property. Then adjust the pixel values of the hazy image using maximum pixel values of the hazy image and original image. Algorithm 2 and 3 shows how to appear artificial rain to make rainy image. This study uses randomly value between (-10, 10) to make the raindrops slant. The height and width of the raindrop are taken 2% and 0.18% subject to the height and width of the original image size respectively. The color of the raindrop is set to <ns0:ref type='bibr'>RGB (196,</ns0:ref><ns0:ref type='bibr'>211,</ns0:ref><ns0:ref type='bibr'>223)</ns0:ref>. All values are taken from the author's perspective. Then the predefined number of raindrops is drawn on the image. To make the image a real rainwater image, this study applies an average kernel of size 4 on the image as a result, the image is a bit blurry. To make the </ns0:p></ns0:div> <ns0:div><ns0:head n='5'>EVALUATION METRICS</ns0:head><ns0:p>In object detection, we should take the high value of both precision <ns0:ref type='bibr' target='#b20'>(Padilla et al., 2021)</ns0:ref> and recall <ns0:ref type='bibr' target='#b20'>(Padilla et al., 2021)</ns0:ref>. Unfortunately, sometimes this is a matter of concern to take the best value of both the Manuscript to be reviewed Computer Science metrics. In this regard, the Precision-Recall (PR) curve may help us to select the best value for evaluating a model. In the PR curve, precision is plotted on Y-axis, and recall is plotted on X-axis, therefore the optimum value for both the metrics can be acquired in the right up corner.</ns0:p><ns0:p>In object detection, NMS is calculated for selecting one entity from many overlapping entities e.x. one bounding box is to be selected from numerous overlapping bounding boxes. Intersection over Union (IoU) calculation may help us to pick up the best bounding box from multiple boxes. Precision, recall and IoU can be calculated according to <ns0:ref type='bibr' target='#b20'>(Padilla et al., 2021)</ns0:ref> and <ns0:ref type='bibr' target='#b19'>(Padilla et al., 2020)</ns0:ref>. The IoU calculation has another effective use in the performance measurement of an object detection model. When we try to measure the performance of a model must look at the appropriate object and perfect bounding box alignment around the object. The IoU computation could assist us in choosing a better bounding box for indefectible alignment. The IoU calculation can be performed using the ground truth bounding box and the predicted bounding box. IoU calculation tells us how much the predicted bounding boxes are related to the ground truth bounding boxes i.e. the percentage of overlap between two bounding boxes.</ns0:p><ns0:p>The bigger the overlap area, the higher the IoU. TP, FP, and FN are calculated according to <ns0:ref type='bibr' target='#b20'>(Padilla et al., 2021)</ns0:ref>. In this study, the authors consider a detection as TP if the model predicts true class and the IoU calculation of bounding boxes is greater than 50%. Inversely, if the IoU is smaller than 50% and the detection emits the right class according to ground truth then it is considered as FP. FP detects a ground truth class however, the ground truth box and the predicted bounding box is not in the same position. FP yields an improper detection case. In the case of FN, the system won't be able to detect any class where ground truth boxes exist. We can calculate FN by subtracting TP from the total positive. For calculating the TP, FP and FN this study uses object confidence score is greater than or equal to 50%.</ns0:p><ns0:p>AP is another metric to evaluate an object detection model. This study tries to evaluate the performance of the model using the AP. AP is a single value that illustrates the average of all precision. AP is also a scheme to encapsulate the PR curve. Higher precision is a piece of clear evidence that a model is confident while it classifies instances among the detections. On the other hand, higher recall is an indicator of the power of a model, it tells us how many correct detections are performed among all the ground truths.</ns0:p><ns0:p>Moreover, both precision and recall are major metrics of an object detection model. If a model has high recall yet low precision is an obvious referential that the model emits maximum positive example truly but it has many false positives i.e. classify many negative examples as positive. On the contrary, low recall yet high precision is an indicator that the model appropriately classifies positive examples however, it may contain only a few positive examples. Hence, it is necessary to choose a threshold, as if both precision and recall will be maximized. The PR curve helps to select the threshold among the different threshold values. Using the precision and recall value, the PR curve can be plotted <ns0:ref type='bibr' target='#b20'>(Padilla et al., 2021)</ns0:ref>. AP is the Area Under the Curve (AUC) of the PR curve. AP and mAP can be calculated according to <ns0:ref type='bibr' target='#b19'>(Padilla et al., 2020)</ns0:ref> and <ns0:ref type='bibr' target='#b20'>(Padilla et al., 2021)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head n='6'>RESULTS</ns0:head><ns0:p>All models of the YOLOX architecture are trained and tested using three different combinations of the dataset to check the robustness of the model. Table <ns0:ref type='table' target='#tab_4'>4</ns0:ref> represents the AP and mAP of the eight different classes. The YOLOX-m model yields the highest mAP applying the second combination of the dataset.</ns0:p><ns0:p>Moreover, averaging the mAP of the three different dataset combinations of every model, the YOLOX-m model performs better than the other two combinations of the dataset and generates the highest average of mAP is 89.84%.</ns0:p><ns0:p>Figure <ns0:ref type='figure' target='#fig_15'>7</ns0:ref> represents the PR curve of the YOLOX architecture. From this curve, it is seen that the best performance by the model yields at the top right corner i.e. whether the value of the top right corner of the curve is chosen then both the precision and recall will be maximized.</ns0:p><ns0:p>Figure <ns0:ref type='figure' target='#fig_16'>8</ns0:ref> displays the TP, FP, and FN rate of every class of the YOLOX-m model. The Highest TP rate is 95.94% for the person object and the lowest TP rate is 86.15% for the safety glass object. The highest FP rate is 7.38% for the white object and the lowest FP rate is 2.74% for the head objects. The highest FN rate is 13.85% for glass objects and the lowest FN rate is 4.06% for the person objects. Manuscript to be reviewed</ns0:p><ns0:p>Computer Science Manuscript to be reviewed</ns0:p><ns0:p>Computer Science the image size. Moreover, in our case 35% size increasing subject to the original image size i.e. increased image size is 864×864 yields 88.89% mAP. Whereas increasing the image size subject to original image size i.e. increased image size is 768×768 yields 88.55% mAP. Most of the images in this study are taken from real-time construction sites where the camera position is not closer to the scene. This may be due to the fact that the mAP did not decrease significantly, though the image size increases. When we decrease the image size of 20% and 35%, the mAP also decreases by 2.48% and 5.24% respectively. Image resize and corresponding annotations are done using roboflow 2 . Manuscript to be reviewed Manuscript to be reviewed </ns0:p><ns0:note type='other'>Computer Science</ns0:note><ns0:note type='other'>Computer Science</ns0:note></ns0:div> <ns0:div><ns0:head n='6.1'>Comparison with the state-of-the-art</ns0:head><ns0:p>In this section, a comparison is illustrated with the state-of-the-art method. According to the background study, this study found that <ns0:ref type='bibr' target='#b28'>(Wang et al., 2021b)</ns0:ref> proposed method performs better than the other state-ofthe-art methods. Therefore, this study shows a comparison with the <ns0:ref type='bibr' target='#b28'>(Wang et al., 2021b)</ns0:ref> achievement.</ns0:p><ns0:p>While we apply YOLOX-m to <ns0:ref type='bibr' target='#b28'>(Wang et al., 2021b)</ns0:ref> Manuscript to be reviewed</ns0:p><ns0:p>Computer Science increases than their proposed method named YOLOv5x. The analogy is shown in Table <ns0:ref type='table' target='#tab_7'>6</ns0:ref>. In addition, YOLOv5x is both trained and tested with our CHVG dataset. YOLOv5x yields 86.24% mAP on the testing dataset, whereas this study's proposed method YOLOX-m delivers 89.84% mAP on the testing dataset which is approximately 3.60% improvement than the YOLOv5x method. Symbol: * From this study. × Not available.</ns0:p><ns0:p>Table <ns0:ref type='table' target='#tab_8'>7</ns0:ref> shows a disparity of blurring face tests between YOLOv5x and YOLOX-m. Moreover, for the blurring face test, this study ignores the safety glass detection case. <ns0:ref type='bibr' target='#b28'>(Wang et al., 2021b)</ns0:ref> reported 79.55% mAP yields using the YOLOv5x model on their CHV dataset. However, this study's proposed method YOLOX-m generates 88.78% mAP. More generally which is nearly 9.23% higher than YOLOv5x. This study blurs a face using the Pixelization process on a certain face region. </ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>introduces YOLOv5 architecture for PPE detection into the construction site which detects six classes including four colored helmets, vests, and person. The authors of this study try to increase the reliability of CV and ensure more safety gear detection in a construction site by detecting eight classes. Therefore a new dataset is generated by extending (Wang et al., 2021b) proposed dataset. An anchor-free training architecture is introduced to detect PPE, person body, and person head in a construction site. Several photometric changes into the images are shown to create artificially rainy, hazy, and low-light conditioned images since the aforementioned situation would appear in a real construction site. YOLOX architecture yields better performance than the other state-of-the-art method.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>1 https://github.com/Megvii-BaseDetection/YOLOX 2/18 PeerJ Comput. Sci. reviewing PDF \| (CS-2021:11:68217:1:2:NEW 22 Apr 2022)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>Figure 1 .</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1. Several images from CHVG dataset.</ns0:figDesc><ns0:graphic coords='5,164.27,63.78,368.50,99.21' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head /><ns0:label /><ns0:figDesc>this study, both training and testing are performed in an anchor-free manner object detection model of YOLO architecture named YOLOX. First of all, an image is fed into the trained model. Distinguishable features are extracted from the backbone of the architecture and fabricate a feature pyramid of the extracted features. Backbone is a feature extractor that represents the input image as a feature map. In this study, DarkNet53 (Redmon and Farhadi, 2018) is used as the backbone of the YOLOX architecture. Darknet-53 is a Convolutional Neural Network (CNN) that serves as the backbone of the darknet YOLO architecture, which consists of 53 convolutional layers. More convolutional layers can learn more complex objects and works with higher accuracy.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head>Figure 2 .</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2. Framework of the proposed system.</ns0:figDesc><ns0:graphic coords='5,164.27,486.64,368.51,219.66' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head /><ns0:label /><ns0:figDesc>, which provides an overview of how the objects are detected from the images. The output can be any combination of the desired eight classes i.e. person body, person head, vest, red, white, yellow, blue, and safety glass. The YOLOX architecture is shown in Figure3. In the object detection field, bounding box regression/object localization and object classification are done in parallel using approximately the same parameter<ns0:ref type='bibr' target='#b31'>(Wu et al., 2020)</ns0:ref><ns0:ref type='bibr' target='#b22'>(Song et al., 2020)</ns0:ref>. A percussion rises up between these two tasks while trying to do object localization and object classification simultaneously. Basically regression and classification sub-network uses nearly the same parameter to do their prediction task. Hence, two separate branches, one for classification and the other for localization i.e. double-headed network was proposed in Double head R-CNN<ns0:ref type='bibr' target='#b0'>(Chao et al., 2018)</ns0:ref> to untangle the head of siblings. In a twin-headed network, object classification is done using a fully connected head network, and the object localization is done utilizing another convolutional head network<ns0:ref type='bibr' target='#b31'>(Wu et al., 2020)</ns0:ref>. Due to having facilities in a double-headed network for object classification and localization many one-stage and two-stage object detection models follow dual-headed architecture<ns0:ref type='bibr' target='#b12'>(Lin et al., 2017)</ns0:ref><ns0:ref type='bibr' target='#b13'>(Liu et al., 2018)</ns0:ref><ns0:ref type='bibr' target='#b22'>(Song et al., 2020)</ns0:ref><ns0:ref type='bibr' target='#b0'>(Chao et al., 2018)</ns0:ref>.According to Figure3, if we divide YOLO families architecture then it has three portions: backbone, head, and prediction.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head>Figure 3 .</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Figure 3. Architecture of YOLOX.</ns0:figDesc><ns0:graphic coords='6,150.09,350.76,396.85,355.55' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_7'><ns0:head /><ns0:label /><ns0:figDesc>continuously emits feature pyramids to the head. Using this feature, the head network classifies the objects and localizes the bounding boxes such that bounding boxes are aligned correct position of the objects. It is easy to switch YOLO architecture to an anchor-free mode. The head network needs to be changed slightly than previously published architecture. Reducing the predictions for each location from three to one and making them directly predict four values, i.e., two offsets in terms of the left-top corner of the grid, and the height and width of the predicted box. Differently sized features are generated from the backbone of the architecture. Feature size can be defined as H × W × P where H and W are the height and width of the input image. The value of P can be 256, 512, or 1024. Every feature map is sent to a 1 × 1 convolution layer. The output is added two parallel branches with two 3 × 3 convolution layers i.e. one for classification and the other for regression. IoU branches are added to the regression branches for the IoU. The output of the classification, regression, and IoU branches are added to another 1 × 1 convolution layer for object classification, box regression, and IoU respectively.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_8'><ns0:head /><ns0:label /><ns0:figDesc>CHVG dataset is divided into three different parts i.e. training, testing, and validation. To check the robustness of the model we created three different combinations of the dataset by keeping the same amount of the images into the training, testing, and validation set. Figure 4 shows the object distribution of the dataset of a combination among the set. Figure 5 represents the distribution of the objects or instances according to eight classes of the dataset of a combination into the three different sets. For training the model this study ascertains the weight decay is 0.0005. Stochastic Gradient Descent (SGD) is used for function optimization, momentum is set to 0.9. The Non-Maximum Suppression (NMS) threshold is adjusted to 0.65. NMS value is used to select an object's most appropriate bounding box. We can select the most appropriate bounding box at the testing time if we train the model using the most appropriate box. If we use a high NMS value, there is a possibility to detect more false negatives. Otherwise, a low NMS value yields more false positive. For this reason, the authors use an NMS value of 0.65 at the time of training for validation process. The authors set the parameters from their interests. In addition, several parameters are set empirically. The first 5 epochs are warm-up epochs and the learning rate is 0.01. These warm-up epochs help the network to train gradually, making a basic sense of the dataset. The authors have experimented with different values however, the aforementioned setup performed well. Then the learning rate changes consecutively over time intervals according to the cosine learning rate schedule following the Equation 1. A cosine learning rate schedule is used because it has a rapidly decreasing nature to a minimum learning rate value before being increased rapidly again. The resetting of the learning rate acts as a simulated restart of the learning process. Input image size is 640×640. The training process can be found in this GitHub repository https://github.com/Md-Ferdous/YOLOX. There are four versions of the YOLOX architecture: YOLOX-s (small), YOLOX-m (medium), YOLOX-l (large), and YOLOX-x (extra large). Batch size is agglutinate to 16, 12, 8, and 6 for the YOLOX-s, YOLOX-m, YOLOX-l, and YOLOX-x model respectively. All models are trained in the PyTorch environment. Table 2 represents the platform parameters. In this study, 200 epochs is performed to train the model. Table 3 represents the model description of YOLOX architecture. Different versions of the YOLOX model depend on the model depth and width. Training parameter increases according to the model depth and width also. In this study, the training time of the YOLOX-m, YOLO-l, and YOLOX-x models increase approximately 2, 3, and 4 times subject to the YOLOX-s model training time. lr = 0.5 × 1.0 + cos pi × iteration total Sci. reviewing PDF \| (CS-2021:11:68217:1:2:NEW 22 Apr 2022)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_9'><ns0:head>Figure 4 .</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Figure 4. Object distribution into training, testing and validation set.</ns0:figDesc><ns0:graphic coords='8,164.27,63.78,368.50,225.56' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_10'><ns0:head>Figure 5 .</ns0:head><ns0:label>5</ns0:label><ns0:figDesc>Figure 5. Per class object distribution in each set.</ns0:figDesc><ns0:graphic coords='8,164.27,343.65,368.51,236.12' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_11'><ns0:head /><ns0:label /><ns0:figDesc>Sci. reviewing PDF \| (CS-2021:11:68217:1:2:NEW 22 Apr 2022) Manuscript to be reviewed Computer Science image a little shady we reduce the brightness by 30% less than the real image because the rainy image is shady than the non-rainy image. Algorithm 2 GENERATE-RANDOM-LINES Require: imageShape, slant, dropLength Ensure: drops ← Φ, i = 0 numDrops ← Total number of rain-drops noiseImage ← Generate 2D random array of same size of the input image while i < numDrops do if slant < 0 then x ← random(slant, imageShape[1]) else x ← random(slant, imageShape[1] − slant) end if y ← random(0, imageShape[0]-dropLength) drops ← append(x, y) end while Algorithm 3 RAINY-IMAGE Require: image Ensure: rainDrop = 0 imageShape ← shape(image) slant ← random(−10, 10) dropLength ← int(imageShape[0] × 0.02) ▷ 2%o f height dropWidth ← int(imageShape[1] × 0.0018) ▷ 0.18%o f width rainDrops ← GENERAT E − RANDOM − LINES(imageShape, slant, dropLength) while rainDrop ̸ = rainDrops do rainyImage ← Draw rain drops on image end while rainyImage ← Apply blur filter to rainyImage rainyImage ← Reduce brightness of rainyImage This study creates a low-light image by controlling the brightness of the original image. Reducing 60% brightness subject to the original image to create a low-light conditioned image. Although lightangles may differ in real-time, however, this study reduces the brightness of every pixel linearly. Figure6shows artificially created hazy, rainy and low-light image.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_12'><ns0:head>Figure 6 .</ns0:head><ns0:label>6</ns0:label><ns0:figDesc>Figure 6. Artificially created rainy (B), hazy (C) and low-light (D) images. (A) is the original image.</ns0:figDesc><ns0:graphic coords='10,153.64,527.83,389.77,113.57' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_13'><ns0:head /><ns0:label /><ns0:figDesc>Sci. reviewing PDF \| (CS-2021:11:68217:1:2:NEW 22 Apr 2022)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_14'><ns0:head>Figure 9</ns0:head><ns0:label>9</ns0:label><ns0:figDesc>Figure9represents the inference time on both the GPU and CPU for a single image. From this figure, it is seen that increasing the model size increases the inference time on both the GPU and CPU. In the GPU, YOLOX-s takes almost 0.08s time to infer an image whereas in the CPU it needs 0.7s to accomplish inference of a single image which is 8.75 times slower than the GPU. GPU inference time may differ based on the architecture of the GPU. The reported time is generated in NVIDIA Tesla K80 GPU.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_15'><ns0:head>Figure 7 .</ns0:head><ns0:label>7</ns0:label><ns0:figDesc>Figure 7. Precision-recall curve of YOLOX architecture. (A) YOLOX-s, (B) YOLOX-m, (C) YOLOX-l and (D) YOLOX-x.</ns0:figDesc><ns0:graphic coords='12,153.64,411.22,389.76,273.17' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_16'><ns0:head>Figure 8 .</ns0:head><ns0:label>8</ns0:label><ns0:figDesc>Figure 8. TP, FP and FN of YOLOX-m.</ns0:figDesc><ns0:graphic coords='13,178.44,63.78,340.16,194.38' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_17'><ns0:head>Figure 9 .</ns0:head><ns0:label>9</ns0:label><ns0:figDesc>Figure 9. Per image inference time on both GPU and CPU.</ns0:figDesc><ns0:graphic coords='13,192.61,302.34,311.82,237.91' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_18'><ns0:head>Figure 10</ns0:head><ns0:label>10</ns0:label><ns0:figDesc>Figure 10 represents several qualitative results of different versions of the YOLOX architecture. According to the Figure 10 a misdetection appears by the YOLOX-l and YOLOX-x model. Although YOLOX-s and YOLOX-m model soothing this misdetection. A white hardhat with a headphone is detected correctly by the YOLOX-s and YOLOX-m model whereas YOLOX-l and YOLOX-x model does not detect this object i.e. a false negative occurs.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_19'><ns0:head>Figure 11</ns0:head><ns0:label>11</ns0:label><ns0:figDesc>Figure11shows the mAP variations subject to the different sizes of the test image set. The YOLOX-m both trained and tested with the 640×640 image size. To check the robustness of the model this study both increase and decrease the ratio of 20% and 35% size of the test image subject to the original image size of 640×640. Basically, reducing the size of the image, the smaller objects become smaller hence, there is a possibility of under-detection of an object by the model. Furthermore, sometimes false detection arises which affects the performance of the model. Therefore, the mAP reduces with the decreasing of the image size. On the contrary, when we increase the image size, the nearest objects move closer, consequently, they are undetected or false detection arises by the model. Therefore, the mAP reduces with increasing</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_20'><ns0:head>Figure 12</ns0:head><ns0:label>12</ns0:label><ns0:figDesc>Figure 12 represents several satisfactory results of the YOLOX-m architecture. In the image (A) objects are in dense condition i.e. objects are occluded, even after they are properly detected. Objects are lined up one after another with an angle into the image (G) yet they are detected accurately. In the image (B), (C) and (H) looks like a picture taken by a CCTV camera i.e. the camera position and image scene are not closer, as a result, objects are small even though unerring detection yields by the model. Objects are natural working mode e.x. kneeling down, bending the spine and different angles, even several objects are on their keens into the image (D), (E), (F) , and (I) still accurate detection is generated by the model.</ns0:figDesc><ns0:graphic coords='14,164.26,304.21,368.52,340.17' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_21'><ns0:head>Figure 13</ns0:head><ns0:label>13</ns0:label><ns0:figDesc>Figure13represents several incorrect detections of the YOLOX-m architecture. In the image (A) a false detection appears, something is detected as a vest that is not actually a vest. In the image (B) a vest object is not detected. in the image (C) a yellow color part of a machine is detected as a yellow hardhat.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_22'><ns0:head>Figure 10 .</ns0:head><ns0:label>10</ns0:label><ns0:figDesc>Figure 10. Several qualitative measures of YOLOX. (A) YOLOX-s, (B) YOLOX-m, (C) YOLOX-l and (D) YOLOX-x.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_23'><ns0:head /><ns0:label /><ns0:figDesc>2 https://roboflow.com/ 13/18 PeerJ Comput. Sci. reviewing PDF \| (CS-2021:11:68217:1:2:NEW 22 Apr 2022)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_24'><ns0:head>Figure 11 .</ns0:head><ns0:label>11</ns0:label><ns0:figDesc>Figure 11. mAP variations on different test image sizes.</ns0:figDesc><ns0:graphic coords='15,249.31,63.78,198.42,163.70' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_25'><ns0:head>Figure 12 .</ns0:head><ns0:label>12</ns0:label><ns0:figDesc>Figure 12. Satisfactory result of YOLOX-m architecture.</ns0:figDesc><ns0:graphic coords='15,153.63,327.83,389.78,368.52' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_26'><ns0:head>Figure 13 .</ns0:head><ns0:label>13</ns0:label><ns0:figDesc>Figure 13. Incorrect detection of YOLOX-m architecture.</ns0:figDesc><ns0:graphic coords='16,153.64,63.78,389.76,143.06' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_27'><ns0:head>Figure 14 .</ns0:head><ns0:label>14</ns0:label><ns0:figDesc>Figure 14. Several qualitative measures of low-light, hazy and rainy effect images on YOLOX-m.</ns0:figDesc><ns0:graphic coords='16,153.64,478.41,389.76,146.01' type='bitmap' /></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head>Table 1 .</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Dataset description Class No. of objects Total No. of objects No. of images</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Person</ns0:cell><ns0:cell>4674</ns0:cell><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>Vest</ns0:cell><ns0:cell>2137</ns0:cell><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>Glass</ns0:cell><ns0:cell>532</ns0:cell><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>Head</ns0:cell><ns0:cell>730</ns0:cell><ns0:cell>11604</ns0:cell><ns0:cell>1699</ns0:cell></ns0:row><ns0:row><ns0:cell>Red</ns0:cell><ns0:cell>496</ns0:cell><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>Yellow</ns0:cell><ns0:cell>1200</ns0:cell><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>Blue</ns0:cell><ns0:cell>543</ns0:cell><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>White</ns0:cell><ns0:cell>1292</ns0:cell><ns0:cell /><ns0:cell /></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_1'><ns0:head>Table 2 .</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Platform parameters.</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Platform</ns0:cell><ns0:cell>GPU</ns0:cell><ns0:cell cols='3'>GPU size CPU core RAM</ns0:cell></ns0:row><ns0:row><ns0:cell>Training</ns0:cell><ns0:cell>NVIDIA GeForce RTX 2080 Ti</ns0:cell><ns0:cell>11GB</ns0:cell><ns0:cell>20</ns0:cell><ns0:cell>128GB</ns0:cell></ns0:row><ns0:row><ns0:cell>Testing</ns0:cell><ns0:cell>NVIDIA Tesla K80</ns0:cell><ns0:cell>11GB</ns0:cell><ns0:cell>2</ns0:cell><ns0:cell>12GB</ns0:cell></ns0:row></ns0:table><ns0:note>7/18PeerJ Comput. Sci. reviewing PDF \| (CS-2021:11:68217:1:2:NEW 22 Apr 2022)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_2'><ns0:head>Table 3 .</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Model description.</ns0:figDesc><ns0:table><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>Training</ns0:cell></ns0:row><ns0:row><ns0:cell>Model</ns0:cell><ns0:cell cols='3'>Depth Width Parameters (M)</ns0:cell><ns0:cell>time</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>(hours)</ns0:cell></ns0:row><ns0:row><ns0:cell>YOLOX-s</ns0:cell><ns0:cell>0.33</ns0:cell><ns0:cell>0.50</ns0:cell><ns0:cell>9</ns0:cell><ns0:cell>4</ns0:cell></ns0:row><ns0:row><ns0:cell>YOLOX-m</ns0:cell><ns0:cell>0.67</ns0:cell><ns0:cell>0.75</ns0:cell><ns0:cell>25.3</ns0:cell><ns0:cell>8</ns0:cell></ns0:row><ns0:row><ns0:cell>YOLOX-l</ns0:cell><ns0:cell>1.0</ns0:cell><ns0:cell>1.0</ns0:cell><ns0:cell>54.2</ns0:cell><ns0:cell>11</ns0:cell></ns0:row><ns0:row><ns0:cell>YOLOX-x</ns0:cell><ns0:cell>1.33</ns0:cell><ns0:cell>1.25</ns0:cell><ns0:cell>99.1</ns0:cell><ns0:cell>15</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_4'><ns0:head>Table 4 .</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Performance of YOLOX in the different dataset combinations.</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Com-bina-tions</ns0:cell><ns0:cell>Per-son</ns0:cell><ns0:cell>Head</ns0:cell><ns0:cell>Vest</ns0:cell><ns0:cell>Red</ns0:cell><ns0:cell cols='2'>Yellow Blue White Glass mAP</ns0:cell><ns0:cell>Avg. of the mAP</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell cols='2'>YOLOX-s</ns0:cell></ns0:row><ns0:row><ns0:cell>1</ns0:cell><ns0:cell cols='4'>93.19 80.21 93.55 89.63</ns0:cell><ns0:cell>86.25</ns0:cell><ns0:cell>90.01 91.12 79.95 87.99</ns0:cell></ns0:row><ns0:row><ns0:cell>2</ns0:cell><ns0:cell cols='4'>92.89 86.90 88.14 89.72</ns0:cell><ns0:cell>92.77</ns0:cell><ns0:cell>90.19 93.43 78.28 89.04</ns0:cell><ns0:cell>88.19</ns0:cell></ns0:row><ns0:row><ns0:cell>3</ns0:cell><ns0:cell cols='4'>92.76 90.61 88.72 85.33</ns0:cell><ns0:cell>91.17</ns0:cell><ns0:cell>85.15 92.55 74.12 87.55</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell cols='2'>YOLOX-m</ns0:cell></ns0:row><ns0:row><ns0:cell>1</ns0:cell><ns0:cell cols='4'>94.54 90.42 93.41 90.65</ns0:cell><ns0:cell>89.14</ns0:cell><ns0:cell>84.69 93.71 78.93 89.44</ns0:cell></ns0:row><ns0:row><ns0:cell>2</ns0:cell><ns0:cell cols='4'>94.72 89.85 90.28 85.54</ns0:cell><ns0:cell>92.06</ns0:cell><ns0:cell>88.08 93.17 85.00 89.84</ns0:cell><ns0:cell>89.47</ns0:cell></ns0:row><ns0:row><ns0:cell>3</ns0:cell><ns0:cell cols='4'>93.74 87.45 89.88 90.85</ns0:cell><ns0:cell>91.29</ns0:cell><ns0:cell>86.66 92.39 81.86 89.27</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell cols='2'>YOLOX-l</ns0:cell></ns0:row><ns0:row><ns0:cell>1</ns0:cell><ns0:cell cols='4'>93.53 90.84 90.07 89.60</ns0:cell><ns0:cell>89.14</ns0:cell><ns0:cell>87.35 94.52 77.85 89.11</ns0:cell></ns0:row><ns0:row><ns0:cell>2</ns0:cell><ns0:cell cols='4'>94.86 92.37 89.54 88.51</ns0:cell><ns0:cell>92.29</ns0:cell><ns0:cell>86.57 93.29 79.28 89.59</ns0:cell><ns0:cell>89.11</ns0:cell></ns0:row><ns0:row><ns0:cell>3</ns0:cell><ns0:cell cols='4'>93.39 86.04 88.28 89.51</ns0:cell><ns0:cell>92.45</ns0:cell><ns0:cell>86.29 93.86 79.28 88.64</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell cols='2'>YOLOX-x</ns0:cell></ns0:row><ns0:row><ns0:cell>1</ns0:cell><ns0:cell cols='4'>92.82 90.16 89.47 85.49</ns0:cell><ns0:cell>89.00</ns0:cell><ns0:cell>88.91 96.18 84.25 89.53</ns0:cell></ns0:row><ns0:row><ns0:cell>2</ns0:cell><ns0:cell cols='4'>95.41 78.19 90.67 86.80</ns0:cell><ns0:cell>95.45</ns0:cell><ns0:cell>92.56 96.45 81.66 89.65</ns0:cell><ns0:cell>89.35</ns0:cell></ns0:row><ns0:row><ns0:cell>3</ns0:cell><ns0:cell cols='4'>90.69 86.79 88.77 88.47</ns0:cell><ns0:cell>90.50</ns0:cell><ns0:cell>86.30 95.18 84.40 88.88</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_5'><ns0:head>Table 5 .</ns0:head><ns0:label>5</ns0:label><ns0:figDesc>Performance of YOLOX-m in the low-light, hazy and rainy effect image.</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Category</ns0:cell><ns0:cell>Per-son</ns0:cell><ns0:cell>Head</ns0:cell><ns0:cell>Vest</ns0:cell><ns0:cell>Red</ns0:cell><ns0:cell cols='2'>Yellow Blue White Glass mAP</ns0:cell></ns0:row><ns0:row><ns0:cell cols='5'>Low-light 90.01 89.77 87.12 85.06</ns0:cell><ns0:cell>87.66</ns0:cell><ns0:cell>86.82 93.47 70.79 86.33</ns0:cell></ns0:row><ns0:row><ns0:cell>Hazy</ns0:cell><ns0:cell cols='4'>90.55 88.97 88.27 85.20</ns0:cell><ns0:cell>88.12</ns0:cell><ns0:cell>87.09 92.70 82.74 87.95</ns0:cell></ns0:row><ns0:row><ns0:cell>Rainy</ns0:cell><ns0:cell cols='4'>87.96 80.16 80.86 75.63</ns0:cell><ns0:cell>78.36</ns0:cell><ns0:cell>78.20 73.35 69.38 77.98</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_7'><ns0:head>Table 6 .</ns0:head><ns0:label>6</ns0:label><ns0:figDesc>Comparison between YOLOX-m and YOLOv5-x<ns0:ref type='bibr' target='#b28'>(Wang et al., 2021b)</ns0:ref> based on the same dataset and this study.</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Dataset</ns0:cell><ns0:cell>CHV</ns0:cell><ns0:cell /><ns0:cell>CHVG</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell cols='5'>Method YOLOv5-x YOLOX-m YOLOv5-x YOLOX-m</ns0:cell></ns0:row><ns0:row><ns0:cell>person</ns0:cell><ns0:cell>83.77</ns0:cell><ns0:cell>95.59</ns0:cell><ns0:cell>88.4</ns0:cell><ns0:cell>94.72</ns0:cell></ns0:row><ns0:row><ns0:cell>head</ns0:cell><ns0:cell>×</ns0:cell><ns0:cell>×</ns0:cell><ns0:cell>85.5</ns0:cell><ns0:cell>89.85</ns0:cell></ns0:row><ns0:row><ns0:cell>vest</ns0:cell><ns0:cell>81.47</ns0:cell><ns0:cell>85.94</ns0:cell><ns0:cell>91.5</ns0:cell><ns0:cell>90.28</ns0:cell></ns0:row><ns0:row><ns0:cell>blue</ns0:cell><ns0:cell>80.76</ns0:cell><ns0:cell>91.98</ns0:cell><ns0:cell>88.7</ns0:cell><ns0:cell>88.08</ns0:cell></ns0:row><ns0:row><ns0:cell>red</ns0:cell><ns0:cell>91.91</ns0:cell><ns0:cell>83.26</ns0:cell><ns0:cell>85.5</ns0:cell><ns0:cell>85.54</ns0:cell></ns0:row><ns0:row><ns0:cell>yellow</ns0:cell><ns0:cell>91.41</ns0:cell><ns0:cell>87.95</ns0:cell><ns0:cell>92.6</ns0:cell><ns0:cell>92.06</ns0:cell></ns0:row><ns0:row><ns0:cell>white</ns0:cell><ns0:cell>89.96</ns0:cell><ns0:cell>95.65</ns0:cell><ns0:cell>79.9</ns0:cell><ns0:cell>93.17</ns0:cell></ns0:row><ns0:row><ns0:cell>glass</ns0:cell><ns0:cell>×</ns0:cell><ns0:cell>×</ns0:cell><ns0:cell>77.8</ns0:cell><ns0:cell>85.00</ns0:cell></ns0:row><ns0:row><ns0:cell>mAP</ns0:cell><ns0:cell>86.55</ns0:cell><ns0:cell>90.06</ns0:cell><ns0:cell>86.24</ns0:cell><ns0:cell>89.84</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_8'><ns0:head>Table 7 .</ns0:head><ns0:label>7</ns0:label><ns0:figDesc>Blurring face test.</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Dataset</ns0:cell><ns0:cell>CHV</ns0:cell><ns0:cell>CHVG</ns0:cell></ns0:row><ns0:row><ns0:cell cols='3'>Model YOLOv5-x YOLOX-m</ns0:cell></ns0:row><ns0:row><ns0:cell>Person</ns0:cell><ns0:cell>82.78</ns0:cell><ns0:cell>91.83</ns0:cell></ns0:row><ns0:row><ns0:cell>head</ns0:cell><ns0:cell>×</ns0:cell><ns0:cell>88.97</ns0:cell></ns0:row><ns0:row><ns0:cell>Vest</ns0:cell><ns0:cell>82.46</ns0:cell><ns0:cell>88.27</ns0:cell></ns0:row><ns0:row><ns0:cell>Red</ns0:cell><ns0:cell>74.59</ns0:cell><ns0:cell>85.20</ns0:cell></ns0:row><ns0:row><ns0:cell>Yellow</ns0:cell><ns0:cell>85.17</ns0:cell><ns0:cell>86.12</ns0:cell></ns0:row><ns0:row><ns0:cell>Blue</ns0:cell><ns0:cell>73.78</ns0:cell><ns0:cell>87.09</ns0:cell></ns0:row><ns0:row><ns0:cell>White</ns0:cell><ns0:cell>78.46</ns0:cell><ns0:cell>93.70</ns0:cell></ns0:row><ns0:row><ns0:cell>mAP</ns0:cell><ns0:cell>79.55</ns0:cell><ns0:cell>88.73</ns0:cell></ns0:row></ns0:table><ns0:note>Symbol: From this study. × Not available.</ns0:note></ns0:figure> <ns0:note place='foot' n='18'>/18 PeerJ Comput. Sci. reviewing PDF \| (CS-2021:11:68217:1:2:NEW 22 Apr 2022)Manuscript to be reviewed Computer Science</ns0:note> </ns0:body> "	"April 18th, 2022 Dear Editors We thank the reviewers for their generous comments on the manuscript and have edited the manuscript to address their concerns. In particular, all of the code repository and the dataset link are provided through the paper. We believe that the manuscript is now suitable for publication in PeerJ Computer Science. Md. Ferdous Dept. of Computer Science and Engineering Khulna University of Engineering & Technology (KUET) Khulna-9203, Bangladesh On behalf of all authors Reviewer 1 Basic reporting The paper is, in general, written well, used formalism is explained, and the problem is stated clearly. Also, the application seems to be interesting. Unfortunately, there are three points that need to be addressed. a) Abstract: the sentence 'For the detection algorithm, this study has used the You Only Look Once (YOLO) family's anchor-free architecture, YOLOX, which yields better performance than the other object detection models within a satisfactory time interval.' is not valid. The study does not bring a comparison with other SOTA detectors. The only comparison is made with YOLOv5-x. Based on general knowledge and benchmarks such as https://paperswithcode.com/sota/object-detection-on-coco benchmark, scaled YOLOv4 should yield better performance than YOLOx. The currently best detector is Swin, which still performs with a reasonable time. Indeed, this study does not bring a comparison with other SOTA detectors. Instead, this study compares the model used for object detection on construction sites. For this reason, the authors said YOLOX yields better performance than the other object detection models within a satisfactory time interval. b) Section 4: there is no motivation to implement your own haze/rain/low-light augmentations when all of them and many more are available through Albumentations? See https://albumentations-demo.herokuapp.com/ Based on it, Section 4 is useless and can be removed. The authors are grateful to the reviewer because we did not know about herokuapp.com before. Even though Streamlit is a modern open-source library, we have tried to create synthetic images from our method. However, the authors did not find a way of making a haze image on herokuapp.com. There is a way to make RandomFog images, but fog and haze are different. For making a rainy image, the authors try to make the image more realistic. However, it seems to the authors that herokuapp.com does this exceptionally well but is not realistic. Hence, the authors try to do these augmentations using their way. c) Section 5: it describes widely used evaluation metrics that are a gold standard in object detection and are well known by researchers. Therefore, there is no need to explain it in detail. It is enough to state you use IOU and mAP; thus, the section can be removed. The authors remove the precision, recall, IoU, AP, and mAP calculation equation in the revised version. We do not remove this section but refurnish it such that new researchers can understand this term at a glance. Experimental design no comment Validity of the findings Here, it is necessary to state that two major issues were found. It is needed to address them carefully, mainly the second issue. a) Repository: - There is no readme in the repository regarding the described functionality. There is only a general readme forked from the original YOLOx repository. It must be described how to run the training script (which one is it) to replicate your results. - I did not find training data or link to them. - The model_test.ipynb functionality in the repository is nothing but a long error report. In the end, there is a test on seven images only without their visualization or mAP evaluation. In summary, the correctness of the scheme cannot be confirmed. The authors provided only the testing dataset and testing python script at the time of the first submission. In this resubmission, we provided: • The whole dataset link, line number 139. • Training and testing python script in the provided GitHub repository, line number 217. • A short video named ‘manual-less.mp4’ where PPE is detected from the construction site using the trained model in the provided GitHub repository. • Readme in GitHub repository. b) Data: there is a question about the correctness of the labels. I examined several images from 'test-n.zip' and observed that: - gettyimages-88655418-612x612_jpg.rf.6f0a5bd6a8ac6cf7a77d8a24b814e8df there are two person, two glasses, two helmets, and two vests. Label includes only one glass and one person. - ppe_0579_jpg.rf.17a45729452bdb75971919d2630f0e21.jpg one orange helmet and one head are missing in the label - ppe_0994_jpg.rf.98234bc61dda7a1fb4e54e7e02ade2a0.jpg the label for the bottom-right person is missing - vitolda-klein-lAqSzwr5eQc-unsplash_jpg.rf.774701a69123aa712e43b4fc4deb1ed0.jpg the label misses orange helmet - 000221_jpg.rf.8dff87700f9373a3eff79e1f9f55f273.jpg label person is missing for all people. That holds for all (8) images from this 'subseries.' - gettyimages-83455052-612x612_jpg.rf.ea5cd5e8c3044737116b908a6139fd6b.jpg the label misses two helmets ...and many more. Based on this fact, the results published in the papers are not trustworthy because they are computed for a highly noisy and inconsistent dataset. The dataset must be fixed, and the whole experiment section must be recalculated. Many thanks to the reviewer for pointing us out. We apologize for our unintentional mistake. The authors rechecked and relabeled the whole dataset. Previously we ignored several partial and tiny objects to do annotation. Due to the lack of a sufficient number of orange-colored hard hat we did not consider this class in our experiment. The authors will try to collect this image from the realtime construction site, and it is one of the further investigations. Authors train and test the model after rechecking and relabeling the dataset. Recalculated results are updated in the ‘Results’ section of the paper. The overall performance does not change remarkably concerning the previously mentioned result. Additional comments There are three minor comments: a) Is 'Md. Ferdous' full name? Yes. 'Md. Ferdous' is my full name. b) The sentence 'In construction sites more than 71% injury appears than 32 in all other industries.' needs to be rephrased. The sentence is reshaped as ' In construction site there are more than 71% injury appears compared to all other industry.' Line number 32. c) Formula 4: there is written 'Where, G and P are the prediction and ground truth bounding boxes respectively'. It is formally correct, but it is better to mark P as prediction and G as ground truth and not contrariwise. The IoU calculation equation has been removed; this line was also removed. Reviewer 2 Basic reporting This review paper demonstrates a YOLO-based architecture to detect personal protective equipment for construction sites. The structure of the article is organized well however, some changes need to be done to improve it. Some suggestions for further improvement: The Introduction section is well-written, and I propose to schematize all the discussion in several paragraphs (to facilitate the reading): (1) motivations, (2) the overall approach, (3) main contributions. However, the main challenges to the field and the needs and benefits of this study are missed in the introduction. The authors schematize all the discussion in several paragraphs according to reviewers suggestion. The main challenges to the field and the needs and benefits of this study are included in the introduction section, specifically from line 52 to 56. A general discussion of the limitations and expectations of the proposed model should be inserted. Limitations and expectations of the proposed model are inserted into the ‘Discussion’ section from line number 387 to 391. Experimental design Adding some effects such as haze or low light on images, will not change the features of the image significantly. I suggest creating a night-time dataset and adding it to the existing one. The authors are very much grateful to the reviewer for a potential suggestion. Working with nighttime images is our further investigation. The authors mentioned that the effected images are used only for testing. What about the original images of the effected images? If the same original images are used for training, then testing with the same images with just a small effect is not a good idea because in that case, the model will just memorize the images. The authors use the original image and the effected image only for testing purposes. Low-light, Haze and Rain effected images are created from the testing dataset. Low-light, Haze, and Rain effects have not been used for the training images. Please specify what is the main differences between CHV and CHVG datasets. Is it the same datasets just the number of classes are increased or the HAZE, RAIN, and LOW-LIGHT images are included as well? The statements are included in the ‘Dataset preparation’ subsection, 140 to 144 as below: “The CHVG and CHV datasets are partially similar, but CHVG contains more 369 images than the CHV dataset. However, the CHVG dataset also contains extra two classes, such as safety glass and a head without a hard hat. In machine learning, more challenges for more classes. For this reason, we try to increase the class number. Besides, safety glass and head without hardhat detection on construction sites is essential, like other objects.” The HAZE, RAIN and LOW-LIGHT images are created from the test dataset to see the model’s robustness. The effected images are not included in our dataset. Has exactly the same YOLOX model been used in this article or have the authors made any changes to it? This needs to be explained in the paper clearly. The exact model of the YOLOX is used in this study, and only hyperparameters are tuned. For example, batch size, IoU, and NMS values are changed to set a better combination. Line number 203 to 205 and 219 represents some hyperparameters value. Line 120: there are two “also” which are not necessary and can be removed. We removed it in our revised version. Line number is 125. Line 146: Please explain why DarkNet53 has been used as pre-trained weights in this model. The statements are included in the ‘The proposed framework’ subsection, 156 to 163 as below: “Darknet-53 is a Convolutional Neural Network (CNN) that serves as the backbone of the darknet YOLO architecture, which consists of 53 convolutional layers. More convolutional layers can learn more complex objects and works with higher accuracy. Moreover, it can do more floating points operations than other models within less time. However, the dataset contains complex objects we need to do feature extraction with less time and better accuracy. For this reason, this study uses Darknet53 as the backbone of the experimented network. Line 186 and 188: Authors need to determine why they set these parameters. The authors set the parameters from their interests. In addition, several parameters are set empirically, line number 209 to 212. Recent researcher also set these parameters in this way. Line 194: The image size needed to be explained in the dataset section. The image size is explained into the ‘Dataset preparation’ subsection in our revised submission, line number 138. Line 259: Please determine why the NMS value is set to 0.65 The statements are included in the ‘Training Process’ section, line number 205 to 209 as below “NMS value is used to select an object's most appropriate bounding box. We can select the most appropriate bounding box at the testing time if we train the model using the most appropriate box. If we use a high NMS value, there is a possibility to detect more false negatives. Otherwise, a low NMS value yields more false positives. For this reason, the authors use an NMS value of 0.65 at the time of training for the validation process and 0.5 at testing.” Validity of the findings The evaluation metrics section is explained very well, however, the discussion section is too short and needs to be expanded. The authors have tried to enlarge the ‘Discussion’ section in the revised version. It would be good if authors upload their code on GitHub and add the link in the paper. The authors provided the code link, line number 217 and full dataset link , line number 139 in the revised version. "	Here is a paper. Please give your review comments after reading it.
435	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Ammonium perchlorate (AP) is the universal oxidizer in use for all the solid rocket propellant motors used for space exploration due to its high available oxygen content and thermal decomposition without any solid residue. The inclusion of reactive species in AP directly affect the viscoelastic and ballistic properties of the propellant. Variations in lattice configuration of AP change its physical and thermal characteristics dramatically. In the present work AP was doped with Copper perchlorate and Iron perchlorate through co crystallisation. The impact of inclusion of these ionic species in the lattice on the thermal decomposition characteristics of AP was examined. The incorporation affected the physical as well as the ballistic characteristics of the resultant AP. The incorporation of foreign ions into AP crystals significantly changed the crystal morphology. The decomposition temperature decreased vis-a-vis with normal AP. The activation energy remarkably decreased for the doped AP crystals.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head n='1'>INTRODUCTION</ns0:head><ns0:p>The important chemical formulation used in rocketry is Composite Solid Propellant (CSP). CSP gives driving force to the rocket. Its important parameters are thermal stability, burning rate, friction and impact sensitivity. AP used as oxidizer is the major component (about 70%) in CSP. Thermal decomposition of AP directly influences the combustion behaviour of the propellant. The reduction in particle size of AP or metallic fuel may increase the burning surface area which in turn increases the burn rate of a propellant. The high thrust requirement of missions are satisfied by either increasing the burning surface area or by the addition of burn rate modifiers (BRM). The commonly used BRM are iron oxide and copper chromite (<3%). The doping of AP with transition metals and their oxides has shown to decrease the activation energy and decomposition temperature of AP, thereby acting as good catalysts <ns0:ref type='bibr' target='#b15'>(Schumacher, 1960;</ns0:ref><ns0:ref type='bibr' target='#b1'>Boldyrev, 2006;</ns0:ref><ns0:ref type='bibr' target='#b7'>Jacobs and Whitehead, 1969;</ns0:ref><ns0:ref type='bibr' target='#b8'>Kishore and Sunitha, 1979)</ns0:ref>. The mechanical mixing of AP with compounds of copper and iron is highly effective in reducing the thermal decomposition of Ammonium perchlorate <ns0:ref type='bibr' target='#b18'>(Wang et al., 2013;</ns0:ref><ns0:ref type='bibr' target='#b13'>Patil et al., 2008;</ns0:ref><ns0:ref type='bibr' target='#b4'>Chen et al., 2008b;</ns0:ref><ns0:ref type='bibr' target='#b0'>Alizadeh-Gheshlaghi et al., 2012;</ns0:ref><ns0:ref type='bibr' target='#b3'>Chen et al., 2008a;</ns0:ref><ns0:ref type='bibr' target='#b19'>Yang et al., 2014;</ns0:ref><ns0:ref type='bibr' target='#b11'>Liu et al., 2004;</ns0:ref><ns0:ref type='bibr' target='#b2'>Chaturvedi and Dave, 2013;</ns0:ref><ns0:ref type='bibr' target='#b17'>Styborski et al., 2015;</ns0:ref><ns0:ref type='bibr' target='#b16'>Song et al., 2010;</ns0:ref><ns0:ref type='bibr' target='#b5'>Costa et al., 2009)</ns0:ref>.</ns0:p><ns0:p>The crystallization being the critical step in the manufacturing of AP, the modifications made in crystallization parameters alter the crystal strength, purity and particle size distribution of AP <ns0:ref type='bibr' target='#b12'>(Mullin, 1997;</ns0:ref><ns0:ref type='bibr' target='#b10'>Lakshmi et al., 2016)</ns0:ref>. Previous works have reported the inclusion of foreign metal ions into AP crystal and its impact on the thermal decomposition characteristics; but the physical and crystallographic aspects were not discussed <ns0:ref type='bibr' target='#b1'>(Boldyrev, 2006;</ns0:ref><ns0:ref type='bibr' target='#b7'>Jacobs and Whitehead, 1969;</ns0:ref><ns0:ref type='bibr' target='#b14'>Pelly, 1982)</ns0:ref> </ns0:p></ns0:div> <ns0:div><ns0:head>EXPERIMENTAL Materials</ns0:head><ns0:p>Ammonium perchlorate crystals of purity >99.0% processed at Ammonium Perchlorate Experimental Plant, Aluva, Kerala was used for the preparation of doped AP in the present work. Analytical grade reagent copper perchlorate hexahydrate, and ferrous perchlorate nonhydrate from MERCK was used as received for doping of AP.</ns0:p></ns0:div> <ns0:div><ns0:head>Co-crystallization</ns0:head><ns0:p>A glass jacketed crystallizer of 5L capacity with glass agitator and teflon paddle is used for crystallization.</ns0:p><ns0:p>The super saturated solution of AP was prepared in distilled water at 75 • C, and the secondary nucleation was prevented by increasing the temperature of the solution by 5 • C. The separation of AP crystals from solution was done by cooling crystallisation method owing to the high temperature coefficient of solubility for AP. The concentration of copper perchlorate / iron perchlorate in mother liquor was varied from 10% to 20% by weight of AP. While the rpm of the agitator was set at slow rate i.e. 50 rpm; a fast cooling mode of 0.6 • C/minute was used to get doped crystal of substantial crystal properties and concentration.</ns0:p><ns0:p>Two different concentrations each of copper doped AP and iron doped AP were prepared. Copper doped AP was prepared by co-crystallisation of AP with copper perchlorate, and iron doped AP was prepared by co-crystallisation of AP with iron perchlorate. The doped samples were named ACuP-1(Cu-0.36%), ACuP-2(Cu-0.49%), AFeP-1(Fe-0.35%), and AFeP-2 (Fe-0.51%) respectively. The prepared doped AP crystals were filtered out and dried in glass trays and dried at 60 • C for 4 hours in laboratory hot air oven.</ns0:p></ns0:div> <ns0:div><ns0:head>Instrumentation</ns0:head><ns0:p>The particle size distribution (PSD) of the doped AP was measured by sieve analysis method using Ro-Tap sieve shaker. The weighted average particle size was calculated by sieve analysis, using sieves of size 45-500 µm. The copper and iron content in the doped AP crystals were measured using ICP-AES. The specific surface areas of the particles were measured by multipoint BET method using Quantachrome NOVA 1200e Surface Area Analyzer. The friability of AP crystals were measured using crystals of particle size >125 µm. The crystals were given rotation and gyration for 30 minutes along with 100 numbers of 3 mm glass beads. The percentage weight loss is measured as the friability of the crystals. The bulk density measurement was done with DBK bulk density apparatus having 100 ml measuring cylinder for filling the AP crystals. The volume change after 100 tapping is used for bulk density calculation. IR Moisture Analyser is used for the estimation moisture content in the sample. Field emission scanning electron microscopy (FESEM) observations were performed to examine the morphology of the samples using Carl Zeiss, Supra 55 model field emission scanning electron microscope. The crystallographic properties of AP samples were examined by collecting the powder X-ray diffraction data of the samples on a Bruker D8-Discover powder X-ray diffractometer with Cu Kα (λ =1.5418 Å) at a scan rate of 2.5 deg per min. Static and Dynamic imaging techniques were performed to study the crystal characteristics.</ns0:p><ns0:p>The Ankersmid Eyetech particle size and shape analyzer measured the shape factor of the crystals. Perkin Elmer Simultaneous Thermogravimetry -Differential Scanning Calorimetry (TG-DSC), TA Instruments Q600, was employed for thermal characterization. The thermal analysis was done at three different heating rates -3 • C/minute, 5 • C/minute and 10 • C/minute -and kinetic analysis was done by Flynn-Wall-Ozawa Method (FWO).</ns0:p></ns0:div> <ns0:div><ns0:head>RESULTS AND DISCUSSIONS Physical Characteristics</ns0:head><ns0:p>The dopants present in the mother liquor affect the crystal formation, and growth of the crystal. The crystal growth was inhibited slightly by the presence of dopant ion which results in decrease in particle Manuscript to be reviewed Chemistry Journals size. The weight average particle size of normal AP is 312 µm. The average particle size of copper doped and iron doped AP is found to be 284 µm and 292 µm respectively. The dopant ion inclusion in AP lattice resulted in point defects, and the strength of the crystals got reduced. The reduction in strength of crystal increases the friability of the crystal. The friability of copper/iron doped AP crystals were found to be 0.72% and 0.71% respectively, slightly higher than friability of normal AP 0.68%. The specific surface area of normal and doped AP crystals were measured using multipoint BET and it is in the range 0.17-0.19 m2/g m 2 /g for normal AP and 0.22 -0.24 m 2 /g for copper/iron doped AP. The surface roughness of doped crystals increases the surface area. The reduction in particle size with doping is a factor for increasing the surface area, and bulk density. The bulk density usually increases with decrease in the particle size of the material, due to better packing and compactness for smaller particles and reduction in the number of voids or pores. Thus the doped AP is a better option to increase the compactness of the particle during propellant mixing. The moisture content increases with increase in dopant concentration, it is within allowed limits for propellant grade AP. For propellant grade AP the maximum allowed moisture content is 0.25%, for copper doped AP it is 0.16% and for iron doped AP it is 0.15%. The physical characteristics of normal and doped AP are given in Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref>. The crystal surface morphology for normal AP and doped AP samples were studied by taking the scanning electron microscopy image of a single particle. The elemental mapping of the doped crystal was conducted to check the uniform distribution of</ns0:p><ns0:p>Copper/Iron in the crystals. The images in Figure <ns0:ref type='figure' target='#fig_4'>2</ns0:ref> give the clear idea that the dopant ions are uniformly distributed throughout the AP crystal.</ns0:p></ns0:div> <ns0:div><ns0:head>Crystallographic characteristics</ns0:head><ns0:p>The XRD patterns of the standard AP samples were given by the JCPDS pattern with reference code:</ns0:p><ns0:p>000080451. The major peaks appear for AP crystals were at 2θ values of 15.3 Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science <ns0:ref type='formula'>101</ns0:ref>), ( <ns0:ref type='formula'>011</ns0:ref>), ( <ns0:ref type='formula'>201</ns0:ref>), ( <ns0:ref type='formula'>002</ns0:ref>), ( <ns0:ref type='formula'>210</ns0:ref>), ( <ns0:ref type='formula'>211</ns0:ref>), ( <ns0:ref type='formula'>112</ns0:ref>), ( <ns0:ref type='formula'>202</ns0:ref>) and ( <ns0:ref type='formula'>212</ns0:ref>) crystal planes respectively. Figure <ns0:ref type='figure' target='#fig_5'>3</ns0:ref> compares the XRD peaks of normal AP and the AP doped with copper and iron perchlorate respectively. The needle like growth of the crystals resulted in reduction in number of peaks in the XRD pattern. For ACuP-2, the relative intensity at 23.9 • was remarkably high, and only two peaks -23.9 and 30.1 -are present. For iron doped AP, the peak at 24.6 got intensified.</ns0:p><ns0:p>It shows that the doping of AP crystal with copper perchlorate and iron perchlorate have a crystal habit modification effect on AP.</ns0:p></ns0:div> <ns0:div><ns0:head>Thermal characteristics</ns0:head><ns0:p>The AP with compounds catalyse the HTD only. For the lattice modified AP, catalysing both the thermal decomposition stages result in a substantial increase in burn rate in AP containing propellant <ns0:ref type='bibr' target='#b6'>(Dey et al., 2015)</ns0:ref>.</ns0:p><ns0:p>The DSC curve given by Figure <ns0:ref type='figure'>4</ns0:ref>(b) shows the catalytic nature of copper and iron on thermal decomposition of AP, and the heat release for the doped samples were found to be higher than that of normal AP. The DTG curve shown in figure <ns0:ref type='figure'>4</ns0:ref>(c) clearly shows the shift in decomposition peaks.</ns0:p><ns0:p>The doped AP samples ACuP-2 and AFeP-2 were taken for kinetic analysis. For the calculation of activation energy by Flynn-Wall-Ozawa method, the thermal analysis were done at three different heating rate 3 • C/minute, 5 • C/minute and 10 • C/minute. The activation energy for these samples were less than that of normal AP samples and it is given in Table <ns0:ref type='table'>3</ns0:ref>. The activation energy versus conversion plot is given in Figure <ns0:ref type='figure'>5</ns0:ref>. There is a decrease in activation energy supporting the effectiveness of catalytic process. The kinetic analysis result shows a prominent decrease in the activation energy which indicates copper is a good catalyst compared to iron in this particular case. The experimental results on AP doped with Cu/Fe showed that the lattice modification was effective to reduce both the low temperature and high temperature decomposition. The solid phase reactions occurs only in LTD, while gas phase reactions occurs in both LTD and HTD. Earlier works on the</ns0:p></ns0:div> <ns0:div><ns0:head>5/8</ns0:head><ns0:p>PeerJ Inorganic Chem. reviewing PDF \| (ICHEM-2020:03:46368:1:1:NEW 5 Aug 2020)</ns0:p><ns0:p>Manuscript to be reviewed <ns0:ref type='bibr' target='#b1'>(Boldyrev, 2006;</ns0:ref><ns0:ref type='bibr' target='#b7'>Jacobs and Whitehead, 1969;</ns0:ref><ns0:ref type='bibr' target='#b8'>Kishore and Sunitha, 1979;</ns0:ref><ns0:ref type='bibr' target='#b18'>Wang et al., 2013;</ns0:ref><ns0:ref type='bibr' target='#b13'>Patil et al., 2008)</ns0:ref>. Manuscript to be reviewed</ns0:p><ns0:note type='other'>Chemistry Journals</ns0:note></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>energy is found to be increased during LTD, which can be due to the conversion of Cu to CuO, after the complete conversion of CuO the activation energy is decreased. Positive holes in CuO can accept electrons and it act as a surface for high temperature decomposition.</ns0:p><ns0:p>In iron doped AP crystals, the surface area is found to be increased due to the porous nature of surface of the crystals. Iron content in the crystal promotes both low temperature and high temperature decompositions. The Fe 2+ /Fe 3+ transition facilitates the electron transfer mechanism. Since the metal ion is present in the lattice it can act as an initiation point for thermal decomposition to start. During thermal decomposition it is converted to Fe 2 O 3 and favours HTD by acting as a catalytic surface. The doped iron converted to Fe 2 O 3 increases the surface area and act as a surface for further thermal decomposition of AP as HTD is a surface phenomenon.</ns0:p><ns0:p>The catalytic activity can be attributed to the presence of Cu/Fe ions throughout the crystal lattice, as well as the synergetic effect of the oxides of copper/ iron during the thermal decomposition of AP. The formation of cuprous/cupric oxides or ferrous/ferric oxides will enhance the redox reaction taking place during the decomposition of AP by acting as a better carrier or conductor of electrons.</ns0:p></ns0:div> <ns0:div><ns0:head>CONCLUSION</ns0:head><ns0:p>The decomposition characteristics of AP crystals were improved by lattice modification with iron perchlorate and copper perchlorate. The lattice modified AP crystal has better decomposition characteristics. The average particle size of copper/iron doped AP was decreased and an increase in friability and bulk density was observed. The inclusion of copper/iron perchlorate into AP lattice via co-crystallisation resulted in the production of AP with good particle size distribution and friability. The doped crystals with low decomposition temperature form the basis of its application as high burn rate propellant. Lattice inclusion of copper ion is found to be an efficient method for improving the thermal decomposition characteristics of AP, and in turn the ballistic properties of the propellant.</ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>. The addition of BRM to AP can be done by either physical mixing or by co-crystallisation. The co-crystallisation of AP with salts results in the lattice inclusion of compounds into AP lattice. Variations in lattice configuration of AP changes its physical, thermal and ballistic characteristics dramatically, while the basic thermodynamic properties could remain unaltered. The present work focus on the alteration in lattice PeerJ Inorganic Chem. reviewing PDF \| (ICHEM-2020:03:46368:1:1:NEW 5 Aug 2020) Manuscript to be reviewed Chemistry Journals Analytical, Inorganic, Organic, Physical, Materials Science configuration of AP by co-crystallisation with perchlorates of copper and iron. The doped AP crystals were analysed for studying the impact of lattice inclusion of Cu and Fe ions on the lattice, physical and thermal characteristics of AP. The incorporation of foreign ions into AP crystals significantly changed the crystal morphology, bulk density, moisture content and the decomposition behaviour compared with normal AP. The decomposition temperature and activation energy remarkably decreased for the doped AP crystals.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ Inorganic Chem. reviewing PDF \| (ICHEM-2020:03:46368:1:1:NEW 5 Aug 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head /><ns0:label /><ns0:figDesc>Figure. 1 (a), (b) and (c) show the SEM images of normal AP, ACuP-2 and AFeP-2. The regularity and spherical nature of the particles get worsened by doping with copper/iron perchlorate due to the fast cooling mode. The SEM results show homogeneous doping of copper/iron perchlorate. Since the particles are of average particle size ∼300 µm, the SEM image with high resolution (magnification 1000X) were taken to cover the surface image of the particles for checking the homogeneity of doping. The doped crystals have more needle like crystal growth compared with normal AP.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head>Figure 1 .</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1. SEM images of (a) Normal AP, (b) ACuP-2, and (c) AFeP-2</ns0:figDesc><ns0:graphic coords='4,141.73,512.17,413.58,92.96' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head>Figure 2 .</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2. Elemental mapping of (a) ACuP-2 and (b) AFeP-2</ns0:figDesc><ns0:graphic coords='5,141.73,63.78,413.59,259.41' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head>Figure 3 .</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Figure 3. XRD Pattern of normal AP, ACuP-2 and AFeP-2</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head>Figure 4 .Figure 5 .</ns0:head><ns0:label>45</ns0:label><ns0:figDesc>Figure 4. (a) TGA Curve of AP and Doped AP(ACuP-1, ACuP-2, AFeP-1, AFeP-2), (b) DSC Curve of AP and (ACuP-1, ACuP-2, AFeP-1, AFeP-2) and (c) DTG Curve of AP and (ACuP-1, ACuP-2, AFeP-1, AFeP-2)Table 3. Activation energy for thermal decomposition of AP, ACuP-2 and AFeP-2</ns0:figDesc><ns0:graphic coords='7,141.89,64.18,413.17,114.27' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_7'><ns0:head /><ns0:label /><ns0:figDesc>formed in the lattice in first step, followed by proton transfer from ammonium ion (cation) to perchlorate ion (anion) via a molecular complex formation. The last step is decomposition proceeds to form ammonia and perchloric acid. The low temperature decomposition reactions start from the core and proceeds to the surface, often involve both bond-breaking and bond-forming steps.The inclusion of copper ion and change in crystallisation parameters resulted in increased surface area of copper doped AP. The lattice included copper acts as the initiation point for the thermal decomposition it get oxidised to CuO due to the increased heat release during thermal decomposition of AP. The presence of ions of copper with two different valencies promote the electron transfer mechanism. The activation6/8PeerJ Inorganic Chem. reviewing PDF \| (ICHEM-2020:03:46368:1:1:NEW 5 Aug 2020)</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='6,203.96,64.68,289.18,235.11' type='bitmap' /></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head>Table 1 .</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Physical characteristics of normal and doped AP</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Sample ref.</ns0:cell><ns0:cell>Weight % of Cu/Fe</ns0:cell><ns0:cell>Mole % of Cu/Fe</ns0:cell><ns0:cell>Average Particle Size (µm)</ns0:cell><ns0:cell>Friability (%)</ns0:cell><ns0:cell>Bulk Density</ns0:cell><ns0:cell>Moisture (%)</ns0:cell><ns0:cell>Specific surface area (m 2 /g)</ns0:cell></ns0:row><ns0:row><ns0:cell>AP</ns0:cell><ns0:cell>0</ns0:cell><ns0:cell>0</ns0:cell><ns0:cell>312</ns0:cell><ns0:cell>0.68</ns0:cell><ns0:cell>1.30</ns0:cell><ns0:cell>0.10</ns0:cell><ns0:cell>0.18</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>ACuP-1 0.36</ns0:cell><ns0:cell>0.007</ns0:cell><ns0:cell>291</ns0:cell><ns0:cell>0.71</ns0:cell><ns0:cell>1.28</ns0:cell><ns0:cell>0.13</ns0:cell><ns0:cell>0.22</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>ACuP-2 0.49</ns0:cell><ns0:cell>0.009</ns0:cell><ns0:cell>284</ns0:cell><ns0:cell>0.72</ns0:cell><ns0:cell>1.27</ns0:cell><ns0:cell>0.16</ns0:cell><ns0:cell>0.22</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>AFeP-1 0.35</ns0:cell><ns0:cell>0.007</ns0:cell><ns0:cell>296</ns0:cell><ns0:cell>0.70</ns0:cell><ns0:cell>1.33</ns0:cell><ns0:cell>0.12</ns0:cell><ns0:cell>0.24</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>AFeP-2 0.51</ns0:cell><ns0:cell>0.01</ns0:cell><ns0:cell>292</ns0:cell><ns0:cell>0.71</ns0:cell><ns0:cell>1.33</ns0:cell><ns0:cell>0.15</ns0:cell><ns0:cell>0.24</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_2'><ns0:head>Table 2 .</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Phenomenological data for the decomposition of normal AP and Doped AP</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Sample</ns0:cell><ns0:cell>Dopant conc.</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell>LTD</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>HTD</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>ref.</ns0:cell><ns0:cell>(%)</ns0:cell><ns0:cell>Ti</ns0:cell><ns0:cell>Tp</ns0:cell><ns0:cell>Tf</ns0:cell><ns0:cell>Mass-loss (%)</ns0:cell><ns0:cell>Ti</ns0:cell><ns0:cell>Tp</ns0:cell><ns0:cell>Tf</ns0:cell><ns0:cell>Mass-loss (%)</ns0:cell></ns0:row><ns0:row><ns0:cell>AP</ns0:cell><ns0:cell>0</ns0:cell><ns0:cell cols='3'>244 285 312</ns0:cell><ns0:cell>30</ns0:cell><ns0:cell cols='3'>312 374 388</ns0:cell><ns0:cell>70</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>ACuP-1 0.36</ns0:cell><ns0:cell cols='3'>225 260 274</ns0:cell><ns0:cell>29</ns0:cell><ns0:cell cols='3'>274 317 341</ns0:cell><ns0:cell>71</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>ACuP-2 0.49</ns0:cell><ns0:cell cols='3'>211 253 269</ns0:cell><ns0:cell>29</ns0:cell><ns0:cell cols='3'>269 314 336</ns0:cell><ns0:cell>71</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>AFeP-1 0.35</ns0:cell><ns0:cell cols='3'>216 263 273</ns0:cell><ns0:cell>31</ns0:cell><ns0:cell cols='3'>273 338 355</ns0:cell><ns0:cell>69</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>AFeP-2 0.51</ns0:cell><ns0:cell cols='3'>213 262 270</ns0:cell><ns0:cell>31</ns0:cell><ns0:cell cols='3'>270 337 353</ns0:cell><ns0:cell>69</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:note place='foot'>Analytical, Inorganic, Organic, Physical, Materials Science </ns0:note> </ns0:body> "	"From Savitha Nair Scientific Officer-C Vikram Sarabhai Space Centre Thiruvananthapuram-695022 23.07.2020 Dear Editors We thank the reviewers for their constructive comments on the manuscript. We have edited the manuscript considering all the reviewers’ comments very seriously and corrections have been brought out in the article in a way to answer the referees and for the readers to have a better understanding. We believe that the manuscript is now suitable for publication in PeerJ. With regards Savitha Nair (On behalf of all authors) Reviewer 1 Comments for the author Main logic: Comment: How do you determine it is co-crystal? What are the types of interaction to form co-crystals? Does it affect the melting point or phase transition of the AP? Why the EDS show separation of different elements? Response: The interaction of AP with copper /iron perchlorate resulted in the formation of ionic co-crystal. The modelling of the complex structure of ionic crystals and ballistic property prediction by theoretical method are discussed in a separate manuscript under submission. The incorporation of a few numbers of the ions of Fe or Cu in the lattice of AP is not enough to result in a significant change in melting point or any phase transition temperature, but was enough to cause a significant change in decomposition pattern of the co-crystals especially because it happens at high temperature. The sensitivity is less for the low temperature phenomenon (i. e. phase transitions) than the high temperature ones. This was confirmed from a study of the difference in behaviours of the co-crystallized versus co-mingled (physical blend) material (of similar overall composition) of the two ionic species. This has been clearly mentioned in our previous article. Since it is a moisture sensitive ionic crystal, the EDS Spectrum shows separation of different elements, and all the elements are uniformly distributed in the crystals. Manuscript preparation: 1. Comment: Figure 1, the scale bar is too small. And most of the information under the bottom of the SEM figures are not necessary, which should be cut off to make them clear. Response: The figures are enlarged so that the scale bars are visible. The unwanted details at the bottom are now cut off to make the SEM images clearer. 2. Comment: Figure 2, once again, the EDS images should have a scale bar or scale bars are too small to be seen. Also the labelling of elements should be clear and the colour of same element from different samples should keep the same to avoid confusing. The original SEM images should be given beside these EDS results. Response: We understand that the scale bars of EDS images are too small, and we have enlarged the images so that the scale bars are clearly seen. The reviewer is thanked for the observation and remark. 3. Comment: Figure 3, standard AP pattern peaks should be given and marked. The peaks for the doped AP should be assigned. Response: We have mentioned about the standard AP pattern lines in the text and the lines for doped AP have also been assigned. 4. Comment: Figure 4, the markers should be hollow to avoid overlapping to each other. Such as, it is hard to distinguish the APFe-1 and APFe-2. DSC data are too overwhelming and the important peaks should be labelled. Response: The markers are made hollow in the revised manuscript as suggested. The doping of AP with iron perchlorate resulted in thermal property modification, and a change in concentration of iron perchlorate in the narrow concentration range investigated had practically no effect on the thermal decomposition. The DSC peaks were labelled in the revised manuscript. 5. Comment: What method was used for the activation energy calculations? What is the different heating rate when do the calculations? The related information should be given in the main text. Response: Flynn-Wall-Ozawa method was used for the activation energy calculations and it has been cited in the text. Three different heating rates -3°C/min, 5°C/min and 10°C/min. were used for the thermal analysis. 6. Comment: How do you know it “in turn improving the ballistic properties of the propellant”? (In abstract and conclusion). Any evidence? Response: The incorporation of a few numbers of the ions of Fe or Cu in the lattice of AP makes a significant change in decomposition pattern of the co-crystals especially because it happens at high temperature. The sensitivity is less for the low temperature phenomenon (i.e. phase transitions) than the high temperature ones. The catalyst decreasing the decomposition temperature of AP is found to improve the burn rate of a propellant by reducing the activation energy for decomposition of propellant as given by S Chattopadhyay et.al. [Ref:19] 7. Comment: Table 1. The mole percentage of Cu or Fe should be given in addition to weight%. Response: The corresponding mole percentage is also included in Table 1, now 8. Comment: Table 1. How did you measure the bulk density and moisture and specific surface area? Response: The procedure to find bulk density and friability were already mentioned in the text. The moisture content in the crystals were measured using IR moisture analyser. 9. Comment: Please use µm instead of µ when describe the size. Response: Yes, this is done throughout in the text while revising the manuscript. 10. Comment: Table 2 and Figure 4. Important peaks should be labelled clearly in Figure 4. Response: There are 3 peaks in the DSC curve, phase transition, low temperature decomposition and high temperature decomposition and they have been labelled clearly. 11. Comment: Line 160, “Copper present in the lattice is the initiation point for LTD and it get oxidized to CuO”, any evidence? Considering the oxidation temperature of Cu, it doesn’t make sense. Response: The heat release during the thermal decomposition of AP is high and it favours the conversion of copper into copper oxide. Moreover, the oxidation potential for the pair Cu/AP should be less than that for Cu/O2 (air), accounting for the difference. 12. Comment: The authors didn’t talk much about the numbers in their tables. Response: The explanation is included in the manuscript now. Reviewer 2 Comments for the author 1. Comment: The authors provided the SEM images, Elemental mapping and XRD Patterns of Normal AP, ACuP-2 and AFeP-2, which cannot evidence these materials as co-crystal compounds especially the XRD Patterns. I think single-crystal structures and elemental analysis results of these materials are needed. Response: We appreciate the well-thought suggestion of the reviewer. The single crystal growth of the doped AP is not possible in this case, as it results in non-uniform inclusion of elements, as the crystal grows. This happens because the concentration of the dopant ions in the crystal evolves as that in the liquid phase varies during co-crystallisation unless one works in the azeotropic concentration ( concentration of the particular ions are the same in both the phases, crystal as well as liquid phases). The XRD pattern of doped AP is different from normal AP, implying the change in lattice due to co-crystallisation. Elemental mapping explains the uniform distribution of dopant in the AP crystal. 2. Comment: In abstract, the authors mentioned “the Ammonium perchlorate (AP) is the universal oxidizer in use ... due to ... thermal decomposition without any solid residue”, which is inconsistent with the introduction of copper or Iron ions. Obviously, the solid residue created by copper or iron ions is not welcome in all the solid rocket propellant motors Response: The dopant percentage is very less, and the composite propellant currently uses the mixed oxide, copper chromite as burn rate modifier. Doping with transition metal ion can dispense with the use of burn rate modifier. Reviewer 3 Comments for the author 1. Comment: In co-crystallisation section, the detailed speed of agitator and cooling rate should be added. Response: The crystallization of AP was done at fast cooling rate of - 0.60C/minute and the agitator rpm was maintained at 50. 2. Comment: For better understanding of the physical characteristics of AP, the particle size distribution of AP, AP doped with Cu and Fe should be given like D50 and distribution diagrams. Response: The particle size distribution of AP was calculated as weight average using ro-tap sieve shaker. Sieves of size 45-500μm was used for determining the weighted average particle size. 3. Comment: In Line 107, the authors said that the friability of the doped samples are higher compared to normal AP, but there is no evidence and how to prove this. Response: Friability of the crystals were determined by calculation the weight loss percentage after applying rotation and gyration to the crystals along with 3mm glass beads. The procedure and obtained results were already included in the text. 4. Comment: Figure.1 only shows the local SEM images at 10 μm, while single particle of AP, ACuP and AFeP should also be provided for better understanding of whole appearance and size. Response: Since the size of the particles are in the range 300μm, the single particle image using SEM is difficult and hence we regret the inability to comply with the recommendation of the reviewer. 5. Comment: As listed in Table 1, the weight percent of Cu or Fe is low, however, why is the crystal structure of AP doped with Cu/Fe quite different from normal AP? Response: The Cu/Fe is interacting with the lattice of AP, and resulted in crystal habit modification. The XRD patterns are found modified due to the lattice interaction. 6. Comment: It is hard to distinguish the line for each sample in Figure 4. Response: For very small changes in concentration of dopant ion, the differential effect on thermal decomposition temperature is less, with the result the thermal analysis curves are hard to distinguish. The modification of AP with higher concentration of dopant is in progress. 7. Comment: Throughout the paper there are many cases that words are either awkward or grammatically incorrect and some are listed below. These confuse the reader and the authors need to check the whole paper carefully. Response: The English language has been carefully edited now to almost do away with such errors as commented on by the reviewer. "	Here is a paper. Please give your review comments after reading it.
436	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Ammonium perchlorate (AP) is the universal oxidizer in use for all the solid rocket propellant motors used for space exploration due to its high available oxygen content and thermal decomposition without any solid residue. The inclusion of reactive species in AP directly affect the viscoelastic and ballistic properties of the propellant. Variations in lattice configuration of AP change its physical and thermal characteristics dramatically. In the present work AP was doped with Copper perchlorate and Iron perchlorate through co crystallisation. The impact of inclusion of these ionic species in the lattice on the thermal decomposition characteristics of AP was examined. The incorporation affected the physical as well as the ballistic characteristics of the resultant AP. The incorporation of foreign ions into AP crystals significantly changed the crystal morphology. The decomposition temperature decreased vis-a-vis with normal AP. The activation energy remarkably decreased for the doped AP crystals.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head n='1'>INTRODUCTION</ns0:head><ns0:p>The important chemical formulation used in rocketry is Composite Solid Propellant (CSP). CSP gives driving force to the rocket. Its important parameters are thermal stability, burning rate, friction and impact sensitivity. AP used as oxidizer is the major component (about 70%) in CSP. Thermal decomposition of AP directly influences the combustion behaviour of the propellant. The reduction in particle size of AP or metallic fuel may increase the burning surface area which in turn increases the burn rate of a propellant. The high thrust requirement of missions are satisfied by either increasing the burning surface area or by the addition of burn rate modifiers (BRM). The commonly used BRM are iron oxide and copper chromite (<3%). The doping of AP with transition metals and their oxides has shown to decrease the activation energy and decomposition temperature of AP, thereby acting as good catalysts <ns0:ref type='bibr' target='#b17'>(Schumacher, 1960;</ns0:ref><ns0:ref type='bibr' target='#b1'>Boldyrev, 2006;</ns0:ref><ns0:ref type='bibr' target='#b7'>Jacobs and Whitehead, 1969;</ns0:ref><ns0:ref type='bibr' target='#b8'>Kishore and Sunitha, 1979)</ns0:ref>. The mechanical mixing of AP with compounds of copper and iron is highly effective in reducing the thermal decomposition of Ammonium perchlorate <ns0:ref type='bibr' target='#b20'>(Wang et al., 2013;</ns0:ref><ns0:ref type='bibr' target='#b15'>Patil et al., 2008;</ns0:ref><ns0:ref type='bibr' target='#b4'>Chen et al., 2008b;</ns0:ref><ns0:ref type='bibr' target='#b0'>Alizadeh-Gheshlaghi et al., 2012;</ns0:ref><ns0:ref type='bibr' target='#b3'>Chen et al., 2008a;</ns0:ref><ns0:ref type='bibr' target='#b21'>Yang et al., 2014;</ns0:ref><ns0:ref type='bibr' target='#b10'>Liu et al., 2004;</ns0:ref><ns0:ref type='bibr' target='#b2'>Chaturvedi and Dave, 2013;</ns0:ref><ns0:ref type='bibr' target='#b19'>Styborski et al., 2015;</ns0:ref><ns0:ref type='bibr' target='#b18'>Song et al., 2010;</ns0:ref><ns0:ref type='bibr' target='#b5'>Costa et al., 2009)</ns0:ref>.</ns0:p><ns0:p>The crystallization being the critical step in the manufacturing of AP, the modifications made in crystallization parameters alter the crystal strength, purity and particle size distribution of AP <ns0:ref type='bibr' target='#b11'>(Mullin, 1997;</ns0:ref><ns0:ref type='bibr' target='#b9'>Lakshmi et al., 2016)</ns0:ref>. Previous works have reported the inclusion of foreign metal ions into AP crystal and its impact on the thermal decomposition characteristics; but the physical and crystallographic aspects were not discussed <ns0:ref type='bibr' target='#b1'>(Boldyrev, 2006;</ns0:ref><ns0:ref type='bibr' target='#b7'>Jacobs and Whitehead, 1969;</ns0:ref><ns0:ref type='bibr' target='#b16'>Pelly, 1982)</ns0:ref> </ns0:p></ns0:div> <ns0:div><ns0:head>EXPERIMENTAL Materials</ns0:head><ns0:p>Ammonium perchlorate crystals of purity >99.0% processed at Ammonium Perchlorate Experimental Plant, Aluva, Kerala was used for the preparation of doped AP in the present work. Analytical grade reagent copper perchlorate hexahydrate, and ferrous perchlorate nonhydrate from MERCK was used as received for doping of AP.</ns0:p></ns0:div> <ns0:div><ns0:head>Co-crystallization</ns0:head><ns0:p>A glass jacketed crystallizer of 5L capacity with glass agitator and teflon paddle is used for crystallization.</ns0:p><ns0:p>The super saturated solution of AP was prepared in distilled water at 75 • C, and the secondary nucleation was prevented by increasing the temperature of the solution by 5 • C. The separation of AP crystals from solution was done by cooling crystallisation method owing to the high temperature coefficient of solubility for AP. The concentration of copper perchlorate / iron perchlorate in mother liquor was varied from 10% to 20% by weight of AP. While the rpm of the agitator was set at slow rate i.e. 50 rpm; a fast cooling mode of 0.6 • C/minute was used to get doped crystal of substantial crystal properties and concentration.</ns0:p><ns0:p>The crystallisation condition optimisation was done through a number of experimental trials and statistical calculation <ns0:ref type='bibr'>(Nair et al., 2020a,b)</ns0:ref>. Two different concentrations each of copper doped AP and iron doped AP were prepared. Copper doped AP was prepared by co-crystallisation of AP with copper perchlorate, and iron doped AP was prepared by co-crystallisation of AP with iron perchlorate. The doped samples were named ACuP-1(Cu-0.36%), ACuP-2(Cu-0.49%), AFeP-1(Fe-0.35%), and AFeP-2 (Fe-0.51%) respectively. The prepared doped AP crystals were filtered out and dried in glass trays and dried at 60 • C for 4 hours in laboratory hot air oven.</ns0:p></ns0:div> <ns0:div><ns0:head>Instrumentation</ns0:head><ns0:p>The particle size distribution (PSD) of the doped AP was measured by sieve analysis method using Ro-Tap sieve shaker. The weighted average particle size was calculated by sieve analysis, using sieves of size 45-500 µm. The copper and iron content in the doped AP crystals were measured using ICP-AES. The specific surface areas of the particles were measured by multipoint BET method using Quantachrome NOVA 1200e Surface Area Analyzer. The friability of AP crystals were measured using crystals of particle size >125 µm. The crystals were given rotation and gyration for 30 minutes along with 100 numbers of 3 mm glass beads. The percentage weight loss is measured as the friability of the crystals. The bulk density measurement was done with DBK bulk density apparatus having 100 ml measuring cylinder for filling the AP crystals. The volume change after 100 tapping is used for bulk density calculation. IR Moisture Analyser is used for the estimation moisture content in the sample. Field emission scanning electron microscopy (FESEM) observations were performed to examine the morphology of the samples using Carl Zeiss, Supra 55 model field emission scanning electron microscope. The crystallographic properties of AP samples were examined by collecting the powder X-ray diffraction data of the samples on a Bruker D8-Discover powder X-ray diffractometer with Cu Kα (λ =1.5418 Å) at a scan rate of 2.5 deg per min. Static and Dynamic imaging techniques were performed to study the crystal characteristics.</ns0:p><ns0:p>The Ankersmid Eyetech particle size and shape analyzer measured the shape factor of the crystals. Perkin Elmer Simultaneous Thermogravimetry -Differential Scanning Calorimetry (TG-DSC), TA Instruments Q600, was employed for thermal characterization. The thermal analysis was done at three different heating rates -3 • C/minute, 5 • C/minute and 10 • C/minute -and kinetic analysis was done by Flynn-Wall-Ozawa Method (FWO).</ns0:p></ns0:div> <ns0:div><ns0:head>2/9</ns0:head><ns0:p>PeerJ Inorganic Chem. reviewing PDF \| (ICHEM- <ns0:ref type='table' target='#tab_3'>2020:03:46368:2:0:NEW 22 Oct 2020)</ns0:ref> Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:p></ns0:div> <ns0:div><ns0:head>RESULTS AND DISCUSSIONS Physical Characteristics</ns0:head><ns0:p>The dopants present in the mother liquor affect the crystal formation, and growth of the crystal. The crystal growth was inhibited slightly by the presence of dopant ion which results in decrease in particle size. The weight average particle size of normal AP is 312 µm. The average particle size of copper doped and iron doped AP is found to be 284 µm and 292 µm respectively. The dopant ion inclusion in AP lattice resulted in point defects, and the strength of the crystals got reduced. The reduction in strength of crystal increases the friability of the crystal. The friability of copper/iron doped AP crystals were found to be 0.72% and 0.71% respectively, slightly higher than friability of normal AP 0.68%. The specific surface area of normal and doped AP crystals were measured using multipoint BET and it is in the range 0.17-0.19 m2/g m 2 /g for normal AP and 0.22 -0.24 m 2 /g for copper/iron doped AP. The surface roughness of doped crystals increases the surface area. The reduction in particle size with doping is a factor for increasing the surface area, and bulk density. The bulk density usually increases with decrease in the particle size of the material, due to better packing and compactness for smaller particles and reduction in the number of voids or pores. Thus the doped AP is a better option to increase the compactness of the particle during propellant mixing. The moisture content increases with increase in dopant concentration, it is within allowed limits for propellant grade AP. For propellant grade AP the maximum allowed moisture content is 0.25%, for copper doped AP it is 0.16% and for iron doped AP it is 0.15%. The physical characteristics of normal and doped AP are given in Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref>. The crystal surface morphology for normal AP and doped AP samples were studied by taking the scanning electron microscopy image of a single particle. Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science distributed throughout the AP crystal. Since the stability of the ionic co-crystal formed is less than those of pure AP, the EDS spectra showed separation of elements when compared to other compounds. Manuscript to be reviewed Chemistry Journals <ns0:ref type='formula'>101</ns0:ref>), ( <ns0:ref type='formula'>011</ns0:ref>), ( <ns0:ref type='formula'>201</ns0:ref>), ( <ns0:ref type='formula'>002</ns0:ref>), ( <ns0:ref type='formula'>210</ns0:ref>), ( <ns0:ref type='formula'>211</ns0:ref>), ( <ns0:ref type='formula'>112</ns0:ref>), ( <ns0:ref type='formula'>202</ns0:ref>) and ( <ns0:ref type='formula'>212</ns0:ref>) crystal planes respectively. Figure <ns0:ref type='figure'>3</ns0:ref> compares the XRD peaks of normal AP and the AP doped with copper and iron perchlorate respectively. The needle like growth of the crystals resulted in reduction in number of peaks in the XRD pattern. For ACuP-2, the relative intensity at 23.9 • was remarkably high, and only two peaks -23.9 and 30.1 -are present. For iron doped AP, the peak at 24.6 got intensified.</ns0:p><ns0:p>It shows that the doping of AP crystal with copper perchlorate and iron perchlorate have a crystal habit modification effect on AP.</ns0:p><ns0:p>The interpretation of XRD data for details related to spacing between the lattice planes and shape of the crystal lattice were done using Bragg's law. The Bragg's law relates the distance between lattice planes d, wavelength of radiation λ , and the diffraction angle θ , as 2d sin θ = nλ .</ns0:p><ns0:p>Due to co-crystallisation the spacing between atoms in the crystal increases, the 2θ angle vary, resulting in a shorter path length for the X-rays to interfere constructively. According to Bragg's law, the spacing between planes d, and by extension the lattice parameters must then increase to compensate for the reduction of θ , as λ is not changing. The doping of AP with metal ions resulted in the expanding d distance between lattice planes. The AP crystals were existing in an orthorhombic shape requiring the need to determine the lattice parameters a, b, and c. Boldyrev reports these lattice parameters as 0.9202, 0.5816, and 0.7449 nm for a, b, and c, respectively <ns0:ref type='bibr' target='#b1'>(Boldyrev, 2006)</ns0:ref>. Choosing h, k, and l values along with using the cited lattice parameters allows for the generation of approximate 2θ values associated with the chosen Miller index. The estimation of peak locations were done using Equation 1</ns0:p><ns0:formula xml:id='formula_0'>d 2 = h 2 a 2 + k 2 b 2 + l 2 c 2 .</ns0:formula><ns0:p>So the peaks of ( <ns0:ref type='formula'>201</ns0:ref>), ( <ns0:ref type='formula'>002</ns0:ref>), and (210) were selected to solve for lattice parameters a, b, and c for each samples. The lattice parameters determined were shown in the Table <ns0:ref type='table' target='#tab_2'>2</ns0:ref>. The inclusion of copper ion into AP lattice increased lattice parameter a and c slightly, and reduced lattice parameter b. For AFeP-2, the cation inclusion reduced the lattice parameter a, and increased lattice parameter b and c, resulting in a substantial increase in the lattice volume. This shows that the inclusion of cations into AP lattice causes distortion of the orthorhombic structure. This strained state increases the enthalpy of the system thus bringing down the numerical value of the free energy change (∆G). </ns0:p></ns0:div> <ns0:div><ns0:head>Thermal characteristics</ns0:head><ns0:p>The Thermo Gravimetric (TG) and Differential Scanning Calorimetry Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science thermal decomposition and the peaks are found to be closer to each other, and the same can be realised from the data given in Table <ns0:ref type='table' target='#tab_3'>3</ns0:ref>. The mass loss during thermal decomposition is slightly more for LTD of iron doped AP, and HTD of copper doped AP. It may be due to the conversion of copper to copper oxide during the thermal decomposition which further catalyses the reaction. The physical mixing of AP with compounds catalyse the HTD only. For the lattice modified AP, catalysing both the thermal decomposition stages result in a substantial increase in burn rate in AP containing propellant <ns0:ref type='bibr' target='#b6'>(Dey et al., 2015)</ns0:ref>.</ns0:p><ns0:p>The DSC curve given by Figure <ns0:ref type='figure' target='#fig_5'>4</ns0:ref>(b) shows the catalytic nature of copper and iron on thermal decomposition of AP, and the heat release for the doped samples were found to be higher than that of normal AP. The DTG curve shown in figure <ns0:ref type='figure' target='#fig_5'>4</ns0:ref>(c) clearly shows the shift in decomposition peaks.</ns0:p><ns0:p>The doped AP samples ACuP-2 and AFeP-2 were taken for kinetic analysis. For the calculation of activation energy by Flynn-Wall-Ozawa method, the thermal analysis were done at three different heating rate 3 • C/minute, 5 • C/minute and 10 • C/minute. The activation energy for these samples were less than that of normal AP samples and it is given in Table <ns0:ref type='table'>4</ns0:ref>. The activation energy versus conversion plot is given in Figure <ns0:ref type='figure' target='#fig_6'>5</ns0:ref>. There is a decrease in activation energy supporting the effectiveness of catalytic process. The kinetic analysis result shows a prominent decrease in the activation energy which indicates copper is a good catalyst compared to iron in this particular case. The experimental results on AP doped with Cu/Fe showed that the lattice modification was effective to reduce both the low temperature and high temperature decomposition. The solid phase reactions occurs only in LTD, while gas phase reactions occurs in both LTD and HTD. Earlier works on the thermal decomposition of AP proposed the electron transfer from ClO − 4 to NH + 4 as the scheme of mechanism <ns0:ref type='bibr' target='#b1'>(Boldyrev, 2006;</ns0:ref><ns0:ref type='bibr' target='#b7'>Jacobs and Whitehead, 1969;</ns0:ref><ns0:ref type='bibr' target='#b8'>Kishore and Sunitha, 1979;</ns0:ref><ns0:ref type='bibr' target='#b20'>Wang et al., 2013;</ns0:ref><ns0:ref type='bibr' target='#b15'>Patil et al., 2008)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>6/9</ns0:head><ns0:p>PeerJ Inorganic Chem. reviewing PDF \| (ICHEM-2020:03:46368:2:0:NEW 22 Oct 2020)</ns0:p><ns0:p>Manuscript to be reviewed The catalytic activity can be attributed to the presence of Cu/Fe ions throughout the crystal lattice, as well as the synergetic effect of the oxides of copper/ iron during the thermal decomposition of AP. The formation of cuprous/cupric oxides or ferrous/ferric oxides will enhance the redox reaction taking place during the decomposition of AP by acting as a better carrier or conductor of electrons.</ns0:p><ns0:note type='other'>Chemistry Journals Analytical, Inorganic, Organic, Physical, Materials Science Table 4.</ns0:note></ns0:div> <ns0:div><ns0:head>7/9</ns0:head><ns0:p>PeerJ Inorganic Chem. reviewing PDF \| (ICHEM-2020:03:46368:2:0:NEW 22 Oct 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>. The addition of BRM to AP can be done by either physical mixing or by co-crystallisation. The co-crystallisation of AP with salts results in the lattice inclusion of compounds into AP lattice. Variations in lattice configuration of AP changes its physical, thermal and ballistic characteristics dramatically, while the basic thermodynamic properties could remain unaltered. The present work focus on the alteration in lattice PeerJ Inorganic Chem. reviewing PDF \| (ICHEM-2020:03:46368:2:0:NEW 22 Oct 2020) Manuscript to be reviewed Chemistry Journals Analytical, Inorganic, Organic, Physical, Materials Science configuration of AP by co-crystallisation with perchlorates of copper and iron. The doped AP crystals were analysed for studying the impact of lattice inclusion of Cu and Fe ions on the lattice, physical and thermal characteristics of AP. The incorporation of foreign ions into AP crystals significantly changed the crystal morphology, bulk density, moisture content and the decomposition behaviour compared with normal AP. The decomposition temperature and activation energy remarkably decreased for the doped AP crystals.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>Figure. 1 (a), (b) and (c) show the SEM images of normal AP, ACuP-2 and AFeP-2. The regularity and spherical nature of the particles get worsened by doping with copper/iron perchlorate due to the fast cooling mode. The SEM results show homogeneous doping of copper/iron perchlorate. Since the particles are of average particle size ∼300 µm, the SEM image with high resolution (magnification 1000X) were taken to cover the surface image of the particles for checking the homogeneity of doping. The doped crystals have more needle like crystal growth compared with normal AP.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>Figure 1 .</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1. SEM images of (a) Normal AP, (b) ACuP-2, and (c) AFeP-2</ns0:figDesc><ns0:graphic coords='4,141.73,569.51,413.58,92.96' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head>Figure 2 .Figure 3 .</ns0:head><ns0:label>23</ns0:label><ns0:figDesc>Figure 2. Elemental mapping of (a) ACuP-2 and (b) AFeP-2</ns0:figDesc><ns0:graphic coords='5,141.73,99.85,413.59,259.41' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head /><ns0:label /><ns0:figDesc>(DSC) measurements were used to study the thermal characteristics of the normal AP and doped AP. The thermal decomposition of AP occurs in two-stages -the low temperature decomposition (LTD) starting at around 240 • C (accounting for 30% mass loss) is immediately followed by the second stage high temperature decomposition (HTD) that completes at around 380 • C. The crystallographic transition from orthorhombic to cubic form is given by the endotherm at 240 • C. The overlaid TG curves of Cu/Fe doped AP and normal AP are shown in Figure 4(a). There is a substantial decrease in the decomposition temperature for LTD and HTD which shows the catalytic nature of Cu/Fe on thermal decomposition of AP. The phenomenological data for the thermal decomposition is shown in Table 3, and it shows that the thermal decomposition peaks completely shift to a lower regime and it was clear from the lowering of initial temperature(Ti), peak temperature(Tp) and final temperature (Tf) of LTD and HTD. The high catalytic activity of copper ion for the thermal decomposition of AP resulted in a lowering of LTD by 32 • C and HTD by 60 • C. However, the efficiency of iron in accelerating the thermal decomposition of AP is less compared to copper where the LTD decrease by 23 • C and the HTD by 37 • C. Moreover, the effect of change in copper concentration on thermal decomposition of AP is found to be more than that for iron. The change in iron concentration does not make much effect on the 5/9 PeerJ Inorganic Chem. reviewing PDF \| (ICHEM-2020:03:46368:2:0:NEW 22 Oct 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head>Figure 4 .</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Figure 4. (a) TGA Curve of AP and Doped AP(ACuP-1, ACuP-2, AFeP-1, AFeP-2), (b) DSC Curve of AP and (ACuP-1, ACuP-2, AFeP-1, AFeP-2) and (c) DTG Curve of AP and (ACuP-1, ACuP-2, AFeP-1, AFeP-2)</ns0:figDesc><ns0:graphic coords='7,141.89,473.83,413.17,114.27' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head>Figure 5 .</ns0:head><ns0:label>5</ns0:label><ns0:figDesc>Figure 5. (a) E a versus conversion plot for Normal AP and Doped AP: (a) LTD, (b) HTD</ns0:figDesc><ns0:graphic coords='8,203.91,204.47,289.00,120.45' type='bitmap' /></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head>Table 1 .</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Physical characteristics of normal and doped AP</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Sample ref.</ns0:cell><ns0:cell>Weight % of Cu/Fe</ns0:cell><ns0:cell>Mole % of Cu/Fe</ns0:cell><ns0:cell>Average Particle Size (µm)</ns0:cell><ns0:cell>Friability (%)</ns0:cell><ns0:cell>Bulk Density</ns0:cell><ns0:cell>Moisture (%)</ns0:cell><ns0:cell>Specific surface area (m 2 /g)</ns0:cell></ns0:row><ns0:row><ns0:cell>AP</ns0:cell><ns0:cell>0</ns0:cell><ns0:cell>0</ns0:cell><ns0:cell>312</ns0:cell><ns0:cell>0.68</ns0:cell><ns0:cell>1.30</ns0:cell><ns0:cell>0.10</ns0:cell><ns0:cell>0.18</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>ACuP-1 0.36</ns0:cell><ns0:cell>0.007</ns0:cell><ns0:cell>291</ns0:cell><ns0:cell>0.71</ns0:cell><ns0:cell>1.28</ns0:cell><ns0:cell>0.13</ns0:cell><ns0:cell>0.22</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>ACuP-2 0.49</ns0:cell><ns0:cell>0.009</ns0:cell><ns0:cell>284</ns0:cell><ns0:cell>0.72</ns0:cell><ns0:cell>1.27</ns0:cell><ns0:cell>0.16</ns0:cell><ns0:cell>0.22</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>AFeP-1 0.35</ns0:cell><ns0:cell>0.007</ns0:cell><ns0:cell>296</ns0:cell><ns0:cell>0.70</ns0:cell><ns0:cell>1.33</ns0:cell><ns0:cell>0.12</ns0:cell><ns0:cell>0.24</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>AFeP-2 0.51</ns0:cell><ns0:cell>0.01</ns0:cell><ns0:cell>292</ns0:cell><ns0:cell>0.71</ns0:cell><ns0:cell>1.33</ns0:cell><ns0:cell>0.15</ns0:cell><ns0:cell>0.24</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_1'><ns0:head /><ns0:label /><ns0:figDesc>The major peaks appear for AP crystals were at 2θ values of 15.3 • , 19.4 • , 22.7 • , 23.9 • , 24.6 • , 27.4 • , 30.1 • , 30.8 • and 34.6 • assigned to the (</ns0:figDesc><ns0:table /><ns0:note>Analytical, Inorganic, Organic, Physical, Materials Science 000080451.</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_2'><ns0:head>Table 2 .</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Lattice parameters and lattice volume of AP, ACuP-2 and AFeP-2</ns0:figDesc><ns0:table><ns0:row><ns0:cell cols='3'>Sl. No. Sample Ref: a (nm) b (nm) c (nm) V (nm 3 )</ns0:cell></ns0:row><ns0:row><ns0:cell>1</ns0:cell><ns0:cell>AP</ns0:cell><ns0:cell>0.9169 0.5824 0.7424 0.3964</ns0:cell></ns0:row><ns0:row><ns0:cell>2</ns0:cell><ns0:cell>ACuP-2</ns0:cell><ns0:cell>0.9172 0.5797 0.7427 0.3949</ns0:cell></ns0:row><ns0:row><ns0:cell>3</ns0:cell><ns0:cell>AFeP-2</ns0:cell><ns0:cell>0.8928 0.6238 0.7609 0.4238</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_3'><ns0:head>Table 3 .</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Phenomenological data for the decomposition of normal AP and Doped AP</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Sample</ns0:cell><ns0:cell>Dopant conc.</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell>LTD</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>HTD</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>ref.</ns0:cell><ns0:cell>(%)</ns0:cell><ns0:cell>Ti</ns0:cell><ns0:cell>Tp</ns0:cell><ns0:cell>Tf</ns0:cell><ns0:cell>Mass-loss (%)</ns0:cell><ns0:cell>Ti</ns0:cell><ns0:cell>Tp</ns0:cell><ns0:cell>Tf</ns0:cell><ns0:cell>Mass-loss (%)</ns0:cell></ns0:row><ns0:row><ns0:cell>AP</ns0:cell><ns0:cell>0</ns0:cell><ns0:cell cols='3'>244 285 312</ns0:cell><ns0:cell>30</ns0:cell><ns0:cell cols='3'>312 374 388</ns0:cell><ns0:cell>70</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>ACuP-1 0.36</ns0:cell><ns0:cell cols='3'>225 260 274</ns0:cell><ns0:cell>29</ns0:cell><ns0:cell cols='3'>274 317 341</ns0:cell><ns0:cell>71</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>ACuP-2 0.49</ns0:cell><ns0:cell cols='3'>211 253 269</ns0:cell><ns0:cell>29</ns0:cell><ns0:cell cols='3'>269 314 336</ns0:cell><ns0:cell>71</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>AFeP-1 0.35</ns0:cell><ns0:cell cols='3'>216 263 273</ns0:cell><ns0:cell>31</ns0:cell><ns0:cell cols='3'>273 338 355</ns0:cell><ns0:cell>69</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>AFeP-2 0.51</ns0:cell><ns0:cell cols='3'>213 262 270</ns0:cell><ns0:cell>31</ns0:cell><ns0:cell cols='3'>270 337 353</ns0:cell><ns0:cell>69</ns0:cell></ns0:row></ns0:table></ns0:figure> </ns0:body> "	"18.10.2020 From Savitha Nair Scientific Officer-C Vikram Sarabhai Space Centre Thiruvananthapuram-695022 Dear Editors We thank the reviewers for their constructive comments on the manuscript. We have edited themanuscript considering all the reviewers’ comments very seriously and corrections have beenbrought out in the article in a way to answer the referees and for the readers to have a betterunderstanding. We believe that the manuscript is now suitable for publication in PeerJ Inorganic Chemistry. With regards Savitha Nair (On behalf of all authors) Response to Review comments of 46368-R2: Impact of Inclusion of Cu and Fe ions in Lattice on the Thermal decomposition characteristics of Ammonium Perchlorate The authors would like to thank the reviewers for their fruitful comments through constructive criticisms. While revising, we have considered all the reviewers’ comments very seriously and corrections have been brought out in the article in a way also to answer the referees’ comments and for the readers to have a better understanding. Such corrections are specifically marked in red font in the manuscript. The responses to the reviewers’ comments are given below point by point: Reviewer 1 Comments for the author 1. Comment: The current results could not support their co-crystal conclusion. The EDS results showed a separation of elements. Response: The incorporation of these additives in the lattice was evidenced from the XRD pattern ofthe doped AP. The difference in the XRD pattern of AP and co-crystallised AP shows that the doping of AP crystal with copper perchlorate and iron perchlorate have a crystal habit modification effect on AP.For the co-crystallised product, the relative intensity of the(101), (002), (210) and (112) planes were remarkably high compared to other planes. The AP crystals were existing in an orthorhombicwith their lattice parameters defined asa, b, and c. The inclusion of copper ions into AP lattice increased lattice parameter a and c, and reduced lattice parameter b. For AFeP-2, the cation inclusion reduced the lattice parameter a, and increased lattice parameter b and c, resulting in a substantial change in the lattice volume [refer table 1].This shows that the inclusion of cations into AP lattice causes distortion of the orthorhombic structure. This strained state increases the enthalpy of the system thus bringing down the numerical value of the free energy change (G). Since the stability of the ionic co-crystal formed is less than those of pure AP, the EDS spectra showed separation of elements when compared to other compounds. The strained crystals showed lowering of the decomposition temperature, unlike the physically mixed product. Since the concentration of copper /iron perchlorate is very less, it is not enough to cause a separate distinct co-lattice to exist as a mixed phase to cause a change in the physical parameters like melting point, phase transition temperature of AP, but it did diminish the decomposition temperature as the triggering of decomposition happens the doped point. Once triggered the remaining decomposition is self-sustained. These aspects have been detailed in the revised article. Sl. No. Sample Ref: a(nm) b(nm) c(nm) V(nm3) 1 AP 0.9169 0.5824 0.7424 0.3964 2 ACuP-2 0.9172 0.5797 0.7427 0.3949 3 AFeP-2 0.8928 0.6238 0.7609 0.4238 2. Comment: Why the authors didn’t cite their previous paper: https://pubs.acs.org/doi/10.1021/acsomega.9b03893 Response: The paper was under communication, and not published at the time of submission of manuscript to PeerJ. The published papers by the authors are now cited in the manuscript. Reviewer 2 Comments for the author 1. Comment: I noted that three reviewers have raised doubts about the formation of co-crystals in this work, but it is obvious that the author failed to provide sufficient evidence to support this in the improved version. Therefore, I insist on rejecting this submission. Response: We regret to note that we could not put forward the arguments in support of the doping at lattice level in a convincing manner. These shortages are now overcome by a detailed convincing statement as in answer 1 to referee 1 [Please see above]. Reviewer 3 Comments for the author 1. Comment: All of my comments have been well answered and this paper can be accepted now. Response: We thank the reviewer for appreciating our work. "	Here is a paper. Please give your review comments after reading it.
437	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Single crystals of 2H and 3R niobium diselenide were grown by a chemical transport reaction. The current determinations by single crystals X-ray diffraction reveal a nonstoichiometric composition. The structures are built from Se-Nb-Se slabs with Nb in trigonal prismatic coordination whereas the extra or additional Nb atoms are located in the octahedral holes between the slabs giving rise to the formula 2H and 3R-Nb 1+x Se 2 with 0.07< x <0.118. In particular, vacancy and Nb-Nb interactions may play an important role on the non-stoichiometry and the stacking mode in NbSe 2 . By increasing the number of additional Nb atoms in the pure 2H-NbSe 2 , a transition 2H to 3R polytype should occur in order to minimize the Nb layer -Nb extra -Nb layer repulsions between these adjacent slabs. The theoretical study shows that both 2H and 3R-Nb 1+x Se 2 are thermodynamically stable in the range 0< x <0.1.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>NbSe 2 belongs to the transition metal dichalcogenides TMDC's with chemical formula TX 2 (T = Nb, Ta; X = S, Se), which have recently renewed interest because of their quasi 2D nature similar to graphene making them very promising in novel electronic devices applications <ns0:ref type='bibr' target='#b43'>(Vogel & Robinson, 2015)</ns0:ref>. The system NbSe 2 has been the subject of many investigations since it exhibits incommensurate charge density waves (CDW) and superconductivity phenomenon below to 4K <ns0:ref type='bibr' target='#b44'>(Wilson, Disalvo & Mahajan, 1975)</ns0:ref>.</ns0:p><ns0:p>The structure consists in hexagonally arranged Se-Nb-Se sandwiches with metal atoms <ns0:ref type='bibr'>(Nb)</ns0:ref> that are located between two layers of chalcogen atoms <ns0:ref type='bibr'>(Se)</ns0:ref> in a trigonal prismatic coordination.</ns0:p><ns0:p>The bonding within each sandwich is covalent while the bonding among sandwiches themselves is a weak Van-der Waals type. Although the stacking along the c axis gives rise to several polytypes usually described as 1T-NbSe 2 , 2H-NbSe 2 , 3R-NbSe 2 and 4H-NbSe 2 <ns0:ref type='bibr' target='#b18'>(Kalikhman & Umanskii, 1973;</ns0:ref><ns0:ref type='bibr' target='#b2'>Brown & Beerntsen, 1965)</ns0:ref>, it seems that only the 2H and 3R are frequently obtained in practice.</ns0:p><ns0:p>Until date, there are many reports on polymorphism in the TMDC's by tuning synthesis temperature such as the high temperature monoclinic MoTe 2 <ns0:ref type='bibr' target='#b1'>(Brown, 1966)</ns0:ref>, the layered telluride Mo 1-x Nb x Te 2 <ns0:ref type='bibr' target='#b15'>(Ikeura et al., 2015)</ns0:ref>, polytypism in TiS 2 which was first observed by <ns0:ref type='bibr'>Tronc et al.,</ns0:ref> PeerJ Inorganic Chem. reviewing PDF \| (ICHEM-2020:10:53874:1:1:NEW 1 Jan 2021)</ns0:p><ns0:p>Manuscript to be reviewed Chemistry Journals <ns0:ref type='bibr'>Analytical, Inorganic, Organic, Physical, Materials Science (1973)</ns0:ref> and <ns0:ref type='bibr'>Legendre et al. (1975)</ns0:ref>, the highly polymorphic TaSe 2 <ns0:ref type='bibr' target='#b2'>(Brown & Beerntsen, 1965;</ns0:ref><ns0:ref type='bibr' target='#b14'>Huisman, Kadijk & Jellinek, 1970)</ns0:ref> and the polymorph TaSe 2-x Te x <ns0:ref type='bibr' target='#b24'>(Luo et al., 2015)</ns0:ref>. This structural anisotropy enables the intercalation of species to form stoichiometric and nonstoichiometric compounds with different guests. A number of authors <ns0:ref type='bibr'>(Huisman, Kadijk & Jellinek, 1970 and references therein)</ns0:ref> reported the existence of the Nb self-intercalation (SI) in NbSe 2 phases where the additional Nb metal atoms lie in octahedral holes between NbSe 2 layers.</ns0:p><ns0:p>Most of the structures were characterized by X-ray powder diffraction method, and very few investigations using single crystal X-ray diffraction technique were performed.</ns0:p><ns0:p>The Nb-Se phase diagram has been established on the basis of the homogeneity range of several polytypes which mostly depend on the method and the synthesis conditions <ns0:ref type='bibr' target='#b34'>(Predel, 1997)</ns0:ref>.</ns0:p><ns0:p>However, and according to the recent findings <ns0:ref type='bibr' target='#b16'>(Ivanova et al., 2019)</ns0:ref>, the diagram may contain some ambiguous data and should be revisited</ns0:p><ns0:p>The selenide 2H-NbSe 2 revisited by single crystals <ns0:ref type='bibr' target='#b29'>(Meerschaut & Deudon, 2001)</ns0:ref> and the selenide 3R-NbSe 2 investigated on the basis of the precession photograph <ns0:ref type='bibr' target='#b14'>(Huisman, Kadijk & Jellinek, 1970)</ns0:ref> have been both found stoichiometric; while the sulfur polytype 3R-Nb 1+x S 2 (x = 0.06 and 0.09) <ns0:ref type='bibr' target='#b29'>(Meerschaut & Deudon, 2001</ns0:ref><ns0:ref type='bibr' target='#b33'>., Powell & Jacobson, 1981)</ns0:ref> is non-stoichiometric.</ns0:p><ns0:p>According to <ns0:ref type='bibr' target='#b6'>Fisher and Sienko (1980)</ns0:ref>, these compounds are non-stoichiometric. Indeed, in the 2H-NbSe 2 , the niobium atoms are stacked directly one above the other along the c axis, which is not the case for 3R-NbSe 2 . In the case of an excessive intercalate niobium a transition from 2H to 3R polytype should then occur in order to minimize the Nb layer -Nb extra -Nb extra repulsions between these adjacent slabs. This transition was explained by an NbS 2 -layers rotation mechanism involving stacking faults probabilities between 15-18% <ns0:ref type='bibr' target='#b19'>(Katzke, 2002;</ns0:ref><ns0:ref type='bibr' target='#b23'>Leroux et al., 2018)</ns0:ref>.</ns0:p><ns0:p>In this work, three polytypes in the Nb-Se system: 2H-Nb 1.031 Se 2 , 3R-Nb 1.071 Se 2 and 3R-Nb 1.085 Se 2 were synthesized by CVT and investigated by single crystal X-ray diffraction.</ns0:p><ns0:p>In particular, the role of vacancy and Nb-Nb interactions on the non-stoichiometry and stacking mode in NbSe 2 has been examined.</ns0:p><ns0:p>Both 2H and 3R polytypes may co-exist in the range 0< x <0.07 and above this limit where only the form 3R predominates as a transition 2H-3R will take place.</ns0:p><ns0:p>PeerJ Inorganic Chem. reviewing PDF \| (ICHEM-2020:10:53874:1:1:NEW 1 Jan 2021)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science Furthermore, by using DFT with CASTEP code, a comparative study involving thermodynamic polytype stability of 2H and 3R-Nb 1+x Se 2 (x = 0, 0.1) has been attempted in order to support the X-ray diffraction conclusions.</ns0:p></ns0:div> <ns0:div><ns0:head>Materials & Methods</ns0:head></ns0:div> <ns0:div><ns0:head>Crystal growth and chemical analysis</ns0:head><ns0:p>Single crystals of 2H and 3R-Nb 1+x Se 2 were obtained by chemical vapor transport (CVT) method during our attempts to prepare ternary HgNbSe 2 and SnNbSe 2 . A stoichiometric amount of pure elements with an excess of Se were mixed and sealed in an evacuated quartz tube (length ~ 18 cm) using iodine (<5 mg/cm 3 ) as a transport agent to favour crystallization. The mixtures (charge ~ 1g) were placed into a zone tube furnace. The temperature was first increased slowly at 500 °C and held then for 48h in order to avoid thermal runaway caused by the exothermic reaction between Nb and Se. After that the temperature is turned up between 760-1050 °C for 15 days. </ns0:p></ns0:div> <ns0:div><ns0:head>Single crystal structure determination</ns0:head><ns0:p>Single crystals data were recorded on a Kappa Apex II CCD X-ray diffractometer <ns0:ref type='bibr'>(Bruker AXS 2006)</ns0:ref> with graphite-monochromated MoKα (λ = 0.71071 Å) radiation. The reflection intensities were integrated with the SAINT <ns0:ref type='bibr'>(Bruker APEX2, 2006)</ns0:ref>; SADABS was used for empirical absorption correction <ns0:ref type='bibr' target='#b40'>(Sheldrick , 2002)</ns0:ref>, and Jana 2006 <ns0:ref type='bibr' target='#b32'>(Petříček et al., 2006)</ns0:ref> was performed for the structure refinement.</ns0:p><ns0:p>The crystal structure of 3R-Nb 1.071 Se 2 was obverse/reverse twinned while the 3R-Nb 1.085 Se 2 was twinned by inversion; with a refined twin domain fraction of 0.611(5) : 0.389(5) and 0.85(8) : 0.15(8) respectively. The CIF files containing details of the structure refinements are available in the Supplemental Files.</ns0:p><ns0:p>PeerJ Inorganic Chem. reviewing PDF \| (ICHEM- <ns0:ref type='table' target='#tab_1'>2020:10:53874:1:1:NEW 1 Jan 2021)</ns0:ref> Manuscript to be reviewed Chemistry Journals <ns0:ref type='bibr'>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:ref> In the final cycles of refinement, all the atoms in the different structures were refined anisotropically.</ns0:p><ns0:p>Details concerning the structure refinement and final results are presented in Table <ns0:ref type='table'>1</ns0:ref> while atomic coordinates, anisotropic displacement parameters and selected bond distances are listed in Tables S1, S2 and S3 of the Supplemental Files.</ns0:p></ns0:div> <ns0:div><ns0:head>Theoretical calculations</ns0:head><ns0:p>The DFT calculations were performed using the plane-wave pseudopotential method implemented in the Cambridge Sequential Total Energy Package (CASTEP) code <ns0:ref type='bibr' target='#b5'>(Clark et al., 2005)</ns0:ref> of the Material Studio program from Accelrys (Materials Studio CASTEP, 2010). The GGA-PBE functional <ns0:ref type='bibr' target='#b7'>(Hammer, Hansen & Nørskov, 1999</ns0:ref>) was used to model the exchange and correlation interactions. The Broyden-Fletcher-Goldfrab-Shanno (BFGS) method was used to carry out the geometrical optimization <ns0:ref type='bibr' target='#b38'>(Shanno, 1985)</ns0:ref>. The plane-wave cut-off energy was adopted to be 500 eV and the Monkhorst-Pack scheme <ns0:ref type='bibr' target='#b35'>(Perdew, Burke & Ernzerhof, 1996)</ns0:ref>, kpoint grid sampling was set to 7x1x1 in the Brillouin zone. As far as self-consistent condition setting is concerned, the total energy was less than 3.10 -2 eV atom, and the maximum displacement and maximum stress allowed was 10 -3 Å and 5.10 -2 GPa respectively. The valence electronic configurations considered for atomic pseudopotential calculation are Nb: 4s 2 4p 6 4d 4 5s 1 and Se: 4s 2 4p 4 .</ns0:p><ns0:p>To investigate the relative stability of the Nb-SI in the 2H and 3R polytype with an excess of niobium (x = 0.1), 5x1x1 and 5x2x1 supercells respectively are adopted, corresponding to the ordered configurations Nb 10 Se 20 (x = 0) and Nb 11 Se 20 (x = 0.1) to simulate both pure and SI systems.</ns0:p><ns0:p>The formation enthalpy ΔE at T = 0 K is expressed as the difference in total energy of the supercell calculated by DFT and the chemical potentials E of Nb (cubic Fm-3m) and Se (trigonal P3 1 21) calculated from their respective bulks in the same computation conditions.</ns0:p><ns0:formula xml:id='formula_0'>∆𝐸 = 1 (𝑥 + 𝑦) [𝐸 𝐷𝐹𝑇 -𝑥𝐸 𝑁𝑏 -𝑦𝐸 𝑆𝑒 ]</ns0:formula><ns0:p>where x and y, respectively represent the Nb and Se atoms number in the supercell structure model.</ns0:p><ns0:p>PeerJ Inorganic Chem. reviewing PDF \| (ICHEM-2020:10:53874:1:1:NEW 1 Jan 2021)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:p><ns0:p>The free energy ΔG of the reactions involving the Nb-SI system at typical synthesis temperatures of 760 and 1050 °C was estimated according to <ns0:ref type='bibr' target='#b16'>Ivanova et al., 2019</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Results and Discussion</ns0:head></ns0:div> <ns0:div><ns0:head>X-ray experimental crystal structure</ns0:head><ns0:p>The polytype 2H-Nb 1.031 Se 2 is almost stoichiometric (Fig. <ns0:ref type='figure'>1A</ns0:ref>). Additional metal Nb2 atoms with a refined occupancy about 7.3% are located into the octahedral sites within the van der Waals VDW gap with the stacking sequence AcAδBcB ( A,B refer to the selenide layers; c to the completely filled niobium layers and δ to the partially filled niobium layers).</ns0:p><ns0:p>Polyhedrons around the Nb atoms are not distorted with six equivalent Nb-Se distances. The Nb1-6Se = 2.597(2) Å around Nb1 (in a trigonal prismatic coordination) are somewhat larger than the Nb2-6Se = 2.477(2) Å, observed around Nb2.</ns0:p><ns0:p>A characteristic feature of this 2H-structure is the short Nb1-Nb2 = 3.1425(6) Å distance, suggesting the formation of strong metal-metal repulsions between the adjacent layers, but can also be analyzed in terms of electrostatic repulsion (Fig. <ns0:ref type='figure'>2A</ns0:ref>).</ns0:p><ns0:p>The relaxation probably occurs following a defect model by introducing vacancy (about 4%) in the Nb1 filled layers. The Nb1 atoms are then displaced forward the created vacancy to minimize the formation of Nb1-Nb2 pairs interactions <ns0:ref type='bibr' target='#b42'>(Tronc & Moret, 1981;</ns0:ref><ns0:ref type='bibr' target='#b0'>Amzallag et al., 2007)</ns0:ref>.</ns0:p><ns0:p>The In the 3R-Nb 1.085 Se 2 (Fig. <ns0:ref type='figure'>1B</ns0:ref>), the refinement reveals a high occupancy, about 11.8%, of additional Nb2 atoms. By comparing with the previous 3R structure, the Nb2Se 6 octahedrons are less distorted Nb2-Se = 2.362(11)-2.621( <ns0:ref type='formula'>14</ns0:ref>) Å and the Nb1-Nb2 distance (3.36(2) Å) becomes shorter (Fig. <ns0:ref type='figure'>2B</ns0:ref>). This can be explained by a shrink of the Nb-Nb layers with high content of extra Nb2 atoms.</ns0:p><ns0:p>In this case, both models exhibit relaxation: 2H-3R transition followed by a defect model introducing about 3% of vacancy in the Nb filled layers. A comparable defect was observed in the 3R-Nb 1.06 S 2 <ns0:ref type='bibr' target='#b33'>(Powell & Jacobson, 1981)</ns0:ref>.</ns0:p><ns0:p>The composition obtained with the structure refinement is : 3R-(Nb 0.97(2) Δ 0.03 ) Nb 0.118(13) Se2 ( Δ denotes Nb vacancy in the fully layers) (ie) 3R-Nb 1.085 Se 2 .</ns0:p><ns0:p>In Nb 1+x Se 2 , as in the layered transition metal dichalcogenides, vacancies in the sublattice of the metal (Nb) and chalcogen (Se) are expected.</ns0:p><ns0:p>it is quite clear that the increase in the additional Nb induces changes in the pressure of saturated Se vapor above the surface of the growing crystal, and then affects the concentration of selenium vacancies. However, a free refinement of the occupancy of Se atoms in the three polytypes leads to a fully occupied sites, possibly due to the excess of Se used as starting material. Therefore, the generation of vacancies in the sublattice of Nb should be correlated to the location of Nb interlayer.</ns0:p><ns0:p>By the presence of extra Nb atoms, a significant residual electron densities are observed in the crystal structure refinements stacking in a way that violates the ideal 2H (ABAB..) and 3R (ABCABC..), about 7.3% for the former and 7.1-11.8% for the latter. The remaining 92.7% and 92.9-88.2% are unfaulted (see CIFs in Electronic Supplemental Files). Certainly, a stacking fault is a common feature in this kind of layered structures, with strong diffuse scattering observed along the c * axis in electron diffraction patterns (see Fig. <ns0:ref type='figure'>S1</ns0:ref>).</ns0:p><ns0:p>parameters and n the number of layers in the stacking sequence; n = 2 and 3 for 2H and 3R respectively.</ns0:p><ns0:p>As shown in Fig. <ns0:ref type='figure'>3</ns0:ref>, this ratio change is relatively small with the increase of x for both polytypes with a discontinuity around x = 0.07. It is assumed that both polytypes may co-exist in the range 0< x <0.07, and above this limit only the form 3R predominates. Comparable results were observed in the study of the non-stoichiometric 2H and 3R-Nb 1+x S 2 (0.07 < x < 0.18) by powder diffraction <ns0:ref type='bibr' target='#b6'>(Fisher & Sienko, 1980)</ns0:ref>. The mechanism involving the 2H-3R transition is still unclear, and may be attributed to the phase limit of the non-stoichiometric Nb 1+x Se 2 . The difference in energy between polytypes is small, thus the possibility to obtain a mixture of polytypes by changing the conditions of synthesis cannot be ruled out.</ns0:p><ns0:p>The present results are in agreement with earlier studies by <ns0:ref type='bibr' target='#b14'>Huisman, Kadijk & Jellinek (1970)</ns0:ref> and <ns0:ref type='bibr' target='#b37'>Selte, Bjerkelund & Kjekshus (1966)</ns0:ref>. The mixture 2H and 3R polytype were prepared at temperature of 760°C in the range 0.07< x <0.118, yet the 3R phase at x = 0.07 required higher temperature 1050-1100°C. Further experimental and theoretical works are mandatory to determine the x range of 2H-3R transition accurately.</ns0:p></ns0:div> <ns0:div><ns0:head>Theoretical results</ns0:head><ns0:p>To corroborate the single X-ray conclusions, the stability of four structures: the pure 2H-NbSe 2 and 3R-NbSe 2 , 2H-Nb 1.1 Se 2 and 3R-Nb 1.1 Se 2 with 10% of extra Nb-SI in the pure 2H and 3R, respectively, have been investigated. The optimized structural parameters of the supercell structures are summarized in Table <ns0:ref type='table' target='#tab_1'>2</ns0:ref>.</ns0:p><ns0:p>The calculated c lattice parameters of 2H and 3R-NbSe 2 are slightly overestimated compared to the experimental values by 5.28 % and 4.38% respectively. This discrepancy is due to the failure of GGA (local and semi-local approximations for the exchange-correlation) in describing the van der Waals interactions.</ns0:p><ns0:p>Structural geometrical optimization revealed that additional Nb2 atoms incorporated in 2H and 3R-Nb 1+x Se 2 with x = 0.1, obviously increase the c parameter by 7.61% and 9.73% respectively while the a parameter remains almost constant.</ns0:p><ns0:p>The Nb1-Se distance slightly increases in pure 2H and 3R-NbSe 2 (2.587 -2.600 Å and 2.597 -2.600 Å) compared to 2H and 3R-Nb 1.1 Se 2 (2.569 -2.687 Å and 2.592 -2.697 Å) while the Nb2-Se distances range from 2.564 -2.759 Å and 2.610 -2.797 Å respectively.</ns0:p><ns0:p>PeerJ Inorganic Chem. reviewing PDF \| (ICHEM-2020:10:53874:1:1:NEW 1 Jan 2021)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:p><ns0:p>The calculated formation energies at T = 0 K (Table <ns0:ref type='table' target='#tab_1'>2</ns0:ref>) indicate that the four structures polytypes can be thermodynamically stable. However, due to the strong Nb-Nb interactions the Nb (x = 0.1) SI system is found to be the least favorable. The close free energy ΔG values at high temperature suggest that facile phase transition between the two polytypes may occur, as established between Nb 2 Se 3 and Nb 1.33 Se 2 polytypes <ns0:ref type='bibr' target='#b16'>(Ivanova et al., 2019)</ns0:ref>. These results can explain the experimental stability of both 2H and 3R between 0 < x < 0.07, and confirm the hypothesis of a phase transition from 2H to 3R beyond x = 0.1.</ns0:p><ns0:p>The band structures and density of states (PDOS) of the foregoing four cases have been presented to study the impact of the Nb-SI on the electronic structure for both 2H and 3R-NbSe 2. . It is worth noticing, that the PDOS of Nb-4d is rather broad and almost located at the same energy with Se-4p, suggesting strong interactions. These phenomenon have been observed in some doped and intercalated 2H-NbSe 2 <ns0:ref type='bibr' target='#b4'>(Chen et al., 2014;</ns0:ref><ns0:ref type='bibr'>Hongping et al., 2014a</ns0:ref><ns0:ref type='bibr'>Hongping et al., , 2014b;;</ns0:ref><ns0:ref type='bibr' target='#b20'>Kouarta, Zanat & Belkhir, 2019;</ns0:ref><ns0:ref type='bibr' target='#b30'>Pervin et al., 2020;</ns0:ref><ns0:ref type='bibr' target='#b46'>Xiao-Chen et al., 2020)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions</ns0:head><ns0:p>In conclusion, the non-stoichiometric polytypes 2H-Nb 1.031 Se 2 , 3R-Nb 1.071 Se 2 and 3R-Nb 1.085 Se 2 have been successfully synthesized and investigated by single crystal X-ray diffraction.</ns0:p><ns0:p>Although the form 3R predominates for values of x greater than 0.07, both 2H and 3R polytypes may co-exist in the range 0< x <0.07. A transition 2H to 3R polytypes, followed by model PeerJ Inorganic Chem. reviewing PDF \| (ICHEM-2020:10:53874:1:1:NEW 1 Jan 2021)</ns0:p><ns0:p>Manuscript to be reviewed Further work involving experimental and theoretical investigations on the Nb-Se system is needed to elucidate the 2H-3R transition mechanism. Manuscript to be reviewed</ns0:p><ns0:note type='other'>Chemistry Journals Analytical, Inorganic</ns0:note><ns0:note type='other'>Chemistry Journals Figure 1</ns0:note><ns0:p>Crystal structure of (A) 2H-Nb 1.031 Se 2 ; (B) 3R-Nb 1.085 Se 2 showing the prismatic packing of Nb1Se 6 polyhedron (drawn in blue) and Nb2Se 6 (drawn in purple).</ns0:p><ns0:p>The extra Nb2 atoms are located into the octahedral sites (Nb2Se 6 drawn in purple) within the van der Waals gap.</ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head>Finally</ns0:head><ns0:label /><ns0:figDesc>the furnace was allowed to cool slowly to room temperature. Crystals of appreciable sizes were grown in the cold end of the tube; 2H+3R crystals are obtained at ~ 760 °C while the 3R crystals are obtained at ~ 1050°C. The analysis results by X-ray energy dispersive spectroscopy (XEDS) with the TEM (EDX Oxford Instruments) on several different crystallites of each polytype sample gave an approximate atomic ratio of 1:2 for Nb and Se [for 3R-Nb 1.085 Se 2 : Nb (at%) = 29,9 and Se (at%) = 70,1].</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>composition obtained with the structure refinement is : 2H-(Nb 0.96(2) Δ 0.04 ) Nb 0.073(13) Se 2 (Δ denotes Nb vacancy in the fully layers) (ie) 2H-Nb 1.031 Se 2 . The Nb1-Se distances in the 3R-Nb 1.071 Se 2 polytype are very comparable [2.594(9) -2.601(8) Å] and close to the values [2.59(8) -2.62(8) Å] reported by Brown & Beerntsen (1965). The refined occupancy (sof) of the additional Nb2 atoms (7.1 %) is almost equal to that observed in the previous 2H structure with the stacking sequence AbAδBcBαCaCβ (A,B,C refer to the selenide layers; a,b,c to the completely filled niobium layers and α,β,δ to the partially filled niobium layers). The octahedrons Nb2Se 6 are slightly distorted with three short [2.288(10) Å] and three long [2.698(14) Å] distances. The Nb2 atoms are then shifted from the center of the octahedrons in order to minimize the Nb1-Nb2 [3.425(17) Å] interactions. PeerJ Inorganic Chem. reviewing PDF \| (ICHEM-2020:10:53874:1:1:NEW 1 Jan 2021) Manuscript to be reviewed Chemistry Journals Analytical, Inorganic, Organic, Physical, Materials Science In order to avoid the direct stacking Nb1-Nb2-Nb1, a relaxation occurs following the transition 2H-3R model and consequently the Nb1-Nb2 distance increases to 3.425(17) Å. Indeed, in the 2H structure the relaxation by introducing high concentration of vacancy in the filled Nb layers may destabilize the structure by breaking the Nb-Nb bonding across the face sharing polyhedrons.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>Figs.</ns0:head><ns0:label /><ns0:figDesc>Figs. 4A and 4B show the band structure of the pure 2H and 3R respectively. The presence of several bands across the Fermi level E F reveals the metallic nature of the pure polytypes. Partial density of states (PDOS) (see Figs. S2A and S2C), indicates that the major contribution in DOS at E F comes from the hybridization between the Nb-4d and Se-4p orbitals which is responsible for the covalent Nb-Se bonds.For both 2H and 3R Nb-SI, the number of electronic bands around E F apparently increases compared to the pure 2H and 3R (Figs.4C and 4D), and consequently the gap around 2eV in the Conduction Band disappears. Moreover, the PDOS at E F decreases and upshifts (see Figs.S2B and S2D) compared to the pure polytypes which implies that a large degree of electrons is transferred by Nb extra atoms into these pure polytypes.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head /><ns0:label /><ns0:figDesc>, Organic, Physical, Materials Science vacancies in the host Nb sublattice should take place in order to minimize the Nb layer -Nb extra -Nb layer repulsions between these adjacent slabs. The calculated formation energies of 2H and 3R-Nb 1+x Se 2 (x = 0, 0.1) by DFT, indicate that both pure and Nb-SI systems can be thermodynamically stable, and suggest an easy phase transition between polytypes. The theoretical outcomes reveal the metallic nature of these polytypes with an overwhelming number of electrons transferred by Nb extra atoms into pure polytypes.</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='19,42.52,258.07,525.00,330.75' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='22,42.52,252.82,525.00,218.25' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='23,42.52,204.52,525.00,213.75' type='bitmap' /></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head /><ns0:label /><ns0:figDesc>Nb 1+x Se 2 Polytype 2H-Nb 1.031 Se 2 3R-Nb 1.071 Se 2 3R-Nb 1.085 Se 2</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Molar mass (g.mol -1 )</ns0:cell><ns0:cell>253.7</ns0:cell><ns0:cell>257.3</ns0:cell><ns0:cell>258.1</ns0:cell></ns0:row><ns0:row><ns0:cell>Crystal size (mm 3 )</ns0:cell><ns0:cell>0.36 x 0.16 x 0.05</ns0:cell><ns0:cell>0.33x 0.31 x 0.015</ns0:cell><ns0:cell>0.20 x 0.19 x 0.03</ns0:cell></ns0:row><ns0:row><ns0:cell>Space group, Z</ns0:cell><ns0:cell>P6 3 /mmc, 2</ns0:cell><ns0:cell>R3m, 3</ns0:cell><ns0:cell>R3m, 3</ns0:cell></ns0:row><ns0:row><ns0:cell>Unit cell dimensions (Å)</ns0:cell><ns0:cell>a = 3.4475(3)</ns0:cell><ns0:cell>a = 3.4512(4)</ns0:cell><ns0:cell>a = 3.4670(4)</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>c = 12.5702(11)</ns0:cell><ns0:cell>c = 18.827(3)</ns0:cell><ns0:cell>c = 18.866(2)</ns0:cell></ns0:row><ns0:row><ns0:cell>c/a</ns0:cell><ns0:cell>3.646</ns0:cell><ns0:cell>5.455</ns0:cell><ns0:cell>5.441</ns0:cell></ns0:row><ns0:row><ns0:cell>(c/n)/a</ns0:cell><ns0:cell>1.823</ns0:cell><ns0:cell>1.818</ns0:cell><ns0:cell>1.836</ns0:cell></ns0:row><ns0:row><ns0:cell>Volume (Å 3 )</ns0:cell><ns0:cell>129.38(2)</ns0:cell><ns0:cell>194.21(4)</ns0:cell><ns0:cell>196.39(4)</ns0:cell></ns0:row><ns0:row><ns0:cell>Calculated density (g.cm -3 )</ns0:cell><ns0:cell>6.5101</ns0:cell><ns0:cell>6.5987</ns0:cell><ns0:cell>6.5442</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>Absorption coefficient (mm -1 ) 32.515</ns0:cell><ns0:cell>32.658</ns0:cell><ns0:cell>32.33</ns0:cell></ns0:row><ns0:row><ns0:cell>Angular range θ (º)</ns0:cell><ns0:cell>3.24-27.48</ns0:cell><ns0:cell>6.50-39.95</ns0:cell><ns0:cell>3.24 -29.86</ns0:cell></ns0:row><ns0:row><ns0:cell>Index ranges</ns0:cell><ns0:cell>-3<h<4 ; -4<k<4</ns0:cell><ns0:cell>-6<h<6 ; -6< k<4</ns0:cell><ns0:cell>-4<h<4 ; -4<k<4</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>-16<l<16</ns0:cell><ns0:cell>-32<l<33</ns0:cell><ns0:cell>-25<l<25</ns0:cell></ns0:row><ns0:row><ns0:cell>Total recorded reflections</ns0:cell><ns0:cell>1568</ns0:cell><ns0:cell>1707</ns0:cell><ns0:cell>947</ns0:cell></ns0:row><ns0:row><ns0:cell>Independent reflections, Rint</ns0:cell><ns0:cell>80, 0.0505</ns0:cell><ns0:cell>547, 0.0498</ns0:cell><ns0:cell>188, 0.054</ns0:cell></ns0:row><ns0:row><ns0:cell>Reflections with I > 3(I)</ns0:cell><ns0:cell>75</ns0:cell><ns0:cell>534</ns0:cell><ns0:cell>176</ns0:cell></ns0:row><ns0:row><ns0:cell>T min /T max</ns0:cell><ns0:cell>0.2910/0.7456</ns0:cell><ns0:cell>0.000/0.615</ns0:cell><ns0:cell>0.368/0.746</ns0:cell></ns0:row><ns0:row><ns0:cell>Number parameters</ns0:cell><ns0:cell>9</ns0:cell><ns0:cell>14</ns0:cell><ns0:cell>15</ns0:cell></ns0:row><ns0:row><ns0:cell>R 1 , wR 2 (all)</ns0:cell><ns0:cell>0.0882/ 0.1033</ns0:cell><ns0:cell>0.0232/ 0.0271</ns0:cell><ns0:cell>0.057/ 0.084</ns0:cell></ns0:row><ns0:row><ns0:cell>Flack parameter</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell>0.15(8)</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_1'><ns0:head>Table 2</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>The optimized structural constants, selected bond lengths, calculated formation energy ΔE (eV) at 0 K and free energy ΔG 1033,1323 (eV) at 1033 and 1323 K for both pure and Nb-SI 2H and 3R-NbSe 2 respectively. : same notation for Nb and Se atoms as used in X-ray refinement tables. The distortion δ (Å) is the difference between the longest and shortest Nb-Se bond distance.</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Polytype</ns0:cell><ns0:cell>a , c (Å)</ns0:cell><ns0:cell>c/a</ns0:cell><ns0:cell>(c/n)/a</ns0:cell><ns0:cell>Nb1-Se , Nb2-Se (Å)</ns0:cell><ns0:cell>δ (Å)</ns0:cell><ns0:cell>ΔE</ns0:cell><ns0:cell>ΔG 1033 , 1323</ns0:cell></ns0:row><ns0:row><ns0:cell>2H-NbSe 2</ns0:cell><ns0:cell>a = 3.463 , c = 13.210</ns0:cell><ns0:cell>3.814</ns0:cell><ns0:cell>1.907</ns0:cell><ns0:cell>2.587-2.600</ns0:cell><ns0:cell>0.013</ns0:cell><ns0:cell>-1.11</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>2H-Nb 1.1 Se 2</ns0:cell><ns0:cell>a = 3.470 , c = 13.807</ns0:cell><ns0:cell>3.979</ns0:cell><ns0:cell>1.990</ns0:cell><ns0:cell>2.569-2.687, 2.564-2.579</ns0:cell><ns0:cell>0.118 , 0.195</ns0:cell><ns0:cell>-0.93</ns0:cell><ns0:cell>-0.96 , -0.97</ns0:cell></ns0:row><ns0:row><ns0:cell>3R-NbSe 2</ns0:cell><ns0:cell>a = 3.506 , c = 19.708</ns0:cell><ns0:cell>5.621</ns0:cell><ns0:cell>1.873</ns0:cell><ns0:cell>2.597-2.600</ns0:cell><ns0:cell>0.03</ns0:cell><ns0:cell>-1.25</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>3R-Nb 1.1 Se 2</ns0:cell><ns0:cell>a = 3.412 , c = 21.679</ns0:cell><ns0:cell>6.300</ns0:cell><ns0:cell>2.100</ns0:cell><ns0:cell>2.592-2.697, 2.610-2.797</ns0:cell><ns0:cell>0.105 , 0.187</ns0:cell><ns0:cell>-0.94</ns0:cell><ns0:cell>-0.97 , -0.98</ns0:cell></ns0:row></ns0:table><ns0:note>NotePeerJ Inorganic Chem. reviewing PDF \| (ICHEM-2020:10:53874:1:1:NEW 1 Jan 2021)</ns0:note></ns0:figure> <ns0:note place='foot'>Analytical, Inorganic, Organic, Physical, Materials Science </ns0:note> </ns0:body> "	"Dear Pr. Jordi Cirera Academic Editor, PeerJ Inorganic Chemistry Thank you for your e-mail enclosing the reviewers' comments. We greatly appreciate your time dealing with the manuscript, and the opportunity to revise our paper entitled ‘X-ray diffraction and theoretical study of the transition 2H-3R polytypes in Nb1+xSe2 (0 < x <0.1)’ Article ID: 53874. The suggestions offered by the reviewers have been helpful for revising and improving our manuscript. We have carefully reviewed the comments and have revised the manuscript accordingly. Please find attached a point-by-point response to the reviewers' concerns. Sincerely yours M. Kars Dear Reviewers', We highly appreciate your contribution to this study with all your constructive comments. The comments and suggestions are very helpful for revising and improving our manuscript. Much Thanks for reviewing our paper, we have tried to answer all of your points below. We hope our responses are satisfactory. Reviewer 1 (Anonymous) Basic reporting no comment Experimental design no comment' Validity of the findings no comment' Comments for the Author Title: X-ray diffraction and theoretical study of the transition 2H-3R polytypes in Nb1+xSe2 (0 < x <0.1) The main conclusion for this paper is different structure switching between 2H to 3R by self-intercalation in the niobium diselenide system. Although this behavior is not new, but the data is useful for further study the polymorphism in the TMDs. Therefore, I suggest it can be accepted to be published in PeeJChemistry Journals after major revisions with answering the following questions: Some issues to be address: 1. Introduction, it is well-known that 2H-NbSe2 is one of the most famous TMDs, where charge density wave CDW） in coexistence with superconductivity. And the superconducting transition temperature is around 7.2 K which is the highest Tc among the pristine TMDs. However, the authors state that 'The system NbSe2 has been the subject of many investigations, since it exhibits incommensurate charge density waves (CDW) and superconductivity phenomenon below to 4K (Wilson, Disalvo & Mahajan, 1975).' is obviously wrong. Evidently, the superconductivity can be seen above 4 K in 2H-NbSe2. Response : Thank you for catching this confusing error. We have corrected this error. “The system NbSe2 exhibits incommensurate charge density waves (CDW) and superconductivity phenomenon below 4K (Wilson, Disalvo & Mahajan, 1975)” 2. Introduction, up to now, there are innumerable reports on polymorphism in the TMDs by tuning synthesis temperature (such as MoTe2, from ref. 1966 Acta. Cryst. 20 268.), chemical doping (such as Mo1-xNbxTe2 from Ref. 2015, APL Materials 3(4); TaSe2-xTex from Ref. Proc Natl Acad Sci USA, 2015, 112 E1174) or self-intercalation (such as TaxSe2) and so on. The authors should mention these cases and make the readers easily understanding. Response : We have added a new paragraph reporting several examples of polytypes existing in the TMDs, as suggested by the reviewer. “Until date, there are many reports on polymorphism in the TMDC’s by tuning synthesis temperature such as the high temperature monoclinic MoTe2 (Brown, 1966), the layered telluride Mo1-xNbxTe2 (Ikeura et al., 2015), polytypism in TiS2 which was first observed by Tronc et al., (1973) and Legendre et al. (1975), the highly polymorphic TaSe2 (Brown & Beerntsen, 1965; Huisman & Jellinek, 1969) and the polymorph TaSe2-xTex (Luo et al., 2015)”. 3. There are a lot mistakes and typos in the whole manuscript. The authors should go through it and fix them, such as 'formulaeTX2' in the introduction, 7x1x1 in line 131, Nb2Se6 in 173 and so on. Response : Thank you for catching these errors. The manuscript was checked again and we tried our best to correct typographical errors and mistakes. 4. The authors should perform the resistivity and magnetic susceptibility measurements to check the CDW and superconductivity on these 2H and 3R-Nb1-xSe2 samples, compared with that of the 2H-NbSe2. Response: Thanks for the comment. Most studies about CDW are focused in the 2H polytype; and we agree with the reviewer that it will be interesting to compare the behavior of CDW in the 3R-Nb1-xSe2 polytype. Unfortunately most of these characterizations are not available for us; we hope that we can perform them with some other collaboration. Annotated manuscript The reviewer has also provided an annotated manuscript as part of their review: Reviewer 2 (Anonymous) Basic reporting 1. Numerous language deficiencies are present, (e.g. out-of-place commas, adjectives without corresponding nouns or missing verbs, such as 'Both have been found stoichiometric;', etc.). Response : Thank you for catching these errors. The manuscript was checked again and we tried our best to correct typographical errors and mistakes. Experimental design 1. Methods should be explained in more detail. For example, authors state: 'The mixture was heated between 760-1050 C for 15 days' Does this mean that the temperature was changed from 760 to 1050 C over a span of 15 days, that several synthesis were performed at different temperatures (each constant during 15 days), or that the temperature was allowed to fluctuate between 760-1050 during that time? Please clarify. I also cannot tell which conditions gave rise to each of the polytypes, or whether a mixture was obtained and then the specific crystals were picked individually for further study. Response : Much thanks for your remarks. More details on the synthesis by CVT were added in the crystal growth and chemical analysis section of the manuscript. “Single crystals of 2H and 3R-Nb1+xSe2 were obtained by chemical vapor transport (CVT) method during our attempts to prepare ternary HgNbSe2 and SnNbSe2. A stoichiometric amount of pure elements with an excess of Se were mixed and sealed in an evacuated quartz tube (length ~ 18 cm) using iodine (<5 mg/cm3) as a transport agent to favour crystallization. The mixtures (charge ~ 1g) were placed into a zone tube furnace. The temperature was first increased slowly at 500 °C and held then for 48h in order to avoid thermal runaway caused by the exothermic reaction between Nb and Se. After that the temperature is turned up between 760-1050 °C for 15 days. Finally the furnace was allowed to cool slowly to room temperature. Crystals of appreciable sizes were grown in the cold end of the tube; 2H+3R crystals are obtained at ~ 760 °C while the 3R crystal are obtained at ~ 1050°C. The analysis results by X-ray energy dispersive spectroscopy (XEDS) with the TEM (EDX Oxford Instruments) on several different crystallites of each polytype sample gave an approximate atomic ratio of 1:2 for Nb and Se [for 3R-Nb1.085Se2: Nb (at%) = 29,9 and Se (at%) = 70,1]”. 2. ' ...then slowly cooled to room temperature. Crystals of appreciable sizes were grown in the cold end of the tube. 'This seems to imply that the cooling was not homogeneous, and a description of the non-homogeneity of the cooling process is therefore needed. Response : At the end of the synthesis, we can choose a slowed or a rapid cooling or just turn off the furnace, depending on the system. We have turned off the furnace to cool slowly to room temperature (see crystal growth and chemical analysis section). 3. ' an approximate ratio of 1:2 for Nb and Se [for 3R-Nb1.085Se2 : Nb (at%) = 29,9 and Se (at%) = 70,1]' Authors should clarify whether the stated ratio is mass or molar ratio. If those are molar ratios, that translates to a 0.85:2 Nb/Se ratio. If those are mass ratios, they translate to a 0.725:2 Nb/Se ratio. In any case those are quite different from the claimed 1.085:2 Nb/Se ratio. Response : The XEDS is a quantitative method analysis with an atomic ratio of approximately 1:2. The formula determined by XEDS is close to the one obtained by the refinement of the crystal structure. We have corrected accordingly. 4. Some extra detail is needed for the theoretical computations: please provide (as Supporting information) the DFT energies of all systems, as well as their geometries. I also notice that the formation enthalpy you compute is not the actual enthalpy, but the energy divided by the number of atoms. Why? Also, at what temperature was enthalpy computed? Authors should state that. Why was enthalpy selected rather than free energy? Doesn't CASTEP allow the computation of hessians and vibrational/rotational contributions, ZPE, etc. , and hence the entropy and Cv/Cp values needed to compute G? Response : Thanks for the comment. We stated that the theoretical computations are at 0K, and as supporting information we provided the CASTEP output files for the four polytypes. In most doped/intercalation studies using DFT calculations, ΔH (considered as temperature-independent constant) is equal to the difference in the sums of the DFT calculated total energies between the products and the reactants (see references Hongping et al., 2014; Hongping et al., 2014., Lin Chen 2013., Mariia N. Ivanova et al., 2018; Xiao-Chen Liu et al., 2020). The free energy at T=0K is equal to the enthalpy ΔH = ΔG0 [ΔG(T) = ΔH – TΔS]. According to the study of Ivanova et al., 2018, the free energy can also be estimated at typical synthesis temperatures. The calculated of the entropy contribution at 750°C (1033 K) and at 1050°C (1323K) was estimated at -0,03 and -0,037eV respectively. We add this estimate to Table 2 and provide more explanation in the discussion of the computed stabilities of the different polytypes. Validity of the findings See below Comments for the Author 1. in line 56 ' since it exhibits [...] superconductivity phenomenon below to 4K (Wilson, Disalvo & Mahajan, 1975)' I guess authors mean 'below 4 K', but the quoted reference shows Tc as 7.3 K (in its table 5, page 189) Response : Thank you for catching this confusing error. We have corrected the sentence accordingly. 2. line 74 'revisited by single crystals '. I guess authors mean 'revisited by single crystal X-ray diffraction' Response: Thank you for this observation. We have corrected it accordingly. 3. line 80 'By the fact of an excess of niobium, a transition 2H to 3R polytype should occur in order to minimize the Nblayer—Nbextra—Nbextra repulsions between these adjacent slabs. ' I am afraid that the language here is again cryptic: aren't both the 2H and 3R polytypes NbSe2? Do authors mean instead that as additional (supra-stoichoiometric) Nb is added the 3R polytype becomes energetically favored relative to the 2H? Response : In the case of an excessive intercalated niobium the 3R polytype becomes energetically favorable compared to the 2H polytype because of the Nblayer—Nbextra—Nbextra repulsions between these adjacent slabs. We have revised the sentence in order to remove this ambiguity. 4. lines 193-195 'it is quite clear, that the increase in the additional Nb , induces changes in the pressure of saturated Se vapor above the surface of the growing crystal, and then affects the concentration of selenium vacancies.' Authors may suspect that, but I am afraid I do not think that is at all clear: did the authors monitor the Se vapor profile above the crystals duting growth? (Is that even technically possible?) I may be misreading, bu it seems to me that authors have only mentioned Nb vacancies before this sentence, and therefore I do not see how they can posit an explanation of the Se vacancies that (apparently) are not reported to be present (an on the next paragraph are stated to not exist 'A free refinement of the occupancy of Se atoms in the three polytypes, leads to a fully occupied sites') Response : We have not grown the crystals by controlling the partial pressure of selenium; the sentence was revised to remove this ambiguity. 5. Figure 3 is not immediately readable, as the reader cannot tell whether the points at the upper right of each graph are calculated/experimental points or are a legend instead. Please correct that. I also fail to see a discontinuity around x=0.07, since (apart from the point that may (or not) be a legend, I see no other 2H points, and the 3R points are not discontinuous). Response : Thank you for this observation. We have moved the legend of the Figure 3. 12. The legends in Figure 4 are barely readable. Please correct. Response : All the Figures are submitted separately, this is probably due to the final building pdf. Nevertheless we have submitted a new Figure 4. 13. The discussion of the computed stabilities is deficient: from the data in table 2, I compute formation enthalpies also of - 66.5 kcal/mol for 2H-Nb1.1Se2 and -67.2 kcal/mol for 3R-Nb1.1Se2 (indeed indistiguishable with the computational methods used). The difference between the stoichoiometric compounds is much larger -76.8 kcal/mol for 2H-NbSe2 and 86.5 kcal/mol for 3R-NbSe2. Interesting as enthalpy differences are, stability cannot be directly inferred from that, but requires the computation of free energies. Response : The free energy at T=0K is equal to the enthalpy ΔH = ΔG0 [ΔG(T) = ΔH – TΔS]. The free energy can be estimated at typical synthesis temperatures according to the study of Ivanova et al., 2018. The calculated of the entropy contribution at 750°C (1033 K) and at 1050°C (1323K) was estimated at -0,03 and -0,037eV respectively. We add this estimate to Table 2 and provide more explanation in the discussion of the computed stabilities of the different polytype. 14. line 252 'Moreover, the PDOS at EF decreases and upshifts (see Figs. S2B and S2D) compared to the pure polytypes, which implies that a large degree of electrons is transferred by Nb extra atoms into these pure polytypes' This should be explained more fully (o at least a citation for a reference detailing how the DOS around Fermi level E(F) relates to the metallic/chalcogenide electron distribution). Response : References on theoretical studies of some doped/intercalation NbSe2 compounds explaining the upshifts of the PDOS at Fermi level EF and the large degree of electron transferred by Nb extra atoms are cited at the end of the paragraph, as references included all phenomena mentioned in the text. We have revised accordingly. 15. There is an important typo in table 2: authors wrote Nb1.01Se2, where they really should have written Nb1.1. Response : Thank you for this observation. We have replaced Nb1.01Se2 by Nb1.1Se2 in Table 2. "	Here is a paper. Please give your review comments after reading it.
438	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Single crystals of 2H and 3R niobium diselenide were grown by a chemical transport reaction. The current determinations by single crystals X-ray diffraction reveal a nonstoichiometric composition. The structures are built from Se-Nb-Se slabs with Nb in trigonal prismatic coordination whereas the extra or additional Nb atoms are located in the octahedral holes between the slabs giving rise to the formula 2H and 3R-Nb 1+x Se 2 with 0.07< x <0.118. In particular, vacancy and Nb-Nb interactions may play an important role on the non-stoichiometry and the stacking mode in NbSe 2 . By increasing the number of additional Nb atoms in the pure 2H-NbSe 2 , a transition 2H to 3R polytype should occur in order to minimize the Nb layer -Nb extra -Nb layer repulsions between these adjacent slabs. The theoretical study shows that both 2H and 3R-Nb 1+x Se 2 are thermodynamically stable in the range 0< x <0.1.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>NbSe 2 belongs to the transition metal dichalcogenides TMDC's with chemical formula TX 2 (T = Nb, Ta; X = S, Se), which have recently renewed interest because of their quasi 2D nature similar to graphene making them very promising in novel electronic devices applications <ns0:ref type='bibr' target='#b45'>(Vogel & Robinson, 2015)</ns0:ref>. The system NbSe 2 has been the subject of many investigations since it exhibits incommensurate charge density waves (CDW) and superconductivity phenomenon above 4K <ns0:ref type='bibr' target='#b46'>(Wilson, Disalvo & Mahajan, 1975)</ns0:ref>.</ns0:p><ns0:p>The structure consists in hexagonally arranged Se-Nb-Se sandwiches with metal atoms <ns0:ref type='bibr'>(Nb)</ns0:ref> that are located between two layers of chalcogen atoms <ns0:ref type='bibr'>(Se)</ns0:ref> in a trigonal prismatic coordination.</ns0:p><ns0:p>The bonding within each sandwich is covalent while the bonding among sandwiches themselves is a weak Van-der Waals type. Although the stacking along the c axis gives rise to several polytypes usually described as 1T-NbSe 2 , 2H-NbSe 2 , 3R-NbSe 2 and 4H-NbSe 2 <ns0:ref type='bibr' target='#b18'>(Kalikhman & Umanskii, 1973;</ns0:ref><ns0:ref type='bibr' target='#b2'>Brown & Beerntsen, 1965)</ns0:ref>, it seems that only the 2H and 3R are frequently obtained in practice.</ns0:p><ns0:p>Until date, there are many reports on polymorphism in the TMDC's by tuning synthesis temperature such as the high temperature monoclinic MoTe 2 <ns0:ref type='bibr' target='#b1'>(Brown, 1966)</ns0:ref>, the layered telluride Mo 1-x Nb x Te 2 <ns0:ref type='bibr' target='#b15'>(Ikeura et al., 2015)</ns0:ref>, polytypism in TiS 2 which was first observed by <ns0:ref type='bibr'>Tronc et al.,</ns0:ref> PeerJ Inorganic Chem. reviewing PDF \| (ICHEM-2020:10:53874:2:0:NEW 28 Jan 2021)</ns0:p><ns0:p>Manuscript to be reviewed Chemistry Journals <ns0:ref type='bibr'>Analytical, Inorganic, Organic, Physical, Materials Science (1973)</ns0:ref> and <ns0:ref type='bibr'>Legendre et al. (1975)</ns0:ref>, the highly polymorphic TaSe 2 <ns0:ref type='bibr' target='#b2'>(Brown & Beerntsen, 1965;</ns0:ref><ns0:ref type='bibr' target='#b14'>Huisman, Kadijk & Jellinek, 1970)</ns0:ref> and the polymorph TaSe 2-x Te x <ns0:ref type='bibr' target='#b26'>(Luo et al., 2015)</ns0:ref>. This structural anisotropy enables the intercalation of species to form stoichiometric and nonstoichiometric compounds with different guests. A number of authors <ns0:ref type='bibr'>(Huisman, Kadijk & Jellinek, 1970 and references therein)</ns0:ref> reported the existence of the Nb self-intercalation (SI) in NbSe 2 phases where the additional Nb metal atoms lie in octahedral holes between NbSe 2 layers.</ns0:p><ns0:p>Most of the structures were characterized by X-ray powder diffraction method, and very few investigations using single crystal X-ray diffraction technique were performed.</ns0:p><ns0:p>The Nb-Se phase diagram has been established on the basis of the homogeneity range of several polytypes which mostly depend on the method and the synthesis conditions <ns0:ref type='bibr' target='#b35'>(Predel, 1997)</ns0:ref>.</ns0:p><ns0:p>However, and according to the recent findings <ns0:ref type='bibr' target='#b16'>(Ivanova et al., 2019)</ns0:ref>, the diagram may contain some ambiguous data and should be revisited</ns0:p><ns0:p>The selenide 2H-NbSe 2 revisited by single crystals <ns0:ref type='bibr' target='#b30'>(Meerschaut & Deudon, 2001)</ns0:ref> and the selenide 3R-NbSe 2 investigated on the basis of the precession photograph <ns0:ref type='bibr' target='#b14'>(Huisman, Kadijk & Jellinek, 1970)</ns0:ref> have been both found stoichiometric; while the sulfur polytype 3R-Nb 1+x S 2 (x = 0.06 and 0.09) <ns0:ref type='bibr' target='#b30'>(Meerschaut & Deudon, 2001</ns0:ref><ns0:ref type='bibr' target='#b34'>., Powell & Jacobson, 1981)</ns0:ref> is non-stoichiometric.</ns0:p><ns0:p>According to <ns0:ref type='bibr' target='#b6'>Fisher and Sienko (1980)</ns0:ref>, these compounds are non-stoichiometric. Indeed, in the 2H-NbSe 2 , the niobium atoms are stacked directly one above the other along the c axis, which is not the case for 3R-NbSe 2 . In the case of an excessive intercalate niobium a transition from 2H to 3R polytype should then occur in order to minimize the Nb layer -Nb extra -Nb extra repulsions between these adjacent slabs. This transition was explained by an NbS 2 -layers rotation mechanism involving stacking faults probabilities between 15-18% <ns0:ref type='bibr' target='#b19'>(Katzke, 2002;</ns0:ref><ns0:ref type='bibr' target='#b25'>Leroux et al., 2018)</ns0:ref>.</ns0:p><ns0:p>In this work, three polytypes in the Nb-Se system: 2H-Nb 1.031 Se 2 , 3R-Nb 1.071 Se 2 and 3R-Nb 1.085 Se 2 were synthesized by CVT and investigated by single crystal X-ray diffraction.</ns0:p><ns0:p>In particular, the role of vacancy and Nb-Nb interactions on the non-stoichiometry and stacking mode in NbSe 2 has been examined.</ns0:p><ns0:p>Both 2H and 3R polytypes may co-exist in the range 0< x <0.07 and above this limit where only the form 3R predominates as a transition 2H-3R will take place.</ns0:p><ns0:p>PeerJ Inorganic Chem. reviewing PDF \| (ICHEM-2020:10:53874:2:0:NEW 28 Jan 2021)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science Furthermore, by using DFT with CASTEP code, a comparative study involving thermodynamic polytype stability of 2H and 3R-Nb 1+x Se 2 (x = 0, 0.1) has been attempted in order to support the X-ray diffraction conclusions.</ns0:p></ns0:div> <ns0:div><ns0:head>Materials & Methods</ns0:head></ns0:div> <ns0:div><ns0:head>Crystal growth and chemical analysis</ns0:head><ns0:p>Single crystals of 2H and 3R-Nb 1+x Se 2 were obtained by chemical vapor transport (CVT) method during our attempts to prepare ternary HgNbSe 2 and SnNbSe 2 . A stoichiometric amount of pure elements with an excess of Se were mixed and sealed in an evacuated quartz tube (length ~ 18 cm) using iodine (<5 mg/cm 3 ) as a transport agent to favour crystallization. The mixtures (charge ~ 1g) were placed into a zone tube furnace. The temperature was first increased slowly at 500 °C and held then for 48h in order to avoid thermal runaway caused by the exothermic reaction between Nb and Se. After that the temperature is turned up between 760-1050 °C for 15 days. </ns0:p></ns0:div> <ns0:div><ns0:head>Single crystal structure determination</ns0:head><ns0:p>Single crystals data were recorded on a Kappa Apex II CCD X-ray diffractometer <ns0:ref type='bibr'>(Bruker AXS 2006)</ns0:ref> with graphite-monochromated MoKα (λ = 0.71071 Å) radiation. The reflection intensities were integrated with the SAINT <ns0:ref type='bibr'>(Bruker APEX2, 2006)</ns0:ref>; SADABS was used for empirical absorption correction <ns0:ref type='bibr' target='#b41'>(Sheldrick , 2002)</ns0:ref>, and Jana 2006 <ns0:ref type='bibr' target='#b33'>(Petříček et al., 2006)</ns0:ref> was performed for the structure refinement.</ns0:p><ns0:p>The crystal structure of 3R-Nb 1.071 Se 2 was obverse/reverse twinned while the 3R-Nb 1.085 Se 2 was twinned by inversion; with a refined twin domain fraction of 0.611(5) : 0.389(5) and 0.85(8) : 0.15(8) respectively. The CIF files containing details of the structure refinements are available in the Supplemental Files.</ns0:p><ns0:p>PeerJ Inorganic Chem. reviewing PDF \| (ICHEM- <ns0:ref type='table' target='#tab_1'>2020:10:53874:2:0:NEW 28 Jan 2021)</ns0:ref> Manuscript to be reviewed Chemistry Journals <ns0:ref type='bibr'>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:ref> In the final cycles of refinement, all the atoms in the different structures were refined anisotropically.</ns0:p><ns0:p>Details concerning the structure refinement and final results are presented in Table <ns0:ref type='table'>1</ns0:ref> while atomic coordinates, anisotropic displacement parameters and selected bond distances are listed in Tables S1, S2 and S3 of the Supplemental Files.</ns0:p></ns0:div> <ns0:div><ns0:head>Theoretical calculations</ns0:head><ns0:p>The DFT calculations were performed using the plane-wave pseudopotential method implemented in the Cambridge Sequential Total Energy Package (CASTEP) code <ns0:ref type='bibr' target='#b5'>(Clark et al., 2005)</ns0:ref> of the Material Studio program from Accelrys (Materials Studio CASTEP, 2010). The GGA-PBE functional <ns0:ref type='bibr' target='#b8'>(Hammer, Hansen & Nørskov, 1999</ns0:ref>) was used to model the exchange and correlation interactions. The Broyden-Fletcher-Goldfrab-Shanno (BFGS) method was used to carry out the geometrical optimization <ns0:ref type='bibr' target='#b39'>(Shanno, 1985)</ns0:ref>. The plane-wave cut-off energy was adopted to be 500 eV and the Monkhorst-Pack scheme <ns0:ref type='bibr' target='#b36'>(Perdew, Burke & Ernzerhof, 1996)</ns0:ref>, kpoint grid sampling was set to 7x1x1 in the Brillouin zone. As far as self-consistent condition setting is concerned, the total energy was less than 3.10 -2 eV atom, and the maximum displacement and maximum stress allowed was 10 -3 Å and 5.10 -2 GPa respectively. The valence electronic configurations considered for atomic pseudopotential calculation are Nb: 4s 2 4p 6 4d 4 5s 1 and Se: 4s 2 4p 4 .</ns0:p><ns0:p>To investigate the relative stability of the Nb-SI in the 2H and 3R polytype with an excess of niobium (x = 0.1), 5x1x1 and 5x2x1 supercells respectively are adopted, corresponding to the ordered configurations Nb 10 Se 20 (x = 0) and Nb 11 Se 20 (x = 0.1) to simulate both pure and SI systems.</ns0:p><ns0:p>The formation enthalpy ΔE at T = 0 K is expressed as the difference in total energy of the supercell calculated by DFT and the chemical potentials E of Nb (cubic Fm-3m) and Se (trigonal P3 1 21) calculated from their respective bulks in the same computation conditions.</ns0:p><ns0:formula xml:id='formula_0'>∆𝐸 = 1 (𝑥 + 𝑦) [𝐸 𝐷𝐹𝑇 -𝑥𝐸 𝑁𝑏 -𝑦𝐸 𝑆𝑒 ]</ns0:formula><ns0:p>where x and y, respectively represent the Nb and Se atoms number in the supercell structure model.</ns0:p><ns0:p>PeerJ Inorganic Chem. reviewing PDF \| (ICHEM-2020:10:53874:2:0:NEW 28 Jan 2021)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:p><ns0:p>The calculated free energy ΔG at typical synthesis temperatures of 760 and 1050 °C only include configurational entropy due to intercalation, according to <ns0:ref type='bibr' target='#b16'>Ivanova et al., 2019</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Results and Discussion</ns0:head></ns0:div> <ns0:div><ns0:head>X-ray experimental crystal structure</ns0:head><ns0:p>The polytype 2H-Nb 1.031 Se 2 is almost stoichiometric (Fig. <ns0:ref type='figure'>1A</ns0:ref>). Additional metal Nb2 atoms with a refined occupancy about 7.3% are located into the octahedral sites within the van der Waals VDW gap with the stacking sequence AcAδBcB ( A,B refer to the selenide layers; c to the completely filled niobium layers and δ to the partially filled niobium layers).</ns0:p><ns0:p>Polyhedrons around the Nb atoms are not distorted with six equivalent Nb-Se distances. The Nb1-6Se = 2.597(2) Å around Nb1 (in a trigonal prismatic coordination) are somewhat larger than the Nb2-6Se = 2.477(2) Å, observed around Nb2.</ns0:p><ns0:p>A characteristic feature of this 2H-structure is the short Nb1-Nb2 = 3.1425(6) Å distance, suggesting the formation of strong metal-metal repulsions between the adjacent layers, but can also be analyzed in terms of electrostatic repulsion (Fig. <ns0:ref type='figure'>2A</ns0:ref>).</ns0:p><ns0:p>The relaxation probably occurs following a defect model by introducing vacancy (about 4%) in the Nb1 filled layers. The Nb1 atoms are then displaced forward the created vacancy to minimize the formation of Nb1-Nb2 pairs interactions <ns0:ref type='bibr' target='#b44'>(Tronc & Moret, 1981;</ns0:ref><ns0:ref type='bibr' target='#b0'>Amzallag et al., 2007)</ns0:ref>.</ns0:p><ns0:p>The In the 3R-Nb 1.085 Se 2 (Fig. <ns0:ref type='figure'>1B</ns0:ref>), the refinement reveals a high occupancy, about 11.8%, of additional Nb2 atoms. By comparing with the previous 3R structure, the Nb2Se 6 octahedrons are less distorted Nb2-Se = 2.362(11)-2.621( <ns0:ref type='formula'>14</ns0:ref>) Å and the Nb1-Nb2 distance (3.36(2) Å) becomes shorter (Fig. <ns0:ref type='figure'>2B</ns0:ref>). This can be explained by a shrink of the Nb-Nb layers with high content of extra Nb2 atoms.</ns0:p><ns0:p>In this case, both models exhibit relaxation: 2H-3R transition followed by a defect model introducing about 3% of vacancy in the Nb filled layers. A comparable defect was observed in the 3R-Nb 1.06 S 2 <ns0:ref type='bibr' target='#b34'>(Powell & Jacobson, 1981)</ns0:ref>.</ns0:p><ns0:p>The composition obtained with the structure refinement is : 3R-(Nb 0.97(2) Δ 0.03 ) Nb 0.118(13) Se2 ( Δ denotes Nb vacancy in the fully layers) (ie) 3R-Nb 1.085 Se 2 .</ns0:p><ns0:p>In Nb 1+x Se 2 , as in the layered transition metal dichalcogenides, vacancies in the sublattice of the metal (Nb) and chalcogen (Se) are expected.</ns0:p><ns0:p>it is quite clear that the increase in the additional Nb induces changes in the pressure of saturated Se vapor above the surface of the growing crystal, and then affects the concentration of selenium vacancies. However, a free refinement of the occupancy of Se atoms in the three polytypes leads to a fully occupied sites, possibly due to the excess of Se used as starting material. Therefore, the generation of vacancies in the sublattice of Nb should be correlated to the location of Nb interlayer.</ns0:p><ns0:p>By the presence of extra Nb atoms, a significant residual electron densities are observed in the crystal structure refinements stacking in a way that violates the ideal 2H (ABAB..) and 3R (ABCABC..), about 7.3% for the former and 7.1-11.8% for the latter. The remaining 92.7% and 92.9-88.2% are unfaulted (see CIFs in Electronic Supplemental Files). Certainly, a stacking fault is a common feature in this kind of layered structures, with strong diffuse scattering observed along the c * axis in electron diffraction patterns (see Fig. <ns0:ref type='figure'>S1</ns0:ref>).</ns0:p><ns0:p>parameters and n the number of layers in the stacking sequence; n = 2 and 3 for 2H and 3R respectively.</ns0:p><ns0:p>As shown in Fig. <ns0:ref type='figure'>3</ns0:ref>, this ratio change is relatively small with the increase of x for both polytypes with a discontinuity at the 2H to 3R transition around x = 0.07. It is assumed that both polytypes may co-exist in the range 0< x <0.07, and above this limit only the form 3R predominates.</ns0:p><ns0:p>Comparable results were observed in the study of the non-stoichiometric 2H and 3R-Nb 1+x S 2 (0.07 < x < 0.18) by powder diffraction <ns0:ref type='bibr' target='#b6'>(Fisher & Sienko, 1980)</ns0:ref>. The mechanism involving the 2H-3R transition is still unclear, and may be attributed to the phase limit of the nonstoichiometric Nb 1+x Se 2 . The difference in energy between polytypes is small, thus the possibility to obtain a mixture of polytypes by changing the conditions of synthesis cannot be ruled out.</ns0:p><ns0:p>The present results are in agreement with earlier studies by <ns0:ref type='bibr' target='#b14'>Huisman, Kadijk & Jellinek (1970)</ns0:ref> and <ns0:ref type='bibr' target='#b38'>Selte, Bjerkelund & Kjekshus (1966)</ns0:ref>. The mixture 2H and 3R polytype were prepared at temperature of 760°C in the range 0.07< x <0.118, yet the 3R phase at x = 0.07 required higher temperature 1050-1100°C. Further experimental and theoretical works are mandatory to determine the x range of 2H-3R transition accurately.</ns0:p></ns0:div> <ns0:div><ns0:head>Theoretical results</ns0:head><ns0:p>To corroborate the single X-ray conclusions, the stability of four structures: the pure 2H-NbSe 2 and 3R-NbSe 2 , 2H-Nb 1.1 Se 2 and 3R-Nb 1.1 Se 2 with 10% of extra Nb-SI in the pure 2H and 3R, respectively, have been investigated. The optimized structural parameters of the supercell structures are summarized in Table <ns0:ref type='table' target='#tab_1'>2</ns0:ref>.</ns0:p><ns0:p>The calculated c lattice parameters of 2H and 3R-NbSe 2 are slightly overestimated compared to the experimental values by 5.28 % and 4.38% respectively. This discrepancy is due to the failure of GGA (local and semi-local approximations for the exchange-correlation) in describing the van der Waals interactions.</ns0:p><ns0:p>Structural geometrical optimization revealed that additional Nb2 atoms incorporated in 2H and 3R-Nb 1+x Se 2 with x = 0.1, obviously increase the c parameter by 7.61% and 9.73% respectively while the a parameter remains almost constant.</ns0:p><ns0:p>PeerJ Inorganic Chem. reviewing PDF \| (ICHEM-2020:10:53874:2:0:NEW 28 Jan 2021)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:p><ns0:p>The Nb1-Se distance slightly increases in pure 2H and 3R-NbSe 2 (2.587 -2.600 Å and 2.597 -2.600 Å) compared to 2H and 3R-Nb 1.1 Se 2 (2.569 -2.687 Å and 2.592 -2.697 Å) while the Nb2-Se distances range from 2.564 -2.759 Å and 2.610 -2.797 Å respectively.</ns0:p><ns0:p>The calculated formation energies at T = 0 K (Table <ns0:ref type='table' target='#tab_1'>2</ns0:ref>) indicate that the four structures polytypes can be thermodynamically stable. However, due to the strong Nb-Nb interactions the Nb (x = 0.1) SI system is found to be the least favorable. The close free energy ΔG values at high temperature suggest that facile phase transition between the two polytypes may occur, as established between Nb 2 Se 3 and Nb 1.33 Se 2 polytypes <ns0:ref type='bibr' target='#b16'>(Ivanova et al., 2019)</ns0:ref>. These results can explain the experimental stability of both 2H and 3R between 0 < x < 0.07, and confirm the hypothesis of a phase transition from 2H to 3R beyond x = 0.1.</ns0:p><ns0:p>The band structures and density of states (PDOS) of the foregoing four cases have been presented to study the impact of the Nb-SI on the electronic structure for both 2H and 3R-NbSe 2. . It is worth noticing, that the PDOS of Nb-4d is rather broad and almost located at the same energy with Se-4p, suggesting strong interactions. These phenomenon have been observed in some doped and intercalated 2H-NbSe 2 <ns0:ref type='bibr' target='#b4'>(Chen et al., 2014;</ns0:ref><ns0:ref type='bibr'>Hongping et al., 2014a</ns0:ref><ns0:ref type='bibr'>Hongping et al., , 2014b;;</ns0:ref><ns0:ref type='bibr' target='#b20'>Kouarta, Zanat & Belkhir, 2019;</ns0:ref><ns0:ref type='bibr' target='#b31'>Pervin et al., 2020;</ns0:ref><ns0:ref type='bibr' target='#b48'>Xiao-Chen et al., 2020)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions</ns0:head><ns0:p>In conclusion, the non-stoichiometric polytypes 2H-Nb 1.031 Se 2 , 3R-Nb 1.071 Se 2 and 3R-Nb 1.085 Se 2 have been successfully synthesized and investigated by single crystal X-ray diffraction.</ns0:p><ns0:p>PeerJ Inorganic Chem. reviewing PDF \| (ICHEM-2020:10:53874:2:0:NEW 28 Jan 2021)</ns0:p><ns0:p>Manuscript to be reviewed Further work involving experimental and theoretical investigations on the Nb-Se system is needed to elucidate the 2H-3R transition mechanism. Manuscript to be reviewed</ns0:p><ns0:note type='other'>Chemistry Journals Analytical, Inorganic</ns0:note><ns0:note type='other'>Chemistry Journals Figure 1</ns0:note><ns0:p>Crystal structure of (A) 2H-Nb 1.031 Se 2 ; (B) 3R-Nb 1.085 Se 2 showing the prismatic packing of Nb1Se 6 polyhedron (drawn in blue) and Nb2Se 6 (drawn in purple).</ns0:p><ns0:p>The extra Nb2 atoms are located into the octahedral sites (Nb2Se 6 drawn in purple) within the van der Waals gap.</ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head>Finally</ns0:head><ns0:label /><ns0:figDesc>the furnace was allowed to cool slowly to room temperature. Crystals of appreciable sizes were grown in the cold end of the tube; 2H+3R crystals are obtained at ~ 760 °C while the 3R crystals are obtained at ~ 1050°C. The analysis results by X-ray energy dispersive spectroscopy (XEDS) with the TEM (EDX Oxford Instruments) on several different crystallites of each polytype sample gave an approximate atomic ratio of 1:2 for Nb and Se [for a selected 3R-crystal: Nb (at%) = 29,9 and Se (at%) = 70,1].</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>composition obtained with the structure refinement is : 2H-(Nb 0.96(2) Δ 0.04 ) Nb 0.073(13) Se 2 (Δ denotes Nb vacancy in the fully layers) (ie) 2H-Nb 1.031 Se 2 . The Nb1-Se distances in the 3R-Nb 1.071 Se 2 polytype are very comparable [2.594(9) -2.601(8) Å] and close to the values [2.59(8) -2.62(8) Å] reported by Brown & Beerntsen (1965). The refined occupancy (sof) of the additional Nb2 atoms (7.1 %) is almost equal to that observed in the previous 2H structure with the stacking sequence AbAδBcBαCaCβ (A,B,C refer to the selenide layers; a,b,c to the completely filled niobium layers and α,β,δ to the partially filled niobium layers). The octahedrons Nb2Se 6 are slightly distorted with three short [2.288(10) Å] and three long [2.698(14) Å] distances. The Nb2 atoms are then shifted from the center of the octahedrons in order to minimize the Nb1-Nb2 [3.425(17) Å] interactions. PeerJ Inorganic Chem. reviewing PDF \| (ICHEM-2020:10:53874:2:0:NEW 28 Jan 2021) Manuscript to be reviewed Chemistry Journals Analytical, Inorganic, Organic, Physical, Materials Science In order to avoid the direct stacking Nb1-Nb2-Nb1, a relaxation occurs following the transition 2H-3R model and consequently the Nb1-Nb2 distance increases to 3.425(17) Å. Indeed, in the 2H structure the relaxation by introducing high concentration of vacancy in the filled Nb layers may destabilize the structure by breaking the Nb-Nb bonding across the face sharing polyhedrons.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>Figs</ns0:head><ns0:label /><ns0:figDesc>Figs. 4A and 4B show the band structure of the pure 2H and 3R respectively. The presence of several bands across the Fermi level E F reveals the metallic nature of the pure polytypes. Partial density of states (PDOS) (see Figs. S2A and S2C), indicates that the major contribution in DOS at E F comes from the hybridization between the Nb-4d and Se-4p orbitals which is responsible for the covalent Nb-Se bonds.For both 2H and 3R Nb-SI, the number of electronic bands around E F apparently increases compared to the pure 2H and 3R (Figs.4C and 4D), and consequently the gap around 2eV in the Conduction Band disappears. Moreover, the PDOS at E F decreases and upshifts (see Figs.S2B and S2D) compared to the pure polytypes which implies that a large degree of electrons is transferred by Nb extra atoms into these pure polytypes.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head /><ns0:label /><ns0:figDesc>, Organic, Physical, Materials Science Although the form 3R predominates for values of x greater than 0.07, both 2H and 3R polytypes may co-exist in the range 0< x <0.07. A transition 2H to 3R polytypes, followed by model vacancies in the host Nb sublattice should take place in order to minimize the Nb layer -Nb extra -Nb layer repulsions between these adjacent slabs.The calculated formation energies of 2H and 3R-Nb 1+x Se 2 (x = 0, 0.1) by DFT, indicate that both pure and Nb-SI systems can be thermodynamically stable, and suggest an easy phase transition between polytypes. The theoretical outcomes reveal the metallic nature of these polytypes with an overwhelming number of electrons transferred by Nb extra atoms into pure polytypes.</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='19,42.52,258.07,525.00,330.75' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='22,42.52,252.82,525.00,218.25' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='23,42.52,204.52,525.00,213.75' type='bitmap' /></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head /><ns0:label /><ns0:figDesc>Nb 1+x Se 2 Polytype 2H-Nb 1.031 Se 2 3R-Nb 1.071 Se 2 3R-Nb 1.085 Se 2</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Molar mass (g.mol -1 )</ns0:cell><ns0:cell>253.7</ns0:cell><ns0:cell>257.3</ns0:cell><ns0:cell>258.1</ns0:cell></ns0:row><ns0:row><ns0:cell>Crystal size (mm 3 )</ns0:cell><ns0:cell>0.36 x 0.16 x 0.05</ns0:cell><ns0:cell>0.33x 0.31 x 0.015</ns0:cell><ns0:cell>0.20 x 0.19 x 0.03</ns0:cell></ns0:row><ns0:row><ns0:cell>Space group, Z</ns0:cell><ns0:cell>P6 3 /mmc, 2</ns0:cell><ns0:cell>R3m, 3</ns0:cell><ns0:cell>R3m, 3</ns0:cell></ns0:row><ns0:row><ns0:cell>Unit cell dimensions (Å)</ns0:cell><ns0:cell>a = 3.4475(3)</ns0:cell><ns0:cell>a = 3.4512(4)</ns0:cell><ns0:cell>a = 3.4670(4)</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>c = 12.5702(11)</ns0:cell><ns0:cell>c = 18.827(3)</ns0:cell><ns0:cell>c = 18.866(2)</ns0:cell></ns0:row><ns0:row><ns0:cell>c/a</ns0:cell><ns0:cell>3.646</ns0:cell><ns0:cell>5.455</ns0:cell><ns0:cell>5.441</ns0:cell></ns0:row><ns0:row><ns0:cell>(c/n)/a</ns0:cell><ns0:cell>1.823</ns0:cell><ns0:cell>1.818</ns0:cell><ns0:cell>1.836</ns0:cell></ns0:row><ns0:row><ns0:cell>Volume (Å 3 )</ns0:cell><ns0:cell>129.38(2)</ns0:cell><ns0:cell>194.21(4)</ns0:cell><ns0:cell>196.39(4)</ns0:cell></ns0:row><ns0:row><ns0:cell>Calculated density (g.cm -3 )</ns0:cell><ns0:cell>6.5101</ns0:cell><ns0:cell>6.5987</ns0:cell><ns0:cell>6.5442</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>Absorption coefficient (mm -1 ) 32.515</ns0:cell><ns0:cell>32.658</ns0:cell><ns0:cell>32.33</ns0:cell></ns0:row><ns0:row><ns0:cell>Angular range θ (º)</ns0:cell><ns0:cell>3.24-27.48</ns0:cell><ns0:cell>6.50-39.95</ns0:cell><ns0:cell>3.24 -29.86</ns0:cell></ns0:row><ns0:row><ns0:cell>Index ranges</ns0:cell><ns0:cell>-3<h<4 ; -4<k<4</ns0:cell><ns0:cell>-6<h<6 ; -6< k<4</ns0:cell><ns0:cell>-4<h<4 ; -4<k<4</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>-16<l<16</ns0:cell><ns0:cell>-32<l<33</ns0:cell><ns0:cell>-25<l<25</ns0:cell></ns0:row><ns0:row><ns0:cell>Total recorded reflections</ns0:cell><ns0:cell>1568</ns0:cell><ns0:cell>1707</ns0:cell><ns0:cell>947</ns0:cell></ns0:row><ns0:row><ns0:cell>Independent reflections, Rint</ns0:cell><ns0:cell>80, 0.0505</ns0:cell><ns0:cell>547, 0.0498</ns0:cell><ns0:cell>188, 0.054</ns0:cell></ns0:row><ns0:row><ns0:cell>Reflections with I > 3(I)</ns0:cell><ns0:cell>75</ns0:cell><ns0:cell>534</ns0:cell><ns0:cell>176</ns0:cell></ns0:row><ns0:row><ns0:cell>T min /T max</ns0:cell><ns0:cell>0.2910/0.7456</ns0:cell><ns0:cell>0.000/0.615</ns0:cell><ns0:cell>0.368/0.746</ns0:cell></ns0:row><ns0:row><ns0:cell>Number parameters</ns0:cell><ns0:cell>9</ns0:cell><ns0:cell>14</ns0:cell><ns0:cell>15</ns0:cell></ns0:row><ns0:row><ns0:cell>R 1 , wR 2 (all)</ns0:cell><ns0:cell>0.0882/ 0.1033</ns0:cell><ns0:cell>0.0232/ 0.0271</ns0:cell><ns0:cell>0.057/ 0.084</ns0:cell></ns0:row><ns0:row><ns0:cell>Flack parameter</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell>0.15(8)</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_1'><ns0:head>Table 2</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>The optimized structural constants, selected bond lengths, calculated formation energy ΔE (eV) at 0 K and free energy ΔG 1033,1323 (eV) at 1033 and 1323 K for both pure and Nb-SI 2H and 3R-NbSe 2 respectively. : same notation for Nb and Se atoms as used in X-ray refinement tables. The distortion δ (Å) is the difference between the longest and shortest Nb-Se bond distance.</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Polytype</ns0:cell><ns0:cell>a , c (Å)</ns0:cell><ns0:cell>c/a</ns0:cell><ns0:cell>(c/n)/a</ns0:cell><ns0:cell>Nb1-Se , Nb2-Se (Å)</ns0:cell><ns0:cell>δ (Å)</ns0:cell><ns0:cell>ΔE</ns0:cell><ns0:cell>ΔG 1033 , 1323</ns0:cell></ns0:row><ns0:row><ns0:cell>2H-NbSe 2</ns0:cell><ns0:cell>a = 3.463 , c = 13.210</ns0:cell><ns0:cell>3.814</ns0:cell><ns0:cell>1.907</ns0:cell><ns0:cell>2.587-2.600</ns0:cell><ns0:cell>0.013</ns0:cell><ns0:cell>-1.11</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>2H-Nb 1.1 Se 2</ns0:cell><ns0:cell>a = 3.470 , c = 13.807</ns0:cell><ns0:cell>3.979</ns0:cell><ns0:cell>1.990</ns0:cell><ns0:cell>2.569-2.687, 2.564-2.579</ns0:cell><ns0:cell>0.118 , 0.195</ns0:cell><ns0:cell>-0.93</ns0:cell><ns0:cell>-0.96 , -0.97</ns0:cell></ns0:row><ns0:row><ns0:cell>3R-NbSe 2</ns0:cell><ns0:cell>a = 3.506 , c = 19.708</ns0:cell><ns0:cell>5.621</ns0:cell><ns0:cell>1.873</ns0:cell><ns0:cell>2.597-2.600</ns0:cell><ns0:cell>0.03</ns0:cell><ns0:cell>-1.25</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>3R-Nb 1.1 Se 2</ns0:cell><ns0:cell>a = 3.412 , c = 21.679</ns0:cell><ns0:cell>6.300</ns0:cell><ns0:cell>2.100</ns0:cell><ns0:cell>2.592-2.697, 2.610-2.797</ns0:cell><ns0:cell>0.105 , 0.187</ns0:cell><ns0:cell>-0.94</ns0:cell><ns0:cell>-0.97 , -0.98</ns0:cell></ns0:row></ns0:table><ns0:note>NotePeerJ Inorganic Chem. reviewing PDF \| (ICHEM-2020:10:53874:2:0:NEW 28 Jan 2021)</ns0:note></ns0:figure> <ns0:note place='foot'>Analytical, Inorganic, Organic, Physical, Materials Science </ns0:note> </ns0:body> "	"Dear Pr. Jordi Cirera Academic Editor, PeerJ Inorganic Chemistry Thank you for your e-mail enclosing the reviewers' latest comments. We greatly appreciate your time dealing with the manuscript, and the opportunity to revise our paper entitled ‘X-ray diffraction and theoretical study of the transition 2H-3R polytypes in Nb1+xSe2 (0 < x <0.1)’ Article ID: 53874. The suggestions offered by the reviewers have been helpful for revising and improving our manuscript. We have carefully reviewed the latest comments and have revised the manuscript accordingly. Please find attached a point-by-point response to the reviewers' concerns. Sincerely yours M. Kars Dear Reviewers', We highly appreciate your contribution to this study with all your constructive comments. The new comments and suggestions are very helpful for revising and improving our manuscript. We apologize to the reviewers if some their comments in our first revision were not correctly answered. Much Thanks for reviewing our paper, we have tried to answer all of your points below. We hope our responses are satisfactory. Reviewer 1 (Anonymous) Basic reporting It is clear than before. Experimental design The data is enough for published. Validity of the findings It is meet the standards. Comments for the Author 1. Introduction, it is well-known that 2H-NbSe2 is one of the most famous TMDs, where charge density wave CDW）in coexistence with superconductivity. And the superconducting transition temperature is around 7.2 K which is the highest Tc among the pristine TMDs. However, the authors state that 'The system NbSe2 has been the subject of many investigations, since it exhibits incommensurate charge density waves (CDW) and superconductivity phenomenon below to 4K (Wilson, Disalvo & Mahajan, 1975).' is obviously wrong. Evidently, the superconductivity can be seen above 4 K in 2H-NbSe2. So, the revised version still need to be fixed. “The system NbSe2 exhibits incommensurate charge density waves (CDW) and superconductivity phenomenon above 4K (Wilson, Disalvo & Mahajan, 1975)”. The Tc for 2H-NbSe2 is 7.2 K, it is above 4 K not below 4K. Response : Again thank you for catching this confusing error. We apologize, this should have been corrected in the first revision We have corrected this error and replaced “below” by “above” at line 57 of the manuscript. “The system NbSe2 exhibits incommensurate charge density waves (CDW) and superconductivity phenomenon above 4K (Wilson, Disalvo & Mahajan, 1975)” Reviewer 2 (Anonymous) Basic reporting see below Experimental design see below Validity of the findings see below Comments for the Author I am generally satisfied with the authors responses. There are still a few issues to address: A) I would have preferred the authors to explicitly state that their computations of free energy (performed using the methodology of Ivanova et al. 2019) only include configurational entropy due to intercalation . Response : We thank the reviewer for this suggestion. We have rephrased the sentence accordingly at lines 160-161 of the manuscript. “The calculated free energy ΔG at typical synthesis temperatures of 760 and 1050 °C only include configurational entropy due to intercalation, according to Ivanova et al. 2019. B) I still do not understand how the Nb1.085:Se2 sample could afford a 29.9%Nb - 70.1 % Se mass (or molar) ratio. That should be clarified. Response : We agree with reviewer that there is some confusion between the formula Nb1.085 Se2 determined by crystal structure refinement from a single crystal X-ray diffraction and the one deduced using the average atomic concentration of the elements analyzed by EDX as accurately as possible. We have rephrased the sentence at lines 116-117 of the manuscript to remove this confusion. “(for a selected 3R-crystal: Nb (at%) = 29,9 and Se (at%) = 70,1).” C) In line 224 authors still state ' As shown in Fig. 3, this ratio change is relatively small with the increase of x for both polytypes with a discontinuity around x = 0.07. ' but no discontinuity can be seen. Please consider rephrasing this, for clarification of what you mean. Response : We thank the reviewer for pointing this out. We have completed the sentence at lines 223-224 of the manuscript. “As shown in Fig. 3, this ratio change is relatively small with the increase of x for both polytypes with a discontinuity at the 2H to 3R transition around x = 0.07.” "	Here is a paper. Please give your review comments after reading it.
439	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>A new bismuth macrocycle complex (Bi-edtabz) was synthesized from a mixture of solutions of the ligand (EDTA-based phenylene) and bismuth under acidic conditions. Its anti-virulence properties were evaluated against Escherichia coli O157: H7, Listeria monocytogenes, Pseudomonas aeruginosa, Salmonella enterica sub. enterica serovar Typhimurium and Staphylococcus aureus. The bismuth complex was characterized by NMR, UV-Vis, FTIR, ESI/MS and TG. Furthermore, Bi-edtabz complex at 0.25 -1 mM presented better antibiofilm properties against E. coli O157: H7 and S. aureus with values of biomass reduction of 30.1 -57.1% and 37.8 -55.5%, respectively, compared with the ligand edtabz. While biofilm formation of L. monocytogenes, P. aeruginosa and Salmonella Typhimurium was most impaired by edtabz (biomass reduction of 66.1 -100%, 66.4 -88.0% and 50.9 -67.1%, respectively. Additionally, Bi-edtabz inhibited the swimming motility of E. coli O157: H7 (12.5%) and colony spread of S. aureus (47.2%) at 1 mM and inhibited violacein production, a quorum-sensing related pigment of the biosensor strain Chromobacterium violaceum. Hence, edtabz and the Bi-edtabz complex can be used as novel anti-virulence agents against pathogenic bacteria.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>PeerJ Inorganic Chem. reviewing PDF \| (ICHEM-2021:11:68130:1:2:NEW 8 Jun 2022)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p><ns0:p>The increasing spread of antibiotic resistance has driven the search for new classes of antimicrobials. Metal complexes that have been used for years in the medical chemistry area have been largely ignored for antibiotic development <ns0:ref type='bibr' target='#b36'>(Ndagi et al. 2017)</ns0:ref>. This class of compounds can adopt a wide range of three-dimensional conformations than other organic compounds leading to unique modes of action <ns0:ref type='bibr' target='#b5'>(Bar et al. 2016</ns0:ref>). These properties make them interesting starting points for the development of new antimicrobials (Frei 2020). Bismuth(III) is a borderline metal ion according to the acid-base theory of Pearson, but it has a high affinity for multidentate ligands containing O and N donor atoms <ns0:ref type='bibr' target='#b11'>(Briand and Burford 2000)</ns0:ref>. EDTA-based ligands have donor atoms that can coordinate with bismuth. It is well known that increasing the number of donor atoms of the ligands and the number of chelating rings usually results in higher stability of the complexes <ns0:ref type='bibr' target='#b8'>(Beltran-Torres et al. 2019</ns0:ref>). Nevertheless, this is not only the factor that regulates the metal coordination; other factors such as the preorganization, the charge of the ligand, the steric efficiency in which the ligand surrounds the Bi(III) ion to form a cage-like structure also play an important role <ns0:ref type='bibr' target='#b42'>(Stavila et al. 2006)</ns0:ref>. Several bismuth compounds have been synthesized for different purposes, from semiconductors to antimicrobials <ns0:ref type='bibr' target='#b32'>(Marzano et al. 2013)</ns0:ref>. Bismuth coordination compounds inhibited the growth of pathogenic bacteria such as Helicobacter pylori, Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, Salmonella Typhimurium, Shigella sonnet and S. dysenteriae <ns0:ref type='bibr' target='#b49'>(Vazquez-Munoz et al. 2020</ns0:ref><ns0:ref type='bibr' target='#b50'>, Wang et al. 2018</ns0:ref><ns0:ref type='bibr' target='#b32'>, Marzano et al. 2013)</ns0:ref>. The antimicrobial mode of action of bismuth compounds is not yet completely elucidated. However, several studies indicated that this property is related to the interference with the cell wall synthesis, inhibition of ATP synthesis, and inhibition of key enzymes in the tricarboxylic acid cycle <ns0:ref type='bibr' target='#b51'>(Wang et al. 2020</ns0:ref><ns0:ref type='bibr' target='#b32'>, Marzano et al. 2013</ns0:ref>). However, the new trends in antibacterial agents are focused beyond their biocidal effect, and attention is being directed to their anti-virulence and pathogenicity activity. This approach includes the interruption of biofilm formation and intercellular communication, inhibiting toxin production and motility, necessary for the colonization of surfaces and tissues infection <ns0:ref type='bibr' target='#b15'>(Defoirdt 2018</ns0:ref><ns0:ref type='bibr' target='#b30'>, Luna-Solorza et al. 2020)</ns0:ref>. Even when the antimicrobial activity of bismuth and its coordination complexes is recognized, their effects on bacterial virulence have not been evaluated. In previous work, iron and copper complexes with an EDTA-based phenylene macrocycle (edtaod) and its open-chain derivative (edtabz) have been synthesized and evaluated as antibiofilm agents against food and clinical-related pathogens <ns0:ref type='bibr' target='#b47'>(Vázquez-Armenta et al. 2021)</ns0:ref>. In general, the open-chain ligand edtabz and their metal complexes showed better anti-biofilm properties without biocide effects than the macrocycle ligand. Correlation analysis showed a positive relationship between molecular properties of compounds such as molecular weight, volume and the number of rotatable bonds and its antibiofilm activity <ns0:ref type='bibr' target='#b47'>(Vázquez-Armenta et al. 2021)</ns0:ref>. Those results demonstrated the potential of coordination complexes in anti-virulence therapy to fight biofilm-related bacterial infections. Based on such findings, this study aimed to synthesize a bismuth(III) complex with an EDTA derivative ligand and characterize it through spectroscopic techniques. In addition, their effects on virulence factors of pathogenic bacteria such as biofilm formation, motility, and cell-to-cell communication were also investigated.</ns0:p></ns0:div> <ns0:div><ns0:head>Materials & Methods</ns0:head></ns0:div> <ns0:div><ns0:head n='1.1'>Synthesis of the ligand</ns0:head><ns0:p>The edtabz ligand was synthesized as previously described by <ns0:ref type='bibr'>Beltran-Torres et al. 2019. Specifically, 2.4 g (9.</ns0:ref>3 mmol) of EDTA dianhydride dissolved in 8 mL of dry dimethylformamide (DMF) was added to 2 mL (20 mmol) of aniline previously distilled. The resulting reaction mixture was left to stand overnight and any solids were filtered out. The filtrate was concentrated to 5 mL by a rotary evaporator, into which acetone was added. Precipitates formed were filtered off, washed with acetone several times until a colorless solid was obtained. Finally, it was vacuum-dried for 8 h at 25 °C. The purity was confirmed by IR and 1 H NMR spectra and determining the decomposition point.</ns0:p></ns0:div> <ns0:div><ns0:head n='1.2'>Synthesis of the bismuth complex</ns0:head><ns0:p>The bismuth complex was synthesized by reaction of the appropriate nitrate salt and ligand. In 15 mL of water, 1 mmol of edtabz was suspended and solubilized by adding Li 2 CO 3 . This solution was mixed with 1 mmol of nitrate bismuth (10 mL aqueous solution, dissolved with a few drops of nitric acid), no visible change of color was observed. After stirring was continued for 30 min and the solution was heated at 40-50 °C. When it was concentrated to half the initial volume and stabilized to 25 °C, the product precipitated. A white powder was filtered off and vacuum-dried for 8 h at 25 °C. Yield: 0.4146g, 54%. mp 238 °C (dec). Found: C, 33.18 %; H, 3.12 %; N, 10.49%. Calcd for BiC 22 H 24 O 6 N 4 • 2NO 3 : C, 33.39 %; H, 3.31 %; N, 10.62%. Mass spectrum m/z: 709.6 (100%), [(M (The mass spectrum of the complex is given in the Supporting Information, S1).</ns0:p></ns0:div> <ns0:div><ns0:head n='1.3'>Spectroscopic Measurements</ns0:head><ns0:p>The bismuth complex was characterized in terms of 1 H and 13 C NMR, UV-Vis, Fouriertransform infrared (FTIR), mass spectra of electrospray ionization (ESI/MS) and thermogravimetric analysis (TGA) <ns0:ref type='bibr' target='#b8'>(Beltran-Torres et al. 2019)</ns0:ref>. The 1 H and 13 C NMR spectra were obtained with a Bruker AVANCE 400 spectrometer for D 2 O solutions at 25 °C. The internal reference was sodium 2,2-dimethyl-2-silanpentane-5-sulfonate (DSS). IR spectra were recorded on a Perkin-Elmer FT-IR Spectrometer Model Frontier equipped with an ATR accessory. UV-Visible spectroscopy was carried out using Perkin-Elmer Lambda 20. For the pHvariable experiment of the Bi-edtabz mixture, the sample was dissolved in 0.1 M NaCl, and the pH values of the sample solutions were adjusted with 0.1 M HCl or 0.1 M NaOH to keep the ionic strength and sample concentration constant. A quartz cuvette of the spectrometer was loaded with the basic solution of the Bi-edtabz, and proper amounts of the acid complex solution were added to have a pH gradient of 0.5 in the final reading of the acid complex solution.</ns0:p><ns0:p>Mass Spectra of Electrospray Ionization (ESI/MS) were obtained on 6130 Quadrupole LC/MS of Agilent Technologies in the negative ionization mode. Thermogravimetric Analysis (TGA) was carried out on a thermogravimetric analyzer Perkin Elmer Pyris 1 TGA to study the complex's thermal stability and composition. Five mg of sample was set in a ceramic pan and analyzed at a °C range under an O 2 atmosphere.</ns0:p></ns0:div> <ns0:div><ns0:head n='1.4'>Antimicrobial activity of ligand and bismuth complex</ns0:head><ns0:p>The antibacterial activity of edtabz and the bismuth complex was evaluated as previously reported by <ns0:ref type='bibr' target='#b47'>Vázquez-Armenta et al. (2021)</ns0:ref>. The tested pathogenic bacteria were Escherichia coli O157: H7 (ATCC 43895), Listeria monocytogenes (ATCC 7644), Pseudomonas aeruginosa (ATCC 10154), Salmonella enterica sub. enterica serovar Typhimurium (ATCC 14028) and Staphylococcus aureus (ATCC 6538). An inoculum of 1x10 8 CFU/mL for each bacterium was obtained from exponential phase cultures in nutrient broth (Luria-Bertani, Brain Heart Infusion or Tryptic Soy broths). Then, 5 µL of inoculum were added to a sterile 96-well microplate (Costar 96), followed by 295 µL of each compound at different concentrations (0-1 mM) diluted in the corresponding nutrient broth to achieve a final inoculum level of 1x10 6 CFU/mL. The microplate was incubated for 24 h at 37 °C. Bacterial growth in the presence of ligand or bismuth complex was inspected visually. The highest tested concentration (1 mM) did not inhibit the growth of the evaluated bacteria; thus, 0.25, 0.5 and 1 mM were selected for further analyze the anti-virulence activity.</ns0:p></ns0:div> <ns0:div><ns0:head n='1.5'>Effect of ligand and bismuth complex on biofilm formation of pathogenic bacteria</ns0:head><ns0:p>The capacity of bismuth complex and edtabz to prevent biofilm formation of pathogenic bacteria was evaluated by the crystal violet staining procedure as previously reported by <ns0:ref type='bibr' target='#b47'>Vázquez-Armenta et al. (2021)</ns0:ref>. Bacterial inoculum was prepared at 1x10 8 CFU/mL in their corresponding broth from an exponential phase culture. Two µL of the bacterial inoculum and 150</ns0:p><ns0:p>of each compound at different concentrations (0, 0.25, 0.5 and 1 mM) were taken and placed in sterile 96-well polystyrene microplates (Costar 96) and incubated for 24 h at 37 °C. After the incubation period, the culture was removed by aspiration, and the wells were washed three times with distilled water and let dry for 15 min. Subsequently, the formed biofilms were stained by adding 200 µL of 0.1% (w/v) crystal violet to each well (45 min at 25 °C). Then, wells were washed gently three times with distilled water to remove the unbound dye and dried for an additional 15 min. The bound dye to biofilms was solubilized by adding 200 µL of 20% acetic acid for 15 min. Finally, the optical density (OD) was measured at 580 nm in FLUOstar Omega spectrophotometer (BMGLabtech, Chicago, IL, USA). Nutrient broth without any compound and bacteria was used as a blank, and the OD values were subtracted from treatment readings. Each experiment was carried out in triplicate, and results were expressed as percentages of inhibition compared with control samples (biofilms grown without compounds) following the formula:</ns0:p><ns0:p>Percentage inhibition (%) = -100 1.6 Effect of bismuth complex on swimming motility</ns0:p><ns0:p>The effect of bismuth complex and ligand on swimming motility of pathogenic bacteria was evaluated by exposing 10 of bacterial suspensions growth overnight (18 h, at 30 °C) to the presence of 1 mM of each compound. The treated bacteria were placed in the center of Petri dishes that contained soft agar (0.3% agar), and untreated bacteria were used as controls. The Petri dishes were incubated at 30 °C, and the diameter (mm) of bacterial motility halos was measured after 24 h of incubation <ns0:ref type='bibr' target='#b9'>(Bernal-Mercado et al. 2020)</ns0:ref>. Each experiment was performed in triplicate.</ns0:p></ns0:div> <ns0:div><ns0:head n='1.7'>Screening for anti-quorum sensing activity</ns0:head><ns0:p>The biomonitor strain Chromobacterium violaceum (ATCC 12472) was used for preliminary screening of anti-quorum sensing activity of the bismuth complex. For this purpose, the disk diffusion assay was employed where the inoculum of C. violaceum was grown aerobically in Lauria-Bertani (LB) broth at 30 °C for 18 h and adjusted to 1x10 8 CFU/mL. Then LB agar plates were spread with 100 of C. violaceum inoculum, and 20 µL of 2 mM bismuth complex or ligand solution was loaded to the sterile disks and placed on the surface of inoculated LB agar plates. The plates were incubated upright for 24 h at 30 °C, and a colorless appearance indicated the inhibition zone of QS <ns0:ref type='bibr'>(Alvarez et al. 2014</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head n='1.8'>Experimental design and statistical analysis</ns0:head><ns0:p>A complete randomized design was performed, the evaluated factor was the concentration of compounds (0.25, 0.5 or 1 mM), and the response variable was biomass reduction of biofilms (%) and motility halos (mm). An analysis of variance (ANOVA) was carried out to estimate significant differences among treatments (p 0.05), and the Tukey-Kramer test was used for means comparison (p 0.05) in the software NCSS 2007.</ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head></ns0:div> <ns0:div><ns0:head n='1'>H NMR spectra edtabz and Bi-edtabz</ns0:head><ns0:p>PeerJ Inorganic Chem. reviewing PDF \| (ICHEM-2021:11:68130:1:2:NEW 8 Jun 2022)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>The edtabz ligand shows the typical derivative EDTA signals, Ha, Hb and Hc. Due to the symmetry of the ligand, only five signals in the 1 H spectrum are seen (Figure <ns0:ref type='figure' target='#fig_1'>1</ns0:ref>). Once the bismuth ion is coordinated, the equivalence of the aliphatic protons and the aromatic ones is lost. In Figure <ns0:ref type='figure' target='#fig_1'>1a</ns0:ref>, protons from the aromatic ring in the free ligand are chemically equivalent, and just two signals for both the aniline rings are shown. Meanwhile, in the Bi-edtabz, the two aromatic rings can be distinguished (Figure <ns0:ref type='figure' target='#fig_1'>1b</ns0:ref>). The 13 C NMR spectra Bi-edtabz complex also confirms such a phenomenon. Nine carbon signals are observed in the 13 C spectrum of the edtabz ligand (Figure <ns0:ref type='figure'>2a</ns0:ref>), while seventeen signals are shown in the complex spectrum (Figure <ns0:ref type='figure'>2b</ns0:ref>). To further understand the chemical environment, coordination mode of the edtabz with Bi 3+ and the proper protons assignment, a Heteronuclear Simple Quantum Coherence (HSQC) experiment was performed (Full HSQC spectrum is given in the Supporting Information, S2).</ns0:p><ns0:p>In the y-axis, the aromatic carbons (Figure <ns0:ref type='figure'>3</ns0:ref>), Cortho, Cmeta and Cpara are shown. Six signals can be seen, three for each aniline ring present in the ligand. In the x-axis, a series of multiplets are shown, which are difficult to assign, but the 2D experiment makes it easier. The multiplets that seem to appear as a doublet at ~7.5 ppm come from the aromatic protons in the ortho position. The multiplet at 7.43 ppm is a contribution of ortho as well as the meta protons. Following the dots in the HSQC experiment, the protons from para and meta form the multiplet at 7.28 ppm, and finally, the multiplet at 7.20 ppm comes from para protons.</ns0:p><ns0:p>The aliphatic protons can be observed in the graph's x-axis in the region from 5.2 to 3.5 ppm (Figure <ns0:ref type='figure'>4</ns0:ref>). As previously mentioned, the number of signals observed, and the pattern displayed can be attributed to a system that has gone from a flexible backbone to a rigid one. The proton at 3.65 and 4.50 ppm are assigned to Ha protons. The Hb protons are assigned to the signals at 4.29 and 4.37 ppm. These protons are sign to the methylene protons neighboring the carboxylate group in the acid; based on previous information, it can be speculated that one arm coordinates from above while the other coordinates from below or a side (Beltran-Torres, 2019). These protons are the ones that suffer the most from the complexation; hence the pattern of the doublet with uneven height can be seen. At 4.02, and 5.05 ppm, the doublets are attributed to Hc protons (J = 16Hz). A doublet can be seen at 3.84 ppm with a J = 16Hz, but no correlation in the spectrum can be seen. The small doublets signals at 3.86 and 3.41 ppm (J = 12Hz) can be attributed to an equilibrium of species when instead of the two carbonyl groups are coordinating, only one is.</ns0:p></ns0:div> <ns0:div><ns0:head>Thermal analysis</ns0:head><ns0:p>The thermal stability of the Bi-edtabz was analyzed by TG analysis. The complex weight loss was monitored while the temperature increased at 10 °C min from 25 °C up to 800 °C, under an air atmosphere (Figure <ns0:ref type='figure'>5</ns0:ref>). The thermal analysis of the bismuth complex included four decompositions steps ( <ns0:ref type='formula'>180</ns0:ref> Manuscript to be reviewed Chemistry Journals °C) involves a loss mass of 7.4%, which could be attributed to another nitrate ion. This result indicates that one NO 3 molecule is part of the outer sphere of the complex (the nitrate molecule in the first decomposition step), and the other nitrate molecule is part of the inner sphere coordination; therefore, the second decomposition step is attributed to this nitrate molecule <ns0:ref type='bibr' target='#b44'>(Taha et al. 2011</ns0:ref><ns0:ref type='bibr' target='#b39'>, Radecka-Paryzek et al. 2007</ns0:ref><ns0:ref type='bibr' target='#b4'>, Anjaneyulu et al. 2010)</ns0:ref>. The third and fourth stages of decomposition (242-374 °C and 374-562 °C) with 38.6% and 16.7% mass loss are attributed to the ligand evolution. The remaining 29% loss corresponds to bismuth residues.</ns0:p></ns0:div> <ns0:div><ns0:head>FTIR spectra of Bi-edtabz</ns0:head><ns0:p>The ligand and the complex's infrared spectrum were taken to understand further the structure of the complex and functional groups involved in the bismuth coordination. Some informative bands of the ligand and its complex are given in Table <ns0:ref type='table'>1</ns0:ref>. In the free ligand, the strong characteristic stretching band C=O of carboxylic acid appeared at 1700 cm -1 and moved to the lower wavenumber side by 80 cm -1 upon complexation. This band shift indicates that the carboxylate groups participated in the formation of the complex. Other bands displaced to lower wavenumber sides upon complexation are the vibrational bands of amide C=O and bonds, which indicates that the amide groups were also involved in the coordination metal ion (The FTIR spectra of bismuth complex and free ligand are given in the Supporting Information, S3).</ns0:p></ns0:div> <ns0:div><ns0:head>UV-Visible spectra of Bi-edtabz</ns0:head><ns0:p>At 242 nm, the edtabz ligand shows a band attributed to transition proper from the aromatic rings. In the bismuth complex, there are two bands, one attributed to the interaction of the ligand's aromatic rings, which shows a hyperchromic effect at 242 nm and 263 nm at a pH of 1.0 (Figure <ns0:ref type='figure'>6a</ns0:ref>). The band at 263 nm is attributed to the metal-ligand transfer charge. The tendency of bismuth to form hydroxides prevented the attempt to titrate bismuth nitrate to ligand to corroborate the molar ratio of complex because the bismuth hydroxide precipitated as a white powder. In Figure <ns0:ref type='figure'>6b</ns0:ref>, the pH-variable experiment is presented. At acid pH, the complex showed two shoulders, and as the pH became basic, the metal-ligand transfer charge band decreased, and the spectrum at pH 12 is the same as the free ligand. A dissociation occurred, which could be to the formation of bismuth hydroxides.</ns0:p></ns0:div> <ns0:div><ns0:head>Effect of Bi-edtabz and edtabz on pathogenic bacteria in planktonic state and biofilm</ns0:head><ns0:p>Visual inspection of the bacterial cultures that grew in the presence of ligand or bismuth complex showed that both compounds at the highest concentration evaluated (1 mM) did not affect the viability of the bacterial cells. Thus, concentrations of 0.25, 0.5 and 1 mM were chosen to evaluate its effect on biofilm formation of pathogenic bacteria using the crystal violet assay where the amount of bound dye is proportional to the total biomass of biofilms <ns0:ref type='bibr' target='#b12'>(Burton et al. 2007</ns0:ref>). Figure <ns0:ref type='figure'>7</ns0:ref> shows the percentages of biomass reduction of biofilms formed in the presence of edtabz or Bi-edtabz complex. Both compounds inhibited biofilm development in a dosedependent manner and presented different inhibition patterns among bacterial species. Bi-edtabz complex at 1 mM showed higher biomass reduction against E. coli O157: H7 (p = 0.0029) and S. aureus (p = 0.014) (58 and 55%, respectively) than the ligand (43 and 37%, respectively). The ligand edtabz caused higher biomass reduction in L. monocytogenes (p = 0.000067), P. aeruginosa (p = 0.0021) and Salmonella Typhimurium (p = 0.0409) than Bi-edtabz complex. At 1 mM, edtabz completely prevented biofilm formation of L. monocytogenes and reduced by 88% and 67% the biomass of P. aeruginosa and Salmonella Typhimurium biofilm.</ns0:p></ns0:div> <ns0:div><ns0:head>Effect of Bi-edtabz and edtabz on swimming motility</ns0:head><ns0:p>In Table <ns0:ref type='table'>2</ns0:ref>, the effect of edtabz and Bi-edtabz on the swimming motility of pathogenic bacteria is presented. After 24 h of incubation at 30 °C untreated pathogenic bacteria displaced on the surface of soft agar showing motility zones from 40.9 to 78.1 mm. The presence of Bi-edtabz complex in the culture media impaired the motility of E. coli O157: H7 (p = 0.00029) and S. aureus (p = 0.00001) with reductions of 12.5 and 47.2%, respectively. While the motility of L. monocytogenes, P. aeruginosa and Salmonella Typhimurium was not affected (p > 0.05) by the bismuth complex. On the other hand, edtabz only affected (p = 0.00001) the motility of Salmonella Typhimurium, showing 41.9% inhibition compared to untreated bacteria.</ns0:p></ns0:div> <ns0:div><ns0:head>Anti-quorum sensing activity of Bi-edtabz and edtbaz in Chromobacterium violaceum model</ns0:head><ns0:p>To explore the anti-quorum sensing activity of the bismuth complex as preliminary screening, biomonitor strain C. violaceum was used. This bacterial model produces a purple pigment (violacein) during the quorum-sensing activation; this production is regulated in response to selfproduced acyl-homoserine lactones (AHL). Thereby, analyzing the pigment production in the bismuth complex presence, it is possible to evaluate the interference of a given substance in this process <ns0:ref type='bibr'>(Alvarez et al. 2014)</ns0:ref>. As shown in Figure <ns0:ref type='figure'>8b</ns0:ref>, Bi-edtabz inhibited the violacein production of C. violaceum as evidenced by the colorless halo (11.9±0.4 mm), absent in edtabz treatment (Figure <ns0:ref type='figure'>8a</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>Discussion</ns0:head><ns0:p>In this study, a bismuth complex with an open-chained EDTA-based phenylene ligand was synthesized and characterized. Few studies of this type of compounds had been reported until date, specially the 1 H NMR analysis. The diamagnetic property of bismuth made it possible to elucidate the coordination through this technique. As can be seen in Figure <ns0:ref type='figure' target='#fig_1'>1</ns0:ref>, in the 1 H spectrum of the Bi-edtabz many signals are presented. The number of signals increased in the complex, and some are seen as doublets of doublets; this response is characteristic of a rigid system. The edtabz ligand is known as a podand, this open structure provides flexibility to the ligand, which provides the facility to coordinate different metal ions because it is not restricted to a preorganized framework. Anyhow, once the ligand coordinates a metal ion, this open structure changes to chelate the metal. In this case, the chelation of the bismuth ion generates a rigid system. The bismuth ion presents an ionic radius of 103 pm (Bi 3+ ), which is considered big compared to other metal ions studied for this ligand (Cu 2+ and Fe 3+ ). The bismuth ion bulkiness comprises the ligand flexibility, and therefore almost all signals are seen doubled due to the loss of chemical equivalence of its protons.</ns0:p><ns0:p>In the pH-variable experiment, it was observed that a disassociation of the complex occurs as the pH becomes basic and the complex seems to be stable in acid media which can be a good property if the aim of this compound is to be use as a pharmaceutical. Therefore, the effect on biofilm formation, motility and cell-to-cell communication of pathogenic bacteria was evaluated. As shown in Figure <ns0:ref type='figure'>7</ns0:ref>, edtabz and Bi-edtabz reduced the biomass of bacterial biofilms and this effect was dose-and species-dependent. Bi 3+ complexation resulted in an enhancement of antibiofilm activities of the edtabz ligand against E. coli O157: H7 and S. aureus. In a previous study Cu 2+ complexation improved inhibitory activities of edtabz against S. aureus while Cu 2+ and Fe 3+ complexation reduced the antibiofilm properties of edtabz against E. coli O157: H7 (Vázquez-Armenta et al. 2021).</ns0:p><ns0:p>According to Tweedy's chelation theory, in a metal complex formation a reduction of polarity of the central metal atom occurs due to the sharing of its positive charge with the ligand; therefore, it favors the permeation of the complex through the lipid layer of the cell membrane <ns0:ref type='bibr' target='#b46'>(Tweedy 1964)</ns0:ref>. In this case, the edtabz and Bi-edtabz do not present a direct correlation; thus, further investigation should be done to elucidate its antibiofilm mode of action. In this sense, studies focused on understanding the transition from planktonic free-living cells to complex bacterial communities in biofilms have been evidenced multiple factors involved in this process, such as the ability of bacteria to attach and colonize abiotic surfaces, the physicochemical properties of bacterial surface, the production of extracellular polymeric substances, and cell-to-cell communication <ns0:ref type='bibr' target='#b10'>(Borges et al. 2016</ns0:ref><ns0:ref type='bibr' target='#b3'>, Andrade et al. 2020)</ns0:ref>. These factors could be considered as starting points to get insight into the mode of action.</ns0:p><ns0:p>Biofilm formation is considered a virulence factor in some pathogenic bacteria since it helps establish infections. For example, P. aeruginosa can establish chronic biofilm-associated infections in patients with cystic fibrosis, where biofilm matrix confers resistance to antimicrobial treatments increasing mortality rate <ns0:ref type='bibr' target='#b22'>(Harrington et al. 2020)</ns0:ref>. In uropathogenic E. coli, biofilm formation keeps bacteria in the urinary tract and hinders its eradication in catheterassociated infections <ns0:ref type='bibr' target='#b54'>(Zhao et al. 2020)</ns0:ref>. In comparison, S. aureus can form biofilms in medical implants causing nosocomial bacteremia that are difficult and expensive to treat <ns0:ref type='bibr' target='#b19'>(Garzón et al. 2019</ns0:ref><ns0:ref type='bibr' target='#b38'>, Oliveira et al. 2018)</ns0:ref>. Therefore, preventing the formation of biofilms is one of the PeerJ Inorganic Chem. reviewing PDF \| (ICHEM-2021:11:68130:1:2:NEW 8 Jun 2022)</ns0:p><ns0:p>Manuscript to be reviewed Chemistry Journals strategies to combat bacterial infections, thus obtained results open the possibility of using edtabz and Bi-edtabz as selective inhibitors against the biofilm formation of pathogenic bacteria.</ns0:p><ns0:p>Another important virulence factor is bacterial motility; pathogenic bacteria use different types of movements mediated by flagella and pili, such as swimming, swarming, or twitching motilities involved in bacterial pathogenesis and biofilm formation. For this reason, bacterial motility is considered a common target in anti-virulence therapy <ns0:ref type='bibr' target='#b24'>(Khan et al. 2019)</ns0:ref>. Swimming motility is defined as the individual movement of bacteria in liquid or low-viscosity surfaces without the need for biosurfactants <ns0:ref type='bibr' target='#b31'>(Martínez et al. 2013</ns0:ref><ns0:ref type='bibr' target='#b20'>, Ha et al. 2014)</ns0:ref>. Despite the recognized antimicrobial activity of bismuth-containing compounds <ns0:ref type='bibr' target='#b16'>(Duffin et al. 2020)</ns0:ref>, few reports evaluated the effect on virulence factors of pathogenic bacteria, such as bacterial motility <ns0:ref type='bibr' target='#b0'>(Alipour et al. 2010</ns0:ref>). Thus, results obtained in the present study showed that the bismuth complex effectively interferes with this virulence factor in E. coli O157: H7 and S. aureus. In contrast, edtabz was effective only against Salmonella Typhimurium while the bismuth complex did not interfere with bacterial motility in this bacterium. This discrepancy can be attributed to the complex regulatory network of flagellar mediated motility of each bacterial specie that involves different transcriptional factors, signal molecules and/or regulatory small RNAs (sRNAs) <ns0:ref type='bibr'>(Mika and Hengge 2013)</ns0:ref>.</ns0:p><ns0:p>In E. coli and Salmonella spp., swimming motility is involved in the adherence of bacteria to host cells and the subsequent colonization <ns0:ref type='bibr' target='#b53'>(Wolfson et al. 2020</ns0:ref><ns0:ref type='bibr' target='#b52'>, Westerman et al. 2021</ns0:ref><ns0:ref type='bibr' target='#b6'>, Barbosa et al. 2017</ns0:ref>). On the other hand, S. aureus does not present swimming motility; instead, it performs a surface displacement named colony spreading <ns0:ref type='bibr' target='#b23'>(Kaito and Sekimizu 2007)</ns0:ref>. This movement is regulated by the agr locus, which regulates the expression of toxins and adhesion proteins and is also involved in its virulence <ns0:ref type='bibr' target='#b28'>(Kizaki et al. 2016)</ns0:ref>.</ns0:p><ns0:p>The mode of action of these compounds has not been described. Bacterial motility is a virulence trait tightly regulated, and each bacterial species has its regulatory system <ns0:ref type='bibr' target='#b13'>(Chaban et al. 2015)</ns0:ref>. Nevertheless, to carry out this physiological process, a proper function of the cell membrane is required <ns0:ref type='bibr' target='#b34'>(Ménard et al. 2014</ns0:ref><ns0:ref type='bibr' target='#b26'>, Khan et al. 2017)</ns0:ref>. In this sense, the impairment of bacterial motility caused by bismuth complexes is attributed to cell membrane damage and further internalization <ns0:ref type='bibr' target='#b51'>(Wang et al. 2020)</ns0:ref>. For this reason, it would be interesting if the bismuth complex obtained in the present study could affect the bacterial membrane functionality.</ns0:p><ns0:p>The production of virulence factors in pathogenic bacteria is regulated in multiple ways; one of them is quorum-sensing, a cell-to-cell communication process in which bacteria coordinates its behavior in a population-dependent manner <ns0:ref type='bibr' target='#b21'>(Haque et al. 2018)</ns0:ref>. Figure <ns0:ref type='figure'>8</ns0:ref> shown that Bi-edtabz was able to interfere with the production of the quorum-sensing regulated pigment (violacein) in the biosensor strain C. violaceum that uses AHL as signal molecules. In pathogenic bacteria such as P. aeruginosa, the signal molecule 3-oxo-C12-AHL triggers the expression of several extracellular virulence factors, promotes biofilm maturation, and regulates the expression of antibiotic efflux pumps; thereby, it is considered a key target in the pathogenesis of P. aeruginosa <ns0:ref type='bibr' target='#b29'>(Lee and Zhang 2015</ns0:ref><ns0:ref type='bibr' target='#b45'>, Tapia-Rodriguez et al. 2019</ns0:ref><ns0:ref type='bibr' target='#b40'>, Sarabhai et al. 2016</ns0:ref>).</ns0:p><ns0:p>In the other hand, E. coli and Salmonella spp. does not produce AHL, but they can sense and respond to external AHL produced by other bacteria that influence biofilm formation and the adhesion to epithelial cells during pathogenesis <ns0:ref type='bibr' target='#b27'>(Kim et al. 2014</ns0:ref><ns0:ref type='bibr' target='#b43'>, Suzuki et al. 2002</ns0:ref><ns0:ref type='bibr' target='#b41'>, Smith et al. 2011</ns0:ref><ns0:ref type='bibr' target='#b14'>, Crago and Koronakis 1999)</ns0:ref>. However, L. monocytogenes and S. aureus use a polypeptide signal molecule instead of the small diffusible molecules used by the other quorum-sensing systems <ns0:ref type='bibr' target='#b37'>(Novick and Geisinger 2008)</ns0:ref>. For this reason, C. violaceum model is not useful to relate the treatment effect on these bacteria. This preliminary screening of anti-quorum sensing activity showed that bismuth complex could interfere with this cell-to-cell communication process; however, more studies are required to clarify the anti-quorum sensing mechanism and its relationship with the biofilm and motility inhibitory activities described here.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions</ns0:head><ns0:p>In this study, a bismuth complex with an open-chained EDTA-based phenylene ligand was synthesized and characterized. 1 H NMR analysis confirmed that the chelation of the bismuth ion to the ligand created a rigid system. Further studies should be made to ensure the coordination sphere of the complex, for example, the X-ray diffraction of a monocrystal of this complex. On the other hand, the pH-variable experiment showed that the complex disassociation occurs as the pH becomes basic and the complex seems to be stable in acid media, which is a good property to consider its use as a pharmaceutical. In addition, the evaluation of anti-virulence properties showed that the bismuth complex and the ligand presented a dose-dependent response and a specific compound-bacteria relationship against biofilm formation, motility, and quorum sensing. It is important to highlight that Bi-edtabz presented higher biomass reduction in biofilm formation assay and impaired the motility of E. coli O157: H7 and S. aureus and inhibited violacein production in the quorum-sensing biomonitor strain C. violaceus. Thus, edtabz and Biedtabz complex can be used as novel anti-virulence agents against pathogenic bacteria. </ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>-562 °C). The first decomposition step (180-220 °C) involves a 7.4% mass loss due to the evolution of a nitrate molecule. The second decomposition step (220-242 PeerJ Inorganic Chem. reviewing PDF \| (ICHEM-2021:11:68130:1:2:NEW 8 Jun 2022)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head>Figure 1 1H</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='18,42.52,188.32,525.00,339.75' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='19,42.52,178.87,525.00,427.50' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='21,42.52,199.12,525.00,405.00' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='22,42.52,199.12,525.00,405.00' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='25,42.52,178.87,525.00,285.75' type='bitmap' /></ns0:figure> <ns0:note place='foot'>Analytical, Inorganic, Organic, Physical, Materials Science </ns0:note> </ns0:body> "	"Rebuttal Letter Dear editor, thanks for all the comments and suggestions to improve the content and quality of our manuscript. We amended it as suggested and it seems now in better shape; we expect that after these modifications you can considered it for publication. Response to reviewers' comments: Reviewer 1 1. The authors mention that the synthetic procedure for the edtabz ligand has been taken from literature. However, the reference to this is missing. The authors should include the appropriate reference in the ‘Materials and Methods: Synthesis of the ligand’ section. A: The edtabz ligand was synthesized by a method previously reported by Beltran-Torres et al, 2019 as is written in the description of how the ligand was synthesized. Please see line 90. 2. In the infrared spectra of the complex, peaks at medium to high wavenumber values have been assigned to specific bonds for the organic counterpart of the complex. If possible, the authors should try to ascertain characteristic peaks in the fingerprint region, especially with respect to metal-oxygen bond(s). A: We appreciate your observation. Coordination complexes frequently contain metal-oxygen or metal-nitrogen bonds, but the absorption bands associated with these bonds are normally difficult to assign since their position is dependent not only on the metal but also on the ligand and coupling with other vibrations modes often occurs. Some ligand vibrations may become infrared or Raman active on forming de complex that is why it is not uncommon for no clear assignments to be made. (Socrates, G. (2015). Infrared and Raman characteristic group frequencies: Tables and Charts. John Wiley & Sons LTD.) 3. In the swimming mobility data provided (Table 2), a decrease in the value(s) is observed from free ligand to the bismuth-ligand complex; except for the pathogen Salmonella Typhimurium, where the value is observed to increase significantly. The authors should try and provide and explanation for such observations. A: We appreciate your comments. An explanation of that observation was included in lines 385 – 388. 4. Solvents used in NMR experiments have not been mentioned. The authors should provide the information about the particular solvent used for such experiments in the Experimental section A: Solvent used in the NMR experiments is mentioned in the Spectroscopic Measurements as well in the description of the 1H and 13C figures of the ligand and the complex. The experiments were carried in D2O. Please see lines 117 and 118. Reviewer 2 1. There is no consistency between the proposed formula (%. Calcd for BiC22H24O12N6 • 2NO3) and m/z 709.6 (the base peak). Also, in this same context it is convenient to review the elemental analysis C, 33.39 %; H, 3.31 %; N, 10.62%. A: We appreciate your observation, the calculated formula written in the manuscript is wrong. A mistake in the formula was made. The two nitrogen as well as the six oxygens from the two nitrate ions were added to the general molecular formula. The correct formula is BiC22H24O6N4 • 2NO3. Please see lines 108 – 110. This formula does fit the elemental analysis obtained. The m/z 709.6 observed in the ESI-MS is considering a nitrate ion being part of the coordination inner sphere while the other nitrate is considered to be part of the outer sphere, which is no directly coordinating with Bi3+ and do to the characteristic of the ESI experiment, the second nitrate ion can be lost during the ionization process. This conclusion was corroborated with the TG analysis. The thermal analysis of the bismuth complex included four decompositions steps (180-562 °C). The first decomposition step (180-220 °C) involves a 7.4% mass loss due to the evolution of a nitrate molecule. The second decomposition step (220-242 °C) involves a loss mass of 7.4%, which could be attributed to another nitrate ion. This result indicates that one NO3 molecule is part of the outer sphere of the complex (the nitrate molecule in the first decomposition step), and the other nitrate molecule is part of the inner sphere coordination; therefore, the second decomposition step is attributed to this nitrate molecule. 2. The integrals of the NMR signals are not shown, and the doubt remains as to whether they correspond to the proton in the ortho (2H), meta (2H) and para (1H) positions as suggested. A: Integrals of the bismuth complex are presented in the following figure. Due to the second order spectrum obtained, the protons equivalence is not the same, that is why they do not integrate to the exact numbers of protons. The signals seen in the 1H spectrum are a contribution of the ortho, meta and para protons, and this will depend in the structural geometry that the ligand opts to when coordinating the bismuth ion. Even though the signals do not exactly match the theorical expectations of a mono substituted ring, it does show a relation of 8.97H and 10.89H aromatic-aliphatic protons. A difference of 1.92H between them can be seen which is within the 10% error that normally is expected in NMR experiments. Theorically, 10H protons are expected to integrate in the aromatic region and 12H in the aliphatic region. 3. The Hb protons are assigned to the signals at 4.31 and 4.23 ppm. These protons are from the carboxylate group in the acid' I assume that the text refers to the methylene protons neighboring the carboxylate and not to those of the carboxylic acid A. We agree with your observation. A correction in the manuscript was made. "	Here is a paper. Please give your review comments after reading it.
440	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Therapeutic nucleic acids provide versatile treatment options for hereditary or acquired diseases. Ionic complexes with basic polymers are frequently used to facilitate nucleic acid's transport to intracellular target sites. Usually, these polyplexes are prepared manually by mixing two components: polyanionic nucleic acids and polycations. However, parameters such as internal structure, size, polydispersity and surface charge of the complexes sensitively affect pharmaceutical efficiency. Hence a controlled assembly is of paramount importance in order to ensure high product quality. In the current study, we present a microfluidic platform for controlled, sequential formulation of polyplexes. We use oligo-amidoamines (termed 'oligomers') with precise molecular weight and defined structure due to their solid phase supported synthesis. The assembly of the polyplexes was performed in a microfluidic chip in two steps employing a design of two successive Y junctions: first, siRNA and core oligomers were assembled into core polyplexes. These core oligomers possess compacting, stabilizing, and endosomal escape mediating motifs.</ns0:p><ns0:p>Second, new functional motifs were mixed to the core particles and integrated into the core polyplex. The iterative assembly formed multi-component polyplexes in a highly controlled manner and enabled us to investigate structure -function relationships. We chose nanoparticle shielding PEG and cell targeting folic acid (termed 'PEG-ligands') as functional components. The PEG-ligands were coupled to lipid anchor oligomers via strain promoted azide -alkyne click chemistry. The lipid anchors feature four cholanic acids for inserting various PEG-ligands into the core polyplex by non-covalent hydrophobic interactions. These core -lipid anchor -PEG-ligand polyplexes containing folate as cell binding ligand were used to determine the optimal PEG-ligand length for transfecting folate receptor-expressing KB cells in vitro. We found that polyplexes with 20 mol % PEGligands (relative to n core oligomer ) showed optimal siRNA mediated gene knock-down when</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>Together with deepened understanding of molecular pathways in hereditary and acquired human diseases comes a growing field of possible applications for nucleic acid-based drugs <ns0:ref type='bibr' target='#b58'>(Shi, Kantoff, Wooster, & Farokhzad, 2016)</ns0:ref>. Recent clinical trials have hinted at the therapeutic potential of non-viral gene carriers <ns0:ref type='bibr' target='#b52'>(Sardh et al., 2018;</ns0:ref><ns0:ref type='bibr' target='#b61'>Titze-de-Almeida, David, & Titze-de-Almeida, 2017)</ns0:ref>, culminating in the approval of patisiran <ns0:ref type='bibr' target='#b21'>(Haussecker, 2018;</ns0:ref><ns0:ref type='bibr' target='#b63'>Tournev et al., 2018)</ns0:ref>. Payloads of these synthetic carriers are usually various nucleic acids that are condensed into particles by cationic lipids <ns0:ref type='bibr' target='#b33'>(Kulkarni, Cullis, & van der Meel, 2018)</ns0:ref> or polycations <ns0:ref type='bibr' target='#b34'>(Lächelt & Wagner, 2015)</ns0:ref>. These polycations have been the focus of extensive research in the past and have been continuously improved to find the optimal balance between various properties, for example compaction, intracellular release <ns0:ref type='bibr' target='#b42'>(Leong & Grigsby, 2010)</ns0:ref>, cell uptake, serum stability, and toxicity <ns0:ref type='bibr' target='#b18'>(Hall, Lächelt, Bartek, Wagner, & Moghimi, 2017)</ns0:ref>. Formulation, however, is only gradually seen to be of importance as well <ns0:ref type='bibr' target='#b67'>(Valencia, Farokhzad, Karnik, & Langer, 2012)</ns0:ref>. Analogous to approval procedures of protein-based drugs, manufacturing processes are an integral part of the product and therefore control over them is critical <ns0:ref type='bibr' target='#b75'>(Wilhelm et al., 2016)</ns0:ref>. Polyplexes <ns0:ref type='bibr' target='#b11'>(Felgner et al., 1997)</ns0:ref> are often prepared by batch wise mixing polycations with nucleic acids either by vigorous pipetting or shaking. Although this method is convenient, particle formation is kinetically controlled and charge neutralization in polyplexes occurs in around 50 ms <ns0:ref type='bibr' target='#b5'>(Braun et al., 2005)</ns0:ref>. Consequently, limited batch-to-batch reproducibility inevitably leads to variable particle properties, which in turn complicate the establishment of precise structure -function relationships. Size and shape, for example, play a major role in deciding the uptake route into cells <ns0:ref type='bibr' target='#b49'>(Rejman, Oberle, Zuhorn, & Hoekstra, 2004;</ns0:ref><ns0:ref type='bibr' target='#b60'>Sykes, Chen, Zheng, & Chan, 2014)</ns0:ref>. They are, however, heavily influenced by assembly conditions. Moreover, each additional component included in the formulation complicates the preparation of defined nanoparticles. Generally, there are two distinct approaches to standardized nanoparticle production. The top-down process, on the one side, produces particles from larger materials, for example with the PRINT method developed by DeSimone and co-workers <ns0:ref type='bibr' target='#b50'>(Rolland et al., 2005)</ns0:ref>. The advantage of this approach is a high control over size and shape of printed particles, although particle purification can be difficult. The bottom-up process, on the other side, produces particles from smaller building units or starting materials which assemble into bigger objects. Here, particle size and shape are controlled during the assembly process and additionally influenced by the design of the educts. In case of ionic polyplexes, the self-assembly process is based on electrostatic interaction between oppositely charged materials. However, control over size and shape is challenging. A widely used macrofluidic approach to standardize nanoparticle production is the application of a T-junction. It enables the continuous production of large quantities of, for example, lipoplexes <ns0:ref type='bibr' target='#b24'>(Kiefer, Kimpfler, Peschka-Süss, Garidel, & Clement, 2004)</ns0:ref> or polyplexes <ns0:ref type='bibr' target='#b23'>(Kasper, Schaffert, Ogris, Wagner, & Friess, 2011)</ns0:ref> with a turbulent mixing regime. Microfluidic approaches <ns0:ref type='bibr' target='#b43'>(Liu, Zhang, Fontana, Hirvonen, & Santos, 2017)</ns0:ref> to the bottom-up production of polyplexes can be broadly divided in droplet <ns0:ref type='bibr' target='#b57'>(Seemann, Brinkmann, Pfohl, & Herminghaus, 2012)</ns0:ref> -and hydrodynamic focussing (C. Y. <ns0:ref type='bibr' target='#b37'>Lee, Wang, Liu, & Fu, 2016)</ns0:ref> -based systems. Both methods are suitable, since polyplex production is performed in aqueous systems and requires fast reaction times. Emulsion based systems have the advantage of discrete reaction chambers with picolitre volumes, but they are usually unstable and need additional surfactants and oily phases to stabilize droplets <ns0:ref type='bibr' target='#b22'>(Ho, Grigsby, Zhao, & Leong, 2011)</ns0:ref>. Laminar flow-based systems have the advantage of producing carriers continuously while mixing of reactants is diffusion controlled only. Mixing speeds can be manipulated by employing baffle structures, organic solvents or external energy sources to influence the time scales reactants need to reach their counterparts allowing for a greater control over particle properties. It has been shown in previous studies that microfluidic-based assembly improves physicochemical properties of produced particles <ns0:ref type='bibr' target='#b4'>(Belliveau et al., 2012;</ns0:ref><ns0:ref type='bibr' target='#b17'>Grigsby, Ho, Lin, Engbersen, & Leong, 2013;</ns0:ref><ns0:ref type='bibr' target='#b29'>Koh et al., 2009)</ns0:ref>. Besides control over the assembly process, control over the precise structure of nanoparticle's components is essential as well. Solid phase supported synthesis of sequence defined oligo(ethanamino)amides <ns0:ref type='bibr' target='#b54'>(Schaffert et al., 2011)</ns0:ref> in our lab has the potential to integrate any functional element at any place in the oligomer's structure. The crucial parameter is the biological performance of polyplexes assembled from these oligomers. We have identified key units in the oligomer's structure: polycationic succinoyl tetra-ethylene pentamine (Stp) units for complexing nucleic acids and tyrosines and fatty acids for stabilizing <ns0:ref type='bibr' target='#b15'>(Fröhlich et al., 2012;</ns0:ref><ns0:ref type='bibr' target='#b64'>Troiber, Edinger, et al., 2013)</ns0:ref> the resulting nanoparticle. Usually, additional chemical moieties, e.g. for shielding the nanoparticle and targeting <ns0:ref type='bibr' target='#b25'>(Klein et al., 2018)</ns0:ref> certain receptors, are integrated into the oligomer's structure to increase biological performance. These additional units, albeit required for efficient nucleic acid delivery, can alter polyplex formation processes <ns0:ref type='bibr' target='#b14'>(Freund, Lächelt, Gruber, Rühle, & Wuttke, 2018)</ns0:ref>. We set out to combine the advantages of sequence defined oligomers with enhanced control over the formulation process in order to generate precise multi-component polyplexes. To this end, we have used a laminar flow-based micromixer to generate polyplexes continuously. We show that the production of three and four component polyplexes is feasible. These polyplexes are assembled on-chip from siRNA, cationic core oligomers (CO), and PEG-ligands with zero to 48 ethylene oxide (EO) repetitions, which are integrated non-covalently into core polyplexes by lipid anchors containing an additional 12 EO repetitions. We use this system to identify the best PEG-ligand length for transfecting KB cells. Additionally, we compared and tested the same set of PEG-ligands on previously published two component polyplexes.</ns0:p></ns0:div> <ns0:div><ns0:head>Materials & Methods</ns0:head></ns0:div> <ns0:div><ns0:head>Materials</ns0:head><ns0:p>Main suppliers: Biochrom (Biochrom GmbH, Berlin, Germany), Iris (Iris Biotech GmbH, Marktredwitz, Germany), Promega (Promega GmbH, Mannheim, Germany), Roth (Carl Roth GmbH + Co. KG, Karlsruhe, Germany), Sigma (Sigma-Aldrich Chemie GmbH, Munich, Germany, now part of Merck KGaA, Darmstadt, Germany), Thermo (Thermo Fisher Scientific GmbH, Schwerte, Germany), VWR <ns0:ref type='bibr'>(VWR International GmbH, Darmstadt, Germany)</ns0:ref>. Solvents: Purified water (produced with Ultra Clear ® GP UV UF, Evoqua Water Technologies GmbH, Günzburg, Germany), acetone HPLC grade (VWR), dichloromethane ACS reagent <ns0:ref type='bibr'>(DCM, Bernd Kraft GmbH, Duisburg)</ns0:ref>, dimethylformamide peptide grade (DMF, Iris), dimethyl sulfoxide for synthesis (DMSO, Acros Organics, Geel, Belgium), N-methyl pyrrolidon peptide </ns0:p></ns0:div> <ns0:div><ns0:head>Oligomer Synthesis</ns0:head><ns0:p>All oligomers have been synthesized by solid phase supported synthesis (SPSS). The synthesis of the core oligomers CO (id: 991) and CON (id: 1106) has been described in detail by <ns0:ref type='bibr'>Klein et al. and</ns0:ref> their analytical data can be found there. <ns0:ref type='bibr' target='#b25'>(Klein et al., 2018</ns0:ref><ns0:ref type='bibr' target='#b28'>(Klein et al., , 2016) )</ns0:ref> The synthesis of DBCOdiscrete PEG(dPEG)-folic acid oligomers (termed 'PEG-ligands') has also been reported in detail by <ns0:ref type='bibr' target='#b25'>Klein et al. (Klein et al., 2018)</ns0:ref>, however only for PEG-ligands with PEG24 (id: 1139) or PEG48 (id: 1140). Here, PEG-ligands without PEG (PEG0, id: 1323), with STOTDA (N″succinyl-4,7,10-trioxa-1,13-tridecanediamine, named 'PEG3' in this manuscript, id: 1324) and PEG12 (id: 1325) were synthesized analogous to the PEG-ligands with longer PEG chains. Basically, Fmoc-Glu-O-2-PhiPr was coupled to the α-amine of a Lys(Dde)-loaded resin followed by N10-(trifluoroacetyl)pteroic acid to produce functional folic acid. The trifluoroacetyl group was deprotected with 25 % aqueous ammonia solution : DMF = 1 : 1. After standard Dde deprotection (2 vol % hydrazine in DMF), the lysine's εamine was modified with the designated dPEG chain followed by a DBCO-acid (dibenzocyclooctyne-acid). For PEG0, DBCO-acid is directly coupled to the lysine's ε amine. For PEG3, STOTDA's (N-Fmoc-N″succinyl-4,7,10-trioxa-1,13-tridecanediamine) succinic acid is coupled to the lysine's ε amine and the DBCO-acid to STOTDA's terminal amine after Fmoc deprotection. Special care needs to be taken when cleaving the final product from the resin, since DBCO is sensitive to high concentrations of TFA and can be converted into unreactive side-products (X. <ns0:ref type='bibr' target='#b70'>Wang, Gobbo, Suchy, Workentin, & Hudson, 2014)</ns0:ref>. Therefore, a cleavage cocktail with only 5 % TFA was used (DCM : TFA : TIS = 92.2 : 5 : 2.5). Cleavage duration was 60 min. The synthesis and analysis of the lipid anchor oligomers LA (id: 1203) and LAE (id: 1223) is described in the supplemental information (1. Lipid Anchor Oligomer Synthesis, 2. Chemical structures).</ns0:p></ns0:div> <ns0:div><ns0:head>Polyplex Preparation</ns0:head></ns0:div> <ns0:div><ns0:head>Core</ns0:head><ns0:p>The amount of siRNA is the key parameter determining quantities of all other reagents in polyplex formation. For measurements and in vitro experiments, polyplexes with a final concentration of 0.025 mg/ml siRNA were produced. A nitrogen to phosphate (N/P) ratio of 12 was used to determine the amount of core oligomer CO (Fig. <ns0:ref type='figure' target='#fig_9'>1A</ns0:ref>) relative to the amount of siRNA. The N/P ratio sets the number of primary and secondary amines in the oligomer's structure in relation to the number of phosphates in the RNA's backbone. The azide-bearing core oligomer CON was handled the same way as CO and is described when the reference system is introduced (cf. Characterization of Core -PEG-Ligand Polyplexes) The conventional method of polyplex preparation was done with pipettes and rapid mixing in a batch wise process. The solvent -if not noted differently -was HEPES buffer pH 7.4 with 5 % glucose (HBG). This buffer was used because it is it does not rely on salts to be isotonic, since polyplex formation relies on charge interactions that could be hampered by ions. Here, CO solution (c CO = 0.504 mg/ml) was added quickly to a siRNA solution (c siRNA = 0.05 mg/ml) of equal volume and mixed by rapid pipetting, achieving a final siRNA concentration of 0.025 mg/ml. Subsequently, the formulation has been incubated for 45 min. For automated polyplex production at a T-junction, siRNA in HBG (c siRNA = 0.05 mg/ml) and CO in HBG (c CO = 0.504 mg/ml) or HBG with 50 % acetone were loaded into two separate syringes (1 ml, Hamilton) that were connected with silicon tubes (SE-200, ProLiquid) to a Tjunction (PP-T-Tüllenverbinder, ProLiquid). Each syringe was driven by a separate syringe pump (LA-120, LA-160) that run at the same speed (flowrates (FR) for each pump were 0.5, 1.0, 2.0, 5.0 and 30.0 ml/h) except for experiments with a final acetone concentration of 2.5 % (c siRNA = 0.027 mg/ml; FR siRNA = 0.917, 1.833, 4.583, 9.167, 55.000 ml/h; c CO = 3.026 mg/ml; FR CO = 0.083, 0.167, 0.417, 0.833, 5.000 ml/h). The final product was collected and incubated for 45 min before use. siRNA concentration in the final formulation was 0.025 mg/ml. For controlled core polyplex production using microfluidics, the double meander channel in Fig. <ns0:ref type='figure' target='#fig_9'>1B</ns0:ref> was used albeit without the second meander and without both S2 inlets. siRNA in HBG (c siRNA = 0.033 mg/ml) was loaded into S4 and CO in HBG or HBG with 50 % acetone (c CO = 3.025 mg/ml) was loaded into S3. Both syringes were driven by separate syringe pumps. Flow rates (FR) were 100 µl/h for S3 and 900 µl/h for S4, respectively. The final product was diluted with HBG to reach c siRNA = 0.025 mg/ml.</ns0:p></ns0:div> <ns0:div><ns0:head>Addition of Lipid Anchor and Lipid Anchor -PEG-Ligand Oligomers</ns0:head><ns0:p>It was determined before that 20 mol % lipid anchor oligomer (LA or LAE) or lipid anchor -PEG-ligand oligomer in relation to n CO offered an optimal balance between efficacy and aggregation of the final product (data not shown).</ns0:p><ns0:p>Lipid anchor or lipid anchor -PEG-ligand oligomers were added in two different ways to core polyplexes. If the complete product is assembled in one continuous process, the double meander channel (DMC) in Fig. <ns0:ref type='figure' target='#fig_9'>1B</ns0:ref> will be used. siRNA in HBG (c siRNA = 0.033 mg/ml) was loaded into S4 (FR = 900 µl/h) and CO in HBG or HBG with 50 % acetone to retard siRNA compaction (c CO = 3.025 mg/ml) was loaded into S3 (FR = 100 µl/h). Lipid anchor or lipid anchor -PEG-ligand oligomers in HBG with 50 % acetone to facilitate solvent exchange were loaded into S2. The flow rate of each syringe S2 was 50 µl/h at a total flow rate of 1100 µl/h, resulting in a flow rate ratio of lipid anchor oligomer to core polyplex of 1:11. The final product was diluted with HBG to c siRNA = 0.025 mg/ml. Alternatively, conventionally (i.e. with pipettes) prepared core polyplexes (c siRNA = 0.032 mg/ml, c CO = 0.319 mg/ml) were fed into both inlets connected to syringe S1 (single meander channel, SMC) with the lipid anchor oligomers filled into syringe S2. In this case, flow rates were 126.5 µl/h for S2 and 600 µl/h for each S1 resulting in a flow rate ratio of 1:10.5. The final product was diluted with HBG to c siRNA = 0.025 mg/ml. The difference in flow rates between the two set-ups is due to separate optimization steps. Both set-ups resulted in large volumes of core solution and only a thin stream (see Fig. <ns0:ref type='figure' target='#fig_9'>1B</ns0:ref>) of lipid anchor solution at the junction, accelerating the solvent exchange from 50 % to 4.8 % acetone and facilitating the association of the hydrophobic lipid anchor with the fatty acids in the core's structure. It is always indicated which method for producing core -lipid anchor -PEG-ligand polyplexes was used.</ns0:p></ns0:div> <ns0:div><ns0:head>Characterization DLS Measurement</ns0:head><ns0:p>For DLS measurements samples were prepared to contain 1.5 µg siRNA in 60 µl HEPES buffered glucose pH 7.4 (HBG) at 25 °C and the corresponding amount of oligomer. Refractive index and viscosity of the solution were calculated using the solvent builder integrated into the software (Zetasizer family software update v7.12). Viscosities and refractive indices (RI) are reported in Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref>. RI of all particles was estimated to be 1.45. In case of a CO core with N/P 12 and 20 mol % of LA, 16.6 µg and 2.8 µg were used, respectively. For size measurements, light scattering was measured at a 173° angle (backscatter) with a flexible attenuator with a Zetasizer Nano ZS ZEN 3600 (Malvern Panalytical Ltd) in DTS1070 micro cuvettes. (Malvern Panalytical Ltd) Samples were measured three times with 12 -15 sub runs each. The mean z-average in nm of those three runs is reported with error bars corresponding to the 95 % confidence interval of the three runs. The underlying intensity distribution is depicted as violin plots in order to gain a better understanding of the formulation's size distribution. The extension of the violin plot in x direction corresponds to the percentage of the total intensity measured at the specific hydrodynamic diameter depicted on the y axis. If zeta potential is measured, the sample will be taken from the cuvette after the size measurement, diluted with HBG to 800 µl and reloaded into the same cuvette. Light scattering was measured at a 90° angle with a flexible attenuator. Samples were measured three times (main runs) with enough sub runs to gather more than 10000 total counts (usually 12-15). The mean zeta potential of those three runs is reported with error bars corresponding to the zeta deviation's mean of each main run. </ns0:p></ns0:div> <ns0:div><ns0:head>Stability of the Core Formulation</ns0:head><ns0:p>Core polyplex formulations were prepared using the single meander channel (Fig. <ns0:ref type='figure' target='#fig_9'>1B</ns0:ref>) set up as described above. CO was diluted in HBG with 50 % acetone and siRNA was diluted in HBG only. c siRNA of the final solution was 0.025 mg/ml. Size, PDI and zeta potential were measured as described under 'DLS Measurement'. This protocol, however, was changed in the following way to allow for multiple measurements over time: Two samples with 60 µl each were prepared. The first sample was used to measure size and PDI. The second sample was diluted with HBG to 800 µl to enable zeta potential measurements. Both samples were measured directly after each other for 90 min.</ns0:p></ns0:div> <ns0:div><ns0:head>Gel Shift</ns0:head><ns0:p>A 1 % [w/w] suspension of agarose in TBE buffer (149 mM TRIS, 89 mM boric acid, 2 mM EDTA in demineralized water) was heated until the agarose was dissolved. After a short cooling period, 0.1 % GelRed® 10000x (Biotium Inc., Fremont CA) was added. The mixture was cast into its mold and a comb was added to create wells. After 30 min, the solidified gel was placed in an electrophoresis chamber and completely immersed in TBE buffer. Polyplexes were prepared as described above. Naked siRNA was used as positive control. C siRNA was 0.025 mg/ml in all samples, sample volume was 20 µl. 4 µl loading buffer (8.21 mM glycerol, 60 mM EDTA, 0.003 mM bromophenol blue in purified water) was added to every sample (V total = 24 µl) and each was pipetted in a well in the solidified gel. The gel was run for 60 min at 80 V. For serum gel shifts, polyplexes were produced with higher siRNA concentration (c siRNA = 0.25 mg/ml) and diluted afterwards with fetal bovine serum (FBS) 1:10 to reach the desired c siRNA = 0.025 mg/ml. Samples containing FBS were incubated at 37 °C up to 24 h until the loading buffer was added and they were pipetted into the gel's wells. ImageJ (v. 1.52n) <ns0:ref type='bibr' target='#b55'>(Schindelin et al., 2012)</ns0:ref> was used to conduct a densitometry analysis of the siRNA bands. To this end, ImageJ was used to extract gray values from the respective siRNA stains. The sum of gray values as a function of the gel's extension in y (width of the stains) and x (length of the whole gel) direction was plotted with ImageJ to produce the desired analysis. The plot's arbitrary values on the y-axis correspond to the sum of all gray values over the full width (y) at a given length position (x). The length position x is plotted on the x axis.</ns0:p></ns0:div> <ns0:div><ns0:head>FRET -Experiments</ns0:head><ns0:p>Polyplexes were prepared conventionally (cf. Polyplex Preparation), albeit with a 1:2 siRNA-Cyanine 5 (Cy5):siRNA mixture. Lipid anchors (LA or LAE) were incubated with 0.75 eq. DBCO-PEG4-Atto488 (relative to azide content) over night at room temperature. Afterwards, the modified lipid anchor solution was diluted 1:2 with unmodified lipid anchor solution, resulting in a theoretical degree of labelling of 37.5 %. The lipid anchor was added to the polyplexes using the SMC (cf. Addition of Lipid Anchor and Lipid Anchor -PEG-ligand Oligomers). The final siRNA concentration was c siRNA = 0.1 mg/ml. Therefore, the final Cy5 and Atto488 concentrations were 6.1 and 21.3 µmol/l, respectively. 30 µl of each sample was filled into a 96 well plate and measured with a TEKAN pleat reader (Tecan Trading AG, Switzerland, Spark 10M, SparkControl V 2.1) with the following set of filters: Cy5: excitation wavelength: 625 nm, bandwidth 35 nm; emission wavelength: 680 nm, bandwidth 30 nm; Atto488: excitation wavelength: 485 nm, bandwidth 20 nm; emission wavelength: 535 nm, bandwidth 25 nm; FRET: excitation wavelength: 485 nm, bandwidth 20 nm; emission wavelength: 680 nm, bandwidth 30 nm. Measured fluorescence is divided by gain's value to exclude amplifier effects.</ns0:p></ns0:div> <ns0:div><ns0:head>Polyplex Compaction and Heparin Competition Assay</ns0:head><ns0:p>Core polyplexes were prepared conventionally (cf. Polyplex Preparation). Solvents were HBG and HBG with 50 % acetone for core polyplexes and lipid anchor oligomers, respectively. 20 mol % of indicated lipid anchor oligomers were attached to the polyplexes via solvent exchange inside the microchannel (Fig. <ns0:ref type='figure' target='#fig_9'>1B</ns0:ref>, single meander channel). The final solvent was HBG with 3.3 % acetone. 20 µl of this mixture containing siRNA (0.025 mg/ml), CO (0.252 mg/ml), lipid anchor (LA: 0.022, LAE: 0.023 mg/ml) were pipetted into a 96 well plate and incubated with 10 µl heparin solution (11.0; 55.0; 110.0; 165.0 IU/ml in HBG) or HBG for 15 min. Afterwards, 80 µl of a 0.5 µg/ml ethidium bromide (EtBr) solution in HBG were added and the samples were incubated for another 5 min. When EtBr intercalates into DNA or RNA it emits a strong signal when excited. This process can be inhibited by compacting the nucleic acid with polycations. Therefore, EtBr's fluorescence correlates with the compaction efficiency of target oligomers. The addition of heparin tests the formulation's resistance against anionic stress. The fluorescence of all samples was measured with a TEKAN plate Reader (Tecan Trading AG, Switzerland, Spark 10M, SparkControl V 2.1) utilizing the following set of filters: Excitation wavelength: 535 nm, bandwidth 25 nm; emission wavelength: 590 nm, bandwidth 20 nm. The well containing only siRNA and EtBr served as positive control and was also used to choose optimal gain and Zposition settings. All readings were normalized to samples containing free siRNA and EtBr only (positive control) and are presented here in '[%] of positive control'.</ns0:p></ns0:div> <ns0:div><ns0:head>Transmission Electron Microscopy</ns0:head><ns0:p>Core polyplexes were prepared conventionally or inside the single meander channel (cf. Polyplex Preparation). Solvents were HBG and HBG with 50 % acetone for core polyplexes and lipid anchor oligomers, respectively. 20 mol % of indicated lipid anchor oligomers were attached to the polyplexes using solvent exchange inside the microchannel (Fig. <ns0:ref type='figure' target='#fig_9'>1B</ns0:ref>, single meander channel). The final solvent was HBG with 3.3 % acetone. Carbon coated copper grids (Ted Pella, Inc. USA, 300 mesh, 3.0 mm O. D.) were hydrophilized with a plasma cleaner under argon atmosphere (420 V, 1 min). The grid's activated surface was placed face down on a 10 µl sample droplet for 3 min. Afterwards, the sample was removed with a filter paper and 5 µl staining solution (1.0 % uranyl formate in purified water) was placed on the grid and immediately removed to wash the sample off. Staining was performed with the same staining solution for 5 s. Afterwards, it was siphoned off with a filter paper and the remaining liquid was left to evaporate for 20 min. Grids were stored at room temperature. Samples were measured with a JEOL JEM-1100 electron microscope at 80 kV acceleration voltage.</ns0:p></ns0:div> <ns0:div><ns0:head>Preparation of Microfluidic Channels</ns0:head></ns0:div> <ns0:div><ns0:head>PDMS Channels</ns0:head><ns0:p>The microfluidic channels design was realized on a silica wafer with soft lithographic methods. The master microstructure was designed with the LPKF CAD/CAM software (LPKF Laser and Electronics) and made using SU8 process on silicon wafer. The microstructure of ~ 72 µm and ~ 90 µm thickness for single-and double-meandering channel, respectively, was rastered using LPKF ProtoLaser LDI UV-laser (LPKF Laser and Electronics). Utilized SU-8 3000 photoresist was processed in accordance with the manufacturer's instructions. The SU-8 master was subsequently silanized in an evacuated desiccator for 12 h with tri-chloro(1H,1H,2H,2Hperfluorooctyl)silane. The PDMS elastomer was mixed with 10 % [w/w] crosslinker, degassed, poured onto the wafer, and cured (75 °C, 4 h). Subsequently, PDMS was peeled from the wafer, holes for the inlets were pierced at the designated positions with a biopsy puncher, and it was bonded to a glass slide by oxygen plasma-induced oxidation (10 W high frequency generator power, 12 seconds, Pico Model E, Diener Electronic). The chip was left alone for 1 h to allow the reaction to complete. Afterwards, polyethylene tubes (length = 110 mm, inner diameter = 0.38 mm) were fitted into the holes in the PDMS and everything was covered with another layer of PDMS treated in the same way as mentioned above to seal the in-and outlets completely. A to-scale model of both channels' layout can be found in the supplemental information together with a detailed description of the channel's dimensions and calculations of Reynold's and Dean's numbers (3. Channel layout, supplemental figure S10, S11).</ns0:p></ns0:div> <ns0:div><ns0:head>In Vitro</ns0:head></ns0:div> <ns0:div><ns0:head>Culture</ns0:head><ns0:p>We used KB cells (cervix carcinoma, derived from HeLa cells) for all in vitro experiments. KB wild type cells were bought from DSZM (Braunschweig, Germany) and they were subsequently modified to code for a GFP-luciferase fusion mRNA by A. Cengizeroglu. The modified cell line is stably transcribing and translating the fusion mRNA to an eGFP-Luciferase fusion protein, which consists of two functional proteins, GFP and luciferase. The fusion protein's expression can be silenced by any siRNA that is complementary to the GFP-luciferase fusion mRNA. Here, we used siGFP. The construct's transfection process was described in A. Cengizeroglu's thesis <ns0:ref type='bibr' target='#b7'>(Cengizeroglu, 2012)</ns0:ref> and first use was demonstrated by Dohmen et al. <ns0:ref type='bibr' target='#b10'>(Dohmen et al., 2012)</ns0:ref>. For each experiment, cells were freshly thawed from a liquid nitrogen storage tank and passaged at least four times before experiments were conducted. Cells were subcultured when 70 -90 % confluency was reached. Culture conditions were 37 °C and 5 % CO 2 . KB cells were cultured in RPMI-1640 supplemented with 10 % fetal bovine serum (FBS) and 1 % penicillin / streptomycin (5 ml with 100 U/ml and 100 µg/ml, respectively).</ns0:p></ns0:div> <ns0:div><ns0:head>Transfection</ns0:head><ns0:p>Cells were seeded into 96 well plates one day prior to transfection. All wells were pre-treated with 40 µl collagen solution per well (0.1 mg/ml, removed after 30 min, 37 °C). Afterwards, cells were seeded with 4000 cells / well in 100 µl folate free Gibco™ RPMI 1640 (Fisher scientific) supplemented with 10 % FBS. The next day, the medium in all wells was replaced with 80 µl fresh medium (RMPI 1640, FolA free) and 20 µl sample solution or HBG (negative control) was added. Samples were prepared completely inside the microfluidic channel (cf. Polyplex Preparation & Fig. <ns0:ref type='figure' target='#fig_9'>1B</ns0:ref>, double meander channel). siRNA concentration was 5 µg/ml in each well. Samples were always prepared in quintuplicates. Medium was exchanged again after 4 h, total incubation time was 48 h at 37 °C, 5 % CO 2 .</ns0:p></ns0:div> <ns0:div><ns0:head>Luciferase Assay</ns0:head><ns0:p>Plates were taken from the incubator and all media was removed. 100 µl / well lysis buffer (Luciferase Cell Culture Lysis 5X Reagent, Promega, diluted 1:10 with purified water) was added and incubated for another 45 min. Plates were frozen at -80 °C until measurement. 35 µl / well of the cell lysate were transferred to white, opaque 96 well plates (BertholdTech) and measured with a Centro LB 960 luminometer (BertholdTech CENTRO, Driver V. 1.21, MikroWin, V. 5.2, 10 s integration / well). 100 µl LAR buffer per well (20 mM glycylglycine, 1.0 mM MgCl 2 , 0.1 mM EDTA, 3.29 mM DTT, 0.548 mM ATP, 1.30 µM coenzyme A, adjusted to pH 8.5 with NaOH)) were automatically added by the machine. The output of this measurement is relative light units (RLUs) per well. The raw data was handled the following way. The mean value from each sample was calculated and was set in relation to the mean value of the respective negative control. Results are depicted in 'RLU [%] of HBG'. Error bars represent 95 % confidence intervals of five samples.</ns0:p></ns0:div> <ns0:div><ns0:head>MTT Assay</ns0:head><ns0:p>Plates were taken from the incubator and 10 µl / well 3-(4,5-dimethylthiazol-2-yl)-2,5diphenyltetrazolium bromide (MTT, Carl Roth, 5 mg/ml in PBS) were added and everything was incubated for another 2 h at 37 °C. Afterwards, the fluids were removed and the plates were frozen at -80 °C for at least 1 h. 100 µl / well dimethylsulfoxide (DMSO) were added and the plates were gently shaken at 37 °C for 20 min to dissolve the purple formazan dye. The absorbance at 590 nm of each well against the reference wavelength (630 nm) was measured with a TEKAN plate reader (Tecan Trading AG, Switzerland, Spark 10M, SparkControl V 2.1). The raw data was handled the following way. The mean value from each sample was calculated and was set in relation to the mean value of the respective negative control. Therefore, results are depicted in '[%] of HBG'. Error bars represent 95 % confidence intervals of five samples.</ns0:p></ns0:div> <ns0:div><ns0:head>Dose Titration</ns0:head><ns0:p>Core polyplexes were prepared conventionally with pipettes as described under 'Polyplex Preparation'. siRNA concentrations were chosen to have a final amount of 100, 250, 500, 750, and 1000 ng / well. CO concentrations were adjusted accordingly. To be precise, siRNA concentrations in 20 µl transfection volume were [mg/ml]: 0.0050, 0.0125, 0.0250 0.0375, 0.0500. CO concentrations were [mg/ml]: 0.0458, 0.1145, 0.2291, 0.3436, 0.5041. 20 µl / well of each sample was transfected as described under 'Transfection'. Samples were transfected in quintuplicates. The formulations' effect on luciferase activity and metabolic activity was evaluated with a luciferase assay and a MTT assay as described above.</ns0:p></ns0:div> <ns0:div><ns0:head>Data Analysis</ns0:head><ns0:p>Data was analyzed with R (V. 3.5.1) and RStudio (V 1.1.463). We always report means with 95 % confidence intervals, except for zeta potential measurements. Mean zeta potential was reported +-mean of zeta deviations to allow for a better understanding of the underlying zeta distribution. Data from cell culture experiments was normalized to its negative control, which was always on the same well plate as the respective samples. A multifactorial two -way ANOVA was used to compare mean relative light unit reduction of core (CO + siRNA) polyplex formulations with two different lipid anchor oligomers and six different PEG-ligand oligomers. A multifactorial two -way ANOVA was used to compare mean relative light unit reduction of core (CON + siRNA) polyplex formulations with six different PEG-ligand oligomers at four different concentrations.</ns0:p><ns0:p>After each ANOVA, post-hoc two-sided student's t tests were conducted between all samples. Test results were corrected for the family-wise error with Holm's method. Significance was set to α < 0.05. R code and raw data are made available here: https://doi.org/10.6084/m9.figshare.7971329.v1</ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head><ns0:p>The aim of our study is to demonstrate the precise production of multi-component polyplexes with a modular two-step microfluidic set-up. The device employs flow-focusing in combination with solvent exchange to allow for the successive assembly of multi-component nanoparticles. We show that the approach results in well-defined and reproducible polyplexes with controlled surface characteristics. It is used here to vary the surface layer in order to identify structure activity relationships between PEG-ligand length and transfection efficiency. Finally, we compare the findings with conventionally (educts are mixed manually with pipettes) prepared polyplexes.</ns0:p></ns0:div> <ns0:div><ns0:head>Design of the Delivery Systems</ns0:head><ns0:p>Oligomers for the formation of core polyplexes are designed to bind siRNA via electrostatic interactions and stabilize the resulting particle with its hydrophobic domains. Solid phase supported synthesis (SPSS) is used to allow for precise control over the oligomers' sequence. (Fig. <ns0:ref type='figure' target='#fig_9'>1A</ns0:ref>) Core oligomers (CO) feature four cationic succinoyl tetra-ethylene pentamine (Stp) units that are flanked by three tyrosines (Y) on each side for aromatic and hydrophobic stabilization <ns0:ref type='bibr'>(Troiber et al., 2013)</ns0:ref>. Lysines (K) are used to introduce a branch in the main chain for the attachment of two cholanic acids (CholA) for further stabilization <ns0:ref type='bibr' target='#b15'>(Fröhlich et al., 2012;</ns0:ref><ns0:ref type='bibr' target='#b54'>Schaffert et al., 2011)</ns0:ref> and to provide attachment points for lipid anchor oligomers. Glycine (G) is used as a spacer. B: Production methods for polyplexes with CO oligomers: Formulations used are depicted between both channels with the id of their corresponding syringe (S1-4). Two different channels were used to produce nanoparticles during solvent exchange, a single meander channel and a double meander channel. In the single meander channel pre-assembled core particles were mixed with lipid anchors or lipid anchor PEG-ligand oligomers. In the double meander channel, the complete polyplex was assembled from its starting components.</ns0:p><ns0:p>The lipid anchor oligomers (Fig. <ns0:ref type='figure' target='#fig_9'>1A</ns0:ref>, LA, LAE) are designed to adsorb to the core polyplexes via hydrophobic interactions between cholanic acids. In addition, they feature a histidine -lysinehistidine (H-K-H) motif to adjust solubility. The PEG12 -chain exposes the terminal azide to the surrounding solution, increasing its accessibility to alkyne-bearing entities. The two glutamic acids (E) in LAE's sequence increase attachment to positively charged core polyplexes and further adjust solubility. Formulation of core -lipid anchor polyplexes requires lipid anchors to be deposited on the core polyplex's hydrophobic patches during solvent exchange inside the microchannel. This step is crucial for controlling the hydrodynamic diameter of generated nanoparticles, since adding lipid anchor oligomers manually yields a suspension of polydisperse aggregates. (Supplemental information, 4. Manual formulation of core -lipid anchor polyplexes, supplemental figure S12) Functional structures of interest can be coupled to lipid anchor oligomers by azide -alkyne click chemistry. This modification is possible either before deposition of lipid anchors on core polyplexes or afterwards. Here, PEG-ligand oligomers (Fig. <ns0:ref type='figure' target='#fig_9'>1A</ns0:ref>) were attached to lipid anchors 24 h before formulation with core polyplexes. The PEG-ligands have been used to investigate the influence of PEG length on transfection efficiency. They feature one dibenzocyclooctyne (DBCO) moiety, a PEG chain, and one molecule folic acid (FolA). The DBCO group enables the rapid and copper free reaction with azide groups, while the folic acid moiety facilitates binding folic acid receptors. PEG chains serve two purposes in this design: firstly, to shield the core polyplexes' positive charge, and secondly to expose folic acid to the environment. Their influence is investigated by using PEG chains of various lengths (number of ethylene oxide (EO) repetitions: 0, 3, 12, 24, or 48). Lipid anchors and PEG-ligands were coupled 24 h prior to polyplex formulation. Since lipid anchors already feature a PEG12 chain, lipid anchor -PEGligand oligomers have a total number of 12, 15, 24, 36, or 60 EO repetitions. Polyplexes from CO oligomers with siRNA and lipid anchor -PEG-ligands are named after their total number of EO repetitions, for example 'P12-24F' for polyplexes with lipid anchors with DBCO-PEG24-FolA modification.</ns0:p></ns0:div> <ns0:div><ns0:head>Polyplex Characterization</ns0:head><ns0:p>Multiple experiments characterizing polyplexes can only contribute viable information about a formulation, if it is ensured that the starting formulation is in equilibrium at the time of each experiment. <ns0:ref type='bibr' target='#b66'>Troiber, Kasper, et al., 2013</ns0:ref> have found particles assembled from the same class of oligomers to be stable over three weeks. Here, we have investigated the changes in size, PDI and zeta potential of our core formulation over 90 min (Supplemental information, 4. Core Polyplex Stability, supplemental figure <ns0:ref type='figure' target='#fig_12'>S13</ns0:ref>). We saw no changes in size and PDI. We did note some changes in zeta potential up to 40 min, which is the reason why formulations were always used after 45 min incubation time.</ns0:p></ns0:div> <ns0:div><ns0:head>Size</ns0:head><ns0:p>Core polyplexes (CO + siRNA) with comparable properties were generated either by conventional bulk mixing, or at a T-junction, or with microfluidics. Integrating lipid anchor or lipid anchor PEG-ligand oligomers increased size and PDI moderately. Hydrodynamic diameter, zeta potential, and PDI of polyplexes was measured by dynamic light scattering (DLS) (Fig. <ns0:ref type='figure' target='#fig_2'>2</ns0:ref>, Fig. <ns0:ref type='figure' target='#fig_3'>3</ns0:ref>) and sizes were confirmed with transmission electron microscopy (TEM) (Fig. <ns0:ref type='figure' target='#fig_4'>4</ns0:ref>). Compacting siRNA conventionally with core oligomers (CO) by rapid pipetting yields particles with a mean hydrodynamic diameter (d Z ) of 84 nm (Fig. <ns0:ref type='figure' target='#fig_11'>2A</ns0:ref>). The polydispersity index is very low (PDI < 0.20; Fig. <ns0:ref type='figure' target='#fig_11'>2B</ns0:ref>). Increasing control over this process either at a Tjunction or with a microfluidic device, however, needs certain additional conditions to be met in order to produce similar particles. At a T-junction, the total flow rate needs to be very high (here: 60 ml/h) to generate particles with a hydrodynamic diameter of 97 nm and a PDI < 0.20. Addition of acetone does only lead to comparable particles and PDIs when flow rates of both components are identical, and CO is dissolved in 50 % acetone as depicted in Fig. <ns0:ref type='figure' target='#fig_11'>2 (d Z</ns0:ref> = 104 nm, PDI < 0.23). This approach, however, results in an acetone concentration of 25 % in the final product requiring additional efforts by evaporation or dialysis to remove the organic solvent when using it in vitro or in vivo. The complete data set (influence of flow rate, acetone and flow rate differences of 1:2 or 1:10) can be found in the supplemental information (6. Polyplex production at a T-junction, supplemental figure <ns0:ref type='figure' target='#fig_13'>S14</ns0:ref>). When preparing polyplexes, it is paramount to decrease diffusion lengths or to increase time needed for efficient siRNA compaction. Otherwise, influence on kinetically controlled polyplex formation is decreased and nanoparticles' size and polydispersity increases. Diffusion lengths inside the micro channel were minimized by flow rate differences > 1:10 and acetone was used to retard siRNA compaction. Previous experiments have shown that feeding the two outer channels with diluted siRNA solution and the middle channel with concentrated CO solution generates particles in an acceptable size range (data not shown). Here, a substantial difference in PDI and hydrodynamic diameter was observed when polyplexes were generated with (d Z = 95 nm, PDI < 0.14) or without (d Z = 149 nm, PDI < 0.11) additional acetone (Fig. <ns0:ref type='figure' target='#fig_2'>2</ns0:ref>). The influence of lipid anchor and lipid anchor -PEG-ligand oligomers on core polyplexes was investigated. To this end, LA or LAE with or without their respective PEG-ligand oligomers (Fig. <ns0:ref type='figure' target='#fig_9'>1A</ns0:ref>) were attached to conventionally prepared core (CO + siRNA) polyplexes inside the microchannel (single meander channel, Fig. <ns0:ref type='figure' target='#fig_9'>1B</ns0:ref>). As described in detail in the methods section, it is essential to use concentrated lipid anchor solutions and diluted core polyplex solutions. This setting ensured that only a thin stream of lipid anchor solution is flowing through the Yjunction, accelerating the solvent exchange from 50 % to 4.8 % acetone and facilitating the association of the hydrophobic lipid anchor with the fatty acids in the core's structure. Polyplexes' particle size, PDI, and zeta potential were measured by DLS (Fig. <ns0:ref type='figure' target='#fig_3'>3</ns0:ref>). In order to gain a better understanding of the intensity distribution, violin plots are provided in addition to zaverage values. Therefore, z-average values can be better assessed based on the underlying distribution, be it mono-or multimodal. The expansion in x direction represents the relative frequency the respective size has been measured. The z-average is located close to the position with the largest expansion in x direction for mono modal distributions (e.g. in the panel labeled 'core'). When the distribution is multimodal, however, z-average's position can be quite misleading (e.g. in the panel core -LAE, sample P12-48F) and the intensity distribution needs to be considered. The effect of adding 20 mol % (relative to n CO ) lipid anchor or lipid anchor -PEG-ligand oligomers to core polyplexes depended on the PEG-ligand's length on the respective lipid anchor. The addition of LA containing oligomers to the core formulation (d Z = 123 nm, PDI < 0.13) increased hydrodynamic diameters of resulting nanoparticles moderately from 131 nm (LA alone) to 169 nm (LA: P12-48F). Additionally, PDI decreased with the addition of LA (PDI < 0.11) or LAE (PDI < 0.10) oligomers and increased from PDI < 0.12 to PDI > 0.20 with longer PEG-ligands. The hydrodynamic diameter of LAE containing polyplexes was generally ~ 15 nm smaller than in LA containing formulations. Although, LAE oligomers with longer PEG-ligands were more likely to form aggregates (LAE: P12-48F). As expected, zeta potential of core polyplexes alone in HBG was positive with ZP = 24 mV due to the high N/P charge ratio. Incorporation of 20 mol % LA or LAE with or without PEG-ligands had only limited effect on the particles' zeta potential except for particles with P12-48F PEG-ligands. (Fig. <ns0:ref type='figure' target='#fig_12'>3C</ns0:ref>) Incorporation of LA:P12-48F or LAE: P12-48F decreased mean zeta potential to 14 mV and 10 mV, respectively. Finally, after having scrutinized all steps independently, core -lipid anchor -PEG-ligand polyplex production from its single components inside one microchannel was investigated. The double meander channel (DMC, Fig. <ns0:ref type='figure' target='#fig_9'>1B</ns0:ref>) was used. Syringe S3 was filled with siRNA in HBG and S4 with CO in HBG with or without 50 % acetone. Syringes S2 were loaded with four different oligomers in HBG with 50 % acetone: LA, LA: P12-24F, LAE, or LAE: P12-12F. Eight runs were conducted, each lipid anchor or lipid anchor -PEG-ligand oligomer was mixed with core polyplexes produced with or without the aid of acetone. Sizes were comparable to corelipid anchor -PEG-ligand polyplexes with conventionally prepared cores when no acetone was used in the core production step. When CO was dissolved in 50 % acetone, however, polyplexes completely prepared with microfluidics had a smaller hydrodynamic diameter and PDI. (Supplemental information, 7. Controlled Production of Core -Lipid Anchor -PEG-Ligand Polyplexes from their Single Components, supplemental figure S15) </ns0:p></ns0:div> <ns0:div><ns0:head>Lipid Anchor Integration</ns0:head><ns0:p>LA and LAE integrate into core polyplexes. Investigation of LA and LAE integration into core polyplexes was carried out by transmission electron microscopy (TEM) and Förster resonance energy transfer (FRET) measurements. TEM measurements revealed that formulations from siRNA and CO form spherical particles with a diameter < 100 nm (Fig. <ns0:ref type='figure' target='#fig_13'>4B</ns0:ref>) which is in good agreement with DLS measurements (Fig. <ns0:ref type='figure' target='#fig_12'>3A</ns0:ref>). There was no obvious difference when particles were produced conventionally or with microfluidics (supplemental information, 8. TEM: Comparisons of polyplexes produced with pipettes or with the double meander channel (DMC), supplemental figure <ns0:ref type='figure' target='#fig_15'>S16</ns0:ref>). LA and LAE with or without covalently bound PEG-ligands alone form tubular or fibrous structures on the TEM grid that could not be found when formulated together with core polyplexes. This finding suggests that lipid anchor oligomers are indeed interacting with core polyplexes. These findings obtained by TEM were supported by FRET measurements. Receiving measurable FRET signals implies a distance < 10 nm between chromophores <ns0:ref type='bibr' target='#b9'>(Clegg, 1996;</ns0:ref><ns0:ref type='bibr' target='#b13'>Förster, 1948)</ns0:ref>. Here, 50 % siRNA with one molecule Cy5 on the sense strand was used for conventional core polyplex formation. Lipid anchor oligomers were modified with 0.75 equivalents (relative to lipid anchors' azide) DBCO-PEG4-Atto488 and subsequently deposited on the conventionally prepared core polyplex using solvent exchange inside the micro-channel. These polyplexes emitted strong FRET signals when Atto488 dyes were excited and fluorescence was measured from Cy5 dyes alone. (Fig. <ns0:ref type='figure' target='#fig_13'>4A</ns0:ref>) When CO was missing from the formulation, polyplex formation did not occur making energy transfer between dyes a function of their dilution only (sample 'siRNA + LA' in panel 'FRET' in Fig. <ns0:ref type='figure' target='#fig_13'>4A</ns0:ref>). All control experiments (FRET measurements from polyplexes with only one dye and fluorescence measurements of both dyes separately) can be found in the supplemental information (9. FRET control experiments, supplemental figure S17). </ns0:p></ns0:div> <ns0:div><ns0:head>Stability</ns0:head><ns0:p>Lipid anchors do not influence stability of core polyplexes. Polyplex stability was assessed with two different methods. The general ability of polyplexes to compact and hold siRNA back under the influence of an electric field was investigated with an agarose gel shift assay and its densitometry analysis to simplify comparison of bands. Ability to compact siRNA and resist polyanionic stress was tested with an ethidium bromide displacement assay with or without additional heparin. Polyplexes from CO and siRNA were prepared conventionally in HBG and lipid anchors ± PEGligands were attached inside the micro channel. Samples were diluted 1:10 with HBG or serum (FBS). Additionally, samples containing serum were incubated at 37 °C for up to 24 h to assess stability under the influence of body temperature and serum components. There was no visible difference between all formulations at t = 0 h with or without additional FBS. At the 4 h mark, only small differences between samples were visible, while all samples retained most of their payload. After 24 h, core -lipid anchor or core -lipid anchor -PEG-ligand formulations revealed a slight decrease in siRNA retention capability in comparison to the core formulation alone. At this time, the core -LAE formulation seemed to be better at retaining siRNA than the core -LA formulation. When lipid anchors coupled with PEG-ligands were used, however, core -LA -PEG-ligand formulations retained siRNA better than their LAE containing counterparts. (Supplemental information, 9. Gel shift assay, supplemental figure S18, S19) Polyplexes (CO + siRNA) were prepared conventionally and lipid anchors were added inside the SMC for the ethidium bromide displacement assay with and without heparin competition. In this assay, LA and LAE containing polyplexes showed an unaltered protection against dye displacement behavior. Fluorescence without additional heparin for core formulation, core -LA formulation and core -LAE formulation was 14 %, 18 %, and 11 % of the positive control, respectively. 1 IU/ml heparin increased fluorescence to 37 %, 39 %, and 22 %. Total displacement was observed at heparin concentrations above 5 IU/ml (supplemental information, 9. Ethidium bromide displacement assay, supplemental figure S20).</ns0:p></ns0:div> <ns0:div><ns0:head>Toxicity</ns0:head><ns0:p>Core (CO + siRNA) -lipid anchor -PEG-ligand polyplexes do not alter the metabolic activity profile of KB cells in comparison to core polyplexes alone. Different fatty acids in oligo-amidoamines have been shown to induce membrane leakage in erythrocytes and to increase cell death in in vitro cell assays <ns0:ref type='bibr' target='#b28'>(Klein et al., 2016;</ns0:ref><ns0:ref type='bibr' target='#b48'>Reinhard, Zhang, & Wagner, 2017)</ns0:ref>. Influence of target formulations completely prepared with microfluidics on metabolic activity of KB cells was assessed by MTT assay to account for any apparent effects on cell survivability. The MTT assay correlates metabolic activity to the amount of formazan dye produced by oxidoreductase enzymes while consuming NAD(P)H. All formulations tested in this assay showed no reduction of formazan absorption relative to untreated KB cells (supplemental information: 11. MTT assay of core -lipid anchor -ligand polyplexes, supplemental figure S21).</ns0:p><ns0:p>Transfection of Core -Lipid Anchor -PEG-Ligand Nanoparticles Core (CO + siRNA) -lipid anchor -PEG-ligand nanoparticles with LA: P12-24F or LAE: P12-12F showed the largest effect on luciferase reporter gene silencing activity in KB cells. KB cells possessing an eGFP-luciferase fusion gene controlled by a constitutively active promoter were used in all cell experiments. Gene expression can be modulated by RNA interference (RNAi): if a siRNA (here: siGFP) that is complementary to any part of the target mRNA (here: eGFP-luciferase fusion mRNA) reaches the cytosol and is incorporated into the RISC complex the corresponding mRNA will be degraded selectively. In this case, the eGFPluciferase fusion protein expression is reduced which in turn leads to a decrease in GFP and luciferase enzymatic activity. Using an in vitro bioluminescence assay, gene silencing efficacy of the siRNA formulation can be correlated to the reduction of, in our case, luciferase activity as measured in relative light units (RLUs) as shown in Fig. <ns0:ref type='figure' target='#fig_5'>5</ns0:ref>. Non-siRNA dependent effects on luciferase activity were monitored with cells treated with identical polyplexes containing control siRNA only. Polyplexes were prepared from their starting materials using microfluidics (Fig. <ns0:ref type='figure' target='#fig_9'>1B</ns0:ref>, double meander channel). Amount of siRNA / well was optimized and set to 500 ng / well. (Supplemental information: 13. Dose Titration, supplemental figure S22) Effects of lipid anchor and PEG-ligands on luciferase activity were estimated using a multifactorial two -way ANOVA. All calculated effects were statistically significant. Main effect of lipid anchors: F(1, 48) = 8.91, p = .032, ω² =.02 , main effect of PEG-ligands: F(5, 48) = 14.78, p < .001, ω² =.43, and the interaction effect between PEG-ligands and lipid anchors: F(5, 48) = 17.02, p < .001, ω² =.32. After it was established that including lipid anchors and PEG-ligands influenced luciferase enzyme activity, post-hoc student's t tests (HOLM corrected) were conducted to identify the statistical significance of each comparison (Table <ns0:ref type='table' target='#tab_1'>2</ns0:ref>). Samples with LA are shown in figure <ns0:ref type='figure' target='#fig_14'>5A</ns0:ref>, samples with LAE in Fig. <ns0:ref type='figure' target='#fig_14'>5B</ns0:ref>. Supplemental figure S23 compares siGFP containing samples from Fig. <ns0:ref type='figure' target='#fig_14'>5A</ns0:ref> and 5B against each other to gauge the lipid anchor's influence on the polyplexes gene silencing efficacy. Both sets showed an effect on eGFP-luciferase gene silencing activity that is dependent on the PEG-ligand's length. For LA containing formulations, relative light units decreased with increasing PEG length, reached their base with P12-24F and rose again with P12-48F. The same pattern was observed with LAE containing formulations, except that the base was already reached with P12-12F and effects of polyplexes with P12-48F are comparable to the siCtrl containing particles. </ns0:p></ns0:div> <ns0:div><ns0:head>Characterization of CON -PEG-Ligand Polyplexes</ns0:head><ns0:p>CON oligomers, in contrast to CO oligomers, feature an additional azidolysine N-terminally (Fig. <ns0:ref type='figure' target='#fig_15'>6A</ns0:ref>). Consequently, PEG-ligands can be coupled covalently to CON containing core polyplexes. Generally, azide-bearing core oligomers were modified with PEG-ligands 45 min after polyplex formation (Fig. <ns0:ref type='figure' target='#fig_15'>6B</ns0:ref>) because coupling PEG-ligands to core oligomers before polyplex formation hampers siRNA compaction <ns0:ref type='bibr' target='#b47'>(Morys et al., 2017)</ns0:ref>. This method has already been established by <ns0:ref type='bibr' target='#b25'>Klein et al. (Klein et al., 2018)</ns0:ref> and was used here to validate results generated with core polyplexes that had PEG-ligands attached by lipid anchors. Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:p></ns0:div> <ns0:div><ns0:head>formation. Building blocks represent natural amino acids (E = glutamic acid, G = glycine, H = histidine, K = lysine, Y = tyrosine), synthetic building blocks (Stp = succinoyl-tetraethylenepentamine, PEG = polyethylene glycol), fatty acids (CholA = cholanic acid), and moieties for bio-orthogonal click chemistry (N3 = azide, DBCO = dibenzocyclooctyne). B: Manual production method for CON -PEG-ligand polyplexes.</ns0:head><ns0:p>Increasing PEG-ligand length and molar amounts promotes aggregation. We covalently bound PEG-ligands to CON core polyplexes prepared as described in Klein et al. <ns0:ref type='bibr' target='#b25'>(Klein et al., 2018)</ns0:ref> and depicted here in Fig. <ns0:ref type='figure' target='#fig_15'>1A and 6A</ns0:ref>. In brief, CON oligomers and siRNA were mixed manually and incubated for 45 min. Afterwards, PEG-ligands were added, and the azide-alkyne click reaction was allowed to complete for 4 h. Results from these covalently modified polyplexes were used to confirm results generated with the lipid anchor containing system. The main difference between both formulations is the mode of incorporation of target PEG-ligands. On the one hand, CO based core polyplexes need lipid anchor oligomers for the non-covalent attachment of PEG-ligands. PEG-ligands are coupled covalently to lipid anchor oligomers before the polyplex formulation process. On the other hand, CON based core polyplexes feature azides that enable the PEG-ligand's covalent integration into core polyplexes after core polyplex formulation. Additionally, we increased PEG-ligand concentrations to investigate their influence on particle size as well. We found that core polyplexes modified with 25 mol % PEG-ligand were all in the same size range (d H ~ 120 nm, Fig. <ns0:ref type='figure' target='#fig_16'>7A</ns0:ref>) and PDI (~ 0.15, Fig. <ns0:ref type='figure' target='#fig_16'>7B</ns0:ref>), except for formulations with P48F (d H = 136 nm, PDI = 0.20). These results were comparable to CO based core -lipid anchor polyplexes with 20 mol % PEG-ligands (Fig. <ns0:ref type='figure' target='#fig_12'>3A</ns0:ref>, 3B), except with LAE which showed a substantial increase in size and PDI with P48F. Increasing PEG-ligand concentration up to 100 mol % did not substantially alter size and PDI of polyplexes with F (d H = 122 nm, PDI = 0.16), P3F (d H = 115 nm, PDI = 0.14), and P12F (d H = 135 nm, PDI = 0.11), but had a large effect on size and PDI of P24F (d H = 1817 nm, PDI = 0.62) and P48F (d H = 8393 nm, PDI = 0.67) containing particles which basically showed aggregation when functionalized with more than 25 mol % PEG-ligands. Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head></ns0:div> <ns0:div><ns0:head>Transfection of CON -PEG-Ligand Polyplexes</ns0:head><ns0:p>The optimal PEG-ligand length is PEG12 or PEG24. The influence of molar amount and PEG length of PEG-ligands on luciferase activity was estimated using a multifactorial two -way ANOVA. siCtrl polyplexes were included in addition to siGFP polyplexes to detect apparent toxicity and to attribute it to either PEG length, molar amount or both. Significant terms suggest an influence of the tested variable (PEG length and molar amount) on transfection efficiency. A significant interaction term indicates that both variables influence each other. Main effect of PEG length for siGFP: F(4, 90) = 3.71, p < 0.008, ω² =.32, main effect of molar amount used with siGFP: F(1, 90) = 24.96, p < .001, ω² =.36, interaction effect between PEG length and molar amounts with siGFP: F(4, 90) = 4.15, p = .004, ω² =.04. The ANOVA with siCtrl polyplexes yielded the following results: Main effect of PEG length for siCtrl: F(4, 90) = 4.37, p < 0.003, ω² =.23, main effect of molar amount used with siCtrl: F(1, 90) = 2.48, p = .119, ω² =.20, and the interaction effect of PEG length with molar amount with siCtrl: F(4, 90) = 13.52, p < .001, ω² =.19. Post-hoc tests were used to quantify the influence of separate PEG-ligands on luciferase knockdown in comparison to the core polyplex formulation (Table <ns0:ref type='table' target='#tab_3'>3 and 4</ns0:ref>). Cells treated with conventionally prepared CON polyplexes with siGFP showed a non-significant decrease in relative light units (RLUs) compared to polyplexes with siCtrl. Incubating polyplexes for 4 h with targeting PEG-ligands of various lengths decreased luciferase activity significantly compared to core formulation without PEG-ligands (Table <ns0:ref type='table' target='#tab_2'>3</ns0:ref>). Increasing PEG-ligand concentration up to 100 mol % (relative to n CON ) increased siGFP's effect as well, but toxicity and aggregation tendency increased simultaneously. (Fig. <ns0:ref type='figure' target='#fig_7'>7</ns0:ref></ns0:p></ns0:div> <ns0:div><ns0:head>, Fig 8B)</ns0:head><ns0:p>There was, however, a 'sweet spot' for the positive influence of PEG-ligand length and molar amount. Increasing the number of PEG repetitions per PEG-ligand decreased RLUs up to P12F when 25 mol % PEG-ligand was added. Longer PEG-ligands were not as powerful (Fig. <ns0:ref type='figure' target='#fig_17'>8A</ns0:ref>, panel 25 mol %). Gradually increasing total PEG-ligand amount relative to free azides increased efficacy but lead to aggregation (Fig. <ns0:ref type='figure' target='#fig_7'>7</ns0:ref>) with associated toxicity (Fig. <ns0:ref type='figure' target='#fig_17'>8B, P24F</ns0:ref>) and loss of function (Fig. <ns0:ref type='figure' target='#fig_17'>8A, P48F</ns0:ref>) for some polyplexes with > 50 mol % PEG-ligands as well. PEGligands with less than 24 PEG units did not exhibit aggregation or toxicity independent from the amount used. Producing CON -PEG-ligand polyplexes completely inside the double meander channel yielded similar results to conventionally produced polyplexes. The results from the luciferase activity assay and the MTT assay from CON polyplexes with 75 mol% PEG ligands are presented in the supplemental information (14. Luciferase and metabolic activity assay of CON polyplexes with PEG-ligands produced with the double meander channel (DMC), supplemental figure S24). Magnitude: < 0.2: negligible. < 0.5: small. < 0.8: medium. < 1.20: large. > 1.20: very large. Bold values are significant at α < .05. PEG-ligands are covalently bound to core polyplexes with siCtrl and CON. Mol %: n PEG-ligand : n CON . Px: PEGx, F: Folic acid.</ns0:p></ns0:div> <ns0:div><ns0:head>Discussion</ns0:head><ns0:p>We have shown that controlled production of simple two component polyplexes is feasible and that it can be extended to generate more sophisticated products. It depends on the aim of the experiment which method is most suitable. Conventional bulk mixing with pipettes is best chosen when polyplexes must be prepared quickly and high control over mixing parameters is not an issue. It is problematic, however, if bulk mixing is the default method for preparing polyplexes, since size and polydispersity are heavily dependent on the concentration of its components and their respective volumes. T-junctions are best for continuously preparing larger volumes of polyplex solutions with some control over mixing speed. Since the mixing is turbulent and flows are fast, however, mixing vastly different volumes can be challenging. Moreover, the high mixing speed required would limit the further automated processing of prepared polyplexes, if the next step involved pressure sensitive components. Microfluidics excel in producing polyplexes with a high degree of control over external mixing parameters and additional reactants, which is reflected in polydispersity indices around 0.1 for these polyplexes. Increasing throughput to T-junction levels, however, would entail parallelization of the whole set-up, which is only feasible when proto typing and sample preparation can be automated.</ns0:p><ns0:p>To demonstrate the advantages of this approach, we have produced multi-component polyplexes from their single components in one continuous experiment which would have been impossible with bulk mixing or at a T-junction. Morphology of core -lipid anchor polyplexes was shown with TEM and FRET experiments. TEM pictures revealed fibrous structures for samples containing only LA or LAE alone, with or without PEG-ligands. These structures, however, were not visible when core -lipid anchor polyplexes were examined. Moreover, FRET experiments showed a strong signal for labeled core (CO + siRNA -Cy5) -lipid anchor (Atto488) polyplexes that could not be observed in mixtures containing only siRNA-Cy5 and lipid anchor-Atto488 without CO. Taken together, both results indicate successful integration of lipid anchor oligomers into core structures. The investigation of size, PDI and zeta potential of core -lipid anchor and core -lipid anchor -PEG-ligand polyplexes showed matching results. Mean hydrodynamic diameter (d H ) and mean PDI increased with increasing PEG-ligand length while zeta potential was gradually reduced. Zeta potential reduction could also be one reason for particles with longer PEG chains forming aggregates, since electrostatic repulsion was diminished. Similarly, polyplexes that had their PEG-ligands directly coupled to CON showed an increase in PDI and d H with PEG24 and PEG48 containing PEG-ligands, specifically with PEG-ligand content > 25 mol %. There is evidence that integration of PEG chains into electrostatically formed nanoparticles decreases its stability <ns0:ref type='bibr' target='#b47'>(Morys et al., 2017)</ns0:ref>. On the one hand, this could be a critical problem if the polyplex disintegrates before it delivers its payload. On the other hand, it has been shown that increased stability has the potential to inhibit delivery as well, if the polyplex does not release its payload once inside the target cell <ns0:ref type='bibr' target='#b42'>(Leong & Grigsby, 2010;</ns0:ref><ns0:ref type='bibr' target='#b53'>Schaffer, Fidelman, Dan, & Lauffenburger, 2000)</ns0:ref>. Therefore, a balance needs to be found between both extremes. Here, core -lipid anchor polyplexes and core polyplexes alone produced similar results, both when treated with poly-anions in an ethidium bromide displacement assay with up to 5 IU/ml heparin and when siRNA compaction and retention is tested with a gel shift assay with or without incubation in 90 % serum at 37 °C. These findings suggest an unaltered stability profile of core -lipid anchor particles compared to its naked core polyplex formulation. Even the addition of PEGligands to core -lipid anchor polyplexes did not alter serum gel shift results exceedingly until the formulation had been incubated for 24 h at 37 °C. Biological activity of core -lipid anchor -PEG-ligand particles was investigated by silencing luciferase protein expression in KB cells in vitro. From previous studies, we anticipated that changing the PEG-ligand on the polyplexes would have the biggest impact on luciferase activity. ANOVA's results confirmed our hypothesis. Additionally, the results revealed a barely significant influence of the lipid anchors used. The small effect can be explained by the lipid anchor's function: since lipid anchors are designed to facilitate association with the core polyplex only, their effect pales in comparison to PEG-ligands which are especially designed to enhance uptake. However, lipid anchors apparently influence the effect of PEG length in PEGligands by shifting the most efficient spacer from PEG12 for LA (LA: P12-12F) to PEG24 for LAE (LAE: P12-24F) containing polyplexes. Additionally, tendency for aggregation seems to be increased with LAE. One could speculate, whether the small, non-significant reduction in mean zeta potential serves and suffices as trigger for aggregation. Nevertheless, the additional 'E's in LAE's structure make the compound's purification easier which might be the decisive argument for the 'E''s integration. The biological activity of polyplexes containing 20 mol % lipid anchor -PEG-ligands is comparable to polyplexes from CON + siRNA with 25 or 50 mol % covalently bound PEG-ligands without lipid anchors. The predictive value of the lipid anchor containing systems has been assessed with the system published by <ns0:ref type='bibr' target='#b25'>Klein et al. (Klein et al., 2018)</ns0:ref>. Here, 25 mol % PEG-ligands were covalently coupled to conventionally prepared polyplexes from CON and siRNA. Subsequently, KB cells were transfected. Indeed, the silencing pattern visible with core -lipid anchor -polyplexes was reproduced and the hinted-on problems with longer PEG chains -aggregations, toxicity -were also visible when PEG-ligand concentration was increased. The most striking resemblance between both systems is the U-shaped pattern when looking at the luciferase activity relative to PEG ligand length. We speculate that at least two effects influence the formulation's efficacy and their interplay leads to the observed pattern. First, if the distance between core oligomer and folic acid is too short, an effective interaction between folic acid and its receptor will be hampered, effectively decreasing the efficacy of formulations with short PEG chains. It has also been suggested that folate receptors need to be crosslinked to facilitate uptake of nanoparticles <ns0:ref type='bibr' target='#b44'>(Mayor, Rothberg, & Maxfield, 1994)</ns0:ref>. Second, polyplexes usually lose their internal stability with increasing PEG length. This could be the reason behind the decrease in transfection efficacy with polyplexes with longer PEG chains. All in all, results of this study suggest that lipid anchors could serve as a tool to investigate structure activity relationships on a wide variety of core polyplexes, especially when core oligomers lack functionalities for covalently binding additional structures. Application of microfluidic devices for various tasks <ns0:ref type='bibr' target='#b74'>(Whitesides, 2006)</ns0:ref> and especially for producing delivery systems <ns0:ref type='bibr' target='#b43'>(Liu et al., 2017)</ns0:ref> usually improves quality of products. For example, Abstiens et al. <ns0:ref type='bibr' target='#b0'>(Abstiens & Goepferich, 2019)</ns0:ref> demonstrated that the continuous production of core -lipid anchor nanoparticles from PLGA and PLA-PEG with microfluidics leads to increased control over the production process which in turn generates nanoparticles with decreased size and polydispersity. Automated production of pDNA PEI polyplexes in Tjunctions has been shown by <ns0:ref type='bibr' target='#b23'>Kasper et al. (Kasper et al., 2011)</ns0:ref> in our lab. Continuous or batch wise production of PEI pDNA polyplexes with active mixing by surface acoustic waves (SAWs) has been demonstrated by <ns0:ref type='bibr' target='#b72'>Westerhausen et al. (Westerhausen et al., 2016)</ns0:ref> and Schnitzler et al. <ns0:ref type='bibr' target='#b56'>(Schnitzler et al., 2019)</ns0:ref>. The decrease in size and polydispersity is most impressive in lipid nanoparticle formulations with siRNA <ns0:ref type='bibr' target='#b8'>(Chen et al., 2012;</ns0:ref><ns0:ref type='bibr' target='#b30'>Krzysztoń et al., 2017)</ns0:ref>. They all show that increasing mixing speeds decrease size and polydispersity. We show that similar improvements can be gained with a passive micromixer and sophisticated sequence-defined oligomers and that multi-component polyplexes can easily be prepared automatically from their starting materials. These core -lipid anchor -PEG-ligand polyplexes were used to investigate the influence of PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2019:04:36565:1:1:NEW 8 Aug 2019)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>PEG length on in vitro efficacy and to see if using lipid anchors had a predictive value for formulations with covalently bound PEG-ligands without lipid anchors. Luciferase assays revealed the influence of PEG length and PEG-ligand concentration on transfection efficiency. It has already been shown by <ns0:ref type='bibr' target='#b25'>Klein et al. (Klein et al., 2018)</ns0:ref> that their shortest PEG-ligand (P24F) in combination with CON was most efficient in their study and that increasing PEG-ligand concentration increased efficiency. Additionally, they demonstrated that even if polyplexes with longer PEG-FolA ligands (P48F, P72F) had bound to FolA receptors, uptake is strongly decreased. They did not show, however, how PEG-ligands with shorter PEG chains compete. We strengthen the results generated by <ns0:ref type='bibr'>Klein et al. by</ns0:ref> showing that there is indeed a strong association between PEG length and transfection efficiency with peak performance with PEG12 and PEG24 containing polyplexes. These two are also statistically highly significant different from the core -LA (p < .001) or core -LAE (p < .001) formulation alone. In this work, however, lipid anchor oligomers with an additional PEG12 chain were used, raising the total number of PEG monomer repetitions in LA: P12-24F containing polyplexes to 36. All in all, our results suggest that using lipid anchors for investigating PEG-ligand performance is a valid way to screen core polyplex PEG-ligand combinations before synthesizing new structures. Our results also suggest that transfection efficiency does not only depend on the PEG-ligand alone but on its chemical environment as well, since changing LA to LAE did significantly increase silencing efficiency of the P12F PEG-ligand (supplemental figure S23, p < .001). It did also increase silencing efficiency of P24F PEG-ligand on LA against LAE, albeit not significantly (p = .237). We also observed that increasing PEG length and PEG-ligand concentration increased aggregation disposition and decreased efficacy. This is in line with results from Abstiens et al. <ns0:ref type='bibr' target='#b2'>(Abstiens, Gregoritza, & Goepferich, 2018)</ns0:ref> who argue that increasing PEG-ligand length and PEG-ligand concentration lead to clustering of nanoparticles and a higher probability for PEG-ligand entanglement and shrouding and therefore decreased efficacy. The microfluidic system presented here has been designed for producing multi-component siRNA polyplexes from its starting materials. During the development process, two modules have been excessively tested: The first one to produce core polyplexes, the other one for the attachment of lipid anchors and PEG-ligands. Further development should focus on the implementation of additional modules for different task, for example for producing pDNA polyplexes. Additionally, producing polyplexes without the help of organic solvents, which possibly alters the kinetically controlled assembly process, which needs mixing speeds in the order of 50 ms <ns0:ref type='bibr' target='#b5'>(Braun et al., 2005)</ns0:ref> to yield small particles, could facilitate the integration of this method into acetone intolerant applications. One solution could be the utilization of surface acoustic waves <ns0:ref type='bibr' target='#b72'>(Westerhausen et al., 2016)</ns0:ref> to avoid usage of organic solvents. In the end, there could be a small set of modules researchers could choose from according to the desired properties of their particles. Furthermore, these modules should be integrated into a system that can automatically select from different starting materials and distribute polyplexes produced under controlled conditions to various containers. This approach would have the advantage to enable faster production of various samples in a controlled manner while producing less waste in a shorter period of time in comparison to conventionally produced polyplexes. The advantage of high throughput production of many different formulations, albeit with a completely different system, has already been shown by <ns0:ref type='bibr' target='#b69'>Wang et al. (H. Wang et al., 2010)</ns0:ref> The PEG-ligands presented here are successful in facilitating transfection when their density on the nanoparticle's surface is large enough. Research by, for example, <ns0:ref type='bibr'>Lee et al. (H. Lee et al., 2012)</ns0:ref>, A. Antony <ns0:ref type='bibr' target='#b3'>(Antony, 1992)</ns0:ref> and S. Major et al. <ns0:ref type='bibr' target='#b44'>(Mayor et al., 1994)</ns0:ref>, show that folic acid receptors might require a certain number and distance of folic acid PEG-ligands to successfully interact with their respective nanoparticles. Therefore, this system could be employed to test various multi-folate receptors on various optimized core structures to finally get an optimized product with ideal PEG-ligands for the target cell type.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions</ns0:head><ns0:p>In conclusion, we have shown that the controlled, continuous formulation of polyplexes from two and three components is advantageous. The subsequent anchoring of DBCO-PEGx-folic acid (PEG-ligands) coupled to lipid anchors on core polyplexes enabled us to investigate the influence of PEG length on transfection efficiency, eliminating the need to alter siRNA complexing oligomers synthetically. We found that core (CO + siGFP) polyplexes -lipid anchor -PEG-ligands with 12 + 12 to 12 + 24 ethylene oxide repetitions had the largest silencing effect on luciferase activity in KB cells. PEG-ligands with 12 + 48 EOs, however, were prone to forming aggregates. These results were validated on a previously published system which binds PEG-ligands covalently. We confirmed that the optimal number of EO repetitions in PEGligands was 24. PEG-ligands with less than 24 EO repetitions are advantageous at PEG-ligand concentrations > 50 mol % (relative to n core oligomer ), because formulations containing ≥ 50 mol % PEG-ligands with ≥ 24 ethylene oxide repetitions tended to form aggregates. Two different channels were used to produce nanoparticles during solvent exchange, a single meander channel and a double meander channel. In the single meander channel preassembled core particles were mixed with lipid anchors or lipid anchor PEG-ligand oligomers.</ns0:p><ns0:p>In the double meander channel, the complete polyplex was assembled from its starting Manuscript to be reviewed Chemistry Journals <ns0:ref type='bibr'>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:ref> </ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>Further materials: Microreactors (MultiSynTech, Witten, Germany), carbon coated copper grids (Ted Pella, Inc. USA, 300 mesh, 3.0 mm O. D.), 96 well plates (TPP 92096, Faust Lab Science GmbH, Klettgau, Germany), cell culture flasks (TPP90075, Faust Lab Science GmbH, Klettgau, Germany), Versilon™-Inert-Schlauch SE-200, 1.6 x 3.2 mm, Wd 0.8 mm, PP-T-Tüllenverbinder 1.6 mm, PP-Luer connector, female, PP-Luer connector, male (ProLiquid GmbH, Überlingen, Germany), Hamilton syringes: syr 1 ml 1001 TLL, d inner = 4.61 mm, syr 500 µl 1750 TLL-XL, d inner = 3.26 mm, syr 100 µl 1710 TLL-XL, d inner = 1.46 mm, needles: NDL ga27, 90 mm, pst4 (Hamilton Bonaduz AG, Switzerland), syringe pumps: LA-122, LA-120, LA-160 (Landgraf Laborsysteme HLL GmbH, Langenhagen, Germany), LabView 2017 (National Instruments, Austin, Texas, USA), biopsy puncher (World precision instruments; ID = 0.96 mm; OD = 1.26 mm)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head>Figure 1 :</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1: Sequence-defined oligomers and their corresponding nanoparticle production methods A: Oligomers used in polyplex formation: Lipid anchors were coupled to PEG-ligands before polyplexes were formulated. Building blocks represent natural amino acids (E = glutamic acid, G = glycine, H = histidine, K = lysine, Y = tyrosine), synthetic building blocks (Stp = succinoyltetraethylene-pentamine, PEG = polyethylene glycol), fatty acids (CholA = cholanic acid), and moieties for bio-orthogonal click chemistry (N3 = azide, DBCO = dibenzocyclooctyne).B: Production methods for polyplexes with CO oligomers: Formulations used are depicted between both channels with the id of their corresponding syringe (S1-4). Two different channels were used to produce nanoparticles during solvent exchange, a single meander channel and a double meander channel. In the single meander channel pre-assembled core particles were mixed with lipid anchors or lipid anchor PEG-ligand oligomers. In the double meander channel, the complete polyplex was assembled from its starting components.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>Figure 2 :</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2: Comparison of core polyplex (CO + siRNA) production methods. A: Mean hydrodynamic diameter in nm. B: Mean polydispersity index (PDI). Method key: hand: Mixing equal volumes of CO and siRNA solution by vigorous pipetting. T-junc.: Mixing equal volumes of CO and siRNA solution (with or without 50 % acetone) at a T-junction at 60 ml/h total flow rate. Micro: Mixing an 11x larger volume of CO with siRNA solution (with or without 50 % acetone) inside the single meander channel at 1.326 ml/h total flow rate. Grey spheres: no acetone was used. Blue cubes: acetone was used. Error bars correspond to 95 % confidence intervals; n = 3.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head>Figure 3 :</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Figure 3: Hydrodynamic diameter (d H ), PDI, and zeta potential of core, core -lipid anchor, and core -lipid anchor -PEG-ligand polyplexes. Subfigures are divided in three panels. 'core' (green) depicts particle properties of the core polyplex formulation used for all subsequent modifications with 20 mol % lipid anchor and lipid anchor-PEG-ligands. 'core-LA' (blue) and 'core-LAE' (orange) indicate the lipid anchor oligomer used for attaching PEGligands to the core polyplex. Formulation key: P12-xxF: number of ethylene oxide repetitions from lipid anchors + PEG-ligands, F: Folate. Detailed PEG-ligand description in Fig. 1A. A: Polyplexes' hydrodynamic diameter with mean z-average (red dots) and respective intensity distribution depicted as violin plot (extension in x direction corresponds to the percentage of the total intensity measured at the specific size depicted on the y axis). B: Polydispersity index (PDI). C: Zeta potential measured in HBG pH 7.4. Caption states assembly method: core polyplexes were prepared with pipettes, lipid anchors were added with the single meander channel (SMC). Statistics: A, B: Error bars correspond to 95 % confidence intervals. C: Error bars correspond to mean zeta deviations. N = 3.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head>Figure 4 :</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Figure 4: FRET and TEM measurements of core (CO + siRNA) -lipid anchor polyplexes and their components. A: Title of each panel indicates dye measured. FRET: excites Atto488 (485 nm), measures Cy5 (680 nm). Color indicates dyes used in this formulation. 'Sample' specifies formulation composition: 'core + LA': core polyplex with 20 mol % LA oligomers. 'siRNA + LA': control formulation without core oligomers, i.e. no particle formation. Cy5 is coupled to sense strand of siRNA. Atto488 is coupled via azide -alkyne click chemistry to the azide of LA or LAE oligomers. Measured fluorescence is divided by gain's value to exclude amplifier effects. Assembly: core polyplexes were prepared with pipettes, lipid anchors were added with the single meander channel (SMC). B: Vertical label: Scale represented by white bar of respective row. Horizontal label: Formulation visible in the respective column. 'Core': Core polyplex. 'Core -LA/LAE: P12': Core-lipid anchor polyplex. 'Core + LA/LAE: P12-24F': Core-lipid anchor -PEG-ligand polyplex. Columns without 'core' depict unformulated lipid anchors or lipid anchor -PEG-ligand oligomers in solution.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head>Figure 5 :</ns0:head><ns0:label>5</ns0:label><ns0:figDesc>Figure 5: Luciferase activity assay of core (CO + siRNA) -lipid anchor -PEG-ligand polyplexes. Luciferase enzyme activity was measured in relative light units (RLU) and is shown relative to values of buffer treated cells. Colors indicate type of siRNA used: Light color: control siRNA, saturated color: siGFPLuc siRNA. lipid anchor -PEG-ligand key: 'none' (green bars): core polyplex formulation alone; used for all subsequent modifications with 20 mol % lipid anchors and lipid anchor -PEG-ligands. P12: core polyplex with unmodified lipid anchor. P12-xxF: PEG12 from the lipid anchor + PEGxx from the PEG-ligand, F: Folate. Detailed PEG-ligand description in Fig. 1A. A: Polyplexes with LA (blue bars). B: Polyplexes with LAE (orange bars). Assembly: completely inside the double meander channel (DMC). Statistics: Tips of horizontal lines indicate compared samples. Comparison: two-sided student's t -test with HOLM correction. N = 5. Key: NS: not significant at α = .05; ***: α < .001. Error bars correspond to 95 % confidence intervals.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head>Figure 6 :</ns0:head><ns0:label>6</ns0:label><ns0:figDesc>Figure 6: Sequence-defined oligomers and their corresponding nanoparticle production methods A: Core oligomer featuring an azide (CON): PEG-ligands were coupled to CON after polyplex</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_7'><ns0:head>Figure 7 :</ns0:head><ns0:label>7</ns0:label><ns0:figDesc>Figure 7: Hydrodynamic diameter (d H ), PDI, and zeta potential of core (CON + siRNA) and core -PEG-ligand polyplexes. Subfigures are divided into five panels. Numbers indicate the amount of PEG-ligand used in mol % relative to n CON . Formulation key: 'core polyplex': Unmodified CON -siGFP polyplex. Px: PEGx, F: Folate. Detailed oligomer description in Fig. 1A (PEG-ligands) and Fig. 6A (CON). Assembly: conventionally with pipettes. A: Hydrodynamic diameter (d H ) and mean z-average (red dots) with respective intensity distribution depicted as violin plot (extension in x direction corresponds to the percentage of the total intensity measured at the specific size depicted on the y axis). B: Polydispersity index (PDI). C: Zeta potential measured in HBG pH 7.4. Statistics: A, B: Error bars correspond to 95 % confidence intervals. C: Error bars correspond to mean zeta deviations. N = 3.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_8'><ns0:head>Figure 8 :</ns0:head><ns0:label>8</ns0:label><ns0:figDesc>Figure 8: Luciferase activity assay and MTT assay of core (CON + siRNA) -PEG-ligand polyplexes. Polyplexes were prepared conventionally. Colors indicate type of siRNA used: Light color:</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_9'><ns0:head>Figure 1</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_10'><ns0:head /><ns0:label /><ns0:figDesc>components.PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2019:04:36565:1:1:NEW 8 Aug 2019)Manuscript to be reviewed Chemistry JournalsAnalytical, Inorganic, Organic, Physical, Materials Science </ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_11'><ns0:head>Figure 2</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_12'><ns0:head>Figure 3</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Figure 3</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_13'><ns0:head>Figure 4 FRET</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Figure 4</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_14'><ns0:head>Figure 5</ns0:head><ns0:label>5</ns0:label><ns0:figDesc>Figure 5</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_15'><ns0:head>Figure 6</ns0:head><ns0:label>6</ns0:label><ns0:figDesc>Figure 6</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_16'><ns0:head>Figure 7</ns0:head><ns0:label>7</ns0:label><ns0:figDesc>Figure 7</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_17'><ns0:head>Figure 8</ns0:head><ns0:label>8</ns0:label><ns0:figDesc>Figure 8</ns0:figDesc></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head>Table 1 : Solvents used for DLS measurements</ns0:head><ns0:label>1</ns0:label><ns0:figDesc /><ns0:table><ns0:row><ns0:cell>Refractive indices (RI) and viscosities in centi poise (cP).</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_1'><ns0:head>Table 2 : Results of post-hoc tests of core (CO + siRNA) -lipid anchor -PEG-ligand polyplexes.</ns0:head><ns0:label>2</ns0:label><ns0:figDesc /><ns0:table><ns0:row><ns0:cell>Two-sided student's t-test with and without HOLM correction. Cohen's d: Effect size.</ns0:cell></ns0:row><ns0:row><ns0:cell>Magnitude: < 0.2: negligible. < 0.5: small. < 0.8: medium. < 1.20: large. > 1.20: very large. Bold values are significant at α < .05. Core: Core polyplex with siGFP and CO. LA/LAE: lipid</ns0:cell></ns0:row><ns0:row><ns0:cell>anchor oligomers. [ ] no PEG-ligand. P12-xxF: number of PEGx from lipid anchor + PEG-</ns0:cell></ns0:row><ns0:row><ns0:cell>ligand, F: Folic acid.</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_2'><ns0:head>Table 3 : Results of post-hoc tests between core (siGFP + CON) polyplex formulations with and without PEG-ligands.</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Two-sided student's t-test with and without HOLM correction. Cohen's d: Effect size. Magnitude: < 0.2: negligible. < 0.5: small. < 0.8: medium. < 1.20: large. > 1.20: very large. Bold values are significant at α < .05. PEG-ligands are covalently bound to core polyplexes with siGFP and CON. Mol %: n PEG-ligand : n CON . Px: PEGx, F: Folic acid.</ns0:figDesc><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_3'><ns0:head>Table 4 : Results of post-hoc tests between core (siCtrl + CON) polyplex formulations with and without PEG-ligands.</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Two-sided student's t-test with and without HOLM correction. Cohen's d: Effect size.</ns0:figDesc><ns0:table /></ns0:figure> <ns0:note place='foot'>Analytical, Inorganic, Organic, Physical, Materials Science </ns0:note> <ns0:note place='foot'>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2019:04:36565:1:1:NEW 8 Aug 2019)Manuscript to be reviewed Chemistry JournalsAnalytical, Inorganic, Organic, Physical, Materials Science </ns0:note> </ns0:body> "	"Response to Reviews Dear Dr. Blank, dear reviewers, This letter accompanies our revised manuscript “A microfluidic approach for sequential assembly of siRNA polyplexes with defined structure – activity relationship”. We are grateful for the reviewer’s helpful comments and hope our revision addresses them all. In particular, we have revised the nomenclature to improve clarity (reviewer 1, reviewer 2, editor) and moved subfigure 1 C and D to the end of the manuscript where this system is employed to prepare control polyplexes to benchmark the efficacy of the hydrophobic attachment of PEG-ligands. Major changes have been made to the following figures. Figure 1 has been restructured and divided in two independent subfigures (Fig. 1 and Fig 6). Supplemental figures S13, S16, S19, S22 and S24 are based on new data and have been added to the manuscript. All figures and supplemental figures have been revised to include the reviewers’ suggestions. Below we detail the changes made in our revision. We include the text of the reviews in Roman font; our responses are in italics. References to line numbers are for the revised manuscript. Substantial changes made to the main manuscript are highlighted in yellow. We believe the manuscript is now suitable for publication. Dominik Loy On behalf of all authors. Dr. Blank: Having read the manuscript myself, I agree with the comments of the reviewers. Also, in my view, the nomenclature is confusing at times and I further missed a clear explanation of why the system shown in Fig. 1c/d is investigated in addition. Please provide some more background on this. Thank you for your evaluation of our manuscript. We address your points together with comment 2 from Reviewer 1 and Minor Points – comment 1 from Reviewer 2. Reviewer 1 Basic reporting In this submission, Loy et al. describe the use of a microfluidic assembly approach to produce functionalized siRNA polyplexes. Improving control during the bottom-up manufacturing of polymer-nucleic acid particles is a worthwhile aim that could help solve the issues of batch-to-batch variability and convoluted structure-function relationships that persist. By incorporating sequence-defined oligocations and sequential chip-based assembly steps, the authors have made a nice step toward achieving fully defined polyplex micromanufacturing. The language is clear throughout, appropriate context is provided through referenced literature, and the experimental methods are both appropriate and well-described. Unfortunately, the modularity and adaptability of the system and its components is a bit of a double-edged sword - the many combinations of assembly conditions and components can cause confusion. This could be alleviated by editing some of the figures to improve clarity. Thank you for your positive and constructive feedback. We have indeed struggled to be as clear and precise about the assembly conditions and components. We hope that we succeeded in improving clarity in the revised manuscript. Please see our answer to your comment 2 on Fig. 1 for further details. Experimental design The methods chosen by the authors are appropriate, well-described, and well-controlled. The statistical analysis is transparent and thorough. However, there are a few issues with the results and their presentation that hinder interpretation. Thank you again for your positive feedback. We hope that our revision of the results resolved these issues. 1) Portions of the results are derived from nanoparticles assembled by loading ‘core polyplexes’ onto a microfluidic chip for subsequent functionalization (most of the characterization figures). Other results involve nanoparticles generated entirely on a chip from their molecular constituents (e.g. the MTT and RNAi figures). It is not clear without delving deep into the text which data is derived from which assembly paradigm. It is conceivable that different degrees of condensation could affect the ratio of available:embedded cholanic acid binding sites, thereby changing the effective molar ratio of lipid anchors and PEG ligands provided. Ideally, the characterization assays and functional assessments would be done with identical batches of particles. At the very least, the figures and captions should clearly indicate which assembly method was used in each specific case. Without such disambiguation, the results can neither be accurately interpreted nor reproduced. You are right; the ideal approach would have been to use identical batches of particles for every experiment. It is very well conceivable that the particle’s internal structure varies with the assembly procedure. However, preparing every formulation inside the microchannel puts a huge strain on expensive reagents. Additionally, the increased precision that is gained by using microfluidics is bought with time that is needed for each experiment, especially in the early phase of the project when many roads were explored that turned out to be dead ends. Therefore, we compromised. Experiments that we deemed uncritical regarding cholanic acid binding sites were done with manually prepared core polyplexes, while the experiments that already had a high degree of variability were conducted with polyplexes prepared inside the microchannel. The first group consists of almost all characterization assays (Fig.3, 4, 7, 8, S12, S17, S18, S19, S20, S22), while the second group holds most of the in vitro assays (Fig. 5, S13, S15, S16, S21, S23, S24) Nevertheless, we repeated TEM measurements with CO and CO – LAE polyplexes completely prepared inside the microchannel and compare them with their manually prepared counterparts. The results are in the supplemental information (Fig. S16) and we inserted a reference to this figure in line 637-639: “There was no obvious difference when particles were produced conventionally or with microfluidics (supplemental information, 8. TEM: Comparisons of polyplexes produced with pipettes or with the double meander channel (DMC), supplemental figure S16).” In addition, we partially repeated our benchmarking experiment (Fig. S24) with CON – PEG-ligand (75 mol%) particles that were completely prepared inside the microchannel. We found small differences in luciferase activity suggesting a small benefit for the microfluidic formulation. The overall trend, however, remained unchanged. See Supporting Figure S24, and lines 848 – 852: “Producing CON – PEG-ligand polyplexes completely inside the double meander channel yielded similar results to conventionally produced polyplexes. The results from the luciferase activity assay and the MTT assay from CON polyplexes with 75 mol% PEG ligands are presented in the supplemental information (14. Luciferase and metabolic activity assay of CON polyplexes with PEG-ligands produced with the double meander channel (DMC), supplemental figure S24).” We also implemented your suggestion to indicate the assembly method in the caption of each figure. 2) Figure 1 is extremely confusing. It should be redrawn with descriptive labels in the figure itself instead of the caption only. Many of the acronyms used are only explained much later in the text. The rationale for including LA and LAE in the study is not discussed until Line 477, nearly halfway through the manuscript. Abbreviations like DP48F are not immediately obvious. Figure 1B should be changed to clarify that it illustrates two different assembly schemes. It also lists “Shell-ligand” where everywhere else it is called “Lipid anchor-ligand”. The abstract refers to optimized PEG lengths of 36, but the methods and Figure 1 refer only to 12, 24, 48, etc. It is not clarified until much later that this sum includes both the lipid anchor and PEG ligand. An unambiguous system should be adopted early and used consistently throughout. Fig 1C & D could be moved elsewhere in the text, perhaps nearer to the sections describing the results using the CON assembly system. Thank you for your suggestions to improve the clarity of our system description. We have redrawn Figure 1 and tried to implement your suggestions into the figure and in describing the assembly system. We made the following changes and hope to be as clear as possible: • Figure 1 now only consists of sub-figures A and B. • Labels in figure 1A now describe the core oligomer CO and the composition of Lipid Anchor – PEG-Ligand Oligomers from the lipid anchor moiety (LA or LAE) and from the PEGn-Ligand; with n = 0, 3, 12, 24, 48 ethylene oxide repetitions • Labels in figure 1B now discriminate between the single meander channel and the double meander channel and state their main difference. We have also removed the name “Shell-ligand” and updated all names to the new convention. • We added a subclause in line 45/46 to explain the Lipid Anchor – PEG-ligands composition: “[..], 12 EOs each from the lipid anchor and 12 or 24 EOs from the PEG ligand, respectively.” • We state the rationale for using lipid anchors in the introduction, lines 117/121: ”We show that the production of three and four component polyplexes is feasible. These polyplexes are assembled on-chip from siRNA, cationic core oligomers (CO), and PEG-ligands with zero to 48 ethylene oxide (EO) repetitions, which are integrated non-covalently into core polyplexes by lipid anchors.” • Figure 1C/D is now Figure 6A/B. It is placed directly before the introduction of the CON assembly system: Lines 773 - 780. Figure 1: Sequence-defined oligomers and their corresponding nanoparticle production methods A: Oligomers used in polyplex formation: Lipid anchors were coupled to PEG-ligands before polyplexes were formulated. Building blocks represent natural amino acids (E = glutamic acid, G = glycine, H = histidine, K = lysine, Y = tyrosine), synthetic building blocks (Stp = succinoyl-tetraethylene-pentamine, PEG = polyethylene glycol), fatty acids (CholA = cholanic acid), and moieties for bio-orthogonal click chemistry (N3 = azide, DBCO = dibenzocyclooctyne). B: Production methods for polyplexes with CO oligomers: Formulations used are depicted between both channels with the id of their corresponding syringe (S1-4). Two different channels were used to produce nanoparticles during solvent exchange, a single meander channel and a double meander channel. In the single meander channel pre-assembled core particles were mixed with lipid anchors or lipid anchor PEG-ligand oligomers. In the double meander channel, the complete polyplex was assembled from its starting components. Validity of the findings The adequate sample sizes and acceptable variability of the data enable robust statistical comparison. The trends identified by the authors are generally supported by the results. A few additional controls would strengthen some of the structure-function relationships identified. Thank you for your assessment of our work. 3) It should be confirmed that the physicochemical characterizations are performed on stabilized and equilibrated particles. If size and charge continue to change over time, comparisons between samples are less reliable. We agree with your comment. Based on the findings of Troiber et al., 2013, who had used similar oligomers to form siRNA polyplexes and observed stability over time, we did not expect our formulation to change substantially over the time we need to work with them. Moreover, intermittent DLS measurements during experiments to assure particle conformity gave us no cause for doubt. However, we acknowledge the need for an additional control experiment. We have investigated the change in size, pdi and zeta potential of our core formulation over 90 min and added a supplementary figure S 21 to our manuscript. The control experiment confirmed the core polyplexes’ stability over the timespan of a typical experiment (90 min) We added a section to the Method section describing the experiment. Lines 290 - 298: “Stability of the Core Formulation Core polyplex formulations were prepared using the single meander channel (Fig. 1B) set up as described above. CO was diluted in HBG with 50 % acetone and siRNA was diluted in HBG only. csiRNA of the final solution was 0.025 mg/ml. Size, PDI and zeta potential were measured as described under ‘DLS Measurement’. This protocol, however, was changed in the following way to allow for multiple measurements over time: Two samples with 60 µl each were prepared. The first sample was used to measure size and PDI. The second sample was diluted with HBG to 800 µl to enable zeta potential measurements. Both samples were measured directly after each other for 90 min.” We added a paragraph referring to the experiment in lines 528-535: “Multiple experiments characterizing polyplexes can only contribute viable information about a formulation, if it is ensured that the starting formulation is in equilibrium at the time of each experiment. Troiber et al., 2013 have found particles assembled from the same class of oligomers to be stable over three weeks. Here, we have investigated the changes in size, PDI and zeta potential of our core formulation over 90 min (Supplemental information, 4. Core Polyplex Stability, supplemental figure S13). We saw no changes in size and PDI. We did not note any changes in zeta potential up to 40 min, which is the reason why formulations were always used after 45 min incubation time.” 4) It is not stated whether the 500 ng dose/well was optimized. With zero toxicity observed in most conditions, it would be interesting to see a dose escalation test. Not only would this perhaps enable more robust gene silencing, but it would elucidate any differences in cytotoxicity between formulations. It would also be nice to see a validation of FA activity following microfluidic assembly. You raise a good point. There are two reasons behind us using this exact amount of siRNA. First, this amount was optimized to be able to differentiate between formulations with various oligomers without masking the oligomer’s effect by toxicity or other factors. For example, if we increase the siRNA concentration, we automatically increase oligomer concentration leading to a larger number of polyplexes that invariably have a greater effect due to their number alone on our cell system. Therefore, increasing the siRNA amount will likely increase the general activity. However, it is generally favored to also have high activity at low concentrations and in this regards, differences of formulations can be hard to detect by increasing the siRNA amount. Or – to quote the movie “the Incredibles”: “if everyone is super, no-one is”. Moreover, using larger amounts of siRNA is more expensive without any added benefits. Second, we agreed on this dose in our lab to enable the comparison between experiments and projects to be able to design better oligomers. Nevertheless, we have done an additional cell experiment to prove our statements. We have escalated the siRNA dose from 100 to 1000 ng / well and added a figure to the supporting information (Fig. S22). We added a paragraph to the Methods describing the experiment. Lines 439-447. “Core polyplexes were prepared conventionally with pipettes as described under ‘Polyplex Preparation’. siRNA concentrations were chosen to have a final amount of 100, 250, 500, 750, and 1000 ng / well. CO concentrations were adjusted accordingly. To be precise, siRNA concentrations in 20 µl transfection volume were [mg/ml]: 0.0050, 0.0125, 0.0250 0.0375, 0.0500. CO concentrations were [mg/ml]: 0.0458, 0.1145, 0.2291, 0.3436, 0.5041. 20 µl / well of each sample was transfected as described under ‘Transfection’. Samples were transfected in quintuplicates. The formulations’ effect on luciferase activity and metabolic activity was evaluated with a luciferase assay and a MTT assay as described above.” We added a sentence referring to the experiment in lines 724-725 About your request about validation of FA activity: (Dohmen et al., 2012; Klein et al., 2018; Müller, Kessel, Klein, Hoehn, & Wagner, 2016) have all shown the activity of various folic acid containing systems on the same cell line. Additionally, the system evaluated by Klein et al. was used in this manuscript as benchmark and our core – lipid anchor – PEG-ligand polyplexes produced by microfluidics showed a similar luciferase activity pattern compared to the Klein et al. particles with validated folic acid activity. Therefore, we refrained from doing this experiment again and hope that our argumentation convinces you. 5) The authors should speculate on the reasons a U-shape effect is observed in gene silencing relative to PEG ligand length. We added a paragraph to the discussion section to address this request. Lines: 945-954: “The most striking resemblance between both systems is the U-shaped pattern when looking at the luciferase activity relative to PEG ligand length. We speculate that at least two effects influence the formulation’s efficacy and their interplay leads to the observed pattern. First, if the distance between core oligomer and folic acid is too short, an effective interaction between folic acid and its receptor will be hampered, effectively decreasing the efficacy of formulations with short PEG chains. It has also been suggested that folate receptors need to be crosslinked to facilitate uptake of nanoparticles (Mayor, Rothberg, & Maxfield, 1994). Second, polyplexes usually lose their internal stability with increasing PEG length. This could be the reason behind the decrease in transfection efficacy with polyplexes with longer PEG chains.” Comments for the author Minor Issues: 6) A MALDI analysis of the CO starting material could help validate its synthesis and monodispersity. The MALDI analysis of CO has already been published by Klein et al. 2016 and its MALDI data can be found there (id: 991, Fig. 3A). We added a sub clause in lines 187-189 stressing this point: ” The synthesis of the core oligomers CO (id: 991) and CON (id: 1106) has been described in detail by Klein et al. and their analytical data can be found there. (Klein et al., 2018, 2016)” 7) Consider representing hydrodynamic diameter as Dz instead of Dh, as it is derived from the intensity-weighted Z-average. This is a good suggestion. We have changed the labels from Dh to Dz in Fig. 2, S12, S14 and S15. We did not change the labels in Fig. 3 and Fig. 7, because the violin plots are directly derived from the intensity-based distribution. However, we changed the legend to include Dz. 8) Figures scales should be adjusted to fit the data (e.g. Figure 2, Figure 3A). We appreciate your comment but also want to make a case for our plot design. We assume that the changes you want us to make are the following: On the y-axis: Fig. 2A from 0-160 to 70 – 160, Fig. 2B from 0-1 to 0-0.3, Fig. 3A: 1 – 10000 to 20 – 10000. We have designed our plots (hopefully) according to the principles outlined in Prof. Edward Tuftes: “The Visual Display of Quantitative Information” 2nd ed., Graphics Press, Connecticut and Prof. Andy Fields “Discovering Statistics Using R” Sage Pub., London. There are two reasons for the y-axis in our size plots starting at 0 (or 1 in the log scale): Firstly, we do not want to exaggerate differences by scaling the plot around our data. However, we also do not want to obscure meaningful differences by inadequate scaling. Secondly, we think it is easier to compare data between plots if the scale is starting at 0. The main reason behind the y-axis in the PDI plot (Figure 2B and others) ranging from zero to one is that the PDI itself can only take values from zero to one. Fields states in his book that if the depicted value can only take certain values, the y-axis should represent the whole scope. We hope that our arguments convinced you of our plot design. Nevertheless, we will change it, if you insist. 9) The asterisks/lines in Figure 5 are ambiguous. Consider using alternate symbols or tick marks to indicate significance between groups. We have changed the layout of the plot by moving the asterisk closer to their respective lines and included tick marks to indicate significance between groups. 10) The methods provided for the gel shift experiments indicate 4uL of loading dye was used, but the sample volume is not stated. Thank you for detecting this missing piece of information. We have added it to the gel shift’s method, lines 307 – 308: “4 µl loading buffer (8.21 mM glycerol, 60 mM EDTA, 0.003 mM bromophenol blue in purified water)” 11) Channel heights should be included in Figures S10 and S11 We included the channel heights in Figures S10 and S11. 12) Cell source is not provided. Are the KB cells derived in-house or obtained commercially? We added a sentence to the Methods section to answer this question. Lines: 391 – 393: “KB wild type cells were bought from DSZM (Braunschweig, Germany) and they were subsequently modified to code for a GFP-luciferase fusion mRNA by A. Cengizeroglu.” 13) Consider using the more-standard PDI instead of pdi We (hopefully) changed every instance of pdi to PDI. 14) The role of acetone in the synthesis should be clarified - is it to retard siRNA compaction (line 524) or facilitate lipid anchor deposition (line 476)? We assume that acetone fulfils both roles. We added two subclauses to the Methods section to clarify the role of acetone. Lines 248 & 250.: “[…] CO in HBG or HBG with 50 % acetone to retard siRNA compaction (cCO = 3.025 mg/ml) was loaded into S3 (FR = 100 µl/h). Lipid anchor or lipid anchor – PEG-ligand oligomers in HBG with 50 % acetone to facilitate solvent exchange were connected to S2.” 15) Consider adding densitometry analysis to the gel results, as the reader is asked to compare relatively minor differences in band intensity. Thank you for this excellent suggestion. We conducted a densitometry analysis and added it to the supplemental information as separate supplemental figure S19. We added a paragraph to the gel shift’s method describing the densitometry analysis, lines: 313 – 319: “ImageJ (v. 1.52n) (Schindelin et al., 2012) was used to conduct a densitometry analysis of the siRNA bands. To this end, ImageJ was used to extract gray values from the respective siRNA stains. The sum of gray values as a function of the gel’s extension in y (width of the stains) and x (length of the whole gel) direction was plotted with ImageJ to produce the desired analysis. The plot’s arbitrary values on the y-axis correspond to the sum of all gray values over the full width (y) at a given length position (x). The length position x is plotted on the x axis.” We added a subclause to the results section to point at the densitometry analysis for simplified comparison of the bands. Line 674: “[…] and its densitometry analysis to simplify comparison of bands” 16) Typos: Line 77: _ _ Line 97: oppositional -> oppositely Line 229: hepes should be capitalized (changed also in line 276) Line 367: to-scale Line 464: lipdid Line 494: and -> an Line 296/301/305: well(s) is more standard than pocket(s) All corrected. Thanks. Reviewer 2 Basic reporting This manuscript is well written. There is a nice general introduction of the topic and the results section begins with a first section in which the function of the different part of each compound is well explained and made accessible to non-specialist. All important information are given making possible to reproduce the work and possibly to use the described technology. Note that some figures and nomenclature are misleading, and I strongly advise to revise the manuscript to take this into account (see my General comments to the authors). Thank you for your critical assessment and encouraging feedback of our work. We hope that the revisions of our manuscript resolved the issues. Please see our answer to comment 1) from Reviewer 1 for further details on misleading figures and nomenclature. Experimental design Overall, the experiments are well conducted. My only main concern was about the decorrelation between the siRNA used in the ex vivo study and the reporter monitored. Indeed, it is not clear for me why the authors measure luciferase activity whereas they used an siRNA targeting GFP. Indeed, if measuring luciferase activity, it would have made more sense to treat cells with siRNA targeting Luciferase gene; or if targeting GFP gene, why not measuring GFP fluorescence? This is a minor comment, but it would be great if the authors justify their choice? Methods section is in general well written and complete. Especially schematic of the microfluidic devices are given as supporting material which is essential for being able to reproduce the results and use the technology. Yet, couple of minor (yet important) information are missing and should be added (see General comments below). Thank you again for your feedback to our design of the experiments. Please excuse the unclear description of our reporter system. We have added more information to clarify your question about our reporter system. Referring to comment 16) of Reviewer 1, we state the origin of the KB cells and refer to the transformation procedure that introduced a stable gene mutation into the cells. The gene codes for a GFP-luciferase fusion mRNA that is translated into a functional GFP-luciferase fusion protein. That means GFP expression as well as luciferase expression is silenced when a siRNA complementary to the GFP-luciferase fusion mRNA is delivered to the cell’s cytosol. siGFP is used in this manuscript as a shorthand for the siRNA targeting the GFP-luciferase fusion mRNA. We added this information to the Methods section, lines 393 – 397. We have also added some more information to the results part to clarify the reporter system. Lines: 715 – 719. The reason behind us measuring the luciferase protein expression and not the GFP expression is that we have a working system for this reporter in our lab, therefore, we used it. Lines 393 – 397: “The modified cell line is stably transcribing and translating the fusion mRNA to an eGFP-Luciferase fusion protein, which consists of two functional proteins, GFP and luciferase. The fusion protein’s expression can be silenced by any siRNA that is complementary to the GFP-luciferase fusion mRNA. Here, we used siGFP.” Lines: 715 – 719: “if a siRNA (here: siGFP) that is complementary to any part of the target mRNA (here: eGFP-luciferase fusion mRNA) reaches the cytosol and is incorporated into the RISC complex the corresponding mRNA will be degraded selectively. In this case, the eGFP-luciferase fusion protein expression is reduced which in turn leads to a decrease in GFP and luciferase enzymatic activity.” Validity of the findings The conclusions and explanations proposed are in line with the results obtained from the experiments. Overall, I agree with all the conclusions exposed by the authors. Moreover, when necessary results were confirmed by an independent approach (e.g. Microscopy and FRET; electrophoresis and Ethidium Bromide assay…). Thank you very much for your positive assessment of our findings Comments for the author In their manuscript, Loy et al present a new class a transfection agent made of three blocks together with the possibility of using microfluidics to generate homogeneous nanoparticles made two to three components and characterized by controlled structure and composition. The originality of these reagents is the use of a lipid layer to self-assemble the particle through non-covalent interaction. Moreover, the authors introduce a microfluidic device allowing for preparing multilayer nanoparticles of controlled size and dispersity made of three different components in a single step. This is an elegant application of microfluidics and I’m pretty supportive to this work. However, prior to accepting this manuscript for being published in PeerJ-Materials Science, I would recommend the following points to be addressed. Major points 1.) Perhaps the biggest issue with this paper is the lack of consistency between the figures and the text regarding compounds naming. For instance, on Line 562-565 the authors talk about LA-DP48F but the reader needs to understand that this corresponds to P60F (12 Eos from LA + 48 Eos from DPF) on the figure. I acknowledge that an explanation is given in Lines 491-493, but still I have been unsure several times about this point. Another example is on line 692 where the authors state that “…reached their base at DP24F…”. Again, the reader has to understand that, if I’m correct, the authors are not talking about the bars labelled P24F, but rather P36F (ie 24 EOs from the PEG-ligand + 12 EOs from the anchor). If my understanding of the discrepancy between the text and the figure is correct, I would definitely ask the authors to change one of them and use a single nomenclature throughout their manuscript and figure. You are absolutely right in your criticism of our work and we thank you for pointing out this problem to us. We have addressed this problem in the answer to Reviewer 2’s comment 2). We hope that the adoption of an unambiguous nomenclature resolved this problem. 2.) I have been confused with the figures (Figure 4A and S15) showing FRET results. Indeed, in the captions, the authors say that “FRET: excites Atto488 (485 nm), measures CY5 (680 nm). This is misleading for me because we do not know (unless digging the information from the Method section) if this set of wavelengths was apply only to FRET or to all the measurement. Therefore, I would propose that the authors up-date the figures and add next to the name of the experiment (e.g. Atto488) the set of wavelengths they used for this particular measurement (in this case “Atto488 (ex./em.; 485-20 nm / 535-25 nm)”). Thank you for this comment and your suggestion. We added wavelengths to both plots as you suggested. (Fig. 4 and Fig. S17) 3.) The authors mention several times “conventionally prepared core”. Even though, methods section informs that this corresponds to manually prepared nanoparticles, I think it would be wise that clarify this point the first time the sentence is used in the result section (probably line 540). This is a good idea, since ‘conventionally’ can mean many things. We added an explanation in line 472 as you suggested: “[…] conventionally (educts are mixed manually with pipettes) […]” Minor points 1.) In general, I wondered how efficient the transfection agents used in this work are with respect to other commercial ones frequently used for cell transfection (e.g. lipofectamine and Fugene). Do the authors have any clue on that? Such a benchmarking might be of great interest for biologist readers and would allow to better justify these new developments. This is a valid question. In our case, we used the oligomer CON (id: 1106), a previously published positive control reagent for siRNA delivery.(Klein et al., 2018), and compare it with the newer system. We have previously compared the evolving class of T-shape oligomers repeatedly with positive control reagents for siRNA delivery (such as Succ-PEI), see recent (Klein et al., 2018) and previous papers.(Fröhlich et al., 2012; Schaffert et al., 2011). 2.) Line 94: The authors state that PRINT method has only advantages. If so, why searching for alternatives? It might be useful to cite disadvantages/limitations of PRINT as well. We use the PRINT method as an example for the top-down method which has very high control over particle size and shape, but we employ the bottom-up approach for our system where control over particle size cannot be established so easily. We think it is not the scope of the paper to engage in a discussion about the advantages and disadvantages of all the methods mentioned in the introduction. Nevertheless, we added a subclause to point out a common limitation of top-down processes in line 81: ”[…] although particle purification can be difficult.“ 3.) Line 360: could the author give the supplier of the fluoro-silane they used? Thanks for paying such close attention to the materials used. It is Sigma. Line: 159 4.) Lines 363-364: the authors use a plasma treatment to bind their PDMS device onto glass slides. It is known that the gas conditions as well as the type of plasma cleaner are important as well. Can the authors indicate if the plasma was generated from air or from oxygen? Also, model and supplier of the plasma cleaner should be given. Included the requested details into the Method section, lines 380 & 381: “[…] oxygen plasma-induced oxidation (10 W high frequency generator power, 12 seconds, Pico Model E, Diener Electronic).” 5.) Line 517: the authors mention that “additional efforts” are required in case 25% acetone is used. Could they specify what these efforts correspond to? Added a subclause to clarify this question in line 551. (“[…] by evaporation or dialysis to remove the organic solvent […]”) 6.) Lines 523-524: acetone was used to retard siRNA compaction. Could the authors shortly explain why this is necessary and how it works? This is necessary to produce smaller particles. (See Figure 2 for the data). We have no prove why it works, we can only speculate. We describe in our manuscript how the polyplexes’ particle size can be influenced. We know, that the average diffusion length between siRNA and oligomer plays a critical role and we know that oligomers with ionic components as well as hydrophobic components will form micelles at the concentrations we use them in. We suppose that the acetone disrupts the oligomer micelles, solvates the hydrophobic parts and therefore increases the average diffusion time which in turn leads to smaller, less polydisperse polyplexes. 7.) Lines 571-572: “CO in HBG with and without 50% acetone… LA or LAE with and without 50% acetone… with and without their respective PEG-ligands…”. From this sentence I was expecting 16 conditions to be tested. However, it turns out that only 8 conditions are found on Fig S14. Therefore, either data are missing or the reader should understand “CO combined with LA or LAE with and without 50% acetone… with and without their respective PEG-ligands…”, in which case the sentence should be corrected. Please excuse our sloppy writing, we wanted to describe an experiment with eight conditions in total. We changed lines 606 – 610 to be more precise: “Syringe S3 was filled with siRNA in HBG and S4 with CO in HBG with or without 50 % acetone. Syringes S2 were loaded with four different oligomers in HBG with 50 % acetone: LA, LA: P12-24F, LAE, or LAE: P12-12F. Eight runs were conducted, each lipid anchor or lipid anchor – PEG-ligand oligomer was mixed with core polyplexes produced with or without the aid of acetone.” 8.) On figure S13.A, third panel, the point at 10 mL/hr indicates a size reliably twice higher than the other points. Do the authors have an explanation to propose for this striking behavior? The explanation is hinted at in lines 548 & 549. The full explanation is that we were unable to properly control the assembly process without wasting a large amount of reagent solution when flow rates were high, and the two flowrates differed by a factor of 10. This probably led to high pressure differences at the T – junction and therefore an excess of the siRNA solution which in turn decreases the effective N/P ratio. We know that low N/P ratios lead to larger particles. The error bars are small probably purely by chance. 9.) Figure S17. It might be useful for a broad readership if the authors could explain how the Ethidium Bromide displacement assay they used works. We added an explanation to the Methods section, lines 344 – 347: “When EtBr intercalates into DNA or RNA it emits a strong signal when excited. This process can be inhibited by compacting the nucleic acid with polycations. Therefore, EtBr’s fluorescence correlates with the compaction efficiency of target oligomers. The addition of heparin tests the formulation’s resistance against anionic stress.” 10.) Transfection assessment. I found odd that the authors used an siRNA targeting GFP mRNA and monitor Luciferase activity as a readout. Why not directly quantifying GFP fluorescence or using siRNA targeting Luciferase mRNA? I think an explanation should be given. Please see our answer to your comment about our experimental design. 11.) It is argued that Glutamic acids (E) were included in LAE to increase attachment. Yet, this point is not addressed anymore in the final discussion of the article which left the reader with an open question on “how useful was finally the addition of Es”. I would suggest that the authors had a statement on this point in their discussion. We address this point in our discussion, lines 925 – 937. We state that the lipid anchors effect is very small in comparison to the effect of the PEG-ligands, but it might be advantageous for the compound’s purification. 12.) Line 815: The authors say that T-junction is best for large scale preparation. How about their microfluidics? Could this technology also be used for large scale preparation? We added a comment to this question in lines 892 – 893: “Increasing throughput to T-junction levels, however, would entail parallelization of the whole set-up, which is only feasible when proto typing and sample preparation can be automated.” 13.) In the text, or at least in the caption of figure 1, it would be great to explain what HBG stands for and why using this particular buffer. Added an explanation in lines 220 – 222: “The solvent – if not noted differently – was HEPES buffer pH 7.4 with 5 % glucose (HBG). This buffer was used because it is it does not rely on salts to be isotonic, since polyplex formation relies on charge interactions that could be hampered by ions.” 14.) Typos: Line 123: “for or for” should read “for” Line 675: repeated “in” Caption Fig. 1: “lipdid” should read “lipid” All corrected, thanks! References Dohmen, C., Edinger, D., Fröhlich, T., Schreiner, L., Lächelt, U., Troiber, C., … Wagner, E. (2012). Nanosized multifunctional polyplexes for receptor-mediated SiRNA delivery. ACS Nano, 6(6), 5198–5208. https://doi.org/10.1021/nn300960m Fröhlich, T., Edinger, D., Kläger, R., Troiber, C., Salcher, E., Badgujar, N., … Wagner, E. (2012). Structure-activity relationships of siRNA carriers based on sequence-defined oligo (ethane amino) amides. Journal of Controlled Release, 160(3), 532–541. https://doi.org/10.1016/j.jconrel.2012.03.018 Klein, P. M., Kern, S., Lee, D.-J., Schmaus, J., Höhn, M., Gorges, J., … Wagner, E. (2018). Folate receptor-directed orthogonal click-functionalization of siRNA lipopolyplexes for tumor cell killing in vivo. Biomaterials, 1–13. https://doi.org/10.1016/j.biomaterials.2018.03.031 Klein, P. M., Reinhard, S., Lee, D.-J., Müller, K., Ponader, D., Hartmann, L., & Wagner, E. (2016). Precise redox-sensitive cleavage sites for improved bioactivity of siRNA lipopolyplexes. Nanoscale, 8(42), 18098–18104. https://doi.org/10.1039/C6NR05767E Müller, K., Kessel, E., Klein, P. M., Hoehn, M., & Wagner, E. (2016). Post-PEGylation of siRNA lipo-oligoamino amide polyplexes using tetra-glutamylated folic acid as ligand for receptor-targeted delivery. Molecular Pharmaceutics, acs.molpharmaceut.6b00102. https://doi.org/10.1021/acs.molpharmaceut.6b00102 Schaffert, D., Troiber, C., Salcher, E. E., Fröhlich, T., Martin, I., Badgujar, N., … Wagner, E. (2011). Solid-phase synthesis of sequence-defined T-, i-, and U-shape polymers for pDNA and siRNA delivery. Angewandte Chemie (International Ed. in English), 50(38), 8986–8989. https://doi.org/10.1002/anie.201102165 Troiber, C., Kasper, J. C., Milani, S., Scheible, M., Martin, I., Schaubhut, F., … Wagner, E. (2013). Comparison of four different particle sizing methods for siRNA polyplex characterization. European Journal of Pharmaceutics and Biopharmaceutics, 84(2), 255–264. https://doi.org/10.1016/j.ejpb.2012.08.014 "	Here is a paper. Please give your review comments after reading it.
441	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>A set of experiments was conducted to synthesize doped and undoped sodium tantalates with carbon and sulfur in energy efficient single-step hydrothermal process. Undoped sodium tantalate nanocubes were synthesized at 140 o C and doped one at 180 o C for 12 hours in rich alkaline atmosphere. The sizes of undoped, carbon-doped, and sulfur-doped sodium tantalate nanocubes were 38 nm, 45 nm, and 40 nm, respectively. The morphological, elemental, compositional, structural, thermal, and photophysical properties of as-synthesized doped and undoped sodium tantalate (NaTaO 3 ) were characterized using scanning electron microscope (SEM), energy dispersive x-ray spectroscope (EDS), Raman spectroscopy, X-ray powder diffraction (XRD), thermal gravimetric analysis (TGA), Fourier transform infrared spectrophotometer (FTIR), and UV-vis spectrophotometer. The sulfur doped NaTaO 3 shows a higher photocatalytic activity in degradation of methylene blue than carbon doped and the undoped NaTaO 3 . The band gaps of undoped NaTaO 3 , carbon doped c-NaTaO 3 , and sulfur doped s-NaTaO 3 were calculated to be 3.94 eV, 3.8 eV, and 3.52 eV, respectively using Tauc plot.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>Sodium tantalates are perovskite compounds of sodium bonded with tantalum and oxygen atoms with definite proportion. A material that obeys the crystallographic structure of calcium titanate (CaTiO 3 ) is usually known as perovskite material. The perovskite structure (usually ABC 3 type) simply consists of a large cation A with a 12-fold coordination at the center of a cubic lattice. The corners of the cube are relatively smaller cation B with 6-fold coordination, and the midpoint of each edge are occupied by smaller anions C (halides or oxides). Alternately, large crystal system has cations A at corners, cation B at the center of the cube, and anions C or (O 2-) in the middle of each face as shown in Figure <ns0:ref type='figure' target='#fig_9'>1</ns0:ref> <ns0:ref type='bibr' target='#b0'>(Ward, 2005)</ns0:ref>. In Figure <ns0:ref type='figure' target='#fig_9'>1</ns0:ref>, Na stands for A, Ta stands for B, and O stands for C. A unit cell of NaTaO 3 shown in Figure <ns0:ref type='figure' target='#fig_9'>1</ns0:ref>(A) has Ta at octahedron center and the Figure <ns0:ref type='figure' target='#fig_9'>1</ns0:ref>(B) has Na + shared with 12 Oions of 8 octahedra. Figure <ns0:ref type='figure' target='#fig_9'>1</ns0:ref>(C) is a probable molecular structure of sodium tantalate <ns0:ref type='bibr'>(PubChem, 2008)</ns0:ref>.</ns0:p><ns0:p>Perovskites with transition metal ions at site B exhibit interesting physical properties because of the distortion in crystals made by the dipole moment of central cation B <ns0:ref type='bibr' target='#b0'>(Ward, 2005;</ns0:ref><ns0:ref type='bibr'>Johnsson, 2005)</ns0:ref>. The distortion in an ideal cubic form of perovskite resulted in orthorhombic, rhombohedral, hexagonal, and tetragonal forms. The structural evolution and change in electronic structure of a compound are responsible for their functional properties. These functionalities can be utilized in catalysis, fuel cells, and electrochemical sensing <ns0:ref type='bibr' target='#b0'>(Ward, 2005;</ns0:ref><ns0:ref type='bibr'>Johnsson, 2005;</ns0:ref><ns0:ref type='bibr' target='#b3'>Gregory, 2002;</ns0:ref><ns0:ref type='bibr' target='#b4'>Okoye, 2005;</ns0:ref><ns0:ref type='bibr' target='#b5'>Atta, 2016)</ns0:ref>. Figure <ns0:ref type='figure' target='#fig_9'>1</ns0:ref>. Perovskite crystal structure of NaTaO 3 (A), a 3D framework of corner sharing [TaO 6 ] octahedra with Na + ions in the twelve-fold cavities in between the polyhedral (B), and its molecular structure (C). Crystal structure of NaTaO 3 was plotted using VESTA-software <ns0:ref type='bibr' target='#b6'>(Momma, 2011)</ns0:ref>.</ns0:p><ns0:p>Metal oxide based nano perovskite exhibits a high activity for the photocatalytic decomposition of water and photodegradation of organic pollutants under ultraviolet irradiation, which could help minimize the environmental pollution and find alternative energy resources <ns0:ref type='bibr' target='#b7'>(Smith, 2013)</ns0:ref>. The photocatalyst such as NaTaO 3 utilizes only few fractions of visible light spectrum for useful work because of its wide band gap of about 4 eV. Fortunately, it has been observed that the features of NaTaO 3 can be tuned by doping of cations or anions into the stoichiometric phase of its base structure to change their physical and chemical behavior <ns0:ref type='bibr' target='#b8'>(Li, 2009;</ns0:ref><ns0:ref type='bibr' target='#b9'>Kang, 2010;</ns0:ref><ns0:ref type='bibr' target='#b10'>Li, 2015;</ns0:ref><ns0:ref type='bibr' target='#b11'>Kanhere, 2014;</ns0:ref><ns0:ref type='bibr'>Lan, 2016)</ns0:ref>. Doping of metal cations or anions on NaTaO 3 reduces its band gap and enhances the visible light response. The process of cation doping on NaTaO 3 are, however, a bit complicated and would limit the economical utilization of the photocatalyst <ns0:ref type='bibr' target='#b9'>(Kang, 2010;</ns0:ref><ns0:ref type='bibr' target='#b10'>Li, 2015)</ns0:ref>. Anion doping into the oxygen site of metal oxides can be useful and easily controlled during synthesis process. Thus, photocatalytic activities of NaTaO 3 can also be enhanced by doping of non-metal anions such as nitrogen, sulfur, or carbon <ns0:ref type='bibr' target='#b11'>(Kanhere, 2014;</ns0:ref><ns0:ref type='bibr'>Lan, 2016;</ns0:ref><ns0:ref type='bibr'>Liu, 2010;</ns0:ref><ns0:ref type='bibr' target='#b14'>Shi, 2012;</ns0:ref><ns0:ref type='bibr' target='#b16'>Kudo, 2007;</ns0:ref><ns0:ref type='bibr' target='#b17'>Fu, 2008)</ns0:ref>. The doping of anions in metal oxides forms an acceptor level because of its electronegativity which increases the valance band-edge potential and thus reduces the band gap. The band gap of the metal oxides reduces significantly if the dopant has P-orbital energy higher than the O 2P orbitals <ns0:ref type='bibr' target='#b10'>(Li, 2015)</ns0:ref>. The sulfur has low electronegativity than oxygen, but a higher electron affinity due to its larger size. Sulfur is also a divalent atom that can replace oxygen atom at substitutional site. Such property of sulfur makes it an efficient dopant of NaTaO 3 . Carbon is also less electronegative than oxygen, but it can contribute more holes in valance band. The C dopants promote separation of photogenerated electrons-holes and thus reduce the rate of recombination <ns0:ref type='bibr' target='#b18'>(Lavand, 2015)</ns0:ref>. The S and C are thus considered to be promising dopants for visible light induced photocatalysis. It has also been reported that the dopant like N (nitrogen) leaves excessive numbers of holes in valance band which forms the recombination centers for electron-hole trapping, thus reduces the lifespan of photogenerated charge carriers <ns0:ref type='bibr' target='#b19'>(Wang, 2013)</ns0:ref>.</ns0:p><ns0:p>Appreciable amount of work has been done in mono-doping or co-doping of NaTaO 3 with dopants like La, Co, Sm, Bi, N, P, and F but a very little work has been devoted to the C or S PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2020:06:50208:1:1:NEW 28 Aug 2020)</ns0:p><ns0:p>Manuscript to be reviewed Chemistry Journals doping. Hence, through this experiment we have synthesized and doped sodium tantalates (NaTaO 3 ) with C and S to study their comparative photophysical properties. An environmentalfriendly low temperature chemical process has been used to synthesize all the samples in this study. In order to optimize growth parameters of the nanostructures we have characterized them for morphological, compositional, structural, thermal, and optical properties using SEM, EDS, Raman, FTIR, XRD, TGA, and UV-Vis Spectroscopy.</ns0:p></ns0:div> <ns0:div><ns0:head>Materials & Methods</ns0:head></ns0:div> <ns0:div><ns0:head n='1.'>Materials Synthesis</ns0:head><ns0:p>There are various factors that affect the structure of perovskite crystals such as increase of temperature, particle size, tiltation of octahedral, synthesis process, concentration of reaction mixture, and reaction time <ns0:ref type='bibr'>(Ahmad, 2017)</ns0:ref>. Various methods have already been developed to synthesize perovskite materials such as ball milling, thermal evaporation, sol-gel process, and hydrothermal process <ns0:ref type='bibr' target='#b0'>(Ward, 2005)</ns0:ref>. Among all these processes, hydrothermal process is one of the most suitable chemical processes in terms of energy consumption and environmental friendliness for the varieties of perovskite compounds. Size and chemical compositions of oxides type perovskite in hydrothermal process (HTs) can be controlled by adjusting the concentration of precursors, reaction time, and temperature. This process is based on a dissolution/precipitation mechanism. We have used a low temperature HTs to synthesize and dope sodium tantalates (NaTaO 3 ) by optimizing the concentration of the reactant precursors.</ns0:p><ns0:p>The NaTaO 3 perovskite were synthesized by reacting tantalum penta-oxide precursor Ta 2 O 5 in high alkaline environment NaOH under hydrothermal conditions at low temperature. The reaction mechanism is given as</ns0:p><ns0:formula xml:id='formula_0'>(i) 2 5 3 2 2 2 o t C NaOH Ta O NaTaO H O   </ns0:formula><ns0:p>For sodium tantalate (NaTaO 3 ), 0.442 g of Ta 2 O 5 powder was dissolved in 0.75 M of NaOH for 5 hours with magnetic stirring. The 50 mL solution was then kept in a 100 mL capacity teflon lined autoclave and heated for 12h at 140 o C <ns0:ref type='bibr' target='#b8'>(Li, 2009)</ns0:ref>. The autoclaves were naturally cooled down to the room temperature before milky-white product was collected, centrifuged washed with water and ethanol many times, and dried at 80 o C overnight.</ns0:p><ns0:p>For C-doped sodium tantalate (c-NaTaO 3 ), 2g of glucose (C 6 H 12 O 6 ) was dispersed in a mixture of 30 ml deionized water and 20 ml polyethelene glycol (400) for 15 minutes with ultrasonication. Then, 1.5g (0.75M) of NaOH and 0.442 g of Ta 2 O 5 powder were added in the mixture and stirred on a magnetic stirrer for 5 hours. The solution was then kept in teflon lined autoclave and heated for 12h at 180 o C <ns0:ref type='bibr' target='#b9'>(Kang, 2010;</ns0:ref><ns0:ref type='bibr' target='#b21'>Wu, 2014)</ns0:ref>. The same cleaning procedure as above was used to collect light-brownish product.</ns0:p><ns0:p>For S-doped sodium tantalate (s-NaTaO 3 ), 0.442 g of Ta 2 O 5 powder and 0.200g of sodium thiosulfate (Na 2 S 2 O 3 :5H 2 O) were dissolved in 1.5 g of NaOH and 50ml deionized water by magnetic stirring for 5 hours and 5 minutes of ultrasonication. The solution was then kept in teflon lined autoclave and heated for 12h at 180 o C <ns0:ref type='bibr' target='#b10'>(Li, 2015)</ns0:ref>. The same cleaning procedure as PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2020:06:50208:1:1:NEW 28 Aug 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science previous cases was used to collect white product. The reagent grade tantalum pentaoxide powder bought from Sigma Aldric was used in this experiment.</ns0:p></ns0:div> <ns0:div><ns0:head n='2.'>Photocatalytic Test</ns0:head><ns0:p>The photocatalytic activities of doped and undoped NaTaO 3 were evaluated by degrading aqueous solution of methylene blue (MB) in visible light irradiation. The photo reactor chamber was locally improvised where the solution was kept cool using water circulating jacketed beaker of 100 ml and was being magnetically stirred during the process. The stock solution of methylene blue of concentration 20 mg/L was prepared by dissolving 20 mg of methylene blue powder in 1 liter of deionized water and stored in dark room for future use.</ns0:p><ns0:p>As synthesized photocatalyst powder of 0.05 g was dispersed in 50 ml of 20 mg/L concentrated methylene blue (MB) aqueous solution. The suspension was then ultrasonicated for 10 minutes and stored in dark for 30 minutes to allow adsorption-desorption equilibrium between photocatalyst and the MB. The suspension was being stirred magnetically and kept cool during this process. The white light LED of 50 Watt was taken as a source of visible light and was kept at about 20 cm above the solution. The suspensions and the aqueous MB solution were kept in dark for 30 minutes. The UV/Vis spectra of these solutions were taken before and after visible light irradiation. At every 50 minutes of irradiation about 2 ml of solution was withdrawn from the reactor cell (beaker), centrifuged for 10 minutes at 3000 rpm and the supernatant of the solution was taken for UV/Vis spectrum. The percentage photo degradation, of MB was taken </ns0:p></ns0:div> <ns0:div><ns0:head>as (ii) 100%</ns0:head><ns0:formula xml:id='formula_1'>o A A   </ns0:formula><ns0:p>where A o is the absorbance before irradiation, and A is the absorbance obtained after every 50 min of irradiation of sample in visible light. The intensity of light on the exposed surface of MB was calculated to be about 10 mW/cm 2 .</ns0:p></ns0:div> <ns0:div><ns0:head>Results and Discussion</ns0:head><ns0:p>The table-top scanning electron microscope (SEM) was used to study the morphology of the samples. The cubic morphology of undoped and doped nanostructures is shown in Figures <ns0:ref type='figure' target='#fig_0'>2(A)</ns0:ref>, 2(B), and 2(C). The morphology of the samples seem similar may be because of the range of doping is very little as can be depicted by XRD of the samples. However, morphology of carbon doped sodium tantalates without using glucose in a reaction mixture along with ethylene glycol during synthesis is shown in Figure <ns0:ref type='figure' target='#fig_0'>2(D)</ns0:ref>. Manuscript to be reviewed Raman spectra were taken to verify EDS and XRD study. The bands between 400 to 1100 cm -1 related to the internal vibrational mode of Ta 2 O 6 in NaTaO 3 structure <ns0:ref type='bibr'>(Vishnu,2009;</ns0:ref><ns0:ref type='bibr' target='#b23'>Hernandez, 2018)</ns0:ref>. The major bands at 450, 500, 620, and 720 cm -1 in NaTaO 3 are reported earlier <ns0:ref type='bibr' target='#b24'>(Longjie, 2015)</ns0:ref>. One additional band appeared at 860cm -1 which is associated with doping of sulfur and may be related to the phase transition of Ta 2 O 6 octahedra tiltation as shown in Figure <ns0:ref type='figure' target='#fig_2'>4(A)</ns0:ref>. In Figure <ns0:ref type='figure' target='#fig_2'>4</ns0:ref>(B), the broad mask between 1100 to 1800 cm -1 engulfing the characteristics D and G bands is related to the amorphous carbon and explains the formation of composite between carbon and NaTaO 3 <ns0:ref type='bibr' target='#b23'>(Hernandez, 2018)</ns0:ref>. The excessive amount of carbon in host material acts as a recombination center and reduces the photocatalytic activity. The average crystallites sizes (L) of NaTaO 3 , s-NaTaO 3 , and c-NaTaO 3 are 38 nm, 40 nm, and 45 nm, respectively as measured from full width at half maximum (FWHM) of prominent XRD peaks at about 32 o of 2θ peak position using Scherrer's formula <ns0:ref type='bibr' target='#b25'>(Langford, 1978)</ns0:ref> Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:formula xml:id='formula_2'>, ,<ns0:label>(iii) cos(</ns0:label></ns0:formula></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>where K = 0.89 for cubical symmetry, FWHM (β) at 2θ in radian unit, and λ = 0.1540598 nm. XRD data indicates highly crystalline cubic nanoparticles with d value of (100) plane is about 0.3903 nm. A detailed inspection of peaks displays the slight shift of position towards lower angle in doped samples, indicating the tendency of modifying cubic phase of NaTaO 3 crystal.</ns0:p><ns0:p>TGA curves were obtained in nitrogen atmosphere at a heating rate of 10 o C/min as shown in Figure <ns0:ref type='figure' target='#fig_5'>6</ns0:ref>. TGA curve determines thermal stability and monitors decomposition behavior of synthesized nanocubes. Each sample produces different sigmoidal shapes of TGA curves. Gradual but slow weight loss up to 150 o C indicates dehydration and evaporation of volatile contents in the sample (such as residual ethanol contents from cleaning). A little incremental weight below 100 o C may be due to buoyancy effect of atmosphere inside the TG system. The samples were hygroscopic in nature due to the synthesis process, and it is expected to have weight loss below 200 o C. The higher weight loss was observed between 300 -700 o C associated with residual low molecular weight substance, decomposition reaction, and coordinated water elimination. Since TGA curves are dependent on heating rate hence any experimental parameters that effect the reaction rate will change the shape of curve e.g. type of materials, purge gas, sample mass, volume, and morphology <ns0:ref type='bibr' target='#b26'>(Froberg, 2020)</ns0:ref>. Sodium tantalate, NaTaO 3 sample has shown very little weight loss altogether of about 2.37%. The first derivative of TGA curve (DTG) reveals several steps of mass loss. The DTG curve shows that 0.87% and 0.52% mass loss happen at 660 o C and 550 o C, respectively. The peak at 110 o C corresponds to evaporation of volatiles as shown in Figure <ns0:ref type='figure' target='#fig_5'>6</ns0:ref>(A). The TGA of s-NaTaO 3 and c-NaTaO 3 samples in Figure <ns0:ref type='figure' target='#fig_5'>6</ns0:ref>(B) has shown the high degradation of impurities from sodium thiosulphate residue and the glucose residue and cause their weight loss of about 12.4% and 10%, respectively. The c-NaTaO 3 also shows oxidation process between 250 o C to 460 o C and become thermally stable after 560 o C. Figure <ns0:ref type='figure' target='#fig_6'>7</ns0:ref> shows FTIR spectra of as synthesized samples. The spectrum of NaTaO 3 comprises of a strong band at 560 cm -1 which corresponds to Ta -O stretching bond. The spectrum of s-NaTaO 3 displays similar spectrum as undoped NaTaO 3 without any bands related to SO 3 2or SO 4 2indicating no residue of sulfur during washing. The bands around 1000 to 1630 cm -1 corresponds to Na -O vibrations. A broad peak around 3400-3500 cm -1 corresponds to H 2 O bands and strong stretching modes of the O-H band. The intense peak at 1068 cm -1 corresponds to CO 3 -2 vibration band in spectrum of c-NaTaO 3 <ns0:ref type='bibr' target='#b10'>(Li, 2015)</ns0:ref>. The visible light photo degradation of MB was analyzed using UV/Vis spectrometer. The peak at 664 nm was chosen to study the degradation of MB with and without catalysts and has been shown in Figure <ns0:ref type='figure' target='#fig_8'>8(A)</ns0:ref>. Not an appreciable change has been observed in UV/Vis spectra of blank MB solution after keeping 30 minutes in dark, depicting the reaching of the adsorption desorption equilibrium. The photoactivity of blank MB solution was very low, only 1.7% degradation was observed after 300 minutes. The degradation of MB and other dyes in visible light photolysis has been reported by previous authors <ns0:ref type='bibr' target='#b27'>(Esparza, 2020;</ns0:ref><ns0:ref type='bibr' target='#b10'>Li, 2015</ns0:ref><ns0:ref type='bibr' target='#b12'>, Lan, 2015;</ns0:ref><ns0:ref type='bibr' target='#b28'>Hou, 2018;</ns0:ref><ns0:ref type='bibr' target='#b18'>Lavand, 2015;</ns0:ref><ns0:ref type='bibr' target='#b29'>Khaneghah, 2018)</ns0:ref>. The MB with catalysts NaTaO 3 , c-NaTaO 3 , and s-NaTaO 3 has shown 3.4%, 5.1%, and 7.8% degradation, respectively within 300 minutes of visible light irradiation. Due to large optical band gap of these catalysts and consequently very poor absorption of visible light its degradation efficiency was very poor. </ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions</ns0:head><ns0:p>Sulfur and carbon mono-doped sodium tantalate nanocubes were grown successfully in rich alkaline atmosphere by low temperature hydrothermal process. The sodium tantalate (NaTaO 3 ), carbon doped sodium tantalate (c-NaTaO 3 ), and sulfur doped sodium tantalate (s-NaTaO 3 ) have found perovskite crystal structure of Pm-3m cubic phase with an average size of 38 nm, 45 nm, and 40 nm, and their band gaps were calculated as 3.94 eV, 3.8 eV, and 3.52 eV, respectively. The slight shift in prominent XRD peaks position of S-doped NaTaO 3 are found towards lower angle but no noticeable shift has been observed in C-doped NaTaO 3 . Bandgap of S-doped NaTaO 3 is found narrower than C-doped and undoped NaTaO 3 . S-doped NaTaO 3 shows comparatively higher visible light photocatalytic activity than C-doped and undoped NaTaO 3 . Due to high band gap the photocatalytic activities of these photocatalysts were poor, however, enhancement in adsorption of methylene blue solution has been observed with the use of these photocatalysts. </ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head>Figure 2 :</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2: SEM images of the pure NaTaO 3 (A), c-NaTaO3 (B), s-NaTaO 3 (C), and c-NaTaO 3 without glucose as a reaction component (D).The compositions of catalyst are shown in Figure3by the EDS images. The plot indicates presence of dopants such as Sulfur and Carbon effectively incorporated into the sodium tantalate matrix during hydrothermal process.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head>Figure 3 :</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Figure 3: EDS spectra of NaTaO 3 (A), c-NaTaO 3 (B), and s-NaTaO 3 (C), respectively.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>Figure 4 :</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Figure 4: Raman spectra of (A) NaTaO 3 and s-NaTaO 3 and (B) c-NaTaO 3 samples.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head>Figure 5 :</ns0:head><ns0:label>5</ns0:label><ns0:figDesc>Figure 5: XRD patterns of NaTaO 3 (A), Rietveld refinement profile of the sample NT using Rex software. Experimental pattern (red), calculated data fit (cyan), and difference curve are shown in pink in the refined data (B), XRD pattern of s-NaTaO 3 , c-NaTaO 3 , and inset shows shift in a peak (C).</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head></ns0:head><ns0:label /><ns0:figDesc>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2020:06:50208:1:1:NEW 28 Aug 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head>Figure 6 :</ns0:head><ns0:label>6</ns0:label><ns0:figDesc>Figure 6: TGA/DTG curves of NaTaO3 (A), c-NaTaO3 and s-NaTaO3 (B).</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head>Figure 7 :</ns0:head><ns0:label>7</ns0:label><ns0:figDesc>Figure 7: FTIR curves of doped and undoped NaTaO3. The plot is offset by 10 points for clarity.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_7'><ns0:head /><ns0:label /><ns0:figDesc>Figure 8(B) shows the UV/Vis absorption spectra of NaTaO 3 , c-NaTaO 3 , and s-NaTaO 3 catalysts. The Tauc plot of (αhυ) 2 ploted against energy from UV/vis diffused reflectance spectra shows the photocatalysts NaTaO 3 , c-NaTaO 3 , and s-NaTaO 3 have direct band gap energy of 3.94 eV, 3.8 eV, and 3.52 eV, respectively.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_8'><ns0:head>Figure 8 :</ns0:head><ns0:label>8</ns0:label><ns0:figDesc>Figure 8: Photodegradation of methylene blue (MB) in solution containing NaTaO 3 , c -NaTaO 3 , and s-NaTaO 3 catalysts (A), and Tauc plot with inset UV-vis diffused reflectance spectra of NaTaO 3 , c -NaTaO 3 , and s -NaTaO 3 (B).</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_9'><ns0:head>Figure 1</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='14,42.52,181.57,525.00,153.00' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='15,42.52,181.57,525.00,192.75' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='18,42.52,178.87,525.00,344.25' type='bitmap' /></ns0:figure> <ns0:note place='foot'>Analytical, Inorganic, Organic, Physical, Materials Science </ns0:note> </ns0:body> "	" Dear Editor, 1/20/2023 We are very thankful to the reviewers for their critical comments on the manuscripts. We have edited the manuscripts to address their concerns and believe that it is now suitable for the publication in PeerJ. Regards Dr. Sunil Karna Assistant professor of Physics On behalf of all authors. Editors comment. 1. Equation number is required in the equation provided in Material synthesis section. Reply: Equation numbers are assigned in the manuscripts for all mathematical equations. Reviewer 1 Comments Basic reporting No Comments Experimental design No Comments Validity of the findings No Comments Comments for the author The manuscript discusses the sulfur and carbon mono-doped sodium tantalate nanocubes in rich alkaline atmosphere by low temperature hydrothermal process. Sodium tantalate, carbon doped sodium tantalite and sulfur doped sodium tantalate were found to have perovskite cubic crystal structure (Pm-3m space group) with an average size of 38 nm, 45 nm and 40 nm, and band gap values of 3.94 eV, 3.8 eV and 3.52 eV respectively. The manuscript is written well and composed of the justified studies, however, it would be good addition if authors may justify the sulphur and carbon doping using XPS studies and changes in Raman studies (if possible) over sodium tantalates. Lack of some relevant references could be seen whose addition may increase the impact of the studies such as Industrial & Engineering Chemistry Research, 57 (1), 18-41, 2018; ACS Omega, 4, 19408-19419, 2019; Scientific Reports, 9, 4488 (1-17), 2019; Desalination and Water Treatment, 162, 303-312, 2019. The manuscript may be considered after addressing the above issues. Comment 1: For the comments on the manuscript is written well and composed of the justified studies, however, it would be good addition if authors may justify the sulphur and carbon doping using XPS studies and changes in Raman studies (if possible) over sodium tantalates. Reply: We have included the Raman spectra to justify the sulfur and carbon doping in the sodium tantalate crystals along with the EDS report. However, XPS study was not possible at this time to further dig into the characterization on doping. Comment 2: For the comments on Lack of some relevant references could be seen whose addition may increase the impact of the studies such as Industrial & Engineering Chemistry Research, 57 (1), 18-41, 2018; ACS Omega, 4, 19408-19419, 2019; Scientific Reports, 9, 4488 (1-17), 2019; Desalination and Water Treatment, 162, 303-312, 2019. Reply: We have included the following citation from the reviewer 1 suggestion in the manuscript at the proper reference point to make the article more relevant. The citation is Industrial & Engineering Chemistry Research, 57 (1), 18-41, 2018. Reviewer 2 Comments 1. License number of VESTA-software is required 2. EDS analysis required the atomic percent ratios of the synthesized photocatalysts. 3. XPS, Raman analyses or alternative techniques are required to support the doping of C and S successfully as the XRD is the only technique used for the purpose and it seems insufficient. 4. Line 140 stoke solution should be stock solution 5. What is the intensity used for the source light and how is it calibrated? 6. In XRD, C doping should shrink the lattice structure and S should Expand the lattice structure of NaTaO3 but the peaks shiftings are not showing the stated trend and not confirming the doping in lattice structure but may be at the interstices. 7. Thermal gravimetric analysis (TGA) and DTA in Figure 15 shows the degradation of impurities from the glucose residue and sodium thiosulphate residue and may refer as impurity not doping! 8. In figure 17, how can the MB degrade without catalyst? In other words, why balnk run showing degradation? 9. In any case if it shows degradation, will the blank run effects or data was subtracted from the photocatalysts data? 10. PL and IPCE studies should also be present. Comment 1. License number of VESTA-software is required Reply: https://jp-minerals.org/vesta/en/download.html The website states “this software is distributed free of charge for academic, scientific, educational, and noncommercial users. Users belonging to commercial enterprises may also use this software at no cost until a license for business users is established. Permission to use this software is hereby granted under the following conditions: Drawings produced by VESTA may be used in any publications provided that its use is explicitly acknowledged.” The website says to reference the following citation: K. Momma and F. Izumi, 'VESTA 3 for three-dimensional visualization of crystal, volumetric and morphology data,' J. Appl. Crystallogr., 44, 1272-1276 (2011). Comment 2: EDS analysis required the atomic percent ratios of the synthesized photocatalysts. Reply: The comment was considered seriously and was corrected in EDS data by reporting atomic percent ratios. Comment 3: XPS, Raman analyses or alternative techniques are required to support the doping of C and S successfully as the XRD is the only technique used for the purpose and it seems insufficient. Reply: Raman analysis is included to support C and S doping in addition to EDS and XRD characterizations. Comment 4: Line 140 stoke solution should be stock solution Reply: The spelling mistake for stock has been corrected in the manuscript. Comment 5: What is the intensity used for the source light and how is it calibrated? Reply: The intensity of light exposed to the MB solution was calculated as 10 mW/cm2 and has been included in the manuscript. Comment 6: In XRD, C doping should shrink the lattice structure and S should Expand the lattice structure of NaTaO3 but the peaks siftings are not showing the stated trend and not confirming the doping in lattice structure but may be at the interstices. Reply: S doping showed slight shift of XRD peak toward the lower diffraction angle. C doping has higher atomic percentage according EDS. However, not much shift has been observed in the C doped in contrast to S doped. But the shift in C-doping has been observed toward lower angle with respect to undoped NaTaO3. Excessive carbon in NaTaO3 structure forms a partial composite and is showing amorphous carbon contents in Raman studies. This issue has been discussed at Raman studies and XRD section in the manuscript. Comment 7: Thermal gravimetric analysis (TGA) and DTA in Figure 15 shows the degradation of impurities from the glucose residue and sodium thiosulphate residue and may refer as impurity. Reply: This comment was taken as neutral. It was already referred as impurity. The TGA of s-NaTaO3 and c-NaTaO3 samples in Figure 6(b) has shown the high degradation of impurities from sodium thiosulphate residue and the glucose residue and cause their weight loss of about 12.4% and 10%, respectively. Comment 8: In figure 17, how can the MB degrade without catalyst? In other words, why blank run showing degradation? Reply: Many citations have been included in the manuscripts as a reference for similar trend. The degradation of MB and other dyes in visible light photolysis has been reported by previous authors (Esparza, 2020; Li, 2015, Lan, 2015; Hou, 2018; Lavand, 2015; Khaneghah, 2018). Here is one citation: 1. Lan, N. T., Phan, L. G., Hoang, L. H., Huan, B. D., Hong, L. V., Anh, T. X., & Chinh, H. D. (2015). Hydrothermal synthesis, structure and photocatalytic properties of La/BiCo-doped NaTaO3, Materials Transactions 57 (1), 1-4. doi:10.2320/matertrans.MA201517. Comment 9: In any case if it shows degradation, will the blank run effects or data was subtracted from the photocatalysts data? Reply: No data has been subtracted in the photocatalysis report because all the samples went through the same physical conditions during the irradiation. Comment 10: PL and IPCE studies should also be present. Reply: Instead of PL and IPCE, we have provided Raman spectra to support C and S doping of NaTaO3. The XRD, EDS, and Raman data are evident of sulfur and carbon doping in NaTaO3 crystals. "	Here is a paper. Please give your review comments after reading it.
442	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>New photocatalysts based on TiO 2 were synthesized and characterized. The synthesis involved the controlled hydrolysis of titanium tetraisopropoxide using water containing different proportions of acetone. X-ray diffraction analyses combined with Raman spectroscopy revealed crystalline oxides characterized by the coexistence of the anatase and brookite phases. The Rietveld refinement of diffractograms showed that the presence of acetone in the synthesis process influenced the composition of these crystalline phases, with the proportion of brookite growing from 13 to 22% with the addition of this solvent in the synthesis process. The BET isotherms revealed that these materials are mesoporous with surface area approximately 12% higher than that of the oxide prepared from hydrolysis using pure water. The photocatalytic potential of these oxides was evaluated by means degradation tests using the dyes Ponceau 4R and Reactive Red 120 as oxidizable substrates. The values achieved using the most efficient photocatalyst among the synthesized oxides were, respectively, 83% and 79% for mineralization, and 100% for discoloration of these dyes. This same oxide loaded with 0.5% of platinum and suspended in a 5:1 v/v water/methanol mixture, produced 56 mmol of gaseous hydrogen in five hours of reaction, a specific hydrogen production rate of 138.5 mmol h -1 g -1 , a value 60% higher than that achieved using TiO 2 P25 under similar conditions.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>New photocatalysts based on TiO 2 were synthesized and characterized. The synthesis involved the controlled hydrolysis of titanium tetraisopropoxide using water containing different proportions of acetone. X-ray diffraction analyses combined with Raman spectroscopy revealed crystalline oxides characterized by the coexistence of the anatase and brookite phases. The Rietveld refinement of diffractograms showed that the presence of acetone in the synthesis process influenced the composition of these crystalline phases, with the proportion of brookite growing from 13 to 22% with the addition of this solvent in the synthesis process. The BET isotherms revealed that these materials are mesoporous with surface area approximately 12% higher than that of the oxide prepared from hydrolysis using pure water. The photocatalytic potential of these oxides was evaluated by means degradation tests using the dyes Ponceau 4R and Reactive Red 120 as oxidizable substrates. The values achieved using the most efficient photocatalyst among the synthesized oxides were, respectively, 83% and 79% for mineralization, and 100% for discoloration of these dyes. This same oxide loaded with 0.5% of platinum and suspended in a 5:1 v/v water/methanol mixture, produced 56 mmol of gaseous hydrogen in five hours of reaction, a specific hydrogen production rate of 138.5 mmol h -1 g -1 , a value 60% higher than that achieved using TiO 2 P25 under similar conditions.</ns0:p></ns0:div> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2020:08:51687:1:1:NEW 24 Sep 2020)</ns0:p><ns0:p>Manuscript to be reviewed Energy and environment are essential and challenging themes for humanity. The growing demand for energy combined with environmental contamination, particularly water contamination, has driven the search for sustainable resources and alternative processes aimed at minimizing negative impacts related to these issues <ns0:ref type='bibr' target='#b7'>(Cunha et al., 2018;</ns0:ref><ns0:ref type='bibr' target='#b80'>Tractz et al., 2019)</ns0:ref>.</ns0:p><ns0:p>Heterogeneous photocatalysis has proving to be a good alternative. Studies have shown its effectiveness in environmental remediation of contaminated waters <ns0:ref type='bibr' target='#b43'>(Machado et al., 2008;</ns0:ref><ns0:ref type='bibr' target='#b11'>França et al., 2016;</ns0:ref><ns0:ref type='bibr' target='#b19'>Hurtado, Sader & Delgado, 2019)</ns0:ref>, as well as in hydrogen production (H 2 ), an important energy vector <ns0:ref type='bibr' target='#b2'>(Bahnemann & Schneider, 2013;</ns0:ref><ns0:ref type='bibr' target='#b71'>Rusinque, Escobedo & Lasa, 2020;</ns0:ref><ns0:ref type='bibr' target='#b13'>Galvão et al., 2019)</ns0:ref>.</ns0:p><ns0:p>Much of the efforts spent on expanding the use of heterogeneous photocatalysis were based on the development of new semiconductor materials with increased photocatalytic activity. Among the possible photocatalysts, TiO 2 stands out due to its abundance, insolubility in water, low toxicity, good chemical stability in a wide pH range, and photostability <ns0:ref type='bibr' target='#b29'>(Kandiel et al., 2010)</ns0:ref>. Despite these desirable characteristics, TiO 2 may present low surface area (depending on the size and shape of crystallites), fast recombination rate of the photogenerated charge carriers (electron/hole) and absorption of radiation in ultraviolet <ns0:ref type='bibr'>(Bahadori et al., 2020)</ns0:ref>. However, such limitations may be circumvented through structural modifications or by the introduction of dopants <ns0:ref type='bibr' target='#b47'>(Machado, Alves & Machado, 2019;</ns0:ref><ns0:ref type='bibr' target='#b77'>Santos et al., 2015a;</ns0:ref><ns0:ref type='bibr' target='#b49'>Martin-Somer et al., 2020)</ns0:ref>. TiO 2 presents itself according to three distinct crystalline phases: brookite, with orthorombic structure, anatase and rutile, both with tetragonal structure, widely used in heterogeneous photocatalysis <ns0:ref type='bibr' target='#b12'>(Fujishima, Zhang & Tryk, 2008)</ns0:ref>. Experimental and theoretical studies suggest that a high percentage of anatase phase and small fraction of brookite guarantees greater photocatalytic activity to TiO 2 , compared to pure anatase, due the existence of structural defects that end up delaying the displacement of electrons and holes, minimizing the recombination between load carriers, making more reactive the surface of the photocatalyst <ns0:ref type='bibr' target='#b25'>(Jiang et al., 2014;</ns0:ref><ns0:ref type='bibr'>Di Paola, Berllardita & Palmisano, 2013)</ns0:ref>.</ns0:p><ns0:p>Efforts have been spent on improving methods that allow the control and reproducibility of the synthesis of this kind of material, which allows the obtaining of particles with mixed crystalline phase, with high photocatalytic yield <ns0:ref type='bibr' target='#b38'>(Luevano-Hipolito et al., 2014;</ns0:ref><ns0:ref type='bibr'>Mohammadi, Harvery & Boodhoo, 2014;</ns0:ref><ns0:ref type='bibr' target='#b51'>Myilsamy, Murugesan & Mahalakshmi, 2015)</ns0:ref>. In this sense, an approach that has proved feasible is the use of solvent combinations in the manipulation of the material mesostructure. Kumar and collaborators showed that sol-gel synthesis in a system involving the combination of different solvents strongly interferes with precursor hydrolysis, improving the structural properties of oxides <ns0:ref type='bibr' target='#b30'>(Kumar et al., 1999)</ns0:ref>.</ns0:p><ns0:p>In the present study, we performed the modified sol-gel synthesis of TiO 2 -based photocatalysts aiming to improve their photocatalytic activities. The precursor (titanium tetraisopropoxide) hydrolysis rate was controlled by the use of different proportions of acetone as co-solvent, reducing the availability of water in the process. With this, greater control of the formation and growth of critical nuclei was possible, avoiding the formation of very crowded particles. The synthesized oxides were characterized by X-ray diffraction (XRD), Raman spectroscopy, diffuse reflectance, specific surface area measurements (BET) and transmission electron microscopy (TEM). The photocatalytic activity of these compounds was evaluated in promoting the photodegradation of two azo dyes, used as oxidizable substrates: Ponceau 4R (P4R) and Reactive Red 120 (RR120). The best and least efficient photocatalyst, along with the TiO2 P25, were confronted in terms of hydrogen production capacity. The reuse potential of the best photocatalyst was also evaluated.</ns0:p></ns0:div> <ns0:div><ns0:head>Materials & Methods</ns0:head></ns0:div> <ns0:div><ns0:head>Experimental</ns0:head><ns0:p>All reagents used (titanium tetraisopropoxide, 97%; isopropanol, 99.5%; ponceau 4R (P4R), 75%; reactive red 120 (RR120) -purity not informed by the supplier; Methanol, 99.8%; hexahydrated hexachloroplatinic acid, 37.5%; hydrochloric acid, 37% and sodium hydroxide, 98%) were of analytical grade, provided by Sigma-Aldrich. Acetone 99.5%, was provided by Synth. All solutions were prepared with ultrapure water obtained from an Elix 5 Milli-Q® water purification system.</ns0:p></ns0:div> <ns0:div><ns0:head>Preparation of photocatalysts</ns0:head><ns0:p>The oxides were obtained by the sol-gel method, involving the solubilization of titanium tetraisopropoxide in isopropanol at 3°C under ultrasonic stirring for 20 minutes, followed by its hydrolysis by the addition of water/acetone mixture by drip and precipitation under ultrasonic stirring.</ns0:p><ns0:p>The water/acetone mixtures were prepared with deionized water and different proportions of acetone (0%, 25%, 50% and 75% v/v). The resulting amorphous solids were washed with distilled water, centrifuged and sintered using a conventional oven at 400 ºC for 5 hours.</ns0:p><ns0:p>The standard photocatalyst, synthesized in aqueous medium, was called W1. The other oxides, synthesized by hydrolysis using different water/acetone mixtures (25%, 50%, 75% v/v of acetone), were named W1-25, W1-50, W1-75, respectively.</ns0:p></ns0:div> <ns0:div><ns0:head>Characterization of the photocatalysts</ns0:head><ns0:p>The photocatalysts were characterized by different methodologies. By X-ray diffraction (XRD) using a XDR600 (Shimadzu) powder diffractometer operating at 40 kV and 120 mA, using Cu Kα radiation. The diffractograms were scanned in the range between 10 and 80º under a rate of 0.5º min -1 . Finally, they were refined by the method of Rietveld using the software 'FullProf ' <ns0:ref type='bibr'>(Roisnel & Rodriguez-Carvajal, 2011)</ns0:ref>. As criteria of mounting, the S factors were maintained between 1.22 and 1.31 (Table <ns0:ref type='table' target='#tab_3'>S1</ns0:ref>, Supplementary Information).</ns0:p><ns0:p>The Raman spectra were acquired at room temperature using a Bruker RFS 100/S spectrometer coupled to a 1064 nm laser operating at 100 mW. Each Raman spectrum, with spectral resolution of 4 cm -1 , is the result of the accumulation of 128 scans.</ns0:p><ns0:p>The diffuse reflectance spectra were obtained using a double beam UV-1650 (Shimadzu) spectrophotometer, estimating the band energy by Kubelka-Munk treatment <ns0:ref type='bibr'>(Patterson, Shelden & Stockton, 1997)</ns0:ref>. In these measures, barium sulfate was used as reference.</ns0:p><ns0:p>The N 2 adsorption-dessorption isotherms were obtained using an ASAP 2020 (Micrometrics) analyser. The adsorption data were analysed by the method proposed by Brunauer, Emmett and Teller (BET) for the surface area and the method of Barrett-Joyner-Halenda (BJH) for pore volume.</ns0:p><ns0:p>Transmission electron microscopy (TEM) images were obtained using a JEM-2100 (Jeol) microscope. In the preparation of the samples, suspensions containing the powders dispersed in acetone were used with the aid of a cutting-edge ultrasound. These suspensions were deposited on copper grids and air dried. From the images, obtained with the aid of the image editing software 'ImageJ', it was possible to calculate the particle size randomly selecting approximately 100 particles per image.</ns0:p></ns0:div> <ns0:div><ns0:head>Photocatalytic assays</ns0:head><ns0:p>4 L of an aqueous solution containing 100 mg L -1 of the photocatalyst were used in the photodegradation assay, in combination with a concentration equivalent to 12.0 ppm of dissolved organic carbon of the dye -corresponding to 31.3 mg L -1 of P4R or 43.5 mg L -1 of RR120 -used as oxidizable substrates. Detailed experimental assembly for the photodegradation assays was described in a previous study <ns0:ref type='bibr' target='#b40'>(Oliveira et al., 2012)</ns0:ref>.</ns0:p><ns0:p>A commercial high-pressure mercury lamp (HPLN) of 400 W (Philips, 2015) without the protective bulb was employed as radiation source. Under this condition, its estimated photonic flux in the UVA was of 3.3 x 10 -6 Einstein/s <ns0:ref type='bibr' target='#b43'>(Machado et al., 2008)</ns0:ref>, with an irradiance inside the reactor of 100 W / m 2 . During discoloration and dye mineralization monitoring, aliquots were collected every 20 minutes, in a total reaction interval of 140 minutes. The dyes discoloration was monitored by varying the absorbance of the solutions with the reaction time, without pH correction. Monitoring was done in the maximum absorbance wavelength in the visible of each dye -507 nm for P4R and 512 nm for RR120 -using a UV-1201 (Shimadzu) spectrophotometer. Mineralization was monitored from dissolved organic carbon (DOC) measurements, using a TOC-VCPH/CPN (Shimadzu) analyser, aiming to identify the most efficient photocatalyst. For this, the experiments were restricted to the monitoring of P4R photodegradation. The most efficient photocatalyst was also submitted to photodegradation tests using Remazol Red (RR120), comparing its performance with that presented by the commercial catalyst Evonik Degussa TiO 2 . These assays were conducted at least in triplicate and separately for each dye.</ns0:p><ns0:p>The reuse of the most efficient photocatalyst was evaluated using P4R as oxidizable substrate. For this, after each reaction the photocatalyst was separated from the supernatant by decanting, washed with distilled water, centrifuged and dried at 70°C for 24 hours, and then reused under the same described conditions using a new load of the same dye. Each test was performed in quadruplicate in order to compensate for the losses that occurred during the washing of the catalyst, in order to ensure a constant mass of this in each cycle. Subsequently, hydrogen production assays were done using the most effective synthesized photocatalyst, as well as the commercial catalyst Evonik Degussa TiO 2 and the W1 oxide. In these experiments, the concentration of catalyst was similar to that used in the assays of dye degradation, being this oxide loaded by photoreduction with 0.5% m/m of Pt, furnished by a solution of hexachloroplatinic acid. So, the Pt-loaded photocatalyst was then suspended in 750 ml of a water/methanol mixture containing 20% v/v of methanol, this last being used as sacrificial reagent. These assays occurred under continuous stirring. The pH of the reaction medium was adjusted in 6.2 using solutions 0.1 mol L -1 of HCl or NaOH. Finally, the potential of reuse of the photocatalyst used in such assays was evaluated in at least three photocatalytic cycles. In the reuse assays, only the pH adjustment of the reaction medium was performed at the beginning of each new cycle. The first cycle was equivalent to the first hydrogen production test, carried out for five hours. Thus, the total reaction time was 15 hours. For all photocatalytic assays the results are the averages of at least three individual experiments.</ns0:p><ns0:p>For operator protection and better use of radiation produced by the lamp, the reactor was positioned in a box internally covered with aluminum film, Fig 1. The reactor, built in borosilicate glass, has a cooling jacket connected to a thermostat bath on its outside which keeps the temperature of the reaction medium stabilized at 20ºC throughout the reaction. Before each experiment, the reactor was purged with N 2 for 20 minutes to eliminate dissolved gases, especially oxygen. The same HPLN lamp reported above was used as radiation source. For analysis of the gases produced during the reaction, aliquots of 1 mL of these gases were collected at intervals of 30 minutes of reaction, in a total period of 5 hours. These samples were analyzed at 230°C in a Shimadzu GC-17A gas-phase chromatograph equipped with thermal conductivity detector (TCD) and a Carboxen™ 1010 Plot capillary column. Argon, with flow of 40 ml min -1 , was employed as carrier gas.</ns0:p></ns0:div> <ns0:div><ns0:head>Results and Discussion</ns0:head></ns0:div> <ns0:div><ns0:head>Characterizations</ns0:head><ns0:p>By analyzing the X-ray diffraction (XRD) data, Fig 2, it is possible to infer that all oxides have well-defined diffraction peaks suggesting crystallinity for these materials most likely due to the heat treatment used in the synthesis process. In addition, according to reports found in the literature and the crystallographic files JCPDS 21-1272 (anatase) and 29-1360 (brookite), all oxides exhibit major peaks characteristic of the anatase phase, and secondary peaks related to the brookite phase <ns0:ref type='bibr' target='#b9'>(Di Paola, Bellardita & Palmisano, 2013;</ns0:ref><ns0:ref type='bibr' target='#b56'>Neto et al., 2017;</ns0:ref><ns0:ref type='bibr' target='#b63'>Patrocinio et al., 2015;</ns0:ref><ns0:ref type='bibr' target='#b18'>Hu, Tsai & Huang, 2003)</ns0:ref>. The mean size and mean deformation of crystallite were calculated from the data obtained from the Rietveld refinement, as presented in Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref>. The diffractograms, accompanied by the respective calculated diffraction profiles, experimentally obtained profiles, and residual curves and Bragg diffractions adjusted by Rietveld method can be seen in Supplementary Figure <ns0:ref type='figure' target='#fig_0'>1</ns0:ref>, in the Supplementary Information. Rietveld refinement data demonstrates that the percentage of brookite phase increases from 13% to 22% with the addition of acetone as co-solvent in the hydrolysis of titanium tetraisopropoxide. Despite this, the increase in the proportion of acetone from 25% to 75% did not result in an equivalent increase in the percentage of brookite phase, suggesting that the use of acetone only interfered in hydrolysis, affecting the organization of critical nuclei in the oligomeric network of titanium, in order to preorder the crystallization of the mentioned phase. On the other hand, the average crystallite size of the anatase phase was about 30% lower for W1-75, compared to the other oxides, including the W1, where there was no addition of acetone during its synthesis. This suggests that the excess of acetone should promote a significant reduction in the average crystallite size of the anatase phase, favoring the increase in the average crystallite size of brookite. Thus, the mean deformation of the crystallite follows the same trend, i.e., if the secondary phase becomes larger it will present larger deformations, when compared with the primary phase.</ns0:p><ns0:p>As in the X-ray diffractograms, the Raman spectra also evidence the mixed composition of two crystalline phases, Fig 3 <ns0:ref type='figure'>.</ns0:ref> In all oxides, five main bands attributed to the anatase phase are observed respectively at 145 cm -1 (E g ), 198 cm -1 (E g ), 399 cm -1 (B 1g ), 519 cm -1 (B 1g ) and 640 cm -1 (E g ) <ns0:ref type='bibr' target='#b74'>(Sahoo et al., 2009)</ns0:ref>. Between 200 and 500 cm -1 four bands of lower intensity are observed: at 247 cm -1 (A 1g ), 323 cm -1 (B 1g ), 368 cm -1 (B 2g ) and 456 cm -1 (B 2g ), attributed to the phase brookite. In addition to these bands, this phase features a band of greater intensity around 150 cm -1 which may be superimposed with the band identified at 145 cm -1 , attributed to anatase, thus influencing the width of the E g Raman mode <ns0:ref type='bibr' target='#b9'>(Di Paola, Bellardita & Palmisano, 2013;</ns0:ref><ns0:ref type='bibr' target='#b20'>Iliev, Hadjiev & Litvinchuk, 2013)</ns0:ref>. Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science be related to the smaller particle size since the lifetime of the vibrational mode tends to be shorter as particle size decreases, which ends up resulting in band enlargement <ns0:ref type='bibr'>(Liu et al., 2012;</ns0:ref><ns0:ref type='bibr' target='#b88'>Zhu et al., 2012)</ns0:ref>. <ns0:ref type='bibr' target='#b36'>(Liu & Li., 2012)</ns0:ref>. The estimated Eg were as follows: 3.23 eV for W1, 3.24 eV for W1-25, 3.22 eV for W1-50, and 3.23 eV for W1-75, indicating that the estimated band gap energies have not undergone major changes, which agree with the data reported in the literature for pure TiO 2 <ns0:ref type='bibr'>(Martin-Somer et al., 2002;</ns0:ref><ns0:ref type='bibr' target='#b56'>Neto et al., 2017;</ns0:ref><ns0:ref type='bibr' target='#b66'>Resende et al., 2017)</ns0:ref>. Most likely, this stems from the synthesis conditions adopted in this work, where none dopant material was added. It is known that the E g displacement to lower energies occurs preferably in synthesis that promote the doping of oxides with metal cations <ns0:ref type='bibr' target='#b77'>(Santos et al., 2015a)</ns0:ref>, non-metallic anions <ns0:ref type='bibr' target='#b35'>(Liu et al., 2014)</ns0:ref>, co-doping <ns0:ref type='bibr'>(Kuvarega, Krause & Momba, 2015)</ns0:ref>, and self-doping <ns0:ref type='bibr' target='#b5'>(Chen et al., 2011)</ns0:ref>.</ns0:p><ns0:p>Figure <ns0:ref type='figure'>5</ns0:ref> -Diffuse reflectance spectra expressed in terms of Kubelka-Munk's function. Insert: % Reflectance vs. wavelength (nm) spectra for the synthesized photocatalysts.</ns0:p><ns0:p>As for N 2 adsorption and desorption of these oxides, Fig 6, the analysis of the adsorptiondesorption isotherms suggests that they are type IV <ns0:ref type='bibr' target='#b22'>(IUPAC., 1985)</ns0:ref>, characteristic of mesoporous materials with an average pore diameter between 2 and 50 nm, Table <ns0:ref type='table'>2</ns0:ref>. Hysteresis profiles are very close to those of type H2, associated with more complex mesoporous structures, in which the distribution of pore sizes and their shape are not well defined <ns0:ref type='bibr' target='#b15'>(Guan-Sajonz et al., 1997)</ns0:ref>. It is also evident that the photocatalysts W1-50 and W1-75, synthesized by hydrolysis using the highest percentages of acetone, present slightly more steeper isotherms compared to the oxides W1 and W1-25, also exhibiting greater heterogeneity in pore distribution compared to these same oxides. Table <ns0:ref type='table'>2</ns0:ref> presents the morphological parameters related to the synthesized oxides. In general, oxides obtained from hydrolysis using water/acetone mixtures did not undergo significant morphological changes, since for W1 the oxide porosity is practically the same presented by W1-50 and W1-75. On the other hand, the surface area of these two oxides is between 10 and 12% larger than that of W1. This may favor the adsorption of organic matter on their surfaces, which can consequently favor the photocatalytic efficiency. In addition, it was observed an inverse correlation between the surface area and the average particle size, except for the W1-25 that presented wide variation on its particle size.</ns0:p><ns0:p>The TEM images, Fig 7, suggest a dense aspect to the particles, which have irregular spherical shape and a strong tendency to aggregation, giving rise to clusters of TiO 2 . This should be related to the high level of hydrolysis provided by the synthesis method <ns0:ref type='bibr' target='#b27'>(Jiang, Herricks & Xia, 2003)</ns0:ref>. However, agglomeration appears to have been minimized by the addition of acetone as co-solvent in hydrolysis, evidencing that its use decreased the hydrolysis rate of the precursor. This, consequently, should favor particle dispersion. On the other hand, the particle sizes estimated from these images do not suggest a role of acetone on this property, as can be seen by the values estimated for the particle size: (14±1) nm, (17±3) nm, (10±1) nm and (13±1) nm, respectively for W1, W1-25, W1-50 and W1-75. The histograms can be viewed in Supplementary Figure <ns0:ref type='figure' target='#fig_1'>2</ns0:ref>, in the Supplementary Information. </ns0:p></ns0:div> <ns0:div><ns0:head>Photocatalytic activity: Degradation/mineralization of organic compounds</ns0:head><ns0:p>The Table <ns0:ref type='table'>3</ns0:ref> presents the photocatalytic performance of the synthesized oxides and of the commercial oxide TiO 2 -P25, in the degradation of the two azo dyes used as oxidizable substrates in this study. For comparative purposes, the dyes were also submitted to direct photolysis, in order to evidence the role of the photocatalysts in the photodegradation.</ns0:p><ns0:p>Table <ns0:ref type='table'>3</ns0:ref> -Photocatalytic performance of synthesized oxides and TiO2-P25 compared with direct photolysis, in the degradation of the dyes Ponceau 4R (P4R) and Remazol Red 120 (RR120).</ns0:p><ns0:p>The discoloration (k dis ) and mineralization (k min ) rate constants were estimated from the application of the kinetic model of Languimir-Hinschelwood <ns0:ref type='bibr' target='#b16'>(Hoffmann et al., 1995;</ns0:ref><ns0:ref type='bibr' target='#b40'>Machado et al., 2012)</ns0:ref>, considering that the kinetic regimen in these photocatalytic processes follows a pseudo-first order kinetics <ns0:ref type='bibr'>(Machado et al., 2003;</ns0:ref><ns0:ref type='bibr' target='#b40'>Machado et al 2012;</ns0:ref><ns0:ref type='bibr' target='#b77'>Santos et al., 2015a;</ns0:ref><ns0:ref type='bibr' target='#b11'>França et al., 2016)</ns0:ref>. Graphs containing the kinetic data corresponding to these values are presented in the Suplementary Information (Supplementary Figures <ns0:ref type='figure' target='#fig_3'>3, 4 and 5</ns0:ref>).</ns0:p><ns0:p>The expected low efficiency both in degradation and discoloration via direct photolysis, compared to the results achieved by the photocatalysts can be related to the energy of the incident photons, provided by the radiation source <ns0:ref type='bibr' target='#b43'>(Machado et al., 2008)</ns0:ref>, and to the very low rate of formation of radical species, produced by homolytic scission of labile bonds present in these dyes <ns0:ref type='bibr' target='#b30'>(Kumar et al., 1999)</ns0:ref>.</ns0:p><ns0:p>In the experiments involving the participation of the photocatalysts, the degradation occurred more efficiently due the participation of reactive oxygen species, among them the hydroxyl radicals (HO . ) and superoxide radical-ions (O 2 .-), generated mainly by water decomposition. Such species, due their low selectivity <ns0:ref type='bibr' target='#b40'>(Machado et al., 2012)</ns0:ref>, together with secondary radical species produced during the photocatalytic process, tend to promote the oxidation of organic substrates present in the reactional medium <ns0:ref type='bibr' target='#b58'>(Oancea & Meltzer, 2014;</ns0:ref><ns0:ref type='bibr' target='#b75'>Santos at al 2015b)</ns0:ref>. The dissolved oxygen, present in the aqueous medium, as example, when reduced by the semiconductor, contributes with the formation of O 2 .and perhydroxyl radicals, which, although less oxidizing than HO . <ns0:ref type='bibr' target='#b40'>(Machado et al., 2012)</ns0:ref>, are very important in promoting the degradation of organic substrates.</ns0:p><ns0:p>The values of the apparent rates of discoloration and mineralization, observed in the reactions mediated by the photocatalysts evaluated in the present study, Supplementary Figure <ns0:ref type='figure' target='#fig_2'>3</ns0:ref> -Supplementary Information, suggest that these reactions occur in two stages, following kinetics of apparent pseudo-first order. Initially, the reaction occurs at a rate lower than in the second stage, when the apparent rate constant, in some cases, is three times higher. The higher rate constant in the second stage should be a consequence of the more favored adsorption of the fragments of organic matter formed in the first stage of the process, combined with the good availability of oxygen and water, important for the formation of radicals responsible for the oxidation of organic matter <ns0:ref type='bibr' target='#b11'>(França et al., 2016)</ns0:ref>.</ns0:p><ns0:p>The mineralization of P4R mediated by the oxides W1-25, W1-50 and W1-75 increased respectively 5.7%, 18.6% and 24.3% more than the result obtained using W1, when 70% mineralization was achieved. It is noteworthy that the hydrolysis process which gave rise to this oxide, occurred exclusively in the presence of water. It should be noted that the mineralization achieved using TiO 2 P25 as photocatalyst was only 8% higher than that obtained when W1-75 was employed.</ns0:p><ns0:p>Although the mineralization and discoloration of P4R conducted using W1-75 presented the best performance among the synthesized oxides, the result observed was only 4.8% higher than that achieved using W1-50. Considering the proportion of acetone used in the synthesis of W1-75 and its limited photocatalytic performance, W1-50 was then considered as the most effective catalyst for mineralizing P4R, being therefore preferably applied in the following stages of the present study. Since W1-25 presented intermediate performance to that observed for the W1 and W1-50 catalysts, evaluating its efficiency, regarding the degradation of RR120, was therefore considered unnecessary.</ns0:p><ns0:p>The good photocatalytic activity presented by these oxides, in particular the W1-50, can be attributed mainly to the mixed composition of the phases and high crystallinity obtained after heat treatment, confirmed by the XRD and Raman spectra. The presence of an additional phase tends to introduce defects that tend to favor the photocatalytic activity of a photocatalyst <ns0:ref type='bibr' target='#b29'>(Kandiel et al., 2010)</ns0:ref>. Brookite, for having conduction band approximately 0.14 eV more negative than anatase, ends up favoring the interfacial electron transfer by imposing an energy barrier for the return of the excited electrons to the valence band of anatase, which tends to favor the coexistence of charge carriers <ns0:ref type='bibr' target='#b29'>(Kandiel et al., 2010;</ns0:ref><ns0:ref type='bibr' target='#b9'>Di Paola, Bellardita & Palmisano, 2013;</ns0:ref><ns0:ref type='bibr' target='#b63'>Patrocinio et al., 2015)</ns0:ref>.</ns0:p><ns0:p>Reuse assays were performed using the recycled W1-50 in the photocatalytic degradation of the dye P4R. The recycled W1-50 was separated by decantation after the first photocatalytic test. It was then washed with distilled water, centrifuged and dried at 70°C for 24 hours. After this procedure, the recycled oxide was used to promote the degradation of P4R present in a new solution. The discoloration level remained at 100% while the mineralization performance decreased about 30%. This loss of performance should be related to photocatalyst poisoning caused by species adsorbed on the catalyst at the end of each photocatalytic cycle, compromising the availability of active sites <ns0:ref type='bibr'>(Nakhjavani et al., 2015)</ns0:ref>. It is important to consider that the recycled catalyst was not submitted to any prior purification procedure aiming the removal of contaminants incorporated by adsorption the previous cycles. The discoloration and mineralization profiles, as well as the kinetics of discoloration and mineralization in this reuse assay, are available in the Supplementary Information, Supplementary Figure <ns0:ref type='figure' target='#fig_3'>4</ns0:ref>.</ns0:p><ns0:p>Table 3 also presents the performance of the oxides W1, W1-50 and TiO 2 P25 in the mineralization and discoloration of the dye RR120. In this case, although RR120 has a more complex chemical structure than P4R, presenting two azo groups and two triazine groups, the performance achieved by W1-50 was comparable to that presented when using TiO 2 P25 differing only by the kinetic constants of mineralization (k min ). The residual total organic carbon (TOC) observed after degradation of both P4R and RR120 (Supplementary Figures <ns0:ref type='figure' target='#fig_2'>3 and 5</ns0:ref>, Supplementary Information), should be related to the presence of short-chain carboxylic acids, recalcitrant to photocatalytic degradation <ns0:ref type='bibr' target='#b11'>(França et al., 2016)</ns0:ref>. Studies have shown that the triazine groups present in the chemical structure of RR120, when photocatalytically oxidized, give rise to cyanuric acid, very resistant to degradation <ns0:ref type='bibr' target='#b17'>(Hu & Wang, 1999;</ns0:ref><ns0:ref type='bibr' target='#b82'>Wang, 2000;</ns0:ref><ns0:ref type='bibr'>Camarillo & Ricon, 2011)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Photocatalytic hydrogen production</ns0:head><ns0:p>The profiles of hydrogen production as function of the reaction time, The process mediated by W1-50 produced approximately 56 mmols of gaseous H 2 , while in the same period TiO 2 P25 produced 43% less. On the other hand, W1 produced approximately 3% less hydrogen than W1-50. In addition, it is explicit that the production of H 2 using the oxides presented in this study increased until the end of the assay, suggesting that the photocatalytic process was still in its propagation stage. H 2 production using TiO 2 P25 presented a different profile, suggesting typical accommodation of processes in stages near termination. It is known that TiO 2 P25 is the result of the crystalline composition between anatase and rutile. The advantage of the photocatalysts presented in this work should be in the combination of anatase and brookite that tends to increase the photocatalytic efficiency of the semiconductor. Liu and coworkers <ns0:ref type='bibr' target='#b35'>(Liu et al., 2014)</ns0:ref>, for example, demonstrated that the recombination of the photoinduced charge carriers is minimized when the semiconductor oxide have a structure based on this kind of phase composition. This behavior occurs due to the most negative cathode potential of the conduction band of the brookite phase, more negative than the proton reduction potential and the cathode potential of the conduction band of anatase, thus favoring its conversion to H 2 <ns0:ref type='bibr' target='#b29'>(Kandiel et al., 2010;</ns0:ref><ns0:ref type='bibr' target='#b63'>Patrocinio et al., 2015;</ns0:ref><ns0:ref type='bibr' target='#b79'>Tay et al., 2013)</ns0:ref>. Besides, this phenomenon facilitates the interfacial transfer of electrons while an energy barrier is established, which hinders their return, thereby prolonging the coexistence of the charge carriers. With this, both the oxidative (metanol oxidation) and the reductive process (H 2 production) end up being favored.</ns0:p><ns0:p>In terms of specific hydrogen production rate (SHPR), the production mediated by W1-50 (138.5 mmol h -1 g -1 ) was 60 % higher than the achieved using TiO 2 P25 (86.4 mmol h -1 g -1 ). Even the SHPR of W1 (126.5 mmol h -1 g -1 ) was higher than that of the commercial photocatalyst. It is observed, therefore, that the variant of the sol-gel synthesis proposed in this study resulted in pure photocatalysts, such as the W1-50, which present SHPR much higher than that of TiO 2 P25, as well as of photocatalysts recently reported in the literature. Selcuk and coworkers <ns0:ref type='bibr' target='#b78'>(Selcuk, Boroglu & Boz, 2012)</ns0:ref>, in a study involving a catalyst resulting from TiO 2 codopage with platinum and nitrogen, reported, under the best operating conditions, a TEPH of 13 µmol h -1 g -1 , a value significantly lower than the achieved using W1-50. This study involved the use of a 400 W mercury lamp as a source of radiation and a solution containing 10% methanol. In another study, Lin and Shih, <ns0:ref type='bibr' target='#b33'>(Lin & Shih, 2016)</ns0:ref> using a TiO 2 doped with copper and nitrogen, obtained a TEPH equal to 27.4 mmol h -1 g -1 , a value approximately 5 times lower than the achieved using the W1-50 in the present study. These authors also used a 400 W mercury lamp as a source of radiation. In this case, the catalyst was suspended in a solution containing 20% methanol.</ns0:p><ns0:p>In addition, the reuse of the W1-50 was evaluated for the collection of information related to its photostability. These tests consisted in evaluating the reproducibility of the catalytic action of this oxide by performing three consecutive photocatalytic cycles of five hours each using the same initial conditions applied to the system, with the exception of the pH of the medium, adjusted at the beginning of each additional cycle. These results are presented in Although there is an increase in H 2 production in the other cycles, compared to the first cycle, during the photocatalytic cycles W1-50 presented a similar profile of H 2 production in the three cycles, Fig 9 <ns0:ref type='figure'>.</ns0:ref> In the second cycle, the SHPR increased by about 10% (154.7 mmol h -1 g -1 ) compared to the first cycle, whereas in the third cycle this increase was of 9%. The good photostability, reproducibility and significant yield in H 2 production during these experiments may be related to the absence of contaminants in the catalyst in the different cycles. Certain oxides based on TiO 2 , obtained from associations, anchoring and doping with other substances, show losses in the capability of H 2 production as the photocatalytic cycles succeed. The reason for this has been pointed out as being due the photodesorption of compounds associated or anchored or by photoreduction of metals on TiO 2 surface, thus contaminating the reaction sites <ns0:ref type='bibr' target='#b86'>(Zhang et al., 2013;</ns0:ref><ns0:ref type='bibr' target='#b83'>Yuan et al., 2015)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions</ns0:head><ns0:p>In the present study, we show the use of acetone as a cosolvent in the hydrolysis of titanium tetraisopropoxide interfered favorably in the organization of critical nuclei in the oligomeric network of Titanium, in order to preorder phase crystallization brookite, thereby expanding the photocatalytic activity of the synthesized oxides. The results obtained by DRX analysis, together with the subsequent Rietveld refinement, demonstrated that the synthesized oxides are crystalline, with the percentage of brookite phase ranging from 13% to 22%, from W1 to W1-50. The changes in the surface area is influenced by the presence of acetone during the hydrolysis process, verified by the increase of 12% for W1-50 compared to that of W1. On the other hand, the estimated band gap energies have not undergone significant changes in view of the synthesis conditions.</ns0:p><ns0:p>During the photodegradation assays, the W1-50 was defined as the most effective photocatalyst based on P4R degradation, when 83% mineralization and 100% discoloration were achieved. In reuse assays using the same catalyst and new charges of the same dye, it was possible to achieve the same level of discolouration. However, the mineralization was impaired by the lack of previous treatment of the catalyst between the cycles of reuse, reaching only 58% of mineralization. On the other hand, in the degradation of the dye RR120 the performance of W1-50 was comparable to that obtained using TiO 2 P25, with 100% discoloration and 79% mineralization.</ns0:p><ns0:p>Regarding the photocatalytic production of hydrogen using W1-50 as a catalyst, 56 mmols of gaseous hydrogen were produced in 5 hours of reaction, which corresponds to a specific hydrogen production rate (SHPR) of 138.5 mmol h -1 g -1 , a value 60% higher than that achieved when TiO 2 P25 was employed. In addition, the reuse assays demonstrated the very good photostability and effectiveness of W1-50, which also ensured an increase of 10% in SHPR in the succession of cycles. Manuscript to be reviewed</ns0:p><ns0:p>Chemistry Journals</ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head>Figure 1 -</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1 -Image of the assembly used in hydrogen production assays: a) external view, b) internal view.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head>Figure 2 -</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2 -X-Ray diffratogram of the studied oxides.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>Figure 3 -</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Figure3-Raman spectra of the synthesized photocatalysts. Insert: peaks at 247 cm -1 , 323 cm -1 , 368 cm -1 , 456 cm -1 attributed to brookite.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head>Figure 4 -</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Figure 4 -Expanded Raman spectra in the region between 120 and 180 cm -1 for the synthesized photocatalysts.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head>Figure 6 -</ns0:head><ns0:label>6</ns0:label><ns0:figDesc>Figure 6 -N 2 adsorption-desorption isotherms obtained for the studied photocatalysts.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head>Figure 7 -</ns0:head><ns0:label>7</ns0:label><ns0:figDesc>Figure 7 -Images obtained by TEM for synthesized oxides a) W1, b) W1-25, c) W1-50, d) W1-75.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head /><ns0:label /><ns0:figDesc>Fig 8, show a superior performance of W1 and W1-50 compared to TiO 2 -P25.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_7'><ns0:head>Figure 8 -</ns0:head><ns0:label>8</ns0:label><ns0:figDesc>Figure 8 -Photocatalytic hydrogen production vs reaction time.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_9'><ns0:head>Figure 9 -</ns0:head><ns0:label>9</ns0:label><ns0:figDesc>Figure 9 -Amount of H 2 produced by W1-50 in three photocatalytic cycles.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_10'><ns0:head /><ns0:label /><ns0:figDesc>Figure 1</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_11'><ns0:head /><ns0:label /><ns0:figDesc>Figure 4</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_12'><ns0:head>Figure 7</ns0:head><ns0:label>7</ns0:label><ns0:figDesc>Figure 7</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_13'><ns0:head>Figure 7 -</ns0:head><ns0:label>7</ns0:label><ns0:figDesc>Figure 7 -Images obtained by TEM for synthesized oxides a) W1, b) W1-25, c) W1-50, d) W1-75.</ns0:figDesc><ns0:graphic coords='26,42.52,229.87,525.00,362.25' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='21,42.52,204.37,525.00,367.50' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='22,42.52,276.37,525.00,367.50' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='24,42.52,250.12,525.00,367.50' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='25,42.52,209.62,525.00,367.50' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='27,42.52,204.37,525.00,367.50' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='28,42.52,209.62,525.00,367.50' type='bitmap' /></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head>Table 1 -</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Percentage of crystalline phase, crystallite size and medium deformation, obtained by Rietveld refinement for synthesized oxides.</ns0:figDesc><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_1'><ns0:head>Table 1 .</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Percentage of crystalline phase, crystallite size and medium deformation, obtained by Rietveld refinement for the synthesized oxides.</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Oxide</ns0:cell><ns0:cell cols='2'>Crystalline phase</ns0:cell><ns0:cell>Crystallite medium</ns0:cell><ns0:cell>Crystallite medium</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>(%)</ns0:cell><ns0:cell /><ns0:cell>size (nm)</ns0:cell><ns0:cell>deformation (%)</ns0:cell></ns0:row><ns0:row><ns0:cell>W1</ns0:cell><ns0:cell>Anatase</ns0:cell><ns0:cell>87</ns0:cell><ns0:cell>61</ns0:cell><ns0:cell>4.0</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>Brookite</ns0:cell><ns0:cell>13</ns0:cell><ns0:cell>16</ns0:cell><ns0:cell>8.0</ns0:cell></ns0:row><ns0:row><ns0:cell>W1-25</ns0:cell><ns0:cell>Anatase</ns0:cell><ns0:cell>78</ns0:cell><ns0:cell>64</ns0:cell><ns0:cell>6.0</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>Brookite</ns0:cell><ns0:cell>22</ns0:cell><ns0:cell>10</ns0:cell><ns0:cell>6.0</ns0:cell></ns0:row><ns0:row><ns0:cell>W1-50</ns0:cell><ns0:cell>Anatase</ns0:cell><ns0:cell>78</ns0:cell><ns0:cell>63</ns0:cell><ns0:cell>6.0</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>Brookite</ns0:cell><ns0:cell>22</ns0:cell><ns0:cell>14</ns0:cell><ns0:cell>10</ns0:cell></ns0:row><ns0:row><ns0:cell>W1-75</ns0:cell><ns0:cell>Anatase</ns0:cell><ns0:cell>78</ns0:cell><ns0:cell>44</ns0:cell><ns0:cell>5.0</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>Brookite</ns0:cell><ns0:cell>22</ns0:cell><ns0:cell>22</ns0:cell><ns0:cell>11</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_2'><ns0:head>Table 1 .</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Morphological parameters related to the synthesized photocatalysts.</ns0:figDesc><ns0:table><ns0:row><ns0:cell cols='2'>Photocatalyst Surface area</ns0:cell><ns0:cell>Porosity</ns0:cell><ns0:cell>Mean pore</ns0:cell><ns0:cell>Average particle size</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>(m 2 /g)</ns0:cell><ns0:cell>(%)</ns0:cell><ns0:cell>diameter (nm)</ns0:cell><ns0:cell>(nm)</ns0:cell></ns0:row><ns0:row><ns0:cell>W1</ns0:cell><ns0:cell>84 ± 2</ns0:cell><ns0:cell>21</ns0:cell><ns0:cell>8</ns0:cell><ns0:cell>14±1</ns0:cell></ns0:row><ns0:row><ns0:cell>W1-25</ns0:cell><ns0:cell>80 ± 2</ns0:cell><ns0:cell>17</ns0:cell><ns0:cell>7</ns0:cell><ns0:cell>17±3</ns0:cell></ns0:row><ns0:row><ns0:cell>W1-50</ns0:cell><ns0:cell>94 ± 2</ns0:cell><ns0:cell>21</ns0:cell><ns0:cell>7</ns0:cell><ns0:cell>10±1</ns0:cell></ns0:row><ns0:row><ns0:cell>W1-75</ns0:cell><ns0:cell>92 ± 2</ns0:cell><ns0:cell>20</ns0:cell><ns0:cell>7</ns0:cell><ns0:cell>13±1</ns0:cell></ns0:row></ns0:table><ns0:note>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2020:08:51687:1:1:NEW 24 Sep 2020)Manuscript to be reviewed Chemistry JournalsAnalytical, Inorganic, Organic, Physical, Materials Science </ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_3'><ns0:head>Table 1 .</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Photocatalytic performance of the synthesized oxides and TiO2-P25 compared with direct photolysis, in the degradation of the dyes Ponceau 4R (P4R) and Remazol Red 120 (RR120).</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Dye/Reaction</ns0:cell><ns0:cell>Direct</ns0:cell><ns0:cell>W1</ns0:cell><ns0:cell>W1-25</ns0:cell><ns0:cell>W1-50</ns0:cell><ns0:cell>W1-75</ns0:cell><ns0:cell>P25</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>photolysis</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>P4R</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>Mineralization (%)</ns0:cell><ns0:cell>13±1</ns0:cell><ns0:cell>70±3</ns0:cell><ns0:cell>74±3</ns0:cell><ns0:cell>83±3</ns0:cell><ns0:cell>87±3</ns0:cell><ns0:cell>94±3</ns0:cell></ns0:row><ns0:row><ns0:cell>1 st k min (x10 3 min -1 )</ns0:cell><ns0:cell>0.8</ns0:cell><ns0:cell>5.0</ns0:cell><ns0:cell>5.5</ns0:cell><ns0:cell>6.0</ns0:cell><ns0:cell>8.0</ns0:cell><ns0:cell>10</ns0:cell></ns0:row><ns0:row><ns0:cell>R 2</ns0:cell><ns0:cell>0.977</ns0:cell><ns0:cell>0.982</ns0:cell><ns0:cell>0.967</ns0:cell><ns0:cell>0.973</ns0:cell><ns0:cell>0.992</ns0:cell><ns0:cell>0.982</ns0:cell></ns0:row><ns0:row><ns0:cell>2 nd k min (x10 3 min -1 )</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>12</ns0:cell><ns0:cell>14</ns0:cell><ns0:cell>21</ns0:cell><ns0:cell>23</ns0:cell><ns0:cell>33</ns0:cell></ns0:row><ns0:row><ns0:cell>R 2</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>0.979</ns0:cell><ns0:cell>0.981</ns0:cell><ns0:cell>0.990</ns0:cell><ns0:cell>0.998</ns0:cell><ns0:cell>0.974</ns0:cell></ns0:row><ns0:row><ns0:cell>Discoloration (%)</ns0:cell><ns0:cell>30±1</ns0:cell><ns0:cell>100</ns0:cell><ns0:cell>100</ns0:cell><ns0:cell>100</ns0:cell><ns0:cell>100</ns0:cell><ns0:cell>100</ns0:cell></ns0:row><ns0:row><ns0:cell>1 st k disc (x10 3 min -1 )</ns0:cell><ns0:cell>2.0</ns0:cell><ns0:cell>25</ns0:cell><ns0:cell>31</ns0:cell><ns0:cell>30</ns0:cell><ns0:cell>31</ns0:cell><ns0:cell>51</ns0:cell></ns0:row><ns0:row><ns0:cell>R 2</ns0:cell><ns0:cell>0.989</ns0:cell><ns0:cell>0.999</ns0:cell><ns0:cell>0,998</ns0:cell><ns0:cell>0.999</ns0:cell><ns0:cell>0.998</ns0:cell><ns0:cell>0.997</ns0:cell></ns0:row><ns0:row><ns0:cell>2 nd k disc (x10 3 min -1 )</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>43</ns0:cell><ns0:cell>73</ns0:cell><ns0:cell>73</ns0:cell><ns0:cell>97</ns0:cell><ns0:cell>-</ns0:cell></ns0:row><ns0:row><ns0:cell>R 2</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>0.972</ns0:cell><ns0:cell>0.937</ns0:cell><ns0:cell>0.932</ns0:cell><ns0:cell>0.876</ns0:cell><ns0:cell>-</ns0:cell></ns0:row><ns0:row><ns0:cell>RR120</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>Mineralization (%)</ns0:cell><ns0:cell>17±2</ns0:cell><ns0:cell>78±2</ns0:cell><ns0:cell></ns0:cell><ns0:cell>79±2</ns0:cell><ns0:cell></ns0:cell><ns0:cell>81±1</ns0:cell></ns0:row><ns0:row><ns0:cell>1 st k min (x10 3 min -1 )</ns0:cell><ns0:cell>1.4</ns0:cell><ns0:cell>9.0</ns0:cell><ns0:cell></ns0:cell><ns0:cell>8.5</ns0:cell><ns0:cell></ns0:cell><ns0:cell>5.6</ns0:cell></ns0:row><ns0:row><ns0:cell>R 2</ns0:cell><ns0:cell>0.959</ns0:cell><ns0:cell>0.995</ns0:cell><ns0:cell></ns0:cell><ns0:cell>0.997</ns0:cell><ns0:cell></ns0:cell><ns0:cell>0.992</ns0:cell></ns0:row><ns0:row><ns0:cell>2 nd k min (x10 3 min -1 )</ns0:cell><ns0:cell></ns0:cell><ns0:cell>13</ns0:cell><ns0:cell></ns0:cell><ns0:cell>14</ns0:cell><ns0:cell></ns0:cell><ns0:cell>20</ns0:cell></ns0:row><ns0:row><ns0:cell>R 2</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>0.989</ns0:cell><ns0:cell></ns0:cell><ns0:cell>0.961</ns0:cell><ns0:cell></ns0:cell><ns0:cell>0.993</ns0:cell></ns0:row><ns0:row><ns0:cell>Discoloration (%)</ns0:cell><ns0:cell>21±1</ns0:cell><ns0:cell>100</ns0:cell><ns0:cell></ns0:cell><ns0:cell>100</ns0:cell><ns0:cell></ns0:cell><ns0:cell>100</ns0:cell></ns0:row><ns0:row><ns0:cell>1 st k disc (x10 3 min -1 )</ns0:cell><ns0:cell>1.5</ns0:cell><ns0:cell>40</ns0:cell><ns0:cell></ns0:cell><ns0:cell>44</ns0:cell><ns0:cell></ns0:cell><ns0:cell>52</ns0:cell></ns0:row><ns0:row><ns0:cell>R 2</ns0:cell><ns0:cell>0.979</ns0:cell><ns0:cell>0.979</ns0:cell><ns0:cell></ns0:cell><ns0:cell>0.977</ns0:cell><ns0:cell></ns0:cell><ns0:cell>0.997</ns0:cell></ns0:row><ns0:row><ns0:cell>2 nd k disc (x10 3 min -1 )</ns0:cell><ns0:cell></ns0:cell><ns0:cell></ns0:cell><ns0:cell></ns0:cell><ns0:cell></ns0:cell><ns0:cell>*</ns0:cell><ns0:cell></ns0:cell></ns0:row><ns0:row><ns0:cell>R 2</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell></ns0:cell><ns0:cell>-</ns0:cell><ns0:cell></ns0:cell><ns0:cell>-</ns0:cell></ns0:row></ns0:table><ns0:note>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2020:08:51687:1:1:NEW 24 Sep 2020)</ns0:note></ns0:figure> <ns0:note place='foot'>Analytical, Inorganic, Organic, Physical, Materials Science </ns0:note> </ns0:body> "	"Uberlândia, September 28, 2020. Dear Editor, Firstly, we hope you and your family are well. We are submitting a revised version of our article in which some suggestions and/or comments made have been incorporated or considered. The added changes were marked in yellow. Corrected figures (Figure 2, Figure S1, Figure S5a and S5b) and Tables 1 to 3 are being sent in substitution. Finally, we did, in the best way we could, the additional adjustments recommended after the analysis made by you and the two Referee. As for dismembering, at this point, the section containing the results together with the discussion in a section only for results and another containing only the discussions, will not be an easy task, and it is very likely that our work suffers damage in its impaired quality. Moreover, this work has already gone through the sieve of two reviewers and an editor, and now we will have to reformulate the most sensitive parts of the work, already reformulated following their recommendations? On the other hand, we have seen in PeerJ many publications where the two topics are also presented together (see for example https://peerj.com/articles/matsci-2/, also published in the section ‘Materials Science’, in november 15, 2019, https://peerj.com/articles/achem-7/, published in the section ‘Analytical Chemistry’ in july 06, 2020, and https://peerj.com/articles/4464/ - of which I am co-author, published in march 6, 2018). There are also other studies where the authors chose to present the results and the discussion in a section that they called 'Results'. And that did not compromise the quality of these works. In view of the above, we ask you to reconsider the requirement made. Sincerely, Antonio Eduardo da Hora Machado, Prof. Dr. Universidade Federal de Uberlândia Laboratório de Fotoquímica e Ciência de Materiais _____________________________________________ Responses to comments and/or suggestions: Editor Responses to comments done in the body of the text: 1) Would 'bleaching' be a better term in this manuscript? (Abstract Line 14) We prefer to keep the term 'discoloration' because it is more widespread in this field of knowledge, which encompasses both studies concerning the photocatalytic detoxification of aqueous effluents and those that refer to the production of H2 also via photocatalysis. 2) This is a parenthetical statement and should be punctuated accordingly (Line 51) We agree. The sentence was changed as suggested. 3) Well-defined diffraction peaks is a direct observation, not an inference. The high crystallinity is an inference, however. (Line 185) We agree. The term “high” was removed. Reviwer#1 Answers to specific topics: 1) The sentence is confused. I suggest that the authors rewrite the sentence. “The dyes discoloration was monitored without pH correction, by varying the absorbance of the solutions with the reaction time”. (Line 139-140). Our apologies. We agree that this sentence was confused. We changed it as suggested. 2) The reactor's boron silicate glass blocks a part of UV radiation. How intense is the radiation inside the reactor? (line 173). The irradiance inside the reactor is 100 W/m2, measured using a Detta OHM model HD 2102.2 radiometer equipped with a UV-A detector. The sentence was changed to: “Under this condition, its estimated photonic flux in the UVA was of 3.3 x 10-6 Einstein/s (Machado et al., 2008), with an irradiance inside the reactor of 100 W/m2”. (Lines 136138). 3) I suggest adding Figure 1 to the supplemental material We prefer to keep it as suggested in the original version of the work, because this figure portrays a photocatalytic system developed especially for the essays presented, thus deserving to be highlighted in the manuscript body. 4) I suggest removing the following sentence, as it does not contain any additional information. “The average size of crystallites could not be calculated using the Debye-Scherrer method, since the presence of a second phase in the crystalline network creates considerable uncertainty in estimating this property (Kibombo et al., 2011)”. (line195-197). We agree. Thank you very much for the observation. This sentence and the related reference (lines 195-197 e 559-561, respectively, in the previous version of the manuscript) were removed. 5) Rewrite the following paragraph according to the discussion of the photocatalysis tests (323-332). “This may favor the adsorption of organic matter on their surfaces, which can consequently favor the photocatalytic efficiency. In addition, it was observed an inverse correlation between the surface area and the average particle size, except for the W1-25 that presented wide variation on its particle size”. (line 274-277). This sentence was rewritten (lines 323 – 327 in the new version). Thank you for the contributions that have certainly given greater clarity to the presentation of our work. 6) TEM images show clusters of partially sintered nanoparticles with irregular shapes. What parameters were used in counting the particle size? We considered that all nanoparticles were spheres-like, and we used the software 'ImageJ', indicated in line 125 in the previous version of the article. This software allows through the zoom tool to measure small particles at the end of the cluster without loss resolution, as exemplified in the following figure, Figure - Example of measurements of measurements taken at the end of the image. 7) Has the adsorption capacity of the materials been analyzed? In the present study, no adsorption measurements of the synthesized materials were done because we considered it irrelevant for the analysis of the results. 8) Was any characterization of the material carried out after the incorporation of the Pt? An in-depth study on the photodeposition of Pt on the surface of TiO2 was not developed, since this theme has been widely explored in other studies already published by other authors related to the production of H2 via water splitting. The focus of this study was to demonstrate that the sol-gel synthesis combined with the use of acetone as co-solvent in the hydrolysis of the precursor (titanium tetraisopropoxide) causes a significant change in the proportion of crystalline phases (anatase/broquite), thus influencing the photocatalytic action of synthesized materials with regard to the ability to mediate the photocatalytic production of H2. Pt was employed as a co-catalyst. We used a fixed concentration so as not to mask the results of H2 production of the different synthesized catalysts. Reviwer#2 Answers to specific topics: 1) On page 5, line 123. The sentence was corrected. Excuse us for this lack of attention, and thank you very much for the contribution. 2) On page 5, lines 141 and 142. The experiments using the dyes P4R and RR120 were carried out separately and not using mixtures of these dyes. Thus, the monitoring of absorbance was performed separately. Therefore, there was no need to use calibration curves in spectrophotometric analyses of the discoloration of these dyes by photocatalytic route. We added in line 141 a sentence to clarify this. 3) On page 5, lines 150 to 152 In fact, losses occurred during washing and drying of the material after each photocatalytic cycle. However, there was no need to reduce the initial concentration of dyes proportionally to the recovered TiO2 mass, because we used in all reuse tests the same mass used in the first assay. For this, four batch tests were performed for each cycle, in order to obtain an accumulated mass of recovered TiO2 that guaranteed the same catalyst mass in the following cycles. We added to these lines a sentence to clarify this. 4) Figure 1 We agree. However, we believe that the reviewer here refers to Figure 2. We changed the colors to make the results presented adequately distinguishable. The internal captions have also changed in the new version of the figure. Rietveld refinement for TiO2-P25 was not performed since the constitution of this catalyst has already been widely studied in the literature. The focus of this work was the characterization of the new synthesized photocatalysts, which consists of anatase and broquite. 5) About Table 2 The error pointed out is persistent and occurred during the conversion of some files at the time of submission. On the substitution of the term 'morphological parameters' by 'textural parameters', we do not agree because the first is the most appropriate when studies involve solid materials. 6) About Table 3 For each entry wherever the rate constants of discoloration or mineralization where presented, there are two values. It is not clear what these two values mean. Is it the result of two equal trials? If yes, I think it is more appropriate to present the data as an average and standard deviation. Or are those two values related to the two-stages pseudo-first-order rate constant? If yes, I recommend the authors to present these values in different columns, and clearly label the columns as first-stage degradation and second-stage degradation, for instance. We did changes to the text between lines 323-331 to make it clearer. The kinetics presented for the discoloration and mineralization of the dyes used in this work as models of oxidizable substrates are all pseudo first order constants, representing processes that occur in two stages, except in the direct photolysis. Regarding the data, all constants presented are mean values related to at least three equivalent experiments, as stated in the experimental section (lines 133-153). Table 3 was redone including the R2 of each regression analysis, as suggested. Second, for the RR120 dye, why is there only one value for the discoloration rate constant? The reason why there is only one value should be clearly explained by the authors. The RR120 dye was completely discolored in just 80 minutes of reaction. Most likely, different from what occurs for P4R, the fragments, formed by the action of the radical species produced during the photocatalytic process, do not absorb or absorb radiation with very low efficiency at the wavelength in which the monitoring was performed (512 nm). It should be emphasized that mineralization measures were conducted monitoring the dissolved organic carbon, much more accurate than discoloration measurements. Third, why were the samples W1-25 and W1-75 not tested neither for mineralization nor discoloration for RR120? If the authors have the possibility to perform this analysis, they should timely do it for the revisions of this paper. If the authors are unable to do it, the reason why should be clearly explained in the manuscript text. Although the mineralization and discoloration of P4R conducted using W1-75 presented the best performance among the synthesized oxides, this result was only 4.8% higher than that achieved when using the W1-50, to the detriment of the expressive amount of acetone used in the synthesis of W1-75. In view of this, W1-50 was considered as the most effective catalyst for mineralizing P4R, being therefore preferably applied in the following stages of the present study. W1-25 and W1-75 did not have their discoloration and mineralization efficiencies evaluated by the reason exposed between lines 337-346, in the body of the article, whose sentences were duly improved. As W1-50 was defined as the catalyst with the best performance, only it was evaluated in the reuse and photocatalytic production tests of H2. Fourth, to complement the data, I recommend the authors to present the R2 values obtained from the pseudo-first-order model fit to the experimental data. Table was improved. Fifth, the caption should be checked, as it is showing Table 1 instead of Table 3. As pointed out above, this error is persistent and occurred during the conversion of some files at the time of submission. 7) About the mineralization and discolaration kinetics: We appreciate the suggestion, but we do not see the need to use non-linear fitting to the data to confirm that both discoloration (excluding data for RR120 using W1, W-50 and P25) as mineralization usually occurs in two stages in pseudo-first order kinetics, as suggested by the kinetic treatment we applied to the collected data. We emphasize that this form of data processing is the result of previous studies conducted by our research group. A more detailed discussion of this treatment can be found, for example in the reference (França et al., 2016; doi: 10.5935/0103-5053.20160007) cited in the present study. We are sure that, through these tests, our goal of defining the most appropriate catalyst, among which we synthesize, for application in hydrogen production tests, was achieved. However, we do not rule out the possibility of applying the suggested treatment in future studies. "	Here is a paper. Please give your review comments after reading it.
443	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>The efficiencies of rice flour (RF) and rice husk (RH) as agents of the controlled release of methyl salicylate (RF-MeSA and RH-MeSA, respectively) were investigated. The adsorption percentage of RH-MeSA was significantly higher (2-fold) than that of RF-MeSA owing to its higher specific surface area and total pore volume. However, both materials are classified as mesoporous materials. Scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, and thermogravimetric analysis (TGA) confirmed that MeSA diffused toward the pores and covered the surfaces of RF and RH. A temperature increase from 25 °C to 40 °C and an increase in relative humidity from 75 % to 95 % stimulated the release of MeSA. The kinetically controlled release of RF-MeSA and RH-MeSA was in line with a Fickian diffusion mechanism. Both RF-MeSA and RH-MeSA significantly delayed the ripening of banana fruit compared to the control. The results indicate that RF and RH can be used as biosorbent materials for the adsorption and controlled release of MeSA without chemical and mechanical modification.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>Physiological and biochemical changes during fruit ripening lead to changes in color, a peak in respiration, a burst in ethylene production, softening and declines in acidity <ns0:ref type='bibr' target='#b40'>(Gray et al, 1994)</ns0:ref>.</ns0:p><ns0:p>Ethylene is an endogenous plant hormone responsible for fruit ripening and senescence <ns0:ref type='bibr' target='#b79'>(Pratt & Goeschl, 1969)</ns0:ref>. Ethylene is synthesized from methionine through the intermediaries S-adenosyl methionine (SAM) and 1-aminocyclopropane-1-carboxylic acid (ACC). The enzyme that converts methionine to SAM is SAM synthase, while ACC synthase converts SAM to ACC, and ACC oxidase catalyzes the oxidation of ACC to ethylene <ns0:ref type='bibr' target='#b105'>(Yang & Hoffman, 1984)</ns0:ref>. release <ns0:ref type='bibr' target='#b34'>(Geera et al., 2006)</ns0:ref>. Potato and corn starch can adsorb volatile compounds <ns0:ref type='bibr'>(Bermiller & Pratt, 1981)</ns0:ref>, and amylose extracted from potato starch can bind with n-butyl alcohol, iso-amyl alcohol, menthone, and other compounds <ns0:ref type='bibr' target='#b92'>(Takeo & Kuge, 1969)</ns0:ref>. Moreover, rice starch has been used as an ingredient in dry shampoo because it absorbs oil in the hair <ns0:ref type='bibr'>(Santander-Ortega et al., 2010)</ns0:ref>. Assam Bora rice starch can be used as a carrier for controlled-release drug delivery <ns0:ref type='bibr' target='#b0'>(Ahmad et al., 2012)</ns0:ref>. Rice starch-konjac glucomannan (KGM) blend films with MeSA have shown potential as agents of controlled release of bioactive compounds <ns0:ref type='bibr' target='#b84'>(Satirapipathkul & Meesukanun, 2013)</ns0:ref>.</ns0:p><ns0:p>Rice husk (RH), an agricultural waste product that is generated in the milling process of rice grain, is normally used as feed for livestock. The major constituents of RH are cellulose (35 %), lignin (25 %), silica (20 %), crude protein (3 %), and ash (17 %) <ns0:ref type='bibr' target='#b99'>(Ugheoke & Mamat, 2012)</ns0:ref>. The latter constituent imbues the husks with high surface area and porosity, two important attributes that facilitate adsorption and desorption <ns0:ref type='bibr' target='#b8'>(Basha et al., 2005)</ns0:ref>. Rice husk ash (RHA) is a source of silica, which is used in slow-release drug delivery systems <ns0:ref type='bibr' target='#b80'>(Prawingwong et al., 2009)</ns0:ref>. Urea coated with rice husk charcoal (RHC) has the potential to slow the release of nitrogen fertilizer <ns0:ref type='bibr' target='#b104'>(Xiaoyu et al., 2013)</ns0:ref>. To the best of our knowledge, there are no reports in the literature that examine the applicability of RF and RH to the adsorption and controlled release of MeSA.</ns0:p><ns0:p>Therefore, the objectives of this study were to investigate the efficacy of RF and RH without a modified surface in adsorbing MeSA and their ability to slowly release this compound, as well as to understand the release kinetics of these two biosorbents. RF and RH biosorbents were used to examine the delay in fruit ripening, using bananas as a model. Bananas are a climacteric fruit which show a sharp increase in ethylene production with a high respiration rate during the time The X-ray patterns of RF, RH, RF-MeSA, and RH-MeSA samples (0.5 g) were analyzed using an XRD analyzer (Bruker AXS, Model D8 Discover, Billerica, MA, USA) with copper radiation at a voltage of 40 kV and 40 mA. The RF, RH, RF-MeSA, and RH-MeSA samples were scanned between 2θ = 5°-60° with a scanning speed of 2° min -1 .</ns0:p></ns0:div> <ns0:div><ns0:head>Fourier transform infrared (FTIR) spectroscopy</ns0:head><ns0:p>For functional group analysis, 2 mg samples of RF, RH, RF-MeSA, and RH-MeSA were mixed with 100 mg of potassium bromide powder; the mixture was compressed at 10 psi. The pellets were transferred into a FTIR spectrometer (Perkin Elmer, Spectrum One, USA), and their spectra were recorded at a resolution of 4 cm -1 in a range of 400-4000 cm -1 .</ns0:p></ns0:div> <ns0:div><ns0:head>Thermogravimetric analysis (TGA)</ns0:head><ns0:p>To determine thermal decomposition of RF, RH, RF-MeSA, and RH-MeSA, 5 mg samples were scanned using a TGA analyzer (Perkin-Elmer, Model Pyris Diamond, USA). The samples were heated from 30 °C to 800 °C, with a temperature ramp rate of 10 °C min -1 . Nitrogen was used as the purge gas at a flow rate of 10 mL min -1 .</ns0:p></ns0:div> <ns0:div><ns0:head>Desorption of rice flour-methyl salicylate (RF-MeSA) and rice husk-methyl salicylate (RH-</ns0:head></ns0:div> <ns0:div><ns0:head>MeSA)</ns0:head><ns0:p>The biosorbents (RF and RH) were weighed and placed into 10 mL glass vials with aluminum caps. MeSA was added to each of the biosorbents at a ratio of 2:1 (w/w; MeSA:biosorbent) and </ns0:p></ns0:div> <ns0:div><ns0:head>Effect of temperatures on desorption</ns0:head><ns0:p>Samples of RF-MeSA and RH-MeSA (1.0 g) in 10 mL airtight glass vials were heated using a heating box (Gemmy DB-006E, Taipei, Taiwan) at 25 °C and 40 °C respectively, at 70-75 % relative humidity. Methyl salicylate gas (5 mL) from the headspace was assayed at 0, 1, 2, 3, 6, 9, 12, and 24 h after heating commenced by gas chromatography (GC; GC-14B Shimadzu, Japan). The GC was equipped with a DB-5 column (30 m × 0.250 mm) and a flame ionization detector. The column temperature was 50 °C for 5 min, then heated to 130 °C at 12 °C min -1 , and then increased to 200 °C at 15 °C min -1 . Helium was used as the carrier gas at a flow rate of 1.0 mL min -1 . Injection and detector temperatures were set at 250 °C and 240 °C, respectively.</ns0:p><ns0:p>Methyl salicylate was used as a standard compound. The percentage desorption of MeSA was calculated as follows: Desorption (%) = A t /A eq × 100</ns0:p><ns0:p>(2)</ns0:p><ns0:p>where A t is the peak area of MeSA at time t and A eq is peak area of MeSA at equilibrium.</ns0:p></ns0:div> <ns0:div><ns0:head>Effect of relative humidity on desorption</ns0:head><ns0:p>Samples of RF-MeSA and RH-MeSA (1.0 g) in 10 mL glass vials were placed in desiccators that were pre-equilibrated with saturated solution sodium chloride and potassium sulfate to attain relative humidities of 75 % and 95 %, respectively, at 25 °C <ns0:ref type='bibr' target='#b41'>(Greenspan, 1977)</ns0:ref>. The equilibrium time for the samples in the desiccator was 1 h. Headspace gas (5 mL) was collected in 1 min intervals and removed at 0, 1, 2, 3, 6, 9, 12, and 24 h for the analysis of MeSA gas by GC.</ns0:p><ns0:p>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2020:07:51183:1:0:NEW 23 Sep 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:p></ns0:div> <ns0:div><ns0:head>Release kinetics</ns0:head><ns0:p>The release kinetics of MeSA from RF-MeSA and RH-MeSA were investigated according to the methods of Ho, Joyce & Bhandari (2011) with some modifications.</ns0:p><ns0:p>The Korsmeyer-Peppas model <ns0:ref type='bibr' target='#b57'>(Korsmeyer et al., 1983)</ns0:ref> was employed to describe the release kinetics by Eq. 3.</ns0:p><ns0:formula xml:id='formula_0'>M t /M ∞ = kt n (3)</ns0:formula><ns0:p>where M t and M ∞ represent MeSA released at time t and at equilibrium, respectively; k is the rate constant; and n is the release exponent calculated from the slope of the straight line. Controlled release was further evaluated by other models. First, the data were assessed as a zero-order reaction (Eq. 4), as the cumulative amount of MeSA released vs. time:</ns0:p><ns0:formula xml:id='formula_1'>C = k 0 t (4)</ns0:formula><ns0:p>where k 0 is the zero-order rate constant expressed in units of concentration/time and t is the time in hours. Second, the data were assessed as a first order reaction (Eq. 5) as the log cumulative percentage of MeSA remaining vs. time:</ns0:p><ns0:formula xml:id='formula_2'>log C = log C 0 -kt /2.303 (5)</ns0:formula><ns0:p>where C 0 is the initial concentration of MeSA released, k is the first-order constant, and t is the time in hours. Lastly, the data were assessed using the Higuchi model (Eq. 6) as the cumulative percentage of MeSA released vs. square root of time:</ns0:p><ns0:formula xml:id='formula_3'>Q = kt 1/2 (6)</ns0:formula><ns0:p>where k is the rate constant and t is the time in hours.</ns0:p><ns0:p>PeerJ Ethylene production and respiration rate were determined by placing one banana fruit in a 600 mL air-tight plastic container and incubating at 25 ± 2 °C for 1 h. A 1.0 mL sample of headspace gas was withdrawn and injected into the gas chromatograph (GC-2014B, Shimadzu, Japan). The ethylene production is expressed as ng kg -1 s -1 and the respiration rate is expressed as µg kg -1 s -1 .</ns0:p><ns0:p>The color change of the banana peel was measured in three locations on each fruit using a colorimeter (CR-300, Minolta, Tokyo, Japan). The measurements were expressed as yellowness (b* value). Fruit firmness was measured by a texture analyzer (TA-XT plus, Stable Micro Systems, UK), and the results were expressed as force (N).</ns0:p><ns0:p>The half-life (t 1/2 ) release, which indicated the time it took for 50 % of the active compound to release from the biosorbent, was determined by Eq. 7 (Ho, Joyce & Bhandari, 2011) <ns0:ref type='table'>2020:07:51183:1:0:NEW 23 Sep 2020)</ns0:ref> Manuscript to be reviewed</ns0:p><ns0:formula xml:id='formula_4'>t 1/2 = exp(-(lnk+ln(0.5))/n) (7) PeerJ Mat. Sci. reviewing PDF \| (MATSCI-</ns0:formula></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>where k is the rate constant and n is the release exponent calculated from the slope.</ns0:p></ns0:div> <ns0:div><ns0:head>Statistical analysis</ns0:head><ns0:p>All experiments were arranged in a randomized complete block design (RCBD) with three replicates. The data were analyzed by analysis of variance (ANOVA) using SAS (SAS Institute;</ns0:p><ns0:p>Cary, NC, USA); significant differences (p ≤ 0.05) among means were determined by Duncan's multiple range test (DMRT).</ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head></ns0:div> <ns0:div><ns0:head>Characteristics of biosorbents</ns0:head><ns0:p>We found that RH had a higher specific surface area and total pore volume (4.24 m 2 g -1 , 0.0051 cm 3 g -1 ) than RF (2.85 m 2 g -1 , 0.0043 cm 3 g -1 ), while the average pore diameter of RH (4.824 nm)</ns0:p><ns0:p>was smaller than that of RF (5.997 nm), as indicated in Table <ns0:ref type='table'>1</ns0:ref>. RH before and after MeSA adsorption. Prior to adsorption, the peak at 3460 cm -1 for RF was assigned to the vibration of O-H stretching, and the peak at 2900 cm -1 was assigned to C-H stretching. The carbonyl group (C=O) of the esterified acetyl group was verified by the peak at 1740 cm -1 , and the peak at 1080 cm -1 was attributed to C-OH stretching of cellulose. For RH, the adsorption peak at 3460 cm -1 was assigned to free hydroxyl groups present in cellulose, hemicellulose, and lignin. The C-H stretching vibration at 2900 cm -1 indicated the presence of an alkane functional group. The peak around 1740 cm -1 was assigned to C=O stretching, which can be attributed to aromatic groups in the hemicelluloses and lignin. The FTIR spectra of RF-MeSA and RH-MeSA did not show peaks at 3460 cm -1 (O-H stretching), but rather at 3200 cm -1 , which corresponds with the spectrum of MeSA. The peak at 2900 cm -1 was reflective of C-H stretching on both RF-MeSA and RH-MeSA.</ns0:p></ns0:div> <ns0:div><ns0:head>Thermogravimetric analysis</ns0:head><ns0:p>The </ns0:p></ns0:div> <ns0:div><ns0:head>Release kinetics</ns0:head><ns0:p>The correlation coefficient (R 2 ) and release exponent (n) of the zero-order, first-order, Higuchi, and Korsmeyer-Peppas models of RF-MeSA and RH-MeSA are summarized in Table <ns0:ref type='table'>2</ns0:ref>. For polymeric matrices, n ≤ 0.5 corresponds to a Fickian diffusion mechanism, and 0. suggest that the controlled release of MeSA can be described as a diffusion mechanism.</ns0:p></ns0:div> <ns0:div><ns0:head>Postharvest quality of banana fruit</ns0:head><ns0:p>Both RF-MeSA and RH-MeSA were applied as biosorbents to delay the ripening of bananas (Fig <ns0:ref type='figure'>8</ns0:ref>). Bananas are a climacteric fruit and display a sharp increase in both ethylene production and respiration rate after harvest; these are two factors that accelerate ripening <ns0:ref type='bibr' target='#b87'>(Seymour et al., 1993)</ns0:ref>. Our results showed that RH-MeSA and RF-MeSA treatments inhibited ethylene production for 1 d of storage, with the control treatment exhibiting a significantly higher value (34.72 ng kg -1 s -1 ; p ≤ 0.05). On d 3 of storage, the climacteric peak of ethylene production in the control treatment was 1,465.28 ng kg -1 s -1 , while those of RH-MeSA and RF-MeSA were 1,198.62 and 1,144.44 ng kg -1 s -1 , respectively (Fig <ns0:ref type='figure'>9A</ns0:ref>). The respiration rate, which was 1.28 µg kg -1 s -1 at harvest, increased in stored fruit. The respiration rate did not change significantly Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science 7D). Moreover, the half-life release (the time taken to release 50 % MeSA from the RF and RH) was 4 d and 5 d at 25 °C and 75 % relative humidity, respectively. (Table <ns0:ref type='table'>3</ns0:ref>)</ns0:p></ns0:div> <ns0:div><ns0:head>Discussion</ns0:head><ns0:p>These results indicate that RH has higher specific surface area and total pore volume than RF. This is likely due to the fact that the main organic compounds comprising RH are silica, cellulose, hemicellulose, and lignin. This composition results in increased surface area and pore volume <ns0:ref type='bibr' target='#b35'>(Ghosh & Bhattacherjee, 2013)</ns0:ref>. According to the IUPAC classification of pore size as macroporous (>50 nm), mesoporous (2.0-50 nm), and microporous (<2.0 nm), the average pore diameter of RF and RH was 5.997 nm and 4.824, respectively. Therefore, RF and RH were classified as mesoporous materials. The result can be explained because all sites on the biosorbents were available at the initial stage of the process <ns0:ref type='bibr' target='#b101'>(Villacañas et al, 2006)</ns0:ref>, which allowed MeSA molecules to readily occupy them until the adsorption process stabilized, as reflected by the attainment of equilibrium between 12 and 24 h. However, the time required to achieve equilibrium in the adsorption process largely depends on the adsorbent structure and is influenced by the molar mass of the adsorbed compound <ns0:ref type='bibr' target='#b89'>(Singh & Yenkie, 2006)</ns0:ref>. Removing the excess MeSA compound using Whatman ® filter paper No.1 has particle retention at 98 % efficiency <ns0:ref type='bibr' target='#b48'>(Hutten, 2015)</ns0:ref> and it did not affect the loss of active compounds that were trapped in the pores. This method has been used to estimate the removal of heavy metal ions <ns0:ref type='bibr' target='#b56'>(Khokhotva & Waara 2010)</ns0:ref> and olive oil waste <ns0:ref type='bibr' target='#b33'>(Garcia et al. 2006</ns0:ref>). Our results showed a higher specific surface area and total pore volume for RH than for RF, which may account for the higher adsorption percentage recorded for RH-MeSA Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science in the semi-crystalline structure <ns0:ref type='bibr' target='#b58'>(Kusbiantoro et al., 2012)</ns0:ref>. Hence, the SEM images and EDS spectrum confirmed that MeSA was adsorbed on the outer surface of RF and RH. The EDS indicated from the increased amount of carbon after MeSA adsorption on both RF and RH that MeSA was loaded on RF and RH successfully. In addition, the FTIR spectra indicated the conjugated aromatic C-C, C=O, and hydrogen bonds that were the functional groups of the MeSA compound, thereby confirming that RF and RH adsorbed the MeSA compound. The TGA implied that MeSA could be adsorbed on RF and RH, and the amount of weight loss of RF-MeSA and RH-MeSA was larger than the RF and RH samples. This result more clearly indicates that weight loss is related to thermal degradation of adsorbed MeSA molecules. RH-MeSA showed a higher weight loss than RF-MeSA, which can be attributed to the greater specific surface area and total pore volume. During the adsorption process, the MeSA molecule attaches to the active sites of the adsorbent surface and then it diffuses into the pores. Therefore, adsorption increases with the surface area and pore volume of the adsorbent <ns0:ref type='bibr' target='#b107'>(Zhang & Blum, 2003)</ns0:ref>. In addition, the percentage of MeSA released from RH and RF increased with increasing temperature because of the weak adsorptive forces between the binding sites of the adsorbent and adsorbate <ns0:ref type='bibr' target='#b78'>(Ofomaja & Ho, 2007)</ns0:ref>. <ns0:ref type='bibr'>Chen et al. (2010)</ns0:ref> reported that the hydrogen bonds between starch chains are broken and the crystalline region is damaged with an increase in temperature. The humidity also affects the release rate of RF-MeSA and RH-MeSA because RF and RH are hygroscopic materials <ns0:ref type='bibr' target='#b77'>(Navaratne, 2013)</ns0:ref>.</ns0:p><ns0:p>Under high humidity, RF and RH have the ability to absorb and retain water molecules from the air, leading to the displacement of MeSA molecules by water vapor <ns0:ref type='bibr' target='#b90'>(Smith et al., 1987)</ns0:ref>. Therefore, increasing the relative humidity leads to an increase in the release of the MeSA compound. The Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science optimum relative humidity for the majority of fruit and vegetables are <ns0:ref type='bibr'>90-95 % (McGregor, 1989)</ns0:ref>.</ns0:p><ns0:p>Fruit and vegetables remain as living organs after harvested, and they continue to respire, leading to moisture loss via the transpiration process <ns0:ref type='bibr' target='#b11'>(Ben-Yehoshua, 1969)</ns0:ref>. Even though, these biosorbent materials effectively delay the ripening of fruit, their application as biosorbents should be balanced against the accelerated release of MeSA from the moisture from fresh produce.</ns0:p><ns0:p>The efficiencies of RF and RH were assessed as new biosorbents for controlling the release of methyl salicylate (MeSA). In a closed system, 1 g dosages of adsorbents were investigated,</ns0:p><ns0:p>showing a pattern of Fickian diffusion. Therefore, RF-MeSA and RH-MeSA at 1 g adsorbents dosage were applied to two bananas that were placed inside a PP tray. The half-life release (the time for 50 % MeSA to be released from the RF and RH) was 4 and 5 d at 25 ℃ and 75 % relative humidity. The application of RF-MeSA and RH-MeSA for delaying the ripening of 'Namwa' banana fruit indicated that RH-MeSA and RF-MeSA inhibited the ethylene production of 'Namwa' bananas during storage. MeSA inhibits ethylene biosynthesis by blocking the conversion of 1-aminocyclopropane-1-carboxylic acid (ACC) to ethylene <ns0:ref type='bibr' target='#b62'>(Leslie & Romani, 1986</ns0:ref>). Ethylene plays a major role in regulating the ripening and softening of climacteric fruit such as mangos, peaches, and jujubes <ns0:ref type='bibr' target='#b91'>(Srivastava & Dwivedi, 2000;</ns0:ref><ns0:ref type='bibr'>Li et al., 2007)</ns0:ref>. Thus, the inhibition of ethylene production may suppress the ripening process and, thereby, maintain fruit firmness and delay color change. However, the RF-MeSA and RH-MeSA treatment had no significant effect on respiration rate. <ns0:ref type='bibr' target='#b25'>Ding and Wang (2003)</ns0:ref> reported that tomatoes treated with MeSA vapor (0.5 mmol L -1 ) depressed their respiration rate from 18 to 6 µg kg -1 s -1 compared to control fruit at 20 ℃. Similarly, MeSA vapor at 1.0 mmol L -1 reduced the respiration rate (6.94 µg kg -1 s -1 ) of 'Primulat' sweet cherries over 14 d, at 2 °C <ns0:ref type='bibr'>(Castillo et al., 2015)</ns0:ref>. In our results, the banana fruit under both treatments showed a slight change in the peel (red orange color). Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science to a delayed ripening process compared with that of RF-MeSA; this was because the structure of RH showed greater surface area and pore volume than RF, which affected the amount of MeSA adsorbed on the surface <ns0:ref type='bibr' target='#b88'>(Sing et al., 1985)</ns0:ref>. Moreover, a comparison of desorption percentages showed a higher overall release in RF than in RH (at 25 °C, 75 % relative humidity), indicating greater retention and therefore slower release of the MeSA compound on the part of the RH biosorbent. Both RF and RH have already been used as biosorbents without modifying the surface.</ns0:p><ns0:p>RF-MeSA and RH-MeSA at 1 g of adsorbent dosage promoted adsorption of the MeSA at 36.76 % and 58.33 %, respectively and their ability to slowly release MeSA depended upon higher temperatures and higher relative humidity. The half-life clearly confirmed the release of MeSA from the RF and RH biosorbents and the diffusion was observed to be Fickian. Because RF and RH are eco-friendly and innovative they are expected to replace the traditional adsorbents such as β-CD which are obtained from the enzymatic degradation of starch <ns0:ref type='bibr' target='#b59'>(Lee et al., 2020)</ns0:ref>. MeSA inclusion with complex β-CD at 1:1 showed the highest MeSA entrapment efficiency (59 %),</ns0:p><ns0:p>indicating that the MeSA release increased with increasing relative humidity and temperature <ns0:ref type='bibr' target='#b50'>(Kant et al. 2004</ns0:ref>). However, RF and RH have the potential to be similar to β-CD but without the need for modification.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions</ns0:head><ns0:p>MeSA (1.0 g) was applied to RF and RH at a ratio of 2:1 (w/w; MeSA: biosorbent) at 25 °C for 24 h in order to reach equilibrium in the desorption process during postharvest treatment. Both Therefore, further study on pH, optimal dosage and concentration of MeSA are needed before these biosorbents are utilized for commercial treatment with bananas or other fruit.</ns0:p><ns0:note type='other'>Figure 1</ns0:note><ns0:p>Pore size distribution of rice flour (RF) and rice husk (RH).</ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>left at 25 °C for 24 h. Samples of RF-MeSA and RH-MeSA (1.0 g) were placed into 10 mL PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2020:07:51183:1:0:NEW 23 Sep 2020) Manuscript to be reviewed Chemistry Journals Analytical, Inorganic, Organic, Physical, Materials Science airtight glass vials and used to study the effect of temperature and relative humidity on desorption processes.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>Mat. Sci. reviewing PDF \| (MATSCI-2020:07:51183:1:0:NEW 23 Sep 2020) Manuscript to be reviewed Chemistry Journals Analytical, Inorganic, Organic, Physical, Materials Science Application of rice flour-methyl salicylate (RF-MeSA) and rice husk-methyl salicylate (RH-MeSA) on banana fruit Banana (Musa sapientum L. 'Namwa') fruit that were free of physical damage and symptoms of disease, and were at 90 d after bloom, were collected from commercial orchards located in Ratchaburi Province, Thailand. Hands were cut into individual fingers, washed with tap water, dipped for 5 min in sodium-hypochlorite (200 mg L -1 ), and air-dried at 25 ± 2 °C before treatment. For each treatment, two fruit were placed inside a perforated polypropylene (PP) tray (4 holes, 2.0 mm perforations), with 1.0 g of RF-MeSA or RH-MeSA in Whatman ® filter paperNo. 1; a group without any biosorbent was used as a control. The storage period was 5 d at 25 ± 2 °C and at a relative humidity of 75 %. The samples were withdrawn for analysis at 0, 1, 3, and 5 d after initiation of the storage period. Six replicates were conducted per treatment, with two fruit per package.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head /><ns0:label /><ns0:figDesc>The pore size distribution of RF and RH is shown in Fig 1. Adsorption of rice flour-methyl salicylate (RF-MeSA) and rice husk-methyl salicylate (RH-MeSA)Adsorption percentageThe adsorption percentages of both RF-MeSA and RH-MeSA increased rapidly during hours 1-6 and then attained equilibrium between hours 12 and 24. At 24 h, the adsorption percentages of respectively (Fig 2). The adsorption percentage of RH-MeSA was significantly higher than that of RF-MeSA throughout the entire 24 h period (p ≤ 0.05).PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2020:07:51183:1:0:NEW 23 Sep 2020)Manuscript to be reviewedChemistry JournalsAnalytical, Inorganic, Organic, Physical, Materials ScienceSurface morphologyThe surface morphologies of RF and RH before and after MeSA adsorption (RF-MeSA and RH-MeSA) were investigated by SEM, as shown in Fig3A-D. According to the SEM images, RF had pores and cracked surfaces on some parts of the starch granules, whereas pores were significantly reduced on RF-MeSA. In addition, RH showed a longitudinal shape with a very rough texture, while the surface of RH-MeSA was characterized by smooth channels. The EDS spectrum confirmed the MeSA adsorption on the RF and RH surfaces. The RH content showed carbon (71.11 %), oxygen (26.67 %), and potassium (2.22 %) atoms. After MeSA adsorption, the RF-MeSA showed an increase in carbon content (84.91 %) (Fig3E-F). The RH-MeSA showed a higher carbon (29.58 %) and oxygen content (11.83 %) after the adsorption process than RH (carbon=38.46 and oxygen=39.60 %) (Fig 3G-H).X-ray diffractionThe XRD patterns(Fig 4A and B) present the structure of RF and RH before and after MeSA adsorption. The main peaks for RF occurred at 2θ diffraction angles of 15°, 17°, 19°, and 23°; these peaks indicate an A-type pattern of semi-crystalline structural arrangement of the amylose and amylopectin molecules. After adsorption, the diffraction peak intensity of RF-MeSA increased at 17°, 19°, and 23°, while RH-MeSA showed an increase in the peak intensity at 22° and a slight shift in peak position from 18° to 14°. The crystalline structure patterns of both RF-MeSA and RH-MeSA persisted. Functional groups PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2020:07:51183:1:0:NEW 23 Sep 2020) Manuscript to be reviewed Chemistry Journals Analytical, Inorganic, Organic, Physical, Materials Science We used FTIR to monitor vibrational frequency changes in the functional groups of RF-MeSA and RH-MeSA, as shown in Fig 5A and B. The results showed different patterns in both RF and</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head /><ns0:label /><ns0:figDesc>differences in weight loss of RF-MeSA and RH-MeSA were investigated by TGA curves, which were separated into three phases(Fig 6A and B). The weight loss shown by RF within the temperature range of 40-100 °C was attributed to the loss of water; that which occurred in the temperature range of 180-350 °C corresponded to the degradation of the saccharide rings; and thermal decomposition above 400 °C corresponded to char yields. In addition, the TGA curve of RF-MeSA showed two peaks in phase 2. The first peak showed thermal decomposition of the MeSA compound at around 155 °C, which resulted in a weight loss of 40.94 %. The second peak was attributed to degradation of the saccharide rings. For RH, the peak in phase 1 was caused byPeerJ Mat. Sci. reviewing PDF \| (MATSCI-2020:07:51183:1:0:NEW 23 Sep 2020) Manuscript to be reviewed Chemistry Journals Analytical, Inorganic, Organic, Physical, Materials Science water evaporation. The peak in phase 2 indicated the degradation of hemicellulose and cellulose between the temperatures of 180 °C and 400 °C. The degradation of lignin occurred over a temperature range of 350 °C to 800 °C. The TGA curve of RH-MeSA showed a weight loss of 57.38 %, which corresponded to thermal decomposition of the MeSA compound at 155 °C. Similarly, the TGA curve of MeSA confirmed that the weight loss that occurred at 155 °C (96.54 %) could be attributed to the thermal decomposition of the MeSA compound. Desorption of rice flour-methyl salicylate (RF-MeSA) and rice husk-methyl salicylate (RH-MeSA) Effect of temperature and relative humidity on desorption The effect of temperature on percentage of desorption was monitored for 24 h (Fig 7A). The desorption percentages of RF-MeSA and RH-MeSA both significantly increased with temperature increases from 25 °C to 40 °C, at 75 % relative humidity (p ≤ 0.05). The desorption percentage of RF-MeSA and RH-MeSA, induced by relative humidity, is shown in Fig 7B. The percentage of desorption significantly increased with increasing relative humidity (p ≤ 0.05). At 75 % relative humidity, the desorption percentage of RF-MeSA was higher than that of RH-MeSA. At 95 % relative humidity; the maximum desorption percentages for RF-MeSA and RH-MeSA were 52.94 % and 49.03 %, respectively.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head /><ns0:label /><ns0:figDesc>5 < n < 1 to a PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2020:07:51183:1:0:NEW 23 Sep 2020) Manuscript to be reviewed Chemistry Journals Analytical, Inorganic, Organic, Physical, Materials Science non-Fickian mechanism. Table 2 shows that n-values less than 0.5 indicate that RF-MeSA and RH-MeSA released at different temperatures (25 °C and 40 °C, respectively) and relative humidities (75 % and 90 %, respectively); this finding is in line with Fickian diffusion mechanisms. Moreover, a comparison of the R 2 values showed that the model best fitting the release of RF-MeSA and RH-MeSA was the Higuchi model (R 2 : 0.7734-0.9065). These results</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head /><ns0:label /><ns0:figDesc>throughout the storage period (Fig 7B). However, the yellowness (b* value) of the control treatment was significantly higher than that of the other treatments on d 3 and d 5 of storage (p ≤ 0.05; Fig 9C). Similarly, the banana fruit firmness at harvest was 35.16 N and significantly decreased during storage, reaching final values of 2.62 N in control fruit and significantly higher values of 2.84 and 5.32 N in RF-MeSA and RH-MeSA treatments, respectively (p ≤ 0.05; Fig PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2020:07:51183:1:0:NEW 23 Sep 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head /><ns0:label /><ns0:figDesc>relative to RF-MeSA. The XRD of RH showed strong broad peaks of semi-crystalline structures at 2θ angle values of 18° and 22°. These peaks indicate the presence of cellulose fiber and silica PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2020:07:51183:1:0:NEW 23 Sep 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_7'><ns0:head /><ns0:label /><ns0:figDesc>temperature and relative humidity are important factors for controlling the release system. In Thailand, the ambient temperatures are 25-40 °C and relative humidity is 75-85 %, while the PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2020:07:51183:1:0:NEW 23 Sep 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_8'><ns0:head /><ns0:label /><ns0:figDesc>RF and RH have the potential to adsorb the MeSA molecule, as well as to release it. RH has a higher specific surface area and total pore volume than RF, resulting in an increased capacity to adsorb MeSA. Analysis with SEM, XRD, FTIR, and TGA confirmed the adsorption of MeSA on PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2020:07:51183:1:0:NEW 23 Sep 2020)Manuscript to be reviewedChemistry JournalsAnalytical, Inorganic, Organic, Physical, Materials Science the outer surfaces of RF and RH. Moreover, the temperature and relative humidity affected the desorption percentage of RF-MeSA and RH-MeSA. Increased temperature (from 25 °C to 40 °C at a relative humidity of 75 %) and increased relative humidity (from 75 % to 95 % at 25 °C) stimulated the release of MeSA from RH-MeSA and RF-MeSA. In addition, the kinetically controlled release of both RF-MeSA and RH-MeSA suggested that they followed a pattern of Fickian diffusion. Thus, we report for the first time that RF and RH are natural adsorbents that have potential applicability for the adsorption and controlled release of MeSA without chemical or mechanical modifications. Although the application of RF-MeSA and RH-MeSA delayed ripening of 'Namwa' banana fruit, treatments at the tested concentrations induced peel disorder.</ns0:figDesc></ns0:figure> </ns0:body> "	"Date: September 23rd, 2020 To: Editor-in-chief of PeerJ From: “Apiradee Uthairatanakij” apiradee.uth@kmutt.ac.th Title: Controlled release of methyl salicylate by biosorbents delays the ripening of banana fruit Dear Editor-in-chief of PeerJ, We wish to re-submit the manuscript titled “Controlled release of methyl salicylate by biosorbents delays the ripening of banana fruit.” We thank you and the reviewers for your thoughtful suggestions and insights. The manuscript has benefited from these insightful suggestions. I look forward to working with you and the reviewers to move this manuscript closer to publication in the PeerJ. The manuscript has been rechecked and the necessary changes have been made in accordance with the reviewers’ suggestions. The responses to all comments have been prepared and attached herewith. The parts revised based on the comments of the reviewers are indicated in yellow highlighted text. Thank you for your consideration. I look forward to hearing from you. Sincerely Assoc. Prof. Dr. Apiradee Uthairatanakij School of Bioresources and Technology, King Mongkut’s University of Technology Thonburi 49 Soi Tientalay 25, Bangkhuntien-Chaitalay Road, Takham, Bangkhuntien, Bangkok 10150 THAILAND Phone: +66 2 470 7724 Fax: +66 2 452 3750 E-mail: apiradee.uth@kmutt.ac.th Reviewer’s comments and suggestions Reviewer 1# Basic reporting NO. Comments and suggestions Rewritten according to the comments and suggestions 1 Your introduction needs more relevant and detail information about your project materials. I suggest that you consider some changes in first two paragraphs of your introduction and add more related background to clearly show significance of your system. for example, MeSA information about formula, molar mass and non-related application to your paper's goal can be replaced with more relevant MeSA application on fruits and plants. Thank you very much for your good suggestions. The introduction has been rewritten in Page 2, Lines 39-66. Experimental design NO. Comments and suggestions Rewritten according to the comments and suggestions 1 To investigate more, it is suggested to check effect of adsorbent dosage on preventing banana ripening? Thank you for your suggestions. The efficiencies of RF and RH were assessed as new biosorbents for controlling the release of methyl salicylate (MeSA). In a closed system, 1 g dosages of adsorbents were investigated, showing a pattern of Fickian diffusion. Therefore, Rice flour-methyl salicylate (RF-MeSA) and rice husk-methyl salicylate (RH-MeSA) at 1 g adsorbents dosage were used to examine the delay in fruit ripening, using bananas as a model “The correction has been made in Page 5, Lines 110-113”. Although the application of RF-MeSA and RH-MeSA delayed ripening of ‘Namwa’ banana fruit, treatments at the tested concentrations induced peel disorder. Therefore, further study on pH, optimal dosage and concentration of MeSA are needed before these biosorbents are utilized for commercial treatment with bananas or other fruit. “The correction has been made in Page 21, Lines 461-465”. 2 It is suggested to report the change in half time of fruit ripening. Half-life has been added in “Page 11, Lines 245-248” “Page 17, Line 362-363”, and Table 3. 3 How experimentally author confirmed complete removal of free MeSA? Just using filter paper may not be efficient enough (has author tried any chromatography methods/centrifugation). Removing the excess MeSA compound using Whatman® filter paper No.1 has particle retention at 98 % efficiency (Hutten, 2015) and it did not affect the loss of active compounds that were trapped in the pores. This method has been used to estimate the removal of heavy metal ions (Khokhotva & Waara 2010) and olive oil waste (Garcia et al. 2006). “The correction has been made in Page 17, Lines 377-381”. For, centrifugation is a method of separating molecules by spinning them at very high speeds (Stephenson, 2016), centrifugal force may remove MeSA that is entrapped on the adsorbent surfaces. Whereas, chromatography is used in separation, isolation, purification, and identification of components of extremely complex mixtures (Coskun, 2016). 4 Why desorption percentage increased with increasing relative humidity? The correction has been made in Page 18, Lines 401-405. 5 Author need add more background information on respiration and ethylene production to make purpose of experiment clear, also as temperature (and concentration of MeSA) can have effect on respiration rates, it would be informative if author check respiration rates at different temperature and MeSA concentrations. The introduction has been rewritten in Page 2, Lines 39-66 and Page 5, Lines 110-113. The respiration rates at different temperature and MeSA concentrations has been added in Page 19, Lines 425-429. 6 To enhance the absorption capacities of developed formulation, it is suggested to modify surface of biosorbent. Thank you for your suggestions. Both RF and RH have already been used as biosorbents without modifying the surface. RF-MeSA and RH-MeSA at 1 g of adsorbent dosage promoted adsorption of the MeSA at 36.76 % and 58.33 %, respectively. However, for further study may be needed to modify surface for more efficient application. 7 Different factors such as pH, initial concentration, sorbent dosage have effect on biosorbent, therefore considering these factors to optimize system performance by adding more experimental condition can help on performance of system. The suggestion has been added in Page 21, Lines 461-465. 8 Page 7, Line 60. Biosorbent materials part need more references. The correction has been made in Page 4, Lines 71-73. 9 Page 9, line 105, 107. No need for abbreviation as RF and RH abbreviation has been already introduced in above part. The correction has been made in Page 6, Lines 119 and 121. 10 Author used 2:1 mole ratio of MeSA and RF/RH and 24 h incubation, so it is suggested to show the optimized mole ratio and time of reaction for both systems by reporting data for different ratios and time. A preliminary test, the biosorbent:MeSA varied of 1:1, 2:1, and 3:1 mole ratio. The result showed that at 2:1 mole ratio was the best adsorbent, while 1:1 mole ratio, the adsorbed not enough to covered the surface of the adsorbent and 3:1 had amount of MeSA greater than necessary. (data not show). 11 Adsorption percentage experimental design need more explanation. E.g. author mentioned: Excess MeSA was removed from RF-MeSA and RH-MeSA samples with Whatman® filter paper No. 1 at 0, 1, 2, 3, 6, 12, and 24 h. (of what process?) The correction has been made in Page 7, Lines 144-147. 12 Is there any reason author tested just two temperatures of 25 and 40 ℃ and humidities of 75% and 95%? more clarification and explanation would be helpful to understand the experimental design. The correction has been made in Page 18, Lines 406-410. 13 What is part 2.3.1 and 2.3.2? if they are related to the mentioned reference, it is strongly recommended to add that information in the paper. This sentence has been deleted. 14 Data should be reported in text or table format, (not both), it is suggested to add standard deviation information as well. (Table 1). The correction has been made in Page 12, Lines 258-261 and the standard deviation has been made in Table 1. 15 More characterization is recommended (e.g. EDS mapping analysis to show silicon and oxygen maps on surfaces of RH before and after adsorption). The correction has been made in Page 13, Lines 277-282 and in Page 18, Lines 385-388. 16 Page 19, Line 327. reference is needed. The correction has been made in Page 16 Lines 350-351. 17 It is suggested to check postharvest quality of other fruits as well to confirm applicability of developed formulation. Thank you for your suggestions. For further study on postharvest quality of other fruits are needed. 18 Figure 1. Error bars are needed. The error bars have been made in Figure 2. 19 Page 15, line 241. Data should be reported in text or table format, (not both), it is suggested to add standard deviation as well. The correction has been made in Page 12, Lines 258-261 and the standard deviation has been made in Table 1. Validity of the findings NO. Comments and suggestions Rewritten according to the comments and suggestions 1 Comparing you control released system ability with other biosorbent materials, such as β-cyclodextrin can help better understanding on advantages and novelty of your system. The correction has been made in Page 20, Lines 443-446. 2 RF and RH have already been used as a biosorbent, so author should strongly discuss on fundamental advantage/effect of their system compared to available systems. The correction has been made in Page 20, Lines 436-442. Reviewer 2# Basic reporting NO. Comments and suggestions Rewritten according to the comments and suggestions 1 The story presented by Uthairatanakij and colleagues is clear. Further, authors made a good effort explaining their results in the discussion section. However, figures should significantly be improved for reader to fully appreciate the data authors are trying to present. Thank you very much for your comments and suggestions. Experimental design NO. Comments and suggestions Rewritten according to the comments and suggestions 1 Experimental methodology is easy to follow and provides a good flow to the story. Further, the proposed experimental design allows for the story to be understandable by the audience. Thank you very much. Comments for the author NO. Comments and suggestions Rewritten according to the comments and suggestions 1 Experimental methodology is easy to follow and provides a good flow to the story. Further, the proposed experimental design allows for the story to be understandable by the audience. Thank you very much. 2 Reviewer recommends authors to revisit the introduction, some important claims have no reference. Thank you very much. The references have been added in Page 4, Lines 71-73. 3 Reword line 49, the idea authors are trying to convey is not clear. The sentences have been revised in Page 3, Lines 61-66. 4 Verb conjugation in line 77 should be have instead of has. The correction has been made in Page 4, Lines 89. “have” instead of “has.” 5 The reported data could be better interpreted if authors spend time improving the overall quality of reported figures. Thank you very much for your suggestions. 6 For example, in figure 2 remove bottom information and leave the magnification only kV values, align the letters with one another. Further, increase magnification to allow reader to appreciate the structural features discussed in the main text. This can be achieved by having a zoom-in smaller figure within each individual SEM image. The SEM has been already edited in Figure 3. 7 Pore size distribution must be included to further support the absorption findings. The pore size distribution has been added in Figure 1. 8 Figure 3, data is hard to appreciate given the composition of the figure. Color label and combine all data into one figure. Further, provide a control crystallinity pattern of amylose and/or amylopectin molecules. The XRD has been already edited in Figure 4. 9 Data provided by figure 4 is a very big component of the story, as authors use this technique to guarantee the presence of MeSA within RF and RH. Regardless of the peak positioning discussed in the main text, authors should color label each spectrum for better interpretation of the results. FTIR spectrum of pristine MeSA lacks a stretch at approximately 2900 cm-1. Further, given the overlapping of the spectra, the fingerprint of the MeSA is impossible to interpret. The FTIR spectrum has been already edited in Figure 5. 10 Reviewer recommends authors to elaborate on how TGA findings correlate to the absorption data obtained. Further, sample preparation should be revisited to eliminate water loss during the ramping of the temperature. The correction has been made in Page 18, Line 390-397. The correction has been made in Page 18, Line 172 “with a temperature ramp rate of 10 °C min-1”. 11 Reviewer invites authors to include pictures of the bananas before, during, and after the postharvest testing to visually support your findings. The pictures of the bananas before, during, and after the postharvest testing have been made in Figure 8. "	Here is a paper. Please give your review comments after reading it.
444	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>TiO 2 nanoparticles were successfully synthesized by the sol-gel method employing different glycols (ethylene glycol, diethylene glycol or polyethylene glycol 300), which were heat-treated in conventional oven or by hydrothermal via, obtaining photocatalysts with particle sizes and distinct crystalline structures. HRTEM analyses showed that the oxides submitted to hydrothermal treatment featured spherical morphology, being formed by partially aggregated particles with sizes varying between 2 and 5 nm. X-ray diffractograms and Raman spectroscopy confirm that anatase was predominant in all synthesized compounds, with presence of brookite phase for samples that received hydrothermal treatment or were synthesized in the presence of polyethylene glycol with heat treatment in conventional oven. The amount of brookite as well as the cell volume, deformation, network parameters and crystallinity were estimated by Rietveld refinement. The surface area and porosity of the materials were higher when the synthesis involved the use of hydrothermal treatment. These oxides are mesoporous with porosity between 14 and 31%.</ns0:p><ns0:p>The oxide synthesized in the presence of ethylene glycol with hydrothermal thermal treatment (TiO 2 G1HT) exhibited the highest photocatalytic activity in terms of mineralization of azo-dye Ponceau 4R (C.I. 16255), under UV-Vis irradiation. This higher photocatalytic activity can be attributed to the formation of binary oxides composed by anatase and brookite and by its optimized morphological and electronic properties.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>Titanium dioxide (TiO 2 ) is widely employed in technological applications, including solar energy conversion, chemical sensors for gases, environmental depollution and hydrogen production, among others <ns0:ref type='bibr' target='#b37'>(Machado et al., 2015;</ns0:ref><ns0:ref type='bibr' target='#b48'>Riyapan et al., 2016)</ns0:ref>. It is an n-type semiconductor, with band gap energy of the extended solid (bulk) in the ultraviolet region of approximately 3.20, 3.02 and 3.14 eV, respectively for anatase, rutile and brookite, the three natural polymorphs <ns0:ref type='bibr' target='#b16'>(Grätzel & Rotzinger, 1985)</ns0:ref>.</ns0:p><ns0:p>TiO 2 -based materials are the most investigated for photocatalytic application since the discovery by Fujishima e Honda <ns0:ref type='bibr' target='#b15'>(Fujishima & Honda, 1972)</ns0:ref>. It is one of the most commonly used semiconductor oxide for environmental photocatalysis, being of low toxicity, insoluble in water and stable to photo and chemical corrosion over a wide range of pH <ns0:ref type='bibr' target='#b36'>(Machado et al., 2012;</ns0:ref><ns0:ref type='bibr' target='#b49'>Rekulapally et al., 2019)</ns0:ref>. The photocatalytic process involves the electronic excitation from the valence (VB) to the conduction band (CB), when irradiated by ultraviolet light. This process generates charge carriers (e -/h + pairs) that react with molecular oxygen and water, forming reactive oxygen species (ROS), such as superoxide radical ion (O 2 •-) and hydroxyl radical (HO • ). These and other secondary-generated radicals promote the degradation of environmental pollutants <ns0:ref type='bibr' target='#b38'>(Machado et al., 2008;</ns0:ref><ns0:ref type='bibr' target='#b39'>Muthamizhchelvan et al., 2020)</ns0:ref>.</ns0:p><ns0:p>Anatase, the most active polymorph for photocatalytic applications, contains more defects due to its structure and acts as electron trap <ns0:ref type='bibr' target='#b18'>(Gupta & Tripathi, 2011)</ns0:ref>. Already in rutile, there is a high e -/h + recombination rate, which limits the photocatalytic response. On the other hand, the brookite photocatalytic activity seems to be related to the relative position of the electronic bands, where CB energy is 0.14 eV more negative than that of anatase in anatase/brookite associations, favoring the photocatalytic processes <ns0:ref type='bibr' target='#b28'>(Kandiel et al., 2010;</ns0:ref><ns0:ref type='bibr' target='#b45'>Patrocinio et al., 2015)</ns0:ref>.</ns0:p><ns0:p>The physical and chemical properties of TiO 2 depend on the arrangement of the crystalline phase, size and shape of the particles, surface area and crystallinity <ns0:ref type='bibr' target='#b52'>(Tan et al., 2015;</ns0:ref><ns0:ref type='bibr' target='#b8'>El-Sheikh et al., 2017)</ns0:ref>, parameters that can be controlled or adjusted during the synthesis process. This oxide can be obtained by different synthetic routes <ns0:ref type='bibr' target='#b0'>(Ahmadi et al., 2015;</ns0:ref><ns0:ref type='bibr' target='#b3'>Benetti et al., 2016;</ns0:ref><ns0:ref type='bibr' target='#b19'>Hajizadeh-Oghaz, 2019;</ns0:ref><ns0:ref type='bibr' target='#b32'>Kumar et al., 2019)</ns0:ref>. The synthesis via sol-gel methodology can be improved by the use of reagents with long hydrophobic chains favoring the controlled formation of critical nuclei, leading to the obtaining of mesoporous particles in a nanometric scale <ns0:ref type='bibr' target='#b56'>(Wang et al., 2014;</ns0:ref><ns0:ref type='bibr' target='#b6'>Darbandi & Dickerson, 2016;</ns0:ref><ns0:ref type='bibr' target='#b8'>El-Sheikh et al., 2017;</ns0:ref><ns0:ref type='bibr' target='#b4'>Catauro et al., 2018)</ns0:ref>. Crystallization is a necessary step for obtaining oxides with a defined structure, purity and desirable morphology. This process can occur either by conventional heat <ns0:ref type='bibr' target='#b20'>(He et al., 2014;</ns0:ref><ns0:ref type='bibr' target='#b45'>Patrocinio et al., 2015)</ns0:ref> or by hydrothermal treatment <ns0:ref type='bibr' target='#b30'>(Kim & Kwak, 2007;</ns0:ref><ns0:ref type='bibr' target='#b47'>Qin et al., 2016)</ns0:ref>.</ns0:p><ns0:p>The application of TiO 2 as a photocatalyst has some disadvantages that can be overcome. The main disadvantage is the high band gap, followed by the relatively high recombination rate of the charge carriers, this last reducing considerably the quantum yield of the photocatalytic processes <ns0:ref type='bibr' target='#b31'>(Kumar & Devi, 2011;</ns0:ref><ns0:ref type='bibr' target='#b12'>Feng et al., 2012;</ns0:ref><ns0:ref type='bibr' target='#b27'>Jaiswal et al., 2015;</ns0:ref><ns0:ref type='bibr' target='#b14'>França et al., 2016)</ns0:ref>.</ns0:p><ns0:p>The existence of junction between different phases of the same semiconductor, as for example in anatase/brookite (A/B) or anatase/rutile (A/R) mixtures <ns0:ref type='bibr' target='#b5'>(Cihlar et al., 2015)</ns0:ref>, results in synergistic effects that leads to a more efficient separation of the e -/h + pairs, reducing the charge recombination rate. Consequently, while the electrons are trapped in one of the crystalline phases, the holes present in the VB have greater chance to oxidize organic matter, enhancing the photocatalytic activity <ns0:ref type='bibr' target='#b57'>(Yang et al., 2013;</ns0:ref><ns0:ref type='bibr' target='#b51'>Shao et al., 2014;</ns0:ref><ns0:ref type='bibr' target='#b54'>Zhang et al., 2020)</ns0:ref>.</ns0:p><ns0:p>In the present study, photocatalysts based on TiO 2 were synthesized by the sol-gel method. The influence of the use of different structural molds (ethylene glycol, diethylene glycol or polyethylene glycol 300) as well as the effect of thermal treatments by conventional or hydrothermal routes, was evaluated on their photocatalytic activity, and structural optical and morphological properties. The photocatalytic activity was evaluated through the degradation of the azo-dye Ponceau 4R, chosen due to its industrial application and undesirable effects on the environment and human health <ns0:ref type='bibr' target='#b36'>(Oliveira et al., 2012;</ns0:ref><ns0:ref type='bibr'>European Food 2020)</ns0:ref>. The results presented here aim to provide new insights into the synthesis of TiO 2 -based photocatalysts with different crystalline phases and the influence of preparation conditions on the photocatalytic properties of these systems.</ns0:p></ns0:div> <ns0:div><ns0:head>Materials & Methods</ns0:head></ns0:div> <ns0:div><ns0:head>Preparation of different TiO 2 photocatalysts</ns0:head><ns0:p>All chemicals were of analytical or HPLC grade and were used as received. Ultrapure water obtained from an Elix 5 Milli-Q ® water purification system was employed in all experiments. TiO 2 samples were synthesized by the sol-gel method, using different glycols (ethylene glycol, diethylene glycol or polyethylene glycol 300) (Sigma Aldrich), and heat treatment in a conventional oven or hydrothermal system.</ns0:p><ns0:p>The TiO 2 Gx photocatalyst was obtained from the mixture, under magnetic stirring, of 10 mL of Ti (IV) isopropoxide (Aldrich, 97%) and 50 mL of glycol (where x = 1 when 886 mmol of ethylene glycol (Vetec, 99.5%) were used, x = 2 for 527 mmol diethylene glycol (Vetec, 99.5%), and x = 3 when polyethylene glycol 300 (Fluka) was used). After 2 hours of stirring, a mixture containing 10 mL of ultrapure water and 90 ml of acetone (Synth, 99.5%) was added to the suspension and kept under stirring for 2 hours. The white precipitate was separated with the aid of a centrifuge (9000 rpm for 20 minutes), followed by washing several times with ethanol to remove residues of glycol, followed by washing three times with distilled water.</ns0:p><ns0:p>For the preparation of heat-treated photocatalysts in a conventional oven (TiO 2 GxM), after washing the powder was dried at 70°C under reduced pressure and sintered at 400°C for 2 hours. After centrifugation and washing the decanted oxide prepared using hydrothermal treatment, TiO 2 GxHT, was submitted to the hydrothermal reactor under a pressure of approximately 13.8 bar at 200°C for 4 hours. Subsequently, it was dried at 70°C for 24 hours.</ns0:p></ns0:div> <ns0:div><ns0:head>Characterization of the photocatalysts</ns0:head><ns0:p>High resolution electronic transmission images were obtained using a Jeol, JEM-2100, Thermo scientific Transmission Electron Microscope. The particle size and spacing between crystalline planes were calculate with the free software 'ImageJ'.</ns0:p><ns0:p>X-ray diffraction analyses (XRD) using a Shimadzu XRD600 powder diffractometer operating at 40 kV and 120 mA, employing Cu Kα (λ= 1,54148 Å) radiation. The diffractograms were collected between 10°≤ 2θ ≤ 90° under a rate of 0.5º min -1 . Crystalline silicon was used as the diffraction standard. X-ray diffratogram of the oxides were refined by the method of Rietveld using the FullProf software, with fitting criteria (Factor S -Goodness of Fit) was employed as the ratio between the weight factor (R wp ) and the expected factor (R exp ), which should be closer to 1. The fit parameters can be found in the Supplemental Information (Table <ns0:ref type='table'>S1</ns0:ref>).</ns0:p><ns0:p>N 2 adsorption-desorption isotherms were obtained using an ASAP 2010 analyzer (Micrometrics). The specific area were analyzed using the Brunauer, Emmett and Teller (BET) model and the Barrett-Joyner-Halenda (BJH) model for the porous volume <ns0:ref type='bibr' target='#b2'>(Barrett, Joyner & Halenda, 1951)</ns0:ref>.</ns0:p><ns0:p>Raman spectra were acquired at room temperature using a Bruker spectrometer model RFS 100/S, samples were excited at 1064 nm with laser operating at 100 mW. Diffuse reflectance spectra of the synthesized oxides were acquired using an UV-1650PC Spectrometer (Shimadzu), at room temperature and potassium bromide was used as reference. The band gap energy being estimated by the Kubelka-Munk function <ns0:ref type='bibr' target='#b46'>(Patterson, Shelden & Stockton, 1977)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Photocatalytic activity</ns0:head><ns0:p>In all photocatalytic assays, 100 mg L -1 of the catalyst was added to 31 mg L -1 dye Ponceau 4R (trisodium (8Z)-7-oxo-8-[(4-sulfonatonaphthalen-1-yl)hydrazinylidene]naphthalene-1,3disulfonate, CI 16255, Sigma-Aldrich, 75%) aqueous solution (pH = 6.9) under magnetic stirring. The experimental setup was previously described in detail <ns0:ref type='bibr' target='#b36'>(Oliveira et al., 2012)</ns0:ref>. Information about the radiation source and experimental data were available in <ns0:ref type='bibr' target='#b38'>(Machado et al., 2008;</ns0:ref><ns0:ref type='bibr' target='#b50'>Santos et al., 2015)</ns0:ref>.</ns0:p><ns0:p>The photocatalytic system was kept at 40 ± 2 ºC and under stirring for 30 minutes in the dark to reach the adsorption equilibrium. Control measurements in the dark were performed and in the absence of a catalyst to evidence the role of TiO 2 in the photochemical reaction. Aliquots were taken at 20 minutes intervals, filtered and analyzed by spectrophotometry, following the discoloration at 507 nm using a Shimadzu spectrophotometer model 1650PC and by Total Organic Carbon (TOC) measurements, using a Shimadzu TOC-VCPH/CPN analyzer.</ns0:p></ns0:div> <ns0:div><ns0:head>Results & Discussion</ns0:head><ns0:p>TEM images evidence that the heat-treated oxides in a conventional oven and by hydrothermal via are made up of approximately spherical nanoparticles (Fig. <ns0:ref type='figure' target='#fig_1'>1 A-D</ns0:ref>). For the material produced after processing in a conventional oven, the formation of agglomerates was more pronounced than after hydrothermal treatment. The estimated particle size from the HRTEM images (Fig. <ns0:ref type='figure' target='#fig_1'>1 E-H</ns0:ref>) were 10 nm, 2 nm, 3 nm e 4 nm respectively for the oxides TiO 2 G1M, TiO 2 G1HT, TiO 2 G2HT e TiO 2 G3HT. The sample calcined in a conventional oven, TiO 2 G1M, presented the largest particle size, probably due the coalescence process by diffusion of smaller (more unstable) particles, favoring the formation of agglomerates. The formation of smaller, although stable, particles was observed after hydrothermal treatment, which tends to produce particles with larger surface areas. The use of different glycols in the synthesis also influenced the particle size due the increase in the carbon chain (G1 < G2 < G3), which resulted in slightly larger particles.</ns0:p><ns0:p>HRTEM images suggest high crystallinity, mainly for the sample treated in a conventional oven. The spacing between crystalline planes, for all samples, was estimated as being 0.35 nm, corresponding to the (101) plan of the anatase phase, indicating that in all cases this is the preferential growth plan for nanoparticles. The presence of crystallographic planes referring to the brookite and rutile crystalline phases were not verified through the images. In the case of brookite, the spacing between the crystallographic planes, of 0.38 nm, corresponding to the ( <ns0:ref type='formula'>120</ns0:ref>) and ( <ns0:ref type='formula'>111</ns0:ref>) planes, is very close to the spacing of the anatase (0.35 nm) causing ambiguity <ns0:ref type='bibr' target='#b29'>(Kobayashi et al., 2007)</ns0:ref>. These plans, in the presence of higher anatase content, are overlaid by the (101) plan. The spacing between the 0.29 nm crystallographic planes, corresponding to the (121) plane, characteristic of the brookite, was not observed <ns0:ref type='bibr' target='#b7'>(Di Paola, Bellardita & Palmisano, 2013)</ns0:ref> [Fig]</ns0:p><ns0:p>Figure <ns0:ref type='figure' target='#fig_1'>1</ns0:ref> TEM and HRTEM images of mesoporous TiO 2 : (A, E) TiO 2 G1M, (B, F) TiO 2 G1HT, (C, G) TiO 2 G2HT, (D, H) TiO 2 G3HT.</ns0:p><ns0:p>The XRD data (Fig. <ns0:ref type='figure' target='#fig_0'>2</ns0:ref>) confirm that all samples are composed mostly of nanocrystals of anatase, with the (101) phase preferably exposed. In the case of HT processing at 200°C for 4h, the presence of crystalline anatase phases and traces of brookite was observed, being confirmed by the presence of peaks at 2θ equal to 25.38° (101) and 30.80° (121), respectively. Under the treatment conditions to which these materials were submitted, the formation of the rutile phase was not observed. The formation of the brookite phase was probably a crucial factor for the inhibition of the transformation of anatase into rutile.</ns0:p><ns0:p>Rietveld analyses of the diffractograms (Fig. <ns0:ref type='figure' target='#fig_1'>S1</ns0:ref>) confirm the decrease in crystallite size for the oxides obtained after hydrothermal heat treatment (HT), which agrees with the HRTEM images. These quantitative data also confirm the greater presence of brookite phase in samples submitted to hydrothermal treatment. Besides that, it is observed that the percentage of the brookite phase remains practically constant even with the use of different glycols in the synthesis process. Already for materials prepared with heat treatment in a conventional oven, it turns out that the use of different glycols leads to greater deformations only for the TiO 2 G3M sample, PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2020:09:53207:1:1:NEW 13 Jan 2021)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>where polyethylene glycol was used in the synthesis, causing the formation of 17.47% brookite phase <ns0:ref type='bibr' target='#b53'>(Tay et al., 2013)</ns0:ref>.</ns0:p><ns0:p>The TiO 2 G3HT sample had a higher portion of brookite when compared to the TiO 2 G3M sample, because since brookite is featured by its low symmetry, its formation is more efficient under mild conditions such as shorter period and lower preparation temperature, as occurs in hydrothermal treatment conditions <ns0:ref type='bibr' target='#b35'>(Lin et al., 2012)</ns0:ref>. The formation of mesoporous structures was confirmed by N 2 adsorption-desorption isotherms (Fig <ns0:ref type='figure'>3</ns0:ref>). Isotherms follow the type III for samples with 100% anatase phase (TiO 2 G1M e TiO 2 G2M). The other samples, with brookite content, have type IV with a pronounced hysteresis loop of types H3 and H4, according to the IUPAC classification. This suggests that these materials are mesoporous solids formed by agglomerated or aggregated particles <ns0:ref type='bibr' target='#b17'>(Gregg & Sing, 1982)</ns0:ref>. The presence of brookite causes a decrease in the average pore diameters, suggesting that the presence of structural defects influences the adsorption capacity and porosity of the material. The values of surface area and porosity of these materials are presented in Table <ns0:ref type='table'>1</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>[Fig]</ns0:head><ns0:p>Figure <ns0:ref type='figure'>3</ns0:ref> N 2 -adsorption-desorption isotherms obtained for the photocatalysts.</ns0:p><ns0:p>Table <ns0:ref type='table'>1</ns0:ref> Morphologic and electronic parameters to oxides synthesized.</ns0:p><ns0:p>[Table <ns0:ref type='table'>]</ns0:ref> The diffuse reflectance spectra, expressed in terms of F(R) vs. photon energy (E), are presented in Fig. <ns0:ref type='figure'>4</ns0:ref>. The indirect band gap value (E g ) was obtained by extrapolating the linear segment to the X axis, Table <ns0:ref type='table'>1</ns0:ref>. However, a simple inspection of the spectra suggests that the band gap values calculated in this way calculated in this way are deviated from the actual values, since the radiation absorption is not canceled (E<E g ), except from the point where F(R) → 0. This suggests the existence of permitted states with energies lower than the estimated E g , that is, E g(real) < E g . Thus, considering the lower threshold of the conduction band, which occurs when F(R)0, that is, states with energies less than or equal to the energy associated with this threshold, are prohibited. In view of this, E g(real) was also calculated (Table <ns0:ref type='table'>1</ns0:ref>). Based on this information, it appears that all photocatalysts absorb radiation more intensely in the near-UV region. However, these photocatalysts, despite the high band gap energies, have significant photocatalytic activity in the visible region, as suggest the estimated values of E g <ns0:ref type='bibr'>(real)</ns0:ref> . The TiO 2 G1HT, TiO 2 G2HT and TiO 2 G1M photocatalysts show a radiation absorption profile shifted to the visible region, with E<E g , being therefore able to uptake photons in a large range of wavelengths. Related to these factors, the high surface area, crystallinity and mixture of crystalline phases are added, which end up favoring the photocatalytic potential of these oxides.</ns0:p><ns0:p>The electronic properties of the particles change significantly by reducing their size. Thus, new properties can be expected in nanoparticles when compared to bulk <ns0:ref type='bibr' target='#b24'>(Hodes, 2007)</ns0:ref>. The variation of energy as a function of size promotes the quantum confinement and is characterized by an increase in the indirect band gap energy (E g ), as can be seen for TiO 2 G1HT, which has smaller particle and crystallite sizes, as estimated by HRMET and DRX analyses, and E g (3.30 eV) greater than that of the extended solid (3.20 eV for TiO 2 ) <ns0:ref type='bibr' target='#b31'>(Kumar & Devi, 2011)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>[Fig]</ns0:head><ns0:p>Figure <ns0:ref type='figure'>4</ns0:ref> Diffuse reflectance spectra of the TiO 2 photocatalysts. Inset: Diffuse reflectance spectra of the samples.</ns0:p><ns0:p>The catalysts were also evaluated using Raman spectroscopy (Fig. <ns0:ref type='figure' target='#fig_2'>5</ns0:ref>). All samples exhibit vibration modes typical of anatase (3E g + 2B 1g + A 1g ). A 1g symmetry mode was not visualized, probably due the overlap with the band corresponding to the second mode, of B 1g symmetry <ns0:ref type='bibr' target='#b26'>(Iliev et al., 2013;</ns0:ref><ns0:ref type='bibr' target='#b13'>Fang et al., 2015)</ns0:ref>. A slight change in the signs is observed depending on the type of heat treatment used . The bands referring to samples thermally treated by hydrothermal route are broader than those observed for the calcined oxides in a conventional oven. This broadening can be directly correlated to the concentration of oxygen vacancies on the photocatalysts, as previously shown by Parker e Siegel <ns0:ref type='bibr' target='#b43'>(Parker & Siegel, 1990)</ns0:ref>. Thus, Raman analysis indicates that the synthesis of oxides treated by the hydrothermal route, induces the formation of oxygen vacancies on the oxide surface, increasing the system disorder.</ns0:p><ns0:p>[Fig]</ns0:p><ns0:p>Figure <ns0:ref type='figure' target='#fig_2'>5</ns0:ref> Raman spectra, at room temperature, for the synthesized TiO 2 photocatalysts. Inset: Expanded normalized Raman spectra between 100 and 200 cm -1 in the main E g peak region attributed to the broadening of the band according to the type of heat treatment.</ns0:p></ns0:div> <ns0:div><ns0:head>Photocatalytic activity</ns0:head><ns0:p>The photocatalytic activity of the different synthesized oxides was evaluated in terms of the degradation of the azo-dye Ponceau 4R. The control experiment, in the absence of any photocatalyst, reveals extremely low levels of dye discoloration (4.0%) and mineralization (13%) after 140 minutes of irradiation (Fig. <ns0:ref type='figure' target='#fig_0'>S2</ns0:ref>). The degradation efficiency presented by the different photocatalysts is summarized in Table <ns0:ref type='table' target='#tab_0'>2</ns0:ref>. Manuscript to be reviewed Chemistry Journals <ns0:ref type='bibr'>Analytical, Inorganic, Organic, Physical, Materials Science [Table]</ns0:ref> The oxides thermally treated by hydrothermal via were more efficient than conventional heat treatment in promoting the degradation and mineralization of the dye under study. Calcination in a conventional oven led to an increase in the crystallinity of the materials, as seen by the XRD data, and a decrease in the surface area, which ended up compromising the photocatalytic activity of these oxides.</ns0:p><ns0:p>The photocatalytic performance exhibited by the samples synthesized in the presence of different glycols and thermally treated by hydrothermal via can be attributed to the coexistence of anatase and brookite the high surface area, mesoporosity, and more appropriate particle sizes. Crystalline materials with smaller particle sizes are more likely to exhibit expressive photocatalytic properties <ns0:ref type='bibr' target='#b40'>(Ohno et al., 2001)</ns0:ref>.</ns0:p><ns0:p>Although the TiO 2 G3M photocatalyst also presents itself as a mixture of polymorphs anatase and brookite, it did not show significant photocatalytic activity, probably related to its smaller surface area.</ns0:p><ns0:p>The increase in photocatalytic activity of samples that present anatase and brookite can be explained by the synergism between these polymorphs. Although anatase and brookite present a very close E g <ns0:ref type='bibr' target='#b36'>(Machado et al., 2012;</ns0:ref><ns0:ref type='bibr' target='#b45'>Patrocinio et al., 2015)</ns0:ref>, theoretical calculations have shown that the energies of the conduction and valence bands of anatase phase are slightly lower than the corresponding energy levels of brookite <ns0:ref type='bibr' target='#b34'>(Li et al., 2008)</ns0:ref>, suggesting a certain ease of migration of electrons from brookite to anatase. Thus, the holes are more available for oxidation reactions. In addition, the energy barrier between these polymorphs will tend to hinder the recombination among charge carriers. Therefore, with an extended life span, holes in the brookite valence band have a greater chance to oxidize organic matter, while electrons 'trapped' in anatase may favor reduction reactions, leading to an increase in the photocatalytic activity <ns0:ref type='bibr' target='#b33'>(Li, Ishigaki & Sun, 2007;</ns0:ref><ns0:ref type='bibr' target='#b45'>Patrocinio et al., 2015)</ns0:ref>.</ns0:p><ns0:p>A complex degradation mechanism is expected in heterogeneous photocatalysis <ns0:ref type='bibr' target='#b25'>(Hoffmann et al., 1995;</ns0:ref><ns0:ref type='bibr' target='#b1'>Ahmed et al., 2010)</ns0:ref>. The reactions occur initially at the solid-solution interface and involve reactive species generated on the surface of the excited photocatalyst or by direct interaction between the excited photocatalyst and the substrate <ns0:ref type='bibr' target='#b36'>(Oliveira et al., 2012;</ns0:ref><ns0:ref type='bibr'>Santos et al., 2015)</ns0:ref>. In the degradation under study, the discoloration of the dye is probably related to the homolytic scission of the azo group. Hydroxyl radicals (HO • ), formed in the solid-solution interface, may be responsible for this process <ns0:ref type='bibr' target='#b31'>(Kumar & Devi, 2011)</ns0:ref>. Table <ns0:ref type='table' target='#tab_0'>2</ns0:ref> presents data on the percentage of discoloration in the reactions mediated by the oxides synthesized in this study. The best performances occurred using oxides submitted to hydrothermal treatment.</ns0:p><ns0:p>The mineralization process follows a Langmuir-Hinshelwood kinetics <ns0:ref type='bibr' target='#b25'>(Hoffmann et al., 1995)</ns0:ref>, being of pseudo-first order in relation to the dye, as show in Fig. <ns0:ref type='figure'>6</ns0:ref>. The rate constants are listed in Table <ns0:ref type='table' target='#tab_0'>2</ns0:ref>.</ns0:p><ns0:p>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2020:09:53207:1:1:NEW 13 Jan 2021)</ns0:p><ns0:p>Manuscript to be reviewed Chemistry Journals <ns0:ref type='bibr'>Analytical, Inorganic, Organic, Physical, Materials Science [Fig]</ns0:ref> Figure <ns0:ref type='figure'>6</ns0:ref> P4R mineralization kinetics using different photocatalysts: TiO 2 G1HT (A), TiO 2 G2HT (B), TiO 2 G3HT (C), TiO 2 G1M (D), TiO 2 G2M (E) and TiO 2 G3M (F). Inset: Absorption spectrum of the dye solution as a function of the irradiation time (∆t = 20 min), during the action of the TiO 2 G1HT photocatalyst.</ns0:p><ns0:p>In these assays, it was found that only 4.0% of the dye was adsorbed in the photocatalyst (Fig. <ns0:ref type='figure' target='#fig_0'>S2</ns0:ref>), suggesting that the observed mechanism occurs mainly through the photodegradation of organic matter, certainly by the action of reactive oxygen species (ROS), such as HO • or O 2 •-, with the predominant action of the HO • radicals, a very strong oxidizing agent (standard reduction potential of HO • /H 2 O 2.38 V vs. NHE) <ns0:ref type='bibr' target='#b21'>(Hoare, 1985)</ns0:ref>. Accordingly, and based on the characterization of the photocatalysts, we can propose a mechanism, shown in equations (1-9) which must occur at the solid-solution interface, where TiO 2 (A) is the anatase polymorph and TiO 2 (B) is the brookite polymorph. As result of the photoexcitation of the catalyst (1), the e -/h + pairs are generated; recombination processes (2) compete with the electron trapping in the polymorph anatase (3) and holes in the brookite polymorph (4 and 5), generating the reactive species responsible for the degradation of the dye (5, 6 and 7). In the valence and conduction bands, the oxidation (8) and reduction (9) reactions occur, respectively, resulting in degradation products.</ns0:p><ns0:p>(</ns0:p><ns0:formula xml:id='formula_0'>) ) ( ) ( ) ( 2 2       vb cb h e TiO UV h B A TiO  (2) ) ( ) ( ) ( ) ( 2 heat or h B A TiO vb h cb e        (3)       2 2 2 2 ) ( ) ( O TiO O e A TiO cb (4)        HO H TiO O H h B TiO vb 2 2 2 ) ( ) ( (5)       HO TiO OH h B TiO vb 2 2 ) ( ) ( (6)       2 2 HO H O Degradation products (7)    OH R P4 (8) 𝑃4𝑅 + ℎ 𝑣𝑏 + → 𝑝𝑟𝑜𝑑𝑢𝑐𝑡 𝑜𝑥𝑖𝑑𝑎𝑡𝑖𝑜𝑛 (9) 𝑃4𝑅 + 𝑒 𝑐𝑏 -→ 𝑝𝑟𝑜𝑑𝑢𝑐𝑡 𝑟𝑒𝑑𝑢𝑐𝑡𝑖𝑜𝑛<ns0:label>1</ns0:label></ns0:formula><ns0:p>The TiO 2 G1HT oxide, present the best photocatalytic performance (k app = 5.9 × 10 3 min -1 ; R = 0.9824), because the availability of reactive species becomes proportionally higher as the concentration of the dye decreases, since the concentration of these species is practically constant during the photocatalytic process <ns0:ref type='bibr' target='#b14'>(França et al., 2016)</ns0:ref>. Therefore, P4R undergo fragmentation at the beginning of the reaction , which should favor a faster mineralization.</ns0:p><ns0:p>Analyzing the spectrum presented in the Inset of Fig 6, it can be seen that at the end of the photocatalytic process, the band centered at 500 nm, referred to an electronic transition with a major component π → π* <ns0:ref type='bibr' target='#b36'>(Oliveira et al., 2012)</ns0:ref>, involving the naphthalenic structures and the azo group, associated with the coloring of the dye, decreases significantly. The formed products should not present new or significant absorption bands in the analyzed region, suggesting that the degradation not only induces a quick discoloration of the dye (Table <ns0:ref type='table' target='#tab_0'>2</ns0:ref>), as they should also cause a significant fragmentation of the dye structure, whose fragments should not absorb significantly in the monitored region of the electromagnetic spectrum.</ns0:p><ns0:p>The coexistence of anatase and brookite in the TiO 2 synthesized with different glycols and treated by a hydrothermal via at low temperature, minimized the recombination rate of the e -/h + pairs, thus allowing the holes to be available for oxidation reactions. In addition, the correlation of physical and chemical factors, such as high surface areas and porosity, high photon absorption capacity in the UV-visible region and crystallinity considerably improved the photocatalytic activity of these oxides.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions</ns0:head><ns0:p>In this study, we present the preparation of TiO 2 mesoporous nanoparticles using the sol-gel method with different glycols as structural molds. The use of ethylene glycol associated to further hydrothermal heat treatment proved to be the most effective way to obtain nanoparticles with improved photocatalytic activity. The results showed that materials submitted to hydrothermal heat treatment presented smaller particles and greater porosity, with formation of approximately spherical nanoparticles and with sizes up to 5 nm and formation of a binary mixture of anatase and brookite phases. The use of different glycols influenced the size of the particles, promoting the formation of smaller particles. The existence of a junction between different phases of the same semiconductor, accompanied by a decrease in the size of the particles, favored the charge transfer processes and contributed to the delay of the recombination processes, significantly improving the photocatalytic activity, verified by the degradation of the azo-dye Ponceau 4R under UV-Vis light irradiation. This type of photocatalyst that can harness both UV and visible light is a promising candidate for applications in photochemistry, sensors and solar cells, which has motivated us to develop oxides and nanocomposites based on TiO 2 with a wide spectrum of applications. </ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head>Figure 2</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>X-ray diffraction patterns of the studied photocatalysts: (A) TiO 2 G1HT; (B) TiO 2 G2HT and (C) TiO 2 G3HT, (D) TiO 2 G1M; (E) TiO 2 G2M and (F) TiO 2 G3M.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head>Figure 1 TEM</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>Figure 5</ns0:head><ns0:label>5</ns0:label><ns0:figDesc>Figure 5</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='21,42.52,204.52,525.00,367.50' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='22,42.52,181.57,525.00,370.50' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='23,42.52,201.82,525.00,371.25' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='25,42.52,224.77,525.00,370.50' type='bitmap' /></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head>Table 2</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Mineralization, Langmuir-Hinshelwood kinetics and discoloration of the P4R azo-dye mediated by the prepared oxides.</ns0:figDesc><ns0:table /><ns0:note>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2020:09:53207:1:1:NEW 13 Jan 2021)</ns0:note></ns0:figure> <ns0:note place='foot'>Analytical, Inorganic, Organic, Physical, Materials Science </ns0:note> </ns0:body> "	"REFERENCE: Article ID 53207 TITLE: Synthesis of nano-TiO2 assisted by glycols and submitted to hydrothermal or conventional heat treatment with promising photocatalytic activity Journal: PeerJ Materials Science January 12th, 2021 Dear Sir Editor, Thank you for your kindly letter and comments. The manuscript was read carefully for correction considering the comments of the reviewers. The corrected words and sentences were highlighted and marked (computer-generated), being the changes indicated in the revised manuscript. Below are answers to reviewers. We believe that the manuscript is now suitable for publication in PeerJ Materials Science. Dra. Lidiaine Maria dos Santos Professor of Chemistry On behalf of all authors. Reviewer 1: The present manuscript titled 'Synthesis of nano-TiO2 assisted by glycols and submitted to hydrothermal or conventional heat treatment with promising photocatalytic activity' is an interesting and compelling study. The results and discussion presented are interesting and can help other readers/researchers working in this area. I recommend the author(s) to address the following minor issues. The caption in the inset of figure 4 mentioned the Optical 'reflectance' spectra of the samples while in the figure it is indicated as absorbance spectra. Authors: We corrected this. In figure 6, the linear fit in the case of TiO2 G1HT (A) doesn't seem right. Please correct it. Authors: We agree. The linear fit of the TiO2G1HT (A) was corrected, excluding the kapp2 values in the text and Table 2. In view of this, the discussion in lines 379 to 383 was adapted, since with the correction in the graph, there is only a single kinetic constant. There are several typos in the manuscript. I recommend proofread the manuscript again. Authors: The manuscript was completely reviewed. Reviewer 2: The authors demonstrated the preparation of TiO2 mesoporous nanoparticles using the modified sol-gel method with different glycols as structure molds. The authors found the use of ethylene glycol associated with hydrothermal heat treatment proved to be the most effective way to obtain nanoparticles with improved photocatalytic properties. The use of different glycols influenced the size of the particles, promoting the formation of smaller particles. All the conclusions are well stated with the proper experimental studies. All data have been provided. Overall quite good. Authors: We thank the reviewer for their generous comments about the manuscript. Reviewer 3: Authors have attempted to compare the different heat treatment techniques in obtaining a better photocatalyst using a “modified” sol-gel method. As authors stated, Titanium dioxide has been attracting interest in various multidisciplinary applications. In the introduction section, there are a few places there it is The authors tried to explain recent applications of the TiO2 and the use of the language was very ambiguous. Some sentences and paragraphs do not make any sense even after going back-and-forth several times. For example, lines 108 – 109 “….semiconductor oxides can to allow the displacement of electrons…” The above sentence is not only hard to comprehend but also confuses the reader in grasping the authors’ views. There are several of these examples all over the manuscript. Lines 111-112 “…Similar behaviors have the mixtures of crystalline phases of TiO2, such as anatase/brookite (A/B) or anatase/rutile (A/R)…” The authors may want to restructure the above sentence. Authors: We restructured these sentences. The new sentence can be seen in lines 107 to 112. Also, line 114-116 “In the present study, photocatalysts based on TiO2 were synthesized by the modified sol-gel method, evaluating the influence of the use of different structural molds (ethylene glycol, diethylene glycol or polyethylene glycol 300), as well as the effect of thermal treatments by conventional or hydrothermal route on the structural, optical and morphological properties.” It would be easier to break the above sentence and explain it effectively in two separate sentences. Authors: We restructured these sentences. The new sentence can be seen in lines 113 to 117. Line 119-121 “…The results presented provide new ideas about obtaining mesoporous...” please rewrite the sentence Language should be improved to make sure that the international audience can clearly understand your manuscript. Authors: We corrected this. The new sentence can be seen in lines 119 to 122. Experimental methods: Does using different structural molds will qualify to be the modified sol-gel? Authors: The word 'modified' was not adequate and was removed from the manuscript. We agree because a typical sol-gel process involves the production of a solution of several metals, the addition of complexing agents such as carboxylic acids or alcohols (for example, glycols). In the results and discussion section: In line 180: The authors state that the formed agglomerates are spherical nanoparticles, which is conflicting with the TEM images. In Fig. 1, D has a different scale compared to A-C, authors may want to replace it with an image with a similar scale. Including the TEM and HRTEM images for TiO2G2M and TiO2G3M might help the reader to comprehend the Fig.1 better. Authors: Unfortunately, we do not have Fig. 1, D on the same scale compared to A-C. Similarly, we do not have the TEM and HRTEM images of TiO2G2M and TiO2G3M. These analyzes are carried out at another institution and we do not have the financial resources to request them. We believe that the images available on the manuscript provide conditions to evaluate the influence of the heat treatment method through the TEM images of TiO2G1M and TiO2G1HT. Besides that, the influence of the use of different glycols was evaluated only by the hydrothermal route because it showed the best photocatalytic performance when compared to conventional thermal treatment, as verified in the results and discussions of the item 'Photocatalytic activity'. Authors may want to state the reference for their estimated spacing between crystalline planes an the . Authors: We added the reference in lines 208 and 209. Can the authors comment on why the Fig.2. (plot C had greater brookite phase than plot f)? Authors: I have added the comment, new sentece can be seen in lines 231 to 234. "	Here is a paper. Please give your review comments after reading it.
445	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Biomimetic model organisms could be a useful surrogate for live animals in many applications if of sufficient biofidelity. One such application is for use in field and laboratory tests of fish mortality associated with passage through hydropower turbines. Laboratory trials suggest that blade strikes are especially injurious and often causes mortality when fish are struck by thinner blades moving at higher velocities. Dose-response relationships have been created from these data, but the exact relationship between fish mortality and the actual forces enacted on fish during simulated blade strike testing remains unknown.</ns0:p><ns0:p>Here, we describe the methods used to create a prototype biomimetic model fish composed of ballistic gelatin and covered with a surrogate skin to better approximate the natural properties of a fish body. Frozen fish were scanned with high-fidelity laser scanners, and a 3D-printed, reusable mold was created from which to cast our gelatin model. Computed tomography scan data, imaged directly or taken from online data repositories, were also successfully used to create CAD models for use in additive manufacturing of molds. One 3-axis accelerometer was embedded into the gelatin to compare accelerometer data to data from previous laboratory research on live fish. The resulting model known as Gelfish had a statistically similar tissue durometer to that of real fish tissue and preliminary blade strike impact testing suggested its overall flexibility was similar to that of a live fish. Gelfish was fundamentally designed with biofidelity as its guiding principle and our results suggest initial experimentation was successful. Future research will include replication of initial Gelfish test results, quantitative measurement of model flexibility relative to real fish, and surrogate skeletal structures to enhance biofidelity. Use of more sophisticated sensors would also provide better quantification of the physical forces of blade strike impact and determine how said forces correlate with rates of mortality observed during tests on live fish.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>The field of biomimetics often produces revolutionary inventions and innovations that overcome persistent engineering challenges-many such breakthroughs are the result of studying aquatic organisms. Fishes and marine mammals are at the center of many studies because of their unique circumstances, the desired fish species may not be available because it is rare, difficult to capture or keep alive in captivity, or protected by state and federal laws. To successfully receive authorization to use live animals, researchers are usually required to explain why use of an animal model (or computer simulation) is not possible and many fields are beginning to substitute animal models where possible <ns0:ref type='bibr' target='#b58'>(Sloman et al., 2019)</ns0:ref>. In most cases, it is difficult to mimic or recreate an organism without studying it first, but the loop could be closed by creating a biomimetic model for future use in place of live animals. All these facts suggest that if a biomimetic model existed, with sufficiently high biofidelity, the need for live animals would be less necessary in certain fields.</ns0:p><ns0:p>One application for a biomimetic model fish would include field and laboratory tests related to concerns of fish passage through hydropower turbines. There are nearly 2500 hydroelectric dams in the USA (EIA, 2020) and many riverine fishes are at a particularly high risk of turbine passage due to their migratory behavior <ns0:ref type='bibr' target='#b48'>(Pracheil et al., 2016b;</ns0:ref><ns0:ref type='bibr' target='#b57'>Silva et al., 2018)</ns0:ref>.</ns0:p><ns0:p>Live fish are often used during passage survival testing that is a part of the relicensing process for conventional hydropower dams. Hydropower facilities must undergo relicensing every 30-50 years and hundreds of dams are projected to submit relicensing applications within the next decade <ns0:ref type='bibr' target='#b65'>(Uria-Martinez et al., 2018)</ns0:ref>. Exact passage conditions of fish are generally unknown and have relied on insights from computational fluid dynamic (CFD) models to estimate probability of exposure to turbine passage stressors. Impacts from turbine blade runners are one of the most injurious stressors and laboratory tests on live fish suggest it may cause organ damage, skeletal fractures, amputation, and death <ns0:ref type='bibr'>(Bevelhimer et al., 2019;</ns0:ref><ns0:ref type='bibr'>Saylor, Fortner & Bevelhimer, 2019;</ns0:ref><ns0:ref type='bibr' target='#b53'>Saylor et al., 2020)</ns0:ref>. Rates of injury and death are highest with thinner blades, higher impact velocities, and when struck on the lateral surface near the center of gravity of a fish <ns0:ref type='bibr' target='#b64'>(Turnpenny et al., 1992;</ns0:ref><ns0:ref type='bibr'>EPRI, 2008;</ns0:ref><ns0:ref type='bibr'>Bevelhimer et al., 2019;</ns0:ref><ns0:ref type='bibr'>Saylor, Fortner & Bevelhimer, 2019;</ns0:ref><ns0:ref type='bibr' target='#b0'>Amaral et al., 2020)</ns0:ref>. Dose-response relationships generated from these laboratory trials are an important resource for designing more fish-friendly turbines; however, these data are limited in scope to just a few fish species exposed to what is presumed to be the worst-case impact scenarios. Furthermore, technology like the hard-bodied autonomous Sensor Fish™, that records actual hydraulic conditions from within a functioning turbine <ns0:ref type='bibr' target='#b10'>(Carlson, Duncan & Gilbride, 2003;</ns0:ref><ns0:ref type='bibr' target='#b17'>Deng et al., 2007a</ns0:ref><ns0:ref type='bibr'>Deng et al., ,b, 2014))</ns0:ref>, is available but incapable of sufficiently mimicking responses of live fish impacted by turbine blades. To that end, a biomimetic fish would be a useful surrogate for live animal tests because it could be used more than once and be validated using previously generated dose-response data.</ns0:p><ns0:p>Herein we detail the methods used to create a prototype biomimetic model fish (hereafter referred to as Gelfish) composed of ballistic gelatin and containing an embedded sensor. We used 3D scanning and imaging technologies to successfully replicate the general shape and surface features of multiple fish species. Scanned images were used to additively manufacture a reusable mold from which to cast the ballistic gelatin model. Ballistic gelatin was chosen for our initial model because of its extensive use as a tissue simulant in ballistic testing. Tissue durometer (firmness) of the Gelfish was compared to real fish tissue to assess biofidelity of our model. Durometer was chosen because it is easy to measure and is well-established in medicine to assess changes in tissue <ns0:ref type='bibr' target='#b28'>(Falanga & Bucalo, 1993;</ns0:ref><ns0:ref type='bibr' target='#b16'>Cuaderes et al., 2009;</ns0:ref><ns0:ref type='bibr' target='#b45'>Moon et al., 2012)</ns0:ref> and organ <ns0:ref type='bibr' target='#b1'>(Belyaev et al., 2010)</ns0:ref> hardness caused by disease, or to confirm biofidelity of cosmetic surgery <ns0:ref type='bibr' target='#b8'>(Brown, Brown & Murphy, 2017;</ns0:ref><ns0:ref type='bibr' target='#b46'>Murphy et al., 2020)</ns0:ref>, which suggests it is a viable option for fish tissue as well. In addition, preliminary observations of model flexibility were also compared to live fish to better assess Gelfish biofidelity following a simulated turbine blade strike. To our knowledge, 3D printing molds instead of the animal model directly, has not been applied to the production of a whole-organism biomimetic model before. More specifically, the objectives of this study were to 1) test the ability of ballistic gelatin to match whole-body firmness of fish tissues, 2) quantify how preparation temperature and warming time affect gelatin durometer, 3) determine efficacy of Plasti Dip® as a surrogate fish skin, 4) additively manufacture molds and cast gelatin models for at least five species of fish, 5) embed a 3-axis accelerometer into Gelfish to record characteristics of blade strike impact, and 6) compare Gelfish responses to available data from live fish when exposed to simulated blade strike impacts to help assess model biofidelity.</ns0:p></ns0:div> <ns0:div><ns0:head>MATERIALS & METHODS</ns0:head></ns0:div> <ns0:div><ns0:head>Ballistic Gelatin Experiments</ns0:head><ns0:p>To our knowledge, there are no published accounts of ballistic gelatin being used as a surrogate for fish tissue, so we designed several experiments to establish its baseline material properties.</ns0:p><ns0:p>Ballistic gelatin was chosen because of its established use as a human and animal tissue simulant in ballistics research <ns0:ref type='bibr' target='#b35'>(Jussila, 2004;</ns0:ref><ns0:ref type='bibr' target='#b43'>Maiden et al., 2015)</ns0:ref>. Furthermore, there are well established protocols and recipes for ballistic gelatin that were easy to modify to meet our needs. We used ballistic gelatin powder specifically formulated to simulate human body density (Vyse® Professional Grade Ballistic Gelatin; Lot #12953; Custom Collagen, Inc., Addison, Illinois, USA) for all trials and final model preparation. Our main metric to measure the material properties of gelatin was tissue durometer (i.e., material hardness or resistance to indentation) for all ballistic gelatin trials. More specifically, we measured durometer with a Shore Type-OO durometer (Model DD-4 Digital Durometer; Precision = ± 0.1 units; Rex Instruments, Buffalo Grove, Illinois, USA) which is best suited to measure soft gels and animal tissue. An automated stand (Model OS-1 Operating Stand, Rex Instruments, Buffalo Grove, Illinois, USA) lowered the meter to the sample at precisely the same rate under a consistent load pressure for all samples, thereby decreasing measurement error.</ns0:p><ns0:p>In preliminary trials, we tested two methods of ballistic gelatin preparation that were modified from other sources to accommodate our smaller sample volumes <ns0:ref type='bibr' target='#b35'>(Jussila, 2004;</ns0:ref><ns0:ref type='bibr' target='#b13'>Cronin, 2011;</ns0:ref><ns0:ref type='bibr' target='#b43'>Maiden et al., 2015)</ns0:ref>. Method one (referred to as cooling hydration) included heating deionized water to a desired temperature using a water bath (Thermo Scientific Precision Microprocessor Controlled 280 Series Water Bath; thermofisher.com), followed by adding the heated water into a large (~900 mL) polypropylene container containing gelatin powder. The water and gelatin were then mixed with a metal spatula until completely homogenized so that no clumps remained. At this point, up to 150 µL of de-foaming agent (Custom Collagen, Inc., Addison, Illinois, USA) was added to remove foam and excess bubbles. The mixture then cooled to room temperature (~22°C) which allowed the gelatin to hydrate. After this cooling hydration period, the container was covered with a lid and refrigerated for 12 hours at 4°C to allow the gelatin to completely set. Finally, the block of ballistic gelatin could be removed, cut into pieces, re-melted, and distributed as needed into other containers for testing. The second method (referred to as heated hydration) was similar to the previous except the heated water and gelatin mixture was covered with parafilm wax, placed back into the same temperature water-bath, and allowed to hydrate at this temperature for at least 10 minutes. Following the heated hydration period, the gelatin mixture could be distributed into test containers, allowed to cool to room temperature, and refrigerated for 12 hours at 4°C. We preferred the heated hydration method because it allowed the gelatin mixture to hydrate without cooling, avoided evaporative water loss during re-melting, and samples could be poured immediately into test containers. Both methods PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2020:11:55231:1:0:NEW 5 May 2021)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science produced comparable estimates of durometer in our ballistic gelatin samples, but heated hydration was preferred because of more consistent heating and avoided unnecessary re-melting.</ns0:p><ns0:p>Lastly, we used cinnamon oil to increase the shelf-life of our ballistic gelatin samples well beyond the normal 7 to 11-day limitation <ns0:ref type='bibr' target='#b35'>(Jussila, 2004;</ns0:ref><ns0:ref type='bibr' target='#b59'>Staymates & Gillen, 2010)</ns0:ref>. We used cinnamon oil (NOW ® Cinnamon Cassia oil; Item #051210; gnc.com) dissolved in 95% ethanol (1:10) at a concentration of 515 ppm as a microbial growth inhibitor. Cinnamon oil was dissolved in 95% ethanol to make it more miscible in water because pure cinnamon oil extract will separate from the gelatin <ns0:ref type='bibr' target='#b35'>(Jussila, 2004)</ns0:ref>. Use of the heated hydration method and cinnamon oil ensured more consistent durometer measurements during experimental trials.</ns0:p><ns0:p>Most published accounts of ballistic gelatin include use of 10 or 20% solutions (mass to volume) of gelatin powder dissolved in deionized water <ns0:ref type='bibr' target='#b35'>(Jussila, 2004;</ns0:ref><ns0:ref type='bibr' target='#b15'>Cronin & Falzon, 2011)</ns0:ref>; however, we were unsure which concentration best mimicked fish tissue. We tested a total of five concentrations including 10, 15, 20, 25, and 30% to determine which concentration best approximated the durometer of actual fish tissue (see last experiment). Each ballistic gelatin concentration was prepared in triplicate. A heated hydration protocol with a preparation temperature of 65°C was used to create each gel mixture. Following hydration, ~60 mL of each replicate was added to a 100-mL, polystyrene weigh boat and allowed to cool at room temperature. When the gelatin reached room temperature (denoted by solidification of the gelatin) all samples were labelled, placed into a large, 33 × 38 cm, 6-Mil plastic storage bag, and refrigerated over-night. Following refrigeration, three randomly selected weigh boats were removed and allowed to warm to room temperature for 30 minutes. Ten durometer measurements were recorded for each sample by removing it from the weigh boat, flipping it over, and taking measurements across the gelatin's bottom surface. The durometer measurements for each replicate were averaged and the arithmetic mean of all three represented the average durometer of each concentration.</ns0:p><ns0:p>There are conflicting accounts of which water temperature is best for preparation of ballistic gelatin with respect to maintaining optimal material properties of gelatin. Preparation temperatures may range from 40 to 90°C or higher; however, manufacturers recommend temperatures near 40°C to maintain its tissue-simulating properties <ns0:ref type='bibr' target='#b25'>(Fackler & Malinowski, 1987;</ns0:ref><ns0:ref type='bibr' target='#b15'>Cronin & Falzon, 2011)</ns0:ref>. We also experimented with preparation temperature to determine how it affected the durometer of our ballistic gelatin samples. All ballistic gelatin samples in PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2020:11:55231:1:0:NEW 5 May 2021)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science these experiments were made using a 25% gelatin concentration. Three temperature treatments -45, 55, and 65°C -were prepared in triplicate and durometer was measured for each sample. In addition, we also tested how warming time following refrigeration affects durometer measurements. This experiment included preparation of three replicate gelatin samples using a concentration of 25% and a water temperature of 45°C. Following refrigeration, five durometer measurements were made immediately (time 0), every 10 min up to 1 hour, then 15 min up to 2 hours, and finally every 30 minutes for up to 4 hours. Durometer was measured and reported in the same manner as the concentration experiment for each temperature and warming time treatment.</ns0:p><ns0:p>The final set of experiments compared the material properties of ballistic gelatin to that of an actual fish to determine how closely we could mimic natural tissue. In addition to gelatin, we tested the use of an artificial skin surrogate that covered our ballistic gelatin samples. We used commercially available Plasti Dip® as a skin surrogate and found that spraying was preferable over dipping to sufficiently cover the gelatin samples. The first set of experiments was used to determine if the surrogate skin covering would significantly increase durometer compared to uncovered samples. We prepared an additional three replicates of 25% ballistic gelatin at 45°C for use in these tests. After refrigeration, we allowed samples to warm for 30 minutes and took 10 durometer measurements. One layer of surrogate skin was then applied to the gelatin sample and allowed to cure for 30 minutes under a fume hood. The samples were then refrigerated for an additional 12 hours, after which they were removed, allowed to warm for 30 minutes, and durometer measurements were taken. The same protocol was repeated for two, three, and four additional layers of surrogate skin for comparison. Next, we collected durometer data from three bluegill sunfish, Lepomis macrochirus, with a total length of ~16 cm and mass of ~90 grams. All three fish were euthanized via overdose of 250 ppm clove oil in 95% ethanol (1:10) immediately prior to durometer measurements. Durometer was taken for each bluegill at 27 different locations along the entire body surface except the head which was mostly bone and the fins which were too thin to measure (Figure <ns0:ref type='figure'>1</ns0:ref>). In addition, the shapes of the Gelfish models and bluegill specimens required us to take durometer measurements by hand because both surfaces were curved which precluded use of the automated stand used for other ballistic gelatin experiments.</ns0:p><ns0:p>Another set of 27 durometer measurements were taken on the same three fish after removing scales from the entire lateral surface. In this way, we created data sets for bluegill whole-fish PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2020:11:55231:1:0:NEW 5 May 2021)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science durometer with and without scales for comparison of ballistic gelatin with and without a skin surrogate. Durometer was measured and reported in the same manner as the concentration experiment for each surrogate skin layer sample and bluegill tested.</ns0:p><ns0:p>All statistical tests were performed using R v.4.0.2 statistical programing language and Sigma Plot v12. One-way analyses of variance (ANOVA) were used to compare the average durometer of 1) different ballistic gelatin concentrations prepared at 45 °C and 2) preparation temperature groups composed of 25% ballistic gelatin. A one-way repeated measures ANOVA was used to analyze the difference in average durometer between warming time and skin-layer treatment groups. Paired t-tests were used to compare average durometer between 1) Gelfish with and without Plasti Dip skin and 2) bluegill sunfish with and without scales, whereas an unpaired t-test was used to compare average durometer between Gelfish with skin and bluegill sunfish with scales. In the event a significant difference was detected by ANOVA, we used Benjamini-Hochberg post-hoc multiple comparison tests to determine the statistical relationship between treatment groups. Finally, linear regression was used to test the relationship between ballistic gelatin concentration and average durometer. All statistical decisions were based on α = 0.05.</ns0:p></ns0:div> <ns0:div><ns0:head>Fish Scanning and Image Collection</ns0:head><ns0:p>Most biometric image data used to create our Gelfish model originated from 3D scans of freshly caught fish. We scanned four species of fish including bluegill sunfish , rainbow trout (Oncorhynchus mykiss), gizzard shad (Dorosoma cepedianum), and white bass (Morone chrysops) which varied in size (Table <ns0:ref type='table'>1</ns0:ref>). These species were chosen because they represent the range of fish body shapes that may pass through hydropower turbines and because blade strike laboratory data are available for each species <ns0:ref type='bibr' target='#b47'>(Pracheil et al., 2016a;</ns0:ref><ns0:ref type='bibr' target='#b4'>Bevelhimer & Derolph, 2019;</ns0:ref><ns0:ref type='bibr'>Bevelhimer et al., 2019;</ns0:ref><ns0:ref type='bibr'>Saylor, Fortner & Bevelhimer, 2019;</ns0:ref><ns0:ref type='bibr' target='#b53'>Saylor et al., 2020)</ns0:ref>. To prepare for scanning, live fish were euthanized in an overdose of 250 ppm clove oil in 95% ethanol (1:10) for at least 15 minutes. Each fish was secured in an upright position with paired fins placed against the body and with the mouth and operculum closed. Individuals were frozen in this position at -20°C for 12 hours prior to scanning. Freezing was necessary to prevent movement of appendages during scanning which helped minimize image processing time.</ns0:p><ns0:p>Additionally, the frozen fish was secured to a platform in an upright position that prevented PeerJ Mat. Sci. reviewing PDF \| (MATSCI- <ns0:ref type='table' target='#tab_2'>2020:11:55231:1:0:NEW 5 May 2021)</ns0:ref> Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science movement but allowed for complete access to scan the entire fish. Finally, each fish was completely covered with a white, matte-finish spray paint to reduce surface reflections caused by fish scales. Two different scanners were used to capture fish images: a Leica Laser Tracker and Scanner (accuracy ± 0.060 mm; lieca-geosystems.com) and a FARO ® SCANARM blue light laser scanner (accuracy ± 0.075 mm; www.faro.com). During scanning, Verisurf software (verisurf.com/software) was used to convert images into a point cloud file.</ns0:p><ns0:p>All laser-scanned point cloud data were processed and converted into a computer-aided design (CAD) model to be used for 3D printing. The point cloud data were converted to ASCII files and imported into Geomagic Design X (3dsystems.com) software. Some noise and unneeded areas (i.e., scanning platform or fish restraint device) of the point cloud file were manually removed. The dorsal, pelvic, and anal fins were removed from the model to simplify preparation of the mold. We used internal software features like 'reduce noise' with a smoothing level of 1 and default levels of 'sampling' to smooth the point cloud data. After smoothing, we used the 'wrap' command to transform the point cloud into a mesh. If the mesh contained more holes the images went through additional smoothing using the 'fill holes' or 'repair' features to close minor or larger gaps in the mesh, respectively. The final mesh was created by using 'remesh', 'smooth', and 'remove spikes' features. Lastly, the final mesh was converted into a SolidWorks surface image, using the 'auto surface' feature with the specifications of an organic geometry type, target patch count of 500, and default adaptive tolerance. The SolidWorks surface model was exported as a .STL file to be used in 3D printing of the fish mold.</ns0:p><ns0:p>We also investigated two additional forms of image acquisition including use of computed tomography scans of preserved specimens or from online databases. Our fifth and final species, American eel, Anguilla rostrata, was created by scanning a preserved specimen. The eel we used was much smaller than most sizes known to pass through hydropower turbines (Table <ns0:ref type='table'>1</ns0:ref>); however, it was used to test our ability to account for and change fish size during image processing. The eel was scanned using a computed tomography scanner through the Diagnostic Imaging Service available at the University of Tennessee College of Veterinary Medicine (UTCVM). The computed tomography scan of the eel was saved as digital imaging and communications in medicine (DICOM) file. An online digital repository called Morphospace (morphospace.org) was also used to find additional x-ray, computed tomography (also computed axial tomography; CAT), or laser-scanned images for white bass, Morone americana. While we generated our own 3D scan data, we were also interested in how readily available online data might also be used to create fish molds. Computed tomography images (either directly imaged or taken from repositories) are not available in point cloud form, so these images were first converted into .STL files using open source InVesalius 3 (invesalius.github.io) software. A contrast range with a lower bound between 42 and 65 and an upper bound of 255 best captured skin traits and underlying skeletal structures while simultaneously filtering out noise. Next, CloudCompare (daniel.gm.net/cc/) and MeshLab (meshlab.net) or Geomagic Design X were used to convert the .STL file into a point cloud by sampling one million points, which ensured sufficient detail for the CAD model while minimizing computational resources. The conversion of point cloud to a surface model (i.e., an .STL file) followed the same procedure as that described above for laser-scanned images.</ns0:p></ns0:div> <ns0:div><ns0:head>Mold Printing and Construction</ns0:head><ns0:p>Each CAD model was further reviewed, and final modifications were made to ensure clean demolding and purging of air during casting. The thickness of the caudal fin and peduncle was increased so that the final cast model made of ballistic gelatin would not rip when removed from the mold. The bluegill and gizzard shad CAD models only included the caudal fin, whereas the rainbow trout and white bass CAD models also included slightly raised areas on the dorsal and ventral surfaces to represent dorsal and anal fins, respectively. Additional features found in all the species CAD models were raised areas that represented the eyes and operculum on the head which were also important landmarks for positioning sensors. The eel CAD model also went through additional processing to remove its notably longer dorsal, anal, and caudal fins. Other modifications to the eel model included scaling-up body proportions to account for different sizes of eel because the original fish was smaller than most eels known to pass through turbines.</ns0:p><ns0:p>The fish CAD model was then subtracted from a box to create a negative space within it, which serves as the mold for the casting. A fill hole was added to each mold CAD model on the anterior (head region) through the mouth to avoid disrupting the shape of the body and allow easy access for filling the mold with ballistic gelatin. The final mold CAD model was split in half and alignment holes, pry points, and mounting hardware were added that ensured the mold was properly sealed and aligned during casting. To additively manufacture the molds, a build file was created that contains the printer tool path and material extrusion rates. We used the Stratasys PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2020:11:55231:1:0:NEW 5 May 2021)</ns0:p><ns0:p>Manuscript to be reviewed Chemistry Journals <ns0:ref type='bibr'>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:ref> Insight software to slice the molds into layers and generate these build files, which were loaded into the Stratasys Control Center for printing. Finally, molds were printed using a Stratasys Fortus 400mc printing system and were composed of spares infill acrylonitrile butadiene styrene (ABS). The inside of each half of the final molds were polished with acetone to completely seal each surface prior to casting <ns0:ref type='bibr' target='#b56'>(Sikder et al., 2014;</ns0:ref><ns0:ref type='bibr' target='#b37'>Lalehpour & Barari, 2016)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Sensor Specification and Calibration</ns0:head><ns0:p>A 3-axis digital accelerometer (ADXL375, www.analog.com) with a capable measurement range of ± 200 g was used for data acquisition in the ballistic gelatin model during simulated blade strike impact trials. Output data was accessed through the I 2 C interface at the rate of 800Hz. The Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>accelerometer was potted with black epoxy potting compound (3M-DP270, www.3m.com) using a custom mold. This provided the accelerometer, and the connections with the data acquisition system, necessary mechanical rigidity and watertight seal. The potted accelerometer was embedded into the ballistic gelatin model and could be used multiple times, i.e., used in multiple ballistic models without deterioration.</ns0:p></ns0:div> <ns0:div><ns0:head>Gelfish Model Preparation & Testing</ns0:head><ns0:p>For our initial complete Gelfish model, we chose to use rainbow trout because the body depth and width of this species could better accommodate an accelerometer. During Gelfish production, the accelerometer was held in place within the rainbow trout mold using a monofilament line that stretched from head to tail. We positioned the accelerometer posterior to</ns0:p><ns0:p>where the operculum would be on a real fish. This location represents the mid-body area, which is associated with the highest rates of injury and mortality when fish are struck by hydropower turbine blades, including rainbow trout <ns0:ref type='bibr'>(EPRI, 2008;</ns0:ref><ns0:ref type='bibr'>Bevelhimer et al., 2019;</ns0:ref><ns0:ref type='bibr' target='#b0'>Amaral et al., 2020;</ns0:ref><ns0:ref type='bibr' target='#b53'>Saylor et al., 2020)</ns0:ref>. The mold was then securely closed and kept in an upright position to cast the mold. A 25% ballistic gelatin solution was prepared at 45°C and injected into the mold using a 60-mL syringe with an extended tip. The syringe tip was inserted completely into the mold and gelatin was injected from the bottom upwards to avoid formation of bubbles. After it was filled, the ballistic gelatin was cooled for 10 minutes at room temperature, followed by refrigeration at 4°C for 90 minutes. Refrigeration was used to accelerate cooling and decrease the time required for the gelatin to completely set. Following refrigeration, the ballistic gelatin model was removed from the mold, placed into a sealed plastic baggie, and refrigerated again at 4°C overnight. A surrogate skin (i.e., Plasti Dip ® ) was applied after overnight refrigeration such that four separate layers were added with at least 45 minutes of curing time between each layer.</ns0:p><ns0:p>The final Gelfish model was then placed back into the baggie and refrigerated prior to its use in blade strike impact trials.</ns0:p><ns0:p>We used the same simulated blade strike apparatus and procedure described in <ns0:ref type='bibr' target='#b53'>Saylor et al. 2020</ns0:ref>, to strike the rainbow trout Gelfish model. We struck the Gelfish 12 times, with each strike accounting for a different velocity and leading-edge blade width, as well as a different impact location and orientation on the model itself, while all blade strikes with the model occurred at 90° (Table <ns0:ref type='table' target='#tab_0'>2</ns0:ref>). These strike conditions were chosen based on previous laboratory tests Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science which found that mid-body, lateral strikes caused the highest rates of injury and death among rainbow trout <ns0:ref type='bibr'>(Bevelhimer et al., 2019;</ns0:ref><ns0:ref type='bibr' target='#b53'>Saylor et al., 2020)</ns0:ref>, and also based on relative proximity to the embedded accelerometer. We considered impacts a 'direct' sensor strike when the blade made contact with the model at the approximate center of the accelerometer. Alternatively, an indirect impact was considered any strike where the blade made contact with the model posterior (towards caudal fin) and away from the accelerometer (Figure <ns0:ref type='figure' target='#fig_8'>3</ns0:ref>). All of these conditions were used to assess the ability of the accelerometer to detect differences in strike impact location on our model, which is impossible to determine on live fishes that pass through hydropower turbines. All strikes were recorded at 1000 fps with a high-speed video camera (Model IL4, Fastec Imaging, San Diego, California) and integrated stroboscope LED lighting system (Monarch Nova-Pro 300, www.monarchinstrument.com) for later review and to confirm blade strike impact velocity.</ns0:p><ns0:p>Data acquisition from the 3-axis accelerometer was initiated immediately prior to engaging the simulated blade strike apparatus. Estimated blade impact velocity with Gelfish was calculated using the running average of the previous 10 frames (e.g., 10 msec) prior to and including impact. Following blade strike, acceleration data were averaged over 10 ms and 30 ms intervals. Maximum acceleration (a MAX ) was determined using the following equation:</ns0:p><ns0:p>[2]</ns0:p><ns0:formula xml:id='formula_0'>𝑎 𝑀𝐴𝑋 = 𝑀𝐴𝑋 [ 1 𝑡 2 -𝑡 1 ∫ 𝑡 2 𝑡 1 𝑎 ( 𝑡 ) 𝑑𝑡 ]</ns0:formula><ns0:p>according to time t and the desired time interval t 1 to t 2 during the acceleration pulse, which is reported as acceleration of gravity (g). We estimated maximum acceleration using 10 and 30 ms running average intervals to test which interval best captured trends in acceleration. Maximum acceleration is a running average derived from National Highway Traffic Safety Administration (NHTSA) specifications for head injury criteria when using one accelerometer <ns0:ref type='bibr' target='#b22'>(Eppinger et al., 1999)</ns0:ref>. Observed acceleration (α obs ) was converted to overall magnitude (across all three axes) according to the following equation:</ns0:p><ns0:p>[3]</ns0:p><ns0:formula xml:id='formula_1'>𝛼 𝑜𝑏𝑠 = 𝛼 2 𝑥 + 𝛼 2 𝑦 + 𝛼 2 𝑧</ns0:formula><ns0:p>with observed values of gravitation acceleration for the x-axis (α x ), y-axis (α y ), and z-axis (α z ) at each time point, which was plotted as 10 ms and 30 ms running averages of observed acceleration against time (ms). Plots of acceleration were used to determine the relative difference in magnitude between strike impact scenarios (Table <ns0:ref type='table' target='#tab_0'>2</ns0:ref>). In addition, we attempted to Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science link changes in acceleration to rates of injury and mortality reported from previous blade strike impact experiments performed on live rainbow trout <ns0:ref type='bibr' target='#b53'>(Saylor et al., 2020)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head></ns0:div> <ns0:div><ns0:head>Ballistic Gelatin Experiments</ns0:head><ns0:p>The ballistic gelatin concentration could be easily modified to account for different tissue durometers when prepared at 45°C and a standardized durometer measurement protocol was used. In fact, average durometer at 45°C significantly increased with every 5% increase in ballistic gelatin across the entire range tested (one-way ANOVA, F 4,10 = 162.40, p < 0.001). We also detected a significant (one-way ANOVA, F 1,13 = 532.22, p < 0.0001) relationship between ballistic gelatin concentration and average durometer given by the following linear model:</ns0:p><ns0:p>[4] D 30 = 1.48 × BG + 0.33 where D 30 is the durometer following 30 minutes of warming and BG is the percentage of ballistic gelatin. Ballistic gelatin (prepared at 45°C) concentration explained ~97% of the variation in average durometer of the linear model (R 2 = 0.974; Figure <ns0:ref type='figure'>4</ns0:ref>). Preparation temperatures of 45, 55, and 65°C did not significantly impact the durometer of our 25% ballistic gelation samples and all three temperatures produced an average durometer of ~35 units.</ns0:p><ns0:p>Warming time significantly (one-way repeated measures ANOVA; F 14,28 = 378.96, p < 0.001) impacted average durometer for 25% ballistic gelatin prepared at 45°C after 10 minutes of warming at room temperature (22.1°C) according to Benjamini-Hochberg multiple comparison tests. Average durometer continued to decrease significantly in a linear fashion after every 10 minutes for the first hour of warming except between the 20 to 30-min time period The average durometer continued to decrease after each warming period but was not significant again until it warmed for 90 minutes. Eventually, average durometer reached its nadir near 44 units after 150 minutes of warming where it plateaued for the remainder of the warming experiment (Figure <ns0:ref type='figure'>5</ns0:ref>).</ns0:p><ns0:p>Ballistic gelatin temperature increased quickly to 19°C within the first 60 minutes of warming and did not increase above 20°C for the remainder of this experiment (Figure <ns0:ref type='figure'>5</ns0:ref>).</ns0:p><ns0:p>The use of a surrogate skin increased average durometer of ballistic gelatin blanks (Table <ns0:ref type='table' target='#tab_1'>3</ns0:ref>) and initial Gelfish models (Table <ns0:ref type='table' target='#tab_4'>4</ns0:ref>). Addition of just one layer of surrogate skin significantly (one-way repeated measures ANOVA; F 4,8 = 323.96, p < 0.001) increased average durometer by Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science surrogate skin. Each additional layer applied to the ballistic gelatin samples also significantly increased durometer, except between two and three layers, which were both near 57 units (Table <ns0:ref type='table' target='#tab_1'>3</ns0:ref>). Up to four layers of surrogate skin caused average durometer to increase by nearly 20 units, to 60.2 ± 0.9 units, compared to samples without surrogate skin (average durometer = 42.3 ± 0.5). Gelfish without surrogate skin (36.2 ± 0.6) had a significantly (two-tailed, dependent t-test; t = -22.209, p = 0.002) lower average durometer than the Gelfish model with surrogate skin (61.6 ± 1.4; Table <ns0:ref type='table' target='#tab_4'>4</ns0:ref>). Similarly, bluegill sunfish with scales removed (54.0 ± 3.2) had significantly (two-tailed, dependent t-test, t = 4.9391, p = 0.039) lower average durometer than bluegill with scales intact (66.8 ± 1.8; Table <ns0:ref type='table' target='#tab_4'>4</ns0:ref>). Lastly, average durometer of the Gelfish model with a surrogate skin was statistically indistinguishable from bluegill samples with scales according to a two-tailed, independent t-test (Figure <ns0:ref type='figure'>6</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>3D Scanning and Printing Fish Molds</ns0:head><ns0:p>We successfully laser-scanned and printed molds for four species while a fifth was successfully printed from CT scan data. Scanning frozen fish in an upright position and use of Geomagic Design X software decreased image post-processing time from nearly 40 hours (manual processing) down to only 2 to 3 hours (with Geomagic software). The resulting SolidWorks models contained more surface features for the rainbow trout versus the bluegill, which required markedly more processing time (Figure <ns0:ref type='figure'>7</ns0:ref>). The SolidWorks surface models produced using this method were also easier to upload and it was easier to modify features such as fin thickness prior to printing, to ensure the resulting ballistic gelatin model did not tear (Figure <ns0:ref type='figure'>8</ns0:ref>). The time required to complete 3D printing of each mold varied by species (i.e., smaller species took less time) but was between 8 to 12 hours. Printing molds upright (versus lying flat) was necessary to limit warping of the mold halves from thermal stresses and ensured the mold halves sealed completely during casing. Acetone sealing successfully prevented ballistic gelatin infiltration through the mold, which reduced cleaning and ensured consistent casting for each model.</ns0:p><ns0:p>Additional mounting brackets were included on both the dorsal and ventral surface of the final mold, which allowed the accelerometer to be suspended within the ballistic gelatin using a monofilament line (Figure <ns0:ref type='figure'>9</ns0:ref>). Multiple Gelfish were cast from the same mold and there is no indication that casting multiple models deteriorated any of the molds. Total preparation time was ~12 hours, including casting (1.5 hours), model refrigeration at 4°C (8 hours), and application of PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2020:11:55231:1:0:NEW 5 May 2021)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science four layers of surrogate skin (2.5 hours) to the model prior to testing. Many Gelfish models could be created during this time if multiple molds were available.</ns0:p></ns0:div> <ns0:div><ns0:head>Gelfish Model Testing</ns0:head><ns0:p>The Gelfish model was capable of withstanding multiple blade strike impacts at comparably high velocities (i.e., up to 11.5 m/s) without deteriorating. The rainbow trout model was successfully exposed to nine different impact scenarios before the skin separated from gelatin model; however, the accelerometer remained functional for all 12 strike tests. While the surrogate skin did separate from the gelatin during testing, the gelatin did not deteriorate and could be reused after reapplying skin layers. Similarly, the accelerometer maintained its functionality and could also be cast into another model fish. The flexibility of the model also mimicked actual rainbow trout struck under the same conditions (i.e., mid-body, lateral strikes with 52-mm blade at ~6.8 m/s); though overall body curvature appeared to be slightly more pronounced with the Gelfish model (Figure <ns0:ref type='figure'>10</ns0:ref>). For example, body curvature of Gelfish was noticeably more pronounced during (+0.014s) and after (+0.024s) blade strike impact. The model also followed a similar trajectory out of the holding brackets following blade strike impact, which mimicked trials on live rainbow trout. The surrogate skin also allowed the model to maintain its integrity throughout the impact process, allowing the entire model (head to tail) to react similarly to real fish.</ns0:p><ns0:p>Changes in acceleration were detected in all three axes, including just prior to impact, during impact, and as the model moved away following contact with the blade (Figure <ns0:ref type='figure'>11</ns0:ref>). Peak magnitude generally occurred 10 ms after the bow wave produced by the blade pushed the model prior to impact. The entire impact sequence took less than 30 ms to complete. The highest peak magnitude and maximum acceleration were detected from a mid-body lateral strike with a 52mm blade moving at 11.5 m/s (Table <ns0:ref type='table' target='#tab_0'>2</ns0:ref>; Figure <ns0:ref type='figure'>11</ns0:ref>). All indirect strikes had noticeably lower peak magnitudes and maximum accelerations, regardless of other strike impact scenarios. Direct impacts to the mid-body ventral surface produced comparable levels of acceleration as mid-body lateral strikes and only differed in the main axis of movement caused by the strike, i.e., x-axis versus z-axis, respectively. Strikes with the same blade moving slower also had noticeably lower magnitudes-158.73 and 107.38 for the 52-mm blade moving 6.8 m/s and 76-mm blade moving at 5.0 m/s, respectively (Figure <ns0:ref type='figure' target='#fig_11'>12</ns0:ref>). Maximum acceleration detected with a 10 ms time interval was always higher than acceleration averaged across 30 ms, regardless of group. Gelfish trials <ns0:ref type='table' target='#tab_0'>2</ns0:ref>). Trends in magnitude and maximum acceleration suggest that the Gelfish model is also capable of detecting differences in impact scenarios, i.e., indirect strikes versus strikes at slower velocities or with thicker blades. A more detailed analysis of correlation with injury risk and mortality was not possible given that only one Gelfish model was tested.</ns0:p></ns0:div> <ns0:div><ns0:head>Discussion</ns0:head><ns0:p>Ballistic gelatin (25% prepared at 45°C) was used successfully to mimic whole-body tissue firmness of real fish, i.e., bluegill sunfish (Figure <ns0:ref type='figure'>6</ns0:ref>). Furthermore, gelatin concentration can be easily modified to account for differences in durometer (15 to 45 units; Figure <ns0:ref type='figure'>4</ns0:ref>) among species, associated with anatomical disparities in scales and/or muscle tissue. Durometer also appears to be a reproduceable means of estimating the material properties and biofidelity of ballistic gelatin compared to real fish tissue. Durometer varied significantly as a result of warming, so experimental protocols must be standardized to ensure measured values can be compared, i.e., we used a 30-minute warming time at room temperature. The exact warming time does not matter provided it is used consistently during experimentation; however, warming in excess of 60 minutes may cause evaporative water loss and shrinkage of the gelatin. No change in average durometer was detected based on preparation temperatures up to 65°C for 25% ballistic gelatin, but we suggest a 45°C (or lower) preparation temperature is ideal because additional heating is unnecessary. Temperatures greater than 65°C may cause detrimental changes to the material properties of ballistic gelatin prepared at lower concentrations of 10 or 20% <ns0:ref type='bibr' target='#b15'>(Cronin & Falzon, 2011;</ns0:ref><ns0:ref type='bibr' target='#b43'>Maiden et al., 2015)</ns0:ref>. Use of cinnamon oil increased the usable shelf-life of the Gelfish samples, but refrigeration was still required to avoid evaporative water loss associated with prolonged warming or air exposure. Plasti Dip applied over the ballistic gelatin created models that more closely mimicked the durometer of our whole-fish samples, and the number of layers could be used to further refine durometer as necessary (Table <ns0:ref type='table' target='#tab_1'>3</ns0:ref>). The addition of simulated skin also maintained body shape integrity during blade strikes. Overall, ballistic gelatin appears to mimic tissue properties well, is non-toxic and easy to handle, and produces transparent models that are well-suited for implantation of additional sensors. Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Our scanning techniques successfully created realistic 3D models and molds of multiple species that captured species-specific differences in external morphology. To our knowledge, this is first use of high-fidelity laser and computed tomography scans to design and produce a mold of an entire organism from which to cast a biomimetic model. To date, use of additive manufacturing for creation of high biofidelic models has mostly centered around 3D printing the desired animal model directly from scanned data <ns0:ref type='bibr' target='#b49'>(Rhyne et al., 2017;</ns0:ref><ns0:ref type='bibr' target='#b67'>Walker et al., 2019;</ns0:ref><ns0:ref type='bibr' target='#b60'>Tetzla et al., 2020)</ns0:ref>. The cost of printing multiple fish models directly is far greater than multiple models cast from just one mold; consequently, the additive manufacturing industry has focused on printing molds which are less labor intensive, cheaper to produce, and of comparable durability to traditional sand-cast molds <ns0:ref type='bibr' target='#b32'>(Hassen et al., 2016</ns0:ref><ns0:ref type='bibr' target='#b31'>(Hassen et al., , 2020;;</ns0:ref><ns0:ref type='bibr' target='#b33'>Hawaldar & Zhang, 2018)</ns0:ref>. Freezing fish worked well for scanning purposes to minimize movement of the specimen during scanning. The FARO scanning system was the easiest to use and produced a high-fidelity rendered model in about 10 minutes. In contrast, the Leica laser tracker and scanning system was very sensitive to slight deviations in fish positioning (caused by thawing) which made image rendering more difficult and increased post-processing time. Mounting the frozen fish on a turntable and securing the laser scanner may help decrease scanning time without compromising the quality of the 3D rendered images. Use of the software Geomagic Design X decreased post-processing time and produced a final CAD model with more realistic landmarks (Figure <ns0:ref type='figure'>7B</ns0:ref>) compared to a model that required 40 hours of manual image processing (Figure <ns0:ref type='figure'>7A</ns0:ref>). Computed tomography scans of a small American eel (scanned at UTCVM) were also used to create a small and large eel mold. Similar CT scan data from online repositories were not always useful because many images only captured skeletal features and excluded soft tissues (e.g., muscle and skin) which are necessary to model body shape. The success of our 3D scanning and printing techniques suggests these methods can accurately recreate the desired features of any organism scanned directly or rendered from scans available via online databases. Gelfish responses were similar to real fish with respect to overall flexibility during simulated impact trials. The model began to bend immediately prior to impact, followed by whole-body curvature during impact, and free movement after the impact sequence (Figure <ns0:ref type='figure'>10</ns0:ref>) which is similar to responses observed in rainbow trout laboratory trials <ns0:ref type='bibr'>(EPRI, 2008</ns0:ref><ns0:ref type='bibr'>(EPRI, , 2011;;</ns0:ref><ns0:ref type='bibr'>Bevelhimer et al., 2019;</ns0:ref><ns0:ref type='bibr' target='#b53'>Saylor et al., 2020)</ns0:ref>. Body curvature was observed as a spike in acceleration in the z-axis (e.g., lateral, side-to-side movement) and tumbling of the model after impact was observed as noticeable changes in acceleration across all three axes (Figure <ns0:ref type='figure'>11</ns0:ref>). The surrogate skin (Plasti Dip) enhanced overall Gelfish performance by adding stiffness to the model. Analysis of high-speed videos suggested that the amount of curvature in the Gelfish model may have exceeded that of real rainbow trout in its current form (Figure <ns0:ref type='figure'>10A4</ns0:ref> & B4).</ns0:p><ns0:p>Additional flexibility in our model is likely because it lacks an endoskeleton, overlapping myomeres, and imbricated scales of an actual fish which impose limits on natural flexibility. The number and size of vertebral centra, specifically, has a profound effect on flexibility (or stiffness) among fish <ns0:ref type='bibr' target='#b41'>(Lindsey, 1978;</ns0:ref><ns0:ref type='bibr' target='#b42'>Long & Nipper, 1996;</ns0:ref><ns0:ref type='bibr' target='#b7'>Brainerd & Patek, 1998)</ns0:ref> and inclusion of a simulated vertebral column could better mimic natural flexibility. In addition, the lack of a vertebral column and/or other support elements caused a delayed response in the movement of the tail compared to the body of the model, following contact with the blade. The Gelfish model was successfully struck 12 times without disintegrating, but the surrogate skin eventually separated from the gelatin and was removed after the ninth strike trial. The latter suggests that our model could be used more than once without losing its structural integrity and while maintaining consistent responses to multiple impact scenarios. More detailed insights into model behavior or flexibility are not warranted because only one model was tested; however, the overall performance and response of Gelfish compared to actual fish supports the biofidelity of this prototype model.</ns0:p><ns0:p>The single 3-axis accelerometer worked well to capture changes in acceleration that a fish may experience during impacts from turbine blades. We detected changes in acceleration during all aspects of the blade impact sequence, including a rise in acceleration as the blade approached, a peak during impact with the model, and random changes in all axes as the model tumbled after impact (Figure <ns0:ref type='figure'>11</ns0:ref>). The greatest changes in acceleration co-occur with lateral bending of the model along the z-axis, observed during review of high-speed videography (Figure <ns0:ref type='figure'>10</ns0:ref>). While we only tested one complete model, there were notable changes in absolute magnitude and timeaveraged acceleration associated with blade leading-edge width, impact velocity, and orientation of the model (Table <ns0:ref type='table' target='#tab_0'>2</ns0:ref>). More specifically, faster velocities and the thinnest blade had the highest observed changes in peak and time-averaged acceleration-these conditions are also thought to be the most injurious and lethal to rainbow trout struck by turbine blades <ns0:ref type='bibr'>(EPRI, 2008</ns0:ref><ns0:ref type='bibr'>(EPRI, , 2011;;</ns0:ref><ns0:ref type='bibr'>Bevelhimer et al., 2019;</ns0:ref><ns0:ref type='bibr' target='#b53'>Saylor et al., 2020)</ns0:ref>. Trends in time-averaged acceleration (both 10 and 30 ms) also detected higher changes in peak magnitude as a result of mid-body lateral strikes, PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2020:11:55231:1:0:NEW 5 May 2021)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science compared to both tail lateral and mid-body ventral strikes, which is consistent with estimated mortality rates for this species <ns0:ref type='bibr'>(Bevelhimer et al., 2019;</ns0:ref><ns0:ref type='bibr' target='#b53'>Saylor et al., 2020)</ns0:ref>. The exact relationship between acceleration and probability of injury or mortality has yet to be determined; however, development of injury criteria and/or probability of fracture models, similar to automobile safety tests <ns0:ref type='bibr' target='#b26'>(Faerber & Kramer, 1985;</ns0:ref><ns0:ref type='bibr' target='#b20'>Digges, 1998;</ns0:ref><ns0:ref type='bibr' target='#b22'>Eppinger et al., 1999;</ns0:ref><ns0:ref type='bibr' target='#b44'>McHenry, 2004)</ns0:ref>, may help connect accelerometer data to laboratory dose-response relationships. Our sensor detected similar estimates of peak acceleration as the Sensor Fish package (i.e., 213 and 223 g, respectively) struck under the same conditions, and at a higher velocity of 7.5 m/s <ns0:ref type='bibr'>(Bevelhimer et al., 2019)</ns0:ref>. The latter suggests our sampling rate of 800 Hz was capable of detecting comparable levels of peak acceleration, given that the Sensor Fish sampling frequency is 2.5 times higher <ns0:ref type='bibr' target='#b18'>(Deng et al., 2007b</ns0:ref><ns0:ref type='bibr' target='#b19'>(Deng et al., , 2014))</ns0:ref>. More impact trials are needed on multiple Gelfish models to establish repeatability and estimate the variation in peak magnitude before making more detailed comparisons between Gelfish and Sensor Fish.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions</ns0:head><ns0:p>Use of ballistic gelatin and 3D scanning to produce reusable molds worked well to recreate the overall shape and basic biomechanical properties of a real fish. Ballistic gelatin was easy to work with and could be modified to account for small changes in tissue firmness related to different species. Ballistic gelatin does have a limited shelf life (even with preservatives) and the need for refrigeration was important to minimize evaporative water loss. The Plasti Dip surrogate skin also appeared to bond well with ballistic gelatin and its inclusion better captured the natural flexibility of a real fish following impact from a simulated turbine blade. Laser and CT scan image data were successfully used to capture the overall shape and identifying surface details of each fish species. Scanning frozen fish was necessary to limit unwanted movement of the fish, which would significantly increase post-processing time. We successfully used these scanned images to create and print molds using additive manufacturing, which enabled casting of multiple models with no indication of mold deterioration.</ns0:p><ns0:p>The response of the Gelfish model from simulated impact conditions suggests it may be slightly more flexible than real fish, but more tests are required to quantitively confirm its Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science leading edge widths, and locations along the body. These changes were consistent with the responses of actual fish exposed to the same scenarios, i.e., differential rates of injury and mortality as strike conditions change. Changes in time-averaged and peak acceleration will likely be most useful if linked to novel injury criteria or mortality thresholds like those used during impact safety tests in the automobile industry. Initial production of a prototype Gelfish was successful, but more development is needed to assess its biomechanical accuracy and determine how sensor output may be linked to rates of injury or mortality detected during dose-response testing.</ns0:p><ns0:p>The basic Gelfish model and the process used to create it needs further development to augment its biofidelity and make it more versatile for use in other applications. At the least, additional impact trials on multiple models are needed to establish variation in model responses and assess the replicability of sensor output. While our method can produce any desired species, the same model would be more useful if it accurately represented groups of similar fishes (taxonomically or functionally) defined by the intended application. For example, surrogate species are used to represent taxonomic groups of fishes for blade strike trials, yet the functional or biomechanical relevance of these groups has yet to be addressed <ns0:ref type='bibr' target='#b53'>(Saylor et al., 2020)</ns0:ref>.</ns0:p><ns0:p>Ballistic gelatin worked well to mimic fish tissue but refrigeration was necessary to prevent water loss and warming time affected firmness of the model. Newer versions of this model may seek to create a model using synthetic polymers which can be modified to enhance model biofidelity without the need for refrigeration or preservatives. Further development of a simulated skin, and dedicated inclusion of structures that mimic the materials properties of bone and organs, may also better approximate the natural flexibility and responses of the fish body.</ns0:p><ns0:p>All new model developments should be replicated and the biomechanics of impact observed in the model should be compared to that of real fish to maximize biofidelity. Inclusion of more than one accelerometer or the use of new sensors, including strain or fracture gauges, would provide additional information to better link sensor output with biological response data. The next model should also prioritize smaller sensors with higher sampling rates that increase the precision of sensor output while minimizing unnecessary gains in mass to the model. Finally, newer versions of Gelfish would benefit from onboard storage and/or wireless communication technologies to allow it to move more freely and make it recoverable during field tests. Manuscript to be reviewed Chemistry Journals <ns0:ref type='bibr'>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:ref> While we developed this model with hydropower applications in mind, our techniques described here may have other applications well. Similar applications might include 1) testing blade strikes associated with irrigation and water pumping stations, 2) strikes from marine hydrokinetic turbines, 3) impacts from boat impellors on large fishes (e.g., sturgeon or paddlefish) and coastal marine mammals (manatees and whales), 4) mortality of birds and bats caused by impacts from wind turbine blades, and 5) mortality among fish, sea turtles, and other marine life caused by unintended interactions with commercial fishing gear. Regardless, biofidelity remains paramount for future Gelfish development and application, which further distinguishes it from lower-biofidelity technologies, like Sensor Fish, currently used in a similar application.</ns0:p></ns0:div> <ns0:div><ns0:head>Table 2(on next page)</ns0:head><ns0:p>Blade strike impact conditions and changes in acceleration from trials performed on the rainbow trout Gelfish model.</ns0:p><ns0:p>Location of strike was mid-body (M) or tail (T) while orientation was lateral (L) or ventral (V).</ns0:p><ns0:p>Alignment axis refers to which of three axes the Gelfish model aligned when held in place prior to blade strike testing. Impacts relative to the sensor were considered 'Direct' if the blade contacted the model fish at the center of the accelerometer whereas 'Indirect' strikes occurred when the blade made contact with the model posterior (towards the caudal fin) to the accelerometer. The Gelfish model used in trials 10 to 12 was the same as trials 7 to 9 except the surrogate skin was removed from the model prior to strike. Manuscript to be reviewed Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:note type='other'>Chemistry Journals Analytical, Inorganic, Organic, Physical, Materials Science Figure 1</ns0:note><ns0:p>Location of durometer measurements on Gelfish versus bluegill sunfish.</ns0:p><ns0:p>Relative durometer measurement locations (circles) were taken on the left side of (A) Gelfish cast without skin and (B) bluegill with scales. Durometer measurements were replicated for both Gelfish and actual bluegill, i.e., n = 3 for each. The Gelfish models and bluegill were ~16 cm total length and ~90 g mass. We also measured Gelfish with surrogate skin and bluegill without scales at the same approximate locations (not pictured). Manuscript to be reviewed</ns0:p><ns0:note type='other'>Chemistry Journals Analytical, Inorganic, Organic, Physical, Materials Science Figure 4</ns0:note><ns0:p>Ballistic gelatin concentration (%) versus average Shore-OO durometer. Manuscript to be reviewed</ns0:p><ns0:note type='other'>Chemistry Journals Analytical, Inorganic, Organic, Physical, Materials Science Figure 5</ns0:note><ns0:p>Change in Shore-OO durometer and gelatin temperature (°C) caused by periods of warming at room temperature (22.1°C).</ns0:p><ns0:p>Changes in average Shore-OO durometer (•) and gelatin temperature (▲) as a function of warming time (min). Bars for average durometer represent standard error of the mean.</ns0:p><ns0:p>Average durometer decreased significantly (except between time periods indicated with dotted lines which were not significant; ns) as gelatin samples warmed according to one-way repeated measures ANOVA (F 14,28 = 378.96, p < 0.001) and Benjamini-Hochberg multiple comparison tests assuming α = 0.05. Ambient temperature was 22.1°C during experimentation.</ns0:p><ns0:p>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2020:11:55231:1:0:NEW 5 May 2021)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p><ns0:note type='other'>Chemistry Journals Analytical, Inorganic, Organic, Physical, Materials Science Figure 6</ns0:note><ns0:p>Bar plots of average Shore-OO durometer for both Gelfish model and bluegill sunfish samples.</ns0:p><ns0:p>Average Shore-OO durometer for one of four groups including Gelfish with no surrogate skin (GFnoskin) or with surrogate skin (Plasti Dip; GFskin) versus actual bluegill sunfish, Lepomis macrochirus, that were intact (Fishall) or with scales removed (Fnoscale). Average durometer is reported with standard error of the mean for each group (n = 3 samples per group).</ns0:p><ns0:p>Dashed lines (---) represent comparisons between average durometer using two-tailed, dependent t-tests while the solid line (-) refers to a two-tailed, independent t-test between treatment groups. Note: Results of statistic tests were considered significant () or not (ns) assuming α = 0.05.</ns0:p><ns0:p>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2020:11:55231:1:0:NEW 5 May 2021)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:p><ns0:note type='other'>Figure 7</ns0:note><ns0:p>Comparison of SolidWorks surface models used to produce unique Gelfish models.</ns0:p><ns0:p>SolidWorks surface models of bluegill sunfish (A) created after nearly 40 hours of manual user manipulation compared to rainbow trout (B) created in less than 3 hours by using Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:p><ns0:note type='other'>Figure 11</ns0:note><ns0:p>Changes in gravitational acceleration in three dimensions during simulated blade strike testing of the Gelfish model.</ns0:p><ns0:p>Example plot of acceleration (g) for the Gelfish model struck with the 52-mm blade on the mid-body, lateral surface at 11.5 m/s. Magnitude was calculated across all three axes for each time step and reached a peak of nearly 220 g in this trial (#1; Table <ns0:ref type='table' target='#tab_0'>2</ns0:ref>).</ns0:p><ns0:p>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2020:11:55231:1:0:NEW 5 May 2021)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>I 2 C protocol was configured with supply voltage = 3.3V, interface voltage range = 𝑉 𝑆 𝑉 𝐷𝐷/𝐼𝑂 3.3V and external pull-up resistors = 1020Ω (Figure 2). The maximum pull-up resistor value ( 𝑅 𝑃 ) was limited to 1180Ω by the rise time ( ) for SCL and SDA and the capacitive load on 𝑅 𝑃𝑚𝑎𝑥 𝑡 𝑟 each bus line ( ), which is given by the following equation: × 𝐶 𝑏 Data acquisition consisted of NI cRIO 9067 (sine.ni.com), utilized as a target device and NI 9402 (sine.ni.com) module to provide the digital lines for SDA and SCL wires of I 2 C protocol. All hardware was programmed in LabVIEW (sine.ni.com) using the SPI and I 2 C Driver API, which served as the I 2 C master, and used the NI 9402 digital I/O to interface with the accelerometer. The LabVIEW Host code included in the API, in addition to the FPGA code, was used for initializing the accelerometer, configuring the I 2 C protocol parameters, data read/write, and data logging operations. Data logging frequency was set via a timed loop in the host code and stored in a .tdms file with a local time stamp associated with each reading. Calibration of the sensor was achieved by following the single point calibration scheme specified by the original equipment manufacturer. The 0g measurements represent a potential bias in acceleration that can result in incorrect output from the sensor, so 0g measurements were specified for all three axes. This calibration scheme aligned the x-and y-axes to the 0g field, while the z-axis was oriented to the 1g field. Alignment with the 1g field also required additional sensitivity compensation of the zaxis to ensure 0g was registered correctly. All 0g offset values were then stored in the LabVIEW code and written to the dedicated offset registers during sensor initialization. The wired PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2020:11:55231:1:0:NEW 5 May 2021)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head>PeerJ</ns0:head><ns0:label /><ns0:figDesc>Mat. Sci. reviewing PDF \| (MATSCI-2020:11:55231:1:0:NEW 5 May 2021)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>PeerJ</ns0:head><ns0:label /><ns0:figDesc>Mat. Sci. reviewing PDF \| (MATSCI-2020:11:55231:1:0:NEW 5 May 2021)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head /><ns0:label /><ns0:figDesc>~10 units, according to a Benjamini-Hochberg pairwise comparison with samples without PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2020:11:55231:1:0:NEW 5 May 2021)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2020:11:55231:1:0:NEW 5 May 2021) Manuscript to be reviewed Chemistry Journals Analytical, Inorganic, Organic, Physical, Materials Science completed without a surrogate skin (Trials 7 to 9) had lower values than the same trial performed on the Gelfish model with an intact surrogate skin (Trials 10 to 12; Table</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2020:11:55231:1:0:NEW 5 May 2021)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head /><ns0:label /><ns0:figDesc>biomechanical properties. Results of blade strike impact tests suggest that the embedded accelerometer detected changes in acceleration associated with impacts at different velocities, PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2020:11:55231:1:0:NEW 5 May 2021)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_7'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2020:11:55231:1:0:NEW 5 May 2021)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_8'><ns0:head>Figure 3</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Figure 3</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_9'><ns0:head /><ns0:label /><ns0:figDesc>Average Shore-OO durometer versus ballistic gelatin concentration. The dashed line (--) represents a significant linear regression model (F 1,14 = 532.22, p < 0.0001, r 2 = 0.9743) fit to these data. Concentration groups with different letters indicate a significant difference according to Benjamini-Hochberg pairwise comparisons which assumed α = 0.05. PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2020:11:55231:1:0:NEW 5 May 2021)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_10'><ns0:head>Geomagic</ns0:head><ns0:label /><ns0:figDesc>Figure 8</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_11'><ns0:head>Figure 12 Changes</ns0:head><ns0:label>12</ns0:label><ns0:figDesc>Figure 12</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='58,42.52,70.87,525.00,405.00' type='bitmap' /></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head>Table 2 .</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2020:11:55231:1:0:NEW 5 May 2021)</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Manuscript to be reviewed</ns0:cell></ns0:row></ns0:table><ns0:note>1 2 PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2020:11:55231:1:0:NEW 5 May 2021)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_1'><ns0:head>Table 3 (on next page)</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Results of a one-way repeated measures ANOVA (F 4,8 = 323.96, p < 0.001) on average durometer versus number of surrogate skin layers applied to ballistic gelatin samples.</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Durometer is presented as average ± standard error (SE) for each skin-layer group (n = 3</ns0:cell></ns0:row><ns0:row><ns0:cell>replicates per group). Skin layer groups with different letters indicate a significant difference</ns0:cell></ns0:row><ns0:row><ns0:cell>according to Benjamini-Hochberg multiple comparison tests. All statistical decisions were</ns0:cell></ns0:row><ns0:row><ns0:cell>based on α = 0.05.</ns0:cell></ns0:row></ns0:table><ns0:note>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2020:11:55231:1:0:NEW 5 May 2021) Manuscript to be reviewed Chemistry Journals Analytical, Inorganic, Organic, Physical, Materials Science1</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_2'><ns0:head>Table 3 .</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>No.</ns0:figDesc><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_3'><ns0:head>of Surrogate Skin Layers Durometer (± SE) Significance</ns0:head><ns0:label /><ns0:figDesc /><ns0:table><ns0:row><ns0:cell>None</ns0:cell><ns0:cell>42.3 ± 0.5</ns0:cell><ns0:cell>a</ns0:cell></ns0:row><ns0:row><ns0:cell>1-layer</ns0:cell><ns0:cell>53.8 ± 0.4</ns0:cell><ns0:cell>b</ns0:cell></ns0:row><ns0:row><ns0:cell>2-layers</ns0:cell><ns0:cell>57.0 ± 0.1</ns0:cell><ns0:cell>c</ns0:cell></ns0:row><ns0:row><ns0:cell>3-layers</ns0:cell><ns0:cell>57.3 ± 0.4</ns0:cell><ns0:cell>c</ns0:cell></ns0:row><ns0:row><ns0:cell>4-layers</ns0:cell><ns0:cell>60.2 ± 0.4</ns0:cell><ns0:cell>d</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_4'><ns0:head>Table 4 (on next page)</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Results of statistical tests on durometer for Gelfish models and intact bluegill samples.</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Durometer is presented as average ± standard error (SE) for each group (n = 3 replicates</ns0:cell></ns0:row><ns0:row><ns0:cell>per group). Significance tests included paired (dependent) or unpaired (independent) t-</ns0:cell></ns0:row><ns0:row><ns0:cell>tests-groups with different letters were considered statistically significant based on α =</ns0:cell></ns0:row><ns0:row><ns0:cell>0.05. Paired t-tests were only performed between Gelfish (skin versus no skin) or bluegill</ns0:cell></ns0:row><ns0:row><ns0:cell>(intact versus without scales) groups, while one unpaired t-test was used to compare average</ns0:cell></ns0:row><ns0:row><ns0:cell>durometer of Gelfish with skin to intact bluegill.</ns0:cell></ns0:row></ns0:table><ns0:note>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2020:11:55231:1:0:NEW 5 May 2021)</ns0:note></ns0:figure> <ns0:note place='foot'>Analytical, Inorganic, Organic, Physical, Materials Science </ns0:note> <ns0:note place='foot' n='2'>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2020:11:55231:1:0:NEW 5 May 2021)Manuscript to be reviewed Chemistry JournalsAnalytical, Inorganic, Organic, Physical, Materials Science </ns0:note> <ns0:note place='foot' n='2'>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2020:11:55231:1:0:NEW 5 May 2021)Manuscript to be reviewed Chemistry JournalsAnalytical, Inorganic, Organic, Physical, Materials Science </ns0:note> </ns0:body> "	"Editor Comments: Overall, this manuscript describes new and pertinent data for creating a biomimetic fish to replace live fish and collect essential data during hydropower turbine testing. The experimental design and studies provide valuable data to further the progress towards a model with high biofidelity. The experimental design and results presented in the manuscript suggest further progress toward a viable model than is represented. Most of the data support a strong methods article that helps the field continue developing a model with high fidelity. If the authors wish to communicate a functioning model, additional replicates and experimentation is warranted. I believe the authors need to decide whether they should present a methods paper or a fully-develop research article. We wish to thank he editor for these comments. We agree that what has been presented is more of a method-based description of a new biomimetic model, with some descriptions of early testing on the model (with sensors) relative to real fish data. We also agree that a hypothesis driven approach cannot be applied to model tests of biofidelity because tests on multiple models is needed. However, we feel strongly that the ballistic gelatin data presented in our article was rigorously controlled and provides one successful example of initial biofidelity of our model. This statement comes from the fact that ballistic gelatin durometer was compared using inferential statistics that suggests we produced a viable tissue mimic using ballistic gelatin. Since we acknowledge the methods driven approach detailed in our article, we also added the term “prototype” throughout our narrative including the title. We feel that the direct connection of our Gelfish model with a prototype (proof of concept) should clearly convey to PeerJ’s reader base that everything described therein is relevant to the production of one model. To that end, we argue we have met those goals and thoroughly described how we attained initial success in biofidelity using tissue durometer, while also suggesting that more testing is needed to confirm flexibility and biomechanical aspects of the model related to real fish. The options for article submission in PeerJ Material Science are “Research Articles” or “Literature Reviews” and for the reasons presented above we feel strongly that we still meet the scope and justification as a research article. We are up front and honest with the limitations with this prototype but would also like to point out that both reviewers feel that our article presents useful information and is valuable overall to the field of biomimetics. If, however, the editor still feels that the paper needs additional changes to meet a “methods-driven” approach we may be amendable to make those changes if the editor would provide additional guidance (not found on PeerJ’s website) to meet that end. Please refer to the reviewer's comments on how to improve and modify the manuscript. Specifically, reviewer one comments #1 and #4. Along these same lines, the authors should replicate the Gelfish testing, as only one fish was tested, as the authors mentioned. Additional fish testing would provide the community more information about how different fish can be modeled (i.e., larger fish like salmon, or smaller such as the bluegill). The authors should consider how vital direct flexibility measurements of the models would increase the study's viability over the indirect methods the authors used to describe the similarity between the model and the real fish. We are unsure what the editor means by indirect measures of the model relative to the realfish, because all of the methods we described compare the simulated biologically system to the actual biological organism throughout testing. This is also true for unreplicated blade strike impact testing that was discussed as well. We struck the Gelfish model under the exact same conditions we would strike living fishes during laboratory testing as well. The primary author Ryan Saylor and Mark Bevelhimer have spent 5 years testing blade strike impacts on real fish, and bring this knowledge and laboratory data to bear in this article as well. Again, we did not make more detailed direct comparisons of the Gelfish to these data because only one model was tested to date. These data and the dose-response models derived from them have already been published, but are also available for public access via the HydroSource website through the Department of Energy for those interested. There is not, however, currently much information on how to relate these biological response models to exact conditions encountered during turbine passage that have been observed using hard-bodied sensor packages like Sensor Fish. We did have more testing planned, but the COVID-19 pandemic forced to stop experiments indefinitely throughout 2020 and the remaining funding expired within this time frame as well. However, we do not think this makes the data any less valuable or rigorous given the detailed methods listed for preparation and experimentation on ballistic gelatin specifically. Finally, this paper is also being submitted at the same time we are seeking final patent protection for this model and the method used to create it which also highlights its perceived usefulness as well. Again, both reviewers suggest the manuscript is well written and the science presented therein is robust and well-conceived. We have addressed ALL of Reviewer #1 and #2’s comments (some of which are reiterated within the same sections below) and made revisions or additions to the manuscript narrative as needed to better refine the message of this article. We truly appreciate the insights from you and both reviewers which have helped create an even stronger article moving forward. Additional comments from reviewer 2 to focus on when making revisions include: 1. Please add more information about the durometer measurements—including calibration and why this particular method provides enough data to avoid other forms of analysis. The simplest answer to this comment is that durometer was easy to use and it is sufficient for use in the field of medicine as well (with citation support provided in Materials and Methods). We did not employ durometer to avoid using other methods of testing biomechanical properties of our model relative to the real fish. Rather, we chose this method (ballistic gelatin and durometer) as the most practical method from which to begin designing a new biomimetic model. We appreciate that durometer is not the best way to test model material properties and biomechanical traits; however, we decided to focus our early development efforts onto a simple, reproducible method that has been used successfully in other fields (i.e., medicine see introduction). We also want to reinforce to Reviewer #2 that the biomimetic model discussed represents a proof-of-concept/prototype from which we intend to develop additional, more complex and biologically realistic models. To produce a prototype model, we decided that the most important characteristic to mimic was the whole-body tissue properties of an intact fish. Of course, a fish body is a complex biological system composed of muscle, tendons, ligaments, skeleton, integument, and scales (among others). Each of these components has its own material properties based on their organic and inorganic chemical composition. A perfect model would accurately represent each component and the interactions between them all in order to maximize biofidelity. In reality, this is not possible nor warranted for a prototype model, rather we sought to approximate the entire fish body by focusing on whole-body tissue firmness/hardness. In this way, we approximate the firmness of all tissues acting in congress to create the fish for which we intend to make a physical model. We acknowledge that durometer measurements represent a surface effect of the combined biological system only, but it is a quantifiable method of firmness used in other fields (see REFS). We would also like to reiterate that our experiments involving durometer measurements on ballistic gelatin and real fish tissue were rigorously controlled and replicated. Replication showed us that variation in durometer was evident within each treatment; however, we still detected a significant effect between treatment groups in this study. Variation in durometer measurements is likely due in part to the heterogenous nature of the animal-based collagen mixture of the ballistic gelatin used in our experiments. Further, this research was undertaken within an Aquatic Ecology Laboratory and not an engineering facility that has a plethora of dedicated instrumentation to assess more rigorous material and biomechanical properties. Rather, we sought to create a viable tissue simulant with quantifiably similar tissue properties to a real fish that is safe, reproducible, and could be used by anyone with access to basic laboratory equipment. To that end, we feel our experiments were successful and adequately approximated basic tissue properties of a biological complex organism which was supported by robust inferential statistical tests. We have highlighted the fact this article represents how we created the first prototype biomimetic fish using some basic techniques as proof of concept. We argue that this does not diminish the scientific rigor used in ballistic gelatin experiments and model preparation discussed in our paper. 2. Additional comparisons between live fish and the model are warranted to fully understand the testing conditions, the model response, and valid comparisons. The conclusions reached using previously reported data needs strengthening with more testing. We agree that additional testing of the model including comparisons against real fish response is needed; however, as described in previous comments to you and the reviewers, we used what data we did produce to show readers our initial success mimicking biofidelity instead. Again, we maintain throughout this document that this is a prototype model which does not necessarily carry with it more detailed studies that come from additional R&D. All of what has been asked by the reviewers and the editor is planned in future iterations of the Gelfish model. We chose to start with the simplest methods of mimicking biofidelity, provided sufficient/replicable evidence in support of this biofidelity (ballistic gelatin durometer), and showed initial results of trials that will be used to confirm and validate biofidelity in the future. To that end, we feel that the model we produced is still useful in its current form because there must be an initial starting point from innovation can occur in future model iterations. 3. Please elaborate on the decision not to include internal support such as bone and how this might affect the model's biofidelity. We also agree that need for additional anatomical structures is needed to increase the biofidelity of the model and ensure it responds in a similar way to real fish during impact. While the latter is true and will eventually be incorporated into new version of the model, we had to make hard decisions for what to include in the prototype Gelfish model. We find validity in any argument for or against the inclusion of skeletal structures, organs, and the integument. Ultimately, we chose to simulate the muscle and integumental complex first since this represents most of the body mass of fish. We would also like to remind Reviewer #2 that the Gelfish detailed in this article was a prototype and we did not seek, or hope to create, a final biomimetic model from this early research. Rather, we wanted to provide a solid foundation from which to build newer versions of the model that will include additional surrogate structures (cranium and vertebral column), additional accelerometers, new sensors not used before, and novel tissue simulants that are not affected by temperature. To this end, we again feel strongly that we have presented a rigorously detailed method for creating a new biomimetic model that can form the basis of research and development moving forward. We also want to reiterate the usefulness and applicable of the 3D scanning and printing techniques used in this article that have obvious uses for many other applications outside of what we have described here. Additional comments to consider: 4. Please support your decision of fish choice to model with data or references that provide common fish species that interact with turbines. This information was already provided in the Materials and Methods, but we added additional citation support with this specific narrative as well. 5. How do cooling and warming change the hardness of the ballistic gel? Does a freshly prepared model have the same hardness as one that has been stored in a refrigerator, then warmed back to room temperature? The effect of warming was directly tested in this paper by measuring durometer over a 4-hour period when the ballistic gelatin was allowed to warm at room temperature (see comment below). No measurements of ballistic gelatin occurred during the hydration period or refrigeration because the ballistic gelatin must be fully hydrated before testing material properties. Refrigeration (sometimes up to 36 hours or more) is also used in most studies that employ ballistic gelatin for actual ballistics testing, i.e., large blocks of gelatin that directly fired upon. This is a consistent step in gelatin preparation that we augmented because our samples were much smaller which suggests hydration at the deepest parts of the ballistic gelatin would occur more quickly than large blocks used in ballistic testing. 6. Please comment on the decision for using the model fish after warming for 30 minutes, even though the plot of hardness versus time in Figure 6 suggest that the temperature of the gel is not stable until >50 minutes, and the hardness isn't stable until > 110 minutes. We acknowledge that ballistic gelatin durometer is not stable until after nearly two hours of warming; however, the exact warming time does not matter provided the consistent warming time is used before measuring durometer. In this study we chose 30 minutes for the following reasons: 1) it allowed the ballistic gelatin to briefly warm at room temperature to help better mimic its tissue properties, 2) longer time periods of two hours of warming can also lead to noticeable changes in mass caused by evaporative water loss. If you refer to the Figure 5 you will notice that variation in durometer begins to increase during the late warming periods which is likely the result of evaporative water loss. Visually this was confirmed by observing the edges of the gelatin samples shrinking away from containers that housed them. You could argue that placing the samples back into the plastic bags would limit this but condensation could actually become an issue as well when the gelatin warmed and potentially absorbed moisture. So, the answer to this problem was to allow the samples to warm for at least 30 minutes, measure them (or strike the Gelfish model) in this time and return refrigerate them otherwise. To this end, we argue that the exact warming time does not matter as much as consistently applying the same warming time to all trials. PeerJ – Reviewer 1 Comments & Responses: Basic reporting The manuscript was written in professional English. The research background was fully described by using the appropriate references. Experimental design The contents of the research are in the field of the journal, and the research significance is explained in detail. And the study meets ethical standards. Validity of the findings No comment. Comments for the author The research in this paper is very meaningful. It has potential application value to imitate fish similar to live fish by using bionics. However, there are still some shortcomings in the research or questions that readers may have. As shown below: How to evaluate the flexibility of Gelfish, is it reasonable to evaluate only by hardness? In this paper, only the hardness can be used to determine whether the tissue similarity between Gelfish and live fish is correct? I think this is not rigorous. I don't know whether other scholars in previous studies also used hardness to characterize it. We appreciate this comment, but also want to reinforce to Reviewer #1 that the biomimetic model discussed represents a proof-of-concept/prototype from which we intend to develop additional, more complex and biologically realistic models. To produce a prototype model, we decided that the most important characteristic to mimic was the whole-body tissue properties of an intact fish. Of course, a fish body is a complex biological system composed of muscle, tendons, ligaments, skeleton, integument, and scales (among others). Each of these components has its own material properties based on their organic and inorganic chemical composition. A perfect model would accurately represent each component and the interactions between them all in order to maximize biofidelity. In reality, this is not possible nor warranted for a prototype model, rather we sought to approximate the entire fish body by focusing on whole-body tissue firmness/hardness. In this way, we approximate the firmness of all tissues acting in congress to create the fish for which we intend to make a physical model. We acknowledge that durometer measurements represent a surface effect of the combined biological system only, but it is a quantifiable method of firmness used in other fields (see REFS). We would also like to reiterate that our experiments involving durometer measurements on ballistic gelatin and real fish tissue were rigorously controlled and replicated. Replication showed us that variation in durometer was evident within each treatment; however, we still detected a significant effect between treatment groups in this study. Variation in durometer measurements is likely due in part to the heterogenous nature of the animal-based collagen mixture of the ballistic gelatin used in our experiments. Further, this research was undertaken within an Aquatic Ecology Laboratory and not an engineering facility that has a plethora of dedicated instrumentation to assess more rigorous material and biomechanical properties. Rather, we sought to create a viable tissue simulant with quantifiably similar tissue properties to a real fish that is safe, reproducible, and could be used by anyone with access to basic laboratory equipment. To that end, we feel our experiments were successful and adequately approximated basic tissue properties of a biological complex organism which was supported by robust inferential statistical tests. We have highlighted the fact this article represents how we created the first prototype biomimetic fish using some basic techniques as proof of concept. We argue that this does not diminish the scientific rigor used in ballistic gelatin experiments and model preparation discussed in our paper. The hardness value of the ballistic gelatin in the article should be given, is it Shore A? Or something else? We agree that this information should be presented and refer Reviewer #1 to the Materials and Methods section on page 6, lines 166-168 of the revised document. Here, we identify that a Shore Type-OO durometer (designed for biological tissues) was used in our study. The exact model number and manufacturer of the durometer are also provided for the reader’s reference. In the process of preparing the sample, the dosage value of de-foaming agent added should be clear, rather than using a few drops to express it. We agree that dosage for de-foaming agent should be conveyed in normal metric units such that the approximate volume for each drop was 50 µL. This information was added to the narrative and can be found in the Material and Methods on page 6, line 179 of the revised document. Whether it is necessary to vacuum defoam the sample during the fabrication process. I don't know how you solved the air bubbles in the prepared sample. In our actual operation process, vacuum defoaming is required, otherwise there will be bubbles. We were also worried about the formation of bubbles during the molding process specifically, but did not encounter any issues given our protocol. The heated hydration procedure relied on hand mixing using a metal spatula instead of magnetic stir bars which could introduce additional air into the mixture as well. In truth, as little as 150 µL of defoamers needed to remove the few surface air bubbles during preparation. An additional step we took during the mold process was to inject the ballistic gelatin into the mold using a 60 mL syringe with an extended tip. The extended tip allowed us to fill the mold from the bottom up such that we did not introduce bubbles that might have formed if it was poured in using a beaker. These two methods and a little bit of practice is all it took to ensure the that air bubbles were minimized in the final gelatin model. 1. Why did you choose bluegill, rainbow trout, gizzard shad, and white bass as the prototype for your study? We chose these species because all have been tested in our simulated blade strike apparatus and have dose-response data that are readily available. In addition, these species are common reservoir inhabitants or are representative of many migratory species impacted by dams and/or pass through hydropower turbines. We did not highlight these data more or make more direct comparisons because impact testing on our physical model was not replicated; however, future versions of our model will utilize these data to help validate sensor output. 2. The marker points in Figure 1 are similar to those added in the post-image processing process. Please provide the actual biological pictures with marker points during the scanning process. We apologize for any confusion, but the points on each image of Figure 1 do not represent marker points used for scanning. The points in these images simply represent approximate locations of Shore-OO durometer readings that were taken on both the actual bluegill and Gelfish model we created. We felt it was necessary to show that durometer measurements on the surface of the fish and ballistic gelatin model were distributed throughout the body, especially in areas with muscle but avoided the head. Scanning occurred without the need for these marker points by simply mounting the frozen fish to a stationary position that could scan the entire specimen. 3. The scanning accuracy of the scanner in this paper should be given, rather than let the reader find it by himself. We agree that this information should be specifically mentioned in the article and have added it to the Material and Methods on page 10, lines 286-287 of the revised document. 4. Whether the sensor designed by the author has been calibrated. This is very critical to the accuracy of the sensor. We thank the reviewer for bringing sensor calibration to our attention. The sensor was calibrated according to manufacturer recommendations since it is commercially available for use. We have added the appropriate narrative detailing sensor calibration to the corresponding section in the Materials and Methods on page 11 lines 364-371 of the revised document. 5. The authors should check whether the format of literatures meet the requirements of the journal, and check the references in the paper, such as P26 Line 677 and P27 Line 706 the corresponding literatures. Moreover, the paragraphs of the manuscript should be double-spaced, and the font should be times new roman. The format of the formula should be centered and right aligned. We thank the reviewer for addressing Journal formatting conventions in the interest of producing consistent articles. All references have been thoroughly checked and formatted according to our Mendeley reference management software using the PeerJ plugin. The manuscript was prepared and formatted according to the template provided on the PeerJ website which includes Arial font for the title, section headers, and figure/graph labels, while all other test was formatted using Times New Roman size 12 font. In addition, this formatting template included 1.15 spacing between lines and did not clearly indicate that narrative prose was supposed to be double-spaced. If these conventions need to be changed prior to publication we would be happy to do so based on feedback from the editor. However, we believe that all formatting conventions have been followed in accordance with the guide for authors provided on the PeerJ website and available publication templates. PeerJ – Reviewer 2 Comments & Responses: Basic reporting Clear writing is used throughout, with sufficient literature references. The introduction is focused on biomimicry and modeling as the foundation for this work, though some additional focus on how similar models have been used to make decisions or inform scientific questions may be useful. There is a considerable amount of biomechanics literature on things like using 3D printing to understand form or function of organism teeth, armor, etc, to model how adaptations might be used in the actual organism. Adding this background might expand the applicability of the research. We agree that the potential applications of our model/technique to answer basic scientific questions related to the functional significance of biological structures is vast. However, we felt it necessary to focus mainly on the applied research applications of this technology which is currently directing its development. In most cases (and we suspect this is also true for Gelfish), answers to basic scientific inquiries occur hand and hand with applied research endeavors on new technology, especially biomimetics. In this way, we felt it prudent to focus the introductory narrative on how 3D printing can be used to indirectly create realistic models for applied research queries. In this narrative we highlight that there is literature available on the direct additive manufacturing of animal models to study basic biological or ecological questions as well with citations. We also included narrative that shows the 3D printing field has begun to invest heavily into R&D of reusable molds from which to cast the relevant structure needed. This method is now used heavily for construction and manufacturing purposes which have traditionally relied on hand-made molds for production. In contrast, we have not found any literature using a similar methodology to 3D print a mold from which to cast a biomimetic model such that our study will be one of the first. In this way, we are confident that the introduction has adequately provided background for our study that is relevant to the prototype described in our article. Article is well structured This paper is more of a methods paper than one with a self-contained hypothesis and results, but the paper has clear objectives and relevant results. We agree that this article provides clear methods for how we designed and created a new prototype biomimetic model fish that requires more development and testing. We added this fact and the term “prototype” to the title, abstract, introduction, and discussion to clearly convey the intention of our study. Experimental design Research question is relevant and meaningful. The topic, understanding how to replicate fish structure and materials, is a knowledge gap. The methods for creating the materials and iterations are well described, with detailed description of how each step was completed. Additional detail is needed for the durometer measurements. Describe how the model and fish were fixed while taking the measurements. Describe how deep the durometer set up is designed to penetrate. While the durometer is a decent way of understanding the material properties, it might be worthwhile describing why this one was chosen, as opposed to others, like comparing rated elastic modulus, bending or torsion of the full structure, etc. We agree that this information was not clearly conveyed in the current narrative prose so it was added into the revised document. We refer Reviewer #2 to the Materials and Methods section on page 9 lines 251-253 of the revised document. We appreciate that durometer is not the best way to test model material properties and biomechanical traits; however, we decided to focus our early development efforts onto a simple, reproducible method that has been used successfully in other fields (i.e., medicine see introduction). We also want to reinforce to Reviewer #2 that the biomimetic model discussed represents a proof-of-concept/prototype from which we intend to develop additional, more complex and biologically realistic models. To produce a prototype model, we decided that the most important characteristic to mimic was the whole-body tissue properties of an intact fish. Of course, a fish body is a complex biological system composed of muscle, tendons, ligaments, skeleton, integument, and scales (among others). Each of these components has its own material properties based on their organic and inorganic chemical composition. A perfect model would accurately represent each component and the interactions between them all in order to maximize biofidelity. In reality, this is not possible nor warranted for a prototype model, rather we sought to approximate the entire fish body by focusing on whole-body tissue firmness/hardness. In this way, we approximate the firmness of all tissues acting in congress to create the fish for which we intend to make a physical model. We acknowledge that durometer measurements represent a surface effect of the combined biological system only, but it is a quantifiable method of firmness used in other fields (see REFS). We would also like to reiterate that our experiments involving durometer measurements on ballistic gelatin and real fish tissue were rigorously controlled and replicated. Replication showed us that variation in durometer was evident within each treatment; however, we still detected a significant effect between treatment groups in this study. Variation in durometer measurements is likely due in part to the heterogenous nature of the animal-based collagen mixture of the ballistic gelatin used in our experiments. Further, this research was undertaken within an Aquatic Ecology Laboratory and not an engineering facility that has a plethora of dedicated instrumentation to assess more rigorous material and biomechanical properties. Rather, we sought to create a viable tissue simulant with quantifiably similar tissue properties to a real fish that is safe, reproducible, and could be used by anyone with access to basic laboratory equipment. To that end, we feel our experiments were successful and adequately approximated basic tissue properties of a biological complex organism which was supported by robust inferential statistical tests. We have highlighted the fact this article represents how we created the first prototype biomimetic fish using some basic techniques as proof of concept. We argue that this does not diminish the scientific rigor used in ballistic gelatin experiments and model preparation discussed in our paper. Validity of the findings Findings are generally robust. The results of the strike analysis on the Gelfish is somewhat speculative, as it is not compared to a fish response in the paper, though it is modeled off of previous results. If it were compared to a fish, how does the model assist in understanding whether or not the strike is a lethal strike? What characteristics of the strike are the authors looking for? A certain amount of force or deflection? This is discussed a bit in the conclusions, but it is difficult to understand if biofidelity can be achieved without injuring more actual fish. We agree that the discussion of flexibility between our model and real fish could certainly be expanded and replicated to show consistency. The Gelfish models we produced in this study were chose because there are laboratory-derived blade strike data available for these species. The laboratory trials included creation of mathematical dose-response models based on realized strike velocity (the dose) and mortality (the response). These models will allow us to correlate the associated rates of injury and mortality from blade strike to sensor output (in this case acceleration on three axes) in the form of gravitation acceleration. Similar methods are used in the automobile crash test industry to link changes in gravitational acceleration with observed rates of injury (usually to the head). The combination of laboratory and sensor data allows threshold of injury or mortality to be created as well. These head injury criteria have been well developed for anthropomorphic test devices used in automobile safety tests. We envision using a comparable method for impact testing data and Gelfish sensor data as well. The inclusion of additional sensors in new models (already in development) will allow us to estimate force, deformation, and strain rates encountered on the fish body as well. We acknowledged all of this information throughout the document, but kept direct discussion of it to a minimum because we did not generate sufficient data to make those comparisons in this study. While the strike analysis adds value for the potential hydropower industry, I don’t know if the strike analysis adds important conclusions to the results of whether or not the Gelfish has high fidelity as a model. I would recommend strengthening comparisons to the rainbow trout lab trials mentioned in line 562. Throughout this section, compare findings to the expectations of an actual fish. We agree that the experimental design and use of blade strike make the Gelfish prototype more directly applicable to the hydropower industry; however, the biomechanics of impact with turbine blades would also provide useful insights for other organisms struck by boat propellers or wind turbine blades as well. We would like to refer Reviewer #2 to the discussion section of the revised article on page 21 lines 621-631 which describes how the acceleration data and previous laboratory data compared to one another. For example, we discuss how larger magnitudes of acceleration were associated with thinner blades and faster impact velocities. In fish testing, we found that thinner, faster blades are always more injurious to fish compared to slower, thicker blades. The narrative and citations provided make the linkage to rainbow trout laboratory response data specifically, which has been studied more extensively than any other species tested to date. To that end, we feel that the narrative provided sufficiently links the what we detected in our experiments to published accounts of injury and mortality available in the literature. We do not, however, agree that it is necessary or prudent to include more discussion in this article given the changes in acceleration we detected were not replicated in more than model. Line 575 – the paper describes how the Gelfish began to disintegrate after being struck repeatedly. I would guess that a fish would also begin to disintegrate after repeated trauma. A high-fidelity model would need to respond to trauma like the fish. In this case, the fact that the skin separated is an important difference, as that would be an unlikely result in the actual fish. I think the paper would benefit from additional focus on describing how the results compare to actual fish in these sections. Reviewer #2 brings up an interesting point related to model longevity and potential application to real fish data. The reviewer also correctly observed that the surrogate skin eventually separated from the model after it was struck nine separate times. We also agree that the skin of real fish would not separate like this during blade strike impact; however, the probability of one fish being struck twice during the same turbine passage event is very small and the odds of multiple strikes is even smaller. In addition, the chances of one individual passing through a hydropower turbine twice in its life time is also improbable. To that end, there is no natural scenario where multiple strikes are possible which suggests that our model performed above what would be expected from a living fish specimen. In field passage trials (and laboratory studies), live fish are passed only once and, in some cases, injuries can be quite severe and lead to whole-body amputations. The Gelfish was struck at least six times in precisely the same location before losing some integrity, which is further evidence that ballistic gelatin was an ideal candidate to represent fish tissue. The surrogate Plasti-Dip skin also remained intact for the first eight to nine strikes, but eventually separated from the ballistic gelatin after that. Regardless, we also think that Plasti-Dip represented a viable surrogate for the fish integumental complex for our prototype model at-the-least. More importantly, the fact that Gelfish could be used (without failing) more than once also indicates that this model could represent 2 to 9 individual fish during field or laboratory tests as well. Again, we feel that the narrative provided has already made appropriate connections to real fish whenever possible, especially considering living fish are only used once during field or laboratory testing. Comments for the author Overall, I think this a relevant study with useful data toward creating model fish or other marine animals. I would like some additional discussion about the lack of bone structure in the model fish. How does this compare to the way an actual fish behaves? What is the justification for not including some internal structure? We also agree that need for additional anatomical structures is needed to increase the biofidelity of the model and ensure it responds in a similar way to real fish during impact. While the latter is true and will eventually be incorporated into new version of the model, we had to make hard decisions for what to include in the prototype Gelfish model. We find validity in any argument for or against the inclusion of skeletal structures, organs, and the integument. Ultimately, we chose to simulate the muscle and integumental complex first since this represents most of the body mass of fish. We would also like to remind Reviewer #2 that the Gelfish detailed in this article was a prototype and we did not seek, or hope to create, a final biomimetic model from this early research. Rather, we wanted to provide a solid foundation from which to build newer versions of the model that will include additional surrogate structures (cranium and vertebral column), additional accelerometers, new sensors not used before, and novel tissue simulants that are not affected by temperature. To this end, we again feel strongly that we have presented a rigorously detailed method for creating a new biomimetic model that can form the basis of research and development moving forward. We also want to reiterate the usefulness and applicable of the 3D scanning and printing techniques used in this article that have obvious uses for many other applications outside of what we have described here. Errata: Line 200: I think plastic storage bag would be better The change has been made as described. "	Here is a paper. Please give your review comments after reading it.
446	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Respiratory protection device such as face mask and respirator minimize the transmission of infectious diseases by providing physical barrier to respiratory virus particles. The level of protection from a face mask and respirator depends on the nature of filter material, size of infectious particle, breathing and environmental conditions, facial seal, and user compliance. The ongoing COVID-19 pandemics has resulted in the global shortage of surgical face mask and respirator. In such situation, significant global population either have reused the single-use face mask and respirator or used a substandard face mask fabricated from locally available materials. At the same time, researchers are actively exploring filter materials having novel functionalities such as antimicrobial, enhanced charge holding, and heat regulating properties to design potentially better face mask. In this work, we reviewed research papers and guidelines published primarily in last decade focusing on, a) virus filtering efficiency, b) impact of type of filter material on filtering efficiency, c) emerging technologies in mask design, and d) decontamination approaches.</ns0:p><ns0:p>Finally, we provide future prospective on the need of novel filter materials and improved design.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head n='1.'>Introduction</ns0:head><ns0:p>We have witnessed several viral disease outbreaks in recent decades. Few notable examples of such outbreak include severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in 2019 <ns0:ref type='bibr' target='#b9'>(Chen et al., 2020)</ns0:ref>, Ebola virus in 2014 <ns0:ref type='bibr'>(World Health Organization, 2014)</ns0:ref>, Middle East respiratory syndrome coronavirus (MERS-CoV) in 2012 <ns0:ref type='bibr' target='#b0'>(Assiri et al., 2013)</ns0:ref>, influenza pandemic (H1N1) in 2009 <ns0:ref type='bibr'>(Yang et al., 2009, p. 1)</ns0:ref> and severe acute respiratory syndrome coronavirus-1 (SARS-CoV-1) in 2002-2003 <ns0:ref type='bibr' target='#b17'>(Donnelly et al., 2003)</ns0:ref>. Respiratory infection spreads through surface contact, droplet spray, and airborne modes of transmission <ns0:ref type='bibr' target='#b2'>(Atkinson & Wein, 2008;</ns0:ref><ns0:ref type='bibr' target='#b12'>Cowling et al., 2013;</ns0:ref><ns0:ref type='bibr' target='#b92'>Wei & Li, 2016)</ns0:ref>. However, the relative contribution of each mode of transmission is not completely understood for many viruses <ns0:ref type='bibr' target='#b36'>(Janssen et al., 2013)</ns0:ref>. Based on recent evidences on COVID-19 <ns0:ref type='bibr' target='#b33'>(Huang et al., 2020;</ns0:ref><ns0:ref type='bibr' target='#b58'>Peeri et al., 2020)</ns0:ref> and lessons from SARS and MERS, it is believed that COVID-19 is transmitted through contact and droplet modes <ns0:ref type='bibr'>(World Health Organization, 2020)</ns0:ref>. However, our understanding on COVID-19 transmission may change as more new findings evolve in the future. The various mode of transmission can be partly or fully interrupted by a combination of personal hygiene practices such hand washing, use of personal protective equipment (PPE) such as face mask and respirator, and physical distancing <ns0:ref type='bibr' target='#b80'>(Siegel et al., 2007)</ns0:ref>.</ns0:p><ns0:p>Virus containing muco-salivary droplets (>5 μm) and aerosol particles (≤5 μm) are exhaled from an infected individual (both symptomatic and asymptomatic) during speaking, breathing, coughing, and sneezing activities. These particles can travel few meters in air depending on their size, gravitational settling, and evaporation rate <ns0:ref type='bibr' target='#b98'>(Yang et al., 2007a;</ns0:ref><ns0:ref type='bibr' target='#b62'>Prather, Wang & Schooley, 2020)</ns0:ref>. Face masks and respirators significantly minimize and or prevent the spread of infection by creating a physical barrier to the virus particles and droplets <ns0:ref type='bibr' target='#b68'>(Rengasamy, Zhuang & Berryann, 2004;</ns0:ref><ns0:ref type='bibr'>MacIntyre & Chughtai, 2015)</ns0:ref>. Particle exposure is maximum if physical distance is less than six feet and mask is not worn (Figure <ns0:ref type='figure' target='#fig_0'>1</ns0:ref>). Alternatively, maximum protection is achieved if both healthy and infected individuals properly use a recommended mask and physical distancing is maintained <ns0:ref type='bibr'>(World Health Organization, 2020)</ns0:ref>. Manuscript to be reviewed Chemistry Journals <ns0:ref type='bibr'>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:ref> </ns0:p></ns0:div> <ns0:div><ns0:head>Surgical face mask</ns0:head><ns0:p>A properly worn surgical mask creates a physical barrier between the immediate environment and respiratory orifices (mouth and nose) thereby blocking or minimizing in and out movement of infectious droplets and particles. <ns0:ref type='bibr' target='#b74'>(Seale et al., 2009)</ns0:ref>These masks are labeled as isolation, dental, or medical procedure masks and they are generally known as facemasks. However, all facemasks are not regulated as surgical masks. In USA, the Food and Drug Administration (FDA) regulates surgical masks under 21 CFR 878.4040. The FDA regulation requires that the surgical mask must have recommended filtering efficiency for inert particles and biological particles, fluid barrier protection standards and flammability tests (see section 3) (US Food and Drug Administration, 2021). Surgical mask are designed for one time use and generally do not provide good facial seal.</ns0:p><ns0:p>The US FDA approved surgical facemasks are recommended for general public and health care professionals at medium to low risk settings <ns0:ref type='bibr' target='#b74'>(Seale et al., 2009)</ns0:ref>.Three layered flat or cup shaped masks with stretchable ear loops or straps are the most commonly used surgical masks. Each layer in the three-layered surgical mask is designed to have a unique functionality (figure <ns0:ref type='figure' target='#fig_9'>2a</ns0:ref>). Details on virus filtering performance and material design of surgical mask is provided in later sections.</ns0:p><ns0:p>There has been a shortage of surgical masks during viral outbreaks including the ongoing COVID-19 due to high demand in the global market <ns0:ref type='bibr' target='#b96'>(Wu et al., 2020)</ns0:ref>. In such difficult situations, general public wear homemade or locally made cloth face mask (figure <ns0:ref type='figure' target='#fig_9'>2b</ns0:ref>) and or face covering.</ns0:p><ns0:p>However, homemade masks provide limited protection to the user <ns0:ref type='bibr' target='#b11'>(Chughtai, Seale & MacIntyre, 2013;</ns0:ref><ns0:ref type='bibr'>MacIntyre et al., 2015)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Respirators and their classification</ns0:head><ns0:p>Respirators are primarily designed for high risk environments including by health care professionals while treating a COVID patient <ns0:ref type='bibr' target='#b74'>(Seale et al., 2009;</ns0:ref><ns0:ref type='bibr' target='#b60'>Phan et al., 2019;</ns0:ref><ns0:ref type='bibr'>World Health Organization, 2020)</ns0:ref>. The outer rim of the respirator provides better seal or fit around the nose and mouth. Since respirators have special design and advanced filter materials (figure <ns0:ref type='figure' target='#fig_9'>2c</ns0:ref>), a properly worn respirator provides an excellent protection to the user. Details of filtering performance and material design of a respirator is provided in later sections. Commercial respirators are available in different designs. They may or may not have an exhalation valve (EV). The EV helps to minimize excessive dampness and heating and offers decreased breathing resistance. Therefore the respirators are more user friendly. However, when the EV is not working properly, it may contaminate the nearby environment with infectious virus particles through exhaled breath.</ns0:p><ns0:p>There are different names for respirators. For example, they are called N95, N99, and N100 in the USA. The numbers 95, 99, and 100 indicate that the respirator can filter at least 95%, 99%, and 99.97% of 0.3 µm sized particles, respectively. The letter N denotes a not oil resistant respirator. Other letters such as R, P are also used which indicate somewhat oil resistant, and strongly resistant to oil (oil proof), respectively. The medical N95 respirator without EV or equivalent is recommend for MERS-CoV, SARS-CoV-1 and SARS-CoV-2 for health care professionals in high risk environments(CDC-NIOSH, 2020). In the USA, surgical N95 respirators are regulated by National Institute for Occupational Safety and Health (NIOSH) under 42 CFR Part 84 and by the FDA under 21 CFR 878.4040 <ns0:ref type='bibr' target='#b67'>(Rengasamy et al., 2017)</ns0:ref>.</ns0:p><ns0:p>There have been several discussions on the filtering efficiency of facemask and respirator during the COVID-19 pandemic. There are some review articles that covered different aspects of masks <ns0:ref type='bibr' target='#b67'>(Rengasamy et al., 2017;</ns0:ref><ns0:ref type='bibr' target='#b77'>Sharma, Mishra & Mudgal, 2020;</ns0:ref><ns0:ref type='bibr' target='#b56'>Palmieri et al., 2021)</ns0:ref>. This review aims to provide in-depth details on the filter media used in face masks and respirators, their virus filtering efficiency, emerging technologies for better performing masks and respirators, and decontamination approaches. We also provide future prospective on the need of novel filter material and improved design.</ns0:p></ns0:div> <ns0:div><ns0:head n='2.'>Survey methodology</ns0:head><ns0:p>We used Google Scholar and PubMed platforms to search relevant documents published before February 2021. The keyword used were 'facemask and virus filtering efficiency' OR 'facemask and virus' OR 'facemask and material'. We reviewed abstract of the documents and selected documents that provided new insight on the virus filtering performance of either cloth facemask, surgical facemask, or respirators were considered further. Additionally, documents that reported significant advancement in the material design and or the understanding the facemask filter materials were also included. Patents were excluded in the study.</ns0:p></ns0:div> <ns0:div><ns0:head n='3.'>Filtering performance of face mask and respirator</ns0:head></ns0:div> <ns0:div><ns0:head>Filtering efficiency</ns0:head><ns0:p>A filtering device, here referring to both face mask and respirator, provides a barrier protection to the user by capturing infectious droplets, bio-aerosol, and other particles.</ns0:p><ns0:p>Conventional single fiber filtration theory <ns0:ref type='bibr' target='#b64'>(Raist, 1987)</ns0:ref> predicts that the particles bigger than 0.3 µm are captured on the filter mainly by interception and inertial impaction and particles smaller than 0.2 µm are captured by diffusion and electrostatic attraction or polarization effects. None of the capture mechanisms are dominant for intermediate sized particles (0.2-0.3 µm), which are known as the most penetrating particles size (MPPS) <ns0:ref type='bibr' target='#b30'>(Hinds, 1999;</ns0:ref><ns0:ref type='bibr' target='#b26'>Hakobyan, 2015)</ns0:ref>. MPPS depends on the nature of filter material and ranges from 0.03 to 0.1 µm <ns0:ref type='bibr' target='#b75'>(Shaffer & Rengasamy, 2009)</ns0:ref>.</ns0:p><ns0:p>Filtering efficiency of a device depends on multiple parameters such as property of material used in the device, facial fitness, breathing condition, size of particle, and environmental factors.</ns0:p><ns0:p>Filtering efficiency (E) is one the most important parameters to quantify filtering performance and is given by equation 1,</ns0:p><ns0:formula xml:id='formula_0'>[1] 100 1            o i C C E</ns0:formula><ns0:p>where, C i and C o are the concentration of particles inside (downstream) and outside (upstream) the filtering device. Alternatively, the performance is also measured in terms of the penetration efficiency (P) <ns0:ref type='bibr' target='#b37'>(Johnston et al., 1992)</ns0:ref>;</ns0:p><ns0:formula xml:id='formula_1'>[2] 100 100             o i C C E P</ns0:formula><ns0:p>The overall filtering performance of a respirator or class of respirators is also measured in terms of assigned protection factor (APF). APF is defined as the level of respiratory protection that a respirator or class of respirators is expected to provide to the user at the workplace when the employer implements an effective respiratory protection program on continuous basis as specified in the <ns0:ref type='bibr'>29 CFR 1910.134 standard(Occupational Safety and</ns0:ref><ns0:ref type='bibr'>Health Administration, 2009)</ns0:ref> Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science APF <ns0:ref type='bibr' target='#b36'>(Janssen et al., 2013)</ns0:ref>. APF of 10 and 20 means a respirator reduces the exposure level by a factor of 10 and 20, respectively.</ns0:p></ns0:div> <ns0:div><ns0:head>In vitro measurement of filtering efficiency</ns0:head><ns0:p>The filtering efficiency of a filtering device varies with test parameters used such as particle/aerosol size and distribution, face velocity, humidity, and flow rate. For example, the filtering efficiency decreases with increase in relative humidity, face velocity, and flow rate <ns0:ref type='bibr' target='#b99'>(Yang et al., 2007b;</ns0:ref><ns0:ref type='bibr' target='#b84'>Thakur, Das & Das, 2013)</ns0:ref>. A user can use a filtering device in a wide range of particle size and concentration distribution and breathing conditions. The regulatory recommendations for the use of respirator and surgical mask are made from in vitro measurement of filtering efficiency <ns0:ref type='bibr' target='#b3'>(Balazy et al., 2006;</ns0:ref><ns0:ref type='bibr' target='#b5'>Ba\lazy et al., 2006;</ns0:ref><ns0:ref type='bibr'>Shine, Rogers & Goldfrank, 2009, p. 1)</ns0:ref>. To ensure that a device can filter even the most penetrating particles in a workplace, the filtering efficiency is measured at standard testing conditions that mimic the worst-case scenario in the workplace.</ns0:p><ns0:p>NIOSH respirator test method(NIOSH, 2019) recommends following test parameters: a) flow rate of 85 Lmin -1 that simulates the breathing volume during a heavy work load, b) poly-dispersed and charge neutralized sodium chloride aerosol particles having count median diameter (CMD) of 75 ± 20 nm and geometric standard deviation (GSD) of <1.86 or broad range distribution (log-normal distribution) NaCl aerosol particles having mass median aerodynamic diameter (MMAD) and mass median diameter (MMD) about 300 nm 240 nm, respectively <ns0:ref type='bibr' target='#b6'>(Bollinger, 2004;</ns0:ref><ns0:ref type='bibr' target='#b75'>Shaffer & Rengasamy, 2009)</ns0:ref>, c) aerosol particle concentration of <200mg/m 3 , d) pre-conditioning of the filtering device at ~85% relative humidity and ~38° C for 24 hours, and e) proper sealing of the face mask or respirator on the sample holder or mannequin <ns0:ref type='bibr' target='#b67'>(Rengasamy et al., 2017;</ns0:ref><ns0:ref type='bibr'>NIOSH, 2019)</ns0:ref>. ASTM F2229-03 standard method is used for testing material used in surgical facemask <ns0:ref type='bibr'>(ASTM, 2003)</ns0:ref>. This method has less stringent test parameters than the NIOSH respirator test method. In the ASTM method, mask material is challenged with charge neutralized latex spheres having size PeerJ Mat. Sci. reviewing PDF \| (MATSCI- <ns0:ref type='table' target='#tab_1'>2021:03:58955:1:1:NEW 30 Jul 2021)</ns0:ref> Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science range of 0.1-5 µm at airflow test velocities (face velocity) of 0.5 to 25 cm/s. The recommended aerosol concentration is 10 7 -10 8 particles/m 3 and can be diluted if needed. The US FDA guidance document for testing facemask recommends the use 0.1 µm charge un-neutralized polystyrene latex spheres at the air flow velocity of 0.5 to 25 cm/s(US Food and Drug Administration, 2004).</ns0:p><ns0:p>The NIOSH method considers a worst case filtering efficiency since the respirator in this method is challenged with charge neutralized aerosol having size close to the most penetrating particle size (~0.050 μm for N-type respirators) at a relatively higher flow rate. The NIOSH certification of medical N95 respirator requires both filtering efficiency and pressure difference tests. The medical respirators are also cleared by FDA after reviewing the particulate matter filtering efficiency, fluid resistance and differential pressure test results <ns0:ref type='bibr' target='#b67'>(Rengasamy et al., 2017)</ns0:ref>.</ns0:p><ns0:p>The US FDA also provides clearance to medical surgical mask after reviewing the information provided by the manufactures in the 510(k) premarket application(US Food and Drug Administration, 2004). In the 510(k) application, the manufactures are required to provide the results of fluid resistance, polystyrene latex and Staphylococcus aureus bacterial aerosol filtering efficiency, differential pressure, and flammability tests. Size of aerosol particle used in the measurement of filtering efficiency is a critical parameter. But the dead and live status of aerosol does not affect the filtering performance <ns0:ref type='bibr'>(Harnish et al., 2013, p. 1)</ns0:ref>. The diameter of aerosol particles used in the test method is comparable to the size of virus particles. For example, physical size of SARS-CoV and MERS-CoV viruses is around 0.125 µm. Efficiency is measured in a specially designed chamber in which a filtering device wearing mannequin face or a sample holder is placed. Then, aerosol is generated by air compressor and nebulizer. Finally, particle concentration and distribution is measured with a particle spectrometer or equivalent <ns0:ref type='bibr' target='#b24'>(Grinshpun et al., 2009)</ns0:ref>.</ns0:p><ns0:p>Protection factor (PF) of a filtering device is also measured using two mannequins to better mimic the workplace scenario. One mannequin acts as a source that mimics infected individual and the other acts as a receiver that mimics healthy individual <ns0:ref type='bibr' target='#b16'>(Diaz & Smaldone, 2010;</ns0:ref><ns0:ref type='bibr' target='#b57'>Patel et al., 2016)</ns0:ref>. A NIOSH approved N95 respirator on source mannequin without a seal resulted in a protection factor of 250 by maintaining ventilated and tidal breathing condition and 3 feet distance between source and receiver. When the N95 respirator on receiver mannequin was sealed, the PF was only 100 <ns0:ref type='bibr' target='#b16'>(Diaz & Smaldone, 2010)</ns0:ref>. This finding highlights the importance of source control in virus transmission as illustrated in figure 1.</ns0:p><ns0:p>PeerJ Mat. Sci. reviewing PDF \| (MATSCI- <ns0:ref type='table' target='#tab_1'>2021:03:58955:1:1:NEW 30 Jul 2021)</ns0:ref> Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:p></ns0:div> <ns0:div><ns0:head>Virus filtering efficiency of surgical masks and respirators</ns0:head><ns0:p>Performance of filtering device can also be measured using virus aerosol particles to calculate virus filtering efficiency (VFE). Eninger et al. <ns0:ref type='bibr' target='#b19'>(Eninger et al., 2008)</ns0:ref> measured the filtering efficiency of N95 and N99 respirators using three different virus aerosols (enterobacteriophages MS2, T4, and Bacillus subtilis bacteriophage) and NaCl aerosol particles at three different inhalation flow rates, 30, 80, and 150 L min -1 . The filtering efficiency of both N95 and N99 respirator was ≥96% for the 0.02-0.5 µm of aerosol particles. Similar filtering efficiency was reported for virus aerosols suggesting that neutral NaCl aerosols may be appropriate for mimicking the filter penetration of similar size viruses. It is to be noted that the virus efficiency test is not required by FDA or NIOSH for approval process.</ns0:p><ns0:p>Balazy et al. <ns0:ref type='bibr'>(Balazy et al., 2006, p. 95</ns0:ref>) measured the VFE of NIOSH certified N95 respirators and surgical masks using MS2 virus. In the study, a 95% VFE was achieved using virus aerosol particles (10-80 nm) and inhalation flow rate of 85 L/min. The VFE of surgical masks ranged from 80 to 85%, suggesting surgical masks cannot be as effective as N95 respirators for small virus. Shimashaki et al. <ns0:ref type='bibr' target='#b78'>(Shimasaki et al., 2018)</ns0:ref> measured the penetration efficiency of nonwoven surgical masks of SMS type and S (Spunlace) types using ΦX174 phase and inactivated influenza virus aerosols at a flow rate of 15 Lmin -1 . The hydrodynamic diameter of the phase and influenza virus as determined by dynamic light scattering was 28 and 112 nm, respectively. The penetration efficiency for ΦX174 phase and influenza virus was ~6% and 20% for SMS mask and ~30% and 80% for S type mask, respectively. The three layered structure of SMS mask may have provided lower penetration efficiency or higher efficiency. In another study, filtering efficiency of medical N95 respirator was ≥99.6% for all combinations of experiment configurations using influenza A virus, rhinovirus 14, and bacteriophage ΦΧ174 at a flow rate of 28.3 Lmin -1 <ns0:ref type='bibr' target='#b108'>(Zhou et al., 2018)</ns0:ref>.</ns0:p><ns0:p>Even though it is challenging to study the filtering efficiency of masks using viable virus aerosol particles, there are few studies reported. Harnish et al. <ns0:ref type='bibr' target='#b29'>(Harnish et al., 2013)</ns0:ref> measured the VFE of NIOSH approved N95 respirators using viable H1N1 virus aerosolized in artificial saliva buffer (CMD of 0.83 µm) at a flow rates of 85 and 170 Lmin -1 . The respirator was glue sealed in a six inch diameter sample holder. The N95 respirator provided VFR of 99.3% at both flow rates. They also measured the filtering efficiency using 0.8 µm polystyrene latex beads aerosol and got PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2021:03:58955:1:1:NEW 30 Jul 2021)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science similar filtering efficiency. This study suggested that the dead or live status of aerosol does not affect filtering efficiency. In another study <ns0:ref type='bibr' target='#b28'>(Harnish et al., 2016)</ns0:ref>, VFE of five different models of NIOSH certified N95 respirators was measured using viable H1N1 influenza aerosol and polystyrene latex bead aerosols having CMD of 0.1 µm representing MPPS for commonly used filter media at a flow rate of 85 Lmin -1 . The mean VFE of respirators sealed to the sample holder ranged from 99.23% to 99.997% and particle aerosol filtering efficiency ranged from 99.17% to 99.995%. This study suggested that the N95 respirators can be used for protection against H1N1 virus in workplace. They also confirmed the earlier conclusion <ns0:ref type='bibr' target='#b29'>(Harnish et al., 2013)</ns0:ref> that the dead or live status of aerosol does not affect the filtering efficiency of a respirator.</ns0:p></ns0:div> <ns0:div><ns0:head>Virus filtering efficiency of cloth face masks</ns0:head><ns0:p>The particulate matter filtering performance of cloth mask have been found lower than commercially available surgical masks and respirators <ns0:ref type='bibr' target='#b65'>(Rengasamy, Eimer & Shaffer, 2010;</ns0:ref><ns0:ref type='bibr' target='#b76'>Shakya et al., 2017;</ns0:ref><ns0:ref type='bibr' target='#b52'>Neupane et al., 2019)</ns0:ref>. <ns0:ref type='bibr' target='#b14'>Davies et al.(Davies et al., 2013)</ns0:ref> reported the filtering efficiency of two layered cloth mask made from commonly available fabrics using bacteriophase MS2 virus aerosol (~23 nm diameter) at a flow rate of 30 L min -1 . The percentage filtering efficiency of cloth masks made of 100% cotton, scarf, tea towel, pillowcase, cotton mix, linen, and silk were 50.85±16.81, 48.87±19.77, 72.46±22.60, 57.13±10.55, 70.24±0.08, 61.67±2.41, 54.32±29.49, respectively. The filtering efficiency of three ply surgical mask was better (89.52±2.65%). This study suggested that homemade mask should be considered as the last option.</ns0:p><ns0:p>Such masks should be worn only if no better mask is available to prevent the transmission.</ns0:p><ns0:p>A cluster randomized trial of cloth masks in healthcare workers in hospital settings reported that influenza like illness was higher in healthcare workers who wore cloth mask than those who wore surgical mask <ns0:ref type='bibr'>(MacIntyre & Chughtai, 2015)</ns0:ref>. In a recent study <ns0:ref type='bibr' target='#b43'>(Leung et al., 2020)</ns0:ref>, a significantly lower amount of coronavirus RNA in respiratory droplet and aerosols and influenza virus RNA in respiratory droplets was found in patients who wore medical surgical masks than in patients who did not wear surgical masks. This study suggested that surgical face masks could be used by COVID-19 patients to reduce onward transmission.</ns0:p><ns0:p>A summary of virus filtration efficiency of face masks and respirators along with few major test parameters is summarized in table 1. A key component of commercially available surgical masks and respirators is a non-woven filter membrane. The membrane consists of 1-20 µm diameter fibers oriented randomly. The material can be fabricated from synthetic or natural polymers or composites such as polypropylene and polyethylene by melt blowing technique. The membrane is mostly electrostatically charged and called as electret filter. The charge is imparted onto the membrane by corona discharge, induction charging, and tribo-electric techniques during fabrication <ns0:ref type='bibr' target='#b84'>(Thakur, Das & Das, 2013;</ns0:ref><ns0:ref type='bibr' target='#b34'>Hutten, 2015)</ns0:ref>. In contrast to conventional filter, electret filter provides better particle capture efficiently by electrostatic interaction. Also, the downstream air pressure drop in such filter is lower resulting in lower resistance to breathing which is referred to as better breathability <ns0:ref type='bibr' target='#b84'>(Thakur, Das & Das, 2013;</ns0:ref><ns0:ref type='bibr' target='#b105'>Zhang et al., 2018)</ns0:ref>.</ns0:p><ns0:p>The filtering efficiency of electret filter depends on charge density, charge retaining or holding capacity, and size and arrangement of fiber. These parameters depend on the material type and filter manufacturing technique to a great extent. It is established fact that filter having smaller fiber leads to higher filtration efficiency than the lager fiber, but the pressure drop in the former is higher making the filtering device less breathable. In addition, a shorter fiber has larger specific surface area and smaller pores compared to longer fiber. Filtering efficiency increases with increase in filter thickness at the expense of breathing resistance. The columbic and di-electrophoretic forces are also known to be stronger in filter media having smaller fibers. This results in stronger capturing of pathogens and better protection. Spun bonding and melt blowing are the most commonly adopted techniques for the fabrication of fibrous filter membrane. The melt blowing technique produces filters having smaller fiber diameter. Therefore, this is the method of choice in manufacturing of filtering media used in surgical mask and respirators(Thakur, Das & Das, 2013). The charge density on the electret media affects the filtering performance. The charge intensity and storage capacity depends on the dielectric property of a fiber material. In general, the polymeric materials having high electrical resistance, thermal stability, and hydrophobicity (for example; polypropylene, polyethylene) provide better charge storage ability and stability <ns0:ref type='bibr' target='#b88'>(Van Turnhout, Adamse & Hoeneveld, 1980)</ns0:ref>. If charge on the electret media is removed, then the filtering efficiency decreases significantly. The most penetrating particle size (MPPS) for electret media varies with material properties including charge density and is reported in the range of 0.03-0.1 µm <ns0:ref type='bibr' target='#b30'>(Hinds, 1999;</ns0:ref><ns0:ref type='bibr' target='#b75'>Shaffer & Rengasamy, 2009;</ns0:ref><ns0:ref type='bibr' target='#b26'>Hakobyan, 2015)</ns0:ref>. That is why the filtering performance of a filtering device is measured by using particles having size at or close to MPPS.</ns0:p></ns0:div> <ns0:div><ns0:head>Material design of a surgical mask</ns0:head><ns0:p>In the mostly commonly used three layered/ply SMS type surgical mask, for example US FDA approved surgical face mask, the middle layer fabricated by melt blown technique (the M layer) is sandwiched between outer and inner layers fabricated by spun bonded technique (the S layers).</ns0:p><ns0:p>The three layers are designed to have specific functions. In all layers, fibers are randomly oriented (non-woven) so as to form web like arrangement (Figure <ns0:ref type='figure' target='#fig_9'>2a</ns0:ref> and inset). The fiber density in middle layer is higher than in the other two layers resulting low porosity. Since this layer is charged, it can efficiently capture infectious particles above. The outermost layer (typically coded blue) is hydrophobic and limits the penetration of water rich muco-salivary droplets. The innermost layer is hydrophilic and can absorb spit, sweat, and muco-salivary droplets thereby minimizing dampness and increasing user comfort. In recent years, surgical face mask having additional functionality are also being explored (see section 5).</ns0:p></ns0:div> <ns0:div><ns0:head>Material design of a cloth mask</ns0:head><ns0:p>For comparison, we also like to comment on the material property of cloth or fabric face masks.</ns0:p><ns0:p>The cloth mask are made from woven or knitted fabrics and are mostly two layered. In commonly used cloth face mask, the pore size and thread density vary based on the nature of the fabrics(figure <ns0:ref type='figure' target='#fig_9'>2b</ns0:ref> and inset). The lower performance of cloth facemask <ns0:ref type='bibr' target='#b65'>(Rengasamy, Eimer & Shaffer, 2010;</ns0:ref><ns0:ref type='bibr' target='#b76'>Shakya et al., 2017;</ns0:ref><ns0:ref type='bibr' target='#b52'>Neupane et al., 2019;</ns0:ref><ns0:ref type='bibr' target='#b42'>Konda et al., 2020)</ns0:ref> Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science performing cloth face masks <ns0:ref type='bibr' target='#b42'>(Konda et al., 2020;</ns0:ref><ns0:ref type='bibr' target='#b50'>Neupane, Chaudhary & Sharma, 2020)</ns0:ref>. The efficiency can be further increased by increasing the number of fabric layers. However, more fabric layers increase the breathing resistance making the mask uncomfortable for use <ns0:ref type='bibr' target='#b18'>(Drewnick et al., 2020;</ns0:ref><ns0:ref type='bibr' target='#b27'>Hancock et al., 2020;</ns0:ref><ns0:ref type='bibr' target='#b42'>Konda et al., 2020;</ns0:ref><ns0:ref type='bibr' target='#b101'>Zangmeister et al., 2020)</ns0:ref>. Reproduced with permission from Ref. <ns0:ref type='bibr' target='#b52'>(Neupane et al., 2019)</ns0:ref>. c) Multilayered structure of a respirator. Letter A and D represent the spun bonded polypropylene layers, B melt blown layer, and D support layer. The scanning electron microscopic (SEM) images layers A and D and B is also shown. Reproduced with permission from Ref. <ns0:ref type='bibr' target='#b7'>(Borkow et al., 2010)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Material design of a N95 respirator</ns0:head><ns0:p>A typical NIOSH certified N95 respirator consists of four layered structure (labeled A, B, C, D in figure <ns0:ref type='figure' target='#fig_9'>2c</ns0:ref>). The outer most layer A (farthest from the face) and D (closest form the face) are made from spun bonded polypropylene <ns0:ref type='bibr' target='#b7'>(Borkow et al., 2010)</ns0:ref>. These layers contain larger sized fibers and capture course particles and stop moisture entering into the inner layers. The inner layer B is made from melt blown polypropylene. It is charged (electret membrane) and contains highly packed small fibers (i.e., low porosity) and eventually can filter fine particles. The next inner layer C is made from a plain polyester and gives a shape to the respirator. This gradient filtration mechanism in the respirator provides high filtering efficiency. Another important factor for better performance of respirator is its design that provides excellent facial fit. Surgical masks are not designed to fit tightly on the face, so they cannot provide the same level of protection as the respirators(CDC-NIOSH, 2020). It is to be noted that the NIOSH certification does not look at the number of filter media layers and the order of hydrophobic and hydrophilic layers. Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head></ns0:div> <ns0:div><ns0:head n='5.'>Emerging technologies</ns0:head><ns0:p>Several efforts have been reported to make better performing masks and respirators in past. Such efforts involve technologies for making new or modified filter pieces, manufacturing protocols, and disinfecting procedures among others. To contribute to the shortage of standard face mask and respirator in emergency situation, researchers, manufacturers, local hospitals, and even general public have proposed a number of innovative ideas.</ns0:p></ns0:div> <ns0:div><ns0:head>3D printing of mask accessories</ns0:head><ns0:p>Additive manufacturing (AM) including 3-dimensional (3D) printing have gained popularity in manufacturing medical devices <ns0:ref type='bibr' target='#b89'>(Ventola, 2014)</ns0:ref>. 3D printing has been used to make mask components such as mask structure or frame, cover, filter fix, seal etc. A variety of different types of materials including polymax PLA filament, SLS/MJF nylon or flexible SLA resin have been used. Foam or silicone band have been used to print seal with improved airtightness and softer skin touch. Even though the 3D printed masks may look like conventional PPE, they may not provide the same level of barrier protection, fluid resistance, filtration, and infection control <ns0:ref type='bibr' target='#b89'>(Ventola, 2014;</ns0:ref><ns0:ref type='bibr' target='#b49'>Morrison et al., 2015)</ns0:ref>. The new designs are not approved by any regulatory agencies yet and performance may have been compromised. Since the 3D printed masks and accessories provide low-cost, quick, and decentralized and distributed manufacturing, they can be promising during emergency situation. The US FDA has developed preliminary guidance to devices using AM that involves 3D printing <ns0:ref type='bibr' target='#b49'>(Morrison et al., 2015;</ns0:ref><ns0:ref type='bibr' target='#b15'>Di Prima et al., 2016)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Modified face mask filter media</ns0:head><ns0:p>There are several efforts to modify mask filters with various materials such as antibody and nanomaterials to enhance the antimicrobial activity and filtering efficiency of the masks. <ns0:ref type='bibr'>Kamiyama et al.(Kamiyama et al., 2011, p</ns0:ref>. 1) reported a modified nonwoven fabric-based air filters that were impregnated with antibody for avian influenza H5N1 virus. The filters were found to inactivate the virus trapped in the filter due to antigen-antibody interaction. However, these filters were tested only for birds. All birds housed in antibody filter covered boxes did not die.</ns0:p><ns0:p>Similar antibody impregnated filter could be tested for face masks. Such methods may require further research to find out how the antibody impregnated on filters would retain their activity in PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2021:03:58955:1:1:NEW 30 Jul 2021)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science ambient environmental condition during transportation, storage and use of the filter. The performance of such filters while they are used in mask is not known.</ns0:p><ns0:p>Metal oxide and metal nanoparticles display biocidal activities <ns0:ref type='bibr' target='#b90'>(Vincent, Hartemann & Engels-Deutsch, 2016;</ns0:ref><ns0:ref type='bibr' target='#b20'>Fernando, Gunasekara & Holton, 2018)</ns0:ref>. In particular, copper oxide nanoparticle display potent biocidal properties against a range of microbes including bacteriophages, bronchitis virus, poliovirus, herpes simplex virus, human immunodeficiency virus and influenza viruses <ns0:ref type='bibr' target='#b35'>(Ingle, Duran & Rai, 2014;</ns0:ref><ns0:ref type='bibr' target='#b90'>Vincent, Hartemann & Engels-Deutsch, 2016;</ns0:ref><ns0:ref type='bibr' target='#b20'>Fernando, Gunasekara & Holton, 2018)</ns0:ref>. Taking the advantage of the biocidal properties, respiratory face masks containing these materials have been tested for anti-microbial activities.</ns0:p><ns0:p>The use of biocidal masks may significantly reduce the risk of hand or environmental contamination. They reduce infection due to improper handling and disposal of the masks.</ns0:p><ns0:p>A copper oxide impregnated respiratory face mask was reported by Borkow et al. <ns0:ref type='bibr' target='#b7'>(Borkow et al., 2010)</ns0:ref> that demonstrated potent anti-influenza biocidal properties without altering physical barrier properties of the masks. The copper oxide impregnation did not alter the filtering efficiency of N95 masks when tested with aerosolized viruses of human influenza A virus (H1N1) and avian influenza virus (H9N2) under simulated breathing conditions. In these experiments, no infectious H1N1 viral titers were recovered from the copper oxide containing masks within 30 minutes. In case of H9N2 virus, titers were recovered from the copper oxide containing masks but were fivefold lower than the control masks. The copper oxide containing masks successfully passed bacterial filtration efficacy, differential pressure, latex particle challenge, and resistance to penetration <ns0:ref type='bibr' target='#b7'>(Borkow et al., 2010)</ns0:ref>. The metal oxide or nanoparticle impregnated respirator could have four layers of fabric as reported by <ns0:ref type='bibr' target='#b7'>Borkow et al.(Borkow et al., 2010)</ns0:ref>. Out of the four layers (Figure <ns0:ref type='figure' target='#fig_9'>2c</ns0:ref>), outer two and inner layers were metal oxide impregnated polypropylene fabric and the remaining layer was made of plain polyester to give shape to the mask.</ns0:p><ns0:p>A mixture of silver nitrate and titanium dioxide nanoparticles coated facemasks were also tested against infectious agents <ns0:ref type='bibr' target='#b44'>(Li et al., 2006)</ns0:ref>. The minimum inhibitory concentration of the nanoparticles against Escherichia coli and Staphylococcus aureus were 1/128 and 1/512, respectively. A 100% reduction in viable E. coli and S. aureus was observed in the coated mask materials after 48 h of incubation. Skin irritation was not observed in any of the volunteers who wore the facemasks.</ns0:p><ns0:p>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2021:03:58955:1:1:NEW 30 Jul 2021)</ns0:p><ns0:p>Manuscript to be reviewed Chemistry Journals <ns0:ref type='bibr'>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:ref> The efficacy of 4 antimicrobial respirators to decontaminate MS2 virus was evaluated <ns0:ref type='bibr' target='#b66'>(Rengasamy, Fisher & Shaffer, 2010)</ns0:ref> using MS2 as a surrogate for pathogenic viruses.</ns0:p><ns0:p>The MS2 activity of masks with antimicrobial material was significantly reduced when stored at 37 °C and 80% RH for 4 hours than the masks without antimicrobial materials. The antimicrobial materials used in this research included coating of outer layer of mask with silver-copper material, incorporating EnvizO 3 -Shield on the outer layer of respirator, iodinated resin incorporated on filtering layer, and TiO2 coated filtering layer. This study suggested that MS2 virus decontamination efficacy of antimicrobial respirators were dependent on the antimicrobial agent and storage conditions. One should note that substituting conventional filter media of facemasks with nanofiber may reduce the airflow resistance that could lead to enhanced filtration <ns0:ref type='bibr' target='#b81'>(Skaria & Smaldone, 2014)</ns0:ref>.</ns0:p><ns0:p>A temperature sensitive and reusable and recyclable face mask consisting of graphene-coated nonwoven filter was recently reported by <ns0:ref type='bibr' target='#b107'>Zhong et al.(Zhong et al., 2020)</ns0:ref>. This mask provided better protection to aqueous respiratory droplets due to its super-hydrophobic surface. Additionally, the mask can be reusable by sterilizing the surface with solar illumination.</ns0:p><ns0:p>Although, viruses filtering performance of the mask has not been reported, such mask might be available as next generation face mask.</ns0:p></ns0:div> <ns0:div><ns0:head>Virus decontamination methods</ns0:head><ns0:p>Because of increased demand and subsequent shortage during viral outbreaks, surgical mask and respirator are decontaminated and reused. Reuse of these protective gears after proper decontamination may help fulfil supply chain constraints to some extent during the pandemics. However, improper decontamination and reuse of face masks and respirators may pose transmission risk. An ideal decontamination process is expected to inactivate any infectious material without altering the membrane integrity and filtering performance. The decontamination methods can be broadly categorized as self-deactivation and forced de-activation.</ns0:p><ns0:p>In the first approach, partly discussed in earlier section, the mask material is functionalized or additional material having novel property is incorporated so as to deactivate the pathogens. One of the strategies is to functionalize fibrous filtration unit of mask by salts such as sodium chloride <ns0:ref type='bibr' target='#b63'>(Quan et al., 2017)</ns0:ref>. In this experiment, salt coating on the fiber surface dissolved when exposed to virus aerosols. The salt destroyed the pathogens when it recrystallized during drying. The salt-PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2021:03:58955:1:1:NEW 30 Jul 2021)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science coated filters also showed higher filtration efficiency than conventional mask filtration layer. The virus spiked salt treated filters provided 100% survival rate of mice. Viruses captured on saltcoated filters exhibited rapid infectivity loss compared to gradual decrease on bare filters. Saltcoated filters proved highly effective in deactivating influenza viruses regardless of subtypes and following storage in harsh environmental conditions. This simple pathogen deactivation method can be helpful in obtaining a broad-spectrum, airborne pathogen prevention device in preparation for epidemic and pandemic of respiratory diseases. Similarly, a quaternary ammonium based antimicrobial surfactant was evaluated to examine its efficiency to reduce bacterial burden on FDA cleared surgical face mask surface <ns0:ref type='bibr' target='#b85'>(Tseng, Pan & Chang, 2016)</ns0:ref>. The antimicrobial surfactant was covalently bound onto mask surface before use. The antimicrobial mask provided >99.3% efficiency for all three bacterial species tested. Interestingly, the antimicrobial agent on the modified mask the antimicrobial agent reduced the average colony rates by 91.8% for bioaerosols that came into contact with the mask (10 3 CFU/m 3 ). However, the rate decreased with increased bioaerosol concentrations.</ns0:p><ns0:p>In a forced de-contamination approach, pathogen is deactivated by using external agents such as vaporized hydrogen peroxide (VGP) <ns0:ref type='bibr' target='#b39'>(Kenney et al., 2020)</ns0:ref>, ultraviolet germicidal irradiation (UVGI), ethylene oxide (EtO), microwave oven irradiation, autoclaving <ns0:ref type='bibr' target='#b25'>(Grinshpun, Yermakov & Khodoun, 2020)</ns0:ref>, and bleach <ns0:ref type='bibr' target='#b91'>(Viscusi et al., 2009)</ns0:ref>. The details of such methods are described in other reviews <ns0:ref type='bibr' target='#b55'>(Ou et al., 2020;</ns0:ref><ns0:ref type='bibr' target='#b71'>Rodriguez-Martinez, Sossa-Briceño & Cortés-Luna, 2020)</ns0:ref>. Therefore, we only briefly mention some of the selected findings.</ns0:p><ns0:p>Vaporized Hydrogen peroxide (HP) can penetrate the porous fabric that may harbor virus.</ns0:p><ns0:p>The virucidal activity of HP was tested in surgical N95 respirators that were aerosolized with 3 bacteriophages: Pseudomonas phage phi-6, T7, and T1 <ns0:ref type='bibr' target='#b40'>(Kenney et al., 2021)</ns0:ref>. It was found that single HP vapor cycle resulted in complete eradication of the bacteriophages from the respirator.</ns0:p><ns0:p>Viscusi et al <ns0:ref type='bibr' target='#b91'>(Viscusi et al., 2009)</ns0:ref> Manuscript to be reviewed Chemistry Journals Analytical, Inorganic, Organic, Physical, Materials Science health risks to the user were evaluated. They found that microwave oven irradiation melted some of the samples. The scent of bleach low levels of chlorine gas were found in the decontaminated respirators. The VHP, ethylene oxide (EtO), and UVGI, were found to be better decontamination methods.</ns0:p></ns0:div> <ns0:div><ns0:head n='6.'>Conclusions and future perspectives</ns0:head><ns0:p>The filtering efficiency of a face mask and respirator depends on number of parameters such as nature of filter media, size of particle, and environmental parameters. The level of protection also depends on facial seal and user compliance. Several studies have shown that the N95 respirator or equivalent or higher, if worn properly, can provide excellent protection to the user in high risk environments. The filtering efficiency of surgical mask is lower than the N95 respirators, and cloth face mask perform even poorer.</ns0:p><ns0:p>A few issues regarding the use of respirator and face mask are the discomfort to the user in prolonged wearing due to imperfect facial fitness, poor heat management inside the filtering device, filter clogging, and increased breathing resistance. In worst cases, this could even lead to psychological impact <ns0:ref type='bibr' target='#b69'>(Roberge et al., 2010;</ns0:ref><ns0:ref type='bibr' target='#b70'>Roberge, Kim & Coca, 2012)</ns0:ref> and reduced adherence and loss of workplace protection factor. So, there is need of next generation facemask and respirator having improved functionalities. The emerging 3D printing technology along with the development of novel materials such as metal organic framework (MOF)based filters <ns0:ref type='bibr' target='#b45'>(Li et al., 2019)</ns0:ref>, nano-fibrous membrane containing charge enhancer <ns0:ref type='bibr' target='#b46'>(Liu et al., 2015)</ns0:ref>, and use of material having high infrared transparency or reflectance for heat management during summer and winter seasons <ns0:ref type='bibr' target='#b97'>(Yang et al., 2017)</ns0:ref> will be very useful. In recent years, due to significant advancement in electrospinning technology, fabrication of filter membranes having desired fiber size, surface area, porosity, and functionality is possible. It is expected that novel facemask and respirators, which incorporate electrospun membranes, will be available commercially in future <ns0:ref type='bibr' target='#b10'>(Cheng et al., 2017;</ns0:ref><ns0:ref type='bibr' target='#b82'>Tebyetekerwa et al., 2020;</ns0:ref><ns0:ref type='bibr' target='#b102'>Zhang et al., 2021)</ns0:ref>.</ns0:p><ns0:p>Currently used respiratory protection device pose potential risk of primary and secondary infection and transmission due to improper handling and disposing them. In viral outbreaks, because of increased demand and subsequent shortage, surgical mask and respirator are decontaminated and reused. There is still a chance of infection during decontamination process or by ineffective decontamination. Also, device interiority and performance may deteriorate, and level of protection could decrease. So, there is need for better decontamination methods other than PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2021:03:58955:1:1:NEW 30 Jul 2021)</ns0:p><ns0:p>Manuscript to be reviewed Chemistry Journals Analytical, Inorganic, Organic, Physical, Materials Science explored in Ref <ns0:ref type='bibr' target='#b91'>(Viscusi et al., 2009;</ns0:ref><ns0:ref type='bibr' target='#b39'>Kenney et al., 2020)</ns0:ref>. Solution to this issue could the incorporation of filter media that can self-decontaminate, partly explored in Refs. <ns0:ref type='bibr' target='#b7'>(Borkow et al., 2010;</ns0:ref><ns0:ref type='bibr' target='#b21'>Fujimori et al., 2012;</ns0:ref><ns0:ref type='bibr' target='#b85'>Tseng, Pan & Chang, 2016)</ns0:ref>, or design of a device that could incorporate resistive heating element.</ns0:p><ns0:p>Another issue during viral outbreak, including COVID-19, is inevitable use of cloth face mask, especially in low income countries. Lower efficiency of such mask is partly due to loosen facial fitting and the material used. There is a need of low-cost and effective home-made alternative fabric material to the cloth face mask. One of the possibility for better performing cloth facemask <ns0:ref type='bibr' target='#b106'>(Zhao et al., 2020)</ns0:ref> could be the use of fabrics that can be charged electrostatically so that the filtering efficiency can be increased. </ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head>Figure 1 :</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1: A cartoon showing the importance of face mask in reducing transmission. From Ref.(Prather, Wang & Schooley, 2020). Reprinted with permission from the American Association for the Advancement of Science (AAAS).</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>. AFP cannot be measured simply from the known aerosol particles inside (C i ) and outside (C o ) the respirator. Several factors such as nature of filter media, length of exposure, facial seal, nature and concentration of contaminants, duration of exposure are considered while assigning the PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2021:03:58955:1:1:NEW 30 Jul 2021)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2021:03:58955:1:1:NEW 30 Jul 2021)Manuscript to be reviewedChemistry Journals4. Filtering efficiency and material propertyAn important question one can have at this point is: what makes the filtering efficiency different?Provided the same test parameters used, the observed difference in filtering efficiency of face masks and respirators is due to: a) inherent property of the filter material, and b) facial fitness i.e. how well the filtering device fits onto the face, and c) breathing condition.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2021:03:58955:1:1:NEW 30 Jul 2021)Manuscript to be reviewed Chemistry JournalsAnalytical, Inorganic, Organic, Physical, Materials Science </ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head /><ns0:label /><ns0:figDesc>is also due to poor facial fitness and performance of material used. Studies have suggested to use tightly woven fabrics having high thread count and low porosity, such as quilting cotton and cotton sheets, to design relatively better PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2021:03:58955:1:1:NEW 30 Jul 2021)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head>Figure 2 :</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2: Material used in a face mask and respirator. a) SMS type surgical mask and optical microscopic images of the outermost and innermost layers (the S layers) and middle layer (the M layer). (b) A typical cloth face mask and optical microscopic image of the cloth face mask surface.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head /><ns0:label /><ns0:figDesc>But the N95 respirators should meet the standard test requirements as described in the 42 CFR Part 84. PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2021:03:58955:1:1:NEW 30 Jul 2021)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_7'><ns0:head /><ns0:label /><ns0:figDesc>compared the effectiveness five different decontamination methods [ethylene oxide, bleach, microwave oven irradiation, germicidal irradiation (UVGI), and vaporized hydrogen peroxide (VHP)] in nine different models of NIOSH-certified respirators (surgical N95 respirators, N95 FFRs, and P100 FFRs). Each respirator was tested for five decontamination methods and the change in ordor, physical appearance, airflow resistance and aerosol penetration was studied. Also, change in material properties of the respirator and possible PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2021:03:58955:1:1:NEW 30 Jul 2021)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_8'><ns0:head>Figure 1 A</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_9'><ns0:head>Figure 2</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2</ns0:figDesc></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head>Table 1 (on next page)</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Manuscript to be reviewed Chemistry JournalsAnalytical, Inorganic, Organic, Physical, Materials Science </ns0:figDesc><ns0:table><ns0:row><ns0:cell>Filtering efficiency</ns0:cell></ns0:row><ns0:row><ns0:cell>A comparison of virus filtration efficiencies.</ns0:cell></ns0:row></ns0:table><ns0:note>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2021:03:58955:1:1:NEW 30 Jul 2021)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_1'><ns0:head>Table 1 :</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Comparison of virus filtration efficiencies</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Filtering device</ns0:cell><ns0:cell>Filtration</ns0:cell><ns0:cell>Major test parameters</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>efficiency</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>≥95%</ns0:cell><ns0:cell>MS2 virus aerosol, flow rate 85 L min -1 (Balazy et al.,</ns0:cell></ns0:row><ns0:row><ns0:cell>N95</ns0:cell><ns0:cell /><ns0:cell>2006)</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>≥99.2%</ns0:cell><ns0:cell>H1N1 viable virus, flow rate 85 L min -1 (Harnish et</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>al., 2016)</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>97.1-97.8%</ns0:cell><ns0:cell>bacteriophage phiX174, 28.3 L min -1 (Rengasamy et</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>al., 2017)</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>~85%</ns0:cell><ns0:cell>MS2 virus aerosol, flow rate 85 L min -1 (Balazy et al.,</ns0:cell></ns0:row><ns0:row><ns0:cell>Three layered surgical</ns0:cell><ns0:cell /><ns0:cell>2006)</ns0:cell></ns0:row><ns0:row><ns0:cell>mask</ns0:cell><ns0:cell>~94%</ns0:cell><ns0:cell>bacteriophage phiX174, flow rate 15 Lmin -1</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>(Shimasaki et al., 2018)</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>~80%</ns0:cell><ns0:cell>Influenza virus, flow rate 15 Lmin -1 (Shimasaki et al.,</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>2018)</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>~90%</ns0:cell><ns0:cell>bacteriophase MS2, flow rate 30 Lmin -1</ns0:cell></ns0:row><ns0:row><ns0:cell>Two layered cloth mask</ns0:cell><ns0:cell>50-70%</ns0:cell><ns0:cell>bacteriophase MS2, flow rate 30 Lmin -1</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:note place='foot'>Analytical, Inorganic, Organic, Physical, Materials Science </ns0:note> </ns0:body> "	"7/24/2021 Dear Editor‒in‒Chief PeerJ Materials Science Thank you for providing an extensive feedback on our manuscript 'Review of materials and testing methods for virus filtering performance of face mask and respirator' submitted to PeerJ Materials Science. We are happy to get the opportunity to revise the manuscript. We have revised the manuscript by addressing all comments received from the reviewers. Please find one-by-one response in the next pages. The summary of major changes are listed below. • Professional English editor proofread and copyedited the manuscript. • We added few clarification in the introduction section, especially the importance of face mask, and virus capture and penetration mechanism. • We also provided a clear distinction between filtering efficiency and protection factor. • Additional clarification has been added on the filtering efficiency test methods. • Additional texts and references related to the electro-spinning method are included. We believe that the incorporation of the reviewers' comments has significantly improved the quality of the manuscript. We will be very excited to hear the acceptance notification of our paper in the PeerJ Materials Science. Best regards, Bhanu B. Neupane, Ph.D. Assistant Professor Central Department of Chemistry Tribhuvan University, Kathmandu, Nepal Email: bbneupane@cdctu.edu.np, newbhanu@gmail.com In the following texts, please find the reviewers comments (C1-C21) and our responses (R1-R21) appear in black and blue texts respectively. Reviewer 1 C1: In this paper, the authors have presented a review of various materials and testing methods for virus filtration of face masks and respirators. The topic is hot and this article can provide in-depth details on the face masks and respirators. After careful reading, I concluded that the manuscript should be published. I suggest a minor change to the manuscript. R1: Thank you for appreciating the importance of the review paper. C2: The surface area and porosity of the fibers are important features that dictate the breathability and filterability of the face masks. Electro‒spinning has been considered as one of the best techniques for fabricating nano‒fiber membranes with desired porosity and surface area. However, the electro‒spinning technique has been ignored in this paper. It would be better if the authors could include some examples/ or recent trends in electro‒spinning. We appreciate the feedback. Considering the current focus of the review paper, we have included the following text in the conclusion section of the revised manuscript. 'In recent years, due to significant advancement in electro-spinning technology, fabrication of filter membranes having desired fiber size, surface area, porosity, and functionality is possible. It is expected that novel facemask and respirators, which incorporate electrospun membranes, will be available commercially in future'. C3: There are few typing errors: Line no 70, Line no 409 We have corrected the typos. Reviewer 2 Authors reviewed the virus filtering efficiency of the face masks and respirators in this MS. The manuscript is well written and presented clearly. There are some issues to be discussed more before published. The comments are as follows. C4: The importance of the face mask and respirator should be discussed in introduction part to create the background for their study. We have included additional information in the introduction section under the sections 'surgical facemask and respirator' C5: The special terms such as face velocity, charge holding, etc. used in MS should be defined. Thank you for the feedback. We have provided additional clarification for special terms such as filtering efficiency, assigned protection factor, face velocity, charge holding capacity, and most penetrating particle size in the revised manuscript. C6: The proven filters/mask such as N95 are expensive to used, in this case, authors should think discussion briefly about recycling by using suitable sterilization techniques. We appreciate the reviewer for bringing this important issue. We have briefly mentioned the sterilization techniques under the section 'Virus decontamination methods' in lines 504-526 and also provided the relevant literatures. C7: Authors should discuss how the virus penetrates the mask/respirator briefly. We have provided a brief discussion on capture mechanism of particles through a face mask and respirator in section 3. C8: The viruses are very much smaller than the pores of the filter materials, in this case how these materials can be able to prevent them from entering? The physical size of virus is smaller than the pores in filter materials. But, virus come with muco-salivary droplets and aerosols so the effective size of virus is higher. We have also provided the capture mechanism of the virus particles in the revised text in section 3. C9: I suggest making a table mentioning filter materials (possibly polymers), their filtration efficiency, their general name such as N95, surgical face mask, etc., the application, and so on. We appreciate the comment made by reviewer. Considering the limited information available in the literature and the current scope of our review, we are not able to include the information. Reviewer 3 C10: This review describes the material and testing methods for face masks and N95 respirators, and their virus ﬁltering performance. The difference in properties of face masks and respirators to reduce/prevent virus transmission has been described. To address respirator shortage during the COVID-19 pandemic, research on filter material with novel functionalities, new mask designs and decontamination methods have been discussed. We appreciate the reviewer for nicely summarizing the focus area of the review. C11: Aerosol particles are captured by fibrous filter materials of N95 respirators and face masks. Inert particles and virus particles are captured by the same mechanisms and literature shows there is no difference in filter efficiency. In the US, neither NIOSH nor FDA requires viral efficiency tests for approval process. The developments in this area appear to have limited applications. We appreciate the reviewer for this important comment. In the original manuscript, we had cited and briefly described few literatures that show that filtering efficiency measured with the inert and virus particles is similar. We have also added the additional text 'In USA, neither FDA nor NIOSH requires Virus efficiency tests for approval process'. C12: The information on face masks including the approval agency and testing requirements is lacking. Facemask test methods have been mixed up with N95 respirator test methods. The distinction between filter efficiency and OSHA assigned protection factor should be well defined. Thank you for the comment. In the revised manuscript, we have made the significant changes on the NIOSH NaCl aerosol and ASTM testing methods. We have also made a clear distinction between filtering efficiency and OSHA assigned protection factor. Please read section 3 in the revised manuscript. Minor comments C13: Lines 66-68 – Recent studies showed COVID-19 transmission by aerosol mode. Reference shows “Organization”, which should be “World Health Organization” – change it throughout the manuscript. We have made the suggested changes throughout the manuscript. C14: Lines 66-68 – Surgical face mask. Few other names include surgical, isolation, dental, and medical procedure masks. What facemasks are described in the manuscript? US FDA cleared surgical masks (SMs) are regulated under 21 CFR 878.4040 and they meet certain efficiency for inert particles and biological particles as well as fluid barrier protection standards and standards for Class I or Class II flammability tests. The surgical mask described in the study should be identified. We have included this important information in the manuscript. Whenever available, we have included the surgical mask type. Please read section 3 of the revised manuscript. C15: Lines 89 – “surgical masks” are recommended for protection. Is this US FDA approved or other agency approved masks? Different types of masks under the name “surgical masks” are on the market. The approval standard or agency should be mentioned. FDA surgical mask information can be obtained from reference “Guidance for Industry and FDA Staff Surgical Masks - Premarket Notification [510(k)] Submissions Document issued on: March 5, 2004 and a correction posted on July 14, 2004.” Similarly, 42CFR Part 84 explains NIOSH approval of N95 masks. As per the reviewer's recommendation, we have included these important points in 'Introduction' and 'In vitro measurement of filtering efficiency' sections. C16: Lines 113 – In the US, respirators are certified by the NIOSH under 42 CFR Part 84. We have clarified it C17: Lines 143-158 – Filter efficiency and assigned protection factor have been described. Assigned protection factor (APF) applies to only NIOSH approved respirators not respirators for other countries. APF is not applicable to surgical masks. It appears that filter efficiency and APF have been misunderstood and simplified. Filtration efficiency and APF are not the same. One cannot simply measure the filtration efficiency and report the APF value. The overall performance of a respirator is described by the OSHA assigned protection factor (APF). APF is defined as the workplace level of respiratory protection that a respirator or class of respirators is expected to provide to employees when the employer implements a continuing, effective respiratory protection program as specified by the 29 CFR 1910.134 standard. APF accounts for factors including filter media, faceseal, valve leakage, fit factor and others. OSHA assigns these numbers after thoroughly reviewing the available literature including the various analysis by respirator authorities, as well as quantitative analysis of data from workplace protection factor and simulated workplace protection factor studies, comments submitted to the record, and public hearing testimony. No such considerations exist for surgical masks and APF does not apply to SM. We appreciate the reviewer for providing the important clarification. We have made the suggested changes and cited the relevant literature. Please find them in section 3 in the revised manuscript. C18: Lines 169 – Again the test parameters are all mixed up. N95 respirator filtration test is done at 85 L/min using charge neutralized NaCl aerosol. Additional tests are needed for certification under a set of test conditions described in 42CFR Part 84. Face velocity 0.5 to 25 cm/sec is described in the ASTM standard. These two cannot be mixed up. This section should be revised. We appreciate the reviewer for providing the important clarification. We have made the suggested changes and clarification with appropriate references. C19: Lines 181-183 – I assume the size of virus particles refer to physical diameters not MMAD. In the case of N95 respirators, virus particles smaller and larger than the most penetrating particle size (MPPS) (~50 nm [physical diameter] for N95 respirators and most of the surgical masks). The virus particles are captured up to 95%. Yes, the size refer to the physical size. We made this clear in the revised text. C20: Lines 293-303 – specify if this is a US FDA cleared surgical mask. We have provided the additional information. C21: Lines 328-340 – For NIOSH certification, N95 respirators should meet the test requirements described in the 42 CFR Part 84 standard. There is no requirement for the number of filter media layers and the order of hydrophobic and hydrophilic layers. We have added this important information. "	Here is a paper. Please give your review comments after reading it.
447	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Respiratory protection device such as face mask and respirator minimize the transmission of infectious diseases by providing physical barrier to respiratory virus particles. The level of protection from a face mask and respirator depends on the nature of filter material, size of infectious particle, breathing and environmental conditions, facial seal, and user compliance. The ongoing COVID-19 pandemics has resulted in the global shortage of surgical face mask and respirator. In such situation, significant global population either have reused the single-use face mask and respirator or used a substandard face mask fabricated from locally available materials. At the same time, researchers are actively exploring filter materials having novel functionalities such as antimicrobial, enhanced charge holding, and heat regulating properties to design potentially better face mask. In this work, we reviewed research papers and guidelines published primarily in last decade focusing on, a) virus filtering efficiency, b) impact of type of filter material on filtering efficiency, c) emerging technologies in mask design, and d) decontamination approaches.</ns0:p><ns0:p>Finally, we provide future prospective on the need of novel filter materials and improved design.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head n='1.'>Introduction</ns0:head><ns0:p>We have witnessed several viral disease outbreaks in recent decades. Few notable examples of such outbreak include severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in 2019 <ns0:ref type='bibr'>(Chen et al., 2020)</ns0:ref>, Ebola virus in 2014 <ns0:ref type='bibr'>(World Health Organization, 2014)</ns0:ref>, Middle East respiratory syndrome coronavirus (MERS-CoV) in 2012 <ns0:ref type='bibr' target='#b0'>(Assiri et al., 2013)</ns0:ref>, influenza pandemic (H1N1) in 2009 <ns0:ref type='bibr' target='#b40'>(Yang et al., 2009)</ns0:ref> and severe acute respiratory syndrome coronavirus-1 (SARS-CoV-1) in <ns0:ref type='bibr'>2002</ns0:ref><ns0:ref type='bibr'>-2003</ns0:ref><ns0:ref type='bibr'>(Donnelly et al., 2003))</ns0:ref>. Respiratory infection spreads through surface contact, droplet spray, and airborne modes of transmission <ns0:ref type='bibr'>(Atkinson & Wein, 2008;</ns0:ref><ns0:ref type='bibr'>Cowling et al., 2013;</ns0:ref><ns0:ref type='bibr'>Wei & Li, 2016)</ns0:ref>. However, the relative contribution of each mode of transmission is not completely understood for many viruses <ns0:ref type='bibr' target='#b5'>(Janssen et al., 2013)</ns0:ref>. The recent evidences have shown that COVID-19 is transmitted through contact, respiratory droplets <ns0:ref type='bibr' target='#b2'>(Huang et al., 2020;</ns0:ref><ns0:ref type='bibr'>Peeri et al., 2020;</ns0:ref><ns0:ref type='bibr'>World Health Organization, 2020)</ns0:ref>, and aerosol routes <ns0:ref type='bibr'>(Greenhalgh et al., 2021)</ns0:ref>. The various mode of transmission can be partly or fully interrupted by a combination of personal hygiene practices such hand washing, use of personal protective equipment (PPE) such as face mask and respirator, and physical distancing <ns0:ref type='bibr'>(Siegel et al., 2007)</ns0:ref>.</ns0:p><ns0:p>Virus containing muco-salivary droplets (>5 μm) and aerosol particles (≤5 μm) are exhaled from an infected individual (both symptomatic and asymptomatic) during speaking, breathing, coughing, and sneezing activities. These particles can travel few meters in air depending on their size, gravitational settling, and evaporation rate <ns0:ref type='bibr'>(Yang et al., 2007a;</ns0:ref><ns0:ref type='bibr' target='#b29'>Prather, Wang & Schooley, 2020)</ns0:ref>. Face masks and respirators significantly minimize and or prevent the spread of infection by creating a physical barrier to the virus particles and droplets <ns0:ref type='bibr' target='#b36'>(Rengasamy, Zhuang & Berryann, 2004;</ns0:ref><ns0:ref type='bibr'>MacIntyre & Chughtai, 2015)</ns0:ref>. Particle exposure is maximum if physical distance is less than six feet and mask is not worn (Figure <ns0:ref type='figure' target='#fig_0'>1</ns0:ref>). Alternatively, maximum protection is achieved if both healthy and infected individuals properly use a recommended mask and physical distancing is maintained <ns0:ref type='bibr'>(World Health Organization, 2020)</ns0:ref>. Ref. <ns0:ref type='bibr' target='#b29'>(Prather, Wang & Schooley, 2020)</ns0:ref>. Reprinted with permission from the American Association for the Advancement of Science (AAAS).</ns0:p><ns0:p>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2021:03:58955:2:0:NEW 16 Aug 2021)</ns0:p><ns0:p>Manuscript to be reviewed Chemistry Journals <ns0:ref type='bibr'>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:ref> </ns0:p></ns0:div> <ns0:div><ns0:head>Surgical face mask</ns0:head><ns0:p>A properly worn surgical mask creates a physical barrier between the immediate environment and respiratory orifices (mouth and nose) thereby blocking or minimizing in and out movement of infectious droplets and particles. <ns0:ref type='bibr' target='#b40'>(Seale et al., 2009)</ns0:ref>These masks are labeled as isolation, dental, or medical procedure masks and they are generally known as facemasks. However, all facemasks are not regulated as surgical masks. In USA, the Food and Drug Administration (FDA) regulates surgical masks under 21 CFR 878.4040. The FDA regulation requires that the surgical mask must have recommended filtering efficiency for inert particles and biological particles, fluid barrier protection standards and flammability tests (see section 3) (US Food and Drug Administration, 2021). Surgical mask are designed for one time use and generally do not provide good facial seal.</ns0:p><ns0:p>The US FDA approved surgical facemasks are recommended for general public and health care professionals at medium to low risk settings <ns0:ref type='bibr' target='#b40'>(Seale et al., 2009)</ns0:ref>.Three layered flat or cup shaped masks with stretchable ear loops or straps are the most commonly used surgical masks. Each layer in the three-layered surgical mask is designed to have a unique functionality (figure <ns0:ref type='figure' target='#fig_12'>2a</ns0:ref>). Details on virus filtering performance and material design of surgical mask is provided in later sections.</ns0:p><ns0:p>There has been a shortage of surgical masks during viral outbreaks including the ongoing COVID-19 due to high demand in the global market <ns0:ref type='bibr'>(Wu et al., 2020)</ns0:ref>. In such difficult situations, general public wear homemade or locally made cloth face mask (figure <ns0:ref type='figure' target='#fig_12'>2b</ns0:ref>) and or face covering.</ns0:p><ns0:p>However, homemade masks provide limited protection to the user(Chughtai, <ns0:ref type='bibr'>Seale & MacIntyre, 2013;</ns0:ref><ns0:ref type='bibr'>MacIntyre et al., 2015)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Respirators and their classification</ns0:head><ns0:p>Respirators are recommended for high risk environments; for example for health professionals when providing care to COVID-19 patients <ns0:ref type='bibr' target='#b40'>(Seale et al., 2009;</ns0:ref><ns0:ref type='bibr' target='#b28'>Phan et al., 2019;</ns0:ref><ns0:ref type='bibr'>World Health Organization, 2020)</ns0:ref>. The outer rim of the respirator provides better seal or fit around the nose and mouth. Since respirators have special design and advanced filter materials (figure <ns0:ref type='figure' target='#fig_12'>2c</ns0:ref>), a properly worn respirator provides an expected protection to the user. Details of filtering performance and material design of a respirator is provided in later sections.</ns0:p><ns0:p>A proper respirator must be selected for a specific hazards. In the USA, filtering facepiece respirators (FFRs) (disposable half-facepiece respirators), elastomeric halfand full-facepiece respirators, powered air-purifying respirators, supplied air respirators, self-contained breathing apparatus and combination respirators are available. The FFRs are available in N, P and R series having minimum filtering efficiency of 95 (N95, P95 and R95), 99(N99, P99 and R99) and 99.97% (N100, P100 and R100) respectively for particles having aerodynamic mass median diameter of 0.3 µm. The letters N, P, and R refer to resistant to oil, partially resistant, and resistant (oil proof); respectively <ns0:ref type='bibr' target='#b23'>(OSHA, 1996)</ns0:ref>. Other countries have different types of respirators. In UK, FFRs are available as FFP1, FFP2, and FFP3 having minimum filtering efficiency of 80%, 94% and 99.95%; The filtering efficiency of facemask and respirator for neutral NaCl aerosol and other particles are well documented in multiple studies <ns0:ref type='bibr' target='#b35'>(Rengasamy et al., 2017;</ns0:ref><ns0:ref type='bibr'>Sharma, Mishra & Mudgal, 2020;</ns0:ref><ns0:ref type='bibr' target='#b26'>Palmieri et al., 2021)</ns0:ref> but the information on virus filtering efficiency is not well described. Also, during the COVID-19 pandemic virus filtering efficiency of facemask and respirator can be of special interest to readers. This review aims to provide in-depth details on the filter media used in face masks and respirators, their virus filtering efficiency, emerging technologies for better performing masks and respirators, and decontamination approaches. We also provide future prospective on the need of novel filter material and improved design.</ns0:p></ns0:div> <ns0:div><ns0:head n='2.'>Survey methodology</ns0:head><ns0:p>We used Google Scholar and PubMed platforms to search relevant documents published before February 2021. The keyword used were 'facemask and virus filtering efficiency' OR 'facemask and virus' OR 'facemask and material'. We reviewed abstract of the documents and selected documents that provided new insight on the virus filtering performance of either cloth facemask, surgical facemask, or respirators were considered further. Additionally, documents that reported significant advancement in the material design and or the understanding the facemask filter materials were also included. Patents were excluded in the study.</ns0:p></ns0:div> <ns0:div><ns0:head n='3.'>Filtering performance of face masks and respirators</ns0:head></ns0:div> <ns0:div><ns0:head>Filtering efficiency</ns0:head><ns0:p>A filtering device, here referring to both face mask and respirator, provides a barrier protection to the user by capturing infectious droplets, bio-aerosol, and other particles.</ns0:p><ns0:p>Conventional single fiber filtration theory <ns0:ref type='bibr' target='#b31'>(Raist, 1987)</ns0:ref> predicts that the particles bigger than 0.3 µm are captured on the filter mainly by interception and inertial impaction and particles smaller than 0.2 µm are captured by diffusion and electrostatic attraction or polarization effects. None of the capture mechanisms are dominant for intermediate sized particles (0.2-0.3 µm), which are known as the most penetrating particles size (MPPS) <ns0:ref type='bibr' target='#b1'>(Hinds, 1999;</ns0:ref><ns0:ref type='bibr'>Hakobyan, 2015)</ns0:ref>. MPPS depends on the nature of filter material and ranges from 0.03 to 0.1 µm <ns0:ref type='bibr'>(Shaffer & Rengasamy, 2009)</ns0:ref>.</ns0:p><ns0:p>Filtering efficiency of a device depends on multiple parameters such as property of material used in the device, size of particle, and environmental factors. Filtering efficiency (E) is one the most important parameters to quantify filtering performance of face mask and respirator and is given by equation 1,</ns0:p><ns0:formula xml:id='formula_0'>[1] 100 1            o i C C E</ns0:formula><ns0:p>where, C i and C o are the concentration of particles inside (downstream) and outside (upstream) the filtering device. Alternatively, the performance is also measured in terms of the penetration efficiency (P) <ns0:ref type='bibr' target='#b6'>(Johnston et al., 1992)</ns0:ref> Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:p><ns0:formula xml:id='formula_1'>[2] 100 100             o i C C E P</ns0:formula><ns0:p>The overall filtering performance of a respirator or class of respirators is also measured in terms of assigned protection factor (APF). APF is defined as the level of respiratory protection that a respirator or class of respirators is expected to provide to the user at the workplace when the employer implements an effective respiratory protection program on continuous basis as specified in the <ns0:ref type='bibr'>29 CFR 1910</ns0:ref><ns0:ref type='bibr'>.134 standard(OSHA, 2009)</ns0:ref>. AFP of a respirator cannot be measured simply from the known aerosol particles inside (C i ) and outside (C o ) the respirator. Several factors such as nature of filter media, length of exposure, facial seal, nature and concentration of contaminants, duration of exposure are considered while assigning the APF <ns0:ref type='bibr' target='#b5'>(Janssen et al., 2013)</ns0:ref>. APF of 10 and 20 means a respirator reduces the exposure level by a factor of 10 and 20, respectively.</ns0:p></ns0:div> <ns0:div><ns0:head>In vitro measurement of filtering efficiency</ns0:head><ns0:p>The filtering efficiency of a filtering device varies with test parameters used such as particle/aerosol size and distribution, filter media charge and particle charge, face velocity, humidity, and flow rate. For example, the filtering efficiency decreases with increase in relative humidity, face velocity, and flow rate <ns0:ref type='bibr'>(Yang et al., 2007b;</ns0:ref><ns0:ref type='bibr'>Thakur, Das & Das, 2013)</ns0:ref>.The regulatory recommendations for the use of respirator and surgical mask are made from in vitro measurement of filtering efficiency <ns0:ref type='bibr'>(NIOSH, 1996)</ns0:ref>. To ensure that a device can filter even the most penetrating particles in a workplace, the filtering efficiency is measured at standard testing conditions that mimic the worst-case scenario in the workplace. Different test methods are used for testing the performance of material used in surgical face mask.</ns0:p></ns0:div> <ns0:div><ns0:head>NIOSH uses</ns0:head><ns0:p>In the ASTM F2229-03 method, surgical face mask material is challenged with charge neutralized latex spheres having size range of 0.1-5 µm at airflow test velocities (face velocity) of 0.5 to 25 cm/s <ns0:ref type='bibr'>(ASTM, 2003)</ns0:ref>. The recommended aerosol concentration is 10 7 -10 8 particles/m 3 and can be diluted if needed. The US FDA recommends slightly different test parameters for testing material (not the entire mask) used in surgical face mask. This method recommends the use 0.1 µm charge un-neutralized polystyrene latex spheres at the air flow velocity of 0.5 to 25 cm/s(US Food and Drug Administration, 2004). The bacterial filtering efficiency (BFE) for the material used in surgical facemask is measured as per FDA guidance and ASTM F2101 method <ns0:ref type='bibr'>(ASTM, 2001)</ns0:ref>.</ns0:p><ns0:p>The mask material (not the whole mask) is challenged with un-neutralized S. aureus bacterial aerosol (mean particle size of 3 ± 0.3 μm diameter) at a flow rate of 28.3 L/min. The US FDA provides clearance to surgical mask after reviewing the information provided by the manufactures in the 510(k) premarket application(US Food and Drug Administration, 2004). In the 510(k) application, the manufactures are required to provide the results of fluid resistance, polystyrene latex and S. aureus bacterial aerosol filtering efficiency, differential pressure, and flammability tests.</ns0:p></ns0:div> <ns0:div><ns0:head>Virus filtering efficiency of surgical masks and respirators</ns0:head><ns0:p>Performance of filtering device can also be measured using virus aerosol particles to calculate virus filtering efficiency (VFE). Eninger et al. <ns0:ref type='bibr'>(Eninger et al., 2008)</ns0:ref> measured the filtering efficiency of N95 and N99 respirators using three different virus aerosols (enterobacteriophages MS2, T4, and Bacillus subtilis bacteriophage) and NaCl aerosol particles at three different inhalation flow rates, 30, 80, and 150 L min -1 . The filtering efficiency of both N95 and N99 respirator was ≥96% for the 0.02-0.5 µm of aerosol particles. Similar filtering efficiency was reported for virus aerosols suggesting that neutral NaCl aerosols may be appropriate for mimicking the filter penetration of similar size viruses. It is to be noted that the virus efficiency test is not required by FDA or NIOSH for approval process.</ns0:p><ns0:p>PeerJ Mat. Sci. reviewing PDF \| (MATSCI- <ns0:ref type='table' target='#tab_2'>2021:03:58955:2:0:NEW 16 Aug 2021)</ns0:ref> Manuscript to be reviewed Chemistry Journals <ns0:ref type='bibr'>Analytical, Inorganic, Organic, Physical, Materials Science Balazy et al.(Balazy et al., 2006)</ns0:ref> measured the VFE of NIOSH certified N95 respirators and surgical masks using MS2 virus. In the study, VFE of not less than 95% was achieved using virus aerosol particles (10-80 nm) and inhalation flow rate of 85 L/min. For the test conditions, VFE of two types of surgical masks was found to be ~80% and ~ 15%, suggesting surgical masks cannot be as effective as N95 respirators for small virus. <ns0:ref type='bibr'>Shimashaki et al.(Shimasaki et al., 2018)</ns0:ref> measured the penetration efficiency of two types of nonwoven fabrics [SMS type and S (Spunlace)] that are used in commercial surgical masks using ΦX174 phase and inactivated influenza virus aerosols at a flow rate of 15 Lmin -1 . The hydrodynamic diameter of the phase and influenza virus as determined by dynamic light scattering was 28 and 112 nm, respectively. The penetration efficiency for ΦX174 phase and influenza virus was ~6% and 20% for SMS type and ~30% and 80% for S type, respectively. The three layered in SMS type may have provided lower penetration efficiency or higher efficiency. In another study, filtering efficiency of surgical N95 respirator was ≥99.6% for all combinations of experiment configurations using influenza A virus, rhinovirus 14, and bacteriophage ΦΧ174 at a flow rate of 28.3 Lmin -1 <ns0:ref type='bibr'>(Zhou et al., 2018)</ns0:ref>.</ns0:p><ns0:p>Even though it is challenging to study the filtering efficiency of masks using viable virus aerosol particles, there are few studies reported. Harnish et al. <ns0:ref type='bibr'>(Harnish et al., 2013)</ns0:ref> measured the VFE of NIOSH approved N95 respirators using viable H1N1 virus aerosolized in artificial saliva buffer (CMD of 0.83 µm) at a flow rates of 85 and 170 Lmin -1 . The respirator was glue sealed in a six inch diameter sample holder. The N95 respirator provided VFR of 99.3% at both flow rates. They also measured the filtering efficiency using 0.8 µm polystyrene latex beads aerosol and got similar filtering efficiency. This study suggested that the dead or live status of aerosol does not affect filtering efficiency. In another study <ns0:ref type='bibr'>(Harnish et al., 2016)</ns0:ref>, VFE of five different models of NIOSH certified N95 respirators was measured using viable H1N1 influenza aerosol and polystyrene latex bead aerosols having CMD of 0.1 µm representing MPPS for commonly used filter media at a flow rate of 85 Lmin -1 . The mean VFE of respirators sealed to the sample holder ranged from 99.23% to 99.997% and particle aerosol filtering efficiency ranged from 99.17% to 99.995%. This study suggested that the N95 respirators can be used for protection against H1N1 virus in workplace. They also confirmed the earlier conclusion <ns0:ref type='bibr'>(Harnish et al., 2013)</ns0:ref> that the dead or live status of aerosol does not affect the filtering efficiency of a respirator. </ns0:p></ns0:div> <ns0:div><ns0:head>Virus filtering efficiency of cloth face masks</ns0:head><ns0:p>The particulate matter filtering performance of cloth mask have been found lower than commercially available surgical masks and respirators <ns0:ref type='bibr' target='#b33'>(Rengasamy, Eimer & Shaffer, 2010;</ns0:ref><ns0:ref type='bibr'>Shakya et al., 2017;</ns0:ref><ns0:ref type='bibr' target='#b21'>Neupane et al., 2019)</ns0:ref>. <ns0:ref type='bibr'>Davies et al.(Davies et al., 2013)</ns0:ref> reported the filtering efficiency of two layered cloth mask made from commonly available fabrics using bacteriophase MS2 virus aerosol (~23 nm diameter) at a flow rate of 30 L min -1 . The percentage filtering efficiency of cloth masks made of 100% cotton, scarf, tea towel, pillowcase, cotton mix, linen, and silk were 50.85±16.81, 48.87±19.77, 72.46±22.60, 57.13±10.55, 70.24±0.08, 61.67±2.41, 54.32±29.49, respectively. The filtering efficiency of three ply surgical mask was better (89.52±2.65%). This study suggested that homemade mask should be considered as the last option.</ns0:p><ns0:p>Such masks should be worn only if no better mask is available to prevent the transmission.</ns0:p><ns0:p>A cluster randomized trial of cloth masks in healthcare workers in hospital settings reported that influenza like illness was higher in healthcare workers who wore cloth mask than those who wore surgical mask <ns0:ref type='bibr'>(MacIntyre & Chughtai, 2015)</ns0:ref>. In a recent study <ns0:ref type='bibr' target='#b13'>(Leung et al., 2020)</ns0:ref>, a significantly lower amount of coronavirus RNA in respiratory droplet and aerosols and influenza virus RNA in respiratory droplets was found in patients who wore surgical masks than in patients who did not wear surgical masks. This study suggested that surgical face masks could be used by COVID-19 patients to reduce onward transmission.</ns0:p><ns0:p>A summary of virus filtration efficiency of face masks and respirators along with few major test parameters is summarized in table 1. <ns0:ref type='bibr'>2015)</ns0:ref>. In contrast to conventional filter, electret filter provides better particle capture efficiently by electrostatic interaction. Also, the downstream air pressure drop in such filter is lower resulting in lower resistance to breathing which is referred to as better breathability <ns0:ref type='bibr'>(Thakur, Das & Das, 2013;</ns0:ref><ns0:ref type='bibr'>Zhang et al., 2018)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head n='4.'>Filtering efficiency and material property</ns0:head><ns0:p>The filtering efficiency of electret filter depends on charge density, charge retaining or holding capacity, and size (length and diameter) and arrangement of fiber. These parameters depend on the material type and filter manufacturing technique to a great extent. It known that filter having smaller fiber diameter leads to higher filtration efficiency than the fiber having lager diameter, but the pressure drop in the former is higher making the filtering device less breathable. In addition, shorter fiber form more porous filter than longer fiber. Filtering efficiency increases with increase in filter thickness at the expense of breathing resistance. The columbic and di-electrophoretic forces are also known to be stronger in filter having smaller fiber diameter. This results in stronger capturing of pathogens and better protection. Spun bonding and melt blowing are the most commonly adopted techniques for the fabrication of fibrous filter membrane. The melt blowing technique produces filters having smaller fiber diameter. Therefore, this is the method of choice in manufacturing of filtering media used in surgical mask and respirators <ns0:ref type='bibr'>(Thakur, Das & Das, 2013)</ns0:ref>.</ns0:p><ns0:p>The charge density on the electret media affects the filtering performance. The charge intensity and storage capacity depends on the dielectric property of a fiber material. In general, the polymeric materials having high electrical resistance, thermal stability, and hydrophobicity (for example; polypropylene, polyethylene) provide better charge storage ability and stability <ns0:ref type='bibr'>(Van Turnhout, Adamse & Hoeneveld, 1980)</ns0:ref>. If charge on the electret media is removed, then the filtering efficiency decreases significantly. The most penetrating particle size (MPPS) for electret media varies with material properties including charge density and is reported in the range of 0.03-0.1 µm <ns0:ref type='bibr' target='#b1'>(Hinds, 1999;</ns0:ref><ns0:ref type='bibr'>Shaffer & Rengasamy, 2009;</ns0:ref><ns0:ref type='bibr'>Hakobyan, 2015)</ns0:ref>. That is why the filtering performance of a filtering device is measured by using particles having size at or close to MPPS.</ns0:p><ns0:p>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2021:03:58955:2:0:NEW 16 Aug 2021)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:p></ns0:div> <ns0:div><ns0:head>Material design of a surgical mask</ns0:head><ns0:p>In the mostly commonly used three layered/ply SMS type surgical mask, for example US FDA approved surgical face mask, the middle layer fabricated by melt blown technique (the M layer) is sandwiched between outer and inner layers fabricated by spun bonded technique (the S layers).</ns0:p><ns0:p>The three layers are designed to have specific functions. In all layers, fibers are randomly oriented (non-woven) so as to form web like arrangement (Figure <ns0:ref type='figure' target='#fig_12'>2a</ns0:ref> and inset). The fiber density in middle layer is higher than in the other two layers resulting low porosity. Since this layer is charged, it can efficiently capture infectious particles above. The outermost layer (typically coded blue) is hydrophobic and limits the penetration of water rich muco-salivary droplets. The innermost layer is hydrophilic and can absorb spit, sweat, and muco-salivary droplets thereby minimizing dampness and increasing user comfort. In recent years, surgical face mask having additional functionality are also being explored (see section 5).</ns0:p></ns0:div> <ns0:div><ns0:head>Material design of a cloth mask</ns0:head><ns0:p>For comparison, we also like to comment on the material property of cloth or fabric face masks.</ns0:p><ns0:p>The cloth mask are made from woven or knitted fabrics and are mostly two layered. In commonly used cloth face mask, the pore size and thread density vary based on the nature of the fabrics(figure <ns0:ref type='figure' target='#fig_12'>2b</ns0:ref> and inset). The lower performance of cloth facemask <ns0:ref type='bibr' target='#b33'>(Rengasamy, Eimer & Shaffer, 2010;</ns0:ref><ns0:ref type='bibr'>Shakya et al., 2017;</ns0:ref><ns0:ref type='bibr' target='#b21'>Neupane et al., 2019;</ns0:ref><ns0:ref type='bibr' target='#b10'>Konda et al., 2020)</ns0:ref> is also due to poor facial fitness and performance of material used. Studies have suggested to use tightly woven fabrics having high thread count and low porosity, such as quilting cotton and cotton sheets, to design relatively better performing cloth face masks <ns0:ref type='bibr' target='#b10'>(Konda et al., 2020;</ns0:ref><ns0:ref type='bibr' target='#b20'>Neupane, Chaudhary & Sharma, 2020)</ns0:ref>. The efficiency can be further increased by increasing the number of fabric layers. However, more fabric layers increase the breathing resistance making the mask uncomfortable for use <ns0:ref type='bibr'>(Drewnick et al., 2020;</ns0:ref><ns0:ref type='bibr'>Hancock et al., 2020;</ns0:ref><ns0:ref type='bibr' target='#b10'>Konda et al., 2020;</ns0:ref><ns0:ref type='bibr'>Zangmeister et al., 2020)</ns0:ref>. </ns0:p></ns0:div> <ns0:div><ns0:head>Material design of a N95 respirator</ns0:head><ns0:p>A typical NIOSH certified N95 respirator consists of four layered structure (labeled A, B, C, D in figure <ns0:ref type='figure' target='#fig_12'>2c</ns0:ref>). The outer most layer A (farthest from the face) and D (closest form the face) are made from spun bonded polypropylene <ns0:ref type='bibr'>(Borkow et al., 2010)</ns0:ref>. These layers contain larger sized fibers and capture course particles and stop moisture entering into the inner layers. The inner layer B is made from melt blown polypropylene. It is charged (electret membrane) and contains highly packed small fibers (i.e., low porosity) and eventually can filter fine particles. The next inner layer C is made from a plain polyester and gives a shape to the respirator. This gradient filtration mechanism in the respirator provides high filtering efficiency. Another important factor for better performance of respirator is its design that provides excellent facial fit. Surgical masks are not designed to fit tightly on the face, so they cannot provide the same level of protection as the respirators(CDC-NIOSH, 2020). It is to be noted that the NIOSH certification does not look at the number of filter media layers and the order of hydrophobic and hydrophilic layers. But the N95 respirators should meet the standard test requirements as described in the 42 CFR Part 84.</ns0:p></ns0:div> <ns0:div><ns0:head n='5.'>Emerging technologies</ns0:head><ns0:p>Several efforts have been reported to make better performing masks and respirators in past. Such efforts involve technologies for making new or modified filter pieces, manufacturing protocols, and disinfecting procedures among others. To contribute to the shortage of standard face mask and respirator in emergency situation, researchers, manufacturers, local hospitals, and even general public have proposed a number of innovative ideas.</ns0:p></ns0:div> <ns0:div><ns0:head>3D printing of mask accessories</ns0:head><ns0:p>Additive manufacturing (AM) including 3-dimensional (3D) printing have gained popularity in manufacturing medical devices <ns0:ref type='bibr'>(Ventola, 2014)</ns0:ref>. 3D printing has been used to make mask components such as mask structure or frame, cover, filter fix, seal etc. A variety of different types of materials including polymax PLA filament, SLS/MJF nylon or flexible SLA resin have been Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science used. Foam or silicone band have been used to print seal with improved airtightness and softer skin touch. Even though the 3D printed masks may look like conventional PPE, they may not provide the same level of barrier protection, fluid resistance, filtration, and infection control <ns0:ref type='bibr'>(Ventola, 2014;</ns0:ref><ns0:ref type='bibr' target='#b19'>Morrison et al., 2015)</ns0:ref>. The new designs are not approved by any regulatory agencies yet and performance may have been compromised. Since the 3D printed masks and accessories provide low-cost, quick, and decentralized and distributed manufacturing, they can be promising during emergency situation. The US FDA has developed preliminary guidance to devices using AM that involves 3D printing <ns0:ref type='bibr' target='#b19'>(Morrison et al., 2015;</ns0:ref><ns0:ref type='bibr'>Di Prima et al., 2016)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Modified face mask filter media</ns0:head><ns0:p>There are several efforts to modify mask filters with various materials such as antibody and nanomaterials to enhance the antimicrobial activity and filtering efficiency of the masks. <ns0:ref type='bibr' target='#b7'>Kamiyama et al.(Kamiyama et al., 2011)</ns0:ref> reported a modified nonwoven fabric-based air filters that were impregnated with antibody for avian influenza H5N1 virus. The filters were found to inactivate the virus trapped in the filter due to antigen-antibody interaction. However, these filters were tested only for birds. All birds housed in antibody filter covered boxes did not die. Similar antibody impregnated filter could be tested for face masks. Such methods may require further research to find out how the antibody impregnated on filters would retain their activity in ambient environmental condition during transportation, storage and use of the filter. The performance of such filters while they are used in mask is not known.</ns0:p><ns0:p>Metal oxide and metal nanoparticles display biocidal activities <ns0:ref type='bibr'>(Vincent, Hartemann & Engels-Deutsch, 2016;</ns0:ref><ns0:ref type='bibr'>Fernando, Gunasekara & Holton, 2018)</ns0:ref>. In particular, copper oxide nanoparticle display potent biocidal properties against a range of microbes including bacteriophages, bronchitis virus, poliovirus, herpes simplex virus, human immunodeficiency virus and influenza viruses <ns0:ref type='bibr' target='#b4'>(Ingle, Duran & Rai, 2014;</ns0:ref><ns0:ref type='bibr'>Vincent, Hartemann & Engels-Deutsch, 2016;</ns0:ref><ns0:ref type='bibr'>Fernando, Gunasekara & Holton, 2018)</ns0:ref>. Taking the advantage of the biocidal properties, respiratory face masks containing these materials have been tested for anti-microbial activities.</ns0:p><ns0:p>The use of biocidal masks may significantly reduce the risk of hand or environmental contamination. They reduce infection due to improper handling and disposal of the masks. Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science barrier properties of the masks. The copper oxide impregnation did not alter the filtering efficiency of N95 masks when tested with aerosolized viruses of human influenza A virus (H1N1) and avian influenza virus (H9N2) under simulated breathing conditions. In these experiments, no infectious H1N1 viral titers were recovered from the copper oxide containing masks within 30 minutes. In case of H9N2 virus, titers were recovered from the copper oxide containing masks but were fivefold lower than the control masks. The copper oxide containing masks successfully passed bacterial filtration efficacy, differential pressure, latex particle challenge, and resistance to penetration <ns0:ref type='bibr'>(Borkow et al., 2010)</ns0:ref>. The metal oxide or nanoparticle impregnated respirator could have four layers of fabric as reported by <ns0:ref type='bibr'>Borkow et al.(Borkow et al., 2010)</ns0:ref>. Out of the four layers (Figure <ns0:ref type='figure' target='#fig_12'>2c</ns0:ref>), outer two and inner layers were metal oxide impregnated polypropylene fabric and the remaining layer was made of plain polyester to give shape to the mask.</ns0:p><ns0:p>A mixture of silver nitrate and titanium dioxide nanoparticles coated facemasks were also tested against infectious agents <ns0:ref type='bibr' target='#b14'>(Li et al., 2006)</ns0:ref>. The minimum inhibitory concentration of the nanoparticles against Escherichia coli and Staphylococcus aureus were 1/128 and 1/512, respectively. A 100% reduction in viable E. coli and S. aureus was observed in the coated mask materials after 48 h of incubation. Skin irritation was not observed in any of the volunteers who wore the facemasks.</ns0:p><ns0:p>The efficacy of 4 antimicrobial respirators to decontaminate MS2 virus was evaluated <ns0:ref type='bibr' target='#b34'>(Rengasamy, Fisher & Shaffer, 2010</ns0:ref>) using MS2 as a surrogate for pathogenic viruses.</ns0:p><ns0:p>The MS2 activity of masks with antimicrobial material was significantly reduced when stored at 37 °C and 80% RH for 4 hours than the masks without antimicrobial materials. The antimicrobial materials used in this research included coating of outer layer of mask with silver-copper material, incorporating EnvizO 3 -Shield on the outer layer of respirator, iodinated resin incorporated on filtering layer, and TiO2 coated filtering layer. This study suggested that MS2 virus decontamination efficacy of antimicrobial respirators were dependent on the antimicrobial agent and storage conditions. One should note that substituting conventional filter media of facemasks with nanofiber may reduce the airflow resistance that could lead to enhanced filtration <ns0:ref type='bibr'>(Skaria & Smaldone, 2014)</ns0:ref>.</ns0:p><ns0:p>A temperature sensitive and reusable and recyclable face mask consisting of graphene-coated nonwoven filter was recently reported by <ns0:ref type='bibr'>Zhong et al.(Zhong et al., 2020)</ns0:ref>. This Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science mask provided better protection to aqueous respiratory droplets due to its super-hydrophobic surface. Additionally, the mask can be reusable by sterilizing the surface with solar illumination.</ns0:p><ns0:p>Although, viruses filtering performance of the mask has not been reported, such mask might be available as next generation face mask.</ns0:p></ns0:div> <ns0:div><ns0:head>Virus decontamination methods</ns0:head><ns0:p>Because of increased demand and subsequent shortage during viral outbreaks, surgical mask and respirator are decontaminated and reused. Reuse of these protective gears after proper decontamination may help fulfil supply chain constraints to some extent during the pandemics.</ns0:p><ns0:p>However, improper decontamination and reuse of face masks and respirators may pose transmission risk. An ideal decontamination process is expected to inactivate any infectious material without altering the membrane integrity and filtering performance. The decontamination methods can be broadly categorized as self-deactivation and forced de-activation.</ns0:p><ns0:p>In the first approach, partly discussed in earlier section, the mask material is functionalized or additional material having novel property is incorporated so as to deactivate the pathogens. One of the strategies is to functionalize fibrous filtration unit of mask by salts such as sodium chloride <ns0:ref type='bibr' target='#b30'>(Quan et al., 2017)</ns0:ref>. In this experiment, salt coating on the fiber surface dissolved when exposed to virus aerosols. The salt destroyed the pathogens when it recrystallized during drying. The saltcoated filters also showed higher filtration efficiency than conventional mask filtration layer. The virus spiked salt treated filters provided 100% survival rate of mice. Viruses captured on saltcoated filters exhibited rapid infectivity loss compared to gradual decrease on bare filters. Saltcoated filters proved highly effective in deactivating influenza viruses regardless of subtypes and following storage in harsh environmental conditions. This simple pathogen deactivation method can be helpful in obtaining a broad-spectrum, airborne pathogen prevention device in preparation for epidemic and pandemic of respiratory diseases. Similarly, a quaternary ammonium based antimicrobial surfactant was evaluated to examine its efficiency to reduce bacterial burden on FDA cleared surgical face mask surface <ns0:ref type='bibr'>(Tseng, Pan & Chang, 2016)</ns0:ref>. The antimicrobial surfactant was covalently bound onto mask surface before use. The antimicrobial mask provided >99.3% efficiency for all three bacterial species tested. Interestingly, the antimicrobial agent on the modified mask the antimicrobial agent reduced the average colony rates by 91.8% for bioaerosols that came into contact with the mask (10 3 CFU/m 3 ). However, the rate decreased with increased bioaerosol concentrations. Manuscript to be reviewed Chemistry Journals <ns0:ref type='bibr'>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:ref> In a forced de-contamination approach, pathogen is deactivated by using external agents such as vaporized hydrogen peroxide (VGP) <ns0:ref type='bibr' target='#b8'>(Kenney et al., 2020)</ns0:ref>, ultraviolet germicidal irradiation (UVGI), ethylene oxide (EtO), microwave oven irradiation, autoclaving <ns0:ref type='bibr'>(Grinshpun, Yermakov &</ns0:ref><ns0:ref type='bibr'>Khodoun, 2020), and</ns0:ref><ns0:ref type='bibr'>bleach(Viscusi et al., 2009)</ns0:ref>. The details of such methods are described in other reviews <ns0:ref type='bibr' target='#b25'>(Ou et al., 2020;</ns0:ref><ns0:ref type='bibr' target='#b39'>Rodriguez-Martinez, Sossa-Briceño & Cortés-Luna, 2020)</ns0:ref>. Therefore, we only briefly mention some of the selected findings.</ns0:p><ns0:p>Vaporized Hydrogen peroxide (HP) can penetrate the porous fabric that may harbor virus.</ns0:p><ns0:p>The virucidal activity of HP was tested in surgical N95 respirators that were aerosolized with 3 bacteriophages: Pseudomonas phage phi-6, T7, and T1 <ns0:ref type='bibr' target='#b9'>(Kenney et al., 2021)</ns0:ref>. It was found that single HP vapor cycle resulted in complete eradication of the bacteriophages from the respirator.</ns0:p><ns0:p>Viscusi et al <ns0:ref type='bibr'>(Viscusi et al., 2009)</ns0:ref> compared the effectiveness five different decontamination methods [ethylene oxide, bleach, microwave oven irradiation, germicidal irradiation (UVGI), and vaporized hydrogen peroxide (VHP)] in nine different models of NIOSH-certified respirators (surgical N95 respirators, N95 FFRs, and P100 FFRs). Each respirator was tested for five decontamination methods and the change in ordor, physical appearance, airflow resistance and aerosol penetration was studied. Also, change in material properties of the respirator and possible health risks to the user were evaluated. They found that microwave oven irradiation melted some of the samples. The scent of bleach low levels of chlorine gas were found in the decontaminated respirators. The VHP, ethylene oxide (EtO), and UVGI, were found to be better decontamination methods.</ns0:p></ns0:div> <ns0:div><ns0:head n='6.'>Conclusions and future perspectives</ns0:head><ns0:p>The filtering efficiency of a face mask and respirator depends on number of parameters such as nature of filter media, size of particle, and environmental parameters. The level of protection also depends on facial seal and user compliance. Several studies have shown that the N95 respirator or equivalent or higher, if worn properly, can provide expected protection to the user in high risk environments. The filtering efficiency of surgical mask is lower than the N95 respirators, and cloth face mask perform even poorer.</ns0:p><ns0:p>A few issues regarding the use of respirator and face mask are the discomfort to the user in prolonged wearing due to imperfect facial fitness, poor heat management inside the filtering Manuscript to be reviewed Chemistry Journals Analytical, Inorganic, Organic, Physical, Materials Science device, filter clogging, and increased breathing resistance. In worst cases, this could even lead to psychological impact <ns0:ref type='bibr' target='#b37'>(Roberge et al., 2010;</ns0:ref><ns0:ref type='bibr' target='#b38'>Roberge, Kim & Coca, 2012)</ns0:ref> and reduced adherence and loss of workplace protection factor. So, there is need of next generation facemask and respirator having improved functionalities. The emerging 3D printing technology along with the development of novel materials such as metal organic framework (MOF)based filters <ns0:ref type='bibr' target='#b15'>(Li et al., 2019)</ns0:ref>, nano-fibrous membrane containing charge enhancer <ns0:ref type='bibr' target='#b16'>(Liu et al., 2015)</ns0:ref>, and use of material having high infrared transparency or reflectance for heat management during summer and winter seasons <ns0:ref type='bibr'>(Yang et al., 2017)</ns0:ref> will be very useful. In recent years, due to significant advancement in electrospinning technology, fabrication of filter membranes having desired fiber size, surface area, porosity, and functionality is possible. It is expected that novel facemask and respirators, which incorporate electrospun membranes, will be available commercially in future <ns0:ref type='bibr'>(Cheng et al., 2017;</ns0:ref><ns0:ref type='bibr'>Tebyetekerwa et al., 2020;</ns0:ref><ns0:ref type='bibr'>Zhang et al., 2021)</ns0:ref>.</ns0:p><ns0:p>Currently used respiratory protection device pose potential risk of primary and secondary infection and transmission due to improper handling and disposing them. In viral outbreaks, because of increased demand and subsequent shortage, surgical mask and respirator are decontaminated and reused. There is still a chance of infection during decontamination process or by ineffective decontamination. Also, device interiority and performance may deteriorate, and level of protection could decrease. So, there is need for better decontamination methods other than explored in Ref <ns0:ref type='bibr'>(Viscusi et al., 2009;</ns0:ref><ns0:ref type='bibr' target='#b8'>Kenney et al., 2020)</ns0:ref>. Solution to this issue could the incorporation of filter media that can self-decontaminate, partly explored in Refs. <ns0:ref type='bibr'>(Borkow et al., 2010;</ns0:ref><ns0:ref type='bibr'>Fujimori et al., 2012;</ns0:ref><ns0:ref type='bibr'>Tseng, Pan & Chang, 2016)</ns0:ref>, or design of a device that could incorporate resistive heating element.</ns0:p><ns0:p>Another issue during viral outbreak, including COVID-19, is inevitable use of cloth face mask, especially in low income countries. Lower efficiency of such mask is partly due to loosen facial fitting and the material used. There is a need of low-cost and effective home-made alternative fabric material to the cloth face mask. One of the possibility for better performing cloth facemask <ns0:ref type='bibr' target='#b2'>(Zhao et al., 2020)</ns0:ref> could be the use of fabrics that can be charged electrostatically so that the filtering efficiency can be increased. </ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head>Figure 1 :</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1: A cartoon showing the importance of face mask in reducing transmission. From</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>have reasonable cost and are widely used. Surgical N95 respirator are a subset of N95 FFRs which are used in healthcare settings for infectious particles. In USA, surgical N95 respirators are NIOSH certified under 42 CFR Part 84(US FDA, 1995). NIOSH reviews the results for fluid resistance, flammability, pressure difference, and biocompatibility supplied by the manufacturer for certification. The FDA also provides clearance to surgical N95 (under 21 CFR 878.4040) for fluid resistance, flammability, and biocompatibility properties. Commercial respirators come with or without exhalation valve (EV). The EV helps to minimize excessive dampness and heating and offers decreased breathing resistance. However, a faulty EV could contaminate the nearby environment with infectious virus particles thereby decreasing the level of protection. The surgical N95 respirator without EV or equivalent is recommend for MERS-CoV, SARS-CoV-1 and SARS-CoV-2 for health care professionals in high risk environments(CDC-NIOSH, 2020).</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head /><ns0:label /><ns0:figDesc>NaCl aerosol method for testing N95 FFRs(NIOSH, 2019). The test parameters are: a) flow rate of 85 Lmin -1 that simulates the breathing volume during a heavy work load, b) poly-dispersed and charge neutralized sodium chloride aerosol particles having count median diameter (CMD) of 75 ± 20 nm and geometric standard deviation (GSD) of <1.86 or broad range distribution (log-normal distribution) NaCl aerosol particles having mass median aerodynamic diameter (MMAD) and mass median diameter (MMD) about 300 nm 240 nm, respectively(Bollinger, 2004; Shaffer & Rengasamy, 2009), c) aerosol particle concentration of <200mg/m 3 , PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2021:03:58955:2:0:NEW 16 Aug 2021) Manuscript to be reviewed Chemistry Journals d) pre-conditioning of the filtering device at ~85% relative humidity and ~38° C for 24 hours.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2021:03:58955:2:0:NEW 16 Aug 2021)Manuscript to be reviewed Chemistry JournalsAnalytical, Inorganic, Organic, Physical, Materials Science </ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head /><ns0:label /><ns0:figDesc>An important question one can have at this point is: what makes the filtering efficiency different?Provided the same test parameters used, the observed difference in filtering efficiency of face masks and respirators is due to: a) inherent property of the filter material, and b) facial fitness i.e. how well the filtering device fits onto the face, and c) breathing condition.A key component of commercially available surgical masks and respirators is a non-woven filter membrane. The membrane consists of 1-20 µm diameter fibers oriented randomly. The material can be fabricated from synthetic or natural polymers or composites such as polypropylene andPeerJ Mat. Sci. reviewing PDF \| (MATSCI-2021:03:58955:2:0:NEW 16 Aug 2021) Manuscript to be reviewed Chemistry Journals polyethylene by melt blowing technique. The membrane is mostly electrostatically charged and called as electret filter. The charge is imparted onto the membrane by corona discharge, induction charging, and tribo-electric techniques during fabrication(Thakur, Das & Das, 2013; Hutten,</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head>Figure 2 :</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2: Material used in a face mask and respirator. a) SMS type surgical mask and optical microscopic images of the outermost and innermost layers (the S layers) and middle layer (the M layer). (b) A typical cloth face mask and optical microscopic image of the cloth face mask surface.Reproduced with permission from Ref.<ns0:ref type='bibr' target='#b21'>(Neupane et al., 2019)</ns0:ref>. c) Multilayered structure of a</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2021:03:58955:2:0:NEW 16 Aug 2021)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_7'><ns0:head>A</ns0:head><ns0:label /><ns0:figDesc>copper oxide impregnated respiratory face mask was reported by Borkow et al. (Borkow et al., 2010) that demonstrated potent anti-influenza biocidal properties without altering physical PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2021:03:58955:2:0:NEW 16 Aug 2021)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_8'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2021:03:58955:2:0:NEW 16 Aug 2021)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_9'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2021:03:58955:2:0:NEW 16 Aug 2021)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_10'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2021:03:58955:2:0:NEW 16 Aug 2021)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_11'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2021:03:58955:2:0:NEW 16 Aug 2021)Manuscript to be reviewed Chemistry JournalsAnalytical, Inorganic, Organic, Physical, Materials Science </ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_12'><ns0:head>Figure 2</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2</ns0:figDesc></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head /><ns0:label /><ns0:figDesc>;</ns0:figDesc><ns0:table><ns0:row><ns0:cell>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2021:03:58955:2:0:NEW 16 Aug 2021)</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_1'><ns0:head>Table 1 (on next page)</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Manuscript to be reviewed Chemistry JournalsAnalytical, Inorganic, Organic, Physical, Materials Science </ns0:figDesc><ns0:table><ns0:row><ns0:cell>Filtering efficiency</ns0:cell></ns0:row><ns0:row><ns0:cell>A comparison of virus filtration efficiencies.</ns0:cell></ns0:row></ns0:table><ns0:note>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2021:03:58955:2:0:NEW 16 Aug 2021)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_2'><ns0:head>Table 1 :</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Comparison of virus filtration efficiencies</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Filtering device</ns0:cell><ns0:cell>Filtration</ns0:cell><ns0:cell>Major test parameters</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>efficiency</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>≥95%</ns0:cell><ns0:cell>MS2 virus aerosol, flow rate 85 L min -1 (Balazy et al.,</ns0:cell></ns0:row><ns0:row><ns0:cell>N95</ns0:cell><ns0:cell /><ns0:cell>2006)</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>≥99.2%</ns0:cell><ns0:cell>H1N1 viable virus, flow rate 85 L min -1 (Harnish et</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>al., 2016)</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>97.1-97.8%</ns0:cell><ns0:cell>bacteriophage phiX174, 28.3 L min -1 (Rengasamy et</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>al., 2017)</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>~85%</ns0:cell><ns0:cell>MS2 virus aerosol, flow rate 85 L min -1 (Balazy et al.,</ns0:cell></ns0:row><ns0:row><ns0:cell>Three layered surgical</ns0:cell><ns0:cell /><ns0:cell>2006)</ns0:cell></ns0:row><ns0:row><ns0:cell>mask</ns0:cell><ns0:cell>~94%</ns0:cell><ns0:cell>bacteriophage phiX174, flow rate 15 Lmin -1</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>(Shimasaki et al., 2018)</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>~80%</ns0:cell><ns0:cell>Influenza virus, flow rate 15 Lmin -1 (Shimasaki et al.,</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>2018)</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>~90%</ns0:cell><ns0:cell>bacteriophase MS2, flow rate 30 Lmin -1</ns0:cell></ns0:row><ns0:row><ns0:cell>Two layered cloth mask</ns0:cell><ns0:cell>50-70%</ns0:cell><ns0:cell>bacteriophase MS2, flow rate 30 Lmin -1</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:note place='foot'>Analytical, Inorganic, Organic, Physical, Materials Science </ns0:note> </ns0:body> "	"8/16/2021 Dear Editor‒in‒Chief PeerJ Materials Science Thank you for providing an extensive feedback on our manuscript 'Review of materials and testing methods for virus filtering performance of face mask and respirator' submitted to PeerJ Materials Science. We are happy to get the opportunity to revise the manuscript for the second time. We have revised the manuscript by addressing all comments received from the reviewer 3. Please find one-by-one response in the next pages. The summary of changes are listed below. • We have added new information in the introduction specially under the section 'respirator and its classification' • Additional clarification has been added on the filtering efficiency test methods. • All other minor suggestions and corrections incorporated in the text. We believe that the incorporation of the reviewers' comments has significantly improved the quality of the manuscript. We will be very excited to hear the acceptance notification of our paper in the PeerJ Materials Science. Best regards, Bhanu B. Neupane, Ph.D. Assistant Professor Central Department of Chemistry Tribhuvan University, Kathmandu, Nepal Email: bbneupane@cdctu.edu.np, newbhanu@gmail.com In the following texts, please find the reviewer 3 comments (C1-C26) and our responses (R1-R26) appear in black and blue texts, respectively. C1: Lines 61 – (Yang et al., 2009, p. 1). What is the reason for adding p.1? just delete throughout the manuscript. R1: We have made the suggested change throughout the manuscript. C2: Lines 66-68 – Recent studies showed COVID-19 transmission by aerosol mode. This point has not been included in the revised manuscript. R2: Thank you for important suggestion. We have made the suggested change and the revised text reads as: 'The recent evidence have showed that COVID‒19 is transmitted through contact, respiratory droplets, and aerosol routes'. C3: Line 109- ‘Respirators are primarily designed for high risk environments including by health care professionals while treating a COVID patient’ – revise the sentence R3: We have made the suggested change. The revised text reads as: 'Respirators are recommended for high risk environments; for example for health professionals when providing care to COVID‒19 patients.' C4: Lines 112 – ‘Since respirators have special design and advanced filter materials (figure 2c), a properly worn respirator provides an excellent protection to the user’. delete ‘excellent’, better to say ‘expected’ because N95 respirator can give 95% protection level for particulates when fit tested and properly used in workplaces with a written respiratory program in place. (A P100 respirators can give 99.97% protection level, and higher level respirators can give higher protection levels.) R4: We have made the suggested change. C5: Lines 117 – However, when the EV is not working properly, it may contaminate the nearby environment with infectious virus particles through exhaled breath. Delete ‘not’. This is the reason why NIOSH recommends respirators without EV for infectious diseases. Revise the sentence. R5: As per recommendation, we have revised the text. We found that that the text fits better in the next paragraph. The revised text reads as: 'Commercial respirators come with or without exhalation valve (EV). The EV helps to minimize excessive dampness and heating and offers decreased breathing resistance. However, a faulty EV could contaminate the nearby environment with infectious virus particles thereby decreasing the level of protection. The medical surgical N95 respirator without EV or equivalent is recommend for MERS‒CoV, SARS‒CoV‒1 and SARS‒CoV‒2 for health care professionals in high risk environments.' C6: Line 119- There are different names for respirators. Gather more information before writing this section. It is full of mistakes. What do you mean? There are only N95, N99 and N100 in the whole world? You may want to say that there are different types of particulate respirators available in different countries. In the US, Filtering facepieces respirators (FFRs) (disposable FFRs), elastomeric half-mask, full facepiece, powered air-purifying respirators, supplied air-purifying and self-contained breathing apparatus and others are available. N, P and R series FFRs are available at 95, 99 and 99.95% efficiency levels (total 9 different types). (approval - code of federal regulations Title 42, Part 84). Then describe N95 FFRs, (widely used, reasonable cost). Surgical N95 masks are a subset of N95 FFRs which are used in healthcare for infectious aerosols. They are NIOSH approved N95 FFRs and also cleared by FDA for fluid resistance, flammability, and biocompatibility properties. Presently, NIOSH reviews the results for fluid resistance, flammability, and biocompatibility supplied by the manufacturer for certification. Other countries have different types of respirators. FFRs in the UK include FFP1, FFP2 and FFP3. Revise the paragraph. R6: As suggested, we have revised the paragraph significantly. The new paragraphs read as: 'A proper respirator must be selected for a specific hazards. In the USA, filtering facepiece respirators (FFRs) (disposable half‒facepiece respirators), elastomeric half‒ and full‒facepiece respirators, powered air‒purifying respirators, supplied air respirators, self-contained breathing apparatus and combination respirators are available. The FFRs are available in N, P and R series having minimum filtering efficiency of 95 (N95, P95 and R95), 99(N99, P99 and R99) and 99.97% (N100, P100 and R100) respectively for particles having aerodynamic mass median diameter of 0.3 µm. The letters N, P, and R refer to resistant to oil, partially resistant, and resistant (oil proof); respectively. Other countries have different types of respirators. In UK, FFRs are available as FFP1, FFP2, and FFP3 having minimum filtering efficiency of 80%, 94% and 99.95%, respectively. The N95 FFRs have reasonable cost and are widely used. Surgical N95 respirator are a subset of N95 FFRs which are used in healthcare settings for infectious particles. In USA, surgical N95 respirators are NIOSH certified under 42 CFR Part 84(US FDA, 1995). NIOSH reviews the results for fluid resistance, flammability, pressure difference, and biocompatibility supplied by the manufacturer for certification. The FDA also provides clearance to surgical N95 (under 21 CFR 878.4040) for fluid resistance, flammability, and biocompatibility properties. Commercial respirators come with or without exhalation valve (EV). The EV helps to minimize excessive dampness and heating and offers decreased breathing resistance. However, a faulty EV could contaminate the nearby environment with infectious virus particles thereby decreasing the level of protection. The surgical N95 respirator without EV or equivalent is recommend for MERS‒CoV, SARS‒CoV‒1 and SARS‒CoV‒2 for health care professionals in high risk environments. C7: Line 121- N-not resistant to oil, R-partially resistant and P-resistant (oil proof) R7: We have made the recommend changes. C8: Line 123- The ‘medical N95’ ---------- delete. It is ‘Surgical N95’ and make the change throughout the manuscript. R8: As suggested, we have replaced ‘medical N95’ by ‘surgical N95’ throughout the text. C9: Line 127- wrong reference. For certification refer; code of fed regulations. 42 CFR Part 84. R9: We have made the recommend changes. C10: Line 131- come up with a reason why filtering efficiency for virus? filter efficiency for inert particles such as NaCl aerosol and other particles are well documented and info on virus particle filtration is not well described.?? R10: We have this important change in the revised manuscript. The revised text appears as: 'The filtering efficiency of facemask and respirator for neutral NaCl aerosol and other particles are well documented in multiple studies but the information on virus filtering efficiency is not well described. Also, during the COVID‒19 pandemic virus filtering efficiency of facemask and respirator can be of special interest to readers. ' C11: 147- may be better to use ‘---------masks and respirators ------- ‘? R11: We have made the suggested change. C12: 159- delete ‘facial fitness, breathing condition’- not applicable for efficiency measurement. R12: We have made the suggested change. C13: Line 164-176- delete this part ‘Alternately------respectively’. APF does not apply to SMs. How is the APF of N95 compared with SM? R13: We kindly like to remind the reviewer that this section was revised as per the reviewer suggestion in the first review. In literature, the terms filtering efficiency and penetration efficiency are used in literature to discuss performance of both facemask and respirator. APF is frequently used to discuss overall performance of a respirator. These terms will also make reader easy to follow the text in later sections. So, we decided to keep this part. However, we have added few clarification in the section. C14: Line 180- include filter media charge and particle charge R14: We have made the suggested change. C15: Line 182- Delete it. ‘A user can use a filtering device in a wide range of 183 particle size and concentration distribution and breathing conditions………(Balazy et al., 2006; Ba\lazy et al., 2006; Shine, Rogers & Goldfrank, 2009, p. 1).’ R15: As suggested, we have deleted the text. C16: Line 183- 186 -‘The regulatory recommendations for the use of respirator and surgical mask are made from in vitro measurement of filtering efficiency(Balazy et al., 2006; Ba\lazy et al., 2006; Shine, Rogers & Goldfrank, 2009, p. 1).’Refer 42CFR Part 84 NIOSH particulate respirator standard for certification. R16: We have made the correction. C17: Line 189- ‘NIOSH respirator test method(NIOSH, 2019)’ Do not generalize test conditions because different tests are required for different types of respirators. NIOSH uses NaCl aerosol method (not recommending) for testing only N95 FFRs, not other respirators. revise the sentence. R17: We have made the suggested revision. C18: Line 199-200- Please delete. No need to describe the sealing of a respirator because the test methods includes this step. Manikin system is not used for this testing. R18: As per recommendation, we have deleted the sentence. C19: Line 201- Make a paragraph for surgical mask testing. C19: As per recommendation, we have made a separate paragraph for surgical mask testing. C20: Line 208-226. Delete these sentences. The NIOSH -------- Grinshpun et al 2009) R20: As per recommendation, we have deleted the sentence. C21: 214. The US FDA also provides clearance to medical surgical mask after reviewing the FDA clears SM after reviewing the test results and supporting information 215 provided by the manufactures in the 510(k) premarket application(US Food and Drug 216 Administration, 2004). In the 510(k) application, the manufactures are required to provide the 217 results of fluid resistance, polystyrene latex and Staphylococcus aureus bacterial aerosol filtering 218 efficiency, differential pressure, and flammability tests. Read the FDA document and include all the tests required for surgical masks. Do not confuse with surgical N95 FFRs tests. Read the literature - Surgical N95 respirators are recommended in healthcare. These are NIOSH certified N95s and also cleared for fluid resistance, flammability and biocompatibility by NIOSH. See reference. Rengasamy et al. 2021. American Journal of Infection Control, online 24 March 2021. R21: We have included the tests required for surgical mask under a separate paragraph. The revised text appears as: 'Different test methods are used for testing the performance of materials used in surgical face mask. In the ASTM F2229-03 method, surgical face mask material is challenged with charge neutralized latex spheres having size range of 0.1‒5 µm at airflow test velocities (face velocity) of 0.5 to 25 cm/s. The recommended aerosol concentration is 107‒108 particles/m3 and can be diluted if needed. The US FDA recommends slightly different testing parameters for material (not the entire mask) used in surgical face mask. This method recommends the use 0.1 µm charge un-neutralized polystyrene latex spheres at the air flow velocity of 0.5 to 25 cm/s(US Food and Drug Administration, 2004). The bacterial filtering efficiency (BFE) for the material used in surgical facemask measured as per FDA guidance and ASTM F2101 method. The mask material (not the whole mask) is challenged with un‒neutralized S. aureus bacterial aerosol (mean particle size of 3 ± 0.3 μm diameter) at a flow rate of 28.3 L/min. The US FDA also provides clearance to surgical mask after reviewing the information provided by the manufactures in the 510(k) premarket application(US Food and Drug Administration, 2004). In the 510(k) application, manufactures are required to provide the results of fluid resistance, polystyrene latex and S. aureus bacterial aerosol filtering efficiency, differential pressure, and flammability tests.' C22: Lines 218 –234 delete ‘Size of aerosol particle -----------illustrated in Fig 1. Neither NIOSH nor FDA requires this test. What is the purpose? R22: As suggested, we have deleted the text. C23: Line 247- The VFE of surgical masks 248 ranged from 80 to 85%, suggesting surgical masks cannot be as effective as N95 respirators for 249 small virus. Make sure it is correct. One SM shows penetration of ~30 to 85% for ~5 to 80 nm particles while the other SM showed 5 to 20% . R23: We have made the necessary changes in the revised manuscript. C24: Line 250- Specify the name of the SM used in the study. R24: We have provided the addition information reported in the literature. C25: Line 256 – 258 In another study, filtering efficiency of medical 257 N95 respirator was ≥99.6% for all combinations of experiment configurations using influenza A  virus, rhinovirus 14, and bacteriophage ΦΧ174 at a flow rate of 28.3 Lmin-1(Zhou et al., 2018). There is no medical N95. The name of the mask is ‘Surgical N95’ R25: As suggested, we have replaced ‘medical N95’ by ‘surgical N95’ throughout the text. C26: Line 309-321- smaller fiber, longer fiber, shorter fiber, - use smaller and larger with diameter R26: We have made the suggested change. C27: Line 511- excellent protection. delete ‘excellent’ and use expected as suggested previously. R27: We have made the suggested change. "	Here is a paper. Please give your review comments after reading it.
448	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Background.The NOVARON, a silver-based antimicrobial agent derived from inorganic ion exchangers developed by Toagosei and registered by FDA, has effectively indicated the antimicrobial power of silver against a variety of microbes. The objective of this study was to investigate the effect of a silver-supported material (Novaron (N)) on the mechanical behaviour, antimicrobial properties, cytotoxicity and colour of light-cured resin composites.</ns0:p><ns0:p>Methods. Silanized aluminum borate whisker (ABWs) (4 wt%) and nano-zirconia (nano-ZrO2) (2 wt%) were mixed with the resin matrix to obtain the control groups; 4 wt% surface-modified Novaron particles were incorporated into the above matrices as the experimental groups. The surface hardness was tested. Furthermore, the antimicrobial abilities evaluated in vitro with Streptococcus mutans (S. mutans), Fusobacterium nucleatum (F. nucleatum) and Candida albicans (C. albicans) using the live/dead, MTT and colony-forming units (CFUs) assay. Furthermore, the effects on fibroblast growth and colour were test in this study.</ns0:p><ns0:p>Results.The data of the Novaron and control groups were analyzed by Student's t-test. The results showed that the activities of S. mutans, F. nucleatum and C. albicans biofilms on the composites surface were greatly reduced (p<0.05) and no significant difference was found in the culture medium (p>0.05). Extracts taken from the cell culture medium of the specimens were used to evaluate cell viability. The composites did not have an adverse effect on fibroblast growth and colour in this study . The results showed that 4 wt% Novaron incorporated into the resin composites could increase the surface hardness (p<0.05). Therefore, Novaron is a potential antimicrobial agent applying in light-cured and inorganic nanoparticles reinforced dental resin materials.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>The development of light-cured dental restorative composites has increased rapidly due to their better aesthetic properties, fewer safety concerns, ease of handling, physical properties similar to dentin and reasonably satisfactory clinical results compared with those of metallic dental amalgams <ns0:ref type='bibr' target='#b37'>(Wang et al. 2019</ns0:ref>). Dental composites have been reported to be used in more than 95% of all anterior tooth direct restorations and in approximately 50% of all posterior tooth direct restorations <ns0:ref type='bibr' target='#b13'>(He et al. 2015)</ns0:ref>. Generally, light-cured dental resin composites consist of an organic resin matrix, inorganic fillers, photo-initiators and accelerators. The most commonly used organic matrices are Bis-GMA and TEGDMA <ns0:ref type='bibr' target='#b44'>(Zhang et al. 2014a)</ns0:ref>. Inorganic fillers such as zirconium dioxide, silicon dioxide and other glass particles are popularly used to improve the mechanical properties of resin composites <ns0:ref type='bibr' target='#b38'>(Wille et al. 2016)</ns0:ref>. It is well known that there are different surface properties between the matrix and the fillers; the former is hydrophilic and highly polar, while the latter is generally relatively hydrophobic and non-polar. Surface modification with silane coupling agents is commonly used to increase the interfacial interaction between inorganic fillers and the organic resin matrix <ns0:ref type='bibr' target='#b21'>(Lung et al. 2016</ns0:ref>).</ns0:p><ns0:p>However, resin composites have some disadvantages, including excessive wear, inadequate strength, technique sensitivity, dimensional shrinkage, poor marginal adaptation, distortion, and lower resistance to caries <ns0:ref type='bibr' target='#b7'>(Cherchali et al. 2017;</ns0:ref><ns0:ref type='bibr' target='#b14'>Ibrahim et al. 2020)</ns0:ref>. The main reason for failure is still secondary caries, followed by fracture of restoration. Compared to other restorative materials, dental resin composites restorative materials have been reported to accumulate more bacteria or plaque and more easily form dental biofilms <ns0:ref type='bibr' target='#b0'>(Almousa et al. 2019;</ns0:ref><ns0:ref type='bibr' target='#b30'>Pietrokovski et al. 2016</ns0:ref>). Among various bacterial species, Streptococcus mutans is considered to play a major role in the formation and development of plaque biofilms <ns0:ref type='bibr' target='#b9'>(De Paula et al. 2018)</ns0:ref>. However, other oral microorganisms such as Enterococcus faecalis, Candida albicans and Fusobacterium nucleatum also play an important role in the development and progression of this disease <ns0:ref type='bibr' target='#b23'>(Melo et al. 2018)</ns0:ref>.</ns0:p><ns0:p>Therefore, the development of antibacterial restorative filling materials requires a potent antimicrobial agent that acts against a wide range of oral micro-organisms. Recent studies have paid growing attention to resin composites materials with antibacterial properties, combating microbial growth/proliferation and secondary caries to improve the longevity of restorations. The most common method is incorporation of the filler with an inorganic antibacterial agent <ns0:ref type='bibr' target='#b2'>(Boaro et al. 2019;</ns0:ref><ns0:ref type='bibr' target='#b26'>Münchow et al. 2020)</ns0:ref>. Compared to organic antibacterial materials, inorganic antibacterial agents have better compatibility as well as long-lasting and wide broad-spectrum antibacterial properties <ns0:ref type='bibr' target='#b35'>(Wang et al. 2017)</ns0:ref>. For the antibacterial properties of dental resin composites, Ag(slilver)-based agents are one of the most commonly used inorganic agents, and silver-zeolite, silver-apatite and other silver-supported materials have been reported to achieve good antibacterial effects <ns0:ref type='bibr' target='#b24'>(Mocanu et al. 2014</ns0:ref>).</ns0:p><ns0:p>In our previous research <ns0:ref type='bibr' target='#b6'>(Chen et al. 2017)</ns0:ref>, we had chosen four different inorganic antibacterial agents (titanium dioxide (TiO 2 ), silver-supported titanium dioxide (Ag/TiO 2 ), silver-supported zirconium phosphate (Navaron), and tetrapod-like zinc oxide whiskers (T-ZnOw)) to fabricated antibacterial composites and investigated their antibacterial activities against oral microorganisms.</ns0:p><ns0:p>Novaron had the highest antibacterial property. Novaron consists of uniform fine particles with low moisture absorption and good heat-resistant properties and is easy to mix with matrices. The antimicrobial mechanism of Novaron involves either or both of the following steps: silver ions enter into the cell membranes of bacteria and then inhibit the crosslinked action of polysaccharide to lightly destroy the cell membranes of bacteria; silver ions combine with DNA, interfere with the synthesis of DNA and RNA, inhibition the replication and proliferation of DNA and finally results in bacterial death <ns0:ref type='bibr' target='#b40'>(Yeluri et al. 2012)</ns0:ref>. Therefore, the objective of this study was to investigate the antimicrobial properties of the silver-supported material Novaron in dental composites. Following our previous research, ABWs and Nano-ZrO 2 were used as fillers to improve the mechanical properties of resin composites <ns0:ref type='bibr' target='#b46'>(Zhang et al. 2014b)</ns0:ref>. In this study, we applied silanization methods to modify the fillers, aiming to modify the surface of Novaron. Additionally, as previous research discovered that 4% Novaron offered good antibacterial and mechanical properties and lower cytotoxicity in acrylic resin composites <ns0:ref type='bibr' target='#b12'>(Han et al. 2015)</ns0:ref>. In this study, we added 4% Novaron into light-cured and nanoparticles reinforced resin to fabricate novel composites, and evaluated the antimicrobial properties against three different microorganisms as well as the mechanical and cytotoxic properties in vitro.</ns0:p></ns0:div> <ns0:div><ns0:head>Materials and methods</ns0:head></ns0:div> <ns0:div><ns0:head>Materials</ns0:head><ns0:p>A light-cured resin composites system was used as the parent resin system to test the effect of Novaron incorporation. The composites contain two matrices, BIS-GMA (Sigma-Aldrich, USA)</ns0:p><ns0:p>and TEGDMA (Sigma-Aldrich,USA);CQ (Sigma-Aldrich,USA), DMAEMA (Sigma-Aldrich, USA), and BHT(Sigma-Aldrich, USA). Two types of filler materials were purchased to reinforce the mechanical properties of the resin composites: Nano-ZrO 2 (granularity: 50-90 nm, Tosoh Co., Ltd., Tokyo, Japan) and ABWs (diameter <1.5 µm, length: 5-30 µm, Shanghai Whisker Composites Co., Ltd., Shanghai, China). The antimicrobial materials were white silver-supported powders: Novaron AG300 (N, average particle size: 0.9 µm, Toagosei Co., Ltd., Tokyo, Japan) in this study. Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>was purchased to silanize the mixed materials. The other chemicals in this study were analytical grade reagents.</ns0:p></ns0:div> <ns0:div><ns0:head>Surface modification of the antimicrobial materials</ns0:head><ns0:p>For the surface treatment of the Novaron, 1.5% γ-MPS (Z-6030) and 10 g of acetone were mixed, and the pH value was adjusted to 4.0-5.0 to control the hydrolysis reaction using acetic acid. The solution was stirred continuously for 1 h to pre-hydrolyse the silane using a magnetic stirring apparatus. Meanwhile, the Novaron powders were immersed in distilled water (Novaron/water weight ratio of 1:10) and completely dispersed with an ultrasonic vibration apparatus for 1 h. Then, the pre-hydrolysed silane was slowly added dropwise into the Novaron suspension, and the mixture was agitated for 1 h at 80 °C. After hot agitation, the suspension was cooled stepwise from room temperature to -80 °C and dried in a vacuum freeze dryer for 24 h to obtain the powders. The surface analyses of unsilanized and silanized Novaron were examined using XPS (AXIS UltraDLD, Kratos, Japan) with an A1 Ka source (1486.6 eV). The morphology of the powder and the dispersion in composites were investigated using SEM (NOVA NanoSEM230, FEI Company, Netherlands).</ns0:p></ns0:div> <ns0:div><ns0:head>Specimens</ns0:head><ns0:p>The two resin matrices BIS-GMA (24.8875%) and TEGDMA (74.6625%) at a 1:3 weight ratio were mixed, and 0.25% photosensitizer CQ, 0.15% activator DMAEMA and 0.05% polymerization inhibitor BHT were added. The silanization of nano-ZrO 2 and ABWs was according to the previous study <ns0:ref type='bibr' target='#b12'>(Han et al. 2015)</ns0:ref>. Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science resin paste. The experimental groups containing Novaron, while the control groups did not. Table <ns0:ref type='table'>1</ns0:ref> lists the different groups of composition prepared in this study. Then, the resin paste was poured into a round mould and light-cured layer-by-layer. Every layer was 0.2mm, and the curing light (Elipar TM S10 LED, 3M, USA) was used to cure the resin layer in 10s. As our previous research <ns0:ref type='bibr' target='#b12'>(Han et al. 2015)</ns0:ref>, the specimens were standardized to dimensions of 10 mm diameter and 2 mm height. All the specimens were mechanically polished to a high gloss using a grinder-polisher (Phoenix Beta, Buehler Ltd., Germany) and wet abrasive paper. To conduct the biofilm experiments and cytotoxicity tests, the specimen surface was polished to a roughness of 0.18±0.03 µm for the antimicrobial and cytotoxicity tests and 0.02±0.005 µm for the Vickers hardness tests. The specimens were sterilized under ultraviolet light for 2 h on each side for the antimicrobial and cytotoxicity tests.</ns0:p></ns0:div> <ns0:div><ns0:head>Mechanical properties test</ns0:head><ns0:p>After the specimens were polished to a surface roughness of 0.02±0.005 µm, the Vickers hardness of specimens randomly chosen from each test group was measured with a micro-hardness tester (HX-1000, Shanghai Taiming Optical Instruments Co., Ltd., Shanghai, China). Three specimens were chosen in each group and three points on each specimen were tested by applying a 50 g (0.49 N) load for 10 s. The results were recorded with PC software. Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Live/dead assay of different microorganisms</ns0:head></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science our previous research <ns0:ref type='bibr' target='#b12'>(Han et al. 2015)</ns0:ref>. Then, the bacterium suspension was adjusted to 1.0 × 10 6</ns0:p><ns0:p>CFUs/mL for further use. Six disc-shaped specimens for each group were placed in a 48-well plate with 500 µL of BHI and 50 µL of inoculated cell suspension in each well. After 24 h inoculation, the discs with biofilm were transferred to a new 48-well plate, and the planktonic cells in the medium were used in the following experiments.</ns0:p><ns0:p>The method of Live/dead assay was as same as our previous research <ns0:ref type='bibr' target='#b17'>(Liu et al. 2020)</ns0:ref>. The cells in the biofilms on discs were harvested with 1mL of BHI with mild sonication and pipetting and stained using a live/dead cell viability kit (Molecular Probes, Invitrogen, USA) for 15 min in darkness. Live cells were stained with Syto 9 to produce green fluorescence, while dead cell membranes were stained with propidium iodide to produce red fluorescence. Separately, the planktonic cells in the medium were collected and similarly live/dead stained. Each test was performed at n = 6. The stained specimens were examined by CLSM (Leica TCS SP2, Germany).</ns0:p></ns0:div> <ns0:div><ns0:head>MTT assay of cell metabolic activity</ns0:head><ns0:p>The MTT assay is a colorimetric assay to estimate the metabolic activity of cells. The 24 h biofilm discs were transferred to a new 48-well plate, and 300 µL of MTT dye (0.5 mg/mL MTT in PBS) was added to each well. Meanwhile, the collected medium with planktonic cells from each well was transferred to a tube containing 30 µL of MTT dye. The specimens of S. mutans and C.</ns0:p><ns0:p>albicans were incubated at 37 °C in an aerobic incubator and F. nucleatum in an anaerobic incubator for 4 h. During this process, metabolically active cells reduced the MTT to purple formazan. After 4 h, the discs were transferred to a new 48-well plate, and 300 µL of DMSO (Sigma, USA) was added to solubilize the formazan crystals, while the planktonic cells were Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science transferred to a new 96-well plate. The absorbance at 590 nm (OD590) was recorded via a multiwell microplate reader (Labsystem Multiskan EX, USA). The higher absorbance indicated a higher formazan concentration, which indicated a higher metabolic activity of the cells. Six replicates were tested for each group (n = 6).</ns0:p></ns0:div> <ns0:div><ns0:head>Colony forming unit counts (CFUs)</ns0:head><ns0:p>Two types of agar plates were prepared in this part: the BHI agar plates were for S. mutans and C. albicans, and the BHI blood agar plates were used to culture F. nucleatum. The cells in the 24 h biofilms on discs were rinsed via mild sonication and were pipetted. Then, the cell suspensions were serially diluted to 10 -6 , and 50 µL of the diluted cell liquid was spread onto a BHI agar plate or BHI blood agar plate and cultured at 37 °C for 48 h for CFUs analysis (n = 6). Separately, the CFUs of the planktonic cells from each well were also measured.</ns0:p></ns0:div> <ns0:div><ns0:head>Cytotoxicity of the composites eluent</ns0:head><ns0:p>For the cell cytotoxicity test, eluent solutions of specimens were prepared according to ISO 10993-5 and ISO 10993-12. Sterile specimens were immersed in DMEM and agitated for 24 ± 2 h at 37 °C to obtain the extracts from the specimens. The surface/volume ratio of the specimen and the medium was 1.25 cm 2 /mL. After incubation, the extracts were filtered by 0.22 µm filters into sterile tubes and diluted 2-fold with fresh DMEM for testing. The negative control groups were DMEM without the eluent solution.</ns0:p><ns0:p>Gingival fibroblasts cultured in DMEM supplemented with 10% foetal calf serum with 100 U/ml penicillin and 100 mg/ml streptomycin (Gibco BRL, USA) at 37 °C in an air atmosphere containing 5% CO 2 at 100% relative humidity. A seeding density of 4000 cells/well was used in 96-well plates, with 200 µL per well. After 24 h incubation at 37 °C with 5% CO 2 in air, the culture medium was removed and replaced with equal volumes of the eluent solution and a 2-fold dilution.</ns0:p><ns0:p>Meanwhile, the negative groups were treated with DMEM. The cells were cultured for another 24 h, and then, 20 µL of sterile-filtered MTT was added to each well. After incubation in a darkroom for 4 h at 37 °C, the unreacted dye was removed, and 200 µL/well of DMSO was added. The plates were then slightly stirred at room temperature for 10 min, and the solution absorbance was measured via a microplate reader (Labsystem Multiskan EX, USA) at 490 nm. The absorbance of the negative groups was set as 100%. The fibroblast viability for cells cultured with eluents = absorbance with eluents/absorbance of negative control.</ns0:p></ns0:div> <ns0:div><ns0:head>Colour change measurement</ns0:head><ns0:p>To evaluate the colour changes, specimens measuring 10 mm in diameter and 2 mm in thickness were prepared. Six samples were prepared from each material. After preparation, the samples were polished to a surface roughness of 0.02±0.005 µm and immersed in stilled water in </ns0:p><ns0:formula xml:id='formula_0'>( △ L * )＋( △ a * )＋( △ b * )</ns0:formula><ns0:p>where L, △a, and △b* represent the difference values of L, a, and b* between the Novaron △ groups and the control groups, respectively. △E<1.0 indicated that the change in colour could not be detected; 1.0<△E<3.3 indicated that the change in colour could not be distinguished; and</ns0:p><ns0:p>△E>3.3 showed that the difference in colour was obvious.</ns0:p></ns0:div> <ns0:div><ns0:head>Statistical analysis</ns0:head><ns0:p>The normal distribution and homogeneity of all the data were checked using the Shapiro-Wilk test. Then, the data of the Novaron and control groups were analysed by Student's t-test using SPSS 19.0 statistical software at a significance level of p < 0.05.</ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head><ns0:p>Fig. <ns0:ref type='figure' target='#fig_5'>1A</ns0:ref> shows the XPS data of the Novaron surface before and after silanization. The band at approximately 102 eV in the spectrum of the Si-2p groups observed was attributed to silanized Novaron. Fig. <ns0:ref type='figure' target='#fig_5'>1B</ns0:ref> and C present the SEM images of the unsilanized and silanized Novaron samples, respectively. These images showed good dispersibility of the fillers after surface treatment, and this property could increase the interfacial contact between fillers and the matrix. Fig. <ns0:ref type='figure' target='#fig_6'>2</ns0:ref> presents the surface hardness results. Statistical analysis revealed that the surface hardness was enhanced significantly with the addition of Novaron compared to that of the control groups (p<0.05). Fig. <ns0:ref type='figure' target='#fig_7'>3</ns0:ref> shows the live/dead images are for biofilms on resin discs; the left columns represent the control groups with different cells, while the right columns represent the Novaron groups. For S. mutans (A), F. nucleatum (B) and C. albicans (C), the resin composites containing Novaron had more compromised cells than the control groups did (D, E, F). Fig. <ns0:ref type='figure' target='#fig_8'>4</ns0:ref> shows the live/dead results of planktonic cells in the medium. There were no obvious differences between the Novaron and control groups for any of the different microbes. These results indicate that the Novaron-containing resin inhibited cell growth on its surface, but the cells distant from its surface were still primarily alive. Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science that the Novaron groups achieved a significantly lower MTT absorbance than that observed in the control groups (p<0.05), while there were no significant differences between the control and Novaron groups in terms of planktonic cells in the culture medium (p>0.05). Fig. <ns0:ref type='figure' target='#fig_10'>6A</ns0:ref> (S. mutans), B (F. nucleatum) and C (C. albicans) present the CFU results on the Novaron groups resin surface, and the results for the planktonic cells in the culture medium are presented in Fig. <ns0:ref type='figure' target='#fig_10'>6D-F</ns0:ref>. The results were the same as the MTT results. The Novaron groups achieved significantly lower CFUs than the control groups did (p<0.05), while there were no significant differences between the control groups and Novaron groups planktonic cells in the culture medium (p>0.05). Fig. <ns0:ref type='figure' target='#fig_11'>7</ns0:ref> shows the results regarding the fibroblast cytotoxicity of resin composites, indicating that there were no significant differences in relative cell viability between the undiluted extracts and 2-fold diluted eluents (p>0.05).</ns0:p><ns0:p>The results of the initial colour measurements of the L, a and b* axes are presented in Table <ns0:ref type='table' target='#tab_4'>2</ns0:ref>. The colour change of the chromaticity of the specimen after the addition of 4% Novaron is</ns0:p><ns0:p>shown in Table <ns0:ref type='table' target='#tab_5'>3</ns0:ref>. The △E values were less than 1.0, indicating that humans could not detect the differences between these two groups.</ns0:p></ns0:div> <ns0:div><ns0:head>Discussion</ns0:head><ns0:p>Uncoated inorganic materials, especially nanoparticles, normally have a tendency to agglomerate. The effect of aggregation of the materials is hindered by combining them into the resin composites <ns0:ref type='bibr' target='#b8'>(Chouirfa et al. 2019</ns0:ref>).Surface modification with silane coupling agents has been one way to stabilize the fillers and disperse the fillers into uniform resin composites <ns0:ref type='bibr' target='#b42'>(Zane et al. 2016)</ns0:ref>. Because of the different surface properties between the matrix and the fillers, the interphase quality between them plays a major role in the ultimate properties of the composites materials (Jiao Manuscript to be reviewed Chemistry Journals <ns0:ref type='bibr'>Analytical, Inorganic, Organic, Physical, Materials Science et al. 2014)</ns0:ref>. There are several methods available to quantify the interfacial adhesion of fillers in composites materials. Surface modification with silane coupling agents is commonly used to improve interfacial adhesion in filler-reinforced resin composites <ns0:ref type='bibr' target='#b1'>(Aydinoglu & Yoruc 2017)</ns0:ref>. The most common silane used in dental composites is γ-MPS, which is a bi-functional monomer, with hydroxymethyl groups substituted by hydroxyl groups attaching to the fillers <ns0:ref type='bibr' target='#b12'>(Han et al. 2015;</ns0:ref><ns0:ref type='bibr' target='#b19'>Liu et al. 2018)</ns0:ref>. γ-MPS also contains C=C bonds, which react with the resin matrices during the curing process. Therefore, the silane coupling agent γ-MPS established chemical bonds between the fillers and the resin composites as a bridge. Surface modification with γ-MPS resulted in the observed Si absorption peak, indicating that the silane coupling agents were successfully grafted onto the filler surface <ns0:ref type='bibr' target='#b12'>(Han et al. 2015;</ns0:ref><ns0:ref type='bibr' target='#b22'>Matinlinna et al. 2018)</ns0:ref>. After silanization by the vacuum freeze-drying method, the morphology of the fillers was evaluated. The images of Fig. <ns0:ref type='figure' target='#fig_5'>1</ns0:ref> showed good dispersibility of the fillers after surface treatment after silanization.</ns0:p><ns0:p>Based on previous research, ABWs and Nano-ZrO 2 were selected as reinforced filler materials incorporated into the resin matrix. Aluminium borate whiskers with a single crystal structure have been successfully used as a reinforcement for metal or resin matrix composites. Nano-ZrO 2 containing nanoparticles exhibit the best anti-wear properties, and zirconium dioxide possesses excellent properties such as high hardness, strength and fracture toughness as well as outstanding wear and chemical corrosion resistance performance <ns0:ref type='bibr' target='#b12'>(Han et al. 2015)</ns0:ref>. Novaron exhibited uniform fine particles with low moisture absorption capability and good heat-resistant properties. This material can be easily mixed into matrices. In addition, it has high physical and chemical stability along with superior discoloration resistance during processing or use <ns0:ref type='bibr' target='#b40'>(Yeluri et al. 2012</ns0:ref>).</ns0:p><ns0:p>In the present study, S. mutans, C. albicans and F. nucleatum were used to examine the antimicrobial activities of Novaron in a resin matrix with direct contact and the planktonic cell Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science test. Streptococcus mutans is a gram-positive bacterium and represents one of the main species in cariogenic biofilms responsible for secondary caries <ns0:ref type='bibr' target='#b11'>(Florez et al. 2016</ns0:ref>). Oral C. albicans has been recognized as one of the contributing factors of denture stomatitis. Biofilm formation on dental materials and the subsequent colonization of microbial cells may cause secondary caries and gingivitis <ns0:ref type='bibr' target='#b29'>(Oktay et al. 2019)</ns0:ref>. F. nucleatum is a gram-negative anaerobe correlated with increased probing depth and progressive periodontal ligament reduction in periodontitis. Periodontitis enhances the loss of tooth attachment and the development of root caries and leads to the failure of Class V restorations <ns0:ref type='bibr' target='#b36'>(Wang et al. 2016</ns0:ref>). The biofilm composition may influence the outcome of caries treatments and the killing efficacy of antibacterial agents. Therefore, new antimicrobial restorative materials should be tested against multispecies biofilms. Novaron is a silver-supported inorganic agent. Silver is well known for its broad-spectrum antimicrobial properties and good biocompatibility with human cells; thus, it has been widely used in medical and other fields. Silver is antimicrobial against a wide range of microorganisms: bacteria, fungi and certain viruses, including antibiotic-resistant strains <ns0:ref type='bibr' target='#b3'>(Cao et al. 2017</ns0:ref>). Silver ions, as released antimicrobial agents, have been incorporated into composites mixtures in an attempt to achieve significant antimicrobial performance. However, as these silver ions are released, the generation of voids can negatively affect the mechanical properties of the composites.</ns0:p><ns0:p>Burst release is another concern of this technique <ns0:ref type='bibr' target='#b5'>(Chatzistavrou et al. 2015)</ns0:ref>. Conversely, nonreleased agents, such as silver-supported fillers, are known to maintain remarkable mechanical properties after ageing since the antimicrobial component is not released over time. Novaron is a silver-supported inorganic antibacterial agent and has excellent antimicrobial efficacy against a wide range of microorganisms <ns0:ref type='bibr' target='#b4'>(Cao et al. 2018)</ns0:ref>. The antimicrobial mechanism of Novaron is presumed to involve either or both of the following steps: silver ions form metal-organic Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science complexes with sulfhydryl groups in the cell walls of bacteria and fungi, generally inactivating essential enzymes responsible for energy metabolism <ns0:ref type='bibr' target='#b10'>(Dias et al. 2019)</ns0:ref>. Silver ions also activate oxygen, which is converted into oxygen free radicals by the action of light energy in air or water as a result of the catalytic action of silver, attacking the respiratory chain and cell division, leading to cell death <ns0:ref type='bibr' target='#b39'>(Xu et al. 2016)</ns0:ref>. Therefore, a further study should investigate the antimicrobial activity over a long period. We chose different methods (Fig. <ns0:ref type='figure' target='#fig_10'>3-6</ns0:ref>) to test the composites antimicrobial ability of biofilms and planktonic bacteria and yeast. These results indicated that the Novaron-containing resin inhibited cell growth on its surface, but the cells distant from its surface were still primarily alive. Previous studies suggested that there was no release of silver ions for a long time <ns0:ref type='bibr' target='#b41'>(Yoshida et al. 1999)</ns0:ref>, which was in agreement with the results of this study. The antibacterial activity of the composites incorporating Novaron can possibly last for long periods because the composites inhibit the growth of cells not by releasing the silver ions from the composites but through their direct contact with the bacteria <ns0:ref type='bibr' target='#b16'>(Kuroki et al. 2010)</ns0:ref>.</ns0:p><ns0:p>It is equally important for new antibacterial composites to be non-cytotoxic and have good biocompatibility. Many studies have reported similar results that Ag+ is nontoxic to the human body at a low concentration <ns0:ref type='bibr' target='#b18'>(Liu & Man 2017;</ns0:ref><ns0:ref type='bibr' target='#b20'>Lu et al. 2017;</ns0:ref><ns0:ref type='bibr' target='#b27'>Natarajan et al. 2016)</ns0:ref>. Cells from various organs or tissues usually display differential susceptibility. Gingival fibroblasts are accessible if the composites are to be applied in the clinic <ns0:ref type='bibr' target='#b31'>(Ren et al. 2019)</ns0:ref>. To exclude the effect of resin monomers on the cytotoxicity assay, the resin discs were cleaned by ultrasound, dried and left undisturbed for 24 h before sterilization. Typical saliva flow is approximately 1000-1500 mL/day for an average person. Hence, diluting the original extract 128-fold yields a total of 1280 mL of culture medium, which can be used to approximate the amount of saliva in the mouth over 24 h <ns0:ref type='bibr' target='#b45'>(Zhang et al. 2013)</ns0:ref>. The present study in Fig. <ns0:ref type='figure' target='#fig_11'>7</ns0:ref> demonstrated that the relative cell viability percentages of the 2-fold diluted and undiluted eluent groups were all greater than 90% after 24 h incubation and were classified as non-cytotoxic and slightly cytotoxic, respectively. In the present study, even at the 2-fold dilution, with a total solution volume of approximately 1/2 of the saliva volume per day in vivo, the Novaron groups still exhibited nearly 100% fibroblast viability <ns0:ref type='bibr' target='#b12'>(Han et al. 2015)</ns0:ref>. According to ISO 10993-5:2009, the resin composites could achieve potential antimicrobial activities without compromising fibroblast cytotoxicity, the material was qualified. Broad optical properties depending on the nanoparticle diameter, refractive index near the nanoparticle surface, and aggregation are also beneficial features of this material <ns0:ref type='bibr' target='#b32'>(Stencel et al. 2018)</ns0:ref>. Previous investigations have suggested some thresholds of perceptible color difference .</ns0:p><ns0:p>△E values lower than approximately 3.3 are acceptable <ns0:ref type='bibr' target='#b34'>(Vichi et al. 2004</ns0:ref>). The high surface area to mass ratio of Novaron allows better antimicrobial activity at a lower concentration without significantly compromising the composites colour.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions</ns0:head><ns0:p>The present study investigated the effects of Novaron addition in nano-ZrO 2 /ABW resin composites on the mechanical activity, antimicrobial properties, cytotoxicity and colour. The results showed that 4 wt% Novaron incorporated into the resin composites could increase the surface hardness. Antimicrobial functions were obtained without compromising the biocompatibility or colour. Therefore, 4 wt% Novaron may have wide applicability in other composites, bonding systems, sealants and cements. These novel antimicrobial resin composites may be promising for inhibiting oral biofilms and secondary caries. Manuscript to be reviewed Manuscript to be reviewed than the control groups (p<0.05) while there were no significant differences between control groups and the Novaron groups of planktonic bacteria in culture medium (p>0.05). The asterisks means there were significant differences between control groups and the Novaron groups and the red lines means there were no significant differences between control groups and the Novaron groups.</ns0:p><ns0:note type='other'>Figure 1</ns0:note><ns0:note type='other'>Chemistry Journals Figure 2</ns0:note><ns0:note type='other'>Chemistry Journals Analytical, Inorganic, Organic, Physical, Materials Science Figure 3</ns0:note><ns0:note type='other'>Figure 4</ns0:note><ns0:p>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2021:07:63904:1:2:NEW 22 Dec 2021)</ns0:p><ns0:p>Manuscript to be reviewed A (S. mutans), B (C. albicans and C (F. nucleatum) were the results of biofilms on the resin surface and planktonic bacteria in culture medium (D-F). Novaron groups achieved significantly lower CFUs than the control groups (p<0.05) while there were no significant differences between control groups and the Novaron groups of planktonic bacteria in culture medium (p>0.05). The asterisks means there were significant differences between control groups and the Novaron groups and the red lines means there were no significant differences between control groups and the Novaron groups.</ns0:p><ns0:note type='other'>Chemistry Journals Figure 6</ns0:note><ns0:p>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2021:07:63904:1:2:NEW 22 Dec 2021)</ns0:p><ns0:p>Manuscript to be reviewed The results indicated that there were no significant differences of relative cell viability between the undiluted extracts and 2-fold diluted eluents (p>0.05).The red lines means there were no significant differences between control groups and the Novaron groups. Manuscript to be reviewed</ns0:p><ns0:note type='other'>Chemistry Journals Figure 7</ns0:note></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>Three strains of microbes, namely, S. mutans (UA159, gram-positive bacterium), C. albicans (76615) and F. nucleatum (AT25586 gram-negative bacterium) (Shanghai Key Laboratory of Stomatology, China), were used in this study. S. mutans and C. albicans were cultivated under aerobic conditions and F. nucleatum was cultivated under anaerobic conditions in 5 mL of BHI (BD, Franklin Lakes, NJ) at 37 °C for 24 h. The methods of bacterium co-culture were as same as PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2021:07:63904:1:2:NEW 22 Dec 2021)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>collected by centrifugation at 5000 × g for 4 min, adding 300 µL of DMSO. After incubation for 20 min with gentle mixing at room temperature in the dark, 200 µL of the DMSO solution was PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2021:07:63904:1:2:NEW 22 Dec 2021)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head /><ns0:label /><ns0:figDesc>darkness at 55 ± 2 °C for approximately 30 min. A spectrophotometer (Color i7, X-Rite, USA) was used to record the CIE Lab* parameters with a D65 illuminant on a white ceramic tile. The CIEL ab system is composed of three respective axes: L* is the lightness from 0 (black) to 100 (white), a* represents the red (+a* value)-green (-a* value) axis, and b* represents the blue (-b* value)-yellow (+b* value) axis. The colour change (△E) was calculated according to the following equation (1) (Tsubone et al. 2012):</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head /><ns0:label /><ns0:figDesc>Fig. 5A (S. mutans), B (F. nucleatum) and C (C. albicans) present the MTT results for the</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head>PeerJ</ns0:head><ns0:label /><ns0:figDesc>Mat. Sci. reviewing PDF \| (MATSCI-2021:07:63904:1:2:NEW 22 Dec 2021)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head>Fig. 1 .</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Fig. 1. Surface silanization.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head>Fig. 2 .</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Fig. 2. Surface hardness.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_7'><ns0:head>Fig. 3 .</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Fig. 3. Represented live/dead images for biofilms on resin disks.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_8'><ns0:head>Fig. 4 .</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Fig. 4. Showed the live/dead results of planktonic bacteria in the medium.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_9'><ns0:head>Figure 5 Fig. 5 .</ns0:head><ns0:label>55</ns0:label><ns0:figDesc>Figure 5</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_10'><ns0:head>Fig. 6 .</ns0:head><ns0:label>6</ns0:label><ns0:figDesc>Fig. 6. CFUs results.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_11'><ns0:head>Fig. 7 .</ns0:head><ns0:label>7</ns0:label><ns0:figDesc>Fig. 7. Human fibroblast cytotoxicity.</ns0:figDesc></ns0:figure> <ns0:figure type='table' xml:id='tab_4'><ns0:head>Table 2 .</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>The chromaticity value of the specimen with 4% Novaron</ns0:figDesc><ns0:table><ns0:row><ns0:cell>1</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>2</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>Groups</ns0:cell><ns0:cell>L</ns0:cell><ns0:cell>a</ns0:cell><ns0:cell>b</ns0:cell></ns0:row><ns0:row><ns0:cell>Control groups</ns0:cell><ns0:cell>88.240±0.493</ns0:cell><ns0:cell>-0.763±0.061</ns0:cell><ns0:cell>1.125±0.172</ns0:cell></ns0:row><ns0:row><ns0:cell>Novaron groups</ns0:cell><ns0:cell>89.273±0.456</ns0:cell><ns0:cell>-0.808±0.082</ns0:cell><ns0:cell>1.103±0.266</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_5'><ns0:head>Table 3 .</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>The change in chromaticity of the specimen after the addition of 4% Novaron</ns0:figDesc><ns0:table><ns0:row><ns0:cell /><ns0:cell>△L</ns0:cell><ns0:cell>△a</ns0:cell><ns0:cell>△b*</ns0:cell><ns0:cell>△E</ns0:cell></ns0:row><ns0:row><ns0:cell>Novaron groups</ns0:cell><ns0:cell>1.033</ns0:cell><ns0:cell>0.045</ns0:cell><ns0:cell>0.022</ns0:cell><ns0:cell>0.535</ns0:cell></ns0:row><ns0:row><ns0:cell>2</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row></ns0:table><ns0:note>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2021:07:63904:1:2:NEW 22 Dec 2021)</ns0:note></ns0:figure> <ns0:note place='foot'>Analytical, Inorganic, Organic, Physical, Materials Science </ns0:note> </ns0:body> "	" Prof. Dr. Youcheng Yu Department of Stomatology Zhongshan Hospital Fudan University 180 Fenglin Road Shanghai, China, 200032 Tel.: +86-21-64041990 Fax: +86-21-64041990 E-mail: yu.youcheng@zs-hospital.sh.cn Dear editors, We thank the reviewers for their generous comments on the manuscript and we have edited the manuscript to address their concerns. We believe that the manuscript is now suitable for publication in PeerJ. Sincerely yours, Prof. Dr. Youcheng Yu On behalf of all authors. Reviewer 1 Basic reporting a. English usage 1. Please check some English usage. For example, Materials, p.8, 'Two types of enhanced materials were ....'. It should be 'Two types of silanized filler materials were ....'. Thank you for your advice, we have replaced 'enhanced materials' for 'filler materials'. We purchased two types of filler materials and slianized the materials. Experimental design Methods 1. The silanization of both filler particles, ABWs and ZrO2, was not mentioned in the methods. Are you referring the procedure according to your previous study [Zhang et al. 2014b]? if yes, please mention it in the text. Yes. The silanization of nano-ZrO2 and ABWs was according to the previous study (Han et al. 2015). We have mentioned it in the text. 2. The total weight % of the filler particles and Novaron was 64%. Was it difficult to wet enough of the fillers with the resin matrices? There were two resin matrices BIS-GMA and TEGDMA. The silanized nano-ZrO2, ABWs and Novaron were incorporated into the resin matrice BIS-GMA first. The BIS-GMA was power. So they could be mixed well. 3. How long did it take for the light curing each time? Thank you for your advice, we have mentioned it in the text. Every layer was 0.2mm, and the curing light (EliparTM S10 LED, 3M, USA) was used to cure the resin layer in 10s. 4. A surface roughness of 0.18 um for biological test and 0.02 um for Vickers hardness tests, why? According to your previous studies? Thank you for your question. Yes, according to our previous studies, the surface roughness of 0.18 um is appropriate for the adhesion and growth of bacterium. While, the Vickers hardness tests need smoother surface. So, we polished the specimen surface to 0.02 um for Vickers hardness tests. Validity of the findings All raw data are provided. Some comments are given: 1. Please check no SD values are provided. Thank you for your advice. We have provided the SD values in the supplemental files of raw data. 2. The raw data need more descriptive information. e.g., surface hardness test, there are 9 values measured for each group. Thank you for your advice, we have added more descriptive information in the manuscript. Three specimens were chosen in each group and three points on each specimen were tested. So there were 9 values measured for each group. Additional comments Figures Represented live/dead images for biofilms on resin disks 1. It is recommended to combine Figs. 5 to 10 into one figure. The live/dead results of planktonic bacteria in the medium Thank you for your advice, we have combined Figs. 5 to 10 into one figure and resubmitted the figures. 1. It is recommended to combine Figs. 11 to 16 into one figure. The MTT results Thank you for your advice, we have combined Figs. 11 to 16 into one figure and resubmitted the figures. 1. It is recommended to combine Figs. 17 to 22 into one figure. CFUs results Thank you for your advice, we have combined Figs.17 to 22 into one figure and resubmitted the figures. 1. It is recommended to combine Figs. 23 to 28 into one figure. Thank you for your advice, we have combined Figs. 23 to 28 into one figure and resubmitted the figures. Other comments 1. p.13, “Representative live/dead images are shown in Fig. 3 for…..”. Please check if it is Fig. 5. Thank you for your advice, we have checked it and resubmitted the figures. 2. p.14, “Fig. 5A (S. mutans), B (F. nucleatum) and C (C. albicans) present the MTT results for the biofilms on the resin surfaces of the Novaron groups, and Fig. 5D-F are….”. Please check the figure numbers. Thank you for your advice, we have checked it and resubmitted the figures. 3. p.14, “……. presented in Fig. 6A (S. mutans), B (F. nucleatum) and C (C. albicans), and the results for the planktonic cells in the culture medium are presented in Fig. 6D-F. The results were the same as the MTT results.”. Please check the figure numbers. Thank you for your advice, we have checked it and resubmitted the figures. 4. p.15, “……. cytotoxicity of resin composites are shown in Fig. 7,……”. Please check the figure numbers Thank you for your advice, we have checked it and resubmitted the figures. Reviewer 2 Basic reporting This study evaluated some properties of a silver-containing resin composite. Novaron is a silver-supported antibacterial material with promising inhibit activity, which can be useful in dental restorative materials for preventing secondary caries around restorations. Thus, the results of this study strengthen the body of evidence regarding the effectiveness of Novaron incorporation. However, from a dental perspective, several improvements are needed. Herein, a list of criticisms and related suggestions is described. Experimental design TITLE The current title does not fully express the objective of the study, since it was not only evaluated the antimicrobial properties, but also the color and mechanical properties. Please, rewrite the title. Thank you for your advice, we have rewritten the title. ABSTRACT The abstract does not report all the tests performed in this study. Please, mention that color change was also evaluated and include a brief description of the statistical analysis used. Thank you for your advice, we have mentioned that color change and added a brief description of the statistical analysis used. INTRODUCTION In general, the introduction is clear and concise. However, I assume that the novelty of the study should be more emphasized. The current introduction does not approach its main topic: Novaron material. For instance, why Novaron is interesting? How Novaron works? Why Novaron should be incorporated in resin composites? What are the advantages of incorporating Novaron compared to other silver-based agents? These questions must be briefly answered through the introduction. Thank you for your advice, we have added the introduction of Novaron material in this part. Some minor revisions are addressed below. Line 71: The term “lower resistance to caries” is not adequate. Please, reformulate the sentence explaining the risk for secondary caries due to microbial invasion and proliferation in the tooth/restoration interface. Add more references if necessary. Thank you for your advice, we have explained the secondary caries and the reasons why the resin composites had lower resistance to caries in the lines 72-74. We have added more references here. Lines 71-72: Please, include a reference that supports this statement. Thank you for your advice, we have added reference here. Lines 80-82: Please, include reference of studies that evaluated resin composites containing antimicrobial agents. Thank you for your advice, we have added reference here. Line 81: Replace “microbial destruction” for “microbial growth/proliferation”. Thank you for your advice, we have replaced “microbial destruction” for “microbial growth/proliferation” in line 81. Line 86: Please, indicate that Ag means silver, since it was mention in the text for the first time. Thank you for your advice, we have indicated that Ag means silver. Line 89-98: This paragraph is confusing for me. I suggest that the authors firstly mention the previous researches using Novaron. After that, the introduction must end with the objective of the study. Thank you for your advice, we have added the previous researches using Novaron in this part. MATERIALS AND METHODS In my understanding, the authors prepared resin matrices with and without Novaron. From a clinical perspective, why Novaron particles were not incorporated into a commercial resin composite? If it is not possible, would not be interesting to include a control group using a commercial resin composite? Please, include sample size calculation. Thank you. The commercial resin composite is semi-transparent paste. Novaron particles were not incorporated into a commercial resin composite uniformly. The resin composite concludes different proportion of resin matrices, filler materials, photosensitizer and polymerization inhibitor，etc. The performances of resin composite are varied with different proportion of component. Therefore, the study did not include a control group using a commercial resin composite. We would research it in the future. Lines 100-111: To enhance the readability of the paper, I suggest the inclusion of a Table giving detailed information regarding the composition of the resin composites evaluated in the study. Agreed, we have added a table e giving detailed information regarding the composition of the resin composites Lines 131-132: Describe how the light-curing was performed in more detail. What light-curing unit was used? The irradiance? Light exposure time? Thank you for your advice, we have added the description of the light-curing in more detail. Line 137: How the specimens were sterilized? Please, describe it. Thank you for your advice, we have described it in the manuscript. Line 145: Replace “three kinds” for “three strains”. Thank you for your advice, we have replaced “three kinds” for “three strains”. Line 201: I suggest the calculation of the Whiteness Index for Dentistry (WID) since it has been widely used in recent studies. WID is a CIELAB-based system, thus the authors can calculate it using CIELAB values previously obtained. Please, have a look at the published paper: Peréz MM et al. Development of a customized whiteness index for dentistry based on CIELAB color space. Dent Mater. 2016;32(3):461-7. doi: 10.1016/j.dental.2015.12.008. Thank you for your advice, we have learned the published paper of Whiteness Index for Dentistry (WID). There were only two groups in our research, we chose the △E values as a qualitative index. according to references, the △E values lower than approximately 3.3 are acceptable. Line 216: Considering the sample size (n = 6), the most adequate normality test would be Shapiro-Wilk. Why the authors did not use that test instead of Kolmogorov-Smirnov? Please, include statistical significances for Tables 1 and 2. Thank you for your advice. We have use the Shapiro-Wilk test for the normality test. And the p values were provided in the supplemental files of raw data. RESULTS AND DISCUSSION All the changes suggested in previous sections should be considered for the Results and discussion section. This study inspires several questions that could be deeply explained through the results and discussion section. Herein, authors should report comments upon only report the obtained results. Moreover, the clinical extrapolation of the results is missing and it is crucial for the dental audience. I suggest rewriting all this section. Thank you for your advice, we have rewritten the section of results and discussion FIGURES Please, include in all figure captions what the asterisks and red lines mean in the charts. Thank you for your advice, we have explained the asterisks and red lines in all figure captions. "	Here is a paper. Please give your review comments after reading it.
449	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Background.The NOVARON, a silver-based antimicrobial agent derived from inorganic ion exchangers developed by Toagosei and registered by FDA, has effectively indicated the antimicrobial power of silver against a variety of microbes. The objective of this study was to investigate the effect of a silver-supported material (Novaron (N)) on the mechanical behaviour, antimicrobial properties, cytotoxicity and colour of light-cured resin composites.</ns0:p><ns0:p>Methods. Silanized aluminum borate whisker (ABWs) (4 wt%) and nano-zirconia (nano-ZrO2) (2 wt%) were mixed with the resin matrix to obtain the control groups; 4 wt% surface-modified Novaron particles were incorporated into the above matrices as the experimental groups. The surface hardness was tested. Furthermore, the antimicrobial abilities evaluated in vitro with Streptococcus mutans (S. mutans), Fusobacterium nucleatum (F. nucleatum) and Candida albicans (C. albicans) using the live/dead, MTT and colony-forming units (CFUs) assay. Furthermore, the effects on fibroblast growth and colour were test in this study.</ns0:p><ns0:p>Results.The data of the Novaron and control groups were analyzed by Student's t-test. The results showed that the activities of S. mutans, F. nucleatum and C. albicans biofilms on the composites surface were greatly reduced (p<0.05) and no significant difference was found in the culture medium (p>0.05). Extracts taken from the cell culture medium of the specimens were used to evaluate cell viability. The composites did not have an adverse effect on fibroblast growth and colour in this study . The results showed that 4 wt% Novaron incorporated into the resin composites could increase the surface hardness (p<0.05). Therefore, Novaron is a potential antimicrobial agent applying in light-cured and inorganic nanoparticles reinforced dental resin materials.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>The development of light-cured dental restorative composites has increased rapidly due to their better aesthetic properties, fewer safety concerns, ease of handling, physical properties similar to dentin and reasonably satisfactory clinical results compared with those of metallic dental amalgams <ns0:ref type='bibr' target='#b37'>(Wang et al. 2019</ns0:ref>). Dental composites have been reported to be used in more than 95% of all anterior tooth direct restorations and in approximately 50% of all posterior tooth direct restorations <ns0:ref type='bibr' target='#b13'>(He et al. 2015)</ns0:ref>. Generally, light-cured dental resin composites consist of an organic resin matrix, inorganic fillers, photo-initiators and accelerators. The most commonly used organic matrices are Bis-GMA and TEGDMA <ns0:ref type='bibr' target='#b44'>(Zhang et al. 2014a)</ns0:ref>. Inorganic fillers such as zirconium dioxide, silicon dioxide and other glass particles are popularly used to improve the mechanical properties of resin composites <ns0:ref type='bibr' target='#b38'>(Wille et al. 2016)</ns0:ref>. It is well known that there are different surface properties between the matrix and the fillers; the former is hydrophilic and highly polar, while the latter is generally relatively hydrophobic and non-polar. Surface modification with silane coupling agents is commonly used to increase the interfacial interaction between inorganic fillers and the organic resin matrix <ns0:ref type='bibr' target='#b21'>(Lung et al. 2016</ns0:ref>).</ns0:p><ns0:p>However, resin composites have some disadvantages, including excessive wear, inadequate strength, technique sensitivity, dimensional shrinkage, poor marginal adaptation, distortion, and lower resistance to caries <ns0:ref type='bibr' target='#b7'>(Cherchali et al. 2017;</ns0:ref><ns0:ref type='bibr' target='#b14'>Ibrahim et al. 2020)</ns0:ref>. The main reason for failure is still secondary caries, followed by fracture of restoration. Compared to other restorative materials, dental resin composites restorative materials have been reported to accumulate more bacteria or plaque and more easily form dental biofilms <ns0:ref type='bibr' target='#b0'>(Almousa et al. 2019;</ns0:ref><ns0:ref type='bibr' target='#b30'>Pietrokovski et al. 2016</ns0:ref>). Among various bacterial species, Streptococcus mutans is considered to play a major role in the formation and development of plaque biofilms <ns0:ref type='bibr' target='#b9'>(De Paula et al. 2018)</ns0:ref>. However, other oral microorganisms such as Enterococcus faecalis, Candida albicans and Fusobacterium nucleatum also play an important role in the development and progression of this disease <ns0:ref type='bibr' target='#b23'>(Melo et al. 2018)</ns0:ref>.</ns0:p><ns0:p>Therefore, the development of antibacterial restorative filling materials requires a potent antimicrobial agent that acts against a wide range of oral micro-organisms. Recent studies have paid growing attention to resin composites materials with antibacterial properties, combating microbial growth/proliferation and secondary caries to improve the longevity of restorations. The most common method is incorporation of the filler with an inorganic antibacterial agent <ns0:ref type='bibr' target='#b2'>(Boaro et al. 2019;</ns0:ref><ns0:ref type='bibr' target='#b26'>Münchow et al. 2020)</ns0:ref>. Compared to organic antibacterial materials, inorganic antibacterial agents have better compatibility as well as long-lasting and wide broad-spectrum antibacterial properties <ns0:ref type='bibr' target='#b35'>(Wang et al. 2017)</ns0:ref>. For the antibacterial properties of dental resin composites, Ag(silver)-based agents are one of the most commonly used inorganic agents, and silver-zeolite, silver-apatite and other silver-supported materials have been reported to achieve good antibacterial effects <ns0:ref type='bibr' target='#b24'>(Mocanu et al. 2014</ns0:ref>).</ns0:p><ns0:p>In our previous research <ns0:ref type='bibr' target='#b6'>(Chen et al. 2017)</ns0:ref>, we had chosen four different inorganic antibacterial agents (titanium dioxide (TiO 2 ), silver-supported titanium dioxide (Ag/TiO 2 ), silver-supported zirconium phosphate (Novaron), and tetrapod-like zinc oxide whiskers (T-ZnOw)) to fabricated antibacterial composites and investigated their antibacterial activities against oral microorganisms.</ns0:p><ns0:p>Novaron had the highest antibacterial property. Novaron consists of uniform fine particles with low moisture absorption and good heat-resistant properties and is easy to mix with matrices. The antimicrobial mechanism of Novaron involves either or both of the following steps: silver ions enter into the cell membranes of bacteria and then inhibit the crosslinked action of polysaccharide to lightly destroy the cell membranes of bacteria; silver ions combine with DNA, interfere with the synthesis of DNA and RNA, inhibition the replication and proliferation of DNA and finally results in bacterial death <ns0:ref type='bibr' target='#b40'>(Yeluri et al. 2012)</ns0:ref>. Therefore, the objective of this study was to investigate the antimicrobial properties of the silver-supported material Novaron in dental composites. Following our previous research, ABWs and Nano-ZrO 2 were used as fillers to improve the mechanical properties of resin composites <ns0:ref type='bibr' target='#b46'>(Zhang et al. 2014b)</ns0:ref>. In this study, we applied silanization methods to modify the fillers, aiming to modify the surface of Novaron. Additionally, as previous research discovered that 4% Novaron offered good antibacterial and mechanical properties and lower cytotoxicity in acrylic resin composites <ns0:ref type='bibr' target='#b12'>(Han et al. 2015)</ns0:ref>. In this study, we added 4% Novaron into light-cured and nanoparticles reinforced resin to fabricate novel composites, and evaluated the antimicrobial properties against three different microorganisms as well as the mechanical and cytotoxic properties in vitro.</ns0:p></ns0:div> <ns0:div><ns0:head>Materials and methods</ns0:head></ns0:div> <ns0:div><ns0:head>Materials</ns0:head><ns0:p>A light-cured resin composites system was used as the parent resin system to test the effect of Novaron incorporation. The composites contain two matrices, BIS-GMA (Sigma-Aldrich, USA)</ns0:p><ns0:p>and TEGDMA (Sigma-Aldrich,USA);CQ (Sigma-Aldrich,USA), DMAEMA (Sigma-Aldrich, USA), and BHT(Sigma-Aldrich, USA). Two types of filler materials were purchased to reinforce the mechanical properties of the resin composites: Nano-ZrO 2 (granularity: 50-90 nm, Tosoh Co., Ltd., Tokyo, Japan) and ABWs (diameter <1.5 µm, length: 5-30 µm, Shanghai Whisker Composites Co., Ltd., Shanghai, China). The antimicrobial materials were white silver-supported powders: Novaron AG300 (N, average particle size: 0.9 µm, Toagosei Co., Ltd., Tokyo, Japan) in this study. Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>was purchased to silanize the mixed materials. The other chemicals in this study were analytical grade reagents.</ns0:p></ns0:div> <ns0:div><ns0:head>Surface modification of the antimicrobial materials</ns0:head><ns0:p>For the surface treatment of the Novaron, 1.5% γ-MPS (Z-6030) and 10 g of acetone were mixed, and the pH value was adjusted to 4.0-5.0 to control the hydrolysis reaction using acetic acid. The solution was stirred continuously for 1 h to pre-hydrolyse the silane using a magnetic stirring apparatus. Meanwhile, the Novaron powders were immersed in distilled water (Novaron/water weight ratio of 1:10) and completely dispersed with an ultrasonic vibration apparatus for 1 h. Then, the pre-hydrolysed silane was slowly added dropwise into the Novaron suspension, and the mixture was agitated for 1 h at 80 °C. After hot agitation, the suspension was cooled stepwise from room temperature to -80 °C and dried in a vacuum freeze dryer for 24 h to obtain the powders. The surface analyses of unsilanized and silanized Novaron were examined using XPS (AXIS UltraDLD, Kratos, Japan) with an A1 Ka source (1486.6 eV). The morphology of the powder and the dispersion in composites were investigated using SEM (NOVA NanoSEM230, FEI Company, Netherlands).</ns0:p></ns0:div> <ns0:div><ns0:head>Specimens</ns0:head><ns0:p>The two resin matrices BIS-GMA (24.8875%) and TEGDMA (74.6625%) at a 1:3 weight ratio were mixed, and 0.25% photosensitizer CQ, 0.15% activator DMAEMA and 0.05% polymerization inhibitor BHT were added. The silanization of nano-ZrO 2 and ABWs was according to the previous study <ns0:ref type='bibr' target='#b12'>(Han et al. 2015)</ns0:ref>. Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science resin paste. The experimental groups containing Novaron, while the control groups did not. Table <ns0:ref type='table'>1</ns0:ref> lists the different groups of composition prepared in this study. Then, the resin paste was poured into a round mould and light-cured layer-by-layer. Every layer was 0.2mm, and the curing light (Elipar TM S10 LED, 3M, USA) was used to cure the resin layer in 10s. As our previous research <ns0:ref type='bibr' target='#b12'>(Han et al. 2015)</ns0:ref>, the specimens were standardized to dimensions of 10 mm diameter and 2 mm height. All the specimens were mechanically polished to a high gloss using a grinder-polisher (Phoenix Beta, Buehler Ltd., Germany) and wet abrasive paper. To conduct the biofilm experiments and cytotoxicity tests, the specimen surface was polished to a roughness of 0.18±0.03 µm for the antimicrobial and cytotoxicity tests and 0.02±0.005 µm for the Vickers hardness tests. The specimens were sterilized under ultraviolet light for 2 h on each side for the antimicrobial and cytotoxicity tests.</ns0:p></ns0:div> <ns0:div><ns0:head>Mechanical properties test</ns0:head><ns0:p>After the specimens were polished to a surface roughness of 0.02±0.005 µm, the Vickers hardness of specimens randomly chosen from each test group was measured with a micro-hardness tester (HX-1000, Shanghai Taiming Optical Instruments Co., Ltd., Shanghai, China). Three specimens were chosen in each group and three points on each specimen were tested by applying a 50 g (0.49 N) load for 10 s. The results were recorded with PC software. Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Live/dead assay of different microorganisms</ns0:head></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science our previous research <ns0:ref type='bibr' target='#b12'>(Han et al. 2015)</ns0:ref>. Then, the bacterium suspension was adjusted to 1.0 × 10 6</ns0:p><ns0:p>CFUs/mL for further use. Six disc-shaped specimens for each group were placed in a 48-well plate with 500 µL of BHI and 50 µL of inoculated cell suspension in each well. After 24 h inoculation, the discs with biofilm were transferred to a new 48-well plate, and the planktonic cells in the medium were used in the following experiments.</ns0:p><ns0:p>The method of Live/dead assay was as same as our previous research <ns0:ref type='bibr' target='#b17'>(Liu et al. 2020)</ns0:ref>. The cells in the biofilms on discs were harvested with 1mL of BHI with mild sonication and pipetting and stained using a live/dead cell viability kit (Molecular Probes, Invitrogen, USA) for 15 min in darkness. Live cells were stained with Syto 9 to produce green fluorescence, while dead cell membranes were stained with propidium iodide to produce red fluorescence. Separately, the planktonic cells in the medium were collected and similarly live/dead stained. Each test was performed at n = 6. The stained specimens were examined by CLSM (Leica TCS SP2, Germany).</ns0:p></ns0:div> <ns0:div><ns0:head>MTT assay of cell metabolic activity</ns0:head><ns0:p>The MTT assay is a colorimetric assay to estimate the metabolic activity of cells. The 24 h biofilm discs were transferred to a new 48-well plate, and 300 µL of MTT dye (0.5 mg/mL MTT in PBS) was added to each well. Meanwhile, the collected medium with planktonic cells from each well was transferred to a tube containing 30 µL of MTT dye. The specimens of S. mutans and C.</ns0:p><ns0:p>albicans were incubated at 37 °C in an aerobic incubator and F. nucleatum in an anaerobic incubator for 4 h. During this process, metabolically active cells reduced the MTT to purple formazan. After 4 h, the discs were transferred to a new 48-well plate, and 300 µL of DMSO (Sigma, USA) was added to solubilize the formazan crystals, while the planktonic cells were Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science transferred to a new 96-well plate. The absorbance at 590 nm (OD590) was recorded via a multiwell microplate reader (Labsystem Multiskan EX, USA). The higher absorbance indicated a higher formazan concentration, which indicated a higher metabolic activity of the cells. Six replicates were tested for each group (n = 6).</ns0:p></ns0:div> <ns0:div><ns0:head>Colony forming unit counts (CFUs)</ns0:head><ns0:p>Two types of agar plates were prepared in this part: the BHI agar plates were for S. mutans and C. albicans, and the BHI blood agar plates were used to culture F. nucleatum. The cells in the 24 h biofilms on discs were rinsed via mild sonication and were pipetted. Then, the cell suspensions were serially diluted to 10 -6 , and 50 µL of the diluted cell liquid was spread onto a BHI agar plate or BHI blood agar plate and cultured at 37 °C for 48 h for CFUs analysis (n = 6). Separately, the CFUs of the planktonic cells from each well were also measured.</ns0:p></ns0:div> <ns0:div><ns0:head>Cytotoxicity of the composites eluent</ns0:head><ns0:p>For the cell cytotoxicity test, eluent solutions of specimens were prepared according to ISO 10993-5 and ISO 10993-12. Sterile specimens were immersed in DMEM and agitated for 24 ± 2 h at 37 °C to obtain the extracts from the specimens. The surface/volume ratio of the specimen and the medium was 1.25 cm 2 /mL. After incubation, the extracts were filtered by 0.22 µm filters into sterile tubes and diluted 2-fold with fresh DMEM for testing. The negative control groups were DMEM without the eluent solution.</ns0:p><ns0:p>Gingival fibroblasts cultured in DMEM supplemented with 10% fetal calf serum with 100 U/ml penicillin and 100 mg/ml streptomycin (Gibco BRL, USA) at 37 °C in an air atmosphere containing 5% CO 2 at 100% relative humidity. A seeding density of 4000 cells/well was used in 96-well plates, with 200 µL per well. After 24 h incubation at 37 °C with 5% CO 2 in air, the culture medium was removed and replaced with equal volumes of the eluent solution and a 2-fold dilution.</ns0:p><ns0:p>Meanwhile, the negative groups were treated with DMEM. The cells were cultured for another 24 h, and then, 20 µL of sterile-filtered MTT was added to each well. After incubation in a darkroom for 4 h at 37 °C, the unreacted dye was removed, and 200 µL/well of DMSO was added. The plates were then slightly stirred at room temperature for 10 min, and the solution absorbance was measured via a microplate reader (Labsystem Multiskan EX, USA) at 490 nm. The absorbance of the negative groups was set as 100%. The fibroblast viability for cells cultured with eluents = absorbance with eluents/absorbance of negative control.</ns0:p></ns0:div> <ns0:div><ns0:head>Colour change measurement</ns0:head><ns0:p>To evaluate the colour changes, specimens measuring 10 mm in diameter and 2 mm in thickness were prepared. Six samples were prepared from each material. After preparation, the samples were polished to a surface roughness of 0.02±0.005 µm and immersed in stilled water in △E>3.3 showed that the difference in colour was obvious.</ns0:p></ns0:div> <ns0:div><ns0:head>Statistical analysis</ns0:head><ns0:p>The normal distribution and homogeneity of all the data were checked using the Shapiro-Wilk test. Then, the data of the Novaron and control groups were analysed by Student's t-test using SPSS 19.0 statistical software at a significance level of p < 0.05.</ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head><ns0:p>Fig. <ns0:ref type='figure' target='#fig_4'>1A</ns0:ref> shows the XPS data of the Novaron surface before and after silanization. The band at approximately 102 eV in the spectrum of the Si-2p groups observed was attributed to silanized Novaron. Fig. <ns0:ref type='figure' target='#fig_4'>1B</ns0:ref> and C present the SEM images of the unsilanized and silanized Novaron samples, respectively. These images showed good dispersibility of the fillers after surface treatment, and this property could increase the interfacial contact between fillers and the matrix. Fig. <ns0:ref type='figure' target='#fig_5'>2</ns0:ref> presents the surface hardness results. Statistical analysis revealed that the surface hardness was enhanced significantly with the addition of Novaron compared to that of the control groups (p<0.05). Fig. <ns0:ref type='figure' target='#fig_6'>3</ns0:ref> shows the live/dead images are for biofilms on resin discs; the left columns represent the control groups with different cells, while the right columns represent the Novaron groups. For S. mutans (A), F. nucleatum (B) and C. albicans (C), the resin composites containing Novaron had more compromised cells than the control groups did (D, E, F). Fig. <ns0:ref type='figure' target='#fig_7'>4</ns0:ref> shows the live/dead results of planktonic cells in the medium. There were no obvious differences between the Novaron and control groups for any of the different microbes. These results indicate that the Novaron-containing resin inhibited cell growth on its surface, but the cells distant from its surface were still primarily alive. Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science that the Novaron groups achieved a significantly lower MTT absorbance than that observed in the control groups (p<0.05), while there were no significant differences between the control and Novaron groups in terms of planktonic cells in the culture medium (p>0.05). Fig. <ns0:ref type='figure' target='#fig_9'>6A</ns0:ref> (S. mutans), B (F. nucleatum) and C (C. albicans) present the CFU results on the Novaron groups resin surface, and the results for the planktonic cells in the culture medium are presented in Fig. <ns0:ref type='figure' target='#fig_9'>6D-F</ns0:ref>. The results were the same as the MTT results. The Novaron groups achieved significantly lower CFUs than the control groups did (p<0.05), while there were no significant differences between the control groups and Novaron groups planktonic cells in the culture medium (p>0.05). Fig. <ns0:ref type='figure' target='#fig_10'>7</ns0:ref> shows the results regarding the fibroblast cytotoxicity of resin composites, indicating that there were no significant differences in relative cell viability between the undiluted extracts and 2-fold diluted eluents (p>0.05).</ns0:p><ns0:p>The results of the initial colour measurements of the L, a and b* axes are presented in Table <ns0:ref type='table' target='#tab_3'>2</ns0:ref>. The colour change of the chromaticity of the specimen after the addition of 4% Novaron is</ns0:p><ns0:p>shown in Table <ns0:ref type='table' target='#tab_4'>3</ns0:ref>. The △E values were less than 1.0, indicating that humans could not detect the differences between these two groups.</ns0:p></ns0:div> <ns0:div><ns0:head>Discussion</ns0:head><ns0:p>Uncoated inorganic materials, especially nanoparticles, normally have a tendency to agglomerate. The effect of aggregation of the materials is hindered by combining them into the resin composites <ns0:ref type='bibr' target='#b8'>(Chouirfa et al. 2019</ns0:ref>).Surface modification with silane coupling agents has been one way to stabilize the fillers and disperse the fillers into uniform resin composites <ns0:ref type='bibr' target='#b42'>(Zane et al. 2016)</ns0:ref>. Because of the different surface properties between the matrix and the fillers, the interphase quality between them plays a major role in the ultimate properties of the composites materials (Jiao Manuscript to be reviewed Chemistry Journals <ns0:ref type='bibr'>Analytical, Inorganic, Organic, Physical, Materials Science et al. 2014)</ns0:ref>. There are several methods available to quantify the interfacial adhesion of fillers in composites materials. Surface modification with silane coupling agents is commonly used to improve interfacial adhesion in filler-reinforced resin composites <ns0:ref type='bibr' target='#b1'>(Aydinoglu & Yoruc 2017)</ns0:ref>. The most common silane used in dental composites is γ-MPS, which is a bi-functional monomer, with hydroxymethyl groups substituted by hydroxyl groups attaching to the fillers <ns0:ref type='bibr' target='#b12'>(Han et al. 2015;</ns0:ref><ns0:ref type='bibr' target='#b19'>Liu et al. 2018)</ns0:ref>. γ-MPS also contains C=C bonds, which react with the resin matrices during the curing process. Therefore, the silane coupling agent γ-MPS established chemical bonds between the fillers and the resin composites as a bridge. Surface modification with γ-MPS resulted in the observed Si absorption peak, indicating that the silane coupling agents were successfully grafted onto the filler surface <ns0:ref type='bibr' target='#b12'>(Han et al. 2015;</ns0:ref><ns0:ref type='bibr' target='#b22'>Matinlinna et al. 2018)</ns0:ref>. After silanization by the vacuum freeze-drying method, the morphology of the fillers was evaluated. The images of Fig. <ns0:ref type='figure' target='#fig_4'>1</ns0:ref> showed good dispersibility of the fillers after surface treatment after silanization.</ns0:p><ns0:p>Based on previous research, ABWs and Nano-ZrO 2 were selected as reinforced filler materials incorporated into the resin matrix. Aluminium borate whiskers with a single crystal structure have been successfully used as a reinforcement for metal or resin matrix composites. Nano-ZrO 2 containing nanoparticles exhibit the best anti-wear properties, and zirconium dioxide possesses excellent properties such as high hardness, strength and fracture toughness as well as outstanding wear and chemical corrosion resistance performance <ns0:ref type='bibr' target='#b12'>(Han et al. 2015)</ns0:ref>. Novaron exhibited uniform fine particles with low moisture absorption capability and good heat-resistant properties. This material can be easily mixed into matrices. In addition, it has high physical and chemical stability along with superior discoloration resistance during processing or use <ns0:ref type='bibr' target='#b40'>(Yeluri et al. 2012</ns0:ref>).</ns0:p><ns0:p>In the present study, S. mutans, C. albicans and F. nucleatum were used to examine the antimicrobial activities of Novaron in a resin matrix with direct contact and the planktonic cell PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2021:07:63904:2:0:NEW 7 Jan 2022)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science test. Streptococcus mutans is a gram-positive bacterium and represents one of the main species in cariogenic biofilms responsible for secondary caries <ns0:ref type='bibr' target='#b11'>(Florez et al. 2016</ns0:ref>). Oral C. albicans has been recognized as one of the contributing factors of denture stomatitis. Biofilm formation on dental materials and the subsequent colonization of microbial cells may cause secondary caries and gingivitis <ns0:ref type='bibr' target='#b29'>(Oktay et al. 2019)</ns0:ref>. F. nucleatum is a gram-negative anaerobe correlated with increased probing depth and progressive periodontal ligament reduction in periodontitis. Periodontitis enhances the loss of tooth attachment and the development of root caries and leads to the failure of Class V restorations <ns0:ref type='bibr' target='#b36'>(Wang et al. 2016</ns0:ref>). The biofilm composition may influence the outcome of caries treatments and the killing efficacy of antibacterial agents. Therefore, new antimicrobial restorative materials should be tested against multispecies biofilms. Novaron is a silver-supported inorganic agent. Silver is well known for its broad-spectrum antimicrobial properties and good biocompatibility with human cells; thus, it has been widely used in medical and other fields. Silver is antimicrobial against a wide range of microorganisms: bacteria, fungi and certain viruses, including antibiotic-resistant strains <ns0:ref type='bibr' target='#b3'>(Cao et al. 2017</ns0:ref>). Silver ions, as released antimicrobial agents, have been incorporated into composites mixtures in an attempt to achieve significant antimicrobial performance. However, as these silver ions are released, the generation of voids can negatively affect the mechanical properties of the composites.</ns0:p><ns0:p>Burst release is another concern of this technique <ns0:ref type='bibr' target='#b5'>(Chatzistavrou et al. 2015)</ns0:ref>. Conversely, nonreleased agents, such as silver-supported fillers, are known to maintain remarkable mechanical properties after ageing since the antimicrobial component is not released over time. Novaron is a silver-supported inorganic antibacterial agent and has excellent antimicrobial efficacy against a wide range of microorganisms <ns0:ref type='bibr' target='#b4'>(Cao et al. 2018)</ns0:ref>. The antimicrobial mechanism of Novaron is presumed to involve either or both of the following steps: silver ions form metal-organic PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2021:07:63904:2:0:NEW 7 Jan 2022)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science complexes with sulfhydryl groups in the cell walls of bacteria and fungi, generally inactivating essential enzymes responsible for energy metabolism <ns0:ref type='bibr' target='#b10'>(Dias et al. 2019)</ns0:ref>. Silver ions also activate oxygen, which is converted into oxygen free radicals by the action of light energy in air or water as a result of the catalytic action of silver, attacking the respiratory chain and cell division, leading to cell death <ns0:ref type='bibr' target='#b39'>(Xu et al. 2016)</ns0:ref>. Therefore, a further study should investigate the antimicrobial activity over a long period. We chose different methods (Fig. <ns0:ref type='figure' target='#fig_9'>3-6</ns0:ref>) to test the composites antimicrobial ability of biofilms and planktonic bacteria and yeast. These results indicated that the Novaron-containing resin inhibited cell growth on its surface, but the cells distant from its surface were still primarily alive. Previous studies suggested that there was no release of silver ions for a long time <ns0:ref type='bibr' target='#b41'>(Yoshida et al. 1999)</ns0:ref>, which was in agreement with the results of this study. The antibacterial activity of the composites incorporating Novaron can possibly last for long periods because the composites inhibit the growth of cells not by releasing the silver ions from the composites but through their direct contact with the bacteria <ns0:ref type='bibr' target='#b16'>(Kuroki et al. 2010)</ns0:ref>.</ns0:p><ns0:p>It is equally important for new antibacterial composites to be non-cytotoxic and have good biocompatibility. Many studies have reported similar results that Ag+ is nontoxic to the human body at a low concentration <ns0:ref type='bibr' target='#b18'>(Liu & Man 2017;</ns0:ref><ns0:ref type='bibr' target='#b20'>Lu et al. 2017;</ns0:ref><ns0:ref type='bibr' target='#b27'>Natarajan et al. 2016)</ns0:ref>. Cells from various organs or tissues usually display differential susceptibility. Gingival fibroblasts are accessible if the composites are to be applied in the clinic <ns0:ref type='bibr' target='#b31'>(Ren et al. 2019)</ns0:ref>. To exclude the effect of resin monomers on the cytotoxicity assay, the resin discs were cleaned by ultrasound, dried and left undisturbed for 24 h before sterilization. Typical saliva flow is approximately 1000-1500 mL/day for an average person. Hence, diluting the original extract 128-fold yields a total of 1280 mL of culture medium, which can be used to approximate the amount of saliva in the mouth over 24 h <ns0:ref type='bibr' target='#b45'>(Zhang et al. 2013)</ns0:ref>. The present study in Fig. <ns0:ref type='figure' target='#fig_10'>7</ns0:ref> demonstrated that the relative cell viability percentages of the 2-fold diluted and undiluted eluent groups were all greater than 90% after 24 h incubation and were classified as non-cytotoxic and slightly cytotoxic, respectively. In the present study, even at the 2-fold dilution, with a total solution volume of approximately 1/2 of the saliva volume per day in vivo, the Novaron groups still exhibited nearly 100% fibroblast viability <ns0:ref type='bibr' target='#b12'>(Han et al. 2015)</ns0:ref>. According to ISO 10993-5:2009, the resin composites could achieve potential antimicrobial activities without compromising fibroblast cytotoxicity, the material was qualified. Broad optical properties depending on the nanoparticle diameter, refractive index near the nanoparticle surface, and aggregation are also beneficial features of this material <ns0:ref type='bibr' target='#b32'>(Stencel et al. 2018)</ns0:ref>. Previous investigations have suggested some thresholds of perceptible color difference .</ns0:p><ns0:p>△E values lower than approximately 3.3 are acceptable <ns0:ref type='bibr' target='#b34'>(Vichi et al. 2004</ns0:ref>). The high surface area to mass ratio of Novaron allows better antimicrobial activity at a lower concentration without significantly compromising the composites colour.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions</ns0:head><ns0:p>The present study investigated the effects of Novaron addition in nano-ZrO 2 /ABW resin composites on the mechanical activity, antimicrobial properties, cytotoxicity and colour. The results showed that 4 wt% Novaron incorporated into the resin composites could increase the surface hardness. Antimicrobial functions were obtained without compromising the biocompatibility or colour. Therefore, 4 wt% Novaron may have wide applicability in other composites, bonding systems, sealants and cements. These novel antimicrobial resin composites may be promising for inhibiting oral biofilms and secondary caries. Manuscript to be reviewed The results indicated that the surface hardness were significantly enhanced upon the addition of 4 wt% Novaron groups (p<0.05).The asterisks means there were significant differences between control groups and the Novaron groups.</ns0:p><ns0:note type='other'>Figure 1</ns0:note><ns0:note type='other'>Chemistry Journals Figure 2</ns0:note><ns0:p>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2021:07:63904:2:0:NEW 7 Jan 2022)</ns0:p><ns0:p>Manuscript to be reviewed than the control groups (p<0.05) while there were no significant differences between control groups and the Novaron groups of planktonic bacteria in culture medium (p>0.05). The asterisks means there were significant differences between control groups and the Novaron groups and the red lines means there were no significant differences between control groups and the Novaron groups.</ns0:p><ns0:note type='other'>Chemistry Journals Analytical, Inorganic, Organic, Physical, Materials Science Figure 3</ns0:note><ns0:note type='other'>Figure 4</ns0:note><ns0:p>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2021:07:63904:2:0:NEW 7 Jan 2022)</ns0:p><ns0:p>Manuscript to be reviewed A (S. mutans), B (C. albicans and C (F. nucleatum) were the results of biofilms on the resin surface and planktonic bacteria in culture medium (D-F). Novaron groups achieved significantly lower CFUs than the control groups (p<0.05) while there were no significant differences between control groups and the Novaron groups of planktonic bacteria in culture medium (p>0.05). The asterisks means there were significant differences between control groups and the Novaron groups and the red lines means there were no significant differences between control groups and the Novaron groups.</ns0:p><ns0:note type='other'>Chemistry Journals Figure 6</ns0:note><ns0:p>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2021:07:63904:2:0:NEW 7 Jan 2022)</ns0:p><ns0:p>Manuscript to be reviewed The results indicated that there were no significant differences of relative cell viability between the undiluted extracts and 2-fold diluted eluents (p>0.05).The red lines means there were no significant differences between control groups and the Novaron groups. Manuscript to be reviewed</ns0:p><ns0:note type='other'>Chemistry Journals Figure 7</ns0:note></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>Three strains of microbes, namely, S. mutans (UA159, gram-positive bacterium), C. albicans (76615) and F. nucleatum (AT25586 gram-negative bacterium) (Shanghai Key Laboratory of Stomatology, China), were used in this study. S. mutans and C. albicans were cultivated under aerobic conditions and F. nucleatum was cultivated under anaerobic conditions in 5 mL of BHI (BD, Franklin Lakes, NJ) at 37 °C for 24 h. The methods of bacterium co-culture were as same as PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2021:07:63904:2:0:NEW 7 Jan 2022)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>collected by centrifugation at 5000 × g for 4 min, adding 300 µL of DMSO. After incubation for 20 min with gentle mixing at room temperature in the dark, 200 µL of the DMSO solution was PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2021:07:63904:2:0:NEW 7 Jan 2022)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head /><ns0:label /><ns0:figDesc>darkness at 55 ± 2 °C for approximately 30 min. A spectrophotometer (Color i7, X-Rite, USA) was used to record the CIE Lab* parameters with a D65 illuminant on a white ceramic tile. The CIEL ab system is composed of three respective axes: L* is the lightness from 0 (black) to 100 (white), a* represents the red (+a* value)-green (-a* value) axis, and b* represents the blue (-b* value)-yellow (+b* value) axis. The colour change (△E) was calculated according to the following equation (1) (Tsubone et al. 2012): △E= (1) ( △ L * )＋( △ a * )＋( △ b * ) where L, △a, and △b* represent the difference values of L, a, and b* between the Novaron △ groups and the control groups, respectively. △E<1.0 indicated that the change in colour could not be detected; 1.0<△E<3.3 indicated that the change in colour could not be distinguished; and</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head /><ns0:label /><ns0:figDesc>Fig. 5A (S. mutans), B (F. nucleatum) and C (C. albicans) present the MTT results for the</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head>Fig. 1 .</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Fig. 1. Surface silanization.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head>Fig. 2 .</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Fig. 2. Surface hardness.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head>Fig. 3 .</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Fig. 3. Represented live/dead images for biofilms on resin disks.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_7'><ns0:head>Fig. 4 .</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Fig. 4. Showed the live/dead results of planktonic bacteria in the medium.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_8'><ns0:head>Figure 5 Fig. 5 .</ns0:head><ns0:label>55</ns0:label><ns0:figDesc>Figure 5</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_9'><ns0:head>Fig. 6 .</ns0:head><ns0:label>6</ns0:label><ns0:figDesc>Fig. 6. CFUs results.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_10'><ns0:head>Fig. 7 .</ns0:head><ns0:label>7</ns0:label><ns0:figDesc>Fig. 7. Human fibroblast cytotoxicity.</ns0:figDesc></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head /><ns0:label /><ns0:figDesc>The silane coupling agent (Z-6030), namely, γ-MPS(Dow Chemical Company, USA),</ns0:figDesc><ns0:table><ns0:row><ns0:cell>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2021:07:63904:2:0:NEW 7 Jan 2022)</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_3'><ns0:head>Table 2 .</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>The chromaticity value of the specimen with 4% Novaron</ns0:figDesc><ns0:table><ns0:row><ns0:cell>1</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>2</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>Groups</ns0:cell><ns0:cell>L</ns0:cell><ns0:cell>a</ns0:cell><ns0:cell>b</ns0:cell></ns0:row><ns0:row><ns0:cell>Control groups</ns0:cell><ns0:cell>88.240±0.493</ns0:cell><ns0:cell>-0.763±0.061</ns0:cell><ns0:cell>1.125±0.172</ns0:cell></ns0:row><ns0:row><ns0:cell>Novaron groups</ns0:cell><ns0:cell>89.273±0.456</ns0:cell><ns0:cell>-0.808±0.082</ns0:cell><ns0:cell>1.103±0.266</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_4'><ns0:head>Table 3 .</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>The change in chromaticity of the specimen after the addition of 4% Novaron</ns0:figDesc><ns0:table><ns0:row><ns0:cell /><ns0:cell>△L</ns0:cell><ns0:cell>△a</ns0:cell><ns0:cell>△b</ns0:cell><ns0:cell>△E</ns0:cell></ns0:row><ns0:row><ns0:cell>Novaron groups</ns0:cell><ns0:cell>1.033</ns0:cell><ns0:cell>0.045</ns0:cell><ns0:cell>0.022</ns0:cell><ns0:cell>0.535</ns0:cell></ns0:row><ns0:row><ns0:cell>2</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row></ns0:table><ns0:note>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2021:07:63904:2:0:NEW 7 Jan 2022)</ns0:note></ns0:figure> <ns0:note place='foot'>Analytical, Inorganic, Organic, Physical, Materials Science </ns0:note> </ns0:body> "	" Prof. Dr. Youcheng Yu Department of Stomatology Zhongshan Hospital Fudan University 180 Fenglin Road Shanghai, China, 200032 Tel.: +86-21-64041990 Fax: +86-21-64041990 E-mail: yu.youcheng@zs-hospital.sh.cn Dear editors, We thank the reviewers for their generous comments on the manuscript and we have edited the manuscript to address their concerns. We believe that the manuscript is now suitable for publication in PeerJ. Sincerely yours, Prof. Dr. Youcheng Yu On behalf of all authors. Reviewer 1 (Anonymous) Validity of the findings In Fig. 1, the XPS spectrum. please check the XPS curve of unsilanized Novaron. It is not clear. Thank you for your advice, we have uploaded the Fig.1. Additional comments Please check the English throughout the text. For example, line 201, the spelling: 'foetal calf serum'. Thank you for your advice, we have replaced 'foetal calf serum' for 'fetal calf serum'. Reviewer 2 (Anonymous) Experimental design - “Ag(slilver)-based”: replace for “Ag(silver)-based”. Thank you for your advice, we have replaced“Ag(slilver)-based” for “Ag(silver)-based”. - “Navaron”: replace for Novaron Thank you for your advice, we have replaced“Navaron”for “Novaron” - It was not described the irradiance emitted by the light curing unit. Moreover, it was added that increments of 0.2 mm in thickness were used. Why such a fine layer? From a clinical perspective, increments of 2 mm in thickness would be used. Thank you for your question, we used the curing light (EliparTM S10 LED, 3M, USA) in the study to cure the resin layer. Its operating voltage was 127V50/60Hz and power input was15 W. The ingredients and proportion of specimens were different from the clinical resin. We added that increments of 0.2 mm in thickness to ensure the fully curing for specimens according our previous research (Han et al. 2015). "	Here is a paper. Please give your review comments after reading it.
450	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Vaesite, a nickel chalcogenide with NiS 2 formula, has been synthetized and studied by theoretical and experimental methods. NiS 2 was prepared by solid-state reaction under vacuum and densified by hot-pressing, at different consolidation conditions. Dense singlephase pellets (relative densities >94%) were obtained, without significant lattice distortions for different hot-pressing conditions. The thermal stability of NiS 2 was studied by thermogravimetric analysis. Both as-synthetized and hot-pressed NiS 2 have a single</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>phase nature, although some hot-pressed samples had traces of the sulfur deficient phase, Ni 1-x S (<1%vol), due to the strong desulfurization at T > 340ºC. The electronic band structure and density of states were calculated by Density Functional Theory (DFT), indicating a metallic behavior. However, the electronic transport measurements showed ptype semiconductivity for bulk NiS 2 , verifying its characteristic behavior has a Mott insulator. The consolidation conditions strongly influence the electronic properties, with the best room-temperature Seebeck coefficient, electrical resistivity and power factor being 182µVK , respectively, pointing this compound as a good starting point for a new family of thermoelectric materials.</ns0:p></ns0:div> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>The search for new, clean, energy sources, as well as the optimization of their use, has become a major issue in contemporary societies. According to the European Environment Agency, current conventional thermal power plants have an energy efficiency around 35-45%, most of the energy being lost as wasted heat <ns0:ref type='bibr'>[EEA 2013</ns0:ref>]. Thermoelectric (TE) materials, which convert thermal energy into electric energy (Seebeck effect) and vice-versa (Peltier effect), are a promising solution to increase the efficiency of many devices and equipment. The potential of a material for thermoelectrics can be evaluated by its figure of merit, zT= α 2 T/ρλ, where α, T, ρ and λ are the Seebeck coefficient, absolute temperature, electrical resistivity and thermal conductivity, respectively <ns0:ref type='bibr' target='#b9'>[Gonçalves & Godart, 2014]</ns0:ref>. Current commercially available TE materials contain rare, expensive and toxic elements, being necessary to develop new, cheap, abundant and environment-friendly alternatives. Metal sulfides are interesting candidates, as they fulfill these requirements <ns0:ref type='bibr' target='#b7'>[Ge et al., 2016]</ns0:ref>. Tetrahedrites are cheap and easily available mineral sulfosalts that present large figures of merit and are seen as having good potential for thermoelectric applications <ns0:ref type='bibr' target='#b18'>[Lu et al., 2016]</ns0:ref>. Pyrite (FeS 2 ) is low cost sulfide with simple synthesis and moderate thermoelectric properties <ns0:ref type='bibr' target='#b11'>[Harada, 1998;</ns0:ref><ns0:ref type='bibr'>Zuñiga-Puelles et al., 2019]</ns0:ref>. In this compound, the large electrical resistivity and thermal conductivity observed in the pristine material are the major constraints to their practical use. These properties can be tuned to much lower values by changing both the composition and microstructure <ns0:ref type='bibr' target='#b23'>[Uhlig, 2014]</ns0:ref>. Vaesite (NiS 2 ), another transition mineral sulfide with pyrite structure <ns0:ref type='bibr' target='#b15'>[Krill et al., 1976]</ns0:ref>, was reported, but its thermoelectric properties were only poorly explored. At equilibrium conditions, NiS 2 is a stoichiometric compound, stable up to 1020 ºC <ns0:ref type='bibr' target='#b26'>[Waldner & Pelton, 2004]</ns0:ref>. However, previous studies also suggested that vaesite is an intrinsic non-stoichiometric compound, with a variable metal concentration and a stable anion content. These deviations from stoichiometry, corroborated by a change of the cell parameters, have important consequences in the electrical and magnetic properties <ns0:ref type='bibr' target='#b6'>[Gautier et al., 1972;</ns0:ref><ns0:ref type='bibr' target='#b15'>Krill et al., 1976]</ns0:ref>. NiS 2 was reported to order antiferromagnetically below T N ~50 K, which is followed by a spin reorientation at ~30 K that leads to a week ferromagnetic ground state <ns0:ref type='bibr' target='#b27'>[Yao et al., 1996]</ns0:ref>. Measurements on single crystals, natural materials and samples prepared by high-pressure synthesis indicated a semiconducting behavior for this compound <ns0:ref type='bibr' target='#b0'>[Bither et al., 1968;</ns0:ref><ns0:ref type='bibr' target='#b13'>Kautz et al., 1972;</ns0:ref><ns0:ref type='bibr' target='#b6'>Gautier et al., 1972;</ns0:ref><ns0:ref type='bibr' target='#b15'>Krill et al., 1976]</ns0:ref>, which pointed to the possibility of using it as thermoelectric material. Nevertheless, thermoelectric measurements made on thin films showed a p-type semiconducting behavior, but small room temperature Seebeck coefficients (4.5-14 µV/K), which contrasts with the large values obtained on single crystals prepared by halogen transport (311-400 µV/K) <ns0:ref type='bibr' target='#b0'>[Bither et al., 1968;</ns0:ref><ns0:ref type='bibr' target='#b15'>Krill et al., 1976;</ns0:ref><ns0:ref type='bibr' target='#b6'>Gautier et al., 1972;</ns0:ref><ns0:ref type='bibr'>Ferrer & Sanchéz, 1999;</ns0:ref><ns0:ref type='bibr' target='#b4'>Clamagirand et al., 2012;</ns0:ref><ns0:ref type='bibr' target='#b13'>Kautz et al., 1972;</ns0:ref><ns0:ref type='bibr' target='#b16'>Kwizera & Dresselhaus & Adler, 1980;</ns0:ref><ns0:ref type='bibr' target='#b20'>Matsuura et al., 2000]</ns0:ref>. Reference values of Seebeck coefficient, resistivity and power factor of vaesite at room temperature can be found in Table <ns0:ref type='table'>1</ns0:ref>. Moreover, albeit the preparation of synthetic bulk NiS 2 by solid state route has been previously described <ns0:ref type='bibr' target='#b15'>[Krill et al., 1976;</ns0:ref><ns0:ref type='bibr' target='#b20'>Matsuura et al., 2000]</ns0:ref>, it resulted in highly porous pellets, easily disaggregated, not suitable for the electrical transport properties study. In this work, we explored the solid-state route followed by hot-press to prepare dense vaesite samples, suitable for their characterization, including the electrical transport properties (electrical resistivity and Seebeck coefficient) investigation. Density functional theory calculations were also performed and their results compared with the experimental data.</ns0:p></ns0:div> <ns0:div><ns0:head>Materials & Methods</ns0:head><ns0:p>NiS 2 samples, with an average mass of ~1.5 g, were prepared by high-temperature reacting the elements inside quartz ampoules. The desired quantities of Ni and S were put inside the quartz ampoules (8 mm inner diameter, 1 mm wall thickness), which were evacuated down to 6×10 5 mbar and sealed. An excess of 5wt% of the chalcogenide element was considered in order to compensate eventual evaporation losses. The ampoules were placed in a horizontal tube furnace pre-heated at 150 ºC, and heated at 800 ºC for 12 h, with a heating speed of 0.3 ºCmin -1 and two intermediate dwells at 400 ºC and 650 ºC for 8 h. Afterwards, they were slowly cooled inside the furnace. The samples were then manually ground, cold-pressed, sealed in evacuated quartz ampoules and heated again in the same conditions. Finally, the samples were once more manually powdered, a ∼30wt% excess of S was added, and ∼0.6 g of the resulting powder was charged in a high-density graphite mould that was used in the hot-pressing procedure. The hotpress was made under inert atmosphere (Ar), increasing the pressure at 3 MPa•min -1 up to 56 MPa and the temperature at 25 ºCmin -1 up to three different dwell temperatures, 700 ºC, 720 ºC and 750 ºC, staying there for 1h30 min. Temperature was then decreased to <100 ºC at 25 ºCmin -1 and the pressure removed at 3 MPa•min -1 . Part of each pellet was manually ground and characterized by powder X-ray diffraction (XRD). A PANalytical X'Pert PRO diffractometer (Bragg-Brentano geometry, Cu Kαradiation) was used. The powders were placed in a low-noise Si single crystal XRD holder and 2θ was scanned from 20º to 90º, with a step size of 0.033º and a time per step of 50 s. Phase identification was made through comparison of the collected diffractograms with reference patterns taken from the literature. Cell parameters and theoretical density were calculated and refined from the powder diffraction data, using the Unit-Cell software <ns0:ref type='bibr' target='#b12'>[Holland & Redfern, 1997]</ns0:ref>. Experimental values of density were determined by the Archimedes method. Porosity was estimated by image analysis, using the ImageJ software. Optical microscopy, scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS) were used for microstructure characterization and chemical composition analysis. It was used an optical microscope ZEISS SteREO Discovery V20 and a JEOL JSM-7001F field emission gun scanning electron microscope (accelerating voltage of 25kV), with an Oxford Instruments EDS spectroscopy system attached. Thermal stability was evaluated with thermogravimetric analysis (TGA). A Dupont 951 Thermogravimetric Analyzer was used. Samples were manually grounded, placed in platinum pans, and heated from 25 ºC to 950 ºC, at a heating rate of 10 ºCmin -1 in an inert atmosphere (N 2 ) flowing at a rate of 60 mL•min -1 . The nature of chemical bonding was analysed by Raman spectroscopy, using a Horiba LabRam HR Evolution Raman microspectometer (laser with λ=532 nm and 10 mW power). Raman spectra were collected from 150 to 1800 cm -1 , the laser light being focused with a 100x objective. 4 scans, with 30 seconds each, were made for each spectrum. Lower laser powers (25-50% of maximum) were required in some measurements to avoid the surface damage. Electrical transport properties were measured between 20-300 K, at a rate of 0.3 Kmin -1 for the Seebeck coefficient and 0.5 Kmin -1 for electrical resistivity, using a closed-cycle cryostat. A system based on the Chaikin's device to measure organic single crystals <ns0:ref type='bibr' target='#b3'>[Chaikin & Kwak, 1975]</ns0:ref> was used to measure the Seebeck coefficient. The samples were first shaped to a needlelike geometry (~0.5x0.5x3.5 mm 3 ) and glued with GE varnish to two gold foils (located in two single crystal quartz blocks heated independently), and the foils are glued with GE varnish to the quartz blocks, so that each side of the sample is thermally anchored to one of the blocks. Two gold wires connected to the sample, then establishing the electrical contacts. The voltage was measured with a low frequency AC technique, with a maximum temperature gradient in the sample of 1 K, controlled by two Au-Fe-Chromel thermocouples connected to the quartz blocks. The electrical resistivity was measured in the same bar-shaped samples through the four-point technique, using an AC resistance bridge and a current of 1 mA. Activation energies were obtained from the electrical resistivity data.</ns0:p></ns0:div> <ns0:div><ns0:head>Band structure calculations</ns0:head><ns0:p>The band structure and density of states of NiS 2 were calculated with the help of the WIEN2k package <ns0:ref type='bibr'>[Blaha et al., 2018]</ns0:ref>. Calculations were performed within the density functional theory (DFT), using linear augmented plane wave (LAPW) method to solve the Kohn-Sham equations. Lattice parameters and atomic positions were taken from experimental data <ns0:ref type='bibr' target='#b25'>[Villars & Calvert, 1986]</ns0:ref>. Both local spin density approximation (LSDA) and generalized gradient approximation with a modified Becke-Johnson potential (GGA+mBJ) were used to approach the exchangecorrelation energy <ns0:ref type='bibr'>[Koller, Trans & Blaha, 2012]</ns0:ref>. The parametrization developed by Perdew-Burke-Ernzerhof was applied for the generalized gradient approximation (PBE-GGA) <ns0:ref type='bibr' target='#b21'>[Perdew, Burke & Ernzerhof, 1996]</ns0:ref>. A cut-off energy of 6 Ry and 1000 k-points in the irreducible part of the Brillouin zone were used for the self-consistent calculations. The criteria of convergence was set at 0.0001 Ry.</ns0:p></ns0:div> <ns0:div><ns0:head>Results and discussion</ns0:head><ns0:p>The powder X-ray diffraction results always point to single phase samples, both after solid-state reaction and hot-pressing (Figure <ns0:ref type='figure'>1</ns0:ref>). All peaks are indexed to the NiS 2 crystal structure, of cubic Pa3 space group. The lattice parameter after solid-state reaction is a=5.685(2) Å. Lattice constants were also calculated for the pellets densified at different consolidation conditions (Table <ns0:ref type='table'>2</ns0:ref>), remaining unchanged. The pellets obtained after the initial heating cycle were highly porous and easily disaggregated, being unsuitable for the electrical transport properties measurements, in agreement with the previous results <ns0:ref type='bibr' target='#b15'>[Krill et al., 1976;</ns0:ref><ns0:ref type='bibr' target='#b20'>Matsuura et al., 2000]</ns0:ref>. Representative microstructures of NiS 2, as-synthetized and hot-pressed, were captured by SEM (Figure <ns0:ref type='figure'>2</ns0:ref>). As-synthetized samples have several pores at the surface, visible to the naked eye, and poorly agglomerated grains. On the other hand, after hot-pressing there is no distinguishable grain boundaries and the porosity decreased substantially (only small closed pores are present) indicating a successful sintering of the grains. The measured compositions, analyzed by EDS, as well as secondary phases detected, are summarized in Table <ns0:ref type='table'>3</ns0:ref>. The microstructure of all samples is homogeneous, being mainly composed of NiS 2 . In samples consolidated at 700 ºC/56 MPa and 750 ºC/56 MPa there is the presence of a sulfur deficient phase, Ni 1-x S, but in minor amounts (< 1vol%). A small deviation from the nominal composition was observed in all samples. The relative density of the consolidated samples increased with increasing hot-pressing temperature, achieving 97% of the theoretical density when processed at 750 ºC and 56 MPa. The high relative densities, >94%, and low estimated porosity, <6% (obtained by image analysis), indicate a successful consolidation of the pellets.</ns0:p><ns0:p>Raman spectroscopy was used to characterize the vibrational frequencies specific of the chemical bonds on the hot-pressed samples. Due to their similarity, only one spectrum is shown (Figure <ns0:ref type='figure'>3</ns0:ref>). The spectra are in qualitative agreement with literature reports <ns0:ref type='bibr' target='#b19'>[Marini et al., 2011]</ns0:ref>. Vaesite has five Raman active modes: A g , E g and three T g modes. Since Ni atoms are located at the center of inversion, all Raman active modes correspond to displacements of the sulfur atoms. Two T g and E g modes correspond to S-S pairs libration. A g and a T g modes correspond, respectively, to in-phase and out-of-phase stretching of the S-S dimers <ns0:ref type='bibr' target='#b19'>[Marini et al., 2011]</ns0:ref>. In the collected spectra only four peaks were detected: two peaks at 268 and 278 cm -1 , corresponding to T g ( <ns0:ref type='formula'>1</ns0:ref>) and E g symmetries (S-S libration); a peak at 474 cm -1 and a shoulder at 485 cm -1 , corresponding to A g and T g (2) vibrational modes (stretching vibrations). The fifth Raman active mode, T g <ns0:ref type='bibr' target='#b10'>(3)</ns0:ref>, is not visible in the spectra and has never been reported before in previous Raman data available in the literature <ns0:ref type='bibr' target='#b19'>[Marini et al., 2011]</ns0:ref>. In all the consolidation conditions, the peaks are located at the same shift values and have similar widths, suggesting that the different sintering temperatures do not introduce distortions or strains in the crystal lattice and that the type and number of bonds are similar. The thermal stability of NiS 2 was evaluated by thermogravimetric analysis under N 2 atmosphere, before and after hot-pressing. The results are shown in Figure <ns0:ref type='figure'>4</ns0:ref>. There is a small mass loss at ~80 ºC for both non-consolidated and consolidated samples, of 4wt% and <1wt%, respectively, due to dehydration and desorption of chemical species formed during storage under air (the nonconsolidated materials was stored for ~6 months, while the consolidated was characterized just after the preparation). At higher temperatures, a significant mass drop (~35%) is observed for both samples, due to desulfurization of NiS 2 . The desulfurization starts at ~340 ºC in the hotpressed sample and at ~440 ºC in non-consolidated powders. This difference of almost 100 ºC is most likely related with the excess of 30% of sulfur added to the samples prior hot-pressing. The real amount of lost sulfur during the hot consolidation was not controlled and therefore, it is possible that not all the sulfur in excess has been evaporated from the pellet, originating a sulfursaturated vaesite structure. If that happened, then the early sulfur loss can be caused by the excess of sulfur. Previously reported thermogravimetric analysis of elemental sulfur indicate that sulfur starts to evaporate at 200 ºC and by 320 ºC the analyzed mass is lost in its total <ns0:ref type='bibr' target='#b22'>[Takahashi et al., 2015]</ns0:ref>. If there is excess of sulfur in vaesite structure, then sulfur might start being released at lower temperatures. In order to avoid thermal degradation of NiS 2 and formation of S-deficient phases, the service temperature of these materials should not surpass ~340 ºC. There are no previous studies on the mechanisms of decomposition of vaesite but the similarity with pyrite TGA results <ns0:ref type='bibr' target='#b17'>[Lambert, Simkovich & Walker, 1998</ns0:ref>] suggests that NiS 2 might decompose by similar mechanisms of sulfur direct escape from vaesite lattice, followed by a decomposition of NiS 2 into Ni 1-x S and subsequently, into NiS. The band structure and density of states of NiS 2 calculated using GGA+mBJ (the LSDA give similar results) are shown in Figure <ns0:ref type='figure'>5</ns0:ref>. From the DFT calculations, one could expect NiS 2 to be metallic due to the partly filled e g band, in agreement with the previous band structure calculation results <ns0:ref type='bibr' target='#b24'>[Vaughan & Tossell, 1983;</ns0:ref><ns0:ref type='bibr' target='#b8'>Gibbs et al., 2005]</ns0:ref>. The temperature dependence of Seebeck coefficient and electrical resistivity, for the different consolidation conditions, are indicated in Figures <ns0:ref type='figure'>6 and 7</ns0:ref>. Seebeck coefficient, electrical resistivity and power factors at room temperature are shown in Table <ns0:ref type='table'>4</ns0:ref>. In all samples, the Seebeck coefficient is positive, indicating that the major charge carriers are holes (p-type semiconductor). The incoherence between the theoretical and experimental results can be related to the electron-electron interactions that lead to a Mott insulator, i.e., an insulator material due to strong correlation effects originated by electrostatic repulsion between electrons, which are not accounted for by conventional band theories. The bandgap of vaesite has been reported to be 0.27 eV <ns0:ref type='bibr' target='#b13'>[Kautz et al., 1972]</ns0:ref>. The highest Seebeck coefficient and power factor were obtained for the pellet hot-pressed at 720 ºC and 56 MPa. Unlike the other samples, this pellet did not show evidences of secondary sulfur-deficient NiS. No experimental work regarding NiS electrical properties was found, but DFT calculations predict a metallic character <ns0:ref type='bibr'>[Persson, 2014]</ns0:ref>. The presence of a metallic phase, even if in small amounts, is expected to be detrimental to the vaesite thermoelectric properties and can be the reason for the lower power factors on the pellets hot-pressed at 700 ºC and 750 ºC. Conversely, the non-presence of NiS in the pellet hot-pressed at 720 ºC points to a higher sulfur content on it (that due to the small difference to the 1:2 stoichiometry could not be detected), which is able to affect the Seebeck coefficient. Therefore, a higher Seebeck coefficient does not seem to be related with the aggregation of the samples, but with the sensitivity of the electronic properties (carrier concentration and type, conductivity and mobility) to stoichiometric variations and crystal defects (grain boundaries and impurities). The values of ρ correspond to the values reported in the literature for polycrystalline samples <ns0:ref type='bibr' target='#b6'>[Gautier et al., 1972;</ns0:ref><ns0:ref type='bibr' target='#b15'>Krill et al., 1976]</ns0:ref>, being lower than those observed on single crystals and higher than those measured in thin films (see Table <ns0:ref type='table'>1</ns0:ref>). A decrease of resistivity is verified with the increase of the hot-pressing temperature. A higher hot-pressing temperature led to a higher aggregation of the grains, translated into a higher relative density and less grain boundary area, resulting in a decrease of the resistivity. The activation energy of these materials is slightly smaller than the one observed in Bi 2 Te 3 (Table <ns0:ref type='table'>4</ns0:ref>) pointing to a possible use as thermoelectric materials close to room temperature. To the best of our knowledge, there are no reported measurements of the Seebeck coefficient of bulk samples. Reported values of room temperature Seebeck coefficient values obtained in this work are of the same order of magnitude of those previously obtained in single crystals (Table <ns0:ref type='table'>1</ns0:ref>), which point to a good quality of the prepared materials. Moreover, this good quality is corroborated by the observation of an anomaly in the electrical resistivity data at the spin reorientation temperature, T sr ~25 K, (insert of Figure <ns0:ref type='figure'>7</ns0:ref>), similarly to those observed on single crystals <ns0:ref type='bibr' target='#b27'>[Yao et al., 1996]</ns0:ref>. The electrical resistivity increases with decreasing temperature, indicating a semiconducting behavior. On the other hand, the decrease of the Seebeck coefficient with decreasing temperature contrasts with the resistivity results and could point to multiple bands, with two types of charge carriers.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions</ns0:head><ns0:p>The preparation of NiS 2 by solid-state route, followed by hot-pressing resulted in single phase pellets. Relative densities superior to 94% were achieved. No significant changes in chemical bonds and lattice distortions were verified for the different hot-pressing conditions. Thermogravimetric analysis of these compounds indicates a strong desulfurization above 340 ºC, which limits their service temperature. As opposite to the band structure calculations that suggested a metallic behavior, bulk NiS 2 is a p-type semiconductor. The maximum power factor obtained for vaesite (14.1 μWK -2 m -1 ), which is significantly higher than the pristine pyrite (0.06-1.65 μWK -2 m -1 ) <ns0:ref type='bibr' target='#b23'>[Uhlig et al, 2014]</ns0:ref> but still far from commercial thermoelectric materials (2250 μWK -2 m -1 ) <ns0:ref type='bibr' target='#b10'>[Han et al., 2017]</ns0:ref>, is a good starting point for further improvements. This work indicates that the consolidation conditions had a notable influence on the resistivity, with denser pellets showing a higher electrical conduction, pointing to an intimate relation between the electronic transport properties and the processing conditions, defects (stoichiometric deviations, grain boundaries) and changes in the chemical composition. Therefore, we can expect that, with a proper optimization of the chemical composition and microstructure, these sulfides could become viable thermoelectric materials. Several aspects were left unexplored in this work. Since the potential of a material for thermoelectricity is also related with its thermal transport properties, a further study of the thermal conductivity is required. The selection of the optimal chemical composition of vaesite, through elemental substitutions, is also necessary. The coupling of these materials in a thermoelectric module also demands good mechanical properties, which so far were never studied. In this project, the thermal stability of vaesite under inert atmosphere was studied but it would be interesting to also evaluate its stability in air (oxidation testing). 5 -16 <ns0:ref type='bibr' target='#b13'>Kautz et al, 1972;</ns0:ref><ns0:ref type='bibr' target='#b16'>Kwizera et al, 1980;</ns0:ref><ns0:ref type='bibr' target='#b20'>Matsuura et al, 2000;</ns0:ref><ns0:ref type='bibr' target='#b0'>Bither et al, 1968</ns0:ref>.</ns0:p><ns0:p>1 2</ns0:p></ns0:div><ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='21,42.52,204.52,525.00,371.25' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='22,42.52,263.17,525.00,294.75' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='23,42.52,181.57,525.00,239.25' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='24,42.52,181.57,525.00,371.25' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='26,42.52,201.82,525.00,368.25' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='27,42.52,201.82,525.00,377.25' type='bitmap' /></ns0:figure> <ns0:note place='foot'>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2019:06:38759:1:1:NEW 7 Oct 2019)Manuscript to be reviewedChemistry JournalsAnalytical, Inorganic, Organic, Physical, Materials Science</ns0:note> <ns0:note place='foot'>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2019:06:38759:1:1:NEW 7 Oct 2019)Manuscript to be reviewed</ns0:note> </ns0:body> "	"Dear Editor, We thank your letter and the Revisors comments, which were most valuable and we think have led to a much better manuscript. All comments have been considered in the manuscript, as you can confirm by the answers below. Best regards, António Pereira Gonçalves Revisor 1 If the values of Seebeck coefficients, resistivity or power factor were already reported in thin film or single crystal study, the authors should note them and compare with those in your hot-pressed NiS2. A new table (Table 1), containing the reported values of Seebeck coefficients, resistivity and power factors, was included in the “Introduction” and compared, in the “Results and discussion” section, with the results obtained in this work. NiS2 is known to show magnetic ordering at 30-40 K. It is not clear that your hot-pressed samples exhibit a magnetic transition in the temperature dependence of resistivity in figure 7. Since the sharpness of the anomalies by transition may be closely associated with the quality of your samples, authors should show the resistivity data around transition temperature, more clearly, for example by using the enlarged view. NiS2 orders antiferromagnetically below TN ~50K, which is followed by a spin reorientation at ~30K that leads to a week ferromagnetic ground state [Yao, J. M. Honig, T. Hogan, C. Kannewurf, J. Spalek, Phys. Rev. B 54 (1996) 17469]. While the higher temperature transition is not evident in the electrical resistivity measurements, the lower one is visible as a small peek. The present results, on polycrystalline materials, clearly show an anomaly at ~25K, confirming the reported observations on single crystals and indicating the good quality of the prepared materials. This information and discussion are now included in the manuscript, together with an enlarged view of the resistivity versus temperature of the 20-50K region. Before hot-press, the authors synthesized the NiS2 samples (as grown samples). What is the properties of your as-grown sample? I recommend to show the properties for comparison, for example, lattice constant, compositions and electrical resistivity (or the activation energy) of your as-grown sample. The lattice constant of the as-prepared material is reported in the manuscript, at the beginning of the “Results and Discussion” section, but unfortunately it was not possible to measure neither the composition nor the electrical resistivity and Seebeck coefficient due to the easy disaggregation of the material. This is now clearly indicated in the manuscript. The authors showed the band calculation of NiS2 with metallic band structures. I am not sure that this work by authors is the first report. If it was already reported, the authors should explain the originality of your band calculation. Up to the authors best knowledge, only brief references on the band structure calculation results of NiS2 were reported (see https://link.springer.com/article/10.1007/BF00309575 https://pubs.acs.org/doi/abs/10.1021/jp054109a), with no presentation of figures. In this work, modern methods were used to calculate the NiS2 band structure and density of states, which are shown in Figure 5 and compared with the experimental results. We have also now included on the manuscript the references on the previous calculations. The authors described that the NiS2 is good starting points for a new family of thermoelectric materials. However, it is not evidently, for readers, that the values of Seebeck coefficients and power factor in your hot-pressed NiS2 are large or not. I suggests these values of typical examples of thermoelectric materials are described in the introduction. These values are now included in the manuscript and compared with those of NiS2. Revisor 2 Fig. 6 and Fig. 7 showed that the samples sintered by different temperatures exhibited different transport properties. It should be further confirmed and given explanation. The measurements were experimentally confirmed and an explanation is now included in the manuscript. This is a new phase and the transport properties are worthy to be studied more deeply. We agree with Revisor 2 and this work will be a subject of future work. "	Here is a paper. Please give your review comments after reading it.
451	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>The COVID-19 pandemic caused by SARS-CoV-2 has become a global public health concern. Recently, vaccines have been developed to treat this infectious disease. However, these newly developed vaccines are not widely available and not suitable for all age groups. In such circumstances, it is wise to wear personal protective equipment (PPE) such as masks, gloves, and gowns to better protect against COVID-19. Face masks have long been recommended as a means of preventing respiratory infections. However, inappropriate use of masks may undermine their effectiveness. The antimicrobial and antiviral properties of graphene have sparked interest in the development of medical devices such as face masks, gloves, and gowns with extra filtering ability to curb the effects of the coronaviruses. Their hydrophobicity, nanosize, large surface area, high electrical and thermal conductivities, and virulence are notable features that reduce the transmission of viruses from person to person via respiratory routes. Graphene-enhanced face masks are intended to encourage travelers to wear them at work and during recreational activities. Moreover, graphene can pose health hazards if inhaled during respiration. In this review, we summarize the current status of graphene and its promising applications for combating COVID-19. Additionally, this review aims to explore the quality of this biomaterial and possible suggestions for the better and safer use of graphene structured respirators.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>The COVID-19 pandemic caused by SARS-CoV-2 has become a global public health concern. Recently, vaccines have been developed to treat this infectious disease.</ns0:p><ns0:p>However, these newly developed vaccines are not widely available and not suitable for all age groups. In such circumstances, it is wise to wear personal protective equipment (PPE) such as masks, gloves, and gowns to better protect against COVID-19. Face masks have long been recommended as a means of preventing respiratory infections. However, inappropriate use of masks may undermine their effectiveness. The antimicrobial and antiviral properties of graphene have sparked interest in the development of medical devices such as face masks, gloves, and gowns with extra filtering ability to curb the effects of the coronaviruses. Their hydrophobicity, nanosize, large surface area, high electrical and thermal conductivities, and virulence are notable features that reduce the transmission of viruses from person to person via respiratory routes. Graphene-enhanced face masks are intended to encourage travelers to wear them at work and during recreational activities. Moreover, graphene can pose health hazards if inhaled during respiration. In this review, we summarize the current status of graphene and its promising applications for combating COVID-19. Additionally, this review aims to explore the quality of this biomaterial and possible suggestions for the better and safer use of graphene structured respirators.</ns0:p></ns0:div> <ns0:div><ns0:head>INTRODUCTION</ns0:head><ns0:p>The current COVID-19 pandemic, caused by the SARS-CoV-2 virus, has become the most debated infectious disease of the 21 st century. It poses an unprecedented threat to human health, food habits, travel routes, financial resources, and the work environment <ns0:ref type='bibr' target='#b17'>(Buonsenso et al., 2021;</ns0:ref><ns0:ref type='bibr' target='#b52'>Guan et al., 2020;</ns0:ref><ns0:ref type='bibr' target='#b59'>Huizar et al., 2020;</ns0:ref><ns0:ref type='bibr' target='#b171'>Shaikh, 2021;</ns0:ref><ns0:ref type='bibr' target='#b192'>Varma et al., 2021)</ns0:ref>. According to the WHO's last updated COVID-19 record (by <ns0:ref type='bibr'>January 11, 2022), 308,458,509 confirmed cases, 5,492,595 confirmed deaths, and 9,138,211,378</ns0:ref> vaccine doses administered have been recorded worldwide. Although the number of deaths per day has decreased, COVID-19 has not been fully controlled and we are bound to follow precautionary measures to prevent its spread. Many countries have declared complete control over COVID-19, but they are still preparing for a worse situation, as they are more likely to mutate and develop new variants. The early stages of COVID-19 were panic, and we were looking for options to stop its spread. Physical distancing, maintenance of well-ventilated rooms, avoidance of crowds, sanitizing hands, coughing into bent Graphene ensures better respiration when embedded in air-filtering membranes of respirators <ns0:ref type='bibr' target='#b48'>(Gope et al., 2021;</ns0:ref><ns0:ref type='bibr' target='#b50'>Goswami et al., 2021)</ns0:ref>. It has a two-dimensional structure in which sp 2 hybridized carbons are arranged hexagonally in a honeycomb lattice (Fig. <ns0:ref type='figure'>1</ns0:ref>). Because of its single layer of carbon atoms, it has a very high surface-to-mass ratio. A key feature of graphene is its large surface area, which makes it suitable for interfacial interactions <ns0:ref type='bibr' target='#b61'>(Innocenzi and Stagi, 2020;</ns0:ref><ns0:ref type='bibr' target='#b123'>Nguyen et al., 2019;</ns0:ref><ns0:ref type='bibr' target='#b139'>Pranno et al., 2020;</ns0:ref><ns0:ref type='bibr' target='#b153'>Reina et al., 2021;</ns0:ref><ns0:ref type='bibr' target='#b220'>Zou et al., 2016)</ns0:ref>. The high electrical conductivity, large surface area, photocatalytic activity, and hydrophobic nature of graphene have attracted the interest of many researchers for the design of high-quality respirators <ns0:ref type='bibr' target='#b24'>(Cheng et al., 2017;</ns0:ref><ns0:ref type='bibr' target='#b77'>Kasbe et al., 2021;</ns0:ref><ns0:ref type='bibr' target='#b105'>Maqbool et al., 2021;</ns0:ref><ns0:ref type='bibr' target='#b180'>Stanford et al., 2019)</ns0:ref>. As they are extremely hydrophobic and microporous, they do not allow aerosols, water droplets, particles, or pathogens to remain in the outer layer of the respirators for long periods (Fig. <ns0:ref type='figure' target='#fig_1'>2</ns0:ref>). In addition, graphene-derived nanomaterials such as graphene oxide (GO) and reduced graphene oxide (RGO) contain -COOH, -OH, -CONH 2 , and -C-OH moieties (Fig. <ns0:ref type='figure'>1</ns0:ref>), which effectively interact with bacterial and viral cell membranes and rupture their outer envelopes. The pore size of the graphene membrane (5.7-25.2 Å) is smaller than the virus size (0.05-0.14 m) <ns0:ref type='bibr' target='#b64'>(Jayaweera et al., 2020)</ns0:ref>. Therefore, it has a greater tendency to serve as a selectively permeable membrane to separate the pernicious SARS-CoV-2 <ns0:ref type='bibr' target='#b19'>(Castelletto and Boretti, 2021;</ns0:ref><ns0:ref type='bibr' target='#b96'>Liu et al., 2015)</ns0:ref>. It is a light-sensitive material that can absorb 2.3% of the incident visible light <ns0:ref type='bibr' target='#b180'>(Li et al., 2019)</ns0:ref>. The amount of light absorbed can increase the temperature of graphene by more than 56 °C, which is sufficient to expel SARS-CoV-2 from the outer surface of graphene-coated face masks within 30 min <ns0:ref type='bibr' target='#b206'>(Yang and Wang, 2020)</ns0:ref>. Exposure of functionalized graphene face masks to sunlight for 10 min can increase the antibacterial efficiency by 8% to 99.99% <ns0:ref type='bibr' target='#b58'>(Huang et al., 2020)</ns0:ref>. Additionally, the sanitization and washing of graphene-loaded face masks are less tedious than those of other mask types. Furthermore, the eco-friendly and reusable features of graphene-based surgical and nonsurgical masks appear to be popular among users.</ns0:p><ns0:p>Several graphene functionalized materials have shown antibacterial properties, and their effectiveness in killing bacteria is encouraging <ns0:ref type='bibr' target='#b81'>(Krishnamoorthy et al., 2012;</ns0:ref><ns0:ref type='bibr' target='#b88'>Li et al., 2014;</ns0:ref><ns0:ref type='bibr' target='#b97'>S. Liu et al., 2011;</ns0:ref><ns0:ref type='bibr' target='#b135'>Perreault et al., 2015;</ns0:ref><ns0:ref type='bibr' target='#b207'>Yang et al., 2017;</ns0:ref><ns0:ref type='bibr' target='#b209'>Zhang and Tremblay, 2020;</ns0:ref><ns0:ref type='bibr' target='#b213'>Zhao et al., 2013)</ns0:ref>. Despite the enormous potential of graphene in a wide variety of biomedical applications, such as drug delivery, chemotherapeutic agents, electron transport systems, enzyme-induction, and bone defect repair <ns0:ref type='bibr'>(Abbasi et al., 2016;</ns0:ref><ns0:ref type='bibr' target='#b12'>Behbudi, 2020;</ns0:ref><ns0:ref type='bibr'>Dhinakaran et al., 2020;</ns0:ref><ns0:ref type='bibr' target='#b41'>Du et al., 2020;</ns0:ref><ns0:ref type='bibr' target='#b85'>Kumar and Chatterjee, 2016;</ns0:ref><ns0:ref type='bibr' target='#b134'>Perini et al., 2020)</ns0:ref>, only a few studies have addressed its application in virus filtering membranes <ns0:ref type='bibr' target='#b8'>(Barbhuiya et al., 2020;</ns0:ref><ns0:ref type='bibr' target='#b107'>Matharu et al., 2020;</ns0:ref><ns0:ref type='bibr' target='#b118'>Musico et al., 2014)</ns0:ref>. An overview of face masks made from graphene and its derivatives is presented in this paper, highlighting their antimicrobial characteristics to reduce the spread of infectious and fatal diseases such as COVID-19. Moreover, this review addresses the benefits, challenges, and future outlook of masks functionalized with graphene and its derivatives.</ns0:p></ns0:div> <ns0:div><ns0:head>SURVEY METHODOLOGY</ns0:head><ns0:p>The literature referenced in this study was systematically reviewed and searched using PubMed, Google Scholar, and various internet websites. We set no time limits for the search. A manual search was performed to collect appropriate literature. This search was conducted based on title, author name, journal scope, and year of publication. The keywords used to search the literature were 'face mask and types' or 'face mask and graphene' or 'face mask and COVID-19'.</ns0:p></ns0:div> <ns0:div><ns0:head>AIR FILTRATION BY GRAPHENE FACE MASK</ns0:head><ns0:p>Face masks are considered safety gear since they protect the respiratory system from airborne droplets and particles. Developing a mask with adequate comfort and high efficacy for removing bio-aerosols, airborne particles, microorganisms, and the particulate matter requires the selection of novel materials and an understanding of the filtering mechanisms in various environments. The face mask efficiency can be affected by many factors, including the inherent properties of the material, chemical composition of the filter, fiber thickness in the filter membrane, and packaging density <ns0:ref type='bibr' target='#b80'>(Konda et al., 2020;</ns0:ref><ns0:ref type='bibr' target='#b87'>Leung et al., 2020)</ns0:ref>. Moreover, many external factors, such as gravitational force, air velocity, electrostatic charge, frequency of respiration, relative humidity, temperature, loading time, and particle interception, contribute to disturbances in air filtration <ns0:ref type='bibr' target='#b155'>(Rengasamy et al., 2018;</ns0:ref><ns0:ref type='bibr' target='#b184'>Tcharkhtchi et al., 2021)</ns0:ref>.</ns0:p><ns0:p>Scientists have used natural and synthetic polymers for decades to make standard-grade face masks. Owing to the failure of polymer-based face masks to meet the standard values and norms, attempts have been made to replace them with graphene, graphene oxide, reduced graphene oxide, and metal-based nanoparticles.</ns0:p><ns0:p>Graphene is a hydrophobic material that is used in face masks to quickly remove respiratory droplets <ns0:ref type='bibr' target='#b35'>(Deng et al., 2021)</ns0:ref>. Aerosolized particles ranging in size from 1 to 10 mm were trapped inside the pores of the outer layer of a traditional face mask. Graphene-based face masks have a unique filtration system that prevents water droplets from attaching to their surfaces and remaining there for an extended period. To verify the filtering efficiency of the 3D printed face mask, Goswami et al. used functionalized graphene to fabricate filtering membranes of 20, 10, and 3 mm made from polypropylene <ns0:ref type='bibr' target='#b50'>(Goswami et al., 2021)</ns0:ref>. Aerosol particles containing viruses and bacteria were allowed to pass through three layers of the membrane: the outer and inner layers without graphene, and the middle layer with graphene, and they found that bacteria and viruses were trapped more in the middle layer. They explained the air filtration process in three ways, based on the materials used and the results obtained. Initially, they believed that graphene had a sharp edge similar to a nano blade that would tear apart the virus' spike protein. Additionally, they hypothesized that electrostatic interactions with living particles may have played a significant role in trapping them, and third, they suggested that the pore size and hydrophobicity of functionalized graphene may have resulted in superior filtration.</ns0:p><ns0:p>A key part of the material is the functional groups of activated graphene oxide, which improves the filtration process <ns0:ref type='bibr' target='#b27'>(Chung et al., 2021;</ns0:ref><ns0:ref type='bibr' target='#b157'>Rhazouani et al., 2020;</ns0:ref><ns0:ref type='bibr' target='#b179'>Song et al., 2015)</ns0:ref>. In contrast to pristine graphene, graphene oxide and reduced graphene oxide interact more rapidly with the outer lipoprotein layer of bacteria and viruses as they pass through the different layers of the face mask. As bacteria interact rapidly with graphene oxide, other factors such as breathing speed, germ size, and droplet diffusion do not influence how well it filters the air. Furthermore, the addition of graphene oxide to the filtering membrane of the face mask increases the charge density and has a stronger electrostatic effect on microorganisms. Donskyi et al. developed a platform to investigate the electrostatic interactions between functionalized graphene and herpes simplex virus type 1 (HSV-1) <ns0:ref type='bibr' target='#b40'>(Donskyi et al., 2019)</ns0:ref>. Their study showed that electrostatic forces were the primary driving force behind the virus trapping. Therefore, graphene-based face masks are believed to provide the maximum protection against disease-causing microbiological particles by acting as excellent filters.</ns0:p><ns0:p>Pal et al. measured the filtration efficiency of laser-induced graphene face masks in light <ns0:ref type='bibr' target='#b127'>(Pal et al., 2021)</ns0:ref>. Light is the main source of photothermal energy that is used to heat the filtering membrane of the face mask. Exposure of face masks to light with a wavelength of 1085 nm for 15-20 minutes improves filtering efficiency by 99.98%. Therefore, the filtration of air depends not only on the materials used in the membrane, such as graphene nanoparticles but also on the light source used. Lin et al. evaluated the performance of a face mask using the hydrophobicity of graphene material <ns0:ref type='bibr' target='#b93'>(Lin et al., 2021)</ns0:ref>. Graphene nanosheet-embedded carbon face masks are believed to exhibit excellent performance in air filtration owing to the hydrophobic nature of graphene.</ns0:p></ns0:div> <ns0:div><ns0:head>RECENT ADVANCES IN GRAPHENE-BASED FACE MASKS AND BENEFITS</ns0:head><ns0:p>Recently, interest in graphene-derived 2D nanomaterials such as nanoporous graphene, graphene oxide (GO), reduced graphene oxide (RGO), graphene quantum dots (GQDs), and other graphene-derived materials has increased significantly <ns0:ref type='bibr' target='#b21'>(Catania et al., 2021;</ns0:ref><ns0:ref type='bibr' target='#b67'>Jiang et al., 2021;</ns0:ref><ns0:ref type='bibr' target='#b159'>Saleh and Fadillah, 2019;</ns0:ref><ns0:ref type='bibr'>Yuxin Yan et al., 2021)</ns0:ref>. These materials are particularly well suited for various ionic sieves, molecular separation, desalination, gas-phase separation, dialysis, hemofiltration, ultra-filtration, water sterilization, sensors, protein separation, viral extraction, and other biomedical applications <ns0:ref type='bibr'>(Ali et al., 2020</ns0:ref><ns0:ref type='bibr'>(Ali et al., , 2019;;</ns0:ref><ns0:ref type='bibr' target='#b186'>Thebo et al., 2018)</ns0:ref>. Studies have also demonstrated the synergistic effect of graphene when used in air filtration masks. However, there are very few commercially available graphene-functionalized nanofiber respirators. Face masks would be effective and acceptable only if aerosolized particles are promptly prevented from entering the respiratory tract.</ns0:p><ns0:p>To ensure superior air filtration in respiratory devices, designers must be knowledgeable about the best practices for seeding, polishing, coating, and synthesizing graphene nanoparticles on the nanofibers. Of the many approaches used to seed graphene nanoparticles on a polymer matrix, electrospinning is one of the most versatile and viable. Electrospinning is used to disperse nanoparticles into ultrafine nanofibers with minimal diameters and produce very fine fibers. This is a very reliable method for storing electrical charges in membranes to improve their air filtering performance <ns0:ref type='bibr' target='#b16'>(Bortolassi et al., 2019;</ns0:ref><ns0:ref type='bibr' target='#b89'>Li et al., 2018;</ns0:ref><ns0:ref type='bibr'>Zhang et al., 2020)</ns0:ref>. <ns0:ref type='bibr' target='#b50'>Goswami et al. (2021)</ns0:ref> fabricated a 3D-printed face mask from polylactic acid and coated it with functionalized graphene ink, and measured the virus arresting, capturing, and filtering efficiencies of the mask. This result was exciting and supported the use of functionalized graphene as a filtering and antibacterial agent.</ns0:p><ns0:p>Graphene, a material with antimicrobial and antiviral properties, has increased the interest of the scientific community in investigating its use in preventive measures, detection, and diagnosis of COVID-19 <ns0:ref type='bibr' target='#b30'>(Damiati et al., 2021;</ns0:ref><ns0:ref type='bibr' target='#b112'>Mojsoska et al., 2021;</ns0:ref><ns0:ref type='bibr' target='#b132'>Payandehpeyman et al., 2021;</ns0:ref><ns0:ref type='bibr' target='#b137'>Pinals et al., 2021;</ns0:ref><ns0:ref type='bibr'>Raval et al., 2020;</ns0:ref><ns0:ref type='bibr' target='#b189'>Torrente-Rodríguez et al., 2020)</ns0:ref>. Graphene-based face masks are newly developed biocompatible medicinal weapons that seem worthy of facing the COVID-19 pandemic. What makes the graphene face masks extraordinary compared to others is shown in Fig. <ns0:ref type='figure' target='#fig_2'>3</ns0:ref>. The antibacterial, antistatic, large surface area, sharp edge, photosensitivity, and electrical superconducting nature of graphene nanomaterials are well suited for designing a shielding membrane in face masks and provide a lot of benefits to face mask holders (Fig. <ns0:ref type='figure' target='#fig_3'>4</ns0:ref>). Private companies, BonBouton have developed reusable, non-disposable, electrothermally and photothermally self-sterilizing, and rechargeable graphene face masks with a functional graphene-infused film that can quickly block viruses from getting inside the respiratory trachea <ns0:ref type='bibr' target='#b105'>(Maqbool et al., 2021)</ns0:ref>. To reduce the pain stacked and mourning situation of the COVID-19 pandemic, ZEN Graphene Solution Ltd. and Graphene Composite Ltd. (GC) have also developed a graphene-based composite ink for manufacturing mouth-nose-covering devices and other personal protective equipment <ns0:ref type='bibr' target='#b48'>(Gope et al., 2021)</ns0:ref>. Using silver and graphene nanoparticle composite inks, they have modified the working mechanism of earlier cotton-and textile-based face masks, which can now efficiently disable SARS-CoV-2 and influenza A and B virus strains <ns0:ref type='bibr' target='#b22'>(Chaudhary et al., 2021)</ns0:ref>. Planar TECH and IDEATI have also manufactured graphene-coated cotton fabric 2 AM face masks <ns0:ref type='bibr' target='#b48'>(Gope et al., 2021;</ns0:ref><ns0:ref type='bibr' target='#b105'>Maqbool et al., 2021)</ns0:ref>. Moreover, Goswami and his coworkers have successfully developed and tested a graphene-based 3D-printed facial protection device active against the SARS-CoV-2 virus <ns0:ref type='bibr' target='#b50'>(Goswami et al., 2021)</ns0:ref>. From the imperial findings, they have inferred that the working principle of the face mask to filter the virus is quite interesting. Likewise, Directa Plus has also used thin atom allotropes of carbon in face masks extracted from graphite to reduce the spread of viral diseases. A skin-tested hypoallergenic G+ mask can offer consumers a range of benefits to protect themselves and others from viral infections <ns0:ref type='bibr' target='#b86'>(Lea, 2020)</ns0:ref>.</ns0:p><ns0:p>Zhong et al. reported a dual-mode laser fabrication technique for depositing graphene onto temperature-sensitive surgical masks <ns0:ref type='bibr' target='#b214'>(Zhong et al., 2020)</ns0:ref>. They found that functionalized graphene face masks with super hydrophobic surfaces offered enhanced protection against coronaviruses from respiratory droplets. Furthermore, Shan et al. revealed that electrothermal graphene-modified masks (GMMs) exhibited good performance in preventing particulate matter and viruses from entering the nose. The findings also showed that GMM is far more efficient than the photothermal face mask for purging breathing air <ns0:ref type='bibr' target='#b172'>(Shan et al., 2020)</ns0:ref>. The most attractive features of the graphene-engineered face masks are their fast-charging capabilities, ability to maintain a temperature of 80 °C by supplying 3V power for 5 h, and reusability and biodegradability. Currently, researchers are well-versed in the many valuable characteristics of graphene. Some researchers have used the unique properties of graphene, such as photosensitivity, to create photothermally self-sterilizing and reusable face masks to reduce the financial and environmental costs associated with the subsequent use of disposable face masks. Graphene nanosheet-embedded carbon (GNEC) film face masks are perfect examples of ways to meet the necessary conditions for improving air filtration quality <ns0:ref type='bibr' target='#b93'>(Lin et al., 2021)</ns0:ref>. Another private company, Medisevo, claimed that their graphene-based face mask developed by them could filter 98% of COVID-19 particles. Medisevo evaluated graphene face masks using the medical face mask standards from the American Society for Testing and Materials <ns0:ref type='bibr' target='#b161'>(Sandle, 2021)</ns0:ref>. LIGC Technology has developed a face mask called the 'Guardian G-Volt' made from laser-induced microporous graphene. The originality of this facemask is that it maintains an electrical charge to kill the microbes trapped in the filter, effectively blocking 99% of contaminants with sizes greater than 0.3 m. The graphene face mask G1 Wonder, developed by Nanometric Materials Pvt. Ltd. with a composite membrane of graphene and silver nanoparticles, can kill 99% of bacteria and viruses <ns0:ref type='bibr' target='#b114'>(Moore, 2021)</ns0:ref>. Laboratory tests have shown that graphene-silver, a composite membrane made from a collection of microscopic razor-sharp blades of graphene with a high electrically charged potential, has the power to break, open, and destroy bacterial and viral cells. Therefore, masks can prevent COVID-19 transmission by preventing the virus from passing through the mask membrane. Table 1 reports the different types of graphene face masks and their empirically justified outstanding air-filtering features. In addition, graphene face masks can act as antimicrobial agents even after 10 washes <ns0:ref type='bibr' target='#b151'>(Ray and Bandyopadhyay, 2021)</ns0:ref>. This is a unique features of this mask, that is not present in many trivial face masks. Other notable features of the graphene-enhanced face masks are shown in Fig. <ns0:ref type='figure'>5</ns0:ref>. Consequently, the users have numerous advantages. Laser-induced graphene face masks have several excellent physical and chemical mechanisms for fighting SARS-CoV-2 infections (Fig. <ns0:ref type='figure' target='#fig_3'>4</ns0:ref>) <ns0:ref type='bibr' target='#b127'>(Pal et al., 2021)</ns0:ref>.</ns0:p><ns0:p>Graphene-modified face masks can be sterilized using photothermal or electrothermal energy, which is a remarkable feature, not found in surgical masks like N95, FFP3, P100, KN 95, and N99 masks. Owing to its high electrostatic charge retention capacity, it is several times more effective in filtering air than other popular face masks. Furthermore, graphene face masks minimize the use of non-biodegradable materials, thereby ensuring a clean and pollution-free environment. The free electrons of graphene nanoparticles are used to trap positively charged bacteria and viruses in face masks <ns0:ref type='bibr' target='#b33'>(De Maio et al., 2021;</ns0:ref><ns0:ref type='bibr' target='#b167'>Seifi and Reza Kamali, 2021)</ns0:ref>. Their high electrical conductivity and the flow of electrons from graphene-based materials cause oxidative stress in bacteria and viruses. As a result, protein denaturation and the destruction of cellular components occur rapidly. Furthermore, the mutual Van der Waals attraction between the embedded graphene nanomaterials and the germs in the droplets prevents the spread of microorganisms from person to person via air roots <ns0:ref type='bibr' target='#b84'>(Kumar et al., 2019)</ns0:ref>. The electrical conductivity of graphene-derived nanomaterials also supports biosensors in detecting, trapping, inactivating, and preventing viruses from spreading. Several studies have emphasized the importance of graphene in the manufacture of sensors as a virus detection agent in clinical settings <ns0:ref type='bibr' target='#b9'>(Bardhan et al., 2021;</ns0:ref><ns0:ref type='bibr' target='#b71'>Jung et al., 2010;</ns0:ref><ns0:ref type='bibr' target='#b94'>F. Liu et al., 2011;</ns0:ref><ns0:ref type='bibr' target='#b169'>Seo et al., 2020)</ns0:ref>. Furthermore, good electrical conductivity boosts charging speed. Thermal energy also plays an important role in the denaturation of the S-protein and the inactivation of the virus. Thermal exposure of the virus to temperatures of 75 °C for 3 min, 65 °C for 5 min, and 60 °C for 20 min left no option for its survival.</ns0:p></ns0:div> <ns0:div><ns0:head>LIMITATIONS, CHALLENGES, AND THE RISK OF USING GRAPHENE FACE MASKS</ns0:head><ns0:p>As graphene-functionalized face masks are gaining popularity owing to the dire situation of the COVID-19 pandemic, the attention of many researchers has turned to safety awareness, exploring the potential dangers of graphene-seeded respiratory masks <ns0:ref type='bibr' target='#b43'>(Fadeel et al., 2018)</ns0:ref>. Many analysts believe that such face masks can significantly disrupt the spread of the COVID-19 pandemic by breaking the chain of virus transmission from one infected person to another. A mask containing graphene and its derivatives may cause long-term adverse effects on the user's skin, vital respiratory, circulatory, excretory, and digestive organs <ns0:ref type='bibr' target='#b5'>(Arvidsson et al., 2013)</ns0:ref>. The lungs may be damaged when grapheme particles reach them after breathing through a mask made of graphene filters. A scientific report has shown that inhalation of graphene nanoparticles can pose serious unexpected risks to lung tissues and blood circulation (Fig. <ns0:ref type='figure' target='#fig_4'>6</ns0:ref>) <ns0:ref type='bibr' target='#b60'>(Ingle et al., 2013;</ns0:ref><ns0:ref type='bibr' target='#b166'>Schinwald et al., 2012;</ns0:ref><ns0:ref type='bibr' target='#b195'>Wang et al., 2016)</ns0:ref>. Based on empirical results, the ability of graphene and its derivatives to inhibit the activation of living cells and the circulation of blood is dose and particle size-dependent <ns0:ref type='bibr' target='#b116'>(Mukherjee et al., 2018</ns0:ref>). An experimental study conducted by Zhang et al. found a decrease in metabolic activity with 0.1 mg/L graphene in mice, but no effect with a 0.01 mg/L concentration <ns0:ref type='bibr' target='#b211'>(Zhang et al., 2010)</ns0:ref>. Mice exposed to aerosolized graphene environments develop lung damage, inflammation, lung granulomas, pulmonary edema, and persistent lung injury <ns0:ref type='bibr' target='#b31'>(Das et al., 2020;</ns0:ref><ns0:ref type='bibr'>Parlin et al., 2020)</ns0:ref>. Inhalation of graphene in mice caused more severe lung injury than asbestos inhalation in humans. Although it helps reduce the number of COVID-19 patients, the effects of graphene on the cellular level of living organisms are considered hazardous rather than benign. The toxicity of graphene nanomaterials at the cellular level is summarized in Table <ns0:ref type='table'>2</ns0:ref>.</ns0:p><ns0:p>Li et al. exposed the skin of some selected people in vitro to a suspended graphene atmosphere and found that the plasma membrane invasion of primary human keratinocytes is due to the aggregation of few-layer graphene-derived material on the dermal layer <ns0:ref type='bibr' target='#b90'>(Li et al., 2013)</ns0:ref>. Therefore, it is thought that this may also be a possible cause of keratinocytes in people who wear graphene, graphene oxide, or reduced graphene oxide integrated respirators. If masks structured with graphene and its derivatives are worn continuously for a long time, the possibility of nanoparticle adsorption into the skin of the mask covering the area increases <ns0:ref type='bibr' target='#b166'>(Schinwald et al., 2012)</ns0:ref>. Thus, long-term use of graphene has the potential to cause skin allergies and damage epidermal cells. The effects of different GO panels (lateral dimension: 871−1678 nm; thickness: 1nm -10 nm) and graphene sheets (average lateral dimensions: 4312 nm; thickness: up to 10 nm) on fibroblast skin were evaluated by <ns0:ref type='bibr' target='#b92'>Liao et al. (Liao et al., 2011)</ns0:ref>. Their results demonstrated that graphene is more invasive and lethal to dermal cells than graphene oxide because it tends to aggregate within the cells. The MTT method, widely used to assess the toxicity of nanomaterials in cell culture has demonstrated that the metabolic activity of PC12 cells decreases in a concentration-dependent manner after 24 h of exposure to graphene nanoparticles. Along with promising results, graphene nanoparticles are highly cytotoxic, and the cytotoxicity of nanoparticles varies based on particle dimensions. Furthermore, the toxicity of graphene-based nanoparticles became more apparent in studies of histopathological changes elicited after exposing mice to graphene aerosol environments for five days (6 h per day). Histopathological inspection successful explained the rupture of macrophage cells in the presence of 3.05 and 10.1 mg/m 3 graphene <ns0:ref type='bibr' target='#b103'>(Ma-Hock et al., 2013)</ns0:ref>.</ns0:p><ns0:p>As a source of material for manufacturing of graphene impregnated face masks, graphene nanoparticles may be risky, unrealistic, and perilous <ns0:ref type='bibr' target='#b44'>(Farahani et al., 2020;</ns0:ref><ns0:ref type='bibr' target='#b129'>Palmieri and Papi, 2020;</ns0:ref><ns0:ref type='bibr' target='#b219'>Zhou and Gao, 2014)</ns0:ref>. Over the past few decades, numerous scientific investigations have alerted investors, researchers, material scientists, chemists, pharmacists, and mask producers to the risks associated with using graphene and its derivatives for multidisciplinary purposes. Several studies have highlighted the harmful effects of graphene and its products on the endocrine, reproductive, immune, nervous, gastrointestinal, and other physiological systems of animals, including humans <ns0:ref type='bibr' target='#b82'>(Kucki et al., 2017;</ns0:ref><ns0:ref type='bibr' target='#b126'>Orecchioni et al., 2017;</ns0:ref><ns0:ref type='bibr' target='#b143'>Rajakumari et al., 2020;</ns0:ref><ns0:ref type='bibr' target='#b145'>Ramal-Sanchez et al., 2021;</ns0:ref><ns0:ref type='bibr' target='#b177'>Shin et al., 2015)</ns0:ref>. The negative impact of nanographene materials on aquatic, marine, and terrestrial animals and plants suggests that graphene toxicity depends significantly on its concentration and particle size. Some studies have revealed the carcinogenic activities of graphene-based materials <ns0:ref type='bibr' target='#b7'>(Banerjee, 2016)</ns0:ref>. The toxicity of graphene was also influenced by other physicochemical parameters, as shown in Fig. <ns0:ref type='figure'>5</ns0:ref>. Exposure of graphene oxide at varying concentrations to the protozoan Euglena gracilis further demonstrated the toxic effect of graphene oxide in the aquatic environment. Hu et al. discovered that when Euglena gracilis was exposed to graphene oxide at a concentration of 2.5 mg L -1 for 96 h, it had devastating effects. This concentration increases the level of malondialdehyde, which reduces the growth rate of E. gracilis and causes oxidative stress <ns0:ref type='bibr' target='#b56'>(Hu et al., 2015)</ns0:ref>. Additionally, some research has revealed the perilous consequences of graphene-based materials on the marine environment. An experiment with Artemia salina showed that the availability of graphene oxide in water at a concentration of 1 mg ml -1 affects the swimming behavior and survival of its larvae <ns0:ref type='bibr' target='#b101'>(Lu et al., 2018)</ns0:ref>. Graphene derivatives, such as graphene oxide (GO), quantum dot particles, and reduced graphene oxide (RGO), have also been found to severely affect the metabolic activity, photosynthesis, germination, seedling, growth rate, and flowering of plants <ns0:ref type='bibr' target='#b62'>(Jastrzębska et al., 2012;</ns0:ref><ns0:ref type='bibr' target='#b202'>Xu et al., 2020;</ns0:ref><ns0:ref type='bibr' target='#b208'>Zhang et al., 2015</ns0:ref><ns0:ref type='bibr' target='#b210'>Zhang et al., , 2021))</ns0:ref>. Consequently, graphene-based face masks are considered an unhealthy practice in the manufacture of medical protective equipment such as goggles, gloves, aprons, shoes, and filter membranes.</ns0:p><ns0:p>According to recent reports, the Canadian government has issued an awareness notice from Health Canada declaring that graphene-based face masks can endanger the health of users <ns0:ref type='bibr' target='#b23'>(Cheng et al., 2021)</ns0:ref>. For a certain month, in Canada, graphene-based face masks were prohibited from being used. Additionally, the director of the French hospital asked people to stop using face masks that included biomass graphene as the elementary material in the filtering membrane until detailed reports of this face mask were obtained. Owing to the lack of comprehensive studies and insufficient evidence to define graphene as a protective material for use in face masks, suggesting that people use graphene engineered face masks for protection purposes would not be appropriate.</ns0:p></ns0:div> <ns0:div><ns0:head>FUTURE OUTLOOK</ns0:head><ns0:p>Graphene and its derived materials improve inappropriate and less efficient personal protective equipment (PPE) containing cotton, silk, chiffon, flannel-based woven, and non-woven fibrous face masks. Graphene is a promising material for enhancing the filtering efficiency of traditional clothing, surgical, non-surgical, and N95 face masks. Recently graphene nanomaterials have been introduced in the production of air-breathing filter membranes to guarantee the superior quality of air purification. Major private companies developing large-scale production of graphene face masks to meet global demand are First Graphene, Planar Tech, Zen Graphene Solutions, and Graphene Composites (GC). Recently, many graphene-related face masks are still in the testing phase, which is why no statistical information is available regarding the positive progress of graphene-related masks. Owing to insufficient information about the scope, demand, challenges, and response to graphene-related face masks; it is difficult to immediately estimate the pros and cons of graphene-fabricated face masks. We believe that the future of graphenebased face masks remains unclear. The incredible power of this face mask to eliminate respiratory droplets, particulate matter, toxic pollutants, bacteria, viruses, pathogens, and aerosolized microorganisms indicates that graphene face masks could play a game-changing role in the future when people face the upcoming waves of the COVID-19 pandemic. However, the possibility of getting sick due to the inhalation of graphene nanoparticles from integrated graphene face masks poses a serious threat that could jeopardize its future scope. Moreover, the restricted use of graphene masks in some countries due to toxicity and dangerous effects on the wearer's cellular level suggests that the use of this personal protective equipment may be worthwhile to some extent <ns0:ref type='bibr' target='#b3'>(Arvidsson et al., 2018;</ns0:ref><ns0:ref type='bibr' target='#b43'>Fadeel et al., 2018)</ns0:ref>. It is believed that the future of graphene-related face masks is difficult to pinpoint and that portraying them as friends or foes is uncertain.</ns0:p></ns0:div> <ns0:div><ns0:head>CONCLUSION AND FUTURE PERSPECTIVES</ns0:head><ns0:p>Graphene is an exciting material that may offer multiple benefits for establishing effective mitigation strategies to improve healthcare services against SARS-CoV-2. It is a suitable nanomaterial for embedding coated clothing in PPE, face masks, and gloves to make medical devices more manageable, and efficiently inhibit the spread of SARS-CoV-2. It is used to manufacture mouth-nose-covering masks using the latest technology. Many masks made by obsolete mechanization are useless for filtering small aerosols, ranging in size from 10 nm to 10 m, whereas face masks related to graphene can filter such ultra-fine particles with excellent success. Hence, graphene-enhanced face masks receive hopeful responses from the users. Several charismatic aspects, such as simplicity of use, auto sterilization, ultra filtration of aerosol particles, reusable type, quick charging process, hydrophobic nature, not suffocating while breathing, durability, and cost-effectiveness, have made it very popular among people who are being hunted by the contagious SARS-CoV-2 virus. However, the interaction of graphene nanoparticles with viable cells and biochemical is considered unsuitable and dangerous to the human body. Although the toxicity of graphene nanoparticles varies depending on the concentration, the number of layers, surface charge density, purity, exposure time, stability, and particle size, even a minor presence of this nanomaterial inside the body can lead to serious chronic diseases, such as cancer. Reports have also shown that its interaction with microphagous cells can weaken the immune system of our body. Hence, we believe that the use of grapheneenhanced face masks is unfriendly. Therefore, there should be a surplus of investigations and studies on the use of such materials before commercialization.</ns0:p></ns0:div> <ns0:div><ns0:head>ADDITIONAL INFORMATION AND DECLARATION</ns0:head><ns0:p>graphene oxide-in-polyacrylonitrile composite with low filtration resistance for the effective capture of PM2.5. J. <ns0:ref type='bibr'>Memb. Sci. 551, 85-92. https://doi.org/10.1016</ns0:ref><ns0:ref type='bibr'>/j.memsci.2018</ns0:ref><ns0:ref type='bibr'>.01.025 Li, Q., Lu, J., Gupta, P., Qiu, M., 2019</ns0:ref> The mask has an excellent hydrophobic property, incredible bacterial filtering efficiency, and prominent photo-sterilized performance. Masks have great potential to work against the spread of the COVID-19 pandemic. <ns0:ref type='bibr' target='#b93'>(Lin et al., 2021)</ns0:ref> </ns0:p></ns0:div> <ns0:div><ns0:head>2.</ns0:head><ns0:p>Graphene mounted 3D printed facial mask</ns0:p><ns0:p>The bacterial filtration efficiency of the mask is 98.2 % and the breathing resistance is 1.10 mbar. Transmission of the SARS-CoV-2 virus through graphene filters was not reported. <ns0:ref type='bibr' target='#b50'>(Goswami et al., 2021)</ns0:ref> 3.</ns0:p></ns0:div> <ns0:div><ns0:head>Flextrapower graphene mask</ns0:head><ns0:p>The mask is carefully made and is safe to wear. The mask follows unique and sophisticated hydrophobic nanotechnology in which virus aerosolized droplets are unable to remain on the exposed layer for long periods.</ns0:p><ns0:p>(Nacinopa, 2020)</ns0:p></ns0:div> <ns0:div><ns0:head>4.</ns0:head><ns0:p>Laser-Induced graphene mask</ns0:p><ns0:p>Most of the bacteria remain alive even after the mask is exposed to sunlight for 8 hours. The mask has shown superior antibacterial action and can be enhanced by photothermal energy. <ns0:ref type='bibr' target='#b58'>(Huang et al., 2020)</ns0:ref> 5.</ns0:p></ns0:div> <ns0:div><ns0:head>Endothermal mask</ns0:head><ns0:p>It is very easy to raise the temperature of the face mask above 80 °C by supplying 3V of energy. The mask filters the air at this temperature by killing all known types of bacteria and viruses. It preserves high particulate matter efficiency and is reusable. <ns0:ref type='bibr' target='#b172'>(Shan et al., 2020)</ns0:ref> 6.</ns0:p></ns0:div> <ns0:div><ns0:head>Graphene oxidebased rechargeable respiratory mask</ns0:head><ns0:p>The mask comes at a very low price (~$1/mask). Its rechargeability and filtering efficiency of more than 95%. The electrostatic charge retention capacity is very high (~1nC/cm2). Even in high humid conditions, the mask recharges very quickly. <ns0:ref type='bibr' target='#b45'>(Figerez et al., 2020)</ns0:ref> 7.</ns0:p></ns0:div> <ns0:div><ns0:head>G+ masks</ns0:head><ns0:p>The G+ mask has been certified to the European standard EN 14683 as an excellent air filtering biocompatible device. It is naturally bacteriostatic and hypoallergenic. The ability to filter while breathing with this mask is very high. It is reusable, washable and the filtering membrane is replaceable. <ns0:ref type='bibr' target='#b13'>(Bhattacharjee et al., 2019)</ns0:ref> 8.</ns0:p></ns0:div> <ns0:div><ns0:head>G1 wonder mask</ns0:head><ns0:p>Graphene-silver nanomaterials are used to design the filtering membrane. This increases the filtering efficiency of the G1 Wonder Mask. The mask is reusable, washable, breathable, and eco-friendly. It can kill 99% of bacteria and viruses in just one second, and also prevent the volatile organic compound from entering inside respiratory organs. <ns0:ref type='bibr' target='#b114'>(Moore, 2021)</ns0:ref> 9.</ns0:p></ns0:div> <ns0:div><ns0:head>Graphene masks</ns0:head><ns0:p>The mask is super-hydrophobic due to the embedded graphene nanoparticles. Exposure to sunlight can raise its temperature to 80°C which is enough to kill bacteria and viruses. In this mask, monolayered nanographene particles are deposited on a non-woven surface at low melting temperatures by a laser-induced forward transfer method. <ns0:ref type='bibr' target='#b214'>(Zhong et al., 2020)</ns0:ref> 10.</ns0:p></ns0:div> <ns0:div><ns0:head n='2'>AM graphene enhanced facemask</ns0:head><ns0:p>This type of face mask consists of three-layered materials such as graphene (outer layer), polyester (middle layer), and 100% cotton (inner layer). Graphene material is anti-static, dust repellent, and filters out PM2.5 airborne particulates. It is washable and also bacteria resistant. The even distribution of heat energy from graphene provides additional comfort to the users of this mask. <ns0:ref type='bibr' target='#b105'>(Maqbool et al., 2021)</ns0:ref> 11.</ns0:p></ns0:div> <ns0:div><ns0:head>Graphene facemask</ns0:head><ns0:p>This is an antibacterial face mask, and the antibacterial properties remain the same even after washing the mask 10 times. It comes in a variety of shapes and sizes, and the mask is flexible as well. In addition, users will feel comfortable while breathing in hot or cold weather. <ns0:ref type='bibr' target='#b79'>(Kilgannon, 2020)</ns0:ref> 12.</ns0:p></ns0:div> <ns0:div><ns0:head>Medieval facemask with graphene</ns0:head><ns0:p>The mask is comfortable, durable, and wearable without fogging glasses. Antibacterial and hypoallergenic ingredients have been used in this mask. From the inner side, the organic lining has been retained for soft and comfortable wear. <ns0:ref type='bibr' target='#b161'>(Sandle, 2021)</ns0:ref> 13.</ns0:p></ns0:div> <ns0:div><ns0:head>Guardian G-Volt masks</ns0:head><ns0:p>The mask is rechargeable and can be sterilized and reusable. It shows antimicrobial properties and repels the microorganism by attachment to <ns0:ref type='bibr' target='#b142'>(Pullangott et al., 2021)</ns0:ref> PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2021:10:66874:1:1:NEW 15 Jan 2022)</ns0:p><ns0:p>Manuscript to be reviewed Chemistry Journals Analytical, Inorganic, Organic, Physical, Materials Science the exposed surface of the face mask.</ns0:p></ns0:div> <ns0:div><ns0:head>14.</ns0:head><ns0:p>G/GOfunctionalized polyurethane or cotton facemask</ns0:p><ns0:p>The G/GO nanoparticles functionalized cotton face mask has significantly enhanced protection against the SARS-CoV-2 virus. It has shown antibacterial properties when the material was tested against E. coli. <ns0:ref type='bibr' target='#b33'>(De Maio et al., 2021)</ns0:ref> </ns0:p></ns0:div> <ns0:div><ns0:head>15.</ns0:head><ns0:p>Anti-COVID laserinduced graphene mask</ns0:p><ns0:p>The mask has superhydrophobic and reusable properties. Sunlight empowers the sterilization of facemask. Exposure of a mask to sunlight can increase its temperature by more than 80 °C. </ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head>(</ns0:head><ns0:label /><ns0:figDesc>Figure 1</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head>Figure 2 Graphene</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>Figure 3 An</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Figure 3</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head>Figure 4</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Figure 4</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head>Figure 6</ns0:head><ns0:label>6</ns0:label><ns0:figDesc>Figure 6</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='30,42.52,255.37,525.00,360.75' type='bitmap' /></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head>Table 1 features of different types of graphene face masks</ns0:head><ns0:label /><ns0:figDesc>Liu, Y., Fu, Y., Wei, T., Le Guyader, L., Gao, G., Liu, R.S., Chang, Y.Z., Chen, C., 2012. </ns0:figDesc><ns0:table><ns0:row><ns0:cell cols='3'>S.N. List of grapheme</ns0:cell><ns0:cell>Features</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>Ref.</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>facemask</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>1.</ns0:cell><ns0:cell>Graphene</ns0:cell><ns0:cell>nano</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='2'>sheet-embedded</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>carbon</ns0:cell><ns0:cell>(GNEC)</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>facemask</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell cols='5'>. Engineering optical absorption in graphene and other 2D</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>materials:</ns0:cell><ns0:cell cols='2'>advances</ns0:cell><ns0:cell>and</ns0:cell><ns0:cell>applications.</ns0:cell><ns0:cell>Adv.</ns0:cell><ns0:cell>Opt.</ns0:cell><ns0:cell>Mater.</ns0:cell><ns0:cell>7,</ns0:cell><ns0:cell>1-23.</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='5'>https://doi.org/10.1002/adom.201900595</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell cols='10'>Li, Y., The triggering of apoptosis in macrophages by pristine graphene through the MAPK and</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>TGF-beta</ns0:cell><ns0:cell /><ns0:cell>signaling</ns0:cell><ns0:cell /><ns0:cell>pathways.</ns0:cell><ns0:cell cols='2'>Biomaterials</ns0:cell><ns0:cell>33,</ns0:cell><ns0:cell>402-411.</ns0:cell></ns0:row></ns0:table><ns0:note>1</ns0:note></ns0:figure> <ns0:note place='foot'>Analytical, Inorganic, Organic, Physical, Materials Science </ns0:note> </ns0:body> "	"Dr. Narendra Kumar Chaudhary, Ph.D. Department of Chemistry Mahendra Morang Adarsh Multiple Campus, Biratnagar, Nepal (Tribhuvan University) Tel : +977-9842020096 E-mail : chem_narendra@yahoo.com Date: - January 14, 2021 . Dear Editors, PeerJ Materials Science We thank the reviewers for their generous comments on the manuscript entitled 'Review on benefits, toxicity, challenges, and future of graphene-based face masks in the prevention of COVID-19 pandemic'. The manuscript has been revised accordingly and all corrections are highlighted in yellow for the convenience of your review. We believe that the manuscript is now suitable for publication in PeerJ. Dr. Narendra Kumar Chaudhary Asst. Professor of Chemistry On behalf of all authors. Reviewer 1 (Anonymous) Basic reporting No comment Experimental design No comment Validity of the findings No comment Additional comments The review paper presented intends to demonstrate the prospects and challenges of graphene-based face masks in preventing COVID-19 Pandemic. This is a very timely review and significant study since highly infectious COVID-19 has become a global public health concern. Graphene-enhanced face masks have demonstrated some hopeful results due to the incredible properties of graphene. However, there are health risks associated with the use of this graphene-enhanced face masks. This review paper has systematically reviewed recently published papers in relevant area and presented scientifically. However, it would be good to make few minor amendments: Comment 1: Line 217: Check if the figure number is accurate. We have corrected the figure (Fig. number is same but figure has changed) Comment 2: It would be great to include some other relevant papers such as: https://doi.org/10.1021/acsnano.0c05537, https://doi.org/10.1002/adfm.202107407, https://doi.org/10.1002/adsu.202100176 Agreed. We have added some more relevant references to the manuscript. Reviewer 3 (Anonymous) Basic reporting The topic is interesting and hot; however, the article needs significant modification. Experimental design no comments Validity of the findings no comments Additional comments 1. How does the electrical and thermal conductivity of graphene reduce the virus transmission? Graphene and derived materials have free electrons which make them have superior electrical and thermal conductivity. This nature of graphene helps to capture viral particles easily. Details are added to the last paragraph of the section 'RECENT ADVANCES IN GRAPHENE-BASED FACE MASKS AND BENEFITS'. 2. Techniques for the Incorporation of graphene into the polymer matrix should be highlighted. for example; electrospinning, 3D printing, coatings............etc A separate paragraph that describes the incorporation of graphene into the polymer matrix has been added to the section titled 'RECENT ADVANCES IN GRAPHENE-BASED FACE MASKS AND BENEFITS' of the manuscript. Hope we answered this comment. 3. The possible environmental impacts associated with the use of graphene-based face masks should be covered. Graphene nano-particles if inhaled can give hazardous effect. It is also toxic for aquatic lives. The details of the toxicity of graphene has elaborated in the section titled 'LIMITATIONS, CHALLENGES, AND THE RISK OF USING GRAPHENE FACE MASKS'. 4. Filtration process should be elaborated. Also, the benefits of using graphene in the face mask should be explained. Agreed. A subsection 'AIR FILTRATION BY GRAPHENE FACE MASK' has been added to the manuscript to describe the filtration process and the benefits of using graphene-based face mask. "	Here is a paper. Please give your review comments after reading it.
452	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Background. Extracellular polymeric substances (EPS) extracted from waste activated sludge (WAS) have previously shown its potential in corrosion inhibition. The aim of this study is to design a synthetic EPS formulation as a surrogate of natural WAS EPS to overcome the corrosion inhibition inconsistency in WAS EPS. The adsorption behavior of the designed inhibitor was studied by kinetic, thermodynamic, and adsorption isotherm analyses.</ns0:p><ns0:p>Methods. Synthetic EPS was formulated based on the typical chemical compositions of natural WAS EPS, i.e. proteins, carbohydrates, humic substances, nucleic acids, and uronic acids. It is a mixture of glutamic acid, carboxymethylcellulose, humic acid, thymine, and alginic acid. Its corrosion inhibition performance was tested with carbon steel in 3.64% NaCl saturated with CO 2 , using the potentiodynamic polarization scanning technique. The corrosion kinetic parameters were evaluated using Arrhenius relationships while the thermodynamic adsorption parameters were examined using the Langmuir isotherm and Van't Hoff plot.</ns0:p><ns0:p>Results. The inhibition efficiency improved with increasing inhibitor concentration and temperature. The optimum performance was 94% with 204 mg/L of inhibitor applied at 70°C. The inhibition performance was controlled by both the concentration of inhibitor and temperature. Chemisorption of the inhibitor molecules contributed to the overall inhibition performance by adhering to Langmuir isotherm, deducing that the synthetic EPS formed a monolayer of protection film on the metal surface, reducing the contact of metal with the corrosive environment, thus, slowing down the overall corrosion rate.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>Extracellular polymeric substances (EPS) are the metabolic products produced by most microorganisms. They accumulate on the surface of microorganisms, acting as protective barriers against the microorganisms' external environment <ns0:ref type='bibr' target='#b0'>[1]</ns0:ref>. Typically, carbohydrates have been identified as the major constituents in the EPS of many pure cultures <ns0:ref type='bibr' target='#b1'>[2]</ns0:ref>[3], whereas proteins were found in substantial quantities in the sludge of many wastewater treatment reactors <ns0:ref type='bibr' target='#b0'>[1]</ns0:ref> <ns0:ref type='bibr' target='#b3'>[4]</ns0:ref>. Small amounts of humic substances <ns0:ref type='bibr' target='#b4'>[5]</ns0:ref>, uronic acids, and nucleic acids <ns0:ref type='bibr' target='#b0'>[1]</ns0:ref>[6] <ns0:ref type='bibr' target='#b6'>[7]</ns0:ref> were also detected in EPS. A previous study <ns0:ref type='bibr' target='#b7'>[8]</ns0:ref> <ns0:ref type='bibr' target='#b8'>[9]</ns0:ref> showed the potential of EPS extracted from waste activated sludge (WAS) of wastewater treatment operations as a green corrosion inhibitor for CO 2 corrosion. A maximum inhibition performance of about 80% was achieved with the application of 1000 mg/L of this inhibitor. The corrosion inhibition mechanism of WAS EPS was explained by the formation of a biofilm on the metal surface, shielding the metal surface from the corrosive environment. Even though the inhibition performance is comparable to commercial products, the nature of WAS caused inconsistency in inhibition efficiency. The composition of WAS is dependent on wastewater treatment operational parameters, such as inlet biochemical oxygen demand and sludge residence time.</ns0:p><ns0:p>This study focused on the evaluation of a corrosion inhibitor from the surrogate of WAS EPS. The reason of making the surrogate was to have control on the chemical composition of the corrosion inhibitor used and ensure consistent inhibition performance. This study hypothesized that the designed synthetic EPS will demonstrate similar corrosion inhibition behavior as the natural WAS EPS because it was formulated based on the major chemical compositions of natural WAS EPS. The novelty of this research was the design of a surrogate biomass-based corrosion inhibitor inspired by sources with varied chemical compositions. To the knowledge of the authors, this line of work has not been reported elsewhere. This study is unique in the way that it is a multidisciplinary work. Bio-inspired systems and materials are not uncommon in the literature. Yet, this concept is the pioneer of the field of corrosion inhibitor formulation development. For instance, some of the most commonly applied programming algorithms in computer science and engineering today are bio-inspired. Algorithms like genetic and ant colony mimic the natural biological systems to solve research problems. This study is adopting the bioinspired concept into the corrosion inhibitor formulation development. It is believed that this line of multidisciplinary work could benefit and advance the research in corrosion inhibitor development, especially the renewable type.</ns0:p><ns0:p>The present study seeks to investigate the corrosion inhibitive properties of synthetic EPS for carbon steel in 3.64% NaCl solution saturated with CO 2 gas using the potentiodynamic polarization technique. The corrosion kinetic parameters and thermodynamic adsorption parameters are calculated and reported.</ns0:p><ns0:p>Pitting Cell Kit, connecting to the Gamry Potentiostat Interface 1000. The reference, counter, and working electrodes used were saturated calomel electrode (SCE), graphite rod, and the metal specimen, respectively. The setup was equipped with a heating jacket connected to TDC4 Omega temperature controller to maintain the test solution at a desired temperature, in this case, 25°C, 50°C, and 70°C. The Glas-Col GT Series stirrer was connected to the setup externally and adjusted to 50 rpm to get the desired shear and to ensure even heating. The working solution volume was 1 L. The working area of the metal specimens had a circular form of 5 cm 2 .</ns0:p><ns0:p>The potentiodynamic polarization scans were carried out in potential range of -0.25 to +0.25 V versus corrosion potential (E corr ) at a scan rate of 3 V/hr. Corrosive medium was added into the reactor with carbon dioxide gas sparging constantly at 20 psi throughout the experiment. The reactor was allowed to equalize for 30 minutes prior to the beginning of experiment. After the system was equalized, Tafel plots were graphed with Gamry DC105 DC Corrosion Technique Software until three relatively similar readings were obtained. Next, corrosion inhibitor was added into the reactor. The reactor was again allowed to equalize for 30 minutes, then Tafel plots were graphed. This step was repeated until three consecutive graphs with similar trends were yielded, to ensure the stability of the system. Subsequently, the concentration of the corrosion inhibitor was increased. Again, the system was being equalized for 30 minutes, followed by the graphing of Tafel plots.</ns0:p><ns0:p>The Tafel plot was plotted with the mean values of corrosion potential (E corr ) and corrosion current density (I corr ) from the triplicates of the experiments, while the electrochemical parameters obtained from the curves were reported with mean and standard deviation. The corrosion current densities were found by extrapolating the linear Tafel segment of the anodic and cathodic curves to the corrosion potential. The corrosion inhibition efficiency was then calculated with Equation <ns0:ref type='formula'>1</ns0:ref>.</ns0:p><ns0:p>(1) 𝐼𝑛ℎ𝑖𝑏𝑖𝑡𝑖𝑜𝑛 𝐸𝑓𝑓𝑖𝑐𝑖𝑒𝑐𝑛𝑦 (%) = 𝐼 𝑐𝑜𝑟𝑟, 𝑢𝑛𝑖𝑛ℎ𝑖𝑏𝑖𝑡𝑒𝑑 -𝐼 𝑐𝑜𝑟𝑟, 𝑖𝑛ℎ𝑖𝑏𝑖𝑡𝑒𝑑 𝐼 𝑐𝑜𝑟𝑟, 𝑢𝑛𝑖𝑛ℎ𝑖𝑏𝑖𝑡𝑒𝑑</ns0:p></ns0:div> <ns0:div><ns0:head>× 100%</ns0:head></ns0:div> <ns0:div><ns0:head>Fourier-transform infrared spectroscopy (FTIR)</ns0:head><ns0:p>Agilent Cary 630 FTIR incorporated with MicroLab software were used for the FTIR analysis in this study. This equipment worked based on Attenuated Total Reflection (ATR) Method. The scanning was range between 4000 to 400 cm -1 with resolution of 4 cm -1 .</ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head></ns0:div> <ns0:div><ns0:head>Corrosion inhibition performance</ns0:head><ns0:p>The Tafel plots generated from the potentiodynamic polarization measurements for carbon steel in 3.64% NaCl saturated with CO 2 gas with synthetic EPS range from 51 mg/L to 204 mg/L at 25°C, 50°C, and 70°C (298 K, 323 K, 343 K) are presented in Figure <ns0:ref type='figure'>1</ns0:ref>, Figure <ns0:ref type='figure'>2</ns0:ref>, and Figure <ns0:ref type='figure'>3</ns0:ref>, respectively. The details of electrochemical parameters obtained from the curves, namely corrosion potential (E corr ), corrosion current density (I corr ), and inhibition efficiency, are listed in Table <ns0:ref type='table'>2</ns0:ref>. Moreover, the effects of inhibitor concentration and media temperature are addressed in the discussion section. It is also worth noting that the significance of operation and economics of the synthetic EPS as an oil field corrosion inhibitor formulation is also included in the discussion section.</ns0:p></ns0:div> <ns0:div><ns0:head>Corrosion kinetic parameters</ns0:head><ns0:p>Corrosion kinetic parameters, i.e. apparent activation corrosion energy (E a ), enthalpy of activation (∆H a °), and entropy of activation (∆S a °), are listed in Table <ns0:ref type='table' target='#tab_1'>3</ns0:ref>. Two Arrhenius plots used for the evaluation of these corrosion kinetic parameters are shown in Figure <ns0:ref type='figure'>4</ns0:ref> and Figure <ns0:ref type='figure'>5</ns0:ref>. The details of calculations are discussed in the discussion section.</ns0:p></ns0:div> <ns0:div><ns0:head>Thermodynamic adsorption parameters</ns0:head><ns0:p>The standard free energy of adsorption (∆G°a ds ), enthalpy of adsorption (∆H ads °), and the entropy of adsorption (∆S ads °) are listed in Table <ns0:ref type='table'>4</ns0:ref>. The Langmuir isotherm and the Van't Hoff plots are shown in Figure <ns0:ref type='figure'>6</ns0:ref> and Figure <ns0:ref type='figure'>7</ns0:ref>, respectively. The equations and graphs involved for the thermodynamic adsorption parameters are explained in the discussion section.</ns0:p></ns0:div> <ns0:div><ns0:head>FTIR</ns0:head><ns0:p>The IR spectra is shown in Figure <ns0:ref type='figure'>8</ns0:ref> Error! Reference source not found.and the characteristic IR absorption frequencies of the responding organic functional groups of synthetic EPS is tabulated in Table <ns0:ref type='table'>5</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Discussion</ns0:head></ns0:div> <ns0:div><ns0:head>Properties of synthetic EPS</ns0:head><ns0:p>Synthetic EPS is a mixture of several major groups of chemicals in natural WAS EPS. Although there are many ways to extract EPS and each of the methods give different chemical composition <ns0:ref type='bibr' target='#b0'>[1]</ns0:ref>[7] <ns0:ref type='bibr' target='#b9'>[10]</ns0:ref>; the composition of synthetic EPS formulated in this study will be based on the method of heating. Typically, the EPS extracted by heating has the highest proteins concentration, followed by carbohydrates, humic substances, nucleic acids, and uronic acids <ns0:ref type='bibr' target='#b0'>[1]</ns0:ref>[7] <ns0:ref type='bibr' target='#b9'>[10]</ns0:ref>. Therefore, proteins will be the basis of the synthetic EPS and the ratio of different chemicals will be based on the proteins. The compounds were mixed in ratios that were realistic (small enough concentration to be able to measure accurately using an analytical balance) to be acted as a corrosion inhibitor. They were mixed according to the following ratios: a. Proteins:carbohydrates = 2.5:1 b. Proteins:humic substances = 6:1 c. Proteins:nucleic acids = 15:1 d. Proteins:uronic acids =15:1 One compound was selected from each chemical group. They were chosen based on their structures and their chemical inhibition performances in the literature. Structure wise, compounds with nitrogen, oxygen, or sulfur atoms were preferred since all organic corrosion inhibitors typically contain at least one of these atoms, almost without exception. In addition, bigger compounds are also typically preferred as corrosion inhibitors because bigger compounds are more effective in separating the metal surface from its corrosive environment when adsorbed on the metal surface. The compounds chosen for the synthetic EPS mixture fulfilled these descriptions, as illustrated in Figure <ns0:ref type='figure'>9</ns0:ref>. Furthermore, those chemicals that had demonstrated corrosion inhibition were prioritized to be the candidates in the pool of selection. For protein, an amino acid, which is the building block of a protein was chosen. Glutamic acid, a common component of bacterial cell wall <ns0:ref type='bibr' target='#b10'>[11]</ns0:ref>, made an excellent candidate as an amino acid for the purpose of this study since it has also been proven to be an effective corrosion inhibitor in several studies <ns0:ref type='bibr'>[12][13]</ns0:ref>. Glutamic acid showed approximately 54 to 90% of inhibition efficiency in 0.5 M HCl with copper <ns0:ref type='bibr'>[12][13]</ns0:ref>. Due to its potential in corrosion inhibition, it was chosen as the main component of the synthetic EPS. The second biggest composition was carbohydrate. For an organic corrosion inhibitor, typically, a bigger molecule is preferred. Carboxymethylcellulose (CMC), a relatively big molecular weight packed with multiple oxygen atoms, was selected as the candidate for the chemical group of carbohydrate. Its corrosion inhibition capability has also been proven excellent in various investigations <ns0:ref type='bibr'>[14][15]</ns0:ref>. Inhibition efficiencies of about 65 to 72% were observed when CMC was used with mild steel in acid solutions H 2 SO 4 <ns0:ref type='bibr' target='#b13'>[14]</ns0:ref> and HCl <ns0:ref type='bibr' target='#b16'>[15]</ns0:ref>, respectively. However, corrosion inhibition studies on the rest of the chemical groups have no record in the literature to date. For humic substances and uronic acids, there are not many chemicals from these groups, so, humic acid and alginic acid were picked for each group, respectively. In the case of nucleic acids, there are only four choices in this group, namely thymine, guanine, adenine, and cytosine. Making a decision based on an economical point of view, the most affordable choice was thymine. Thymine is a relatively smaller compound compared to other chosen chemicals, but it contains both nitrogen and oxygen atoms, making it a desirable option. Hence, glutamic acid, CMC, humic acid, thymine, and alginic acid were chosen as the formulation for synthetic EPS. Their chemical structures are shown in Figure <ns0:ref type='figure'>9</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>The potential of utilizing biomass sources directly as corrosion inhibitors are undeniable.</ns0:head><ns0:p>There is an enormous amount of studies on the application of plant extracts as corrosion inhibitors, but these products are still relatively rare in the market. One of the main reasons that is delaying the commercialization of these inhibitors could be the current immature resource recovery techniques. A lot of extraction methods are still economical infeasible these days. Therefore, in order to promote the use of renewable corrosion inhibitors, as well as to improve the marketability of these products, a bio-inspired corrosion inhibitor formulation is introduced in this study. Compared to the traditional plant extracts corrosion inhibitors, this type of renewable corrosion inhibitor is more market-ready because of several advantages: (1) renewable sources, (2) economic feasibility, (3) chemical composition consistency, as well as (4) corrosion inhibition performance consistency.</ns0:p></ns0:div> <ns0:div><ns0:head>Effect of concentration</ns0:head><ns0:p>The curves in Figure <ns0:ref type='figure'>1</ns0:ref>, Figure <ns0:ref type='figure'>2</ns0:ref>, and Figure <ns0:ref type='figure'>3</ns0:ref> revealed well defined anodic and cathodic polarization Tafel regions. Note that only one set of experimental data was reported because the differences in triplicates were insignificant. The results for the triplicates can be found in the raw data section.</ns0:p><ns0:p>As observed in these figures, both cathodic and anodic reactions of carbon steel electrode corrosion were inhibited by the increase concentration of synthetic EPS in 3.64% NaCl saturated with CO 2 gas. This observation indicates that the addition of synthetic EPS reduced anodic dissolution as well as the hydrogen evolution reaction <ns0:ref type='bibr' target='#b17'>[16]</ns0:ref>. This can be explained by the adsorption of inhibitor over the corroded surface <ns0:ref type='bibr' target='#b18'>[17]</ns0:ref>. Tafel lines of nearly equal slopes were obtained, indicating that the hydrogen evolution reaction was activated-controlled <ns0:ref type='bibr' target='#b19'>[18]</ns0:ref>.</ns0:p><ns0:p>The details of electrochemical parameters obtained from the Tafel plots such as the values of corrosion potential, E corr , corrosion current density, I corr , corrosion protection efficiency, and surface coverage degree, θ, are presented in Table <ns0:ref type='table'>2</ns0:ref>. The corrosion inhibition efficiency was calculated using Equation <ns0:ref type='formula'>1</ns0:ref>, based on the I corr values, where I corr,blank and I corr were the corrosion current density without and with inhibitor, respectively. These values were obtained by the extrapolation of the cathodic and anodic Tafel lines to the corrosion potentials. The data showed that the I corr values decreased in the presence of synthetic EPS. These values also dropped as the concentration of inhibitor increased, meaning that the corrosion reaction was slowing down as the inhibitor concentration was increasing. This phenomenon can be attributed to the adsorption of synthetic EPS on the metal surface <ns0:ref type='bibr' target='#b19'>[18]</ns0:ref>.</ns0:p><ns0:p>There was no definite pattern observed in E corr values in the presence of different concentrations of synthetic EPS. This result indicated that synthetic EPS may be considered as a mixed-type corrosion inhibitor <ns0:ref type='bibr' target='#b20'>[19]</ns0:ref> in the presence of CO 2 gas saturated 3.64% NaCl solution. The maximum displacement in E corr of less than 0.085 V suggests a mixed mode of inhibition <ns0:ref type='bibr' target='#b21'>[20]</ns0:ref>. Mixed-type corrosion inhibitor retards corrosion rate by suppressing both anodic and cathodic corrosion reactions, typically by adsorbing on a metal surface, forming a protective film to reduce contact of metal surface from the corrosive environment <ns0:ref type='bibr' target='#b22'>[21]</ns0:ref>.</ns0:p><ns0:p>The inhibition efficiency increased as the concentration of synthetic EPS increased. The maximum inhibition was about 94% with an optimum inhibitor concentration of 204 mg/L at 70°C. At 25°C, the maximum inhibition protection of synthetic EPS was 82% at a concentration of 153 mg/L. The previous study of WAS EPS inhibitor demonstrated an optimum inhibition performance of about 79% at a concentration of 1000 mg/L <ns0:ref type='bibr' target='#b7'>[8]</ns0:ref>. Even though the inhibition performance showed only a mere improvement of 3%, the inhibitor concentration was reduced by about 6.5 times. It is known that the natural WAS EPS is rich in a variety of compounds. These compounds could have posed stearic hindrance on the adsorption of inhibition molecules on the metal surface, bring down the efficiency of the overall inhibition performance, so, higher concentrations of inhibitors were required to demonstrate the corrosion inhibition capability. Unlike the natural WAS EPS, the synthetic EPS was formulated specifically on the EPS groups that are known to perform as corrosion inhibitors. Hence, it is expected that the corrosion inhibition efficiency of synthetic EPS to be higher than the natural WAS EPS. Furthermore, in the case of commercial corrosion inhibitors, their corrosion protection performances are typically above 70%. Synthetic EPS has a corrosion inhibition performance that is within the range of commercial corrosion inhibitors. One advantage compared to natural WAS EPS is that its inhibition performance is consistent. The results obtained from this study strongly suggest the great potential commercialization value of synthetic EPS as a valuable material to inhibit corrosion issues in oilfield operations.</ns0:p></ns0:div> <ns0:div><ns0:head>Effect of temperature</ns0:head><ns0:p>The effect of temperature on the inhibited solution-metal reaction is highly complex because many changes could occur on the metal surface such as rapid etching and desorption of inhibitor, also, the inhibitor itself may undergo decomposition and/or rearrangement <ns0:ref type='bibr' target='#b23'>[22]</ns0:ref>. The effect of corrosion inhibition by synthetic EPS in NaCl solution saturated with CO 2 gas was studied with three different temperatures, i.e. 25°C, 50°C, and 70°C. Since the corrosion rate is greatly affected by the concentration of inhibitor as well as the temperature of the working environment, these factors have an important operational impact.</ns0:p><ns0:p>At different temperatures and inhibitor concentrations, the corrosion inhibition efficiencies varied. It was apparent that the rates of carbon steel corrosion, both in the blank solution of 3.64% NaCl saturated with CO 2 gas and with the presence of corrosion inhibitor, increased with increasing temperature. The impact of temperature on the overall corrosion reaction was more pronounced than the effect of inhibitor concentration. The inhibition efficiency increased with temperature. Typically, a decrease in inhibition efficiency with a rise in temperature suggests physisorption of the corrosion inhibitor. In contrast, an increase in inhibition efficiency with rise in temperature is indicative of a chemisorption mechanism <ns0:ref type='bibr' target='#b24'>[23]</ns0:ref>. Therefore, the results clearly indicate a chemisorption mechanism of synthetic EPS on the carbon steel surface.</ns0:p></ns0:div> <ns0:div><ns0:head>Corrosion kinetic parameters</ns0:head><ns0:p>Corrosion kinetics parameters can be evaluated with different approaches. This study seeks to quantitatively evaluate the performance of the studied corrosion inhibitor (synthetic EPS) using an engineering calculation approach that yields empirical results. Instead of focusing mechanistically on the chemistry of the corrosion and corrosion inhibitor reactions to obtain the corrosion kinetics parameters, it is looking at an engineering perspective that is tailored to the studied system. The chemistry of the corrosion and corrosion inhibitor reactions are unutterably important, but it is not the center of the study. This study is not set up to investigate the mechanistic values of the corrosion kinetic and thermodynamic adsorption parameters. This paper emphasizes on the engineering significance of the synthetic EPS as a corrosion inhibitor by applying engineering equations that are based on basic corrosion theory but adapted heuristically to the studied system. Since the reported numbers (i.e. apparent activation corrosion energy, enthalpy of activation, entropy of activation) are empirical values that are only relevant to the studied system, these numbers may not be the duplicated with other system set up (e.g. traditional weight loss method with beaker testing). Even though the reported numbers are not the mechanistic values with respect to the theoretical corrosion reactions and the theoretical adsorption of the inhibitors, it serves a purpose to screen the performance of the studied corrosion inhibitor quantitatively using engineering calculations. In addition, the empirical values are also helpful in determining the behavior of the inhibitor in the studied system. For example, the enthalpy of activation could be used to describe whether the metal dissolution process is endothermic or exothermic. The idea is that, by adhering to the same experimental set up and procedure, the same engineering calculations can be performed to estimate the same parameters for other inhibitors, serving as a comparison model. This is a particularly valuable heuristic tool, where the corrosion inhibitor can be screened rapidly. Thus, if the results obtained from the experiment are encouraging, further corrosion testing such as sparged beaker test and wheel test could be considered. Furthermore, the same approach has previously been demonstrated in the literature <ns0:ref type='bibr' target='#b25'>[24]</ns0:ref>[25], proven its usability and reliability.</ns0:p><ns0:p>The activation parameters for the corrosion reaction were calculated using an Arrheniustype plot according to Equation <ns0:ref type='formula'>2</ns0:ref>. It is worth mentioning that the Arrhenius equations applied were tailored to the studied system, as a heuristic approach to estimate the empirical values of the apparent activation corrosion energy, enthalpy of activation, and entropy of activation that are true to the system. E a in the equation denotes the apparent activation corrosion energy, R is the universal gas constant, and k is the Arrhenius pre-exponential factor. The values of apparent activation energy of corrosion were determined from the slope of ln I corr versus 1/T plot, shown in Figure <ns0:ref type='figure'>4</ns0:ref>. The data showed lower activation energy in the presence of inhibitors than in its absence, which is a typical pattern of chemisorption <ns0:ref type='bibr' target='#b19'>[18]</ns0:ref>.</ns0:p><ns0:p>An alternative formulation of Arrhenius equation, i.e. transition-state equation shown in Equation <ns0:ref type='formula'>3</ns0:ref>, was used to calculate the change of enthalpy (∆H a °) and entropy (∆S a °) of activation for the activation complex formation in the transition state. In this equation, the h is the Planck's constant, N is the Avagadro's number, ∆S a ° is the entropy of activation, and ∆H a ° is the enthalpy of activation. Figure <ns0:ref type='figure'>5</ns0:ref> shows a plot of ln (I corr /T) against 1/T for synthetic EPS. A straight line was obtained with a slope of ∆H a °/R and an intercept of ln (R/Nh + ∆S a °/R), from which the values of ∆H a ° and ∆S a ° were calculated. The positive enthalpy values reflected the endothermic nature of metal dissolution process. Large and negative values of entropy imply that the activated complex in the rate determining step represents an association rather than a dissociation step <ns0:ref type='bibr' target='#b19'>[18]</ns0:ref>.</ns0:p><ns0:p>(2)</ns0:p><ns0:formula xml:id='formula_0'>𝐼 𝑐𝑜𝑟𝑟 = 𝑘𝑒 - 𝐸 𝑎 𝑅𝑇 (3) 𝐼 𝑐𝑜𝑟𝑟 = 𝑅𝑇 𝑁ℎ 𝑒𝑥𝑝 ( ∆𝑆 0 𝑎 𝑅 ) 𝑒𝑥𝑝 ( -∆𝐻 0 𝑎 𝑅𝑇 )</ns0:formula></ns0:div> <ns0:div><ns0:head>Thermodynamic adsorption parameters</ns0:head><ns0:p>Adsorption isotherms provide insights into the interaction among the adsorbed molecules and the metal surface, which can help to better understand the corrosion inhibition mechanism. The values of surface coverage (θ) to different concentrations of inhibitor, obtained from the polarization measurements in the temperature range of 25 to 70°C (298 to 343 K) were used to explain the best isotherm to determine the adsorption mechanism. The values of θ were assumed to be the corrosion inhibition efficiencies. The reason being, without the presence of inhibitor compound, an inhibition efficiency of 0% is expected, so, when an inhibitor compound is introduced to a corrosive environment, the improved corrosion inhibition efficiency is believed to be solely contributed by the coverage of the inhibitor compound on the metal surface. The surface coverage, θ, were used in a series of equations shown in Equation <ns0:ref type='formula'>4</ns0:ref>, Equation <ns0:ref type='formula'>5</ns0:ref>, and Equation 6 <ns0:ref type='bibr' target='#b27'>[26]</ns0:ref>. Equation <ns0:ref type='formula'>4</ns0:ref>showed the relationship of I corr , I corr,blank , I sat , and θ. I sat is the current density of entirely covered surface. This equation was then be rearranged into Equation <ns0:ref type='formula'>5</ns0:ref>. As I corr was greater than I sat , Equation <ns0:ref type='formula'>5</ns0:ref>was simplified to Equation <ns0:ref type='formula'>6</ns0:ref>. ( <ns0:ref type='formula'>4</ns0:ref>)</ns0:p><ns0:formula xml:id='formula_1'>𝐼 𝑐𝑜𝑟𝑟 = (1 -𝜃)𝐼 𝑐𝑜𝑟𝑟,𝑏𝑙𝑎𝑛𝑘 + 𝜃𝐼 𝑠𝑎𝑡 (5) 𝜃 = 𝐼 𝑐𝑜𝑟𝑟, 𝑏𝑙𝑎𝑛𝑘 -𝐼 𝑐𝑜𝑟𝑟 𝐼 𝑐𝑜𝑟𝑟,𝑏𝑙𝑎𝑛𝑘 -𝐼 𝑠𝑎𝑡 (6) 𝜃 = 𝐼 𝑐𝑜𝑟𝑟, 𝑏𝑙𝑎𝑛𝑘 -𝐼 𝑐𝑜𝑟𝑟 𝐼 𝑐𝑜𝑟𝑟,𝑏𝑙𝑎𝑛𝑘</ns0:formula><ns0:p>In the range of temperature and inhibitor concentration studied, the best correlation between the experimental results and the adsorption isotherm functions was obtained using Langmuir adsorption isotherm. The Langmuir isotherm for monolayer adsorption is given by Equation <ns0:ref type='formula'>7</ns0:ref>. By linearizing this equation, Equation 8 was obtained. In Equation <ns0:ref type='formula'>7</ns0:ref>and Equation <ns0:ref type='formula'>8</ns0:ref>, θ is the surface coverage degree, C is the inhibitor concentration in the NaCl solution, and K is the equilibrium constant of the adsorption process. The correlation coefficient, R 2 , was used to describe how close the isotherm fits the experimental data. The plot of C/θ against C gave a straight line and the linear correlation coefficients were fairly close to 1, indicating good fit to the data. This graph is shown in Figure <ns0:ref type='figure'>6</ns0:ref>. The adsorption behavior of synthetic EPS conformed to Langmuir isotherm, suggesting monolayer adsorption, which is a typical behavior of chemisorption <ns0:ref type='bibr' target='#b28'>[27]</ns0:ref>.</ns0:p><ns0:p>In general, Langmuir isotherm is not recommended to be used to describe a mixture system because the individual components in a mixture can each be adsorbed in different ways. However, it is applicable to this study because the synthetic EPS, as a corrosion inhibitor formulation, was treated as an entity. The individual contribution of compounds in the mixture of synthetic EPS were considered unimportant, therefore, being omitted. These are the basic assumptions of Langmuir isotherm: (1) surface of the adsorbent (metal) is uniform, (2) adsorption sites are equivalent, (3) adsorbed molecules do not interact, (4) all adsorption occurs through the same mechanism. Assuming that the metal surface is uniform, the adsorption sites are equivalent, and the inhibitor formulation is being treated as an entity, Langmuir isotherm is appropriate to be used to describe the overall adsorption mechanism. There are numerous studies in the literature where Langmuir was used to describe the adsorption mechanism of an inhibitor mixture, especially plant extracts <ns0:ref type='bibr' target='#b29'>[28]</ns0:ref> <ns0:ref type='bibr' target='#b31'>[29]</ns0:ref>. K values were calculated from the intercepts of the same plot (Figure <ns0:ref type='figure'>6</ns0:ref>). The constant of adsorption, K, can be related to the standard free energy of adsorption, ∆G°a ds , using Equation <ns0:ref type='formula'>9</ns0:ref>. The constant 1 x 10 6 in the equation is the concentration of water molecules expressed in mg/L, R is the universal gas constant, T is the absolute temperature. On the other hand, ∆H°a ds can be deduced from the integrated version of the Van't Hoff equation expressed by Equation 10Error! Reference source not found.. Figure <ns0:ref type='figure'>7</ns0:ref> shows the plot of ln K versus 1/T which yield a straight line with a slope of -∆H°a ds /R. The value obtained was used to find the ∆H°a ds . The calculated ∆H°a ds was then used to calculate the values of ∆S°a ds by using Equation <ns0:ref type='formula'>11</ns0:ref>. ( <ns0:ref type='formula'>9</ns0:ref>)</ns0:p><ns0:formula xml:id='formula_2'>∆𝐺 °𝑎𝑑𝑠 =-𝑅𝑇𝑙𝑛 (1 × 10 6 𝐾) (10) 𝑙𝑛 𝐾 =- ∆𝐻 °𝑎𝑑𝑠 𝑅𝑇 + ∆𝑆 °𝑎𝑑𝑠 𝑅 (11) ∆𝐺 °𝑎𝑑𝑠 = ∆𝐻 °𝑎𝑑𝑠 -𝑇∆𝑆 °𝑎𝑑𝑠</ns0:formula><ns0:p>A more in-depth study of the inhibitor adsorption mechanism was investigated using the values of thermodynamic parameters. The details can be found in Table <ns0:ref type='table'>4</ns0:ref>. The spontaneity of the adsorption of inhibitor on the metal surface as well as the stability of the adsorbed layer on the metal surface was demonstrated by the resulted negative values of ∆G°a ds . Typically, an endothermic adsorption process that has a positive value of ∆H°a ds is attributed unequivocally to chemisorption, while an exothermic adsorption process with ∆H°a ds of negative value may involve either physisorption or chemisorption, or a combination of both the processes <ns0:ref type='bibr' target='#b23'>[22]</ns0:ref>. In this study, the ∆H°a ds was positive, once again implying a chemisorption mechanism. The value of ∆S°a ds decreased with increased temperature, implying that the reaction of adsorption was PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2019:09:41197:1:0:NEW 14 Nov 2019)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science getting less disordered.</ns0:p></ns0:div> <ns0:div><ns0:head>FTIR</ns0:head><ns0:p>The trend in the IR spectrum of the synthetic EPS followed closely to the natural WAS EPS <ns0:ref type='bibr' target='#b7'>[8]</ns0:ref> as expected because it is formulated based on the chemical composition of natural WAS EPS. Similar to the natural WAS EPS, the FTIR results of synthetic EPS showed that functional groups O-H, N-H, C-N, C=O, and C-H were present. Since the synthetic EPS and natural WAS EPS both have the same functional group, it can be deduced that these functional groups play major roles in corrosion inhibition. Other authors have also suggested the contribution of these functional groups in corrosion inhibition <ns0:ref type='bibr' target='#b29'>[28]</ns0:ref> <ns0:ref type='bibr' target='#b31'>[29]</ns0:ref>.</ns0:p><ns0:p>The electrochemical theory of corrosion holds that the metal surface corroding in an electrolyte is covered with local electrolytic cells. Some areas of the metal can act as anodes and other areas can act as cathodes, shown in Figure <ns0:ref type='figure'>10</ns0:ref>, depending upon the history of the metal regarding heat treatment, presence of imperfections, scratches, greases, paint coatings, fingerprint smudges, etc. At anodic sites, the metal usually dissolves into solution. Electrons given from these sites are transported to local cathodes and collected by electron acceptors such as hydrogen ions and oxygen. As previously suggested, synthetic EPS acts as a mixed-type corrosion inhibitor, meaning that the molecules in the synthetic EPS chemisorbed on both the anodic and cathodic sites of metal surface to form a monolayer protection film. The functional groups rich in nitrogen and oxygen atoms acted as the polar head of organic corrosion inhibitors, adsorbing on metal surface by forming chemical bonds between the inhibitor molecules and metal ions, while the non-polar hydrocarbon chain attached to the polar head isolated the metal surface from the corrosive surrounding, suppressing both anodic and cathodic corrosion reactions, reducing the overall corrosion rate.</ns0:p></ns0:div> <ns0:div><ns0:head>Operation and economic significance of synthetic EPS</ns0:head><ns0:p>The corrosion inhibition performance of the synthetic EPS shown in this study is promising. Other areas that are interesting to be investigated are the operational and economical sides of this corrosion inhibitor formulation.</ns0:p><ns0:p>Similar to most bio-inspired materials, this corrosion inhibitor formulation can be formulated with commercial renewable resources or extracted from natural resources (waste activated sludge), leaving a lesser environmental impact compared to the commercial petroleumbased corrosion inhibitors. Besides having corrosion inhibition performance comparable to commercial products (commercial products usually show inhibition performance above 70%), the synthetic EPS is also just as easy to be applied like commercial corrosion inhibitors, making it an excellent alternative.</ns0:p><ns0:p>The economic analysis of synthetic EPS was evaluated in this study. The production cost of the synthetic EPS is about $4.23 for every 10,000-inhibition treatment (assuming 1 L system/treatment), while the market price of a typical commercial oil and gas corrosion inhibitor costs about $2.38 per 10,000 applications. It is worth mentioned that the synthetic EPS is formulated based solely on the composition of natural EPS. The economic feasibility can be improved in future studies by product optimization to reduce the applied inhibitor concentration and enhance the inhibition performance.</ns0:p><ns0:p>It is evident that bio-inspired systems/materials have high potential in revolutionizing the current market to reduce dependence on fossil fuel-based products as well as to promote innovative product development approach. This transformation is not only applicable in the corrosion inhibitor industry but should also be extended to benefit other research and development areas.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions</ns0:head><ns0:p>The studied inhibitor, synthetic EPS, is a surrogate of biomass-based corrosion inhibitor inspired by sources with varied chemical compositions to overcome the composition inconsistency in biomass that can cause unreliable corrosion inhibition performance. Synthetic EPS is a mixture of glutamic acid, carboxymethylcellulose, humic acid, thymine, and alginic acid, following the chemical composition of natural WAS EPS extracted by heating method. Unlike the natural WAS EPS that is rich in assorted of molecules that could promote stearic hindrance on the adsorption of inhibitor molecules, synthetic EPS was designed specifically based on the EPS groups that are known to perform as corrosion inhibitors. With concentration of 204 mg/L in 3.64% NaCl saturated with CO 2 gas, synthetic EPS showed maximum corrosion inhibitions of 82.41%, 89.65%, and 93.99% at 25°C, 50°C, and 70°C, respectively. Its performance compared favorably with natural WAS EPS and commercial corrosion inhibitors.</ns0:p><ns0:p>The adsorption mechanism adhered to Langmuir isotherm, implying monolayer adsorption. It was found that the inhibition performance was controlled by both the concentration of inhibitor and temperature.</ns0:p><ns0:p>The corrosion inhibition capability was due to chemisorption shown by several evidences:</ns0:p><ns0:p>(1) An increase in corrosion inhibition efficiency with increase temperature (2) A decrease in activation energy in the presence of inhibitor (3) The adsorption isotherm conforms to the Langmuir monolayer mechanism (4) Endothermic adsorption Since the formulation of synthetic EPS was designed solely based on the chemical composition of natural WAS EPS, it was not optimized to meet the purpose of corrosion inhibition. Based on the results presented and the needs and requirements of corrosion protection service providers, the future direction of the current research will focus on optimizing the formulation in order to reduce the required applied concentration of corrosion inhibitor while achieving the maximum attainable corrosion inhibition performance. This could be done by first reducing the number of compounds in the formulation, then optimizing the concentration of the compounds and the inhibition performance statistically. This bio-inspired material is developed in the hope to promote the commercialization of renewable corrosion inhibitors. According to the Google Scholar database, there are as many as 17,600 publications on the topic of green corrosion inhibitors from the year 1980 to 2018. A significant portion of these publications were made up by phytochemical-based compounds. Plant extracts are gaining popularity as green corrosion inhibitors candidates not only because they are renewable sources, but also their potential in corrosion mitigation. On average, the attainable corrosion protection efficiencies of these inhibitors can range from 70% to as high as 98% <ns0:ref type='bibr' target='#b32'>[30]</ns0:ref>[31] <ns0:ref type='bibr' target='#b34'>[32]</ns0:ref>[33] <ns0:ref type='bibr' target='#b36'>[34]</ns0:ref>. Despite the overwhelming research evidence that suggests the impressive performance of these inhibitors, there are still drastic uneven numbers of research reports and products on the shelves. Needless to say, resource recovery efforts take time to improve. Before these techniques are optimized, or if there are enough products to be added to the product line to improve the cost issue <ns0:ref type='bibr' target='#b37'>[35]</ns0:ref>, until then, bio-inspired material could be an alternative to balance the environmental problems bring by petroleum-based corrosion inhibitors and the economic complications raise by plant extracts corrosion inhibitors. As a matter of course, the idea of benefiting from bio-inspired systems and materials will not only benefit the corrosion inhibitor sector but is prompt to be extended to any other applicable research area. Manuscript to be reviewed</ns0:p><ns0:note type='other'>Chemistry Journals Figure 1</ns0:note><ns0:p>Tafel plot for carbon steel in 3.64% NaCl concentrated with CO 2 with different concentrations of synthetic EPS at 25°C</ns0:p></ns0:div><ns0:figure xml:id='fig_1'><ns0:head>1 Characteristic 1 PeerJ</ns0:head><ns0:label>11</ns0:label><ns0:figDesc>Mat. Sci. reviewing PDF \| (MATSCI-2019:09:41197:1:0:NEW 14 Nov 2019)</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='28,42.52,201.82,525.00,315.00' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='29,42.52,201.82,525.00,320.25' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='30,42.52,201.82,525.00,309.00' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='31,42.52,181.57,525.00,300.00' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='32,42.52,201.82,525.00,319.50' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='33,42.52,178.87,525.00,346.50' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='34,42.52,178.87,525.00,313.50' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='35,42.52,178.87,525.00,318.75' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='36,42.52,178.87,525.00,192.75' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='37,42.52,178.87,525.00,259.50' type='bitmap' /></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2019:09:41197:1:0:NEW 14 Nov 2019)Manuscript to be reviewedChemistry JournalsAnalytical, Inorganic, Organic, Physical, Materials Science</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Temperature (°C)</ns0:cell><ns0:cell>Temperature (K)</ns0:cell><ns0:cell>Concentration (mg/L)</ns0:cell><ns0:cell>E corr (V)</ns0:cell><ns0:cell>I corr (µA/cm 2 )</ns0:cell><ns0:cell>Inhibition efficiency (%)</ns0:cell><ns0:cell>Surface coverage degree, θ</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>0</ns0:cell><ns0:cell>-0.73</ns0:cell><ns0:cell>52.48</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>51</ns0:cell><ns0:cell>-0.71</ns0:cell><ns0:cell>25.12</ns0:cell><ns0:cell>68.72</ns0:cell><ns0:cell>0.6872</ns0:cell></ns0:row><ns0:row><ns0:cell>25</ns0:cell><ns0:cell>298</ns0:cell><ns0:cell>102</ns0:cell><ns0:cell>-0.71</ns0:cell><ns0:cell>18.20</ns0:cell><ns0:cell>77.34</ns0:cell><ns0:cell>0.7734</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>153</ns0:cell><ns0:cell>-0.71</ns0:cell><ns0:cell>14.45</ns0:cell><ns0:cell>82.00</ns0:cell><ns0:cell>0.8200</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>204</ns0:cell><ns0:cell>-0.70</ns0:cell><ns0:cell>14.13</ns0:cell><ns0:cell>82.41</ns0:cell><ns0:cell>0.8241</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>0</ns0:cell><ns0:cell>-0.74</ns0:cell><ns0:cell>125.89</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>51</ns0:cell><ns0:cell>-0.76</ns0:cell><ns0:cell>37.15</ns0:cell><ns0:cell>86.03</ns0:cell><ns0:cell>0.8603</ns0:cell></ns0:row><ns0:row><ns0:cell>50</ns0:cell><ns0:cell>323</ns0:cell><ns0:cell>102</ns0:cell><ns0:cell>-0.76</ns0:cell><ns0:cell>34.67</ns0:cell><ns0:cell>86.96</ns0:cell><ns0:cell>0.8696</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>153</ns0:cell><ns0:cell>-0.76</ns0:cell><ns0:cell>33.11</ns0:cell><ns0:cell>87.55</ns0:cell><ns0:cell>0.8755</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>204</ns0:cell><ns0:cell>-0.76</ns0:cell><ns0:cell>27.54</ns0:cell><ns0:cell>89.65</ns0:cell><ns0:cell>0.8965</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>0</ns0:cell><ns0:cell>-0.73</ns0:cell><ns0:cell>223.87</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>51</ns0:cell><ns0:cell>0.76</ns0:cell><ns0:cell>45.71</ns0:cell><ns0:cell>91.70</ns0:cell><ns0:cell>0.9170</ns0:cell></ns0:row><ns0:row><ns0:cell>70</ns0:cell><ns0:cell>343</ns0:cell><ns0:cell>102</ns0:cell><ns0:cell>-0.76</ns0:cell><ns0:cell>39.81</ns0:cell><ns0:cell>92.77</ns0:cell><ns0:cell>0.9277</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>153</ns0:cell><ns0:cell>-0.76</ns0:cell><ns0:cell>34.67</ns0:cell><ns0:cell>93.71</ns0:cell><ns0:cell>0.9371</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>204</ns0:cell><ns0:cell>0.75</ns0:cell><ns0:cell>33.11</ns0:cell><ns0:cell>93.99</ns0:cell><ns0:cell>0.9399</ns0:cell></ns0:row></ns0:table><ns0:note>1 PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2019:09:41197:1:0:NEW 14 Nov 2019)Manuscript to be reviewedChemistry JournalsAnalytical, Inorganic, Organic, Physical, Materials Science</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_1'><ns0:head>Table 3 (on next page)</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Corrosion kinetic parameters for carbon steel in different concentrations of the synthetic ∆H a ° (kJ/mol) ∆S a ° (J/mol K)</ns0:figDesc><ns0:table><ns0:row><ns0:cell>0</ns0:cell><ns0:cell>27.46</ns0:cell><ns0:cell>24.81</ns0:cell><ns0:cell>-71.28</ns0:cell></ns0:row><ns0:row><ns0:cell>51</ns0:cell><ns0:cell>12.54</ns0:cell><ns0:cell>8.75</ns0:cell><ns0:cell>-131.24</ns0:cell></ns0:row><ns0:row><ns0:cell>102</ns0:cell><ns0:cell>15.21</ns0:cell><ns0:cell>12.56</ns0:cell><ns0:cell>-120.83</ns0:cell></ns0:row><ns0:row><ns0:cell>153</ns0:cell><ns0:cell>17.24</ns0:cell><ns0:cell>14.59</ns0:cell><ns0:cell>-115.63</ns0:cell></ns0:row><ns0:row><ns0:cell>204</ns0:cell><ns0:cell>16.47</ns0:cell><ns0:cell>13.82</ns0:cell><ns0:cell>-118.73</ns0:cell></ns0:row></ns0:table><ns0:note>EPSPeerJ Mat. Sci. reviewing PDF \| (MATSCI-2019:09:41197:1:0:NEW 14 Nov 2019) Manuscript to be reviewed Chemistry Journals Analytical, Inorganic, Organic, Physical, Materials Science Inhibitor concentration (mg/L) Ea (kJ/mol)</ns0:note></ns0:figure> <ns0:note place='foot'>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:note> </ns0:body> "	"October 10, 2019 Dr. Alexey Kabalnov Academic Editor of PeerJ Materials Science Dear Dr. Kabalnov: The authors appreciate all the reviewers’ suggestions to enhance the quality of the paper. Below is a table describing actions to each of the reviewers’ comments. Some of the major changes included emphasizing the novelty of the study, clarifying experimental techniques and validity, as well as application of results. We feel that the revisions have improved the paper and it is now in better form for publication. We would like the paper to be published in your esteemed journal. This paper is a continuation of a previous paper published in PeerJ on the use of waste activated sludge EPS as a corrosion inhibitor. We would like to show the evolution of the research concepts in this area in your esteemed journal, even if it is not PeerJ-Material Science. We were very satisfied with the fairness, clarity, and comprehensive nature of the previous reviews. Please let us know of your decision at your earliest convenience. Sincerely, Liew Chien Go, Dilip Depan, William Holmes, August Gallo, Kathleen Knierim, Tre Bertrand, and Rafael Hernandez Table of actions to reviewers’ comments. Review Revision/Response Editor Both reviewers raise concerns that have to be addressed. I may suggest to follow the suggestions of Reviewer 1 and to perhaps conduct standard corrosion tests instead/in addition to Tafel plots. In order Tafel plots to be publishable, you may consider to address the comments 1- 5 of Reviewer 1 and to address the experimental concerns of Reviewer 2 (old vs new sample for every run, etc) Thank you for the comment. The authors have edited the paper following most of the suggestions of Reviewer 1 and 2. As a clarification point, it was not the intention of the paper to collect and analyze data associated with the basic theoretical principles of corrosion and measurement of chemical and physical properties of corrosion inhibitors. The proposed contribution is a demonstration of engineering efforts to demonstrate the performance of a corrosion inhibitor mixture. All the methods and analysis tool applied in the proposed contribution have been used elsewhere by other investigators evaluating the effectiveness of other corrosion inhibitors. The current compounds and mixture are unique as corrosion inhibitors and have been validated by a commercial partner as having market potential. The authors of the paper understand that the results obtained cannot be generalized and may only applied to the experimental condition evaluated, which are supposed to simulate oil field applications. A standard electrochemical test (potentiodynamic test) was conducted as an accelerated method for corrosion inhibitor screening, to determine if the inhibitors are able to passivate the metal surface by estimating the corrosion rate as a parameter to measure performance compared to commercial inhibitors, which is the objective of this study. This project was sponsored by industry. The proposed corrosion inhibitor formulation evaluated in the study was compared with the best commercially available corrosion inhibitor manufactured and distributed by the sponsoring company. All the calculations conducted had the objective of understanding performance compared to the established corrosion inhibitor and set the foundation for specific field applications. Even though we appreciate the reviewers’ comments, other issues to consider were time and reliability of the data. For example: 1. A standard weight loss test does not exhibit (or very rarely) constant rate over time. In order to obtain a reliable corrosion rate, a year of metal exposure to the corrosive environment is recommended by ISO 9223. This could certainly be considered on the next phase of the research and a future publication. 2. The experiment could take a longer time to get a reliable corrosion inhibition result since the corrosion rate is usually the highest at the beginning of the experiment. Furthermore, the constant purging of CO2 into the system was considered uneconomical. The authors will consider the reviewers’ comments regarding experimental configurations in future studies by applying sparged beaker test and wheel test to estimate the corrosion rate, study film persistency, and the thermal stability of the corrosion inhibitors. Reviewer 1 Basic reporting Language is satisfactory, when all other basic issues are completely unsatisfactory. (1) The authors misunderstand the origin of Equations which they apply to treat the data. (2) The authors' background in kinetics of corrosion processes is very poor. (3) Presentation of electrochemical data and corresponding quantities is misleading. (4) There is no any scientific result or hypothesis. Instead, we see a number of observations for very complex and less characterized system accompanied by incorrect data treatment, see below. Thank you for the comment. This study seeks to quantitatively evaluate the performance of the studied corrosion inhibitor (synthetic EPS) using an engineering calculation approach that yields empirical results. Instead of focusing mechanistically on the chemistry of the corrosion and corrosion inhibitor reactions, it is looking at an engineering perspective that is tailored to the studied system. The chemistry of the corrosion and corrosion inhibitor reactions are undeniably important, but it is not the center of the study. This study is not set up to investigate the mechanistic values of the corrosion kinetic and thermodynamic adsorption parameters. It was not the intention of the authors to determine physical and chemical properties for specific reaction and compounds. Calculations of these parameters were performed to compare with other values in the literature as performance metrics. This paper focuses on the engineering significance of the synthetic EPS as a corrosion inhibitor by applying engineering equations that are based on basic corrosion theory but adapted heuristically to the studied system (e.g. Arrhenius equations in terms of Icorr). Since the reported numbers (i.e. apparent activation corrosion energy, enthalpy of activation, entropy of activation, standard free energy of adsorption, enthalpy of adsorption, entropy of adsorption) are empirical values that are only relevant to the studied system, these numbers may not be the duplicated with different system set up (e.g. traditional weight loss method with beaker testing). These explanations were added from line 279 to 304 and line 306 to 309 to clarify the confusion. Even though the reported numbers are not the mechanistic values with respect to the theoretical corrosion reactions and the theoretical adsorption of the inhibitors, they are useful to screen the performance of the studied corrosion inhibitor quantitatively using engineering calculations. In addition, the empirical values are also helpful in determining the behavior of the inhibitor in the studied system (e.g. spontaneous/non-spontaneous reaction, physical sorption/chemical sorption). The idea is that, by adhering to the same experimental set up and procedure, these engineering calculations can be performed to estimate the parameters for other inhibitors, serving as a comparison model. This is a particularly useful heuristic tool, where the corrosion inhibitor can be screened rapidly. Thus, if the results obtained from the experiment are encouraging, further corrosion testing such as sparged beaker test and wheel test could be considered. Furthermore, the same approach has previously been demonstrated in the literature, proven its usability and reliability. For examples, “Caffeic acid as a green corrosion inhibitor for mild steel” by authors F.S. de Souza and A. Spinelli, and “Inhibition of mild steel corrosion in sulfuric acid solution by new schiff base” by Ahmed A. Al-Amiery, et al. 1. Original Arrhenius equations were tailored to be used heuristically for the polarization study in this paper, yielding empirical results that are relevant to the studied system. The empirical values of the apparent activation corrosion energy, enthalpy of activation, entropy of activation, standard free energy of adsorption, enthalpy of adsorption, and entropy of adsorption are reported, instead of the mechanistic values that are based solely on the chemistry of corrosion and corrosion inhibitors. The same approach has previously been demonstrated in “Caffeic acid as a green corrosion inhibitor for mild steel” by authors F.S. de Souza and A. Spinelli, and “Inhibition of mild steel corrosion in sulfuric acid solution by new schiff base” by Ahmed A. Al-Amiery, et al. The explanation was added from line 306 to 309. 2. Similarly, the kinetics study in this paper does not focus on the chemistry of corrosion and corrosion inhibitor reactions theoretically. Instead, tailored to the studied system by applying a set of engineering equations, resulting in empirical results that are relevant to the studied system. Therefore, the kinetics interpretation can be different in: (1) in different system and (2) if approach the problem mechanistically with basic chemistry theory. As mentioned before, the same approach was portrayed in “Caffeic acid as a green corrosion inhibitor for mild steel” by authors F.S. de Souza and A. Spinelli, and “Inhibition of mild steel corrosion in sulfuric acid solution by new schiff base” by Ahmed A. Al-Amiery, et al. The explanation was added from line 279 to 304. 3. The presentation of electrochemical data and the corresponding quantities followed the typical electrochemical corrosion inhibitor studies in the literature associated with engineering performance, which could be different from basic chemistry applications. The electrochemical data and the corresponding quantities are obtained empirically with a set of heuristic engineering equations that are relevant to the studied system. Likewise, as mentioned previously, the same approach has been applied in “Caffeic acid as a green corrosion inhibitor for mild steel” by authors F.S. de Souza and A. Spinelli, and “Inhibition of mild steel corrosion in sulfuric acid solution by new schiff base” by Ahmed A. Al-Amiery, et al. The explanation was added from line 279 to 304. It is a common practice in corrosion inhibitor studies to assume the inhibition efficiency as the surface coverage. The reason being, without the presence of inhibitor compound, an inhibition efficiency of 0% is expected, so, when an inhibitor compound is introduced to a corrosive environment, the improved corrosion inhibition efficiency is believed to be solely contributed by the coverage of the inhibitor compound on the metal surface. Some studies regarding the same topic have previously been demonstrated such as “The effect of temperature on the acidic dissolution of steel in the presence of inhibitors” by E. Khamis and “Apricot juice as green corrosion inhibitor of mild steel in phosphoric acid” by Aprael S. Yaro et al. the explanation was added from line 332 to 335. 4. The hypothesis was stated from line 58 to 61. This study hypothesized that the designed synthetic EPS will demonstrate similar corrosion inhibition behavior as the natural waste activated sludge (WAS) EPS because it was formulated based on the major chemical compositions of natural WAS EPS. The scientific results were summarized in the conclusion section from line 437 to 456. The result of the study showed that a surrogate of a biomass-based material (bio-inspired material) could be formulated to perform comparably or better to a natural biomass-based material. The resulting mixture has the advantage of a more consistent performance and independence from the variability of wastewater treatment operations. Experimental design The arrangement of experiment is more or less traditional for corrosion research, based mostly on the measurements of polarization curves. Very important information about the type of carbon steel is absent (the authors report only elemental composition, but do not consider phase composition which is crucial for corrosion behavior) and escape to describe the samples surface pretreatment (which is crucial for kinetics of interfacial processes). The most dramatic problem is the choice of the inhibitor under study. For practical applications, this mixture can probably work, but it is impossible to consider the mixture of 5 substances in terms of adsorption isotherms. Any of the components is characterized by certain isotherm when adsorbs in the absence of other species. However the adsorption from the mixture is typically competitive, and cannot be considered in any additive manner. The parameter of Langmuir adsorption isotherm has a meaning of surface-adsorbate interaction energy and cannot be extracted from the data for mixtures. For FTIR experiments, I failed to understand under what conditions these spectra were registered. To consider FTIR information in the context of adsorption from solution, one needs to measure the spectra in situ. The usual problem in this case is the overlap of bands for solute and adsorbate, and there are many specific techniques to separate these contributions, but nothing is written about this principle technical issue. Most probably Fig.8 demonstrate the spectra of the mixture as is, so it can tell us nothing about participation of certain functional groups in adsorption. There is no indication of solution pH, which is important in respect to protonation constants for acids. The adsorption properties of organic acids and their anions are completely different. Thank you for the comment. The phase composition of the used metal is not commonly reported in traditional corrosion inhibitor peer reviewed publications. However, the tested carbon steels in this study consist of a mixture of two phases, cementite and ferrite. The surface pretreatment procedure was described from line 82 to 84: The pre-treatment of the specimens’ surface was carried out by grinding with sandpapers of 40, 220, 320 grits, rinsing with deionized water, then drying with paper towel. The specimens were used immediately after pre-treatment. The authors agree that any of the five substances in the mixture could be characterized by certain isotherm when adsorbs in the absence of other species. However, this study is not looking at the individual compound as a corrosion inhibitor, instead, the five-compound mixture is treated as an inhibitor formulation and treated as an entity. Thus, competition among the compounds are omitted. This leads the authors to believe that the overall adsorption of the inhibitor formulation corresponds to a Langmuir isotherm. The basic assumptions of Langmuir isotherms include: 1. Surface of the adsorbent (metal) is uniform 2. Adsorption sites are equivalent 3. Adsorbed molecules do not interact 4. All adsorption occurs through the same mechanism Assuming that the metal surface is uniform, the adsorption sites are equivalent, and the inhibitor formulation is being treated as an entity, Langmuir isotherm is suitable to be used to describe the overall adsorption mechanism. There are numerous studies, at least more than 10, in the literature where Langmuir was used to describe the adsorption mechanism of an inhibitor mixture, especially plant extracts. Some of the examples include: “Corrosion inhibitive properties and adsorption behaviour of ethanol extract of Piper guinensis as a green corrosion inhibitor for mild steel in H2SO4” by E.E. Ebenso et al. and “Leaves extract of Ananas sativum as green corrosion inhibitor for aluminium in hydrochloric acid solutions” by E.I. Ating, et al. Explanation regarding this issue was added from line 323 to 332. The FTIR spectrum is to show the functional groups present in the five-component mixture, demonstrating the similarities between the synthetic EPS and natural EPS. Another reason to demonstrate the FTIR spectrum is to show the functional groups that have possibly contributed to the overall adsorption. These functional groups have showed their metal adsorption potential in the existing literature. For example: “Corrosion inhibition of mild steel using potato peel extract in 2 M HCl solution” by Taleb H. Ibrahim et al. and “A study on corrosion inhibitor of mild-steel in hydrochloric acid using cashew waste” by O. Olawale et al. The authors see no value in obtaining the FTIR spectrum in situ because these five compounds share the same established corrosion inhibition functional groups, that said, the data of the in situ spectra will not be enough to distinguish the contributed inhibition compounds. Chemical analysis such as gas chromatography/liquid chromatography could be used but for a mixture of five compounds with distinctly different structures, complications of chemical analytical technique occur. Treating the inhibitor formulation as an entity, the solution pH brings no value to the study. Since this study is not focusing on individual compound inhibition, the protonation of the individual compounds in the synthetic EPS is not important. Validity of the findings All the findings resulting from the treatment of polarization curves are senseless because of the following reasons. (0) There are no linear parts in Tafel plots, so I wonder how the authors extrapolated the anodic and anodic curves in their Tafel plots to obtain I_corr. I can assume that one of the reasons of non-linear behavior is the Ohmic drop because the authors use a huge (5cm^2) electrode, and the currents in rather wide potential intervals are above 1 mA. For any electrochemical experiments, much smaller electrodes must be applied, and Ohmic contribution should be examined from the very beginning. (1) Arrhenius Eq (2) is valid for the rates of particular processes, and cannot be applied to I_corr, which presents combination of at least two (anodic and cathodic) coupled processes. It is very easy to see (Figs 1-3) that the inhibitor effects on cathodic and anodic branches are different, so the contribution in I_corr are also different for solutions of various concentrations. (2) It is impossible to determine the surface coverage from I_corr using Eq. (4) or even from the currents at cathodic and anodic branches because the rates of reactions are not obligatory proportional to adsorbate-free surface. In particular, anodic dissolution includes chloride adsorption step, which can preferentially occur at some active centers. Adsorption of large molecules like carbohydrates does not obligatory suppress the adsorption of low-molecular species participating in corrosion. For hydrogen evolution, the rate can be proportional either to teta, or to teta^2, depending on the nature of the limiting step. (3) It is impossible to apply Langmuir isotherm to high surface coverages (0.69 an even much higher) because this isotherm assumes the absence of lateral interactions in adlayer. These interactions are unavoidable for molecules with any functional groups. It is also impossible to apply Langmuir isotherm to large molecules which tend to poly-layer adsorption. Moreover, before starting with any isotherm one needs to clarify whether the adsorption is reversible or irreversible. Correspondingly, 'adsorption part' is completely mistaken. (4) I can assume that 'local electrolytic cells' mentioned by the authors is the notion related to local galvanic pairs. However this mechanism of corrosion is not universal, it can take place only for strongly inhomogeneous surface and depends on distribution of minor components in the steel sample. In any case, in NaCl solution the basic mechanism is related to chloride, not to any galvanic pairs. (5) The data tabulated by the authors do not allow to determine any quantities with the accuracy presented now (up to five significant digits). What is tabulated for K (three points in Table 4) does not allow to calculate anything even with low accuracy. Thank you for the comment. 0. There are linear parts in the Tafel plots. The authors estimated the corrosion rate using the software attached to the experimental equipment (Gamry DC105 DC Corrosion Technique Software) and have compared the corrosion rate adhering to the standard procedure showed by Gamry: https://www.gamry.com/application-notes/corrosion-coatings/basics-of-electrochemical-corrosion-measurements/ The equipment setup is purchased, not built, the corrosion rates estimated using the attached software were assumed to be reliable. This system is regularly used by industry to determine corrosion inhibition performance. 1. As mentioned previously, this study seeks to quantitatively evaluate the performance of the studied corrosion inhibitor (synthetic EPS) using an engineering calculation approach that yields empirical results. Instead of focusing mechanistically on the chemistry of the corrosion and corrosion inhibitor reactions, it is looking at an engineering perspective that is tailored to the studied system. Icorr was incorporated into the Arrhenius equations to be tailored to the studied system. The explanation was added from line 279 to 304 and line 306 to 309. The authors think that Arrhenius equation is appropriate because of the nature of corrosion. Corrosion is a redox reaction which involves anodic and cathodic half reactions. Thus, Icorr resulted from the coupled anodic and cathodic reactions was deemed as one complete reaction that is appropriate to be used in the Arrhenius equations. The inhibitor may have affected the anodic and cathodic branches differently, yet, that does not make Arrhenius equation unsuitable for the purpose of this study. The contribution of Icorr are only different for solutions of various concentrations in term of intensity. It is expected that higher concentration of inhibitor will impact the Icorr more significantly than low concentration of inhibitor. A similar approach has previously been demonstrated in “Caffeic acid as a green corrosion inhibitor for mild steel” by authors F.S. de Souza and A. Spinelli, and “Inhibition of mild steel corrosion in sulfuric acid solution by new schiff base” by Ahmed A. Al-Amiery, et al. 2. It is a common practice for corrosion inhibitor study to assume the inhibition efficiency as the surface coverage. The reason being, without the presence of inhibitor compound, an inhibition efficiency of 0% is expected, so, when an inhibitor compound is introduced to a corrosive environment, the improved corrosion inhibition efficiency is believed to be solely contributed by the coverage of the inhibitor compound on the metal surface. Some studies regarding the same topic have previously been demonstrated such as “The effect of temperature on the acidic dissolution of steel in the presence of inhibitors” by E. Khamis and “Apricot juice as green corrosion inhibitor of mild steel in phosphoric acid” by Aprael S. Yaro et al. the explanation was added from line 332 to 335. The adsorption of chloride is possible, especially in an acidic (CO2 rich) environment. This adsorption is expected to have happened before the inhibitor compounds are introduced to the system. Thus, when the inhibitor compounds are presence in the system, they are most likely protonated by the solution, which would in turn either replacing the chloride ions, or forming complexes with chloride ions. For these reasons, the authors think that it is appropriate to assume the inhibition efficiency as the surface coverage. 3. There is no evidence showing that lateral interaction was present in the inhibitor compounds. Treating the inhibitor formulation as an entity, lateral interaction is omitted. Large molecules could form poly-layer film on the metal surface but that is not necessarily the case. Langmuir isotherm has been widely used in the literature, at least more than 10, to describe the adsorption of high surface coverage (higher than 0.69), large molecules, and mixtures. For examples, “Corrosion inhibitive properties and adsorption behaviour of ethanol extract of Piper guinensis as a green corrosion inhibitor for mild steel in H2SO4” by E.E. Ebenso et al. and “Leaves extract of Ananas sativum as green corrosion inhibitor for aluminium in hydrochloric acid solutions” by E.I. Ating, et al. This study shows that chemisorption was contributing to the overall adsorption, implying monolayer formation and irreversible adsorption. 4. The phrase “local electrolytic cell” refers to the anodic and cathodic sites on the metal grain. Due to the various states of stress that the metal has underwent (e.g. heat treatment, presence of imperfections, scratches, etc.), some grains are anodic to the other. It was explained from line 398 to 403. In order to clarify the confusion, the authors have added an image (Figure 10) to help the readers to get a better picture. 5. The values of K did not paste right from the Excel spreadsheet on Table 4, a few more significant figures were missing. The significant figures of K have been fixed in the table. The calculations were done using K values with four significant figures, which are consistent with the significant figures of other parameters. The accuracy concern should be resolved. Comments for the author If the authors consider their mixture as useful for the practical corrosion protection, they should arrange standard corrosion tests instead of measuring polarization curves. If the authors plan to develop corrosion science and to publish articles, they should start from the very beginning. Thank you for the comment. A standard electrochemical test (potentiodynamic test) was done as an accelerated method for corrosion inhibitor screening, to determine if the inhibitors are able to passivate the metal surface by estimating the corrosion rate, which is the objective of this study. This project was sponsored by industry. The proposed corrosion inhibitor formulation evaluated in the study were compared with the best commercially available corrosion inhibitor manufactured and distributed by the sponsoring company. All the calculations conducted had the objective of understanding performance compared to the established corrosion inhibitor and set the foundation for specific field applications. Even though we appreciate the Reviewers’ comments, other issues to consider were time and reliability of the data. For example: 1. A standard weight loss test does not exhibit (or very rarely) constant rate over time. In order to obtain a reliable corrosion rate, a year of metal exposure to the corrosive environment is recommended by ISO 9223. 2. The experiment could take a longer time to get a reliable corrosion inhibition result since the corrosion rate is usually the highest in the beginning and the constant purging of CO2 into the system was considered uneconomical. The authors will consider the reviewers’ comments regarding experimental configurations in future studies by applying sparged beaker test and wheel test to estimate the corrosion rate, study film persistency, and the thermal stability of the corrosion inhibitors. Reviewer 2 Basic reporting In this work English is suitable, clear and technically correct. However the introduction is not enough described and does not clearly introduce the innovative aspects of the study and the literature can be more descriptive on the actual background. (The authors shoud be careful when citing the literature with a specific program since sometimes the references can be missing) The structure of the article is not good in the results part where no description is given and should be more emphasized, maybe with a joint results and discussion section. The number of figures is adequate for the present study and are relevant for the clarity of the paper, however they can be more thoroughly described throughout the text helping the reader to follow the line of the work. In particular figure9 is not described in the text The results could be relevant to the work if more elaborated Thank you for the comment. The innovative aspects of the study and the literature more descriptive on the actual background have been added in the introduction (line 63 to 71), discussion (line 200 to 210), and conclusion (line 465 to 480). The citation issue has been fixed. The descriptions of the results are addressed in the discussion section. Results and discussion sections need to be separated according to the journal guideline. Figure 9 shows the structure of the compounds present in the synthetic EPS. The description of this figure was added from line 173 to 177. Experimental design Validity of the findings The impact and the novelty of the work is undeniable but the work lacks some rationale in the methodology used. For instance in the section where the authors discussed the properties of synthetic EPS is a good introduction part but is not supported by the results previously described, thus should be moved or both in the introduction and in the methodology section. The data provided in the text can be questionable if the Tafel experiments were performed always on the same sample. The analyses changed the interface and the behavior described does not reproduce the actual conditions, inducing an anomalous variability. It would be better perform experiments using for each condition a 'new' sample (3 samples for each condition). Some parts of the results are missing and are described in the discussion section. I would suggest to join the results and discussion section in order to have a text that results more clear to the reader. Thank you for the comment. More existing literature was added in the discussion from line 182 to 183 and line 188 to 190. Triplicates were done but only one set of results were reported because of the minor variation. The explanation was added from line 214 to 216. The results of triplicate were added in the raw data section. The journal requires the results and discussion sections to be separated. Comments for the author In this work the author investigated the role of specific synthetic EPS to be evaluated as corrosion inhibitors for mild steel in an anaerobic chloride-rich environment. The present study is within the scope of the journal, could be of a particular scientific interest and is quite innovative for the use of synthetic complex polymers that can be found in nature. However the work needs more thorough investigations and is not enough elaborated to be published as a scientific article. I suggest the authors to rewrite the article focusing on the results and discussion section, clearly indicating the results ad describing the trends found and after that trying to justify the data obtained according to the parameters chosen. In terms of scientific rigor, the units should always be the same throughout the paper (use K or °C and not both) Thank you for the comment. The innovative aspects of the study and the literature more descriptive on the actual background have been added in the introduction (line 63 to 71), discussion (line 200 to 210), and conclusion (line 465 to 480). In addition, the discussion section is also expanded to explain the modification of Arrhenius equation and the assumption of Langmuir isotherm for mixture. This study seeks to quantitatively evaluate the performance of the studied corrosion inhibitor (synthetic EPS) using an engineering calculation approach that yields empirical results. Instead of focusing mechanistically on the chemistry of the corrosion and corrosion inhibitor reactions, it is looking at an engineering perspective that is tailored to the studied system. The chemistry of the corrosion and corrosion inhibitor reactions are undeniably important, but it is not the center of the study. This study is not set up to investigate the mechanistic values of the corrosion kinetic and thermodynamic adsorption parameters. This paper emphasizes on the engineering significance of the synthetic EPS as a corrosion inhibitor by applying engineering equations that are based on basic corrosion theory but adapted heuristically to the studied system (e.g. Arrhenius equations in terms of Icorr). Since the reported numbers (i.e. apparent activation corrosion energy, enthalpy of activation, entropy of activation, standard free energy of adsorption, enthalpy of adsorption, entropy of adsorption) are empirical values that are only relevant to the studied system, these numbers may not be duplicated with different system set up (e.g. traditional weight loss method with beaker testing). This explanation is added to the paper to clarify the confusion. Even though the reported numbers are not the mechanistic values with respect to the theoretical corrosion reactions and the theoretical adsorption of the inhibitors, it serves a purpose to screen the performance of the studied corrosion inhibitor quantitatively using engineering calculations. In addition, the empirical values are also helpful in determining the behavior of the inhibitor in the studied system (e.g. spontaneous/non-spontaneous reaction, physical sorption/chemical sorption). The idea is that, by adhering to the same experimental set up and procedure, these engineering calculations can be performed to estimate the similar parameters for other inhibitors, serving as a comparison model. This is a particularly useful heuristic tool, where the corrosion inhibitor can be screened rapidly. Thus, if the results obtained from the experiment are encouraging, further corrosion testing such as sparged beaker test and wheel test could be considered. Furthermore, the same approach has previously been demonstrated in the literature, proven its usability and reliability. For examples, “Caffeic acid as a green corrosion inhibitor for mild steel” by authors F.S. de Souza and A. Spinelli, and “Inhibition of mild steel corrosion in sulfuric acid solution by new schiff base” by Ahmed A. Al-Amiery, et al. These explanations were added from line 279 to 304 and from line 306 to 309. Furthermore, operational and economical significance of synthetic EPS as a corrosion inhibitor formulation was also added to the discussion section from line 413 to 434. This paper covers the kinetic, thermodynamic, and adsorption analyses of the corrosion inhibition of synthetic EPS in detail. Besides the innovative concept of bio-inspired material application and the detailed discussion of the inhibitor behavior, the operational and economical significance of this inhibitor was also addressed. For these reasons, the authors think that this paper is suitable to be published as a scientific article. The unit for temperature has been changed to K throughout the paper. "	Here is a paper. Please give your review comments after reading it.
453	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Background. Extracellular polymeric substances (EPS) extracted from waste activated sludge (WAS) have previously shown its potential in corrosion inhibition. The aim of this study is to design a synthetic EPS formulation as a surrogate of natural WAS EPS to overcome the corrosion inhibition inconsistency in WAS EPS. The adsorption behavior of the designed inhibitor was studied by kinetic and thermodynamic analyses.</ns0:p><ns0:p>Methods. Synthetic EPS is a bio-inspired material that was formulated based on the most typical chemical compositions of natural WAS EPS, i.e. proteins, carbohydrates, humic substances, nucleic acids, and uronic acids, which was not optimized for corrosion inhibition performance. It is a mixture of glutamic acid, carboxymethylcellulose, humic acid, thymine, and alginic acid. Its corrosion inhibition performance was tested with carbon steel in 3.64% NaCl saturated with CO 2 , using the potentiodynamic polarization scanning technique. The resulted electrochemical parameters were used to evaluate the empirical corrosion kinetic and thermodynamic adsorption parameters.</ns0:p><ns0:p>Results. Addition of synthetic EPS showed significant decrease in corrosion rate as compared to the control. The inhibition efficiency improved with increasing inhibitor concentration and temperature. The optimum performance was 94% with 204 mg/L of inhibitor applied at 70°C (343 K). The inhibition performance was controlled by both the concentration of inhibitor and temperature. Chemisorption of the inhibitor molecules contributed to the overall inhibition performance, reducing the contact of metal with the corrosive environment, thus, slowing down the overall corrosion rate.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>Extracellular polymeric substances (EPS) are the metabolic products produced by most microorganisms. They accumulate on the surface of microorganisms, acting as protective barriers against the microorganisms' external environment <ns0:ref type='bibr' target='#b0'>[1]</ns0:ref>. Typically, carbohydrates have been identified as the major constituents in the EPS of many pure cultures <ns0:ref type='bibr' target='#b1'>[2]</ns0:ref>[3], whereas proteins were found in substantial quantities in the sludge of many wastewater treatment reactors <ns0:ref type='bibr' target='#b0'>[1]</ns0:ref> <ns0:ref type='bibr' target='#b3'>[4]</ns0:ref>. Small amounts of humic substances <ns0:ref type='bibr' target='#b4'>[5]</ns0:ref>, uronic acids, and nucleic acids <ns0:ref type='bibr' target='#b0'>[1]</ns0:ref>[6] <ns0:ref type='bibr' target='#b6'>[7]</ns0:ref> were also detected in EPS. A previous study <ns0:ref type='bibr' target='#b7'>[8]</ns0:ref> <ns0:ref type='bibr' target='#b8'>[9]</ns0:ref> showed the potential of EPS extracted from waste activated sludge (WAS) of wastewater treatment operations as a green corrosion inhibitor for CO 2 corrosion. A maximum inhibition performance of about 80% was achieved with the application of 1000 mg/L of this inhibitor. The corrosion inhibition mechanism of WAS EPS was explained by the formation of a biofilm on the metal surface, shielding the metal surface from the corrosive environment. Even though the inhibition performance is comparable to commercial products, the nature of WAS caused inconsistency in inhibition efficiency. The composition of WAS is dependent on wastewater treatment operational parameters, such as inlet biochemical oxygen demand and sludge residence time.</ns0:p><ns0:p>EPS. The reason of making the surrogate was to have control on the chemical composition of the corrosion inhibitor used and ensure consistent inhibition performance. This study hypothesized that the designed synthetic EPS will demonstrate similar corrosion inhibition behavior as the natural WAS EPS because it was formulated based on the major chemical compositions of natural WAS EPS. The novelty of this research was the design of a surrogate biomass-based corrosion inhibitor inspired by sources with varied chemical compositions. To the knowledge of the authors, this line of work has not been reported elsewhere. This study is unique in the way that it is a multidisciplinary work. Bio-inspired systems and materials are not uncommon in the literature. Yet, this concept is the pioneer of the field of corrosion inhibitor formulation development. For instance, some of the most commonly applied programming algorithms in computer science and engineering today are bio-inspired. Algorithms like genetic and ant colony mimic the natural biological systems to solve research problems. This study is adopting the bioinspired concept into the corrosion inhibitor formulation development. It is believed that this line of multidisciplinary work could benefit and advance the research in corrosion inhibitor development, especially the renewable type.</ns0:p><ns0:p>The present study seeks to investigate the corrosion inhibitive properties of synthetic EPS for carbon steel in 3.64% NaCl solution saturated with CO 2 gas using the potentiodynamic polarization technique. The corrosion kinetic parameters and thermodynamic adsorption parameters are calculated and reported.</ns0:p></ns0:div> <ns0:div><ns0:head>Materials & Methods</ns0:head></ns0:div> <ns0:div><ns0:head>Metal specimen preparation</ns0:head><ns0:p>Potentiodynamic polarization scans were performed on carbon steels of the following weight percentage composition: 0.17 C, 0.08 Mn, 0.014 P, 0.002 S, 0.022 Si, 0.02 Cu, 0.01 Ni, 0.04 Cr, 0.002 Sn, 0.042 Al, 0.006 N, 0.001 V, 0.0001 B, 0.001 Ti, 0.001 Cb, and the remainder iron. The pre-treatment of the specimens' surface was carried out by grinding with sandpapers of 40, 220, 320 grits, rinsing with deionized water, then drying with paper towel. The specimens were used immediately after pre-treatment.</ns0:p></ns0:div> <ns0:div><ns0:head>Corrosive medium preparation</ns0:head><ns0:p>The corrosive medium was prepared with 36.4 g of NaCl (Fisher Scientific, Hampton, NH, USA) in 1 L of deionized water to make up 3.64% of NaCl solution. The deionized water used was drinking water filtered with Milli-Di Water Purification System (Merck Millipore, Burlington, MA). Prior to starting of each experiment, CO 2 gas was sparged in the test solution at 30 psi for 30 minutes. Then, the solution was transferred into the reactor, with CO 2 gas continuously sparging throughout the experiment at 20 psi.</ns0:p></ns0:div> <ns0:div><ns0:head>Corrosion inhibitor preparation</ns0:head><ns0:p>A mixture of several chemical compounds was labelled as synthetic EPS. It was used as the test corrosion inhibitor in this study. The details of each compound, i.e. chemical type, compound identity, vendor, specification, and composition, are listed in Table <ns0:ref type='table'>1</ns0:ref>. These compounds were mixed in the given composition as synthetic EPS. The concentrations of inhibitors used in the following runs were doubled, tripled, and quadrupled.</ns0:p></ns0:div> <ns0:div><ns0:head>Potentiodynamic polarization method</ns0:head><ns0:p>Potentiodynamic polarization experiments were carried out with Gamry Flexcell Critical Pitting Cell Kit, connecting to the Gamry Potentiostat Interface 1000. The reference, counter, and working electrodes used were saturated calomel electrode (SCE), graphite rod, and the metal specimen, respectively. The setup was equipped with a heating jacket connected to TDC4 Omega temperature controller to maintain the test solution at a desired temperature, in this case, 25°C, 50°C, and 70°C. The Glas-Col GT Series stirrer was connected to the setup externally and adjusted to 50 rpm to get the desired shear and to ensure even heating. The working solution volume was 1 L. The working area of the metal specimens had a circular form of 5 cm 2 .</ns0:p><ns0:p>The potentiodynamic polarization scans were carried out in potential range of -0.25 to +0.25 V versus corrosion potential (E corr ) at a scan rate of 3 V/hr. Corrosive medium was added into the reactor with carbon dioxide gas sparging constantly at 20 psi throughout the experiment. The reactor was allowed to equalize for 30 minutes prior to the beginning of experiment. After the system was equalized, Tafel plots were graphed with Gamry DC105 DC Corrosion Technique Software until three relatively similar readings were obtained. Next, corrosion inhibitor was added into the reactor. The reactor was again allowed to equalize for 30 minutes, then Tafel plots were graphed. This step was repeated until three consecutive graphs with similar trends were yielded, to ensure the stability of the system. Subsequently, the concentration of the corrosion inhibitor was increased. Again, the system was being equalized for 30 minutes, followed by the graphing of Tafel plots.</ns0:p><ns0:p>The Tafel plot was plotted with the mean values of corrosion potential (E corr ) and corrosion current density (I corr ) from the triplicates of the experiments, while the electrochemical parameters obtained from the curves were reported with mean and standard deviation. The corrosion current densities were found by extrapolating the linear Tafel segment of the anodic and cathodic curves to the corrosion potential. The corrosion inhibition efficiency was then calculated with Equation <ns0:ref type='formula'>1</ns0:ref>.</ns0:p><ns0:p>(1) 𝐼𝑛ℎ𝑖𝑏𝑖𝑡𝑖𝑜𝑛 𝐸𝑓𝑓𝑖𝑐𝑖𝑒𝑐𝑛𝑦 (%) = 𝐼 𝑐𝑜𝑟𝑟, 𝑢𝑛𝑖𝑛ℎ𝑖𝑏𝑖𝑡𝑒𝑑 -𝐼 𝑐𝑜𝑟𝑟, 𝑖𝑛ℎ𝑖𝑏𝑖𝑡𝑒𝑑 𝐼 𝑐𝑜𝑟𝑟, 𝑢𝑛𝑖𝑛ℎ𝑖𝑏𝑖𝑡𝑒𝑑</ns0:p></ns0:div> <ns0:div><ns0:head>× 100%</ns0:head></ns0:div> <ns0:div><ns0:head>Fourier-transform infrared spectroscopy (FTIR)</ns0:head><ns0:p>Agilent Cary 630 FTIR incorporated with MicroLab software were used for the FTIR analysis in this study. This equipment worked based on Attenuated Total Reflection (ATR) Method. The scanning was range between 4000 to 400 cm -1 with resolution of 4 cm -1 .</ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head></ns0:div> <ns0:div><ns0:head>Corrosion inhibition performance</ns0:head><ns0:p>The Tafel plots generated from the potentiodynamic polarization measurements for carbon steel in 3.64% NaCl saturated with CO 2 gas with synthetic EPS range from 51 mg/L to 204 mg/L at 25°C, 50°C, and 70°C (298 K, 323 K, 343 K) are presented in Figure <ns0:ref type='figure'>1</ns0:ref>, Figure <ns0:ref type='figure'>2</ns0:ref>, and Figure <ns0:ref type='figure'>3</ns0:ref>, respectively. The details of electrochemical parameters obtained from the curves, namely corrosion potential (E corr ), corrosion current density (I corr ), and inhibition efficiency, are listed in Table <ns0:ref type='table'>2</ns0:ref>. Moreover, the effects of inhibitor concentration and media temperature are addressed in the discussion section. It is also worth noting that the significance of operation and economics of the synthetic EPS as an oil field corrosion inhibitor formulation is also included in the discussion section.</ns0:p></ns0:div> <ns0:div><ns0:head>Corrosion kinetic parameters</ns0:head><ns0:p>Corrosion kinetic parameters, i.e. apparent activation corrosion energy (E a ), enthalpy of activation (∆H a °), and entropy of activation (∆S a °), are listed in Table <ns0:ref type='table' target='#tab_1'>3</ns0:ref>. Two Arrhenius plots used for the evaluation of these corrosion kinetic parameters are shown in Figure <ns0:ref type='figure'>4</ns0:ref> and Figure <ns0:ref type='figure'>5</ns0:ref>. The details of calculations are discussed in the discussion section.</ns0:p></ns0:div> <ns0:div><ns0:head>Thermodynamic adsorption parameters</ns0:head><ns0:p>The standard free energy of adsorption (∆G°a ds ), enthalpy of adsorption (∆H ads °), and the entropy of adsorption (∆S ads °) are listed in Table <ns0:ref type='table'>4</ns0:ref>. The Langmuir isotherm and the Van't Hoff plots are shown in Figure <ns0:ref type='figure'>6</ns0:ref> and Figure <ns0:ref type='figure'>7</ns0:ref>, respectively. The equations and graphs involved for the thermodynamic adsorption parameters are explained in the discussion section.</ns0:p></ns0:div> <ns0:div><ns0:head>FTIR</ns0:head><ns0:p>The IR spectra is shown in Figure <ns0:ref type='figure'>8</ns0:ref> and the characteristic IR absorption frequencies of the responding organic functional groups of synthetic EPS is tabulated in Table <ns0:ref type='table'>5</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Discussion</ns0:head></ns0:div> <ns0:div><ns0:head>Properties of synthetic EPS</ns0:head><ns0:p>Synthetic EPS is a mixture of several major groups of chemicals in natural WAS EPS. Although there are many ways to extract EPS and each of the methods give different chemical composition <ns0:ref type='bibr' target='#b0'>[1]</ns0:ref>[7] <ns0:ref type='bibr' target='#b9'>[10]</ns0:ref>; the composition of synthetic EPS formulated in this study will be based on the method of heating. Typically, the EPS extracted by heating has the highest proteins concentration, followed by carbohydrates, humic substances, nucleic acids, and uronic acids <ns0:ref type='bibr' target='#b0'>[1]</ns0:ref>[7] <ns0:ref type='bibr' target='#b9'>[10]</ns0:ref>. Therefore, proteins will be the basis of the synthetic EPS and the ratio of different chemicals will be based on the proteins. The compounds were mixed in ratios that were realistic (small enough concentration to be able to measure accurately using an analytical balance) to be acted as a corrosion inhibitor. They were mixed according to the following ratios: In order for the synthetic EPS to resemble the natural WAS EPS while keeping the complexity of the mixture low, one compound was selected from each chemical group. Since five major groups are generally being studied in the natural WAS EPS, five components were selected for the synthetic EPS formulation. They were chosen based on their structures and their chemical inhibition performances in the literature. Structure wise, compounds with nitrogen, oxygen, or sulfur atoms were preferred since all organic corrosion inhibitors typically contain at least one of these atoms, almost without exception. In addition, bigger compounds are also typically preferred as corrosion inhibitors because bigger compounds are more effective in separating the metal surface from its corrosive environment when adsorbed on the metal surface. The compounds chosen for the synthetic EPS mixture fulfilled these descriptions, as illustrated in Figure <ns0:ref type='figure' target='#fig_2'>9</ns0:ref>. Furthermore, those chemicals that had demonstrated corrosion inhibition were prioritized to be the candidates in the pool of selection. For protein, an amino acid, which is the building block of a protein was chosen. Glutamic acid, a common component of bacterial cell wall <ns0:ref type='bibr' target='#b10'>[11]</ns0:ref>, made an excellent candidate as an amino acid for the purpose of this study since it has also been proven to be an effective corrosion inhibitor in several studies <ns0:ref type='bibr'>[12][13]</ns0:ref>. Glutamic acid showed approximately 54 to 90% of inhibition efficiency in 0.5 M HCl with copper <ns0:ref type='bibr'>[12][13]</ns0:ref>. Due to its potential in corrosion inhibition, it was chosen as the main component of the synthetic EPS. The second biggest composition was carbohydrate. For an organic corrosion inhibitor, typically, a bigger molecule is preferred. Carboxymethylcellulose (CMC), a relatively big molecular weight packed with multiple oxygen atoms, was selected as the candidate for the chemical group of carbohydrate. Its corrosion inhibition capability has also been proven excellent in various investigations <ns0:ref type='bibr' target='#b13'>[14]</ns0:ref> <ns0:ref type='bibr' target='#b16'>[15]</ns0:ref>. Inhibition efficiencies of about 65 to 72% were observed when CMC was used with mild steel in acid solutions H 2 SO 4 <ns0:ref type='bibr' target='#b13'>[14]</ns0:ref> and HCl <ns0:ref type='bibr' target='#b16'>[15]</ns0:ref>, respectively. However, corrosion inhibition studies on the rest of the chemical groups have no record in the literature to date. For humic substances and uronic acids, there are not many chemicals from these groups, so, humic acid and alginic acid were picked for each group, respectively. In the case of nucleic acids, there are only four choices in this group, namely thymine, guanine, adenine, and cytosine. Making a decision based on an economical point of view, the most affordable choice was thymine. Thymine is a relatively smaller compound compared to other chosen chemicals, but it contains both nitrogen and oxygen atoms, making it a desirable option. Hence, glutamic acid, CMC, humic acid, thymine, and alginic acid were chosen as the formulation for synthetic EPS. Their chemical structures are shown in Figure <ns0:ref type='figure' target='#fig_2'>9</ns0:ref>.</ns0:p><ns0:p>The formulation of synthetic EPS was designed solely based on the chemical composition of natural WAS EPS, which was not optimized to meet the purpose of corrosion inhibition. Interestingly, this corrosion inhibitor showed corrosion inhibition performance comparable to the natural WAS EPS as well as commercial corrosion inhibitor. In order to improve the corrosion inhibition efficiency of synthetic EPS, the future direction of the current research will focus on optimizing the formulation to reduce the required applied concentration of corrosion inhibitor while achieving the maximum attainable corrosion inhibition performance. This could be done by first reducing the number of compounds in the formulation, followed by optimizing the concentration of the compounds and the inhibition performance statistically.</ns0:p></ns0:div> <ns0:div><ns0:head>The potential of utilizing biomass sources directly as corrosion inhibitors are undeniable.</ns0:head><ns0:p>There is an enormous amount of studies on the application of plant extracts as corrosion inhibitors, but these products are still relatively rare in the market. One of the main reasons that is delaying the commercialization of these inhibitors could be the current immature resource recovery techniques. A lot of extraction methods are still economical infeasible these days. Therefore, in order to promote the use of renewable corrosion inhibitors, as well as to improve the marketability of these products, a bio-inspired corrosion inhibitor formulation is introduced in this study. Compared to the traditional plant extracts corrosion inhibitors, this type of renewable corrosion inhibitor is more market-ready because of several advantages: (1) renewable sources, (2) economic feasibility, (3) chemical composition consistency, as well as ( <ns0:ref type='formula'>4</ns0:ref> </ns0:p></ns0:div> <ns0:div><ns0:head>Effect of concentration</ns0:head><ns0:p>The curves in Figure <ns0:ref type='figure'>1</ns0:ref>, Figure <ns0:ref type='figure'>2</ns0:ref>, and Figure <ns0:ref type='figure'>3</ns0:ref> revealed well defined anodic and cathodic polarization Tafel regions. Note that only one set of experimental data was reported because the differences in triplicates were insignificant. The results for the triplicates can be found in the raw data section.</ns0:p><ns0:p>As observed in these figures, both cathodic and anodic reactions of carbon steel electrode corrosion were inhibited by the increase concentration of synthetic EPS in 3.64% NaCl saturated with CO 2 gas. This observation indicates that the addition of synthetic EPS reduced anodic dissolution as well as the hydrogen evolution reaction <ns0:ref type='bibr' target='#b17'>[16]</ns0:ref>. This can be explained by the adsorption of inhibitor over the corroded surface <ns0:ref type='bibr' target='#b18'>[17]</ns0:ref>. Tafel lines of nearly equal slopes were obtained, indicating that the hydrogen evolution reaction was activated-controlled <ns0:ref type='bibr' target='#b19'>[18]</ns0:ref>.</ns0:p><ns0:p>The details of electrochemical parameters obtained from the Tafel plots such as the values of corrosion potential, E corr , corrosion current density, I corr , corrosion protection efficiency, and surface coverage degree, θ, are presented in Table <ns0:ref type='table'>2</ns0:ref>. The corrosion inhibition efficiency was calculated using Equation <ns0:ref type='formula'>1</ns0:ref>, based on the I corr values, where I corr,blank and I corr were the corrosion current density without and with inhibitor, respectively. These values were obtained by the extrapolation of the cathodic and anodic Tafel lines to the corrosion potentials. The data showed that the I corr values decreased in the presence of synthetic EPS. These values also dropped as the concentration of inhibitor increased, meaning that the corrosion reaction was slowing down as the inhibitor concentration was increasing. This phenomenon can be attributed to the adsorption of synthetic EPS on the metal surface <ns0:ref type='bibr' target='#b19'>[18]</ns0:ref>.</ns0:p><ns0:p>There was no definite pattern observed in E corr values in the presence of different concentrations of synthetic EPS. This result indicated that synthetic EPS may be considered as a mixed-type corrosion inhibitor <ns0:ref type='bibr' target='#b20'>[19]</ns0:ref> in the presence of CO 2 gas saturated 3.64% NaCl solution. The maximum displacement in E corr of less than 0.085 V suggests a mixed mode of inhibition <ns0:ref type='bibr' target='#b21'>[20]</ns0:ref>. Mixed-type corrosion inhibitor retards corrosion rate by suppressing both anodic and cathodic corrosion reactions, typically by adsorbing on a metal surface, forming a protective film to reduce contact of metal surface from the corrosive environment <ns0:ref type='bibr' target='#b22'>[21]</ns0:ref>.</ns0:p><ns0:p>The inhibition efficiency increased as the concentration of synthetic EPS increased. The maximum inhibition was about 94% with an optimum inhibitor concentration of 204 mg/L at 70°C. At 25°C, the maximum inhibition protection of synthetic EPS was 82% at a concentration of 153 mg/L. The previous study of WAS EPS inhibitor demonstrated an optimum inhibition performance of about 79% at a concentration of 1000 mg/L <ns0:ref type='bibr' target='#b7'>[8]</ns0:ref>. Even though the inhibition performance showed only a mere improvement of 3%, the inhibitor concentration was reduced by about 6.5 times. It is known that the natural WAS EPS is rich in a variety of compounds. These compounds could have posed stearic hindrance on the adsorption of inhibition molecules on the metal surface, bring down the efficiency of the overall inhibition performance, so, higher concentrations of inhibitors were required to demonstrate the corrosion inhibition capability. Unlike the natural WAS EPS, the synthetic EPS was formulated specifically on the EPS groups that are known to perform as corrosion inhibitors. Hence, it is expected that the corrosion inhibition efficiency of synthetic EPS to be higher than the natural WAS EPS. Furthermore, in the case of commercial corrosion inhibitors, their corrosion protection performances are typically above 70%. Synthetic EPS has a corrosion inhibition performance that is within the range of commercial corrosion inhibitors. One advantage compared to natural WAS EPS is that its inhibition performance is consistent. The results obtained from this study strongly suggest the great potential commercialization value of synthetic EPS as a valuable material to inhibit corrosion issues in oilfield operations.</ns0:p></ns0:div> <ns0:div><ns0:head>Effect of temperature</ns0:head><ns0:p>The effect of temperature on the inhibited solution-metal reaction is highly complex because many changes could occur on the metal surface such as rapid etching and desorption of inhibitor, also, the inhibitor itself may undergo decomposition and/or rearrangement <ns0:ref type='bibr' target='#b23'>[22]</ns0:ref>. The effect of corrosion inhibition by synthetic EPS in NaCl solution saturated with CO 2 gas was studied with three different temperatures, i.e. 25°C, 50°C, and 70°C. Since the corrosion rate is greatly affected by the concentration of inhibitor as well as the temperature of the working environment, these factors have an important operational impact.</ns0:p><ns0:p>At different temperatures and inhibitor concentrations, the corrosion inhibition efficiencies varied. It was apparent that the rates of carbon steel corrosion, both in the blank solution of 3.64% NaCl saturated with CO 2 gas and with the presence of corrosion inhibitor, increased with increasing temperature. The impact of temperature on the overall corrosion reaction was more pronounced than the effect of inhibitor concentration. The inhibition efficiency increased with temperature. Typically, a decrease in inhibition efficiency with a rise in temperature suggests physisorption of the corrosion inhibitor. In contrast, an increase in inhibition efficiency with rise in temperature is indicative of a chemisorption mechanism <ns0:ref type='bibr' target='#b24'>[23]</ns0:ref>. Therefore, the results clearly indicate a chemisorption mechanism of synthetic EPS on the carbon steel surface.</ns0:p></ns0:div> <ns0:div><ns0:head>Corrosion kinetic parameters</ns0:head><ns0:p>Corrosion kinetics parameters can be evaluated with different approaches. This study seeks to quantitatively evaluate the performance of the studied corrosion inhibitor (synthetic EPS) using an engineering calculation approach that yields empirical results. Instead of focusing mechanistically on the chemistry of the corrosion and corrosion inhibitor reactions to obtain the corrosion kinetics parameters, it is looking at an engineering perspective that is tailored to the studied system. The chemistry of the corrosion and corrosion inhibitor reactions are unutterably important, but it is not the center of the study. This study is not set up to investigate the mechanistic values of the corrosion kinetic and thermodynamic adsorption parameters. This paper emphasizes on the engineering significance of the synthetic EPS as a corrosion inhibitor by applying engineering equations that are based on basic corrosion theory but adapted heuristically to the studied system. Since the reported numbers (i.e. apparent activation corrosion energy, enthalpy of activation, entropy of activation) are empirical values that are only relevant to the studied system, these numbers may not be the duplicated with other system set up (e.g. traditional weight loss method with beaker testing). Even though the reported numbers are not the mechanistic values with respect to the theoretical corrosion reactions and the theoretical adsorption of the inhibitors, it serves a purpose to screen the performance of the studied corrosion inhibitor quantitatively using engineering calculations. In addition, the empirical values are also helpful in determining the behavior of the inhibitor in the studied system. For example, the enthalpy of activation could be used to describe whether the metal dissolution process is endothermic or exothermic. The idea is that, by adhering to the same experimental set up and procedure, the same engineering calculations can be performed to estimate the same parameters for other inhibitors, serving as a comparison model. This is a particularly valuable heuristic tool, where the corrosion inhibitor can be screened rapidly. Thus, if the results obtained from the experiment are encouraging, further corrosion testing such as sparged beaker test and wheel test could be considered. Furthermore, the same approach has previously been demonstrated in the literature <ns0:ref type='bibr' target='#b25'>[24]</ns0:ref>[25], proven its usability and reliability.</ns0:p><ns0:p>The activation parameters for the corrosion reaction were calculated using an Arrheniustype plot according to Equation <ns0:ref type='formula'>2</ns0:ref>. It is worth mentioning that the Arrhenius equations applied were tailored to the studied system, as a heuristic approach to estimate the empirical values of the apparent activation corrosion energy, enthalpy of activation, and entropy of activation that are true to the system. E a in the equation denotes the apparent activation corrosion energy, R is the universal gas constant, and k is the Arrhenius pre-exponential factor. The values of apparent activation energy of corrosion were determined from the slope of ln I corr versus 1/T plot, shown in Figure <ns0:ref type='figure'>4</ns0:ref>. The data showed lower activation energy in the presence of inhibitors than in its absence, which is a typical pattern of chemisorption <ns0:ref type='bibr' target='#b19'>[18]</ns0:ref>.</ns0:p><ns0:p>An alternative formulation of Arrhenius equation, i.e. transition-state equation shown in Equation <ns0:ref type='formula'>3</ns0:ref>, was used to calculate the change of enthalpy (∆H a °) and entropy (∆S a °) of activation for the activation complex formation in the transition state. In this equation, the h is the Planck's constant, N is the Avagadro's number, ∆S a ° is the entropy of activation, and ∆H a ° is the enthalpy of activation. Figure <ns0:ref type='figure'>5</ns0:ref> shows a plot of ln (I corr /T) against 1/T for synthetic EPS. A straight line was obtained with a slope of ∆H a °/R and an intercept of ln (R/Nh + ∆S a °/R), from which the values of ∆H a ° and ∆S a ° were calculated. The positive enthalpy values reflected the endothermic nature of metal dissolution process. Large and negative values of entropy imply that the activated complex in the rate determining step represents an association rather than a dissociation step <ns0:ref type='bibr' target='#b19'>[18]</ns0:ref>.</ns0:p><ns0:p>(2)</ns0:p><ns0:formula xml:id='formula_0'>𝐼 𝑐𝑜𝑟𝑟 = 𝑘𝑒 - 𝐸 𝑎 𝑅𝑇 (3) 𝐼 𝑐𝑜𝑟𝑟 = 𝑅𝑇 𝑁ℎ 𝑒𝑥𝑝 ( ∆𝑆 0 𝑎 𝑅 ) 𝑒𝑥𝑝 ( -∆𝐻 0 𝑎 𝑅𝑇 )</ns0:formula></ns0:div> <ns0:div><ns0:head>Thermodynamic adsorption parameters</ns0:head><ns0:p>Adsorption isotherms provide insights into the interaction among the adsorbed molecules and the metal surface, which can help to better understand the corrosion inhibition mechanism. Similar to the corrosion kinetic parameters, the thermodynamic adsorption parameters reported in this section are only empirical values relevant to this studied system. The values of surface coverage (θ) to different concentrations of inhibitor, obtained from the polarization measurements in the temperature range of 25 to 70°C (298 to 343 K) were used to explain the best isotherm to determine the adsorption mechanism. The values of θ were assumed to be the corrosion inhibition efficiencies. The reason being, without the presence of inhibitor compound, an inhibition efficiency of 0% is expected, so, when an inhibitor compound is introduced to a corrosive environment, the improved corrosion inhibition efficiency is believed to be solely contributed by the coverage of the inhibitor compound on the metal surface. The surface coverage, θ, were used in a series of equations shown in Equation <ns0:ref type='formula'>4</ns0:ref>, Equation <ns0:ref type='formula'>5</ns0:ref>, and Equation 6 <ns0:ref type='bibr' target='#b27'>[26]</ns0:ref>. Equation <ns0:ref type='formula'>4</ns0:ref>showed the relationship of I corr , I corr,blank , I sat , and θ. I sat is the current density of entirely covered surface. This equation was then be rearranged into Equation <ns0:ref type='formula'>5</ns0:ref>. As I corr was greater than I sat , Equation <ns0:ref type='formula'>5</ns0:ref>was simplified to Equation <ns0:ref type='formula'>6</ns0:ref>. ( <ns0:ref type='formula'>4</ns0:ref>)</ns0:p><ns0:formula xml:id='formula_1'>𝐼 𝑐𝑜𝑟𝑟 = (1 -𝜃)𝐼 𝑐𝑜𝑟𝑟,𝑏𝑙𝑎𝑛𝑘 + 𝜃𝐼 𝑠𝑎𝑡 (5) 𝜃 = 𝐼 𝑐𝑜𝑟𝑟, 𝑏𝑙𝑎𝑛𝑘 -𝐼 𝑐𝑜𝑟𝑟 𝐼 𝑐𝑜𝑟𝑟,𝑏𝑙𝑎𝑛𝑘 -𝐼 𝑠𝑎𝑡 (6) 𝜃 = 𝐼 𝑐𝑜𝑟𝑟, 𝑏𝑙𝑎𝑛𝑘 -𝐼 𝑐𝑜𝑟𝑟 𝐼 𝑐𝑜𝑟𝑟,𝑏𝑙𝑎𝑛𝑘</ns0:formula><ns0:p>In the range of temperature and inhibitor concentration studied, the best correlation between the experimental results and the adsorption isotherm functions was obtained using Langmuir adsorption isotherm. The Langmuir isotherm for monolayer adsorption is given by Equation <ns0:ref type='formula'>7</ns0:ref>. By linearizing this equation, Equation <ns0:ref type='formula'>8</ns0:ref>was obtained. ( <ns0:ref type='formula'>7</ns0:ref>)</ns0:p><ns0:formula xml:id='formula_2'>𝜃 1 -𝜃 = 𝐾𝐶 𝐶 𝜃 = 1 𝐾 + 𝐶 (8)</ns0:formula><ns0:p>In Equation <ns0:ref type='formula'>7</ns0:ref>and Equation <ns0:ref type='formula'>8</ns0:ref>, θ is the surface coverage degree, C is the inhibitor concentration in the NaCl solution, and K is the equilibrium constant of the adsorption process. The correlation coefficient, R 2 , was used to describe how close the isotherm fits the experimental data. The plot of C/θ against C gave a straight line and the linear correlation coefficients were fairly close to 1, indicating good fit to the data. This graph is shown in Figure <ns0:ref type='figure'>6</ns0:ref>. The adsorption behavior of synthetic EPS conformed to Langmuir isotherm, suggesting monolayer adsorption, which is a typical behavior of chemisorption <ns0:ref type='bibr' target='#b28'>[27]</ns0:ref>.</ns0:p><ns0:p>In general, Langmuir isotherm is not recommended to be used to describe a mixture system because the individual components in a mixture can each be adsorbed in different ways. However, it is applicable to this study because the synthetic EPS, as a corrosion inhibitor formulation, was treated as an entity. The individual contribution of compounds in the mixture of synthetic EPS were considered unimportant, therefore, being omitted. These are the basic assumptions of Langmuir isotherm: (1) surface of the adsorbent (metal) is uniform, (2) adsorption sites are equivalent, (3) adsorbed molecules do not interact, (4) all adsorption occurs through the same mechanism. Assuming that the metal surface is uniform, the adsorption sites are equivalent, and the inhibitor formulation is being treated as an entity, Langmuir isotherm is appropriate to be used to describe the overall adsorption mechanism. There are numerous studies in the literature where Langmuir was used to describe the adsorption mechanism of an inhibitor mixture, especially plant extracts <ns0:ref type='bibr' target='#b29'>[28]</ns0:ref> <ns0:ref type='bibr' target='#b31'>[29]</ns0:ref>. K values were calculated from the intercepts of the same plot (Figure <ns0:ref type='figure'>6</ns0:ref>). The constant of adsorption, K, can be related to the standard free energy of adsorption, ∆G°a ds , using Equation <ns0:ref type='formula'>9</ns0:ref>. The constant 1 x 10 6 in the equation is the concentration of water molecules expressed in mg/L, R is the universal gas constant, T is the absolute temperature. On the other hand, ∆H°a ds can be deduced from the integrated version of the Van't Hoff equation expressed by Equation <ns0:ref type='formula'>10</ns0:ref>. Figure <ns0:ref type='figure'>7</ns0:ref> shows the plot of ln K versus 1/T which yield a straight line with a slope of -∆H°a ds /R.</ns0:p><ns0:p>The value obtained was used to find the ∆H°a ds . The calculated ∆H°a ds was then used to calculate the values of ∆S°a ds by using Equation <ns0:ref type='formula'>11</ns0:ref>. A more in-depth study of the inhibitor adsorption mechanism was investigated using the values of thermodynamic parameters. The details can be found in Table <ns0:ref type='table'>4</ns0:ref>. The spontaneity of the adsorption of inhibitor on the metal surface as well as the stability of the adsorbed layer on the metal surface was demonstrated by the resulted negative values of ∆G°a ds . Typically, an endothermic adsorption process that has a positive value of ∆H°a ds is attributed unequivocally to chemisorption, while an exothermic adsorption process with ∆H°a ds of negative value may involve either physisorption or chemisorption, or a combination of both the processes <ns0:ref type='bibr' target='#b23'>[22]</ns0:ref>. In this study, the ∆H°a ds was positive, once again implying a chemisorption mechanism. The value of ∆S°a ds decreased with increased temperature, implying that the reaction of adsorption was getting less disordered.</ns0:p></ns0:div> <ns0:div><ns0:head>FTIR</ns0:head><ns0:p>The corrosion inhibition capability of synthetic EPS was proven significant in this study. The trend in the IR spectrum of the synthetic EPS followed closely to the natural WAS EPS <ns0:ref type='bibr' target='#b7'>[8]</ns0:ref> as expected because it is formulated based on the chemical composition of natural WAS EPS. Similar to the natural WAS EPS, the FTIR results of synthetic EPS showed that functional groups O-H, N-H, C-N, C=O, and C-H were present. Since the synthetic EPS and natural WAS EPS both have the same functional group, it can be deduced that these functional groups play major roles in corrosion inhibition. Other authors have also suggested the contribution of these functional groups in corrosion inhibition <ns0:ref type='bibr'>[28][29]</ns0:ref>.</ns0:p><ns0:p>This study treated the mixture of synthetic EPS as an entity, meaning that the contribution to the overall inhibition cannot be ascribed to any single component in the mixture. A suggestive corrosion inhibition mechanism of the synthetic EPS can be explained by the electrochemical theory. The electrochemical theory of corrosion holds that the metal surface corroding in an electrolyte is covered with local electrolytic cells. Some areas of the metal can act as anodes and other areas can act as cathodes, shown in Figure <ns0:ref type='figure'>10</ns0:ref>, depending upon the history of the metal regarding heat treatment, presence of imperfections, scratches, greases, paint coatings, fingerprint smudges, etc. At anodic sites, the metal usually dissolves into solution. Electrons given from these sites are transported to local cathodes and collected by electron acceptors such as hydrogen ions and oxygen. As previously suggested, synthetic EPS acts as a mixed-type corrosion inhibitor, meaning that the molecules in the synthetic EPS chemisorbed on both the anodic and cathodic sites of metal surface to form a monolayer protection film. The functional groups rich in nitrogen and oxygen atoms acted as the polar head of organic corrosion inhibitors, adsorbing on metal surface by forming chemical bonds between the inhibitor molecules and metal ions, while the non-polar hydrocarbon chain attached to the polar head isolated the metal surface from the corrosive surrounding, suppressing both anodic and cathodic corrosion reactions, reducing the overall corrosion rate. Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:p></ns0:div> <ns0:div><ns0:head>Engineering, operation, and economic significance of synthetic EPS</ns0:head><ns0:p>The corrosion inhibition performance of the synthetic EPS shown in this study is promising. Other areas that are interesting to be investigated are the engineering, operational, and economical sides of this corrosion inhibitor formulation. This article is an extension of a previous study that showed the potential of EPS extracted from waste activated sludge (WAS) of wastewater treatment operations as a green corrosion inhibitor for CO 2 corrosion. A maximum inhibition performance of about 80% was achieved with the application of 1000 mg/L of this inhibitor <ns0:ref type='bibr' target='#b7'>[8]</ns0:ref> <ns0:ref type='bibr' target='#b8'>[9]</ns0:ref>. The major flaw in this corrosion inhibitor is that the inhibition performance is not consistent due to the variation in wastewater compositions. Thus, synthetic EPS was formulated in this study based on the natural composition of WAS EPS, as a bio-inspired material, that resemble the inhibition performance of natural WAS EPS, but with consistent inhibition performance. This approach is novel in research and development of green corrosion inhibitor development. Commercial corrosion inhibitor for the oil and gas industry are generally petroleum-based, while most green corrosion inhibitors reported in the literature were directly extracted from agricultural sources. The technique used in this study for the development of bio-inspired material as a corrosion inhibitor can be further expanded in the area of green corrosion inhibitors development and potentially be extended to other research fields.</ns0:p><ns0:p>In terms of operation significance, similar to most bio-inspired materials, this corrosion inhibitor formulation can be formulated with commercial renewable resources or extracted from natural resources (waste activated sludge), leaving a lesser environmental impact compared to the commercial petroleum-based corrosion inhibitors. Besides having corrosion inhibition performance comparable to commercial products (commercial products usually show inhibition performance above 70%), the synthetic EPS is also just as easy to be applied like commercial corrosion inhibitors, making it an excellent alternative.</ns0:p><ns0:p>The economic analysis of synthetic EPS was evaluated in this study. The production cost of the synthetic EPS is about $4.23 for every 10,000-inhibition treatment (assuming 1 L system/treatment), while the market price of a typical commercial oil and gas corrosion inhibitor costs about $2.38 per 10,000 applications. It is worth mentioned that the synthetic EPS is formulated based solely on the composition of natural EPS. The economic feasibility can be improved in future studies by product optimization to reduce the applied inhibitor concentration and enhance the inhibition performance.</ns0:p><ns0:p>It is evident that bio-inspired systems/materials have high potential in revolutionizing the current market to reduce dependence on fossil fuel-based products as well as to promote innovative product development approach. This transformation is not only applicable in the corrosion inhibitor industry but should also be extended to benefit other research and development areas. The studied corrosion inhibitor, synthetic EPS, showed corrosion inhibition capability for carbon steel when tested in 3.64% NaCl saturated with CO 2 gas. Synthetic EPS is a surrogate of biomass-based corrosion inhibitor inspired by sources with varied chemical compositions to overcome the composition inconsistency in biomass that can cause unreliable corrosion inhibition performance. Synthetic EPS is a mixture of glutamic acid, carboxymethylcellulose, humic acid, thymine, and alginic acid, following the chemical composition of natural WAS EPS extracted by heating method. Unlike the natural WAS EPS that is rich in assorted of molecules that could promote stearic hindrance on the adsorption of inhibitor molecules, synthetic EPS was designed specifically based on the EPS groups that are known to perform as corrosion inhibitors. The electrochemical testing used in this study showed that the corrosion rates were significantly reduced with the addition of synthetic EPS. With concentration of 204 mg/L in 3.64% NaCl saturated with CO 2 gas, synthetic EPS showed maximum corrosion inhibitions of 82.41%, 89.65%, and 93.99% at 25°C, 50°C, and 70°C, respectively. Its performance compared favorably with natural WAS EPS and commercial corrosion inhibitors. It was found that the inhibition performance was controlled by both the concentration of inhibitor and temperature.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions</ns0:head><ns0:p>The corrosion inhibition capability was due to chemisorption shown by several evidences:</ns0:p><ns0:p>(1) An increase in corrosion inhibition efficiency with increase temperature (2) A decrease in activation energy in the presence of inhibitor (3) Endothermic adsorption Since the formulation of synthetic EPS was designed solely based on the chemical composition of natural WAS EPS, it was not optimized to meet the purpose of corrosion inhibition. Based on the results presented and the needs and requirements of corrosion protection service providers, the future direction of the current research will focus on optimizing the formulation in order to reduce the required applied concentration of corrosion inhibitor while achieving the maximum attainable corrosion inhibition performance. This could be done by first reducing the number of compounds in the formulation, then optimizing the concentration of the compounds and the inhibition performance statistically. This bio-inspired material is developed in the hope to promote the commercialization of renewable corrosion inhibitors. According to the Google Scholar database, there are as many as 17,600 publications on the topic of green corrosion inhibitors from the year 1980 to 2018. A significant portion of these publications were made up by phytochemical-based compounds. Plant extracts are gaining popularity as green corrosion inhibitors candidates not only because they are renewable sources, but also their potential in corrosion mitigation. On average, the attainable corrosion protection efficiencies of these inhibitors can range from 70% to as high as 98% <ns0:ref type='bibr' target='#b32'>[30]</ns0:ref>[31] <ns0:ref type='bibr' target='#b34'>[32]</ns0:ref>[33] <ns0:ref type='bibr' target='#b36'>[34]</ns0:ref>. Despite the overwhelming research evidence that suggests the impressive performance of these inhibitors, there are still drastic uneven numbers of research reports and products on the shelves. Needless to say, resource recovery efforts take time to improve. Before these techniques are optimized, or if there are enough products to be added to the product line to improve the cost issue <ns0:ref type='bibr' target='#b37'>[35]</ns0:ref>, until then, bio-inspired material could be an alternative to balance the environmental problems bring by petroleum-based corrosion inhibitors and the economic complications raise by plant extracts corrosion inhibitors. As a matter of course, the idea of benefiting from bio-inspired systems and materials will not only benefit the corrosion inhibitor sector but is prompt to be extended to any other applicable research area. Manuscript to be reviewed</ns0:p><ns0:note type='other'>Chemistry Journals Figure 1</ns0:note><ns0:p>Tafel plot for carbon steel in 3.64% NaCl concentrated with CO 2 with different concentrations of synthetic EPS at 25°C</ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>a. Proteins:carbohydrates = 2.5:1 b. Proteins:humic substances = 6:1 c. Proteins:nucleic acids = 15:1 d. Proteins:uronic acids =15:1</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>) corrosion PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2019:09:41197:2:1:NEW 17 Dec 2019) Manuscript to be reviewed Chemistry Journals Analytical, Inorganic, Organic, Physical, Materials Science inhibition performance consistency.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>( 9 )</ns0:head><ns0:label>9</ns0:label><ns0:figDesc>∆𝐺 °𝑎𝑑𝑠 =-𝑅𝑇𝑙𝑛 (1 × 10 6 𝐾) ∆𝐺 °𝑎𝑑𝑠 = ∆𝐻 °𝑎𝑑𝑠 -𝑇∆𝑆 °𝑎𝑑𝑠</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2019:09:41197:2:1:NEW 17 Dec 2019)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2019:09:41197:2:1:NEW 17 Dec 2019)Manuscript to be reviewedChemistry JournalsAnalytical, Inorganic, Organic, Physical, Materials Science</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head>1 Characteristic 1 PeerJ</ns0:head><ns0:label>11</ns0:label><ns0:figDesc>Mat. Sci. reviewing PDF \| (MATSCI-2019:09:41197:2:1:NEW 17 Dec 2019)</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='29,42.52,201.82,525.00,315.00' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='30,42.52,201.82,525.00,320.25' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='31,42.52,201.82,525.00,309.00' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='32,42.52,181.57,525.00,300.00' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='33,42.52,201.82,525.00,319.50' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='34,42.52,178.87,525.00,346.50' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='35,42.52,178.87,525.00,313.50' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='36,42.52,178.87,525.00,318.75' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='37,42.52,229.87,525.00,192.75' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='38,42.52,178.87,525.00,259.50' type='bitmap' /></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2019:09:41197:2:1:NEW 17 Dec 2019)Manuscript to be reviewedChemistry JournalsAnalytical, Inorganic, Organic, Physical, Materials Science</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Temperature (°C)</ns0:cell><ns0:cell>Temperature (K)</ns0:cell><ns0:cell>Concentration (mg/L)</ns0:cell><ns0:cell>E corr (V)</ns0:cell><ns0:cell>I corr (µA/cm 2 )</ns0:cell><ns0:cell>Inhibition efficiency (%)</ns0:cell><ns0:cell>Surface coverage degree, θ</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>0</ns0:cell><ns0:cell>-0.73</ns0:cell><ns0:cell>52.48</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>51</ns0:cell><ns0:cell>-0.71</ns0:cell><ns0:cell>25.12</ns0:cell><ns0:cell>68.72</ns0:cell><ns0:cell>0.6872</ns0:cell></ns0:row><ns0:row><ns0:cell>25</ns0:cell><ns0:cell>298</ns0:cell><ns0:cell>102</ns0:cell><ns0:cell>-0.71</ns0:cell><ns0:cell>18.20</ns0:cell><ns0:cell>77.34</ns0:cell><ns0:cell>0.7734</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>153</ns0:cell><ns0:cell>-0.71</ns0:cell><ns0:cell>14.45</ns0:cell><ns0:cell>82.00</ns0:cell><ns0:cell>0.8200</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>204</ns0:cell><ns0:cell>-0.70</ns0:cell><ns0:cell>14.13</ns0:cell><ns0:cell>82.41</ns0:cell><ns0:cell>0.8241</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>0</ns0:cell><ns0:cell>-0.74</ns0:cell><ns0:cell>125.89</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>51</ns0:cell><ns0:cell>-0.76</ns0:cell><ns0:cell>37.15</ns0:cell><ns0:cell>86.03</ns0:cell><ns0:cell>0.8603</ns0:cell></ns0:row><ns0:row><ns0:cell>50</ns0:cell><ns0:cell>323</ns0:cell><ns0:cell>102</ns0:cell><ns0:cell>-0.76</ns0:cell><ns0:cell>34.67</ns0:cell><ns0:cell>86.96</ns0:cell><ns0:cell>0.8696</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>153</ns0:cell><ns0:cell>-0.76</ns0:cell><ns0:cell>33.11</ns0:cell><ns0:cell>87.55</ns0:cell><ns0:cell>0.8755</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>204</ns0:cell><ns0:cell>-0.76</ns0:cell><ns0:cell>27.54</ns0:cell><ns0:cell>89.65</ns0:cell><ns0:cell>0.8965</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>0</ns0:cell><ns0:cell>-0.73</ns0:cell><ns0:cell>223.87</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>51</ns0:cell><ns0:cell>0.76</ns0:cell><ns0:cell>45.71</ns0:cell><ns0:cell>91.70</ns0:cell><ns0:cell>0.9170</ns0:cell></ns0:row><ns0:row><ns0:cell>70</ns0:cell><ns0:cell>343</ns0:cell><ns0:cell>102</ns0:cell><ns0:cell>-0.76</ns0:cell><ns0:cell>39.81</ns0:cell><ns0:cell>92.77</ns0:cell><ns0:cell>0.9277</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>153</ns0:cell><ns0:cell>-0.76</ns0:cell><ns0:cell>34.67</ns0:cell><ns0:cell>93.71</ns0:cell><ns0:cell>0.9371</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>204</ns0:cell><ns0:cell>0.75</ns0:cell><ns0:cell>33.11</ns0:cell><ns0:cell>93.99</ns0:cell><ns0:cell>0.9399</ns0:cell></ns0:row></ns0:table><ns0:note>1 PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2019:09:41197:2:1:NEW 17 Dec 2019)Manuscript to be reviewedChemistry JournalsAnalytical, Inorganic, Organic, Physical, Materials Science</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_1'><ns0:head>Table 3 (on next page)</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Corrosion kinetic parameters for carbon steel in different concentrations of the synthetic ∆H a ° (kJ/mol) ∆S a ° (J/mol K)</ns0:figDesc><ns0:table><ns0:row><ns0:cell>0</ns0:cell><ns0:cell>27.46</ns0:cell><ns0:cell>24.81</ns0:cell><ns0:cell>-71.28</ns0:cell></ns0:row><ns0:row><ns0:cell>51</ns0:cell><ns0:cell>12.54</ns0:cell><ns0:cell>8.75</ns0:cell><ns0:cell>-131.24</ns0:cell></ns0:row><ns0:row><ns0:cell>102</ns0:cell><ns0:cell>15.21</ns0:cell><ns0:cell>12.56</ns0:cell><ns0:cell>-120.83</ns0:cell></ns0:row><ns0:row><ns0:cell>153</ns0:cell><ns0:cell>17.24</ns0:cell><ns0:cell>14.59</ns0:cell><ns0:cell>-115.63</ns0:cell></ns0:row><ns0:row><ns0:cell>204</ns0:cell><ns0:cell>16.47</ns0:cell><ns0:cell>13.82</ns0:cell><ns0:cell>-118.73</ns0:cell></ns0:row></ns0:table><ns0:note>EPSPeerJ Mat. Sci. reviewing PDF \| (MATSCI-2019:09:41197:2:1:NEW 17 Dec 2019) Manuscript to be reviewed Chemistry Journals Analytical, Inorganic, Organic, Physical, Materials Science Inhibitor concentration (mg/L) Ea (kJ/mol)</ns0:note></ns0:figure> <ns0:note place='foot'>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:note> </ns0:body> "	"November 27, 2019 Dr. Alexey Kabalnov Academic Editor of PeerJ Materials Science Dear Dr. Kabalnov: The authors appreciate the comments to enhance the quality of the paper. Below is a table describing actions to the comments. Based on the editor’s comments, additional information was included in the article for clarification. We feel that the revisions have improved the paper and it is now in better form for publication. We would like the paper to be published in your esteemed journal. This paper is a continuation of a previous paper published in PeerJ on the use of waste activated sludge EPS as a corrosion inhibitor. We would like to show the evolution of the research concepts in this area in this journal. We were very satisfied with the fairness, clarity, and comprehensive nature of the previous reviews. Please let us know of your decision at your earliest convenience. Sincerely, Liew Chien Go, Dilip Depan, William Holmes, August Gallo, Tre Bertrand, and Rafael Hernandez Table of actions to reviewers’ comments. Review Revision/Response Editor Some additions and changes were made but they are not sufficient to make the manuscript publishable as is. I may suggest to downplay the thermodynamic and kinetic analysis, as pure empirical data; remove mentioning of Langmuir isotherm and 'monolayer coverage' from the abstract and conclusions. It would help to simplify the message (e.g. the formulation as tested provided a significant decrease in corrosion rate as compared to control'). Also, provide some more rational on how this complex blend of 5 components was arrived to. Was it optimized? Was it based on a natural composition of some kind? Is the observed reduction of corrosion practically important and significant? Any insights on which component of the blend is responsible for the observed effect? Note that the components cannot be treated as a single component, as their chemistries are quite different, and the polymer absorption is irreversible (which is contrary to the assumption of Langmuir isotherm). Thank you for the comment. The authors have edited the paper following the suggestions. The authors would like to emphasize that the thermodynamic and kinetic analyses are empirical. This information was added in the abstract from line 32 to 34. The empirical data were used for quantitative measurement of corrosion inhibition performance and may not apply outside of the experimental conditions described in the paper. This statement was previously mentioned from line 300 to 317 and was added in lines 342 to 343 of the manuscript. The authors have removed the mentioning of Langmuir isotherm and ‘monolayer coverage’ from the abstract and conclusions. In order to emphasize the fact that synthetic EPS can reduce corrosion rate, this message was added to the beginning of the results portion in the abstract and the conclusion section added in line 36 to 37 and line 479 to 480. The fact that the synthetic EPS mixture demonstrated better performance compared to some common commercial inhibitors was also mentioned from line 208 to 209 and from line 452 to 455. The rational on how the blend was arrived to was previously described in the “Properties of synthetic EPS” portion under the discussion section, from line 161 to 225. The specific reason five components were selected for the synthetic EPS blend was because natural WAS EPS is typically made up of five most common chemical groups, i.e. proteins, carbohydrates, humic substances, and uronic acids. In order for the synthetic EPS to resemble the natural WAS EPS while reducing the complexity of the mixture, one compound was selected from each chemical group. Since five major groups are generally being studied in the natural WAS EPS, five components were selected for the synthetic EPS formulation. This explanation was added from line 174 to 177. The fact that synthetic EPS was not optimized for corrosion inhibition has previously described in the conclusion from line 490 to 497. This information was added in the “Properties of synthetic EPS” portion under the discussion section, from line 206 to 214 for clarification. The authors are preparing another paper on the optimization of synthetic EPS mixture for the submission to an archival journal. Synthetic EPS was formulated based on the natural composition of waste activated sludge extracellular polymeric substances (WAS EPS). This was previously described in the “Properties of synthetic EPS” portion under the discussion section in line 161. More information was added in the “Engineering, operation, and economic significance of synthetic EPS” portion under the discussion section, from line 440 to 442. The observed reduction of corrosion contributed by synthetic EPS is important and significant. This message was added to the beginning of the results portion in the abstract and the conclusion section, from line 36 to 37 and from line 479 to 480, respectively. Since the synthetic EPS in this study was treated as an entity, meaning that the contribution to the overall inhibition cannot be ascribed to any single component in the mixture. This information was added from line 413 to 416 for clarification. "	Here is a paper. Please give your review comments after reading it.
454	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Environmental and energy conservation pressure has led to a dramatic increase in the need for economically feasible lightweight materials that can be better substitutes for nonbiodegradable materials in reinforced composites. In this study, the mechanical and thermal properties of polyester resin composites hybridized with a blend of untreated and alkali treated sisal (Agave sisalana) and cattail (Typha angustifolia) fibers were evaluated.</ns0:p><ns0:p>Composites were fabricated by a hand lay-up technique at an optimal hybrid fiber weight fraction of 20 wt% and a constant sisal/cattail fiber blend ratio of 75/25. Flexural, tensile, compressive and impact strengths and moduli, as well as thermal conductivity of the composites were evaluated following ASTM and ISO test methods. Analytical results indicated that alkali pre-treatment of the fibers enhanced the mechanical properties of the hybrid polyester composites though only marginal differences were recorded in the thermal conductivity of the composites fabricated with treated and untreated fiber blends.</ns0:p><ns0:p>Morphological examination revealed that the major failure modes were fiber pull-outs and fiber fracture in composites fabricated with untreated and treated fiber blends respectively. The composites produced could find non-structural applications as ceiling boards, electronic and food packaging materials but their properties such as wettability, crystallinity, flammability and other thermal properties need to be further investigated.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>Reinforcement of construction materials such as mud bricks using natural fibers (grass and straw) have been witnessed since the beginning of the era of human civilization <ns0:ref type='bibr' target='#b57'>(Rizal et al., 2018)</ns0:ref>. With the industry 4.0 revolution, natural fibers from sisal, cattail, flax, date palm, coir, oil palm empty fruit bunch, ramie, sugar palm, bamboo, hemp, rubber wood, jute, pineapple leaf and kenaf have been subject of obsessive research for their possible use in reinforcement of composite materials <ns0:ref type='bibr' target='#b7'>(Bongomin, Ocen, Nganyi, Musinguzi, & Omara, 2020;</ns0:ref><ns0:ref type='bibr' target='#b22'>Gupta, Akash, Rao, & Kumar, 2016;</ns0:ref><ns0:ref type='bibr' target='#b29'>Khanam, Reddy, Raghu, John, & Naidu, 2007;</ns0:ref><ns0:ref type='bibr' target='#b46'>Nasir, Gupta, Hossen Beg, Chua, & Asim, 2014;</ns0:ref><ns0:ref type='bibr' target='#b66'>Venkateshwaran, ElayaPerumal, Alavudeen, & Thiruchitrambalam, 2011)</ns0:ref>. This is primarily because they possess superior properties such as high specific strength and stiffness, low density and toxicity, carbon dioxide neutrality, are biodegradable and are readily available. This make them good substitutes for expensive glass, carbon and aramid when used in composites for low load bearing and thermal applications <ns0:ref type='bibr' target='#b2'>(Asim et al., 2017;</ns0:ref><ns0:ref type='bibr' target='#b32'>Malkapuram, Kumar, & Negi, 2009;</ns0:ref><ns0:ref type='bibr' target='#b49'>Pickering, Efendy, & Le, 2016;</ns0:ref><ns0:ref type='bibr' target='#b61'>Sanjay, Arpitha, & Yogesha, 2015;</ns0:ref><ns0:ref type='bibr'>Sliseris, Yan, & Kasal, 2016)</ns0:ref>. Moreover, their inclusion in composites economizes the volume of the polymeric matrix consumed, conferring additional logistic advantages <ns0:ref type='bibr' target='#b18'>(Frollini, Bartolucci, Sisti, & Celli, 2013)</ns0:ref>. The salient drawbacks with natural fibers when commingled in composites is their relatively high moisture absorption tendency and poor compatibility with the matrix <ns0:ref type='bibr' target='#b22'>(Gupta et al., 2016;</ns0:ref><ns0:ref type='bibr' target='#b66'>Venkateshwaran et al., 2011)</ns0:ref>. These negative characteristics are primarily due to their inherent heterogenous content such as carbohydrates (cellulose, hemicellulose, starch), lignin, pectin, waxes, fats and other polar compounds <ns0:ref type='bibr' target='#b40'>(Mortazavi & Moghaddam, 2010;</ns0:ref><ns0:ref type='bibr' target='#b65'>Sun & Cheng, 2002)</ns0:ref>. Thus, weak interface adhesion between natural fibers and polymer matrices are due to the differences in the wettability of natural fibers (inherently hydrophilic) and the polymer matrix (usually hydrophobic). This phenomenon reduces the efficiency of stress distribution from the matrix to the fiber and simultaneously diminishes the mechanical properties of such natural fiber-reinforced composites <ns0:ref type='bibr' target='#b50'>(Punyamurthy, Sampathkumar, Srinivasa, & Bennehalli, 2012)</ns0:ref>. So often, alkali treatment (typically with sodium hydroxide solution) or treatment with coupling agents are preferred for reworking the properties of natural fibers to improve interfacial bonding between the fibers and the matrices in the resultant composites <ns0:ref type='bibr' target='#b3'>(Aziz & Ansell, 2004;</ns0:ref><ns0:ref type='bibr' target='#b8'>Boopathi, Sampath, & Mylsamy, 2012;</ns0:ref><ns0:ref type='bibr'>El-Abbassi, Assarar, Ayad, & Lamdouar;</ns0:ref><ns0:ref type='bibr' target='#b21'>Goud & Rao, 2011;</ns0:ref><ns0:ref type='bibr' target='#b33'>Manalo, Wani, Zukarnain, Karunasena, & Lau, 2015;</ns0:ref><ns0:ref type='bibr'>Reddy, Uma Maheswari, Shukla, Song, & Varada Rajulu, 2013;</ns0:ref><ns0:ref type='bibr' target='#b58'>Rout, Misra, Tripathy, Nayak, & Mohanty, 2001;</ns0:ref><ns0:ref type='bibr' target='#b68'>Verma & Gope, 2013)</ns0:ref>. Both sisal (Agave sisalana) and cattail (Typha angustifolia) are readily available plants in Kenya, the latter being a wild marginal weed <ns0:ref type='bibr' target='#b11'>(Colbers et al., 2017;</ns0:ref><ns0:ref type='bibr'>Committee on Commodity Problems, 2017;</ns0:ref><ns0:ref type='bibr' target='#b41'>Mukherjee & Satyanarayana, 1984;</ns0:ref><ns0:ref type='bibr' target='#b48'>Phologolo, Yu, Mwasiagi, Muya, & Li, 2012)</ns0:ref>. Sisal fibers are widely used due to their availability with each plant producing 200-250 leaves and each leaf producing 1000-1200 fiber bundles. Thus, a normal leaf weighing about 600 g yields about 3% by weight of fibers <ns0:ref type='bibr' target='#b41'>(Mukherjee & Satyanarayana, 1984)</ns0:ref>. Due to its better mechanical properties and abundance in most parts of Kenya, sisal fibers could be a promising reinforcement material in hybrid composites. On the other hand, fibers from cattail plant leaves are identical to hemp (jute) fibers and thus can be similarly utilized in textile and composite applications <ns0:ref type='bibr' target='#b4'>(Baldwin & Cannon, 2007;</ns0:ref><ns0:ref type='bibr' target='#b40'>Mortazavi & Moghaddam, 2010;</ns0:ref><ns0:ref type='bibr'>Ramanaiah, RatnaPrasad, & Reddy, 2011)</ns0:ref>. The use of cattail fibers could control the invasive cattail weed, generate employment opportunities while conserving the environment. Investigation of the properties of sisal and cattail fibers when used singly or in combination, with other fibers or materials in composites have been done by preceding researchers. <ns0:ref type='bibr' target='#b28'>Joseph et al. (2003)</ns0:ref> evaluated the dynamic mechanical properties of polypropylene composites reinforced with treated and untreated short sisal fibers. They examined the resultant composites with reference to fiber loading, fiber length, chemical treatment, frequency and temperature and deduced that inclusion of sisal fibers increased the storage and loss moduli of the composites as the reinforcement imparted by the fibers allowed stress transfer from the matrix to the fiber. A fiber length of 2 mm was cited by the team as necessary for attaining maximum dynamic and loss moduli. Another study by <ns0:ref type='bibr' target='#b22'>Gupta et al. (2016)</ns0:ref> which evaluated the mechanical properties of alkaline treated sisal/hemp fiber reinforced hybrid epoxy composites indicated that increase in tensile and flexural strengths were registered at 40 wt% of sisal/hemp fiber blend, and increase in the weight percentage of fiber blend increased the hardness strength of the composites. In another such concerted investigation, <ns0:ref type='bibr'>Bichang'a et al. (2017)</ns0:ref> evaluated the effect of alkali treatment on the mechanical properties of a woven sisal fabric reinforced epoxy composite fabricated at 40% fiber weight fraction. The team inferred that chemical treatment of sisal fabric with 4% (w/v) sodium hydroxide solution for 1 hour at room temperature improved the mechanical properties of the resultant composite. Further, <ns0:ref type='bibr' target='#b59'>Samuel et al. (2012)</ns0:ref> bewrayed that the mechanical properties of ukam and sisal fiber reinforced composites were greatly influenced by alkali treatment of the fibers. For cattail plant, a patent for using its parts for production of thermal insulation materials was filed in 1962 (http://www.google.com/patents/US3063125) and several others have been filed with promising results from feasibility studies indicating its possible use in the manufacture of insulation plates and blow-in insulation boards <ns0:ref type='bibr' target='#b11'>(Colbers et al., 2017;</ns0:ref><ns0:ref type='bibr'>Naporo, 2013)</ns0:ref>. In an investigation by <ns0:ref type='bibr' target='#b57'>Rizal et al. (2018)</ns0:ref>, it was reported that treatment of cattail (Typha species) with 5% (w/v) sodium hydroxide solution improved the interfacial shear strength of hybrid epoxy composites from 2.240 MPa to 2.718, 3.753, 3.960 and 4.185 after 1, 2, 3 and 4 hours of treatment respectively. Composite specimens after 4 hours of alkali treatment had the highest tensile strength of 37.4 MPa compared to 29.2 Mpa in untreated Typha reinforced epoxy composites. <ns0:ref type='bibr' target='#b56'>Rizal et al. (2019)</ns0:ref> inferred that the mechanical properties and crystallinity index of composites reinforced with cattail fibers previously treated with 5% (w/v) sodium hydroxide solution increased with processing time. The current study investigates the effect of alkali treatment on the mechanical and thermal properties of sisal/cattail reinforced polyester composites.</ns0:p></ns0:div> <ns0:div><ns0:head>Materials & Methods</ns0:head><ns0:p>Fiber Samples and Chemicals Used Sisal fibers, traditionally popular for making twine and ropes, were generously supplied by Lomolo Sisal Estate Ltd, Mogotio, Baringo county, Kenya. Green mature cattail plant leaves were obtained from cattail (Typha angustifolia) plants from a swamp in the propinquity of Moi University staff quarters, Uasin Gishu county, Eldoret, Kenya. Analytical reagents: unsaturated polyester resin (UPR), methyl ethyl ketone peroxide (MEKP) and acetic acid, double distilled water and Sodium hydroxide pearls (extra pure) were supplied by Henkel Chemicals (E.A.) Ltd, Industrial area, Nairobi, Kenya and Moi University Textile Laboratory, Uasin Gishu county, Eldoret, Kenya respectively. Mechanical properties of the neat unsaturated polyester resin (UPR, GP 1778) were density-1.23 g/cm 3 , tensile strength-29.20 MPa, tensile modulus-2,194.70 MPa, flexural strength-70.00 MPa, impact strength-9.00 kJ/m 2 and an elongation at break of 4.20%.</ns0:p></ns0:div> <ns0:div><ns0:head>Preparation of Sisal and Cattail Fibers</ns0:head><ns0:p>Cattail leaves were separated from the stalk grouping at the leaf base followed by mechanical decortication process to extract the fibers. Fibers were subsequently dried at 80 0 C in an oven to constant weight to eliminate excess moisture that would otherwise result in poor fiber-matrix adhesion. Sisal fibers as supplied were cleaned with warm distilled water (for those not to be alkali treated) to remove chlorophyll, leaf juices, adhesive solids and soluble impurities after which they were dried.</ns0:p></ns0:div> <ns0:div><ns0:head>Fiber Surface Treatment</ns0:head><ns0:p>Fiber surface modification was achieved by submersing sisal and cattail fibers in 4% and 5% (w/v) sodium hydroxide solution at room temperature for 1 hour respectively <ns0:ref type='bibr'>(Bichanga & Ayub, 2017;</ns0:ref><ns0:ref type='bibr' target='#b15'>Dedeepya, Raju, & Kumar, 2012;</ns0:ref><ns0:ref type='bibr' target='#b56'>Rizal et al., 2019)</ns0:ref>. After treatment, the fibers were rinsed thoroughly with distilled water acidified with acetic acid (1 %w/v) to neutralize excess sodium hydroxide in the fibers. The pH of the rinses was monitored using Hanna 211 digital microprocessor-based bench top pH/mV/ 0 C meter (Hanna instruments, Italy) previously calibrated using pH 4.01, 7.01, 10 buffers. The pH electrode was thoroughly rinsed with distilled water in between different measurements. The rinsed fibers were subsequently dried.</ns0:p></ns0:div> <ns0:div><ns0:head>Characterization of Sisal and Cattail Fibers</ns0:head><ns0:p>Both treated and untreated sisal and cattail fibers (pre-dried in an oven for one hour at 80 0 C to remove excess moisture that could lead to poor fiber-matrix adhesion) were characterized by determining their linear densities and tensile properties (tenancies). Linear densities of the fibers were determined as per ASTM D1577-2001 by weighing out known lengths of the fibers. Thirty (30) fibers, each from treated and untreated sisal and cattail fibers were picked randomly and then cut to a length of 300 mm (as per the universal tensile testing machine gauge length and the manufacturer's instructions) to form four (4) bundles of fibers. Each of the four test specimen bundles were separately weighed. From the measured fiber weights and the number of fiber specimen in each bundle, the weight of each fiber in the four bundles were determined. With these weights in grams, linear density in tex was determined by dividing the fiber weight by its length in kilometers. Tensile strength of the fibers were determined as per ASTM D3822M-2001 under ambient conditions using a universal tensile testing machine (UTM-TH2730, Rycobel, Belgium) at a gauge length of 300 mm and speed of 5 mm/min. Tensile strength was determined for the four bundles of treated and untreated fibers by taking an average of 30 tests for each. From these tests, fiber tensile strength in terms of breaking tenacity (cN/tex) were determined by dividing the breaking force (cN) by the linear density (tex) of the treated and untreated sisal and cattail fibers. The assumptions made were that the fibers are cylindrical in shape.</ns0:p></ns0:div> <ns0:div><ns0:head>Composite Fabrication</ns0:head><ns0:p>Sisal/cattail fiber reinforced polyester composites were prepared by simple hand (a wet) lay-up technique as described by <ns0:ref type='bibr' target='#b9'>Borah et al. (2016)</ns0:ref> with slight modifications. A mould measuring 310 × 310 × 25 mm was fabricated using a polished iron metal sheet from which composites of dimensions 300 × 300 mm were prepared. The mould was cleaned using acetone, followed by application of mould release agent (MR8) on the inner surfaces. The inner surfaces were then covered with aluminium foil to avoid the chances of composites sticking onto the mould surface and to provide good surface finish. The experimental design for the amount of matrix material and the reinforcements used in the composites follows from a preceding study <ns0:ref type='bibr' target='#b35'>(Mbeche, Wambua, & Githinji, 2020)</ns0:ref> which indicated that the optimal weight fraction was 20% with 75/25 sisal/cattail fiber blend for optimal mechanical properties of the resultant polyester composites. Unsaturated polyester resin (UPR) and hardener (MEKP) were mixed in a ratio of 0.02:1 by mass as per the manufacturer's instructions and stirred thoroughly. The resin was mixed with blended fibers and stirred for 15 minutes to ensure uniform dispersion of fibers within the resin. The content was then poured into the mould and then spread gently to ensure uniform thickness of the resultant composite. To prevent air entrapment during fabrication, a thin plastic sheet (velvex) was used to cover the mould and then pressed gently and uniformly using a pressure roller. The composites were allowed to cure at ambient conditions for 6 hours under 3.27 kN/m 2 compressive pressure after which they were trimmed prior to mechanical tests. Analytical weighings were done using a calibrated Mettler PM200 digital analytical balance (Marshall Scientific, Hampton, NH, USA).</ns0:p></ns0:div> <ns0:div><ns0:head>Evaluation of Mechanical Properties and Fractography Studies</ns0:head><ns0:p>Composite samples for various mechanical tests were conditioned for 48 hours at ambient conditions of temperature (23 ± 2 0 C) and relative humidity (65%) prior to evaluation at the Materials Engineering Laboratory of Multimedia University, Nairobi, Kenya. Three-point flexure tests were conducted in accordance with ASTM D790-2003 standard using a universal material testing machine (Model UT-10, Enkay Enterprises, India) at a loading rate of PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2020:01:45030:1:1:NEW 19 Feb 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science 2 mm/min at Rivatex East Africa Limited Textile laboratory. Tensile, flexural and compressive moduli were computed from the stress-strain curves. Impact strength was estimated using a Charpy impact tester (Model HLE, Enkay Enterprises, India) as per ISO 179-1:2000 standard. Tensile and compression tests were conducted using a universal testing machine (UTM-TH2730, Rycobel, Belgium) with a maximum load cell of 5 kN. The tensile and compressive properties were determined in accordance with ASTM D638-2014 and ASTM D3410M-2003 standards at loading rates of 2 mm/min and 5 mm/min respectively. Surface morphology of untreated and treated sisal/cattail polyester hybrid composites were investigated using MSX-500Di Scopeman Digital Microscope (Herter Instruments, Barcelona, Spain).</ns0:p></ns0:div> <ns0:div><ns0:head>Evaluation of Thermal Conductivity</ns0:head><ns0:p>Thermal conductivity tests were done using a thermal conductivity apparatus (Model P5687, Cussons Technology, UK) in the Thermodynamic Engineering Laboratory of Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya in accordance with ASTM C518-1998.</ns0:p></ns0:div> <ns0:div><ns0:head>Analytical Quality Assurance and Quality Control</ns0:head><ns0:p>All reagents used were of high analytical purity. Equipment such as pH meter and analytical balance were calibrated prior to use. All samples were analyzed at least in triplicate to obtain a relative uncertainty of less than 5% <ns0:ref type='bibr' target='#b47'>(Omara et al., 2019)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Statistical Analysis of Results</ns0:head><ns0:p>Analytical data were captured in Microsoft Excel 2016 (Microsoft Corporation, USA) for preliminary analysis. Data were checked for normality prior to statistical evaluation using the Kolmogorov-Smirnov test and subsequently presented as means of quintuples with errors as standard deviations attached. Paired t test was performed to identify any significant differences between groups. All analyses were performed at a 95% confidence interval (with differences in mean values accepted as being significant at p < 0.05) using Sigma Plot statistical software (v14.0, Systat Software Inc., San Jose, CA, USA) <ns0:ref type='bibr' target='#b47'>(Omara et al., 2019)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Results & Discussion</ns0:head></ns0:div> <ns0:div><ns0:head>Properties of Sisal and Cattail Fibers</ns0:head><ns0:p>Linear density (in tex) and tenacity (in cN/tex) were used to characterize the fibers. Higher mean linear densities (26.17 ± 8.33 tex and 35.17 ± 54.96 tex) were recorded for untreated sisal and cattail fibers as compared to 10 ± 5.07 tex and 12.33 ± 5.42 tex recorded in the alkali treated fibers. On the other hand, the mean tenacity of treated sisal and cattail fibers were 146.26 cN/tex and 35.35 cN/tex compared to 23.52 cN/tex and 9.46 cN/tex recorded in the untreated fibers respectively (Table <ns0:ref type='table'>S1</ns0:ref>). These differences could be attributed to the reduction in fibre diameter due to the loss of weight resulting from the removal of carbonaceous materials after alkali treatment (Figure <ns0:ref type='figure' target='#fig_0'>1</ns0:ref>) <ns0:ref type='bibr' target='#b19'>(Gañan, Garbizu, Llano-Ponte, & Mondragon, 2005;</ns0:ref><ns0:ref type='bibr'>Ikramullah et al., 2019;</ns0:ref><ns0:ref type='bibr' target='#b53'>Reddy et al., 2013)</ns0:ref>. The micrographs (Figure <ns0:ref type='figure' target='#fig_0'>1</ns0:ref>) confirm that there were reductions in the diameter of sisal and cattail fibers after alkali treatment for 1 hour. The high tenacity values of alkali treated sisal fibers can be attributed to the removal of lignin and other soluble impurities thereby increasing the aspect ratio and thus tenacity of the fibers <ns0:ref type='bibr' target='#b31'>(Mahato, Goswami, & Ambarkar, 2014)</ns0:ref>. Paired t test indicated that there were significant differences (p < 0.05) between the tenacity of treated and untreated sisal and cattail fibers. A comparable value of 7.83 tex for treated sisal fibres was reported by <ns0:ref type='bibr' target='#b31'>Mahato et al. (2014)</ns0:ref>. <ns0:ref type='bibr' target='#b55'>Rezig et al. (2014)</ns0:ref> reported a linear density of 32 tex for untreated cattail fibers and a linear density of between 10 to 30 tex for alkali treated cattail fibers. <ns0:ref type='bibr' target='#b54'>Rezig et al. (2016)</ns0:ref> recorded a tenacity of 12.41 cN/tex in an optimized cattail fibre extraction process from cattail plant leaves using 20 g/L of sodium hydroxide at 100 0 C. <ns0:ref type='bibr'>Mortazavi and Moghaddam (2009;</ns0:ref><ns0:ref type='bibr'>2010)</ns0:ref> reported tenacities of 30.17 ± 4.7 cN/tex and 34.87 cN/tex for untreated cattail fibres and those treated with 6% sodium hydroxide in 3% ethylenediaminetetraacetic acid (EDTA).</ns0:p></ns0:div> <ns0:div><ns0:head>Effect of Alkali treatment on Flexural, Tensile and Compressive Strengths of the Polyester Hybrid Composites</ns0:head><ns0:p>Figure <ns0:ref type='figure' target='#fig_1'>2</ns0:ref> illustrates the effect of alkali treatment on flexural, tensile and compressive strengths of the composites at 20 wt% hybrid fiber weight fraction and 75/25 sisal/cattail fiber in the hybrid composites. As illustrated, hybrid composites prepared from a blend of treated sisal and cattail fiber had better mechanical properties than those from untreated fibers. The flexural, tensile and compressive strengths improved by 24.64%, 19.56% and 18.70% respectively with alkali treatment. Paired t test results revealed that there were significant differences (p < 0.05) between the mean values of the evaluated mechanical properties of composites reinforced with untreated and treated fiber blends. High-resolution fracture behavior images after tensile testing of the composites (Figure <ns0:ref type='figure' target='#fig_2'>3 a-b</ns0:ref>) depicts that there were fiber pull outs from the matrix in untreated hybrid composites signifying that there was poor adhesion between the fibers and the matrix <ns0:ref type='bibr' target='#b2'>(Asim et al., 2017)</ns0:ref>. Cattail fiber-pull outs were observed, and this could be due to the lower strength of cattail fibers which resulted in their breakage as compared to sisal fibers. Cellulose, hemicellulose and lignin are the main components of natural fibers. Whereas cellulose is the principal component in natural fibers, hemicellulose on the other hand is a cementing matrix between cellulose and lignin that confer rigidity to plants. Further, cellulose (a semi crystalline polysaccharide) and hemicellulose are hydrophilic, while lignin is relatively hydrophobic <ns0:ref type='bibr' target='#b69'>(Zhou, Fan, & Chen, 2016)</ns0:ref>. Interface bonding in fiber reinforced composites is through electrostatic, chemical bonding and mechanical interlocking mechanisms <ns0:ref type='bibr' target='#b34'>(Matthews & Rawlings, 1999)</ns0:ref>. The latter is dominant when fibers surfaces are rough. This mechanism increases shear strength of the fiber-matrix interface. On the other hand, chances are that different types of bonding between the fiber and the matrix interfaces may occur and act synergistically <ns0:ref type='bibr' target='#b49'>(Pickering et al., 2016)</ns0:ref>. Thus, alkali treatment in this study might have additionally interfered with hydrogen bonds in the chemical structure of the fibers, increasing fiber surface roughness. Further, alkali treatment increases crystallinity index of fibers, enhancing formation of hydrogen bonds between cellulose chains and hence chemical bonding between the fibers in composites <ns0:ref type='bibr' target='#b20'>(Gassan & Bledzki, 1999;</ns0:ref><ns0:ref type='bibr' target='#b43'>Mylsamy & Rajendran, 2011a;</ns0:ref><ns0:ref type='bibr'>Sreekala, Kumaran, & Thomas, 2011)</ns0:ref>. The surface roughness of natural fibers interestingly increases with increase in the duration of alkali treatments <ns0:ref type='bibr' target='#b60'>(Sangappa, Rao, Asha, Kumar, & Somashekar, 2014)</ns0:ref>. However, it should be over emphasized that treatment at high alkali concentrations or subjection to long alkali treatment periods which have been avoided in this study often lead to significant reduction in the mechanical performance of the treated fibers <ns0:ref type='bibr' target='#b27'>(Jacob, Thomas, & Varughese, 2004;</ns0:ref><ns0:ref type='bibr' target='#b36'>Mishra et al., 2003;</ns0:ref><ns0:ref type='bibr' target='#b57'>Rizal et al., 2018)</ns0:ref>. Therefore, in hybrid composites with treated fibers (Figure <ns0:ref type='figure' target='#fig_2'>3 c-d</ns0:ref>), there were fewer fiber pullouts but more fiber fracture and twisting at the broken ends indicating a relatively strong bond between the fibers and the matrix. Higher breakages of cattail fibers were also observed in treated hybrid composites, and this could be due to their low strengths as compared to sisal fibers. The lower strength of cattail fibers in this study may be attributed to the extraction process used. Cattail fibers were manually decorticated from their leaves which could have resulted in some breakages and thus compromising their strength. Furthermore, high cattail fiber breakages in treated hybrid composites may be attributed to their poor alkali treatment, since most of these extracted fibers were in bundles leading to poor impregnation of sodium hydroxide during treatment. Improvement in mechanical properties following alkali treatment can be attributed to better fiber/matrix interface, due to changes in surface topography of the fibers, leading to increased mechanical interactions with the matrix as well as increasing fiber wettability by removal of some of the cementing components (lignin, pectin, hemicellulose, fats) on the fiber surfaces <ns0:ref type='bibr'>(Bichang'a et al., 2017;</ns0:ref><ns0:ref type='bibr' target='#b19'>Gañan et al., 2005;</ns0:ref><ns0:ref type='bibr' target='#b42'>Mwaikambo & Ansell, 1999;</ns0:ref><ns0:ref type='bibr' target='#b57'>Rizal et al., 2018)</ns0:ref>. This trend is comparable to that reported by <ns0:ref type='bibr' target='#b1'>Alavudeen et al. (2011)</ns0:ref> in which alkali treatment of randomly mixed banana/kenaf hybrid polyester composites increased the tensile strength of the composites by 14.1% from 31.9 MPa to 36.4 MPa at 50 wt%. <ns0:ref type='bibr' target='#b62'>Senthilkumar and Ravi (2017)</ns0:ref> also reported an improvement in flexural strength by 47.78% from 82.74 MPa to 122.27 MPa for untreated and 6% sodium hydroxide sisal fiber treated composites respectively in hybrid epoxy composites. <ns0:ref type='bibr' target='#b57'>Rizal et al. (2018)</ns0:ref> got similar findings in which alkali treatment of cattail fibers recorded flexural strengths of 44.50 <ns0:ref type='bibr'>MPa and 69.50,</ns0:ref><ns0:ref type='bibr'>77.20,</ns0:ref><ns0:ref type='bibr'>50.30 and 49.80</ns0:ref> MPa for epoxy composites with untreated and treated cattail fibers for 1, 2, 4 and 8 hours respectively. The highest tensile strength recorded was 37.40 MPa for fibers treated for 4 hours, while composites with untreated fibers had tensile strength of 29.20 MPa.</ns0:p></ns0:div> <ns0:div><ns0:head>Effects of Alkali Treatment on Flexural, Tensile, Compressive Moduli of the Polyester Hybrid Composites</ns0:head><ns0:p>The effect of alkali treatment on flexural, tensile, compressive moduli of sisal/cattail hybrid polyester reinforced composites at a constant hybrid fiber weight fraction of 20 wt% and sisal/cattail fiber blend of 75/25 in the hybrid composites are shown in Figure <ns0:ref type='figure'>4</ns0:ref>. It is evident that treatment of sisal and cattail fibers resulted in an increase in flexural, tensile and compressive moduli of the hybrid composites. At 20 wt% fraction and 75/25 sisal/cattail fiber content in the hybrid, flexural, tensile and compressive moduli increased by 10.44%, 12.97% and 17.26% respectively. Paired t test indicated that there were significant differences (p < 0.05) in the means of flexural, tensile and compressive moduli of the treated and untreated hybrid composites. The observed trend, therefore, is a clear indication that alkali treatment of sisal and cattail fiber improved the flexural, tensile and compressive moduli of the hybrid polyester composites. This could be because alkali treatment increases surface roughness (exposes hydroxyl groups) of the fibers to the matrix due to the removal of lignin and other impurities from the fiber surface, thereby improving fiber-matrix adhesion <ns0:ref type='bibr'>(Ikramullah et al., 2019;</ns0:ref><ns0:ref type='bibr' target='#b40'>Mortazavi & Moghaddam, 2010)</ns0:ref>. A comparable pattern of the effect of alkali treatment on flexural, tensile and compressive moduli was reported by <ns0:ref type='bibr'>Bichang'a et al. (2017)</ns0:ref> for woven sisal reinforced epoxy composites in which 12.97%, 31.19% and 34.98% increments in the flexural, tensile and compressive moduli of the composites respectively were registered. <ns0:ref type='bibr' target='#b57'>Rizal et al. (2018)</ns0:ref> also noted a similar enhancement of tensile modulus of epoxy composites reinforced with alkali treated cattail fibers.</ns0:p></ns0:div> <ns0:div><ns0:head>Effects of Alkali Treatment on Impact Strength of the Hybrid Polyester Composites</ns0:head><ns0:p>Impact strength performance of natural fiber reinforced composites is influenced by factors such as fiber and matrix properties, as well as fiber and matrix interface properties <ns0:ref type='bibr' target='#b57'>(Rizal et al., 2018)</ns0:ref>. In this study, the average impact strength of the composites reinforced with untreated fibers was 23.19 ± 0.10 kJ/m 2 . Thus, it improved with alkali treatment of sisal and cattail fibers by 16.73% to an average maximum value of 27.08 ± 0.30 kJ/m 2 . Paired t test indicated that there was a significant difference (p < 0.05) in the impact strengths of polyester composites fabricated with untreated and treated sisal and cattail fibers. This difference in impact strengths could be due to the increase in the energy required to break the specimen because of the strong fiber-matrix bonds created by exposing hydroxyl groups to the matrix as surface impurities were removed by alkali treatment <ns0:ref type='bibr' target='#b40'>(Mortazavi & Moghaddam, 2010)</ns0:ref>. <ns0:ref type='bibr' target='#b1'>Alavudeen et al. (2011)</ns0:ref> recorded 24% improvement in impact strength (from 0.50 to 0.62 kJ/m 2 ) for untreated and alkali treated composites at 30 wt% fiber weight fractions for randomly mixed banana/kenaf hybrid polyester composites. Similarly, <ns0:ref type='bibr'>Bichang'a et al. (2017)</ns0:ref> bewrayed that impact strength of a woven sisal/epoxy composite after alkali treatment improved significantly. <ns0:ref type='bibr' target='#b57'>Rizal et al. (2018)</ns0:ref> also reported that alkali treatment of cattail fibers treated for 1 and 2 hours increased the impact strength of epoxy composites from around 10.70 kJ/m 2 to 12.40 and 14.20 kJ/m 2 respectively. However, treatment for 4 and 8 hours recorded a decrease from 14.20 kJ/m 2 to 12.80 kJ/m 2 in both cases.</ns0:p><ns0:p>It is reported that composites with weak interfacial compatibility may have poor mechanical properties due to crack propagation at the matrix interface <ns0:ref type='bibr' target='#b10'>(Chen et al., 2012)</ns0:ref>. Weak interfacial compatibility in composites also accelerates matrix crack propagation, resulting in debonding of fibers and matrices <ns0:ref type='bibr' target='#b57'>(Rizal et al., 2018)</ns0:ref>. Further, fiber-matrix interface conditions often affect energy absorption in composites, in that composites with good interfacial compatibility always have the impact load received by the polymer matrix transferred to the fiber <ns0:ref type='bibr' target='#b23'>(Hao, Wu, Qiu, & Wang, 2018;</ns0:ref><ns0:ref type='bibr' target='#b45'>Nair, Wang, & Hurley, 2010)</ns0:ref> which is its role in matrix reinforcement. Impact failure of composites are caused by fiber and matrix damage, fiber pull-outs from the matrix, and debonding between the fiber and matrix. Debonding is inevitable in the event that the load transferred to the fiber exceeds the fiber-matrix interface strength <ns0:ref type='bibr'>(Mylsamy & Rajendran, 2011b)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Effects of Alkali Treatment on Thermal Conductivity of the Hybrid Polyester Composites</ns0:head><ns0:p>The average thermal conductivity of untreated and treated sisal/cattail hybrid reinforced unsaturated polyester resin composites were 0.66 ± 0.06 and 0.72 ± 0.07 W/mK respectively (Table <ns0:ref type='table'>S2</ns0:ref>). Thus, thermal conductivity of the composites improved by 8.96% with alkali treatment. However, t test indicated that there was no statistically significant difference (p < 0.05) between the thermal conductivities of treated and untreated hybrid composites. This marginal improvement in thermal conductivity with alkali treatment can be attributed to the removal of cementing materials from the fiber surfaces that destroys the aerenchyma tissues within the fibers especially for cattail fibers, reducing the thermal contact resistance of the resultant composites <ns0:ref type='bibr' target='#b11'>(Colbers et al., 2017)</ns0:ref> (Figure <ns0:ref type='figure' target='#fig_3'>5</ns0:ref>). In addition, alkali treatment improves fiber diameters as well as make fiber surfaces rough. This results in better interlocking between the fibers and the matrix, improving the thermal conductivity of the composites <ns0:ref type='bibr' target='#b0'>(Agrawal, Saxena, Sreekala, & Thomas, 1999;</ns0:ref><ns0:ref type='bibr' target='#b69'>Zhou et al., 2016)</ns0:ref>. The recorded thermal conductivity of the composites is close to those previously reported for the individual fibers. Fraunhofer Institute designed a thermal insulation panel made of cattail fibers which had a thermal conductivity of 0.052 W/mK (https://www.fraunhofer.de/en/press/research-news/2013/may/using-cattailsforinsulation.html). <ns0:ref type='bibr' target='#b30'>Luamkanchanaphan et al. (2012)</ns0:ref> concluded that narrow-leaved cattail fiber (hot-pressed) biocomposite produced with a Methylene Diphenyl Diisocyanate binder had lower thermal conductivities (between 0.0438 to 0.0606 W/mK for density between 200 and 400 kg/m 3 ) and 11-15% moisture diffusion coefficient. <ns0:ref type='bibr' target='#b11'>Colbers et al. (2017)</ns0:ref> reported thermal conductivity of 1.53 W/mK for blow-in cattail insulation board. Furthermore, <ns0:ref type='bibr' target='#b51'>Ramanaiah et al. (2011)</ns0:ref> studying thermal behavior of cattail reinforced polyester composites reported a similar trend with thermal conductivity values between 0.32-0.39 W/mK at a fiber volume fraction between 0.15-0.32. A close thermal conductivity value of 0.16 W/mK at 85% clay was reported by <ns0:ref type='bibr' target='#b16'>Dieye et al. (2017)</ns0:ref> while investigating the effects of binder (clay) weight on the thermal conductivity of Typha australis fiber reinforced composites.</ns0:p></ns0:div> <ns0:div><ns0:head>Fractography Studies</ns0:head><ns0:p>Fracture mode in natural fibers can be intracellular or intercellular. The former is often reported in fibers having large elongation that are tested at low speeds. Such a fracture is generally accompanied by tearing of cell walls as well as pull-out of the fibrils <ns0:ref type='bibr' target='#b41'>(Mukherjee & Satyanarayana, 1984)</ns0:ref>. The intercellular fracture is commonly observed in low elongation fibers tested at high speed and occurs typically with separation of the bonding materials between the cells and very little pull-out of the fibrils <ns0:ref type='bibr' target='#b41'>(Mukherjee & Satyanarayana, 1984)</ns0:ref>. In this study, failure mechanism was investigated by inspecting both treated and untreated specimens after the impact test. It was found that the fracture surface of the untreated impact specimens was almost flat while that of treated specimens had saw-like fractured surfaces. Further, more fiber pull-outs were observed in untreated composite specimens, and more fiber breakages in treated specimens (Figure <ns0:ref type='figure' target='#fig_2'>3</ns0:ref>). And thus, the high values of impact strengths reported for treated hybrid composites in this study may also be attributed to their failure modes (larger saw-like fractured surfaces and higher fiber breakages) as more impact energy is absorbed. Therefore, the major composite failure modes identified were fiber pull-outs (in untreated fibers) and fiber fracture (in treated fibers) corroborating a previous observation <ns0:ref type='bibr' target='#b35'>(Mbeche et al., 2020)</ns0:ref>. Similarly, <ns0:ref type='bibr' target='#b57'>Rizal et al. (2018)</ns0:ref> reported that failure in Typha fiber reinforced epoxy composites was due to fiber and matrix debonding, fiber pull-outs, and fiber damage.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusion</ns0:head><ns0:p>Alkali treatment of sisal and cattail fibers enhanced the mechanical properties of sisal/cattail fiber reinforced polyester composites. However, there was insignificant enhancement of thermal conductivity of the hybrid composites due to alkali treatment. Fractography studies unveiled that the major failure modes in the resultant composites with untreated and treated fiber blends respectively were fiber pull-outs and fiber fracture. Though the composites produced could be put to non-structural use as ceiling boards, electronic and food packaging materials, further research evaluating properties such as water absorption tendency (wettability), crystallinity, flammability and other thermal properties are inevitable to enhance their efficient practical applications. The effect of extracting sisal and cattail fibers using chelating agents such as sodium tripolyphosphate (STPP) and Ethylenediamine tetraacetic acid (EDTA) on the properties of a similar fiber blend polyester hybrid composite should be investigated. Manuscript to be reviewed Chemistry Journals <ns0:ref type='bibr'>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:ref> </ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head>Figure 1</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head>Figure 2</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>Figure 3</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Figure 3</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head>Figure 5</ns0:head><ns0:label>5</ns0:label><ns0:figDesc>Figure 5</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='23,42.52,199.12,525.00,342.00' type='bitmap' /></ns0:figure> <ns0:note place='foot'>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2020:01:45030:1:1:NEW 19 Feb 2020)</ns0:note> <ns0:note place='foot'>Analytical, Inorganic, Organic, Physical, Materials Science </ns0:note> </ns0:body> "	"Africa Center of Excellence II in Phytochemicals, Textiles and Renewable Energy (ACE II PTRE), Department of Chemistry and Biochemistry, School of Biological and Physical Sciences, Moi University, P.O. Box 3900-30100, Eldoret, Kenya www.mu.ac.ke prof.timo2018@mu.ac.ke Wednesday, 19th February 2020 Dear Professor Alexey Kabalnov, We are grateful to the Editorial team and the Reviewers for the valuable time, useful contributions and generous comments accorded to our manuscript ‘‘Effects of alkali treatment on the mechanical and thermal properties of sisal/cattail polyester commingled composites’’ (Article ID: 45030). We have revised the manuscript in response to the extensive and insightful review comments. Particularly, we have revised in-text citations to match the journals’ style. We have responded to the review comments point-counter-point and as advised, we have attempted to succinctly explain the changes made in reaction to all the review comments. Changes in the revised manuscript are highlighted using Microsoft word tracked changes feature. Concerns raised by Reviewers are shown as normal text while our responses appear as text (italicized). We look forward to hearing from you regarding our resubmission. We shall be glad to respond to any further questions and comments that the review team may raise to improve the quality of our manuscript. Thank you for your time and consideration in advance, Timothy Omara (On behalf of both authors) EDITOR COMMENTS (ALEXEY KABALNOV) MINOR REVISIONS Great paper; please address minor revisions suggested by Reviewer 2, after which the paper can be published. Thank you for this complement and suggestion. We have addressed the concerns raised by the reviewer. REVIEWER 1 (ANONYMOUS) Basic reporting The article is clear and written using professional English language. The article is quite interesting and I thank you the authors for providing extensive references of similar work done by others. If there is a comment I would like to make is that the general reference listed on line 79 (another study) and line 93 (In an investigation) could be improved by adding either the tittle of the study or investigation on question or just the author involved, despite of the fact that it is well reference (4) and (1) respectively. Thank you for this insight. We have revised these accordingly by adding the names of the authors involved. I would like to add that I have not found Tables S1 listed on line 221 and Table S2 listed on line 344. The figures are very illustrative and reveal well the findings. Thank you for this comment. We are sorry that you were unable to retrieve the supplementary data. However, they were uploaded during submission, and to this end, we have reuploaded them. Experimental design The methodology used is consistent with a comprehensive coverage of the subject. However, I would like to state that the tittle mentions the mechanical and thermal properties, and, only the thermal conductivity was tested. I expected to see more than just one thermal property. Nevertheless, the research is quite meaningful and methods are well described with sufficient information to replicate. This is quite correct. Only thermal conductivity was studied as one of the thermal properties. And because we had both thermal and mechanical properties, the plural ‘‘properties’’ could not be omitted by any means in the tittle. We have addressed this by modifying our recommendation to include investigation of other thermal properties as well (L39 in the abstract) and the conclusion. Validity of the findings I commend the authors for their extensive data set and again comparison with other similar work performed by others. This comparison makes it easier for the reader to understand what to expect from the study. I would like to mention that perhaps there is an error on line 218 in which mentions 26.17 ± 8.33 and 35.17 ± 54.96 tex. I think it might be 26.17 ± 8.33 tex and 35.17 ± 5.496 tex. Thank you for your complement. On the units, this is quite correct. We have corrected this omission of the unit. This check has been done throughout the manuscript. Conclusions are well stated and limited to supporting results. Overall the study is well performed, well understood, data have been provided and they are statistically sound. Thank you for your time, sincere evaluation and recommendation of our manuscript. REVIEWER 2 (ANONYMOUS) Basic reporting The manuscript “Effects of alkali treatment on the mechanical and thermal properties of sisal/cattail polyester commingled composites” is a well-written and comprehensive report. The authors provide a good review of relevant literature and compare their own results to those presented in relevant studies. - The authors should add scale bars to all microscopic images Thank you for this insight. Our attempts to embed scale bars in the micrographs was impeded by instrumental limitation. - In the paragraph “fractography studies” the authors refer to Figure 2 but describe Figure 3. Please change reference to Figure 3. Thank you for this keen observation. We have revised this as suggested. - The authors are inconsistent with respect to the number of decimals reported for modulus and strength values and should revise accordingly. Thank you for this technical observation. All values have been revised to 2 decimal places. - For the caption of Figure 3, I suggest adding a comment that clarifies that fracture surfaces are shown. Thank you for this insight. We have revised this to Figure 3. Micrographs of polyester hybrid composites showing the fracture surfaces in the composites. We hope this is agreeable. - The unit “MPa” is reported as “Mpa” several times, e.g. in lines 283, 287, and 290. This was yet quite observant of you! We truly appreciate this. We have revised these accordingly. This check has been done throughout the mansucript. Experimental design The experiments were designed to thoroughly investigate the posed research question, and the conclusions are supported by data. All methods and experimental procedures are well described. Validity of the findings Conclusions are generally supported by data and the authors compare their own findings to results reported in relevant studies. A few things to address: - The authors discuss that the thermal conductivity does not change significantly between composites with treated and untreated fibers. This contradicts the statement in the conclusion that thermal properties were enhanced for composites contain treated fibers. The authors should change the conclusion so that it matches their findings. We appreciate this technical suggestion. We have revised this accordingly (L33-35 in the abstract and the conclusion) - The impact strength data was not provided in the form of a graph or raw data, numbers are mentioned in the text. This is true. However, raw data for impact strengths, along with all the other mechanical and thermal properties were supplied in the supplementary materials (Data S2) as per the journal policy on open data sharing. Data S2 have been resubmitted to that effect. "	Here is a paper. Please give your review comments after reading it.
455	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Cross-coupling at aryl halide thin films has been well-established as a technique for the surface-initiated Kumada catalyst transfer polymerization (SI-KCTP), used to produce covalently bound conjugated polymer thin films. In this work, we report that the spontaneous grafting of 4-iodobenzenediazonium tetrafluoroborate on gold substrates creates a durable iodoarene layer which is effective as a substrate for cross-coupling reactions including SI-KCTP. Using cyclic voltammetry of a surface-coupled ferrocene terminating agent, we have measured initiator surface coverage produced by oxidative addition of Pd(t-Bu 3 P) 2 and estimated the rate constant of the termination reaction in the SI-KCTP system with 2-chloromagnesio-5-bromothiophene and Pd(t-Bu 3 P) 2 . We used this system to prepare uniform polythiophene thin films averaging 90 nm in thickness.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>INTRODUCTION</ns0:head><ns0:p>Since the development of the first polyacetylenes by Shirakawa and MacDiarmid in the 1970s <ns0:ref type='bibr' target='#b11'>(Chiang et al. (1977</ns0:ref><ns0:ref type='bibr' target='#b10'>(Chiang et al. ( , 1978))</ns0:ref>), applications of pi-conjugated polymers (CPs) have proliferated along with a number of refined synthetic approaches for forming these polymers. CPs based on arene repeat units are the single largest category of CP with current practical applications, due to the inherent stability of the aromatic system vs. oxidation. To prepare polyarenes, oxidative polymerization approaches are common. <ns0:ref type='bibr' target='#b27'>(Kaloni et al. (2017)</ns0:ref>; <ns0:ref type='bibr' target='#b53'>Niemi et al. (1992)</ns0:ref>) Oxidative polymerization is difficult to control and applicable only to electron-rich arene monomers. More recently, cross-coupling strategies have been developed <ns0:ref type='bibr' target='#b74'>(Yamamoto (2002)</ns0:ref>; <ns0:ref type='bibr' target='#b26'>Heeger (2001)</ns0:ref>), most commonly coupling of a aryl dihalide with an arene bearing two nucleophilic groups, e.g., an 'AA/BB' strategy. Stille and Suzuki coupling are commonly employed in these syntheses, although others are reported. <ns0:ref type='bibr' target='#b4'>(Bao et al. (1999)</ns0:ref>; <ns0:ref type='bibr' target='#b2'>Argun et al. (2004)</ns0:ref>; <ns0:ref type='bibr'>Yiu et al. (</ns0:ref> <ns0:ref type='formula'>2012</ns0:ref>))</ns0:p><ns0:p>The single greatest refinement in the cross-coupling synthesis of polyarene CPs came in the mid-2000s from the Yokozawa and McCullough groups <ns0:ref type='bibr' target='#b76'>(Yokoyama and Yokozawa (2007)</ns0:ref>; <ns0:ref type='bibr' target='#b62'>Sheina et al. (2004)</ns0:ref>), in which 'AB' monomers were prepared by monometallation of an aryl dihalide. The most common metallation approach uses magnesium-halogen exchange of an alkylmagnesium halide to prepare an arylmagnesium halide monomer. This modification yielded a dramatic improvement in polydispersity of the prepared polymers, especially polythiophenes and poly(p-phenylenes), relative to an AA/BB approach.</ns0:p><ns0:p>The improvement is attributed to a change in mechanism, from a typical cross-coupling catalytic cycle to the so-called 'catalyst-transfer' (CT) mechanism. <ns0:ref type='bibr' target='#b49'>(Miyakoshi et al. (2005)</ns0:ref>) In this mechanism, the zerovalent, coordinatively unsaturated transition metal does not dissociate from the growing polymer chain, but remains complexed to the pi-system. (Figure <ns0:ref type='figure'>1</ns0:ref>) Due to this complexation, the zerovalent center reacts next with the halide endgroup of the same polymer chain, and chain-growth, pseudoliving polymerization ensues. <ns0:ref type='bibr' target='#b6'>(Bryan and McNeil (2013)</ns0:ref>)</ns0:p><ns0:p>Figure <ns0:ref type='figure'>1</ns0:ref>. The Kumada catalyst-transfer polymerization (CTP) of a magnesiated dihaloarene proceeds in a chain-growth fashion, yielding a polyarene chain bound to the initiator.</ns0:p><ns0:p>The existence of this CT effect has been borne out by a number of theoretical studies <ns0:ref type='bibr' target='#b25'>(He et al. (2018)</ns0:ref>), as well as exploits in which CTP is used to prepare block copolymers and end-functionalized polymers. <ns0:ref type='bibr' target='#b79'>(Zhang et al. (2018)</ns0:ref>; <ns0:ref type='bibr' target='#b77'>Yokozawa and Yokoyama (2009)</ns0:ref>; <ns0:ref type='bibr' target='#b1'>Aplan and Gomez (2017)</ns0:ref>) Many of the most common applications of CPs involve their deposition in the form of thin films, including uses in photovoltaics, electrochromics, and sensors. <ns0:ref type='bibr' target='#b44'>(Marshall et al. (2011a))</ns0:ref> In the late 2000s, we and others used CTP to prepare conjugated polymer films grafted from a surface (SI-CTP). <ns0:ref type='bibr' target='#b59'>(Senkovskyy et al. (2007)</ns0:ref>; <ns0:ref type='bibr' target='#b63'>Sontag et al. (2009)</ns0:ref>; <ns0:ref type='bibr' target='#b45'>Marshall et al. (2011b)</ns0:ref>; <ns0:ref type='bibr' target='#b30'>Kiriy et al. (2011)</ns0:ref>) Typically, this feat is accomplished by formation of a self-assembled monolayer (SAM) bearing an aryl halide, followed by reaction of the halide film surface with a zerovalent metal precatalyst to form a surface-bound organometallic complex. Silane (on oxide) or thiol (on metals) SAMs are the most common aryl halide films used. Many interesting surface structures have been prepared using this effective approach. However, limitations of SAMs exist; purification of silane materials is often problematic and the quality of the thin film varies based on difficult-to-control factors such as moisture content. <ns0:ref type='bibr' target='#b36'>(Lessel et al. (2012)</ns0:ref>) The formation of thiol SAMs on noble metal surfaces is convenient and robust towards atmosphere and water (it is common practice for thiol SAMs to be prepared in the lab atmosphere using an ethanol/water mixture as solvent) but thiol SAMs are not especially durable. With all SAM techniques, the most effective coupling agents for SAM formation contain a long central alkyl chain which limits electronic coupling between the surface and the endgroup <ns0:ref type='bibr'>(Trammell et al. (2007)</ns0:ref>), an undesirable property for electronic applications. So, we sought to develop a more convenient initiator system for a SI-CTP reaction, specifically the surface-initiated Kumada polymerization (SI-KCTP).</ns0:p><ns0:p>Reductive electrografting of aryl diazonium salts is well-established as a surface modification protocol. <ns0:ref type='bibr' target='#b41'>(Mahouche-Chergui et al. (2011)</ns0:ref>; <ns0:ref type='bibr' target='#b3'>Assresahegn et al. (2015)</ns0:ref>) While careful controls such as use of antioxidant additives <ns0:ref type='bibr' target='#b0'>(Anariba et al. (2003)</ns0:ref>; <ns0:ref type='bibr' target='#b46'>Menanteau et al. (2013)</ns0:ref>) or incorporation of a bulky protecting group <ns0:ref type='bibr' target='#b15'>(Combellas et al. (2008)</ns0:ref>) are necessary to ensure formation of a well-defined monolayer, this method is known even in the absence of these precautions to form highly functionalized thin films composed of a conductive arene multilayer. <ns0:ref type='bibr' target='#b33'>(Lee et al. (2012)</ns0:ref>) While deposition of thin films from aryldiazonium salts using electrochemical reduction of the diazonium salt is a well-known technique for surface functionalization, spontaneous reaction of aryldiazonium salts with a surface is less common.</ns0:p><ns0:p>However, a number of groups have explored this approach in recent years. <ns0:ref type='bibr' target='#b65'>(Stewart et al. (2004)</ns0:ref>; <ns0:ref type='bibr' target='#b56'>Podvorica et al. (2009)</ns0:ref>; <ns0:ref type='bibr' target='#b47'>Mesnage et al. (2012)</ns0:ref>) Terminal functional groups (as the para substituent) which have been deposited using a spontaneous method include NO 2 <ns0:ref type='bibr' target='#b32'>(Laurentius et al. (2011);</ns0:ref><ns0:ref type='bibr' target='#b17'>Cullen et al. (2012)</ns0:ref>), COOH <ns0:ref type='bibr' target='#b57'>(Polsky et al. (2008)</ns0:ref>), n-alkyl <ns0:ref type='bibr' target='#b13'>(Combellas et al. (2005a)</ns0:ref>), perfluoroalkyl <ns0:ref type='bibr' target='#b14'>(Combellas et al. (2005b)</ns0:ref>), diazonium <ns0:ref type='bibr' target='#b43'>(Marshall et al. (2018)</ns0:ref>), and amine <ns0:ref type='bibr' target='#b29'>(Kesavan and Abraham John (2014)</ns0:ref>). The Tour group has prepared a great variety of conjugated linkers deposited spontaneously from organic solvents, particularly on silicon surfaces. <ns0:ref type='bibr' target='#b31'>(Kosynkin and Tour (2001)</ns0:ref>) In particular, halide-functionalized thin films formed by spontaneous diazonium grafting from organic solutions have not been reported, and only a few In this work, we report a useful instance of spontaneous aryl diazonium salt grafting to a gold surface to prepare a functionalized surface which can serve as an initiator platform for the Pd-catalyzed SI-KCTP reaction. (Figure <ns0:ref type='figure' target='#fig_2'>3</ns0:ref>) XPS survey scans of the functionalized surface revealed no nitrogen in the film, supporting the hypothesis that gold can directly catalyze the dissociation of the diazonium salt to give dinitrogen. The resulting surface has a high density of reactive groups as measured under standard conditions for evaluation of SI-KCTP initiator surfaces, and a remarkably thick, brushlike polythiophene film is formed on the surface when used in the SI-KCTP reaction. Tracking of reactive group concentration during the progress of the polymerization reaction reveals that the majority of surface-bound polymer is formed after most of the endgroups have been eliminated from the surface, explaining the rough morphology of the film and possibly shedding light on similar morphology observed by other groups. The protocol we developed for these experiments uses an air-stable diazonium salt to form the aryl halide base layer and easily handled, commercially available Pd complexes to generate the surface-bound catalyst.</ns0:p><ns0:p>We expect these simplifying advances in technique for this state-of-the-art conjugated polymer synthesis Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science method to render SI-KCTP more accessible for practical applications.</ns0:p></ns0:div> <ns0:div><ns0:head>MATERIALS AND METHODS</ns0:head></ns0:div> <ns0:div><ns0:head>General experimental remarks.</ns0:head><ns0:p>Gold-coated slides were standard float glass microscope slides coated with roughly 100 nm Au on a Cr adhesion layer, purchased from Sigma-Aldrich. HPLC-grade acetonitrile was purchased from Alfa Aesar and stored over 4 Å molecular sieves. Other chemicals were purchased directly from well-known chemical suppliers and used as received. The ferrocene coupling agent FcCH 2 ThBr was prepared according to the procedure freely available in the Supporting Information of our previous report. <ns0:ref type='bibr' target='#b64'>(Sontag et al. (2011)</ns0:ref>).</ns0:p><ns0:p>Surface profilometry was performed at the College of Engineering and Computing, University of South Carolina Columbia, on a Veeco Dektak 3ST surface profiler using 1 mg stylus force.</ns0:p><ns0:p>1,3-bis(2,6-Diisopropylphenyl)imidazol-2-ylidene](3-chloropyridyl)palladium(II) dichloride (i-PrPEPPSI, referred to in this manuscript as PEPPSI) <ns0:ref type='bibr' target='#b54'>(O'Brien et al. (2006)</ns0:ref>), 2,5-dibromo-3-hexylthiophene <ns0:ref type='bibr' target='#b7'>(Bäuerle et al. (1993)</ns0:ref>), and 2,5-diiodothiophene <ns0:ref type='bibr' target='#b21'>(Ebai and Marshall (2014)</ns0:ref>) were prepared according to literature procedures. 2,5-diiodothiophene was distilled under a 5 Torr vacuum before use.</ns0:p></ns0:div> <ns0:div><ns0:head>Synthetic procedures</ns0:head><ns0:p>Synthesis of 4-halobenzenediazonium tetrafluoroborate salts.</ns0:p><ns0:p>4-iodoaniline was prepared by iodination of aniline with molecular iodine in aqueous sodium bicarbonate solution according to Vogel's procedure <ns0:ref type='bibr' target='#b71'>(Vogel and Furniss (1989)</ns0:ref>), and recrystallized from boiling heptane to yield yellow needles. 4-bromoanilinium hydrochloride was obtained from Eastman and used as received. Recrystallized 4-iodoaniline or as-received 4-bromoanilinium hydrochloride (2.5 mmol)</ns0:p><ns0:p>was suspended in 2 mL deionized water and cooled below 0 </ns0:p></ns0:div> <ns0:div><ns0:head>Deposition of aryl iodide thin films</ns0:head><ns0:p>A 50 mM solution of freshly recrystallized 4-iodobenzenediazonium tetrafluoroborate was prepared in dry acetonitrile, typically a 10 mL sample in a clean 20 mL scintillation vial. 1-3 pieces of cut gold-coated glass slide were cleaned. Cleaning included sonication in isopropyl alcohol, washing with deionized water, followed by 5 min immersion in freshly prepared sulfuric acid/30% hydrogen peroxide 'piranha' solution, 3:1 v:v. (Caution: this mixture is violently reactive, especially with organic materials; the smallest amount practical should be prepared, and piranha should be handled using exclusively glass materials in a clean, uncluttered hood. The operator should wear body protection, gloves, and face protection.) Freshly cleaned slide substrates were blown free of moisture in a stream of nitrogen gas, and added immediately to a freshly prepared iodobenzenediazonium salt solution. Substrates were allowed to stand without stirring for 1-2h, then removed and washed with copious amounts of acetonitrile and dried in a stream of nitrogen gas.</ns0:p><ns0:p>Functionalized gold substrates were stored in a covered Petri dish in lab atmosphere with no additional precautions until use.</ns0:p></ns0:div> <ns0:div><ns0:head>Surface coupling procedure</ns0:head><ns0:p>In a nitrogen-filled glove box, 10 mM solutions of Pd initiators were prepared in toluene. Pd(t-Bu 3 P) 2 solution was prepared directly from the solid, available commercially from TCI as a beige powder and</ns0:p></ns0:div> <ns0:div><ns0:head>4/18</ns0:head><ns0:p>PeerJ Mat. Sci. reviewing PDF \| (MATSCI- <ns0:ref type='table'>2019:12:43994:1:1:NEW 26 Feb 2020)</ns0:ref> Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science yielding a faintly yellow solution. The air-stable Pd(II) complex i-Pr-PEPPSI was suspended in toluene in an amount calculated to yield a 10 mM solution, and 2 equiv. freshly titrated i-PrMgCl in 2-MeTHF was added dropwise while agitating. A clear yellow solution was obtained, which gave a grey precipitate after a few minutes. This solution was used as formed.</ns0:p><ns0:p>Grignard coupling agents were prepared by addition at room temperature while stirring in the glove box of 1.0 equivalent of freshly titrated i-PrMgCl.LiCl solution to 10 mL of a 50 mmol solution of the aryl halide (2,5-dibromothiophene or FcCH 2 ThBr) in THF. Aliquots of the polymerization solution were saved, removed from the glove box, and quenched with water to confirm the presence of excess halide; excess i-PrMgCl is detrimental to the cross-coupling reaction.</ns0:p><ns0:p>Aryl iodide functionalized slides were placed in a Pd initiator solution within the glove box and the solution heated to 60 • C on a hot plate in a sealed vial for 2 hours. Slides were removed from the vial, washed 3x with toluene, and transferred to a 50 mM solution of Grignard reactant prepared as above (either 2-chloromagnesio-5-bromothiophene or ferrocene probe ClMgTh-CH 2 -Fc) and heated to 60 • C overnight. After reaction, samples were removed from the glove box, exposed to atmosphere, and washed with THF, ethanol, water, and acetone. For samples termed 'rigorously cleaned,' PT-grafted films were briefly sonicated (ca. 30s) in acetone and were transferred to a scintillation vial half filled with chloroform, heated to boiling for several minutes, and allowed to cool. This cycle was repeated twice. The samples were removed from the film and dried in air.</ns0:p></ns0:div> <ns0:div><ns0:head>Electrochemistry</ns0:head><ns0:p>Cyclic voltammetry (CV) and alternating-current voltammetry were performed using a BASi PalmSens 3 potentiostat/galvanostat in a three-electrode cell configuration. A 100 mM solution of tetrabutylammonium hexafluorophosphate in dichloromethane was used as electrolyte. A silver wire pseudoreference electrode was used for which the Fc/Fc + redox couple on gold appeared at 0.489V, and a platinum wire was used as the counter electrode. Electrochemical surface coverages were determined by direct integration of CV peaks, and are reported as an average of anodic and cathodic peaks. Surface area of electrodes was estimated by imaging of the electrode surface with a 2.83 cm 2 standard and image analysis using ImageJ to determine working area.</ns0:p></ns0:div> <ns0:div><ns0:head>X-ray photoelectron spectroscopy</ns0:head></ns0:div> <ns0:div><ns0:head>General instrumental information</ns0:head><ns0:p>XPS measurements were performed in the USC Center for Engineering and Computing. The instrument used was a Kratos Axis Ultra DLD with a hemispherical analyzer and monochromator-equipped Al Kα source. The Au 4f 7/2 peak was used as a binding energy reference at 84.0 eV, with spectra corrected so that this reference peak appeared at the standard value.</ns0:p></ns0:div> <ns0:div><ns0:head>XPS thickness estimates</ns0:head><ns0:p>For film thickness estimates using XPS, we assume that Au is covered by an homogeneous carbon layer in order to calculate the Inelastic Mean Free Path (IMFP) of photoelectrons through the deposited layer.</ns0:p><ns0:p>For photoelectrons with kinetic energy higher than 150 eV a good approximation for the IMFP (λ ) in nanometers is λ = BE 1/2 , where B has a numerical value of 0.087 for organic layers and E is the photoelectron kinetic energy. Using a Al Kα excitation photon beam, kinetic energies for the C 1s and Au 4f electrons are 1202 and 1402 eV respectively. Experimental XPS peak intensities, including the attenuation of Au signal by C layer, are described by the following equations:</ns0:p><ns0:p>(1)</ns0:p><ns0:formula xml:id='formula_0'>I C = I C (∞) × (1 − e d C /λ C cos(θ ) )</ns0:formula><ns0:p>(2)</ns0:p><ns0:formula xml:id='formula_1'>I Au = I Au (0) × (1 − e d C /λ Au cos(θ ) )</ns0:formula><ns0:p>where I C and I Au are the experimental intensities, I C (∞) = 0.25 and I Au (0) = 4.95 are atomic sensitivity factors, θ = 0 is the angle between the electron analyzer entrance and the surface normal of the analysis sample, and d C is the thickness of the carbon overlayer being modeled.</ns0:p></ns0:div> <ns0:div><ns0:head>Scanning electron microscopy</ns0:head><ns0:p>SEM and EDX were performed on an Hitachi Cold Field Emission 8200 Series FE-SEM at the Applied Research Center, Aiken, SC, USA. A 9 kV accelerating voltage was used for typical images shown.</ns0:p></ns0:div> <ns0:div><ns0:head>5/18</ns0:head><ns0:p>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2019:12:43994:1:1:NEW 26 Feb 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:p></ns0:div> <ns0:div><ns0:head>Vibrational and NMR spectroscopy</ns0:head><ns0:p>A Thermo Electron Nicolet 4700 Fourier transform IR spectrometer using a DTGS detector and fitted with a grazing angle accessory was used to collect IR spectra of thin films. Bulk IR spectra of samples were collected on a Nicolet 380 spectrometer with a Smart Orbit diamond ATR attachment. 64 scans were summed at a 4 cm -1 resolution to produce a typical spectrum.</ns0:p><ns0:p>Routine 1H and 13C NMR of monomers and initiators were taken using a Anasazi EFT 60 MHz FT-NMR spectrometer referenced to tetramethylsilane at 0 ppm.</ns0:p></ns0:div> <ns0:div><ns0:head>RESULTS</ns0:head><ns0:p>Preparation of aryl iodide surface: characterization of the aryl iodide pre-initiator layer <ns0:ref type='formula'>2011</ns0:ref>)), we developed this process as an easy entry to surface-directed cross-coupling reactions.</ns0:p><ns0:p>We can estimate an upper bound on the efficiency of the oxidative addition step (Figure <ns0:ref type='figure' target='#fig_2'>3</ns0:ref>) by reaction of a Pd(0) complex followed by cross-coupling with a ferrocene probe. We have previously shown that Pd(t-Bu 3 P) 2 , developed by Fu and co-workers <ns0:ref type='bibr' target='#b38'>(Littke (2002)</ns0:ref>), is an effective Pd(0) precatalyst for forming surface-bound initiators for Kumada catalyst transfer polymerization through oxidative addition. Reaction of this precatalyst with our spontaneously grafted aryl iodide layer, followed by quenching with the ferrocene-bearing Grignard probe species FcCH 2 ThMgCl, gave a ferrocene-coated gold surface. Using cyclic voltammetry (CV) (Figure <ns0:ref type='figure' target='#fig_4'>5</ns0:ref>), the surface coverage of ferrocene was estimated to be Γ = 2.8x10 -10 mol/cm 2 .</ns0:p><ns0:p>An important ancillary finding of this work is that the standard procedure <ns0:ref type='bibr' target='#b64'>(Sontag et al. (2011);</ns0:ref><ns0:ref type='bibr' target='#b78'>Youm et al. (2016)</ns0:ref>; VonWald et al. ( <ns0:ref type='formula'>2018</ns0:ref>)) for forming FcCH 2 ThMgCl, using 1 equivalent of isopropylmagnesium chloride (iPrMgCl), does not completely convert the precursor halide into the probe species FcCH 2 ThMgCl. (Figure <ns0:ref type='figure' target='#fig_3'>S4</ns0:ref>) While the use of 3 equivalents of iPrMgCl does accomplish this conversion, the resulting Grignard solution yields a lower surface coverage after cross-coupling than the partially converted material. (Figure <ns0:ref type='figure' target='#fig_4'>S5</ns0:ref>) Groups using this procedure should be aware that this complication exists, and surface coverage values generated using this protocol should be regarded as lower bounds.</ns0:p></ns0:div> <ns0:div><ns0:head>6/18</ns0:head><ns0:p>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2019:12:43994:1:1:NEW 26 Feb 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science <ns0:ref type='formula'>2016</ns0:ref>)) The surface-bound Ar-PdL 2 I complex produced from our film's reaction with the Fu catalyst is effective in the SI-KCTP reaction, yielding a thick (avg. 90 nm) PT film with a purple color, visible as a rough coating on gold in scanning electron microscopy (SEM) (Figure <ns0:ref type='figure'>6</ns0:ref>). Reflectance UV-visible spectroscopy of the films is consistent with the presence of a thinner PT film produced by PEPPSI. (Figure <ns0:ref type='figure'>7</ns0:ref>) The regular decoration of physisorbed PT nanoparticles visible in the post-polymerization film (Figure <ns0:ref type='figure'>6a</ns0:ref>) was an unexpected discovery, since the polymer film was washed well with water and organic solvents after the reaction was complete.</ns0:p><ns0:p>XPS of the Fu and PEPPSI PT films ( Figure <ns0:ref type='figure'>8</ns0:ref>) after reaction reveal several critical aspects of the SI-KCTP reaction. First, both samples give spectra consistent with the presence of thick polythiophene films, with the Au peak largely effaced due to the thick covering layer. The relative ratios of C to S are not significant here due to the disproportionate effect of surface adventitious carbon as in the initiator layer (Figure <ns0:ref type='figure' target='#fig_3'>4</ns0:ref>) but the much larger sulfur signal in the Fu film is certainly due to this film's greater thickness, consistent with UV-Vis and IR measurements. (Figure <ns0:ref type='figure'>7</ns0:ref>). Since we can tell from the S signal as well as IR and absorption spectroscopy that the Fu film is thicker, Au signal (roughly equal in both samples) is likely due to defects in the film, rendering the thickness fitting procedure used for the initiator layer inapplicable here. Fits of the C 1s region of both films (Figure <ns0:ref type='figure' target='#fig_2'>S13</ns0:ref>) confirm the larger proportion of the C sp 2 component of the Fu film, consistent with this film's greater thickness.</ns0:p></ns0:div> <ns0:div><ns0:head>Quenching studies of the growing Fu-PT film</ns0:head><ns0:p>We characterized the film growth and catalyst activity as a function of time by quenching a series of growing Fu PT films with FcCH 2 ThMgCl. Ferrocene surface coverage in the films after quenching gives a reasonably direct measure of active chain ends at the time of quenching. (Figure <ns0:ref type='figure'>9</ns0:ref>) Formation of these ferrocene-terminated PT films allows characterization of the PT films by alternating-current voltammetry (ACV), revealing the gradual evolution of k ET in the growing film. (Figure <ns0:ref type='figure'>10</ns0:ref>) Each timepoint was also characterized by IR and reflectance UV-vis. (Figure <ns0:ref type='figure'>11</ns0:ref>)</ns0:p><ns0:p>We attempted the SI-KCTP procedure with two other common monomers, 2,5-diiodobenzene and 2,5dibromo-3hexylthiophene, and repeated the preparation of the Fu film with 4-bromobenzenediazonium tetrafluoroborate used to form the initiator layer. (Figure <ns0:ref type='figure'>S9</ns0:ref> and S10) Magnesiation and polymerization of 2,5-diiodothiophene for 12h yielded a thick (70 nm) polythiophene film which was measurably more uniform (rms roughness of 31 nm) by stylus profilometry than the film produced by the dibromothiophene monomer (42 nm) (Figures <ns0:ref type='figure' target='#fig_4'>S14 and S15</ns0:ref>). 2,5-dibromo-3-hexylthiophene did not yield a film of P3HT detectable at 12 h by reflectance UV/Vis, but did give enough grafted material to be detectable by IR.</ns0:p><ns0:p>(Figure <ns0:ref type='figure'>S17</ns0:ref>) On the other hand, a gold surface prepared with a C18 SAM gave no substantial film of PT by UV-vis, an important negative control. (Figure <ns0:ref type='figure'>S11</ns0:ref>) Briefly, XPS of the spontaneously grafted film is consistent with a thin layer comprised primarily of C sp 2 atoms with some adventitious carbon, including residual carbonate from oxidative cleaning. (Figure <ns0:ref type='figure' target='#fig_3'>4c</ns0:ref>) This adventitious material, which is practically inescapable in samples exposed to the air and is present in a freshly cleaned 'bare' gold substrate (Figure <ns0:ref type='figure' target='#fig_8'>S12</ns0:ref>), explains the deviation from ideal I:C ratio and the presence of the C=O peak near 288.5 eV.</ns0:p><ns0:p>The observed surface coverage for the Pd initiator on a spontaneously grafted iodophenyl layer is identical to that determined by the Tour group for the ferrocene surface coverage of a directly electrografted ferrocenyl arenediazonium salt on ITO <ns0:ref type='bibr' target='#b39'>(Lu et al. (2008)</ns0:ref>) and is not significantly different from the reported (Γ = 3.0x10 -10 mol/cm 2 ) achieved by carbodiimide coupling of ferrocenecarboxylic acid to a aminoterminated thiol SAM. <ns0:ref type='bibr' target='#b61'>(Seo et al. (2004)</ns0:ref>) CV of the cross-coupled ferrocene layer gives a very low (30 mV) separation between anodic and cathodic peaks consistent with a fast, ideal surface-confined redox reaction. The full width at half max (FWHM) of each peak is 90 mV, indistinguishable from the Nernstian ideal value of 90.6 mV. It is worth noting that unlike many observed close-packed monolayers, this CV does not display broadening of the peak due to repulsive interactions between the redox sites. <ns0:ref type='bibr' target='#b72'>(Vogel et al. (2017)</ns0:ref>) This observation is consistent with our proposed structure for the iodophenyl surface, in which the reactive iodine groups are distributed over a thin, disordered 3-dimensional polyphenylene film rather than packed more closely in a true 2-dimensional SAM.</ns0:p><ns0:p>For contrast, we performed the same coupling reaction using an indium tin oxide substrate functionalized with the commercially available silane 4-bromophenyltrimethoxysilane. (Figure <ns0:ref type='figure' target='#fig_4'>5</ns0:ref>) The surface coverage of the resulting ferrocene layer was significantly lower, (ca. 1x10 -11 mol/cm 2 , Figure <ns0:ref type='figure' target='#fig_4'>5</ns0:ref>) and the 50 mV peak-to-peak separation of the redox waves in the silane-based film indicates a slower rate of the redox reaction vs. the iodophenyl surface, which displays only a 30 mV separation. Downard has correlated layer thickness of nitrophenyl groups with NO 2 group concentration in spontaneously deposited nitrophenyl films, giving concentrations of 6-14x10 -10 mol/cm 2 . <ns0:ref type='bibr' target='#b35'>(Lehr et al. (2009)</ns0:ref>) Surprisingly, our electrochemically measured surface coverages for the iodophenyl layer based on cross-coupling with the ferrocene probe are reasonably consistent with these values, despite the much greater steric demands of the cross-coupling reaction compared to the electrochemical reduction of a -NO 2 group. Overall, cyclic voltammetry of the cross-coupled aryl iodide substrate indicates efficient conversion of surface aryl iodide groups in the arenediazonium-based film to Ar-Pd(II) groups and subsequent reaction with an aryl Grignard reagent in solution, effectively proving the formation of a surface-bound Pd(II) catalyst for Kumada coupling.</ns0:p></ns0:div> <ns0:div><ns0:head>Insights into PT structure and catalyst fate from absorption spectroscopy and XPS.</ns0:head><ns0:p>The film produced by the Fu catalyst (PT-Fu) has an intense and narrow visible-spectrum transition redshifted relative to the fundamental absorption of the PEPPSI film, conferring a purple color on the film and indicating in this case that the chains are sufficiently close in origin point to be forced into an ordered configuration with a high conjugation length. <ns0:ref type='bibr' target='#b48'>(Mitchell (2007)</ns0:ref>) This observation is consistent with the surface coverage observed by ferrocene probe, which matches values for a close-packed monolayer.</ns0:p><ns0:p>(Figure <ns0:ref type='figure' target='#fig_4'>5</ns0:ref>) . Additionally, copious amounts of PT are observed in solution with the PEPPSI catalyst, but little or none with the Fu catalyst. These observations are consistent with the polymerization reaction catalyzed by PEPPSI complex having a much higher reaction rate than the Fu catalyst, but the surfaceconfined reaction with PEPPSI undergoing rapid termination such that the catalyst soon dissociates, ending surface-directed chain growth of the polymer brush. The high conjugation length in the Fu film may be primarily due to increased grafting density relative to the PEPPSI film and to literature reports of thinner unsubstituted PT films. <ns0:ref type='bibr' target='#b9'>(Chatterjee et al. (2018)</ns0:ref>) Thick polythiophene films prepared by cross-coupling methods have been reported to form ordered structures, yielding a similar sharp fundamental transition resembling the visible spectrum of monodisperse solution polymers <ns0:ref type='bibr' target='#b23'>(Fuks-Janczarek et al. (2006)</ns0:ref>) and vibronic structures tailing off into the near-infrared. <ns0:ref type='bibr' target='#b78'>(Youm et al. (2016)</ns0:ref>) Ordered structure in other polythiophenes has been reported to yield a purple color <ns0:ref type='bibr' target='#b48'>(Mitchell (2007)</ns0:ref>), similar to our own Fu film.</ns0:p><ns0:p>(Figure <ns0:ref type='figure'>6</ns0:ref>, inset) A reasonable explanation for the interesting 500 nm diameter low-polydispersity (Figure <ns0:ref type='figure'>S7</ns0:ref>) features is that they were formed from free Pd catalyst centers, not bound to the surface, which were present during the reaction. Such centers could have been due to (1) physisorbed Pd(0) not removed by washing Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science after catalyst deposition, (2) chain transfer of Pd(0) off the growing chain during the reaction, or (3) detachment of the growing chain from the surface at the original point of attachment (i.e., desorption of an initiator phenyl group after the reaction has started). The presence of these catalyst centers, through whatever means, resulted in the formation of solution polymer. As the polymer grew, the solubility and swellability of the chain decreased quickly, and aggregates formed, eventually physisorbing onto the surface. Attributing these nanoparticles to loose Pd is consistent with our observation that the nanoparticles give a strong palladium signal as observed by energy-dispersive X-ray spectroscopy (EDS) (Figure <ns0:ref type='figure'>S1</ns0:ref>), and the observation that a small amount of polythiophene is detectable by reflectance UV-vis even in the control slide coated only with an alkanethiol SAM. (Figure <ns0:ref type='figure'>S11</ns0:ref>) Relatively few SI-KCTP films in the literature have been characterized by SEM such that the micron-scale morphology is known, and aggregated solution polymer features may be relatively common in low-solubility polyarene films prepared by SI-KCTP. The particles remain physisorbed after rinsing with water and acetone to quench the polymerization, but a rigorous cleaning by sonication in acetone followed by exhaustive extraction in boiling chloroform removed the particles, leaving the film intact.</ns0:p><ns0:p>Examination of the halogen signals in XPS yields insight into the course of the polymerization. The PEPPSI-treated film is completely scoured free of both iodine and bromine signals. The absence of iodine strongly suggests that the Pd(0) active species in the PEPPSI-catalyzed polymerization undergoes oxidative addition very efficiently, since oxidative addition of Pd(0) is the likeliest mechanism by which iodine can be eliminated from the initiator. The complete absence of bromine in the PEPPSI film is equally suggestive. This result implies that dissociation of Pd(0) during the catalyst-transfer step (Figure <ns0:ref type='figure'>1</ns0:ref>) must not be the means of chain termination in the PEPPSI-catalyzed reaction. Dissociation would leave bromine-terminated chains. The other possibility for termination is by disproportionation of Pd(0) catalyst ends. This process would lead to C-terminated, looped brushes, consistent with Nesterov's result from Ni(0) catalyzed PT formation studied by small-angle neutron scattering. <ns0:ref type='bibr' target='#b9'>(Chatterjee et al. (2018)</ns0:ref>)</ns0:p><ns0:p>Alternatively, the surface-confined chain-growth polymerization could simply be slow, such that active Pd(II) endgroups remained on the film and resulted in H-terminated chains on exposure to water during cleaning. We view this possibility as unlikely, due to (a) the fast production of copious amounts of solution polymer we observed during the reaction and (b) the fact that, unlike the Fu PT film, we did not observe residual Pd in the PEPPSI PT film by SEM/EDS. Based on the XPS results, we believe that the PEPPSI catalyst gives a very active Pd(0) species which undergoes oxidative addition rapidly but also terminates rapidly by disproportionation, yielding mostly solution polymer.</ns0:p><ns0:p>A small amount of residual iodine is visible in the Fu film, indicating that the oxidative addition step is not 100% efficient for this catalyst. However, the iodine signal is dramatically decreased vs. the initiator layer, (Figure <ns0:ref type='figure' target='#fig_3'>4</ns0:ref>) so that this result is not necessarily inconsistent with the high surface coverage observed by ferrocene probe (Figure <ns0:ref type='figure' target='#fig_4'>5</ns0:ref>). A substantial bromine signal is seen in the Fu film, indicating that dissociation is a significant termination process in the growing Fu film, although not ruling out disproportionation and the presence of still-active chain ends as alternative fates for individual chains.</ns0:p></ns0:div> <ns0:div><ns0:head>Polymerization, redox, and termination kinetics from quenching studies.</ns0:head><ns0:p>Quenching of growing PT films with ferrocenyl Grignard yields insights into kinetics of both the polymerization reaction and the termination reaction(s).The induction period seen in Figure <ns0:ref type='figure' target='#fig_8'>12</ns0:ref> is particularly interesting. Why does this feature appear? Comparison of these data to the evolution of the IR spectrum over time (Figure <ns0:ref type='figure'>11</ns0:ref>) reveals a likely answer. The induction period is not observed in IR. Rather, the intensity of in-plane and out-of-plane thienyl C-H bending modes grows smoothly starting at t = 0. A reasonable interpretation of these two observations is that the growing polymer chains at t = 15' and t = 1h have not reached the length required to fold into ordered structures <ns0:ref type='bibr' target='#b78'>(Youm et al. (2016)</ns0:ref>) which yield the consistent conjugation length (about n = 8) giving rise to the absorption at 480 nm and the optical band gap (matching literature for polythiophene per <ns0:ref type='bibr' target='#b28'>Kaloni et al. (2016)</ns0:ref> near 1.9 eV. (Figure <ns0:ref type='figure'>S16</ns0:ref>). The latter value is consistent with literature values for polythiophene. This interpretation is also consistent with voltammetric measurements of these samples, where only the 3h and 12h samples show the doping/dedoping process of polythiophene, (Figure <ns0:ref type='figure'>9</ns0:ref>) and thickness as each evolves over time is that the majority of catalyst centers are removed by termination before an appreciable thickness of polymer has formed. (Figure <ns0:ref type='figure' target='#fig_8'>12</ns0:ref>) This result implies that the observed film thicknesses are the product of an initiator density significantly lower than that measured by crosscoupling probes of the initiator surface alone. A perfectly uniform and brushlike surface would be very smooth <ns0:ref type='bibr' target='#b58'>(Pyun et al. (2005)</ns0:ref>), and a surface coverage of actual polymer chains equal to the initial measured initiator surface coverage (Figure <ns0:ref type='figure' target='#fig_4'>5</ns0:ref>) would be sufficiently closely packed to force a brushlike structure. Without knowledge of the observed reduction in initiator surface coverage, it would be difficult to explain the observed fibrillar or nodular morphology of the surface, which is found in all reported nanoscale imaging of surface-bound conjugated polymers lacking solubilizing sidechains. We interpret individual fibrils or nodules as features formed by chains grown at the longest-surviving catalyst sites.</ns0:p><ns0:p>Typical feature sizes (as measured by SEM and rms roughness of stylus profilometry, Figure <ns0:ref type='figure' target='#fig_3'>S14</ns0:ref>-S15) are 30-50 nm. An 50 nm PT feature must have a degree of polymerization (DP) of roughly 130 using a rigid-rod model, or roughly 200 using a wormlike chain model with a 12-15 nm persistence length.</ns0:p><ns0:p>This reasonably high molecular weight is not inconsistent with FTIR measurements. The degree of polymerization in polythiophene can be estimated from IR <ns0:ref type='bibr' target='#b24'>(Furukawa et al. (1987)</ns0:ref>) by fitting the ratio of absorbances at 670 and 790 cm -1 (corresponding to endgroup and repeat unit out-of-plane C-H bending modes respectively,) but the endgroup peak is not visible in the Fu and PEPPSI films with our instrument.</ns0:p><ns0:p>To be indistinguishable from noise in our system, the absorbance value must be <0.001, (Figure <ns0:ref type='figure'>7</ns0:ref>) giving a lower bound of DP = 56 by comparison to the observed repeat unit out-of-plane bend intensity of 0.05.</ns0:p><ns0:p>The low-frequency regions of the ACV data are not well enough defined to extract a precise measurement of k ET for the Fc-functionalized films, but the data allows a reasonable overview of the trend in the rate constant in the growing film. The substrate consisting of FcCH 2 ThMgCl coupled directly to the 2.5 nm iodoarene layer has k ET near 25 s -1 , while the 3h sample is bounded by k ET = 5 s -1 . These values are comparable to long-chain alkylthiol-based ferrocene monolayers <ns0:ref type='bibr' target='#b22'>(Eckermann et al. (2010)</ns0:ref>), despite the iodoaryl/PT film's several-fold greater thickness. ACV of the 1h film, in particular, shows a relatively low slope at low frequencies, suggesting that the film is made up of a heterogeneous collection of redox sites with different individual k ET values. (ibid.) <ns0:ref type='bibr' target='#b12'>(Chidsey (1991)</ns0:ref>) The slow decrease of observed rate constants as the film grows is reasonable in that polythiophene is a conductive polymer and becomes doped by oxygen in a matter of a few minutes <ns0:ref type='bibr' target='#b16'>(Cook et al. (2012)</ns0:ref>), providing a hopping-based conductivity mechanism to allow communication of electroactive endgroups with the electrode.</ns0:p><ns0:p>The likeliest interpretation of the results from attempted 3-HT polymerization is that termination processes were complete in the 'induction period' (Figure <ns0:ref type='figure' target='#fig_8'>12</ns0:ref>) before a sufficiently large fraction of surface chains had grown to a conjugation length sufficient to yield the characteristic absorption of a Manuscript to be reviewed</ns0:p><ns0:note type='other'>Chemistry Journals Analytical, Inorganic, Organic, Physical, Materials Science</ns0:note><ns0:p>polythiophene. This result is consistent with our previous finding that monomers with bulky groups on the monomer do not polymerize well in the SI-KCTP reaction using SAM-based initiators even in cases which are known to work in solution. <ns0:ref type='bibr' target='#b45'>(Marshall et al. (2011b)</ns0:ref>) This limitation has been overcome by the use of grafted macroinitiator layers <ns0:ref type='bibr' target='#b30'>(Kiriy et al. (2011))</ns0:ref> or by initiating the polymerization from a highly curved, colloidal nanoparticle rather than a flat, monolithic surface. Based on our results, it seems that the steric restriction of the diazonium-based arene layer is similar enough to that of a SAM to result in the same limitation on SI-KCTP reactions. The coupling of ferrocene probe to the bromoarene layer was not as efficient as that observed in the iodoarene layer, giving roughly 10% of the coverage (and a non-ideal voltammogram) compared to iodophenyl under the same conditions. (Figures <ns0:ref type='figure' target='#fig_8'>S10 and 12</ns0:ref>) However, a substantial polythiophene film was still produced with the bromophenyl initiator surface, ca. 20 nm as roughly estimated by comparison of the UV-Vis absorbance to that of the known brush. This result is reasonable, given our earlier finding that a relatively small fraction of the original catalyst endgroups are responsible for the majority of polymer production.</ns0:p><ns0:p>Overall, microscopy, voltammetry, and spectroscopy of substrates produced from the reaction of polyarene films with Pd(0) complexes followed by BrThMgCl reveals a roughly 90 nm thick, regular film of polythiophene, consistent in morphology with other films of polythiophenes with small or no side chains reported in the literature (VonWald et al. ( <ns0:ref type='formula'>2018</ns0:ref>)), and which is robust to rigorous cleaning procedures. In our experience, the use of the commercially available Fu Pd catalyst is very convenient and reproducible relative to approaches based on Ni(COD) 2 with various ligands, and the surface coverages attained by our method are as good or better than other state-of-the-art techniques. <ns0:ref type='bibr' target='#b78'>(Youm et al. (2016)</ns0:ref>; Sontag et al. ( <ns0:ref type='formula'>2011</ns0:ref>)) The film produced by the Fu catalyst is among the thickest PT films produced by cross-coupling methods (rather than electrochemical oxidative polymerization) currently reported in the literature, and this combination of catalyst and monomer may be particularly useful for preparation of polymer films for electronic devices. The film produced by the reduced PEPPSI Pd catalyst was not as thick, likely due to disproportionation of the Pt(0) catalytic center, with chain-transfer implied by the copious production of PT in solution, and confirmed to be caused by disproportionation by XPS observation of elimination of Br in the growing chain. However, the fact that the approach based on the in situ reduction of PEPPSI succeeded in producing a uniform film is worth reporting, as Pd(II) complexes (and PEPPSI in particular) are much easier to handle and prepare than the air-unstable Pd(0) precatalysts. To the best of our knowledge, this work is the first report in the literature of the preparation of unsubstituted PT films using PEPPSI or the popular Fu catalyst. The quantification of active catalytic chain ends by quenching the polymerization with a ferrocene probe is a useful approach, allowing the collection of kinetic data for surface-based termination processes and redox transport properties of the layer in one set of voltammetric experiments. Overall, microscopy, voltammetry, and spectroscopy of the polymerized substrates demonstrates that the spontaneously grafted aryl iodide film produced from 4-iodobenzenediazonium tetrafluoroborate is an effective and mechanically/chemically robust precatalyst for the SI-KCTP reaction.</ns0:p></ns0:div> <ns0:div><ns0:head>CONCLUSIONS</ns0:head><ns0:p>A robust aryl iodide thin film is spontaneously deposited on gold surfaces by an acetonitrile solution of 4-iodobenzenediazonium tetrafluoroborate. This is the first report of thin film formation based on an aryl halide diazonium salt which is (a) deposited from an organic solution and (b) spontaneous rather than reductively electrografted. The resulting film contains no nitrogen, in notable contrast with spontaneously deposited films from aqueous diazonium salt solutions. The aryl iodide film can be efficiently reacted with Pd(0) complexes to give surface-bound Pd(II) initiator complexes, including a complex generated in situ from the well-known air-stable Pd(II) cross-coupling catalyst i-Pr-PEPPSI. The surface-bound Pd(II) complex produced from the 'Fu catalyst,' Pd(t-Bu 3 P) 2 , initiates polymerization with 2-chloromagnesio-5-halothiophene solutions to yield densely grafted and durable polythiophene brushes on the order of 100 nm in thickness. Catalyst concentration on the surface is observed to decline over time during the polymerization, with a half-life on the order of 10 minutes. This decline may explain the rough morphology typically observed in SI-KCTP polymer films. Our reported initiator system is synthetically convenient both in the formation of the original aryl halide layer and its conversion into the Pd(II) surface-bound catalyst, and may find immediate use in organic electronic device construction.</ns0:p></ns0:div> <ns0:div><ns0:head>14/18</ns0:head><ns0:p>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2019:12:43994:1:1:NEW 26 Feb 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>(2,4,6-trichlorophenyl and 4-bromophenyl)<ns0:ref type='bibr' target='#b47'>(Mesnage et al. (2012)</ns0:ref>) from aqueous solution.Most work in the area of spontaneous surface modification with diazonium salts uses aqueous solutions of the salt, exploiting the inherent instability of arenediazonium salts in water due to diazonium hydrolysis2/18 PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2019:12:43994:1:1:NEW 26 Feb 2020) Manuscript to be reviewed Chemistry Journals Analytical, Inorganic, Organic, Physical, Materials Science to diazohydroxide compounds. (Lewis and Johnson (1960)) Diazonium salt hydrolysis reliably forms thin films on metallic, oxide, and carbon surfaces. (Combellas et al. (2005a); Podvorica et al. (2009); Lehr et al. (2010); Berisha et al. (2016)) These films contain a substantial fraction of azo R-N=N-R' linkages, and XPS evidence indicates that the aryl film is sometimes linked to metal surfaces through a nitrogen-metal bond. (Combellas et al. (2005a)) However, evidence is beginning to emerge that spontaneous deposition of diazonium salts in acetonitrile (and likely other polar aprotic solvents) on gold proceeds by a different mechanism, possibly by direct Au catalysis of C-N 2 + bond cleavage yielding C-Au bonds. (Mesnage et al. (2012)) Bolstering this hypothesis, we have found in this work and past studies that arenediazonium-based films formed spontaneously from acetonitrile generally do not contain any nitrogen at all, in sharp contrast to aqueous-based films driven by diazohydroxide deposition. As a component of this work, we sought to demonstrate the spontaneous grafting of the aryldiazonium halide 4-iodobenzenediazonium tetrafluoroborate, and determine whether the resulting layer reacts to form a surface-bound initiator for SI-KCTP. We found that 4-iodobenzenediazonium salt spontaneously forms a thin film at a clean gold surface, and that the resulting aryl iodide layer is convenient and effective for cross-coupling. (Figure 2) In particular, this iodoarene layer yields a high density of surface-bound Pd(II) sites active in the cross-coupling reaction as measured electrochemically using a ferrocenyl probe, FcCH 2 ThMgCl, previously reported by our group (Sontag et al. (2011)), and used by others for similar systems. (Youm et al. (2016); VonWald et al. (2018)). This surface-bound cross-coupling initiator also reacts effectively with Grignard-based thiophene AB monomers to yield polythiophene brushes.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head>Figure 2 .</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2. The aryl diazonium salt 4-iodobenzenediazonium tetrafluoroborate reacts spontaneously with freshly cleaned Au surfaces to yield an aryl iodide-functionalized surface.</ns0:figDesc><ns0:graphic coords='4,276.14,303.30,144.74,54.72' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>Figure 3 .</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Figure3. Reaction of an aryl iodide layer on gold with Pd(0) followed by Grignard reagents yields a ferrocene-functionalized layer using a Fc-bearing Grignard reagent. Use of a magnesiated dihaloarene results in a thick polythiophene film through the SI-KCTP reaction.</ns0:figDesc><ns0:graphic coords='4,183.09,416.05,330.80,100.96' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head>Figure 4 .</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Figure 4. XPS of the spontaneously deposited 4-iodophenyl layer. a) Survey scan of the film reveals no nitrogen near 400 eV, and a strong Au 4f peak indicating a thin (ca. 2.5 nm) organic film. b) I 3d XPS of the film gives a strong signal. c) The breadth of the C 1s peak is consistent with two major species present.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head>Figure 5 .</ns0:head><ns0:label>5</ns0:label><ns0:figDesc>Figure 5. a) Cyclic voltammetry of PhI thin film on gold (0.1M TBAPF 6 /DCM solution, 100 mV/s scan rate, Ag wire reference electrode, second cycle shown) after reaction with the ferrocene cross-coupling probe Fc-CH 2 -ThMgCl shows a densely packed surface (2.8x10 -10 mol/cm 2 ) and a near-ideal surface redox couple consistent with a thin arene layer. b) A commercial bromophenyl silane on ITO yields an order of magnitude lower surface coverage, larger FWHM of redox peaks, and larger peak-to-peak separation under the same electrochemical conditions.</ns0:figDesc><ns0:graphic coords='8,162.41,63.78,372.21,153.21' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head>Figure 6 .Figure 7 .</ns0:head><ns0:label>67</ns0:label><ns0:figDesc>Figure 6. (a) SEM of a PT film produced by SI-KCTP according to Figure 2 shows a morphology consistenting of a rough PT coating. Physisorbed PT particles are also visible. Inset: The PT film produced on gold is a dark purple color, indicating closely packed PT chains. (b) A lower-magnification image of the same PT film (in which a crack in the metal coating is also visible) reveals a regular scattering of narrow-size-distribution (Figure S7) PT particles. EDS mapping of this region (Figure S6) shows carbon and sulfur signals associated with the film and particles but not the glass below, confirming the presence of polythiophenes specifically coating the gold surface. (c) Rigorous cleaning removes physisorbed polythiophene, but leaves the surface-bound PT brush (roughly 90 nm by profilometry) intact. (Figure S2)</ns0:figDesc><ns0:graphic coords='8,162.41,417.22,372.22,108.67' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head>Figure 8 .Figure 9 .Figure 10 .Figure 11 .</ns0:head><ns0:label>891011</ns0:label><ns0:figDesc>Figure 8. X-ray photoelectron spectroscopy (XPS) of polythiophene films prepared using (t-Bu 3 P) 2 Pd ('Fu film') and PEPPSI Pd ('PEPPSI film') catalysts. (a) Some bromine chain ends are present in the Fu film, implying that termination can occur by catalyst dissociation from the growing chain. Br is not observed in the PEPPSI film; therefore, termination must occur by disproportionation. (b) Due to the substantial (ca. 80 nm) thickness of the Fu film, the C sp 2 component is clearly visible in its C 1s spectrum. Higher binding energy peaks are also visible due to atmospheric oxidation of the polymer, yielding COO and C-O groups. (c) The S 2p peak, near 165 eV and consistent with a thiophene sulfur, is very strong in the Fu film. (d) Again PEPPSI shows its reactivity, with the iodine initiator group completely eliminated from the PEPPSI film. A faint but recognizable iodine signal is seen in the Fu film, showing that oxidative addition of the Pd(0) catalyst is not 100% efficient despite high observed electrochemical surface coverage.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_7'><ns0:head /><ns0:label /><ns0:figDesc>Sci. reviewing PDF \| (MATSCI-2019:12:43994:1:1:NEW 26 Feb 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_8'><ns0:head>Figure 12 .</ns0:head><ns0:label>12</ns0:label><ns0:figDesc>Figure 12. (a) Dissociation of the Pd(0) species in the SI-KCTP reaction with the Fu catalyst takes place with k on the order of 10 -3 s -1 . (b) Tracking absorbance at 480 nm is a good measure of growth of the polythiophene film, which takes place at roughly 10-11 nm/h after an induction period corresponding to a shorter conjugation length.</ns0:figDesc></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head>3/18 PeerJ</ns0:head><ns0:label /><ns0:figDesc /><ns0:table /><ns0:note>Mat. Sci. reviewing PDF \| (MATSCI-2019:12:43994:1:1:NEW 26 Feb 2020)</ns0:note></ns0:figure> <ns0:note place='foot'>Analytical, Inorganic, Organic, Physical, Materials Science </ns0:note> <ns0:note place='foot' n='18'>/18 PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2019:12:43994:1:1:NEW 26 Feb 2020)Manuscript to be reviewed</ns0:note> </ns0:body> "	"Nicholas Marshall Assistant Professor of Chemistry University of South Carolina Aiken Dr. Jeremiah Gassensmith Academic Editor, PeerJ Materials Science Dear Dr. Gassensmith, Thank you for providing the reviewers' comments and suggestions in response to our manuscript 'Cross-coupling polymerization at iodophenyl thin films prepared by spontaneous grafting of a diazonium salt.' We are pleased to hear that each reviewer finds our work potentially interesting, and have made every effort to address the reviewers' concerns including doing many new experiments. Below is a point-by-point response. For your convenience and the reviewers', a summary of new work in the revision is as follows: ● We have performed a time study of the polymerization reaction with the main monomer, 2-bromo-5-chloromagnesiothiophene, demonstrating the growth of the film by IR and UV-vis over the 12h timeframe. ● We quenched the above polymerization at various timepoints using a Grignard-based ferrocene capping agent, allowing us to monitor the coverage of active surface catalyst during the polymerization course. We estimated the first-order rate constant of the catalyst loss process(es). ● We characterized these ferrocene-functionalized polythiophene films with cyclic and alternating-current voltammetry, extracting estimates of electron-transfer rate constants through the films and observing the time (ca. 3h) at which the doping-dedoping process of PT began to appear. ● We characterized the polymer films with XPS. Examination of Br and I peaks in the completed films led us to infer that chains made with the PEPPSI catalyst terminate by disproportionation, while those made with the Fu catalyst terminate partially by dissociation. ● We performed the polymerization with two other monomers - magnesiated 2,5-diiodothiophene (giving PT) and 2,5-dibromo-3-hexylthiophene (giving P3HT) - and another diazonium salt surface, produced using 4-bromobenzenediazonium tetrafluoroborate. We found that P3HT was formed at the surface in only small amounts, barely detectable by IR, and that the bromoarene surface was not as effective at forming the initiator under these conditions. However, the use of diiodothiophene monomer gave a smoother film and might be a good improvement on the technique. ● We characterized the PT films by stylus profilometry, finding that the 1000 nm feature shown in the previous version was not representative of the overall film. A more representative set of measurements gives an average thickness of around 90 nm. We also used this method to estimate surface roughness of PT films prepared from dibromothiophene and diiodothiophene monomers. ● We performed many other small revisions, additions of data, and clarifications at reviewers' requests. These are listed below in the point-by-point summary. ● At journal staff's request, we made a major formatting change, separating Results and Discussion section, and revised many figures. We believe that the revised manuscript with new data and analyses is a far stronger work than the original version. In particular, the findings about SI-KCTP termination mechanisms and polymer growth rates will be important for other researchers in this growing area. Thank you for your work in editing our manuscript. We believe that it may be suitable for acceptance now, and we look forward to hearing from PeerJ Materials Science. Best, Nicholas Marshall Note to Editors and Reviewers: Line numbers in the response below refer to the revised document, revision 1_1. NM Basic reporting The study by Marshall et al looks at the surface-initiated Kumada catalyst transfer polymerization of polythiophene after grafting of 4-iodobenzenediazonium tetrafluoroborate on gold substrates. Overall the study is interesting, well done and worthy of publication. A few minor comments should be addressed prior to publication: Line 54. Sentence ends with “it” consider revising. The sentence has been revised to read 'Many interesting surface structures have been prepared using this effective approach.' (Line 56) Figure 5 – more details on the CV (electrodes, solution salt concentration, etc..) should be include in the caption. The conditions (0.1M TBAPF6/DCM solution, 100 mV/s scan rate, Ag wire reference electrode) have been added to the figure caption. (pg. 7) Line 163 define “PEPPSI” The full name of this compound has been added here. (Line 126) Line 175 and 178: Figure 6. Figure 6 “a” should be included for clarity. Done. (Line 268) Line 178 this is the film after polymerization correct? Not before? This is not clear and should be identified. The phrase 'in the post-polymerization film' has been added to this description in line 268. Line 187 define “EDS” The definition has been added. (Line 356) Experimental design good experimental design Validity of the findings L:114. XPS analysis: how does XPS provide thickness measurements? It provides an elemental compositional analysis. Where does the 2.5nm come from? The author should describe the assumptions used to make this analysis an provide more details (either in main text or in ESI). A complete description of the estimation process has been added to the Experimental section in the XPS subsection beginning on line 200. For the CV (L152) can the authors comment on the stability of the films through repetitive CV measurements? Are the responses consistent after 5, 10 ,50 cycles? Is Figure 5 the first run? The figure shows the second cycle in all cases. Typically, we observe some alteration after the first cycle, but subsequent cycles are reasonably consistent, showing only a slight decline between cycles of CV. We have also observed degradation of the Fc-functionalized films while simply sitting in the laboratory. CV of a well-covered slide which has been in the lab environment for a few days shows only a few percent of the original Fc signal. Because of this decline, we always took electrochemical measurements immediately on removing slides from the glove box. A typical CV figure has been added to the Supporting Information (Fig. S8) with 3 subsequent cycles to support this claim. Alternating-current voltammetry, which requires many cycles, has been performed on the samples, and Figure 10 has been added to the manuscript summarizing results from this technique. Reviewer 2 Basic reporting * The Results & Discussion section would be easier to follow if a brief description of the experimental procedures used to prepare the samples were added to the beginning of each section (preparation of aryl iodide surfaces before line 112, polymerization conditions between the sentences in Line 156) The subsection headings have been revised to read 'Preparation of aryl iodide surface: characterization of the aryl iodide pre-initiator layer' (referring to the comment on line 111-112, now on line 224) and 'SI-KCTP using the surface-bound initiator: establishment of polymerization conditions' referring to the comment on line 156, with the new section on line 253. * Throughout the manuscript, there are points where added detail would make the science easier to follow, for example: * Abstract (lines 7-8): 'thick, well-defined polythiophene thin films' -- what range of thicknesses? what is meant by well-defined? The relevant passage has been revised to read '...this system to prepare uniform polythiophene brushes averaging 90 nm in thickness.' * Line 55: 'the surface produced is highly variable' -- what aspect of the surface is variable? This statement was reworded to read '...purification of silane materials is often problematic and the quality of the thin film varies based on difficult-to-control factors such as moisture content.' (Line 57) * Line 56: 'Thiol SAMs are quite reproducible' -- what aspect of the SAMs is reproducible? This statement was reworded to read 'The formation of thiol SAMs on noble metal surfaces is convenient and robust towards atmosphere and water (it is common practice for thiol SAMs to be prepared in the lab atmosphere using an ethanol/water mixture as solvent) but thiol SAMs are not especially durable.' (Line 58) * Line 131: '...coverage...consistent with a closely packed ferrocene monolayer.' -- what coverage level or range is reported in the cited reference? This section has been substantially re-written to cite a second relevant value for surface coverage reported in the literature, and state both reported coverages in the text, as well as to state explicitly that the surface coverages were estimated by cyclic voltammetry. * Line 136: 'the surface coverage...was significantly lower' -- state measure value to show how much lower. We have added a good bit of content here, including the requested surface coverage. We have also expanded this discussion, including the following text: 'The full width at half max (FWHM) of each peak is 90 mV, indistinguishable from the Nernstian ideal value of 90.6 mV. It is worth noting that unlike many observed close-packed monolayers, this CV does not display broadening of the peak due to repulsive interactions between the redox sites. (Vogel et al., 2017) This observation is consistent with our proposed structure for the iodophenyl surface, in which the reactive groups are distributed over a thin, disordered iodine-functionalized polyphenylene film rather than packed in a true SAM.' (Line 309) * Line 137: 'the peak-to-peak separation...indicates a slower rate' -- state the two different peak-to-peak separation values so they can be directly compared. We have explicitly stated the values as follows. 'The surface coverage of the resulting ferrocene layer was significantly lower, (Figure S5) and the 50 mV peak-to-peak separation of the redox waves in the silane-based film indicates a slower rate of the redox reaction \textit{vs.} the iodophenyl surface, which displays only a 30 mV separation.' (Line 316) To avoid vagueness, we also revised the following statement, (added text is starred) 'Surprisingly, our electrochemically measured surface coverages for the iodophenyl layer based on cross-coupling with the ferrocene probe are reasonably consistent with these values, despite the much greater steric demands of the cross-coupling reaction compared to the electrochemical reduction of a -NO2 group.' to add the explicit statement that this claim refers to the iodophenyl layer. (Line 321) * Lines 146-150: 'An important ancillary finding of this work...' -- This discussion is confusing. Sources for the standard procedure should be cited. What role does reaction time play? What specific method for preparing thienylmagnesium chlorides was used in this work? How much lower is the observed surface coverage if an excess of isopropylmagnesium chloride is used? Because the substrates are immersed into a solution of the Grignard reagent, presumably the concentration of thienylmagnesium chloride is much larger than the concentration of surface-bound aryl halides regardless of the preparation method used. Is the implication that the excess isopropylmagnesium chloride can also react with the surface-bound aryl halides? Were any ratios of iPrMgCl:FcCH2ThBr between 2 and 3 examined to find the lowest ratio where complete conversion could be observed (presumably this would lead to the highest yields)? The reviewer's hypothesis (that excessive iPrMgCl reacts with the surface) is what we suppose as well, and similarly to the reviewer we assume that the partially converted material works because there's plenty of thienylmagnesium chloride even at a low conversion rate. As Figure S4 shows, reaction time between 15m and 4h does not change the conversion of the thienyl bromide starting material enough to be obvious in TLC. We observed a similar non-effect of time at 1 equivalent iPrMgCl. We plan to exhaustively characterize this key transmetallation reaction in future work. Right now, we know only enough to know that it's not a clean 1:1 conversion. We have referred again here to examples of this procedure in the literature and added the detailed procedure to the caption of Figure S4. * Line 165: 'highly uniform' -- How was the uniformity of the film assessed? This passage has been revised as follows. 'This sample was coated with a thinner, red polythiophene film with the typical regular morphology (with ca. 50 nm surface features as measured by image analysis) of an unsubstituted conjugated polymer (Sontag et al. 2009) after reaction was complete. (Figure S3)' (Line 264) * Line 180: 'low-polydispersity' -- How was the dispersity of the particles assessed? Image analysis in ImageJ was applied to test our claim here. After applying reasonable filtering settings to remove artifacts and exclude obvious aggregates from the calculation, the particles were found to be about 500 nm in diameter (Feret, i.e., caliper diameter) with the reasonably narrow distribution shown. This analysis is summarized in Figure S7. The revised claim (beginning on Line 347) reads 'A reasonable explanation for the interesting 500 nm diameter low-polydispersity (Figure S7) features is that they were formed from Pd catalyst centers, not bound to the surface, which were present during the reaction. Such centers could have been due to (1) physisorbed Pd(0) not removed by washing, (2) chain transfer of Pd(0) off the growing chain during the reaction, or (3) detachment of the growing chain from the surface at the original point of attachment (i.e., desorption of an initiator phenyl group after the reaction has started). The presence of these catalyst centers, through whatever means, resulted in the formation of solution polymer. As the polymer grew, the solubility and swellability of the chain decreased quickly, and aggregates formed, eventually physisorbing onto the surface. Attributing these nanoparticles to loose Pd is consistent with our observation that the nanoparticles give a strong palladium signal as observed by energy-dispersive X-ray spectroscopy (EDS) (Figure S1), and the observation that a small amount of polythiophene is detectable by reflectance UV-vis even in the control slide coated only with an alkanethiol SAM. (Figure S11).' * Line 187: 'the brush thickness on the 2D surface is close to the particle radius.' -- How were thickness and particle radius assessed? How close were the values to each other? After careful consideration of the reviewer's, this section has been substantially rewritten. We believe that the very thick feature in the original SEM figure was an edge artifact (as mentioned elsewhere in this response document). This claim has been eliminated in the revision. * Fig 6b is described as showing 'a regular distribution of narrow-size distribution PT particles' -- how were the regularity of the distribution and the dispersity of the particles assessed? This particular (kind of redundant) statement was eliminated in the rewritten paragraph. * Line 205: 'The film produced by the reduced PEPPSI Pd catalyst was not as thick nor the reaction as controlled...' -- What is meant by 'controlled' here? Does it refer to molecular weight? dispersity? fraction of chains bound to the surface rather than in solution? A clearer description would be helpful. The reviewer is right - this statement is vague. The revised sentence reads (beginning Line 464) 'The film produced by the reduced PEPPSI Pd catalyst was not as thick, likely due to chain transfer of the Pt(0) catalytic center (as seen by the copious production of PT in solution) but the fact that this approach succeeded in producing a uniform film is worth reporting.' * Figure 3: PEPPSI is a catalyst (Pd + ligand), not a ligand, so the definition of 'L' is not accurate The scheme in Figure 3, leftmost arrow, has been revised to precisely describe the catalyst system. * Figure 5: The caption should specify that the sample used for the CV in part (b) is on an ITO surface whereas the one in part (a) is on a gold surface. This information has been added to the Figure 5 caption. * Lines 154-156: 'Reaction...with surface-bound...catalysts has been established as a state-of-the-art technique' -- References should be cited (or re-cited) here. We have added appropriate citations, including to Neo et al, 2016, and clarified that this sentence is referring to the SI-KCTP reaction so that the following sentence makes more sense. (Line 256) * Line 174: Extraneous '(C)'? There was an error in the LaTeX for the manuscript here which caused a reference (Chatterjee et al, 2018) to be mis-formatted. It has been fixed. * Figure 6: The caption for Figure S1 describes the film in Figure 6b as being cracked, but that is not mentioned in the caption to Figure 6. More information has been added to this caption. It now reads '(a) SEM of a PT film produced by SI-KCTP according to Figure 2 shows a morphology consistenting of a rough PT coating. Physisorbed PT particles are also visible. Inset: The PT film produced on gold is a dark purple color, indicating closely packed PT chains. (b) A lower-magnification image of the same PT film (in which a crack in the metal coating is also visible) reveals a regular scattering of narrow-size-distribution (Figure S7) PT particles. EDS mapping of this region (Figures S1 and S6) shows carbon and sulfur signals associated with the film and particles but not the glass below, confirming the presence of polythiophenes specifically coating the gold surface. (c) Rigorous cleaning removes physisorbed polythiophene, but leaves the surface-bound PT brush intact. (Figure S2).' * Figure 7: The caption for Part B does not appear to be correct. We have corrected this error - 7(b) now refers to the grazing-angle IR spectrum of the film, not the reflectance UV spectrum. * Lines 129-130/216: The ferrocene-functionalized thienyl Grignard reagent is described as being 'well-known' earlier in the text, but in the experimental section the reader is referred to a prior publication (20% yield in 4 steps from dibromothiophene) from the author. Perhaps 'well-known' was too strong a statement here? The Nesterov (LSU) and You (UNC) groups have reported using the probe as well, although the latter was in collaboration with the current author's doctoral supervisor. At any rate, we rephrased this part to be more specific, as follows: 'a s measured electrochemically using a ferrocenyl probe,FcCH2 T hMgCl, previously reported by our group, (Sontag et al. (2011)) and used by others for similar systems. (Youm et al. (2016); VonWald et al. (2018))' (Line 99) * Figure S1: The caption refers to Figure 7b (IR spectra) instead of 6b. The main conclusion from these images seems to be that there is no Au/S/C/Pd in the cracked area of the film. The error in the caption has been corrected. * Figure S2: It is difficult to tell what conclusion is being supported by the element map--is there supposed to be a correlation between areas of S & C density? Dots corresponding to Au seem most abundant--would this be expected for a thick film of polymer? In the spectrum at the bottom, it would be useful if the region where N atoms were expected to appear were labeled so that the loss of nitrogen during grafting can be easily confirmed. We have clarified in the caption of S2, 'The carbon and sulfur signals are strongly associated with the surface of the film and are negligible in the crack, consistent with a layer of polythiophene grown on the upper surface of the gold film.' Unfortunately, EDS is not surface-sensitive enough to provide a cross-check against our XPS results in Fig. 4, especially for a light atom such as N. It is barely able to detect the sulfur, which shows up so clearly in XPS of the Fu film in the new Figure 8! * Figure S3: The text cites this figure as evidence that a highly uniform film was prepared. It is difficult to tell from the SEM image, but it could also be interpreted as being made up of smaller fibrillar units that do not look particularly regular. Some more explanation in the text would help. This morphology is quite typical of low-substitution conjugated polymers such as PT, polyphenylene, PEDOT, etc. We have changed the phrasing both here, describing the film from PEPPSI catalyst as having 'the typical bumpy morphology of an unsubstituted conjugated polymer.' (Line 264) In the final paragraph of the results and discussion, we have added text to this effect with an additional citation to VonWald and coworkers (2018): 'microscopy of the substrate produced from the reaction of polyarene films with Pd(0) complexes followed by BrThMgCl reveals a roughly 90 nm thick, regular film of polythiophene, consistent in morphology with other polythiophene films with small or no side chains reported in the literature, ( VonWald 2018)' (beginning Line 450) Finally, we have provided a proposed explanation for this ubiquitous type of morphology (the rapid drop-off of active SI-KCTP catalyst sites, seen in the new Figure 9) and cited Doubina, 2012, as a great example of the same morphology in a similar polymer. * Figure S4: This figure is confusing. The starting material (SM) should be identified as FcCH2ThBr. What was the TLC solvent system? The intensity of the baseline spot seems to increase with reaction time--is this important? The ratio of reagents should be clearly defined (equivalents of iPrMgCl to FcCH2ThBr). We have added the identity of the mobile phase (hexanes) and provided an unambiguous legend for this figure. In solvent with a few percent ethyl acetate, the baseline spot moves, implying that it is a characterizable side product. We plan to characterize it this summer. Experimental design The experiments are mostly well-designed, but the absence of any characterization data for the key molecule, 4-iodobenzenediazonium tetrafluoroborate, is very concerning. Basic characterization data should be included. The reviewer is right - this molecule is critical and the data should have been reported even though it is a commercially available compound. We have added spectral data to the manuscript (beginning at line 131) and spectra to a supporting document. Additionally, there are several points where control experiments would be useful for supporting the conclusions: * Line 121: 'XPS of the spontaneously grafted film is consistent with a thin layer of C sp2 atoms with some adventitious carbon, including residual carbonate from oxidative cleaning at 288.5 eV.' - XPS of the substrate after cleaning but before grafting would be useful to confirm the source of the sp2 C signal. (Also, Figure 4c should be be referred to here.) We have added this spectrum (Figure S12), the figure reference, and the following text: 'This adventitious material, which is practically inescapable in samples exposed to the air and is present in a freshly cleaned 'bare' gold substrate (Figure S12), explains the deviation from ideal I:C ratio and the presence of the C=O peak near 288.5 eV.' (Line 300) * Lines 134-136: It is not clear that using trimethoxysilane grafting to an ITO surface should be directly comparable to benzenediazonium salt coupling to a gold surface. Is there any literature to support the idea that they should have comparable levels of surface coverage? There are also likely to be differences in efficacy of coupling to in iodophenyl surface (diazonium/Au system) vs a bromophenyl surface (silane/ITO system). Controls with a bromophenyldiazonium salt/Au surface and/or with an appropriate iodophenyl thiol/Au surface would be more likely to reveal more about these systems. We have included a measurement of the bromophenyldiazonium salt, which as both we and the reviewer suspected yields lower coverage under the same conditions. Also, to provide a more reproducible measurement for a basis of comparison between systems, we obtained order-of-magnitude estimates of the electron-transfer rate constant with alternating current voltammetry. Ferrocene-thiol-Au systems are well-known in the literature, and our rate constants compare favorably to longer nonconjugated systems despite the diazonium-based film being thicker. The ACV results can be seen in the new Fig. 10 and results from the bromophenyl salt in Fig. S9 and S10. * Line 195: XPS (or other analysis of the film) after sonication and chloroform extraction would confirm that the film remained intact after the particle removal process. This analysis has been performed and added to the text as Fig. 8 and Fig. S13 with the associated text. We thank the reviewer for the suggestion, as we were able to derive extremely interesting inferences about the termination mechanism from the data, as seen in the discussion beginning at line 270. Briefly: PEPPSI terminates only by disproportionation, while Fu terminates with some chain-transfer dissociation. Line 263: The 'rigorous cleaning' procedure is described here to be extraction with boiling chloroform, but in the main text (Lines 194-195), the procedure is described as including both sonication in acetone and chloroform extraction. The omission in the experimental section has been corrected. The new text mentions the sonication step explicitly, line 182. Validity of the findings The presentation of the findings is generally reasonable, but it leaves open a number of questions: * Line 114: 'thickness of the thin film of 2.5 nm, consistent with other reported spontaneously grafted films.' A monolayer of iodophenyl groups should be much thinner than 2.5 nm (< 1 nm)--can the author comment on the accuracy of this value and what it implies about surface coverage if it is accurate? References should be cited here for other reported films. Text explaining both the atomic ratio difference and comparing the layer thickness to relevant literature values (Touze, 2018 and Muri, 2011) has been added at line 226 and the following text near line 300. 'This adventitious material, which is practically inescapable in samples exposed to the air and is present in a freshly cleaned 'bare' gold substrate (Figure S12), explains the deviation from ideal I:C ratio and the presence of the C=O peak near 288.5 eV.' * Lines 115-116: The measured 1:8 I/C ratio is significantly lower than the expected 1:5 ratio (implying that only 62.5% of phenyl rings still have an iodine that can participate in grafting reactions). Can the authors comment on what this would imply about potential surface coverage? Since the anomaly is likely due to overestimation of the carbon signal due to adventitious carbon, we do not think this ratio directly translates into an effect on the maximum surface coverage. We have added the reviewer's requested XPS control experiment showing bare gold, which supports our interpretation that 'miscellaneous' C-H and COO species are present even in brand new, commercially produced, freshly oxidatively cleaned gold. As mentioned previously, we have added a statement to this effect. * The studies with the ferrocene probe are used to suggest the formation of a closely packed monolayer--how does this finding relate to the later conclusions about polymer surface coverage? We have greatly expanded these studies, including using the ferrocene probe to track coverage during the actual polymerization. We extensively discussed the relationship between catalyst surface coverage and polymer morphology/thickness in the new section beginning at line 387. * Line 159: How were sample colors determined and compared? Can the color change be influenced by the gold surface? Citing absorption spectra here would be helpful and more quantitative. We have added a couple of useful citations (including more specific details drawn from Youm, 2014 for vibronic structure in ordered polythiophenes, one to Mitchell, 2007, for the actual color of ordered polythiophenes, and one to Fuks-Janczarek, 2006, for the origin of the sharp absorption peak observed in the Fu film). We reworded this paragraph to refer more to specific absorptions in our spectra and in literature, e.g., 'Thick polythiophene films prepared by cross-coupling methods have been reported to form ordered structures, yielding a similar sharp fundamental transition resembling the visible spectrum of monodisperse solution polymers (Fuks-Janczarek, 2006) and vibronic structures tailing off into the near-infrared. (Youm, 2016) (Line 341) * Lines 179-182: 'A reasonable explanation for these...features is that they were formed from Pd(O) catalyst centers that were physisorbed onto the substrate surface' -- If this is true, then it should also happen in the ferrocene studies--what effect would excess Pd catalyst have on the surface coverage studies? Could physisorbed catalyst react with thienylmagnesium chloride and/or thienyl bromide to either give detectable solution species or excess surface-bound ferrocene? This is a valid concern that directly impacts a conclusion of our study, since we operate under the assumption that only surface-bound Pd(II) centers will lead to grafted ferrocene or grafted polythiophene chains. However, we believe that we can safely neglect the impact of physisorbed catalyst on the observed grafting density. First, we note that a steady decline in active endgroups measured by Fc coupling is observed during the polymerization (new Figure 9) eventually leading to no observable Fc endgroups at the 12 h endpoint. We know that film at the 12 h point still contains I and Br groups (new Figure 8), and we infer from XPS that catalyst is being released into solution by chain-transfer dissociation and possibly disproportionation, which both yield Pd(0) species capable of re-initiation. However, zero measurable Fc is seen at the 12h mark. The conclusion: 'loose' catalyst does not re-add to surface halide groups. We can justify this observation with a simple calculation. Even if ten times as many catalyst centers were released from physisorption as were present on the actual slide (an n=10 multilayer on a roughly 1 cm2 slide) that would be roughly 3x10-9 mol of catalyst. In a typical 10 mL volume used in the ferrocene coupling experiment, that would be a catalyst concentration of 3x10-7M, which is probably negligible compared to the 0.01M solution that we used to deliberately react with the surface. The volume of the cross-coupling solutions has been added to the experimental section in the revised manuscript. We have also completed another control experiment, (Fig. S11). where we measure the UV-vis absorption of the physisorbed polymer seen in Figure 6 a-b (before rigorous cleaning). The physisorbed polymer is detectable, but not on the order of the surface-grown films. * Lines 183-185: '[upon precipitation] the active catalyst site was inaccessible and polymerization effectively ceased.' -- Is this necessarily true? This would depend upon the porosity of the particles formed upon polymer precipitation, the location of catalyst sites (particle interior vs particle surface), and solubility of monomer in polymer particle. The intended initiation sites are also bound to an insoluble material (the gold substrate), so it is difficult to argue that polymerization will occur from the gold surface and not from a polymer particle. We agree with this critique. The factors that led to the particular size and shape of the particles formed are not understood at this time. The rewritten text calls attention to what is known of their size, dispersity, and composition without overstating our understanding of their formation. For instance, a key passage of the rewritten text (starting at line 349 of the revised text) reads: 'Such centers could have been due to (1) physisorbed Pd(0) not removed by washing after catalyst deposition, (2) chain transfer of Pd(0) off the growing chain during the reaction, or (3) detachment of the growing chain from the surface at the original point of attachment (i.e., desorption of an initiator phenyl group after the reaction has started). The presence of these catalyst centers, through whatever means, resulted in the formation of solution polymer. As the polymer grew, the solubility and swellability of the chain decreased quickly, and aggregates formed, eventually physisorbing onto the surface.' We hope to spin off a separate project studying these interesting microparticles in another work. * Lines 187-188: 'the brush thickness on the 2D surface is close to the particle radius, implying that the features may have grown as part of the same process.' -- It is not clear why this would be true. By the authors' arguments, the thickness of the brush should depend upon polymer growth rate, while the particle size should depend upon whatever stage of agglomeration the precipitating solution polymer chains (which can grow from both ends and should have a different degree of polymerization than the surface bound chains) achieve by the time they adsorb to the substrate--these seem to be two very different factors. Agreed. We have removed this claim of implication in the rewritten section, as we have concluded that the 1 micron thick section observed in the original Figure 6d was an artifact rather than a representative section. See the next paragraph. * Lines 196-198: '...microscopy of the substrate...reveals a roughly 1 micron thick, regular film of polythiophene...' -- If the assumption is that each surface-bound aryl iodide results in one polymer chain and that there is a high density of aryl iodide sites, does a thickness of 1 micron fit with accessible molecular weights for polythiophene prepared by Pd catalysts? My instinct is that preparing polythiophene chains with a chain length of around 1 micron would mean reaching much higher molecular weights than can typically be prepared by these methods. This is a good question and one which we should have considered more carefully in the first submission, as the feature observed in Figure 6 implies a world-record PT film thickness. In a helpful discussion with Dr. Mikhail Gaevski (USC Columbia), he cautioned us that fracture measurements of soft materials can give artifacts. Profilometry measurements (also carried out by Dr. Gaevski) showed that while 1-micron regions exist in the film, (S14-S15) representative thicknesses for a film at 12 h polymerization time in the dibromothiophene film are roughly 70-100 nm. We have revised the text in several places to reflect this very significant change. The real trouble is that no one really knows what the molecular weights are of unsubstituted polythiophenes. However, solution 3-hexylthiophenes can reach degrees of polymerization of 400-500 using cross-coupling polymerization techniques; such polymers are available from commercial suppliers such as Rieke Metals. We have added an analysis of theoretical film thicknesses vs. PT molecular weight, starting near line 413 in the revised manuscript: 'We interpret individual fibrils or nodules as features formed by chains grown at the longest-surviving catalyst sites. Typical feature sizes as measured by SEM and rms roughness of stylus profilometry (Fig. S14-S15) are 30-50 nm. An 50 nm PT feature must have a degree of polymerization (DP) of roughly 130 using a rigid-rod model, or roughly 200 using a wormlike chain model with a 12-15 nm persistence length.' * Line 232: The yield for the key molecule 4-iodobenzenediazonium tetrafluoroborate is described as '0.090 g, 28%', but 0.090 g of the product (317.82 g/mol) corresponds to 0.283 mmol of product, which, starting from 2.5 mmol of 4-iodoaniline, seems to correspond to an 11.3% yield. The absence of any additional characterization data is concerning. We have added a basic characterization data set to the SI (13C and 1H NMR and IR spectroscopy) for this material. The material on which these characterization measurements was based was the actual batch of 4-iodobenzenediazonium tetrafluoroborate from which the portion used to prepare the film characterized in Figure 4. The corresponding author (NM) apologizes for the mistake in the yield calculation. I misread my own spreadsheet. The error has been corrected. Comments for the author This manuscript describes some potentially interesting results, but there is significant room for improvement in the interpretation and presentation of the results. All of the areas of concern seem addressable if the authors are allowed sufficient time for revisions. Reviewer 3 Basic reporting The paper is well written and well referenced. Figures and table are good. Experimental design The experimental design is presented in detailed procedures. Methods are described in detail and information will be easy to replicate. Validity of the findings The findings are supported by experimental evidence. Comments for the author This manuscript reports the synthesis of polythiophene films by surface-initiated Kumada cross-coupling polymerization. The experimental methods are well described and the analysis of the polymer film by SEM is proper because it allows the determination of the film thickness. I appreciate the originality of the reported work but I do not see much potential in terms of potential applications. By using non-substituted thiophene as the monomer, there is no regioregularity problem. The authors should determine the optical bang gap from the UV-vis spectrum of the film shown in Figure 7. From the manuscript, it is not clear if the polythiophene film thickness can be controlled. The authors should discuss if the film thickness can be controlled because this is very useful. We have added the band gap estimation as Fig. S16. We prepared a time course of the polymerization, with the results summarized in Fig. 9 of the revised document. Based on the growth curve of the polymer, it seems very likely that the thickness can be tuned. The authors could try to adjust the method to grow the polythiophene film on transparent ITO which will allow the characterization of the film to determine optical and electronic properties. The molecular weight of the polythiophene cannot be determined because non-substituted polythiophene is insoluble in organic solvents. I would see a lot more value to this work if 3-hexylthiophene would be used as the starting monomer to generate P3HT. This would also allow a discussion of regioregularity. At the reviewer's suggestion, we prepared 2,5-dibromo-3-hexylthiophene and attempted its polymerization using the technique described in this work. The resulting surface showed detectable polymer by FTIR, but not a substantial thickness of material. This did not surprise us. It's a little-known fact in the surface-initiated Kumada polymerization world that monomers with bulky groups generally do not polymerize well from self-assembled monolayers due to the high steric demands of the surface reaction. Researchers in this area have circumvented this problem by the use of smaller (e.g., methyl) sidechains, by using a swellable crosslinked polymer instead of a SAM as a macroinitiator, or by using the sharply curved surface of a colloidal nanomaterial as the surface platform. We were hoping that this catalyst system would not display this limitation (since a diazonium-based film is not a true SAM) but it seems to be affected as well. We summarized these results as follows: (Line 437) 'This result is consistent with our previous finding that monomers with bulky groups on the monomer do not polymerize well in the SI-KCTP reaction using SAM-based initiators even in cases which are known to work in solution. (Marshall, 2011) This limitation has been overcome by the use of grafted macroinitiator layers (Kiriy, 2011) or by initiating the polymerization from a highly curved, colloidal nanoparticle rather than a flat, monolithic surface. Based on our results, it seems that the steric restriction of the diazonium-based arene layer is similar enough to that of a SAM to result in the same limitation on SI-KCTP reactions.' The authors recognize the formation of polymer in solution which is physisorbed on the surface of the film. The authors should try to analyze the films using TMAFM analysis to determine the rms. The morphology of the film is important, and TMAFM analysis would give some answers regarding the surface morphology of the polythiophene films. Since we do not have direct access to AFM (we are actually preparing a grant to purchase one at this time) we measured rms roughness using stylus profilometry. We think that this information, in combination with visible morphology in the SEM images, (Figures 6, S3, S14, S15) gives a satisfactory picture of the surface morphology. The authors could try to measure the conductivity of the film upon doping with iodine using a four point probe. We are actually planning a followup study where we look at the effect of various dopants using computation and electrochemistry. We will have to identify a collaborator to perform conductivity measurements for that work, since we do not have that instrument at our institution. However, we were able to demonstrate electrochemical PF6- doping and dedoping of the formed polymer, and have included this content as Fig. 9 and as a supplemental video. The polymer shows considerable electrochromism on doping/dedoping. "	Here is a paper. Please give your review comments after reading it.
456	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Glucose oxidating enzymes have a tremendous potential for various energy, healthcare and environmental sensing applications. In this work, we studied the effect of reducing the size of pyranose 2-oxidase (POx) on stability and enzymatic activity of proteolyzed POx.</ns0:p><ns0:p>Limited proteolysis of the POx was performed using trypsin to remove flexible structural regions without significant damage to the overall conformation and catalytic activity of the enzyme. Enzymatic activities of the modified and wild-type POx were measured by colorimetric coupled reaction assay and compared. The enzymatic activity of the modified POx showed 90% activity compared to the wild-type POx. This result indicates that reducing the size of the protein can be done without losing enzymatic activity and such enzymes potentially could provide a larger gain in electrochemical activity compared with wild-type enzymes.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>The combination of proteins and nanomaterials to produce various functional enzyme electrodes is important for both understanding the fundamental protein-nanomaterial interactions and the development of high performance bioelectronic devices <ns0:ref type='bibr' target='#b0'>1</ns0:ref> . An establishment of an efficient electrochemical communication between the active site of protein and the electrode surfaces, while maintaining a long-term protein stability, is a critical step for developments of any practical bioelectronics devices (e.g., biosensors 2 , biofuel cells 3 , neuronsemiconductor hybrid systems for dynamic memory 1 , etc.). To achieve these goals, previous investigators have created functional enzyme electrodes by immobilizing the enzyme on the external surface of nanomaterials <ns0:ref type='bibr' target='#b3'>[4]</ns0:ref><ns0:ref type='bibr' target='#b4'>[5]</ns0:ref><ns0:ref type='bibr' target='#b5'>[6]</ns0:ref><ns0:ref type='bibr' target='#b6'>[7]</ns0:ref><ns0:ref type='bibr' target='#b7'>[8]</ns0:ref><ns0:ref type='bibr' target='#b8'>[9]</ns0:ref><ns0:ref type='bibr' target='#b9'>[10]</ns0:ref> ; by entrapping it in sol-gel <ns0:ref type='bibr' target='#b5'>6,</ns0:ref><ns0:ref type='bibr' target='#b10'>[11]</ns0:ref><ns0:ref type='bibr' target='#b11'>[12]</ns0:ref><ns0:ref type='bibr' target='#b12'>[13]</ns0:ref> or polymers <ns0:ref type='bibr' target='#b2'>3,</ns0:ref><ns0:ref type='bibr' target='#b13'>[14]</ns0:ref><ns0:ref type='bibr' target='#b14'>[15]</ns0:ref><ns0:ref type='bibr' target='#b15'>[16]</ns0:ref><ns0:ref type='bibr' target='#b16'>[17]</ns0:ref><ns0:ref type='bibr' target='#b17'>[18]</ns0:ref><ns0:ref type='bibr' target='#b18'>[19]</ns0:ref><ns0:ref type='bibr' target='#b19'>[20]</ns0:ref><ns0:ref type='bibr' target='#b20'>[21]</ns0:ref><ns0:ref type='bibr' target='#b21'>[22]</ns0:ref> , by using bulk composite <ns0:ref type='bibr' target='#b5'>6</ns0:ref> ; by chemically modifying structures of enzymes <ns0:ref type='bibr' target='#b22'>[23]</ns0:ref><ns0:ref type='bibr' target='#b23'>[24]</ns0:ref><ns0:ref type='bibr' target='#b24'>[25]</ns0:ref><ns0:ref type='bibr' target='#b25'>[26]</ns0:ref><ns0:ref type='bibr' target='#b26'>[27]</ns0:ref> . However, either a poor electrochemical communication of these immobilized enzymes to electrodes or their short lifetime hinders the development of practical bioelectronic devices <ns0:ref type='bibr' target='#b2'>3,</ns0:ref><ns0:ref type='bibr' target='#b27'>28,</ns0:ref><ns0:ref type='bibr' target='#b28'>29</ns0:ref> .</ns0:p><ns0:p>Effective direct, non-mediated electrical wiring of enzyme on electrodes remains a challenging task <ns0:ref type='bibr' target='#b29'>30,</ns0:ref><ns0:ref type='bibr' target='#b30'>31</ns0:ref> . Newly developed approaches that can enhance direct electron transfer include improving the surface properties of the electrode by introducing highly conductive nanoparticles and controlling enzyme orientation by changing surface charge properties <ns0:ref type='bibr' target='#b31'>[32]</ns0:ref><ns0:ref type='bibr' target='#b32'>[33]</ns0:ref><ns0:ref type='bibr' target='#b33'>[34]</ns0:ref> . Several recent studies developed modified enzyme supports for better wiring by surface grafting of nanoparticle supports with mediators <ns0:ref type='bibr' target='#b13'>14,</ns0:ref><ns0:ref type='bibr' target='#b31'>32,</ns0:ref><ns0:ref type='bibr' target='#b33'>[34]</ns0:ref><ns0:ref type='bibr' target='#b34'>[35]</ns0:ref><ns0:ref type='bibr' target='#b35'>[36]</ns0:ref><ns0:ref type='bibr' target='#b36'>[37]</ns0:ref><ns0:ref type='bibr' target='#b37'>[38]</ns0:ref><ns0:ref type='bibr' target='#b38'>[39]</ns0:ref><ns0:ref type='bibr' target='#b39'>[40]</ns0:ref><ns0:ref type='bibr' target='#b40'>[41]</ns0:ref><ns0:ref type='bibr' target='#b41'>[42]</ns0:ref> . Some research groups were able to modify the surface charge properties of enzymes to control the orientation of their adsorption on electrode surface <ns0:ref type='bibr' target='#b31'>32,</ns0:ref><ns0:ref type='bibr' target='#b32'>33</ns0:ref> .</ns0:p><ns0:p>Glucose oxidase (GOx) is one of the most frequently used enzymes for glucose based biofuel cells and biosensors due to its high and selective reactivity towards glucose oxidation, well-known crystal structure and easy availability. Recently, another flavin-dependent enzyme, pyranose 2-oxidase (POx), has gained increased attention due to its attractive structural features <ns0:ref type='bibr' target='#b42'>43,</ns0:ref><ns0:ref type='bibr' target='#b43'>44</ns0:ref> . POx catalyzes the oxidation of D-glucose at C2 position to yield 2-dehydro Dglucose while producing electrons that can be utilized in either power generation for biofuel cells or glucose detection for the biosensors <ns0:ref type='bibr' target='#b44'>[45]</ns0:ref><ns0:ref type='bibr' target='#b45'>[46]</ns0:ref><ns0:ref type='bibr' target='#b46'>[47]</ns0:ref> . Unlike GOx, the POx protein is not surrounded by electrochemically nonconductive glycosylation layer in its native state. This structural characteristic offers better mass transport of glucose and better potential for efficient electron transfer from the active site to the electrode. Therefore, POx is considered a good candidate for biofuel cell and biosensor applications.</ns0:p><ns0:p>POx is a homotetrameric enzyme, which contains one flavin adenine dinucleotide (FAD) per subunit <ns0:ref type='bibr' target='#b47'>48,</ns0:ref><ns0:ref type='bibr' target='#b48'>49</ns0:ref> . POx tetramer structure has a hydrodynamic radius of 6.2 nm where each of the 4 FAD/FADH 2 centers is buried ~1.4 nm below the enzyme surface <ns0:ref type='bibr' target='#b48'>49,</ns0:ref><ns0:ref type='bibr' target='#b49'>50</ns0:ref> . In our current study, we aimed to remove its exposed flexible polypeptide regions. The main idea was to decrease the POx dimensions in such a way that the protein core remains intact . We chose POx from Phanerochaete chrysosporium because it is the most stable POx compared to POxs from other organisms <ns0:ref type='bibr' target='#b50'>51</ns0:ref> . The exposed flexible regions were removed from the surface of POx by limited proteolysis. We demonstrated that the POx structure minimization by limited trypsinolysis does not eliminate its enzymatic activity and the tryptic fragments still maintain a stable quaternary structure.</ns0:p></ns0:div> <ns0:div><ns0:head>Materials & Methods</ns0:head></ns0:div> <ns0:div><ns0:head>POx recombinant plasmid construct</ns0:head><ns0:p>The DNA sequence encoding POx from Phanerochaete chrysosporium (GenBank: AAS93628.1) was optimized for Escherichia coli expression using the online tool OPTIMIZER <ns0:ref type='bibr' target='#b51'>52</ns0:ref> . The optimized sequence was synthesized and cloned into a pET-21b(+) vector between NdeI and XhoI restriction sites at GenScript (Piscataway, NJ).</ns0:p></ns0:div> <ns0:div><ns0:head>Expression and purification of POx</ns0:head><ns0:p>The recombinant pET-21b(+) plasmid with a confirmed POx insert sequence was used to transform competent BL21(DE3) E. coli cells (Life Technologies). A freshly transformed E. coli colony was used to inoculate 5 ml of LB medium supplied with 100 μg/ml ampicillin. The culture was grown in an incubator shaker at 37°C for 4 hours at 250 rpm, then it was transferred into 500 ml of the same growth medium and shaken at 37°C and 220 rpm. Once the optical density of the culture reached OD 600 = 0.5, the temperature was lowered to 25°C, and after 1 hour protein expression was induced by the addition of 0.1 mM isopropyl β-D-1thiogalactopyranoside (IPTG). The induced cells were incubated at 25°C and 220 rpm overnight and harvested by centrifugation at 4000 g for 20 minutes. Harvested cells were resuspended in 30 ml of 50 mM sodium phosphate buffer at pH 7.8 with 300 mM NaCl, 10 mM imidazole and 0.1 mM PMSF supplied with an EDTA-free complete Protease Inhibitor Cocktail tablet (Roche, Mannheim, Germany). The cells were disrupted by sonication on ice for a total of 10 minutes. Cell lysate was cleared of cell debris by centrifugation at 16000 rpm (Beckman JA-17 rotor) for 30 minutes at 4°C and mixed with 15 ml of Qiagen Ni-NTA agarose. The suspension was shaken at 4°C for 1 hour, loaded onto a column and washed with 6 bed volumes of 50 mM sodium phosphate buffer at pH 7.8 with 300 mM NaCl, 10 mM imidazole, 0.1 mM PMSF at 4°C. The resin was washed (1) with 50 mM sodium phosphate buffer at pH 7.8 containing 300 mM NaCl and 50 mM imidazole, and (2) with 50 mM sodium phosphate buffer at pH 7.8 containing 300 mM NaCl and 100 mM imidazole. The POx protein was eluted with 50 mM sodium phosphate buffer at pH 7.8 with 300 mM NaCl and 250 mM imidazole and was kept either in ice or flash-frozen in liquid nitrogen with 10% glycerol and stored at -80°C until use. For further experimentations, stock POx was dialyzed against 50 mM sodium phosphate buffer at pH 7 with 500 mM NaCl.</ns0:p><ns0:p>Protein concentration was determined using bicinchoninic acid assay (Thermo Scientific, Waltham, MA) according to the manufacturer's protocol. Molecular mass calculations were done using ExPASy ProtParam tool <ns0:ref type='bibr' target='#b52'>53</ns0:ref> . The calculated molecular mass of full-length POx was 72,198 Da and the molecular mass of proteolyzed POx was 66,529 Da, considering that fragments ii, iv and vi (Figure <ns0:ref type='figure' target='#fig_0'>1c</ns0:ref>) remain intact after limited proteolysis.</ns0:p></ns0:div> <ns0:div><ns0:head>Molecular Dynamics Simulations (MDS)</ns0:head><ns0:p>The crystal structure of POx (PDB ID: 4mig, chains A, B, C and D) was used for molecular dynamics simulations. Loop polypeptide fragments between peptide bonds that were assumed to be cleaved by trypsin were removed from the chain A in silico, and a pdb file for proteolyzed POx was generated using UCSF CHIMERA 1.9 <ns0:ref type='bibr' target='#b53'>54</ns0:ref> . Simulations of the proteolyzed POx tetramer were conducted using AMBER 11 <ns0:ref type='bibr' target='#b54'>55</ns0:ref> . Hydrogen atoms were first added and the peptide was then placed in a simulated box of TIP3P water molecules with a 10 Å minimal distance from the outermost side chains to the edge of the box. The charge of the protein was neutralized by adding Na + ions in the protein-water simulated box. The system was energy-minimized to overcome the effects of steric overlap between atoms. The motion of each peptide was simulated as a function of time using the SHAKE algorithm 56 with a time step of 2 femtoseconds. The simulations were run at 300 K for 80 nanoseconds in order to reach steady states. Structural comparison was done using UCSF CHIMERA 1.9.</ns0:p></ns0:div> <ns0:div><ns0:head>Limited proteolysis</ns0:head><ns0:p>To remove the flexible unstructured regions of POx structure, limited proteolysis was performed using trypsin. Trypsin is a highly specific serine protease, which cleaves peptide chains at the carbonyl side of lysine and arginine. The limited proteolysis was done in 50 mM sodium phosphate buffer at pH 7, containing 500 mM NaCl at room temperature. POx to trypsin mass ratio of 50:1 was used. At several time points, 10 µl aliquots were removed, mixed with the sample buffer (2x Laemmli buffer composition: 0.125M Tris-HCl, 20% Glycerol, 4% SDS, 0.004% bromophenol blue, 10% 2-mercaptoethanol) for analysis and boiled to terminate trypsinolysis. The results were analyzed by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE). Molecular masses of protein bands were calculated using Image Lab software (BioRad).</ns0:p></ns0:div> <ns0:div><ns0:head>Purification of POx proteolytic fragments</ns0:head><ns0:p>To prepare large quantities of proteolytic fragments for further enzyme testing, trypsinolysis was terminated by adding 1 mM Pefabloc in 5.5 hours. Proteolytic fragments were purified either by Fast Protein Liquid Chromatography (FPLC) using a QFF anion exchange column (for CV experiments) or Size Exclusion Chromatography using a PD-10 desalting column (GE Healthcare Life Sciences, Pittsburgh, PA) (for cross-linking and CD experiments). For FPLC based purification, we used 20 mM Tris and 1mM DTT at pH 8.5 with 0-1 M NaCl gradient to elute samples. POx fragments were eluted from 0.30 M to 0.36 M NaCl concentration range. For desalting column based purification, we used 50 mM sodium phosphate buffer at pH 7, containing 500 mM NaCl.</ns0:p><ns0:p>POx cross-linking with glutaraldehyde For cross-linking experiments, we used full-length POx and proteolyzed POx (0.415 mg/ml) in 50 mM sodium phosphate buffer at pH 7, containing 500 mM NaCl. Full-length POx was treated with 0.02% (v/v) glutaraldehyde at room temperature and 10 µl aliquots were removed at 10 and 60 minutes time points. Crosslinking reaction was stopped by adding 10 µl of the sample buffer for SDS-PAGE. POx fragments were treated with glutaraldehyde (GA) (0.05 % and 0.10 % (v/v)) at room temperature and 10 µl aliquots were removed at 30 and 60 minutes time points. Crosslinking reactions were stopped by adding 10 µl sample buffer for SDS/PAGE.</ns0:p></ns0:div> <ns0:div><ns0:head>Size exclusion chromatography</ns0:head><ns0:p>For size exclusion chromatography of POx, we used Superdex 75 10/300 GL column (GE Healthcare, Pittsburgh, PA). 200 µl (~0.4 mg/ml) of a sample was loaded for each protein. For eluting the protein samples, we used 50 mM phosphate buffer at pH 7 containing 150 mM NaCl at flow rate of 0.5 ml/min.</ns0:p><ns0:p>Circular dichroism (CD) measurements CD spectra of 0.1 mg/ml full length POx and proteolyzed POx in 12.5 mM sodium phosphate buffer at pH 7 with 125 mM NaCl were recorded at 20 o C from 195 to 250 nm at 0.5 nm intervals, on an Aviv CD spectrometer Model 400 (Aviv Biomedical Inc., Lakewood, NJ). Hellma Analytics (Plainview, NY) quartz cuvettes with 1 mm path length were used for CD measurements.</ns0:p></ns0:div> <ns0:div><ns0:head>Enzyme activity assay</ns0:head><ns0:p>Color reagent for activity assay was prepared by mixing 20.3 mg 4-aminoantipyrine, 9.5 mg Phenol, 1 mg Peroxidase, 10 ml Tris-HCl buffer (0.1 M, pH 7) in a light protected tube. For enzyme activity assay, 800 µl deionized water, 500 µl Tris-HCl (0.1M, pH 7), 100 µl color reagent, 50 µl of 1M glucose, 50 µl 0.02 mg/ml POx sample were added and mixed thoroughly. During the reaction, quinoneimine dye was produced which absorbs light at 500 nm. The rate of production of quinonimine dye is directly proportional to the enzymatic oxidation of glucose. The reactions associated in the experiment are:</ns0:p><ns0:formula xml:id='formula_0'>D-Glucose + O 2 2-Dehydro-D-glucose + H 2 O 2 2H 2 O 2 + 4-Aminoantipyrine + Phenol Quinoneimine dye + 4 H 2 O</ns0:formula><ns0:p>The absorbance of the reaction mixture was recorded immediately at 500 nm for 4 minutes. We performed 10 enzyme activity assay measurements for three separately obtained samples of proteolyzed POx and reported the mean and standard deviation as error bars in the corresponding figure.</ns0:p></ns0:div> <ns0:div><ns0:head>Statistical analysis</ns0:head><ns0:p>We ran two tailed statistical t-test between the two groups of enzyme activity results (10 experiments per replicates x 3 replicates each = 30 data points for each) of wild-type and modified POx. The test result gave the p-value of 0.0079. We considered p-value below 0.05 as statistically significant difference.</ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head><ns0:p>In silico removal of exposed flexible regions in POx had no significant effect on POx structure.</ns0:p><ns0:p>Initial inspection of the POx amino acid sequence and its three-dimensional structure (pdb#4mig) demonstrated presence of several exposed disordered regions. Disordered flexible regions occupy more space than ordered ones, so we hypothesize that by removing them we will be able to reduce distance between the surface of the protein and its active site. We chose limited proteolysis by trypsin to remove exposed flexible regions of POx with the purpose of overall structural reduction. Trypsin cleaves peptide chains at the carbonyl side of lysine (K) and arginine (R). Exposed disorder and flexible regions in proteins are typically the most susceptible to proteolytic cleavage. Removal of the exposed flexible regions will cut the protein primary sequence into several fragments (Figure <ns0:ref type='figure' target='#fig_0'>1c</ns0:ref>), but given the high stability of the intact POx, the secondary and tertiary structural components (e.g., alpha helices, beta sheets, hydrogen bonds, disulfide bonds) may still maintain the overall globular protein conformation.</ns0:p><ns0:p>To predict the effects of trypsinolysis on POx conformation, we analyzed the crystal structure of POx shown in Figure <ns0:ref type='figure' target='#fig_0'>1</ns0:ref>. We identified five exposed disordered regions that are available in the POx structure. The regions are Met1-Pro13 (N-terminal residue), Met55-Gly70, Leu307-Ser319, His345-Pro374, and Arg618-His629 (C-terminal residue), all highlighted in gray in Figure <ns0:ref type='figure' target='#fig_0'>1c</ns0:ref>. As trypsin is highly specific in cleaving after Arg and Lys residues, we identified potential trypsin cleavage sites within these exposed disordered regions (Figure <ns0:ref type='figure' target='#fig_0'>1c</ns0:ref>). Figure <ns0:ref type='figure' target='#fig_0'>1c</ns0:ref> also shows the sequences of POx tryptic fragments that we predicted to be produced after limited proteolysis. Upon treatment with trypsin, two exposed flexible fragments, residues Gly57-Arg63 and Gly361-Arg367 (iii-magenta and v-black, respectively), together with the N-(iorange) and C-(vii-purple) terminal flexible fragments were expected to be removed from the POx structure. Although the removal of these four exposed flexible regions will create gaps within the POx structure, we expected the other three fragments (ii-blue, iv-green and vi-red) to keep most of the non-covalent contacts existing in the intact protein. Therefore, we assumed that these three fragments would remain bound together in a compact globular structure and maintain the surrounding of the central FAD molecule in a way appropriate for efficient enzymatic catalysis.</ns0:p><ns0:p>To mimic the trypsinolysis in silico, we removed the chosen regions (region i, iii, v, vii in Figure <ns0:ref type='figure' target='#fig_0'>1c</ns0:ref>) from the wild-type POx. Then, we performed molecular dynamic simulation for the modified POx tetramer molecules to predict the effect of trypsinolysis on the modified POx conformation. The final simulated structure of the modified POx showed very small overall backbone shifts with respect to the wild-type POx (Figure <ns0:ref type='figure' target='#fig_1'>2a</ns0:ref>). These shifts had a root mean square deviation over C-α atoms (rmsd) of 1.26 Å, which is comparable to the rmsd calculated for the wild-type POx with respect to a homologue of POx from different organism Trametes multicolor, which is 1.07 Å (between C-α atoms). Therefore, we did not expect to significantly alter the structure of the POx active center and thus the functionality of POx.</ns0:p><ns0:p>We analyzed the positions of the residues that are important for the catalysis of glucose oxidation. The residues from His553 to Asn596 (Figure <ns0:ref type='figure' target='#fig_1'>2c</ns0:ref>) are active site residues and residues from Arg457 to Glu467 (Figure <ns0:ref type='figure' target='#fig_1'>2f</ns0:ref>) are dynamic substrate recognition loops that are important for the catalysis and substrate recognition <ns0:ref type='bibr' target='#b56'>57</ns0:ref> . We compared these two important regions in the wildtype and modified POx structures and found no significant differences. There were slight backbone shifts with rmsd over C-α atoms of 0.754 Å and 2.48 Å respectively, and helices formation in regions Thr564-Lys566 and Val465-Glu467 (shown in gray circles in Figure <ns0:ref type='figure' target='#fig_1'>2c</ns0:ref> and 2f respectively, rmsd calculated between C-α atoms for these circled regions are 0.633 Å and 3.45 Å respectively). All these differences are far away from the FAD (yellow) and substrate binding sites (cyan). For this reason, we expected that these changes will not have significant effect on enzyme functionality. In addition to that, His158 and Thr160 are reported important in catalysis and binding to the FAD group <ns0:ref type='bibr' target='#b50'>51</ns0:ref> . Figure <ns0:ref type='figure' target='#fig_1'>2e</ns0:ref> shows His158 residue and Figure <ns0:ref type='figure' target='#fig_1'>2b</ns0:ref> shows the Thr160 residue where wild-type (blue) and modified (brown) proteins are superimposed to compare their side chain conformations. There is no significant difference other than small backbone shift in His158 (rmsd over C-α atoms = 0.827Å) and Thr160 (rmsd over C-α atoms = 1.133 Å) residues.</ns0:p><ns0:p>We also compared positions of the POx residues that are reported to participate in tetramer formation. A long oligomerization arm comprising residues Ile105 to Asn134 helps to form POx homotetramer <ns0:ref type='bibr' target='#b56'>57</ns0:ref> . Figure <ns0:ref type='figure' target='#fig_1'>2d</ns0:ref> shows superimposed oligomerization arms of wild-type (blue) and modified (brown) POx structure. We observed a small backbone shift with the rmsd of 1.82 Å between C-α atoms that may not be significant enough to alter the tetramer formation. These MDS results suggested that removal of disordered regions should not significantly affect the active center or formation of a tetramer and therefore should not affect the enzyme functionality.</ns0:p><ns0:p>Limited proteolysis of POx resulted in formation of several fragments. Based on our prediction, limited proteolysis by trypsin will result in formation of seven fragments with theoretical molecular masses of fragment (i) through (vii): 1.127, 4.907, 0.725, 33.154, 0.618, 28.503, 1.589 kDa, respectively (Figure <ns0:ref type='figure' target='#fig_0'>1c</ns0:ref>), three of which (fragments ii, iv and vi) will maintain a globular polypeptide structure, whereas the rest (fragments i, iii, v and vii) will be separated and exist as unbound and unstructured polypeptides in solution.</ns0:p><ns0:p>To find conditions and time for removal of flexible regions, trypsin was added to POx in 1:50 w/w ratio and proteolysis was stopped in 3, 10, 30 and 60 minutes using protease inhibitor pefabloc. These are the typical time range used for limited proteolysis of proteins <ns0:ref type='bibr' target='#b57'>58</ns0:ref> . Within the first three minutes, a band of ~65 kDa appeared corresponding to a ~5 kDa decrease in the POx molecular mass (Figure <ns0:ref type='figure' target='#fig_2'>3a</ns0:ref>, lane 3). By the 60-minute time point, further proteolysis of 65 kDa fragment resulted in the formation of two major bands with molecular masses of 34 kDa and 27 kDa and a minor band of 32 kDa (Figure <ns0:ref type='figure' target='#fig_2'>3a</ns0:ref>, lane 6). At this point we performed an enzyme functionality assay and found that the enzyme was still highly active.</ns0:p><ns0:p>Next, we decided to increase the time of proteolysis until the 65 kDa band disappears. For that, limited trypsinolysis was repeated and stopped at 4, 5, 5.5, 6, 6.5 and 7 hours. After 5 hours of treatment, most of the 65 kDa band disappeared and the bands with the molecular masses 32 and 27 became the major ones, while the band with the MW of 34 kDa became a minor band. This mixture of proteolytic fragments was very stable and did not undergo any further digestion for up to 7 hours (Figure <ns0:ref type='figure' target='#fig_2'>3b</ns0:ref>). Also, the reaction mixture was tested for POx functionality and it was found highly active.</ns0:p><ns0:p>Theoretically predicted molecular masses of tryptic fragments matched with actual molecular masses.</ns0:p><ns0:p>Comparing molecular masses of the obtained fragments with the predicted molecular weights (Figure <ns0:ref type='figure' target='#fig_0'>1c</ns0:ref>) and considering a ~5% uncertainty in determining molecular mass of a protein using SDS-PAGE, we interpreted the 5 kDa decrease in molecular mass appearing in the early stages of the trypsin digestion as originating from the removal of the N-and Cterminal flexible regions of the protein (fragments i and vii). The predicted combined molecular mass of the N-and C-terminal regions is 2.716 kDa, which is less than the observed 5 kDa mass decrease. However, our interpretation is consistent with the fact that the removal of the terminal regions should result in a single polypeptide and therefore a single band as in Figure <ns0:ref type='figure' target='#fig_2'>3a</ns0:ref> (lane 3).</ns0:p><ns0:p>The 34 kDa fragment corresponds to the predicted tryptic fragment-iv (green) flanked by the flexible region-iii (magenta) and the flexible region-v (black) (predicted combined molecular mass is 34.5 kDa). The 32 kDa band corresponds to the predicted tryptic fragment-iv (green) (predicted molecular mass 33.2 kDa) after the flexible regions iii and v were removed from the 34 kDa fragment. The 27 kDa major band corresponds to the structured region vi (red) (theoretical molecular mass 28.5 kDa) (Figure <ns0:ref type='figure' target='#fig_0'>1c</ns0:ref>). Fragments with molecular masses smaller than 5 kDa typically cannot be seen in 12 % SDS-polyacrylamide gels due to their high mobility, fast diffusion and poor staining. Therefore, we did not expect to observe fragments that correspond to structured region ii, and flexible regions i, iii, v, vii.</ns0:p><ns0:p>POx tryptic fragments form a tetramer.</ns0:p><ns0:p>Our MDS data indicated that removing the flexible regions by limited proteolysis should not affect the tetramer formation. Also during limited proteolysis, the oligomerization arm should not be affected by trypsin, since this segment is not exposed to the outer surface of POx. To verify experimentally that the tryptic fragments still form a tetramer, we cross-linked the protein by glutaraldehyde. Cross-linking by glutaraldehyde is often used to obtain preliminary information on quaternary structure of a protein <ns0:ref type='bibr' target='#b58'>59</ns0:ref> . When protein oligomers are treated with glutaraldehyde cross-linker, the polypeptide chains form inter-subunit covalent cross-links holding the chains together in a denaturing environment. These cross-linked chains can be further examined by SDS-PAGE and polypeptide chains interacting with each other can be identified. To perform this experiment, both full-length POx and POx fragments after 5.5 hours of trypsinolysis (denoted as proteolyzed POx thereafter) were crosslinked with different amounts of glutaraldehyde (0.02%, 0.05%, 0.1% v/v) and analyzed by SDS-PAGE.</ns0:p><ns0:p>SDS-PAGE image in Figure <ns0:ref type='figure' target='#fig_4'>4</ns0:ref> shows that, upon glutaraldehyde treatment, the proteolysed POx and the uncleaved full-length POx formed bands with molecular masses higher than a single polypeptide chain of the uncleaved POx. For the uncleaved POx, we observed bands with the molecular mass of 70, 100, 126, 220 and a band above 250 kDa (Figure <ns0:ref type='figure' target='#fig_4'>4a</ns0:ref>). Based on the sequence of POx predicting the molecular mass of a POx monomer as 70 kDa, the band >250 kDa corresponds to a cross-linked POx tetramer. Bands with lower molecular masses could represent cross-linked dimers and trimers. For the POx cleaved with trypsin, we observed bands with the highest molecular mass of above 250 kDa. Similar to the full-length POx, we assigned the band >250 kDa in Figure <ns0:ref type='figure' target='#fig_4'>4b</ns0:ref> to a tetramer. The bands with lower molecular masses could correspond to intermediate cross-linked complexes of the proteolytic fragments.</ns0:p><ns0:p>We also performed size exclusion chromatography to estimate and compare the molecular sizes of the full-length and proteolyzed POx. Our results (Figure <ns0:ref type='figure' target='#fig_5'>5</ns0:ref>) showed that the two POx elution profile overlapped with each other confirming that they have similar molecular sizes that were not resolved on the column. The data we obtained from the glutaraldehyde treatment and the chromatography, therefore, show that after proteolysis the inter-subunit interactions in POx were not affected and the protein exists in solution in a tetrameric form.</ns0:p><ns0:p>Circular dichroism (CD) spectra analysis showed no increase in disorder after proteolysis.</ns0:p><ns0:p>Based on our MDS results, we expected that the proteolysis does not damage the enzyme structural conformation and therefore its functionality. Gel filtration data and the SDS-PAGE analysis of the proteolytic reaction mixture and the products of the glutaraldehyde treatment suggested that the limited trypsinolysis removed disordered regions of POx, while the globular structure of the POx core remained unaffected. Therefore, we expected that there would be no increase in the disordered secondary structure content of proteolyzed POx. CD spectroscopy is a powerful technique to study changes in the secondary structure content of any proteins. To prepare a sample of proteolyzed POx for CD analysis, we separated small cleaved fragments from the globular POx core by size exclusion chromatography. Figure <ns0:ref type='figure'>6</ns0:ref> shows the CD spectra of the full-length and purified proteolyzed POx. There are no drastic changes in the spectrum, however, intersection of the spectrum of the proteolyzed POx with the x-axis slightly shifted to higher wavelength compared to the spectrum of wild-type POx. This indicates the slight increase in ordered secondary structure content and suggests that the main core of cleaved POx maintained its native three-dimensional structure after removal of disordered regions during limited trypsinolysis.</ns0:p><ns0:p>Proteolyzed POx is functionally active. Enzymatic activity is highly dependent on the structural conformation of its active site. Using CD, size exclusion chromatography and cross-linking we demonstrated that proteolysis did not affect the secondary and the quaternary structures of the enzyme, and therefore, most probably the tertiary structure, including the active site conformation, remains unchanged. In such case, the proteolyzed POx should still be functional. To test if removal of disordered flexible regions has an impact on its enzymatic activity, we measured the POx activity after proteolysis and compared it with the wild-type POx activity. The rate of enzymatic reaction was measured using a coupled reaction method that produces quinoneimine dye. Oxidation of glucose is directly coupled to the quinonimine dye production and the rate of change in quinoneimine dye concentration was a direct measure of the enzymatic activity. The activity test showed that the wild-type POx had specific activity of 39.5±5.8 ∆Absorbance . sec -1 /µmole POx whereas modified POx had specific activity of 35.5±5.3 ∆Absorbance . sec -1 / µmole POx (the absorbance of the quinoneimine dye was measured at 500 nm). Most of the enzyme activity (~90 %) was retained after trypsin treatment (Figure <ns0:ref type='figure' target='#fig_7'>7</ns0:ref>) confirming that there are no drastic changes in the active site conformation.</ns0:p><ns0:p>Ethics.</ns0:p><ns0:p>The study was carried out in accordance with the policies of the WSU Institutional Biosafety Committee (approval reference number 01131).</ns0:p></ns0:div> <ns0:div><ns0:head>Discussion</ns0:head><ns0:p>One of the recent studies has performed deglycosylation of an enzyme named glucose oxidase (GOx) to reduce the hydrodynamic diameter of the enzyme molecule and therefore the critical separation distance between active site and the surface <ns0:ref type='bibr' target='#b59'>60</ns0:ref> . As a result, the GOx active site was brought closer to its surface, and the electron transfer efficiency was significantly improved According to Marcus theory 31 , the kinetics of direct electron transfer between the active site of enzyme and the electrode surface is highly dependent on their separation distance. The probability for the electrons to 'jump' from the active site of the enzyme to the electrode increases exponentially as the separation distance decreases. Therefore, for the proteins with deeply buried active sites, an approach involving protein size minimization can be a great solution to improve their electron transfer efficiency.</ns0:p><ns0:p>The difficulty with protein size minimization lies with the fact that enzymes are highly structurally organized molecules. Their three-dimensional (tertiary) globular structure is defined by their amino acid sequence, or primary structure, which evolved over considerable time. When enzyme primary structures are modified from their natural form, some important intramolecular interactions can be lost, which can change the overall conformation (tertiary structure) or even lead to a complete loss of structure rendering them unfolded. This will lead to the decrease or complete loss of their enzymatic activities. Consequently, the protein engineering effort aiming at the protein size minimization requires taking into consideration major forces holding the protein in its globular folded state and keeping the active site operational.</ns0:p><ns0:p>The removal of the flexible disordered regions of POx is an initial effort of the long-term goal of an enzyme structure minimization process to improve the electron transfer efficiency. In this study, we identified several exposed disordered regions that are available in the POx structure. Our molecular dynamic simulation results suggested that removal of these disordered regions from the wild-type POx should not significantly affect its active center or formation of its tetramer structure and therefore should not affect its functionality. The limited proteolysis of the POx was then performed using trypsin to remove the identified flexible structural regions. As our molecular dynamic simulation predicts, the experimental data obtained from the glutaraldehyde treatment and the chromatography show that after proteolysis the inter-subunit interactions in POx were not affected and the protein exists in solution in a tetrameric form. Circular dichroism spectra analysis also indicates that there are no decrease in ordered secondary structure content and confirms that the main core of cleaved POx maintained its native three-dimensional structure. The enzymatic activity of the modified POx showed only 10% reduced activity compared to the wild-type POx. Hence, we demonstrated that limited proteolysis removed the disordered regions from POx structure, while the protein core is structurally intact, stable and catalytically active. Enzyme structure minimization may be done not only using limited proteolysis but also by gene editing to remove secondary structure elements that may not affect active site formation. In this study, we removed not entire loops/termini but 34 residues only (5.4% of total protein mass) due to trypsin specificity. In future studies, whole loops can be removed by changing them to short linkers (2-3 residue) by modifying the nucleotide sequence. This means that up to 70 residues can be removed and replaced by 6-9 residues resulting in 10% decrease of the enzyme molecular mass. In addition, flexible loops occupy more space then ordered elements and the decrease in size may be even more than 10%. MDS is a great tool to test results of such modifications. Modifications may decrease enzymatic activity, therefore the big challenge will be to ensure that any loss of enzymatic activity resulting from protein minimization is much smaller than the increase in the electron transfer efficiency. Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science Manuscript to be reviewed </ns0:p><ns0:note type='other'>Figure 5</ns0:note><ns0:note type='other'>Chemistry Journals Figure 6</ns0:note></ns0:div><ns0:figure xml:id='fig_0'><ns0:head>Figure 1 Crystal</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head>Figure 2 Molecular</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>Figure 3 SDS</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Figure 3</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head /><ns0:label /><ns0:figDesc>(a) Molecular mass standards (lane 1); full-length POx (lane 2); POx treated with trypsin for different time points: 3 min (lane 3), 10 min (lane 4), 30 min (lane 5), 60 min (lane 6); (b) Molar mass standards (lane 1); POx treated with trypsin for different time points: 4 hr (lane 2), 5 hr (lane 3), 5.5 hr (lane 4), 6 hr (lane 5), 6.5 hr (lane 6), 7 hr (lane 7); full-length POx (lane 8). Arrowheads indicate standard proteins, arrows indicate POx (full-length and fragments). PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2019:08:40593:1:1:NEW 8 Apr 2020)Manuscript to be reviewedChemistry JournalsAnalytical, Inorganic, Organic, Physical, Materials Science</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head>Figure 4 Results</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Figure 4</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head>Figure 5 .</ns0:head><ns0:label>5</ns0:label><ns0:figDesc>Figure 5. Elution profiles for dextran blue, full-length POx, proteolyzed POx after 5.5 h of trypsinolysis, and tropomodulin (40 kDa) obtained using size exclusion chromatography.Note, that elution profiles overlap for full-length and proteolyzed POx.</ns0:figDesc><ns0:graphic coords='22,42.52,219.37,525.00,410.25' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head /><ns0:label /><ns0:figDesc>CD spectra measured for full-length and proteolyzed POx (after 5.5 hours of trypsinolysis) in 12.5 mM sodium phosphate buffer, pH 7, 125 mM NaCl. PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2019:08:40593:1:1:NEW 8 Apr 2020)Manuscript to be reviewedChemistry JournalsAnalytical, Inorganic, Organic, Physical, Materials Science</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_7'><ns0:head>Figure 7 Enzymatic</ns0:head><ns0:label>7</ns0:label><ns0:figDesc>Figure 7</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='15,42.52,70.87,525.00,373.50' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='17,42.52,70.87,525.00,384.00' type='bitmap' /></ns0:figure> <ns0:note place='foot'>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2019:08:40593:1:1:NEW 8 Apr 2020)Manuscript to be reviewedChemistry JournalsAnalytical, Inorganic, Organic, Physical, Materials Science</ns0:note> <ns0:note place='foot'>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2019:08:40593:1:1:NEW 8 Apr 2020)Manuscript to be reviewed</ns0:note> <ns0:note place='foot'>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:note> </ns0:body> "	"We thank reviewers for their comments. Here are our responses Reviewer 1. Q1. Line 123: 53 check citation format Our Response: The citation was inserted correctly now. Q2. Figure 3 has an embedded legend as 'Figure 1', a possible editing error. Figure 3 a) and b) are mainly the same at different time points. Both can be a single figure, maybe merging lines 2 and 7 from 3 b) Our Response: The idea in showing the entire range of different time points was to find the minimal period of time after which we see no changes. Different time points for proteolysis were obtained by gel-electrophoresis using two different gels. Unfortunately, gels are too wide to put them next to each other in one figure. They cannot be merged because protein mobility is slightly different from one run to another (this is typical for different gels and/or SDS-PAGE runs). Also bands in lines 2 to 7 are different, there are more 70K band in 2 and 3 compared to 5.5-7, so we cannot merge these lines, therefore we would like to leave this figure as it is. While submitting we will check that legends correspond to correct figures. Q3. Legend of Figure 4 is confusing and should be rewritten for clarity. Our Response: Thank you for this comment, some information inadvertently disappeared from the legend. We now have supplied the corrected legend: “Figure 4. Results of crosslinking of POx by glutaraldehyde (GA) shown by SDS-polyacrylamide gel electrophoresis. (A) Time dependence of crosslinking full-length POx by 0.02 % GA: molecular mass standards (lane 1); full-length POx (lane 2), full-length POx treated for 10 min (lane 3) or for 60 min (lane 4). (B) Time dependence of crosslinking POx after 5.5 h of trypsinolysis by 0.05 or 0.1 % of GA: molecular mass standards (lane 1); proteolyzed POx (lane 2) proteolyzed POx treated for 30 min with 0.05% (lane 3) or 0.1% GA (lane 4), and for 60 min with 0.1% (lane 5) or 0.05% GA (lane 6). Arrowheads indicate molecular mass standard proteins, arrows indicate cross-linked polypeptides.” Q4. Figure 5 will be clearer if full length and proteolyzed POx lines are shown with solid lines, whereas markers in dashed or thinner lines. Our Response: The profile plot is modified as reviewer requests. Note, that elution peaks for full-length and proteolyzed POx overlap with each other. Q5. Did secondary structure content analysis of full and proteolyzed POx match with other results? Authors discussed secondary structure content, but values are not presented nor the deconvolution method used is described. How do the HT values behave? How many spectra were recorded or averaged? CD measurements are valuable for testing the main objective of this research, but authors use a visual inspection of the CD spectra as evidence. Our Response: We agree that showing changes in the POx secondary structure content would be useful, however, this deconvolution analysis is only reliable for comparatively big changes. All programs that calculate secondary structure content depend on concentration. Also for better deconvolution, data points below 200 nm are necessary. First, we could not perform measurements below 200 nm with our 1 mm cuvettes because we had 125 mM NaCl in the sample (to keep POx in solution, and the HT voltage at 200 nm already exceeded 500. Second, methods to determine concentrations have typical errors of up to 5-10 %, therefore this error will give similar errors in calculating the secondary structure. Here, we expect very small changes because we removed only 32 residues from 629 res POx monomer (621 res of POX sequence +8 res of the tag), which is ~5 % of the entire protein sequence. We consider secondary content changes not exceeding 5 % below a typical secondary content error caused by variability in concentration determination. HT values (dynode voltage) are ~350. We added error bars to the graph. When measuring the spectra, we do not need repeats to get statistical data. The software on the instrument that we used works by repeating the measurement at each wavelength point. One measurement takes 25 microseconds, and 1 sec of data accumulation produces 40000 measurements. We used the averaging time of 5 sec for each wavelength point. Standard errors were calculated and plotted by the software during data collection. There would not be much difference between averaging 5 repeats of 1 sec collection or collecting 5 sec per point. After adding errors we can’t say that there are changes so we changed text in this section to “There are no drastic changes in the spectrum. This indicates no change in the secondary structure content andsuggests that the main core of proteolyzed POx maintained its native three-dimensional structure . Q6. Did the authors made blank reactions on enzyme activity without POx? I think an enzyme kinetics approach will give an insight of the effect of removing loops to the enzyme's turnover or substrate affinity. Our response: All enzyme assay reported are associated with blank subtraction (Glucose conversion without enzyme). For the experimental time window (4 mins) for enzyme assay, we do not see any reaction happening without enzyme. There was no color change detected confirming glucose conversion reaction is extremely slow without the presence of enzyme. We agree with the reviewer that Michaelis-Menten Kinetics study will give us more insight of the effect of removing loops to the enzyme’s turnover and substrate affinity. For the second part of the comment, see our answer to Q4 of Reviewer 2. Q7. The major goal of this research is to reduce the enzyme's size, without losing its enzymatic activity for further applications. But the authors do not expect that proteolysis by trypsin alters the active site or its surroundings significantly. Actually, they assume that two small loops of six residues are removed. I think it is not clear whether this is a proof of concept if the protein is not significantly size reduced or its active site is more exposed. In fact, the main finding is that two flexible/exposed loops can be removed without singnificantly loss of POx function. Our response: The reviewer is correct, our main finding is that flexible/exposed loops (regions from two internal loops, N-terminal and C-terminal regions) can be removed from POx without loss of its tertiary structure and therefore its enzymatic activity function. Based on our MDS data, we had a reason to assume that its tertiary structure would not be affected but without testing it experimentally it was impossible to know this with certainty. Typically, when polypeptide chains in proteins are broken, an entropic penalty for forming a compact structure is so high that many proteins fall apart. In this study we removed not entire loops but 34 residues only (5.4% of total protein mass) due to trypsin specificity. The aim of our study is to demonstrate that such approach works. After showing that this change does not affect tertiary structure and activity, in future experiments, next step would be to remove the loops by changing them to short linkers (2-3 res) in the nucleotide sequence. This means that 70 res may be removed and replaced by ~6-9 residues and 10% of a protein will be removed. Flexible loops take more space then ordered elements so from the point of protein volume it will be even more than 10%. We added these explanations to Conclusions. Q8. From the biochemical approach of the experimental work, this manuscript is more suited for the biological and environmental section of this journal, than the materials science one. Our Response: We originally submitted this manuscript to biological section, but the PeerJ team suggested to switch it to one of their Chemistry journals (including Material Science) and after checking papers in these journals we chose Material Science. Reviewer 2. Q1. The manuscript needs information on replicates and analysis. For all experiments, please provide number of replicates and what the errors are. This is critical for evaluation of the data. This needs to be fully evaluated and the data shown Our Response: Data errors are now shown for the CD analysis (see our answer to Q5 for the reviewer 1). There are no more data that need statistical analysis besides activity tests that had errors in our first submitted version. Q2. The authors present some potentially interesting data in this work, however, there is no detailed biochemical and biophysical analysis of the proteolyzed enzymes. Some data are not clear with awkward phrases here and there. Our Response: It would be possible to address this comment if the reviewer 2 was more specific about his/her comments. If the reviewer meant that Legend to Fig. 4 is an example of an awkward phrase, we have addressed it in the revised version (see our answer to Q3 for the reviewer 1). Q3. Only 1 proteolyzed enzyme was showed similar kinetic rate with WT? The authors state that this method is effective. I would challenge this concept by the authors expressed and purified all proteolyzed enzymes, which is by no means a low-effort endeavor. Our Response: It was not our intention to show the effect of removing disordered regions on structure and activity of different enzymes. We wanted to show that this particular enzyme known for its properties can be a target for further manipulations with structure. Q4. Figure 7: I would like to see the kcat and KM values and the error in these values. Which step in the catalytic cycle of proteolyzed enzyme is rate-limiting? For completeness it would be good to present the full kinetics parameters of proteolyzed enzyme. Our Response: Although we agree that Michaelis-Menten Kinetics study will give us more insight into the effect of removing loops on the enzyme’s turnover and substrate affinity, the main objective of this manuscript was a feasibility study. It was to demonstrate that we can selectively remove flexible/exposed loops (regions from two internal loops, and N/C-terminal regions) from POx without the loss of its tertiary structure and enzymatic activity. Since in the future we plan to work with genetically modified rather than with the proteolyzed enzyme (see answer to Q7 of Reviewer 1), this level of detail will not change our major conclusion on feasibility and has no bearing on our future studies. Detailed kinetic studies are more appropriate for future genetically modified constructs. Q5. Author should explain why proteolyzed enzymes can convert similar efficiently with WT? Our Response: We explain this by the fact that its tertiary structure is not affected. This explanation is clarified in the corresponding section. Q6. Results are a bit overselling and there is relatively little discussion about the usefulness/success rate of the applied method. The paper attempting to rationalise the observations is rather speculative and it is not clear what we have learned from the current study about making small proteins stable. Our response: Our aim was not to find a universal way to make small proteins stable. We demonstrate that removing flexible regions does not affect tertiary structure and functionality of this particular enzyme (also, please, see our answer to Q7 of the reviewer 1). However, given our results, a similar but not necessarily identical approach can be used for other protein systems. "	Here is a paper. Please give your review comments after reading it.
457	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Background. The effect of oxide scale composition of hot-rolled strip (Q235) on shot blasting is studied in this paper. The properties of the oxide scale on the strip surface change during storage. The shot blasting is an important on-line acid-less descaling technology. The effect of shot blasting is affected by many factors, among which the composition of oxide scale may play an important role. However, there are few studies on the relationship between the oxide layer content and the descaling effect.</ns0:p><ns0:p>Methods. The morphologies of oxide scales at different storage times are observed by scanning electron microscopy, and the compositions are analyzed by X-ray diffraction. These strips are then shot blasted and descaled with different amounts of abrasive, and the descaling effects are compared by scanning electron microscopy.</ns0:p></ns0:div> <ns0:div><ns0:head>Results.</ns0:head><ns0:p>The results show that the eutectoid structure Fe 3 O 4 / Fe in the oxide scale will gradually transform into Fe 3 O 4 . In the case of short storage time, the content of the eutectoid structure is high, and it is difficult to remove the oxide scale. While the strip with a long storage time has no eutectoid structure Fe 3 O 4 / Fe and FeO, so it is easy to remove the oxide scale during the shot blasting process. The composition of the oxide scale has a significant effect on the effect of shot blasting, and it provides significant guidance to the optimization of the descaling process parameters.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>During the steel strip rolling and cooling process, a dense and brittle oxide scale will form on the surface. Before the further cold rolling or galvanizing process, the oxide scale is usually removed by pickling to ensure the surface quality of the finished product <ns0:ref type='bibr' target='#b0'>(Sun et al., 2003;</ns0:ref><ns0:ref type='bibr' target='#b1'>Bin S, Guang-ming C & Zhen-yu L, 2010)</ns0:ref>.</ns0:p><ns0:p>Due to the serious environmental pollution problem caused by the pickling process, scholars have long been committed to the research of acid-free descaling technology to replace the pickling process, and have achieved valuable theoretical and experimental achievements. The principle of high-pressure water descaling is to use high-pressure pumps to generate high-pressure water. The high-pressure water jets cause thermal changes, shocks, vibrations, and scouring on the surface of the strip. The dynamic pressure of high-pressure water becomes the hydrostatic pressure and invades the bottom of the oxide scale, causing the oxide scale to peel off from the surface of the substrate (Choi J W & Choi J W, 2002). This technology is widely applied in hot rolling process, but it can't be used to the cold rolling procedure since the energy of water is too small to remove scales.</ns0:p><ns0:p>Abrasive water jet descaling technology uses high-pressure water to accelerate steel sand, quartz sand and other discrete bodies, and sprays the mixed abrasive stream to the strip surface at a certain angle through a nozzle to crush the oxide scale. Both the discrete body and water in this method can be recycled, and the descaling effect is obvious. However, due to the large water flow of the system, the high-pressure plunger pump requires higher cleanliness of the water, the water circulation system is always in a high load state, and the nozzle wears severely under longterm service, so this technology can only be applied to narrow band steel descaling or surface treatment of small parts <ns0:ref type='bibr' target='#b2'>(Meng, Wei, & Ma, 2016)</ns0:ref>.</ns0:p><ns0:p>Tensioning descaling is a mechanical method of repeatedly bending the strip steel. After the metal substrate is subjected to stress, a certain elastoplastic deformation occurs. The oxide scale on the surface of the metal substrate is broken due to brittleness and the purpose of descaling is achieved. Tensioning descaling is generally used in cases where the material is not seriously hardened and the product quality requirements are not strict <ns0:ref type='bibr' target='#b3'>(Tongqing, 1998;</ns0:ref><ns0:ref type='bibr' target='#b4'>Bakhmatov et al., 2014)</ns0:ref>. Smooth-Clean Surface (SCS) technology is used in a closed space to automatically adjust the roll gap of the grinding roller according to the thickness of the strip. At the same time, the surface of the steel plate is continuously washed by circulating filtered water, and the ground iron oxide is taken away to achieve surface cleaning. Finally, a 7 μm thick anti-rust layer is formed on the surface of the metal substrate. This method is not suitable for cold rolling, deep drawing and rotary deep drawing <ns0:ref type='bibr' target='#b5'>(Tamura et al., 2020)</ns0:ref>.</ns0:p><ns0:p>In order to realize the application of on-line acidless descaling for broad steel strip production, the Material Works Ltd. of the USA developed EPS (Eco-Pickled Surface) system. In this system, the abrasive shot blasting was applied to the industrial fields and proved to be an effective method to ensure the strip surface quality after descaling <ns0:ref type='bibr'>(Voges & Mueth, 2012;</ns0:ref><ns0:ref type='bibr' target='#b7'>Voges, Mueth & Lehane, 2008)</ns0:ref>. However, the energy consumption and processing cost of the system is so high that it cannot replace the pickling process yet.</ns0:p><ns0:p>Our research group is very interested in the non-acid oxide scale removal technology, and proposed the oxide scale removal technology combining shot blasting with high-pressure water, and designed the relevant experimental equipment. The most important research direction is to optimize the process parameters of shot blasting to reduce system energy consumption and processing costs. We have studied the effect of shot blasting speed on the descaling effect, and the results show with the increase of the projectile velocity, the damage area of the oxide scale is increased, and the damage area is composed of the direct destruction area and the indirect failure area. <ns0:ref type='bibr'>(Wang et al. 2017</ns0:ref><ns0:ref type='bibr'>(Wang et al. , 2017</ns0:ref><ns0:ref type='bibr' target='#b10'>(Wang et al. , 2018))</ns0:ref>.</ns0:p><ns0:p>Actually, the effect of shot blasting is affected by many factors, among which the composition of oxide scale also has an important effect on the removal of scales. Parameters such as steel type, rolling speed, rolling temperature, cooling speed and coiling temperature etc. will affect the oxide scale composition <ns0:ref type='bibr' target='#b11'>(Zhou et al., 2011;</ns0:ref><ns0:ref type='bibr' target='#b11'>Gong et al., 2009)</ns0:ref>. The oxide scale layer of ordinary carbon steel generally consists of three layers <ns0:ref type='bibr' target='#b12'>(Bonnet et al., 2003)</ns0:ref>: the inner layer is a solid solution of FeO and Fe 3 O 4 , the middle layer is Fe 3 O 4 , and the outer layer is Fe 2 O 3 . During the hot rolling process, the main component of the oxide scale layer is FeO. According to the Fe-O equilibrium phase diagram <ns0:ref type='bibr'>(Chen & Yeun, 2000</ns0:ref><ns0:ref type='bibr'>, 2002</ns0:ref><ns0:ref type='bibr'>, 2003)</ns0:ref>, the eutectoid reaction of FeO can produce a mixed product of Fe and Fe 3 O 4 below 570 °C. After laminar cooling and air cooling, a large amount of FeO will transfer into precipitates by eutectoid reaction. After being exposed to the air for a long time, the outermost layer of the oxide layer continues to be oxidized to Fe 2 O 3 . Therefore, in the process of the exposure in the air, the oxide scale composition is varying continually. However, there are few studies on the relationship between the oxide layer content and the descaling effect, which is a key factor affecting the descaling effect, and is also the research objective of this paper.</ns0:p><ns0:p>In this paper, two kinds of Q235 strip steels with different air cooling time are selected for the research. Firstly, the difference in scale composition on the strip surface is obtained through energy dispersive spectrometer and X-ray diffraction analysis. Then the shot blasting experiments are carried out, and the descaling effect is observed by scanning electron microscopy (SEM). Moreover, the influence of the variation of the scale's composition caused by air cooling time on the descaling effect of shot blasting is analyzed, which provide important guidance to the improvement of acidless descaling process in industrial production. The research route is shown in Fig. <ns0:ref type='figure'>1</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Materials & Methods</ns0:head><ns0:p>The test samples are two Q235 strips stored at different times. Firstly, the oxide scale morphologies and compositions are measured by energy dispersive spectrometer (EDS) and Xray diffraction (XRD). Then, the descaling experiments are performed using the shot blasting descaling experimental device developed by NERCFRE. The electron microscope is used to observe the removal effect in the experiments.</ns0:p><ns0:p>The object of XRD inspection is the surface of the sample after shot blasting. The bottom of the sample is fixed on the platform by means of bonding. The model of the XRD device is Ultima IV, and the type of Tube is ceramic X-ray tube.</ns0:p><ns0:p>As for scanning electron microscope (SEM), the sample was cut into 10mm×10mm squares and then embedded into the resin. The SEI mode is used to observe the surface morphology, and the BSEI mode is used for element detection. The type of equipment used is ULTRA 55.</ns0:p></ns0:div> <ns0:div><ns0:head n='1'>Experimental Materials</ns0:head><ns0:p>The experimental samples were taken from the Q235 hot rolled strip of the practical production line of a steel company. During the hot rolling process, the temperature dropped from 1050℃ to 870℃. Both the samples are 1m × 1m in size and 3mm in thickness. One of the samples was produced one year ago and stored in the room environment, the sample and the PeerJ Mat. Sci. reviewing PDF \| (MATSCI- <ns0:ref type='table' target='#tab_2'>2020:02:45849:1:1:NEW 29 Jun 2020)</ns0:ref> Manuscript to be reviewed Chemistry Journals group of subsequent specimens from which were labeled as No. 1. The other was produced within one week before the experiment, the sample and the group of subsequent specimens from which were labeled as No. 2. The reason for using samples that have been stored for a long time is to explore the descaling effect of samples with different oxide layer compositions, and the composition of the oxide layer can also be changed in other ways. Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref> shows the chemical composition of the two samples. It can be seen there are little difference between them, and the influence on the mechanical properties can be neglected. 2 Experimental Method.</ns0:p></ns0:div> <ns0:div><ns0:head n='2.1'>Oxide scale's composition analysis.</ns0:head><ns0:p>The procedure was as follows: a. Both sample No. 1 and No. 2 were cut in the middle area to obtain 8 specimens with size of 20 mm × 10 mm respectively. The surface of the sample was cleaned by ultrasonic cleaner, then wiped with alcohol and dried with a dryer. b. 4 specimens from both No. 1 and No. 2 groups were made into mounts with the cutting surface as the front side and polished, respectively. Then the oxide scale morphology observation and energy dispersive spectrometer (EDS) were conducted by the ZEISS ULTRA 55 scanning electron microscope. c. Other 4 specimens from both No. 1 and No. 2 groups were taken to the phase analysis by the Ultima IV X-ray diffractometer (XRD), respectively.</ns0:p></ns0:div> <ns0:div><ns0:head n='2.2'>Shot blasting experiments</ns0:head><ns0:p>(1)The slot blasting descaling experimental facility.</ns0:p><ns0:p>The acidless descaling experimental facility designed by NERCFRE is shown in Fig. <ns0:ref type='figure'>2</ns0:ref>. This device mainly includes six major units, which are uncoiler, 5-roller tension leveler, slot descaling, high-pressure jet, sweeping-drying and coiler. The main process parameters include: impact angle, impact line speed, particle size and abrasive weight. The shot blasting experiments with a small amount of abrasive a. Both sample No. 1 and No. 2 were cut in the middle area to obtain 1 specimen with size of 200mm× 200mm respectively. The surface of the sample was cleaned by ultrasonic cleaner, then wiped with alcohol and dried with a dryer. b. The slot blasting experiments were performed by the acidless descaling experimental facility. The impact angle θ is set to 60 °, the impact line speed v is 40 m / s, the particle size D is 0.6 mm, and the abrasive weight W is 2 kg. c. Both the samples in step b were cut in the middle area to obtain 2 specimens with size of 20mm×10mm respectively, and the specimens obtained by cutting were divided into two groups. One group of specimens was observed by a ZEISS ULTRA 55 scanning electron microscope for the descaling effect on the front of the specimens. Another group of specimens was mounted with the cut surface, and the descaling effect from the cut surface was observed.</ns0:p><ns0:p>(2)The shot blasting experiments with a large amount of abrasive.</ns0:p><ns0:p>In order to analyze whether a sufficient amount of abrasive can remove the scales clearly, a total weight of 20 kg of abrasive was used in the experiment, and the remaining parameters were unchanged.</ns0:p></ns0:div> <ns0:div><ns0:head>Results 1 Results of scale composition experiments</ns0:head><ns0:p>The cross-section morphologies of the oxide scale observed by SEM are shown in Fig. <ns0:ref type='figure'>3</ns0:ref>. The energy dispersive spectrometer of the iron and oxygen elements at the outside, intermediate and inside positions of the oxide scale by the ZEISS ULTRA 55 scanning electron microscopy are shown in Table <ns0:ref type='table' target='#tab_1'>2</ns0:ref>. The results of the phase analysis by X-ray diffractometer are shown in Fig.</ns0:p></ns0:div> <ns0:div><ns0:head n='4.'>2 Experiments of the shot blasting experiments with a small amount of abrasive</ns0:head><ns0:p>The descaling effects from the front surface's scanning after the shot blasting with 2kg of abrasive are shown by Fig. <ns0:ref type='figure'>5</ns0:ref>.</ns0:p><ns0:p>The oxide scales are layered and have a certain thickness. It is difficult to determine whether the oxide scale is completely peeled off from the base body only from the front surface's scanning. Therefore, it is necessary to observe the effect of descaling from the cross section, as shown by Fig. <ns0:ref type='figure' target='#fig_3'>6</ns0:ref>.</ns0:p><ns0:p>The SEM results of No. 1and No.2 groups after shot blasting with 20kg abrasive are shown in Fig. <ns0:ref type='figure'>7</ns0:ref> and Fig. <ns0:ref type='figure' target='#fig_1'>8</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head n='3'>Experiments of the shot blasting experiments with a large amount of abrasive</ns0:head><ns0:p>The 4000 times magnifications of descaling effect from the section's scanning after the shot blasting with 20kg of abrasive for No. 1 and No. 2 group are shown in Fig. <ns0:ref type='figure'>9</ns0:ref> and Fig. <ns0:ref type='figure'>10</ns0:ref>.</ns0:p><ns0:p>Since Fig. <ns0:ref type='figure'>9</ns0:ref> is the 4000-times magnification result of the SEM, the field of view is very small In order to improve the reliability of the research, a larger view field was chosen and the area scanning of energy dispersive spectrometer was conducted, as is shown in Fig. <ns0:ref type='figure'>11</ns0:ref>. Similarly, a larger view field was chosen and the area scanning of energy dispersive spectrometer for No. 2 group was conducted, as is shown in Fig. <ns0:ref type='figure'>12</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Discussion 1 Scale composition analysis 1.1Scale morphology analysis</ns0:head><ns0:p>As is shown in Fig. <ns0:ref type='figure'>3(A)</ns0:ref>, for the No. 1 group, the thickness of oxide scale is relatively uniform and is about 9.5μm, and the structure is compact and well combined with the basal body, which indicates that the oxidation of the strip surface is uniform and adequate during the hotrolling and long-time air cooling process. In Fig. <ns0:ref type='figure'>3(B)</ns0:ref>, for the No. 2 group, the uniformity of the oxide scale thickness is worse than that of No. 1 and the average thickness is about 12μm. It is obvious that there are many defects in the structure of oxide scale. By the above comparison, there are apparent differences of scale morphology with the rolling and cooling conditions difference. And as the chemical composition changes, the density of the oxide scale gradually increases.</ns0:p></ns0:div> <ns0:div><ns0:head n='1.2'>The energy dispersive spectrometer</ns0:head><ns0:p>As is shown in Table <ns0:ref type='table' target='#tab_1'>2</ns0:ref>, the values are the average of multiple measurements. The results show that there is almost no difference in the iron and oxygen content at different positions of the oxide scale for each group. In addition, the content of oxygen element at all positions of the oxide scale of No. 1 group is higher than that of the No.2 group, which indicates the different oxidation effect caused by the storage time in the air.</ns0:p></ns0:div> <ns0:div><ns0:head n='1.3'>The phase analysis of scale</ns0:head><ns0:p>As is shown in Fig. <ns0:ref type='figure'>4(A</ns0:ref> </ns0:p></ns0:div> <ns0:div><ns0:head n='1'>Analysis of the descaling effects from the front surface's scanning</ns0:head><ns0:p>As is shown in Fig. <ns0:ref type='figure'>5(A)</ns0:ref>, for the No. 1 group, at the edge of the hitting pit, a large area of the oxide scale fall off, and a large number of cracks appeared on the surface of the remaining scale layer. The peeled areas are large and the descaling effect is good. As is shown in Fig. <ns0:ref type='figure'>5(B)</ns0:ref>, for the No. 2 group, only a few oxide scale fall off at the junction of the hitting pit edge, and there are only a few tiny cracks on the remaining oxide scale layer. The peeled areas are small and the descaling effect is worse compared with the No.1 group.</ns0:p></ns0:div> <ns0:div><ns0:head n='2.2'>Analysis of the descaling effects from the section's scanning</ns0:head><ns0:p>As shown in Fig. <ns0:ref type='figure' target='#fig_3'>6(A)</ns0:ref>, for the No. 1 group, after the shot blasting with a small amount of abrasive, the oxide scale at the pit's edge fall off completely and the basal body is revealed and the peeled areas are large. There are not obvious cracks of the oxide scales in and around the pits, but there are tiny gap between the scale layer and the basal body near the peeled areas.</ns0:p><ns0:p>As is shown in Fig. <ns0:ref type='figure' target='#fig_3'>6(B)</ns0:ref>, for the No. 2 group, the peeled areas of the scale layer is small, and the basal body is not completely revealed. However, there are obvious cracks of the oxide scales in the pits. Thus, it can be deduced that compared with No. 1 group, the oxide scale of the specimens of No. 2 group has lower hardness and better combination with the basal body. The descaling effect of No. 1 group is better when the impact force of the projectile reaches a certain level.</ns0:p><ns0:p>3 Analysis of the shot blasting experiments with a large amount of abrasive 3.1 Analysis of the descaling effects from the front surface's scanning Fig. <ns0:ref type='figure'>7 (A)</ns0:ref> shows the descaling effect at 100x magnification in the backscattering mode. The darker part represents the area where the oxide scale has not fallen off, and the lighter part represents the area where the oxide scale has fallen off. It can be seen that most of the oxide scale has been peeled and only a few remains after the shot blasting with a large amount of abrasive. The 500 times magnification of the peeled areas is shown by Fig. <ns0:ref type='figure'>7(B)</ns0:ref>, and it can be observed that the pits of the basal body have become relatively smooth due to multiple hits. Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science that the oxygen content is 30.28%, the iron content is 69.72%, which indicates that the layer is the remaining oxide scale rather than the basal body. It can be obtained that the outer oxide scale layer falls off during the shot blasting process, but the inner oxide scale layer still exists on the substrate, which also confirms that the oxide scale is a layered structure.</ns0:p></ns0:div> <ns0:div><ns0:head n='3.2'>Analysis of the descaling effects from the section's scanning</ns0:head><ns0:p>As is shown in Fig. <ns0:ref type='figure'>9</ns0:ref>, the oxide scale after the shot blasting with 20kg of abrasive for No. 1 group has been peeled cleanly without obvious residue, and the surface is smooth after a large number of random hits. As is shown in Fig. <ns0:ref type='figure'>10</ns0:ref>, the oxide scale after the shot blasting with 20kg of abrasive for No. 2 group has not been peeled completely, but the thickness is reduced from12μm to 6μm, which means that the outer oxide scale falls off with the shot blasting, but the internal scale layer still exits. In addition, obvious cracks appeared on the surface of the remaining oxide scale.</ns0:p><ns0:p>As is shown in Fig. <ns0:ref type='figure'>11</ns0:ref>, where a larger view field was chosen compared with Fig. <ns0:ref type='figure'>9</ns0:ref>, the result of the area scan can indicate the content of the element by the depth of the color. The scanning area is shown by the green line frame in Fig. <ns0:ref type='figure'>11 (A)</ns0:ref>, and the scanning result of oxygen element is shown in Fig. <ns0:ref type='figure'>11 (B</ns0:ref>). It can be seen that there is no large amount of oxygen between the mounting powder and the basal body, which indicate that the oxide scale has fallen off after a large number of shot blasting and there is no oxide scale remaining.</ns0:p><ns0:p>As is shown in Fig. <ns0:ref type='figure'>12</ns0:ref>, where a larger view field was chosen compared with Fig. <ns0:ref type='figure'>10</ns0:ref>, the scanning area is shown by the green line frame in Fig. <ns0:ref type='figure'>12</ns0:ref> (A), and the scanning result of oxygen element is shown in Fig. <ns0:ref type='figure'>12 (B</ns0:ref>). It can be seen that there is a significant area of oxygen accumulation between the mounting powder and the substrate, which indicates that after a large amount of shot blasting, the oxide scale still exists.</ns0:p><ns0:p>It can be known from the above experiments that the difficulty of oxide scale removal is related to the content of Fe 3 O 4 in it. For steel strip that has been stored for a long time, the main components of the oxide scale are Fe 2 O 3 and Fe 3 O 4 , and the oxide scale can be more easily removed by shot blasting; while for the steel strip with shorter storage time, the oxide scale contains Fe 3 O 4 , shot blasting can reduce the thickness of the scale layer, but only much longer shot blasting time can make the oxide scale completely fall off.</ns0:p><ns0:p>For oxide scale without eutectoid structure, in the case of only descaling by shot blasting, as the thickness of oxide scale gradually decrease, the efficiency of descaling will be greatly reduced, resulting in increased costs. Therefore, after the shot blasting and descaling, an additional high-pressure water jet process can be added. Firstly, a large area of oxide scales is removed by shot blasting. At this time, the binding capacity between the remaining oxide scales and the basal body becomes weak, and then it can be completely removed by direct spraying with high pressure water further.</ns0:p><ns0:p>For oxide scale with eutectoid structure, using shot blasting to remove oxide scale is less effective. The method of combining shot blasting and pickling should be explored. By studying the best process, it can reduce pollution emissions and production costs and improve production efficiency.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions</ns0:head><ns0:p>In this paper, two kinds of Q235 strips stored at different times were selected to analyze the difference of surface oxide scale composition and the effect of shot blasting descaling, which provided a basis for the optimization of shot blasting process. The main research contents and conclusions are as follows:</ns0:p><ns0:p>(1) The EDS and XRD were used to observe and analyze the composition of the two Q235 steel scales stored at different times. It is found that the composition of the steel strip after hotrolling is significantly different during long-term storage. During the storage of the strip, the oxide scale will continue to be oxidized, and the eutectoid structure Fe 3 O 4 /Fe of the inner layer will be oxidized to Fe 3 O 4 . The hot-rolled strip scale with long storage time will have no eutectoid structure Fe 3 O 4 /Fe and FeO.</ns0:p><ns0:p>(2) The descaling experimental facility designed by NERCFRE was used to perform shot blasting and descaling treatment. The scanning electron microscope was used to observe the effect of a small number of shot blasting effects of two Q235 strip steels. Although Fe 2 O 3 and Fe 3 O 4 have high hardness, they are easy to fall off during shot blasting, and the strips that have not been stored for a long time are prone to scaly fracture due to the presence of Fe 3 O 4 /Fe eutectoids. However, it is more firmly bonded to the basal body, and it is relatively difficult to remove the oxide scale.</ns0:p><ns0:p>(3) The scanning electron microscope was used to observe the effect of a large number of shot blasting effects of two Q235 strip steels. It is found that for strips that have been stored for a long time, the main components of the oxide scale are Fe 2 O 3 and Fe 3 O 4 , which can be more easily removed by shot blasting; while for strips that have been stored for a short time, the scales contain eutectoids structure Fe 3 O 4 / Fe, shot blasting can reduce the thickness of the oxide scale, but it is more difficult to completely remove it.</ns0:p><ns0:p>(4) According to the experimental analysis in this paper, it is found that due to the presence of the eutectoid structure Fe 3 O 4 / Fe in the oxide scale, it is more difficult to remove the oxide scale. The subsequent research should adjust the shot blasting descaling process for different oxide scale components, such as the combination of shot blasting and high-pressure water direct injection. At the same time, it is also possible to explore the descaling process combined with pickling and find the optimal ratio of shot blasting descaling and pickling to achieve the comprehensive optimization of reducing pollution emissions, reducing production costs and improving production efficiency. Manuscript to be reviewed Manuscript to be reviewed</ns0:p><ns0:note type='other'>Chemistry Journals Figure 6</ns0:note><ns0:note type='other'>Chemistry Journals Figure 9</ns0:note><ns0:p>The descaling effects from t he section's scanning after the shot blasting with a large amount of abrasive of No. 1 group (WD =12.4mm)</ns0:p><ns0:p>The 4000 times magnification of descaling effect from the section's scanning after the shot blasting with 20kg of abrasive for No. 1 group is in this figure <ns0:ref type='figure'>.</ns0:ref> PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2020:02:45849:1:1:NEW 29 Jun 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p><ns0:note type='other'>Chemistry Journals Figure 10</ns0:note><ns0:p>The descaling effects from the section's scanning after the shot blasting with a large amount of abrasive of No. 2 group (WD =12.4mm)</ns0:p><ns0:p>The 4000 times magnification of descaling effect from the section's scanning after the shot Manuscript to be reviewed Energy dispersive spectrometer of iron and oxygen elements of the oxide scale</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>The energy dispersive spectrometer of the iron and oxygen elements at the outside, Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>), the phase composition of the oxide scale is mainly Fe 3 O 4 and Fe 2 O 3 for the No. 1 group, and FeO and Fe are almost absent. It indicates that FeO is converted into Fe 3 O 4 and Fe by the eutectoid reaction, and the eutectoid structure Fe 3 O 4 /Fe is oxidized to Fe 3 O 4 subsequently during the long-time storage in the air. Thus, the scale's composition is mainly Fe 3 O 4 with a small amount of Fe 2 O 3 . As is shown in Fig. 4(B), for the No. 2 group, the phase composition of the oxide scale is mainly Fe 3 O 4 , Fe 2 O 3 and the eutectoid structure Fe 3 O 4 /Fe. The obvious difference from No. 1 group is the existence of the eutectoid structure Fe 3 O 4 /Fe due to the short storage time in the air. 2 Analysis of the shot blasting experiments with a small amount of abrasive 2.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head>Fig. 8</ns0:head><ns0:label>8</ns0:label><ns0:figDesc>Fig. 8 (A) shows the descaling effect at a magnification of 50 times, and Fig. 8 (B) is a partial enlarged view of Fig. 8 (A). The relatively uniform color in the figures indicates there is only one kind of material in the surface. And the energy dispersive spectrometer results show</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>Figure 1 Figure 5</ns0:head><ns0:label>15</ns0:label><ns0:figDesc>Figure 1</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head>Figure 6</ns0:head><ns0:label>6</ns0:label><ns0:figDesc>Figure 6 The descaling effects from the front section's scanning after the shot blasting with a small amount of abrasive (A) No. 1 group (B) No. 2 group (WD = 12.5mm)</ns0:figDesc><ns0:graphic coords='16,42.52,224.62,525.00,179.25' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head /><ns0:label /><ns0:figDesc>Figure 11</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head /><ns0:label /><ns0:figDesc>intermediate and inside positions of the oxide scale by the ZEISS ULTRA 55 scanning electron microscopy are shown in this figure.</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='12,42.52,204.37,525.00,219.75' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='13,42.52,224.62,525.00,179.25' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='14,42.52,204.37,525.00,204.75' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='15,42.52,250.12,525.00,177.00' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='17,42.52,270.37,525.00,177.00' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='18,42.52,270.37,525.00,174.75' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='19,42.52,250.12,525.00,354.00' type='bitmap' /></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head>Table 1 :</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>The chemical composition of the samples Effect of Oxide Scale Composition of Hot-Rolled Strip Steel on Shot Blasting/table-1</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Sample No.</ns0:cell><ns0:cell>Fe/%</ns0:cell><ns0:cell>C/%</ns0:cell><ns0:cell>Mn/%</ns0:cell><ns0:cell>Si/%</ns0:cell><ns0:cell>S/%</ns0:cell><ns0:cell>P/%</ns0:cell></ns0:row><ns0:row><ns0:cell>No. 1</ns0:cell><ns0:cell>＞97</ns0:cell><ns0:cell>0.17</ns0:cell><ns0:cell>0.31</ns0:cell><ns0:cell>0.15</ns0:cell><ns0:cell>0.03</ns0:cell><ns0:cell>0.020</ns0:cell></ns0:row><ns0:row><ns0:cell>No. 2</ns0:cell><ns0:cell>＞97</ns0:cell><ns0:cell>0.19</ns0:cell><ns0:cell>0.26</ns0:cell><ns0:cell>0.13</ns0:cell><ns0:cell>0.028</ns0:cell><ns0:cell>0.017</ns0:cell></ns0:row><ns0:row><ns0:cell>DO1:</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row></ns0:table><ns0:note>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2020:02:45849:1:1:NEW 29 Jun 2020)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_1'><ns0:head>Table 2 (on next page)</ns0:head><ns0:label>2</ns0:label><ns0:figDesc /><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_2'><ns0:head>Table 2 : Energy dispersive spectrometer of iron and oxygen elements of the oxide scale</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Effect of Oxide Scale Composition of Hot-Rolled Strip Steel on Shot Blasting/table-2</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Positions of scale</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>No. 1</ns0:cell><ns0:cell>No. 2</ns0:cell><ns0:cell>No. 1</ns0:cell><ns0:cell>No. 2</ns0:cell></ns0:row><ns0:row><ns0:cell>Outside</ns0:cell><ns0:cell>76.34</ns0:cell><ns0:cell>85.10</ns0:cell><ns0:cell>23.67</ns0:cell><ns0:cell>14.90</ns0:cell></ns0:row><ns0:row><ns0:cell>intermediate</ns0:cell><ns0:cell>76.96</ns0:cell><ns0:cell>85.77</ns0:cell><ns0:cell>23.04</ns0:cell><ns0:cell>14.23</ns0:cell></ns0:row><ns0:row><ns0:cell>Inside</ns0:cell><ns0:cell>79.50</ns0:cell><ns0:cell>85.21</ns0:cell><ns0:cell>20.50</ns0:cell><ns0:cell>14.19</ns0:cell></ns0:row><ns0:row><ns0:cell>DO2:</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row></ns0:table><ns0:note>Fe  O  PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2020:02:45849:1:1:NEW 29 Jun 2020)</ns0:note></ns0:figure> <ns0:note place='foot'>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:note> </ns0:body> "	"Rebuttal Letter Dear Editors and Reviewers: Thank you very much for sending us the Reviewers’ reports on our manuscript entitled “Effect of Oxide Scale Composition of Hot-Rolled Strip Steel on Shot Blasting”. Particularly, we would like to thank the Reviewers for their valuable comments and criticisms. According to the Reviewers and Editors’ recommendations, we: •Endeavored to revise the paper according to all the comments. •Added the necessary explanation in our study. •Modified the format of the figures and paper. We have revised carefully our manuscript with red text. The following is a detailed list of response to all comments, and changes we have made. We look forward to hearing from you regarding our submission and we would be glad to respond to any further questions and comments that you may have. Sincerely yours, Xiaochen Wang ----------------------------------- University of Science and Technology Beijing wangxiaochen@ustb.edu.cn RESPONSES TO REVIEWER#1’s COMMENTS: COMMENT 1: Please indicate resin, oxide scale and steel substrate in all the SEM images. RESPONSE: Resin, oxide scale and steel substrate have been indicated in all SEM images. COMMENT 2: In Fig. 3, the thickness of oxide scale in sample groups 1 and 2 is quite different. After using the abrasive under the same weight and shot blasting time, it is easy to understand that sample 2 has more oxides retained due to thicker oxide scale. Except for oxide scale composition, other influences such as hardness and compactness of oxide scale on shot blasting should also be considered. RESPONSE: Thank you very much for your suggestions. It can be seen from Figure 6 that the difference in brittleness of the oxide layer is the main reason for the different descaling effect. In fact, if the sample shown in Figure 8 continues to increase the time of shot blasting in our experiments, the descaling effect will not be significantly improved. We have studied the effect of shot blasting speed on the descaling effect, which has been added in the manuscript. Other factors such as hardness and compactness will be considered in our further research. COMMENT 3: In Fig. 4, please correct the Chinese characters in Y axes. RESPONSE: Thanks for your suggestion, and those characters have been corrected. COMMENT 4: In Fig. 5, it is hard to distinguish the different amounts of cracks after shot blasting with 2 kg of abrasive in sample groups 1 and 2. Please indicate the cracks in the SEM images. RESPONSE: Thanks for your suggestion. Cracks have been indicated in Fig. 5. COMMENT 5: In Fig. 6, please use the same magnification in the images for a clear comparison. To see the cracks more clearly, it is better to add enlarged images focusing on the interface of oxide scale/steel substrate. RESPONSE: Thanks for your suggestion. Two figures in Fig. 6 have been adjusted to the same magnifications. There are descriptions of cracks in the later figures. COMMENT 6: In Figs. 7 and 8, please use consistent magnifications to well compare the descaling effects in sample groups 1 and 2. Please also indicate the exact zones in Figs. 7A and 8A for an enlarged observation in Figs. 7B and 8B, respectively. RESPONSE: Thanks for your suggestion. This article mainly focuses on the influence of the amount of abrasive and scale composition on the descaling effect, it can be seen from Fig. 7 that the scale layer is indeed cleaned and the sample scale layer in Fig. 8 still exists. The following Figures 9 to 12 can also be used as evidence. COMMENT 7: In Figs. 10 and 12, it is better to use consistent magnification to compare the EDS mapping of Oxygen. RESPONSE: Thanks for your suggestion. This article mainly focuses on the influence of the amount of abrasive and scale composition on the descaling effect. In the renumbered pictures, Figure 9 and Figure 10 can prove that the scale is removed, and Figure 11 and Figure 12 can prove that there is still scale left. RESPONSES TO REVIEWER#2’s COMMENTS: COMMENT 1: Title should be reorganized, if possible, for readability and clarity – I recommend “Effect of oxide scale structure on shot-blasting of hot-rolled strip steel.” The introduction needs a little bit of readability work; but the literature cited seems relevant and the establishment of the background – oxide scaling approaches and their uses and limitations – is strong and provides good detail. In several places the authors say that “research is not sufficient” on particular topics. It would strengthen the paper to explain how this insufficiency impacts productivity, and/or how this insufficiency will be addressed by the present research. Most figures are of acceptable quality. Interpretation and results could be improved through the inclusion of some additional data, specific recommendations below. Scale bars are included on all images. I believe there is a repeated typographical error in Figure 1, I recommend the authors carefully review figures and tables to catch any other such errors. I would like to see significantly more detail on SEM and XRD settings either in the captions or in the main body of the text, as well as details on sample preparation. The omission of these elements considerably weakens the conclusions – see section 3 of this review. RESPONSE: Thanks very much for your suggestions. The title has been changed to “Effect of oxide scale structure on shot-blasting of hot-rolled strip steel”. It is a quite novelty subject on the parameters optimization for the shot blasting in order to replace the pickling process, and our group has dealt with this research for recent 7 or 8 years. Meanwhile, we have not found the research on the effect of oxide scale structure on shot-blasting of hot-rolled strip steel yet. The details on SEM and XRD settings and the sample preparation have been supplemented in the manuscript. COMMENT 2: The introduction could do a better job of framing the research question. It was clear by the end of the paper what the question was, but I would have liked a clearer picture at the outset of why the “storage time” or “air-cooling time” is relevant in the broader scope of steel oxide layer removal. Would it be better for the authors’ stated goals of efficiency and lowering emissions to have a short storage time or a long one, perhaps considering the manufacturing and supply chain? More discussion on this topic would help frame the purpose of the paper for the reader. My concerns about rigor and detail are constrained to the analytical methods and sample preparation, which I will cover in detail in the following sections. Replication of these findings will be difficult without additional information on the samples themselves as well as the analysis methods. A thermal history of the steel samples during hot-rolling would help. This is partially addressed in the introduction, but never fully explained. A simple figure showing representative dwell temperature vs. time for Q235 during the hot-rolling process would make the article more accessible to the general materials science audience, not just those versed in steel manufacturing. RESPONSE: Thanks very much for your suggestions. Originally, we were analyzing the optimization of the shot blasting descaling effect, and how to reduce the energy consumption is quite important in this area. In this process, it was found that the mechanical properties of the surface’s iron oxide scale had a significant effect on the descaling effect. Therefore, we carried out the shot blasting experiments using steel strips with different Fe3O4 content. So the conclusion of the effect of Fe3O4 content on the descaling effect is given, as is shown in this manuscript. The real purpose of this experiment in this paper is to compare the effect of Fe3O4 content on the descaling effect. The use of strip steel stored for 1 year is only to change the proportion of Fe3O4 content. If there are other methods to change the content of Fe3O4 in the oxide layer, they can also be applied to oxide scale removal. The corresponding explanation has been added in the manuscript: The reason for using samples that have been stored for a long time is to explore the descaling effect of samples with different oxide layer compositions, and the composition of the oxide layer can also be changed in other ways. COMMENT 3: My main concerns with this paper have to do with the robustness and controlled nature of the data presented. I believe the data is legitimate and supports the authors’ conclusions (with some exceptions, see Section 4), but because these conclusions depend purely on interpretation of SEM images and XRD phase IDs, much more background is needed. General improvements for presented figures: • XRD data should include: o Sample preparation & mounting  Were samples powdered/pulverized? How? How much was analyzed? • If not, how were the samples mounted? How were orientation effects filtered from the data? o Instrument model & make o Tube/anode material and/or x-ray wavelength  Tube excitation parameters (KV/mA) would also be preferable o Calculated/literature phase data (“stick patterns”) and source thereof (ICDD, COD, card number, etc.) • SEM data should include: o Sample preparation (sectioning, mounting) o Accelerating voltage o Working distance o Imaging mode (SEI, BSEI)  If a backscattered electron image (BSEI): • Imaging mode (i.e. compositional vs. topographical) • Tilt angle This information can be supplied either in the Experimental Methods section or in the figure captions. Detailed review on individual figures can be found in the general comments. I believe the compositional assertions about the eutectoid phase must be identified as speculation, without the inclusion of additional data or literature support. RESPONSE: Thanks very much for your suggestions. The work distance has been added in the manuscript. The descriptions of XRD and SEM have been supplemented in the manuscript, as follows: The object of XRD inspection is the surface of the sample after shot blasting. The bottom of the sample is fixed on the platform by means of bonding. The model of the XRD device is Ultima IV, and the type of Tube is ceramic X-ray tube. As for scanning electron microscope (SEM), the sample was cut into 10mm×10mm squares and then embedded into the resin. The SEI mode is used to observe the surface morphology, and the BSEI mode is used for element detection. The type of equipment used is ULTRA 55. COMMENT 4: Figure 1. Desaling -> Descaling Figure 3. Labeling the figures with their approximate air exposure time (1 year vs. 1 week) will help readability. Combining this figure with some of the later cross-sectional analysis may also help readability. Figure 4. In addition to the general notes from section 3: the y-axis should be in English, I am assuming this is “Intensity (counts)” or similar. The data could use a background correction, as there is some scattering contributing to the background in the 10-30 2θ region. Additionally, there are some phase-identification discrepancies that would be resolved by the inclusion of the literature patterns – with ICDD or COD card catalog numbers – on the figure itself. This typically requires reproduction in color, so alternative presentations may be necessary if color is not available. Peak ID inconsistencies that need correction:  30° 2θ – M, O, or both?  38° 2θ – M or O?  58° 2θ – M, O, or both?  90° 2θ – M or I? The primary issue with this figure is the claimed ID of a F3O4/Fe phase. The data as presented show that the authors have observed an Fe phase (having a tight correlation with Q235 literature patterns) and a magnetite (Fe3O4) phase in both samples. However, in order to identify a eutectoid (i.e. a solid solution of two phases) more careful analysis of peak shifts and peak intensities would have to be performed, as the interatomic distances should change as a result of varying Fe inclusion, compared to the parent phases. For the record, I do not believe that the presence or non-presence of the eutectoid phase is necessary to explain the differences in behavior of the oxide scale, so references to this phase could be removed, the inconsistencies above could be corrected, and the data could be presented as-is. The important difference I do see is that Figure 4B shows a higher percentage of the Fe phase, but without further explanation of the samples’ preparation and mounting, there is no way to tell that the variance in the Fe phase is not simply due to variation arising from the presumed pulverization/grinding process, or possibly due to the thicker deposit of oxides (i.e. higher mass fraction) in the No. 2 group. Inclusion and discussion of the sample preparation would strengthen the central claims about the different oxidation times requiring different descaling methods. Figure 5. This figure could be improved by adding a reference surface, i.e. before blasting. Figure 6. This figure could be combined with Figure 5, so that the surface and cross-sectional views are next to each other. The claims about No.2/6B having “better combination with the basal body” in line 199 could use additional support, this does not seem obvious to me from the image. An alternate theory could simply be that the No.1 film is more cohesive and densified, therefore when shot-blasted, it comes off in larger flakes rather than being crushed into smaller pieces. A higher magnification image or, better, a compositional BSE image showing a density gradient in this zone might provide evidence of this “better combination” claim. HRTEM would provide conclusive proof, but these experiments are difficult and lengthy. Figure 7. Additional notes about the imaging modes are needed – 7A is clearly a compositional BSE image but 7B may be topographical BSE, or even SEI. SEM firing parameters and working distance are needed to allow for replication. Figure 8. The claim about “color uniformity” on lines 213-214 is only true if this is a compositional BSE image. Please specify, the contrast variations at edges indicate that these are SE images, which carry no information about composition. Additionally, if these are compositional BSE images, these contrast variations will need to be addressed in the text. Figures 9-12. I would recommend combining these for direct comparison – figures 9 and 11 can be combined so that they can be compared side-by-side. Same with 10 and 12. The imaging parameters included in figures 10/12 should be included for other images in the manuscript, though usually these are in the caption or the Experimental Method section. The data would also be stronger if EDS mapping of Fe for these regions were included. Also – figure 9/11 does a better job (but still not entirely conclusive) demonstrating the “better combination” claim from Figure 6. This is the type of magnification needed, preferably with before-and-after images. Table 1. How was the chemical composition determined? Table 2. How was position in the sample determined? Was this paired with a depth-profiling technique? Details are needed! RESPONSE: Thanks for your suggestions. Figure 1: The wrong spelling has been corrected. Figure 3: The labels have been added in the figures. Figure 4: The Chinese label in the figure has been changed to English. M and O are calibrated according to PDF standard diffraction card, so: 30° 2θ – M and O 38° 2θ – M and O 58° 2θ – M and O 90° 2θ – I Figure 5: Areas where the scales have fallen off and where they have not can be distinguished from the current figures. Figure 6: Figure 5 and figure 6 are images obtained from different experimental batches, so they are not combined. Figure 7: The parameters related to SEM have been added to the manuscript. Figure 8: Figure 8 is SE images. It can be clearly seen from Fig. 7 that the morphology of the surface after the scale layer has fallen off, which is in sharp contrast to the surface scale layer in Fig. 8 that has not fallen off. Figures 9-12: The order of figures 9 to 12 in section 3.2 has been adjusted, and the structure of section 3.2 has also been adjusted. Table 1: The chemical composition was determined by EDS. Table 2: The content of Fe and O in Table 2 is the average value of multiple measurements, in accordance with the statistical law. COMMENT 5: Line 19 – explain what “the related researches are not sufficient” means Line 33 – specify that we are talking about steel. Line 45 – “impact of water is not sufficient” – specify, for what? Line 72-73 – English usage Line 90 – “research… on the blasting descaling effect is not sufficient” – specify what information is missing – what do we not know that this paper will address? Line 106-107 – include thermal history of the samples here Lines 116-147 – include sample preparation, analysis method settings, additional detail from figure commentary here Line 131 – unless an equation is going to be shown using these parameters, symbols are not necessary Lines 154-155 – Why does the sample with the longer oxidation time have a thinner scale? I would like to see some discussion of density here, not just composition. Line 157 – this “well combined” claim shows up several times, it is not obvious from the data presented. Either provide more analytical support or remove. Line 216-218 – the behavior described here could also result from poorly-densified, morphologically heterogeneous oxide crystals, rather than a layered structure. Layering should be clear from the cross-sectional SEM in Figure 3, but it simply looks disorganized and poorly densified. I think this is the real conclusion from the data – somewhat counterintuitively, well-formed, highly dense oxide scales are easier to shatter and remove under shot-blasting. Line 225 – don’t state that results are accidental. Just describe what is learned from the high-magnification image, and what is learned from lower-mag/EDS. Lines 246-252 – I think this will need to be rewritten, unless the data more conclusively demonstrates the presence of this eutectoid phase. Lines 253-259 – it seems odd to spend much of the paper showing how the No.1 oxide scales are more completely removed and then to turn around here and say that the descaling will be less efficient and more expensive. I am not sure this is what the authors intend. RESPONSE: Thanks for your suggestions. Line 19 – This sentence has been changed to “However, at present, there are few studies on the relationship between the oxide layer content and the descaling effect”. Line 33 –This sentence has been changed to “During the steel strip rolling and cooling process, a dense and brittle oxide scale will form on the surface”. Line 45 –This sentence has been changed to “This technology is widely applied in hot rolling process, but it can’t be used to the cold rolling procedure since the energy of water is too small to remove scales”. Line 72-73 –This sentence has been changed to “Our research group is very interested in the non-acid oxide scale removal technology, and proposed the oxide scale removal technology combining shot blasting with high-pressure water, and designed the relevant experimental equipment.”. Line 90 –This sentence has been changed to “However, there are few studies on the relationship between the oxide layer content and the descaling effect, which is a key factor affecting the descaling effect, and is also the research objective of this paper”. Line 106-107 –A sentence describing the temperature change was added to the manuscript: during the hot rolling process, the temperature dropped from 1050℃ to 870℃. Lines 116-147 –Sample preparation and analysis method have already been mentioned in the context. Line 131 – This sentence has been changed to “The main process parameters include: impact angle, impact line speed, particle size and abrasive weight.”. Lines 154-155 – A sentence has been added to the manuscript: And as the chemical composition changes, the density of the oxide scale gradually increases. Line 157 –This sentence has been changed to “It is obvious that there are many defects in the structure of oxide scale”. Line 216-218 –This may be related to the bonding strength of different components and the matrix. This provides ideas for further research on the subject. Line 225 – This sentence has been changed to “Since Fig. 9 is the 4000-times magnification result of the SEM, the field of view is very small In order to improve the reliability of the research, a larger view field was chosen and the area scanning of energy dispersive spectrometer was conducted, as is shown in Fig. 11”. Lines 246-252 – This paragraph has been changed to “It can be known from the above experiments that the difficulty of oxide scale removal is related to the content of Fe3O4 in it. For steel strip that has been stored for a long time, the main components of the oxide scale are Fe2O3 and Fe3O4, and the oxide scale can be more easily removed by shot blasting; while for the steel strip with shorter storage time, the oxide scale contains Fe3O4, shot blasting can reduce the thickness of the scale layer, but only much longer shot blasting time can make the oxide scale completely fall off”. Lines 253-259 – The purpose of this paragraph is to propose a method of combining shot descaling with high pressure water to further reduce the cost of descaling when the Fe3O4 content is low. For the case of higher Fe3O4 content, the effect of shot blasting is more significant. The description in this paragraph is consistent with the purpose of improving efficiency and reducing costs in actual production. RESPONSES TO REVIEWER#3’s COMMENTS: COMMENT 1: The abstract suggested that the properties of the scale would explain the effectiveness of the descaling process. I was disappointed by the very limited experimental design of the investigation which only looked at two aging conditions of the steel and its surface scale which was 1) no aging and 2) aging at ambient after one year. I cannot believe that manufacturers or consumers would want to wait for a year to be able to effectively descale these materials so that they could avoid various other methods including pickling. This work did not directly compare shot blasting and pickling to show the difference. In the methods section, the experimental equipment was described where the process parameters were detailed to include 1) angle of impingement, 2) velocity, 3) shot diameter and 4) amount of shot used. Only the last variable was apparently used, and no other attempt was done to determine it other factors might have contributed to the success of the investigation. There was no optimization of the process claimed in the conclusions because there were no other factors that were tested. There was only one type of shot blasting material used. RESPONSE: Thanks very much for your suggestions. Originally, we were analyzing the optimization of the shot blasting descaling effect, and how to reduce the energy consumption is quite important in this area. In this process, it was found that the mechanical properties of the surface’s iron oxide scale had a significant effect on the descaling effect. Therefore, we carried out the shot blasting experiments using steel strips with different Fe3O4 content. Besides, other methods of increasing the Fe3O4 content can be used to enhance the descaling effect, and it is not necessary to store the steel for one year. We have already studied the influences by other factors on the descaling effect. These factors include angle and velocity of impingement, shot diameter and amount of shot, and the related papers on the influence of the velocity have been added to the list of references. Other papers related to angle of impingement, shot diameter and amount of shot are still under preparation. The sentences added in the manuscript are: We have studied the effect of shot blasting speed on the descaling effect, and the results show with the increase of the projectile velocity, the damage area of the oxide scale is increased, and the damage area is composed of the direct destruction area and the indirect failure area. COMMENT 2: I was looking forward to see the properties of the scale explained by the morphology and chemical composition, however it did not go far enough. Only the average chemical composition of the scale was used as a characterization. The Fe / O phase diagram was referred to in the quoted references, but was not discussed in enough detail to establish what was expected in the present investigation. Obviously the scale present in fresh material was not in equilibrium, and it was implied that material stored for a year was nearly in equilibrium. If it was important to the success of the investigation to produce the equilibrium phase and distribution, why then was no attempt made to accelerate the formation of the equilibrium product? While the morphology was shown in layer thickness, there was no quantitative expression of grain size or other distribution of the phases present. No attempt was made to assess the mechanism of adhesion or bonding or the strength of the adhesion of the scale layers to help explain the observed behavior. I would imagine that a micro shear test or bend tests as described for tension descaling would be appropriate methods to derive the properties of the scale needed for this characterization. The introduction reviewed an extensive list of competing descaling processes used in the industry compared to shot blasting, but no attempt was made to make an assessment of the relative cost, material aging or logistical tradeoffs between these methods. There was also no pass / fail criteria expressed to help judge the ultimate success of failure of the results. RESPONSE: Thanks very much for your suggestions. In the following studies, the study on accelerating the generation of Fe3O4 will be added, and the influence of different compositions on adhesion will be taken into account. In this manuscript, we only study the effect of shot peening dosage and components on the descaling effect, and do not consider the comparison with pickling cost. We are also considering whether the method combined with pickling after spraying will further reduce the descaling cost. In this manuscript, we carried out the shot blasting experiment using steel strips with different Fe3O4 content, and the conclusion of the effect of Fe3O4 content on the descaling effect is given. The real purpose of this experiment in this paper is to compare the effect of Fe3O4 content on the descaling effect. The use of strip steel stored for 1 year is only to change the proportion of Fe3O4 content. If there are other methods to change the content of Fe3O4 in the oxide layer, it can also be applied to this method of oxide scale removal. "	Here is a paper. Please give your review comments after reading it.
458	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Background. The effect of oxide scale composition of hot-rolled strip (Q235) on shot blasting is studied in this paper. The properties of the oxide scale on the strip surface change during storage. The shot blasting is an important on-line acid-less descaling technology. The effect of shot blasting is affected by many factors, among which the composition of oxide scale may play an important role. However, there are few studies on the relationship between the oxide layer content and the descaling effect.</ns0:p><ns0:p>Methods. The morphologies of oxide scales at different storage times are observed by scanning electron microscopy, and the compositions are analyzed by X-ray diffraction. These strips are then shot blasted and descaled with different amounts of abrasive, and the descaling effects are compared by scanning electron microscopy.</ns0:p></ns0:div> <ns0:div><ns0:head>Results.</ns0:head><ns0:p>The results show that the eutectoid structure Fe 3 O 4 / Fe in the oxide scale will gradually transform into Fe 3 O 4 . In the case of short storage time, the content of the eutectoid structure is high, and it is difficult to remove the oxide scale. While the strip with a long storage time has no eutectoid structure Fe 3 O 4 / Fe and FeO, so it is easy to remove the oxide scale during the shot blasting process. The composition of the oxide scale has a significant effect on the effect of shot blasting, and it provides significant guidance to the optimization of the descaling process parameters.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>During the steel strip rolling and cooling process, a dense and brittle oxide scale will form on the surface. Before the further cold rolling or galvanizing process, the oxide scale is usually removed by pickling to ensure the surface quality of the finished product <ns0:ref type='bibr' target='#b0'>(Sun et al., 2003;</ns0:ref><ns0:ref type='bibr' target='#b1'>Bin S, Guang-ming C & Zhen-yu L, 2010)</ns0:ref>.</ns0:p><ns0:p>Due to the serious environmental pollution problem caused by the pickling process, scholars have long been committed to the research of acid-free descaling technology to replace the pickling process, and have achieved valuable theoretical and experimental achievements. The principle of high-pressure water descaling is to use high-pressure pumps to generate high-pressure water. The high-pressure water jets cause thermal changes, shocks, vibrations, and scouring on the surface of the strip. The dynamic pressure of high-pressure water becomes the hydrostatic pressure and invades the bottom of the oxide scale, causing the oxide scale to peel off from the surface of the substrate (Choi J W & Choi J W, 2002). This technology is widely applied in hot rolling process, but it can't be used to the cold rolling procedure since the energy of water is too small to remove scales.</ns0:p><ns0:p>Abrasive water jet descaling technology uses high-pressure water to accelerate steel sand, quartz sand and other discrete bodies, and sprays the mixed abrasive stream to the strip surface at a certain angle through a nozzle to crush the oxide scale. Both the discrete body and water in this method can be recycled, and the descaling effect is obvious. However, due to the large water flow of the system, the high-pressure plunger pump requires higher cleanliness of the water, the water circulation system is always in a high load state, and the nozzle wears severely under longterm service, so this technology can only be applied to narrow band steel descaling or surface treatment of small parts <ns0:ref type='bibr' target='#b2'>(Meng, Wei, & Ma, 2016)</ns0:ref>.</ns0:p><ns0:p>Tensioning descaling is a mechanical method of repeatedly bending the strip steel. After the metal substrate is subjected to stress, a certain elastoplastic deformation occurs. The oxide scale on the surface of the metal substrate is broken due to brittleness and the purpose of descaling is achieved. Tensioning descaling is generally used in cases where the material is not seriously hardened and the product quality requirements are not strict <ns0:ref type='bibr' target='#b3'>(Tongqing, 1998;</ns0:ref><ns0:ref type='bibr' target='#b4'>Bakhmatov et al., 2014)</ns0:ref>. Smooth-Clean Surface (SCS) technology is used in a closed space to automatically adjust the roll gap of the grinding roller according to the thickness of the strip. At the same time, the surface of the steel plate is continuously washed by circulating filtered water, and the ground iron oxide is taken away to achieve surface cleaning. Finally, a 7 μm thick anti-rust layer is formed on the surface of the metal substrate. This method is not suitable for cold rolling, deep drawing and rotary deep drawing <ns0:ref type='bibr' target='#b5'>(Tamura et al., 2020)</ns0:ref>.</ns0:p><ns0:p>In order to realize the application of on-line acidless descaling for broad steel strip production, the Material Works Ltd. of the USA developed EPS (Eco-Pickled Surface) system. In this system, the abrasive shot blasting was applied to the industrial fields and proved to be an effective method to ensure the strip surface quality after descaling <ns0:ref type='bibr'>(Voges & Mueth, 2012;</ns0:ref><ns0:ref type='bibr' target='#b7'>Voges, Mueth & Lehane, 2008)</ns0:ref>. However, the energy consumption and processing cost of the system is so high that it cannot replace the pickling process yet.</ns0:p><ns0:p>Our research group is very interested in the non-acid oxide scale removal technology, and proposed the oxide scale removal technology combining shot blasting with high-pressure water, and designed the relevant experimental equipment. The most important research direction is to optimize the process parameters of shot blasting to reduce system energy consumption and processing costs. We have studied the effect of shot blasting speed on the descaling effect, and the results show with the increase of the projectile velocity, the damage area of the oxide scale is increased, and the damage area is composed of the direct destruction area and the indirect failure area. <ns0:ref type='bibr'>(Wang et al. 2017</ns0:ref><ns0:ref type='bibr'>(Wang et al. , 2017</ns0:ref><ns0:ref type='bibr' target='#b10'>(Wang et al. , 2018))</ns0:ref>.</ns0:p><ns0:p>Actually, the effect of shot blasting is affected by many factors, among which the composition of oxide scale also has an important effect on the removal of scales. Parameters such as steel type, rolling speed, rolling temperature, cooling speed and coiling temperature etc. will affect the oxide scale composition <ns0:ref type='bibr' target='#b11'>(Zhou et al., 2011;</ns0:ref><ns0:ref type='bibr' target='#b11'>Gong et al., 2009)</ns0:ref>. The oxide scale layer of ordinary carbon steel generally consists of three layers <ns0:ref type='bibr' target='#b12'>(Bonnet et al., 2003)</ns0:ref>: the inner layer is a solid solution of FeO and Fe 3 O 4 , the middle layer is Fe 3 O 4 , and the outer layer is Fe 2 O 3 . During the hot rolling process, the main component of the oxide scale layer is FeO. According to the Fe-O equilibrium phase diagram <ns0:ref type='bibr'>(Chen & Yeun, 2000</ns0:ref><ns0:ref type='bibr'>, 2002</ns0:ref><ns0:ref type='bibr'>, 2003)</ns0:ref>, the eutectoid reaction of FeO can produce a mixed product of Fe and Fe 3 O 4 below 570 °C. After laminar cooling and air cooling, a large amount of FeO will transfer into precipitates by eutectoid reaction. After being exposed to the air for a long time, the outermost layer of the oxide layer continues to be oxidized to Fe 2 O 3 . Therefore, in the process of the exposure in the air, the oxide scale composition is varying continually. However, there are few studies on the relationship between the oxide layer content and the descaling effect, which is a key factor affecting the descaling effect, and is also the research objective of this paper.</ns0:p><ns0:p>In this paper, two kinds of Q235 strip steels with different air cooling time are selected for the research. Firstly, the difference in scale composition on the strip surface is obtained through energy dispersive spectrometer and X-ray diffraction analysis. Then the shot blasting experiments are carried out, and the descaling effect is observed by scanning electron microscopy (SEM). Moreover, the influence of the variation of the scale's composition caused by air cooling time on the descaling effect of shot blasting is analyzed, which provide important guidance to the improvement of acidless descaling process in industrial production. The research route is shown in Fig. <ns0:ref type='figure'>1</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Materials & Methods</ns0:head><ns0:p>The test samples are two Q235 strips stored at different times. Firstly, the oxide scale morphologies and compositions are measured by energy dispersive spectrometer (EDS) and Xray diffraction (XRD). Then, the descaling experiments are performed using the shot blasting descaling experimental device developed by NERCFRE. The electron microscope is used to observe the removal effect in the experiments.</ns0:p><ns0:p>The object of XRD inspection is the surface of the sample after shot blasting. The bottom of the sample is fixed on the platform by means of bonding. The model of the XRD device is Ultima IV, and the type of Tube is ceramic X-ray tube.</ns0:p><ns0:p>As for scanning electron microscope (SEM), the sample was cut into 10mm×10mm squares and then embedded into the resin. The SEI mode is used to observe the surface morphology, and the BSEI mode is used for element detection. The type of equipment used is ULTRA 55.</ns0:p></ns0:div> <ns0:div><ns0:head n='1'>Experimental Materials</ns0:head><ns0:p>The experimental samples were taken from the Q235 hot rolled strip of the practical production line of a steel company. During the hot rolling process, the temperature dropped from 1050℃ to 870℃. Both the samples are 1m × 1m in size and 3mm in thickness. One of the samples was produced one year ago and stored in the room environment, the sample and the PeerJ Mat. Sci. reviewing PDF \| (MATSCI- <ns0:ref type='table' target='#tab_2'>2020:02:45849:2:0:NEW 30 Aug 2020)</ns0:ref> Manuscript to be reviewed Chemistry Journals group of subsequent specimens from which were labeled as No. 1. The other was produced within one week before the experiment, the sample and the group of subsequent specimens from which were labeled as No. 2. The reason for using samples that have been stored for a long time is to explore the descaling effect of samples with different oxide layer compositions, and the composition of the oxide layer can also be changed in other ways. Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref> shows the chemical composition of the two samples. It can be seen there are little difference between them, and the influence on the mechanical properties can be neglected. 2 Experimental Method.</ns0:p></ns0:div> <ns0:div><ns0:head n='2.1'>Oxide scale's composition analysis.</ns0:head><ns0:p>The procedure was as follows: a. Both sample No. 1 and No. 2 were cut in the middle area to obtain 8 specimens with size of 20 mm × 10 mm respectively. The surface of the sample was cleaned by ultrasonic cleaner, then wiped with alcohol and dried with a dryer. b. 4 specimens from both No. 1 and No. 2 groups were made into mounts with the cutting surface as the front side and polished, respectively. Then the oxide scale morphology observation and energy dispersive spectrometer (EDS) were conducted by the ZEISS ULTRA 55 scanning electron microscope. c. Other 4 specimens from both No. 1 and No. 2 groups were taken to the phase analysis by the Ultima IV X-ray diffractometer (XRD), respectively.</ns0:p></ns0:div> <ns0:div><ns0:head n='2.2'>Shot blasting experiments</ns0:head><ns0:p>(1)The slot blasting descaling experimental facility.</ns0:p><ns0:p>The acidless descaling experimental facility designed by NERCFRE is shown in Fig. <ns0:ref type='figure'>2</ns0:ref>. This device mainly includes six major units, which are uncoiler, 5-roller tension leveler, slot descaling, high-pressure jet, sweeping-drying and coiler. The main process parameters include: impact angle, impact line speed, particle size and abrasive weight. The shot blasting experiments with a small amount of abrasive a. Both sample No. 1 and No. 2 were cut in the middle area to obtain 1 specimen with size of 200mm× 200mm respectively. The surface of the sample was cleaned by ultrasonic cleaner, then wiped with alcohol and dried with a dryer. b. The slot blasting experiments were performed by the acidless descaling experimental facility. The impact angle θ is set to 60 °, the impact line speed v is 40 m / s, the particle size D is 0.6 mm, and the abrasive weight W is 2 kg. c. Both the samples in step b were cut in the middle area to obtain 2 specimens with size of 20mm×10mm respectively, and the specimens obtained by cutting were divided into two groups. One group of specimens was observed by a ZEISS ULTRA 55 scanning electron microscope for the descaling effect on the front of the specimens. Another group of specimens was mounted with the cut surface, and the descaling effect from the cut surface was observed.</ns0:p><ns0:p>(2)The shot blasting experiments with a large amount of abrasive.</ns0:p><ns0:p>In order to analyze whether a sufficient amount of abrasive can remove the scales clearly, a total weight of 20 kg of abrasive was used in the experiment, and the remaining parameters were unchanged.</ns0:p></ns0:div> <ns0:div><ns0:head>Results 1 Results of scale composition experiments</ns0:head><ns0:p>The cross-section morphologies of the oxide scale observed by SEM are shown in Fig. <ns0:ref type='figure'>3</ns0:ref>. The energy dispersive spectrometer of the iron and oxygen elements at the outside, intermediate and inside positions of the oxide scale by the ZEISS ULTRA 55 scanning electron microscopy are shown in Table <ns0:ref type='table' target='#tab_1'>2</ns0:ref>. The results of the phase analysis by X-ray diffractometer are shown in Fig.</ns0:p></ns0:div> <ns0:div><ns0:head n='4.'>2 Experiments of the shot blasting experiments with a small amount of abrasive</ns0:head><ns0:p>The descaling effects from the front surface's scanning after the shot blasting with 2kg of abrasive are shown by Fig. <ns0:ref type='figure'>5</ns0:ref>.</ns0:p><ns0:p>The oxide scales are layered and have a certain thickness. It is difficult to determine whether the oxide scale is completely peeled off from the base body only from the front surface's scanning. Therefore, it is necessary to observe the effect of descaling from the cross section, as shown by Fig. <ns0:ref type='figure' target='#fig_3'>6</ns0:ref>.</ns0:p><ns0:p>The SEM results of No. 1and No.2 groups after shot blasting with 20kg abrasive are shown in Fig. <ns0:ref type='figure'>7</ns0:ref> and Fig. <ns0:ref type='figure' target='#fig_1'>8</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head n='3'>Experiments of the shot blasting experiments with a large amount of abrasive</ns0:head><ns0:p>The 4000 times magnifications of descaling effect from the section's scanning after the shot blasting with 20kg of abrasive for No. 1 and No. 2 group are shown in Fig. <ns0:ref type='figure'>9</ns0:ref> and Fig. <ns0:ref type='figure'>10</ns0:ref>.</ns0:p><ns0:p>Since Fig. <ns0:ref type='figure'>9</ns0:ref> is the 4000-times magnification result of the SEM, the field of view is very small In order to improve the reliability of the research, a larger view field was chosen and the area scanning of energy dispersive spectrometer was conducted, as is shown in Fig. <ns0:ref type='figure'>11</ns0:ref>. Similarly, a larger view field was chosen and the area scanning of energy dispersive spectrometer for No. 2 group was conducted, as is shown in Fig. <ns0:ref type='figure'>12</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Discussion 1 Scale composition analysis 1.1Scale morphology analysis</ns0:head><ns0:p>As is shown in Fig. <ns0:ref type='figure'>3(A)</ns0:ref>, for the No. 1 group, the thickness of oxide scale is relatively uniform and is about 9.5μm, and the structure is compact and well combined with the basal body, which indicates that the oxidation of the strip surface is uniform and adequate during the hotrolling and long-time air cooling process. In Fig. <ns0:ref type='figure'>3(B)</ns0:ref>, for the No. 2 group, the uniformity of the oxide scale thickness is worse than that of No. 1 and the average thickness is about 12μm. It is obvious that there are many defects in the structure of oxide scale. By the above comparison, there are apparent differences of scale morphology with the rolling and cooling conditions difference. And as the chemical composition changes, the density of the oxide scale gradually increases.</ns0:p></ns0:div> <ns0:div><ns0:head n='1.2'>The energy dispersive spectrometer</ns0:head><ns0:p>As is shown in Table <ns0:ref type='table' target='#tab_1'>2</ns0:ref>, the values are the average of multiple measurements. The results show that there is almost no difference in the iron and oxygen content at different positions of the oxide scale for each group. In addition, the content of oxygen element at all positions of the oxide scale of No. 1 group is higher than that of the No.2 group, which indicates the different oxidation effect caused by the storage time in the air.</ns0:p></ns0:div> <ns0:div><ns0:head n='1.3'>The phase analysis of scale</ns0:head><ns0:p>The diffraction peaks are identified according to the PDF2004 standard card. As is shown in Fig. <ns0:ref type='figure'>4(A</ns0:ref> </ns0:p></ns0:div> <ns0:div><ns0:head n='1'>Analysis of the descaling effects from the front surface's scanning</ns0:head><ns0:p>As is shown in Fig. <ns0:ref type='figure'>5(A)</ns0:ref>, for the No. 1 group, at the edge of the hitting pit, a large area of the oxide scale fall off, and a large number of cracks appeared on the surface of the remaining scale layer. The peeled areas are large and the descaling effect is good. As is shown in Fig. <ns0:ref type='figure'>5(B)</ns0:ref>, for the No. 2 group, only a few oxide scale fall off at the junction of the hitting pit edge, and there are only a few tiny cracks on the remaining oxide scale layer. The peeled areas are small and the descaling effect is worse compared with the No.1 group.</ns0:p></ns0:div> <ns0:div><ns0:head n='2.2'>Analysis of the descaling effects from the section's scanning</ns0:head><ns0:p>As shown in Fig. <ns0:ref type='figure' target='#fig_3'>6(A)</ns0:ref>, for the No. 1 group, after the shot blasting with a small amount of abrasive, the oxide scale at the pit's edge fall off completely and the basal body is revealed and the peeled areas are large. There are not obvious cracks of the oxide scales in and around the pits, but there are tiny gap between the scale layer and the basal body near the peeled areas.</ns0:p><ns0:p>As is shown in Fig. <ns0:ref type='figure' target='#fig_3'>6(B)</ns0:ref>, for the No. 2 group, the peeled areas of the scale layer is small, and the basal body is not completely revealed. However, there are obvious cracks of the oxide scales in the pits. Thus, it can be deduced that compared with No. 1 group, the oxide scale of the specimens of No. 2 group has lower hardness and better combination with the basal body. The descaling effect of No. 1 group is better when the impact force of the projectile reaches a certain level.</ns0:p><ns0:p>3 Analysis of the shot blasting experiments with a large amount of abrasive 3.1 Analysis of the descaling effects from the front surface's scanning Fig. <ns0:ref type='figure'>7 (A)</ns0:ref> shows the descaling effect at 100x magnification in the backscattering mode. The darker part represents the area where the oxide scale has not fallen off, and the lighter part represents the area where the oxide scale has fallen off. It can be seen that most of the oxide scale has been peeled and only a few remains after the shot blasting with a large amount of abrasive. The 500 times magnification of the peeled areas is shown by Fig. <ns0:ref type='figure'>7(B)</ns0:ref>, and it can be observed that the pits of the basal body have become relatively smooth due to multiple hits. Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science only one kind of material in the surface. And the energy dispersive spectrometer results show that the oxygen content is 30.28%, the iron content is 69.72%, which indicates that the layer is the remaining oxide scale rather than the basal body. It can be obtained that the outer oxide scale layer falls off during the shot blasting process, but the inner oxide scale layer still exists on the substrate, which also confirms that the oxide scale is a layered structure.</ns0:p></ns0:div> <ns0:div><ns0:head n='3.2'>Analysis of the descaling effects from the section's scanning</ns0:head><ns0:p>As is shown in Fig. <ns0:ref type='figure'>9</ns0:ref>, the oxide scale after the shot blasting with 20kg of abrasive for No. 1 group has been peeled cleanly without obvious residue, and the surface is smooth after a large number of random hits. As is shown in Fig. <ns0:ref type='figure'>10</ns0:ref>, the oxide scale after the shot blasting with 20kg of abrasive for No. 2 group has not been peeled completely, but the thickness is reduced from12μm to 6μm, which means that the outer oxide scale falls off with the shot blasting, but the internal scale layer still exits. In addition, obvious cracks appeared on the surface of the remaining oxide scale.</ns0:p><ns0:p>As is shown in Fig. <ns0:ref type='figure'>11</ns0:ref>, where a larger view field was chosen compared with Fig. <ns0:ref type='figure'>9</ns0:ref>, the result of the area scan can indicate the content of the element by the depth of the color. The scanning area is shown by the green line frame in Fig. <ns0:ref type='figure'>11 (A)</ns0:ref>, and the scanning result of oxygen element is shown in Fig. <ns0:ref type='figure'>11 (B</ns0:ref>). It can be seen that there is no large amount of oxygen between the mounting powder and the basal body, which indicate that the oxide scale has fallen off after a large number of shot blasting and there is no oxide scale remaining.</ns0:p><ns0:p>As is shown in Fig. <ns0:ref type='figure'>12</ns0:ref>, where a larger view field was chosen compared with Fig. <ns0:ref type='figure'>10</ns0:ref>, the scanning area is shown by the green line frame in Fig. <ns0:ref type='figure'>12</ns0:ref> (A), and the scanning result of oxygen element is shown in Fig. <ns0:ref type='figure'>12 (B</ns0:ref>). It can be seen that there is a significant area of oxygen accumulation between the mounting powder and the substrate, which indicates that after a large amount of shot blasting, the oxide scale still exists.</ns0:p><ns0:p>It can be known from the above experiments that the difficulty of oxide scale removal is related to the content of Fe 3 O 4 in it. For steel strip that has been stored for a long time, the main components of the oxide scale are Fe 2 O 3 and Fe 3 O 4 , and the oxide scale can be more easily removed by shot blasting; while for the steel strip with shorter storage time, the oxide scale contains Fe 3 O 4 , shot blasting can reduce the thickness of the scale layer, but only much longer shot blasting time can make the oxide scale completely fall off.</ns0:p><ns0:p>For oxide scale without eutectoid structure, in the case of only descaling by shot blasting, as the thickness of oxide scale gradually decrease, the efficiency of descaling will be greatly reduced, resulting in increased costs. Therefore, after the shot blasting and descaling, an additional high-pressure water jet process can be added. Firstly, a large area of oxide scales is removed by shot blasting. At this time, the binding capacity between the remaining oxide scales and the basal body becomes weak, and then it can be completely removed by direct spraying with high pressure water further.</ns0:p><ns0:p>For oxide scale with eutectoid structure, using shot blasting to remove oxide scale is less effective. The method of combining shot blasting and pickling should be explored. By studying the best process, it can reduce pollution emissions and production costs and improve production efficiency.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions</ns0:head><ns0:p>In this paper, two kinds of Q235 strips stored at different times were selected to analyze the difference of surface oxide scale composition and the effect of shot blasting descaling, which provided a basis for the optimization of shot blasting process. The main research contents and conclusions are as follows:</ns0:p><ns0:p>(1) The EDS and XRD were used to observe and analyze the composition of the two Q235 steel scales stored at different times. It is found that the composition of the steel strip after hotrolling is significantly different during long-term storage. During the storage of the strip, the oxide scale will continue to be oxidized, and the eutectoid structure Fe 3 O 4 /Fe of the inner layer will be oxidized to Fe 3 O 4 . The hot-rolled strip scale with long storage time will have no eutectoid structure Fe 3 O 4 /Fe and FeO.</ns0:p><ns0:p>(2) The descaling experimental facility designed by NERCFRE was used to perform shot blasting and descaling treatment. The scanning electron microscope was used to observe the effect of a small number of shot blasting effects of two Q235 strip steels. Although Fe 2 O 3 and Fe 3 O 4 have high hardness, they are easy to fall off during shot blasting, and the strips that have not been stored for a long time are prone to scaly fracture due to the presence of Fe 3 O 4 /Fe eutectoids. However, it is more firmly bonded to the basal body, and it is relatively difficult to remove the oxide scale.</ns0:p><ns0:p>(3) The scanning electron microscope was used to observe the effect of a large number of shot blasting effects of two Q235 strip steels. It is found that for strips that have been stored for a long time, the main components of the oxide scale are Fe 2 O 3 and Fe 3 O 4 , which can be more easily removed by shot blasting; while for strips that have been stored for a short time, the scales contain eutectoids structure Fe 3 O 4 / Fe, shot blasting can reduce the thickness of the oxide scale, but it is more difficult to completely remove it.</ns0:p><ns0:p>(4) According to the experimental analysis in this paper, it is found that due to the presence of the eutectoid structure Fe 3 O 4 / Fe in the oxide scale, it is more difficult to remove the oxide scale. The subsequent research should adjust the shot blasting descaling process for different oxide scale components, such as the combination of shot blasting and high-pressure water direct injection. At the same time, it is also possible to explore the descaling process combined with pickling and find the optimal ratio of shot blasting descaling and pickling to achieve the comprehensive optimization of reducing pollution emissions, reducing production costs and improving production efficiency. Manuscript to be reviewed Manuscript to be reviewed</ns0:p><ns0:note type='other'>Chemistry Journals Figure 6</ns0:note><ns0:note type='other'>Chemistry Journals Figure 9</ns0:note><ns0:p>The descaling effects from t he section's scanning after the shot blasting with a large amount of abrasive of No. 1 group (WD =12.4mm)</ns0:p><ns0:p>The 4000 times magnification of descaling effect from the section's scanning after the shot blasting with 20kg of abrasive for No. 1 group is in this figure <ns0:ref type='figure'>.</ns0:ref> PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2020:02:45849:2:0:NEW 30 Aug 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p><ns0:note type='other'>Chemistry Journals Figure 10</ns0:note><ns0:p>The descaling effects from the section's scanning after the shot blasting with a large amount of abrasive of No. 2 group (WD =12.4mm)</ns0:p><ns0:p>The 4000 times magnification of descaling effect from the section's scanning after the shot Manuscript to be reviewed Energy dispersive spectrometer of iron and oxygen elements of the oxide scale</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>The energy dispersive spectrometer of the iron and oxygen elements at the outside, Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>), the phase composition of the oxide scale is mainly Fe 3 O 4 and Fe 2 O 3 for the No. 1 group, and FeO and Fe are almost absent. It indicates that FeO is converted into Fe 3 O 4 and Fe by the eutectoid reaction, and the eutectoid structure Fe 3 O 4 /Fe is oxidized to Fe 3 O 4 subsequently during the long-time storage in the air. Thus, the scale's composition is mainly Fe 3 O 4 with a small amount of Fe 2 O 3 . As is shown in Fig. 4(B), for the No. 2 group, the phase composition of the oxide scale is mainly Fe 3 O 4 , Fe 2 O 3 and the eutectoid structure Fe 3 O 4 /Fe. The obvious difference from No. 1 group is the existence of the eutectoid structure Fe 3 O 4 /Fe due to the short storage time in the air. 2 Analysis of the shot blasting experiments with a small amount of abrasive 2.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head>Fig. 8</ns0:head><ns0:label>8</ns0:label><ns0:figDesc>Fig.8(A) shows the descaling effect at a magnification of 50 times, and Fig.8 (B) is a partial enlarged view of Fig.8 (A). The relatively uniform color in the figures indicates there is</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>Figure 1 Figure 5</ns0:head><ns0:label>15</ns0:label><ns0:figDesc>Figure 1</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head>Figure 6</ns0:head><ns0:label>6</ns0:label><ns0:figDesc>Figure 6 The descaling effects from the front section's scanning after the shot blasting with a small amount of abrasive (A) No. 1 group (B) No. 2 group (WD = 12.5mm)</ns0:figDesc><ns0:graphic coords='16,42.52,224.62,525.00,179.25' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head /><ns0:label /><ns0:figDesc>Figure 11</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head /><ns0:label /><ns0:figDesc>intermediate and inside positions of the oxide scale by the ZEISS ULTRA 55 scanning electron microscopy are shown in this figure.</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='12,42.52,204.37,525.00,219.75' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='13,42.52,224.62,525.00,179.25' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='14,42.52,204.37,525.00,204.75' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='15,42.52,250.12,525.00,177.00' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='17,42.52,270.37,525.00,177.00' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='18,42.52,270.37,525.00,174.75' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='19,42.52,250.12,525.00,354.00' type='bitmap' /></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head>Table 1 :</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>The chemical composition of the samples Effect of Oxide Scale Composition of Hot-Rolled Strip Steel on Shot Blasting/table-1</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Sample No.</ns0:cell><ns0:cell>Fe/%</ns0:cell><ns0:cell>C/%</ns0:cell><ns0:cell>Mn/%</ns0:cell><ns0:cell>Si/%</ns0:cell><ns0:cell>S/%</ns0:cell><ns0:cell>P/%</ns0:cell></ns0:row><ns0:row><ns0:cell>No. 1</ns0:cell><ns0:cell>＞97</ns0:cell><ns0:cell>0.17</ns0:cell><ns0:cell>0.31</ns0:cell><ns0:cell>0.15</ns0:cell><ns0:cell>0.03</ns0:cell><ns0:cell>0.020</ns0:cell></ns0:row><ns0:row><ns0:cell>No. 2</ns0:cell><ns0:cell>＞97</ns0:cell><ns0:cell>0.19</ns0:cell><ns0:cell>0.26</ns0:cell><ns0:cell>0.13</ns0:cell><ns0:cell>0.028</ns0:cell><ns0:cell>0.017</ns0:cell></ns0:row><ns0:row><ns0:cell>DO1:</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row></ns0:table><ns0:note>PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2020:02:45849:2:0:NEW 30 Aug 2020)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_1'><ns0:head>Table 2 (on next page)</ns0:head><ns0:label>2</ns0:label><ns0:figDesc /><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_2'><ns0:head>Table 2 : Energy dispersive spectrometer of iron and oxygen elements of the oxide scale</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Effect of Oxide Scale Composition of Hot-Rolled Strip Steel on Shot Blasting/table-2</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Positions of scale</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>No. 1</ns0:cell><ns0:cell>No. 2</ns0:cell><ns0:cell>No. 1</ns0:cell><ns0:cell>No. 2</ns0:cell></ns0:row><ns0:row><ns0:cell>Outside</ns0:cell><ns0:cell>76.34</ns0:cell><ns0:cell>85.10</ns0:cell><ns0:cell>23.67</ns0:cell><ns0:cell>14.90</ns0:cell></ns0:row><ns0:row><ns0:cell>intermediate</ns0:cell><ns0:cell>76.96</ns0:cell><ns0:cell>85.77</ns0:cell><ns0:cell>23.04</ns0:cell><ns0:cell>14.23</ns0:cell></ns0:row><ns0:row><ns0:cell>Inside</ns0:cell><ns0:cell>79.50</ns0:cell><ns0:cell>85.21</ns0:cell><ns0:cell>20.50</ns0:cell><ns0:cell>14.19</ns0:cell></ns0:row><ns0:row><ns0:cell>DO2:</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row></ns0:table><ns0:note>Fe  O  PeerJ Mat. Sci. reviewing PDF \| (MATSCI-2020:02:45849:2:0:NEW 30 Aug 2020)</ns0:note></ns0:figure> <ns0:note place='foot'>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:note> </ns0:body> "	"Rebuttal Letter Dear Editors and Reviewers: Thank you very much for sending us the Reviewers’ reports on our manuscript entitled “Effect of Oxide Scale Structure on Shot-blasting of Hot-rolled Strip Steel”. Particularly, we would like to thank the Reviewers for their valuable comments and criticisms. According to the Reviewers and Editors’ recommendations, we: •Added the explanation of the standard we used when identify those peaks. •Modified the peak identifications in Figure 4. We have revised carefully our manuscript with red text. The following is a detailed list of response to all comments, and changes we have made. We look forward to hearing from you regarding our submission and we would be glad to respond to any further questions and comments that you may have. Sincerely yours, Xiaochen Wang ----------------------------------- University of Science and Technology Beijing wangxiaochen@ustb.edu.cn RESPONSES TO REVIEWER#2’s COMMENTS: COMMENT 1: I do not feel that my commentary on the XRD data has been appropriately addressed. The authors indicate the samples are simply attached to the XRD sample holder and scanned, without any processing. This results in the potential for changing intensities due to orientation of the crystallites within the sample. Generally samples are powderized before XRD analysis. Without this step, there is no way to be assured that the entire pattern of any particular phase is appearing, because the method for phase identification depends on having the analyzed crystallites randomly oriented within the sample volume. In the specific case here - an oxide film growing off of a flat substrate - it is likely that grains will grow in a particular direction, resulting in preferred (and unknown) orientations within the sample. It introduces many questions about the data. This should be addressed in the experimental method - how these data were refined to address the orientation questions. If they were not refined, they should be. RESPONSE: Because it is necessary to avoid extracting to the matrix during the powder production process, the experimental operation is more difficult. In this paper, the relationship between wavelength and thickness is used to ensure the accuracy of the iron oxide scale extraction location. COMMENT 2: In the rebuttal letter, the authors mention PDF cards. I'm assuming this means ICDD data - please include the card numbers for the specific patterns used somewhere in the article. The authors say the X-ray tube is 'ceramic' - this may be the housing, but the actual anode material is, in virtually all cases, a metal target. What I am really asking for here is which wavelength(s) the samples were illuminated with. If it is standard Cu k-alpha (1.541 angstroms) this should be stated somewhere. The name of the instrument is insufficient, most suppliers offer many tube options, though 95% of the instruments have Cu anodes. This is critical, along with the PDF numbers, for repeatability and comprehension of the data. Finally, the peak identifications themselves are inconsistent from Figure 4A to Figure 4B. This may be a result of the orientation issues mentioned above, which can cause peaks in literature powder patterns to not appear in scans. However, if that is the case, additional data must be presented to justify assigning a peak to one pattern in one sample and a separate pattern for the second. The most egregious cases are, again, as noted in my previous review: 30° 2θ - assigned to both M/O in 4A, assigned to O in 4B. 38° 2θ - assigned M in 4A, assigned O in 4B. 58° 2θ - assigned to both M/O in 4A, assigned to M in 4B. 90° 2θ - assigned I in 4A, assigned M in 4B. RESPONSE: Both the two figures use the same PDF card in this paper, which is PDF2004, so there is no problem about the standard utilized in this paper. The peaks in those pictures are relatively close, and some peaks appear to be at close angles, but there are deviations in reality. COMMENT 3: The authors claimed to address these in the rebuttal, but the uploaded images have not been changed. Additionally, they simply assign 3 of the peaks to both patterns (highly improbable, based on what I've seen of Fe3O4 and Fe2O3 patterns in the literature), and I believe they misidentify the peak at 90 as Fe - I suspect the actual Fe peak is at 82, again, based on my own very limited searching of the literature. RESPONSE: Our goal is to find the main peaks. Some peaks that do not affect the conclusion of the experiment are not marked. 82 should be Fe, but because this peak is relatively small and does not affect the conclusion, so it is not marked. COMMENT 4: Lastly, the literature patterns themselves are not shown in the image nor referenced in the document. There is additionally a prominent peak at 33° 2θ assigned to phase O in 4A that disappears in 4B, despite still identifying the O phase for that sample. If the authors simply include the literature patterns (as 'sticks' underneath the scan itself, or in a separate chart) on the image, these questions are resolved, and the phase ID will be more-or-less obvious. RESPONSE: We use the same standard throughout the experiment, so it is indeed a different material composition. COMMENT 5: The reason the discussion above (on powders vs. bulk) is relevant here, is that it is not easy to determine that the disappearance of the peak at 33 is because the sample lacks Fe2O3, or because the Fe2O3 crystals are oriented in such a way that this peak is not revealed in that sample. RESPONSE: According to our specific analysis in the article, the reason for this phenomenon is the composition of the material, not the powder. COMMENT 6: Similarly the changes in intensity for the I phase may be related to preferred orientation, as the ratio between the two peaks does appear to change from sample to sample - it is simply not possible to know, without further data treatment and explanation in the paper. Finally, the eutectoid phase mentioned still has no literature pattern nor phase ID. I am not familiar with this subfield, so 'eutectoid Fe/Fe3O4' may be a misnomer, but generally, this refers to a solid solution, not the mere simultaneous presence of two distinct phases (this would be something like 'heterogeneous polycrystal' not eutectic or eutectoid). RESPONSE: Eutectoid refers to the phase change that occurs when two or more solid phases precipitate together from the same solid phase. This phenomenon does exist in this experiment. COMMENT 7: In my opinion, it may be better to rely more (if not entirely) on the EDS data from Table 2 to make the point about the oxygen composition of the scale and how it affects shot peening. On the other hand, I feel the electron microscopy work has been clarified well, and have no further issues. RESPONSE: The difference of the content of O is actually to explain that the two compositions are Fe2O3 and Fe3O4. Because Fe2O3 exhibits brittleness, while Fe3O4 exhibits toughness, so the use of projectiles to hit iron oxide scale will have different effects. "	Here is a paper. Please give your review comments after reading it.
459	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>The ability to identify and reject bitter molecules may determine evolutionary fitness. These molecules might be in potentially toxic or contaminated food. Surprisingly, the ability to identify but tolerate or even enjoy bitter foods and medicines may be beneficial. For example, the tolerance of bitterness as a spice or as a medicine may lead to better nutritional, immunological and health outcomes. More recently the ability of intensely bitter compounds to induce innate immune responses to counter infection has inspired the screening of new drugs and the repurposing of safe, known drugs to new uses. These avenues of study may also help to address long-standing questions regarding unexpected side-effects and placebo/nocebo effects. Therefore, to distinguish all these effects ranging from desire to aversion, there is a need to quantitatively determine the concentration thresholds and to position these bitter substances on a unified taste threshold spectrum. Such an understanding may help elucidate the concentrationbased molecular drivers for the chemoreceptive response to bitter substances. This article reports the development of a gradient boosting machine (GBM) that enables a direct interrogation of molecular structure with no intermediary chemical properties. Using molecularly engineered simulations, it is shown that potassium acesulfame has a hidden bitterness motif that is centered on the chemoreceptive spectrum uniting bitterness and sweetness molecular motifs. The resultant shifted perception from a touchstone bitterness sensation to a bitter after-taste is attributable to this cached molecular motif.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>The ability to identify and reject bitter molecules may determine evolutionary fitness. These molecules might be in potentially toxic or contaminated food. Surprisingly, the ability to identify but tolerate or even enjoy bitter foods and medicines may be beneficial.</ns0:p><ns0:p>For example, the tolerance of bitterness as a spice or as a medicine may lead to better nutritional, immunological and health outcomes. More recently the ability of intensely bitter compounds to induce innate immune responses to counter infection has inspired the screening of new drugs and the repurposing of safe, known drugs to new uses. These avenues of study may also help to address long-standing questions regarding unexpected side-effects and placebo/nocebo effects. Therefore, to distinguish all these effects ranging from desire to aversion, there is a need to quantitatively determine the concentration thresholds and to position these bitter substances on a unified taste threshold spectrum.</ns0:p><ns0:p>Such an understanding may help elucidate the concentration-based molecular drivers for the chemoreceptive response to bitter substances. This article reports the development of a gradient boosting machine (GBM) that enables a direct interrogation of molecular structure with no intermediary chemical properties. Using molecularly engineered simulations, it is shown that potassium acesulfame has a hidden bitterness motif that is centered on the chemoreceptive spectrum uniting bitterness and sweetness molecular motifs. The resultant shifted perception from a touchstone bitterness sensation to a bitter after-taste is attributable to this cached molecular motif.</ns0:p></ns0:div> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>Gustation is a critical sense for our daily living. The ability to taste specific compounds is intimately linked to our dietary choices, safety and health.</ns0:p><ns0:p>Human dietary choices have also evolved according to cues drawn from bitter tastes. The adoption of bitter spices into cuisine is also noteworthy since there is a range of spices that bears a distinct bitterness. Some examples include mustard and chervil. It is also notable that traditional vegetables such as the African Spider Plant and the Malabar Spinach are cherished for their bitterness just as much as they are for their robust nutritional value <ns0:ref type='bibr' target='#b0'>(1)</ns0:ref>. There is now a substantial collection of research showing that the systemic responses to glucose differ between meals containing bitter foods from those without bitter foods. These reports suggest that satiety and the non-diabetic glycemic response can be induced in diabetic respondents when bitter compounds are ingested. These findings appear to support the premise that human feeding response and endocrinology are co-evolved <ns0:ref type='bibr' target='#b1'>(2)</ns0:ref>.</ns0:p><ns0:p>'Bitters' have also been used as a flavoring agent for alcoholic beverages globally as means of fortification; in some instances, these bitter cocktails are selected for their medicinal effect e.g. in the historic use of quinine for the preparation of tonics in the tropics. The medicinal and gustatory effects have therefore been conflated in recent times becoming part of the barman's lore (3).</ns0:p><ns0:p>In spite of the unsupported conflation of gustatory and medicinal effects, there is reason to believe that the bitterness receptors (hTAS2R family) may have some links to immunity at a molecular level. It is known that there are extra-oral taste receptors especially in the respiratory system (4) and in the intestinal mucosa <ns0:ref type='bibr' target='#b3'>(5)</ns0:ref>. Within the respiratory system, ciliated airway epithelia express hTAS2R receptors. These receptors are known to respond to the presence of bacterial quorum sensing molecules by initiating a Ca2+-dependent signaling pathway that increases mucociliary clearance and production of antimicrobial products including peptides and Reactive Nitrogen Species (4). Gut epithelial tissues are known to contain Tuft cells which have taste-chemosensory capacities enabling the promotion of type-2 immunity in the event of ingress by foreign bodies <ns0:ref type='bibr' target='#b4'>(6)</ns0:ref>.</ns0:p><ns0:p>The ability to distinguish fresh from spoilt food depends on our ability to associate freshness with gustatory cues. This relationship is partly innate and partly learnt <ns0:ref type='bibr' target='#b5'>(7)</ns0:ref>. The genetic origins of taste have been confirmed within the human species and more broadly across the animal kingdom. This constitutes the innate bitterness sense. However, the ability to sense a bitter substance is only the beginning as the appropriate learnt response may vary from simple reduction in the amount consumed (e.g. the use of a spice) or the complete avoidance (e.g. aversion). The most intense bitter foods elicit an aversive response which may culminate in an emetic response. Both aversion and emesis are primary immune responses protecting the body from far worse consequences from imbibing poisons <ns0:ref type='bibr' target='#b6'>(8)</ns0:ref>.</ns0:p><ns0:p>Not surprisingly, bitter tastes are now demonstrated to have been evolutionarily sectioned according to biogeography. The taste receptor frequency appears to be latitudinally ordered according to the global biogeography. The ability of a species to gustatorily identify poisonous from non-poisonous plants is itself a measure of fitness which is linked to survival <ns0:ref type='bibr' target='#b7'>(9)</ns0:ref>. However, even within a confined latitudinal window, the diversity in the ability to sense the full spectrum of bitterness is now understood to be driven by the zygosity and the epigenetic profile of the taster giving rise to the neologism of a 'supertaster' <ns0:ref type='bibr' target='#b8'>(10)</ns0:ref>. These studies have shown the undervalued importance of bitterness beyond taste but have also raised questions on the precise molecular theory driving bitterness thresholds and responses. Similarly, testing and design principles for bitter compounds require refinement against the often-noisy clinical data. In this regard, the focus of this article is the construction of model forms that may permit the identification of the relevant molecular space and the structural heuristics for further investigation.</ns0:p></ns0:div> <ns0:div><ns0:head>Materials & Methods</ns0:head><ns0:p>Metadata Collection. Data was sourced from previous studies by Meyerhof et al. of molecular receptive thresholds drawn from the calcium-signaling responses of hTAS2R transfected cells <ns0:ref type='bibr' target='#b9'>(11)</ns0:ref>. Cells were designed to express the hTAS2R epitopes on the cell surface, exposed to the bitter compounds, calcium-sensitive signaling dye and an inhibitor for anion transport (cellular vitality tests were co-evaluated alongside the hTAS2R-mediated calcium signaling). The hTAS2R receptors were coupled to intracellular calcium signaling by the chimeric G-protein subunit, Gα16gust44 <ns0:ref type='bibr' target='#b10'>(12)</ns0:ref>. Adequate controls were provided by means of empty transfection vectors <ns0:ref type='bibr' target='#b9'>(11)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Relative Quinine Index (RQI):</ns0:head><ns0:p>The RQI is calculated by dividing the detection concentration of a given compound and dividing it by the detection concentration of quinine. The RQI helps to organically communicate how bitter a compound is because quinine is a familiar compound commonly used to make tonic water (a consumer product). Therefore, a molecule that is more bitter than quinine will have a an RQI less than one (the converse is true). Sample Size & Data Description: There were in total 82 bitter compounds. These compounds were pre-determined to be bitter based on psychophysical tests <ns0:ref type='bibr' target='#b9'>(11)</ns0:ref>. The RQI ranges from 0.00013 to 99.9 representing almost six orders of change in magnitude. Generation of Molecular Structures. Structures were generated using ChemSpider Variable Transformation. Molecular structures were checked for consistency before they were used to generate descriptors using R ChemoInformatic packages: Rcpi (13), ChemmineR <ns0:ref type='bibr' target='#b12'>(14)</ns0:ref> and ChemmineOB <ns0:ref type='bibr' target='#b13'>(15)</ns0:ref>. Components include: molecular fingerprints (graph, FP4, MACCS)`, electron structure, spatial and topological descriptors. See Table <ns0:ref type='table'>1</ns0:ref> for the descriptor set. Chemical Scaffold Determination. The chemical scaffolds were identified using Scaffold Hunter <ns0:ref type='bibr' target='#b14'>(16)</ns0:ref>. Briefly, chemical identifiers, Names, SMILES and RQI were imported into an HSQLDB database. Thereafter, scaffold clouds and tree maps were generated to represent the inner structural relationships between members of the chemical library. Scaffolds were obtained by using a deterministic structural reduction process that prunes terminal branches revealing inner shared structures referred to as scaffolds. Scaffolds were clustered via the sequential agglomerative hierarchical non-overlapping clustering (SAHN) algorithm <ns0:ref type='bibr' target='#b15'>(17,</ns0:ref><ns0:ref type='bibr' target='#b16'>18)</ns0:ref>. Description of the SAHN Algorithm. The algorithm has four main steps: first, the chemical structures are fingerprinted using bit arrays that capture both existing and missing chemical features. The chemical fingerprints extended with descriptors are then used to calculate a similarity matrix. Pairs of similar molecules are used to create nodes in a sequential fashion that ultimately captures all the members of the dataset in a single mathematical construct (this is the linkage routine). Finally, visual representations of the data are presented using dimensional reduction methods allowing users to have compact slices of the data conveying the structural chemical motifs in a semantically compelling snapshot <ns0:ref type='bibr' target='#b15'>(17,</ns0:ref><ns0:ref type='bibr' target='#b16'>18)</ns0:ref>. Model Development. The computational technique had three steps. The first step was to transform the molecular SMILES into structures and generate the descriptors for structure, electronic state and topology. The second was to generate a high-variance reduced form of the molecular structures capturing the input space. This step involved removing the low and zerovariance vectors in the input space. Low and zero variance vectors are removed because they are uninformative and therefore do not contribute to the determinacy of the system. The third step involved using a gradient boosting machine (GBM) to regress the bitterness thresholds against the input space. A GBM was chosen because it generates initially weak learners and subsequent stronger learners will only improve on weak learners in areas of chemical space where residuals are large. When applied to this bit array representation of molecular descriptors, they will be run on an eight-fold (8x) cross-validated train-test sample split. The training sets are validated by comparing the resulting trained model predictions against the experimental data in the test set. The chosen model will have the lowest overall cross-validated error. The hyperparameters used were: 199 trees, maximum tree depth of 7 levels; minimum tree depth of 2 rows and a learning rate of 0.2 (detailed information included in the code attachments). The choices are driven by the rate of convergence of the algorithm; in turn convergence is driven by the attainment of a sustained minimum in the residuals of the model (see Fig. <ns0:ref type='figure'>1</ns0:ref>). Additionally, the learning rate served to minimize the test set residuals which means that the overall model performance is within specified metrics. While this means that the convergence is slowed, the other chosen parameters balance this decreased velocity with an aggressively parameterized chain of increasingly complex learners. Model selection was based on a set of 4 values: Coefficient of Determination (R.SQ.), model error, convergence and sustained metrics during validation. The full dataset and the outlined code have been provided and can be run on R version 3.5.0 or higher.</ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head><ns0:p>Model generation. The model convergence was demonstrably quick having constructed the input space using molecular fingerprints and descriptors as shown in Fig. <ns0:ref type='figure'>1</ns0:ref>. Model convergence is directly linked to the choice of hyperparameters which accelerate the reduction in residuals to a sustained minimum. The residuals' magnitudes are captured on the y-axis while the algorithm's process time is captured on the x-axis in seconds. The attainment of the minimum is shown in the asymptote of the residual-duration curve. Molecular patterns have previously been observed amongst bitter molecules when contrasted against an intuitive diametrical opposite (sweet molecules). This observation was proven true when molecular changes to moieties on the sweet molecule gave bitter molecules (19). Therefore, the molecular structure was used to provide structural information divisible into three areas: geometric/connective, electro-topological and structural representations. A summary of the descriptor choice is provided in Table <ns0:ref type='table'>1</ns0:ref>.</ns0:p><ns0:p>The RQI was then regressed against the input space generating a cross-validated model with the following metrics: Coefficient of Determination (R 2 ) (97%), Root Mean Square Error (RMSE) (2.34) for a Mean Response (15.39). The model accuracy is shown in Fig. <ns0:ref type='figure'>2</ns0:ref> by plotting the predicted vs. the empirical bitterness index. The points are straddled across the identity line (y=x) showing the model tries to match the empirical reality and the errors (deviations away from the identity line) are random. Overall, the mathematical construct confirmed the existence of a robust deterministic relationship between the input space and the RQI. This is consistent with previous observations of the strong relationship between molecular structure, molecular formula and geometry ( <ns0:ref type='formula'>19</ns0:ref>) because both the model and observational treatments map from the same foundational chemical features onto measures of bitterness with observations being descriptive while the model is a compact mathematical representation of the same insights.</ns0:p></ns0:div> <ns0:div><ns0:head>Discussion. Can we develop heuristics from the structural information?</ns0:head><ns0:p>The observations made in Table <ns0:ref type='table'>1</ns0:ref> are striking given that they offer an abstraction of the drivers of bitterness with a moiety-based adjustment to contrast between configurations on specific molecular structure with shared motifs. Given that the model could sufficiently predict these changes, it can be inferred that the full dataset should lead us to similar conclusions. From Fig. <ns0:ref type='figure'>3</ns0:ref>, the amine-containing groups contribute greatly to the scaffold cloud to the tune of 8X relative to the sulfinyl groups. This is consistent with previous observations for alkylamines, amides and azacycloalkanes (19). The representational model predicts that molecules with pyridyl and amine substructures will have significant bitterness quotients. The centrality of amine-containing bitterants appears to demonstrate a path of modification towards molecular clusters with increasing aversive gustatory qualities as shown in Fig. <ns0:ref type='figure'>4</ns0:ref>. Clustering approaches are especially vivid where hierarchical and progressive modification are concerned. Not surprisingly, these modifications allude to the origins of bitter substances being the armsrace between plant and animal kingdoms and the search for optimality between fitness targets of protection and dispersal. These observations raise the hope that further research into the bitterness-driven screening libraries and the extensional indications for known and well-tested drugs can be expected to be fecund.</ns0:p></ns0:div> <ns0:div><ns0:head>Why might Potassium Acesulfame (AceK) have a bitter after-taste?</ns0:head><ns0:p>The model was used to explore what changes drive the bitterness threshold of known compounds. AceK has a bitter aftertaste but the existence of some bitterness signaling (high panelist variance) throughout the taste experience indicates that the molecule possesses inherent bitterness <ns0:ref type='bibr' target='#b18'>(20)</ns0:ref>. Using the model, the hypothesis could be tested by changing moieties that are likely to be transformation sites. The new molecular structures are then used as inputs into the GBM whose output is the predicted RQI which represents a bitterness threshold relative to quinine (the chosen bitterness standard). On Table <ns0:ref type='table'>2</ns0:ref>, AceK has a respectable bitterness threshold. The RQI does not change meaningfully across similar molecules with a -CNSO-motif; they all have high RQI (i.e. they are less bitter than quinine). In contrast, maintaining an -NCS-(thiocyanate) motif alone lowers the bitterness threshold even to below that of quinine as exemplified by the PTC (phenylthiocarbamide) standard <ns0:ref type='bibr' target='#b9'>(11)</ns0:ref>. This finding may indicate that molecular structure is critical to bitterness sensing and that motifs can be altered by introduction of additional polar atoms to the identified molecular 'trigger motif' to increase the RQI. Additionally, from a 'molecular structure' perspective there are gradations of taste uniting bitter and sweet on the same scale. Where such gradations meet, as in the AceK case, the hidden bitterness motif is likely altered resulting in a shifting of perceptions to after-tastes rather than a touchstone bitterness sensation. This model-driven conclusion is supported by the observation that the heteromeric sweetness receptor hTAS1R2-hTAS1R3 and bitterness receptors hTAS2R43 and hTAS2R44 are all activated by AceK at higher concentrations for the sweetness receptors implying bitterness would be recognizable at lower concentrations typical of a post-evacuation state within the inundated gustatory bulb i.e. as an after-taste <ns0:ref type='bibr' target='#b18'>(20)</ns0:ref>. These in vitro observations confirm the ability for the AceK 'master' molecular key to unlock both sweet and bitter sensations in that order <ns0:ref type='bibr' target='#b18'>(20)</ns0:ref>.</ns0:p><ns0:p>Examining mycotic compounds: Evidence of evolutionary cooperativity. It is observable that homologous compounds demonstrate an evolutionary pressure exerted by saprophytes and autotrophs against herbivorous and omnivorous heterotrophs. A fine example is the link between phenylketonuria and ochratoxin A; the former being linked to fetal protection against the latter (21). Therefore, bitter ochratoxin A is linked to changes in the human genome driven by its toxicity (21). Additionally, for the same cognate molecular set, the detection threshold (RQI) for the poisonous strychnine variant molecule (LD50 at 5 mg; RQI at 0.01) is lower than that of its edible and sometimes therapeutic counterpart brucine (LD50 at 1000 mg; RQI at 1) <ns0:ref type='bibr' target='#b9'>(11)</ns0:ref>. These representative LD50-RQI relationships are nonlinear. Much interest in this observation has been driven by the desire to find more powerful and durable antifungals, antibiotics and antivirals. Therefore, the case study here looks to compare cognate molecular sets to identify which among them are likely to fall in the edible-therapeutic group. More specifically, ergolines are of interest given that their somatic and psychiatric effects can span the spectrum of beneficence to toxicity.</ns0:p><ns0:p>We examined the class of ergoline mycotic chemistries known to have pharmaceutical value in humans. These chemistries are known to have vascular <ns0:ref type='bibr' target='#b22'>(22)</ns0:ref> and nervous system effects <ns0:ref type='bibr' target='#b23'>(23)</ns0:ref>. They are valued for treating a range of conditions including post-partum bleeding <ns0:ref type='bibr' target='#b22'>(22)</ns0:ref> and migraines <ns0:ref type='bibr' target='#b24'>(24)</ns0:ref>. The molecular structures of three ergolines were entered as inputs, converted to chemical fingerprints and descriptors and subsequently predicted by the GBM to have quantified predicted RQIs (column 3, Table <ns0:ref type='table'>3</ns0:ref>). Looking at Table <ns0:ref type='table'>3</ns0:ref>, the demarcation between the compounds on the toxicity measures (LD50) broadly matched with the RQI as predicted by the model. Low RQI thresholds correspond with low LD50 thresholds supporting the going hypothesis that evolutionary directions for human bitterness receptors appear to follow the surrounding environmental pressures. This motivates the derivation of a ratio being the LD50-to-RQI (toxicity-bitterness) ratio. This Toxicity-Bitterness Ratio approximates 1500 (ergometrine), 150 (methylergometrine) and 15 (methysergide). This shows an intervallic spread between each evolutionary terminus uniting human and mycotic adaptations. There is a non-linear decrease in the toxicity-bitterness ratio which is attributable to the non-linearity of psychophysical (RQI-Concentration) <ns0:ref type='bibr' target='#b9'>(11)</ns0:ref> and toxicity (LD50-Concentration) (25) curves for compounds including ergolines which are phenomenologically modeled using non-linear regressive methods.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions.</ns0:head><ns0:p>In conclusion, a molecular-theoretic approach to predicting bitterness thresholds for the human T2R receptor has been developed demonstrating exquisite model quality diagnostics. The outcomes may be usable in the testing and design of bitter compounds targeted at taste-chemosensory receptors. Model assessments have also allowed us to identify the importance of electro-topological, structural and geometric properties of the molecular space. Further, the model was usable in developing verifiable structural heuristics for bitterness, explaining aftertaste sensations chemometrically and separating toxic from non-toxic therapeutic molecular cognates. It is proposed that future work may focus on the mechanistic drivers of receptor-driven immune responses addressed to the greater challenge of identification of scaffolds for immunotherapeutic small molecules and next-generation adjuvants. Manuscript to be reviewed </ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:note type='other'>Figure 3</ns0:note></ns0:div><ns0:figure xml:id='fig_0'><ns0:head>Figure 1 TrainingFigure 2 All</ns0:head><ns0:label>12</ns0:label><ns0:figDesc>Figure 1</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head>A</ns0:head><ns0:label /><ns0:figDesc>visualized structural contribution to the data set A cloud visualization indicates that the parametric coefficients that drive bitterness are greater for amine groups than they are for the sulfinyl groups with a greater than 8X preponderance</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='12,42.52,256.49,525.00,177.75' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='15,42.52,250.12,525.00,324.00' type='bitmap' /></ns0:figure> <ns0:note place='foot'>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:note> </ns0:body> "	"Sammy Sambu Computational Chemistry, Bartanel Discovery, Zaventem, Belgium Editorial and Review Board, PeerJ, 23-Oct-2019 Dear Sir/Madam, RE: On proposed revisions to the article titled “The determinants of chemoreception as evidenced by Machine Learning” (#OCHEM-2019:09:40853: 0:1) by Sammy Sambu I would like to thank the editor and reviewers for taking the time to read the article and to provide valuable comments and suggestions to improve the article. Improving the clarity of the article will go a long way to delivering the intended message of the article to current and future readers. I have updated much of the original illustrations and corresponding text – the overall script reads clearer and easier with a lucid flow from section to section. I have provided a summarized table capturing each concern and the changes made to accommodate the concern (references are made to the section of the text with the changes tracked for ease). Reviewer Section Start Line End Line Response Action Rohit G. Abstract 1 1 The abstract does not contain details of the study and needs to be re-written to include the results. Specifically, the AceK insights should be a part of the abstract. AceK summary added to abstract Rohit G. Abstract 1 1 Machine learning in the title is too vague. Consider being more specific, e.g. 'as evidenced by machine learning' -> 'as evidenced by gradient boosting models in molecular fingerprint spaces' title updated to mention GBMs Rohit G. 15-18 15 18 the sentence is overly long sentence split into 3 short ones Amrita 15-18 15 The first line of the abstract (lines 15:18) is a little long and could be split into two sentences. sentence split into shorter sentences Amrita 17 17 line 17 ('have' --> 'has') sentence changed to directly mention “foods and medicines' Amrita 17 17 line 136 ('relative the' --> 'relative to the'); added 'the' Amrita 19 19 line 19 ('ability for intensely bitter molecules' --> 'ability of intensely bitter molecules'); switched to 'of' Rohit G. 30 30 30 'the cues drawn' -> 'cues drawn' changed to 'cues drawn' Amrita 31 31 line 31 ('are a range' --> 'is a range'); changed to emphasize 'range': there is a range of spices that bears …bitterness. Rohit G. 36 36 36 'It appears' -> 'It has been reported' Changed to 'These reports suggest …' to build on the previous statement Rohit G. 41 41 'used as a flavouring' -> 'used as a flavouring agent' 'agent' added for clarity Rohit G. 47 47 Unclear why nevertheless is used 'Nevertheless' replaced with reference to the conflation of gustatory v. medicinal effects Amrita 47 47 line 47 ('to believe the bitterness receptors...' --> 'to believe that the bitterness receptors...'); added 'that' (only one such occurrence in paper) Rohit G. 54 54 'have a taste-chemosensory capacity' -> 'have taste-chemosensory capabilities' changed to reflect multiple dimensions to the tuft cell capacities Amrita 69 69 line 69 ('The ability for' --> 'The ability of'); changed to 'of' Amrita 75 75 line 75 ('questions on the precise a molecular theory' --> 'questions regarding the precision of a molecular theory'); changed to denote improvement/sharpening of the prevailing understanding: questions on 'the precise molecular theory' driving bitterness…responses. Amrita M&M 81 I was wondering how the bitterness index quantifies the gradations of bitterness, and more details of the same could be provided in the Methods or Results sections. Materials and Methods section now has a paragraph defining the RQI and has an example of how to interpret it Rohit G. 82-89 82 The data-set used should be described in more detail, including at least the description of how many data points were obtained section on Sample Size and Dataset description included Rohit G. 90 90 'a ChemSpider' -> 'ChemSpider' errant 'a' removed Amrita 90 90 line 90 ('using a ChemSpider' --> 'using ChemSpider'); errant 'a' removed Rohit G. 98-100 98 The scaffold process needs to be described in more detail, including the SAHN algorithm and how this is relevant to reduce and cluster the underlying chemical structures. section on description of SAHN algorithm added Rohit G. 98 Why has the gradient boosting model been used? How has the model been generated? What is the rationale behind the parameters? How was the data validated? What were the hyper-parameters? Figures 1. and 2. need more of an explanation in the text. a boosting algorithm will generate initially weak learners and improve on them in areas of chemical space where residuals are large The model generation starts with a random 8X cross-validated train-test sample split; initial models are built using weak (low-parameter) models and gradually improved in areas of large residuals using more complex learners the model was validated by testing performance against the test sample the hyper parameters used were: 199 trees, maximum tree depth of 7 levels; minimum tree depth of 2 rows and a learning rate of 0.2 (detailed information included in the attachments) additional explanation for the training convergence (fig.1) and the accuracy/fidelity tests (fig.2) as measures of model suitability given in the results section Rohit G. 98 What is the relation between the RQI and LD50.human? (Table 3) Has the LD50 been correlated with the RQI in this study or others? Has it been factored into the model development? The scaling is clearly non-linear (RQI v/s LD50) so the correlation should be described in more detail. the link between RQI and LD50 has been clarified by adding RQI & LD50 values for brucine and strychnine which are used in the dataset for the GBM model A referenced study by Meyerhof identifies this correlation A referenced study by Woolf also establishes causation between a known bitter compound and genomic-level effects the structural foundations for these relationships is captured by the model since brucine and strychnine structures & RQI are in the dataset used for building the model Amrita 98-103 98 the sentence in lines 98:103 could be broken into two. sentences broken to encode one idea per sentence Amrita validity 98 The sample size of the data, controls and scientific methods adopted in this work are adequate. The bitterness index and the relationship between RQI and the results could be expounded further. Additional Sections on the RQI as a measure of bitterness added Rohit G. 104-111 104 This section should be expanded on with an example, detailing the transformation, descriptor generation and other transformation metrics. section has been expanded explaining the algorithmic steps and choices for model types and hyperparameters have been explained the full code for the model development have been included in the attachment as well as the dataset allowing reproduction of the algorithm Note that these steps cannot be carried out with a single compound, rather it requires the full data set to illustrate (these will be available to readers for trial and reproduction) Rohit G. 107-108 107 Why have the low and zero variance vectors been removed? sentence added explaining that these vectors are uninformative (they do not add new & useful information to the model) Amrita 124 124 The expansions of the acronyms RQI (line 124), R2 (line 125), RMSE (line 125) should be included once. RQI is now explained in the methods section R.SQ (R2) is expounded as Coefficient of Determination and Root Mean Square Error for RMSE Amrita 125 125 The expansions of the acronyms RQI (line 124), R2 (line 125), RMSE (line 125) should be included once. RQI is now explained in the methods section R2 is expounded as Coefficient of Determination and Root Mean Square Error for RMSE Rohit G. 127 127 Further comment on why the regressed model correlates well with the observations would add to the manuscript. additional comment added referencing the common foundations of observations & of the mathematical model Rohit G. 130 Table 2 130 It is unclear as to how the Predicted RQI performs here without information on the actual RQI. Table 2 should include the actual RQI. Line 130 refers to the descriptor choices in Table 1 -- now corrected *NB that figs. 3 & 4 are visual representations of Table 1 Table 2 now includes the empirical RQI of better-recognizable compounds Amrita 131 131 line 131 ('given they' --> 'given that they'); 'that' added Amrita 133 133 133 ('Give the model could sufficiently...' --> 'Given that the model could sufficiently...' or 'Since the model could sufficiently...'); 'that' added also changed line 176 to match grammatical pattern Amrita 134 134 line 134 ('it reasons' --> 'it stands to reason' or 'it is reasonable to suppose' or 'it can be inferred'); changed to 'it can be inferred' also removed repeated emphasis of 'similar' Amrita 136 136 line 136 ('relative the' --> 'relative to the'); added 'to' Rohit G. 156 156 156 Well known' should be backed by a citation removed 'well known' and introduced 'standard' and a reference Rohit G. 162 162 Though a reasonable conclusion from the data, any supporting evidence would help in terms of establishing the distinction between after tastes and initial taste sensations. added a reference (20) that shows how in vitro data confirms that AceK differentially activates sweetness and bitterness simultaneously in that order Rohit G. 184 184 3 data points which do not show linear ordering is insufficient to confirm a hypothesis. Consider using different language if more data is not available. changed the language to reflect the plausibility: removed 'certain' *added ref. (25) which explains the nonlinearity of toxicity measures while (11) has bitterness curves & fittings confirming nonlinearity of bitterness receptors Rohit G. fig.2 260 Figure 2 is unclear. What do the hollow dots and the filled in dots signify? there is no difference: all points are hollow but, in some areas, they are stacked on-top of each other **A new image has been created with crosses 'x' for clarity I am grateful for the level of attention to detail and the speed of the review given to the submission. I hope the article will be of use to both mentioned articles and to groups looking for ways leverage chemoreception in their developmental research in industry and academia. Best Regards, Sambu SK. "	Here is a paper. Please give your review comments after reading it.
460	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Background: Literatures revealed that 1,4-pentadien-3-one and triazine derivatives exhibited a wide variety of biological activities. In order to develop highly bioactive molecules, in this study, a series of novel1,4-pentadien-3-one derivatives containing triazine moieties were synthesized and their antibacterial and antiviral activities were investigated. Method: A series of novel 1,4-pentadien-3-one derivatives containing triazine moieties were synthesized and characterized in detail via 1 H NMR, 13 C NMR and HRMS spectra. The antibacterial activities against Xanthomonas axonopodispv. citri (Xac), Xanthomonas oryzaepv. oryzae (Xoo) and Ralstonia solanacearum (R.s) were evaluated at 100 and 50 µg/mL using a turbidimeter and N. tabacun L. leaves under the same age as that of test subjects. The curative, protective and inactivation activities against tobacco mosaic virus (TMV) at a concentration of 500 μg/mL were evaluated by the half-leaf blight spot method. Results: The bioassay results showed that some of the target compounds exhibited fine antibacterial activities against Xac and R.s. Particularly, with half maximal effective concentration (EC 50 ) values of some target compounds against R.s are visibly better than that of the positive control bismerthiazol (BT). Notably, compound 4a showed excellent inactivation activity against TMV, the EC 50 values of 12.5 μg/mL, which was superior to that of ningnanmycin (NNM,13.5 μg/mL). Besides, molecular docking studies for 4a with tobacco mosaic virus coat protein (TMV-CP) showed that the compound was embedded well in the pocket between the two subunits of TMV-CP. These findings indicate that 1,4-pentadien-3-one derivatives containing a triazine may be potential antiviral and antibacterial agents.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>INTRODUCTION</ns0:head><ns0:p>Plant pathogens have become one of the world's largest agricultural problems because they exhibit a significant threat not only to agricultural products but also to human health <ns0:ref type='bibr'>( Li et al.,2011;</ns0:ref><ns0:ref type='bibr' target='#b2'>Lorenzo et al.,2017)</ns0:ref>. Plant pathogens diseases, such as citrus canker, rice bacterial leaf blight and tobacco bacterial wilt, were caused by Xanthomonas axonopodispv. citri (Xac), Xanthomonas oryzaepv. oryzae <ns0:ref type='bibr'>(Xoo)</ns0:ref> and Ralstonia solanacearum (R.s), respectively. They are difficult to control in agricultural production <ns0:ref type='bibr' target='#b3'>(Zou et al., 2011;</ns0:ref><ns0:ref type='bibr' target='#b4'>Li et al., 2017)</ns0:ref>. In addition, tobacco mosaic virus (TMV) can infect more than 885 plant species, causing nearly $100 million in damage worldwide <ns0:ref type='bibr' target='#b5'>(Su et al., 2016;</ns0:ref><ns0:ref type='bibr' target='#b7'>Bos et al., 2000)</ns0:ref>. Therefore, the discovery and development of new antiviral and antibacterial agents with a novel mode of action are of great importance to the medical community.</ns0:p><ns0:p>1,4-Pentadien-3-one derivatives, derived from plant metabolic products curcumin, were found to have a good range of biological activities such as antiviral <ns0:ref type='bibr' target='#b8'>(Zhang et al., 2018)</ns0:ref>, antibacterial <ns0:ref type='bibr' target='#b9'>(Long et al., 2015)</ns0:ref>, anticancer <ns0:ref type='bibr' target='#b10'>(Luo et al., 2014)</ns0:ref>, anti-inflammatory <ns0:ref type='bibr' target='#b11'>(Liu et al., 2014)</ns0:ref>, anti-oxidative <ns0:ref type='bibr'>(Masuda et al., 2015)</ns0:ref>, and anti-HIV activities <ns0:ref type='bibr' target='#b13'>(Sharma et al., 2019)</ns0:ref>. Over the past few years, the synthesis and study of pharmacological activity of 1,4-pentadien-3-one derivatives attracted the attention of many chemists <ns0:ref type='bibr' target='#b14'>(Wang et al., 2017;</ns0:ref><ns0:ref type='bibr'>Zhou et al., 2017)</ns0:ref>.</ns0:p><ns0:p>Further study on the structural optimization of 1,4-pentadien-3-one found that introducing benzotriazin-4(3H)-one <ns0:ref type='bibr' target='#b8'>(Zhang et al., 2018)</ns0:ref>, imidazole <ns0:ref type='bibr' target='#b17'>(Samaan et al., 2014)</ns0:ref>, thiazole <ns0:ref type='bibr'>(Wang et al., 2015)</ns0:ref>, or chromone <ns0:ref type='bibr' target='#b19'>(Chen et al., 2015)</ns0:ref> moieties (Figure <ns0:ref type='figure' target='#fig_0'>1</ns0:ref>. A1-A4), could greatly enhance biological activities. Notably, Chen et al. verified the anti-TMV mechanism of 1,4-pentadien-3one derivatives (Figure <ns0:ref type='figure' target='#fig_1'>2</ns0:ref>. B), and found 5-position of 1,4-pentadien-3-one nucleus plays a key role in antiviral activities <ns0:ref type='bibr' target='#b20'>(Chen et al., 2019)</ns0:ref>. In addition, triazine scaffold has been associated with diversified pharmacological activities <ns0:ref type='bibr' target='#b21'>(Irannejad et al., 2010)</ns0:ref>, such as antioxidant <ns0:ref type='bibr' target='#b22'>(Khoshneviszadeh et al., 2016)</ns0:ref>, antithrombotic <ns0:ref type='bibr' target='#b23'>(Tamboli et al., 2015</ns0:ref><ns0:ref type='bibr'>), antiplatelet (Konno et al.,1993</ns0:ref><ns0:ref type='bibr'>), anticancer (Fu et al., 2017)</ns0:ref>, thromboxane synthetase inhibition <ns0:ref type='bibr'>(Monge et al., 2010)</ns0:ref>, antimalarial <ns0:ref type='bibr' target='#b23'>(Tamboli et al., 2015)</ns0:ref>, α-glucosidase inhibition <ns0:ref type='bibr' target='#b5'>(Wang et al., 2016)</ns0:ref>, antiviral and antibacterial activities <ns0:ref type='bibr' target='#b20'>(Tang et al., 2019)</ns0:ref>. Recently, chemical research on triazine derivatives showed that the heterocyclic nitrogen had tremendous application foregrounds in the development of novel agricultural bactericides and virucides <ns0:ref type='bibr' target='#b8'>(Zhang et al., 2018)</ns0:ref>. Sangshetti et al. reported potent inhibitory effect of triazine and their derivatives against three fungals ((Candida albicans (MIC-25), Aspergillus niger (MIC-12.5) and Cryp tococcus neoformans (MIC-25)) similar to miconazole (Figure <ns0:ref type='figure' target='#fig_2'>3</ns0:ref>. C) <ns0:ref type='bibr'>(Sangshetti et al., 2010)</ns0:ref>. Therefore, triazine group was introduced into the 5-position of 1,4-pentadien-3-one nucleus to build a new molecular structure and their potential biological activities were tested (Figure <ns0:ref type='figure' target='#fig_3'>4</ns0:ref>). </ns0:p></ns0:div> <ns0:div><ns0:head>MATERIALS & METHODS</ns0:head></ns0:div> <ns0:div><ns0:head>Instruments and chemicals</ns0:head><ns0:p>Melting points were determined using an XT-4 digital melting-point apparatus (Beijing Tech. Instrument Co., China) and readings were uncorrected. 1 H NMR, 13 C NMR and 19 F NMR spectra were recorded on a 400 MHz spectrometer (Swiss Bruker) with DMSO and CDCl 3 as the solvent and tetramethylsilane as the internal standard. The course of the reaction was monitored by thin-layer-chromatography analysis on silica gel GF 254 <ns0:ref type='bibr'>(Qingdao Haiyang Chemical Company, Ltd., Qingdao, China)</ns0:ref>, and spots were visualized with ultraviolet (UV) light. Highresolution mass spectrometry (HRMS) was conducted by using a Thermo Scientific Q Exactive </ns0:p></ns0:div> <ns0:div><ns0:head>General procedure for the synthesis of intermediates</ns0:head><ns0:p>A synthetic route to 1,4-pentadien-3-one derivatives containing a triazine moiety was designed and shown in Figure <ns0:ref type='figure' target='#fig_5'>5</ns0:ref>. According to previously reported methods <ns0:ref type='bibr' target='#b20'>(Chen et al., 2019;</ns0:ref><ns0:ref type='bibr' target='#b20'>Tang et al., 2019;</ns0:ref><ns0:ref type='bibr'>, Gan et al., 2017)</ns0:ref>, intermediates 1 and 2 could be obtained. Using benzyl, biacetyl and thio-semicarbazide as the initial materials in acetic acid and water was stirred at 100-110 o C for 6-8 h to obtain the intermediate 3 <ns0:ref type='bibr' target='#b20'>(Tang et al., 2019)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>General procedure for the synthesis of target compounds 4a-4r</ns0:head><ns0:p>Reaction mixture was added to a solution of intermediate 2 (12 mmol), intermediate 3 (10 mmol) and K 2 CO 3 (30 mmol) in dimethylformamide and stirred at room temperature for 6-8 h. Upon completion of reaction (indicated by TLC), and ethyl acetate was used to extract three times (30 mL×3). the solvent was removed under reduced pressure. Residue was purified by silica-gel column chromatography using petroleum ether/ethyl acetate (3:1 v/v) to obtain target compounds 4a-4r. The 1 H NMR, 13 C NMR, 19 F NMR and HMRS spectra of the target compounds 4a-4r are also provided in the Supporting Information. </ns0:p></ns0:div> <ns0:div><ns0:head>Bioactivity assay</ns0:head></ns0:div> <ns0:div><ns0:head>Antibacterial activity in vitro</ns0:head><ns0:p>The in vitro antibacterial activities of target compounds 4a-4r against rice bacterial leaf blight, tobacco wilt and citrus canker caused by the pathogens of Xanthomonas axonopodispv. citri (Xac), Xanthomonas oryzaepv. oryzae (Xoo) and Ralstonia solanacearum (R.s), respectively, by the turbidimeter test <ns0:ref type='bibr' target='#b20'>(Tang et al., 2019;</ns0:ref><ns0:ref type='bibr' target='#b14'>Zhang et al., 2017)</ns0:ref>.This test method is provided in the Supporting Information.</ns0:p></ns0:div> <ns0:div><ns0:head>Antiviral activities in vivo</ns0:head><ns0:p>The in vivo antibacterial activities of target compounds 4a-4r against tobacco mosaic virus (TMV) by the half-leaf blight spot method <ns0:ref type='bibr' target='#b20'>(Chen et al., 2019)</ns0:ref>.This test method is provided in the Supporting Information.</ns0:p><ns0:p>PeerJ O. Chem. reviewing PDF \| (OCHEM- <ns0:ref type='table' target='#tab_7'>2019:06:38615:1:1:REVIEW 4 Oct 2019)</ns0:ref> Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:p></ns0:div> <ns0:div><ns0:head>Molecular docking</ns0:head><ns0:p>The molecular docking was performed by using DS-CDocker implemented in Discovery Studio (version 4.5). This test method is provided in the Supporting Information.</ns0:p></ns0:div> <ns0:div><ns0:head>RESULTS</ns0:head></ns0:div> <ns0:div><ns0:head>Antibacterial activities in vitro</ns0:head><ns0:p>The antibacterial activities of target compounds have been evaluated by the turbidimeter test <ns0:ref type='bibr' target='#b8'>(Zhang et al.,2018;</ns0:ref><ns0:ref type='bibr' target='#b20'>Tang et al.,2019)</ns0:ref>. Results in Table <ns0:ref type='table'>1</ns0:ref> indicated that some of synthesized compounds exhibited appreciable antibacterial activities against Xoo, R.s and Xac at the concentrates of 100 µg/mL. Among these derivatives, 4n and 4p exhibited excellent bactericidal effect against Xoo, with inhibition rates of 60.5 % and 56.5 %, respectively, which were superior to bismerthiazol (BT, 56.1 %). In addition, as Table <ns0:ref type='table'>1</ns0:ref> demonstrated that the designed compounds displayed certain bactericidal effect toward R.s. Studies on the inhibition effect of title compounds suggested that 4a, 4b, 4j and 4k exerted the excellent inhibition effect against R.s with the inhibition rates of 58.2, 53.9, 53.5 and 61.9 %, respectively, which were better than that of BT (52.1 %). It was noted that compounds 4k (91.8 %) and 4l (95.4 %) exposed better antibacterial activity toward Xac than that of BT (70.5 % ).</ns0:p><ns0:p>To further understand antibacterial activity of title compounds, the EC 50 values of some title compounds were calculated and summarized in Table <ns0:ref type='table'>2</ns0:ref>. Notably, compounds 4a, 4b, 4j and 4k exerted admirable inhibition effects against R.s, with half maximal effective concentration (EC 50 ) values of ranging from 0.43-4.76 μg/mL, which were better than that of BT (EC 50 =49.5 μg/mL).</ns0:p><ns0:p>Meanwhile, compounds 4j and 4k showed remarkable antibacterial activities against Xac with the EC 50 values of 55.53 and 129.1 μg/mL, which were better than that of BT (EC 50 =153.7 μg/mL).</ns0:p><ns0:p>Table <ns0:ref type='table'>1</ns0:ref>. Inhibition effect of the some title compounds against Xoo, R.s and Xac. a Table <ns0:ref type='table'>2</ns0:ref>. EC 50 values of some title compounds against Xoo, Xac and R.s.</ns0:p></ns0:div> <ns0:div><ns0:head>Antiviral activities against TMV in vivo</ns0:head><ns0:p>The antiviral activities of the title compounds 4a-4r against tobacco mosaic virus (TMV ) were evaluated by the half leaf method <ns0:ref type='bibr' target='#b20'>(Chen et al., 2019)</ns0:ref> and the results were summarized in Manuscript to be reviewed Chemistry Journals activity against TMV in vivo. Compounds 4f, 4k and 4l showed remarkable curative activity against TMV, with values of 53.8, 66.3 and 59.9 %, respectively. Which were better than that of ningnanmycin (NNM, 45.7 %). Meanwhile, compound 4h (61.4 %) exhibited excellent protection activity, also superior to NNM (53.4 %). Overall, most of the compounds indicated general inactivation activity against TMV at 500 µg/mL.</ns0:p><ns0:p>Based on the previous bioassays, the EC 50 values of some the title compounds were tested and are listed in Table <ns0:ref type='table' target='#tab_2'>4</ns0:ref>. Compound 4a exhibited excellent inactivation activity against TMV, with the EC 50 values of 12.5 μg/mL, which was better than that of NNM (EC 50 =13.5 μg/mL).</ns0:p><ns0:p>Moreover, compounds 4k and 4l exhibited the preferably curative activity against TMV, with EC 50 values of 11.5 and 12.1 μg/mL, respectively, which were superior to that of NNM (EC 50 =82.2 μg/mL). </ns0:p></ns0:div> <ns0:div><ns0:head>Molecular docking studies</ns0:head><ns0:p>Molecular docking studies (Figure <ns0:ref type='figure' target='#fig_17'>7 and 8</ns0:ref>) for 4a with tobacco mosaic virus coat protein (TMV-CP) (PDB code:1EI7). Molecular docking results revealed that compound 4a was the most preferred compound based on the analysis followed by 4d and so on (Table <ns0:ref type='table' target='#tab_0'>3</ns0:ref>). Compound 4a binding orientation clearly is described by Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:p></ns0:div> <ns0:div><ns0:head>DISCUSSION</ns0:head></ns0:div> <ns0:div><ns0:head>Structure-activity relationships of antibacterial activities</ns0:head><ns0:p>The antibacterial results in Tables <ns0:ref type='table'>1 and 2</ns0:ref> also indicated that the different groups on R had significant effects on the antibacterial activity of the title compounds. Obviously, the presence of -Cl-Ph group can effectively enhance the antibacterial activity against Xac. For example, the compounds 4k and 4l, which contain R=4-Cl-Ph and R=2-Cl-Ph groups respectively, exhibited EC 50 values of 55.53 and 129.1 μg/mL, which were better than that of BT (EC 50 =153.7 μg/mL).</ns0:p><ns0:p>Meanwhile, when R was substituted with thiophene-2-yl and 4-Cl-Ph groups, the corresponding compounds 4a, 4b, 4j and 4k exhibit remarkable antibacterial activities against R.s, with the EC 50 values of ranging from 0.43-4.76 μg/mL, which were better than that of BT (EC 50 =49.5 μg/mL).</ns0:p></ns0:div> <ns0:div><ns0:head>Structure-activity relationships of antiviral activities</ns0:head><ns0:p>The antiviral bioassay results indicated that the title compounds showed excellent antiviral activity against TMV. The preliminary SAR results were dropped based on the anti-TMV activity (as shown in Table <ns0:ref type='table' target='#tab_7'>3 and 4</ns0:ref>). The results indicated that when R was the 4-NO 2 -Ph (4f), 4-Cl-Ph (4k) or 2-Cl-Ph (4l) group, the corresponding title compounds exhibited good curative activity. Furthermore, when the R was 4-OMe-Ph group, the protective activity of corresponding compound 4h, with the EC 50 values of 32.1 μg/mL, which was better than that of NNM (EC 50 = 82.2 μg/mL).</ns0:p></ns0:div> <ns0:div><ns0:head>CONCLUSIONS</ns0:head><ns0:p>In short, a series of 1,4-pentadien-3-one derivatives containing a triazine scaffold were synthesized. The obtained bioassay results revealed that some of the title compounds exhibited excellent antibacterial or antiviral activities that were better than the commercial agents. In particular, compound 4a showed prominent inactivation activity against TMV. Furthermore, compound 4a had strong binding capability with TMV-CP. These results proved that the 1,4pentadien-3-one derivatives containing a triazine scaffold possess antiviral and antibacterial agents. Manuscript to be reviewed Manuscript to be reviewed Manuscript to be reviewed Manuscript to be reviewed Manuscript to be reviewed Manuscript to be reviewed Manuscript to be reviewed </ns0:p><ns0:note type='other'>Chemistry Journals Figure 1</ns0:note><ns0:note type='other'>Chemistry Journals Analytical, Inorganic, Organic, Physical, Materials Science Figure 2</ns0:note><ns0:note type='other'>Chemistry Journals Figure 3</ns0:note><ns0:note type='other'>Chemistry Journals Figure 5</ns0:note><ns0:note type='other'>Chemistry Journals Figure 6</ns0:note><ns0:note type='other'>Chemistry Journals Figure 7</ns0:note><ns0:note type='other'>Chemistry Journals Figure 8</ns0:note></ns0:div><ns0:figure xml:id='fig_0'><ns0:head>Figure 1 .</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1. Chemical structures of bioactive molecules bearing 1,4-pentadien-3-one fragment.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head>Figure 2 .</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2. The anti-TMV mechanism of 1,4-pentadien-3-one derivatives.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>Figure 3 .</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Figure 3. 1,2,4-triazine fragment against three fungals (Candida albicans, Aspergillus niger and Cryp tococcus neoformans).</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head>Figure 4 .</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Figure 4. Design strategy of title compounds.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head /><ns0:label /><ns0:figDesc>Figure 5.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head>Figure 5 .</ns0:head><ns0:label>5</ns0:label><ns0:figDesc>Figure 5. Synthesis route for the target compounds.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head>Figure 6 .</ns0:head><ns0:label>6</ns0:label><ns0:figDesc>Figure 6. Tobacco leaf morphology effects of the NNM and 4k, 4h and 4a against TMV in vivo</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_7'><ns0:head>Figure 7 and 8 ,</ns0:head><ns0:label>8</ns0:label><ns0:figDesc>it forms one hydrogen bond with PHEA:12 with highest docking score (2.49 Å) among the designed molecules and the glide energy was also less compared to others showing few hydrophobic interactions with specific residues like as TYRA:139, VALA:75, LYSB:268 etc.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_8'><ns0:head>Figure 7 .</ns0:head><ns0:label>7</ns0:label><ns0:figDesc>Figure 7 . Three dimensional diagrams of compound 4a docked with TMV-CP.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_9'><ns0:head>Figure 8 .</ns0:head><ns0:label>8</ns0:label><ns0:figDesc>Figure 8. Two dimensional diagrams of compound 4a docked with TMV-CP. The twodimensional diagram contains conventional hydrogen bonds, carbon-hydrogen bonds, Pi-Pi Tshaped bonds and Pi-Alkyl bonds.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_10'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ O. Chem. reviewing PDF \| (OCHEM-2019:06:38615:1:1:REVIEW 4 Oct 2019) Manuscript to be reviewed Chemistry Journals Analytical, Inorganic, Organic, Physical, Materials Science morpholinoethylamine moiety as potential antithrombotic agents*. Journal of Enzyme Inhibition and Medicinal Chemistry 5:1475-6366 DOI:10.3109/14756366.2015.1060480. Konno S, Matsuya Y, Kumazawa M, Amano M, Kokubo T, Sagi M. 1993. Studies on astriazine derivatives. xix. synthesis of 2, 3-diarylpyrazine and 2, 3-diarylpyridine derivatives as blood platelet aggregation inhibitors. Yakugaku Zasshi 113:40-52 DOI:10.1248/yakushi1947.113.1-40. Fu DJ, Zhang L, Song J, Mao RW, Zhao RH, Liu YC, Hou YH, Yang JJ, Zhang YB. 2017. Design and synthesis of formononetin-dithiocarbamate hybrids that inhibit growth and migration of pc-3Â cells via mapk/wnt signaling pathways. European Journal of Medicinal Chemistry 127:87-99 DOI:10.1016/j.ejmech.2016.12.027. Monge A, Palop J, Ramirez C, Font M, Fernandez-Alvarez E. 2010. Cheminform abstract: new 5H-1,2,4-triazino(5,6-b)indole and aminoindole derivatives. synthesis and studies as inhibitors of blood platelet aggregation, anti-hypertensive agents and thromboxane synthetase inhibitors. Cheminform 22:48 DOI:10.1002/chin.199129148. Tamboli RS, Giridhar R, Gandhi HP, Kanhed AM, Mande HM, Yadav MR. 2015. Design, green synthesis and pharmacological evaluation of novel 5,6-diaryl-triazine bearing 3morpholinoethylamine moiety as potential antithrombotic agents. Journal of Enzyme Inhibition and Medicinal Chemistry 5:1475-6366 DOI:10.3109/14756366.2015.1060480. Wang GC, Peng ZY, Wang J, Li J, Li X. 2016. Synthesis and biological evaluation of novel 2,4,5-triarylimidazole-1,2,3-triazole derivatives via click chemistry as α-glucosidase inhibitors. Bioorganic Medicinal Chemistry Letters 26:5719 DOI:10.1016/j.bmcl.2016.10.057. Tang X, Su SJ, Chen M, He J, Xia RJ, Guo T, Chen Y, Zhang C, Xue W. 2019. Novel chalcone derivatives containing a triazine moiety: design, synthesis, antibacterial and antiviral activities. RSC Advances 9:6011-6020 DOI:10.1039/C9RA00618D. Sangshetti JN, Shinde DB. 2010. One pot synthesis and sar of some novel 3-substituted 5,6diphenyl-triazine as antifungal agents. Cheminform 20:742-745 DOI:10.1016/j.bmcl.2009.11.048. Gan XH, Hu DY, Wang Y, Yu L, Song BA.2017. Novel trans-ferulic acid derivatives PeerJ O. Chem. reviewing PDF \| (OCHEM-2019:06:38615:1:1:REVIEW 4 Oct 2019)Manuscript to be reviewed Chemistry JournalsAnalytical, Inorganic, Organic, Physical, Materials Science </ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_11'><ns0:head>Figure 1 .</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1. Chemical structures of bioactive molecules bearing 1,4-pentadien-3-one fragment.</ns0:figDesc><ns0:graphic coords='22,42.52,224.62,525.00,221.25' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_12'><ns0:head>Figure 2 .</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2. The anti-TMV mechanism of 1,4-pentadien-3-one derivatives</ns0:figDesc><ns0:graphic coords='23,42.52,204.37,525.00,491.25' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_13'><ns0:head>Figure 3 Figure 4 Figure 4 .</ns0:head><ns0:label>344</ns0:label><ns0:figDesc>Figure 3. 1,2,4-triazine fragment against three fungals (Candida albicans, Aspergillus niger and Cryp tococcus neoformans)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_14'><ns0:head>Figure 5 .</ns0:head><ns0:label>5</ns0:label><ns0:figDesc>Figure 5. Synthesis route for the target compounds</ns0:figDesc><ns0:graphic coords='27,42.52,204.37,525.00,324.00' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_15'><ns0:head>Figure 6 .Figure 6 .</ns0:head><ns0:label>66</ns0:label><ns0:figDesc>Figure 6. Tobacco leaf morphology effects of the NNM and 4k, 4h and 4a against TMV in vivo(Right leaf: not treated with compound,Left leaf: smeared with compound)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_16'><ns0:head>Figure 7 .</ns0:head><ns0:label>7</ns0:label><ns0:figDesc>Figure 7. Three dimensional diagrams of compound 4a docked with TMV-CP.</ns0:figDesc><ns0:graphic coords='29,42.52,204.37,525.00,358.50' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_17'><ns0:head>Figure 8 .</ns0:head><ns0:label>8</ns0:label><ns0:figDesc>Figure 8. two dimensional diagrams of compound 4a docked with TMV-CP. The twodimensional diagram contains conventional hydrogen bonds, carbon-hydrogen bonds, Pi-Pi T-shaped bonds and Pi-Alkyl bonds.</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='26,42.52,255.37,525.00,157.50' type='bitmap' /></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head>Table 3 and</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Figure6. It was found that some of the title compounds exhibited good antiviral</ns0:figDesc><ns0:table /><ns0:note>PeerJ O. Chem. reviewing PDF \| (OCHEM-2019:06:38615:1:1:REVIEW 4 Oct 2019)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_1'><ns0:head>Table 3 .</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Antiviral activities of the target compounds against TMV in vivo at 500 μg/mL.</ns0:figDesc><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_2'><ns0:head>Table 4 .</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>EC 50 values of the 4a, 4d, 4h, 4k and 4l against TMV in vivo.</ns0:figDesc><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_3'><ns0:head>Table 3 (on next page)</ns0:head><ns0:label>3</ns0:label><ns0:figDesc /><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_4'><ns0:head>Table 3 .</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Antiviral activities of the target compounds against TMV in vivo at 500 μg/mL</ns0:figDesc><ns0:table><ns0:row><ns0:cell>a</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_5'><ns0:head>Table 4 (on next page)</ns0:head><ns0:label>4</ns0:label><ns0:figDesc /><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_6'><ns0:head>Table 4</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>EC 50 values of the 4a, 4d, 4h,4k and 4l against TMV in vivoTable 4 shown that the EC 50 values some of the title compounds against TMV in vivo PeerJ O. Chem. reviewing PDF \| (OCHEM-2019:06:38615:1:1:REVIEW 4 Oct 2019)</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Manuscript to be reviewed</ns0:cell></ns0:row></ns0:table><ns0:note>a</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_7'><ns0:head>Table 4</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>EC 50 values of the 4a, 4d, 4h,4k and 4l against TMV in vivo a Average of three replicates; b: A commercial agricultural antiviral agent ningnanmycin was used for comparison of antiviral activities; NNM: ningnanmycin.</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Compd.</ns0:cell><ns0:cell>against TMV</ns0:cell><ns0:cell>regression equation</ns0:cell><ns0:cell>r 2</ns0:cell><ns0:cell>EC 50</ns0:cell></ns0:row><ns0:row><ns0:cell>4a</ns0:cell><ns0:cell>Inactivation activity</ns0:cell><ns0:cell>y = 0.6712x + 4.2637</ns0:cell><ns0:cell>0.9234</ns0:cell><ns0:cell>12.5</ns0:cell></ns0:row><ns0:row><ns0:cell>4d</ns0:cell><ns0:cell>Inactivation activity</ns0:cell><ns0:cell>y = 0.8253x + 3.7000</ns0:cell><ns0:cell>0.9279</ns0:cell><ns0:cell>37.6</ns0:cell></ns0:row><ns0:row><ns0:cell>4h</ns0:cell><ns0:cell>Protection activity</ns0:cell><ns0:cell>y = 0.4739x + 4.2865</ns0:cell><ns0:cell>0.9833</ns0:cell><ns0:cell>32.1</ns0:cell></ns0:row><ns0:row><ns0:cell>4k</ns0:cell><ns0:cell>Curative activity</ns0:cell><ns0:cell>y = 0.4261x + 4.5479</ns0:cell><ns0:cell>0.9382</ns0:cell><ns0:cell>11.5</ns0:cell></ns0:row><ns0:row><ns0:cell>4l</ns0:cell><ns0:cell>Curative activity</ns0:cell><ns0:cell>y = 0.6542x + 4.2925</ns0:cell><ns0:cell>0.9191</ns0:cell><ns0:cell>12.1</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>Curative activity</ns0:cell><ns0:cell>y = 0.4415x + 4.1563</ns0:cell><ns0:cell>0.9720</ns0:cell><ns0:cell>81.4</ns0:cell></ns0:row><ns0:row><ns0:cell>NNM b</ns0:cell><ns0:cell>Protection activity</ns0:cell><ns0:cell>y = 0.4732x + 4.0939</ns0:cell><ns0:cell>0.9097</ns0:cell><ns0:cell>82.2</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>Inactivation activity</ns0:cell><ns0:cell>y = 0.8498x + 4.0381</ns0:cell><ns0:cell>0.9702</ns0:cell><ns0:cell>13.5</ns0:cell></ns0:row><ns0:row><ns0:cell>a:</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row></ns0:table><ns0:note>PeerJ O. Chem. reviewing PDF \| (OCHEM-2019:06:38615:1:1:REVIEW 4 Oct 2019)</ns0:note></ns0:figure> <ns0:note place='foot'>Analytical, Inorganic, Organic, Physical, Materials Science </ns0:note> </ns0:body> "	"Dear editor and reviewers: Thank you very much for your letter about our paper entitled “Synthesis and biological activity of 1,4-pentadien-3-one derivatives containing a triazine scaffold” (ID: 38615). We have carefully revised it according to the review opinions. These revisions marked with yellow background in the manuscript and also listed below for your reference. Attached please find the revised version, which we would like to submit for your kind consideration. We would like to express our great appreciation to you for comments on our paper. Looking forward to hearing from you. Yours sincerely, Wei Xue Below is the address of the corresponding author: Prof. Wei Xue Guizhou University, Huaxi District, Guiyang, Guizhou Province P. R. China 550025 Fax: 0086-0851-88292920 E-mail: wxue@gzu.edu.cn Answer reviewer 1 1. However, there are some mistakes and deficiencies. Answer: thank you for your suggestion. I have made carefully modifications according to the PDF version you provided and please professionals to modify the article grammar. Please refer to the modified details in the revised version. 2. line 65: 'chemists studies' Answer: thank you for your suggestion. The 'chemists studies' has been changed to “chemical research”. Please refer to the modified details in the revised version. 3. line 29: 'besides' Answer: thank you for your suggestion. 'besides' has been changed to 'Besides'. Please refer to the modified details in the revised version. 4. line 96-99: 12mmol, 10mmol, et al, add spaces for numbers and units, and check the full article. Answer: thank you for your suggestion. I have double-checked the full-text unit and added spaces. Please refer to the modified details in the revised version. Answer reviewer 2 1. There are some grammar problems in the article that need to be carefully corrected, for example the sentence “In addition, tobacco mosaic virus (TMV) can cause more than 885 plants to infected the virus, resulting in a worldwide loss of $100 million worldwide Answer: thank you for your suggestion. I have modified the sentence. Please refer to the modified details in the revised version. 2. The first letter of each sentence should be capitalized. Please carefully check the upper and lower case of the text; Answer: thank you for your suggestion. I have carefully checked the case of the letters in the whole text. Please refer to the modified details in the revised version. 3. In the Line 28 and 29: BT and TC first appeared, should be give full name; Answer: thank you for your suggestion. The full name has been added to the article. Please refer to the modified details in the revised version. 4. In the Line 91: the sentence “A synthetic route to 1,4-pentadien-3-one derivatives containing a triazine moiety was designed and is shown in Scheme 1.” tenses should be consistent; Answer: thank you for your suggestion. I have carefully revised the sentence. Please refer to the modified details in the revised version. 5. In the Line 116: “Molecular docking.” should be delete; Answer: thank you for your suggestion. “Molecular docking.” has been deleted. 6. In the article, author should pay attention to the Tables and Figures should be unified format; Answer: thank you for your suggestion. I have carefully checked the Tables and Figures, and unified format. 7. The sentence “Table 1. Inhibition effect of the sme title compounds against Xoo, R.s and Xac. a” pay attention to spelling; Answer: thank you for your suggestion. I have carefully checked the spelling, Please refer to the modified details in the revised version. 8. Figure 5 sick spots can't be seen clearly, please provide a clear picture; Answer: thank you for your suggestion. I have replaced the clear figure 5. Please refer to the modified details in the revised version. 9. Figure 6 of docking research the caption for the interaction is too small. A correction is necessary for the descriptions of “Conventional Hydrogen Bond”, “Carbon Hydrogen Bond”, “Pi-Sulfur”, “Pi-Pi T-shaped” and “Pi-Alkyl” in the lower left and lower right in the figure. Answer: thank you for your suggestion. I have added the chemical bond to figure 6, and the detailed modification has been marked with yellow background. Answer reviewer 3 1. However, the results on the biological activity should be provided in international units, i.e. microMolar and nor micrograms/mL, which does not allow for comparison purposes. Another major concern is the presentation of the actibacterial data (Tables 1 and 2) in non conventional units. The widely accepted parameter to measure antibacterial activity is MIC, and not EC50. Answer: thank you for your suggestion. I know that the microMolar and the MIC international units, but the uniform use of micrograms/mL and EC50 in our field is acceptable. Many articles use this unit such as Design, synthesis and antibacterial activities against Xanthomonasoryzaepv.oryzae, Xanthomonasaxonopodispv. Citri and Ralstoniasolanacearum of novel myricetin derivatives containing sulfonamide moiety. (doi:10.1002/ps.5587) and Design, synthesis, and antibacterial activity of novel Schiff base derivatives of quinazolin-4(3H)-one. (doi:10.1016/j.ejmech.2014.02.053) etc. "	Here is a paper. Please give your review comments after reading it.
462	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Extensive computational studies of the imine synthesis from amines and aldehydes in water have shown that the large-scale structure of water is needed to afford appropriate charge delocalisation and enable sufficient transition state stabilisation. These insights cannot, however, be applied to the understanding of the reaction pathway in apolar solvents due their inability to form extensive hidrogen-bonding networks. In this work, we perform the first computational studies of this reaction in nonpolar conditions. This density-functional study of the reaction of benzaldehyde with four closely related aromatic amines (aniline, o-toluidine, m-toluidine and p-toluidine) shows that, although an additional molecule of amine may provide some stabilization of the first transition state even in the absence of a hydrogen bonding network, this is insufficient to achieve high reaction rates.</ns0:p><ns0:p>Our computations also show that when an extra proton is added to the spectator amine, the activation energies become so low that even picomolar amounts of protonated base are enough to achieve realistic rates. Additional computations show that those minute amounts of protonated base may be obtained under reaction conditions without the addition of extraneous acid through the auto-protolysis of the amines themselves. To our knowledge, this is the first report of a role for the auto-protolysis of anilines in their extensive reactional repertoire.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>Imines can be readily synthesized through the reversible reaction of amines with aldehydes. This reaction proceeds through an addition step which forms a carbinolamine intermediate, which is then dehydrated to the imine in the rate-determining step. The released water is usually removed from the system to shift the equilibrium towards the products. The reaction rate is quite sensitive to pH: moderate amounts of acid greatly accelerate it <ns0:ref type='bibr' target='#b25'>(Santerre, Hansrote & Crowell, 1958)</ns0:ref>, but excess acid prevents it <ns0:ref type='bibr'>(Jencks, 1959 and references therein)</ns0:ref>. The decrease in reaction rate at very low pH is due to the protonation of the amine, which renders it unable to directly attack the carbonyl, whereas protonation of the carbinolamine is required to achieve high rates of dehydration <ns0:ref type='bibr' target='#b19'>(Jencks, 1964)</ns0:ref> (Figure <ns0:ref type='figure' target='#fig_1'>1</ns0:ref>). Acid catalysis has also been postulated to proceed through protonation of the carbonyl group, rendering it more susceptible to nucleophilic attack by the amine <ns0:ref type='bibr' target='#b16'>(Hammett, 1940)</ns0:ref>. The reaction may also take place in the absence of acid catalysis <ns0:ref type='bibr' target='#b22'>(Law, 1912;</ns0:ref><ns0:ref type='bibr' target='#b4'>Campbell et al., 1948;</ns0:ref><ns0:ref type='bibr' target='#b8'>Crowell & Peck, 1953)</ns0:ref>, especially with primary amines.</ns0:p><ns0:p>Computational studies of this reaction have shown that in the absence of charge stabilization by solvent the activation energies of the formation of the carbinolamine <ns0:ref type='bibr' target='#b15'>(Hall & Smith, 1998;</ns0:ref><ns0:ref type='bibr' target='#b9'>Ding, Cui & Li, 2015;</ns0:ref><ns0:ref type='bibr' target='#b5'>Ćmikiewicz, Gordon & Berski, 2018)</ns0:ref> are prohibitively high (above 25 kcal•mol -1 ) and the activation energies of its dehydration to imine <ns0:ref type='bibr' target='#b15'>(Hall & Smith, 1998;</ns0:ref><ns0:ref type='bibr' target='#b5'>Ćmikiewicz, Gordon & Berski, 2018)</ns0:ref> are even higher (between 45 and 55 kcal•mol -1 ).</ns0:p><ns0:p>Incorporation of one <ns0:ref type='bibr' target='#b15'>(Hall & Smith, 1998;</ns0:ref><ns0:ref type='bibr' target='#b9'>Ding, Cui & Li, 2015)</ns0:ref> or two <ns0:ref type='bibr' target='#b15'>(Hall & Smith, 1998)</ns0:ref> water molecules as proton transfer assistants greatly facilitates the formation of the carbinolamine by decreasing the activation energy to 8-16 kcal•mol -1 but still affords large barriers incompatible with room-temperature reaction (26.7 kcal•mol -1 ) for the dehydration step <ns0:ref type='bibr' target='#b15'>(Hall & Smith, 1998)</ns0:ref>. Realistic barriers are, however, obtained when a large number of explicit water molecules (from 9 to 29) are included in the model <ns0:ref type='bibr' target='#b28'>(Solís-Calero et al., 2012)</ns0:ref>, enabling extensive stabilization of the nascent charges present in the transition state of the dehydration step. Since so far all the computational work on this reaction has been performed on systems including only water as solvent, the aforementioned insights cannot be directly applied to reactions in nonpolar or aprotic solvents, such as the condensation of benzaldehyde with aniline (or toluidines), which is experimentally observed to proceed readily and exothermically in the absence of an acid catalyst <ns0:ref type='bibr' target='#b22'>(Law, 1912;</ns0:ref><ns0:ref type='bibr' target='#b4'>Campbell et al., 1948;</ns0:ref><ns0:ref type='bibr' target='#b8'>Crowell & Peck, 1953)</ns0:ref> or protic solvents. In the computational study described in the present manuscript we found, for the first time, a reaction pathway that affords realistic reaction barrier in the absence of hydrogenbonding stabilization by protic solvent molecules, and consequently an explanation of how this classic reaction can proceed in nonpolar solvents.</ns0:p></ns0:div> <ns0:div><ns0:head>Computational methods</ns0:head><ns0:p>The reaction mechanism was investigated using the widely used PBE0 functional <ns0:ref type='bibr' target='#b0'>(Adamo & Barone, 1999)</ns0:ref>, which we have earlier shown to be a very good choice for the description of mechanisms involving the protonation or deprotonation of ketones and amines <ns0:ref type='bibr' target='#b27'>(Silva & Ramos, 2011)</ns0:ref>. All geometry optimizations were performed with the Firefly(Granovsky) quantum chemistry package, which is partially based on the GAMESS (US) <ns0:ref type='bibr' target='#b26'>(Schmidt et al., 1993)</ns0:ref> code, using autogenerated delocalized coordinates <ns0:ref type='bibr' target='#b3'>(Baker, Kessi & Delley, 1996)</ns0:ref>. In geometry optimizations, the aug-pcseg-1 basis set <ns0:ref type='bibr' target='#b20'>(Jensen, 2014)</ns0:ref> was used for heavy atoms and the pcseg-1 basis set was used for hydrogen. Zero-point and thermal effects on the free energies at 298.15 K were computed at the optimized geometries. DFT energies of the optimized geometries were then computed using the pcseg-2 basis set <ns0:ref type='bibr' target='#b20'>(Jensen, 2014)</ns0:ref>, which is expected to be close to the complete basis set limit for DFT. The double-hybrid functional DSD-BLYP <ns0:ref type='bibr' target='#b21'>(Kozuch, Gruzman & Martin, 2010)</ns0:ref> supplemented with DFT-D3-BJ corrections <ns0:ref type='bibr' target='#b14'>(Grimme, Ehrlich & Goerigk, 2011)</ns0:ref>was chosen for these single-point energies due to its superlative performance in the computation of total energies vs. the highest quality benchmarks available <ns0:ref type='bibr'>(Goerigk & Grimme, 2011;</ns0:ref><ns0:ref type='bibr' target='#b11'>Goerigk et al., 2017)</ns0:ref>. Auto-protolysis constants (pKs) of various amines, ethylene carbonate, acetonitrile, and nitromethane were computed by comparing the energies of separately optimized neutral clusters of each molecule to clusters of the same size which included one single instance of protonated (or deprotonated) molecule. In all cases, intra-and inter-molecular dispersion effects were included in the geometry optimization, frequency calculation, and highlevel single point steps using the DFT-D3 formalism with Becke-Johnson damping developed by <ns0:ref type='bibr' target='#b13'>Grimme et al. (Grimme et al., 2010;</ns0:ref><ns0:ref type='bibr' target='#b14'>Grimme, Ehrlich & Goerigk, 2011)</ns0:ref>. Solvation effects in aniline were computed using the Polarizable Continuum Model <ns0:ref type='bibr'>(Tomasi & Persico, 1994;</ns0:ref><ns0:ref type='bibr' target='#b23'>Mennucci & Tomasi, 1997;</ns0:ref><ns0:ref type='bibr' target='#b7'>Cossi et al., 1998)</ns0:ref> <ns0:ref type='table' target='#tab_2'>2019:01:34538:1:0:NEW 9 Oct 2020)</ns0:ref> Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science interactions with the continuum solvent were computed using the method developed by Amovili and Mennucci <ns0:ref type='bibr' target='#b2'>(Amovilli & Mennucci, 1997)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head><ns0:p>The reactions of benzaldehyde with aniline and its three mono-methylated derivatives (otoluidine, m-toluidine, and p-toluidine) were studied in the gas phase. In all cases, the most stable initial arrangement of aldehyde and the aromatic amine finds both molecules parallel to each other due to the interaction between their aromatic clouds (Figure <ns0:ref type='figure' target='#fig_2'>2</ns0:ref>). In the traditional description of this reaction mechanism, the subsequent formation of the carbinolamine intermediate proceeds through the simultaneous attack of the carbonyl carbon atom by the amine lone pair and proton transfer from the amine to the carbonyl oxygen atom. The geometry of this transition state (Figure <ns0:ref type='figure' target='#fig_2'>2</ns0:ref>) is virtually identical for the four aromatic amines studied, with a N-C distance of 1.608-1.613 Å, a NH-O distance of 1.384-1.389 Å and a C-O distance of 1.336 Å almost exactly between that of a C-O double bond (1.215 Å ) and a C-O single bond (1.411 Å). The transition states are, however, very hard to reach as they lie 55.4-70.6 kcal•mol -1 above the pre-reactional complex state (Table <ns0:ref type='table'>1</ns0:ref>). Since these high activation energies are incompatible with the experimentally observed syntheses of imines from aldehyde and aromatic amines at temperatures between 0 and 60 ºC <ns0:ref type='bibr'>(Allen & VanAllan, 1941;</ns0:ref><ns0:ref type='bibr' target='#b4'>Campbell et al., 1948;</ns0:ref><ns0:ref type='bibr' target='#b8'>Crowell & Peck, 1953)</ns0:ref>, the actual reaction mechanism must be more complex than commonly postulated.</ns0:p><ns0:p>Additional computations showed that the inclusion of an additional molecule of amine greatly facilitates the formation of the carbinolamine by assisting the proton transfer from the amine to the carbonyl oxygen (Figure <ns0:ref type='figure'>3A</ns0:ref>). The increased energetic stabilization (between 21 and 32 kcal•mol -1 , relative to the unaided reaction) yields barriers of 34.4-38.8 kcal•mol -1 above the pre-reactional complex state (Table <ns0:ref type='table' target='#tab_1'>2</ns0:ref>), still far above the values required for detectable reaction rates. The reaction now becomes exergonic (vs.</ns0:p><ns0:p>PeerJ O. Chem. reviewing PDF \| (OCHEM-2019:01:34538:1:0:NEW 9 Oct 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science pre-reactional complex) in almost all cases, with the reaction with o-toluidine as the sole exception due to the geometric constraints entailed by the close proximity of the methyl substituents in the aromatic amines. Carbinolamine dehydration proved to be difficult (Table <ns0:ref type='table' target='#tab_1'>2</ns0:ref>), with barriers ranging between 28 and 45 kcal•mol -1 relative to the carbinolamine. Previous computational studies of this reaction <ns0:ref type='bibr' target='#b15'>(Hall & Smith, 1998;</ns0:ref><ns0:ref type='bibr' target='#b28'>Solís-Calero et al., 2012)</ns0:ref> showed that this step is also difficult in water models unless large solvent cages are used, which allow very efficient charge delocalization throughout the hydrogen-bonded network <ns0:ref type='bibr' target='#b28'>(Solís-Calero et al., 2012)</ns0:ref>. Since such stabilization is exceedingly unlikely to be available in aromatic amine solvents due to their inability to form such extended networks, we analysed other possibilities of achieving acceptable reaction rates for the dehydration step. Inspired by the observation of dimeric derivatives of imines obtained through electrochemical reduction <ns0:ref type='bibr' target='#b22'>(Law, 1912)</ns0:ref> we evaluated the feasibility of stabilizing the carbinolamine dehydration step with a second molecule of carbinolamine. Interaction of two carbinolamines with each other to form a bimolecular pre-reactional complex is energetically very favorable (by 80-95 kcal•mol -1 ), and the subsequent barriers range from 5.2 (for m-toluidine) to 18-21 kcal•mol -1 (for the other aromatic amines) (Table <ns0:ref type='table'>3 and Figure</ns0:ref> <ns0:ref type='figure'>4</ns0:ref>). These activation energies are well into the range of experimental feasibility, provided that a mechanism for the initial formation of the carbinolamine (that circumvents the high barriers encountered previously) can be found.</ns0:p><ns0:p>The feasibility of acid-assisted catalysis was therefore explored. Our computations showed that the addition of one protonated molecule of amine grealy facilitated the atack of the carbonyl by the neutral amine, through the strong stabilization of the nascent negative charge on the carbonyl oxygen (Figure <ns0:ref type='figure'>5</ns0:ref>). The N-protonated carbinolamine formed in this step may then transfer the extra proton to the assisting base, which in turn funnels it to the leaving hydroxyl group, yielding the protonated Schiff base and a water molecule. For the aliphatic amines tested, the results obtained were not very promising: although the initial formation of the N-protonated carbinolamine did indeed proceed without an energetic barrier, the subsequent proton transfer to the leaving hydroxyl group proved to be quite endergonic due to the relative instability of the produced N-protonated imine. Still, barriers of at most 23 kcal•mol -1 (implying reaction rates of Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science at least 0.3 h -1 ) were obtained in all cases. The height of the barrier decreased markedly when the amine was changed from ethylamine to ethenylamine and ethynylamine, confirming that the introduction of π-delocalization stabilizes the product (and that the endergonicity of the reactions can be overcome by facilitating the spreading of the anscent positive charge throughout the molecule). In agreement with this interpretation, the reaction barriers obtained (Tables <ns0:ref type='table' target='#tab_2'>4 and 5</ns0:ref>) for the reactions of acetaldehyde or benzaldehyde with the four aromatic amines tested are very small (from 2.5 to 10.5•kcal•mol -1 ) and therefore have extremely high reaction rates (1.24 × 10 5 -9.1 × 10 10 s -1 ). The experimentally observed reaction rates (on the order of 1 h -1 ) can therefore be achieved with minute concentrations of protonated base (10 -15 -10 -9 mol•dm -3 ).</ns0:p><ns0:p>We hypothesized that, even without the addition of acid catalysts, such minute amounts of protonated amine might be available through the auto-protolysis of the amine. Indeed, even some solvents generally regarded as aprotic or only weakly protic have been experimentally</ns0:p><ns0:p>shown to auto-ionize to a limited extent <ns0:ref type='bibr' target='#b24'>(Mihajlović et al., 1996)</ns0:ref>. To ascertain the likelihood of auto-protolysis of aniline and toluidines, we performed additional computations using small clusters of amine molecules, one of which was kept protonated (or deprotonated). Since very accurate results would require the simulation of very large solvent clusters to account for possible long-range structural rearrangements around the ionized structures, which are unfortunately not possible with our current computational resources, we compared our results with the auto-protolysis constants, computed in the same way, of other solvents which have been studied experimentally. Our results (Table <ns0:ref type='table' target='#tab_3'>6</ns0:ref>) show that the auto-protolysis of most of the amines tested is much more favourable than that of ethylene carbonate (pKs=21.5) nitromethane (pKs=23.7) or acetonitrile (pKs=28.8), and that therefore self-ionization of aniline or toluidines can easily afford concentrations of protonated amine at least as high as 10 -21.5/2 , which render accessible the mechanism postulated above.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions</ns0:head><ns0:p>Like the analogous reaction in water <ns0:ref type='bibr' target='#b9'>(Ding, Cui & Li, 2015)</ns0:ref>, imine formation from benzaldehyde and anilines in nonpolar solvent cannot occur without the intervention of a base which facilitates the transfer of one proton from the amine nitrogen atom to the PeerJ O. Chem. reviewing PDF \| (OCHEM-2019:01:34538:1:0:NEW 9 Oct 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science carbonyl oxygen. Although the energetic stabilization provided by this assistance decreases activation energy by more than 20 kcal•mol -1 relative to the reaction in the gas phase, this is not sufficient to enable reasonable rates of formation of the carbinolamine.</ns0:p><ns0:p>The carbinolamine dehydration step is also prohibitively expensive, but can be made more accessible if a bimolecular mechanism (where one carbinolamine catalyzes the dehydration of the other) is taken into account. Both steps can be made much more accessible if the nascent negative charge in the attacked carbonyl (or the leaving hydroxyl in the dehydration step) are stabilized through interaction with the protonated forms of the reacting bases. Our computations, in turn, show that auto-protolysis of the amines to generate these species is feasible and that the low activation energies of the protonated amine-assisted mechanism fully enable the observation of good reaction rates even from the minute concentrations of protonated amine predicted to exist in water-free aniline/benzaldehyde mixtures. Manuscript to be reviewed Relative free energies vs. pre-reactional complex (kcal•mol-1)of the species involved in the base-assisted formation of carbinolamines from benzaldehyde and aniline derivatives Energies computed at the DSD-BLYP-D3(BJ)/pcseg2//PBE0-D3(BJ)/(aug)-pcseg1 theory level.</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Solvation effects in aniline were included with the PCM formalism.</ns0:p><ns0:p>PeerJ O. Chem. reviewing PDF \| (OCHEM-2019:01:34538:1:0:NEW 9 Oct 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science Manuscript to be reviewed Relative free energies (kcal•mol-1) of the species involved in the aromatic aminiumassisted dehydration of the carbinolamines produced from the reaction of benzadelhyde with aromatic amines</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Energies computed at the DSD-BLYP-D3(BJ)/pcseg2//PBE0-D3(BJ)/(aug)-pcseg1 theory level.</ns0:p><ns0:p>Solvation effects in aniline were included with the PCM formalism. ) of different solvents.</ns0:p><ns0:p>Geometries optimized at the PBE0-D3(BJ)/(aug)-pcseg1 level. Energies computed with DSD-BLYP-D3(BJ)/pcseg-2. Solvation effects were included with the PCM formalism. Experimental values were taken from <ns0:ref type='bibr' target='#b24'>(Mihajlović et al., 1996)</ns0:ref> PeerJ O. Chem. reviewing PDF \| (OCHEM-2019:01:34538:1:0:NEW 9 Oct 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science Manuscript to be reviewed Manuscript to be reviewed</ns0:p><ns0:note type='other'>Chemistry Journals Analytical, Inorganic, Organic, Physical, Materials Science Figure 1</ns0:note><ns0:note type='other'>Chemistry Journals Figure 5</ns0:note><ns0:p>Reaction profile of the protonated-amine-assisted formation of Schiff bases.</ns0:p><ns0:p>A) N-protonated carbinolamine and neutral amine; B) carbinolamine and protonated amine; Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ O. Chem. reviewing PDF \| (OCHEM-2019:01:34538:1:0:NEW 9 Oct 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head>Table 1 :</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Relative free energies vs. pre-reactional complex (kcal•mol -1 )of the species involved in the formation of carbinolamines from benzaldehyde and aniline derivatives, computed at the DSD-BLYP-D3(BJ)/ pcseg2//PBE0-D3(BJ)/(aug)-pcseg1 theory level. Solvation effects in aniline were included with the PCM formalism. Chem. reviewing PDF \| (OCHEM-2019:01:34538:1:0:NEW 9 Oct 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>Table 2 :</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Relative free energies vs. pre-reactional complex (kcal•mol -1 )of the species involved in the base-assisted formation of carbinolamines from benzaldehyde and aniline derivatives, computed at the DSD-BLYP-D3(BJ)/pcseg2//PBE0-D3(BJ)/(aug)-pcseg1 theory level. Solvation effects in aniline were included with the PCM formalism. Chem. reviewing PDF \| (OCHEM-2019:01:34538:1:0:NEW 9 Oct 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head /><ns0:label /><ns0:figDesc>Figure 2</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head>C</ns0:head><ns0:label /><ns0:figDesc>) transition state of the dehydration step; D) Potential energy surfaces of the reactions of acetaldehyde with methylamine (blue), ethylamine (red), ethenylamine (green) and ethynylamine (violet); E) Potential energy surfaces of the reactions of acetaldehyde with aniline (blue), m-toluidine (red), o-toluidine (green) and p-toluidine (violet) PeerJ O. Chem. reviewing PDF \| (OCHEM-2019:01:34538:1:0:NEW 9 Oct 2020)</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='25,42.52,255.37,525.00,263.25' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='26,42.52,255.37,525.00,350.25' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='27,42.52,255.37,525.00,322.50' type='bitmap' /></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head /><ns0:label /><ns0:figDesc>implemented in Firefly. Dispersion and repulsion</ns0:figDesc><ns0:table><ns0:row><ns0:cell>PeerJ O. Chem. reviewing PDF \| (OCHEM-</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_1'><ns0:head>Table 2 (on next page)</ns0:head><ns0:label>2</ns0:label><ns0:figDesc /><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_2'><ns0:head>Table 5 (on next page)</ns0:head><ns0:label>5</ns0:label><ns0:figDesc /><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_3'><ns0:head>Table 6 (on next page) Computed</ns0:head><ns0:label>6</ns0:label><ns0:figDesc /><ns0:table /><ns0:note>auto-protolysis energies (kcal•mol -1</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_4'><ns0:head>Table 6 : Computed auto-protolysis energies (kcal•mol -1 ) of different solvents. Geometries optimized at the PBE0-D3(BJ)/(aug)-pcseg1 level. Energies computed with DSD-BLYP- D3(BJ)/pcseg-2. Solvation effects were included with the PCM formalism. Experimental values were taken from (Mihajlović et al., 1996)</ns0:head><ns0:label>6</ns0:label><ns0:figDesc /><ns0:table><ns0:row><ns0:cell /><ns0:cell>Auto-protolysis energies ( kcal•mol -1 )</ns0:cell><ns0:cell>Experimental auto-</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>protolysis constant</ns0:cell></ns0:row><ns0:row><ns0:cell>ethenylamine</ns0:cell><ns0:cell>69.4</ns0:cell><ns0:cell>n.a.</ns0:cell></ns0:row><ns0:row><ns0:cell>ethynylamine</ns0:cell><ns0:cell>72.5</ns0:cell><ns0:cell>n.a.</ns0:cell></ns0:row><ns0:row><ns0:cell>p-toluidine</ns0:cell><ns0:cell>80.7</ns0:cell><ns0:cell>n.a.</ns0:cell></ns0:row><ns0:row><ns0:cell>aniline</ns0:cell><ns0:cell>82.2</ns0:cell><ns0:cell>n.a.</ns0:cell></ns0:row><ns0:row><ns0:cell>o-toluidine</ns0:cell><ns0:cell>83.4</ns0:cell><ns0:cell>n.a.</ns0:cell></ns0:row><ns0:row><ns0:cell>ethylamine</ns0:cell><ns0:cell>85.1</ns0:cell><ns0:cell>n.a.</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>ethylene carbonate 88.2</ns0:cell><ns0:cell>10 -21.5</ns0:cell></ns0:row><ns0:row><ns0:cell>acetonitrile</ns0:cell><ns0:cell>95.8</ns0:cell><ns0:cell>10 -28.8</ns0:cell></ns0:row><ns0:row><ns0:cell>methylamine</ns0:cell><ns0:cell>100.9</ns0:cell><ns0:cell>n.a.</ns0:cell></ns0:row><ns0:row><ns0:cell>nitromethane</ns0:cell><ns0:cell>105.9</ns0:cell><ns0:cell>10 -23.7</ns0:cell></ns0:row><ns0:row><ns0:cell>m-toluidine</ns0:cell><ns0:cell>106.1</ns0:cell><ns0:cell>n.a.</ns0:cell></ns0:row></ns0:table><ns0:note>PeerJ O. Chem. reviewing PDF \| (OCHEM-2019:01:34538:1:0:NEW 9 Oct 2020)</ns0:note></ns0:figure> <ns0:note place='foot'>Analytical, Inorganic, Organic, Physical, Materials Science </ns0:note> <ns0:note place='foot' n='6'>PeerJ O. Chem. reviewing PDF \| (OCHEM-2019:01:34538:1:0:NEW 9 Oct 2020)Manuscript to be reviewed Chemistry JournalsAnalytical, Inorganic, Organic, Physical, Materials Science </ns0:note> </ns0:body> "	"Dear Editor, I apologize for the large delay in my resubmission. This was brought about by my realization (prompted by the reviewers’ suggestions) that the basis set used for the optimizations in the original work was of insufficient quality: contrary to my assumption, pcseg-0 was not comparable to 6-31G* (a standard basis set for optimizations) but was rather closer to 4-31G, which has long been deprecated. I have remade the whole work using pcseg-1, which is a triple-zeta basis set, as well as provided single-point energies using DSD-BLYP (which according to the benchmarks by Goerigk et al. is as close as “gold standard” as a DFT method can currently be). My responses to the reviewers follow: Reviewer 1 Basic reporting This article appears to meet the stated standards. Experimental design This article appears to meet the stated standards. Validity of the findings I have concerns about the results. While the calculations were rather straightforward, the conclusions seem to be drawn on particular orientations of a single water molecule. To my mind, the authors would need to sample a multitude of possible orientations to assure that configuration space is adequately covered to draw meaningful conclusions. In addition, I worry about carrying out such sampling in the presence of a continuum rather than a large number of explicit solvent molecules. My response: Use of a large number of solvent molecules unfortunately could not be performed due to the lack of QM/MM codes. I have, however, carried out molecular dynamics simulations of benzaldehyde in a box of aniline, which showed that the required geometric arrangement (as evaluated from the N-O distances) was reasonably frequent (around 7.7% of the time). To obtain estimates of the stability of the postulated bimolecular complexes, the potential of mean force was obtained through umbrella sampling simulations performed by constraining the distance between the C=O in benzaldehyde (or carbinolamine) and the NH3+ of anilinium (or N-H in a second carbinolamine) with a harmonic potential of the form with k equal to 10.0 kcal/mol/Å2. Molecular dynamics wimulations were performed in YASARA(Krieger & Vriend, 2015)⁠ using the AMBER14 forcefield(Maier et al., 2015)⁠. Charge assigment was performed with the AM1BCC protocol(Jakalian et al., 2000; Jakalian, Jack & Bayly, 2002)⁠ . Sampling was performed in bins 0.5 Å apart, for 6 ns per bin. In each bin, the first full ns was discarded from the analysis. The unbiased distributions were obtained through the weighted histogram analysis method (WHAM)(Kumar et al., 1992; Grossfield, 2018)⁠ using a bin size of 0.2 Å. The resulting PMF graphs are shown below: Although the relation between PMFs and actual DeltaG is not completely straightforward (and these values can therefore be somewhat exaggerated relative to the actual DeltaGs), these data do show that the postulated complexes can arise in solution. Reviewer 2: Basic reporting Manuscript is very well written. I suggest addition of discussion to the introduction regarding importance of understanding Schiff base formation. I would also suggest substituting 'apolar' for 'nonpolar' throughout. I have performed this replacement, as requested. Experimental design Methods are likely to be adequate. However, I suggest the authors add discussion on the appropriateness of the PBE0 method for such systems. Citation of a relevant benchmark study has been added. Validity of the findings All data provided are appropriate. Reviewer 3 Basic reporting This is a purely computational study investigating pathways towards imine synthesis with specific emphasis on solvent effects. Prior work has been referenced appropriately. I have no problem with this being an entirely computational study without any experimental evidence, however, as outlined further below, I do have some concerns regarding the methodology that has been used and the reliability of the results. I also find the discussion relatively short and feel the author could elaborate more on the findings. A few things, which I will mention below, are not quite clear and hence it would be difficult to reproduce the results. I think a major revision is warranted. Experimental design The positive thing is that the author took into account London-dispersion interactions, something that has been often ignored in computational organic chemistry in the past. That being said, it is surprising that the author uses an outdated version of the DFT-D3 correction instead of the more reliable and accurate 2011 version with Becke-Johnson damping. There is also no reason given why PBE0 was chosen as the functional (more on this in the next point). The basis sets seem to be OK. As also requested by reviewer #2, I have added a citation to a benchmark supporting the use of PBE0 in similar systems. The author mentions explicit solvation, but even after rereading various sections of the manuscript multiple times, I would not be able to reproduce those results if I wanted to. How many explicit solvent molecules were included? Only one? If that is the case, this would not qualify as a reliable study. There was indeed a single stabilizing solvent molecule, since I feared that including more would (besides all the additional computational expense due to the steep scaling of these computations with system size) lead me into a problem of many different conformational minima, and that might make it more difficult to ensure that I was indeed comparing TS and reactants from the same conformational basins. I think the reliability of this study is not hampered greatly by this choice, since the bulkiness of the aromatic rings renders unlikely the simultaneous stabilization of the system by more than one solvent molecule at any ge given time. I have, nonetheless, changed the text to highlight the fact that I could get good rates in the absence of polar molecules, instead (the text was changed from “The present manuscript fills this gap by describing the first computational study of this reaction in the presence of explicit non-aqueous solvent molecules” to “In the computational study described in the present manuscript we found, for the first time, a reaction pathway that affords realistic reaction barrier in the absence of hydrogen-bonding stabilization by protic solvent molecules, and consequently an explanation of how this classic reaction can proceed in apolar solvents.” ) Validity of the findings 1)I already commented on how difficult it was to understand how exactly solvent molecules were treated explicitly? The author does not mention any sampling of the solvent shell, how many molecules were included etc. Please check my response to similar concerns by reviewer #1 2) The discrepancy between experiments and calculated high activation barriers could not only be due to the postulated reaction mechanism (see ll. 71-74), but due to the chosen methodology. However, such critical analysis is missing. In fact, The reliance on PBE0 is a bit worrying. Major benchmark studies have shown that it is only a mediocre functional for reaction energies and barrier heights (see the GMTKN55 and MGCDB84 databases and subsequent recommendations and insights). While I can accept this functional for geometry optimisations, the systems (at least in the gas phase) are not prohibitively large for dispersion corrected double hybrids or at least the better hybrid functionals recommended in the aforementioned studies. As such, I do not agree that PBE0-D3/aug-pcseg-2 can be considered a “high level” (line 49). If the author decides to stick with the chosen methodology, then a paragraph is needed that puts it into context with larger benchmark studies. An expected error range should be provided and it should be made clear that this is only a very qualitative study. This comment has been very important to correct this manuscript: the use of a double hybrid to obtain single-point energies showed that the corresponding data made no sense at all. The search for the origin of the discrepancy allowed me to ascertain that the optimized geometries themselves were to blame, and I have therefore remade the whole work using a triple-zeta basis set. The improved work has important mechanistic differences relative to the earlier version: specifically, the initial step remained out of reach even in the presence of a stabilizing amine molecule. Protonated amines, however, can readily catlyze the whole reaction. 3) What type charges are reported in l. 78 and the lines thereafter? Mulliken charges or more reliable ones? Those were Löwdin charges. Since their precise values are not important to the qualitative interpreation of the potential surfaces, I have removed those mentions. "	Here is a paper. Please give your review comments after reading it.
464	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Opuntia megarrhiza is an endemic plant used in Mexican traditional medicine for the treatment of bones fractures in humans and domestic animals. One of the most used technique for the detection and characterization of the structure of phytochemical compounds is the Gas Chromatography Coupled to Mass Spectrometry. The goals of the present study were to identify and characterize the phytochemical compounds present in wild individuals of O. megarrhiza using this analysis. We used chloroform and methanol extracts from cladodes, and they were analyzed by gas chromatography-electron impactmass spectrometry. We obtained 53 phytochemical compounds, 19 have previously identified with some biological activity. Most of these compounds are alkanes, alkenes, aromatic hydrocarbons, fatty acids, and ketones. We detected some fragmentation patterns that are described for the first time for this species. The variety of metabolites presents in O. megarrhiza justifies the medicinal use of this plant in traditional medicine and highlight it as a source of phytochemical compounds with potential in medicine and biotechnology.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>INTRODUCTION</ns0:head><ns0:p>The members of Cactaceae represent a diverse evolutionary lineage endemic to America, over 1,450 species belonging to ca. 127 genera <ns0:ref type='bibr'>(Barthlott & Hunt, 1993;</ns0:ref><ns0:ref type='bibr'>Hunt et al., 2006;</ns0:ref><ns0:ref type='bibr'>Hernández-Hernández et al., 2011)</ns0:ref>. They are successful plants adapted to arid and semiarid environments, where the conditions imply a constantly stress, so they have developed different phytochemical compounds with an important biological activity such as alkaloids, amino acids, antioxidant phenol components (betalains and flavonoids), carotenoids, coumarins, esters, fibers, phytosterols, tannins, terpenes, tocopherols, and vitamins C and E <ns0:ref type='bibr' target='#b9'>(Piattelli et al., 1965;</ns0:ref><ns0:ref type='bibr'>Stintzing et al., 2001;</ns0:ref><ns0:ref type='bibr'>Strack et al., 2003;</ns0:ref><ns0:ref type='bibr'>Paiz et al., 2010;</ns0:ref><ns0:ref type='bibr'>Sim et al., 2010;</ns0:ref><ns0:ref type='bibr'>Osorio-Esquivel et al., 2011;</ns0:ref><ns0:ref type='bibr'>Harlev et al., 2012;</ns0:ref><ns0:ref type='bibr'>Aruwa et al., 2018;</ns0:ref><ns0:ref type='bibr'>Araujo et al., 2021)</ns0:ref>. Bioactive phytochemical concentration of 5 ppm and were analyzed in the GC-MS using the same parameters than in the samples.</ns0:p></ns0:div> <ns0:div><ns0:head>RESULTS</ns0:head><ns0:p>A total of 53 phytochemical compounds were detected based on the analyses of the obtained chromatograms (Tables <ns0:ref type='table' target='#tab_7'>1 to 4</ns0:ref>). The (CH 3 ) 2 CO/MeOH extract showed eleven peaks with the PVDF membrane filter and twelve with the PTFE. The (CH 3 ) 2 CO/CHCl 3 extract showed twentythree peaks with PVDF and seven with PTFE (Figure <ns0:ref type='figure'>2</ns0:ref>).</ns0:p><ns0:p>The analysis from (CH 3 ) 2 CO/MeOH extract with PVDF membrane filter showed the presence of eleven phytochemical constituents. Five alkanes: Octane, 2,3,6,7-tetramethyl (1.17%), Decane, 2,3,5,8-tetramethyl-(1.78%), Heptadecane (1.03%), Hexadecane, 7-methyl-(1%), Decane, 2,3,6trimethyl-(1.80%). One aromatic hydrocarbon: Benzene, 1,3-bis(1,1-dimethylethyl)-(4.19%).</ns0:p><ns0:p>One ester: R-(+)-Methyl-2-isopropyl-5-oxohexanoate (2.78%). One ketone: 4-Isopropyl-1,3cyclohexanedione (2.19%). One Halogenated hydrocarbons: Tetrapentacontane, 1,54-dibromo-(1.41%). Two fatty acids: n-Hexadecanoic acid (4.67%), Octadecanoic acid (2.47%) (Figure <ns0:ref type='figure'>2a</ns0:ref>, Table <ns0:ref type='table'>1</ns0:ref>).</ns0:p><ns0:p>The analysis from the (CH 3 ) 2 CO/MeOH extract with PTFE membrane filter twelve phytochemical constituents were detected. Eight alkanes: Decane, 4-methyl-(1.44%), Heptacosane (0.98%), Pentacosane (1.66%), Heneicosane (0.98%), Nonacosane (2.28%), Triacontane (1.4%), Hentriacontane (1.63%), Octacosane (1.28%). Two aromatic hydrocarbons:</ns0:p><ns0:p>Benzene, 1,3-bis(1,1-dimethylethyl)-(3.53%), Phenol, 2,4-bis(1,1-dimethylethyl)-(2.13%). One ketone: 4-Isopropyl-1,3-cyclohexanedione (1.84%). One lipid: gamma-Sitosterol (1.76%) (Figure <ns0:ref type='figure'>2b</ns0:ref>, Table <ns0:ref type='table' target='#tab_2'>2</ns0:ref>). Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>The analysis from the (CH 3 ) 2 CO/CHCl 3 extract with PVDF membrane filter showed the presence of twenty-three phytochemical constituents. Three alkanes: Tetratriacontane (0.92%), Tetrapentacontane (0.93%), Eicosane (1.34%). Three aromatic hydrocarbons: Benzene, 4-ethyl-1,2-dimethyl-(2.27%), Benzene, 1,2,4,5-tetramethyl-(0.51%), Benzothiazole (5.33%). Three ketones: 2H-Pyran-2-one, 6-hexyltetrahydro-(0.58%), 7,9-Di-tert-butyl-1-oxaspiro (4,5) deca-6,9-diene-2,8-dione (1.61%), . Two alcohols: Phytol (0.45%), 2-Methyl-Z,Z-3,13-octadecadienol (0.45%). One aldehyde: Tridecanedial (0.58%). Five alkenes: 1,3-Cyclopentadiene, 1,2,3,4-tetramethyl-5methylene-(0.82%), 5-Octadecene, (E)-(0.67%), 9-Octadecene, (E)-(1.65%), <ns0:ref type='bibr'>2,</ns0:ref><ns0:ref type='bibr'>6,</ns0:ref><ns0:ref type='bibr'>10,</ns0:ref><ns0:ref type='bibr'>14,</ns0:ref><ns0:ref type='bibr'>2,</ns0:ref><ns0:ref type='bibr'>6,</ns0:ref><ns0:ref type='bibr'>10,</ns0:ref><ns0:ref type='bibr'>14,</ns0:ref>. Six fatty acids: Hexadecanoic acid, methyl ester (1.1%), 9,12-Octadecadienoic acid (Z,Z)-, methyl ester (0.65%), Octadecanoic acid, methyl ester (0.97%), Hexadecanoic acid, butyl ester (4.83%), Phosphonic acid, dioctadecyl ester ( 0.55%), Octadecanoic acid, 2-methylpropyl ester (3.10%) (Figure <ns0:ref type='figure'>2c</ns0:ref>, Table <ns0:ref type='table' target='#tab_4'>3</ns0:ref>).</ns0:p><ns0:p>The analysis from (CH 3 ) 2 CO/CHCl 3 extract with PTFE membrane filter seven compounds were observed. One alkane: Cyclohexane, 1-(1,5-dimethylhexyl)-4-(4-methylpentyl)-(1.20%). Four</ns0:p><ns0:formula xml:id='formula_0'>aromatic hydrocarbons: Benzene, 1-ethyl-2,4-dimethyl-(2.51%), Benzene, 4-ethyl-1,2-dimethyl- (4.24%), Benzene, 2-ethyl-1,3-dimethyl-(0.83%), Phenol, 2,4-bis(1,1-dimethylethyl)-(5.28%).</ns0:formula><ns0:p>One ester: 1,2-Benzenedicarboxylic acid, mono(2-ethylhexyl) ester (7.55%). One fatty acid: Octadecanoic acid, 2-methylpropyl ester (10.83%) (Figure <ns0:ref type='figure'>2d</ns0:ref>, Table <ns0:ref type='table' target='#tab_6'>4</ns0:ref>).</ns0:p><ns0:p>The analyses reveled different nature kinds for the identified compounds alkanes, aromatic hydrocarbons, esters, ketones halogenated hydrocarbons, alcohols, aldehydes, alkenes, lipids, and fatty acids, some of them with a biological activity previously reported (Tables <ns0:ref type='table' target='#tab_11'>5, 6, and 7</ns0:ref> Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science activities previously reported (Tables <ns0:ref type='table' target='#tab_11'>6 and 7</ns0:ref>); their mass spectra resulting from the GC-MS analyses and chemical structures are presented in Figures <ns0:ref type='figure'>S1 to S4</ns0:ref>.</ns0:p><ns0:p>Ten phytochemical compounds shown in Figure <ns0:ref type='figure'>3</ns0:ref> were the most prevailing in the two extracts (CHCl 3 and MeOH) and both membrane filters (PVDF and PTFE): Benzene, 1,3-bis(1,1dimethylethyl) (4.19 %) in MeOH/PVDF and (3.53 %) in (MeOH/PTFE), n-Hexadecanoic acid (4.67 %) in MeOH/PVDF; Benzothiazole (5.33 %), Hexadecanoic acid, butyl ester (4.83 %), Octadecanoic acid, 2-methylpropyl ester (3.10 %), <ns0:ref type='bibr'>2,</ns0:ref><ns0:ref type='bibr'>6,</ns0:ref><ns0:ref type='bibr'>10,</ns0:ref><ns0:ref type='bibr'>14,</ns0:ref><ns0:ref type='bibr'>6,</ns0:ref><ns0:ref type='bibr'>10,</ns0:ref><ns0:ref type='bibr'>14,</ns0:ref><ns0:ref type='bibr'>18eicosapentaene (4.80 %)</ns0:ref>, and Stigmastan-3,5-diene (13.80%) in CHCL 3 / PVDF; and Benzene, 4ethyl-1,2-dimethyl-(4.24 %), Phenol, 2,4-bis(1,1-dimethylethyl)-(5.28 %) and 1,2-Benzenedicarboxylic acid, mono (2-ethylhexyl) ester (7.55 %) in CHCl 3 /PTFE (Figure <ns0:ref type='figure'>3</ns0:ref>).</ns0:p><ns0:p>Identity from five compounds that showed a similarity percentage above 95%, was supported by comparison of their retention times with pure commercial standards (Figure <ns0:ref type='figure'>S5</ns0:ref>).</ns0:p><ns0:p>Benzothiazole was found at Tr of 14.61 min., with ions (m/z) of 135 and 107.9. Heneicosane at Tr of 33.5 min., with ions (m/z) of 57, 113. Hentriacontane at Tr of 48.7 min., with ions (m/z) of 57, 85, and 113. Hexadecanoic acid, methyl ester at Tr of 30.5 min., with ions (m/z) of <ns0:ref type='bibr'>74, 143, 227, and 55.</ns0:ref> Triacontane was detected at Tr of 33.5 min., with ions (m/z) 57, 85 and 113.</ns0:p><ns0:p>Finally, 34 compounds with no identified biological activity were found, eight in the CH 3 ) 2 CO/ MeOH extract with PVDF membrane filter (MeOH/PVDF), five in the CH 3 ) 2 CO/MeOH extract with PTFE (MeOH/PTFE), 16 in the CH 3 ) 2 CO/CHCl 3 extract with PVDF (CHCL 3 /PVDF), and five in the (CH 3 ) 2 CO/CHCl 3 extract with PTFE (CHCL 3 /PTFE). Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>DISCUSSION</ns0:head><ns0:p>The use of Opuntia megarrhiza in traditional medicine in Mexico has been reported previously <ns0:ref type='bibr'>(Segura-Venegas & Rendón-Aguilar, 2016)</ns0:ref>, however, this is the first study that demonstrate the presence of phytochemical compounds with biological activities. Previously, it has been reported that several Opuntia species are used in the world as local medicinal interventions for chronic diseases and as food sources, mainly because they possess nutritional properties and biological activities that has been recently reviewed <ns0:ref type='bibr'>(Aruwa et al., 2018)</ns0:ref>. Here we report for the first time, the identification of several phytochemical compounds in O. megarrhiza with biological activities. Our findings highlight the relevance of this species in developing of new drugs, trough future chemical studies, and encourage of planting this species once this one is listed as endangered in the Red List of the IUCN.</ns0:p><ns0:p>Biotechnological methods are reliable and provide continuous sources of raw material and natural products for food, pharmaceutical, and cosmetic industries <ns0:ref type='bibr'>(Rao & Ravishankar, 2002;</ns0:ref><ns0:ref type='bibr' target='#b4'>Nalawade et al., 2003;</ns0:ref><ns0:ref type='bibr'>Julsing et al., 2007)</ns0:ref>. Previously, it has been indicated that more than 50,000 plant species are used in phytotherapy and medicine, and around 66% of them are harvested from nature leading to local extinction of many species or degradation of their habitats <ns0:ref type='bibr'>(Tasheva & Kosturkova, 2012)</ns0:ref>. Alternatives to protect these useful plants, should be directed to both preservation of the plant populations and elevating the level of knowledge for sustainable utilization of these plants in medicine have been previously indicated <ns0:ref type='bibr'>(WHO, 2010)</ns0:ref>.</ns0:p><ns0:p>Biotechnological methods offer possibilities not only for faster cloning and conservation of the genotype of the plants <ns0:ref type='bibr'>(Verpoorte et al., 2002;</ns0:ref><ns0:ref type='bibr'>Tripathi & Tripathi, 2003)</ns0:ref> but for modification of their gene information, regulation, and expression for production of valuable substances in higher amounts or with better properties <ns0:ref type='bibr'>(Rao & Ravishankar, 2002;</ns0:ref><ns0:ref type='bibr'>Khan et al., 2009)</ns0:ref>.</ns0:p><ns0:p>GC-MS is one of the most precise methods to identify various metabolites present in plant extracts <ns0:ref type='bibr'>(Fiehn et al., 2000;</ns0:ref><ns0:ref type='bibr'>Roessner et al., 2000;</ns0:ref><ns0:ref type='bibr'>Roessner et al., 2001;</ns0:ref><ns0:ref type='bibr'>Kopka, 2006a;</ns0:ref><ns0:ref type='bibr'>Kopka,</ns0:ref> PeerJ O. Chem. reviewing PDF \| (OCHEM- <ns0:ref type='table' target='#tab_7'>2021:08:64433:1:1:NEW 3 Dec 2021)</ns0:ref> Manuscript to be reviewed Chemistry Journals <ns0:ref type='bibr'>Analytical, Inorganic, Organic, Physical, Materials Science 2006b;</ns0:ref><ns0:ref type='bibr'>Fiehn, 2006;</ns0:ref><ns0:ref type='bibr'>Fernie, 2007;</ns0:ref><ns0:ref type='bibr'>Saito & Matsuda, 2010;</ns0:ref><ns0:ref type='bibr'>Tiago et al., 2016;</ns0:ref><ns0:ref type='bibr'>Dinesh-Kumar & Rajakumar, 2018)</ns0:ref> since some of these chromatographs include preloaded libraries or databases (NIST and Pubchem) that allows to know the possible identity of the metabolites by comparing the resulting mass spectra with those found as reference in these libraries <ns0:ref type='bibr'>(Kim et al., 2019;</ns0:ref><ns0:ref type='bibr'>Wei et al., 2014)</ns0:ref>. Several studies indicate that Opuntia plants contain different phytochemical groups such as phenolic acids, sterols, esters, coumarins, terpenoids, and alkaloids with several health benefits <ns0:ref type='bibr' target='#b9'>(Piattelli et al., 1965;</ns0:ref><ns0:ref type='bibr'>Stintzing, et al., 2001;</ns0:ref><ns0:ref type='bibr'>Strack et al., 2003;</ns0:ref><ns0:ref type='bibr'>Paiz et al., 2010;</ns0:ref><ns0:ref type='bibr'>Osorio-Esquivel et al., 2011;</ns0:ref><ns0:ref type='bibr'>Aruwa et al., 2018)</ns0:ref>. However, the nature of the compound extracted depends largely on their solubility in the extraction solvent, the degree of polymerization of the phenols, and the interaction of the phenols with other constituents of the plant <ns0:ref type='bibr'>(Choi et al., 2002;</ns0:ref><ns0:ref type='bibr'>El Cadi et al., 2020)</ns0:ref>. But the use of different membrane filters allows to identified chemical compounds with different hydrophobicity and molecule sizes. Previously, it has been indicated that PTFE has less hydrophobic adsorption but more size exclusion <ns0:ref type='bibr' target='#b12'>(Xiao et al., 2014)</ns0:ref>.</ns0:p><ns0:p>In addition, identity of five of the compounds found was corroborated using pure commercial standards. The ions obtained from each of the standards corresponded to those found in the extracts according to the NIST base of the equipment. GC-MS has a library of mass spectra, which makes it easy to obtain compounds that have the most similar mass to the library spectrum <ns0:ref type='bibr'>(Kim et al., 2019)</ns0:ref>. However, the attribution of a GC-MS chromatographic peak should be confirmed whenever possible by comparison with a standard compound analyzed under the same experimental conditions <ns0:ref type='bibr'>(Sturaro et al., 1994)</ns0:ref>. We identified five compounds in the extracts performed through the use of standards. In this context, the analytical standard is used as a reference in the qualitative, quantitative and identity determinations of a compound, it must also have high purity and stability <ns0:ref type='bibr'>(Sun et al., 2015)</ns0:ref>. Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science On the other hand, the major phytochemical compounds found in our study have been reported to possess several biological activities. Some alkanes like Hentriacontane and Triacontane have antibacterial activity <ns0:ref type='bibr'>(Boussaada et al., 2008;</ns0:ref><ns0:ref type='bibr'>Olubunmi et al., 2009;</ns0:ref><ns0:ref type='bibr'>Hsouna et al., 2011;</ns0:ref><ns0:ref type='bibr'>Tiagy & Agarwal, 2017)</ns0:ref>. Heptadecane have antifungal activity <ns0:ref type='bibr'>(Adeleye et al., 2010)</ns0:ref>. Eicosane has both antibacterial and antifungal activity <ns0:ref type='bibr'>(Hsouna et al., 2011)</ns0:ref>. Heneicosane, Heptadecane, Octacosane, and Pentacosane have antimicrobial activity <ns0:ref type='bibr'>(Rahbar et al., 2012;</ns0:ref><ns0:ref type='bibr' target='#b1'>Marrufo et al., 2013;</ns0:ref><ns0:ref type='bibr'>Usha Nandhini et al., 2015;</ns0:ref><ns0:ref type='bibr'>Jun et al., 2018)</ns0:ref>. Heptadecane and Hentriacontane have antiinflammatory activity <ns0:ref type='bibr'>(Kim et al., 2011;</ns0:ref><ns0:ref type='bibr'>Kim et al., 2013)</ns0:ref>. Heptacosane, Heptadecane, Octacosane, and Pentacosane have antioxidant activity <ns0:ref type='bibr'>(Kim et al., 2013;</ns0:ref><ns0:ref type='bibr' target='#b1'>Marrufo et al., 2013;</ns0:ref><ns0:ref type='bibr'>Jun et al., 2018)</ns0:ref>. Eicosane and Triacontane have antitumor activity <ns0:ref type='bibr'>(Hsouna et al., 2011;</ns0:ref><ns0:ref type='bibr'>Tiagy & Agarwal, 2017)</ns0:ref>. Triacontane has antidiabetic activity <ns0:ref type='bibr'>(Tiagy & Agarwal, 2017)</ns0:ref>. Heneicosane has been reported as an antiasthmatic and urine acidifier <ns0:ref type='bibr'>(Usha Nandhini et al., 2015)</ns0:ref>. Eicosane, Octadecane, Hexadecanoic acid, and Eicosane has been previously identified in Opuntia stricta <ns0:ref type='bibr'>(Izuegbuna et al., 2019)</ns0:ref>.</ns0:p><ns0:p>Fatty acids like Octadecanoic acid have antibacterial or antifungal activity <ns0:ref type='bibr'>(Gehan et al., 2009;</ns0:ref><ns0:ref type='bibr'>Hsouna et al., 2011)</ns0:ref>, and it has previously identified in Opuntia dillenii <ns0:ref type='bibr'>(Ben-Lataief et al., 2020)</ns0:ref>. The n-Hexadecanoic acid have antialopecic, anti-androgenic, antifibrinolytic, antioxidant, antipsychotic, hemolytic, hypocholesterolemic, nematicide, pesticide, 5-Alpha reductase inhibitor (USDA [U.S. Department of Agriculture, Agricultural Research Service] 1992-2016), and antiinflammatory <ns0:ref type='bibr'>(Aparna et al., 2012)</ns0:ref>. Octadecanoic acid have been reported as anticarcinogen or antitumoral <ns0:ref type='bibr'>(Hsouna et al., 2011;</ns0:ref><ns0:ref type='bibr'>Gehan et al., 2009)</ns0:ref>. Fatty acid like Hexadecanoic acid, butyl ester, Hexadecanoic acid, methyl ester, and Octadecanoic acid, methyl ester, has antimicrobial activity <ns0:ref type='bibr'>(Sujatha et al., 2014;</ns0:ref><ns0:ref type='bibr'>Abubakar & Majinda, 2016)</ns0:ref> <ns0:ref type='bibr'>Pongprayoon et al., 1992;</ns0:ref><ns0:ref type='bibr'>Lee et al., 1999;</ns0:ref><ns0:ref type='bibr'>Okiei et al., 2009;</ns0:ref><ns0:ref type='bibr'>Saikia et al., 2010;</ns0:ref><ns0:ref type='bibr'>Hema et al., 2011;</ns0:ref><ns0:ref type='bibr'>Costa et al., 2012;</ns0:ref><ns0:ref type='bibr'>Silva et al., 2014;</ns0:ref><ns0:ref type='bibr' target='#b3'>Mohammad et al., 2016;</ns0:ref><ns0:ref type='bibr'>Islam et al., 2018)</ns0:ref>, and the 1,2-Benzenedicarboxylic acid, mono (2-ethylhexyl) ester has anti-inflammatory, antimicrobial, antioxidant, antiviral, and cytotoxicity activities <ns0:ref type='bibr'>(Krishnan et al., 2014)</ns0:ref>.</ns0:p><ns0:p>Additionally, phytochemical compounds we found in Opuntia megarrhiza with no reports of biological activity, have been previously identified in other Opuntia species. For example, Oleic acid (9-Octadecene) and Stigmastan-3,5-diene was identified in O. dillenii <ns0:ref type='bibr'>(Ben-Lataief et al., 2020)</ns0:ref>. Additionally, β-Sitosterol is the major sterol extracted from different parts of the fruit oils of Opuntia ficus-indica <ns0:ref type='bibr'>(Ramadan & Mörsel, 2003a;</ns0:ref><ns0:ref type='bibr'>Ramadan & Mörsel, 2003b)</ns0:ref>. Herein, we identify gamma-Sitosterol in O. megarrhiza.</ns0:p></ns0:div> <ns0:div><ns0:head>CONCLUSIONS</ns0:head><ns0:p>The GC-MS analysis of cladode extracts of Opuntia megarrhiza conducted here proves the presence of several phytochemical compounds responsible for biological activities previously reported support the medicinal use of this plant in traditional medicine. Particularly, the antiinflammatory activity in compounds with a high similarity percentage in our results (e.g. n-Hexadecanoic acid, Phenol, 2,4-bis(1,1-dimethylethyl)-, Hentriacontane, Benzothiazole, and Hexadecanoic acid, methyl ester), support its use treating bone fractures. Hence, O. megarrhiza Manuscript to be reviewed Manuscript to be reviewed Manuscript to be reviewed Manuscript to be reviewed Manuscript to be reviewed Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Chemistry Journals Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ O. Chem. reviewing PDF \| (OCHEM-2021:08:64433:1:1:NEW 3 Dec 2021)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>). From the identified compounds, 19 shown similarities to phytochemical compounds with biological PeerJ O. Chem. reviewing PDF \| (OCHEM-2021:08:64433:1:1:NEW 3 Dec 2021)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ O. Chem. reviewing PDF \| (OCHEM-2021:08:64433:1:1:NEW 3 Dec 2021)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ O. Chem. reviewing PDF \| (OCHEM-2021:08:64433:1:1:NEW 3 Dec 2021)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head>.</ns0:head><ns0:label /><ns0:figDesc>Benzenoids like 1,2-Benzenedicarboxylic acid, mono 2-ethylhexyl) ester has been reported as cytotoxic (Krishnan et PeerJ O. Chem. reviewing PDF \| (OCHEM-2021:08:64433:1:1:NEW 3 Dec 2021) Manuscript to be reviewed Chemistry Journals Analytical, Inorganic, Organic, Physical, Materials Science al., 2014). The diterpene Phytol has been reported to have multiple activities like anticarcinogen, anticonvulsant, antifungal, anti-inflammatory, antimalaria, antimicrobial, antinociceptive, antioxidant, antitubercular, antispasmodic, anxiolytic, autophagy and apoptosis inducing, cytotoxic, immune-modulating, metabolism-modulating, resistant to gonorrhea, and stimulant (USDA [U.S. Department of Agriculture, Agricultural Research Service] 1992-2016;</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head /><ns0:label /><ns0:figDesc>Figure 1</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head /><ns0:label /><ns0:figDesc>Phytochemical compounds identified by GC-MS from MeOH extract with PVDF membrane filter. RT: Retention time. Percentage of similarity to the reference spectrum of the NIST library. PeerJ O. Chem. reviewing PDF \| (OCHEM-2021:08:64433:1:1:NEW 3 Dec 2021)</ns0:figDesc></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head>Table 1 (on next page)Table 1 .</ns0:head><ns0:label>11</ns0:label><ns0:figDesc /><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_2'><ns0:head>Table 2 (on next page)</ns0:head><ns0:label>2</ns0:label><ns0:figDesc /><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_3'><ns0:head>Table 2 .</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Phytochemical compounds identified by GC-MS from MeOH extract with PTFE membrane filter. RT: Retention time. Percentage of similarity to the reference spectrum of the NIST library.</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Peak</ns0:cell><ns0:cell>RT</ns0:cell><ns0:cell>Name of the</ns0:cell><ns0:cell>Molecular</ns0:cell><ns0:cell>Peak</ns0:cell><ns0:cell>Similarity</ns0:cell><ns0:cell>Molecular</ns0:cell><ns0:cell>Compound</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>(%)</ns0:cell><ns0:cell>formula</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>No.</ns0:cell><ns0:cell>(min)</ns0:cell><ns0:cell>compound</ns0:cell><ns0:cell>Weight</ns0:cell><ns0:cell>area</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell>nature</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>(g/mol)</ns0:cell><ns0:cell>(%)</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>8.33</ns0:cell><ns0:cell>Decane, 4-methyl-</ns0:cell><ns0:cell>156.30</ns0:cell><ns0:cell>1.44</ns0:cell><ns0:cell>64</ns0:cell><ns0:cell>C11H24</ns0:cell><ns0:cell>Alkane</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='2'>15.35 Benzene, 1,3-bis(1,1-dimethylethyl)-</ns0:cell><ns0:cell>190.32</ns0:cell><ns0:cell>3.53</ns0:cell><ns0:cell>95</ns0:cell><ns0:cell>C14H22</ns0:cell><ns0:cell>Aromatic Hydrocarbon</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='2'>17.20 4-Isopropyl-1,3-cyclohexanedione</ns0:cell><ns0:cell>154.21</ns0:cell><ns0:cell>1.84</ns0:cell><ns0:cell>53</ns0:cell><ns0:cell>C9H14O2</ns0:cell><ns0:cell>Ketone</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='2'>21.61 Heptacosane</ns0:cell><ns0:cell>380.73</ns0:cell><ns0:cell>0.98</ns0:cell><ns0:cell>83</ns0:cell><ns0:cell>C27H56</ns0:cell><ns0:cell>Alkane</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='2'>22.05 Phenol, 2,4-bis(1,1-dimethylethyl)-</ns0:cell><ns0:cell>206.32</ns0:cell><ns0:cell>2.13</ns0:cell><ns0:cell>97</ns0:cell><ns0:cell>C14H22O</ns0:cell><ns0:cell>Aromatic Hydrocarbon</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='2'>22.69 Pentacosane</ns0:cell><ns0:cell>352.68</ns0:cell><ns0:cell>1.66</ns0:cell><ns0:cell>64</ns0:cell><ns0:cell>C25H52</ns0:cell><ns0:cell>Alkane</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='2'>26.55 Heneicosane</ns0:cell><ns0:cell>296.57</ns0:cell><ns0:cell>0.98</ns0:cell><ns0:cell>90</ns0:cell><ns0:cell>C21H44</ns0:cell><ns0:cell>Alkane</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='2'>46.73 Nonacosane</ns0:cell><ns0:cell>408.8</ns0:cell><ns0:cell>2.28</ns0:cell><ns0:cell>95</ns0:cell><ns0:cell>C29H60</ns0:cell><ns0:cell>Alkane</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='2'>48.04 Triacontane</ns0:cell><ns0:cell>422.81</ns0:cell><ns0:cell>1.4</ns0:cell><ns0:cell>96</ns0:cell><ns0:cell>C30H62</ns0:cell><ns0:cell>Alkane</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='2'>49.33 Hentriacontane</ns0:cell><ns0:cell>436.83</ns0:cell><ns0:cell>1.63</ns0:cell><ns0:cell>93</ns0:cell><ns0:cell>C31H64</ns0:cell><ns0:cell>Alkane</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='2'>50.57 Octacosane</ns0:cell><ns0:cell>394.76</ns0:cell><ns0:cell>1.28</ns0:cell><ns0:cell>95</ns0:cell><ns0:cell>C28H58</ns0:cell><ns0:cell>Alkane</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='2'>52.22 gamma-Sitosterol</ns0:cell><ns0:cell>414.70</ns0:cell><ns0:cell>1.76</ns0:cell><ns0:cell>90</ns0:cell><ns0:cell>C29H50O</ns0:cell><ns0:cell>Lipids</ns0:cell></ns0:row></ns0:table><ns0:note>PeerJ O. Chem. reviewing PDF \| (OCHEM-2021:08:64433:1:1:NEW 3 Dec 2021) Manuscript to be reviewed Chemistry Journals Analytical, Inorganic, Organic, Physical, Materials Science PeerJ O. Chem. reviewing PDF \| (OCHEM-2021:08:64433:1:1:NEW 3 Dec 2021)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_4'><ns0:head>Table 3 (on next page)</ns0:head><ns0:label>3</ns0:label><ns0:figDesc /><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_5'><ns0:head>Table 3 .</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Phytochemical compounds identified by GC-MS from CHCl 3 extract with PVDF membrane filter. RT: Retention time. Percentage of similarity to the reference spectrum of the NIST library.</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Peak</ns0:cell><ns0:cell>RT</ns0:cell><ns0:cell>Name of the</ns0:cell><ns0:cell>Molecular</ns0:cell><ns0:cell>Peak</ns0:cell><ns0:cell>Similarity</ns0:cell><ns0:cell>Molecular</ns0:cell><ns0:cell>Compound</ns0:cell></ns0:row><ns0:row><ns0:cell>No.</ns0:cell><ns0:cell>(min)</ns0:cell><ns0:cell>compound</ns0:cell><ns0:cell>Weight</ns0:cell><ns0:cell>area</ns0:cell><ns0:cell>(%)</ns0:cell><ns0:cell>formula</ns0:cell><ns0:cell>nature</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>(g/mol)</ns0:cell><ns0:cell>(%)</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>1</ns0:cell><ns0:cell cols='2'>10.45 Benzene, 4-ethyl-1,2-dimethyl-</ns0:cell><ns0:cell>134.21</ns0:cell><ns0:cell cols='2'>2.27 93</ns0:cell><ns0:cell>C10H14</ns0:cell><ns0:cell>Aromatic Hydrocarbon</ns0:cell></ns0:row><ns0:row><ns0:cell>2</ns0:cell><ns0:cell cols='2'>11.34 Benzene, 1,2,4,5-tetramethyl-</ns0:cell><ns0:cell>134.21</ns0:cell><ns0:cell cols='2'>0.51 81</ns0:cell><ns0:cell>C10H14</ns0:cell><ns0:cell>Aromatic Hydrocarbon</ns0:cell></ns0:row><ns0:row><ns0:cell>3</ns0:cell><ns0:cell cols='2'>11.47 1,3-Cyclopentadiene, 1,2,3,4-tetramethyl-5-</ns0:cell><ns0:cell>134.21</ns0:cell><ns0:cell cols='2'>0.82 95</ns0:cell><ns0:cell>C10H14</ns0:cell><ns0:cell>Alkene</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>methylene-</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>4</ns0:cell><ns0:cell cols='2'>14.53 Benzothiazole</ns0:cell><ns0:cell>135.18</ns0:cell><ns0:cell cols='2'>5.33 95</ns0:cell><ns0:cell>C7H5NS</ns0:cell><ns0:cell>Aromatic Hydrocarbon</ns0:cell></ns0:row><ns0:row><ns0:cell>5</ns0:cell><ns0:cell cols='2'>18.59 Phytol</ns0:cell><ns0:cell>296.53</ns0:cell><ns0:cell cols='2'>0.45 42</ns0:cell><ns0:cell>C20H40O</ns0:cell><ns0:cell>Alcohol</ns0:cell></ns0:row><ns0:row><ns0:cell>6</ns0:cell><ns0:cell cols='2'>22.70 2H-Pyran-2-one, 6-hexyltetrahydro-</ns0:cell><ns0:cell>184.27</ns0:cell><ns0:cell cols='2'>0.58 59</ns0:cell><ns0:cell>C11H20O2</ns0:cell><ns0:cell>Ketone</ns0:cell></ns0:row><ns0:row><ns0:cell>7</ns0:cell><ns0:cell cols='2'>30.09 5-Octadecene, (E)-</ns0:cell><ns0:cell>252.47</ns0:cell><ns0:cell cols='2'>0.67 78</ns0:cell><ns0:cell>C18H3</ns0:cell><ns0:cell>Alkene</ns0:cell></ns0:row><ns0:row><ns0:cell>8</ns0:cell><ns0:cell cols='2'>30.95 7,9-Di-tert-butyl-1-oxaspiro (4,5) deca-6,9-</ns0:cell><ns0:cell>276.37</ns0:cell><ns0:cell cols='2'>1.61 50</ns0:cell><ns0:cell>C17H24O3</ns0:cell><ns0:cell>Ketone</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>diene-2,8-dione</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>9</ns0:cell><ns0:cell cols='2'>31.01 Hexadecanoic acid, methyl ester</ns0:cell><ns0:cell>270.45</ns0:cell><ns0:cell cols='2'>1.1 93</ns0:cell><ns0:cell>C17H34O2</ns0:cell><ns0:cell>Fatty acid</ns0:cell></ns0:row><ns0:row><ns0:cell>10</ns0:cell><ns0:cell cols='2'>33.98 9-Octadecene, (E)-</ns0:cell><ns0:cell>252.5</ns0:cell><ns0:cell cols='2'>1.65 89</ns0:cell><ns0:cell>C18H36</ns0:cell><ns0:cell>Alkene</ns0:cell></ns0:row><ns0:row><ns0:cell>11</ns0:cell><ns0:cell cols='2'>34.20 9,12-Octadecadienoic acid (Z,Z)-, methyl</ns0:cell><ns0:cell>294.47</ns0:cell><ns0:cell cols='2'>0.65 96</ns0:cell><ns0:cell>C19H34O2</ns0:cell><ns0:cell>Fatty acid</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>ester</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>12</ns0:cell><ns0:cell cols='2'>34.31 2-Methyl-Z,Z-3,13-octadecadienol</ns0:cell><ns0:cell>280.5</ns0:cell><ns0:cell cols='2'>0.45 53</ns0:cell><ns0:cell>C19H36O</ns0:cell><ns0:cell>Alcohol</ns0:cell></ns0:row><ns0:row><ns0:cell>13</ns0:cell><ns0:cell cols='2'>34.78 Octadecanoic acid, methyl ester</ns0:cell><ns0:cell>298.50</ns0:cell><ns0:cell cols='2'>0.97 93</ns0:cell><ns0:cell>C19H38O2</ns0:cell><ns0:cell>Fatty acid</ns0:cell></ns0:row><ns0:row><ns0:cell>14</ns0:cell><ns0:cell cols='2'>34.88 Tridecanedial</ns0:cell><ns0:cell>212.33</ns0:cell><ns0:cell cols='2'>0.58 62</ns0:cell><ns0:cell>C13H24O2</ns0:cell><ns0:cell>Aldehyde</ns0:cell></ns0:row><ns0:row><ns0:cell>15</ns0:cell><ns0:cell cols='2'>34.97 Aspidospermidin-17-ol, 1-acetyl-19,21-</ns0:cell><ns0:cell>414.5</ns0:cell><ns0:cell cols='2'>0.84 52</ns0:cell><ns0:cell cols='2'>C23H30N2O5 Ketone</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>epoxy-15,16-dimethoxy-</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>16</ns0:cell><ns0:cell cols='2'>35.84 Hexadecanoic acid, butyl ester</ns0:cell><ns0:cell>312.53</ns0:cell><ns0:cell cols='2'>4.83 87</ns0:cell><ns0:cell>C20H40O2</ns0:cell><ns0:cell>Fatty acid</ns0:cell></ns0:row><ns0:row><ns0:cell>17</ns0:cell><ns0:cell cols='2'>36.24 Phosphonic acid, dioctadecyl ester</ns0:cell><ns0:cell>585.9</ns0:cell><ns0:cell cols='2'>0.55 53</ns0:cell><ns0:cell cols='2'>C36H74O3P + Fatty acid</ns0:cell></ns0:row><ns0:row><ns0:cell>18</ns0:cell><ns0:cell cols='2'>39.21 Octadecanoic acid, 2-methylpropyl ester</ns0:cell><ns0:cell>340.58</ns0:cell><ns0:cell cols='2'>3.10 87</ns0:cell><ns0:cell>C22H44O2</ns0:cell><ns0:cell>Fatty acid</ns0:cell></ns0:row><ns0:row><ns0:cell>19</ns0:cell><ns0:cell cols='2'>39.39 Tetratriacontane</ns0:cell><ns0:cell>478.91</ns0:cell><ns0:cell cols='2'>0.92 90</ns0:cell><ns0:cell>C34H70</ns0:cell><ns0:cell>Alkane</ns0:cell></ns0:row><ns0:row><ns0:cell>20</ns0:cell><ns0:cell cols='2'>40.96 Tetrapentacontane</ns0:cell><ns0:cell>759.45</ns0:cell><ns0:cell cols='2'>0.93 80</ns0:cell><ns0:cell>C54H110</ns0:cell><ns0:cell>Alkane</ns0:cell></ns0:row><ns0:row><ns0:cell>21</ns0:cell><ns0:cell cols='2'>42.48 Eicosane</ns0:cell><ns0:cell>282</ns0:cell><ns0:cell cols='2'>1.34 68</ns0:cell><ns0:cell>C20H42</ns0:cell><ns0:cell>Alkane</ns0:cell></ns0:row><ns0:row><ns0:cell>22</ns0:cell><ns0:cell cols='2'>45.81 2,6,10,14,18-Pentamethyl-2,6,10,14,18-</ns0:cell><ns0:cell>350.6</ns0:cell><ns0:cell cols='2'>4.80 74</ns0:cell><ns0:cell>C25H50</ns0:cell><ns0:cell>Alkene</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>eicosapentaene</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>23</ns0:cell><ns0:cell cols='2'>52.21 Stigmastan-3,5-diene</ns0:cell><ns0:cell>396.7</ns0:cell><ns0:cell cols='2'>13.80 60</ns0:cell><ns0:cell>C29H48</ns0:cell><ns0:cell>Alkene</ns0:cell></ns0:row></ns0:table><ns0:note>PeerJ O. Chem. reviewing PDF \| (OCHEM-2021:08:64433:1:1:NEW 3 Dec 2021) Manuscript to be reviewed Chemistry Journals Analytical, Inorganic, Organic, Physical, Materials Science PeerJ O. Chem. reviewing PDF \| (OCHEM-2021:08:64433:1:1:NEW 3 Dec 2021)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_6'><ns0:head>Table 4 (on next page)</ns0:head><ns0:label>4</ns0:label><ns0:figDesc /><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_7'><ns0:head>Table 4 .</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Phytochemical compounds identified by GC-MS from CHCl 3 extract with PTFE membrane filter. RT: Retention time. Percentage of similarity to the reference spectrum of the NIST library.</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Manuscript to be reviewed</ns0:cell></ns0:row></ns0:table><ns0:note>PeerJ O. Chem. reviewing PDF \| (OCHEM-2021:08:64433:1:1:NEW 3 Dec 2021) PeerJ O. Chem. reviewing PDF \| (OCHEM-2021:08:64433:1:1:NEW 3 Dec 2021)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_8'><ns0:head>Table 5 (on next page)</ns0:head><ns0:label>5</ns0:label><ns0:figDesc /><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_9'><ns0:head>Table 5 .</ns0:head><ns0:label>5</ns0:label><ns0:figDesc /><ns0:table><ns0:row><ns0:cell>Chemistry Journals Analytical, Inorganic, Organic, Physical, Materials Science</ns0:cell></ns0:row><ns0:row><ns0:cell>Number and type of phytochemical compounds in Opuntia megarrhiza by extract and</ns0:cell></ns0:row><ns0:row><ns0:cell>membrane filter. Alkanes (A), Aromatic hydrocarbons (Ah), esters (E), ketones (K),</ns0:cell></ns0:row><ns0:row><ns0:cell>Halogenated hydrocarbons (Hh), Alcohols (Al), Aldehydes (Ad), Alkenes (Ak), Lipids (L), Fatty</ns0:cell></ns0:row><ns0:row><ns0:cell>acids (Fa).</ns0:cell></ns0:row></ns0:table><ns0:note>PeerJ O. Chem. reviewing PDF \| (OCHEM-2021:08:64433:1:1:NEW 3 Dec 2021) Manuscript to be reviewed Antimicrobial and antioxidant (Jun et al., 2018) PeerJ O. Chem. reviewing PDF \| (OCHEM-2021:08:64433:1:1:NEW 3 Dec 2021)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_10'><ns0:head>Table 7 (on next page)</ns0:head><ns0:label>7</ns0:label><ns0:figDesc /><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_11'><ns0:head>Table 7 .</ns0:head><ns0:label>7</ns0:label><ns0:figDesc>Phytochemical compounds with biological activity identified by GC-MS in CHCl 3 extract. m/z: mass-to-charge ratio. *Percentage of similarity to the reference spectrum of the NIST library.</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Chemistry Journals Analytical, Inorganic, Organic, Physical, Materials Science</ns0:cell></ns0:row></ns0:table><ns0:note>PeerJ O. Chem. reviewing PDF \| (OCHEM-2021:08:64433:1:1:NEW 3 Dec 2021) Manuscript to be reviewed 206 Aromatic hydrocarbon Anti-inflammatory, antimicrobial and Antioxidant USDA [U.S. Department of Agriculture, Agricultural Research Service] PeerJ O. Chem. reviewing PDF \| (OCHEM-2021:08:64433:1:1:NEW 3 Dec 2021)</ns0:note></ns0:figure> <ns0:note place='foot'>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:note> </ns0:body> "	"December 2nd, 2021. Dear Professor Eder Lenardao PeerJ Editor Please find the files of the revised version of the Manuscript ID: 64433 entitled 'GC-MS analysis of phytochemical compounds of Opuntia megarrhiza (Cactaceae), an endangered plant of Mexico' submitted to the PeerJ Organic Chemestry. Changes were highlighted in red. All persons entitled to authorship have been named and all authors have seen and agreed to the submitted version of the manuscript. In the following pages we explain how we considered every suggestion by editor and the two reviewers. Dr. Pablo Delgado Sánchez Facultad de Agronomía y Veterinaria-Universidad Autónoma de San Luis Potosí. pablo.delgado@uaslp.mx Dr. José Arturo de Nova Vázquez Instituto de Investigación de Zonas Desérticas-Universidad Autónoma de San Luis Potosí. arturo.denova@gmail.com Editor Comments: I have received the comments of three experts in natural compounds analysis. As you can see by the reviews, your work needs considerable adjustments to be acceptable for publication. However, if you are able to address the questions and suggestions of each reviewer (mainly reviewers #1 and #3) I could reconsider my decision. R: We attended the questions of Reviewer 1 and 3 as follows: Reviewer 1 Basic reporting In the manuscript entitled as GC-MS analysis of phytochemical compounds of Opuntia megarrhiza (Cactaceae), an endangered plant of Mexico the authors describe the identification and characterization of the phytochemical compounds present in wild individuals of O. megarrhiza using Gas Chromatography Coupled to Mass Spectrophotometry. In this study, the authors reported 53 phytochemical compounds, 18 have previously identified with some biological activity. The variety of metabolites presents in O. megarrhiza justifies the medicinal use of this plant in traditional medicine and highlight it as a source of phytochemical compounds with potential in medicine and biotechnology. The journal PeerJ Organic Chemistry publishes articles exploring subjects including biochemistry, polymers, green chemistry, photochemistry, and synthetic organic chemistry. I believe that the present study is in accordance with the profile of the journal, that the manuscript is relatively free of error and demonstrates a quality of the paper in terms of scientific value. Therefore, I recommend this manuscript for publication in PeerJ Organic Chemistry after consideration of the issues and corrections. 1) The whole manuscript is clearly written in professional, unambiguous language. The introduction section is full of information about the phytochemical compounds, however, I think you should do an update in some references as at lines 37-43, to show the topic is more current. Some examples: Sci Rep 11, (2021) 10041 (https://doi.org/10.1038/s41598-021-89437-4); Food Chemistry 362 (2021) 130196 (https://doi.org/10.1016/j.foodchem.2021.130196); Biotechnology Reports 30 (2021) e00633 (https://doi.org/10.1016/j.btre.2021.e00633). R: We include the recommended references to show the topic is of current research in lines 40 to 49. 2) I strongly recommend that the authors draw the chemical structures to the demonstrate the phytochemical compounds with biological activities (in the Tables 6 1 and 7). In this Tables (6 and 7) you should exclude the Similarity (%) and RT (min) columns, they are a duplicate information. R: The chemical structures were included in Supplementary figures S1 to S4 and referred in text in lines 213. Experimental design 1) Some studies in current literature report the use of LC-MS–MS to identification and characterization of components in plant extracts. With this kind of Cactaceae plant, could the authors use this technique? Or is the GC-MS the most appropriate for this situation? R: We include the next lines in introduction (lines 84-86): The LC-MS is a robust technique for general unknown screening, however its major drawback is the lack of universal reference libraries obtained with different instrument types (Marquet, 2012), as in GC-MS. 2) The method was described with sufficient detail and information to replicate. However, the authors need to correct some abbreviations of the solvents. Methanol is correctly represented as MeOH, not MeOTH. Still, acetone isn’t CH₃CH₃, this is ethane molecule. The correct acetone representation is (CH3)2CO. R: Abbreviations were corrected to MeOH and (CH3)2CO in the whole document. Line135 3) In some point of the manuscript (line 159, 172 and go on) the authors starting described the phytochemical constituents from the CH3CN extract. Is this information correct? The use of acetonitrile wasn’t described in the Materials and Methods section. R: We do not use acetonitrile, but acetone. Corrected to (CH3)2CO line 134,135 Validity of the findings 1) Only the compounds and their respective activities were reported in the Discussion section, and the obtained results support the medicinal use of this plant in traditional medicine. However, the authors should explore more the importance of why find out what kind of phytochemical compounds are present in O. megarrhiza. For example, you should discuss about a future developing of new drugs, new chemical studies, or about the encouragement of planting this species once this one is listed as endangered in the Red List of the IUCN. We include Lines 247-263 Here we report for the first time, several phytochemical compounds in O. megarrhiza with biological activities. Our findings highlight the relevance of the species in developing of new drugs, trough future chemical studies, and encourage of planting this species once this one is listed as endangered in the Red List of the IUCN. 2 Biotechnological methods are reliable and provide continuous sources of raw material and natural products for food, pharmaceutical, and cosmetic industries (Ramachandra & Ravishankar, 2002; Nalawade et al., 2003; Julsing et al., 2007). Previously, it has been indicated that more than 50,000 plant species are used in phytotherapy and medicine, and around 66% of them are harvested from nature leading to local extinction of many species or degradation of their habitats (Tasheva & Kosturkova, 2012). Alternatives to protect these useful plants, should be directed to both preservation of the plant populations and elevating the level of knowledge for sustainable utilization of these plants in medicine have been previously indicated (WHO, 2010). Biotechnological methods offer possibilities not only for faster cloning and conservation of the genotype of the plants (Verpoorte et al., 2002; Tripathi & Tripathi, 2003) but for modification of their gene information, regulation, and expression for production of valuable substances in higher amounts or with better properties (Ramachandra Rao &. Ravishankar, 2002; Khan et al 2009). 2) In the line 198 the authors described the “the presence of 18 phytochemical compounds with biological activities, with similarity values of identification above 93%.” Considering the tables 1-4, there are only 15 phytochemical compounds with similarity values of identification above 93%. Is this count correct? Still, a short sentence about the similarity values of identification and if there are a percentage acceptable to characterize a molecule, should be add in the discussion. R: Lines 160–168 were modified to indicate that similarity index of 90% and above was consider according to previous studies. 3) For better molecular characterization, the MS spectrum of compounds at the Table 6 and 7, should be add in the Supplemental files. R: The MS spectrum of compounds were included in Supplementary Figures S1 to S4. Additional comments Please correct following typos and errors: - change from 5ºC to 5 ºC (line 129) R: Done (line 146) - change from 300ºC to 300 ºC (line 129) R: Done (line 146) - the number 3 in the compound is subscript (e.g. CHCl3) (lines 130,134, 172, 189, 204,219,224,226,236,237,238, and go on and go on, Figure, Tables)) R: Done in the whole document - the letter H in 2H-Pyran-2-one (line 162) is in italic form. R: Done (line 193) - change from MetOH3 to MeOH (lines 129, 135, 171,174,182,219,220,221, 235,236, and go on, Figure 3, Table 5) 3 R: Done in the text, Figure 3, and Table 5 - change from CHCl to CHCl3 (lines 30,134, 172, 189, 204,219,224,226,236,237,238) R: Done in all the text and tables - the quality of Figure 2 should be improved. R: Done, the jpeg file has a resolution of 900 dpi. Revisor 2 The MS “GC-MS analysis of phytochemical compounds of Opuntia megarrhiza (Cactaceae), an endangered plant of Mexico”, authored by Arturo De-Nova and Pablo Delgado-Sánchez et al., describes the identification and characterize the phytochemical compounds present in individuals of O. megarrhiza using the gas chromatography coupled to mass spectrometry. The authors used organic solvents to prepare the extracts and 53 phytochemical compounds were identified. The protocol used in the chemical characterization agrees with literature procedures. However, the text is not clear and need to an extensive revision for types. The quality of figures and chemical structures also need to be improved. After reading of the manuscript, it is possible to identify a confusion in the nomenclature/abbreviation of the solvents used to prepare the extracts, which makes it difficult to understand the text. This work could be a relevant contribution for Chemistry of Natural Products, but I don’t recommend the acceptance of this manuscript in Peer J. this way. I suggest a careful review of the text and addition of a biological application study for the extracts before a new submission. R: Text was reviewed, sections were clarified and typo errors. Quality of figures and chemical structures were improved. Nomenclature/abbreviation was corrected as indicated by the reviewer 1. Experimental design The research agrees with the Scope of the journal. However, in some cases the methods are not described with sufficient details or information to replicate. I suggest a detail review. R: Methods were described with more details (see changes in lines: 116-168). Validity of the findings The results obtained are promising. However, they need to be better discussed. I suggest adding another reference parameter to complete the characterization of the compounds present in the extracts since the similarity of the spectra in many cases is less than 75%. This comment applies to the results described in tables. R: Discussion was improved to highlight the importance of our findings and were restricted to compounds with percentage similarity above 90% and confirmed with standards.lines 160-168 Reviewer: Thiago Barcellos da Silva 4 Basic reporting While the work is interesting and relevant regarding the phytochemical analysis of Opuntia megarrhiza, I have some concerns about the manuscript that make me not to recommend publication in its current form. Thus, it needs major revisions before publication, including English language improvement. R: English writing was reviewed and improved. Experimental design In my opinion, the main concern is the tentative identification of the extracted phytochemicals based only on the similarity approach from the Mass Spectral Libraries. Although the libraries are an important tool to help in the identification, additionally analysis should be used. At least, I recommend a Linear retention index analysis and comparison with standard samples. Lines 160-168, 227-233 R: As highlighted by the reviewer the similarity approach from the Mass Spectral Libraries are important, however we have included comparisons with standard samples for those above 90% similarity. Validity of the findings The authors mention that the extracts were filtered using two different membrane filter (PTFE and PVDF) and as consequence, the chemical composition of the extracts are different. Thus, the authors should clarify the difference between both membranes and discuss the results regarding the composition. Lines 277-280 R: Before depositing in a vial, extracts were filtered through a polytetrafluoroethylene (PTFE) and polyvinylidene (PVDF) membranes (with different hydrophobic adsorption ranges and size exclusion pores). R: In discussion“Use of different membrane filters allows to identified chemical compounds with different hydrophobicity and molecule sizes. Previously, it has been indicated that PTFE has less hydrophobic adsorption but more size exclusion (Xiao et al. 2014)”. Regarding the chromatograms showed in the Figure 2, some of the labeled peaks are nearly in the noise, which difficult the appropriated identification of the phytochemicals. R: The are not in the noice as it can be corroborated in actual figures with more resolution. I also suggest improving the discussion comparing the identified compounds and the identified compounds reported in other studies for the Opuntia Species lines 304-306, 308.309, 328-333 5 R: Compounds identified in other studies for Opuntia species have been included in “Eicosane, Octadecane, Hexadecanoic acid, and Eicosane has been previously identified in Opuntia stricta (Izuegbuna et al., 2019)”, lines 316-317 “and it has previously identified in Opuntia dillenii (Ben-Lataief et al., 2020).”; and lines 335-340 “Additionally, phytochemical compounds we found in Opuntia megarrhiza with no reports of biological activity, have been previously identified in other Opuntia species. For example, Oleic acid (9-Octadecene) and Stigmastan-3,5-diene was identified in O. dillenii (Ben-Lataief et al., 2020). Additionally, β-sitosterol, the major sterol extracted from different parts of the fruit oils of Opuntia ficus-indica (Ramadan et al., 2003; Ramadan & Mörsel, 2003), is a similar to sterols we found in O. megarrhiza.” The authors reposted that a” exhaustive search” was made for the biological activity based in the literature. But, if possible, a small biological activity screening against bacteria or fungi will considerably improve the study. As suggestion too, due to the large number of cited references in the manuscript (more the 100 references) a revision manuscript about the phytochemical composition and biological activity from Opuntia Species could be organized. R: We thank the suggestion, and we are preparing future studies with biological activity tests from different extracts of Opuntia megarrhiza again phytopathogens and cellular lines. Here we want to open future lines in research on the phytochemical compounds of the species depending on their previous recognized activities. As an exhaustive review of the activity of phytochemical compounds of this Opuntia species it is a great idea you recommendation of to organize a Review of bioactivity on genera Opuntia for future manuscripts. Additional comments Some minor revision to improve the manuscript are suggested as follow: Mass analysis is a spectrometric technique and not a ”Spectrophotometry” technique. Also, use the word “technique” instead of the word “technic”. R: Done (lines 19, 83, 84) In the Abstract, the sentence “and they were analyzed in a mass spectrometry detector with eletronic impact” could be replaced with “and they were analyzed by gas chromatography-electron impact-mass spectrometry”. R: Done (line 23-24) The introduction should be revised. It is very repetitive. lines 31-114 In line 30, the sentence seems incomplete. “127 general…” R: It was corrected to the taxonomic category “genera”, no “general”, referring to species belonging this taxonomic category (line 32) 6 In line 71, the authors should replace de “UV resonance“, with “UV-Vis spectrophotometry”. UV-Vis spectrophotometry is not a resonance spectroscopy. R: Done (line 79,80) Line 113: correct “past” to “paste”. R: Done (line 129) Line 114: The for methanol is MeOH and not MeOTH or MetOH3 as shown in Table 5. Check all the acronyms along the manuscript. Also, check all the molecular formulas in the mains text and Tables, such as for acetone which represented as CH3CH3 R: Done MeOH: lines 129, 135, 171,174,182,219,220,221, 235,236, corrected in whole text and tables, (CH3)2CO line 134,135 Line 132: Correct the misspelt word “specters” R: Done (line 150). All the compounds nomenclature should be revised. R: Done in whole document The symbol for mass/charge (m/z) is always italicized. Check it in the main manuscript and Tables R: Done in whole document and tables 7 "	Here is a paper. Please give your review comments after reading it.
466	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Imines and their derivatives are of great interest to organic synthetic chemists due to their involvement as key intermediates which facilitate the construction of nitrogen heterocycles, particularly the formation of alkaloids. Imine formation by condensation of primary amines with aromatic aldehydes and cyclohexanone has been investigated under environmentally-friendly solventless heterogeneous catalysis. An array of different imines was obtained in excellent yields in appreciably short reaction times using Amberlyst® 15 as a heterogeneous catalyst. The latter was used owing to its high commercial availability, recyclability, ease of separation from the reaction mixture, and versatility.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head n='1.'>Introduction</ns0:head><ns0:p>Imines are fundamental intermediates in the synthesis of N-containing organic molecules which are biologically active (particularly alkaloids) or used in various industrial procedures. <ns0:ref type='bibr' target='#b0'>1</ns0:ref> Their formation via the condensation of amines with aldehydes or ketones is a reversible process. <ns0:ref type='bibr' target='#b0'>1</ns0:ref> In fact, the use of imines as intermediates in multicomponent reactions is very ubiquitous due to the same equilibrium which exists. <ns0:ref type='bibr' target='#b1'>2</ns0:ref> Imine-based multicomponent reactions which have gained considerable attention in the past few years include: formation of quinolizidines and indolizidines via A 3 coupling, other Mannich-type reactions (nitro-Mannich, aza-Friedel-Crafts, Petasis), and the intramolecular aza-Diels-Alder reaction. <ns0:ref type='bibr'>3,</ns0:ref><ns0:ref type='bibr' target='#b4'>4,</ns0:ref><ns0:ref type='bibr' target='#b5'>5</ns0:ref> In order to be able to drive the condensation process to completion, the classical method required azeotropic distillation by a Dean-Stark apparatus which obviously necessitated the use of excess amounts of solvents. <ns0:ref type='bibr' target='#b6'>6</ns0:ref> Subsequently, alternative eco-friendly procedures were developed including the Schmidt reaction, the oxidative-dehydrogenation of amines and the oxidative coupling of alcohols and amines just to name a few. <ns0:ref type='bibr' target='#b7'>7</ns0:ref> Such procedures still may suffer from one or more of the following disadvantages: require expensive metallic catalysts, have a lower atom economy, require long reaction times, require the use of toxic solvents and show overall low environmental benignity. <ns0:ref type='bibr' target='#b6'>6,</ns0:ref><ns0:ref type='bibr' target='#b7'>7</ns0:ref> After the incorporation of the green chemistry principles into the synthetic chemistry modus operandi, following the works of Anastas and Warner, 8 the above mentioned syntheses were improved further and rendered more eco-friendly through the use of heterogeneous catalysts such as: sulfated-TiO 2 , montmorillonite K-10 clay, sulfated nano-ordered silica and zeolites. <ns0:ref type='bibr' target='#b9'>[9]</ns0:ref><ns0:ref type='bibr' target='#b10'>[10]</ns0:ref><ns0:ref type='bibr' target='#b12'>[11]</ns0:ref><ns0:ref type='bibr' target='#b14'>[12]</ns0:ref> Further recently developed sustainable methodologies have been reported using irradiation techniques. <ns0:ref type='bibr' target='#b15'>13</ns0:ref> Moreover the addition of dehydrating agents such as phosphorous-pentoxide-silica have also been shown to drive the reaction to completion by removing the condensation product, ergo water. <ns0:ref type='bibr' target='#b16'>14</ns0:ref> Obviously such agents require oven drying and reactivation for future re-use. Another difficulty associated with this synthetic reaction is that in general, ketones are much less reactive when compared with aldehydes rendering ketimine synthesis even more unfavourable and non-green. In the following research, in continuation of our interest in the application of heterogeneous catalysts, <ns0:ref type='bibr' target='#b17'>15</ns0:ref> herein we report how various heterogeneous catalysts and some desiccants were tried and compared in terms of activity and efficiency for the imine synthesis reaction. Amberlyst® 15, a cheap commercially available catalyst is found to give the highest yields in short reaction times under neat conditions. In addendum, the unprecedented synthesis of the ketimine from the condensation of cyclohexanamine and cyclohexanone proved successful.</ns0:p></ns0:div> <ns0:div><ns0:head n='2.'>Experimental</ns0:head></ns0:div> <ns0:div><ns0:head>2.1.</ns0:head><ns0:p>Materials used All commercially available chemicals were purchased from Aldrich and used without further purification.</ns0:p></ns0:div> <ns0:div><ns0:head>2.2.</ns0:head><ns0:p>Instrumentation For the characterization of final products and monitoring of the reactions the same procedure described in detail in our previous publications was followed in order to obtain FTIR, NMR and MS and GC spectra. 16</ns0:p></ns0:div> <ns0:div><ns0:head>2.3.</ns0:head><ns0:p>General procedure The general procedure for the imine-synthesis reaction involved stirring the aldehyde (5 mmol) and the amine (5.5 mmol) in the presence of 0.2 g of A15 catalyst under neat conditions at room temperature in a nitrogen-dried 25 mL one-neck round bottomed flask. The reaction was monitored via both TLCs and/or GC analysis. The catalyst was filtered off by suction and washed appropriately with diethyl ether solvent (approximately 5 -10 mL). The filtrate was concentrated by rotary evaporation and by a double-stage vacuum oil pump in order to remove the unreacted amine for reactions involving low-boiling amines. The products of aromatic amines were purified by recrystallization from ethanol or by column chromatography using a 9:1, 8:2 or 7:3 hexane/ethyl acetate eluant ratio. The TLC plates used for monitoring were composed of silica on PET with fluorescent indicator. Plates were observed under a UV lamp at a wavelength of 254 nm before staining in an iodine-saturated chamber. Analytical data for all products are reported in the Supporting Information File.</ns0:p></ns0:div> <ns0:div><ns0:head n='3.'>Results and discussion</ns0:head><ns0:p>During the initial screening, the reaction between benzaldehyde (1a) and t-butylamine (2a) was chosen as the model reaction (Scheme 1). The latter was always performed at room temperature under quasi-solvent-free conditions in the presence/absence of a number of heterogeneous catalysts and desiccants. In addition, the molar ratios of the reagents and catalyst quantities were also varied. Table <ns0:ref type='table'>1</ns0:ref> show the results of the preliminary catalyst/drying agent screening trials.</ns0:p></ns0:div> <ns0:div><ns0:head>Scheme 1.</ns0:head></ns0:div> <ns0:div><ns0:head>Table 1</ns0:head><ns0:p>As evidenced, out of the catalysts and desiccants tried and tested, the best yields were obtained using Montmorillonite K-10, Amberlyst® 15 and acidic alumina. However, the work up of the reactions involving either MK-10 or acidic alumina required the addition of more solvent than that involving Amberlyst® 15 owing to their physical state (powder). Henceforth, Amberlyst® 15 (being in the form of beads) was selected for the subsequent optimization trials especially considering its ease of separation from the reaction mixture. In addition, it was discovered that a smaller amount of Amberlyst® 15 could result in even higher yields (Table <ns0:ref type='table' target='#tab_1'>2</ns0:ref>) possibly owing to easier mechanical stirring and less product adsorption onto the catalyst beads.</ns0:p></ns0:div> <ns0:div><ns0:head>Table 2.</ns0:head><ns0:p>One of the main limitations of the condensation of primary amines and aldehydes/ketones is the equilibrium which exists between the products and the substrates. This explains why in the initial trials the amine was used in excess, ergo, to shift the equilibrium forward. Yet, as outlined in Table <ns0:ref type='table' target='#tab_2'>3</ns0:ref>, the yields remained practically the same even when the latter mentioned excess was decreased to 0.1 equivalents only.</ns0:p></ns0:div> <ns0:div><ns0:head>Table 3.</ns0:head><ns0:p>Subsequently, having identified the ideal conditions (neat, room temperature, 0.2 g per 5 mmol Amberlyst® 15, 0.1 equivalent excess of amine), the substrate scope could be expanded by varying the aldehydes and the amines. In general, the best outcomes were obtained (Table <ns0:ref type='table' target='#tab_3'>4</ns0:ref>) when using aromatic aldehydes and aliphatic primary amines due to the higher reactivity of the Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science aldehydes as opposed to ketones and the greater nucleophilic character of aliphatic amine as opposed to aromatic amines.</ns0:p></ns0:div> <ns0:div><ns0:head>Table 4.</ns0:head><ns0:p>Positively, despite their inherent lack of reactivity, aromatic amines also gave appreciable yields as outlined in Table <ns0:ref type='table' target='#tab_4'>5</ns0:ref>. Not only, but the primary aliphatic amine, c-hexylamine, was able to react successfully with the cyclic ketone, cyclohexanone (Scheme 2) to give the product (3s) in 85% yield.</ns0:p></ns0:div> <ns0:div><ns0:head>Table 5.</ns0:head></ns0:div> <ns0:div><ns0:head>Scheme 2</ns0:head><ns0:p>Lastly, the catalyst exhibited good recyclability because the model reaction could be repeated up to 5 times with the same catalyst with the yield decreasing by 10% between the first and last trial (Figure <ns0:ref type='figure' target='#fig_1'>1</ns0:ref>). The latter decrease is most probably a result of sulfonic acid group inactivation by the reaction with the amine reactant. </ns0:p></ns0:div> <ns0:div><ns0:head n='4.'>Conclusions</ns0:head><ns0:p>The heterogeneous, safe-to-handle, relatively-cheap and commercially-available Amberlyst® 15 was found to be the ideal catalyst for the synthesis of various imines using both aliphatic and aromatic amines and aromatic aldehydes (72 -99% yields, 17 examples) in significantly short reaction times (2 -4 hours) at room temperature in neat conditions. The catalyst morphology, i.e. being in the form of microporous beads, enabled it to be easily recovered with minimal solvent use during work up and reused for up to 5 times. a. All reactions were carried out in the presence of 1 mL of diethyl ether on a 5 mmol scale using a 1 : 3 molar ratio of benzaldehyde 1a : amine2a at room temperature (circa 15 -25 0 C). The addition of diethyl ether was required because reaction mixture soon thickened significantly after reaction initiation. Manuscript to be reviewed</ns0:p><ns0:formula xml:id='formula_0'>O + C H 3 C H 3 CH 3 NH 2 RT, neat -H 2 O (1a) (2a) N C H 3 CH 3 C H 3 (3a)</ns0:formula></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science </ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ O. Chem. reviewing PDF \| (OCHEM-2022:04:72527:1:1:NEW 3 Jul 2022)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head>Figure 1 .</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head /><ns0:label /><ns0:figDesc>b. Yield of pure isolated product unless otherwise indicated c. No desiccant or catalyst added d. Effective pore size in Angstrom e. A smaller amount of MK-10 (compared to other catalysts) was used because on addition of larger amounts of MK-10, reaction mixture dried up immediately and the addition of 1 mL of diethyl ether was not enough to aid stirring.f. When a larger amount of catalyst was used (1.5 g), the Nafion beads kept moving out of reaction mixture and adhering to reactant flask walls. g. Reaction carried out under solventless conditions owing to the small amount of catalyst h. Copper iodide leaching was noted due to residual green colour in the crude reaction mixture following catalyst filtration.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head /><ns0:label /><ns0:figDesc>reactions carried out under solventless conditions at RT using 0.2 g Amberlyst® 15 on a 5 mmol scale using an amine equivalent excess of 0.1 b. Yield of pure isolated product collected after work-up and purification c. Reactions carried out in a 1:1.5 aldehyde/ketone : amine molar ratio d. No pure product collected</ns0:figDesc></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head>Table 21 . Optimization trials involving various heterogeneous catalysts Entry a Catalyst (Amount in g) Reaction time (h)</ns0:head><ns0:label>21</ns0:label><ns0:figDesc /><ns0:table><ns0:row><ns0:cell>Yield b 3a</ns0:cell></ns0:row><ns0:row><ns0:cell>(%)</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_1'><ns0:head>Table 2 . Optimization trials involving changing the amount oo Amberlyste 15 Entry a ¡¢£¤¥¤¦ oo Amberlyste 15 (g)</ns0:head><ns0:label>2</ns0:label><ns0:figDesc /><ns0:table><ns0:row><ns0:cell>Reaction time (h)</ns0:cell><ns0:cell>Yield b 3a</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>(%)</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_2'><ns0:head>Table 3 . Optimization trials involving varying the reagent ratios Entry a Aldehyde X amine molar ratio Reaction time (h) Yield b 3a (%)</ns0:head><ns0:label>3</ns0:label><ns0:figDesc /><ns0:table><ns0:row><ns0:cell>1</ns0:cell><ns0:cell>1 : 2</ns0:cell><ns0:cell>2</ns0:cell><ns0:cell>99%</ns0:cell></ns0:row><ns0:row><ns0:cell>2</ns0:cell><ns0:cell>1 : 1.5</ns0:cell><ns0:cell>2</ns0:cell><ns0:cell>99%</ns0:cell></ns0:row><ns0:row><ns0:cell>3</ns0:cell><ns0:cell>1 : 1.1</ns0:cell><ns0:cell>2</ns0:cell><ns0:cell>99%</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='3'>a. All reactions carried out under solventless conditions at room temperature on a 5 mmol scale</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>b. Yield of pure isolated product</ns0:cell><ns0:cell /><ns0:cell /></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_3'><ns0:head>Table 4 : Condensation of primary aliphatic amines with aromatic aldehydes using 2 Amberlyst® 15 as catalyst Entry a Aldehyde/Ketone</ns0:head><ns0:label>4</ns0:label><ns0:figDesc /><ns0:table><ns0:row><ns0:cell>Amine</ns0:cell><ns0:cell>Product</ns0:cell><ns0:cell>Time (h)</ns0:cell><ns0:cell>Yield (%) b</ns0:cell></ns0:row></ns0:table><ns0:note>PeerJ O. Chem. reviewing PDF \| (OCHEM-2022:04:72527:1:1:NEW 3 Jul 2022) Manuscript to be reviewed Chemistry Journals Analytical, Inorganic, Organic, Physical, Materials Science PeerJ O. Chem. reviewing PDF \| (OCHEM-2022:04:72527:1:1:NEW 3 Jul 2022)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_4'><ns0:head>Table 5 .</ns0:head><ns0:label>5</ns0:label><ns0:figDesc>Condensation of aromatic p §¨© § amines w¨ aromatic aldehydes . All reactions carried out under solventless conditions using 0.2 g Amberlyst® 15 on a 5 mmol scale using an aldehyde/ketone : amine molar ratio of 1 : 1.1 b. Yield of pure isolated product collected by recrystallization from ethanol c. Reactions carried out using a 1:1.5 aldehyde/ketone : amine ratio</ns0:figDesc><ns0:table><ns0:row><ns0:cell>i § a</ns0:cell><ns0:cell>Aldehyde</ns0:cell><ns0:cell>Amine</ns0:cell><ns0:cell>Product</ns0:cell><ns0:cell>Time (h)</ns0:cell><ns0:cell>Yield</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>(%) b</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>NH 2</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>1</ns0:cell><ns0:cell>1a</ns0:cell><ns0:cell /><ns0:cell>3o</ns0:cell><ns0:cell>3</ns0:cell><ns0:cell>72</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>2e</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>2</ns0:cell><ns0:cell>1b</ns0:cell><ns0:cell>2e</ns0:cell><ns0:cell>3p</ns0:cell><ns0:cell>2.5</ns0:cell><ns0:cell>81</ns0:cell></ns0:row><ns0:row><ns0:cell>3</ns0:cell><ns0:cell>1c</ns0:cell><ns0:cell>2e</ns0:cell><ns0:cell>3q</ns0:cell><ns0:cell>4</ns0:cell><ns0:cell>75</ns0:cell></ns0:row><ns0:row><ns0:cell>4 c</ns0:cell><ns0:cell>1e</ns0:cell><ns0:cell>NH 2</ns0:cell><ns0:cell>3r</ns0:cell><ns0:cell>3</ns0:cell><ns0:cell>90</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>2f</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row></ns0:table><ns0:note>a</ns0:note></ns0:figure> </ns0:body> "	"Dear Editor, Thank you for your comments and suggestions. We are submitting our revised manuscript addressing all the reviewers’ comments. We are thankful to the reviewers for their valuable comments and suggestions. All suggestions have been incorporated in the revised manuscript. The following is a point-by-point response to the comments made by the reviewers. We hope to have satisfied all reviewers’ expectations. Sincerely Giovanna Bosica (Corresponding author) Response to Reviewer Reports: Editor's Comments MINOR REVISIONS Dear Prof. Bosica Your manuscript was evaluated by three experts in organic synthesis. As you can see, one of the reviewers did not recommend publication. Still, I'm willing to offer you the opportunity to make the adjustments recommended by the reviewers. As soon as you are in a position to submit a new version, I will be happy to re-evaluate your work. [# PeerJ Staff Note: Please ensure you address the axis labels in Figure 1 #] - Thanks for offering us this opportunity. The axis labels in Figure 1 have been added. Reviewer 1 (Anonymous) Basic reporting The work is written confidentially Experimental design Could be interesting to use also aliphatic aldehydes or other aliphatic ketones in addition to the cyclic one 1i. Validity of the findings The procedure has several advantages including simplicity, irreversibility and the green and environmentally friendly conditions (room temperature, recyclable catalyst). Moreover immines are important intermediates in the synthesis of biologically active N-heterocyclic products and in industrial synthetic processes. Additional comments The work reports a simple and efficient procedure to obtain imines. A variety of aromatic aldehydes containing electron-withdrawing and electron-donating groups, with aliphatic and aromatic ammines can be employed; could be interesting to use also aliphatic aldehydes or other aliphatic ketones in addition to the cyclic one 1i. I think that the manuscript can be published in PeerJ because it has several advantages including irreversibility and the green and environmentally friendly conditions (room temperature, recyclable catalyst). - Thanks for positive remarks and for the suggestion. Albeit the fact that other combinations of aliphatic aldehydes/ketones and amines have been tried out and in some cases a pure product was collected, due to current instrumental failure in our department (namely GC which is needed to follow the course of the reaction and MS for final product characterisation), we feel that it is better if we do not include such trials. IR and NMR spectra in some cases are still not enough to confirm product structure especially in case of molecules with a large aliphatic portion that result in overlapping multiplets at low chemical shifts. Some typing errors: In the line 15 of abstract is reported “aliphatic and aromatic aldehydes” change with “aromatic aldehydes” and also in line 120 change “aldehydes” with “aromatic aldehydes”. - changed In line 43 the reference could be added: “Green Chemistry Theory and Practice Paul T. Anastas and John C. Warner Oxford University Press 1998” – added, new ref. 8 Line 108: change “Scheme 1” with “Scheme 2 ”- it was already Scheme 2 Line 185: change “Subash, B.” with “Subash, B.;” - changed Line 195: change “Kodumuri, S.” with “Kodumuri, S.;” and “Gajula, K.S.” with “Gajula, K.S.;” - changed Schemes and tables: note a of Table 2: change “benzaldehyde:amine” with “1a:2a” – changed, now in Table 1 (see Reviewer 2) Scheme 2: change “5hrs” in “5h” - changed Supporting information: line 25: change “748 ” with “748. ” - changed lines 52 and 72: change “H NMR” with “1H NMR” - changed - changed lines 80: superscript 1 in “1H NMR” – changed put lines 82 and 83 (white solid and IR) before line 81 – changed Reviewer 2 (Anonymous) Basic reporting Dr. Giovanna Bosica and coauthors described the synthesis of imine by condensation of primary amines with aliphatic and aromatic aldehydes and cyclohexanone under environmentally-friendly solventless heterogeneous catalysis. They reported that Amberlyst® 15 is a performant heterogeneous catalyst in this transformation. Furthermore, the commercial availability and recyclability of this catalyst, the ease of separation from reaction mixture and versatility, makes this synthetic process an innovative and interesting approach for the synthesis of imine derivatives. The paper is generally well referenced but, in my opinion, when critical points are reported to describe the disadvantages of other procedures (rows 38-41), related references should be added. Anyway, some changes to improve the quality of this paper are required. – Thanks for positive remarks and for the suggestions, references have been inserted Preliminary experiments (table 1 and 2) should be more detailed, and the related results reported in just one table (Table 1 + Table 2). The header of all tables (all named as 'table 1') and the caption of the schemas (all named as 'Scheme 1') need to be corrected. -Table 1 and Table 2 have been combined in one table (Table 1) and corrected accordingly, additional details have been added Cyclic aliphatic amines have a nitrogen comprised in the ring (i.e., piperidine, pyridine, etc....), cyclohexylamine is just a primary aliphatic amine with a cyclic substituent (row 103). - corrected To evaluate the effectiveness of using this amine, the reaction with cyclohexanone must also be compared with other amines. Further data could be obtained using cyclohexylamine also with other carbonyl substrates. - Some extra trials have been performed (see response to Reviewer 1) Instead of “catalyst’s physical state” (row 121), the term “catalyst morphology” is preferable. – changed In reference 2, an author is missed (Wei Zhang). Please check. – corrected In reference 11, the article title is “Hβ Catalyzed condensation reaction between aromatic ketones and anilines: To access ketimines (imines)”. Please check. – corrected Some changes in the Supporting Information file are also required. Some signals that refer to a singlet are reported as multiplet and/or with ppm expressed as a range (i.e., methoxy and tBu groups, etc….), why? (For example, see compounds 3c, 3d, 3e, 3f, 3k ….). - The software used to process the data picked up minor solvent peaks and included them along with the main peak. Changes have been made. The 1H-NMR data of compound 3i are reported in reverse order of all the others. – changed The 1H-NMR data reported for compound 3j, in my opinion, don’t fit structural characteristics; furthermore, the indicated reference (18), refer to the 4-nitro derivative NMR data; please check. Agreed. It is the 4-nitro derivative. – corrected Compound 3k was described with one more aromatic hydrogen (5 ArH instead of 4 ArH); a comma is missed before 8.20 (row 49). - There was an extra H in one of the multiplets at 7.15 – 7.34 ppm due to chloroform solvent peak. Change has been made. Compound 3m: a comma instead of “and” is required (row 59). – changed Compound 3p: the J for peaks at 8.21 and 8.70 ppm are missed (row 73). - J values inserted Compound 3r: (row 81), please erase the bracket after Hz; IR data should be moved before NMR data; – changed the signal of iminic hydrogen is probably missed. Agreed, iminic hydrogen was missing, now it has been included. Experimental design No comment Validity of the findings The authors have to check carefully NMR data and the way they have to be reported. Scanned spectra could be added to the Supporting information file in order to help the reader. - NMR data have been checked and amended where necessary. Copies of selected scanned spectra have been added to the Supporting information file. Reviewer 3 (Anonymous) Basic reporting The manuscript titled “Facile imine synthesis under green conditions using Amberlyst® 15” describes the synthesis of sixteen aldimines obtained from de reaction of arylaldehydes with alkyl/aryl amines catalyzed by Amberlyst® 15 at room temperature under solventless conditions. Still, one example of a ketimine is presented. In my opinion, the reaction concept is well known as previously published by several authors (J. Org. Chem., 1971, 86, 1570, Synthesis 1985; 679, Tetrahedron Letters 1997, 38, 2039, Synlett 2004, 2135, J. Chem. Res. 2005, 299, Synthesis 2006, 1652, etc). I have several observations that justify why the manuscript cannot be accepted for publication on PeerJ Journal at this moment. Please, see the following points. - Although it is true that ample studies are available on the synthesis of imines, the ease of use and recoverability of Amberlyst 15® makes the catalyst a viable cheap alternative especially in cases when the imine which is synthesized would need to be then used in a more complex synthetic pathway. In fact, in research done by our group (yet to be published), Amberlyst 15® has proved to be able to be used in tandem with other catalysts in a one-pot methodology for the synthesis of more complex end-products without interfering with the mechanism of the reaction. The reason behind this is that there are no metal centres (such as in sulfated-titania or clays) or hydroxyl groups (such as in zeolites) that can cause side-product formation after the imine has formed. 1) I did not fully understand from the Table 1 and 2 why Amberlyst-15 was chosen as the optimal catalyst. Overall yields of 3a in the case of acid alumina, M K-10, molecular sieves, and anhydrous MgSO4 were higher. - The morphology of the catalyst allows for easier retrieval at the end of the reaction. No centrifugation is needed whilst the pore size of the filter paper doesn’t need to be too small allowing for faster filtration rates. Complete mass recovery of catalyst is possible due to its morphology (macroporous beads). 2) For me, the section “Results and Discussion” is poor, and it is limited to aryl aldehydes and few amines. - The amines used are either aliphatic or aromatic in nature which helps to show that the catalyst has a wide applicability and is not restricted to either one or the other. 3) General Procedure: The purification method needs to be better elucidated. Which products were purified by column chromatography? Which were crystallized? Details on crystallization method need to be provided when appropriate. - Such details have been entered into the supporting information. 4) One of the steps in typical procedures for the synthesis of compounds 3 is washing the reaction mixture with diethyl ether. Authors should add the information concerning the amount of diethyl ether used in this step. - The amount has been added. 5) Supporting information: The supporting information has twenty-one references with no need. - References referring to the “in text manuscript” have been removed from the supporting information file. 6) Authors should thoroughly check the values of spin-spin coupling constants through all the Supporting Information taking into account the fact that the spin-spin coupling constants of the protons, which are coupled to each other, should be the same. - Some changes have been made. In some cases, although peaks appear as triplets for example, they are in fact fused dds or ddds resulting in erroneous J constant measurements. 7) The melting point analysis is missing for compounds 3o, 3p and 3r. - Melting points have been inserted Experimental design No comment. Validity of the findings Totally, I don’t think this work has big impact for organic synthesis research. The synthesized compounds are known and the manuscript lacks novelty. So, I do not recommend this manuscript to be published in PeerJ. - We have developed a green approach for the synthesis of imines that is an alternative one to the traditional way reported in literature and with the advantage of being a simple and efficient procedure. We hope our answers match your expectations. Thank you and best regards, Giovanna Bosica (Corresponding author) "	Here is a paper. Please give your review comments after reading it.
467	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Imines and their derivatives are of great interest to organic synthetic chemists due to their involvement as key intermediates which facilitate the construction of nitrogen heterocycles, particularly the formation of alkaloids. Imine formation by condensation of primary amines with aromatic aldehydes and cyclohexanone has been investigated under environmentally-friendly solventless heterogeneous catalysis. An array of different imines was obtained in excellent yields in appreciably short reaction times using Amberlyst® 15 as a heterogeneous catalyst. The latter was used owing to its high commercial availability, recyclability, ease of separation from the reaction mixture, and versatility.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head n='1.'>Introduction</ns0:head><ns0:p>Imines are fundamental intermediates in the synthesis of N-containing organic molecules which are biologically active (particularly alkaloids) or used in various industrial procedures. <ns0:ref type='bibr' target='#b0'>1</ns0:ref> Their formation via the condensation of amines with aldehydes or ketones is a reversible process. <ns0:ref type='bibr' target='#b0'>1</ns0:ref> In fact, the use of imines as intermediates in multicomponent reactions is very ubiquitous due to the same equilibrium which exists. <ns0:ref type='bibr' target='#b1'>2</ns0:ref> Imine-based multicomponent reactions which have gained considerable attention in the past few years include: formation of quinolizidines and indolizidines via A 3 coupling, other Mannich-type reactions (nitro-Mannich, aza-Friedel-Crafts, Petasis), and the intramolecular aza-Diels-Alder reaction. <ns0:ref type='bibr'>3,</ns0:ref><ns0:ref type='bibr' target='#b4'>4,</ns0:ref><ns0:ref type='bibr' target='#b5'>5</ns0:ref> In order to be able to drive the condensation process to completion, the classical method required azeotropic distillation by a Dean-Stark apparatus which obviously necessitated the use of excess amounts of solvents. <ns0:ref type='bibr' target='#b6'>6</ns0:ref> Subsequently, alternative eco-friendly procedures were developed including the Schmidt reaction, the oxidative-dehydrogenation of amines and the oxidative coupling of alcohols and amines just to name a few. <ns0:ref type='bibr' target='#b7'>7</ns0:ref> Such procedures still may suffer from one or more of the following disadvantages: require expensive metallic catalysts, have a lower atom economy, require long reaction times, require the use of toxic solvents and show overall low environmental benignity. <ns0:ref type='bibr' target='#b6'>6,</ns0:ref><ns0:ref type='bibr' target='#b7'>7</ns0:ref> After the incorporation of the green chemistry principles into the synthetic chemistry modus operandi, following the works of Anastas and Warner, 8 the above mentioned syntheses were improved further and rendered more eco-friendly through the use of heterogeneous catalysts such as: sulfated-TiO 2 , montmorillonite K-10 clay, sulfated nano-ordered silica and zeolites. <ns0:ref type='bibr' target='#b9'>[9]</ns0:ref><ns0:ref type='bibr' target='#b10'>[10]</ns0:ref><ns0:ref type='bibr' target='#b12'>[11]</ns0:ref><ns0:ref type='bibr' target='#b14'>[12]</ns0:ref> Further recently developed sustainable methodologies have been reported using irradiation techniques. <ns0:ref type='bibr' target='#b15'>13</ns0:ref> Moreover the addition of dehydrating agents such as phosphorous-pentoxide-silica have also been shown to drive the reaction to completion by removing the condensation product, ergo water. <ns0:ref type='bibr' target='#b16'>14</ns0:ref> Obviously such agents require oven drying and reactivation for future re-use. Another difficulty associated with this synthetic reaction is that in general, ketones are much less reactive when compared with aldehydes rendering ketimine synthesis even more unfavourable and non-green. In the following research, in continuation of our interest in the application of heterogeneous catalysts, <ns0:ref type='bibr' target='#b17'>15</ns0:ref> herein we report how various heterogeneous catalysts and some desiccants were tried and compared in terms of activity and efficiency for the imine synthesis reaction. Amberlyst® 15, a cheap commercially available catalyst is found to give the highest yields in short reaction times under neat conditions. In addendum, the unprecedented synthesis of the ketimine from the condensation of cyclohexanamine and cyclohexanone proved successful.</ns0:p></ns0:div> <ns0:div><ns0:head n='2.'>Experimental</ns0:head></ns0:div> <ns0:div><ns0:head>2.1.</ns0:head><ns0:p>Materials used All commercially available chemicals were purchased from Aldrich and used without further purification.</ns0:p></ns0:div> <ns0:div><ns0:head>2.2.</ns0:head><ns0:p>Instrumentation For the characterization of final products and monitoring of the reactions the same procedure described in detail in our previous publications was followed in order to obtain FTIR, NMR and MS and GC spectra. 16</ns0:p></ns0:div> <ns0:div><ns0:head>2.3.</ns0:head><ns0:p>General procedure The general procedure for the imine-synthesis reaction involved stirring the aldehyde (5 mmol) and the amine (5.5 mmol) in the presence of 0.2 g of A15 catalyst under neat conditions at room temperature in a nitrogen-dried 25 mL one-neck round bottom flask. The reaction was monitored via both TLCs and/or GC analysis. The catalyst was filtered off by suction and washed appropriately with diethyl ether solvent (approximately 5 -10 mL). The filtrate was concentrated by rotary evaporation and by a double-stage vacuum oil pump in order to remove the unreacted amine for reactions involving low-boiling amines. The products of aromatic amines were purified by recrystallization from ethanol or by column chromatography using a 9:1, 8:2 or 7:3 hexane/ethyl acetate eluant ratio. The TLC plates used for monitoring were composed of silica on PET with fluorescent indicator. Plates were observed under a UV lamp at a wavelength of 254 nm before staining in an iodine-saturated chamber. Analytical data for all products are reported in the Supporting Information File.</ns0:p></ns0:div> <ns0:div><ns0:head n='3.'>Results and discussion</ns0:head><ns0:p>During the initial screening, the reaction between benzaldehyde (1a) and t-butylamine (2a) was chosen as the model reaction (Scheme 1). The latter was always performed at room temperature under quasi-solvent-free conditions in the presence/absence of a number of heterogeneous catalysts and desiccants. In addition, the molar ratios of the reagents and catalyst quantities were also varied. Table <ns0:ref type='table'>1</ns0:ref> show the results of the preliminary catalyst/drying agent screening trials.</ns0:p></ns0:div> <ns0:div><ns0:head>Scheme 1.</ns0:head></ns0:div> <ns0:div><ns0:head>Table 1</ns0:head><ns0:p>As evidenced, out of the catalysts and desiccants tried and tested, the best yields were obtained using Montmorillonite K-10, Amberlyst® 15 and acidic alumina. However, the work up of the reactions involving either MK-10 or acidic alumina required the addition of more solvent than that involving Amberlyst® 15 owing to their physical state (powder). Henceforth, Amberlyst® 15 (being in the form of beads) was selected for the subsequent optimization trials especially considering its ease of separation from the reaction mixture. In addition, it was discovered that a smaller amount of Amberlyst® 15 could result in even higher yields (Table <ns0:ref type='table' target='#tab_1'>2</ns0:ref>) possibly owing to easier mechanical stirring and less product adsorption onto the catalyst beads.</ns0:p></ns0:div> <ns0:div><ns0:head>Table 2.</ns0:head><ns0:p>One of the main limitations of the condensation of primary amines and aldehydes/ketones is the equilibrium which exists between the products and the substrates. This explains why in the initial trials the amine was used in excess, ergo, to shift the equilibrium forward. Yet, as outlined in Table <ns0:ref type='table' target='#tab_2'>3</ns0:ref>, the yields remained practically the same even when the latter mentioned excess was decreased to 0.1 equivalents only.</ns0:p></ns0:div> <ns0:div><ns0:head>Table 3.</ns0:head><ns0:p>Subsequently, having identified the ideal conditions (neat, room temperature, 0.2 g per 5 mmol Amberlyst® 15, 0.1 equivalent excess of amine), the substrate scope could be expanded by varying the aldehydes and the amines. In general, the best outcomes were obtained (Table <ns0:ref type='table' target='#tab_3'>4</ns0:ref>) when using aromatic aldehydes and aliphatic primary amines due to the higher reactivity of the Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science aldehydes as opposed to ketones and the greater nucleophilic character of aliphatic amine as opposed to aromatic amines.</ns0:p></ns0:div> <ns0:div><ns0:head>Table 4.</ns0:head><ns0:p>Positively, despite their inherent lack of reactivity, aromatic amines also gave appreciable yields as outlined in Table <ns0:ref type='table' target='#tab_4'>5</ns0:ref>. Not only, but the primary aliphatic amine, c-hexylamine, was able to react successfully with the cyclic ketone, cyclohexanone (Scheme 2) to give the product (3s) in 85% yield.</ns0:p></ns0:div> <ns0:div><ns0:head>Table 5.</ns0:head></ns0:div> <ns0:div><ns0:head>Scheme 2</ns0:head><ns0:p>Lastly, the catalyst exhibited good recyclability because the model reaction could be repeated up to 5 times with the same catalyst with the yield decreasing by 10% between the first and last trial (Figure <ns0:ref type='figure' target='#fig_1'>1</ns0:ref>). The latter decrease is most probably a result of sulfonic acid group inactivation by the reaction with the amine reactant. </ns0:p></ns0:div> <ns0:div><ns0:head n='4.'>Conclusions</ns0:head><ns0:p>The heterogeneous, safe-to-handle, relatively-cheap and commercially-available Amberlyst® 15 was found to be the ideal catalyst for the synthesis of various imines using both aliphatic and aromatic amines and aromatic aldehydes (72 -99% yields, 17 examples) in significantly short reaction times (2 -4 hours) at room temperature in neat conditions. The catalyst morphology, i.e. being in the form of microporous beads, enabled it to be easily recovered with minimal solvent use during work up and reused for up to 5 times. a. All reactions were carried out in the presence of 1 mL of diethyl ether on a 5 mmol scale using a 1 : 3 molar ratio of benzaldehyde 1a : amine2a at room temperature (circa 15 -25 0 C). The addition of diethyl ether was required because reaction mixture soon thickened significantly after reaction initiation. Manuscript to be reviewed</ns0:p><ns0:formula xml:id='formula_0'>O + C H 3 C H 3 CH 3 NH 2 RT, neat -H 2 O (1a) (2a) N C H 3 CH 3 C H 3 (3a)</ns0:formula></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science </ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ O. Chem. reviewing PDF \| (OCHEM-2022:04:72527:2:0:NEW 21 Jul 2022)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head>Figure 1 .</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head /><ns0:label /><ns0:figDesc>b. Yield of pure isolated product unless otherwise indicated c. No desiccant or catalyst added d. Effective pore size in Angstrom e. A smaller amount of MK-10 (compared to other catalysts) was used because on addition of larger amounts of MK-10, reaction mixture dried up immediately and the addition of 1 mL of diethyl ether was not enough to aid stirring.f. When a larger amount of catalyst was used (1.5 g), the Nafion beads kept moving out of reaction mixture and adhering to reactant flask walls. g. Reaction carried out under solventless conditions owing to the small amount of catalyst h. Copper iodide leaching was noted due to residual green colour in the crude reaction mixture following catalyst filtration.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head /><ns0:label /><ns0:figDesc>reactions carried out under solventless conditions at RT using 0.2 g Amberlyst® 15 on a 5 mmol scale using an amine equivalent excess of 0.1 b. Yield of pure isolated product collected after work-up and purification c. Reactions carried out in a 1:1.5 aldehyde/ketone : amine molar ratio d. No pure product collected</ns0:figDesc></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head>Table 21 . Optimization trials involving various heterogeneous catalysts Entry a Catalyst (Amount in g) Reaction time (h)</ns0:head><ns0:label>21</ns0:label><ns0:figDesc /><ns0:table><ns0:row><ns0:cell>Yield b 3a</ns0:cell></ns0:row><ns0:row><ns0:cell>(%)</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_1'><ns0:head>Table 2 . Optimization trials involving changing the amount oo Amberlyste 15 Entry a ¡¢£¤¥¤¦ oo Amberlyste 15 (g)</ns0:head><ns0:label>2</ns0:label><ns0:figDesc /><ns0:table><ns0:row><ns0:cell>Reaction time (h)</ns0:cell><ns0:cell>Yield b 3a</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>(%)</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_2'><ns0:head>Table 3 . Optimization trials involving varying the reagent ratios Entry a Aldehyde X amine molar ratio Reaction time (h) Yield b 3a (%)</ns0:head><ns0:label>3</ns0:label><ns0:figDesc /><ns0:table><ns0:row><ns0:cell>1</ns0:cell><ns0:cell>1 : 2</ns0:cell><ns0:cell>2</ns0:cell><ns0:cell>99%</ns0:cell></ns0:row><ns0:row><ns0:cell>2</ns0:cell><ns0:cell>1 : 1.5</ns0:cell><ns0:cell>2</ns0:cell><ns0:cell>99%</ns0:cell></ns0:row><ns0:row><ns0:cell>3</ns0:cell><ns0:cell>1 : 1.1</ns0:cell><ns0:cell>2</ns0:cell><ns0:cell>99%</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='3'>a. All reactions carried out under solventless conditions at room temperature on a 5 mmol scale</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>b. Yield of pure isolated product</ns0:cell><ns0:cell /><ns0:cell /></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_3'><ns0:head>Table 4 : Condensation of primary aliphatic amines with aromatic aldehydes using 2 Amberlyst® 15 as catalyst Entry a Aldehyde/Ketone</ns0:head><ns0:label>4</ns0:label><ns0:figDesc /><ns0:table><ns0:row><ns0:cell>Amine</ns0:cell><ns0:cell>Product</ns0:cell><ns0:cell>Time (h)</ns0:cell><ns0:cell>Yield (%) b</ns0:cell></ns0:row></ns0:table><ns0:note>PeerJ O. Chem. reviewing PDF \| (OCHEM-2022:04:72527:2:0:NEW 21 Jul 2022) Manuscript to be reviewed Chemistry Journals Analytical, Inorganic, Organic, Physical, Materials Science PeerJ O. Chem. reviewing PDF \| (OCHEM-2022:04:72527:2:0:NEW 21 Jul 2022)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_4'><ns0:head>Table 5 .</ns0:head><ns0:label>5</ns0:label><ns0:figDesc>Condensation of aromatic p §¨© § amines w¨ aromatic aldehydes . All reactions carried out under solventless conditions using 0.2 g Amberlyst® 15 on a 5 mmol scale using an aldehyde/ketone : amine molar ratio of 1 : 1.1 b. Yield of pure isolated product collected by recrystallization from ethanol c. Reactions carried out using a 1:1.5 aldehyde/ketone : amine ratio</ns0:figDesc><ns0:table><ns0:row><ns0:cell>i § a</ns0:cell><ns0:cell>Aldehyde</ns0:cell><ns0:cell>Amine</ns0:cell><ns0:cell>Product</ns0:cell><ns0:cell>Time (h)</ns0:cell><ns0:cell>Yield</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>(%) b</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>NH 2</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>1</ns0:cell><ns0:cell>1a</ns0:cell><ns0:cell /><ns0:cell>3o</ns0:cell><ns0:cell>3</ns0:cell><ns0:cell>72</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>2e</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>2</ns0:cell><ns0:cell>1b</ns0:cell><ns0:cell>2e</ns0:cell><ns0:cell>3p</ns0:cell><ns0:cell>2.5</ns0:cell><ns0:cell>81</ns0:cell></ns0:row><ns0:row><ns0:cell>3</ns0:cell><ns0:cell>1c</ns0:cell><ns0:cell>2e</ns0:cell><ns0:cell>3q</ns0:cell><ns0:cell>4</ns0:cell><ns0:cell>75</ns0:cell></ns0:row><ns0:row><ns0:cell>4 c</ns0:cell><ns0:cell>1e</ns0:cell><ns0:cell>NH 2</ns0:cell><ns0:cell>3r</ns0:cell><ns0:cell>3</ns0:cell><ns0:cell>90</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>2f</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row></ns0:table><ns0:note>a</ns0:note></ns0:figure> </ns0:body> "	"Dear Editor, Thank you for your comments and suggestions. We are submitting our re-revised manuscript addressing all the reviewers’ comments. We are thankful to the reviewers for their valuable comments and suggestions. All suggestions have been incorporated in the revised manuscript. The following is a point-by-point response to the comments made by the reviewers. We hope to have satisfied all reviewers’ expectations. Sincerely Giovanna Bosica (Corresponding author) Response to Reviewer Reports: Editor's Comments MINOR REVISIONS Dear Dr. Bosica, Thank you for your submission to PeerJ Organic Chemistry. It is my opinion as the Academic Editor for your article - Facile imine synthesis under green conditions using Amberlyst® 15 - that it requires a number of Minor Revisions. My suggested changes and reviewer comments are shown below and on your article 'Overview' screen. Please address these changes and resubmit. Although not a hard deadline please try to submit your revision within the next 40 days. Thanks for providing the revised version of your manuscript. Before a final decision, however, I would like to request special attention to what was raised by Reviewer 2, regarding the characterization of compound 3j and the spectra figures in SI material. - Thanks for offering us this opportunity. Regarding the missing spectra in the supporting info file, I don’t know why it was not the updated file I had uploaded, maybe during the several uploadings required by the staff precheck changes something went wrong (I have that previous version if you need). We have now further updated that S.I. file including also 3j spectrum. All suggestions have been addressed. Reviewer 1 (Anonymous) Basic reporting The revised manuscript considered most of the reviewers’ comments. The Tables have been corrected and more literature data has been added. Despite the unfortunate failure of the instrument, which did not allow the insertion of the preliminary experiments with other aldehydes/aliphatic ketones, many substrates are reported and characterized. Experimental design I think that the manuscript is suitable for publication in Peer J in the present form because represents a very simple, eco-friendly, efficient, and alternative approach to the synthesis of imines. Validity of the findings The ease of use and recoverability of Amberlyst 15® makes the catalyst a viable cheap alternative - Thanks for positive remarks. Reviewer 2 (Anonymous) Basic reporting Dr. Giovanna Bosica and coauthors submitted the revised manuscript “Facile imine synthesis under green conditions using Amberlyst® 15” accordingly to reviewer’s suggestions. Unfortunately, there are still problems with the 1H-NMR data reported for compound 3j. The chemical shifts of aliphatic protons do not correspond to the isobutyl group of 3j and they are in absolute disagreement with those reported in reference 20. In my opinion, the derivative 3j is neither the N-(3-nitrobenzylidene)-2-methylpropan- 1-amine nor N-(4-nitrobenzylidene)-2-methylpropan-1-amine. The authors have to check the starting reagents and repeat the experiment. Agreed and corrected. Furthermore, I did not find copies of selected scanned spectra in the Supporting information file. You are right, there was a problem during the several uploadings of the final document in the staff pre-check. All selected spectra are now showing and 3j spectrum has also been included. Row 73: round bottom flask. – corrected Row 155: (ref 2) please add a semicolon between P. and Zhang. – corrected Experimental design No comment Validity of the findings no comment – Thanks for positive remarks and for the suggestions. Reviewer 3 (Anonymous) Basic reporting In my previous review, several were the comments made to the authors. The authors have properly revised the manuscript and SI which are better now. Therefore, I would recommend accepting this manuscript in its current form. Experimental design No comment. Validity of the findings No comment. We hope our answers match your expectations. Thank you and best regards, Giovanna Bosica (Corresponding author) "	Here is a paper. Please give your review comments after reading it.
469	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>In regard to the actual public health global emergency and, based on the state of the art about the ways to inhibit the SARS-CoV-2 treating the COVID19, a family of 1,5disubstituted tetrazole-1,2,3-triazoles, previously synthesized, have been evaluated through in silico assays against the main protease of the mentioned virus (CoV-2-M Pro ). The results show that three of these compounds present a more favorable interaction with the selected target than the co-crystallized molecule, which is a peptide-like derivative. It was also found that also hydrophobic interactions play a key role in the ligand-target molecular couplings, due to the higher hydrophobic surfaces into the active site. Finally, a pharmacophore model has been proposed based on the results below, and a family of 1,5-DT derivatives has been designed and tested with the same methods employed in this work. It was concluded that the compound with the isatin as a substituent (P8) present the higher ligand-target interaction, which makes this a strong drug candidate against COVID19, due can inhibit the CoV-2-M Pro protein.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>I. Introduction</ns0:head><ns0:p>Recently, a new kind of coronavirus strain was discovered in Wuhan city in Hubei province, central China. This virus is known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and has caused the coronavirus disease 2019 (COVID-19), which has now become a pandemic threat <ns0:ref type='bibr' target='#b9'>(Gabutti et al. 2020;</ns0:ref><ns0:ref type='bibr' target='#b10'>Gralinski & Menachery 2020;</ns0:ref><ns0:ref type='bibr' target='#b13'>Jin et al. 2020)</ns0:ref>. At present, SARS-CoV-2 has caused thousands of deaths and more than 5 million people have been infected worldwide, becoming a global public health emergency <ns0:ref type='bibr' target='#b34'>(Sohrabi et al. 2020)</ns0:ref>. Despite the fact that there are no specific antivirals to treat the COVID-19, the scientific community is using the drug repurposing of some FDA approved drugs such as lopinavir, remdesivir, and chloroquine as a rapid strategy to find a cure <ns0:ref type='bibr' target='#b16'>(Kandeel & Al-Nazawi 2020;</ns0:ref><ns0:ref type='bibr' target='#b20'>Li & Clercq 2020;</ns0:ref><ns0:ref type='bibr'>Li et al. 2020;</ns0:ref><ns0:ref type='bibr' target='#b33'>Shah et al. 2020)</ns0:ref>. Nonetheless, the need to develop a new specific antiviral drug is still urgent.</ns0:p><ns0:p>A quick and efficient way to find a new drug candidate is through computer-aided drug design (CADD) which is a powerful tool used to find new compound by reducing risk, time, and cost of research in the drug discovery process <ns0:ref type='bibr' target='#b2'>(Baig et al. 2016;</ns0:ref><ns0:ref type='bibr' target='#b3'>Bisht & Singh 2018;</ns0:ref><ns0:ref type='bibr' target='#b5'>Ferreira et al. 2015;</ns0:ref><ns0:ref type='bibr' target='#b12'>Hoque et al. 2017)</ns0:ref>. Moreover, in February 2020, the first high-resolution crystal structure of the main protease of SARS-CoV-2 was published (PDB code: 6lu7) <ns0:ref type='bibr' target='#b23'>(Liu et al. 2020b</ns0:ref>). Such protein is essential in the virus life cycle, thus making it a key target in the quest of developing novel antiviral agents.</ns0:p><ns0:p>Moreover, regards other studies of the main protease of SARS-CoV-2 (CoV-2-M Pro ), which presents a similar structure to the M Pro of the SARS-CoV <ns0:ref type='bibr' target='#b11'>(He et al. 2020;</ns0:ref><ns0:ref type='bibr' target='#b22'>Liu et al. 2020a;</ns0:ref><ns0:ref type='bibr' target='#b38'>Vellingiri et al. 2020)</ns0:ref>, has been reported that the hydrogen bonds play a key role in the ligandtarget interactions <ns0:ref type='bibr' target='#b4'>(Chou et al. 2003)</ns0:ref>, as well as in the present year have been perform several studies about the interactions of organic molecules with the CoV-2-M Pro <ns0:ref type='bibr' target='#b26'>(Peele et al. 2020a;</ns0:ref><ns0:ref type='bibr' target='#b27'>Peele et al. 2020b)</ns0:ref>. Also, accord to the state of the art concerning potential candidates that target this specific enzyme highlights 1,2,3-triazoles, synthesized by the Dehaen group, were proposed as potential anti-coronavirus agents <ns0:ref type='bibr' target='#b17'>(Karypidou et al. 2018)</ns0:ref>. In this context, the present work aimed to evaluate in silico a series of 1,5-disubstituted-tetrazole derivatives which were previously synthesized in our research group <ns0:ref type='bibr' target='#b0'>(Aguilar-Morales et al. 2019</ns0:ref>) and whose biological and theoretical essays have not been reported.</ns0:p><ns0:p>Recent reports have shown the plethora of molecules that can interact with the selected target (CoV-2-M Pro ); ranging from alkaloids found in medicinal plants <ns0:ref type='bibr' target='#b31'>(Qamar et al. 2020)</ns0:ref>, as well as in other plants with known health properties, as garlic <ns0:ref type='bibr' target='#b30'>(Phuong-Thuy et al. 2020)</ns0:ref>, to the use of alpha ketoamide <ns0:ref type='bibr' target='#b13'>(Zhang et al. 2020)</ns0:ref>. Based on this context, the present work proposes several 1,5disubstituted tetrazole-1,2,3-triazoles as some novel plausible inhibitors of the CoV-2-M Pro . The above-mentioned compounds were evaluated through docking assays to obtain the target-ligand interactions that take place. Additionally, a pharmacophore model was performed based on the target (considering the electrostatic, hydrophobic and hydrogen bond interactions) leading to the to design of a novel family of molecules that can potentially inhibit the CoV-2-M Pro .</ns0:p></ns0:div> <ns0:div><ns0:head>II. Computational Methods</ns0:head><ns0:p>The Cartesian coordinates from the selected target, CoV-2-M Pro , were obtained from the protein data bank (PDB code: 6lu7), which was one of the first crystallized structures of the main protease of the SARS-CoV-2 virus. Furthermore, the Chimera package was used to add charges, remove solvents and correct residues of the target structure <ns0:ref type='bibr' target='#b29'>(Pettersen et al. 2004</ns0:ref>).</ns0:p><ns0:p>Moreover, the 1,5-disubstituted tetrazole-1,2,3-triazoles and the co-crystallized compound (into the CoV-2-M Pro structure), see Fig. <ns0:ref type='figure'>1</ns0:ref>, which are considered as ligands, were modeled using the Avogadro software <ns0:ref type='bibr' target='#b13'>(Jin et al. 2020)</ns0:ref> and charged with the Chimera package <ns0:ref type='bibr' target='#b29'>(Pettersen et al. 2004</ns0:ref>). However, to obtain a better approach, the ligands were optimized at the UFF level <ns0:ref type='bibr' target='#b32'>(Rappe et al. 1992</ns0:ref>) using the Gaussian 09 (G09) package <ns0:ref type='bibr'>(Frisch et al. 2009)</ns0:ref>. Note that, the UFF method was employed due to the good distance and angle bonds it provides to organic molecules, as well as the low computational cost.</ns0:p><ns0:p>Ligand-target interactions were obtained using the Molegro MVD package and the in silico molecular couplings, so-called molecular docking, were performed through the MolDock score function <ns0:ref type='bibr' target='#b35'>(Thomsen & Christensen 2006)</ns0:ref>. Also, the electrostatic, hydrogen bond and hydrophilic surfaces interactions were obtained with the same MVD software. Although several methods are frequently reported when studying ligand-target interactions such as the DFTB <ns0:ref type='bibr' target='#b1'>(Allec et al. 2019;</ns0:ref><ns0:ref type='bibr' target='#b24'>Morao et al. 2017)</ns0:ref>, the selected method was used as a preliminary approach to evaluate these interactions.</ns0:p><ns0:p>Finally, the pharmacophore model was developed using the ZINCPharmer server(Koes & Camacho 2012), considering the obtained properties obtained throughout the whole study. It is noteworthy to mention that the proposed novel inhibitors underwent the same process as the 1,5disubstituted tetrazole-1,2,3-triazoles and were evaluated using the selected target with the above method.</ns0:p></ns0:div> <ns0:div><ns0:head>III. Results</ns0:head></ns0:div> <ns0:div><ns0:head n='3.1'>Molecular Docking</ns0:head><ns0:p>The specific docking of all the modeled ligands, see Fig. <ns0:ref type='figure'>1</ns0:ref>, into the active site of the protein CoV-2-M Pro is shown in Fig. <ns0:ref type='figure'>2</ns0:ref>. It is evident that all the ligands docked similarly to the selected target PeerJ Phy. Chem. reviewing PDF \| (PCHEM- <ns0:ref type='table' target='#tab_0'>2020:05:48566:1:2:NEW 1 Jun 2020)</ns0:ref> Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science and interacted with the catalytic triad residues, such interaction will be explained in the discussion section.</ns0:p><ns0:p>Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref> shows the hydrogen bond energies, the electrostatic interactions, and the LE values obtained for the selected ligands. Additionally, Fig. <ns0:ref type='figure'>3</ns0:ref> shows the active site of the target, with the co-crystalized ligand and two of the best ligands interacting.</ns0:p></ns0:div> <ns0:div><ns0:head n='3.2'>Hydrogen bonds, electrostatic interactions and hydrophobic interactions</ns0:head><ns0:p>The hydrogen bond and electrostatic interactions between the selected target and the ligand 1e, which is the compound with the most favorable ligand-target interactions, are depicted in Fig. <ns0:ref type='figure'>4</ns0:ref>. The principal interactions are those with histidine residues, as well as one with serine and glutamine.</ns0:p><ns0:p>The hydrophobicity surfaces of the active site show that in the deep zone of the cavity a hydrophobic zone can be found, as shown in Fig. <ns0:ref type='figure'>5</ns0:ref> in blue colored surfaces. However, the front of the cavity and one site in the upper-left zone shows a higher hydrophilic site, depicted in red color in Fig. <ns0:ref type='figure'>5</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head n='3.3'>Pharmacophore model</ns0:head><ns0:p>Taken into account the results gathered, especially the docking results obtained by both, the 1e ligand and the co-crystallized ligand, and taken the hydrophobic surfaces as one of the main interactions, a pharmacophore model was developed. The principal aim in developing this model was to predict and propose the molecular fragments that are necessary to interact with CoV-2-M Pro , which would result in virus inhibition.</ns0:p><ns0:p>The proposed pharmacophore model is shown in Fig. <ns0:ref type='figure'>6</ns0:ref>, and consists of ten principal components: two hydrophobic fragments (Hy, depicted in green color), one aromatic fragment (Ar, colored in blue color), three hydrophobic-aromatic fragments (Hy-Ar, represented in purple color), two hydrogen donor fragments (HD, depicted in gray color) and two hydrogen acceptor fragments (colored in orange color).</ns0:p></ns0:div> <ns0:div><ns0:head n='3.4'>Proposing molecules</ns0:head><ns0:p>Based on the obtained results and considering the pharmacophore model, a series of ten 1,5disubstituted tetrazole-1,2,3-triazoles have been proposed as inhibitors of the CoV-2-M Pro , which are depicted in Fig. <ns0:ref type='figure'>7</ns0:ref>. Furthermore, Fig. <ns0:ref type='figure'>8A</ns0:ref> shows that that molecule P8 prefers to interact in the right side of the molecule in a similar manner as the co-crystallized ligand and 1e compounds. The structure of compound P8 is located in the deep of the active site.</ns0:p><ns0:p>Finally, Fig. <ns0:ref type='figure' target='#fig_4'>9</ns0:ref> shows the molecules 1e and P8 in the pharmacophore model, and reveals the occupied space by these molecules into the pharmacophore model.</ns0:p></ns0:div> <ns0:div><ns0:head>IV. Discussion</ns0:head></ns0:div> <ns0:div><ns0:head n='4.1'>Molecular Docking</ns0:head><ns0:p>To evaluate the best ligand docked in the selected target, CoV-2-M Pro , the MolDock score energy was considered as a parameter of measurement. Furthermore, the ligand efficiency (LE = Energy/No. of heavy atoms) was used to determine with better precision the ligand-target binding strength. This parameter gives the energy provided per atom in the ligand-target interaction, making it a better way of comparison between ligands with different number of atoms, see Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref>.</ns0:p><ns0:p>In comparison with the co-crystallized ligand reported in the PDB file, 1e is 0.04 kcal/mol more stable <ns0:ref type='bibr' target='#b23'>(Liu et al. 2020b)</ns0:ref>. Also, P8 is the molecule with highest ligand-target interaction energy. Moreover, the hydrogen bond (H bond ), electrostatic (Elstat) interactions and the Van der Waals energies (VdW) are shown in Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref>, demonstrating that the VdW the limiting energy in obtaining a better ligand-target interaction for this kind of systems. In other hand, best ligands show the most favorable VdW energies. However, the state of the art regarding this protease indicates that the hydrogen bond interactions are one of the most important energies, especially with the amides of the catalytic triad residues (Gly143, Cys145, and Ser144) <ns0:ref type='bibr' target='#b13'>(Zhang et al. 2020)</ns0:ref>. Note that table 1 includes compounds P1-10, which are the designed potential inhibitors presented in this work and will be boarded in section 4.4</ns0:p><ns0:p>In respect to the docked cavity, our results are in line with the results obtained by other authors, and show the interactions of the ligands into the active site of the protein, in the so-called catalytic triad (Gly143, Ser144, and Cys145), see Fig. <ns0:ref type='figure'>3</ns0:ref>. At the same time, the His41 and the Asp187 are important residues in the onset of the electronic transfer, which is the key mechanism in peptide bond rupture for this kind of protease. Other key fragments in the oxyanion hole includes the Gly143 and Ser144 residues, which according to Warshel and co-workers <ns0:ref type='bibr' target='#b14'>(Kamerlin et al. 2010;</ns0:ref><ns0:ref type='bibr' target='#b25'>Mukherjee & Warshel 2012)</ns0:ref>, stabilize the anionic intermediary. This helps identify the active site and is a way to corroborate that this cavity is the target site of the protein.</ns0:p><ns0:p>Fig. <ns0:ref type='figure'>3</ns0:ref> shows first the co-crystallized molecule (a peptide like derivative) which directly interacts with the catalytic triad and part of the molecule dock perfectly in almost the whole cavity. Furthermore, Fig. <ns0:ref type='figure'>3B</ns0:ref> depicts the two best ligands interacting in a similar manner to the cocrystallized molecule, but filling the right site of the computed cavity. This behavior might explain the most favorable interactions seen when comparing them with the co-crystallized molecule, see Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head n='4.2'>Hydrogen bonds, electrostatic interactions and hydrophobic interactions</ns0:head><ns0:p>The 1e ligand presents hydrogen bond interactions with Ser144 and His164, see Fig. <ns0:ref type='figure'>4A</ns0:ref>. Note that these are residues of the catalytic site. Furthermore, Fig <ns0:ref type='figure'>4B</ns0:ref> shows the same ligand interacting with His41, His163, Glu166, and His172 via electrostatic forces, in which the repulsive electrostatic interactions are more prominent than the attractive ones. This, as a result of the similar partial charge (positive-positive or negative-negative) between 1e and histidine residues. This kind of interaction plays a key role in the final conformation of the bioactive posse. Generally, the electrostatic interactions are one of the limiting energies in the ligand-target coupling and ligand 1e shows a value of -0.65, being this one of the highest ones of the table.</ns0:p><ns0:p>To better evaluate the ligand-target interactions, it is necessary to carry out an analysis of the hydrophobic surfaces of the selected cavity. Fig 5A shows the interactions between the cocrystallized ligand and the selected target, from a hydrophobic behavior. The ligand takes a conformation into the cavity occupying only the deep zone of the active site (the more hydrophobic site) and avoids the interactions with the front site of the cavity. Note that ligand 1e shows a similar interaction with the active site, see Fig. <ns0:ref type='figure'>5B</ns0:ref>.</ns0:p><ns0:p>Contrary to the co-crystallized ligand, compound 1e does not cross the cavity space in the right site. However, in interacts in the deep site of the cavity, docking less in the left site of the surface. Note that ligand 1e presents higher L.E., than the co-crystallized ligand, see Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref>. Therefore, it is clear that the presence of aromatic and hydrophobic rings in both molecules is essential and key for better interactions. The hydrophilic interactions are practically negligible.</ns0:p></ns0:div> <ns0:div><ns0:head n='4.3'>Pharmacophore model</ns0:head><ns0:p>Analyzing Fig6, it is clear that the low side of the model needs mostly hydrophobic fragments, and in the top, the fragments are HD and HA. The volume of the pharmacophore model makes us think that to tackle the M Pro of the SARS-Cov-2 virus, it is necessary a molecule with rings along their whole structure.</ns0:p><ns0:p>Highlights that with the proposed pharmacophore model can be design a family of compounds which can inhibit the M Pro , avoiding the virus replication and promoting the cure for the COVID19.</ns0:p></ns0:div> <ns0:div><ns0:head n='4.4'>Proposing molecules</ns0:head><ns0:p>Based on the obtained results and considering the pharmacophore model, a series of ten 1,5disubstituted tetrazole-1,2,3-triazoles have been proposed as inhibitors of the CoV-2-M Pro , which are depicted in Fig. <ns0:ref type='figure'>7</ns0:ref>. As shown in Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref>, the P8 and P10 designed molecules present the more favorable interactions with the selected target as they show a more negative LE value than the other evaluated compounds. Note that, six of the ten designed molecules exhibit better interaction with the CoV-2-M Pro than the co-crystallized molecules, which is the reference molecule.</ns0:p><ns0:p>On the other hand, the compound P8 has an isatin scaffold (1H-indole-2,3-dione) as part of its structure, which is considered a privileged structure given its broad biological and pharmacological activity. Some of which include antibacterial, anticancer, antitubercular, antimalarial, antifungal, anticancer, anti-HIV, and in general antiviral <ns0:ref type='bibr' target='#b36'>(Varun et al. 2019)</ns0:ref>. Analyzing the bio-active possess of compound P8, it is seen that it promotes an intramolecular stabilization due to two stacking interactions: one with the triazole ring and the other with the benzene ring, face-to-edge, and faceto-face, respectively. Fig. <ns0:ref type='figure'>8</ns0:ref> shows the principal hydrophobic interactions between P8 and the Cov-2-M Pro , depicted in blue surfaces. These results can be explained by the higher quantity of rings in the P8 structures, which could promote hydrophobic interactions.</ns0:p><ns0:p>In the case of the hydrogen bond interactions, molecule P8 interacts not only with the catalytic triad, specifically with the Ser144 and Cys145, and presents a higher number of interactions with other residues that include Ser1 and Asn142. The last one promotes a higher H bond energy than compound 1e, see Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref>. In fact, <ns0:ref type='bibr' target='#b23'>(Liu et al. 2020b</ns0:ref>) mention that hydrogen bond interactions play a key role in the ligand-target interaction as highlighted in the state of the art. Moreover, the electrostatic interactions between P8 and the selected target take place at the residues His41, His163, Glu166, and His172 residues, which in terms of Elstat energy, do not promote a favorable interaction (To understand better the behavior of the electrostatic interactions as a function of the different moieties in the studied molecules, the molecular electrostatic potential surfaces are depicted in Supplemental Files).</ns0:p><ns0:p>On the other hand, molecule 1e, which was previously synthesized by some of us, and the designed compound P8 were evaluated into the pharmacophore model and analyzed through the segments docked with the proposed structure. Fig. <ns0:ref type='figure' target='#fig_4'>9A</ns0:ref> shows the molecule 1e in the pharmacophore model, which reveals that this molecule needs some components to complete all the pharmacophore fragments. Specifically, it needs an aromatic moiety, as well as an HD and HA fragments in the top of the molecule.</ns0:p><ns0:p>Finally, Fig. <ns0:ref type='figure' target='#fig_4'>9B</ns0:ref> depicts the P8 structure into the pharmacophore model and shows that this molecule only an Hy and one HD fragments in order to complete all the requirements. Analyzing the results, it is clear that to obtain some better molecules that could inhibit the Cov-2-M Pro it is necessary to have a system that includes some rings in their structure. Also, the right side is the more important site of the cavity, as long as the size of the molecule does not overpass the size of the cavity.</ns0:p></ns0:div> <ns0:div><ns0:head>V. Conclusions</ns0:head><ns0:p>A family of compounds previously synthesized by some of us was tested to inhibit the protein Cov-2-M Pro , the results show that three of these compounds present a more favorable interaction with the selected target than the co-crystallized molecule, which is a peptide-like derivative. Moreover, although the fact that hydrogen bond interactions are mentioned in the state of the art about the selected protease, it can also be found that the electrostatic interactions and main the hydrophobic interactions play a key role in the ligand-target molecular couplings.</ns0:p><ns0:p>At the same time, the results reveal that a molecule can couple into the active site, which presents higher hydrophobic surfaces. Thus, in the quest to develop potential candidates it is essential to synthesize some molecules with a higher number of aromatic rings in their structures. Note that the residues of the active site interact in a stronger way with the best coupled ligand.</ns0:p><ns0:p>Finally, a pharmacophore model has been designed and used to propose a new family of 1,5disubstituted tetrazole-1,2,3-triazoles derivatives. These compounds are potential candidates to be synthesized as a perspective of this work. Based on the obtained results, the best ligands were coupled with the pharmacophore model, highlights the importance of the isatin moiety. Also, the pharmacophore model revealed that derivatives bearing the isatin substituent have a higher potential in the design of new drugs against the SARS-Cov-2. Hydrophobic and stacking interactions also play a key role in the design of new drug candidates to treat the COVID19.</ns0:p><ns0:p>PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2020:05:48566:1:2:NEW 1 Jun 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science Manuscript to be reviewed</ns0:p><ns0:note type='other'>Chemistry Journals Analytical, Inorganic, Organic, Physical, Materials Science Figure 1</ns0:note><ns0:p>Modeled co-crystallized and 1,5-disubstituted tetrazole-1,2,3-triazoles Manuscript to be reviewed Manuscript to be reviewed Manuscript to be reviewed</ns0:p><ns0:note type='other'>Chemistry Journals Figure 5</ns0:note><ns0:note type='other'>Chemistry Journals Figure 6</ns0:note><ns0:note type='other'>Chemistry Journals Figure 7</ns0:note><ns0:p>Designed compounds that present favorable interactions with the CoV-2-M Pro protein.</ns0:p><ns0:p>PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2020:05:48566:1:2:NEW 1 Jun 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science </ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>Figure 4</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>) the co-crystallized ligand, and B) 1e. Blue surfaces represent hydrophobic sites, red surfaces are hydrophilic zones PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2020:05:48566:1:2:NEW 1 Jun 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head /><ns0:label /><ns0:figDesc>and B) lateral view for the pharmacophore model for the Cov-2-M Pro protein. Green spheres depict the hydrophobic segments (Hy), blue spheres are the aromatic segments (Ar), purple spheres represent the hydrophobic and aromatic segments (Hy-Ar), gray spheres depict the hydrogen donor segments (HD) and the orange sphere represents the hydrogen acceptor segments (HA). PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2020:05:48566:1:2:NEW 1 Jun 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head /><ns0:label /><ns0:figDesc>Figure 8</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head>Figure 9</ns0:head><ns0:label>9</ns0:label><ns0:figDesc>Figure 9</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='17,42.52,178.87,525.00,425.25' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='19,42.52,242.99,525.00,249.00' type='bitmap' /></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head>Table 1 (on next page)</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Interaction energies between the modeled ligands and the protein CoV-2-M</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Pro</ns0:cell><ns0:cell>.</ns0:cell></ns0:row></ns0:table><ns0:note>PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2020:05:48566:1:2:NEW 1 Jun 2020)</ns0:note></ns0:figure> <ns0:note place='foot'>Analytical, Inorganic, Organic, Physical, Materials Science </ns0:note> </ns0:body> "	"Tierra Blanca, Gto., México, May-24-2020 COVER LETTER FOR THE REVISED VERSION (PeerJ- PCHEM-2020:05:48566:1:0:NEW) Ho Leung Ng (Academic Editor, PeerJ Physical Chemistry) Dear Editor, thank you very much for the opportunity to revise our manuscript entitled ‘Tackling the SARS-Cov-2 main protease using hybrid derivatives of 1,5-disubstituted tetrazole-1,2,3-triazoles: an in silico assay”. Then, all points, suggestions and concerns from the three experts were considered as much as it was possible for reviewing our manuscript, highlighting the changes in green. In the same way, point-by-point replies are provided. It is worthy to note that some typos, grammar and/or style-format mistakes were found and corrected by a colleague, highlighting the changes in yellow and others remarked with the word track change tool. Additionally, following the editorial support team recommendations, the results and the discussion were separated in different sections, treating to avoid as possible to modify the information of the manuscript. To Reviewer #1 Reviewer #1: “I consider this manuscript to be of interest to readers of this PeerJ journal, and I am supportive of publication with a minor note. There has actually be prior work using advanced methods (such as DFTB) for understanding ligand-protein interactions, which should also be mentioned: J. Comput. Chem. 38, 1987–1990 (2017); J. Chem. Theory Comput. 15, 2807-2815 (2019). In particular, these prior works have shown that DFTB is more accurate than MD calculations for ligand-protein interactions, and is faster than DFT for large systems, which should be mentioned as previous studies relevant to this field. With this minor revision, I would be receptive towards publication.” Authors: We are grateful for your comments and recommendations. The DFTB method has mentioned in the Computational Methods sections (lines 301-304), citing the recommended previous studies about it. To Reviewer #2 (Md Sharif Khan) Reviewer #2: “This is highly requested that authors should increase the understandability of the sentences all over the manuscript” Authors: We are grateful for your comments and recommendations. Some typos, grammar and/or style-format mistakes were found and corrected by a colleague, highlighting the changes in yellow Reviewer #2: “In the introduction section please mention the specific benefit for the substitution conducted in this work. In 64 – 67 please mention the abbreviation and please provide information about what kind of biological theoretical understanding is necessary and why it is important and improve the understanding from the previously published works. In the last paragraph of the introduction please arrange relevant information about your study and mention the methodology used in this work.” Authors: The request by the reviewer was attended (see Introduction section). Reviewer #2: “Line 44- please update the number according to the present situation.” Authors: Has been done (see Line 121) Reviewer #2: “Line 114 – insert (-) sign” Authors: Done (See line 316). Reviewer #2: “Line 139 – why SER144 is capital?” Authors: Changed to Ser144 (Now line 434). Reviewer #2: “In table 1 – Please insert the unit accordingly in the main text.” Authors: The units are mentioned in the title of the table (highlighted in green color). Reviewer #2: “In line 88 – 89, optimization was conducted at the UFF level, what is the reason for choosing UFF, authors should define the reason and explain the validity of the choosing such methods.” Authors: Was added an explanation about the use of UFF in lines 294-296. Reviewer #2: “Electrostatic charge distribution on the surface of the 1,5 – disubstituted tetrazole – 1,2,3-triazoles is important for this study and read will like to see the distribution of the change which could provide easy understandings, authors can provide such information by making a surface grid figure at least for the co-crystallized, 1e, and P8 compounds.” Authors: The molecular electrostatic surfaces for the co-crystallized, 1e, and P8 compounds are depicted in the supplemental files (see 655-657). Reviewer #2: “A clear representation of co-crystallized ligand and the 1e ligand is necessary for easy understandings.” Authors: The structure of the co-crystallized ligand has been added to the new Fig. 1. Reviewer #2: In figure 4 the reason for the repulsive interactions has to be explained, further, it is worthy to provide an explanation about the difference of the interaction energies for different target sites. Authors: The explanation was added in lines 510-511. To Reviewer #3 (Abraham Peele) Reviewer #3: “Results section authors have given the name recombinant strain COV-2MA” Authors: Thank you very much for your comments. The COV-2MA term has been changed to the correct term. Reviewer #3: “Authors required to include the following references at appropriate places in the manuscript” Authors: The references have been added to the current version. Additional comments for PeerJ production staff If our article is accepted for publication, please feel free to make any changes that you consider suitable to improve the manuscript, grammar and/or style. With regards and on behalf of my co-workers, Erik Díaz-Cervantes, PhD Corresponding author "	Here is a paper. Please give your review comments after reading it.
470	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>We explain why search algorithms can find molecules with particular properties in an enormous chemical space (ca 10 60 molecules) by considering only a tiny subset (typically 10 3−6 molecules). Using a very simple example, we show that the number of potential paths that the search algorithms can follow to the target is equally vast. Thus, the probability of randomly finding a molecule that is on one of these paths is quite high and from here a search algorithm can follow the path to the target molecule. A path is defined as a series of molecules that have some non-zero quantifiable similarity (score) with the target molecule and that are increasingly similar to the target molecule. The minimum path length from any point in chemical space to the target corresponds is on the order of 100 steps, where a step is the change of and atom-or bond-type. Thus, a perfect search algorithm should be able to locate a particular molecule in chemical space by screening on the order of 100s of molecules, provided the score changes incrementally. We show that the actual number for a genetic search algorithm is between 100 and several millions, and depending on the target property and its dependence on molecular changes, the molecular representation, and the number of solutions to the search problem.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>INTRODUCTION</ns0:head><ns0:p>Chemical space is the number of possible small organic molecules, which has been estimated to be on the order of 10 60 molecules. Many techniques have been developed to search this chemical space for molecules with desirable properties, <ns0:ref type='bibr'>[1;2]</ns0:ref> including genetic algorithms, <ns0:ref type='bibr'>[3;4]</ns0:ref> variational autoencoders, <ns0:ref type='bibr'>[5;6]</ns0:ref> recurrent neural networks, <ns0:ref type='bibr'>[7;8;9]</ns0:ref> and generative adversarial networks. <ns0:ref type='bibr'>[10;11;12]</ns0:ref> Rather than screening a user defined library, these methods automatically select a subset of chemical space for screening, usually in an iterative fashion. The size of the subsets typically range between 10,000 and several million, i.e. a tiny fraction of chemical space yet usually produce good candidates.</ns0:p><ns0:formula xml:id='formula_0'>10 60 − → 10 3−6 − → 1<ns0:label>(1)</ns0:label></ns0:formula><ns0:p>In this paper we discuss how this is possible using genetic algorithms (GAs) as the search algorithm. We use GAs as they are relatively simple and thus easy to interpret, but our general conclusions should also be valid for machine learning-based methods.</ns0:p><ns0:p>The paper is organized as follows. First we discuss a related non-chemistry search problem that is conceptually easier to understand but is of roughly similar difficulty: finding a specific sequence of characters. Then we discuss the chemical equivalent, which is finding a specific molecule among the 10 60 possible. Finally, we discuss an example of the more usual molecular discovery problem where there are many solutions.</ns0:p></ns0:div> <ns0:div><ns0:head>COMPUTATIONAL METHODOLOGY</ns0:head><ns0:p>The graph-based GA code used in this study is that described by Jensen <ns0:ref type='bibr' target='#b17'>[13]</ns0:ref> except that, inspired by Brown et al. <ns0:ref type='bibr'>[</ns0:ref> Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>from the current and previous generation). The GAs use roulette wheel selection to choose parents for mating. The string-based GA code is the same as the graph-based GA code, except for the crossover and mutation operations. For string-based GA the crossover is performed by picking a random cut point between two characters for each parent string and then combining the left and right sub-string from the first and second parent, respectively. In the case of the Shakespeare example described below the same cut point was used for both parents and the fragments are combined so that the children are the same length as the parents. In the case of SMILES and DeepSMILES the local syntax is not considered when choosing the cut-point, e.g. a cut within [C@H] is allowed. In the case of SELFIES, each unit is enclosed with square brackets, so only cuts between a closing and opening square bracket is allowed. If the crossover does not lead to a valid molecule according to RDKit the process is repeated up to 50 times, after which a new pair of parents are chosen. After crossover the child is mutated at a specified rate (the mutation rate),</ns0:p><ns0:p>i.e. if the mutation rate is 50% then there is a 50% chance that one character in the string is replaced by a randomly chosen character. The allowed characters are those found among 250,000 molecules found in the ZINC data base used in previous studies <ns0:ref type='bibr'>[14;5;13]</ns0:ref> (see SI for more information). If the mutation does not lead to a valid molecule according to RDKit the process is repeated up to 50 times, after which the original molecule is returned. In all cases the molecules are Kekulized, meaning that aromaticity is not removed, before mating and mutation operations are applied to increase the chances of making a string that corresponds to a molecule. The string-based molecular representations are not re-canonicalized after mating and mutation operations are applied.</ns0:p><ns0:p>The Tanimoto score used for rediscovery is computed using RDKit <ns0:ref type='bibr' target='#b19'>[15]</ns0:ref> based on ECFP4 circular fingerprints, following Brown et al. <ns0:ref type='bibr' target='#b12'>[8]</ns0:ref> . The first excitation energy and associated oscillator strength is computed using the semiempirical sTDA-xTB <ns0:ref type='bibr' target='#b20'>[16]</ns0:ref> method based on an MMFF94 <ns0:ref type='bibr'>[17;18;19;20;21]</ns0:ref> optimized geometry. The geometry is chosen by generating and energy-minimizing twenty random conformations using RDKit and choosing the geometry with the lowest energy. Manuscript to be reviewed </ns0:p><ns0:note type='other'>Chemistry Journals</ns0:note></ns0:div> <ns0:div><ns0:head>RESULTS AND DISCUSSION</ns0:head><ns0:p>A simple example from Shakespeare</ns0:p><ns0:p>We start by considering a very simple search problem <ns0:ref type='bibr' target='#b26'>[22]</ns0:ref> for which the various factors contributing to successful searches can be demonstrated analytically (see SI). The sentence 'to be or not to be that is the question' has 39 lower case characters including spaces. It is one of 27 39 = 6.7×10 55 39-character sequences, which is roughly the same size as chemical space. Despite this vast search space a simple genetic algorithm (GA) can easily identify the target: using an initial population of 100 randomly generated phrases and a mutation rate of 20% the target phrase is identified after no more than ∼300 generations (median ∼200), i.e. the solution is consistently found by evaluating only ca 10,000 to 30,000 sequences out of 6.7× 10 55 possible (Figure <ns0:ref type='figure' target='#fig_0'>1</ns0:ref>)</ns0:p><ns0:p>This remarkable feat can be explained as follows: 1-(26/27) 39 or 77% of the 6.7×10 55 possible sequences have at least one correctly placed character compared to the target sequence (Eq S1). This means that for an initial mating pool of 100 random sequences, an average of 77 sequences will have a score of at least 1. An average 38±1 of the 39 positions are correctly represented in at least one gene (Eqs S4-S5). Since the score is additive, it is very likely that a crossover will result in a child with a higher score.</ns0:p><ns0:p>Indeed simulations show that the tends score increases by 1 with every generation until the score reaches about 20, i.e. until about half the letters are correctly placed. This makes sense because, on average, each parent contributes half the genetic information and the correctly placed letters are evenly distributed in the initial population. After half the letters and spaces are placed correctly, the score increases more slowly and it can take many generations to place the last character since that tends to occur solely through random mutations in the current GA implementation.</ns0:p><ns0:p>So rather than picturing 6.7×10 55 random sequences that one must sift through, one should picture an enormous number of interconnected paths that connects low-scoring sequences to the target sequence (Figure <ns0:ref type='figure' target='#fig_1'>2</ns0:ref>). Since 77% sequences have a score of at least 1, one is very likely to encounter such a path by chance and one can then follow the path to the target sequence using a search algorithm such as a GA. However, such paths only exist if the score increases in a relatively smooth fashion as one gets closer to the target. </ns0:p></ns0:div> <ns0:div><ns0:head>Rediscovery</ns0:head></ns0:div> <ns0:div><ns0:head>String-based approaches</ns0:head><ns0:p>The closest chemical equivalent to the Shakespeare example described in the previous section is locating a predefined molecule in chemical space, i.e. rediscovery. Brown et al. <ns0:ref type='bibr' target='#b12'>[8]</ns0:ref> have demonstrated this for three drug molecules: celecoxib, troglitazone, and tiotixene (Figure <ns0:ref type='figure' target='#fig_3'>3</ns0:ref>). Here similarity to the target is measured by the Tanimoto similarity, which is computed by decomposing each molecule into overlapping fragments up to a certain size and then counting how many fragments the two molecules have in common and dividing by the combined total number of fragments. Thus, the Tanimoto score ranges from 0 (no similarity) to 1 (very similar or identical).</ns0:p></ns0:div> <ns0:div><ns0:head>4/19</ns0:head><ns0:p>PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2020:05:49104:1:1:NEW 2 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Since molecules can be represented as strings (e.g. SMILES strings) we start by using our string-based GA used in the previous example with some minor modifications as described in the Methods section.</ns0:p><ns0:p>Otherwise we follow the same procedure as Brown et al. <ns0:ref type='bibr' target='#b12'>[8]</ns0:ref> .</ns0:p><ns0:p>The results of 40 SMILES-based GA searches for each of the three molecules are shown in Figure <ns0:ref type='figure' target='#fig_4'>4</ns0:ref>.</ns0:p><ns0:p>45%, 0%, and 7% of the searches succeed for celecoxib, troglitazone, and tiotixene, respectively, which all are significantly lower than the 100% success rate for the Shakespeare example. Why do so many of the searches fail? The SMILES strings range in length from 56 to 61 characters and the search uses 25 different characters so the search spaces are larger than in the Shakespeare example. However, the GA used in that example has no problem finding longer sentences (Figure <ns0:ref type='figure' target='#fig_1'>S2</ns0:ref>). The other main difference between the SMILES-based rediscovery and the Shakespeare example is the score. In order to compute the Tanimoto score the SMILES string is first converted to a molecular graph, and this conversion fails for a larger portion of the SMILES strings generated using the mating and mutation operations. The failure is primarily due to incorrect SMILES syntax, such as unmatched parentheses or integers denoting ring-closures. Thus, the rediscovery search can only follow paths through sequence space leading to the target molecules that are composed of valid SMILES strings, which is a small subset of all possible paths (Figure <ns0:ref type='figure' target='#fig_1'>2</ns0:ref>). This makes the rediscovery task intrinsically harder than the Shakespeare example, where the score can be evaluated for nonsensical strings.</ns0:p><ns0:p>Table <ns0:ref type='table'>S1</ns0:ref> show the (non-canonical) SMILES strings for the successful SMILES-based GA searches. In the case of troglitazone there were no successful searches, but 15 of the searches resulted in a maximum score of 0.79, which is the second highest score observed, so these string are counted as successful for the current discussion. Many of the SMILES strings show similar patterns. For celecoxib, all but two SMILES strings start with 'NS(=O)(=O)C1=..' and end with '..C=C1'. For troglitazone all but two SMILES strings start with 'CC1=..' and end with '..C1O', or vice versa, and similarly for tiotixene. The most likely explanation is that each respective search starts from the same or similar SMILES strings in the initial population. Indeed inspection of the SMILES strings in the initial population reveal strings with similar patterns (Figure <ns0:ref type='figure'>5</ns0:ref>). In the case of celecoxib there are 13 different molecules with the same phenyl-X-benzenesulfonamide architecture, which helps explain why celecoxib is rediscovered more frequently than tiotixene, where the SMILES pattern shown in Figure <ns0:ref type='figure'>5</ns0:ref> is the only example in the initial population. In the case of troglitazone, the search has to place a more complicated syntax (COC2=CC=C(CC3SC(=O)NC3=O)C=C2), compared to celecoxib and tiotixene, at the correct position in the string. While this can be done at the 5 position (Figure <ns0:ref type='figure'>5</ns0:ref>), it is more difficult at the 2 position (which would result in troglitazone) due to the SMILES syntax of the chromane moiety that is most common in the initial population (Figure <ns0:ref type='figure' target='#fig_3'>S3</ns0:ref>). This observation could help explain why none of the SMILES-based troglitazone rediscovery searches are successful.</ns0:p><ns0:p>In the case of troglitazone, the success rate can be improved significantly by using DeepSMILES <ns0:ref type='bibr' target='#b27'>[23]</ns0:ref> (Figure <ns0:ref type='figure' target='#fig_4'>4</ns0:ref>), another string-based molecular representation that doesn't involved matched parenthesis and integers denoting ring-closures. However, using DeepSMILES does not increase the success rate for celecoxib or tiotixene. Finally, using SELFIES <ns0:ref type='bibr' target='#b28'>[24]</ns0:ref> does not increase the success rate for any of the three molecules. It is very likely that the performance of the string-based GA searches can be improved significantly by using more sophisticated algorithms. <ns0:ref type='bibr'>[25;26;27]</ns0:ref> The main point for the purposed of thus study is that the molecular representation is one of the factors that can complicate the exploration of chemical space.</ns0:p></ns0:div> <ns0:div><ns0:head>Graph-based approach</ns0:head><ns0:p>The success rate for rediscovery can be improved significantly by performing the mating and mutation operations directly on the molecule (formally a graph with nodes and edges corresponding to atoms and bonds, respectively) rather than a string representation (Figure <ns0:ref type='figure' target='#fig_4'>4</ns0:ref>). The success rate for tiotixene (38%) is noticeably lower than those for celecoxib (82%) and troglitazone (100%). The reason is that two of the fragments found in tiotixene are not found in the initial population, while the corresponding numbers for celecoxib and troglitazone are zero and one, respectively. The missing fragment for troglitazone relates to the connection between the chromane and benzene group (Figure <ns0:ref type='figure' target='#fig_4'>S4</ns0:ref> Manuscript to be reviewed Manuscript to be reviewed</ns0:p><ns0:note type='other'>Chemistry Journals</ns0:note></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:p><ns0:p>At first impression our results indicate that our graph-based GA is able to find a specific molecule in chemical space by evaluating only a very small subset of between ≤35,400 molecules (troglitazone) and ≤1,000,000 molecules (tiotixene). Troglitazone is rediscovered with a 100% certainty in 354 generations or less, where 100 molecules is evaluated for each generation. Tiotixene is rediscovered successfully in 38% of the GA searches, meaning that a minimum of 10 GA searches have to be performed to rediscover tiotixene with a 99% certainty, where each search requires up to 100,000 molecules to be evaluated. However, the initial mating pool was constructed following Brown et al. <ns0:ref type='bibr' target='#b12'>[8]</ns0:ref> , i.e. the 100 top-scoring molecules in a 1.6 million molecule ChEMBL subset, where molecules with an ECFP4 Tanimoto similarity of >0.323 are removed. So constructing the initial population itself requires 1.6 million molecules to be evaluated and this 'cost' must be added. If instead the initial population is constructed as before but from 10,000 molecules chosen from the 1.6 million ChEMBL subset, the success rates are 93%, 70%, and 25%, respectively, meaning that at least 2, 4, and 17 GA searches are needed for rediscovery to succeed with >99% certainty (Figure <ns0:ref type='figure' target='#fig_7'>6</ns0:ref>).</ns0:p><ns0:p>Thus, between 210,000 and 1,710,000 molecules need to be evaluated to find one particular molecule in chemical space using our GA. All the fragments in celecoxib and troglitazone are in the respective initial population, while two fragments are missing for tiotixene (Figure <ns0:ref type='figure' target='#fig_4'>S4(c)</ns0:ref>). This indicates the substructures that make up the three molecules are relatively common in the ChEMBL data set.</ns0:p><ns0:p>In summary, at least in the case of drug-like molecules it is possible to locate specific molecules in chemical space by evaluating a relatively small subset (10 5−6 ). Similarly to the Shakespeare example, the reason is that many of the molecules in chemical space have some structural motifs in common with the target molecule. Search algorithms like GAs can then combine these structural motifs to create molecules that are increasingly similar to the target molecules. The order in which these fragments are combined correspond to different (interconnected) paths in chemical space that all lead to the target molecules (Figure <ns0:ref type='figure' target='#fig_1'>2</ns0:ref>). There are a vast number of such paths, so it is highly likely to randomly encounter at least one such path, which can then be followed to the target. In this particular case, the search is frustrated by the use of the Tanimoto similarity as the scoring function, since it is only a semi-continuous function (cf.</ns0:p><ns0:p>Figure <ns0:ref type='figure' target='#fig_0'>S1</ns0:ref>) of the molecular structure in the sense that all atoms and bonds in a fragment must be placed correctly before the fragment is counted as found.</ns0:p></ns0:div> <ns0:div><ns0:head>Absorbance</ns0:head><ns0:p>Rediscovery is mainly interesting because there is only one solution (or very few solutions) and thus serves to test the limits of chemical space search algorithms. Most target properties will have several solutions in chemical space and may thus be easier to find. To illustrate this, we search for molecules that absorb light at 200, 400, and 600 nm with an oscillator strength (ω) of ≥0.3. The score is given by Manuscript to be reviewed</ns0:p><ns0:formula xml:id='formula_1'>score = e − 1 2 ⇣ λ −λ t σ ⌘ 2 + min(ω, 0.3)/0.3<ns0:label>(2</ns0:label></ns0:formula></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>where λ is the computed absorption wavelength of the molecule, λ t is the target wavelength, and σ is 50 nm. The GA searches are terminated if the top score in the population is within 0.01 of the maximum possible score of 2.0. The absorption wavelength and oscillator strength is computed using the xTB-sTDA method <ns0:ref type='bibr' target='#b20'>[16]</ns0:ref> based on a low-energy molecular structure computed using the MMFF94 force field as implemented in RDKit. The low-energy structure is computed by generating and energy-minimising 20 random conformations using RDKit and choosing the conformer with the lowest MMFF94 energy. The molecules for the initial population are chosen randomly from the first 1000 molecules in 250,000-molecule subset of the ZINC data base that we have used previously. <ns0:ref type='bibr' target='#b17'>[13]</ns0:ref> Molecules that absorbed within 100 nm of the target wavelength were excluded from the initial population. The goal of these simulations is to illustrate the use of GAs with a scoring function that has a complex dependence on the molecular structure and a target property with multiple solutions, not to find stable, synthetically accessible molecules for experimental testing. The results for 40 50-generation GA searches with a population size of 20 are shown in Figure <ns0:ref type='figure' target='#fig_9'>7</ns0:ref>. The success rates are 100% and 97% for 400 and 600 nm, while only 30% for 200 nm. For 400 and 600 nm, the median number of generations needed to find to find a molecule with the target property is 6 and 20 generations, respectively, which corresponds to screening only 120 and 400 different molecules. While the success rate is comparatively low for 200 nm (requiring up to 13,000 molecule evaluations) it is still impressive given the small population size, initially constructed from randomly chosen molecules (i.e.</ns0:p><ns0:p>no pre-screening like for rediscovery). Inspection of the molecules (Figure <ns0:ref type='figure'>8</ns0:ref> and Figures <ns0:ref type='figure' target='#fig_9'>S5-S7</ns0:ref>) show that, as expected, they are all different. Thus, there are many molecules in chemical space that satisfy the search criterion with, presumably, many different paths leading to each target, as shown for rediscovery, which increases the chances of success (Figure <ns0:ref type='figure' target='#fig_1'>2</ns0:ref>)).</ns0:p><ns0:p>For 200 nm, all but two GA searches achieved a score of >1.85, which corresponds to wavelength with 28.5 nm of the target value (assuming the oscillator strength is >0.3). So the majority of the searches get reasonably close to the target, but fail to reach the success criterion of 1.99, which corresponds to a wavelength within 7 nm of the target value. The most likely explanation is that it requires a larger change in excitation energy to shift low wavelength excitations. For example, a shift from 207 to 200 nm requires a change in excitation energy of 0.21 eV, compared to 0.05 eV (ca 1 kcal/mol) for a shift from 407 to 400 nm. Thus, absorption wavelengths around 400 nm are easier to fine-tune using relatively modest molecular changes, compared to 200 nm. Conversely, in the case of 600 nm, almost any change to the structure can easily change the excitation wavelength by 7 nm, so it becomes more difficult to hit the target wavelength exactly compared to 400 nm. This underscores the importance of smooth, incremental scoring functions for search efficiency (Figure <ns0:ref type='figure' target='#fig_0'>S1</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>8/19</ns0:head><ns0:p>PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2020:05:49104:1:1:NEW 2 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p><ns0:note type='other'>Chemistry Journals Figure 8</ns0:note><ns0:p>. Some of the molecules found by the GA searches for molecules that absorb at a certain wavelength. Below each molecules is the computed absorption wavelength (in nm) and oscillator strength. We recognise that some of these molecules may not be stable (e.g. cyclopentadiene groups tend to dimerise) or represent the most stable tautomer. We merely use absorbance as a score that has a complex dependence on the molecular structure.</ns0:p></ns0:div> <ns0:div><ns0:head>CONCLUSIONS</ns0:head><ns0:p>This paper explains how search algorithms can find particular molecules in an enormous chemical space (10 60 molecules) by considering only a tiny subset (typically 10 3−6 molecules). We use a simple, nonchemistry related search problem that is easy to interpret quantitatively. We show that a genetic algorithm (GA) can find one particular 39-character sequence by considering at most 30,000 out of 6.7×10 55 possible sequences (Figure <ns0:ref type='figure' target='#fig_0'>1</ns0:ref>). The reason is that 77% of the 6.7×10 55 possible sequences have at least one correctly placed character (with a score of 1), so it is easy to find such sequences by random chance.</ns0:p><ns0:p>Search algorithms like GAs then combine these sequences to make higher-scoring sequences, in an iterative fashion, until the target sequence is obtained. If we view closely related sequences with correctly placed characters as being 'connected' then we can envisage the search space as being filled with an enormous number of interconnected paths that connect sequences with few correctly placed characters to the target sequence (Figure <ns0:ref type='figure' target='#fig_1'>2</ns0:ref>). It is easy to find a distant point on one of these paths and relatively easy to follow the path to the target using search methods such as GAs, provided the score changes incrementally as the sequence is changed (Figure <ns0:ref type='figure' target='#fig_0'>S1</ns0:ref>). As step along the path represents an edit of the sequence so the length of a given path from a given point to the target is the so-called edit distance, where the change in a single character corresponds to an edit distance of one. This means that the shortest possible path from a sequence with only one correctly placed character to the target is only 38. So while the sequence space is vast, the shortest distance between any pair of points involves at most 39 changes.</ns0:p><ns0:p>The closest chemical equivalent to the simple string search example is locating a predefined molecule in chemical space, i.e. rediscovery. Rediscovery, using text strings (SMILES, DeepSMILES, and SELF-IES) to represent the molecules, is shown to be significantly more challenging even though the mechanics of the GA search (i.e. the mating and mutation operations) are very similar. Most string-based searches fail to find the target after 100,000 molecule evaluations starting from initial populations made by prescreening over 1.6 million molecules (Figure <ns0:ref type='figure' target='#fig_4'>4</ns0:ref>). The primary difference between the simple phrase search Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science and rediscovery is that in the latter case the score can only be evaluated for strings that correspond to valid molecules, while in the former case all strings can be scored. Since most string-based mating a mutation operations lead to strings with invalid syntax and zero scores for the rediscovery search, there are many fewer paths leading to the target (Figure <ns0:ref type='figure' target='#fig_1'>2</ns0:ref>) compared to the simple string search.</ns0:p><ns0:p>In one of the the three rediscovery targets (troglitazone) the success rate can be improved by using DeepSMILES, a string-based molecular representation with simpler syntax compared to SMILES. It is also quite likely that the success can be improved further by more sophisticated mating a mutation operations designed specifically for particular syntax associated with each molecular representation. The success rate can be improved by performing mating and mutation operations directly in the molecular graph (i.e. the atom and bonds, Figure <ns0:ref type='figure' target='#fig_7'>4 and 6</ns0:ref>), where a particular molecule can be rediscovered with >99% certainty by evaluating between 210,000 and 1.7 million (10 5−6 ) molecules -a very small fraction of chemical space. In analogy with the simple phrase example, the reason is that the chemical 'alphabet' of organic chemistry is relatively small ca ten different atoms and three different bonds. So it is quite likely that a randomly chosen molecule has something in common (e.g. a C-C bond or a pyridine ring)</ns0:p><ns0:p>with the target molecule and thus lies on a path that a search algorithm can follow to the target (Figure <ns0:ref type='figure' target='#fig_1'>2</ns0:ref>).</ns0:p><ns0:p>For example, more than 99.9% of the 1.6 million molecules in the ChEMBL data set, used to construct the initial populations, have a non-zero Tanimoto similarity with the three rediscovery targets (Figure <ns0:ref type='figure' target='#fig_3'>3</ns0:ref>).</ns0:p><ns0:p>Most drug-like molecules have at most 50 atoms and bonds, so the number of changes needed to inter-convert two very different molecules (the so called graph edit distance) is generally less than 100.</ns0:p><ns0:p>So, while chemical space is vast, the ideal search algorithm can traverse it very quickly -as long as the desired property changes incrementally.</ns0:p><ns0:p>While rediscovering one molecule in chemical space can require the screening of 10 5−6 ) molecules, finding a molecule with a particular property, such as absorbance at a particular wavelength, can often be accomplished more efficiently since there tends to be many different molecules with the desired property (Figures <ns0:ref type='figure' target='#fig_9'>7, 8</ns0:ref>, and 2b). For example, finding molecules that absorb at 200±7, 400±7, and 600±7 requires the screening of up to 13,000, 120, and 400 different molecules.</ns0:p><ns0:p>This study focuses on GAs as they are relatively simple and thus easy to interpret, but our general conclusions should also be valid for other generative models aimed at searching for molecules with specific properties. Such generative models typically combine a machine-learned molecular representation with a standard search algorithm such as swarm optimization, <ns0:ref type='bibr' target='#b8'>[6]</ns0:ref> hill climb, <ns0:ref type='bibr' target='#b12'>[8]</ns0:ref> or Monte Carlo tree search. <ns0:ref type='bibr' target='#b13'>[9]</ns0:ref> Like most search algorithms, these algorithms are designed to find and incrementally follow paths through search space towards the desired goal (Figure <ns0:ref type='figure' target='#fig_1'>2</ns0:ref>), similarly to GAs.</ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head>Figure 1 .</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1. The Shakespeare example. Plot of the top score found in the population for each generation for 10 different GA searches. The population size is 100, so up to 10,000 different sequences are evaluated in 100 generations.</ns0:figDesc><ns0:graphic coords='3,141.73,383.63,413.60,275.73' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head>Figure 2 .</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2. Pictorial representation of paths (lines) through chemical space leading to target(s), i.e. molecules with the desired property. A path connects molecules with non-zero scores and the scores increase incrementally as one gets to the target. In the left panel only one molecule has the desired property, while in the right panel several molecules have the desired property.</ns0:figDesc><ns0:graphic coords='4,146.90,63.78,196.44,110.50' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head /><ns0:label /><ns0:figDesc>Figure S1 shows plots similar to Figure 1, but where there the score only increases if the number of correctly placed characters increases by 2. After about half of the characters are placed correctly, it becomes less likely that a mating operation or mutation increases the score and none of 10 simulations manages to find the correct sequence in 1000 generations. If the score only increases if the number of correctly placed characters increases by 5, then the the GA fails to increase the score beyond 15 (Figure S1). A chemical example of non-continuous scores is discussed below.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head>Figure 3 .</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Figure 3. The three target molecules for rediscovery.</ns0:figDesc><ns0:graphic coords='5,141.73,63.78,413.58,110.81' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head>Figure 4 .</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Figure 4. Rediscovery. Plot of the top score found in the population for each generation for 40 different GA searches for each target molecule (celecoxib, troglitazone, and tiotixene (Figure3)) and molecular representation (graph, SMILES, DeepSMILES, and SELFIES). The score is the Tanimoto similarity to the target molecule computed using ECFP4 circular fingerprints. The population size is 100, so up to 100,000 different molecules are evaluated in 1000 generations. The mutation rate is 50%. For each plot we show the success rate and the median number of generations for successful runs.</ns0:figDesc><ns0:graphic coords='5,141.73,210.13,413.57,295.28' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head>/ 19 PeerJ</ns0:head><ns0:label>19</ns0:label><ns0:figDesc>(a)). Inspection of the initial population for the troglitazone GA searches shows that it contains several molecules with chromane 5Phy. Chem. reviewing PDF \| (PCHEM-2020:05:49104:1:1:NEW 2 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head>Figure 5 ./ 19 PeerJ</ns0:head><ns0:label>519</ns0:label><ns0:figDesc>Figure 5. Examples of SMILES strings obtained by successful SMILES-based GA searches. In the case of troglitazone none of the searches were successful, so SMILES with a Tanimoto similarity of 0.79 are shown.</ns0:figDesc><ns0:graphic coords='7,141.73,63.78,413.58,536.81' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_7'><ns0:head>Figure 6 .</ns0:head><ns0:label>6</ns0:label><ns0:figDesc>Figure 6. Same as for Figure 4 but using only the graph based approach and an initial population based on pre-screening 10,000 molecules rather than 1.6 million.</ns0:figDesc><ns0:graphic coords='8,141.73,218.44,413.58,120.63' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_8'><ns0:head>) 7/ 19 PeerJ</ns0:head><ns0:label>19</ns0:label><ns0:figDesc>Phy. Chem. reviewing PDF \| (PCHEM-2020:05:49104:1:1:NEW 2 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_9'><ns0:head>Figure 7 .</ns0:head><ns0:label>7</ns0:label><ns0:figDesc>Figure 7. Plot of the top score found in the population for each generation for 40 different GA searches for molecules that absorb at 200, 400, and 600 nm, all using a graph-based molecular representation. The population size is 20, so up to 1000 different molecules are evaluated in 50 generations. The mutation rate is 5%. For each plot we show the success rate and the median number of generations for successful runs.</ns0:figDesc><ns0:graphic coords='9,141.73,233.67,413.58,120.63' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_10'><ns0:head>9 / 19 PeerJ</ns0:head><ns0:label>919</ns0:label><ns0:figDesc>Phy. Chem. reviewing PDF \| (PCHEM-2020:05:49104:1:1:NEW 2 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:note place='foot'>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:note> </ns0:body> "	"Editor comments As noted by reviewer 1, the list of references is quite short. Given that molecular design and global optimization have been very active fields in computational chemistry for the last 20 years, some additional context of how the presented results relate to previous research would be appropriate. The short bibliography is due to the fact that the central question (why search algorithms can find molecules with particular properties in an enormous chemical space (ca 1060 molecules) by considering only a tiny subset (typically 103−6 molecules)) addressed in this study have not been considered in the literature so far. Furthermore, we note that reference 1 is a review article with an excellent overview of the field. Nevertheless, we have now added a reference to another review (2) as well as some references to the most relevant examples of chemical exploration methods: Many techniques have been developed to search this chemical space for molecules with desirable properties,[1;2] including genetic algorithms,[3;4] variational autoencoders,[5;6] recurrent neural networks,[7;8;9] and generative adversarial networks.[10;11;12] Furthermore, we have added the following paragraph in the Conclusions: This study focuses on GAs as they are relatively simple and thus easy to interpret, but our general conclusions should also be valid for other generative models aimed at searching for molecules with specific properties. Such generative models typically combine a machine-learned molecular representation with a standard search algorithm such as swarm optimization,[6] hill climb,[8] or Monte Carlo tree search.[9] Like most search algorithms, these algorithms are designed to find and incrementally follow paths through search space towards the desired goal (Figure 2), similarly to GAs. Reviewer 1 (Anonymous) Basic reporting This paper cites only 18 references even though it addresses a rather 'classic' problem using rather 'classic' methods. 6 of the 18 references correspond to potential energy methods which are not the point of this study. Comparison to performance of other optimization runs published in design papers in the literature is lacking entirely. As such, I am skeptical about the scholarship level of this manuscript. For example, Geerlings and co-workers published some best first search molecular design results a couple of years ago where they also found surprisingly rapid convergence in chemical space. The authors offer little to no context regarding this and other related work in the field. Studies on methods that search the entire chemical space (which is the focus of this study) have not really focussed on efficiency, i.e minimising the number of search steps. The studies by Geerlings (e.g. 10.1002/cphc.201501189) and related studies such as 10.1063/1.2987711, that focus on efficiency, study very constrained optimization problems involving a common core with X substituents that can be placed on Y position in a common molecular scaffold. This represents a much smaller search space. So with regard to efficiency, there are no appropriate studies to compare to. Furthermore, efficiency is not really the central focus of this article. It is well established that search algorithms can locate molecules in the vastness of chemical space in a finite number of steps. The main point of our study is to explain why that is possible at all and not whether it happens in 10,000 or 1 million steps. With regard to context, we have added the paragraph in the Conclusions as described above. Experimental design I see two problems with this manuscript. First, the research question is, to the best of my understanding, not entirely well defined. Typically, when performing molecular design, some target property value should be given, and the optimisation algorithm has to search chemical space in order to minimise deviation from target property. This is what the authors do for the absorbance. It is less clear to the reader what is the actually target in their 'Graph-based approach' section. If it is just the Tanimoto similarity I do not really understand the point since the target is then known already and a simple greedy best first search algorithm should trivially lead to the result in (practically) no time. Or is this interesting because only a GA should be used? In any case, I do not think that this is sufficiently well explained. We feel the following explanation from the original version is sufficiently clear. The closest chemical equivalent to the Shakespeare example described in the previous section is locating a predefined molecule in chemical space, i.e. rediscovery. Brown et al.[3] have demonstrated this for three drug molecules: celecoxib, troglitazone, and tiotixene (Figure 3). Here similarity to the target is measured by the Tanimoto similarity, which is computed by decomposing each molecule into overlapping fragments up to a certain size and then counting how many fragments the two molecules have in common and dividing by the combined total number of fragments. Thus, the Tanimoto score ranges from 0 (no similarity) to 1 (very similar or identical). Furthermore, we do not agree that a greedy best first search algorithm would succeed in practically no time. For example, Brown et al. (DOI:10.1021/acs.jcim.8b00839) applied a LSTM RNN model combined with a simple hill-climb algorithm (which is very similar to a greedy algorithm), which required up 163,840 (20x8192) molecule evaluation to succeed. Within chemistry the greedy best first search algorithms have been applied to search spaces of around 1010 molecules, while the search space for rediscovery is the entire chemical space (1060), as we discussed above. Secondly, the example target values for the absorbance (200, 400, and 600 nm) seem ad hoc, and it is hard to decipher for the reader how 'hard' they really are. If the absorbance distribution of their molecular space had, for example, peaks at those three values, it should be relatively easy to rapidly find examples that get close. As such, it is not clear how such a hidden bias might have affected the results and the conclusions drawn. I think that a distribution plot of some representative sub-sample should be shown, and the authors should also include target values which lie outside of that distribution, in order to see if their conclusions also hold for the extremes. As we write in the paper: “Molecules that absorbed within 100 nm of the target wavelength were excluded from the initial population.” So any “bias” in the initial population has been removed and the target values “lie outside” of the distribution of initial absorbance values. Comments for the Author This is an interesting study with interesting findings. There are some points which should be adressed, but I think that it can be easily done. It might also be worthwhile to include some speculations in how far the conclusions depend on the choice of GA as an optimization algorithm, and how they could change if other algorithms were used. We have added a discussion of how our findings relate to other optimisation/ML algorithms. Reviewer 2 (Anonymous) Basic reporting The authors use precise English and the professional words. However, some mistakes still exist and the authors should proofread the paper carefully. The background may lack some proper description. We have added some more discussion of background in the introduction and proofread the paper carefully Validity of the findings In this manuscript, the authors describe the genetic algorithms to find particular molecules in an enormous chemical space by considering only a tiny subset. The method has been proved exactly in authors’ previous work. Here, the rediscovery of specific molecules is discussed by strings-based and graph-based approach and the search for the target properties is also analyzed in detail. The explanation about particular molecules can be easier found by search algorithms. No changes requested. Comments for the Author (1) The mutation rates used in string-based and graph-based genetic algorithms should be specified in manuscript. We have specified the mutation rates. (2) It would be better to demonstrate that whether the search for molecules with target properties depends on the properties of initial population or not. It is well known that the GA search depends on the initial population and it is an implicit assumption in the study. For rediscovery the initial mating pool was constructed following Brown et al., i.e. the 100 top-scoring molecules in a 1.6 million molecule ChEMBL subset, where molecules with an ECFP4 Tanimoto similarity of >0.323 are removed (to provide some medium level of difficulty). We also test a similar approach where we use a 10,000-molecule subset of the ChEMBL subset. Given that the string-based algorithms already have low success rates with the 0.323 cutoff, it is not clear what one would learn by decreasing it further. For absorbance the initial population is chosen randomly and molecules with absorbance within 100 nm of the target wavelength (again, to not make it too easy). It’s not clear what one could change about the initial population to make the search even more challenging. (3) For each generation of different searches, the scores have different degrees of improvement. It would be better to show the processes of molecules changes. It is not clear what the reviewer means by “the processes of molecules changes” "	Here is a paper. Please give your review comments after reading it.
471	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Background. The interactions between colloidal particles in the binary system or mixture colloids containing clay minerals and bacteria have important influences on formations and stabilities of soil aggregates, transportations of soil water, as well as biological activities of microorganisms. How the interfacial reaction of metal ions affects their interaction therefore becomes an important scientific issue.</ns0:p></ns0:div> <ns0:div><ns0:head>Methods.</ns0:head><ns0:p>Dynamic light scattering studies on the aggregation kinetics of mixture colloids containing kaolinite and Pseudomonas putida (P. putida) were conducted in this study.</ns0:p><ns0:p>Results. Aggregation could be observed between kaolinite and kaolinite, between kaolinite and P. putida when P. putida content was less than 33.3%. Additionally, aggregation rates decreased with increasing P. putida content. The critical coagulation concentrations and activation energies indicated that there were strong specific ion effects on the aggregation of mixture colloids. Most importantly, the activation energy increased sharply with increasing P. putida content , which might result from the lower Hamaker constant of P. putida compared with that of kaolinite.</ns0:p></ns0:div> <ns0:div><ns0:head>Contributions.</ns0:head><ns0:p>(1) Strong specific ion effects on mixture colloids aggregation of kaolinite-Pseudomonas putida were observed; (2) the aggregation behavior of mixture colloidal system was determined by the average effects of mixture colloids, rather than the specific component in mixture systems. This finding provides an important methodological guide for further studies on the colloidal aggregation behavior of mixture systems with organic and inorganic materials.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>Colloids in soil can be divided into inorganic colloids represented by minerals as well as organic colloids represented by microorganisms and organic macromolecules <ns0:ref type='bibr' target='#b54'>(Xiong 1983)</ns0:ref>. The interactions between soil colloidal particles have important influences on the formation and stability of soil aggregates, the transportation of soil water, the biological activities of microorganisms, and so on <ns0:ref type='bibr' target='#b3'>(Borgnino 2013;</ns0:ref><ns0:ref type='bibr' target='#b28'>Loosli et al. 2019)</ns0:ref>. Dynamic light scattering <ns0:ref type='bibr'>(DLS)</ns0:ref> technique has been widely used in the study with respect to aggregation processes of soil mineral and organic colloids <ns0:ref type='bibr' target='#b0'>(Artemyeva et al. 2017;</ns0:ref><ns0:ref type='bibr' target='#b10'>Derrendinger & Sposito 2000;</ns0:ref><ns0:ref type='bibr' target='#b31'>Nguyen et al. 2013;</ns0:ref><ns0:ref type='bibr' target='#b32'>Nguyen et al. 2017;</ns0:ref><ns0:ref type='bibr' target='#b43'>Tian et al. 2014;</ns0:ref><ns0:ref type='bibr' target='#b56'>Yan et al. 2019;</ns0:ref><ns0:ref type='bibr' target='#b61'>Zhu et al. 2017)</ns0:ref>. Moreover, parameters with regarding to aggregation kinetics including the critical coagulation concentration (CCC) <ns0:ref type='bibr' target='#b23'>(Jia et al. 2013)</ns0:ref>, aggregation activation energy <ns0:ref type='bibr' target='#b43'>(Tian et al. 2014)</ns0:ref>, ionic polarization <ns0:ref type='bibr' target='#b18'>(Gao et al. 2014)</ns0:ref>, and Hamaker constant <ns0:ref type='bibr' target='#b30'>(Luo et al. 2018</ns0:ref>) can be obtained by DLS measurement based on the determination of the hydrodynamic diameter of aggregates. ionic electrostatic effects, the difference from ionic radius and ionic hydration radius is merely a quatratic term <ns0:ref type='bibr' target='#b8'>(Chen 2001)</ns0:ref>, which cannot be used to interpret the above-mentioned up-to-10 times differences between CCC values. Recent study indicated that the ionic dispersion forces might be an important reason for the specific ion effects <ns0:ref type='bibr' target='#b4'>(Bostrom et al. 2001</ns0:ref>), which could be important especially when the Coulomb/electrostatic effect was weak (i.e., when the electrostatic field was sufficiently shielded because of high electrolyte concentrations) <ns0:ref type='bibr' target='#b34'>(Parsons et al. 2011</ns0:ref>). However, <ns0:ref type='bibr' target='#b43'>Tian et al. (2014)</ns0:ref> observed more pronounced specific ion effects at lower electrolyte concentrations, which obviously cannot be explained by the dispersion forces. A series of studies on the effects of ion interfacial reaction on colloidal particle aggregation have been carried out recently <ns0:ref type='bibr' target='#b15'>(Du et al. 2017;</ns0:ref><ns0:ref type='bibr' target='#b18'>Gao et al. 2014;</ns0:ref><ns0:ref type='bibr' target='#b22'>Hu et al. 2015;</ns0:ref><ns0:ref type='bibr' target='#b25'>Li et al. 2015;</ns0:ref><ns0:ref type='bibr' target='#b27'>Liu et al. 2014;</ns0:ref><ns0:ref type='bibr' target='#b44'>Tian et al. 2015;</ns0:ref><ns0:ref type='bibr' target='#b55'>Xu et al. 2015)</ns0:ref>. They suggested that the strong non-classical polarization, which was caused by the strong electric field formed by the surface charge of clay minerals in water system, could give a rational explanation for specific ion effects on mineral colloids aggregation. These new studies indicated that this non-classical polarization effects could be as strong as the Coulomb force, and were up to 10 4 times that of the classical polarization <ns0:ref type='bibr' target='#b27'>(Liu et al. 2014)</ns0:ref>. Moreover, this non-classical polarization not only greatly enhanced the adsorption intensity of the ions onto the surface, but also deeply affected the interaction between the colloidal particles <ns0:ref type='bibr' target='#b15'>(Du et al. 2017)</ns0:ref>.</ns0:p><ns0:p>There are a large number of microorganisms in the soil. Although the volume scale of these microorganisms is larger than the scale defined by colloidal particles, their colloidal properties are still obvious since the particle size of the microorganisms would be 1-1000 nm in at least one direction <ns0:ref type='bibr' target='#b41'>(Sumner 2000)</ns0:ref>. This might be the reason why the soil scientists always identified microorganisms as microbial colloids <ns0:ref type='bibr' target='#b35'>(Peng et al. 2018;</ns0:ref><ns0:ref type='bibr' target='#b36'>Pisarcik et al. 2016</ns0:ref>). It's worth noting that microbial colloids are neither mineral colloids nor soil humus colloids. Take the bacterial cell as an example, a large number of functional groups and surface charges, existing on both interior and exterior hydrophilic surfaces, have similarities with those generating from the general organic macromolecules and even mineral colloidal surfaces; however, there is a lipid bilayer existing between the interior and exterior surfaces of the bacterial cell membrane. There have been plenty of studies on 'mineral-bacteria' interactions so far <ns0:ref type='bibr' target='#b14'>(Diao et al. 2014;</ns0:ref><ns0:ref type='bibr' target='#b21'>Hong et al. 2014;</ns0:ref><ns0:ref type='bibr' target='#b38'>Qu et al. 2018;</ns0:ref><ns0:ref type='bibr' target='#b52'>Wu et al. 2012;</ns0:ref><ns0:ref type='bibr' target='#b58'>Zhao et al. 2018;</ns0:ref><ns0:ref type='bibr' target='#b59'>Zhao et al. 2012;</ns0:ref><ns0:ref type='bibr' target='#b60'>Zhao et al. 2014</ns0:ref>), but the effects of ionic polarization, especially non-classical ionic polarization, on the 'mineral-bacteria' interaction are rarely reported. A large number of studies have shown that addition of natural organic matter (NOM) leads to changes in the aggregation of inorganic colloids <ns0:ref type='bibr' target='#b39'>(Ramirez et al. 2019;</ns0:ref><ns0:ref type='bibr' target='#b42'>Sun et al. 2018)</ns0:ref>. However, there is little study when it comes to microorganisms. A large number of studies have shown that the addition of natural organic matter leads to changes in the aggregation of inorganic colloids. It has been found that the adsorption of bacteria onto the mineral surfaces is mainly affected by factors such as pH, ionic strength <ns0:ref type='bibr' target='#b52'>(Wu et al. 2012)</ns0:ref>, clay type <ns0:ref type='bibr'>(Bellou et al. 2015)</ns0:ref>, bacteria types and its cell surface properties <ns0:ref type='bibr' target='#b37'>(Poortinga et al. 2002)</ns0:ref>, bacteria growth cycle <ns0:ref type='bibr' target='#b51'>(Wu et al. 2014)</ns0:ref>, bacteria/mineral mass ratio <ns0:ref type='bibr' target='#b57'>(Yee et al. 2000)</ns0:ref>, and so on <ns0:ref type='bibr' target='#b47'>(Tsagkari & Sloan 2018)</ns0:ref>. Many theories were used to explain these phenomena. The attachment process of microorganisms onto minerals was described by the Langmuir isotherm equation <ns0:ref type='bibr' target='#b48'>(Vasiliadou & Chrysikopoulos 2011;</ns0:ref><ns0:ref type='bibr' target='#b48'>Vasiliadou et al. 2011)</ns0:ref> and by the Freundlich isotherm equation <ns0:ref type='bibr' target='#b9'>(Chrysikopoulos & Syngouna 2012)</ns0:ref>. <ns0:ref type='bibr' target='#b24'>Jiang et al. (2007)</ns0:ref> Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science with Na + , Mg 2+ was more effective in promoting the adsorption of Pseudomonas putida (P. putida) on minerals, indicating that electrostatic interaction plays an important role in the adsorption process. <ns0:ref type='bibr' target='#b59'>Zhao et al. (2012)</ns0:ref> demonstrated that Derjaguin-Landau-Verwey-Overbeek (DLVO) theory can be used to explain the adsorption of clay minerals (i.e., montmorillonite and kaolinite)</ns0:p><ns0:p>by Escherichia. coli and Streptococcus. suis under very low electrolyte conditions. The adsorption of Bacillus subtilis on the phyllosilicates surface could be well explained by the extended DLVO theory <ns0:ref type='bibr' target='#b21'>(Hong et al. 2014)</ns0:ref>. <ns0:ref type='bibr' target='#b33'>Parikh and Chorover (2006)</ns0:ref> found that P-OFe covalent bonds can be formed between the carboxyl or phosphate groups on the bacteria (P. putida) surface and the iron atoms on the surface of hematite and goethite. Meanwhile, the adsorption thermodynamic principles have been widely used in the study on 'mineral-bacteria' interactions <ns0:ref type='bibr' target='#b5'>(Chen et al. 2003;</ns0:ref><ns0:ref type='bibr' target='#b6'>Chen & Zhu 2005;</ns0:ref><ns0:ref type='bibr' target='#b20'>Hendricks et al. 1979)</ns0:ref>.</ns0:p><ns0:p>The above analysis indicates that it would be a valuable scientific question to clarify the role of non-classical ionic polarization on the 'mineral-bacteria' interaction. In this work, the aggregation behaviors of 'kaolinite-P. putida'induced by LiNO 3 , KNO 3 and CsNO 3 were studied by D LS technique. Whether the aggregation of 'mineral-bacteria' mixture colloids would be affected by the specific ion effects was identified firstly, and then how the non-classical ionic polarization effects affect the 'mineral-bacteria' interaction was clarified. Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Materials and Methods</ns0:head></ns0:div> <ns0:div><ns0:head>Bacterial suspension</ns0:head></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>selected as the test strains. The strain P. putida was cultured at 30 °C on Luria-Bertani (LB) liquid medium. The LB medium per liter (pH = 7) contained 10 g Tryptone, 5 g Yeast and 10 g NaCl. P.</ns0:p><ns0:p>putida was stored in a glycerol tube (30 %) at -80 °C, and activated in LB medium at 30 °C and 120 r/min for 7 h until an optical density at 600nm (OD 600 ) of 0.5 was reached before the experiment. 1 mL of the activation solution was incubated in 250 mL LB medium at 30 °C, 120 r/min for 14 hours when the cell reached the stationary phase (OD 600 =2.0). Then the suspension was freeze-dried (Dong, Zhou 2019). During the aggregation experiments, 10 mg of freeze-dried P. putida powders were identically dispersed in the pH = 8.0 sterile water (pre-adjusted by 10 mmol/L NaOH). The bacterial suspension was ultrasonically dispersed with a KQ5200DE ultrasonic disruptor at 40 kHz for 15 min. The hydrodynamic diameter of P. putida was measured as 1600±100 nm through dynamic light measurement before adding electrolyte.</ns0:p></ns0:div> <ns0:div><ns0:head>Kaolinite colloidal suspension</ns0:head><ns0:p>Kaolinite (> 99.99% pure, purchased from Xuzhou, Jiangsu Province, China) was used in this study. The cation exchange capacity (CEC) and specific surface area were determined to be 37.5 mequiv./kg and 57 m 2 /g, respectively. The kaolinite colloidal suspension were prepared according to the following procedure <ns0:ref type='bibr' target='#b54'>(Xiong 1983)</ns0:ref>. 50.0 g kaolinite particles and 10 mL 100 mmol/L KOH solutions were successively added into a 500 mL beaker, and then diluted with sterile water to 500 mL. After 15 min of intensive sonication, the suspension was further diluted to 5 L using sterile water. The kaolinite colloidal particles with the effective hydrodynamic diameter of less than 300 nm were extracted and collected using the static sedimentation method.</ns0:p><ns0:p>The particle density was estimated to be 7. Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science kaolinite colloidal suspensions were diluted 10 times and the pH value was measured to be around 8.0. The hydrodynamic diameter of kaolinite was measured to be 320±20 nm.</ns0:p></ns0:div> <ns0:div><ns0:head>Experimental conditions</ns0:head><ns0:p>The experiment was carried out at a temperature of 25 °C and pH = 8.0, which can keep the dispersion of both the bacteria and the kaolinite colloids as well as the cell integrity of the bacteria.</ns0:p><ns0:p>The selected electrolytes and their concentrations were LiNO 3 (0 -900 mmol/L), KNO 3 (0 -700 mmol/L), and CsNO 3 (0 -450 mmol/L), respectively. The total mass concentration of the colloidal suspension were 300 mg/L, and the mass concentrations of the bacteria in the mixed suspension were set as 0, 10, 20, 50, and 100 mg/L, respectively, indicating the P. putida contents were 0%, 3.33%, 6.67%, 16.67%, and 33.33%, respectively. Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Dynamic light scattering measurement</ns0:head></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>automatically by DLS measurement. The duration for each experiment was 30 min. We chose 30 min to be the duration time, because after 30 min aggregation the light scattering intensity gradually became unstable. CCC, the minimum electrolyte concentration required for the Diffusion-Limited colloid aggregation (DLCA) regime <ns0:ref type='bibr' target='#b26'>(Lin et al. 1989)</ns0:ref>, is an important parameter for characterizing the colloidal aggregation. The CCC value directly reflects the difference in the colloidal stability.</ns0:p><ns0:p>Based on the research of <ns0:ref type='bibr' target='#b23'>Jia et al. (2013)</ns0:ref>, the aggregation kinetics of colloidal particles in electrolyte solutions can be described by the total average aggregation (TAA) rate, which was expressed as,</ns0:p><ns0:formula xml:id='formula_0'>0 0 0 0 0 ( ) 1 ( ) (1) t T D t D v f dt t t    %</ns0:formula><ns0:p>where T (f 0 ) (nm/min) is the TAA rate from t = 0 to a given time t = t 0 (t 0 > 0) which is committed ~ v to a time limit of the aggregation process; f 0 (mmol/L) is the electrolyte concentration; D 0 and D (t)</ns0:p><ns0:p>(nm) are respectively the effective hydrodynamic diameters of aggregates at the beginning and at time t 0 . <ns0:ref type='bibr' target='#b43'>Tian et al. (2014)</ns0:ref> showed that the activation energy (ΔE) and the TAA rate ( T (f 0 )) are ~ v correlated with the expressions as</ns0:p><ns0:formula xml:id='formula_1'>    0 -ln (2) T T v f E RT v CCC   % %</ns0:formula><ns0:p>where, R (J/mol K) is the gas constant and T (K) is the absolute temperature.</ns0:p></ns0:div> <ns0:div><ns0:head>Zeta potential measurements</ns0:head><ns0:p>The zeta potentials of 'bacteria-kaolinite' mixture colloids as functions of the bacteria PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2020:03:47222:1:2:NEW 4 Aug 2020)</ns0:p><ns0:p>Manuscript to be reviewed Chemistry Journals contents were measured in DI water using ZETA Plus (Brookhaven Instruments Corporation, New York, USA) at 25 ℃, pH 8.0. The samples for the zeta potential measurements were prepared in a similar manner as those for the aggregation experiments. The total mass concentration of the sample suspension was 300 mg/L, and the mass concentrations of the bacteria in the mixed suspension were set as 0, 10, 20, 50, and 100 mg/L, respectively. Triplicate measurements were performed with ten runs per measurement.</ns0:p></ns0:div> <ns0:div><ns0:head>Results and discussion</ns0:head></ns0:div> <ns0:div><ns0:head>Kinetics of 'kaolinite-P. putida' aggregation</ns0:head><ns0:p>The curves describing the hydrodynamic diameter growth of 'kaolinite-P. putida' mixture colloids in LiNO 3 solutions were shown in Fig. <ns0:ref type='figure' target='#fig_4'>1</ns0:ref>. It could be seen that, the hydrodynamic diameters of 'kaolinite-P. putida' aggregates increased with increasing LiNO 3 concentrations. For example, the hydrodynamic diameters of mixture colloids containing 100% kaolinite and 0% P.</ns0:p><ns0:p>putida indicated that aggregation occurred between kaolinite and kaolinite. However, the hydrodynamic diameter of the mixture colloids containing 0% kaolinite and 100% P. putida suggested that aggregation would not occur between P. putida and P. putida. The mixture colloids aggregated when the proportion of P. putida was 0%, 3.33%, 6.67%, 16.67% and 33.33%, but the aggregation rate decreased with increasing proportion of P. putida.</ns0:p><ns0:p>The curves describing the hydrodynamic diameters of different 'kaolinite-P. putida' mixture as a function of time in KNO 3 solutions were shown in Fig. <ns0:ref type='figure' target='#fig_5'>2</ns0:ref>. Similar to the aggregation in LiNO 3 solutions, the hydrodynamic diameters of 'kaolinite-P. putida' mixture colloidal aggregates increased with increasing KNO 3 concentrations. Additionally, the aggregation can occur between PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2020:03:47222:1:2:NEW 4 Aug 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science kaolinite and kaolinite but not occur between P. putida and P. putida. The mixture colloids aggregated when the proportion of P. putida was 0%, 3.33%, 6.67%, 16.67% and 33.33%, but the aggregation rate decreased with increasing proportion of P. putida.</ns0:p><ns0:p>The curves describing the hydrodynamic diameter of different proportions of 'kaolinite-P.</ns0:p><ns0:p>putida' as a function of time in CsNO 3 were shown Fig. <ns0:ref type='figure' target='#fig_6'>3</ns0:ref>. Similar to colloidal aggregation in LiNO 3 and KNO 3 solutions, the hydrodynamic diameters of 'kaolinite-P. putida ' mixture colloidal aggregates increased with the increase of CsNO 3 concentration. The aggregation can occur between kaolinite and kaolinite but not between P. putida and P. putida.</ns0:p></ns0:div> <ns0:div><ns0:head>TAA rates and CCC</ns0:head><ns0:p>Using the experimental data given in Figs. 1, 2, and 3, the TAA rates T (f 0 ) of mixture colloids ~ v aggregation in various alkali ion solutions were calculated according to Eq. ( <ns0:ref type='formula'>1</ns0:ref>), and their relationships with the alkali ion concentration f 0 were given in Table <ns0:ref type='table' target='#tab_1'>1</ns0:ref>. The more detailed results of the TAA rates changing with electrolyte concentrations has been given in the Supporting</ns0:p></ns0:div> <ns0:div><ns0:head>Information (Figs. S1~S5).</ns0:head><ns0:p>As can be seen, the CCC of the mixture colloids under different bacterial contents can be obtained (Table <ns0:ref type='table' target='#tab_1'>1</ns0:ref>). For any given electrolyte solutions, the CCC for the mixture colloids increased as the bacterial content increased. For the aggregation of the mixture colloids with the same bacterial contents, the CCC increased in the order of Li + > K + > Cs + , exhibiting strong specific ion effects. For example, when the P. putida content was 3.33%, the CCCs for the aggregation of mixture colloids were 18.7,16.7 and 9.4 mmol/L for Li + , K + and Cs + , respectively. Moreover, the specific ion effects reflected by the CCC values increased as the bacterial content increased.</ns0:p><ns0:p>PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2020:03:47222:1:2:NEW 4 Aug 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:p><ns0:p>When the electrolyte concentrations were less than the CCC, the aggregation belongs to the RLCA regime, and the repulsive potential energy between the colloidal particles was higher than the attractive potential energy. Table <ns0:ref type='table' target='#tab_1'>1</ns0:ref> showed the fitting equation for aggregation of 'kaolinite-P. putida ' mixture colloids in different electrolyte solutions when the electrolyte concentrations were less than the CCC. As can be seen, given the same electrolyte conditions, the TAA rates of'kaolinite-P. putida' mixture colloids decreased as the bacterial content increased. On the other hand, for the same bacterial contents, the TAA rates for the aggregation of'kaolinite-P. putida' mixture colloids increased in the order of Li + < K + < Cs + , exhibiting strong specific ion effects.</ns0:p><ns0:p>For example, when the P. putida content was 3.33 %, the TAA rates for the aggregation of mixture colloids were 1.21, 48.2, and 80.5 nm/min for Li + , K + and Cs + , respectively.</ns0:p></ns0:div> <ns0:div><ns0:head>Interaction between P. putida and kaolinite and the aggregation mechanism</ns0:head><ns0:p>The activation energies for the aggregation of the mixture colloids in the various alkali ion solutions were calculated using Eq. 2 and plotted in Fig. <ns0:ref type='figure' target='#fig_7'>4</ns0:ref>. At any given same electrolyte concentrations below CCC, the activation energies for the mixture colloids with different bacteria contents were significantly different. In addition, the activation energies increased as the bacterial content increased. At any given different alkali ion concentrations below CCC, the activation energies for the mixture colloids with the same bacteria contents increased in the order of Li + > K + > Cs + . For example, when the P. putida content was 3.33 % and the electrolyte concentration was 5 mmol/L, the activation energy for the aggregation of mixture colloids were respectively 1.5RT, 1.4RT, and 0.7RT for Li + , K + and Cs + . The activation energies for the mixture colloids aggregation might come from the electrostatic separate colloidal particle. The experimental results shown in Table <ns0:ref type='table' target='#tab_1'>1</ns0:ref> could be satisfactorily explained by this inference. Each mixture colloidal system can only have one specific CCC, which is determined by each physical parameter (such as determination of the average value of potential, electric field strength, charge density, Hamaker constant, etc.) since each physical parameter of the mixed system (such as potential, electric field strength, charge density, Hamaker constant, etc.) has only one average value. What's more, the average Hamaker constant of the mixture colloids decreased with the increase of P. putida content due to higher Hamker constant of kaolinite and lower Hamaker constant of P. putida. Eventually, the CCC of mixed system increases with the increase of P. putida content. Finally, we would like to give a speculated explanation for the experimental results for the kinetics of pure P. putida aggregation. We know that, P. putida is a gram-negative, rod-shaped bacteria, and with the polar flagellum or with positively and negatively charges at cell surfaces; in addition, generally, bacteria prefer to survive in cell groups but not a single cell. Therefore, P.</ns0:p><ns0:p>putida could aggregate easily. In our experiments, however, (1) the net charge of P. putida was negative, therefore, the electrostatic fore between two cells must be repulsive as the distance between two cells was relative long; (2) the adopted cations in this study were monovalent cation, those cations just could produce relative weak screening effect on the electric field around cell, Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science divalent cation presented in the suspension, the cell aggregation might be possibly observed.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions</ns0:head><ns0:p>In this paper, the aggregation behaviors of P. putida, kaolinite and 'kaolinite-P. putida' mixed colloids in various concentrations of LiNO 3 , KNO 3 and CsNO 3 electrolytes were studied.</ns0:p><ns0:p>Aggregation occured between kaolinite and kaolinite whereas no aggregation occured between P.</ns0:p><ns0:p>putida and P. putida in the presence of any kind of the three electrolytes. Additionally, the aggregation occured in the mixture colloids when the bacterial content in the mixed system was less than 50%. TAA rate decreased with the increasing bacterial content. Additionally, specific ion effects affected the aggregation of 'P. putida -kaolinite' mixture colloids. The TAA rates for the aggregation of the mixture colloids with same bacteria content increased in the order of Cs + > K + > Li + . For example, when the P. putida content equaled 3.33 %, the TAA rates for the aggregation of the mixture colloids were 1.21, 48.2, 80.5 nm/min for Li + , K + and Cs + respectively. The CCC for the aggregation of the mixture colloids with the same bacteria content increased in the order of Li + > K + > Cs + . The CCC for the aggregation of the mixture colloids were 18.7, 16.7 and 9.4 mmol/L for Li + , K + and Cs + , respectively, when the P. putida content equaled 3.33 %. The aggregation activation energy for the aggregation of the mixture colloids with the same bacteria contents increased in the order Li + > K + > Cs + . The increasing of aggregation activation energy and decreasing of TAA rates for the aggregation of the mixture colloids as P. putida content increased might come from the lower Hamaker constant of P. putida than that of kaolinite, which further gave rise to lower molecular gravitational potential between colloidal particles. What's more, the aggregation behavior of a mixed colloidal system was determined by the average effect PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2020:03:47222:1:2:NEW 4 Aug 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science of electrostatic potential and molecular gravitational potential produced by the various colloidal particles instead of separate colloidal particle. This finding provides an important methodological guide for studying the aggregation behavior of 'bacterial-clay' mixed systems. Manuscript to be reviewed Manuscript to be reviewed Manuscript to be reviewed Manuscript to be reviewed</ns0:p><ns0:note type='other'>Figure 1</ns0:note><ns0:note type='other'>Chemistry Journals Analytical, Inorganic, Organic, Physical, Materials Science Figure 2</ns0:note><ns0:note type='other'>Chemistry Journals Analytical, Inorganic, Organic, Physical, Materials Science Figure 3</ns0:note><ns0:note type='other'>Chemistry Journals Analytical, Inorganic, Organic, Physical, Materials Science Figure 4</ns0:note></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science Manuscript to be reviewed</ns0:p><ns0:note type='other'>Figure 5</ns0:note></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science Manuscript to be reviewed</ns0:p><ns0:note type='other'>Figure 6</ns0:note></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:p></ns0:div> <ns0:div><ns0:head>Table 1(on next page)</ns0:head><ns0:p>Table <ns0:ref type='table' target='#tab_1'>1</ns0:ref>. Expressions of the TAA rates for the aggregation of mixed particles in the various alkali ion solutions when the electrolyte concentration is less than the CCC.</ns0:p><ns0:p>PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2020:03:47222:1:2:NEW 4 Aug 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>P. putida (Gram-negative), purchased from China Center for Type Culture Collection, was PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2020:03:47222:1:2:NEW 4 Aug 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>35 g/L by the oven drying method. Then the prepared PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2020:03:47222:1:2:NEW 4 Aug 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>A</ns0:head><ns0:label /><ns0:figDesc>BI-200SM multi-angle laser light scattering instrument (Brookhaven Instruments Corporation, New York, USA) with a BI-9000AT auto-correlator (Brookhaven Instruments Corporation) was used for the measurement of the aggregates' particle size (e.g., effective hydrodynamic diameter) which will change during the aggregation process. The power of the laser device equals 15 mW and the laser is vertically polarized with a wavelength of 532 nm. Experimentally, ultrapure water, kaolinite suspensions, bacteria suspensions and the electrolyte (LiNO 3 , KNO 3 or CsNO 3 ) solutions of different concentrations were mixed in the scattering bottle with the total volume of 10 mL. The procedure of aggregation kinetic measurement was that: each colloidal (including single and mixed colloids) suspension was prepared with a 2 min sonication before adding the electrolyte. After adding electrolyte, the information about particles size and the size distribution was recorded PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2020:03:47222:1:2:NEW 4 Aug 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head /><ns0:label /><ns0:figDesc>thus a long range electrostatic repulsive force between two cells could present; (3) the density of cell particles in this experiments was very low, thus the long range electrostatic repulsive force dominate cells interaction. Those three points might be the reasons for explaining the kinetics of pure P. putida aggregation. Therefore, on the contrary, if the cell density was very high, and if PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2020:03:47222:1:2:NEW 4 Aug 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head>Figure. 1</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure. 1 Growth of hydrodynamic diameters of 'kaolinite-P. putida' aggregates in LiNO 3 solutions</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head>Figure. 2</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure. 2 Growth of hydrodynamic diameters of 'kaolinite-P. putida' aggregates in KNO 3 solutions</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head>Figure. 3</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Figure. 3 Growth of hydrodynamic diameters of 'kaolinite-P. putida' aggregates in CsNO 3 solutions.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_7'><ns0:head>Figure 4 .</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Figure 4. The activation energies for the aggregation of the mixture colloids with different bacteria contents in LiNO 3 (A), KNO 3 (B), and CsNO 3 (C) solutions.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_8'><ns0:head>Figure 5 .</ns0:head><ns0:label>5</ns0:label><ns0:figDesc>Figure 5. Zeta potential of 'kaolinite-P. putida' mixed particle as a function of P. putida content at pH 8.0.</ns0:figDesc><ns0:graphic coords='30,42.52,199.12,525.00,401.25' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_9'><ns0:head>Figure 6 .</ns0:head><ns0:label>6</ns0:label><ns0:figDesc>Figure 6. Schematic diagram of 'kaolinite-P. putida' TAA rates as a function of electrolyte concentration.</ns0:figDesc><ns0:graphic coords='31,42.52,199.12,525.00,401.25' type='bitmap' /></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head /><ns0:label /><ns0:figDesc>found that, compared</ns0:figDesc><ns0:table /><ns0:note>PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2020:03:47222:1:2:NEW 4 Aug 2020)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_1'><ns0:head>Table 1 .</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Expressions of the TAA rates for the aggregation of mixed particles in the various alkali ion solutions when the electrolyte concentration is less than the CCC.</ns0:figDesc><ns0:table><ns0:row><ns0:cell>P. putida</ns0:cell><ns0:cell cols='2'>CCC values</ns0:cell><ns0:cell /><ns0:cell cols='3'>Expressions of TAA rates vs. electrolyte</ns0:cell></ns0:row><ns0:row><ns0:cell>contents</ns0:cell><ns0:cell cols='2'>(mmol/L)</ns0:cell><ns0:cell /><ns0:cell>concentration</ns0:cell><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>(%)</ns0:cell><ns0:cell>Li +</ns0:cell><ns0:cell>K +</ns0:cell><ns0:cell>Cs +</ns0:cell><ns0:cell>Li +</ns0:cell><ns0:cell>K +</ns0:cell><ns0:cell>Cs +</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>40.05 -0 f</ns0:cell><ns0:cell>54.64 -0 f</ns0:cell><ns0:cell>195.09 -0 f</ns0:cell></ns0:row><ns0:row><ns0:cell>0</ns0:cell><ns0:cell>3.2</ns0:cell><ns0:cell>2.8</ns0:cell><ns0:cell>0.89</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>18.52</ns0:cell><ns0:cell>23.68</ns0:cell><ns0:cell>16.64</ns0:cell></ns0:row><ns0:row><ns0:cell>3.33</ns0:cell><ns0:cell>18.7</ns0:cell><ns0:cell>16.7</ns0:cell><ns0:cell>9.4</ns0:cell><ns0:cell>8.77 -8.92 0 f</ns0:cell><ns0:cell>13.02 -0 f</ns0:cell><ns0:cell>18.24 -0 f</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>16.92</ns0:cell><ns0:cell>10.66</ns0:cell></ns0:row><ns0:row><ns0:cell>6.67</ns0:cell><ns0:cell>38.4</ns0:cell><ns0:cell>30.3</ns0:cell><ns0:cell>26.2</ns0:cell><ns0:cell>4.11 -9.56 0 f</ns0:cell><ns0:cell>0 5.65 -f</ns0:cell><ns0:cell>8.22 -14.67 0 f</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>13.07</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>16.67</ns0:cell><ns0:cell>71.2</ns0:cell><ns0:cell>61.4</ns0:cell><ns0:cell>51.3</ns0:cell><ns0:cell>1.67 -6.12 0 f</ns0:cell><ns0:cell>0 1.90 -f</ns0:cell><ns0:cell>2.40 -15.05 0 f</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>14.25</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>0.12 -0 f</ns0:cell><ns0:cell>0.54 -0 f</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>33.33</ns0:cell><ns0:cell cols='3'>372.2 187.9 182.6</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell>0.40 -5.01</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>4.697</ns0:cell><ns0:cell>18.23</ns0:cell><ns0:cell /></ns0:row></ns0:table><ns0:note>0 f PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2020:03:47222:1:2:NEW 4 Aug 2020)</ns0:note></ns0:figure> <ns0:note place='foot'>Analytical, Inorganic, Organic, Physical, Materials Science </ns0:note> </ns0:body> "	"Cover letter for revision Date: 28-Mar-2020 Manuscript ID: peerj (#47222) Manuscript Title: Aggregation kinetics of binary systems containing kaolinite and Pseudomonas putida induced by 1:1 electrolyte Authors: Zhaoxuan Yu; Rui Tian; Dian Liu, Yekun Zhang, Hang Li Dear editor, Thank you so much for giving us the opportunity to revise the manuscript entitled “Aggregation kinetics of binary systems containing kaolinite and Pseudomonas putida induced by 1:1 electrolyte” peerj (#47222). We have carefully studied all the comments of Editor and reviewer, and we think that all the concerns have been addressed. A point-by-point responses to the comments are included in this cover letter. All the authors have read the manuscript carefully and approved the submission. If there are any questions, please contact us freely. We are looking forward to hearing from you. Yours sincerely, Dr. Rui Tian & Prof. Hang Li Chongqing Key Laboratory of Soil Multi-Scale Interfacial Process, College of Resources and Environment, Southwest University, Chongqing, 400715, P. R. CHINA Rui Tian E-mail: tr2016@swu.edu.cn Hang Li E-mail: lihangswu@163.com. Phone: 086-023-68250674. Fax:086-023-68250444. Response to the Comments Reviewer #1 (Anonymous) Basic reporting The paper appeared with interesting results and new messages are given. The paper is of a satisfactory level, quite clear and well organized. Experimental design All the methods used are well established and the discussion part is complete. Validity of the findings The contribution of this piece of research is valuable Comments for the Author The aim of this work was to study the aggregation of 'mineral-bacteria' mixture colloids of “kaolinite-P. putida” induced by LiNO3, KNO3 and CsNO3. Dynamic light scattering studies on the aggregation kinetics of mixture colloids containing kaolinite and P. putida were conducted in this study. The experimental study is well organized, providing new insides in the field of colloidal aggregation behavior of binary systems. Author Reply: Thanks for the positive comments! Minor comments 1. The XDLVO theory should be used to explain the aggregation between kaolinite-kaolinite, kaolinite-P. putida, and P. putida-P. putida. Author Reply: Yes, the XDLVO should be employed to quantitatively explain the aggregation. However, at present the quantitative calculations of the three forces in XDLVO theory for those two materials are impossible. Most importantly, the purpose of this study was mainly to reveal the specific ion effects of monovalent cations on the aggregation of the mixture colloids. The title has been changed to: Aggregation kinetics of binary systems containing kaolinite and Pseudomonas putida induced by different 1:1 electrolytes: Specific ion effects 2. L123: The hydrodynamic diameter of P. putida was measured as 1600 ± 100 nm. How did you measure the diameter? Author Reply: The method has been given, please refer to Page 7, Lines 131-132. 3. L143: ….indicating the P. putida contents were 0%, 144 3.33%, 6.67%, 16.67%, and 33.33%. Based on weight? Please explain. Author Reply: The detailed description of experimental conditions has been given. Please refer to Page 8, Lines 150-153. 4. Eqs. 1 and 2: \s\up5(~)T(f0) does not appear in the equation 1. Present clearly the \s\up5(~)T(f0). Author Reply: \s\up5(~)T(f0) appears in Eqs. 1 and 2, and the definition of \s\up5(~)T(f0) is given in the recised manuscript. 5. The procedure for the Kinetics of “kaolinite-P. putida” aggregation, should be described in the methods section. Author Reply: The procedure is given in the revised manuscript; please refer to Pages 8-9, Lines 162-167. 6. Please correct the typo mistakes throughout the text. E.g. scattering, Oncsik, et al (Oncsik et al. 2015). Author Reply: Typo mistakes has been checked. 7. L261: We can speculate that there may … three different conditions since different electrostatic repulsion … kaolinite-kaolinite, kaolinite-P. putida, and P. putida-P. putida. Please use literature findings to support the statements appeared in the text. Author Reply: The expressions have been essentially changed; please refer to Pages 13-14, Lines 269-289. 8. L282: Based on this, we can deduce that the aggregation behaviour…….average effect produced by the various colloidal …….of separate colloidal particle. Please use literature findings to support the statements appeared in the text. Author Reply: The expressions have been essentially changed; please refer to Pages 13-14, Lines 269-289. From the discussion on Pages 13-14, Lines 269-286, we can find that, the aggregation behavior of the mixture colloids is determined by the average effect produced by the different types of colloidal particles. 9. Please consider also the publications in this field: doi:10.1029/2010WR009560, doi:10.1016/j.colsurfb.2011.01.026. Author Reply: The Introduction have been revised accordingly. Please refer to Page 5, Lines 97-99. Reviewer 2 (Jianying Shang) Basic reporting Clear English; Good literature; Professional article structure, figures, tables. Raw data shared; Experimental design Well enxperimental design Validity of the findings This paper shows the novelty; All underlying data are robust, statistically sound, & controlled; Conclusions are well stated; Comments for the Author This study showed dynamic light scattering studies on the aggregation kinetics of mixture colloids containing kaolinite and Pseudomonas putida(P.putida) . This is a very good topic to study. The results showed that the aggregation behavior of mixture colloids or binary systems was determined by the average effects, rather than the specific component, of binary systems. The experiment design is good and English is good. I suggest that this paper can be accepted. Author Reply: Thanks for the positive comments! Reviewer 3 (Anonymous) Basic reporting In this paper, the authors investigated the colloidal interactions between P. putida and kaolinite in the different mass ration by dynamic light scattering. The results showed that aggregation happened in kaolinite colloidal particles, and between kaolinite and bacterial cells, but not happened in bacterial cells. This study provides interesting data for understanding the aggregation phenomena in soil and sediment. However, several aspects should be improved before publication. Experimental design This paper is original research within the Aims and Scope of the journal. Validity of the findings Speculation is welcome, but should be identified as such. Author Reply: Thanks for the positive comments! Comments for the Author Firstly, all the aggregation data were obtained by dynamic light scattering in 30 min. The authors don't tell us why 30 min, and is it enough for aggregation experiment? And what would happen if conducted a more prolonged test, for example, 60 min or 120 min? Author Reply: The explanation is given; please refer to Pages 8-9, Lines 162-167. Secondly, P. putida is a gram-negative, rod-shaped bacteria, and with the polar flagellum. Generally, bacteria prefer to survive in cell groups but not a single cell. Furthermore, plenty of positively and negatively charged surface sites existed on the cell surfaces. Therefore, it is hard to understand why the bacteria could not aggregate in the present study. The authors need to provide more explanation on this point. Author Reply: A speculated explanation is given in the revised version; please refer to Page 15, Lines 303-316. In our experiments, (1) the net charge of P. putida was negative, therefore, as the distance between two cells was relative long, the electrostatic fore between two cells must be repulsive; (2) our adopted cations were monovalent cation, those cations just could produce relative weak screening effect on the electric field around cell, thus a long range electrostatic repulsive force between two cells could present; (3) the density of cell particles in our experiments is very low, thus the long range electrostatic repulsive force dominate cells interaction. Those three points might be the reasons for explaining the kinetics of pure P. putida aggregation. Therefore, on the contrary, if the cell density was very high, and if divalent cation presented in the suspension, the cell aggregation might be possibly observed. Thirdly, the aggregation between mineral colloid and bacteria could be the first step in forming soil aggregates. It is essential but not enough to explain the total aggregation phenomena in soil. More aggregation work is needed to conduct, e.g. at different pHs, to explain the aggregation better. Author Reply: Yes, we agree. We will conduct those studies in the future. Reviewer 4 (Anonymous) Basic reporting no comment Experimental design The experimental design is thoroughly described Validity of the findings The conclusions are very well stated Comments for the Author This study examines the interactions between kaolinite colloidal particles and P. putida. Certainly, the research topic is of great interest because the migration of a mixture of clay particles and bacteria in environmental systems is not fully understood. The manuscript is very well written and organized. The experimental procedures are thoroughly presented. The figures are clear. Consequently, this reviewer recommends publication of this manuscript in PeerJ after a relatively minor revision. The following is a short list of minor suggestions: Author Reply: Thanks for the positive comments! (a) The abstract should clearly list all the novel contributions of this work. Author Reply: The Abstract have been revised accordingly. Please refer to Page 2, lines 29-33. (b) The introduction section of the manuscript is very informative; however, the interaction of kaolinite colloidal particles with various biocolloids as well as numerous contaminants has been studied extensively in the literature. Therefore, the introduction should be expanded to mention previous studies such as the works by Chrysikopoulos and Syngouna (Colloids and Surfaces B: Biointerfaces, 92, 74–83, 2012), Bellou et al (Science of the Total Environment, 517, 86–95, 2015) and Fountouli et al (Environmental Earth Sciences, 78, 152, 2019), Fountouli et al (Environmental Earth Sciences, 78, 152, doi:10.1007/s12665-019-8147-x, 2019). Author Reply: The Introduction have been revised accordingly. Please refer to Pages 5-6, lines 92-111. (c) Note that Vasiliadou et al (Colloids and Surfaces B: Biointerfaces, 84(2), 354–359, 2011) have studied the interactions between different structured kaolinite materials and P. putida. Also Vasiliadou and Chrysikopoulos (Water Resources Research, 47(2), W02543, doi:10.1029/2010WR009560, 2011) investigated the interactions between kaolinite and P. putida during transport in porous media. Author Reply: The Introduction has been revised accordingly. Please refer to Pages 5-6, lines 92-111. (d) How do the measured zeta potentials compare with literature values? Author Reply: Changed. Please refer to Pages 12-13, Lines 253-256. (e) The captions could be more descriptive. The captions for Figures 1-3 need more attention. Author Reply: The captions for Figures 1-3 have been revised accordingly. (f) Can Figure 6 contain some quantitative information? Currently Figure 6 is not very informative. Author Reply: Figure 6 is a conceptual diagram. "	Here is a paper. Please give your review comments after reading it.
472	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Malaria is a disease with debilitating health and negative economic impacts in regions at high risk of infection. Parasitic resistance and side effects of current antimalarial drugs are major setbacks to the successful campaigns that have reduced malaria incidence by 40 % in the last decade. The parasite's dependence on glycolysis for energy requirements makes pathway enzymes suitable targets for drug development. Specifically, triose phosphate isomerase (TPI) from Plasmodium falciparum (pTPI) and human (hTPI) cells show striking structural features that can be used in development of new antimalarial agents. In this study MD simulations were used to characterize binding sites on hTPI and pTPI interactions with sulfonamides. The molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) method was used to estimate the interaction energies of four sulfonamide-TPI docked complexes. A unique combination of key residues at the dimer interface of pTPI is responsible for the observed selective affinity to pTPI compared to hTPI.</ns0:p><ns0:p>The representative sulfonamide; 4-amino-N-(3,5-dimethylphenyl)-3fluorbenzenesulfonamide (sulfaE) shows a strong affinity with pTPI (dimer interface, -80.45 kJ/mol and active site region, -144.59 kJ/mol). The interaction is considerably weaker with hTPI (dimer interface, -14.99 kJ/mol and active site region, -143.79 kJ/mol). Both enzyme bind the ligand with same affinity at active site binding mode. The data however indicates that the dimer interface of triose phosphate isomerase (TPI) glycolytic enzyme is vital for development of sulfonamide based antimalarial drugs.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>Parasitic organisms, such as Plasmodium with a fully compartmentalized glycolytic pathway, are responsible for the world scourge of malaria prevalent in tropical and sub-tropical regions of the world <ns0:ref type='bibr' target='#b21'>(Kehr, Sturm, Rahlfs, Przyborski, & Becker, 2010)</ns0:ref>. Malaria, if untreated, can lead to very debilitating conditions such as coma, brain damage, loss of muscle function and death <ns0:ref type='bibr' target='#b36'>(Ringwald, Sukwa, Basco, Bloland, & Mendis, 2002)</ns0:ref>. Global mapping data estimates that about 3.2 billion people are at risk of contracting malaria every year <ns0:ref type='bibr' target='#b14'>(Guerra, Snow, & Hay, 2006)</ns0:ref>. Aniline and sulfonamide based drugs like sulfanilamide and sulfadoxine have been shown to interfere with the production of cellular components (amino acids and nucleotides), important for cell growth in parasitic organisms (Plasmodium and bacteria). <ns0:ref type='bibr' target='#b17'>(Hyde, 2007)</ns0:ref> Folate pathway enzymes like dihydropteroate synthase and dihydrofolate reductase are major targets for malaria treatment using combination therapy of sulfadoxine-pyrimethamine (SP). <ns0:ref type='bibr' target='#b26'>(Matondo et al., 2014;</ns0:ref><ns0:ref type='bibr'>Mulenga et al., 2006)</ns0:ref>. The continued application of the SP combination in place of the more effective artemisinin-based combination therapy is due to minimal side effects. Despite the limited efficacy of SP because of parasitic resistance resulting from genetic mutations, WHO continues to recommend their use in heavily affected regions mainly for safety concerns. <ns0:ref type='bibr' target='#b8'>(Djaman, Abouanou, Basco, & Kone, 2004;</ns0:ref><ns0:ref type='bibr' target='#b15'>Heinberg & Kirkman, 2015)</ns0:ref>. One of the goals of this study is to explore whether the sulfonamides we have designed can selectively interact with target receptors critical to the Plasmodium parasite. The Plasmodium parasite's sole dependence on glycolysis for energy needs makes the pathway enzymes potential targets for development of antimalarial chemotherapies <ns0:ref type='bibr' target='#b22'>(Kim & Dang, 2005;</ns0:ref><ns0:ref type='bibr' target='#b42'>Velanker et al., 1997;</ns0:ref><ns0:ref type='bibr'>Verlinde et al., 2001)</ns0:ref>. The decrease in efficiency of current antimalarial agents in many affected regions of the world due to toxic side effects, parasitic resistance caused by mutation has increased the cost and complexity of treating malaria <ns0:ref type='bibr' target='#b2'>(Bray, Barrett, Ward, & de Koning, 2003;</ns0:ref><ns0:ref type='bibr' target='#b3'>Briolant et al., 2010;</ns0:ref><ns0:ref type='bibr' target='#b31'>Petersen, Eastman, & Lanzer, 2011)</ns0:ref>. The limited number of new and effective antimalarial drugs, coupled with parasitic resistance to almost every available therapeutic combination continues to spur the search for novel, cheaper and better analogues <ns0:ref type='bibr'>(Plowe et al., 2007;</ns0:ref><ns0:ref type='bibr' target='#b39'>Triglia, Menting, Wilson, & Cowman, 1997)</ns0:ref>. Triosephosphate isomerase (TPI) is a key dimeric enzyme that speeds up the final investment phase of glycolysis, especially necessary for energy production in Plasmodium parasite the causative agent of malaria. TPI is an efficient enzyme that catalyzes the reversible interconversion between two triose phosphates; dihydroxyacetone phosphate (DHAP and glyceraldehyde-3-phosphate (G3P) <ns0:ref type='bibr' target='#b35'>(Richard, 1985)</ns0:ref>. The three-dimensional structures of human TPI (hTPI) with PDB accession code 4POC, <ns0:ref type='bibr'>(Roland et al., 2015)</ns0:ref> and Plasmodium TPI (pTPI) with PDB accession code-2VFI, <ns0:ref type='bibr' target='#b13'>(Gayathri et al., 2009)</ns0:ref> share similar structural folds (0.825 Å root mean square deviation in atomic positions), despite the 58 % difference in sequence identity (Figure <ns0:ref type='figure'>1</ns0:ref>). TPI enzymes do however, have key amino acid residues located in key binding motifs with different side chain polarities (Figure <ns0:ref type='figure'>1B</ns0:ref>). The two motifs of significant interest so far in the literature include; the dimer interface and active site regions. For example, position 96 that is proximal to TPI active site residues (K12, H95 and E165) in many TPI sequences is usually occupied by serine. This is replaced by Phenylalanine (Phe) in pTPI sequence <ns0:ref type='bibr' target='#b30'>(Parthasarathy, Ravindra, Balaram, Balaram, & Murthy, 2002)</ns0:ref>. In TPI the dimer interface comprises of residues (Y48, D49, V46, S45,) that face each other and extend the electrostatic field of this binding motif (Figure <ns0:ref type='figure'>2A and B</ns0:ref>). We also observe some subtle substitutions that affect the size and polarity of interface residues. For example, A46, I48 in hTPI PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2019:09:41562:1:2:NEW 4 Jun 2020)</ns0:p><ns0:p>Manuscript to be reviewed Chemistry Journals are replaced by V46, Y48 in pTPI. As a result, the dimer interface in pTPI seems more polar and less tight compared to hTPI (Figure <ns0:ref type='figure'>2</ns0:ref>) In our previous work, molecular docking calculations of the interaction between glycolytic enzymes with sulfanilamide, antimalarial drugs (primaquine, pyrimethamine, chloroquine) and 8 fluorinated sulfonamides <ns0:ref type='bibr' target='#b7'>(Dizala-Mukinay, Babalola, Sloop, & Forlemu, 2017;</ns0:ref><ns0:ref type='bibr' target='#b11'>Forlemu, Watkins, & Sloop, 2017)</ns0:ref> classified the compounds in low and high affinity groups with potential of selectivity. The sulfonamides we tested are derivatives of sulfadoxine that is used in combination with pyrimethamine to treat malaria in children and pregnant women. <ns0:ref type='bibr' target='#b27'>(Menard & Dondorp, 2017)</ns0:ref> Our docking calculations identified 3 novel sulfonamide ligands with strong binding affinity in micromolar range, and enhance interactions with pTPI over to hTPI (Table <ns0:ref type='table'>1</ns0:ref>). The affinity of the three novel sulfonamides docked with TPI was also significantly stronger than a number of antimalarial drugs (quinine, pyrimethamine and primaquine). <ns0:ref type='bibr' target='#b11'>(Forlemu et al., 2017)</ns0:ref> Despite the initial success of the docking studies to identify TPI as a target receptor, with potential of enhanced selectivity between hTPI and pTPI, the inhibition mechanism, dynamic motions of enzymes upon binding and contribution of residues is not well understood. In the current study, we have performed molecular dynamics simulations on four ternary complexes with the representative sulfonamide docked in the identified binding modes. Our goal is to gain insights on the binding process and explanation of the impact of residue substitution like S96 in hTPI to F96 in pTPI and dimer interface residues substitutions. We hypothesize that the size and changes in polarity of the amino acid substitution A46, I48 in hTPI to V46, Y48 in pTPI, enhances affinity sulfonamides. Chemical structures of the sulfonamide ligands bound to the TPI receptor in the docking study are shown in Figure <ns0:ref type='figure'>3</ns0:ref>. The binding energy (G), residue contribution to energy between a representative sulfonamide ligand 4-amino-N-(3,5-dimethylphenyl)-3-fluorbenzenesulfonamide (sulfaE) with hTPI and pTPI was calculated by the molecular mechanics/Poisson-Boltzmann and surface area solvation method. We expect to obtain answers to two key questions; (1) Is there some selective enhancement for the binding of sulfonamide to hTPI as opposed to pTPI. (2) What structural motifs and residues are critical for the binding, and are the key TPI residue substitutions critical for binding sulfonamides? Docked complexes obtained from using AutoDock 4.2 served as initial configurations for molecular dynamics simulations (Gromacs software package) to screen impactful interactions and dynamically refined the complexes formed. <ns0:ref type='bibr' target='#b40'>(Van Der Spoel et al., 2005)</ns0:ref> </ns0:p></ns0:div> <ns0:div><ns0:head>Molecular Systems and Computational Methods A. Docked Complexes and Ligands:</ns0:head><ns0:p>The interactions between 8 novel sulfonamides with hTPI and pTPI were characterized using blind docking <ns0:ref type='bibr' target='#b11'>(Forlemu et al., 2017)</ns0:ref>. The docking scores for the best performing sulfonamides 4-amino-N-(3,5-dimethylphenyl)-3-fluorbenzenesulfonamide (sulfaE), 4-amino-N-(2-fluoro-3,5dimethylphenyl)-benzenesulfonamide (sulfaC), and 4-amino-N-(2,6-difluorophenyl)-2,6dimethoxybenzenesulfonamide (sulfaH) are shown in Table <ns0:ref type='table'>1</ns0:ref>. The three-dimensional structures of the ligands tested were built using GuassView, and then geometry optimized with Gaussian 09 using a B3LYP/6-311g basis set <ns0:ref type='bibr' target='#b5'>(Dennington, Keith, & Millam, 2009;</ns0:ref><ns0:ref type='bibr' target='#b25'>M. J. Frisch, 2016)</ns0:ref>. The derivatives have the same basic structure, but differ in the substitution pattern of polar fluorine, methoxy and alkyl functional groups (Table <ns0:ref type='table'>1</ns0:ref> and Figure <ns0:ref type='figure'>3</ns0:ref>). The binding energies from docking calculations for all three high affinity sulfonamides are not significantly different as a result only sulfaE configuration is used as a representative sulfonamide for the MD simulations. SulfaE docked complexes were used as initial MD input structures based on enhanced selectivity in terms of affinity (binding energies and dissociation constant) with pTPI compared to hTPI. <ns0:ref type='bibr'>(Forlemu et al., 2017)</ns0:ref>. The molecular topologies and parameters for sulfaE were obtained from the fast force field generating tool called SwissParam <ns0:ref type='bibr' target='#b48'>(Zoete, Cuendet, Grosdidier, & Michielin, 2011)</ns0:ref>, based on the Merck molecular force field. The molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) method was used to estimate the interaction energies of four sulfonamide-TPI docked complexes <ns0:ref type='bibr' target='#b16'>(Homeyer & Gohlke, 2012)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>B. Molecular Dynamics Simulations:</ns0:head><ns0:p>MD simulations were carried out on four sulfaE/TPI docked complexes. The first two complexes involved sulfaE docked in the dimer or active site region of pTPI and the other two involving sulfaE docked in the dimer or active site region of hTPI. Each docked complex was relaxed using two stages of 1000 steps of steepest decent energy minimization, followed by 50,000 steps of conjugate gradient energy minimization. Subsequently a 4-ns long MD simulation was used to equilibrate each system at 300 K using an NVT ensemble, with GROMACS MD code and a Vrescale thermostat to keep the temperature fixed <ns0:ref type='bibr' target='#b4'>(Bussi, Donadio, & Parrinello, 2007)</ns0:ref>. This was followed by a 10 ns NPT ensemble simulation to equilibrate the pressure at 1 bar and 300 K. A final production MD was run for 500 ns, with atomic coordinates saved after every 100 ps. All the simulations were performed using the TIP3P water model and the protein described using the CHARMM27 force field <ns0:ref type='bibr' target='#b10'>(Foloppe & MacKerell, 2000;</ns0:ref><ns0:ref type='bibr' target='#b18'>Jorgensen, Chandrasekhar, Madura, Impey, & Klein, 1983)</ns0:ref>. The simulations were performed in an orthorhombic box with periodic boundary conditions and dimensions at least 8 Å from the solute (protein ligand complex). The complex was soaked in water molecules for a total system size of 93 Å x 87 Å x 76 Å. The total system charge was kept neutral by using an appropriate combination of chloride and sodium ions. The PME method was used to estimate the long-range electrostatic interactions, with short range nonbonding interactions estimated using a 14 Å cutoff. The time step for each simulation was set 2 fs and the hydrogens restrained using the SHAKE algorithm <ns0:ref type='bibr' target='#b1'>(Berendsen, Postma, Gunsteren, DiNola, & Haak, 1984;</ns0:ref><ns0:ref type='bibr' target='#b41'>van Gunsteren & Berendsen, 1977)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>C. Binding Free Energy Calculation.</ns0:head><ns0:p>The MM/PBSA (molecular mechanics [MM] with Poisson-Boltzmann [PB] and surface area solvation) method was used estimate the binding free energy between sulfaE and different binding pockets on pTPI and hTPI <ns0:ref type='bibr'>(Kollman et al., 2000)</ns0:ref>. The single-trajectory MM/PBSA method was used to post-process the binding energy (G bind ) of 4 sulfaE (L)/TPI(P) complexes to determine impact of binding pocket and residues substitutions (Scheme 1).</ns0:p><ns0:p>P + L PL Scheme 1 The binding free energy was computed as Equation <ns0:ref type='formula'>1</ns0:ref>∆𝐺 𝑏𝑖𝑛𝑑 = 𝐺 𝑃𝐿 -𝐺 𝑃 -𝐺 𝐿 PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2019:09:41562:1:2:NEW 4 Jun 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:p><ns0:p>Where G is computed from molecular mechanics (MM) force field expressions Equation <ns0:ref type='formula'>2</ns0:ref>𝐺 = 𝐸 𝑏𝑜𝑛𝑑 + 𝐸 𝑒𝑙 + 𝐸 𝑣𝑑𝑤 + 𝐺 𝑝𝑜𝑙 + 𝐺 𝑛𝑝 -𝑇𝑆 Here E bond represents standard molecular mechanics force field terms describe stretching, bending, and torsional bonded interactions (Equation <ns0:ref type='formula'>3</ns0:ref>) Equation 3</ns0:p><ns0:formula xml:id='formula_0'>𝐸 𝑏𝑜𝑛𝑑 = ∑ 𝑏𝑜𝑛𝑑 𝑘 𝐵 (𝑏 -𝑏 0 ) 2 + ∑ 𝑎𝑛𝑔𝑙𝑒 𝑘 𝜃 (𝜃 -𝜃 0 ) 2 + ∑ 𝑑𝑖ℎ 𝑘 ∅ [(1 + cos (𝑛∅ -𝛿)]</ns0:formula><ns0:p>Where k b is a bond stretching constant, b is the actual length of the bond, and b 0 is the equilibrium bond length, k  is the angle bending constant, θ is the actual angle and θ 0 the equilibrium or unstrained angle. The phase angle shifts for the torsional angle is represented by δ. The constant k  controls the amplitude of the bond twist (rotation curves), n is an integer (2, 3, 4 or 6) that describes the periodicity of the bond twist.</ns0:p><ns0:p>Equation 4</ns0:p><ns0:formula xml:id='formula_1'>𝐸 𝑒𝑙 = ∑ 𝑖 ∑ 𝑗 > 𝑖 𝑞 𝑖 𝑞 𝑗 4𝜋𝜀 0 𝑟 𝑖,𝑗 Equation 5 𝐸 𝑣𝑑𝑊 = ∑ 𝑖 ∑ 𝑗 > 𝑖 4𝜀 𝑖𝑗[( 𝜎 𝑖𝑗 𝑟 𝑖𝑗 ) 12 -( 𝜎 𝑖𝑗</ns0:formula><ns0:p>𝑟 𝑖𝑗 ) 6 ] The nonbonded interactions include the electrostatic interactions approximated by the coulomb potential and van der Waals interactions approximated with the 6-12 Lennard-Jones type potential (Equation 4 and 5). The constants  j are characteristic of the atoms involved, σ ij represents the attractive and repulsive parameters for atoms i and j respectively, and r ij the distance between the centers of the two atoms. The electrostatic interaction is defined by assigning partial charges (q i and q j ) to each van der Waals atom. The effective dielectric constant is represented by . The 𝜀 0 solvation energy is captured by an electrostatic polar contribution (G pol ) and a non-polar contribution (G np ) term. The G pol term is obtained from the solution to the Poisson-Boltzmann equation as describe below (Equation <ns0:ref type='formula'>6</ns0:ref>) within the implicit solvent model and describe the free energy solvation contribution Equation <ns0:ref type='formula'>6</ns0:ref>∇[𝜀(𝑟)∇𝜙(𝑟)] -𝜀(𝑟)𝜅 2 sinh (𝜙(𝑟)) + 4𝜋𝜌(𝑟) = 0 where 𝜅 2 = 8𝜋𝑒 2 𝐼 𝜀 𝜈 𝑘 𝐵 𝑇 where I represent Ionic strength (0.15 M), the dielectric constant (1 and 80 for receptor and 𝜀 𝜈 water respectively) and thus implicitly accounting for solvent properties. G np describes the nonpolar contributions to the solvation energy and can be determined from the solvent accessible surface area approximation (SASA) <ns0:ref type='bibr' target='#b16'>(Homeyer & Gohlke, 2012)</ns0:ref>. The MM-PBSA is an intermediate free energy calculation method that has been shown to estimate ligand binding affinities with correlation coefficients comparable to experiments and also discriminate between ligand protein complexes better that the widely used docking methods. In this study, the average binding energies and other quantities of interest are computed using structures from 625 snapshots randomly selected after one half microsecond of production simulation. The single trajectory simulation of the complex (ligand and TPI enzyme) in explicit solvent, was then decomposed by removing appropriate atoms to obtain energy parameters for the free ligand (sulfaE) and TPI enzyme (Equation <ns0:ref type='formula'>1</ns0:ref>). <ns0:ref type='bibr' target='#b34'>(Ren et al., 2020)</ns0:ref> The uncertainties for thermodynamic parameters of interest are obtained from statistical analysis using energy data from the 625 sampled conformations. The entropic contributions (Equation <ns0:ref type='formula'>2</ns0:ref>), are not computed because it's the ligand and proteins are similar and inclusion has limited impact on ranking of relative binding affinities as observed by multiple studies. <ns0:ref type='bibr' target='#b45'>(Wang, Greene, Xiao, Qi, & Luo, 2018;</ns0:ref><ns0:ref type='bibr'>Yang et al., 2011)</ns0:ref> </ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head><ns0:p>In order to elucidate the selectivity and affinity essential to the binding of sulfaE to Plasmodium and human TPI glycolytic enzymes, an energetic analysis using a combination of MD simulations and MM/PBSA free energy computation method was used. To calculate the binding free energies, molecular conformations obtained from the MD simulations of the four AutoDock initial complexes were used. After 500 ns of MD simulations, 625 conformations from each equilibrated complex was used to compute the average binding energy and energetic contribution for each amino acid residue in enzyme binding sites using the MM-PBSA method.</ns0:p></ns0:div> <ns0:div><ns0:head>A. Structural stability of complexes:</ns0:head><ns0:p>To monitor the stability of the systems, the total energy (E T ) and root means square deviation (RMSD) was investigated (Figure <ns0:ref type='figure'>4</ns0:ref>). The average values of E T within the first 100 ns and rest of simulations not shown in Figure <ns0:ref type='figure'>4</ns0:ref> for are: (hTPI-active site complex; -8.36 x 10 5 kJ/mol, hTPIdimer interface complex; -5.20 x 10 5 kJ/mol, pTPI-active site complex; -5.27 x 10 5 kJ/mol, pTPIdimer interface complex; -7.72 x 10 5 kJ/mol). The 6 systems (apo forms of the enzymes and four complexes) were also stable during simulation with deviations from average structures ranging from 1.5 to 3.5 Å Figure <ns0:ref type='figure'>4</ns0:ref>. Five of the systems rapidly reach equilibrium and are stable during the simulation. The average backbone RMSD ranges from 1.5 Å (unbound hTPI and hTPI-active site complex), 2.5 Å for the unbound pTPI and pTPI complexes, to 3.5 Å for the hTPI dimer interface. It is evident from Figure <ns0:ref type='figure'>4</ns0:ref> that the presence of SulfaE ligand in the different binding pockets causes slight structural changes. The pTPI complexes do fluctuate less compared to the hTPI (Figure <ns0:ref type='figure'>4</ns0:ref>). The hTPI dimer complex shows most of the fluctuation, indicating that the presence of the ligand does cause some structural changes. An estimated measure of flexibility for each residue in all four complexes is shown in Figure <ns0:ref type='figure'>5</ns0:ref> below. This measure of residue flexibility is similar hTPI, pTPI and the four complexes with some regions showing minimal residue fluctuations (within 1.5 Å) while some show significant residue fluctuations (2 -4 Å). In general, we observed more significant fluctuations in regions or with residues in close or direct contact with the binding ligand sulfaE. The residues around loop 6 of the TPI enzyme (160 to 200) are known to be involved with catalysis and showed more fluctuations in the neighborhood of 1.7 Å. The sulfaE/pTPI active site complex also shows fluctuations between 2.0 and 2.7 Å. The dimer interface residues (40 to 80) for interaction with pTPI show dynamic fluctuations within 2 Å. It is important to determine whether these overall residue movements contribute to molecular binding or are just random molecular motions. The fluctuations are also more prevalent within TPI loop 6 residues and dimer interface residues and therefore indicate some impact in the ligand binding process.</ns0:p></ns0:div> <ns0:div><ns0:head>B. Binding Free Energy:</ns0:head><ns0:p>Six hundred and twenty-five conformations from 200 to 300 ns of the simulations were collected and used for the MM-PBSA calculations. The pairwise intramolecular, intermolecular and desolvation energies of the interaction between the ligands and enzymes are presented in Table <ns0:ref type='table'>2</ns0:ref> and Figure <ns0:ref type='figure'>6</ns0:ref>. More negative binding energies, correlate with a favorable binding of sulfonamide to the TPI binding pocket. According to the binding energies (G bind ), the complexes at the active site of the TPI enzyme are more favorable than the dimer interface complexes. The binding energies indicate a preference for binding to pTPI (-80.45 kJ/mol) as opposed to hTPI (-14.99 kJ/mol). Favorable binding is enhanced by energetic contributions from hydrogen bonding, intermolecular electrostatic, van der Waals interactions and the non-polar component of the free energy of solvation (SASA) including hydrophobic effects (Table <ns0:ref type='table'>2</ns0:ref>). The active site (AS) region for both enzyme species show strong affinity with sulfaE (Figure <ns0:ref type='figure'>6</ns0:ref>) throughout the entire simulation. Enhanced binding to pTPI compared to hTPI is more pronounced at the dimer interface. The highly unfavorable desolvation energy for the polar groups is one of the main reasons we observe difference in binding between hTPI (216.66 kJ/mol) and pTPI (162.82 kJ/mol).</ns0:p></ns0:div> <ns0:div><ns0:head>C. Binding Modes and Residue Contributions to binding process</ns0:head><ns0:p>The interactions of sulfaE with the binding sites of hTPI and pTPI are shown in Figure <ns0:ref type='figure'>7 and 8</ns0:ref>. The complexes are stabilized in each pocket by a combination of polar and non-polar amino acid residues located within 5 Å directed by hydrogen bonding, electrostatics and van der Waals interactions. The MD simulations revealed close contact interactions of sulfaE with THR216, LEU226, SER211, GLU212, P178, THR175, THR177, TYR208 at the active site of hTPI. For the dimer interface of hTPI GLU77, ASN65, PHE102, ARG98 were revealed For pTPI complexes, GLN64, ASN65, SER45, VAL44, TYR48, VAL78, and LYS112 interact with ligand in dimer interface, while LEU113, LYS122, LEU162. ILE161, VAL125 and PHE150 interact at the active site. The mix in polarity of close contact residues as well as the dipolar nature of the sulfonamide ligand structure is in line with the strong contributions of the van der Waal and electrostatic interactions to the overall binding energy. Multiple hydrogen bonds and formed between sulfaE and TPI enzymes. The hydrogen bond occupancy is however higher in the active site complexes compared to the dimer interface complexes. In addition, the perceived bias in affinity towards pTPI maybe a result dimer interface complex been stabilized by the 98% hydrogen bond occupancy between sulfaE nitrogen atom acceptors and GLN64. For the hTPI dimer interface the ASN65 hydrogen bond only shows a 33.6 % occupancy during the simulation (Figure <ns0:ref type='figure'>9</ns0:ref>). The active site complexes for both enzymes (hTPI and pTPI) form multiple hydrogen bonds averaging above 95 % occupancy during the simulations (Figure <ns0:ref type='figure'>9</ns0:ref>). Pairwise energy decomposition over the same time intervals used to determine the binding energy calculations, was to estimate the impact of residue substitutions between hTPI and pTPI on binding. The decomposed energy contributions from individual TPI residues are presented in the sulfaE-residue interaction map (Figure <ns0:ref type='figure'>10 and 11</ns0:ref>). The maps rapidly vividly show the cluster of residues in each enzyme pocket and their contribution to binding. For example, we observe that the dimer interface residues <ns0:ref type='bibr'>(46)</ns0:ref><ns0:ref type='bibr'>(47)</ns0:ref><ns0:ref type='bibr'>(48)</ns0:ref><ns0:ref type='bibr'>(49)</ns0:ref><ns0:ref type='bibr'>(50)</ns0:ref><ns0:ref type='bibr'>(75)</ns0:ref><ns0:ref type='bibr'>(76)</ns0:ref><ns0:ref type='bibr'>(77)</ns0:ref><ns0:ref type='bibr'>(78)</ns0:ref><ns0:ref type='bibr'>(79)</ns0:ref><ns0:ref type='bibr'>(80)</ns0:ref> in pTPI contribute more to binding compared to hTPI dimer interface residues (Figure <ns0:ref type='figure'>10</ns0:ref>,11 and Table <ns0:ref type='table'>3</ns0:ref>). The active site binding residues (95-98, 160-167) also show strong contributions in both species. The maps suggest that the stronger affinity observed with the pTPI dimer interface compared to the hTPI dimer interface is due to some cooperative effect between residues from both monomeric units of TPI.</ns0:p></ns0:div> <ns0:div><ns0:head>Discussion</ns0:head><ns0:p>The observations from this study also mirror literature that the glycolytic enzyme TPI is a potential target for refinement of antimalarial chemotherapies. <ns0:ref type='bibr' target='#b22'>(Kim & Dang, 2005)</ns0:ref> A number of experiments have shown that glycolytic enzyme like triose phosphate isomerase can be selectively targeted by antimalarial agents <ns0:ref type='bibr' target='#b0'>(Astorga, Ekins, Morales, & Wright, 2012;</ns0:ref><ns0:ref type='bibr' target='#b13'>Gayathri et al., 2009)</ns0:ref>. In a previous study using blind docking with the AutoDock4.2 software, differences in the interactions between a number of sulfonamides and hTPI/pTPI were observed in the micromolar subrange, suggesting two main binding motifs. The first goal of this work was to obtain an energetic description of the interactions between a novel sulfonamide ligand representative and hTPI or pTPI. Specifically, we questioned the nature of the interactions given those similarities in binding domains between TPI enzymes across different species: Is there some selective enhancement for the binding of sulfonamide to hTPI as opposed to pTPI? The mechanism of substrate/ligand interaction by TPI has been studied extensively and shows that the 11 residues in loop 6 and catalytic residues (E165, H95, K12, Y208, A176) are responsible for strong affinity to substrate <ns0:ref type='bibr' target='#b6'>(Derreumaux & Schlick, 1998;</ns0:ref><ns0:ref type='bibr' target='#b19'>Joseph, Petsko, & Karplus, 1990;</ns0:ref><ns0:ref type='bibr'>Roland et al., 2015)</ns0:ref>. Gao et al showed that sulfonated dyes (suramin, Direct red 23, Direct Violet 51) interfere with the dimerization process to form biological functional units of TPI thus inhibiting the enzymes <ns0:ref type='bibr' target='#b12'>(Gao et al., 1998)</ns0:ref>. The affinities shown by these sulfonated dyes when interacting with active site residues of TPI ranges from -17.7 to -38.8 kcal/mol with IC50 values ranging from 41.9 to 49.7 M <ns0:ref type='bibr' target='#b20'>(Joubert, Neitz, & Louw, 2001)</ns0:ref>. In this study, using a representative sulfonamide sulfaE, the simulations revealed that sulfaE binds TPI with binding free energies ranging from -80.45 kJ/mol (-19.2 kcal/mol) to -144.59 kJ/mol (-34.4 kcal/mol). The molecular dynamics simulations and free energy calculations using the MM-PBSA method also suggest that there is selective enhancement in interactions between sulfaE and triose phosphate isomerase (TPI) from human and Plasmodium species. The bias in interactions is mainly because of the difference in affinity at the dimer interface of both enzymes differs significantly (Table <ns0:ref type='table'>2, 3 and 4</ns0:ref>). For the dimer interface complexes, the van der Waals interaction energy, nonpolar solvation energy, is more favorable for sulfaE-pTPI dimer complex than sulfaE-hTPI complex, shifted by -86.29 kJ/mol, and -2.21 kJ/mol respectively (Table <ns0:ref type='table'>2</ns0:ref>). The polar solvation energy of sulfaE-hTPI complex is shifted by 53.84 kJ/mol relative to the sulfaE-pTPI dimer interface complex. The intermolecular electrostatic interactions are similar for both dimer complexes but more favorable for the sulfaE-hTPI complex especially for the active site complexes. The hydrogen bond occupancy map (Figure <ns0:ref type='figure'>9</ns0:ref>) also shows the formation 4 hydrogen bonds with active site complexes with over 90 % occupancy during the simulation. The probability of forming hydrogen bonds is lowest with the hTPI-dimer interface complex. The unfavorable polar solvation energy and low H-bond occupancy explains why sulfaE seems to prefer the parasite to the human enzyme. The second major goal of this study was to understand the structural motifs responsible for the binding, and whether key TPI residue substitutions are critical for binding sulfonamides. The dimer interface of pTPI with polar and hydrophobic amino acid residues (V44, S45, V46, Y48, I63, Q64, N65, V66, E77, V78) of appropriate sizes seems to form an important binding pocket. The dimer interface for hTPI does have some residue substitutions that make binding difficult (P44, T45, A46, I48 and F74, F102). For example, the V44P substitution leads to a less favorable contribution to van der Waals, electrostatic and non-polar contributions to the binding energy for this residue in hTPI (Table <ns0:ref type='table'>3 and 4</ns0:ref>). A drastic reduction in contribution to these intermolecular interactions is also observed for the S45T, V46A substitutions. The contributions from H47Y, Y48I substitutions are not significantly different across the species. In all these substitutions, the polar solvation energy is more favorable for the hTPI complex but not enough to overcome strong favorable contributions from the other energy terms. The binding energy individual residue map also shows that more residues contribute favorably to the binding in pTPI compared to hTPI in the active site binding pocket (Figure <ns0:ref type='figure'>10</ns0:ref> and 11). For active site region complexes, only chain A residues where the ligand was docked show active contribution to binding. For the dimer interface pocket, we observe residue contributions from both chains. This is an indication that the ligand is forming multiple contacts with key residues on both chains A and B. The dimer interface residues for pTPI, however, contribute more favorably to the binding energy compared to hTPI residues. For example, the switch in residue from S45T in pTPI to hTPI has a significant effect in contributions from intermolecular electrostatic forces, van der Waal forces, non-polar and polar desolvation energies. Specifically, S45 in pTPI contributes favorably to binding with favorable electrostatic and van der Waals energies (-7.04 chain A and -8.04 chain B) compared to T45 with contributions (-0.14 chain A and 0.055 chain B) Table <ns0:ref type='table'>3 and 4</ns0:ref>. The affinity of hTPI dimer interface residues is likely dampened by steric factors of the pocket as shown with strong polar solvation energies for some residues like Glu77. The contribution from each residue indicates that strong and favorable electrostatics and van der Waals interaction overcome the polar solvation energies for interaction between sulfaE and pTPI more readily, explaining the favorable strong total binding energies relative to those between sulfaE and hTPI. Structures of the proposed binding conformations also explain why pTPI seemingly interacts more with sulfaE (Figure <ns0:ref type='figure'>7 and 8</ns0:ref>). The ligand sulfaE seems more tightly packed and fits well in the dimer interface of pTPI permitting stronger electrostatic and van der Waals interactions with the protein residues. The strength of interactions is also bolstered by the contributions from residues in both chains A and B of pTPI. The sulfaE ligand does not benefit from strong contributions from residues in both chains in the hTPI interface (Table <ns0:ref type='table'>3, 4 and Figure 10, 11</ns0:ref>). There is a slight shift in the ligand position in hTPI compared to pTPI Figure (Figure <ns0:ref type='figure'>7 and 8</ns0:ref>). The ligand sulfaE is also not tightly packed in the dimer interface due to size of hTPI and poor fit because of unfavorable interactions with anchor residues (E77). The fact that mostly residues on one of the monomers contribute significantly towards overall binding energy is also an indicative of fewer favorable interactions with hTPI (Figure <ns0:ref type='figure'>7</ns0:ref> and Table <ns0:ref type='table'>4</ns0:ref>). We observed that van der Waals and electrostatic interactions are key components explaining the stronger affinity towards pTPI as opposed to hTPI. In addition, the overall charge of pTPI of (-8e) as opposed to (-6e) for hTPI indicates that subtle residue substitutions do have an observable effect on charge variation between hTPI and pTPI. This charge difference in protein receptor and the dipolar nature of the amine-based sulfaE can lead to selectivity in sulfaE hTPI/pTPI complexes.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions:</ns0:head><ns0:p>In this article, we have studied the binding of sulfaE and TPI from human and Plasmodium species with complexes formed at two binding pockets; dimer interface and active site region. The total binding energy of interactions was obtained from 500 ns MD simulations in explicit water. This was followed by implicit solvent free energy calculations using the MM-PBSA method. Many experiments have shown that TPI is a potential glycolytic enzyme for the development of antimalarial medication. The similarity in structural folds of TPI enzyme from human and Plasmodium species has, however, slowed down the progress in this field. The models of interaction between a representative sulfonamide and TPI enzyme from Plasmodium and human species suggested in this article show that subtle substitutions of residues even with similar polarity and just minimal size effect can lead to variations in contributions to the total binding energy from van der Waal and electrostatic forces. Strong and favorable intermolecular electrostatic, van der Waals interactions and increases in non-polar solvation energies are responsible for the selectivity of pTPI with sulfaE compared to hTPI at the dimer interface. The importance of polar solvation energies on a per residue basis shows why structural inspection from our previous docking studies is not enough to characterize such interactions. The huge increase in polar solvation energies, especially for some hTPI dimer interface residues (E77), is also responsible for discriminating between complexes formed. We think this molecule can serve as a pharmacophore for the design of new inhibitors using the identified and subtle differences at the dimer interface and differences in interactions around loop 6 and active site residues. </ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head>2</ns0:head><ns0:label /><ns0:figDesc>PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2019:09:41562:1:2:NEW 4 Jun 2020)Manuscript to be reviewedChemistry JournalsAnalytical, Inorganic, Organic, Physical, Materials Science</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>Figure 1</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='28,42.52,229.87,525.00,378.75' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='31,42.52,229.87,525.00,274.50' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='32,42.52,255.37,525.00,367.50' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='33,42.52,255.37,525.00,312.75' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='34,42.52,255.37,525.00,305.25' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='35,42.52,204.37,525.00,325.50' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='36,42.52,255.37,525.00,178.50' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='37,42.52,255.37,525.00,186.00' type='bitmap' /></ns0:figure> <ns0:note place='foot'>PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2019:09:41562:1:2:NEW 4 Jun 2020)Manuscript to be reviewedChemistry JournalsAnalytical, Inorganic, Organic, Physical, Materials Science</ns0:note> <ns0:note place='foot'>PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2019:09:41562:1:2:NEW 4 Jun 2020)</ns0:note> <ns0:note place='foot'>PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2019:09:41562:1:2:NEW 4 Jun 2020)Manuscript to be reviewed Chemistry Journals</ns0:note> </ns0:body> "	"School of Science & Technology Dr. Neville Y. Forlemu School of Science & Technology Georgia Gwinnett College 1000 University Center Lane Lawrenceville, Georgia, 30043 404-991-8837 Email: nforlemu@ggc.edu June 4th , 2020 Reviewers and Editors PeerJ Physical Chemistry Dear Editors, I am writing to resubmit our manuscript entitled, “Molecular Dynamics Simulations of the interactions between triose phosphate isomerase and sulfonamides:” for further consideration for publication by PeerJ Physical Chemistry. Many antimalarial drugs are currently facing increasing resistance from Plasmodium falciparum putting about 3.5 billion people worldwide at risk of malaria infection. In a recent CNN news story based on scientific literature, there are increasing reports that even the best combination antimalarial therapy of artemisinin derivatives is facing alarming failure rates ranging from 35 to 70% in South East Asia. This article discusses a class of ligands called sulfonamides that have been successfully used in the past to fight bacterial infections. The paper demonstrates that the glycolytic enzyme triosephosphate isomerase (TPI) is a suitable target for the development of new antimalarial therapies with sulfonamide as basic pharmacophore. The article highlights the interactions of a representative sulfonamide with human and Plasmodium TPI. We identify dimer interface of the TPI as the major binding domain responsible for potential selective affinity towards the Plasmodium enzyme compared to its human isoform. The interdisciplinary nature of this research spans fields such as structural biology, biochemistry, physics, chemistry and computational science disciplines, and should be of broad interest. Our hope is that this work will engender potential collaborations with experimentalists our teaching campus and off campus as well. Here is a summary of some of the comments from reviewers that we have addressed: Reviewer 1: Basic reporting 1. The hypothesis for the study is not clear based only on the introduction. The authors should revise the last paragraph of the into to include a clear hypothesis and/or a summary of main results/conclusions. a) We have included this statement in the last paragraph 2. The English is unclear in certain places. I have tried to note most of them below. b) Thank you and the changes suggested have been applied Experimental design 1000 University Center Lane Lawrenceville, GA 30043 Phone: 678-407-5602 www.ggc.edu I have ordered the flawed sections of the experimental design below by priority (1 being highest priority). 1. The authors should run simulations of the apo (i.e. no ligand) pTPI and hTPI. Several comments in the text (see lines 225-227, 236-237, 245-247), make conclusions on what happens to the structure of TPI when the ligand binds. However, since the authors did not simulate the apo TPI structures, they do not have a set of simulations to compare with their ligand-bound simulations. Thus, the conclusions based on those findings cannot be validated. c) The new set of simulations now include the apo forms of the enzymes: 2. Why do the authors only run 100 ns for each simulation? With the size of the systems, the authors could easily run each to at least 1000 ns. The RMSD plots (Figure 3) suggest that none of the simulations have reached a stable structural conformation with the bound ligand. Instead, the RMSD values continue to increase as the simulation progresses up until the end (100 ns). This suggests the protein-ligand complexes have not reached a stable structure (i.e. converged). Thus, the authors should extend their simulations well beyond 100 ns until the structures reach a stable conformation that the authors can trust and verify. d) We have extended our simulations to 1000 ns using the resources we have at an only teaching undergraduate institution 3. The authors should perform a hydrogen bonding analysis on their simulations. On multiple occasions (Lines 361 and 349) they mention the stabilizing interactions as polar and/or hydrogen bonds, but the authors do not support these statements with the corresponding analysis. The authors should report percent occupancies and lifetimes for any hydrogen bonds relevant to protein-ligand stability. e) The hydrogen bond occupancies have now been included 4. Why was sulfaE chosen as the representative ligand to test? The authors should make this clear. And if there is no obvious reason, the reviewers should run additional simulations to include the other two ligands mentioned from the previous work. a) This has been explained in the text, but sulfaE chosen because the other ligands are not significantly different, the variations are in position of fluorine 5. It is unclear how many frames/snapshots were used for MM-PBSA calculations for each simulation. Line 210 says that 20 snapshots were used. from each simulation. However, line 217 says that about 2000 conformations were used for MM-PBSA (is this per simulation or total across the four simulations?). If only 20 snapshots were used per simulation, that's only 80 snapshots. So where did the 2000 come from? In my experience, a minimum of 200 frames must be analyzed to get reasonable results from MM-PBSA. b) This has now been addressed in the paper. In this study, the average binding energies and other quantities of interest are computed using structures from 625 snapshots randomly selected after one half microsecond of production simulation. 6. At the very least, they authors should explain why they chose not to include entropic calculations in their methods. On lines 338-340, the authors mention the potential effect of entropy on ligand affinity. The authors should calculate the entropy of the systems using snapshots from their simulations to support their conclusion here. c) This has now been addressed in the paper. The entropic contributions (Equation 2), are not computed because it’s the ligand and proteins are similar and inclusion has limited impact on ranking of relative binding affinities as observed by multiple studies. (Wang, Greene, Xiao, Qi, & Luo, 2018; Yang et al., 2011) 7. Line 143 states that 6000 water molecules were added for each simulation. Was it precisely 6000, or is that an approximation? If it is not exact, the authors should clarify. It was exact and the authors have adjusted the paper to reflect this comment. The supplemental files include the coordinate files with the numbers of waters used for each complex. 8. Line 192. The Poisson-Boltzmann equation. Did the authors use the linear or non-linear form of the PB equation? And why? The authors should justify the reasoning for their choice. Linear was used for simplicity Validity of the findings Most of the conclusions drawn from the current data meets the journals standards. However, there are certain areas where conclusions are drawn and/or statements are made that do not have data to back up the claims. These areas have all been highlighted in the comments on the Experimental Design, but I will restate them here for completeness. 1. I am skeptical of the validity of one of the simulations and/or the MM-PBSA results. Specifically, the simulation with sulfaE bound to the hTPI dimer interface. The MM-PBSA results state the average binding free energy is +35.86 kJ/mol. This value suggests the ligand and protein are incompatible and are energetically unfavorable for binding. If that is the case, it would be expected that the ligand should dissociate from the binding pocket of the dimer interface to mitigate this unfavorable bound state. However, the authors make no note or mention of the dynamics of this simulation, or the physical meaning of this energetic unfavorability. The polar solvation term is clearly the leading cause of this energetic instability. However, the authors do not explain why the polar solvation would tend to be so positive for these complexes, and especially so for the hTPI dimer interface simulation. Such a high positive binding free energy suggests 1) something is fundamentally wrong with the simulation, or 2) the MM-PBSA calculation did not use the proper variables to accurately represent the complex. Without seeing the simulations up close, it's difficult to suggest which of these may be the culprit of these poor results. Either way, I'm highly skeptical of these energies as they are being reported. The simulations were extended and still currently running but we think we have achieve equilibrium for the system especially in terms of potential energy not shown> 2. Several comments in the text (see lines 225-227, 236-237, 245-247), make conclusions on what happens to the structure of TPI when the ligand binds. However, since the authors did not simulate the apo TPI structures, they do not have a set of simulations to compare with their ligand-bound simulations. Thus, the conclusions based on those findings cannot be validated. The apo enzymes have been included and the paper sections updated 3. The RMSD plots (Figure 3) suggest that none of the simulations have reached a stable structural conformation with the bound ligand. Instead, the RMSD values continue to increase as the simulation progresses up until the end (100 ns). This suggests the protein-ligand complexes have not reached a stable structure (i.e. converged). Thus, the authors should extend their simulations well beyond 100 ns until the structures reach a stable conformation that the authors can trust and verify. The simulations have been extended to 500 ns and we think we have achieved stability and any artifacts are periodic boundary issues due to dimer 4. On multiple occasions (Lines 361 and 349) mention the stabilizing interactions as polar and/or hydrogen bonds, but the authors do not support these statements with the corresponding analysis. The authors should report percent occupancies and lifetimes for any hydrogen bonds relevant to protein-ligand stability. We have included H-bond occupancy data during the simulations, and the confirm our conclusions that interactions are stronger in Plasmodium overall compared to hTPI again because of the complex at dimer interface. 5. On lines 338-340, the authors mention the potential effect of entropy on ligand affinity. The authors should calculate the entropy of the systems using snapshots from their simulations to support their conclusion here. We have not computed entropy and we think this does not matter since we are using the same ligand in different binding pockets and thing the differences will be similar and does not significantly impact the calculations Reviewer 2: Basic reporting 1. Professional English is used throughout the manuscript. There are some grammatical and formatting errors in the manuscript. a) These errors have been largely fixed based on recommendations from reviewer 1 2. The citations are sufficient and the information presented in the introduction is broad and somewhat vague. a) We have retooled the introduction to also include studies of sulfonamide derivatives used in the fight against malaria: “Aniline and sulfonamide based drugs like sulfanilamide and sulfadoxine have been shown to interfere with the production of cellular components (amino acids and nucleotides), important for cell growth in parasitic organisms (Plasmodium and bacteria).(Hyde, 2007)” b) Our discussion also describes the foliate pathway were sulfonamide derivatives like sulfadoxine are currently been used in the treatment of malaria c) We note some drawbacks and issues of parasitic resistance that have push us to test our novel sulfonamides on multiple pathways. The current paper address one sulfonamide and triose phosphate isomerase a glycolytic enzyme that has extensively been studied for its role in the fight against malaria Experimental design 1. The research is within the aims and scope of the journal. 2. The research question is well-defined but it is not particularly meaningful, and it does not fill any knowledge gap as far as I know. a) Our Paper shows that despite subtle changes in the active site of the enzyme the affinity at this binding mode is not different between the two species b) Our data points to the dimer interface for development of new drugs because we see some clear differences in affinity consistent with other studies 3. The methods are sufficiently described. Validity of the findings The focus of the work is to explore the interactions between pTPI/hTPI and sulfonamides using docking and MD simulations. There is no experimental evidence that 'sulfonamides' in general and in particular the 'antimalarial sulfonamides' used in the work can inhibit or bind to TPI. Without such premise, using modeling to explore interactions quite frankly does not contribute any new useful knowledge or hypothesis. If the compounds were being consider as potential inhibitors of TPI, it would be better to use modeling and/or experimental tools to compare them with known inhibitors and non-inhibitors. a) Our MD Simulations seek to assess the impact of the residues in these two binding modes to explain the affinity with sulfonamides b) We have published modeling papers that have shown sulfonamides showing stronger affinities compared to known inhibitors like (Primaquine, Chloroquine,) http://dx.doi.org/10.4236/ojbiphy.2017.71004 c) We have also begun work on using gel electrophoresis and capillary electrophoresis to study these interactions experimentally. Sincerely Dr. Neville Forlemu "	Here is a paper. Please give your review comments after reading it.
473	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Malaria is a disease with debilitating health and negative economic impacts in regions at high risk of infection. Parasitic resistance and side effects of current antimalarial drugs are major setbacks to the successful campaigns that have reduced malaria incidence by 40 % in the last decade. The parasite's dependence on glycolysis for energy requirements makes pathway enzymes suitable targets for drug development. Specifically, triose phosphate isomerase (TPI) from Plasmodium falciparum (pTPI) and human (hTPI) cells show striking structural features that can be used in development of new antimalarial agents. In this study MD simulations were used to characterize binding sites on hTPI and pTPI interactions with sulfonamides. The molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) method was used to estimate the interaction energies of four sulfonamide-TPI docked complexes. A unique combination of key residues at the dimer interface of pTPI is responsible for the observed selective affinity to pTPI compared to hTPI.</ns0:p><ns0:p>The representative sulfonamide; 4-amino-N-(3,5-dimethylphenyl)-3fluorbenzenesulfonamide (sulfaE) shows a strong affinity with pTPI (dimer interface, -42.91 kJ/mol and active site region, -71.62 kJ/mol), hTPI (dimer interface, -41.32 kJ/mol and active site region, -84.40 kJ/mol). Strong and favorable van der Waals interactions and increases in non-polar solvation energies explain the difference in affinity between pTPI with sulfaE compared to hTPI at the dimer interface. This is an indication that the dimer interface of triose phosphate isomerase (TPI) glycolytic enzyme is vital for development of sulfonamide based antimalarial drugs.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>Parasitic organisms, such as Plasmodium with a fully compartmentalized glycolytic pathway, are responsible for the world scourge of malaria prevalent in tropical and sub-tropical regions of the world <ns0:ref type='bibr' target='#b22'>(Kehr, Sturm, Rahlfs, Przyborski, & Becker, 2010)</ns0:ref>. Malaria, if untreated, can lead to very debilitating conditions such as coma, brain damage, loss of muscle function and death <ns0:ref type='bibr' target='#b37'>(Ringwald, Sukwa, Basco, Bloland, & Mendis, 2002)</ns0:ref>. Global mapping data estimates that about 3.2 billion people are at risk of contracting malaria every year <ns0:ref type='bibr' target='#b15'>(Guerra, Snow, & Hay, 2006)</ns0:ref>. Aniline and sulfonamide based drugs like sulfadoxine have been shown to interfere with the production of cellular components (amino acids and nucleotides), important for cell growth in parasitic organisms (Plasmodium and bacteria). <ns0:ref type='bibr' target='#b18'>(Hyde, 2007)</ns0:ref> Folate pathway enzymes like dihydropteroate synthase and dihydrofolate reductase are major targets for malaria treatment using combination therapy of sulfadoxine-pyrimethamine (SP). <ns0:ref type='bibr' target='#b27'>(Matondo et al., 2014;</ns0:ref><ns0:ref type='bibr'>Mulenga et al., 2006)</ns0:ref>. The continued application of the SP combination in place of the more effective artemisininbased combination therapy is due to minimal side effects. Despite the limited efficacy of SP because of parasitic resistance resulting from genetic mutations, WHO continues to recommend their use in heavily affected regions mainly for safety concerns. <ns0:ref type='bibr' target='#b8'>(Djaman, Abouanou, Basco, & Kone, 2004;</ns0:ref><ns0:ref type='bibr' target='#b16'>Heinberg & Kirkman, 2015)</ns0:ref>. One of the goals of this study is to explore whether the sulfonamides we have designed can selectively interact with target receptors critical to the Plasmodium parasite. The Plasmodium parasite's sole dependence on glycolysis for energy needs makes the pathway enzymes potential targets for development of antimalarial chemotherapies <ns0:ref type='bibr' target='#b23'>(Kim & Dang, 2005;</ns0:ref><ns0:ref type='bibr' target='#b43'>Velanker et al., 1997;</ns0:ref><ns0:ref type='bibr'>Verlinde et al., 2001)</ns0:ref>. The decrease in efficiency of current antimalarial agents in many affected regions of the world due to toxic side effects, parasitic resistance caused by mutation has increased the cost and complexity of treating malaria <ns0:ref type='bibr' target='#b2'>(Bray, Barrett, Ward, & de Koning, 2003;</ns0:ref><ns0:ref type='bibr' target='#b3'>Briolant et al., 2010;</ns0:ref><ns0:ref type='bibr' target='#b32'>Petersen, Eastman, & Lanzer, 2011)</ns0:ref>. The limited number of new and effective antimalarial drugs, coupled with parasitic resistance to almost every available therapeutic combination continues to spur the search for novel, cheaper and better analogues <ns0:ref type='bibr'>(Plowe et al., 2007;</ns0:ref><ns0:ref type='bibr' target='#b40'>Triglia, Menting, Wilson, & Cowman, 1997)</ns0:ref>. Triosephosphate isomerase (TPI) is a key dimeric enzyme that speeds up the final investment phase of glycolysis, especially necessary for energy production in Plasmodium parasite the causative agent of malaria. TPI is an efficient enzyme that catalyzes the reversible interconversion between two triose phosphates; dihydroxyacetone phosphate (DHAP and glyceraldehyde-3-phosphate (G3P) <ns0:ref type='bibr' target='#b36'>(Richard, 1985)</ns0:ref>. The three-dimensional structures of human TPI (hTPI) with PDB accession code 4POC, <ns0:ref type='bibr'>(Roland et al., 2015)</ns0:ref> and Plasmodium TPI (pTPI) with PDB accession code-2VFI, <ns0:ref type='bibr' target='#b13'>(Gayathri et al., 2009)</ns0:ref> share similar structural folds (0.825 Å root mean square deviation in atomic positions), despite the 58 % difference in sequence identity (Figure <ns0:ref type='figure'>1</ns0:ref>). TPI enzymes do however, have key amino acid residues located in key binding motifs with different side chain polarities (Figure <ns0:ref type='figure'>1B</ns0:ref>). The two motifs of significant interest so far in the literature include; the dimer interface and active site regions. For example, position 96 that is proximal to TPI active site residues (K12, H95 and E165) in many TPI sequences is usually occupied by serine. This is replaced by Phenylalanine (Phe) in pTPI sequence <ns0:ref type='bibr' target='#b31'>(Parthasarathy, Ravindra, Balaram, Balaram, & Murthy, 2002)</ns0:ref>. In TPI the dimer interface comprises of residues (Y48, D49, V46, S45,) that face each other and extend the electrostatic field of this binding motif (Figure <ns0:ref type='figure'>2A and B</ns0:ref>). We also observe some subtle substitutions that affect the size and polarity of interface residues. The red and blue regions PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2019:09:41562:2:1:NEW 14 Aug 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>correspond to extreme values (low and high) electrostatic potential energy representative of surfaces occupied by polar acidic and basic residues. The green to white regions indicate intermediary electrostatic potential, energy representative surfaces occupied by nonpolar residues For example, A46, I48 in hTPI are replaced by V46, Y48 in pTPI. As a result, the dimer interface in pTPI seems more polar and less tight compared to hTPI (Figure <ns0:ref type='figure'>2</ns0:ref>) In our previous work, molecular docking calculations of the interaction between glycolytic enzymes with sulfanilamide, antimalarial drugs (primaquine, pyrimethamine, chloroquine) and 8 fluorinated sulfonamides <ns0:ref type='bibr' target='#b7'>(Dizala-Mukinay, Babalola, Sloop, & Forlemu, 2017;</ns0:ref><ns0:ref type='bibr' target='#b11'>Forlemu, Watkins, & Sloop, 2017)</ns0:ref> classified the compounds in low and high affinity groups with potential of selectivity. The sulfonamides we tested are derivatives of sulfadoxine that is used in combination with pyrimethamine to treat malaria in children and pregnant women. <ns0:ref type='bibr' target='#b28'>(Menard & Dondorp, 2017)</ns0:ref> Our docking calculations identified 3 sulfonamide ligands with strong binding affinity in micromolar range, and enhance interactions with pTPI over to hTPI (Table <ns0:ref type='table'>1</ns0:ref>). The affinity of the three novel sulfonamides docked with TPI was also significantly stronger than a number of antimalarial drugs (quinine, pyrimethamine and primaquine). <ns0:ref type='bibr' target='#b11'>(Forlemu et al., 2017)</ns0:ref> Despite the initial success of the docking studies to identify TPI as a target receptor, with potential of enhanced selectivity between hTPI and pTPI, the inhibition mechanism, dynamic motions of enzymes upon binding and contribution of residues is not well understood. In the current study, we have performed molecular dynamics simulations on four ternary complexes with the representative sulfonamide docked in the identified binding modes. Our goal is to gain insights on the binding process and explanation of the impact of residue substitutions like S96 in hTPI to F96 in pTPI and dimer interface residues substitutions. We hypothesize that the size and changes in polarity of the amino acid substitution A46, I48 in hTPI to V46, Y48 in pTPI, enhances affinity for sulfonamides. Chemical structures of the sulfonamide ligands bound to the TPI receptor in the docking study are shown in Figure <ns0:ref type='figure'>3</ns0:ref>. The binding energy (G), residue contribution to energy between a representative sulfonamide ligand 4-amino-N-(3,5-dimethylphenyl)-3-fluorbenzenesulfonamide (sulfaE) with hTPI and pTPI was calculated by the molecular mechanics/Poisson-Boltzmann and surface area solvation method. We expect to obtain answers to two key questions; (1) Is there some selective enhancement for the binding of sulfonamide to hTPI as opposed to pTPI. (2) What structural motifs and residues are critical for the binding, and are the key TPI residue substitutions critical for binding sulfonamides? Docked complexes obtained from using AutoDock 4.2 served as initial configurations for molecular dynamics simulations (Gromacs software package) to screen impactful interactions and dynamically refined the complexes formed. <ns0:ref type='bibr' target='#b41'>(Van Der Spoel et al., 2005)</ns0:ref> </ns0:p></ns0:div> <ns0:div><ns0:head>Molecular Systems and Computational Methods A. Docked Complexes and Ligands:</ns0:head><ns0:p>The interactions between 8 novel sulfonamides with hTPI and pTPI were characterized using blind docking <ns0:ref type='bibr' target='#b11'>(Forlemu et al., 2017)</ns0:ref>. The docking scores for the best performing sulfonamides 4-amino-N-(3,5-dimethylphenyl)-3-fluorbenzenesulfonamide (sulfaE), 4-amino-N-(2-fluoro-3,5dimethylphenyl)-benzenesulfonamide (sulfaC), and 4-amino-N-(2,6-difluorophenyl)-2,6dimethoxybenzenesulfonamide (sulfaH) are shown in Table <ns0:ref type='table'>1</ns0:ref>. The three-dimensional structures of the ligands tested were built using GuassView, and then geometry optimized with Gaussian 09 using a B3LYP/6-311g basis set <ns0:ref type='bibr' target='#b5'>(Dennington, Keith, & Millam, 2009;</ns0:ref><ns0:ref type='bibr' target='#b26'>M. J. Frisch, 2016)</ns0:ref>. The derivatives have the same basic structure, but differ in the substitution pattern of polar fluorine, methoxy and alkyl functional groups (Table <ns0:ref type='table'>1</ns0:ref> and Figure <ns0:ref type='figure'>3</ns0:ref>). The binding energies from docking calculations for all three high affinity sulfonamides are not significantly different as a result only sulfaE configuration is used as a representative sulfonamide for the MD simulations. SulfaE docked complexes were used as initial MD input structures based on enhanced selectivity in terms of affinity (binding energies and dissociation constant) with pTPI compared to hTPI. <ns0:ref type='bibr'>(Forlemu et al., 2017)</ns0:ref>. The molecular topologies and parameters for sulfaE were obtained from the fast force field generating tool called SwissParam <ns0:ref type='bibr' target='#b49'>(Zoete, Cuendet, Grosdidier, & Michielin, 2011)</ns0:ref>, based on the Merck molecular force field. The molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) method was used to estimate the interaction energies of four sulfonamide-TPI docked complexes <ns0:ref type='bibr' target='#b17'>(Homeyer & Gohlke, 2012)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>B. Molecular Dynamics Simulations:</ns0:head><ns0:p>MD simulations were carried out on four sulfaE/TPI docked complexes. The first two complexes involved sulfaE docked in the dimer or active site region of pTPI and the other two involving sulfaE docked in the dimer or active site region of hTPI. Each docked complex was relaxed using two stages of 1000 steps of steepest decent energy minimization, followed by 50,000 steps of conjugate gradient energy minimization. Subsequently a 4-ns long MD simulation was used to equilibrate each system at 300 K using an NVT ensemble, with GROMACS MD code and a Vrescale thermostat to keep the temperature fixed <ns0:ref type='bibr' target='#b4'>(Bussi, Donadio, & Parrinello, 2007)</ns0:ref>. This was followed by a 10 ns NPT ensemble simulation to equilibrate the pressure at 1 bar and 300 K. A final production MD was run for 500 ns, with atomic coordinates saved after every 100 ps. All the simulations were performed using the TIP3P water model and the protein described using the CHARMM27 force field <ns0:ref type='bibr' target='#b10'>(Foloppe & MacKerell, 2000;</ns0:ref><ns0:ref type='bibr' target='#b19'>Jorgensen, Chandrasekhar, Madura, Impey, & Klein, 1983)</ns0:ref>. The simulations were performed in an orthorhombic box with periodic boundary conditions and dimensions at least 8 Å from the solute (protein ligand complex). The complex was soaked in water molecules for a total system size of 93 Å x 87 Å x 76 Å. The total system charge was kept neutral by using an appropriate combination of chloride and sodium ions. The PME method was used to estimate the long-range electrostatic interactions, with short range nonbonding interactions estimated using a 14 Å cutoff. The time step for each simulation was set 2 fs and the hydrogens restrained using the SHAKE algorithm <ns0:ref type='bibr' target='#b1'>(Berendsen, Postma, Gunsteren, DiNola, & Haak, 1984;</ns0:ref><ns0:ref type='bibr' target='#b42'>van Gunsteren & Berendsen, 1977)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>C. Binding Free Energy Calculation.</ns0:head><ns0:p>The MM/PBSA (molecular mechanics [MM] with Poisson-Boltzmann [PB] and surface area solvation) method was used estimate the binding free energy between sulfaE and different binding pockets on pTPI and hTPI <ns0:ref type='bibr'>(Kollman et al., 2000)</ns0:ref>. The single-trajectory MM/PBSA method was used to post-process the binding energy (G bind ) of 4 sulfaE (L)/TPI(P) complexes to determine impact of binding pocket and residues substitutions (Scheme 1).</ns0:p><ns0:p>P + L PL Scheme 1 The binding free energy was computed as PeerJ Phy. Chem. reviewing PDF \| (PCHEM- <ns0:ref type='table' target='#tab_1'>2019:09:41562:2:1:NEW 14 Aug 2020)</ns0:ref> Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science Equation 1</ns0:p><ns0:p>∆𝐺 𝑏𝑖𝑛𝑑 = 𝐺 𝑃𝐿 -𝐺 𝑃 -𝐺 𝐿 Where G is computed from molecular mechanics (MM) force field expressions Equation <ns0:ref type='formula'>2</ns0:ref>𝐺 = 𝐸 𝑏𝑜𝑛𝑑 + 𝐸 𝑒𝑙 + 𝐸 𝑣𝑑𝑤 + 𝐺 𝑝𝑜𝑙 + 𝐺 𝑛𝑝 -𝑇𝑆 Here E bond represents standard molecular mechanics force field terms describe stretching, bending, and torsional bonded interactions (Equation <ns0:ref type='formula'>3</ns0:ref>) Equation 3</ns0:p><ns0:formula xml:id='formula_0'>𝐸 𝑏𝑜𝑛𝑑 = ∑ 𝑏𝑜𝑛𝑑 𝑘 𝐵 (𝑏 -𝑏 0 ) 2 + ∑ 𝑎𝑛𝑔𝑙𝑒 𝑘 𝜃 (𝜃 -𝜃 0 ) 2 + ∑ 𝑑𝑖ℎ 𝑘 ∅ [(1 + cos (𝑛∅ -𝛿)]</ns0:formula><ns0:p>Where k b is a bond stretching constant, b is the actual length of the bond, and b 0 is the equilibrium bond length, k  is the angle bending constant, θ is the actual angle and θ 0 the equilibrium or unstrained angle. The phase angle shifts for the torsional angle is represented by δ. The constant k  controls the amplitude of the bond twist (rotation curves), n is an integer (2, 3, 4 or 6) that describes the periodicity of the bond twist.</ns0:p><ns0:p>Equation 4</ns0:p><ns0:formula xml:id='formula_1'>𝐸 𝑒𝑙 = ∑ 𝑖 ∑ 𝑗 > 𝑖 𝑞 𝑖 𝑞 𝑗 4𝜋𝜀 0 𝑟 𝑖,𝑗 Equation 5 𝐸 𝑣𝑑𝑊 = ∑ 𝑖 ∑ 𝑗 > 𝑖 4𝜀 𝑖𝑗[( 𝜎 𝑖𝑗 𝑟 𝑖𝑗 ) 12 -( 𝜎 𝑖𝑗</ns0:formula><ns0:p>𝑟 𝑖𝑗 ) 6 ] The nonbonded interactions include the electrostatic interactions approximated by the coulomb potential and van der Waals interactions approximated with the 6-12 Lennard-Jones type potential (Equation 4 and 5). The constants  j are characteristic of the atoms involved, σ ij represents the attractive and repulsive parameters for atoms i and j respectively, and r ij the distance between the centers of the two atoms. The electrostatic interaction is defined by assigning partial charges (q i and q j ) to each van der Waals atom. The effective dielectric constant is represented by . The 𝜀 0 solvation energy is captured by an electrostatic polar contribution (G pol ) and a non-polar contribution (G np ) term. The G pol term is obtained from the solution to the Poisson-Boltzmann equation as describe below (Equation <ns0:ref type='formula'>6</ns0:ref>) within the implicit solvent model and describe the free energy solvation contribution Equation 6</ns0:p><ns0:formula xml:id='formula_2'>∇[𝜀(𝑟)∇𝜙(𝑟)] -𝜀(𝑟)𝜅 2 sinh (𝜙(𝑟)) + 4𝜋𝜌(𝑟) = 0 where 𝜅 2 = 8𝜋𝑒 2 𝐼</ns0:formula><ns0:p>𝜀 𝜈 𝑘 𝐵 𝑇 where I represent Ionic strength (0.15 M), the dielectric constant (1 and 80 for receptor and 𝜀 𝜈 water respectively) and thus implicitly accounting for solvent properties. G np describes the nonpolar contributions to the solvation energy and can be determined from the solvent accessible surface area approximation (SASA) <ns0:ref type='bibr' target='#b17'>(Homeyer & Gohlke, 2012)</ns0:ref>. The MM-PBSA is an intermediate free energy calculation method that has been shown to estimate ligand binding affinities with correlation coefficients comparable to experiments and also discriminate between ligand protein complexes better than the widely used docking methods. In this study, the average binding energies and other quantities of interest are computed using structures from 500 snapshots selected from the last 400 ns of the 1 microsecond of production simulation. The single trajectory simulation of the complex (ligand and TPI enzyme) in explicit solvent, was then decomposed by removing appropriate atoms to obtain energy parameters for the free ligand (sulfaE) and TPI enzyme (Equation <ns0:ref type='formula'>1</ns0:ref>). <ns0:ref type='bibr' target='#b35'>(Ren et al., 2020)</ns0:ref> The uncertainties for thermodynamic parameters of interest are obtained from statistical analysis using energy data from the 500 sampled conformations. The entropic contributions (Equation <ns0:ref type='formula'>2</ns0:ref>), are not computed because the ligand and proteins are similar and inclusion has limited impact on ranking of relative binding affinities as observed by multiple studies. <ns0:ref type='bibr' target='#b46'>(Wang, Greene, Xiao, Qi, & Luo, 2018;</ns0:ref><ns0:ref type='bibr'>Yang et al., 2011)</ns0:ref> </ns0:p></ns0:div> <ns0:div><ns0:head>Results and Discussion</ns0:head><ns0:p>In order to elucidate the selectivity and affinity essential to the binding of sulfaE to Plasmodium and human TPI glycolytic enzymes, an energetic analysis using a combination of MD simulations and MM/PBSA free energy computation method was used. To calculate the binding free energies, molecular conformations obtained from the MD simulations of the four AutoDock initial complexes were used. After 500 ns of MD simulations, 500 conformations from each equilibrated complex was used to compute the average binding energy and energetic contribution for each amino acid residue in enzyme binding sites using the MM-PBSA method.</ns0:p></ns0:div> <ns0:div><ns0:head>A. Structural stability of complexes:</ns0:head><ns0:p>To monitor the stability of the systems, the total energy (E T ) and root means square deviation (RMSD) was investigated (Figure <ns0:ref type='figure'>4</ns0:ref>). The average values of E T within the first 100 ns and rest of simulations not shown in Figure <ns0:ref type='figure'>4</ns0:ref> are: (hTPI-active site complex; -8.36 x 10 5 kJ/mol, hTPI-dimer interface complex; -5.20 x 10 5 kJ/mol, pTPI-active site complex; -5.27 x 10 5 kJ/mol, pTPI-dimer interface complex; -7.72 x 10 5 kJ/mol). The 6 systems (apo forms of the enzymes and four complexes) were also stable during simulation with deviations from average structures ranging from 1.0 to 2.5 Å Figure <ns0:ref type='figure'>4</ns0:ref>. The average backbone RMSD ranges from 1.0 Å (unbound hTPI, pTPI, and hTPI-active site complex), 1.5 Å for the dimer interface complexes, to 2.0 Å for the pTPI dimer interface. It is evident from Figure <ns0:ref type='figure'>4</ns0:ref> that the presence of SulfaE ligand in the different binding pockets causes slight structural changes. The hTPI complexes fluctuate less compared to the pTPI (Figure <ns0:ref type='figure'>4</ns0:ref>). An estimated measure of flexibility for each residue in all four complexes is shown in Figure <ns0:ref type='figure'>5</ns0:ref> below. This measure of residue flexibility is similar hTPI, pTPI and the four complexes with some regions showing minimal residue fluctuations (within 1.5 Å) while some show significant residue fluctuations (2 -4 Å). In general, we observed more significant fluctuations in regions or with residues in close or direct contact with the binding ligand sulfaE. The residues around loop 6 of the TPI enzyme (160 to 200) are known to be involved with catalysis and showed more fluctuations in the neighborhood of 2.0-3.5 Å with the apo forms of the enzymes. The sulfaE/pTPI active site complex also shows fluctuations between 2.0 and 2.7 Å. The dimer interface residues (40 to 80) for interaction with pTPI show dynamic fluctuations within 2 Å. It is important to determine whether these overall residue movements contribute to molecular binding or are just random molecular motions. The seems to some stabilization of residue fluctuations prevalent within TPI loop 6 residues and dimer interface residues and therefore indicate some impact in the ligand binding process.</ns0:p><ns0:p>PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2019:09:41562:2:1:NEW 14 Aug 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science B. Binding Free Energy:</ns0:p><ns0:p>Five hundred conformations from 500 to 900 ns of the simulations were collected and used for the MM-PBSA calculations. The binding energies of the interaction between the ligands and enzymes are presented in Table <ns0:ref type='table'>2 and Figure 6</ns0:ref>. More negative binding energies, correlate with a favorable binding of sulfonamide to the TPI binding pocket. According to the binding energies (G bind ), the complexes at the active site of the TPI enzymes are more favorable than the dimer interface complexes. There is a slight preference for binding to pTPI as opposed to hTPI. The binding energy contributions from electrostatics, van der Waals and hydrophobic effects are also presented in Table <ns0:ref type='table'>2</ns0:ref>. Overall favorable binding is enhanced by energetic contributions from hydrogen bonding, intermolecular electrostatic, van der Waals interactions and the non-polar component of the free energy of solvation (SASA) including hydrophobic effects (Table <ns0:ref type='table'>2</ns0:ref>). The active site (AS) region for both enzyme species show strong affinity with sulfaE (Figure <ns0:ref type='figure'>6</ns0:ref>) that is sustained throughout the entire simulation. The electrostatic component of the binding energy is responsible for stronger interactions at the active site of hTPI compared to pTPI. The enhanced binding observed with pTPI compared to hTPI is more pronounced at the dimer interface. The unfavorable desolvation energy for the polar groups, the van der Waals, polar solvation energies and hydrophobic effects are the reasons we observe difference in binding between hTPI and pTPI.</ns0:p></ns0:div> <ns0:div><ns0:head>Binding Modes and Residue Contributions to binding process</ns0:head><ns0:p>The interactions of sulfaE with the binding sites of hTPI and pTPI are shown in Figure <ns0:ref type='figure'>7 and 8</ns0:ref>. The complexes are stabilized in each pocket by a combination of polar and non-polar amino acid residues located within 5 Å directed by hydrogen bonding, electrostatics and van der Waals interactions. The MD simulations revealed close contact interactions of sulfaE with T216, L226, S211, E212, P178, T175, T177, Y208 at the active site of hTPI. For the dimer interface of hTPI E77, N65, F102, R98 were revealed For pTPI complexes, Q64, N65, S45, V44, Y48, V78, and K112 interact with ligand in dimer interface, while L113, K122, L162, I161, V125 and F150 interact at the active site. The mix in polarity of close contact residues as well as the dipolar nature of the sulfonamide ligand structure is in line with the strong contributions of the van der Waal and electrostatic interactions to the overall binding energy. Multiple hydrogen bonds are formed between sulfaE and TPI enzymes. The hydrogen bond occupancy is however higher in the active site complexes compared to the dimer interface complexes. In addition, the perceived bias in affinity towards pTPI maybe a result dimer interface complex been stabilized by the 98% hydrogen bond occupancy between sulfaE nitrogen atom acceptors and Q64. For the hTPI dimer interface the N65 hydrogen bond only shows a 33.6 % occupancy during the simulation (Table <ns0:ref type='table'>3</ns0:ref>). The active site complexes for both enzymes (hTPI and pTPI) form multiple hydrogen bonds averaging above 95 % occupancy during the simulations (Table <ns0:ref type='table'>3</ns0:ref>). Per-residue energy decomposition over the same time intervals used to determine the binding energy calculations, was to estimate the impact of residue substitutions between hTPI and pTPI on binding. The decomposed energy contributions from individual TPI residues are presented in the sulfaE-residue interaction map (Figure <ns0:ref type='figure'>9</ns0:ref> and 10). The maps vividly show the cluster of residues in each enzymes pocket and their contribution to binding. For example, we observe that the dimer interface residues <ns0:ref type='bibr'>(46)</ns0:ref><ns0:ref type='bibr'>(47)</ns0:ref><ns0:ref type='bibr'>(48)</ns0:ref><ns0:ref type='bibr'>(49)</ns0:ref><ns0:ref type='bibr'>(50)</ns0:ref><ns0:ref type='bibr'>(75)</ns0:ref><ns0:ref type='bibr'>(76)</ns0:ref><ns0:ref type='bibr'>(77)</ns0:ref><ns0:ref type='bibr'>(78)</ns0:ref><ns0:ref type='bibr'>(79)</ns0:ref><ns0:ref type='bibr'>(80)</ns0:ref> in pTPI contribute more to binding compared to hTPI dimer interface residues (Figure <ns0:ref type='figure'>9</ns0:ref>,10 and Table <ns0:ref type='table' target='#tab_1'>4</ns0:ref>). The active site binding residues (95-98, 160-167) also show strong contributions in both species. The maps suggest that the stronger affinity observed with the pTPI dimer interface compared to the hTPI dimer interface is due to some cooperative effect between residues from both monomeric units of TPI.</ns0:p></ns0:div> <ns0:div><ns0:head>Discussion:</ns0:head><ns0:p>The observations from this study also mirror literature findings that the glycolytic enzyme TPI is a potential target for refinement of antimalarial chemotherapies. <ns0:ref type='bibr' target='#b23'>(Kim & Dang, 2005)</ns0:ref> A number of experiments have shown that glycolytic enzyme like triose phosphate isomerase can be selectively targeted by antimalarial agents <ns0:ref type='bibr' target='#b0'>(Astorga, Ekins, Morales, & Wright, 2012;</ns0:ref><ns0:ref type='bibr' target='#b13'>Gayathri et al., 2009)</ns0:ref>. In a previous study using blind docking with the AutoDock4.2 software, differences in the interactions between a number of sulfonamides and hTPI/pTPI were observed in the micromolar subrange, suggesting two main binding motifs. The first goal of this work was to obtain an energetic description of the interactions between a novel sulfonamide ligand representative and hTPI or pTPI. Specifically, we questioned the nature of the interactions given those similarities in binding domains between TPI enzymes across different species: Is there some selective enhancement for the binding of sulfonamide to hTPI as opposed to pTPI? The mechanism of substrate/ligand interaction by TPI has been studied extensively and shows that the 11 residues in loop 6 and catalytic residues (E165, H95, K12, Y208, A176) are responsible for strong affinity to substrate <ns0:ref type='bibr' target='#b6'>(Derreumaux & Schlick, 1998;</ns0:ref><ns0:ref type='bibr' target='#b20'>Joseph, Petsko, & Karplus, 1990;</ns0:ref><ns0:ref type='bibr'>Roland et al., 2015)</ns0:ref>. Gao et al showed that sulfonated dyes (suramin, Direct red 23, Direct Violet 51) interfere with the dimerization process to form biological functional units of TPI thus inhibiting the enzymes <ns0:ref type='bibr' target='#b12'>(Gao et al., 1998)</ns0:ref>. The affinities shown by these sulfonated dyes when interacting with active site residues of TPI ranges from -17.7 to -38.8 kcal/mol with IC 50 values ranging from 41.9 to 49.7 M <ns0:ref type='bibr' target='#b21'>(Joubert, Neitz, & Louw, 2001)</ns0:ref>. In this study, using a representative sulfonamide sulfaE, the simulations revealed that sulfaE binds TPI with binding free energies ranging from -80.45 kJ/mol (-19.2 kcal/mol) to -144.59 kJ/mol (-34.4 kcal/mol) Figure <ns0:ref type='figure'>6</ns0:ref>. The molecular dynamics simulations and free energy calculations using the MM-PBSA method also suggest that there is selective enhancement in interactions between sulfaE and triose phosphate isomerase (TPI) from human and Plasmodium species. The observed bias in interactions is mainly because of the difference in electrostatic and van der Waal interactions at the active site and the dimer interface of both enzymes (Table <ns0:ref type='table' target='#tab_1'>2, 3 and 4</ns0:ref>). For the dimer interface complexes, the van der Waals interaction energy, nonpolar solvation energy, is more favorable for sulfaE-pTPI dimer complex than sulfaE-hTPI complex, shifted by -46.48 kJ/mol, and -2.79 kJ/mol respectively (Table <ns0:ref type='table'>2</ns0:ref>). The polar solvation energy of sulfaE-hTPI complex is shifted by 15.31 kJ/mol relative to the sulfaE-pTPI dimer interface complex. This suggest that a bigger penalty is paid for desolvating the ligand in hTPI compared to pTPI. The intermolecular electrostatic interactions are more favorable for the hTPI complexes especially for the active site complexes. The hydrogen bond occupancy map (Table <ns0:ref type='table'>3</ns0:ref>) also shows the formation of four hydrogen bonds with active site complexes with over 90 % occupancy during the simulation. The probability of forming hydrogen bonds is lowest with the hTPI-dimer interface complex. The unfavorable polar solvation energy and low H-bond occupancy explains the slight preference sulfaE to interact with the parasitic enzyme compared to its human counterpart. This can open doors for fine-tuning and developing selective and potent ligands the other goal of this study. The second major goal of this study was to understand the structural motifs responsible for the binding, and whether key TPI residue substitutions are critical for binding sulfonamides. The dimer interface of pTPI with polar and hydrophobic amino acid residues (V44, S45, V46, Y48, I63, Q64, N65, V66, E77, V78) of appropriate sizes seems to form an important binding pocket. The dimer interface for hTPI does have some residue substitutions that make binding difficult (P44, T45, A46, I48 and F74, F102). For example, the V44P substitution leads to a less favorable contribution to van der Waals, and non-polar contributions to the binding energy for this residue in hTPI (Table <ns0:ref type='table' target='#tab_1'>4 and 4</ns0:ref>). A drastic reduction in contribution to these intermolecular interactions is also observed for the S45T, V46A substitutions. The contributions from H47Y, Y48I substitutions are not significantly different across the species. In all these substitutions, the polar solvation energy is less favorable for the hTPI complex. The binding energy individual residue map also shows that more residues contribute favorably to the binding in pTPI compared to hTPI in the active site binding pocket (Figure <ns0:ref type='figure'>9</ns0:ref> and 10). For active site region complexes, only chain A residues where the ligand was docked show active contribution to binding. For the dimer interface pocket, we observe residue contributions from both chains. This indicates that the ligand is forming multiple contacts with key residues on both chains A and B. The dimer interface residues for pTPI, however, contribute more favorably to the binding energy compared to hTPI residues. For example, the switch in residue from S45T in pTPI to hTPI has a significant effect in contributions from intermolecular electrostatic forces, van der Waal forces, non-polar and polar desolvation energies. Specifically, S45 in pTPI contributes favorably to binding with favorable electrostatic and van der Waals energies (-7.04 chain A and -8.04 chain B) compared to T45 with contributions (-0.14 chain A and 0.055 chain B) Table <ns0:ref type='table' target='#tab_1'>4 and 5</ns0:ref>. This suggest the larger size of T45 in hTPI is negatively impacting the binding. The affinity of hTPI dimer interface residues is likely dampened by steric factors of the pocket as shown with strong polar solvation energies for some residues like E77. The contribution from each residue indicates that strong and favorable electrostatics and van der Waals interaction overcome the polar solvation energies for interaction between sulfaE and pTPI more readily, explaining the favorable strong total binding energies relative to those between sulfaE and hTPI. Structures of the proposed binding conformations also explain why pTPI seemingly interacts more with sulfaE (Figure <ns0:ref type='figure'>7 and 8</ns0:ref>). The ligand sulfaE seems more tightly packed and fits well in the dimer interface of pTPI permitting stronger electrostatic and van der Waals interactions with the protein residues. The strength of interactions is also bolstered by the contributions from residues in both chains A and B of pTPI. The sulfaE ligand does not benefit from strong contributions from residues in both chains in the hTPI interface (Table <ns0:ref type='table' target='#tab_1'>4</ns0:ref>, 5 and Figure <ns0:ref type='figure'>9</ns0:ref>, 10). There is a slight shift in the ligand position in hTPI compared to pTPI Figure (Figure <ns0:ref type='figure'>7 and 8</ns0:ref>). The ligand sulfaE is also not tightly packed in the dimer interface due to size of hTPI and poor fit because of unfavorable interactions with anchor residues (E77). The fact that mostly residues on one of the monomers contribute significantly towards overall binding energy is also an indicative of fewer favorable interactions with hTPI (Figure <ns0:ref type='figure'>7</ns0:ref> and Table <ns0:ref type='table'>5</ns0:ref>). We observed that van der Waals and electrostatic interactions are key components explaining the stronger affinity towards pTPI as opposed to hTPI. In addition, the overall charge of pTPI of (-8e) as opposed to (-6e) for hTPI indicates that subtle residue substitutions do have an observable effect on charge variation between hTPI and pTPI. This charge difference in protein receptor and the dipolar nature of the amine-based sulfaE can lead to selectivity in sulfaE hTPI/pTPI complexes.</ns0:p><ns0:p>PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2019:09:41562:2:1:NEW 14 Aug 2020)</ns0:p><ns0:p>Manuscript to be reviewed Chemistry Journals Analytical, Inorganic, Organic, Physical, Materials Science Conclusions: In this article, we have studied the binding of sulfaE and TPI from human and Plasmodium species with complexes formed at two binding pockets; dimer interface and active site region. The total binding energy of interactions was obtained from 900 ns MD simulations in explicit water. This was followed by implicit solvent free energy calculations using the MM-PBSA method. Many experiments have shown that TPI is a potential glycolytic enzyme for the development of antimalarial medication. The similarity in structural folds of TPI enzyme from human and Plasmodium species has, however, slowed down the progress in this field. The models of interaction between a representative sulfonamide and TPI enzyme from Plasmodium and human species suggested in this article show that subtle substitutions of residues even with similar polarity and just minimal size effect can lead to variations in contributions to the total binding energy from van der Waal and electrostatic forces. Strong and favorable intermolecular electrostatic, van der Waals interactions and increases in non-polar solvation energies are responsible for the selectivity of pTPI with sulfaE compared to hTPI at the dimer interface. The importance of polar solvation energies on a per residue basis shows why structural inspection from our previous docking studies is not enough to characterize such interactions. The huge increase in polar solvation energies, especially for some hTPI dimer interface residues (E77), is also responsible for discriminating between complexes formed. We think this molecule can serve as a pharmacophore for the design of new inhibitors using the identified and subtle differences at the dimer interface and differences in interactions around loop 6 and active site residues. References: Manuscript to be reviewed Chemistry Journals Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science </ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head>2</ns0:head><ns0:label /><ns0:figDesc>PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2019:09:41562:2:1:NEW 14 Aug 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>Figure 1</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='31,42.52,229.87,525.00,378.75' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='34,42.52,229.87,525.00,274.50' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='35,42.52,255.37,525.00,367.50' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='40,42.52,255.37,525.00,191.25' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='41,42.52,255.37,525.00,182.25' type='bitmap' /></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head /><ns0:label /><ns0:figDesc>Analytical, Inorganic, Organic, Physical, Materials Science 1</ns0:figDesc><ns0:table><ns0:row><ns0:cell cols='3'>Hydrogen Bond Occupancy Active Site</ns0:cell><ns0:cell cols='3'>Hydrogen Bond Occupancy Dimer Interface</ns0:cell></ns0:row><ns0:row><ns0:cell>Ligand</ns0:cell><ns0:cell>hTPI</ns0:cell><ns0:cell>%</ns0:cell><ns0:cell>Ligand</ns0:cell><ns0:cell>hTPI</ns0:cell><ns0:cell>%</ns0:cell></ns0:row><ns0:row><ns0:cell>249-H2</ns0:cell><ns0:cell>A-176-O</ns0:cell><ns0:cell>97.6</ns0:cell><ns0:cell>249-H2</ns0:cell><ns0:cell>E-77-OE1</ns0:cell><ns0:cell>26.7</ns0:cell></ns0:row><ns0:row><ns0:cell>249-H</ns0:cell><ns0:cell>K-174-O</ns0:cell><ns0:cell>38.1</ns0:cell><ns0:cell>249-H</ns0:cell><ns0:cell>F-102-O</ns0:cell><ns0:cell>24.4</ns0:cell></ns0:row><ns0:row><ns0:cell>249-01</ns0:cell><ns0:cell>T-216-HG1</ns0:cell><ns0:cell>95.7</ns0:cell><ns0:cell>249-O</ns0:cell><ns0:cell>R-98-H11</ns0:cell><ns0:cell>33.9</ns0:cell></ns0:row><ns0:row><ns0:cell>249-0</ns0:cell><ns0:cell>Y-208-HH</ns0:cell><ns0:cell>94.2</ns0:cell><ns0:cell>249-O</ns0:cell><ns0:cell>N-65-D21</ns0:cell><ns0:cell>33.6</ns0:cell></ns0:row><ns0:row><ns0:cell>Ligand</ns0:cell><ns0:cell>pTPI</ns0:cell><ns0:cell>%</ns0:cell><ns0:cell>Ligand</ns0:cell><ns0:cell>pTPI</ns0:cell><ns0:cell>%</ns0:cell></ns0:row><ns0:row><ns0:cell>249-H2</ns0:cell><ns0:cell>I-161-O</ns0:cell><ns0:cell>98.4</ns0:cell><ns0:cell>249-O</ns0:cell><ns0:cell>G-75-H</ns0:cell><ns0:cell>20.8</ns0:cell></ns0:row><ns0:row><ns0:cell>249-H</ns0:cell><ns0:cell>Q-146-O</ns0:cell><ns0:cell>37.2</ns0:cell><ns0:cell>249-O</ns0:cell><ns0:cell>P-43-O</ns0:cell><ns0:cell>29.8</ns0:cell></ns0:row><ns0:row><ns0:cell>249-01</ns0:cell><ns0:cell>V-125-HN</ns0:cell><ns0:cell>94.0</ns0:cell><ns0:cell>249-O</ns0:cell><ns0:cell>N-65-D21</ns0:cell><ns0:cell>46.5</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>249-N</ns0:cell><ns0:cell>Q-64-HN</ns0:cell><ns0:cell>98.6</ns0:cell></ns0:row><ns0:row><ns0:cell>2</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row></ns0:table><ns0:note>PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2019:09:41562:2:1:NEW 14 Aug 2020)Manuscript to be reviewed Chemistry JournalsAnalytical, Inorganic, Organic, Physical, Materials Science </ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_1'><ns0:head>Table 4 (on next page)</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Decomposition of Binding Free Energies ΔG (kJ/mol) for the four SulfaE−pTPI Complexes into Contributions from Individual Residues</ns0:figDesc><ns0:table /><ns0:note>PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2019:09:41562:2:1:NEW 14 Aug 2020)</ns0:note></ns0:figure> </ns0:body> "	" Dr. Neville Y. Forlemu August 15th , 2020 School of Science & Technology Georgia Gwinnett College 1000 University Center Lane Lawrenceville, Georgia, 30043 404-991-8837 Email: nforlemu@ggc.edu Reviewers and Editors PeerJ Physical Chemistry Dear Editors, I am writing to resubmit our manuscript entitled, “Molecular Dynamics Simulations of the interactions between triose phosphate isomerase and sulfonamides:” for further consideration for publication by PeerJ Physical Chemistry. Many antimalarial drugs are currently facing increasing resistance from Plasmodium falciparum putting about 3.5 billion people worldwide at risk of malaria infection. In a recent CNN news story based on scientific literature, there are increasing reports that even the best combination antimalarial therapy of artemisinin derivatives is facing alarming failure rates ranging from 35 to 70% in South East Asia. This article discusses a class of ligands called sulfonamides that have been successfully used in the past to fight bacterial infections. The paper demonstrates that the glycolytic enzyme triosephosphate isomerase (TPI) is a suitable target for the development of new antimalarial therapies with sulfonamide as basic pharmacophore. The article highlights the interactions of a representative sulfonamide with human and Plasmodium TPI. We identify dimer interface of the TPI as the major binding domain responsible for potential selective affinity towards the Plasmodium enzyme compared to its human isoform. The interdisciplinary nature of this research spans fields such as structural biology, biochemistry, physics, chemistry and computational science disciplines, and should be of broad interest. Our hope is that this work will engender potential collaborations with experimentalists our teaching campus and off campus as well. Here is a summary of some of the comments from reviewers that we have addressed: Reviewer 1: Basic reporting 1. Figure 1B, 1D, 2B, and 2D need explanations of the color scheme being used for the electrostatic potential surfaces. The coloring scheme has now been described in the text. 2. Some of the residue labels in Figure 2A and 2C are not easily readable, especially the D49.A because of the red text over red VDW atoms. We have modified the coloring scheme to provide better contrast 3. Figure 4F appears to be missing some data. The graph does not fully extend all the way to 400 ns. 4. Why are only 400 ns of the simulation data shown in Figure 4? Based on lines 292-293, I assume the first 100 ns of the simulation are not shown, I just do not understand the reasoning behind this omission. 5. For consistency, all the graphs in Figure 4 should have the same range for the y-axis. There is no clear reason for this, the next revision has data for the first 900 ns as the last 100 ns still running for to set of complexes 6. The authors should be consistent with how they identify specific amino acids. For example, in lines 132, 135, 383, 444, 471, and several times in lines 409-415, the authors use one-letter codes and residue number (e.g. Y48). Contrarily, on lines 339-344, the authors use all-caps 3-letter codes with residue number (e.g. TYR48). This same inconsistency is found in the figures. These have now been addressed in the paper 7. Figure 9 is probably best represented as a table instead. This now Table 3 8. The resolution of the graphs in Figures 10 and 11 is poor and should be improved prior to publication. We have worked to accommodate these requested changes 9. The authors refer to their energy decomposition as ‘Pairwise’, which would suggest the energies correspond to the interaction energy between two different residues. However, the data appears to show a ‘per-residue’ decomposition since the energies are reported as the energy of a single residue, and not it paired with another residue. This should be clarified by the authors. The reviewers are correct and this is reflected in tables 3 and 4. So we now use the per-residue terminology as the idea was to assess the contribution of each residue in contact in the ligands. Experimental design a) For MM-PBSA calculations, why were exactly 625 snapshots chosen? And why only 625 snapshots? And why randomly? And how were the frames “randomly” chosen? And why did these snapshots only come from 200-300 ns of the trajectory (as described on line 319). The 625 was a representative sample chosen arbitrally and that is where the idea of randomness came in. The 200-300 ns was also picked because it was the furthest point of the simulations at the time. The other choice was to perform calculations on set collected at different time intervals, which we think we have eventually done based on reviewer questions and recommendations. For example, the current iteration sample 500 conformation in the last 500 ns of the simulations, and we observe similar trends with the 200-300 ns time frame. This is also in line with literature reports suggesting that large sample of conformations does not necessarily produce data significantly different from small samples. We have also run simulations extracting 2500 confirmations to sample for free energy calculations and those are expensive and the first two data sets we have also correlate well with the 500 confirmations. We are reporting the 500 confirmations data since we have data for all complex, but simulations still running for 2 of 4 complexes with the 25000 conformations choice. Validity of the findings b) Lines 331-332 – The authors mention the importance of the desolvation free energy term contributes to the observed enhanced binding for pTPI over hTPI, but the authors neglect to mention that the ΔE vdW term also contributes significantly (Table 2), and possibly more so than the desolvation free energy. We do elsewhere but maybe not stress enough. We fix that in this iteration Sincerely Dr. Neville Forlemu "	Here is a paper. Please give your review comments after reading it.
474	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Mathematical models of the dynamics of infectious disease transmission are used to forecast epidemics and assess mitigation strategies. In this article, we highlight the analogy between the dynamics of disease transmission and chemical reaction kinetics while providing an exposition on the classic Susceptible-Infectious-Removed (SIR) epidemic model. Particularly, the SIR model resembles a dynamic model of a batch reactor carrying out an autocatalytic reaction with catalyst deactivation. This analogy between disease transmission and chemical reactions enables the exchange of ideas between epidemic and chemical kinetic modeling communities.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head n='1'>INTRODUCTION</ns0:head><ns0:p>Mathematical models of the dynamics of infectious disease transmission <ns0:ref type='bibr' target='#b16'>(Brauer, 2017;</ns0:ref><ns0:ref type='bibr' target='#b36'>Hethcote, 2000)</ns0:ref> are useful for forecasting epidemics, evaluating public health interventions, and inferring properties of diseases.</ns0:p><ns0:p>In compartmental epidemic models <ns0:ref type='bibr' target='#b15'>(Brauer, 2008)</ns0:ref>, each member of the population is categorized based on their disease status in addition to, possibly, their attributes and/or the treatment they received. The dynamics of disease transmission are then typically modeled with differential equations that describe the flow of individuals between the compartments as the population mixes, the disease spreads, infected individuals progress through the stages of the disease, and public health interventions are implemented.</ns0:p><ns0:p>The parsimonious, classic SIR (Susceptible-Infectious-Removed) compartmental model <ns0:ref type='bibr' target='#b40'>(Kermack and McKendrick, 1927;</ns0:ref><ns0:ref type='bibr' target='#b5'>Anderson, 1991)</ns0:ref> gives insights into the dynamics of epidemics and shows utility for understanding how public health interventions affect the trajectory of an epidemic <ns0:ref type='bibr' target='#b11'>(Bertozzi et al., 2020)</ns0:ref>.</ns0:p><ns0:p>In this article, we highlight the analogy between the dynamics of disease transmission and chemical reaction kinetics while formulating and analyzing the SIR epidemic model. This article is pedagogical in nature and its aim, in making the connection between chemical kinetics and the spread of infectious disease, is knowledge exchange across the two disciplines of chemical kinetic and epidemic modeling. </ns0:p></ns0:div> <ns0:div><ns0:head n='2'>RESULTS</ns0:head></ns0:div> <ns0:div><ns0:head n='2.1'>The SIR model</ns0:head><ns0:p>In the classic SIR model of an epidemic <ns0:ref type='bibr' target='#b40'>(Kermack and McKendrick, 1927;</ns0:ref><ns0:ref type='bibr' target='#b49'>Murray, 1993;</ns0:ref><ns0:ref type='bibr' target='#b36'>Hethcote, 2000;</ns0:ref><ns0:ref type='bibr' target='#b22'>Edelstein-Keshet, 1988;</ns0:ref><ns0:ref type='bibr' target='#b32'>Frauenthal, 2012;</ns0:ref><ns0:ref type='bibr' target='#b17'>Brauer et al., 2019)</ns0:ref>, each member of the population belongs to one of three compartments: Susceptible, Infectious, or Removed.</ns0:p></ns0:div> <ns0:div><ns0:head n='2.1.1'>The reactions</ns0:head><ns0:p>Susceptible folks can contract the disease if they come into contact with an infectious individual.</ns0:p><ns0:p>Once infected, they move into the infectious compartment, assuming zero delay between infection and the ability to transmit the disease. This is analogous to an irreversible, autocatalytic chemical reaction <ns0:ref type='bibr' target='#b56'>(Schuster, 2019;</ns0:ref><ns0:ref type='bibr' target='#b57'>Scott, 1983)</ns0:ref> between a reactant, S, and catalyst, I:</ns0:p><ns0:formula xml:id='formula_0'>S + I −−→ 2 I {1}</ns0:formula><ns0:p>Infectious individuals eventually recover or die from the disease, entering the removed compartment. Folks in the removed compartment do not participate in disease transmission. i.e., they cannot transmit the disease, nor can they contract it again, assuming that recovery from the disease confers immunity to reinfection. This is analogous to a reaction where the catalyst, I, irreversibly degrades or converts to a deactivated form, R:</ns0:p><ns0:formula xml:id='formula_1'>I −−→ R {2}</ns0:formula><ns0:p>We assume permanent protective immunity is conferred upon recovery from the disease, thus neglecting the possibility of an R −−→ S reaction.</ns0:p><ns0:p>So, the SIR model of an epidemic is analogous to an autocatalytic reaction (rxn. {1}) with catalyst deactivation (rxn. {2}). An infectious individual (the catalyst, I), (i) upon contacting (colliding with) a susceptible individual (the reactant, S), can convert them into another infectious individual (another catalyst particle) and (ii) recovers or dies (deactivates) with time. Fig. <ns0:ref type='figure' target='#fig_0'>1</ns0:ref> depicts the flow of individuals between compartments under the SIR model, induced by rxns. {1} and {2}.</ns0:p></ns0:div> <ns0:div><ns0:head n='2.1.2'>The dynamic mathematical model</ns0:head><ns0:p>Mathematically, the SIR model <ns0:ref type='bibr' target='#b40'>(Kermack and McKendrick, 1927;</ns0:ref><ns0:ref type='bibr' target='#b22'>Edelstein-Keshet, 1988;</ns0:ref><ns0:ref type='bibr' target='#b49'>Murray, 1993;</ns0:ref><ns0:ref type='bibr' target='#b45'>Martcheva, 2015a)</ns0:ref> is equivalent to a dynamic model of a well-mixed, isothermal batch reactor carrying out the two homogeneous, elementary rxns. {1} and {2}.</ns0:p><ns0:p>As in a (closed) batch reactor, we neglect immigration and emigration (hence, the absence of flow to/from external populations in Fig. <ns0:ref type='figure' target='#fig_0'>1</ns0:ref>). Moreover, we take births and deaths not caused by the disease to be negligible over the (short) time scale of the epidemic. The incidence rate. Assuming their spatial mixing is uniform <ns0:ref type='bibr' target='#b66'>(Wilson and Worcester, 1945;</ns0:ref><ns0:ref type='bibr' target='#b65'>Weiss, 2013)</ns0:ref>, we invoke the law of mass action to model the rate at which susceptible and infectious individuals 'react' via bimolecular, autocatalytic rxn. {1}. The incidence rate of the disease, i.e. the number of new infections per unit time <ns0:ref type='bibr' target='#b45'>(Martcheva, 2015a)</ns0:ref> The recovery rate. We model the rate at which infectious individuals 'deactivate' (are removed) via rxn. {2} with first-order kinetics, i.e., as γ[I] (per capita). The inverse of the first-order recovery rate constant γ > 0 is the average time period that an infected individual is infectious <ns0:ref type='bibr' target='#b39'>(Keeling and Rohani, 2011)</ns0:ref>.</ns0:p><ns0:p>The assumptions above are summarized by the rates of flow between the compartments shown in Fig. <ns0:ref type='figure' target='#fig_0'>1</ns0:ref>. We arrive at the set of nonlinear, coupled differential equations that comprise the SIR dynamic model of infectious disease transmission by writing a mass balance on each compartment in Fig. <ns0:ref type='figure' target='#fig_0'>1</ns0:ref>:</ns0:p><ns0:formula xml:id='formula_2'>d[S] dt = −β [S][I]</ns0:formula><ns0:p>(1)</ns0:p><ns0:formula xml:id='formula_3'>d[I] dt = β [S][I] − γ[I] (2) d[R] dt = γ[I].<ns0:label>(3)</ns0:label></ns0:formula><ns0:p>The only two parameters in the SIR model are the transmission and recovery rate constants, β and γ, respectively. The average duration of infectiousness, γ −1 , could be estimated from contact tracing or shedding studies <ns0:ref type='bibr' target='#b29'>(Fine, 2003)</ns0:ref>. The transmission rate constant, β , could be identified by fitting differential eqns. 1-3 to epidemic time series data (case counts) <ns0:ref type='bibr' target='#b45'>(Martcheva, 2015a;</ns0:ref><ns0:ref type='bibr' target='#b65'>Weiss, 2013)</ns0:ref>, much like identifying a reaction rate constant from concentration time series <ns0:ref type='bibr' target='#b31'>(Fogler, 2010)</ns0:ref>.</ns0:p><ns0:p>Addition of eqns. 1-3 confirms the population is closed and demography is neglected, i.e.,</ns0:p><ns0:p>[S](t) + [I](t) + [R](t) = 1, ∀t ≥ 0. As a consequence, eqns. 1 and 2 fully determine the SIR</ns0:p><ns0:formula xml:id='formula_4'>model dynamics, with [R](t) = 1 − [S](t) − [I](t).</ns0:formula></ns0:div> <ns0:div><ns0:head n='2.1.3'>The replacement and basic reproduction numbers, r and R 0</ns0:head><ns0:p>Two important numbers aid our characterization and understanding of SIR model dynamics: the replacement number, r, and the basic reproduction number, R 0 .</ns0:p><ns0:p>The replacement number, r = r(t), is the expected number of folks (directly) infected by a typical infectious individual, mixing in the population, over the course of their infectiousness <ns0:ref type='bibr' target='#b36'>(Hethcote, 2000)</ns0:ref>. Because the concentration of susceptible folks [S] = [S](t) influences the frequency that a typical infectious individual contacts a susceptible individual, r changes over the course of an epidemic. In the SIR model, a typical infectious individual is expected to produce β [S](t) new infections per unit time (incidence rate per infectious individual) for an infectious duration of γ −1 . The replacement number is therefore:</ns0:p><ns0:formula xml:id='formula_5'>r = r(t) = β γ [S](t).<ns0:label>(4)</ns0:label></ns0:formula><ns0:p>The basic reproduction number, R 0 , is defined as the initial replacement number when one infectious individual is introduced into an all-susceptible population <ns0:ref type='bibr' target='#b22'>(Edelstein-Keshet, 1988;</ns0:ref><ns0:ref type='bibr' target='#b36'>Hethcote, 2000)</ns0:ref>. In this context, R 0 in the SIR model is the replacement number in eqn. 4 when [S] ≈ 1:</ns0:p><ns0:formula xml:id='formula_6'>R 0 = β γ .<ns0:label>(5)</ns0:label></ns0:formula><ns0:p>i.e., R 0 is the expected number of infections directly caused by a single infectious individual introduced into an entirely susceptible population <ns0:ref type='bibr' target='#b36'>(Hethcote, 2000)</ns0:ref>.</ns0:p><ns0:p>The dimensionless numbers r and R 0 are properties of both the disease and the population <ns0:ref type='bibr' target='#b19'>(Delamater et al., 2019)</ns0:ref>. While r = r(t) changes with time, R 0 is constant and defined only at the initial stage of a particular context: when one infectious individual is introduced to an all-susceptible population. Notably, the two numbers are related via r(t) = R 0 [S](t).</ns0:p><ns0:p>If the basic reproduction number R 0 in eqn. 5 is large (small), the infected are infectious for a long (short) period of time, the disease is (not) easily transmitted, and/or the average frequency of contacts in the population is high (low). Under the analogy with chemical kinetics, since the activity and longevity of the catalyst, I, are embedded in β and γ, respectively: R 0 is large (small) if the catalyst has a high (low) activity and/or remains active for a long (short) time.</ns0:p><ns0:p>These remarks also hold for the replacement number, r = R 0 [S]. However, r decreases as the concentration of the reactant, [S], decreases, owing to the reduced frequency that any given catalyst particle encounters a reactant particle to catalyze its conversion into another catalyst particle by rxn. {1}.</ns0:p></ns0:div> <ns0:div><ns0:head n='2.2'>SIR model dynamics</ns0:head><ns0:p>In the SIR model, what happens if we introduce a small number of infectious individuals into a large population of susceptible individuals? This is akin to injecting our deactivating autocatalyst, I, into a well-mixed batch reactor containing pure S. The corresponding initial conditions are:</ns0:p><ns0:formula xml:id='formula_7'>[S](0) = [S] 0 (6) [I](0) = [I] 0 (7) [R](0) = 0, (8) with [S] 0 + [I] 0 = 1, [I] 0 << 1, and [S] 0 , [I] 0 > 0. We consider [R](0) = 0 for the interesting case</ns0:formula><ns0:p>where a population is exposed to a novel pathogen to which it has no immunity.</ns0:p></ns0:div> <ns0:div><ns0:head n='2.2.1'>The replacement number r(t) determines the sign of</ns0:head><ns0:formula xml:id='formula_8'>[I] ′ (t)</ns0:formula><ns0:p>The replacement number r(t) in eqn. 4 is key to understanding SIR model dynamics. By inspection of eqn. 2, [I](t) is increasing at time t if the replacement number r(t) > 1 and decreasing if r(t) < 1. This is intuitive: if the typical infectious person mixing in the population is expected to infect less than one susceptible person before they recover, they are not expected to replace themselves with a new infectious individual to propagate the disease, and, consequently,</ns0:p><ns0:formula xml:id='formula_9'>[I](t) is decreasing. Note r(t) < 1 ⇐⇒ [S](t) < R −1 0 .</ns0:formula></ns0:div> <ns0:div><ns0:head n='2.2.2'>Does an epidemic ensue?</ns0:head><ns0:p>We first address a qualitative question: given the initial conditions in eqn. 6-8, does the disease invade the population? The outcome depends on the initial replacement number Under the chemical reaction analogy, if r 0 < 1 (r 0 > 1), the injected catalyst particles deactivate via rxn. {2} faster (slower) than they catalyze rxn. {1} to convert the reactant, S, into more catalyst, I, to propagate autocatalytic rxn. {1}; the reaction dies out (proceeds).</ns0:p><ns0:formula xml:id='formula_10'>r 0 := r(0) = R 0 [S] 0 in a threshold manner. If r 0 > 1, [I](t)</ns0:formula></ns0:div> <ns0:div><ns0:head>4/15</ns0:head><ns0:p>PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2020:07:51040:1:1:NEW 22 Aug 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science [I](t)</ns0:p><ns0:p>[R](t) For our remaining analysis, we take r 0 > 1 and further analyze the dynamics of an SIR epidemic.</ns0:p><ns0:formula xml:id='formula_11'> 0 = 2 [I] 0 = 10 −5</ns0:formula></ns0:div> <ns0:div><ns0:head>SIR model dynamics</ns0:head></ns0:div> <ns0:div><ns0:head n='2.2.3'>Initial exponential growth</ns0:head><ns0:p>Early in the epidemic, the number of infectious folks grows approximately exponentially with growth rate (r 0 − 1)γ:</ns0:p><ns0:formula xml:id='formula_12'>[I](t) ≈ [I] 0 e (r 0 −1)γt . (<ns0:label>9</ns0:label></ns0:formula><ns0:formula xml:id='formula_13'>)</ns0:formula><ns0:p>This follows from eqn. 2 if we neglect the depletion of the susceptible pool by approximating</ns0:p><ns0:p>[S](t) ≈ [S] 0 , valid only in the initial stage of the epidemic; as the disease spreads, [S] decreases and diminishes the replacement number. Eqn. 9 is thus an overestimate.</ns0:p><ns0:p>Since eqn. 9 is also a valid initial approximation for r 0 < 1, it reinforces that an epidemic will not ensue if r 0 < 1, since [I](t) would then decay approximately exponentially. decreases monotonically as the disease invades the population. As a result, the frequency with which any given infectious individual comes into contact with a susceptible individual decreases.</ns0:p></ns0:div> <ns0:div><ns0:head n='2.2.4'>A simulation of an SIR model epidemic</ns0:head><ns0:p>In conjunction with the infectious folks recovering, this eventually causes the (net) growth rate of [I] to diminish and, when the replacement number r in eqn. 4 drops below one, causes <ns0:ref type='bibr'>[I]</ns0:ref> to decay. The epidemic self-extinguishes, i.e., lim t→∞ [I](t) = 0 <ns0:ref type='bibr' target='#b12'>(Bjornstad et al., 2020a)</ns0:ref>. The R category accumulates the folks that have been infected by and have recovered or died from the disease, [R](t) = γ t 0 [I](τ)dτ (see eqn. 3 with initial condition 8). Notably, the disease does not infect the entire population, even after an infinite amount of time (lim t→∞ [S](t) = 0). i.e., the epidemic self-extinguishes not because the population is depleted of susceptible folks, but rather because it is depleted of infectious folks <ns0:ref type='bibr' target='#b49'>(Murray, 1993;</ns0:ref><ns0:ref type='bibr' target='#b65'>Weiss, 2013)</ns0:ref>. An alternative visualization of SIR model dynamics in Fig. <ns0:ref type='figure' target='#fig_5'>S2</ns0:ref> below one (i.e. once [S] drops below R −1 0 ), the catalyst, I, is deactivating via rxn. {2} faster than it is converting the reactant, S, into more catalyst to replenish itself via rxn. {1}. Consequently, the catalyst concentration, [I], begins to drop and decays to zero. Owing to catalyst deactivation (rxn. {2}), not all reactant, S, is consumed, even after an infinite amount of time.</ns0:p></ns0:div> <ns0:div><ns0:head n='2.2.5'>Solution trajectory in the ([S], [I]) phase space</ns0:head><ns0:p>We can analytically find the trajectory of the solution to eqns. Separating, integrating, and applying the initial conditions in eqns. 6 and 7, we arrive at the solution path <ns0:ref type='bibr' target='#b15'>(Brauer, 2008)</ns0:ref>:</ns0:p><ns0:formula xml:id='formula_14'>[I](t) = 1 − [S](t) + 1 R 0 log [S](t) [S] 0 . (<ns0:label>10</ns0:label></ns0:formula><ns0:formula xml:id='formula_15'>)</ns0:formula><ns0:p>Fig. <ns0:ref type='figure' target='#fig_6'>3</ns0:ref> shows the trajectory of the solution given by eqn. 10 (R 0 = 2, [I] 0 = 10 −5 ), traveling from the initial condition in the bottom right corner to the final condition on the bottom left.</ns0:p><ns0:p>The trajectory reinforces that [S](t) decreases monotonically with time; that [I](t) increases, peaks, then diminishes to zero; and that a fraction of the population remains susceptible after the epidemic dies out.</ns0:p></ns0:div> <ns0:div><ns0:head n='2.2.6'>Final size of the epidemic</ns0:head><ns0:p>Given the epidemic (autocatalytic reaction) dies out before all of the susceptible folks (reactant)</ns0:p><ns0:p>have been infected, what fraction of the population (reactant) remains susceptible (unreacted)</ns0:p><ns0:p>after the epidemic ends?</ns0:p><ns0:p>We find an implicit equation for [S] ∞ := lim t→∞ [S](t) by taking the limit t → ∞ in eqn. 10: We used the fact that the epidemic eventually dies out, lim t→∞ [I](t) = 0.</ns0:p><ns0:formula xml:id='formula_16'>0 = 1 − [S] ∞ + 1 R 0 log [S] ∞ [S] 0 . (<ns0:label>11</ns0:label></ns0:formula><ns0:p>[S] ∞ is the unique root of eqn. 11 in (0, R −1 0 ) <ns0:ref type='bibr' target='#b36'>(Hethcote, 2000)</ns0:ref>. The fraction of the population infected over the course</ns0:p><ns0:formula xml:id='formula_17'>of the epidemic is lim t→∞ [R](t) =: [R] ∞ = 1 − [S]</ns0:formula><ns0:p>∞ since the R category accumulates those that have recovered or died from the disease. Fig. <ns0:ref type='figure' target='#fig_8'>4</ns0:ref>(a) shows that, as R 0 increases from one, more of the population will be infected over the course of the epidemic.</ns0:p></ns0:div> <ns0:div><ns0:head n='2.2.7'>Peak prevalence of infectious folks</ns0:head><ns0:p>The peak prevalence of infectious folks, max t [I](t), is important because it determines the maximum strain on the healthcare system. Both eqn. 10 and eqn. 2 show that the maximum in [I](t) (look for d [I] d[S] = 0 or d [I] dt = 0, respectively) occurs when the replacement number r(t) = 1. Before the peak is reached, the replacement number r(t) > 1 and [I](t) is increasing; after the peak, r(t) < 1 and [I](t) is decreasing to zero. Substituting [S] = R −1 0 into eqn. 10, we find:</ns0:p><ns0:formula xml:id='formula_18'>max t [I](t) = 1 − 1 R 0 [1 + log (R 0 [S] 0 )] .<ns0:label>(12)</ns0:label></ns0:formula><ns0:p>Fig. <ns0:ref type='figure' target='#fig_8'>4</ns0:ref>(b) shows how the peak prevalence of infectious folks increases as R 0 increases from one. In addition, Fig. <ns0:ref type='figure' target='#fig_8'>4</ns0:ref>(c) shows that the (non-dimensional) time to reach peak prevalence, arg max t α[I](t), decreases as R 0 increases from one.</ns0:p></ns0:div> <ns0:div><ns0:head n='2.2.8'>Herd immunity</ns0:head><ns0:p>A population achieves herd immunity when a sufficient fraction are immune to the disease so as to confer indirect, population-level protection from an invasion of the disease upon the introduction of an infectious individual. Notably, the immunity could be acquired by either previous infection or by vaccination. <ns0:ref type='bibr' target='#b36'>(Hethcote, 2000)</ns0:ref> The administration of a vaccine that confers perfect and permanent immunity to a susceptible individual is modeled by introducing an S −−→ R reaction to the SIR model, i.e., by allowing flow in Fig. <ns0:ref type='figure' target='#fig_0'>1</ns0:ref> from the susceptible category directly to the removed category <ns0:ref type='bibr' target='#b67'>(Yusuf and Benyah, 2012;</ns0:ref><ns0:ref type='bibr' target='#b45'>Martcheva, 2015a;</ns0:ref><ns0:ref type='bibr' target='#b58'>Shulgin et al., 1998)</ns0:ref>.</ns0:p><ns0:p>What fraction v of a population must be immune to the disease in order to achieve herd immunity? [I](t) will decrease upon introducing an infectious individual if the replacement</ns0:p></ns0:div> <ns0:div><ns0:head>7/15</ns0:head><ns0:p>PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2020:07:51040:1:1:NEW 22 Aug 2020)</ns0:p><ns0:p>Manuscript to be reviewed herd immunity</ns0:p><ns0:note type='other'>Chemistry Journals Analytical, Inorganic</ns0:note><ns0:formula xml:id='formula_19'>[R] ∞ = 1 − [S] ∞ v > 1 −  −1 0 Figure 5.</ns0:formula><ns0:p>Herd immunity. The gray region shows the fraction v > 1 − R −1 0 of the population that must be immune to achieve herd immunity, as a function of R 0 . Such immunity could be conferred by vaccination and/or by previous infection. For comparison, the orange line shows [R] ∞ , the number of folks that will be infected over the course of an SIR epidemic without any vaccination. number r(t) < 1, i.e. if [S] < R −1 0 . Thus, a fraction v > 1 − R −1 0 of the population must be immune to achieve herd immunity. Fig. <ns0:ref type='figure'>5</ns0:ref> shows the region v > 1 − R −1 0 (gray) and illustrates that, as R 0 increases from one, more of the population must be immune to prevent an epidemic via herd immunity.</ns0:p><ns0:p>In the chemical kinetics analogy, herd immunity results from reducing the concentration of the reactant, [S], so that a catalyst particle, I, fed to the reactor is expected to deactivate before it encounters an S particle and autocatalyzes rxn. {1} to replace itself. i.e., to achieve herd immunity, [S] must be reduced sufficiently to make the replacement number r less than one.</ns0:p><ns0:p>We now make a comparison between herd immunity achieved exclusively by (i) by vaccination (S −−→ R) and (ii) by infection and removal (recovery or death) (S −−→ I −−→ R).</ns0:p><ns0:p>Consider an all-susceptible population. Under strategy (i), vaccinating a fraction v = 1 − R −1 0 of the population will suffice to confer herd immunity. Under strategy (ii), by introducing infectious individuals and allowing the epidemic to run its course, a fraction [R] ∞ of the population will be infected over the course of the epidemic. Though, herd immunity will be achieved when</ns0:p><ns0:p>[S] = R −1 0 . At this point, [I](t) is maximal (see eqn. 10 and Fig. <ns0:ref type='figure' target='#fig_6'>3</ns0:ref>). These infectious individuals will infect more susceptible folks before they all recover/die, even though, on average, each is expected to recover/die before they infect a susceptible individual (r < 1 after this point). The <ns0:ref type='figure'>5</ns0:ref> confirms that a significantly greater fraction of the population will be infected by the disease if we let it run its course than would have needed vaccination to achieve herd immunity. However, the most obvious benefit of the path S −−→ R over S −−→ I −−→ R is avoidance of disease-induced suffering and, possibly, death.</ns0:p><ns0:formula xml:id='formula_20'>comparison of v = 1 − R −1 0 and [R] ∞ in Fig.</ns0:formula></ns0:div> <ns0:div><ns0:head n='2.2.9'>Conclusion: R 0 is a useful tool</ns0:head><ns0:p>The dimensionless basic reproduction number R 0 is a property of an infectious disease within a population. The average frequency of contacts of an individual in the population, the transmissibility of the disease, and the average duration of infectiousness are all embedded in R 0 . In the SIR model, R 0 influences whether or not the disease invades the population, the initial exponential growth rate of infectious folks, how many are infected over the course of the epidemic, the peak prevalence of infectious folks, and how many must be vaccinated to achieve herd immunity.</ns0:p></ns0:div> <ns0:div><ns0:head>8/15</ns0:head><ns0:p>PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2020:07:51040:1:1:NEW 22 Aug 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:p></ns0:div> <ns0:div><ns0:head n='2.3'>Extensions to the SIR model</ns0:head><ns0:p>The is maintained at a non-zero value). See Fig. <ns0:ref type='figure' target='#fig_11'>6</ns0:ref>(a) (bottom). At an endemic equilibrium, the replacement number is maintained at one so that [I](t) is neither increasing nor decreasing. <ns0:ref type='bibr' target='#b36'>(Hethcote, 2000;</ns0:ref><ns0:ref type='bibr' target='#b13'>Bjornstad et al., 2020b;</ns0:ref><ns0:ref type='bibr' target='#b21'>Earn, 2008)</ns0:ref> Time-varying parameters. A time-varying transmission rate constant β = β (t) can model (voluntary or policy-induced) changes in behavior during an epidemic that affect (i) the average frequency of contacts of an individual in the population or (ii) the transmissibility of the disease.</ns0:p><ns0:p>Examples include staying home, social distancing, and taking hygiene measures. <ns0:ref type='bibr' target='#b25'>(Fenichel et al., 2011;</ns0:ref><ns0:ref type='bibr' target='#b55'>Reluga, 2010;</ns0:ref><ns0:ref type='bibr' target='#b12'>Bjornstad et al., 2020a;</ns0:ref><ns0:ref type='bibr' target='#b14'>Bootsma and Ferguson, 2007)</ns0:ref>. A periodic β (t)</ns0:p><ns0:p>can model seasonality of an infectious disease <ns0:ref type='bibr' target='#b30'>(Fisman, 2007;</ns0:ref><ns0:ref type='bibr' target='#b8'>Aron and Schwartz, 1984)</ns0:ref>. A time-varying recovery rate constant γ = γ(t) can model changes in the average time period of infectiousness, e.g., a reduction of γ −1 by administering a drug to infected patients <ns0:ref type='bibr' target='#b12'>(Bjornstad et al., 2020a)</ns0:ref>. Evolution of the pathogen could also cause β and γ to change with time <ns0:ref type='bibr' target='#b42'>(Lion and Metz, 2018)</ns0:ref>. Reducing the transmission rate constant β and/or the average time period of infectiousness γ −1 reduces the replacement number r (see eqn. 4); if r is reduced below one, the prevalence of infectious folks will decrease. Analogously, in chemical kinetics, time-varying reaction rate constants arise when the temperature in the reactor changes with time <ns0:ref type='bibr' target='#b31'>(Fogler, 2010)</ns0:ref>.</ns0:p><ns0:p>Stochasticity. As opposed to the deterministic differential eqns. 1-3, we can introduce randomness into the SIR model to account for the stochastic and uncertain nature of human interaction and disease transmission <ns0:ref type='bibr' target='#b2'>(Allen, 2008;</ns0:ref><ns0:ref type='bibr' target='#b18'>Britton, 2010;</ns0:ref><ns0:ref type='bibr' target='#b3'>Allen, 2017)</ns0:ref>. Stochastic epidemic models aim to describe the probabilistic distribution of outcomes, e.g., the distributions of [R] ∞ and max t [I](t) <ns0:ref type='bibr' target='#b2'>(Allen, 2008;</ns0:ref><ns0:ref type='bibr' target='#b18'>Britton, 2010)</ns0:ref>. Stochasticity can be particularly important to model for small populations and in the early stage of an epidemic, when there are small numbers of infectious individuals <ns0:ref type='bibr' target='#b18'>(Britton, 2010)</ns0:ref>. Analogously, when modeling chemical reaction dynamics, if the reactants are not abundant, such as in a biological cell, discreteness and stochasticity can be important to model <ns0:ref type='bibr' target='#b23'>(Erban et al., 2007;</ns0:ref><ns0:ref type='bibr' target='#b33'>Gillespie et al., 2013)</ns0:ref>. See Fig. <ns0:ref type='figure' target='#fig_11'>6</ns0:ref>(b) for an example of a stochastic simulation of the SIR model.</ns0:p><ns0:p>More realistic probabilistic distributions of the infectious time period. The probabilistic implication of first-order decay in eqn. 3 for a single infectious individual is that their time period of infectiousness is an exponentially distributed random variable with mean γ −1 <ns0:ref type='bibr' target='#b15'>(Brauer, 2008)</ns0:ref>.</ns0:p><ns0:p>More realistic probabilistic distributions of the time period of infectiousness can be built into the SIR model <ns0:ref type='bibr' target='#b37'>(Hethcote and Tudor, 1980;</ns0:ref><ns0:ref type='bibr' target='#b43'>Lloyd, 2001)</ns0:ref>. In the realm of biochemical kinetics,</ns0:p></ns0:div> <ns0:div><ns0:head>9/15</ns0:head><ns0:p>PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2020:07:51040:1:1:NEW 22 Aug 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science Manuscript to be reviewed</ns0:p><ns0:formula xml:id='formula_21'>S I R β[S][I] γ[I] µ µ[S] µ[I] µ[R] 0.4 0.6 0.8 1.0 [S](t) 0.0 0.1 0.2 0.3 0.4 [I](t) β(μ + γ) −1 = 2 μ(μ + γ) −1 = 0.</ns0:formula></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science several steps involving transcription factors, cofactors, chromatin remodeling, etc. <ns0:ref type='bibr' target='#b51'>(Pedraza and Paulsson, 2008;</ns0:ref><ns0:ref type='bibr' target='#b35'>Haberle and Stark, 2018)</ns0:ref>.</ns0:p><ns0:p>Additional compartments. We can introduce additional compartments and further divide the population to (i) account for heterogeneity in the population, (ii) model public health interventions, and (iii) include other characteristics of the disease. Members of different compartments can then have different contact patterns, transmissibilities, and/or recovery times. For example, the SEQIJR model <ns0:ref type='bibr' target='#b34'>(Gumel et al., 2004)</ns0:ref> introduces three additional compartments: Exposed, Quarantined, and Isolated (J). Usually, the E compartment is included to model the latent period of a disease; exposed individuals have been exposed to and infected by the disease but are not yet infectious <ns0:ref type='bibr' target='#b15'>(Brauer, 2008;</ns0:ref><ns0:ref type='bibr' target='#b46'>Martcheva, 2015b)</ns0:ref>. The Q and J compartments are included to model the control measures of quarantining exposed individuals and isolating infectious individuals, respectively. Members of the Q and J compartments contact other members of the population with reduced frequencies, compared to those in the E and I compartments, respectively <ns0:ref type='bibr' target='#b15'>(Brauer, 2008)</ns0:ref>. See Fig. <ns0:ref type='figure' target='#fig_11'>6</ns0:ref> Notably, a more precise definition of R 0 is needed if compartments are introduced to account for heterogeneity in the population <ns0:ref type='bibr' target='#b20'>(Diekmann et al., 1990)</ns0:ref>.</ns0:p><ns0:p>Spatial heterogeneity. We can model spatial heterogeneity of an epidemic, i.e., spatiallydependent [S], [I], and [R], by treating space as discrete (Van den Driessche, 2008) or continuous <ns0:ref type='bibr' target='#b45'>(Martcheva, 2015a)</ns0:ref>. Modeling the spatial movement of susceptible and infectious individuals in a continuous space as a diffusive process results in reaction-diffusion equations <ns0:ref type='bibr' target='#b45'>(Martcheva, 2015a)</ns0:ref>, also commonly used in the chemical sciences. Compartmental, epidemic models of metapopulations allow travel between spatially segregated regions and resemble models of multiple reactors connected with pipes that permit flow between them (Van den Driessche, 2008; <ns0:ref type='bibr' target='#b6'>Arino et al., 2005;</ns0:ref><ns0:ref type='bibr' target='#b9'>Balcan et al., 2010)</ns0:ref>.</ns0:p><ns0:p>More detail/structure in the contact patterns. Network epidemiological models allow us to study how structure and heterogeneity in the contact patterns within a population impact the dynamics of an epidemic <ns0:ref type='bibr' target='#b41'>(Kiss et al., 2017;</ns0:ref><ns0:ref type='bibr' target='#b10'>Bansal et al., 2007;</ns0:ref><ns0:ref type='bibr' target='#b38'>Keeling and Eames, 2005;</ns0:ref><ns0:ref type='bibr' target='#b50'>Newman, 2002)</ns0:ref>. In a typical contact network model, each individual in a population is represented by a node, and each interaction/contact between a pair of individuals is represented by an edge.</ns0:p><ns0:p>The state of an SIR network at a given time is specified by the disease status (S, I, or R) of each node. The disease can be transmitted along an edge if one of the nodes is infectious and the other is susceptible. <ns0:ref type='bibr'>(Kiss et al., 2017) See Fig. 6(d)</ns0:ref>.</ns0:p><ns0:p>The dynamics of disease transmission on a contact network depend significantly on the structure of the network, e.g., on its degree distribution, node clustering, and correlations between the degrees of connected nodes <ns0:ref type='bibr' target='#b10'>(Bansal et al., 2007)</ns0:ref>. Intuitively, the dynamics of an SIR model on large, static, random k-regular contact networks (a random network where each node has k neighbors) closely approximate those of eqns. 1-3, which assume homogeneous mixing <ns0:ref type='bibr' target='#b10'>(Bansal et al., 2007)</ns0:ref>. In contrast, the dynamics of SIR models on highly heterogeneous contact networks, such as those with heavy-tailed degree distributions that capture 'superspreaders' <ns0:ref type='bibr' target='#b44'>(Lloyd-Smith et al., 2005;</ns0:ref><ns0:ref type='bibr' target='#b48'>Meyers et al., 2005)</ns0:ref>, deviate significantly from eqns. 1-3.</ns0:p><ns0:p>Interestingly, directed networks can model asymmetric contacts that can only transmit the disease one-way, such as with donated blood transfusions <ns0:ref type='bibr' target='#b38'>(Keeling and Eames, 2005;</ns0:ref><ns0:ref type='bibr' target='#b1'>Allard et al., 2020)</ns0:ref>.</ns0:p><ns0:p>A contact network model such as in Fig. <ns0:ref type='figure' target='#fig_11'>6</ns0:ref>(d) only implicitly accounts for spatial heterogeneity. To more explicitly account for spatial heterogeneity, contact networks can be encoded as a dynamic, bipartite graph with two classes of nodes: individuals and locations. Edges then connect individuals with locations, and disease transmission can occur between people that are in the same location. <ns0:ref type='bibr' target='#b24'>(Eubank et al., 2004)</ns0:ref> Other agent-based models are fully spatially explicit and track the location of individuals as they move between households, schools, and workplaces <ns0:ref type='bibr'>(Ferguson et al., 2005)</ns0:ref>. Notably, to construct such contact network models, very detailed data is required <ns0:ref type='bibr' target='#b0'>(Ajelli et al., 2010)</ns0:ref>.</ns0:p><ns0:p>Vectors that transmit the pathogen from host to host. Some infectious diseases are primarily transmitted from one host to another host by living vectors that can acquire and carry the infectious agent. For example, mosquitoes can acquire an infectious agent (e.g., the virus that causes dengue fever or the parasite that causes malaria) from feeding on the blood of an infected human, then transmit the infectious agent to another, susceptible human when feeding on their blood. SIR-like models of diseases transmitted by vectors include an incidence term</ns0:p><ns0:formula xml:id='formula_22'>β [S][I v ],</ns0:formula><ns0:p>where [I v ] is the concentration of infectious vectors and β includes the frequency that the vector bites hosts and the probability of transmission conditioned upon a bite. <ns0:ref type='bibr' target='#b47'>(Martcheva, 2015c;</ns0:ref><ns0:ref type='bibr' target='#b59'>Smith et al., 2012)</ns0:ref> </ns0:p></ns0:div> <ns0:div><ns0:head n='3'>CONCLUSIONS</ns0:head><ns0:p>Mathematical models of the dynamics of disease transmission are used to forecast epidemics and assess mitigation strategies. We provided an exposition on the classic SIR dynamic model of disease transmission and highlighted the analogy between disease transmission and an autocatalytic reaction with catalyst deactivation. This analogy links together the fields of chemical kinetic and epidemic modeling to enable knowledge exchange between the two research communities.</ns0:p><ns0:p>Moreover, the analogy could be the basis for an engaging, experience-based approach to learning chemical kinetics in the classroom (Sucre-Rosales et al., 2020) and illustrate how concepts in one field can transfer, aid understanding, and generate insights to/in another field.</ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head>Figure 1 .</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1. The SIR model. The boxes represent the set of Susceptible, Infectious, and Removed individuals. The arrows represent flow and are annotated with per capita flow rates.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>Let [S](t), [I](t), and [R](t) be the fraction of the population that is susceptible, infectious, and removed, respectively, at time t. Considering a large population, we treat [S], [I], and [R] as continuous variables. So, [S], [I], [R] ∈ [0, 1].</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head /><ns0:label /><ns0:figDesc>, is then β [S][I] (per capita). A symmetric, bilinear function of [S] and [I], intuitively, the incidence rate doubles if [I] ([S]) doubles while [S] ([I]) is fixed. The second-order transmission rate constant β > 0 is the product of the average frequency of contacts of an individual in the population and the transmissibility of the disease (the probability of transmission conditioned upon contact).</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head>Figure 2 .</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2. Numerical approximation (Rackauckas and Nie, 2017) of the solution to the SIR model in eqns. 1-3 with initial conditions in eqns. 6-8.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head>Fig. 2</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Fig. 2 shows the numerical solution to eqns. 1-3 with initial conditions in eqns. 6-8 for R 0 = 2 and [I] 0 = 10 −5 . Initially, the concentration of infectious folks, [I], grows approximately exponentially (see Fig. S1 for a comparison of [I](t) with eqn. 9). The concentration of susceptible folks, [S],</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head>Figure 3 .</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Figure 3. The trajectory of the solution to the SIR model, with initial conditions in eqns 6-8, in the ([S], [I]) phase plane, given by eqn. 10. The trajectory is colored according to time. The initial conditions, peak prevalence of infectious folks, and final conditions are marked. Since [S](t) + [I](t) ≤ 1, points in the gray region are not feasible.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_7'><ns0:head /><ns0:label /><ns0:figDesc>1-2 in the ([S], [I]) phase plane. Dividing eqn. 2 by eqn. 1 takes us into the phase plane by giving a differential equation with a d[I] d[S] derivative, with the view of [I] as a function of [S].</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_8'><ns0:head>Figure 4 .</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Figure 4. Final size and peak prevalence of an SIR epidemic. As a function of R 0 , (a) the fraction of the population that will be infected over the course of the epidemic,[R] ∞ = 1 − [S] ∞ , with[S] ∞ obtained from eqn. 11, (b) the fraction of the population that is infectious at peak prevalence, max t [I](t), via eqn. 12, and (c) the (non-dimensional) time to reach peak prevalence, arg max t α[I](t), computed by a grid search using the numerical solution to the SIR model. All correspond to initial conditions in eqns. 6-8.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_10'><ns0:head /><ns0:label /><ns0:figDesc>SIR model is a very simple epidemic model, but we can extend it to model other epidemiological factors and prevention/control measures by introducing: Births, deaths, and loss of immunity. To model infectious disease transmission over longer time scales, we can modify the SIR model to account for births and deaths not caused by the disease by allowing flow into the S compartment and flow out of all compartments, respectively. See Fig. 6(a) (top). While eqns. 1-3 resemble a dynamic model of a closed batch reactor carrying out rxns. 1 and 2, the modified SIR model with demographics resembles a dynamic model of a continuously stirred tank reactor (Fogler, 2010): births are represented by a feed stream of pure S flowing into the reactor, while deaths are represented by an effluent stream. In addition, as opposed to assuming recovery from infection confers permanent immunity, we can model temporary immunity by introducing an R −−→ S reaction to represent loss of immunity. Because births and the loss of immunity continuously add to the susceptible pool, [I](t) in the SIR model with births, deaths, and loss of immunity can exhibit damped oscillations that settle on an endemic equilibrium, where the disease remains in the population indefinitely (i.e., [I]</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_11'><ns0:head>Figure 6 .</ns0:head><ns0:label>6</ns0:label><ns0:figDesc>Figure 6. Extensions to the SIR model. (a) Births and natural deaths (balanced, with rate constant µ) are introduced to the SIR model through the flows in/out of the compartments denoted by the dashed arrows (top). With births and deaths, the trajectory of the solution in the phase plane (bottom) shows [I](t) can exhibit damped oscillations that settle on an endemic equilibrium (µ(µ + γ) −1 unrealistically large to clearly see lim t→∞ [I](t) > 0). (Hethcote, 2000) (b) A simulation of the (stochastic) SIR continuous-time Markov chain model (Allen, 2017) for a small population of 50 individuals, with one initially infectious. The state variables S, I, and R are discrete counts of individuals in the compartments. (c) Introducing additional compartments. e.g., Exposed individuals have been exposed to and infected by the disease, but cannot yet transmit the disease, due to a latent period. Owing to control measures, quarantined (Q) and isolated (J) individuals make contacts with a reduced frequency. (d) Contact network models. (top) Nodes represent individuals, and each is in the S, I, or R state. Edges, along which disease transmission can occur, represent contacts. (bottom) The state changes of nodes in an SIR contact network model<ns0:ref type='bibr' target='#b41'>(Kiss et al., 2017)</ns0:ref>.</ns0:figDesc></ns0:figure> <ns0:figure type='table' xml:id='tab_1'><ns0:head /><ns0:label /><ns0:figDesc>(c). As another example, we can partition the infectious compartment into two distinct categories of infectious folks: symptomatic and asymptomatic<ns0:ref type='bibr' target='#b64'>(Vivas-Barber et al., 2014)</ns0:ref>, which can have different frequencies of contacts, transmissibilities, and recovery rates. Finally, to account for different mixing patterns among different age groups<ns0:ref type='bibr' target='#b15'>(Brauer, 2008)</ns0:ref>, age-structured compartmental models partition the S and I compartments into age cohorts. Notably, introducing additional compartments to the SIR model is much like introducing additional reactive chemical species into a chemical kinetics model. Additional compartments complicate the derivation of the basic reproduction number R 0 from the model (van den Driessche, 2017). Still, the peak prevalence of infectious folks and final size of the epidemic increase with R 0 in most models (Van den Driessche and Watmough, 2008).</ns0:figDesc><ns0:table /></ns0:figure> <ns0:note place='foot' n='3'>/15 PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2020:07:51040:1:1:NEW 22 Aug 2020)Manuscript to be reviewed</ns0:note> <ns0:note place='foot' n='11'>/15 PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2020:07:51040:1:1:NEW 22 Aug 2020)Manuscript to be reviewed</ns0:note> </ns0:body> "	"Dear Prof. Margraf and reviewers, Thank you for reviewing my article and providing me with feedback to improve it. I am excited to publish in Peer J Physical Chemistry because of its modern, open access publishing model and commenting functionality. Below is my point-by-point response to the reviews. Improvements since submission: ● Fig. 4c shows the time to peak prevalence as a function of R0. ● Fig. 6d shows the reaction scheme in contact network models to clarify the dynamics of an SIR epidemic on a contact network. ● I remade all plots with a more mature plot theme and printed the relevant parameter settings inside the plot windows. ● Stimulated by the reviewer comments, I found additional interesting analogies between some of the extended SIR models and phenomena in the chemical sciences. ● Adopted the “removed” nomenclature for the R compartment, instead of “recovered”, which better clarifies that the R compartment includes both folks that recovered from the disease and folks that died from the disease. ● Clearer sentence structure in many places. ● Reordered the discussion of the extensions of the SIR model according to increasing complexity. Cory M. Simon Assistant Professor School of Chemical, Biological, and Environmental Engineering Oregon State University w: simonensemble.github.io e: Cory.Simon@oregonstate.edu Editor comments (Johannes Margraf) MINOR REVISIONS Please particularly take into account the comments of reviewer 1 regarding the discussion of chemical analogies to extended SIR models. Also, all subsection titles should be capitalized. I capitalized all subsection titles and addressed the comments of reviewer 1 (see below). Reviewer 1 (Anonymous) Basic reporting no comment Experimental design The manuscript examines the analogy between differential equations and their solutions in chemical kinetics and epidemiological models. This goal is accomplished satisfactorily. The discussion could be further improved by giving engineering/chemical kinetics analogues to the extensions of the SIR model, if such analogues exist. Perhaps quarantine in the SEQIJR model could be related to the presence of an absorber material in a reactor? I am unsure how time-varying parameters and births, death, and loss of immunity could be related to the chemical kinetics/engineering, but perhaps the author has an idea. Thank you for the good suggestion! In the section on extensions of the SIR model, I found chemical kinetic analogies for all extensions, with the exception of contact network models and models of vector-bourne diseases, for which I could not identify any strong analogies. The new analogies I identified, motivated by your feedback, are below. Thanks, I am particularly happy that you encouraged me to think more about the SIR model with demographics, since this has a strong analogy with a CSTR! Introducing additional compartments “Introducing additional compartments to the SIR model is much like introducing additional reactive chemical species into a chemical kinetics model.” More realistic probabilistic distributions of the infectious time period “In the realm of biochemical kinetics, such non-exponential waiting times could arise e.g. between gene transcription events, owing to several steps involving transcription factors, cofactors, chromatin remodeling, etc. [].” Time-varying parameters “Analogously, in chemical kinetics, time-varying reaction rate constants arise when the temperature in the reactor changes with time [].” Births and deaths “While eqns. 1-3 resemble a dynamic model of a closed batch reactor carrying out rxns. 1 and 2, the modified SIR model with demographics in Fig. 6a resembles a dynamic model of a continuously stirred tank reactor []: births are represented by a feed stream of pure S flowing into the reactor, while deaths are represented by an effluent stream.” Validity of the findings no comment Comments for the Author I found the analogy between kinetic and epidemiological models very unexpected, but also very interesting. The manuscript presents this analogy in a very instructive way and I think it is a valuable read for anyone involved in physical chemistry in some way. Thank you for the praise! Reviewer 2 (Anonymous) Basic reporting This paper offers a pedagogical introduction to the Susceptible-Infectious-Recovered (SIR) epidemic model for readers familiar with chemical kinetic models. By drawing analogies between chemical kinetic and compartmental epidemic models, the author aims to foster the exchange of ideas between disparate research communities. The paper is well-organized, clearly written, and technically accurate. I would be happy to share a published version with my colleagues to use in lectures for senior undergraduate chemical engineering students. Moreover, the highlighted extensions of the simple SIR model may serve to stimulate research professionals in the area of chemical kinetics to seek opportunities to apply their expertise in “new” domains. Thank you for the praise! Yes, I hope my article will: (i) spread ideas from mathematical epidemiology into the chemical kinetic modeling community, (ii) motivate chemical kinetic modelers to contribute to developing epidemic models, and (iii) provide an engaging learning experience for undergraduate students in a chemical reaction engineering course. Experimental design This paper is a review of prior work. Validity of the findings The presentation of the SIR model is clear and accurate. "	Here is a paper. Please give your review comments after reading it.
475	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>We present a method for the automatic determination of transition states (TSs) that is based on Grimme's RMSD-PP semiempirical tight binding reaction path method (J. Chem.</ns0:p><ns0:p>Theory Comput. 2019, 15, 2847-2862), where the maximum energy structure along the path serves as an initial guess for DFT TS searches. The method is tested on 100 elementary reactions and located a total of 89 TSs correctly. Of the 11 remaining reactions, nine are shown not to be elementary reactions after all and for one of the two true failures the problem is shown to be the semiempirical tight binding model itself.</ns0:p><ns0:p>Furthermore, we show that the GFN2-xTB RMSD-PP barrier is a good approximation for the corresponding DFT barrier for reactions with DFT barrier heights up to about 30 kcal/mol. Thus, GFN2-xTB RMSD-PP barrier heights, which can be estimated at the cost of a single energy minimisation, can be used to quickly identify reactions with low barriers, although it will also produce some false positives.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>INTRODUCTION</ns0:head><ns0:p>The computational determination of chemical reaction networks <ns0:ref type='bibr'>[1;2;3;4;5;6]</ns0:ref> requires that the estimation of barrier heights and/or location of transition states (TSs) be automated. Many methods for automated barrier height estimation and TS location have been proposed. <ns0:ref type='bibr'>[7;8;9;10;11;12;13;2;14]</ns0:ref> However, the computational demand of these methods are significantly higher than for locating minima.</ns0:p><ns0:p>Recently, Grimme <ns0:ref type='bibr' target='#b14'>[15]</ns0:ref> presented a method (root mean square deviation-push-pull or RMSD-PP) for the rapid estimation of reaction paths based on a semiempirical tight-binding model (GFN2-xTB <ns0:ref type='bibr'>[16;17]</ns0:ref> ).</ns0:p><ns0:p>The predicted path can be used in a barrier estimate and the maximum energy structure as a TS guess in more expensive methods. Here, the performance of both are tested. This method is attractive to use when screening large amounts of reactions, as it is not much more expensive than a geometry optimization and the GFN2-xTB method has been parameterised for the entire periodic table up to Z = 86. However, for it to be practically useful it needs to work in an automated framework. Furthermore, we investigate whether the RMSD-PP reaction path can be used to distinguish reactions that have high and low barriers at the DFT level. If so, the RMSD-PP method could be used to increase the efficiency of the high throughput determination of reaction networks, where one is usually interested in relatively low-energy barriers.</ns0:p><ns0:p>The paper is organized as follows. First, the automated procedure for locating transition states is presented. Then, the method is tested on 100 elementary reactions suggested by Zimmerman <ns0:ref type='bibr'>[13;18]</ns0:ref> . Next we test whether the DFT barrier heights can be estimated using the GFN2-xTB RMSD-PP reaction path and test some commonly used methods for validating transitions states. Finally, we summarize our conclusions.</ns0:p><ns0:p>PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2020:07:50973:1:1:CHECK 1 Sep 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:p></ns0:div> <ns0:div><ns0:head>METHOD</ns0:head><ns0:p>The idea behind the RMSD-PP method is to add a Gaussian biasing potential to the electronic energy (E el tot ) 'pushing' the molecule away from the reactant structure and a Gaussian biasing potential 'pulling' the molecule towards the product structure.</ns0:p><ns0:formula xml:id='formula_0'>E tot = E el tot + k push e −α∆ 2 r + k pull e −α∆ 2 p (1)</ns0:formula><ns0:p>Here k push > 0 and k pull < 0, and ∆ r and ∆ p are the RMSDs between the current structure and the reactant and product, respectively. A geometry optimisation with this energy function is performed starting with the reactant structure and the geometry of each optimisation step is saved, re-optimised with three steps without the biasing potentials, and E el tot recorded. All these structures (typically 30-200) and the corresponding energies represent the reaction path and the associated computational cost thus corresponds to that of a geometry optimisation. Representative timings are shown in Table <ns0:ref type='table'>S1</ns0:ref>.</ns0:p><ns0:p>Figure <ns0:ref type='figure'>1</ns0:ref> shows a flowchart of the automated procedure for locating transition states (TSs). The reactant and product structures with same atomic ordering are required as input. The procedure starts with an RMSD-PP path search run with respective k pull and k push values of -0.02 and 0.01 Hartrees (E h ) and an α of 0.6 Bohr −1 (1/a 0 ) (parameter set 1, Table <ns0:ref type='table'>S2</ns0:ref>). In addition to this run, two additional runs are performed where the k pull and k push values are multiplied by 1.5 and 2.25. A run is deemed successful if the root mean square deviation (RMSD) of the end structure compared to the product structure is less than 0.3 Bohr and the reaction path with the smallest absolute values of k pull and k push is selected. If the reaction does not complete, the setup for the path search is changed: the last structure of the run is saved and used as product structure in the next run while the product structure is used as reactant structure (trial 2, parameter set 1, Table <ns0:ref type='table'>S2</ns0:ref>). The same procedure is then repeated for trials 3-5 (Table <ns0:ref type='table'>S2</ns0:ref>) until completion is achieved. If all five attempts fail, then the entire procedure is repeated with an electronic temperature of 6000 K (increased from 300 K). If the reaction again fails to complete then the method is deemed to fail for the reaction, although we did not observe this for the reactions considered in this paper.</ns0:p><ns0:p>We also test a slightly different parameter set (parameter set 2, Table <ns0:ref type='table'>S2</ns0:ref>), where k push is lowered to 0.008 E h for the first try.</ns0:p><ns0:p>Once the reaction has completed and the path found, the maximum energy structure along the path is extracted along with the two neighbouring structures. A linear interpolation (10 points from maximum energy structure to both neighbours) is performed and the interpolated structures are subjected to single point energy calculations using both Density Functional Theory (DFT) and GFN2-xTB. All DFT calculations are performed with the Gaussian 16 program <ns0:ref type='bibr' target='#b20'>[19]</ns0:ref> . The maximum GFN2-xTB energy along the interpolated path is used to estimate the GFN2-xTB barrier (orange part of the flow chart, Figure <ns0:ref type='figure'>1</ns0:ref>). The maximum energy structure based on DFT calculations is used as initial guess for the TS structure in a DFT TS search using the Berny optimization algorithm <ns0:ref type='bibr' target='#b22'>[20]</ns0:ref> [opt=(calcall, ts, noeigen)]. Whether the correct TS is found is evaluated based on an intrinsic reaction coordinate (IRC <ns0:ref type='bibr' target='#b23'>[21]</ns0:ref> ) path search in both forward and reverse direction from the found TS. From the endpoint structures of the IRC, the adjacency matrices are extracted. The adjacency matrix for an N atom system is an N × N matrix with 1 on the off-diagonal elements linking atoms that are bonded and 0 if the atoms are not bonded. The structures are converted from coordinates to adjacency matrix using xyz2mol. <ns0:ref type='bibr' target='#b24'>[22]</ns0:ref> The assignment of bond/no bond is done using the xyz2mol program based on a simple extended Hückel theory (EHT) calculation and the Mulliken overlap population between each pair of atoms as implemented in RDKit <ns0:ref type='bibr' target='#b25'>[23]</ns0:ref> . The adjacency matrices for the endpoints of the IRC are compared with the adjacency matrices for the intended reactant and product structures to determine if a TS for the intended reaction is found. If the adjacency matrices of the IRC endpoint structures do not match those of the input reactant and product structures it may be due to the IRC not having completed as the IRC calculations often terminate before converging to reactant/product structures. Thus, the endpoints of the IRC are geometry optimized, and these structures checked by the same procedure. If either sets of structures (based on adjacency matrices) match the input structures, the TS for the given reaction is concluded to have been found and the search procedure terminated.</ns0:p><ns0:p>If the IRC did not result in a path connecting the input reactant and product, a constrained optimization on the TS guess, obtained as the maximum energy structure of the interpolated structures, is performed.</ns0:p><ns0:p>The bond constraints are set up automatically by considering the difference in adjacency matrices of input Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science reactant and product structure, resulting in a set of bonds being formed/broken during the reaction. only connectivity changes are considered, meaning that, e.g., going from a double bond to a single bond is not considered bond breaking. The length of the set of bonds are fixed to the values in the guess structure from the interpolation, and the remaining structure relaxed. The new TS guess is taken through the same procedure with TS optimization, IRC and check. If the TS is still not found, the entire procedure is repeated but using an electronic temperature of 6000 K in the RMSD-PP reaction path step.</ns0:p></ns0:div> <ns0:div><ns0:head>Dataset</ns0:head><ns0:p>To test the TS localizer protocol, a preexisting data set from the literature is chosen to avoid bias in the choice of reactions studied. The data set used by Zimmerman to test his double-ended growing string method (GSM), consisting of 105 elementary reactions is used <ns0:ref type='bibr'>[18;13]</ns0:ref> . Only reactions of neutral molecules and reactions where bond breaking/formation take place are included (i.e. excluding conformational changes). Thus, the test set consists of 100 elementary reactions including both simple and complicated reactions with between 1 and 6 bond changes (Table <ns0:ref type='table'>S3</ns0:ref>). To be able to use the TSs located by Zimmerman, the same level of theory for the DFT part of the procedure is used: UB3LYP/6-31G <ns0:ref type='bibr'>[24;25;26;27;28]</ns0:ref> . This level of theory lacks dispersion corrections, which are included in GFN2-xTB. This issue is unlikely to have any impact for the small compounds used in this study. However, for large compounds this method should be used in conjunction with dispersion corrected DFT.</ns0:p><ns0:p>All reactant and product structures were reoptimised using GFN2-xTB to verify that the structures have corresponding minima on the GFN2-xTB potential energy surface. This is the case for all reactions but reaction 16, as discussed further below. The DFT geometries for the reactant and product are used as input for the procedure described above.</ns0:p></ns0:div> <ns0:div><ns0:head>Approximate TS validation procedures</ns0:head><ns0:p>A popular approach in automated TS procedures is to either skip the IRC step and use alternative validation procedures for the TS or first screen the TS with alternative validation procedures before doing the IRC in an effort to save computational time <ns0:ref type='bibr'>[29;30;13]</ns0:ref> . Though the TS validation here is based on the IRC path and whether it connects the reactant and product, some of these alternative approaches are also tested. In particular, the TS vetting requirements suggested by Jacobson et al. <ns0:ref type='bibr' target='#b31'>[29]</ns0:ref> are tested. The three requirements are: 1) There should be exactly 1 imaginary frequency of the Hessian, 2) at least one of the active bonds (bonds being broken or formed during the reaction) should have an intermediate length, and 3) that the eigenvector corresponding to the imaginary frequency should have motion along at least one of the active bond stretching modes. We use the same cutoff values for when a bond length is considered intermediate and when it is considered that the eigenvector has motion along a bond stretching mode as in the original article, that is: A bond length r ij between atom i and j is considered intermediate if</ns0:p><ns0:formula xml:id='formula_1'>1.2 ≤ r ij r cov i + r cov j ≤ 1.7<ns0:label>(2)</ns0:label></ns0:formula><ns0:p>where r cov i is the covalent radius of atom i <ns0:ref type='bibr' target='#b25'>[23]</ns0:ref> . The eigenvector corresponding to the imaginary frequency, v v v TS is considered to move along the stretching mode of bond i, v v v stretch i i i (unit vector), if the absolute value of the scalar projection of v v v TS on v v v stretch i i i is larger than 0.33:</ns0:p><ns0:formula xml:id='formula_2'>\|v v v stretch i i i • • • v v v TS \|≥0.33<ns0:label>(3)</ns0:label></ns0:formula></ns0:div> <ns0:div><ns0:head>RESULTS AND DISCUSSION</ns0:head></ns0:div> <ns0:div><ns0:head>Success rate</ns0:head><ns0:p>For each of the 100 reactions, the procedure is run three times with two different but similar parameter strategies for the GFN2-xTB path calculations (Table <ns0:ref type='table'>S2</ns0:ref>) for a total of 6 runs. The reason for running three times per parameter set is that the RMSD-PP procedure includes a random 'initial distortion parameter' which can lead to slightly different reactions paths for each run. Manuscript to be reviewed If that fails to find the TS, a constraint optimization is done and the TS optimization tried again. Finally, for the failed searches, the entire procedure is run at a higher electronic temperature (HT) of 6000 K. Run 1-3 is done with parameter set 1 and Run 4-6 done with parameter set 2 (SI).</ns0:p><ns0:note type='other'>Chemistry Journals</ns0:note><ns0:p>Figure <ns0:ref type='figure' target='#fig_2'>2</ns0:ref> shows the distribution of success rates for each of the 6 runs. Run 1-3 are with the same parameters (parameter set 1 in Table <ns0:ref type='table'>S2</ns0:ref>) and run 4-6 are with the same parameters (parameter set 2 in Table <ns0:ref type='table'>S2</ns0:ref>). The parameter sets are almost identical, the only difference is that the first run in parameter set 2 is initiated with a smaller push strength. The total number of successes is quite similar within the 6 runs (ranging between 81 and 83 TSs located) and the majority of the TSs are located using the guess structure from the RMSD-PP path directly. Combining all TSs located during the 6 runs, a total of 89 TSs are found. For the first parameter set (run 1-3) 85 TSs are located and for the second parameter set (run 4-6) 88 of the TSs are located. It is possible, that exploring a larger part of the parameter space allows localization of the last reactions.</ns0:p><ns0:p>For the reactions not located by the procedure (reactions 6, 10, 11, 16, 20, 35, 54, 68, 84, 90, and 96), the TS structures proposed by Zimmerman were further analysed. However, they were first put through the same IRC validation procedure (with and without reoptimization of the TS). Only two of the remaining 11 reactions (reactions 16 and 84) went to minima corresponding to the proposed reactant and product structures, while the majority of the 9 reactions found an intermediate minimum structure</ns0:p><ns0:p>along the way (Table <ns0:ref type='table'>S4</ns0:ref>), indicating that the reaction (at least for UB3LYP/6-31G) is not an elementary reaction. The 9 reactions are not used in the following analysis, where the data set is now reduced to 91 reactions (89 of which the procedure managed to locate a TS for).</ns0:p><ns0:p>The two reactions, for which the TS search was unsuccessful, are shown in Figure <ns0:ref type='figure' target='#fig_3'>3</ns0:ref>. The product in reaction 16 was the only structure that reacted when optimized with GFN2-xTB. After optimization the product became NH 3 + BH 3 + NH 2 BH 2 and the product is thus not stable on the GFN2-xTB potential energy surface, which can affect the path optimization and thus the TS guess. During the DFT TS optimization the TS guess structure instead goes to the TS of reaction 9 (Table <ns0:ref type='table'>S3</ns0:ref>), which has a ≈ 8 kcal/mol lower barrier than reaction 16. The other reaction not found, reaction 84, is a simple reaction and it is not clear why the TS of this reaction would be difficult to locate. Instead the TS of the reaction in Figure <ns0:ref type='figure'>4</ns0:ref> is found every time. Comparing the found TS with the true TS (Figure <ns0:ref type='figure'>5</ns0:ref>) shows that the TSs are quite similar. The important difference seems to be the orientation of the methylene group in the middle.</ns0:p></ns0:div> <ns0:div><ns0:head>5/25</ns0:head><ns0:p>PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2020:07:50973:1:1:CHECK 1 Sep 2020)</ns0:p><ns0:p>Manuscript to be reviewed A taken from the first run, coordinates for B from SI of the work of Zimmerman <ns0:ref type='bibr' target='#b12'>[13]</ns0:ref> .</ns0:p><ns0:note type='other'>Chemistry Journals</ns0:note></ns0:div> <ns0:div><ns0:head>Comparison of xTB barrier estimates and DFT barriers 154</ns0:head><ns0:p>In this section we test whether the RMSD-PP reaction path can be used to distinguish reactions that have Part A, C and E of Figure <ns0:ref type='figure'>S1</ns0:ref> show the correlation between the barrier estimated with GFN2-xTB and that calculated by DFT for the first parameter set (run 1-3). For each point is indicated the pull strength (color) and the push strength (size). Reactions, where the search was unsuccessful are labelled with red edges. Similarly, part B, D and F of Figure <ns0:ref type='figure'>S1</ns0:ref> show the GFN2-xTB barrier estimate vs. DFT barriers for the three runs with parameter set 2 (run 4-6). As one would expect, higher pull and push values are needed for higher barriers. The mean absolute error (MAE) is between 14.9 and 19.2 kcal/mol for all six runs, and there is a wide spread of values and several outliers. So, generally speaking, the GFN2-xTB barrier from the RMSD-PP reaction path is a poor estimate of DFT barrier heights. However, in many reaction network studies the goal is to identify reactions that proceed at measurable rates at room temperature, which translates into barrier heights of no more than 30 kcal/mol. The correlation between xTB and DFT is considerably better for these reactions. Figure <ns0:ref type='figure'>6A</ns0:ref> shows GFN2-xTB barrier estimates for all 91 reactions for run 1, while Figure <ns0:ref type='figure'>6B</ns0:ref> includes only reactions with a DFT barrier of less than 30 kcal/mol. For these 7 reactions the MAE is lowered to 5.3 kcal/mol (the MAE is 5.5 kcal/mol for the 7 reactions when calculated including all 6 runs). Reactions where the GFN2-xTB barrier is less than 40 kcal/mol, includes all seven reactions with DFT barriers less than 30 kcal/mol, in addition to 14-20 false positives (14, 17, 20, 14, 17, and 15 false positives for runs 1-6) where the DFT barrier is higher than 30 kcal/mol. If one excludes points where the absolute pull values are higher than 0.03 then the number of Manuscript to be reviewed false positives drops to 11-14 (12, 11, 14, 11, 12 and 11 false positives for runs 1-6).</ns0:p><ns0:p>Recomputing the barriers using DFT single point calculations (Figures <ns0:ref type='figure' target='#fig_2'>7 and S2</ns0:ref>) leads to a slight better correlation for the higher barriers but a worse correlation for the lower barriers (MAE=17.8 kcal/mol including all 6 runs), so the GFN2-xTB barriers are actually more useful.</ns0:p></ns0:div> <ns0:div><ns0:head>TS validation procedure</ns0:head><ns0:p>Here we test the performance of the validation procedures described in the Methods section: An effective validation procedure should discard as many wrong TSs as possible while not removing true transition states. The requirement, that the found transition state should have exactly 1 imaginary frequency is fulfilled for all 83 found TSs, but is also fulfilled for all but 1 (TS optimization failed) of the wrong transition states. Though the requirement can be applied without fear of throwing away true TSs, it is not very effective in filtering out wrong TSs. The requirement, that the TS structure should have at least one of the active bonds at an intermediate distance is fulfilled for 77 out of 83 true transition states and not fulfilled for three out of eight wrong transition states. Thus, applying this validation test to the transition state structures would have resulted in six correct TSs being filtered out. The last validation test, that the displacement vector of the imaginary frequency should be along at least one of the active bonds given the cutoff value above, is not fulfilled for nine of the transition states confirmed to be true by an IRC. Requiring all three validation tests to be fulfilled would have resulted in 13 of the 83 true transition states to have been filtered out. Four out of eight of the wrong transition states would also have been filtered out, but one needs to be very careful when applying these alternative validation tests, considering whether the saved computational time is worth more than the wrongly rejected transition states.</ns0:p><ns0:p>and products by a geometry optimisation using an energy function augmented by two Gaussian biasing potentials, one 'pushes' the structure away from the reactant and the other 'pulls' the structure towards the product. Our method starts with a series of RMSD-PP calculations with increasingly larger push and pull strengths until reaction completion. The additional structures near the highest point on the reaction path are generated by interpolation and used for DFT single points and the highest energy structure is then used as an initial guess for a TS search. Upon convergence the TS is tested by an IRC calculation and if the TS is found to be incorrect then the initial guess structure is reoptimised with key bond lengths constrained and used as an initial guess for a new TS search. If that fails, the entire procedure is repeated but using an electronic temperature of 6000K for the RMSD-PP calculations.</ns0:p><ns0:p>The method is tested on 100 elementary reactions used previously by Zimmerman and co-workers (Table <ns0:ref type='table'>S3</ns0:ref>). <ns0:ref type='bibr'>[18;13]</ns0:ref> For each of the 100 reactions, the procedure is run three times with two different but similar parameter strategies for the GFN2-xTB path calculations (Table <ns0:ref type='table'>S1</ns0:ref>) for a total of 6 runs.</ns0:p><ns0:p>Combining all TSs located during the six runs, a total of 89 TSs are found. Only two of the remaining 11 reactions (reactions 16 and 84) went to minima corresponding to the proposed reactant and product structures, while the majority of the 9 reactions found an intermediate minimum structure along the way, indicating that the reaction (at least for UB3LYP/6-31G) is not an elementary reaction. Thus our method failed for only two reactions (Figure <ns0:ref type='figure' target='#fig_3'>3</ns0:ref>), where for one of them the product is not a stable structure on the GFN2-xTB potential energy surface.</ns0:p><ns0:p>Furthermore, we show that the RMSD-PP barrier is a good approximation for the corresponding DFT barrier for reactions with DFT barrier heights up to about 30 kcal/mol. Thus, RMSD-PP barrier heights, which can be computed at the cost of a single energy minimisation, can be used to quickly identify reactions with low barriers, although it will also produce some false positives.</ns0:p><ns0:p>Finally, we show that various tests of whether the correct TSs have been found, produce several false positives and false negatives and should be used with care.</ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head>Figure 1 ./ 25 PeerJ</ns0:head><ns0:label>125</ns0:label><ns0:figDesc>Figure 1. Flowchart describing the automated workflow implemented. Orange steps depend solely on GFN2-xTB calculations, while purple steps rely on DFT calculations</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head>4 / 25 PeerJ</ns0:head><ns0:label>425</ns0:label><ns0:figDesc>Phy. Chem. reviewing PDF \| (PCHEM-2020:07:50973:1:1:CHECK 1 Sep 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>Figure 2 .</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2. Distribution of the successful TSs localized for each of the 6 runs. The GFN2-xTB TS guess structure is first used directly (without optimization) in a UB3LYP/6-31G TS optimization. If that fails to find the TS, a constraint optimization is done and the TS optimization tried again. Finally, for the failed searches, the entire procedure is run at a higher electronic temperature (HT) of 6000 K. Run 1-3 is done with parameter set 1 and Run 4-6 done with parameter set 2 (SI).</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head>Figure 3 .</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Figure 3. The two reactions not found by the procedure. Bonds broken in the reaction are indicated in red, bonds formed in blue.</ns0:figDesc><ns0:graphic coords='7,152.07,63.78,392.89,161.61' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head>Figure 4 .Figure 5 .</ns0:head><ns0:label>45</ns0:label><ns0:figDesc>Figure 4. The reaction of the TS located when searching for the TS for reaction 84. Bonds broken in the reaction are indicated in red, bonds formed in blue.</ns0:figDesc><ns0:graphic coords='7,214.11,274.27,268.81,80.78' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head>155</ns0:head><ns0:label /><ns0:figDesc>high and low barriers at the DFT level. If so, the RMSD-PP method could be used in the high throughput 156 determination of reaction networks, where one is usually interested in relatively low-energy barriers. The</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head>Figure 6 .Figure 7 .</ns0:head><ns0:label>67</ns0:label><ns0:figDesc>Figure 6. Barrier estimate from GFN2-xTB compared to DFT (UB3LYP/6-31G**) barriers for the first run shown in Figure 2. k pull and k push values are given in Hartree per atom. For each point is indicated the pull strength (color) and the push strength (size). Reactions where the search was unsuccessful are labelled with red edges. Part A of the figure shows estimates for all (91) reactions while part B shows barrier estimates for reactions with a barrier of less than 30 kcal/mol.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_7'><ns0:head>7 / 25 PeerJ</ns0:head><ns0:label>725</ns0:label><ns0:figDesc>Phy. Chem. reviewing PDF \| (PCHEM-2020:07:50973:1:1:CHECK 1 Sep 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_8'><ns0:head>1 ) exactly 1 imaginary</ns0:head><ns0:label>11</ns0:label><ns0:figDesc>Figure8Bfor the failed transition states of run 1.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_9'><ns0:head>1 i m f r e q l e n g t h o f a c t i v e b o n d m o v e m e n t a l o n g a c t i v e b o nl e n g t h o f a c t i v e b o n d m o v e m e n t a l o n g a c t i v e b o nFigure 8 .</ns0:head><ns0:label>18</ns0:label><ns0:figDesc>Figure 8. Test of different TS validation methods for the (A) True TSs; and (B) wrong TSs of run 1.</ns0:figDesc></ns0:figure> <ns0:note place='foot'>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:note> </ns0:body> "	"Reviewer 1 Basic reporting A) The article is well written, and mostly, citations to the directly related works are given. In the introduction, one should add a citation to H.B. Schlegel’s WIREs article (DOI: 10.1002/wcms.34), which gives a good overview over different TS optimization techniques. Furthermore, some of the TS optimization techniques mentioned therein might be worth citing as well – for sure the one that has been used in the DFT re-optimization using Gaussian should be cited here (not just the Gaussian suite itself). We have added these references B) The introduction itself could be longer in the sense that the importance for high throughput barrier calculations could be highlighted (kinetic model generation for example). Something like this is written at the bottom of p.5, (below “Comparison of xTB barrier estimates and DFT barriers”) and could be also given in the Introduction. We have added the following paragraph to the introduction “Furthermore, we investigate whether the RMSD-PP reaction path can be used to distinguish reactions that have high and low barriers at the DFT level. If so, the RMSD-PP method could be used to increase the efficiency of the high throughput determination of reaction networks, where one is usually interested in relatively low-energy barriers.” C) The color codings should be explained in the figure captions of figures 3 & 4. We have explained the color coding in Figures 3 and 4 Minor: a) The abbreviation RMSD-PP is actually never introduced, i.e., that “PP” stands for push-pull. In the same context, I find “Grimme’s RMSD-PP semiempirical tight binding reaction path method” in the abstract too coarse, as there are two components here: The path search mechanism based on an atomic RMSD-based bias potential and the underlying electronic structure method, which has been the semiempirical GFN2-xTB method in Grimme’s work as well as this one. It would not harm to be more explicit, like “RMSD-PP in combination with the semiempirical tight-binding method…”, since we could, in principle, also combine RMSD-PP with DFT. We have defined the RMSD-PP acronym. While it is true that RMSD-PP can be used with DFT, we only test its utility with GFN2-xTB. Thus we feel that “We present a method for the automatic determination of transition states (TSs) that is based on Grimme’s RMSD-PP semiempirical tight binding reaction path method…” is most accurate and concise. 1 b) It says “GFN-xTB […] models” in the summary, but the paper reports exclusively GFN2-xTB. Were other methods tested? If not, I would just use GFN2-xTB here as well. This has been fixed. c) The x-axes in Figure 6 and 7 should be more explicit (“DFT barrier” or so). This has been fixed d) p.1: There is a period after “Grimme” in the introduction that needs to be removed. p.2: “haven” -> “having” p.4: “TS structures proposed by Zimmerman was further analysed”, change “was” to “were”. These have been fixed. Experimental design A) The “2-4 optimization steps without any bias” on p.1 need to be explained further. How many states are exactly taken or what is the criterion for 2 vs 4 steps. It could be emphasized that this is not a path optimization, but an unconstrained geometry optimization, is that right? We have given are more detailed description of the RMSD-PP method that answers these questions: The idea behind the RMSD-PP method is to add a Gaussian biasing potential to the electronic energy (Eel tot) ”pushing” the molecule away from the reactant structure and a Gaussian biasing potential ”pulling” the molecule towards the product structure. Here kpush > 0 and kpull < 0, and ∆r and ∆p are the RMSDs between the current structure and the reactant and product. respectively. A geometry optimisation with this energy function is performed starting with the reactant structure and the geometry of each optimisation step is saved, re-optimised with three steps without the biasing potentials, and Eel tot recorded. All these structures (typically 30-200) and the corresponding energies represent the reaction path and the associated computational cost thus corresponds to that of a geometry optimisation. Representative timings are shown in Figure S5. B) Is the interpolation (p.2) done in Cartesian coordinates? Depending on how far the next neighbor is, this could be a bad choice, maybe not for the systems in this benchmark here, but for rotating substituents (phenyl for example). 2 The interpolation is done between the two structures on either side of the energy maximum. They are thus very similar in structure and unphysical artifacts such as those mentioned by the reviewer have not been observed. C) p.2: “the endpoints of the IRC are geometry optimized, and these structures checked by the same procedure”. This needs further clarification. Due to the computational cost, I am assuming that this means that the structures are all optimized at the GFN2-xTB level. If so: does one actually ever need the adjacency matrix scheme or couldn’t this reoptimization procedure replace that entirely? The optimisation is done at the DFT level - the same level used for the IRC. However, if the incorrect TS is found then these optimisations could lead to compounds that are different from the reactant and product structures, so the adjacency matrices must be compared. D) p.4: the “random ‘initial distortion parameter’” needs further explanation. Why can this not be done more deterministic? There is no information in the xtb manual about this, but the “initial distortion parameter” is mentioned in the output files. According to our email exchange with Prof Grimme: “a tiny change in the gradient alters the optimisation path and this is caused by the restart file.” Prof. Grimme informs us that the RMSD-PP method will be deterministic in a future version. We will adjust and retest our method accordingly when this version is released. Optional: E) Did the authors test their procedure in combination with GFN2-xTB and an implicit solvation model (like GBSA)? This may perform better for some of the charged species – this is just a suggestion. Our reference data from Zimmerman was in the gas phase, so we did not test the method with implicit solvent models. Validity of the findings A) This is a point that needs further elaboration: Figure 7 shows promising results (DFT singlepoints on the xTB geometries), but it is not discussed in the text at all. I don’t know why. Here it is also important to note what that means. Are xTB geometries used throughout or only for the TS estimate, it looks like the latter, since the deviations all indicate an overestimation of the barrier. Maybe using xTB geometries (for the minima) could help here. I think this deserves more attention as it might give access to fast screening (a DFT singlepoint is mostly bearable). We have added the following discussion of Figure 7: 3 Recomputing the barriers using DFT single point calculations (Figure 7) leads to a slight better correlation for the higher barriers but a worse correlation for the lower barriers, so the xTB barriers are actually more useful. Note that the barrier is computed from the highest energy among the 20 DFT single point calculations using the interpolated structure. So the cost is somewhat high for little, if any, gain in accuracy. The RMSD-PP calculations use the DFT structures of the reactant and product to force the TS guess to be as “DFT-like” as possible. It is possible that a separate RMSD-PP run with xTB optimised reactants and products give results in a better correlation and we plan to investigate this in a future study where we include more low-barrier examples (which is of prime interest). B) The discussion/methodology around Figure 8 could be elaborated a bit more. Particularly, how are “wrong TS” classified if they fulfill all the 3 criteria? For example, are they higher in energy than the ones presented by Zimmerman, or is there a possibility that they actually found a lower TS? The TS is wrong if the IRC does not lead to the correct (input) reactant and products.The wrong TS structures match those of Zimmerman but the IRCs do not lead to the reactants and products used by Zimmerman and us to find the IRCs (Zimmerman didn’t verify his TSs with IRCs). One of our findings is that the 3 criteria cannot be used in place of IRCs in such cases. C) The systems are mostly too small for this to be relevant, but the authors should mention that there exists an difference in the included physics: GFN2-xTB contains a London dispersion correction, while the DFT level does not. I understand that for consistency with Zimmerman, a dispersion-devoid model was used. But the authors should clarify that this could expectedly(!) lead to differences between both levels of theory for larger systems. We have added the following “To be able to use the TSs located by Zimmerman, the same level of theory for the DFT part of the procedure is used: UB3LYP/6-31G*. This level of theory lacks dispersion corrections, which are included in GFN2-xTB. This issue is unlikely to have any impact for the small compounds used in this study. However, for large compounds this method should be used in conjunction with dispersion corrected DFT.” D) Is the tool/workflow available to the community? As we state in SI: The code and data resulting from this study can be found here https://github.com/jensengroup/RMSD_PP_TS and https://sid.erda.dk/sharelink/EPvv68fOTp, respectively. 4 Optional: E) For the TS geometries in Figure 5: Did the authors try to compute S^2 expectation values with UB3LYP on both geometries? It would be interesting to know if the DFT geometry is spin-contaminated or if the xTB geometry has related issues (GFN2-xTB always favors low-spin configurations). Maybe a simple S^2 expectation value on both geometries shows something here (maybe not). We have not looked into this. Reviewer 2 (Marc van der Kamp) Basic reporting 2) Thank you for providing a repository with data at https://sid.erda.dk/sharelink/EPvv68fOTp. The Supporting Information statement indicates that these are “data resulting from this study”. However, I note that: a) Only reactant and product structures are provided, whereas (approximate) transition state structures are the key data resulting from this study. Would it be possible to include those? Including both the TS geometries obtained with the method and those optimized with DFT would allow other researchers to properly evaluate the work and results. We have added this data b) It is not described whether the provided structures are DFT geometries obtained from Zimmerman’s work, or geometries reoptimized with GFN2-xTB by the authors of this work. Please add this important meta-data. We have clarified this in the beginning of the SI with a description of each datafile provided. c) I note that only 77 reactant structures (r.xyz) and 91 product structures (*p.xyz) are included in the reactant_product_structures.tar.gz archive. They include also the reactant and product structures for the 5 reactions from Zimmerman’s set that were not used in this study (reactions 94, 95 and 102-105). This means that many reactant and product structures are missing. This has been fixed. 3) The results for the barrier height prediction are not very clearly presented/quantified. a) The authors state that “the RMSD-PP barrier is a good approximation for the corresponding DFT barrier for reactions with DFT barrier heights up to about 30 kcal/mol”, but they provide little/no direct quantification of this. They do state “The correlation between xTB and DFT is considerably better for these reactions.”, so please include a measure for this correlation here. 5 We have added the following “ Figure 6A shows GFN2-xTB barrier estimates for all 91 reactions for run 1, while Figure 6B includes only reactions with a DFT barrier of less than 30 kcal/mol. For these 7 reactions the MAE is lowered to 5.3 kcal/mol (the MAE is 5.5 kcal/mol for the 7 reactions when calculated including all 6 runs).“ b) The presentation of Figures 6 and 7 is not optimal. I assumed that the six plots are for the data obtained from the 6 runs, with Runs 1-3 (obtained with parameter set 1) shown on one side, and Runs 4-6 (obtained with parameter set 2) on the other. This is probably the case (as indicated in the text), but the Figure legend itself states: “Figures (a), (d), and (e) correspond to runs 1, 2, and 3”, and it is not clarified what panels b,c,f show. This would be made much clearer by adding labels of “Run 1” etc. above the individual panels. Further, axis titles should be clarified, e.g. “Barrier xTB” --> “Barrier estimate from GFN2-xTB” and “Barrier” --> “DFT Barrier”. Further, the different ranges on the y-axis make visual comparison between runs difficult. Could the same range be employed throughout (e.g. 0-200), perhaps with outliers beyond 200 kcal/mole indicated using arrows? The “old” Figures 6 and 7 have been moved to SI (now Figues S1 and S2) and the suggested changes have been made: Run 1-6 labels above panels, axis titles and constant y-range with outliers indicated using arrows. c) The authors could consider placing the full Figure 6 and 7 in the Supporting Information (in which case point b) above could be ignored), and replacing them with plots for 1 run only, where they can show two plots for each: the total set of reactions as well as the reactions with DFT barriers below 30 kcal/mole only. This may help to better deliver a main message of the paper (“the RMSD-PP barrier is a good approximation for the corresponding DFT barrier for reactions with DFT barrier heights up to about 30 kcal/mol”, but not/less so for those with higher barriers). This will have the additional benefit of making the labels better readable (as they are currently quite small). We have made the suggested changes 4) The presentation of the reactions is not very clear / may contain mistakes. a) In the main text, Figures 3 and 4 would be much improved by including standard (electron pushing) arrows to describe the reaction. At least, the following should be added to the legend: 'Bonds broken in the reaction are indicated in red, bonds formed in blue'. This has been added b) Table S2 (and deposited data file reactions.txt) contain several identical reactions, i.e. reactants and products are identical (e.g. 1 and 2; 4,5 and 6; 10, 11, 14). This needs to be 6 corrected. Further, the minus signs indicating atoms with formal negative charge are often difficult to spot. Can they be enlarged, or (ideally) be placed in circles (as in Figure 3)? Table S2 has been updated to include atom labelling indicating which bonds are broken/formed (a lot of the reactions have the same product and reactant pair but still represent different reactions). Furthermore charges are now placed in circles. Experimental design 5) Some further clarification/information on the methods could be added. a) I very much appreciate the short description of the RMSD-PP method, so that original paper(s) by Grimme describing the method do not have to be studied in detail. To clarify further how the method works, as relevant to the current manuscript, can you please describe: 1) How are points on the path defined (i.e. the 'distance' between them, and/or 2) how is it defined how many points there are on the full path? We have given are more detailed description of the RMSD-PP method that answers these questions: The idea behind the RMSD-PP method is to add a Gaussian biasing potential to the electronic energy (Eeltot) ”pushing” the molecule away from the reactant structure and a Gaussian biasing potential ”pulling” the molecule towards the product structure. Here kpush > 0 and kpull < 0, and ∆r and ∆p are the RMSDs between the current structure and the reactant and product. respectively. A geometry optimisation with this energy function is performed starting with the reactant structure and the geometry of each optimisation step is saved, re-optimised with three steps without the biasing potentials, and Eeltot recorded. All these structures (typically 30-200) and the corresponding energies represent the reaction path and the associated computational cost thus corresponds to that of a geometry optimisation. Representative timings are shown in Figure S5. b) Could there be a short indication/description of how the k_pull and k_push and alpha values used in the parameter sets were arrived at? Trial-and-error? Based on previous work? The values were chosen based on trial and error, starting from the values used in Grimme’s original publication. This has now been clarified in SI. Validity of the findings 7 1) The authors correctly describe their method as ‘fast’ in the title, however, exactly what this means is not described or reported clearly. a) Table S5 indicates (for just two reactions) the wall-time required for TS estimation by their procedure, which is impressive. However, no reference is made to this table in the main text, nor is any approximate timing given for TS estimation. I suggest that the authors include somewhere (perhaps even in the abstract), that TS estimation can be obtained “within a few minutes on a single CPU”. Where such a statement is added to the text, Table S5 can be referred to. Further, it would be better if the CPU used for the Table S5 timings is described (e.g. speed in GHz, perhaps also type). We have now referred to Table S5 as described above and specified the CPU in the table caption. b) In the abstract and summary, the authors only describe that “RMSD-PP barrier heights [..] can be estimated at the cost of a single energy minimisation”. What this means is not very well defined, and is certainly hard to interpret by reading the abstract only. With RMSD-PP barrier height, to the authors mean barrier height estimation/calculation after TS estimation, or the whole procedure to obtain the estimate TS? With “single energy minimisation”, do the authors refer to a single energy calculation or a single structure optimisation, or the procedure of 'start with the reactants, turn on push/pull potentials, minimize, and check, whether the system falls into products'? If energy calculation or optimisation, at what level of theory? This statement should be changed/clarified. We have now clarified that we refer to GFN2-xTB barrier heights Comments for the Author The github repository is well structured and the code is extensively commented, which is highly appreciated. A suggestion: currently, the implementation can only be used with SLURM job scheduler. It would be nice to refactor the implementation to allow running the scripts with other job schedulers or at least directly on a local machine. We only have access to the SLURM scheduler, so we cannot test other job schedulers. Finally, minor points regarding textual/language errors and small clarifications are annotated in the attached PDF. I would appreciate if the authors could address these too, to further improve the readability. These issues have been fixed. 8 "	Here is a paper. Please give your review comments after reading it.
476	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>The dihydroazulene/vinylheptafulvene (DHA/VHF) thermocouple is a promising candidate for thermal heat batteries that absorb and store solar energy as chemical energy without the need for insulation. However, in order to be viable the energy storage capacity and lifetime of the high energy form (i.e. the free energy barrier to the back reaction) of the canonical parent compound must be increased significantly to be of practical use. We use semiempirical quantum chemical methods, machine learning, and density functional theory to virtually screen over 230 billion substituted DHA molecules to identify promising candidates. We identify a molecule with a predicted energy density of 0.38 kJ/g, which is significantly larger than the 0.14 kJ/g computed for the parent compound. The free energy barrier to the back reaction is 11 kJ/mol higher than the parent compound, which should correspond to a half-life of about 10 days -4 months. This is considerably longer than the 3-39 hours (depending on solvent) observed for the parent compound and sufficiently long for many practical applications. Our paper makes two main important contributions: 1) a novel and generally applicable methodological approach that makes screening of huge libraries for properties involving chemical reactivity with modest computational resources, and 2) a clear demonstration that the storage capacity of the DHA/VHF thermocouple cannot be increased to >0.5 kJ/g by combining simple substituents.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>The Sun is the most abundant source of energy, but periods of supply do not always match periods of demand. Therefore finding solutions for storing solar energy is one of the major challenges for a sustainable society. One approach is to employ light-induced isomerization of photoactive molecules <ns0:ref type='bibr' target='#b0'>[1,</ns0:ref><ns0:ref type='bibr' target='#b1'>2]</ns0:ref> as exemplified by the DHA/VHF thermocouple in Figure <ns0:ref type='figure' target='#fig_0'>1</ns0:ref>. Upon irradiation, a molecule is converted to a high-energy photo-isomer and upon a certain stimulus, the high-energy isomer returns to the original molecule, and the excess energy is released as heat. This corresponds to a closed energy cycle of light-harvesting, energy storage and release, with no emission of CO 2 or other chemical products. Such systems are termed molecular solar-heat batteries. A suitable molecule 1) must absorb sunlight by converting to a higher energy form, 2) must absorb as much energy as possible (where ca 1 kJ/g is considered the practical maximum limit <ns0:ref type='bibr' target='#b2'>[3]</ns0:ref>, but 0.3 kJ/g has been considered a reasonable target for some applications <ns0:ref type='bibr' target='#b3'>[4]</ns0:ref>), and 3) must be stable in the high-energy form for days or weeks. Dihydroazulenes (DHAs) are one class of promising candidates for solar heat batteries. The parent system (shown in Figure <ns0:ref type='figure' target='#fig_0'>1</ns0:ref>) absorbs at the right wavelength with good quantum yield. However, the energy density is only 0.14 kJ/g and the half-life of VHF is only 3-39 hours (depending on solvent). <ns0:ref type='bibr' target='#b4'>[5]</ns0:ref> Removal of one cyano group increases the storage density to 0.25 kJ/g and the half-life to years. <ns0:ref type='bibr' target='#b5'>[6]</ns0:ref> Unfortunately, the back reaction could not be triggered without causing degradation. With both cyano groups present the storage density can be increased by up to 0.38 kJ/g but not without decreasing the half-life significantly. <ns0:ref type='bibr' target='#b6'>[7]</ns0:ref> The goal of this study is to identify substituted DHAs with both higher energy density and longer half life through high throughput virtually screening.</ns0:p><ns0:p>We consider 42 common substituents (including H) at seven different positions, as shown in Figure <ns0:ref type='figure' target='#fig_2'>2</ns0:ref>, resulting in more than 230 billion molecules (slightly less than 42 7 due to permutational symmetry). To our knowledge this compound library is the largest library considered thus far. For example, it is three orders of magnitude larger than the 'ultra-large' library of 170 million compounds used by Shoichet, Irwin, and coworkers. <ns0:ref type='bibr' target='#b8'>[8]</ns0:ref> While there has been a few virtual screening studies of thousands of reaction energies, <ns0:ref type='bibr' target='#b9'>[9]</ns0:ref> corresponding screening studies of barrier heights typically involve less than 100 molecules (e.g. <ns0:ref type='bibr' target='#b10'>[10]</ns0:ref>). Thus, screening barrier heights for billions or even thousands of molecules represents a fundamental challenge.</ns0:p><ns0:p>Figure <ns0:ref type='figure' target='#fig_2'>2</ns0:ref>: The substituents and positions considered in this study. Substitutions at position 8 is not considered, since removal of one cyano group has been shown to cause problems with the back reaction. The substituents are separated into electron withdrawing groups (EWG) and electron donating groups (EDG). There are a total of 42 different substituents counting hydrogen and phenyl resulting in more than 230 billion molecules (slightly less than 42 7 due to permutational symmetry). This paper is organized as follows. First we demonstrate the utility of semiempirical quantum mechanics (SQM) by screening all 35,588 singly and doubly substituted DHAs. Then we use a simple linear regression model and a gradient boosting decision tree (GBDT) trained on SQM data, to screen all 230 billion molecules (a flowchart of the exhaustive screening procedure is shown Figure <ns0:ref type='figure' target='#fig_1'>S1</ns0:ref>). Finally, the linear regression model is then used to demonstrate that the best molecules predicted by that model can be found efficiently using a genetic algorithm (GA), suggesting that a GA could be used to screen even larger chemical spaces, perhaps using SQM directly rather than machine learning.</ns0:p></ns0:div> <ns0:div><ns0:head>Results and Discussion</ns0:head><ns0:p>The goal of this study is to identify molecules with an energy storage density that is as high as possible and a half-life of that is at least as long as the parent compound (shown in Figure <ns0:ref type='figure' target='#fig_0'>1</ns0:ref>) and preferably longer. We note that, according to transition state theory, even a modest 10 kJ/mol increase in the activation free energy corresponds to a 56 fold increase in the half life, which is 3-39 hours for the parent compound (depending on solvent). Even high level ab initio calculations can easily give an error of ±10 kJ/mol for barrier heights, so molecules with large storage densities but computed barrier heights similar to the parent compound are potentially worth testing experimentally.</ns0:p></ns0:div> <ns0:div><ns0:head>Screening all singly and doubly substituted molecules</ns0:head><ns0:p>We start by screening all 35,588 singly and doubly substituted DHAs because they are most synthetically accessible and can be screened without machine learning models using semiempirical electronic structure methods (SQM). Figure <ns0:ref type='figure' target='#fig_6'>3</ns0:ref>(a) shows a plot of the SQM barriers plotted vs the storage densities for 32,623 singly and doubly substituted DHA/VHF couples. We want to select roughly 100 of the most promising molecules for further study with M06-2X/6-31G(d). As discussed in Supplementary Information, PM3 tends to overestimate the barrier relative to the parent molecule (the horizontal red line) so the barrier should be significantly higher than that. Similarly, GFN2-xTB tends to underestimate the reaction energy relative to the parent system (the vertical red line) and, hence, the energy storage density, so molecules with only somewhat higher storage density are potentially promising candidates. After some experimentation we found that cutoffs of 178 kJ/mol (15 kJ/mol higher than the parent compound) and 0.33 kJ/g leads to 109 molecules for further study using DFT (green box in Figure <ns0:ref type='figure' target='#fig_6'>3(a)</ns0:ref>). For all 109 molecules we optimize the lowest energy GFN2-xTB DHA, VHF, and PM3 TS structures with M06-2X/6-31G(d) and the results are shown in Figure <ns0:ref type='figure' target='#fig_6'>3(b)</ns0:ref>. As expected, the majority of the molecules have higher energy storage densities than the parent compound, but some have lower barriers with DFT. This indicates that by using PM3 energies, we are more likely to get false-positives than false-negatives. More false-positives will increase the number of redundant calculations; however, this is preferred over missing potentially excellent candidates.</ns0:p><ns0:p>From this set we choose ten molecules (the purple box in Figure <ns0:ref type='figure' target='#fig_6'>3</ns0:ref>(b), and Table <ns0:ref type='table'>S1</ns0:ref>) for more thorough examination by performing a systematic conformational search (see Computational Methodology section) and computing enthalpies and free energies for the lowest energy conformers using M06-2X/6-31G(d).</ns0:p><ns0:p>The resulting DFT storage densities and free energy barrier heights are shown in Table 1 for the five molecules with largest storage density. The best five are all predicted to have an almost two-fold increase in storage density (0.24-0.25 kJ/g) compared to the parent system. Of the five, all but one have predicted back reaction-barriers that are between 4.7 and 16.5 kJ/mol higher than the parent compound.</ns0:p><ns0:p>Figure <ns0:ref type='figure' target='#fig_6'>3</ns0:ref>(a) shows three molecules with storage densities of nearly 0.6 kJ/g that we initially discounted because they are likely to have low barriers to the back reaction. To ensure that this is indeed the case, we perform the same systematic conformational search as for the ten promising molecules. The results are summarized in Table <ns0:ref type='table'>2</ns0:ref>.</ns0:p><ns0:p>All three systems have an electron donating amino group at position 2 and an electron withdrawing group which allow for a hydrogen bond to the amino group in position 1. The three high energy density systems have very low back reaction barriers making them unsuitable for storage purposes. The amino group in position 2 have been shown to yield al a r g ei n c r e a s ei ns t o r a g ee n e r g y ,b u ta l s or e s u l t si nas i g n i fi c a n td e c r e a s eo ft h eb a c k reaction barrier <ns0:ref type='bibr' target='#b11'>[11]</ns0:ref>. The hydrogen bond between the amino group and the electron withdrawing group stabilizes the DHA system increasing the storage energy, also locks VHF in the s-cis-VHF conformer. This means that the most stable VHF conformer is structurally Figure <ns0:ref type='figure' target='#fig_6'>3</ns0:ref>: (a) Electronic barrier heights and storage energies computed for 32,623 singlyand doubly substituted molecules using SQM and '5 + 5n rot ' conformations as described in the Computational Methodology section. (b) Electronic barrier heights and storage energies computed for the 109 molecules in the green box in (a) computed using M06-2X/6-31G(d). The geometry optimizations are started from the SQM structures and the purple box highlights 10 molecules selected for further study (Table <ns0:ref type='table'>S1</ns0:ref>). The vertical and horizontal lines mark the storage energy and barrier height for the parent molecule illustrated in Figure <ns0:ref type='figure' target='#fig_0'>1</ns0:ref>.</ns0:p><ns0:p>very similar to the transition state structure, making the back reaction barrier very small.</ns0:p><ns0:p>To summarise, given the set of ligands shown in Figure <ns0:ref type='figure' target='#fig_2'>2</ns0:ref> the largest energy density one can hope to achieve with a singly or doubly substituted DHA molecules is about 0.5 kJ/g. But the half-life of the high energy states are much to short (sub-second) to be of practical use. The largest possible energy density for molecules with half-lives on the order of hours or greater is around 0.25 kJ/g.</ns0:p></ns0:div> <ns0:div><ns0:head>Screening all 230 billion molecules</ns0:head><ns0:p>To exhaustively screen the entire chemical space a fast estimation of the storage density and back-reaction barrier is needed. We represent each molecule as a 287 (7 x 41) bit vector, where each row <ns0:ref type='bibr' target='#b6'>(7)</ns0:ref> represents one of the seven open positions on the DHA motif, and each column (41) represents a non-hydrogen substituent. A more through description of the representation is shown in Figure <ns0:ref type='figure' target='#fig_2'>S2</ns0:ref>. Using this representation, we fit two linear regression models to SQM storage densities. One model predicts storage densities for molecules with ≤4 substituents and the other model for molecules with more than 4 substituents. We found that two different models are needed to get an acceptably low error (MAEs of 0.02 and 0.04 kJ/g, respectively). Each model is trained on ∼25,000 molecules and tested on another ∼25,000 molecules that represent a wide range of storage densities (Figure <ns0:ref type='figure' target='#fig_6'>S3</ns0:ref>). Figure <ns0:ref type='figure' target='#fig_3'>S4</ns0:ref> shows the number of times a ligand is found at a given position in the training set for the linear regression models. Though the distribution is somewhat uneven, all ligands are represented at all positions at least 40 times, so the training set is reasonably representative of the entire chemical space The RMSD values of the 1-4 substituent model and the 5-7 substituent model are 0.025 and 0.050 kJ/g, respectively (Figure <ns0:ref type='figure' target='#fig_4'>S5</ns0:ref>).</ns0:p><ns0:p>The simplicity of the linear ML-models means that they can be used to perform an exhaustive search of all 230 billion molecules in only 12 hours using a single CPU. During the exhaustive search, all molecules with predicted storage densities smaller than 0.30 kJ/g are discarded (Figure <ns0:ref type='figure' target='#fig_3'>4(a)</ns0:ref>). With the linear regression model, we reduce the chemical space of interest from 230 billion to roughly 421 million molecules. As seen from Table <ns0:ref type='table'>S2</ns0:ref>, molecules with 5-7 substituents greatly outnumber molecules with 1-4 substituents.</ns0:p><ns0:p>The 421 million molecules are still far too many to screen using SQM methods, and the results from the linear model of 5-7 molecules indicate some degree of non-linearity (which are the group that the majority of the remaining molecules belong to). Thus, to more accurately predict the storage densities of the remaining molecules, we train a new model using the gradient boosted tree method, LightGBM <ns0:ref type='bibr' target='#b12'>[12]</ns0:ref>. The LightGBM model is trained on new SQM data chosen among the remaining 421 million molecules. The new SQM data is chosen, such that the storage densities are evenly distributed between the minimum and maximum storage densities (0.30 kJ/g -0.57 kJ/g) as predicted by the linear regression model (Figure <ns0:ref type='figure' target='#fig_5'>S6</ns0:ref>). The LightGBM model is trained on 39,057 molecules and validated on 6,510 molecules, respectively. Finally, the new model is tested on 6,510 molecules and shown to have an RMSD value of 0.036 kJ/g (Figure <ns0:ref type='figure' target='#fig_4'>5a</ns0:ref>), lower than the 0.050 kJ/g RMSD for the linear regression model for molecules with 5-7 substituents. The LightGBM model is then used to predict storage densities of all 421 million molecules in approximately 10 hours.</ns0:p><ns0:p>Based on the storage densities predicted using the LightGBM model, we select 36,000 molecules and compute PM3 back reaction barrier heights to train and test an new Light-GBM model. There is a rough inverse correlation between storage densities and barriers to the back reaction, so the molecules are selected in such a way that all storage density ranges are represented (Figure <ns0:ref type='figure' target='#fig_5'>S6</ns0:ref>). It proved challenging to converge all 36,000 transition state calculations, therefore the barrier heights are estimated using adiabatic scans along with the breaking bond. A LightGBM model was trained, validated, and tested using a 83/7/10 split of the data and yielded a RMSD of 21.21 kJ/mol (Figure <ns0:ref type='figure' target='#fig_4'>5b</ns0:ref>). The model is used to predict the back reaction barriers of all the 421 million remaining molecules.</ns0:p><ns0:p>The LightGBM energies are then used to select molecules with a storage density of ≥0.40 kJ/g and a barrier height of ≥165 kJ/mol (the PM3 barrier for the parent system), of which there are 957,587. The highest storage density found in this set is 0.58 kJ/g so the main conclusion is that there are no molecules among the 230 billion molecules considered here with a storage density approaching 1 kJ/g. From these roughly 1 million molecules we need to select at most 50,000 molecules for SQM refinement. Figure <ns0:ref type='figure' target='#fig_5'>6</ns0:ref> shows an overview of the number of molecules with various ranges of barrier heights and storage densities. After much deliberation we chose two sets for SQM refinement. The first set is the 1969 molecules with barriers and storage densities of >165 kJ/mol and >0.50 kJ/g. Given the tendency of PM3 (and, hence, the LightGBM model) to overestimate the barrier height, these are almost certainly false positives but, given their high storage densities, we include them 'just in case' The second set is the 33,085 molecules with barriers and storage densities of >185 kJ/mol and >0.45 kJ/g (Figure <ns0:ref type='figure' target='#fig_3'>4</ns0:ref>(b)), which, given the higher barriers, are more likely to include true positives. The combined sets of molecules contain 34,241 unique molecules, and out of these it was possible to compute the SQM storage density for 24,388 molecules. The remaining molecules did either not converge, or the connectivity changed during the optimization, and are therefore unlikely to be good candidates. For the remaining 22,258 molecules with storage densities above 0.4 kJ/g, we performed an adiabatic scan with PM3 to rapidly estimate the back reaction barrier height. Of the 954 molecules the majority have back reaction barriers that are smaller than the parent molecule. However, we do find one molecule, shown in Table <ns0:ref type='table'>3</ns0:ref>, with a relatively high storage density and barrier height (Figures <ns0:ref type='figure' target='#fig_7'>4(d) and S7</ns0:ref>). The molecule is subjected to a more thorough DFT investigation, as described in the supporting information, and the resulting reaction enthalpy and Gibbs free activation energy is shown in Table <ns0:ref type='table'>3</ns0:ref>. The best thermal heat battery candidate is therefore 9 with a predicted energy density of 0.38 kJ/g and a barrier height of 130.0 kJ/mol. For comparison the energy density of the parent system (0.14 kJ/g) is almost three times lower, while the barrier height (119.1 kJ/mol) is 11 kJ/mol lower. According to TS theory, an 11 kJ/mol increase in barrier height increases the half life by a factor of 85, i.e. from 3-39 hours (depending on solvent) to about 10 days -4 months. The computed absorption spectrum of 9 is shown in Figure <ns0:ref type='figure' target='#fig_7'>7</ns0:ref>, together with that of the parent system. Compound 9 retains an absorption peak abound 350 nm, but the intensity is reduced by about a factor of three.</ns0:p></ns0:div> <ns0:div><ns0:head>Test of genetic algorithm search</ns0:head><ns0:p>While it is possible to exhaustively screen 42 substituents at seven positions, it will not be possible for future screening efforts that involve all nine positions and/or more than 42 substituents since the number of molecules will number in the trillions or more. We therefore test whether a genetic algorithm (GA) can find the best candidate in such a large data set efficiently, using the linear regression model since we know what the correct answer is. To screen for both high storage density and long half life, we train a linear regression model to predict the back reaction barrier height using same molecules we used to train the storage density model (Figure <ns0:ref type='figure'>S8</ns0:ref>). Since the model is only used to test the efficiency of GA searches, we trained one model (instead of two as is done for the storage density).</ns0:p><ns0:p>The best candidate in the search space is defined as the molecule with the largest storage density and a barrier height ≥180 kJ/mol. From the exhaustive search we know that this molecule is 10 shown in Table <ns0:ref type='table'>4</ns0:ref> with a storage density of 0.531 kJ/g. Table <ns0:ref type='table'>4</ns0:ref>: Structure and rediscovery p ercentage for the molecule with largest storage density (0.531 kJ/g) and barrier height (190.37 kJ/mol) above 180.0 kJ/mol as predicted by the linear regression models. The last columns gives the rediscovery percentage during 1,000 GA runs.</ns0:p></ns0:div> <ns0:div><ns0:head>Identifier</ns0:head><ns0:p>Structure Rediscovery % 10 74.0</ns0:p><ns0:p>For the genetic algorithm each gene is defined as a vector with seven numb ers (bases) ranging from 0 to 41, representing the seven possible positions and 42 possible substituents. The score for each gene is computed as score =min(∆E rxn ,t rxn )/t rxn +min(∆E ‡ ,t ‡ )/t ‡ <ns0:ref type='bibr' target='#b0'>(1)</ns0:ref> where t ‡ and t rxn are set to 180 kJ/mol and 1.0 kJ/g, respectively. <ns0:ref type='bibr' target='#b13'>[13]</ns0:ref> The GA search starts with an initial population of 100 randomly generated genes of singly substituted molecules, for which the scores are computed using the linear regression models. Pairs of genes are then selected with a probability proportional to their score (roulette selection) and mated, by choosing a random cut point between bases for two parent genes and recombining. After each mating a mutation is performed 20% of the time by randomly changing one of the bases. This process is repeated 100 times and the 100 best scoring genes before and after mating is selected as the new population and the process is repeated for 100 generations.</ns0:p><ns0:p>We p erform 1,000 separate GA searches and record the highest scoring molecule. 10 is found 74% of the time. Thus, one should be able to find the best molecule with 99% certainty by running only four GA searches, i.e. by testing at most 40,000 different molecules. For comparison, we calculated storage densities and back-reaction barriers for >100,000 and >70,000 molecules as part of training and testing the machine learning models and their predictions. Thus, it may be computationally more efficient to search the chemical space using GA combined with SQM calculations rather than developing ML models for the properties of interest.</ns0:p></ns0:div> <ns0:div><ns0:head>Computational Methodology Semiempirical Calculations</ns0:head><ns0:p>∆H rxn is approximated as the difference in electronic energy (∆E rxn )c o m p u t e du sing GFN2-xTB <ns0:ref type='bibr' target='#b14'>[14]</ns0:ref> while ∆G , ‡ is approximated as the difference in heat of formation (∆∆H ‡ f ) computed using PM3 <ns0:ref type='bibr' target='#b15'>[15]</ns0:ref> (collectively referred to as 'SQM'). GFN2-xTB is chosen due to its computational efficiency, while PM3 is chosen because it is available in both ORCA 4.0 <ns0:ref type='bibr' target='#b16'>[16]</ns0:ref> and Gaussian09 <ns0:ref type='bibr' target='#b17'>[17]</ns0:ref> (see below). For each DHA structure the VHF structure is automatically generated using RDKit. 5 + 5n rot random conformations (where n rot is the number of rotateable bonds in the molecule, see Figure <ns0:ref type='figure'>S9</ns0:ref> for benchmark) are generated using RDKit and optimized using GFN2-xTB (which are sufficient for screening purposes, illustrated in S10). Optimizations that result in discrepancies between the input and output connectivity are discarded. The lowest energy conformers of DHA and VHF are used to compute ∆E rxn . In some cases the conformational search is done systematically (see next subsection) at the SQM as described below.</ns0:p><ns0:p>To compute the energy barrier an adiabatic scan is performed (using ORCA) for the breaking CC bond, which is constrained to 12 values out to 3.5 Å starting from the DHA structure with the lowest energy. When screening all 230 billion molecules the highest energy structure and energy is used as an estimate for the transition state. However, for the single and double substituted case the highest energy structure is used as starting point for a transition state (TS) search using PM3 in Gaussian09 while computing the Hessian in each step. For the molecules where the TS search converged it is verified that the normal mode associated with the imaginary frequency lies along the reacting CC-bond. The low energy VHF GFN2-xTB conformer is reoptimized using PM3, and the PM3 barrier is computed. This TS connects DHA with with s-cis-VHF while the lowest energy VHF conformer is usually s-trans-VHF, so the implicit assumption is that s-cis-VHF and s-trans-VHF are in thermal equilibrium, i.e. that the barrier between the two VHF conformations is lower than the barrier between s-cis-VHF and DHA (Figure <ns0:ref type='figure' target='#fig_0'>1</ns0:ref>).</ns0:p><ns0:p>Figure <ns0:ref type='figure' target='#fig_1'>S11</ns0:ref> shows a comparison of barriers computed by locating the TS to barriers estimated by scans. There is a good correlation between the two barriers for medium sized barriers, but the scan tends to overestimate high barriers and underestimate low barriers. The use of scans to estimate barriers is this likely to lead to false positives, which are subsequently eliminated by DFT calculations, but is unlikely to lead to false negatives (i.e. we are unlikely to miss any promising candidates by using estimated barriers).</ns0:p></ns0:div> <ns0:div><ns0:head>DFT refinement</ns0:head><ns0:p>Select structures are investigated further at the M06-2X[18]/6-31G(d) <ns0:ref type='bibr' target='#b19'>[19]</ns0:ref> level of theory(using Gaussian09). This level of theory was chosen as a good compromise between computational efficiency and accuracy as judged by comparison to DLPNO-CCSD(T) and CCSD(T)-F12a calculations <ns0:ref type='bibr' target='#b20'>[20]</ns0:ref>. DFT is used in one of three ways. The first quick approach estimates DFT energies by reoptimizing the structure with the lowest SQM energy for DHA, VHF, and the TS and the electronic energy is used to compute the storage density and barrier. A benchmark investigation has been carried out by <ns0:ref type='bibr' target='#b11'>[11]</ns0:ref>. The second approach is a more thorough DFT searches, used to obtain the final DFT energies. In the second approach a systematic conformer search is performed using SQM where each rotateable bond is rotated by ±120 starting from the lowest energy structure found using the RDKit conformer generating algorithm. Each structure is energyminimized and in the case of the TSs the reacting bond is constrained. The minimized TS structure is then used as an initial guess for an unconstrained TS search. All unique conformers (conformers with Torsion Fingerprint Deviation <ns0:ref type='bibr' target='#b21'>[21]</ns0:ref> that are less than 0.001 are considered identical) are then reoptimized with M06-2X/6-31G(d) and the structures with the lowest free energy are used to compute the storage density and barrier.</ns0:p></ns0:div> <ns0:div><ns0:head>Machine learning models</ns0:head><ns0:p>We use both linear regression and a Gradient Boosted tree method (LightGBM) in this study. For the linear regression, a molecule is represented by positional seven-hot encoding, i.e. vector with 287 (41 × 7) binary elements, where each chunk of 41 is one-hot encoded representation of a non-H substituent at a particular position (Figure <ns0:ref type='figure' target='#fig_2'>S2</ns0:ref>. This representation was used to train three different machine learning (ML)-models using SQM data. Linear regression and kernel ridge regression as implemented in Scikit-learn and a gradient boosted tree method as implemented in LightGBM. We found very little difference in performance between regression and kernel ridge regression and use the former, simpler, model in this study.</ns0:p><ns0:p>The combined use of positional binary encoding (X)andlinearregressionamoun tsto an additive model</ns0:p><ns0:formula xml:id='formula_0'>∆E = w • X + b = 7 X i=1 w ij + b (2)</ns0:formula><ns0:p>where the regression coefficient w ij is the effect of placing substituent j on position i on either the reaction energy or barrier height and b is the corresponding value for the unsubstituted molecule.</ns0:p></ns0:div> <ns0:div><ns0:head>Summary and outlook</ns0:head><ns0:p>We virtually screen 42 different substituents (including hydrogen) and 7 p ossible substituent positions (Figure <ns0:ref type='figure' target='#fig_2'>2</ns0:ref>) of the dihydroazulene/vinylheptafulvene (DHA/VHF) thermal heat battery (Figure <ns0:ref type='figure' target='#fig_0'>1</ns0:ref>) for molecules with high storage density (∆H rxn /MW) and stability (∆G , ‡ ) The size of the chemical space is roughly 230 billion molecules. We start by screening all 35,588 singly and doubly substituted DHAs using semiempirical methods (SQM): GFN2-xTB for the storage density and PM3 for the barrier height of the back reaction. Compared to M06-2X/6-31G(d), PM3 tends to significantly overestimate the barrier relative to the reference compound, while GFN2-xTB tends somewhat underestimate the storage energy, but the methods are sufficiently accurate to identify promising molecules for further refinement (Figure <ns0:ref type='figure' target='#fig_1'>S10</ns0:ref>).</ns0:p><ns0:p>The storage density and back reaction barrier of all 35,588 singly-and doubly-substituted DHA molecules are evaluated using SQM and used to identify 109 molecules for further study using M06-2X/6-31G(d) (Figure <ns0:ref type='figure' target='#fig_6'>3(a)</ns0:ref>). The energy densities and barrier heights computed by reoptimising the lowest energy SQM-conformations are then used to select 10 molecules for further study using a thorough conformational search (Figure <ns0:ref type='figure' target='#fig_6'>3</ns0:ref>(b) and Table <ns0:ref type='table'>S1</ns0:ref>). Five of the molecules are predicted to have an almost two-fold increase in storage density (0.24-0.25 kJ/g) compared to the parent system and all but one of these have predicted back reaction-barriers that are between 4.7 and 16.5 kJ/mol higher than the parent compound.</ns0:p><ns0:p>In order to screen the entire chemical space we generate additional SQM-data for higher degrees of substitution and use it to fit linear regression models that reproduce the storage energies to within 0.017 and 0.038 kJ/g depending on degree of substitution (Figure <ns0:ref type='figure' target='#fig_4'>S5</ns0:ref>). These models are then used to estimate the storage density of all 230 billion molecules and the 421 million molecules with storage densities higher than 0.30 kJ/g are selected for further study (Figure <ns0:ref type='figure' target='#fig_3'>4(a)</ns0:ref>). Gradient boosted tree method (LightGBM) models for the storage density and back reaction barrier are trained on new SQM data chosen among the these 421 million molecules, with respective MAEs of 0.026 kJ/g and 16.4 kJ/mol. These models are used to predict the storage densities and barrier heights for all 421 million molecules, and 34,241 molecules with storage densities larger than 0.45 kJ/g and high barrier heights are chosen for further study (Figures <ns0:ref type='figure' target='#fig_5'>4(b) and 6</ns0:ref>). The highest storage density found in this set is 0.58 kJ/g so it is already clear at this point that there are no molecules among the 230 billion molecules considered here with a storage density approaching 1 kJ/g. The storage densities of the 34,241 molecules are computed using SQM and the barrier heights for the 22,258 molecules with SQM storage densities above 0.4 kJ/g are estimates using PM3.</ns0:p><ns0:p>Next, 1177 molecules with storage densities ≥0.54 kJ/g (Figure <ns0:ref type='figure' target='#fig_3'>4</ns0:ref>)) and barriers ≥185 kJ/mol are selected for further refinement using DFT. The lowest energy geometry of DHA and VHF is reoptimized using M06-2X/6-31G(d), which also used to estimate the barrier height using adiabatic scans. Of the 954 molecules for which the DFT calculations succeed, the majority have back reaction barriers that are smaller than the parent molecule. However, one molecule, shown in Table <ns0:ref type='table'>3</ns0:ref>, has a relatively high storage density and barrier height (Figures <ns0:ref type='figure' target='#fig_3'>4(d</ns0:ref>) and S7) and is subjected to a more thorough DFT investigation.</ns0:p><ns0:p>Our conclusion is that the best thermal heat battery candidate among the 230 billion is 9 (3) with a predicted energy density of 0.38 kJ/g and a barrier height of 130.0 kJ/mol. For comparison the energy density of the parent system (0.14 kJ/g) is almost three times lower, while the barrier height (119.1 kJ/mol) is 11 kJ/mol lower. According to TS theory, an 11 kJ/mol increase in barrier height increases the half life by a factor of 85, i.e. from 3-39 hours (depending on solvent) to about 10 days -4 months. The computed absorption spectrum of 9 is shown in Figure <ns0:ref type='figure' target='#fig_7'>7</ns0:ref>, together with that of the parent system. Compound 9 retains an absorption peak abound 350 nm, but the intensity is reduced by about a factor of three.</ns0:p><ns0:p>The main conclusion of our work is that it is unlikely that the storage density of DHA can be increased to a value above 0.5 kJ/g by substitution at positions 1-7 (at least without decreasing the barrier to back reaction). Yet, storage densities above 0.3 kJ/g of both molecules 9 and 10 may be sufficient for some applications when considering also their long storage times. There are, however, other drawbacks with these molecules. For example, their absorption profiles are not optimum since there is reduced absorption in the visible region. And maybe more problematic, these molecules have rather complicated substitution patterns. There are today synthetic protocols available for functionalizing selectively at positions 1, 2, 3, and 7 (and in part position 6) of DHA, <ns0:ref type='bibr' target='#b1'>[2]</ns0:ref> but so far these protocols have only been used to introduce substituents efficiently at maximum three positions (see SI for further synthetic considerations). So new synthetic protocols need to be developed for introducing substituents around the entire DHA core, and some suitable protecting groups need to be installed, for example to allow both amino and aldehyde functionalities in the same molecule. To avoid intermolecular reactions between such groups, it could be attractive to simply make 9 and 10 part of the monomeric repeat units of a polymer scaffold via the amine functionality. Organization of DHA units along a polymer may in fact possibly also enhance the energy density further as observed for some azobenzene-based materials. <ns0:ref type='bibr' target='#b22'>[22]</ns0:ref> A Other design strategies will also be pursued in future work, such as replacement of the cyano groups at position 1 or the introduction of non-carbon atoms in the DHA scaffold (heterocyclic structures).</ns0:p><ns0:p>To our knowledge this compound library is the largest library considered thus far and the first to include barrier heights as a screening parameter. Notably, we show that the substituent effects on barrier heights can be estimated using ML and a very simple representation with sufficient accuracy to be useful. Despite the huge library size the screen is carried out using comparatively modest computational resources by using SQM as an intermediate step between ML and DFT calculations. SQM allows for large data sets for ML models to be constructed in a matter of days and the pre-screening tens of thousands of candidates so that DFT calculations are only performed on the most promising candidates. This is important because the prediction of reliable reaction energies and barrier heights requires thorough conformational searches that require significant computational resources at the DFT level. The overall methodological approach outlined in this paper is, in principle, generally applicable to lead optimisation of properties involving chemical reactivity. However, the accuracy of the SQM predictions relative to DFT and the accuracy of the ML models must of course be tested for each new system and not be adequate in all cases.</ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head>Figure 1 :</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1: Schematic representation of the dihydroazulene/vinylheptafulvene (DHA/VHF) thermal heat battery. DHA is converted to VHF photochemically and the excess chemical energy (∆H rxn ) is released as heat when needed. The half life of VHF is determined by the free energy barrier of the back reaction to DHA. The molecule shown has been studied experimentally and is referred to as the 'parent' molecule.</ns0:figDesc><ns0:graphic coords='3,104.71,184.58,385.86,120.96' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head>Table 1 :</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>M06-2X/6-31G(d) predicted storage densities and back reaction barrier heights for the five molecules with largest storage densities among the molecules highlighted in Figures 3(b), based on the lowest free energy structures. The corresponding M06-2X/6-31G(d)-values for the parent molecule are 0.14 kJ/g and 119.1 kJ/mol, respectively.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>Table 2 :</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>M06-2X/6-31G(d) predicted storage densities and back reaction barrier heights for the three molecules with near 0.6 kJ/g storage density shown in Figures3(a), based on the lowest free energy structures. The corresponding M06-2X/6-31G(d)-values for the parent molecule are 0.14 kJ/g and 119.1 kJ/mol, respectively Identifier Structure ∆H rxn /M W ∆G , ‡ [kJ/g] [kJ/mol]</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head>Figure 4 :</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Figure 4: Distribution of storage densities for a) the linear regression model, b) the LightGBM regression model, c) GFN2-xTB, and d) DFT M06-2X/6-31G(d). The black arrow indicates the storage density for 9 and the dashed line the cut-off used to select molecules for further study. The distribution of a) and b) does not show all 421 million molecules, but only a subset of 1 million randomly chosen molecules.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head>Figure 5 :</ns0:head><ns0:label>5</ns0:label><ns0:figDesc>Figure 5: Illustration of the performance of the LightGBM models for the test sets. a) shows the actual storage density computed using GFN2-xTB with 5+5n rot conformers compared to the predicted storage density using the LightGBM. b) compares the back barrier energy found using an PM3 adiabatic scan along the breaking bond to the predicted LightGBM model.</ns0:figDesc><ns0:graphic coords='10,72.00,72.00,451.29,219.67' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head>Figure 6 :</ns0:head><ns0:label>6</ns0:label><ns0:figDesc>Figure 6: Number of molecules with barriers and energy densities above certain thresholds. The inset on the right shows the number of unique molecules for various combinations of subsets.</ns0:figDesc><ns0:graphic coords='10,72.00,385.82,451.26,172.74' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head>Table 3 :</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>M06-2X/6-31G(d) predicted storage densities and back reaction barrier heights for the two molecules, based on the lowest free energy structure. The corresponding M06-2X/6-31G(d)-values for the parent molecule are 0.14 kJ/g and 119.1 kJ/mol, respectively Identifier Structure ∆H rxn /M W ∆G , ‡ [kJ/g] [kJ/mol] is to select around 1500 molecules for DFT calculations. Through experimenting with cut-offs, we pick 1177 molecules with storage densities ≥0.54 kJ/g (Figure 4(c)) and barriers ≥185 kJ/mol. The ground state SQM structures and transition state guess for the chosen molecules are then reoptimized with DFT. The optimizations succeed for 954 molecules, while the remaining 223 molecules fail due to the location of a wrong transition state (202 molecules) or because the connectivity of the product molecule changed during the optimization (21 molecules). We select random 20 molecules with wrong transition states and the 21 molecules with wrong products and perform a 15 point adiabatic scan along with the breaking bond in steps of 0.1 Å, staring from DHA. None of the 41 molecules showed any indication of a high-energy transition state by visual inspection of the scans, which indicates that the back reaction barrier is very low. Thus, the 223 molecules are not investigated any further.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_7'><ns0:head>Figure 7 :</ns0:head><ns0:label>7</ns0:label><ns0:figDesc>Figure 7: Computed (CAM-B3LYP/6-311+G(d)//M06-2X/6-31G(d)) absorption spectra of compound 9 and the parent compound.</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='4,121.92,72.00,351.43,194.40' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='6,75.41,72.00,444.47,230.78' type='bitmap' /></ns0:figure> <ns0:note place='foot'>PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2020:11:55144:1:2:NEW 9 Dec 2020)Manuscript to be reviewed</ns0:note> <ns0:note place='foot'>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:note> </ns0:body> "	"Reviewer 1 (Kjell Jorner) The GitHub link is broken: https://github.com/jensengroup/dhashould be https://github.com/jensengroup/dha_htvs The link in the manuscript works fine for us. However, upon publication in PeerJ the link will be moved to the journal website so this should solve any problems. It is a bit confusing that compound 9 in Table 3 and compound 10 in Table 4 have storage energies and reaction barriers that are not comparable. As I understand it, the properties of 9 are predicted by the DFT calculations, while those of 10 are given by the linear model, but a quick look might lead to the belief that 10 is actually better than 9. We agree that it is confusing. We have moved the energy storage and barrier info in Table 4 into the table legend to make it less prominent as these values are not the main result of the study. Regarding the machine learning, it is not clear to me what is the result of the train, validation and test sets. I would assume that the data in Figure 5, Figure S6 and Figure S8 is for the test set, but this should be clarified in the figures or the captions. We have clarified that the plots are made using the test set. The molecules in Table 2 (6-7) are said to have low barriers for back reaction due to the hydrogen-bonding favoring the s-cis VHF form. Compound 10 has the same substitution pattern with an amine at position 2 and an aldehyde at position 1. Is compound 10 expected to have a similarly inflated reaction barrier as 6-7? In this case it should be highlighted as a limitation of the approach. This especially in light of the suggestion to replace the high-throughput virtual screening approach by the GA, which could get stuck in a false minimum. Replacing the virtual screening with GA would only be done if the reaction energies and barriers are computed using SQM. This would involve a conformational search which would locate the hydrogen bond in question. The conclusion that “The overall methodological approach outlined in this paper is generally applicable to lead optimisation of properties involving chemical reactivity.” is too strong and should be qualified. Not all reactivity problems will be amenable to semi-empirical methods, particularly if challenging quantum-mechanically or if there are strong solvent effects. Also, it’s not certain that similar machine learning models employed will be equally successful to other reactivity problems. We agree. We have added the following qualifications: “The overall methodological approach outlined in this paper is, in principle, generally applicable to lead optimisation of properties involving chemical reactivity. However, the accuracy of the SQM predictions relative to DFT and the accuracy of the ML models must of course be tested for each new system and not be adequate in all cases. ” The GA model finds compound 10 74% of the times, but it would be interesting to know the difference in predicted performance between 10 and the best molecule in the rest of the runs. If something nearly as good is found, that would increase the perceived usefulness of the GA even more. In runs where the global maximum is not found the GA search often gets stuck in other regions of chemical space. The reason is that the molecules with the highest scores are quite similar, so if the GA search encounters this area it easily finds the best score. If one wanted to find, say, the top 10 one would use a thresholded score modifier similar to that used for the barrier. Reviewer 2 (Anonymous) I would suggest that the authors please be more explicit in how is the process of vectorizing the molecules to be feed to the ML methods are. Or if they are using a standard algorithm such as Morgan fingerprints. We have added an example in SI (Figure S2). On synthetic accessibility, I’m aware that a quite small number of the possible combinatorial functionalizations could lead to synthetic viable molecules. I may suggest that, at least for the small screening library (the one tested using PM3 and TB methods), the authors could evaluate the synthetic complexity score (https://github.com/connorcoley/scscore) to have a complexity metric that can enrich the discussion of their results. It is not clear to use what is gained by that. All but one of these molecules (compound 9) do not have the desired properties, so why is the synthetic accessibility important? For compound 9 we do discuss the synthetic accessibility in SI. Finally, scores like that are designed to give a rough measure of relative synthetic accessibility for very different molecules. It is not clear that they say anything meaningful for molecules with a common core with different (relatively small) substituents. In Figure 3, the labels of electronic barriers and storage energies do not coincide as the same variables in the first paragraph of the Computational Methodology, please uniform the notation. We have changed the label of the y-axis in Figure 3a to ΔΔHf‡. The rest of the labels are correct. Reviewer 3 (Adrian Jinich) The computational pipeline introduced in this paper is very impressive and clearly well thought out and implemented. However, it is complex, with several steps involved, each using different subsets of data with different methods and models. Therefore perhaps a schematic diagram of the entire pipeline, either as a main or SI figure, would help guide the reader. We have included a flow chart in SI (Figure S1). In the manuscript, when the authors mention the number of substituents considered, they sometimes write “42” and other times “41” Please make this consistent. We found one instance of incorrect usage, which has now been fixed. We now consistently use “42 substituents” (counting hydrogen). The remaining occurrences of “41” refers to the 7-hot encoded bit vector where one counts from 0. Can the authors add a comment somewhere in the main text on the possibilities or outlooks for comparing the predictions to experimental data? The project hinges on the assumption that the quantum chemistry calculations are an accurate representation of the ground truth values for storage energies and reaction barriers. So additional comments on how those DFT calculations are expected to correlate with experimental data would be valuable. (More specifically: I have read the section “Synthetic considerations”. However, what I’m referring to is the possibility of connecting the predicted thermodynamic/kinetic parameters to a database of experimental data.) The enthalpy difference between DHA and VHF have not been measured experimentally, so it is not possible to do a comparison. The DFT method we chose is based on CCSD(T) benchmark calculations as mentioned in Computational Methodology. In the summary and outlook, pg. 15, the authors mention “For example, their absorption profiles are not optimum.” Can they add an extra sentence/phrase to explain why? The sentence now reads “For example, their absorption profiles are not optimum since there is reduced absorption in the visible region.” - In summary and outlook, pg. 14, third paragraph, there is a typo: “421 molecules” should be “421 million molecules”. This has been fixed. - Figure S6: The acronym “ML” appears in the figure legend, but the exact meaning of ML is not specified in the caption. Please fix this. This has been fixed - Figure S2: Fig. S2 (A) has green data points that are circled in black. However, no mention of what this means appears in the figure legend. I know this is mentioned in the SI text, but adding this to the figure legend might help with clarity. This has been fixed. "	Here is a paper. Please give your review comments after reading it.
477	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>A bio-based Silica/Calcium Carbonate (CS-SiO 2 /CaCO 3 ) nanocomposite was synthesized in this study using waste eggshells (ES) and rice husks (RH). The adsorbents (ESCaCO 3 , RHSiO 2 and, CS-SiO 2 /CaCO 3 ) characterized using XRD show crystallinity associated with the calcite and quartz phase. The FTIR of ESCaCO 3 shows the CO 3 -2 group of CaCO 3, while the spectra of RHSiO2 show majorly the siloxane bonds (Si-O-Si) in addition to the asymmetric and symmetric bending mode of SiO 2 . The spectra for Chitosan (CS) show peaks corresponding to the C=O vibration mode of amides, C-N stretching, and C-O stretching. The CS-SiO 2 /CaCO 3 nanocomposite shows the spectra pattern associated with ESCaCO 3 and RHSiO 2. The FESEM micrograph shows a near monodispersed and spherical CS-SiO 2 /CaCO 3 nanocomposite morphology, with an average size distribution of 32.15±6.20 nm. The corresponding EDX showed the representative peaks for Ca, C, Si, and O. The highest removal efficiency of phenol over the adsorbents was observed over CS-SiO 2 /CaCO 3 nanocomposite compared to other adsorbents. Adsorbing 84-89% of phenol in 60-90 min at a pH of 5.4, and a dose of 0.15 g in 20 ml of 25 mg/L phenol concentration. The result of the kinetic model shows the adsorption processes to be best described by pseudo-secondorder. The highest correlation coefficient (R 2 ) of 0.99 was observed in CS-SiO 2 /CaCO 3 nanocomposite, followed by RHSiO 2 and ESCaCO 3 . The result shows the equilibrium data for all the adsorbents fitting well to the Langmuir isotherm model. And follow the trend CS-SiO 2 /CaCO 3 > ESCaCO 3 >RHSiO 2 . The Langmuir equation and Freundlich model in this study show a higher correlation coefficient (R 2 =0</ns0:p><ns0:p>.9912 and 0.9905) for phenol adsorption onto CS-SiO 2 /CaCO 3 nanocomposite with a maximum adsorption capacity (q m ) of 14.06 mg/g compared to RHSiO 2 (10.64 mg/g) and ESCaCO 3 (10.33 mg/g). The results suggest good monolayer coverage on the adsorbent's surface (Langmuir) and heterogeneous surfaces</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div> <ns0:ref type='bibr'>al., 2015;</ns0:ref><ns0:ref type='bibr' target='#b49'>Pandey, Shukla & Singh, 2017;</ns0:ref><ns0:ref type='bibr'>Husein et a.l, 2019)</ns0:ref><ns0:p>. These emerging pollutants, if not removed in water sources for human consumption, could, in the long run, interferes and mimic physiological processes that regulate the metabolic functions of enzymes, hormones, and several biochemical indices <ns0:ref type='bibr'>(Ali, Al-Othman & Al-Hakkani, 2016)</ns0:ref>. One of the emerging chemicals classified as a priority pollutant is phenol <ns0:ref type='bibr'>(USEPA, 1985)</ns0:ref>, with a recommended permissible limit of 1.0 µg/L allowed in drinking water <ns0:ref type='bibr' target='#b39'>(Khare & Kumar, 2012)</ns0:ref>. Phenol is a common raw material for a range of pharmaceutical products, pesticides, fertilizers, disinfectants, and a host of household preservatives and detergents <ns0:ref type='bibr' target='#b23'>(Ge et al., 2019)</ns0:ref>. The fingerprint of phenolic compounds is manifested in humans through several exposure pathways, exerting their toxicity by inducing protoplasmic poison, denaturing proteins, and creating an acid-base imbalance amongst many <ns0:ref type='bibr'>(ATSDR, 2014)</ns0:ref>.</ns0:p><ns0:p>In response to these health-related complications, scientist reported several techniques such as chemical oxidation, electrocoagulation, solvent extraction, membrane separation, and adsorption methods to be efficient in removing phenol from polluted water <ns0:ref type='bibr' target='#b7'>(Burghoff &de Haan, 2009;</ns0:ref><ns0:ref type='bibr' target='#b4'>Amin, Akhtar & Rai, 2010;</ns0:ref><ns0:ref type='bibr' target='#b20'>El-Ashtoukhy et al., 2013)</ns0:ref>. Besides the adsorption methods, the other techniques highlighted above are characterized to be cost-driven, energy-intensive techniques, and generate toxic sludge in the process <ns0:ref type='bibr' target='#b20'>(El-Ashtoukhy et al., 2013)</ns0:ref>. The adsorption processes are practically in vogue and utilized by many researchers to remove pollutants in water. Chief among the adsorption materials is activated carbon, which later becomes less economically viable as an adsorbent due to its costly regenerating processes, and relatively expensive starting material <ns0:ref type='bibr' target='#b39'>(Khare & Kumar, 2012;</ns0:ref><ns0:ref type='bibr' target='#b33'>Jin & Zhu, 2014;</ns0:ref><ns0:ref type='bibr' target='#b23'>Ge et al., 2019)</ns0:ref>. Furthermore, adsorbent prepared from nature-based carbon materials (clay, peat, wood, sawdust, fly ash, coal reject, bagasse, and pine cones) has to undergo surface modifications to be efficient <ns0:ref type='bibr' target='#b17'>(Djebbar et al., 2012;</ns0:ref><ns0:ref type='bibr' target='#b11'>Chen et al., 2017)</ns0:ref>.</ns0:p><ns0:p>Therefore, this study attempts to create a biobased organic-inorganic framework to synthesize adsorbent materials using available nature-based materials that are sustainable <ns0:ref type='bibr' target='#b27'>(Habte et al., 2019)</ns0:ref>. The study integrates the high surface area and facile/tunable adsorption-desorption characteristics of silica <ns0:ref type='bibr' target='#b11'>(Chen et al., 2017)</ns0:ref>, the compatibility, better adhesion, and thermal stability of CaCO 3 <ns0:ref type='bibr' target='#b29'>(Hassan, Rangari & Jeelani, 2014)</ns0:ref>, the natural cationic potential, and rich hydroxyl (-OH) and amino (-NH 2 ) groups of chitosan <ns0:ref type='bibr'>(Dehaghi et al., 20114)</ns0:ref> in the synthesis of the biosorbent material. Studies show that some plant species' tissues contain high biogenic silica deposits, mostly in its hydrated silica (SiO 2 . nH 2 O) form <ns0:ref type='bibr' target='#b25'>(Ghorbani, Sanati & Maleki, 2015)</ns0:ref>. Similarly, eggshells, among other sources (cockle shells), are among the most readily available calcium carbonate sources in nature <ns0:ref type='bibr' target='#b29'>(Hassan, Rangari & Jeelani, 2014)</ns0:ref>. Another material of choice used in the synthesis is chitosan, a natural cationic cellulose biopolymer derived from Crustaceans <ns0:ref type='bibr' target='#b8'>(Bwatanglang et al., 2016)</ns0:ref>. These materials are of great environmental importance and are widely covered in green chemistry in the synthesis of adsorbent materials for the removal of phenol <ns0:ref type='bibr'>(Giraldo & Moreno-Pirajan, 2014;</ns0:ref><ns0:ref type='bibr' target='#b13'>Chraibi et al., 2016;</ns0:ref><ns0:ref type='bibr' target='#b53'>Sarker and Fakhruddin 2017;</ns0:ref><ns0:ref type='bibr' target='#b5'>Asgharnia et al., 2019;</ns0:ref><ns0:ref type='bibr' target='#b43'>Mandal, Mukhopadhyay & Das, 2019)</ns0:ref>.</ns0:p><ns0:p>Other studies also reported using the same raw materials to synthesize biosilica and calcium carbonate SiO 2 /CaCO 3 nanocomposites. <ns0:ref type='bibr' target='#b45'>Morsy et al. (2019)</ns0:ref> used semi-burned rice straw ash to synthesize modified papermaking fillers nanocomposites by sol-gel technique. Similarly, <ns0:ref type='bibr' target='#b29'>Hassan et al. (2014)</ns0:ref> used waste eggshell to synthesize CaCO 3 nanoparticles reinforcement fillers prepared using a combination of mechanochemical and ultrasonic irradiation techniques. In the light of the above studies, this present work utilizes RH to synthesis SiO 2 and waste eggshells to derive the CaCO 3. The SiO 2 /CaCO 3 was synthesis by stabilizing the particles in chitosan to form the CS-SiO 2 /CaCO 3 nanocomposite not as fillers but instead as an adsorbent material for the removal of phenol. Therefore, this study's primary focus is to utilize these nature-based waste materials to design, synthesize, and formulate the adsorbing materials consisting of the organic phase (chitosan) and an inorganic phase (calcium carbonate and silica) for the removal of phenol in water.</ns0:p></ns0:div> <ns0:div><ns0:head>2.0.Materials and Methods: 2.1.1. Chemical and reagents:</ns0:head><ns0:p>The waste eggshells (ES) and rice husks (RH) used in this work were obtained from local fastfood restaurants in Mubi, Adamawa state Nigeria. The Low-molecular-weight Chitosan (CS) (75%-85% degree of deacetylation) was purchased from Sigma-Aldrich (St Louis, MO, USA). Ethanol (C 2 H 5 OH, 99.7%), hydrochloric acid (HCl, 37%), sulphuric acid (H 2 SO 4 , 98.5%), hydrogen peroxide (H 2 O 2 ), sodium hydroxide (NaOH, 97%), acetone (C 3 H 6 O, 99%), acetic acid (CH 3 COOH, 99.85%) and phenol (C 6 H 6 O, 99%) were purchased from BDH Chemical Ltd England.</ns0:p></ns0:div> <ns0:div><ns0:head n='2.1.2.'>Preparation and synthesis of the adsorbents:</ns0:head><ns0:p>The preparation of the adsorbent follows sequential steps. Briefly, a thoroughly washed RH was subjected to acid pretreatment using HCl: H 2 SO 4 (10:30 wt%) and further treatment with H 2 O 2 (30% v/v) at 70 o C for 60 min <ns0:ref type='bibr' target='#b41'>(Lu & Hsieh, 2012;</ns0:ref><ns0:ref type='bibr'>Le et al., 2013)</ns0:ref>. The formed slurry was rinsed with distilled water, oven-dried at 600 o C for 4h. The sieved RH is then dispersed in an aqueous 0.5M NaOH solution under vigorous stirring for 1 h to form the RH-derived sodium silicate (RH-Na 2 SiO 3 ). The RH-silicate (RHSiO 2 ) is obtained by subjecting RH-Na 2 SiO 3 , in 12 wt% H 2 SO 4 under vigorous stirring for 15 min. The formed RHSiO 2 is washed in water and aged in ethanol at 60 o C for 60 min. The final material is further rinsed in water and dried under ambient temperature <ns0:ref type='bibr' target='#b41'>(Lu & Hsieh, 2012;</ns0:ref><ns0:ref type='bibr'>Le et al., 2013;</ns0:ref><ns0:ref type='bibr' target='#b25'>Ghorbani, Sanati & Maleki, 2015)</ns0:ref>. The proteins in the Chicken eggshells are deactivated/denatured by boiling after subjecting the same to washing using acetone/alcohol (1:1 ratio), dried, and made into powder. The powdered samples were further dispersed in distilled water under sonication, collected and heated in an oven at 600 o C for 3h, and finally sieved using a 40-75µm to form the ES-calcium carbonate (ESCaCO 3 ) particle <ns0:ref type='bibr' target='#b42'>(Minakshi et al., 2019)</ns0:ref>.</ns0:p><ns0:p>An aqueous solution of ESCaCO 3 (5 wt%) was prepared under vigorous stirring at 3000 rpm for 30 min. To the formed precursor, 1g of RHSiO 2 in 20 ml of distilled water was added gradually under heating at 80 0 C and stirred for 60 min to form the composite mixture of RHSiO 2 /ESCaCO 3 particles <ns0:ref type='bibr'>(Morsy, El-Sheikh & Barhoun, 2019)</ns0:ref>. The Bio-based CS-SiO 2 /CaCO 3 nanocomposites were prepared by the dropwise addition of aqueous RHSiO 2 /ESCaCO 3 (1g/20 mL) solution into chitosan (CS) suspensions (0.1 g in 10 mL of 1% acetic acid) under stirring for 30 min in other to stabilize the particles. The formed precursor was incubated at 60 °C for 3 h, collected by centrifugation, and further allowed to age in 2.0M NaOH solution for 2h. Then wash using distilled water and filtered to obtain the CS-SiO 2 /CaCO 3 nanocomposites.</ns0:p></ns0:div> <ns0:div><ns0:head n='2.1.3.'>Instrumental analysis:</ns0:head><ns0:p>In this study, different instruments were used at various stages of the analysis to characterize the prepared samples. The structure and phase identification of the prepared RHSiO 2 , ESCaCO 3 , and the CS-SiO 2 /CaCO 3 nanocomposites were established using an X-ray diffraction (XRD) instrument (Malvern PAAnalytical Empyrean, Nederland) equipped with Cu anode (CuKα radiation source) X-ray tube. The identification of the functional groups was determined using Fourier Transform Infrared (FTIR) spectroscopy using PerkinElmer (Waltham, MA, USA. N3895) with a range covering 4,000-500 cm -1 . The elemental composition and morphology were studied using Field Emission Scanning Electron Microscopy and Energy Dispersive X-ray (FESEM/EDX) on JEOL JSM-7600F (JEOL, Tokyo, Japan).</ns0:p></ns0:div> <ns0:div><ns0:head n='2.1.4.'>Adsorption experiment</ns0:head><ns0:p>The adsorbents were brought into contact with aqueous phenol with a concentration ranging from 10-30 mg/L and agitated at a speed of 120 rpm at room temperature. The percentage of phenol in each of the aliquot samples taken were determined after 30, 45, 60, 70, 80, and 90 min by measuring the difference in absorbance using UV-Vis Spectrophotometer (PerkinElmer Lambda 35 spectrometer). The study was conducted using changes in pH <ns0:ref type='bibr'>(4, 5.4, 6, 7.4, and 8</ns0:ref>) and adsorbent dose (0.05, 0.1, 0.15, 0.2, 0.25g). The adsorption kinetics were determined using the Pseudo-first-order and Pseudo-second-order kinetic model. The Pseudo-first-order kinetic model was determined using equation (1):</ns0:p><ns0:formula xml:id='formula_0'>𝐿𝑜𝑔 (𝑞𝑒 -𝑞𝑡) = 𝐿𝑜𝑔𝑞𝑒 - 𝐾 1 𝑡 2.303 (1)</ns0:formula><ns0:p>Where K 1 (mg/g min) is the rate constant, qe (mg/g) is the adsorption capacity at equilibrium, and qt (mg/g) is the adsorption capacity at time t. The slope of the Log (qe -qt) vs. t was used for the determination of the equilibrium rate constant K 1 . The Pseudo-second order kinetic was determined using equation ( <ns0:ref type='formula'>2</ns0:ref>):</ns0:p><ns0:formula xml:id='formula_1'>𝑡 𝑞𝑡 = 1 𝐾 2 𝑞𝑒 2 + 1 𝑞𝑡 𝑡 (2)</ns0:formula><ns0:p>The rate constant (K 2 ) and qe are calculated from the intercept and slope of the plot of t/qt vs. t <ns0:ref type='bibr'>(Husein et al.et al., 2019)</ns0:ref>. The percentage removal efficiency of phenol and the amount adsorbed over the adsorbent were determined using equation (3):</ns0:p><ns0:p>.</ns0:p><ns0:formula xml:id='formula_2'>𝑅% = [ 𝐶𝑖 -𝐶𝑒 𝐶𝑖 ] 𝑥 100</ns0:formula><ns0:p>(3)</ns0:p><ns0:p>PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2020:10:53668:1:1:NEW 22 Dec 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Where Ci and Ce are the initial and the equilibrium concentrations of phenol in mg/L. The uptake capacity (mg/g) of the sorbent for each concentration of phenol at equilibrium were determined using equation ( <ns0:ref type='formula'>4</ns0:ref>):</ns0:p><ns0:formula xml:id='formula_3'>𝑞𝑒(𝑚𝑔/𝑔) = [𝐶𝑖 -𝐶𝑒)/𝑀] 𝑥 𝑉 (4)</ns0:formula><ns0:p>Where V is the volume of the solution in L, while M is the mass of the biosorbent (g). The adsorption behavior was analyzed using The Langmuir <ns0:ref type='bibr' target='#b40'>(Langmuir, 1916)</ns0:ref> and Freundlich <ns0:ref type='bibr' target='#b22'>(Freundlich, 1906)</ns0:ref> isotherms models. The following relation represents the linear form of the Langmuir isotherm model:</ns0:p><ns0:formula xml:id='formula_4'>𝐶𝑒 𝑞𝑒 = 1 𝑞𝑚𝐾𝑒 + 𝐶𝑒 𝑞𝑚 (<ns0:label>5</ns0:label></ns0:formula><ns0:formula xml:id='formula_5'>)</ns0:formula><ns0:p>Where qe is the amount adsorbed at equilibrium (mg/g), C e is the equilibrium concentration of the adsorbate (mg/L), and q m (mg/g) and Ke (L/mg) are the Langmuir constants related to the maximum adsorption capacity and the energy of adsorption, respectively. These constants are evaluated from the intercept and slope of the linear plot experimental data of Ce/qe (g/mg) versus Ce (L/mg). The following relation gives the linear form of the Freundlich isotherm model:</ns0:p><ns0:formula xml:id='formula_6'>𝐿𝑜𝑔𝑞𝑒 = 𝐿𝑜𝑔𝐾 𝑓 + 𝐼 𝑛 𝐿𝑜𝑔𝐶𝑒<ns0:label>(6)</ns0:label></ns0:formula><ns0:p>Where K f and 1/n are the Freundlich constants related to adsorption capacity and adsorption intensity of the adsorbent. The values of K f and 1/n were derived from the intercept and slope of the linear plot of Logqe versus LogCe. The dimensionless constant (separation factor, R L ) taking from the Langmuir isotherm was estimated using equation ( <ns0:ref type='formula'>7</ns0:ref>):</ns0:p><ns0:p>.</ns0:p><ns0:formula xml:id='formula_7'>𝑅 𝐿 = 1 [𝐾 𝐿 𝐶𝑖 + 1] (7)</ns0:formula><ns0:p>Where Ci is the initial concentration and K L the concentration of Langmuir. If R L = 0, the adsorption is irreversible, is favorable when 0 < R L <1, linear when R L = 1 and unfavorable when R L >1 <ns0:ref type='bibr' target='#b13'>(Chraibi et al., 2016)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>3.0.</ns0:head><ns0:p>Results and Discussion: 3.1. Characterization of adsorbent: 3.1.1. FTIR analysis:</ns0:p><ns0:p>The fingerprints or functional groups involved in the various stages of the synthesis identified by FTIR are shown in Fig. <ns0:ref type='figure' target='#fig_0'>1</ns0:ref>. In the figure, the broadband in the spectra of ESCaCO 3 centered around 3104-3630 cm -1 is an O-H bond from water, while the peak at 1651 cm -1 is the adsorption band of H-O-H bending vibrations from adsorbed water molecules <ns0:ref type='bibr' target='#b31'>(Ihli et al., 2014;</ns0:ref><ns0:ref type='bibr' target='#b27'>Habte et al., 2019)</ns0:ref>.</ns0:p><ns0:p>The three peaks at 1413 cm -1 , 873 cm -1 , and 712 cm -1 are absorption peaks due to the CO 3 -2 group in CaCO 3 <ns0:ref type='bibr' target='#b47'>(Nyquist & Kagel, 2012;</ns0:ref><ns0:ref type='bibr'>Morsy, El-Sheikh & Barhoun, 2019;</ns0:ref><ns0:ref type='bibr'>Du and Amstad, 2020)</ns0:ref>. The peaks at 3285-3629 cm -1 , 1651 cm -1 , 1056 cm -1 , and 796 cm -1 in the spectra of RHSiO2 are from the silanol OH groups of water, the H-O-H bending vibration, siloxane asymmetric bending vibrations bands (Si-O-Si), and the symmetric bending mode of silanol (Si-O) <ns0:ref type='bibr' target='#b34'>(Kamath & Proctor, 1998;</ns0:ref><ns0:ref type='bibr' target='#b25'>Ghorbani, Sanati & Maleki, 2015)</ns0:ref>. Chitosan's spectra show stretching vibration of OH group around 3372 cm -1 and a -CH 2 symmetric vibration at 2878 cm -1 . The peaks at 1645 cm -1 , 1378 cm -1 , and the prominent peak at 1031 cm -1 are C=O vibration mode of amides, C-N stretching, and C-O stretching vibrations, respectively <ns0:ref type='bibr' target='#b51'>(Saifuddin & Dinara, 2012;</ns0:ref><ns0:ref type='bibr' target='#b16'>Dehaghi et al., 2014)</ns0:ref>.</ns0:p><ns0:p>The broadband around 3312-3618 cm -1 and the peak centered at 1643 cm -1 are fingerprints from O-H bond and H-O-H bending vibrations of ESCaCO 3 , and RHSiO 2 , in the CS-SiO 2 /CaCO 3 nanocomposite. The absorption peaks centered at 1412 cm -1 and 873 cm -1 are from the CO3 -2 group in ESCaCO 3 , while the peaks at 1065 cm -1 , 795 cm -1 , and 713 cm -1 are from the Si-OH stretching, Si-O-Si asymmetric, and Si-O-Si symmetric stretching vibrations from the RHSiO 2 respectively <ns0:ref type='bibr' target='#b47'>(Nyquist & Kagel, 2012;</ns0:ref><ns0:ref type='bibr'>Morsy, El-Sheikh & Barhoun, 2019)</ns0:ref>. The Si-O-Si asymmetric vibrational mode from the spectra of RHSiO 2 centered at 1056 cm -1 slightly shifted to 1065 cm -1 in the nanocomposites, suggesting a possible interaction with the C-O stretching vibrations of CS (1031 cm -1 ).</ns0:p></ns0:div> <ns0:div><ns0:head>INSERT FIGURE. 1.</ns0:head></ns0:div> <ns0:div><ns0:head n='3.1.2.'>XRD analysis:</ns0:head><ns0:p>Fig. <ns0:ref type='figure'>2</ns0:ref> represents the XRD patterns of (a) ESCaCO 3 , (b) RHSiO 2 , (c) CS and, (d) CS-SiO 2 /CaCO 3 nanocomposite. Fig. <ns0:ref type='figure'>2a</ns0:ref> shows the XRD patterns for ESCaCO 3 having all the diffraction peaks corresponding to the standard pattern for calcite (JCPDS card no. 005-0586), indicating 81% CaCO 3 <ns0:ref type='bibr' target='#b29'>(Hassan, Rangari & Jeelani, 2014;</ns0:ref><ns0:ref type='bibr' target='#b42'>Minakshi et al., 2019)</ns0:ref>. The maximum peaks at 2θ=29.46˚ reflect the calcite phase of CaCO 3 . This result agrees with the findings reported by <ns0:ref type='bibr' target='#b13'>Chraibi et al. (2016)</ns0:ref>, showing the peak at 2θ=29.48˚ for CaCO 3 derived from the eggshell. Fig. <ns0:ref type='figure'>2b</ns0:ref> shows the XRD patterns of silica prepared from rice husk (RHSiO 2 ). The peak corresponding to the semi-crystalline phase of Si appeared at a broad peak centered at 2θ=22.08˚. The analysis of the particles indicates the presence of silica (96%). The result agrees with the study by <ns0:ref type='bibr' target='#b45'>Morsy et al. (2019)</ns0:ref> showing 82% silica and a broad peak centered at 2θ=22.5˚ corresponding to silica nanoparticles' semi-crystalline phase. A strong peak centered at ~20 o was observed in the spectra of pure chitosan (Fig. <ns0:ref type='figure'>2c</ns0:ref>). This high degree of crystallinity was observed to transcend to a very weak peak that is not noticeable in the spectra of CS-SiO 2 /CaCO 3 nanocomposite, arising from possible disarray in the chain alignment of chitosan by the diffraction peaks of calcite and the semi-crystalline phase of Si <ns0:ref type='bibr' target='#b16'>(Dehaghi et al., 2014)</ns0:ref>. Furthermore, Fig. <ns0:ref type='figure'>2d</ns0:ref> shows the XRD patterns of the CS-SiO 2 /CaCO 3 nanocomposites. The results show the diffraction peaks corresponding to the calcite phase of CaCO 3 at 2θ=29.51˚ and a semi-crystalline phase related to silica at 2θ=22.79˚.</ns0:p><ns0:p>The result further shows that CS-SiO 2 /CaCO 3 nanocomposite predominantly consists of 9% silica and 52% CaCO 3 .</ns0:p></ns0:div> <ns0:div><ns0:head>INSERT FIGURE. 2.</ns0:head></ns0:div> <ns0:div><ns0:head n='3.1.3.'>FESEM/EDX analysis:</ns0:head><ns0:p>Fig. <ns0:ref type='figure'>3</ns0:ref> shows FESEM/EDX micrographs with the corresponding particle size distribution of ESCaCO 3 , RHSiO 2 , and CS-SiO 2 /CaCO 3 nanocomposites. The micrograph of ESCaCO 3 , as shown in Fig. <ns0:ref type='figure'>3a</ns0:ref>, reveals an irregular surface structure with a random assembly of aggregated grains that form a densely packed structure and size distribution up to 100 nm (Fig. <ns0:ref type='figure'>3b</ns0:ref>). Similar morphological features were reported by <ns0:ref type='bibr' target='#b42'>Minakshi et al. (2019)</ns0:ref> and <ns0:ref type='bibr'>Ahmed et al. (2020)</ns0:ref>, showing the structure of eggshells-CaCO 3 having an irregular surface structure with a size distribution ~200 nm and 89 nm, respectively. The morphology of the RHSiO 2 , as shown in Fig. <ns0:ref type='figure'>3c</ns0:ref>, shows nearly spherical densely packed grains with an average particle size of 19.07±4.63 nm (Fig. <ns0:ref type='figure'>3d</ns0:ref>). The images are similar to those reported by <ns0:ref type='bibr'>Le et al. (2013)</ns0:ref>, <ns0:ref type='bibr'>Morsy, El-Sheikh & Barhoun (2019)</ns0:ref>, and <ns0:ref type='bibr' target='#b50'>Phoohinkong and Kitthawee (2014)</ns0:ref>. The result in Fig. <ns0:ref type='figure'>3e</ns0:ref> shows that the CS-SiO 2 /CaCO 3 nanocomposite have better and good monodispersed spherical morphology than those of the single EsCaCO 3 particles. The dispersion of the SiO 2 /CaCO 3 into CS suspension increases the uniformity of the grain size of CS-SiO 2 /CaCO 3 , with an average size distribution of 32.15±6.20 nm (Fig. <ns0:ref type='figure'>3f</ns0:ref>). The result falls near to the findings reported by <ns0:ref type='bibr'>Morsy, El-Sheikh & Barhoun (2019)</ns0:ref>, showing SiO 2 /CaCO 3 nanocomposites to be nearly spherical with particle sizes ranging 70-110 nm. The corresponding EDX results, as presented in Fig. <ns0:ref type='figure'>3a, c</ns0:ref>, and e, showed representative peaks for Ca, Si, and O for all the samples. The EDX of ESCaCO 3 consists mainly of C (36%), O (52%), Ca (10%), while the spectra of RHSiO 2 show majorly the peaks of Si (30%) and O (69). The CS-SiO 2 /CaCO 3 nanocomposite show similar peaks corresponding to the spectra of SiO2 and CaCO3, which indicate a successful formation of CS-SiO 2 /CaCO 3 nanocomposite.</ns0:p></ns0:div> <ns0:div><ns0:head>INSERT FIGURE. 3.</ns0:head></ns0:div> <ns0:div><ns0:head n='3.2.'>Factors affecting adsorption behaviors:</ns0:head><ns0:p>The Phenol removal efficiency of the adsorbent, as shown in Fig. <ns0:ref type='figure'>4a</ns0:ref>, shows the adsorption capacity increases in the order of CS-SiO 2 /CaCO 3 >RHSiO 2 >ESCaCO 3 with an increase in the adsorbent dose. The synthesized CS-SiO 2 /CaCO 3 nanocomposite due to additional binding sites from SiO 2 , CaCO 3, and CS adsorbed more phenol than the RHSiO 2 and ESCaCO 3 particles . The study was conducted at a varying initial dosage of 0.05-0.25g, pH5.4, and initial phenol concentration of 25 mg/L at 120 rpm for 60 min. The highest removal efficiency of 80% was achieved at a dose of 0.15g of CS-SiO 2 /CaCO 3 nanocomposite and remain more or less the same up to 0.25g. Suggesting that the removal efficiency at the onset of the adsorption processes was higher due to the more available surface area, additional adsorption sites, and then remained the same due to saturating absorbent sites following an increase in the dose <ns0:ref type='bibr' target='#b43'>(Mandal, Mukhopadhyay & Das, 2019)</ns0:ref>. The RHSiO 2 and ESCaCO 3 at varying initial dosage show an increase in phenol removal efficiency. The highest removal efficiency of phenol was observed at a dose of 0.25g of RHSiO 2 (75%) and ESCaCO 3 (76%), respectively. An increase in phenol removal efficiency with an increase in adsorbent dosage was also observed and reported by Sarker and Fakhruddin (2017) and Asgharnia et al. (2019) using rice straw as adsorbent. Similar observations were reported using CaCO 3 derived from the eggshell. The study reported an optimum dosage of eggshell powder adsorbent at 4 g <ns0:ref type='bibr' target='#b35'>(Kashi, 2017)</ns0:ref>.</ns0:p><ns0:p>Fig. <ns0:ref type='figure'>4b</ns0:ref> described the adsorption efficiency of the adsorbent at varying pH environments. The study was carried out at varying pH 4, 5.4, 6, 7.4, and 8, with an initial phenol concentration of 25 mg/L and an adsorbent dose of 0.15g at 120 rpm for 60 min. The plots show higher removal efficiency occurring at acidic pH range (4-6) and decreasing at pH of 7.4 and 8. At these pH ranges, the acidic environment substantially increases the electrostatic attraction with the phenolates' negative ions, increasing phenol adsorption in the process <ns0:ref type='bibr'>(Khare & Kymar, 2012;</ns0:ref><ns0:ref type='bibr' target='#b15'>Daraei et al., 2013;</ns0:ref><ns0:ref type='bibr' target='#b48'>Ouallal et al., 2019)</ns0:ref>. The adsorption processes following the pH variation were observed to follow the trend CS-SiO 2 /CaCO 3 >RHSiO 2 >ESCaCO 3 . The highest percentage removal of 83% was observed in the final nanocomposite (CS-SiO 2 /CaCO 3 ) at a pH of 5.4. The presence of available exchangeable ions <ns0:ref type='bibr' target='#b35'>(Kashi, 2017)</ns0:ref>, multiple adsorption sites on the adsorbent at this pH range leads to the observed removal efficiency in the CS-SiO 2 /CaCO 3 nanocomposite compared to the other adsorbents. <ns0:ref type='bibr' target='#b5'>Asgharnia et al. (2019)</ns0:ref>, while investigating the effect of pH on phenol removal efficiency, observed higher efficiency for rice husk activated carbon (RHAC) at pH6 and rice husk carbon (RHC) at pH5. The result further agrees with the findings reported by <ns0:ref type='bibr' target='#b35'>Kashi (2017)</ns0:ref> and <ns0:ref type='bibr' target='#b15'>Daraei et al. (2013)</ns0:ref> using powdered eggshell.</ns0:p><ns0:p>Varying the initial concentration of phenol from 5-30 mg/L, as shown in Fig. <ns0:ref type='figure'>4c</ns0:ref>, shows the removal efficiency increases as the concentration increases from 5-25 mg/L and remains the same at 30 mg/L. The CS-SiO 2 /CaCO 3 nanocomposite gives the highest removal efficiency (87%) at a concentration range of 25 mg/L and 30 mg/L. The removal efficiency follows the trend of CS-SiO 2 /CaCO 3 >RHSiO 2 >ESCaCO 3 , at a fixed adsorbent dose of 0.15g, pH5.4 at 120 rpm for 60 min. An increase in concentration leads to a rise in phenol adsorption on the surface of the sorbent. But above 25 mg/L, the removal efficiency remains the same, which suggests a saturation of the sorption sites available for sorption at this concentration of the sorbate molecules <ns0:ref type='bibr' target='#b16'>(Dehaghi et al., 2014;</ns0:ref><ns0:ref type='bibr' target='#b53'>Sarker and Fakhruddin, 2017)</ns0:ref>. Fig. <ns0:ref type='figure'>4d</ns0:ref> shows the effect of contact time <ns0:ref type='bibr'>(30, 45, 60, 70, 80, and 90)</ns0:ref> on the percentage removal of phenol using 0.15g of adsorbent in 20 ml of 25 mg/L phenol, pH5.4 at 120 rpm. The result shows an increase in phenol removal efficiency after 60 min of contact with CS-SiO 2 /CaCO 3 nanocomposite and RHSiO 2 particles. The CS-SiO 2 /CaCO 3 nanocomposite shows removal efficiency of 84% after 60 min and 89% at 90 min of contact time. The result agrees with findings reported by <ns0:ref type='bibr' target='#b5'>Asgharnia et al. (2019)</ns0:ref> and Sarker and Fakhruddin (2017) using RH as an adsorbent.</ns0:p><ns0:p>INSERT FIGURE. 4.</ns0:p></ns0:div> <ns0:div><ns0:head n='3.3.'>Kinetic adsorption study:</ns0:head><ns0:p>To further understand phenol adsorption behavior and mechanism onto CS-SiO 2 /CaCO 3 , RHSiO 2 , and ESCaCO3, the study was subjected to kinetic analysis using pseudo-first-order and pseudo-second-order models. The models help to predict possible interaction mechanisms between the adsorbent and the adsorbate. The result is shown in Fig. <ns0:ref type='figure'>5</ns0:ref> and further elaborated in Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref>. The plot from the graph of t/qt versus t fit well with pseudo-second-order with the highest correlation coefficient (R 2 ) of 0.99 and qm values of 3.80 mg/g observed in CS-SiO 2 /CaCO 3 nanocomposite (Fig. <ns0:ref type='figure'>5a</ns0:ref>). The result further shows phenol adsorption over RHSiO 2 (R 2 =0.98) and ESCaCO 3 (R 2 =0.95) fitting well with the pseudo-second-order. The outcome suggests that the adsorption process is chemisorption and the adsorbent particles are heterogeneous <ns0:ref type='bibr' target='#b43'>(Mandal, Mukhopadhyay & Das, 2019)</ns0:ref>. On the other hand, the pseudo-first-order graph of log (qe-qt) against t, as shown in Fig. <ns0:ref type='figure'>5b</ns0:ref>, poorly described the absorption processes of ESCaCO 3 (R 2 =0.72), but a bit linear for CS-SiO 2 /CaCO 3 nanocomposite (R 2 =0.91) and RHSiO 2 (R 2 =0.90) respectively. Pseudo-first-order suggests that the adsorption process is physisorption and the adsorbent particles are homogeneous <ns0:ref type='bibr' target='#b30'>(Husein et al., 2019)</ns0:ref>. This study's result agrees with the findings reported by <ns0:ref type='bibr' target='#b43'>Mandal, Mukhopadhyay & Das (2019)</ns0:ref>. Reporting pseudo-second-order supporting the adsorption processes of phenol using rice husk as adsorbent. Though pseudo-second-order best described the adsorption processes, the R 2 derived from the pseudo-first-order model of CS-SiO 2 /CaCO 3 and RHSiO 2 , though poorly described the adsorption process further characterize the homogeneity or the heterogeneity of the adsorption sites <ns0:ref type='bibr' target='#b48'>(Ouallal et al., 2019)</ns0:ref>. These observations also suggest some semblance of pseudo-first-order mechanism influencing the adsorption process of CS-SiO 2 /CaCO 3 nanocomposite and RHSiO 2 particles. Which further aggress with isotherm result from this study, showing the adsorption processes to possess good monolayer coverage on the surface and heterogeneous surface with different binding sites for CS-SiO 2 /CaCO 3 nanocomposite <ns0:ref type='bibr' target='#b55'>(Song, Johnson & Elimelech, 1994)</ns0:ref>. The higher values of K 2 , compared to K 1 (Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref>), further suggest a pseudo-first-order reaction's applicability <ns0:ref type='bibr'>(Ali, Al-Othman & Al-Hakkani, 2016)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>INSERT FIGURE. 5. INSERT TABLE. 1</ns0:head></ns0:div> <ns0:div><ns0:head n='3.4.'>Adsorption isotherms study:</ns0:head><ns0:p>As presented in Fig. <ns0:ref type='figure'>6</ns0:ref>, the result showed the equilibrium data for all the adsorbents at an initial concentration of 5-30 mg/L, demonstrating a better matching to the Langmuir isotherm model. Observed to follow the trend CS-SiO 2 /CaCO 3 > ESCaCO 3 >RHSiO 2 . <ns0:ref type='bibr' target='#b13'>Chraibi et al. (2016)</ns0:ref> and <ns0:ref type='bibr' target='#b35'>Kashi (2017)</ns0:ref>, in a separate study, show Langmuir isotherm describing the adsorption of phenol onto a calcined eggshell. <ns0:ref type='bibr'>Giraldo & Moreno-Pirajan (2014)</ns0:ref> observed similar findings over activated carbons derived from eggshell particles. <ns0:ref type='bibr' target='#b5'>Asgharnia et al. (2019)</ns0:ref> also reported a better matching of phenol adsorption with the Langmuir isotherm model using RHS and RHAC as adsorbents.</ns0:p><ns0:p>Except for CS-SiO 2 /CaCO 3 nanocomposite (Fig. <ns0:ref type='figure'>6a</ns0:ref>), the adsorption data for ESCaCO 3 (Fig. <ns0:ref type='figure'>6b</ns0:ref>) and RHSiO 2 (Fig. <ns0:ref type='figure'>6c</ns0:ref>) base on their R 2 values, fitted poorly to the Freundlich isotherm model. However, the magnitude of their K F (4.60 and 3.3) showed the tendency of phenol adsorption over the adsorbents <ns0:ref type='bibr'>(Khare & Kumar, 2013)</ns0:ref>. The Langmuir equation and Freundlich model in this study show a higher correlation coefficient (R 2 =0.9912 and 0.9905) for phenol adsorption onto CS-SiO 2 /CaCO 3 nanocomposite (Fig. <ns0:ref type='figure'>6a</ns0:ref>). Suggesting good monolayer coverage on the surface of the adsorbent <ns0:ref type='bibr'>(Langmuir)</ns0:ref> and equally possess heterogeneous surfaces with available binding sites <ns0:ref type='bibr'>(Freundlich)</ns0:ref> <ns0:ref type='bibr' target='#b37'>(Kermani et al., 2012)</ns0:ref>. The K F (3.4) and 1/n (2.5) values calculated from the Freundlich isotherm for CS-SiO 2 /CaCO 3 nanocomposite further suggest that the sorption process is complimentary and physicochemical <ns0:ref type='bibr' target='#b55'>(Song, Johnson & Elimelech, 1994)</ns0:ref>. The magnitude of 1/n indicates that phenol was favorably adsorbed by CS-SiO 2 /CaCO 3 nanocomposite compared to the other adsorbent <ns0:ref type='bibr' target='#b48'>(Ouallal et al., 2019)</ns0:ref>. From the data, the magnitude of q m , which defines the amount of phenol per unit weight of sorbent, was higher for CS-SiO 2 /CaCO 3 nanocomposite in comparison to RHSiO 2 and ESCaCO 3 particles. Similar results were also reported by <ns0:ref type='bibr' target='#b37'>Kermani et al. (2012)</ns0:ref> using rice husk ash as an adsorbent. In the study, both the Langmuir and Freundlich isotherm model adequately described the adsorption process. <ns0:ref type='bibr' target='#b17'>Djebbar et al. (2012)</ns0:ref> reported similar findings for phenol's adsorption using natural clay.</ns0:p></ns0:div> <ns0:div><ns0:head>INSERT FIGURE. 6.</ns0:head><ns0:p>The R L estimation at various C i further shows the adsorption to be more favored by the Langmuir isotherm model, having values of <1 for all the adsorbents. The results indicate favorable adsorption in all cases. Similar observations have been reported for phenol's sorption on rice husk and eggshells <ns0:ref type='bibr'>(Milhome et al., 2012;</ns0:ref><ns0:ref type='bibr' target='#b13'>Chraibi et al., 2016)</ns0:ref>. Table <ns0:ref type='table' target='#tab_1'>2</ns0:ref> shows the correlation coefficients (R 2 ), the Langmuir constants (q m , and K L ), the Freundlich parameters (K F , 1/n), and the separation factors (R L ) estimated from this study.</ns0:p></ns0:div> <ns0:div><ns0:head>INSERT TABLE. 2</ns0:head><ns0:p>Some authors reported materials derived from rice husk/straw to show an adsorption capacity of 13.9 <ns0:ref type='bibr' target='#b43'>(Mandal, Mukhopadhyay & Das, 2019)</ns0:ref>. <ns0:ref type='bibr' target='#b53'>Sarker and Fakhruddin (2017)</ns0:ref> reported an adsorption capacity of 3.8 and 5.8 mg/g from a similar study using rice straw. <ns0:ref type='bibr' target='#b46'>Nadavala et al. (2009)</ns0:ref> and <ns0:ref type='bibr'>Du et al. (2020)</ns0:ref> reported an adsorption capacity of 1.26 and 8.55 mg/g using chitosan as adsorbents. About 30-58 mg/g were also reported using calcine eggshells by <ns0:ref type='bibr' target='#b13'>Chraibi et al. (2016)</ns0:ref> and 191 mg/g from activated carbon derived from eggshells <ns0:ref type='bibr'>(Giraldo & Moreno-Pirajan, 2014)</ns0:ref>. The studies reported and presented in Table <ns0:ref type='table'>3</ns0:ref> show phenol adsorption capacity that is either higher or lower than the values recorded in this study. However, research shows that the adsorption capacity of an adsorbent is a function of so many parameters used in the synthesis and adsorption studies. Parameters influencing adsorption processes include the following; the initial phenol concentration, the adsorbent's characteristics, the adsorbent dose, the particle size of the adsorbent, temperature, pH, and contact time <ns0:ref type='bibr' target='#b43'>(Mandal, Mukhopadhyay & Das, (2019)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>INSERT TABLE. 3</ns0:head><ns0:p>PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2020:10:53668:1:1:NEW 22 Dec 2020)</ns0:p><ns0:p>Manuscript to be reviewed Chemistry Journals Analytical, Inorganic, Organic, Physical, Materials Science</ns0:p></ns0:div> <ns0:div><ns0:head n='3.5.'>Adsorption and removal mechanism of phenol over CS-SiO 2 /CaCO 3 nanocomposite</ns0:head><ns0:p>The starting materials used in the adsorbents' synthesis (ESCaCO 3 . RHSiO 2 and, CS-SiO 2 /CaCO 3 ) are low-cost, readily available, and naturally derived from agricultural waste. Phenol adsorbed on the adsorbent can be decomposed upon re-calcination or incubation in an alkaline medium <ns0:ref type='bibr' target='#b56'>(Tabana et al., 2020)</ns0:ref>. The removal of phenol was carried out by incubating the spent CS-SiO 2 /CaCO 3 nanocomposite in 0.1MNaOH solution for 60 min at 45 ˚C followed by washing using DI water <ns0:ref type='bibr' target='#b16'>(Dehaghi et al., 2014;</ns0:ref><ns0:ref type='bibr' target='#b58'>Tural et al., 2016)</ns0:ref>. Fig. <ns0:ref type='figure'>7</ns0:ref> shows the FTIR result for CS-SiO 2 /CaCO 3 nanocomposite. The study shows broadband at 3500-3000 cm -1, and the small peak at 1643 cm -1 disappeared in CS-SiO 2 /CaCO 3 nanocomposite spectra after phenol adsorption. Suggests electrostatic interaction of the phenolate with the adsorbent's hydroxide layers <ns0:ref type='bibr' target='#b48'>(Ouallal et al., 2019)</ns0:ref>. The CS-SiO 2 /CaCO 3 nanocomposite on contact with phenol further shows a change in peak intensity of CO 3 2at 1410 cm -1 from 1412 cm -1 before the adsorption. The phenol adsorption influences the Si-OH stretching vibration activity at 1065 cm -1 to 1045 cm -1 . Similarly, a change in the asymmetric and symmetric stretching mode at 795 cm -1 associated with Si-O-Si was observed on interaction with phenol to 790 cm -1 .</ns0:p><ns0:p>Furthermore, the spectra on the phenol's desorption from the CS-SiO 2 /CaCO 3 nanocomposite show a broad contour's appearance, suggesting to be from moisture-induced crystallization <ns0:ref type='bibr' target='#b48'>(Ouallal et al., 2019)</ns0:ref>. Moisture induced crystallization could arise from a humid environment <ns0:ref type='bibr' target='#b54'>(Singer et al., 2012)</ns0:ref> or at least in parts related to moisture contents directly bound to the atoms <ns0:ref type='bibr' target='#b31'>(Ihli et al., 2014;</ns0:ref><ns0:ref type='bibr' target='#b32'>Jensen et al., 2018)</ns0:ref> or structurally trapped in the crystals or due to fracture of hydrogen bonds formed by H 2 O…CO 3 2hydrogen bonds. <ns0:ref type='bibr' target='#b12'>(Cheng, Sun & Wu, 2019;</ns0:ref><ns0:ref type='bibr'>Du and Amstard, 2020)</ns0:ref>. Certain soluble additives also facilitate moisture-induced crystallization formation <ns0:ref type='bibr' target='#b12'>(Cheng, Sun & Wu, 2019;</ns0:ref><ns0:ref type='bibr'>Du and Amstard, 2020)</ns0:ref>. The CS-SiO 2 /CaCO 3 nanocomposite on phenol adsorption shows a shift in the peak at 1410 cm -1 , 1045 cm -1 , and 874 cm -1 , which corresponds to the peaks at 1427 cm -1 , 1074 cm -1 , and 873 cm -1 after the desorption.</ns0:p></ns0:div> <ns0:div><ns0:head>INSERT FIGURE. 7.</ns0:head></ns0:div> <ns0:div><ns0:head n='4.'>Conclusion:</ns0:head><ns0:p>A bio-based CS-SiO 2 /CaCO 3 nanocomposite derived from low-cost waste materials (chicken eggshell, rice husk, and chitosan) were synthesized and characterized using FTIR, XRD, FESEM, and EDX. The as-synthesized materials were able to remove 84-89% of phenol in 60-90 min at a pH of 5.4, a dose of 0.15g in 20 ml of 25 mg/L phenol concentration. The adsorption processes were observed to be complemented by both Langmuir and Freundlich adsorption processes. Suggesting good monolayer coverage on the surface of the adsorbent in addition to some heterogeneous surfaces with different available binding sites. The adsorption capacity observed in this study is 14.06 mg/g for CS-SiO 2 /CaCO 3 nanocomposite. The FTIR analysis revealed changes in functional groups' activity before adsorption and after adsorption, suggesting good anchorage of phenol on the adsorbent. The estimation of the separation factor (R L ) at various initial concentrations (Ci) further shows the adsorption to be more favored by the Langmuir isotherm model, having values of 0 < R L <1 for all the adsorbents. The research indicates that bio-based CS-SiO 2 /CaCO 3 nanocomposite could adsorb phenol at both the monolayer and heterogeneous surfaces of CS-SiO 2 /CaCO 3 nanocomposite. Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head>Figure 1 .</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='20,42.52,201.82,525.00,332.25' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='21,42.52,201.82,525.00,498.75' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='22,42.52,201.82,525.00,370.50' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='23,42.52,219.37,525.00,168.00' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='25,42.52,201.82,525.00,345.00' type='bitmap' /></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head>Table 1 : Pseudo-first-order and Pseudo-second order constants for adsorption of phenol Pseudo-first-order constants Pseudo-second order constants Adsorbents q m (mg/g) K 1(min-1) R 2 qm (mg/g) K 2 (g/mg min) R 2 CS-SiO 2 /CaCO 3</ns0:head><ns0:label>1</ns0:label><ns0:figDesc /><ns0:table><ns0:row><ns0:cell /><ns0:cell>2.49</ns0:cell><ns0:cell>-1.1E-02</ns0:cell><ns0:cell>0.91</ns0:cell><ns0:cell>3.80</ns0:cell><ns0:cell>0.01</ns0:cell><ns0:cell>0.99</ns0:cell></ns0:row><ns0:row><ns0:cell>RHSiO 2</ns0:cell><ns0:cell>2.20</ns0:cell><ns0:cell>-9.0E-03</ns0:cell><ns0:cell>0.90</ns0:cell><ns0:cell>2.80</ns0:cell><ns0:cell>0.01</ns0:cell><ns0:cell>0.98</ns0:cell></ns0:row><ns0:row><ns0:cell>ESCaCO 3</ns0:cell><ns0:cell>1.80</ns0:cell><ns0:cell>-7.8E-03</ns0:cell><ns0:cell>0.72</ns0:cell><ns0:cell>2.26</ns0:cell><ns0:cell>0.02</ns0:cell><ns0:cell>0.95</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_1'><ns0:head>Table 2 : Langmuir and Freundlich isotherm constants for adsorption of phenol Langmuir constants Freundlich constants Adsorbents q m (mg/g) K L(L/mg) R 2 K F (mg/g) 1/n R 2 CS-SiO 2 /CaCO 3 14</ns0:head><ns0:label>2</ns0:label><ns0:figDesc /><ns0:table><ns0:row><ns0:cell /><ns0:cell>.06</ns0:cell><ns0:cell>0.19</ns0:cell><ns0:cell>0.99</ns0:cell><ns0:cell>3.38</ns0:cell><ns0:cell>2.52</ns0:cell><ns0:cell>0.99</ns0:cell></ns0:row><ns0:row><ns0:cell>RHSiO 2</ns0:cell><ns0:cell>10.64</ns0:cell><ns0:cell>0.52</ns0:cell><ns0:cell>0.98</ns0:cell><ns0:cell>4.60</ns0:cell><ns0:cell>3.80</ns0:cell><ns0:cell>0.88</ns0:cell></ns0:row><ns0:row><ns0:cell>ESCaCO 3</ns0:cell><ns0:cell>10.31</ns0:cell><ns0:cell>0.31</ns0:cell><ns0:cell>0.99</ns0:cell><ns0:cell>3.29</ns0:cell><ns0:cell>2.87</ns0:cell><ns0:cell>0.</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_2'><ns0:head>96 Separation factor (R L ) at various C i CS-SiO 2 /CaCO 3</ns0:head><ns0:label /><ns0:figDesc /><ns0:table><ns0:row><ns0:cell /><ns0:cell>0.51</ns0:cell><ns0:cell>0.34</ns0:cell><ns0:cell>0.26</ns0:cell><ns0:cell>0.21</ns0:cell><ns0:cell>0.17</ns0:cell><ns0:cell>0.15</ns0:cell></ns0:row><ns0:row><ns0:cell>RHSiO 2</ns0:cell><ns0:cell>0.79</ns0:cell><ns0:cell>0.85</ns0:cell><ns0:cell>0.88</ns0:cell><ns0:cell>0.90</ns0:cell><ns0:cell>0.92</ns0:cell><ns0:cell>0.93</ns0:cell></ns0:row><ns0:row><ns0:cell>ESCaCO 3</ns0:cell><ns0:cell>0.80</ns0:cell><ns0:cell>0.79</ns0:cell><ns0:cell>0.79</ns0:cell><ns0:cell>0.78</ns0:cell><ns0:cell>0.78</ns0:cell><ns0:cell>0.79</ns0:cell></ns0:row></ns0:table><ns0:note>PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2020:10:53668:1:1:NEW 22 Dec 2020)</ns0:note></ns0:figure> <ns0:note place='foot'>Analytical, Inorganic, Organic, Physical, Materials Science </ns0:note> </ns0:body> "	"ADAMAWA STATE UNIVERSITY, MUBI P.M.B. 25, MUBI, ADAMAWA STATE, NIGERIA DEPARTMENT OF PURE AND APPLIED CHEMISTRY 17th December, 2020. Dear Editors, We are sincerely grateful to the reviewers for their expert review comments on the manuscript “Adsorption of phenol over Bio-based silica/calcium carbonate (CS-SiO2/CaCO3) nanocomposite synthesized from waste eggshells and rice husks”. The manuscript is thoroughly revised and all concerns raised by the reviewers are edited accordingly Based on the revised copy edited as advised, we believe the manuscript is now suitable for publication in PeerJ. Below are the responses to the reviewer’s comments. We are grateful for your consideration. Yours sincerely, Ibrahim Birma Bwatanglang (Ph.D) Department of Pure & Applied Chemistry, Faculty of Science, Adamawa State University, Mubi, Nigeria. Tel.: +2348038264580. ibbbirma@gmail.com On behalf of all authors Editor comments (P. Davide Cozzoli) MAJOR REVISIONS On the basis of the comments from 5 independent referees, we find that your article could be of interest to the readership of Peer J. Physical Chemsitry. However, the reviewers raised concerns regarding the completeness of information provided, the adequacy of the experiments undertaken and the appropriateness of data discussion, which need proper attention. Therefore, it appears that MAJOR REVISIONS should be made before the manuscript may be considered further for publication. We would like to invite a carefully revised manuscript that adequately addresses the concerns raised by all referees on a point-to-point basis. You are requested to explicitly address the questions raised by reviewers and clearly point out the changes made in the manuscript. You may also include explanations for disagreeing with any of the suggestions that you have chosen not to follow. Improvement of English grammar and usage is also recommended the manuscript may be accepted. Provide all of the above-requested information in a detailed Cover Letter when you submit the Revised Manuscript. Please also submit a Marked Manuscript in which any changes made are clearly recognizable. We anticipate that your revised manuscript may be sent back to the original reviewers for further assessment. [# PeerJ Staff Note: Please ensure that all review comments are addressed in a rebuttal letter and any edits or clarifications mentioned in the letter are also inserted into the revised manuscript where appropriate. It is a common mistake to address reviewer questions in the rebuttal letter but not in the revised manuscript. If a reviewer raised a question then your readers will probably have the same question so you should ensure that the manuscript can stand alone without the rebuttal letter. Directions on how to prepare a rebuttal letter can be found at: https://peerj.com/benefits/academic-rebuttal-letters/ #] [# PeerJ Staff Note: The Academic Editor has identified that the English language must be improved. PeerJ can provide language editing services - please contact us at copyediting@peerj.com for pricing (be sure to provide your manuscript number and title) #] Author’s response: We have reviewed the manuscript and responded to the reviewers comments in the rebuttal letter and address in the revised manuscript. Reviewer 1: 1. Basic reporting No comment 2. Experimental design No comment 3. Validity of the findings No comment 4. Comments for the Author It is not a particularly innovative work. However the work is complete and well written. So it deserves publication. Author’s response: Though synthesis of adsorbent using waste ES and RH are widely captured in literature, the design and application of the synthesized chitosan-based SiO2/CaCO3 nanocomposite as reported in this study was tested as adsorbing materials for the practical removal of phenol. Reviewer 2: 1. Basic reporting NO comment and NO changes necessary. The article was written in conformity with any required professional standards and in accordance to the stated aim of the journal itself. Technically correct, clear and professional English was used throughout in the article. The abstract provides a concise and a complete summary of the content of the paper. Besides, the introduction provides a good, generalized background of the topic that quickly gives the reader an appreciation of the wide range of applications for this technology. Anyway, the study objectives, scientific data are clear, understandable and explicitly defined. The conclusions are mostly well supported by the results. 2. Experimental design The experimental design and the scientific methods used are appropriate to the aims of the study. The article provides sufficient information for another capable researcher to reproduce the experiments described. 3. Validity of the findings NO comments and NO any additional experiments are necessary to validate the results presented here. The conclusions of the study are supported by appropriate scientific-technical methods. The data on which these conclusions are based are solid, statistically sound, and controlled. Reviewer 3: 1. Basic reporting Throughout the manuscript, the writing needs to be professional and consistent. a. There are several incomplete or fragment sentences (e.g. lines 49, 398). Author’s response: The incomplete sentence/fragment was rephrased accordingly (line 48-50 and 450 in the revised manuscript) b. There are many inconsistencies with spaces (e.g. for wt.% and wt. % in lines 94 and 107, for “°C” in line 105 and 113, for “=” in lines 198 and 205, for “Fig.” in lines 166 and 192, after “CS-” in lines 402 and 409). Author’s response: The inconsistencies are corrected and effected in the revised manuscript. c. Inconsistency in subscript fonts for parameters (K1 in line 135, qm in Table 1). Author’s response: The inconsistency in subscript fonts in the text and Table are harmonized accordingly. (Line 149 in the revised manuscript and Table 1) d. The font of parameter symbols is not consistent with italic vs non-italic fonts. (e.g. qe in line 135 and 137). Author’s response: Corrected and effected (line 149 and 151 in the revised manuscript) e. Theme and bold font dissimilarities in line 138 Author’s response: Corrected and effected in the revised manuscript f. First bracket missing in equation (line 141). Author’s response: Corrected equation inserted (Line 163 in the revised manuscript) g. Be consistent with theme fonts for all the equations (e.g. lines 138, 147 and more) Author’s response: The theme fonts for all the equations are harmonized h. Degree symbol is missing (lines 197, 198). Author’s response: Inserted i. Capital and small letters (Adsorption in line 72) Author’s response: Corrected j. The period or full stop mark was twice used (lines 129). Author’s response: Repeated full stop mark deleted accordingly k. Re-write the sentence in line 296-297 Author’s response: The sentence is rephrased as evident in line 333-334 in the revised manuscript l. FT-IR and FTIR (lines 120, 164, 166 and more) Author’s response: Corrected across the entire manuscript 2. Experimental design a. The authors need to clarify the type of eggshell they used in the section of Synthesis of the Adsorbents and Characterization Author’s response: Chicken eggshells was used to synthesize the eggshells-calcium carbonate (ESCaCO3) particle. (Line 114 in the revised manuscript) b. The authors are suggested to provide more factors for the adsorption capacity of an adsorbent. Author’s response: The factors such as initial phenol concentration, the characteristics of the adsorbent, the adsorbent dose, particle size of the adsorbent, the temperature, pH, and contact time influences the maximum adsorption capacity of an adsorbent (line 398-403 in the revised manuscript) 3. Validity of the findings The knowledge gap being filled in this study should be clarified in Introduction section Author’s response: The study reported by Morsy et al, (2019) used semi-burned rice straw ash to synthesize SiO2/CaCO3. This present study however utilizes RH to synthesis SiO2 and waste eggshells to derive the CaCO3. The SiO2/CaCO3 was synthesis by stabilizing the particles in chitosan to form the CS-SiO2/CaCO3 nanocomposite not as a reinforcement fillers but rather as an adsorbent material for the removal of phenol. (Included in the introduction in line 85-95 in the revised manuscript) 4. General comments The authors synthesized bio-based adsorbing materials silica/calcium carbonate (CS-SiO2/CaCO3) from nature-based waste materials for removal of one of contaminant chemicals phenol from water. Though the principle behind the synthesis is not quite new, there are novelty in design and demonstration of usage for the adsorbing materials for practical phenol removal applications. The writing needs to be more scientific and technical. There is a high chance that the reader may misinterpret or cannot understand what the authors want to explain and report. The plots in the figures have major scopes to be improved both in quality and presentation. Author’s response: The manuscript was further rephrase, detailing scientific and technical understanding. All figures are rerun to improve quality and clarity of presentations Reviewer Comments 4: 1. Basic reporting The paper is well written and interesting. The materials are not very new the synthesis is well known in the literature but the application part can be useful for the readers 2. Experimental design a. The experiments were well designed and executed. It was not clear: on line 107 A solution of 5 wt. % ESCaCO3 was prepared under vigorous stirring at 3000 rpm for 30 min. What was the solvent used to prepare the 5wt% solution Author’s response: An aqueous solution of ESCaCO3. (Line 120 in the revised manuscript) b. It would be added advantage if authors could provide the surface area measurements of all the samples to see pore volume of the particles Author’s response: Particle size measurements for the remaining samples were added. Details in Fig. 3 and in text; line 254, 258 and 263 in the revised manuscript c. Add the CS : chitosan in the abstract Author’s response: added 3. Validity of the findings a. The research finding are validated with several experiments and data collected. From the experiments its not clear that the CaCO3/ SiO2 is a physical mixture? or any formation of calcium silicate traces. Author’s response: The CaCO3/SiO2 is a formation prepared through stepwise synthesis from eggshells (ES) and rice husk (RH). RH and ES contains high deposits of biogenic silica and calcium. These waste materials were first subjected to series of pretreatment, protein denaturing, oven treatment and washing to obtained the RHSiO2 and ESCaCO3. The obtained the RHSiO2 and ESCaCO3 were used in this work to prepare a material consist of SiO2 and CaCO3 (Line 106-128 in the revised manuscript). To an aqueous solution of ESCaCO3 prepared under vigorous stirring at 3000 rpm for 30 min, a gram of RHSiO2 in 20 ml of distilled water was added gradually under heating at 80 0C and stirred for 60 min to form the composite RHSiO2/ESCaCO3. The presence CaCO3 and SiO2 in the composite were identified using different instruments. The FTIR analysis shows a characteristic peaks corresponding to the CO3-2 group of CaCO3 in the spectra of ESCaCO3. The XRD patterns also shows diffraction peaks for calcite in the same sample. Furthermore, bands corresponding to siloxane (Si–O–Si) and silanol (Si-O) were detected in the spectra of RHSiO2, indicating the presence of silica in the composites. Further identification was also observed in the XRD spectra, showing a pattern corresponding to the semi-crystalline phase of Si. The EDX analysis also shows the presence of C (36%), O (52%), Ca (10%) Si (30%) and O (69) in the formation. b. Also in XRD of composite with Chitosan the peaks related to the Chitosan are missing, pl explain the reasons. Author’s response: Only one prominent peak at ~20 degree observed in the spectra of the pure CS. This high degree of crystallinity was not visible in the nanocomposite which indicate low crystallinity, reflecting a disarray in the chain alignment of CS by the overwhelming peaks of calcite and silica. (Line 239-243 in the revised manuscript) c. The percentages CaCO3 and SiO2 in the mixture are advised to report and The percentage of mixture of CaCO3/ SiO2 in Chitosan should be reported Author’s response: The percentage of silica in the RHSiO2 is about 96% while that of CaCO3 in the ESCaCO3 is 81%. Following the synthesis of the CS-SiO2/CaCO3 nanocomposites, the percentage of the SiO2/CaCO3 stabilized in CS show 9% silica and 56% CaCO3. (Line 231-247 in the revised manuscript) 4. Comments for the Author The paper is well thought and experiment are well designed. It is advised to address all the comments before final publication Reviewer Comments 5: 1. Basic reporting This manuscript is well organized. It was well written with clear and unambiguous, professional English used throughout 2. Experimental design Original primary research within Aims and Scope of the journal. Research question well defined, relevant & meaningful. It is stated how research fills an identified knowledge gap. 3. Validity of the findings Obtained results are original. The conclusions are appropriately stated based on investigated data. 4. Comments for the Author a. Some specific words should be corrected or explained, such as: ESCaCO3, RHSiO2, what are ES and/or RH meaning? Author’s response: Corrected accordingly (line 15, 98, 111, and 118 in the revised manuscript). Eggshells (ES) and rice husk (RH). Eggshells calcium carbonate (ESCaCO3), rice husk silica (RHSiO2) b. A subsection “Chemicals and Reagents” is needed to add for indicating purity and listing of all chemicals used Author’s response: Inserted (Line 97 in the revised manuscript) c. A subsection “Characterizations” is needed to add for listing all techniques have been used for characterization of synthesized materials Author’s response: Inserted under instrumental analysis (Line 129 in the revised manuscript) d. All equations should be numbered and cited into body text Author’s response: Numbered and cited accordingly (Line 149-193 in the revised manuscript) e. A table must be added to compare the working conditions and the maximum adsorption capacity (Qmax) of the CS-SiO2/CaCO3 nanocomposites to other pervious reports Author’s response: Table 3 was added summarizing working conditions and the maximum adsorption capacity from literatures to the value obtained in this study (In the text in line 392-398 in the revised manuscript) f. Authors must check again the manuscript to correct all mistakes for examples: line 147, “ Author’s response: Corrected accordingly "	Here is a paper. Please give your review comments after reading it.
478	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>A graph-based genetic algorithm (GA) is used to identify molecules (ligands) with high absolute docking scores as estimated by the Glide software package, starting from randomly chosen molecules from the ZINC database, for four different targets: Bacillus subtilis chorismate mutase (CM), human β 2 -adrenergic G protein-coupled receptor (β 2 AR), the DDR1 kinase domain (DDR1), and β-cyclodextrin (BCD). By the combined use of functional group filters and a score modifier based on a heuristic synthetic accessibility (SA) score our approach identifies between ca 500 and 6000 structurally diverse molecules with scores better than known binders by screening a total of 400,000 molecules starting from 8000 randomly selected molecules from the ZINC database. Screening 250,000 molecules from the ZINC database identifies significantly more molecules with better docking scores than known binders, with the exception of CM, where the conventional screening approach only identifies 60 compounds compared to 511 with GA+Filter+SA. In the case of β 2 AR and DDR1 the GA+Filter+SA approach finds significantly more molecules with docking scores lower than -9.0 and -10.0. The GA+Filters+SA docking methodology is thus effective in generating a large and diverse set of synthetically accessible molecules with very good docking scores for a particular target. An early incarnation of the GA+Filter+SA approach was used to identify potential binders to the COVID-19 main protease and submitted to the early stages of the COVID Moonshot project, a crowdsourced initiative to accelerate the development of a COVID antiviral.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>INTRODUCTION</ns0:head><ns0:p>Docking of molecules to protein targets is an important part of computer aided drug discovery. <ns0:ref type='bibr' target='#b0'>[1]</ns0:ref> One use of molecular docking is high throughput virtual screening (HTVS) of libraries of known molecules. Recent studies have show that such HTVS of hundreds of millions <ns0:ref type='bibr' target='#b1'>[2]</ns0:ref> or even billions of molecules <ns0:ref type='bibr' target='#b2'>[3]</ns0:ref> are possible. However, such large numbers pale in comparison with the estimated 10 60 small molecules that make up chemical space.</ns0:p><ns0:p>The only practical way to search this space is to use search algorithms to identify interesting subspaces of manageable sizes. Historically, most work in this area as it relates to drug discovery have used evolutionary search algorithms to address this problem and such methods have also been applied to docking. The use of evolutionary algorithms in drug discovery has been reviewed by Devi et al. <ns0:ref type='bibr' target='#b3'>[4]</ns0:ref> . Examples involving docking include work by Pegg et al. <ns0:ref type='bibr' target='#b4'>[5]</ns0:ref> , Nicolaou et al. <ns0:ref type='bibr' target='#b5'>[6]</ns0:ref> , and Daeyaert and Deem <ns0:ref type='bibr' target='#b6'>[7]</ns0:ref> who all use genetic algorithms (GA) to optimise docking scores obtained by the DOCK, <ns0:ref type='bibr' target='#b7'>[8]</ns0:ref> , Glamdock, <ns0:ref type='bibr' target='#b8'>[9]</ns0:ref> Autodoc-Vina <ns0:ref type='bibr' target='#b9'>[10]</ns0:ref> programs, respectively. All three methods combine predefined molecular fragments in order to help ensure that the final molecules are synthetically accessible. Very recently, Cofala et al. <ns0:ref type='bibr' target='#b10'>[11]</ns0:ref> and Nigam et al. <ns0:ref type='bibr' target='#b11'>[12]</ns0:ref> presented SELFIES <ns0:ref type='bibr' target='#b12'>[13]</ns0:ref> -based (mutations only) GA approaches for optimising docking scores. Cofala et al. <ns0:ref type='bibr' target='#b10'>[11]</ns0:ref> optimised QuickVina 2 <ns0:ref type='bibr' target='#b13'>[14]</ns0:ref> docking scores for COVID-19 main protease (M Pro ). However, the several of the presented molecules in this study appear synthetically inaccessible. Nigam et al. <ns0:ref type='bibr' target='#b11'>[12]</ns0:ref> optimised docking scores to 5-hydroxytryptamine receptor 1B and Cytochrome P450 2D6 by interpolating between a known binder to each target. Finally, Cieplinski et al. <ns0:ref type='bibr' target='#b14'>[15]</ns0:ref> and Boitreaud et al. <ns0:ref type='bibr' target='#b15'>[16]</ns0:ref> have used variational autoencoders <ns0:ref type='bibr'>[17;18]</ns0:ref> to optimise SMINA <ns0:ref type='bibr' target='#b18'>[19]</ns0:ref> and Autodoc-Vina docking scores for several targets. Cieplinski et al. <ns0:ref type='bibr' target='#b14'>[15]</ns0:ref> noted difficulties in finding good binders using this approach while Boitreaud et al. <ns0:ref type='bibr' target='#b15'>[16]</ns0:ref> achieved some success.</ns0:p><ns0:p>In this paper we show that a non-fragment based GA <ns0:ref type='bibr' target='#b19'>[20]</ns0:ref> can be used to find more synthetically accessible molecules with good Glide <ns0:ref type='bibr'>[21;22]</ns0:ref> docking scores compared to conventional HTVS of libraries. We note that our study does not address whether docking is useful for drug discovery.</ns0:p></ns0:div> <ns0:div><ns0:head>COMPUTATIONAL METHODOLOGY</ns0:head><ns0:p>A graph-based genetic algorithm <ns0:ref type='bibr' target='#b19'>[20]</ns0:ref> (GA) is used to identify molecules (ligands) with high absolute docking scores as estimated by the Glide software package <ns0:ref type='bibr'>[21;22]</ns0:ref> using either the faster HTVS or the slower SP scoring methodology. While the scoring functions for HTVS and SP are the same, SP samples more intermediate conformations throughout the docking funnel, and also reduces the thoroughness of the final torsional refinement and sampling. For the COVID Moonshot project we also rescore some ligands using the XP scoring function, which has greater requirements for ligand-receptor shape complementarity and weeds out false positives that SP lets through. Five conformations of each molecule are generated using RDKit and minimized with the MMFF94 force field. <ns0:ref type='bibr'>[23;24;25;26;27]</ns0:ref> The lowest energy conformer is used for docking. The population size is 400 molecules, the mutation rate is 50% (meaning that a mutation operation is applied to 50% of the offspring-molecules), and the number of generations is 50. The maximum molecule size allowed is 30 ± 5 non-hydrogen atoms. Molecules are chosen for mating with a probability proportional to their scores (roulette selection) and the 400 best-scoring molecules are advanced to the next generation (elitism). The initial population is chosen randomly from a 250,000molecule subset of the ZINC database <ns0:ref type='bibr' target='#b27'>[28]</ns0:ref> used in a previous study. <ns0:ref type='bibr' target='#b19'>[20]</ns0:ref> As noted by Gao and Coley <ns0:ref type='bibr' target='#b28'>[29]</ns0:ref> and Brown et al. <ns0:ref type='bibr' target='#b29'>[30]</ns0:ref> generative models in general and GAs in particular often generate molecules with known chemically unstable bonds or molecules that are difficult to synthesise. We address this issue in three ways: we use Walters rd filters code (following Brown et al. <ns0:ref type='bibr' target='#b29'>[30]</ns0:ref> ), a score modifier suggested by Gao and Coley <ns0:ref type='bibr' target='#b28'>[29]</ns0:ref> based on a heuristic synthetic accessibility (SA) score <ns0:ref type='bibr' target='#b30'>[31]</ns0:ref> , and a combination of the two. The rd filers code contains several sets of SMARTS strings defining unstable bonds or groups. We use all the sets and eliminate any molecule with any of these moieties from the population. In the score modifier approach, the docking score is multiplied by a modified Gaussian function that ranges from 0 to 1 for high and low values of the SA score, respectively (a low SA score indicates a synthetically accessible molecule):</ns0:p><ns0:formula xml:id='formula_0'>score := score • e − 1 2 ⇣ max(SA score,µ)−µ σ ⌘ 2<ns0:label>(1)</ns0:label></ns0:formula><ns0:p>where µ = 2.230044 and σ = 0.6526308. <ns0:ref type='bibr' target='#b28'>[29]</ns0:ref> We found that the heuristic SA score depends on the protonation state of acid/base groups in is lower (better) for the neutral protonation state, so we neutralise such groups before computing the SA score.</ns0:p><ns0:p>The synthetic accessibility of the molecules in the final populations are estimated using the Molecule.one software package. <ns0:ref type='bibr' target='#b31'>[32]</ns0:ref> The calculations are submitted remotely to Molecule.one servers using a license generously provided by Molecule.one for this project. Just as for the heuristic SA score it is important to supply this algorithm with the neutralised forms of the molecules.</ns0:p><ns0:p>The docking targets are Bacillus subtilis chorismate mutase (CM), human β 2 -adrenergic G proteincoupled receptor (β 2 AR), the DDR1 kinase domain (DDR1), and β -cyclodextrin (BCD). For the three proteins we use the 2CHT, <ns0:ref type='bibr' target='#b32'>[33]</ns0:ref> 2RH1, <ns0:ref type='bibr' target='#b33'>[34]</ns0:ref> and 3ZOS <ns0:ref type='bibr' target='#b34'>[35]</ns0:ref> crystal structures from the Protein Data Bank <ns0:ref type='bibr' target='#b35'>[36]</ns0:ref> , respectively. The proteins are prepared for docking with the Protein Preparation Wizard <ns0:ref type='bibr' target='#b36'>[37]</ns0:ref> in the Maestro <ns0:ref type='bibr'>[38]</ns0:ref> software by protonating all residues assuming pH 7 with PropKa. <ns0:ref type='bibr' target='#b37'>[39]</ns0:ref> All three protein structures contain co-crystallized ligands: a transition state analog (TSA) for CM, carazolol for β 2 AR, and ponabtidin for DDR1. All water molecules in the crystal structures were removed before constructing a docking grid. For the three proteins, the position of each co-crystalized ligand is used as the centroid for the docking grid whereas for BCD we used the center of mass. A 20 Å buffer around the centroid was employed when constructing the docking grid. We re-dock these ligands to their respectively targets to get an idea of what docking score one would expect for known binders. We determined the protonation state and overall charge of each ligand (-2 for TSA, 0 for carazolol, and +1 for ponabtidin) by visual inspection of the crystal structure. In addition, we dock the known BCD-binder 6-(phenylamino)naphthalene-2-sulfonate (2,6-ANS) to BCD in the anionic form based on the typical pKa of the sulfonate group. The structures of the ligands are displayed in Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref>. <ns0:ref type='table' target='#tab_0'>1</ns0:ref>) without any structural screening (GA), with the group-filters (+Filter), with a heuristic synthetic accessibility score (SA), and with both Filter and SA. The HTVS scoring methodology is used except for CM(SP) where the SP scoring methodology is used. Columns 6 and 7 list the corresponding number molecules with scores lower than -9.0 kcal/mol and -10.0 kcal/mol obtained with +Filter+SA. The last three columns list the corresponding number of molecules obtained by docking all the molecules from the 250K ZINC subset.</ns0:p><ns0:p>GA +Filter +SA +Filter+SA <-9.0 <-10.0 ZINC <-9.0 <-10.0 </ns0:p><ns0:formula xml:id='formula_1'>CM</ns0:formula></ns0:div> <ns0:div><ns0:head>RESULTS AND DISCUSSION</ns0:head><ns0:p>We perform 20 different GA searches using the HTVS scoring function for each target. With a population size of 400 this generates up to 8000 different potential binders for each target in the final populations Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Table <ns0:ref type='table'>3</ns0:ref>. The average docking score and standard deviation (in kcal/mol) of all the molecules in the combined final populations of 20 GA searches, except for 'ZINC' which lists the corresponding values for the 8000 top scoring molecules obtained using the 250K ZINC subset. (there are a few duplicates for some targets). Column 2 in Table <ns0:ref type='table' target='#tab_1'>2</ns0:ref> shows the number of molecules that have better (more negative) scores than known binders to the four targets (Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref>). Virtually all the molecules in the final populations have better scores than the known binders using the simpler HTVS scoring methodology. The average scores shown in Table <ns0:ref type='table'>3</ns0:ref> show that the scores are not only better, but considerably better, than for the known binders, except for CM where the decrease is more modest (0.8 kcal/mol compared to 3.1-4.7 kcal/mol). When using the more complex SP scoring function for CM the number of molecule with better scores drops to 4638 compared to 7963, indicating that any conclusions drawn below is likely to depend on the scoring function. However, given the computational expense of the SP scoring function and the relatively large number of docking simulations performed in this study we continue using the HTVS scoring function below.</ns0:p><ns0:p>While these results are encouraging, visual inspection of some of the best scoring molecules (first column of molecules in Figure <ns0:ref type='figure' target='#fig_1'>1</ns0:ref>) show that they do not resemble drug-like molecules, indicating that they may be unstable and/or synthetically inaccessible. To quantify this property we compute a synthetic accessibility score using the Molecule.one retrosynthesis package which is based on machine learning algorithms and trained on a large number of reactions. Molecule.one returns a synthetic accessibility score ranging from 1 (very synthetically accessible) to 10 (not synthetically accessible) and the fraction of the 100 best-scoring molecules with a Molecule.one score below 10 is shown in Column 2 of Table <ns0:ref type='table' target='#tab_4'>4</ns0:ref>. DDR1 is the only case for which a non-negligible fraction of molecules (26%) may be synthetically viable. This problem has been observed before for generative models by, for example, Gao and Coley <ns0:ref type='bibr' target='#b28'>[29]</ns0:ref> , Brown et al. <ns0:ref type='bibr' target='#b29'>[30]</ns0:ref> , and Renz et al. <ns0:ref type='bibr' target='#b38'>[40]</ns0:ref> . We address this issue in three ways: we use Walters rd filters code (following Brown et al. <ns0:ref type='bibr' target='#b29'>[30]</ns0:ref> ), a score modifier based on a heuristic synthetic accessibility (SA) score <ns0:ref type='bibr' target='#b30'>[31]</ns0:ref> suggested by Gao and Coley <ns0:ref type='bibr' target='#b28'>[29]</ns0:ref> , and a combination of the two. The results are shown in Table <ns0:ref type='table' target='#tab_1'>2</ns0:ref> and show that the use of filters has relatively little effect on the number of molecules with better scores than the known binders and their average docking score . Unfortunately, there is also a negligible effect on the fraction of molecules deemed synthetically accessible molecules by Molecules.one (Table <ns0:ref type='table' target='#tab_4'>4</ns0:ref>). Inspection of the best scoring molecules for each target (second column of molecules in Figure <ns0:ref type='figure' target='#fig_1'>1</ns0:ref>) reveals that while the filters successfully prevented unstable bonding patterns, they do not prevent other reactive moieties such as cyclopentadienes and cyclohexadiene-like motifs.</ns0:p><ns0:p>The use of the SA score modifier has a much bigger effect on the number of molecules with better scores than the known binders and their average docking score. The effect is most pronounced for CM where the number of good binders drops more than an order of magnitude to only 181 molecules, while the decrease is about 70% for both β 2 AR and DDR1 and 23% for BCD. The most likely explanation is that ligands that bind well in the CM binding pocket tend to have high (bad) SA scores compared to the other targets. This is supported by the fact that the SA scores for the known binders TSA, carazolol, ponatinib, and 2,6-ANS (Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref>) are 5.4, 2.6, 2.9, and 1.8, respectively. The corresponding Molecule.one scores are 10.0, 2.9, 2.3, and 2.2, which indicates that the heuristic scores correlate well with the more sophisticated ML approach used by Molecule.one. With fewer molecules in the final population with high scores the average score necessarily increases (becomes less negative). The good news is that the fraction of molecules in the final population that Molecule.one deems synthetically accessible increases significantly to between 0.24 to 0.64. These fraction can be further increased to between 0.76 and 0.91, with only negligible effect on the number of good binders in the final population, by using the score modifier together with the filters. The increase in good binders (to 511) in the case of CM is most likely due to the stochastic nature of the GA searches.</ns0:p><ns0:p>A plot of the Molecule.one score vs the docking score (Figure <ns0:ref type='figure' target='#fig_3'>2a</ns0:ref>) obtained using Filters+SA shows Manuscript to be reviewed no correlation. Better scoring molecules are thus not necessarily harder to synthesise and the top scoring molecules for each target all have relatively low (good) synthetic accessibility scores. Furthermore, the fractions of synthetically accessible molecules computed using the top 100 scoring molecules are thus expected to be representative of the corresponding fractions for the entire final population.</ns0:p><ns0:note type='other'>Chemistry Journals</ns0:note><ns0:p>A similar plot of the ECFP4 Tanimoto similarity to the best scoring molecule for each target vs docking score (Figure <ns0:ref type='figure' target='#fig_3'>2b</ns0:ref>) also shows no correlation. The minimum and maximum similarity to the best scoring molecules are in the range of about 0.2-0.6 and indicting a great deal of structural diversity among the 100 best scoring molecules for each target. The GA+Filters+SA docking methodology is thus effective in generating a large and diverse set of synthetically accessible molecules with high docking scores for a particular target.</ns0:p><ns0:p>Inspection of the top scoring molecules obtained with both filters and a score modifier (Figures 1 (final column) and S2) reveal fairly ordinary looking organic molecules except that the charged states for CM and BCD (Figures <ns0:ref type='figure' target='#fig_1'>1d and p</ns0:ref>) are not reasonable for a pH of 7. Future studies using this approach will need to correct this by, for example, including additional filters or adding a term to the score that penalizes large deviations from empirically estimated pK a values. Finally, while the accuracy of the chosen docking methodology is not a focus of this paper we do note some encouraging signs for the HTVS scoring function in Glide. For example, all the top scoring molecules for CM (Figure <ns0:ref type='figure' target='#fig_1'>1</ns0:ref>) are dianions just like the known binder TSA (Figure <ns0:ref type='figure' target='#fig_1'>1</ns0:ref>) and the CM substrate chorismate. Only 327 out of the 250,000 molecules in the ZINC subset that the initial population is randomly drawn from are dianions so the dianion motif is most likely generated by the GA. Similarly, for the fluorene moiety seen in both carazolol and Figure <ns0:ref type='figure' target='#fig_1'>1g</ns0:ref> as well as two ethylene linked aromatic moieties seen in ponatinib and Figures <ns0:ref type='figure' target='#fig_1'>1i and k</ns0:ref>, but with only 242 and 40 occurrences in the 250K ZINC subset, respectively. For BCD there is a clear preference for both lipophilic (adamantane-like in Figure <ns0:ref type='figure' target='#fig_1'>1m</ns0:ref>) moieties favoring binding to the pocket as well as hydrophilic moieties which are also representative of the structures that bind favorably according to experiments.</ns0:p></ns0:div> <ns0:div><ns0:head>Comparison to conventional HTVS</ns0:head><ns0:p>While the GA+Filter+SA results are encouraging they do involve the docking of 400,000 compounds and the question whether equally good results can be obtained by simply screening a library of molecules of similar size. To answer this question we dock all 250,000 molecules in the ZINC subset from which we sample the initial population for the GA searches. This approach identifies significantly more molecules with better docking scores than known binders, with the exception of CM, where the conventional HTVS approach only identifies 60 compounds compared to 511 with GA+Filter+SA (Table <ns0:ref type='table' target='#tab_1'>2</ns0:ref>). However, the known binders for the remaining targets have relatively low docking scores of less than -7.0 kcal/mol. In the case of β 2 AR and DDR1 the GA+Filter+SA approach finds significantly more molecules with docking scores lower than -9.0 kcal/mol and -10.0 kcal/mol. Here, GA+Filter+SA finds 1.9 times as many molecules with a docking score lower than -9.0 kcal/mol by docking only 1.6 times as many molecules. Overall, the number of molecules deemed synthetically accessible by Molecule.one is about the same as for GA+Filter+SA: somewhat higher for β 2 AR and BCD and somewhat lower for CM and DDR1 (Table <ns0:ref type='table' target='#tab_4'>4</ns0:ref>, the top-10 scoring molecules for each target is shown in Figure <ns0:ref type='figure'>S3</ns0:ref>). The GA+Filter+SA therefore seems like a promising approach for finding molecules with very good docking scores compared to conventional HTVS of libraries.</ns0:p></ns0:div> <ns0:div><ns0:head>The COVID Moonshot project</ns0:head><ns0:p>The COVID Moonshot project, <ns0:ref type='bibr' target='#b39'>[41]</ns0:ref> a crowd-sourced initiative to accelerate the development of a COVID antiviral, was announced in mid-March 2020. The organizers of the project provided several crystal structures of the COVID-19 main protease (M Pro ) in complex with several small fragments <ns0:ref type='bibr' target='#b40'>[42]</ns0:ref> (Figure <ns0:ref type='figure' target='#fig_1'>S1</ns0:ref>) and invited the scientific community use this data to construct and submit potential M Pro inhibitors for further experimental verification. Though we were in a relatively early stage of this project we decided to build upon our methodology as it was then, to construct candidates for the second and third rounds of submissions.</ns0:p><ns0:p>For Round 2 we perform 20 GA searches with a population size of 400 using the HTVS+Filter+SA as above and the 6LU7 crystal structure of M Pro . <ns0:ref type='bibr' target='#b42'>[43]</ns0:ref> However, at that early stage we sampled our initial population from the first 1000 molecules of the 250K ZINC database subset. Also, we were not yet aware of the importance of neutralizing acid base groups before computing the SA score and checking synthetic accessibility, but this does not seem to be important for this target. Based on experience with the other targets we did increase the maximum molecule size to 50 ± 5 non-hydrogen atoms. All molecules in the final population are re-scored using the Glide XP scoring function <ns0:ref type='bibr' target='#b43'>[44]</ns0:ref> . The 128 molecules with a score better than or equal to -7.0 kcal/mol are selected and subjected to retrosynthetic analysis using the ASKCOS software package <ns0:ref type='bibr' target='#b44'>[45]</ns0:ref> using the settings suggested by Gao and Coley <ns0:ref type='bibr' target='#b28'>[29]</ns0:ref> . Molecules with less than 4 synthetic steps are selected and re-docked using the XP scoring methodology. The six molecules with XP scores better than -7.5 kcal/mol (Figure <ns0:ref type='figure'>3</ns0:ref>)a-f were then submitted to Round 2 in March 30th, 2020. The feedback on Twitter was that the molecules were rather small and more fragment-like than drug-like.</ns0:p><ns0:p>For Round 3 we use Glide SP rather than HTVS for the GA search, use Molecule.one in addition to ASKCOS to determine synthetic accessibility for molecules with XP scores better than -7.0 kcal/mol, and eliminate all molecules with for which both ASKCOS and Molecule.one fail to find a retrosynthetic route. The remaining 136 molecules are then re-docked using the XP scoring methodology and molecules are selected from among the top-scoring molecules. We selected four molecules for submission to Round 3 (Figure <ns0:ref type='figure'>3g-j</ns0:ref>) based on their score, diversity (also relative to our Round 2 submissions), and size and submitted them to Round 3 on April 2nd, 2020. (We also submitted four molecules selected based purely on their score, i.e. with synthetic accessibility considerations based on ASKCOS and Molecule.one, which are not discussed further.) Of the 10 submitted molecules, one was selected by the organizers (Figure <ns0:ref type='figure'>3b</ns0:ref>) for synthesis and assay, but showed relatively low inhibition (10% average inhibition at 20 µM) and was not pursued further. Many of our submissions feature a urea linkage or amide linkage that are also present in many of the fragment binders identified by the COVID Moonshot organizers (Figure <ns0:ref type='figure' target='#fig_1'>S1</ns0:ref>). In fact one of our submissions (Figure <ns0:ref type='figure'>3f</ns0:ref>) differs by only a few atoms from one of the fragments (Figure <ns0:ref type='figure' target='#fig_1'>S1(l)</ns0:ref>) as well as one the submissions selected for further study. Overall, these results are quite encouraging given that our submissions are generated starting from randomly selected molecules.</ns0:p></ns0:div> <ns0:div><ns0:head>CONCLUSION AND OUTLOOK</ns0:head><ns0:p>A graph-based genetic algorithm <ns0:ref type='bibr' target='#b19'>[20]</ns0:ref> (GA) is used to identify molecules (ligands) with high absolute docking scores as estimated by the Glide software package, <ns0:ref type='bibr'>[21;22]</ns0:ref> starting from randomly chosen molecules Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Figure <ns0:ref type='figure'>3</ns0:ref>. Molecules submitted to the second and third round of the COVID moonshot project (with the corresponding XP docking score in parenthesis). Next to each submitted molecule is a molecule that was selected for further study by the project organizers that most closely matches our submissions (with the corresponding ECFP4 Tanimoto similarity below). All docking scores in kcal/mol. from the ZINC database. We perform 20 different GA searches using the HTVS scoring function each for four different targets: Bacillus subtilis chorismate mutase (CM), human -adrenergic G protein-coupled receptor (β 2 AR), the DDR1 kinase domain (DDR1), and β -cyclodextrin (BCD). With a population size of 400 this approach generates up to 8000 different potential binders for each target, almost all of which have a better docking score than known binders (Figures <ns0:ref type='figure' target='#fig_3'>1 and 2</ns0:ref>). However, many of these molecules do not resemble drug-like molecules (Figures <ns0:ref type='figure' target='#fig_3'>1 and S2</ns0:ref>) and virtually none of the top-100 scoring molecules Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science are deemed synthetically accessible by the retrosynthetic software package Molecule.one <ns0:ref type='bibr' target='#b31'>[32]</ns0:ref> (Table <ns0:ref type='table' target='#tab_4'>4</ns0:ref>).</ns0:p><ns0:p>Following suggestions by Brown et al. <ns0:ref type='bibr' target='#b29'>[30]</ns0:ref> and Gao and Coley <ns0:ref type='bibr' target='#b28'>[29]</ns0:ref> we show that the synthetic accessibility can be increased significantly by the combined use of Walters rd filters code and a score modifier based on a heuristic synthetic accessibility (SA) score <ns0:ref type='bibr' target='#b30'>[31]</ns0:ref> (GA+Filter+SA). However, this also leads to a drop in the number of molecules with scores better than known binders of between 22% (BCD) and 95% (CM). The GA+Filter+SA approach thus identifies between roughly 500 and 6000 structurally diverse (Table <ns0:ref type='table' target='#tab_1'>2</ns0:ref> and Figure <ns0:ref type='figure' target='#fig_3'>S2</ns0:ref>) molecules with scores better than known binders by screening a total of 400,000 molecules starting from 8000 randomly selected molecules from the ZINC database. However, screening 250,000 molecules from the ZINC database identifies significantly more molecules with better docking scores than known binders, with the exception of CM, where the conventional HTVS approach only identifies 60 compounds compared to 511 with GA+Filter+SA (Table <ns0:ref type='table' target='#tab_1'>2</ns0:ref>). The known binders for the remaining targets have relatively low docking scores of less than -7.0 kcal/mol. In the case of β 2 AR and DDR1 the GA+Filter+SA approach finds significantly more molecules with docking scores lower than -9.0 kcal/mol and -10.0 kcal/mol. The GA+Filters+SA docking methodology is thus effective in generating a large and diverse set of synthetically accessible molecules with very good docking scores for a particular target. However, for targets such as CM that predominantly binds charged ligands this approach will need to correct for unphysical protonation states by, for example, including additional filters or adding a term to the score that penalizes large deviations from empirically estimated pK a values.</ns0:p><ns0:p>An early incarnation of the GA+Filter+SA approach was used to identify potential binders to the COVID-19 main protease (M Pro ) and submitted to the early stages of the COVID Moonshot project, <ns0:ref type='bibr' target='#b39'>[41]</ns0:ref> a crowd-sourced initiative to accelerate the development of a COVID antiviral. Of the 10 submitted molecules, one was selected by the COVID Moonshot organizers (Figure <ns0:ref type='figure'>3b</ns0:ref>) for synthesis and assay, but showed relatively low inhibition (10% average inhibition at 20 µM) and was not pursued further. Many of our submissions feature a urea linkage or amide linkage that are also present in many of the fragment binders identified by the COVID Moonshot organizers (Figure <ns0:ref type='figure' target='#fig_1'>S1</ns0:ref>). In fact one of our submissions (Figure <ns0:ref type='figure'>3f</ns0:ref>) differs by only a few atoms from one of the fragments (Figure <ns0:ref type='figure' target='#fig_1'>S1l</ns0:ref> as well as one the submissions selected for further study. Overall, these results are quite encouraging given that our submissions are generated starting from randomly selected molecules.</ns0:p><ns0:p>As pointed out by Cieplinski et al. <ns0:ref type='bibr' target='#b14'>[15]</ns0:ref> docking scores may also be used as a challenging test for generative models that 'reflect the complexity of real discovery problems'. <ns0:ref type='bibr' target='#b45'>[46]</ns0:ref> Our study suggests that finding synthetically accessible molecules with good docking scores for CM presents an especially challenging objective function, and more so if the SP docking score is used. However, a benchmark based on a commercial software package such as Glide is not ideal and it remains to be seen whether this target is equally challenging using open source docking software such as SMINA. <ns0:ref type='bibr' target='#b18'>[19]</ns0:ref> 9 </ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>0±0.6 -9.9±0.5 -6.6±1.5 -6.4±1.6 -7.7±0.5 BCD -9.1±0.2 -8.2±0.5 -5.6±0.8 -5.3±0.8 -5.7±0.2</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head>Figure 1 .</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1. Best scoring molecules from the final population of GA-docking runs (see text for explanation) for the four different targets: CM (a-d), β 2 AR (e-h), DDR1 (i-l), and BCD (m-p). The scores (in kcal/mol) are shown in parentheses.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>5 / 16 PeerJ</ns0:head><ns0:label>516</ns0:label><ns0:figDesc>Phy. Chem. reviewing PDF \| (PCHEM-2021:03:58722:1:1:NEW 19 Apr 2021)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head>Figure 2 .</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2. (a) Molecule.one score versus docking score for the 100 best binders predicted for the four different targets using Filters and SA. (b) ECFP4 Tanimoto similarity to the best scoring molecule for each target vs docking score for the 100 best binders predicted for the four different targets using Filters and SA.</ns0:figDesc><ns0:graphic coords='7,141.73,63.78,413.55,137.85' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head>7 / 16 PeerJ</ns0:head><ns0:label>716</ns0:label><ns0:figDesc>Phy. Chem. reviewing PDF \| (PCHEM-2021:03:58722:1:1:NEW 19 Apr 2021)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head>/ 16 PeerJ</ns0:head><ns0:label>16</ns0:label><ns0:figDesc>Phy. Chem. reviewing PDF \| (PCHEM-2021:03:58722:1:1:NEW 19 Apr 2021)Manuscript to be reviewedChemistry JournalsAnalytical, Inorganic, Organic, Physical, Materials Science</ns0:figDesc></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head>Table 1 .</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Docking scores in kcal/mol, heuristic synthetic accessibility scores, and Molecule.one scores for known binders.</ns0:figDesc><ns0:table><ns0:row><ns0:cell cols='2'>Target Molecule</ns0:cell><ns0:cell cols='2'>Charge HTVS</ns0:cell><ns0:cell cols='3'>SP SA score Molecule.one</ns0:cell></ns0:row><ns0:row><ns0:cell>CM</ns0:cell><ns0:cell>TSA</ns0:cell><ns0:cell>-2</ns0:cell><ns0:cell cols='2'>-7.5 -8.1</ns0:cell><ns0:cell>5.4</ns0:cell><ns0:cell>10.0</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>β 2 AR Carazolol</ns0:cell><ns0:cell>0</ns0:cell><ns0:cell>-6.8</ns0:cell><ns0:cell /><ns0:cell>2.6</ns0:cell><ns0:cell>2.9</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>DDR1 Ponatinib</ns0:cell><ns0:cell>+1</ns0:cell><ns0:cell>-6.9</ns0:cell><ns0:cell /><ns0:cell>2.9</ns0:cell><ns0:cell>2.3</ns0:cell></ns0:row><ns0:row><ns0:cell>BCD</ns0:cell><ns0:cell>2,6-ANS</ns0:cell><ns0:cell>-1</ns0:cell><ns0:cell>-4.4</ns0:cell><ns0:cell /><ns0:cell>1.8</ns0:cell><ns0:cell>2.2</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_1'><ns0:head>Table 2 .</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Columns</ns0:figDesc><ns0:table /><ns0:note>2-5 list the number of molecules (out of a total of about 8000) with docking scores higher than known binders (Table</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_4'><ns0:head>Table 4 .</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Fraction of the 100 top scoring molecules that are deemed synthetically accessible by Molecule.one.</ns0:figDesc><ns0:table><ns0:row><ns0:cell /><ns0:cell cols='4'>GA +Filter +SA +Filter+SA ZINC</ns0:cell></ns0:row><ns0:row><ns0:cell>CM</ns0:cell><ns0:cell>0.00</ns0:cell><ns0:cell>0.03 0.45</ns0:cell><ns0:cell>0.91</ns0:cell><ns0:cell>0.83</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>β 2 AR 0.00</ns0:cell><ns0:cell>0.05 0.55</ns0:cell><ns0:cell>0.76</ns0:cell><ns0:cell>0.84</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>DDR1 0.26</ns0:cell><ns0:cell>0.29 0.64</ns0:cell><ns0:cell>0.88</ns0:cell><ns0:cell>0.82</ns0:cell></ns0:row><ns0:row><ns0:cell>BCD</ns0:cell><ns0:cell>0.03</ns0:cell><ns0:cell>0.02 0.24</ns0:cell><ns0:cell>0.85</ns0:cell><ns0:cell>0.89</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:note place='foot' n='2'>/16 PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2021:03:58722:1:1:NEW 19 Apr 2021)Manuscript to be reviewed</ns0:note> <ns0:note place='foot'>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:note> <ns0:note place='foot' n='4'>/16 PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2021:03:58722:1:1:NEW 19 Apr 2021)Manuscript to be reviewed</ns0:note> <ns0:note place='foot' n='6'>/16 PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2021:03:58722:1:1:NEW 19 Apr 2021)Manuscript to be reviewed</ns0:note> </ns0:body> "	"Reviewer 1 (Akshat Kumar Nigam) In one of the experiments, the authors report the following fitness function “In the score modifier approach, the docking score is multiplied by a modified Gaussian function that ranges from 0 to 1 for high and low values of the SA score”. Here, it is my opinion that explicitly stating the fitness functions (using equations) for all experiments will help a reader. I found it challenging to understand the equation for this particular example. We have included an equation describing the function (eq 1 on page 2). “the mutation rate is 50%”: Can the authors please clarify how the mutation rate is determined?. For example, for a given molecule, are 50% of the atoms and bond modified?. In my opinion, this will help readers that have not read the GB-GA paper. Additionally, was this 50% mutation rate also used for crossovers?. We have now clarified this point: The population size is 400 molecules, the mutation rate is 50% (meaning that a mutation operation is applied to 50% of the offspring-molecules), and the number of generations is 50. “While these results are encouraging visual inspection of some of the best scoring molecules (first column of molecules in Figure 1) show that they do not resemble drug-like molecules, indicating that they may be unstable and/or synthetically inaccessible.” For better readability, please add a comma after “encouraging” (or break the sentence into a few parts). Done “and the fraction of the 100 best-scoring molecules with a Molecule.one score below 10 is shown in Column 2 of Table 4” In the previous part of this sentence, the authors state that Molecule.one returns a score between 1 and 10. As such, re-stating that “100 best-scoring molecules with a Molecule.one score below 10” might be redundant. Maybe the authors wanted to convey something else? We have changed “between 1 and 10” to “ranging from 1 to 10” “The only case for which a non-negligible fraction of molecules may be synthetically viable is DDR1 with 26% while for the rest that fraction is very close to 0%.”. Please consider rewriting this sentence. It is hard to read. We have rewritten the sentence as follows: “DDR1 is the only case for which a non-negligible fraction of molecules (26%) may be synthetically viable.” “A similar plot of the Tanimoto similarity to the best scoring molecule for each target vs docking score (Figure 2b) also shows no correlation”. The authors should explicitly state which fingerprint was utilized for calculating the similarity. We have now specified that we use ECFP4 fingerprints for the Tanimoto similarity Reviewer 2 (Christof Jäger) Is there a specific reason that Glide has been chosen as docking software? This is the software package we have most experience with. It might be valuable for the reader to briefly comment on the difference between Glide’s HTVS and SP (and XP) scoring. We have added the following discussion: “While the scoring functions for HTVS and SP are the same, SP samples more intermediate conformations throughout the docking funnel, and also reduces the thoroughness of the final torsional refinement and sampling. For the COVID Moonshot project we also rescore some ligands using the XP scoring function, which has greater requirements for ligand-receptor shape complementarity and weeds out false positives that SP lets through.” Why have five conformations been generated for each molecule at the start and how does this compare to e.g. a standard combination of Glide SP + Glide XP conformational search and optimisation approach? (also in terms of computational costs). Also in relation to the different number of flexibly rotating bonds in different molecules, could this fixed number lead to a bias in conformer selection? The standard algorithm for docking with Glide includes flexible ligands where ligand conformations are sampled by for example dihedral rotations over bonds before docking. In our initial trials this increased the calculation time considerably over rigid docking but we observed no clear indication that rigid docking decreased accuracy using the approach that was used in this study. The approach we settled on with five conformations were chosen as a compromise between speed and “accuracy”. “the docking score is multiplied by a modified Gaussian function that ranges from 0 to 1 for high and low values of the SA score”: Would it be possible to inform very briefly on the type of modification? We have included an equation describing the function (eq 1 on page 2). On the receptors: Do the receptors contain any “specialities” like metal ions or water molecules in the binding pockets and have they been treated (or removed)? No special metal ions are present in any of the hosts, but all water molecules were removed before a docking grid was constructed. We have clarified this in the manuscript: “All water molecules in the crystal structures were removed before constructing a docking grid. For the three proteins, the position of each co-crystalized ligand is used as the centroid for the docking grid whereas for BCD we used the center of mass. A 20 Å buffer around the centroid was employed when constructing the docking grid.” Has a docking grid been defined and what was its centroid and size? We have clarified this in the manuscript as well. For the proteins, the centroid used for docking is the position of the co-crystalized ligands. For BCD we used the center of mass as the centroid. As the authors might know, this referee is a strong supporter of open and FAIR data sharing principles. Thus, I appreciate that github links have been included in the supporting information. However, the links seem to be broken and might need corrections. (Is the interface with graph-based GA algorithm for this application also included there?) This appears to be caused by changes made to the submitted pdf by the PeerJ submission system. When the article is published the links will appear on the web page and should work. Yes, there is also a link to the GA code. The link between the new approach and the Covid Moonshot project could be established (on page 7) a bit better. It appears that “An early incarnation of the GA+Filter+SA approach” has been used for this part of the study and it might be good to better understand why this then has been added to this manuscript and if the developed approach would result in different outcomes. The Covid Moonshot section was added to the manuscript because it gives some insight into how our approach (albeit an early incarnation) can be used for a real-life application. Especially given the fact that the early incarnation is not too different from the final method. As we write in the paper: “However, at that early stage we sampled our initial population from the first 1000 molecules of the 250K ZINC database subset. Also, we were not yet aware of the importance of neutralizing acid base groups before computing the SA score and checking synthetic accessibility, but this does not seem to be important for this target.“ We thus don’t expect the results would differ significantly with the final version of the method. Please add units to values in Tables Units for Glide scores (kcal/mol) have been included now. The remaining entries in the tables are either number of molecules (as indicated in the table captions) or fractions, which do not have units. Figure 1 Caption: Replace “Highest scoring” with “best scoring”? Page 6 (Comparison to…): “equally good results can obtained” … “can be obtained” This has been fixed. "	Here is a paper. Please give your review comments after reading it.
479	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>The substitution of Ile to Val at residue 117 (I117V) of neuraminidase (NA) reduces the susceptibility of the A/H5N1 influenza virus to oseltamivir (OTV). However, the molecular mechanism by which the I117V mutation affects the intermolecular interactions between NA and OTV has not been fully elucidated. In this study, we performed molecular dynamics (MD) simulations to analyze the characteristic conformational changes that contribute to the reduced binding affinity of NA to OTV after the I117V mutation. The results of MD simulations revealed that after the I117V mutation in NA, the changes in the secondary structure around the mutation site had a noticeable effect on the residue interactions in the OTV-binding site. In the case of the WT NA-OTV complex, the positively charged side chain of R118, located in the β-sheet region, frequently interacted with the negatively charged side chain of E119, which is an amino acid residue in the OTV-binding site. This can reduce the electrostatic repulsion of E119 toward D151, which is also a negatively charged residue in the OTV-binding site, so that both E119 and D151 simultaneously form hydrogen bonds with OTV more frequently, which greatly contributes to the binding affinity of NA to OTV. After the I117V mutation in NA, the side chain of R118 interacted with the side chain of E119 less frequently, likely because of the decreased tendency of R118 to form a β-sheet structure. As a result, the electrostatic repulsion of E119 toward D151 is greater than that of the WT case, making it difficult for both E119 and D151 to simultaneously form hydrogen bonds with OTV, which in turn reduces the binding affinity of NA to OTV. Hence, after the I117V mutation in NA, influenza viruses are less susceptible to OTV because of conformational changes in residues of R118, E119, and D151 around the mutation site and in the binding site.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>Influenza A viruses infect a variety of avian and mammalian species, including humans <ns0:ref type='bibr' target='#b49'>(Webster et al., 1992)</ns0:ref>. Influenza A viruses are divided into subtypes based on antigenic differences of two virus surface glycoproteins, hemagglutinin (HA) and neuraminidase (NA) <ns0:ref type='bibr' target='#b15'>(Gamblin & Skehel, 2010)</ns0:ref>. Sixteen HA (H1-H16) and nine NA (N1-N9) subtypes have been isolated from wild waterfowl so far <ns0:ref type='bibr' target='#b13'>(Fouchier et al., 2005)</ns0:ref>. HA mediates virus entry into the host cell by binding to a terminal sialic acid on the host cell surface. NA is responsible for removing sialic acid to facilitate the release of progeny viruses from infected cells. Several NA inhibitors, such as oseltamivir (OTV), zanamivir, laninamivir, and peramivir, are currently available for the treatment of influenza virus infection <ns0:ref type='bibr' target='#b33'>(McKimm-Breschkin, 2012)</ns0:ref>. Among them, OTV is the most widely used anti-influenza drug <ns0:ref type='bibr' target='#b25'>(Kim et al., 1997)</ns0:ref>.</ns0:p><ns0:p>OTV-resistant H1N1 and H5N1 viruses have been isolated from humans as well as avian or swine species <ns0:ref type='bibr' target='#b37'>(Monto et al., 2006;</ns0:ref><ns0:ref type='bibr' target='#b38'>Rameix-Welti et al., 2006;</ns0:ref><ns0:ref type='bibr' target='#b32'>McKimm-Breschkin et al., 2007)</ns0:ref>. This suggests that viruses could acquire reduced sensitivity to OTV not only by drugselective pressure but also by natural genetic variation. In the mid-2000s, several H5N1 viruses with an Ile-to-Val substitution at position 117 of NA (I117V) were isolated from some avian species <ns0:ref type='bibr' target='#b19'>(Hurt et al., 2007;</ns0:ref><ns0:ref type='bibr' target='#b32'>McKimm-Breschkin et al., 2007;</ns0:ref><ns0:ref type='bibr' target='#b16'>Govorkova et al., 2009;</ns0:ref><ns0:ref type='bibr' target='#b6'>Chen et al., 2010;</ns0:ref><ns0:ref type='bibr' target='#b21'>Ilyushina et al., 2010;</ns0:ref><ns0:ref type='bibr' target='#b31'>McKimm-Breschkin et al., 2013;</ns0:ref><ns0:ref type='bibr'>Takano et al., 2013;</ns0:ref><ns0:ref type='bibr' target='#b30'>Marinova-Petkova et al., 2014;</ns0:ref><ns0:ref type='bibr' target='#b10'>Creanga et al., 2017;</ns0:ref><ns0:ref type='bibr' target='#b27'>Kode et al., 2019)</ns0:ref>. In vitro and in vivo experiments have shown that the I117V mutant NA conferred a reduction in susceptibility to OTV as compared to the wild-type (WT) <ns0:ref type='bibr' target='#b19'>(Hurt et al., 2007;</ns0:ref><ns0:ref type='bibr' target='#b32'>McKimm-Breschkin et al., 2007;</ns0:ref><ns0:ref type='bibr' target='#b6'>Chen et al., 2010;</ns0:ref><ns0:ref type='bibr' target='#b21'>Ilyushina et al., 2010;</ns0:ref><ns0:ref type='bibr' target='#b31'>McKimm-Breschkin et al., 2013;</ns0:ref><ns0:ref type='bibr'>Takano et al., 2013;</ns0:ref><ns0:ref type='bibr' target='#b10'>Creanga et al., 2017;</ns0:ref><ns0:ref type='bibr' target='#b27'>Kode et al., 2019)</ns0:ref>. Interestingly, residue 117 is not contained in the drug-binding site of NA, which consists of eight functional residues (R118, D151, R152, R224, E276, R292, R371, and Y406; N2 numbering) and eleven framework residues (E119, R156, W178, S179, D198, I222, E227, H274, E277, N294, and E425; N2 numbering) <ns0:ref type='bibr' target='#b9'>(Colman, Varghese & Laver, 1983;</ns0:ref><ns0:ref type='bibr' target='#b8'>Colman, Hoyne & Lawrence, 1993)</ns0:ref>. The molecular mechanism underlying how the mutation of residue 117, which is not part of the drug-binding site of NA, indirectly affects the molecular interaction between NA and OTV has not been fully elucidated.</ns0:p><ns0:p>Several computational studies using molecular dynamics (MD) simulations have reported on the molecular mechanism of reduced susceptibility to OTV in the I117V mutant <ns0:ref type='bibr'>(Takano et al., 2013;</ns0:ref><ns0:ref type='bibr' target='#b34'>Mhlongo & Soliman, 2015)</ns0:ref>. <ns0:ref type='bibr'>Takano et al. (2013)</ns0:ref> evaluated the effects of the I117V mutation in NA on OTV susceptibility in vitro, in vivo, and in silico. Their experimental results showed that the I117V mutation caused a slight reduction in the susceptibility of NA to OTV in vitro and dramatically in vivo. They also analyzed a single 2.5-ns trajectory obtained from MD simulations to further investigate the molecular mechanism by which the I117V mutation reduces the susceptibility of NA to OTV. Their computational results showed that the I117V mutation decreased the binding affinity for OTV because of the loss of hydrogen bonds between the carboxyl group of OTV and the side chain of R118 of NA. <ns0:ref type='bibr' target='#b34'>Mhlongo and Soliman (2015)</ns0:ref> analyzed four distinctive 25-ns trajectories obtained from MD simulations to investigate the molecular mechanism of the reduced susceptibility of the I117V mutant NA to OTV. Their computational results showed that the I117V mutation distorts the orientation of OTV in the drug-binding site of NA because of the loss of hydrogen bonds between the amino group of OTV and the side chain of E119 of NA, resulting in reduced binding affinity of NA to OTV. In these previous computational studies, the production trajectories of the MD simulations were too short to reach reliable statistical results. In addition, they focused on changes in the direct interactions between OTV and amino acid residues in the drug-binding site of NA. However, it was not clear how the I117V mutation of NA at a point outside its drug-binding site could cause changes in the intermolecular interaction with OTV.</ns0:p><ns0:p>In this study, we performed four distinctive 100-ns MD simulations for the WT and I117V mutant NA-OTV complexes in the A/H5N1 influenza virus. Based on the multiple production trajectories obtained from MD simulations, we analyzed the characteristic conformational changes around the I117V mutation site of NA, which greatly affected the intermolecular interactions with OTV. The results showed that after the I117V mutation in NA, the binding affinity between NA and OTV was reduced due to the conformational change of R118 adjacent to the mutation site, which affected the interactions of E119 and D151 with OTV. Thus, the present study successfully clarified the molecular mechanism by which the I117V mutation reduces the susceptibility of NA to OTV.</ns0:p></ns0:div> <ns0:div><ns0:head>Methods</ns0:head></ns0:div> <ns0:div><ns0:head>Initial structures</ns0:head><ns0:p>The coordinate of WT avian influenza virus A/H5N1 NA in complex with OTV was obtained from the Protein Data Bank (PDB code: 2HU4) <ns0:ref type='bibr' target='#b40'>(Russell et al., 2006)</ns0:ref>. The complex structure of the I117V mutant NA and OTV was generated by replacing isoleucine (Ile) 117 in the WT complex with valine (Val). H5N1 NA contains one calcium ion, which is necessary for structural stability <ns0:ref type='bibr' target='#b43'>(Smith et al., 2006)</ns0:ref>, but no calcium ions were found in the crystal structure registered as 2HU4. The coordinate of the calcium ion in NA was obtained from the structure of A/H5N1 NA registered as 3CL0 <ns0:ref type='bibr' target='#b7'>(Collins et al., 2008)</ns0:ref>. The protonation state of histidine (His) in NA at pH 7 was determined using the PDB2PQR server <ns0:ref type='bibr' target='#b12'>(Dolinsky et al., 2004)</ns0:ref>. The other ionized residues, such as arginine (Arg), lysine (Lys), aspartic acid (Asp), and glutamic acid (Glu), were treated as charged entities. The missing hydrogen atoms in NA and OTV were added using the LEaP program in the Amber 20 package <ns0:ref type='bibr' target='#b4'>(Case et al., 2020)</ns0:ref>. For each disulfide bond in NA, a covalent bond was created between the proximate cysteine residues using the LEaP program. The FF14SB variant of the AMBER force field was used to describe NA <ns0:ref type='bibr' target='#b29'>(Maier et al., 2015)</ns0:ref>. The parameters of the generalized AMBER force field (GAFF) were applied to OTV <ns0:ref type='bibr' target='#b48'>(Wang et al., 2004)</ns0:ref>. The partial atomic charges in OTV were determined on the basis of ab initio quantum chemistry calculations at the HF/6-31G(d) level with the Gaussian 16 program package <ns0:ref type='bibr' target='#b14'>(Frisch et al., 2016)</ns0:ref>, following the restrained electrostatic potential fitting procedure <ns0:ref type='bibr' target='#b2'>(Bayly et al., 1993)</ns0:ref>. The complexes of NA and OTV were dissolved in a truncated octahedral box filled with water molecules, where the box size was set so that there was a distance of at least 10 Å between the complexes and the boundary of the box. The TIP3P model was used to represent water molecules <ns0:ref type='bibr' target='#b22'>(Jorgensen et al., 1983)</ns0:ref>. The total charge of the systems was neutralized by the addition of sodium counter ions. Periodic boundary conditions were adopted. MD simulations MD simulations were performed using the PMEMD module in the Amber 20 package <ns0:ref type='bibr' target='#b4'>(Case et al., 2020)</ns0:ref>. The geometry of each system was optimized (energy minimized) using the steepest descent algorithm for 500 steps, followed by the conjugate gradient algorithm for 4,500 steps. After geometry optimization, each system was heated until the temperature (T) reached 300 K over a period of 200 ps in the NVT ensemble, while applying a harmonic restraint of 2 kcal mol −1 Å −2 on the complexes of NA and OTV, except for the hydrogen atoms. The temperature was regulated using the weak-coupling algorithm <ns0:ref type='bibr' target='#b3'>(Berendsen, Postma & Funsteren, 1984)</ns0:ref>. After heating, 10 ns of MD simulations were performed to equilibrate the system in the NpT ensemble at T = 300 K and a pressure (p) of 1.0 atm. The pressure was maintained using a Berendsen barostat. After equilibration, additional 10-ns MD simulations were performed in the NpT ensemble at T = 300 K and p = 1.0 atm. During MD simulations, all covalent bond lengths were constrained using the SHAKE algorithm <ns0:ref type='bibr' target='#b41'>(Ryckaert, Ciccotti, & Berendsen, 1977)</ns0:ref>. The time step of MD simulations was set to 2 fs. A cutoff for the non-bonded intermolecular interactions was set to 8 Å. Long-range electrostatic interactions were treated using the particle-mesh Ewald method <ns0:ref type='bibr' target='#b11'>(Darden, York, & Pedersen, 1993)</ns0:ref>. Finally, four copied MD simulations were performed for 100 ns starting with different coordinates and velocities in the NpT ensemble at T = 300 K and p = 1.0 atm, where the initial coordinates were randomly selected from the additional 10-ns trajectories after equilibration. The production phase to be analyzed was the last 80 ns of MD simulations, which was determined based on the root mean square displacement (RMSD) of the backbone atoms in the proteins with respect to the initial structure along the simulation time. The time series of RMSD and radius of gyration for the backbone atoms in the WT and I117V mutant NA are shown in Figures <ns0:ref type='figure' target='#fig_1'>S1 and S2</ns0:ref>. The changes in RMSD were almost constant after 20 ns, indicating that the MD simulations properly converged in the region of 20-100 ns.</ns0:p></ns0:div> <ns0:div><ns0:head>Binding free energy calculations</ns0:head><ns0:p>Binding free energies were determined for 400 frames extracted from the four distinctive production phases of the MD simulations, based on the Molecular Mechanics Poisson Boltzmann Surface Area (MM-PBSA) continuum solvation method <ns0:ref type='bibr' target='#b28'>(Kollman et al., 2000)</ns0:ref>. The MM-PBSA calculations were performed using the MMPBSA.py program in the Amber 20 package <ns0:ref type='bibr'>(Miller et al., 2012;</ns0:ref><ns0:ref type='bibr' target='#b4'>Case et al., 2020)</ns0:ref>. The adaptive Poisson Boltzmann (PB) solver was used to estimate the electrostatic contribution to the solvation free energy <ns0:ref type='bibr' target='#b1'>(Baker et al., 2001)</ns0:ref>. The linear PB equation was solved using a maximum of 1,000 iterations. The surface area for the nonpolar solvation energy term was determined using the Linear Combination of Pairwise Overlap (LCPO) algorithm <ns0:ref type='bibr' target='#b50'>(Weiser et al., 1999)</ns0:ref>. In calculations using continuum methods, the dielectric properties of the protein interior and solvent are represented in terms of the dielectric constants. In this study, the dielectric constant of the protein interior was set to 4, as a relatively large dielectric constant is desirable for NA, considering that its binding site contains many charged residues <ns0:ref type='bibr' target='#b18'>(Hou et al., 2011)</ns0:ref>. The dielectric constant of the solvent phase was set to 80. The ionic strength was set at 150 mM. The ratio between the longest dimension of the rectangular finitedifference grid and that of the solute was set to four.</ns0:p><ns0:p>Entropies due to the vibrational degrees of freedom were calculated for 100 configurations by normal mode analysis using the NAB program in the Amber 20 package <ns0:ref type='bibr' target='#b4'>(Case et al., 2020)</ns0:ref>. The geometry of each configuration was optimized (energy minimized) with a generalized Born solvent model, using a maximum of 10,000 steps with a target root-meansquare gradient of 10 −3 kcal mol −1 Å −1 .</ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head></ns0:div> <ns0:div><ns0:head>Binding structures and energies</ns0:head><ns0:p>Figure <ns0:ref type='figure' target='#fig_0'>1</ns0:ref> shows the snapshot images obtained from the MD simulations for the WT and I117V mutant NA-OTV complexes, which show the OTV binding site and the region adjacent to residue 117. As shown in Fig. <ns0:ref type='figure' target='#fig_0'>1</ns0:ref>, OTV bound to the WT or I117V mutant NA by forming hydrogen bonds with two negatively charged residues, E119 and D151, and three positively charged residues, R152, R292, and R371. Residue R118 has a positively charged side chain PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2021:06:62231:1:1:NEW 1 Oct 2021)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science similar to R292 and R371 in the binding site, but no hydrogen bond formation with OTV was observed. This is supported by the co-crystal structure of WT A/H5N1 NA with OTV (PDB code: 2HU4) <ns0:ref type='bibr' target='#b40'>(Russell et al., 2006)</ns0:ref> showing that R118 is not in a position to form hydrogen bonds with OTV.</ns0:p><ns0:p>Table <ns0:ref type='table'>1</ns0:ref> summarizes the computed binding free energies (∆G) of OTV for the WT and I117V mutant NA obtained from the MM-PBSA calculations, along with the enthalpy (∆H) and entropy (T∆S). The binding free energies of OTV were computed to be −14.60 and −11.88 kcal mol -1 for the WT and I117V mutant NA, respectively. The 2.72 kcal mol -1 increase in the binding free energy of OTV due to the I117V mutation could slightly reduce the susceptibility of this inhibitor to NA. This is supported by the fact that the I117V mutant NA has a 3-to approximately 50-fold decrease in the relative susceptibility to OTV compared with the WT NA in H5N1 viruses <ns0:ref type='bibr' target='#b19'>(Hurt et al., 2007;</ns0:ref><ns0:ref type='bibr' target='#b32'>McKimm-Breschkin et al., 2007;</ns0:ref><ns0:ref type='bibr' target='#b6'>Chen et al., 2010;</ns0:ref><ns0:ref type='bibr' target='#b21'>Ilyushina et al., 2010;</ns0:ref><ns0:ref type='bibr' target='#b31'>McKimm-Breschkin et al., 2013;</ns0:ref><ns0:ref type='bibr'>Takano et al., 2013;</ns0:ref><ns0:ref type='bibr' target='#b10'>Creanga et al., 2017;</ns0:ref><ns0:ref type='bibr' target='#b27'>Kode et al., 2019)</ns0:ref>. According to the WHO's antiviral working group criteria, influenza A viruses with < 10-fold change in the half maximal inhibitory concentration (IC 50 ) value were characterized as exhibiting normal inhibition, while those with 10-to 100-fold and > 100-fold changes exhibited reduced and highly reduced inhibition, respectively <ns0:ref type='bibr'>(WHO, 2012)</ns0:ref>. The relationship between ΔG and IC 50 can be approximated using ΔG  RT ln IC 50 , where R is the ideal gas constant and T is the temperature. The experimentally observed 3-to 50-fold change in the IC 50 value after I117V mutation corresponds to a binding free energy difference of 0.7−2.3 kcal mol −1 . The current results are qualitatively consistent with the experimental studies, indicating that the MD simulations, which form the basis for subsequent analyses, are reliable.</ns0:p><ns0:p>In this study, we adopted the single-trajectory MM-PBSA calculational approach, which has been widely used in previous studies, to determine binding free energy differences because of its good balance between computational cost and reliability <ns0:ref type='bibr' target='#b47'>(Wang et al., 2019)</ns0:ref>. In some cases the single-trajectory approach used in this study is less reliable for determining binding free energies than the multiple-trajectory approach that accounts for conformational changes upon drug binding. However, we emphasize that the binding free energy difference of 2.72 kcal mol −1 between the WT and I117V mutant NA determined in the present study is in good agreement with the experimentally determined value of 0.7-2.3 kcal mol −1 , which reveals that our MD simulations are sufficiently reliable for analyzing changes in various intra-protein interactions, such as hydrogen bonding, and secondary structures, which is the focus of this study.</ns0:p><ns0:p>As shown in Fig. <ns0:ref type='figure' target='#fig_0'>1</ns0:ref>, residue 117 did not interact directly with OTV in either WT or I117V mutant NA. Thus, the decrease in binding affinity between OTV and NA due to the I117V mutation could be the result of indirect effects due to changes in the interaction network of amino acid residues inside the protein. As shown in Table <ns0:ref type='table'>1</ns0:ref>, the change in the entropic component (TΔS) upon I117V mutation is almost zero, which indicates that the difference in the binding free energies (ΔΔG) is mostly enthalpy-driven rather than entropy-driven. Based on this observation, further analyses that focus on the factors responsible for changes in the direct interactions between OTV and NA are expected to be helpful. To elucidate the molecular mechanism by which the I117V mutation of NA at a point outside its drug-binding site could reduce the susceptibility to OTV, we performed the following detailed analysis based on the results of MD simulations.</ns0:p></ns0:div> <ns0:div><ns0:head>Hydrogen bond analysis</ns0:head><ns0:p>Figure <ns0:ref type='figure' target='#fig_1'>2</ns0:ref> shows the hydrogen bond occupancies of OTV for the WT and I117V mutant NAs during the MD simulations. The standard errors in hydrogen bond occupancies are small (less than 1%); the 95% confidence intervals for hydrogen bond occupancies are shown as error bars in Fig. <ns0:ref type='figure' target='#fig_1'>2</ns0:ref>. Hydrogen bonds were assigned using PYTRAJ <ns0:ref type='bibr' target='#b38'>(Nguyen et al., 2016)</ns0:ref>, a Python frontend package of the CPPTRAJ program <ns0:ref type='bibr' target='#b39'>(Roe & Cheatham, 2013)</ns0:ref>. As shown in Fig. <ns0:ref type='figure' target='#fig_1'>2</ns0:ref>, OTV bound to NA by forming hydrogen bonds with five charged amino acid residues, E119, D151, R152, R292, and R371. In hydrogen bonding with OTV, the side chains of E119 and D151 acted as hydrogen acceptors, while the side chains of R152, R292, and R371 acted as hydrogen donors. The occupancies of the hydrogen bonds formed by R292 and R371 with OTV were almost 100% in both the WT and I117V mutant NA-OTV complexes, indicating that the interactions were extremely stable. The hydrogen bond formed between OTV and R152 was found to be relatively unstable, with an occupancy of approximately 60% in both WT and I117V mutant cases. Notable changes caused by the I117V mutation in NA were observed in the hydrogen bonds formed by E119 and D151 with OTV. Because of the I117V mutation in NA, the hydrogen bond occupancy of the E119-OTV pair increased by approximately 10%, whereas the hydrogen bond occupancy of the D151-OTV pair decreased by approximately 30%. D151 formed hydrogen bonds with the adjacent positively charged R156 amino acid residue when not bound to OTV. Thus, the instability of the hydrogen bond with D151 after the I117V mutation might be the major reason for the reduced binding affinity of NA to OTV. Secondary structure analysis Figures <ns0:ref type='figure' target='#fig_2'>3A and 3B</ns0:ref> show the secondary structure occupancies in the region containing the 100th to 150th residues of NA for the WT and I117V mutant NA-OTV complexes, respectively. Figure <ns0:ref type='figure' target='#fig_2'>3C</ns0:ref> shows the changes in secondary structure occupancy after the I117V mutation. The secondary structure occupancies for all the residues in the NA are shown in Figure <ns0:ref type='figure' target='#fig_2'>S3</ns0:ref>. The standard errors for secondary structure occupancies are small (less than 1%); the 95% confidence intervals for secondary structure occupancies are shown as error bars in Figs. 3A, 3B and 3C. The secondary structures were classified into three simplified categories (helix, sheet, and coil) using the PYTRAJ package <ns0:ref type='bibr' target='#b38'>(Nguyen et al., 2016)</ns0:ref> based on the DSSP program <ns0:ref type='bibr' target='#b23'>(Kabsch & Sander, 1983)</ns0:ref>. The occupancies of secondary structures were calculated based on the assignment results for 3,200 three-dimensional structures extracted from four distinctive 80-ns trajectories in the production phase of MD simulations. In Fig. <ns0:ref type='figure' target='#fig_2'>3</ns0:ref>, the helix and sheet components are represented by red and blue bars, respectively, while the rest correspond to the coil.</ns0:p><ns0:p>As shown in Fig. <ns0:ref type='figure' target='#fig_0'>1</ns0:ref>, NA has an overall β-sheet-rich structure with partial helices. In the WT NA, as shown in Fig. <ns0:ref type='figure' target='#fig_2'>3A</ns0:ref>, the helix moieties were found in the region from the 105th to 111th residues and from the 143rd to 149th residues of NA. The 117th residue of interest in this study was located in the β-sheet region formed by residues between the 115th and 124th residues. After the I117V mutation, the secondary structure of the NA was significantly altered.</ns0:p><ns0:p>As shown in Fig. <ns0:ref type='figure' target='#fig_2'>3C</ns0:ref>, the occupancy of the helix component was significantly reduced in the region between residues the 147th and 149th residues with the I117V mutation. The secondary structure near the mutation site was also changed due to the I117V mutation, indicating that the β-sheet occupancy of R118 was reduced by approximately 12%. This may be due to a change in the orientation of R118 caused by the mutation of the bulkier Ile to the smaller Val at the 117th residue, which reduces the hydrogen-bonding interaction with residue L134 located in the adjacent antiparallel β-sheet moiety. Such conformational changes of R118 at a point inside the drug-binding site of NA would lead to a decrease in the binding affinity of the I117V mutant for OTV, due to the indirect effect of the Ile-to-Val mutation at residue 117.</ns0:p></ns0:div> <ns0:div><ns0:head>Residue-residue and residue-drug interactions</ns0:head><ns0:p>Figure <ns0:ref type='figure'>4</ns0:ref> shows the correlations between the distances of the R118-E119 pair (R R118-E119 ) and the D151-OTV pair (R D151-OTV ) in the WT and I117V mutant NA-OTV complexes as scatter plots and probability densities. The value of R R118-E119 was determined by measuring the inter-atomic distance between the carbon atom in the guanidino group of R118 and the carbon atom in the carboxyl group of E119. The value of R D151-OTV was determined by measuring the inter-atomic distance between the carbon atom in the carboxyl group of D151 and the nitrogen atom in the amino group of OTV. Figure <ns0:ref type='figure'>5</ns0:ref> shows the conformational fluctuations of the OTV binding site and adjacent I117V mutation site in the WT and I117V mutant NA-OTV complexes by superimposing 100 snapshot images obtained from the MD simulations.</ns0:p><ns0:p>In the case of the WT NA-OTV complex, as shown in Fig. <ns0:ref type='figure'>4</ns0:ref>, the distribution of R D151-OTV was generally localized in a monomodal manner around 3.6 Å, indicating that D151 tends to interact with OTV by forming hydrogen bonds. On the other hand, the distribution of R R118-E119 was bimodal, with one strongly localized around 4.2 Å and the other weakly distributed around 6.0 Å, indicating that the side chains of R118 and E119 tended to interact closely, but were sometimes too far apart to interact. These characteristic conformational fluctuations of R118, E119, and D151 can also be seen in the snapshot images of the three-dimensional structure shown in Fig. <ns0:ref type='figure'>5A</ns0:ref>.</ns0:p><ns0:p>In the case of the I117V mutant NA-OTV complex, as shown in Fig. <ns0:ref type='figure'>4</ns0:ref>, the distribution of R D151-OTV was bimodal, such that in addition to the peak observed around 3.6 Å, a peak also appeared around 6.2 Å, unlike in the WT case. This indicates that, after the I117V mutation, the frequency of hydrogen bond formation between D151 and OTV was reduced, which is also supported by the results shown in Fig. <ns0:ref type='figure' target='#fig_1'>2</ns0:ref>. The relatively large peak around 6.2 Å also suggests that the 150 loop of NA was frequently opened after the I117V mutation, similar to what has been observed in the NA mutants of many drug-resistant strains <ns0:ref type='bibr' target='#b17'>(Han, Liu & Mu, 2012;</ns0:ref><ns0:ref type='bibr' target='#b24'>Kar & Knecht, 2012;</ns0:ref><ns0:ref type='bibr' target='#b50'>Woods et al., 2012;</ns0:ref><ns0:ref type='bibr' target='#b42'>Schaduangrat et al., 2016;</ns0:ref><ns0:ref type='bibr' target='#b52'>Yadav, Igarashi & Yamamoto, 2021)</ns0:ref>. The distribution of R R118-E119 was multimodal, with peaks near 4.2 Å and 6.0 Å as in the WT case, and an additional weak peak appearing near 7.4 Å. Here, compared to the WT case, the probability density of the main component at around 4.2 Å decreased, while that of the components at around 6.0 Å and 7.4 Å increased. This shows that after the I117V mutation, the side chains of R118 and E119 tended to separate frequently, thus not interacting with each other, compared to the WT case.</ns0:p></ns0:div> <ns0:div><ns0:head>Discussion</ns0:head></ns0:div> <ns0:div><ns0:head>WT NA-OTV complex</ns0:head><ns0:p>In the WT NA-OTV complex, the residue-residue interaction between R118 and E119 may play a key role in enhancing the residue-drug interaction between D151 and OTV to increase the binding affinity of the WT NA to OTV. As shown in Figs. <ns0:ref type='figure' target='#fig_0'>1A and 5A</ns0:ref>, the negatively charged E119 interacts with the positively charged amino group of OTV, together with the negatively charged D151. Here, E119 and D151 tend to approach each other when interacting with OTV simultaneously, but the closer they are, the stronger the electrostatic repulsion between the negatively charged side chains. However, as shown in Fig. <ns0:ref type='figure' target='#fig_1'>2</ns0:ref>, the hydrogen bond occupancy of the E119-OTV and D151-OTV pairs was approximately 80% for both, indicating that E119 and D151 can form relatively stable hydrogen bonds with OTV. As shown in Fig. <ns0:ref type='figure' target='#fig_2'>3A</ns0:ref>, R118 is in the β-sheet region, indicating that its side chain can be rigidly oriented. Owing to the strong directivity of R118, derived from its secondary structure formation, its positively charged side chain can frequently interact in parallel with the negatively charged side chain of the adjacent E119. When R118 and E119 interact, the positive and negative charges of their side chains neutralize each other, thereby suppressing the electrostatic repulsion between E119 and D151. Thus, both E119 and D151 can simultaneously form hydrogen-bonding interactions with OTV, which contributes to the enhancement of the binding affinity of NA to OTV.</ns0:p></ns0:div> <ns0:div><ns0:head>I117V mutant NA-OTV complex</ns0:head><ns0:p>In the I117V mutant NA-OTV complex, the binding affinity of NA to OTV may be reduced by the weakening of the residue-drug interaction between D151 and OTV, accompanied by a decrease in the opportunity for residue-residue interaction between R118 and E119. As shown in Fig. <ns0:ref type='figure' target='#fig_2'>3C</ns0:ref>, the occupancy of R118 forming the β-sheet structure decreased after the I117V mutation, indicating that the directionality of its side chain was weakened. The weakening of the directionality of its positively charged side chain reduces the opportunity for interaction with the negatively charged side chain of the adjacent E119. The reduced interactions between the side chains of R118 and E119 are shown in Fig. <ns0:ref type='figure'>4</ns0:ref>. As mentioned earlier, in the WT NA-OTV complex, the interaction between R118 and E119 can contribute to reducing the electrostatic repulsion between E119 and D151. However, in the I117V mutant NA-OTV complex, the electrostatic repulsion between E119 and D151 can be enhanced, since R118 has less opportunity for interaction with E119. This inhibits both E119 and D151 from simultaneously forming hydrogen-bonding interactions with the same positively charged amino group of OTV, resulting in a decrease in the binding affinity between NA and OTV. Thus, the change in the interactions of these residues after the I117V mutation slightly reduces the binding affinity of NA to OTV, resulting in a reduction in OTV drug susceptibility to influenza viruses.</ns0:p><ns0:p>As mentioned in the Introduction, several computational studies that use MD simulations have reported on the molecular mechanism associated with the lower susceptibility of the I117V mutant to OTV <ns0:ref type='bibr'>(Takano et al., 2013;</ns0:ref><ns0:ref type='bibr' target='#b34'>Mhlongo & Soliman, 2015)</ns0:ref>. <ns0:ref type='bibr'>Takano et al. (2013)</ns0:ref> analyzed a single 2.5-ns MD trajectory to show that the loss of hydrogen bonding between the R118 side chain in NA and the OTV carboxyl group after I117V mutation is responsible for the reduced susceptibility of NA to OTV. A previous study by <ns0:ref type='bibr'>Takano et al. (2013)</ns0:ref> showed that hydrogen bonds are formed between the R118 residue of WT NA and OTV; however this was not observed in the previous study by <ns0:ref type='bibr' target='#b34'>Mhlongo and Soliman (2015)</ns0:ref> or in the current study. We speculate that this discrepancy is due the trajectory used for analysis in the previous study by <ns0:ref type='bibr'>Takano et al. (2013)</ns0:ref>, which was too short to adequately sample the conformational space of the system. <ns0:ref type='bibr' target='#b34'>Mhlongo and Soliman (2015)</ns0:ref> analyzed four distinctive 25-ns MD trajectories and suggested that the I117V mutation affects residue-residue interactions in NA that cause the drugbinding site to change its conformation, thereby altering residue-drug interactions between NA and OTV; however, the details were not clear. In the current study, we elucidated correlations between the residue-residue interaction of the R118-E119 pair and the residue-drug interaction of the D151-OTV pair in NA-OTV complexes by analyzing four distinctive 80-ns trajectories obtained from MD simulations, as shown in Fig. <ns0:ref type='figure'>4</ns0:ref>. As a result, we clarified the detailed molecular mechanism by which the I117V mutation in NA alters the inter-residue interactions between R118, E119, and D151, and destabilizes the residue-drug interaction between D151 and OTV, thereby reducing the susceptibility of NA to OTV.</ns0:p><ns0:p>We speculate that the I117V mutation not only affects the susceptibility of NA to OTV, but also viral fitness. With regard to viral fitness, we expect to extend the present study in the future to clarify the effects of the I117V mutation on the binding affinity of the natural sialic acid substrate to NA. However, according to <ns0:ref type='bibr' target='#b0'>Adams et al. (2019)</ns0:ref>, viral fitness not only depends on the binding affinity between the substrate and the enzyme, but also on the catalytic efficiency of the enzyme. Hence, clarifying the catalytic reaction mechanism of NA using expensive computational methods, such as the QM/MM method <ns0:ref type='bibr' target='#b44'>(Sousa et al., 2017)</ns0:ref>, is required to study the effect of NA mutations on viral fitness. Since analyzing the catalytic reaction of NA is far beyond the scope of this study, we simply mention it here as a future subject.</ns0:p></ns0:div> <ns0:div><ns0:head>Designing a potential drug design against I117V mutant strains</ns0:head><ns0:p>As summarized in Table <ns0:ref type='table'>1</ns0:ref>, the I117V mutation in NA reduces the binding free energy of NA to OTV by 2.72 kcal mol −1 , which corresponds to an approximate 100-fold decrease in the relative susceptibility of the I117V mutant NA to OTV compared to that of WT NA. The IC 50 value of OTV has been experimentally observed to change by a factor of 3-50 upon I117V mutation in NA <ns0:ref type='bibr' target='#b19'>(Hurt et al., 2007;</ns0:ref><ns0:ref type='bibr' target='#b32'>McKimm-Breschkin et al., 2007;</ns0:ref><ns0:ref type='bibr' target='#b6'>Chen et al., 2010;</ns0:ref><ns0:ref type='bibr' target='#b21'>Ilyushina et al., 2010;</ns0:ref><ns0:ref type='bibr' target='#b31'>McKimm-Breschkin et al., 2013;</ns0:ref><ns0:ref type='bibr'>Takano et al., 2013;</ns0:ref><ns0:ref type='bibr' target='#b10'>Creanga et al., 2017;</ns0:ref><ns0:ref type='bibr' target='#b27'>Kode et al., 2019)</ns0:ref>. Compared with substitutions that are selected under drug pressure and increase IC 50 by more than 600-fold, such as H274Y <ns0:ref type='bibr' target='#b20'>(Hurt et al., 2012)</ns0:ref>, the I117V mutation does not dramatically affect susceptibility to OTV. Therefore, OTV treatment may be effective against the I117V mutant strain of the influenza virus. However, the I117V mutation may affect OTV resistance in synergism with other mutations. For example, Hurt et al. <ns0:ref type='bibr' target='#b36'>(2012)</ns0:ref> found that the introduction of the dual I117V + H274Y mutation in NA significantly decreased susceptibility to OTV (a 1896-fold increase in IC 50 ) compared to that resulting from the H274Y mutation alone (a 650-fold increase in IC 50 ). Therefore, based on the new knowledge gained in this study, we propose guidelines for drug design that avoid the loss of drug sensitivity associated with the I117V mutation in preparation for the possible emergence of potent drug-resistant strains.</ns0:p><ns0:p>Based on our study, we suggest that an inhibitor with a longer positively charged group is better than one with a shorter positively charged group, such as the amino group in OTV, to avoid resistance from the I117V mutation that affects interactions between the inhibitor and the E119 and D151 NA binding site residues. A longer positively charged group in the inhibitor helps to reduce electrostatic repulsion between the negatively charged E119 and D151 side chains. For example, OTV has a short positively charged amino group that interacts with residues E119 and D151, while zanamivir has a long positively charged guanidino group. In fact, the I117V mutation in NA resulted in a significant 50-fold change in the IC 50 value for OTV but only a 1.6-fold change in the IC 50 value for zanamivir, which indicates that zanamivir is effective against the I117V mutant strain <ns0:ref type='bibr' target='#b31'>(McKimm-Breschkin et al., 2013)</ns0:ref>. In this study, we used molecular simulations to understand the molecular mechanism of OTV resistance associated with the I117V mutation in NA in detail, which led to the establishment of new molecular design guidelines that effectively solve the drug resistance problem.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions</ns0:head><ns0:p>In this study, we theoretically investigated the molecular mechanism of reduced OTV drug susceptibility in the A/H5N1 influenza virus harboring the NA I117V mutation using MD simulations.</ns0:p><ns0:p>In the WT NA-OTV complex, the interaction between R118 and E119 can play an important role in increasing the binding affinity of NA to OTV. In this case, the positively charged side chain of R118, located in the β-sheet region, can frequently interact with the negatively charged side chain of E119, preventing the electrostatic repulsion between E119 and D151. This enables both the negatively charged side chains of E119 and D151 to simultaneously form hydrogen bonding interactions with the positively charged amino group of OTV, thereby contributing significantly to the binding affinity between NA and OTV.</ns0:p><ns0:p>In the I117V mutant NA-OTV complex, the binding affinity of NA to OTV can be reduced by decreasing the opportunity for interaction between R118 and E119. In this case, the mutation reduces the tendency of R118 to form the β-sheet structure, leading to less frequent interaction between its positively charged side chain and the negatively charged side chain of E119. This increases the electrostatic repulsion between E119 and D151, making it difficult for both to simultaneously form hydrogen bonds with OTV, which in turn reduces the binding affinity between NA and OTV. Thus, after the I117V mutation in NA, influenza viruses are less susceptible to OTV because of changes in the residue interactions between R118, E119, and D151.</ns0:p><ns0:p>The present study has successfully clarified the molecular mechanism by which the I117V mutation in NA reduces the OTV drug susceptibility of the A/H5N1 influenza virus. </ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head>Figure 1</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head>Figure 2</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>Figure 3</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Figure 3</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='19,42.52,70.87,525.00,426.00' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='25,42.52,280.87,525.00,198.75' type='bitmap' /></ns0:figure> </ns0:body> "	"October 2, 2021 Professor Carsten Baldauf Academic Editor, PeerJ Physical Chemistry Dear Professor Baldauf: re: “Theoretical insights into the molecular mechanism of I117V mutation in neuraminidase mediated reduction of oseltamivir drug susceptibility in A/H5N1 influenza virus”; Article ID: 62231. We are most grateful to you and the reviewers for the helpful comments on the original version of our manuscript. We have taken all these comments into account and submit a revised version of our paper. We have addressed all the comments by Reviewers 1 and 2, as indicated in the attached “Response to Reviewers” files, and we hope that the explanations and revisions of our work are satisfactory. In addition, the overlap with the authors' previous paper in the Methods section, pointed out by PeerJ staff, has been appropriately addressed. Together with the revised manuscript, we uploaded a copy of the manuscript to show revisions. We hope that the revised version of our paper is now suitable for publication in PeerJ Physical Chemistry. I understand that we are going through a difficult time in response to COVID-19, so I’m afraid to ask, but I would like to know the journal's decision as soon as possible, because this paper is significant for my student’s career development. Considering that my student’s PhD registration is due in a few weeks, it would be really great to know the decision before that. According to the journal’s announcement, it takes about 35 days on average for the first decision. However, for this manuscript, 50 days passed before we received the first decision, which means that a lot of time has already passed. We look forward to hearing from you at your earliest convenience. Sincerely, Norifumi Yamamoto Chiba Institute of Technology 2-17-1 Tsudanuma, Narashino, Chiba 275-0016, Japan Tel & Fax: +81-47-478-0375 E-mail: norifumi.yamamoto@it-chiba.ac.jp Response to Reviewer 1 Reviewer’s comments (comments that require authors’ reply are numbered) Reviewer 1 (Anonymous) Basic reporting This is a clearly written manuscript that highlights the relevant literature in the introduction. The figures are relevant, clearly labelled and of high quality. There was no raw data supplied, which would be the simulation trajectories and secondary data derived from trajectory analysis. These raw data would take a lot of space and re-analysis would be beyond the scope of this review. Experimental design The experimental design is evident of original primary research. It is made clear how the research question fills the knowledge gap of understanding how mutations outside the active site of an enzyme can influence the binding affinity of an inhibitor. The Methods are state-of-the art and described with sufficient detail that an expert would be able to replicate the methods. Validity of the findings [Comment #1-1] All relevant data have been provided, however no statistical analysis was provided. It should be assessed, if the difference in hydrogen bond occupancy in figure 2 is statistically significant. At least error bars based on confidence intervals should be shown in figure 2 as well as in figure 3. [Comment #1-2] The description of the hydrogen bond results in lines 213, 214 (and elsewhere) should distinguish between thermodynamic and kinetic effects by using the correct terms stable/unstable and inert/labile. [addressed in Comments #1-3 to #1-5] Relevant and clear conclusions are made, which are linked to the research question. However, the discussion lacks depth as pointed out in section 4. Additional comments [Comment #1-3] The discussion is quite limited in scope. It is completely focussed at the results, without references to the wider literature and comparison with similar computational and/or experimental studies. [Comment #1-4] Furthermore, the discussion should refer to the significance of the finding for the future prospects of OTV as an anti-influenza drug and any implications for future drug discovery. [Comment #1-5] An appreciation that the mutation may affect viral fitness (with references to literature) as well as opportunities for further work should be shown. With regards to viral fitness, the present study may even be extended to include a comparison of the computed binding affinities of the natural sialic acid substrate to the mutant and wild-type enzyme. Reply to Reviewer’s comments We are grateful to Reviewer 1 for the critical comments and useful suggestions that have helped us to improve our paper. As indicated in the responses that follow, we have taken all these comments and suggestions into account in the revision of our paper. Comment #1-1: All relevant data have been provided, however no statistical analysis was provided. It should be assessed, if the difference in hydrogen bond occupancy in figure 2 is statistically significant. At least error bars based on confidence intervals should be shown in figure 2 as well as in figure 3. Authors’ reply: We agree with the reviewer's comment that the statistical errors should be clarified for hydrogen bond occupancy and secondary structure occupancy shown in Figures 2 and 3. The standard errors for hydrogen bond occupancy and secondary structure occupancy were small, less than 1%. In the revised manuscript, the 95% confidence intervals for hydrogen bond occupancy and secondary structure occupancy are shown as error bars in Figures 2 and 3. Changes to the manuscript: The standard errors in hydrogen bond occupancies are small (less than 1%); the 95% confidence intervals for hydrogen bond occupancies are shown as error bars in Fig. 2. [lines 229−231] The standard errors for secondary structure occupancies are small (less than 1%); the 95% confidence intervals for secondary structure occupancies are shown as error bars in Figs. 3A, 3B and 3C. [lines 252−254] Comment #1-2: The description of the hydrogen bond results in lines 213, 214 (and elsewhere) should distinguish between thermodynamic and kinetic effects by using the correct terms stable/unstable and inert/labile. Authors’ reply: We agree with the reviewer's comment that the description of the hydrogen bond results should distinguish between thermodynamic and kinetic effects. In the revised manuscript, the hydrogen bonding results are described so that thermodynamic and kinetic effects can be clearly distinguished. Changes to the manuscript: The hydrogen bond formed between OTV and R152 was found to be relatively unstable, … [lines 238, 239] Comment #1-3: The discussion is quite limited in scope. It is completely focussed at the results, without references to the wider literature and comparison with similar computational and/or experimental studies. Authors’ reply: We agree with the reviewer's comment that the discussion should be broadened to include comparisons with similar computational and/or experimental studies. In the revised manuscript, we referred to similar computational studies to broaden the discussion. Changes to the manuscript: As mentioned in the Introduction, several computational studies that use MD simulations have reported on the molecular mechanism associated with the lower susceptibility of the I117V mutant to OTV (Takano et al., 2013; Mhlongo & Soliman, 2015). Takano et al. (2013) analyzed a single 2.5-ns MD trajectory to show that the loss of hydrogen bonding between the R118 side chain in NA and the OTV carboxyl group after I117V mutation is responsible for the reduced susceptibility of NA to OTV. A previous study by Takano et al. (2013) showed that hydrogen bonds are formed between the R118 residue of WT NA and OTV; however this was not observed in the previous study by Mhlongo and Soliman (2015) or in the current study. We speculate that this discrepancy is due the trajectory used for analysis in the previous study by Takano et al. (2013), which was too short to adequately sample the conformational space of the system. Mhlongo and Soliman (2015) analyzed four distinctive 25-ns MD trajectories and suggested that the I117V mutation affects residue-residue interactions in NA that cause the drug-binding site to change its conformation, thereby altering residue-drug interactions between NA and OTV; however, the details were not clear. In the current study, we elucidated correlations between the residue-residue interaction of the R118-E119 pair and the residue-drug interaction of the D151-OTV pair in NA-OTV complexes by analyzing four distinctive 80-ns trajectories obtained from MD simulations, as shown in Fig. 4. As a result, we clarified the detailed molecular mechanism by which the I117V mutation in NA alters the inter-residue interactions between R118, E119, and D151, and destabilizes the residue-drug interaction between D151 and OTV, thereby reducing the susceptibility of NA to OTV. [lines 345−364] Comment #1-4: Furthermore, the discussion should refer to the significance of the finding for the future prospects of OTV as an anti-influenza drug and any implications for future drug discovery. Authors’ reply: We agree with the reviewer's comment that we should refer to the prospects of OTV as an anti-influenza drug and any implications for future drug discovery. In the revised manuscript, we mentioned these points to broaden the discussion. Changes to the manuscript: Designing a potential drug design against I117V mutant strains As summarized in Table 1, the I117V mutation in NA reduces the binding free energy of NA to OTV by 2.72 kcal mol−1, which corresponds to an approximate 100-fold decrease in the relative susceptibility of the I117V mutant NA to OTV compared to that of WT NA. The IC50 value of OTV has been experimentally observed to change by a factor of 3–50 upon I117V mutation in NA (Hurt et al., 2007; McKimm-Breschkin et al., 2007; Chen et al., 2010; Ilyushina et al., 2010; McKimm-Breschkin et al., 2013; Takano et al., 2013; Creanga et al., 2017; Kode et al., 2019). Compared with substitutions that are selected under drug pressure and increase IC50 by more than 600-fold, such as H274Y (Hurt et al., 2012), the I117V mutation does not dramatically affect susceptibility to OTV. Therefore, OTV treatment may be effective against the I117V mutant strain of the influenza virus. However, the I117V mutation may affect OTV resistance in synergism with other mutations. For example, Hurt et al. (2012) found that the introduction of the dual I117V + H274Y mutation in NA significantly decreased susceptibility to OTV (a 1896-fold increase in IC50) compared to that resulting from the H274Y mutation alone (a 650-fold increase in IC50). Therefore, based on the new knowledge gained in this study, we propose guidelines for drug design that avoid the loss of drug sensitivity associated with the I117V mutation in preparation for the possible emergence of potent drug-resistant strains. Based on our study, we suggest that an inhibitor with a longer positively charged group is better than one with a shorter positively charged group, such as the amino group in OTV, to avoid resistance from the I117V mutation that affects interactions between the inhibitor and the E119 and D151 NA binding site residues. A longer positively charged group in the inhibitor helps to reduce electrostatic repulsion between the negatively charged E119 and D151 side chains. For example, OTV has a short positively charged amino group that interacts with residues E119 and D151, while zanamivir has a long positively charged guanidino group. In fact, the I117V mutation in NA resulted in a significant 50-fold change in the IC50 value for OTV but only a 1.6-fold change in the IC50 value for zanamivir, which indicates that zanamivir is effective against the I117V mutant strain (McKimm-Breschkin et al., 2013). In this study, we used molecular simulations to understand the molecular mechanism of OTV resistance associated with the I117V mutation in NA in detail, which led to the establishment of new molecular design guidelines that effectively solve the drug resistance problem. [lines 375−404] Comment #1-5: An appreciation that the mutation may affect viral fitness (with references to literature) as well as opportunities for further work should be shown. With regards to viral fitness, the present study may even be extended to include a comparison of the computed binding affinities of the natural sialic acid substrate to the mutant and wild-type enzyme. Authors’ reply: We agree with the reviewer's comment that we should discuss how the mutation affects viral fitness. In order to study the effect of mutations in neuraminidase on viral fitness, it is necessary to clarify the catalytic reaction mechanism of neuraminidase using expensive computational methods such as the QM/MM method. Analyzing the catalytic reaction of enzyme is far beyond the scope of this study, and we believe that the actual analysis is a future work. In the revised manuscript, we included these points and discussed how the I117V mutation in neuraminidase affects the fitness of influenza viruses. Changes to the manuscript: We speculate that the I117V mutation not only affects the susceptibility of NA to OTV, but also viral fitness. With regard to viral fitness, we expect to extend the present study in the future to clarify the effects of the I117V mutation on the binding affinity of the natural sialic acid substrate to NA. However, according to Adams et al. (2019), viral fitness not only depends on the binding affinity between the substrate and the enzyme, but also on the catalytic efficiency of the enzyme. Hence, clarifying the catalytic reaction mechanism of NA using expensive computational methods, such as the QM/MM method (Sousa et al., 2017), is required to study the effect of NA mutations on viral fitness. Since analyzing the catalytic reaction of NA is far beyond the scope of this study, we simply mention it here as a future subject. [lines 365−373] Response to Reviewer 2 Reviewer’s comments (comments that require authors’ reply are numbered) Reviewer 2 (Chris Oostenbrink) Basic reporting [Comment #2-1] 1. The manuscript is clearly written. Only in the introduction around lines 66-68, I would suggest to use the present tense, as the residues are still located at the described positions, this was not only the case when the structures were reported. Experimental design [Comment #2-2] 2. The authors report that they performed four independent simulations, with different starting conformations. How were these different starting conformations obtained? [Comment #2-3] 3. I am typically not a fan of MM-PBSA calculations, but I laud the authors for the clear description of the methodology. Could they still say something about how the SA term was obtained and (more importantly) why they think a single-trajectory approach is appropriate? This is typically only the case if no conformational changes upon binding are to be expected. However, as the whole manuscript is about conformational changes due to the mutation, it is not unlikely that the indicated sidechains would perform very differently in an apo simulation of the protein? The argumentation that the triple trajectory approach leads to a lot of noise does not make the single-trajectory approach more appropriate. Validity of the findings [Comment #2-4] 4. At the end of the introduction, the authors make a rather bold statement that in previous studies the ‘simulations were too short to reach reliable statistical results’. This may be true, but after such a harsh statement, I would expect that the authors would go out of their way to show proper statistical tests for their analyses. As you have done four independent simulations, you could add error bars to the bar plots of figures 2 and 3. Is the decrease of the secondary structure elements of 12% or the change in the occurrence of hydrogen bonds by 10% or 30% actually statistically relevant? [Comment #2-5] 5. For the free energy differences in table 1, the authors may want to emphasize what the difference in free energy is for an effect of a factor 3-50. This would be between 0.6 and 2.3 kcal/mol. [Comment #2-6] 6. The authors may also want to emphasize that, based on table 1, they predict that the difference in binding affinity is mostly enthalpy driven and not entropy driven. Based on that analysis, it makes sense to focus on direct interactions, such as hydrogen bonds. Additional comments [Comment #2-7] 7. In what kind of interactions does D151 engage when it is not hydrogen bonded to OTV? [Comment #2-8] 8. The authors may want to spend a few thoughts on how their newly derived knowledge on the mechanism of the I117V mutation can be put to practical use. Do they expect that drugs that rely less on the interactions with E119 and D151 would suffer less from this mutation? Could they give suggestions of such molecules? Reply to Reviewer 2’s comments We are grateful to Reviewer 2 for the critical comments and useful suggestions that have helped us to improve our paper. As indicated in the responses that follow, we have taken all these comments and suggestions into account in the revision of our paper. Comment #2-1: 1. The manuscript is clearly written. Only in the introduction around lines 66-68, I would suggest to use the present tense, as the residues are still located at the described positions, this was not only the case when the structures were reported. Authors’ reply: We agree with the reviewer's comment that the lines 66-68 should be written in the present tense. In the revised manuscript, the parts pointed out in the reviewer’s comment are written in the present tense. Changes to the manuscript: Interestingly, residue 117 is not contained in the drug-binding site of NA, which consists of eight functional residues… [lines 67, 68] Comment #2-2: 2. The authors report that they performed four independent simulations, with different starting conformations. How were these different starting conformations obtained? Authors’ reply: We agree with the reviewer's comment that it should be clarified how the starting conformations of simulations were obtained. In the revised manuscript, we described more details about how the initial coordinates for the four independent simulations were obtained. Changes to the manuscript: Finally, four copied MD simulations were performed for 100 ns starting with different coordinates and velocities in the NpT ensemble at T = 300 K and p = 1.0 atm, where the initial coordinates were randomly selected from the additional 10-ns trajectories after equilibration. [lines 144−147] Comment #2-3: 3. I am typically not a fan of MM-PBSA calculations, but I laud the authors for the clear description of the methodology. Could they still say something about how the SA term was obtained and (more importantly) why they think a single-trajectory approach is appropriate? This is typically only the case if no conformational changes upon binding are to be expected. However, as the whole manuscript is about conformational changes due to the mutation, it is not unlikely that the indicated sidechains would perform very differently in an apo simulation of the protein? The argumentation that the triple trajectory approach leads to a lot of noise does not make the single-trajectory approach more appropriate. Authors’ reply (1): We agree with the reviewer's comment that it should be clarified how the surface area (SA) term was obtained. In the revised manuscript, we described more details about the SA term of the MM-PBSA calculations. Changes to the manuscript (1): The surface area for the nonpolar solvation energy term was determined using the Linear Combination of Pairwise Overlap (LCPO) algorithm (Weiser et al., 1999). [lines 161−163] Authors’ reply (2): We understand the reviewer’s comment that the single-trajectory approach may be less reliable in terms of the binding free energy estimation than the multiple-trajectory approach, which can account for conformational changes upon drug binding. The focus of the current study is to clarify the details of the molecular mechanism of reduced susceptibility associated with the I117V mutation in neuraminidase by analyzing changes in various intra-protein interactions such as hydrogen bonds and secondary structure. Thus, in this study, we adopted the single-trajectory approach, which has been widely used in previous studies for estimating the binding free energy difference because of its good balance between computational cost and reliability. Here, following the Reviewer's Comment 2-5, we can emphasize that the binding free energy difference between the wild-type and I117V mutant estimated in the present study was in good agreement with the experimentally observed changes in the IC50 values. This indicates that our MD simulations are sufficiently reliable as a basis for analyzing changes in various intra-protein interactions, such as hydrogen bonds and secondary structure, which is the focus of this study. In the revised manuscript, we described the above point about the reliability of the computational method used in this study. Changes to the manuscript (2): In this study, we adopted the single-trajectory MM-PBSA calculational approach, which has been widely used in previous studies, to determine binding free energy differences because of its good balance between computational cost and reliability (Wang et al., 2019). In some cases the single-trajectory approach used in this study is less reliable for determining binding free energies than the multiple-trajectory approach that accounts for conformational changes upon drug binding. However, we emphasize that the binding free energy difference of 2.72 kcal mol−1 between the WT and I117V mutant NA determined in the present study is in good agreement with the experimentally determined value of 0.7–2.3 kcal mol−1, which reveals that our MD simulations are sufficiently reliable for analyzing changes in various intra-protein interactions, such as hydrogen bonding, and secondary structures, which is the focus of this study. [lines 205−214] Comment #2-4: 4. At the end of the introduction, the authors make a rather bold statement that in previous studies the ‘simulations were too short to reach reliable statistical results’. This may be true, but after such a harsh statement, I would expect that the authors would go out of their way to show proper statistical tests for their analyses. As you have done four independent simulations, you could add error bars to the bar plots of figures 2 and 3. Is the decrease of the secondary structure elements of 12% or the change in the occurrence of hydrogen bonds by 10% or 30% actually statistically relevant? Authors’ reply: We agree with the reviewer's comment that the statistical errors should be clarified for hydrogen bond occupancy and secondary structure occupancy shown in Figures 2 and 3. In the revised manuscript, the 95% confidence intervals for hydrogen bond occupancy and secondary structure occupancy are shown as error bars in Figures 2 and 3. The standard errors for hydrogen bond occupancy and secondary structure occupancy were very small, less than 1%. Thus, a 12% decrease in the secondary structure elements, or a 10% or 30% change in the occurrence of hydrogen bonds, is statistically meaningful. Changes to the manuscript: The standard errors in hydrogen bond occupancies are small (less than 1%); the 95% confidence intervals for hydrogen bond occupancies are shown as error bars in Fig. 2. [lines 229−231] The standard errors for secondary structure occupancies are small (less than 1%); the 95% confidence intervals for secondary structure occupancies are shown as error bars in Figs. 3A, 3B and 3C. [lines 252−254] Comment #2-5: 5. For the free energy differences in table 1, the authors may want to emphasize what the difference in free energy is for an effect of a factor 3-50. This would be between 0.6 and 2.3 kcal/mol. Authors’ reply: We thank Reviewer 2 for this useful suggestion. In the revised manuscript, following the reviewer's suggestion, we emphasized that the binding free energy difference was in good agreement with the IC50 value change after the mutation. Changes to the manuscript: The relationship between ΔG and IC50 can be approximated using ΔG  RT ln IC50, where R is the ideal gas constant and T is the temperature. The experimentally observed 3- to 50-fold change in the IC50 value after I117V mutation corresponds to a binding free energy difference of 0.7−2.3 kcal mol−1. [lines 199−202] Comment #2-6: 6. The authors may also want to emphasize that, based on table 1, they predict that the difference in binding affinity is mostly enthalpy driven and not entropy driven. Based on that analysis, it makes sense to focus on direct interactions, such as hydrogen bonds. Authors’ reply: We thank Reviewer 2 for this useful suggestion. In the revised manuscript, following the reviewer's suggestion, we emphasized that the difference in binding affinity is mostly enthalpy driven and not entropy driven. Changes to the manuscript: As shown in Table 1, the change in the entropic component (TΔS) upon I117V mutation is almost zero, which indicates that the difference in the binding free energies (ΔΔG) is mostly enthalpy-driven rather than entropy-driven. Based on this observation, further analyses that focus on the factors responsible for changes in the direct interactions between OTV and NA are expected to be helpful. [lines 218−222] Comment #2-7: 7. In what kind of interactions does D151 engage when it is not hydrogen bonded to OTV? Authors’ reply: We agree with the reviewer's comment that we should clarify the interaction of D151 when it is not bound to OTV. D151 formed hydrogen bonds with the adjacent positively charged amino acid residue, R156, when it was not bound to OTV. In the revised version, this point has been clearly mentioned. In addition, changes have been made in Figures 1 and 5 to show the side chains of R156. Changes to the manuscript: D151 formed hydrogen bonds with the adjacent positively charged R156 amino acid residue when not bound to OTV. [lines 243, 244] Comment #2-8: 8. The authors may want to spend a few thoughts on how their newly derived knowledge on the mechanism of the I117V mutation can be put to practical use. Do they expect that drugs that rely less on the interactions with E119 and D151 would suffer less from this mutation? Could they give suggestions of such molecules? Authors’ reply: We agree with the reviewer's comment that we should give some thought on how to put our newly driven knowledge on the mechanism of the I117V mutation to practical use. In the revised manuscript, we described ideas for designing effective inhibitors for drug resistance caused by the I117V mutation based on our newly derived knowledge. Changes to the manuscript: Designing a potential drug design against I117V mutant strains As summarized in Table 1, the I117V mutation in NA reduces the binding free energy of NA to OTV by 2.72 kcal mol−1, which corresponds to an approximate 100-fold decrease in the relative susceptibility of the I117V mutant NA to OTV compared to that of WT NA. The IC50 value of OTV has been experimentally observed to change by a factor of 3–50 upon I117V mutation in NA (Hurt et al., 2007; McKimm-Breschkin et al., 2007; Chen et al., 2010; Ilyushina et al., 2010; McKimm-Breschkin et al., 2013; Takano et al., 2013; Creanga et al., 2017; Kode et al., 2019). Compared with substitutions that are selected under drug pressure and increase IC50 by more than 600-fold, such as H274Y (Hurt et al., 2012), the I117V mutation does not dramatically affect susceptibility to OTV. Therefore, OTV treatment may be effective against the I117V mutant strain of the influenza virus. However, the I117V mutation may affect OTV resistance in synergism with other mutations. For example, Hurt et al. (2012) found that the introduction of the dual I117V + H274Y mutation in NA significantly decreased susceptibility to OTV (a 1896-fold increase in IC50) compared to that resulting from the H274Y mutation alone (a 650-fold increase in IC50). Therefore, based on the new knowledge gained in this study, we propose guidelines for drug design that avoid the loss of drug sensitivity associated with the I117V mutation in preparation for the possible emergence of potent drug-resistant strains. Based on our study, we suggest that an inhibitor with a longer positively charged group is better than one with a shorter positively charged group, such as the amino group in OTV, to avoid resistance from the I117V mutation that affects interactions between the inhibitor and the E119 and D151 NA binding site residues. A longer positively charged group in the inhibitor helps to reduce electrostatic repulsion between the negatively charged E119 and D151 side chains. For example, OTV has a short positively charged amino group that interacts with residues E119 and D151, while zanamivir has a long positively charged guanidino group. In fact, the I117V mutation in NA resulted in a significant 50-fold change in the IC50 value for OTV but only a 1.6-fold change in the IC50 value for zanamivir, which indicates that zanamivir is effective against the I117V mutant strain (McKimm-Breschkin et al., 2013). In this study, we used molecular simulations to understand the molecular mechanism of OTV resistance associated with the I117V mutation in NA in detail, which led to the establishment of new molecular design guidelines that effectively solve the drug resistance problem. [lines 375−404] "	Here is a paper. Please give your review comments after reading it.
480	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>The substitution of Ile to Val at residue 117 (I117V) of neuraminidase (NA) reduces the susceptibility of the A/H5N1 influenza virus to oseltamivir (OTV). However, the molecular mechanism by which the I117V mutation affects the intermolecular interactions between NA and OTV has not been fully elucidated. In this study, we performed molecular dynamics (MD) simulations to analyze the characteristic conformational changes that contribute to the reduced binding affinity of NA to OTV after the I117V mutation. The results of MD simulations revealed that after the I117V mutation in NA, the changes in the secondary structure around the mutation site had a noticeable effect on the residue interactions in the OTV-binding site. In the case of the WT NA-OTV complex, the positively charged side chain of R118, located in the β-sheet region, frequently interacted with the negatively charged side chain of E119, which is an amino acid residue in the OTV-binding site. This can reduce the electrostatic repulsion of E119 toward D151, which is also a negatively charged residue in the OTV-binding site, so that both E119 and D151 simultaneously form hydrogen bonds with OTV more frequently, which greatly contributes to the binding affinity of NA to OTV. After the I117V mutation in NA, the side chain of R118 interacted with the side chain of E119 less frequently, likely because of the decreased tendency of R118 to form a β-sheet structure. As a result, the electrostatic repulsion of E119 toward D151 is greater than that of the WT case, making it difficult for both E119 and D151 to simultaneously form hydrogen bonds with OTV, which in turn reduces the binding affinity of NA to OTV. Hence, after the I117V mutation in NA, influenza viruses are less susceptible to OTV because of conformational changes in residues of R118, E119, and D151 around the mutation site and in the binding site.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>Influenza A viruses infect a variety of avian and mammalian species, including humans <ns0:ref type='bibr' target='#b49'>(Webster et al., 1992)</ns0:ref>. Influenza A viruses are divided into subtypes based on antigenic differences of two virus surface glycoproteins, hemagglutinin (HA) and neuraminidase (NA) <ns0:ref type='bibr'>(Gamblin & Skehel, 2010)</ns0:ref>. Sixteen HA (H1-H16) and nine NA (N1-N9) subtypes have been isolated from wild waterfowl so far <ns0:ref type='bibr' target='#b12'>(Fouchier et al., 2005)</ns0:ref>. HA mediates virus entry into the host cell by binding to a terminal sialic acid on the host cell surface. NA is responsible for removing sialic acid to facilitate the release of progeny viruses from infected cells. Several NA inhibitors, such as oseltamivir (OTV), zanamivir, laninamivir, and peramivir, are currently available for the treatment of influenza virus infection <ns0:ref type='bibr' target='#b31'>(McKimm-Breschkin, 2012)</ns0:ref>. Among them, OTV is the most widely used anti-influenza drug <ns0:ref type='bibr' target='#b24'>(Kim et al., 1997)</ns0:ref>.</ns0:p><ns0:p>OTV-resistant H1N1 and H5N1 viruses have been isolated from humans as well as avian or swine species <ns0:ref type='bibr' target='#b35'>(Monto et al., 2006;</ns0:ref><ns0:ref type='bibr' target='#b36'>Rameix-Welti et al., 2006;</ns0:ref><ns0:ref type='bibr' target='#b30'>McKimm-Breschkin et al., 2007)</ns0:ref>. This suggests that viruses could acquire reduced sensitivity to OTV not only by drugselective pressure but also by natural genetic variation. In the mid-2000s, several H5N1 viruses with an Ile-to-Val substitution at position 117 of NA (I117V) were isolated from some avian species <ns0:ref type='bibr' target='#b17'>(Hurt et al., 2007;</ns0:ref><ns0:ref type='bibr' target='#b30'>McKimm-Breschkin et al., 2007;</ns0:ref><ns0:ref type='bibr' target='#b14'>Govorkova et al., 2009;</ns0:ref><ns0:ref type='bibr' target='#b5'>Chen et al., 2010;</ns0:ref><ns0:ref type='bibr' target='#b19'>Ilyushina et al., 2010;</ns0:ref><ns0:ref type='bibr' target='#b29'>McKimm-Breschkin et al., 2013;</ns0:ref><ns0:ref type='bibr'>Takano et al., 2013;</ns0:ref><ns0:ref type='bibr' target='#b28'>Marinova-Petkova et al., 2014;</ns0:ref><ns0:ref type='bibr' target='#b9'>Creanga et al., 2017;</ns0:ref><ns0:ref type='bibr' target='#b25'>Kode et al., 2019)</ns0:ref>. In vitro and in vivo experiments have shown that the I117V mutant NA conferred a reduction in susceptibility to OTV as compared to the wild-type (WT) <ns0:ref type='bibr' target='#b17'>(Hurt et al., 2007;</ns0:ref><ns0:ref type='bibr' target='#b30'>McKimm-Breschkin et al., 2007;</ns0:ref><ns0:ref type='bibr' target='#b5'>Chen et al., 2010;</ns0:ref><ns0:ref type='bibr' target='#b19'>Ilyushina et al., 2010;</ns0:ref><ns0:ref type='bibr' target='#b29'>McKimm-Breschkin et al., 2013;</ns0:ref><ns0:ref type='bibr'>Takano et al., 2013;</ns0:ref><ns0:ref type='bibr' target='#b9'>Creanga et al., 2017;</ns0:ref><ns0:ref type='bibr' target='#b25'>Kode et al., 2019)</ns0:ref>. Interestingly, residue 117 is not contained in the drug-binding site of NA, which consists of eight functional residues (R118, D151, R152, R224, E276, R292, R371, and Y406; N2 numbering) and eleven framework residues (E119, R156, W178, S179, D198, I222, E227, H274, E277, N294, and E425; N2 numbering) <ns0:ref type='bibr' target='#b8'>(Colman, Varghese & Laver, 1983;</ns0:ref><ns0:ref type='bibr' target='#b7'>Colman, Hoyne & Lawrence, 1993)</ns0:ref>. The molecular mechanism underlying how the mutation of residue 117, which is not part of the drug-binding site of NA, indirectly affects the molecular interaction between NA and OTV has not been fully elucidated.</ns0:p><ns0:p>Several computational studies using molecular dynamics (MD) simulations have reported on the molecular mechanism of reduced susceptibility to OTV in the I117V mutant <ns0:ref type='bibr'>(Takano et al., 2013;</ns0:ref><ns0:ref type='bibr' target='#b32'>Mhlongo & Soliman, 2015)</ns0:ref>. <ns0:ref type='bibr'>Takano et al. (2013)</ns0:ref> evaluated the effects of the I117V mutation in NA on OTV susceptibility in vitro, in vivo, and in silico. Their experimental results showed that the I117V mutation caused a slight reduction in the susceptibility of NA to OTV in vitro and dramatically in vivo. They also analyzed a single 2.5-ns trajectory obtained from MD simulations to further investigate the molecular mechanism by which the I117V mutation reduces the susceptibility of NA to OTV. Their computational results showed that the I117V mutation decreased the binding affinity for OTV because of the loss of hydrogen bonds between the carboxyl group of OTV and the side chain of R118 of NA. <ns0:ref type='bibr' target='#b32'>Mhlongo and Soliman (2015)</ns0:ref> analyzed four distinctive 25-ns trajectories obtained from MD simulations to investigate the molecular mechanism of the reduced susceptibility of the I117V mutant NA to OTV. Their computational results showed that the I117V mutation distorts the orientation of OTV in the drug-binding site of NA because of the loss of hydrogen bonds between the amino group of OTV and the side chain of E119 of NA, resulting in reduced binding affinity of NA to OTV. In these previous computational studies, the production trajectories of the MD simulations were too short to reach reliable statistical results. In addition, they focused on changes in the direct interactions between OTV and amino acid residues in the drug-binding site of NA. However, it was not clear how the I117V mutation of NA at a point outside its drug-binding site could cause changes in the intermolecular interaction with OTV.</ns0:p><ns0:p>In this study, we performed four distinctive 100-ns MD simulations for the WT and I117V mutant NA-OTV complexes in the A/H5N1 influenza virus. Based on the multiple production trajectories obtained from MD simulations, we analyzed the characteristic conformational changes around the I117V mutation site of NA, which greatly affected the intermolecular interactions with OTV. The results showed that after the I117V mutation in NA, the binding affinity between NA and OTV was reduced due to the conformational change of R118 adjacent to the mutation site, which affected the interactions of E119 and D151 with OTV. Thus, the present study successfully clarified the molecular mechanism by which the I117V mutation reduces the susceptibility of NA to OTV.</ns0:p></ns0:div> <ns0:div><ns0:head>Methods</ns0:head></ns0:div> <ns0:div><ns0:head>Initial structures</ns0:head><ns0:p>The coordinate of WT avian influenza virus A/H5N1 NA in complex with OTV was obtained from the Protein Data Bank (PDB code: 2HU4) <ns0:ref type='bibr' target='#b38'>(Russell et al., 2006)</ns0:ref>. The complex structure of the I117V mutant NA and OTV was generated by replacing isoleucine (Ile) 117 in the WT complex with valine (Val). H5N1 NA contains one calcium ion, which is necessary for structural stability <ns0:ref type='bibr' target='#b41'>(Smith et al., 2006)</ns0:ref>, but no calcium ions were found in the crystal structure registered as 2HU4. The coordinate of the calcium ion in NA was obtained from the structure of A/H5N1 NA registered as 3CL0 <ns0:ref type='bibr' target='#b6'>(Collins et al., 2008)</ns0:ref>. The protonation state of histidine (His) in NA at pH 7 was determined using the PDB2PQR server <ns0:ref type='bibr' target='#b11'>(Dolinsky et al., 2004)</ns0:ref>. The other ionized residues, such as arginine (Arg), lysine (Lys), aspartic acid (Asp), and glutamic acid (Glu), were treated as charged entities. The missing hydrogen atoms in NA and OTV were added using the LEaP program in the Amber 20 package <ns0:ref type='bibr' target='#b4'>(Case et al., 2020)</ns0:ref>. For each disulfide bond in NA, a covalent bond was created between the proximate cysteine residues using the LEaP program. The FF14SB variant of the AMBER force field was used to describe NA <ns0:ref type='bibr' target='#b27'>(Maier et al., 2015)</ns0:ref>. The parameters of the generalized AMBER force field (GAFF) were applied to OTV <ns0:ref type='bibr' target='#b48'>(Wang et al., 2004)</ns0:ref>. The partial atomic charges in OTV were determined on the basis of ab initio quantum chemistry calculations at the HF/6-31G(d) level with the Gaussian 16 program package <ns0:ref type='bibr' target='#b13'>(Frisch et al., 2016)</ns0:ref>, following the restrained electrostatic potential fitting procedure <ns0:ref type='bibr' target='#b2'>(Bayly et al., 1993)</ns0:ref>. The complexes of NA and OTV were dissolved in a truncated octahedral box filled with water molecules, where the box size was set so that there was a distance of at least 10 Å between the complexes and the boundary of the box. The TIP3P model was used to represent water molecules <ns0:ref type='bibr' target='#b20'>(Jorgensen et al., 1983)</ns0:ref>. The total charge of the systems was neutralized by the addition of sodium counter ions. Periodic boundary conditions were adopted. MD simulations MD simulations were performed using the PMEMD module in the Amber 20 package <ns0:ref type='bibr' target='#b4'>(Case et al., 2020)</ns0:ref>. The geometry of each system was optimized (energy minimized) using the steepest descent algorithm for 500 steps, followed by the conjugate gradient algorithm for 4,500 steps. After geometry optimization, each system was heated until the temperature (T) reached 300 K over a period of 200 ps in the NVT ensemble, while applying a harmonic restraint of 2 kcal mol −1 Å −2 on the complexes of NA and OTV, except for the hydrogen atoms. The temperature was regulated using the weak-coupling algorithm <ns0:ref type='bibr' target='#b3'>(Berendsen, Postma & Funsteren, 1984)</ns0:ref>. After heating, 10 ns of MD simulations were performed to equilibrate the system in the NpT ensemble at T = 300 K and a pressure (p) of 1.0 atm. The pressure was maintained using a Berendsen barostat. After equilibration, additional 10-ns MD simulations were performed in the NpT ensemble at T = 300 K and p = 1.0 atm. During MD simulations, all covalent bond lengths were constrained using the SHAKE algorithm <ns0:ref type='bibr' target='#b39'>(Ryckaert, Ciccotti, & Berendsen, 1977)</ns0:ref>. The time step of MD simulations was set to 2 fs. A cutoff for the non-bonded intermolecular interactions was set to 8 Å. Long-range electrostatic interactions were treated using the particle-mesh Ewald method <ns0:ref type='bibr' target='#b10'>(Darden, York, & Pedersen, 1993)</ns0:ref>. Finally, four copied MD simulations were performed for 100 ns starting with different coordinates and velocities in the NpT ensemble at T = 300 K and p = 1.0 atm, where the initial coordinates were randomly selected from the additional 10-ns trajectories after equilibration and the initial velocities were randomly reassigned. The production phase to be analyzed was the last 80 ns of MD simulations, which was determined based on the root mean square displacement (RMSD) of the backbone atoms in the proteins with respect to the initial structure along the simulation time. The time series of RMSD and radius of gyration for the backbone atoms in the WT and I117V mutant NA are shown in Figures <ns0:ref type='figure' target='#fig_3'>S1 and S2</ns0:ref>. The changes in RMSD were almost constant after 20 ns, indicating that the MD simulations properly converged in the region of 20-100 ns.</ns0:p></ns0:div> <ns0:div><ns0:head>Binding free energy calculations</ns0:head><ns0:p>Binding free energies were determined for 400 frames extracted from the four distinctive production phases of the MD simulations, based on the Molecular Mechanics Poisson Boltzmann Surface Area (MM-PBSA) continuum solvation method <ns0:ref type='bibr' target='#b26'>(Kollman et al., 2000)</ns0:ref>. The MM-PBSA calculations were performed using the MMPBSA.py program in the Amber 20 package <ns0:ref type='bibr'>(Miller et al., 2012;</ns0:ref><ns0:ref type='bibr' target='#b4'>Case et al., 2020)</ns0:ref>. The adaptive Poisson Boltzmann (PB) solver was used to estimate the electrostatic contribution to the solvation free energy <ns0:ref type='bibr' target='#b1'>(Baker et al., 2001)</ns0:ref>. The linear PB equation was solved using a maximum of 1,000 iterations. The surface area for the nonpolar solvation energy term was determined using the Linear Combination of Pairwise Overlap (LCPO) algorithm <ns0:ref type='bibr' target='#b50'>(Weiser et al., 1999)</ns0:ref>. In calculations using continuum methods, the dielectric properties of the protein interior and solvent are represented in terms of the dielectric constants. In this study, the dielectric constant of the protein interior was set to 4, as a relatively large dielectric constant is desirable for NA, considering that its binding site contains many charged residues <ns0:ref type='bibr' target='#b16'>(Hou et al., 2011)</ns0:ref>. The dielectric constant of the solvent phase was set to 80. The ionic strength was set at 150 mM. The ratio between the longest dimension of the rectangular finitedifference grid and that of the solute was set to four.</ns0:p><ns0:p>Entropies due to the vibrational degrees of freedom were calculated for 100 configurations by normal mode analysis using the NAB program in the Amber 20 package <ns0:ref type='bibr' target='#b4'>(Case et al., 2020)</ns0:ref>. The geometry of each configuration was optimized (energy minimized) with a generalized Born solvent model, using a maximum of 10,000 steps with a target root-meansquare gradient of 10 −3 kcal mol −1 Å −1 .</ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head></ns0:div> <ns0:div><ns0:head>Binding structures and energies</ns0:head><ns0:p>Figure <ns0:ref type='figure' target='#fig_2'>1</ns0:ref> shows the snapshot images obtained from the MD simulations for the WT and I117V mutant NA-OTV complexes, which show the OTV binding site and the region adjacent to residue 117. As shown in Fig. <ns0:ref type='figure' target='#fig_2'>1</ns0:ref>, OTV bound to the WT or I117V mutant NA by forming hydrogen bonds with two negatively charged residues, E119 and D151, and three positively Manuscript to be reviewed Chemistry Journals charged residues, R152, R292, and R371. Residue R118 has a positively charged side chain similar to R292 and R371 in the binding site, but no hydrogen bond formation with OTV was observed. This is supported by the co-crystal structure of WT A/H5N1 NA with OTV (PDB code: 2HU4) <ns0:ref type='bibr' target='#b38'>(Russell et al., 2006)</ns0:ref> showing that R118 is not in a position to form hydrogen bonds with OTV.</ns0:p><ns0:p>Table <ns0:ref type='table'>1</ns0:ref> summarizes the computed binding free energies (∆G) of OTV for the WT and I117V mutant NA obtained from the MM-PBSA calculations, along with the enthalpy (∆H) and entropy (T∆S). The binding free energies of OTV were computed to be −14.60 and −11.88 kcal mol -1 for the WT and I117V mutant NA, respectively. The 2.72 kcal mol -1 increase in the binding free energy of OTV due to the I117V mutation could slightly reduce the susceptibility of this inhibitor to NA. This is supported by the fact that the I117V mutant NA has a 3-to approximately 50-fold decrease in the relative susceptibility to OTV compared with the WT NA in H5N1 viruses <ns0:ref type='bibr' target='#b17'>(Hurt et al., 2007;</ns0:ref><ns0:ref type='bibr' target='#b30'>McKimm-Breschkin et al., 2007;</ns0:ref><ns0:ref type='bibr' target='#b5'>Chen et al., 2010;</ns0:ref><ns0:ref type='bibr' target='#b19'>Ilyushina et al., 2010;</ns0:ref><ns0:ref type='bibr' target='#b29'>McKimm-Breschkin et al., 2013;</ns0:ref><ns0:ref type='bibr'>Takano et al., 2013;</ns0:ref><ns0:ref type='bibr' target='#b9'>Creanga et al., 2017;</ns0:ref><ns0:ref type='bibr' target='#b25'>Kode et al., 2019)</ns0:ref>. According to the WHO's antiviral working group criteria, influenza A viruses with < 10-fold change in the half maximal inhibitory concentration (IC 50 ) value were characterized as exhibiting normal inhibition, while those with 10-to 100-fold and > 100-fold changes exhibited reduced and highly reduced inhibition, respectively <ns0:ref type='bibr'>(WHO, 2012)</ns0:ref>. If experiments are done under the same conditions, the relative binding free energy of ΔΔG = ΔG (1) -ΔG (2) can be approximated using ΔΔG  RT ln (IC 50 (1) / IC 50 (2) ), where R is the ideal gas constant and T is the temperature. The experimentally observed 3-to 50-fold change in the IC 50 value after I117V mutation corresponds to a binding free energy difference of 0.7−2.3 kcal mol −1 . The current results are qualitatively consistent with the experimental studies, indicating that the MD simulations, which form the basis for subsequent analyses, are reliable.</ns0:p><ns0:p>In this study, we adopted the single-trajectory approach in the MM-PBSA calculation, because it assumes that no significant conformational changes occur upon ligand binding. The single-trajectory MM-PBSA approach has been widely used in previous studies, to determine binding free energy differences because of its good balance between computational cost and reliability <ns0:ref type='bibr' target='#b47'>(Wang et al., 2019)</ns0:ref>. In some cases the single-trajectory approach used in this study is less reliable for determining binding free energies than the multiple-trajectory approach that accounts for conformational changes upon drug binding. However, we emphasize that the binding free energy difference of 2.72 kcal mol −1 between the WT and I117V mutant NA determined in the present study is in good agreement with the experimentally determined value of 0.7-2.3 kcal mol −1 , which reveals that our MD simulations are sufficiently reliable for analyzing changes in various intra-protein interactions, such as hydrogen bonding, and secondary structures, which is the focus of this study.</ns0:p><ns0:p>As shown in Fig. <ns0:ref type='figure' target='#fig_2'>1</ns0:ref>, residue 117 did not interact directly with OTV in either WT or I117V mutant NA. Thus, the decrease in binding affinity between OTV and NA due to the I117V mutation could be the result of indirect effects due to changes in the interaction network of amino acid residues inside the protein. As shown in Table <ns0:ref type='table'>1</ns0:ref>, the change in the entropic component (TΔS) upon I117V mutation is almost zero, which indicates that the difference in the binding free energies (ΔΔG) is mostly enthalpy-driven rather than entropy-driven. Based on this observation, further analyses that focus on the factors responsible for changes in the direct interactions between OTV and NA are expected to be helpful. To elucidate the molecular mechanism by which the I117V mutation of NA at a point outside its drug-binding site could reduce the susceptibility to OTV, we performed the following detailed analysis based on the results of MD simulations.</ns0:p></ns0:div> <ns0:div><ns0:head>Hydrogen bond analysis</ns0:head><ns0:p>Figure <ns0:ref type='figure' target='#fig_3'>2</ns0:ref> shows the hydrogen bond occupancies of OTV for the WT and I117V mutant NAs during the MD simulations. The standard errors in hydrogen bond occupancies are small (less than 1%); the 95% confidence intervals for hydrogen bond occupancies are shown as error bars in Fig. <ns0:ref type='figure' target='#fig_3'>2</ns0:ref>. Hydrogen bonds were assigned using PYTRAJ <ns0:ref type='bibr' target='#b36'>(Nguyen et al., 2016)</ns0:ref>, a Python frontend package of the CPPTRAJ program <ns0:ref type='bibr' target='#b37'>(Roe & Cheatham, 2013)</ns0:ref>. As shown in Fig. <ns0:ref type='figure' target='#fig_3'>2</ns0:ref>, OTV bound to NA by forming hydrogen bonds with five charged amino acid residues, E119, D151, R152, R292, and R371. In hydrogen bonding with OTV, the side chains of E119 and D151 acted as hydrogen acceptors, while the side chains of R152, R292, and R371 acted as hydrogen donors. The occupancies of the hydrogen bonds formed by R292 and R371 with OTV were almost 100% in both the WT and I117V mutant NA-OTV complexes, indicating that the interactions were extremely stable. The hydrogen bond formed between OTV and R152 was found to be relatively unstable, with an occupancy of approximately 60% in both WT and I117V mutant cases. Notable changes caused by the I117V mutation in NA were observed in the hydrogen bonds formed by E119 and D151 with OTV. Because of the I117V mutation in NA, the hydrogen bond occupancy of the E119-OTV pair increased by approximately 10%, whereas the hydrogen bond occupancy of the D151-OTV pair decreased by approximately 30%. D151 formed hydrogen bonds with the adjacent positively charged R156 amino acid residue when not bound to OTV. Thus, the instability of the hydrogen bond with D151 after the I117V mutation might be the major reason for the reduced binding affinity of NA to OTV. Secondary structure analysis Figures <ns0:ref type='figure' target='#fig_4'>3A and 3B</ns0:ref> show the secondary structure occupancies in the region containing the 100th to 150th residues of NA for the WT and I117V mutant NA-OTV complexes, respectively. Figure <ns0:ref type='figure' target='#fig_4'>3C</ns0:ref> shows the changes in secondary structure occupancy after the I117V mutation. The secondary structure occupancies for all the residues in the NA are shown in Figure <ns0:ref type='figure' target='#fig_4'>S3</ns0:ref>. The standard errors for secondary structure occupancies are small (less than 1%); the 95% confidence intervals for secondary structure occupancies are shown as error bars in Figs. 3A, 3B and 3C. The secondary structures were classified into three simplified categories (helix, sheet, and coil) using the PYTRAJ package <ns0:ref type='bibr' target='#b36'>(Nguyen et al., 2016)</ns0:ref> based on the DSSP program <ns0:ref type='bibr' target='#b21'>(Kabsch & Sander, 1983)</ns0:ref>. The occupancies of secondary structures were calculated based on the assignment results for 3,200 three-dimensional structures extracted from four distinctive 80-ns trajectories in the production phase of MD simulations. In Fig. <ns0:ref type='figure' target='#fig_4'>3</ns0:ref>, the helix and sheet components are represented by red and blue bars, respectively, while the rest correspond to the coil.</ns0:p><ns0:p>As shown in Fig. <ns0:ref type='figure' target='#fig_2'>1</ns0:ref>, NA has an overall β-sheet-rich structure with partial helices. In the WT NA, as shown in Fig. <ns0:ref type='figure' target='#fig_4'>3A</ns0:ref>, the helix moieties were found in the region from the 105th to 111th residues and from the 143rd to 149th residues of NA. The 117th residue of interest in this study was located in the β-sheet region formed by residues between the 115th and 124th residues. After the I117V mutation, the secondary structure of the NA was significantly altered.</ns0:p><ns0:p>As shown in Fig. <ns0:ref type='figure' target='#fig_4'>3C</ns0:ref>, the occupancy of the helix component was significantly reduced in the region between residues the 147th and 149th residues with the I117V mutation. The secondary structure near the mutation site was also changed due to the I117V mutation, indicating that the β-sheet occupancy of R118 was reduced by approximately 12%. This may be due to a change in the orientation of R118 caused by the mutation of the bulkier Ile to the smaller Val at the 117th residue, which reduces the hydrogen-bonding interaction with residue L134 located in the adjacent antiparallel β-sheet moiety. Such conformational changes of R118 at a point inside the drug-binding site of NA would lead to a decrease in the binding affinity of the I117V mutant for OTV, due to the indirect effect of the Ile-to-Val mutation at residue 117.</ns0:p></ns0:div> <ns0:div><ns0:head>Residue-residue and residue-drug interactions</ns0:head><ns0:p>Figure <ns0:ref type='figure'>4</ns0:ref> shows the correlations between the distances of the R118-E119 pair (R R118-E119 ) and the D151-OTV pair (R D151-OTV ) in the WT and I117V mutant NA-OTV complexes as scatter plots and probability densities. The value of R R118-E119 was determined by measuring the inter-atomic distance between the carbon atom in the guanidino group of R118 and the carbon atom in the carboxyl group of E119. The value of R D151-OTV was determined by measuring the inter-atomic distance between the carbon atom in the carboxyl group of D151 and the nitrogen atom in the amino group of OTV. Figure <ns0:ref type='figure'>5</ns0:ref> shows the conformational fluctuations of the OTV binding site and adjacent I117V mutation site in the WT and I117V mutant NA-OTV complexes by superimposing 100 snapshot images obtained from the MD simulations.</ns0:p><ns0:p>In the case of the WT NA-OTV complex, as shown in Fig. <ns0:ref type='figure'>4</ns0:ref>, the distribution of R D151-OTV was generally localized in a monomodal manner around 3.6 Å, indicating that D151 tends to interact with OTV by forming hydrogen bonds. On the other hand, the distribution of R R118-E119 was bimodal, with one strongly localized around 4.2 Å and the other weakly distributed around 6.0 Å, indicating that the side chains of R118 and E119 tended to interact closely, but were sometimes too far apart to interact. These characteristic conformational fluctuations of R118, E119, and D151 can also be seen in the snapshot images of the three-dimensional structure shown in Fig. <ns0:ref type='figure'>5A</ns0:ref>.</ns0:p><ns0:p>In the case of the I117V mutant NA-OTV complex, as shown in Fig. <ns0:ref type='figure'>4</ns0:ref>, the distribution of R D151-OTV was bimodal, such that in addition to the peak observed around 3.6 Å, a peak also appeared around 6.2 Å, unlike in the WT case. This indicates that, after the I117V mutation, the frequency of hydrogen bond formation between D151 and OTV was reduced, which is also supported by the results shown in Fig. <ns0:ref type='figure' target='#fig_3'>2</ns0:ref>. The relatively large peak around 6.2 Å also suggests that the 150 loop of NA was frequently opened after the I117V mutation, similar to what has been observed in the NA mutants of many drug-resistant strains <ns0:ref type='bibr' target='#b15'>(Han, Liu & Mu, 2012;</ns0:ref><ns0:ref type='bibr' target='#b22'>Kar & Knecht, 2012;</ns0:ref><ns0:ref type='bibr' target='#b50'>Woods et al., 2012;</ns0:ref><ns0:ref type='bibr' target='#b40'>Schaduangrat et al., 2016;</ns0:ref><ns0:ref type='bibr' target='#b52'>Yadav, Igarashi & Yamamoto, 2021)</ns0:ref>. The distribution of R R118-E119 was multimodal, with peaks near 4.2 Å and 6.0 Å as in the WT case, and an additional weak peak appearing near 7.4 Å. Here, compared to the WT case, the probability density of the main component at around 4.2 Å decreased, while that of the components at around 6.0 Å and 7.4 Å increased. This shows that after the I117V mutation, the side chains of R118 and E119 tended to separate frequently, thus not interacting with each other, compared to the WT case.</ns0:p></ns0:div> <ns0:div><ns0:head>Discussion</ns0:head></ns0:div> <ns0:div><ns0:head>WT NA-OTV complex</ns0:head><ns0:p>In the WT NA-OTV complex, the residue-residue interaction between R118 and E119 may play a key role in enhancing the residue-drug interaction between D151 and OTV to increase the binding affinity of the WT NA to OTV. As shown in Figs. <ns0:ref type='figure' target='#fig_2'>1A and 5A</ns0:ref>, the negatively charged E119 interacts with the positively charged amino group of OTV, together with the negatively charged D151. Here, E119 and D151 tend to approach each other when interacting with OTV simultaneously, but the closer they are, the stronger the electrostatic repulsion between the negatively charged side chains. However, as shown in Fig. <ns0:ref type='figure' target='#fig_3'>2</ns0:ref>, the hydrogen bond occupancy of the E119-OTV and D151-OTV pairs was approximately 80% for both, indicating that E119 and D151 can form relatively stable hydrogen bonds with OTV. As shown in Fig. <ns0:ref type='figure' target='#fig_4'>3A</ns0:ref>, R118 is in the β-sheet region, indicating that its side chain can be rigidly oriented. Owing to the strong directivity of R118, derived from its secondary structure formation, its positively charged side chain can frequently interact in parallel with the negatively charged side chain of the adjacent E119. When R118 and E119 interact, the positive and negative charges of their side chains neutralize each other, thereby suppressing the electrostatic repulsion between E119 and D151. Thus, both E119 and D151 can simultaneously form hydrogen-bonding interactions with OTV, which contributes to the enhancement of the binding affinity of NA to OTV.</ns0:p></ns0:div> <ns0:div><ns0:head>I117V mutant NA-OTV complex</ns0:head><ns0:p>In the I117V mutant NA-OTV complex, the binding affinity of NA to OTV may be reduced by the weakening of the residue-drug interaction between D151 and OTV, accompanied by a decrease in the opportunity for residue-residue interaction between R118 and E119. As shown in Fig. <ns0:ref type='figure' target='#fig_4'>3C</ns0:ref>, the occupancy of R118 forming the β-sheet structure decreased after the I117V mutation, indicating that the directionality of its side chain was weakened. The weakening of the directionality of its positively charged side chain reduces the opportunity for interaction with the negatively charged side chain of the adjacent E119. The reduced interactions between the side chains of R118 and E119 are shown in Fig. <ns0:ref type='figure'>4</ns0:ref>. As mentioned earlier, in the WT NA-OTV complex, the interaction between R118 and E119 can contribute to reducing the electrostatic repulsion between E119 and D151. However, in the I117V mutant NA-OTV complex, the electrostatic repulsion between E119 and D151 can be enhanced, since R118 has less opportunity for interaction with E119. This inhibits both E119 and D151 from simultaneously forming hydrogen-bonding interactions with the same positively charged amino group of OTV, resulting in a decrease in the binding affinity between NA and OTV. Thus, the change in the interactions of these residues after the I117V mutation slightly reduces the binding affinity of NA to OTV, resulting in a reduction in OTV drug susceptibility to influenza viruses.</ns0:p><ns0:p>As mentioned in the Introduction, several computational studies that use MD simulations have reported on the molecular mechanism associated with the lower susceptibility of the I117V mutant to OTV <ns0:ref type='bibr'>(Takano et al., 2013;</ns0:ref><ns0:ref type='bibr' target='#b32'>Mhlongo & Soliman, 2015)</ns0:ref>. <ns0:ref type='bibr'>Takano et al. (2013)</ns0:ref> analyzed a single 2.5-ns MD trajectory to show that the loss of hydrogen bonding between the R118 side chain in NA and the OTV carboxyl group after I117V mutation is responsible for the reduced susceptibility of NA to OTV. A previous study by <ns0:ref type='bibr'>Takano et al. (2013)</ns0:ref> showed that hydrogen bonds are formed between the R118 residue of WT NA and OTV; however this was not observed in the previous study by <ns0:ref type='bibr' target='#b32'>Mhlongo and Soliman (2015)</ns0:ref> or in the current study. We speculate that this discrepancy is due the trajectory used for analysis in the previous study by <ns0:ref type='bibr'>Takano et al. (2013)</ns0:ref>, which was too short to adequately sample the conformational space of the system. <ns0:ref type='bibr' target='#b32'>Mhlongo and Soliman (2015)</ns0:ref> analyzed four distinctive 25-ns MD trajectories and suggested that the I117V mutation affects residue-residue interactions in NA that cause the drugbinding site to change its conformation, thereby altering residue-drug interactions between NA and OTV; however, the details were not clear. In the current study, we elucidated correlations between the residue-residue interaction of the R118-E119 pair and the residue-drug interaction of the D151-OTV pair in NA-OTV complexes by analyzing four distinctive 80-ns trajectories obtained from MD simulations, as shown in Fig. <ns0:ref type='figure'>4</ns0:ref>. As a result, we clarified the detailed molecular mechanism by which the I117V mutation in NA alters the inter-residue interactions between R118, E119, and D151, and destabilizes the residue-drug interaction between D151 and OTV, thereby reducing the susceptibility of NA to OTV.</ns0:p><ns0:p>We speculate that the I117V mutation not only affects the susceptibility of NA to OTV, but also viral fitness. With regard to viral fitness, we expect to extend the present study in the future to clarify the effects of the I117V mutation on the binding affinity of the natural sialic acid substrate to NA. However, according to <ns0:ref type='bibr' target='#b0'>Adams et al. (2019)</ns0:ref>, viral fitness not only depends on the binding affinity between the substrate and the enzyme, but also on the catalytic efficiency of the enzyme. Hence, clarifying the catalytic reaction mechanism of NA using expensive computational methods, such as the QM/MM method <ns0:ref type='bibr' target='#b42'>(Sousa et al., 2017)</ns0:ref>, is required to study the effect of NA mutations on viral fitness. Since analyzing the catalytic reaction of NA is far beyond the scope of this study, we simply mention it here as a future subject.</ns0:p></ns0:div> <ns0:div><ns0:head>Designing a potential drug design against I117V mutant strains</ns0:head><ns0:p>As summarized in Table <ns0:ref type='table'>1</ns0:ref>, the I117V mutation in NA reduces the binding free energy of NA to OTV by 2.72 kcal mol −1 , which corresponds to an approximate 100-fold decrease in the relative susceptibility of the I117V mutant NA to OTV compared to that of WT NA. The IC 50 value of OTV has been experimentally observed to change by a factor of 3-50 upon I117V mutation in NA <ns0:ref type='bibr' target='#b17'>(Hurt et al., 2007;</ns0:ref><ns0:ref type='bibr' target='#b30'>McKimm-Breschkin et al., 2007;</ns0:ref><ns0:ref type='bibr' target='#b5'>Chen et al., 2010;</ns0:ref><ns0:ref type='bibr' target='#b19'>Ilyushina et al., 2010;</ns0:ref><ns0:ref type='bibr' target='#b29'>McKimm-Breschkin et al., 2013;</ns0:ref><ns0:ref type='bibr'>Takano et al., 2013;</ns0:ref><ns0:ref type='bibr' target='#b9'>Creanga et al., 2017;</ns0:ref><ns0:ref type='bibr' target='#b25'>Kode et al., 2019)</ns0:ref>. Compared with substitutions that are selected under drug pressure and increase IC 50 by more than 600-fold, such as H274Y <ns0:ref type='bibr' target='#b18'>(Hurt et al., 2012)</ns0:ref>, the I117V mutation does not dramatically affect susceptibility to OTV. Therefore, OTV treatment may be effective against the I117V mutant strain of the influenza virus. However, the I117V mutation may affect OTV resistance in synergism with other mutations. For example, Hurt et al. <ns0:ref type='bibr' target='#b34'>(2012)</ns0:ref> found that the introduction of the dual I117V + H274Y mutation in NA significantly decreased susceptibility to OTV (a 1896-fold increase in IC 50 ) compared to that resulting from the H274Y mutation alone (a 650-fold increase in IC 50 ). Therefore, based on the new knowledge gained in this study, we propose guidelines for drug design that avoid the loss of drug sensitivity associated with the I117V mutation in preparation for the possible emergence of potent drug-resistant strains.</ns0:p><ns0:p>Based on our study, we suggest that an inhibitor with a longer positively charged group is better than one with a shorter positively charged group, such as the amino group in OTV, to avoid resistance from the I117V mutation that affects interactions between the inhibitor and the E119 and D151 NA binding site residues. A longer positively charged group in the inhibitor helps to reduce electrostatic repulsion between the negatively charged E119 and D151 side chains. For example, OTV has a short positively charged amino group that interacts with residues E119 and D151, while zanamivir has a long positively charged guanidino group. In fact, the I117V mutation in NA resulted in a significant 50-fold change in the IC 50 value for OTV but only a 1.6-fold change in the IC 50 value for zanamivir, which indicates that zanamivir is effective against the I117V mutant strain <ns0:ref type='bibr' target='#b29'>(McKimm-Breschkin et al., 2013)</ns0:ref>. In this study, we used molecular simulations to understand the molecular mechanism of OTV resistance associated with the I117V mutation in NA in detail, which led to the establishment of new molecular design guidelines that effectively solve the drug resistance problem. Manuscript to be reviewed Chemistry Journals <ns0:ref type='bibr'>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:ref> In this study, we theoretically investigated the molecular mechanism of reduced OTV drug susceptibility in the A/H5N1 influenza virus harboring the NA I117V mutation using MD simulations.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions</ns0:head><ns0:p>In the WT NA-OTV complex, the interaction between R118 and E119 can play an important role in increasing the binding affinity of NA to OTV. In this case, the positively charged side chain of R118, located in the β-sheet region, can frequently interact with the negatively charged side chain of E119, preventing the electrostatic repulsion between E119 and D151. This enables both the negatively charged side chains of E119 and D151 to simultaneously form hydrogen bonding interactions with the positively charged amino group of OTV, thereby contributing significantly to the binding affinity between NA and OTV.</ns0:p><ns0:p>In the I117V mutant NA-OTV complex, the binding affinity of NA to OTV can be reduced by decreasing the opportunity for interaction between R118 and E119. In this case, the mutation reduces the tendency of R118 to form the β-sheet structure, leading to less frequent interaction between its positively charged side chain and the negatively charged side chain of E119. This increases the electrostatic repulsion between E119 and D151, making it difficult for both to simultaneously form hydrogen bonds with OTV, which in turn reduces the binding affinity between NA and OTV. Thus, after the I117V mutation in NA, influenza viruses are less susceptible to OTV because of changes in the residue interactions between R118, E119, and D151.</ns0:p><ns0:p>The present study has successfully clarified the molecular mechanism by which the I117V mutation in NA reduces the OTV drug susceptibility of the A/H5N1 influenza virus. </ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2021:06:62231:2:0:NEW 20 Oct 2021)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2021:06:62231:2:0:NEW 20 Oct 2021)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>Figure 1</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head>Figure 2</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head>Figure 3</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Figure 3</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='19,42.52,70.87,525.00,426.00' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='25,42.52,280.87,525.00,198.75' type='bitmap' /></ns0:figure> <ns0:note place='foot'>Analytical, Inorganic, Organic, Physical, Materials Science </ns0:note> </ns0:body> "	"October 20, 2021 Professor Carsten Baldauf Academic Editor, PeerJ Physical Chemistry Dear Professor Baldauf: re: “Theoretical insights into the molecular mechanism of I117V mutation in neuraminidase mediated reduction of oseltamivir drug susceptibility in A/H5N1 influenza virus”; Article ID: 62231. We are most grateful to you and the reviewers for the helpful comments on the first revised version of our manuscript. We have taken all the remaining comments by Reviewer 2 into account and submit a revised version. We hope that revisions of our work are satisfactory. We hope that the revised version of our paper is now suitable for publication in PeerJ Physical Chemistry. I also appreciate your understanding of the importance of this paper to the career development of my students. Considering that my student’s PhD registration is due in a few days, it would be really great to know the decision before that. We look forward to hearing from you at your earliest convenience. Sincerely, Norifumi Yamamoto Chiba Institute of Technology 2-17-1 Tsudanuma, Narashino, Chiba 275-0016, Japan Tel & Fax: +81-47-478-0375 E-mail: norifumi.yamamoto@it-chiba.ac.jp Response to Reviewer 2 Reviewer’s comments (comments that require authors’ reply are numbered) Reviewer 2 (Chris Oostenbrink) Basic reporting The authors have responded to my comments and have made modifications to the manuscript. It is basically acceptable for publications, but I would urge the authors to make a few small clarifications. Let me follow my original comments: 1. OK Experimental design [Comment #2-2] 2. The authors write that the four independent simulations started from randomly selected conformations from the initial 10-ns equilibration. Were the velocities reassigned completely? If not, then you have actually performed the same simulation 4 times with a slightly different shift in starting time: picking a random configuration (positions and velocities) and starting a simulation is basically the same as continuing the simulation at that point. I guess you have reassigned velocities for the four simulations, but this is not stated in the manuscript. [Comment #2-3] 3. The authors seem to brush away the argumentation that the single-trajectory approach in MMPBSA may not be appropriate when conformational changes are involved by stating that it is widely used and that it leads to results that match the experimental values. I would like to emphasize that this still does not make the single-trajectory approach appropriate. For the current manuscript and the aims of the authors, it may be sufficient, and I appreciate that the authors at least mention the possibility that the single-trajectory approach is sometimes not reliable. If they really want to make me happy, they would add ‘because it assumes that no significant conformational changes occur upon ligand binding’ to that statement. Validity of the findings 4. OK [Comment #2-5] 5. Here, I think that the authors should be more careful. The IC50 is a property that depends on the experimental setup (protein and substrate concentration), and the equation that the authors give (ΔG ~= RT ln IC50) is not correct. If experiments are done under the same conditions, however, a similar equation often does hold for the relative binding free energies: ΔΔG ~= RT ln [IC50(1) / IC50(2)]. This is also what the authors actually use in the manuscript. 6. OK Additional comments 7. OK 8. OK Reply to Reviewer 2’s comments We are grateful to Reviewer 2 for the useful comments. As indicated in the responses that follow, we have taken all these comments into account in the revision of our paper. Comment #2-2: 2. The authors write that the four independent simulations started from randomly selected conformations from the initial 10-ns equilibration. Were the velocities reassigned completely? If not, then you have actually performed the same simulation 4 times with a slightly different shift in starting time: picking a random configuration (positions and velocities) and starting a simulation is basically the same as continuing the simulation at that point. I guess you have reassigned velocities for the four simulations, but this is not stated in the manuscript. Authors’ reply: We agree with the reviewer’s comment. In the revised manuscript, we described more details about how the initial velocities for the four independent simulations were determined. Changes to the manuscript: …the initial coordinates were randomly selected from the additional 10-ns trajectories after equilibration and the initial velocities were randomly reassigned. [lines 146, 147] Comment #2-3: 3. The authors seem to brush away the argumentation that the single-trajectory approach in MMPBSA may not be appropriate when conformational changes are involved by stating that it is widely used and that it leads to results that match the experimental values. I would like to emphasize that this still does not make the single-trajectory approach appropriate. For the current manuscript and the aims of the authors, it may be sufficient, and I appreciate that the authors at least mention the possibility that the single-trajectory approach is sometimes not reliable. If they really want to make me happy, they would add ‘because it assumes that no significant conformational changes occur upon ligand binding’ to that statement. Authors’ reply: We understand the reviewer’s opinion. In the revised manuscript, we have added sentences that follow the reviewer’s advice. Changes to the manuscript: In this study, we adopted the single-trajectory approach in the MM-PBSA calculation, because it assumes that no significant conformational changes occur upon ligand binding. [lines 207, 208] Comment #2-5: 5. Here, I think that the authors should be more careful. The IC50 is a property that depends on the experimental setup (protein and substrate concentration), and the equation that the authors give (ΔG ~= RT ln IC50) is not correct. If experiments are done under the same conditions, however, a similar equation often does hold for the relative binding free energies: ΔΔG ~= RT ln [IC50(1) / IC50(2)]. This is also what the authors actually use in the manuscript. Authors’ reply: We agree with the reviewer’s comment. In the revised manuscript, we based our explanation on the equation for the relative binding free energy. Changes to the manuscript: If experiments are done under the same conditions, the relative binding free energy of ΔΔG = ΔG(1) – ΔG(2) can be approximated using ΔΔG  RT ln (IC50(1) / IC50(2)),… [lines 200−202] "	Here is a paper. Please give your review comments after reading it.
481	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>There is abundant theoretical evidence indicating that a mirror image of Protein A may occur during the protein folding process. However, as to whether such mirror image exists in solution is an unsolved issue. Here we provide outline of an experimental design aimed to detect the mirror image of Protein A in solution. The proposal is based on computational simulations indicating that the use of a mutant of protein A, namely Q10H, could be used to detect the mirror image conformation in solution. Our results indicate that the native conformation of the protein A should have a pKa, for the Q10H mutant, at ≈6.2, while the mirror-image conformation should have a pKa close to ≈7.3. Naturally, if all the population is in the native state for the Q10H mutant, the pKa should be ≈6.2, while, if all are in the mirror-image state, it would be ≈7.3, and, if it is a mixture, the pKa should be larger than 6.2, presumably in proportion to the mirror population. In addition, evidence is provided indicating the tautomeric distribution of H10 must also change between the native and mirror conformations. Although this may not be completely relevant for the purpose of determining whether the protein A mirror image exists in solution, it could provide valuable information to validate the pKa findings. We hope this proposal will foster experimental work on this problem either by direct application of our proposed experimental design or serving as inspiration and motivation for other experiments.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>INTRODUCTION</ns0:head><ns0:p>A mirror image conformation is one that looks approximately like the specular image of the native state.</ns0:p><ns0:p>We say approximately because we do not require the amino acids to be specular images, but only the overall topology of the molecule. At least for some proteins, the mirror image will be energetically very close to the native state and thus it could also exist in solution. Among these proteins, we will focus our attention on the B-domain of staphylococcal protein A [PDB ID: 1BDD; a three-helix bundle] <ns0:ref type='bibr' target='#b9'>Gouda et al. (1992)</ns0:ref>. This protein has been the subject of extensive theoretical <ns0:ref type='bibr' target='#b19'>Olszewski et al. (1996)</ns0:ref>; <ns0:ref type='bibr' target='#b30'>Vila et al. (2003)</ns0:ref>; <ns0:ref type='bibr' target='#b8'>Garcia and Onuchic (2003)</ns0:ref>; <ns0:ref type='bibr' target='#b15'>Lee et al. (2006)</ns0:ref>; <ns0:ref type='bibr' target='#b11'>Kachlishvili et al. (2014)</ns0:ref> and experimental <ns0:ref type='bibr' target='#b6'>Deisenhofer (1981)</ns0:ref>; <ns0:ref type='bibr' target='#b9'>Gouda et al. (1992)</ns0:ref>; <ns0:ref type='bibr' target='#b0'>Bai et al. (1997)</ns0:ref>; <ns0:ref type='bibr' target='#b17'>Myers and Oas (2001)</ns0:ref>; <ns0:ref type='bibr' target='#b22'>Sato et al. (2004)</ns0:ref>; <ns0:ref type='bibr' target='#b7'>Dimitriadis et al. (2004)</ns0:ref>; <ns0:ref type='bibr' target='#b18'>Noel et al. (2012)</ns0:ref> studies because of its biological importance and small size. In contrast to this, the mirror-image conformation has been subject of limited discussion <ns0:ref type='bibr' target='#b19'>Olszewski et al. (1996)</ns0:ref>; <ns0:ref type='bibr' target='#b30'>Vila et al. (2003)</ns0:ref>; <ns0:ref type='bibr' target='#b8'>Garcia and Onuchic (2003)</ns0:ref>; <ns0:ref type='bibr' target='#b18'>Noel et al. (2012)</ns0:ref>; <ns0:ref type='bibr' target='#b11'>Kachlishvili et al. (2014)</ns0:ref>. The reason for this might be that the mirror image conformation of this protein has been observed only in some theoretical studies with different force fields but it has never been detected experimentally. As to whether this conformation is an artifact of the simulations or is difficult to observe the conformation experimentally, remains to be solved.</ns0:p><ns0:p>Difficulties for experiments to detect the mirror-image conformation arise precisely because the PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2019:04:36440:1:2:NEW 26 Jul 2019)</ns0:p><ns0:p>Manuscript to be reviewed Chemistry Journals <ns0:ref type='bibr'>Analytical, Inorganic, Organic, Physical, Materials</ns0:ref> Science secondary structures of the mirror-image and the native conformation of protein A are identical and the structural difference between these conformations are subtle <ns0:ref type='bibr' target='#b11'>Kachlishvili et al. (2014)</ns0:ref>. Because of this, use of simple experiments such as circular dichroism, used to estimate the fraction of secondary-structure content, or more sophisticative technique, such as nuclear magnetic resonance (NMR) spectroscopy, e.g., to monitor the 13 C chemical shift changes that may occur at residue-level <ns0:ref type='bibr' target='#b11'>Kachlishvili et al. (2014)</ns0:ref>, are useless for an accurate characterization of the mirror image conformation. A strong motivation to propose alternative methods to explore the possible coexistence in solution of the native and mirror-image conformation of protein A, comes from older evidence indicating that the mirror-image conformation could be a possible solution to the NMR-determined structure of protein A <ns0:ref type='bibr' target='#b9'>Gouda et al. (1992)</ns0:ref>. Indeed, according to <ns0:ref type='bibr' target='#b9'>Gouda et al. Gouda et al. (1992)</ns0:ref>, '. . . distance-geometry calculations resulted in 41 solutions, which had correct polypeptide folds excluding 14 mirror-image substructures. . . ' However, the mirrorimage structures were excluded from the analysis of Gouda et al. <ns0:ref type='bibr' target='#b9'>Gouda et al. (1992)</ns0:ref> without providing any reason. It seems that the decision was adopted because the 'mirror-image' satisfies the NOE constraints but contain D-amino acid residues (personal communication with Ichio Shimada).</ns0:p><ns0:p>Overall, we propose here a proof-of-concept of an experimental design aimed to solve this problem.</ns0:p><ns0:p>Initially we will show, by using ROSETTA, <ns0:ref type='bibr' target='#b4'>Bradley et al. (2005)</ns0:ref> that a mutant of protein A, hereafter the Q10H protein, exhibits the ability to fold into the native conformation (see Figure <ns0:ref type='figure' target='#fig_3'>1</ns0:ref>) as well as into the mirror-image conformation (see Figure <ns0:ref type='figure'>2</ns0:ref>). Later, we estimate the fraction of the native and mirror-image populations of the protein Q10H by using a recently introduced method, that take into account the protein dynamics in water by using a constant-pH MD simulation, to accurately determine the pKa values of ionizable residues, and fractions of ionized and tautomeric forms of histidine (His), in proteins at a given fixed pH <ns0:ref type='bibr' target='#b33'>Vorobjev et al. (2018)</ns0:ref>. Indeed, we explore the dependence of the electrostatically-calculated pKa and fractions of the imidazole ring forms of H10 as a function of pH for both the native-like and mirror-image conformations.</ns0:p></ns0:div> <ns0:div><ns0:head>MATERIALS AND METHODS</ns0:head><ns0:p>In this section we will give a brief reference to existent theoretical methods aimed to predict (i) the 3D structure of proteins accurately; <ns0:ref type='bibr' target='#b3'>Bradley et al. (2003)</ns0:ref> or determine (ii) the pKa values of ionizable residues and fractions of ionized and tautomeric forms of histidine (His) and acid residues in proteins, at a given fixed pH <ns0:ref type='bibr' target='#b34'>Vorobjev et al. (2008</ns0:ref><ns0:ref type='bibr' target='#b33'>Vorobjev et al. ( , 2018))</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Determination of the native and image-mirror conformations of protein Q10H</ns0:head><ns0:p>To generate the native and mirror-image conformations of protein A Q10H we used the fast-relax protocol from Rosetta, <ns0:ref type='bibr' target='#b5'>Chaudhury et al. (2010)</ns0:ref>; <ns0:ref type='bibr' target='#b4'>Bradley et al. (2005)</ns0:ref> this is an all-atom refinement protocol consisting of several rounds of repacking and energy minimization. The repulsive part of the Van der Waals energy function is annealed from 2% to 100%. Essentially the algorithm explores the local conformational space around the starting structure with a radius of 2 to 3 Å of rmsd (for the C α ). We performed several rounds of fast-relax using the following genetic-like algorithm:</ns0:p><ns0:p>1. For a given conformation of protein A mutate it by replacing Q10 with H10 2. Use the mutant as the starting point of 200 independent rounds of the fast-relaxation protocol 3. Choose 10 conformations; 2 at random and the 8 lowest-energy conformations 4. For each one of those conformations use fast-relaxation to generate 100 independent rounds (for a total of 1000 conformations) 5. repeat, from step 3, 40 times 6. keep the lowest energy conformation from all the rounds We started from 2 different conformations. For the native conformation we used 1BDD <ns0:ref type='bibr' target='#b9'>Gouda et al. (1992)</ns0:ref>. For the mirror image we started from a mirror-image conformation previously obtained by <ns0:ref type='bibr' target='#b30'>Vila et al, Vila et al. (2003)</ns0:ref>.</ns0:p><ns0:p>The Rosetta energy score of the lowest energy conformations for the native and image-mirror of protein Q10H was on par.</ns0:p></ns0:div> <ns0:div><ns0:head>2/11</ns0:head><ns0:p>PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2019:04:36440:1:2:NEW 26 Jul 2019)</ns0:p><ns0:p>Manuscript to be reviewed Chemistry Journals <ns0:ref type='bibr'>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:ref> </ns0:p></ns0:div> <ns0:div><ns0:head>Computation of the pKa and the tautomeric fractions of the imidazole ring of H10</ns0:head><ns0:p>The native-like and mirror-image conformations of protein Q10H, generated as describe in the previous section, were used as input files for the calculations of the pKa of all ionizable residues in the sequence as well as the fractions of the ionized H + and the tautomeric N ε2 − H and N δ 1 − H forms of the imidazole ring of H10. In particular, as it is well known, the tautomeric determination of the imidazole ring of His is both a very important problem in structural biology <ns0:ref type='bibr' target='#b24'>Schnell and Chou (2008)</ns0:ref>; <ns0:ref type='bibr' target='#b1'>Bermúdez et al. (2014)</ns0:ref> and a challenging task <ns0:ref type='bibr' target='#b16'>Machuqueiro and Baptista (2011)</ns0:ref>. For this reason, a recently introduced electrostatic-based method to determine the pKa values of ionizable residues and fractions of ionized and tautomeric forms of histidine (His) and acid residues in proteins <ns0:ref type='bibr' target='#b33'>Vorobjev et al. (2018)</ns0:ref>, is applied here to the analysis of protein A mutant Q10H. Protein dynamics in water, at a given pH=7.0, was taken into account by constant-pH MD simulation <ns0:ref type='bibr' target='#b32'>Vorobjev et al. (2017</ns0:ref><ns0:ref type='bibr' target='#b33'>Vorobjev et al. ( , 2018) )</ns0:ref> of both the native and mirror-image conformations of the Q10H mutant.</ns0:p><ns0:p>Protein dynamics in water was modeled by MD simulations with implicit solvent, namely using the Lazaridis-Karplus solvent model <ns0:ref type='bibr' target='#b13'>Lazaridis and Karplus (1999)</ns0:ref> with the BioPASED program <ns0:ref type='bibr' target='#b21'>Popov and Vorob'ev (2010)</ns0:ref>. For the MD simulation, the following three-step protocol was used. First step, determination of an equilibrium protein structure at temperature 300 K and pH 7.0 using the next three step procedure: (i) building a full atomic protein structure, i.e. with all hydrogen atoms added; this means, for example, that each His residue needs to be built up in the most probable form, i.e. in the ionized H + form or in the most probable neutral tautomer, N δ 1 − H (HD1 or HID) and N ε2 − H (HE2 or HIE); (ii) the crystal structure with all the assigned hydrogen atoms and histidine forms was energy optimized in implicit solvent using a conjugate gradient method; (iii) the system is heated slowly from 1 to 300 K during 250 ps; and (iv) a final equilibration at 300 K, during 0.5-1 ns, was carried out.</ns0:p><ns0:p>Step 2: generation of a representative set of 3D protein structures as a collections of snapshots each 50 ps along equilibrium MD trajectory during 25 ns snapshots taken every 50 ps time-interval. Step 3: for each snapshot, the pKa's of all ionizable residues is computed, as well as the fractions of two neutral tautomers of His and the acid residues, by carrying out an MC calculation with GB-MSR6c as an implicit solvent model. Finally, an average pKa's for each ionizable residue as well as the fraction of ionized and two tautomers of histidine and neutral form of acid residues of the protein are calculated.</ns0:p><ns0:p>The ionization constants pKa and the fractions of ionized and two neutral tautomers of histidine at constant pH 7.0 are modeled by MD simulations at constant pH <ns0:ref type='bibr' target='#b32'>Vorobjev et al. (2017</ns0:ref><ns0:ref type='bibr' target='#b33'>Vorobjev et al. ( , 2018))</ns0:ref>. During the pH-constant MD simulations all acid (Asp, Glu) and base (Lys, Arg) residues were kept in the ionized state because their respective pKo's (3.5, 4.0, and 10.5, 12.5, respectively) are shifted by more than 2.5 pK units from the pH (7.0) at which the calculations were carried out (see Table <ns0:ref type='table'>S1</ns0:ref> in supplemental files). On the other hand, the two existent histidine residues, namely H10 and H19, were considered to be electrostatically couple residues having nine ionization states, namely, 00, <ns0:ref type='bibr'>01,</ns0:ref><ns0:ref type='bibr'>02,</ns0:ref><ns0:ref type='bibr'>10,</ns0:ref><ns0:ref type='bibr'>11,</ns0:ref><ns0:ref type='bibr'>12,</ns0:ref><ns0:ref type='bibr'>20,</ns0:ref><ns0:ref type='bibr'>21,</ns0:ref><ns0:ref type='bibr'>22</ns0:ref>, where 0,1,2 represents the ionized and two neutral tautomer states respectively (see Table <ns0:ref type='table'>S2</ns0:ref> in supplemental files). The average potential energy values and it's thermal fluctuations due to molecular dynamics in solvent are estimated along 25ns MD equilibrium trajectory for each of the nine ionization states (see Table <ns0:ref type='table'>S2</ns0:ref> in supplemental files). Low energy states, which have occupation number large than 0.01, for histidine residues H10 and H19 along 25ns constant-pH MD trajectory, are shown in Table <ns0:ref type='table'>S3</ns0:ref> (see supplemental files). Energy fluctuations of the Q10H protein in solvent along 25 ns MD trajectory for native-like and mirror-image structures are shown in Figure <ns0:ref type='figure' target='#fig_3'>S1</ns0:ref> (see supplemental files). It can be seen, from this figure, that fluctuation of the native-like and mirror image structures are overlapping, i.e. spontaneous transition between native-like and mirror-image structures can occur. The average range of fluctuations of the atomic positions, i.e. in terms of the RMSD, observed along the MD trajectories were 1.4 and 1.3 Å for the native-like and mirror-image structures, respectively. Variation of pKa constant along MD trajectory is presented on Figure <ns0:ref type='figure'>S2</ns0:ref> (see supplemental files). It can be seen that pKa shift for histidine His10 are -0.3 and +0.8 pK units for the native-like and mirror-image protein structures. Such relatively large pKa shift for relatively small proteins can serves as a mark of native-like and mirror-image structures. Occupations of ionization states of His10 residue versus MD time are shown in Figure <ns0:ref type='figure' target='#fig_4'>S3a</ns0:ref> and Figure <ns0:ref type='figure' target='#fig_4'>S3b</ns0:ref> (see supplemental files) for native-like and mirror-mage structures, respectively. It should be noticed, that occupation of different ionization states of His10 show a large variation, i.e. RMSD from it's average values.</ns0:p><ns0:p>One challenge question is how meaningful the pK difference computed with our method are. In this regards, we would like to mention that the accuracy of the pK calculations have been carefully analyzed Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science through a series of applications. Indeed, a comparison with experimental data show the method is accurate enough, in terms of a NMR-based methodology, to predict the pK and tautomeric fractions of six histidine forms on the enzime DFPase from Loligo vulgaris, a 314-residues all-β protein containing 94 ionizable residues <ns0:ref type='bibr' target='#b33'>Vorobjev et al. (2018)</ns0:ref>. In addition, a large test on 297 ionized residues from 34 proteins show that a 57%, 86% and 95% of the pK prediction are with an accuracy better than 0.5, 1.0 and 1.5 pK unit respectively <ns0:ref type='bibr' target='#b32'>Vorobjev et al. (2017)</ns0:ref>. Such range of accuracy is comparable or better than state of the art predictive methods such as the electrostatic-based MCCI2 method <ns0:ref type='bibr' target='#b26'>Song et al. (2009)</ns0:ref>. Moreover, the H10 pKa differences between the native-like and the mirror-image conformations of Q10H protein does not disappear but kept constant (≈1.1 pK units) between 9ns-25ns of the pH-constant MD simulation (see Figure <ns0:ref type='figure'>S2</ns0:ref> and table <ns0:ref type='table'>S3</ns0:ref> in supplemental files), hence, given further confidence on the accuracy of the pK shift predictions.</ns0:p></ns0:div> <ns0:div><ns0:head>4/11</ns0:head><ns0:p>PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2019:04:36440:1:2:NEW 26 Jul 2019)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:p></ns0:div> <ns0:div><ns0:head>RESULTS AND DISCUSSION</ns0:head></ns0:div> <ns0:div><ns0:head>ANALYSIS OF THE PKA VARIATIONS AS A FUNCTION OF PH</ns0:head><ns0:p>Figure <ns0:ref type='figure'>2</ns0:ref> shows a superposition of the lowest-energy conformations for both the native-like (green-ribbon) and the mirror-image (yellow-ribbon) of protein Q10H obtained by using <ns0:ref type='bibr'>ROSETTA Bradley et al. (2005)</ns0:ref>.</ns0:p><ns0:p>These two structures were used to compute for each ionizable residue along the sequence the value of the pKa variations (∆ = [pK a Native − pK a Mirror ]) at pH 7.0 <ns0:ref type='bibr' target='#b34'>Vorobjev et al. (2008)</ns0:ref>. The result of this analysis is shown in Figure <ns0:ref type='figure' target='#fig_1'>3</ns0:ref> (as blue dots) where one of the largest change in ∆, namely larger than ±1.0 pK units, occurs for H10. This large shift on the pKa of residue H10 appears to be a consequence of the close proximity of H10 to D38 in the mirror-image conformation of protein Q10H (see Figure <ns0:ref type='figure'>2</ns0:ref>).</ns0:p><ns0:p>There is another change of ∆ larger than ±1.0 pKa unit and it occurs for residue K8 (see blue dots in Figure <ns0:ref type='figure' target='#fig_1'>3</ns0:ref>), a residue belonging to the flexible N-terminal region of the mutant protein Q10H, viz., ranging from residues T1 trough E9. The origin of the large computed shift in the pKa of residue K8 is the following. In the native structure of protein Q10H residue K8 is well exposed to the solvent. On the other hand, in the mirror image of Q10H residue K8 is close to E16, making a favorable electrostatic interaction. However a close inspection of these two structures indicates that the favorable electrostaticinteraction between K8 and E16, observed in the mirror image conformation, could also occur on the native conformation, e.g., by a rearrangement of the backbone-torsional angles of the flexible N-terminal region of the protein Q10H. If this were feasible, the computed pKa shift for K8 should be ≈ 0. Consequently, monitoring the pKa shift of K8 does not appear to be the right choice for the purpose of an accurate determination of the coexistence between the native and the mirror image states in solution. Unlike the origin of the pKa shift for K8, the interaction between H10 and D38 cannot take place in both the native and the mirror-image conformations (see Figure <ns0:ref type='figure'>2</ns0:ref>) and, hence, from here on we will focus our attention on H10 only.</ns0:p><ns0:p>Consideration of the protein dynamics in water is very important for an accurate computation of conformational-dependent values, such as the pKa's. However, this effect was not taken into account in the computation of the ∆ values shown, as blue-dots, in the Figure <ns0:ref type='figure' target='#fig_1'>3</ns0:ref>. Consequently, we carried out a constant-pH MD simulation of both the native-like Q10H mutant and its mirror-image conformations <ns0:ref type='bibr' target='#b33'>Vorobjev et al. (2018)</ns0:ref>. As mentioned in the Materials and Methods section, during the simulations at constant-pH 7.0 it is reasonable to consider all acid (Asp, Glu) and base (Lys, Arg) residues in the ionized state, because their respective pK 0 's (3.5, 4.0, and 10.5, 12.5, respectively) are shifted by more than 2.5 pK units from the pH (7.0) at which the calculations were carried out. For the same reason, the only Tyr in the sequence was consider as neutral. However, histidine residue pKa's (6.5) can vary considerably at pH 7.0 at which the calculations are carried out and, hence, consideration of histidine ionization states for each of the imidazole ring of His forms must be considered explicitly. Consequently during the calculations the nine ionizations states of the two interacting His, namely between H10 and H19, were explicitly considered (see Table <ns0:ref type='table'>S2</ns0:ref> of supplemental files). The average ∆ change for H10, computed from the native-like and mirror-image conformations after 25ns MD simulations, is shown as an orange dot in In general, the results shown in Figures <ns0:ref type='figure' target='#fig_5'>3 and 4</ns0:ref> and Table <ns0:ref type='table'>S3</ns0:ref> (supplemental files) are decisive for the determination of the fraction of native and mirror image conformations in solution. Indeed, if the dominant conformation in solution is the native like then the pKa of H10 will be 6.2±0.2. On the other hand, if the dominant conformation in solution is the mirror image then the pKa will be 7.3±0.2. Any other in-between value may indicate coexistence of these two conformations in solution.</ns0:p></ns0:div> <ns0:div><ns0:head>Validation of the H10 pKa-based predictions</ns0:head><ns0:p>Small changes around the computed average pKa value for H10 in the native-like conformation (6.2) are of course possible. In such a case additional experiments are necessary to determine whether such shift is due to expected fluctuations of the native conformation (around ±0.3 in pKa units) or to the presence of a small fraction of the mirror-image conformation. One such additional experiment could be the determination of the tautomers of the imidazole ring of H10. In this section we analyze this possibility by using two NMR-based methods.</ns0:p></ns0:div> <ns0:div><ns0:head>5/11</ns0:head><ns0:p>PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2019:04:36440:1:2:NEW 26 Jul 2019)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science Let us explain this in detail. Under the only condition that His is non-protonated, we have been able to show that the fraction of the N δ 1 − H tautomeric form ( f δ 1 ) of the imidazole ring of His can be estimated by using the following equation: f δ 1 = (J obs − 165.0)/15.0, <ns0:ref type='bibr' target='#b31'>Vila and Scheraga (2017)</ns0:ref> where J refers to 1 J Cδ 2H SSCC, and here obs is the observed value in solution for H10. Naturally,</ns0:p><ns0:formula xml:id='formula_0'>f ε2 = 1 − f δ 1 . Hence, if</ns0:formula><ns0:p>the native-like structure is the dominant topology in solution, then the following inequality should hold: <ns0:ref type='figure' target='#fig_5'>4</ns0:ref>) otherwise there would be coexistence of the native-like structure with other topology in solution.</ns0:p><ns0:formula xml:id='formula_1'>f ε2 ≫ f δ 1 (see Figure</ns0:formula><ns0:p>A second, and less restrictive, validation test will be to use a recently proposed NMR-based methodology aimed to determine the tautomeric forms as a function of the ionization state of the imidazole ring of histidine <ns0:ref type='bibr' target='#b29'>Vila et al. (2011)</ns0:ref>. In this approach, the average tautomeric fraction of the N ε2 − H form of His ( f ε2 ) can be determined by using the following equation:</ns0:p><ns0:formula xml:id='formula_2'>f ε2 = ∆ obs (1 − f H+ )/∆ ε</ns0:formula><ns0:p>where f H+ is the experimentally determined fraction for the ionized form of H10, at a given fix pH;</ns0:p><ns0:formula xml:id='formula_3'>∆ obs =\| 13 C δ 2 − 13 C γ \|,</ns0:formula><ns0:p>where 13 C δ 2 and 13 C γ are the NMR-observed chemical shifts for the imidazole ring of H10 at that pH; and ∆ ε is the first-order absolute shielding difference, \| 13 C δ 2 − 13 C γ \| ε , between the 13 C δ 2 and 13 C γ nuclei for the N ε2 − H tautomer, i.e., present to the extent of 100%. ∆ ε is a parameter which must be estimated <ns0:ref type='bibr' target='#b29'>Vila et al. (2011)</ns0:ref>. As a first approximation, a ∆ ε = 27.0ppm, obtained from the analysis of a His-rich protein, <ns0:ref type='bibr' target='#b32'>Vorobjev et al. (2017)</ns0:ref> namely Loligo vulgaris (pdb id 1E1A), a 314-residue all-β protein, <ns0:ref type='bibr' target='#b23'>Scharff et al. (2001)</ns0:ref> should be used. Naturally, the f δ 1 fraction, viz., for the N δ 1 − H tautomer, is obtained straightforwardly as: f δ 1 = 1 − f H+ − f ε2 . Although this second approach to compute the tautomers of H10 it is more general than the previous one, i.e., by using the 1 J Cδ 2H SSCC, the determination of the 13 C γ chemical shift it is not always feasible. Indeed, only 213 13 C γ , versus 6,984 13 C δ 2 , chemical shifts of the imidazole ring of histidine have been deposited in the Biological Magnetic Resonance data Bank (BMRB) <ns0:ref type='bibr' target='#b27'>Ulrich et al. (2008)</ns0:ref>. Overall, if it were feasible to observe the 13 C γ chemical shift we suggest to use both approaches to validate the pKa predictions.</ns0:p><ns0:p>Although this work is not intended to be a revision of all existing methods used to determine the tautomeric forms of the imidazole ring of His, the use of the tautomeric identification by direct observation of 15 N chemical shifts of the imidazole ring of His, which is a common practice in NMR spectroscopy, <ns0:ref type='bibr' target='#b20'>Pelton et al. (1993)</ns0:ref>; <ns0:ref type='bibr' target='#b25'>Shimahara et al. (2007)</ns0:ref>; <ns0:ref type='bibr' target='#b10'>Hass et al. (2008)</ns0:ref> should be mentioned. This method requires, as a necessary condition, knowledge of the canonical limiting values of the 15 N chemical shift of the imidazole ring of His in which each form of His is present to the extent of 100%. In this regard, there is theoretical evidence indicating that a considerable difference for the average tautomeric equilibrium constant, K T , can be obtained if DFT-computed 15 N limiting values rather than canonical limiting values are used <ns0:ref type='bibr' target='#b28'>Vila (2012)</ns0:ref>, Because these results raise concerns about the magnitude of the uncertainty associated with the predictions we did not consider this method as an alternative to the above-proposed tests to validate the pKa predictions.</ns0:p><ns0:p>All in all, the estimated tautomeric forms of the imidazole ring of His are certainly not enough to accurately determine whether the coexistence of native-like and mirror-image structures occurs in solution but it could be of valuable assistance to validate the determination made by the pKa analysis.</ns0:p></ns0:div> <ns0:div><ns0:head>CONCLUSIONS</ns0:head><ns0:p>We provided a proof-of-concept of an experimental design that could be used to detect the coexistence of native and mirror-image conformations for the Q10H mutant of protein A in solution. Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>of the mirror image, published so far, would be only of Academic interest, perhaps, reduced only to show a possible intermediate conformational state in the pathway of protein folding. On the other hand, if the experiments provide evidence that there is structural coexistence, then the theoretical predictions will have a sound basis and, even more important, it may spur significant progress in the conformational analysis of proteins with mirror-images. Manuscript to be reviewed</ns0:p><ns0:note type='other'>Chemistry Journals Analytical, Inorganic, Organic, Physical, Materials Science Figure 2</ns0:note><ns0:p>. Green-and yellow-ribbon diagrams for the native and 'mirror' image conformations of Protein A, respectively. The position of the side-chain of H10 is highlighted for each of these conformations. Moreover, the side-chain of D38 is also displayed to point out the close proximity between D38 and H10 in the 'mirror' image conformation. The favorable electrostatic interaction between D38 and H10 may be responsible for the large (∆ ≈ −1.1) change in the computed pKa between the native-like and the 'mirror-image conformations. Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>Chem. reviewing PDF \| (PCHEM-2019:04:36440:1:2:NEW 26 Jul 2019)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head>Figure 3 .</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Figure 3. Similarly, the computed average change for the imidazole ring forms of H10 as a function of pH for both the native like and the mirror image conformations are display in Figure 4. As shown in this Figure at a given fix pH, e.g., at pH=8.0, there are significant changes among the computed fractions of the imidazole ring forms of H10.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>First</ns0:head><ns0:label /><ns0:figDesc>, as shown in Figures 4, there is a large change in the average fractions of H10 tautomers as a function of pH. In particular, if the population of the native-like conformation is dominant in solution (≈ 100%) then, as shown in Figure4, the fraction of the protonated form should be ≈ 0% at pH ≈ 8.0. In other words, only the imidazole ring of H10 tautomers will be present in solution at this pH. Therefore, their relative populations can be determined accurately by measuring the one-bond CH, 1 J CH , Spin-Spin Coupling Constants (SSCC) of the imidazole ring of H10.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head>Figure 1 .</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure1. Red-and white-ribbon diagrams for the native structures of protein A (PDB ID1BDD Gouda et al. (1992)) and the equivalent for protein Q10H, respectively. The position of the side-chain of Q10 and H10 for protein A and protein Q10H are highlighted. The C α rmsd between the two native structures is 1.4 Å.</ns0:figDesc><ns0:graphic coords='10,141.73,247.19,413.61,239.84' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head>Figure 3 .</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Figure 3. Dots indicate the pKa change (∆), computed at pH 7.0, for each ionizable residue along the protein Q10H sequence. The blue-dots were computed from the single lowest-energy generated conformations of both the native-like and mirror-image topology, respectively. The orange-dots were computed for the two histidines in the sequence, namely H10 and H19, as an average over 25ns MD simulations for both the native-like and mirror-image conformations; vertical orange-lines denotes the standard deviations of the computed average ∆ values</ns0:figDesc><ns0:graphic coords='11,141.73,505.11,413.58,137.86' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head>Figure 4 .</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Figure 4. Fractions of the imidazol ring forms of H10 as a function of pH, for the 'Native' (panel A) and 'Mirror' (panel B) topologies of the Q10H mutant of protein A. The values, for each topology, are estimated along 25ns MD equilibrium trajectory for each of nine ionization states of two electrostatically-coupled histidines residues, namely H10 and H19</ns0:figDesc><ns0:graphic coords='12,141.73,146.71,413.59,440.79' type='bitmap' /></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head /><ns0:label /><ns0:figDesc>Determination of the pKa values of the ionizable residue H10 should provide a quick answer to this problem. Additionally</ns0:figDesc><ns0:table /><ns0:note>the NMR-determination of the one-bond vicinal coupling constant or the chemical-shifts of the imidazole ring of H10 could be used to validate this finding. There are two main advantages of the proposed methodology. Firstly, there is no need for 3D structural information and, secondly, a validation test can be carried out by standard NMR-based experiments.Whatever the output of the proposed experiments is, we will find them interesting. Indeed, if the results don't indicate the presence of the mirror image, all the theoretical predictions about the existence6/11 PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2019:04:36440:1:2:NEW 26 Jul 2019)</ns0:note></ns0:figure> </ns0:body> "	"Rebuttal letter: Does protein A mirror image exist in solution? In boldface you will find our answers to your comments as well as the reviewers comments. We want to notice that we have extended our simulations from 10 ns to 25 ns and we have also added an extensive discussion about the pKa calculation. We have also added a sumplemental file with new tables and figures. Last, but not least we have also made other minor revisions to the document to increase its redability. We want to thanks the editor and the reviewers for their comments as they have being useful to improve the quality of the manuscript. Editor • The main concern that has to be addressed is whether the pKa values can be predicted accurately and precisely enough to make a predicted pKa difference of 1.3 units meaningful. Evidence for and against this needs to be discussed carefully and extensively, especially in light of the short simulation time. This is the crux of the paper so a fair, balanced, and extensive discussion is needed. We agree with the Editor and the following sentence was added to the revised version of the manuscript (between lines 149-161): One challenge question is how meaningful the pK difference computed with our method are. In this regards, we would like to mention that the accuracy of the pK calculations have been carefully analyzed through a series of applications. Indeed, a comparison with experimental data show that the method is accurate enough, in terms of a NMR-based methodology, to predict the pK and tautomeric fractions of six histidine forms on the enzime DFPase from Loligo vulgaris, a 314-residues all-b protein containing 94 ionizable residues (DOI10.1080/07391102.2017.1377636). In addition, in another application (DOI10.1080/07391102.2017.1288169), a large test on 297 ionized residues from 34 proteins show that a 57%, 86% and 95% of the pK prediction are with an accuracy better than 0.5, 1.0 and 1.5 pK unit, respectively. Such range of accuracy is comparable or better than state of the art predictive methods such as the electrostaticbased MCCI2 method (DOI10.1002/jcc.21222). Moreover, the H10 pKa differences between the native-like and the mirror-image conformations of Q10H protein does not disappear but kept constant (~1.1 pK units) between 9ns-25ns of the MD simulation (see Figure 6 and Table S3 in supplemental files of the revised version of the manuscript), hence, given further confidence on the accuracy of the pK shift predictions. • Sufficient detail is needed regarding the MD simulations, so that the 1 calculations can be reproduced and the reviewers questions in this regard (such a choice of protonation state for “spectator” groups) needs to be addressed. We have augmented and revised the section “Computation of the pKa and the tautomeric fractions of the imidazole ring of H10” with details on how the MD simulations were set-up and processed. Lines 108-148 on the revised version of the manuscript. • There seems to be a lot of manual intervention in creating the starting structures, so the coordinates of these need to made accessible, at a minimum. Only a single initial structure was generated by manual intervention and this structure was early discarded because it’s energy was higher than the other two starting points. To avoid confusion we have removed any mention to this starting structure and in the revised manuscript we now mention the two initial structures we actually use. Lines 91-93 on the revised version of the manuscript. We have uploaded the two starting conformation to the GitHub repository https://github.com/BIOS-IMASL/protein_a_mirror Reviewer 1 (Anonymous) According to the methods section, “As mentioned in the main text, all acid 110 (Asp, Glu) and base (Lys, Arg) residues were kept in the ionized state because their respective pKo’s 111 (3.5, 4.0, and 10.5, 12.5, respectively) are shifted by more than 2.5 pK units from the pH (7.0) at which 112 the calculations were carried out.” but pKa’s can shift depending on the protein environment. Why was no check done to make sure these residues have pKas safely far away from 7? Seems like that should be straightforward given your other calculations. The pKa shift of all residues were calculated for both the native-like and the mirror-image protein. The results are shown in Table S1 (supplemental files) in the revised version of the manuscript. It can be seen that all ASP, GLU, LYS residues have pKa shifted by more than 2.5 pK units from pH=7.0. In other words, these shifts are not large enough to be considered as variable during the MD simulations carry out at pH 7.0. Lines 125-129 of the main text of the revised version of the manuscript. Precisely how the MD simulations were set-up and processed is not detailed. Details are necessary for replication. We have augmented and revised the section “Computation of the pKa and the tautomeric fractions of the imidazole ring of H10” with details on how the MD simulations were set-up and processed. Lines 2 108-148 on the revised version of the manuscript. Reviewer 2 (Anonymous) I think the writing was clear, but the formatting should be improved, particularly because PeerJ uses parenthetical citations. Thanks for the comment. We write the manuscript using a LaTeX template provided by PeerJ, we have no decision on the format used by the journal. There are several non-standard writing choices, like beginning a paragraph with “At this point is worth noting the following.” Thanks for the comment. We have revised our writing choices and grammar. Also, the proposed experiment would distinguish between the conformations of a histidine-containing mutant of the protein as a function of pH. Protonation states can have a large effect on the conformation of a protein, so replacement of a glutamine residue with a histidine residue and then titrating the protonation state of the histidine. Much of the generation of the protein conformations are done “by hand” and there is an acknowledged subjective element. e.g., 'For the native conformation we used 1BDD. Gouda et al. (1992) For the mirror image we started from 2 points: a mirror-image conformation previously obtained by Vila et al, Vila et al. (2003) an a mirror image folded by hand. We discarded all the conformations obtained from this last starting point as their energies were much more higher than for the other two starting points. Only a single initial structure was generated by manual intervention and this structure was early discarded because it’s energy was higher than the other two starting points. To avoid confusion we have removed any mention to this starting structure and in the revised version of the manuscript we only mention the two initial structures we actually used. Lines 91-93 on the revised version of the manuscript. The technical details of their molecular dynamics simulations are incomplete, but it seems clear that the simulations were only performed for 10 ns. Much longer simulations are necessary to calculate rigorous pKa differences. I suspect the observed differences might disappear if the simulations were performed longer. We agree with the reviewer and, consequently, we had extended the MD simulations from 10ns to 25ns. As shown in Figure 6 (suplemental files of the revised version of the manuscript) there is a pKa equilibration after ~9ns for both the native-like and the mirror-image structures of mutant Q10H of Protein A, respectively. In other words, 3 the pKa differences (as shown in Table S3 supplemental files of the revised version of the manuscript) does not disappear but kept constant (~1.1 pK units) between 9ns-25ns of the pH-constant MD simulation. See whole Section “Computation of the pKa and the tautomeric fractions of the imidazole ring of H10 ”; lines 96-161 in the revised version of the manuscript. Also, pKa prediction methods of all types rarely more accurate than plus or minus one unit relative to the experimental values, so there difference of 6.1 vs 7.3 is within the margin of error of the calculations. The authors report a statistical error ~0.2-0.3 pK units, but this is likely a major underestimate due to limited sampling. At minimum, this result would have to be reproduced by other constant pH methods, such as the Brooks, Roitberg, Shen, or Roux methods. We disagree with the reviewer comments that “ . . . pKa prediction methods of all types rarely more accurate than plus or minus one unit relative to the experimental values. . . ” because it is an imprecise statement and without solid grounds. Instead, please, see our comment below. Moreover, we do not introduce here a new method of calculations, we just use a method which have been previously developed and extensively tested against other accurate methods. For all these reasons, the following sentence was added to the revised version of the manuscript (between lines 149-161): One challenge question is how meaningful the pK difference computed with our method are. In this regards, we would like to mention that the accuracy of the pK calculations have been carefully analyzed through a series of applications. Indeed, a comparison with experimental data show that the method is accurate enough, in terms of a NMR-based methodology, to predict the pK and tautomeric fractions of six histidine forms on the enzime DFPase from Loligo vulgaris, a 314-residues all-b protein containing 94 ionizable residues (DOI10.1080/07391102.2017.1377636). In addition, in another application (DOI 10.1080/07391102.2017.1288169), a large test on 297 ionized residues from 34 proteins show that a 57%, 86% and 95% of the pK prediction are with an accuracy better than 0.5, 1.0 and 1.5 pK unit, respectively. Such range of accuracy is comparable or better than state of the art predictive methods such as the electrostaticbased MCCI2 method (DOI10.1002/jcc.21222). Moreover, the H10 pKa differences between the native-like and the mirror-image conformations of Q10H protein does not disappear but kept constant (~1.1 pK units) between 9ns-25ns of the MD simulation (see Figure 6 and Table S3 in supplemental files of the revised version of the manuscript), hence, given further confidence on the accuracy of the pK shift predictions. Simulation in an explicit solvent and demonstrating convergence are essential for this. 4 We disagree with the reviewer suggestion. Indeed, Figure 2 shows that residues H10 and D38 in the native-like and mirror-image structures are well exposed into water solvent. i.e. they are not in a protein cavity. Therefore mobility of water molecules will be similar to that of regular bulk water molecules, i.e. all water solvent can be treated as a continuum media. Also, long-timescale simulations would be needed to show that the histidine mutant as the same conformation as the wild-type in both the A and mirror conformations as well as their relative Gibbs energies. It would also need to be shown that titration of the histidine and its fractional population in a protonated state does not induce a conformational change (which is unlikely to be true). We have extended the simulation time to 25ns. We argue that Q10H mutant will not induce a change in the protein conformation, mainly, because Q10H is well exposed into the solvent, and is not involved in any important/critical atom-atom interactions. Proof of this assumption is the stable value of the potential energy along the 25ns of the MD simulation (see Figure S1 in the supplemental files of the revised version of the manuscript). Comments for the Author My feeling is that there is not to much to be gained by further theoretical studies of this problem. Without experimental validation, it’s difficult to be conclusive. We agree, this problem can only be verified by experimental intervention. For that reason here we prose a potentially useful experiment that could be helpful to provide answers to this question or at least contribute to the development of alternative (even better) experimental proposals. To the best of our knowledge there is no experimental paper in the literature aims to elucidate as to whether the Protein A mirror image could exist in solution despite the large amount of theoretical evidence suggesting it. From this point of view, we consider our contribution as seminal. The essential design of the proposed experiment introduces an amino acid that will undergo a change in charge as part of the experiment and is also fractionally populated. This could totally change the conformation of a small protein, so I don’t believe the problem is well-defined. We do not believe a conformational change is highly likely for Q10H, as explained above, although we can not completely rule-out such possibility. However, our main motivation is to encourage experimentalists to work on this problem, and in this manuscript we provided a plausible experiment and invite others to provide alternative experiments. The computational pKa prediction methods would also need to be shown to be able to predict experimental values with a higher accuracy of any previous 5 method. If this method can do that, there are many more exciting problems to study with that tool. We have been able to prove that “. . . our method show, in a comparison with other two tested methods, the lowest percentage of the largest wrong predictions, i.e., prediction with an error, DpKa, > 2.0 pK units, among the tested set of 34 proteins . . . ” (DOI 10.1080/07391102.2017.1288169) which is, by itself, a large accomplishment among method develop for accurate pK predictions. Moreover, as previously discussed this is not a paper about methods for pKa prediction, those methods have already been discussed elsewhere. This is an application of one of such methods. We are glad to hear that the reviewer consider our method can be used to solve other exciting problems. 6 "	Here is a paper. Please give your review comments after reading it.
482	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>There is abundant theoretical evidence indicating that a mirror image of Protein A may occur during the protein folding process. However, as to whether such mirror image exists in solution is an unsolved issue. Here we provide outline of an experimental design aimed to detect the mirror image of Protein A in solution. The proposal is based on computational simulations indicating that the use of a mutant of protein A, namely Q10H, could be used to detect the mirror image conformation in solution. Our results indicate that the native conformation of the protein A should have a pKa, for the Q10H mutant, at ≈6.2, while the mirror-image conformation should have a pKa close to ≈7.3. Naturally, if all the population is in the native state for the Q10H mutant, the pKa should be ≈6.2, while, if all are in the mirror-image state, it would be ≈7.3, and, if it is a mixture, the pKa should be larger than 6.2, presumably in proportion to the mirror population. In addition, evidence is provided indicating the tautomeric distribution of H10 must also change between the native and mirror conformations. Although this may not be completely relevant for the purpose of determining whether the protein A mirror image exists in solution, it could provide valuable information to validate the pKa findings. We hope this proposal will foster experimental work on this problem either by direct application of our proposed experimental design or serving as inspiration and motivation for other experiments.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>INTRODUCTION</ns0:head><ns0:p>A mirror image conformation is one that looks approximately like the specular image of the native state.</ns0:p><ns0:p>We say approximately because we do not require the amino acids to be specular images, but only the overall topology of the molecule. At least for some proteins, the mirror image will be energetically very close to the native state and thus it could also exist in solution. Among these proteins, we will focus our attention on the B-domain of staphylococcal protein A [PDB ID: 1BDD; a three-helix bundle] <ns0:ref type='bibr' target='#b9'>Gouda et al. (1992)</ns0:ref>. This protein has been the subject of extensive theoretical <ns0:ref type='bibr' target='#b19'>Olszewski et al. (1996)</ns0:ref>; <ns0:ref type='bibr' target='#b30'>Vila et al. (2003)</ns0:ref>; <ns0:ref type='bibr' target='#b8'>Garcia and Onuchic (2003)</ns0:ref>; <ns0:ref type='bibr' target='#b15'>Lee et al. (2006)</ns0:ref>; <ns0:ref type='bibr' target='#b11'>Kachlishvili et al. (2014)</ns0:ref> and experimental <ns0:ref type='bibr' target='#b6'>Deisenhofer (1981)</ns0:ref>; <ns0:ref type='bibr' target='#b9'>Gouda et al. (1992)</ns0:ref>; <ns0:ref type='bibr' target='#b0'>Bai et al. (1997)</ns0:ref>; <ns0:ref type='bibr' target='#b17'>Myers and Oas (2001)</ns0:ref>; <ns0:ref type='bibr' target='#b22'>Sato et al. (2004)</ns0:ref>; <ns0:ref type='bibr' target='#b7'>Dimitriadis et al. (2004)</ns0:ref>; <ns0:ref type='bibr' target='#b18'>Noel et al. (2012)</ns0:ref> studies because of its biological importance and small size. In contrast to this, the mirror-image conformation has been subject of limited discussion <ns0:ref type='bibr' target='#b19'>Olszewski et al. (1996)</ns0:ref>; <ns0:ref type='bibr' target='#b30'>Vila et al. (2003)</ns0:ref>; <ns0:ref type='bibr' target='#b8'>Garcia and Onuchic (2003)</ns0:ref>; <ns0:ref type='bibr' target='#b18'>Noel et al. (2012)</ns0:ref>; <ns0:ref type='bibr' target='#b11'>Kachlishvili et al. (2014)</ns0:ref>. The reason for this might be that the mirror image conformation of this protein has been observed only in some theoretical studies with different force fields but it has never been detected experimentally. As to whether this conformation is an artifact of the simulations or is difficult to observe the conformation experimentally, remains to be solved.</ns0:p><ns0:p>Difficulties for experiments to detect the mirror-image conformation arise precisely because the secondary structures of the mirror-image and the native conformation of protein A are identical and the PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2019:04:36440:2:1:NEW 8 Aug 2019)</ns0:p><ns0:p>Manuscript to be reviewed Chemistry Journals structural difference between these conformations are subtle <ns0:ref type='bibr' target='#b11'>Kachlishvili et al. (2014)</ns0:ref>. Because of this, use of simple experiments such as circular dichroism, used to estimate the fraction of secondary-structure content, or more sophisticative technique, such as nuclear magnetic resonance (NMR) spectroscopy, e.g., to monitor the 13 C chemical shift changes that may occur at residue-level <ns0:ref type='bibr' target='#b11'>Kachlishvili et al. (2014)</ns0:ref>, are useless for an accurate characterization of the mirror image conformation. A strong motivation to propose alternative methods to explore the possible coexistence in solution of the native and mirror-image conformation of protein A, comes from older evidence indicating that the mirror-image conformation could be a possible solution to the NMR-determined structure of protein A <ns0:ref type='bibr' target='#b9'>Gouda et al. (1992)</ns0:ref>. Indeed, according to <ns0:ref type='bibr' target='#b9'>Gouda et al. Gouda et al. (1992)</ns0:ref>, '. . . distance-geometry calculations resulted in 41 solutions, which had correct polypeptide folds excluding 14 mirror-image substructures. . . ' However, the mirrorimage structures were excluded from the analysis of <ns0:ref type='bibr' target='#b9'>Gouda et al. Gouda et al. (1992)</ns0:ref> without providing any reason. It seems that the decision was adopted because the 'mirror-image' satisfies the NOE constraints but contain D-amino acid residues (personal communication with Ichio Shimada).</ns0:p><ns0:p>Overall, we propose here a proof-of-concept of an experimental design aimed to solve this problem.</ns0:p><ns0:p>Initially we will show, by using ROSETTA, <ns0:ref type='bibr' target='#b4'>Bradley et al. (2005)</ns0:ref> that a mutant of protein A, hereafter the Q10H protein, exhibits the ability to fold into the native conformation (see Figure <ns0:ref type='figure' target='#fig_3'>1</ns0:ref>) as well as into the mirror-image conformation (see Figure <ns0:ref type='figure' target='#fig_4'>2</ns0:ref>). Later, we estimate the fraction of the native and mirror-image populations of the protein Q10H by using a recently introduced method, that take into account the protein dynamics in water by using a constant-pH MD simulation, to accurately determine the pKa values of ionizable residues, and fractions of ionized and tautomeric forms of histidine (His), in proteins at a given fixed pH <ns0:ref type='bibr' target='#b33'>Vorobjev et al. (2018)</ns0:ref>. Indeed, we explore the dependence of the electrostatically-calculated pKa and fractions of the imidazole ring forms of H10 as a function of pH for both the native-like and mirror-image conformations.</ns0:p></ns0:div> <ns0:div><ns0:head>MATERIALS AND METHODS</ns0:head><ns0:p>In this section we will give a brief reference to existent theoretical methods aimed to predict (i) the 3D structure of proteins accurately; <ns0:ref type='bibr' target='#b3'>Bradley et al. (2003)</ns0:ref> or determine (ii) the pKa values of ionizable residues and fractions of ionized and tautomeric forms of histidine (His) and acid residues in proteins, at a given fixed pH <ns0:ref type='bibr' target='#b34'>Vorobjev et al. (2008</ns0:ref><ns0:ref type='bibr' target='#b33'>Vorobjev et al. ( , 2018))</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Determination of the native and image-mirror conformations of protein Q10H</ns0:head><ns0:p>To generate the native and mirror-image conformations of protein A Q10H we used the fast-relax protocol from Rosetta, <ns0:ref type='bibr' target='#b5'>Chaudhury et al. (2010)</ns0:ref>; <ns0:ref type='bibr' target='#b4'>Bradley et al. (2005)</ns0:ref> this is an all-atom refinement protocol consisting of several rounds of repacking and energy minimization. The repulsive part of the Van der Waals energy function is annealed from 2% to 100%. Essentially the algorithm explores the local conformational space around the starting structure with a radius of 2 to 3 Å of rmsd (for the C α ). We performed several rounds of fast-relax using the following genetic-like algorithm:</ns0:p><ns0:p>1. For a given conformation of protein A mutate it by replacing Q10 with H10 2. Use the mutant as the starting point of 200 independent rounds of the fast-relaxation protocol 3. Choose 10 conformations; 2 at random and the 8 lowest-energy conformations 4. For each one of those conformations use fast-relaxation to generate 100 independent rounds (for a total of 1000 conformations) 5. repeat, from step 3, 40 times 6. keep the lowest energy conformation from all the rounds We started from 2 different conformations. For the native conformation we used 1BDD <ns0:ref type='bibr' target='#b9'>Gouda et al. (1992)</ns0:ref>. For the mirror image we started from a mirror-image conformation previously obtained by <ns0:ref type='bibr' target='#b30'>Vila et al, Vila et al. (2003)</ns0:ref>.</ns0:p><ns0:p>The Rosetta energy score of the lowest energy conformations for the native and image-mirror of protein Q10H was on par.</ns0:p></ns0:div> <ns0:div><ns0:head>2/11</ns0:head><ns0:p>PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2019:04:36440:2:1:NEW 8 Aug 2019)</ns0:p><ns0:p>Manuscript to be reviewed Chemistry Journals <ns0:ref type='bibr'>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:ref> </ns0:p></ns0:div> <ns0:div><ns0:head>Computation of the pKa and the tautomeric fractions of the imidazole ring of H10</ns0:head><ns0:p>The native-like and mirror-image conformations of protein Q10H, generated as describe in the previous section, were used as input files for the calculations of the pKa of all ionizable residues in the sequence as well as the fractions of the ionized H + and the tautomeric N ε2 − H and N δ 1 − H forms of the imidazole ring of H10. In particular, as it is well known, the tautomeric determination of the imidazole ring of His is both a very important problem in structural biology <ns0:ref type='bibr' target='#b24'>Schnell and Chou (2008)</ns0:ref>; <ns0:ref type='bibr' target='#b1'>Bermúdez et al. (2014)</ns0:ref> and a challenging task <ns0:ref type='bibr' target='#b16'>Machuqueiro and Baptista (2011)</ns0:ref>. For this reason, a recently introduced electrostatic-based method to determine the pKa values of ionizable residues and fractions of ionized and tautomeric forms of histidine (His) and acid residues in proteins <ns0:ref type='bibr' target='#b33'>Vorobjev et al. (2018)</ns0:ref>, is applied here to the analysis of protein A mutant Q10H. Protein dynamics in water, at a given pH=7.0, was taken into account by constant-pH MD simulation <ns0:ref type='bibr' target='#b32'>Vorobjev et al. (2017</ns0:ref><ns0:ref type='bibr' target='#b33'>Vorobjev et al. ( , 2018) )</ns0:ref> of both the native and mirror-image conformations of the Q10H mutant.</ns0:p><ns0:p>Protein dynamics in water was modeled by MD simulations with implicit solvent, namely using the Lazaridis-Karplus solvent model <ns0:ref type='bibr' target='#b13'>Lazaridis and Karplus (1999)</ns0:ref> with the BioPASED program <ns0:ref type='bibr' target='#b21'>Popov and Vorob'ev (2010)</ns0:ref>. For the MD simulation, the following three-step protocol was used. First step, determination of an equilibrium protein structure at temperature 300 K and pH 7.0 using the next three step procedure: (i) building a full atomic protein structure, i.e. with all hydrogen atoms added; this means, for example, that each His residue needs to be built up in the most probable form, i.e. in the ionized H + form or in the most probable neutral tautomer, N δ 1 − H (HD1 or HID) and N ε2 − H (HE2 or HIE); (ii) the crystal structure with all the assigned hydrogen atoms and histidine forms was energy optimized in implicit solvent using a conjugate gradient method; (iii) the system is heated slowly from 1 to 300 K during 250 ps; and (iv) a final equilibration at 300 K, during 0.5-1 ns, was carried out.</ns0:p><ns0:p>Step 2: generation of a representative set of 3D protein structures as a collections of snapshots each 50 ps along equilibrium MD trajectory during 25 ns snapshots taken every 50 ps time-interval. Step 3: for each snapshot, the pKa's of all ionizable residues is computed, as well as the fractions of two neutral tautomers of His and the acid residues, by carrying out an MC calculation with GB-MSR6c as an implicit solvent model. Finally, an average pKa's for each ionizable residue as well as the fraction of ionized and two tautomers of histidine and neutral form of acid residues of the protein are calculated.</ns0:p><ns0:p>The ionization constants pKa and the fractions of ionized and two neutral tautomers of histidine at constant pH 7.0 are modeled by MD simulations at constant pH <ns0:ref type='bibr' target='#b32'>Vorobjev et al. (2017</ns0:ref><ns0:ref type='bibr' target='#b33'>Vorobjev et al. ( , 2018))</ns0:ref>. During the pH-constant MD simulations all acid (Asp, Glu) and base (Lys, Arg) residues were kept in the ionized state because their respective pKo's (3.5, 4.0, and 10.5, 12.5, respectively) are shifted by more than 2.5 pK units from the pH (7.0) at which the calculations were carried out (see Table <ns0:ref type='table'>S1</ns0:ref> in supplemental files). On the other hand, the two existent histidine residues, namely H10 and H19, were considered to be electrostatically couple residues having nine ionization states, namely, 00, <ns0:ref type='bibr'>01,</ns0:ref><ns0:ref type='bibr'>02,</ns0:ref><ns0:ref type='bibr'>10,</ns0:ref><ns0:ref type='bibr'>11,</ns0:ref><ns0:ref type='bibr'>12,</ns0:ref><ns0:ref type='bibr'>20,</ns0:ref><ns0:ref type='bibr'>21,</ns0:ref><ns0:ref type='bibr'>22</ns0:ref>, where 0,1,2 represents the ionized and two neutral tautomer states respectively (see Table <ns0:ref type='table'>S2</ns0:ref> in supplemental files). The average potential energy values and it's thermal fluctuations due to molecular dynamics in solvent are estimated along 25ns MD equilibrium trajectory for each of the nine ionization states (see Table <ns0:ref type='table'>S2</ns0:ref> in supplemental files). Low energy states, which have occupation number large than 0.01, for histidine residues H10 and H19 along 25ns constant-pH MD trajectory, are shown in Table <ns0:ref type='table'>S3</ns0:ref> (see supplemental files). Energy fluctuations of the Q10H protein in solvent along 25 ns MD trajectory for native-like and mirror-image structures are shown in Figure <ns0:ref type='figure' target='#fig_3'>S1</ns0:ref> (see supplemental files). It can be seen, from this figure, that fluctuation of the native-like and mirror image structures are overlapping, i.e. spontaneous transition between native-like and mirror-image structures can occur. The average range of fluctuations of the atomic positions, i.e. in terms of the RMSD, observed along the MD trajectories were 1.4 and 1.3 Å for the native-like and mirror-image structures, respectively. Variation of pKa constant along MD trajectory is presented on Figure <ns0:ref type='figure' target='#fig_4'>S2</ns0:ref> (see supplemental files). It can be seen that pKa shift for histidine His10 are -0.3 and +0.8 pK units for the native-like and mirror-image protein structures. Such relatively large pKa shift for relatively small proteins can serves as a mark of native-like and mirror-image structures. Occupations of ionization states of His10 residue versus MD time are shown in Figure <ns0:ref type='figure' target='#fig_5'>S3a</ns0:ref> and Figure <ns0:ref type='figure' target='#fig_5'>S3b</ns0:ref> (see supplemental files) for native-like and mirror-mage structures, respectively. It should be noticed, that occupation of different ionization states of His10 show a large variation, i.e. RMSD from it's average values.</ns0:p><ns0:p>One challenge question is how meaningful the pK difference computed with our method are. In this regards, we would like to mention that the accuracy of the pK calculations have been carefully analyzed Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science through a series of applications. Indeed, a comparison with experimental data show the method is accurate enough, in terms of a NMR-based methodology, to predict the pK and tautomeric fractions of six histidine forms on the enzime DFPase from Loligo vulgaris, a 314-residues all-β protein containing 94 ionizable residues <ns0:ref type='bibr' target='#b33'>Vorobjev et al. (2018)</ns0:ref>. In addition, a large test on 297 ionized residues from 34 proteins show that a 57%, 86% and 95% of the pK prediction are with an accuracy better than 0.5, 1.0 and 1.5 pK unit respectively <ns0:ref type='bibr' target='#b32'>Vorobjev et al. (2017)</ns0:ref>. Such range of accuracy is comparable or better than state of the art predictive methods such as the electrostatic-based MCCI2 method <ns0:ref type='bibr' target='#b26'>Song et al. (2009)</ns0:ref>.</ns0:p><ns0:p>Moreover, the H10 pKa differences between the native-like and the mirror-image conformations of Q10H protein does not disappear but kept constant (≈1.1 pK units) between 9ns-25ns of the pH-constant MD simulation (see Figure <ns0:ref type='figure' target='#fig_4'>S2</ns0:ref> and table <ns0:ref type='table'>S3</ns0:ref> in supplemental files), hence, given further confidence on the accuracy of the pK shift predictions.</ns0:p><ns0:p>Since the error of our pKa predictive method can be positive or negative, there is a non-negligible chance that the computed difference of (≈1.1 pK units) is in fact practically null. However, there is also a non-negligible chance that the pKa difference is even larger than 1.1 pK units. Thus, we hope this result are enough to encourage experimentalists to perform the experimental design we propose.</ns0:p></ns0:div> <ns0:div><ns0:head>4/11</ns0:head><ns0:p>PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2019:04:36440:2:1:NEW 8 Aug 2019)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:p></ns0:div> <ns0:div><ns0:head>RESULTS AND DISCUSSION</ns0:head></ns0:div> <ns0:div><ns0:head>ANALYSIS OF THE PKA VARIATIONS AS A FUNCTION OF PH</ns0:head><ns0:p>Figure <ns0:ref type='figure' target='#fig_4'>2</ns0:ref> shows a superposition of the lowest-energy conformations for both the native-like (green-ribbon) and the mirror-image (yellow-ribbon) of protein Q10H obtained by using <ns0:ref type='bibr'>ROSETTA Bradley et al. (2005)</ns0:ref>.</ns0:p><ns0:p>These two structures were used to compute for each ionizable residue along the sequence the value of the pKa variations (∆ = [pK a Native − pK a Mirror ]) at pH 7.0 <ns0:ref type='bibr' target='#b34'>Vorobjev et al. (2008)</ns0:ref>. The result of this analysis is shown in Figure <ns0:ref type='figure' target='#fig_1'>3</ns0:ref> (as blue dots) where one of the largest change in ∆, namely larger than ±1.0 pK units, occurs for H10. This large shift on the pKa of residue H10 appears to be a consequence of the close proximity of H10 to D38 in the mirror-image conformation of protein Q10H (see Figure <ns0:ref type='figure' target='#fig_4'>2</ns0:ref>).</ns0:p><ns0:p>There is another change of ∆ larger than ±1.0 pKa unit and it occurs for residue K8 (see blue dots in Figure <ns0:ref type='figure' target='#fig_1'>3</ns0:ref>), a residue belonging to the flexible N-terminal region of the mutant protein Q10H, viz., ranging from residues T1 trough E9. The origin of the large computed shift in the pKa of residue K8 is the following. In the native structure of protein Q10H residue K8 is well exposed to the solvent. On the other hand, in the mirror image of Q10H residue K8 is close to E16, making a favorable electrostatic interaction. However a close inspection of these two structures indicates that the favorable electrostaticinteraction between K8 and E16, observed in the mirror image conformation, could also occur on the native conformation, e.g., by a rearrangement of the backbone-torsional angles of the flexible N-terminal region of the protein Q10H. If this were feasible, the computed pKa shift for K8 should be ≈ 0. Consequently, monitoring the pKa shift of K8 does not appear to be the right choice for the purpose of an accurate determination of the coexistence between the native and the mirror image states in solution. Unlike the origin of the pKa shift for K8, the interaction between H10 and D38 cannot take place in both the native and the mirror-image conformations (see Figure <ns0:ref type='figure' target='#fig_4'>2</ns0:ref>) and, hence, from here on we will focus our attention on H10 only.</ns0:p><ns0:p>Consideration of the protein dynamics in water is very important for an accurate computation of conformational-dependent values, such as the pKa's. However, this effect was not taken into account in the computation of the ∆ values shown, as blue-dots, in the Figure <ns0:ref type='figure' target='#fig_1'>3</ns0:ref>. Consequently, we carried out a constant-pH MD simulation of both the native-like Q10H mutant and its mirror-image conformations <ns0:ref type='bibr' target='#b33'>Vorobjev et al. (2018)</ns0:ref>. As mentioned in the Materials and Methods section, during the simulations at constant-pH 7.0 it is reasonable to consider all acid (Asp, Glu) and base (Lys, Arg) residues in the ionized state, because their respective pK 0 's (3.5, 4.0, and 10.5, 12.5, respectively) are shifted by more than 2.5 pK units from the pH (7.0) at which the calculations were carried out. For the same reason, the only Tyr in the sequence was consider as neutral. However, histidine residue pKa's (6.5) can vary considerably at pH 7.0 at which the calculations are carried out and, hence, consideration of histidine ionization states for each of the imidazole ring of His forms must be considered explicitly. Consequently during the calculations the nine ionizations states of the two interacting His, namely between H10 and H19, were explicitly considered (see Table <ns0:ref type='table'>S2</ns0:ref> of supplemental files). The average ∆ change for H10, computed from the native-like and mirror-image conformations after 25ns MD simulations, is shown as an orange dot in In general, the results shown in Figures <ns0:ref type='figure' target='#fig_6'>3 and 4</ns0:ref> and Table <ns0:ref type='table'>S3</ns0:ref> (supplemental files) are decisive for the determination of the fraction of native and mirror image conformations in solution. Indeed, if the dominant conformation in solution is the native like then the pKa of H10 will be 6.2±0.2. On the other hand, if the dominant conformation in solution is the mirror image then the pKa will be 7.3±0.2. Any other in-between value may indicate coexistence of these two conformations in solution.</ns0:p></ns0:div> <ns0:div><ns0:head>Validation of the H10 pKa-based predictions</ns0:head><ns0:p>Small changes around the computed average pKa value for H10 in the native-like conformation (6.2) are of course possible. In such a case additional experiments are necessary to determine whether such shift is due to expected fluctuations of the native conformation (around ±0.3 in pKa units) or to the presence of a small fraction of the mirror-image conformation. One such additional experiment could be the determination of the tautomers of the imidazole ring of H10. In this section we analyze this possibility by using two NMR-based methods.</ns0:p></ns0:div> <ns0:div><ns0:head>5/11</ns0:head><ns0:p>PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2019:04:36440:2:1:NEW 8 Aug 2019)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science Let us explain this in detail. Under the only condition that His is non-protonated, we have been able to show that the fraction of the N δ 1 − H tautomeric form ( f δ 1 ) of the imidazole ring of His can be estimated by using the following equation: f δ 1 = (J obs − 165.0)/15.0, <ns0:ref type='bibr' target='#b31'>Vila and Scheraga (2017)</ns0:ref> where J refers to 1 J Cδ 2H SSCC, and here obs is the observed value in solution for H10. Naturally,</ns0:p><ns0:formula xml:id='formula_0'>f ε2 = 1 − f δ 1 . Hence, if</ns0:formula><ns0:p>the native-like structure is the dominant topology in solution, then the following inequality should hold: <ns0:ref type='figure' target='#fig_6'>4</ns0:ref>) otherwise there would be coexistence of the native-like structure with other topology in solution.</ns0:p><ns0:formula xml:id='formula_1'>f ε2 ≫ f δ 1 (see Figure</ns0:formula><ns0:p>A second, and less restrictive, validation test will be to use a recently proposed NMR-based methodology aimed to determine the tautomeric forms as a function of the ionization state of the imidazole ring of histidine <ns0:ref type='bibr' target='#b29'>Vila et al. (2011)</ns0:ref>. In this approach, the average tautomeric fraction of the N ε2 − H form of His ( f ε2 ) can be determined by using the following equation: f ε2 = ∆ obs (1 − f H+ )/∆ ε where f H+ is the experimentally determined fraction for the ionized form of H10, at a given fix pH;</ns0:p><ns0:formula xml:id='formula_2'>∆ obs =\| 13 C δ 2 − 13 C γ \|,</ns0:formula><ns0:p>where 13 C δ 2 and 13 C γ are the NMR-observed chemical shifts for the imidazole ring of H10 at that pH; and ∆ ε is the first-order absolute shielding difference, \| 13 C δ 2 − 13 C γ \| ε , between the 13 C δ 2 and 13 C γ nuclei for the N ε2 − H tautomer, i.e., present to the extent of 100%. ∆ ε is a parameter which must be estimated <ns0:ref type='bibr' target='#b29'>Vila et al. (2011)</ns0:ref>. As a first approximation, a ∆ ε = 27.0ppm, obtained from the analysis of a His-rich protein, <ns0:ref type='bibr' target='#b32'>Vorobjev et al. (2017)</ns0:ref> namely Loligo vulgaris (pdb id 1E1A), a 314-residue all-β protein, <ns0:ref type='bibr' target='#b23'>Scharff et al. (2001)</ns0:ref> should be used. Naturally, the f δ 1 fraction, viz., for the N δ 1 − H tautomer, is obtained straightforwardly as: f δ 1 = 1 − f H+ − f ε2 . Although this second approach to compute the tautomers of H10 it is more general than the previous one, i.e., by using the 1 J Cδ 2H SSCC, the determination of the 13 C γ chemical shift it is not always feasible. Indeed, only 213 13 C γ , versus 6,984 13 C δ 2 , chemical shifts of the imidazole ring of histidine have been deposited in the Biological Magnetic Resonance data Bank (BMRB) <ns0:ref type='bibr' target='#b27'>Ulrich et al. (2008)</ns0:ref>. Overall, if it were feasible to observe the 13 C γ chemical shift we suggest to use both approaches to validate the pKa predictions.</ns0:p><ns0:p>Although this work is not intended to be a revision of all existing methods used to determine the tautomeric forms of the imidazole ring of His, the use of the tautomeric identification by direct observation of 15 N chemical shifts of the imidazole ring of His, which is a common practice in NMR spectroscopy, <ns0:ref type='bibr' target='#b20'>Pelton et al. (1993)</ns0:ref>; <ns0:ref type='bibr' target='#b25'>Shimahara et al. (2007)</ns0:ref>; <ns0:ref type='bibr' target='#b10'>Hass et al. (2008)</ns0:ref> should be mentioned. This method requires, as a necessary condition, knowledge of the canonical limiting values of the 15 N chemical shift of the imidazole ring of His in which each form of His is present to the extent of 100%. In this regard, there is theoretical evidence indicating that a considerable difference for the average tautomeric equilibrium constant, K T , can be obtained if DFT-computed 15 N limiting values rather than canonical limiting values are used <ns0:ref type='bibr' target='#b28'>Vila (2012)</ns0:ref>, Because these results raise concerns about the magnitude of the uncertainty associated with the predictions we did not consider this method as an alternative to the above-proposed tests to validate the pKa predictions.</ns0:p><ns0:p>All in all, the estimated tautomeric forms of the imidazole ring of His are certainly not enough to accurately determine whether the coexistence of native-like and mirror-image structures occurs in solution but it could be of valuable assistance to validate the determination made by the pKa analysis.</ns0:p></ns0:div> <ns0:div><ns0:head>CONCLUSIONS</ns0:head><ns0:p>We provided a proof-of-concept of an experimental design that could be used to detect the coexistence of native and mirror-image conformations for the Q10H mutant of protein A in solution. Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>of the mirror image, published so far, would be only of Academic interest, perhaps, reduced only to show a possible intermediate conformational state in the pathway of protein folding. On the other hand, if the experiments provide evidence that there is structural coexistence, then the theoretical predictions will have a sound basis and, even more important, it may spur significant progress in the conformational analysis of proteins with mirror-images. Manuscript to be reviewed Manuscript to be reviewed</ns0:p><ns0:note type='other'>Chemistry Journals</ns0:note></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>Chem. reviewing PDF \| (PCHEM-2019:04:36440:2:1:NEW 8 Aug 2019)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head>Figure 3 .</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Figure 3. Similarly, the computed average change for the imidazole ring forms of H10 as a function of pH for both the native like and the mirror image conformations are display in Figure 4. As shown in this Figure at a given fix pH, e.g., at pH=8.0, there are significant changes among the computed fractions of the imidazole ring forms of H10.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>First</ns0:head><ns0:label /><ns0:figDesc>, as shown in Figures 4, there is a large change in the average fractions of H10 tautomers as a function of pH. In particular, if the population of the native-like conformation is dominant in solution (≈ 100%) then, as shown in Figure4, the fraction of the protonated form should be ≈ 0% at pH ≈ 8.0. In other words, only the imidazole ring of H10 tautomers will be present in solution at this pH. Therefore, their relative populations can be determined accurately by measuring the one-bond CH, 1 J CH , Spin-Spin Coupling Constants (SSCC) of the imidazole ring of H10.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head>Figure 1 .</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1. Red-and white-ribbon diagrams for the native structures of protein A (PDB ID 1BDD Gouda et al. (1992)) and the equivalent for protein Q10H, respectively. The position of the side-chain of Q10 and H10 for protein A and protein Q10H are highlighted. The C α rmsd between the two native structures is 1.4 Å.</ns0:figDesc><ns0:graphic coords='10,141.73,77.07,413.56,224.30' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head>Figure 2 .</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure2. Green-and yellow-ribbon diagrams for the native and 'mirror' image conformations of Protein A, respectively. The position of the side-chain of H10 is highlighted for each of these conformations. Moreover, the side-chain of D38 is also displayed to point out the close proximity between D38 and H10 in the 'mirror' image conformation. The favorable electrostatic interaction between D38 and H10 may be responsible for the large (∆ ≈ −1.1) change in the computed pKa between the native-like and the 'mirror-image conformations.</ns0:figDesc><ns0:graphic coords='10,141.73,393.71,413.59,239.53' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head>Figure 3 .</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Figure 3. Dots indicate the pKa change (∆), computed at pH 7.0, for each ionizable residue along the protein Q10H sequence. The blue-dots were computed from the single lowest-energy generated conformations of both the native-like and mirror-image topology, respectively. The orange-dots were computed for the two histidines in the sequence, namely H10 and H19, as an average over 25ns MD simulations for both the native-like and mirror-image conformations; vertical orange-lines denotes the standard deviations of the computed average ∆ values</ns0:figDesc><ns0:graphic coords='11,141.73,286.22,413.58,137.86' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head>Figure 4 .</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Figure 4. Fractions of the imidazol ring forms of H10 as a function of pH, for the 'Native' (panel A) and 'Mirror' (panel B) topologies of the Q10H mutant of protein A. The values, for each topology, are estimated along 25ns MD equilibrium trajectory for each of nine ionization states of two electrostatically-coupled histidines residues, namely H10 and H19</ns0:figDesc><ns0:graphic coords='12,141.73,146.71,413.59,440.79' type='bitmap' /></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head /><ns0:label /><ns0:figDesc>Determination of the pKa values of the ionizable residue H10 should provide a quick answer to this problem. Additionally</ns0:figDesc><ns0:table /><ns0:note>the NMR-determination of the one-bond vicinal coupling constant or the chemical-shifts of the imidazole ring of H10 could be used to validate this finding. There are two main advantages of the proposed methodology. Firstly, there is no need for 3D structural information and, secondly, a validation test can be carried out by standard NMR-based experiments.Whatever the output of the proposed experiments is, we will find them interesting. Indeed, if the results don't indicate the presence of the mirror image, all the theoretical predictions about the existence6/11 PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2019:04:36440:2:1:NEW 8 Aug 2019)</ns0:note></ns0:figure> <ns0:note place='foot'>Analytical, Inorganic, Organic, Physical, Materials Science </ns0:note> </ns0:body> "	"Rebuttal letter: Outline of an experimental design aimed to detect a protein A mirror image in solution In boldface you will find our answer to your last comment Editor The manuscript has improved significantly, and you need to make one small change in order for me to accept the paper. And that is a brief discussion of the following point You write that “In addition, a large test on 297 ionized residues from 34 proteins show that a 57%, 86% and 95% of the pK prediction are with an accuracy better than 0.5, 1.0, and 1.5 pK unit respectively” Since the error can be both positive and negative, there is a non-negligible chance that your computed values of 6.2 and 7.3 are in fact (for example) 6.7 and 6.8 respectively, i.e. there is no measurable difference in pKa. In fact there probably isn’t any computational pKa prediction method that can deliver the accuracy needed to unequivocally say whether there is a measurable pKa difference. However, there is also a non-negligible chance that the pKa difference is even larger, so your work may convince some experimentalists to perform the experiment. We agree with the Editor and the following sentence was added to the revised version of the manuscript (lines 161-164): Since the error of our pkA predictive method can be positive or negative, there is a non-negligible chance that the computed difference of (≈ 1.1 pK units) is in fact practically null. However, there is also a non-negligible chance that the pKa difference is even larger than 1.1 pK units. Thus, we hope this result are enough to encourage experimentalists to perform the experimental design we propose. 1 "	Here is a paper. Please give your review comments after reading it.
483	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Background. Intrinsically disordered proteins (IDPs) have been shown to exhibit cryoprotective activity toward other cellular enzymes without any obvious conserved sequence motifs. This study investigated relationships between the physical properties of several human genome-derived IDPs and their cryoprotective activities.</ns0:p><ns0:p>Methods. Cryoprotective activity of three human-genome derived IDPs and their truncated peptides toward lactate dehydrogenase (LDH) and glutathione S-transferase (GST) was examined. After the shortest cryoprotective peptide was defined (named FK20), cryoprotective activity of all-D-enantiomeric isoform of FK20 (FK20-D) as well as a racemic mixture of FK20 and FK20-D was examined. In order to examine the lack of increase of thermal stability of the target enzyme, the CD spectra of GST and LDH in the presence of a racemic mixture of FK20 and FK20-D at varying temperatures were measured and used to estimate T m .</ns0:p><ns0:p>Results. Cryoprotective activity of IDPs longer than 20 amino acids was nearly independent of the amino acid length. The shortest IDP-derived 20 amino acid length peptide with sufficient cryoprotective activity was developed from a series of TNFRSF11B fragments (named FK20) was identified. FK20, FK20-D, and an equimolar mixture of FK20 and FK20-D also showed similar cryoprotective activity toward LDH and GST. T m of GST in the presence and absence of an equimolar mixture of FK20 and FK20-D are similar, suggesting that IDPs' cryoprotection mechanism stems partly from a molecular shielding effect rather than a direct interaction with the target enzymes.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>Intrinsically disordered proteins (IDPs) are an important class of proteins, that is widely associated with broad biological processes <ns0:ref type='bibr' target='#b34'>(Wright & Dyson, 1999;</ns0:ref><ns0:ref type='bibr' target='#b26'>Romero et al., 2001;</ns0:ref><ns0:ref type='bibr' target='#b8'>Dunker et al., 2008;</ns0:ref><ns0:ref type='bibr'>Tompa & Fersht, 2009;</ns0:ref><ns0:ref type='bibr' target='#b33'>Uversky & Dunker, 2010)</ns0:ref>. The most unique feature of IDPs is that they lack stable and compact tertiary structures alone under physiological conditions, while some of them eventually fold into stable structures during specific interaction. Currently, at least two major biological functions of IDPs have been proposed: 'coupling folding and binding' <ns0:ref type='bibr' target='#b9'>(Dyson & Wright, 2002;</ns0:ref><ns0:ref type='bibr' target='#b27'>Sugase, Dyson & Wright, 2007;</ns0:ref><ns0:ref type='bibr' target='#b18'>Higo, Nishimura & Nakamura, 2011)</ns0:ref> and 'accelerated association with partner molecules by the fly-casting mechanism' <ns0:ref type='bibr' target='#b23'>(Levy, Onuchic & Wolynes, 2007;</ns0:ref><ns0:ref type='bibr' target='#b27'>Sugase, Dyson & Wright, 2007;</ns0:ref><ns0:ref type='bibr' target='#b6'>Chen, 2009)</ns0:ref>. The former function is amino acid sequence dependent, because at least some part of the IDP must adopt into a fixed conformation with specific molecular contacts upon target binding. The latter function is assumed as less sequence dependent; a higher flexibility in the extended conformation of the IDP region that interconnects two functional domains, is seemingly more important. Recently, we and other researchers proposed a third physiological function of IDPs as protectants from environmental stresses, such as freezing and desiccation <ns0:ref type='bibr' target='#b20'>(Hughes & Graether, 2011;</ns0:ref><ns0:ref type='bibr' target='#b21'>Hughes et al., 2013;</ns0:ref><ns0:ref type='bibr' target='#b4'>Boothby et al., 2017;</ns0:ref><ns0:ref type='bibr' target='#b24'>Matsuo et al., 2018)</ns0:ref>. In this study, we focused on the cryoprotective activity of IDPs.</ns0:p><ns0:p>Plant dehydrins (DHNs) are extensively well studied examples of cryoprotective IDPs <ns0:ref type='bibr' target='#b2'>(Allagulova et al., 2003;</ns0:ref><ns0:ref type='bibr' target='#b17'>Hanin et al., 2011;</ns0:ref><ns0:ref type='bibr' target='#b16'>Graether & Boddington, 2014)</ns0:ref>. DHNs belong to a family of late embryogenesis abundant (LEA) proteins that are major contributors to the development of desiccation tolerance during plant seed maturation <ns0:ref type='bibr' target='#b15'>(Goyal, Walton & Tunnacliffe, 2005;</ns0:ref><ns0:ref type='bibr' target='#b19'>Hincha & Thalhammer, 2012;</ns0:ref><ns0:ref type='bibr' target='#b3'>Amara et al., 2014)</ns0:ref>. DHNs are characterized by the presence of one or more uniquely conserved sequence motifs, Lys-rich (K-), Tyr-rich (Y-), and Ser-rich (S-) segments <ns0:ref type='bibr' target='#b20'>(Hughes & Graether, 2011)</ns0:ref>. Many in vitro studies have demonstrated that DHNs effectively prevent inactivation of the model reporter enzyme, lactate dehydrogenase (LDH), during repeated freeze/thaw cycles <ns0:ref type='bibr' target='#b20'>(Hughes & Graether, 2011;</ns0:ref><ns0:ref type='bibr' target='#b7'>Cuevas-Velazquez, Rendon-Luna & Covarrubias, 2014)</ns0:ref>. A possible mechanism underlying DHN cryoprotection involves 'molecular shields' that prevent stochastic direct contacts between enzyme molecules <ns0:ref type='bibr' target='#b5'>(Chakrabortee et al., 2012;</ns0:ref><ns0:ref type='bibr' target='#b21'>Hughes et al., 2013)</ns0:ref>. Two independent research groups found that the cryoprotective activity of DHNs and their artificial variants roughly correlated with their hydrodynamic radius (R H ) rather than their amino acid sequences <ns0:ref type='bibr' target='#b21'>(Hughes et al., 2013;</ns0:ref><ns0:ref type='bibr' target='#b7'>Cuevas-Velazquez, Rendon-Luna & Covarrubias, 2014;</ns0:ref><ns0:ref type='bibr' target='#b10'>Ferreira et al., 2018)</ns0:ref>, partly supporting the molecular shielding hypothesis.</ns0:p><ns0:p>Strongly encouraged by Graether and colleagues' work <ns0:ref type='bibr' target='#b20'>(Hughes & Graether, 2011;</ns0:ref><ns0:ref type='bibr' target='#b21'>Hughes et al., 2013;</ns0:ref><ns0:ref type='bibr' target='#b10'>Ferreira et al., 2018)</ns0:ref>, we have uncovered evidence supporting the molecular shielding hypothesis of cryoprotective IDPs through a different approach <ns0:ref type='bibr' target='#b24'>(Matsuo et al., 2018)</ns0:ref>. If the cryoprotective action is driven solely by the molecular shielding effect, any other IDPs could also exert cryoprotective activity. Indeed, we found a potential example in the literature, silk worm sericin, a Ser-rich IDP <ns0:ref type='bibr' target='#b32'>(Tsujimoto et al., 2001)</ns0:ref>. We expanded the cryoprotective IDP concept to include other evolutionarily unrelated IDPs, such as human genome-derived IDPs of 36 to 44 amino acid residues, rather than DHNs or LEAs. In our previous study <ns0:ref type='bibr' target='#b24'>(Matsuo et al., 2018)</ns0:ref>, we demonstrated that all the examined IDPs derived from the human genome exerted cryoprotective activity toward not only the model enzyme LDH, but also glutathione-S transferase (GST) and green fluorescent protein (GFP). In detail, 53 candidate IDP genes from human genome were first predicted by a bioinformatics method, and then, among them, 35 IDP peptides systematically proven as the flexible disordered peptide segments by the NMR-based indirect IDP-assessment methods developed by us <ns0:ref type='bibr' target='#b14'>(Goda et al., 2015b)</ns0:ref>. Accordingly, the five randomly-selected human genome-derived IDP peptides among the 35 peptides were demonstrated to have substantial cryoprotective activities against LDH, GST, and green fluorescent protein <ns0:ref type='bibr' target='#b24'>(Matsuo et al., 2018)</ns0:ref>. However, in that study, all five IDPs showed similar levels of cryoprotective activity; we could not identify further sequence-activity relationships in the human genome-derived IDPs.</ns0:p><ns0:p>In this study, we selected three IDPs, C1, D10, and E1, for further analysis. We succeeded in minimizing the length of the human-genome-derived IDPs with practical cryoprotective activity. We found that FK20-the 20 amino acid fragment (position 24-43) from the tumor necrosis factor receptor superfamily member 11B (TNFRS11B) precursor-showed substantial cryoprotective activity toward LDH. Then we compared the cryoprotective activity of FK20 and its all-D-enantiomeric isomer and found that the cryoprotective activity of FK20 is independent to its chirality. Accordingly, we employed a new technique to use the racemic mixture of FK20 and FK20-D, that do not show CD signal, to investigate the absence of specific molecular effect toward the GST and LDH reporter enzyme by CD spectroscopy. These results suggest a potential use of human genome-derived IDP as a cryopreserving agent for cryosensitive enzymes and proteins.</ns0:p></ns0:div> <ns0:div><ns0:head>Materials & Methods</ns0:head><ns0:p>Expression and preparation of the IDP samples Human genome-derived IDP samples and their truncation mutants were prepared by an E. coli expression system optimized for preparing IDP samples, as previously described <ns0:ref type='bibr' target='#b11'>(Goda et al., 2015a)</ns0:ref>. In brief, the N-terminal autoprotease N(pro) from bovine viral diarrhea virus was selected as a fusion partner for protein expression using the pET-based N(pro) fusion protein expression system <ns0:ref type='bibr' target='#b0'>(Achmüller et al., 2007)</ns0:ref>. The IDPs were expressed, N-terminal tags were removed, and finally purified by reversed phase HPLC (COSMOSIL® 5C4-AR-300, Nacalai Tesque, ϕ4.6mm x 250 mm) with 0.1% trifluoroacetic acid-acetonitrile solvent system. All the peptides were quantified by UV absorbance at 280 nm, lyophilized, and stored at -30°C until use. FK20, a 20 amino acid peptide, and its all-D-enantiomeric isomer, FK20-D, were chemically synthesized (Biologica Co. Ltd. Nagoya, Aichi, Japan) with at least 80% purity, and purified by reversed phase HPLC.</ns0:p></ns0:div> <ns0:div><ns0:head>Cryoprotection assay for LDH</ns0:head><ns0:p>Rabbit muscle lactate dehydrogenase (LDH) (Sigma-Aldrich, L-2500) was selected as the reporter enzyme for cryoprotection activity of shorter IDP peptides, using a slightly modified protocol based on Hughes and Graether <ns0:ref type='bibr' target='#b20'>(Hughes & Graether, 2011)</ns0:ref>. The initial LDH solution contained 50 µg/ml in 10 mM sodium phosphate (pH 7.4). In a 1.5-ml microfuge tube, we mixed a 10-µl aliquot of the LDH solution with 10 µl of solution containing each of the individual protectants (IDP peptides) at concentrations ranging from 2.5 µg/ml to 500 µg/ml. Five cycles of freezing in liquid nitrogen for 30 s and thawing in a water bath at 4°C for 5 min were applied to each sample. Subsequently, LDH activity was measured using a standard NADH oxidase coupled-enzyme system according to the our previous report <ns0:ref type='bibr' target='#b24'>(Matsuo et al., 2018)</ns0:ref>. For the analysis, we set the LDH activity of the untreated sample (the enzyme without freeze and thaw processes and without the addition of a cryoprotectant) as 100%. All measurements were performed in triplicate.</ns0:p></ns0:div> <ns0:div><ns0:head>Cryoprotection assay for GSH</ns0:head><ns0:p>PeerJ Phy. Chem. reviewing PDF \| (PCHEM- <ns0:ref type='table'>2020:12:56382:1:0:NEW 28 Oct 2021)</ns0:ref> Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science Glutathione S-transferase from Schistosoma japonicum (GST) was selected as the second reporter enzyme for cryoprotection activity of shorter IDP peptides because this enzyme was easily prepared in the laboratory and also suited for the subsequent CD spectroscopy experiments. The cryoprotection assay was performed according to our previous report <ns0:ref type='bibr' target='#b24'>(Matsuo et al., 2018)</ns0:ref>. After five freeze/thaw cycles, GST activity was measured using a standard 1chloro-2,4-dinitrobenzene (CDNB, Sigma-Aldrich, 138630) assay with a 96-well plate and 2300 EnSpire Microplate Reader (Perkin Elmer). For the analysis, we set the GST activity of the untreated sample (the enzyme without freeze and thaw processes and without the addition of a cryoprotectant) as 100%. All measurements were performed three times and the standard deviations were calculated.</ns0:p></ns0:div> <ns0:div><ns0:head>Circular dichroism (CD) measurements</ns0:head><ns0:p>CD spectra between 190 and 300 nm were collected on a J-805 spectropolarimeter (JASCO, Tokyo, Japan). The time constant, scan speed, bandwidth/resolution, and sensitivity of the spectropolarimeter were set at 1 s, 100 nm/min, 1 nm, and 100 mdeg, respectively. We measured a 300 μl solution of 250 µg/ml of FK20, FK20-D or 125 µg/ml each of the FK20/FK20-D mixture with a 10 mM sodium phosphate buffer (pH 7.4) in a quartz cuvette with a 1 mm light path length at 20°C. Accordingly, we measured CD spectra of a 300 μl solution of 100 µg/ml of GST with 0, 25, 50 and 100 µg/ml each of FK20/FK20-D mixture in the same buffer. Similarly, we measured CD spectra of a 300 μl solution of 100 µg/ml of LDH with 0, 25, 50 and 100 µg/ml each of FK20/FK20-D mixture in the same buffer.</ns0:p><ns0:p>The GST and LDH denaturation temperatures (T m ) were determined by CD spectra at varying temperatures from 25°C to 75°C or 85°C by monitoring the ellipticity at 222 nm at the speed of heating by 1°C/min. Every 10°C, CD spectra between 200 and 300 nm were automatically collected. For LDH, CD spectra of LDH were measured at 25°C and 85°C. A moving average of CD values of each five temperature points were plotted, and three straight lines corresponding to the baseline, the plateau, and the slope, were indicated. T m was determined as the temperature of 50% denatured state.</ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head></ns0:div> <ns0:div><ns0:head>Cryoprotective activity of shorter IDPs toward LDH and GST</ns0:head><ns0:p>To determine the minimal length of IDPs showing a substantial cryoprotective activity toward LDH and GST, we constructed several recombinant plasmids containing coding regions of the human genome-derived IDPs C1, D10, E1, and a series of their C-terminal truncated mutants. Amino acid sequences with schematic diagrams of IDPs and their truncated mutants are shown in Figure <ns0:ref type='figure' target='#fig_1'>1</ns0:ref>. Human genome-derived IDPs C1, D10, and E1 consisted of 36, 37 and 39 amino acids, respectively. These peptides were proven as IDPs by both CD spectra and 1 H-15 N 2D-NMR spectra in our previous studies <ns0:ref type='bibr' target='#b14'>(Goda et al., 2015b;</ns0:ref><ns0:ref type='bibr' target='#b24'>Matsuo et al., 2018)</ns0:ref>. We used the PONDR server (http://pondr.com) <ns0:ref type='bibr' target='#b25'>(Obradovic et al., 2005)</ns0:ref> to predict whether the series of Cterminal truncated peptides were also disordered. Accordingly, the charge-hydropathy plots (Uversky plots) were shown in Supplementary Figure <ns0:ref type='figure' target='#fig_1'>S1</ns0:ref>. Cryoprotective activity of all peptides toward LDH at concentrations of 5, 10, 25, 50 and 500 μg/mL were measured. All peptides showed stronger cryoprotective activity than BSA in a concentration-dependent manner, with none showing less than 90% cryoprotection at the highest concentration (500 μg/mL) (data not shown). We compared these cryoprotective effects at the intermediate concentration (50 μg/mL) (Figure <ns0:ref type='figure' target='#fig_3'>2A</ns0:ref>) and found that the cryoprotective activity of most of IDPs was independent of their amino acid lengths, except the three shorter peptides, C1 20 , D10 15 and E1 20 . These peptides showed decreased cryoprotection activity of less than 60%. As a result, D10 20 was the shortest peptide with practical cryoprotective activity toward LDH at 50 μg/mL. Subsequently, we examined the cryoprotective activity of these peptides toward GST (Figure <ns0:ref type='figure' target='#fig_3'>2B</ns0:ref>) and the results were similar to those found with LDH. Also, D10 20 was again the shortest peptide with reasonably sufficient cryoprotective activity toward GSH at 50 μg/mL. We further analyzed the normalized cryoprotective activity by amino acid lengths and calculated isoelectric points, pI <ns0:ref type='bibr'>(Figures 2C and 2D,</ns0:ref><ns0:ref type='bibr'>respectively)</ns0:ref>. In order to compare the cryoprotective activities for the different reporter enzymes, both the maximum preserved enzymatic activities with the cryoprotective peptide, LDH with E1 31 and GST with D10 20 , were set to 100%, respectively. Peptides shorter than 20 amino acids were less cryoprotective, which is consistent with the proposed cryoprotection mechanism of DHNs as a molecular shield effect, where cryoprotective activity of various DHNs were roughly proportional to R H logarithms of the cryoprotectants (Cuevas-Velazquez, <ns0:ref type='bibr' target='#b7'>Rendon-Luna & Covarrubias, 2014;</ns0:ref><ns0:ref type='bibr' target='#b10'>Ferreira et al., 2018)</ns0:ref>. Since we only examined cryoprotective IDPs smaller than 42 residues, only the lower limit of the correlation has been observed. In addition, we found that the IDP peptides with either extremely high or low pI were less cryoprotective, whereas the peptides with neutral pI were more potent.</ns0:p><ns0:p>Hereafter, our shortest cryoprotective IDP-derived peptide D10 20 is referred to as FK20.</ns0:p></ns0:div> <ns0:div><ns0:head>Cryoprotective activity of FK20 and its all-D-enantiomeric isomer FK20-D</ns0:head><ns0:p>We further characterized cryoprotective activity and its FK20 mechanism by using its all-Denantiomeric isomer, FK20-D. Based on this study and our previous study, the cryoprotective activity of IDP-derived peptides is likely not amino acid sequence specific. For example, we measured and compared 1 H-15 N HSQC spectra of the target molecule Aβ(1-42) in the absence and the presence of the cryoprotective IDPs in our previous study, and we observed almost no spectra change <ns0:ref type='bibr' target='#b22'>(Ikeda et al., 2020)</ns0:ref>. Thus, the absence of any specific molecular interaction between IDPs and the target enzymes is expected. It is generally saying that proving nonexistence is always difficult. Although it is difficult to completely prove the absence of any specific interaction between the IDPs and the reporter enzymes, we challenged to provide indirect evidence to support this assumption as many as possible. In this study, we examined the cryoprotective activity of FK20-D and compared to that of the parent FK20. The cryoprotective activities of FK20, FK20-D, and the same total concentration of the racemic equimolar mixture of FK20 and FK20-D (FK20-LD) toward LDH and GST at varying concentrations were examined (Figure <ns0:ref type='figure' target='#fig_5'>3A</ns0:ref> and 3B, respectively). FK20-D showed similar concentration-dependent cryoprotective profiles toward the reporter enzymes. The results suggested that specific molecular interaction between the reporter enzymes and either the FK20 or FK20-D peptides was absent, because the contribution of any residue-specific interaction of FK20 is not reproducible with FK20-D. We assumed that the effect of FK20 and FK20-D toward LDH and GST were more 'environmental'. Indeed, FK20-LD showed similar concentration-dependent cryoprotective profiles (Figures <ns0:ref type='figure' target='#fig_5'>3A and 3B</ns0:ref>). Note that the additive property of different cryoprotective IDPs was also observed between full length C1 and D10 (Supplementary Figure <ns0:ref type='figure' target='#fig_3'>S2</ns0:ref>). A 1:1 mixture (in weight) of C1 and D10 showed a concentration-dependent cryoprotective profile similar to that of C1.</ns0:p></ns0:div> <ns0:div><ns0:head>Effect of cryoprotective IDP peptide toward structure and thermal stability of GST and LDH</ns0:head><ns0:p>Next we tried to show that the cryoprotective activity was not due to either structural change or thermal stabilization of GST, but rather that reporter enzyme cryoprotection can be explained by avoiding denaturation of the enzymes by structural or thermal stabilization through either specific or non-specific IDP interactions. In our the other previous study, we succeeded in demonstrating the amyloid formation inhibitory activity of the same human genome-derived IDP peptides against Aβ(1-42) <ns0:ref type='bibr' target='#b22'>(Ikeda et al., 2020)</ns0:ref>. At that time, we employed solution NMR techniques to monitor existence of a specific molecular interaction between 15 N-labelled Aβ(1-42) in the presence of non-labelled IDPs. However, in this study, the molecular weight of the reporter enzyme (for example, GST) seems not suitable for solution NMR. Therefore, we employed CD spectroscopy, since FK20-LD, the equimolar mixture of FK20 and FK20-D, is silent in CD measurement. Figure <ns0:ref type='figure' target='#fig_8'>4A</ns0:ref> presents the experimental evidence of this unique feature of FK20-LD (grey), showing no significant CD band. FK-20 (black) showed the typical CD spectrum of a disordered state, whereas FK20-D (black, dashed line) showed the exact same CD spectrum with an inverted sign, as expected.</ns0:p><ns0:p>Figure <ns0:ref type='figure' target='#fig_8'>4B</ns0:ref> shows the CD spectra of GST in the absence (dashed line) and presence (solid line) of FK20-LD. There was no significant change in CD spectra, indicating that GST did not change its three-dimensional structure. Thus, FK20-LD cryoprotective activity was not a result of GST structural change. Figures <ns0:ref type='figure' target='#fig_8'>4C and 4E</ns0:ref> show a series of GST CD spectra at increasing temperatures from 25°C to 75°C with and without FK20-LD. Figure <ns0:ref type='figure' target='#fig_8'>4D and 4F</ns0:ref> show GST thermal denaturation plots taken from Figures <ns0:ref type='figure' target='#fig_8'>4C and 4E</ns0:ref>, respectively. The GST thermal denaturation temperature (T m ) was 56.5±1.2°C for GST alone, and 58.2±1.1°C in the presence of 0.2 mg/mL of FK20-DL. Similarly, Figure <ns0:ref type='figure' target='#fig_9'>5A</ns0:ref> shows the CD spectra of LDH in the absence (dashed line) and presence (solid line) of FK20-LD. Figures <ns0:ref type='figure' target='#fig_9'>5B and 5D</ns0:ref> show initial and final LDH CD spectra at increasing temperatures from 25°C to 75°C with and without FK20-LD. Figure <ns0:ref type='figure' target='#fig_9'>5C and 5E</ns0:ref> show LDH thermal denaturation plots taken from Figures <ns0:ref type='figure' target='#fig_9'>5B and 5D s</ns0:ref>, respectively. The LDH thermal denaturation temperature (T m ) was 61.2°C for LDH alone, and 62.0°C in the presence of 0.2 mg/mL of FK20-DL. The results suggest that the thermal stability gains of GST and LDH by FK20-DL are small. Thus, we concluded that the FK20-DL cryoprotective activity was likely not a result of a thermal stabilization effect on the reporter enzymes.</ns0:p></ns0:div> <ns0:div><ns0:head>Discussion</ns0:head><ns0:p>In this study, we succeeded in minimizing the amino acid length of human-genome-derived IDPs with a reasonable cryoprotective effect into 20 amino acid residues. The shortest cryoprotective peptide, FK20, showed 100% cryoprotective action toward LDH at a concentration of 100 μg/mL (0.01%). We observed that the peptides shorter than 20 amino acids were less cryoprotective. The result is consistent with the proposed cryoprotection mechanism of DHNs as a molecular shield effect, where cryoprotective activity of various DHNs were roughly proportional to hydrodynamic radius R H logarithms of the cryoprotectants (Cuevas-Velazquez, <ns0:ref type='bibr' target='#b7'>Rendon-Luna & Covarrubias, 2014;</ns0:ref><ns0:ref type='bibr' target='#b10'>Ferreira et al., 2018)</ns0:ref>.The mechanism of molecular shield is to inhibit a core of protein aggregation during freeze-thaw cycles of the proteins by disturbing the direct collision between the reporter enzymes. Thus, R H of cryoprotectant is considered for explaining the molecular shield efficacy.</ns0:p><ns0:p>We demonstrated that the cryoprotective activity of FK20 is independent to its molecular chirality, that suggests the absence of the specific interaction between the reporter enzymes and FK20. This assumption is consistent with our observation that the addition of IDP (in this time, FK20 / FK20-D mixture) did not affect the thermal stability of GST and LDH. The GST thermal denaturation temperature (T m ) was 56.6±1.1°C for GST alone, and 58.1±1.1°C in the presence of 0.2 mg/mL of FK20-DL (Figure <ns0:ref type='figure' target='#fig_8'>4D and 4F</ns0:ref>). Similarly, the T m of LDH was 61.2°C for LDH alone, and 62.0°C in the presence of 0.2 mg/mL of FK20-DL, and the remarkable gain of thermal stability was not observed (Figure <ns0:ref type='figure' target='#fig_9'>5C and 5E</ns0:ref>). Thus, we concluded that the FK20-DL cryoprotective activity was likely not a result of a thermal stabilization effect on the reporter enzymes.</ns0:p><ns0:p>The FK20 mechanism of cryoprotective activity toward the reporter enzymes seems to be a molecular shield effect, in which the peptide may prevent stochastic contacts with the reporter enzyme molecules upon desiccation during the freezing process. The schematic diagrams of cryoprotecting mechanisms toward the reporter enzymes are summarized in Figures 6. Recently, a new class of functional IDPs that exhibits a potent protective activity against protein aggregation following to heat-denaturation, named heat-resistant obscure (Hero) proteins was discovered <ns0:ref type='bibr'>(Tsuboyama et al., 2020)</ns0:ref>. Although the mechanism of aggregation protection of Hero proteins is not yet unraveled, the molecular shield mechanism is one of the candidates. We recently reported that human genome-derived IDPs also can inhibit the nucleation phase of the amyloid fibril formation of Aβ(1-42) <ns0:ref type='bibr' target='#b22'>(Ikeda et al., 2020)</ns0:ref>, and the fibril formation inhibition is likely based on the molecular shield mechanism.</ns0:p><ns0:p>In conclusion, we have developed a cryoprotective peptide, FK20, with 20 amino acid residues. The sequence of FK20 was taken from human genome encoded intrinsically disordered proteins. The mechanism of cryoprotection toward the reporter enzymes, LDH and GST, was assumed to be a molecular shield effect with no specific molecular interaction. Taking into consideration of these facts, the molecular shield becomes one of noteworthy properties of IDPs.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions</ns0:head><ns0:p>We have developed a cryoprotective peptide, FK20, with 20 amino acid residues. The sequence of FK20 was taken from human genome encoded intrinsically disordered proteins. FK20, its all-D-enantiomeric isomer FK20-D, and the equimolar mixture of FK20 / FK20-D exhibited the similar level of cryoprotective activity toward the reporter enzymes, LDH and GST. No marked thermal stabilization of GST was observed in the presence of the equimolar mixture of FK20 / FK20-D. The mechanism of cryoprotection toward the reporter enzymes, LDH and GST, was assumed to be a molecular shield effect with no (stereo-)specific molecular interaction. </ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head>Figure 1 Figure 1 .</ns0:head><ns0:label>11</ns0:label><ns0:figDesc>Figure 1</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head>Fig. 1 .</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Fig. 1. Amino acid sequences and schematic diagrams of human genome-derived IDPs and their deletion mutants. Molecular weight (M.W.) and calculated isoelectric point (pI) are also shown. D10 20 was renamed FK20. Residues of D-amino acids are shown in italics. The Ref_seq accession codes, protein names, and the corresponding residue numbers of the human genome-derived IDPs are as follows; C1 (NP_570859.1 obsolete, WW domain-containing oxidoreductase isoform 3, 1-36), D10 (NP_002537.3, tumor necrosis factor receptor superfamily member 11B precursor, 24-62), and E1 (NP_005222.2, src substrate cortactin isoform a, 305-342).</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>Figure 2 Figure 2 .</ns0:head><ns0:label>22</ns0:label><ns0:figDesc>Figure 2</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head>Fig. 2 .</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Fig. 2. Cryoprotective activity of human genome-derived IDPs and control proteins towards LDH (A) and GST (B). Dependency of the normalized cryoprotective activity against amino acid lengths (C) and calculated isoelectric points, pI (D). (A) LDH activities (final concentration of 50 µg/ml) after freeze-thawing in the presence of 50 μg/ml indicated additive IDP peptides. The LDH activity of the untreated sample was set to 100%. Protein(-) indicates LDH activity without cryoprotectant. The error bars indicate standard deviation. (B) GST activities (final concentration of 280 µg/ml) after freeze-thawing in the presence of 50 μg/ml indicated additive IDP peptides. The GST activity of the untreated sample was set to 100%. IDPs and peptides are colored black. Protein(-) indicates GST activity without cryoprotectant. The error bars indicate standard deviation. (C) Cryoprotective activities of each cryoprotectants towards LDH and GST are normalized and plotted against their amino acid length. (D) Normalized cryoprotective activities are plotted against pI of each cryoprotectants.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head>Figure 3 Figure 3 .</ns0:head><ns0:label>33</ns0:label><ns0:figDesc>Figure 3</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head>Fig. 3 .</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Fig.3. Cryoprotective activity of IDP-derived peptide FK20 and its all D-enantiomeric isomer FK20-D towards LDH (A) and GST (B) (A) LDH activities were plotted (final concentration of 50 µg/ml) after freeze-thawing in the presence of additive IDP peptides of concentrations ranging from 2.5 to 500 μg/mL. The LDH activity of the untreated sample was set to 100%. FK20: filled circle, FK20-D: grey square. (B) GST activities were plotted (final concentration of 280 µg/mL) after freeze-thawing in the presence of additive IDP peptides of the concentration ranging 2.5 to 500 μg/mL. The GST activity of the untreated sample was set to 100%. FK20: filled circle, FK20-D; grey square, FK20-DL (equimolar mixture of FK20 and FK20-D): filled triangle with dashed line.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head>Figure 4</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Figure 4</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_7'><ns0:head>Figure 4 .</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Figure 4. Influence of IDP-derived peptide FK20 and its all D-enantiomeric isomer FK20-D to the structure and thermal stability of GST.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_8'><ns0:head>Fig. 4 .</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Fig. 4. Influence of IDP-derived peptide FK20 and its all D-enantiomeric isomer FK20-D to the structure and thermal stability of GST. (A) CD spectra of 250 μg/mL of FK20 (solid line), FK20-D (dashed line) and equimolar mixture (125 μg/mL each) FK20-LD (grey line) at 25°C. (B) CD spectra of 100 μg/mL GST without (dashed line) or with (solid lines) 50, 100, and 200 μg/mL of FK20-LD at 25°C. (C) CD spectra of 100 μg/mL GST alone at various temperatures. (E) CD spectra of 100 μg/mL GST with 200 μg/mL of FK20-LD at various temperatures ranging 25 to 75°C. (D, F) Melting curve of GST with (F) or without (D) FK20-LD monitored by the residual molar ellipticity [theta] at 222 nm.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_9'><ns0:head>Fig. 5 .</ns0:head><ns0:label>5</ns0:label><ns0:figDesc>Fig. 5. Influence of IDP-derived peptide FK20 and its all D-enantiomeric isomer FK20-D to the structure and thermal stability of LDH. (A) CD spectra of 100 μg/mL LDH without (dashed line) or with (solid lines) 50, 100, and 200 μg/mL of FK20-LD at 25°C. (B) CD spectra of 100 μg/mL LDH alone at two temperatures, 25 and 85°C. (D) CD spectra of 100 μg/mL LDH with 200 μg/mL of FK20-LD at two temperatures, 25 and 85°C. (C, F) Melting curve of LDH with (E) or without (C) FK20-LD monitored by the residual molar ellipticity [theta] at 222 nm.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_10'><ns0:head>Figure. 6 .</ns0:head><ns0:label>6</ns0:label><ns0:figDesc>Figure. 6. Schematic representation of the mechanisms for cryoprotection of enzyme by FK20.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_11'><ns0:head>Fig. 6 .</ns0:head><ns0:label>6</ns0:label><ns0:figDesc>Fig. 6. Schematic representation of the mechanisms for cryoprotection of enzyme by FK20. (A) Illustration of the mechanism of stochastic protein collisions upon repeated freeze/thaw cycles. The GST molecule (dimer) is represented by the ribbon diagram. The filled block arrow indicates stochastic direct collision of proteins during freeze/thaw cycles. (B) Illustration of the molecular shield model of FK20 cryoprotective activity toward the enzymes, originally proposed by Chakrabortee et al. (see text)<ns0:ref type='bibr' target='#b5'>(Chakrabortee et al., 2012)</ns0:ref> . FK20 is represented as bold strings.</ns0:figDesc></ns0:figure> <ns0:note place='foot'>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:note> </ns0:body> "	"Dear editor and the reviewers, I am very pleased to send you the revised version of our manuscript of “Cryoprotective activities of FK20, a human genome-derived intrinsically disordered peptide against cryosensitive enzymes without a stereospecific molecular interaction” by Naoki Matsuo et al as an original research article for PeerJ Physical Chemistry. This manuscript is not being considered to submit elsewhere. The authors sincerely thank all the reviewers for their insightful comments and constructive suggestions, all of which are indeed helpful for improving the manuscript. In the revised version, Figure 1 was reproduced according to the two reviewer s’ suggestion. Original Figure 5 was totally removed and replaced with the newly-derived supportive data according to reviewer 2’s suggestion. In addition, title was changed according to the reviewer 1’s recommendation. Many minor inappropriate expressions in the original manuscript are now rephrased adequately. They are colored in red in the file PeerJ-211012-MatsuoFK20RED2.docx”. Black and white version of the same file is also sent. As a result, the authors believe that our revised manuscript has become much informative and highly readable. The authors greatly appreciate both the editor and reviewers' effort for improving our manuscript. We now believe that this manuscript has become acceptable for PeerJ Phys Chem. We are looking forward to hear good news from the editor. Thank you for your consideration in advance. Best regards, Hidekazu Hiroaki (corresponding author) (submitter) hiroaki.hidekazu@f.mbox.nagoya-u.ac.jp Professor, Laboratory of Structural Molecular Pharmacology, Graduate School of Pharmaceutical Sciences, Nagoya University. Point-to-point responses Reviewer 1 Basic reporting This manuscript reports cryoprotective activities of three human-genome derived IDPs and their truncated peptides for LDH and GST. CD analysis by using D-enantiomeric isoform suggested that FK20 might not interact to GST and not affect the GST's structure during the cryoprotection. FK20 could also successfully cryopreserve two mammalian cell systems. Experimental design The experimental design is modern and appropriate. Validity of the findings I have major difficulties to understand this finding. Details are shown in the following 'General comments.' Additional comments 1) It is my doubt thorough the manuscript that the information of the gene (protein) is limited. The gene information supports readers to correctly understand this work. First, it is needed to note the reason why authors used these protein sequences. More than 10% of open reading frames in eukaryotic genomes have been estimated as intrinsically disordered proteins or proteins containing intrinsically disordered regions. Authors should show the intension of choosing the gene from abundant IDPs existing in the human genome in the 'Introduction' section or the 'Results' section. RESPONSE: The authors sincerely thank this reviewer for his/her kind advice for improving our incomplete introduction. The selection of IDP genes from human genome were done in our previous report, but the detail was not clearly described in the first version of the manuscript. Accordingly, the following phrases were now added. (page 3 lines 88-94) In detail, 53 candidate IDP genes from human genome were first predicted by a bioinformatics method, and then, among them, 35 IDP peptides systematically proven as the flexible disordered peptide segments by the NMR-based indirect IDP-assessment methods developed by us (Goda et al., 2015b). Accordingly, the five randomly-selected human genome-derived IDP peptides among the 35 peptides were demonstrated to have substantial cryoprotective activities against LDH, GST, and green fluorescent protein (Matsuo et al., 2018). Second, it is highly recommended that the accession number, the function (unless it has determined, putative), and the expression patterns (organ, age, stress specificities) of the protein are documented in the text. RESPONSE: Thank you very much for the comment. The authors agree that the additional description of the accession numbers may improve the readers’ convenience to reproduce our results. Since the authors are not the expert of the reliable resource for the expression patterns of these genes since these genes are really randomly selected from human genome. Since the authors cannot trust the reliability of such information, only the Ref_seq gene accession numbers with the residue numbers were added in the Figure 1 legend. “The Ref_seq accession codes, protein names, and the corresponding residue numbers of the human genome-derived IDPs are as follows; C1 (NP_570859.1 obsolete, WW domain-containing oxidoreductase isoform 3, 1-36), D10 (NP_002537.3, tumor necrosis factor receptor superfamily member 11B precursor, 24-62), and E1 (NP_005222.2, src substrate cortactin isoform a, 305-342). ” 2) Figure 1. This figure is difficult to understand. What does the green, blue, and magenta bars represent? Although some bars have amino acid sequences above them, they did not fit to the lengths of the corresponding color bars (or each amino acid sequence is a part of the original sequence that was exhibited by a bar?). Are these sequences parts of some proteins? if so, show which part is taken from the original protein. RESPONSE: Thank you for the reviewer’s advice. Now the authors corrected the size of the fonts and the lengths of the bars in Figure 1. 3) Figures 2, 3, and 5. Please show basic information in the legends to understand the figures. How many times the experiments are repeated? What is the mean of error bars? Statistically significant? RESPONSE: For the Figure 2 and 3, the number of the repeated times of the experiments were already given in the Materials and Methods section. For example, (Line 132) “All measurements were performed in triplicate”. The authors further added new information and rephrased this sentence into the following new one. (Line 132) “For the analysis, we set the LDH activity of the untreated sample (the enzyme without freeze and thaw processes and without the addition of a cryoprotectant) as 100%. All measurements were performed in triplicate. ”. The explanation of the error bars (indicating the standard deviation) are also mentioned in the figure legend. Finally, Figure 5 was removed according to the other reviewer’s comment. 4) Figure 6. A) the paper (Chakrabortee et al. 2012) proposed two types of molecular shield models, original one and extended one. Figure 6A seems to show the extended one. However, applying extended model needs weak association between cryoprotective peptides and protected proteins. Authors should describe the reason why they adopted the extended molecular shield model to their results. B) I think that relevance of the data is not enough to make Figure 6B. RESPONSE: Thank you for criticizing our Figure 6 carefully. Indeed, although we observed just a slight increase of Tm when IDP peptide FK20-LD were added to GST and LDH, we decided not to claim the presence of the loose association between the reporter enzymes and IDP peptides. As the reviewer kindly pointed out, our data DID NOT examine the presence of the loose association suggesting the advanced model, although only the four molecular equivalents of IDP-derived peptides were enough for exhibiting cryoprotective activity. Accordingly, Figure 6 was now revised as to follow the “basic” model. 5) The title is somewhat ambiguous. A title which directly represents the cryoprotective activities of FK20 for cryosensitive enzymes and mammalian cells seems to be better. RESPONSE: The authors agree to change the title into more appropriate one. The new article title is : “Cryoprotective activities of FK20, a human genome-derived intrinsically disordered peptide against cryosensitive enzymes without a stereospecific molecular interaction”. Thank you very much for the advice. 6) L45-46. The phrase 'but also non-functional proteins' can be omitted because the following sentence mentioned 'IDPs are widely associated with biological processes'. RESPONSE: According to this reviewer’s suggestion, the corresponding sentences were rephrased. (page 2, 45-46) Intrinsically disordered proteins (IDPs) are an important class of not only functional, but also non-functional proteins, that is widely associated with broad biological processes Reviewer 2 Basic reporting The manuscript contains numerous small grammatical errors that needs correction by a native English speaker. Below are corrections for text that are not necessarily grammatically incorrect: Line 29 Change “if all-D-enantiomeric” to “of all-D-enantiomeric” RESPONSE: The typo was corrected. Thank you very much. Line 34 Change “IDPs larger than 20 amino acids” to “IDPs longer than 20 amino acids” RESPONSE: The corresponding words were corrected (Line 35) “ Cryoprotective activity of IDPs longer than 20 amino acids was nearly independent …” Line 46 What is meant by “non-functional” proteins? Is this in the sense of not yet identified, or that they have zero biological function? RESPONSE: Thank you very much for the suggestion. The point was also mentioned by the reviewer 1. Accordingly, such the misleading expression was omitted as follows, (page 2, Line 45-46) Intrinsically disordered proteins (IDPs) are an important class of not only functional, but also non-functional proteins, that is widely associated with broad biological processes Line 49 Not all IDPs will fold into a stable structure, or gain any structure, when bound to a ligand. Please reword this sentence to reflect that. RESPONSE: Thank you very much for the suggestion. The sentence was rephrased as followd; (page 2, Line 47-49) The most unique feature of IDPs is that they lack stable and compact tertiary structures alone under physiological conditions, while some of them eventually fold into stable structures during specific interaction. Line 91-104 This section could be shortened since it reads like a very detailed summary of the results. I would also suggest using the present tense to avoid confusion. RESPONSE: We tried to shorten the corresponding section. Some sentences were omitted and/or rephrased. (page 3, Line 97 – 107) We succeeded in minimizing the length of the human-genome-derived IDPs with practical cryoprotective activity. We found that FK20—the 20 amino acid fragment (position 24-43) from the tumor necrosis factor receptor superfamily member 11B (TNFRS11B) precursor—showed substantial cryoprotective activity toward LDH. Then we compared the cryoprotective activity of FK20 and its all-D-enantiomeric isomer and found that the cryoprotective activity of FK20 is independent to its chirality. Accordingly, we employed a new technique to use the racemic mixture of FK20 and FK20-D, that do not show CD signal, to investigate the absence of specific molecular effect toward the GST and LDH reporter enzyme by CD spectroscopy. These results suggest a potential use of human genome-derived IDP as a cryopreserving agent for cryosensitive enzymes and proteins. Line 213 “It is generally saying that proving non-existence is always difficult.” To be clear, it is impossible to prove something does not exist because we cannot distinguish between something not existing or that we cannot observe it. Please rewrite this sentence. RESPONSE: The authors agree this comment. The corresponding sentences were now rephrased with an introduction of our another previous study (Ikeda et al, 2020), in which 15N-labeled Aβ(1-42) was examined by 1H-15N 2D NMR spectra with and without the non-labeled IDP peptides. The authors think this observation partly enhance our assumption of the absence of the strong interaction between the reporter enzymes and FK20-LD. (page 6, Line 198-205) For example, we measured and compared 1H-15N HSQC spectra of the target molecule Aβ(1-42) in the absence and the presence of the cryoprotective IDPs in our previous study, and we observed almost no spectra change (Ikeda et al., 2020). Thus, the absence of any specific molecular interaction between IDPs and the target enzymes is expected. It is generally saying that proving non-existence is always difficult. Although it is difficult to completely prove the absence of any specific interaction between the IDPs and the reporter enzymes, we challenged to provide indirect evidence to support this assumption as many as possible. Line 222 “atmospheric” - perhaps the author means environmental? RESPONSE: Thank you for the comment. The word “atmospheric” was changed into “environmental”. Line 216+ I assume the equimolar mixture concentration is the total concentration of the protein? RESPONSE: Thank you for the comment. In order to avoid this confusion, the authors now used the term “racemic mixture” for the equimolar mixture of L and D peptides. (Line 267-270) The cryoprotective activities of FK20, FK20-D, and the same total concentration of the racemic equimolar mixture of FK20 and FK20-D (FK20-LD) toward LDH and GST at varying concentrations were examined (Figure 3A and 3B, respectively). The corresponding term were used for the other part of the document. There are several problems with the figures. Fig. 1 is very confusing to me - the protein sequence text is not the same length as any of the coloured bars, making it difficult for me to tell which sequence corresponds to what bar. PONDR is mentioned in the text on line 185, but I could not find any such plot (Fig. S1 is actually a Uversky plot). The supplemental filenames should be revised to make it clearer which files correspond to Fig. S1 etc. RESPONSE: The authors now carefully redraw the Figure 1 without any format crash, and the font size and the length of the bars were adjusted. For supplementary figure S1, this was totally our mistake and just we confused PONDR plot and Uversky plot, because the corresponding Uversky plots were made by the PONDR server with the Charge-Hydropathy option in the website “http://www.pondr.com/”. The corresponding text was corrected from PONDR to Charge-Hydropathy plot (Uversky plot) (Line 194-197). “We used the PONDR server (http://pondr.com) (Obradovic et al., 2005) to predict whether the series of C-terminal truncated peptides were also disordered. Accordingly, the charge-hydropathy plots (Uversky plots) were shown in Supplementary Figure S1.” In addition, the file for the supplementary figures was renamed. Experimental design No comment Validity of the findings The paper has a considerable amount of interesting data and the use of all-D amino acids is a very clever way to address the molecular shield mechanism of IDPs, and an especially smart way to eliminate the IDP signal in the GST CD studies. RESPONSE: Thank you very much for evaluating our experiment high. By encouraging the reviewer’s comment, we further dried to demonstrate the validity of this experimental technique for the other reporter enzyme, LDH. Accordingly, the new Figure 5 was now added. The data (Tm observation of LDH with and without FK20-LD) was similarly obtained by that of the GSH (Figure 4). The corresponding experimental section was also added. I am having some difficulty interpreting Fig. 2C and 2D. Normalized activity is not defined in the Methods and I do not see the calculation in the Excel files. Because of the sigmoidal shape of the LDH cryoprotection curves, using a single protein concentration does not provide an accurate view of the efficiency. I would strongly suggest plotting the normalized activity as PD50 (concentration of protein required to recover 50% of LDH activity) to get a hopefully better view of how these IDPs are working. RESPONSE: Thank you very much for pointing out our incomplete description of the normalization process. The reviewer’s suggestion, the authors agree that the use of PD50 for comparing all the data is ideal. However, because of the limited resources of the synthetic peptides and the commercially purchased LDH, it will be a cost to determine PD50 for all the truncated peptides. Instead, we tried to roughly estimate the tendency of the cryoprotective activities of these truncated IDP-peptides. The lack of definition of the normalized enzymatic activities was just our mistake. In the deposited excel file, the spreadsheet Figure2CD, LDH and GSH activities were normalized. Accordingly, we describe this process as follows; (Page 6, Line 192-195) “In order to compare the cryoprotective activities for the different reporter enzymes, both the maximum preserved enzymatic activities with the cryoprotective peptide, LDH with E131 and GST with D1020, were set to 100%, respectively. Peptides shorter than 20 amino acid”. The cell cryoprotection data are interesting, but I think they are very out of context in this manuscript. I think the data should be removed, because at this point it is not clear if the protection is due to the protein stabilization effect seen in the in vitro experiments, or due to membrane stabilization. The membrane stabilization is a possibility, because the IDPs were added to the culture media and there is no specific evidence that these peptides are internalized. RESPONSE: According to this reviewer 2’s suggestion, we finally decided to omit all the data of the cryoprotection of cells. Accordingly, two paragraphs in the experimental section for cell culture and cell cryopreservation were omitted. The previous figure 5 and figure 6B (the model of the mode of action for cell cryopreservation) were also omitted. Thank you very much for improving our manuscript more clear and concise form. "	Here is a paper. Please give your review comments after reading it.
484	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Lead (Pb) is a pervasive contaminant and poses a serious threat to living beings. The present study aims at batch and fixed bed column scale potential of commercial compost (CCB) and peanut shells biosorbents (PSB) for the sequestration of Pb from contaminated aqueous systems. The PSB and CCB were characterized with FTIR, SEM and Brunauer Emmett-Teller (BET) to get insight of the adsorption behavior of both materials. Fixed bed column scale experiments were performed at steady state flow (2.5 and 5.0 ml/min), initial Pb concentrations (25 and 50 mg/L) and dosage of each adsorbent (3.0 and 6.0 g/column).</ns0:p><ns0:p>Columns packed (15.9 cm 2 ) with PSB and CCB have revealed excellent adsorption of Pb with PSB as compared with CCB. The total volume of injected contaminated water was 1500 ml and 3000 ml at 2.5 and 5.0 ml/min, respectively while total bed volume number was 157. A series of batch experiments with CCB and PSB was conducted at adsorbent dosage (1.25-5.0 g/L), initial Pb level (25-100 mg/L), interaction time (0-180 min) and solution pH (4-10) at room temperature. Batch scale results revealed that PSB removed 92% Pb from water at 25 mg Pb/L concentration as compared with CCB (79%). The presence of competing ions in groundwater showed less Pb removal as compared with synthetic water. The experimental data were simulated with equilibrium isothermal models: Langmuir, Freundlich, and kinetic models: pseudo first order, pseudo second order and intra-particle diffusion. The Freundlich and pseudo second order models better described the equilibrium and kinetic experimental data, respectively with maximum sorption of 42.5 mg/g by PSB which is also evident from FTIR functional groups and SEM results. While equilibrium sorption of Pb onto CCB was equally explained by Freundlich and Langmuir models. These findings indicate that PSB could be an active and ecofriendly biosorbent for the sequestration of metals from contaminated aqueous systems.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>Water is very crucial for all the living organisms and is a basic requirement of life. Safe drinking water is essential for sustaining life <ns0:ref type='bibr'>(Shahid et al. 2021;</ns0:ref><ns0:ref type='bibr'>Natasha et al. 2021</ns0:ref>). With the passage of time, many natural and anthropogenic activities are polluting freshwater bodies. Several organic and inorganic pollutants are continuously discharged into surface and subsurface water bodies, thereby adversely affecting the ecosystem. In comparison of these water pollutants, heavy metal (HMs) contamination is a major concern. Heavy metals are known as dangerous metals having 63.5-200.6 g atomic weight and five times higher density than water <ns0:ref type='bibr'>(Abas et al. 2013)</ns0:ref>. Because of their toxicity to plants, animals, and humans, HMs are categorized under environmental pollutants. Heavy metals like Lead, Cadmium, Chromium, Mercury, Arsenic, and Nickel cause a serious threat to human health. These HMs are highly soluble and enter the food chain in variety of ways. These are non-degradable and persistence in nature. Natural (weathering of rocks, volcanos, floods etc.,) and human activities (mining, petroleum refining, tanneries, battery production, metal plating, glass production and pesticides production etc.) induce accumulation of these HMs into the environment <ns0:ref type='bibr'>(Kadirvelu et al. 2001)</ns0:ref>.</ns0:p><ns0:p>Lead (Pb) is an abundant pollutant in the environment and due to its implications it becomes a major concern worldwide. In America, Pb is ranked 2 nd position among all known hazardous substances <ns0:ref type='bibr'>(ATSDR 2007)</ns0:ref>. Some sources of Pb in wastewater are effluents from smelting, tanneries, metal plating, radiator manufacturing, alloy, and battery industries <ns0:ref type='bibr'>(Kadirvelu et al. 2001)</ns0:ref>. In drinking water, acceptable level of Pb is 0.05 mg/L set by <ns0:ref type='bibr'>WHO and EPA (Arbabi et al. 2015)</ns0:ref>. Moreover, effluents from manufacturing process such as pigments, television tubes, fuel, paints, explosives are also causes of the water contamination with <ns0:ref type='bibr'>Pb (Ahalya et al. 2005)</ns0:ref>. When this Pb contaminated water interacts with ecosystem, the metal ions can accumulate into human body through the consumption of Pb contaminated food chain or water <ns0:ref type='bibr'>(Abdelsalam 2011)</ns0:ref>. A small amount of Pb continuously accumulating for extended period results in chronic toxicity and damages to organs <ns0:ref type='bibr'>(Badmus et al. 2007</ns0:ref>). Moreover, Pb causes kidney damage, reproductive system, and central nervous system (CNS) in human and causes oxidative stress, damages photosynthetic pigments etc. in plants.</ns0:p><ns0:p>Many approaches have been introduced to remove Pb from wastewater. Among these techniques, ion exchange <ns0:ref type='bibr'>(Dabrowski et al. 2004</ns0:ref><ns0:ref type='bibr'>), electrolysis, electro-coagulation (Milhajlovic et al. 2015)</ns0:ref>, chemical precipitation <ns0:ref type='bibr'>(Charerntanyarak 1999)</ns0:ref>, solvent extraction, and adsorption <ns0:ref type='bibr'>(Iqbal et al. 2021;</ns0:ref><ns0:ref type='bibr' target='#b19'>Imran et al. 2020)</ns0:ref> etc. are commonly used. Moreover, most of these treatment technologies are expensive, generate secondary pollution, require large number of workers and experts, and show less effectiveness <ns0:ref type='bibr'>(Abas et al. 2013)</ns0:ref>. However, adsorption is efficient for the sequestration of HMs from contaminated water <ns0:ref type='bibr'>(Imran et al. 2019)</ns0:ref>.</ns0:p><ns0:p>Metal ions can be removed from wastewater by the adsorption process due to its effective sequestration on the surface of an adsorbent materials <ns0:ref type='bibr'>(Tariq et al. 2020;</ns0:ref><ns0:ref type='bibr'>Milhajlovic et al. 2015)</ns0:ref>.</ns0:p><ns0:p>In this phenomenon, low-cost adsorbents that have higher adsorption potential are utilized. A diversity of indigenously available constituents, e.g., agricultural wastes, natural wastes, and industrial by-products can be employed as economical adsorbents <ns0:ref type='bibr'>(Atkinson et al. 1998)</ns0:ref>. <ns0:ref type='bibr'>Huang and Wu (1975)</ns0:ref> reported that activated carbon has been extensively employed as an adsorbent for the sequestration of Pb from contaminated aqueous systems. Activated carbon, on the other hand, is still a costly material due to its widespread application in water and wastewater treatment sectors <ns0:ref type='bibr'>(Iqbal et al. 2021)</ns0:ref>). Therefore, for this purpose, more efficient, economically feasible and Infrared spectra of pristine PSB and CCB were obtained in absorbance mode from wavenumber 700-4000 cm -1 at resolution of 2 cm -1 . The FTIR spectra were achieved using a Matson Polaris FTIR spectrophotometer. Prior to FTIR analysis, PSB and CCB were milled with KBr to form fine powder and compressed into thin pellets. The SEM images for surface morphology were obtained using a TESCAN Vega TS 5136LM at 20 kV. The samples for SEM analysis were coated in gold with a Balzers' Spluttering device. The Brunauer-Emmett-Teller (BET) surface area and pores related information of both PSB and CCB were measured with a Tristar 3000 (Micromeritics) analyzer. Porosity (%) of the PSB and CCB was measured following <ns0:ref type='bibr'>Shah et al. (2019)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Preparation of synthetic wastewater</ns0:head><ns0:p>Lead nitrate (Pb (NO 3 ) 2 ) salt was used for the preparation of Pb stock solution. For 1000 mg/L Pb stock solution in a 1000 mL volumetric flask, 1.60 g of Pb (NO 3 ) 2 was thoroughly mixed in 500 mL distilled water and final volume was made 1 L. Different sub-stocks of 25-100 mg/L were prepared from this stock solution. For the pH adjustment, a few drops of 0.5 M NaOH/HCl solution were added to maintain the pH 6.0. Because dissolved Pb ions precipitate at high pH, maximal adsorption of Pb occurs at 6.0 pH <ns0:ref type='bibr' target='#b19'>(Imran et al. 2020;</ns0:ref><ns0:ref type='bibr'>Ahmed et al. 2016)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Fixed bed column scale removal of Pb and reusability of PSB</ns0:head><ns0:p>The fixed bed column scale experiments for Pb removal were carried out in plexiglass columns.</ns0:p><ns0:p>The experiment was conducted at steady state in duplicate sets. The column scale arrangement with the dimensions (14.5 cm length and 4.5 cm internal diameter) is presented in Figure <ns0:ref type='figure' target='#fig_10'>1</ns0:ref>. The adsorbent biomass (3.0 and 6.0 g/column) was packed as a central layer between acid-washed quartz sand (0.1-0.2 mm). Wet packing was adopted to release the trapped air between the particles and to get rid of disturbance during flow of contaminated water in the column. At the inner bottom and topmost position, cloth filter was used to improve the flow distribution, prevent adsorbent loss, and avoid clogging of the openings used for capillaries carrying Pb contaminated water at inlet and effluent at outlet. Filter was also used at the bottom and top of the adsorbent layer as shown in Figure <ns0:ref type='figure' target='#fig_10'>1</ns0:ref>. At the start, distilled water was run for 30 min through the columns to flush the columns. Two different initial concentrations of Pb (25 and 50 mg/L) were injected through the columns at flow rate 2.5 and 5.0 ml/min at pH 6 (optimum pH level at batch scale). At outlet side, effluent was collected in plastic bottles by gravity. The effluent was collected after <ns0:ref type='bibr'>15,</ns0:ref><ns0:ref type='bibr'>30,</ns0:ref><ns0:ref type='bibr'>60,</ns0:ref><ns0:ref type='bibr'>120,</ns0:ref><ns0:ref type='bibr'>180,</ns0:ref><ns0:ref type='bibr'>240,</ns0:ref><ns0:ref type='bibr'>300,</ns0:ref><ns0:ref type='bibr'>360,</ns0:ref><ns0:ref type='bibr'>420,</ns0:ref><ns0:ref type='bibr'>480,</ns0:ref><ns0:ref type='bibr'>540</ns0:ref> </ns0:p></ns0:div> <ns0:div><ns0:head>Batch experiments</ns0:head><ns0:p>Batch experiments were performed in the Environmental Sciences laboratory of COMSATS University Islamabad (CUI), Vehari Campus. Digital weighing balance was used to weigh three different adsorbent doses (1.25, 2.5, and 5.0 g/ L) of both adsorbents (PSB and CCB). In 250 mL conical flasks, 100 mL of different concentrations (25, 50, 75, and 100 mg/L) of Pb contaminated solution and weighed doses of adsorbents were added. Duplicate sets were prepared and placed on a mechanical shaker at 150 rpm and room temperature (28 o C). At different time intervals (15, 30, 60, and 120 min), samples were taken and filtered using Whatman filter papers-42. The filtered samples were used to determine the residual concentration of Pb in water samples. Samples of initial concentrations of sub solution (without addition of biosorbents) were also stored for analysis. For the determination of pH effect on the sequestration of Pb from contaminated water, different pH values (4-10) were adjusted using 0.5M NaOH/HCl solutions rest while of the parameters e.g., initial concentration (IC), temperature and adsorbent dose were kept constant.</ns0:p><ns0:p>Atomic Absorption Spectrophotometer (AAS) was used to measure the concentration of residual Pb in samples.</ns0:p></ns0:div> <ns0:div><ns0:head>Impact of coexisting ions on Pb removal from water</ns0:head><ns0:p>In groundwater and wastewater, there exists several other ions which interfere in the adsorption of contaminant from water onto the adsorbent surface. Therefore, in the present study two groundwater samples (GW1 and GW2) having different concentration of cations and anions collected from Vehari were used to evaluate the impact of coexisting ions in water for the sequestration of Pb. The groundwater samples were analysed for electrical conductivity (EC), total PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2021:07:64090:1:0:NEW 24 Nov 2021)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science dissolved salts (TDS), pH, sodium (Na), potassium (K), calcium (Ca), magnesium (Mg), carbonate (CO 3 ), bicarbonate (HCO 3 ), Chlorine (Cl), sulphate (SO 4 ), nitrate (NO 3 ) and Pb. The EC, TDS and pH were measured with respective EC, TDS, and pH meters. The Na, K and Ca were measured using Flame photometer while SO 4 and NO 3 concentration was measured with calorimetric method. The concentration of Cl, CO 3 and HCO 3 was measured with titration method Estefan (2013) while Mg and Pb were measured using atomic absorption spectrophotometer.</ns0:p><ns0:p>First groundwaters containing Pb (GW1= 80 ug/L, GW2=57 ug/L) were treated with PSB and CCB at optimum conditions and subsequently Pb concentration (25 mg/L) was developed in both groundwater samples and Pb removal was evaluated in the presence of different cations and anions in water. The characteristics of GW1 and GW2 have been presented in Table <ns0:ref type='table' target='#tab_2'>1</ns0:ref>. The results of batch scale adsorption experiments carried out in distilled water (DW) and groundwater samples at Pb concentration (25 mg/L and 50 mg/L) were compared to evaluate the impact of competing ions in water on Pb removal.</ns0:p></ns0:div> <ns0:div><ns0:head>Data analysis</ns0:head><ns0:p>The initial and final concentrations of Pb ions in water samples were measured using AAS. The equilibrium adsorption (mg/g) of Pb ions onto PSB and CCB was estimated with Eq. 1 (Imran (1)</ns0:p><ns0:formula xml:id='formula_0'>i e e C C q V W         </ns0:formula><ns0:p>In Eq. ( <ns0:ref type='formula'>1</ns0:ref>), C i is the IC and C e is the residual concertation (mg/L) of Pb ions in water, V is the volume of Pb contaminated water (L), W is the mass (g) of the adsorbents (PSB and CCB) and e q represents the amount of adsorbate (Pb) ions attached on the surface of PSB and CCB at equilibrium. To determine the kinetic adsorption q t at time t, Ce in Eq. ( <ns0:ref type='formula'>1</ns0:ref>) was replaced with residual concentration (C t ) at time t.</ns0:p><ns0:p>The percentage removal (R %) of Pb ions from contaminated water by PSB and CCB at column and batch scale was found from initial and final concentration ( ) of Pb using Eq. ( <ns0:ref type='formula'>2</ns0:ref> Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:p><ns0:p>(2) (%) 100</ns0:p><ns0:formula xml:id='formula_1'>i f i C C R C         </ns0:formula><ns0:p>The total volume of Pb containing solution injected to each column was calculated from the ( ) t V multiplication of flow rate and total injection time ( ). The total mass ( )</ns0:p><ns0:formula xml:id='formula_2'>Q total t t total</ns0:formula><ns0:p>V Qt  of metal ions (mg) injected to the column was found using Eq. ( <ns0:ref type='formula'>3</ns0:ref>) while column scale ( ) total m adsorption was estimated from Eq. ( <ns0:ref type='formula'>1</ns0:ref>) by replacing V with .</ns0:p><ns0:p>total Qt</ns0:p><ns0:p>(3) 1000</ns0:p><ns0:formula xml:id='formula_3'>i total total C Qt m </ns0:formula></ns0:div> <ns0:div><ns0:head>Adsorption kinetics modeling</ns0:head><ns0:p>Kinetic studies are employed for the optimization of different operating conditions for the adosorption process. Literature shows that different kinetic models are proposed to explain the reaction sequence and adsorption behavior <ns0:ref type='bibr' target='#b19'>(Imran et al. 2020;</ns0:ref><ns0:ref type='bibr'>Iqbal et al. 2021</ns0:ref>). In the present study, pseudo first order (PFO), pseudo-second order (PSO), and intra-particle diffusion (IPD) kinetic models were used for the kinetics of Pb ions adsorption onto PSB and CCB. The correlation coefficients (R 2 ) were used to determine the applicability of these kinetic models. The model is most relevant to data when the R 2 value is high (close to 1.0) <ns0:ref type='bibr'>(Imran et al. 2019)</ns0:ref>.</ns0:p><ns0:p>The PFO kinetic model is based on the notion that the change in contaminant concentration overtime is proportional to power one which has been presented in linear form (Eq. 4). ( <ns0:ref type='formula'>4</ns0:ref>)</ns0:p><ns0:formula xml:id='formula_4'>log (q e -q t ) = 𝑙𝑜𝑔𝑞 𝑒 + 𝑘 𝑡 2.303 𝑡</ns0:formula><ns0:p>In Eq. ( <ns0:ref type='formula'>4</ns0:ref>), q e and q t represent the adsorption capacity of PSB and CCB (mg g -1 ) at equilibrium and time t, respectively, k 1 represents rate constant (L min -1 ) and t represents time duration (min). Table <ns0:ref type='table' target='#tab_3'>2</ns0:ref> shows the values of k 1 , q e , and R 2 which is a correlation coefficient between experimental and model values. The findings indicated that a plot of log(q e -q t ) vs t using the Lagergren PFO model yields a straight line with a low correlation coefficient (R 2 ). In comparison to the experimental results, the PFO kinetic model projected much lower values of the equilibrium adsorption capacity</ns0:p><ns0:p>(q e ). The results showed that the PFO kinetic model is incompatible with the kinetic data of Pb adsorption. The PFO kinetic model is not usually appropriate for entire data range of interaction PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2021:07:64090:1:0:NEW 24 Nov 2021)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science time, but it explains well the adsorption mechanism at the start of the interaction between the adsorbent and contaminant <ns0:ref type='bibr'>(McKay and Ho 1999)</ns0:ref>.</ns0:p><ns0:p>The PSO kinetic model explains the contaminant adsorption mechanism throughout the whole interaction time. The PSO may be expressed in linear form by Eq. ( <ns0:ref type='formula'>5</ns0:ref>):</ns0:p><ns0:p>(5)</ns0:p><ns0:formula xml:id='formula_5'>t q t = 1 k 2 q e 2 + 1 q e 𝑡</ns0:formula><ns0:p>Where k 2 (g mg -1 min -1 ) indicates the PSO rate constant of the contaminant adsorption process.</ns0:p><ns0:p>The values of k 2 (g mg -1 min -1 ) and q e (mg g -1 ) were determined by making a plot of t/q t against t. The values of PSO model parameters and R 2 for adsorption of Pb have been presented in Table <ns0:ref type='table' target='#tab_3'>2</ns0:ref>. The estimated and experimental q e values were found to be closer to each other with high R 2 .</ns0:p><ns0:p>Thereafter, PSO kinetic model fits well with the kinetic adsorption of Pb ions on PSB and CCB as compared with PFO kinetic model. These findings corroborate the previously published findings (Sadaf and Bhatti 2011). Several stages are involved in the attachment of Pb molecules from contaminated water to the surface of adsorbent materials. The rate-controlling step in a batch experiment system includes quick and continuous stirring might be film diffusion, intra-particle diffusion (Eq. 6), or a combination of both processes. . ( <ns0:ref type='formula'>6</ns0:ref>)</ns0:p><ns0:formula xml:id='formula_6'>q t = 𝐾 𝑑𝑖𝑓 𝑡 0.5 + 𝐶</ns0:formula><ns0:p>In Eq. ( <ns0:ref type='formula'>6</ns0:ref>), C=intercept which characterizes boundary layer thickness and k dif (mg g -1 min -1/2 ) = IPD rate constant. Table <ns0:ref type='table' target='#tab_3'>2</ns0:ref> shows the k dif and C values for Pb adsorption onto PSB and CCB. According to IPD hypothesis, curve between q t and t 0.5 should be linear. If IPD is involved in the adsorption of a contaminant, there exists straight line between a plot of contaminant adsorbed per unit mass of biosorbent (q t ) and t 0.5 <ns0:ref type='bibr'>(Bhattacharyya and Gupta 2006)</ns0:ref>. The low value of R 2 suggests that the Pb ion attachment onto the PSB and CCB is not influenced IPD.</ns0:p></ns0:div> <ns0:div><ns0:head>Adsorption equilibrium isotherms</ns0:head><ns0:p>The adsorption isotherms are employed to test the adsorbents adsorption capacity for a certain contaminant <ns0:ref type='bibr' target='#b19'>(Imran et al. 2020)</ns0:ref>. The adsorption isotherm is a property of a system at a given temperature <ns0:ref type='bibr'>(Iqbal et al. 2021;</ns0:ref><ns0:ref type='bibr'>Shah et al. 2019;</ns0:ref><ns0:ref type='bibr'>Poulopoulos and Inglezakis 2006)</ns0:ref>. Adsorption isotherms may be predicted using a variety of models. Langmuir and Freundlich equilibrium In Langmuir isotherm (Eq. 7), monolayer adsorption of a contaminant onto the surface with a limited number of sites available for contaminant adsorption is considered.</ns0:p><ns0:formula xml:id='formula_7'>q e = q max K L C e 1 + 𝐾 𝐿 𝐶 𝑒 (7)</ns0:formula><ns0:p>Where q max represents the maximum adsorption of Pb ions onto PSB and CCB, K L =Langmuir model constant and C e = Pb concentration in solution (mg/L) at equilibrium Langmuir isotherm model in linearized form has been given in Eq. ( <ns0:ref type='formula'>8</ns0:ref>) to find the model parameters. The values of the Langmuir model parameters, q max and K L were found by using the slope and intercept of the linear plot of C e /q e vs C e . C e q e = 1 𝑞 𝑚𝑎𝑥 𝐾 𝐿 + 1 𝑞 𝑚𝑎𝑥 𝐶 𝑒 (8) The biosorbent surface might be monolayer or/and multilayer. It is reported that Freundlich isotherm model (Eq. 9) is applicable for multilayer adsorption and it assumes contaminant interaction with a heterogeneous surface having a non-uniform sorption energy distribution throughout the surface.</ns0:p><ns0:formula xml:id='formula_8'>𝑞 𝑒 = 𝐾 𝑓 𝐶 𝑒 1/n (9)</ns0:formula><ns0:p>Where equilibrium sorption of Pb per mass of the PSB and CCB (mg/g), Ce= aqueous</ns0:p><ns0:formula xml:id='formula_9'>𝑞 𝑒 =</ns0:formula><ns0:p>concentration of Pb at equilibrium (mg/L), K f and n are Freundlich model constants related to the adsorption capacity and the adsorption intensity, respectively.</ns0:p><ns0:p>To determine the values of model parameters, linearized form of Freundlich isotherm (Eq. 10) model was used. The slope and intercept of the curve of ln qe vs ln Ce were considered to derive the Freundlich equilibrium constants K f and n. </ns0:p></ns0:div> <ns0:div><ns0:head>Results and Discussion</ns0:head></ns0:div> <ns0:div><ns0:head>Characterization of Adsorbents</ns0:head><ns0:p>The surface morphology of PSB and CCB using scanning electron microscope has been presented</ns0:p><ns0:p>in Figure <ns0:ref type='figure' target='#fig_11'>2a</ns0:ref> Brunauer Emmet-Teller (BET) analysis was performed to find the surface area and pore size of PSB and CCB adsorbents. The N 2 -adsorption/desorption isotherms obtained from BET analysis were used to determine the pores related information (size, volume, porosity) and BET surface area of PSB and CCB (Figure <ns0:ref type='figure' target='#fig_11'>2d</ns0:ref>). There is difference in adsorption/desorption curves which reveals the porous nature of both adsorbents. The BET results revealed that PSB exhibits surface area of 226.8 m 2 /g. The average particle and pore size of PSB was 26.5 and 3.3 nm, respectively.</ns0:p><ns0:p>While PSB has 5.11% porosity which contributes in the electrostatic attachment and diffusive movement of Pb onto PSB surface. The surface area of CCB was 126.1 m 2 /g with particle size of 45 nm and pore size of 3.5 nm.</ns0:p></ns0:div> <ns0:div><ns0:head>Breakthrough curves for Pb removal at fixed bed column</ns0:head><ns0:p>It was important to assess the impact of controlling parameters on the removal of Pb from fixed bed column with both adsorbents. The column scale removal of contaminants is governed by flow rate, inlet concentration, bed depth and inner diameter of the column <ns0:ref type='bibr'>(Canteli et al. 2014</ns0:ref>). In the present study, the impact of flow rate (2.5 and 5.0 ml/L), initial concentration of Pb (25 and 50 mg/L) and dose (bed height= 0.65 and 1.2 cm/column) of PSB and CCB (3.0 and 6.0 g/column)</ns0:p><ns0:p>was evaluated on the shape of breakthrough curves. and CCB (first 30 min) while later on e.g. at 300 min, PSB removed 47% Pb and CCB removed 32.4% Pb from contaminated water at constant flow rate (2.5 ml/min). However, results showed that at the same (300 min) PSB and CCB showed just 23.7% and 2.5% removal, respectively when at flow rate 5.0 ml/min. Similarly, <ns0:ref type='bibr' target='#b38'>Yahya et al. (2020)</ns0:ref> studied that the decline in metal ions onto the cashew nutshell adsorbent was observed when flow rate was increased at a fixed adsorbent bed. It is due to the less time available between adsorbents (PSB and CCB) and metal ions for interaction which reduces the adsorbent saturation. While at lower flow rate metal ions were removed efficiently. <ns0:ref type='bibr' target='#b37'>Xu et al. (2013)</ns0:ref> reported that at lower flow rate film resistance and thickness was increased which contributes to higher metal removal from contaminated water. Priya and</ns0:p></ns0:div> <ns0:div><ns0:head>Impact of flow rate on column scale Pb sequestration</ns0:head><ns0:p>Radha (2016) reported that the adsorption was significantly affected by varying flow rate. As flow rate increases the film resistance is decreased which reduces the sequestration of contaminants from aqueous systems.</ns0:p></ns0:div> <ns0:div><ns0:head>Effect of initial concentration of Pb on column scale sequestration</ns0:head><ns0:p>Figure <ns0:ref type='figure' target='#fig_12'>3b</ns0:ref> displays the effect of Pb initial concentration (25 and 50 mg/L) on its removal by PSB and CCB at column scale when flow rate is 2.5 ml/min, bed height is 0.65 cm (3.0 g/column) and pH of Pb containing water is pH-6. The mass of Pb fed to the adsorbents was 37.5 mg and 150 mg when water was injected at 25 and 50 mg/L, respectively. The results show that the change in concentration gradient has significant impact on the saturation of PSB and CCB and their Pb removal (%) from contaminated water. There is decline in Pb removal when its concentration was changed from 25-50 mg/L. This decline in Pb removal at elevated concentration of Pb (50 mg/L) is accredited to the saturation of available sorption sites on PSB and CCB beyond a certain initial level of metal ions. At time 240 min, PSB and CCB showed 63.4% and 50.8% Pb removal, respectively when initial Pb concentration was 25 mg/L while these adsorbents showed 54.8% and 43.2% Pb removal, respectively at the same time (240 min) but at 50 mg/L initial Pb concentration.</ns0:p><ns0:p>However, Pb adsorption onto PSB and CCB increased with increase in initial concentration of Pb in water. This enhanced Pb adsorption at elevated initial Pb level is because of higher utilization of active sites at elevated Pb concentration. This earlier exhaustion of the PSB and CBB active sites at elevated Pb level is attributed to the fact that high Pb concentration caused quick saturation of the sorbent. Results (Figure <ns0:ref type='figure' target='#fig_12'>3b</ns0:ref>) reveal that the shape of curves by PSB and CCB at higher concentration (50 mg/L) is more pronounced and steepness of the slope is also increased. Swapna</ns0:p><ns0:p>Priya and Radha (2016) reported that the adsorption capacity decreased from 10.26 to 9.90 μg g −1 when the solution initial concentration decreased from 600 to 200 μg L −1 . However, there is slower saturation of adsorbent materials at lower initial concentration as compared with high initial concentration. Similarly, <ns0:ref type='bibr' target='#b5'>Bharathi and Ramesh (2013)</ns0:ref> reported the removal of contaminant decreased with increasing initial concentration which is due to decline in exhaustion and breakthrough time. <ns0:ref type='bibr' target='#b9'>Dorado et al. (2014)</ns0:ref> reported that the breakthrough time is decreased at enhanced metal concentration.</ns0:p></ns0:div> <ns0:div><ns0:head>Effect of dose (Bed height) on column scale Pb removal</ns0:head><ns0:p>Figure <ns0:ref type='figure' target='#fig_12'>3</ns0:ref> c presents the impact of bed height of PSB and CCB filled in the column for Pb removal at steady state (constant flow rate) when column was wet packed with 3.0 g and 6.0 g PSB and CCB separately. The impact of bed height was evaluated at constant initial Pb concentration (25 mg/L) and flow rate 2.5 ml/min. The results revealed that when dose was changed from 3.0 to 6.0 g/column, there was improvement in Pb removal at a given time which is attributed to higher number of adsorption sites and more height of bed for the attachment of Pb at higher dosage (6.0 g/column which is equal to 1.2 cm bed height) as compared with 3.0 g/column which is equal to 0.65 cm/column. The PSB and CCB showed 63.4% and 50.8% Pb removal, respectively at dose 3.0 g/column while at higher dose (6.0 g/column), PSB and CCB showed higher removal (73.2% and 59.3%, respectively). The increase in bed height results in more service time for breakthrough curve and exhaustion time for a given concentration <ns0:ref type='bibr' target='#b19'>(Naeem et al. 2020;</ns0:ref><ns0:ref type='bibr' target='#b22'>Imran et al. 2021;</ns0:ref><ns0:ref type='bibr'>Yaya et al. 2020;</ns0:ref><ns0:ref type='bibr'>Canteli et al. 2014)</ns0:ref>. <ns0:ref type='bibr' target='#b5'>Bharathi and Ramesh (2013)</ns0:ref> reported that the removal increased with increasing bed height/dose. It is attributed to more adsorption sites available for the contaminant sequestration. <ns0:ref type='bibr' target='#b11'>El-Mouhri et al. (2020)</ns0:ref> observed that the removal enhanced from 95.4% -97.4% when the bed height was increased from 3 -5 cm. The increase in removal percentage is attributed to the active sites and interaction time available for the contaminant with the adsorbent material. Swapna Priya and Radha (2016) studied that the quantity of adsorbent is very crucial for the breakthrough point because adsorbent quantity determines the bed height. As bed height is increased, the breakthrough would also be higher because increase in bed height increases the surface area significantly which favors the adsorption.</ns0:p></ns0:div> <ns0:div><ns0:head>Batch scale Pb sequestration from contaminated water</ns0:head></ns0:div> <ns0:div><ns0:head>Impact of initial Pb level on its adsorption and removal</ns0:head><ns0:p>The impact of various concentrations of Pb with constant adsorbent mass on Pb removal (%) are shown in Figure <ns0:ref type='figure' target='#fig_13'>4a</ns0:ref>. Pb removal (Figure <ns0:ref type='figure' target='#fig_13'>4b</ns0:ref>). The maximum values of Pb ion sorbed on PSB and CCB surface were found at mass of 1.25 g/L of contaminated water. Overall, Pb adsorption showed a decline with the increase when adsorbent dose was increased. As adsorbent dose is increased, surface area also increases and the interaction between Pb and adsorbent becomes strong therefore, the removal increased with increasing adsorbent dosage. Initially, a sharp increase (45.4 % to 79.8%) in Pb removal was observed By PSB when dosage was changed from 1.25 to 2.5 g/L of Pb contaminated water. While with the further increase in adsorbent dosage the impact was small (79.8-87.6%) which is due to the possible aggregation of adsorbent particles at higher dose <ns0:ref type='bibr' target='#b22'>(Imran et al. 2021)</ns0:ref>.</ns0:p><ns0:p>Similarly, <ns0:ref type='bibr'>Iqbal et al. (2021)</ns0:ref> reported that with the addition of adsorbent dosage the removal was elevated which is attributed to the competition between adsorbent and adsorbate. Initially there are limited number of active available sites and higher number of contaminant ions therefore, the removal was decreased. Moreover, at lower adsorbent dosage the active sites becomes saturated PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2021:07:64090:1:0:NEW 24 Nov 2021)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science with contaminant earlier <ns0:ref type='bibr' target='#b19'>(Imran et al. 2020)</ns0:ref>. At higher adsorbent dosage, the removal was not significantly increased which is due to the overlapping/aggregation of adsorbent material at higher concentration of dose <ns0:ref type='bibr' target='#b36'>(Wang et al. 2018)</ns0:ref>. Similar results were reported by many researchers <ns0:ref type='bibr' target='#b1'>(Adenuga et al. 2019;</ns0:ref><ns0:ref type='bibr'>Heraldy et al. 2018;</ns0:ref><ns0:ref type='bibr'>Iqbal et al. 2021;</ns0:ref><ns0:ref type='bibr' target='#b27'>Kostić et al. 2014)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Effect of interaction time on Pb removal</ns0:head><ns0:p>Effective adsorption is reflected when the equilibrium is attained with adsorbent and adsorbate.</ns0:p><ns0:p>Furthermore, a certain time is crucial that confirms that the equilibrium has been attained <ns0:ref type='bibr' target='#b8'>(Dakhil 2015)</ns0:ref>. Therefore, impact of interaction time was evaluated as a main parameter that affects the adsorption capacity of PSB and CCB adsorbents. Figure <ns0:ref type='figure' target='#fig_15'>5a</ns0:ref> displays the behavior of Pb removal with different time interval on PSB and CCB at constant pH-6, dose 2.5 g/L, and concentration 50 mg/L. Adsorption of Pb increased when interaction time was increased but a significant changes happened in first 30 minutes, subsequently minor variations in adsorption were noted. The adsorption of Pb ions became constant when at equilibrium (60 min) by both PSB and CCB adsorbents. At equilibrium, highest Pb ions removal efficiency was 79.8% when 50 mg Pb/L interacted with PSB and CCB showed 48.80% removal under same conditions. The increase in removal with increasing interaction time is attributed to the interaction time between adsorbent and Pb ions. Initially the adsorption sharply increased but with the passage of time, the Pb removal was slower than initial period of time. After 60 min, change in removal is low due to occupation of most of the adsorption sites during first 60 min. The removal onto PSB increased from 49.4 to 73.0 % when the interaction time was changed from 15 to 60 min. Results show that when interaction time further increased from 60 to 180 min, only 6.8% more Pb was removed. Therefore, experiment was run for 180 min to attain equilibrium. <ns0:ref type='bibr' target='#b31'>Mukherjee et al. (2020)</ns0:ref> reported that the Pb and Cd removal gradually increased with increasing interaction time and then a sharp decline was noticed after 30 min, which is due to the intra-particle diffusion process. Similarly, Gaur et al.</ns0:p><ns0:p>(2018) reported that the equilibrium was attained in first 60 min for the adsorption of Pb and As by using soya been biosorbent. The results were also consistent with other relevant studies <ns0:ref type='bibr' target='#b2'>(Arimurti et al. 2016;</ns0:ref><ns0:ref type='bibr' target='#b4'>Ben-Ali et al. 2017;</ns0:ref><ns0:ref type='bibr' target='#b39'>Yogeshwaran and Priya 2021)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Effect of solution pH on Pb removal</ns0:head><ns0:p>PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2021:07:64090:1:0:NEW 24 Nov 2021)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:p><ns0:p>In adsorption studies, pH plays an important role for the removal of contaminants from wastewater.</ns0:p><ns0:p>The impact of pH for the removal Pb onto PSB and CCB from contaminated water was evaluated.</ns0:p><ns0:p>Therefore, the solution pH was changed from 4 to 10, while all other parameters were kept constant (50 mg/L). Figure <ns0:ref type='figure' target='#fig_15'>5b</ns0:ref> demonstrates the impact of pH on Pb removal from contaminated water. It was noticed that with the increase in pH, the removal percentage also increased. The removal percentage with PSB changed from 62.0-79.9% when the solution pH was increased from 4 to 6.</ns0:p><ns0:p>The increase in removal percentage is due to fluctuations in pH which might result from the surface properties of the adsorbent material as well as contaminant molecules. <ns0:ref type='bibr'>Heraldy et al. (2018)</ns0:ref> reported that the removal enhanced with the increasing pH of the Pb contaminated solution.</ns0:p><ns0:p>Previously reported that the effectiveness of the adsorbent materials were low at lower pH and increased with increasing pH <ns0:ref type='bibr' target='#b33'>(Pang et al. 2011</ns0:ref>). This might be due to the fact that lower pH can charge the active sites with H+ ions and makes the adsorbent surface more positive that cause electrostatic repulsion between adsorbent and Pb ions. Moreover, at basic pH electrostatic attraction occurred between adsorbent and adsorbate and enhanced removal was obtained <ns0:ref type='bibr' target='#b14'>(Hafshejani et al. 2015;</ns0:ref><ns0:ref type='bibr' target='#b35'>Taşar et al. 2014)</ns0:ref>. When the solution pH >6, the Pb hydrolysis and precipitation occurs as Pb hydroxides <ns0:ref type='bibr'>(Heraldy et al. 2018)</ns0:ref>. As pH was further changed from 6-10, there is improvement of Pb removal but removal at pH beyond 6 is attributed to Pb precipitation in solution as we observed clear precipitates at this higher pH (8 and 10). So, pH-6 was selected as optimum pH and all other cases were conducted at this pH.</ns0:p></ns0:div> <ns0:div><ns0:head>Impact of coexisting ions on Pb removal</ns0:head><ns0:p>The presence of competing ions in water may affect the contaminant removal onto the adsorbent surface <ns0:ref type='bibr'>(Imran et al. 2019;</ns0:ref><ns0:ref type='bibr'>Vilvanathan and Shanthakumar 2015)</ns0:ref>. Figure <ns0:ref type='figure' target='#fig_14'>5</ns0:ref> c presents the results regarding the impact of cations and anions on the removal of Pb when initial Pb concentrations (25 and 50 mg/L) were developed in two groundwater samples (GW1 and GW2). The characteristics of GW1 and GW2 are given in Table <ns0:ref type='table' target='#tab_2'>1</ns0:ref>. The results (Figure <ns0:ref type='figure' target='#fig_15'>5c</ns0:ref>) reveal that Pb removal with DW, GW1 and GW2 onto PSB at 25 mg/L was 90, 85.2 and 88.4%, respectively while PSB showed 79.8, 71.0 and 75.8% Pb removal in DW, GW1 and GW2, respectively at 50 mg/L. The GW1 and GW2 showed 4.8% and 1.6% lower Pb removal than DW when experiment was run at 25 mg/L while GW1 and GW2 showed 8. and GW2, respectively at 50 mg/L. The GW1 and GW2 showed 7.6% and 4.0% lower Pb removal than DW when experiment was run at 25 mg/L while GW1 and GW2 showed 9.8 and 5.4% less Pb removal as compared with DW at 50 mg/L. The GW1 showed more difference in Pb removal onto both PSB and CCB as compared with GW2. This higher difference in Pb removal by GW2 is attributed to higher concentration of cations (Na, K, Ca, and Mg) as compared with GW1 (Table <ns0:ref type='table' target='#tab_2'>1</ns0:ref>). The higher concentration of coexisting cations (especially Ca and Mg) competed for adsorption sites with Pb ions <ns0:ref type='bibr'>(Imran et al. 2019;</ns0:ref><ns0:ref type='bibr'>Meseguer et al. 2016)</ns0:ref> to occupy the surface of PSB and CCB adsorbents. Similar response of competing ions in groundwater was found by <ns0:ref type='bibr'>Imran et al. (2019)</ns0:ref> on the removal of Pb from contaminated water. Likewise, many other studies have reported that with increasing ionic strength the removal decreased <ns0:ref type='bibr' target='#b19'>(Imran et al. 2020;</ns0:ref><ns0:ref type='bibr' target='#b22'>Imran et al. 2021;</ns0:ref><ns0:ref type='bibr' target='#b25'>Kariuki et al. 2017)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Equilibrium and kinetic isotherms for Pb adsorption onto PSB</ns0:head><ns0:p>The equilibrium data for Pb adsorption by PSB and CCB was mathematically explained by adsorption isotherm models as described earlier in section 2.7. The kinetic adsorption data obtained at 50 mg Pb/L was validated with PFO, PSO and Intra-particle diffusion model. The fitting behavior of kinetic models has been displayed in Figure <ns0:ref type='figure' target='#fig_16'>6a-c</ns0:ref> </ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions</ns0:head><ns0:p>In the present work, the fixed bed column and batch scale adsorption potential of Peanut shells and commercial compost as biosorbents (PSB and CCB) were successfully evaluated for the sequestration of Pb from contaminated work. The maximum adsorption of Pb onto PSB (42.5 mg/g) was higher than CCB (26.39 mg/g) at optimum dose and pH, which is better than several previously explored adsorbents. The more Pb sequestration onto PSB is attributed to its improved surface characterization (SEM, FTIR, and BET) as compared with CCB under same conditions.</ns0:p><ns0:p>The presence of competing ions in groundwater (GW1 and GW2) showed 9.8 and 5. Manuscript to be reviewed Manuscript to be reviewed Manuscript to be reviewed</ns0:p><ns0:note type='other'>Chemistry Journals Figure 1</ns0:note><ns0:note type='other'>Chemistry Journals Figure 2</ns0:note></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science Manuscript to be reviewed</ns0:p><ns0:note type='other'>Figure 3</ns0:note></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science Manuscript to be reviewed Manuscript to be reviewed</ns0:p><ns0:note type='other'>Figure 4</ns0:note><ns0:note type='other'>Chemistry Journals Figure 6</ns0:note></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>environment-friendly adsorbents are required. Several scientists have attempted batch scale experiments to examine the adsorption capacity of orange peels (El-Said et al. 2012), cocoa shells (Meunier et al. 2003), banana peels (Annadurai et al. 2002), rice husks (Asrari et al. 2010), saponified melon peels (Chaudhary and Ijaz 2014), eggshell (Hussain and Shariff 2014) and bentonite (Naseem and Tahir 2001) for the elimination of Pb from polluted water. However, literature shows that there are limited studies performed on column scale for the remediation of Pb from contaminated water.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>and 600 min to determine the residual Pb concentration in the PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2021:07:64090:1:0:NEW 24 Nov 2021) Manuscript to be reviewed Chemistry Journals Analytical, Inorganic, Organic, Physical, Materials Science water after attachment of Pb at PSB and CCB surface. A peristaltic pump was used to inject contaminated water at a uniform flow rate at inlet side while at outside pump was not used just effluent was collected by gravity. In Figure 1, acronym R1 represents replication one and R2 indicates replication two of the column experimentation. To estimate the reusability of PSB and CCB, the columns were emptied after experimental run at 25 mg/L and the used PSB and CCB were rinsed with 2% HCl solution, filtered by adding distilled water and used for repacking before starting injection of 25 mg/L concentration again in the columns. The column scale removal and reusability of PSB and CCB for Pb sequestration were calculated based on the residual concentration and initial concentration of Pb in water.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head /><ns0:label /><ns0:figDesc>2021; Shah 2019; Edokpayi et al. 2015).</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head>C</ns0:head><ns0:label /><ns0:figDesc>) f PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2021:07:64090:1:0:NEW 24 Nov 2021)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2021:07:64090:1:0:NEW 24 Nov 2021) Manuscript to be reviewed Chemistry Journals Analytical, Inorganic, Organic, Physical, Materials Science sorption isotherms are the most widely utilized models for pollutant adsorption from wastewater (Tariq et al. 2020).</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head /><ns0:label /><ns0:figDesc>-b. The SEM results illustrated that PSB and CCB have porous surface with pores having variation in size and possessed irregular shapes containing capillary tubes that might help to trap metal ions through diffusion. Overall, there is more aggregation of particles in case of CCB as compared with PSB which might lead to lower Pb adsorption by CCB.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head>Figure</ns0:head><ns0:label /><ns0:figDesc>Figure2cdisplays the Fourier transform infrared (FTIR) spectroscopic results to find the functional groups existing on PSB and CCB surface. As shown in Figure2c, the FTIR spectra indicated broad band at 3308 cm -1 which represented -OH groups. The absorbance peak observed at wavenumber 2920-2849 cm -1 indicates aliphatic C-H group. The one peak observed at 1400 cm -1 represents alkanes (C-H) bending. The absorption peaks observed at wavenumber 1518-1634 cm -1 represent stretching of C=C. The presence of these functional groups on PSB and CCB surface is responsible for the Pb adsorption from contaminated water. However, there is difference in absorbance peak and wavenumber between PSB and CCB which might help in their different Pb adsorption potential.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_7'><ns0:head>Figure</ns0:head><ns0:label /><ns0:figDesc>Figure3apresents the breakthrough curves for the removal of Pb onto PSB and CCB at two different flow rates (2.5 and 5 ml/min) while other parameters; dose of PSB and CCB was 3.0 g/column (0.65 cm/column) were kept constant constant. The results revealed that when flow rate is increased, there is decline in the uptake of Pb ions by both adsorbents (PSB and CCB) due to negative effect of flow rate on mass transfer efficiency. The volume of injected Pb containing water at 2.5 and 5.0 ml/min was 1500 ml and 3000 ml, respectively. While bed volume (BV) number which is the ratio of total volume of contaminated water fed to the column and volume of adsorbent bed was 157. The results show that volume of treated effluent is reduced at higher flow rate because breakthrough curve is obtained earlier, and adsorbents get saturated in short time at high flow rate of injected water. It is attributed to the low interaction time between the adsorbent material and Pb ions which ultimately prevents complete saturation of the active sites thereby leading a decline in adsorption of Pb ions. Similar trend was reported by other investigators in literature(Naeem et al. 2019; Cantelli et al. 2014; Caprine et al. 2013). The results show that Pb ions adsorption on PSB strongly depends on the flow rate at which contaminated water is being injected to the column. The PSB and CCB takes more time to get saturated when there is low flow rate and sorption increased steadily as compared with injection of Pb containing water at high flow rate. The injection time must be extended when contaminant is injected at low flow rate because adsorption sites are saturated gradually(Naeem et al. 2019). Overall, PSB showed more removal of Pb ions as compared with CCB. At the start, there is negligible difference in Pb removal by PSB</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_8'><ns0:head /><ns0:label /><ns0:figDesc>Figure 4b depicted that effect of PSB and CCB adsorbents dose for the removal of Pb from contaminated water by varying adsorbent dose from 1.25 to 5.0 g/L of contaminated water when Pb initial concertation was 50 mg/L. Results revealed a clear impact of PSB and CCB biomass on</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_9'><ns0:head /><ns0:label /><ns0:figDesc>8 and 4.0% less Pb removal as compared with DW at 50 mg/L. Similarly, Pb removal with DW, GW1 and GW2 onto CCB at 25 mg/L was 70.2, PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2021:07:64090:1:0:NEW 24 Nov 2021) Manuscript to be reviewed Chemistry Journals Analytical, Inorganic, Organic, Physical, Materials Science 71.6 and 75.42, respectively while CCB showed 69.8, 60.0 and 64.4% Pb removal in DW, GW1</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_10'><ns0:head>Figure 1 :</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1: Schematic representation of column scale arrangement for Pb removal from water</ns0:figDesc><ns0:graphic coords='32,42.52,199.12,525.00,525.00' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_11'><ns0:head>Figure 2 :</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2: SEM image of Peanut shells (a), CCB (b) and (c) FTIR spectra of both adsorbents, (d) N 2 adsorption/desorption during BET analysis of both CCB and PSB used for the removal of Pb from water at fixed bed column scale and batch scale</ns0:figDesc><ns0:graphic coords='33,42.52,222.07,525.00,397.50' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_12'><ns0:head>Figure 3 :</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Figure 3: Breakthrough curves obtained from fixed bed column scale removal of Pb using Peanut shells (PSB) and commercial compost biosorbent (CCB); (a) effect of flow rate (2.5 and 5.0 ml/min) when dose of PSB and CCB is 3.0 g/column pH-6 and Ci=25 mg/L,</ns0:figDesc><ns0:graphic coords='34,42.52,239.62,525.00,346.50' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_13'><ns0:head>Figure 4 :</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Figure 4: (a) Effect of initial concentration of Pb (25-100 mg/L) on its removal (%) at equilibrium, pH-6, dose 0.5 g/L and room temperature; (b) Impact of PSB and CCB dose on Pb removal at equilibrium, Ci= 50 mg/L, pH-6;</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_14'><ns0:head>Figure 5</ns0:head><ns0:label>5</ns0:label><ns0:figDesc>Figure 5</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_15'><ns0:head>Figure 5 :</ns0:head><ns0:label>5</ns0:label><ns0:figDesc>Figure 5: Effect of interaction time on (a) Pb removal when Ci= 50 ppm, PSB dose= 0.5 g/100 ml), (b) impact of solution pH of contaminated water on Pb removal at equilibrium, dose 0.5 g/100 ml, Ci=50 mg/L, (c) impact of competing ions in groundwater sampl</ns0:figDesc><ns0:graphic coords='37,42.52,239.62,525.00,352.50' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_16'><ns0:head>Figure 6 :</ns0:head><ns0:label>6</ns0:label><ns0:figDesc>Figure 6: Correlation of experimental data with kinetic adsorption models: Pseudo first order (a), pseudo second order (b) and intra-particle diffusion (c); equilibrium adsorption isotherms: Langmuir (d), Freundlich (e).</ns0:figDesc><ns0:graphic coords='38,42.52,219.37,525.00,525.00' type='bitmap' /></ns0:figure> <ns0:figure type='table' xml:id='tab_1'><ns0:head /><ns0:label /><ns0:figDesc>. The comparison of modeling results with kinetic experimental data shows that pseudo second order well explained the kinetic adsorption of Pb onto PSB and CCB as is evident from the value of coefficient of determination (R 2 ) which is 0.99 for Pseudo second order model. The values of kinetic and equilibrium model parameters have been shown in Table1. For equilibrium data, values of coefficient of determination show that Pb ion sorption well fitted with Freundlich adsorption isotherm (R 2 = 0.98, , Inorganic, Organic, Physical, Materials Science A comparison of Pb adsorption onto PSB and CCB with previous studies is important to determine the effectiveness and real time applications of proposed materials. Table3highlights the comparison of PSB and CCB with previous studies. Current study depicted that the adsorption of Pb was 50.0 mg/g. Hence, it was noticed that the proposed material (PSB) is more efficient than other adsorbents such as corncobs(Mendoza-Castillo et al. 2015), soya bean(Gaur et al. 2018), Eupatorium adenophorum spreng(Guo et al. 2009), Okra waste(Hashem 2007), Tea waste(Mondal 2010), and Agave sisalana (DosSantosa et al. 2011). It was also noticed that many other adsorbents are available that have been more efficient such as tomato waste and apple juice residue(Heraldy et al. 2018), lignin biosorbent(Klapiszewski et al. 2017), Pineapple waste(Mopoung and Kengkhetkit 2016), Palm fruit fiber(Ideriah et al. 2012) etc. than PSB. However, the availability and cost effectiveness of the adsorbent materials is also very important. Although many adsorbents are more efficient, but the process and cost effectiveness makes PSB attractive for the remediation of Pb from contaminated water.</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Table 1). The fitting behavior of equilibrium models (Langmuir and Freundlich) has been</ns0:cell></ns0:row><ns0:row><ns0:cell>presented in Figure 6d-e. The respective values of coefficient of determination R 2 in case of</ns0:cell></ns0:row><ns0:row><ns0:cell>Langmuir adsorption isotherm fitted with PSB was 0.89 while adsorption by CCB was equally</ns0:cell></ns0:row><ns0:row><ns0:cell>fitted with Langmuir model as its R 2 =0.98. The value of q max for PSB and CCB as per Langmuir</ns0:cell></ns0:row><ns0:row><ns0:cell>model was 42.05 mg/g and 26.39 mg/g, respectively. Moreover, respective n values of Freundlich</ns0:cell></ns0:row><ns0:row><ns0:cell>model for PSB and CCB was 1.92 and 2.27.</ns0:cell></ns0:row></ns0:table><ns0:note>Comparison of Pb removal by PSB with literaturePeerJ Phy. Chem. reviewing PDF \| (PCHEM-2021:07:64090:1:0:NEW 24 Nov 2021)Manuscript to be reviewedChemistry Journals Analytical</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_2'><ns0:head>Table 1 :</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Characteristics of groundwater samples (GW1 and GW2) used to evaluate the impact of interfering inorganic ions on the removal of Pb (II) from contaminated water.</ns0:figDesc><ns0:table><ns0:row><ns0:cell>4 % lower,</ns0:cell></ns0:row></ns0:table><ns0:note>is more effective and environment friendly at both column and batch scale for the removal of Pb as compared with CCB PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2021:07:64090:1:0:NEW 24 Nov 2021)Manuscript to be reviewed Chemistry JournalsAnalytical, Inorganic, Organic, Physical, Materials Science </ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_3'><ns0:head>Table 2 :</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Parameters of kinetic and equilibrium adsorption models</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Type</ns0:cell><ns0:cell>Model</ns0:cell><ns0:cell>Parameter</ns0:cell><ns0:cell>PSB</ns0:cell><ns0:cell>CCB</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>K L (L/mg)</ns0:cell><ns0:cell>0.080</ns0:cell><ns0:cell>0.077</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>Langmuir</ns0:cell><ns0:cell>q max (mg/g)</ns0:cell><ns0:cell>42.5</ns0:cell><ns0:cell>26.39</ns0:cell></ns0:row><ns0:row><ns0:cell>Equilibrium</ns0:cell><ns0:cell /><ns0:cell>R 2 (-)</ns0:cell><ns0:cell>0.89</ns0:cell><ns0:cell>0.99</ns0:cell></ns0:row><ns0:row><ns0:cell>Adsorption models</ns0:cell><ns0:cell /><ns0:cell>K F ([mg/g(L/mg) 1/n ])</ns0:cell><ns0:cell>5.36</ns0:cell><ns0:cell>3.96</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>Freundlich</ns0:cell><ns0:cell>n (-)</ns0:cell><ns0:cell>1.92</ns0:cell><ns0:cell>2.27</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>R 2 (-)</ns0:cell><ns0:cell>0.98</ns0:cell><ns0:cell>0.99</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>k 1 (1/min)×10 -2</ns0:cell><ns0:cell>0.046</ns0:cell><ns0:cell>0.069</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>Pseudo first</ns0:cell><ns0:cell>q e (mg/g)</ns0:cell><ns0:cell>9.58</ns0:cell><ns0:cell>5.47</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>order</ns0:cell><ns0:cell>R 2 (-)</ns0:cell><ns0:cell>0.889</ns0:cell><ns0:cell>0.92</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>k 2 (g/mg/min)×10 -2</ns0:cell><ns0:cell>0.0075</ns0:cell><ns0:cell>0.0028</ns0:cell></ns0:row><ns0:row><ns0:cell>Kinetic models</ns0:cell><ns0:cell>Pseudo second order</ns0:cell><ns0:cell>q e (mg/g) R 2 (-)</ns0:cell><ns0:cell>16.7 0.99</ns0:cell><ns0:cell>15.6 0.99</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>Intra-particle</ns0:cell><ns0:cell>k dif</ns0:cell><ns0:cell>0.51</ns0:cell><ns0:cell>0.83</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>diffusion</ns0:cell><ns0:cell>C</ns0:cell><ns0:cell>9.85</ns0:cell><ns0:cell>4.11</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>R 2 (-)</ns0:cell><ns0:cell>0.67</ns0:cell><ns0:cell>0.77</ns0:cell></ns0:row></ns0:table><ns0:note>PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2021:07:64090:1:0:NEW 24 Nov 2021)Manuscript to be reviewed Chemistry JournalsAnalytical, Inorganic, Organic, Physical, Materials Science </ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_4'><ns0:head>Table 3 (on next page)</ns0:head><ns0:label>3</ns0:label><ns0:figDesc /><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_5'><ns0:head>Table 3 :</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Comparison of different adsorbents for the removal of Pb from contaminated</ns0:figDesc><ns0:table /><ns0:note>water PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2021:07:64090:1:0:NEW 24 Nov 2021) Manuscript to be reviewed Chemistry Journals Analytical, Inorganic, Organic, Physical, Materials Science 1</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_6'><ns0:head>Table 3 :</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Comparison of different adsorbents for the removal of Pb from contaminated water</ns0:figDesc><ns0:table><ns0:row><ns0:cell>No.</ns0:cell><ns0:cell>Materials</ns0:cell><ns0:cell cols='2'>Adsorption/Removal References</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>lignin biosorbent</ns0:cell><ns0:cell>89.02 mg/g</ns0:cell><ns0:cell>(Klapiszewski et al.</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>2017)</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>soya bean</ns0:cell><ns0:cell>0.72 mg/g</ns0:cell><ns0:cell>(Gaur et al. 2018)</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>Pineapple waste</ns0:cell><ns0:cell>77.16 mg/g</ns0:cell><ns0:cell>(Mopoung and</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>Kengkhetkit 2016)</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>Melon peel</ns0:cell><ns0:cell>72 mg/g</ns0:cell><ns0:cell>(Gour et al. 2016)</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>Palm fruit fiber</ns0:cell><ns0:cell>73 mg/g</ns0:cell><ns0:cell>(Ideriah et al. 2012)</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>metallothionein</ns0:cell><ns0:cell>39.02 mg/g</ns0:cell><ns0:cell>(Mwandira et al. 2020)</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>Modified corncobs</ns0:cell><ns0:cell>4.34 mg/g</ns0:cell><ns0:cell>(Mendoza-Castillo et</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>al. 2015)</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>Okra waste</ns0:cell><ns0:cell>5.00 mg/g</ns0:cell><ns0:cell>(Hashem 2007)</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>Agave sisalana</ns0:cell><ns0:cell>1.34 mg/g</ns0:cell><ns0:cell>(Dos Santosa et al.</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>2011)</ns0:cell></ns0:row><ns0:row><ns0:cell>10</ns0:cell><ns0:cell>Tea waste</ns0:cell><ns0:cell>1.35 mg/g</ns0:cell><ns0:cell>(Mondal 2010)</ns0:cell></ns0:row><ns0:row><ns0:cell>11</ns0:cell><ns0:cell>Eupatorium</ns0:cell><ns0:cell>3.46 mg/g</ns0:cell><ns0:cell>(Guo et al. 2009)</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>adenophorum spreng</ns0:cell><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>12</ns0:cell><ns0:cell>Peanut shells biosorbent</ns0:cell><ns0:cell>50.0 mg/g</ns0:cell><ns0:cell>Current study</ns0:cell></ns0:row><ns0:row><ns0:cell>2</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>3</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row></ns0:table><ns0:note>PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2021:07:64090:1:0:NEW 24 Nov 2021)</ns0:note></ns0:figure> <ns0:note place='foot'>Analytical, Inorganic, Organic, Physical, Materials Science </ns0:note> </ns0:body> "	"Editor comments (Claudio Della Volpe) MAJOR REVISIONS The basic idea of your experiment is acceptable and interesting but the development of your job is questionable, as fully explained in the comments of the reviewers. Your paper is not acceptable in its present form so it should be revised; you need further experimental work and revised analysis as explained in detail in the comment of the reviewers, especially the first and the third reviewers. The problem of using only distilled water is a strong limitation. Read with attention the critical comments of the reviewers and be ready to perform further experiments if you want to publish your idea. Regards. Response: Thanks to the editor for positive remarks. This limitation of using distilled water has been resolved by performing experiments in two groundwater samples having different concentration of cations and anions in addition to Pb ions in water. The groundwater samples were analysed for electrical conductivity (EC), total dissolved salts (TDS), pH, sodium (Na), potassium (K), calcium (Ca), magnesium (Mg), carbonate (CO3), bicarbonate (HCO3), Chlorine (Cl), sulphate (SO4), nitrate (NO3) and Pb. The EC, TDS and pH were measured with respective EC, TDS, and pH meters. The Na, K and Ca were measured using Flame photometer while SO4 and NO3 concentration was measured with calorimetric method. The concentration of Cl, CO3 and HCO3 was measured with titration method Estefan (2013) while Mg and Pb were measured using atomic absorption spectrophotometer. First groundwaters containing Pb (GW1= 80 ug/L, GW2=57 ug/L) were treated with PSB and CCB at optimum conditions and subsequently Pb concentration (25 mg/L) was developed in both groundwater samples and Pb removal was evaluated in the presence of different cations and anions in water. The characteristics of GW1 and GW2 have been presented in Table 1. The results of batch scale adsorption experiments carried out in distilled water (DW) and groundwater samples at Pb concentration (25 mg/L and 50 mg/L) were compared to evaluate the impact of competing ions in water on Pb removal. Please find highlighted text in results and methodology about this impact in the main file of revised version. Reviewer 1 (Anonymous) Basic reporting This submission deals with the adsorption of lead from water onto a biosorbent based on peanut shells. Some equilibrium batch tests were conducted and conventional models were applied to the data. The approach applied is very conventional and the only new aspect is the use of a relatively new biosorbent for a specific contaminant. I think that it is a normal approach in a laboratory to evaluate a commercial product and to propose it for the treatment of a specific type of water. There are many studies and textbooks giving detailed research and insights about the experimental evaluation of adsorbents. I don’t see any advancement in the science in this aspects. It can further not be excluded that such a waste product may have the potential to contaminate the treated water because of pesticides or other heavy metals used in various part of the world. Because of its low informative value and no innovative approaches proposed, I have to recommend to reject the submission. Response: Thanks to the reviewer for positive remarks and highlighting few points to improve the manuscript. In revised version, groundwater experiments were conducted to evaluate the impact of competing ions on Pb sequestration from contaminated water. The groundwater samples were analysed for electrical conductivity (EC), total dissolved salts (TDS), pH, sodium (Na), potassium (K), calcium (Ca), magnesium (Mg), carbonate (CO3), bicarbonate (HCO3), Chlorine (Cl), sulphate (SO4), nitrate (NO3) and Pb. The EC, TDS and pH were measured with respective EC, TDS, and pH meters. The Na, K and Ca were measured using Flame photometer while SO4 and NO3 concentration was measured with calorimetric method. The concentration of Cl, CO3 and HCO3 was measured with titration method Estefan (2013) while Mg and Pb were measured using atomic absorption spectrophotometer. First groundwaters containing Pb (GW1= 80 ug/L, GW2=57 ug/L) were treated with PSB and CCB at optimum conditions and subsequently Pb concentration (25 mg/L) was developed in both groundwater samples and Pb removal was evaluated in the presence of different cations and anions in water. The characteristics of GW1 and GW2 have been presented in Table 1. The results of batch scale adsorption experiments carried out in distilled water (DW) and groundwater samples at Pb concentration (25 mg/L and 50 mg/L) were compared to evaluate the impact of competing ions in water on Pb removal. Experimental design - Line 125: in this research a synthetic wastewater was used, based on a very simple composition by the dilution Pb in distilled water. This is one of the main concerns in this research, because the use of synthetic wastewater does not permit to evaluate the effects of non-competitive and competitive adsorption and desorption (mutual interference effects, multiple adsorption mechanisms, ...). - Please reconsider the extensive use of the words 'biosorption' - they refer to filling materials able to develop a microbial community or biofilm. Response: thanks to the reviewer for positive remarks. In revised version, groundwater experiments were conducted to evaluate the impact of competing ions on Pb sequestration from contaminated water. The concentration of different cations and anions in two groundwater samples were measured and their impact on the Pb removal was evaluated. The concentration of different cations and anions in groundwater samples has been presented in Table 1 and results regarding the impact of interfering ions on Pb removal have been shown in Figure 5c. Validity of the findings - Figure 4: It is very questionable to apply these models on 3-4 data points. - A very important aspect which is not covered by the authors is that distilled water was examined. If, as the authors suggest, the tested material should be useful for contaminant removal from wastewaters etc. why were no tests carried out on such waters? This would be highly necessary because in real/ natural waters, competition for adsorption sites occurs, reducing the available adsorption capacities for single adsorbates by orders of magnitude. - With the extremely high dosages reported here (for instance see figure 4 with 25-100 mg/L), it appears largely unrealistic that the tested material could be applied in practice. Response: Thanks for raising such point. Dear reviewer, in the revised version of the manuscript, we have evaluated the impact of competing cations on the adsorption of Pb by using two groundwater samples collected from Vehari. These groundwater samples were measured for Pb, electrical conductivity (EC), total dissolved salts (TDS), pH, Na, K, Ca, Cl, CO3, HCO3. Further detail is given in methodology and results sections. Reviewer 2 (Luca Fiori) Basic reporting The paper is written in an English that needs improvement. It's not bad, but there are some mistakes that need to be corrected. Just one example: line 468 <<The more Pb sequestration onto PSB is attributed to its improved surface characterization>>. Some words are spelled wrong (e.g., line 177 concertation instead of concentration). Literature references look fine, and sufficient field background/context is provided. The article structure, figures, and tables are fine, but a few things should be corrected: • Figure 3: the caption is incomplete: (b) effect • Figure 3a: The experimental data symbols for Q = 5.0 ml/min (CCB) are missing - there is only the curve. • Figure 5a: the line connecting the experimental points must be made either broken (straight lines) or curved: not a mix of the two options. Response: Thanks to the reviewer for the positive remarks and suggestions to improve the manuscript. These changes have been made in the Figures Experimental design The experimental design is fine, even if basic. The scientific and methodological approaches are fine. Methods are well described. Response: thanks for the positive remarks Validity of the findings Findings are OK, although often quite trivial. I do not see any statistics on the experimental data: it seems all the tests have been performed only once. However, considering the type of tests carried out, I think this can be accepted. Conclusions seem fine. Response: thanks for the positive feedback. In fact, experiments were conducted in triplicate and the values used for the removal and adsorption are mean values of n=3. Additional comments Abstract: Indicating the pump flow rate has no physical meaning if the value of other parameters is not reported: bed volume and/or vessel cross-section. Line 64: define the CNS acronym. Response: thanks to the reviewer asking for clarity. The other parameter have also been mentioned in the abstract of the revised version. Area of the column, bed volume number have been mentioned. CNS indicates central nervous system CNS. Its full description has been provided in the main file. Line 110: if possible, report about the residual biomasses used to produce the commercial compost. Response: The commercial compost (CCB) was obtained from a company ‘PlantFert”. According to the company, CCB was prepared from the vegetables waste. Line 134: specify what do you mean with steady state – I guess the fluid-dynamics. Line 144: avoid @ Response: Yes, it was related to the flow rate of the fluid being injected with peristaltic pumps. Steady state means, the contaminated water is continuously being injected with same speed. Line 177: the IC acronym should be defined. Response: It is initial concentration of the contaminant (Pb in current study). Its full description has been given in Line 170 when it was mentioned at first place. Line 203 << Table 1 shows the values of k1, qe (calculated from model), qe-experimental,>>. In Table 1 I do not see any experimental data. Response: Reviewer is right. This confusion in text has been clarified. The above-mentioned text has been revised (please find highlighted text in main file). This Table contains only the values of models’ parameters and value of correlation between experimental and simulated values (modeling results). Line 225 <<Kp>>: in Table 1 the authors wrote <<Kdif>> referring to the same variable: decide which symbol to use. The IPD acronym should be defined. Response: This confusion has been clarified. In fact, it is kdif in theory and results. This change has been made in the revised version throughout the manuscript. IPD is intra-particle diffusion which has been mentioned in line 194. Line 326 <<capacity decreased from 10.26 to 9.9015μg g−1>> too many values after the comma in the second data. Response: Revised it for clarity just upto two decimals Line 374 << The maximum values of Pb ion sorbed on PSB and CCB surface were found at mass of 1.25 g/L of contaminated water. Overall, Pb adsorption showed a decline with the increase when adsorbent dose was increased.>>. Looking at the Fig, the trend seems reversed. Response: Respected reviewer, the removal of Pb increased with increase in adsorbent dosage. Figure 4 a presents the impact of initial concentration of Pb on its removal. Results indicate that with increase in initial concentration at constant dose of CCB and PSB. At lower initial concentration, the removal was highest because PSB contains enough available spaces that favors the adsorption of Pb on PSB and CCB. While at higher concentration, the available spaces become saturated with Pb ions and no more sites available for the adsorption of Pb ions. The decline in the Pb removal with increasing solution concentration is attributed to the concentration gradient or split in the flux in solute concentration and sorbent surface. The decline in removal with increasing concentration was due to the decline in available sorption sites (Boudrahem et al. 2019). While this trend is reversed when the impact of dose was evaluated at constant initial concentration (50 mg/L) and dose was varied from 1.25 to 5.0 g/L (Fig. 4b). This Figure shows that removal of Pb increased with increase in dose of both adsorbents. While there is decline in Pb adsorption with increase in adsorbent dose. In the revised version, adsorption has also been shown in Figure 4b. As per Equation 1, adsorption contains adsorbent mass is in denominator (inverse relation) and shows that with increase in mass adsorption should decrease. So, removal is directly related with adsorbent mass while adsorption is inversely related with the adsorbent dose. Line 447: << for PSB and CCB was 2.27 and 1.92>>. The values were reversed. Response: Reviewer is right. These values have been revised as per Table 1. In revised version, it is Table 2 because one new Table (1) has been added for the properties of groundwater samples used to evaluate the impact of cations and anions on Pb removal from contaminated water. S Reviewer 3 (Paolo Ciambelli) Basic reporting The manuscript deals with the very serious problem of aqueous systems contamination by lead, a very toxic and hazardous contaminant. The solution investigated to obtain pure and safe water for drinking is based on the adsorption performance of a peanuts shell biosorbent. In the latest years many papers, aiming at finding an efficient, economically feasible and environment-friendly adsorbent, have been published, the most of them addressed to claim very high adsorption capacity. Less attention was addressed to verify the performance of such adsorbents in flow systems based on adsorption packed columns for contaminated water remediation. The authors claim that main goal of the study was to assess the biosorption potential of peanut shells wastes (PSB) to remove Pb from wastewater through experimental and modeling approaches, using batch and flow systems, operated under various conditions in terms of adsorbate concentration, adsorbent dosage, solution pH and contact time. A comparison with a commercial compost biosorbent (CSB) was also performed. Response: Thanks to the reviewer, he also agrees that in literature less attention was addressed to verify the performance of such adsorbents in flow systems based on adsorption packed columns for contaminated water remediation. Experimental design Experimental characterization of PSB has been carried out through FTIR, SEM, and nitrogen adsorption for surface area/pore volume size. Really, the results obtained in the latter case (surface area of 226.8 m2 /g, average particle and pore size 26.5 and 3.3 nm, respectively, 5.11% porosity) are not presented as adsorption isotherms, while they are essential to support the reported, rather surprising, size values. Response: Thanks to the reviewer asking for adsorption/desorption isotherms. In the revised version, adsorption/desorption curves for nitrogen by both adsorbents (CCB and PSB) have been presented in Figure 2d. These curves with PSB are different and showed relatively higher adsorption/desorption of nitrogen as compared with CCB. Validity of the findings The work is medium quality, and could be improved by adding some results, discussion, especially concerning the potential application, following the below suggestions. Concerning the adsorption equilibrium measurements and modeling: line 404…… “The removal onto PSB increased from 49.4 to 73.0 % when the contact time was changed from 15 to 60 min. Moreover, only 6.8% removal was increased when the contact time further increased from 60 to 180 min”. Response: Regarding equilibrium, text has been revised. After 60 min, change in removal is low due to occupation of most of the adsorption sites during first 60 min. The removal onto PSB increased from 49.4 to 73.0 % when the interaction time was changed from 15 to 60 min. Results show that when interaction time further increased from 60 to 180 min, only 6.8% more Pb was removed. Therefore, equilibrium was attained slowly, and experiment was run for 180 min. Comments : If the purpose is to determine the real equilibrium values, further 6.8 % increase of adsorbed amount is not negligible. The approach to the equilibrium can be very slow, especially in the experiments in which the amount of lead is not enough excess with respect the amount of adsorbent, resulting in a rather high reduction of lead concentration and then driving force to adsorption. Moreover, I suggest that the Authors don’t use the term contact time along all the manuscript to indicate the run time. The modeling approach is acceptable; the same for choosing R2= 0.99 as acceptable values for supporting a model. Response: Thanks to the reviewer for positive remarks and suggestion. Contact time has been replaced with interaction time throughout the manuscript and it has been mentioned that during first 60 min there was fast removal of Pb and after that equilibrium was attained slowly. Concerning the flow column experiments and modeling : I believe an introduction to the comments is necessary. The challenge of using adsorption to remove Pb from wastewater containing 10-100 ppm and over is really very much arduous, if one considers that the current limit for drinking water is 10 ppb. This means that you need a very large amount of very high capacity adsorbent or a very large scale adsorption column. Cheap adsorbent can surely help, but how much the adsorbent cost influences the final cost of the treatment ? And, looking at the above premise, how frequent is the necessity of column regeneration ? How much the operation influences the final cost, compared to the effect of adsorbent cost ? Moreover, the discussion and interpretation of the breakthrough curve sas depending of the operating parameters should be less qualitative. Response: Thanks for reviewer’s practical approach. I agree in most scenarios of contaminated water Pb concentration is not too much and, in such cases, mostly cheap adsorbents surely help. e.g., in revised version of the manuscript we carried out experiments using groundwater samples in which Pb concentration was 80 ppb and 57 ppb in addition to other cations and anions in water (mentioned in Table 1). With both adsorbents (PSB and CCB), Pb was not detected in the samples after treatment of groundwater. But, elevated levels of Pb are found at some places especially near the industry where Pb containing products are produced. In such cases, off course adsorbent with high efficiency are needed and their cost of treatment must be evaluated. To minimize the cost, adsorbents materials are regenerated. In our previous work, Naeem et al. (2019), we had conducted column scale regeneration experiments by injecting acid solution to wash the adsorbents after the removal of Cd from contaminated water using raw and activated biochar. Moreover, discussion regarding breakthrough curves has been improved significantly in the revised version (please find highlighted text). Coming back to the right comment of the Authors on the low attention addressed in the most papers to verify the performance of adsorbents in flow systems, however their experimental approach is rather far from answering the above considerations. First of all, the scale of the experiments: the column is too small and the adsorbent bed geometry is questionable. A too large diameter with respect the height also results in a radial profile of velocity very non uniform, and the entrance effects are critical. From the experimental data it can be seen that to reach the limit of 50 ppb (higher than the current regulation limit) 99.8 or 99.9 % removal are the values fot the breakthrough curves to be stopped. One simple parameter to be evaluated is the corresponding BV (bed volume) number at that value, i.e. the ratio between the total amount of liquid fed to the bed and the adsorbent volume. Few BVs means a very high frequency of regeneration, resulting in high operation cost. Response: Respected reviewer, there is no doubt that the diameter of the column affects adsorbent bed geometry which influences the removal of a contaminant. With increase in diameter, there is decrease in bed height, decline in the removal of contaminant and the time adsorbent takes to get saturated is also reduced. In our scenario, column scale experiments were conducted at constant diameter (4.5 cm) and bed height at two different doses (3.0 and 6.0 g/column) was 0.6 and 1.2 cm, respectively. Reviewer is right, in future column studies, we’ll consider this suggestion to have bed height higher than the diameter of the column (to avoid radial effects) especially by using columns with small diameter. Moreover, bed volume number was calculated using the total amount of contaminated water fed to the bed and adsorbent volume. The calculated BV number was 157. It has also been mentioned in column scale results (find highlighted txt please). One more aspect, the importance of regeneration, underlined by the Authors, is not investigated. The comparison with CCB is winning in terms of adsorption capacity and shape of breakthrough curves, however the flow column behavior of CCB is very poor. It seems difficult to imagine the practical utilization of CCB for lead sequestration in a flow system on the base of the results found by the Authors. English language is of acceptable quality. Response: Thanks to the reviewer for positive remarks about the language quality. Column scale performance of CCB is poor as compared with its batch scale adsorption capacity which is attributed to the packing of CCB in column because in column, adsorbent molecules have not as much access to contaminant as in batch scale where there is more interaction between adsorbent and adsorbate due to continuous shaking. "	Here is a paper. Please give your review comments after reading it.
485	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>We test our meta-molecular dynamics (MD) based approach for finding low-barrier (<30 kcal/mol) reactions on uni-and bimolecular reactions extracted from the barrier dataset developed by Grambow et al. For unimolecular reactions the meta-MD simulations identify 25 of the 26 products found by Grambow et al., while the subsequent semiempirical screening eliminates an additional four reactions due to at an overestimation of the reaction energies or estimated barrier heights relative to DFT. In addition, our approach identifies an additional 36 reactions not found by Grambow et al., 10 of which are <30 kcal/mol. For bimolecular reactions the meta-MD simulations identify 19 of the 20 reactions found by Grambow et al., while the subsequent semiempirical screening eliminates an additional reaction. In addition, we find 34 new low-barrier reactions. For bimolecular reactions we found that it is necessary to 'encourage' the reactants to go to previously undiscovered products, by including products found by other MD simulations when computing the biasing potential as well as decreasing the size of the molecular cavity in which the MD occurs, until a reaction is observed. We also show that our methodology can find the correct products for two reactions that are more representative of those encountered in synthetic organic chemistry. The meta-MD hyperparameters used in this study thus appears to be generally applicable to finding low-barrier reactions.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head n='1'>Introduction</ns0:head><ns0:p>Understanding how molecular systems react, i.e. which reactions are possible under what conditions, is an essential part of chemical research and computational methods for exploring reaction space in an automated manner are continually being proposed. <ns0:ref type='bibr' target='#b0'>[1,</ns0:ref><ns0:ref type='bibr' target='#b1'>2,</ns0:ref><ns0:ref type='bibr' target='#b2'>3,</ns0:ref><ns0:ref type='bibr' target='#b3'>4,</ns0:ref><ns0:ref type='bibr' target='#b4'>5,</ns0:ref><ns0:ref type='bibr' target='#b5'>6,</ns0:ref><ns0:ref type='bibr' target='#b6'>7,</ns0:ref><ns0:ref type='bibr' target='#b7'>8,</ns0:ref><ns0:ref type='bibr' target='#b8'>9,</ns0:ref><ns0:ref type='bibr' target='#b9'>10,</ns0:ref><ns0:ref type='bibr' target='#b10'>11,</ns0:ref><ns0:ref type='bibr' target='#b9'>10,</ns0:ref><ns0:ref type='bibr' target='#b11'>12,</ns0:ref><ns0:ref type='bibr' target='#b12'>13,</ns0:ref><ns0:ref type='bibr' target='#b13'>14,</ns0:ref><ns0:ref type='bibr' target='#b14'>15,</ns0:ref><ns0:ref type='bibr' target='#b15'>16,</ns0:ref><ns0:ref type='bibr' target='#b16'>17]</ns0:ref> These methods can be divided into three different categories: (semi-)exhaustive searches, <ns0:ref type='bibr' target='#b4'>[5,</ns0:ref><ns0:ref type='bibr' target='#b2'>3,</ns0:ref><ns0:ref type='bibr' target='#b0'>1,</ns0:ref><ns0:ref type='bibr' target='#b1'>2,</ns0:ref><ns0:ref type='bibr' target='#b10'>11]</ns0:ref> reaction template methods, <ns0:ref type='bibr' target='#b9'>[10,</ns0:ref><ns0:ref type='bibr' target='#b11'>12,</ns0:ref><ns0:ref type='bibr' target='#b12'>13,</ns0:ref><ns0:ref type='bibr' target='#b13'>14,</ns0:ref><ns0:ref type='bibr' target='#b14'>15]</ns0:ref>, and meta-molecular dynamics (meta-MD) based approaches. <ns0:ref type='bibr' target='#b9'>[10,</ns0:ref><ns0:ref type='bibr' target='#b15'>16,</ns0:ref><ns0:ref type='bibr' target='#b10'>11,</ns0:ref><ns0:ref type='bibr' target='#b16'>17]</ns0:ref> Examples of (semi-)exhaustive searches include graph enumeration of products <ns0:ref type='bibr' target='#b4'>[5,</ns0:ref><ns0:ref type='bibr' target='#b2'>3,</ns0:ref><ns0:ref type='bibr' target='#b0'>1,</ns0:ref><ns0:ref type='bibr' target='#b1'>2,</ns0:ref><ns0:ref type='bibr' target='#b10'>11]</ns0:ref> and enumeration of reaction coordinates. <ns0:ref type='bibr' target='#b13'>[14,</ns0:ref><ns0:ref type='bibr' target='#b14'>15]</ns0:ref> For these methods the size of the search space, and hence the computational cost, grows quickly with the size of the molecules. The reaction template approaches lie at the other extreme in terms of computational efficiency, and work by investigating only pre-determined reaction types. Though efficient and used extensively in atmospheric and combustion chemistry, this approach can be hard to generalise to other areas such as synthetic organic chemistry, though a recent attempt is encouraging. <ns0:ref type='bibr' target='#b12'>[13]</ns0:ref> The meta-MD approaches explore reactivity via biasing potentials that force reactions and exploration of conformational space. The meta-MD approach can also be combined with (semi-)exhaustive search methods as shown by Lavigne et al. <ns0:ref type='bibr' target='#b8'>[9]</ns0:ref> In the non-exhaustive approaches the key question is whether they identify all relevant reactions for the problem at hand. At room temperature, this typically means all reactions with barriers less than ca 30 kcal/mol given the typical accuracy of quantum chemical calculations. Recently, we demonstrated that a combination of product generation using meta-MD and barrier estimation + TS guess generation using the RMSD-PP method <ns0:ref type='bibr' target='#b15'>[16]</ns0:ref>, both relying on semiempirical GFN2-xTB <ns0:ref type='bibr' target='#b17'>[18]</ns0:ref> calculations, could efficiently suggest the three lowest barrier elementary reactions of 3-hydroperoxypropanal. <ns0:ref type='bibr' target='#b10'>[11]</ns0:ref> The results depend strongly on the hyperparameters that control the biasing potential in the meta-MD and it is unclear how well this hyperparameter set generalizes to other unimolecular reactants and to bimolecular reactions in general. Here we test the performance of this hyperparameter set on elementary unimolecular reactions involving 163 reactant molecules and 20 elementary bimolecular reactions, both extracted from the recently published database of elementary reactions involving H, C, N, and O on two different DFT levels. <ns0:ref type='bibr' target='#b18'>[19]</ns0:ref> In addition we test the method on two multi-step reactions related to organic synthesis.</ns0:p></ns0:div> <ns0:div><ns0:head n='2'>Methods</ns0:head><ns0:p>Our method for predicting the kinetically important elementary reactions <ns0:ref type='bibr' target='#b10'>[11]</ns0:ref> is based on three steps (Figure <ns0:ref type='figure' target='#fig_0'>1</ns0:ref>): (a) the generation of possible single-step products which is based on meta-molecular dynamics (meta-MD) simulations followed by (b) screening of the proposed products based on reaction energies and estimated barrier heights computed at the semiempirical (GFN2-xTB) level of theory followed by (c) validation at the DFT level of theory. In this study we use a cutoff of 40 kcal/mol when screening reaction energies and barrier estimates as a compromise between the accuracy of the energies and the number of reactions to be checked. Our approach to product generation is based on the meta-MD approach by Grimme <ns0:ref type='bibr' target='#b15'>[16]</ns0:ref> which is a way of increasing the likelihood of a reaction occurring during an MD simulation by penalising to previously visited structures. This is done by adding additional terms to the energy and gradient that depend on the RMSD from previously visited structures during the current MD simulation or previous MD simulations (selected by the user). As described in <ns0:ref type='bibr' target='#b10'>[11]</ns0:ref> the additional energy terms depend on the hyper-parameters k push , α, and s and we use values that were optimised to promote unimolecular chemical reactions <ns0:ref type='bibr' target='#b10'>[11]</ns0:ref> (k push = 0.05E h ,α = 0.3 Bohr −2 ,s = 0.8) along with an additional set found empirically as part of this study (k push = 0.03E h ,α = 0.7 Bohr −2 ,s = 0.6). For the bimolecular reactions, we change the procedure slightly. Initial runs showed greatly increased run times compared to single-fragment reactants using the original hyperparameters. To decrease run times we decrease s (which scales the size of the molecular cavity) by 0.02 every 5 ps as long as no reaction has occurred, thereby forcing the reactant molecules closer together.</ns0:p><ns0:p>We generate different random Cartesian coordinates ('embedding' in RDKit) for the reactants for each of the meta-MD runs based on their SMILES string. If the reactant consists of multiple molecules, they are first embedded randomly on top of each other using RDKit <ns0:ref type='bibr' target='#b19'>[20]</ns0:ref> with a subsequent force field optimization of each fragment. The second molecule is then moved in a random direction by a distance, d:</ns0:p><ns0:formula xml:id='formula_0'>d = 0.5 ⋅ (D max,1 + D max,2 ) + 2 Å (1)</ns0:formula><ns0:p>where D max,i is the maximum distance between any two atoms in molecule i. The coordinates of the reactants are energy minimised with GFN2-xTB before starting the meta-MD. If a change in atomic connectivity is detected the meta-MD simulation is skipped and the barrier estimation is done using the unoptimised reactant structure. Bond detection is done by xyz2mol based on the overlap density from an extended Hückel calculation, which must be greater than 0.2 for a bond. We perform 100 meta-MD simulations for each reactant unless otherwise noted. Our algorithm checks for changes in atomic connectivity every 5 ps and the simulation is stopped when this is detected. The output of the meta-MD simulations is then a list of all unimolecular reactions and a database of the reactant and product structures.</ns0:p><ns0:p>Barrier estimates and transition state (TS) guess structures are based on the RMSD-PP procedure by Grimme <ns0:ref type='bibr' target='#b15'>[16]</ns0:ref> and is described in detail in <ns0:ref type='bibr' target='#b20'>[21]</ns0:ref> and <ns0:ref type='bibr' target='#b10'>[11]</ns0:ref>. The RMSD-PP method locates TS guess structures by interpolating between reactants and products via biasing potentials and has roughly the same computational cost as a geometry optimisation. Instead of embedding the reactant and product structures from the SMILES saved during the product generation as done in <ns0:ref type='bibr' target='#b10'>[11]</ns0:ref>, we use the optimized structures from the meta-MD procedure as input to the RMSD-PP procedure. For the barrier estimate, the RMSD-PP is run five times, and the lowest barrier estimate is used, as described in <ns0:ref type='bibr' target='#b10'>[11]</ns0:ref>, except two of the five runs are done starting the procedure from product → reactant instead of reactant → product. If available, different conformers of the reactant and product are used in each of the five runs. If the reaction is found in five or more of the 100 meta-MD runs, the reactant/product structures are extracted from a different meta-MD simulation in each of the five RMSD-PP calculations. If the reaction is found four times during the meta-MD simulations, two of the RMSD-PP calculations use reactant/product structures from the same meta-MD simulation, while the remaining three RMSD-PP calculations use reactant/product structures from three different runs and so on for cases where the reaction is found three, two or one time during the 100 meta-MD runs. For the computation of reaction energies and barriers we need energies of the reactant and products. The lowest energy structure encountered for each molecule (each unique canonical SMILES) during the geometry optimizations that follow the meta-MD runs is used.</ns0:p><ns0:p>The last step of the procedure is the DFT-refinement (ωB97X-D/def2-TZVP) of the reactions found at the semiempirical level of theory that have reaction energies and barriers below 40 kcal/mol. Again, our validation procedure is as described in <ns0:ref type='bibr' target='#b20'>[21]</ns0:ref> and <ns0:ref type='bibr' target='#b10'>[11]</ns0:ref> except that we only test one of the five possible TS guess structures that can be extracted from the five RMSD-PP runs in order to reduce the computational cost of the DFT part of the procedure. The barriers and reaction energies at DFT level of theory are computed as stated in <ns0:ref type='bibr' target='#b18'>[19]</ns0:ref>, adding zero-point vibrational energies to the electronic energies of reactant, product and transition states (TSs) before calculating the barrier as the energy difference between TS and reactant and reaction energy as the energy difference between product and reactant.</ns0:p><ns0:p>All Density Functional Theory (DFT) calculations are performed using Gaussian 16 <ns0:ref type='bibr' target='#b21'>[22]</ns0:ref>. The meta-MD calculations and the RMSD-PP barrier estimates are performed with version 6.1.4 of the xtb program <ns0:ref type='bibr' target='#b15'>[16]</ns0:ref> using the GFN2-xTB method. <ns0:ref type='bibr' target='#b17'>[18]</ns0:ref> All structure-to-SMILES and structure-to-adjacency matrix (AC) (N atoms × N atoms dimensional matrix with elements either 1 or 0 depending on whether the atom-pair is bound or not) conversions are done using xyz2mol. 3 Results and discussion</ns0:p><ns0:p>3.1 Unimolecular reactions</ns0:p></ns0:div> <ns0:div><ns0:head n='3.1.1'>The low-barrier reaction dataset</ns0:head><ns0:p>The low-barrier dataset used in this study is extracted from the dataset created by Grambow et al. <ns0:ref type='bibr' target='#b18'>[19]</ns0:ref> The reactants in the Grambow dataset consists of all molecules in the GDB-7 dataset <ns0:ref type='bibr' target='#b23'>[24]</ns0:ref> with less than seven heavy atoms plus ca. 430 randomly selected molecules with seven heavy atoms. Reactions and the corresponding transition states (TSs) are located by performing several hundred single-ended growing string method (GSM <ns0:ref type='bibr' target='#b13'>[14]</ns0:ref>) searches from each reactant at the B97-D3/def2-mSVP level of theory, followed by TS refinement at the ωB97X-D3/def2-TZVP level of theory. The result is 16,365 and 11,961 unimolecular reaction barriers at the B97-D3/def2-mSVP and ωB97X-D3/def2-TZVP level of theory, respectively. The corresponding number of low-barrier reactions with barriers below 30 kcal/mol is 199 and 30, involving 163 (Figure <ns0:ref type='figure' target='#fig_0'>S1</ns0:ref>) and 27 different reactants, respectively. We would thus expect that applying our meta-MD search to these 163 reactants (Figure <ns0:ref type='figure' target='#fig_0'>S1</ns0:ref>) should identify these 30 reactions (Table <ns0:ref type='table'>S1</ns0:ref>) if we use ωB97X-D3/def2-TZVP for the DFT refinement.</ns0:p><ns0:p>Since the D3 dispersion correction is not available with the ωB97X functional in Gaussian16 we start by reoptimising the 30 TSs at the ωB97X-D/def2-TZVP level of theory and verifying them by performing intrinsic reaction coordinate (IRCs) calculations. For two reactions (R1108 and R7201, using the notation of Grambow et al.) the IRCs go to the correct reactant, but to a different stereoisomer. Since that barrier is below 30 kcal/mol we use the newly found structure to initiate the meta-MD instead of the one proposed by Grambow et al. For five reactions (R1084, R2399, R2523, R6490, and R18816), the IRC does not lead to the reactant proposed by Grambow et al. We subsequently find the TS for R2399 using our meta-MD approach, but we exclude R1084, R2523, R6490, and R18816 from our low-barrier dataset. Thus, we expect that applying our meta-MD search to the 163 reactants described above should identify 26 reactions with barriers below 30 kcal/mol at the ωB97X-D/def2-TZVP level of theory.</ns0:p></ns0:div> <ns0:div><ns0:head n='3.1.2'>Meta-MD based search for low-barrier reactions</ns0:head><ns0:p>Using the default hyperparameter set (k push = 0.05, α = 0.3 and s = 0.8) we find 20 of the 26 reactions.</ns0:p><ns0:p>For three of the reactions (R3725, R7207, and R8701) the meta-MD failed to generate the corresponding product structures. Another reaction (R2514) is eliminated due to having a GFN2-xTB reaction energy of 53 kcal/mol, which is significantly higher than the corresponding ωB97X-D3/def2-TZVP-value of -3 kcal/mol. The final two reactions (R4612 and R9011) are eliminated due to high RMSD-PP estimated barrier heights of 48 and 47 kcal/mol, respectively -considerably higher than the corresponding ωB97X-D3/def2-TZVPvalues of 26 and 30 kcal/mol. The actual barrier heights at the GFN2-xTB level are 42 and 45 kcal/mol, which indicates that the problem lies primarily with the GFN2-xTB method itself and not the barrierestimation method. If we perform additional meta-MD simulations with a slightly different hyperparameter set (k push =0.03, α = 0.7, s = 0.6) we locate R3725 and R7207, but R3725 is subsequently eliminated due to a high reaction energy (42 kcal/mol) compared to a DFT value of -21 kcal/mol. So, using two set of hyperparameters, the meta-MD simulations identify 25 of the 26 low-barrier reactions reactions found by The four false negatives that result from errors in the GFN2-xTB reaction energies and estimated barrier height (R2514, R3725, R4612, and R9011) all contain a N-N triple bond in the products, indicating a systematic error in the GFN2-xTB method. In the case of reaction energies such errors can be efficiently corrected by, for example, the connectivity-based hierarchy method. <ns0:ref type='bibr' target='#b24'>[25,</ns0:ref><ns0:ref type='bibr' target='#b25'>26]</ns0:ref>Another option is simply to use DFT instead of GFN2-xTB for the subsequent screening. The meta-MD approach produces between 1 and 51 reactions per reactant (Figure <ns0:ref type='figure' target='#fig_2'>3</ns0:ref>) which is practical to check with DFT, and certainly with DFT//GFN2-xTB single point calculations. In our current approach 1257 and 2392 reactions are eliminated based on semiempirical reaction energies and estimated barriers, respectively, so that only 316 reactions are checked with DFT out of a total of 3965 candidate reactions.</ns0:p><ns0:p>Our meta-MD based approach also identifies 10 low-barrier reactions, shown in Figure <ns0:ref type='figure'>4</ns0:ref>, not found by Grambow et al., as well as 26 new reactions with barriers above 30 kcal/mol (Table <ns0:ref type='table'>S2 and S3</ns0:ref>). All lowbarrier reactions involve new reactants not represented in the low-barrier dataset, which indicates that the reactions in that dataset are likely the ones with the lowest possible barriers for each reactant. Eleven of the new high-barrier reactions (Table <ns0:ref type='table'>S2</ns0:ref>) have barriers that are lower than the lowest barrier found by Grambow et al. for those reactants. Fourteen of the 36 new reactions (N6, N11, N13, N14, N16, N17, N18, N22, N23, N24, N25, N29, N35, and N36) are found by Grambow et al. at the B97-D3/def2-mSVP level of theory but these TS structures are apparently not of sufficent quality for the ωB97X-D3/def2-TZVP refinement. For the remaining reactions, it is not clear whether the new reactions are not found by Grambow et al. due to the growing string method (GSM) itself or the more approximate level of theory used for the GSM calculations. One reaction (N32 in Table <ns0:ref type='table'>S3</ns0:ref>) corresponds to a change in chirality due to a hydrogen transfer and such reactions do not appear to have been reported by Grambow et al., so it is possible that they found it but did not report it.</ns0:p><ns0:p>Two of the new low-barrier reactions (N4 and N7) proceed without a barrier at the GFN2-xTB level of theory, while the DFT barriers are 21 and 12 kcal/mol, respectively and both reactions are endothermic at the DFT level of theory. Both reactions involve a N-N triple bond, though that is also the case for several other new low barrier reactions that were identified successfully.</ns0:p></ns0:div> <ns0:div><ns0:head n='3.2'>Bimolecular reactions</ns0:head><ns0:p>Grambow et al. <ns0:ref type='bibr' target='#b18'>[19]</ns0:ref> only searched for elementary reactions starting from single reactant molecules, but they found a lot of products with two fragments. From these back-reactions, we extract a set of 20 target reactions with barriers below 30 kcal/mol, where two molecules react to create a single molecule (Table <ns0:ref type='table'>S4</ns0:ref>).</ns0:p><ns0:p>The barriers range between 8 and 28 kcal/mol and reaction energies range between -58 and 8 kcal/mol. Unlike for the unimolecular reactants above, these bimolecular reactants were not subjected to a thorough reaction discovery search so it is more likely that will be additional reactions with barriers below 30 kcal/mol. As with the unimolecular reactions above, we re-optimize the TS structures provided by Grambow et al. at the ωB97X-D/def2-TZVP level of theory and confirm that they connect the stated reactant and product with an IRC.</ns0:p><ns0:p>Again, we first run 100 meta-MD simulations for each of the 20 reactant pairs with the primary hyperparameter set (k push =0.05, α = 0.3, s = 0.8), which results in a total of 586 elementary reactions (average of 29.3 per reactant, Figure <ns0:ref type='figure' target='#fig_2'>S3</ns0:ref>). Among these reactions are 16 of the 20 target reactions but R129, R2191, R10077 and R7854 (Table <ns0:ref type='table'>S4</ns0:ref>) are not found. The lower success rate compared to unimolecular reactions (16/20 vs 23/26 for this hyperparameter set) is likely due to the increased number of reactions per reactants (29.3 vs 17.4 reactions per reactant on average). For a reactant system with only a single viable path (low-barrier single-step product) we expect a high probability that at least one of the 100 meta-MD simulations will go to that product. However, for a reactant system with, say, 29 low-barrier single-step reactions there is a good chance that at least one of the 29 reactions will not be found by meta-MD in any of the 100 simulations. Assuming all 29 reactions are found with equal likelihood there is only a 40% chance that 100 meta-MD runs will find all 29 reactions, compared to a 96% chance for 17 low-barrier reactions. To account for this, we try to 'encourage' the reactants to go to previously undiscovered products, by including products found by other MD simulations when computing the biasing potential. After filtering the 586 reactions found in the first set of runs with RMSD-PP estimated barriers, 318 reactions are left for DFT refinement. For the second set of runs (initiated with products from the first set of runs), after filtering based on both RMSD-PP barrier estimates and duplicates of reactions from the first run, 174 reactions are left for DFT refinement. We find one additional target reaction in this way: R129. Two (R7854 and R10077) of the remaining three target reactions can be found with meta-MD by changing the hyperparameter set to our secondary choice (k push = 0.03, α = 0.7 and s = 0.6).</ns0:p><ns0:p>With the three different kind of runs tested here we find 19 of the 20 target reactions shown in Table <ns0:ref type='table'>S4</ns0:ref>. The one reaction not found by meta-MD (R2191) is shown in Figure <ns0:ref type='figure'>5(a)</ns0:ref>. Though R10077 (Figure <ns0:ref type='figure'>5(b)</ns0:ref>) was found by meta-MD with our secondary hyperparameter set, it was predicted to have a too high barrier (≈ 60 kcal/mol) using the GFN2-xTB RMSD-PP estimate. We note that these two target reactions (Figure <ns0:ref type='figure'>5</ns0:ref>) both involve oxiranimines and have similar mechanisms. Unlike the reactions causing problems in the search from single molecule reactants above, these two reactions have low reaction energies at the GFN2-xTB level of theory (-37 kcal/mol for R2191 and -29 kcal/mol for R10077).</ns0:p><ns0:p>The DFT refinement step localizes the 18 target reactions remaining at this point as well as 34 new reactions with barriers below 30 kcal/mol. Twenty-five of the new reactions are located from the reactions found Target reaction R10077 which is found with meta-MD using the secondary hyperparameter set, but where the GFN2-xTB barrier estimate from RMSD-PP is too high (60 kcal/mol). The reaction energy is ∆E = −39 kcal/mol and the barrier is ∆E † = 10 kcal/mol calculated with ωB97X-D3/def2-TZVP <ns0:ref type='bibr' target='#b18'>[19]</ns0:ref> using our default hyperparameter set while the remaining nine reactions are found by penalising products found by the first meta-MD runs. The 34 new reactions are spread across 12 of the 20 reactants. Figure <ns0:ref type='figure' target='#fig_5'>6</ns0:ref> shows the lowest-barrier reaction for each of these 12 reactants and the remaining 22 new low-barrier reactions can be found in Figure <ns0:ref type='figure' target='#fig_1'>S2</ns0:ref>. The new reactions represent a range of reaction energies between -47 and 20 kcal/mol and barriers ranging from 0.5 to 29 kcal/mol. We find both reactions where the two reactant molecules react with each other and unimolecular reactions where one of the reactant molecules goes through an isomerization reaction.</ns0:p><ns0:formula xml:id='formula_1'>N4 21 ΔE † kcal/mol = 16 ΔE kcal/mol = N H O N O O N H O N reactant 88 N5 23 ΔE † kcal/mol = 19 ΔE kcal/mol - = O N H H N C O reactant 116 N6 28 ΔE † kcal/mol = 14 ΔE kcal/mol - = O N N N N O reactant 158 N8 7 ΔE † kcal/mol = 4 ΔE kcal/mol - = O N N NH 2 N N H 2 N O reactant 159 N9 6 ΔE † kcal/mol = 3 ΔE kcal/mol - = N N N N O N 2 + NN O reactant 162 N10 5 ΔE † kcal/mol = 71 ΔE kcal/mol - = 12 ΔE † kcal/mol = 8 ΔE kcal/mol = N7 N O N N N N O N reactant 137</ns0:formula></ns0:div> <ns0:div><ns0:head n='3.3'>Application to organic chemistry</ns0:head><ns0:p>The reactions tested thus far involve relatively small molecules, often with functional groups not usually seen in organic chemistry. We thus test our methodology on two reactions, one unimolecular and one bimolecular, that are more representative of those encountered in synthetic organic chemistry. Our goal here is simply to check whether the correct products can be found with the current hyperparameters rather than an exhaustive computational study of the reaction mechanisms.</ns0:p></ns0:div> <ns0:div><ns0:head n='3.3.1'>A unimolecular reaction</ns0:head><ns0:p>Inspired by Lavigne et al. <ns0:ref type='bibr' target='#b8'>[9]</ns0:ref> we study an important step of the synthesis of Berkeleyone A reported by Elkin et al. (Figure <ns0:ref type='figure' target='#fig_6'>7</ns0:ref>). <ns0:ref type='bibr' target='#b26'>[27]</ns0:ref> We choose the same protonated epoxide reactant structure as Lavigne et al. for the starting point of our analysis (Figure <ns0:ref type='figure' target='#fig_6'>7</ns0:ref>). In practice several protonation sites must be investigated but this process can easily be automated. Figure <ns0:ref type='figure' target='#fig_7'>8</ns0:ref> highlights some pathways found using meta-MD for product generation and RMSD-PP for barrier estimates at the GFN2-xTB level. As our goal with this example is to check the ability of meta-MD + RMSD-PP to give insight into more complicated multi-step reactions compared to the elementary reactions studied until now we skip the DFT validation step and report the GFN2-xTB energies and barrier estimates. Thus, the energetics presented in Figure <ns0:ref type='figure' target='#fig_7'>8</ns0:ref> are not expected to be quantitatively accurate. Doing meta-MD + RMSD-PP starting from the reactant structure (R) produces reactions involving ringopening of the protonated epoxide to produce both secondary and tertiary carbocations, proton transfer from the epoxide to the carbonyl oxygen of the ester group, as well as tautomerization reactions. Instead of restarting the procedure from every one of the produced intermediates, we choose to follow the path involving the tertiary carbocation I1 further. Continuing this process we create reaction profiles as presented in Figure <ns0:ref type='figure' target='#fig_7'>8</ns0:ref>. We locate a possible mechanism for the path to the experimentally observed product (P) through the five steps connected in black. We also show a pathway to one of the other (less stable) stereoisomers of the product (Pa, blue) as well as the path to the most stable product encountered (Pb, purple). We note that both the intermediate I2 and Pb is also found in the study conducted by Lavigne et al. We also find paths to macrocycle structures (from I1) which is also found by them. The most stable intermediate (I2), a bicyclic ether, is a known byproduct when doing this type of epoxide-initiated cyclizations. <ns0:ref type='bibr' target='#b27'>[28,</ns0:ref><ns0:ref type='bibr' target='#b28'>29]</ns0:ref> The competition between carbon cyclisation (P) and oxygen cyclisation (Pb) is also a known problem for these reactions. <ns0:ref type='bibr' target='#b27'>[28,</ns0:ref><ns0:ref type='bibr' target='#b28'>29]</ns0:ref> </ns0:p></ns0:div> <ns0:div><ns0:head n='3.3.2'>A bimolecular reaction example</ns0:head><ns0:p>Next we study the acid catalyzed synthesis of a benzimidazole derivative starting from ortho-phenylenediamine and benzoic acid (Phillips method). <ns0:ref type='bibr' target='#b29'>[30]</ns0:ref> The reaction is acid catalyzed so at each minimum along the path we try the different possible protonated structures and optimize with GFN2-xTB. Structures with energies < 30 kcal/mol relative to the lowest energy protonation structure are considered relevant for the analysis. The result of the search is summarized in Figure <ns0:ref type='figure' target='#fig_9'>9</ns0:ref>. For the reactant system, two protonation structures are possible: protonation at the nitrogen or at the carbonyl oxygen (which is 17 kcal/mol higher than at nitrogen). We follow the path from the carbonyl protonated structure and find step 1 by meta-MD (Figure <ns0:ref type='figure' target='#fig_9'>9</ns0:ref>, step 1) where the lone pair of one of the nitrogen atoms is used to attack the carbonyl carbon. The next step is elimination of water which can be found in two ways: either by manually transferring the proton to one of the hydroxyl groups, resulting in water eliminated upon energy minimization, or by adding a water molecule that can aid in the proton-transfer during the meta-MD simulation (Figure <ns0:ref type='figure' target='#fig_9'>9</ns0:ref>, step 2). Step 3 is a ring closure initiated by attack of the other nitrogen atom. The second water elimination can again be found by manually moving the proton from the ammonium to the hydroxyl group (step 4) and subsequent geometry optimization which eliminates the water molecule (step 5) creating the product 2-phenyl benzamidazole. Contrary to the first water elimination, this second water elimination was only found by manually moving the proton and not by the meta-MD runs with an additional water molecule (the backward reaction is the preferred path). The manual proton transfers can easily be automated.</ns0:p></ns0:div> <ns0:div><ns0:head n='3.4'>Timings</ns0:head><ns0:p>The CPU-time requirements for the semiempirical calculations are relatively modest compared to the DFT refinement calculations. A single meta-MD simulation requires on average 5.4 minutes on a single core of a Intel Xeon E5-2643 v3 (3.4 GHz) for the reactants of the low-barrier reaction dataset. Larger molecules such as the Berkeleyone A precursor (Figure <ns0:ref type='figure' target='#fig_6'>7</ns0:ref>) require about 25 minutes, but the precise value depends greatly on how fast the reaction occurs; for the steps presented in Figure <ns0:ref type='figure' target='#fig_7'>8</ns0:ref> the average run time for a meta-MD simulation was in the range 17-35 minutes. A single semiempirical barrier estimate typically takes about 14 seconds on the same type of core (13 minutes for the Berkeleyone A precursor) and we usually run five of these in parallel with different settings. For comparison, a typical ωB97X-D/def2-TZVP TS search takes about 6.5 hours on 2 cores. We test our meta-MD based approach for finding low-barrier (<30 kcal/mol) reactions for uni-and bimolecular reactions extracted from the barrier dataset developed by Grambow et al. <ns0:ref type='bibr' target='#b18'>[19]</ns0:ref> Based on this dataset it should be possible to locate 26 low-barrier unimolecular reactions at the ωB97X-D/def2-TZVP level of theory starting from 163 reactants. Our method uses Grimme's meta-MD approach, with carefully chosen hyperparameters, to identify possible products, which are subsequently screened using semiempirical reaction energies and barrier heights before being refined with DFT (Figure <ns0:ref type='figure' target='#fig_0'>1</ns0:ref>). The meta-MD simulations identify 25 of the 26 products found by Grambow et al., while the subsequent semiempirical screening eliminates an additional four reactions due to an overestimation of the reaction energies or estimated barrier heights relative to DFT, suggesting that DFT may thus be needed in the screening process. In addition, our approach identifies an additional 36 reactions not found by Grambow et al., 10 of which have barriers <30 kcal/mol. All low-barrier reactions involve new reactants not represented in the low-barrier dataset, which indicates that the reactions in that dataset are likely the ones with the lowest possible barriers for each reactant.</ns0:p><ns0:p>Grambow et al. <ns0:ref type='bibr' target='#b18'>[19]</ns0:ref> only searched for elementary reactions starting from single reactant molecules, but they found a lot of products with two fragments. From these back-reactions, we extract a set of 20 target low-barrier reactions where two molecules react to create a single molecule (Table <ns0:ref type='table'>S4</ns0:ref>). While these reactions are not necessarily the ones with the lowest barrier for a given pair of reactant molecules, our method should be able to identify them along with any reactions with lower barriers. The meta-MD simulations identify 19 of the 20 products found by Grambow et al., while the subsequent semiempirical screening eliminates an additional reaction due to an overestimation of the barrier height relative to DFT. In addition, we find 34 new low-barrier reactions. We found that it is necessary to 'encourage' the reactants to go to previously undiscovered products, by including products found by other MD simulations when computing the biasing potential as well as decreasing the size of the molecular cavity in which the MD occurs, until a reaction is observed.</ns0:p><ns0:p>The reactions in the Grambow et al. data set involve relatively small molecules, often with functional groups not usually seen in organic chemistry. We thus test our methodology on two reactions, one unimolecular and one bimolecular, that are more representative of those encountered in synthetic organic chemistry, with the goal to simply check whether the correct products can be found with the current hyperparameters. The unimolecular reaction is a multi-step triple ring-closure (Figure <ns0:ref type='figure' target='#fig_6'>7</ns0:ref>)-an important step of the synthesis of Berkeleyone A reported by Elkin et al. <ns0:ref type='bibr' target='#b26'>[27]</ns0:ref> We locate a possible mechanism for the path to the observed product through the five steps, together with other known biproducts. The bimolecular reaction is the acid catalyzed syntheses of benzimidazole derivatives starting from ortho-phenylenediamine and benzoic acid (Phillips method) <ns0:ref type='bibr' target='#b29'>[30]</ns0:ref>, where we find all five steps in the generally accepted reaction mechanism. The meta-MD hyperparameters used in this study thus appear to be generally applicable to finding low-barrier reactions. </ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head>Figure 1 :</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure1: Schematics of the method used to predict low-barrier reactions employed in this study. A cutoff value of 40 kcal/mol is used to screen for low-barrier reactions (circles below the dotted line). Abbreviations are meta-MD: meta-molecular dynamics, ∆E rxn : electronic reaction energy, RMSD-PP: root-mean-square deviation-push/pull, DFT: density functional theory, TS: transition state, IRC: intrinsic reaction coordinate.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head>Figure 2 :</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2: The reaction not found by either meta-MD hyperparameter sets. The reaction energy is ∆E = 19 kcal/mol and the barrier is ∆E † = 27 kcal/mol calculated with ωB97X-D3/def2-TZVP [19]</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>Figure 3 :</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Figure 3: Distribution of the number of different reactions found during the 100 meta-MD runs (with default hyperparameter set) for the 163 single-fragment reactants shown in Figures S1. The average is 17.4 reactions/reactant.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head>Figure 4 :Figure 5 :</ns0:head><ns0:label>45</ns0:label><ns0:figDesc>Figure 4: 10 new reactions found with barriers below 30 kcal/mol. The stated barriers (∆E † ) and reaction energies (∆E) are computed at the ωB97X-D/def2-TZVP level of theory.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head>Figure 6 :</ns0:head><ns0:label>6</ns0:label><ns0:figDesc>Figure 6: The lowest-barrier reaction per reactant for the 12 reactants, where new reactions below 30 kcal/mol were found.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head>Figure 7 :</ns0:head><ns0:label>7</ns0:label><ns0:figDesc>Figure 7: Step in the synthesis of Berkeleyone A<ns0:ref type='bibr' target='#b26'>[27]</ns0:ref>. This reaction was previously studied with imposed activation by Lavigne et al.<ns0:ref type='bibr' target='#b8'>[9]</ns0:ref> </ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_7'><ns0:head>Figure 8 :</ns0:head><ns0:label>8</ns0:label><ns0:figDesc>Figure 8: Some highlighted reaction paths found at GFN2-xTB level of theory. Energies are relative to the reactant (R)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_8'><ns0:head>ΔE</ns0:head><ns0:label /><ns0:figDesc>rxn = -8 kcal/mol ΔE † = 12 kcal/mol ΔE rxn = -6 kcal/mol ΔE † = 28 kcal/mol step 2 step 3 ΔE rxn = 5 kcal/mol ΔE † = 33 kcal/mol ΔE rxn = -25 kcal/mol for step 4+5</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_9'><ns0:head>Figure 9 :</ns0:head><ns0:label>9</ns0:label><ns0:figDesc>Figure9: Summary of the reaction path found using meta-MD to follow the acid-catalyzed synthesis from ortho-phenylenediamine and benzoic acid to 2-phenyl benzamidazole. Reaction energies and approximate barriers at GFN2-xTB level of theory.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_10'><ns0:head>12 PeerJ</ns0:head><ns0:label>12</ns0:label><ns0:figDesc>Phy. Chem. reviewing PDF \| (PCHEM-2022:01:69535:1:0:NEW 11 Feb 2022)Manuscript to be reviewedChemistry JournalsAnalytical, Inorganic, Organic, Physical, Materials Science</ns0:figDesc></ns0:figure> <ns0:note place='foot'>PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2022:01:69535:1:0:NEW 11 Feb 2022)Manuscript to be reviewedChemistry JournalsAnalytical, Inorganic, Organic, Physical, Materials Science</ns0:note> <ns0:note place='foot'>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:note> </ns0:body> "	"Reviewer 2 Basic reporting: 1. A mixture of “parameters” and “hyperparameters” is used throughout. Please be consistent with this terminology. Fixed 2. On line 13, the phrase “10 of which are <30 kcal/mol” is ambiguous; is it that 10 of the barrier heights of the new reactions are < 30 kcal/mol? Yes. We feel the current formulation is pretty clear. 3. On line 26, please include refs 12–16 with refs 1–11 if these also fall under the category of “computational methods for exploring reaction space in an automated manner”. It is not clear why refs 12–16 are not included in this first list, but are included in the categories in the subsequent sentences. On line 47, it would be helpful to mention that the data of Grambow et al. includes molecules containing only H, C, N and O. Fixed 4. On line 42, please define TS and RMSD-PP on the first occurrence. Also on line 61, the (hyper)parameters kpush, alpha and s should be defined on the first occurrence, and the equation relating their usage to the RMSD-PP method should be included, as the authors have done in reference 11. On line 63, please include units for kpush and alpha. Following the suggestions of reviewer 3, we have defined the abbreviations in Figure 1. We now refer the reader to reference 11 for a more detailed description of the parameters, and we have now included units for kpush and alpha. 5. On lines 53–55 the list in the text uses Arabic numerals, whereas the Figure 1 uses letters – please use one or the other. Fixed 6. The language is too colloquial in two places: on line 64, please rephrase “we change the procedure a bit”, and on line 181, please rephrase “other new low barrier reaction that worked fine”. We have rephrased the two sentences 7. On lines 69–70, in the RDKit embedding procedure, is connectivity already defined prior to the meta-MD run using a SMILES string input? In other words, does this procedure generate conformers, rather than constitutional isomers? Please clarify this in the text. We have now clarified that the embedding is done based on a SMILES string. 8. On lines 76–78, please include the criteria for the changes in connectivity. According to the ‘check_activity_of_bonds’ function in rmsd_pp_no_slurm.py, this appears to be done using equation 2 in reference 20. It would helpful to the reader to have this defined in this manuscript too, or at least referenced. We have added this sentence to clarify: “Bond detection is done by xyz2mol based on the overlap density from an extended Huckel calculation, which must be greater than 0.2 for a bond.” 9. On lines 159–160, please quote the average number of reactions per reactant (17.3) in the text, and/or in Figure 3, for use when referencing this value in the ‘Bimolecular’ section. Similarly, to aid the comparison with the number of unimolecular reactions per reactant, could the authors include a plot of the equivalent bimolecular data, as has been done for unimolecular reactions in Figure 3 (# of reactions vs count)? We have added the average number of reactions per reactant to Figure 3 and we have added a similar plot for bimolecular reactions in SI. 10. On lines 241 and 323, “berkeleyone” is misspelled as “berkelyone”. Fixed 11. In Figure 9, could the authors include activation and reaction energies for the processes described in Figure 9? For example, in line 269 a 17 kcal/mol penalty is mentioned for protonating the acid instead of the diamine, but this penalty must presumably be compensated by the lower activation energy resulting from acid protonation – however this is not clear from the figure. Done 12. On line 175, the authors refer specifically to reaction N32 in Table S3, however the caption to Table S3 is ambiguous: “Note that Grambow et al reported a reaction to another diastereomer of the product. We include it here because we saw a significantly lower barrier than the reported (55 kcal/mol)”. Please explicitly mention reaction N32 in the caption to Table S3 so that it can be understood without needing to cross-reference the main text. Fixed 13. In the SI tables, please include table headings at the top of each column. For example, in each of the tables it is not immediately clear that the numbers in the second column are referring to the numbering in Figure S1. Done Experimental design: 1. On line 11, the knowledge gap that this article fills would become clearer if it were mentioned that Grambow et al. employed a growing string method to identify reaction paths, to contrast with the meta-MD approach used in this work. Done 2. On line 38, why was 30 kcal/mol chosen as the cut-off for “relevant” activation energies at room temperature? This barrier would suggest a half-life for a unimolecular reaction of over 30 years at room temperature, whereas, for example, a 25 kcal/mol activation barrier corresponds to a half-life of ~2.7 days, which is perhaps more chemically “relevant”. Was 30 kcal/mol chosen to allow for errors in the electronic structure method, so that valid reactions are not omitted? Similarly, why was a 40 kcal/mol screening cut-off chosen (line 98)? The data presented here appear to show that GFN2-xTB may significantly overestimate some reaction barriers – by ~15 kcal/mol according to line 144 – compared with DFT. Could a cut-off of 50 kcal/mol be used as a screening cut-off to minimise this issue? We have now clarified these points: “At room temperature, this typically means all reactions with barriers less than ca 30 kcal/mol given the typical accuracy of quantum chemical calculations” “In this study we use a cutoff of 40 kcal/mol when screening reaction energies and barrier estimates as a compromise between the accuracy of the energies and the number of reactions to be checked.” 3. In Equation 1, why was 2 Å chosen as the empirical displacement parameter? Would this value still be suitable for molecules containing heavier (i.e. larger) atoms than those in the dataset used here (H, C, N, O only)? Could this parameter be replaced by an additional variable that depends on the van der Waals radii of the nearest overlapping atoms? The results are unlikely to be sensitive to the exact choice if this parameter since the molecules are subsequently energy minimised. 4. On lines 65–67 (Methods section), for bimolecular reactions s is said to be decreased “by 0.02 every 5 ps as long as no reaction has occurred”, however this approach is not discussed in the Bimolecular section of the Results and Discussion. Could the authors expand on this point, to clarify at which stage this approach was employed? We feel it is clear that this refers to the meta-MD simulations. 5. On line 97, were DFT calculations run using a spin-unrestricted ansatz, as in the work of Grambow et al. (reference 18)? Yes 6. On line 108, were all GNF2-xTB calculations run in the gas phase? Was there a reason an implicit solvent model was not employed for the cationic reactions, which are likely to be sensitive to solvent effects? As we state in the manuscript: “Our goal here is simply to check whether the correct products can be found with the current parameters rather than an exhaustive computational study of the reaction mechanisms.” 7. On lines 172–174, could the use of the GGA functional B97-D3, rather than the more accurate hybrid GGA functional ωB97X-D3, also be a cause of error, aside from error resulting from the GSM method or the small basis set already mentioned? We have changed “small basis set” to “more approximate level of theory” 8. On line 187, given the approximately 10 kcal/mol entropic penalty accompanying bimolecular reactions (J. Chem. Educ. 2002, 79, 1269), would it be better to choose a smaller electronic activation energy, e.g. ~20-25 kcal/mol, to compensate for the entropic effect? This would ensure the reactions are still “relevant” at room temperature (i.e. do not have barriers significantly above 30 kcal/mol). Not a bad idea. We may look into this in future studies 9. Lavigne et al (reference 9) also use RMSD-PP to generate reactive conformers, although uses imposed activation to bias the system into cleaving the oxirane C–O bond. Could the authors comment on the merits of the meta-MD approach employed here in comparison with the method of Lavigne et al.? As we mention in the Introduction, Lavigne et al. is a hybrid between a (semi-)exhaustive search and meta-MD method, whereas our approach is a “pure” meta-MD method. 10. In Figure 8, could the authors comment on the TS linking I2a to Pa (blue path)? From their curly arrow depiction, it would appear that the process linking I2a to Pa (blue path) is concerted. Conversely, the process from I3 to P (black path) is stepwise, going via I4. The only difference between these two processes would appear to be the relative stereochemistry of the cyclohexane alcohol / alkyl substituents. Is this enough to cause such a drastic change in mechanism? Yes Does the mechanism found here agree with what is proposed in the literature, for example reference 9? We have clarified that the P is the experimentally observed product. Validity of the findings: 1. On line 295, as in the abstract, it would be instructive to mention that reactions were found by Grambow et al. using the growing string method. It is not otherwise clear what comparison is being made with the meta-MD approach. Done 2. Please expand the readme on the GitHub with details on the usage of each Python script used to generate and analyse the data. The “Usage Notes” section in reference 18 provides a useful template that could be used. It would also be helpful to include a readme for the data included with the link, perhaps at the end of the manuscript. Done Reviewer 3 Basic reporting The manuscript is well written and enjoyable to follow. However, some aspects could be improved, making the article accessible to a broader scientific audience: 1) Figure 1 would benefit from including the explanation of abbreviations, and the meaning of colors used for the products (not all cultural backgrounds see red as unfavorable and green as positive). Also, I would suggest not using a combination of red and green concerning the colorblind readers. We have conferred with a color blind colleague who has no problems distinguishing the colors. Without specific suggestions for colors from the reviewer, it’s hard to know what to change the colors to. 2) Since the estimates with RMSD-PP method are critical in the screening stage of the workflow, it would be helpful to have a short, 1-2 sentences, explanation of its principle with its first mention. We have added the following sentence The RMSD-PP method locates TS guess structures by interpolating between reactants and products via biasing potentials and has roughly the same computational cost as a geometry optimisation. 3) On lines 142-143, adding info on the fact that the estimates come from RMSD-PP would make the distinctions between the estimates and the actual semi-empirical barriers easier. Done 4) Figure 9 would report the reaction path in a more comprehensive way when supplemented by data on reaction energies as well as barriers. Done 5) Figure S2 is missing the caption. Done Experimental design The dataset used is rather extensive, given the methodology used, and the extension towards also evaluating bimolecular reactions with meta-MD is valuable. Although, the description of the experimental procedures could be enhanced by providing more details on the following two issues: 1) on line 63, the authors provide an additional set of hyper-parameters used for meat-MD, which were found as in this study. However, there is no data on how were these parameters found, what other sets were used etc. We have clarified that these parameters were found empirically 2) on line 77, the algorithm checks for changes in atomic connectivity every 5 ps. How is this performed? Do such checks rely on semi-empirical energy minimization as described on line 74? We have clarified this in response to reviewer 1. Validity of the findings I wonder if the authors have tried more meta-MD runs, in particular, for bimolecular reactions where the odds of enumerating all expected low-barrier reactions are relatively low. Given the computational costs, it should be feasible to markedly increase the number of runs at least for some of the problematic cases. We may look at this in future studies. Reviewer 4 Mostly, the literature references also appear to be appropriate. Yet, in the beginning, a number of references is given in the context of “computational methods for exploring reaction space in an automated manner”. I suggest to add the paper from the Martínez group (https://doi.org/10.1038/nchem.2099) that also falls into this category. I think that it is worth noting that their “piston-like” pushing of molecules is a harsher predecessor to the here suggested cavity reduction. We have added a reference to this paper Minor issues: line 313 and abstract: “at an” -> “an” line 87 'is' -> 'are' line 178 'GFN-xTB' -> 'GFN2-xTB' Fixed Experimental design The used methodology is described quite well in chapter 2. Due to the additional biases applied, the comp. cost is increased roughly by a factor of 3 compared to a minima search (cf. line 215)? We use the secondary parameter set only for those molecules that are unreactive with the primary parameter set, so the increased cost is less than a factor of 3. line 99-101: About testing only 5 structures. Is there a fall-back option, if more than just these 5 structures are found within a certain energy window? We believe the reviewer misunderstands this point. The 5 structures are TS structure guesses for the same reaction. line 126: To make the comparison with the previous work of Grambow better possible, the authors might consider to compute wB97X-D3 single-point calculations (for example, using ORCA) on the wB97X-D geometries. The effect of using the D-dispersion correction on the reaction energetics is negligible. line 284: an average atom number for the investigated systems should be given. The structure of all molecules are given so the number of atoms is readily apparent to the reader Validity of the findings The data has been provided in an external repository, which is very good. I personally would favor an (additional) upload together with the paper SI to have all the data available in the same place. The large amount of data in the external repository makes this impossible. line 280: It is stated that the manual proton transfers could easily be automated. But the authors should comment on this - would an automatic procedure (exploring all paths) not grow dramatically in comp. Cost? It only grows linearly with the number of protonation sites. Additional comments About line 126: A warning: wB97X, wB97X-D, wB97X-D3, and wB97X-V all have different XC functional parameters! So, wB97X-D3 is not simply the D3-corrected version of wB97X. Noted "	Here is a paper. Please give your review comments after reading it.
486	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>The liquid-vacuum interface of molten alkali carbonate salts is studied with molecular dynamics simulations. Three salts comprised of Li x Na y K z CO 3 near their respective eutectic concentrations are considered to understand the distribution of ions relative to a liquidvacuum interface and their diffusivity. These simulations show that each of the cations accumulate at the interface preferentially compared to carbonate. The cation ordering is found to inversely correspond to cation radius, with K being the most likely occupant at the surface, followed by Na, Li, and then the anion. Similar to other studies, the carbonate is found to diffuse more slowly than the cations, but we do observe small differences in diffusion between compositions that present opportunities to optimize ion transport. These results hold consequences for our understanding of ion behavior in molten carbonate salts and the performance of devices employ these electrolytes.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>Molten carbonate salts have been studied in many contexts because of their occurrence in natural environments, in many engineered materials, and as components in various devices <ns0:ref type='bibr' target='#b12'>(Gaune-Escard & Haarberg, 2014)</ns0:ref>. Alkali carbonate melts are specifically attractive for use in many applications because they have low vapor pressures, are easy to contain, and are generally environmentally safe <ns0:ref type='bibr' target='#b27'>(Maru, 1984;</ns0:ref><ns0:ref type='bibr' target='#b12'>Gaune-Escard & Haarberg, 2014)</ns0:ref>. A significant amount of interest has been motivated by electrochemical devices and chemical separation technologies, which can be designed to have high efficiency, resilience from fouling, and low material costs <ns0:ref type='bibr' target='#b27'>(Maru, 1984;</ns0:ref><ns0:ref type='bibr' target='#b8'>Dicks, 2004;</ns0:ref><ns0:ref type='bibr' target='#b22'>Kirubakaran, Jain & Nema, 2009;</ns0:ref><ns0:ref type='bibr' target='#b38'>Wade et al., 2011;</ns0:ref><ns0:ref type='bibr' target='#b31'>Roest et al., 2017)</ns0:ref>. The performance of such devices have been studied in addition to the bulk behavior of individual components of relevant systems, but the thermodynamics and dynamics of alkali carbonate electrolytes near the relevant interfaces are not as well understood. The behavior of interfaces generally, and ions at liquid-vacuum interfaces, have received significant attention as regions featuring interesting manifestations of physical principles and as domains where distinct chemistries can occur <ns0:ref type='bibr' target='#b0'>(Allara, 2005;</ns0:ref><ns0:ref type='bibr' target='#b26'>Kumar, Knight & Voth, 2013;</ns0:ref><ns0:ref type='bibr' target='#b32'>Soniat, Kumar & Rick, 2015;</ns0:ref><ns0:ref type='bibr' target='#b35'>Tse et al., 2015;</ns0:ref><ns0:ref type='bibr' target='#b1'>Bastos-González et al., 2016;</ns0:ref><ns0:ref type='bibr' target='#b15'>Gutiérrez et al., 2018)</ns0:ref>. The purpose of this work is to apply similar methods to characterize the liquid-vacuum interface of three molten alkali carbonate electrolytes.</ns0:p><ns0:p>These electrolytes present many aspects that are of a general interest to chemists in many subdisciplines. Their performance and feasibility for use in molten carbonate fuel cells is of particular relevance because the operation of such devices relies on both the structure and transport of molecules at interfaces and the bulk within the device. In a typical molten carbonate fuel cell oxygen and carbon dioxide gases are fed to the cathode where O 2 is reduced and carbonate is formed. The carbonate ion is then transported through the electrolyte to the anode where carbonate reacts with hydrogen gas. The hydrogen is reduced, carbon dioxide is reformed, and water is produced. Therefore, the performance of these fuel cells depend on the efficient uptake of feed gases at an interface, transport of carbonate through the bulk electrolyte, and release of product gases at an interface. It is therefore important to characterize the bulk and interfacial behavior of these electrolytes.</ns0:p><ns0:p>Numerous computational studies have considered the bulk behavior of ion transport in these electrolytes <ns0:ref type='bibr' target='#b17'>(Habasaki, 1990;</ns0:ref><ns0:ref type='bibr' target='#b23'>Koishi et al., 2000;</ns0:ref><ns0:ref type='bibr' target='#b6'>Costa, 2008;</ns0:ref><ns0:ref type='bibr' target='#b37'>Vuilleumier et al., 2014;</ns0:ref><ns0:ref type='bibr' target='#b28'>Ottochian et al., 2016;</ns0:ref><ns0:ref type='bibr' target='#b5'>Corradini, Coudert & Vuilleumier, 2016)</ns0:ref>. Habasaki studied carbonate salts with Li or Na cations and found that the anion is significantly more mobile with the smaller cation, which they found is related to the ionic radii <ns0:ref type='bibr' target='#b17'>(Habasaki, 1990)</ns0:ref>. Koishi and coworkers looked at carbonate salts with Li and K cations and found that carbonate diffusion is maximized when Li proportion is highest <ns0:ref type='bibr' target='#b23'>(Koishi et al., 2000)</ns0:ref>. Costa and Ribeiro also found that carbonate diffuses fastest when there is more Li than K, but their trend is less clear, which they attribute to a small box size and higher system density <ns0:ref type='bibr' target='#b6'>(Costa, 2008)</ns0:ref>. Corradini and coworkers generally found behavior of Li and K carbonate salts similar to other works, but interestingly postulated that ionic diffusion of cations and anions might be anticorrelated resulting in smaller ionic PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2019:07:39824:1:1:NEW 28 Aug 2019)</ns0:p><ns0:p>Manuscript to be reviewed Chemistry Journals conductivities than would be anticipated from the diffusion constants and the Nernst-Einstein relationship <ns0:ref type='bibr' target='#b34'>(Tissen, Janssen & Eerden, 1994;</ns0:ref><ns0:ref type='bibr' target='#b5'>Corradini, Coudert & Vuilleumier, 2016)</ns0:ref>, which was also observed by <ns0:ref type='bibr' target='#b37'>Vuilleumier et al. (Vuilleumier et al., 2014)</ns0:ref>. These studies, however, do not consider the thermodynamics or transport of alkali carbonate salts in the interfacial regions that are important for the transport of ions through electrochemical devices.</ns0:p><ns0:p>Interfacial behavior of these molten alkali carbonate salts has been considered in a few studies <ns0:ref type='bibr' target='#b31'>(Roest et al., 2017;</ns0:ref><ns0:ref type='bibr' target='#b15'>Gutiérrez et al., 2018</ns0:ref><ns0:ref type='bibr' target='#b16'>Gutiérrez et al., , 2019))</ns0:ref>. Roest and coworkers studied the behavior of Li, Na, and K carbonate salts at charged and neutral interfaces <ns0:ref type='bibr' target='#b31'>(Roest et al., 2017)</ns0:ref>.</ns0:p><ns0:p>They have identified distinct mass and charge profiles in the molten liquid near the interface and the ions are found to diffuse more slowly in close vicinity of the interface. <ns0:ref type='bibr'>Gutiérrez et al. have</ns0:ref> studied molten LiCO 3 and a eutectic mixture of LiNaKCO 3 at an interface with carbon solids, gases, and vacuum <ns0:ref type='bibr' target='#b15'>(Gutiérrez et al., 2018</ns0:ref><ns0:ref type='bibr' target='#b16'>(Gutiérrez et al., , 2019))</ns0:ref>. They find that the ions do arrange at the interface as defined by the Gibbs dividing surface. The Li and carbonate ions are found to have similar profiles outside of the Gibbs dividing surface (in the vacuum region), but Li and carbonate are found to be weakly layered below the interface in the liquid. In molten LiNaKCO 3 , they find a more complicated distribution of ions. The K is found to be most prevalent in the region outside the Gibbs dividing surface, and Li and Na are found just inside the Gibbs dividing surface. These studies leave questions about how salt composition affect ion and interface structure and dynamics.</ns0:p><ns0:p>It is with this in mind that this study examines the behavior of carbonate ions at a liquidvacuum interface. The liquid-vacuum interface has been selected because sorption and desorption processes of feed and waste gases is poorly understood at a molecular scale, but crucial to the operation of devices using these materials. In this work, the structure and transport of molten Li, Na, and K containing carbonate salts are examined at the liquid-vacuum interface.</ns0:p><ns0:p>We carefully examine local density profiles with respect to the interface using two definitions of the divide between the liquid and vacuum. These two definitions provide complementary perspectives on ion behavior at the boundary between the two phases, which will likely be useful for future studies. We also estimate the slab width, surface tension, and self-diffusion constant, and relate the observed values to the system composition.</ns0:p></ns0:div> <ns0:div><ns0:head>Methods</ns0:head><ns0:p>PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2019:07:39824:1:1:NEW 28 Aug 2019)</ns0:p><ns0:p>Manuscript to be reviewed Chemistry Journals <ns0:ref type='bibr'>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:ref> Simulations were performed of three eutectic systems with chemical formulas of the form Li x Na y K z CO 3 , where . Redox processes are not considered in this work, so Li, Na, 𝑥 + 𝑦 + 𝑧 = 2 and K always refer to the cations and C and O to the constituents of the carbonate anion, which comprise the electrolyte. The fractions of each cation in each system considered in this work are listed in Table <ns0:ref type='table'>1</ns0:ref>. Cation fractions instead of mole fraction or concentration are used to emphasize the relative cation amounts in each system. The three systems considered have unique elemental compositions (LiNaCO 3 , LiKCO 3 , or LiNaKCO 3 ), so the alkali metal subscripts are dropped to simplify identification. These systems have been selected because they are near the eutectic compositions <ns0:ref type='bibr' target='#b20'>(Janz, 1967)</ns0:ref>, which permits for the salt to be molten and for device operation at the lowest temperatures. Initial configurations were prepared by placing 1000 cations (according to the fractions in Table <ns0:ref type='table'>1</ns0:ref>) and 500 carbonate anions randomly on a grid in a simulation cell with dimensions of 40 Å x 40 Å x 100 Å. Ions were placed on evenly spaced, 4 Å grid points by choosing a species randomly within the constraints of the particular composition of interest. Cations were placed on the grid points and the carbon from carbonate was placed at the grid points with the oxygen atoms placed around it. The ions were positioned just below the liquid density so they form a socalled slab of liquid surrounded by a large vacuum region. The system is constructed so that the average liquid-vacuum interface is perpendicular to the z-axis (Fig. <ns0:ref type='figure'>1</ns0:ref>). This study uses the interaction parameters developed by Tissen and Janssen <ns0:ref type='bibr'>(Tissen & Janssen, 1990;</ns0:ref><ns0:ref type='bibr'>Janssen & Tissen, 1990)</ns0:ref>, sometimes called the JT model, which have been employed for many similar studies of molten alkali carbonate salts and as such provides a robust body of literature for validation and reference <ns0:ref type='bibr'>(Tissen & Janssen, 1990;</ns0:ref><ns0:ref type='bibr'>Janssen & Tissen, 1990;</ns0:ref><ns0:ref type='bibr' target='#b34'>Tissen, Janssen & Eerden, 1994;</ns0:ref><ns0:ref type='bibr' target='#b23'>Koishi et al., 2000;</ns0:ref><ns0:ref type='bibr' target='#b28'>Ottochian et al., 2016;</ns0:ref><ns0:ref type='bibr' target='#b41'>Wilding et al., 2016;</ns0:ref><ns0:ref type='bibr' target='#b31'>Roest et al., 2017;</ns0:ref><ns0:ref type='bibr' target='#b10'>Du et al., 2017</ns0:ref><ns0:ref type='bibr' target='#b11'>Du et al., , 2019;;</ns0:ref><ns0:ref type='bibr' target='#b9'>Ding et al., 2018;</ns0:ref><ns0:ref type='bibr' target='#b15'>Gutiérrez et al., 2018)</ns0:ref>. The JT model employs coulombic interactions with long-ranged interactions described by Ewald summation and Borntype repulsion parameterized from quantum mechanical calculations <ns0:ref type='bibr'>(Tissen & Janssen, 1990)</ns0:ref>.</ns0:p><ns0:p>Simulations were performed with the LAMMPS molecular dynamics package <ns0:ref type='bibr' target='#b29'>(Plimpton, 1995)</ns0:ref>.</ns0:p><ns0:p>Electrostatic interactions beyond 11.4 Å were calculated with the particle-particle particle-mesh with an accuracy of 10 -5 . The temperature was held constant using the Nosé-Hoover algorithm with a damping parameter of 500 fs and carbonate was kept rigid using the SHAKE algorithm as implemented in LAMMPS. Each system was heated from 0 to 1200 K in 50 ps and then PeerJ Phy. Chem. reviewing PDF \| (PCHEM- <ns0:ref type='table'>2019:07:39824:1:1:NEW 28 Aug 2019)</ns0:ref> Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science equilibrated at 1200 K for 50 ps with a time step of 0.05 fs in the constant number of particles, temperature, and volume (NVT) ensemble. They were then further equilibrated in the NVT ensemble for 20 ns with a timestep of 0.5 fs. Therefore, the thermodynamic data presented was extracted from 10 ns NVT production simulations and the diffusion constants from 10 ns constant number of particles, volume, and energy (NVE) simulations. In these production simulations, configurations were saved every 0.5 ps and analysis included all frames from the corresponding NVT or NVE trajectory.</ns0:p><ns0:p>Molecular visualization was performed with Visual Molecular Dynamics (VMD) <ns0:ref type='bibr' target='#b18'>(Humphrey, Dalke & Schulten, 1996)</ns0:ref>. Density profiles were calculated with in house scripts.</ns0:p><ns0:p>Densities are normalized by dividing by the average density in the center of the respective slab to facilitate comparison between systems with different compositions. The Gibbs dividing surfaces are determined from the z-dimension density profile to find the two planes (one on each side of the slab) where the density is half the average bulk liquid density <ns0:ref type='bibr' target='#b14'>(Gochenour, Heyert & Lindberg, 2018)</ns0:ref>. The interface, however, need not be viewed as a static plane, but can also be viewed as a dynamic, three-dimensional region specific to the underlying molecular configuration. This is similar to the distinction between 'sea level' and waves on the sea, where sea level is determined by averaging over local fluctuations to obtain a useful, but dramatically simplified, description. Analogously, it is useful to consider an interpretation of the interface that captures the local molecular-scale undulations. In this work, we utilize the instantaneous interface scheme developed by <ns0:ref type='bibr' target='#b42'>Willard and Chandler (Willard & Chandler, 2010)</ns0:ref>. Briefly, this method involves creating a coarse-grained density field and identifying points in this coarse grained density field at a density halfway between that of the two phases. These points can then be connected to identify the interface that separates the two phases. The long time average of the instantaneous interface is analogous to the Gibbs dividing surface. The instantaneous interfaces were calculated with an in-house script using a coarse graining length, ξ, of 1.5 Å. Self-diffusion constants are determined from the mean-squared displacement as calculated by CPPTRAJ with the Einstein relation <ns0:ref type='bibr' target='#b30'>(Roe & Cheatham, 2013)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head><ns0:p>A snapshot of the equilibrated simulation setup for the LiKCO 3 system is shown in Fig. Manuscript to be reviewed Chemistry Journals to the interface in Fig. <ns0:ref type='figure'>2</ns0:ref>. The organization of each element is isolated with atomic density profiles for each system in Fig. <ns0:ref type='figure'>3</ns0:ref>. The contribution of each element to the whole density profile is examined in Fig. <ns0:ref type='figure'>4</ns0:ref>. An example instantaneous interface for the LiKCO 3 system is shown in Fig. <ns0:ref type='figure'>5</ns0:ref> and the distribution of instantaneous interface sites relative to the Gibbs dividing surface for each system are shown in Fig. <ns0:ref type='figure'>6</ns0:ref>. Finally, a histogram of the nearest distance between each element and the closest point on the instantaneous interface is shown in Fig. <ns0:ref type='figure' target='#fig_6'>7</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Discussion</ns0:head><ns0:p>Density profiles reveal interface-induced structure.</ns0:p><ns0:p>Ion behavior in the vicinity of the liquid-vacuum interface was first examined with density distributions perpendicular to the plane of the interface. Fig. <ns0:ref type='figure'>2</ns0:ref> shows the normalized density along the coordinate perpendicular to the interface for each system considered. The most obvious difference observed in the simulations is an increase in slab width. This change in width is directly related to the amount of K, the largest cation considered, in the solution (Fig. <ns0:ref type='figure'>2</ns0:ref>). Near the interface there is structure in the densities. The liquid region is flat indicative of homogeneous liquid structure, the intermediate region between the liquid and vacuum shows two peaks, and finally there is a drastic decrease in density when moving into the vacuum. The peaks are most well-defined in the Na-containing systems (red and blue curves in Fig. <ns0:ref type='figure'>2</ns0:ref>), but these features are present in each system.</ns0:p></ns0:div> <ns0:div><ns0:head>Slab width and surface tension are highly correlated with system composition.</ns0:head><ns0:p>The width of the slab can be defined as the distance between the Gibbs dividing surface on each side of the slab (Table <ns0:ref type='table'>2</ns0:ref>). The slab width is found to be highly correlated with the size of the cations. Using ionic radii of 0.68, 0.97, and 1.33 Å for Li, Na, and K cations (Weast, 1968), an average cation radius can be calculated for each system (1)</ns0:p><ns0:formula xml:id='formula_0'>〈𝑟 𝑖𝑜𝑛 〉 = χ 𝐿𝑖 𝑟 𝐿𝑖 𝑖𝑜𝑛 + χ 𝑁𝑎 𝑟 𝑁𝑎 𝑖𝑜𝑛 + χ 𝐾 𝑟 𝐾 𝑖𝑜𝑛</ns0:formula><ns0:p>where is the average cation radius, is the fraction of the cations that are X, and is</ns0:p><ns0:formula xml:id='formula_1'>〈𝑟 𝑖𝑜𝑛 〉 χ 𝑋 𝑟 𝑋</ns0:formula><ns0:p>𝑖𝑜𝑛 the ionic radius of X. The slab width is found to be highly correlated with the average cation radius, with a linear coefficient of correlation (R 2 ) of 0.999. The individual cation sizes, however, are less correlated with slab width. The width is most weakly correlated with the cation fraction of Li, but significantly stronger correlation is observed with Na and K cation fractions.</ns0:p><ns0:p>PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2019:07:39824:1:1:NEW 28 Aug 2019)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:p><ns0:p>The correlation coefficients are 0.780, 0.975, and 0.998 for Li, Na, and K, respectively. These conclusions are similar to the observation by Habasaki that anion-cation spacing is correlated with the cationic radii of Li and Na in carbonate salts <ns0:ref type='bibr' target='#b17'>(Habasaki, 1990)</ns0:ref>, which indicates that molecular-scale spatial correlations can provide a good indication of larger bulk densities.</ns0:p><ns0:p>The surface tension can be estimated from the cell width and the diagonal components of the pressure tensor according to the expression</ns0:p><ns0:formula xml:id='formula_2'>(2) 𝛾 = 𝐿 𝑧 2 〈 𝑃 𝑧𝑧 -1 2 (𝑃 𝑥𝑥 + 𝑃 𝑦𝑦 ) 〉</ns0:formula><ns0:p>where γ is the surface tension, is the simulation cell width in the dimension, is the 𝐿 𝑧 𝑧 𝑃 𝑧𝑧 component of the pressure tensor normal to the interface, and and are the tangential 𝑃 𝑥𝑥 𝑃 𝑦𝑦 components of the pressure tensor <ns0:ref type='bibr' target='#b21'>(Kirkwood & Buff, 1949)</ns0:ref>. This is similar to the surface tension protocol used by others <ns0:ref type='bibr' target='#b4'>(Bhatt, Newman & Radke, 2004;</ns0:ref><ns0:ref type='bibr' target='#b7'>Desmaele et al., 2019)</ns0:ref>. Surface tension values are shown in Table <ns0:ref type='table'>2</ns0:ref> and have not been reported previously for the JT model.</ns0:p><ns0:p>Similar to the slab width discussed previously, the surface tension is found to be highly correlated with K concentration, slightly less with Na, and significantly less with Li. The surface tensions measured here seem to be systematically less than reported in the experimental literature <ns0:ref type='bibr' target='#b39'>(Ward & Janz, 1965;</ns0:ref><ns0:ref type='bibr' target='#b25'>Kojima et al., 2008)</ns0:ref>. For example, Kojima et al. report the surface tension of LiNaKCO 3 at 1200 K to be approximately 200 mN/m while the JT model yields a surface tension of 130±10 mN/m <ns0:ref type='bibr' target='#b25'>(Kojima et al., 2008)</ns0:ref>. Therefore, the JT model yields surface tension values that are about 35% smaller than those reported in the experimental literature <ns0:ref type='bibr' target='#b39'>(Ward & Janz, 1965;</ns0:ref><ns0:ref type='bibr' target='#b25'>Kojima et al., 2008)</ns0:ref>. The JT model appears to underestimate cohesive interactions, which is similar to previous studies that have shown a similar disagreement when the model density is compared to experiment <ns0:ref type='bibr' target='#b28'>(Ottochian et al., 2016;</ns0:ref><ns0:ref type='bibr' target='#b31'>Roest et al., 2017;</ns0:ref><ns0:ref type='bibr' target='#b7'>Desmaele et al., 2019)</ns0:ref>. Some works have compensated for this discrepancy by performing their simulations at elevated pressure <ns0:ref type='bibr' target='#b28'>(Ottochian et al., 2016;</ns0:ref><ns0:ref type='bibr' target='#b31'>Roest et al., 2017</ns0:ref>), but this is not possible here because the liquid is in contact with vacuum and therefore free to expand. Nevertheless, the literature provides significant evidence that this model provides a generally faithful description of molten alkali carbonates and as such lends confidence that the trends reported here are reliable.</ns0:p><ns0:p>Additionally, the JT model has received such widespread usage that characterization of the surface tension provides important perspective on strengths and weaknesses of the model.</ns0:p></ns0:div> <ns0:div><ns0:head>Elemental structure at the Gibbs dividing surface.</ns0:head><ns0:p>PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2019:07:39824:1:1:NEW 28 Aug 2019)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:p><ns0:p>The normalized density in Fig. <ns0:ref type='figure'>2</ns0:ref> includes all species in the system. It is interesting to decompose the density into the contributions from each elements to see if the ions accumulate differently at the interface. Therefore the density profile of each element was calculated to resolve these distributions near the interface. For example, the complete density distribution of each element in the LiNaKCO 3 system is shown in Fig. <ns0:ref type='figure'>3a</ns0:ref>. Similar to Fig. <ns0:ref type='figure'>2</ns0:ref>, the densities in Fig. <ns0:ref type='figure'>and d</ns0:ref>). In the electrolyte without Na (Fig. <ns0:ref type='figure'>3c</ns0:ref>), K is also found to be depleted near the Gibbs dividing surface. The other K-containing solution (Fig. <ns0:ref type='figure'>3d</ns0:ref>) does not show similar depletion, but instead K has a significant maximum about 7 Å on the liquid side of the Gibbs dividing surface. Lithium is generally observed to be the cation with a maximum closest to the interface, which is generally similar in shape and location to the C peak of the anion. The C and O peaks are generally similar, with the O showing slightly less structuring. While some information about ordering of each element relative to the interface is observed, these features are nevertheless difficult to resolve. Therefore, it would be useful to further examine the local enrichment or depletion of each species relative to the interface for a clearer picture of ion distributions.</ns0:p><ns0:p>While Fig. <ns0:ref type='figure'>3</ns0:ref> shows the structure of each atom at the Gibbs dividing surface in the liquid region, it is difficult to identify the atomic contributions to the total density profile and more specifically to see the atomic ordering within the interfacial region. Therefore, Fig. <ns0:ref type='figure'>4</ns0:ref> depicts the difference between Fig. <ns0:ref type='figure'>3b, c</ns0:ref>, and d and the corresponding total density profiles in Fig. <ns0:ref type='figure'>2</ns0:ref>. Fig. <ns0:ref type='figure'>4</ns0:ref> shows that K has significant density at the surface and is the most prominent, when it is present.</ns0:p><ns0:p>Conversely, as was observed in the analysis of Fig. Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science compared to its bulk density. The behavior of Li is less dramatic. When K is present, Li has a peak near the same distance from the interface. When K is not in the solution (Fig. <ns0:ref type='figure'>4a</ns0:ref>), then Li is the predominant cation at the Gibbs dividing surface. In all cases, C is depleted at the surface compared to the cations, but has a maximum just inside the surface. The error in these density profiles was estimated by breaking up the simulations into ten segments of equal length and calculating the error in the mean from these segments. Fig. <ns0:ref type='figure'>S1</ns0:ref> depicts the normalized atomic density profiles with error for each element in the LiNaKCO 3 system. This shows that the error is smaller than peaks and valleys discussed, which indicates that the observed features are physically meaningful. Additionally, the error would be estimated to be even smaller than depicted because of agreement between the left and right sides of the interface, which are independent from each other. These findings are similar to those reported by Gutiérrez et al. who also showed ionic structuring near the Gibbs dividing surface <ns0:ref type='bibr' target='#b15'>(Gutiérrez et al., 2018)</ns0:ref>. Next, these ionic arrangements will be examined further using a local definition of the interface.</ns0:p></ns0:div> <ns0:div><ns0:head>Instantaneous interface analysis reveals local fluctuations of the liquid surface.</ns0:head><ns0:p>The interfacial analyses so far have been performed with respect to the Gibbs dividing surface. The Gibbs dividing surface is the plane that on average separates two distinct phases <ns0:ref type='bibr' target='#b14'>(Gochenour, Heyert & Lindberg, 2018)</ns0:ref>, but the instantaneous interface method can provide a description of the interface with molecular features of the surface. An example of such an instantaneous interface is shown for the LiKCO 3 system in Fig. <ns0:ref type='figure'>5</ns0:ref>. In this work, we use the instantaneous interface to characterize fluctuations of the surface and elemental distributions in the vicinity of the instantaneous interface.</ns0:p><ns0:p>Elemental structure at the instantaneous interface shows cations preferentially populate the surface.</ns0:p><ns0:p>The instantaneous interface corresponds to the boundary between the molten salt liquid and vacuum for a particular molecular configuration. Therefore, analysis of the instantaneous interface and the underlying atomic distribution provides details about the behavior of the system in light of a specific arrangement of the atoms, rather than the time-averaged, global behavior described by the Gibbs dividing surface. Anions diffuse slower than the cations.</ns0:p><ns0:p>Finally, the transport of the ions is evaluated using the self-diffusion constants. Table <ns0:ref type='table'>3</ns0:ref> shows the diffusion constant of each species in each system. The diffusion constants are in general agreement with previous, similar studies <ns0:ref type='bibr' target='#b17'>(Habasaki, 1990;</ns0:ref><ns0:ref type='bibr' target='#b23'>Koishi et al., 2000;</ns0:ref><ns0:ref type='bibr' target='#b6'>Costa,</ns0:ref> PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2019:07:39824:1:1:NEW 28 Aug 2019)</ns0:p><ns0:p>Manuscript to be reviewed <ns0:ref type='bibr' target='#b6'>2008;</ns0:ref><ns0:ref type='bibr' target='#b37'>Vuilleumier et al., 2014;</ns0:ref><ns0:ref type='bibr' target='#b28'>Ottochian et al., 2016;</ns0:ref><ns0:ref type='bibr' target='#b5'>Corradini, Coudert & Vuilleumier, 2016;</ns0:ref><ns0:ref type='bibr' target='#b31'>Roest et al., 2017)</ns0:ref>, however exact comparison with bulk diffusion constants is difficult since the systems described here are heterogeneous. Nevertheless, this work shows that carbonate diffuses slower than all of the cations. The cation self-diffusion is found to be proportional to cation size, with the larger ions diffusing faster. This is attributed to each cation having the same charge, so larger radii ions experience correspondingly weaker electrostatic interactions.</ns0:p><ns0:note type='other'>Chemistry Journals Analytical, Inorganic, Organic, Physical, Materials Science</ns0:note></ns0:div> <ns0:div><ns0:head>CONCLUSIONS</ns0:head><ns0:p>Molecular dynamics simulations have revealed thermodynamic and dynamic properties of molten alkali carbonates near a liquid-vacuum interface. This work has shown that in three alkali carbonate salts, the alkali cations preferentially accumulate at the interface more than the anion. Additionally, the anions are found to diffuse much more slowly than the cations.</ns0:p><ns0:p>Nevertheless, subtle differences are seen between the three solutions considered, which may be helpful in the selection of electrolyte compositions that yield the best performance in an electrochemical device. Intriguingly, the LiKCO 3 solution is found to yield the fastest carbonate diffusion while also permitting the closest approach of the anion to the interface. These differences could have significant effects on the performance of devices employing such electrolytes and therefore warrant study to understand and confirm these results.</ns0:p></ns0:div> <ns0:div><ns0:head>CONFLICTS OF INTEREST</ns0:head><ns0:p>There are no conflicts to declare.</ns0:p><ns0:note type='other'>Figure 1</ns0:note><ns0:p>Snapshot of the LiNaKCO 3 simulation cell.</ns0:p><ns0:p>Snapshot of the equilibrated LiNaKCO 3 system with the simulation cell depicted with black lines.</ns0:p><ns0:p>PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2019:07:39824:1:1:NEW 28 Aug 2019)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p><ns0:note type='other'>Chemistry Journals Figure 2</ns0:note><ns0:p>Normalized total density profile in the dimension perpendicular to the interface. Manuscript to be reviewed Manuscript to be reviewed</ns0:p><ns0:note type='other'>Chemistry Journals Figure 3</ns0:note><ns0:note type='other'>Chemistry Journals Figure 4</ns0:note><ns0:p>Atomic density differences from the total density profile in the dimension perpendicular to the Gibbs dividing surface. Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:note type='other'>Figure 5</ns0:note><ns0:p>A representative snapshot of the LiKCO 3 system with the corresponding instantaneous interface.</ns0:p><ns0:p>The atoms comprising the salt are depicted in green, purple, blue, and pink and the instantaneous interface is shown in gold.</ns0:p><ns0:p>PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2019:07:39824:1:1:NEW 28 Aug 2019)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p><ns0:note type='other'>Chemistry Journals Figure 6</ns0:note><ns0:p>Distribution of instantaneous interface sites relative to the Gibbs dividing surface.</ns0:p><ns0:p>Shown are LiNaCO 3 (red), LiKCO 3 (black), and LiNaKCO 3 (blue).</ns0:p><ns0:p>PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2019:07:39824:1:1:NEW 28 Aug 2019)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>Analytical, Inorganic, Organic, Physical, Materials Science</ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>1. The distribution of ions within each system are examined with density profiles perpendicular PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2019:07:39824:1:1:NEW 28 Aug 2019)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head>3</ns0:head><ns0:label /><ns0:figDesc>are normalized by dividing by the average density in the middle of the slab. Fig.3areveals the anticipated general trend of high density within the slab and no density outside it in the vacuum and the peaks and valleys indicate interfacial ordering of the individual ions in the elemental density profiles. This is similar to the entire system density profiles shown in Fig.2. These features are difficult to resolve because of the large density difference between the liquid and vacuum, so Figs.3b, c, and dshow detailed views of the liquid region of the elemental density profiles for each system. Each element is observed to have maxima and minima induced by proximity to the interface that are much larger than the subtle, apparently random wiggles in the middle of the slab. The similarity of the right and left interfaces indicates that the profiles are converged. Most notably, Na is observed (red lines) to be depleted compared to the other elements near the Gibbs dividing surface(Figs. 3b </ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head /><ns0:label /><ns0:figDesc>Fig.7shows a histogram of the shortest distance between each element and the instantaneous interface. The curves are normalized so they all have maximum values of 1 to simplify comparisons between species, despite different concentrations. The density profiles relative to the instantaneous interface in Fig.7provide a complementary perspective on ion structure near the interface to those observed with respect to the Gibbs dividing surface in Figs.3 and 4. In Fig.7, the cations are always more likely to be closer to the instantaneous interface than the anions. The ordering is distinct, with K being closest, then Na, Li, O, and finally C. It is interesting to note that the cation trend inversely follows the ionic radii. This indicates that the cations preferentially populate the surface of the instantaneous interface. Interestingly, these distributions are apparently independent of the electrolyte composition, and therefore possibly indicative of broader trends in ion behavior in these molten alkali carbonate electrolytes. The uniformity of the elemental distributions in each panel of Fig.7is distinct from the behavior observed with respect to the Gibbs dividing surface shown in Figs.3 and 4. This indicates that the dynamics of the interface can be affected by the composition, but the actual arrangement of the atoms is less sensitive.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head /><ns0:label /><ns0:figDesc>The densities are normalized by dividing by the average liquid density. For each system, the right Gibbs dividing surface is positioned at 0 Å and the left interface provides a sense of the slab width because the xy area of the slab remains constant. Expansion of the slab is highly correlated with increasing concentration of the larger radius K ion. Each system shows structure near each interface that corresponds to structure within the liquid.PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2019:07:39824:1:1:NEW 28 Aug 2019)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head /><ns0:label /><ns0:figDesc>Normalized densities with respect to the right Gibbs dividing surface. Shown for Li (green), Na (red), K (black), C, (orange), and O (blue) in the a) entire LiNaKCO 3 system and detailed views of the liquid region for b) LiNaCO 3 , c) LiKCO 3 , and d) LiNaKCO 3 . For each system, the right Gibbs dividing surface is positioned at 0 Å. The peaks and valleys are indicative of the atomic structuring near the surface. PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2019:07:39824:1:1:NEW 28 Aug 2019)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head /><ns0:label /><ns0:figDesc>Shown for Li (green), Na (red), K (black), C, (orange), and O (blue) for the a) LiNaCO 3 , b) LiKCO 3 , and c) LiNaKCO 3 systems. These are the profiles in Fig.2with the whole system profiles in Fig.1subtracted. These plots emphasize the contributions of each element to the total density profile. For each system, the right Gibbs dividing surface is positioned at 0 Å.PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2019:07:39824:1:1:NEW 28 Aug 2019)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head>Figure 7</ns0:head><ns0:label>7</ns0:label><ns0:figDesc>Figure 7</ns0:figDesc></ns0:figure> <ns0:note place='foot' n='2'>PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2019:07:39824:1:1:NEW 28 Aug 2019)Manuscript to be reviewed Chemistry JournalsAnalytical, Inorganic, Organic, Physical, Materials Science </ns0:note> <ns0:note place='foot'>Analytical, Inorganic, Organic, Physical, Materials Science </ns0:note> </ns0:body> "	"August 24, 2019 Author response to Reviewers for Structure and diffusion of molten alkali carbonate salts at the liquid-vacuum interface. The Reviewers’ comments are reproduced in their entirety and author responses are provided in blue. Corresponding changes to the manuscript have been highlighted with blue text in the manuscript. I appreciate the thoughts and effort of the Reviewers and I feel that addressing these concerns has made the manuscript clearer and more interesting. Thank you, Gerrick Reviewer 1 (Anonymous) Comments for the Author 1. The methods of the manuscript needs more detail. I suggest that you improve the description at lines 114- 120 to provide more justification for your study (specifically, you should give the specific expression of potential function and detailed numerical value of the interaction parameters ). I have added more information about the Janssen & Tissen (JT) model for alkali carbonates near lines 117-122. I have discussed the model and its origins, but I have decided against including the potential energy function or the interaction parameters. In my opinion, they are outside the purview of this article because they are not a product of this work and the parameters themselves are not directly discussed. As such, I think readers that are interested in the JT model specifically would be best served by looking up the original articles by Janssen & Tissen and subsequent discussion in the literature. However, I do cite these works, so readers should readily be able to find the relevant papers. 2. In the manuscript, the simulation results lack a detailed calculation process. It is only based on similar methods in the literature, but the specific calculation method is not given. This makes it difficult for future readers to understand. Thanks for pointing out this oversight. I have provided more details about the simulation parameters selected on lines 118-122 and the protocol on lines 127-131. 3. The description at lines110-112'Initial configurations were prepared by placing 1000 cations (according to the fractions in Table 1) and 500 carbonate anions randomly on a grid in a simulation cell with dimensions of 40 Å40 Å100 Å.' Please give the answers to the following questions in the appropriate place in the manuscript. a. What kind of random algorithm is used for random placement? I have added more details about the placement algorithm on lines 106-109. b. Are there multiple simulations and statistical analysis of the simulation results? c. Are the results of multiple simulations consistent? Multiple independent simulations were not performed. However, ‘chunking’ of the simulations was used to estimate error within the data. The simulations were split into ten segments of equal length and each segment was analyzed. The result of this analysis is shown in a supplementary figure (Fig. S1). Fig. S1 shows the normalized atomic number density and standard error in the mean for each element in the LiNaKCO3 solution. These plots demonstrate that many of the reported features are significantly larger than the errors. Additionally, the density profiles are symmetric, further indicating that the observed peaks and valleys are real and not a product of random fluctuations. Corresponding discussion has also been added on lines 251-258. Additionally, it’s worth noting that the simulation protocols used in this work are comparable in length to other published studies of similar alkali carbonate systems. d. Whether the micro-simulation results can be extended to the macro-system? These simulation results provide a trustworthy description of the behavior of molten alkali carbonate ions at a liquid-vacuum interface. These molecular-scale results are relevant for the understanding the operation of devices that use these materials as electrolytes, particularly for the absorption of gas into the liquid, or its desorption, since these processes must occur at the relevant interfaces and are mediated by the availability of the relevant species at the surface. I have added discussion to this point on lines 83-85. Reviewer 2 (Anonymous) Basic reporting 1. The English require substantial improvements. For example, line 36: are specifically; Line 81: they have identified; line 105: The considered three systems; line 112: were; line 113:was ; line 129: Visual Molecular Dynamics (VMD) …… I have carefully reread the manuscript and attempted to address these concerns. I believe the manuscript clarity and readability has been improved. 2. In the introduction, paragraph 3 required references to validate the statement. I’m not sure to which statement the Reviewer is referring with this comment. The third paragraph of the Introduction contains a significant amount of literature review, but as far as I can tell all statements include appropriate references. 3. Line 229 to 245: description of identifying the instantaneous interface should go to method part. This does improve the flow of the manuscript. Most of the indicated portion has been moved to lines 138-148. Experimental design 4. The purpose of this work is not very clear. Why are authors focused on liquid-vacuum interface not other interface? The potential application of the findings should be described more specifically. Thanks for the feedback. I have more explicitly stated how the consideration of the liquid-vacuum interface has implications for gas sorption and desorption in devices using these materials on lines 83-85 and I have also pointed out that using the instantaneous interface method provides a new perspective that is not yet present in the literature on ion accumulation at the surface in these systems on lines 88-91. 5. How the total number of ions in the systems are determined? What is the initial liquid density? The number of ions was selected to yield a large enough slab to avoid artifacts that are known to arise from small boxes employing periodic boundary conditions. There isn’t a standard ion count used by similar studies in the literature, so an amount in the middle of other recent studies was used. These systems are created with the interface (the interface is not introduced to a bulk liquid as is common in other protocols), so the whole system density is not well-defined. 6. The description of the system set up is not very clear. It is better to have a representative figure to show the system set up. Thanks for this comment. I have added description on lines 106-109 that hopefully make the set up procedure clearer. Additionally, I have added a snapshot of the equilibrated LiNaKCO3 system as Fig. 1 to show the reader the simulation cell. Validity of the findings 7. The simulation time is very short, and the equilibration of systems should be validated to ensure the sufficient sampling such as analysis of the density change with time Equilibration was validated by examining the total and potential energies with time, as well as checking the reported results using a chunking procedure of the production simulation. The simulation times are short compared to some studies (for example, many biological phenomena occur over much longer timescales and as such require longer simulations), but these simulations must be considered in light of the processes being studied. In this case, the interesting processes occur in picoseconds, and so are captured repeatedly within nanoseconds. Additionally, our simulation lengths are similar to other recent reports, for example Gutiérrez et al. use 10 ns production simulations (10.1021/acs.langmuir.8b02907), Ding et al. use production simulations of at least 10 ns (10.1016/j.apenergy.2017.07.019), and Desmaele et al. use less than 1 ns to obtain densities (10.1063/1.5082731). The work by Desmaele et al. is equilibrated for 0.5 ns, so the long equilibrium simulations in this work are longer than in other recent examples the literature. (It’s worth pointing out that I observed quick relaxation of the energies during equilibration, so the relatively short time used by Desmaele et al. appears reasonable. My equilibration duration is longer than is truly needed). Therefore, examination of the work at hand and comparison with other recent studies provides good reason to expect that these simulations are in equilibrium and that the production simulations are long enough to adequately describe the relevant equilibrium ensemble of structures. 8. The observed peaks and drops are very small, and the fluctuation could be dramatic along the simulation. The density profile evolved with time should be analysis and error bar should be added to time-averaged density profile. Estimation and presentation of error in the density profiles is a great point. I debated how to address this as I prepared the figures and wrote the manuscript. In the end, I decided to not include error for three reasons: 1) Error is usually not included in the presentation of distribution functions (for example, radial distribution functions, density distributions, etc) because those properties are known to converge in relatively short durations for liquid systems. 2) The density profiles include both interfaces of each slab in Figs. 2, 3, and 4 (Figs. 1, 2, and 3 in the initial submission). The differences between a given curve at the left and right interfaces therefore yields information about the error. 3) The ion distributions with respect to the instantaneous interface reported in Fig. 7 (Fig. 6 in the initial submission) show that ion behavior is remarkably similar between solutions (that is, the curves of the same color when comparing panels a, b, and c are very similar). This indicates that the simulations are converged, sampled adequately, and that the observed behavior is a general trend of the ions in these solutions. Nevertheless, I have added a supplementary figure to address this. Fig. S1 shows the normalized atomic number density and standard error in the mean for each element in the LiNaKCO3 solution as estimated by breaking the simulation into ten segments of equal length. These plots demonstrate that many of the reported features are significantly larger than the errors. Additionally, the density profiles are symmetric, further indicating that the observed peaks and valleys are real and not a product of random fluctuations. 9. It is not clear how the results are analyzed. Is it analyzed using the last frame in the simulation or averaged over all the frames? This is certainly an important aspect of the work! The thermodynamic results are averaged over the entire NVT trajectory, while the diffusion constants were obtained from the entire NVE simulation. I have added some language on lines 127-131 to make this clearer. "	Here is a paper. Please give your review comments after reading it.
487	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Background. The generalized ensemble approach with the molecular dynamics (MD) method has been widely utilized. This approach usually has two features. (i) A bias potential, whose strength is replaced during a simulation, is applied. (ii) Sampling can be performed by many parallel runs of simulations. Although the frequency of the bias-strength replacement and the number of parallel runs can be adjusted, the effects of these settings on the resultant ensemble remain unclear.</ns0:p><ns0:p>Method. In this study, we performed multicanonical MD simulations for a foldable mini-protein (Trpcage) and two unstructured peptides (8-and 20-residue poly-glutamic acids) with various settings.</ns0:p><ns0:p>Results. As a result, running many short simulations yielded robust results for the Trp-cage model. Regarding the frequency of the bias-potential replacement, although using a high frequency enhanced the traversals in the potential energy space, it did not promote conformational changes in all the systems.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>In the past several decades, the molecular dynamics (MD) method has been widely applied to investigate the microscopic behavior of molecular systems. Although advances in highperformance computing technology have extended the timescale that is reachable by MD simulations <ns0:ref type='bibr' target='#b34'>(Salomon-Ferrer et al., 2013;</ns0:ref><ns0:ref type='bibr' target='#b35'>Shaw et al., 2014;</ns0:ref><ns0:ref type='bibr' target='#b0'>Abraham et al., 2015)</ns0:ref>, there is still a large gap from experimental measurements. In particular, it is not straightforward to characterize the free-energy landscape (FEL) of a complex molecular system, because the characteristics of conformational ensembles obtained via canonical MD simulations largely depend on the initial conditions. To solve this problem, the generalized ensemble (GE) approach has been extensively developed and applied to the MD method. The GE approach enhances the conformational sampling using some tricks. First, in many GE methods, the conformational sampling can be performed with many parallel runs of simulations in a coupled or independent manner. For example, the replica-exchange MD (REMD) method <ns0:ref type='bibr' target='#b40'>(Sugita & Okamoto, 1999)</ns0:ref> involves performing many simulations of the same system, i.e., replicas, with different temperatures. The replicas with adjacent temperatures are coupled by exchanging their temperatures via Monte Carlo trials. On the other hand, the multicanonical MD (McMD) method <ns0:ref type='bibr' target='#b27'>(Nakajima, Nakamura & Kidera, 1997</ns0:ref>) can be performed by multiple independent runs, and a resultant ensemble is obtained by concatenating the trajectories of these runs <ns0:ref type='bibr' target='#b18'>(Ikebe et al., 2010)</ns0:ref>. Second, the GE approach generates a non-Boltzmann distribution by applying bias potential, e.g., heating/cooling in the entire system or a part of the system, scaling the potential energies, and applying spring potentials for parts of system. These biases enhance the conformational changes of molecules and avoid trapping the molecular system at local minima in the FEL. During a simulation, the strength of the bias is frequently replaced, and the system alternates between different bias conditions. After simulations, a canonical ensemble can be obtained by reweighting each snapshot in the sampled conformational ensemble <ns0:ref type='bibr' target='#b39'>(Souaille & Roux, 2001;</ns0:ref><ns0:ref type='bibr' target='#b36'>Shirts & Chodera, 2008)</ns0:ref>.</ns0:p><ns0:p>For using these two features, users must adjust some settings. First, the number of runs is an adjustable parameter. In the case of the REMD method, using a larger number of replicas allows wider overlaps of the energy distributions between adjacent replicas and results in a higher acceptance probability. However, increasing the number of runs proportionally increases the computational costs. Users must choose the optimal balance between the number of runs and the length of each run according to the available computational resources. Previously, <ns0:ref type='bibr'>Ikebe et al.</ns0:ref> reported that an increase in the number of independent runs of McMD yields efficient exploration of a wider area of the conformational space. <ns0:ref type='bibr' target='#b18'>(Ikebe et al., 2010)</ns0:ref> However, the balance between the number of runs and the length of each run has not been discussed. Second, the frequency of the bias-strength replacement is also adjustable. In the REMD method, the frequency of replica-exchange trials must be specified by users. Other methods using a continuous bias strength, e.g., McMD and adaptive umbrella sampling (AUS), can control the frequency of bias-strength replacement by using the virtual-system coupling scheme, <ns0:ref type='bibr' target='#b17'>(Higo, Umezawa & Nakamura, 2013;</ns0:ref><ns0:ref type='bibr' target='#b15'>Higo et al., 2015)</ns0:ref> as described later. It is reported that the frequency of the bias-strength replacement affects the resultant ensembles for the REMD method. <ns0:ref type='bibr' target='#b30'>(Periole & Mark, 2007;</ns0:ref><ns0:ref type='bibr' target='#b38'>Sindhikara, Meng & Roitberg, 2008;</ns0:ref><ns0:ref type='bibr' target='#b33'>Rosta & Hummer, 2009;</ns0:ref><ns0:ref type='bibr' target='#b37'>Sindhikara, Emerson & Roitberg, 2010;</ns0:ref><ns0:ref type='bibr' target='#b20'>Jani, Sonavane & Joshi, 2014;</ns0:ref><ns0:ref type='bibr'>Iwai, Kasahara & Takahashi, 2018)</ns0:ref> Although higher frequencies enhance the traversals in the temperature space, they are suspected as an origin of artifacts. Although the effects of these features have been examined, these studies were mainly based on simple model peptides with helix-coil transitions. The effects of the features for more practical cases, e.g., a protein folding-unfolding transition, are not fully understood. More importantly, the relationship between these effects and the complexities of molecular systems, e.g., the degree of freedom and ruggedness of the free-energy landscape, are expected to be revealed. In this study, we aim to elucidate the effects of the number of runs and the bias-replacing frequency for the GE method on the resultant conformational ensembles of molecular models including a foldable mini-protein and disordered model peptides. We utilized the trivialtrajectory parallelized virtual-system coupled McMD (TTP-V-McMD) method <ns0:ref type='bibr' target='#b18'>(Ikebe et al., 2010;</ns0:ref><ns0:ref type='bibr' target='#b17'>Higo, Umezawa & Nakamura, 2013)</ns0:ref>, which is a variant of the McMD method, for simulating the three molecular models with an explicit solvent: (i) Trp-cage, (ii) 8-residue polyglutamic acid (PGA8), and (iii) 20-residue poly-glutamic acid (PGA20). We chose these models as test cases to examine the simulation conditions because they are sufficiently small for elucidating their conformational ensembles within a practical computational time in addition to the fact that their structural properties have been well studied thus far. Trp-cage, which is a miniprotein consisting of 20 amino acids, has been widely studied as a prominent model of protein folding. <ns0:ref type='bibr' target='#b1'>(Ahmed et al., 2005;</ns0:ref><ns0:ref type='bibr' target='#b31'>Péter Hudáky et al., 2007;</ns0:ref><ns0:ref type='bibr' target='#b11'>Hałabis et al., 2012)</ns0:ref> Poly-glutamic acids have been used as model peptides to characterize the conformational properties of polypeptides. <ns0:ref type='bibr' target='#b3'>(Clarke et al., 1999;</ns0:ref><ns0:ref type='bibr' target='#b25'>Kimura et al., 2002;</ns0:ref><ns0:ref type='bibr' target='#b6'>Finke et al., 2007;</ns0:ref><ns0:ref type='bibr' target='#b5'>Donten & Hamm, 2013;</ns0:ref><ns0:ref type='bibr' target='#b29'>Ogasawara et al., 2018)</ns0:ref> We analyzed their FELs under various parameter settings to provide a guide for adjusting these parameters for the GE methods. The questions to be answered are as follows: 'Which condition is more efficient: many short simulations or a small number of long-term simulations?' and 'Which is better: frequent or less frequent replacement of the bias strength?'. Moreover, we discuss the relationship between the relaxation of the energy and that of the protein conformation. While the McMD method enhances the relaxation in the energy space, it is not guaranteed to enhance the relaxation in the conformational space. We analyzed these two relaxation processes using the McMD trajectories calculated with the various settings.</ns0:p></ns0:div> <ns0:div><ns0:head>Materials & Methods</ns0:head><ns0:p>We calculated the FELs of the three explicitly solvated molecular models: Trp-cage, PGA8, and PGA20, by using the TTP-V-McMD method with various settings. The theory of <ns0:ref type='bibr'>McMD, virtual-system coupled McMD (V-McMD)</ns0:ref>, and trivial-trajectory parallelization (TTP) is briefly presented in the following subsections. Then, the simulation protocol applied in this study is described.</ns0:p></ns0:div> <ns0:div><ns0:head>McMD</ns0:head><ns0:p>The McMD method efficiently explores the conformational space of a molecular system, by applying a biasing energy term. The Hamiltonian H of the system is Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Chemistry Journals</ns0:head><ns0:p>where K and E mc denote the kinetic energy and multicanonical energy, respectively. E mc is defined as follows: ,</ns0:p><ns0:p>(2) 𝐸 𝑚𝑐 = 𝐸 + 𝑅𝑇ln P c (𝐸,𝑇)</ns0:p><ns0:p>where E is the potential energy, and the second term corresponds to the bias potential. R is the gas constant, and P c (E, T) denotes the canonical distribution at the temperature T:</ns0:p><ns0:formula xml:id='formula_0'>, (<ns0:label>3</ns0:label></ns0:formula><ns0:formula xml:id='formula_1'>) P c (𝐸,𝑇) = n(𝐸)exp ( -𝐸 𝑅𝑇 ) Z c (𝑇)</ns0:formula><ns0:p>where n(E) denotes the density of states, and Z c (T) is the partition function of the canonical distribution at the temperature T. With this definition, the potential energy distribution of an ensemble obtained from the McMD, or the multicanonical distribution, P mc (E), becomes uniform:</ns0:p><ns0:formula xml:id='formula_2'>P mc (𝐸) = n(𝐸)exp ( -𝐸 𝑚𝑐 𝑅𝑇 ) Z mc (𝑇) = n(𝐸)exp ( -𝐸 𝑅𝑇 ) P c (𝐸,𝑇) Z mc (𝑇) = 𝑍 𝑐 (𝑇)</ns0:formula><ns0:p>𝑍 𝑚𝑐 (𝑇) = 𝑐𝑜𝑛𝑠𝑡.</ns0:p><ns0:p>(4) As a result, the McMD method performs a random walk in the potential energy space and generates a uniform distribution of potential energy in a resultant ensemble. After a multicanonical ensemble is obtained, a canonical ensemble at any temperature in a sampled energy range can be generated by reweighting the probability of existence of each snapshot. Eqs. ( <ns0:ref type='formula' target='#formula_0'>3</ns0:ref>) and ( <ns0:ref type='formula'>4</ns0:ref>) include an analytical form of n(E), which is usually unknown a priori. Therefore, n(E) is approximated as a parametric function, e.g., the polynomial function, and its parameters are estimated by iterations of McMD simulations to make P mc (E) near-uniform. In the i th iteration, the bias potential is calculated using Eq. (2) with the canonical distribution obtained from the (i-1) th iteration, i.e.,</ns0:p><ns0:p>. As the result of the i th iteration, we obtain . P 𝑖 -1 c (𝐸,𝑇) P 𝑖 mc (𝐸) P 𝑖 c can be calculated as (𝐸,𝑇)</ns0:p><ns0:p>.</ns0:p><ns0:p>(5) P 𝑖 c (𝐸,𝑇) = P 𝑖 mc (𝐸)P 𝑖 -1 c (𝐸,𝑇) See Ref. <ns0:ref type='bibr' target='#b16'>(Higo et al., 2012)</ns0:ref> for details.</ns0:p></ns0:div> <ns0:div><ns0:head>V-McMD</ns0:head><ns0:p>V-McMD introduces a virtual system, which interacts with the molecular system, and the multicanonical ensemble is calculated for the entire system consisting of these two subsystems. <ns0:ref type='bibr' target='#b17'>(Higo, Umezawa & Nakamura, 2013)</ns0:ref> In practice, this method can be roughly interpreted as a combination of McMD and the simulated tempering method. The simulated tempering method replaces the system temperature with the Metropolis criterion and performs a canonical simulation until the next replacement trial. On the other hand, in V-McMD, the potential energy space is split into several regions (Figure <ns0:ref type='figure' target='#fig_2'>S1</ns0:ref>), and the molecular system is trapped in one of these regions. With a certain time interval (t VST ), the molecular system replaces the region to be trapped. The state variable governing which region traps the molecular system is called the 'virtual state,' and the system defined by the virtual state is called the 'virtual system.' The energy range of each virtual state is defined to be overlapped with the adjacent virtual states. When the molecular system has the potential energy E k in the overlapped region of the i th and (i+1) th virtual states, the state transition between these two virtual states can occur. Because this transition does not change the atomic coordinates or potential energy, the Metropolis criterion of this state transition is always satisfied. The time interval of virtual-state transitions (t VST ) should be determined arbitrarily by users. See Ref. <ns0:ref type='bibr' target='#b17'>(Higo, Umezawa & Nakamura, 2013)</ns0:ref> for details.</ns0:p></ns0:div> <ns0:div><ns0:head>TTP</ns0:head><ns0:p>According to the theory of TTP, trajectories of multiple independent McMD runs with the same molecular system and different initial conditions can be treated as a single trajectory of an McMD simulation by concatenating the trajectories in an arbitrary order. This theory requires the condition that the initial coordinates of each run are sampled from the multicanonical distribution. Because the initial coordinates of production runs can be obtained from the nearuniform potential energy distribution generated by iterative simulations, it is expected that this condition holds. The McMD method with the TTP theory, which is called the TTP-McMD method, can be considered as a hybrid Monte Carlo sampler, by assuming that the system transitions from the last snapshot of the i th run (the microscopic state m il ) to the first snapshot of the j th run (the microscopic state m jf ) via a Monte Carlo step (Figure <ns0:ref type='figure'>S2</ns0:ref>). See Ref. <ns0:ref type='bibr' target='#b18'>(Ikebe et al., 2010)</ns0:ref> for details.</ns0:p></ns0:div> <ns0:div><ns0:head>Simulation protocol</ns0:head><ns0:p>We studied the three molecular systems, which are Trp-cage, PGA8, and PGA20 in an explicitly solvated cubic periodic boundary cell, by using the TTP-V-McMD method. Random coil structures of Trp-cage, PGA8, and PGA20 were constructed using the Modeller software <ns0:ref type='bibr' target='#b41'>(Webb & Sali, 2016)</ns0:ref> without any template. The termini of the PGAs were capped with acetyl and Nmethyl groups, and the termini of the Trp-cage were not capped. Each of these molecular models was plased into a cubic box filled by water molecules; the number of water molecules were 5,097, 2,879, and 3,800 for Trp-cage, PGA8, and PGA20, respectively. In addition, a Clion was added to the Trp-cage model to cancel the net charge of the system. The net charge of the PGA models was zero because all the Glu residues were protonated.</ns0:p><ns0:p>The system was relaxed by using the GROMACS software. <ns0:ref type='bibr' target='#b32'>(Pronk et al., 2013)</ns0:ref> Energy minimizations were successively applied using the steepest descent and conjugate gradient methods. Then, an MD simulation under a constant-pressure ensemble with the Berendsen barostat was performed for 1 ns. In the first half of the simulation, gradual heating from 10 to 300 K was applied. In the simulation, the positions of the heavy atoms of the Trp-cage were restrained, the bond lengths were not constrained, and the integration time step (Δt) was 0.5 fs. Subsequently, an additional constant-pressure relaxation was applied for 1 ns with Δt = 2.0 fs, and the covalent bonds to hydrogen atoms were constrained using the LINCS method. <ns0:ref type='bibr' target='#b14'>(Hess et al., 1997;</ns0:ref><ns0:ref type='bibr' target='#b12'>Hess, 2008)</ns0:ref> The final configuration of each model was used for the TTP-V-McMD simulations. The cell dimensions of these configurations were 54.0378 Å, 44.6116 Å, and 49.1174 Å for Trp-cage, PGA8, and PGA20, respectively.</ns0:p><ns0:p>For each model, the following steps were performed using our MD simulation program, which is called myPresto/omegagene and is tailored for GE simulations. <ns0:ref type='bibr' target='#b24'>(Kasahara et al., 2016)</ns0:ref> The protein conformation was randomized with a constant-temperature simulation at 800 K. By using 30 snapshots taken from a trajectory with an interval of 300 ps, 30 independent runs were simulated with a gradual decrease in the temperature from 629 to 296 K to estimate the density of states. Successively, the TTP-V-McMD simulations were iteratively performed while updating the estimation of the density of states. <ns0:ref type='bibr' target='#b17'>(Higo, Umezawa & Nakamura, 2013</ns0:ref>) A total of 84 production runs were performed (N run = 84) for each of three different interval times for the virtual-state transitions (t VST ) meaning the interval times for bias-potential replacement: t VST = 0.002 ps, t VST = 0.2 ps, and t VST = 20 ps. The simulation length of each run (t run ) was 50 ns except for the Trp-cage model with t VST = 0.2 ps, t run , of which the simulation length was 200 ns. In total, 50.4 μs of trajectories were simulated as production runs. The virtual system was divided into seven states that cover the energy range corresponding to the canonical distribution from 296 to 629 K (Table <ns0:ref type='table' target='#tab_0'>S1</ns0:ref>). The velocity scaling method <ns0:ref type='bibr' target='#b2'>(Berendsen et al., 1984)</ns0:ref> was applied to maintain the system temperature.</ns0:p><ns0:p>For the potential parameters, the AMBER ff99SB-ILDN force field, (Lindorff-Larsen et al., 2010) the ion parameter presented by Joung and Cheatham, <ns0:ref type='bibr' target='#b22'>(Joung & Cheatham, 2008)</ns0:ref> and the TIP3P water model <ns0:ref type='bibr' target='#b21'>(Jorgensen et al., 1983)</ns0:ref> were applied. The electrostatic potential was calculated using the zero-multipole summation method, which is a non-Ewald scheme. <ns0:ref type='bibr' target='#b7'>(Fukuda, 2013;</ns0:ref><ns0:ref type='bibr' target='#b8'>Fukuda, Kamiya & Nakamura, 2014)</ns0:ref> The zero-dipole condition with the damping factor α = 0 was used. <ns0:ref type='bibr' target='#b10'>(Fukuda, Yonezawa & Nakamura, 2011;</ns0:ref><ns0:ref type='bibr' target='#b9'>Fukuda et al., 2012)</ns0:ref> </ns0:p></ns0:div> <ns0:div><ns0:head>Comparison of simulated ensembles among different settings</ns0:head><ns0:p>On the basis of the trajectories obtained from of the TTP-V-McMD production runs, the effects of the simulation conditions, i.e., the time interval for bias-strength replacement (t VST ), the number of independent runs (N run ), and the simulation time of each run (t run ), were assessed. For the Trp-cage model, we analyzed the FEL for various conformational ensembles on the basis of the two structural parameters: the root-mean-square deviation (RMSD) of Cα atoms from the native conformation (PDB ID: 1L2Y, model 1), which is denoted as RMSD native , and the radius of gyration (R g ). The FEL is visualized as the map of the potential of mean forces (PMF) on the plane defined by these two parameters. We defined the reference ensemble as the ensemble calculated for the conditions of t run = 200 ns, N run = 84, and t VST = 0.2 ps, because it is expected to have the highest reliability owing to its abundance of samples (it comprises a total of 16.8 μs of simulations). The FELs analyzed in various conditions were compared with the reference FEL with regard to the Pearson correlation coefficient of the PMF (PCC PMF ). To calculate the PCC PMF for a pair of FELs, bins without samples in one of the two FELs were ignored. In addition, the probability of the existence of the native conformations in each ensemble (P native ) was measured to characterize each ensemble. The native conformations are defined as the conformations with RMSD native ≤ 2.0 Å.</ns0:p><ns0:p>For the PGA models, the FELs were analyzed using principal component analysis (PCA) based on the Cα-Cα distances (28 and 190 dimensions for PGA8 and PGA20, respectively). The PCAs were performed using aggregations of trajectories with all the three t VST conditions for each model. For each t VST condition, the ensemble calculated from the entire trajectory (t run = 50 ns and N run = 84) was considered as the reference ensemble. The FELs were compared with regard to PCC PMF , similar to the Trp-cage case.</ns0:p><ns0:p>To assess the effects of N run and t run , PCC PMF (and P native for the Trp-cage model) were calculated for ensembles with subsets of the reference trajectories. Because there are many possibilities to pick N run runs from 84 runs and t run -length trajectories from the entire set of trajectories, we analyzed them by using the bootstrap approach. We constructed an ensemble by taking a random sampling of N run runs from 84 runs with replacement and repeated it 100 times. The statistics over the 100 ensembles were analyzed via simulation with this N run setting. This process was repeatedly performed for N run = 1, 2, ..., 84. For the case of t run , the trajectories were split into 5-ns bins, and an ensemble was constructed by taking a random sampling of t run /5 bins with replacement. We confirmed that the results of the bootstrap analyses with 100 and 200 samples were consistent (Figure <ns0:ref type='figure'>S3</ns0:ref>).</ns0:p><ns0:p>The sampling efficiency was measured in terms of the frequency of traversals between low-and high-energy regions, which were defined as the ranges [E min , E low ] and [E high , E max ], respectively. Here, E min and E max denote the minimum and maximum potential energies in all the trajectories, respectively, and E low and E high are defined as follows.</ns0:p><ns0:p>(6)</ns0:p><ns0:formula xml:id='formula_3'>𝐸 𝑙𝑜𝑤 = 𝐸 𝑚𝑖𝑛 + 𝑋(𝐸 𝑚𝑎𝑥 -𝐸 𝑚𝑖𝑛 ) (7) 𝐸 ℎ𝑖𝑔ℎ = 𝐸 𝑚𝑎𝑥 -𝑋(𝐸 𝑚𝑎𝑥 -𝐸 𝑚𝑖𝑛 )</ns0:formula><ns0:p>X is an arbitrary parameter in the range of 0 to 0.5. We assessed X = 0.2 and 0.3. The traversal frequency F travers E was calculated as the number of traversals between the two energy regions during 1.0 ns. The traversal frequencies of RMSD native and R g (F travers RMSD and F travers Rg , respectively) were also analyzed.</ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head><ns0:p>In the first part of this section, the results of the Trp-cage model are described. The reference ensemble is characterized in the subsection, 'FEL of folding-unfolding equilibrium of Trpcage'. Next, the effects of the parameters t run, N run , and their balances are discussed in the successive subsections: 'Effects of simulation time for each run,' 'Effects of number of independent runs,' and 'Balance between simulation time and number of runs,' respectively. Subsequently, the effects of the other parameter t VST are discussed in the subsection, 'Effects of frequency of bias-strength replacement.' Additionally, the following subsection, 'Effects of system complexity' describes the results of the PGA8 and PGA20 models and compares them with those of the Trp-cage model.</ns0:p></ns0:div> <ns0:div><ns0:head>FEL of folding-unfolding equilibrium of Trp-cage</ns0:head><ns0:p>For the Trp-cage model, we performed 34 iterations of TTP-V-McMD simulations while updating the estimation of the density of states, n(E), and obtained a near-uniform energy distribution (Figure <ns0:ref type='figure'>S4</ns0:ref>). On the basis of this estimation, we performed production runs with N run = 84, t run = 200 ns, and t VST = 0.2 ps. This is called the reference setting hereinafter. The resultant canonical ensemble reweighted at 300 K is referred to as the reference ensemble.</ns0:p><ns0:p>The FEL of the reference ensemble projected on the RMSD native -R g plane is shown in Figure <ns0:ref type='figure' target='#fig_2'>1A</ns0:ref>. The most stable basin corresponds to the native structure consisting of an α-helix at the N-terminus, a 3 10 -helix at the middle, and a loop region at the C-terminus (the secondary structural elements were recognized by using the DSSP software) <ns0:ref type='bibr' target='#b23'>(Kabsch & Sander, 1983)</ns0:ref>. For example, the RMSD native of one of the most probable structures in this basin was 0.994 Å (Figure <ns0:ref type='figure' target='#fig_2'>1B</ns0:ref>). The energy barrier (approximately 3.3 kcal/mol) was observed at RMSD native ≈ 3 Å in a low-R g regime. Around this barrier, the 3 10 -helix at the middle of the peptide chain was partially deformed; this deformation can be the first step of an unfolding process (Figure <ns0:ref type='figure' target='#fig_2'>1C</ns0:ref>). The details of the unfolding pathway are not discussed in this paper. The second basin was widely spread around RMSD native = 4-7 Å and R g = 7-9 Å. This corresponds to the unfolded state, and examples of the unfolded structures taken from this basin are shown in Figures <ns0:ref type='figure' target='#fig_2'>1E and F</ns0:ref>. The difference in the PMF between the bottoms of the first and second stable basins was 1.014 kcal/mol, and the population of the native conformations (P native ) was 22.37%. The landscape is qualitatively similar to that calculated using the REMD method reported by another group <ns0:ref type='bibr' target='#b4'>(Day, Paschek & Garcia, 2010)</ns0:ref>. Our TTP-V-McMD simulation successfully identified the native structure as the most stable basin in the energy landscape, by using the reference setting.</ns0:p></ns0:div> <ns0:div><ns0:head>Effects of simulation time for each run</ns0:head><ns0:p>The FELs of the Trp-cage model were drawn for a variety of t run values under the condition of N run = 84 and compared with the reference FEL. The FELs based on the trajectories of 0-25, 0-50, and 0-100 ns are shown in Figures <ns0:ref type='figure'>2A, B, and C</ns0:ref>, respectively. The overall geometries of these FELs were qualitatively similar to the reference (Figure <ns0:ref type='figure' target='#fig_2'>1A</ns0:ref>); their PCC PMF values were 0.936, 0.936, and 0.994, respectively. The bootstrap statistics of PCC PMF for each t run value are summarized in Figure <ns0:ref type='figure'>2D</ns0:ref>. For t run = 200 ns, the bootstrap average and the standard deviation (SD) of PCC PMF were 0.990 and 0.007, respectively. Even in the worst case among 100 randomly generated ensembles with t run = 200 ns, PCC PMF was 0.966. From this condition, a decrease in t run yielded a slow decay of PCC PMF , and PCC PMF reached 0.9 at t run ≈ 30 ns, which corresponds to 15% of the samples in the reference. Further decreasing t run resulted in a steep decrease of PCC PMF . Along with the decrease of the bootstrap average of PCC PMF , the SD was increased. This means that an insufficient simulation time causes a loss of robustness of the results.</ns0:p><ns0:p>In contrast to the fact that the PCC PMF decays in a shorter t run than the reference, the balance between the folded and unfolded states (P native ) was almost constant regardless of t run (Figure <ns0:ref type='figure'>2E</ns0:ref>); the bootstrap average of P native for t run = 5-200 ns was in the range of 0.220 to 0.225. However, the SD of P native was reduced with the increase of t run ; the SDs of P native at t run = 5, 50, and 200 ns were 0.07, 0.02, and 0.008, respectively. The loss of robustness due to the insufficiency of the simulation time is demonstrated in terms of not only the similarity of the entire FEL but also the stability of the native fold.</ns0:p></ns0:div> <ns0:div><ns0:head>Effects of number of independent runs</ns0:head><ns0:p>As in the previous subsection, the effects of the reduction of N run on the FELs were assessed under the condition of t run = 200 ns. Examples of FELs with N run = 10, 21, and 42 are shown in Figures <ns0:ref type='figure'>3A, B, and C</ns0:ref>, respectively; the PCC PMF values were 0.637, 0.939, and 0.993, respectively. Although the positions and wideness of the basins were similar to the reference, the FELs with a smaller N run were smoother and lacked small bumps on the landscapes. The bootstrap statistics of PCC PMF for various N run values (Figure <ns0:ref type='figure'>3D</ns0:ref>) were similar to those for t run (Figure <ns0:ref type='figure'>2D</ns0:ref>). The quantity of the samples required for PCC PMF ≥ 0.9 was approximately onefourth of the reference (N run ≈ 21). The average (and the SD) of PCC PMF at N run = 21 and 42 were 0.906 (0.07) and 0.956 (0.04), respectively. Larger N run values are needed to obtain robust results.</ns0:p><ns0:p>Regarding P native , the influence of the reduction of N run (Figure <ns0:ref type='figure'>3E</ns0:ref>) differed from that of the reduction of t run (Figure <ns0:ref type='figure'>2E</ns0:ref>). A lower N run resulted in the underestimation of the population of native conformations. P native reached at plateau for N run ≥ 21. A certain number of runs was needed to obtain robust results, and t run = 200 ns was too short to reach equilibrium with a small number of trajectories for this system.</ns0:p></ns0:div> <ns0:div><ns0:head>Balance between simulation time and number of runs</ns0:head><ns0:p>The evaluation for various t run values with N run = 84 runs (Figure <ns0:ref type='figure'>2</ns0:ref>) and that for various N run values with t run = 200 ns (Figure <ns0:ref type='figure'>3</ns0:ref>) indicate that reducing t run produced better results than reducing N run if the cumulative simulation time (N run × t run ) was the same. Figure <ns0:ref type='figure'>4</ns0:ref> shows direct comparisons of the results, indicating that high-N run conditions resulted in a higher PCC PMF and more similar values of P native to the reference, with lower SDs, than long-t run conditions. In particular, the qualitative difference between the two strategies is shown by the mean of P native .</ns0:p><ns0:p>Reducing N run resulted in the significant underestimation of the fold stability, but reducing t run did not.</ns0:p><ns0:p>In addition, we performed bootstrap analyses for all the combinations of 40-t run settings <ns0:ref type='bibr'>(5, 10, 15, ..., 200 ns)</ns0:ref> and 21-N run settings <ns0:ref type='bibr'>(4, 8, 12, ..., 84)</ns0:ref>. The average values of PCC PMF and P native in all the conditions are presented in Figures <ns0:ref type='figure'>5 and S5</ns0:ref>. The PCC PMF was proportional to log(N run × t run ). While the trend of P native is ambiguous, the use of a larger number of samples resulted in a higher P native . In the case where only small amount of data was available, a lower ratio of t run /N run (purple plots in Figure <ns0:ref type='figure'>5</ns0:ref>) yielded better results.</ns0:p></ns0:div> <ns0:div><ns0:head>Effects of frequency of bias-strength replacement</ns0:head><ns0:p>The parameter t VST controls the frequency of the bias-strength switching in the TTP-V-McMD method. We investigated the effects of this parameter by comparing the TTP-V-McMD simulations of the Trp-cage model under the three conditions-t VST = 0.002, 0.2, and 20 pswith t run = 50 ns for N run = 84.</ns0:p><ns0:p>Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref> summarizes the frequency of traversals between high-and low-potential energy regimes (F trv E ), as defined in Eqs. ( <ns0:ref type='formula'>6</ns0:ref>) and ( <ns0:ref type='formula'>7</ns0:ref>) with X = 0.3 and 0.2, as well as the frequency of traversals between RMSD native (F trv RMSD ) and R g (F trv Rg ). The simulations with a shorter t VST resulted in faster traversals in the potential energy space, indicating that with a shorter t VST , a wider potential energy range can be sampled in a shorter time. However, faster traversal in the potential energy space does not ensure faster transition of the protein conformation. For both X = 0.2 and 0.3, although the setting of t VST = 0.002 ps yielded the highest F trv E , this condition did not yield a higher F trv RMSD and F trv Rg compared to when a longer t VST was used. This result indicates that the relaxation of the conformation requires a longer time than that of the potential energy. If a strong bias is applied and the system takes a high-potential energy state, it can return to lowenergy states before conformational changes. Therefore, a moderate speed for traversals in the potential energy space is ideal for efficient conformational sampling. In the case of X = 0.2, t VST = 0.2 ps exhibited the most frequent conformational changes.</ns0:p><ns0:p>In addition, the resultant ensembles were slightly affected by the setting of t VST . We analyzed P native for ensembles of various t run values with N run = 84 using the bootstrap method (Figure <ns0:ref type='figure' target='#fig_4'>S6</ns0:ref>). The results for all three t VST values showed similar trends, i.e., near-constant average values and the gradual decay of the SD with the increase of t run . While t VST = 0.2 ps showed a smaller P native than the other two t VST settings, the difference was smaller than the SD. On the other hand, higher SD values were observed in the following order: t VST = 0.2 > 20 > 0.002 ps. This is consistent with the order of F trv RMSD and F trv Rg (Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref>). The result indicates that more frequent traversals between high-and low-RMSD native conformations make it possible to explore a wider region of the conformational space; thus, the population of the native conformation decreases, and the SD increases.</ns0:p><ns0:p>Regarding the PCC PMF with the reference setting (t VST = 0.2 ps, N run = 84, and t run = 200 ns), the average PCC PMF values at t run = 50 ns differed among different settings of t VST (Figure <ns0:ref type='figure' target='#fig_4'>S6</ns0:ref>). This indicates that changing t VST yields subtle differences in the resultant ensemble. Regarding the balance between t run and N run , the trends were similar for all the settings of t VST (Figure <ns0:ref type='figure'>S7</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>Effects of system complexity: comparison with the PGA models</ns0:head><ns0:p>We performed the same analyses for the molecular models of PGA8 and PGA20. In contrast to Trp-cage, these peptides did not exhibit a particular fold. The FELs of both PGA8 and PGA20 were unimodal distributions, the basins of which consisted of a variety of collapsed conformations (Figure <ns0:ref type='figure' target='#fig_4'>6</ns0:ref> for t VST = 0.2 ps). The ensembles included short secondary structural elements but they were unstable. Although the shape of the small bumps in the basins differed depending on the simulation conditions, the overall geometries of the FELs were similar (Figure <ns0:ref type='figure'>S8</ns0:ref> for t VST = 0.002 ps and 20 ps).</ns0:p><ns0:p>Regarding the balance between t run and N run , Figure <ns0:ref type='figure'>7</ns0:ref> shows the bootstrap averages of PCC PMF between the ensemble calculated by the full-length trajectory (t run = 50 ns and N run = 84) and those calculated by the reduced trajectories. No clear differences were found between the PCC PMF curve with reduced t run and that with reduced N run for both the PGA8 and PGA20 (Figure <ns0:ref type='figure'>7</ns0:ref> for t VST = 0.2 ps; Figure <ns0:ref type='figure'>S9</ns0:ref> for the other conditions). A small number of long simulations exhibited the similar efficiency as that of many short simulations. In addition, no significant differences were found between the results of PGA8 and PGA20. It is noteworthy that the conformational space of PGA20 is considerably wider than that of PGA8 and similar to that of Trp-cage, because the conformational space volume of polypeptides is determined primarily by their length, Therefore, we concluded that the effects of balance between t run and N run are determined by the complexity of the FEL (e.g., existence of the free-energy barrier) rather than the conformational space volume. An increase in the number of runs is more effective for a system with more complex FEL.</ns0:p><ns0:p>For the PGA models, the frequencies of traversals in the potential energy and R g spaces (F trv E and F trv Rg , respectively) are summarized in Table <ns0:ref type='table' target='#tab_1'>2</ns0:ref>. Both the PGA8 and PGA20 models yielded similar trends as the Trp-cage model (Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref>). Although frequent replacements of biaspotential strength enhanced the traversals in the potential energy space, they did not enhance the conformational changes in terms of R g . This implies that the conformational changes are much slower than the potential energy changes even if there is no free-energy barrier exists in the landscape. However, in contrast to the Trp-cage case, the drawback of the frequent replacement, that is, slow traversals in the conformational space, is unclear in the case of PGA20.</ns0:p></ns0:div> <ns0:div><ns0:head>Discussion</ns0:head><ns0:p>We examined the performance of the TTP-V-McMD method with regard to two adjustable settings: (i) the balance between the number of runs (N run ) and the simulation length in each run (t run ) and (ii) the frequency of the bias-strength switching (t VST ). For (i), in the Trp-cage model including folding-unfolding transition, we found higher robustness of the conditions with a larger number of runs than with longer simulations. In particular, the probability of the existence of native conformations in a resultant ensemble (P native ) was more sensitive to the condition than the entire similarity of the FEL. However, for the cases of PGAs without free-energy barrier in their FELs, no significant effect was shown in the balance between the number and length of simulations. Therefore, the optimal balance depended on the molecular system, and the complexity of the FELs was a key feature rather than the degree of freedom. In any case, increasing the number of simulations was recommended because it is not worse than increasing the length of each run. This result is practically useful because performing many parallel runs is easier than executing a single long simulation. While the result obtained here encourages performing many short runs, it requires the condition that the initial structures of the production runs are uniformly sampled from the multicanonical ensemble, whose energy distribution is uniform. <ns0:ref type='bibr' target='#b18'>(Ikebe et al., 2010)</ns0:ref> As our protocol samples the initial structures of the production runs from the previous iteration of the McMD, it is expected that this condition holds.</ns0:p><ns0:p>For (ii), whereas higher frequencies of bias-strength replacement enhance the sampling of a wider range of potential energy, they do not ensure the enhancement of the sampling of a wider range of conformations. This means that the enhancement of the sampling along one variable (e.g., potential energy or temperature) does not ensure the enhancement of the sampling along another variable (e.g., RMSD and R g ). Rapid traversals in the energy space sometimes obtain a high energy and return to the low-energy regime before conformational change regardless of the existence of free-energy barrier in the FEL. A moderate frequency is needed to maximize the performance for any molecular system.</ns0:p><ns0:p>The findings that we obtained by applying the TTP-V-McMD method provide insight into the characteristics of many other GE methods. (i) For GE methods that involve running independent parallel simulations, e.g., simulated tempering and AUS, performing many short runs can be more effective than increasing the length of each run. For GE methods where parallel runs are coupled, e.g., the REMD method, this conclusion should not be simply applied. For example, an increase in the number of runs in the REMD method resulted in larger overlaps of the distributions of neighboring replicas, along with an increase in the acceptance probability of replica-exchange trials. Our previous evaluation for the REMD method showed that a larger number of replicas does not always yield better results. <ns0:ref type='bibr'>(Iwai, Kasahara & Takahashi, 2018)</ns0:ref> The number of runs should be adjusted independently from the coupling condition of the parallel runs; for example, the number of runs in a REMD simulation could be increased by performing two or more independent REMD simulations with different initial conformations, and aggregating the resultant ensembles. (ii) Regarding the frequency of the bias-strength replacement, the conclusion that the interval should be long enough to relax the conformation could be transferred to other GE methods. For the REMD methods, the effects of the interval for replica-exchange trials have been reported; while some studies recommended shorter intervals <ns0:ref type='bibr' target='#b38'>(Sindhikara, Meng & Roitberg, 2008;</ns0:ref><ns0:ref type='bibr' target='#b37'>Sindhikara, Emerson & Roitberg, 2010)</ns0:ref>, the side effects of highly frequent exchange trials have also been reported and were consistent to our result <ns0:ref type='bibr' target='#b30'>(Periole & Mark, 2007;</ns0:ref><ns0:ref type='bibr'>Iwai, Kasahara & Takahashi, 2018)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions</ns0:head><ns0:p>In this study, the effects of two parameters of GE methods, i.e., (i) the balance between the number of runs (N run ) and the simulation length in each run (t run ) and (ii) the frequency of the bias-strength switching (t VST ) were extensively examined with using all-atom explicit-solvent models of three polypeptides that are a foldable mini-protein and disordered peptides. We suggest a guide to adjust the setting for general molecular systems and GE methods. (i) Increasing in the number of runs should be prioritized rather than increasing the simulation length. (ii) Highly frequent replacements of the bias potentials may yield side effects because conformational relaxation was slower than potential energy relaxation. The time interval for replacement should be longer than or equal to 0.2 ps. Manuscript to be reviewed </ns0:p><ns0:note type='other'>Chemistry Journals Figure 2</ns0:note></ns0:div><ns0:figure xml:id='fig_0'><ns0:head>, ( 1 )</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>𝐻 = 𝐾 + 𝐸 𝑚𝑐 PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2019:07:39005:1:0:NEW 20 Sep 2019)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2019:07:39005:1:0:NEW 20 Sep 2019)Manuscript to be reviewed Chemistry JournalsAnalytical, Inorganic, Organic, Physical, Materials Science </ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>Figure 1 FEL</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head>FELs</ns0:head><ns0:label /><ns0:figDesc>Figure 3</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head>Figure 6 FELs</ns0:head><ns0:label>6</ns0:label><ns0:figDesc>Figure 6</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='19,42.52,237.67,525.00,413.25' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='20,42.52,288.67,525.00,441.00' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='21,42.52,308.92,525.00,275.25' type='bitmap' /></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head>Table 1 .</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Average values (and the standard errors) of the traversal frequencies over 84 runs for the Trp-cage model.</ns0:figDesc><ns0:table><ns0:row><ns0:cell>t VST (ps)</ns0:cell><ns0:cell>0.002</ns0:cell><ns0:cell>0.2</ns0:cell><ns0:cell>20</ns0:cell></ns0:row><ns0:row><ns0:cell>X</ns0:cell><ns0:cell /><ns0:cell>0.3</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>F trv E (ns −1 )</ns0:cell><ns0:cell>1.63 (0.06)</ns0:cell><ns0:cell>1.45 (0.04)</ns0:cell><ns0:cell>1.02 (0.04)</ns0:cell></ns0:row><ns0:row><ns0:cell>F trv RMSD (ns −1 )</ns0:cell><ns0:cell>0.057</ns0:cell><ns0:cell>0.060</ns0:cell><ns0:cell>0.062</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>(0.006)</ns0:cell><ns0:cell>(0.004)</ns0:cell><ns0:cell>(0.006)</ns0:cell></ns0:row><ns0:row><ns0:cell>F trv Rg (ns −1 )</ns0:cell><ns0:cell>0.040 (0.005)</ns0:cell><ns0:cell>0.044 (0.003)</ns0:cell><ns0:cell>0.050 (0.006)</ns0:cell></ns0:row><ns0:row><ns0:cell>X</ns0:cell><ns0:cell /><ns0:cell>0.2</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>F trv E (ns −1 )</ns0:cell><ns0:cell>0.70 (0.03)</ns0:cell><ns0:cell>0.62 (0.02)</ns0:cell><ns0:cell>0.46 (0.02)</ns0:cell></ns0:row><ns0:row><ns0:cell>F trv RMSD (ns −1 )</ns0:cell><ns0:cell>0.005 (0.001)</ns0:cell><ns0:cell>0.011 (0.001)</ns0:cell><ns0:cell>0.006 (0.002)</ns0:cell></ns0:row><ns0:row><ns0:cell>F trv Rg (ns −1 )</ns0:cell><ns0:cell>0.008</ns0:cell><ns0:cell>0.012</ns0:cell><ns0:cell>0.007</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>(0.002)</ns0:cell><ns0:cell>(0.001)</ns0:cell><ns0:cell>(0.002)</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_1'><ns0:head>Table 2 .</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Average values (and standard errors) of the traversal frequencies over 84 runs for PGA models.</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Model</ns0:cell><ns0:cell>PGA8</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell>PGA20</ns0:cell><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>t VST (ps)</ns0:cell><ns0:cell>0.002</ns0:cell><ns0:cell>0.2</ns0:cell><ns0:cell>20</ns0:cell><ns0:cell>0.002</ns0:cell><ns0:cell>0.2</ns0:cell><ns0:cell>20</ns0:cell></ns0:row><ns0:row><ns0:cell>X</ns0:cell><ns0:cell>0.3</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell>0.3</ns0:cell><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>F trv E (ns −1 )</ns0:cell><ns0:cell>2.91 (0.03)</ns0:cell><ns0:cell>2.70 (0.03)</ns0:cell><ns0:cell>0.86 (0.02)</ns0:cell><ns0:cell>1.05 (0.04)</ns0:cell><ns0:cell>0.99 (0.03)</ns0:cell><ns0:cell>0.46 (0.02)</ns0:cell></ns0:row><ns0:row><ns0:cell>F trv Rg (ns −1 )</ns0:cell><ns0:cell>0.44</ns0:cell><ns0:cell>0.47</ns0:cell><ns0:cell>0.47</ns0:cell><ns0:cell>0.045</ns0:cell><ns0:cell>0.044</ns0:cell><ns0:cell>0.049</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>(0.02)</ns0:cell><ns0:cell>(0.02)</ns0:cell><ns0:cell>(0.02)</ns0:cell><ns0:cell>(0.005)</ns0:cell><ns0:cell>(0.005)</ns0:cell><ns0:cell>(0.006)</ns0:cell></ns0:row><ns0:row><ns0:cell>X</ns0:cell><ns0:cell>0.2</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell>0.2</ns0:cell><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>F trv E (ns −1 )</ns0:cell><ns0:cell>1.58 (0.02)</ns0:cell><ns0:cell>1.53 (0.02)</ns0:cell><ns0:cell>0.50 (0.01)</ns0:cell><ns0:cell>0.4 (0.02)</ns0:cell><ns0:cell>0.41 (0.01)</ns0:cell><ns0:cell>0.22 (0.01)</ns0:cell></ns0:row><ns0:row><ns0:cell>F trv Rg (ns −1 )</ns0:cell><ns0:cell>0.117 (0.007)</ns0:cell><ns0:cell>0.147 (0.007)</ns0:cell><ns0:cell>0.146 (0.007)</ns0:cell><ns0:cell>0.011 (0.002)</ns0:cell><ns0:cell>0.013 (0.002)</ns0:cell><ns0:cell>0.015 (0.003)</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:note place='foot'>Analytical, Inorganic, Organic, Physical, Materials Science </ns0:note> </ns0:body> "	"A point-by-point reply with respect to individual comments from the referees is appended. In this document, the reviewers’ comments, our replies, and quoted sentences from the manuscript and their revised parts are written in blue, black, green, and red texts, respectively. Reply to the comments from the Reviewer #1. We appreciate the reviewer for generously positive comments. We have responded all the points kindly raised by the reviewer, and have revised the manuscript. Basic reporting   The authors have written clear and unambiguous sentences, which fully meet the criteria of this paper. However, three minor fixes are required as follows:   1. The context on lines 74-76 'It is reported that .....' doesn't have the reference. By clarifying who did the study, you can avoid the confusion of readers.   The references attached to the next sentence is moved to here.  It is reported that the frequency of the bias-strength replacement affects the resultant ensembles for the REMD method. (Periole & Mark, 2007; Sindhikara, Meng & Roitberg, 2008; Rosta & Hummer, 2009; Sindhikara, Emerson & Roitberg, 2010; Jani, Sonavane & Joshi, 2014; Iwai, Kasahara & Takahashi, 2018) Although higher frequencies enhance the traversals in the temperature space, they are suspected as an origin of artifacts. 2. '0.002 ps ns,' on line 205 is a spelling mistake. I think this is '0.002 ps,'.   We revised it accordingly.  3. The simulation condition t_vst of the data shown in figure 6 is different between line 389 of the text and the caption. I guess that the text is correct.   As the reviewer guessed, 't_vst = 0.2 ps' is correct description. We have revised the legend of Fig 6 accordingly.  Figure 6. FELs calculated by ensembles of (A) PGA8 and (B) PGA20 using trun = 50 ns and Nrun = 84 with tVST = 0.2 ps. (C, D, E) Examples of snapshots in the basins marked in the panels (A) and (B). Comments for the Author   I require two corrections to improve the reader's readability as follows:   1. The types of lines in the line graph as shown in figure S6 should be reconsidered. It is difficult for readers to distinguish between the two dotted lines in the caption.   We have colored the lines with different colors.  Figure S6. The effects of tVST on (A) the average of PCCPMF, (B) the SD of PCCPMF, (C) the average of Pnative, and (D) the SD of Pnative for the Trp-cage model. The horizontal axis indicates trun. The blue, red, and orange lines indicate tVST = 0.002, 0.2, and 20 ps, respectively. 2. There are too many digits in the numerical data shown in the text such as PCC_PMF and P_native. Especially, there is no point in displaying the value of standard deviation with many digits.   The number of digits has been determined based on the standard error. We have decreased the digits from the standard deviation. Reply to the comments from the Reviewer #2. We appreciate the reviewer for generously positive comments. Basic reporting   I do not see any raw data in the SI. As much raw data as is practical should be provided.   We have uploaded raw data on the 3rd party repository, figshare. See the following URL: https://figshare.com/articles/fel_tar_gz/8797895 In addition, we have revised the manuscript according to some points raised by the reviewer 1. See the reply to the reviewer 1. Reply to the comments from the Reviewer #3. We appreciate the reviewer for generously positive comments. We have revised the manuscript according to some points raised by the reviewer 1. See the reply to the reviewer 1. "	Here is a paper. Please give your review comments after reading it.
488	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Anthralin (1,8-dihydroxyanthrone, 1,8-dihydroxy-9(10H)-anthracenone), also known as dithranol and cignolin, is one of the most efficient drugs in the treatment of psoriasis and other skin diseases. The precise mode of biochemical action is not fully understood, but the activity of the drug is increased by the influence of UV radiation. In the present investigation, the UV absorption of anthralin is studied by synchrotron radiation linear dichroism (SRLD) spectroscopy on molecular samples partially aligned in stretched polyethylene, covering the near and vacuum UV regions with wavenumbers ranging from 23000 to 58000 cm -1 (430-170 nm). The observed polarization spectra are well predicted by quantum chemical calculations using time-dependent density functional theory (TD-DFT). About a dozen spectral features are assigned to computed electronic transitions.</ns0:p><ns0:p>The calculations support interpretation of the anomalous fluorescence of anthralin as a result of barrier-less excited state intramolecular proton transfer (ESIPT) to the tautomer 8,9-dihydroxy-1(10H)-anthracenone.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>For more than 100 years anthralin (A), also known as dithranol and cignolin, has been applied as an effective topical agent for the treatment of the skin disease psoriasis <ns0:ref type='bibr' target='#b2'>(Ashton et al., 1983;</ns0:ref><ns0:ref type='bibr' target='#b38'>van de Kerkhof et al., 1991;</ns0:ref><ns0:ref type='bibr' target='#b33'>Sehgal, Verma & Khurana, 2014;</ns0:ref><ns0:ref type='bibr' target='#b15'>Körber et al., 2019)</ns0:ref>. The compound was for many years believed to be anthracene-1,8,9-triol, but spectroscopic and crystallographic analyses indicated that the prevailing constitution is that of the tautomer 1,8-dihydroxy-9(10H)-anthracenone <ns0:ref type='bibr' target='#b13'>(Hellier & Whitefield, 1967;</ns0:ref><ns0:ref type='bibr' target='#b3'>Avdovich & Neville, 1980;</ns0:ref><ns0:ref type='bibr' target='#b0'>Ahmed, 1980)</ns0:ref>, see Fig. <ns0:ref type='figure' target='#fig_2'>1</ns0:ref>. A is a reactive compound characterized by complicated red-ox properties <ns0:ref type='bibr' target='#b5'>(Czerwinska et al., 2006;</ns0:ref><ns0:ref type='bibr'>Psenichnyuk & Komolov, 2014)</ns0:ref> and it exhibits anomalous fluorescence with a Stokes shift of 10500 cm -1 , indicating large rearrangement in the excited state <ns0:ref type='bibr' target='#b27'>(Møller et al., 1998)</ns0:ref>. The precise mode of biochemical action is not fully understood, but the application of UV radiation is known to increase the activity of the drug <ns0:ref type='bibr' target='#b16'>(Lapolla et al., 2011)</ns0:ref>. Bally and coworkers <ns0:ref type='bibr' target='#b5'>(Czerwinska et al., 2006)</ns0:ref> and <ns0:ref type='bibr'>Pshenichnyuk and Komolov (Psenichnyuk & Komolov, 2014)</ns0:ref> thus considered the involvement of excited electronic states generated by UV irradiation.</ns0:p><ns0:p>In the present work, we investigate the excited states of A by synchrotron radiation linear dichroism (SRLD) UV spectroscopy on molecular samples partially aligned in stretched lowdensity polyethylene (PE). The use of synchrotron radiation <ns0:ref type='bibr' target='#b22'>(Miles et al, 2007;</ns0:ref><ns0:ref type='bibr' target='#b23'>Miles et al., 2008)</ns0:ref> provides increased signal-to-noise ratio in the UV region and enables a significant expansion of the accessible spectral range, compared with the use of a traditional light source <ns0:ref type='bibr' target='#b28'>(Nguyen et al., 2018;</ns0:ref><ns0:ref type='bibr' target='#b29'>Nguyen et al., 2019)</ns0:ref>. In the present study, the measurement is extended into the vacuum UV, covering the region up to 58000 cm -1 (170 nm); this is an extension of the previously investigated range by about 11000 cm -1 (1.4 eV) <ns0:ref type='bibr' target='#b1'>(Andersen & Spanget-Larsen, 1997)</ns0:ref>. Linear dichroism (LD) spectroscopy yields experimental information on the molecular transition moment directions of the observed absorption bands <ns0:ref type='bibr' target='#b21'>(Michl & Thulstrup, 1986;</ns0:ref><ns0:ref type='bibr' target='#b37'>Thulstrup & Michl, 1989;</ns0:ref><ns0:ref type='bibr' target='#b32'>Rodger & Nordén, 1997;</ns0:ref><ns0:ref type='bibr' target='#b30'>Nordén, Rodger & Dafforn, 2010)</ns0:ref>. The observed spectra are discussed with reference to the results of time-dependent density functional theory (TD-DFT) calculations <ns0:ref type='bibr' target='#b19'>(Marques et al., 2012;</ns0:ref><ns0:ref type='bibr' target='#b7'>Foresman & Frisch, 2015)</ns0:ref>. Additional information is provided as Supplemental data.</ns0:p></ns0:div> <ns0:div><ns0:head>Materials & Methods</ns0:head></ns0:div> <ns0:div><ns0:head>Sample Preparation</ns0:head><ns0:p>A sample of A <ns0:ref type='bibr'>(CAS No. 1143-38-0)</ns0:ref> was purchased from Sigma-Aldrich and purified by column chromatography as previously described <ns0:ref type='bibr' target='#b1'>(Andersen & Spanget-Larsen, 1997)</ns0:ref>. The near-UV absorbance spectrum of the purified substance in n-heptane solution (Merck Uvasol) is shown in Fig. <ns0:ref type='figure' target='#fig_2'>S1</ns0:ref>. Low-density polyethylene (PE) 100m sheet material was obtained from Hinnum Plast A/S. A 2.5×6 cm PE piece cut from the sheet was purified by extraction with chloroform (Merck Uvasol) at 50 °C for one day. A was introduced by submersion of the dried PE sample into a saturated chloroform solution of the substance in a sealed container at room temperature for three days. After evaporation of the chloroform from the doped sample, the surface was cleaned with ethanol (Merck Uvasol) to remove crystalline deposits. The PE sample was finally uniaxially stretched by 500%. A sample without solute was produced in the same manner for use as a reference. Further details on stretched polyethylene samples can be found in the literature <ns0:ref type='bibr' target='#b21'>(Michl & Thulstrup, 1986;</ns0:ref><ns0:ref type='bibr' target='#b37'>Thulstrup & Michl, 1989)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Linear dichroism (LD) spectroscopy</ns0:head><ns0:p>LD spectra in the range 31300-20000 cm -1 (320-500 nm) were recorded on a UV-2101PC Shimadzu spectrophotometer at Roskilde University. SRLD spectra were measured in the range 58000-31300 cm -1 (170-320 nm) on the CD1 beamline <ns0:ref type='bibr' target='#b22'>(Miles et al, 2007;</ns0:ref><ns0:ref type='bibr' target='#b23'>Miles et al., 2008)</ns0:ref> at the storage ring ASTRID at the Centre for Storage Ring Facilities (ISA). As previously described <ns0:ref type='bibr' target='#b1'>(Andersen & Spanget-Larsen, 1997;</ns0:ref><ns0:ref type='bibr' target='#b28'>Nguyen et al., 2018;</ns0:ref><ns0:ref type='bibr' target='#b29'>Nguyen et al., 2019)</ns0:ref>, two absorbance curves were recorded at room temperature with the electric vector of the sample beam parallel 𝐴 ISO (𝜈) = 𝐸 𝑈 (𝜈) + 2𝐸 𝑉 (𝜈)</ns0:p></ns0:div> <ns0:div><ns0:head>Calculations</ns0:head><ns0:p>Quantum chemical calculations considering isolated molecules in the gas phase were performed by using the Gaussian16 software package <ns0:ref type='bibr'>(Frisch et al., 2016)</ns0:ref>. Optimizations of molecular geometry were performed with B3LYP <ns0:ref type='bibr' target='#b4'>(Becke, 1993;</ns0:ref><ns0:ref type='bibr' target='#b17'>Lee, Yang & Parr, 1988)</ns0:ref>. This DFT was found to be very successful in the prediction of molecular and vibrational structures of compounds like A with intramolecular C=O•••HO hydrogen bonding (Spanget-Larsen, <ns0:ref type='bibr' target='#b36'>Hansen & Hansen, 2011;</ns0:ref><ns0:ref type='bibr' target='#b12'>Hansen, Spanget-Larsen, 2012)</ns0:ref>. Geometry optimizations for the ground state (S 0 ) and the lowest excited singlet state (S 1 ) of A were carried out with B3LYP and TD-B3LYP, respectively, using the 6-31+G(d,p) basis set <ns0:ref type='bibr' target='#b7'>(Foresman & Frisch, 2015;</ns0:ref><ns0:ref type='bibr'>Frisch et al., 2016)</ns0:ref>.</ns0:p><ns0:p>Vertical electronic transitions from the ground state to the 60 lowest excited singlet states were computed with TD-CAM-B3LYP <ns0:ref type='bibr' target='#b39'>(Yanai, Tew & Handy, 2004)</ns0:ref> and the basis set AUG-cc-pVTZ <ns0:ref type='bibr' target='#b6'>(Dunning, 1989;</ns0:ref><ns0:ref type='bibr' target='#b14'>Kendall, Dunning & Harrison, 1992)</ns0:ref>. We recently found that this long-range corrected procedure is adequate in the prediction of electronic transitions of aromatic chromophores in the near and vacuum UV regions <ns0:ref type='bibr' target='#b28'>(Nguyen et al., 2018;</ns0:ref><ns0:ref type='bibr' target='#b29'>Nguyen et al., 2019)</ns0:ref>. A constant term was subtracted from the excitation wavenumbers calculated with TD-CAM-B3LYP in order to facilitate comparison with the observed spectra <ns0:ref type='bibr' target='#b11'>(Grimme, 2004;</ns0:ref><ns0:ref type='bibr' target='#b28'>Nguyen et al., 2018;</ns0:ref><ns0:ref type='bibr' target='#b29'>Nguyen et al., 2019)</ns0:ref>; an empirical correction of 3500 cm −1 was found to be adequate in the present case. The main predicted transitions are listed in Table <ns0:ref type='table'>1</ns0:ref> together with the observed ones. A complete listing of the calculated transitions is provided as Data S1. Graphical representations of transitions to 1 A 1 and 1 B 2 states are shown in Fig. <ns0:ref type='figure'>3</ns0:ref>. Gaussian convolutions considering all allowed transitions are provided as Fig. <ns0:ref type='figure'>S3</ns0:ref>, using previously described procedures <ns0:ref type='bibr'>(Serr & O'Boyle, 2009;</ns0:ref><ns0:ref type='bibr' target='#b29'>Nguyen et al., 2019)</ns0:ref>. Detailed results for the ESIPT photoproduct 8,9-dihydroxy-1(10H)-anthracenone are given in Data S2, results for additional excited state configurations are outlined in Fig. <ns0:ref type='figure'>S4</ns0:ref>. observed spectroscopic features i <ns0:ref type='bibr' target='#b21'>(Michl & Thulstrup, 1986;</ns0:ref><ns0:ref type='bibr' target='#b37'>Thulstrup & Michl, 1989)</ns0:ref>:</ns0:p></ns0:div> <ns0:div><ns0:head>Results and discussion</ns0:head><ns0:p>(1)</ns0:p><ns0:formula xml:id='formula_0'>𝐾 𝑖 = 〈cos 2 (𝑀 𝑖 ,𝑈)〉</ns0:formula><ns0:p>The orientation factor is defined as an average over all solute molecules in the light path, indicated by the pointed brackets in Eqn. (1), where is the angle of the moment vector of (𝑀 𝑖 ,𝑈)</ns0:p><ns0:p>transition i with the polymer stretching direction, . The values may be determined by the</ns0:p></ns0:div> <ns0:div><ns0:head>𝑈 𝐾 𝑖</ns0:head><ns0:p>graphical TEM procedure <ns0:ref type='bibr' target='#b21'>(Michl & Thulstrup, 1986;</ns0:ref><ns0:ref type='bibr' target='#b37'>Thulstrup & Michl, 1989)</ns0:ref> which involves construction of linear combinations of and . In the present application we consider the 𝐸 𝑈 (𝜈)</ns0:p><ns0:p>𝐸 𝑉 (𝜈) reduced absorbance curves <ns0:ref type='bibr' target='#b18'>(Madsen et al., 1992)</ns0:ref>: determined by visual inspection. The molecular point group of A is C 2v <ns0:ref type='bibr' target='#b0'>(Ahmed, 1980)</ns0:ref> and allowed vertical transitions must be polarized along the three symmetry axes x, y, and z, corresponding to excited states of B 1 , B 2 , and A 1 symmetry, respectively. Hence, only three different K i values corresponding to K x , K y , and K z are expected. From the curves in Fig. <ns0:ref type='figure'>2</ns0:ref> 𝑟 𝐾 (𝜈) (middle) K values close to 0.60 can be estimated for the features at 34600, 39200, 44000 and 46100 cm -1 , and values close to 0.25 for those at 37600, 42900, 51000 and 53400 cm -1 . As previously discussed <ns0:ref type='bibr' target='#b1'>(Andersen & Spanget-Larsen, 1997)</ns0:ref>, the relatively broad band with maximum close to 27500 cm -1 (~365 nm) is due to two near-degenerate, differently polarized transitions; their K values cannot be determined directly.</ns0:p><ns0:formula xml:id='formula_1'>𝑟 𝐾 (𝜈) (2) 𝑟 𝐾 (𝜈) = ( 1 -𝐾 ) 𝐸 𝑈 (𝜈) -2𝐾𝐸 𝑉 (</ns0:formula><ns0:p>The K values 0.60 and 0.25 must be assigned to K y and K z , corresponding to transitions polarized along the in-plane long and short molecular axes y and z. Under the assumption that absorbance polarized along the out-of-plane x axis is negligible in the present spectra, it is possible to construct the partial absorbance curves and indicating y-and z-polarized 𝐴 𝑦 (𝜈)</ns0:p><ns0:p>𝐴 𝑧 (𝜈)</ns0:p><ns0:p>intensity <ns0:ref type='bibr' target='#b18'>(Madsen et al., 1992)</ns0:ref>:</ns0:p><ns0:p>(3)</ns0:p><ns0:formula xml:id='formula_2'>𝐴 𝑦 (𝜈) = (𝐾 𝑦 -𝐾 𝑧 ) -1 •𝑟 𝐾 𝑧 (𝜈) 𝐴 𝑧 ( 𝜈 ) = (𝐾 𝑧 -𝐾 𝑦 ) -1 •𝑟 𝐾 𝑦 ( 𝜈 )</ns0:formula><ns0:p>The curves and produced with = (0.60, 0.25) are shown in Fig. <ns0:ref type='figure'>2</ns0:ref> (bottom) 𝐴 𝑦 (𝜈)</ns0:p><ns0:p>𝐴 𝑧 (𝜈)</ns0:p><ns0:formula xml:id='formula_3'>(𝐾 𝑦 ,𝐾 𝑧 )</ns0:formula><ns0:p>and in Fig. <ns0:ref type='figure'>3</ns0:ref>. They are shown on an expanded absorbance scale in Fig. <ns0:ref type='figure'>S2</ns0:ref>, together with the isotropic absorbance curve . The latter is equal to three</ns0:p><ns0:formula xml:id='formula_4'>𝐴 ISO (𝜈) = 𝐴 𝑦 (𝜈) + 𝐴 𝑧 (𝜈) = 𝐸 𝑈 (𝜈) + 2𝐸 𝑉 (𝜈)</ns0:formula><ns0:p>times the absorbance that would have been measured in an isotropic experiment on the same sample. Six long-axis (y) polarized features B, C, E, G, H, and L and six short-axis (z) polarized features A, D, F, I, J, and K are indicated. Observed peak wavenumbers, absorbances, and polarization directions are listed in Table <ns0:ref type='table'>1</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Electronic transitions</ns0:head><ns0:p>The spectrum starts with a relatively broad band system with maximum close to 27500 cm -1 (364 nm). The absorbance is predominantly long-axis (y) polarized, overlapping weaker short-axis (z) polarized intensity. The short-axis polarized absorbance A with maximum at 27300 cm -1 (366 nm) can be assigned to the 2 1 A 1 state computed at 27900 cm -1 (Table <ns0:ref type='table'>1</ns0:ref>, Fig. <ns0:ref type='figure'>3</ns0:ref>). The computed transition is well described by promotion of an electron from the 6b 1 () HOMO to the 7b 1 () LUMO. The intense long-axis polarized band B peaking at 27700 cm -1 (361 nm) is assigned to the 1 1 B 2 state predicted at 27700 cm -1 (Table <ns0:ref type='table'>1</ns0:ref>, Fig. <ns0:ref type='figure'>3</ns0:ref>). This transition is primarily due to the promotion from the 4a 2 () SHOMO to the 7b 1 () LUMO.</ns0:p><ns0:p>Band C with maximum at 34600 cm -1 (289 nm) is purely long-axis (y) polarized and is easily assigned to the 2 1 B 2 state computed at 34600 cm -1 (Table <ns0:ref type='table'>1</ns0:ref>, Fig. <ns0:ref type='figure'>3</ns0:ref>). The following absorbance band is resolved into two differently polarized components, D and E at 37600 and 39200 cm -1 (266 and 255 nm). They can be assigned to the 3 1 A 1 and 3 1 B 2 states computed at 38600 and 40900 cm -1 (Table <ns0:ref type='table'>1</ns0:ref>, Fig. <ns0:ref type='figure'>3</ns0:ref>). Band F at 42900 cm -1 (233 nm) is predominantly shortaxis (z) polarized and is assigned to the 5 1 A 1 state predicted at 43200 cm -1 (Table <ns0:ref type='table'>1</ns0:ref>, Fig. <ns0:ref type='figure'>3</ns0:ref>). The band overlaps the onset of long-axis polarized (y) intensity G around 44000 cm -1 (~227 nm). This feature is possibly due to the 4 1 B 2 state computed at 43500 cm -1 (Table <ns0:ref type='table'>1</ns0:ref>, Fig. <ns0:ref type='figure'>3</ns0:ref>), but it may also involve b 2 symmetric vibrational components of band F, gaining long-axis polarized intensity by vibronic coupling with the strong band H. In the isotropic spectrum recorded in n-heptane solution, transition F apparently corresponds to the shoulder close to 43500 cm -1 and transition G to the peak at 44300 cm -1 (Fig. <ns0:ref type='figure' target='#fig_2'>S1</ns0:ref>).</ns0:p><ns0:p>Assignment of individual transitions in the high-wavenumber region is complicated by the presence of broad, overlapping bands and by a high density of electronic states. Our TD-CAM-B3LYP calculation predicts about 50 electronic transitions between 60000 and 45000 cm -1 (Data S1). In addition, the applied theoretical model may be less accurate in this region and the suggested assignments of observed features to computed transitions are necessarily tentative.</ns0:p><ns0:p>The observed long-axis (y) polarized absorbance in this region displays a strong band H with maximum at 46100 cm -1 (217 nm). According to the calculated transitions, this band can be assigned to the 5 1 B 2 state predicted at 47400 cm -1 (Table <ns0:ref type='table'>1</ns0:ref>, Fig. <ns0:ref type='figure'>3</ns0:ref>). Band H has a long tail into the vacuum UV with an additional peak L at 54100 cm -1 (185 nm). Several states are predicted to contribute to this absorption, primarily 8 1 B 2 and 9 1 B 2 computed at 52500 and 56300 cm -1 (Table <ns0:ref type='table'>1</ns0:ref>, Fig. <ns0:ref type='figure'>3</ns0:ref>).</ns0:p><ns0:p>The short-axis (z) polarized intensity in this region is dominated by a very strong band K with maximum at 53400 cm -1 (187 nm) in the vacuum UV. This is the strongest absorbance observed in the investigated spectral range. The peak K at 53400 cm -1 is preceded by two diffuse shoulders I and J at 47000 and 51000 cm -1 (213 and 196 nm). Several states are predicted to contribute to this band system, such as 7 1 A 1 , 8 1 A 1 , 9 1 A 1 , and 10 1 A 1 , with transition to the 9 1 A 1 state computed at 52800 cm -1 as the most intense (Table <ns0:ref type='table'>1</ns0:ref>, Fig. <ns0:ref type='figure'>3</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>Excited state intramolecular proton transfer (ESIPT)</ns0:head><ns0:p>The observed fluorescence emission of A in n-hexane solution has a maximum at 17500 cm -1 (570 nm) with an unusually large Stokes shift of 10500 cm -1 relative to the excitation observed at 28000 cm -1 (360 nm) <ns0:ref type='bibr' target='#b27'>(Møller et al., 1998)</ns0:ref>. The fluorescence quantum yield is nearly independent of temperature which suggests that the decay from the initially formed excited state to the ground state does not involve barrier crossing <ns0:ref type='bibr' target='#b27'>(Møller et al., 1998)</ns0:ref>.</ns0:p><ns0:p>According to the orbital amplitudes in Fig. <ns0:ref type='figure'>4</ns0:ref>, the promotions 6b 1 ()  7b 1 () and 4a 2 ()  7b 1 () involved in the transitions to the two lowest, near-degenerate singlet states 2 1 A 1 and 1 1 B 2 are associated with substantial transfer of electron density from the phenolic moieties to the carbonyl group. This is expected to affect the balance of forces involved in the intramolecular hydrogen bonding, facilitating excited state intramolecular proton transfer (ESIPT) <ns0:ref type='bibr' target='#b1'>(Andersen & Spanget-Larsen, 1997)</ns0:ref>. Geometry optimization of the excited S 1 state with TD-B3LYP predicts barrier-less transition to the ESIPT product 8,9-dihydroxy-1(10H)anthracenone, see Fig. <ns0:ref type='figure'>5</ns0:ref>, leading to good agreement with the observed fluorescence and excitation wavenumbers (the predicted excitation wavenumber indicated in Fig. <ns0:ref type='figure'>5</ns0:ref> is slightly different from the one listed in Table <ns0:ref type='table'>1</ns0:ref> because of different TD-DFT procedures). The present TD-DFT results thus support the assignment previously suggested on the basis of semiempirical model calculations <ns0:ref type='bibr' target='#b1'>(Andersen & Spanget-Larsen, 1997;</ns0:ref><ns0:ref type='bibr' target='#b27'>Møller et al., 1998)</ns0:ref>. Excited state equilibrium nuclear coordinates and other computational data for the ESIPT product are provided as Data S2; relative energies of this and additional excited state configurations are outlined in Fig. <ns0:ref type='figure'>S4</ns0:ref>.</ns0:p><ns0:p>A similar excited state rearrangement is observed for the closely related compound 1,8dihydroxy-9,10-anthraquinone (chrysazin, an oxidation product of A) as reported by Smulevich and coworkers <ns0:ref type='bibr' target='#b35'>(Smulevich et al., 1987;</ns0:ref><ns0:ref type='bibr' target='#b20'>Marzocchi et al., 1998)</ns0:ref> and recently studied theoretically by <ns0:ref type='bibr'>Mohammed et al. (Mohammad et al., 2014)</ns0:ref> and by <ns0:ref type='bibr' target='#b40'>Zheng et al. (Zheng, Zhang & Zhao, 2017)</ns0:ref>. </ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>and perpendicular to the stretching direction of the PE sample. The observed baseline-(𝑈) (𝑉) corrected LD absorbance curves and are shown in Fig. 2 (top). The isotropic 𝐸 𝑈 (𝜈) 𝐸 𝑉 (𝜈) absorbance curve is shown in Fig. S2.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>𝜈) PeerJ Phy. Chem. reviewing PDF \| (PCHEM-2019:09:41267:1:0:NEW 11 Oct 2019)Manuscript to be reviewedChemistry JournalsA family of curves for A is shown in Fig.2(middle). A spectral peak or shoulder due to 𝑟 𝐾 (𝜈)transition i disappears from the linear combination for K = K i and the K i value can thus be 𝑟 𝐾(𝜈) </ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>Figure 1</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='16,42.52,70.87,256.13,672.95' type='bitmap' /></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head>Linear Dichroism: Orientation Factors and Partial Absorbance Curves</ns0:head><ns0:label /><ns0:figDesc /><ns0:table><ns0:row><ns0:cell>The observed SRLD absorption curves</ns0:cell><ns0:cell>𝐸 𝑈 (𝜈)</ns0:cell><ns0:cell>and</ns0:cell><ns0:cell>𝐸 𝑉 (𝜈)</ns0:cell><ns0:cell cols='2'>for A partially aligned in stretched PE</ns0:cell></ns0:row><ns0:row><ns0:cell cols='6'>are shown in Fig. 2 (top). The directional information that can be extracted from these curves is</ns0:cell></ns0:row><ns0:row><ns0:cell cols='5'>represented by the orientation factors for the molecular transition moment vectors 𝐾 𝑖</ns0:cell><ns0:cell>𝑀 𝑖</ns0:cell><ns0:cell>of the</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:note place='foot'>Analytical, Inorganic, Organic, Physical, Materials Science </ns0:note> </ns0:body> "	"Article ID: 41267 Roskilde, Wednesday, October 11, 2019 Rebuttal Letter Dear Walter de Azevedo, Jr. Thank you for the decision letter of October 7 with the reviewers’ comments on our manuscript “UV synchrotron radiation linear dichroism spectroscopy of the anti-psoriatic drug anthralin” (Duy Duc Nguyen, Nykola C. Jones, Søren V. Hoffmann, Jens Spanget‑Larsen). Please find below our response to the requested changes. Original comments by the reviewers are quoted in italics. Sincerely, Jens Spanget-Larsen Reviewer 1 Line 58: “Previously investigated”. Report references. Reference has been provided: (Andersen & Spanget-Larsen, 1997) Lines 95-99. The authors should explain why they use two different TD-DFT techniques: 1. TD-BRLYP/6-31+g(d,p), in Fig. 5 and Data_S2; 2. TD-CAM-BRLYP/aug-cc-pVTZ, in Fig. 3, Fig. 4, Tab. 1, and Data_S1. Additional text and references have been added to the section ‘Calculations’: Optimizations of molecular geometry were performed with B3LYP (Becke, 1993; Lee, Yang & Parr, 1988). This DFT was found to be very successful in the prediction of molecular and vibrational structures of compounds like A with intramolecular C=O···HO hydrogen bonding (Spanget-Larsen, Hansen & Hansen, 2011; Hansen, Spanget-Larsen, 2012). Geometry optimizations for the ground state (S0) and the lowest excited singlet state (S1) of A were carried out with B3LYP and TD-B3LYP, respectively, using the 6-31+G(d,p) basis set (Foresman & Frisch, 2015; Frisch et al., 2016). We recently found that this long-range corrected procedure is adequate in the prediction of electronic transitions of aromatic chromophores in the near and vacuum UV regions (Nguyen et al., 2018; Nguyen et al., 2019). Line 105. 'Gaussian convolutions' Please, add some details. The pertinent text in section ‘Calculations’ is modified as follows: Gaussian convolutions considering all allowed transitions are provided as Fig. S3, using previously described procedures (Serr & O’Boyle, 2009; Nguyen et al., 2019). The reference to Serr & O’Boyle is added to the reference list. Line 71. “Fig. S1” Did the authors take into account the heptane solvent effect, e.g. via the PCM method? The text in section ‘Calculations’ has been modified: Quantum chemical calculations considering isolated molecules in the gas phase were performed by using the Gaussian16 software package (Frisch et al., 2016). Line 84. “58000-313000 cm-1” But the paper reports observed data down to 25000 cm-1. I am not sure what the reviewer means; the wavenumber 25000 cm–1 is not mentioned anywhere in the manuscript. As stated in the original manuscript, section ‘Linear dichroism LD spectroscopy’: “LD spectra in the range 31300–20000 cm–1 (320–500 nm) were recorded on a UV-2101PC Shimadzu spectrophotometer at Roskilde University. SRLD spectra were measured in the range 58000–31300 cm–1 (170–320 nm) on the CD1 beamline (Miles et al, 2007; Miles et al., 2008) at the storage ring ASTRID at the Centre for Storage Ring Facilities (ISA).” In the ‘Abstract’ we mention the range 23000 to 58000 cm–1 because the actual onset of the electronic absorbance spectrum of A is close to 23000 cm–1 (430 nm). Line 143. “Fig. 3” Correct the caption as pVTZ. Better oscillator strength instead of “f”. The title has been changed as follows: Partial absorbance curves Ay and Az for anthralin (A) and electronic transitions to excited 1B2 and 1A1 states predicted with TD–CAM-B3LYP/AUG-cc-aVTZ (f = oscillator strength). Line 148. “Table 1” Footnote a: Data S3 are missing Footnote g: correct as Fig. 4 The footnotes have been corrected: a Main transitions only. Complete list of calculated transitions provided as Supplemental data S1. g MO energies and diagrams in Fig. 4. Line 193. Fig 4. Report the color meaning. A legend has been added: Different colors indicate amplitudes of different sign. Line 198. “Geometry optimization”. Did the authors take into account the n-hexane solvent effect, e.g. via the PCM method? As mentioned above, the text in section ‘Calculations’ has been modified: Quantum chemical calculations considering isolated molecules in the gas phase were performed by using the Gaussian16 software package (Frisch et al., 2016). Line 200. 'Fig. 5' Anthralin left results are different from those in Tab. 1, Data_S1, and Fig. S4. That's probably due to the different TD-DFT methods employed, and the authors must explain and comment on that. The following text has been inserted: (the predicted excitation wavenumber indicated in Fig. 5 is slightly different from the one listed in Table 1 because of different TD-DFT procedures). Line 205. “Fig. S4” Anthralin energies here are different from those in Fig. 5. Why? The wavenumber 27300 cm–1 in Fig. 5 refers to vertical excitation, while the wavenumber 26300 cm–1 in Fig. S4 refers to vertical emission. This is explained in the legends to the two figures and is also indicated by the arrow symbolism. Reviewer 2 I would be much happier if the units used for energy (cm-1, nm and eV) were converted to the number susually used by spectroscopists in the relevant regions. We prefer wavenumbers (cm–1) because they are proportional to energy. This is in accordance with the layout of the classical collection “UV-Atlas organischer Verbindungen” (Verlag Chemie 1966) where the spectra are displayed on a linear wavenumber scale: “Die Darstellung ist durchgehend in Wellenzahlen linear…”. I foudn it hard to compare everything and to compare with my experience. Throughout our text we give corresponding wavelengths in nm. Also figure legends shoudl go below figures. The placing of the legends in the reviewing document is not our choice, but a result of the online submission procedure. Nothign is achieved in terms of mode of action - which is the motivation for the paper. However, the data will be useful. We do not wish to speculate on possible dermatological modes of action. But we are pleased that the reviewer thinks that our results will be useful. Reviewer 3 The paper can be recommended for publication in the present form. "	Here is a paper. Please give your review comments after reading it.
489	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Background : Diminished ovarian reserve (DOR) is a challenge to fertility treatment, which seriously affects the reproductive potential of female at childbearing age. However, its pathogenesis is still unclear and treatment options are limited. This study aimed to find clues to understand the molecular mechanism of DOR. Methods: The mRNA microarray dataset E-MTAB-391 , downloaded from ArrayExpress , was submitted to R software to screen the differentially expressed genes (DEGs) and perform functional enrichment analyses. The protein-protein interaction (PPI) and miRNAs -mRNAs network also were constructed. Ovarian granulosa cells (GCs) from women with DOR and control group were collected to perform untargeted metabolomics analyses. In addition, the small molecule drugs were identified based on the Connectivity Map database. Results: Finally, 138 DEGs were identified. Gene Ontology (GO) analysis suggested that DEGs mainly enriched in regulation of cytokine biosynthetic process and steroid biosynthetic process. Kyoto Encyclopedia of Genes and Genomes (KEGG) suggested that DEGs mainly enriched in AGE-RAGE signaling pathway and steroid biosynthesis. In PPI network, JUN, EGR1, HMGCR, ATF3 and SQLE were determined as hub genes, which might be involved in steroid biosynthesis and inflammation. MiRNAs also have roles in DOR development through regulating the target genes. The difference of steroid metabolism in GCs was validated by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Furthermore, 31 small molecules were selected, which have potential positive or negative influence on DOR. Conclusions:</ns0:p><ns0:p>Our study found that steroidogenesis and inflammation had critical roles in DOR, which might provide promising insights for its prediction and treatment.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>INTRODUCTION</ns0:head><ns0:p>Diminished ovarian reserve (DOR) is often referred to the decreased number or quality of follicle and oocytes, leading to the decline in reproductive potential of female <ns0:ref type='bibr' target='#b42'>(Sharara et al. 1998)</ns0:ref>.</ns0:p><ns0:p>Previous research has reported that the incidence of DOR varies from 9% to 24% in women undergoing in vitro fertilization <ns0:ref type='bibr' target='#b21'>(Kyrou et al. 2009)</ns0:ref>. There is no effective treatment for women diagnosed DOR besides assisted reproductive techniques. Ovarian reserve declines with advancing age, but some women experience DOR much earlier than usual. DOR in young women means an accelerated process of the normal physiological decline in ovarian reserve.</ns0:p><ns0:p>DOR is one of the greatest challenges to reproductive endocrinologist because of poor ovarian response to gonadotrophin stimulation, low pregnancy rate and high pregnancy loss <ns0:ref type='bibr' target='#b23'>(Levi et al. 2001;</ns0:ref><ns0:ref type='bibr' target='#b28'>Navot et al. 1987)</ns0:ref>.</ns0:p><ns0:p>Ovarian follicle is a precise complex comprised by the oocytes, cumulus cells (CCs), and granulosa cells <ns0:ref type='bibr'>(GCs)</ns0:ref>. The bidirectional communication between oocyte and companion somatic cells is essential for follicular development and oocyte growth <ns0:ref type='bibr' target='#b1'>(Anderson & Albertini 1976;</ns0:ref><ns0:ref type='bibr' target='#b26'>Matzuk et al. 2002)</ns0:ref>. In mouse, transcriptional activity of oocyte could be modulated when cultured with GCs in vitro, but not in the absence of GCs <ns0:ref type='bibr' target='#b9'>(De La Fuente & Eppig 2001)</ns0:ref>. Another study showed granulosa cells apoptosis was increased in DOR patients, associated with worse ovarian response and oocyte yield <ns0:ref type='bibr' target='#b11'>(Fan et al. 2019)</ns0:ref>. Considering this coadjutant relationship, it may provide a deep understanding for DOR pathogenesis to explore the differences of GCs from PeerJ reviewing PDF \| (2020:03:46424:1:1:NEW 5 Jun 2020)</ns0:p><ns0:p>Manuscript to be reviewed women with DOR and normal ovarian reserve (NOR). Previous studies have investigated mRNA expression profile of ovarian CCs <ns0:ref type='bibr' target='#b16'>(Greenseid et al. 2011)</ns0:ref> and GCs <ns0:ref type='bibr' target='#b6'>(Chin et al. 2002;</ns0:ref><ns0:ref type='bibr' target='#b47'>Skiadas et al. 2012</ns0:ref>) from DOR patients, as well as miRNA expression pattern <ns0:ref type='bibr' target='#b5'>(Chen et al. 2017;</ns0:ref><ns0:ref type='bibr' target='#b53'>Woo et al. 2018)</ns0:ref>. The inclusion criteria for DOR patients in these studies were usually different. MiRNAs are small noncoding RNAs that have critical roles in many biological processes and diseases, including reproduction. MiRNAs regulate mRNA translation and stability <ns0:ref type='bibr' target='#b10'>(Fabian et al. 2010)</ns0:ref>.</ns0:p><ns0:p>With the deeper understanding for miRNA roles in female reproduction <ns0:ref type='bibr' target='#b36'>(Sabry et al. 2019)</ns0:ref>, miRNA therapeutics could become one of options for DOR treatment.</ns0:p><ns0:p>Steroid hormones are a type of steroids and are synthesized from cholesterol in gonads and adrenal glands <ns0:ref type='bibr' target='#b15'>(Greaves et al. 2014)</ns0:ref>. The levels of steroid hormones affect the process of follicular growth and development <ns0:ref type='bibr' target='#b7'>(Chou & Chen 2018)</ns0:ref>. Inflammation underlies a wide range of physiological and pathological processes <ns0:ref type='bibr' target='#b27'>(Medzhitov 2008)</ns0:ref>. Aberrant inflammation has a negative effect on folliculogenesis and ovulation, and polycystic ovary syndrome (PCOS) serves as an example which is associated with chronic endogenous production of low-grade proinflammatory cytokines <ns0:ref type='bibr' target='#b4'>(Boots & Jungheim 2015)</ns0:ref>. The abnormalities of inflammation and steroidogenesis may be involved in development of DOR.</ns0:p><ns0:p>In the present study, we obtained raw data of mRNA expression profile of DOR GCs from dataset E-MTAB-391 that is publicly available, and performed bioinformatic analyses to identify differentially expressed genes (DEGs). Protein-protein interaction (PPI) networks were constructed based on DEGs, and differentially expressed miRNAs (DEMs) extracted from previous study <ns0:ref type='bibr' target='#b53'>(Woo et al. 2018)</ns0:ref> were used for the construction of miRNA-mRNA network.</ns0:p><ns0:p>Small molecule drugs having potential synergistic or antagonistic effects on DOR also were screened based on Connectivity Map (CMap) database <ns0:ref type='bibr' target='#b22'>(Lamb et al. 2006)</ns0:ref>. Moreover, liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to explore the metabolic alteration of GCs using our DOR samples. This study might shed some light on the prediction and treatment of DOR.</ns0:p></ns0:div> <ns0:div><ns0:head>MATERIALS & METHODS</ns0:head></ns0:div> <ns0:div><ns0:head>Data collection</ns0:head><ns0:p>We screened GEO and ArrayExpress for expressed profiles of GCs from patient with or without DOR. Those datasets were excluded: (1) the detail information on samples were not available;</ns0:p><ns0:p>(2) the samples based on cell lines or animal models; (3) the sample size was < 10; (4) the definition of DOR and participants age were quite different from ours. Finally, only mRNA microarray dataset E-MTAB-391, including 13 DOR samples and 13 NOR samples <ns0:ref type='bibr' target='#b47'>(Skiadas et al. 2012)</ns0:ref>, met the criteria and was downloaded from ArrayExpress (https://www.ebi.ac.uk/arrayexpress/). The platform of E-MTAB-391 is A-MEXP-1564-IIIumina HumanRef-8 WG-DASL v3 Expression BeadChip. As for the miRNAs involved in DOR, the data were directly extracted from previous study <ns0:ref type='bibr' target='#b5'>(Chen et al. 2017)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Identification of DEGs</ns0:head><ns0:p>The limma package in R software <ns0:ref type='bibr' target='#b33'>(Ritchie et al. 2015)</ns0:ref> was used to identify DEGs between DOR and NOR samples. Adjusted P-value (The false discovery rate) was obtained by Benjamini-Hochberg algorithm to screen the DEGs. Adjusted P < 0.05 and \|log 2 (fold change; FC) \| > 0.58 were set as the DEGs cut-off criteria. Finally, DEGs were divided into upregulated and downregulated DEGs and saved for subsequent analyses.</ns0:p></ns0:div> <ns0:div><ns0:head>Functional and enrichment analyses of DEGs</ns0:head><ns0:p>PeerJ reviewing PDF \| (2020:03:46424:1:1:NEW 5 Jun 2020)</ns0:p><ns0:p>Manuscript to be reviewed Gene Ontology (GO) annotation is widely used to understand biological functions of multiple genes, comprising three independent ontologies: biological process (BP), molecular function (MF) and cellular component (CC) <ns0:ref type='bibr' target='#b2'>(Ashburner et al. 2000)</ns0:ref>. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis is a valuable tool to assess the interaction networks of genes and their products <ns0:ref type='bibr' target='#b20'>(Kanehisa et al. 2017)</ns0:ref>. In our research, the clusterProfiler package in R software was used to obtain the GO and KEGG pathway enrichment for DEGs <ns0:ref type='bibr' target='#b54'>(Yu et al. 2012)</ns0:ref>.</ns0:p><ns0:p>Value of P < 0.05 was set as the threshold for significance.</ns0:p></ns0:div> <ns0:div><ns0:head>PPI network construction and module analysis</ns0:head><ns0:p>The STRING database provides an assessment and integration for PPI <ns0:ref type='bibr' target='#b48'>(Szklarczyk et al. 2017)</ns0:ref>.</ns0:p><ns0:p>For getting a system-wide understanding among DEGs, the PPI network was constructed by the STRING (version 10.5: https://string-db.org/cgi/input.pl) with combined score ≥ 4 as the cut-off.</ns0:p><ns0:p>It was visualized by Cytoscape (version 3.6.1; https://cytoscape.org/), which can integrate biomolecular interaction networks into a unified conceptual framework <ns0:ref type='bibr' target='#b41'>(Shannon et al. 2003</ns0:ref>). In the network, the nodes and edges represent proteins and protein-protein associations, respectively. Module analysis of the PPI network was performed based on Molecular Complex Detection (MCODE) of Cytoscape software with following parameters: degree cut-off = 2, node score cut-off = 0.2, max depth = 100, and k-score = 2. Subsequently, GO and KEGG pathway analyses of selected modules were performed by clusterProfiler package.</ns0:p></ns0:div> <ns0:div><ns0:head>Exploring target genes of DEMs</ns0:head><ns0:p>105 DEMs of GCs from women diagnosed DOR were extracted from previous study <ns0:ref type='bibr' target='#b53'>(Woo et al. 2018)</ns0:ref>. The multiMiR package integrates 11 miRNAs-target databases (3 validated and 8 predicted miRNAs-target databases) and 3 disease-/drug-related miRNA databases <ns0:ref type='bibr' target='#b34'>(Ru et al. 2014)</ns0:ref>, and it was employed to retrieve interactions between DEMs and screened DEGs. Target genes of DEMs were only screened from 11 miRNAs-target databases. Finally, the regulatory network of miRNA-mRNA was visualized by Cytoscape software.</ns0:p></ns0:div> <ns0:div><ns0:head>Identification of small molecules</ns0:head><ns0:p>The CMap is a resource to find connections between diseases, genetics perturbation, and drug action, which contains 7000 gene-expression profiles from cultured human cells treated with bioactive small molecules <ns0:ref type='bibr' target='#b22'>(Lamb et al. 2006</ns0:ref>). The upregulated and downregulated genes were mapped to the CMap database (https://portals.broadinstitute.org/cmap) to identify potential small molecule drugs, which have antagonistic or synergistic effects on DOR. The criteria of n ≥ 4 (the number of instances), enrichment > 0.7 and P-value < 0.01 were regarded as statistically significant.</ns0:p></ns0:div> <ns0:div><ns0:head>Collection of GCs</ns0:head><ns0:p>This study was approved by the ethics committee of The First Hospital of Lanzhou University (LDYYLL2019-44) and written informed consent was obtained from all participants. Ovarian GCs were collected from women with DOR (n=3) and women with NOR (n=3). All participants were ≤ 35 years to eliminate potential confounding caused by aging. DOR were identified by FSH level (12 ≤ FSH < 25) and ovarian response (the number of follicles on the day of the ovulatory hCG (human chorion gonadotropin) trigger ≤ 7). These infertile women that underwent IVF due to male factor or tubal factor were chosen as NOR group. Controlled ovarian stimulation was performed and follicular development was monitored with transvaginal ultrasound. Oocyte retrieval was performed after 36 hours of hCG administration and GCs were isolated from fluid aspirates as described previously <ns0:ref type='bibr' target='#b50'>(Vanacker et al. 2011)</ns0:ref>. <ns0:ref type='table' target='#tab_3'>2020:03:46424:1:1:NEW 5 Jun 2020)</ns0:ref> Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>PeerJ reviewing PDF \| (</ns0:head></ns0:div> <ns0:div><ns0:head>LC-MS/MS experiments</ns0:head><ns0:p>The metabolites extraction and LC-MS/MS analysis were performed at BGI Shenzhen. Each frozen GCs sample was thawed and weighed into 1.5 mL Eppendorf tubes. The internal standard solution and 800 μL solvent of methanol/acetonitrile/water (2:2:1, v/v/v) were added to homogenize. Mixtures were centrifuged at 25,000 rcf for 15 min, and supernatant was transferred and vacuum dried. Then, the metabolite extract was re-extracted in 200 μL of methanol/water mixture (1:9, v/v). After vortexing, the samples were further centrifuged. Supernatant was collected and quality control (QC) for each sample was performed using 20 μL supernatant. The Waters 2D UPLC (Waters, USA) coupled with Q Exactive high-resolution mass spectrometer (Thermo Fisher Scientific, USA) was used. The analytical column was an ACQUITY UPLC BEH C18 (1.7 μm, 2.1×100 mm, Waters, USA). The mobile phase of positive ion mode was MS-grade water with 0.1% formic acid (A) and 100% methanol with 0.1% formic acid (B), and the mobile phase of negative ion mode was MS-grade water with 10 mM ammonium formate (A) and 95% methanol with 10 mM ammonium formate (B). The extracts were gradient-eluted and the flow rate was 0.35 ml/min. The resolutions for full scan and fragment acquisition were 70,000 and 17,500, respectively. The parameters for ESI were setting as following: sheath gas flow rate at 40 L•min-1, auxiliary gas flow rate at 10 L•min-1, spray voltage, 3800 V (positive mode), 3200 V (negative mode), capillary temperature at 320℃ and auxiliary gas heater temperature at 350℃. The LC-MS/MS data were processed by Compound Discoverer 3.0 (Thermo Fisher Scientific, USA) software. The differential metabolites were identified with combination of principal component analysis (PCA) and univariate analysis.</ns0:p></ns0:div> <ns0:div><ns0:head>RESULTS</ns0:head><ns0:p>PeerJ reviewing PDF \| (2020:03:46424:1:1:NEW 5 Jun 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Identification of DEGs related to DOR</ns0:head><ns0:p>In this study, the normalized expression data were downloaded from E-MTAB-391 dataset and were present in Fig. <ns0:ref type='figure'>S1</ns0:ref>. Totally, 18128 genes were available for further identification of DEGs.</ns0:p><ns0:p>Based on the criteria, 138 DEGs were identified from DOR and NOR samples, including 55 upregulated and 83 downregulated genes. The volcano plot in Fig. <ns0:ref type='figure'>1</ns0:ref> indicates the distribution of all screened genes. The heat map of all DEGs using unsupervised hierarchical clustering was demonstrated in Fig. <ns0:ref type='figure'>S2</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>GO and KEGG pathway enrichment analyses of DEGs</ns0:head><ns0:p>According to the GO BP analysis, the upregulated DEGs were mainly enriched in skeletal muscle cell differentiation and regulation of transcription from RNA polymerase II promoter in response to stress, and downregulated DEGs were mainly enriched in steroid biosynthetic process and cholesterol biosynthetic process. The Fig. <ns0:ref type='figure'>2</ns0:ref> showed the top 20 GO BP terms of upand down-regulated DEGs in detail. Additionally, KEGG pathway enrichment analysis was performed and the results were collected in Table <ns0:ref type='table'>1</ns0:ref>. The upregulated DEGs were significantly enriched in AGE-RAGE signaling pathway in diabetic complications and human T-cell leukemia virus 1 infection, and downregulated DEGs mainly enriched in steroid biosynthesis (Fig. <ns0:ref type='figure'>3</ns0:ref>) and terpenoid backbone biosynthesis (Fig. <ns0:ref type='figure'>S3</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>Construction of PPI network and modules analysis</ns0:head><ns0:p>All DEGs related to DOR were imported into STRING database to establish the PPI network, which included 99 nodes and 294 edges when the cut-off was set as combined score ≥ 0.7. Then, PPI network was visualized by Cytoscape software and demonstrated in Fig. <ns0:ref type='figure'>4A</ns0:ref>. These genes had higher node degrees: Jun Proto-Oncogene, AP-1 Transcription Factor Subunit (JUN PeerJ reviewing PDF \| (2020:03:46424:1:1:NEW 5 Jun 2020)</ns0:p><ns0:p>Manuscript to be reviewed ,degree=24), Early Growth Response 1 (EGR1, degree=18), 3-Hydroxy-3-Methylglutaryl-CoA Reductase (HMGCR, degree=17), Activating Transcription Factor 3 (ATF3, degree=15), Squalene Epoxidase (SQLE, degree=15). In addition, the top two modules were filtered out from PPI network to implement further GO and KEGG pathway analyses. Module 1 (Fig. <ns0:ref type='figure'>4B</ns0:ref>) was consisted of 12 downregulated genes that enriched in cholesterol biosynthetic process in GO BP term. Module 2 (Fig. <ns0:ref type='figure'>4C</ns0:ref>) was consisted of 10 upregulated genes that mainly enriched in skeletal muscle cell differentiation in GO BP term. The detailed results of GO and KEGG pathway analyses were collected in Table <ns0:ref type='table' target='#tab_1'>2</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Prediction of target genes of DEMs and construction of DEMs-DEGs regulatory network</ns0:head><ns0:p>The DEMs associated with DOR, including 85 upregulated and 20 downregulated, were submitted to multiMiR package to predict target genes of DEGs. Then, the identified DEMs-DEGs pairs (including 91 DEMs and 109 DEGs) were used to construct the regulatory network.</ns0:p><ns0:p>Among them, miR-155-5p, miR-16-5p, let-7b-5p, miR-107, miR-103a-3p have more target genes, and the details of interactions between DEMs and DEGs were shown in Fig. <ns0:ref type='figure'>5</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Screening of small molecule drugs</ns0:head><ns0:p>In order to screen out small molecule drugs, all DEGs were compared with the gene expression profiles in CMap. Finally, 31 small molecules were identified, in which 7 molecules with negative scores might have potential to reverse DOR. H-7 and estriol were interesting which have been reported to possess potential effects on DOR. Detailed results are shown in Fig. <ns0:ref type='figure'>6</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Metabolic differences of GCs between DOR and NOR</ns0:head><ns0:p>Metabolites are essential for cellular function and untargeted metabolomics analyses can provide fruitful information to understand their associations with diseases. The GCs samples from two PeerJ reviewing PDF \| (2020:03:46424:1:1:NEW 5 Jun 2020)</ns0:p><ns0:p>Manuscript to be reviewed groups were analyzed by LC-MS/MS under both positive and negative ion mode. After data processing and metabolites identification, the differential metabolites were screened with a threshold of p-value < 0.05 and FC ≥ 1.2 or ≤ 0.83. Metabolic difference did exist in GCs of DOR compared with NOR samples. Differences of steroid were observed in GCs of two groups and details of metabolites were listed in Table <ns0:ref type='table' target='#tab_3'>3</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>DISCUSSION</ns0:head><ns0:p>DOR is a challenge to the physician because women with DOR commonly have a reduced number of oocytes retrieved compared with similar age women. In some women, DOR might progress to the primary ovarian insufficiency (POI), a diagnosis of extreme form of ovarian dysfunction <ns0:ref type='bibr' target='#b8'>(Cooper et al. 2011)</ns0:ref>. Given serious damage to reproductive health and physician's chance to intervene, there is an urgent need to elucidate DOR etiology. Many mRNA/miRNA expression profiles of GCs from DOR patients have been determined to explore molecular mechanism in previous studies. In this study, we mainly paid attention to alteration of GCs from young women with DOR, and performed LC-MS/MS experiments and bioinformatic analyses to explore differences between DOR and NOR. We obtained raw data of mRNA expression pattern and DEMs from previous publications of <ns0:ref type='bibr' target='#b47'>(Skiadas et al. 2012</ns0:ref>) and <ns0:ref type='bibr' target='#b53'>(Woo et al. 2018)</ns0:ref>, which had similar inclusion criteria of DOR to ours. The inclusion criteria of these cohorts are presented in Table <ns0:ref type='table'>S1</ns0:ref>.</ns0:p><ns0:p>The process of steroidogenesis is fundamental to sex steroid hormone, such as progestogens, androgens and estrogens. Estrogens play the most important role in female reproduction.</ns0:p><ns0:p>Synthesis and secretion of estrogens are promoted by FSH which is elevated in DOR women <ns0:ref type='bibr' target='#b0'>(2015)</ns0:ref>. However, whether there is difference in estrogens level between DOR and NOR remains controversial. In our study, downregulated DEGs mainly enriched in steroid biosynthetic process in GO BP term (Fig. <ns0:ref type='figure'>2A</ns0:ref>). KEGG pathway analysis (Table <ns0:ref type='table'>1</ns0:ref>) showed that downregulated DEGs mainly enriched in steroid biosynthesis and terpenoid backbone biosynthesis. Therefore, a range of substances of steroidogenesis may play a major role in development of DOR. In consistent with our results of PPI network (Fig. <ns0:ref type='figure'>4</ns0:ref>), several key genes in top 20 (including HMGCR, SQLE, CYP51A, HMGCS1, FDFTI, SC5D, NSDHL, IDI1, EBP, MSMO1) were found in steroid biosynthesis and terpenoid backbone biosynthesis pathways (Figs. <ns0:ref type='figure'>3 and S3</ns0:ref>). All of these genes were downregulated in DOR from our dataset. It has been reported that HMGCR catalyzes the first rate-limiting step in cholesterol synthesis <ns0:ref type='bibr' target='#b18'>(Howe et al. 2017</ns0:ref>), HMGCS1 condenses acetyl-CoA to form 3-hydroxy-3-methylglutaryl CoA which is the substrate for HMGCR <ns0:ref type='bibr' target='#b25'>(Mathews et al. 2014)</ns0:ref>, and CYP51A also participates in the synthesis of cholesterol, which can catalyze the removal of the 14α-methyl group from lanosterol <ns0:ref type='bibr' target='#b43'>(Sharpe & Brown 2013)</ns0:ref>. Upstream biological disruptions lead to a series of metabolomic changes. According to the result of untargeted metabolomics, several steroids (Table <ns0:ref type='table' target='#tab_3'>3</ns0:ref>) were significantly lower in GCs from patients with DOR compared with control group. Among them, progesterone not only has an essential role in female reproductive events (ovulation, implantation, maintenance of pregnancy) but also serves as an intermediate in the estrogen biosynthesis <ns0:ref type='bibr' target='#b13'>(Gellersen et al. 2009)</ns0:ref>. Prior evidence suggests that progesterone can be produced directly by GCs and then enters theca cells to convert into androgens <ns0:ref type='bibr' target='#b29'>(Oktem et al. 2017)</ns0:ref>. Hydroxyprogesterone is an intermediate in the conversion of progesterone to androgens which would be transported into GCs to convert into estrogen. The relationships between ovarian function and other steroids (table 3) are reported rarely and requires more studies. It has been demonstrated that several steroidogenic genes disturbances induced by bisphenol A caused development impairment of the ovary tissues <ns0:ref type='bibr' target='#b24'>(Liu et al. 2019)</ns0:ref>.</ns0:p><ns0:p>Taken together, hormone synthesis might be impaired in DOR, which could be compensated by elevated FSH. The perturbation of steroidogenic genes might be responsible for development of DOR.</ns0:p><ns0:p>Aberrant inflammation has been suggested to influence the follicular growth and development <ns0:ref type='bibr' target='#b4'>(Boots & Jungheim 2015)</ns0:ref>. Our result showed that upregulated genes enriched in AGE-RAGE signaling pathway (Table <ns0:ref type='table'>1</ns0:ref>). It has been proved that AGE-RAGE signaling pathway induced ROS burst and inflammation, eventually leading to POI. <ns0:ref type='bibr' target='#b19'>(Huang et al. 2019</ns0:ref>). EGR1 plays a proinflammatory role in numerous pathological processes and human diseases <ns0:ref type='bibr' target='#b39'>(Schmidt et al. 2008)</ns0:ref>. A recent research has demonstrated that EGR1 was increased in aged ovaries compared with the young in mice <ns0:ref type='bibr' target='#b55'>(Yuan et al. 2016</ns0:ref>), In our results, EGR1 with second degree in PPI network was upregulated in DOR women. It has demonstrated that in cholestasis, EGR1 regulates production of inflammatory mediators, including cytokines, adhesion molecules and others, which promoted accumulation and activation of inflammatory cells, finally leading liver injury <ns0:ref type='bibr' target='#b3'>(Bonetti et al. 2010)</ns0:ref>. According to our results of GO analysis of upregulated DEGs (Fig. <ns0:ref type='figure'>2B</ns0:ref>), cytokines might associate with the development of DOR. Cytokines play a key role in inflammation, and the ovary presents immune cells which are a source of cytokines <ns0:ref type='bibr' target='#b49'>(Tabibzadeh 1994;</ns0:ref><ns0:ref type='bibr' target='#b51'>Vinatier et al. 1995)</ns0:ref>. Accumulated evidence suggested that inflammation was closely related to ovarian functions. The women diagnosed PCOS present with chronic low-grade inflammation due to an overactivity of interleukin-1 (IL-1), a proinflammatory cytokine <ns0:ref type='bibr' target='#b31'>(Popovic et al. 2019</ns0:ref>). In addition, multiple autoimmune diseases have an adverse effect on female fertility via prematurely diminishing ovarian reserve <ns0:ref type='bibr' target='#b40'>(Sen et al. 2014)</ns0:ref>. Therefore, antiinflammation treatment may be able to alleviate the progression of DOR. In a rat model of POI, resveratrol would counteract the inflammatory signaling induced by ionizing radiation and preserve the entire ovarian follicle pool <ns0:ref type='bibr' target='#b37'>(Said et al. 2016</ns0:ref>). Further study is needed to reveal the role of inflammation in DOR development and controlling inflammation may be an option to relieve DOR.</ns0:p><ns0:p>MiRNA is a class of endogenous non-coding small molecule RNA, which plays an important role in modulation of gene expression at the post-transcriptional level. Previous research showed miRNA could control human ovarian GCs steroidogenesis, proliferation and apoptosis <ns0:ref type='bibr' target='#b17'>(Hennebold 2010;</ns0:ref><ns0:ref type='bibr' target='#b45'>Sirotkin et al. 2010;</ns0:ref><ns0:ref type='bibr' target='#b46'>Sirotkin et al. 2009</ns0:ref>). In our study, ,steroidogenic genes are regulated by differentially expressed miRNAs. For example, HMGCS1 is regulated by 25 DEMs such as miR-155-5p, miR-16-5p, let-7b-5p, miR-107, miR-103a-3p. Furthermore, single miRNA can target multiple genes. For example, miR-155-5p targeted 30 DEGs including 6 steroidogenic genes (HMGCS1, FDFT1, CYP51A1, SQLE, SC5D, EBP). MiR-107 targets 21 DEGs including 5 steroidogenic genes (HMGCS1, FDFT1, CYP51A1, SQLE, EBP). The expression of miRNA-107 in murine ovarian GCs exposed to cadmium was significantly different from the control group, miRNA-107 could regulate expression of kitl (kit ligand) <ns0:ref type='bibr' target='#b52'>(Wang et al. 2018)</ns0:ref>. Kitl plays an important role in recruitment of primitive follicle <ns0:ref type='bibr' target='#b30'>(Parrott & Skinner 1999)</ns0:ref>, proliferation and differentiation of GCs, recruitment of theca cells and early steroid hormone synthesis <ns0:ref type='bibr' target='#b12'>(Flanagan et al. 1991)</ns0:ref>. Thus, miRNAs might be involved in development of DOR through regulating target genes. MicroRNA therapeutics has shown promise in management of various diseases and several miRNA-based therapeutics have reached clinical testing <ns0:ref type='bibr' target='#b35'>(Rupaimoole & Slack 2017)</ns0:ref>. Therefore, miRNAs also may be a research direction for DOR treatment in the future.</ns0:p><ns0:p>We also conducted the CMap analysis, which can help us to quickly identify molecule drugs with antagonistic or synergistic effects on DOR based on gene expression profile. In our result PeerJ reviewing PDF \| (2020:03:46424:1:1:NEW 5 Jun 2020)</ns0:p><ns0:p>Manuscript to be reviewed (Fig. <ns0:ref type='figure'>6</ns0:ref>), 7 agents with negative scores may have potential for DOR treatment. Among them, H-7</ns0:p><ns0:p>(1-(5-isoquinolinesulfonyl)-2-methylpiperazine), an inhibitor of protein kinase C, has been reported to reduce the release of oocytes from rat ovaries <ns0:ref type='bibr' target='#b44'>(Shimamoto et al. 1993)</ns0:ref>. Estriol is a form of estrogens. A meta-analysis concluded that the luteal estradiol stimulation in assisted reproduction technology could decrease cycle cancellation rate and increase clinical pregnancy rates in poor responders exposed to controlled ovarian hyperstimulation <ns0:ref type='bibr' target='#b32'>(Reynolds et al. 2013)</ns0:ref>.</ns0:p><ns0:p>Thus, we think that these identified molecule drugs based on bioinformatic analysis may provide novel treatment for DOR, and further validation for their effects is still needed.</ns0:p><ns0:p>Despite the promising findings in our study, there may be a limitation. We identify DEGs possible with \|log2FC\| > 0.58 (i.e. fold change approximately was > 1.5) which may be a relatively lower criterion than other studies. DOR is an early stage of the ovarian reserve impairment and might take several years to develop into POI, and subtle alteration may have wider significance for its development. Thus small changes of gene expressions are also worth attention for exploration. In addition, the data of E-MTAB-391 was derived from large sample size. Furthermore, the difference of steroid metabolism between DOR and NOR was validated using LC-MS/MS in our samples. Therefore, our findings are reliable and can provide valuable insights into DOR development.</ns0:p></ns0:div> <ns0:div><ns0:head>CONCLUSION</ns0:head><ns0:p>We revealed the perturbations of steroidogenic and inflammation-related genes that might be regulated by miRNAs in DOR women by bioinformatic approaches. In further study we found steroid metabolites was reduced in GCs from DOR women using metabolomics. In addition, several small molecule drugs (e.g., estriol, a steroid hormone) with potential antagonistic or synergistic effects on DOR were screened out. This study indicated that steroidogenesis and inflammation had critical roles in DOR, which may be the targets for prediction and treatment of DOR.</ns0:p></ns0:div> <ns0:div><ns0:head>Figure 1</ns0:head><ns0:p>Volcano plot of all DEGs</ns0:p><ns0:p>The orangered and blue dots represent significantly upregulated and downregulated DEGs, respectively. DEGs, differentially expressed genes; FC, fold change.</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:03:46424:1:1:NEW 5 Jun 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p><ns0:note type='other'>Figure 2</ns0:note><ns0:p>The GO enrichment analysis of the DEGs Manuscript to be reviewed Enriched GO BP terms (top 5) and significantly enriched KEGG pathways of genes in the top two modules GO, Gene Ontology; BP, biological process; KEGG, Kyoto Encyclopedia of Genes and Genomes.</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:03:46424:1:1:NEW 5 Jun 2020)</ns0:p><ns0:p>Manuscript to be reviewed Manuscript to be reviewed </ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>(A) Top 20 enriched BP terms of downregulated genes. (B) Top 20 enriched BP terms of upregulated genes. The length of bars represents the number of genes, the color of bars represents corresponding adjusted P-value. GO, Gene Ontology; DEGs, differentially expressed genes; BP, biological process.</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='21,42.52,229.87,525.00,341.25' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='25,42.52,306.37,525.00,371.25' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='28,42.52,229.87,525.00,341.25' type='bitmap' /></ns0:figure> <ns0:figure type='table' xml:id='tab_1'><ns0:head>Table 2 (on next page)</ns0:head><ns0:label>2</ns0:label><ns0:figDesc /><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_3'><ns0:head>Table 3 (on next page)</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>LC-MS/MS detected steroids that varied in GCs of DOR with significant difference LC-MS/MS, liquid chromatography-tandem mass spectrometry; GCs, granulosa cells; DOR, diminished ovarian reserve; FC, fold change.</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Components</ns0:cell><ns0:cell>Formula</ns0:cell><ns0:cell>m/z</ns0:cell><ns0:cell>FC</ns0:cell><ns0:cell cols='2'>p-value Class</ns0:cell><ns0:cell>Sub class</ns0:cell><ns0:cell>label</ns0:cell></ns0:row><ns0:row><ns0:cell>Hydroxyprogesterone</ns0:cell><ns0:cell>C 21 H 30 O 3</ns0:cell><ns0:cell cols='3'>330.2190 0.0404 0.0381</ns0:cell><ns0:cell>Steroids and</ns0:cell><ns0:cell>Pregnane</ns0:cell><ns0:cell>down</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>steroid</ns0:cell><ns0:cell>steroids</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>derivatives</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>Progesterone</ns0:cell><ns0:cell>C 21 H 30 O 2</ns0:cell><ns0:cell cols='3'>314.2244 0.0785 0.0025</ns0:cell><ns0:cell>Steroids and</ns0:cell><ns0:cell>Pregnane</ns0:cell><ns0:cell>down</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>steroid</ns0:cell><ns0:cell>steroids</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>derivatives</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>3 alpha-hydroxydesogestrel</ns0:cell><ns0:cell>C 22 H 30 O 2</ns0:cell><ns0:cell cols='3'>326.2241 0.0657 0.0027</ns0:cell><ns0:cell>Steroids and</ns0:cell><ns0:cell>Estrane steroids</ns0:cell><ns0:cell>down</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>steroid</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>derivatives</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>(6beta,8xi,11beta,14xi,16alpha)-9-</ns0:cell><ns0:cell cols='4'>C 22 H 29 FO 6 408.1960 0.0290 0.0093</ns0:cell><ns0:cell>Steroids and</ns0:cell><ns0:cell cols='2'>Hydroxysteroids down</ns0:cell></ns0:row><ns0:row><ns0:cell>fluoro-6,11,17,21-tetrahydroxy-16-</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>steroid</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>methylpregna-1,4-diene-3,20-dione</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>derivatives</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>(6beta,8xi,11beta,14xi,16alpha)-9-</ns0:cell><ns0:cell cols='4'>C 22 H 29 FO 6 408.1959 0.0340 0.0179</ns0:cell><ns0:cell>Steroids and</ns0:cell><ns0:cell cols='2'>Hydroxysteroids down</ns0:cell></ns0:row><ns0:row><ns0:cell>fluoro-6,11,17,21-tetrahydroxy-16-</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>steroid</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>methylpregna-1,4-diene-3,20-dione</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>derivatives</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>4,6-cholestadien-3-one</ns0:cell><ns0:cell>C 27 H 42 O</ns0:cell><ns0:cell cols='3'>382.3229 0.0343 0.0352</ns0:cell><ns0:cell>Steroids and</ns0:cell><ns0:cell>Cholestane</ns0:cell><ns0:cell>down</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>steroid</ns0:cell><ns0:cell>steroids</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>derivatives</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>Cholest-4-en-3-one</ns0:cell><ns0:cell>C 27 H 44 O</ns0:cell><ns0:cell cols='3'>384.3385 0.0796 0.0273</ns0:cell><ns0:cell>Steroids and</ns0:cell><ns0:cell>Cholestane</ns0:cell><ns0:cell>down</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>steroid</ns0:cell><ns0:cell>steroids</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>derivatives</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>1</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row></ns0:table><ns0:note>PeerJ reviewing PDF \| (2020:03:46424:1:1:NEW 5 Jun 2020)Manuscript to be reviewed</ns0:note></ns0:figure> <ns0:note place='foot'>PeerJ reviewing PDF \| (2020:03:46424:1:1:NEW 5 Jun 2020)Manuscript to be reviewed</ns0:note> </ns0:body> "	"Dear Editors Thank the reviewers for their generous comments on the manuscript entitled “Identification of alteration in granulosa cells of diminished ovarian reserve using bioinformatics and metabolomics” (ID: #46424). We have edited the manuscript to address their concerns. Your comments have helped us a lot in improving the logic and novelty of our article. Thanks again for your comments from my heart. We believe that the manuscript is now suitable for publication in PeerJ. We look forward to hearing from you at your earliest convenience. Ruifen He1, Zhongying Zhao1, Yongxiu Yang2, Xiaolei Liang2 1 The First Clinical Medical College of Lanzhou University, Lanzhou, China 2 Department of Obstetrics and Gynecology, The First Hospital of Lanzhou University, Key Laboratory for Gynecologic Oncology Gansu Province, Lanzhou, China Corresponding Author: Xiaolei Liang No.1, Donggangxi Rd, Chengguan District, Lanzhou City, Gansu Pro., 730000 China Mail address: liangxl07@lzu.edu.cn Dear Editor and Reviewers: Thank you for your letter and for the reviewers’ comments concerning our manuscript entitled “Identification of alteration in granulosa cells of diminished ovarian reserve using bioinformatics and metabolomics” (ID: #46424). Those comments are all valuable and helpful for revising and improving our paper. We have studied comments carefully and have revised the manuscript according to the comments. Revised portion are marked in red in tracked changes manuscript. If there are any other modifications we could make, we would like very much to modify them and we really appreciate your help. The main corrections in the paper and the responds to the reviewer’s comments are listed below. Responds to the reviewer’s comments: Reviewer 1 (Anonymous) Basic reporting The authors conducted this study aiming to understand the molecular mechanisms behind diminished ovarian reserve with bioinformatic approaches. They utilized published gene expression data, including mRNA and miRNA dataset, and further analyzed and integrated them with several bioinformatic tools. They found inflammatory and steroidogenic signalings could responsible for diminished ovarian reserve. And they also supplemented this observation with their own metabolomic experiment which showed decreased steroids in the granulosa cells from patients with diminished ovarian reserve. The manuscript is well-written and logical sound. Several concerns will be listed below. Thank you very much for your review and we appreciate for your warm work earnestly. Experimental design 1. Most of the analysis in this manuscript highly depends on the E-MTAB-391 dataset the authors retrieved from a previous publication (Skiadas CC, et al.). However, there are multiple studies compared the gene expression of granulosa cells between normal and diminished ovarian reserve individuals (Greenseid K, et al., Chin K-V, et al., Pashaiasl M, et al.). These studies should be cited in the manuscript and the authors should justify the reason they only select a specific dataset as their input. For example, couple of the studies observed the changes in expression of IGF1 and IGF2. However, they are not on the DEG list of this manuscript. Why is that? And then why is the current dataset more optimal for the authors' purpose? Thank you very much for your comments. We have read and cited relevant literatures you provided in the comments (line 64-67). We are very sorry that we didn’t make it clear why we selected E-MTAB-391 dataset and we have made supplement to dataset selection (line 94-95). We mainly focused on the alteration of granulosa cells (GCs) from young women with DOR in this study and have highlighted this in introduction and discussion parts (line 50-52, line 228-232). The difference in gene expression of GCs has been demonstrated in young and older women (Chin et al. 2002). Greenseid et al. have identified the gene expression pattern of cumulus cells (CCs) by microarray and validated microarray findings in GCs in participants with DOR (Greenseid et al. 2011). The CCs and GCs are two distinct cell types, although they are derived from a single-cell type in early development. In addition, DOR is not an overt phenotype, as of today, there is no consensus on the definition of DOR(2015). Skiadas et al. explored the alteration of gene expression of GCs between DOR and control group (Skiadas et al. 2012). They defined DOR with FSH level combined with ovarian response, and all participants were ≤35 years. These criteria are similar to ours. Beyond that, their raw data of microarray is publicly available and sample size was the biggest in these studies. As for the another study, Pashaiasl et al. directly extracted up-and down-regulated genes of CCs or GCs from previous publications to perform meta-analysis and computational biology analysis (Pashaiasl et al. 2016). Lastly, IGF1 and IGF2 were identified by Greenseid et al., But we couldn’t find them in DEGs list based on E-MTAB-391 dataset. This difference might due to different experiment platforms and samples. 2. To identify differential expressed genes, the authors use log2 (FC) >0.58 as their cutoff. Several studies used log2 (FC) >1 as filter and it is not necessary the standard way to do it. But the authors has the responsibility to explain how this number (0.58) was chosen. If the filter was log2 (FC) >1, there would be no genes related to steroidogenesis in the list and the steroidogenesis is one of the major players being discussed in this manuscript. Thank you for your suggestions and we have added a description as a limitation in discussion part (line 316-324). There is no an absolute standard in this parameter setting. We set \|log2FC\| >0.58 (i.e. fold change approximately was > 1.5) to identify DEGs as comprehensively as possible. The data of E-MTAB-391 was derived from large sample size (n=26). Beyond that, we did find metabolic difference of steroids in GCs between normal and diminished ovarian reserve individuals by metabolomics. 3. For the differential expressed miRNA, the authors directly used the list from the publication of Chen D., et al. Again, it is not the only study toward this question. For example, Woo I., et al. did deep sequencing on the granulosa cells from normal and diminished ovarian reserve individuals. The differential expressed miRNA in their study are not the same list as in the study of Chen D., et al. So the authors should justify why they chose one over the other one (and please cite the publication) or they should consider to use both of them as input. We feel great thanks for your professional review work on our article and are very sorry for our negligence because we were not critical in selection of differentially expressed miRNA. The list of differential expressed miRNA in publication of Chen et al. was derived from cumulus cells (Chen et al. 2017). Thus, we have re-extracted differential expressed miRNAs of GCs from publication of Woo et al. (Woo et al. 2018) and reconstructed the mRNA-miRNA regulatory network (Fig. 5). We have cited both of them based on your suggestion (line 64-67) and made corrections in results and discussion parts (line 205-209, line 289-305). 4. The authors then utilized the differential expressed genes and miRNAs to study the miRNA-mRNA regulatory network. The intention here is good. However, the authors did not compare the patient selection and sample collection methods in details. The authors should make a table to compare different cohorts of patients in their manuscript which should include their own cohort for the study of metabolomics. And the authors also need to explain how the differences between different selection methods could influence the results of their own if there is any. For example, the FSH level of these three cohorts are different. Thank you for your suggestions. We have made a table to compare inclusion criteria of different cohorts of patients (Table S1). As mentioned above, because there is no consensus on the definition of DOR, it is difficult to achieve same inclusion criteria completely. Despite existing differences in inclusion criteria in these three cohorts, we believe that such differences have little influence on the results, because the key molecular mechanism for DOR might be common in different cohorts of patients. Validity of the findings 1. In general, I appreciate the authors presented their data pretty well as well as discussed their findings in details in the discussion. At the same time, I do think the figures can be upgraded a little. The major concern here is that most of them will not be readable (figure2-6). There are a lot of labels and the fonts are way too small. In Figure 3, as far as I can see, there is only one color. And it is very hard to tell if the darkness of the green are different from each other. Please try to present the figures in a way the readers can read and digest. We have upgraded the figures in the paper (figure 2-6). In Figure 3, we have made some modifications. The CYP51A1 with the darkest blue has largest fold change and the other genes seem to be similar because their changes are very close. 2. I believe the metabonomic study in the end of paper can be interesting and beneficial to the filed. But the authors should provide the whole list (now there is only metabolites related to steroidogenesis) of their discovery as a supplementary file. We have uploaded the whole list with Microsoft Excel file in supplementary file, including two sheets representing metabolites in positive ion mode and negative ion mode. 3. Please also provide the list of differential expressed miRNA used in this publication. We have supplemented the list of differential expressed miRNA in supplementary file. Comments for the Author I believe the study can provide valuable information to the field. But as a manuscript heavily replies on bioinformatics, the selection of input is very critical. The authors need to be very careful on the way they select dataset and provide strong rationale on how they select the data. Thank you about your excellent comments and we all agree with you. All of these are valuable and helpful to improve our article. We have made corrections and supplements in manuscript based on your comments. Your appreciation encouraged us, and we sincerely hope that the correction will meet with approval. 2015. Testing and interpreting measures of ovarian reserve: a committee opinion. Fertil Steril 103:e9-e17. 10.1016/j.fertnstert.2014.12.093 Chen D, Zhang Z, Chen B, Ji D, Hao Y, Zhou P, Wei Z, and Cao Y. 2017. Altered microRNA and Piwi-interacting RNA profiles in cumulus cells from patients with diminished ovarian reserve. Biol Reprod 97:91-103. 10.1093/biolre/iox062 Chin KV, Seifer DB, Feng B, Lin Y, and Shih WC. 2002. DNA microarray analysis of the expression profiles of luteinized granulosa cells as a function of ovarian reserve. Fertil Steril 77:1214-1218. 10.1016/s0015-0282(02)03114-x Greenseid K, Jindal S, Hurwitz J, Santoro N, and Pal L. 2011. Differential granulosa cell gene expression in young women with diminished ovarian reserve. Reprod Sci 18:892-899. 10.1177/1933719111398502 Pashaiasl M, Ebrahimi M, and Ebrahimie E. 2016. Identification of the key regulating genes of diminished ovarian reserve (DOR) by network and gene ontology analysis. Mol Biol Rep 43:923-937. 10.1007/s11033-016-4025-8 Skiadas CC, Duan S, Correll M, Rubio R, Karaca N, Ginsburg ES, Quackenbush J, and Racowsky C. 2012. Ovarian reserve status in young women is associated with altered gene expression in membrana granulosa cells. Mol Hum Reprod 18:362-371. 10.1093/molehr/gas008 Woo I, Christenson LK, Gunewardena S, Ingles SA, Thomas S, Ahmady A, Chung K, Bendikson K, Paulson R, and McGinnis LK. 2018. Micro-RNAs involved in cellular proliferation have altered expression profiles in granulosa of young women with diminished ovarian reserve. J Assist Reprod Genet 35:1777-1786. 10.1007/s10815-018-1239-9 Reviewer 2 (Anonymous) Basic reporting no comment Experimental design no comment Validity of the findings no comment Comments for the Author Comments for the Author: The manuscript by He et al was designed to understand the molecular mechanism of diminished ovarian reserve. They provide some preliminary data on the effects of steroidogenesis and inflammation at diminished ovarian reserve. Although observations made here are of interest, this study is rather too descriptive, and lack of mechanistic insights. Also, the writing is not very competent, with many awkward sentences. We really appreciate your time and patience on our article. First, we feel sorry for our poor writing, however, we have made some modifications to the grammar and formatting errors in the text. We fully understood your suggestions. It is believed that etiology of diminished ovarian reserve is multifactorial(Jin et al. 2012), and it might progress completely unnoticed. However, as of today, key molecular mechanism of diminished ovarian reserve is not identified and fundamental research on this field is insufficient. In our study, we found that steroidogenesis and inflammation might be responsible for development of diminished ovarian reserve, and we further validated the alteration of steroids in granulosa cells by metabolomics. We believe these findings could provide some valuable information on the field and guide the subsequent experimental direction. But it is difficult to deeply explore the mechanism of disease in human due to methodological and ethical limitations. We have tried to investigate the mechanism of diminished ovarian reserve. In our laboratory, the mice model of diminished ovarian reserve has been created and we will perform further studies based on these results. We sincerely hope that the correction will meet with approval. Additional minor comments: 1: The authors need to proof read the entire manuscript carefully, and to make correct statements. Thank you for your comments. We are very sorry for incorrect statement. We have carefully read and revised the entire manuscript. Revised portion are marked in red in tracked changes manuscript. We really appreciate your time and patience on our manuscript. 2: In results part, these titles are not interpret fitly. Once again, thank you very much for your comments. We have made modifications on titles in results part (line 174, 181, 204). We really appreciate your help and hope that the correction will meet with approval. Jin M, Yu Y, and Huang H. 2012. An update on primary ovarian insufficiency. Science China Life sciences 55:677-686. 10.1007/s11427-012-4355-2 "	Here is a paper. Please give your review comments after reading it.
490	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Background: During fertility treatment, diminished ovarian reserve (DOR) is a challenge that can seriously affect a patient's reproductive potential. However, the pathogenesis of DOR is still unclear and its treatment options are limited. This study aimed to explore DOR's molecular mechanisms.</ns0:p></ns0:div> <ns0:div><ns0:head>Methods:</ns0:head><ns0:p>We used R software to analyze the mRNA microarray dataset E-MTAB-391 downloaded from ArrayExpress , screen for differentially expressed genes (DEGs), and perform functional enrichment analyses. We also constructed the protein-protein interaction (PPI) and miRNA-mRNA networks. Ovarian granulosa cells (GCs) from women with DOR and the control group were collected to perform untargeted metabolomics analyses. Additionally, small molecule drugs were identified using the Connectivity Map database.</ns0:p><ns0:p>Results : We ultimately identified 138 DEGs. Our gene ontology (GO) analysis indicated that DEGs were mainly enriched in cytokine and steroid biosynthetic processes. According to the Kyoto Encyclopedia of Genes and Genomes (KEGG), the DEGs were mainly enriched in the AGE (advanced glycation endproduct)-RAGE (receptor for AGE) signaling pathway in diabetic complications and steroid biosynthesis. In the PPI network, we determined that JUN, EGR1, HMGCR, ATF3, and SQLE were hub genes that may be involved in steroid biosynthesis and inflammation. MiRNAs also played a role in DOR development by regulating target genes. We validated the differences in steroid metabolism across GCs using liquid chromatography-tandem mass spectrometry (LC-MS/MS). We selected 31 small molecules with potentially positive or negative influences on DOR development.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusion :</ns0:head><ns0:p>We found that steroidogenesis and inflammation played critical roles in DOR development, and our results provide promising insights for predicting and treating DOR.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>INTRODUCTION</ns0:head><ns0:p>Diminished ovarian reserve (DOR), defined as a decline in the number or quality of follicles and oocytes, reduces a female patient's reproductive potential <ns0:ref type='bibr' target='#b44'>(Sharara et al., 1998)</ns0:ref>. The incidence of DOR ranges from 9% to 24% in women undergoing in vitro fertilization (IVF) <ns0:ref type='bibr' target='#b21'>(Kyrou et al., 2009)</ns0:ref>. The most common effective treatment for DOR is the use of assisted reproductive techniques. Ovarian reserve naturally declines with age, but some women experience DOR much earlier than average. Young women with DOR thus have an accelerated physiological decline in ovarian reserve. DOR is one of the greatest challenges for reproductive endocrinologists because it is characterized by poor ovarian response to gonadotrophin stimulation, low pregnancy rates, and high rates of pregnancy loss <ns0:ref type='bibr' target='#b23'>(Levi et al., 2001;</ns0:ref><ns0:ref type='bibr' target='#b30'>Navot et al., 1987)</ns0:ref>.</ns0:p><ns0:p>An ovarian follicle is a complex structure comprised of oocytes, cumulus cells (CCs), and granulosa cells <ns0:ref type='bibr'>(GCs)</ns0:ref>. The bidirectional communication between the oocyte and its companion somatic cells is essential for follicular development and oocyte growth <ns0:ref type='bibr' target='#b1'>(Anderson & Albertini 1976;</ns0:ref><ns0:ref type='bibr' target='#b27'>Matzuk et al., 2002)</ns0:ref>. In mice, the oocyte's transcriptional activity was modulated when in vitro cultured with GCs, but not without GCs <ns0:ref type='bibr' target='#b9'>(De La Fuente & Eppig 2001)</ns0:ref>. Another study showed that DOR patients had an increase in GC apoptosis, which is associated with a poor ovarian response and oocyte yield <ns0:ref type='bibr' target='#b12'>(Fan et al., 2019)</ns0:ref>. Considering this coadjutant relationship between oocyte and GC, exploring the alteration of GCs from women with DOR may provide a deeper understanding for DOR pathogenesis. Previous studies have investigated the mRNA expression profiles of ovarian CCs <ns0:ref type='bibr' target='#b17'>(Greenseid et al., 2011)</ns0:ref> and GCs <ns0:ref type='bibr' target='#b6'>(Chin et al., 2002;</ns0:ref><ns0:ref type='bibr' target='#b47'>Skiadas et al., 2012)</ns0:ref> in addition to miRNA expression patterns <ns0:ref type='bibr' target='#b5'>(Chen et al., 2017;</ns0:ref><ns0:ref type='bibr' target='#b54'>Woo et al., 2018)</ns0:ref> in DOR patients. These studies typically had different inclusion criteria for DOR patients. MiRNAs are small noncoding RNAs that play critical roles in many diseases and biological processes, including reproduction, and regulate mRNA translation and stability <ns0:ref type='bibr' target='#b11'>(Fabian et al., 2010)</ns0:ref>.</ns0:p><ns0:p>Exploring how miRNA affects female reproduction <ns0:ref type='bibr' target='#b38'>(Sabry et al., 2019)</ns0:ref> could reveal miRNA therapeutics as a possible DOR treatment option.</ns0:p><ns0:p>Steroid hormones are a type of steroid involved in many biological and physiological functions. Cholesterol is the precursor for steroid hormone synthesis <ns0:ref type='bibr' target='#b16'>(Greaves et al., 2014)</ns0:ref>.</ns0:p><ns0:p>Steroid hormone levels affect the follicular growth and development processes <ns0:ref type='bibr' target='#b7'>(Chou & Chen 2018)</ns0:ref>. Inflammation triggers a wide range of physiological and pathological processes <ns0:ref type='bibr' target='#b28'>(Medzhitov 2008)</ns0:ref>. Aberrant inflammation has a negative effect on folliculogenesis and ovulation, and polycystic ovary syndrome (PCOS) is associated with the chronic endogenous production of low-grade pro-inflammatory cytokines <ns0:ref type='bibr' target='#b4'>(Boots & Jungheim 2015)</ns0:ref>. Inflammation and steroidogenesis abnormalities may also be involved in the development of DOR.</ns0:p><ns0:p>In this study, we performed bioinformatic analyses on the mRNA expression profiles of DOR GCs from the publicly available dataset E-MTAB-391 in order to identify differentially expressed genes (DEGs). We constructed protein-protein interaction (PPI) networks based on the DEGs, and a miRNA-mRNA network using the differentially expressed miRNAs (DEMs) extracted from a previous study <ns0:ref type='bibr' target='#b54'>(Woo et al., 2018)</ns0:ref>. Small molecule drugs with potential synergistic or antagonistic effects on DOR were also screened using the Connectivity Map (CMap) database <ns0:ref type='bibr' target='#b22'>(Lamb et al., 2006)</ns0:ref>. Moreover, we applied liquid chromatography-tandem mass spectrometry (LC-MS/MS) on our samples to explore the metabolic alteration of GCs. This study may shed light on the future of DOR prognosis and treatment.</ns0:p></ns0:div> <ns0:div><ns0:head>MATERIALS & METHODS</ns0:head></ns0:div> <ns0:div><ns0:head>Data collection</ns0:head><ns0:p>We screened GEO and ArrayExpress (https://www.ebi.ac.uk/arrayexpress/) for expressed profiles of GCs from patients with and without DOR. We excluded datasets that: (1) were without detailed sample information, (2) used samples based on cell lines or animal models, (3) had sample sizes < 10, and (4) had quite different definitions of DOR or used different participant age ranges than our study. Only one mRNA microarray dataset, E-MTAB-391 (which included 13 DOR samples and 13 normal ovarian reserve (NOR) samples <ns0:ref type='bibr' target='#b47'>(Skiadas et al., 2012)</ns0:ref>), met our criteria and was downloaded from ArrayExpress. E-MTAB-391's platform was the A-MEXP-1564-IIIumina HumanRef-8 WG-DASL v3 Expression BeadChip. We directly extracted the data from the DOR miRNAs used in a previous study <ns0:ref type='bibr' target='#b54'>(Woo et al., 2018)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Identifying DEGs</ns0:head><ns0:p>We used the limma package in R software <ns0:ref type='bibr' target='#b35'>(Ritchie et al., 2015)</ns0:ref> to identify DEGs across the DOR and NOR samples. The adjusted P-value (false discovery rate) was obtained using the Benjamini-Hochberg algorithm when screening the DEGs. We set P < 0.05 and \|log 2 (fold change; FC) \| > 0.58 as the DEG cut-off criteria. The DEGs were divided into upregulated and downregulated DEGs and saved for subsequent analyses.</ns0:p></ns0:div> <ns0:div><ns0:head>Functional and enrichment analyses of DEGs</ns0:head><ns0:p>Gene ontology (GO) annotation is widely used to study the biological functions of multiple genes, and is comprised of three independent ontologies: biological process (BP), molecular function (MF), and cellular component (CC) <ns0:ref type='bibr' target='#b2'>(Ashburner et al., 2000)</ns0:ref>. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis is a valuable tool used to assess the interaction PeerJ reviewing PDF \| (2020:03:46424:2:0:NEW 1 Aug 2020)</ns0:p><ns0:p>Manuscript to be reviewed networks of genes and their products <ns0:ref type='bibr' target='#b20'>(Kanehisa et al., 2017)</ns0:ref>. In this study, we used the clusterProfiler package in R software to obtain the GO and KEGG pathway enrichment for the DEGs <ns0:ref type='bibr' target='#b55'>(Yu et al., 2012)</ns0:ref>. We set the value of P < 0.05 as the threshold for significance.</ns0:p></ns0:div> <ns0:div><ns0:head>PPI network construction and module analysis</ns0:head><ns0:p>The STRING database offers PPI assessment and integration <ns0:ref type='bibr' target='#b48'>(Szklarczyk et al., 2017)</ns0:ref>. To achieve a system-wide understanding across DEGs, we constructed the PPI network using STRING (version 10.5; https://string-db.org/cgi/input.pl) with a combined cutoff score ≥ 4. The network was visualized using Cytoscape (version 3.6.1; https://cytoscape.org/), which can integrate biomolecular interaction networks into a unified conceptual framework <ns0:ref type='bibr' target='#b43'>(Shannon et al., 2003)</ns0:ref>. The nodes and edges of the network represent proteins and protein-protein associations, respectively. We performed a module analysis of the PPI network based on the Molecular Complex Detection (MCODE) feature of the Cytoscape software using the following parameters: degree cut-off = 2, node score cut-off = 0.2, max depth = 100, and k-score = 2. We performed subsequent GO and KEGG pathway analyses of the selected modules using the clusterProfiler package.</ns0:p></ns0:div> <ns0:div><ns0:head>Exploring DEM target genes</ns0:head><ns0:p>We extracted 105 DEMs from the GCs of women diagnosed with DOR in a previous study <ns0:ref type='bibr' target='#b54'>(Woo et al., 2018)</ns0:ref>. The multiMiR package integrated 11 miRNA-target databases (three validated and eight predicted miRNA-target databases) and three disease-/drug-related miRNA databases <ns0:ref type='bibr' target='#b36'>(Ru et al., 2014)</ns0:ref> to retrieve interactions between the DEMs and screened DEGs. The DEM target genes were only screened from the 11 miRNA-target databases. Finally, we visualized the regulatory miRNA-mRNA network using Cytoscape software.</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:03:46424:2:0:NEW 1 Aug 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Identifying small molecules</ns0:head><ns0:p>The CMap database (https://portals.broadinstitute.org/cmap) contains 7,000 gene-expression profiles from cultured human cells treated with bioactive small molecules <ns0:ref type='bibr' target='#b22'>(Lamb et al., 2006)</ns0:ref>, and is a valuable resource when looking for connections between diseases, genetic perturbation, and drug action. We mapped the upregulated and downregulated genes to the CMap database to identify potential small molecule drugs, which either have antagonistic or synergistic effects on DOR. We regarded n (the number of instances) ≥ 4, enrichment > 0.7, and P-value < 0.01 as statistically significant.</ns0:p></ns0:div> <ns0:div><ns0:head>Collecting GCs</ns0:head><ns0:p>This study was approved by the ethics committee of the First Hospital of Lanzhou University (LDYYLL2019-44), and we obtained written informed consent from all participants. Ovarian GCs were collected from women with DOR (n=3) and women with NOR (n=3). All participants were ≤ 35 years old to eliminate age as a potential confounding variable. DOR was identified using FSH levels (12 ≤ FSH < 25) and ovarian response (the number of follicles on the day of the ovulatory human chorion gonadotropin (hCG) trigger injection≤ 7). We selected infertile women undergoing IVF due to male or tubal factor infertility for the NOR group. Controlled ovarian stimulation was performed and follicular development was monitored using a transvaginal ultrasound. Oocyte retrieval was performed 36 hours after hCG administration and GCs were isolated from fluid aspirates using previously-described methods <ns0:ref type='bibr' target='#b51'>(Vanacker et al., 2011)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>LC-MS/MS experiments</ns0:head><ns0:p>PeerJ reviewing <ns0:ref type='table' target='#tab_3'>PDF \| (2020:03:46424:2:0:NEW 1 Aug 2020)</ns0:ref> Manuscript to be reviewed</ns0:p><ns0:p>We performed metabolite extraction and LC-MS/MS analysis at Beijing Genomics Institute (BGI). Each frozen GCs sample was thawed and weighed into 1.5 mL Eppendorf tubes. We added the internal standard solution and 800 μL of methanol/acetonitrile/water solvent (2:2:1, v/v/v) to homogenize. Mixtures were centrifuged at 25,000 rcf for 15 min, and the supernatant was transferred out and vacuum dried. We then re-extracted the metabolite extract in 200 μL of a methanol/water mixture (1:9, v/v). After vortexing, the samples were centrifuged again. We collected the supernatant and inspected each sample using 20 μL of supernatant, the Waters 2D UPLC (Waters, Milford, MA, USA), and a Q Exactive high-resolution mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA). The analytical column we used was an ACQUITY UPLC BEH C18 (1.7 μm, 2.1×100 mm, Waters). In positive ion mode, the mobile phase was MS-grade water with 0.1% formic acid (A) and 100% methanol with 0.1% formic acid (B). In negative ion mode, the mobile phase was MS-grade water with 10 mM of ammonium formate (A) and 95% methanol with 10 mM of ammonium formate (B). The extracts were gradient-eluted with a flow rate of 0.35 ml/min. The full scan and fragment acquisition resolutions were 70,000 and 17,500, respectively. The ESI parameters were set as follows:</ns0:p><ns0:p>sheath gas flow rate of 40 L•min-1, auxiliary gas flow rate of 10 L•min-1, spray voltage of 3800 V (positive mode) and 3200 V (negative mode), capillary temperature of 320℃, and auxiliary gas heater temperature of 350℃. The LC-MS/MS data were processed using Compound Discoverer 3.0 software (Thermo Fisher Scientific). We identified the differential metabolites using a combination of principal component analysis (PCA) and univariate analysis.</ns0:p></ns0:div> <ns0:div><ns0:head>RESULTS</ns0:head></ns0:div> <ns0:div><ns0:head>Identifying DEGs related to DOR</ns0:head><ns0:p>PeerJ reviewing PDF \| (2020:03:46424:2:0:NEW 1 Aug 2020)</ns0:p></ns0:div> <ns0:div><ns0:head>Manuscript to be reviewed</ns0:head><ns0:p>In this study, we downloaded the normalized expression data from the E-MTAB-391 dataset (Fig. <ns0:ref type='figure' target='#fig_2'>S1</ns0:ref>). A total of 18,128 genes were available for further DEG identification. Using our criteria, we selected 138 DEGs from the DOR and NOR samples, including 55 upregulated and 83 downregulated genes. The volcano plot in Fig. <ns0:ref type='figure' target='#fig_2'>1</ns0:ref> shows the distribution of all screened genes.</ns0:p><ns0:p>The heat map of all DEGs based on unsupervised hierarchical clustering is shown in Fig. <ns0:ref type='figure'>S2</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>GO and KEGG pathway enrichment analyses of DEGs</ns0:head><ns0:p>According to the GO BP analysis, the upregulated DEGs were mainly enriched in skeletal muscle cell differentiation and regulation of transcription from RNA polymerase II promoter in response to stress. Downregulated DEGs were mainly enriched in the steroid biosynthetic and cholesterol biosynthetic processes. Fig. <ns0:ref type='figure'>2</ns0:ref> shows the top 20 GO BP up-and down-regulated DEG terms in detail. Additionally, we performed KEGG pathway enrichment analysis and the results can be found in Table <ns0:ref type='table'>1</ns0:ref>. The upregulated DEGs were significantly enriched in the AGE (advanced glycation end-product)-RAGE (receptor for AGE) signaling pathway in diabetic complications and human T-cell leukemia virus 1 infection, and the downregulated DEGs were mainly enriched in steroid biosynthesis (Fig. <ns0:ref type='figure'>3</ns0:ref>) and terpenoid backbone biosynthesis (Fig. <ns0:ref type='figure'>S3</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>Constructing PPI networks and module analysis</ns0:head><ns0:p>We imported all DEGs related to DOR into the STRING database when constructing the PPI network, which included 99 nodes and 294 edges when the cut-off combined score was set at ≥ 0.7. The PPI network was visualized using Cytoscape software (Fig. <ns0:ref type='figure'>4A</ns0:ref>). The genes with higher node degrees were Jun proto-oncogene, AP-1 transcription factor subunit (JUN, degree=24); early growth response 1 (EGR1, degree=18); 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR, degree=17); activating transcription factor 3 (ATF3, degree=15); and squalene epoxidase (SQLE, degree=15). Additionally, we filtered out the top two modules from the PPI network in order to implement further GO and KEGG pathway analyses. Module 1 (Fig. <ns0:ref type='figure'>4B</ns0:ref>) contained 12 downregulated genes enriched in the cholesterol biosynthetic process of the GO BP term. Module 2 (Fig. <ns0:ref type='figure'>4C</ns0:ref>) contained 10 upregulated genes that were mainly enriched in skeletal muscle cell differentiation of the GO BP term. The detailed results of the modules' GO and KEGG pathway analyses are shown in Table <ns0:ref type='table' target='#tab_1'>2</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Predicting DEM target genes and constructing the DEM-DEG regulatory network</ns0:head><ns0:p>We analyzed the DEMs associated with DOR, including 85 upregulated and 20 downregulated genes, using the multiMiR package to predict their target genes. We then used the identified DEM-DEG pairs (comprised of 91 DEMs and 109 DEGs) to construct the regulatory network.</ns0:p><ns0:p>Among the pairs, miR-155-5p, miR-16-5p, let-7b-5p, miR-107, and miR-103a-3p had the most target genes. The detailed interactions between the DEMs and DEGs are shown in Fig. <ns0:ref type='figure'>5</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Screening small molecule drugs</ns0:head><ns0:p>In order to screen out small molecule drugs, we compared all DEGs to the gene expression profiles in CMap. We identified 31 small molecules, seven of which had negative scores with the potential to reverse DOR. The detailed results are shown in Fig. <ns0:ref type='figure'>6</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Metabolic differences between DOR and NOR GCs</ns0:head><ns0:p>Metabolites are essential for cellular function and untargeted metabolomics analyses can provide information on their associations with diseases. We analyzed the GC samples from the DOR and NOR groups using LC-MS/MS in both positive and negative ion modes. After data processing and metabolite identification, we screened the differential metabolites using a threshold p-value < 0.05 and FC ≥ 1.2 or ≤ 0.83. We did detect metabolic differences between the GCs of the DOR PeerJ reviewing PDF \| (2020:03:46424:2:0:NEW 1 Aug 2020)</ns0:p><ns0:p>Manuscript to be reviewed and NOR samples. The detailed differences in the steroids and metabolites observed in the GCs of the two groups are listed in Table <ns0:ref type='table' target='#tab_3'>3</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>DISCUSSION</ns0:head><ns0:p>A patient with DOR has a reduced number of retrieved oocytes compared to other women of a similar age. In some women, DOR can progress to a diagnosis of primary ovarian insufficiency (POI), which is an extreme form of ovarian dysfunction <ns0:ref type='bibr' target='#b8'>(Cooper et al., 2011)</ns0:ref>. Because of this serious threat to a patient's reproductive health, there is an urgent need to further examine DOR etiology. Previous studies have used the GC mRNA/miRNA expression profiles from DOR patients to explore the molecular mechanisms of DOR. In this study, we focused on altered GCs from young women with DOR, and performed LC-MS/MS experiments and bioinformatic analyses to explore the differences between DOR and NOR. We obtained raw mRNA expression patterns and DEM data from previous publications <ns0:ref type='bibr' target='#b47'>(Skiadas et al., 2012)</ns0:ref> <ns0:ref type='bibr' target='#b54'>(Woo et al., 2018)</ns0:ref> with similar inclusion criteria to ours. The inclusion criteria are presented in Table <ns0:ref type='table'>S1</ns0:ref>.</ns0:p><ns0:p>Sex steroid hormones (progestogens, androgens, and estrogens) have a steroid nucleus structure and are typically synthesized from cholesterol in the gonads and adrenal glands <ns0:ref type='bibr' target='#b16'>(Greaves et al., 2014)</ns0:ref>. These hormones play important roles in female reproduction. The synthesis and secretion of estrogen are promoted by the elevated FSH levels found in patients with DOR (Practice Committee of the American Society for Reproductive Medicine, 2015). However, whether there is a difference in the estrogen levels of patients with DOR and NOR remains controversial. In our study, we found that downregulated DEGs were mainly enriched in the steroid biosynthetic process of the GO BP term (Fig. <ns0:ref type='figure'>2A</ns0:ref>). KEGG pathway analysis (Table <ns0:ref type='table'>1</ns0:ref>) showed that downregulated DEGs were mainly enriched in steroid biosynthesis and terpenoid Manuscript to be reviewed backbone biosynthesis. Therefore, a range of steroidogenesis substances may play a major role in DOR development. Consistent with our PPI network results (Fig. <ns0:ref type='figure'>4</ns0:ref>), we found several key genes in the top 20 (including HMGCR, SQLE, CYP51A, HMGCS1, FDFTI, SC5D, NSDHL, IDI1, EBP, and MSMO1) in steroid biosynthesis and terpenoid backbone biosynthesis pathways (Figs.</ns0:p><ns0:p>3 and S3). All these genes were downregulated in patients with DOR from our dataset. Previous studies have found that HMGCR catalyzes the first rate-limiting step in cholesterol synthesis <ns0:ref type='bibr' target='#b18'>(Howe et al., 2017)</ns0:ref>; HMGCS1 condenses acetyl-CoA to form 3-hydroxy-3-methylglutaryl CoA, which is the substrate for HMGCR <ns0:ref type='bibr' target='#b26'>(Mathews et al., 2014)</ns0:ref>; and CYP51A also participates in cholesterol synthesis, which can catalyze the removal of the 14α-methyl group from lanosterol <ns0:ref type='bibr' target='#b45'>(Sharpe & Brown 2013)</ns0:ref>. Upstream biological disruptions lead to a series of metabolomic changes. According to our untargeted metabolomics analysis, several steroids (Table <ns0:ref type='table' target='#tab_3'>3</ns0:ref>) were significantly lower in GCs from patients with DOR compared to the control group. Among these steroids, progesterone plays an essential role in female reproductive events (ovulation, implantation, and pregnancy maintenance) and serves as an intermediate during estrogen biosynthesis <ns0:ref type='bibr' target='#b15'>(Gellersen et al., 2009)</ns0:ref>. Prior evidence suggests that GCs can directly produce progesterone before entering theca cells to convert into androgens <ns0:ref type='bibr' target='#b31'>(Oktem et al., 2017)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Hydroxyprogesterone acts as an intermediate during the conversion of progesterone to androgens</ns0:head><ns0:p>that are transported into GCs and converted into estrogen. The relationships between steroids and ovarian function (Table <ns0:ref type='table' target='#tab_3'>3</ns0:ref>) have been rarely reported on and require more study. It has been demonstrated that several steroidogenic gene disturbances induced by bisphenol A can cause developmental impairments of ovary tissue <ns0:ref type='bibr' target='#b24'>(Liu et al., 2019)</ns0:ref>. Overall, patients with DOR may have impaired hormone synthesis, which could be compensated for by elevated FSH levels. The perturbation of steroidogenic genes may be responsible for DOR development.</ns0:p><ns0:p>Aberrant inflammation has been suggested to influence follicular growth and development <ns0:ref type='bibr' target='#b4'>(Boots & Jungheim 2015)</ns0:ref>. Our results showed that upregulated genes were enriched in the AGE-RAGE signaling pathway (Table <ns0:ref type='table'>1</ns0:ref>). The AGE-RAGE signaling pathway has been shown to induce reactive oxygen species (ROS) burst and inflammation, eventually leading to POI <ns0:ref type='bibr' target='#b19'>(Huang et al., 2019)</ns0:ref>. EGR1 plays a proinflammatory role in numerous pathological processes and human diseases <ns0:ref type='bibr' target='#b41'>(Schmidt et al., 2008)</ns0:ref>. A recent study found that in mice, EGR1 was increased in aged ovaries compared to young ovaries <ns0:ref type='bibr' target='#b57'>(Yuan et al., 2016)</ns0:ref>. Our results showed that EGR1 as a key gene in the PPI network was upregulated in women with DOR. During cholestasis, EGR1 regulates the production of inflammatory mediators, including cytokines and adhesion molecules, that promote the accumulation and activation of inflammatory cells, causing liver injuries <ns0:ref type='bibr' target='#b3'>(Bonetti et al., 2010)</ns0:ref>. According to the results of our GO analysis of upregulated DEGs (Fig. <ns0:ref type='figure'>2B</ns0:ref>), cytokines may be associated with DOR development. Cytokines play a key role in inflammation, and can be found in the immune cells of the ovary <ns0:ref type='bibr' target='#b49'>(Tabibzadeh 1994;</ns0:ref><ns0:ref type='bibr' target='#b52'>Vinatier et al., 1995)</ns0:ref>. Accumulated evidence suggests that inflammation is closely related to ovarian dysfunction. Women diagnosed with PCOS often present with chronic low-grade inflammation due to overactive interleukin-1 (IL-1), a proinflammatory cytokine <ns0:ref type='bibr' target='#b33'>(Popovic et al., 2019)</ns0:ref>.</ns0:p><ns0:p>Additionally, multiple autoimmune diseases have adverse effects on female fertility via premature DOR <ns0:ref type='bibr' target='#b42'>(Sen et al., 2014)</ns0:ref>. Therefore, anti-inflammatory treatment may be able to alleviate the progression of DOR. In a POI rat model, resveratrol counteracted inflammatory signaling induced by ionizing radiation, and preserved the entire ovarian follicle pool <ns0:ref type='bibr' target='#b39'>(Said et al., 2016)</ns0:ref>. More studies are needed to confirm the role of inflammation in DOR development and whether controlling inflammation is an option for DOR treatment.</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:03:46424:2:0:NEW 1 Aug 2020)</ns0:p><ns0:p>Manuscript to be reviewed MiRNAs, a class of endogenous non-coding small molecule RNA, play an important role in gene expression modulation at the post-transcriptional level. Previous studies have shown that miRNAs help regulate reproductive functions, particularly follicular development, oocyte maturation, corpus function, pregnancy establishment, and early embryonic development <ns0:ref type='bibr' target='#b10'>(Eisenberg et al., 2015;</ns0:ref><ns0:ref type='bibr' target='#b50'>Tesfaye et al., 2016)</ns0:ref>. The role of miRNAs in ovarian function has been demonstrated primarily by the conditional knockout of Dicer <ns0:ref type='bibr' target='#b25'>(Luense et al., 2009)</ns0:ref>, a cytoplasmic RNase Ⅲ required for miRNA production in mammals. In a mouse model, the conditional knockout of Dicer in ovarian GCs led to decreased ovulation rates <ns0:ref type='bibr' target='#b29'>(Nagaraja et al., 2008)</ns0:ref> and compromised folliculogenesis and POI in oocytes <ns0:ref type='bibr' target='#b56'>(Yuan et al., 2014)</ns0:ref>. In our study, we found that steroidogenic genes were regulated by differentially expressed miRNAs. HMGCS1 was regulated by 25 DEMs, including miR-155-5p, miR-16-5p, let-7b-5p, miR-107, and miR-103a-3p. Furthermore, a single miRNA can target multiple genes. MiR-107 targets 21 DEGs, including five steroidogenic genes: HMGCS1, FDFT1, CYP51A1, SQLE, and EBP. miRNA-107 expression in murine ovarian GCs exposed to cadmium was significantly different from expression in the control group, and miRNA-107 can regulate kit ligand (kitl) expression <ns0:ref type='bibr' target='#b53'>(Wang et al., 2018)</ns0:ref>. Kitl plays an important role in the recruitment of primitive follicles <ns0:ref type='bibr' target='#b32'>(Parrott & Skinner 1999)</ns0:ref>, the proliferation and differentiation of GCs, the recruitment of theca cells, and early steroid hormone synthesis <ns0:ref type='bibr' target='#b14'>(Flanagan et al., 1991)</ns0:ref>. Therefore, miRNAs may contribute to DOR development by regulating target genes. microRNA therapies for several diseases have reached clinical testing stages with promising results <ns0:ref type='bibr' target='#b37'>(Rupaimoole & Slack 2017)</ns0:ref>. miRNAs should be researched for potential DOR treatments.</ns0:p><ns0:p>We also conducted CMap analysis to quickly identify molecule drugs with antagonistic or synergistic effects on DOR based on their gene expression profiles. We found seven agents with Manuscript to be reviewed negative scores that had potential for DOR treatment (Fig. <ns0:ref type='figure'>6</ns0:ref>). Among these, H-7 (1-(5isoquinolinesulfonyl)-2-methylpiperazine), an inhibitor of protein kinase C, has been found to reduce the release of oocytes from rat ovaries <ns0:ref type='bibr' target='#b46'>(Shimamoto et al., 1993)</ns0:ref>. Estriol is a form of estrogen, and a meta-analysis concluded that luteal estradiol stimulation in assisted reproductive technology could decrease cycle cancellation rates and increase clinical pregnancy rates in poor responders exposed to controlled ovarian hyperstimulation <ns0:ref type='bibr' target='#b34'>(Reynolds et al., 2013)</ns0:ref>. Therefore, we hypothesized that these molecule drugs, identified by bioinformatic analysis, may provide novel DOR treatment. Further validation of their effects is still needed.</ns0:p><ns0:p>Despite our study's promising findings, there were still limitations. We identified possible DEGs using \|log2FC\| > 0.58 (the approximate fold change was > 1.5), which is a relatively lower criterion than those used by other studies. DOR is an early stage ovarian reserve impairment that may take several years to develop into POI, and subtle alterations may have broader significance during its development. Small gene expression changes are also worth noting. In addition, we derived the E-MTAB-391 data from a large sample size. Nevertheless, we validated the steroid metabolism differences between DOR and NOR samples using LC-MS/MS. Therefore, our findings are reliable and provide valuable insight into DOR development.</ns0:p></ns0:div> <ns0:div><ns0:head>CONCLUSION</ns0:head><ns0:p>We used bioinformatics approaches to investigate the perturbed steroidogenic and inflammationrelated genes that may be regulated by miRNAs in women with DOR. Using metabolomics, we found that steroid metabolites were reduced in the GCs from DOR samples. Additionally, several small molecule drugs (e.g., the steroid hormone estriol) with potential antagonistic or synergistic effects on DOR were screened out. Our results suggest that steroidogenesis and inflammation play critical roles in DOR development, and should be pursued in future studies on DOR prediction and treatment. Manuscript to be reviewed</ns0:p><ns0:note type='other'>Figure 2</ns0:note><ns0:p>The GO enrichment analysis of the DEGs Manuscript to be reviewed Enriched GO BP terms (top 5) and significantly enriched KEGG pathways of genes in the top two modules GO, Gene Ontology; BP, biological process; KEGG, Kyoto Encyclopedia of Genes and Genomes.</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:03:46424:2:0:NEW 1 Aug 2020)</ns0:p><ns0:p>Manuscript to be reviewed Manuscript to be reviewed </ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ reviewing PDF \| (2020:03:46424:2:0:NEW 1 Aug 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ reviewing PDF \| (2020:03:46424:2:0:NEW 1 Aug 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>Figure 1</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head /><ns0:label /><ns0:figDesc>(A) Top 20 enriched BP terms of downregulated genes. (B) Top 20 enriched BP terms of upregulated genes. The length of bars represents the number of genes, the color of bars represents corresponding adjusted P-value. GO, Gene Ontology; DEGs, differentially expressed genes; BP, biological process.</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='25,42.52,306.37,525.00,371.25' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='28,42.52,229.87,525.00,341.25' type='bitmap' /></ns0:figure> <ns0:figure type='table' xml:id='tab_1'><ns0:head>Table 2 (on next page)</ns0:head><ns0:label>2</ns0:label><ns0:figDesc /><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_3'><ns0:head>Table 3 (on next page)</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>LC-MS/MS detected steroids that varied in GCs of DOR with significant difference LC-MS/MS, liquid chromatography-tandem mass spectrometry; GCs, granulosa cells; DOR, diminished ovarian reserve; FC, fold change.</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Components</ns0:cell><ns0:cell>Formula</ns0:cell><ns0:cell>m/z</ns0:cell><ns0:cell>FC</ns0:cell><ns0:cell cols='2'>p-value Class</ns0:cell><ns0:cell>Sub class</ns0:cell><ns0:cell>label</ns0:cell></ns0:row><ns0:row><ns0:cell>Hydroxyprogesterone</ns0:cell><ns0:cell>C 21 H 30 O 3</ns0:cell><ns0:cell cols='3'>330.2190 0.0404 0.0381</ns0:cell><ns0:cell>Steroids and</ns0:cell><ns0:cell>Pregnane</ns0:cell><ns0:cell>down</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>steroid</ns0:cell><ns0:cell>steroids</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>derivatives</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>Progesterone</ns0:cell><ns0:cell>C 21 H 30 O 2</ns0:cell><ns0:cell cols='3'>314.2244 0.0785 0.0025</ns0:cell><ns0:cell>Steroids and</ns0:cell><ns0:cell>Pregnane</ns0:cell><ns0:cell>down</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>steroid</ns0:cell><ns0:cell>steroids</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>derivatives</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>3 alpha-hydroxydesogestrel</ns0:cell><ns0:cell>C 22 H 30 O 2</ns0:cell><ns0:cell cols='3'>326.2241 0.0657 0.0027</ns0:cell><ns0:cell>Steroids and</ns0:cell><ns0:cell>Estrane steroids</ns0:cell><ns0:cell>down</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>steroid</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>derivatives</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>(6beta,8xi,11beta,14xi,16alpha)-9-</ns0:cell><ns0:cell cols='4'>C 22 H 29 FO 6 408.1960 0.0290 0.0093</ns0:cell><ns0:cell>Steroids and</ns0:cell><ns0:cell cols='2'>Hydroxysteroids down</ns0:cell></ns0:row><ns0:row><ns0:cell>fluoro-6,11,17,21-tetrahydroxy-16-</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>steroid</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>methylpregna-1,4-diene-3,20-dione</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>derivatives</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>(6beta,8xi,11beta,14xi,16alpha)-9-</ns0:cell><ns0:cell cols='4'>C 22 H 29 FO 6 408.1959 0.0340 0.0179</ns0:cell><ns0:cell>Steroids and</ns0:cell><ns0:cell cols='2'>Hydroxysteroids down</ns0:cell></ns0:row><ns0:row><ns0:cell>fluoro-6,11,17,21-tetrahydroxy-16-</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>steroid</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>methylpregna-1,4-diene-3,20-dione</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>derivatives</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>4,6-cholestadien-3-one</ns0:cell><ns0:cell>C 27 H 42 O</ns0:cell><ns0:cell cols='3'>382.3229 0.0343 0.0352</ns0:cell><ns0:cell>Steroids and</ns0:cell><ns0:cell>Cholestane</ns0:cell><ns0:cell>down</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>steroid</ns0:cell><ns0:cell>steroids</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>derivatives</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>Cholest-4-en-3-one</ns0:cell><ns0:cell>C 27 H 44 O</ns0:cell><ns0:cell cols='3'>384.3385 0.0796 0.0273</ns0:cell><ns0:cell>Steroids and</ns0:cell><ns0:cell>Cholestane</ns0:cell><ns0:cell>down</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>steroid</ns0:cell><ns0:cell>steroids</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>derivatives</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>1</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row></ns0:table><ns0:note>PeerJ reviewing PDF \| (2020:03:46424:2:0:NEW 1 Aug 2020)Manuscript to be reviewed</ns0:note></ns0:figure> <ns0:note place='foot'>PeerJ reviewing PDF \| (2020:03:46424:2:0:NEW 1 Aug 2020)Manuscript to be reviewed</ns0:note> </ns0:body> "	"Dear Editor, Thank you for your letter and patience on the manuscript entitled “Using bioinformatics and metabolomics to identify altered granulosa cells in patients with diminished ovarian reserve” (ID:46424). We thank all the reviewers for their generous comments on the manuscript and have edited the manuscript to address their concerns. All comments have helped us a lot in improving the logic and novelty of our article. We believe that the manuscript is now suitable for publication in PeerJ. We look forward to hearing from you at your earliest convenience. Ruifen He1, Zhongying Zhao1, Yongxiu Yang2, Xiaolei Liang2 1The First Clinical Medical College of Lanzhou University, Lanzhou, China 2Department of Obstetrics and Gynecology, The First Hospital of Lanzhou University, Key Laboratory for Gynecologic Oncology Gansu Province, Lanzhou, China Corresponding Author: Xiaolei Liang Lanzhou City, Gansu Province, 730000 China Mail address: liangxl07@lzu.edu.cn Dear Editor and Reviewers, Thank you for your patience and time concerning our manuscript entitled “Using bioinformatics and metabolomics to identify altered granulosa cells in patients with diminished ovarian reserve” (ID:46424). Those comments are all valuable and helpful for revising and improving our paper. We have studied comments carefully and have made correction which we hope meet with approval. Our manuscript have received language editing service provided by PeerJ. We believe these modifications will not change the meaning of the original text. Revised portion are marked in red in tracked changes manuscript. We really appreciate your help and patience on our manuscript. The main corrections in the paper and the responds to the reviewer’s comments are listed below. Responds to the reviewer’s comments: Reviewer 1 (Anonymous) Basic reporting See below Experimental design See below Validity of the findings See below Comments for the Author I appreciate the responses from the authors to each comments. The current manuscript is better scientifically and logically sound. However, the writing is somehow downgraded in this version, particularly for the newly added context. It is not only that the structure of the sentences is not reader-friendly but the contents are not professionally written. For example, the authors added a paragraph to describe the importance of miRNA in reproductive system. The most compelling evidence is certainly the Dicer-KO experiments, which were done by multiple groups. The description toward importance of miRNA in this current version is very superficial. And similar issues can be found across the manuscript. It is highly recommended that the authors should make the efforts to polish the manuscript and make sure the manuscript can communicate with the scientific community with clear and scientific language. Thank you for your professional suggestions and we are very sorry for our poor writing. According to your suggestion, we have extended the section that describes the role of miRNAs in reproductive system (line282-289) and revised the manuscript carefully to improve the readability. Furthermore, we have asked professional editor who was recommended by PeerJ to conduct language editing on the manuscript. The changes are marked in red in tracked changes manuscript and these changes will not influence the content and framework of the paper. We really appreciate your time and kindness on our manuscript and hope the revision will meet with approval. Once again, thank you very much for your comments and suggestions. Reviewer 2 (Anonymous) Basic reporting no comment Experimental design no comment Validity of the findings no comment Comments for the Author no comment "	Here is a paper. Please give your review comments after reading it.
491	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Background Excessive impact forces and vertical loading rates are associated with running injuries and have been targeted in running retraining strategies. This study aimed to determine the effects of 12-week cadence retraining on impact forces and lower extremity biomechanics during running. Methods Twenty-four healthy male recreational runners were randomised into either a 12-week cadence retraining group (n=12) who ran with a 7.5% increase in preferred cadence or the control group (n=12). Kinematics and ground reaction forces were recorded simultaneously to quantify impact forces and lower extremity kinematics and kinetics. Results Significantly decreased impact forces (1.86±0.30 vs. 1.67±0.27 BW, P = 0.003), average loading rates (91.59±18.91 vs. 77.31±15.12 BW/S, P = 0.001), maximum loading rates (108.8±24.5 vs. 92.8±18.5 BW/S, P = 0.001), impact impulse (19.1±2.6 vs. 17.2 N•s, P = 0.005) and vertical momentum at initial contact (-50.8±9.6 vs. -46.8±7.6 kg•m/s, P = 0.002) were observed after cadence retraining. At the initial contact, the foot angles, vertical velocities of the centre of gravity and distances between ankle and hip in the sagittal plane significantly decreased after retraining. In addition, the vertical excursion of the centre of gravity, peak knee flexion angles, and ranges of motion of the knee significantly decreased after retraining, whilst the lower extremity stiffness significantly increased. Conclusions This increased cadence after the 12-week cadence retraining can effectively reduce the impact force and induced changes to the lower extremity biomechanics both at initial contact and during stance, which may potentially decrease the risk of impact-related running injuries.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>Long-distance running is one of the most popular activities. According to the Chinese Athletics Association Marathon Annual Press Conference, 1,581 marathon events were held in China in 2018, with a total of almost 5.83 million participants <ns0:ref type='bibr' target='#b24'>(Han 2019)</ns0:ref>. Similarly, there were over 50 million recreational runners in the USA <ns0:ref type='bibr' target='#b0'>(2017)</ns0:ref>. However, the incidence of running injuries remains high <ns0:ref type='bibr' target='#b35'>(Messier et al. 2018)</ns0:ref>. A recent study has shown that running has a higher risk of overuse injuries than other types of aerobic exercises, such as walking, swimming, and cycling <ns0:ref type='bibr' target='#b13'>(Francis et al. 2019</ns0:ref>). In addition, 19.4% to 79.3% of long-distance runners experienced lower extremity injuries <ns0:ref type='bibr' target='#b38'>(van Gent et al. 2007</ns0:ref>). Among these injuries, knee injuries, such as patellofemoral pain, are the most common. Also, injuries to the lower leg have bene reported to be just as common as the knee in more recent publications <ns0:ref type='bibr' target='#b3'>(Buist et al. 2010;</ns0:ref><ns0:ref type='bibr' target='#b14'>Franke et al. 2019)</ns0:ref>.</ns0:p><ns0:p>Training history, anatomical characteristics and running biomechanics are the main risk factors that impact overuse injuries in running <ns0:ref type='bibr' target='#b27'>(Hreljac 2004</ns0:ref>). Among various biomechanical factors, excessive impact forces and loading rates are associated with injuries and have been targeted in running retraining strategies <ns0:ref type='bibr' target='#b4'>(Cheung & Davis 2011)</ns0:ref>. In a recent review, excessive accumulation of impact peak force in knee joints could lead to overuse injuries <ns0:ref type='bibr' target='#b18'>(Gijon-Nogueron & Fernandez-Villarejo 2015)</ns0:ref>. Previous prospective studies found that injured runners had greater vertical loading rates than non-injured runners <ns0:ref type='bibr' target='#b5'>(Davis et al. 2016;</ns0:ref><ns0:ref type='bibr' target='#b15'>Fu et al. 2017)</ns0:ref>. In addition, other prospective studies showed that runners with patellofemoral pain <ns0:ref type='bibr' target='#b4'>(Cheung & Davis 2011)</ns0:ref> and chronic exertional compartment syndrome <ns0:ref type='bibr' target='#b8'>(Diebal et al. 2012</ns0:ref>) exhibited lower impact loading after their pain and disability typically associated with these injuries reduced. Impact forces can be modified through several factors, such as speed <ns0:ref type='bibr' target='#b22'>(Hamill et al. 1983)</ns0:ref>, shoe/surface/slope <ns0:ref type='bibr' target='#b9'>(Dixon et al. 2000;</ns0:ref><ns0:ref type='bibr' target='#b19'>Gottschall & Kram 2005)</ns0:ref>, strike pattern <ns0:ref type='bibr'>(Warne et al.</ns0:ref> PeerJ reviewing PDF \| (2019:07:39657:1:0:CHECK 31 Jan 2020)</ns0:p><ns0:p>Manuscript to be reviewed <ns0:ref type='bibr' target='#b0'>2017)</ns0:ref>, and cadence/step length <ns0:ref type='bibr' target='#b26'>(Hobara et al. 2012)</ns0:ref>. Increasing running cadence or decreasing step length at a certain speed (2.5m/s for increasing cadence, 4.58m/s for decreasing step length) could decrease the impact forces and vertical loading rates <ns0:ref type='bibr' target='#b26'>(Hobara et al. 2012;</ns0:ref><ns0:ref type='bibr'>Stergiou et al. 2003)</ns0:ref>, and the reduction in impact force was found to related to the decrease in the vertical velocity of CoG <ns0:ref type='bibr' target='#b6'>(Derrick et al. 1998)</ns0:ref>. Additionally, the impact attenuation and energy absorbed by the hip and knee joints decreased with increasing cadence <ns0:ref type='bibr' target='#b6'>(Derrick et al. 1998;</ns0:ref><ns0:ref type='bibr' target='#b25'>Heiderscheit et al. 2011;</ns0:ref><ns0:ref type='bibr' target='#b34'>Mercer et al. 2003)</ns0:ref>. These results indicate that increasing cadence or reducing step length has an immediate effect on decreasing the impact forces during running. Moreover, other variables, such as peak joint angles during the stance phase (Dos <ns0:ref type='bibr' target='#b10'>Santos et al. 2016)</ns0:ref>, patellofemoral joint contact forces <ns0:ref type='bibr' target='#b28'>(Lenhart et al. 2015;</ns0:ref><ns0:ref type='bibr' target='#b43'>Willy et al. 2016c</ns0:ref>), plantar pressure <ns0:ref type='bibr' target='#b40'>(Wellenkotter et al. 2014)</ns0:ref>, and muscle forces <ns0:ref type='bibr' target='#b28'>(Lenhart et al. 2015)</ns0:ref>, can be reduced with increasing cadence. Also, a decrease in peak joint angles during the stance phase was observed with decreasing step length <ns0:ref type='bibr' target='#b7'>(Dewolf & De 2015)</ns0:ref>.</ns0:p><ns0:p>In regards to long-term cadence retraining, <ns0:ref type='bibr' target='#b39'>Warne et al. (2017)</ns0:ref> reported that impact forces were reduced after a 6-week retraining intervention with a 10% increase in cadence. <ns0:ref type='bibr' target='#b21'>Hafer et al. (2015)</ns0:ref> did not find significant changes in impact forces, but observed significant decreases in loading rates after a 6-week cadence retraining with a 10% increase in cadence. Similarly, reductions in peak loading rates and average loading rates were also observed after an eightsession cadence retraining with a 7.5% increase in cadence <ns0:ref type='bibr' target='#b41'>(Willy et al. 2016a)</ns0:ref>. In summary, cadence retraining decreases loading rates during the impact phase in running. However, due to the small number of participants, short retraining intervention time, and insufficient evaluation variables among those studies, the movement patterns and biomechanical factors, such as lower extremity kinematics and kinetics, are unclear and may result in changes in impact force loading PeerJ reviewing PDF \| (2019:07:39657:1:0:CHECK 31 Jan 2020)</ns0:p><ns0:p>Manuscript to be reviewed rates. Assessing these additional variables may result in better understanding of cadence retraining on running biomechanics and its associated risk of injuries.</ns0:p><ns0:p>Therefore, the aim of this study was to quantify the effects of a 12-week cadence retraining protocol on impact forces, lower extremity kinematics and joint mechanics. We hypothesized that a 12-week cadence retraining intervention would result in significantly lower peak impact forces and peak loading rates. Additionally, decreases in lower extremity kinematics and kinetics at initial contact and during stance phase after cadence retraining would be observed.</ns0:p></ns0:div> <ns0:div><ns0:head>Methods</ns0:head></ns0:div> <ns0:div><ns0:head>Participants</ns0:head><ns0:p>A sample size estimate determined a minimum sample size of 26 participants was required to achieve a minimum effect size of 0.6. Considering a drop-out rate of 15-20%, thirty male recreational runners were recruited through online social media, running clubs, and flyers.</ns0:p><ns0:p>Participants were randomly assigned to either the cadence retraining group (CAD) or control group (CON) based on simple randomization, with 15 participants in each group (Table <ns0:ref type='table'>1</ns0:ref>).</ns0:p><ns0:p>When the participants first came to the lab, they were required to run on a treadmill for 2 minutes with a high-speed camera placed next to the treadmill to record their running posture. They were determined as rearfoot strikers by checking the foot angle (the angle between the foot and ground at initial contact) <ns0:ref type='bibr' target='#b25'>(Heiderscheit et al. 2011)</ns0:ref> in the video frame by frame. All participants ran a minimum of 15 km/week for at least 3 months prior to the study. Participants were excluded if they had any lower limb musculoskeletal injuries in the previous 6 months. In addition, there were no significant differences in age, height, weight, and weekly mileage between two groups.</ns0:p><ns0:p>This study was approved by the Institutional Review Board of the Shanghai University of Sports </ns0:p></ns0:div> <ns0:div><ns0:head>Experimental protocol</ns0:head><ns0:p>The participants visited the laboratory twice, i.e., at baseline and at the end of the program.</ns0:p><ns0:p>Before data collection, participants were required to change their test vest, shorts, socks, and experimental shoes and walk for 2 min and run at 3.33 m/s on a treadmill for 5 min as a warmup. After a warm-up, a total of 40 markers were placed on participants, including 16 markers on anatomical landmarks and 24 tracking markers and a static calibration was then performed (Figure <ns0:ref type='figure'>1</ns0:ref>). The participants were instructed to run over the ground across a 10 m runway at 3.33 m/s with the foot of the preferred kicking leg <ns0:ref type='bibr' target='#b15'>(Fu et al. 2017)</ns0:ref> striking the force platform, whilst kinematic and ground reaction force data were captured. The running speed was considered acceptable if the deviation was within 5%. Three successful running trials were collected for PeerJ reviewing <ns0:ref type='table'>PDF \| (2019:07:39657:1:0:CHECK 31 Jan 2020)</ns0:ref> Manuscript to be reviewed each participant.</ns0:p></ns0:div> <ns0:div><ns0:head>Retraining protocol</ns0:head><ns0:p>All of the participants were required to run at preferred speeds during the cadence retraining <ns0:ref type='bibr' target='#b21'>(Hafer et al. 2015)</ns0:ref>. The running speed and cadence during training were monitored by a commercial running application CODOON © (Chengdu Ledong Information Technology Co., Ltd., China). Each participant received a sport belt bag to place their phones during running, and they were instructed to place the bag above their sacrum. The participants were asked to run outdoors three times (30 min for each run) at a comfortable speed to determine the preferred speed and cadence of the participants. The preferred speed and preferred cadence were the average values obtained from three outdoor trials. Participants in the CAD group were instructed to run with a 7.5% increase in cadence, whilst those in the CON group ran at their preferred cadence <ns0:ref type='bibr' target='#b42'>(Willy et al. 2016b</ns0:ref>). Retraining participants were informed about the conditions set and given access to a mobile phone metronome that can play the beat at the increased cadence. The cadence retraining protocol lasted for 12 weeks with three sessions a week and 5-48 min each session <ns0:ref type='bibr' target='#b37'>(Neal et al. 2018</ns0:ref>) (Figure <ns0:ref type='figure'>2</ns0:ref>). Each participant used his preferred running mode, namely, treadmill or over ground, to complete his retraining. The retraining protocol substituted a part of the participants' running volume with unchanged total weekly running volume. Moreover, all participants were required to upload their running volumes, speed and cadence recorded by the CODOON © running app to the researchers after every retraining session. Participants were excluded if their training protocols were interrupted more than three times, and they were also excluded if their cadence didn't achieve the desired cadence for three weeks since the start. We provided weekly group training three times a week, and participants chose one of weekly group </ns0:p></ns0:div> <ns0:div><ns0:head>Data processing</ns0:head><ns0:p>Visual 3D biomechanical analysis software (v5, C-Motion, Inc., Germantown, MD, USA) was used to compute the 3D kinematic and kinetic variables of the lower extremity during running.</ns0:p><ns0:p>Marker trajectories were filtered with a cut-off frequency of 7 Hz via a fourth-order Butterworth low-pass filter. Kinematic variables of the hip, knee and ankle joints included the following: (1) joint angles (θ 0 ) and foot angles (θ f , the angle between the foot and ground) at initial contact (Figure <ns0:ref type='figure'>3</ns0:ref> Manuscript to be reviewed to the second peak in the curve (Figure <ns0:ref type='figure'>3</ns0:ref>). Loading rate was calculated on the basis of the method described by <ns0:ref type='bibr' target='#b16'>Futrell et al.(2018)</ns0:ref>. In brief, a point of interest (POI) was defined as the first point above 75% of a participant's body weight with instantaneous loading rate less than 15 body weight/s. Average loading rate (the average slope) and maximum loading rate (i.e., the maximum instantaneous slope) were calculated from 20% to 80% and from 20% to 100% of the force at the POI, respectively (Figure <ns0:ref type='figure'>3</ns0:ref>). Kinetic variables included joint stiffness <ns0:ref type='bibr' target='#b23'>(Hamill et al. 2009)</ns0:ref> [k j , Equation ( <ns0:ref type='formula'>2</ns0:ref>)] and peak moments of hip, knee and ankle in the sagittal plane, vertical momentum at initial contact [P, Equation (3)] and lower extremity stiffness <ns0:ref type='bibr' target='#b31'>(Liu et al. 2006)</ns0:ref> [k leg , Equation (4)].</ns0:p><ns0:p>(1)</ns0:p><ns0:formula xml:id='formula_0'>𝐼 = ∫ 𝑡 0 𝐹 𝑧 × ∆𝑡</ns0:formula><ns0:p>where refers to vertical ground reaction force; t refers to the time point of impact peak.</ns0:p><ns0:p>𝐹 𝑧</ns0:p><ns0:p>(2)</ns0:p><ns0:formula xml:id='formula_1'>𝑘 𝑗 = ∆𝑀 ∆𝜃</ns0:formula><ns0:p>where is the joint moment difference between initial contact and midstance and is the ∆𝑀 ∆𝜃 joint angle difference between initial contact and midstance.</ns0:p><ns0:p>(3) 𝑃 = 𝑚 × 𝑣 where m is the participant's mass and v is the vertical velocity of CoG.</ns0:p><ns0:p>(4)</ns0:p><ns0:formula xml:id='formula_2'>𝑘 𝑙𝑒𝑔 = 𝐺𝑅𝐹 𝑖 ∆𝑦</ns0:formula><ns0:p>where GRF i is the vertical ground reaction force at the lowest position of CoG and is the ∆𝑦 maximum vertical displacement of CoG.</ns0:p></ns0:div> <ns0:div><ns0:head>Statistics</ns0:head><ns0:p>The mean and standard deviation for each variable were calculated. A two-way repeated measure paired t-tests and independent sample t-tests results. The 95% confidence interval of the differences for group effects was reported. The criterion α level was set to 0.05. All of the statistical procedures were conducted using SPSS Software (Version 20; SPSS, Inc., Chicago, IL, USA).</ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head></ns0:div> <ns0:div><ns0:head>Dropout rate</ns0:head><ns0:p>Thirty participants (15 in the CAD group and 15 in the CON group) completed the pre-training tests at their first visit to the laboratory (Table <ns0:ref type='table'>1</ns0:ref>). However, in the CAD group, one participant was excluded due to his insufficient training volume, and two participants withdrew for personal reasons or more than three interruptions. In the CON group, two participants were lost to contact, and one participant withdrew for personal reasons. Overall, 24 participants (12 in CAD, 12 in CON) completed the 12-week cadence retraining protocol and had a second visit to the laboratory for post-training tests (Table <ns0:ref type='table'>1</ns0:ref>). In addition, no significant difference was observed in the average running volumes between the CAD and CON groups (CAD: 23.3±3.3 km/week, CON: 22.9±4.3 km/week).</ns0:p></ns0:div> <ns0:div><ns0:head>Cadence and step length</ns0:head><ns0:p>PeerJ reviewing PDF \| (2019:07:39657:1:0:CHECK 31 Jan 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p><ns0:p>Significant training × group interaction effect was observed for the cadence (P < 0.001, 𝜂 2 =0.867) (Figure <ns0:ref type='figure' target='#fig_8'>4</ns0:ref>). Specifically, the cadence significantly increased in the CAD group (P < 0.001, Cohen's d = 3.87), but not in the CON group (P > 0.05) after training. A significant main effect of training was noted for the step length, which decreased (P = 0.011, =0.259) in the 𝜂 2</ns0:p><ns0:p>CAD group after training (Figure <ns0:ref type='figure' target='#fig_8'>4</ns0:ref>). Additionally, the step length in the CAD group was significantly lower than that in the CON group after training (P = 0.04, 95% confidence interval </ns0:p></ns0:div> <ns0:div><ns0:head>Impact forces</ns0:head><ns0:p>Significant training × group interaction effects were observed for the impact forces (P = 0.022, =0.217) and impact impulse (P = 0.003, =0.335) (Figure <ns0:ref type='figure' target='#fig_9'>5</ns0:ref>). Specifically, the impact forces 𝜂 2 𝜂 2 significantly decreased in the CAD group (P = 0.003, Cohen's d = 1.10), but not in the CON group (P > 0.05). Also, the impact forces in the CAD group was significantly lower than that in <ns0:ref type='table'>2</ns0:ref>). In addition, the vertical excursion of CoG in the CAD group was significantly lower than that in the CON group after training (P = 0.025, 95% CI [-0.015, -0.001], Cohen's d = 1.03). Significant main effects of training were observed for the peak knee flexion angle and knee joint range of motion. Specifically, the peak knee flexion angle (P = 0.048, =0.166) and knee joint range of 𝜂 2 motion (P = 0.039, =0.180) decreased in the CAD group after training (Table <ns0:ref type='table'>2</ns0:ref>). For the lower 𝜂 2 extremity stiffness, a significant main effect of training was observed, which increased in the CAD group after training (P = 0.048, =0.166) (Table <ns0:ref type='table'>2</ns0:ref>). However, there was neither 𝜂 2</ns0:p><ns0:p>significant training by group interaction, nor main effects of training with respect to the joint moment and joint stiffness of the ankle, knee, and hip (P > 0.05) (Table <ns0:ref type='table'>2</ns0:ref>). </ns0:p></ns0:div> <ns0:div><ns0:head>Discussion</ns0:head><ns0:p>This study aimed to determine the effects of 12-week cadence retraining on running biomechanics. Significant reductions were observed in the peak impact forces, loading rates and</ns0:p><ns0:p>PeerJ reviewing PDF \| (2019:07:39657:1:0:CHECK 31 Jan 2020)</ns0:p><ns0:p>Manuscript to be reviewed impact impulse in the CAD group, supporting the hypothesis that cadence retraining could decrease the running impact forces. The preferred cadence in the CAD group significantly increased after the 12-week cadence retraining, consistent with the results of previous studies conducted by <ns0:ref type='bibr' target='#b21'>Hafer et al. (2015)</ns0:ref> and <ns0:ref type='bibr' target='#b37'>Neal et al. (2018)</ns0:ref>. However, the average change in the preferred cadence in the present study was +5.7% between pre-and post-training induced by a 7.5% increase in the cadence during retraining; by contrast, the preferred cadence changes in the study of <ns0:ref type='bibr' target='#b21'>Hafer et al. (2015)</ns0:ref> and <ns0:ref type='bibr' target='#b37'>Neal et al. (2018)</ns0:ref> were +2.4% and +7.6%, respectively, with 10% and 7.5% increase in the cadence during retraining. The current CAD protocol employed more participants (12 vs. 5) and longer retraining weeks (12 vs. 6 weeks) than in the study of <ns0:ref type='bibr' target='#b21'>Hafer et al. (2015)</ns0:ref>. Meanwhile, retraining was monitored in the current study. The increased training duration and controlled training compliance may be the reasons for the differences in the increase in cadence after retraining between the present study and previous research.</ns0:p><ns0:p>In this study, the peak impact forces were significantly reduced by 10.2%, which is greater than the 7.6% decrease observed in the study of <ns0:ref type='bibr' target='#b26'>Hobara et al.(2012)</ns0:ref>. This decrease was related to the reduction in the vertical velocity of CoG <ns0:ref type='bibr' target='#b6'>(Derrick et al. 1998</ns0:ref>). In the present study, the vertical momentum at the initial contact significantly decreased in the CAD group after retraining. This finding indicated that the change in the vertical momentum of CoG was reduced during impact attenuation. The time-to-impact peak showed no significant differences between pre-and posttraining, and the decreased peak impact forces in the CAD group were mainly associated with the decrease in impact impulse. In addition, <ns0:ref type='bibr' target='#b33'>Mercer et al. (2015)</ns0:ref> recently found that the impact force of subtle heel strike was smaller than that of obvious heel strike. In the present study, the Manuscript to be reviewed cadence may be another reason for the decrease in the peak impact forces in the CAD group after retraining.</ns0:p><ns0:p>Similarly, the average loading and maximum loading rates in the CAD group were significantly reduced after retraining, consistent with the findings reported by <ns0:ref type='bibr' target='#b21'>Hafer et al. (2015)</ns0:ref> and <ns0:ref type='bibr' target='#b41'>Willy et al. (2016a)</ns0:ref>. Furthermore, <ns0:ref type='bibr' target='#b29'>Lieberman et al. (2010)</ns0:ref> found that the loading rate was smaller in forefoot strike than that in rearfoot strike. In the present study, the decreased foot angle in the CAD group after retraining slightly altered the strike pattern, which may contribute to the reduced loading rate. Runners with a history of stress fractures or other types of injuries had higher loading rates than those of healthy runners <ns0:ref type='bibr' target='#b44'>(Worp et al. 2016;</ns0:ref><ns0:ref type='bibr' target='#b45'>Yong et al. 2018</ns0:ref>). In addition, runners with greater loading rates were more likely to get injured <ns0:ref type='bibr' target='#b2'>(Bredeweg et al. 2013;</ns0:ref><ns0:ref type='bibr' target='#b5'>Davis et al. 2016)</ns0:ref>. Therefore, cadence retraining based on increased cadence may have a positive effect on reducing the risk of running injuries, such as stress fractures.</ns0:p><ns0:p>The main changes in kinematics at touchdown included significant decreases in the foot angle, vertical velocity of the CoG and L ah . The foot angle, which reflected the foot strike pattern during running, significantly decreased with increasing cadence <ns0:ref type='bibr' target='#b1'>(Allen et al. 2016;</ns0:ref><ns0:ref type='bibr' target='#b25'>Heiderscheit et al. 2011)</ns0:ref>. A study found that 17.5% of rearfoot strike runners changed to non-rearfoot strike at +10% of their preferred cadence, and 30% of them changed to non-rearfoot strike at +30% of preferred cadence <ns0:ref type='bibr' target='#b1'>(Allen et al. 2016)</ns0:ref>. Similarly, the foot angle in the CAD group decreased by 4.5°, indicating that the cadence retraining caused a subtle change in the foot strike pattern.</ns0:p><ns0:p>Moreover, the reduction in L ah confirmed the decrease in the foot angle <ns0:ref type='bibr' target='#b30'>(Lieberman et al. 2015)</ns0:ref>.</ns0:p><ns0:p>The knee joint was most sensitive to change in cadence during the stance phase. A negligible increase in cadence induced significant changes in the peak knee flexion angle (Dos <ns0:ref type='bibr' target='#b10'>Santos et al. 2016;</ns0:ref><ns0:ref type='bibr' target='#b37'>Neal et al. 2018</ns0:ref>) and energy absorbed by the knee joint (Heiderscheit et al.</ns0:p><ns0:p>PeerJ reviewing PDF \| (2019:07:39657:1:0:CHECK 31 Jan 2020)</ns0:p><ns0:p>Manuscript to be reviewed 2011). In the CAD group, 5.7% increase in cadence after retraining decreased the peak knee flexion angle and knee joint range of motion, leading to reduced vertical excursion of the CoG.</ns0:p><ns0:p>However, no differences were observed for the hip and ankle joint angles between pre-and posttraining. Therefore, CoG adjustment after increasing cadence was mainly attributed to the knee joint. Additionally, lower extremity stiffness, calculated as the ratio of force and vertical excursion of CoG, is an important variable in the spring-mass model of the muscle-skeleton system <ns0:ref type='bibr' target='#b31'>(Liu et al. 2006</ns0:ref>). In the present study, the lower extremity stiffness significantly increased in the CAD group after training, consistent with previous research obtained by acutely increasing the cadence <ns0:ref type='bibr' target='#b12'>(Farley & González 1996;</ns0:ref><ns0:ref type='bibr' target='#b17'>Giandolini et al. 2013)</ns0:ref>. This finding may be due to the reduced vertical excursion of the CoG during stance phase induced by the decrease in the stance time <ns0:ref type='bibr' target='#b36'>(Morin et al. 2007</ns0:ref>) and the knee joint range of motion.</ns0:p><ns0:p>Although cadence increased by 5.7% in the CAD group after 12-week retraining, the magnitude was only 170.5 step/min. Most running coaches and clinicians considered a cadence below 170 step/min to be low <ns0:ref type='bibr' target='#b21'>(Hafer et al. 2015)</ns0:ref>, leaving additional space for increasing cadence. Thus, a step-by-step approach could be employed to improve the retraining effects with considerable increase in cadence increase. Secondly, the participants in the present study were all male; thus, whether females would show the same effects after 12-week cadence retraining remains unclear. Thirdly, the running biomechanics on a limited run-up (10 meters) with a relatively small area (60cm × 90cm) for foot placement may slightly different from the outdoor over ground running. Moreover, the long-term retention effects caused by retraining changes are unknown. Further, continuous data monitoring and collection of running until the fatigue state between pre-and post-training can be considered in future studies to comprehensively determine the biomechanical adaptation of long-distance running after changes in cadence. Finally, future</ns0:p><ns0:p>PeerJ reviewing PDF \| (2019:07:39657:1:0:CHECK 31 Jan 2020)</ns0:p><ns0:p>Manuscript to be reviewed studies may use cadence retraining as a method to treat participants with impact-related running injuries.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusion</ns0:head><ns0:p>The 12-week cadence retraining significantly increased the cadence by 5.7%. The increased cadence effectively decreased the impact forces, namely, impact peak, average loading rates maximum loading rates, and impact impulse. As the close relationship between impact forces and running injuries, increasing the cadence as a retraining method may reduce the risk of some impact-related injuries. Meanwhile, the foot became 'flatter' when its position was closer to the CoG at the initial contact after training, and the vertical excursion of the CoG was smaller during stance, providing the mechanical explanation for the decreased impact forces. Furthermore, the vertical excursion of the CoG decreased, further leading to an increase in lower extremity stiffness. Hence, cadence retraining can lead to lower extremity biomechanical changes. </ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ reviewing PDF \| (2019:07:39657:1:0:CHECK 31 Jan 2020)Manuscript to be reviewed(No. 2017007). Informed written consent was obtained from each participant. *************Insert Table 1 here*********** Instrumentation A 12-camera motion capture system (100 Hz, T40; Vicon Motion Inc., Oxford, UK) was used to collect kinematic data. Ground reaction force data were captured at 1000 Hz by using two 90cm × 60cm × 10cm 3D force platforms (9287B; Kistler Instruments AG Corp., Winterthur, Switzerland). The kinematics and ground reaction force data were simultaneously collected using the Vicon system. A Photogate system (Witty-wireless training timer, Microgate Corp., Italy) was used to monitor over ground running speed. Conventional running shoes (Nike Air Zoom Pegasus 34) were used in data collection (Figure 1). ***********Insert Figure 1 here***********</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ reviewing PDF \| (2019:07:39657:1:0:CHECK 31 Jan 2020) Manuscript to be reviewed training to participate based on their own time. During the weekly group training, the participants performed an 8-minute warm-up, such as dynamic stretch, under the guidance of the researchers. Then the participants started to run according to their retraining schedule. We didn't give them guidance on running techniques, since the purpose of weekly group training was to keep in touch with them and ensure the quality of retraining. ***********Insert Figure 2 here***********</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head /><ns0:label /><ns0:figDesc>); (2) peak joint extension/dorsiflexion angles (θ max-ext ) and peak joint flexion/plantar flexion angles (θ max-flx ) during stance; and (3) joint ranges of motion (θ ROM , θ ROM =θ max-ext −θ maxflx ) during stance. Other kinematic variables included landing positions of the foot relative to the knee (L ak , Figure 3), landing positions of the foot relative to the hip (L ah , Figure 3), vertical velocities of the centre of gravity (CoG) at initial contact, vertical excursion of the CoG during the stance phase and stance time. ***********Insert Figure 3 here*********** Impact variables included peak impact forces, peak active forces, maximum loading rates, average loading rates, and impact impulse [I, Equation (1)]. In rearfoot strike runners, the impact force was the first peak in the ground reaction force curve, whilst the peak active forces referred PeerJ reviewing PDF \| (2019:07:39657:1:0:CHECK 31 Jan 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ reviewing PDF \| (2019:07:39657:1:0:CHECK 31 Jan 2020)Manuscript to be reviewed ANOVA was used to characterise the effects of training (pre-and post-training) and group (CAD and CON) on each variable. Independent sample t-tests and paired t-tests were used as post-hoc tests when a significant interaction was detected to assess potential group effects between CAD and CON and the retraining effects pre-and post-training, respectively. The observed effect size ( ) values were reported to ANOVAs results and effect size (Cohen's d) values were reported to 𝜂 2</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head>(</ns0:head><ns0:label /><ns0:figDesc>CI) [-0.245, -0.006], Cohen's d = 0.94). ***********Insert Figure 4 here***********</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head /><ns0:label /><ns0:figDesc>the CON group after training (P = 0.038, 95% CI[-0.443, -0.013], Cohen's d = 0.95). The impact impulse didn't change in the CON group (P > 0.05), but decreased in the CAD group (P = 0.005, Cohen's d = 1.01). Significant main effects of training were observed for the average loading rates and maximum loading rates. Specifically, the average loading rates (P = 0.029, 𝜂 2=0.198), and the maximum loading rates (P = 0.025, =0.209) decreased in the CAD group 𝜂 2 after training (Figure 5). ***********Insert Figure 5 here************* Kinematics and joint mechanics PeerJ reviewing PDF \| (2019:07:39657:1:0:CHECK 31 Jan 2020) Manuscript to be reviewed Significant training × group interaction effects were observed for the foot angle (P = 0.04, 𝜂 2 =0.178), vertical velocity of CoG at initial contact (P = 0.035, =0.186), vertical momentum at 𝜂 2 initial contact (P = 0.037, =0.183), stance time (P = 0.012, =0.252), vertical excursion of 𝜂 2 Cohen's d = 1.09), vertical velocity of CoG at initial contact (P = 0.002, Cohen's d = 1.16), vertical momentum at initial contact (P = 0.002, Cohen's d = 1.16), stance time (P = 0.002, Cohen's d = 1.18), vertical excursion of CoG (P < 0.001, Cohen's d = 1.83), and (P < 𝐿 𝑎ℎ 0.001, Cohen's d = 1.45) decreased in the CAD group after training (Figure 6, Table</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head></ns0:head><ns0:label /><ns0:figDesc>***********Insert Figure 6 here*********** ***********Insert Table 2 here*************</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_7'><ns0:head /><ns0:label /><ns0:figDesc>foot angle in the CAD group significantly decreased after retraining, indicating a tendency from rearfoot strike to non-rearfoot strike. The subtle change in the strike pattern induced by increased PeerJ reviewing PDF \| (2019:07:39657:1:0:CHECK 31 Jan 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_8'><ns0:head>Figure 4</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Figure 4</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_9'><ns0:head>Figure 5</ns0:head><ns0:label>5</ns0:label><ns0:figDesc>Figure 5</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_10'><ns0:head>Figure 6</ns0:head><ns0:label>6</ns0:label><ns0:figDesc>Figure 6</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='30,42.52,232.42,525.00,247.50' type='bitmap' /></ns0:figure> </ns0:body> "	"Point-by-Point Response to Reviewers’ Comments We would like to sincerely thank the reviewers for their helpful recommendations. We have seriously considered all the comments and carefully revised the manuscript accordingly. Revisions are highlighted in the manuscript using a red font to indicate where changes have taken place. We feel that the quality of the manuscript has been significantly improved with these modifications and improvements based on the reviewers’ suggestions and comments. We hope our revision will lead to an acceptance of our manuscript for publication in the PeerJ. Response to Reviewer #1 General comments GC: The study assessed the effect of cadence retraining on impact related variables and lower limb kinematics and kinetics. Overall the study is well written and examines an important research question. I have suggested some changes to improve the reporting of the manuscript. My main concern relates to the presentation of results, where I am unclear as to what statistical tests have been used and reported. Please see specific comments in the points below. Response GC: Thank you very much for your positive comments and all the suggestions that you provided. We have revised the manuscript accordingly, especially on the presentation of results that you are concerned about. Specific comments SC1: Abstract – it would be nice see some data reported in the results section of the abstract. If you are tight on word limit perhaps present the most important results. Please see my comments in relation to statistical analysis and results as to what is appropriate to include in the results. Response SC1: Thank you for your advice. We have added some data in the results section of the abstract (lines 24-28). “Significantly decreased impact forces (1.86±0.30 vs. 1.67±0.27 BW, P = 0.003), average loading rates (91.59±18.91 vs. 77.31±15.12 BW/S, P = 0.001), maximum loading rates (108.8±24.5 vs. 92.8±18.5 BW/S, P = 0.001), impact impulse (19.1±2.6 vs. 17.2 N·s, P = 0.005) and vertical momentum at initial contact (-50.8±9.6 vs. -46.8±7.6 kg·m/s, P = 0.002) were observed after cadence retraining.” SC2: Line 44 – please change patellofemoral joint pain synthesis to patellofemoral pain – as that is the current description of this condition. Also injuries to the lower leg have bene reported to be just as common as the knee in more recent publications. Please document this (e.g. Buist et al. BJSM 2010: 44:598-604; Franke et al. JOSPT 2019: 518-529). Response SC2: Thank you for your advice. We have changed the “patellofemoral joint pain synthesis” into “patellofemoral pain” (line 47). Also, we have cited the papers you mentioned (lines 46-49). “Among these injuries, knee injuries, such as patellofemoral pain, are the most common. Also, injuries to the lower leg have bene reported to be just as common as the knee in more recent publications (Buist et al., 2010; Franke, Backx, & Huisstede, 2019).” References: Buist, I., ., Bredeweg, S. W., Bessem, B., ., Mechelen, W., Van, Lemmink, K. A. P. M., & Diercks, R. L. (2010). Incidence and risk factors of running-related injuries during preparation for a 4-mile recreational running event. Br J Sports Med, 44(8), 598. Franke, T. P. C., Backx, F. J. G., & Huisstede, B. M. A. (2019). Running Themselves Into the Ground? Incidence, Prevalence, and Impact of Injury and Illness in Runners Preparing for a Half or Full Marathon. J Orthop Sports Phys Ther, 49(7), 518-528. doi: 10.2519/jospt.2019.8473 SC3: Lines 46-52: In this section please differentiate those studies that have shown an association with injury (i.e. retrospective studies) to prospective studies which provide a greater level of evidence. In other words, is there prospective evidence for excessive impact forces and loading rates as a risk factor for running injuries? Response SC3: Thank you for your comments. We have deleted certain retrospective studies and added related prospective studies in the introduction section (lines 50-60). “Training history, anatomical characteristics and running biomechanics are the main risk factors that affect overuse injuries in running (Hreljac, 2004). Among various biomechanical factors, excessive impact forces and loading rates are associated with injuries and have been targeted in running retraining strategies (Cheung & Davis, 2011). In a recent review, excessive accumulation of impact peak force in knee joints could lead to overuse injuries (Gijon-Nogueron & Fernandez-Villarejo, 2015). Previous prospective studies found that injured runners had greater vertical loading rates than non-injured runners (Davis, Bowser, & Mullineaux, 2016; Fu et al., 2017). In addition, other prospective studies showed that runners with patellofemoral pain (Cheung & Davis, 2011) and chronic exertional compartment syndrome (Diebal, Gregory, Alitz, & Gerber, 2012) exhibited lower impact loading after their pain and disability typically associated with these injuries reduced.” References: Hreljac, A. (2004). Impact and overuse injuries in runners. Med.sci.sports Exerc, 36(5), 845. Cheung, R. T. H., & Davis, I. S. (2011). Landing pattern modification to improve patellofemoral pain in runners: a case series. J Orthop Sports Phys Ther, 41(12), 914-919. Gijon-Nogueron, G., & Fernandez-Villarejo, M. (2015). Risk Factors and Protective Factors for Lower-Extremity Running Injuries A Systematic Review. J Am Podiatr Med Assoc, 105(6), 532-540. doi: 10.7547/14-069.1 Bredeweg, S. W., Kluitenberg, B., Bessem, B., & Buist, I. (2013). Differences in kinetic variables between injured and noninjured novice runners: a prospective cohort study. J Sci Med Sport, 16(3), 205-210. doi: 10.1016/j.jsams.2012.08.002 Davis, I. S., Bowser, B. J., & Mullineaux, D. R. (2016). Greater vertical impact loading in female runners with medically diagnosed injuries: a prospective investigation. 50(14), 887-892. doi: 10.1136/bjsports-2015-094579 Diebal, A. R., Gregory, R., Alitz, C., & Gerber, J. P. (2012). Forefoot running improves pain and disability associated with chronic exertional compartment syndrome. Am J Sports Med, 40(5), 1060-1067. SC4: Line 99 – you have indicated sampling rate twice in this sentence – please remove one duplicate. Response SC4: Thank you for your reminder. We have removed one duplicate as suggested (lines 116-117). “A 12-camera motion capture system (100 Hz, T40; Vicon Motion Inc., Oxford, UK) was used to collect kinematic data.” SC5: Lines 111-118: Please provide more details in regard to the sequence of your experimental protocol. For example, were markers placed on participants and static calibration performed before or after the 5 min run on the treadmill? Response SC5: Thank you for your advice. We have provided more details in the experimental protocol section (lines 126-135). In addition, the markers were placed on the participants and static calibration performed after the 5 minutes run on the treadmill. “The participants visited the laboratory twice, at baseline and at the end of the program. Before data collection, participants were required to change their test vest, shorts, socks, and experimental shoes and walk for 2 min and run at 3.33 m/s on a treadmill for 5 min as a warm-up. After the warm-up, a total of 40 markers were placed on participants, including 16 markers on anatomical landmarks and 24 tracking markers and a static calibration was then performed (Figure 1). The participants were instructed to run over the ground across a 10 m runway at 3.33 m/s with the foot of the preferred kicking leg (Fu et al., 2017) striking the force platform, whilst kinematic and ground reaction force data were captured. The running speed was considered acceptable if the deviation was within 5%. Three successful running trials were collected for each participant.” Reference: Bredeweg, S. W., Kluitenberg, B., Bessem, B., & Buist, I. (2013). Differences in kinetic variables between injured and noninjured novice runners: a prospective cohort study. J Sci Med Sport, 16(3), 205-210. doi: 10.1016/j.jsams.2012.08.002 SC6: Line 132: The Alfredson reference seems to be the incorrect reference here. Response SC6: Sorry about the confusion. We have changed the reference (lines 149-150). “The cadence retraining protocol lasted for 12 weeks with three sessions a week and 5–48 min each session (Neal, Barton, Birn-Jeffrey, Daley, & Morrissey, 2018)(Figure 2).” Reference: Neal, B. S., Barton, C. J., Birn-Jeffrey, A., Daley, M., & Morrissey, D. (2018). The effects & mechanisms of increasing running step rate: A feasibility study in a mixed-sex group of runners with patellofemoral pain. Phys Ther Sport, 32, 244-251. doi: 10.1016/j.ptsp.2018.05.018 SC7: Lines 135-137: How did the researchers ensure compliance to the retraining protocol? You mention that cadence was uploaded, but what was the action if participants were not achieving the desired cadence? Response SC7: Thank you for your comments. The weekly group training was the primary means of ensuring compliance (lines 157-163). Specifically, we reminded the participant to pay attention to the metronome in the next training if they didn’t achieve the desired cadence in certain training. However, participants were excluded if their cadence didn’t achieve the desired cadence for three weeks since the start. We also added this information in the exclusion criteria (lines 155-157). “We provided weekly group training three times a week, and participants chose one of weekly group training to participate based on their own time. During the weekly group training, the participants performed 8-minutes warm-up, such as dynamic stretch, under the guidance of the researchers. Then the participants started to run according to their retraining schedule. We didn’t give them guidance on running techniques, since the purpose of weekly group training was to keep in touch with them and ensure the quality of retraining.” “Participants were excluded if their training protocols were interrupted more than three times, and they were also excluded if their cadence didn’t achieve the desired cadence for three weeks since the start.” SC8: Line 138: please provide details on the weekly group training. What did you do in these sessions? Response SC8: Thank you for your advice. As suggested, we have provided more details on the weekly group training (lines 157-163). Please also refer to our Response SC7. “We provided weekly group training three times a week, and participants chose one of weekly group training to participate based on their own time. During the weekly group training, the participants performed 8-minutes warm-up, such as dynamic stretch, under the guidance of the researchers. Then the participants started to run according to their retraining schedule. We didn’t give them guidance on running techniques, since the purpose of weekly group training was to keep in touch with them and ensure the quality of retraining.” SC9: Overall for your results you should be presenting the interaction effect as your main finding, not the time effects. If there was truly an effect of gait retraining on your variables there should be group differences at follow-up. Reporting changes over time as treatment effects are misleading. I am little confused as to how you present these group differences, for example is the p-value reported for impact peak the interaction effect, or the result of the independent t-test? You have reported in your statistics that independent or paired t-tests were used when a significant interaction was found, yet you haven’t reported where interactions were found, so what is the justification for following up with these tests? As per your methods if you did not find interaction effects, you should not be reporting these post-hoc tests. Response SC9: Thank you for your advice. We have rewritten the results section and presented the interaction effects according to your suggestions (lines 226-267). “Cadence and step length Significant training × group interaction effect was observed for the cadence (P < 0.001, =0.867) (Figure 4). Specifically, the cadence significantly increased in the CAD group (P < 0.001, Cohen’s d = 3.87), but not in the CON group (P > 0.05) after training. A significant main effect of training was noted for the step length, which decreased (P = 0.011, =0.259) in the CAD group after training (Figure 4). Additionally, the step length in the CAD group was significantly lower than that in the CON group after training (P = 0.04, 95% confidence interval (CI) [-0.245, -0.006], Cohen’s d = 0.94). Impact forces Significant training × group interaction effects were observed for the impact forces (P = 0.022, =0.217) and impact impulse (P = 0.003, =0.335) (Figure 5). Specifically, the impact forces significantly decreased in the CAD group (P = 0.003, Cohen’s d = 1.10), but not in the CON group (P > 0.05). Also, the impact forces in the CAD group was significantly lower than that in the CON group after training (P = 0.038, 95% CI [-0.443, -0.013], Cohen’s d = 0.95). The impact impulse didn’t change in the CON group (P > 0.05), but decreased in the CAD group (P = 0.005, Cohen’s d = 1.01). Significant main effects of training were observed for the average loading rates and maximum loading rates. Specifically, the average loading rates (P = 0.029, =0.198), and the maximum loading rates (P = 0.025, =0.209) decreased in the CAD group after training (Figure 5). Kinematics and joint mechanics Significant training × group interaction effects were observed for the foot angle (P = 0.04, =0.178), vertical velocity of CoG at initial contact (P = 0.035, =0.186), vertical momentum at initial contact (P = 0.037, =0.183), stance time (P = 0.012, =0.252), vertical excursion of CoG (P = 0.001,=0.409), and (P = 0.011, =0.258). Specifically, the foot angle (P = 0.003, Cohen’s d = 1.09), vertical velocity of CoG at initial contact (P = 0.002, Cohen’s d = 1.16), vertical momentum at initial contact (P = 0.002, Cohen’s d = 1.16), stance time (P = 0.002, Cohen’s d = 1.18), vertical excursion of CoG (P < 0.001, Cohen’s d = 1.83), and (P < 0.001, Cohen’s d = 1.45) decreased in the CAD group after training (Figure 6, Table 2). In addition, the vertical excursion of CoG in the CAD group was significantly lower than that in the CON group after training (P = 0.025, 95% CI [-0.015, -0.001], Cohen’s d = 1.03). Significant main effects of training were observed for the peak knee flexion angle and knee joint range of motion. Specifically, the peak knee flexion angle (P = 0.048, =0.166) and knee joint range of motion (P = 0.039, =0.180) decreased in the CAD group after training (Table 2). For the lower extremity stiffness, a significant main effect of training was observed, which increased in the CAD group after training (P = 0.048, =0.166) (Table 2). However, there was neither significant training by group interaction, nor main effects of training with respect to the joint moment and joint stiffness of the ankle, knee, and hip (P > 0.05) (Table 2).” SC10: It would be useful if you could also present the 95% confidence interval of the differences for group effects (i.e. impact peak, step length, CoG excursion). Also please provide some magnitude of effect for the difference (i.e. ES) Response SC10: Thank you for your advice. As suggested, we have added the 95% confidence interval of the differences for group effects and provided the effect size in the results section (lines 226-267). For more details, please refer to our response SC9. Meanwhile, we have added the related statements to the statistics section regarding how we conducted the 95% confidence interval and the effect size (lines 208-210). Statistics “The observed effect size () values were reported to ANOVAs results and effect size (Cohen’s d) values were reported to paired t-tests and independent sample t-tests results. The 95% confidence interval of the differences for group effects was reported.” SC11: Did you not find an interaction effect for cadence? The figure indicates that it may have been close to significance? Given this is your intervention it is surprising there was no group differences at follow-up. Response SC11: Thank you for your comments. Yes, there was an interaction effect for cadence. We have rewritten the related text in the results section (lines 227-229). “Significant training × group interaction effect was observed for the cadence (P < 0.001, =0.867) (Figure 4). Specifically, the cadence significantly increased in the CAD group (P < 0.001, Cohen’s d = 3.87), but not in the CON group (P > 0.05) after training.” SC12: As per my comment in the results, overall in the discussion you need to indicate that only peak impact forces, step length and COG excursion exhibited a group by time interaction. Thus these are the only variables that demonstrated differences related to time and group allocation. Much of your discussion is focused on variables that showed a time effect and this should be reported with some caution. Response SC12: Thank you for your advice. We have rewritten the results section, and we have pointed out the variables that had training by group interaction. Cadence, impact forces, impact impulse, foot angle, vertical velocity of CoG at initial contact, stance time, vertical excursion of CoG, and showed significant training × group interaction effects. Therefore, over half of the variables we discussed exhibited training by group interaction. SC13: Line 242: you still have not indicated how you controlled compliance to the cadence retraining so find it hard how you can suggest this is a reason for the reported differences. Please provide details in the methods I regard to how you ensured compliance with the increased cadence. Response SC13: Thank you for your comments. The weekly group training was the primary means of ensuring compliance. We have provided more details on the weekly group training (lines 157-163). Please also refer to our Response SC7. “We provided weekly group training three times a week, and participants chose one of weekly group training to participate based on their own time. During the weekly group training, the participants performed 8-minutes warm-up, such as dynamic stretch, under the guidance of the researchers. Then the participants started to run according to their retraining schedule. We didn’t give them guidance on running techniques, since the purpose of weekly group training was to keep in touch with them and ensure the quality of retraining.” SC14: Line 245: In the results you indicate a reduction of 12% in peak impact forces, which is different to that reported here. Response SC14: Thank you for your comments. A reduction of 12% in peak impact forces in the results section was the difference between pre-training and post-training in the cadence retraining group, while 10.2% in the discussion section is the difference between cadence retraining group and control group at post-training. SC15: Line 307: You need to temper your conclusions based upon my comments related to your statistical, analysis, and presentation of results. Response SC15: Thank you for your advice. We have rewritten the conclusion based on your comments on statistical, analysis, and presentation of results (lines 351-360). “The 12-week cadence retraining significantly increased the cadence by 5.7%. The increased cadence effectively decreased the impact forces, namely, impact peak, average loading rates maximum loading rates, and impact impulse. As the close relationship between impact forces and running injuries, increasing the cadence as a retraining method may reduce the risk of some impact-related injuries. Meanwhile, the foot became “flatter” when its position was closer to the CoG at the initial contact after training, and the vertical excurison of the CoG was smaller during stance, providing the mechanical explanation for the decreased impact forces. Furthermore, the vertical excursion of the CoG decreased, further leading to an increase in lower extremity stiffness. Hence, cadence retraining can lead to lower extremity biomechanical changes.” Response to Reviewer #2 General comments Basic reporting The authors are highlighting an important topic in running injury research especially gait retraining with emphasis on cadence retraining. There is evidence in the literature demonstrating that (Willy et al.2016, Hobara et al. 2012) increasing cadence above one’s natural step rate appears to result in an immediate reduction in load rates. Willy et al. 2016 used a wireless accelerometer as a cue for 8 training sessions, to increase preferred step rate by 7.5% and this was effective at reducing impact forces, peak hip adduction and eccentric knee joint work in healthy runners. Hafer et al. also showed that that increased running cadence using cadence retraining reduces kinematics and kinetics that have been tied to overuse running injuries. GC1: The introduction lacks clear justification for performing the study. The authors do not provide any deficiencies in the running literature to justify a need to do this study. Response GC1: Thank you for your advice. The small number of participants, short retraining intervention time, and insufficient evaluation variables are the main deficiencies for the previous cadence retraining studies. Based on those deficiencies in the running literature, we performed this study. We have added related text in the introduction section (lines 83-88). “However, due to the small number of participants, short retraining intervention time, and insufficient evaluation variables among those studies, the movement patterns and biomechanical factors, such as lower extremity kinematics and kinetics, are unclear and may result in changes in impact force loading rates. Assessing these additional variables may result in better understanding of cadence retraining on running biomechanics and its associated risk of injuries.” GC2: There is no clear distinction between increase in cadence or decrease in step length that is the focus of the study. Hypothesis is not specific and focused enough. Response GC2: Thank you for your comment. This study focused on increase in cadence at a consistent speed. At the consistent speed, the cadence and step length are inversely proportional. Therefore, an increase in the cadence will lead to a decrease in the step length. We have rewritten the hypothesis part. Please refer to response SC14. GC3: There is no justification for the amount of variables that were chosen for analysis in this study (kinematics and kinetics) Response GC3: Thank you for your comment. We have provided the reasons for the amount of variables that were chosen for analysis in this study (kinematics and kinetics). For the details, please refer to response SC13. GC4: The raw data and the figures were helpful to understand the data. The description of the data in the results in the data was muddled because of the lack of clarity if it was talking about the data within the CAD group or CON group (pre and post) or comparing the CAD and CON group. Response GC4: Thank you for your advice. Sorry about the confusion in the results section. We have rewritten the results section, and clearly described the data. For the details, please refer to response SC23. Experimental design GC5: There is no discussion about sample size calculations. There is no information about how the randomization was performed for the two groups. If any of the investigators were blinded and who performed the randomization. Response GC5: Thank you for your advice. As suggested, we have added sample size calculation and randomization protocol in the methods section. For the details, please refer to response SC15. GC6: The marker set used for the gait analysis was not described. There is no justification for using the 12 week retraining period or the 7.5% increase in cadence. They have cited a study but why they felt this protocol was appropriate for their study was not stated. Response GC6: Thank you for your advice. As suggested, we have added the marker set description. For the details, please refer to response SC16. In terms of the 12 week retraining period or the 7.5% increase in cadence, we have cited another study to support our study (lines 149-150). “The cadence retraining protocol lasted for 12 weeks with three sessions a week and 5–48 min each session (Neal et al., 2018) (Figure 2).” Reference: Neal, B. S., Barton, C. J., Birn-Jeffrey, A., Daley, M., & Morrissey, D. (2018). The effects & mechanisms of increasing running step rate: A feasibility study in a mixed-sex group of runners with patellofemoral pain. Phys Ther Sport, 32, 244-251. doi: 10.1016/j.ptsp.2018.05.018 GC7: It is not clear why the runners ran on the treadmill prior to data collection. Were three running trials enough to collect enough data points? It wasn't mentioned for how many seconds every trial was performed. The assessment was performed at fixed speed but retraining occurred at preferred speeds. The authors do not provide details of the weekly group training sessions. Response GC7: Thank you for your comments. Due to the limited space in the laboratory, we required the participants to run on a treadmill for 5 minutes as warm-up. We have collected enough data points in the three trials. Each trial lasted for 3-5 seconds. In terms of the running speed and group training sessions, please refer to our response SC19. GC8: There were many (at least 17) biomechanical variables analyzed in this study and except the impact forces or loading rates there was no justification for the rest of the variables. Which plane were the peak moments of hip, knee and ankle analyzed or was it an overall support moment? Some variables like vertical momentum at initial contact, maximum extension/dorsiflexion angle during the stance phase; maximum flexion/plantar flexion angle during the stance phase; centre of gravity, sagittal plane distance between ankle and knee/ ankle and hip at initial contact are variables which have not been previously reported in the running literature. Response GC8: Thank you for your comments. We have explained the reason for the selected variables in the specific comments section. Please see our response SC20. The peak moments of hip, knee, and ankle were analyzed in the sagittal plane, and we have added this in the manuscript (lines 187-190). GC9: The validity of the overall findings is questionable without knowing the impact of these variables on injury status in runners. I am not sure if center of gravity is what the authors use for center of mass which is used in the running literature. Response GC9: Thank you for your comments. We have added the impact of variables on running injuries in runner in the introduction section (lines 53-60). Besides, the center of gravity (COG) used in this study didn’t refer to center of mass. “In a recent review, excessive accumulation of impact peak force in knee joints could lead to overuse injuries (Gijon-Nogueron & Fernandez-Villarejo, 2015). Previous prospective studies found that injured runners had greater vertical loading rates than non-injured runners (Davis et al., 2016; Fu et al., 2017). In addition, other prospective studies showed that runners with patellofemoral pain (Cheung & Davis, 2011) and chronic exertional compartment syndrome (Diebal et al., 2012) exhibited lower impact loading after their pain and disability typically associated with these injuries reduced.” References: Cheung, R. T. H., & Davis, I. S. (2011). Landing pattern modification to improve patellofemoral pain in runners: a case series. J Orthop Sports Phys Ther, 41(12), 914-919. Gijon-Nogueron, G., & Fernandez-Villarejo, M. (2015). Risk Factors and Protective Factors for Lower-Extremity Running Injuries A Systematic Review. J Am Podiatr Med Assoc, 105(6), 532-540. doi: 10.7547/14-069.1 Bredeweg, S. W., Kluitenberg, B., Bessem, B., & Buist, I. (2013). Differences in kinetic variables between injured and noninjured novice runners: a prospective cohort study. J Sci Med Sport, 16(3), 205-210. doi: 10.1016/j.jsams.2012.08.002 Davis, I. S., Bowser, B. J., & Mullineaux, D. R. (2016). Greater vertical impact loading in female runners with medically diagnosed injuries: a prospective investigation. 50(14), 887-892. doi: 10.1136/bjsports-2015-094579 Diebal, A. R., Gregory, R., Alitz, C., & Gerber, J. P. (2012). Forefoot running improves pain and disability associated with chronic exertional compartment syndrome. Am J Sports Med, 40(5), 1060-1067. GC10: T-tests to look at differences between the two groups were not performed. Height of the participant has been shown to impact step length. Response GC10: Thank you for your advice. As suggested, we performed t-test for demographic variables between two groups. There were no significant differences in age, height, weight, and weekly mileage between two groups. Besides, no significant differences were observed in the height of the participant between CAD and CON group (CAD: 174.8±4.4 cm; CON: 175.5±5.1 cm). We have added these data in the revised manuscript (lines 109-110). “In addition, there were no significant differences in age, height, weight, and weekly mileage between two groups.” Validity of the findings GC11: The authors do a good job using tables and figures to describe the data. The discussion section introduces the role of foot strike pattern which tends to shift the focus from cadence retraining. Previous literature has highlighted the role of forefoot strike pattern and decrease in average and peak loading rates (healthy runners and runners with tibial stress fracture) (Boyer et al., 2014; Shih et al., 2013, Yong et al.2018; Futrell et al.2018). The novelty of this study is not clearly demonstrated by the authors. It rather seems like a validation of other studies or a replication of the protocol. Response GC11: Thank you for your comments. The small number of participants, short retraining intervention time, and insufficient evaluation variables are the main deficiencies for the previous cadence retraining studies. Based on those deficiencies in the running literature, we performed this study, which contained larger number of participants, longer retraining intervention time, and more evaluation variables. We have added related text in the introduction section (lines 83-88). Specific comments SC1: Line 17: Please use injuries not injury status Response SC1: Thank you for your reminder. We have changed “injury status” into “injuries” (lines 16-17). “Excessive impact forces and vertical loading rates are associated with running injuries and have been targeted in running retraining strategies.” SC2: Line 19: during running Response SC2: Thank you for your reminder. We have changed “in running” into “during running” (lines 17-19). “This study aimed to determine the effects of 12-week cadence retraining on impact forces and lower extremity biomechanics during running.” SC3: Abstract results section: This section needs numbers and p-values. It is very wordy. Response SC3: Thank you for your advice. We have added numbers and p-values in this section (lines 24-28). “Significantly decreased impact forces (1.86±0.30 vs. 1.67±0.27 BW, P = 0.003), average loading rates (91.59±18.91 vs. 77.31±15.12 BW/S, P = 0.001), maximum loading rates (108.8±24.5 vs. 92.8±18.5 BW/S, P = 0.001), impact impulse (19.1±2.6 vs. 17.2 N·s, P = 0.005) and vertical momentum at initial contact (-50.8±9.6 vs. -46.8±7.6 kg·m/s, P = 0.002) were observed after cadence retraining.” SC4: Abstract conclusion: Needs to be more impactful and not be similar to the introduction. Response SC4: Thank you for your comments. We have rewritten the conclusion in the abstract to make it to be more impactful (lines 33-36). “This increased cadence after the 12-week cadence retraining can effectively reduce the impact force and induced changes to the lower extremity biomechanics both at initial contact and during stance, which may potentially decrease the risk of impact-related running injuries.” SC5: Line 36: Please provide the name of the report. Response SC5: Thank you for your advice. We have provided the name of the report (lines 39-41). “According to the Chinese Athletics Association Marathon Annual Press Conference, 1,581 marathon events were held in China in 2018, with a total of almost 5.83 million participants (Han, 2019).” Reference: Han, T. (2019). Marathon statistics in China. From http://www.athletics.org.cn/marathon/news/2019/0311/218818.html. SC6: Line 40: Please ensure that the citation is accurate. Typically, all running studies reference the RunningUSA.org for the statistics on participation Response SC6: Thank you for your comments. We have referenced the RunningUSA.org for the statistics on participation (lines 41-42). “Similarly, there were over 50 million recreational runners in the USA (“Running USA. Annual Reports [Internet],” 2017).” Reference: Running USA. Annual Reports [Internet]. (2017). from http://www.runningusa.org/annual-reports. SC7: Line 44: Patellofemoral pain syndrome Response SC7: Thank you for your advice. We have changed the “patellofemoral joint pain synthesis” to “patellofemoral pain” (lines 46-47). “Among these injuries, knee injuries, such as patellofemoral pain, are the most common.” SC8: Line 47: Consider using the word impact rather than affect. Response SC8: Thank you for your advice. We have changed the “affect” into “impact” (lines 50-51). “Training history, anatomical characteristics and running biomechanics are the main risk factors that impact overuse injuries in running.” SC9: References were nor provided for: Line 40, Line 49. Response SC9: Thank you for your advice. As suggested, references were provided for these sentences (lines 42-43, 51-53). “However, the incidence of running injuries remains high (Messier et al., 2018).” “Among various biomechanical factors, excessive impact forces and loading rates are associated with injuries and have been targeted in running retraining strategies (Cheung & Davis, 2011).” References: Messier, S. P., Martin, D. F., Mihalko, S. L., Ip, E., DeVita, P., Cannon, D. W., . . . Seay, J. F. (2018). A 2-Year Prospective Cohort Study of Overuse Running Injuries: The Runners and Injury Longitudinal Study (TRAILS). Am J Sports Med, 46(9), 2211-2221. doi: 10.1177/0363546518773755 Cheung, R. T. H., & Davis, I. S. (2011). Landing pattern modification to improve patellofemoral pain in runners: a case series. J Orthop Sports Phys Ther, 41(12), 914-919. SC10: Line 48-49: Please make sure you change it to running injuries everywhere rather than running injury status. Response SC10: Thank you for your reminder. We have changed “running injury status” into “running injuries” throughout the manuscript (lines 16-17, 51-53). “Excessive impact forces and vertical loading rates are associated with running injuries and have been targeted in running retraining strategies.” “Among various biomechanical factors, excessive impact forces and loading rates are associated with injuries and have been targeted in running retraining strategies (Cheung & Davis, 2011).” Reference: Cheung, R. T. H., & Davis, I. S. (2011). Landing pattern modification to improve patellofemoral pain in runners: a case series. J Orthop Sports Phys Ther, 41(12), 914-919. SC11: Please rewrite the sentences for grammar corrections: Lines 52: compared “to” healthy runners Lines 58: decreased Lines 59: indicate Response SC11: Thank you for your advice. The sentence in line 52 has been deleted. Besides, we have rewritten the other two sentences as suggested (lines 66-68, 68-70). “Additionally, the impact attenuation and energy absorbed by the hip and knee joints decreased with increasing cadence.” “These results indicate that increasing the cadence or reducing the step length has an immediate effect on decreasing the impact forces during running.” SC12: Lines 59-65: There is no clear distinction between increase in cadence or decrease in step length that is the focus of the study. Response SC12: Thank you for your advice. This study focused on increase in cadence at consistent speed. At the consistent speed, the cadence and step length are inversely proportional. An increase in the cadence will lead to a decrease in the step length. Also, we have rewritten those sentences (lines 70-75). “Moreover, other variables, such as peak joint angles during the stance phase (Dos Santos, Nakagawa, Nakashima, Maciel, & Serrao, 2016), patellofemoral joint contact forces (Lenhart et al., 2015; Willy, Willson, Clowers, Baggaley, & Murray, 2016), plantar pressure (Wellenkotter, Kernozek, Meardon, & Suchomel, 2014), and muscle forces (Lenhart et al., 2015), can be reduced with increasing cadence. Also, a decrease in peak joint angles during the stance phase was observed with decreasing step length (Dewolf & De, 2015).” References: Dos Santos, A. F., Nakagawa, T. H., Nakashima, G. Y., Maciel, C. D., & Serrao, F. (2016). The Effects of Forefoot Striking, Increasing Step Rate, and Forward Trunk Lean Running on Trunk and Lower Limb Kinematics and Comfort. Int J Sports Med, 37(5), 369-373. doi: 10.1055/s-0035-1564173 Lenhart, R. L., Smith, C. R., Vignos, M. F., Kaiser, J., Heiderscheit, B. C., & Thelen, D. G. (2015). Influence of step rate and quadriceps load distribution on patellofemoral cartilage contact pressures during running. J Biomech, 48(11), 2871-2878. doi: 10.1016/j.jbiomech.2015.04.036 Willy, R. W., Willson, J. D., Clowers, K., Baggaley, M., & Murray, N. (2016). The effects of body-borne loads and cadence manipulation on patellofemoral and tibiofemoral joint kinetics during running. J Biomech, 49(16), 4028-4033. Wellenkotter, J., Kernozek, T. W., Meardon, S., & Suchomel, T. (2014). The Effects of Running Cadence Manipulation on Plantar Loading in Healthy Runners. Int J Sports Med, 35(09), 779-784. Dewolf, A., & De, J. D. (2015). Effect of stride length on maximal pelvic tilt and hip extension during running. Comput Methods Biomech Biomed Engin, 1926-1927. SC13: Lines 73-77: There is no justification for the amount of variables that were chosen for analysis in this study (kinematics and kinetics). Response SC13: Thank you for your comments. The reason we select those variables is to comprehensively evaluate the changes of the lower extremity biomechanics after cadence retraining. Though some of our variables are not commonly used in the running literature, almost all of them have been used in the previous cadence or step length studies, such as maximum extension/dorsiflexion angle and maximum flexion/plantar flexion angle during the stance phase (Dos Santos et al., 2016; Heiderscheit, Chumanov, Michalski, Wille, & Ryan, 2011), center of mass (center of gravity) (Heiderscheit et al., 2011; Lyght, Nockerts, Kernozek, & Ragan, 2016), sagittal plane distance between ankle and knee/ ankle and hip at initial contact (Lieberman, Warrener, Wang, & Castillo, 2015). We select the vertical momentum at initial contact is to interpret the decrease in impact peak. References: Dos Santos, A. F., Nakagawa, T. H., Nakashima, G. Y., Maciel, C. D., & Serrao, F. (2016). The Effects of Forefoot Striking, Increasing Step Rate, and Forward Trunk Lean Running on Trunk and Lower Limb Kinematics and Comfort. Int J Sports Med, 37(5), 369-373. doi: 10.1055/s-0035-1564173 Heiderscheit, B. C., Chumanov, E. S., Michalski, M. P., Wille, C. M., & Ryan, M. B. (2011). Effects of step rate manipulation on joint mechanics during running. Med Sci Sports Exerc, 43(2), 296-302. doi: 10.1249/MSS.0b013e3181ebedf4 Lyght, M., Nockerts, M., Kernozek, T. W., & Ragan, R. (2016). Effects of Foot Strike and Step Frequency on Achilles Tendon Stress During Running. J Appl Biomech, 32(4), 365-372. doi: 10.1123/jab.2015-0183 Lieberman, D. E., Warrener, A. G., Wang, J., & Castillo, E. R. (2015). Effects of stride frequency and foot position at landing on braking force, hip torque, impact peak force and the metabolic cost of running in humans. J Exp Biol, 218(21), 3406-3414. SC14: Lines 81-83: The hypothesis needs to be clear and specific. Response SC14: Thank you for your advice. We have rewritten the hypothesis to make it clearer and more specific (lines 90-94). “We hypothesized that a 12-week cadence retraining intervention would result in significantly lower peak impact forces and peak loading rates. Additionally, decreases in lower extremity kinematics and kinetics at initial contact and during stance phase after cadence retraining would be observed.” SC15: Lines 85-95: Please perform sample size calculation and describe the randomization protocol. Was there any blinding of the investigators who performed data analysis and collected the data? Please specify if it was lower limb musculoskeletal injuries. Response SC15: Thank you for your advice. In terms of sample size calculation and randomization protocol, we have added sample size calculation and randomization protocol in the methods section (lines 98-102). In terms of blinding, honestly, there was no blinding of the investigators who performed data analysis and collected the data, but participants were blinding of their groups. Regarding your last comment, i.e., “Please specify if it was lower limb musculoskeletal injuries”, participants included in this study were free of any lower limb musculoskeletal injuries in the previous 6 months (lines 108-109). “A sample size estimate determined a minimum sample size of 26 participants was required to achieve a minimum effect size of 0.6. Considering a drop-out rate of 15-20%, thirty male recreational runners were recruited through online social media, running clubs, and flyers. Participants were randomly assigned to either the cadence retraining group (CAD) or control group (CON) based on simple randomization, with 15 participants in each group (Table 1).” “Participants were excluded if they had any lower limb musculoskeletal injuries in the previous 6 months.” SC16: Line 100: Please describe which marker set was used. Response SC16: Thank you for your advice. We have added the marker set (lines 129-131). “After a warm-up, a total of 40 markers were placed on participants, including 16 markers on anatomical landmarks and 24 tracking markers and a static calibration was then performed (Figure 1).” SC17: Line 104: Please remove the word force before ground reaction force data Response SC17: Thank you for your advice. We have removed this word (lines 119-120). “The kinematics and ground reaction force data were simultaneously collected using the Vicon system.” SC18: Line 112: Please describe why you had them run on treadmill. Response SC18: Thank you for your comments. Due to the limited space in the laboratory, we required the participants to run on a treadmill for 5 minutes as a warm-up. SC19: Retraining Protocol: Please describe why you did not collect data at preferred speed if they ran for 12 weeks at their preferred speed. What was the difference in speeds? Decrease in running speed can lead to decrease in kinetic forces and viceversa. Please describe the group training sessions. Response SC19: Thank you for your comments. Firstly, there is previous study such as Crowell et al., that collected data at fixed speed (3.37 m/s) and designed gait retraining at preferred speed (Crowell & Davis, 2011). Secondly, collecting data at fixed speed can remove the effects of speed between subjects. Lastly, participants may better adapt to the increased cadence when they performed cadence retraining at preferred speed. Decrease in running speed can lead to decrease in kinetic forces and variables, but the data collected before and after cadence retraining was at the same speed (3.33m/s). therefore, there was no speed effects on the kinetic forces and variables. Regarding the group training sessions, we have provided more details on the weekly group training (lines 157-163). “We provided weekly group training three times a week, and participants chose one of weekly group training to participate based on their own time. During the weekly group training, the participants performed 8-minutes warm-up, such as dynamic stretch, under the guidance of the researchers. Then the participants started to run according to their retraining schedule. We didn’t give them guidance on running techniques, since the purpose of weekly group training was to keep in touch with them and ensure the quality of retraining.” Reference: Crowell, H. P., & Davis, I. S. (2011). Gait retraining to reduce lower extremity loading in runners. Clin Biomech (Bristol, Avon), 26(1), 78-83. doi: 10.1016/j.clinbiomech.2010.09.003 SC20: Data processing: Please justify your reasons for selecting so many variables for analysis. There are many variables which are not commonly used in the running literature (vertical momentum at initial contact, maximum extension/dorsiflexion angle during the stance phase; maximum flexion/plantar flexion angle during the stance phase; center of gravity, sagittal plane distance between ankle and knee/ ankle and hip at initial contact). Does Center of gravity refer to center of mass? Which plane were the peak moments of hip, knee and ankle analyzed or was it an overall support moment? Response SC20: Thank you for your comments. The reason we select those variables is to comprehensively evaluate the changes of the lower extremity biomechanics after cadence retraining. Though some of our variables are not commonly used in the running literature, almost all of them have been used in the previous cadence or step length studies, such as maximum extension/dorsiflexion angle and maximum flexion/plantar flexion angle during the stance phase (Dos Santos et al., 2016; Heiderscheit et al., 2011), center of mass (center of gravity) (Heiderscheit et al., 2011; Lyght et al., 2016), sagittal plane distance between ankle and knee/ ankle and hip at initial contact (Lieberman et al., 2015). We select the vertical momentum at initial contact is to interpret the decrease in impact peak. The center of gravity (COG) used in this study didn’t refer to center of mass. The peak moments of hip, knee, and ankle were analyzed in the sagittal plane, and we have added in the manuscript (lines 187-190). “Kinetic variables included joint stiffness (Hamill, Moses, & Seay, 2009) [kj, Equation (2)] and peak moments of hip, knee and ankle in the sagittal plane, vertical momentum at initial contact [P, Equation (3)] and lower extremity stiffness (Liu et al., 2006) [kleg, Equation (4)].” References: Dos Santos, A. F., Nakagawa, T. H., Nakashima, G. Y., Maciel, C. D., & Serrao, F. (2016). The Effects of Forefoot Striking, Increasing Step Rate, and Forward Trunk Lean Running on Trunk and Lower Limb Kinematics and Comfort. Int J Sports Med, 37(5), 369-373. doi: 10.1055/s-0035-1564173 Heiderscheit, B. C., Chumanov, E. S., Michalski, M. P., Wille, C. M., & Ryan, M. B. (2011). Effects of step rate manipulation on joint mechanics during running. Med Sci Sports Exerc, 43(2), 296-302. doi: 10.1249/MSS.0b013e3181ebedf4 Lyght, M., Nockerts, M., Kernozek, T. W., & Ragan, R. (2016). Effects of Foot Strike and Step Frequency on Achilles Tendon Stress During Running. J Appl Biomech, 32(4), 365-372. doi: 10.1123/jab.2015-0183 Lieberman, D. E., Warrener, A. G., Wang, J., & Castillo, E. R. (2015). Effects of stride frequency and foot position at landing on braking force, hip torque, impact peak force and the metabolic cost of running in humans. J Exp Biol, 218(21), 3406-3414. Hamill, J., Moses, M., & Seay, J. (2009). Lower extremity joint stiffness in runners with low back pain. Res Sports Med, 17(4), 260-273. doi: 10.1080/15438620903352057 Liu, Y., Peng, C. H., Wei, S. H., Chi, J. C., Tsai, F. R., & Chen, J. Y. (2006). Active leg stiffness and energy stored in the muscles during maximal counter movement jump in the aged. J Electromyogr Kinesiol, 16(4), 342-351. doi: 10.1016/j.jelekin.2005.08.001 SC21: Please perform t-tests for demographic variables between two groups. Height of the participant can impact step length. Response SC21: Thank you for your comments. As suggested, we have performed t-test for demographic variables between two groups. There were no significant differences in age, height, weight, and weekly mileage between two groups (lines 109-110). Besides, no significant differences were observed in the height of the participant between CAD and CON group (CAD: 174.8±4.4cm; CON: 175.5±5.1cm). “There were no significant differences in age, height, weight, and weekly mileage between two groups.” SC22: Line 192-193: Revise for grammar. Response SC22: Thank you for your advice. We have revised the grammar for this sentence (lines 217-219). “However, in the CAD group, one participant was excluded due to his insufficient training volume, and two participants withdrew for personal reasons or more than three interruptions.” SC23: Line 220, 223, 225: which group? Response SC23: Thank you for your advice. Line 220 and 223 referred to the CAD group, and line 225 referred to the CON group. We have rewritten the results section, and the group has been added to the results (lines 226-267). “Cadence and step length Significant training × group interaction effect was observed for the cadence (P < 0.001, =0.867) (Figure 4). Specifically, the cadence significantly increased in the CAD group (P < 0.001, Cohen’s d = 3.87), but not in the CON group (P > 0.05) after training. A significant main effect of training was noted for the step length, which decreased (P = 0.011, =0.259) in the CAD group after training (Figure 4). Additionally, the step length in the CAD group was significantly lower than that in the CON group after training (P = 0.04, 95% confidence interval (CI) [-0.245, -0.006], Cohen’s d = 0.94). Impact forces Significant training × group interaction effects were observed for the impact forces (P = 0.022, =0.217) and impact impulse (P = 0.003, =0.335) (Figure 5). Specifically, the impact forces significantly decreased in the CAD group (P = 0.003, Cohen’s d = 1.10), but not in the CON group (P > 0.05). Also, the impact forces in the CAD group was significantly lower than that in the CON group after training (P = 0.038, 95% CI [-0.443, -0.013], Cohen’s d = 0.95). The impact impulse didn’t change in the CON group (P > 0.05), but decreased in the CAD group (P = 0.005, Cohen’s d = 1.01). Significant main effects of training were observed for the average loading rates and maximum loading rates. Specifically, the average loading rates (P = 0.029, =0.198), and the maximum loading rates (P = 0.025, =0.209) decreased in the CAD group after training (Figure 5). Kinematics and joint mechanics Significant training × group interaction effects were observed for the foot angle (P = 0.04, =0.178), vertical velocity of CoG at initial contact (P = 0.035, =0.186), vertical momentum at initial contact (P = 0.037, =0.183), stance time (P = 0.012, =0.252), vertical excursion of CoG (P = 0.001,=0.409), and (P = 0.011, =0.258). Specifically, the foot angle (P = 0.003, Cohen’s d = 1.09), vertical velocity of CoG at initial contact (P = 0.002, Cohen’s d = 1.16), vertical momentum at initial contact (P = 0.002, Cohen’s d = 1.16), stance time (P = 0.002, Cohen’s d = 1.18), vertical excursion of CoG (P < 0.001, Cohen’s d = 1.83), and (P < 0.001, Cohen’s d = 1.45) decreased in the CAD group after training (Figure 6, Table 2). In addition, the vertical excursion of CoG in the CAD group was significantly lower than that in the CON group after training (P = 0.025, 95% CI [-0.015, -0.001], Cohen’s d = 1.03). Significant main effects of training were observed for the peak knee flexion angle and knee joint range of motion. Specifically, the peak knee flexion angle (P = 0.048, =0.166) and knee joint range of motion (P = 0.039, =0.180) decreased in the CAD group after training (Table 2). For the lower extremity stiffness, a significant main effect of training was observed, which increased in the CAD group after training (P = 0.048, =0.166) (Table 2). However, there was neither significant training by group interaction, nor main effects of training with respect to the joint moment and joint stiffness of the ankle, knee, and hip (P > 0.05) (Table 2).” SC24: Line 240: Please explain why you think your study did not see the increase in cadence that you expected (5.7% vs 7.5%) specifically if any biomechanical adaptations can explain this. Response SC24: Thank you for your comments. During training, participants maintained their cadence by a metronome. During data collection, there was no metronome to help participants maintain their cadence. In addition, previous studies also didn’t see the increase in cadence as they expected, such as 2.4% vs. 10% (Hafer, Brown, Demille, Hillstrom, & Garber, 2015) and 8.6% vs. 10% (Baumgartner, Gusmer, & Hollman, 2019). References: Hafer, J. F., Brown, A. M., Demille, P., Hillstrom, H. J., & Garber, C. E. (2015). The effect of a cadence retraining protocol on running biomechanics and efficiency: a pilot study. J Sports Sci, 33(7), 724-731. Baumgartner, J., Gusmer, R., & Hollman, J. (2019). Increased stride-rate in runners following an independent retraining program: A randomized controlled trial. doi: 10.1111/sms.13509 SC25: Please link your results to cadence retraining and kinetic parameters linked to running injury rather than focusing on foot strike pattern. Can you describe how the results of the study can be used for future studies especially in participants with running injuries. Response SC25: Thank you for your comments. The foot angle is a main variable to evaluate strike pattern, and the decreased foot angle in the CAD group after training just make us link to the slight change in strike pattern. We use the slight change in strike pattern to interpret the decreased impact forces. As we elaborated in the introduction, the impact forces and loading rate are related to running injuries. In terms of the use for future studies, the results of the study indicate that the preferred cadence of runners can be significantly increased by a 12-week cadence retraining. Future studies can use cadence retraining to increase the cadence of their participants. In addition, as cadence retraining can reduce impact forces which are related to running injuries, participants suffered from impact-related running injuries may relieve pain or reduce injury symptoms by cadence retraining. Therefore, future studies may use cadence retraining as a method to treat participants with impact-related running injuries. Relevant text has been added in the revised manuscript (lines 347-348). “Finally, future studies may use cadence retraining as a method to treat participants with impact-related running injuries.” Response to Reviewer #3 General comments GC1: I commend the authors on undertaking and submitting an article on a very clinically relevant topic which should be of interest to readers. The article examines the effect of a 12-week cadence retraining program on impact forces and lower extremity biomechanics. Whilst the authors’ study findings do support the original research goals, their conclusion that reducing impact forces may decrease the risk of impact-related running injuries needs further exploration in the introduction. The link between running injuries and impact forces remains unclear which needs to be indicated. Response GC1: Thank you for your comments. The relation between impact forces and running injuries have been further elaborated in the introduction (lines 51-60). Also, we have rewritten the conclusion (lines 351-360). “Among various biomechanical factors, excessive impact forces and loading rates are associated with injuries and have been targeted in running retraining strategies(Cheung & Davis, 2011). In a recent review, excessive accumulation of impact peak force in knee joints could lead to overuse injuries(Gijon-Nogueron & Fernandez-Villarejo, 2015). Previous prospective studies found that injured runners had greater vertical loading rates than non-injured runners (Davis et al., 2016; Fu et al., 2017). In addition, other prospective studies showed that runners with patellofemoral pain (Cheung & Davis, 2011) and chronic exertional compartment syndrome (Diebal et al., 2012) exhibited lower impact loading after their pain and disability typically associated with these injuries reduced.” “The 12-week cadence retraining significantly increased the cadence by 5.7%. The increased cadence effectively decreased the impact forces, namely, impact peak, average loading rates maximum loading rates, and impact impulse. As the close relationship between impact forces and running injuries, increasing the cadence as a retraining method may reduce the risk of some impact-related injuries. Meanwhile, the foot became “flatter” when its position was closer to the CoG at the initial contact after training, and the vertical excurison of the CoG was smaller during stance, providing the mechanical explanation for the decreased impact forces. Furthermore, the vertical excursion of the CoG decreased, further leading to an increase in lower extremity stiffness. Hence, cadence retraining can lead to lower extremity biomechanical changes.” References: Cheung, R. T. H., & Davis, I. S. (2011). Landing pattern modification to improve patellofemoral pain in runners: a case series. J Orthop Sports Phys Ther, 41(12), 914-919. Gijon-Nogueron, G., & Fernandez-Villarejo, M. (2015). Risk Factors and Protective Factors for Lower-Extremity Running Injuries A Systematic Review. J Am Podiatr Med Assoc, 105(6), 532-540. doi: 10.7547/14-069.1 Bredeweg, S. W., Kluitenberg, B., Bessem, B., & Buist, I. (2013). Differences in kinetic variables between injured and noninjured novice runners: a prospective cohort study. J Sci Med Sport, 16(3), 205-210. doi: 10.1016/j.jsams.2012.08.002 Davis, I. S., Bowser, B. J., & Mullineaux, D. R. (2016). Greater vertical impact loading in female runners with medically diagnosed injuries: a prospective investigation. 50(14), 887-892. doi: 10.1136/bjsports-2015-094579 Diebal, A. R., Gregory, R., Alitz, C., & Gerber, J. P. (2012). Forefoot running improves pain and disability associated with chronic exertional compartment syndrome. Am J Sports Med, 40(5), 1060-1067. GC2: The article is well written although some sections, including tables and figures, need further detail or clarification and I have included specific comments in the General Comments section below which I hope the authors will find helpful. Response GC2: Thank you for your advice. Your comments are very helpful to us. We have revised tables and figures based on your suggestions. Please refer to our response SC35-38. Experimental design GC3: The research is within the Aims and Scope of the journal. However, further elaboration of the introduction to the topic is required to justify the study and research question. Additionally, the link between impact forces and running related injuries needs to be explored further to justify any of the conclusions made in the abstract. Response GC3: Thank you for your advice. We have further elaborated the introduction to the topic to justify the study and research question (lines 83-88). Also, the link between impact forces and running related injuries has been explored in the introduction (lines 53-60). “However, due to the small number of participants, short retraining intervention time, and insufficient evaluation variables among those studies, the movement patterns and biomechanical factors, such as lower extremity kinematics and kinetics, are unclear and may result in changes in impact force loading rates. Assessing these additional variables may result in better understanding of cadence retraining on running biomechanics and its associated risk of injuries.” “In a recent review, excessive accumulation of impact peak force in knee joints could lead to overuse injuries (Gijon-Nogueron & Fernandez-Villarejo, 2015). Previous prospective studies found that injured runners had greater vertical loading rates than non-injured runners (Davis et al., 2016; Fu et al., 2017). In addition, other prospective studies showed that runners with patellofemoral pain (Cheung & Davis, 2011) and chronic exertional compartment syndrome (Diebal et al., 2012) exhibited lower impact loading after their pain and disability typically associated with these injuries reduced.” References: Cheung, R. T. H., & Davis, I. S. (2011). Landing pattern modification to improve patellofemoral pain in runners: a case series. J Orthop Sports Phys Ther, 41(12), 914-919. Gijon-Nogueron, G., & Fernandez-Villarejo, M. (2015). Risk Factors and Protective Factors for Lower-Extremity Running Injuries A Systematic Review. J Am Podiatr Med Assoc, 105(6), 532-540. doi: 10.7547/14-069.1 Bredeweg, S. W., Kluitenberg, B., Bessem, B., & Buist, I. (2013). Differences in kinetic variables between injured and noninjured novice runners: a prospective cohort study. J Sci Med Sport, 16(3), 205-210. doi: 10.1016/j.jsams.2012.08.002 Davis, I. S., Bowser, B. J., & Mullineaux, D. R. (2016). Greater vertical impact loading in female runners with medically diagnosed injuries: a prospective investigation. 50(14), 887-892. doi: 10.1136/bjsports-2015-094579 Diebal, A. R., Gregory, R., Alitz, C., & Gerber, J. P. (2012). Forefoot running improves pain and disability associated with chronic exertional compartment syndrome. Am J Sports Med, 40(5), 1060-1067. GC4: The study primarily attempts to answer whether increasing running cadence can decrease loading rates in runners and should likely focus on this. There are additional studies, such as Yong et al. (2008) and Chan et. al (2018) which have reviewed the impact of gait retraining, including increasing cadence, on loading rates in runners and should be referenced within the article. This will strengthen the authors' findings and can help to justify the association with injury reduction. Response GC4: Thank you for your advice. We have referenced the studies you recommended. Please refer to our response SC28. GC5: Additionally, the methodology requires additional detail. There should be further justification regarding the chosen sample size and elaboration of the participants running experience and training history, where possible. This will assist with the application of the research findings. Response GC5: Thank you for your comments. We have added the sample size calculation in the methods section (lines 98-102) and participants’ weekly mileage in Table 1. “A sample size estimate determined a minimum sample size of 26 participants was required to achieve a minimum effect size of 0.6. Considering a drop-out rate of 15-20%, thirty male recreational runners were recruited through online social media, running clubs, and flyers. Participants were randomly assigned to either the cadence retraining group (CAD) or control group (CON) based on simple randomization, with 15 participants in each group (Table 1).” Table 1 Demographics for participants () Group First visit (n) Second visit (n) Age (years) Height (cm) Weight (kg) Weekly mileage (km) CAD 15 12 23.6±7.5 174.8±4.4 71.8±4.9 23.3±3.3 CON 15 12 23.7±1.2 175.5±5.1 70.8±7.3 22.9±4.3 Note: CAD, cadence retraining group; CON, control group. Validity of the findings GC6: There are some concerns regarding the methodological quality in terms of the reliability of assessing running gait over a force platform where participants have to place the foot. Further information on the experimental set-up would help. Response GC6: Thank you for your comments. Further information on the experimental set-up has been elaborated in the specific comments. Please refer to our response SC17. GC7: Additionally, the validity and reliability of CONDOON app and use phones to measure running metrics will need to be included in the study to justify the findings. Response GC7: Thank you for your comments. We have responded to this comment in detail in response SC19. Specific comments SC1: Line #42-43: “In addition, 19.4% to 79.3% of long-distance runners experienced 43 injuries” – Can you further elaborate why the large variance; for example, novice runners or those who run increased distance show higher injury rates. Response SC1: Thank you for your comments. The training distance per week in male runners and a history of previous injuries are the main reasons for the large variance. SC2: Line #42-43: “whilst 63% of them had a history of lower extremity injury” – This is unclear who it is referring to. There is no reference to this in van Gant et. al. 2007 Response SC2: Sorry about the confusion. We have removed the second half of this sentence to avoid misunderstanding (lines 45-46). “In addition, 19.4% to 79.3% of long-distance runners experienced lower extremity injuries.” SC3: Line#44: “such as patellofemoral joint pain synthesis” – Remove word “synthesis” Response SC3: Thank you for your advice. We have removed the word “synthesis” (lines 46-47). “Among these injuries, knee injuries, such as patellofemoral pain, are the most common. SC4: Line#49: Can please you reference running retraining studies? Response SC4: As suggested, we have referenced a running retraining study (lines 51-53). “Among various biomechanical factors, excessive impact forces and loading rates are associated with injuries and have been targeted in running retraining strategies (Cheung & Davis, 2011).” Reference: Cheung, R. T. H., & Davis, I. S. (2011). Landing pattern modification to improve patellofemoral pain in runners: a case series. J Orthop Sports Phys Ther, 41(12), 914-919. SC5: Line#55: “Increasing the running cadence or decreasing the step length” - Remove “the” Response SC5: As suggested, we have removed the word “the” (lines 62-65). “Increasing running cadence or decreasing step length at a certain speed (2.5m/s for increasing cadence, 4.58m/s for decreasing step length) could decrease the impact forces and vertical loading rates (Hobara et al. 2012; Stergiou et al. 2003).” Reference: Hobara, H., Sato, T., Sakaguchi, M., Sato, T., & Nakazawa, K. (2012). Step frequency and lower extremity loading during running. Int J Sports Med, 33(4), 310-313. doi: 10.1055/s-0031-1291232 SC6: Line#56: “at a certain speed” – What speeds? Response SC6: Thank you for your comments. In these two studies, the speeds in increasing cadence and decreasing step length are 2.5 m/s and 4.58 m/s, respectively (lines 62-65). “Increasing running cadence or decreasing step length at a certain speed (2.5 m/s for increasing cadence and 4.58 m/s for decreasing step length) could decrease the impact forces and vertical loading rates (Hobara et al. 2012; Stergiou et al. 2003).” References: Hobara, H., Sato, T., Sakaguchi, M., Sato, T., & Nakazawa, K. (2012). Step frequency and lower extremity loading during running. Int J Sports Med, 33(4), 310-313. doi: 10.1055/s-0031-1291232 Stergiou, N., Bates, B. T., & Kurz, M. J. (2003). Subtalar and knee joint interaction during running at various stride lengths. Journal of Sports Medicine & Physical Fitness, 43(3), 319-326. SC7: Line#58-59: “decreased” – Grammatical error. Changed to past tense. Response SC7: As suggested, we have changed it to past tense (lines 66-68). “Additionally, the impact attenuation and energy absorbed by the hip and knee joints decreased with increasing cadence.” SC8: Line#60: “the cadence or reducing the step length” - Remove “the”. Also, increasing cadence and reducing step length are the same. Response SC8: Thank you for your advice. We have removed the word “the” (lines 68-70). “These results indicated that increasing cadence or reducing step length has an immediate effect on decreasing the impact forces during running.” SC9: Line#66: “In regard” – Change to in regards. Response SC9: As suggested, we have changed “In regard” into “In regards” (lines 76-77). “In regards to long-term cadence retraining, Warne et al. (2017) reported that impact forces were reduced after a 6-week retraining intervention with a 10% increase in cadence.” Reference: Warne, J. P., Smyth, B. P., Fagan, J. O. C., Hone, M. E., Richter, C., Nevill, A. M., . . . Warrington, G. D. (2017). Kinetic changes during a six-week minimal footwear and gait-retraining intervention in runners. J Sports Sci, 35(15), 1538-1546. SC10: Line#67: Is 6 weeks defined as a “long term change” Response SC10: Thank you for your comments. In the study of Hafer et al. (2015), they regarded 6-week cadence retraining as long-term retraining. Reference: Hafer, J. F., Brown, A. M., Demille, P., Hillstrom, H. J., & Garber, C. E. (2015). The effect of a cadence retraining protocol on running biomechanics and efficiency: a pilot study. J Sports Sci, 33(7), 724-731. SC11: Line#68-72: “Hafer et al. (2015) did not find significant changes in impact forces in another cadence retraining intervention, but the loading rates decreased after 6-week cadence retraining with a 10% increase in cadence. In addition, reductions in peak loading rates and average loading rates were observed after an eight-session cadence retraining intervention with a 7.5% increase in cadence” - Consider re-wording this clearer and separate the two studies. Response SC11: Thank you for your advice. We have rewritten the sentence to make it clearer (lines 78-81). “Hafer et al. (2015) did not find significant changes in impact forces, but observed significant decreases in loading rates after a 6-week cadence retraining with a 10% increase in cadence. Similarly, reductions in peak loading rates and average loading rates were also observed after an eight-session cadence retraining with a 7.5% increase in cadence (Willy et al., 2016).” References: Hafer, J. F., Brown, A. M., Demille, P., Hillstrom, H. J., & Garber, C. E. (2015). The effect of a cadence retraining protocol on running biomechanics and efficiency: a pilot study. J Sports Sci, 33(7), 724-731. Willy, R. W., Buchenic, L., Rogacki, K., Ackerman, J., Schmidt, A., & Willson, J. D. (2016). In-field gait retraining and mobile monitoring to address running biomechanics associated with tibial stress fracture. Scand J Med Sci Sports, 26(2), 197-205. doi: 10.1111/sms.12413. SC12: Line#72: “cadence retraining modifies” - If it decreased say it decreased loading rates. Response SC12: Thank you for your advice. As suggested, we have changed the expression (lines 81-82). “In summary, cadence retraining decreased loading rates during the impact phase in running.” SC13: Line#75-77: “Assessing these additional variables may result in better understanding of cadence retraining on running biomechanics and its associated risk of injuries” - Are you assessing the risk of injury? You need to elaborate on the link between impact forces and running injuries. Response SC13: Thank you for your comments. We have reworded the sentences and elaborated on the link between impact forces and running injuries (lines 53-60). “In a recent review, excessive accumulation of impact peak force in knee joints could lead to overuse injuries (Gijon-Nogueron & Fernandez-Villarejo, 2015). Previous prospective studies found that injured runners had greater vertical loading rates than non-injured runners (Davis et al., 2016; Fu et al., 2017). In addition, other prospective studies showed that runners with patellofemoral pain (Cheung & Davis, 2011) and chronic exertional compartment syndrome (Diebal et al., 2012) exhibited lower impact loading after their pain and disability typically associated with these injuries reduced.” References: Gijon-Nogueron, G., & Fernandez-Villarejo, M. (2015). Risk Factors and Protective Factors for Lower-Extremity Running Injuries A Systematic Review. J Am Podiatr Med Assoc, 105(6), 532-540. doi: 10.7547/14-069.1 Bredeweg, S. W., Kluitenberg, B., Bessem, B., & Buist, I. (2013). Differences in kinetic variables between injured and noninjured novice runners: a prospective cohort study. J Sci Med Sport, 16(3), 205-210. doi: 10.1016/j.jsams.2012.08.002 Davis, I. S., Bowser, B. J., & Mullineaux, D. R. (2016). Greater vertical impact loading in female runners with medically diagnosed injuries: a prospective investigation. 50(14), 887-892. doi: 10.1136/bjsports-2015-094579 Cheung, R. T. H., & Davis, I. S. (2011). Landing pattern modification to improve patellofemoral pain in runners: a case series. J Orthop Sports Phys Ther, 41(12), 914-919. Diebal, A. R., Gregory, R., Alitz, C., & Gerber, J. P. (2012). Forefoot running improves pain and disability associated with chronic exertional compartment syndrome. Am J Sports Med, 40(5), 1060-1067. SC14: Line#89: How did you determine that participants were rearfoot strikers? Response SC14: Thank you for your comments. The rearfoot strike pattern was determined by foot angle (the angle between the foot and ground) at initial contact. When the participants first came to the lab, they were required to run on a treadmill for 2 minutes. At the same time, a high-speed camera was placed next to the treadmill to record their running posture. Their strike patterns were confirmed by looking back at the video frame by frame. We have added this piece of information in the revise manuscript (lines 103-106). “When the participants first came to the lab, they were required to run on a treadmill for 2 minutes with a high-speed camera placed next to the treadmill to record their running posture. They were determined as rearfoot strikers by checking the foot angle (the angle between the foot and ground at initial contact) (Heiderscheit et al. 2011) in the video frame by frame.” References: Heiderscheit, B. C., Chumanov, E. S., Michalski, M. P., Wille, C. M., & Ryan, M. B. (2011). Effects of step rate manipulation on joint mechanics during running. Med Sci Sports Exerc, 43(2), 296-302. doi: 10.1249/MSS.0b013e3181ebedf4 SC15: Line#102-103: Rewrite as 90cm × 60cm × 10cm Response SC15: As suggested, we have rewritten this sentence (lines 117-118). “Ground reaction force data were captured at 1000 Hz by using two 90cm × 60cm × 10cm 3D force platforms.” SC16: Line#111: Remove “that is”. Write at baseline or commencement and at the end of the program. Response SC16: Thank you for your advice. We have rewritten this sentence at the commencement (line 126). “The participants visited the laboratory twice, i.e., at baseline and at the end of the program.” SC17: Line#115: I would be interested to see the experimental design in the appendix with a description of the run-up and force plate location. This is a limitation of the study as you are assessing running biomechanics with a limited run-up with a relatively small area for foot placement. Response SC17: Thank you for your comments. The figure below is the setup of our experiment. Two force plates were placed in the middle of a 10-meter runway, with 12 Vicon cameras around. In addition, we have stated the limited run-up and the relatively small area for foot placement as limitations and added these in the limitation section as well (lines 341-343). “Thirdly, the running biomechanics on a limited run-up (10 meters) with a relatively small area (60cm × 90cm) for foot placement may slightly different from the outdoor over ground running.” SC18: Line#121: How did you determine “preferred speed”? Response SC18: Thank you for your comments. The preferred speed was obtained from three outdoor running trials prior to the cadence retraining (lines 142-145). “The participants were asked to run outdoors three times (30 min for each run) at a comfortable speed to determine the preferred speed and cadence of the participants. The preferred speed and preferred cadence were the average values obtained from the three outdoor running trials.” SC19: Line#123: Need to provide some data regarding the reliability and validity of CODOON running app in tracking running volumes, speed and cadence. How accurate are phones in measuring cadence? Have you controlled for the positioning of the phone and where will it be held during trials? ie. Held in hand or strapped to the arm. Response SC19: Thank you for your comments. We have contacted the engineer of CODOON running app, who responded us that this app could accurately monitor running speed and cadence. In addition, CODOON running app has a high market share in Chinese running app. Currently, however, we do not have available data regarding the reliability and validity of this app. Regarding the position of the phone, we have controlled the position of the phone. Each participant received a sport belt bag to place their phones during running, and they were instructed to place the bag above their sacrum. We have added this piece of information in the methods section (lines 141-142). “Each participant received a sport belt bag to place their phones during running, and they were instructed to place the bag above their sacrum.” SC20: Line#124” “The participants were asked to run outdoors for three times” – remove word “for”. Response SC20: As suggested, we have removed the word “for” (lines 142-144). “The participants were asked to run outdoors three times (30 min for each run) at a comfortable speed to determine the preferred speed and cadence of the participants.” SC21: Line#126 – Grammatical. Consider rewriting Response SC21: Thank you for your advice. We have rewritten this sentence (lines 144-145). “The preferred speed and preferred cadence were the average values obtained from three outdoor running trials.” SC22: Good summary of data processing and statistics. Recommend further statistical assessment prior to publication to validate the analysis. Response SC22: Thank you for your comments. We have performed statistical assessment again to validate the analysis. SC23: Line#191-192: Consider summarising more clearly. Response SC23: Thank you for your advice. We have rewritten this sentence to make it clearer (lines 217-219). “However, in the CAD group, one participant was excluded due to his insufficient training volume, and two participants withdrew for personal reasons or more than three interruptions.” SC24: Line#197: “CAD: 23.3+/-22.9 km/week” – There appears to be an error in standard deviation calculations. I could not validate these numbers as only patient training characteristics at baseline were reported in the supplementary material. If not, can you explain the large variance in the training of the CAD group? Response SC24: Sorry about the confusion. We made a mistake while typing the numbers, and we corrected the numbers (line 224). Now, it reads “CAD: 23.3±3.3 km/week”. SC25: Consider elaborating some of the arguments in the introduction rather than introducing new arguments in the conclusion. Response SC25: Thank you for your advice. We have elaborated some of the arguments in the introduction (lines 53-57, 62-66). “In a recent review, excessive accumulation of impact peak force in knee joints could lead to overuse injuries (Gijon-Nogueron & Fernandez-Villarejo, 2015). Previous prospective studies found that injured runners had greater vertical loading rates than non-injured runners (Davis et al., 2016; Fu et al., 2017).” “Increasing running cadence or decreasing the step length at a certain speed (2.5 m/s for increasing cadence, 4.58 m/s for decreasing step length) could decrease the impact forces and vertical loading rates (Hobara et al. 2012; Stergiou et al. 2003), and the reduction in impact force was found to related to the decrease in the vertical velocity of CoG (Derrick, Hamill, & Caldwell, 1998).” References: Gijon-Nogueron, G., & Fernandez-Villarejo, M. (2015). Risk Factors and Protective Factors for Lower-Extremity Running Injuries A Systematic Review. J Am Podiatr Med Assoc, 105(6), 532-540. doi: 10.7547/14-069.1 Bredeweg, S. W., Kluitenberg, B., Bessem, B., & Buist, I. (2013). Differences in kinetic variables between injured and noninjured novice runners: a prospective cohort study. J Sci Med Sport, 16(3), 205-210. doi: 10.1016/j.jsams.2012.08.002 Davis, I. S., Bowser, B. J., & Mullineaux, D. R. (2016). Greater vertical impact loading in female runners with medically diagnosed injuries: a prospective investigation. 50(14), 887-892. doi: 10.1136/bjsports-2015-094579 Hobara, H., Sato, T., Sakaguchi, M., Sato, T., & Nakazawa, K. (2012). Step frequency and lower extremity loading during running. Int J Sports Med, 33(4), 310-313. doi: 10.1055/s-0031-1291232 Stergiou, N., Bates, B. T., & Kurz, M. J. (2003). Subtalar and knee joint interaction during running at various stride lengths. Journal of Sports Medicine & Physical Fitness, 43(3), 319-326. Derrick, T. R., Hamill, J., & Caldwell, G. E. (1998). Energy absorption of impacts during running at various stride lengths. Med Sci Sports Exerc, 30(1), 128-135. SC26: Lines#247-251: These arguments should be included in the introduction and can be referred back to in the discussion. Response SC26: Thank you for your comments. As suggested, we have moved those arguments to the introduction section and referred back to in the discussion section (lines 53-57, 62-66, 287-289). Introduction “In a recent review, excessive accumulation of impact peak force in knee joints could lead to overuse injuries (Gijon-Nogueron & Fernandez-Villarejo, 2015). Previous prospective studies found that injured runners had greater vertical loading rates than non-injured runners (Davis et al., 2016; Fu et al., 2017).” “Increasing the running cadence or decreasing the step length at a certain speed could decrease the impact forces and vertical loading rates (Hobara, Sato, Sakaguchi, Sato, & Nakazawa, 2012; Stergiou, Bates, & Kurz, 2003), and the reduction in impact force was found to related to the decrease in the vertical velocity of CoG (Derrick et al., 1998).” Discussion “In this study, the peak impact forces were significantly reduced by 10.2%, which is greater than the 7.6% decrease observed in the study of Hobara et al. (2012). This decrease was related to the reduction in the vertical velocity of CoG (Derrick et al., 1998).” References: Gijon-Nogueron, G., & Fernandez-Villarejo, M. (2015). Risk Factors and Protective Factors for Lower-Extremity Running Injuries A Systematic Review. J Am Podiatr Med Assoc, 105(6), 532-540. doi: 10.7547/14-069.1 Bredeweg, S. W., Kluitenberg, B., Bessem, B., & Buist, I. (2013). Differences in kinetic variables between injured and noninjured novice runners: a prospective cohort study. J Sci Med Sport, 16(3), 205-210. doi: 10.1016/j.jsams.2012.08.002 Davis, I. S., Bowser, B. J., & Mullineaux, D. R. (2016). Greater vertical impact loading in female runners with medically diagnosed injuries: a prospective investigation. 50(14), 887-892. doi: 10.1136/bjsports-2015-094579 Hobara, H., Sato, T., Sakaguchi, M., Sato, T., & Nakazawa, K. (2012). Step frequency and lower extremity loading during running. Int J Sports Med, 33(4), 310-313. doi: 10.1055/s-0031-1291232 Stergiou, N., Bates, B. T., & Kurz, M. J. (2003). Subtalar and knee joint interaction during running at various stride lengths. Journal of Sports Medicine & Physical Fitness, 43(3), 319-326. Derrick, T. R., Hamill, J., & Caldwell, G. E. (1998). Energy absorption of impacts during running at various stride lengths. Med Sci Sports Exerc, 30(1), 128-135. SC27: Lines#258-259: Consider re-wording. Response SC27: Thank you for your reminder. We have rewritten this sentence (lines 296-297). “In the present study, the foot angle in the CAD group significantly decreased after retraining, indicating a tendency from rearfoot strike to non-rearfoot strike.” SC28: Lines#269-271: Do we have evidence that reducing loading rates can decrease injuries? Yong et al. (2008) “Acute changes in foot strike pattern and cadence affect running parameters associated with tibial stress fractures” has also explored this and should be referenced in the text. Response SC28: Thank you for your comments. There is evidence that reducing loading rates can decrease injuries which has been mentioned in the introduction section. Prospective studies found the relation between loading and running injuries (lines 53-60). Also, we have referenced the study by Yong et al (lines 305-307). Introduction “In a recent review, excessive accumulation of impact peak force in knee joints could lead to overuse injuries (Gijon-Nogueron & Fernandez-Villarejo, 2015). Previous prospective studies found that injured runners had greater vertical loading rates than non-injured runners (Davis et al., 2016; Fu et al., 2017). In addition, other prospective studies showed that runners with patellofemoral pain (Cheung & Davis, 2011) and chronic exertional compartment syndrome (Diebal et al., 2012) exhibited lower impact loading after their pain and disability typically associated with these injuries reduced.” Discussion “Runners with a history of stress fractures or other types of injuries had higher loading rates than those of healthy runners (Worp, Vrielink, & Bredeweg, 2016; Yong, Silder, Montgomery, Fredericson, & Delp, 2018).” References: Gijon-Nogueron, G., & Fernandez-Villarejo, M. (2015). Risk Factors and Protective Factors for Lower-Extremity Running Injuries A Systematic Review. J Am Podiatr Med Assoc, 105(6), 532-540. doi: 10.7547/14-069.1 Bredeweg, S. W., Kluitenberg, B., Bessem, B., & Buist, I. (2013). Differences in kinetic variables between injured and noninjured novice runners: a prospective cohort study. J Sci Med Sport, 16(3), 205-210. doi: 10.1016/j.jsams.2012.08.002 Davis, I. S., Bowser, B. J., & Mullineaux, D. R. (2016). Greater vertical impact loading in female runners with medically diagnosed injuries: a prospective investigation. 50(14), 887-892. doi: 10.1136/bjsports-2015-094579 Cheung, R. T. H., & Davis, I. S. (2011). Landing pattern modification to improve patellofemoral pain in runners: a case series. J Orthop Sports Phys Ther, 41(12), 914-919. Diebal, A. R., Gregory, R., Alitz, C., & Gerber, J. P. (2012). Forefoot running improves pain and disability associated with chronic exertional compartment syndrome. Am J Sports Med, 40(5), 1060-1067. Worp, H. V. D., Vrielink, J. W., & Bredeweg, S. W. (2016). Do runners who suffer injuries have higher vertical ground reaction forces than those who remain injury-free? A systematic review and meta-analysis. Br J Sports Med, 50(8), 450–457. Yong, J. R., Silder, A., Montgomery, K. L., Fredericson, M., & Delp, S. L. (2018). Acute changes in foot strike pattern and cadence affect running parameters associated with tibial stress fractures. J Biomech, 76, 1-7. doi: 10.1016/j.jbiomech.2018.05.017 SC29: Lines#275-277: Needs reference. Additional grammatical errors. Consider revising sentence. Response SC29: Thank you for your reminder. We have rewritten the sentence and added a reference as suggested (lines 314-316). “A study found that 17.5% of rearfoot strike runners changed to non-rearfoot strike at +10% of their preferred cadence, and 30% of them changed to non-rearfoot strike at +30% of preferred cadence (Allen, Heisler, Mooney, & Kring, 2016).” Reference: Allen, D. J., Heisler, H., Mooney, J., & Kring, R. (2016). The Effect Of Step Rate Manipulation on Foot Strike Pattern Of Long Distance Runners. Int J Sports Phys Ther, 11(1), 54-63. SC30: Lines#296-297: “Although cadence increased by 5.7% in the CAD group after 12-week retraining, the magnitude was only 170.5 step/min, leaving additional space for increasing cadence” - You have not reported on normative cadence values for runners. Why is there additional space? Also is there a ceiling effect or where increasing cadence can have a negative effect or reduce running economy. One participant increased to a cadence of 193 steps/minute. Response SC30: Thank you for your advice. We have added the normative cadence values for runners in the discussion (lines 335-338). Although we are not sure if there a ceiling effect or where increasing cadence can have a negative effect or reduce running economy, a cadence of approximately 170‐200 step/min is currently considered optimal for maintaining running economy while reducing lower limb stress (Bencsik & Zelei, 2015; Bowersock, Willy, Devita, & Willson, 2016; Kirby, Nugent, Marlow, MacLeod, & Marble, 1989; Quinn, Dempsey, LaRoche, Mackenzie, & Cook, 2019). “Although cadence increased by 5.7% in the CAD group after 12-week retraining, the magnitude was only 170.5 step/min. Most running coaches and clinicians considered a cadence below 170 step/min to be low (Hafer et al., 2015), leaving additional space for increasing cadence.” References: Bencsik, L., & Zelei, A. (2015). A study on the effect of human running cadence based on the bouncing ball model. Paper presented at the 13th Annual International Conference on Dynamical Systems Theory and Applications, Lodz, Poland. Bowersock, C. D., Willy, R. W., Devita, P., & Willson, J. D. (2016). Independent effects of step length and foot strike pattern on tibiofemoral joint forces during running. J Sports Sci, 35(20), 1-9. Kirby, R. L., Nugent, S. T., Marlow, R. W., MacLeod, D. A., & Marble, A. E. (1989). Coupling of cardiac and locomotor rhythms. J Appl Physiol (1985), 66(1), 323-329. doi: 10.1152/jappl.1989.66.1.323 Quinn, T. J., Dempsey, S. L., LaRoche, D. P., Mackenzie, A. M., & Cook, S. B. (2019). Step Frequency Training Improves Running Economy in Well-Trained Female Runners. J Strength Cond Res. doi: 10.1519/jsc.0000000000003206 Hafer, J. F., Brown, A. M., Demille, P., Hillstrom, H. J., & Garber, C. E. (2015). The effect of a cadence retraining protocol on running biomechanics and efficiency: a pilot study. J Sports Sci, 33(7), 724-731. SC31: Lines#309-311: “Increasing the cadence as a retraining method may reduce the risk of impact-related injuries” - Again, needs further link in the introduction to justify this. Response SC31: Thank you for your comments. We have elaborated the running injuries and impact forces in the introduction (lines 53-60), and rewritten relevant text (lines 353-355). Introduction “In a recent review, excessive accumulation of impact peak force in knee joints could lead to overuse injuries (Gijon-Nogueron & Fernandez-Villarejo, 2015). Previous prospective studies found that injured runners had greater vertical loading rates than non-injured runners (Davis et al., 2016; Fu et al., 2017). In addition, other prospective studies showed that runners with patellofemoral pain (Cheung & Davis, 2011) and chronic exertional compartment syndrome (Diebal et al., 2012) exhibited lower impact loading after their pain and disability typically associated with these injuries reduced.” “As the close relationship between impact forces and running injuries, increasing the cadence as a retraining method may reduce the risk of some impact-related injuries.” References: Gijon-Nogueron, G., & Fernandez-Villarejo, M. (2015). Risk Factors and Protective Factors for Lower-Extremity Running Injuries A Systematic Review. J Am Podiatr Med Assoc, 105(6), 532-540. doi: 10.7547/14-069.1 Bredeweg, S. W., Kluitenberg, B., Bessem, B., & Buist, I. (2013). Differences in kinetic variables between injured and noninjured novice runners: a prospective cohort study. J Sci Med Sport, 16(3), 205-210. doi: 10.1016/j.jsams.2012.08.002 Davis, I. S., Bowser, B. J., & Mullineaux, D. R. (2016). Greater vertical impact loading in female runners with medically diagnosed injuries: a prospective investigation. 50(14), 887-892. doi: 10.1136/bjsports-2015-094579 Cheung, R. T. H., & Davis, I. S. (2011). Landing pattern modification to improve patellofemoral pain in runners: a case series. J Orthop Sports Phys Ther, 41(12), 914-919. Diebal, A. R., Gregory, R., Alitz, C., & Gerber, J. P. (2012). Forefoot running improves pain and disability associated with chronic exertional compartment syndrome. Am J Sports Med, 40(5), 1060-1067. SC32: Lines#310-312: “Meanwhile, the foot became “flatter” when its position was closer to the CoG at the initial contact after training, providing the mechanical explanation for the decreased impact forces” Elaborate on the mechanical explanation. Also, consider the change in vertical oscillation (excursion). Response SC32: Thank you for your comments. We agree with you point and have revised this sentence (355-358). The mechanical explanation means that the decreased impact forces may due to the decreased foot angle and vertical excurison of the CoG. “Meanwhile, the foot became “flatter” when its position was closer to the CoG at the initial contact after training, and the vertical excurison of the CoG was smaller during stance, providing the mechanical explanation for the decreased impact forces.” SC33: Lines#312-315: “Hence, cadence retraining can lead to the biomechanical adaptation of the entire lower extremity to the running pattern” – I am unsure what you are trying to get across here Response SC33: Thank you for your comments. We have rewritten this sentence to make it clearer (lines 359-360). “Hence, cadence retraining can lead to lower extremity biomechanical changes.” SC34: REFERENCES. The references have some consistent errors. Consider removing DOI unless required. Lines#331 and 408 have minor author errors. Response SC34: Thank you for your advice. Reference in line 408 has been removed. Meanwhile, we have corrected other errors (line 383). SC35: Table 1: Elaborate or use key/legends groups (CAD and COG). Label standard definitions. Response SC35: Thank you for your advice. As suggested, we have added legend groups (CAD and COG) along with standard definitions. Table 1 Demographics for participants () Group First visit (n) Second visit (n) Age (years) Height (cm) Weight (kg) Weekly mileage (km) CAD 15 12 23.6±7.5 174.8±4.4 71.8±4.9 23.3±3.3 CON 15 12 23.7±1.2 175.5±5.1 70.8±7.3 22.9±4.3 Note: CAD, cadence retraining group; CON, control group. SC36: Table 2: Consider using a line chart to illustrate. Response SC36: Thank you for your advice. We have changed the table into a line chart (Figure 2). Figure 2 Cadence retraining protocol SC37: Table 3: Variables are not labelled on the chart and it is difficult to refer to the note below. Consider full label on the table and in landscape mode. Needs consistent use of decimal places. Need to label standard deviations. Response SC37: Thank you for your advice. We have reformed the Table 2 (previous Table 3), including labelling the variables on the chart in landscape mode, consistent using of decimal places (2 digits), and labelling standard deviations. Table 2 Effect of 12-week cadence retraining protocol on joint mechanics () Joint Variables Cadence retraining group Control group Pre-training Post-training Pre-training Post-training Lower extremity stiffness (N/kg/°) 34.34±7.08 38.61±6.51* 38.08±7.35 38.36±5.59 Foot / Ankle Foot angle (°) 18.27±5.59 13.74±2.82* 17.02±6.54 16.97±7.16 Angle at initial contact (°) 8.10±3.91 8.34±3.84 8.45±4.87 9.57±5.28 Maximum dorsiflexion angle during stance (°) 20.10±4.33 20.50±3.91 20.63±3.81 19.74±4.6 Joint range of motion during stance (°) 40.89±5.30 39.76±3.12 39.09±3.87 40.54±3.84 Peak joint moment (N·m/kg) −2.45±0.28 −2.46±0.22 −2.53±0.43 −2.53±0.43 Joint stiffness (N·m/kg/°) 0.22±0.05 0.22±0.04 0.24±0.07 0.25±0.07 Knee Angle at initial contact (°) −10.03±3.96 −11.95±4.27 −10.96±4.05 −11.02±5.61 Maximum flexion angle during stance (°) −38.60±5.00 −36.50±5.45* −37.74±2.78 −37.22±4.42 Joint range of motion during stance (°) 28.47±4.41 25.84±3.43* 27.97±2.47 27.59±3.17 Peak joint moment (N·m/kg) −2.77±0.38 −2.67±0.52 −2.84±0.37 −2.79±0.45 Joint stiffness (N·m/kg/°) 0.02±0.01 0.02±0.01 0.03±0.02 0.03±0.02 Hip Angle at initial contact (°) 23.86±4.41 21.83±8.16 24.71±2.65 25.10±6.32 Maximum flexion angle during stance (°) −14.98±3.27 −14.70±6.27 −14.39±4.06 −13.10±4.41 Joint range of motion during stance (°) 39.58±4.95 37.76±3.08 39.85±3.92 39.57±4.72 Peak joint moment (N·m/kg) 1.43±0.4 1.49±0.42 1.68±0.44 1.75±0.34 Joint stiffness (N·m/kg/°) 0.09±0.04 0.10±0.03 0.08±0.03 0.08±0.03 SC38: Figure 2-5: Need to elaborate abbreviations (eg. POI Lah) Consider expanding CAD and CON Response SC38: Thank you for your advice. As suggested, we have elaborated the abbreviations and expanded CAD and CON. Note: , landing positions of the foot relative to the knee; , landing positions of the foot relative to the hip; POI, point of interest. Note: CAD, cadence retraining group; CON, control group. Note: CAD, cadence retraining group; CON, control group; BW, body weight. Note: CAD, cadence retraining group; CON, control group; CoG, center of gravity, , landing positions of the foot relative to the knee; , landing positions of the foot relative to the hip. "	Here is a paper. Please give your review comments after reading it.
492	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Background Excessive impact peak forces and vertical loading rates are associated with running injuries and have been targeted in gait retraining studies. This study aimed to determine the effects of 12-week cadence retraining on impact peak, vertical loading rates and lower extremity biomechanics during running. Methods Twenty-four healthy male recreational runners were randomised into either a 12-week cadence retraining group (n = 12), which included those who ran with a 7.5% increase in preferred cadence, or a control group, which included those who ran without any changes in cadence (n = 12). Kinematics and ground reaction forces were recorded simultaneously to quantify impact force variables and lower extremity kinematics and kinetics. Results Significantly decreased impact peak (1.86 ± 0.30 BW vs. 1.67 ± 0.27 BW, P = 0.003), average loading rates (91.59 ± 18.91 BW/s vs. 77.31 ± 15.12 BW/s, P = 0.001) and maximum loading rates (108.8 ± 24.5 BW/s vs. 92.8 ± 18.5 BW/s, P = 0.001) were observed in the cadence retraining group. Foot angles (18.27° ± 5.59° vs. 13.74° ± 2.82°, P = 0.003) and vertical velocities of the centre of gravity (CoG) (0.706 ± 0.115 m/s vs. 0.652 ± 0.091 m/s, P = 0.002) significantly decreased in the cadence retraining group at initial contact. In addition, vertical excursions of the CoG (0.077 ± 0.01 m vs. 0.069 ± 0.008 m, P = 0.002) and peak knee flexion angles (38.6° ± 5.0° vs. 36.5° ± 5.5°, P < 0.001) significantly decreased whilst lower extremity stiffness significantly increased (34.34 ± 7.08 kN/m vs. 38.61 ± 6.51 kN/m, P = 0.048) in the cadence retraining group. Conclusion Twelve-week cadence retraining significantly increased the cadence of the cadence retraining group by 5.7%. This increased cadence can effectively reduce impact peak and loading rates and lead to changes in lower extremity biomechanics at initial contact and during stance. This result may potentially decrease the risk of impact-related running injuries.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>Long-distance running is a very popular activity. According to the Chinese Athletics Association Marathon Annual Press Conference, 1,581 marathon events with a total of nearly 5.83 million participants were held in China in 2018 <ns0:ref type='bibr' target='#b21'>(Han 2019)</ns0:ref>. Similarly, 18.1 million runners registered for organised races in the US <ns0:ref type='bibr' target='#b0'>(2019)</ns0:ref>. However, the incidence of running injuries is fairly high <ns0:ref type='bibr' target='#b29'>(Messier et al. 2018)</ns0:ref>. 19.4% to 79.3% of long-distance runners experienced lower extremity injuries <ns0:ref type='bibr' target='#b32'>(van Gent et al. 2007)</ns0:ref>. Amongst these injuries, knee injuries, such as patellofemoral pain, are the most common. Meanwhile, there is data to suggest that injuries to the lower leg have been reported to be just as common as injuries to the knee <ns0:ref type='bibr' target='#b4'>(Buist et al. 2010;</ns0:ref><ns0:ref type='bibr' target='#b12'>Franke et al. 2019)</ns0:ref>.</ns0:p><ns0:p>Training history, anatomical characteristics and running biomechanics are the main risk factors influencing overuse injuries due to running <ns0:ref type='bibr' target='#b24'>(Hreljac 2004</ns0:ref>). Amongst various biomechanical factors, excessive impact peak forces and loading rates are associated with injuries and have been targeted in gait retraining studies <ns0:ref type='bibr' target='#b6'>(Cheung & Davis 2011)</ns0:ref>. In a recent review, excessive accumulation of impact peak forces in knee joints was found to lead to overuse injuries <ns0:ref type='bibr' target='#b16'>(Gijon-Nogueron & Fernandez-Villarejo 2015)</ns0:ref>. Previous prospective studies found that injured runners had greater vertical loading rates than non-injured runners <ns0:ref type='bibr' target='#b7'>(Davis et al. 2016;</ns0:ref><ns0:ref type='bibr' target='#b13'>Fu et al. 2017)</ns0:ref>. Another prospective study showed that runners with patellofemoral pain exhibited lower impact loading after the pain and disability typically associated with these injuries were reduced <ns0:ref type='bibr' target='#b6'>(Cheung & Davis 2011)</ns0:ref>. Impact peak can be influenced by several factors, such as speed <ns0:ref type='bibr' target='#b20'>(Hamill et al. 1983)</ns0:ref>, shoe/surface/slope <ns0:ref type='bibr' target='#b9'>(Dixon et al. 2000;</ns0:ref><ns0:ref type='bibr' target='#b18'>Gottschall & Kram 2005)</ns0:ref>, strike pattern <ns0:ref type='bibr' target='#b33'>(Warne et al. 2017</ns0:ref>) and cadence/step length <ns0:ref type='bibr' target='#b23'>(Hobara et al. 2012)</ns0:ref>. Increasing running cadence at 2.5 m/s or decreasing step length at 4.58 m/s could decrease impact peak and PeerJ reviewing PDF \| (2019:07:39657:2:2:NEW 15 May 2020)</ns0:p><ns0:p>Manuscript to be reviewed vertical loading rates <ns0:ref type='bibr' target='#b23'>(Hobara et al. 2012;</ns0:ref><ns0:ref type='bibr' target='#b31'>Stergiou et al. 2003)</ns0:ref>, and reductions in impact peak were related to decreases in vertical velocity of the centre of gravity (CoG) <ns0:ref type='bibr' target='#b8'>(Derrick et al. 1998)</ns0:ref>.</ns0:p><ns0:p>Other lower extremity variables, such as foot angles at initial contact <ns0:ref type='bibr' target='#b22'>(Heiderscheit et al. 2011)</ns0:ref> and peak joint angles during the stance phase (Dos <ns0:ref type='bibr' target='#b10'>Santos et al. 2016)</ns0:ref>, also showed decreases with increasing cadence. These results indicate that increasing cadence or decreasing step length has an immediate effect on decreasing impact forces and other lower-extremity variables in running.</ns0:p><ns0:p>In regard to cadence retraining, <ns0:ref type='bibr' target='#b19'>Hafer et al. (2015)</ns0:ref> observed significant decreases in loading rates after 6 weeks of cadence retraining with a 10% increase in cadence. However, cadence increased by only 2.4% after retraining, whilst a 10% increase in cadence was prescribed for retraining; no feedback was given as to how well the participants matched their prescribed cadence during retraining. Mobile monitoring was used by <ns0:ref type='bibr' target='#b35'>Willy et al. (2016b)</ns0:ref> to assess adherence to the prescribed cadence during an eight-session cadence retraining with a 7.5% increase in cadence, and significant reductions in maximum and average loading rates were observed after retraining. Nevertheless, an eight-session retraining (13.1 days) is relatively short.</ns0:p><ns0:p>In addition, whether impact peak would decrease after supervised cadence retraining remains unknown. Therefore, a relatively long-term and supervised intervention is needed to evaluate the effects of cadence retaining on impact peak and loading rates. Twelve weeks of gait retraining allows the initial adaptation of musculoskeletal structures to new running patterns <ns0:ref type='bibr' target='#b27'>(McCarthy et al. 2014</ns0:ref>) and may reduce risks of rapid transition <ns0:ref type='bibr' target='#b17'>(Goss & Gross 2012)</ns0:ref>. Meanwhile, previous studies reported significant biomechanics changes after 12-week gait retraining <ns0:ref type='bibr' target='#b27'>(McCarthy et al. 2014;</ns0:ref><ns0:ref type='bibr' target='#b37'>Yang et al. 2020)</ns0:ref>. Increasing one's cadence by over 10% could be metabolically costly, which indicates that considerable increases in cadence are unsustainable and may not be adopted Manuscript to be reviewed by runners <ns0:ref type='bibr' target='#b5'>(Cavanagh & Williams 1982)</ns0:ref>. <ns0:ref type='bibr' target='#b30'>Neal et al. (2018)</ns0:ref> and <ns0:ref type='bibr'>Willy et al. (2016)</ns0:ref> observed certain significant changes after cadence retraining with a 7.5% increase in cadence. As such, we sought to determine whether a relatively small increase in cadence (7.5%) during long-term cadence retraining could significantly reduce impact peak and loading rates.</ns0:p><ns0:p>The present study, therefore, aimed to quantify the effects of a 12-week cadence retraining protocol on impact peak, loading rates and other lower-extremity biomechanical variables. We hypothesised that 12-week cadence retraining would result in remarkably decreased impact peak and loading rates. Additionally, decreases in lower-extremity biomechanics at initial contact and during the stance phase after cadence retraining would be observed.</ns0:p></ns0:div> <ns0:div><ns0:head>Methods</ns0:head></ns0:div> <ns0:div><ns0:head>Participants</ns0:head><ns0:p>Sample size estimation indicated that a minimum sample size of 26 participants was required to achieve a minimum effect size of 0.6. Considering a drop-out rate of 15%-20%, 30 male recreational runners were recruited through online social media, running clubs and flyers.</ns0:p><ns0:p>Participants were randomly assigned to either a cadence retraining group (CAD) or a control group (CON) on the basis of the lottery method of sampling, and 15 participants was included in each group (Table <ns0:ref type='table'>1</ns0:ref>). When the participants first reported to the laboratory, they were required to run on a treadmill for 2 min. A high-speed camera placed next to the treadmill recorded their running patterns. The participants were determined to be rearfoot strikers by checking the foot angle (i.e. the angle between the foot and ground at initial contact) of the dominant leg (the preferred kicking leg) <ns0:ref type='bibr' target='#b13'>(Fu et al. 2017</ns0:ref>) by reviewing the obtained videos frame by frame <ns0:ref type='bibr' target='#b22'>(Heiderscheit et al. 2011)</ns0:ref>. Consequently, all runners were visibly rearfoot strikers. They ran a minimum of 15 km/week for at least 3 months prior to the study. Participants were excluded if they had any lower limb musculoskeletal injuries in the previous 6 months. No significant differences in age, height, weight and weekly mileage were observed between the two groups. </ns0:p></ns0:div> <ns0:div><ns0:head>Experimental protocol</ns0:head><ns0:p>The participants visited the laboratory twice, i.e. at baseline and at the end of the programme.</ns0:p><ns0:p>Prior to data collection, the participants were required to wear uniform clothing, including a vest, socks and shoes, and walk for 2 min and run at 3.33 m/s on a treadmill for 5 min as a warm-up.</ns0:p><ns0:p>Thereafter, a total of 40 markers were placed on the participants, and static calibration was posterior surface of the calcaneus (Fig. <ns0:ref type='figure'>1B</ns0:ref>). In addition, three tracking markers were placed on the thigh and shank. The participants were instructed to run over the ground across a 10 m runway (Fig. <ns0:ref type='figure'>1C</ns0:ref>) at 3.33 m/s during which kinematic and ground reaction force data were captured. The running speed was considered acceptable if the deviation was within 5%. Three successful running trials were collected for each participant.</ns0:p></ns0:div> <ns0:div><ns0:head>Retraining protocol</ns0:head><ns0:p>All of the participants were required to run at their preferred speeds during the cadence retraining protocol <ns0:ref type='bibr' target='#b19'>(Hafer et al. 2015)</ns0:ref>. Running speed and cadence during training were monitored using the commercial running application CODOON © (Chengdu Ledong Information Technology Co., Ltd., China). Each participant received a sport belt bag in which to place their mobile phones during running, and they were instructed to place the bag above their sacrum. The participants were asked to run outdoors three times (30 min/run) at a comfortable speed to determine their preferred speed and cadence. The preferred speed and preferred cadence were the average values obtained from three outdoor trials. Participants in the CAD group were instructed to run with a 7.5% increase in cadence, whereas those in the CON group ran without any change in cadence <ns0:ref type='bibr' target='#b35'>(Willy et al. 2016b)</ns0:ref>. Participants in the retraining group were informed about and given access to a mobile-based metronome application with tempos set to a 7.5% increase in cadence. Figure <ns0:ref type='figure' target='#fig_6'>2</ns0:ref> shows the cadence retraining protocol, which lasted for 12 weeks with three sessions a week and 5-48 min each session <ns0:ref type='bibr' target='#b30'>(Neal et al. 2018)</ns0:ref>. Participants used their preferred running mode, namely, treadmill or over ground, to complete their retraining. The retraining protocol constituted part of the participants' running volume so that their total weekly running volume remained unchanged. After each retraining session, participants could check their average cadence, speed and running volume on the CODOON © running application. They were also required to submit data recorded by the application to the researchers. Participants were excluded if their training protocols were interrupted more than three times or if their cadence did not </ns0:p></ns0:div> <ns0:div><ns0:head>Data processing</ns0:head><ns0:p>The Visual 3D software (v5, C-Motion, Inc., Germantown, MD, USA) was used to compute the 3D kinematic and kinetic variables of the lower extremity during running. Marker trajectories were filtered with a cut-off frequency of 7 Hz via a fourth-order Butterworth low-pass filter <ns0:ref type='bibr' target='#b37'>(Yang et al. 2020)</ns0:ref>. A seven-segment lower extremity model was built via the Visual 3D, and CoG was estimated from this model. Impact force variables included impact peak, maximum Manuscript to be reviewed loading rates and average loading rates. In rearfoot strike runners, impact peak was defined the first peak in the ground reaction force curve (Fig. <ns0:ref type='figure'>3B</ns0:ref>). Loading rate was calculated on the basis of the method described by <ns0:ref type='bibr'>Futrell et al. (2018)</ns0:ref>. In brief, a point of interest (POI) was defined as the first point above 75% of a participant's body weight with an instantaneous loading rate of less than 15 body weight/s. Average (the average slope) and maximum (i.e. the maximum instantaneous slope) loading rates were then calculated from 20% to 80% and from 20% to 100% of the force at POI, respectively (Fig. <ns0:ref type='figure'>3B</ns0:ref>). Kinetic variables included lower extremity stiffness <ns0:ref type='bibr' target='#b26'>(Liu et al. 2006)</ns0:ref>, k leg , as shown in Equation ( <ns0:ref type='formula'>1</ns0:ref>). Kinematic variables of the hip, knee and ankle joints included foot angle (the angle between the foot and ground) at initial contact (Fig. <ns0:ref type='figure'>3A</ns0:ref>) and peak joint extension/dorsiflexion and peak joint flexion/plantar flexion angles during the stance. </ns0:p></ns0:div> <ns0:div><ns0:head>Statistics</ns0:head><ns0:p>The mean and standard deviation for each variable were calculated. Two-way repeated measure ANOVA was used to characterise the effects of training (pre-and post-training) and group (CAD and CON) on each variable. Independent sample and paired t-tests were used as post-hoc tests when a significant interaction was detected to assess potential group effects between CAD and CON and retraining effects pre-and post-training, respectively. The observed effect size ( ) 𝜂 2</ns0:p><ns0:p>was considered in the ANOVA results, and effect size (Cohen's d) was considered in the paired and independent sample t-tests results. The 95% confidence interval (CI) of the differences in group effects was reported. The criterion α level was set to 0.05. All statistical procedures were conducted using SPSS software (Version 20; SPSS, Inc., Chicago, IL, USA).</ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head></ns0:div> <ns0:div><ns0:head>Dropout rate</ns0:head><ns0:p>Thirty participants (15 in the CAD group and 15 in the CON group) completed the pre-training tests on their first visit to the laboratory (Table <ns0:ref type='table'>1</ns0:ref>). However, in the CAD group, one participant was excluded because of insufficient training volume, and two participants withdrew for personal reasons or because their results showed more than three interruptions. In the CON group, two participants were lost to contact, and one participant withdrew for personal reasons.</ns0:p><ns0:p>Overall, 24 participants, 12 in the CAD group and 12 in the CON group, completed the 12-week cadence retraining protocol and reported to the laboratory for post-training tests (Table <ns0:ref type='table'>1</ns0:ref>). No significant difference in average running volumes was observed between the CAD and CON groups (CAD: 23.3±3.3 km/week, CON: 22.9±4.3 km/week).</ns0:p></ns0:div> <ns0:div><ns0:head>Cadence and step length</ns0:head><ns0:p>Figure <ns0:ref type='figure' target='#fig_8'>4A</ns0:ref> shows a significant training × group interaction effect for cadence (P < 0.001, = 𝜂 2 0.867). Specifically, cadence significantly increased by 5.7% (161.3±9. Manuscript to be reviewed 0.011, = 0.259) (Fig. <ns0:ref type='figure' target='#fig_8'>4B</ns0:ref>).</ns0:p><ns0:p>Step length in the CAD group was 4.9% lower (2.39±0.14 m vs. Significant main effects of training were observed for peak knee flexion angle. Specifically, peak knee flexion angle (P = 0.048, = 0.166) was decreased in the CAD group after training (Table <ns0:ref type='table'>𝜂 2</ns0:ref> 2). A significant main effect of training was observed for lower extremity stiffness, which increased in the CAD group after training (P = 0.048, = 0.166) (Table <ns0:ref type='table'>2</ns0:ref>). </ns0:p></ns0:div> <ns0:div><ns0:head>Discussion</ns0:head><ns0:p>This study aimed to determine the effects of 12-week cadence retraining on running biomechanics. Significant reductions in impact peak and loading rates were observed in the CAD group; thus, cadence retraining could decrease running impact force variables. The preferred cadence in the CAD group significantly increased after 12-week cadence retraining, consistent with the results of previous studies conducted by <ns0:ref type='bibr' target='#b19'>Hafer et al. (2015)</ns0:ref> and <ns0:ref type='bibr' target='#b30'>Neal et al. (2018)</ns0:ref>.</ns0:p><ns0:p>However, the average change in preferred cadence in the present study was +5.7% between preand post-training. By contrast, the preferred cadence changes in the studies of <ns0:ref type='bibr' target='#b19'>Hafer et al. (2015)</ns0:ref> and <ns0:ref type='bibr' target='#b30'>Neal et al. (2018)</ns0:ref> were +2.4% and +7.6%, respectively, which were induced by increases of In the present study, impact peak was significantly reduced by 10.2%, which was greater than the 7.6% decrease observed in the study of <ns0:ref type='bibr' target='#b23'>Hobara et al. (2012)</ns0:ref>. Moreover, impact peak in the CAD group was 12% significantly lower than that in the CON group. This decrease may be related to reductions in vertical velocity and vertical excursion of the CoG <ns0:ref type='bibr' target='#b8'>(Derrick et al. 1998)</ns0:ref>.</ns0:p><ns0:p>PeerJ reviewing PDF \| (2019:07:39657:2:2:NEW 15 May 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p><ns0:p>According to the impulse-momentum principle, impulse is equal to the change in the body's momentum. During running, the momentum exchange between the ground and a portion of the body when it comes to a full stop causes an impact peak <ns0:ref type='bibr' target='#b1'>(Addison & Lieberman 2015)</ns0:ref>. In the present study, the vertical velocity of the CoG at initial contact was significantly decreased in the CAD group after retraining, but no difference in t ip was observed between pre-and post-training.</ns0:p><ns0:p>This finding may indicate that the observed decrease in impact peak in the CAD group after retraining may be due to the decreased vertical velocity of the CoG at initial contact after retraining. In addition, <ns0:ref type='bibr' target='#b28'>Mercer et al. (2015)</ns0:ref> recently found that the impact peak of a subtle heel strike was smaller than that of an obvious heel strike. In the present study, foot angles in the CAD group significantly decreased after retraining, thereby indicating a tendency of switching from a rearfoot strike to a non-rearfoot strike. This subtle change in strike pattern induced by an increase in cadence may explain the decrease in impact peak in the CAD group after retraining.</ns0:p><ns0:p>Average loading and maximum loading rates in the CAD group were significantly reduced after retraining, consistent with the findings reported by <ns0:ref type='bibr' target='#b19'>Hafer et al. (2015)</ns0:ref> and <ns0:ref type='bibr' target='#b34'>Willy et al. (2016a)</ns0:ref>. <ns0:ref type='bibr' target='#b25'>Lieberman et al. (2010)</ns0:ref> found that the loading rate was lower in forefoot strikes than that in rearfoot strikes. In the present study, decreased foot angles in the CAD group after retraining slightly altered the strike pattern, which may contribute to reductions in loading rate.</ns0:p><ns0:p>Runners with a history of stress fractures or other types of injuries have higher loading rates than healthy runners <ns0:ref type='bibr' target='#b36'>(Worp et al. 2016;</ns0:ref><ns0:ref type='bibr'>Yong et al. 2018</ns0:ref>). In addition, runners with high loading rates are more likely to develop injuries compared with those with low loading rates <ns0:ref type='bibr' target='#b3'>(Bredeweg et al. 2013;</ns0:ref><ns0:ref type='bibr' target='#b7'>Davis et al. 2016)</ns0:ref>. Therefore, the decrease in loading rates after retraining may indicate that cadence retraining has a positive effect on reducing the risk of running injuries, such as stress fractures.</ns0:p><ns0:p>PeerJ reviewing PDF \| (2019:07:39657:2:2:NEW 15 May 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p><ns0:p>The main changes in kinematics at touchdown included significant decreases in the foot angle and vertical velocity of the CoG in the CAD group. The foot angle, which reflects the foot strike pattern during running, significantly decreased with increasing cadence <ns0:ref type='bibr' target='#b2'>(Allen et al. 2016;</ns0:ref><ns0:ref type='bibr' target='#b22'>Heiderscheit et al. 2011)</ns0:ref>. A previous study found that 17.5% of rearfoot strike runners switched to non-rearfoot strikes at +10% of their preferred cadence and that 30% of these runners switched to non-rearfoot strikes at +30% of their preferred cadence <ns0:ref type='bibr' target='#b2'>(Allen et al. 2016</ns0:ref>). The foot angle in the CAD group significantly decreased by 4.5°, thereby indicating that cadence retraining causes a subtle change in the foot strike pattern.</ns0:p><ns0:p>The knee joint was highly sensitive to changes in cadence during the stance phase. A negligible increase in cadence induced significant changes in peak knee flexion angle (Dos <ns0:ref type='bibr' target='#b10'>Santos et al. 2016;</ns0:ref><ns0:ref type='bibr' target='#b30'>Neal et al. 2018</ns0:ref>). In the CAD group, cadence increased by 5.7% (9.2 step/min) after retraining decreased the peak knee flexion angle and vertical excursion of the CoG; no significant differences were observed for the hip and ankle joint angles between preand post-training. Thus, reduction in the vertical excursion of the CoG may be mainly attributed to the knee joint. Additionally, lower extremity stiffness significantly increased in the CAD group after training, consistent with previous research obtained by acutely increasing the cadence <ns0:ref type='bibr' target='#b11'>(Farley & González 1996;</ns0:ref><ns0:ref type='bibr' target='#b14'>Giandolini et al. 2013)</ns0:ref>. This finding may be due to the reduced vertical excursion of the CoG during the stance phase induced by the decrease in peak knee flexion angle.</ns0:p><ns0:p>Some limitations of this study must be considered when interpreting the results. Firstly, all of the participants were male; whether females would show the same effects after 12-week cadence retraining remains unclear. Secondly, the running biomechanics obtained from a limited run-up (10 m) with a relatively small area (60 cm × 90 cm) for foot placement may slightly differ from that obtained during outdoor over-ground running. Moreover, long-term retention effects caused by retraining changes were not evaluated in this study. Finally, whether the training effect will maintain when individuals reach fatigue is unknown, and should be considered in future studies.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusion</ns0:head><ns0:p>Twelve-week cadence retraining significantly increased runners' cadence by 5.7%. The increased cadence effectively decreased a number of impact force variables, namely, impact peak, average loading rates and maximum loading rates. Given the close relationship between impact force variables and running injuries, increasing the cadence as a retraining method may reduce the risk of some impact-related injuries. Flattening of the foot at initial contact after training and a low vertical velocity of the CoG at initial contact may provide a mechanical explanation for the observed decrease in impact force variables. Furthermore, the vertical excursion of the CoG decreased, thereby increasing lower extremity stiffness. Hence, cadence retraining can lead to lower extremity biomechanical changes. Manuscript to be reviewed Manuscript to be reviewed </ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>This study was approved by the Institutional Review Board of the Shanghai University of Sports (No. 2017007). Informed written consent was obtained from each participant prior to their participation in this study. *************Insert Table 1 here*********** Instrumentation A 12-camera motion capture system (100 Hz, T40; Vicon Motion Inc., Oxford, UK) was used to collect kinematic data. Ground reaction force data were captured by using two 90 cm × 60 cm × 10 cm force platforms (1,000 Hz, 9287B; Kistler Instruments AG Corp., Winterthur, Switzerland). The kinematics and ground reaction force data were simultaneously collected using the Vicon system. A Photogate system (Witty-Wireless Training Timer, Microgate Corp., Italy) was used to monitor over-ground running speed. Conventional running shoes (Nike Air Zoom Pegasus 34) were used by the participants during the experiments (Fig. 1A). ***********Insert Figure 1 here***********</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>performed. The anatomical locations of the markers were the right/left ilium crest tubercle, right/left posterior superior iliac spine, right/left femur greater trochanter, right/left anterior superior iliac spine, right/left femur lateral epicondyle, right/left femur medial epicondyle, right/left fibula apex of the lateral malleolus, right/left tibia apex of the medial malleolus, right/left head of the fifth metatarsus, right/left head of the first metatarsus and right/left</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head /><ns0:label /><ns0:figDesc>achieve the targeted cadence for 3 weeks since the beginning of training. Weekly group trainings were provided three times a week in the CAD group to ensure compliance, and participants chose one of weekly group training sessions in which to participate on the basis of their schedule. During group training, the participants performed an 8 min warm-up, such as dynamic stretching, under the guidance of the researchers. Then, the participants began to run according to their retraining schedule. The participants did not receive guidance on running techniques because the weekly group training aimed to ensure compliance and the quality of retraining. ***********Insert Figure 2 here***********</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ reviewing PDF \| (2019:07:39657:2:2:NEW 15 May 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head /><ns0:label /><ns0:figDesc>The times from initial contact to impact peak (t ip ), vertical velocities of the CoG at initial contact and vertical excursion of the CoG during the stance phase were also evaluated.***********Insert Figure 3 here************* (1) 𝑘 𝑙𝑒𝑔 = 𝐺𝑅𝐹 𝑖 ∆𝑦 where GRF i is the vertical ground reaction force at the lowest position of the CoG and is the ∆𝑦 maximum vertical displacement of the CoG.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head /><ns0:label /><ns0:figDesc>Figure5Ashows significant training × group interaction effects for impact peak (P = 0.022, = 𝜂 2</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head>𝜂 2 </ns0:head><ns0:label>2</ns0:label><ns0:figDesc>***********Insert Figure 6 here*********** ***********Insert Table 2 here*************</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_7'><ns0:head>10% and 7 .</ns0:head><ns0:label>7</ns0:label><ns0:figDesc>5% in cadence during retraining. Compared to the study by Hafer et al., the current study and the study by Neal et al. achieved better training effects in cadence, which was likely due to the improved supervision in training.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_8'><ns0:head>Figure 4</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Figure 4</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_9'><ns0:head>Figure 5</ns0:head><ns0:label>5</ns0:label><ns0:figDesc>Figure 5</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_10'><ns0:head>Figure 6</ns0:head><ns0:label>6</ns0:label><ns0:figDesc>Figure 6</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='29,42.52,178.87,525.00,401.25' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='30,42.52,204.37,525.00,259.50' type='bitmap' /></ns0:figure> <ns0:note place='foot'>PeerJ reviewing PDF \| (2019:07:39657:2:2:NEW 15 May 2020)</ns0:note> </ns0:body> "	"Point-by-Point Response to Reviewers’ Comments We would like to sincerely thank the reviewers for their helpful recommendations. We have seriously considered all the comments and carefully revised the manuscript accordingly. Revisions are highlighted in the manuscript with computer-generated tracked changes. We feel that the quality of the manuscript has been significantly improved with these modifications and improvements based on the reviewers’ suggestions and comments. We hope our revision will lead to an acceptance of our manuscript for publication in the PeerJ. Response to reviewer: Eoin Doyle Basic reporting SC1: Numerous grammatical errors remain within the manuscript. Some examples are: Line #46: “bene”, Line #63: “to related”, Line #64 “CoG” Abbreviation but on 1st reference. I would recommend a full 3rd party grammatical review prior to publishing. Response SC1: Thank you for your advice. We have corrected those grammatical errors (lines 48-50 & lines 65-66). As suggested, we have invited a third party to conduct a thorough language review. The English review was conducted using a two-stage process, in which two editors reviewed the file. Both editors are native English speakers. For more details, please see the review certification below. “Meanwhile, there is data to suggest that injuries to the lower leg have been reported to be just as common as injuries to the knee (Buist et al. 2010; Franke et al. 2019).” “and reductions in impact peak were related to decreases in vertical velocity of the centre of gravity (CoG) (Derrick et al. 1998).” References Buist I., Bredeweg SW, Bessem B., Mechelen W, Van, Lemmink KAPM, and Diercks RL. 2010. Incidence and risk factors of running-related injuries during preparation for a 4- mile recreational running event. Br J Sports Med 44:598. Francis P, Whatman C, Sheerin K, Hume P, and Johnson MI. 2019. The Proportion of LowerLimb Running Injuries by Gender, Anatomical Location and Specific Pathology: A Systematic Review. J Sports Sci Med 18:21-31. Derrick TR, Hamill J, and Caldwell GE. 1998. Energy absorption of impacts during running at various stride lengths. Med Sci Sports Exerc 30:128-135. SC2: There are some areas that still require reference updates. Line #41: “2017” - Needs updated reference. Line #55-56: These are case series so please indicate accordingly. Response SC2: Thank you for your comments. We have updated the references (lines 44-45 & 58-60). “Similarly, 18.1 million runners registered for organised races in the US (2019).” “Another prospective study showed that runners with patellofemoral pain exhibited lower impact loading after the pain and disability typically associated with these injuries were reduced (Cheung & Davis 2011).” References Running USA. 2019. Annual Reports [Internet]. Available at http://www.runningusa.org/annual-reports. Cheung RTH, and Davis IS. 2011. Landing pattern modification to improve patellofemoral pain in runners: a case series. J Orthop Sports Phys Ther 41:914-919. SC3: The figures and tables are significantly improved. Experimental design I thank the authors for expanding on their methodological design. Response SC3: Thank you for your comments. A few smaller concerns are as follows; SC4: Line #64. Derrick used CoM as a description, which is common with biomechanical studies on running. It would be helpful to see how you determined or estimated CoG. What this from a hip marker? If so, please include. Response SC4: Thank you for your comments. The CoG was not determined from a hip marker. It was estimated from the lower extremity segments (pelvis, thigh, shank, and foot) in Visual 3D. We have added this piece of information in the methods section (lines 181-182). “A seven-segment lower extremity model was built via the Visual 3D, and CoG was estimated from this model.” SC5: Line #124: The anatomical locations of the marker set could be described further in the appendix so that the protocol could be reproduced. Response SC5: Thank you for your comments. We have added this information directly in the methods section (lines 134-139). Specifically, the anatomical locations of the markers were right/left ilium crest tubercle, right/left posterior superior iliac spine, right/left femur greater trochanter, right/left anterior superior iliac spine, right/left femur lateral epicondyle, right/left femur medial epicondyle, right/left fibula apex of lateral malleolus, right/left tibia apex of medial malleolus, right/left head of 5th metatarsus, right/left head of first metatarsus, right/left posterior surface of calcaneus. In addition, there were three tracking markers on thigh and shank, respectively. SC6: Line #140: It remains unclear why 7.5% was chosen. This needs further justification for why it was chosen over 10%. Referencing other studies provided support but you still need clear reasoning. Response SC6: Thank you for your comments. The 7.5% increased cadence has been justified in the introduction section (lines 85-91). Briefly, increasing one’s cadence over 10% was metabolically costly (Cavanagh & Williams 1982). Meanwhile, previous studies (Neal et al. 2018; Willy et al. 2016) observed certain significant changes after cadence retraining with a 7.5% increase in cadence. We thus would like to characterize if a relatively small increase in cadence (7.5%) during long-time cadence retraining could significantly reduce impact peak and loading rates. “Increasing one’s cadence by over 10% could be metabolically costly, which indicates that considerable increases in cadence are unsustainable and may not be adopted by runners (Cavanagh & Williams 1982). Neal et al. (2018) and Willy et al. (2016) observed certain significant changes after cadence retraining with a 7.5% increase in cadence. As such, we sought to determine whether a relatively small increase in cadence (7.5%) during long-term cadence retraining could significantly reduce impact peak and loading rates.” References Cavanagh PR, and Williams KR. 1982. The effect of stride length variation on oxygen uptake during distance running. Med Sci Sports Exerc 14:30-35. Willy RW, Meardon SA, Schmidt A, Blaylock NR, Hadding SA, and Willson JD. 2016b. Changes in tibiofemoral contact forces during running in response to in-field gait retraining. J Sports Sci 34:1602-1611. Neal BS, Barton CJ, Birn-Jeffrey A, Daley M, and Morrissey D. 2018. The effects & mechanisms of increasing running step rate: A feasibility study in a mixed-sex group of runners with patellofemoral pain. Phys Ther Sport 32:244-251. SC7: Line #172. The impact variables are well described. However, in Line #229 “impact forces” is used but it is unclear which variable is being referred to. This theme continues in the results section (Lines #231-234) and discussion (Lines #278) impact forces. Response SC7: Thank you for your comments. We have unified the terminology in this study. “Impact force” variables refer to impact peak and loading rates (lines 182-183). Changes have been made throughout the manuscript. Validity of the findings There is one conclusion that needs further refinement. SC8: Lines #281-283. “This finding indicated that the change in the vertical momentum of CoG was reduced during impact attenuation” - This conclusion appears incorrect. Velocity would not change as it was measured at initial contact. Any decreases would more likely be due to less centre of mass displacement. Response SC8: Thank you for your comments. We have rewritten those sentences to clarify this point (lines 278-285). “According to the impulse–momentum principle, impulse is equal to the change in the body’s momentum. During running, the momentum exchange between the ground and a portion of the body when it comes to a full stop causes an impact peak (Addison & Lieberman 2015). In the present study, the vertical velocity of the CoG at initial contact was significantly decreased in the CAD group after retraining, but no difference in tip was observed between pre- and post-training. This finding may indicate that the observed decrease in impact peak in the CAD group after retraining may be due to the decreased vertical velocity of the CoG at initial contact after retraining.” References Derrick TR, Hamill J, and Caldwell GE. 1998. Energy absorption of impacts during running at various stride lengths. Med Sci Sports Exerc 30:128-135. Addison BJ, and Lieberman DE. 2015. Tradeoffs between impact loading rate, vertical impulse and effective mass for walkers and heel strike runners wearing footwear of varying stiffness. J Biomech 48:1318-1324. Comments for the author SC9: The authors have done a good job answering the reviewers’ concerns and amending the manuscript. This shows a significant improvement in many areas. Readability may be improved with an external grammatical review as indicated above. Response SC9: Thank you for your comments. As we mentioned above, according to your suggestion, a thorough language review was conduct by an invited third party. Response to Reviewer #2 Basic reporting GC1: My overall comments about the Introduction: You haven’t highlighted the need for the study. You just summarized the available literature and It seems like because lower extremity biomechanics has plethora of variables available for assessment, you are picking the one that have not been highlighted. I mentioned it in first review, please justify the need for the study and I feel this still lacks that. Response GC1: Thank you for your comments. As suggested, we have justified and highlighted the need for the study, especially regarding the motivation for a relatively long-time and supervised intervention in the introduction section (lines 71-97). “In regard to long-term cadence retraining, Hafer et al. (2015) observed significant decreases in loading rates after 6 weeks of cadence retraining with a 10% increase in cadence. However, cadence increased by only 2.4% after retraining, whilst a 10% increase in cadence was prescribed for retraining; no feedback was given as to how well the participants matched their prescribed cadence during retraining. Mobile monitoring was used by Willy et al. (2016b) to assess adherence to the prescribed cadence during an eight-session cadence retraining with a 7.5% increase in cadence, and significant reductions in maximum and average loading rates were observed after retraining. Nevertheless, an eight-session retraining (13.1 days) is relatively short. In addition, whether impact peak would decrease after supervised cadence retraining remains unknown. Therefore, a relatively long-term and supervised intervention is needed to evaluate the effects of cadence retaining on impact peak and loading rates. Twelve weeks of gait retraining allows the initial adaptation of musculoskeletal structures to new running patterns (McCarthy et al. 2014) and may reduce risks of rapid transition (Goss & Gross 2012). Meanwhile, previous studies reported significant biomechanics changes after 12-week gait retraining (McCarthy et al. 2014; Yang et al. 2020). Increasing one’s cadence by over 10% could be metabolically costly, which indicates that considerable increases in cadence are unsustainable and may not be adopted by runners (Cavanagh & Williams 1982). Neal et al. (2018) and Willy et al. (2016) observed certain significant changes after cadence retraining with a 7.5% increase in cadence. As such, we sought to determine whether a relatively small increase in cadence (7.5%) during long-term cadence retraining could significantly reduce impact peak and loading rates. The present study, therefore, aimed to quantify the effects of a 12-week cadence retraining protocol on impact peak, loading rates and other lower-extremity biomechanical variables. We hypothesised that 12-week cadence retraining would result in remarkably decreased impact peak and loading rates. Additionally, decreases in lower-extremity biomechanics at initial contact and during the stance phase after cadence retraining would be observed.” References Hafer JF, Brown AM, Demille P, Hillstrom HJ, and Garber CE. 2015. The effect of a cadence retraining protocol on running biomechanics and efficiency: a pilot study. J Sports Sci 33:724-731. Willy RW, Meardon SA, Schmidt A, Blaylock NR, Hadding SA, and Willson JD. 2016b. Changes in tibiofemoral contact forces during running in response to in-field gait retraining. J Sports Sci 34:1602-1611. Cavanagh PR, and Williams KR. 1982. The effect of stride length variation on oxygen uptake during distance running. Med Sci Sports Exerc 14:30-35. Warne JP, Smyth BP, Fagan JOC, Hone ME, Richter C, Nevill AM, Moran KA, and Warrington GD. 2017. Kinetic changes during a six-week minimal footwear and gait-retraining intervention in runners. J Sports Sci 35:1538-1546. Neal BS, Barton CJ, Birn-Jeffrey A, Daley M, and Morrissey D. 2018. The effects & mechanisms of increasing running step rate: A feasibility study in a mixed-sex group of runners with patellofemoral pain. Phys Ther Sport 32:244-251. McCarthy C, Fleming N, Donne B, and Blanksby B. 2014. 12 weeks of simulated barefoot running changes foot-strike patterns in female runners. Int J Sports Med 35:443-450. Goss DL, and Gross MT. 2012. Relationships among self-reported shoe type, footstrike pattern, and injury incidence. US Army Med Dep J:25-30. Yang Y, Zhang X, Luo Z, Wang X, Ye D, and Fu W. 2020. Alterations in Running Biomechanics after 12 Week Gait Retraining with Minimalist Shoes. Int J Environ Res Public Health 17 (10): 818. Experimental design GC2: The calculation of variables and statistical tests performed are valid. My suggestion would be that If you still want to include all your variables (kinematics and kinetics) could you use linear regression techniques to evaluate which variables predict impact forces and loading rates? Response GC2: Thank you for your comments. We have seriously considered your recommendation in GC5 and GC6 regarding rewriting this paper to focus on impact peak and loading rates, and your previous viewpoint regarding reducing the numbers of the variables. We then took your suggestions and removed nearly 2/3 of the variables to only focus on impact peaks and loading rates. Specifically, only 11 variables are now included, namely, impact peak, maximum loading rates, average loading rates, foot angles at initial contact, maximum dorsiflexion angle, maximum knee flexion angle, maximum hip flexion angle, lower extremity stiffness, time from initial contact to impact peak (tip), vertical velocities of CoG at initial contact and vertical excursion of CoG during stance (lines 182-195). Based on these variables, we have rewritten this paper to focus on impact peak and loading rates. As a result, we have found that the 12-week cadence retraining significantly reduced the impact peak and loading rates. In addition, thank you very much for your suggestion on linear regression techniques. As the purpose of this study was mainly to evaluate the effects of cadence retraining on impact variables, however, performing linear regression techniques to evaluate which variables predict impact forces and loading rates is now being considered in another manuscript. “Impact force variables included impact peak, maximum loading rates and average loading rates. In rearfoot strike runners, impact peak was defined the first peak in the ground reaction force curve (Fig. 3B). Loading rate was calculated on the basis of the method described by Futrell et al. (2018). In brief, a point of interest (POI) was defined as the first point above 75% of a participant’s body weight with an instantaneous loading rate of less than 15 body weight/s. Average (the average slope) and maximum (i.e. the maximum instantaneous slope) loading rates were then calculated from 20% to 80% and from 20% to 100% of the force at POI, respectively (Fig. 3B). Kinetic variables included lower extremity stiffness (Liu et al. 2006), kleg, as shown in Equation (1). Kinematic variables of the hip, knee and ankle joints included foot angle (the angle between the foot and ground) at initial contact (Fig. 3A) and peak joint extension/dorsiflexion and peak joint flexion/plantar flexion angles during the stance. The times from initial contact to impact peak (tip), vertical velocities of the CoG at initial contact and vertical excursion of the CoG during the stance phase were also evaluated.” References Futrell EE, Jamison ST, Tenforde AS, and Davis IS. 2018. Relationships between Habitual Cadence, Footstrike, and Vertical Loadrates in Runners. Med Sci Sports Exerc. Liu Y, Peng CH, Wei SH, Chi JC, Tsai FR, and Chen JY. 2006. Active leg stiffness and energy stored in the muscles during maximal counter movement jump in the aged. J Electromyogr Kinesiol 16:342-351. Validity of the findings GC3: The Discussion revolves around Impact forces, Loading rates, foot angle and some kinematic variables. In my opinion, it should focus more on mechanisms of changes and differences between the two groups. There is no mention about changes in control group and the differences seen with CAD group. Response GC3: Thank you for your comments. On one hand, we have focused more on mechanistically explaining the effects of cadence retraining on the variables and added the differences in impact peak between two groups in the discussion (lines 274-285 & lines 310-320). On the other hand, as there were no significant differences in control group between pre- and post-retraining, we didn’t include the changes in control group. In addition, we have modified the discussion based on your specific comments. Please also refer to our Response SC31-SC34. “In the present study, impact peak was significantly reduced by 10.2%, which was greater than the 7.6% decrease observed in the study of Hobara et al. (2012). Moreover, impact peak in the CAD group was 12% significantly lower than that in the CON group. This decrease may be related to reductions in vertical velocity and vertical excursion of the CoG (Derrick et al. 1998). According to the impulse–momentum principle, impulse is equal to the change in the body’s momentum. During running, the momentum exchange between the ground and a portion of the body when it comes to a full stop causes an impact peak (Addison & Lieberman 2015). In the present study, the vertical velocity of the CoG at initial contact was significantly decreased in the CAD group after retraining, but no difference in tip was observed between pre- and post-training. This finding may indicate that the observed decrease in impact peak in the CAD group after retraining may be due to the decreased vertical velocity of the CoG at initial contact after retraining.” “The knee joint was highly sensitive to changes in cadence during the stance phase. A negligible increase in cadence induced significant changes in peak knee flexion angle (Dos Santos et al. 2016; Neal et al. 2018). In the CAD group, cadence increased by 5.7% (9.2 step/min) after retraining decreased the peak knee flexion angle and vertical excursion of the CoG; no significant differences were observed for the hip and ankle joint angles between pre- and post-training. Thus, reduction in the vertical excursion of the CoG may be mainly attributed to the knee joint. Additionally, lower extremity stiffness significantly increased in the CAD group after training, consistent with previous research obtained by acutely increasing the cadence (Farley & González 1996; Giandolini et al. 2013). This finding may be due to the reduced vertical excursion of the CoG during the stance phase induced by the decrease in peak knee flexion angle.” References Hobara H, Sato T, Sakaguchi M, Sato T, and Nakazawa K. 2012. Step frequency and lower extremity loading during running. Int J Sports Med 33:310-313. Derrick TR, Hamill J, and Caldwell GE. 1998. Energy absorption of impacts during running at various stride lengths. Med Sci Sports Exerc 30:128-135. Addison BJ, and Lieberman DE. 2015. Tradeoffs between impact loading rate, vertical impulse and effective mass for walkers and heel strike runners wearing footwear of varying stiffness. J Biomech 48:1318-1324. Dos Santos AF, Nakagawa TH, Nakashima GY, Maciel CD, and Serrao F. 2016. The Effects of Forefoot Striking, Increasing Step Rate, and Forward Trunk Lean Running on Trunk and Lower Limb Kinematics and Comfort. Int J Sports Med 37:369-373. Neal BS, Barton CJ, Birn-Jeffrey A, Daley M, and Morrissey D. 2018. The effects & mechanisms of increasing running step rate: A feasibility study in a mixed-sex group of runners with patellofemoral pain. Phys Ther Sport 32:244-251. Farley CT, and González O. 1996. Leg stiffness and stride frequency in human running. J Biomech 29:181-186. Giandolini M, Arnal PJ, Millet GY, Peyrot N, Samozino P, Dubois B, and Morin JB. 2013. Impact reduction during running: efficiency of simple acute interventions in recreational runners. Eur J Appl Physiol 113:599-609. GC4: I would recommend rewriting the limitations. Response GC4: Thank you for your comments. We have rewritten the limitations (lines 321-330). “Some limitations of this study must be considered when interpreting the results. Firstly, all of the participants were male; whether females would show the same effects after 12-week cadence retraining remains unclear. Secondly, the running biomechanics obtained from a limited run-up (10 m) with a relatively small area (60 cm × 90 cm) for foot placement may slightly differ from that obtained during outdoor over-ground running. Moreover, long-term retention effects caused by retraining changes were not evaluated in this study. Finally, whether the training effect will maintain when individuals reach fatigue is unknown, and should be considered in future studies.” GC5: The conclusion was strong and focused on the big picture points from the study and that is why I would recommend rewriting the paper to focus on the changes in impact forces, loading rates rather than including all the 30 variables. Response GC5: Thank you for your comments. We have seriously considered your advice regarding rewriting this paper to focus on impact peak and loading rates, and your previous point regarding reducing the numbers of the variables. Therefore, we took this suggestion and removed nearly 2/3 of our variables to focus on impact peaks and loading rates. Specifically, on 11 variables are now included, namely, impact peak, maximum loading rates, average loading rates, foot angles at initial contact, maximum dorsiflexion angle, maximum knee flexion angle, maximum hip flexion angle, lower extremity stiffness, time from initial contact to impact peak (tip), vertical velocities of CoG at initial contact, and vertical excursion of CoG during stance. Based on these variables, we have rewritten this paper to focus on impact peak and loading rates. As a result, we have found that the 12-week cadence retraining significantly reduced the impact peak and loading rates. Please refer to our revised manuscript (lines 182-195). “Impact force variables included impact peak, maximum loading rates and average loading rates. In rearfoot strike runners, impact peak was defined the first peak in the ground reaction force curve (Fig. 3B). Loading rate was calculated on the basis of the method described by Futrell et al. (2018). In brief, a point of interest (POI) was defined as the first point above 75% of a participant’s body weight with an instantaneous loading rate of less than 15 body weight/s. Average (the average slope) and maximum (i.e. the maximum instantaneous slope) loading rates were then calculated from 20% to 80% and from 20% to 100% of the force at POI, respectively (Fig. 3B). Kinetic variables included lower extremity stiffness (Liu et al. 2006), kleg, as shown in Equation (1). Kinematic variables of the hip, knee and ankle joints included foot angle (the angle between the foot and ground) at initial contact (Fig. 3A) and peak joint extension/dorsiflexion and peak joint flexion/plantar flexion angles during the stance. The times from initial contact to impact peak (tip), vertical velocities of the CoG at initial contact and vertical excursion of the CoG during the stance phase were also evaluated.” References Futrell EE, Jamison ST, Tenforde AS, and Davis IS. 2018. Relationships between Habitual Cadence, Footstrike, and Vertical Loadrates in Runners. Med Sci Sports Exerc. Liu Y, Peng CH, Wei SH, Chi JC, Tsai FR, and Chen JY. 2006. Active leg stiffness and energy stored in the muscles during maximal counter movement jump in the aged. J Electromyogr Kinesiol 16:342-351. Comments for the author GC6: Thank you for your resubmission and for addressing the comments. It is more clear now with what the study intends to do but I am still not sure about the need to include 30 variables (kinematics and kinetics) to evaluate the changes of a 12 week cadence retraining program. In your Introduction, you suggest “insufficient evaluation of variables and small sample size of previous studies” as the need to perform this study but your study also has a small sample size and you did not address why all the variables you included in your study are important to evaluate. I have made specific comments below but I would recommend the focus of the paper be strictly on the kinetic variables (Impact forces and loading rates and how CoG and foot angle play a role to support your findings). You should also highlight your 12 week retraining program and how the compliance tracking and meeting participants was the novel part in this study. Response GC6: Thank you for your comments. We have removed the point in the introduction about insufficient evaluation of variables and small sample size of previous studies. Regarding the 30 variables, as mentioned in GC5, we have removed nearly 2/3 of our variables to only focus on impact peak and loading rates. Currently, only 11 variables are now included (lines 181-194, please refer to GC5). Based on these variables, we have rewritten this paper to focus on impact peak and loading rates. As a result, we have found that the 12-week cadence retraining significantly reduced the impact peak and loading rates. In terms of the 12-week retraining program, we have highlighted the need for the 12-week retraining program in the introduction section (lines 82-85). Meanwhile, the compliance tracking (weekly group trainings) was highlighted in the methods section (lines 167-174). “Twelve weeks of gait retraining allows the initial adaptation of musculoskeletal structures to new running patterns (McCarthy et al. 2014) and may reduce risks of rapid transition (Goss & Gross 2012). Meanwhile, previous studies reported significant biomechanics changes after 12-week gait retraining (McCarthy et al. 2014; Yang et al. 2020).” “Weekly group trainings were provided three times a week in the CAD group to ensure compliance, and participants chose one of weekly group training sessions in which to participate on the basis of their schedule. During group training, the participants performed an 8 min warm-up, such as dynamic stretching, under the guidance of the researchers. Then, the participants began to run according to their retraining schedule. The participants did not receive guidance on running techniques because the weekly group training aimed to ensure compliance and the quality of retraining.” References McCarthy C, Fleming N, Donne B, and Blanksby B. 2014. 12 weeks of simulated barefoot running changes foot-strike patterns in female runners. Int J Sports Med 35:443-450. Goss DL, and Gross MT. 2012. Relationships among self-reported shoe type, footstrike pattern, and injury incidence. US Army Med Dep J:25-30. Yang Y, Zhang X, Luo Z, Wang X, Ye D, and Fu W. 2020. Alterations in Running Biomechanics after 12 Week Gait Retraining with Minimalist Shoes. Int J Environ Res Public Health 17 (10): 818. Specific Comments: Abstract: SC1: Lines 16-17: You only focus on Impact forces and vertical loading rates and then lines 18-19 you talk about Impact forces and lower extremity biomechanics. I would recommend being consistent both the times. Response SC1: Sorry about the confusion it caused. We have changed the expression to clarify the meaning (lines 18-20). “This study aimed to determine the effects of 12-week cadence retraining on impact peak, vertical loading rates and lower extremity biomechanics during running.” SC2: Lines 22: Please describe if the control group ran without any changes in cadence. Response SC2: Yes, the control group ran without any changes in cadence, and we have added this piece of information in the methods section (lines 21-24 & lines 154-156). “Twenty-four healthy male recreational runners were randomised into either a 12-week cadence retraining group (n = 12), which included those who ran with a 7.5% increase in preferred cadence, or a control group, which included those who ran without any changes in cadence (n = 12).” “Participants in the CAD group were instructed to run with a 7.5% increase in cadence, whereas those in the CON group ran without any change in cadence (Willy et al. 2016b).” Reference Willy RW, Meardon SA, Schmidt A, Blaylock NR, Hadding SA, and Willson JD. 2016b. Changes in tibiofemoral contact forces during running in response to in-field gait retraining. J Sports Sci 34:1602-1611. SC3: Lines 24: Please mention in the Cadence training group so we know you are talking about this group. If in the results you are including the data for some variables, then you have to include it for all variables especially if all of them are significant. Response SC3: Thank you for your advice. According to your suggestion, we have included the group information and the data in those variables (lines 26-35). “Significantly decreased impact peak (1.86 ± 0.30 BW vs. 1.67 ± 0.27 BW, P = 0.003), average loading rates (91.59 ± 18.91 BW/s vs. 77.31 ± 15.12 BW/s, P = 0.001) and maximum loading rates (108.8 ± 24.5 BW/s vs. 92.8 ± 18.5 BW/s, P = 0.001) were observed in the cadence retraining group. Foot angles (18.27° ± 5.59° vs. 13.74° ± 2.82°, P = 0.003) and vertical velocities of the centre of gravity (CoG) (0.706 ± 0.115 m/s vs. 0.652 ± 0.091 m/s, P = 0.002) significantly decreased in the cadence retraining group at initial contact. In addition, vertical excursions of the CoG (0.077 ± 0.01 m vs. 0.069 ± 0.008 m, P = 0.002) and peak knee flexion angles (38.6° ±5.0° vs. 36.5° ± 5.5°, P < 0.001) significantly decreased whilst lower extremity stiffness significantly increased (34.34 ± 7.08 kN/m vs. 38.61 ± 6.51 kN/m, P = 0.048) in the cadence retraining group.” SC4: Line 31: Grammar, it should be knee range of motion. Response SC4: Thank you. As we have rewritten our paper to focus on impact peak and loading rates, the knee range of motion has been excluded in this revised manuscript (lines 31-35). Meanwhile, we have invited a third party to conduct a thorough language review. The English review was conducted using a two-stage process, in which two editors reviewed the file. Both editors are native English speakers. For more details, please see the review certification below. SC5: Line 33: I would include the % increase in cadence in the sentence. Response SC5: Thank you for your suggestion. We have included the % increase in cadence accordingly (lines 36-37). “Twelve-week cadence retraining significantly increased the cadence of the cadence retraining group by 5.7%.” SC6: Line 34: Grammar, I would recommend using induce not induced. I would also recommend using the word “leads to” instead. Response SC6: Thank you for your comments. As suggested, we have used the word “leads to” instead of “induce” (lines 36-39). “Twelve-week cadence retraining significantly increased the cadence of the cadence retraining group by 5.7%. This increased cadence can effectively reduce impact peak and loading rates and lead to changes in lower extremity biomechanics at initial contact and during stance. This result may potentially decrease the risk of impact-related running injuries.” Introduction: SC7: Lines 41-43: 5.83 million runners vs. 50 million recreational runners Is a huge difference in participation numbers. Can you cite the either the numbers for participation in running events or recreational runners every year for both the countries? Response SC7: Thank you for your comments. We have cited the numbers for participation in running events for both countries (lines 42-45). “According to the Chinese Athletics Association Marathon Annual Press Conference, 1,581 marathon events with a total of nearly 5.83 million participants were held in China in 2018 (Han 2019). Similarly, 18.1 million runners registered for organised races in the US (2019).” References Han T. 2019. Marathon statistics in China. Running USA. 2019. Annual Reports [Internet]. Available at http://www.runningusa.org/annual-reports. SC8: Lines 43-45: I would consider removing the sentence on running has more injuries than other activities, it sounds redundant. You have already provided injury numbers. Response SC8: Thank you for your advice. We have removed this sentence. SC9: Lines 43-46: I would move this sentence to after the Messier reference. In addition, 19.4% to 79.3% of long-distance runners experienced lower extremity injuries (van Gent et al. 2007). Response SC9: Thank you. As suggested, we have moved this sentence to after the Messier reference (lines 45-47). “However, the incidence of running injuries is fairly high (Messier et al. 2018). 19.4% to 79.3% of long-distance runners experienced lower extremity injuries (van Gent et al. 2007).” References Messier SP, Martin DF, Mihalko SL, Ip E, DeVita P, Cannon DW, Love M, Beringer D, Saldana S, Fellin RE et al. 2018. A 2-Year Prospective Cohort Study of Overuse Running Injuries: The Runners and Injury Longitudinal Study (TRAILS). Am J Sports Med 46:2211-2221. van Gent RN, Siem D, van Middelkoop M, van Os AG, Bierma-Zeinstra SM, and Koes BW. 2007. Incidence and determinants of lower extremity running injuries in long distance runners: a systematic review. Br J Sports Med 41:469-480; discussion 480. SC10: Line 48: I would recommend not using the word in recent publications. Instead use literature or there is data to suggest. Response SC10: Thank you for your advice. We have changed the expression. Now, it reads “Meanwhile, there is data to suggest that injuries to the lower leg have been reported to be just as common as injuries to the knee (Buist et al. 2010; Franke et al. 2019)” (lines 48-50) References Buist I, Bredeweg SW, Bessem B, Mechelen W, Van, Lemmink KAPM, and Diercks RL. 2010. Incidence and risk factors of running-related injuries during preparation for a 4-mile recreational running event. Br J Sports Med 44:598. Franke TPC, Backx FJG, and Huisstede BMA. 2019. Running Themselves Into the Ground? Incidence, Prevalence, and Impact of Injury and Illness in Runners Preparing for a Half or Full Marathon. J Orthop Sports Phys Ther 49:518-528. SC11: Line 51: Grammar, I would recommend using influence not impact. Response SC11: As suggest, we have changed “impact” to “influence” (lines 51-52). “Training history, anatomical characteristics and running biomechanics are the main risk factors influencing overuse injuries due to running (Hreljac 2004).” Reference Hreljac A. 2004. Impact and overuse injuries in runners. Med Sci Sports Exerc 36:845. SC12: Line 52-53: I would recommend using gait retraining studies instead of in running retraining strategies. Response SC12: Thank you. As suggested, we have changed “running retraining strategies” to “gait retraining studies” (lines 52-54). “Amongst various biomechanical factors, excessive impact peak forces and loading rates are associated with injuries and have been targeted in gait retraining studies (Cheung & Davis 2011).” Reference Cheung RTH, and Davis IS. 2011. Landing pattern modification to improve patellofemoral pain in runners: a case series. J Orthop Sports Phys Ther 41:914-919. SC13: Lines 57-60: Please rewrite this sentence to indicate specifically what is the point that you are trying to make. Response SC13: Sorry for the confusion. We are trying to make the point that impact peak can be influenced by several factors. This sentence has been rewritten (lines 60-63). “Impact peak can be influenced by several factors, such as speed (Hamill et al. 1983), shoe/surface/slope (Dixon et al. 2000; Gottschall & Kram 2005), strike pattern (Warne et al. 2017) and cadence/step length (Hobara et al. 2012).” References Hamill J, Bates BT, Knutzen KM, and Sawhill JA. 1983. Variations in ground reaction force parameters at different running speeds. Hum Mov Sci 2:47-56. Dixon SJ, Collop AC, and Batt ME. 2000. Surface effects on ground reaction forces and lower extremity kinematics in running. Med Sci Sports Exerc 32:1919-1926. Gottschall JS, and Kram R. 2005. Ground reaction forces during downhill and uphill running. J Biomech 38:445-452. Warne JP, Smyth BP, Fagan JOC, Hone ME, Richter C, Nevill AM, Moran KA, and Warrington GD. 2017. Kinetic changes during a six-week minimal footwear and gait-retraining intervention in runners. J Sports Sci 35:1538-1546. Hobara H, Sato T, Sakaguchi M, Sato T, and Nakazawa K. 2012. Step frequency and lower extremity loading during running. Int J Sports Med 33:310-313. SC14: Lines 60-66: You talk about Impact forces, then you jump in to energy absorption then you talk about Impact forces again till line 70. Line 70-75 you talk about in general all effects of increasing cadence. I would recommend bringing everything together and justifying why all these things are important and how are they related to your study. in my opinion, listing all changes in gait variables doesn’t support your paper, it seems like you are doing a review of literature rather than telling the reader why these variables are important. Response SC14: Thank you for your comments. We have brought immediate effects of cadence changes together. Immediate increase cadence or decrease step length can decrease impact peak and loading rates, which makes it possible to decrease impact and loading rates by cadence retraining. Also, we have rewritten those sentences (lines 63-70). “Increasing running cadence at 2.5 m/s or decreasing step length at 4.58 m/s could decrease impact peak and vertical loading rates (Hobara et al. 2012; Stergiou et al. 2003), and reductions in impact peak were related to decreases in vertical velocity of the centre of gravity (CoG) (Derrick et al. 1998). Other lower extremity variables, such as foot angles at initial contact (Heiderscheit et al. 2011) and peak joint angles during the stance phase (Dos Santos et al. 2016), also showed decreases with increasing cadence. These results indicate that increasing cadence or decreasing step length has an immediate effect on decreasing impact forces and other lower-extremity variables in running.” References Hobara H, Sato T, Sakaguchi M, Sato T, and Nakazawa K. 2012. Step frequency and lower extremity loading during running. Int J Sports Med 33:310-313. Stergiou N, Bates BT, and Kurz MJ. 2003. Subtalar and knee joint interaction during running at various stride lengths. Journal of Sports Medicine & Physical Fitness 43:319-326. Derrick TR, Hamill J, and Caldwell GE. 1998. Energy absorption of impacts during running at various stride lengths. Med Sci Sports Exerc 30:128-135. Heiderscheit BC, Chumanov ES, Michalski MP, Wille CM, and Ryan MB. 2011. Effects of step rate manipulation on joint mechanics during running. Med Sci Sports Exerc 43:296-302. Dos Santos AF, Nakagawa TH, Nakashima GY, Maciel CD, and Serrao F. 2016. The Effects of Forefoot Striking, Increasing Step Rate, and Forward Trunk Lean Running on Trunk and Lower Limb Kinematics and Comfort. Int J Sports Med 37:369-373. SC15: Line 69: Be consistent using terminology: stick to decrease step length or reduced step length. They mean the same thing but it’s important to be consistent. Response SC15: Thank you for your comments. We have changed “reduce” into “decrease” (lines 68-70). “These results indicate that increasing cadence or decreasing step length has an immediate effect on decreasing impact forces and other lower-extremity variables in running.” SC16: Line 76: I would take out the word long term. There is confusion if you are focused on impact forces or the impact phase of running or both. Response SC16: Thank you for your advice. As suggested, we have removed the word “long term”. Meanwhile, this sentence focused on the loading rates (lines 71-72). “In regard to cadence retraining, Hafer et al. (2015) observed significant decreases in loading rates after 6 weeks of cadence retraining with a 10% increase in cadence.” Reference Hafer JF, Brown AM, Demille P, Hillstrom HJ, and Garber CE. 2015. The effect of a cadence retraining protocol on running biomechanics and efficiency: a pilot study. J Sports Sci 33:724-731. SC17: Lines 83-86: I would specifically list how many participants were there in those studies and why you think that is a small number especially considering your study had 12 in the experimental group. Why do you think 6 weeks is a short training intervention time? Do you think it is practically feasible to retrain someone’s gait for more than 6 weeks and is there literature to support that you need more than 6 weeks for gait retraining to occur? Please explain what do you mean by Insufficient evaluation of variables. Please rewrite these three lines. It is a very long sentence and it’s not clear what is that point that you are trying to make. Response SC17: Thank you for your comments. We have removed the sentence that “previous studies have a small number of participants”. In terms of the retraining period, sorry for the confusion, we originally don’t think 6-week is a short training intervention time, but a period of 13.1 days is a relative short intervention time. In addition, we think it is feasible to conduct a 12-week gait retraining, and we have referenced two studies (McCarthy et al. 2014; Yang et al. 2020). In terms of variables, we accepted your advice in GC5 about rewriting this paper to focus on impact peak and loading rates. Thus, we removed the point of insufficient evaluation of variables in previous studies. Also, we removed nearly 2/3 of our variables and rewrote this paper to focus on impact peak and loading rates. We have modified relevant text. For more details, please refer to our revised manuscript (lines 71-85). “In regard to cadence retraining, Hafer et al. (2015) observed significant decreases in loading rates after 6 weeks of cadence retraining with a 10% increase in cadence. However, cadence increased by only 2.4% after retraining, whilst a 10% increase in cadence was prescribed for retraining; no feedback was given as to how well the participants matched their prescribed cadence during retraining. Mobile monitoring was used by Willy et al. (2016b) to assess adherence to the prescribed cadence during an eight-session cadence retraining with a 7.5% increase in cadence, and significant reductions in maximum and average loading rates were observed after retraining. Nevertheless, an eight-session retraining (13.1 days) is relatively short. In addition, whether impact peak would decrease after supervised cadence retraining remains unknown. Therefore, a relatively long-term and supervised intervention is needed to evaluate the effects of cadence retaining on impact peak and loading rates. Twelve weeks of gait retraining allows the initial adaptation of musculoskeletal structures to new running patterns (McCarthy et al. 2014) and may reduce risks of rapid transition (Goss & Gross 2012). Also, previous studies reported significant biomechanics changes after 12-week gait retraining (McCarthy et al. 2014; Yang et al. 2020).” References Hafer JF, Brown AM, Demille P, Hillstrom HJ, and Garber CE. 2015. The effect of a cadence retraining protocol on running biomechanics and efficiency: a pilot study. J Sports Sci 33:724-731. Willy RW, Meardon SA, Schmidt A, Blaylock NR, Hadding SA, and Willson JD. 2016b. Changes in tibiofemoral contact forces during running in response to in-field gait retraining. J Sports Sci 34:1602-1611. McCarthy C, Fleming N, Donne B, and Blanksby B. 2014. 12 weeks of simulated barefoot running changes foot-strike patterns in female runners. Int J Sports Med 35:443-450. Goss DL, and Gross MT. 2012. Relationships among self-reported shoe type, footstrike pattern, and injury incidence. US Army Med Dep J:25-30. Yang Y, Zhang X, Luo Z, Wang X, Ye D, and Fu W. 2020. Alterations in Running Biomechanics after 12 Week Gait Retraining with Minimalist Shoes. Int J Environ Res Public Health 17: pii: E818. SC18: Lines 86-88: Please explain why you need more variables to understand cadence retraining and injury risk. Do you think the variables that you have listed from lines 60-75 are not enough? Are there any variables linked to Injury that these papers don’t talk about? Response SC18: Thank you for your comments. As mentioned above in GC5, we have seriously considered your advice regarding rewriting this paper to focus on impact peak and loading rates, and your previous point regarding reducing the numbers of the variables. Therefore, we took this suggestion and removed nearly 2/3 of our variables to focus on impact peaks and loading rates. Consequently, only 11 variables are now included. Based on these variables, we have rewritten this paper to focus on impact peak and loading rates. As a result, we have found that the 12-week cadence retraining significantly reduced the impact peak and loading rates. Please refer to our revised manuscript (lines 182-195). Besides, we have removed the viewpoint that we needed more variables to understand cadence retraining and injury risk. “Impact force variables included impact peak, maximum loading rates and average loading rates. In rearfoot strike runners, impact peak was defined the first peak in the ground reaction force curve (Fig. 3B). Loading rate was calculated on the basis of the method described by Futrell et al. (2018). In brief, a point of interest (POI) was defined as the first point above 75% of a participant’s body weight with an instantaneous loading rate of less than 15 body weight/s. Average (the average slope) and maximum (i.e. the maximum instantaneous slope) loading rates were then calculated from 20% to 80% and from 20% to 100% of the force at POI, respectively (Fig. 3B). Kinetic variables included lower extremity stiffness (Liu et al. 2006), kleg, as shown in Equation (1). Kinematic variables of the hip, knee and ankle joints included foot angle (the angle between the foot and ground) at initial contact (Fig. 3A) and peak joint extension/dorsiflexion and peak joint flexion/plantar flexion angles during the stance. The times from initial contact to impact peak (tip), vertical velocities of the CoG at initial contact and vertical excursion of the CoG during the stance phase were also evaluated.” References Futrell EE, Jamison ST, Tenforde AS, and Davis IS. 2018. Relationships between Habitual Cadence, Footstrike, and Vertical Loadrates in Runners. Med Sci Sports Exerc. Liu Y, Peng CH, Wei SH, Chi JC, Tsai FR, and Chen JY. 2006. Active leg stiffness and energy stored in the muscles during maximal counter movement jump in the aged. J Electromyogr Kinesiol 16:342-351. SC19: Lines 89: You haven’t talked about why 12 weeks is important. Response SC19: Thank you for your comments. First, the 12-week gait retraining intervention can allow initial adaption musculoskeletal structures to new running patterns, which may reduce the risk from a rapid transition. Then, previous studies have obtained significant biomechanics changes after 12-week gait retraining. Meanwhile, we would like to evaluate the effects of a relatively long-time cadence retraining on impact peak and loading rates. Based on these considerations, we selected the 12-week intervention. We have clarified this point in the introduction section (lines 80-85). “Therefore, a relatively long-term and supervised intervention is needed to evaluate the effects of cadence retaining on impact peak and loading rates. Twelve weeks of gait retraining allows the initial adaptation of musculoskeletal structures to new running patterns (McCarthy et al. 2014) and may reduce risks of rapid transition (Goss & Gross 2012). Also, previous studies reported significant biomechanics changes after 12-week gait retraining (McCarthy et al. 2014; Yang et al. 2020).” References McCarthy C, Fleming N, Donne B, and Blanksby B. 2014. 12 weeks of simulated barefoot running changes foot-strike patterns in female runners. Int J Sports Med 35:443-450. Goss DL, and Gross MT. 2012. Relationships among self-reported shoe type, footstrike pattern, and injury incidence. US Army Med Dep J:25-30. Yang Y, Zhang X, Luo Z, Wang X, Ye D, and Fu W. 2020. Alterations in Running Biomechanics after 12 Week Gait Retraining with Minimalist Shoes. Int J Environ Res Public Health 17 (10):818. Methods: SC20: Lines 98-99: Why did you choose effect size 0.6? Response SC20: Thank you for your comments. Several previous running training studies chose an effect size of 0.3 when calculating the sample size (Fuller et al. 2017; Fuller et al. 2015). Thus, we chose an effect size of 0.6 (double the number) to calculate the sample size. In terms of the participants, the number of subjects finally included in this study was 12 in each group. Nevertheless, in the 12-week gait retraining study by McCarthy et al. (2014), there were 9 participants in the intervention group and 10 participants in the control group. Therefore, considering the number of participants, we believe the effect size of 0.6 in this study is applicable. References Fuller, J. T., Thewlis, D., Tsiros, M. D., Brown, N. A. T., & Buckley, J. D. (2017). Six-week transition to minimalist shoes improves running economy and time-trial performance. J Sci Med Sport, 20(12), 1117-1122. Fuller JT, Thewlis D, Tsiros MD, et al. The long-term effect of minimalist shoes on running performance and injury: design of a randomised controlled trial. BMJ Open 2015; 5(8): e008307. McCarthy, C., Fleming, N., Donne, B., & Blanksby, B. (2014). 12 weeks of simulated barefoot running changes foot-strike patterns in female runners. Int J Sports Med, 35(5), 443-450. doi:10.1055/s-0033-1353215 SC21: Lines 102: Which Simple randomization procedure. Response SC21: Thank you for your comments. We used the lottery method to create a simple random sample. This piece of information has been added (lines 104-106). “Participants were randomly assigned to either a cadence retraining group (CAD) or a control group (CON) on the basis of the lottery method of sampling, and 15 participants was included in each group (Table 1).” SC22: Lines 103-106: Which limb did you calculate the foot angle? Can you include the mean of the foot angle? Response SC22: Thank you for your comments. The foot angle was determined from the dominant leg (the preferred kicking leg) (Fu et al. 2017). In terms of the foot angle, we didn’t calculate the specific value of the foot angle, but determine the participants’ strike pattern (rearfoot strike or non-rearfoot strike) by checking the foot angle (i.e. the angle between the foot and ground at initial contact) via reviewing the obtained videos frame by frame (Heiderscheit et al. 2011). All runners were visibly rearfoot strikers. We have added this information in the methods section (lines 108-112). “The participants were determined to be rearfoot strikers by visibly checking the foot angle (i.e. the angle between the foot and ground at initial contact) of the dominant leg (the preferred kicking leg) (Fu et al. 2017) via reviewing the obtained videos frame by frame (Heiderscheit et al. 2011). Consequently, all runners were visibly rearfoot strikers.” References Heiderscheit BC, Chumanov ES, Michalski MP, Wille CM, and Ryan MB. 2011. Effects of step rate manipulation on joint mechanics during running. Med Sci Sports Exerc 43:296-302. Fu W, Fang Y, Gu Y, Huang L, Li L, and Liu Y. 2017. Shoe cushioning reduces impact and muscle activation during landings from unexpected, but not self-initiated, drops. J Sci Med Sport 20:915-920. SC23: Line 127: What is test vest? Response SC23: Sorry for the confusion. The test vest was the uniform clothing worn by the participant during the experiment. We have rewritten this sentence to clarify this point (lines 132-134). “Prior to data collection, the participants were required to wear uniform clothing, including a vest, socks and shoes, and walk for 2 min and run at 3.33 m/s on a treadmill for 5 min as a warm-up.” SC24: Line 138: Add “protocol” after cadence retraining. Response SC24: The change has been made accordingly (lines 147-148). “All of the participants were required to run at their preferred speeds during the cadence retraining protocol.” SC25: Lines 145-146: how did the runners know they were running with a 7.5% increase in cadence? Response SC25: Thank you for your comments. The runners can check their average cadence during the session on a mobile running application (CODOON©) (lines 163-165). “After each retraining session, participants could check their average cadence, speed and running volume on the CODOON© running application. They were also required to submit data recorded by the application to the researchers.” SC26: Lines 147-149: Rewrite this sentence. Response SC26: As suggested, we have rewritten this sentence (lines 156-158). “Participants in the retraining group were informed about and given access to a mobile-based metronome application with tempos set to a 7.5% increase in cadence.” SC27: Lines 157-163: It is not clear what is the purpose of the weekly retraining group? Do you think it is feasible to expect runners to come in every week and why did you choose dynamic stretching and what muscles were targeted? Response SC27: Thank you for your comments. The purpose of the weekly retraining group is to assure the compliance of participants achieve better retraining intervention. We have added this in the methods section (lines 167-170). We considered it was feasible to expect runners to come in every week and that’s exactly what we did, because all participants lived in the same area as us. With regard to the dynamic stretch, it can well warm up the targeted muscles (i.e. lower limb muscles, such as glute, thigh and calf), and it was less tedious than the static stretch. “Weekly group trainings were provided three times a week in the CAD group to ensure compliance, and participants chose one of weekly group training sessions in which to participate on the basis of their schedule.” SC28: Lines 169: Why did you choose 7 Hz for filtering the markers? Response SC28: Thank you for your comments. The 7 Hz was referenced from another running study of our group, and we did get smoothed curves after filtering the markers with 7 Hz (lines 179-181). “Marker trajectories were filtered with a cut-off frequency of 7 Hz via a fourth-order Butterworth low-pass filter (Yang et al. 2020).” Reference Yang Y, Zhang X, Luo Z, Wang X, Ye D, and Fu W. 2020. Alterations in Running Biomechanics after 12 Week Gait Retraining with Minimalist Shoes. Int J Environ Res Public Health 17(10): 818. Results SC29: Lines 226-Lines 233: I would recommend using the values and changes in cadences and step length in this paragraph. Response SC29: Thank you for your comments. We have added the values and changes in cadences and step length (lines 226-233). “Figure 4A shows a significant training × group interaction effect for cadence (P < 0.001, = 0.867). Specifically, cadence significantly increased by 5.7% (161.3±9.5 step/min vs. 170.5±9.2 step/min) in the CAD group (P < 0.001, Cohen’s d = 3.87) but not in the CON group (P > 0.05) after training. A significant main effect of training was observed for step length, which decreased by 4.1% (2.49±0.16 m vs. 2.39±0.14 m) in the CAD group after training (P = 0.011, = 0.259) (Fig. 4B). Step length in the CAD group was 4.9% lower (2.39±0.14 m vs. 2.51±0.14 m) than that in the CON group after training (P = 0.04, 95%CI [−0.245, −0.006], Cohen’s d = 0.94).” SC30: Line 240: Please change to CAD group “were” significantly. Response SC30: Thank you for your advice. According to the suggestion from the other reviewer, we have unified the terminology in this study. Here, we changed “impact forces” to “impact peak” (lines 239-241) and thus retained the word “was”. “Meanwhile, impact peak in the CAD group was significantly lower than that in the CON group after training (P = 0.038, 95% CI [−0.443, −0.013], Cohen’s d = 0.95).” Discussion: SC31: Lines 278: Change from in the present study was to “were”. Neal et al. observed higher cadence post training (7.5% compared to your study 5.7%). Please talk about why you think there was a difference. Response SC31: Thank you for your advice. We have changed “was” to “were”. In addition, to be honest, we are not sure if there are substantially differences between a 5.7% increase in our study and a 7.6% increase in the study by Neal et al. Thus, we removed relevant sentences to avoid misleading the readers (lines 268-273). “However, the average change in preferred cadence in the present study was +5.7% between pre- and post-training. By contrast, the preferred cadence changes in the studies of Hafer et al. (2015) and Neal et al. (2018) were +2.4% and +7.6%, respectively, which were induced by increases of 10% and 7.5% in cadence during retraining. Compared to the study by Hafer et al., the current study and the study by Neal et al. achieved better training effects in cadence, which was likely due to the improved supervision in training.” References Hafer JF, Brown AM, Demille P, Hillstrom HJ, and Garber CE. 2015. The effect of a cadence retraining protocol on running biomechanics and efficiency: a pilot study. J Sports Sci 33:724-731. Neal BS, Barton CJ, Birn-Jeffrey A, Daley M, and Morrissey D. 2018. The effects & mechanisms of increasing running step rate: A feasibility study in a mixed-sex group of runners with patellofemoral pain. Phys Ther Sport 32:244-251. SC32: Line 305-308: I would consider removing this because your study did not look at Injured runners and this doesn’t support your study. Instead focus on cadence retraining and impact on loading rates and mechanistically explaining how cadence retraining changes these variables. Response SC32: Thank you for your comments. We totally agree with your point that our study did not look at injured runners. Actually, we referenced those studies to indicate that runners with larger loading rates were more likely to get injured. In our study, the loading rates significantly decreased after retraining, and we consider this may indicate cadence retraining may have a positive effect on reducing the risk of some running injuries. We have rewritten those sentences to clarify the meaning (lines 297-301). “In addition, runners with high loading rates are more likely to develop injuries compared with those with low loading rates (Bredeweg et al. 2013; Davis et al. 2016). Therefore, the decrease in loading rates after retraining may indicate that cadence retraining has a positive effect on reducing the risk of running injuries, such as stress fractures.” References Bredeweg SW, Kluitenberg B, Bessem B, and Buist I. 2013. Differences in kinetic variables between injured and noninjured novice runners: a prospective cohort study. J Sci Med Sport 16:205-210. Davis IS, Bowser BJ, and Mullineaux DR. 2016. Greater vertical impact loading in female runners with medically diagnosed injuries: a prospective investigation. 50:887-892. SC33: Lines 320-321: How much was the change in cadence, please be specific. Response SC33: Thank you for your comments. In the CAD group, 5.7% (9.2 step/min) increased in cadence after training. We have added this piece of information in the discussion section (lines 312-315). “In the CAD group, cadence increased by 5.7% (9.2 step/min) after retraining decreased the peak knee flexion angle and vertical excursion of the CoG; no significant differences were observed for the hip and ankle joint angles between pre- and post-training.” SC34: Lines 320-334: I would recommend talking how your study contributed to the literature rather than restating the literature. I would focus more on mechanisms because its discussion section rather than highlighting the literature. Response SC34: Thank you for your comments. We have rewritten those sentences to focus more on mechanisms as suggested (lines 310-320). “The knee joint was highly sensitive to changes in cadence during the stance phase. A negligible increase in cadence induced significant changes in peak knee flexion angle (Dos Santos et al. 2016; Neal et al. 2018). In the CAD group, cadence increased by 5.7% (9.2 step/min) after retraining decreased the peak knee flexion angle and vertical excursion of the CoG; no significant differences were observed for the hip and ankle joint angles between pre- and post-training. Thus, reduction in the vertical excursion of the CoG may be mainly attributed to the knee joint. Additionally, lower extremity stiffness significantly increased in the CAD group after training, consistent with previous research obtained by acutely increasing the cadence (Farley & González 1996; Giandolini et al. 2013). This finding may be due to the reduced vertical excursion of the CoG during the stance phase induced by the decrease in peak knee flexion angle.” References Dos Santos AF, Nakagawa TH, Nakashima GY, Maciel CD, and Serrao F. 2016. The Effects of Forefoot Striking, Increasing Step Rate, and Forward Trunk Lean Running on Trunk and Lower Limb Kinematics and Comfort. Int J Sports Med 37:369-373. Neal BS, Barton CJ, Birn-Jeffrey A, Daley M, and Morrissey D. 2018. The effects & mechanisms of increasing running step rate: A feasibility study in a mixed-sex group of runners with patellofemoral pain. Phys Ther Sport 32:244-251. Farley CT, and González O. 1996. Leg stiffness and stride frequency in human running. J Biomech 29:181-186. Giandolini M, Arnal PJ, Millet GY, Peyrot N, Samozino P, Dubois B, and Morin JB. 2013. Impact reduction during running: efficiency of simple acute interventions in recreational runners. Eur J Appl Physiol 113:599-609. SC35: Lines 336: This is unclear. Do you mean step frequency? That is not magnitude. This is the first time you mention step frequency. Also, there are no norms in the literature to suggest numbers for preferred step frequency. Response SC35: Thank you for your comments. According to your suggestion, we removed this sentence to avoid misunderstanding. SC36: Lines 338: Please explain step by step approach especially after you suggest the 12 week training improved compliance and had a longer retraining phase compared to other studies. Response SC36: Thank you for your comments. As you mentioned in SC35 that there are no norms in the literature to suggest numbers for preferred step frequency, we then removed the first limitation and the subsequent point that “a step-by-step approach could be employed to improve the retraining effects with considerable increase in cadence increase”. Also, we have rewritten the limitation section (lines 321-328). “Some limitations of this study must be considered when interpreting the results. Firstly, all of the participants were male; whether females would show the same effects after 12-week cadence retraining remains unclear. Secondly, the running biomechanics obtained from a limited run-up (10 m) with a relatively small area (60 cm × 90 cm) for foot placement may slightly differ from that obtained during outdoor over-ground running. Moreover, long-term retention effects caused by retraining changes were not evaluated in this study. Finally, whether the training effect will maintain when individuals reach fatigue is unknown, and should be considered in future studies.” "	Here is a paper. Please give your review comments after reading it.
493	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Background Excessive impact peak forces and vertical load rates are associated with running injuries and have been targeted in gait retraining studies. This study aimed to determine the effects of 12-week cadence retraining on impact peak, vertical load rates and lower extremity biomechanics during running. Methods Twenty-four healthy male recreational runners were randomised into either a 12-week cadence retraining group (n = 12), which included those who ran with a 7.5% increase in preferred cadence, or a control group, which included those who ran without any changes in cadence (n = 12). Kinematics and ground reaction forces were recorded simultaneously to quantify impact force variables and lower extremity kinematics and kinetics. Results Significantly decreased impact peak (1.86 ± 0.30 BW vs. 1.67 ± 0.27 BW, P = 0.003), vertical average load rates (91.59 ± 18.91 BW/s vs. 77.31 ± 15.12 BW/s, P = 0.001) and vertical instantaneous load rates (108.8 ± 24.5 BW/s vs. 92.8 ± 18.5 BW/s, P = 0.001) were observed in the cadence retraining group, while no significant differences were observed in the control group. Foot angles (18.27° ± 5.59° vs. 13.74° ± 2.82°, P = 0.003) and vertical velocities of the centre of gravity (CoG) (0.706 ± 0.115 m/s vs. 0.652 ± 0.091 m/s, P = 0.002) significantly decreased in the cadence retraining group at initial contact, but not in the control group. In addition, vertical excursions of the CoG (0.077 ± 0.01 m vs. 0.069 ± 0.008 m, P = 0.002) and peak knee flexion angles (38.6° ± 5.0° vs. 36.5° ± 5.5°, P < 0.001) significantly decreased whilst lower extremity stiffness significantly increased (34.34 ± 7.08 kN/m vs.</ns0:p><ns0:p>38.61 ± 6.51 kN/m, P = 0.048) in the cadence retraining group. However, no significant differences were observed for those variables in the control group. Conclusion Twelveweek cadence retraining significantly increased the cadence of the cadence retraining group by 5.7%. This increased cadence effectively reduced impact peak and vertical average/instantaneous load rates. Given the close relationship between impact force variables and running injuries, increasing the cadence as a retraining method may</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>Long-distance running is a very popular form of physical activity in China and across the world <ns0:ref type='bibr' target='#b7'>(Clermont et al. 2019)</ns0:ref>. According to the Chinese Athletics Association Marathon Annual Press Conference, 5.83 million participants ran in 1,581 marathon events (5k,10k, half and full marathon) in China in 2018 (Chinese Athletics Association). Similarly, 18.1 million runners registered for organised races in the US (Running USA). However, the incidence of running injuries is fairly high <ns0:ref type='bibr' target='#b29'>(Messier et al. 2018)</ns0:ref>. 19.4% to 79.3% of long-distance runners experienced lower extremity injuries <ns0:ref type='bibr' target='#b32'>(van Gent et al. 2007)</ns0:ref>. Amongst these injuries, knee injuries, such as patellofemoral pain, are the most common. Meanwhile, there is data to suggest that injuries to the lower leg have been reported to be just as common as injuries to the knee <ns0:ref type='bibr' target='#b4'>(Buist et al. 2010;</ns0:ref><ns0:ref type='bibr' target='#b13'>Franke et al. 2019)</ns0:ref>.</ns0:p><ns0:p>Training history, anatomical characteristics and running biomechanics are the main risk factors influencing overuse injuries due to running <ns0:ref type='bibr' target='#b24'>(Hreljac 2004</ns0:ref>). Amongst various biomechanical factors, excessive impact peak forces and load rates are associated with injuries and have been targeted in gait retraining studies <ns0:ref type='bibr' target='#b6'>(Cheung & Davis 2011)</ns0:ref>. In a recent review, excessive accumulation of impact peak forces in knee joints was found to lead to overuse injuries <ns0:ref type='bibr' target='#b16'>(Gijon-Nogueron & Fernandez-Villarejo 2015)</ns0:ref>. Previous prospective studies found that injured runners had greater vertical load rates than non-injured runners <ns0:ref type='bibr' target='#b8'>(Davis et al. 2016;</ns0:ref><ns0:ref type='bibr' target='#b14'>Fu et al. 2017)</ns0:ref>. Another prospective study showed that runners with patellofemoral pain exhibited lower impact loading after the pain and disability typically associated with these injuries were reduced <ns0:ref type='bibr' target='#b6'>(Cheung & Davis 2011)</ns0:ref>. Impact peak can be influenced by several factors, such as speed <ns0:ref type='bibr'>(Hamill</ns0:ref> PeerJ reviewing PDF \| (2019:07:39657:3:0:NEW 17 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed <ns0:ref type='bibr'>et al. 1983)</ns0:ref>, shoe/surface/slope <ns0:ref type='bibr' target='#b11'>(Dixon et al. 2000;</ns0:ref><ns0:ref type='bibr' target='#b18'>Gottschall & Kram 2005)</ns0:ref>, strike pattern <ns0:ref type='bibr' target='#b9'>(Davis et al. 2017</ns0:ref>) and cadence/step length <ns0:ref type='bibr' target='#b22'>(Hobara et al. 2012)</ns0:ref>. Increasing running cadence at 2.5 m/s or decreasing step length at 4.58 m/s could decrease impact peak and vertical load rates <ns0:ref type='bibr' target='#b22'>(Hobara et al. 2012;</ns0:ref><ns0:ref type='bibr' target='#b31'>Stergiou et al. 2003)</ns0:ref>, and reductions in impact peak were related to decreases in vertical velocity of the centre of gravity (CoG) <ns0:ref type='bibr' target='#b10'>(Derrick et al. 1998)</ns0:ref>. Other lower extremity variables, such as foot angles at initial contact <ns0:ref type='bibr' target='#b21'>(Heiderscheit et al. 2011</ns0:ref>) and peak joint angles during the stance phase (Dos <ns0:ref type='bibr' target='#b12'>Santos et al. 2016)</ns0:ref>, also showed decreases with increasing cadence. These results indicate that increasing cadence or decreasing step length has an effect on decreasing impact forces and other lower-extremity variables in running.</ns0:p><ns0:p>With regards to cadence retraining, <ns0:ref type='bibr' target='#b19'>Hafer et al. (2015)</ns0:ref> observed significant decreases in load rates after 6 weeks of cadence retraining with a 10% increase in cadence. However, cadence increased by only 2.4% after retraining, whilst a 10% increase in cadence was prescribed for retraining; no feedback was given as to how well the participants matched their prescribed cadence during retraining. Whether impact peak would decrease after supervised cadence retraining remains unknown. Therefore, a relatively long-term and supervised intervention is needed to evaluate the effects of cadence retaining on impact peak and load rates. Twelve weeks of gait retraining allows the initial adaptation of musculoskeletal structures to new running patterns <ns0:ref type='bibr' target='#b25'>(Latorre-Román et al. 2019</ns0:ref>) and may reduce injury risks of gait transition within a short period <ns0:ref type='bibr' target='#b17'>(Goss & Gross 2012)</ns0:ref>. Increasing one's cadence by over 10% could be metabolically costly, which indicates that considerable increases in cadence are unsustainable and may not be adopted by runners <ns0:ref type='bibr' target='#b5'>(Cavanagh & Williams 1982)</ns0:ref>. Mobile monitoring was used by <ns0:ref type='bibr' target='#b34'>Willy et al. (2016b)</ns0:ref> to assess adherence to the prescribed cadence during cadence retraining with a 7.5% increase in cadence, and significant reductions in maximum and average load rates were observed after retraining. As such, we sought to determine whether a relatively small increase in cadence (7.5%) during long-term cadence retraining could significantly reduce impact peak and load rates.</ns0:p><ns0:p>The present study, therefore, aimed to quantify the effects of a 12-week cadence retraining protocol on impact peak, load rates and other lower-extremity biomechanical variables. We hypothesised that 12-week cadence retraining would result in remarkably decreased impact peak and load rates. Additionally, decreases in lower-extremity biomechanics at initial contact and during the stance phase after cadence retraining would be observed.</ns0:p></ns0:div> <ns0:div><ns0:head>Methods</ns0:head></ns0:div> <ns0:div><ns0:head>Participants</ns0:head><ns0:p>Sample size estimation indicated that a minimum sample size of 26 participants was required to achieve a minimum effect size of 0.6. Considering a drop-out rate of 15%-20%, 30 male recreational runners were recruited through online social media, running clubs and flyers.</ns0:p><ns0:p>Participants were randomly assigned to either a cadence retraining group (CAD) or a control group (CON) on the basis of the lottery method of sampling, and 15 participants were included in each group (Table <ns0:ref type='table'>1</ns0:ref>). When the participants first reported to the laboratory, they were required to run on a treadmill for 2 min. A high-speed camera placed next to the treadmill recorded their foot strike patterns. The participants were determined to be rearfoot strikers by checking the foot angle (i.e. the angle between the foot and ground at initial contact) of the dominant leg (the preferred kicking leg) <ns0:ref type='bibr' target='#b14'>(Fu et al. 2017</ns0:ref>) by reviewing the obtained videos frame by frame <ns0:ref type='bibr' target='#b21'>(Heiderscheit et al. 2011)</ns0:ref>. Consequently, all runners were rearfoot strikers. They ran a minimum of 15 km/week for at least 3 months prior to the study. Participants were excluded if they had any lower limb musculoskeletal injuries in the previous 6 months. No significant differences in age, height, weight and weekly mileage were observed between the two groups. This study was approved by the Institutional Review Board of the Shanghai University of Sports (No. 2017007).</ns0:p><ns0:p>Informed written consent was obtained from each participant prior to their participation in this <ns0:ref type='table'>2019:07:39657:3:0:NEW 17 Jul 2020)</ns0:ref> Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Experimental protocol</ns0:head><ns0:p>The participants visited the laboratory twice, at baseline and at the end of the gait retraining program. Prior to data collection, the participants were required to wear uniform clothing, including a vest, socks and shoes, and walk for 2 min and run at 3.33 m/s on a treadmill for 5 min as a warm-up. Thereafter, a total of 40 markers were placed on the participants, and static calibration was performed. The anatomical locations of the markers were the right/left ilium crest tubercle, right/left posterior superior iliac spine, right/left femur greater trochanter, right/left anterior superior iliac spine, right/left femur lateral epicondyle, right/left femur medial epicondyle, right/left fibula apex of the lateral malleolus, right/left tibia apex of the medial malleolus, right/left head of the fifth metatarsals, right/left head of the first metatarsus and right/left posterior surface of the calcaneus (Fig. <ns0:ref type='figure'>1B</ns0:ref>). In addition, three tracking markers were placed on the thigh and shank. The participants were instructed to run over the ground across a 10 m runway (Fig. <ns0:ref type='figure'>1C</ns0:ref>) at 3.33 m/s during which kinematic and ground reaction force data were captured. The running speed was considered acceptable if the deviation was within 5%. Three successful running trials were collected for each participant.</ns0:p></ns0:div> <ns0:div><ns0:head>Retraining protocol</ns0:head><ns0:p>All of the participants were required to run at their preferred speeds during the cadence retraining protocol <ns0:ref type='bibr' target='#b19'>(Hafer et al. 2015)</ns0:ref>. Running speed and cadence during training were monitored using the commercial running application CODOON © (Chengdu Ledong Information Technology Co., Ltd., China). Each participant received a sport belt bag in which to place their mobile phones Manuscript to be reviewed during running, and they were instructed to place the bag above their sacrum. The participants were asked to run outdoors three times (30 min/run) at a comfortable speed to determine their preferred speed and cadence. The preferred speed and preferred cadence were the average values obtained from three outdoor trials. Participants in the CAD group were instructed to run with a 7.5% increase in cadence, whereas those in the CON group ran without any change in cadence <ns0:ref type='bibr' target='#b34'>(Willy et al. 2016b)</ns0:ref>. Participants in the retraining group were informed about and given access to a mobile-based metronome application with tempos set to a 7.5% increase in cadence. Figure <ns0:ref type='figure' target='#fig_5'>2</ns0:ref> shows the cadence retraining protocol, which lasted for 12 weeks with three sessions a week and 5-48 min each session <ns0:ref type='bibr' target='#b30'>(Neal et al. 2018)</ns0:ref>. Participants used their preferred running mode, namely, treadmill or over ground, to complete their retraining. The retraining protocol constituted part of the participants' running volume so that their total weekly running volume remained unchanged. After each retraining session, participants could check their average cadence, speed and running volume on the CODOON © running application. They were also required to submit data recorded by the application to the researchers. Participants were excluded if their training protocols were interrupted more than three times or if their cadence did not </ns0:p></ns0:div> <ns0:div><ns0:head>Data processing</ns0:head><ns0:p>The Visual 3D software (v5, C-Motion, Inc., Germantown, MD, USA) was used to compute the 3D kinematic and kinetic variables of the lower extremity during running. Marker trajectories were filtered with a cut-off frequency of 7 Hz via a fourth-order Butterworth low-pass filter <ns0:ref type='bibr' target='#b35'>(Yang et al. 2020)</ns0:ref>. A seven-segment lower extremity model was built via the Visual 3D, and CoG was estimated from this model. Impact force variables included impact peak, vertical instantaneous load rates (VILR) and vertical average load rates (VALR). In rearfoot strike runners, impact peak was defined the first peak in the ground reaction force curve (Fig. <ns0:ref type='figure'>3B</ns0:ref>).</ns0:p><ns0:p>Load rates was calculated on the basis of the method described by Futrell et al. <ns0:ref type='bibr' target='#b15'>(Futrell et al. 2018)</ns0:ref>. In brief, a point of interest (POI) was defined as the first point above 75% of a participant's body weight with an instantaneous load rate of less than 15 body weight/s. VALR (the average slope) and VILR (i.e. the maximum instantaneous slope) were then calculated from 20% to 80% and from 20% to 100% of the force at POI, respectively (Fig. <ns0:ref type='figure'>3B</ns0:ref>). We also calculated lower extremity stiffness <ns0:ref type='bibr' target='#b27'>(Liu et al. 2006)</ns0:ref>, k leg , as shown in Equation (1). Kinematic variables of the hip, knee and ankle joints included foot angle (the angle between the foot and ground) at initial contact (Fig. <ns0:ref type='figure'>3A</ns0:ref>) and peak joint extension and peak joint flexion angles during the stance. The times from initial contact to impact peak (t ip ), vertical velocities of the CoG at initial contact and vertical excursion of the CoG during the stance phase were also evaluated. </ns0:p></ns0:div> <ns0:div><ns0:head>Statistics</ns0:head><ns0:p>The mean and standard deviation for each variable was calculated. Two-way repeated measure ANOVA was used to characterise the effects of training (pre-and post-training) and group (CAD and CON) on each variable. Independent sample and paired t-tests were used as post-hoc tests when a significant interaction was detected to assess potential group effects between CAD and CON and retraining effects pre-and post-training, respectively. The observed effect size ( ) 𝜂 2</ns0:p><ns0:p>was considered in the ANOVA results, and effect size (Cohen's d) was considered in the paired and independent sample t-tests results. The 95% confidence interval (CI) of the differences in group effects was reported. The criterion α level was set to 0.05. All statistical procedures were conducted using SPSS software (Version 20; SPSS, Inc., Chicago, IL, USA).</ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head></ns0:div> <ns0:div><ns0:head>Dropout rate</ns0:head><ns0:p>Thirty participants (15 in the CAD group and 15 in the CON group) completed the pre-training tests on their first visit to the laboratory ( Manuscript to be reviewed personal reasons or because their results showed more than three interruptions. In the CON group, two participants were lost to contact, and one participant withdrew for personal reasons.</ns0:p><ns0:p>Overall, 24 participants, 12 in the CAD group and 12 in the CON group, completed the 12-week cadence retraining protocol and reported to the laboratory for post-training tests (Table <ns0:ref type='table'>1</ns0:ref>). No significant difference in average running volumes was observed between the CAD and CON groups (CAD: 23.3±3.3 km/week, CON: 22.9±4.3 km/week).</ns0:p></ns0:div> <ns0:div><ns0:head>Cadence and step length</ns0:head><ns0:p>Figure <ns0:ref type='figure' target='#fig_7'>4A</ns0:ref> shows a significant training × group interaction effect for cadence (P < 0.001, = 𝜂 2 0.867). Specifically, cadence significantly increased by 5.7% (161.3±9. <ns0:ref type='table'>2</ns0:ref>, Fig. <ns0:ref type='figure' target='#fig_8'>5</ns0:ref>).</ns0:p><ns0:p>Moreover, vertical excursion of the CoG in the CAD group was significantly lower than that in the CON group after training (P = 0.025, 95% CI [−0.015, −0.001], Cohen's d = 1.03).</ns0:p><ns0:p>Significant main effects of training were observed for peak knee flexion angle. Specifically, peak knee flexion angle (P = 0.048, = 0.166) was decreased in the CAD group after training (Table <ns0:ref type='table'>𝜂 2</ns0:ref> 2). A significant main effect of training was observed for lower extremity stiffness, which increased in the CAD group after training (P = 0.048, = 0.166) (Table <ns0:ref type='table'>2</ns0:ref>). Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Discussion</ns0:head><ns0:p>This study aimed to characterize the effects of a 12-week cadence retraining protocol on impact peak, load rates and other lower-extremity biomechanical variables. Significant reductions in impact peak and load rates were observed in the CAD group. The preferred cadence in the CAD group significantly increased after 12-week cadence retraining, consistent with the results of previous studies conducted by <ns0:ref type='bibr' target='#b19'>Hafer et al. (2015)</ns0:ref> and <ns0:ref type='bibr' target='#b30'>Neal et al. (2018)</ns0:ref>. However, the average change in preferred cadence in the present study was +5.7% between pre-and post-training. By contrast, the preferred cadence changes in the studies of <ns0:ref type='bibr' target='#b19'>Hafer et al. (2015)</ns0:ref> and <ns0:ref type='bibr' target='#b30'>Neal et al. (2018)</ns0:ref> were +2.4% and +7.6%, respectively, which were induced by increases of 10% and 7.5% in In the present study, impact peak was significantly reduced by 10.2% in the CAD group after training, which was greater than the 7.6% decrease observed <ns0:ref type='bibr'>(pre-training vs. post-training)</ns0:ref> in the study of <ns0:ref type='bibr' target='#b22'>Hobara et al. (2012)</ns0:ref>. Moreover, impact peak in the CAD group after training was 12% significantly lower than that in the CON group after training (CAD vs. CON). This decrease may be related to reductions in vertical velocity and vertical excursion of the CoG <ns0:ref type='bibr' target='#b10'>(Derrick et al. 1998)</ns0:ref>. According to the impulse-momentum principle, impulse is equal to the change in the body's momentum. During running, the momentum exchange between the ground and a portion of the body when it comes to a full stop causes an impact peak <ns0:ref type='bibr' target='#b1'>(Addison & Lieberman 2015)</ns0:ref>. In the present study, the vertical velocity of the CoG at initial contact was significantly decreased in the CAD group after retraining, but no difference in t ip was observed between pre-and posttraining. This finding may indicate that the observed decrease in impact peak in the CAD group after retraining may be due to the decreased vertical velocity of the CoG at initial contact after retraining. The foot angle, which reflects the foot strike pattern during running, significantly decreased with increasing cadence <ns0:ref type='bibr' target='#b2'>(Allen et al. 2016;</ns0:ref><ns0:ref type='bibr' target='#b21'>Heiderscheit et al. 2011</ns0:ref>). In addition, a lower impact peak has been found in a smaller foot angle at foot contact with a rearfoot strike <ns0:ref type='bibr' target='#b28'>(Mercer & Horsch 2015)</ns0:ref>. In the present study, the foot angle in the CAD group significantly decreased by 4.5° after retraining, which may partially explain the decrease in impact peak in the CAD group after retraining.</ns0:p><ns0:p>VALR and VILR in the CAD group were significantly reduced after retraining, consistent with the findings reported by <ns0:ref type='bibr' target='#b19'>Hafer et al. (2015)</ns0:ref> and <ns0:ref type='bibr' target='#b33'>Willy et al. (2016a)</ns0:ref>. <ns0:ref type='bibr' target='#b26'>Lieberman et al. (2010)</ns0:ref> found that the load rates was lower in forefoot strikes than that in rearfoot strikes. In the present study, decreased foot angles in the CAD group after retraining slightly altered the strike pattern, which may contribute to reductions in load rates. Injured runners were reported to have higher load rates than non-injured runners in a prospective investigation <ns0:ref type='bibr' target='#b8'>(Davis et al. 2016)</ns0:ref>. Therefore, the decrease in load rates after retraining indicate that cadence retraining may reduce the risk of running injuries, such as stress fractures.</ns0:p><ns0:p>The knee joint was highly sensitive to changes in cadence during the stance phase <ns0:ref type='bibr' target='#b21'>(Heiderscheit et al. 2011)</ns0:ref>. 5.7% (9.2 step/min) increase in cadence induced significant changes in peak knee flexion angle (Dos <ns0:ref type='bibr' target='#b12'>Santos et al. 2016;</ns0:ref><ns0:ref type='bibr' target='#b30'>Neal et al. 2018)</ns0:ref>. The increase in cadence in the CAD group decreased the peak knee flexion angle and vertical excursion of the CoG; no significant differences were observed for the hip and ankle joint angles between pre-and posttraining. Additionally, lower extremity stiffness significantly increased in the CAD group after training, which may be due to the reduced vertical excursion of the CoG during the stance phase induced by the decrease in peak knee flexion angle.</ns0:p><ns0:p>Some limitations of this study must be considered when interpreting the results. Firstly, all of the participants were male; whether females would show the same effects after 12-week cadence retraining remains unclear. Secondly, the running biomechanics obtained from a limited run-up (10 m) with a relatively small area (60 cm × 90 cm) for foot placement may slightly differ from that obtained during outdoor over-ground running. Moreover, long-term retention effects caused by retraining changes were not evaluated in this study. Finally, whether the training effect will maintain when individuals reach fatigue is unknown, and should be considered in future studies.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusion</ns0:head><ns0:p>Twelve-week cadence retraining significantly increased runners' cadence by 5.7%. The increased cadence effectively decreased a number of impact force variables, namely, impact peak, VALR and VILR. Given the close relationship between impact force variables and running injuries, increasing the cadence as a retraining method may reduce the risk of some impactrelated injuries. A decrease in foot angle at initial contact after training may provide a Manuscript to be reviewed </ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>**********Insert Table 1 here*********** Instrumentation A 12-camera motion capture system (100 Hz, T40; Vicon Motion Inc., Oxford, UK) was used to collect kinematic data. Ground reaction force data were captured by using two 90 cm × 60 cm × 10 cm force platforms (1,000 Hz, 9287B; Kistler Instruments AG Corp., Winterthur, Switzerland). The kinematics and ground reaction force data were simultaneously collected using the Vicon system. A Photogate system (Witty-Wireless Training Timer, Microgate Corp., Italy) was used to monitor over-ground running speed. Conventional running shoes (Nike Air Zoom Pegasus 34) were used by the participants during the experiments (Fig. 1A). ***********Insert Figure 1 here*********** PeerJ reviewing PDF \| (</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ reviewing PDF \| (2019:07:39657:3:0:NEW 17 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head /><ns0:label /><ns0:figDesc>achieve the targeted cadence for 3 weeks since the beginning of training. Weekly group trainings were provided three times a week in the CAD group to ensure compliance, and participants chose one of weekly group training sessions in which to participate on the basis of their schedule. During group training, the participants performed an 8 min warm-up, such as dynamic stretching, under the guidance of the researchers. Then, the participants began to run according to their retraining schedule. The participants did not receive guidance on running techniques PeerJ reviewing PDF \| (2019:07:39657:3:0:NEW 17 Jul 2020) Manuscript to be reviewed because the weekly group training aimed to ensure compliance and the quality of retraining. ***********Insert Figure 2 here***********</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ reviewing PDF \| (2019:07:39657:3:0:NEW 17 Jul 2020) Manuscript to be reviewed ***********Insert Figure 3 here************* (1) 𝑘 𝑙𝑒𝑔 = 𝐺𝑅𝐹 𝑖 ∆𝑦 where GRF i is the vertical ground reaction force at the lowest position of the CoG and is the ∆𝑦 maximum vertical displacement of the CoG.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head /><ns0:label /><ns0:figDesc>Figure5Ashows significant training × group interaction effects for impact peak (P = 0.022, = 𝜂 2</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head>𝜂 2 </ns0:head><ns0:label>2</ns0:label><ns0:figDesc>***********Insert Figure 5 here************* PeerJ reviewing PDF \| (2019:07:39657:3:0:NEW 17 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head /><ns0:label /><ns0:figDesc>cadence during retraining. Compared to the study by Hafer et al. (2015), the cadence after training in the current study and the study by Neal et al. (2018) was closer to the prescribed cadence, which was likely due to the enhanced supervision in training. In addition, real-time feedback was provided in Neal et al.'s study, which may lead to the differences in cadence improvements between Neal et al.'s and present study.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_7'><ns0:head>Figure 4</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Figure 4</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_8'><ns0:head>Figure 5</ns0:head><ns0:label>5</ns0:label><ns0:figDesc>Figure 5</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='30,42.52,178.87,525.00,401.25' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='31,42.52,204.37,525.00,259.50' type='bitmap' /></ns0:figure> </ns0:body> "	"Point-by-Point Response to Reviewers’ Comments We would like to sincerely thank the reviewers for their helpful recommendations. We have seriously considered all the comments and carefully revised the manuscript accordingly. Revisions are highlighted in the manuscript using a red font to indicate where changes have taken place. We feel that the quality of the manuscript has been significantly improved with these modifications and improvements based on the reviewers’ suggestions and comments. We hope our revision will lead to an acceptance of our manuscript for publication in the PeerJ. Reviewer 2 (Anonymous) Basic reporting The authors have done a good job answering the reviewers’ concerns and making changes to the manuscript. There is an improvement in many areas especially with the Introduction to focus on kinetics (impact forces and loading rates) and highlighting why they chose the 7.5% increase in cadence and the 12-week cadence retraining period. I am still concerned about including other lower extremity variables (kinematics) in the paper. Please change average and maximum loading rates to vertical instantaneous load rates (VILR) and vertical average load rates (VALR) everywhere in the manuscript. I am also concerned about the discussion section as some of the points have still haven't been changed or explained from previous submissions. Response GC: Thank you for your positive comments. Regarding your concern about including other lower extremity variables (kinematics), the following are our considerations. On the one hand, since one of the main purposes of the current study is to determine the effects of 12-week cadence retraining on lower extremity biomechanics, only reporting impact-related variables seems not enough to fully explore the changes in lower extremity biomechanics after retraining. For example, in this study, a decrease in foot angle at initial contact after training and a low vertical velocity of the CoG at initial contact may provide a mechanical explanation for the observed decrease in impact force variables (lines 282-296). On the other hand, as we mentioned in the previous point-by-point response letter, we removed nearly 2/3 of the variables to largely focus on impact peaks and load rates. Now only 6 kinematic variables are included, namely, foot angles at initial contact, maximum dorsiflexion angle, maximum knee flexion angle, maximum hip flexion angle, vertical velocities of CoG at initial contact and vertical excursion of CoG during stance. From our understanding and representative running-related biomechanical studies (Dos Santos et al. 2016; Hafer et al. 2015; Heiderscheit et al. 2011; Neal et al. 2018; Willy et al. 2016), these fundamental variables are essential to describe the kinematic changes in lower extremity after retraining. Based on the above considerations, we decided to retain them. In addition, according to your suggestions, we have changed average and maximum loading rates to vertical instantaneous load rates (VILR) and vertical average load rates (VALR), respectively. The discussion section has also been changed or explained based on your specific comments (lines 264-312). References Dos Santos AF, Nakagawa TH, Nakashima GY, Maciel CD, and Serrao F. 2016. The Effects of Forefoot Striking, Increasing Step Rate, and Forward Trunk Lean Running on Trunk and Lower Limb Kinematics and Comfort. Int J Sports Med 37:369-373. Hafer JF, Brown AM, Demille P, Hillstrom HJ, and Garber CE. 2015. The effect of a cadence retraining protocol on running biomechanics and efficiency: a pilot study. J Sports Sci 33:724-731. Heiderscheit BC, Chumanov ES, Michalski MP, Wille CM, and Ryan MB. 2011. Effects of step rate manipulation on joint mechanics during running. Med Sci Sports Exerc 43:296-302. Neal BS, Barton CJ, Birn-Jeffrey A, Daley M, and Morrissey D. 2018. The effects & mechanisms of increasing running step rate: A feasibility study in a mixed-sex group of runners with patellofemoral pain. Phys Ther Sport 32:244-251. Willy RW, Meardon SA, Schmidt A, Blaylock NR, Hadding SA, and Willson JD. 2016. Changes in tibiofemoral contact forces during running in response to in-field gait retraining. J Sports Sci 34:1602-1611. Experimental design Methods section was adequate. Please check my general comments for minor changes. Validity of the findings Please review my general comments for the discussion and conclusion section. I am still concerned that your discussion and conclusion doesn't highlight what your study found and how it links to the results. Please focus on your study rather than only citing previous research and those results. Response GC: Thank you for your comments. We have carefully reviewed your general comments below. According to your suggestions, the discussion and conclusion section has been substantially modified (lines 264-330). Besides, as per your request, we also highlighted the current findings and how it links to the results (lines 299-304). Comments for the Author Abstract: SC1: Please include results from the control group also in the results. Response SC1: Thank you for your comments. We have included the results from the control group (lines 26-37). “Significantly decreased impact peak (1.86 ± 0.30 BW vs. 1.67 ± 0.27 BW, P = 0.003), average load rates (91.59 ± 18.91 BW/s vs. 77.31 ± 15.12 BW/s, P = 0.001) and maximum load rates (108.8 ± 24.5 BW/s vs. 92.8 ± 18.5 BW/s, P = 0.001) were observed in the cadence retraining group, while no significant differences were observed in the control group. Foot angles (18.27° ± 5.59° vs. 13.74° ± 2.82°, P = 0.003) and vertical velocities of the centre of gravity (CoG) (0.706 ± 0.115 m/s vs. 0.652 ± 0.091 m/s, P = 0.002) significantly decreased in the cadence retraining group at initial contact, but not in the control group. In addition, vertical excursions of the CoG (0.077 ± 0.01 m vs. 0.069 ± 0.008 m, P = 0.002) and peak knee flexion angles (38.6° ± 5.0° vs. 36.5° ± 5.5°, P < 0.001) significantly decreased whilst lower extremity stiffness significantly increased (34.34 ± 7.08 kN/m vs. 38.61 ± 6.51 kN/m, P = 0.048) in the cadence retraining group. However, no significant differences were observed for those variables in the control group.” SC2: Please rewrite the conclusion to reword lines 37-39. Response SC2: Thank you for your comments. We have reworded those lines (lines 39-42). “This increased cadence effectively reduced impact peak and vertical average/instantaneous load rates. Given the close relationship between impact force variables and running injuries, increasing the cadence as a retraining method may potentially reduce the risk of impact-related running injuries.” Introduction SC3: Line 42 feels incomplete. I recommend adding form of physical activity or exercise in China and across the world. Response SC3: Sorry for the confusion it caused. According to your suggestion, we have modified this sentence to clarify the meaning. Now it reads “Long-distance running is a very popular form of physical activity in China and across the world.” SC4: Lines 43-44: Change the grammar. 5.83 million participants ran in 1,581 marathon events in China in 2018. I would just check to make sure 1581 marathon events or races that include all distances (5k,10k, half and full marathon). Response SC4: Thank you for your advice. 1581 marathon events included all distances. We have changed the expression and grammar accordingly (lines 46-48). “According to the Chinese Athletics Association Marathon Annual Press Conference, 5.83 million participants ran in 1,581 marathon events (5k,10k, half and full marathon) in China in 2018.” SC5: Line 69: I would remove the word 'immediate'. Response SC5: Thanks for your suggestion. We have removed the word “immediate” (lines 71-73). “These results indicate that increasing cadence or decreasing step length has an effect on decreasing impact forces and other lower-extremity variables in running.” SC6: Line 71: Change 'In regard' to 'With regards to'. Response SC6: As per your request, we have changed “in regard” to “with regards to” (line 74). “With regards to cadence retraining, Hafer et al. (2015) observed significant decreases in load rates after 6 weeks of cadence retraining with a 10% increase in cadence.” Reference Hafer JF, Brown AM, Demille P, Hillstrom HJ, and Garber CE. 2015. The effect of a cadence retraining protocol on running biomechanics and efficiency: a pilot study. J Sports Sci 33:724-731. SC7: Line 82-83: Please elaborate on what do you mean by 'reduce risks of rapid transition'. Response SC7: Thank you for your comments. We have changed the expression to clarify the meaning. Now it reads “reduce injury risks of gait transition within a short period” (lines 82-83). SC8: Line 83-85: I would recommend deleting lines 325-326. It sounds repetitive. Response SC8: Thank you for your advice. If we understand you correctly you recommended deleting lines 83-85 not lines 325-326. We have removed this sentence accordingly. SC9: Lines 87-88 are also a repetition from lines 75-78. I would recommend moving those lines here. Response SC9: Thank you for your comments. As per your request, we have moved lines 75-78 here and deleted previous lines 87-88. Methods SC10: Line 103: Please change 'was included' to 'were included'. Response SC10: Thank you for your careful review. We have changed the expression accordingly (line 104). “Participants were randomly assigned to either a cadence retraining group (CAD) or a control group (CON) on the basis of the lottery method of sampling, and 15 participants were included in each group (Table 1).” SC11: Line 106: Please change 'running' patterns to 'foot strike' patterns. Response SC11: Thank you for your advice. We have changed “running” patterns to “foot strike” patterns (line 107). “A high-speed camera placed next to the treadmill recorded their foot strike patterns.” SC12: Line 109: Please take out the word 'visibly'. Response SC12: As per your request, we have taken out the word “visibly” (line 110). SC13: Line 129: Please add 'gait retraining' program. Response SC13: Thank you for your advice. We have added this term accordingly (lines 130-131). “The participants visited the laboratory twice, at baseline and at the end of the gait retraining program.” SC14: Line 137: Please change 'metatarsus' to 'metatarsals' Response SC14: Thank you. We have changed “metatarsus” to “metatarsals” (line 138). SC15: Line 180-185: Futrell et al. 2018 calculated vertical instantaneous load rates (VILR) and vertical average load rates (VALR). Please ensure that you mention these specific terms. Response SC15: Thank you for your comments. What we mean here are vertical average load rates and vertical instantaneous load rates. Besides, according to your suggestion, we have changed “average” and “maximum” load rates to “vertical average” load rates (VALR) and “vertical instantaneous” load rates (VILR) throughout the manuscript, respectively. SC16: Line 185: Please add 'We also calculated' instead of kinetic variables because impact peak and loading rates are also kinetics. Response SC16: Thank you for your comments. We have added the expression accordingly (line 189). “We also calculated lower extremity stiffness (Liu et al. 2006), kleg, as shown in Equation (1).” Reference Liu Y, Peng CH, Wei SH, Chi JC, Tsai FR, and Chen JY. 2006. Active leg stiffness and energy stored in the muscles during maximal counter movement jump in the aged. J Electromyogr Kinesiol 16:342-351. SC17: Lines 187-188: Please only mention flexion and extension. All lower extremity joints are not dorsiflexion and plantar flexion, only ankle joint is. Response SC17: Thank you for your comments. We have removed “dorsiflexion” and “plantar flexion” from this sentence (lines 190-192). “Kinematic variables of the hip, knee and ankle joints included foot angle (the angle between the foot and ground) at initial contact (Fig. 3A) and peak joint extension and peak joint flexion angles during the stance.” SC18: Line 197: Please change 'were' to 'was' or change the sentence to 'The mean and standard deviations were calculated for all variables'. Response SC18: Thank you for your advice. We have changed “were” to “was” (line 201). “The mean and standard deviation for each variable was calculated.” SC19: Line 222-223: Please report changes if any in cadence in control group. Response SC19: As per your request, we have included the data in control group (lines 226-228). “Specifically, cadence significantly increased by 5.7% (161.3±9.5 step/min vs. 170.5±9.2 step/min) in the CAD group (P < 0.001, Cohen’s d = 3.87) but not in the CON group (164.3±7.7 step/min vs. 165.5±6.8 step/min, P > 0.05) after training.” SC20: Line 225-226: Please indicate the values of step length in control group. Response SC20: As per your request, we have included the values of step length in the control group (lines 230-232). “No significant difference for step length was observed in the CON group (2.54±0.16 m vs. 2.51±0.14 m).” SC21: Lines 230-237: Please refer to my earlier comments about terminologies. Response SC21: Thank you for your comments. As we mentioned in Response SC15, we have changed “average” and “maximum” load rates to “vertical average” load rates (VALR) and “vertical instantaneous” load rates (VILR) throughout the manuscript, respectively (lines 242-244). “Significant main effects of training were observed for VALR and VILR. Specifically, VALR (P = 0.029, = 0.198) and VILR (P = 0.025, = 0.209) decreased in the CAD group after training (Figs. 5C, 5D).” SC22: Lines 256-257: Please align this with study aims. Response SC22: Thank you for your suggestion. We have changed the expression of this sentence in order to align it with study aims (lines 264-265). “This study aimed to characterize the effects of a 12-week cadence retraining protocol on impact peak, load rates and other lower-extremity biomechanical variables.” SC23: Lines 258: Please remove 'cadence retraining could decrease running impact force variables' or explain how you came to this conclusion. Response SC23: As per your request, we have removed this sentence. SC24: Lines 264-266: Please reword this to explain why Neal et al. 2018 had greater improvements in cadence compared to Hafer at al. 2015 and present study. Response SC24: Thank you for your comments. We have reworded these lines to explain why Neal et al. 2018 had greater improvements in cadence compared to Hafer at al. 2015 and present study (lines 272-277). Now it reads “Compared to the study by Hafer et al. (2015), the cadence after training in the current study and the study by Neal et al. (2018) was closer to the prescribed cadence, which was likely due to the enhanced supervision in training. In addition, real-time feedback was provided in Neal et al.’s study, which may lead to the differences in cadence improvements between Neal et al.’s and present study.” References Hafer JF, Brown AM, Demille P, Hillstrom HJ, and Garber CE. 2015. The effect of a cadence retraining protocol on running biomechanics and efficiency: a pilot study. J Sports Sci 33:724-731. Neal BS, Barton CJ, Birn-Jeffrey A, Daley M, and Morrissey D. 2018. The effects & mechanisms of increasing running step rate: A feasibility study in a mixed-sex group of runners with patellofemoral pain. Phys Ther Sport 32:244-251. SC25: Lines 267-270: Please reword this to clearly explain your study variables VS Hobara et al. 2012 study values. It's confusing when you say impact peak decreased by 10.2% but then you mention it decreased by 12% in the CAD group compared to the control group. Response SC25: Thank you for your comments. We have reworded these sentences to clarify the meaning (lines 278-282). Now it reads “In the present study, impact peak was significantly reduced by 10.2% in the CAD group after training, which was greater than the 7.6% decrease observed (pre-training vs. post-training) in the study of Hobara et al. (2012). Moreover, impact peak in the CAD group after training was 12% significantly lower than that in the CON group after training (CAD vs. CON).” Reference Hobara H, Sato T, Sakaguchi M, Sato T, and Nakazawa K. 2012. Step frequency and lower extremity loading during running. Int J Sports Med 33:310-313. SC26: Line 278: Please remove 'recently' Response SC26: According to your comments in SC27, we have modified this sentence. SC27: Line 278-282: This section is muddled. What is subtle heel strike. What do you mean by change from rear foot to non-rearfoot strike? How is that subtle. Please provide values of the change in rear foot angles. Response SC27: Sorry for the confusion it caused. According to your comments, we have removed the terminology of “subtle heel strike” to avoid misleading the readers. Besides, we have reworded these sentences to clarify the meaning. Also, as per your request, the value of the change in foot angle has been provided (lines 292-296). “In addition, a lower impact peak has been found in a smaller foot angle at foot contact with a rearfoot strike (Mercer & Horsch 2015). In the present study, the foot angle in the CAD group significantly decreased by 4.5° after retraining, which may partially explain the decrease in impact peak in the CAD group after retraining.” Reference Mercer JA, and Horsch S. 2015. Heel-toe running: A new look at the influence of foot strike pattern on impact force. J Exerc Sci Fit 13:29-34. SC28: Lines 285-293: Please refrain from using terms like 'Positive effect on reducing risk of running injuries' and highlighting other studies that don't focus on gait retraining and its effects on loading rates and impact peaks. Response SC28: Thank you for your comments. We have changed the expression like “positive effect on reducing risk of running injuries” to “may reduce the risk of running injuries” (line 304). Since there were limited studies on cadence retraining, we referred to other running related studies to interpret the decrease in load rates and the relationship between load rate and running injuries. Also, we have reworded those sentences to refrain from highlighting the studies that don’t focus on gait retraining (lines 299-304). “Lieberman et al. (2010) found that the load rate was lower in forefoot strikes than that in rearfoot strikes. In the present study, decreased foot angles in the CAD group after retraining slightly altered the strike pattern, which may contribute to reductions in load rates. Injured runners were reported to have higher load rates than non-injured runners in a prospective investigation (Davis et al. 2016). Therefore, the decrease in load rates after retraining indicate that cadence retraining may reduce the risk of running injuries, such as stress fractures.” References Lieberman DE, Venkadesan M, Werbel WA, Daoud AI, D'Andrea S, Davis IS, Mang'Eni RO, and Pitsiladis Y. 2010. Foot strike patterns and collision forces in habitually barefoot versus shod runners. Nature 463:531-535. Davis IS, Bowser BJ, and Mullineaux DR. 2016. Greater vertical impact loading in female runners with medically diagnosed injuries: a prospective investigation. 50:887-892. SC29: Lines 294: Please change 'touchdown' to 'initial contact'. Response SC29: Thank you for your advice. According to your comments in SC30, we have removed this sentence. SC30: Line 295-301: This is repetition because you already included changes in foot angle in the earlier section. The study is not focused on foot strike pattern and or foot angle. Please reword this. Response SC30: Thank you for your comments. We have removed these repetitive lines to avoid misleading the readers. Also, we have reworded related sentences in the earlier section (lines 290-296). Please refer to our Response SC27 for more details. “The foot angle, which reflects the foot strike pattern during running, significantly decreased with increasing cadence (Allen et al. 2016; Heiderscheit et al. 2011). In addition, a lower impact peak has been found in a smaller foot angle at foot contact with a rearfoot strike (Mercer & Horsch 2015). In the present study, the foot angle in the CAD group significantly decreased by 4.5° after retraining, which may partially explain the decrease in impact peak in the CAD group after retraining.” References Allen DJ, Heisler H, Mooney J, and Kring R. 2016. The Effect Of Step Rate Manipulation on Foot Strike Pattern Of Long Distance Runners. Int J Sports Phys Ther 11:54-63. Heiderscheit BC, Chumanov ES, Michalski MP, Wille CM, and Ryan MB. 2011. Effects of step rate manipulation on joint mechanics during running. Med Sci Sports Exerc 43:296-302. Mercer JA, and Horsch S. 2015. Heel-toe running: A new look at the influence of foot strike pattern on impact force. J Exerc Sci Fit 13:29-34. SC31: Line 302: please provide a reference. Response SC31: We have added a related reference as suggested (lines 305-306). “The knee joint was highly sensitive to changes in cadence during the stance phase (Heiderscheit et al. 2011).” Reference Heiderscheit BC, Chumanov ES, Michalski MP, Wille CM, and Ryan MB. 2011. Effects of step rate manipulation on joint mechanics during running. Med Sci Sports Exerc 43:296-302. SC32: Lines 302-303: Please mention specific change in cadence. Response SC32: As per your request, we have added the specific change in cadence (lines 306-307). “5.7% (9.2 step/min) increase in cadence induced significant changes in peak knee flexion angle (Dos Santos et al. 2016; Neal et al. 2018).” References Dos Santos AF, Nakagawa TH, Nakashima GY, Maciel CD, and Serrao F. 2016. The Effects of Forefoot Striking, Increasing Step Rate, and Forward Trunk Lean Running on Trunk and Lower Limb Kinematics and Comfort. Int J Sports Med 37:369-373. Neal BS, Barton CJ, Birn-Jeffrey A, Daley M, and Morrissey D. 2018. The effects & mechanisms of increasing running step rate: A feasibility study in a mixed-sex group of runners with patellofemoral pain. Phys Ther Sport 32:244-251. SC33: Lines 304-306: Please reword this to 'The increase in cadence in the CAD group decreased the peak knee flexion angle and vertical excursion of the CoG'. Response SC33: As per your request, we have changed this expression (lines 307-309). Now it reads “The increase in cadence in the CAD group decreased the peak knee flexion angle and vertical excursion of the CoG.” SC34: Lines 307-308: Please remove this sentence 'Thus, reduction in the vertical excursion of the CoG may be mainly attributed to the knee joint' or provide a justification based on your study results why you think this is the case, just because peak knee flexion angle was different doesn't justify that it is the reason why change in vertical COG was observed. Response SC34: We have removed this sentence as suggested. SC35: Lines 309-310: Your study did not 'acutely increase the cadence' Your study was a gait retraining study over 12 weeks. Response SC35: Thank you for your comments. We have removed this expression and modified the sentence (lines 310-312). “Additionally, lower extremity stiffness significantly increased in the CAD group after training, which may be due to the reduced vertical excursion of the CoG during the stance phase induced by the decrease in peak knee flexion angle.” SC36: Lines 327-329: Please remove this sentence because your discussion did not look at vertical velocity of CoG and impact forces. Your discussion focused only on change in foot angle. Also, please refrain from including new terms, please be consistent with terminologies. Throughout the manuscript you mention rear foot, non-rearfoot, subtle but now you include 'flattening'. Response SC36: Thank you for your comments. According to your suggestion, we have removed “vertical velocity of CoG at initial contact” from this sentence. Also, we have changed “flattening of the foot” to “a decrease in foot angle” (lines 327-328). “A decrease in foot angle at initial contact after training may provide a mechanical explanation for the observed decrease in impact force variables.” SC37: Table 2: Please include pre and post values of impact forces and load rates in Table 2. Response SC37: Thank you for your comments. As per your request, we have included pre and post values of impact forces and load rates in Table 2. SC38: Lines 371: Futtrell reference has been deleted from the manuscript reference section. Please add it again. Response SC38: Thank you for your reminder. We have added this reference again to the manuscript reference section (lines 373-374). SC39: Lines 390: Han T. 2019. Marathon statistics in China. THIS REFERENCE IS INCORRECT. PLEASE CITE IT ACCURATELY. Response SC39: Thank you for your suggestion. We have modified this reference. Now it reads “Marathon statistics in China 2019. Available at http://www.athletics.org.cn/marathon/news/2019/0311/218818.html.” (lines 340-341). Reviewer 3 (Eoin Doyle) Comments for the Author I commend the authors on accepting the feedback and for making significant improvements to their article. As mentioned previously the article is on a very clinically relevant topic which should be of interest to readers. Well done. I have a few minor suggestions to improve the paper prior to accepting. Response GC: Thank you for your comments. We have revised the manuscript based on your following suggestions. SC1: Line #45. 2019 is not a reference. Update this to the author (Running USA). Response SC1: Thank you for your suggestion. We have updated this reference accordingly (lines 48-49). “Similarly, 18.1 million runners registered for organised races in the US (Running USA).” Reference Running USA’s Annual Reports 2019 [Internet]. Available at http://www.runningusa.org/annual-reports. SC2: Line #129. Remove i.e. Response SC2: Thank you. We have removed “i.e.” as per your request (lines 130-131). “The participants visited the laboratory twice, at baseline and at the end of the gait retraining program.” SC3: Line#289. 'runners with high loading rates are more likely to develop injuries compared with those with low loading rates' We don't know this. One study is retrospective (Davis). The other reports symmetry angle of impact peak to be different between injured and non-injured runners. Consider revising this line but I think the argument is logical. Response SC3: Thank you for your comments. We have revised this statement according to your suggestion (lines 301-303). Now it reads “Injured runners were reported to have higher load rates than non-injured runners in a prospective investigation (Davis et al. 2016)”. Reference Davis IS, Bowser BJ, and Mullineaux DR. 2016. Greater vertical impact loading in female runners with medically diagnosed injuries: a prospective investigation. Br J Sports Med, 50:887-892. SC4: Line#298-9. '+10% of their preferred cadence'. '+30% of their preferred cadence' Is this and increase? Response SC4: Yes, “+10% of their preferred cadence” and “+30% of their preferred cadence” mean a 10% and a 30% (our mistake, it should be 15%) increase in cadence, respectively. However, according to the comments from the other reviewer, we have removed this sentence. "	Here is a paper. Please give your review comments after reading it.
494	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>This study, concerning the epigeic fauna of carabid beetles (Coleoptera; Carabidae), was conducted in the north-east of Poland, in an area which is part of the Dąbrówka Forest Subdistrict and has been included in the 'Small water retention program for the Province of Warmia and Mazury in 2006-2015'. The purpose of the study was to assess the impact of the water retention implemented within the framework of the above program on assemblages of ground beetles. These insects are highly sensitive to any anthropogenically induced transformations. This analysis was based on the interactions among the analyzed insects caused by changes occurring in their habitat. During the three-year study, 5 807 specimens representing 84 species were captured. The water storage had a significant influence on the structure of the Carabidae assemblages. Before the earthworks were constructed for the project, the beetle assemblages had comprised a large group of xerophilous species, whereas after the small retention reservoirs had been created, an increase in the contribution of hygrophilous species was noticed. The results indicate that the retention works cause alterations in the water and environmental conditions of the habitats, and thereby effect changes in the composition of Carabidae assemblages. Moreover, modification in water relations within a habitat causes long-term changes in the structural and functional diversity of the beetles.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head n='1.'>Introduction</ns0:head><ns0:p>Water, as the principal component modeling the natural environment, plays a significant role in shaping forest ecosystems, in the sense of being both a habitat-forming element and a factor which ensures the stability, sustainability and diversity of habitats <ns0:ref type='bibr' target='#b46'>(Pierzgalski, 2008;</ns0:ref><ns0:ref type='bibr'>Blumenfeld et al., 2010;</ns0:ref><ns0:ref type='bibr' target='#b18'>Koralay & Kara, 2018)</ns0:ref>. Any aberrations from the natural water regime, such as periodic water deficits or excesses, are events that have an adverse effect on the whole ecological system <ns0:ref type='bibr' target='#b50'>(Rulik &White, 2019)</ns0:ref>. Such irregularities can be a consequence of erroneously implemented water retention or naturally occurring hydrological and meteorological processes <ns0:ref type='bibr' target='#b28'>(Liberacki & Szafrański, 2013;</ns0:ref><ns0:ref type='bibr'>Miler et al., 2013;</ns0:ref><ns0:ref type='bibr' target='#b15'>Kędziora et al., 2014)</ns0:ref>. As mentioned by <ns0:ref type='bibr' target='#b32'>Mioduszewski (2010)</ns0:ref>, in compliance with the Framework Water Directive of the European Union and because of the occurrence of water deficits in the entire European Union, all member countries are obligated to maintain an inventory and to protect ecosystems that have an impact on the shaping of proper water balance in nature. They are also required to implement measures to counteract water deficits by improving the retention capacity of the biosphere. In order to take full advantage of forest ecosystems, complex actions are undertaken to decelerate the circulation of water in the catchment while preserving the natural landscape <ns0:ref type='bibr'>(European Environment Agency, 2015)</ns0:ref>. Such measures, called forest retention, are modeled after natural processes occurring in the natural environment <ns0:ref type='bibr' target='#b6'>(Gustafsson et al., 2012)</ns0:ref>.</ns0:p><ns0:p>By restoring the natural retention properties of ecosystems, and thereby improving the availability of water, it is possible to enhance the diversity of flora and fauna in the habitat <ns0:ref type='bibr' target='#b7'>(Hansson et al., 2005;</ns0:ref><ns0:ref type='bibr'>Nietupski, Kosewska & Ciepielewska, 2007;</ns0:ref><ns0:ref type='bibr' target='#b10'>Janusz et al., 2011;</ns0:ref><ns0:ref type='bibr'>Kosewska & Nietupski, 2015)</ns0:ref>.</ns0:p><ns0:p>The modifications which occur in areas where water retention has been introduced can be assessed by analyzing the responses of living organisms. Both intra-and inter-species structures of dependencies between organisms and the area they inhabit provide valuable information about the natural environment <ns0:ref type='bibr' target='#b29'>(Mc Geoch, 1998;</ns0:ref><ns0:ref type='bibr' target='#b4'>Gerlach, Samways & Pryke, 2013)</ns0:ref>. Effective indicator species used in the monitoring of the natural environment are beetles (Coleoptera;</ns0:p><ns0:p>Carabidae) living on the surface of the earth (epigeic) <ns0:ref type='bibr' target='#b61'>(Tőzsér et al., 2019)</ns0:ref>. These insects, as bioindicators of the condition of the environment, are characterized by high sensitivity to changing habitat conditions, especially changes in moisture content. This means that their observation can provide specific data about the current state of the ecosystem in which they live <ns0:ref type='bibr' target='#b47'>(Rainio & Niemelä, 2003;</ns0:ref><ns0:ref type='bibr'>Avgin & Luff, 2010;</ns0:ref><ns0:ref type='bibr' target='#b17'>Koivula, 2011;</ns0:ref><ns0:ref type='bibr'>Kotze et al., 2011</ns0:ref><ns0:ref type='bibr'>, Bednarska et al., 2018)</ns0:ref>.</ns0:p><ns0:p>The aim of this study was to answer the following research hypotheses: 1) long-term modifications of the habitat water relations affect changes in ground beetle diversity, causing the disappearance of xerophilous species and the increase of hygrophilic species, 2) the designated</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:04:48069:1:1:NEW 14 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed transects differ in quantitative and qualitative values for the carabidofauna caught, 3) retention works cause the disappearance of rare Carabidae species found on the Red List of endangered animals in Poland <ns0:ref type='bibr' target='#b43'>(Pawłowski, Kubisz & Mazur, 2002)</ns0:ref>. However, when the retention changes had been made, the research area was expanded by adding a second research study site, located to the east of the retention reservoirs. In 2012 and 2013,</ns0:p></ns0:div> <ns0:div><ns0:head n='2.'>Materials & Methods</ns0:head></ns0:div> <ns0:div><ns0:head>Study</ns0:head><ns0:p>traps were dug at both study sites, 15 traps in each, set up in 3 rows and at the same 10-meter distance as in 2009 (30 traps in total) (figure <ns0:ref type='figure' target='#fig_6'>1</ns0:ref>).</ns0:p><ns0:p>A modified Barber traps method was used for beetle capture, caught into containers with a capacity of 500 ml and a height of 12 cm <ns0:ref type='bibr'>(Barber, 1931)</ns0:ref>. The containers were filled up to 1/3 with a solution of ethylene glycol including a small amount of detergent to decrease the surface tension. The traps were dug in level with the ground surface and emptied regularly every 14 days, collecting the entomological material and replacing the preserving solution. <ns0:ref type='bibr'>Poland, 2010;</ns0:ref><ns0:ref type='bibr'>2013;</ns0:ref><ns0:ref type='bibr' target='#b55'>2014)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Data analysis.</ns0:head><ns0:p>Species diversity and abundance of captured beetles were expressed in real numbers of caught specimens and species. The most popular tool, i.e. the Shannon index <ns0:ref type='bibr' target='#b51'>(Shannon-Weaver, 1949)</ns0:ref>, was used to evaluate the biodiversity of the beetles, while the assessment of any deviations was based on mean individual biomass (MIB) <ns0:ref type='bibr' target='#b57'>(Szyszko et al., 2000)</ns0:ref>.</ns0:p><ns0:p>The ecological analysis was carried out using the following indicators: trophic group, phenology, hygropreferences and habitat types (trophic group -(hemizoophages (Hz), small</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:04:48069:1:1:NEW 14 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed zoophages (Sz): with a body length of no more than 15 mm, large zoophages (Lz): with a body length over 15 mm) <ns0:ref type='bibr' target='#b26'>(Leśniak, 1985;</ns0:ref><ns0:ref type='bibr'>Aleksandrowicz, 2004;</ns0:ref><ns0:ref type='bibr'>Kosewska, Nietupski & Ciepielewska, 2007)</ns0:ref>, phenology -(spring breeders (Sb), autumn breeders (Ab)) <ns0:ref type='bibr' target='#b24'>(Larsson, 1939;</ns0:ref><ns0:ref type='bibr' target='#b60'>Thiele, 1977)</ns0:ref>, hygropreferences -(xerophilous (Xe), mesophilous (M), hygrophilous (H)) <ns0:ref type='bibr' target='#b60'>(Thiele, 1977;</ns0:ref><ns0:ref type='bibr'>Aleksandrowicz, Pakuła & Mazur, 2008)</ns0:ref>, habitat types -(forest (F), open area (Oa), peat bog (Pb), eurytopic (Eu)) <ns0:ref type='bibr'>(Aleksandrowicz, 2004;</ns0:ref><ns0:ref type='bibr'>Kosewska, Nietupski & Ciepielewska, 2007)</ns0:ref>.</ns0:p><ns0:p>To determine the significance of differences between the basic parameters describing the biodiversity of the Carabidae assemblages in the analyzed transects and years, the generalized linear model (GLM) was used, which helped to determine the p values using Statistica 13.1 software (StatSoft, Inc.). A test of the significance of effects comprised in the model was carried out according to Wald's statistics. Jackknife 2 estimator was used for abundance data (using EstimateS v. 9.1.0 statistical software) and the species accumulation curves, were calculated to access the adequacy of the sampling efficiency <ns0:ref type='bibr'>(Zahl, 1977;</ns0:ref><ns0:ref type='bibr'>Colwell, 2005)</ns0:ref>. Assessment of the similarity of the Carabidae assemblages in the examined transects and the years of the study was made with the help of a non-metric multidimensional scaling (NMDS), using Morisita's measure of similarity. Assessment of the significance of differences between the analyzed assemblages in the NMDS method was carried out using the ANOSIM non-parametric statistical test <ns0:ref type='bibr' target='#b14'>(Kenkel & Orloci, 1986;</ns0:ref><ns0:ref type='bibr'>Clarke, 1993)</ns0:ref>. Impact of environmental variables (year, transect) on the species composition of Carabidae was determined using redundancy analysis RDA <ns0:ref type='bibr' target='#b59'>(Ter Braak & Šmilauer, 1998)</ns0:ref>. This method was chosen based on an analysis of the data distribution, which was linear (SD = 1.58).</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:04:48069:1:1:NEW 14 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head n='3.'>Results</ns0:head><ns0:p>During the three-year study, 5 807 specimens representing 84 species of Carabidae were caught (table <ns0:ref type='table'>1</ns0:ref>). Before the small retention program was implemented, 1 377 specimens belonging to 52 species were caught. In 2012, the number of species captured at the study site I did not change, although the number of carabid beetles caught (927 specimens) decreased. In the final year of the study, the number of species rose to 61, and their abundance increased to 1 493 specimens. At study site II, which was a replication, 965 specimens representing 57 species were caught, and the number of carabid individuals caught in 2013 increased to 1 045, while the number of species fell to 43 (table <ns0:ref type='table'>1</ns0:ref>). After the implementation of the water retention, the occurrence of new, hygrophilous species (Patrobus atrorufus STRØM, Bembidion mannerheimii C. R. SAHLBERG) and an increase in the share of rare, disappearing or threatened species (Oodes helopioides FABRICIUS, Carabus convexus FABRICIUS, Carabus marginalis FABRICIUS) was observed. Moreover, following the implementation of the water retention program, the presence of a rare species, such as Philorhizus sigma P. ROSSI, was noted (table <ns0:ref type='table'>1</ns0:ref>). Despite the rise in the percentage of stenotopic species the hydro-technical works had been completed, the extremely endangered species Epaphius rivularis SCHRANK disappeared (table <ns0:ref type='table'>1</ns0:ref>).</ns0:p><ns0:p>The dominant species in both study sites and in all years of the study was the forest <ns0:ref type='table'>1</ns0:ref>).</ns0:p><ns0:p>The results obtained from the generalized linear model (GLM) showed a statistically significant impact of the variable factors (transect, year) and their interaction with respect to the presence of epigeic carabid beetles (table <ns0:ref type='table'>2</ns0:ref>). The GLM model was chosen because the data had a unimodal distribution. The transects chosen in the study differed from each other in the quantities of individuals and number of species of ground beetles caught (table <ns0:ref type='table'>3</ns0:ref>). The highest values concerning the number of species and number of individuals prior to the hydro-technical modifications were noted in transect A (peat meadow), while the lowest ones were in transect C (forest) (figure 2 a, b). This is supported by the biological diversity assessment, which additionally demonstrated the most significant contrast during the three-year research between transects A (1.25) and C (0.4) (table <ns0:ref type='table'>3</ns0:ref>). After the execution of the small water retention project, there were no quantitative or qualitative dependences confirmed proportional to the distance of the determined transects from the retention reservoirs (figure <ns0:ref type='figure' target='#fig_7'>2</ns0:ref> Manuscript to be reviewed were correlated mainly with the second ordination axis, describing almost 10% of the variation.</ns0:p><ns0:p>The highlighted factor -2009 -is correlated with the occurrence of xerophilic hemizoophages with an autumn type of development. The vector describing 2012 showed an inverse correlation, which indicates large changes in ground beetle assemblages as a result of earth transformations.</ns0:p><ns0:p>On the other hand, the vector describing 2013 revealed a tendency corresponding to the state of the carabid assemblages in 2009, which indicates the tendency of the structure of carabid assemblages to return to the state before implemention of the water retention program (figure <ns0:ref type='figure'>5</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head n='4.'>Discussion</ns0:head><ns0:p>This study, conducted in the Dąbrówka Forest Subdistrict, showed that the number of individuals and species diversity of the captured carabid beetles was at a high level in comparison with results achieved on peatlands in Belarus, Germany and Slovakia <ns0:ref type='bibr'>(Aleksandrowicz, 2002;</ns0:ref><ns0:ref type='bibr'>Buchholz, Hannig & Schirmel, 2009;</ns0:ref><ns0:ref type='bibr'>Igondová & Majzlan, 2015)</ns0:ref>. In our study, we observed over 29 % of all carabid species present in the Masurian Lake District, which may suggest that the analyzed area plays a significant role in the preservation of biological diversity <ns0:ref type='bibr'>(Aleksandrowicz, Gawroński & Browarski, 2003;</ns0:ref><ns0:ref type='bibr' target='#b42'>Pacuk & Regulska, 2014)</ns0:ref>. Similar conclusions can be drawn from analysis the quantitative and qualitative composition of carabid beetle assemblages from peatlands located in the north-east of Poland <ns0:ref type='bibr' target='#b36'>(Nietupski et al. 2008a;</ns0:ref><ns0:ref type='bibr' target='#b37'>Nietupski, Ciepielewska & Kosewska, 2008b;</ns0:ref><ns0:ref type='bibr' target='#b40'>Nietupski et al., 2010;</ns0:ref><ns0:ref type='bibr' target='#b41'>Nietupski, Kosewska & Lemkowska, 2015)</ns0:ref>. Because of the poor knowledge of entomofauna inhabiting areas subjected Manuscript to be reviewed conditions and the composition of caught entomofauna. <ns0:ref type='bibr' target='#b16'>Kiryluk (2012)</ns0:ref> and <ns0:ref type='bibr'>Abell et al. (2019)</ns0:ref> confirm that when moisture conditions deteriorate, the diversity of flora decreases, which causes impoverishment of fauna. Hence, this is another parameter which directly affects the species composition and the abundance of captured insects, by being a factor that decides about their ability to survive, thereby differentiating the quality of insect assemblages <ns0:ref type='bibr' target='#b60'>(Thiele, 1977;</ns0:ref><ns0:ref type='bibr' target='#b33'>Nietupski, Kosewska & Ciepielewska, 2006)</ns0:ref>. <ns0:ref type='bibr' target='#b11'>Jaworski & Hilszczański (2013)</ns0:ref> emphasize that temperature and humidity have both direct and indirect effects on all aspects of the life of both the larvae and imagines of insects.</ns0:p><ns0:p>Out of the three years of observations, the entomological material gathered in the first year after the hydro-technical modifications was the least numerous, but this may have been caused by stronger anthropopressure on the analyzed ecosystems. Similar conclusions are drawn by <ns0:ref type='bibr' target='#b53'>Skłodowski and Mądrzejewska (2008)</ns0:ref> and by <ns0:ref type='bibr' target='#b48'>Rosenzweig (1995)</ns0:ref>, who claim that environmental disturbances have a considerable influence, leading to the reduced biodiversity of local ecosystems. Moreover, that year was characterized by relatively high atmospheric precipitation (700 mm), which could also have had an influence on the presence of carabid beetles <ns0:ref type='bibr'>(Central Statistical Office in Poland, 2013)</ns0:ref>. In turn, the increase in the diversity of carabids in the last year of the study ( <ns0:ref type='formula'>2013</ns0:ref>) may be indicative of the natural conditions slowly normalizing after the implementation of changes in the water relations, whose main goal was to improve the moisture conditions. These results are in accord with those reported by <ns0:ref type='bibr' target='#b12'>Jędryczkowski and Kupryjanowicz (2005)</ns0:ref>, who emphasize that species diversity grows proportionally to the increase in the area's moisture content. The effect of soil moisture in forest habitats on beetle assemblages was also demonstrated by <ns0:ref type='bibr' target='#b13'>Kagawa and Maeto (2014)</ns0:ref>.</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:04:48069:1:1:NEW 14 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p><ns0:p>Changes in the habitat induced by the implementation of the small water retention program in the Dąbrówka Forest Subdistrict also affected the distribution of the carabid beetles in the three research transects. Before the earthworks, the diversity of carabid species decreased directly proportionally to the increasing distance of the set transects from the open area of the peatland meadow (table <ns0:ref type='table'>3</ns0:ref>). The earthworks caused certain disturbances in the habitat, associated, inter alia, with increased soil moisture, which caused changes in the spatial structure of carabid assemblages. Fluctuations in the natural environment due to the retention works created stress conditions, which were most distinctly evidenced during the three-year period in transect A.</ns0:p><ns0:p>Transect A, located closest to the retention reservoirs, responded the most to the induced changes, which is reflected in the graphical shift of the polygon describing the similarity of the Carabidae assemblages in this transect in the multidimensional spatial scaling approach (NMDS) (figure 4 a, b, c). Following the hydro-technical modifications, the species-richest study site (except study site 2012 II) was transect B (table <ns0:ref type='table'>3</ns0:ref>). This transect, by being localized at the edge of two different habitats, could have been conducive to a rise in biodiversity <ns0:ref type='bibr'>(Yu et al., 2007;</ns0:ref><ns0:ref type='bibr' /> Banul, <ns0:ref type='bibr'>Kosewska & Borkowski, 2018)</ns0:ref>. <ns0:ref type='bibr' target='#b56'>Szyszko (2002)</ns0:ref> states that transitional zones between two different habitats are an excellent foraging site, and subsequently a good place for the development of carabid beetles. Additionally, the above transect in our study was an area subjected to a lesser influence of environmental disturbances, which may also have contributed to higher species diversity. The lowest diversity was noted in the forest zone (transect C) (table <ns0:ref type='table'>3</ns0:ref>). Forest habitats are characterized by relatively low diversity, but these assemblages are stable and quite resistant to environmental changes. This opinion has been verified by the observations made in the forest ecosystem located in the Dąbrówka Forest Subdistrict (C) (figure <ns0:ref type='figure'>4 a, b, c</ns0:ref>).</ns0:p><ns0:p>The role of the stable woodland ecosystem in shaping species diversity and abundance of insects</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:04:48069:1:1:NEW 14 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed has been highlighted by <ns0:ref type='bibr' target='#b55'>Sushko (2014)</ns0:ref>, who carried out entomological studies in Lithuania.</ns0:p><ns0:p>Sushko showed a greater diversity of carabid fauna in a birch forest than in the adjacent peatland.</ns0:p><ns0:p>Furthermore, he detected similar species diversity of carabid beetles in a pine forest and on peatland, but determined that the number of beetles in the pine forest was twice as high as on the peatland <ns0:ref type='bibr' target='#b54'>(Sushko, 2007)</ns0:ref>. <ns0:ref type='bibr' target='#b2'>Dapkus and Tamutis (2008)</ns0:ref> reported that the number of species on peatland and in a bordering pine forest was similar, but twice as many carabid specimens were detected in the forest than on the peatland. The adaptability of carabid beetles in the course of evolution led to the emergence of individuals with changeable preferences in regard to habitat, moisture and trophy, or representing different development types <ns0:ref type='bibr' target='#b60'>(Thiele, 1977)</ns0:ref>. Our analysis, in line with the division adopted for the sake of this research, showed the presence of representatives of all categories of the carabid fauna living in Poland, in terms of their preferred habitat, moisture conditions and trophy. This may be an argument in favor of the internal diversity of the analyzed habitats, and further indicating the high diversity of insects inhabiting this area. The fluctuations that had appeared shaped the structures of biocenoses and the created environmental factors gave rise to new, often valuable populations <ns0:ref type='bibr' target='#b52'>(Skłodowski & Zdzioch, 2006)</ns0:ref>. <ns0:ref type='bibr' target='#b1'>Czyżyk and Porter (2017)</ns0:ref> undertook a study to assess the influence of small water reservoirs created in woodlands on the surrounding environment. The results they obtained showed the impact of the water bodies on the food base for Carabidae, as a result of which structural changes occurred in carabid assemblages.</ns0:p><ns0:p>Additionally, analysis of transects located at different distances from the water reservoirs revealed the effect of the distance to these study sites on the quantitative and qualitative Manuscript to be reviewed significance and general direction of changes, which were probably effected by habitat transformations caused by water-and earthworks. A more detailed analysis of changes occurring in carabid assemblages is provided by the RDA redundancy analysis (figure <ns0:ref type='figure'>5</ns0:ref>). The completed earthworks caused some changes in the habitat, including higher soil moisture. This resulted in a decrease in the share of xerophilic hemizoophages with autumn type of development and an increase in the share of zoophages with higher water requirements, the spring type of development inhabiting forests, open areas and peat bogs, or being eurytopes.</ns0:p><ns0:p>The character of changes in the structure of epigeic carabid assemblages induced by the land retention works, in general, should be perceived as positive, in agreement with the expectations of what condition this type of habitat ought to be in <ns0:ref type='bibr' target='#b1'>(Czyżyk & Porter, 2017)</ns0:ref>.</ns0:p><ns0:p>Zabrocka-Kostrubiec ( <ns0:ref type='formula'>2008</ns0:ref>) concludes that measures implemented under the umbrella of small water retention programs play a significant and beneficial role in forest management practice and, in the long term, they assure the permanently sustainable development of woodland ecosystems included in such programs. However, the effects are not unidirectional, because water retention transformations also lead to the disappearance of certain habitats <ns0:ref type='bibr'>(Bajkowski et al., 2000)</ns0:ref>, and in the case of peatland and marsh habitats, they can result in considerable ecological transformations <ns0:ref type='bibr' target='#b5'>(Grzywna, 2010)</ns0:ref>. Thus, certain concerns can be raised by the fact that such rapid habitat-related changes may result in the disappearance of rare, stenotopic species, such as E. rivularis in our study. The question of whether this is a permanent outcome, or whether that species will reappear in the habitat after some time, can be resolved only through further observations.</ns0:p></ns0:div> <ns0:div><ns0:head n='5.'>Conclusions</ns0:head><ns0:p>PeerJ reviewing PDF \| (2020:04:48069:1:1:NEW 14 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p><ns0:p>Water as a scarce product is a current problem worldwide. For this reason, programs are being implemented whose main goal is to prevent water shortages. One such program is small water retention. When introducing such changes, however, we must be aware that retention works induce transformations of habitats, and thereby effect changes in the composition of valuable Carabidae fauna. The implementation of this program, on the one hand, causes a decrease in the number of carabid beetles and certain disturbances in the previous structure of their assemblages but, on the other hand, it enables the appearance of Carabidae fauna typical for this type of habitat, with specific habitat and moisture requirements, often valuable in the natural environment. It can, therefore, be concluded that small water retention projects have a strong effect on epigeic fauna, and the transformation mostly involves xerophilous species being replaced by hygrophilous species, with greater ecological adaptability (eurytopic species with the spring type of development). Another demonstrable change is the increase in the number of peat bog carabids, which indicates the direction of habitat-related changes induced by hygrotechnical works. In consequence, the area undergoes transformation and is again settled by hygrophilous organisms, which are otherwise often on the brink of extinction. However, only many further years of research and monitoring of retention areas could give an answer as to whether these processes are short-term and reversible or not. Manuscript to be reviewed</ns0:p><ns0:p>Badister sodalis <ns0:ref type='bibr'>(DUFTSCHMID, 1812)</ns0:ref> Pb, H, Sz, Sb 0 0,00 1 0,11 0 0,00 2 0,13 0 0,00</ns0:p><ns0:p>Bembidion doris (PANZER, 1796) Pb, H, Sz, Sb 0 0,00 0 0,00 0 0,00 1 0,07 0 0,00</ns0:p><ns0:p>Metallina properans (STEPHENS, 1828) Oa, M, Sz, Sb 0 0,00 1 0,11 1 0,10 0 0,00 0 0,00</ns0:p><ns0:p>Bembidion quadrimaculatum (LINNAEUS, 1760) Oa, M, Sz, Sb 0 0,00 0 0,00 0 0,00 1 0,07 0 0,00 Oa, Xe, Lz, Sb 0 0,00 0 0,00 0 0,00 1 0,07 0 0,00</ns0:p><ns0:p>Harpalus laevipes ZETTERSTEDT 1828 F, M, Hz, Sb 0 0,00 0 0,00 1 0,10 1 0,07 1 0,10</ns0:p><ns0:p>Lebia chlorocephala (J. J. HOFFMANN, 1803) Oa, M, Sz, Sb 0 0,00 2 0,22 0 0,00 0 0,00 0 0,00</ns0:p><ns0:p>Limodromus assimilis PAYKULL, 1790 F, H, Sz, Sb 0 0,00 1 0,11 0 0,00 0 0,00 0 0,00</ns0:p><ns0:p>Nebria brevicollis (FABRICIUS, 1792) Eu, M, Sz, Ab 0 0,00 0 0,00 0 0,00 0 0,00 1 0,10</ns0:p><ns0:p>Notiophilus biguttatus (FABRICIUS, 1779) F, M, Sz, Sb 0 0,00 0 0,00 1 0,10 0 0,00 0 0,00</ns0:p><ns0:p>Philorhizus sigma (P. ROSSI, 1790) Oa, Xe, Sz, Sb 0 0,00 0 0,00 1 0,10 0 0,00 0 0,00</ns0:p><ns0:p>Pterostichus anthracinus (ILLIGER, 1798) Pb, H, Sz, Ab 0 0,00 0 0,00 1 0,10 0 0,00 0 0,00 Manuscript to be reviewed Manuscript to be reviewed </ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>area. The study was completed in north-eastern Poland, in the Province of Warmia and Mazury, in the Dąbrówka Forest Subdistrict (UTM DE 66). Entomological observations were carried out on an area formerly used for farming and then afforested and turfed. Two study sites were selected, characterized by different moisture conditions and located at a different distance to retention water reservoirs ('a' and 'b') created as part of the Programme of small retention for the Province of Warmia and Mazury in 2006-2015 (figure 1) (www.sporol.warmia.mazury.pl). The first study site was located 128 meters from a forest road, 36 m from retention water reservoir 'a', and 62 m from retention water reservoir 'b'. The second study site was situated at a distance of 100 m from the first , 47 m from the forest road, 39 m from reservoir 'a' and 15 meters from reservoir 'b' (Forest Data Bank, 2019). Both study areas were characterized by similar habitat conditions. Three transects were chosen at each study site: transect A -on a waterlogged peat meadow, with the highest moisture content; transect B -an ecotone between the peat meadow and a forest, located 20 m away from transect A, and characterized by lower water abundance; transect C -situated in a pine forest, 20 meters away from transect B, with the lowest moisture content. The area covered by extensive meadow was dominated by peat soils of transitional peatland with the soil moisture content classified as a very wet, and very acid reaction. At both study sites, the meadow was adjacent to a ten-year-old mixed coniferous forest with the PeerJ reviewing PDF \| (2020:04:48069:1:1:NEW 14 Jul 2020) Manuscript to be reviewed dominant species being common pine (Pinus sylvestris L.). This area was underlain mostly by Brunic Arenosols with the soil moisture content classified into the category: fresh wet (Forest Data Bank, 2019). Data collection. The water retention project in the Dąbrówka Forest Subdistrict within the framework of the program mentioned above started in 2010 with a design phase, and was continued in the form of earthworks in 2011. The comprehensive project consisted of new water retention reservoirs (www.olsztyn.olsztyn.lasy.gov.pl). Studies on beetles of the Carabidae family were conducted in 2009, 2012 and 2013. The first entomological observations were made prior to the retention works connected with the project's implementation. The main purpose of the investigations carried out in 2009 was to make an assessment of the original state of Carabidae assemblages in the area designated for the project. The research continued in 2012 and 2013 after the hydro-technical works had been completed, aimed at providing information on the extent to which the project affected the presence of the analyzed beetles. In 2009, the field research began on 21 April and finished on 22 September (153 days). In 2012, the field research was carried out from 27 April to 26 October (181 days), and in 2013 it lasted from 25 April to 9 October (166 days). In 2009, traps were placed at study site I, linearly, in 3 transects (A, B, C). In each transect, there were 5 traps spaced at 10 meters (15 traps in total) (figure 1). The position of research study site I in 2009 was chosen intuitively as the exact location of the planned retention reservoirs was not known at that time. After the earthworks had been completed, the PeerJ reviewing PDF \| (2020:04:48069:1:1:NEW 14 Jul 2020) Manuscript to be reviewed entomological observations were continued (in 2012 and 2013) in the area chosen in 2009.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>Evaluation of meteorological conditions was based on measurements and observations carried out at the Meteorological Station in Olsztyn. Values of the mean annual air temperature during the three years of the study fluctuated only a little (7.7 ºC in 2009, 7.5 ºC in 2012 and 7.8 ºC in 2013). The average sum of atmospheric precipitation during the three years of the study was 629 mm (604 mm in 2009, 700 mm in 2012, 582 mm in 2013) (Central Statistical Office in</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head /><ns0:label /><ns0:figDesc>mesophilous beetle Pterostichus niger SCHALLER (2009: 22.15%, 2012 (I): 31.28%, 2012 (II): 32.75%, 2013 (I): 28.80%, 2013 (II): 32.34%). In the first year, another numerous species was the forest xerophilous Calathus erratus C.R. SAHLBERG (17.21%), which was not detected after the water retention project had been implemented. In 2012 and 2013, an increase occurred in the share of hygrophilous species classified as small predators of the spring type of PeerJ reviewing PDF \| (2020:04:48069:1:1:NEW 14 Jul 2020) Manuscript to be reviewed development, they may be eurytopic or they may be associated with open areas (Dyschirius globosus HERBST, Agonum fuliginosum PANZER, Agonum sexpunctatum LINNAEUS) or connected with forests as their habitat (Pterostichus strenuus PANZER, Oxypselaphus obscurus HERBST). In addition, following the water retention, there was a rise in the abundance of peatland hygrophilous beetles (Carabus granulatus LINNAEUS, Oodes helopioides FABRICIUS, Pterostichus diligens STURM, Leistus terminatus PANZER, Patrobus atrorufus STRØM) (table</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head /><ns0:label /><ns0:figDesc>a, b). In 2012, the species richest transect was transect B (forest-meadow ecotone), while the fewest species were caught in transect C (forest). In 2013, the final year of our observations, the above values in individual transects were approximately the same. The MIB analysis showed that the transect PeerJ reviewing PDF \| (2020:04:48069:1:1:NEW 14 Jul 2020)Manuscript to be reviewed localized in a forest habitat (C) was characterized by the highest values in each year of the study (figure2 c).Species accumulation curves for individual transects in the three years of the study confirmed that the sampling effort was adequate (figure3). Only the rarefaction curve from transect A, located closest to the water reservoirs, in the year of transformation (2012) did not reach an asymptote. This was probably due to unstable conditions leading to fauna migration, which is reflected in the number of singletons (over 50%).Graphical and statistical analysis of the interpretation of differences in particular transects was presented with the aid of the non-metric multidimensional scaling (NMDS) analysis. Theanalysis showed that the Carabidae assemblages inhabiting the analyzed transects, before and after the implementation of the small retention program, were significantly different between one another (ANOSIM; A: R=0.30, B: R=0.26, C: R=0.32; p<0.01). The curves in the graphs illustrating the similarity between the captured Carabidae assemblages in the individual years of entomological observations (Stress A=0.22, B=0.16, C=0.14) demonstrated that the statistically most significant difference was in transect A (figure 4 a, b, c). RDA analysis showing the relationships between ecological groups of ground beetles and environmental variables (year, transect) showed that the first ordination axis, determining 82.4% of diversity, is positively correlated with the occurrence of large forest zoophages, whose occurrence determines the growth of MIB. This condition is characteristic of stable habitats, which is typical for forest areas (transect C). An inverse correlation was found for transects A and B. Transect A was correlated with the presence of hygrophilous species associated with peat bogs, while transect B showed a strong correlation with the occurrence of small zoophages belonging to eurytopic and open area species. RDA analysis also showed that the research years PeerJ reviewing PDF \| (2020:04:48069:1:1:NEW 14 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head /><ns0:label /><ns0:figDesc>to water retention programs,<ns0:ref type='bibr' target='#b8'>Homburg et al. (2014)</ns0:ref> suggest that it is necessary to gain more insight into this research question. Our studies conducted in the Olsztyn Forest District have demonstrated that the impact of hydro-technical works directly affect the typical habitat PeerJ reviewing PDF \| (2020:04:48069:1:1:NEW 14 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head /><ns0:label /><ns0:figDesc>composition of epigeic carabid beetle assemblages. The differences observed between Carabidae populations in the years of our study, presented via NMDS analysis, only indicate the PeerJ reviewing PDF \| (2020:04:48069:1:1:NEW 14 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head>Figure 1</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_7'><ns0:head>Figure 2 A</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='38,42.52,70.87,307.92,672.95' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='42,42.52,70.87,262.65,672.95' type='bitmap' /></ns0:figure> <ns0:note place='foot' n='3'>PeerJ reviewing PDF \| (2020:04:48069:1:1:NEW 14 Jul 2020)Manuscript to be reviewed</ns0:note> </ns0:body> "	" University of Warmia and Mazury in Olsztyn Emilia Ludwiczak Faculty of Environmental Management and Agriculture Department of Entomology, Phytopathology and Molecular Diagnostics Prawochenskiego 17, 10-720 Olsztyn, Poland email: emilia.ludwiczak@uwm.edu.pl July 5th, 2020 Dear Editor We thank the reviewers for their generous comments on the manuscript. All proposed changes and suggestions, both of the Editor and Reviewers, have been accepted and implemented. We believe that the manuscript is now suitable for publication in PeerJ. Thank you for your consideration Sincerely Yours Emilia Ludwiczak, PhD REVIEWER 1 (Anonymous) We would like to thank Reviewer 1 for his thoughtful remarks and effort to improve our manuscript. Detailed responses to the reviewer are given below. Basic reporting Title The title is clear and properly reflect the content. Abstract and keywords The abstract is suitable and basically good. Line 17 I think that the aim of your study is not 'to try' but just 'to assess'. Please improve it. Agreed, I corrected it. Line 17-20 This sentence is definitely too long. Please divide it. It will make the goal more understandable and transparent. I divided the sentence. Line 20-21 Sentence need the language improvements. Language improvements have been made. Line 24 Remove 'observed'. Please avoid redundant sentence structure or words. 'Observed' removed. Introduction The introduction is comprehensive, but needs some corrections and explanations. Moreover in the title of the paper you use ' ground beetle (Coleoptera; Carabidae) as an indicator...', please develop this thread in more detail in the introduction. Thread has been developed in the introduction. Line 52-54 Please do the language improvements of this sentence. Language improvements have been made. Line 60 I propose to add some reference to confirm this statement. Reference added. Line 61-65 Too long sentence. Please divide it. I divided my sentences. Line 67 Remove 'to try', it is not a scientific statement. Use just 'evaluate...' 'Try' removed. Discussion and Conclusions Discussion is interesting and all-embracing. References Literature is well-chosen, but only three articles are from the last three years. Can you add more recent references? It will also emphasize that your research is current. Newer references have been added. Information on research relevance has been introduced. Table 2 There is 'zone'. Please clarify it and be consistent throughout the text. The word 'zone' removed, the term 'transect' was used. Table 3. Please do not double Carabid/Carabidae in table caption. Corrected. Figure 2 Please remove the grid lines from the chart. Figure 2 a- axis signature should be 'species richness', b- abundance. It should be the same as in the figure caption. The style of figure 2c differs slightly from a and b. Changes have been made. Figure 3 Please remove the grid lines from the chart. Please sign the axes on figure A, B and C. Changes have been made. Figure 4. Please sign the axes on figure A, B and C. Add the meaning of the abbreviations to the figure caption. Changes have been made. Experimental design Material and Methods Line 75-79 Once again, too long sentence. I divided the sentence. Line 79 'Object' is not appropriate term. Maybe please use for example 'study site' The term “study site” was used Line 85-88 What does it mean 'sphere'. It's a strange term, rather rarely used. For me it looks like transect of five pitfall traps...or three habitats (A, B, C). The terms 'transect' have been used. Line 94-95 Use English name in brackets. English name used. Line 102 'and' should be remove, use comma. 'And' removed, comma added. Line 112 Here you use transect, and it is good expression. Use the same term throughout your work. I think the term sphere is misleading. The word 'transect' used throughout the work. Line 121 'During the capture of beetles...' this is not good English expression. Please improve it. Language improvements have been made. Line 152 It should be GLM, not GML. Did you check the distribution of variables. You don't mention it in Data analysis. GML has been changed to GLM. This model was chosen because the data had a unimodal distribution. The information has been added to the text. Line 157 Why you used the Morisita's measure of similarity, not e.g. Bray-Curtis? Measures of similarity Morisita’s is one of the standard techniques used in NMDS. Line 161 I am not sure that PCA is appropriate analysis. Principal components analysis is a variable-reduction technique that shares many similarities to exploratory factor analysis. Its aim is to reduce a larger set of variables into a smaller set of 'artificial' variables, called 'principal components', which account for most of the variance in the original variables. In your case I propose to use RDA analysis (Redundancy analysis with Monte Carlo permutation test) because it is a method to extract and summarize the variation in a set of response variables that can be explained by a set of explanatory variables. I agree with the comments regarding the PCA analysis. RDA analysis applied. Line 167 'species classified as belonging to...' , please delete 'classified as'. 'Classified as' has been removed. Line 178 Remove 'once' Removed. Line 194-198 Too long sentence Corrected. Line 219 What percentage of variance do the axes describe? As above I think the RDA analysis would be appropriate here not PCA. RDA analysis introduced. REVIEWER 3 (Anonymous) We would like to thank Reviewer 3 for insightful comments on the paper, as these comments led us to an improvement of the work. Basic reporting The manuscript is written in good English. The introduction provides with a sufficient overview of the scientific background of the paper with well-chosen references. The structure of the manuscript is according to the instructions given by PeerJ. All table and figures are relevant for the paper and the figures are of good graphical quality. However, the captions of figs. 2 and 4 have to be improved. In the caption to fig. 2 lacks the information what is shown by the error bars. Did you calculate standard deviation or standard error? This is an important information. The letters “a”, “b, and “c’ should be in capitals as in the figure itself. Please add also the unit for MIB, which is mg. In the caption to fig. 4 it would be helpful for the reader to add at least a reference to the methods with respect to the abbreviations of the ecological groups. Changes have been made. Information on the standard error used has been added to the article. Experimental design The manuscript is based on original primary research, which falls into the general scope of the journal. The basic purpose of the study is well defined (lines 66-69), but the authors might formulate some expectations (research hypotheses). Which kind of changes in carabid assemblages are expected? Which differences regarding the changes are expected between the different studied spheres? Research hypotheses have been added. The methods description should be improved. Information about the basic samples used in statistical analyses is missing. The authors mention that there were 15 traps in each object (5 per transect = sphere), which were emptied every 14 days. However, it is not clear if the material from the14-day-intervals were later pooled for each trap and if each trap was used as sample. Traps were dug in two study sites, 15 traps in each (30 traps in total). Each trap was treated as a replication. This information has been added to the manuscript text. Unfortunately the supplemental files “…raw_data.csv” and “…Statistical_data.csv” also give no precise information concerning this matter. The file, “Statistica_data.csv” contains several lines with data for each trap in each year (for example nine for the year 2009). Thus, I assume that these are nine 14-day-intervals. However, this should be indicated in the file. The file “…raw_data.csv”, however, shows only 5 columns for zone A in 2009. I assume that this are the data for the individual traps pooled. Instead of using “A”, “B”, etc. in this table I propose to use the respective trap numbers. From the file “…Statistical_data.csv” I would conclude that each 14-day-interval was used as basic sample in the statistics, but from fig. 4 I would conclude that the pooled data were used. Therefore, it has to be precisely described in the methods which basic samples were used for which statistical analysis. Supporting files have been corrected. The changes in the methodology were introduced. I also suggest to provide more details regarding the PCA analysis (lines 159-162). Why was a linear method (PCA) selected instead of an unimodal method (CA)? Which settings (for example regarding data transformation) were used in the analyses? According to the reviewer's comments, the PCA analysis was abandoned, while the RDA analysis was used. Results are described accurately, but I like to encourage the authors to rethink the description of the results of the PCA (lines 219-229). PCA analysis has been replaced by RDA analysis with detailed discussion. Even if I agree with the general trends formulated by the authors, the results are complex and not all assemblages of the respective years follow the described trends entirely. Therefore, I recommend to emphasize that the results have more tendency character. Tendency character is underlined. However, only further, many years of research on monitoring of drained areas could answer to whether these processes are short-term and reversible or not. Manuscript text added. I propose also to emphasize the MIB indicator, since there is a clear tendency that assemblages from 2009 are characterized by lower MIB values, what is in accordance with the dominating of large zoophages after the retention project, as stated by the authors. This was emphasized in discussions about RDA analysis. The conclusions formulated by the author are well stated. One or two sentences regarding possibilities of practical application of the results might be added. Information on the practical application of the results has been added. Comments for the author Line 43: Probably it should be “mentioned” instead of “maintained”. Please check. The error has been corrected. Line 55: The reference “Kosewska et al., 2015” is not included in the reference list. Instead, there is a reference “Kosewska & Nietupski, 2015”. Please specify. The error has been corrected. Line 146: Has to be “Pacuła” instead of “Pacuk”. The error has been corrected. Line 524: Please add information when the website was accessed. Information, when the page was visited has been added. Line 525: The reference “Zabrocka-Kostrubiec, 2008” should be shifted to the end of the reference list. The error has been corrected. Reviewer 2 Basic reporting The work is, unfortunately, written in very inadequate English, with several important terms erroneously used (e.g. for feeding type or trophic group, the word 'trophy' is used) that leaves the reader grasping for meaning. Language improvements have been made (native speaker). Terminology have been improved. The conceptual background is murky, and there is considerable confusion about water retention (which is not drainage), as well as what exactly carabids will indicate in this context. The aims of the work are not clear. As there are no specific hypotheses, results are not well structured, and the impression is that the authors themselves are unsure what precisely their findings mean? This leads to very simple and qualitative statements, like the presence of ca. 30% of the regional fauna, the appearance of moisture-preferring species, stb. Diversity analysis is restricted to a very simple and indequate method. Overall, these do not convince the reader that there is much new in this work. Thank you very much for the valuable comments especially for paying attention to the incorrectly used word drainage. Of course, you are right at this issue and it was corrected in the manuscript. It was our huge mistake. Thank you. But on the other hand, we don’t agree with your all opinions about our study. In our opinion, this is very important research, and even on a little local scale, it shows very actual problems of lack of water and ways to solve them with big attention to the environment. Poland classified as a country poor in water is called 'Egypt of Europe'. Therefore, water as an increasingly scarce product has become a subject of special protection, even if the improvement of water relations is associated with temporary, unfavorable changes in biocenosis. The research is not accidental but thought out in terms of using zooindicators, which are ground beetles, to show the directions of fauna changes under the influence of retention works. This has been presented in detail in the aim and hypotheses which have been added. Experimental design There is no real repetition here - only one setup is studied, and the trapping effort is decidedly modest (15 traps). Strange terminology makes me a bit unsure about important details (e.g. what is 'sphere'?) The research question is not well defined, it seems like merely a 'let's see what happened' type. The diversity evaluation is very superficial, and there is no test about the completeness of the study - for example, by species accumulation curves, or an estimation of the number of species present. The claimed information gap is only the scarce studies of peatlands of the study region. No justification is given why does this matter? A single set of traps (15) in 2009 resulted from the fact that the exact location of the retention reservoirs to be constructed was not known at that time. In 2012 and 2013 two such sets were tested. However, even one set showed trends of changes that were confirmed in subsequent years. The number of 15 traps seems to be insufficient in this type of research, but considering that each of them was treated as a single unit and the material was collected continuously for over half a year, the results are representative. This is demonstrated by both the high number of individuals and species of carabids caught, and also the species accumulation curves, made according to the reviewer's suggestion. Attention was also paid to the presence of rare species on the peatlands of these areas, e.g. Epaphius rivularis, which disappeared probably as a result of retention works. This requires further research. Validity of the findings Not a big study with insufficient replication. This undermines the validity. No well-formulated research questions, thus 'results' leave the reader uninterested, and makes difficult to draw meaningful conclusions. In fact, those are very non-specific and qualitative. Imprecise and erroneous terminology adds to the confusion (re. drainage vs. water retention). Comments for the author This work aimed to examine the changes in carabid assemblages triggered by the establishment of a water retention scheme. Unfortunately, the language is so bad that in many places, the reader has to guess the intended meaning. You will have to ask for help from a colleague with a better command of English. You use the term community vs. assemblage as if they were equivalent. I suggest that your study concerns a taxonomic group and not an ecological one, so I'd use assemblage throughout. The correct term then would be 'ground beetle assemblage/s' or 'carabid assemblage/s'. Your trapping intensity is not high - at minimum, you have to prove its completeness by the use of a species-accumulation curve. Diversity evaluation is very primitive - use the scheme suggested by Henderson & Southwood's Ecological methods (2016, Wiley). You also have to think about indication - what do you want to indicate and which parameters of the carabid assemblage would indicate that? This is not mentioned nor elaborated. Thank you very much for your valuable comments. They allowed us to refine and improve our manuscript. Language and terminology have been improved. The researcher's goal was specified by adding research hypotheses. The test results were re-examined and subjected to additional analyzes. I hope that we have also clarified sufficiently the reviewer's doubts. "	Here is a paper. Please give your review comments after reading it.
495	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>This study, concerning the epigeic fauna of carabid beetles (Coleoptera; Carabidae), was conducted in the north-east of Poland, in an area which is part of the Dąbrówka Forest Subdistrict and has been included in the 'Small water retention program for the Province of Warmia and Mazury in 2006-2015'. The purpose of the study was to assess the impact of the water retention implemented within the framework of the above program on assemblages of ground beetles. These insects are highly sensitive to any anthropogenically induced transformations. This analysis was based on the interactions among the analyzed insects caused by changes occurring in their habitat. During the three-year study, 5 807 specimens representing 84 species were captured. The water storage had a significant influence on the structure of the Carabidae assemblages. Before the earthworks were constructed for the project, the beetle assemblages had comprised a large group of xerophilous species, whereas after the small retention reservoirs had been created, an increase in the contribution of hygrophilous species was noticed. The results indicate that the retention works cause alterations in the water and environmental conditions of the habitats, and thereby effect changes in the composition of Carabidae assemblages. Moreover, modification in water relations within a habitat causes long-term changes in the structural and functional diversity of the beetles.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head n='1.'>Introduction</ns0:head><ns0:p>Water, as the principal component modeling the natural environment, plays a significant role in shaping forest ecosystems, in the sense of being both a habitat-forming element and a factor which ensures the stability, sustainability and diversity of habitats <ns0:ref type='bibr' target='#b50'>(Pierzgalski, 2008;</ns0:ref><ns0:ref type='bibr'>Blumenfeld et al., 2010;</ns0:ref><ns0:ref type='bibr' target='#b22'>Koralay & Kara, 2018)</ns0:ref>. Any aberrations from the natural water regime, such as periodic water deficits or excesses, are events that have an adverse effect on the whole ecological system <ns0:ref type='bibr' target='#b54'>(Rulik &White, 2019)</ns0:ref>. Such irregularities can be a consequence of erroneously implemented water retention or naturally occurring hydrological and meteorological processes <ns0:ref type='bibr' target='#b32'>(Liberacki & Szafrański, 2013;</ns0:ref><ns0:ref type='bibr'>Miler et al., 2013;</ns0:ref><ns0:ref type='bibr' target='#b18'>Kędziora et al., 2014)</ns0:ref>. As mentioned by <ns0:ref type='bibr' target='#b36'>Mioduszewski (2010)</ns0:ref>, in compliance with the Framework Water Directive of the European Union and because of the occurrence of water deficits in the entire European Union, all member countries are obligated to maintain an inventory and to protect ecosystems that have an impact on the shaping of proper water balance in nature. They are also required to implement measures to counteract water deficits by improving the retention capacity of the biosphere. In order to take full advantage of forest ecosystems, complex actions are undertaken to decelerate the circulation of water in the catchment while preserving the natural landscape <ns0:ref type='bibr'>(European Environment Agency, 2015)</ns0:ref>. Such measures, called forest retention, are modeled after natural processes occurring in the natural environment <ns0:ref type='bibr' target='#b8'>(Gustafsson et al., 2012)</ns0:ref>.</ns0:p><ns0:p>By restoring the natural retention properties of ecosystems, and thereby improving the availability of water, it is possible to enhance the diversity of flora and fauna in the habitat <ns0:ref type='bibr' target='#b9'>(Hansson et al., 2005;</ns0:ref><ns0:ref type='bibr'>Nietupski, Kosewska & Ciepielewska, 2007;</ns0:ref><ns0:ref type='bibr' target='#b13'>Janusz et al., 2011;</ns0:ref><ns0:ref type='bibr'>Kosewska & Nietupski, 2015)</ns0:ref>.</ns0:p><ns0:p>The modifications which occur in areas where water retention has been introduced can be assessed by analyzing the responses of living organisms. Both intra-and inter-species structures of dependencies between organisms and the area they inhabit provide valuable information about the natural environment <ns0:ref type='bibr' target='#b33'>(Mc Geoch, 1998;</ns0:ref><ns0:ref type='bibr' target='#b6'>Gerlach, Samways & Pryke, 2013)</ns0:ref>. Effective indicator species used in the monitoring of the natural environment are beetles (Coleoptera;</ns0:p><ns0:p>Carabidae) living on the surface of the earth (epigeic) <ns0:ref type='bibr' target='#b65'>(Tőzsér et al., 2019)</ns0:ref>. These insects, as bioindicators of the condition of the environment, are characterized by high sensitivity to changing habitat conditions, especially changes in moisture content. This means that their observation can provide specific data about the current state of the ecosystem in which they live <ns0:ref type='bibr' target='#b51'>(Rainio & Niemelä, 2003;</ns0:ref><ns0:ref type='bibr'>Avgin & Luff, 2010;</ns0:ref><ns0:ref type='bibr' target='#b21'>Koivula, 2011;</ns0:ref><ns0:ref type='bibr'>Kotze et al., 2011</ns0:ref><ns0:ref type='bibr'>, Bednarska et al., 2018)</ns0:ref>.</ns0:p><ns0:p>The aim of this study was to answer the following research hypotheses: 1) long-term modifications of the habitat water relations affect changes in ground beetle diversity, causing the disappearance of xerophilous species and the increase of hygrophilic species, 2) the designated</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:04:48069:2:0:NEW 2 Aug 2020)</ns0:p><ns0:p>Manuscript to be reviewed transects differ in quantitative and qualitative values for the carabidofauna caught, 3) retention works cause the disappearance of rare Carabidae species found on the Red List of endangered animals in Poland <ns0:ref type='bibr' target='#b47'>(Pawłowski, Kubisz & Mazur, 2002)</ns0:ref>. Manuscript to be reviewed study sites, the meadow was adjacent to a ten-year-old mixed coniferous forest with the dominant species being common pine (Pinus sylvestris L.). This area was underlain mostly by Brunic Arenosols with the soil moisture content classified into the category: fresh wet (Forest Data Bank, 2019).</ns0:p></ns0:div> <ns0:div><ns0:head n='2.'>Materials & Methods</ns0:head></ns0:div> <ns0:div><ns0:head>Study</ns0:head></ns0:div> <ns0:div><ns0:head>Data collection.</ns0:head><ns0:p>The water retention project in the Dąbrówka Forest Subdistrict within the framework of Manuscript to be reviewed reservoirs was not known at that time. After the earthworks had been completed, the entomological observations were continued <ns0:ref type='bibr'>(in 2012 and 2013)</ns0:ref> in the area chosen in 2009.</ns0:p><ns0:p>However, when the retention changes had been made, the research area was expanded by adding a second research study site, located to the east of the retention reservoirs. In 2012 and 2013,</ns0:p><ns0:p>traps were dug at both study sites, 15 traps in each, set up in 3 rows and at the same 10-meter distance as in 2009 (30 traps in total) (figure <ns0:ref type='figure' target='#fig_6'>1</ns0:ref>).</ns0:p><ns0:p>A modified Barber traps method was used for beetle capture, caught into containers with a capacity of 500 ml and a height of 12 cm <ns0:ref type='bibr'>(Barber, 1931)</ns0:ref>. The containers were filled up to 1/3 with a solution of ethylene glycol including a small amount of detergent to decrease the surface tension. The traps were dug in level with the ground surface and emptied regularly every 14 days, collecting the entomological material and replacing the preserving solution. <ns0:ref type='bibr'>Poland, 2010;</ns0:ref><ns0:ref type='bibr'>2013;</ns0:ref><ns0:ref type='bibr' target='#b59'>2014)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Data analysis.</ns0:head><ns0:p>Species diversity and abundance of captured beetles were expressed in real numbers of caught specimens and species. The most popular tool, i.e. the Shannon index <ns0:ref type='bibr' target='#b55'>(Shannon-Weaver, 1949)</ns0:ref>, was used to evaluate the biodiversity of the beetles, while the assessment of any deviations was based on mean individual biomass (MIB) <ns0:ref type='bibr' target='#b61'>(Szyszko et al., 2000)</ns0:ref>.</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:04:48069:2:0:NEW 2 Aug 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p><ns0:p>The ecological analysis was carried out using the following indicators: trophic group, phenology, hygropreferences and habitat types (trophic group -(hemizoophages (Hz), small zoophages (Sz): with a body length of no more than 15 mm, large zoophages (Lz): with a body length over 15 mm) <ns0:ref type='bibr' target='#b30'>(Leśniak, 1985;</ns0:ref><ns0:ref type='bibr'>Aleksandrowicz, 2004;</ns0:ref><ns0:ref type='bibr'>Kosewska, Nietupski & Ciepielewska, 2007)</ns0:ref>, phenology -(spring breeders (Sb), autumn breeders (Ab)) <ns0:ref type='bibr' target='#b28'>(Larsson, 1939;</ns0:ref><ns0:ref type='bibr' target='#b64'>Thiele, 1977)</ns0:ref>, hygropreferences -(xerophilous (Xe), mesophilous (M), hygrophilous (H)) <ns0:ref type='bibr' target='#b64'>(Thiele, 1977;</ns0:ref><ns0:ref type='bibr'>Aleksandrowicz, Pakuła & Mazur, 2008)</ns0:ref>, habitat types -(forest (F), open area (Oa), peat bog (Pb), eurytopic (Eu)) <ns0:ref type='bibr'>(Aleksandrowicz, 2004;</ns0:ref><ns0:ref type='bibr'>Kosewska, Nietupski & Ciepielewska, 2007)</ns0:ref>.</ns0:p><ns0:p>To determine the significance of differences between the basic parameters describing the biodiversity of the Carabidae assemblages in the analyzed transects and years, the generalized linear model (GLM) was used, which helped to determine the p values using Statistica 13.1 software (StatSoft, Inc.). A test of the significance of effects comprised in the model was carried out according to Wald's statistics. Jackknife 2 estimator was used for abundance data (using EstimateS v. 9.1.0 statistical software) and the species accumulation curves, were calculated to access the adequacy of the sampling efficiency <ns0:ref type='bibr'>(Zahl, 1977;</ns0:ref><ns0:ref type='bibr' target='#b2'>Colwell, 2005)</ns0:ref>. Assessment of the similarity of the Carabidae assemblages in the examined transects and the years of the study was made with the help of a non-metric multidimensional scaling (NMDS), using Morisita's measure of similarity. Assessment of the significance of differences between the analyzed assemblages in the NMDS method was carried out using the ANOSIM non-parametric statistical test <ns0:ref type='bibr' target='#b17'>(Kenkel & Orloci, 1986;</ns0:ref><ns0:ref type='bibr' target='#b0'>Clarke, 1993)</ns0:ref>. Impact of environmental variables (year, transect) on the species composition of Carabidae was determined using</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:04:48069:2:0:NEW 2 Aug 2020)</ns0:p><ns0:p>Manuscript to be reviewed redundancy analysis RDA (Ter <ns0:ref type='bibr' target='#b63'>Braak & Šmilauer, 1998)</ns0:ref>. This method was chosen based on an analysis of the data distribution, which was linear (SD = 1.58).</ns0:p><ns0:p>Each trap was used as a replication for GLM analysis (mean values of abundance, species richness, mean individual biomass MIB) and for species accumulation curves. For NMDS and RDA analysis the data were pooled. Each sample used for statistical analysis consisted of data from all observations in the studied season.</ns0:p></ns0:div> <ns0:div><ns0:head n='3.'>Results</ns0:head><ns0:p>During the three-year study, 5 807 specimens representing 84 species of Carabidae were caught (table <ns0:ref type='table'>1</ns0:ref>). Before the small retention program was implemented, 1 377 specimens belonging to 52 species were caught. In 2012, the number of species captured at the study site I did not change, although the number of carabid beetles caught (927 specimens) decreased. In the final year of the study, the number of species rose to 61, and their abundance increased to 1 493 specimens. At study site II, which was a replication, 965 specimens representing 57 species were caught, and the number of carabid individuals caught in 2013 increased to 1 045, while the number of species fell to 43 (table <ns0:ref type='table'>1</ns0:ref>). After the implementation of the water retention, the occurrence of new, hygrophilous species (Patrobus atrorufus STRØM, Bembidion mannerheimii C. R. SAHLBERG) and an increase in the share of rare, disappearing or threatened species (Oodes helopioides FABRICIUS, Carabus convexus FABRICIUS, Carabus marginalis FABRICIUS) was observed. Moreover, following the implementation of the water retention program, the presence of a rare species, such as Philorhizus sigma P. ROSSI, was noted (table <ns0:ref type='table'>1</ns0:ref>). Despite the rise in the percentage of stenotopic species the hydro-technical works had been completed, the extremely endangered species Epaphius rivularis SCHRANK disappeared (table <ns0:ref type='table'>1</ns0:ref>).</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:04:48069:2:0:NEW 2 Aug 2020)</ns0:p><ns0:p>Manuscript to be reviewed The results obtained from the generalized linear model (GLM) showed a statistically significant impact of the variable factors (transect, year) and their interaction with respect to the presence of epigeic carabid beetles (table <ns0:ref type='table'>2</ns0:ref>). The GLM model was chosen because the data had a unimodal distribution. The transects chosen in the study differed from each other in the quantities of individuals and number of species of ground beetles caught (table <ns0:ref type='table'>3</ns0:ref>). The highest values concerning the number of species and number of individuals prior to the hydro-technical modifications were noted in transect A (peat meadow), while the lowest ones were in transect C (forest) (figure 2 a, b). This is supported by the biological diversity assessment, which additionally demonstrated the most significant contrast during the three-year research between transects A (1.25) and C (0.4) (table <ns0:ref type='table'>3</ns0:ref>). After the execution of the small water On the other hand, the vector describing 2013 revealed a tendency corresponding to the state of the carabid assemblages in 2009, which indicates the tendency of the structure of carabid assemblages to return to the state before implemention of the water retention program (figure <ns0:ref type='figure'>5</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head n='4.'>Discussion</ns0:head><ns0:p>This study, conducted in the Dąbrówka Forest Subdistrict, showed that the number of individuals and species diversity of the captured carabid beetles was at a high level in comparison with results achieved on peatlands in Belarus, Germany and Slovakia <ns0:ref type='bibr'>(Aleksandrowicz, 2002;</ns0:ref><ns0:ref type='bibr'>Buchholz, Hannig & Schirmel, 2009;</ns0:ref><ns0:ref type='bibr' target='#b12'>Igondová & Majzlan, 2015)</ns0:ref>. In our study, we observed over 29 % of all carabid species present in the Masurian Lake District, which may suggest that the analyzed area plays a significant role in the preservation of biological diversity <ns0:ref type='bibr'>(Aleksandrowicz, Gawroński & Browarski, 2003;</ns0:ref><ns0:ref type='bibr' target='#b46'>Pacuk & Regulska, 2014)</ns0:ref>. Similar conclusions can be drawn from analysis the quantitative and qualitative composition of carabid beetle assemblages from peatlands located in the north-east of Poland <ns0:ref type='bibr' target='#b40'>(Nietupski et al. 2008a</ns0:ref>;</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:04:48069:2:0:NEW 2 Aug 2020)</ns0:p><ns0:p>Manuscript to be reviewed confirm that when moisture conditions deteriorate, the diversity of flora decreases, which causes impoverishment of fauna. Hence, this is another parameter which directly affects the species composition and the abundance of captured insects, by being a factor that decides about their ability to survive, thereby differentiating the quality of insect assemblages <ns0:ref type='bibr' target='#b64'>(Thiele, 1977;</ns0:ref><ns0:ref type='bibr' target='#b37'>Nietupski, Kosewska & Ciepielewska, 2006)</ns0:ref>. <ns0:ref type='bibr' target='#b14'>Jaworski & Hilszczański (2013)</ns0:ref> emphasize that temperature and humidity have both direct and indirect effects on all aspects of the life of both the larvae and imagines of insects.</ns0:p><ns0:p>Out of the three years of observations, the entomological material gathered in the first year after the hydro-technical modifications was the least numerous, but this may have been caused by stronger anthropopressure on the analyzed ecosystems. Similar conclusions are drawn by <ns0:ref type='bibr' target='#b57'>Skłodowski and Mądrzejewska (2008)</ns0:ref> and by <ns0:ref type='bibr' target='#b52'>Rosenzweig (1995)</ns0:ref>, who claim that environmental disturbances have a considerable influence, leading to the reduced biodiversity of local ecosystems. Moreover, that year was characterized by relatively high atmospheric precipitation (700 mm), which could also have had an influence on the presence of carabid beetles <ns0:ref type='bibr'>(Central Statistical Office in Poland, 2013)</ns0:ref>. In turn, the increase in the diversity of carabids in the last year of the study <ns0:ref type='bibr' target='#b35'>(2013)</ns0:ref> may be indicative of the natural conditions slowly normalizing after the implementation of changes in the water relations, whose main goal was to</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:04:48069:2:0:NEW 2 Aug 2020)</ns0:p><ns0:p>Manuscript to be reviewed improve the moisture conditions. These results are in accord with those reported by <ns0:ref type='bibr' target='#b15'>Jędryczkowski and Kupryjanowicz (2005)</ns0:ref>, who emphasize that species diversity grows proportionally to the increase in the area's moisture content. The effect of soil moisture in forest habitats on beetle assemblages was also demonstrated by <ns0:ref type='bibr' target='#b16'>Kagawa and Maeto (2014)</ns0:ref>.</ns0:p><ns0:p>Changes in the habitat induced by the implementation of the small water retention program in the Dąbrówka Forest Subdistrict also affected the distribution of the carabid beetles in the three research transects. Before the earthworks, the diversity of carabid species decreased directly proportionally to the increasing distance of the set transects from the open area of the peatland meadow (table <ns0:ref type='table'>3</ns0:ref>). The earthworks caused certain disturbances in the habitat, associated, inter alia, with increased soil moisture, which caused changes in the spatial structure of carabid assemblages. Fluctuations in the natural environment due to the retention works created stress conditions, which were most distinctly evidenced during the three-year period in transect A.</ns0:p><ns0:p>Transect A, located closest to the retention reservoirs, responded the most to the induced changes, which is reflected in the graphical shift of the polygon describing the similarity of the Carabidae assemblages in this transect in the multidimensional spatial scaling approach (NMDS) (figure 4 a, b, c). Following the hydro-technical modifications, the species-richest study site (except study site 2012 II) was transect B (table <ns0:ref type='table'>3</ns0:ref>). This transect, by being localized at the edge of two different habitats, could have been conducive to a rise in biodiversity <ns0:ref type='bibr'>(Yu et al., 2007;</ns0:ref><ns0:ref type='bibr' /> Banul, <ns0:ref type='bibr'>Kosewska & Borkowski, 2018)</ns0:ref>. <ns0:ref type='bibr' target='#b60'>Szyszko (2002)</ns0:ref> states that transitional zones between two different habitats are an excellent foraging site, and subsequently a good place for the development of carabid beetles. Additionally, the above transect in our study was an area subjected to a lesser influence of environmental disturbances, which may also have contributed to higher species diversity. The lowest diversity was noted in the forest zone (transect C) (table</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:04:48069:2:0:NEW 2 Aug 2020)</ns0:p><ns0:p>Manuscript to be reviewed The role of the stable woodland ecosystem in shaping species diversity and abundance of insects has been highlighted by <ns0:ref type='bibr' target='#b59'>Sushko (2014)</ns0:ref>, who carried out entomological studies in Lithuania.</ns0:p><ns0:p>Sushko showed a greater diversity of carabid fauna in a birch forest than in the adjacent peatland.</ns0:p><ns0:p>Furthermore, he detected similar species diversity of carabid beetles in a pine forest and on peatland, but determined that the number of beetles in the pine forest was twice as high as on the peatland <ns0:ref type='bibr' target='#b58'>(Sushko, 2007)</ns0:ref>. <ns0:ref type='bibr' target='#b4'>Dapkus and Tamutis (2008)</ns0:ref> reported that the number of species on peatland and in a bordering pine forest was similar, but twice as many carabid specimens were detected in the forest than on the peatland. The adaptability of carabid beetles in the course of evolution led to the emergence of individuals with changeable preferences in regard to habitat, moisture and trophy, or representing different development types <ns0:ref type='bibr' target='#b64'>(Thiele, 1977)</ns0:ref>. Our analysis, in line with the division adopted for the sake of this research, showed the presence of representatives of all categories of the carabid fauna living in Poland, in terms of their preferred habitat, moisture conditions and trophy. This may be an argument in favor of the internal diversity of the analyzed habitats, and further indicating the high diversity of insects inhabiting this area. The fluctuations that had appeared shaped the structures of biocenoses and the created environmental factors gave rise to new, often valuable populations <ns0:ref type='bibr' target='#b56'>(Skłodowski & Zdzioch, 2006)</ns0:ref>. <ns0:ref type='bibr' target='#b3'>Czyżyk and Porter (2017)</ns0:ref> undertook a study to assess the influence of small water reservoirs created in woodlands on the surrounding environment. The results they obtained showed the impact of the water bodies on the food base for Carabidae, as a result of which structural changes occurred in carabid assemblages.</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:04:48069:2:0:NEW 2 Aug 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p><ns0:p>Additionally, analysis of transects located at different distances from the water reservoirs revealed the effect of the distance to these study sites on the quantitative and qualitative composition of epigeic carabid beetle assemblages. The differences observed between Carabidae populations in the years of our study, presented via NMDS analysis, only indicate the significance and general direction of changes, which were probably effected by habitat transformations caused by water-and earthworks. A more detailed analysis of changes occurring in carabid assemblages is provided by the RDA redundancy analysis (figure <ns0:ref type='figure'>5</ns0:ref>). The completed earthworks caused some changes in the habitat, including higher soil moisture. This resulted in a decrease in the share of xerophilic hemizoophages with autumn type of development and an increase in the share of zoophages with higher water requirements, the spring type of development inhabiting forests, open areas and peat bogs, or being eurytopes.</ns0:p><ns0:p>The character of changes in the structure of epigeic carabid assemblages induced by the land retention works, in general, should be perceived as positive, in agreement with the expectations of what condition this type of habitat ought to be in <ns0:ref type='bibr' target='#b3'>(Czyżyk & Porter, 2017)</ns0:ref>.</ns0:p><ns0:p>Zabrocka-Kostrubiec (2008) concludes that measures implemented under the umbrella of small water retention programs play a significant and beneficial role in forest management practice and, in the long term, they assure the permanently sustainable development of woodland ecosystems included in such programs. However, the effects are not unidirectional, because water retention transformations also lead to the disappearance of certain habitats <ns0:ref type='bibr'>(Bajkowski et al., 2000)</ns0:ref>, and in the case of peatland and marsh habitats, they can result in considerable ecological transformations <ns0:ref type='bibr' target='#b7'>(Grzywna, 2010)</ns0:ref>. Thus, certain concerns can be raised by the fact that such rapid habitat-related changes may result in the disappearance of rare, stenotopic species, such as E. rivularis in our study. The question of whether this is a permanent outcome, or</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:04:48069:2:0:NEW 2 Aug 2020)</ns0:p><ns0:p>Manuscript to be reviewed whether that species will reappear in the habitat after some time, can be resolved only through further observations.</ns0:p></ns0:div> <ns0:div><ns0:head n='5.'>Conclusions</ns0:head><ns0:p>Water as a scarce product is a current problem worldwide. For this reason, programs are being implemented whose main goal is to prevent water shortages. One such program is small water retention. When introducing such changes, however, we must be aware that retention works induce transformations of habitats, and thereby effect changes in the composition of valuable Carabidae fauna. The implementation of this program, on the one hand, causes a decrease in the number of carabid beetles and certain disturbances in the previous structure of their assemblages but, on the other hand, it enables the appearance of Carabidae fauna typical for this type of habitat, with specific habitat and moisture requirements, often valuable in the natural environment. It can, therefore, be concluded that small water retention projects have a strong effect on epigeic fauna, and the transformation mostly involves xerophilous species being replaced by hygrophilous species, with greater ecological adaptability (eurytopic species with the spring type of development). Another demonstrable change is the increase in the number of peat bog carabids, which indicates the direction of habitat-related changes induced by hygrotechnical works. In consequence, the area undergoes transformation and is again settled by hygrophilous organisms, which are otherwise often on the brink of extinction. However, only many further years of research and monitoring of retention areas could give an answer as to whether these processes are short-term and reversible or not.</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:04:48069:2:0:NEW 2 Aug 2020)</ns0:p><ns0:p>Manuscript to be reviewed Manuscript to be reviewed</ns0:p><ns0:p>Badister sodalis <ns0:ref type='bibr'>(DUFTSCHMID, 1812)</ns0:ref> Pb, H, Sz, Sb 0 0,00 1 0,11 0 0,00 2 0,13 0 0,00</ns0:p><ns0:p>Bembidion doris (PANZER, 1796) Pb, H, Sz, Sb 0 0,00 0 0,00 0 0,00 1 0,07 0 0,00</ns0:p><ns0:p>Metallina properans (STEPHENS, 1828) Oa, M, Sz, Sb 0 0,00 1 0,11 1 0,10 0 0,00 0 0,00</ns0:p><ns0:p>Bembidion quadrimaculatum (LINNAEUS, 1760) Oa, M, Sz, Sb 0 0,00 0 0,00 0 0,00 1 0,07 0 0,00 Oa, Xe, Lz, Sb 0 0,00 0 0,00 0 0,00 1 0,07 0 0,00</ns0:p><ns0:p>Harpalus laevipes ZETTERSTEDT 1828 F, M, Hz, Sb 0 0,00 0 0,00 1 0,10 1 0,07 1 0,10</ns0:p><ns0:p>Lebia chlorocephala (J. J. HOFFMANN, 1803) Oa, M, Sz, Sb 0 0,00 2 0,22 0 0,00 0 0,00 0 0,00</ns0:p><ns0:p>Limodromus assimilis PAYKULL, 1790 F, H, Sz, Sb 0 0,00 1 0,11 0 0,00 0 0,00 0 0,00</ns0:p><ns0:p>Nebria brevicollis (FABRICIUS, 1792) Eu, M, Sz, Ab 0 0,00 0 0,00 0 0,00 0 0,00 1 0,10</ns0:p><ns0:p>Notiophilus biguttatus (FABRICIUS, 1779) F, M, Sz, Sb 0 0,00 0 0,00 1 0,10 0 0,00 0 0,00</ns0:p><ns0:p>Philorhizus sigma (P. ROSSI, 1790) Oa, Xe, Sz, Sb 0 0,00 0 0,00 1 0,10 0 0,00 0 0,00</ns0:p><ns0:p>Pterostichus anthracinus (ILLIGER, 1798) Pb, H, Sz, Ab 0 0,00 0 0,00 1 0,10 0 0,00 0 0,00 Manuscript to be reviewed Manuscript to be reviewed </ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>area.The study was completed in north-eastern Poland, in the Province of Warmia and Mazury, in the Dąbrówka Forest Subdistrict (UTM DE 66). Letter from the Olsztyn Forest District confirming the consent to make the area available, sent to the PeerJ publisher.Entomological observations were carried out on an area formerly used for farming and then afforested and turfed. Two study sites were selected, characterized by different moistureconditions and located at a different distance to retention water reservoirs ('a' and 'b') created as part of the Programme of small retention for the Province of Warmia and Mazury in 2006-2015 (figure 1) (www.sporol.warmia.mazury.pl). The first study site was located 128 meters from a forest road, 36 m from retention water reservoir 'a', and 62 m from retention water reservoir 'b'. The second study site was situated at a distance of 100 m from the first , 47 m from the forest road, 39 m from reservoir 'a' and 15 meters from reservoir 'b' (Forest Data Bank, 2019). Both study areas were characterized by similar habitat conditions. Three transects were chosen at each study site: transect A -on a waterlogged peat meadow, with the highest moisture content; transect B -an ecotone between the peat meadow and a forest, located 20 m away from transect A, and characterized by lower water abundance; transect C -situated in a pine forest, 20 meters away from transect B, with the lowest moisture content. The area covered by extensive meadow was dominated by peat soils of transitional peatland with the soil moisture content classified as a very wet, and very acid reaction. At both PeerJ reviewing PDF \| (2020:04:48069:2:0:NEW 2 Aug 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>the program mentioned above started in 2010 with a design phase, and was continued in the form of earthworks in 2011. The comprehensive project consisted of new water retention reservoirs (www.olsztyn.olsztyn.lasy.gov.pl). Studies on beetles of the Carabidae family were conducted in 2009, 2012 and 2013. The first entomological observations were made prior to the retention works connected with the project's implementation. The main purpose of the investigations carried out in 2009 was to make an assessment of the original state of Carabidae assemblages in the area designated for the project. The research continued in 2012 and 2013 after the hydro-technical works had been completed, aimed at providing information on the extent to which the project affected the presence of the analyzed beetles. In 2009, the field research began on 21 April and finished on 22 September (153 days). In 2012, the field research was carried out from 27 April to 26 October (181 days), and in 2013 it lasted from 25 April to 9 October (166 days). In 2009, traps were placed at study site I, linearly, in 3 transects (A, B, C). In each transect, there were 5 traps spaced at 10 meters (15 traps in total) (figure 1). The position of research study site I in 2009 was chosen intuitively as the exact location of the planned retention PeerJ reviewing PDF \| (2020:04:48069:2:0:NEW 2 Aug 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head /><ns0:label /><ns0:figDesc>Evaluation of meteorological conditions was based on measurements and observations carried out at the Meteorological Station in Olsztyn. Values of the mean annual air temperature during the three years of the study fluctuated only a little (7.7 ºC in 2009, 7.5 ºC in 2012 and 7.8 ºC in 2013). The average sum of atmospheric precipitation during the three years of the study was 629 mm (604 mm in 2009, 700 mm in 2012, 582 mm in 2013) (Central Statistical Office in</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ reviewing PDF \| (2020:04:48069:2:0:NEW 2 Aug 2020)Manuscript to be reviewed retention project, there were no quantitative or qualitative dependences confirmed proportional to the distance of the determined transects from the retention reservoirs (figure 2 a, b). In 2012, the species richest transect was transect B (forest-meadow ecotone), while the fewest species were caught in transect C (forest). In 2013, the final year of our observations, the above values in individual transects were approximately the same. The MIB analysis showed that the transect localized in a forest habitat (C) was characterized by the highest values in each year of the study (figure2 c).Species accumulation curves for individual transects in the three years of the study confirmed that the sampling effort was adequate (figure3). Only the rarefaction curve from transect A, located closest to the water reservoirs, in the year of transformation (2012) did not reach an asymptote. This was probably due to unstable conditions leading to fauna migration, which is reflected in the number of singletons (over 50%).Graphical and statistical analysis of the interpretation of differences in particular transects was presented with the aid of the non-metric multidimensional scaling (NMDS) analysis. Theanalysis showed that the Carabidae assemblages inhabiting the analyzed transects, before and after the implementation of the small retention program, were significantly different between one another (ANOSIM; A: R=0.30, B: R=0.26, C: R=0.32; p<0.01). The curves in the graphs illustrating the similarity between the captured Carabidae assemblages in the individual years of entomological observations (Stress A=0.22, B=0.16, C=0.14) demonstrated that the statistically most significant difference was in transect A (figure 4 a, b, c). RDA analysis showing the relationships between ecological groups of ground beetles and environmental variables (year, transect) showed that the first ordination axis, determining 82.4% of diversity, is positively correlated with the occurrence of large forest zoophages, whose PeerJ reviewing PDF \| (2020:04:48069:2:0:NEW 2 Aug 2020)Manuscript to be reviewed occurrence determines the growth of MIB. This condition is characteristic of stable habitats, which is typical for forest areas (transect C). An inverse correlation was found for transects A and B. Transect A was correlated with the presence of hygrophilous species associated with peat bogs, while transect B showed a strong correlation with the occurrence of small zoophages belonging to eurytopic and open area species. RDA analysis also showed that the research years were correlated mainly with the second ordination axis, describing almost 10% of the variation.The highlighted factor -2009 -is correlated with the occurrence of xerophilic hemizoophages with an autumn type of development. The vector describing 2012 showed an inverse correlation, which indicates large changes in ground beetle assemblages as a result of earth transformations.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head /><ns0:label /><ns0:figDesc><ns0:ref type='bibr' target='#b41'>Nietupski, Ciepielewska & Kosewska, 2008b;</ns0:ref><ns0:ref type='bibr' target='#b44'>Nietupski et al., 2010;</ns0:ref><ns0:ref type='bibr' target='#b45'>Nietupski, Kosewska & Lemkowska, 2015)</ns0:ref>. Because of the poor knowledge of entomofauna inhabiting areas subjected to water retention programs,<ns0:ref type='bibr' target='#b11'>Homburg et al. (2014)</ns0:ref> suggest that it is necessary to gain more insight into this research question. Our studies conducted in the Olsztyn Forest District have demonstrated that the impact of hydro-technical works directly affect the typical habitat conditions and the composition of caught entomofauna.<ns0:ref type='bibr' target='#b20'>Kiryluk (2012)</ns0:ref> andAbell et al. (2019) </ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head /><ns0:label /><ns0:figDesc>3). Forest habitats are characterized by relatively low diversity, but these assemblages are stable and quite resistant to environmental changes. This opinion has been verified by the observations made in the forest ecosystem located in the Dąbrówka Forest Subdistrict (C) (figure 4 a, b, c).</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head>Figure 1</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_7'><ns0:head>Figure 2 A</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='38,42.52,70.87,307.92,672.95' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='42,42.52,70.87,262.65,672.95' type='bitmap' /></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head /><ns0:label /><ns0:figDesc>Calathus erratus C.R. SAHLBERG (17.21%), which was not detected after the water retention project had been implemented. In 2012 and 2013, an increase occurred in the share of hygrophilous species classified as small predators of the spring type of development, they may be eurytopic or they may be associated with open areas (Dyschirius</ns0:figDesc><ns0:table /><ns0:note>The dominant species in both study sites and in all years of the study was the forestmesophilous beetle Pterostichus niger SCHALLER (2009: 22.15%, 2012 (I): 31.28%, 2012 (II): 32.75%, 2013 (I): 28.80%, 2013 (II): 32.34%). In the first year, another numerous species was the forest xerophilous globosus HERBST, Agonum fuliginosum PANZER, Agonum sexpunctatum LINNAEUS) or connected with forests as their habitat (Pterostichus strenuus PANZER, Oxypselaphus obscurus HERBST). In addition, following the water retention, there was a rise in the abundance of peatland hygrophilous beetles (Carabus granulatus LINNAEUS, Oodes helopioides FABRICIUS, Pterostichus diligens STURM, Leistus terminatus PANZER, Patrobus atrorufus STRØM) (table 1).</ns0:note></ns0:figure> <ns0:note place='foot' n='3'>PeerJ reviewing PDF \| (2020:04:48069:2:0:NEW 2 Aug 2020)Manuscript to be reviewed</ns0:note> </ns0:body> "	" University of Warmia and Mazury in Olsztyn Emilia Ludwiczak Faculty of Environmental Management and Agriculture Department of Entomology, Phytopathology and Molecular Diagnostics Prawochenskiego 17, 10-720 Olsztyn, Poland email: emilia.ludwiczak@uwm.edu.pl August 2th, 2020 Dear Editor We thank the reviewers for their comments on the manuscript. All proposed changes and suggestions, both of the Editor and Reviewers, have been accepted and implemented. • information on the consent of the Olsztyn Forest District for access to the research area has been attached to the manuscript methodology, the scan of the permit is included in the supplementary files, • a literal error in the reference by Igondová & Majzlan, 2015 has been corrected, • each trap was treated as a replication - this information was by mistake removed from the manuscript text. It was our mistake. Thank you for your valuable attention. This manuscript text has been added: Each trap was used as a replication for GLM analysis (mean values of abundance, species richness, mean individual biomass MIB) and for species accumulation curves. For NMDS and RDA analysis the data were pooled. Each sample used for statistical analysis consisted of data from all observations in the studied season. We believe that the manuscript is now suitable for publication in PeerJ. Thank you for your consideration Sincerely Yours Emilia Ludwiczak, PhD "	Here is a paper. Please give your review comments after reading it.
496	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Background. Photosynthesis and cellular respiration play major roles in energy metabolism and are important Life Science topics for K16 Biology students. Algae beads are used for photosynthesis and cellular respiration labs. Currently there are a few companies that sell biology educational kits for making algae beads using non-motile green micro-algae to introduce students to photosynthesis. These kits are expensive and, do not come with detailed guidelines for trouble shooting and customizations for different grade levels. Chlamydmonas reinhardtii is a motile green micro-alga and is an excellent model system for photosynthesis studies. In this article, we are presenting the work conducted in the student-driven, American Society of Plant Biologists-funded, Plant-BLOOME educational outreach project. This project is a supervised collaborative effort of three undergraduates and one high school student. We have generated a protocol which can be used to make Chlamydomonas beads. We have used these beads to design two simple and inexpensive plant biology hands-on activities. These laboratory activities have been customized to teach the interplay of photosynthesis and cellular respiration to K4 -K16 Biology students.</ns0:p><ns0:p>Methods Chlamydomonas beads were used for two different laboratory activities that involved monitoring pH changes over time using a pH indicator. Our first activity centers on making and, using light-powered algae bead bracelets to monitor dramatic color/pH changes over time when exposed to darkness or light. Our second activity employs strain-specific algae beads with approximately equal cell numbers to conduct comparative photosynthesis and cellular respiration studies in two Chlamydomonas strains namely, wild type, 4A+ and, a high light-sensitive, photosynthetic mutant, 10E35/lsr1a.</ns0:p><ns0:p>Results. We optimized our experimental protocol using algae beads in a 5.5 mL screw capped glass vials before performing the same experiment in algae bead bracelets. We found that the algal cell density/bead, water type used in the experiment and, the duration of dark exposure of algal beads can affect successful implementation of the lab activities. Light-powered algae bead bracelets showed dramatic color/pH change within 3 hours upon exposure to light or darkness. These bracelets could be switched back and forth between darkness and light multiple times within 48-72 hours to display color/pH changes, provided prior dark exposure time did not exceed 9 hours. Our comparative studies of photosynthesis and cellular respiration in 10E35 and in 4A+ showed that relative respiration rate and photosynthetic rate is higher and lower in 10E35, respectively, compared to that in 4A+. Additionally, 10E35 failed to display the expected photosynthesis-induced pH/color changes in the light after prolonged exposure to darkness which indicated that prolonged dark exposure of 10E35, hindered</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>Photosynthesis, an energy requiring anabolic process, comprises of two sets of reactions that occur in the chloroplast: Light reaction and Calvin cycle. In the light reaction, solar energy captured by photosynthetic pigments is used to photolyze water into electrons and protons. These protons and electrons are ultimately used to generate ATP (adenosine triphosphate), a reducing power and oxygen in the light reaction <ns0:ref type='bibr' target='#b18'>(Tymoczko, Berg & Stryer, 2015)</ns0:ref>. ATP and the reducing power generated in the light reaction and water, are used in the Calvin cycle to reduce atmospheric carbon dioxide to sugar <ns0:ref type='bibr' target='#b18'>(Tymoczko, Berg & Stryer, 2015)</ns0:ref>. Aerobic cellular respiration is a catabolic energy releasing process that oxidizes fixed carbon to generate ATP <ns0:ref type='bibr' target='#b18'>(Tymoczko, Berg & Stryer, 2015)</ns0:ref>. Oxygenic photosynthesis provides not only fixed carbon that is utilized by cellular respiration for energy production but, it is also the only source for generation of oxygen on a mass scale on Earth to support life <ns0:ref type='bibr' target='#b18'>(Tymoczko, Berg & Stryer, 2015)</ns0:ref>. Hence, every Biology students should have a broad understanding of these two complementary, life-supporting, fundamental biochemical reactions. These two biochemical reactions are listed in the Next Generation Science Standards (NGSS) Life Science core idea LS1C: From Molecules to Organisms: Structures & Processes. LS1C aligns with principles 1, 2, 3, 5, 10 and 11 of the 12 Principles of Plant Biology listed by the American Society of Plant Biologists ('The 12 Principles of Plant Biology', ASPB; Article S1). Guidelines for photosynthesis and cellular respiration laboratories using non-motile green microalgae bead are available on the websites of Carolina Biological (Burlington, NC), Bio-Rad (Hercules, CA) and Gene Technology Access Center (GTAC; Victoria, Australia) for classroom use. ('Carolina Quicktips Making Algae Beads', Carolina Biological; 'Photosynthesis and Cellular Respiration Kit for General Biology', Bio-Rad; 'Algae Immobilised in Alginate balls', GTAC, 2016). Traditionally, non-motile algae like Chlorella, Ankistrodesmus and Scenedesmus have been used to generate algae beads as non-motile algae can be trapped and immobilized easily ('Carolina Quicktips Making Algae Beads', Carolina Biological; 'Algae Immobilised in Alginate balls', GTAC, 2016; 'Photosynthesis and Cellular Respiration Kit for General Biology', Bio-Rad). Chlamydomonas reinhardtii is motile green micro-alga and is an excellent model system for photosynthesis and bioenergy researchers <ns0:ref type='bibr' target='#b7'>(Merchant et al., 2007;</ns0:ref><ns0:ref type='bibr' target='#b12'>Scranton et al., 2015;</ns0:ref><ns0:ref type='bibr' target='#b10'>Radakovits et al., 2010)</ns0:ref>. At our research laboratory we use Chlamydomonas as an experimental system to study photo-protection and photosynthetic pigment metabolism. Our ASPB-funded Plant-BLOOME educational outreach student-driven project centers on designing new educational hands-on activities using exclusively, Chlamydomonas and not any other alga. There is one recent report of immobilization of a Chlamydomonas strain for photobiohydrogen production under anaerobic sulfur-deprived conditions in different types of photobioreactors <ns0:ref type='bibr' target='#b1'>(Canbay, Kose & Oncel, 2018)</ns0:ref>. Low yield of biofuel from large-scale algal cultures in bioreactors is a major problem. Sustainable bioenergy production can be improved by immobilizing motile algae like Chlamydomonas and Botryococcus sp. that are employed for bioenergy research <ns0:ref type='bibr' target='#b1'>(Canbay, Kose & Oncel, 2018;</ns0:ref><ns0:ref type='bibr' target='#b10'>Radakovits et al., 2010)</ns0:ref>. Our educational trips to schools in Georgia and our participation at the education booths at the NSTA meeting in Atlanta in 2018 and at the Plant Biology 2019 in San Jose, CA, clearly showed us that K6-K16 students (and even educators) love to make algae beads (Text S1). Bead-making activity is excellent for student engagement in classrooms, which cannot be achieved by using commercially purchased pre-made beads. Unfortunately, anonymous teacher and student surveys that we collected cannot be shared with public because our institution did not submit IRB application materials for this project. Glimpses of our educational outreach activities can be found at several available links shown in Text S1. Commercial kits from Bio-Rad or other vendors are costly when one considers how many students can be served per commercial kit and the duration of the time the kit can be used in classrooms (see Materials and Methods and Text S2 for detailed calculation). Commercial educational kits often do not work well, uses beads with short shelf lives, takes long time to show color change and, sometimes comes with erroneous instructions (For example: Carolina instruction sheet instructs educators to grow dense cultures of Chlorella for 3-4 weeks before harvesting cells for bead-making. This means the company is instructing educators to make algal beads using a culture that is in the late stationary phase; https://www.carolina.com/pdf/activitiesarticles/carolina-qt-making-algae-beads-cb814921806.pdf ). Technical resources that comes with these kits lack specific guidelines for optimizing the experiment and troubleshooting. Hence a well-defined protocol with proper detailed guidelines for conducting lab activities & managing class times and, information for acquiring lab materials inexpensively, will be useful Biology educators at schools and institutions that have very limited resources and funding. Calcium alginate is used to trap and immobilize living cells in industrial procedures ('Carolina Quicktips Making Algae Beads, Carolina Biological; 'Algae Immobilised in Alginate balls', GTAC, 2016; 'Photosynthesis and Cellular Respiration Kit for General Biology', Bio-Rad). For example, immobilized non-motile colonial algae are being tested for biofuel production, immobilized yeast cells are being used for alcoholic wine fermentation, and immobilized bacterial cells are being used for water disinfection <ns0:ref type='bibr' target='#b5'>(Kröger & Müller-Langer, 2012;</ns0:ref><ns0:ref type='bibr' target='#b4'>Gotovtsev et al., 2015)</ns0:ref>. To entrap algae in beads, cell suspension and 2% sodium alginate are mixed at a specific ratio and added drop-wise to chilled calcium chloride solution. Calcium ions link the alginate monomers together to make a gel-like polymer of calcium alginate which trap cells and immobilize them in beads. These algal beads can be used for biological experiments or other biotechnological applications. Cellular respiration oxidizes organic chemicals and releases CO 2 into the environment irrespective of presence/absence of light and, photosynthesis converts CO 2 into fixed carbon only in the presence of light <ns0:ref type='bibr' target='#b18'>(Tymoczko, Berg & Stryer, 2015)</ns0:ref>. Cellular respiration in live cells in the beads will release CO 2 that will dissolve in water in which the beads are immersed to generate carbonic acid ('Photosynthesis and Cellular Respiration Kit for General Biology', Bio-Rad; 'Algae Immobilised in Alginate balls', GTAC, 2016). Conversely in the light, photosynthesis in the algal cells in the beads will remove CO 2 from the water surrounding the beads ('Photosynthesis and Cellular Respiration Kit for General Biology', Bio-Rad; 'Algae Immobilised in Alginate balls', GTAC, 2016). In the light, cellular respiration is still going on in the cells, but the net use of CO 2 by photosynthesis vastly outweighs the CO 2 released during cellular respiration, unlike that in the dark. Hence pH of the water will be acidic in the dark and alkaline in the light. In the two activities designed by us, students will monitor photosynthesis and cellular respiration-induced pH changes in the water by color changes of a pH indicator as well as by measuring the pH with pH testing strips and/or a pH electrode. We have generated a detailed protocol of making Chlamydomonas beads and two simple plant biology hands-on activities. These laboratory activities were used to teach the interplay of photosynthesis and cellular respiration to Biology students in nine schools and two universities in Georgia in a fun and engaging way. The presented educational work is a product of supervised collaborative efforts of three undergraduate students and one high school student in Georgia, USA. In the two designed laboratory activities students make Chlamydomonas beads and use these beads to conduct their own independent experiments. In the first lab activity students make light-powered green algae bead bracelets and use these algae bracelets to perform time course experiments in light and dark to study the interplay of photosynthesis and cellular respiration. In the second activity, students compare relative ratios of photosynthesis and cellular respiration in a Chlamydomonas wild type (4A+) and a chlorophyll-deficient, high-light sensitive mutant strain, 10E35/lsr1a, using strain-specific algae beads. 10E35 is a random insertional mutant generated by our research lab with a mutation in a novel functionally uncharacterized gene, LSR1 and is the center of an on-going research project at our laboratory <ns0:ref type='bibr' target='#b8'>[Nguyen et al., 2017;</ns0:ref><ns0:ref type='bibr'>Article S2]</ns0:ref>. We present in this article our protocol for making Chlamydomonas beads (including some preliminary testing data that helped us to refine the protocol), two new plant biology teaching tools and sample teaching resources for educators. We hope that the teaching resources will help plant biology educators to customize the labs according to grade level, availability of resources, and allow better time management in classrooms. The designed lab activities support active learning and contributes toward the following: 1) NGSS Science and Engineering Practice: Developing and using models; Planning and carrying out investigations and, 2) NGSS Core Idea: Life Science LS1C: From molecules to organisms: Structures and Processes.</ns0:p></ns0:div> <ns0:div><ns0:head>Materials & Methods</ns0:head></ns0:div> <ns0:div><ns0:head>Material information for educators</ns0:head><ns0:p>Information (vendors and catalog numbers) for ordering specific items related to the project like algal strains, algal growth media, inoculating loops, flasks, plastic transfer pipettes, Eppendorf tubes, pH indicators, pH test strips, bracelet tubing, glass vials, yarns for bracelet braids, sodium alginate, calcium chloride, counting chambers etc. are given on pages 1-6 in Text S2 file. On pages 5-6 in Text S2 file, we have shown the pricing of the basic items that one will need to start the lab and the cost comparison of our protocol Vs. the Bio-Rad Photosynthesis and Cellular respiration kit for general Biology. The cost comparison shows that our protocol is inexpensive and will serve more students over a longer period than the Bio-Rad kit (Text S2).</ns0:p></ns0:div> <ns0:div><ns0:head>Algal media and cultures</ns0:head><ns0:p>Chlamydomonas wild type strain 4A+ (CC-4051 4A+ mt+) strain was a gift from Dr. Krishna K. Niyogi (UC Berkeley, CA). 10E35/lsr1a (light-sensitive related 1a) is a random insertional nuclear mutant generated by our lab which has a mutation in a novel gene, LSR1 encoding a protein of unknown function <ns0:ref type='bibr' target='#b8'>[Nguyen et al., 2017;</ns0:ref><ns0:ref type='bibr'>Article S2]</ns0:ref>. 4A+ and 10E35 strains were maintained in the lab on Tris-Acetate Phosphate (TAP) agar media plates (Text S2) in dim light intensities (15-20 µmol m -2 s -1 ) at 25°C. A starter culture of 4A+ was started approximately 11-12 days ahead of the lab activity by inoculating 10 mL of liquid TAP media in a 50 mL flask with 4A+ cells from a 5-day old TAP agar media plate (Text S2). After 5 days of growth, 1 mL of the starter culture was used to inoculate 300 mL of fresh TAP media in a 1L flask. The TAP liquid 4A+ culture was grown for 6-7 days for dense dark green growth. 10E35 grows slower than 4A+. Hence 10E35 liquid TAP cultures should be started at least 3-4 days before starting the 4A+ liquid TAP cultures. Algal liquid cultures were grown under 25°C under continuous illumination of 80-100 µmol photons m -2 s -1 provided by the combined light intensities of four to six cool white fluorescent lights. Cultures were shaken continuously on an open-air orbital shaker at a speed of 150-180 rpm to ensure a uniform illumination of the cells and to prevent cells from settling down. Light intensities were measured using a LI-250A Light Meter (LI-COR, Inc., Lincoln, NE).</ns0:p><ns0:p>Preparation of 2% sodium alginate and 3% calcium chloride solutions 2 grams of sodium alginate (Fisher Scientific, Waltham, MA) was dissolved in 100 mL of E-pure water overnight at room temperature by stirring at a speed of 400 rpm using a magnetic stirrer.</ns0:p><ns0:p>[Note: sodium alginate forms a very viscous solution when dissolved at 1.5% -4%]. 2% sodium alginate solution was stored at room temperature. 30 grams of calcium chloride was dissolved in 1000 mL of E-pure water and stored at 4ºC in a fridge.</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:05:49217:1:1:NEW 14 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Cell counts</ns0:head><ns0:p>Cell density (number of cells per mL of the culture) was determined before harvesting Chlamydomonas cells from the TAP liquid culture to estimate the volume of culture needed to harvest specific number of cells per 50 mL falcon tube. Cell density was calculated by counting the cells using a Hausser Scientific Bright-Line™ Counting Chamber (Hausser Scientific, Philadelphia, PA). A basic protocol on how to use a hemocytometer in a classroom setting is available at https://www.ruf.rice.edu/~bioslabs/methods/microscopy/cellcounting.html. It is to be noted that cell counting is optional. School teachers who do not have access to a hemocytometer/counting chamber, can grow algae culture for 6-7 days and then harvest the cells to make beads. Additionally, teachers can match the green color of the beads with that shown in our article figures.</ns0:p></ns0:div> <ns0:div><ns0:head>Preparation of Chlamydomonas 4A+ and 10E35 beads</ns0:head><ns0:p>A detailed version of the Chlamydomonas bead-making protocol (including trouble shooting) is available at https://www.protocols.io/view/making-inexpensive-light-powered-chlamydomonasrei-bgpyjvpw. Chlamydomonas strain 4A+ or 10E35 cells were harvested by spinning down dense TAP liquid strain-specific cultures at 1,000-1,500 g for 3 minutes in a benchtop centrifuge. The supernatant was discarded and the cell pellet was collected. Harvesting 100 mL of dense Chlamydomonas culture generated 200-300 beads of 4-5 mm in diameter. 2% well mixedsodium alginate was added to the cell pellet in a 4:1 or 5:1 ratio (depending on the total number of cells harvested; see results and detailed protocol on https://www.protocols.io/view/makinginexpensive-light-powered-chlamydomonas-rei-bgpyjvpw). The algae and 2% sodium alginate were gently mixed till the entire cell pellet was completely resuspended without any visible cell clumps. Maximum number of total cells used for resuspension in sodium alginate was either 395 X 10 6 or 790 X 10 6 cells depending on the experiment (see result section). We resuspended the cell pellets containing 395 X 10 6 cells and 790 X 10 6 cells in 5 mL of sodium alginate to get an approximate final cell density of 66 X 10 6 cells/mL and 132 X 10 6 cells/mL in the cell suspension, respectively. 1 mL of sodium alginate-algal cell suspension gave us approximately 32-35 beads depending on pipetting techniques. Hence the cell suspension with cell density of 66 X 10 6 cells/mL will form beads that have approximately 1.89 X 10 6 -2 X 10 6 cells/bead while the cell suspension with cell density of 132 X 10 6 cells/mL will have 3.77 X 10 6 -4.1 X 10 6 cells/bead. We used 8 beads of similar sizes (4-5 mm in diameter) for glass vial experiments. The algae-sodium alginate mix was added drop wise steadily and quickly with uniform pipetting by using a micropipette or a plastic transfer pipette into a beaker of pre-chilled 3% calcium chloride kept on ice. If pipetting is not smooth and regular and, the algae-sodium alginate mixture is not mixed by swirling in between pipetting, irregular shaped and beads with different cell numbers/bead (light and dark green beads) will form (Fig. <ns0:ref type='figure' target='#fig_0'>S1</ns0:ref>). As soon as the algae-sodium alginate mixture touches the chilled calcium chloride liquid surface, the mixture solidified into tiny beads. The calcium chloride beaker containing the beads were kept on ice for 10-15 minutes to allow complete solidification of the algal beads.</ns0:p><ns0:p>The beads are separated from the calcium chloride solution by filtering through an oil strainer. Algal beads on the strainer were washed with tap water. The beads were kept temporarily in a petri dish containing small amount of tap water till the bracelets were made. Surplus beads were stored in tap water in a beaker for future use within 1-2 days. Algae bead making demonstration video clips are available at: https://youtu.be/u4BbZ29qlWQ and at https://youtu.be/eIxbzeHW8IM.</ns0:p></ns0:div> <ns0:div><ns0:head>Preparation of Chlamydomonas 4A+ bead bracelet</ns0:head><ns0:p>Flexible tubing was cut into 10 pieces, each 5 inches long. Caps of 1.5 mL Eppendorf tubes were cut off with a scissor. De-capped Eppendorf tube was used to plug the ends of the bracelet tubing (one de-capped tube at each end of the cut tubing). Colorful cotton yarn was cut according to the wrist width, intertwined and yarn braids were made. One braid was looped tightly onto the mouth of each de-capped Eppendorf tube at each end of the bracelet. Next, one end of the bracelet tubing was unplugged by removing the de-capped Eppendorf tube that was sealing the end. About 3.5 mL of tap water <ns0:ref type='bibr'>[pH 7.2 -7.3</ns0:ref>] was introduced into the bracelet flexible tubing. 15-38 algae beads (depending on the experiment) were gently introduced into the water inside the tubing. 8-10 drops of the bicarbonate indicator (Carolina Biological, Burlington, NC) were added into the water in the tubing and the end of the tubing was plugged back with the de-capped Eppendorf tube. Precautions were taken to avoid acidic or alkaline contamination of the flexible tubing, plastic spoon, transfer pipettes, petri dishes etc. used in our experiments, since the bicarbonate indicator is not directly specific to gases like carbon dioxide. About 0.5 cm-1 cm air gap was left at each end inside the tubing to provide enough air for cells. The bracelet was imaged and the pH of the water inside the bracelet was measured using pH testing strips (Fisher Scientific, Waltham, MA) before shifting it to light or to darkness for the lab activity. All experiments described below were performed with the same batch of beads. A detailed version of the protocol is available at https://www.protocols.io/view/making-inexpensive-light-poweredchlamydomonas-rei-bgpyjvpw. Demonstration of algae bead bracelet making video clips available at: https://youtu.be/A7VIjLDGSCc and https://youtu.be/vh_1ASpQgS8 and https://youtu.be/enctr0yhWQ8.</ns0:p></ns0:div> <ns0:div><ns0:head>Light and dark exposure experiments with Chlamydomonas bead bracelets</ns0:head><ns0:p>For the constant light/dark exposure experiment, one bracelet was kept under 150-200 µmole m -2 s -1 light intensity [equivalent to the combined light intensities of 12 to 14 cool white fluorescent lights] and another one was kept in the dark inside a lab cabinet drawer. After 3 hours of light/dark exposure, bracelets were imaged. pH of the water inside the bracelets were measured using pH testing strips (Fisher Scientific, Waltham, MA). For dark shift experiment, the bracelet was first light-adapted for 4 hours and then shifted to darkness. For light shift experiment, the bracelet was dark-adapted for 4 hours and then shifted to light. After every 1 hour over a period of 4 hours during light exposure or over a period of 3 hours during dark exposure, the bracelet was imaged to monitor the carbon dioxide percentage change inside the bracelet tubing. The carbon dioxide percentage change is monitored indirectly by the color changes of the bicarbonate indicator. pH was not measured for the light/dark shift experiments with algae bead bracelets. For testing the effect of different dark exposure times on photosynthesis, one algae bead bracelet was exposed to 9 hours of darkness and the other was PeerJ reviewing PDF \| (2020:05:49217:1:1:NEW 14 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed exposed to 15 hours of darkness. After the dark exposure, the 9 hours-and 15 hours-darkadapted bracelets were exposed to light for 4 hours and 12 hours, respectively and were imaged after the light exposure. pH of the water inside these light and dark-exposed bracelets was measured using pH testing strips (Fisher Scientific, Waltham, MA).</ns0:p></ns0:div> <ns0:div><ns0:head>Light and dark exposure experiments with Chlamydomonas strain-specific beads in glass vials</ns0:head><ns0:p>For testing the effect of water quality on photosynthesis, eight 4A+ beads were either introduced into 2.5 mL of tap water (pH 7.2 -7.3) or into de-ionized [DI] water (pH 7.1-7.2) in 5.5 mL screw capped glass vials (Fisher Scientific, Waltham, MA). For testing the effect of cell density on photosynthesis, eight 4A+ beads were either introduced into 2.5 mL of tap water (pH 7.2 -7.3). For both stated experiments, 125 µL of the 0.02% phenol red solution (Fisher Scientific, Waltham, MA) was added to the algae bead vials to serve as a pH indicator and the vials were capped tightly. One set of 4A+ bead and the control vials were exposed to 150-200 µmole m -2 s -1 light intensity and the other set to darkness for 2 hours. After 2 hours of light or dark exposure, vials were imaged and pH of the water in the vials was measured using a Thermo Fisher Scientific Orion-3 Star benchtop pH meter (Fisher Scientific, Waltham, MA). For comparative analyses of photosynthesis and cellular respiration in 4A+ and 10E35 strains under constant light/darkness, beads having approximately 2 X 10 6 cells/bead for each strain were used (Fig. <ns0:ref type='figure' target='#fig_1'>S2</ns0:ref>). Eight 4A+ and 10E35 beads were introduced into 2.5 mL of tap water (pH 6.9 -7.3) in 5.5 mL screw capped glass vials (Fisher Scientific, Waltham, MA). 125 µL of the phenol red solution (Fisher Scientific, Waltham, MA) was added to the 4A+ and 10E35 bead vials and the vials were capped tightly. One set of 4A+, 10E35 and control vials was exposed to light intensity of 150-200 µmole m -2 s -1 and the other set was exposed to darkness for 1 hour. The algae bead and control vials were imaged after every 30 minutes over a period of 1 hour and pH of the water in the vials was measured. The 1-hour light adapted 4A+, 10E35 and the control vials were exposed to light for an additional 3 hours and then shifted to dark. The vials were imaged after every 15 minutes for a period of 1 hour during dark exposure. After 1 hour, these dark-exposed vials were kept under dark for additional 5 hours. After 6 hours-of dark exposure, vials were shifted to light [150-200 µmole m -2 s -1 ] and imaged after 30 minutes, 1 hour, 2 hours, 3 hours and 48 hours. pH of the water in the glass vials in the above stated experiments were measured using a Thermo Fisher Scientific Orion-3 Star benchtop pH meter (Fisher Scientific, Waltham, MA).</ns0:p></ns0:div> <ns0:div><ns0:head>Imaging and Data analyses</ns0:head><ns0:p>Images were captured a Samsung Galaxy S5 camera. Statistical analyses of the recorded pH data were performed using Microsoft Excels' t-Test: Paired Two Sample for Means tool in the analysis ToolPak. Both One-Tailed and Two-Tailed Hypothesis Tests were performed. Standard deviations shown in Tables under result section was calculated using Excel. The raw statistical analyses data from three biological replicates per experiment have been deposited in Figshare (https://doi.org/10.6084/m9.figshare.12344024.v1) and are presented in the supplementary Data S1 file. Data S2 file contains raw pH data, mean and standard deviation information. Each PeerJ reviewing PDF \| (2020:05:49217:1:1:NEW 14 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed biological replicate had three internal replicates. The average of three internal replicates from each biological replicate is shown in the data in Data S1 and Data S2 files.</ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head><ns0:p>Effect of total cell numbers in Chlamydomonas 4A+ strain beads on photosynthesis and cellular respiration-induced color/pH changes in tap water. We used two types of beads that have two-fold difference in total cell numbers/bead: 1) beads that have approximately 2 X 10 6 cells/bead and, 2) beads that have approximately 4 X 10 6 cells/bead. It is expected that a high cell number in a bead will increase cellular respiration as a high cell density in the bead will create oxygen stress. The pH in the light-exposed vial containing 4 X 10 6 cells/bead was 6.1 and the pH in the light exposed vial containing 2 x10 6 cells/bead was pH 8.4 for the same duration of light exposure (Fig. <ns0:ref type='figure' target='#fig_1'>2</ns0:ref>; Table <ns0:ref type='table'>S2</ns0:ref>; https://doi.org/10.6084/m9.figshare.12344024.v1 ; Data S1; Data S2). The pH in the light-and PeerJ reviewing PDF \| (2020:05:49217:1:1:NEW 14 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed dark-exposed vials containing 4 x 10 6 cells/bead differed by only 0.1 pH unit while the pH in the light-and dark-exposed vials containing 2 x 10 6 cells/bead differed approximately by 2 pH units for the same duration of light exposure (Fig. <ns0:ref type='figure' target='#fig_1'>2</ns0:ref>; Table <ns0:ref type='table'>S2</ns0:ref>; https://doi.org/10.6084/m9.figshare.12344024.v1 ; Data S1; Data S2). There was a statistically significant pH difference between the light-exposed vials with 4 X 10 6 cells/bead and that with 2 X 10 6 cells/bead (Data S1). The pH difference between the dark-exposed vials with 4 X 10 6 cells/bead and that with 2 x 10 6 cells/bead was statistically significant (Data S1). pH differences between the light and dark control vials were insignificant as the p-values were higher than 5% in both 1-tailed and 2-tailed hypothesis tests (Data S1). Our results show high cell density/bead will hinder observation of pH changes in a photosynthesis lab. In the light-exposed experimental vial, partial buoyancy of one bead can be seen, indicative of O 2 production in photosynthesis (Fig. <ns0:ref type='figure' target='#fig_1'>2B</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>Indirect detection of carbon dioxide concentration in the 4A+ bead bracelet under light and darkness using the bicarbonate indicator.</ns0:head><ns0:p>Bicarbonate indicator is commonly used in photosynthesis and respiration experiments to detect indirectly the percentage of carbon dioxide in a sample. It is a more sensitive pH indicator than phenol red. When the carbon dioxide content in water is higher than 0.04%, pH becomes acidic. Acidic pH changes the red color of the indicator to yellow. If the carbon dioxide content is lower than 0.04%, pH gets alkaline and the indicator changes color from red to magenta and, under very low carbon dioxide concentrations the color of the indicator changes to purple (https://en.wikipedia.org/wiki/Bicarbonate_indicator). The expected color scale at different pH when bicarbonate indicator is used as the pH indicator can be found at https://pmgbiology.com/tag/respiration/. We used three bracelets (with algal beads ranging from 30-38) to monitor color changes of the bracelet water containing the bicarbonate indicator. These are designated as control, darkexposed and light-exposed bracelets in Fig. <ns0:ref type='figure' target='#fig_2'>3</ns0:ref>. The color of the water in the control bracelet (not exposed to dark or light), dark-and light-exposed bracelets were, light red, bright yellow and dark blue, respectively (Fig. <ns0:ref type='figure' target='#fig_2'>3</ns0:ref>). The objective of the experiment was to simply determine the color/pH changes of the water in the experimental bracelets in the light or in the dark relative to the control. The average pH of the water in the control algal bracelet was around 7 (Fig. <ns0:ref type='figure' target='#fig_2'>3A</ns0:ref>; Table <ns0:ref type='table'>S3</ns0:ref>; https://doi.org/10.6084/m9.figshare.12344024.v1 ; Data S1; Data S2). pH of the water in the dark-exposed algal bracelets ranged between 6 and 6.5 with STDEV ± 0.24 (Fig. <ns0:ref type='figure' target='#fig_2'>3B</ns0:ref>; https://doi.org/10.6084/m9.figshare.12344024.v1; Data S1; Data S2) indicating a high percentage of carbon dioxide because of cellular respiration. pH of the water in the light-exposed algal bracelets ranged between 8.5 and 9 with STDEV ± 0.24 indicating a low percentage of carbon dioxide because of photosynthesis (Fig. <ns0:ref type='figure' target='#fig_2'>3C</ns0:ref>; Table <ns0:ref type='table'>S3</ns0:ref>; https://doi.org/10.6084/m9.figshare.12344024.v1 ; Data S1; Data S2). pH difference between the light-and dark-exposed bracelets is statistically significant (Data S1; Data S2; https://doi.org/10.6084/m9.figshare.12344024.v1 ). Our results clearly show that carbon-dioxide PeerJ reviewing PDF \| (2020:05:49217:1:1:NEW 14 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed percentage can be monitored indirectly under light/darkness via sharp pH/color changes in the water in the bracelet in the presence of the bicarbonate indicator.</ns0:p></ns0:div> <ns0:div><ns0:head>Time course monitoring of photosynthesis-induced color changes in the dark adapted-4A+ bead bracelet when shifted to light.</ns0:head><ns0:p>An algal bracelet was dark-adapted for 4 hours. After dark-adaptation the bracelet was exposed to light for 4 hours. This light-exposed bracelet was imaged after every 1 hour during light exposure to monitor the gradient color changes over time without disturbing the bracelet (Fig. <ns0:ref type='figure'>4</ns0:ref>). The results show that if the algal bracelet is left undisturbed, one can pinpoint specifically which beads were actively photosynthesizing from the red-magenta-purple color streaks in the water on top of these beads that were removing carbon-dioxide from the tubing water (Fig. <ns0:ref type='figure'>4 B-D</ns0:ref>). pH was not measured in these bracelets as the objective of this experiment was to determine if differences exist in photosynthetic rates among different beads by visually observing the gradual color change of the water in the bracelet in light.</ns0:p></ns0:div> <ns0:div><ns0:head>Time course monitoring of cellular respiration-induced color changes in the light adapted-4A+ bead bracelet when shifted to darkness.</ns0:head><ns0:p>An algal bracelet was light adapted for 4 hours. After light-adaptation the bracelet was exposed to dark for 3 hours. The dark-exposed bracelet was imaged after every 1 hour during the light exposure to monitor the gradient color changes over time (Fig. <ns0:ref type='figure' target='#fig_4'>5</ns0:ref>). The results show that distinct pH gradient can be observed in a colorful way in an undisturbed algal bracelet (Fig. <ns0:ref type='figure' target='#fig_4'>5B</ns0:ref>). pH was not measured in these bracelets as the objective of this experiment (in conjunction with the Fig. <ns0:ref type='figure'>4</ns0:ref> experiment) was to teach students in a fun way, the 'tug of war' between photosynthesis and cellular respiration by visually observing the dramatic color changes of the water in the bracelet upon exposure to darkness or light.</ns0:p></ns0:div> <ns0:div><ns0:head>Effect of prior dark exposure duration on photosynthesis-induced pH changes in the 4A+ bead bracelet in light.</ns0:head><ns0:p>One algal bracelet was kept in the dark for 9 hours and the other one was kept in the dark for 15 hours. After dark incubation, both bracelets were imaged and the pH was measured using pH testing strips and, then shifted to light. (Fig. <ns0:ref type='figure' target='#fig_5'>6</ns0:ref>). There was no significant difference in pH between the 9-hours dark-adapted and 15-hours-dark adapted bracelets (Data S1). There was a significant difference in pH between 9-hours-dark adapted bracelet and 15-hours-dark adapted bracelet when these were exposed to light for 4 hours and 12 hours, respectively (Data S1). The bracelet that was kept in dark for 9 hours showed increase in pH from pH 6 [STDEV ±0] to pH 8.67 [STDEV ±0.24] within 4 hours under light because of photosynthesis (Fig. <ns0:ref type='figure' target='#fig_5'>6A</ns0:ref> & 6B; Table <ns0:ref type='table'>S4</ns0:ref>; https://doi.org/10.6084/m9.figshare.12344024.v1 ; Data S1; Data S2). The bracelet that was kept in dark for 15 hours showed a small increase in pH from 5.5 [STDEV ±0.41] to 6.3 [STDEV ±0.24], despite being exposed to light for 12 hours. This indicates prior prolonged exposure to darkness hinders photosynthesis in algal beads in light (Fig. <ns0:ref type='figure' target='#fig_5'>6C</ns0:ref> & 6D; Table <ns0:ref type='table'>S4</ns0:ref>; https://doi.org/10.6084/m9.figshare.12344024.v1 ; Data S1; Data S2).</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:05:49217:1:1:NEW 14 Jul 2020)</ns0:p></ns0:div> <ns0:div><ns0:head>Manuscript to be reviewed</ns0:head><ns0:p>Comparative studies of photosynthesis and cellular respiration-induced color/pH changes in vials containing wild type 4A+ and 10E35 mutant beads. 4A+ and 10E35 beads have approximately 2 X 10 6 cells/bead (Fig. <ns0:ref type='figure' target='#fig_1'>S2</ns0:ref>). Each light and dark set comprised of a control and experimental vials of 10E35 and 4A+ (Fig. <ns0:ref type='figure' target='#fig_6'>7</ns0:ref>). Images of the vials in each light and dark set were taken before light or dark exposure (Fig. <ns0:ref type='figure' target='#fig_6'>7A and 7D</ns0:ref>). Each light and dark vial sets were imaged after 30 minutes of light and dark exposures, respectively for a period of 1 hour. Results show a statistically significant slow increase in pH in 10E35 vial under light compared to that in the 4A+ vial (Fig. <ns0:ref type='figure' target='#fig_6'>7B -7C</ns0:ref>; Table <ns0:ref type='table'>S5</ns0:ref>; https://doi.org/10.6084/m9.figshare.12344024.v1 ; Data S1; Data S2). This could be due to a slow rate of photosynthesis or a high rate of cellular respiration or a combination of both phenomena in 10E35 relative to that in 4A+. 10E35 displays relatively a higher rate of cellular respiration in dark compared to that in 4A+ as indicated by the fast pH drop in dark in 10E35 vial over time compared to that in the 4A+ vial that is statistically significant (Fig. <ns0:ref type='figure' target='#fig_6'>7E-7F</ns0:ref>; Table <ns0:ref type='table'>S5</ns0:ref>, https://doi.org/10.6084/m9.figshare.12344024.v1 ; Data S1; Data S2).</ns0:p><ns0:p>Time course monitoring of cellular respiration-induced color/pH changes in the dark in vials containing 4A+ and 10E35 beads that were exposed to light for 4 hours. Light-exposed 10E35, 4A+ and control vials from Fig. <ns0:ref type='figure' target='#fig_6'>7</ns0:ref> experiment were exposed to light for additional three hours. Hence this set was light-exposed for a total of 4 hours. After fours of light exposure, images were taken and the vials were exposed to dark. Images of the dark-exposed vials were taken every 15 minutes over a period of 1 hour during dark exposure (Fig. <ns0:ref type='figure'>8</ns0:ref>). 10E35 shows relatively a higher cellular respiration rate that is statistically significant, compared to that in 4A+, as indicated by the rapid drop in pH in the 10E35 vial compared to that in the 4A+ vial (Fig. <ns0:ref type='figure'>8B -8E</ns0:ref>; Table <ns0:ref type='table'>S6</ns0:ref>; https://doi.org/10.6084/m9.figshare.12344024.v1; Data S1; Data S2). The results re-confirm the results shown in Fig. <ns0:ref type='figure' target='#fig_6'>7E</ns0:ref>-7F (Table <ns0:ref type='table'>S5</ns0:ref>, https://doi.org/10.6084/m9.figshare.12344024.v1 ; Data S1; Data S2).</ns0:p></ns0:div> <ns0:div><ns0:head>Time course monitoring of photosynthesis-induced pH changes in the light in 4A+ and</ns0:head><ns0:p>10E35 bead vials that were exposed to dark for 6 hours. Dark-exposed 10E35, 4A+ and control vials from Fig. <ns0:ref type='figure'>8</ns0:ref> experiment were exposed to dark for additional five hours. Hence this set was dark-exposed for a total of six hours. After six hours of dark exposure, images were taken of the dark-exposed vials and the vials were exposed to light. Images of these light-exposed vials were taken after 30 minutes, 1 hour, 2 hours, 3 hours and 48 hours of light exposure (Fig. <ns0:ref type='figure'>9</ns0:ref>). The results show that 4A+ photosynthesized at a faster rate compared to 10E35 after 6 hours of dark exposure to cause a distinct water color/pH change that was statistically significant (Table <ns0:ref type='table'>S7</ns0:ref>; https://doi.org/10.6084/m9.figshare.12344024.v1 ; Data S1; Data S2). Despite the significant pH difference in these two vials of 10E35, pH in the 48 hours-light exposed 10E35 vial was acidic (pH= 6.43±0.06) compared to the alkaline pH (8.47±0.06) in the 48 hours-exposed 4A+ vial. It is known that 10E35 progressively photobleaches with increase in light intensity <ns0:ref type='bibr' target='#b8'>[Nguyen et al., 2017;</ns0:ref><ns0:ref type='bibr'>Article S2]</ns0:ref>. Figure <ns0:ref type='figure'>9F</ns0:ref> shows that 10E35 beads were photo-bleached in the light-exposed vial after 48 hours of light exposure. Photo-bleaching indicates that there is chlorophyll breakdown in the beads.</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:05:49217:1:1:NEW 14 Jul 2020)</ns0:p></ns0:div> <ns0:div><ns0:head>Manuscript to be reviewed</ns0:head></ns0:div> <ns0:div><ns0:head>Discussion</ns0:head><ns0:p>Chlamydomonas reinhardtii is a unicellular micro-green alga (a Chlorophyte) that retains many of the features of green plants and of the common ancestor of plants and animals, although its lineage diverged from Streptophytes over one billion years ago. Chlamydomonas is used to study eukaryotic photosynthesis because, unlike angiosperms, it can use acetate to grow in the dark while maintaining a functional photosynthetic apparatus <ns0:ref type='bibr' target='#b7'>(Merchant et al., 2007)</ns0:ref>. It is also a model organism for elucidating eukaryotic flagella and basal body structure and functions which can be linked to various ciliopathies <ns0:ref type='bibr' target='#b13'>(Silflow & Lefebvre, 2001)</ns0:ref>. More recently, Chlamydomonas research has been developed for bioremediation purposes, generation of biofuels and has led to breakthroughs in optogenetics <ns0:ref type='bibr' target='#b7'>(Merchant et al., 2007</ns0:ref>; 'Critical tool for brain research derived from 'pond scum' <ns0:ref type='bibr'>NSF, 2013;</ns0:ref><ns0:ref type='bibr' target='#b21'>Zhang, 2015;</ns0:ref><ns0:ref type='bibr' target='#b12'>Scranton et al., 2015)</ns0:ref>. Currently, the Chlamydomonas Resource Center [https://www.chlamycollection.org/] offers number of educational kits ('Resources For Teaching' -Chlamydomonas Resource Center') including instructions and strains on its website; however, these tools barely scratch the surface of what could be taught using Chlamydomonas to students enrolled in K12 Biology and in college Biology undergraduate courses. Hence there is a huge potential to develop Chlamydomonas an under-utilized teaching tool, into a powerful popular teaching tool which will complement existing plant science teaching strategies. Our objective for the American Society of Plant Biologists' (ASPB) Plant-BLOOME project was to design fifteen simple handson activities on different Biology topics that can not only educate and excite high school students about Chlamydomonas but can be also included as a component in college Biology laboratory courses. The activities described in this manuscript is centered on photosynthesis and cellular respiration. We have found that the Chlamydomonas culture should be grown under low light (80-100 micro mol photons m -2 s -1 ) to obtain a healthy culture that is not photo-oxidatively stressed to be used for our lab activities. The culture should be a dense culture and have a cell density ranging from 18 X 10 6 cells/mL to 22 X 10 6 cells/mL to get enough cells for a class of 24 students, working in groups of two to three. Algal beads once made, should be rinsed thoroughly with tap water for at least five minutes to remove residual sodium chloride that is formed as a product in the reaction between sodium alginate and calcium chloride during bead-making step. This step is a very important step and must not be skipped as any residual sodium chloride will hinder photosynthesis in the experiment. As shown in the Fig. <ns0:ref type='figure' target='#fig_1'>2</ns0:ref>, high cell numbers in a bead has a negative effect on photosynthesis. We have found best results can be achieved when the harvested cell numbers are between 250 X 10 6 -395 X 10 6 cells/ 50 mL falcon tube. Cells inside the beads are oxygen-stressed. Hence it is important to leave air gaps inside the bracelet at each end of the flexible tubing (see Materials and Methods). The same rule applies when performing the experiment in a 5.5 mL glass vial. It is important to leave air gap of half the volume of the vial. Results in Fig. <ns0:ref type='figure' target='#fig_5'>6</ns0:ref> showed that prolonged dark exposure of 15 hours has a negative effect on photosynthesis. Algae bead bracelets exposed to 9 hours of darkness (Fig. <ns0:ref type='figure' target='#fig_5'>6</ns0:ref>) can be shifted back PeerJ reviewing PDF \| (2020:05:49217:1:1:NEW 14 Jul 2020) and forth between dark and light to display color changes over a period of 24-48 hours (the color changes slowly after 24 hours; based on observations in different classrooms, no data was collected). We have also found that once the bracelet is assembled, if it is exposed to light for about 3-4 hours (which we call in our lab as the 'light charging of the bracelet') and, then switched to dark for 2-3 hours ['discharging of the bracelet'], the bracelet displays fast color changes as long as the dark exposure time was not exceeded beyond 9 hours (based on observations in different classrooms, no data was collected). But if the assembled bracelet is shifted to dark immediately after assembly, the bracelet fails to display fast color changes. Immobilized oxygen stressed-Chlamydomonas cells in the beads are dependent on photosynthesis for glucose biosynthesis as they are immersed in tap water in the bracelet. Tap water lacks acetate and other nutrients for algal growth. We hypothesize that the initial light exposure allows the cells to synthesize glucose/starch by photosynthesis which is later used to support the high rate of cellular respiration in beads for energy production. If the bracelet is shifted to dark without prior light exposure, the high cellular respiration rate consumes the existing starch in the cells. Hence when this dark-exposed bracelet is exposed to light, the cells will have to synthesize enough glucose/starch via photosynthesis to support the high rate of cellular respiration in the beads and this will take some time. This is reflected in the slow color/pH changes of a bracelet that is exposed to dark immediately after assembly compared to the one that is exposed to light immediately after assembly. Chlamydomonas can take up exogenous acetate from the TAP medium to make net synthesis of glucose via the Glyoxylate/C2 cycle, which is present in many bacteria, micro-algae and plants <ns0:ref type='bibr' target='#b6'>(Kunze et al., 2006)</ns0:ref>. Substituting tap water with acetate containing-TAP growth media (heterotrophic and photo-heterotrophic media; Text S2) inside the bracelet will hinder color change in bracelets/vials as TAP medium has Tris buffer, which has a pKa value of 8.06 at 25°C and a buffering range of pH 7-9. Chlamydomonas grows slower in High Salt (HS) photosynthetic media than in TAP medium as HS medium lacks acetate <ns0:ref type='bibr' target='#b14'>(Sueoka, 1960)</ns0:ref>. HS medium cannot be used as a substitute for tap water inside the bracelet as we have tried it and have found that the bracelets do not show color changes even if the beads are exposed to light for 48 hours (data not shown). During spring 2018-fall 2019, the described laboratory activities were incorporated in Biology classes in nine schools and in Biology labs at the University of West Georgia and at the Perimeter College [Georgia State University] (Text S1). To date, we have targeted of about 947 school students in Georgia and hope to target more college students in future. We are proposing a class workflow in Text S3, which is based on the feedback of 12 school teachers and 2 college instructors who participated in the Plant-BLOOME project. Regardless of the suggested time line, instructors can adjust lab times according to their teaching agenda by either spreading the lab activities across multiple classes or by removing one or more activities (Text S3). This will allow the instructor to involve the class in discussion after each activity. Alternatively, students can perform an outdoor experiment by wearing these bracelets/necklaces (you can also make algae bead necklaces) during day time and exposing these bracelets to strong sunlight or wear them in the night to see the water color changes. Conducting the experiment in a 5.5 mL capped glass vials will expedite the experiment completion within 1.5-2 hours in classrooms. The advantage of performing the experiment in glass vials is that students can clearly monitor oxygen production in photosynthesis by monitoring the buoyancy of the algal beads over time. Bead buoyancy is difficult to clearly visualize in a bracelet because of the narrow diameter of the bracelet tubing. Photosynthetic efficiencies of Chlamydomonas strains are measured in a laboratory by an oxygen electrode. But many financially disadvantaged schools and institutions of higher learning do not have access to an oxygen electrode. Our hands-on activity can be used to compare crudely photosynthetic efficiencies of Chlamydomonas wild type and photosynthetic mutant strains in a classroom setting. This will allow educators at institutions with limited resources and funding to engage students in critical thinking based on observations of a scientific experiment. Our lab activities can be customized for different grade levels by adding or removing layers of lab components. Some suggested activities for Middle school, high school and college undergraduates are shown in Table <ns0:ref type='table'>1</ns0:ref>, Table <ns0:ref type='table'>2 and Table 3</ns0:ref>, respectively. For example, for middle school students the algae bead bracelet or the vial version of the experiment can be used. Students can observe under light microscopes, swimming Chlamydomonas and its bright orange eyespot which is used by the cell for light sensing and aids photo-taxis [Table <ns0:ref type='table'>1</ns0:ref>; <ns0:ref type='bibr' target='#b19'>Ueki et al., 2016)</ns0:ref>. Photosynthesis is modulated by light color and light intensities <ns0:ref type='bibr' target='#b18'>(Tymoczko, Berg & Stryer, 2015)</ns0:ref>. Red and blue light stimulates photosynthesis and other colored light are not utilized for photosynthesis <ns0:ref type='bibr' target='#b18'>(Tymoczko, Berg & Stryer, 2015)</ns0:ref>. Hence algae bead bracelets can be used by high school students to test the effects of different light intensities and colored light using different colored light filters (Table <ns0:ref type='table'>2</ns0:ref>). High school students can also conduct a vial experiment with a wild type strain and any available photosynthetic mutant strain that they have access to. Photosynthetic mutants like the cytochrome f deficient mutant (petA) [CC-3737 petA (N153Q)]; the D1-less mutant (Fud7) [ CC-4147 FUD7 (psbA deletion) mt+] and the D2-less mutant (PsbD) [CC-4385 PsbD (deletion) mt+ are available via Chlamydomonas Resource Center (Table <ns0:ref type='table'>2</ns0:ref>). 10E35 mutant can be obtained from our lab. Additionally, a basic bioinformatic laboratory can be added to the high school Biology lab. The DNA sequence of the mutated gene in the photosynthetic Chlamydomonas mutant can be given to students and they can use the DNA sequence to BLAST the NCBI database to identify the gene and the protein. Students can also check for paralogs/orthologs of the identified gene/protein (Table <ns0:ref type='table'>2</ns0:ref>). For college undergraduate level Biology labs, additional molecular and biochemical layers can be added on top of the high school lab components as shown in Table <ns0:ref type='table'>3</ns0:ref>. We have provided class work-flow, sample pre-and post-lab questions and a rubric for grading pre-and post-lab assignments which can be used by educators (Text S3). The assignments and the rubric can be customized according to the knowledge base of students in the class. In summary, science literacy in young students can be improved by studying a 'pond-scum' which is used by plant biologists, neuroscientists, biomedical and renewable energy researchers and can show them the inter-disciplinary nature of 21 st century Biology.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions</ns0:head><ns0:p>Our designed protocol can be used to make beads using motile micro-alga like Chlamydomonas reinhardtii. These algal beads can be used for basic photosynthesis labs or for comparative studies of relative rates of photosynthesis and cellular respiration in Chlamydomonas wild type and mutant strains. Although our work was performed with the objective of designing engaging hands-on plant biology labs for K16 Biology students, it might be useful to bioenergy researchers who are interested in exploring the use of immobilized Chlamydomonas or other motile green algae for biofuel production <ns0:ref type='bibr' target='#b12'>(Scranton et al., 2015;</ns0:ref><ns0:ref type='bibr' target='#b10'>Radakovits et al., 2010;</ns0:ref><ns0:ref type='bibr' target='#b1'>Canbay, Kose & Oncel, 2018)</ns0:ref>. Our lab activities using the wild type Chlamydomonas strain can be performed both in glass vials and in bracelets. Based on our class room experiences at nine schools and two colleges in Georgia and the enthusiasm of the plant community members at the educational booths at the Plant Biology meetings organized by ASPB, we envision that young students will find the 'bracelet' approach more enjoyable than conducting the same experiment in glass vials (Text S1). Our lab activities are inexpensive and can been customized according to grade levels. <ns0:ref type='table'>S1</ns0:ref>.</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:05:49217:1:1:NEW 14 Jul 2020) <ns0:ref type='table'>S3</ns0:ref>. <ns0:ref type='table'>S5</ns0:ref>.</ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head>Figure 1</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head>Figure 2 Effect</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>Figure 3</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Figure 3</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head /><ns0:label /><ns0:figDesc>Figure 4</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head>Figure 5</ns0:head><ns0:label>5</ns0:label><ns0:figDesc>Figure 5</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head>Figure 6</ns0:head><ns0:label>6</ns0:label><ns0:figDesc>Figure 6</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head>(Figure 7</ns0:head><ns0:label>7</ns0:label><ns0:figDesc>Figure 7</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_7'><ns0:head>(</ns0:head><ns0:label /><ns0:figDesc>A) Control, 10E35 and 4A+ bead vials before light exposure. (B) Control, 10E35 and 4A+ bead vials after 30 minutes of light exposure. (C) Control, 10E35 and 4A+ bead vials after 1 hour of light exposure. (D) Control, 10E35 and 4A+ bead vials before dark exposure. (E) Control, 10E35 and 4A+ bead vials after 30 minutes of dark exposure. (F) Control, 10E35 and 4A+ bead vials after 1 hour of dark exposure. Algal beads of each strain had approximately 2 X 10 6 cells/bead. Eight beads of each strain were used per experimental vial for the experiment. The order of the vials from left to right: control, 10E35 and 4A+ vials. All statistical analyses can be found in https://doi.org/10.6084/m9.figshare.12344024.v1 , Data S1, Data S2 and Table</ns0:figDesc></ns0:figure> <ns0:note place='foot'>PeerJ reviewing PDF \| (2020:05:49217:1:1:NEW 14 Jul 2020)</ns0:note> <ns0:note place='foot' n='6'>cells/bead. The bracelet contained thirty-six 4A+ strain beads. Bicarbonate indicator was used as a pH indicator. PeerJ reviewing PDF \| (2020:05:49217:1:1:NEW 14 Jul 2020)</ns0:note> <ns0:note place='foot' n='6'>cells/bead. Both bracelets contained 38 beads. All statistical analyses can be found in https://doi.org/10.6084/m9.figshare.12344024.v1 , Data S1, Data S2 and TableS4.PeerJ reviewing PDF \| (2020:05:49217:1:1:NEW 14 Jul 2020)</ns0:note> </ns0:body> "	" Department of Biology 1601 Maple Street Carrollton, Georgia 30118 Office Tel: 678-839-5488 Email: mmitra@westga.edu July 3rd, 2020 To, The Editorial Board Peer J Dear Editor and Reviewers, We thank you for your valuable comments and suggestions and, for the time that you have invested in providing thoughtful comments to us. We have revised extensively the manuscript to address your concerns. We have incorporated all suggestions of reviewers in our revised manuscript, except for few suggestions. We have provided clear justifications for our inability to incorporate these suggestions in our manuscript in the rebuttal letter. We believe that with the incorporation of the constructive suggestions from the reviewers, our manuscript is now suitable for publication in Peer J. Sincerely, Associate Professor Biology Department, University of West Georgia On behalf of all authors. Please note that reviewers’ comments are in blue font and our responses are in green font. Please note that line numbers have changed in the revised manuscript because of text addition and deletions. The line numbering in our response is based on the line numbering in the revised document. Reviewer 1 (Anonymous) Basic reporting Questions regarding the introduction: Could the authors explain the advantage of using a motile strain of green algae vs. non motile? I’d like to know if it is the novelty of being able to expand the species of unicellular algae that can be studied, using a well-known model organism, etc. – I see that this is in the discussion, but I think it is helpful to have a brief reference in the introduction as well. Response: We agree. We have added are reasons for using a motile strain for bead production in the Introduction section with added new references. (lines: 96-105) Is there evidence for the available kits not working well? (ie. did your group test them? Survey K16 teachers?) Response: Please see lines:117-125 and also lines 111-114. We did have feedback from school teachers about the kits. Unfortunately, we cannot use the feedback as the Office of Research and Sponsored Operations (ORSP) at our University, UWG, did not submit IRB application materials for this project and the routing form for the Plant-BLOOME grant indicate that there were no human subjects involved. We do not have the IRB permission to share these surveys and feedback (lines 111-114). I have attached a copy of the email from my UWG ORSP which the editor can share with you that confirms this information. we have attached a Text S1 file that has weblinks of glimpses of our educational outreach activities. We did not try out commercial kits as our project was funded by the Plant-BLOOME grant which was development of new Chlamydomonas-based hand-on-activities for Plant Biology/General Biology education. Hence, we did not use commercial kits that use non-motile alga like Chlorella. We have added some sentences to summarize general feedback from teachers about commercial kits erroneous instruction for the Carolina kit to clarify this issue. Writing clarity: Lines 23-25: awkward wording, suggestions in red: These kits are expensive, can be applied to only non-motile algae, and provide protocols that lack detailed specifics for trouble shooting and does not provide customization guidelines for different grade levels. Response: We agree. We have rephrased sentence in lines 24-26. We did not see your comments in red. Lines 47-49: awkward wording, suggestions in red: Our comparative studies of photosynthesis and cellular respiration in the10E35 and in 4A+ strains showed that 10E35 relatively has a higher respiration rate and a lower photosynthetic rate than 4A+. Response: We agree. We have rephrased sentence in lines 55-58 . We did not see your comments in red. Line 90: maybe add “fun and engaging way” to sound more technical? Response: We agree. We have made the change in line 158. Line 93: “conduct their own independent experiments” – this shows the activity is not just a ‘cookie cutter’ lab. Response: We agree. We have added the suggestion in line 162. Line 95: remove “in” before “in dark” Response: We agree. We have removed ‘in” before “in dark” in line 164. Lines 98-100: It seems redundant to have both “functionally uncharacterized” and “unknown function” in this sentence. Response: We agree. We have removed “unknown function” in lines 169. Lines 105-107: Add a statement that acknowledges that cellular respiration is still going on when the cells are in the light, but the net production of O2/use of CO2 from photosynthesis vastly outweighs the O2 consumption/CO2 release during cellular respiration (a very common misconception is that cellular respiration doesn’t occur in the light) Response: We agree. We have added the suggested statement and have rephrased sentences in lines:149-151 to make it clear that respiration occurs in light and darkness while photosynthesis occurs only in light. Please note that line numbers have changed because of a paragraph rearrangement. Lines 113-116: this is just a very long sentence. Maybe according to grade level, availability of resources, and allow better time management? Response: We agree. We have rephrased the entire sentence as suggested in lines 182-185. Experimental design For clarification – when referring to biological replicate – you are meaning 3 separate replications of the experiment, each with 3 internal replicates? Apologies, I got lost in the large amount of data presented in the supplemental data files. If this is not the case, I would suggest having more replicates to strengthen your findings. Response: We mean three separate replications of the experiment, each with three internal replicates. To reduce the volume of data we used the average of the three internal replicates in the Data S1 and Data S2 files. We have provided now a clarification statement about the internal replicates in the two data files (Data S1 and Data S2) and in the Table S1- Table S7 legends. dx.doi.org/10.17504/protocols.io.bgpyjvpw/ Are there copyright issues with having the protocol previously published? Response: No. We submitted this protocol to the Protols.io (a database for protocols) and have published it. The updated link for this published protocol has been added in the manuscript (https://www.protocols.io/view/making-inexpensive-light-powered-chlamydomonas-rei-bgpyjvpw) Text_S1 Item 21: aluminum foil Response: We agree. We have combined previous Text S1 Text S2 and Text S3 into one file and have named it as Text S2.We have added the word aluminum on page 2 of Text S2 file for item 21. Item 30: it seems that calling it a hemocytometer or counting chamber might be more flexible to users? Response: We agree. We have added the word hemocytometer beside the item: Hausser Scientific Bright-Line™ Counting Chamber for item 30 on page 2 of the Text S2 file. Text_S2 In order to have the greatest possible flexibility to educators likely working on a tight budget or who may already have some of these supplies, it would be helpful to have a disclaimer with the supply list that there may be other vendors for the general supplies that are cheaper or that they already work with. Response: We agree. We have added the disclaimer on page 3 in Text S2. The easiest way to do this is adding a column to the left that has the generic term for the item (ie. 1.5 ml centrifuge tubes, yarn, disposable inoculating loops and needles, hemocytometer, etc.) as they may already have these items/be able to find cheaper. For example, the Tygon E3603 laboratory tubing is $211 (I realize you get a ton of tubing, but maybe a school would only want a small amount and look to pick it up at a hardware store) and it could be broadly represented by providing the dimensions. Response: We agree. We have added the generic names of items on pages 1-2 in Text S2 and have added the specific dimensions of the Tygon tubing in the Text S2 on page 2. It may be beneficial to provide a basic protocol on how to use a hemocytometer in a teaching setting? Again, this could be a supplemental text, similar to S1 and S2. Response: We agree. We have provided a web link for using the hemocytometer under “Cell counts” under Materials and Methods section (Lines:230-231). We also added two sentences in lines: 231-235 to state that the cell counting is optional as we understand that many school teachers won’t have access to a counting chamber. Teachers without an access to a counting chamber can grow algae culture for 6-7 days and harvest cells to make beads without counting cells. Additionally, teachers can match the green color of the beads with that shown in our article figures. Validity of the findings Lines 280-282: I don’t typically see p values referred to as percentages in scientific papers? Response: p- values are expressed as decimals but it is easier for students and teachers it to understand what they are if one converts them into percentage. Hence, we kept the values as percentage. Additionally, some of this information regarding the type of tailed test could be removed to condense the results and included in the figure legends (and kept in the data analysis section of the methods) to condense the text in the results. Response: We agree. We have cited the Figshare data DOI, Data S1, Data S2 and respective supplementary Tables in the result section text and, also in the Figure and Table legends, instead of describing the numerical values of the statistically analyses in the result section to condense results, as suggested. Thinking of this article in terms of describing teaching activities, it would be helpful to have a figure/supplemental text for specifically trouble shooting common problems. Response: We admit there is no separate trouble shooting section but critical steps and pitfalls (what to do and what not to do) are described in each step in the published protocol in detail in protocols.io. We have cited this web link under Materials and Method section (https://www.protocols.io/view/making-inexpensive-light-powered-chlamydomonas-rei-bgpyjvpw ). This link was previously not working properly in the original manuscript but has been updated in the revised version. Materials and Method section also addresses some of the important steps where things can go wrong and where one should be extra- careful. I think in terms of audience that teachers using this activity would benefit from the separation of technical details from the learning activities, pre-class assignments/background, assessments aligned to learning goals. Perhaps this could be combined with trouble shooting and supplies into a single supplemental text that is information on teaching? Response: We agree. We have separated technical details from teaching. Text S2 file now contains supply information, cost comparisons and media recipe. As stated earlier, troubleshooting can be found in each step of the protocol at https://www.protocols.io/view/making-inexpensive-light-powered-chlamydomonas-rei-bgpyjvpw. We have combined previous Table 8, Table S1 and Text S4 into one file and have named it as Text S3 which contains class work flow, pre-class assignments/background and grading rubric (teaching materials). Some of the items in the discussion, such as customization by grade level, could be in their own figure to highlight and provide more options (as it sounds like you have them with 14 other activities!) - this could be similar to Table 9. Response: We agree. We have added two tables centered on customization of photosynthesis-based lab for middle school and high school students (Table 1 and Table 2). Clarification about 15 activities: Our objective for the American Society of Plant Biologists’ (ASPB) Plant-BLOOME project was to design fifteen simple hands-on activities on different Biology topics that educate and excite high school students about Chlamydomonas. These additional 14 lab activities are not based on photosynthesis and cellular respiration. For example, we have flagella and motility lab, genetics lab etc. We have added our clarification about 15 activities in lines:572-576. Comments for the Author This manuscript has a huge amount of data! For me, there is important information that has two broad audiences and you will not be able to reach them in a single manuscript. Review instructions state that decisions are not made based on any subjective determination of niche audience, so this may be out of line, but the work seems like 2 different manuscripts that differ on scope and audience: 1) a methods paper providing all data (potentially include data from verification of the other developed activities?) with optimizing this measure of photosynthesis and cellular respiration - color change, pH, time course experiments, and statistical analysis – geared towards the broader scientific community and fitting in the PeerJ journal, and 2) a teaching module designed for K4-16 educators that has the protocols, and much less technical information (suggestions include International Journal of STEM Education, Frontiers in Education: STEM Education, American Biology Teacher). If I am incorrect with regards to my assessment of the scope of PeerJ, I would advise a greater separation between teaching-specific information (simplicity and clarity) and basic data. If PeerJ looks for this type of comprehensive work, I applaud having a platform that is open access and can appeal to multiple, historically separated, audiences. Response: We understand what the reviewer trying to convey. Peer J informed us before our manuscript submission that they do not publish mere protocol/methodology article. Since the activities described in the manuscript were developed for educators and students, we thought that we can combine techniques with some educational aspects, especially, since this entire project was driven by undergrads and high school students. Reviewer 2 (Mitzuko Dautt-Castro) Basic reporting I suggest deepening in the basic concepts of the photosynthesis process and highlight its biological importance. This could help you to strengthen your work justification. Response: We agree. We have added few sentences to highlight the importance of photosynthesis in Lines: 64-76. -I suggest a change in the order of information in the introduction section. Line 87 to 101 is about your results, so, I think that this information should appear after line 110. Response: We agree. We have changed the order of information as suggested in lines:141-170. There is information that doesn't have references, please include the original works from which that information was taken. i.e., lines 103, 107, 334, 541. Response: We have added references in lines 143, 145-146, 429, 651-653 as suggested. The link that you provide for “Carolina QuickTips Making Algae Beads” reference doesn't work, please check it. Response: We apologize for the broken link. We have updated the web link. It's highly recommended that the figures show all the necessary data to can understand them even without seeing the figure legend. In this regard, I suggest including some missed information in the figures. For example, in Fig. 1, please indicates which vials correspond to DI or tap water; Fig 2, please indicates the concentrations of the beads, etc. For more details please check the attached pdf file. Response: We agree. We have re-labeled all Figures to show the necessary data for full understanding of the result. We did not see any attached pdf file but we hope that by re-labeling all figures, we have addressed your concerns. -Tables 1 to 7 are repeated or complementary information of figures, thus, I suggest sending them to supplementary material. Response: We agree. We have moved Tables 1-7 to supplementary materials. These tables are labeled as Tables S1- Table S7. Experimental design -Considering the nature of your investigation and intending to facilitate the reading of your results, I suggest adding one figure to show the color-scale expected when you use the pH indicators as the bicarbonate indicator and the phenol red solution. Response: We agree. We have added a sentence that has two web links for the figures that show the expected color scale at different pH when phenol red is used as a pH indicator in line # 362-365 . We have added a sentence that has the web link for the figures that shows the expected color scale at different pH, when bicarbonate indicator is used as a pH indicator in lines: 429-431 . I suggest describing that phenol red solution is used as a pH indicator too, as a bicarbonate indicator (line 236). Response: We have added the phrase that phenol red is a pH indicator. Phenol red is not considered as a bicarbonate indicator as it is not sensitive enough to show a color change as the concentration of carbon dioxide gas in an aqueous solution increases like the hydrogen carbonate/ bicarbonate indicator. We added a sentence in lines: 424-425 to reflect this. For the evaluation of the effect of cell density in line 234, I suggest writing the two concentrations of beads used. Response: The experiment that you are talking about in line 234, has the same cell density for both Chlamydomonas strains. We used beads that had approximately 1.89 - 2 X 106 cells/bead, as was stated in line 243 of the original manuscript (revised manuscript: Line # 400). We have now rounded off the value on line 400 in the revised manuscript to 2 X 106 cells/bead to avoid confusion and to keep the values consistent throughout the manuscript. About the data analysis, I appreciate that you provide all the statistical analysis, however, I found them difficult to read. I suggest founding a better way to describe them. For instance, you can consider the way you describe it on lines 378-379. In this example, you summarize the information and put them in parentheses. Also, consider that it's not mandatory to describe all the numeric data obtained in your statistical analysis. Because you provide all your analysis in detail in supplementary material; you can always refer to that. Response: We agree. We have cited Figshare published dataset DOI, Data S1, Data S2 and respective supplementary Tables in the result section text and, also in the Figure and Table legends, instead of describing the obtained numeric data. Comments for the Author Response: We did not see any attached file. But we think that we have understood what you wanted us to do. We have re-labeled all the figures so that one can understand the results fully by just looking at the figures. Reviewer 3 (Anonymous) Basic reporting There are some grammatical issues in the paper, in particular those around use of tense. The Background section of the Abstract would benefit from an additional sentence connecting the ideas from sentence1 to 2 (lines 20-23). Response: We agree. We have added a sentence to link sentence 1 and 2 (lines: 21-22). The third sentence in the Abstract Background has grammatical problems and would benefit from the use of colons and semicolons (lines 23-25). Response: We agree. We have replaced the original long sentence with the sentence: These kits are expensive and, do not come with detailed guidelines for trouble shooting and customizations for different grade levels. (lines:24-26). It is unclear to the rationale for including the last sentence of the Abstract Background (lines 28-31). Response: We agree. We have restructured the “Background section” of the Abstract, and have rephrased these sentences in Lines: 28-34. Plant-BLOOME is an educational outreach project and not a hard-core science research project. This project centers on generating enthusiasm and excitement in students for green biology. Student participation is an integral component of our Plant-BLOOME project (Please see Introduction: Lines 104-114 and Text S1). There are grammatical issues with the last sentence of the Abstract Methods (lines 36-39). Response: We agree. We have replaced the sentences in lines 38-45 with: Our first activity centers on making and using light-powered algae bead bracelets to monitor dramatic color/pH changes over time when exposed to darkness or light. Our second activity employs strain-specific algae beads with approximately equal cell numbers to conduct comparative photosynthesis and cellular respiration studies in two Chlamydomonas strains namely, wild type, 4A+ and, a high light-sensitive photosynthetic mutant, 10E35/lsr1a. Tense and verb-subject agreement issues in the second sentence of the Results section of the Abstract (lines 41-44). Response: We agree. We have rephrased the sentence in the following way in lines 48-51: We found that the algal cell density/bead, water type used in the experiment and, the duration of dark exposure of algal beads can affect successful implementation of the lab activities. The fourth and last sentences of the Abstract Results section are a different tense from the rest of the section (lines 45-47, 50-52). Response: We agree. We have changed the tense in the sentences in lines 53 - 61 to match that in the rest of the section. The first sentence of the Introduction needs commas separating the coordinating adjectives and the sentence ends with a phrase that is not grammatically correct (lines 55-56). Response: We agree. We have rephrased the sentence and have added the commas separating adjectives. This sentence is now in line:75-76. The second sentence of the Introduction is a run on and would benefit from being split into two (lines 56-60). Response: We agree. We have split the long sentence into two sentences in lines 76-82. In the Introduction section, BIO-RADs “Photosynthesis and Cellular Respiration Kit for General Biology” should be added to the list of currently available commercial kits, as it offers the most comprehensive activity guidelines and includes detailed troubleshooting prompts (lines 60-64, 70-72). Response: We agree. We have added the reference of BIO-RADs “Photosynthesis and Cellular Respiration Kit for General Biology” in lines 84-85, 87-88, 92. It is unclear what the advantage would be of using motile versus non-motile algae for photosynthesis and respiration assays (lines 67-70). Response: We agree. We have added our explanation of using a motile strain for bead production in the Introduction, with added new references. (lines: 96-105) Authors should articulate the rationale for use of Chlamydomonas over Chlorella, as the commercial kits use the latter. Response: We agree. We have done so in lines: 96-99; also refer to 96-105 Related, the authors should explain the advantage of making the beads over purchasing pre-made beads, such as the ones available from BIO-RAD. Given the number of additional reagents and time needed for making the beads, it is unclear if this protocol is more cost effective in comparison to the BIO-RAD kit, which one can purchase for $181 in the US. The author’s should include a detailed cost comparison to support this claim. Response: We agree. Please see pages 5-6 in Text S2 for cost details and cost comparison. Also see lines:196-200 . The authors should read Canbay, Kose, and Oncel (2018) for a published protocol for making algae beads using Chlamydomonas (lines 67-68). Response: We have read the paper and we thank the reviewer for providing us this reference as we did not know about this article. We have added this reference in line:102 and in line 105. The figures are extensive and well organized. For the sake of reducing confusion in your target audience (K4-12 teachers), it is recommended that the hand-written labels on the glass vials be cropped out. Response: We do agree that by cropping the labels on the vials would make the figures look pretty. We would like to show the full vial pictures as we want the educators to see the full vial in the pictures and we believe in showing the raw data as much as much as possible. Hence, we did not crop the labels. We also have re-labeled all figures so that readers can understand the full result by just looking at the figures without reading the figure legends. Given the importance of subtle color changes, the images should have a consistent white-balance applied. The authors are encouraged to look at Figure 2(A), specifically. Response: In Figure 2A, the dark and light set images were taken separately and merged. Unfortunately, the background light was different color when the two pictures were taken, which students did not realize. Hence it is not possible to apply a consistent white balance. Even without the white balance results can be clearly understood as the colors are distinct (yellow Vs. bright pink color difference in the two light set: DI water and tap water vials). Considering, that we are not aiming to detect very subtle difference in pH in school classrooms but, just aiming to detect obvious color changes that that strikes our eye distinctly, we think that the overall the color changes are depicted well in all figures. Experimental design It is unclear if the focus of this article is the modification of existing protocols for use with Chlamydomonas or the use of this technique as part of a teaching module. If the former, then I would encourage the author’s to read the paper by Canbay, Kose, and Oncel (2018) that describes production and analysis of immobilizing Chlamydomonas in beads. The author’s should describe how their protocol differs and/or is superior to the protocol described in the 2018 paper. Response: We have read the paper and we thank the reviewer for giving us this reference as we did not know about this published article. We generated our protocol of Chlamydomonas bead making at our lab in March 2018 (before we submitted our Plant-BLOOME grant; our BLOOME project started from August 2018). We did not know that there is an article from a Turkish Lab that has made Chlamydomonas beads for biohydrogen production in bioreactors as this article was published in May 2018. We never checked Web of Science or google, after March 2018. Hence there is no question of modifying an existing protocol. Our project is not a research project and, comparison of two technique is not our project goal. Nevertheless, we would like to point out few things. There is a difference between the purpose of making Chlamydomonas beads for hydrogen production in bioreactors under sulfur-deprived anerobic conditions Vs. the purpose of making Chlamydomonas beads for Plant Biology education and classroom engagement to teach students photosynthesis. There are three differences between the bead-making protocol generated by Canbay, Kose and Oncel (2018) and our published protocol (https://www.protocols.io/view/making-inexpensive-light-powered-chlamydomonas-rei-bgpyjvpw ). 1. The percentage sodium alginate (2% [ours] Vs. 4 % sodium alginate [Canbay et al. 2018]) 2. Temperature used for solidifying the beads. (4ºC [ours] Vs. room temperature [Canbay et al. 2018]) and, 3. Time needed for solidification of the beads (5-10 mins [ours] Vs. 30 minutes [Canbay et al. 2018]) Additionally, bead making protocol by Canbay, Kose and Oncel, 2018 cannot be used for comparative studies of two strains that differ in chlorophyll amounts per cell as their bead making protocol is based on chlorophyll amount/ L of algal culture. Photosynthetic mutants are often chlorophyll deficient. Hence, one should not use chlorophyll concentration of culture to make beads if one wants to compare photosynthesis between two strains that differ in chlorophyll amount per cell. One will have to use approximately equal number of cells per bead in such experiments. If the latter, then it is unclear why the author’s would choose to include a mutant strain that is not available from the Chlamy Stock Center. The authors do include a list of strains that are available, but results from this activity using those strains was not provided (lines 544-547). Response: We have made some changes in lines # 656-661 to clarify our statements that led to this confusion. Teachers are free to use any photosynthetic mutants they have access to and, this includes characterized and uncharacterized mutants. We have also stated that 10E35 strain can be obtained from our lab. We mentioned alternative strains to educators as mere suggestions. We did not have any other photosynthetic mutants in our lab. We would have to purchase them from Chlamydomonas Resource Center and our funding is very limited. Student-driven designing of hands-on activities is a key component in the BLOOME project. The PI’s undergraduate students generated the 10E35 mutant. 10E35 mutant is our on-going research project of the undergraduate students in our lab. Hence, we have used 10E35 for the comparative experiments just to show that using approximately equal cell numbers/bead, we can crudely compare photosynthesis in two different Chlamydomonas strains. Providing my lab’s strains to school teachers was a component in the BLOOME project. We have provided the science teachers with the 10E35 and 4A+ strains for the experiment We agree it would have been nice to use other well characterized photosynthetic mutants but we had to work with what we have. Failing to use other well characterized photosynthetic mutants does not change our results. Please see below for further explanation to the sub-parts of this question. For a K6-12 teacher, it will be difficult for them to know what expected results are. The authors can provide a table or figure describing expected outcomes using exemplar strains available from the Chlamy Stock Center. It is to be noted that all we did was to check the color change of the pH indicator, phenol red, which produces two distinct colors: yellow in acidic pH and red or bright pink in alkaline pH. Hence, we can confidently predict that any photosynthetic mutant would take a long time to change phenol red color to red -bright pink (fuchsia) in the light or will very quickly change the phenol red color to yellow in the dark, compared to a wild type strain. This method is not a sophisticated method of measuring actual photosynthetic rates which can be done using an oxygen electrode (lines 641-643). Please look at the web links that we have added in lines: 362-365 and in lines: 429-431, for the expected color scale at different pH, when phenol red and bicarbonate indicator is used as pH indicator, respectively. These expected color scales should help the students/educators to interpret their experimental results, if they want to use other photosynthetic mutants. In the “Preparation of Chlamydmomonas…” subsection, the URLs do not work because of the inclusion of the forward slash at the end of the link (lines 160 and 166). Response: We apologize for the broken link. We have updated the link. The chemical analysis of the “tap water” should be included to define “tap water” for this study and confirm that chemicals that would modify photosynthesis/respiration, such as Acetate or Sulfur-containing compounds, are not present. Response: Plant-BLOOME is an educational outreach project and is not a hardcore science research project. The suggested experiment is beyond the scope of our educational outreach project for the following reasons: 1. Tap water testing is not required as our experiments worked in tap water in light and in darkness as expected but, did not work with the de-ionized water. 2. Tap water in different regions of USA vary and, also varies with the plumbing in the same building. School teachers are not going to test their tap water by elaborate chemical analyses before they use it for their experiments. It is not feasible and practical for them to do so. 3. There is no acetate in our tap water as the pH of tap water is 7.2-7.3 (Data S1 and Data S2). We need acetate as a carbon source for Chlamydomonas but there is not acetate in tap water as depicted by the pH. Also, before bead making, we drain of TAP medium from the cell pellet and the beads are washed thoroughly with tap water before being used for experiments. This is a critical step as described in the Method section and also in the published protocol. 4. Sulfur deficiency inhibits photosynthesis. We need sulfur for photosynthesis. In our tap water experiment, photosynthesis worked. We do not have means to tests for sulfur compounds in tap water in our lab and it is beyond the scope of our educational Plant-BLOOME project. Validity of the findings This paper modifies a published protocol and applies it to existing activities with a new twist, which is the plastic tubing “bracelets”. The tubing allows for better visualization of the pH changes due to the activity of organisms in individual beads, in comparison to glass vials or microcentrifuge tubes. The “bracelet” modification is meant to capture the attention of students, and this should be effective in doing so, however, data was not provided. Response: We agree but unfortunately, we do not have the IRB permission to share surveys with the public (please see the email from the ORSP office at my institution that I provided to the editor). This email is not part of publication but editors can share it with the reviewers. Please also refer to lines: 111 - 114 and Text S1. The existing, commercially-available beads can also be repackaged into this “bracelet” form. Response: True. But none of the companies that sell algal bead kits have done it yet. As stated earlier, students (and also educators) love to make algae beads as it is cool to see the algal cell suspension drops turn into tiny “green caviar” so quickly. Since Chlamydomonas can swim, if the alginate and algal cells are not well mixed, one can get weird shapes like Hershey’s kisses (See Figure S1). Students love that. We would not like to deprive the students of this enjoyable activity. Given the number of institutions this protocol has been used at, the inclusion of student and teacher opinions would have strengthened the argument that the learning module is engaging. Response: We agree but unfortunately, we do not have the IRB permission to share surveys with the public (please see the email from the ORSP office at my institution that I provided to the editor). This email is not part of publication but editor can share it with you. Please also refer to lines: 111 - 114 and Text S1. The authors also claim that this protocol is most cost-effectives than existing protocols or kits. The authors should include a detailed list of all reagents and equipment required, with their respective cost of use, to support this claim. Response: We agree. Detailed information of items is presented in the supplementary Text S2 file. We have shown the price of each items and cost comparison in Text S2 file on pages 5-6. Please note that many of these stated items can be found at local store at a cheaper price than the prices charged by Fisher or other companies. Hence, the cost will further go down. We added a disclaimer on page 3 of Text S2. We have compared the cost of Bio-Rad kit that serves 24 workstations (student number per workstation unspecified on their website) Vs. our protocol that can serve large population of students over a longer period (Text S2 and lines:196 -200). "	Here is a paper. Please give your review comments after reading it.
497	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Background. Photosynthesis and cellular respiration play major roles in energy metabolism and are important Life Science topics for K16 Biology students. Algae beads are used for photosynthesis and cellular respiration labs. Currently there are a few companies that sell biology educational kits for making algae beads using non-motile green micro-algae to introduce students to photosynthesis. These kits are expensive and, do not come with detailed guidelines for trouble shooting and customizations for different grade levels. Chlamydmonas reinhardtii is a motile green micro-alga and is an excellent model system for photosynthesis studies. In this article, we are presenting the work conducted in the student-driven, American Society of Plant Biologists-funded, Plant-BLOOME educational outreach project. This project is a supervised collaborative effort of three undergraduates and one high school student. We have generated a protocol which can be used to make Chlamydomonas beads. We have used these beads to design two simple and inexpensive plant biology hands-on activities. These laboratory activities have been customized to teach the interplay of photosynthesis and cellular respiration to K4 -K16 Biology students.</ns0:p><ns0:p>Methods Chlamydomonas beads were used for two different laboratory activities that involved monitoring pH changes over time using a pH indicator. Our first activity centers on making and, using light-powered algae bead bracelets to monitor dramatic color/pH changes over time when exposed to darkness or light. Our second activity employs strain-specific algae beads with approximately equal cell numbers to conduct comparative photosynthesis and cellular respiration studies in two Chlamydomonas strains namely, wild type, 4A+ and, a high light-sensitive, photosynthetic mutant, 10E35/lsr1a.</ns0:p><ns0:p>Results. We optimized our experimental protocol using algae beads in a 5.5 mL screw capped glass vials before performing the same experiment in algae bead bracelets. We found that the algal cell density/bead, water type used in the experiment and, the duration of dark exposure of algal beads can affect successful implementation of the lab activities. Light-powered algae bead bracelets showed dramatic color/pH change within 3 hours upon exposure to light or darkness. These bracelets could be switched back and forth between darkness and light multiple times within 48-72 hours to display color/pH changes, provided prior dark exposure time did not exceed 9 hours. Our comparative studies of photosynthesis and cellular respiration in 10E35 and in 4A+ showed that relative respiration rate and photosynthetic rate is higher and lower in 10E35, respectively, compared to that in 4A+. Additionally, 10E35 failed to display the expected photosynthesis-induced pH/color changes in the light after prolonged exposure to darkness which indicated that prolonged dark exposure of 10E35, hindered</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>Photosynthesis, an energy requiring anabolic process, comprises of two sets of reactions that occur in the chloroplast: Light reaction and Calvin cycle. In the light reaction, solar energy captured by photosynthetic pigments is used to photolyze water into electrons and protons. These protons and electrons are ultimately used to generate ATP (adenosine triphosphate), a reducing power and oxygen in the light reaction <ns0:ref type='bibr' target='#b20'>(Tymoczko, Berg & Stryer, 2015)</ns0:ref>. ATP and the reducing power generated in the light reaction and water, are used in the Calvin cycle to reduce atmospheric carbon dioxide to sugar <ns0:ref type='bibr' target='#b20'>(Tymoczko, Berg & Stryer, 2015)</ns0:ref>. Aerobic cellular respiration is a catabolic energy releasing process that oxidizes fixed carbon to generate ATP <ns0:ref type='bibr' target='#b20'>(Tymoczko, Berg & Stryer, 2015)</ns0:ref>. Oxygenic photosynthesis provides not only fixed carbon that is utilized by cellular respiration for energy production but, it is also the only source for generation of oxygen on a mass scale on Earth to support life <ns0:ref type='bibr' target='#b20'>(Tymoczko, Berg & Stryer, 2015)</ns0:ref>. Hence, every Biology students should have a broad understanding of these two complementary, life-supporting, fundamental biochemical reactions. These two biochemical reactions are listed in the Next Generation Science Standards (NGSS) Life Science core idea LS1C: From Molecules to Organisms: Structures & Processes. LS1C aligns with principles 1, 2, 3, 5, 10 and 11 of the 12 Principles of Plant Biology listed by the American Society of Plant Biologists ('The 12 Principles of Plant Biology', ASPB; Article S1). Guidelines for photosynthesis and cellular respiration laboratories using non-motile green microalgae beads are available on the websites of Carolina Biological (Burlington, NC), Bio-Rad (Hercules, CA) and Gene Technology Access Center (GTAC; Victoria, Australia) for classroom use. ('Carolina Quicktips Making Algae Beads', Carolina Biological; 'Photosynthesis and Cellular Respiration Kit for General Biology', Bio-Rad; 'Algae Immobilised in Alginate balls', GTAC, 2016). Traditionally, non-motile algae like Chlorella, Ankistrodesmus and Scenedesmus have been used to generate algae beads as non-motile algae can be trapped and immobilized easily ('Carolina Quicktips Making Algae Beads', Carolina Biological; 'Algae Immobilised in Alginate balls', GTAC, 2016; 'Photosynthesis and Cellular Respiration Kit for General Biology', Bio-Rad). Chlamydomonas reinhardtii is motile green micro-alga and is an excellent model system for photosynthesis and bioenergy researchers <ns0:ref type='bibr'>(Merchant et al., 2007;</ns0:ref><ns0:ref type='bibr' target='#b13'>Scranton et al., 2015;</ns0:ref><ns0:ref type='bibr' target='#b11'>Radakovits et al., 2010)</ns0:ref>. At our research laboratory we use Chlamydomonas as an experimental system to study photo-protection and photosynthetic pigment metabolism. Our ASPB-funded Plant-BLOOME educational outreach student-driven project centers on designing new educational hands-on activities using exclusively, Chlamydomonas and not any other alga. There is one recent report of immobilization of a Chlamydomonas strain for photobiohydrogen production under anaerobic sulfur-deprived conditions in different types of photobioreactors <ns0:ref type='bibr' target='#b1'>(Canbay, Kose & Oncel, 2018)</ns0:ref>. Low yield of biofuel from large-scale algal cultures in bioreactors is a major problem. Sustainable bioenergy production can be improved by immobilizing motile algae like Chlamydomonas and Botryococcus sp. that are employed for bioenergy research <ns0:ref type='bibr' target='#b1'>(Canbay, Kose & Oncel, 2018;</ns0:ref><ns0:ref type='bibr' target='#b11'>Radakovits et al., 2010)</ns0:ref>. Our educational trips to schools in Georgia and our participation at the education booths at the NSTA meeting in Atlanta in 2018 and at the Plant Biology 2019 in San Jose, CA, clearly showed us that K6-K16 students (and even educators) love to make algae beads (Text S1). Bead-making activity is excellent for student engagement in classrooms, which cannot be achieved by using commercially purchased pre-made beads. Unfortunately, anonymous teacher and student surveys that we collected cannot be shared with public because our institution did not submit IRB application materials for this project. Glimpses of our educational outreach activities can be found at several available links shown in Text S1. Commercial kits from Bio-Rad or other vendors are costly when one considers how many students can be served per commercial kit and the duration of the time the kit can be used in classrooms (see Materials and Methods and Text S2 for detailed calculation). Commercial educational kits often do not work well, uses beads with short shelf lives, takes long time to show color change and, sometimes comes with erroneous instructions (For example: Carolina instruction sheet instructs educators to grow dense cultures of Chlorella for 3-4 weeks before harvesting cells for bead-making. This means the company is instructing educators to make algal beads using a culture that is in the late stationary phase; https://www.carolina.com/pdf/activitiesarticles/carolina-qt-making-algae-beads-cb814921806.pdf). Technical resources that comes with these kits lack specific guidelines for optimizing the experiment and troubleshooting. Hence a well-defined protocol with proper detailed guidelines for conducting lab activities & managing class times and, information for acquiring lab materials inexpensively, will be useful Biology educators at schools and institutions that have very limited resources and funding. Calcium alginate is used to trap and immobilize living cells in industrial procedures ('Carolina Quicktips Making Algae Beads, Carolina Biological; 'Algae Immobilised in Alginate balls', GTAC, 2016; 'Photosynthesis and Cellular Respiration Kit for General Biology', Bio-Rad). For example, immobilized non-motile colonial algae are being tested for biofuel production, immobilized yeast cells are being used for alcoholic wine fermentation, and immobilized bacterial cells are being used for water disinfection <ns0:ref type='bibr' target='#b5'>(Kröger & Müller-Langer, 2012;</ns0:ref><ns0:ref type='bibr' target='#b4'>Gotovtsev et al., 2015)</ns0:ref>. To entrap algae in beads, cell suspension and 2% sodium alginate are mixed at a specific ratio and added drop-wise to chilled calcium chloride solution. Calcium ions link the alginate monomers together to make a gel-like polymer of calcium alginate which trap cells and immobilize them in beads. These algal beads can be used for biological experiments or other biotechnological applications. Cellular respiration oxidizes organic chemicals and releases CO 2 into the environment irrespective of presence/absence of light and, photosynthesis converts CO 2 into fixed carbon only in the presence of light <ns0:ref type='bibr' target='#b20'>(Tymoczko, Berg & Stryer, 2015)</ns0:ref>. Cellular respiration in live cells in the beads will release CO 2 that will dissolve in water in which the beads are immersed to generate carbonic acid ('Photosynthesis and Cellular Respiration Kit for General Biology', Bio-Rad; 'Algae Immobilised in Alginate balls', GTAC, 2016). Conversely in the light, photosynthesis in the algal cells in the beads will remove CO 2 from the water surrounding the beads ('Photosynthesis and Cellular Respiration Kit for General Biology', Bio-Rad; 'Algae Immobilised in Alginate balls', GTAC, 2016). In the light, cellular respiration is still going on in the cells, but the net use of CO 2 by photosynthesis vastly outweighs the CO 2 released during cellular respiration, unlike that in the dark. Hence pH of the water will be acidic in the dark and alkaline in the light. In the two activities designed by us, students will monitor photosynthesis and cellular respiration-induced pH changes in the water by color changes of a pH indicator as well as by measuring the pH with pH testing strips and/or a pH electrode. We have generated a detailed protocol of making Chlamydomonas beads and two simple plant biology hands-on activities. These laboratory activities were used to teach the interplay of photosynthesis and cellular respiration to Biology students in nine schools and two universities in Georgia in a fun and engaging way. The presented educational work is a product of supervised collaborative efforts of three undergraduate students and one high school student in Georgia, USA. In the two designed laboratory activities students make Chlamydomonas beads and use these beads to conduct their own independent experiments. In the first lab activity students make light-powered green algae bead bracelets and use these algae bracelets to perform time course experiments in light and dark to study the interplay of photosynthesis and cellular respiration. In the second activity, students compare relative ratios of photosynthesis and cellular respiration in a Chlamydomonas wild type (4A+) and a chlorophyll-deficient, high-light sensitive mutant strain, 10E35/lsr1a, using strain-specific algae beads. 10E35 is a random insertional mutant generated by our research lab with a mutation in a novel functionally uncharacterized gene, LSR1 and is the center of an on-going research project at our laboratory <ns0:ref type='bibr' target='#b9'>[Nguyen et al., 2017;</ns0:ref><ns0:ref type='bibr'>Article S2]</ns0:ref>. We present in this article our protocol for making Chlamydomonas beads (including some preliminary testing data that helped us to refine the protocol), two new plant biology teaching tools and sample teaching resources for educators. We hope that the teaching resources will help plant biology educators to customize the labs according to grade level, availability of resources, and allow better time management in classrooms. The designed lab activities support active learning and contributes toward the following: 1) NGSS Science and Engineering Practice: Developing and using models; Planning and carrying out investigations and, 2) NGSS Core Idea: Life Science LS1C: From molecules to organisms: Structures and Processes.</ns0:p></ns0:div> <ns0:div><ns0:head>Materials & Methods</ns0:head></ns0:div> <ns0:div><ns0:head>Material information for educators</ns0:head><ns0:p>Information (vendors and catalog numbers) for ordering specific items related to the project like algal strains, algal growth media, inoculating loops, flasks, plastic transfer pipettes, Eppendorf tubes, pH indicators, pH test strips, bracelet tubing, glass vials, yarns for bracelet braids, sodium alginate, calcium chloride, counting chambers etc. are given on pages 1-6 in Text S2 file. On pages 5-6 in Text S2 file, we have shown the pricing of the basic items that one will need to start the lab and the cost comparison of our protocol Vs. the Bio-Rad Photosynthesis and Cellular respiration kit for general Biology. The cost comparison shows that our protocol is inexpensive and will serve more students over a longer period than the Bio-Rad kit (Text S2).</ns0:p></ns0:div> <ns0:div><ns0:head>Algal media and cultures</ns0:head><ns0:p>Chlamydomonas wild type strain 4A+ (CC-4051 4A+ mt+) strain was a gift from Dr. Krishna K. Niyogi (UC Berkeley, CA). 10E35/lsr1a (light-sensitive related 1a) is a random insertional nuclear mutant generated by our lab which has a mutation in a novel gene, LSR1 encoding a protein of unknown function <ns0:ref type='bibr' target='#b9'>[Nguyen et al., 2017;</ns0:ref><ns0:ref type='bibr'>Article S2]</ns0:ref>. 4A+ and 10E35 strains were maintained in the lab on Tris-Acetate Phosphate (TAP) agar media plates (Text S2) in dim light intensities (15-20 µmol m -2 s -1 ) at 25°C. A starter culture of 4A+ was started approximately 11-12 days ahead of the lab activity by inoculating 10 mL of liquid TAP media in a 50 mL flask with 4A+ cells from a 5-day old TAP agar media plate (Text S2). After 5 days of growth, 1 mL of the starter culture was used to inoculate 300 mL of fresh TAP media in a 1L flask. The TAP liquid 4A+ culture was grown for 6-7 days for dense dark green growth. 10E35 grows slower than 4A+. Hence 10E35 liquid TAP cultures should be started at least 3-4 days before starting the 4A+ liquid TAP cultures. Algal liquid cultures were grown under 25°C under continuous illumination of 80-100 µmol photons m -2 s -1 provided by the combined light intensities of four to six cool white fluorescent lights. Cultures were shaken continuously on an open-air orbital shaker at a speed of 150-180 rpm to ensure a uniform illumination of the cells and to prevent cells from settling down. Light intensities were measured using a LI-250A Light Meter (LI-COR, Inc., Lincoln, NE).</ns0:p><ns0:p>Preparation of 2% sodium alginate and 3% calcium chloride solutions 2 grams of sodium alginate (Fisher Scientific, Waltham, MA) was dissolved in 100 mL of E-pure water overnight at room temperature by stirring at a speed of 400 rpm using a magnetic stirrer.</ns0:p><ns0:p>[Note: sodium alginate forms a very viscous solution when dissolved at 1.5% -4%]. 2% sodium alginate solution was stored at room temperature. 30 grams of calcium chloride was dissolved in 1000 mL of E-pure water and stored at 4ºC in a fridge. Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Cell counts</ns0:head><ns0:p>Cell density (number of cells per mL of the culture) was determined before harvesting Chlamydomonas cells from the TAP liquid culture to estimate the volume of culture needed to harvest specific number of cells per 50 mL falcon tube. Cell density was calculated by counting the cells using a Hausser Scientific Bright-Line™ Counting Chamber (Hausser Scientific, Philadelphia, PA). A basic protocol on how to use a hemocytometer in a classroom setting is available at https://www.ruf.rice.edu/~bioslabs/methods/microscopy/cellcounting.html. It is to be noted that cell counting is optional. School teachers who do not have access to a hemocytometer/counting chamber, can grow algae culture for 6-7 days and then harvest the cells to make beads. Additionally, teachers can match the green color of the beads with that shown in our article figures.</ns0:p></ns0:div> <ns0:div><ns0:head>Preparation of Chlamydomonas 4A+ and 10E35 beads</ns0:head><ns0:p>A detailed version of the Chlamydomonas bead-making protocol (including trouble shooting) is available at https://www.protocols.io/view/making-inexpensive-light-powered-chlamydomonasrei-bgpyjvpw. Chlamydomonas strain 4A+ or 10E35 cells were harvested by spinning down dense TAP liquid strain-specific cultures at 1,000-1,500 g for 3 minutes in a benchtop centrifuge. The supernatant was discarded and the cell pellet was collected. Harvesting 100 mL of dense Chlamydomonas culture generated 200-300 beads of 4-5 mm in diameter. 2% well mixedsodium alginate was added to the cell pellet in a 4:1 or 5:1 ratio (depending on the total number of cells harvested; see results and detailed protocol on https://www.protocols.io/view/makinginexpensive-light-powered-chlamydomonas-rei-bgpyjvpw). The algae and 2% sodium alginate were gently mixed till the entire cell pellet was completely resuspended without any visible cell clumps. Maximum number of total cells used for resuspension in sodium alginate was either 395 X 10 6 or 790 X 10 6 cells depending on the experiment (see result section). We resuspended the cell pellets containing 395 X 10 6 cells and 790 X 10 6 cells in 5 mL of sodium alginate to get an approximate final cell density of 66 X 10 6 cells/mL and 132 X 10 6 cells/mL in the cell suspension, respectively. 1 mL of sodium alginate-algal cell suspension gave us approximately 32-35 beads depending on pipetting techniques. Hence the cell suspension with cell density of 66 X 10 6 cells/mL will form beads that have approximately 1.89 X 10 6 -2 X 10 6 cells/bead while the cell suspension with cell density of 132 X 10 6 cells/mL will have 3.77 X 10 6 -4.1 X 10 6 cells/bead. We used 8 beads of similar sizes (4-5 mm in diameter) for glass vial experiments. The algae-sodium alginate mix was added drop wise steadily and quickly with uniform pipetting by using a micropipette or a plastic transfer pipette into a beaker of pre-chilled 3% calcium chloride kept on ice. If pipetting is not smooth and regular and, the algae-sodium alginate mixture is not mixed by swirling in between pipetting, irregular shaped and beads with different cell numbers/bead (light and dark green beads) will form (Fig. <ns0:ref type='figure' target='#fig_3'>S1</ns0:ref>). As soon as the algae-sodium alginate mixture touches the chilled calcium chloride liquid surface, the mixture solidified into tiny beads. The calcium chloride beaker containing the beads were kept on ice for 10-15 minutes to allow complete solidification of the algal beads.</ns0:p><ns0:p>The beads are separated from the calcium chloride solution by filtering through an oil strainer. Algal beads on the strainer were washed with tap water. The beads were kept temporarily in a petri dish containing small amount of tap water till the bracelets were made. Surplus beads were stored in tap water in a beaker for future use within 1-2 days. Algae bead making demonstration video clips are available at: https://youtu.be/u4BbZ29qlWQ and at https://youtu.be/eIxbzeHW8IM.</ns0:p></ns0:div> <ns0:div><ns0:head>Preparation of Chlamydomonas 4A+ bead bracelet</ns0:head><ns0:p>Flexible tubing was cut into 10 pieces, each 5 inches long. Caps of 1.5 mL Eppendorf tubes were cut off with a scissor. De-capped Eppendorf tube was used to plug the ends of the bracelet tubing (one de-capped tube at each end of the cut tubing). Colorful cotton yarn was cut according to the wrist width, intertwined and yarn braids were made. One braid was looped tightly onto the mouth of each de-capped Eppendorf tube at each end of the bracelet. Next, one end of the bracelet tubing was unplugged by removing the de-capped Eppendorf tube that was sealing the end. About 3.5 mL of tap water <ns0:ref type='bibr'>[pH 7.2 -7.3</ns0:ref>] was introduced into the bracelet flexible tubing. 15-38 algae beads (depending on the experiment) were gently introduced into the water inside the tubing. 8-10 drops of the bicarbonate indicator (Carolina Biological, Burlington, NC) were added into the water in the tubing and the end of the tubing was plugged back with the de-capped Eppendorf tube. Precautions were taken to avoid acidic or alkaline contamination of the flexible tubing, plastic spoon, transfer pipettes, petri dishes etc. used in our experiments, since the bicarbonate indicator is not directly specific to gases like carbon dioxide. About 0.5 cm-1 cm air gap was left at each end inside the tubing to provide enough air for cells. The bracelet was imaged and the pH of the water inside the bracelet was measured using pH testing strips (Fisher Scientific, Waltham, MA) before shifting it to light or to darkness for the lab activity. All experiments described below were performed with the same batch of beads. A detailed version of the protocol is available at https://www.protocols.io/view/making-inexpensive-light-poweredchlamydomonas-rei-bgpyjvpw. Demonstration of algae bead bracelet making video clips available at: https://youtu.be/A7VIjLDGSCc and https://youtu.be/vh_1ASpQgS8 and https://youtu.be/enctr0yhWQ8.</ns0:p></ns0:div> <ns0:div><ns0:head>Light and dark exposure experiments with Chlamydomonas bead bracelets</ns0:head><ns0:p>For the constant light/dark exposure experiment, one bracelet was kept under 150-200 µmole m -2 s -1 light intensity [equivalent to the combined light intensities of 12 to 14 cool white fluorescent lights] and another one was kept in the dark inside a lab cabinet drawer. After 3 hours of light/dark exposure, bracelets were imaged. pH of the water inside the bracelets were measured using pH testing strips (Fisher Scientific, Waltham, MA). For dark shift experiment, the bracelet was first light-adapted for 4 hours and then shifted to darkness. For light shift experiment, the bracelet was dark-adapted for 4 hours and then shifted to light. After every 1 hour over a period of 4 hours during light exposure or over a period of 3 hours during dark exposure, the bracelet was imaged to monitor the carbon dioxide percentage change inside the bracelet tubing. The carbon dioxide percentage change is monitored indirectly by the color changes of the bicarbonate indicator. pH was not measured for the light/dark shift experiments with algae bead bracelets. For testing the effect of different dark exposure times on photosynthesis, one algae bead bracelet was exposed to 9 hours of darkness and the other was exposed to 15 hours of darkness. After the dark exposure, the 9 hours-and 15 hours-darkadapted bracelets were exposed to light for 4 hours and 12 hours, respectively and were imaged after the light exposure. pH of the water inside these light and dark-exposed bracelets was measured using pH testing strips (Fisher Scientific, Waltham, MA).</ns0:p></ns0:div> <ns0:div><ns0:head>Light and dark exposure experiments with Chlamydomonas strain-specific beads in glass vials</ns0:head><ns0:p>For testing the effect of water quality on photosynthesis, eight 4A+ beads were either introduced into 2.5 mL of tap water (pH 7.2 -7.3) or into de-ionized [DI] water (pH 7.1-7.2) in 5.5 mL screw capped glass vials (Fisher Scientific, Waltham, MA). For testing the effect of cell density on photosynthesis, eight 4A+ beads were either introduced into 2.5 mL of tap water (pH 7.2 -7.3). For both stated experiments, 125 µL of the 0.02% phenol red solution (Fisher Scientific, Waltham, MA) was added to the algae bead vials to serve as a pH indicator and the vials were capped tightly. One set of 4A+ bead and the control vials were exposed to 150-200 µmole m -2 s -1 light intensity and the other set to darkness for 2 hours. After 2 hours of light or dark exposure, vials were imaged and pH of the water in the vials was measured using a Thermo Fisher Scientific Orion-3 Star benchtop pH meter (Fisher Scientific, Waltham, MA). For comparative analyses of photosynthesis and cellular respiration in 4A+ and 10E35 strains under constant light/darkness, beads having approximately 2 X 10 6 cells/bead for each strain were used (Fig. <ns0:ref type='figure' target='#fig_4'>S2</ns0:ref>). Eight 4A+ and 10E35 beads were introduced into 2.5 mL of tap water (pH 6.9 -7.3) in 5.5 mL screw capped glass vials (Fisher Scientific, Waltham, MA). 125 µL of the phenol red solution (Fisher Scientific, Waltham, MA) was added to the 4A+ and 10E35 bead vials and the vials were capped tightly. One set of 4A+, 10E35 and control vials was exposed to light intensity of 150-200 µmole m -2 s -1 and the other set was exposed to darkness for 1 hour. The algae bead and control vials were imaged after every 30 minutes over a period of 1 hour and pH of the water in the vials was measured. The 1-hour light adapted 4A+, 10E35 and the control vials were exposed to light for an additional 3 hours and then shifted to dark. The vials were imaged after every 15 minutes for a period of 1 hour during dark exposure. After 1 hour, these dark-exposed vials were kept under dark for additional 5 hours. After 6 hours-of dark exposure, vials were shifted to light [150-200 µmole m -2 s -1 ] and imaged after 30 minutes, 1 hour, 2 hours, 3 hours and 48 hours. pH of the water in the glass vials in the above stated experiments were measured using a Thermo Fisher Scientific Orion-3 Star benchtop pH meter (Fisher Scientific, Waltham, MA).</ns0:p></ns0:div> <ns0:div><ns0:head>Imaging and Data analyses</ns0:head><ns0:p>Images were captured a Samsung Galaxy S5 camera. Statistical analyses of the recorded pH data were performed using Microsoft Excels' t-Test: Paired Two Sample for Means tool in the analysis ToolPak. Both One-Tailed and Two-Tailed Hypothesis Tests were performed. Standard deviations shown in Tables under result section was calculated using Excel. Raw statistical analyses data from three biological replicates per experiment have been deposited in Figshare </ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head><ns0:p>PeerJ reviewing PDF \| (2020:05:49217:2:0:NEW 27 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed Effect of total cell numbers in Chlamydomonas 4A+ strain beads on photosynthesis and cellular respiration-induced color/pH changes in tap water. We used two types of beads that have two-fold difference in total cell numbers/bead: 1) beads that have approximately 2 X 10 6 cells/bead and, 2) beads that have approximately 4 X 10 6 cells/bead. It is expected that a high cell number in a bead will increase cellular respiration as a high cell density in the bead will create oxygen stress. The pH in the light-exposed vial containing 4 X 10 6 cells/bead was 6.1 and the pH in the light exposed vial containing 2 x10 6 cells/bead was pH 8.4 for the same duration of light exposure (Fig. <ns0:ref type='figure' target='#fig_4'>2</ns0:ref>; Table <ns0:ref type='table'>S2</ns0:ref>; https://doi.org/10.6084/m9.figshare.12344024.v1; Data S1; Data S2). The pH in the light-and dark-exposed vials containing 4 x 10 6 cells/bead differed by only 0.1 pH unit while the pH in the light-and dark-exposed vials containing 2 x 10 6 cells/bead differed approximately by 2 pH units for the same duration of light exposure (Fig. <ns0:ref type='figure' target='#fig_4'>2</ns0:ref>; Table <ns0:ref type='table'>S2</ns0:ref>; https://doi.org/10.6084/m9.figshare.12344024.v1; Data S1; Data S2). There was a statistically significant pH difference between the light-exposed vials with 4 X 10 6 cells/bead and that with 2 X 10 6 cells/bead (p-values from the 1-tailed and 2-tailed hypothesis tests for the light-exposed vials were 0.0001 and 0.0002, respectively; Data S1). The pH difference between the darkexposed vials with 4 X 10 6 cells/bead and that with 2 x 10 6 cells/bead was statistically significant (p-values from the 1-tailed and 2-tailed hypothesis tests were 0.01 and 0.02, respectively; Data S1). pH differences between the light and dark control vials were insignificant as the p-values were higher than 0.05 in both 1-tailed and 2-tailed hypothesis tests (Data S1). Our results show high cell density/bead will hinder observation of pH changes in a photosynthesis lab. In the lightexposed experimental vial, partial buoyancy of one bead can be seen, indicative of O 2 production in photosynthesis (Fig. <ns0:ref type='figure' target='#fig_4'>2B</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>Indirect detection of carbon dioxide concentration in the 4A+ bead bracelet under light and darkness using the bicarbonate indicator.</ns0:head><ns0:p>Bicarbonate indicator is commonly used in photosynthesis and respiration experiments to detect indirectly the percentage of carbon dioxide in a sample. It is a more sensitive pH indicator than phenol red. When the carbon dioxide content in water is higher than 0.04%, pH becomes acidic. Acidic pH changes the red color of the indicator to yellow. If the carbon dioxide content is lower than 0.04%, pH gets alkaline and the indicator changes color from red to magenta and, under very low carbon dioxide concentrations the color of the indicator changes to purple (https://en.wikipedia.org/wiki/Bicarbonate_indicator). The expected color scale at different pH when bicarbonate indicator is used as the pH indicator can be found at https://pmgbiology.com/tag/respiration/. We used three bracelets (with algal beads ranging from 30-38) to monitor color changes of the bracelet water containing the bicarbonate indicator. These are designated as control, darkexposed and light-exposed bracelets in Fig. <ns0:ref type='figure' target='#fig_5'>3</ns0:ref>. The color of the water in the control bracelet (not exposed to dark or light), dark-and light-exposed bracelets were, light red, bright yellow and dark blue, respectively (Fig. <ns0:ref type='figure' target='#fig_5'>3</ns0:ref>). The objective of the experiment was to simply determine the color/pH changes of the water in the experimental bracelets in the light or in the dark relative to Manuscript to be reviewed the control. The average pH of the water in the control algal bracelet was around 7 (Fig. <ns0:ref type='figure' target='#fig_5'>3A</ns0:ref>; Table <ns0:ref type='table'>S3</ns0:ref>; https://doi.org/10.6084/m9.figshare.12344024.v1; Data S1; Data S2). pH of the water in the dark-exposed algal bracelets ranged between 6 and 6.5 with STDEV ± 0.24 (Fig. <ns0:ref type='figure' target='#fig_5'>3B</ns0:ref>; https://doi.org/10.6084/m9.figshare.12344024.v1; Data S1; Data S2) indicating a high percentage of carbon dioxide because of cellular respiration. pH of the water in the light-exposed algal bracelets ranged between 8.5 and 9 with STDEV ± 0.24 indicating a low percentage of carbon dioxide because of photosynthesis (Fig. <ns0:ref type='figure' target='#fig_5'>3C</ns0:ref>; Table <ns0:ref type='table'>S3</ns0:ref>; https://doi.org/10.6084/m9.figshare.12344024.v1; Data S1; Data S2). pH difference between the light-and dark-exposed bracelets is statistically significant (p-values from the 1-tailed and 2tailed hypothesis tests were 0.001 and 0.003, respectively; Data S1; Data S2; https://doi.org/10.6084/m9.figshare.12344024.v1). Our results clearly show that carbon-dioxide percentage can be monitored indirectly under light/darkness via sharp pH/color changes in the water in the bracelet in the presence of the bicarbonate indicator.</ns0:p></ns0:div> <ns0:div><ns0:head>Time course monitoring of photosynthesis-induced color changes in the dark adapted-4A+ bead bracelet when shifted to light.</ns0:head><ns0:p>An algal bracelet was dark-adapted for 4 hours. After dark-adaptation the bracelet was exposed to light for 4 hours. This light-exposed bracelet was imaged after every 1 hour during light exposure to monitor the gradient color changes over time without disturbing the bracelet (Fig. <ns0:ref type='figure'>4</ns0:ref>). The results show that if the algal bracelet is left undisturbed, one can pinpoint specifically which beads were actively photosynthesizing from the red-magenta-purple color streaks in the water on top of these beads that were removing carbon-dioxide from the tubing water (Fig. <ns0:ref type='figure'>4 B-D</ns0:ref>). pH was not measured in these bracelets as the objective of this experiment was to determine if differences exist in photosynthetic rates among different beads by visually observing the gradual color change of the water in the bracelet in light.</ns0:p></ns0:div> <ns0:div><ns0:head>Time course monitoring of cellular respiration-induced color changes in the light adapted-4A+ bead bracelet when shifted to darkness.</ns0:head><ns0:p>An algal bracelet was light adapted for 4 hours. After light-adaptation the bracelet was exposed to dark for 3 hours. The dark-exposed bracelet was imaged after every 1 hour during the light exposure to monitor the gradient color changes over time (Fig. <ns0:ref type='figure' target='#fig_7'>5</ns0:ref>). The results show that distinct pH gradient can be observed in a colorful way in an undisturbed algal bracelet (Fig. <ns0:ref type='figure' target='#fig_7'>5B</ns0:ref>). pH was not measured in these bracelets as the objective of this experiment (in conjunction with the Fig. <ns0:ref type='figure'>4</ns0:ref> experiment) was to teach students in a fun way, the 'tug of war' between photosynthesis and cellular respiration by visually observing the dramatic color changes of the water in the bracelet upon exposure to darkness or light.</ns0:p></ns0:div> <ns0:div><ns0:head>Effect of prior dark exposure duration on photosynthesis-induced pH changes in the 4A+ bead bracelet in light.</ns0:head><ns0:p>One algal bracelet was kept in the dark for 9 hours and the other one was kept in the dark for 15 hours. After dark incubation, both bracelets were imaged and the pH was measured using pH testing strips and, then shifted to light. (Fig. <ns0:ref type='figure' target='#fig_8'>6</ns0:ref>). There was no significant difference in pH between the 9-hours dark-adapted and 15-hours-dark adapted bracelets (p values from 1-tailed PeerJ reviewing PDF \| (2020:05:49217:2:0:NEW 27 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed and 2-tailed tests were 0.11 and 0.22, respectively; Data S1). There was a significant difference in pH between 9-hours-dark adapted bracelet and 15-hours-dark adapted bracelet when these were exposed to light for 4 hours and 12 hours, respectively (p values from 1-tailed and 2-tailed tests were 0.002 and 0.005, respectively; Data S1). The bracelet that was kept in dark for 9 hours showed increase in pH from pH 6 [STDEV ±0] to pH 8.67 [STDEV ±0.24] within 4 hours under light because of photosynthesis (Fig. <ns0:ref type='figure' target='#fig_8'>6A</ns0:ref> & 6B; Table <ns0:ref type='table'>S4</ns0:ref>; https://doi.org/10.6084/m9.figshare.12344024.v1; Data S1; Data S2). The bracelet that was kept in dark for 15 hours showed a small increase in pH from 5.5 [STDEV ±0.41] to 6.3 [STDEV ±0.24], despite being exposed to light for 12 hours. This indicates prior prolonged exposure to darkness hinders photosynthesis in algal beads in light (Fig. <ns0:ref type='figure' target='#fig_8'>6C</ns0:ref> & 6D; Table <ns0:ref type='table'>S4</ns0:ref>; https://doi.org/10.6084/m9.figshare.12344024.v1; Data S1; Data S2).</ns0:p></ns0:div> <ns0:div><ns0:head>Comparative studies of photosynthesis and cellular respiration-induced color/pH changes in vials containing wild type 4A+ and 10E35 mutant beads.</ns0:head><ns0:p>4A+ and 10E35 beads have approximately 2 X 10 6 cells/bead (Fig. <ns0:ref type='figure' target='#fig_4'>S2</ns0:ref>). Each light and dark set comprised of a control and experimental vials of 10E35 and 4A+ (Fig. <ns0:ref type='figure' target='#fig_9'>7</ns0:ref>). Images of the vials in each light and dark set were taken before light or dark exposure (Fig. <ns0:ref type='figure' target='#fig_9'>7A and 7D</ns0:ref>). Each light and dark vial sets were imaged after 30 minutes of light and dark exposures, respectively for a period of 1 hour. Results show a statistically significant slow increase in pH in 10E35 vial under light compared to that in the 4A+ vial (Fig. <ns0:ref type='figure' target='#fig_9'>7B</ns0:ref> -7C; Table <ns0:ref type='table'>S5</ns0:ref>; https://doi.org/10.6084/m9.figshare.12344024.v1; Data S1; Data S2) (p-values from the 1-tailed and the 2-tailed tests ranged from 0 -0.015 and 0 -0.03, respectively). This could be due to a slow rate of photosynthesis or a high rate of cellular respiration or a combination of both phenomena in 10E35 relative to that in 4A+. 10E35 displays relatively a higher rate of cellular respiration in dark compared to that in 4A+ as indicated by the fast pH drop in dark in 10E35 vial over time compared to that in the 4A+ vial that is statistically significant (Fig. <ns0:ref type='figure' target='#fig_9'>7E-7F</ns0:ref>; Table <ns0:ref type='table'>S5</ns0:ref>, https://doi.org/10.6084/m9.figshare.12344024.v1; Data S1; Data S2) (p-values from the 1tailed and 2-tailed tests ranged from 0.002 -0.011 and 0.005 -0.022, respectively).</ns0:p></ns0:div> <ns0:div><ns0:head>Time course monitoring of cellular respiration-induced color/pH changes in the dark in vials containing 4A+ and 10E35 beads that were exposed to light for 4 hours.</ns0:head><ns0:p>Light-exposed 10E35, 4A+ and control vials from Fig. <ns0:ref type='figure' target='#fig_9'>7</ns0:ref> experiment were exposed to light for additional three hours. Hence this set was light-exposed for a total of 4 hours. After fours of light exposure, images were taken and the vials were exposed to dark. Images of the dark-exposed vials were taken every 15 minutes over a period of 1 hour during dark exposure (Fig. <ns0:ref type='figure'>8</ns0:ref>). 10E35 shows relatively a higher cellular respiration rate that is statistically significant, compared to that in 4A+, as indicated by the rapid drop in pH in the 10E35 vial compared to that in the 4A+ vial (p-values from the 1-tailed and 2-tailed tests ranged from 0.0005 -0.001 and from 0.001-0.003, respectively) (Fig. <ns0:ref type='figure'>8B -8E</ns0:ref>; Table <ns0:ref type='table'>S6</ns0:ref>; https://doi.org/10.6084/m9.figshare.12344024.v1; Data S1; Data S2). The results re-confirm the results shown in Fig. <ns0:ref type='figure' target='#fig_9'>7E</ns0:ref>-7F (Table <ns0:ref type='table'>S5</ns0:ref>, https://doi.org/10.6084/m9.figshare.12344024.v1; Data S1; Data S2).</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:05:49217:2:0:NEW 27 Jul 2020)</ns0:p></ns0:div> <ns0:div><ns0:head>Manuscript to be reviewed</ns0:head></ns0:div> <ns0:div><ns0:head>Time course monitoring of photosynthesis-induced pH changes in the light in 4A+ and</ns0:head><ns0:p>10E35 bead vials that were exposed to dark for 6 hours. Dark-exposed 10E35, 4A+ and control vials from Fig. <ns0:ref type='figure'>8</ns0:ref> experiment were exposed to dark for additional five hours. Hence this set was dark-exposed for a total of six hours. After six hours of dark exposure, images were taken of the dark-exposed vials and the vials were exposed to light. Images of these light-exposed vials were taken after 30 minutes, 1 hour, 2 hours, 3 hours and 48 hours of light exposure (Fig. <ns0:ref type='figure'>9</ns0:ref>). The results show that 4A+ photosynthesized at a faster rate compared to 10E35 after 6 hours of dark exposure to cause a distinct water color/pH change that was statistically significant (p-values from the 1-tailed and 2-tailed tests ranged from 0.0001-0.0175 and from 0.0002 -0.035, respectively) (Table <ns0:ref type='table'>S7</ns0:ref>; https://doi.org/10.6084/m9.figshare.12344024.v1; Data S1; Data S2). Despite the significant pH difference in these two vials of 10E35, pH in the 48 hours-light exposed 10E35 vial was acidic (pH= 6.43±0.06) compared to the alkaline pH (8.47±0.06) in the 48 hours-exposed 4A+ vial. It is known that 10E35 progressively photo-bleaches with increase in light intensity <ns0:ref type='bibr' target='#b9'>[Nguyen et al., 2017;</ns0:ref><ns0:ref type='bibr'>Article S2]</ns0:ref>. Figure <ns0:ref type='figure'>9F</ns0:ref> shows that 10E35 beads were photo-bleached in the light-exposed vial after 48 hours of light exposure. Photo-bleaching indicates that there is chlorophyll breakdown in the beads.</ns0:p></ns0:div> <ns0:div><ns0:head>Discussion</ns0:head><ns0:p>Chlamydomonas reinhardtii is a unicellular micro-green alga (a Chlorophyte) that retains many of the features of green plants and of the common ancestor of plants and animals, although its lineage diverged from Streptophytes over one billion years ago. Chlamydomonas is used to study eukaryotic photosynthesis because, unlike angiosperms, it can use acetate to grow in the dark while maintaining a functional photosynthetic apparatus <ns0:ref type='bibr'>(Merchant et al., 2007)</ns0:ref>. It is also a model organism for elucidating eukaryotic flagella and basal body structure and functions which can be linked to various ciliopathies <ns0:ref type='bibr' target='#b14'>(Silflow & Lefebvre, 2001)</ns0:ref>. More recently, Chlamydomonas research has been developed for bioremediation purposes, generation of biofuels and has led to breakthroughs in optogenetics <ns0:ref type='bibr'>(Merchant et al., 2007</ns0:ref>; 'Critical tool for brain research derived from 'pond scum' <ns0:ref type='bibr'>NSF, 2013;</ns0:ref><ns0:ref type='bibr' target='#b23'>Zhang, 2015;</ns0:ref><ns0:ref type='bibr' target='#b13'>Scranton et al., 2015)</ns0:ref>. Currently, the Chlamydomonas Resource Center [https://www.chlamycollection.org/] offers number of educational kits ('Resources For Teaching' -Chlamydomonas Resource Center') including instructions and strains on its website; however, these tools barely scratch the surface of what could be taught using Chlamydomonas to students enrolled in K12 Biology and in college Biology undergraduate courses. Hence there is a huge potential to develop Chlamydomonas an under-utilized teaching tool, into a powerful popular teaching tool which will complement existing plant science teaching strategies. Our objective for the American Society of Plant Biologists' (ASPB) Plant-BLOOME project was to design fifteen simple handson activities on different Biology topics that can not only educate and excite high school students about Chlamydomonas but can be also included as a component in college Biology laboratory courses. The activities described in this manuscript is centered on photosynthesis and cellular respiration.</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:05:49217:2:0:NEW 27 Jul 2020)</ns0:p></ns0:div> <ns0:div><ns0:head>Manuscript to be reviewed</ns0:head><ns0:p>We have found that the Chlamydomonas culture should be grown under low light (80-100 micro mol photons m -2 s -1 ) to obtain a healthy culture that is not photo-oxidatively stressed to be used for our lab activities. The culture should be a dense culture and have a cell density ranging from 18 X 10 6 cells/mL to 22 X 10 6 cells/mL to get enough cells for a class of 24 students, working in groups of two to three. Algal beads once made, should be rinsed thoroughly with tap water for at least five minutes to remove residual sodium chloride that is formed as a product in the reaction between sodium alginate and calcium chloride during bead-making step. This step is a very important step and must not be skipped as any residual sodium chloride will hinder photosynthesis in the experiment. As shown in the Fig. <ns0:ref type='figure' target='#fig_4'>2</ns0:ref>, high cell numbers in a bead has a negative effect on photosynthesis. We have found best results can be achieved when the harvested cell numbers are between 250 X 10 6 -395 X 10 6 cells/ 50 mL falcon tube. Cells inside the beads are oxygen-stressed. Hence it is important to leave air gaps inside the bracelet at each end of the flexible tubing (see Materials and Methods). The same rule applies when performing the experiment in a 5.5 mL glass vial. It is important to leave air gap of half the volume of the vial. Results in Fig. <ns0:ref type='figure' target='#fig_8'>6</ns0:ref> showed that prolonged dark exposure of 15 hours has a negative effect on photosynthesis. Algae bead bracelets exposed to 9 hours of darkness (Fig. <ns0:ref type='figure' target='#fig_8'>6</ns0:ref>) can be shifted back and forth between dark and light to display color changes over a period of 24-48 hours (the color changes slowly after 24 hours; based on observations in different classrooms, no data was collected). We have also found that once the bracelet is assembled, if it is exposed to light for about 3-4 hours (which we call in our lab as the 'light charging of the bracelet') and, then switched to dark for 2-3 hours ['discharging of the bracelet'], the bracelet displays fast color changes as long as the dark exposure time was not exceeded beyond 9 hours (based on observations in different classrooms, no data was collected). But if the assembled bracelet is shifted to dark immediately after assembly, the bracelet fails to display fast color changes. Immobilized oxygen stressed-Chlamydomonas cells in the beads are dependent on photosynthesis for glucose biosynthesis as they are immersed in tap water in the bracelet. Tap water lacks acetate and other nutrients for algal growth. We hypothesize that the initial light exposure allows the cells to synthesize glucose/starch by photosynthesis which is later used to support the high rate of cellular respiration in beads for energy production. If the bracelet is shifted to dark without prior light exposure, the high cellular respiration rate consumes the existing starch in the cells. Hence when this dark-exposed bracelet is exposed to light, the cells will have to synthesize enough glucose/starch via photosynthesis to support the high rate of cellular respiration in the beads and this will take some time. This is reflected in the slow color/pH changes of a bracelet that is exposed to dark immediately after assembly compared to the one that is exposed to light immediately after assembly. Chlamydomonas can take up exogenous acetate from the TAP medium to make net synthesis of glucose via the Glyoxylate/C2 cycle, which is present in many bacteria, micro-algae and plants <ns0:ref type='bibr' target='#b6'>(Kunze et al., 2006)</ns0:ref>. Substituting tap water with acetate containing-TAP growth media (heterotrophic and photo-heterotrophic media; Text S2) inside the bracelet will hinder color change in bracelets/vials as TAP medium has Tris buffer, which has a pKa value of 8.06 at 25°C and a buffering range of pH 7-9. Chlamydomonas grows slower in High Salt (HS) photosynthetic media than in TAP medium as HS medium lacks acetate <ns0:ref type='bibr' target='#b15'>(Sueoka, 1960)</ns0:ref>. HS medium cannot be used as a substitute for tap water inside the bracelet as we have tried it and have found that the bracelets do not show color changes even if the beads are exposed to light for 48 hours (data not shown). During spring 2018-fall 2019, the described laboratory activities were incorporated in Biology classes in nine schools and in Biology labs at the University of West Georgia and at the Perimeter College [Georgia State University] (Text S1). To date, we have targeted of about 947 school students in Georgia and hope to target more college students in future. We are proposing a class workflow in Text S3, which is based on the feedback of 12 school teachers and 2 college instructors who participated in the Plant-BLOOME project. Regardless of the suggested time line, instructors can adjust lab times according to their teaching agenda by either spreading the lab activities across multiple classes or by removing one or more activities (Text S3). This will allow the instructor to involve the class in discussion after each activity. Alternatively, students can perform an outdoor experiment by wearing these bracelets/necklaces (you can also make algae bead necklaces) during day time and exposing these bracelets to strong sunlight or wear them in the night to see the water color changes. Conducting the experiment in a 5.5 mL capped glass vials will expedite the experiment completion within 1.5-2 hours in classrooms. The advantage of performing the experiment in glass vials is that students can clearly monitor oxygen production in photosynthesis by monitoring the buoyancy of the algal beads over time. Bead buoyancy is difficult to clearly visualize in a bracelet because of the narrow diameter of the bracelet tubing. Photosynthetic efficiencies of Chlamydomonas strains are measured in a laboratory by an oxygen electrode. But many financially disadvantaged schools and institutions of higher learning do not have access to an oxygen electrode. Our hands-on activity can be used to compare crudely photosynthetic efficiencies of Chlamydomonas wild type and photosynthetic mutant strains in a classroom setting. This will allow educators at institutions with limited resources and funding to engage students in critical thinking based on observations of a scientific experiment. Our lab activities can be customized for different grade levels by adding or removing layers of lab components. Some suggested activities for Middle school, high school and college undergraduates are shown in Table <ns0:ref type='table'>1</ns0:ref>, Table <ns0:ref type='table'>2 and Table 3</ns0:ref>, respectively. For example, for middle school students the algae bead bracelet or the vial version of the experiment can be used. Students can observe under light microscopes, swimming Chlamydomonas and its bright orange eyespot which is used by the cell for light sensing and aids photo-taxis [Table <ns0:ref type='table'>1</ns0:ref>; <ns0:ref type='bibr' target='#b21'>Ueki et al., 2016)</ns0:ref>. Photosynthesis is modulated by light color and light intensities <ns0:ref type='bibr' target='#b20'>(Tymoczko, Berg & Stryer, 2015)</ns0:ref>. Red and blue light stimulates photosynthesis and other colored light are not utilized for photosynthesis <ns0:ref type='bibr' target='#b20'>(Tymoczko, Berg & Stryer, 2015)</ns0:ref>. Hence algae bead bracelets can be used by high school students to test the effects of different light intensities and colored light using different colored light filters (Table <ns0:ref type='table'>2</ns0:ref>).</ns0:p><ns0:p>High school students can also conduct a vial experiment with a wild type strain and any available photosynthetic mutant strain that they have access to. Photosynthetic mutants like the cytochrome f deficient mutant (petA) [CC-3737 petA (N153Q)]; the D1-less mutant (Fud7) [ CC-4147 FUD7 (psbA deletion) mt+] and the D2-less mutant (PsbD) [CC-4385 PsbD (deletion) mt+ are available via Chlamydomonas Resource Center (Table <ns0:ref type='table'>2</ns0:ref>). 10E35 mutant can be obtained from our lab. Additionally, a basic bioinformatic laboratory can be added to the high school Biology lab. The DNA sequence of the mutated gene in the photosynthetic Chlamydomonas mutant can be given to students and they can use the DNA sequence to BLAST the NCBI database to identify the gene and the protein. Students can also check for paralogs/orthologs of the identified gene/protein (Table <ns0:ref type='table'>2</ns0:ref>). For college undergraduate level Biology labs, additional molecular and biochemical layers can be added on top of the high school lab components as shown in Table <ns0:ref type='table'>3</ns0:ref>. We have provided class work-flow, sample pre-and post-lab questions and a rubric for grading pre-and post-lab assignments which can be used by educators (Text S3). The assignments and the rubric can be customized according to the knowledge base of students in the class. In summary, science literacy in young students can be improved by studying a 'pond-scum' which is used by plant biologists, neuroscientists, biomedical and renewable energy researchers and can show them the inter-disciplinary nature of 21 st century Biology.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions</ns0:head><ns0:p>Our designed protocol can be used to make beads using motile micro-alga like Chlamydomonas reinhardtii. These algal beads can be used for basic photosynthesis labs or for comparative studies of relative rates of photosynthesis and cellular respiration in Chlamydomonas wild type and mutant strains. Although our work was performed with the objective of designing engaging hands-on plant biology labs for K16 Biology students, it might be useful to bioenergy researchers who are interested in exploring the use of immobilized Chlamydomonas or other motile green algae for biofuel production <ns0:ref type='bibr' target='#b13'>(Scranton et al., 2015;</ns0:ref><ns0:ref type='bibr' target='#b11'>Radakovits et al., 2010;</ns0:ref><ns0:ref type='bibr' target='#b1'>Canbay, Kose & Oncel, 2018)</ns0:ref>. Our lab activities using the wild type Chlamydomonas strain can be performed both in glass vials and in bracelets. Based on our class room experiences at nine schools and two colleges in Georgia and the enthusiasm of the plant community members at the educational booths at the Plant Biology meetings organized by ASPB, we envision that young students will find the 'bracelet' approach more enjoyable than conducting the same experiment in glass vials (Text S1). Our lab activities are inexpensive and can been customized according to grade levels. <ns0:ref type='table'>S3</ns0:ref>. <ns0:ref type='table'>S5</ns0:ref>.</ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ reviewing PDF \| (2020:05:49217:2:0:NEW 27 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>(https://doi.org/10.6084/m9.figshare.12344024.v1) and are presented in the supplementary Data S1 file. p-values of experiments can be found in the Data S1 file. Data S2 file contains raw pH PeerJ reviewing PDF \| (2020:05:49217:2:0:NEW 27 Jul 2020) data, mean and standard deviation information. Each biological replicate had three internal replicates. The average of three internal replicates from each biological replicate is shown in the data in Data S1 and Data S2 files.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ reviewing PDF \| (2020:05:49217:2:0:NEW 27 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head>Figure 1</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head>Figure 2 Effect</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head>Figure 3</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Figure 3</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head /><ns0:label /><ns0:figDesc>Figure 4</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_7'><ns0:head>Figure 5</ns0:head><ns0:label>5</ns0:label><ns0:figDesc>Figure 5</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_8'><ns0:head>Figure 6</ns0:head><ns0:label>6</ns0:label><ns0:figDesc>Figure 6</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_9'><ns0:head>(Figure 7</ns0:head><ns0:label>7</ns0:label><ns0:figDesc>Figure 7</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_10'><ns0:head>(</ns0:head><ns0:label /><ns0:figDesc>A) Control, 10E35 and 4A+ bead vials before light exposure. (B) Control, 10E35 and 4A+ bead vials after 30 minutes of light exposure. (C) Control, 10E35 and 4A+ bead vials after 1 hour of light exposure. (D) Control, 10E35 and 4A+ bead vials before dark exposure. (E) Control, 10E35 and 4A+ bead vials after 30 minutes of dark exposure. (F) Control, 10E35 and 4A+ bead vials after 1 hour of dark exposure. Algal beads of each strain had approximately 2 X 10 6 cells/bead. Eight beads of each strain were used per experimental vial for the experiment. The order of the vials from left to right: control, 10E35 and 4A+ vials. All statistical analyses can be found in https://doi.org/10.6084/m9.figshare.12344024.v1 , Data S1, Data S2 and Table</ns0:figDesc></ns0:figure> <ns0:note place='foot'>PeerJ reviewing PDF \| (2020:05:49217:2:0:NEW 27 Jul 2020)</ns0:note> <ns0:note place='foot'>PeerJ reviewing PDF \| (2020:05:49217:2:0:NEW 27 Jul 2020)Manuscript to be reviewed</ns0:note> <ns0:note place='foot' n='6'>cells/bead. The bracelet contained thirty-six 4A+ strain beads. Bicarbonate indicator was used as a pH indicator. PeerJ reviewing PDF \| (2020:05:49217:2:0:NEW 27 Jul 2020)</ns0:note> <ns0:note place='foot' n='6'>cells/bead. Both bracelets contained 38 beads. All statistical analyses can be found in https://doi.org/10.6084/m9.figshare.12344024.v1 , Data S1, Data S2 and TableS4.PeerJ reviewing PDF \| (2020:05:49217:2:0:NEW 27 Jul 2020)</ns0:note> </ns0:body> "	" Department of Biology 1601 Maple Street Carrollton, Georgia 30118 Office Tel: 678-839-5488 Email: mmitra@westga.edu July 27th, 2020 To, The Editorial Board Peer J Dear Editor and Reviewer, We thank you for the time that you have invested in providing thoughtful comments to us. We have incorporated the reviewer’s suggestions in the manuscript. We believe that with the revisions, our manuscript is now ready for publication in Peer J. Sincerely, Associate Professor Biology Department, University of West Georgia On behalf of all authors. Please note that reviewers’ comments are in blue font and our responses are in green font. The line numbering in our response is based on the line numbering in the document with track changes. Reviewer 2 (Mitzuko Dautt-Castro) Basic reporting No revision is required by the reviewer. Experimental design The changes made to the descriptions of statistical analyses improve considerably your manuscript. However, I think that it is very important to mention which was the p-value used in your analyses. You can mention it in the 'Imaging and Data analyses' section or, you can point it out in the results section, by enclosing the p-value in parentheses when necessary. Nevertheless, it is important to note that the p-values usually are presented as decimals, as already mentioned by another reviewer. So, I suggest writing in these ways in the manuscript, and if you want to conserve the percentages data in the Data files, you can do it, too. We have introduced a sentence in line 318 to indicate the p-values are in the Data S1 file. We have conserved the p-values expressed in percentages in the Data S1 file but have introduced the relevant p-values in decimal form in the Result section in line # s: 339 - 341 343 - 346 349 - 350 372 - 373 375 - 376 377 406 - 407 437 - 438 440 - 441 457 - 458 463 - 464 473 - 474 485 - 486 Validity of the findings I consider that this work will have an important impact on the educational and academic level. I think that the experiments were properly developed and the conclusions are well stated. Also, the changes made to the 'Materials and methods' section improve considerably the manuscript. No revision is required by the reviewer. Comments for the Author I appreciate the changes made. I think you improved the manuscript a lot. I consider that it is almost ready for publication. Thanks for the valuable suggestions which improved the quality of the manuscript. "	Here is a paper. Please give your review comments after reading it.
498	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Background:</ns0:head><ns0:p>The current cultivation and plant breeding of Honeybush tea (produced from members of Cyclopia Vent.) do not consider the genetic diversity nor structuring of wild populations. Thus, wild populations may be at risk of genetic contamination if cultivated plants are grown in the same landscape. Here, we investigate the spatial distribution of genetic diversity within Cyclopia intermedia E. Mey. -this species is widespread and endemic in the Cape Floristic Region (CFR) and used in the production of Honeybush tea.</ns0:p></ns0:div> <ns0:div><ns0:head>Methods:</ns0:head><ns0:p>We applied High Resolution Melt analysis (HRM), with confirmation Sanger sequencing, to screen two non-coding chloroplast DNA regions (two fragments from the atpI-aptH intergenic spacer and one from the ndhA intron) in wild C. intermedia populations. A total of 156 individuals from 17 populations were analyzed for phylogeographic structuring. Statistical tests included analyses of molecular variance and isolation-by-distance, while relationships among haplotypes were ascertained using a statistical parsimony network.</ns0:p><ns0:p>Results: Populations were found to exhibit high levels of genetic structuring, with 62.8 % of genetic variation partitioned within mountain ranges. An additional 9 % of genetic variation was located amongst populations within mountains, suggesting limited seed exchange among neighboring populations. Despite this phylogeographic structuring, no isolation-by-distance was detected (p > 0.05) as nucleotide variation among haplotypes did not increase linearly with geographic distance; this is not surprising given that the configuration of mountain ranges dictates available habitats and, we assume, seed dispersal kernels.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions:</ns0:head><ns0:p>Our findings support concerns that the unmonitored redistribution of Cyclopia genetic material may pose a threat to the genetic diversity of wild populations, and ultimately the genetic resources within the species. We argue that 'duty of care' principles be used when cultivating Honeybush and that seed should not be translocated outside of the mountain range of origin. Secondarily, given the genetic uniqueness of wild populations, cultivated populations should occur at distance from wild populations that is sufficient to prevent unintended gene flow; however, further research is needed to assess gene flow within mountain ranges.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>The Cape Floristic Region (CFR), located along the southern Cape of Africa, hosts over 9000 species of flowering plants, of which nearly 70% are endemic to the region <ns0:ref type='bibr' target='#b22'>(Goldblatt and Manning, 2002)</ns0:ref>. With such diversity, it is unsurprising that the Cape flora has a long history of commercial harvesting, cultivation and trade, dating back to seventeenth century European settlers <ns0:ref type='bibr' target='#b70'>(Scott and Hewett, 2008)</ns0:ref>. This includes members of the CFR endemic genus Cyclopia Vent., used for the production of a herbal infusion referred to as Honeybush tea <ns0:ref type='bibr' target='#b25'>(Hofmeyer and Phillips, 1922;</ns0:ref><ns0:ref type='bibr' target='#b35'>Kies, 1951)</ns0:ref>. The Honeybush industry relies predominantly on raw material sourced from wild populations, but this approach is unable to meet consumer demand and a transition to agricultural production is underway to avoid over-exploitation of natural resources <ns0:ref type='bibr' target='#b30'>(Joubert et al., 2011)</ns0:ref>. This expansion of the cultivated Honeybush sector should reduce harvesting pressure on wild populations and provide opportunities for employment in economically depressed rural communities. However, the underlying distribution and level of genetic diversity present in wild populations is rarely considered during the transition to cultivation plants, and often involves a period of screening individuals for commercially favourable traits <ns0:ref type='bibr' target='#b26'>(Hyten et al., 2006;</ns0:ref><ns0:ref type='bibr' target='#b67'>Schipmann et al., 2005)</ns0:ref>. This has been the case for Honeybush, with individuals sourced from multiple populations and species for initial breeding trials <ns0:ref type='bibr' target='#b30'>(Joubert et al., 2011)</ns0:ref>. When establishing cultivated populations, failing to represent the genetic diversity patterns of local populations can potentially place wild populations at risk of contamination by non-local genetic lineages <ns0:ref type='bibr' target='#b23'>(Hammer et al., 2009;</ns0:ref><ns0:ref type='bibr' target='#b39'>Laikre et al., 2010)</ns0:ref>. Potential issues of genetic contamination associated with the cultivation Honeybush have recently been raised <ns0:ref type='bibr' target='#b55'>(Potts, 2017a)</ns0:ref>. Based on the limited dispersal abilities of Cyclopia species (seed dispersed by dehiscent seed pods and ants, and pollen by carpenter bees, Xylocopa spp., <ns0:ref type='bibr' target='#b68'>Schutte, 1997)</ns0:ref>, <ns0:ref type='bibr' target='#b55'>Potts (2017a)</ns0:ref> argues that Cyclopia populations are likely to exhibit geographically structured genetic diversity, which may be lost or disrupted if not considered during the transition to cultivation. Here we apply High Resolution Melt analysis (HRM; <ns0:ref type='bibr' target='#b78'>Wittwer et al., 2003)</ns0:ref>, coupled with confirmation Sanger sequencing <ns0:ref type='bibr' target='#b65'>(Sanger et al., 1977)</ns0:ref>, of chloroplast genetic lineages in the widespread Cyclopia intermedia E. Mey to test whether spatial structuring exists in this species.</ns0:p><ns0:p>Genetic diversity provides the basis for evolutionary change, including acclimation and adaptive responses to changes in local environmental conditions <ns0:ref type='bibr' target='#b54'>(Pauls et al., 2012)</ns0:ref>. Genetic diversity is not, however, evenly distributed across a species' range. Rather, demographic history shapes the spatial structuring and extent of molecular variation within a species <ns0:ref type='bibr' target='#b7'>(Charlesworth, 2009;</ns0:ref><ns0:ref type='bibr' target='#b19'>Excoffier et al., 2009;</ns0:ref><ns0:ref type='bibr' target='#b37'>Klopfstein et al., 2005)</ns0:ref>, providing the basis for phylogeography <ns0:ref type='bibr' target='#b1'>(Avise et al., 1987)</ns0:ref>. As biogeographic processes (climate and geomorphic changes) fragment and isolate populations, neutral genetic processes (mutation and drift) lead to genetic divergence among populations (if isolated for sufficient time), producing patterns of phylogeographic structuring. Large interconnected populations are likely to exhibit lower levels of genetic drift <ns0:ref type='bibr' target='#b36'>(Kimura and Crow, 1963)</ns0:ref> as the large effective population size facilitates the persistence of rare alleles (generated by mutation or migration) at low frequencies <ns0:ref type='bibr' target='#b72'>(Tajima, 1989)</ns0:ref> and therefore have higher levels of genetic diversity. Alternatively, small populations are often isolated and prone to genetic drift and inbreeding, reducing overall genetic diversity <ns0:ref type='bibr' target='#b15'>(Ellstrand and Elam, 1993)</ns0:ref>. As argued in <ns0:ref type='bibr' target='#b55'>Potts (2017a)</ns0:ref>, it is likely that these processes have driven phylogeographic structuring in the CFR given its topographic and environmental heterogeneity -and thus high-levels of genetic differentiation amongst populations is likely the norm, rather than the exception. These demographic processes, and their impacts on intraspecific genetic diversity, are rarely considered when wild crop or medicinal plants are introduced to an agricultural setting. This has resulted in genetic divergence between neighbouring wild and cultivated populations <ns0:ref type='bibr' target='#b81'>(Yuan et al., 2010;</ns0:ref><ns0:ref type='bibr'>Otero-Arnaiz et al., 2005)</ns0:ref>, creating opportunities for genetic pollution of wild populations <ns0:ref type='bibr' target='#b3'>(Bredeson et al., 2016;</ns0:ref><ns0:ref type='bibr' target='#b2'>Bartsch et al., 1999;</ns0:ref><ns0:ref type='bibr' target='#b84'>Vanden Broeck et al., 2004;</ns0:ref><ns0:ref type='bibr' target='#b47'>Millar and Byrne, 2007)</ns0:ref> and potentially disrupting species boundaries through the formation of hybrid taxa <ns0:ref type='bibr' target='#b44'>(Macqueen and Potts, 2018;</ns0:ref><ns0:ref type='bibr'>Lexer et al., 2003)</ns0:ref>. For example, wild harvesting of the Chinese medicinal plant Scutellaria baicalensis (Lamiaceae) has resulted in rapid declines in population sizes and, subsequently, widespread cultivation was promoted to meet demands and reduce harvesting pressure on wild populations <ns0:ref type='bibr' target='#b81'>(Yuan et al., 2010)</ns0:ref>. By screening genetic diversity and structuring of 28 wild and 22 cultivated S. baicalensis populations, <ns0:ref type='bibr' target='#b81'>Yuan et al. (2010)</ns0:ref> demonstrated that although cultivated populations supported similar levels of genetic diversity, they did not represent the phylogeographic structuring of wild populations, as genetic types were widely redistributed outside of their natural ranges.</ns0:p><ns0:p>Moreover, if genetically divergent wild and cultivated variants occur within pollination distance from one another, have overlapping flowering times, and are sexually compatible, then gene flow is likely to occur <ns0:ref type='bibr' target='#b0'>(Arias and Rieseberg, 1994;</ns0:ref><ns0:ref type='bibr' target='#b2'>Bartsch et al., 1999;</ns0:ref><ns0:ref type='bibr' target='#b52'>Papa and Gepts, 2003;</ns0:ref><ns0:ref type='bibr'>Otero-Arnaiz et al., 2005)</ns0:ref>. Genetic pollution of crop wild relatives has been widely documented since the advent of genetically modified (GM) crop plants, with the majority of globally important GM crops exhibiting hybridization with at least one wild relative <ns0:ref type='bibr' target='#b16'>(Ellstrand et al., 1999;</ns0:ref><ns0:ref type='bibr' target='#b17'>Ellstrand et al., 2002)</ns0:ref>. It is therefore suggested that 'duty of care' be taken when introducing new crops into agricultural systems, so as to minimise any potential negative ecological effects <ns0:ref type='bibr' target='#b5'>(Byrne and Stone, 2011)</ns0:ref>.</ns0:p><ns0:p>Like the majority of members of the highly-diverse CFR flora, the extent of geographic structuring of chloroplast genetic diversity of members of Cyclopia is unknown, and no precautionary limit to the redistribution of seed material exists to guide cultivation. This study therefore sets out to test the postulation put forward by <ns0:ref type='bibr' target='#b55'>Potts (2017a)</ns0:ref>, that -due to the topographic complexity of the Cape landscape coupled with limited seed dispersal by ants -members of Cyclopia will exhibit highly structured chloroplast genetic diversity; such a pattern has already been observed in the preliminary haplotype screening of wild Cyclopia subternata Vogel. populations <ns0:ref type='bibr' target='#b21'>(Galuszynski and Potts, 2020)</ns0:ref>.</ns0:p><ns0:p>Through a combination of High Resolution Melt analysis (HRM; <ns0:ref type='bibr' target='#b78'>Wittwer et al., 2003)</ns0:ref> and Sanger sequencing <ns0:ref type='bibr' target='#b65'>(Sanger et al., 1977)</ns0:ref> of two non-coding chloroplast DNA (cpDNA) regions (two fragments from the atpI-aptH intergenic spacer and one from the ndhA intron), an applied phylogeographic approaches is used to describe the levels and distribution of haplotype variation in the widespread and commercially important Honeybush species C. intermedia. The findings of this study provide baseline data for the development of guidelines for the management and protection of wild genetic diversity in C. intermedia.</ns0:p></ns0:div> <ns0:div><ns0:head>Materials & Methods</ns0:head><ns0:p>PeerJ reviewing PDF \| (2020:04:47838:1:2:NEW 13 Jul 2020)</ns0:p></ns0:div> <ns0:div><ns0:head>Manuscript to be reviewed</ns0:head></ns0:div> <ns0:div><ns0:head>Study species</ns0:head><ns0:p>Cyclopia intermedia is the most widespread member of the genus (consisting of 23 species; <ns0:ref type='bibr' target='#b68'>Schutte, 1997)</ns0:ref>, occurring in isolated patches across all major mountain ranges within the eastern CFR <ns0:ref type='bibr' target='#b68'>(Schutte, 1997</ns0:ref>, sampling locations provided in Fig. <ns0:ref type='figure' target='#fig_3'>1</ns0:ref> and Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref>; further descriptions of these mountain rages are provided in S1); the species has a broad habitat tolerance occurring at elevations ranging from 500 -1700 m on rocky, loamy, and sandy soils of adequate depth. After fires, it can resprout from a woody rootstock to form a dense shrub (see figures in S1). This species has poor post-fire recruitment from seed compared to obligate reseeding members of the genus <ns0:ref type='bibr' target='#b69'>(Schutte et al., 1995)</ns0:ref>. Although C. intermedia was not initially targeted for cultivation, it currently accounts for the majority of wild harvested <ns0:ref type='bibr'>Honeybush (McGregor, 2017)</ns0:ref>, and commercial cultivation of this species is on the rise <ns0:ref type='bibr'>(N.C.G. pers. obs., 2018)</ns0:ref>. As such, it is unlikely that genetic material has been widely translocated for this species and opportunities exist to guide future conservation and management of genetic diversity.</ns0:p></ns0:div> <ns0:div><ns0:head>Sample collection and DNA extraction</ns0:head><ns0:p>Over the period of 2015-2018, samples were collected from wild populations across the natural range of C. intermedia. Due to many widespread fires across the CFR during this period, many populations were limited to individuals that were either in the process of resprouting or those found in micro-refugia (such as rocky outcrops) where they were protected from fire. Thus, sample size is highly variable among populations (ranging between 1 and 16). In populations with less than 10 plants, all individuals were sampled, in large populations plants were collected across the full extent of the stand with a minimum distance of five meters between sampled individuals. All sampling was approved by the relevant permitting agencies: Cape Nature (Permit number: CN35-28-4367), the Eastern Cape Department of Economic Development, Environmental Affairs and Tourism (Permit numbers: CRO 84/ 16CR, CRO 85/ 16CR), and the Eastern Cape Parks and Tourism Agency (Permit number: RA_0185). Fresh leaf material was collected from the healthy growing tips of individuals and placed directly into a silica desiccating medium for a minimum of two weeks prior to DNA extraction. A modified CTAB DNA extraction approach was adapted from the methods outlined by <ns0:ref type='bibr' target='#b14'>Doyle and Doyle (1987)</ns0:ref>. Once extracted, genomic DNA was quantified using a NanoDrop 2000c Spectrophotometer (Thermo Fisher Scientific, Wilmington, DE19810r Scientific, USA) and 5 ng/L DNA dilutions were made for PCR amplification and HRM analysis.</ns0:p></ns0:div> <ns0:div><ns0:head>Haplotype detection and sequence alignment</ns0:head><ns0:p>High Resolution Melt analysis involves the gradual heating of PCR products amplified in the presence of a DNA saturating dye. As the double stranded DNA is melted, it dissociates at a rate based on the nucleotide binding chemistry of the double stranded DNA molecule under analysis. As such, different nucleotide sequences usually produce distinct melt curves when plotting the normalised fluorescence differences among samples against temperature (not every unique sequence has a differentiable melt curve). The application of HRM has been demonstrated to be a rapid and cost effective means of detecting haplotype variation in wild Cyclopia populations <ns0:ref type='bibr' target='#b21'>(Galuszynski and Potts, 2020)</ns0:ref> and other plant species (e.g. Amphicarpaea bracteata (L.) Fernald, Fabaceae: <ns0:ref type='bibr' target='#b34'>Kartzinel et al., 2016</ns0:ref>; Arenaria ciliata L. and Arenaria norvegica Gunnerus, Caryophyllaceae: <ns0:ref type='bibr' target='#b13'>Dang et al., 2012;</ns0:ref><ns0:ref type='bibr'>Olea europaea L., Oleaceae: Muleo et al., 2009;</ns0:ref><ns0:ref type='bibr' /> and, Alnus glutinosa (L.) Gaertn., Betulaceae: <ns0:ref type='bibr' target='#b12'>Cubry et al., 2015)</ns0:ref>.</ns0:p><ns0:p>As the application of HRM to members of Cyclopia populations has been described elsewhere <ns0:ref type='bibr' target='#b21'>(Galuszynski and Potts, 2020)</ns0:ref>, only a brief overview of the method is provided here. Two non-coding cpDNA regions (two fragments from the atpI-aptH intergenic spacer and one from the ndhA intron) were amplified using Cyclopia specific primers (provided in Table <ns0:ref type='table' target='#tab_1'>2</ns0:ref>) and subsequently screened for nucleotide variation as detected by HRM curve analysis (i.e. samples with different melt curves). Samples were run in replicates of two, and grouped by population using the well group option in the CFX Manager Software (Bio-Rad Laboratories, Hercules, California, U.S.A.) to allow for HRM clustering to be conducted on a per population basis following the workflow suggested by <ns0:ref type='bibr' target='#b13'>Dang et al. (2012)</ns0:ref>. All reactions (PCR amplification and subsequent HRM) took place in a 96 well plate CFX Connect (Bio-Rad Laboratories, Hercules, California, USA) and PCR and HRM conditions are provided in Table <ns0:ref type='table' target='#tab_2'>3</ns0:ref>. All haplotype melt curve grouping was performed on normalized fluorescence differences curves using the automated clustering algorithm of the High Precision Melt software (Bio-Rad Laboratories, Hercules, California, U.S.A.) (Tm threshold = 0.05; curve shape sensitivity = 70 %; temperature correction = 20).</ns0:p><ns0:p>As HRM analysis does not provide insights into the specific nucleotide motifs under analysis, the haplotype identity of each HRM cluster was confirmed by sequencing a subset of individuals belonging to each HRM cluster per population (PCR amplification following the protocols of <ns0:ref type='bibr' target='#b71'>Shaw et al. [2007]</ns0:ref>, which are described in Table <ns0:ref type='table' target='#tab_0'>S1</ns0:ref>). Since the HRM analysis targeted a subsection of the cpDNA regions investigated, unidirectional sequencing of the full region (using the reverse primers indicated in Table <ns0:ref type='table' target='#tab_1'>2</ns0:ref>), proved sufficient for verifying the sequence motifs amplified by the genus specific HRM primers. In cases where sample replicates were classified as two different putative haplotypes by HRM clustering, confirmation sequencing of the sample was employed to ensure correct haplotype assignment. Additionally, in cases where HRM clusters represented false negative haplotype assignments (i.e. different haplotypes, as determined from confirmation sequencing, being assigned to the same HRM cluster), all the individuals assigned to the HRM cluster were sequenced for haplotype assignment; this, however, was only required in one instance (described in the results).</ns0:p><ns0:p>Sanger confirmation sequences were assembled using CondonCode <ns0:ref type='bibr'>Aligner [v2.0.1]</ns0:ref> (Codon Code Corp, http://www. codoncode.com). Each base-call was assigned a quality score using the PHRED base-calling program <ns0:ref type='bibr' target='#b18'>(Ewing et al., 1998)</ns0:ref>. Following this, sequences were automatically aligned using ClustalW <ns0:ref type='bibr' target='#b75'>(Thompson et al., 1994)</ns0:ref> and visually inspected for quality, all small (2-3 bp) indels occurring in homopolymer repeat regions that proved difficult to score were removed. All C. intermedia samples that underwent HRM analysis were then assigned the haplotype identity of the HRM cluster they belonged to using a custom R script (provided as S2). The cpDNA regions under investigation are maternally inherited in tandem and not subject to recombination <ns0:ref type='bibr' target='#b63'>(Reboud and Zeyl, 1994)</ns0:ref>, and were therefore combined for subsequent analysis.</ns0:p><ns0:p>Haplotype distribution and within populations frequency was mapped using QGIS [3.2.2] <ns0:ref type='bibr' target='#b38'>(Lacaze et al., 2018)</ns0:ref> (Fig. <ns0:ref type='figure' target='#fig_3'>1</ns0:ref>, Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref>). <ns0:ref type='table' target='#tab_3'>2020:04:47838:1:2:NEW 13 Jul 2020)</ns0:ref> Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>PeerJ reviewing PDF \| (</ns0:head></ns0:div> <ns0:div><ns0:head>Phylogeographic analysis</ns0:head><ns0:p>The genealogical relationship among the C. intermedia cpDNA haplotypes was ascertained using Statistical Parsimony (SP) network construction in TCS [v1.2.1] <ns0:ref type='bibr' target='#b10'>(Clement et al., 2000)</ns0:ref>. Five outgroup taxa (C. aurescens Kies, C. buxifolia (Burm.f.) Kies, C. genistoides (L) R.Br., C. longifolia Vogel, and C. sessiliflora (L) R.Br.) that were previously sequenced to develop the genus specific HRM markers <ns0:ref type='bibr' target='#b21'>(Galuszynski and Potts, 2020)</ns0:ref> were included in the SP network. Default options were used to build the network, although each indel event was reduced to a single base pair (TCS counts each base as an independent evolutionary event). Spatial partitioning of genetic diversity across mountain ranges (mountain range assignment for each population is provided in Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref>) and populations was tested using an Analysis of Molecular Variance (AMOVA; <ns0:ref type='bibr' target='#b20'>Excoffier et al., 1992)</ns0:ref>, conducted using the poppr [v2.8.3] library <ns0:ref type='bibr' target='#b33'>(Kamvar et al., 2014;</ns0:ref><ns0:ref type='bibr' target='#b32'>Kamvar et al., 2015)</ns0:ref>. Isolation By Distance <ns0:ref type='bibr' target='#b80'>(Wright, 1943)</ns0:ref> was calculated using untransformed geographic distance, as well as the log natural algorithm of geographic distance; the latter accounts for overdispersion in the data resulting from non-linear patterns of genetic divergence among populations <ns0:ref type='bibr' target='#b64'>(Rousset, 1997)</ns0:ref>. The genetic divergence indices used to calculate isolation-by-distance (IBD) included: Jost's D <ns0:ref type='bibr' target='#b28'>(Jost, 2008)</ns0:ref> (calculated using the mmod [v1.3.3] library; <ns0:ref type='bibr' target='#b77'>Winter, 2012)</ns0:ref>, which measures haplotype differentiation between populations, and Prevosti's distance <ns0:ref type='bibr' target='#b60'>(Prevosti et al., 1975)</ns0:ref> (calculated using the poppr library), a measure of pairwise population genetic distance that treats indels as a fifth character state (all indels were reduced to single base events for the same reason described above). Significance of the IBD analyses were tested via a Mantel tests, with 9999 replicates <ns0:ref type='bibr' target='#b45'>(Mantel, 1967)</ns0:ref>, implemented via the randtest function in ade [v4 1.7] <ns0:ref type='bibr' target='#b15'>(Dray and Dufour, 2007)</ns0:ref>.</ns0:p><ns0:p>Expected haplotype richness, that corrects for unequal sample sizes among populations, was calculated using the poppr function in the poppr library, and mean haplotype fixation was calculated for each population using pairwise G'st <ns0:ref type='bibr' target='#b24'>(Hedrick, 2005)</ns0:ref>, calculated in the mmod [v1.3.3] library <ns0:ref type='bibr' target='#b77'>(Winter, 2012)</ns0:ref> and plotted against longitude. This longitudinal trend in haplotype turnover was investigated as the Cape Fold mountains that support C. intermedia populations consist of linear arrangement of ranges that are dissected by deep river gorges (Fig. <ns0:ref type='figure' target='#fig_3'>1</ns0:ref> and described further in S1) that PeerJ reviewing PDF \| (2020:04:47838:1:2:NEW 13 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed are likely major barriers to seed dispersal. Genetic clustering of populations was estimated from a Neighbour Joining dendrogram constructed from pairwise population genetic distance <ns0:ref type='bibr' target='#b60'>(Prevosti et al., 1975</ns0:ref>) calculated above. Branch support was tested via a bootstrap analysis conducted with 9999 replicates using the aboot function in the poppr library. All analyses were conducted in R [v3.5.1] (R Core Team, 2018).</ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head></ns0:div> <ns0:div><ns0:head>High Resolution Melt haplotype detection</ns0:head><ns0:p>High Resolution Melt proved to be an effective tool for detecting haplotype variation in wild C. intermedia populations -with a total of 156 individuals screened across 17 populations -detecting 23 haplotypes (confirmed by sequencing a total of 86 and 79 individuals across the two non-coding cpDNA regions respectively). Only one false negative was detected in the study (HRM clustering two different haplotypes, amplified by the MLT S3 -MLT S4 primer combination, as the same putative haplotype), which was resolved by sequencing the atpI-atpH intergenic spacer for both individuals assigned to that cluster. (Note that this is why sequencing of a random subset of samples from each HRM curve cluster was conducted -to detect such possible false negatives).</ns0:p><ns0:p>The final merged cpDNA dataset consists of 844 base pairs, 505 from the atpI-atpH intergenic spacer and 339 from the ndhA intron, with an overall GC content of 28.1%.</ns0:p><ns0:p>The data contained 23 polymorphic sites including four indels, nine transversions, and 13 transitions. Nucleotide differences between C. intermedia haplotypes are reported in Table <ns0:ref type='table' target='#tab_3'>4</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Phylogeographic analysis</ns0:head><ns0:p>The SP network revealed no distinct haplotype groups. Rather, most of the haplotype variation radiates from an ancestral haplotype (M), located at the center of the network (Fig. <ns0:ref type='figure' target='#fig_4'>2</ns0:ref>). Haplotypes exhibited little divergence, many differing by a single substitution.</ns0:p><ns0:p>Clustering of populations (based on pairwise genetic distance) in the NJ tree (Fig. <ns0:ref type='figure' target='#fig_6'>3</ns0:ref>) was largely consistent with a shared mountain of origin; for example, populations from Langkloof formed a well-supported group with 96.7% bootstrap support. However, PeerJ reviewing PDF \| (2020:04:47838:1:2:NEW 13 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed notable deviations from this were apparent as genetically unique populations from the Swartberg Mountains (BF and SBM in Fig. <ns0:ref type='figure' target='#fig_3'>1</ns0:ref>) formed independent branches outside of the core Swartberg Mountains cluster, and the Baviaanskloof population (BAV in Fig. <ns0:ref type='figure' target='#fig_3'>1</ns0:ref>) was grouped in the core Swartberg cluster.</ns0:p><ns0:p>Genetic divergence across mountain ranges was further supported by the AMOVA analysis (results reported in Table <ns0:ref type='table'>5</ns0:ref>), detecting a significant trend (p < 0.05) for genetic diversity to be structured among mountain ranges, accounting for 62.8% of genetic variation, with an additional 9.0% structured among populations within mountain ranges.</ns0:p><ns0:p>No IBD was detected for any of the population differentiation measures (p > 0.05 across all measures of geographic distance). Plots of expected haplotype richness and G''st against longitude suggest that edge populations of C. intermedia tend to be fixed for a small number locally unique haplotypes (Fig. <ns0:ref type='figure'>4</ns0:ref>).</ns0:p><ns0:p>We note that outgroup samples nest within the statistical parsimony network. This may be due to a recently shared common ancestor, incomplete lineage sorting or chloroplast capture (i.e. hybridization). Identifying which driver or drivers gave rise to this observed pattern requires further sampling of species, populations and genomes -sampling the nuclear genome is crucial for identifying hybridization events. At present, little is known about the proclivity for hybridization amongst the species in this genus.</ns0:p></ns0:div> <ns0:div><ns0:head>Discussion</ns0:head><ns0:p>Although Cyclopia intermedia is the most widely distributed member of its genus (occurring across all major mountain ranges within the eastern CFR), populations of this species are generally found in localized low density patches on south facing slopes. As stated above, wild harvesting of C. intermedia still forms the bulk of the Honeybush industry in the Eastern Cape of South Africa <ns0:ref type='bibr' target='#b46'>(McGregor, 2017)</ns0:ref>, but the recent transition to agricultural production of Honeybush has seen increased cultivation of Cyclopia species <ns0:ref type='bibr' target='#b30'>(Joubert et al., 2011)</ns0:ref>, including C. intermedia. With no information regarding the spatial structuring of wild Cyclopia genetic diversity available, no general rules exist to guide the translocation of seed and seedlings. This poses a potential threat of exposing wild populations to non-local genetic lineages through pollen and seed flow. The escape of non-local genetic lineages may place wild Cyclopia at the risk of genetic pollution <ns0:ref type='bibr' target='#b39'>(Laikre et al., 2010;</ns0:ref><ns0:ref type='bibr' target='#b55'>Potts, 2017a)</ns0:ref>. The phylogeographic patterns detected for C. </ns0:p></ns0:div> <ns0:div><ns0:head>Phylogeography of C. intermedia</ns0:head><ns0:p>The phylogeography of C. intermedia was explored using HRM coupled with sequencing to screen haplotype variation across two non-coding cpDNA regions. High levels of haplotype turnover among populations and genetic structuring was detected.</ns0:p><ns0:p>Phylogeographic work in the CFR has, for the most part, detected phylogeographic structuring of biota regardless of taxonomy or life form (reviewed in <ns0:ref type='bibr' target='#b40'>Lexer et al., 2013;</ns0:ref><ns0:ref type='bibr' target='#b76'>Tolley et al., 2014)</ns0:ref>. Unfortunately, few widespread plant taxa have been targeted for phylogeographic work in the region, and even less attention has been given to eastern CFR species. Two studies of widespread CFR plant taxa that naturally occur in the Topographic complexity has been widely cited as an important driver of population isolation in the CFR and adjacent biomes <ns0:ref type='bibr' target='#b4'>(Britton et al., 2014;</ns0:ref><ns0:ref type='bibr' target='#b57'>Potts et al., 2013a;</ns0:ref><ns0:ref type='bibr' target='#b58'>Potts et al., 2013b;</ns0:ref><ns0:ref type='bibr' target='#b61'>Prunier et al., 2017;</ns0:ref><ns0:ref type='bibr' target='#b56'>Potts et al., 2017b;</ns0:ref><ns0:ref type='bibr' target='#b62'>Prunier and Holsinger, 2010)</ns0:ref>.</ns0:p><ns0:p>Disjunct population distributions in the CFR has been attributed to Pleistocene climate cycles fragmenting populations into climate refugia <ns0:ref type='bibr' target='#b4'>(Britton et al., 2014;</ns0:ref><ns0:ref type='bibr' target='#b56'>Potts, 2017b;</ns0:ref><ns0:ref type='bibr' target='#b57'>Potts et al., 2013a)</ns0:ref> as vegetation dynamics in the region shifted in response to changes in rainfall seasonality <ns0:ref type='bibr'>(Bar-Matthews et al., 2010;</ns0:ref><ns0:ref type='bibr' target='#b9'>Chase and Meadows, 2007)</ns0:ref>. Furthermore, since seed dispersal is generally limited to short distances in CFR taxa long distance dispersal is unlikely to be responsible for the distribution of populations across adjacent mountain ranges or drainage basins.</ns0:p><ns0:p>In the case of C. intermedia, topographic complexity appears to have played a pivotal role in shaping the distribution of genetic diversity, with 62.8% of haplotype variation structured based on mountain ranges (AMOVA), and multiple cases of complete haplotype turnover occurring across adjacent ranges (Fig. <ns0:ref type='figure' target='#fig_3'>1</ns0:ref>, Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref>). This was most PeerJ reviewing PDF \| (2020:04:47838:1:2:NEW 13 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed dramatic across the Anysberg, Besemfontein and Swartberg Pass populations that, despite their close proximity, shared no haplotypes. These patterns of intraspecific divergence are similar to those of T. triangularis <ns0:ref type='bibr' target='#b4'>(Britton et al., 2014)</ns0:ref> and P. repens <ns0:ref type='bibr' target='#b61'>(Prunier et al., 2017)</ns0:ref>, where populations sampled from these mountains exhibited high levels of divergence. The deeply incised gorges and expanses of inhospitable lowland habitat separating mountain ranges (see S1) are barriers dispersal in the CFR, promoting vicariance among upland populations that may have once been more connected during past climate conditions. Additionally, the vegetation composition of the eastern CFR may have seesawed between C3 Mediterranean shrub-lands (that support C. intermedia) and alternative woodlands and/or C4 grasslands <ns0:ref type='bibr'>(Bar-Matthews et al., 2010;</ns0:ref><ns0:ref type='bibr' target='#b11'>Cowling and Lombard, 2002)</ns0:ref>. While this would have fragmented populations across mountain ranges, as described above, it may have also created opportunities for admixture among populations within mountain ranges and perhaps even across mountain ranges via habitat corridors, especially in the eastern reaches of species ranges. This may explain the widespread distributions of haplotypes M and F in the eastern C. intermedia populations (Fig. <ns0:ref type='figure' target='#fig_3'>1</ns0:ref>). These haplotypes, while dominant in the Langkloof and Groot Swartberg mountains respectively (suggesting some degree of connectivity among populations within these ranges), are also found in the eastern most populations located in the Cockscomb (haplotype M) and Baviaanskloof (haplotype F) mountains that both support unique local haplotypes that make them genetically distinct.</ns0:p><ns0:p>Considering the slow mutation rate of the chloroplast genome <ns0:ref type='bibr' target='#b66'>(Schaal et al., 1998)</ns0:ref>, the presence of unique haplotypes at low frequencies within populations should be viewed as an additional level of genetic structuring in C. intermedia, with 9% of haplotype variation localized in populations within mountain ranges.</ns0:p><ns0:p>Of the 23 haplotypes detected in C. intermedia, four are shared among populations (Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref>), possibly suggesting limited dispersal among and within mountain ranges.</ns0:p><ns0:p>These rare haplotypes may result from mutations generated in-situ, with limited migration among adjacent populations preventing rare haplotypes from becoming more widespread. Short distance seed dispersal by ants, where 25 m is considered a long distance dispersal event (Gómez and Espadaler, 2013), coupled with poor recruitment, would be sufficient to maintain isolation among populations, promoting genetic PeerJ reviewing PDF \| (2020:04:47838:1:2:NEW 13 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed divergence over short distances. Limited seed dispersal capability has frequently been associated with genetic differentiation among populations of CFR taxa in the past <ns0:ref type='bibr' target='#b4'>(Britton et al., 2014;</ns0:ref><ns0:ref type='bibr'>Lexer et al., 2003;</ns0:ref><ns0:ref type='bibr' target='#b57'>Potts et al., 2013a;</ns0:ref><ns0:ref type='bibr' target='#b82'>Zietsman et al., 2009)</ns0:ref>. <ns0:ref type='bibr' target='#b4'>Britton et al. (2014)</ns0:ref> detected a number of instances of near complete haplotype turnover among neighboring populations of the high elevation sedge T. triangularis sampled from the eastern CFR. While, in the case of the Little Karoo endemic Berkheya cuneata (Asteraceae), populations occurring in the western and eastern sub-basins of the Gouritz basin (located between the Groot Swartberg and Outeniqua mountains) have been isolated for a sufficient period to produce highly divergent west and eastern cpDNA lineages <ns0:ref type='bibr' target='#b57'>(Potts et al., 2013a)</ns0:ref>. Limited seed dispersal across environmental and physical barriers (expanses of unsuitable lowland habitat in the case of T. triangularis and the Rooiberg mountain in the case of B. cuneata) was evoked as possible mechanisms for prolonged population isolation. We postulate that the distribution patterns of rare haplotypes in C. intermedia may be a consequence of the same process and represents an important aspect of Cyclopia wild genetic diversity in need of protection.</ns0:p><ns0:p>Distribution patterns of haplotype richnessThe lack of IBD <ns0:ref type='bibr' target='#b64'>(Rousset, 1997)</ns0:ref> and starburst SP network <ns0:ref type='bibr' target='#b8'>(Charlesworth, 2003)</ns0:ref> suggests that C. intermedia has experienced a non-linear processes of population isolation and subsequent genetic divergence (i.e. not a single direction stepping stone colonization path), with haplotype M identified as a putative ancestral form due to its central position in the network <ns0:ref type='bibr' target='#b6'>(Cann et al., 1987;</ns0:ref><ns0:ref type='bibr' target='#b74'>Templeton et al., 1995)</ns0:ref>. Some consequences of this are that co-occurring haplotypes have as many, if not more, nucleotide differences separating them than haplotypes originating from geographically distant populations (Fig. <ns0:ref type='figure'>4</ns0:ref>). This results from novel haplotypes being generated directly from the ancestral form, rather than accumulating mutations along a colonization path (Fig. <ns0:ref type='figure' target='#fig_4'>2</ns0:ref>), thus no spatially structured haplotype groups were detected in the SP network and haplotypes located in edge populations are not more genetically differentiated than populations from the center of the species range. For instance, haplotypes A, E and C, while occurring in different mountain ranges, are genetically more similar to haplotype M than haplotypes L, O, Q, S, and T that occur within the same mountain range as haplotype M (Fig. <ns0:ref type='figure' target='#fig_4'>1 and 2</ns0:ref>). The nonlinear pattern of population isolation (described above) has a marked impact on within population genetic diversity <ns0:ref type='bibr' target='#b19'>(Excoffier et al., 2009)</ns0:ref>. Populations located in the geographic source of a species' range, (i.e. where the ancestral haplotypes are generally dominant, such as the Groot Swartberg and Langkloof in the case of C. intermedia) are likely to have reduced impacts of drift, due to a larger effective population size and long term persistence, promoting the accumulation of genetic diversity <ns0:ref type='bibr' target='#b19'>(Excoffier et al., 2009)</ns0:ref>. This is evident in C. intermedia, with the longitudinal center of the species range having higher expected haplotype richness (Fig. <ns0:ref type='figure'>4</ns0:ref>). In contrast, populations at the edge of a species range may exhibit reduced genetic diversity <ns0:ref type='bibr' target='#b37'>(Klopfstein et al., 2005)</ns0:ref> and increase fixation of unique local haplotypes (Fig. <ns0:ref type='figure'>4</ns0:ref>). Novel mutations and rare haplotypes become rapidly fixed in edge populations that are prone to genetic bottlenecks resulting from founder effects or population fragmentation and isolation due to climate changes <ns0:ref type='bibr' target='#b37'>(Klopfstein et al., 2005)</ns0:ref>. The potential role of Pleistocene climate instability in isolating C. intermedia populations has been discussed, but we reiterate here the potential role of vicariance in isolating of these genetically distinct edge populations, such as the Anysberg, Garcias Pass, PeerJ reviewing PDF \| (2020:04:47838:1:2:NEW 13 Jul 2020)</ns0:p></ns0:div> <ns0:div><ns0:head>Manuscript to be reviewed</ns0:head><ns0:p>Besemfontein and Lady Slipper populations.</ns0:p></ns0:div> <ns0:div><ns0:head>Management of C. intermedia genetic diversity</ns0:head><ns0:p>The goal of identifying historically isolated sets of genetically similar populations is that these populations can then be viewed as distinct management or conservation unitstermed 'evolutionarily significant units' <ns0:ref type='bibr' target='#b48'>(Moritz, 1994)</ns0:ref>. For example, evolutionarily significant units are restricted to clearly defined drainage basins in some plant species <ns0:ref type='bibr' target='#b57'>(Potts et al., 2013a;</ns0:ref><ns0:ref type='bibr' target='#b58'>Potts et al., 2013b)</ns0:ref>. Such units may be especially valuable for guiding the translocation of genetic material in C. intermedia, and Cyclopia as a whole.</ns0:p><ns0:p>Unfortunately, the biogeographic history of C. intermedia described here has not partitioned genetic variation into neat geographic units. And despite the coarse pattern of genetic diversity being structured within mountain ranges -populations tend to support unique haplotypes (Fig. <ns0:ref type='figure' target='#fig_3'>1</ns0:ref>, Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref>) that make them genetically distinct (e.g. the Prince Alfred's Pass and Swartberg Mountains populations, Fig. <ns0:ref type='figure' target='#fig_6'>3</ns0:ref>) and in need of individual management and protection. Furthermore, a single study sampling only two non-coding regions cannot be considered to represent the entirety of a species genetic diversity. Thus, the fact that 13 of the 17 populations screened here supported unique haplotypes suggests that grouping neighbouring populations as genetically homogenous spatial units for conservation and management would poorly represent the species' cpDNA genetic diversity. Rather than relying on a few Cyclopia populations to represent the genetic diversity, or evolutionary significance, of broad areas, all wild populations should be safeguarded from potential genetic pollution to some degree.</ns0:p></ns0:div> <ns0:div><ns0:head>Recommendations for 'duty of care' of C. intermedia genetic diversity</ns0:head><ns0:p>Currently, the Honeybush industry relies on raw material sourced predominantly from wild populations, with an estimated 85% of wild harvested Honeybush collected from C. intermedia populations <ns0:ref type='bibr' target='#b46'>(McGregor, 2017)</ns0:ref>. With a rise in popularity of natural products in recent years, the Honeybush industry has seen a surge in consumer demand <ns0:ref type='bibr' target='#b29'>(Joubert et al., 2008;</ns0:ref><ns0:ref type='bibr' target='#b30'>Joubert et al., 2011)</ns0:ref>, placing these wild populations under additional harvesting pressure. Agricultural cultivation has therefore been encouraged as a means of protecting Cyclopia from unsustainable harvesting activities.</ns0:p><ns0:p>The initial Honeybush boom in the 1990s <ns0:ref type='bibr' target='#b30'>(Joubert et al., 2011;</ns0:ref><ns0:ref type='bibr'>du Toit et al., 1998)</ns0:ref> spurred on investigations into the cultivation potential of various species of Cyclopia , including C. intermedia. These investigations involved outcrossing experiments, resulting in successful interspecific crossing (Hannes de Lange, unpublished data summarised in the South African Honeybush Tea Association Newsletter No. 14, 8-15), and the introduction of cultivated material into wild populations (N.C.G. pers. obs. 2016; S Nortje, pers. com. 2019). Furthermore, Honeybush cultivation has been encouraged to take place near to wild Cyclopia populations <ns0:ref type='bibr' target='#b27'>(Jacobs, 2008)</ns0:ref> that, based on the evidence presented here and elsewhere <ns0:ref type='bibr' target='#b21'>(Galuszynski and Potts, 2020)</ns0:ref>, are likely to be genetically distinct -as previously suspected <ns0:ref type='bibr' target='#b55'>(Potts, 2017a;</ns0:ref><ns0:ref type='bibr' target='#b68'>Schutte, 1997)</ns0:ref>.</ns0:p><ns0:p>Commercial populations are therefore likely to pose a threat to the genetic integrity of wild Cyclopia if gene flow occurs.</ns0:p><ns0:p>Cultivation, as well as interest in augmentation of wild C. intermedia populations, is on the rise. The high levels of interpopulation haplotype turnover reported here, most of which coincides with transitions between mountains ranges, should be used to guide future conservation and management of C. intermedia seed material -specifically, seed material should be kept within mountain ranges and cultivation should never be used to replace wild populations. While further work is required to describe gene flow patterns within mountain ranges, particularly pollen flow. What can be inferred from the results presented here and existing literature, is that gene flow is likely to be limited to relatively short distances. Currently it is assumed that pollen flow is likely to be limited to a 6km radius (representing the maximum foraging distance of Xylocopa species, <ns0:ref type='bibr' target='#b53'>Pasquet et al., 2008</ns0:ref><ns0:ref type='bibr'>, the common pollinators of Honeybush, Schutte, 1997)</ns0:ref> All haplotypes are colorized based on Fig. <ns0:ref type='figure' target='#fig_3'>1</ns0:ref> and labeled as in Table <ns0:ref type='table' target='#tab_0'>1 and the</ns0:ref> Branches with greater than 50% bootstrap support are labeled and populations that deviate from being grouped with population occurring from the same mountain ranges are denoted in bold typeface; Baviaanskloof (BAV), and Swartberg mountains (SBM), originating from the Groot Swartberg mountains. Population and mountain range naming follows that of Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Figure 4</ns0:head><ns0:p>Haplotype diversity (expected haplotype richness) and differentiation <ns0:ref type='bibr'>(Hedrick's Gst)</ns0:ref> measures plotted against the longitudinal position (decimal degrees) of populations.</ns0:p><ns0:p>Broken lines were manually drawn around the distribution of points.</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:04:47838:1:2:NEW 13 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed Population location and haplotype assignment.</ns0:p><ns0:p>Population names and mountain range of origin (following the abbreviated format used in Fig. <ns0:ref type='figure' target='#fig_3'>1 and 4</ns0:ref>, full names provided in table footer), and GPS coordinates of each population in decimal degree format. The number of accessions sampled per population (N) ranges between 1 and 16 based on the number of individuals found in the field. The number of accessions assigned to each haplotype (A -W) is also provided.</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:04:47838:1:2:NEW 13 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed Manuscript to be reviewed Manuscript to be reviewed Manuscript to be reviewed Manuscript to be reviewed </ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ reviewing PDF \| (2020:04:47838:1:2:NEW 13 Jul 2020)Manuscript to be reviewed intermedia (discussed below) support the concerns raised by<ns0:ref type='bibr' target='#b55'>Potts (2017a)</ns0:ref>, and should provide preliminary guidance to the management and protection of Cyclopia wild genetic resources.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>western and eastern CFR (Tetraria triangularis (Boeck.) C.B.Clarke: Britton et al., 2014; and Protea repens (L.) L.: Prunier et al., 2017) do, however, highlight the potential for population divergence to occur at a range of spatial scales, including over relatively short distances in heterogeneous landscape of the CFR.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head /><ns0:label /><ns0:figDesc>and seed dispersal being limited to under 180 m (globally the longest measured seed dispersal event by ants, Gómez and Espadaler, 2013). Honeybush cultivation should therefore operate at a localized scale and facilitate in preserving the genetic uniqueness of Cylopia populations within mountain ranges.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head>Figure 1</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head>Figure 2</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head /><ns0:label /><ns0:figDesc>putative ancestral haplotype (M) is indicated as a square. Frequency of haplotypes that were detected five times or more in the study are provided in parenthesis, while the area of circles is scaled based on haplotype frequency. Black circles indicate 'missing' haplotypes, while haplotypes connected by a single line differ by one base pair. When the relationship between haplotypes is uncertain a broken line is used. White circles indicate the outgroup taxa: aur = Cyclopia aurescens, bux = C. buxifolia, gen = C. genistoides, lon = C. longifolia, ses = C. sessiliflora.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head>Figure 3 Neighbour</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Figure 3</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_7'><ns0:head>4</ns0:head><ns0:label /><ns0:figDesc>---1 ---------15 Mountain ranges: Anysberg (AB), Klein Swartberg (KSB), Rooiberg (RB), Groot Swartberg (SB), Outeniqua (OUT), Langkloof (LK), Baviaanskloof (B), Cockscomb (CC).PeerJ reviewing PDF \| (2020:04:47838:1:2:NEW 13 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_8'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ reviewing PDF \| (2020:04:47838:1:2:NEW 13 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='27,42.52,70.87,525.00,425.25' type='bitmap' /></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head>Table 1 (on next page)</ns0:head><ns0:label>1</ns0:label><ns0:figDesc /><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_1'><ns0:head>Table 2 (on next page)</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Polymerase Chain Reaction primers used to detect haplotype variation across two noncoding cpDNA regions in wild Cyclopia intermedia populations.Primers used for High Resolution Melt analysis are denoted in bold typeface and primers used to amplify the full cpDNA region are denoted in italic typeface. The primers used for unidirectional sequencing of PCR products for HRM cluster confirmation are underlined and italicised. The average number of length of PCR products amplified by the various primer combinations are given in base pairs (bp), followed by the primers nucleotide motif, and the annealing temperature used for PCR.</ns0:figDesc><ns0:table /><ns0:note>PeerJ reviewing PDF \| (2020:04:47838:1:2:NEW 13 Jul 2020)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_2'><ns0:head>Table 3 (on next page)</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Polymerase Chain Reaction and High Resolution Melt condition used to screen haplotype variation in wild Cyclopia intermedia populations.</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Manuscript to be reviewed</ns0:cell></ns0:row><ns0:row><ns0:cell>Primer annealing temperature (Tm) for the various primer combinations used in this study</ns0:cell></ns0:row><ns0:row><ns0:cell>are provided in Table 2.</ns0:cell></ns0:row></ns0:table><ns0:note>PeerJ reviewing PDF \| (2020:04:47838:1:2:NEW 13 Jul 2020) 2 PeerJ reviewing PDF \| (2020:04:47838:1:2:NEW 13 Jul 2020)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_3'><ns0:head>Table 4 (on next page)</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Nucleotide differences among the 23 cpDNA haplotypes detected in wild Cyclopia intermedia populations.The three cpDNA fragments that were individually screened via High Resolution Melt analysis are presented as separate sets of columns, with the nucleotide position within the context of the concatenated haplotype provided for each base pair variation provided. The indels that differentiate haplotypes are reported below the table, with the variation among indel 2a -2e indicated by bold italicised typeface.</ns0:figDesc><ns0:table><ns0:row><ns0:cell>1</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell cols='4'>MLT S3 -MLT S4</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell cols='4'>MLT S1 -MLT S2</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell cols='3'>MLT U1 -MLT U2</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell cols='6'>(atpI-atpH intergenic spacer)</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell cols='6'>(atpI-atpH intergenic spacer)</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell cols='3'>(ndhA intron)</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>34</ns0:cell><ns0:cell /><ns0:cell>44</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>79</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>2</ns0:cell><ns0:cell /><ns0:cell>2</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>7</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>-</ns0:cell><ns0:cell /><ns0:cell>-</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>-</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>23</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>13</ns0:cell><ns0:cell>13</ns0:cell><ns0:cell>14</ns0:cell><ns0:cell>19</ns0:cell><ns0:cell>19</ns0:cell><ns0:cell>19</ns0:cell><ns0:cell>23</ns0:cell><ns0:cell>27</ns0:cell><ns0:cell>30</ns0:cell><ns0:cell>30</ns0:cell><ns0:cell>32</ns0:cell><ns0:cell>33</ns0:cell><ns0:cell>44</ns0:cell><ns0:cell>43</ns0:cell><ns0:cell>44</ns0:cell><ns0:cell>46</ns0:cell><ns0:cell>49</ns0:cell><ns0:cell>53</ns0:cell><ns0:cell>56</ns0:cell><ns0:cell>58</ns0:cell><ns0:cell>61</ns0:cell><ns0:cell>67</ns0:cell><ns0:cell>71</ns0:cell><ns0:cell>72</ns0:cell><ns0:cell>73</ns0:cell><ns0:cell>79</ns0:cell><ns0:cell>80</ns0:cell></ns0:row><ns0:row><ns0:cell>Position</ns0:cell><ns0:cell cols='8'>-29 30 35 38 66 91 99 116</ns0:cell><ns0:cell>0</ns0:cell><ns0:cell>6</ns0:cell><ns0:cell>7</ns0:cell><ns0:cell>1</ns0:cell><ns0:cell>2</ns0:cell><ns0:cell>7</ns0:cell><ns0:cell>3</ns0:cell><ns0:cell>2</ns0:cell><ns0:cell>4</ns0:cell><ns0:cell>6</ns0:cell><ns0:cell>1</ns0:cell><ns0:cell>8</ns0:cell><ns0:cell>8</ns0:cell><ns0:cell>7</ns0:cell><ns0:cell>8</ns0:cell><ns0:cell>2</ns0:cell><ns0:cell>8</ns0:cell><ns0:cell>8</ns0:cell><ns0:cell>9</ns0:cell><ns0:cell>7</ns0:cell><ns0:cell>3</ns0:cell><ns0:cell>7</ns0:cell><ns0:cell>2</ns0:cell><ns0:cell>6</ns0:cell><ns0:cell>9</ns0:cell><ns0:cell>4</ns0:cell><ns0:cell>1</ns0:cell></ns0:row><ns0:row><ns0:cell>Consensus</ns0:cell><ns0:cell>1</ns0:cell><ns0:cell>T</ns0:cell><ns0:cell>T</ns0:cell><ns0:cell>T</ns0:cell><ns0:cell>A</ns0:cell><ns0:cell>C</ns0:cell><ns0:cell>C</ns0:cell><ns0:cell>G</ns0:cell><ns0:cell>A</ns0:cell><ns0:cell>T</ns0:cell><ns0:cell>T</ns0:cell><ns0:cell>C</ns0:cell><ns0:cell>G</ns0:cell><ns0:cell>A</ns0:cell><ns0:cell>G</ns0:cell><ns0:cell>C</ns0:cell><ns0:cell>A</ns0:cell><ns0:cell>C</ns0:cell><ns0:cell>G</ns0:cell><ns0:cell cols='3'>G 2a G</ns0:cell><ns0:cell>3</ns0:cell><ns0:cell>T</ns0:cell><ns0:cell>G</ns0:cell><ns0:cell>C</ns0:cell><ns0:cell>G</ns0:cell><ns0:cell>T</ns0:cell><ns0:cell>T</ns0:cell><ns0:cell>G</ns0:cell><ns0:cell>C</ns0:cell><ns0:cell>G</ns0:cell><ns0:cell>G</ns0:cell><ns0:cell>C</ns0:cell><ns0:cell>-</ns0:cell></ns0:row><ns0:row><ns0:cell>Haplotype</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>A</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>a</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>2b</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>3</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>-</ns0:cell></ns0:row><ns0:row><ns0:cell>B</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>t</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>2d</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>3</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>4b</ns0:cell></ns0:row><ns0:row><ns0:cell>C</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>a</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>a</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>2a</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>3</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>-</ns0:cell></ns0:row><ns0:row><ns0:cell>D</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>a</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>a</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>2a</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>3</ns0:cell><ns0:cell>c</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>-</ns0:cell></ns0:row><ns0:row><ns0:cell>E</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>a</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>2a</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>3</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>-</ns0:cell></ns0:row><ns0:row><ns0:cell>F</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>2a</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>3</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>4a</ns0:cell></ns0:row><ns0:row><ns0:cell>G</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>2a</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>3</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>a</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>t</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>-</ns0:cell></ns0:row><ns0:row><ns0:cell>H</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>a</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>2a</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>3</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>a</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>t</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>-</ns0:cell></ns0:row><ns0:row><ns0:cell>I</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>2a</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>4a</ns0:cell></ns0:row><ns0:row><ns0:cell>J</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>2a</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>3</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>t</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>t</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>-</ns0:cell></ns0:row><ns0:row><ns0:cell>K</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>2a</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>3</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>a</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>t</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>t</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>-</ns0:cell></ns0:row><ns0:row><ns0:cell>L</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>g</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>2a</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>3</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>a</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>t</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>-</ns0:cell></ns0:row><ns0:row><ns0:cell>M</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>2a</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>3</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>.</ns0:cell><ns0:cell>-</ns0:cell></ns0:row></ns0:table><ns0:note>PeerJ reviewing PDF \| (2020:04:47838:1:2:NEW 13 Jul 2020)Manuscript to be reviewed</ns0:note></ns0:figure> <ns0:note place='foot'>PeerJ reviewing PDF \| (2020:04:47838:1:2:NEW 13 Jul 2020)Manuscript to be reviewed</ns0:note> <ns0:note place='foot'>PeerJ reviewing PDF \| (2020:04:47838:1:2:NEW 13 Jul 2020)</ns0:note> <ns0:note place='foot' n='2'>PeerJ reviewing PDF \| (2020:04:47838:1:2:NEW 13 Jul 2020)</ns0:note> </ns0:body> "	"Applied phylogeography of Cyclopia intermedia (Fabaceae) highlights the need for duty of care when cultivating Honeybush. - Response to reviewers Review comments are presented in colour type face (red and green for reviewers 1 and 2 respectively) with the authors responses in black type face. Editors comment: Please consider suggestions by the two reviewers included below. Crucial information that should be included is to detail methods, for example in results the figure shows the outgroups and they were not mentioned previously. AMOVA methods are explained, however they are not presented in results. In addition, results should be considered to propose priority based in the phylogeographic network. - We thank the handling editor for managing our manuscript in a timely and professional manner. We feel that the reviewers selected are representative of our intended audience and have provided thoughtful comments that have improved the overall quality of the manuscript. We have followed many of the reviewers suggestions relating to the details of the methods (such as the introduction of the outgroup taxa and specifics of some analyses) and have provided additional AMOVA results. We have not however, proposed priority populations based on our results. As this study presents a snap-shot of the potential genetic variation in wild C. intermedia populations – revealing that many populations are genetically distinct, supporting unique haplotypes – we feel that there is still a great deal of unsampled genetic variation. Furthermore, due to the uneven sampling within mountain ranges and populations some of the apparently unique populations may be a consequence of incomplete sampling. Reviewer 1 Basic reporting The authors present a well-written manuscript with a well-referenced introduction that suitably sets the scene for the study. The structure of the paper confirms to PeerJ standards. - We thank you for your encouraging comments and for setting time aside to review our manuscript. We have changed the haplotype colours to be more distinct as suggested. Overall the figures are good and present the data well. However, there are a few errors in the captions (detailed below) and I found the colours used for haplotypes P and M are very similar and difficult to distinguish. - The Figures have been updated for greater clarity and the haplotype colours have been changed to be more contrasting. Thank you for bringing this to our attention. The raw data produced is supplied in Tables 2 and 4 and has also been deposited in GenBank. Additional information on the PCR protocol and the custom R script that were used is provided in the Supplemental Files. - No comment needed Minor comments Ln 74 – Change ‘I’ to ‘we’ (since there are two authors). - Sorry for this mistake, the manuscript was based on a chapter from NCG’s thesis and we must have missed this during editing. I has been replaced with we throughout. Ln 222- Change ‘all indels were reduced to single base pairs’ to ‘each indel event was reduced to a single base pair’ - This correction was made as it does read more smoothly, thanks. Ln 258 - Change ‘atpI-aptH intergenic spacer’ to ‘atpI-atpH intergenic spacer’ - My mistake, thanks. Ln 283 – should G’’’’’ts be Gst? - Corrected as G””st. Ln 298 – Change (Potts, 2017a) to Potts (2017a) - Corrected, thank you. Ln 351 - Change ‘I’ to ‘we’ (since there are two authors). - Corrected, thank you. Ln 376 – Change to ‘Novel mutations and rare haplotypes become rapidly fixed in populations….’ - We don’t follow, there would be no change? Ln 433 – Acknowledgments – Change ‘me’ to ‘us’ (since there are two authors). - Corrected, thank you. Ln 478 – The title of the Dray reference should start with ‘The’ - Corrected, thank you. Ln 498 – This reference is missing the journal. - The citation was to a preprint that has subsequently been accepted since the initial submission of this manuscript :) The citation has been updated. Ln 518 – insert space between ‘GST’ and ‘and’ - Corrected, thank you. Ln 559 - Restio capensis should be in italics. - Corrected, thank you. Ln 665 - Ambystoma tigrinum should be in italics and be followed by only one full stop. - Corrected, thank you. Figure 1 caption – should ascensions be accessions? - Our apologies, we have corrected this typo Figure 2 caption – Change – ‘haplotypes connected by a single line differ by a one base pair’ to ‘haplotypes connected by a single line differ by one base pair’ - “a” deleted Figure 4 caption – change demoted to denoted. - Corrected, thanks Table 3 caption – italicize C. intermedia - Corrected, thanks Table 4 – delete the word ‘various’ - Deleted Experimental design The research presented fits within the scope of PeerJ. The study has clear aims and examines these with appropriate sampling and analyses. The methods are clearly explained, although I would like to see details of the outgroup samples added to the ‘Sample collection and DNA extraction’ section. The use of sequencing to confirm the HRM clusters and determine the level of false negative haplotype assignments is particularly commendable and it gives confidence in the quality of the data. - The outgroup samples were sourced from a previous study (Galuszynski and Potts 2020). We failed to mention this in the paper and have included a statement linking these samples to the above mentioned reference. Validity of the findings Overall, the conclusions are appropriate given the results and link back to the original research question in the introduction. - Thank you for these kind comments. I have one question about the interpretation of the network. I found it interesting that three of the outgroup species (C. aurescens, C. longifolia and C. sessiflora) have haplotypes that, in the network, are nested within the genetic diversity detected in the study species. Are these species known to hybridise with the study species? Could chloroplast capture from these, or other closely-related species, be the source of some of the rare local haplotypes found in your target species? Chloroplast capture has been found in many overseas studies of plant phylogeography. I don’t think it would change the interpretation of the results or the conclusions but it is something that should be considered. - Thank you for this comment, hybridization and chloroplast capture is an important issue that should be explored in Cyclopia. Considering the relatively small amount of sequence data presented in the manuscript and the fact that none of the outgroup taxa represent a more parsimonious route to generating any of the haplotypes detected in C. intermedia we do not feel that we have sufficient evidence to discuss chloroplast capture as a source of haplotype richness in C. intermedia. These issues of hybridization are however raised in a separate manuscript (currently being prepared for submission) comparing haplotype variation between wild and cultivated populations of three Honeybush species, where stronger evidence for chloroplast capture is detected. Thus, we cannot answer the reviewer, but have provided the following text in the results to highlight this point: “We note that outgroup samples nest within the statistical parsimony network. This may be due to a recently shared common ancestor, incomplete lineage sorting or chloroplast capture (i.e. hybridization). Identifying which driver or drivers gave rise to this observed pattern requires further sampling of species, populations and genomes — sampling the nuclear genome is crucial for identifying hybridization events. At present, little is known about the proclivity for hybridization amongst the species in this genus.” Comments for the author Overall a good solid study and a nicely written paper. I particularly like that clear conservation recommendations are provided. - Thank you very much for your comments and for reviewing the manuscript. We know reviewing manuscripts can be a time consuming task and we appreciate the concise and helpful tone used throughout. Reviewer 2 Basic reporting I think it is an important contribution because adds useful information about phylogeographic patterns of Cyclopia intermedia, used to produce the Honeybush tea. This species is endemic from the Cape Floristic Region (CFR), located along the southern Cape of Africa. Since the cultivation of this species is relatively recent, opportunities exist to guide future conservation and management based on the retrieved genetic patterns. However, I consider there are some aspects of the manuscript that should be revised. Authors should revise considerably the English editing. I think some ideas are confused because of wording. In some places you used 'I' instead of 'We', please check. In general terms I consider the introduction is sufficient and clear, the manuscript are appropriately referenced and is also well structured. Authors do not mentioned if they have deposited or will deposit the sequences in the Genebank; they should do it. -We thank you for the time you made aside to complete a thorough review of our manuscript and for highlighting a number of important issues. You feedback has definitely improved the quality of the overall paper. We hope that the changes made to the manuscript are sufficient and address the issues you have highlighted. The manuscript was based on a chapter from NCG’s thesis and we must have missed the “I” s during editing, I has been replaced with we throughout. All sequences have been deposited in GenBank, accession numbers MN930803 - MN930855 and MN930856 – MN930919. The Peer J format has this information provided as part of the declarations rather than in text and may therefore have been missed during the review process. Experimental design I think the research question is well defined and the knowledge gap is well formulated. The hypothesis should be improved and Materials and Methods should include more detailed information about some issues. Authors hypothesize that due to limited seed dispersal by ants, Cyclopia species will exhibit highly structured chloroplast genetic diversity as was observed in a previous study of another wild Cyclopia species. In other words, the hypothesis is raised in relation to an autoecological characteristic of the species. On the other hand, the topographic complexity and climatic heterogeneity of the study area are also mentioned in the introduction, but authors did not present an integrated hypothesis considering both factors (i.e. autecology and landscape and environmental characteristics). Thus, I suggest developing a hypothesis that includes both elements. Furthermore, I suggest including in Materials and Methods a description of the study area, since it is not mentioned how high these mountains are, which is the direction of the mountain ranges, and how is the distribution of Cyclopia intermedia within the mountain range. It is not clear for me if there could be altitudinal corridors for the species? Please, explicit how the topography could be a possible barrier for Cyclopia intermedia? I consider that this will contribute to a better discussion of the results. For example, in the Discussion section the different basins and sub-basins are mentioned and it is difficult to follow it. I suggest including a DEM map (modifying Fig. 1) indicating the location of each basin, mountains, etc. to better understand and discuss the retrieved genetic patterns. - While we do acknowledge that the role of topography was not adequately highlighted when introducing the study (this has been corrected), the study does not develop an independent hypothesis, but rather tests the postulation put forward by Potts 2017. For this reason, the role of past climate variability, while important in the phylogeographic discussion, is not emphasized in the introduction as it does not feature in heavily in Potts 2017 postulation. Future work focusing specifically on the impact of climate history on genetic divergence within Cyclopia will focus more on this topic. Thank you for bringing this to our attention, we have replaced the basemap in the haplotype distribution figure (Fig. 1) with a high contrast DEM to better display the topography of the study area. Additionally, rather than adding to the methods section we have opted to include a brief description of the study site as an appendix, including a topographically descriptive map. We feel that this provides the option of further exploring the study context for those interested without making the manuscript overly descriptive and possible detracting from the core message – C. intermedia haplotype variation is rich and geographically structured. We did however provide additional contextualization of the distribution of C. intermedia with respect to the Cape Fold mountains when describing the analysis of the longitudinal haplotype turnover pattern in the methods section. Materials and Methods Please introduce better the focal species. Is it an herb? a shrub? - The species description has been updated to better describe C. intermedia Please specify the distance among sampled individuals and the minimum and maximum distance among sampled localities. - Due to the frequent and widespread fires in the Cape at the time of sampling it was not possible to set some predefined limit on the distance between individuals sampled. For example, the populations with fewer than 10 samples represent all individuals found in the site with some individuals being collected less than 2 m apart and others over 200 m apart. In more dense stands however, samples were collected across the full extent of stand with some samples being collected between 5 and 10 m apart and the furthest samples collected over 500 m apart. The methods text has been updated to describe the minimum distances between plants collected. Author say that they amplified three non-coding chloroplast DNA loci. However, as you say in line 221, chloroplast DNA are maternally inherited and in tandem, thus the chloroplastidial DNA molecule is considered a single locus. In addition, in my opinion you amplified two non-coding regions (the atpI-aptH intergenic spacer and the ndhA intron), not three as is mentioned in the manuscript; the atpI-aptH intergenic spacer was amplified in two parts. Please modify. - Thank you for bringing it to our attention that it would be preferable to report the two segments of the atpI-atpH region as a single loci. We no longer use the term locus/loci, but region. Considering phylogeographic analyses, I think with this dataset authors could go further in unravelling the evolutionary history of the species, for example including demographic analyses. But, for the question posed, inferring haplotypes genealogy, their spatial distribution, and characterize the spatial genetic structure may be enough. The comment I have in this regard is in relation to the use of AMOVA. In this analysis, you define a priori groups of populations; so while partitioning can be significant, perhaps there is another barrier that further explains the genetic pattern obtained. Thus, I suggest to include some analysis such as BASP or SAMOVA where the population groups are identified a posteriori. Given that the patterns found were not expected, this type of analysis can help to infer some factor / barrier not previously considered. - We agree and thank you for these suggestions, C. intermedia would be an excellent model for exploring evolutionary processes in the CFR. Initially the goal was to collect a large number of individuals per site, however, as mentioned before fire history prevented this. This resulted in uneven sampling that may limit the statistical value of more complex analyses, evident in the preliminary BEAST and SPREAD analyses we conducted and subsequently scrapped. We did however, followed your suggestions and tested whether the populations underwent any expansion or contraction (via Tajima’s D, Fu and Li’s D, and Ramos-Onsins and Rozas's R2), but found no significant results (p > 0.05 in all three cases). Furthermore, conducting a SAMOVA did not provide any new insights into the spatial structuring of populations, this is either a consequence of uneven sampling that may undermine some of the assumptions made by the analysis or due to the high levels of haplotype turnover among populations. Thus, the SAMOVA results appear to suggest that populations tend to be genetically unique, supporting the patterns evident in the NJ clustering of populations based on pairwise genetic distance (Fig. 4). We did not add these tests to the manuscript as we feel they do not add any substantial advances in understanding beyond the results already reported Line 187-188 – How many replicates per sample? - We apologize that this was unclear. The manuscript text has been edited to reflect that two replicates were run per sample. Line 205-207- Please, inform how common was this situation - This is reported on in the results section. Only one population was found to have different haplotypes with the same melt curve. Line 226 - Did sequences retrieved many indels? Why indels were not coded? As suggested for example by Simmon and Ochoterena 2000, Gaps as Characters in Sequence-Based Phylogenetic Analyses - Please refer to Table 2 for the four indels encountered. We do not believe that coding these as a presence absence matrix, as suggested by Simmon and Ochoterena (2000), would provide additional insight into the relationships between haplotypes and therefore opted for a genetic distance measure that treats gaps as a fifth character state. Line 237 - But you don’t have alleles. You mean, haplotypes? It is necessary to give more detail about the genetic distances and the richness estimator used. Why did not use a genetic distance that consider nucleotide differentiation and haplotype frequency among populations? They are implemented in Arlequin or DNAsp and sure in R. - Thank your for bringing this to our attention, alleles has been removed and replaced with haplotypes. Genetic distance and richness estimators are rarely fully described in application focused papers. If readers are interested in any particular component of the methods the original source is provided for further reading. A number of different genetic differentiation measures (Fst, Gst, G””st, Jost’s D) were, however, tested and did not impact the observed trends. Thus, the methods selected were based on the recommendations of Jost et al., 2018. As the measures implemented in R are calculated at the population level, the frequency of haplotypes are inherently considered. The measures are based on the extent to which aligned haplotype sets (with the haplotypes detected in a population representing a set in this case) differ from one another, thus the proportion of genetic differences across all haplotypes within a population are compared across all the haplotypes from another populations. Line 239 - It takes indels as a fifth state? -That is the case, the text has been updated to clarify this. Line 241 - Which analyses? Do you mean that significant association between these genetic and geographic distances were tested via Mantel Test? Please reword this sentence. - Thank you for highlighting this for us, after reading the statement we see that it was unclear that the mantel test was testing the significance of the IBD tests. The text has been updated and hopefully reads more clearly now. Line 244 - Did you correct this estimation based on sample sizes? This is important because of the difference in the sampling sizes among populations. - Yes, the estimation is corrected for to account for unequal sampling and the text has been updated to indicate this. Line 247 - Why did you consider only the longitude? And the latitude? Probably you can better explain how the study area and the area of distribution is to a better understanding - We had hoped that from the sample distribution map it was clear that the distribution of C. intermedia followed a linear pattern along longitude. The methods have been updated to better describe this pattern and the topographic context of the CFR. “This longitudinal trend in haplotype turnover was investigated as the Cape Fold mountains that support C. intermedia populations consist of linear arrangement of ranges that are dissected by deep river gorges (Fig. 2 and described further in S1) that are likely major barriers to seed dispersal.” Validity of the findings RESULTS I consider results are not well stated and that there are some inconsistencies with the methodology. In the Result section, Phylogeographic analysis, please include a description of haplotypes geographical distribution. I recommended to characterize the spatial distribution of haplotypes, while mentioning how genealogically close they are. This makes easier to build the underlying biogeographic scenario. - We agree that some further contextualization of haplotype distribution and genealogical closeness may be relevant for some populations (e.g. GAR, AB, BF, LMF/LS), but to describe the distribution of 23 haplotypes seems rather tedious and unlikely to appeal to the reader. We feel that this information is best interpreted from the colour coding of figures 1 and 2 (i.e. no clear colour groupings in the SP net, rather the colours are somewhat spread around, suggests that co-occuring haplotypes are not close genealogical relatives). Haplotype network include out-groups as shown in Fig. 2, but it was not mentioned either in M&M or discussion. I suggest taking them out. - Thank you for bringing this to our attention, rather than removing the outgroups we have introduced them in the methods and contextualized them in the discussion. “Five outgroup taxa (C. aurescens Kies, C. buxifolia (Burm.f.) Kies, C. genistoides (L) R.Br., C. longifolia Vogel, and C. sessiliflora (L) R.Br.) that were previously sequenced to develop the genus specific HRM markers (Galuszynski and Potts, 2020) were included in the SP network.” Could you please show the AMOVA results (FCT, FST, FIS, p values? In a table in the main manuscript or as supplementary material. - The AMOVA implemented in R does not provide these additional statistics as standard, but the analyses were performed to test the significance of Fst and have been included in the AMOVA results presented in Table 5. Line 258 – Please put “regions” instead of “loci” - Changed as requested Line 259 - ndh or ndhA, please uniform throughout the manuscript - Corrected Line 259-260 Please move the line about haplotype spatial distribution to M&M - Moved Line 287 – Among instead of within - Corrected Line 288 - I suggest 9.0% structured among populations within mountain ranges. - Thank you for the suggestion, it was been implemented. Fig. 1. The study area it is not clearly indicated for me. What is it the white map? An amplification of the white are in the Africa map? What is obscure haplotypes? Please explain better. I also recommend to inset a picture showing the mountain ranges more clearly. - Thank you for bringing this to our attention, we have updated the map inset to only show the South African context and the caption to be more descriptive. The mountain ranges are described in more detain in the new appendix. Fig. 2. Haplotype network include out-groups as shown in Fig. 2, but it was not mentioned either in M&M or discussion. I do not see any broken line as is mentioned in figure legend - The out groups have now been introduced in the methods and the spacing on the broken lines increased to be more visible. We apologize for not checking that the figures were clear in the review manuscript. DISCUSSION AND CONCLUSION Authors conclude that all wild populations should be safeguarded from potential genetic pollution to some degree. Of course that this would be the best option, but I think author should propose an order of priority based on their study. For example considering haplotype diversity, private haplotypes, etc. - We intentionally decided not to focus too heavily on targeting priority populations as we do not consider our study to represent the totality of the species haplotype variation and nearly every population supports some private haplotype that is in need of protection. We do however, agree that the original manuscript may underplay this fact and we have expanded on our discussion of the management implications of our data to better reflect the high levels of haplotype turnover detected. Moreover, based on Fig. 1, it is evident that to the western side of the distribution range, there would be evidence of isolation among populations, but toward the eastern side there is some evidence of a greater connectivity among some populations. I think this pattern has management implications. I consider the conclusion could be more connected with the results obtained. - We agree with this observation to some degree. Yes there is complete haplotype turnover among the western most populations (GAR, AB, however BF – RB and DR must be excluded from this discussion due to small sample size), these populations occur in different mountain ranges (the Langeberg, Anysberg and Klein Swartberg respectively). While fixation may not be as high in the eastern populations, the unique haplotypes detected in the east are highly divergent (e.g. haplotype W) and are also restricted to mountain ranges and thus should be managed in the same manner as western populations (i.e. don’t move genetic material outside of its mountain range of origin). The fact that the BAV populations shares so much similarity with the Swartberg does however warrant further discussion and the manuscript has been updated to that accord. In the discussion section, basins and mountain range, are mentioned but they have not been well presented in the previous sections, so it is a little difficult to follow the reasoning of the discussion. As I mentioned above it is necessary to introduce better the geography and the biogeography of the study area. Including a figure where different mountain ranges, basins, etc be shown. - We thank you for bringing this to our attention, we have updated the manuscript to better contextualize the study in relation to the regions geography by including an additional appendix. We hope this will facilitate contextualization of the study to an international audience. Line 294-297 I suggest moving these lines to M&M - This information is already in the methods section and is provided here to re-contextualize the study discussion. Line 338-339 - Could you give a putative explanation for this pattern? - The manuscript text has been edited to provide better explanation of the climate history in the eastern CFR, that may have lead to periods of connectivity among populations and generated the observed haplotype compositions within populations. “the vegetation composition of the eastern CFR may have seesawed between C3 Mediterranean shrub -lands (that support C. intermedia) and alternative woodlands and/or C4 grasslands (Bar-Matthews et al., 2010; Cowling and Lombard, 2002). While this would have fragmented populations across mountain ranges, as described above, it may have also created opportunities for admixture among populations within mountain ranges and perhaps even across mountain ranges via habitat corridors, especially in the eastern reaches of species ranges. This may explain the widespread distributions of haplotypes M and F in the eastern C. intermedia populations (Fig. 1). These haplotypes, while dominant in the Langkloof and Groot Swartberg mountains respectively (suggesting some degree of connectivity among populations within these ranges), are also found in the eastern most populations located in the Cockscomb (haplotype M) and Kouga (haplotype F) mountains that both support unique local haplotypes that make them genetically distinct. Considering the slow mutation rate of the chloroplast genome (Schaal et al., 1998), the presence of unique haplotypes at low frequencies within populations should be viewed as an additional level of genetic structuring in C. intermedia, with 9% of haplotype variation localized within populations within mountain ranges. Of the 23 haplotypes detected in C. intermedia, four are shared among populations (Table 1), possibly suggesting limited dispersal among and within mountain ranges. These rare haplotypes may result from mutations generated in-situ, with limited migration among adjacent populations preventing rare haplotypes from becoming more widespread.“ Line 370 – What process? Please, explain better what would be a physical barrier for C. intermedia…This idea should be also presented in the introduction - Poor dispersal across topographic barriers as a process isolating populations has been discussed in the manuscript. Line 379-380 -This could be a consequence of secondary contact area.. - If secondary contact had occurred would one not expect to find more cases of haplotypes being shared among mountain ranges and populations? Line 405 - Probably this idea should be also proposed in the introduction, in the hypothesis - As the study is largely descriptive of phylogeographic trends and lacks the sample design to test demographic hypotheses we are apprehensive to link the ideas of (Excoffier et al., 2009 and Klopfstein et al., 2005) to a hypothesis in the introduction, we do however introduce the concepts that facilitated the development of the ideas presented by Excoffier et al. (2009) and Klopfstein et al. (2005) in paragraph 3 (lines 84-101) of the manuscripts introduction. "	Here is a paper. Please give your review comments after reading it.
499	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>The differences in small noncoding RNAs (sncRNAs), including miRNAs, piRNAs, and tRNAderived fragments (tsRNAs), between X and Y sperm of mammals remain unclear. Here, we employed high-throughput sequencing to systematically compare the sncRNA profiles of X and Y sperm from bulls (n=3), which may have a wider implication for the whole mammalian class. For the comparison of miRNA profiles, we found that the abundance of bta-miR-652 and bta-miR-378 were significantly higher in X sperm, while nine miRNAs, including bta-miR-204 and bta-miR-3432a, had greater abundance in Y sperm (p < 0.05). qPCR was then used to further validate their abundances. Subsequent functional analysis revealed that their targeted genes in sperm were significantly involved in nucleosome binding and nucleosomal DNA binding. In contrast, their targeted genes in mature oocyte were significantly enriched in 11 catabolic processes, indicating that these differentially abundant miRNAs may trigger a series of catabolic processes for the catabolization of different X and Y sperm components during fertilization. Furthermore, we found that X and Y sperm showed differences in piRNA clusters distributed in the genome as well as piRNA and tsRNA abundance, two tsRNAs (tRNA-Ser-AGA and tRNA-Ser-TGA) had lower abundance in X sperm than Y sperm (p < 0.05). Overall, our work describes the different sncRNA profiles of X and Y sperm in cattle and enhances our understanding of their potential roles in the regulation of sex differences in sperm and early embryonic development .</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>Several previous studies have considered the question of diversity between X and Y sperm, demonstrating significant variation in their structure, morphology, motility, and energy metabolism <ns0:ref type='bibr' target='#b15'>(Cui and Matthews, 1993;</ns0:ref><ns0:ref type='bibr' target='#b59'>Sarkar et al., 1984;</ns0:ref><ns0:ref type='bibr' target='#b64'>Shettles, 1960)</ns0:ref>. With the advent of computer-assisted sperm analysis, which allows the objective evaluation of kinetic parameters, most of the detected variations have been considered controversial <ns0:ref type='bibr' target='#b32'>(Hossain et al., 2001;</ns0:ref><ns0:ref type='bibr' target='#b52'>Penfold et al., 1998)</ns0:ref>. Sperm carry different sex chromosomes (either an X-or a Y-chromosome), which provide clues for the discovery of other differences between X and Y sperm. Indeed, such differences have been identified in several different types of profiles <ns0:ref type='bibr' target='#b72'>(Yadav et al., 2017)</ns0:ref>, including protein profiles <ns0:ref type='bibr' target='#b12'>(Chen et al., 2012;</ns0:ref><ns0:ref type='bibr' target='#b18'>De Canio et al., 2014)</ns0:ref> and messenger RNA (mRNA) profiles <ns0:ref type='bibr' target='#b11'>(Chen et al., 2014)</ns0:ref>, by using high-throughput measurement technologies. However, whether there is the difference in the profiles of sncRNAs is still unknown.</ns0:p><ns0:p>Recently, an increasing number of studies in several species have shown that mature ejaculate sperm carry thousands of sncRNA, including tRNA-derived fragments (tsRNAs), ribosomal RNAs (rRNAs) and small nucleolar RNAs (snoRNAs), especially microRNAs (miRNA) and Piwi-interacting RNAs (piRNA) <ns0:ref type='bibr' target='#b60'>(Sellem et al., 2020)</ns0:ref>. As the best-studied type of small noncoding RNA, miRNA has been implicated in posttranscriptional control, by binding to an Argonaute (AGO) protein to stabilize its target through binding to the 3' untranslated region(UTR), and in the regulation of translation by targeting amino acid coding (CDS) regions <ns0:ref type='bibr' target='#b29'>(Hausser et al., 2013)</ns0:ref>. Even though the complexity of sperm miRNA has been well characterized in several mammalian species, including humans <ns0:ref type='bibr' target='#b50'>(Pantano et al., 2015;</ns0:ref><ns0:ref type='bibr' target='#b67'>Tay et al., 2008)</ns0:ref>, mice <ns0:ref type='bibr' target='#b49'>(Nixon et al., 2015)</ns0:ref>, pigs <ns0:ref type='bibr' target='#b9'>(Chen et al., 2017;</ns0:ref><ns0:ref type='bibr' target='#b75'>Zhang et al., 2017)</ns0:ref> and bulls <ns0:ref type='bibr' target='#b7'>(Capra et al., 2017)</ns0:ref>, the functions of most of the sperm miRNAs remain enigmatic. On one hand, sperm miRNAs seem to be required by the sperm themselves and may have a function that impacts sperm motility. As a snapshot of what remains after spermatogenesis, the sperm miRNA profile was shown to be altered in different types of motile sperm in bulls <ns0:ref type='bibr' target='#b7'>(Capra et al., 2017)</ns0:ref>. On the other hand, sperm miRNAs may play roles in processes, such as fertilization <ns0:ref type='bibr' target='#b74'>(Yuan et al., 2016)</ns0:ref> and, subsequently embryonic development <ns0:ref type='bibr' target='#b74'>(Yuan et al., 2016)</ns0:ref>, potentially even transmitting paternally acquired phenotypes <ns0:ref type='bibr' target='#b26'>(Grandjean et al., 2015;</ns0:ref><ns0:ref type='bibr' target='#b56'>Rodgers et al., 2015;</ns0:ref><ns0:ref type='bibr' target='#b63'>Sharma et al., 2015)</ns0:ref> after they are carried into the fertilized oocyte. The major reason why sperm miRNAs execute these roles is that in the fertilized oocyte they regulate target maternal mRNAs <ns0:ref type='bibr' target='#b56'>(Rodgers et al., 2015;</ns0:ref><ns0:ref type='bibr' target='#b70'>Wang et al., 2017)</ns0:ref>. miRNAs carried by sperm may control maternal mRNAs expression levels to affect epigenetic reprogramming, the cleavage, and apoptosis of somatic cell nuclear transfer (SCNT) embryosin cattle <ns0:ref type='bibr' target='#b70'>(Wang et al., 2017)</ns0:ref>. Transgenerational effects associated with parental diet were also proposed to be mediated, at least in part, by sperm tsRNAs <ns0:ref type='bibr' target='#b10'>(Chen et al., 2016)</ns0:ref>. In contrast to miRNAs (~22nt), piRNAs (24-31 nucleotides ) are marginally longer, expressed primarily in the germline and binding to the Piwi class as compared to the Ago-class <ns0:ref type='bibr' target='#b2'>(Aravin et al., 2006;</ns0:ref><ns0:ref type='bibr' target='#b25'>Girard et al., 2006;</ns0:ref><ns0:ref type='bibr' target='#b65'>Siomi et al., 2011)</ns0:ref>. piRNAs are assumed to be produced from the polycistronic RNAs that are transcribed in the genome from a small number of specific regions named piRNA clusters. It has been proposed that piRNAs may protect genome integrity from the deleterious effects of repetitive and transposable elements by binding to the elements <ns0:ref type='bibr' target='#b37'>(Krawetz et al., 2011)</ns0:ref>. piRNAs were shown to be the more abundant set of regulatory sncRNAs than other types of sncRNAs in human sperm <ns0:ref type='bibr' target='#b50'>(Pantano et al., 2015)</ns0:ref>, their expression in sperm correlated to sperm concentration and fertilization rate <ns0:ref type='bibr' target='#b16'>(Cui et al., 2018)</ns0:ref>. These groundbreaking detection of sperm sncRNA led us to question whether there are any differences in the sncRNA profiles of two types of sperm, and if so, what are the specific functions of sncRNA showing differences between X and Y sperm?</ns0:p><ns0:p>Here, we systematically compared the abundance of several kinds of sncRNA species between X and Y sperm, especially in miRNAs, piRNAs, and tsRNAs. To explore the roles that differentially abundant (DA) miRNAs play in sperm and fertilized oocyte, we predicted their target binding sites in 3'UTRs and CDS regions and performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the target genes presented in sperm and mature oocyte. To our knowledge, this study provides the first description of the differences in the sncRNA profiles of X and Y sperm, which could improve our understanding of their possible functions in the regulation of sex differences in sperm and early embryonic development.</ns0:p></ns0:div> <ns0:div><ns0:head>Materials & Methods</ns0:head></ns0:div> <ns0:div><ns0:head>Bull X and Y sperm collection</ns0:head><ns0:p>Semen samples were obtained from the Saikexing Institute (Hohhot, China). Briefly, samples were collected from three Holstein bulls at three years of age. These bulls were fed the same diet daily and reared in the same conditions and environments. The semen was sampled from them using an artificial vagina and stored at room temperature (18°C) for 1 h. Subsequently, the samples were passed through a 50 µm filter to remove debris or clumped sperm, and the sperm were stained with the Hoechst-33342 fluorophore (Sigma, St Louis, USA) via incubation at 34°C for 45 min in darkness. After staining, the three sperm samples were separated into three X sexsorted semen and three Y sex-sorted semen using a high-speed MoFlo SX XDP flow cytometer (DakoCytomation, Fort Collins, USA). The purity of X and Y sex-sorted semen were tested by using the sort reanalysis method <ns0:ref type='bibr' target='#b71'>(Welch and Johnson, 1999)</ns0:ref>. In brief, 20 µl semen from each sample sonicated to remove the sperm tail and stained with 20 µl Hoechst-33342 fluorophore via incubation at 34°C for 20 min in darkness. These samples were input into the high-speed MoFlo SX XDP flow cytometer to measure the purity of X and Y sex-sorted semen by performing the resorting procedure. Then, the sorted semen was washed twice in phosphate buffered saline (PBS, GE Healthcare Life Sciences, USA) through centrifugation at 700 g for 10 min at 20°C. We removed the supernatants and mixed the sperm pellets with TRIzol by incubation for 5 min at RT (Sigma; 0.5 ml per 1 × 10 7 sperm). After that, the samples were store on dry ice for next day use. The Scientific Ethics Committee of Huazhong Agricultural University approved the experimental design and animal treatments for the present study (permit number: HZAUSW-2017-012), and all experimental protocols were conducted in accordance with the guidelines.</ns0:p></ns0:div> <ns0:div><ns0:head>Sperm total RNA isolation, libraries preparation, and sequencing</ns0:head><ns0:p>The TRIzol method has been used to extract the sperm total RNA of the six samples <ns0:ref type='bibr' target='#b17'>(Das et al., 2010)</ns0:ref>. Comprehensive protocols were outlined in our earlier report <ns0:ref type='bibr' target='#b62'>(Shangguan et al., 2020)</ns0:ref>. Using a 2100 Bioanalyzer, the RNA integrity was assessed after extraction of the RNA (Agilent Technologies, USA). The validated RNAs of X and Y sperm (10 ng RNA from each sample) were sequenced on the BGISEQ-500 platform <ns0:ref type='bibr' target='#b21'>(Fehlmann et al., 2016)</ns0:ref> at the BGI company (Shenzhen, China). A sequencing library was prepared and sequenced according to a standard protocol established by the BGI <ns0:ref type='bibr' target='#b21'>(Fehlmann et al., 2016)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Preprocessing of small noncoding RNA data</ns0:head><ns0:p>After sequencing, the raw data were obtained from X and Y sperm samples. The quality of sequencing reads was tested by fastQC (http://www.bioinformatics.babraham.ac.uk/projects/ fastqc/). The adapters were initially detached from the raw sequence data (3' adapters: AGTCGGAGGCCAAGCGGTCTTAGGAAGACAA, 5' adapters: GAACGACATGGCTACGATCCGACTT). Also, in order to obtain clean data, we used Trimmomatic to trim the low-quality bases of each sequence <ns0:ref type='bibr' target='#b6'>(Bolger et al., 2014)</ns0:ref>. The options below were used to trim: SLIDINGWINDOW 4:15, MINLEN 15, MAXINFO 15:0.8. The sequencing data of X and Y sperm sncRNA have been deposited in the Sequence Read Archive (https://www.ncbi.nlm.nih.gov/sra) with the accession codes PRJNA624261.</ns0:p></ns0:div> <ns0:div><ns0:head>Small noncoding RNA annotation</ns0:head><ns0:p>Analysis of the small noncoding RNA data was performed using Unitas with options: -tail 1mismatch 0 <ns0:ref type='bibr' target='#b24'>(Gebert et al., 2017)</ns0:ref> ( Unitas is a software for the classification and annotation of mature miRNAs, rRNAs, piRNAs, tsRNAs, protein-coding RNAs, small nucleolar RNAs (snoRNAs), small nuclear RNAs (snRNAs), low complexity RNAs, non-annotated RNAs, and miscellaneous RNAs (miscRNAs). To reduce false-positive results for miRNA annotation, Mirdeep2 <ns0:ref type='bibr' target='#b22'>(Friedländer et al., 2011)</ns0:ref> was also used to predict putative known mature miRNAs of Bos taurus. We chose 17-23 nt sequences (based on the most of the length of mature miRNA <ns0:ref type='bibr' target='#b61'>(Sendler et al., 2013)</ns0:ref> from the clean data from which the Bos taurus rRNA and tRNA sequences had been removed as the input for Mirdeep2. The known mature Bos taurus miRNAs dataset (miRBase v.21, http://www.miRbase.org/) was used for miRNA detection. Then, the relative abundance of all miRNAs annotated from two software was standardized to the transcripts per million reads value (RPM) according to the formula: RPM = (mapped reads × 10 6 ) / total reads, and miRNAs with an RPM > 5 that were found in at least 2 samples were identified as miRNAs expressed.</ns0:p><ns0:p>The information of piRNA cluster and tsRNAs of X and Y sperm was obtained from the output files of Unitas (version 2.1) <ns0:ref type='bibr' target='#b57'>(Rosenkranz and Zischler, 2012)</ns0:ref>. We obtained piRNA clusters data of each sample. For the piRNA cluster, if the genomic regions of two clusters identified in one sample were overlapped on the genome, they were considered as the same cluster. The same clusters found in all 3 replications were identified as the conserved cluster in each group. Genes and repeats falling within the detected clusters were retrieved using bovinemine (http://bovinegenome.org). Furthermore, reads mapped within the detected clusters were also retrieved to map to all available piRNA database on piRBase (http://regulatoryrna.org/database/piRNA/download.html) to identify the putative piRNA using the Bowtie software <ns0:ref type='bibr' target='#b39'>(Langmead et al., 2009)</ns0:ref>. piRNAs with an RPM > 10 that were annotated in at least three samples were defined as expressed piRNAs.</ns0:p></ns0:div> <ns0:div><ns0:head>Analysis of differential miRNA abundance</ns0:head><ns0:p>Differentially abundant analysis was carried out using the Bioconductor DEseq2 R package <ns0:ref type='bibr' target='#b45'>(Love et al., 2014)</ns0:ref>. By applying thresholds of a P-value < 0.05 and \|log2(fold change)\| > 1, the remaining miRNAs were defined as significantly differentially abundant (DA) miRNAs. Furthermore, the analysis of the differential abundance of piRNAs and tsRNAs between X and Y sperm was the same as that of miRNAs.</ns0:p></ns0:div> <ns0:div><ns0:head>Functional annotation of DA miRNAs</ns0:head><ns0:p>Two datasets named transcriptome data sequenced from single bull metaphase II oocyte (GSE59186) (n = 2) and Bull sperm transcriptome data(SRA055325) (n = 1) that have been earlier published were applied to explore the function of X and Y sperm DA miRNAs. The raw sequencing data were collected from the Sequence Read Archive (SRA) and were reanalyzed following these processes: <ns0:ref type='bibr' target='#b76'>(1)</ns0:ref> We used Cutadapt (https://code.google.com/p/cutadapt/) to cut the sequencing adapters and FASTX-Toolkit (http://hannonlab.cshl.edu/fastx_toolkit/) was used to filter sequences of low quality with the options 'fastq_quality_trimmer -v -Q 33 -l 30 -i -t 20';</ns0:p><ns0:p>(2) Clean reads were aligned to the reference genome (Btau_4.6.1) by Tophat software <ns0:ref type='bibr'>(Trapnell et al., 2012)</ns0:ref>; (3) For each gene model the mapped sequencing was counted and recorded via Cufflinks in Fragments Per Kilobase Million (FPKM) <ns0:ref type='bibr'>(Trapnell et al., 2012)</ns0:ref>. Moreover, sperm transcripts were filtered with FPKM < 50, and oocyte genes with FPKM > 50 were retained in at least one sample. Ultimately, we obtained 1,036 sperm genes and 2,584 oocyte genes (Table <ns0:ref type='table'>S5</ns0:ref>). miRwalk was applied to identify the targets of the DA miRNAs with TarPmiR-algorithms (http://129.206.7.150/) <ns0:ref type='bibr' target='#b19'>(Dweep and Gretz, 2015)</ns0:ref>. miRNA binding sites including CDS and 3' UTR within the complete sequences of all Bos taurus genes were investigated. Only target genes with binding P-values > 0.8 were retained for further analysis. The mature oocyte and sperm gene sets were further overlapped with the target genes set of DA miRNAs. Functional annotations of the target genes found in sperm and matured oocyte were carried out by Clusterprofiler software, respectively <ns0:ref type='bibr' target='#b73'>(Yu et al., 2012)</ns0:ref>. Genes acquired were subjected to enrichment analyses by GO and KEGG to detect the significantly enriched terms in target genes. Also, terms with an adjusted p < 0.05 by Benjamini-Hochberg (BH) multiple testing were deemed significant. <ns0:ref type='table'>PDF \| (2020:06:49991:1:1:NEW 20 Jul 2020)</ns0:ref> Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Quantitative real-time PCR (qPCR) validation of the sequencing results</ns0:head></ns0:div> <ns0:div><ns0:head>PeerJ reviewing</ns0:head><ns0:p>To verify the accuracy of high throughput sequencing results, we randomly selected and confirmed the abundance of four miRNAs <ns0:ref type='bibr'>(bta-miR-204, bta-miR-3432a, bta-miR-652, and bta-miR-378)</ns0:ref> in X and Y sperms by qPCR. Sperm RNA was produced from another three bulls semen following the aforementioned protocol. Using the miScript II RT Kit (Qiagen), total RNA of each sample was reverse-transcribed into cDNA. qPCR was carried out on an ABI 7500 Real-Time PCR system (Applied Biosystem) by using miScript SYBR Green PCR Kit (Qiagen) with a miRNA-specific forward primer. The relative abundant levels of the miRNAs were normalized to U6 and calculated by 2 (−ΔΔCt) approach. Table <ns0:ref type='table'>S11</ns0:ref> shows the primer information.</ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head></ns0:div> <ns0:div><ns0:head>Evaluation of sperm RNA quality</ns0:head><ns0:p>After sorting, we obtained around 0.9 billion sex-sorted sperm per sample for RNA extraction. Information for the sex-sorted semen samples and sperm RNA quality data are shown in Table <ns0:ref type='table'>S1</ns0:ref>. It reveals two well-known characteristics of sperm RNA (an absence of intact ribosomal RNA (rRNA) and predominance of short-length RNA molecules) <ns0:ref type='bibr' target='#b34'>(Johnson et al., 2011)</ns0:ref>. All the samples exhibited 28S/18S values of 0, indicating a lack of intact foreign RNA in sperm RNA samples. The RNA integrity number (RIN) was approximately 2.5, which was conformed to the characteristic of RNA in sperm <ns0:ref type='bibr' target='#b48'>(Mao et al., 2014;</ns0:ref><ns0:ref type='bibr' target='#b61'>Sendler et al., 2013;</ns0:ref><ns0:ref type='bibr' target='#b74'>Yuan et al., 2016)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Read counts of each RNA class</ns0:head><ns0:p>After sequencing, we obtained <ns0:ref type='bibr'>33,974,607, 30,027,769 and 30,422,125 raw reads (31,474,834 ± 2,173,828</ns0:ref>, mean ± SD) for X4069, <ns0:ref type='bibr'>X4118 and X4131 sample, respectively and 30,532,748, 29,907,341, and 29,594,201 raw reads (30,011,430 ± 477,853)</ns0:ref> for Y4069, Y4118 and Y4131 sample, respectively. The quality control results show that bases of these raw sequences are with high quality score and the raw sequence lengths are 50 nt in all samples, suggesting the good quality of sequencing data we obtained (Additional material 1). After removing low-quality reads, <ns0:ref type='bibr'>25,037,734, 26,837,611, and 24,848,674 clean reads (25,574,673 ± 1,097,814)</ns0:ref> for <ns0:ref type='bibr'>X4069, X4118 and X4131 sample, respectively, and 28,164,513, 27,819,402, and 26,946,322 clean reads (27,643,412 ± 627,875)</ns0:ref> for Y4069, Y4118 and Y4131 sample, respectively, remained. The proportion of Bos taurus miRNA (11.9% vs. 20.2%, t-test, p = 0.003786) and non-annotated sequence (68.1 % vs. 52.7%, t-test, p < 0.0001) were significantly different between X and Y sperm, while the other small noncoding RNA species (miRNAs of other species, rRNAs, tsRNAs, protein-coding RNAs, snoRNAs, miscRNAs, snRNAs, and piRNAs) presented similar proportions (Table <ns0:ref type='table'>S1</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>Identification of DA tsRNAs in X and Y sperm</ns0:head><ns0:p>PeerJ reviewing PDF \| (2020:06:49991:1:1:NEW 20 Jul 2020)</ns0:p></ns0:div> <ns0:div><ns0:head>Manuscript to be reviewed</ns0:head><ns0:p>We identified 52 tsRNAs in X and Y sperm (Table <ns0:ref type='table'>S10</ns0:ref>), The comparison of X and Y sperm revealed only 2 significantly DA tsRNAs, including tRNA-Ser-AGA and tRNA-Ser-TGA, both they have lower abundance in the X sperm (p < 0.05).</ns0:p></ns0:div> <ns0:div><ns0:head>Identification of DA miRNAs in X and Y sperm</ns0:head><ns0:p>In total, 490 known Bos taurus miRNAs were detected by Unitas, and 202 known Bos taurus miRNAs were identified by Mirdeep2, respectively. Among these miRNAs, 49 and 21 miRNAs were differentially abundant at a significant level (p < 0.05 and \|log2fold change\| > 1) (Table <ns0:ref type='table'>S2</ns0:ref>). We obtained 12 DA miRNAs that overlapped between 49 and 21 DA miRNAs identified by two different software, including 3 highly abundant miRNAs in X sperm (bta-miR-15a, bta-miR-652, and bta-miR-378) and 9 more enriched miRNAs in Y sperm (bta-miR-204, bta-miR-1271, bta-miR-211, bta-miR-375, bta-miR-3432a, bta-miR-127, bta-miR-6529a, bta-miR-369-5p, and bta-miR-196a) (Table <ns0:ref type='table'>1</ns0:ref>). Among these DA miRNAs, bta-miR-204, bta-miR-375, and bta-miR-378 were the most significantly DA miRNAs (p < 0.005). Bta-miR-204 (log2FC = -2.36, P = 0.0002) was the most abundant miRNA in both fractions and was previously identified in human, pig, and mouse epididymal sperm (Table <ns0:ref type='table'>2</ns0:ref>). The abundance of bta-miR-652 (log2FC = 2.26, P = 0.0092) that was greater in X sperm was the only DA miRNA detected on the X chromosome (Table <ns0:ref type='table'>1</ns0:ref>), whose abundance has been detected in human and mouse epididymal sperm but not in boar sperm (Table <ns0:ref type='table'>2</ns0:ref>) <ns0:ref type='bibr' target='#b49'>(Nixon et al., 2015;</ns0:ref><ns0:ref type='bibr' target='#b50'>Pantano et al., 2015)</ns0:ref>. Furthermore, the DA miRNAs (4/12) showed a higher preference for chromosome 21 (Table <ns0:ref type='table'>1</ns0:ref>).</ns0:p><ns0:p>In addition to the 12 DA miRNAs, another 118 Bos taurus miRNAs included the 10 most abundant non -DA miRNAs between X and Y sperm: bta-miR-100, bta-let-7a-5p, bta-miR-22-3p, bta-miR-151-5p, bta-miR-21-5p, bta-miR-99a-5p, bta-miR-16b, bta-miR-7, and bta-miR-27a-3p (Table <ns0:ref type='table'>S2</ns0:ref>). Interestingly, these miRNAs together accounted for 92% of the RPM values of all non-DA miRNAs, indicating that the levels of the miRNAs differed sharply. We also compared our findings with recent studies and found that bta-miR-100 is also present at high levels in porcine, bull, human, and mouse epididymal sperm miRNA profiles <ns0:ref type='bibr' target='#b7'>(Capra et al., 2017;</ns0:ref><ns0:ref type='bibr' target='#b9'>Chen et al., 2017;</ns0:ref><ns0:ref type='bibr' target='#b49'>Nixon et al., 2015;</ns0:ref><ns0:ref type='bibr' target='#b50'>Pantano et al., 2015)</ns0:ref> (Table <ns0:ref type='table'>2</ns0:ref>), suggesting that its biological functions are conserved across these species.</ns0:p></ns0:div> <ns0:div><ns0:head>Prediction of DA miRNA target genes in mature oocyte and sperm</ns0:head><ns0:p>We predicted target sites in the 3'UTRs and CDS regions, of 1,677 and 4,028 genes, respectively, for nine upregulated DA miRNAs in Y sperm, and in 510 and 1224 genes, respectively, for three upregulated DA miRNAs in X sperm (Figure <ns0:ref type='figure' target='#fig_0'>1</ns0:ref>, Table <ns0:ref type='table'>S4</ns0:ref>). We found that a greater number of target genes were predicted to be bound in CDS regions than in 3'UTRs.</ns0:p><ns0:p>Similar to the prediction results, the number of predicted target genes present in mature oocyte and sperm that were bound at CDS sites was greater than the number bound at 3'UTR sites. In the mature oocyte gene sets (Table <ns0:ref type='table'>S5</ns0:ref>), 602 and 248 genes were targeted by nine upregulated DA miRNAs at CDS and 3'UTR binding sites, respectively, in Y sperm. Three upregulated DA miRNAs in X sperm targeted 163 and 70 genes in CDS and 3'UTR binding sites, respectively. In contrast, in the sperm gene sets (Table S 4), nine upregulated DA miRNAs of Y sperm targeted 146 and 37 genes at CDS and 3'UTR binding sites, respectively, and three upregulated DA miRNAs of X sperm targeted 33 and 11 genes at CDS and 3'UTR binding sites, respectively. (Figure <ns0:ref type='figure' target='#fig_0'>1</ns0:ref>, Table <ns0:ref type='table'>S6</ns0:ref>). Taken together, the results suggested that the phenomenon of miRNA regulation of gene expression through CDS regions may be widely present in sperm and fertilized oocyte. In addition, the DA miRNAs may exhibit one or more target genes, due to interacting with different target regions <ns0:ref type='bibr' target='#b29'>(Hausser et al., 2013)</ns0:ref>. By removing these repeated target genes, we eventually obtained 887 target genes in the mature oocyte and 210 target genes in sperm (Figure <ns0:ref type='figure' target='#fig_0'>1</ns0:ref>). Among these target genes, 79% (697/887) and 82% (172/210) were bound at CDS regions in mature oocyte and sperm, respectively (Table <ns0:ref type='table'>S6</ns0:ref>). Furthermore, we found that 6.3% and 23.3% of oocyte genes were targeted by X and Y sperm upregulated DA miRNAs, respectively, on CDS sites, which was greater than 3.2% and 14.1% of sperm genes targeted by DA miRNAs on CDS sites (Figure <ns0:ref type='figure' target='#fig_0'>1</ns0:ref>). Similarly, on the 3'UTR sites, 2.7% and 9.6% of oocyte genes were targeted by X and Y sperm highly abundant miRNAs, respectively, which was higher than 1.1% and 3.6% of sperm genes targeted by DA miRNAs (Figure <ns0:ref type='figure' target='#fig_0'>1</ns0:ref>). These results suggested that DA miRNAs were more prone to target genes in matured oocyte than in sperm, which was consistent with the previous finding that miRNA targets are likely absent in sperm <ns0:ref type='bibr'>(Krawetz et al., 2011)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Functional analysis of DA miRNA targets in sperm and mature oocyte</ns0:head><ns0:p>In the functional enrichment analysis of the 210 targets in sperm, the top significantly enriched GO categories were mainly related to mRNA processing, nucleosome binding, and nucleosomal DNA binding (adjusted p < 0.05, Figure <ns0:ref type='figure' target='#fig_1'>2A</ns0:ref>, Table <ns0:ref type='table'>S7</ns0:ref>). Surprisingly, the 887 targets in mature oocyte were significantly related to 11 catabolic processes, including macromolecule catabolic processes, cellular protein catabolic processes, and organonitrogen compound catabolic processes (adjusted p < 0.05, Figure <ns0:ref type='figure' target='#fig_1'>2A</ns0:ref>, Table <ns0:ref type='table'>S7</ns0:ref>). The 17 genes (MAGOH, PSMC5, DICER1, KCTD13, TRIP12, EIF3E, PSMA5, UBE2H, PSMD11, USP1, SKP1, ARIH1, EZR, IDE, TIMP3, and TRIM13) related to catabolic processes were targeted by seven Y sperm upregulated DA miRNAs: bta-miR-127, bta-miR-1271, bta-miR-196a, bta-miR-204, bta-miR-3432a, bta-miR-375 and bta-miR-6529a (Figure <ns0:ref type='figure' target='#fig_1'>2B</ns0:ref>, Table <ns0:ref type='table'>S 7</ns0:ref>). This finding may indicate that these DA miRNAs and their targets in the oocyte are involved in a series of catabolic processes. Here, no enriched GO terms overlapped between the sperm and mature oocyte (Figure <ns0:ref type='figure' target='#fig_1'>2A</ns0:ref>).</ns0:p><ns0:p>On the other hand, in the KEGG analysis of the 210 targets in sperm, seven KEGG pathways were significantly enriched, including the cell cycle and RNA transport (adjusted p < 0.05, Table <ns0:ref type='table'>S7</ns0:ref>). In contrast, only the endometrial cancer pathway was significantly enriched by 887 targets in the mature oocyte (adjusted p < 0.05, Table <ns0:ref type='table'>S7</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>Different distributions of piRNA cluster between X and Y sperm</ns0:head><ns0:p>Here, a total of 21 and 12 unique piRNA clusters loci were identified in X and Y sperm, respectively, and 71 clusters loci were shared between two fractions, cluster XY69 that was located in the region of 25,287,459bp to 25,335,935bp on chromosome 28 was reported to be conserved deeply in Eutherian mammals (Table <ns0:ref type='table'>S8</ns0:ref>).</ns0:p><ns0:p>To best understand the potential functions of piRNAs in X and Y sperm, we searched genes and transposons falling within their unique cluster loci. 362 repeats and 14 genes were within the unique cluster region of X sperm. Of them, BovB, Bov-tA3, and Bov-A2 were the top three repeat elements detected in unique piRNA clusters of X sperm. Cluster X9 located on 14 chromosomes contained the greatest number of repeat elements (102) in all the clusters identified (Figure <ns0:ref type='figure'>3A</ns0:ref>, Table <ns0:ref type='table'>S9</ns0:ref>). 14 genes enriched in 14 GO terms (adjusted p < 0.05), including biology function of galactosyl ceramide catabolic process, and galactolipid metabolic process. On the other hand, 169 repeat elements and 7 genes are within unique cluster loci of X sperm, among them, 23 BovB was identified, which was the greatest number of repeat elements identified, followed by Bov-tA2 ( <ns0:ref type='formula'>21</ns0:ref>) and BOV-A2 (13). Cluster Y11 located on chromosome 24 contained the greatest number of 77 repeat elements (Figure <ns0:ref type='figure'>3A</ns0:ref>, Table <ns0:ref type='table'>S9</ns0:ref>). 13 GO terms were enriched by 7 genes (adjusted p < 0.05), including nucleosome assembly and histone H3-K27 trimethylation. Details of X and Y unique piRNA clusters including genes, repeats, and function of genes falling within the cluster regions are given in Figure3A and Table <ns0:ref type='table'>S9</ns0:ref>. Furthermore, the expressed piRNAs in X and Y sperm were explored, we identified 582 piRNAs. Of them, 28 piRNAs were differentially abundant (p < 0.05), 15 piRNAs had higher abundance in Y sperm and 13 piRNAs were enriched in X sperm. The most significantly enriched piRNAs in X and Y sperm are piR-5346348 (p = 1.22×10 -12 ) and piR-5342466 (p = 1.13×10 -12 ), respectively. (Figure <ns0:ref type='figure'>3B</ns0:ref>, Table <ns0:ref type='table'>S10</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>qPCR validation of DA miRNAs</ns0:head><ns0:p>To validate the high-throughput sequencing results, we randomly selected four miRNAs <ns0:ref type='bibr'>(bta-miR-204, bta-miR-3432a, bta-miR-652, and bta-miR-378)</ns0:ref> to perform the qPCR experiment. The relative fold changes of these miRNAs in qPCR were concordant with the sequencing results (Figure <ns0:ref type='figure'>4</ns0:ref>, Table <ns0:ref type='table'>S11</ns0:ref>), indicating that the miRNA identification and abundance estimation were reliable.</ns0:p></ns0:div> <ns0:div><ns0:head>Discussion</ns0:head><ns0:p>In mammals, X sperm contains more DNA than Y sperm, and these DNA differences might result in differences in small RNA abundance. In our study, the differential abundance of miRNAs, piRNAs, and tsRNAs between the two types of sperm were identified. Previous studies have revealed that adjacent sperm cells can share gene products through intercellular bridges during spermatogenesis, suggesting that sncRNA molecules may be shared between X and Y sperm cell during spermatogenesis <ns0:ref type='bibr' target='#b20'>(Fawcett et al., 1959)</ns0:ref> and retained within mature sperm, which may explain why a part of non-DA small RNAs was identified between X and Y sperm. However, these products are probably not all shared through the intercellular bridge <ns0:ref type='bibr' target='#b68'>(Ventelä et al., 2003)</ns0:ref>. Indeed, differences between X and Y sperm have been identified through several types of analyses, such as protein analysis <ns0:ref type='bibr' target='#b12'>(Chen et al., 2012;</ns0:ref><ns0:ref type='bibr' target='#b18'>De Canio et al., 2014)</ns0:ref> and transcript analysis <ns0:ref type='bibr' target='#b11'>(Chen et al., 2014)</ns0:ref>. As in previous studies <ns0:ref type='bibr' target='#b11'>(Chen et al., 2014;</ns0:ref><ns0:ref type='bibr' target='#b12'>Chen et al., 2012;</ns0:ref><ns0:ref type='bibr' target='#b18'>De Canio et al., 2014)</ns0:ref>, we used sex-sorted X and Y semen (including ~90% X(Y) sperm and ~10%Y(X) sperm ) as the sequencing sample, which may also produce fewer DA miRNAs, due to the inevitable presence of false-negative DA miRNAs in the analytical results. Finally, by employing a conservative approach to identify DA miRNAs (only the DA miRNAs annotated from both Mirdeep2 and Unitas were included in the further analysis), the accuracy of DA miRNA identification was improved, but other DA miRNAs might have been missed.</ns0:p><ns0:p>In the present study, the total numbers of DA and highly abundant non-DA miRNAs were detected from previously reported data obtained from bull sperm <ns0:ref type='bibr' target='#b7'>(Capra et al., 2017;</ns0:ref><ns0:ref type='bibr' target='#b60'>Sellem et al., 2020)</ns0:ref>. Moreover, nine highly enriched non-DA miRNAs <ns0:ref type='bibr'>(bta-miR-100, let-7a-5p,bta-miR-22-3p, bta-miR-151-5p, bta-miR-21-5p, bta-miR-99a-5p,bta-miR-16b, bta-miR-7 and bta-miR-27a-3p)</ns0:ref> and two DA miRNAs <ns0:ref type='bibr'>(bta-miR-211, bta-miR-204, and bta-miR-375)</ns0:ref> were present among the top 20 abundant miRNAs of a previous study <ns0:ref type='bibr' target='#b7'>(Capra et al., 2017;</ns0:ref><ns0:ref type='bibr' target='#b60'>Sellem et al., 2020)</ns0:ref> (Table <ns0:ref type='table'>2</ns0:ref>). Some of the differences in sperm miRNA profiles observed in the current study may be due to the use of differences in the treatment of the samples. For example, in Capra et al.'s study, the sperm were cryopreserved in straws for AI <ns0:ref type='bibr' target='#b7'>(Capra et al., 2017)</ns0:ref>, while sperm were stored on dry ice after sorting in the current study. Among the most abundant non-DA miRNAs, bta-miR-100 and bta-miR-151-5p have been shown to be associated with sperm motility in bulls <ns0:ref type='bibr' target='#b7'>(Capra et al., 2017)</ns0:ref>. bta-miR-100, which exhibited the highest abundance in our study, has been previously reported as the most abundant in bull sperm <ns0:ref type='bibr' target='#b66'>(Stowe et al., 2014)</ns0:ref> and has been identified in sperm of other species: human, pig, and mouse (Table <ns0:ref type='table'>2</ns0:ref>) <ns0:ref type='bibr' target='#b9'>(Chen et al., 2017;</ns0:ref><ns0:ref type='bibr' target='#b49'>Nixon et al., 2015;</ns0:ref><ns0:ref type='bibr' target='#b50'>Pantano et al., 2015)</ns0:ref>. Additionally, bta-miR-100 was shown to exhibit low abundance in the semen of infertile males with semen abnormalities <ns0:ref type='bibr' target='#b43'>(Liu et al., 2012a)</ns0:ref>, suggesting an important role of bta-miR-100 in regulating fertility across mammalian species.</ns0:p><ns0:p>Although sperm miRNA contents have been most extensively explored via high-throughput sequencing in mammals, the function of miRNAs in sperm itself essentially remains controversial. The main cause of this uncertainty is that mature sperm are widely thought to be translationally inactive in the cytoplasm <ns0:ref type='bibr' target='#b51'>(Park et al., 2012;</ns0:ref><ns0:ref type='bibr' target='#b54'>Rahman et al., 2013)</ns0:ref>. In a previously reported study of DA sperm proteins and mRNAs, we found that there were no DA proteins corresponding to mRNA which are differentially expressed between X and Y sperm <ns0:ref type='bibr' target='#b12'>(Chen et al., 2012;</ns0:ref><ns0:ref type='bibr' target='#b18'>De Canio et al., 2014)</ns0:ref>, which may provide indirect evidence of silencing of translation in sperm. Increasing evidence regarding miRNA-target interactions has revealed a new mode of miRNAs action through which gene translation may be regulated by miRNAs targeting CDS regions <ns0:ref type='bibr' target='#b29'>(Hausser et al., 2013;</ns0:ref><ns0:ref type='bibr' target='#b67'>Tay et al., 2008)</ns0:ref>. For instance, inhibition of translation in somatic cells was previously demonstrated to be due to miRNA binding sites located in CDS regions <ns0:ref type='bibr' target='#b29'>(Hausser et al., 2013)</ns0:ref>. Similarly, miRNAs have been shown to cotarget the 3' UTRs and CDS regions of maternally expressed mRNAs to regulate embryonic development in early zebrafish embryos <ns0:ref type='bibr' target='#b29'>(Hausser et al., 2013)</ns0:ref>. Furthermore, the application of high-throughput approaches for isolating argonaute-bound target sites indicates that CDS sites are as numerous as those located in 3′ UTRs <ns0:ref type='bibr' target='#b13'>(Chi et al., 2009;</ns0:ref><ns0:ref type='bibr' target='#b27'>Hafner et al., 2010)</ns0:ref>. In our study, we found that most sperm mRNAs (82%) and mature oocyte mRNAs (79%) were predicted to be targeted by DE miRNAs through binding to CDS regions (Figure <ns0:ref type='figure' target='#fig_0'>1</ns0:ref>). In addition, the argonaute 2 complex, which is crucial for miRNA function, was found to be bound to miRNAs in mouse sperm <ns0:ref type='bibr' target='#b44'>(Liu et al., 2012b)</ns0:ref>. Here, it is likely that DA sperm miRNAs bind to CDS regions and act as translationinhibiting factors in sperm <ns0:ref type='bibr' target='#b31'>(Hosken and Hodgson, 2014)</ns0:ref>, and mRNAs are regulated by the CDS regions, which are widespread in sperm and fertilized oocyte. Furthermore, functional analysis of DA miRNA-targeted genes in sperm showed that these genes were involved in nucleosome binding and nucleosomal DNA binding. Mature sperm retains some fraction of residual nucleosomes <ns0:ref type='bibr' target='#b4'>(Balhorn et al., 1977)</ns0:ref>. The X chromosome in X sperm was demonstrated to exhibit strong enrichment of nucleosome-binding sites, and the Y chromosome in Y sperm exhibited a strong depletion in bovine sperm <ns0:ref type='bibr' target='#b58'>(Samans et al., 2014)</ns0:ref>. Overall, the results suggest that X and Y sperm, with different sex chromosomes, may contain genes targeted by DA miRNAs that perform different functions in nucleosome binding and nucleosomal DNA binding.</ns0:p><ns0:p>The egg is the ultimate destination for sperm, along with its miRNAs. Mammalian sperm carry subsets of miRNAs into oocyte during fertilization. However, whether sperm miRNAs can play the roles after fertilization is still controversial. One argument relevant to this issue is that the levels of sperm miRNA are low relative to those of unfertilized MII (Metaphase II) oocyte, and fertilization does not alter the MII oocyte miRNA repertoire, suggesting that it plays a limited role in mammalian fertilization or early preimplantation development <ns0:ref type='bibr' target='#b1'>(Amanai et al., 2006)</ns0:ref>. However, an increasing number of studies have shown that sperm-borne miRNAs are indeed important for preimplantation embryonic development <ns0:ref type='bibr' target='#b26'>(Grandjean et al., 2015;</ns0:ref><ns0:ref type='bibr' target='#b56'>Rodgers et al., 2015;</ns0:ref><ns0:ref type='bibr' target='#b63'>Sharma et al., 2015)</ns0:ref>. Shuiqiao Yuan et al found that sperm with altered miRNAs could fertilize wild-type eggs. However, embryos derived from these partial small noncoding RNA-deficient sperm displayed a significant reduction in developmental potential, which could be rescued by injecting wild-type sperm-derived total or small RNAs into ICSI (Intracytoplasmic sperm injection) embryos, whereas maternal miRNAs were found to be dispensable for both fertilization and preimplantation development <ns0:ref type='bibr' target='#b74'>(Yuan et al., 2016)</ns0:ref>. According to recent studies, even when the content of sperm miRNAs is low, miRNAs can be involved in initiating a cascade of molecular events after fertilization, through targeted degradation of stored maternal mRNAs <ns0:ref type='bibr' target='#b56'>(Rodgers et al., 2015)</ns0:ref>. In addition, the sperm-borne miR-449b can improve the first cleavage division, involve in epigenetic reprogramming and apoptotic status of preimplantation cloned bovine embryos through regulating maternal mRNAs <ns0:ref type='bibr' target='#b70'>(Wang et al., 2017)</ns0:ref>. In the current study, DA miRNAs were more prone to target genes in matured oocyte than in sperm. Taken together, the results seem to indicate that one of the ways in which sperm miRNA perform their roles after fertilization is by regulating maternal genes. Based on this hypothesis, we carried out the functional analysis of the putative targets of DA miRNAs in the fertilized oocyte. The analysis of GO term annotations indicated that maternal mRNAs in oocyte targeted by DA miRNAs were significantly enriched in 11 catabolic processes. <ns0:ref type='bibr'>Seven DA miRNAs (bta-miR-127, bta-miR-1271, bta-miR-196a, bta-miR-204, bta-miR-3432a, bta-miR-375 and bta-miR-6529a)</ns0:ref>, along with their 17 target genes were found to be involved in catabolic processes in the mature oocyte (Figure <ns0:ref type='figure'>3B</ns0:ref>). Among these miRNAs, miR-204 and miR-375 with their related target genes have been well established to play a clear inhibitory role in catabolic processes in cancer <ns0:ref type='bibr' target='#b42'>(Lin et al., 2017;</ns0:ref><ns0:ref type='bibr' target='#b47'>Mao et al., 2016)</ns0:ref>. Indeed, spermatozoon fertilization of oocyte was previously demonstrated to trigger a selective process that recognizes and degrades paternally inherited organelles <ns0:ref type='bibr' target='#b0'>(Al Rawi et al., 2011)</ns0:ref>. Furthermore, X and Y sperm were reported to exhibit different protein profiles <ns0:ref type='bibr' target='#b12'>(Chen et al., 2012;</ns0:ref><ns0:ref type='bibr' target='#b18'>De Canio et al., 2014)</ns0:ref> and sex chromosome structures. Based on these enlightening findings, we postulated that, when an X sperm or Y sperm enters the oocyte, sperm-carried DA miRNAs probably have discriminating catabolic functions for X and Y sperm involving different components through regulating related genes. In the present study, thyroid hormone receptor interactor 12 (Trip12), a maternal gene that is putatively targeted by bta-miR-204, bta-miR-1271, bta-miR-375, and bta-miR-3432a, plays an important role in embryogenesis <ns0:ref type='bibr' target='#b36'>(Kajiro et al., 2011)</ns0:ref>. Moreover, prolonged stress in mice was demonstrated to alter the expression of nine sperm miRNAs, including miR-204 and miR-375, which can regulate maternal mRNAs, resulting in changes in offspring hypothalamic-pituitaryadrenal (HPA) axis responses to stress <ns0:ref type='bibr' target='#b55'>(Rodgers et al., 2013;</ns0:ref><ns0:ref type='bibr' target='#b56'>Rodgers et al., 2015)</ns0:ref>. Interestingly, one of their targeted maternal mRNAs identified in mouse fertilized oocyte, Serine and arginine rich splicing factor 2 (Srsf2), was predicted to be targeted by another DA miRNAs bta-miR-378 in bull mature oocyte <ns0:ref type='bibr' target='#b55'>(Rodgers et al., 2013;</ns0:ref><ns0:ref type='bibr' target='#b56'>Rodgers et al., 2015)</ns0:ref>. HPA response patterns differ markedly in males and females <ns0:ref type='bibr' target='#b28'>(Handa et al., 1994;</ns0:ref><ns0:ref type='bibr' target='#b35'>Kajantie and Phillips, 2006;</ns0:ref><ns0:ref type='bibr' target='#b38'>Kudielka and Kirschbaum, 2005;</ns0:ref><ns0:ref type='bibr' target='#b69'>Verma et al., 2011)</ns0:ref>. These finding suggest that bta-miR-204, bta-miR-375 and bta-miR-378, three most significantly DE miRNAs, carried by X and Y sperm to the fertilized oocyte may regulate maternal mRNAs to potentially influence stress reactivity in the offspring. In addition to sperm miRNA, tsRNAs from sperm could act as acquired epigenetic factors and contribute to offspring phenotypes such as metabolic traits . In current study, two tsRNAs (tRNA-Ser-AGA and tRNA-Ser-TGA) were differentially abundant between X and Y sperm. The expression of tRNA-Ser-TGA was shown to be positively correlated with cell proliferation in prostate cancer cell, which could promote the transition of these cells from the gap 2 phase of the cell cycle to the mitotic phase <ns0:ref type='bibr' target='#b41'>(Lee et al., 2009)</ns0:ref>. This finding suggest the possibility that tRNA-Ser-TGA may act in a similar manner upon delivery to the oocyte <ns0:ref type='bibr' target='#b53'>(Peng et al., 2012)</ns0:ref>. However, the studies involved in the fields of embryo development related to these two DA tsRNAs remain limited, which still require further exploration.</ns0:p><ns0:p>PiRNAs are small noncoding RNAs that can have significant implications for germ cell development and function. piRNAs are shown to be the most abundant class of small RNAs in human sperm <ns0:ref type='bibr' target='#b50'>(Pantano et al. 2015)</ns0:ref>. Sellem and colleagues found that 26% of reads were annotated by piRNAs in bull sperm <ns0:ref type='bibr' target='#b60'>(Sellem et al., 2020)</ns0:ref>. In the present study, the percentage of clean reads mapping to piRNAs database was about 6.8 % and 8.7% for X and Y sperm, respectively (Table <ns0:ref type='table'>S2</ns0:ref>). The differences in piRNA proportion observed between two studies may be mainly due to the use of different analysis strategies. The high modifications of a single nucleotide at either the 5p or 3p end were the most frequent changes <ns0:ref type='bibr' target='#b60'>(Sellem et al., 2020)</ns0:ref>. In this study, the high threshold that allowed 0 mismatch when mapping the sequences to reference piRNA sequences was used for piRNAs annotation, which would increase the credibility of the conservative piRNA annotated results but decrease the proportion of piRNAs annotated. Genome mapping of such piRNA sequences revealed that piRNAs mostly originate from distinct genome clusters, termed piRNA clusters <ns0:ref type='bibr' target='#b3'>(Aravin et al., 2007)</ns0:ref>, which are a few to hundreds of kb in length. The genomic locations of these loci are often conserved between related species such as mouse and human <ns0:ref type='bibr' target='#b2'>(Aravin et al., 2006;</ns0:ref><ns0:ref type='bibr' target='#b23'>Gan et al., 2011)</ns0:ref>, but the sequences of the piRNAs themselves have evolved rapidly differ even between closely related species such as human and chimpanzee <ns0:ref type='bibr' target='#b46'>(Lukic and Chen, 2011</ns0:ref>). In the present study, one piRNA cluster was reported to conserve deeply in eutherian mammals which are located between the CCAR1 and DDX50 genes were also identified in the present study and were named CXY69, it was conserved in X and Y sperm and also contain STOX1 gene. STOX1 transcript was antisense to the many piRNAs generated in CXY69 cluster <ns0:ref type='bibr' target='#b14'>(Chirn et al., 2015)</ns0:ref>. The different distribution of piRNA clusters, containing different genes and transposons, and abundance of piRNAs between X and Y sperm were identified in this study, suggesting these piRNAs may play different regulatory roles between them. Because of the low level of piRNAs conservation between even closely related species <ns0:ref type='bibr' target='#b25'>(Girard et al., 2006;</ns0:ref><ns0:ref type='bibr' target='#b30'>Hong et al., 2016;</ns0:ref><ns0:ref type='bibr' target='#b37'>Krawetz et al., 2011;</ns0:ref><ns0:ref type='bibr' target='#b40'>Lau et al., 2006)</ns0:ref>, and studies deciphering the functions of piRNAs were still limited, the potential role of they played in X and Y sperm remain to be further understood.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions</ns0:head><ns0:p>In conclusion, the present study revealed the sncRNA contents of X and Y sperm and highlighted the differences in the abundance and diversity of several common sncRNAs across two types of sperm. Additionally, we comprehensively discussed the roles of the DA miRNAs in sperm and fertilized oocyte, which could enhance our understanding of their potential functions involved in sex differences in sperm and early embryonic development. <ns0:ref type='bibr' target='#b7'>(Capra et al., 2017)</ns0:ref>, Bull 2 (top 20) <ns0:ref type='bibr' target='#b7'>(Capra et al., 2017)</ns0:ref>, Human <ns0:ref type='bibr'>(Pantano et al., 5 2015)</ns0:ref>, Boar <ns0:ref type='bibr' target='#b9'>(Chen et al., 2017)</ns0:ref>, and Mouse <ns0:ref type='bibr' target='#b49'>(Nixon et al., 2015)</ns0:ref>.</ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head>Figure 1 Upset</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head>Figure 2 Functional</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>(</ns0:head><ns0:label /><ns0:figDesc>A) Bar plot shows the significant (adjusted P-value < 0.05) gene ontology (GO) terms enriched by the target genes in sperm and mature oocyte. Bar height shows the enrichment scores (-log adjusted P-value) of the GO terms. Line plot depicts the number of genes that belong to each category. Blue and yellow bar show GO terms of biological process and cellular component, respectively. (B) Interactions between DA miRNAs and their mature oocyte target genes involved in catabolic processes.</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='23,42.52,255.37,525.00,318.00' type='bitmap' /></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head /><ns0:label /><ns0:figDesc>'+' and '-' refer to the miRNAs presented and absented in the datasets of DE miRNAs, non-DE miRNAs,Bull 1 4<ns0:ref type='bibr' target='#b60'>(Sellem et al.,2020)</ns0:ref> ,Bull 2</ns0:figDesc><ns0:table><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>Bull 2</ns0:cell><ns0:cell>Bull 2</ns0:cell><ns0:cell>Human</ns0:cell><ns0:cell>Boar</ns0:cell><ns0:cell>Mouse</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>(Top 20)</ns0:cell><ns0:cell>(Top 20)</ns0:cell><ns0:cell>(All)</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>miR-100</ns0:cell><ns0:cell>128833</ns0:cell><ns0:cell>99447</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>+</ns0:cell></ns0:row><ns0:row><ns0:cell>let-7a-5p</ns0:cell><ns0:cell>15372</ns0:cell><ns0:cell>13679</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>+</ns0:cell></ns0:row><ns0:row><ns0:cell>miR-22-3p</ns0:cell><ns0:cell>18459</ns0:cell><ns0:cell>9877</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>+</ns0:cell></ns0:row><ns0:row><ns0:cell>miR-151-5p</ns0:cell><ns0:cell>16214</ns0:cell><ns0:cell>11325</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>+</ns0:cell></ns0:row><ns0:row><ns0:cell>miR-21-5p</ns0:cell><ns0:cell>15621</ns0:cell><ns0:cell>5607</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>+</ns0:cell></ns0:row><ns0:row><ns0:cell>miR-449a</ns0:cell><ns0:cell>8210</ns0:cell><ns0:cell>9565</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>+</ns0:cell></ns0:row><ns0:row><ns0:cell>miR-99a-5p</ns0:cell><ns0:cell>9363</ns0:cell><ns0:cell>6399</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>+</ns0:cell></ns0:row><ns0:row><ns0:cell>miR-16b</ns0:cell><ns0:cell>9903</ns0:cell><ns0:cell>3619</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell></ns0:row><ns0:row><ns0:cell>miR-7</ns0:cell><ns0:cell>4595</ns0:cell><ns0:cell>6283</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>-</ns0:cell></ns0:row><ns0:row><ns0:cell>miR-27a-3p</ns0:cell><ns0:cell>7530</ns0:cell><ns0:cell>2925</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>+</ns0:cell></ns0:row><ns0:row><ns0:cell>miR-127</ns0:cell><ns0:cell>49</ns0:cell><ns0:cell>78</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>+</ns0:cell></ns0:row><ns0:row><ns0:cell>miR-1271</ns0:cell><ns0:cell>30</ns0:cell><ns0:cell>72</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell></ns0:row><ns0:row><ns0:cell>miR-15a</ns0:cell><ns0:cell>162</ns0:cell><ns0:cell>33</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>+</ns0:cell></ns0:row><ns0:row><ns0:cell>miR-196a</ns0:cell><ns0:cell>130</ns0:cell><ns0:cell>205</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>+</ns0:cell></ns0:row><ns0:row><ns0:cell>miR-204</ns0:cell><ns0:cell>44384</ns0:cell><ns0:cell>159903</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>+</ns0:cell></ns0:row><ns0:row><ns0:cell>miR-211</ns0:cell><ns0:cell>67</ns0:cell><ns0:cell>153</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell></ns0:row><ns0:row><ns0:cell>miR-3432a</ns0:cell><ns0:cell>2042</ns0:cell><ns0:cell>3114</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell></ns0:row><ns0:row><ns0:cell>miR-369-5p</ns0:cell><ns0:cell>117</ns0:cell><ns0:cell>189</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell></ns0:row><ns0:row><ns0:cell>miR-375</ns0:cell><ns0:cell>147</ns0:cell><ns0:cell>329</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>+</ns0:cell></ns0:row><ns0:row><ns0:cell>miR-378</ns0:cell><ns0:cell>51</ns0:cell><ns0:cell>2</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>+</ns0:cell></ns0:row><ns0:row><ns0:cell>miR-652</ns0:cell><ns0:cell>295</ns0:cell><ns0:cell>28</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>+</ns0:cell></ns0:row><ns0:row><ns0:cell>miR-6529a</ns0:cell><ns0:cell>1415</ns0:cell><ns0:cell>2697</ns0:cell><ns0:cell>+</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>-</ns0:cell></ns0:row><ns0:row><ns0:cell>3</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row></ns0:table></ns0:figure> <ns0:note place='foot'>PeerJ reviewing PDF \| (2020:06:49991:1:1:NEW 20 Jul 2020)</ns0:note> <ns0:note place='foot'>PeerJ reviewing PDF \| (2020:06:49991:1:1:NEW 20 Jul 2020)Manuscript to be reviewed</ns0:note> <ns0:note place='foot' n='6'>PeerJ reviewing PDF \| (2020:06:49991:1:1:NEW 20 Jul 2020)Manuscript to be reviewed</ns0:note> </ns0:body> "	"Dear Dr. Korakot Nganvongpanit: We are resubmitting the revised manuscript, 'Differences in small noncoding RNAs profile between bull X and Y sperm', for your consideration to publish in PeerJ. Thank you very much for sending us these valuable comments of three experts in the field on our manuscript. These comments have helped us to greatly improve the quality of our paper. We have thoroughly revised our manuscript according to the three reviewers’ helpful comments and suggestions. We sincerely hope that the revisions can meet your requirements and make the revised manuscript more suitable to be published in PeerJ. Editor comments (Korakot Nganvongpanit) Thank you very much for your excellent work. This manuscript has been much improved from the previous version. However, I am writing to let you know that your manuscript has been reviewed by three experts in the field and we request that you make major revisions before it is processed further. Please find your manuscript and the review reports at the following link. I am waiting for your revision version. Response: Thank you very much for your kind efforts for improving this article. We have thoroughly revised our manuscript based on the comments. In addition, the re-used text of our recently published paper (https://www.frontiersin.org/articles/10.3389/fgene.2020.00419/full) in the manuscript was paraphrased and marked in blue (Line 74-78, Line 80-84, Line 126-157, Line 176-187, and Line 193-206). The comments and critiques of the reviewers have been addressed as listed below: Reviewer 1 (Anonymous) Basic reporting 1. The authors have consistently used the term “abundance” in place of “expression” to describe the presence of sperm-borne sncRNA in their data. Similarly, the use of “differential accumulation” instead of “differentially expressed” throughout the revised manuscript is corrected. Response: We thank the reviewer for the previous valuable advice. 2. To verify the enrichment level of X and Y bearing spermatozoa in the current study, the authors have added the statements in line 112-119 of the revised manuscript to show the detailed method of sort reanalysis and thus provided with reference. The use of aforementioned method is certainly recommended, and the study design allows it. The results of resorting procedure are quite acceptable, as shown in Table S1. Using this approach, the replication of information could be achieved by another researcher. Response: We thank the reviewer for the previous suggestions that greatly improve the quality of our manuscript. 3. The RNA integrity number (RIN) of 2.4-2.6 (in the current study) is quite controversial whether they are already degraded or not. Most RNA-seq experiments rely on the RIN above 5 to be considered as high quality RNAs. From Georgiadis et al (2015), they considered that the mature sperm samples have RIN of 2.7 are poor quality for downstream application. It will be better if the authors could provide some pictures of FastQC for raw sequence data. Ref:http://dx.doi.org/10.1016/j.juro.2014.07.107 Response: Thank you for your kind suggestion. The paper reviewer suggested is instructive. We added two parts of results (Per Base Sequence Quality and Distribution of Sequence Lengths) of FastQC for the raw sequencing data in the new manuscript (Additional material 1). The results show that the base calls mostly fall into the green area and the lengths of raw sequences are 50nt in all samples, suggesting the good quality of raw sequencing data we obtained. The method and results of FastQC were added in Line 134-136 and Line 222-224, respectively. Validity of the findings 1.The real-time PCR experiments (qPCRs) to validate the findings of abundantly presence of miRNAs were conducted and the results are included in the revised version of the paper. The methods (Quantitative real-time PCR (qPCR) validation of the sequencing results) and results are shown in line 210-219 and line 343-348, respectively. Response: Thank you very much for your previous suggestions. Comments for the Author The typo errors of previous version of manuscript have already been solved. However, a few typo and punctuation errors, such as (HOSSAIN et al. 2001; Penfold et al. 1998) of line 50-51; using the sort reanalysis method (Welch & Johnson 1999). of line 111 are found in this version. Response: Thank you for your careful reading. We revised the citations in Line 50 and Line 113. We have rechecked and revised this kind of issues throughout the manuscript. Reviewer 2 (Anonymous) Basic reporting Zhou et al described the difference of sncRNA between X and Y sperms of the bull which may play an important role in the regulation of mature mRNA in fertilized oocytes and subsequent embryogenesis. The general content was well organized with professional English. The figure and table here were suitable and sufficient. I find some mistakes of citation in the text, for example, in line 50, (HOSSAIN et al. 2001) should be edited in the proper format. Please carefully check it throughout MS. Response: Thank you for your careful reading. We revised the citation in Line 50. We have rechecked and revised this kind of issues throughout the manuscript. Experimental design The detailed background and methodology gave were sufficient to understand the main objective. I have some suggestions as below; (i) The information of bull used in this study should be given in necessary detailed such as age or breed because these factors may influence the DA. Response: We thank you for your suggestion. The semen samples were collected from three Holstein bulls at 3 years of age. They were born on 16-6-2014, 23-6-2014, and 27-6-2014, respectively. These bulls were fed the same diet daily and reared in the same conditions and environments. The information of these bulls was added in Line 105-107 and Table S1. (ii) The author employed NGS with the BGISEQ-500 platform for sequencing; however, the author should provide the reference of the platforms used. Also, RNA integrity must be fall in the high-quality criterion for doing NGS. The author should provide the information on RNA integrity obtained from this investigation in MS. Please specify where these data were detailed in Table S1? Response: Thank you for your suggestions. The reference (Fehlmann et al. 2016) about BGISEQ-500 platform was added in Line 130. The RNA integrity number (RIN) was specified in Line 215-216 (Table S1, Sheet 1). To our knowledge, most of the sperm studies showed the low RNA Integrity Number (RIN) values measured for sperm RNA (RIN <4) (Mao et al. 2014; Sendler et al. 2013), because the 28S to 18S rRNA in sperm are highly fragments (RIN value was mainly affected by the degree of 28S to 18S rRNA degradation)( Schroeder et al. 2006). The previous study of human sperm reported that the RNA integrity number (RIN) of 2-4 could indicate good sperm RNA quality (Yuan et al. 2016). In our study, the RNA integrity number (RIN) was approximately 2.5, which was consistent with previous studies (Mao et al., 2014; Sendler et al., 2013; Yuan et al., 2016). Indeed, the small RNA sequencing focus on the RNA in length arranged from 18 to 30 nt, the low RIN values may have little impact on the sequencing results. We added two parts of results (Per Base Sequence Quality and Distribution of Sequence Lengths) of FastQC for raw sequencing data in the new manuscript (Figure S1). The results show that base calls mostly fall into the green area and the raw sequence lengths are 50nt in all samples, suggesting the good quality of raw sequencing data we obtained. 1. Mao S, Sendler E, Goodrich RJ, Hauser R, and Krawetz SA. 2014. A comparison of sperm RNA-seq methods. Systems biology in reproductive medicine 60:308-315. 2. Sendler E, Johnson GD, Mao S, Goodrich RJ, Diamond MP, Hauser R, and Krawetz SA. 2013. Stability, delivery and functions of human sperm RNAs at fertilization. Nucleic Acids Research 41:4104-4117. 3. Fehlmann T, Reinheimer S, Geng CY, Su XS, Drmanac S, Alexeev A, Zhang CY, Backes C, Ludwig N, Hart M, An D, Zhu ZZ, Xu CJ, Chen A, Ni M, Liu J, Li YX, Poulter M, Li YP, Stahler C, Drmanac R, Xu X, Meese E, and Keller A. 2016. cPAS-based sequencing on the BGISEQ-500 to explore small non-coding RNAs. Clinical Epigenetics 8:11. 10.1186/s13148-016-0287-1 4. Schroeder A, Mueller O, Stocker S, et al. The RIN: an RNA integrity number for assigning integrity values to RNA measurements. BMC Mol Biol. 2006;7:3. 5. Yuan S, Schuster A, Tang C, et al. Sperm-borne miRNAs and endo-siRNAs are important for fertilization and preimplantation embryonic development. Development. 2016;143(4):635-647. (iii) the author did validation of sequencing result by qPCR for four miRNAs including bta-miR-204, bta-miR-3432a, bta-miR-652, and bta-miR-378. The author gave the information of primers for these miRNAs but I note the primers of each miRNA have only an oligonucleotide. Should they have forward and reverse sense? please verify them. Response: Thank you for your careful review. In this study, qPCR was carried out using miScript SYBR Green PCR Kit (Qiagen). This kit includes the universal primer which allows the detection of miRNAs in combination with a miRNA-specific primer. However, the universal primer sequence is not disclosed. So only the forward primers were listed in Table S11. Validity of the findings The finding of this study was well-written with a logic style. I have some suggestions a bit in the results section. I find the author provides few discussion points in the result context, for example, in line 270-271, “The RNA integrity number (RIN) was approximately 2.5, which was consistent with previous studies (Mao et al. 2014; Sendler et al. 2013).” and in line 286-288 “A considerable portion of the sperm small noncoding RNA could not be annotated in existing public databases, which was consistent with a previous study (Krawetz et al. 2011).”. These statements should be moved in the discussion parts. Besides, the author found the difference in tsRNA between X and Y sperm, resulting in tRNA-Ser-AGA and tRNA-Ser-TGA. In the discussion, why this point has not been mentioned? Response: We totally agree with the reviewer. Because these two sentences are not the focuses of discussion in this study. In the new manuscript, the sentence “The RNA integrity number (RIN) was approximately 2.5, which was consistent with previous studies” was revised to “The RNA integrity number (RIN) was approximately 2.5, which was conformed to the characteristic of RNA in sperm”. Also, after careful consideration, we think that the expression of the sentence “A considerable portion of the sperm small noncoding RNA could not be annotated in existing public databases, which was consistent with a previous study” may be inappropriate, so we removed it in the manuscript. In addition, we added the discussion related to tsRNAs in Line 462-471, as followed: “In addition to sperm miRNA, tsRNAs from sperm could act as acquired epigenetic factors and contribute to offspring phenotypes such as metabolic traits (Chen et al., 2016). In the current study, two tsRNAs (tRNA-Ser-AGA and tRNA-Ser-TGA) were differentially abundant between X and Y sperm. The expression of tRNA-Ser-TGA was shown to be positively correlated with cell proliferation in the prostate cancer cell, which could promote the transition of these cells from the gap 2 phase of the cell cycle to the mitotic phase (Lee et al., 2009). This finding suggests the possibility that tRNA-Ser-TGA may act in a similar manner upon delivery to the oocyte (Peng et al., 2012). However, the studies involved in the fields of embryo development related to these two DA tsRNAs remain limited, which still require further exploration” Comments for the Author (i) The scientific name must be written in an italic style, please check in the supplemental table. Response: Thank you for your kind suggestions. We corrected these issues in the supplemental table. (ii) The abbreviation should be written with full name in the first place such as ICSI and MII in the discussion. Response: Thank you for your kind suggestions. We correct these issues in the new manuscript. Reviewer 3 (Anonymous) Basic reporting The role of sperm borne sncRNAs are more and more studied as key elements for embryo development, in many species. In bulls, more results have to be produced to paint a clear overview of the content/variation/involvement of these sncRNAs in different biological pathways. Establishing a comparison of sncRNAs content of X and Y sperm cells, represent an interesting way to increase our knowledge around these elements. Some minor modifications have to be brought to your manuscripts, but to my point of view also some important lacks have to be filled in your study (as the used of adjusted or corrected p values instead p values). The number of significant miRNAs will change and potentially the interpretation of results. Experimental design Line 103 : The number/origin of sample are not clear. You talked about 3 bulls, but how many samples? 6 (X and Y fractions for each bulls) or 2 (X and Y fractions polled) ? Response: Thank you for pointing this problem out. We sequenced six samples including three X sperm samples and three Y sperm samples. We clarified the sample information in Line 105, Line 110-111, and Line 126. Line 124 : “In brief, the samples were thawed on ice and added to TRIzol…..” Do you started from frozen semen? It was not mentioned on the previous paragraph (semen collection). How many sperm cells have been used for the total RNA extraction? According to several works published recently in bovine (example: Sellem et al., 2020), the lysis step of bull sperm cell is quite difficult, and Trizol alone is not enough to lyze properly the cell. Have you met some difficulties with your approach? Have you some pictures of the sperm cell after the treatment? Response: In this study, we started from fresh sorted semen. In order to the extraction of sperm RNA from fresh sperm, after sorting, we removed the supernatants of sperm samples and treated the sperm pellets with TRIzol via incubation for 5 min at RT. After that, the samples were stored on dry ice for next day use. Then, the samples were thawed on ice and added TRIzol again. We apologize for the unclear description. The description was added in Line 120-121. We totally agree with the reviewer. Sperm have very low RNA content and resist lysis by detergents. In our experiment, we referred to the previously published protocol (Das et al. 2010) that used to extract the RNA from stallion sperm with TRIzol method. The issue we met is that the RNA quantity obtained is quite low using TRIzol method. In order to obtain enough RNA quantity to sequence, we increased the number of sperm cells to 8-10 billion sperm. The detailed information of sperm number was shown in Table S1. Unfortunately, we didn’t take the pictures of the sperm cell after the treatment. In addition, thank you for suggesting the paper (Sellem et al., 2020). In the future study, we will try the optimized guanidinium-Trizol total RNA extraction protocol mentioned in this paper to extract the sperm RNA. Also, the lysed sperm cell will be checked after the treatment. 1. Das PJ, Paria N, Gustafson-Seabury A, Vishnoi M, Chaki SP, Love CC, Varner DD, Chowdhary BP, and Raudsepp T. 2010. Total RNA isolation from stallion sperm and testis biopsies. Theriogenology 74:1099-1106. e1092. 2. Sellem E, Marthey S, Rau A, Jouneau L, Bonnet A, Perrier J-P, Fritz S, Le Danvic C, Boussaha M, Kiefer H, Jammes H, and Schibler L. 2020a. A comprehensive overview of bull sperm-borne small non-coding RNAs and their diversity across breeds. Epigenetics & chromatin 13:19. 10.1186/s13072-020-00340-0 Line 162 : “…putative known mature miRNAs…” You have to add the word “and” between putative and known. Response: Corrected. Line 183: “…By applying thresholds of a P-value < 0.05….”: you have qualified miRNAs as “differentially expressed” based on the FC and the p-value. Due to the statistic hazards on important number of tests, you will be able to detect a false significant tests (p-value < 5%)… that why it’s important to use the adjusted p-values and not just the p-values. Please to re-analyse your data under this new approach. Response: We totally agree with the reviewer that we can obtain a more credible list of DA miRNAs by applying the adjusted p-values threshold. In this study, the results of the adjusted p-value calculated by Benjamini method were shown in Table S3. Because there were only a few DA miRNAs with the adjusted p-value < 0.05 or 0.1, we worried that some important DA miRNAs may be missed using the threshold of adjusted p-value. Thus, to validate the DA miRNAs, we further performed the qPCR experiment for four DA miRNAs. The relative fold changes of these miRNAs in qPCR were concordant with the sequencing results (Figure 4, Table S11), indicating that the miRNA identification and abundance estimation were reliable. Line 187: “Functional analysis of DA miRNAs”. May I ask you to change the word “analysis” by “annotation”? The work done here was only in silico, while the expression “functional analysis” tent to thing about real lab work. Response: We agree with the reviewer. The “analysis” was corrected to “annotation” in Line 175. Validity of the findings Line 227: “…indicating a lack of foreign RNA in sperm RNA samples…” Indicating a lack of intact foreign RNA…. Response: Thank you for pointing this out. The sentence was corrected in Line 214. Lines 343 to 348: May I ask you to put this paragraph before the one about the identification of DA miRNA? May I ask you to be less “positive” about the concordance of results between the two technologies? The results for the miR-204 and 652 are not so equal…and they represent the half of your tests. Response: Thank you for your suggestions. This paragraph was put before the paragraph related to the identification of DA miRNAs in Line 235-238. We agree with the reviewer’s opinion about the concordance of results between the two technologies. In this study, after overlapping the miRNAs annotated from two software, we only chose the results annotated by Unitas as the final miRNA results. As shown in Table 1, the Log2FC, p-value, and RPM value are calculated from the results annotated by Unitas. We clarified the footnote under Table 1: The Log2FC, P-value, and RPM values are calculated from the results annotated by Unitas (Table S2). Line 242: “…A considerable portion of the sperm small noncoding RNA could not be annotated in existing public databases, which was consistent with a previous study (Krawetz et al. 2011)…” May I ask you more details? “A considerable portion” means how many in your case? The Krawetz et al., work was published in 2011…The reference database (miRbase) has been updated. May I ask you to compare your results with another publication (example: Sellem et al., 2020)? Response: Thank you for recommending us such great paper. We learned that 13% unmapped reads, 15% outmapped reads, and 6% unknown sequences (Non annotation) found in this study. However, in our study, 68.1% of sequences in X sperm and 52.7% of sequences in Y sperm could not be annotated (Table S1, Sheet 2). Except for the reason mentioned, we think that there are still two main reasons to explain the different results obtained between the two studies. The first one is that these two studies used different analysis strategies. We calculated the percentage of reads mapping to each sncRNA species based on the annotated output of Unitas. The high percentage (more than 50%) of non-annotated sequences were also found in our recently published paper (Shangguan et al., 2020). Because we didn’t remove the sequences either unmapped or outmapped to bull genome before annotation, it may cause the unmapped and outmapped sequences were identified as non-annotated sequences. The other reason may be that we used a high threshold (allowed 0 mismatch when mapping the sequences to reference ncRNA sequences) for small RNA annotation. After careful consideration, we think that the expression of this sentence may be inappropriate, so we removed it in the manuscript. In addition, we compared our miRNAs results with Sellem et al study, the results were shown in Table 2. The relevant discussion was added in Line 368-370. 1. Shangguan A, Zhou H, Sun W, et al. Cryopreservation Induces Alterations of miRNA and mRNA Fragment Profiles of Bull Sperm. Front Genet. 2020;11:419. doi:10.3389/fgene.2020.00419 Lines 245 to 247: “…In total, 490 and 202 known Bos taurus miRNAs were detected by Unitas and Mirdeep2, respectively… » It’s not clear….May I ask you to rephrase these 2 sentences? Response: We agree with the reviewer. The sentence was revised in Line 240-241, as followed, “In total, 490 known Bos taurus miRNAs were detected by Unitas and 202 known Bos taurus miRNAs were identified by Mirdeep2.” Lines 247 to 259: Once again, the use of “p value” is not recommended due to the high number of tests…You need to use the “adjusted p value” (whatever the correction applied). Response: We thank you for this valuable suggestion again. We totally agree with the reviewer that we can obtain a more credible list of DA miRNAs by applying the adjusted p-values threshold. In this study, the results of the adjusted p-value calculated by Benjamini method were shown in Table S3. Because there were only a few DA miRNAs with the adjusted p-value < 0.05 or 0.1, we worried that some important DA miRNAs may be missed using the threshold of the adjusted p-value. To validate the DA miRNAs, we further performed the qPCR experiment for four DA miRNAs. The relative fold changes of these miRNAs in qPCR were concordant with the sequencing results (Figure 4, Table S11), indicating that the miRNA identification and abundance estimation were reliable. Lines 297 to 313: The number of targets could be very important per miRNA. Do you have applied of filter to choose the most relevant ones? Response: Yes, we do. We used a relatively high threshold to filter the target of DA miRNAs. Only targets of DA miRNAs with binding P-values > 0.8 were collected for further analysis. (This binding p-values means binding probability, which is calculated from a random-forest-based approach with TarPmiR for miRNA target site prediction). Also, after filtering, the 1,036 sperm genes and 2,584 oocyte genes (Table S5) were overlapped with the DA miRNA target genes, respectively. These overlapped genes were considered as the most relevant target genes expressed in sperm or the oocyte. Discussion Lines 353 to 357 : “…Previous studies have revealed that adjacent sperm cells can share gene products through intercellular bridges during spermatogenesis, suggesting that miRNA molecules may be shared between X and Y sperm cell during spermatogenesis (Fawcett et al. 1959), which may explain why a part (118) of non-DE small RNAs were identified between X and Y sperm…” This supposition is too hazardous. Several other explanations could be involved...May I ask you to change your sentences or to bring more details? Response: Thank you for your suggestion. We clarified this sentence as “Previous studies have revealed that adjacent sperm cells can share gene products through intercellular bridges during spermatogenesis, suggesting that sncRNA molecules may be shared between X and Y sperm cell during spermatogenesis (Fawcett et al., 1959) and retained within mature sperm, which may explain why a part of non-DA small RNAs was identified between X and Y sperm.” in Line 350-355. Lines 368 to 383: New studies have been published between 2017 and 2020, and especially Sellem et al.,2020 in bull semen. May I ask you to compare also your results to theirs? Response: Thank you for your suggestion. We compared our miRNAs results with Sellem et al study, the results were shown in Table 2. Also, the relevant discussion was added in Line 368-370. Line 413 to 450: We would expect more discussion about the differences between X and Y sperm cells. You have highlighted several interesting genes with your KEEG analysis. So please may I ask you to bring more information about their potential role in embryo development (in a X and Y context)? Response: Thank you for your suggestions. We added more discussion related to the potential roles of DA miRNAs in Line 451-462, as follows: “Moreover, prolonged stress in mice was demonstrated to alter the expression of nine sperm miRNAs, including miR-204 and miR-375, which can regulate maternal mRNAs, resulting in changes in offspring hypothalamic–pituitary–adrenal (HPA) axis responses to stress (Rodgers et al., 2013; Rodgers et al., 2015). Interestingly, one of their targeted maternal mRNAs identified in mouse fertilized oocyte, Serine, and arginine rich splicing factor 2 (Srsf2), was predicted to be targeted by another DA miRNAs bta-miR-378 in the bull mature oocyte (Rodgers et al., 2013; Rodgers et al., 2015). HPA response patterns differ markedly in males and females (Handa et al., 1994; Kajantie and Phillips, 2006; Kudielka and Kirschbaum, 2005; Verma et al., 2011). These findings suggest that bta-miR-204, bta-miR-375, and bta-miR-378, three most significantly DE miRNAs, carried by X and Y sperm to the fertilized oocyte may regulate maternal mRNAs to potentially influence stress reactivity in the offsprings. However, whether they are authentically influenced by sperm DA miRNAs will require further exploration.” Lines 451 to 467: The number of piRNAs identified in your studies is quite different from those obtained in another ones. May I ask you to add some comparisons with freshly published studies? What about the tsRNAs? May I ask you to add a paragraph in the discussion part? Response: Thank you for your careful review. In our study, we found that most of piRNAs are with a very low value of per million reads (RPM) which are consistent with the previous study (Sellem et al, 2020). Also, most of the piRNAs expressed unevenly across the samples. So piRNAs with an RPM < 10 that were annotated in less than three samples were filtered in this study. After that, we identified 582 piRNAs. Also, we compared the percentage of piRNAs annotated with freshly published study (Sellem et al., 2020) and added relevant discussion in Line 473-483, as follows:” piRNAs are shown to be the most abundant class of small RNAs in human sperm (Pantano et al. 2015). Sellem and colleagues found that 26% of reads were annotated by piRNAs in bull sperm. In the present study, the percentage of clean reads mapping to piRNAs database was about 6.8 % and 8.7% for X and Y sperm, respectively (Table S2). The differences in piRNA proportion observed between the two studies may be mainly due to the use of different analysis strategies. The high modifications of a single nucleotide at either the 5p or 3p end were the most frequent changes (Sellem et al., 2020). In this study, the high threshold that allowed 0 mismatch when mapping the sequences to reference piRNA sequences was used for piRNAs annotation, which would increase the credibility of the conservative piRNA annotated results, but decrease the proportion of piRNAs annotated.” In addition, the discussion related to tsRNAs was added in Line 462-471, as followed: “In addition to sperm miRNA, tsRNAs from sperm could act as acquired epigenetic factors and contribute to offspring phenotypes such as metabolic traits (Chen et al., 2016). In the current study, two tsRNAs (tRNA-Ser-AGA and tRNA-Ser-TGA) were differentially abundant between X and Y sperm. The expression of tRNA-Ser-TGA was shown to be positively correlated with cell proliferation in the prostate cancer cell, which could promote the transition of these cells from the gap 2 phase of the cell cycle to the mitotic phase (Lee et al., 2009). This finding suggests the possibility that tRNA-Ser-TGA may act in a similar manner upon delivery to the oocyte (Peng et al., 2012). However, the studies involved in the fields of embryo development related to these two DA tsRNAs remain limited, which still require further exploration.” 1. Sellem E, Marthey S, Rau A, Jouneau L, Bonnet A, Perrier J-P, Fritz S, Le Danvic C, Boussaha M, Kiefer H, Jammes H, and Schibler L. 2020a. A comprehensive overview of bull sperm-borne small non-coding RNAs and their diversity across breeds. Epigenetics & chromatin 13:19. 10.1186/s13072-020-00340-0 Conclusions You mainly discussed about miRNAs…It’s just a suggestion but maybe you should change the term “sncRNA” into “miRNAs”. Response: Thank you for your kind suggestion. In the new version of the manuscript, we added the discussion about piRNAs and tsRNAs. After careful consideration, we retain the “sncRNA” term in the manuscript. Thank you very much for your attention and consideration. Yours sincerely, Shujun Zhang, PhD College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China E-mail: sjxiaozhang@mail.hzau.edu.cn "	Here is a paper. Please give your review comments after reading it.
500	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Public interest in ecological landscaping and gardening is fueling a robust market for native plants. Most plants available to consumers through the horticulture trade are cultivated forms that have been selected for modified flowers or foliage, compactness, or other ornamental characteristics. Depending on their traits, some native plant cultivars seem to support pollinators, specialist insect folivores, and insect-based vertebrate food webs as effectively as native plant species, whereas others do not. There is particular need for information on whether native cultivars can be as effective as true or 'wild-type' native species for supporting specialist native insects of conservation concern. Herein we compared the suitability of native milkweed species and their cultivars for attracting and supporting one such insect, the iconic monarch butterfly (Danaus plexippus L.), as well as native bees in urban pollinator gardens. Wild-type Asclepias incarnata L. (swamp milkweed) and Asclepias tuberosa L. (butterfly milkweed) and three additional cultivars of each that vary in stature, floral display, and foliage color were grown in a replicated common garden experiment at a public arboretum. We monitored the plants for colonization by wild monarchs, assessed their suitability for supporting monarch larvae in greenhouse trials, measured their defensive characteristics (leaf trichome density, latex, and cardenolide levels), and compared the proportionate abundance and diversity of bee families and genera visiting their blooms. Significantly more monarch eggs and larvae were found on A. incarnata than A. tuberosa in both years, but within each milkweed group, cultivars were colonized to the same extent as wild types. Despite some differences in defense allocation, all cultivars were as suitable as wild-type milkweeds in supporting monarch larval growth. Five bee families and 17 genera were represented amongst the 2436 total bees sampled from blooms of wild-type milkweeds and their cultivars in the replicated gardens. Bee assemblages of A. incarnata were dominated by Apidae (Bombus, Xylocopa spp., and Apis mellifera), whereas A. tuberosa attracted relatively more</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>Burgeoning interest in ecological landscaping to support pollinators, birds, and other urban wildlife is fueling an enthusiastic and active plant movement <ns0:ref type='bibr'>(Kendle & Rose, 2000;</ns0:ref><ns0:ref type='bibr' target='#b77'>Tallamy, 2008;</ns0:ref><ns0:ref type='bibr' target='#b38'>Jones, 2019;</ns0:ref><ns0:ref type='bibr'>USFS, 2020;</ns0:ref><ns0:ref type='bibr'>USFWS, 2020)</ns0:ref> and a robust market for native plant species in the nursery, landscape, and gardening trades <ns0:ref type='bibr' target='#b31'>(Hanson, 2017;</ns0:ref><ns0:ref type='bibr'>ASLA, 2018;</ns0:ref><ns0:ref type='bibr' target='#b17'>Curry, 2018)</ns0:ref>. Native plants can be defined as those that share an evolutionary history with regional insects and other organisms, whereas non-native or exotic plants evolved someplace other than where they have been introduced <ns0:ref type='bibr' target='#b85'>(Wilde et al., 2015)</ns0:ref>. A compelling ecological argument for prioritizing the locally native flora over otherwise desirable (e.g., non-invasive) exotic species is its greater capacity to support local biodiversity, particularly of co-adapted native insect herbivores that are critical food for higher-order consumers including the many species of terrestrial birds that rear their young partly or wholly on insects <ns0:ref type='bibr' target='#b78'>(Tallamy & Shropshire, 2009;</ns0:ref><ns0:ref type='bibr' target='#b12'>Burghardt, Tallamy & Shriver, 2009;</ns0:ref><ns0:ref type='bibr' target='#b59'>Narango, Tallamy & Marra, 2018)</ns0:ref>. Native plants also support numerous species of pollen-specialist native bees <ns0:ref type='bibr' target='#b24'>(Fowler, 2016)</ns0:ref>.</ns0:p><ns0:p>Besides promoting plants of local provenance, the horticultural industry has introduced many native plant cultivars, natural variants of native species that are deliberately collected, selected, cross-bred, or hybridized for desirable traits; e.g., disease resistance, plant stature, leaf color, floral display, or extended bloom period, that can be maintained through propagation <ns0:ref type='bibr' target='#b85'>(Wilde et al., 2015)</ns0:ref>. Although use of cultivars is generally discouraged in ecological restoration projects <ns0:ref type='bibr' target='#b46'>(Lesica & Allendorf, 1999;</ns0:ref><ns0:ref type='bibr' target='#b42'>Kettenring et al., 2014)</ns0:ref>, they are attractive to consumers seeking novel plants that combine the attributes of natives and ornamentals, and open the door to new introductions and vast market potential <ns0:ref type='bibr' target='#b31'>(Hanson, 2017;</ns0:ref><ns0:ref type='bibr' target='#b17'>Curry, 2018)</ns0:ref>. Indeed, a survey of nurseries in the Mid-Atlantic region, probably representative of the industry overall, found that only 23% of native plant taxa being marketed are true or 'wild type', the rest being available only as cultivated forms <ns0:ref type='bibr' target='#b15'>(Coombs & Gilchrist, 2017)</ns0:ref>.</ns0:p><ns0:p>Native plant cultivars are not without controversy, however, even for managed landscapes and gardens. Some environmental organizations decry them, arguing that their mass-marketing and use will diminish the genetic diversity of flora in urban ecosystems that are already degraded by preponderance of exotic ornamental plants, further reducing their capacity to adapt to change, support wildlife, or provide other ecosystem services <ns0:ref type='bibr' target='#b88'>(Wild Ones, 2013)</ns0:ref>. Cultivar traits that could potentially affect pollinator visitation include conversion of anthers and pistils to petals ('double flowered'), color, size, and shape of flowers, floral density, and possibly plant stature <ns0:ref type='bibr' target='#b14'>(Comba et al., 1999;</ns0:ref><ns0:ref type='bibr' target='#b16'>Corbet et al., 2001;</ns0:ref><ns0:ref type='bibr' target='#b71'>Ricker, Lubell & Brand, 2019)</ns0:ref>. While some floral traits that humans may find attractive in native cultivars, e.g., double flowers or an unusual color, may decrease the quantity, quality, and accessibility of nectar and pollen, making those plants unattractive or of little value to pollinators <ns0:ref type='bibr' target='#b14'>(Comba et al., 1999;</ns0:ref><ns0:ref type='bibr' target='#b25'>Garbuzov, Alton & Ratnieks, 2017;</ns0:ref><ns0:ref type='bibr' target='#b48'>Mach & Potter, 2018)</ns0:ref>, other native plant cultivars, and many non-natives, do provide highquality nectar and pollen and can be equally or more attractive to pollinators as native plant species <ns0:ref type='bibr' target='#b56'>(Masierowska 2006</ns0:ref><ns0:ref type='bibr' target='#b73'>, Salisbury et al., 2015;</ns0:ref><ns0:ref type='bibr' target='#b84'>White, 2016;</ns0:ref><ns0:ref type='bibr' target='#b48'>Mach & Potter, 2018;</ns0:ref><ns0:ref type='bibr' target='#b71'>Ricker, Lubell & Brand, 2019)</ns0:ref>. Thus, the value of native cultivars for pollinators must be evaluated on a case-by-case basis <ns0:ref type='bibr' target='#b71'>(Ricker, Lubell & Brand, 2019)</ns0:ref>.</ns0:p><ns0:p>Compared to studies focused on pollinators, little work has addressed the question of whether native plant cultivars are the ecological equivalent to their parent species in supporting native insect folivores. Breeding for traits that change a plant's form, foliage color, floral display, or phytochemistry could alter cues used by specialist insects in host recognition or acceptance, perhaps to the extent that the insect no longer recognizes or accepts the cultivar as food <ns0:ref type='bibr' target='#b5'>(Baisden et al., 2018)</ns0:ref>. Alternatively, because there may be tradeoffs in plants' allocation of resources to defense or growth, selection for traits such as enhanced floral display may make cultivars more palatable to herbivores by reducing their investment in defenses <ns0:ref type='bibr' target='#b35'>(Herms & Mattson, 1992)</ns0:ref>. Limited research to date suggests the extent to which that may happen depends on the herbivore in question and the particular characteristics of the cultivar that distinguish it from the parent species <ns0:ref type='bibr' target='#b85'>(Wilde, Gandhi & Colson, 2015)</ns0:ref>. Some cultivar traits, e.g., leaf variegation or leaves altered from green to red or purple, seem to change host suitability for some insects, whereas selection for other traits seems to make little difference insofar as host use by particular herbivores or biodiversity of folivorous insects supported by those plants <ns0:ref type='bibr' target='#b79'>(Tencazar & Krischik, 2007;</ns0:ref><ns0:ref type='bibr' target='#b5'>Baisden et al., 2018;</ns0:ref><ns0:ref type='bibr' target='#b66'>Poythress & Affolter, 2018)</ns0:ref>. There is particular need for information on whether cultivars of native plants can be as effective as their parental species for supporting specialist native folivores of conservation concern.</ns0:p><ns0:p>The monarch butterfly (Danaus plexippus L.) is arguably the most well-known and beloved native North American insect <ns0:ref type='bibr' target='#b29'>(Gustafsson et al., 2015)</ns0:ref>. Every fall, hundreds of millions of monarch butterflies make their long-distance journey south from the United States and Canada to overwintering sites in Mexico and California. Both the eastern and western monarch populations declining <ns0:ref type='bibr' target='#b11'>(Brower et al., 2012;</ns0:ref><ns0:ref type='bibr' target='#b52'>Malcolm, 2018;</ns0:ref><ns0:ref type='bibr'>Rendón-Salinas, Pelton et al. 2019</ns0:ref>) fueling concern that it may face extirpation unless habitat conservation are enacted across North America.. Planting milkweeds (Asclepias spp.), the monarch's obligate larval host plants, is a key part of the international conservation strategy to return this iconic butterfly to sustainable status <ns0:ref type='bibr' target='#b80'>(Thogmartin et al., 2017;</ns0:ref><ns0:ref type='bibr'>Monarch Joint Venture, 2020;</ns0:ref><ns0:ref type='bibr'>US Fish and Wildlife Service, 2020)</ns0:ref>. Restoring sufficient milkweed to ensure a stable monarch population will likely require contributions from all land use sectors including urban and suburban areas <ns0:ref type='bibr' target='#b80'>(Thogmartin et al., 2017;</ns0:ref><ns0:ref type='bibr' target='#b37'>Johnston et al., 2019)</ns0:ref>. In cities and towns, many initiatives are underway, with myriad gardens being planted in residential, educational,and recreational properties <ns0:ref type='bibr' target='#b64'>(Phillips, 2019;</ns0:ref><ns0:ref type='bibr' target='#b58'>MonarchWatch, 2020;</ns0:ref><ns0:ref type='bibr'>National Pollinator Garden Network, 2020)</ns0:ref>. Milkweed flowers produce abundant nectar and, in addition to monarchs, are highly attractive to bees and numerous other native insects including butterflies, moths, skippers, beetles, and flies. <ns0:ref type='bibr' target='#b72'>(Robertson, 1891;</ns0:ref><ns0:ref type='bibr' target='#b49'>Macior, 1965;</ns0:ref><ns0:ref type='bibr' target='#b10'>Borders & Lee-Mäder, 2015;</ns0:ref><ns0:ref type='bibr' target='#b3'>Baker & Potter, 2018</ns0:ref>) so urban butterfly gardens can also play a role in supporting their biodiversity. Conservation gardens also provide urban citizens with the opportunity to reconnect with the natural world, helping to foster a greater awareness of conservation issues <ns0:ref type='bibr' target='#b27'>(Goddard, Dougill & Benton, 2010;</ns0:ref><ns0:ref type='bibr' target='#b44'>Lepczyk et al., 2017;</ns0:ref><ns0:ref type='bibr' target='#b7'>Bellamy et al., 2017)</ns0:ref>.</ns0:p><ns0:p>Native plant cultivars, including milkweeds selected for novel floral display, longer blooming duration, compact growth form, and other consumer-attractive traits, are increasingly available in the wholesale nursery trade and at local garden centers <ns0:ref type='bibr' target='#b6'>(Baumle, 2018)</ns0:ref> so it is important to determine if such plants have equivalent value as native species if used for ecological gardening. Different species of milkweeds present a spectrum of palatability across the monarch's host range <ns0:ref type='bibr' target='#b21'>(Erickson, 1973;</ns0:ref><ns0:ref type='bibr' target='#b76'>Schroeder, 1976;</ns0:ref><ns0:ref type='bibr' target='#b3'>Baker & Potter, 2018)</ns0:ref>. Milkweed cultivars within a single parental species group may offer a similar spectrum. In this study, we used the highprofile system of milkweeds, monarch butterflies, and bees to test the hypothesis that commercial cultivars provide equivalent ecological benefits as wild-type milkweeds in the context of small urban gardens.</ns0:p></ns0:div> <ns0:div><ns0:head>Materials & Methods</ns0:head><ns0:p>Garden study site Six replicated gardens <ns0:ref type='bibr'>(1.22</ns0:ref> x 9.75 m) were established in public areas of the Arboretum State Botanical Garden of Kentucky, Lexington, in May 2018. The surrounding landscape consisted of restored prairie, formal gardens, and trees. Patches of open, low-maintenance grassland were sprayed with glyphosate to kill existing vegetation, tilled, and covered with weed barrier cloth. Each garden was subdivided into eight randomized 1.22 × 1.22 m plots, one for each of eight milkweed types which included Asclepias incarnata L. (swamp milkweed) and Asclepias tuberosa L. (butterfly milkweed) grown from seedlings produced from commercial openpollinated seed production fields and hereafter called 'wild type' for convenience, and three additional cultivars of each species including A. incarnata 'Cinderella', 'Ice Ballet', and 'Soulmate', and A. tuberosa 'Blonde Bombshell', 'Gay butterflies' and 'Hello Yellow', produced via controlled pollination or tissue culture (Fig. <ns0:ref type='figure'>1</ns0:ref>, Table <ns0:ref type='table'>S1</ns0:ref>). The milkweeds were purchased from various producers (American Meadows, Shelburne, VT; Centerton Nurseries, Bridgeton, NJ; Prairie Moon, Winona, MN) as bare root 2-year old plants which were started in our greenhouse. Four plants of a single type (16-30 cm height, depending on species and cultivar) were transplanted 0.6 m apart within each plot. Each garden was then covered with dark brown hardwood mulch (5 cm depth). Plants were watered twice per week for the first three weeks to aid establishment and during a period of drought in 2019. We replaced a few of the less-vigorous milkweeds with healthier greenhouse-grown transplants in May 2019 at the start of the second growing season.</ns0:p></ns0:div> <ns0:div><ns0:head>Monarch colonization of wild-type milkweeds and cultivars in gardens</ns0:head><ns0:p>Milkweeds in each garden were monitored for monarch eggs and larvae twice monthly from June to September 2018 and May to August 2019. At each visit all plants were inspected by turning over all leaves, and also examining all stems and flowering portions of the plant. All observations took place between 1000 to 1400 h, on clear warm days. Eggs and larvae were left in place after counting.</ns0:p></ns0:div> <ns0:div><ns0:head>Physical and defensive characteristics of wild-type milkweeds and cultivars</ns0:head><ns0:p>Bloom period was assessed in the field for each milkweed type. Plant height and canopy width were measured after bloom when plants had reached maturity. Six leaves (2 each from the upper, middle, and lower thirds of the plant canopy, per milkweed type) were collected from each garden in July 2018, frozen at -80°C, and lyophilized. Cardenolide analysis followed methods of <ns0:ref type='bibr' target='#b87'>Wiegrebe & Wichtl (1993)</ns0:ref> and <ns0:ref type='bibr' target='#b53'>Malcolm & Zalucki, (1996)</ns0:ref>.</ns0:p><ns0:p>Trichome densities and latex exudation were compared among milkweeds by methods of <ns0:ref type='bibr' target='#b0'>Agrawal & Fishbein (2006)</ns0:ref>. Four upper canopy leaves from each replicate (24 total per plant type) were collected in June 2019, leaf discs (28 mm 2 ) were cut about 2 cm from their tips, and trichomes on adaxial and abaxial surfaces were counted under a binocular microscope. Latex exudation was sampled in the field by cutting the tips (0.5 cm) off intact leaves (24 total per plant type), collecting the exuding latex into pre-weighed tubes with a filter paper wick, and weighing the samples on a microbalance.</ns0:p></ns0:div> <ns0:div><ns0:head>Monarch larval performance on wild-type milkweeds and cultivars</ns0:head><ns0:p>Growth and survival of monarch larvae was tested in the greenhouse in July 2019. This trial included two year-old rootstock of the same milkweed species and cultivars in the gardens except for A. tuberosa 'Blonde Bombshell' which was excluded because of poor regeneration and market unavailability. All plants were grown in 5.6 liter pots, using a soil and bark mix (SunGro, Quincy, MI), and were 30-60 cm tall. Temperature was regulated between 20-27°C and no artificial light was used. Cohorts of newly-molted second instars from our greenhouse colony were placed on plants (one per plant; 10 replicates each) and confined by placing a white finemesh bag (25 x 40 cm) over each plant. Larvae were initially within 12 h of molting, and blocked by slight variation in initial size when allocated to replicates. Potential positional bias was minimized by rotating the position of the plants on the greenhouse bench within each replicate once per day. Larvae were left in place for 7d and then evaluated for amount of weight gained and larval instar level attained.</ns0:p></ns0:div> <ns0:div><ns0:head>Bee assemblages of wild-type milkweeds and cultivars</ns0:head><ns0:p>We collected samples of 50 or more bees from blooms of each milkweed type in at least four and in most cases all six of the replicated gardens. Because of sparse blooming of certain milkweed types (mainly A. tuberosa straight species and 'Hello Yellow') in one or two of the plots, it was not possible to collect a full sample from every garden. Bees were collected by knocking them into plastic containers containing 70% EtOH, or sometimes caught with aerial nets held over an Manuscript to be reviewed umbel so that bees would fly up into the net, or by gently sweeping the blooms without damaging the plant. We collected the first 50 bees encountered on a given milkweed species per replicate which required multiple visits to each garden during peak bloom. At each visit, we placed eight bee collection containers (one for each milkweed type) in each garden, and then worked our way through all replicates, starting at a different garden on each visit, collecting bees systematically throughout. Bee samples were washed with water and dish soap, rinsed, then dried using a fan-powered dryer for 30-60 min and pinned. Specimens were identified to genus <ns0:ref type='bibr' target='#b62'>(Packer, Genaro & Sheffield, 2007)</ns0:ref>, with honey bees and bumble bees taken to species <ns0:ref type='bibr' target='#b86'>(Williams et al., 2014)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Data analyses</ns0:head><ns0:p>We used separate two-way analyses of variance (ANOVA) for a randomized complete block design to compare numbers of monarch eggs and larvae in gardens, larval performance, and plant characteristics between all milkweed types, and within milkweed species. Two-tailed Dunnett's tests were used when the F-statistic was significant to test for differences among individual cultivars and their parental milkweed species.</ns0:p><ns0:p>Bee genus richness and diversity (Simpson Index of Diversity 1-D; Magurran 2004) were similarly compared. Statistical analyses were performed with Statistix 10 (Analytical Software 2013). Chi-square analyses were used to compare proportionate representation of bee families in samples from the wild type and cultivars within each milkweed species. Data are reported as means ± standard error (SE).</ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head></ns0:div> <ns0:div><ns0:head>Monarch colonization of wild-type milkweeds and cultivars in gardens</ns0:head><ns0:p>Each of the six gardens attracted monarchs, with eggs and larvae found throughout the 2018 and 2019 growing seasons (238 and 207 total individuals, respectively). Monarch immature life stages were first found in the gardens in May, peaking in August and persisting into September. Significantly more eggs and larvae were found on A. incarnata than A. tuberosa in 2018 (F 7,47 = 5.25, P < 0.001) and 2019 (F 6,41 = 6.29, P < 0.001) but within species, there were no differences in extent of colonization of the wild types versus their cultivars in either year (Table <ns0:ref type='table'>1</ns0:ref>). The A. tuberosa cultivar 'Blonde Bombshell' was excluded in 2019 due to poor regeneration of the inground plants and market unavailability for replacements. <ns0:ref type='table'>2</ns0:ref>). There was no overall significant difference in latex expression between the two milkweed species, but A. tuberosa, as a group, had relatively more trichomes and higher cardenolide concentrations (Table <ns0:ref type='table'>2</ns0:ref>). Within the A. incarnata group, 'Cinderella' had significantly higher latex expression than the PeerJ reviewing PDF \| (2020:06:49796:1:1:NEW 27 Jul 2020)</ns0:p></ns0:div> <ns0:div><ns0:head>Defensive and physical characteristics of wild-type milkweeds and cultivars Expression of defensive characteristics differed among milkweed types (Table</ns0:head><ns0:p>Manuscript to be reviewed wild types, and 'Ice Ballet' had the highest number of trichomes and highest cardenolide concentrations. Within the A. tuberosa group 'Gay Butterflies' and 'Hello Yellow' had significantly higher latex expression than the wild type.</ns0:p><ns0:p>Asclepias incarnata, as expected, were taller than A. tuberosa (Table <ns0:ref type='table'>S2</ns0:ref>). Plant stature was similar within the A. incarnata group except for cultivar 'Soulmate' which had a wider canopy than the wild type. Within A. tuberosa, 'Gay Butterflies' and 'Hello Yellow' were taller and wider than the wild type. All of the milkweeds bloomed in June and July (Table <ns0:ref type='table'>S2</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>Larval performance on of wild-type milkweeds and cultivars</ns0:head><ns0:p>Monarch larvae grew and developed on all milkweeds tested (Table <ns0:ref type='table'>1</ns0:ref>). Growth and development were faster overall on A. tuberosa than on A. incarnata, but within groups was similar on wild types and their respective cultivars.</ns0:p></ns0:div> <ns0:div><ns0:head>Bee assemblages of garden milkweeds</ns0:head><ns0:p>Five families and 17 genera were represented amongst the total of 2436 bees sampled from milkweed blooms in the replicated garden plots (Table <ns0:ref type='table'>3</ns0:ref>). Within the A. incarnata group, bee genus diversity was similar (F 3,15 = 1.74, P = 0.2) but genus richness was greater for 'Soulmate' than for the wild type (F 3,15 = 4.14, P = 0.03; Table <ns0:ref type='table'>3</ns0:ref>). Bee genus diversity was similar within the A. incarnata group (F 3,15 = 1.74, P = 0.2, Table <ns0:ref type='table'>3</ns0:ref>). Bee assemblages of A. incarnata were dominated by Apid bees (Fig. <ns0:ref type='figure' target='#fig_3'>2</ns0:ref>), particularly bumble bees (Bombus spp.), carpenter bees (Xylocopa spp.), and honey bees (Apis mellifera). Representation of particular families and genera was similar among the four types except for 'Soulmate' which attracted proportionately few Bombus spp. compared to the wild type (χ 2 = 29.5, P < 0.001).</ns0:p><ns0:p>Asclepias tuberosa attracted a somewhat more even distribution of bee families and genera, with proportionately more Halictidae and Megachilidae compared to the A. incarnata group, and each cultivar attracting diverse bee genera in varying proportions (Table <ns0:ref type='table'>3</ns0:ref>, Fig. <ns0:ref type='figure' target='#fig_4'>3</ns0:ref>). Although A. tuberosa 'Blonde Bombshell' attracted bees from 11 different genera, most (71%) of them were Halictidae, genus Lasioglossum, accounting for that cultivar having lower genus diversity than the wild type (F 3,15 = 5.82, P = 0.007). There was significant variation in bee genus richness within the A. tuberosa group (F 3,15 = 6.31, P < 0.01) but none of the cultivars had higher or lower richness than did the wild type (Table <ns0:ref type='table'>3</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>Discussion</ns0:head><ns0:p>A major challenge to scaling up the use of native species in landscaping and gardening is providing plants that are both ecologically functional and profitable for the horticulture industry PeerJ reviewing PDF \| (2020:06:49796:1:1:NEW 27 Jul 2020) <ns0:ref type='bibr'>(Wilde, Ganghi & Colson, 2015)</ns0:ref>. Native plants are mainly introduced into urban ecosystems through a market system that satisfies consumer preferences for ornamental traits. Consequently, many native plant species have been selected or bred for extended flowering, novel color, size, or morphology of flowers or foliage, compactness, or other aesthetic characteristics, with frequent new cultivar introductions <ns0:ref type='bibr'>(Wilde, Ganghi & Colson, 2015)</ns0:ref>. Depending on their traits, some native plant cultivars seem to support specific folivorous insects, or insect-based food webs, as effectively as native plant species, whereas others do not (e.g., <ns0:ref type='bibr' target='#b79'>Tencazar & Krischik, 2007;</ns0:ref><ns0:ref type='bibr' target='#b5'>Baisden et al., 2018;</ns0:ref><ns0:ref type='bibr'>Poythress & Affolter, 2019;</ns0:ref><ns0:ref type='bibr' target='#b71'>Ricker, Lubell & Brand, 2019)</ns0:ref>. There is particular need for information on whether or not cultivars can support native insects of conservation concern.</ns0:p><ns0:p>Among such insects, none approaches the power of the monarch butterfly as a catalyst for public interest in ecological gardening <ns0:ref type='bibr' target='#b29'>(Gustafsson et al., 2015)</ns0:ref>. Our results suggest that, at least in urban pollinator gardens, cultivars of A. incarnata and A. tuberosa, two of the most widely-sold garden-friendly native milkweeds <ns0:ref type='bibr' target='#b3'>(Baker & Potter, 2018)</ns0:ref>, are as suitable as their respective parental species for attracting and supporting monarch butterflies. Over two growing seasons, we found similar numbers of naturally-occurring eggs and larvae on cultivars and straight species within each group. Despite some differences in plant defensive characteristics (trichomes, latex, and cardenolides), larval growth, development, and survival were similar on milkweeds within each group. Monarch larvae are capable of dealing with a range of milkweed defenses <ns0:ref type='bibr' target='#b19'>(Dussourd & Eisner, 1987;</ns0:ref><ns0:ref type='bibr' target='#b0'>Agrawal & Fishbein, 2006)</ns0:ref>. It is not unexpected, therefore, that cultivation at least within A.incarnata and A. tuberosa does not result in changes in defense that are too severe for monarch larvae to overcome. Shared evolutionary history with plants has led to widespread host specificity in phytophagous insects <ns0:ref type='bibr' target='#b9'>(Bernays & Graham, 1988)</ns0:ref>. Many Lepidoptera have narrow host ranges, often restricted to a single genus <ns0:ref type='bibr'>(Dyer et.al., 2007)</ns0:ref>, so a plant breeder selecting for modified plant phenotypes could potentially alter the cues such insect specialists rely upon to recognize their hosts. Butterflies, in general, use a combination of visual, olfactory, and gustatory cues to find and accept host plants <ns0:ref type='bibr' target='#b70'>(Renwick & Chew, 1994)</ns0:ref>. Monarchs move extensively between habitat patches, but the relative distances over which they use vision or olfaction to locate milkweeds or nectar sources is uncertain <ns0:ref type='bibr' target='#b94'>(Zalucki, Parry & Zalucki, 2016)</ns0:ref>.</ns0:p><ns0:p>Monarch females foraging in natural habitat tend to lay more eggs on taller, more isolated milkweed plants than on shorter, less accessible ones <ns0:ref type='bibr' target='#b92'>(Zalucki & Kitching, 1982;</ns0:ref><ns0:ref type='bibr' target='#b94'>Zalucki, Parry & Zalucki, 2016)</ns0:ref>, and the same patterns occur in butterfly gardens <ns0:ref type='bibr' target='#b3'>(Baker & Potter, 2018;</ns0:ref><ns0:ref type='bibr'>2019)</ns0:ref>. The relatively short stature of all cultivars of A. tuberosa (Table <ns0:ref type='table'>S2</ns0:ref>) compared to A. incarnata may account, in part, for why we found fewer eggs and larvae on the former species in both years despite them both being suitable as larval food <ns0:ref type='bibr'>(Erikson, 1973)</ns0:ref>. Shorter milkweeds may go unnoticed by the butterflies because they are less visually apparent and accessible than taller milkweeds, especially when surrounded by non-host plants <ns0:ref type='bibr' target='#b4'>(Baker & Potter, 2019)</ns0:ref>. Some other butterfly species form a visual search image for host plants with a particular leaf shape that facilitates host-finding in the field <ns0:ref type='bibr' target='#b8'>(Benson, Brown & Gilbert, 1975;</ns0:ref><ns0:ref type='bibr' target='#b68'>Rausher, 1978;</ns0:ref><ns0:ref type='bibr' target='#b18'>Dell'Aglio, Lasada & Jiggins, 2016</ns0:ref>), but it is not known if monarchs do this. The estimated 100 milkweed species native to North America vary in leaf size and shape <ns0:ref type='bibr' target='#b89'>(Woodson, 1954)</ns0:ref>, and several studies suggest that those with narrow leaves (e.g., A. verticillata) are less preferred for oviposition <ns0:ref type='bibr' target='#b3'>(Baker & Potter 2018</ns0:ref><ns0:ref type='bibr' target='#b65'>, Pocius et al., 2018)</ns0:ref>. All native cultivars used in our study had leaves seemingly similar to their parental species, but if plant breeders were to select for cultivars having modified leaf shape, color, or variegation, such changes could potentially affect monarchs' visual perception of them as hosts.</ns0:p><ns0:p>Native bee populations are declining <ns0:ref type='bibr' target='#b13'>(Cameron et al., 2011;</ns0:ref><ns0:ref type='bibr' target='#b43'>Koh et al., 2016)</ns0:ref> and millions of urban pollinator gardens are being planted to help their plight <ns0:ref type='bibr' target='#b64'>(Phillips, 2019)</ns0:ref>. Milkweed flowers produce abundant nectar <ns0:ref type='bibr' target='#b90'>(Wyatt & Broyles, 1994)</ns0:ref>, and are highly attractive to bees and other nectar-feeding insects <ns0:ref type='bibr'>(Fishbein & Venable, 1996;</ns0:ref><ns0:ref type='bibr' target='#b50'>MacIvor et al., 2017</ns0:ref><ns0:ref type='bibr' target='#b3'>Baker & Potter, 2018)</ns0:ref>. Because milkweed pollen is enclosed within pollinia, nectar is the only reward that milkweeds offer their pollinators <ns0:ref type='bibr' target='#b90'>(Wyatt & Broyles, 1994)</ns0:ref>. Large bees and wasps are the most effective milkweed pollinators, whereas most of the smaller visitors are unable to transfer pollina and do not provide pollination services to milkweed <ns0:ref type='bibr' target='#b41'>(Kephart, 1983;</ns0:ref><ns0:ref type='bibr' target='#b36'>Ivey et al., 2003;</ns0:ref><ns0:ref type='bibr'>Maclvor et al., 2017)</ns0:ref>.</ns0:p><ns0:p>In the present study, large-bodied, eusocial Apidae dominated the bee assemblages of A. incarnata whereas A. tuberosa attracted proportionately more Halictidae, Megachilidae, and other relatively small native bees. Both patterns are consistent with an earlier study in which only wild-type milkweeds were compared <ns0:ref type='bibr' target='#b3'>(Baker & Potter, 2018)</ns0:ref>. Large apid bees have high energy demands <ns0:ref type='bibr' target='#b33'>(Heinrich, 1976)</ns0:ref>, so may favor milkweeds such as A. incarnata having large flowers and abundant nectar rewards, whereas the relatively smaller flowers of A. tuberosa may provide a sufficient nectar reward for relatively smaller native bees <ns0:ref type='bibr' target='#b3'>(Baker and Potter, 2018)</ns0:ref>. Unlike garden plants wherein cultivar selection has reduced or eliminated floral rewards for pollinators <ns0:ref type='bibr' target='#b25'>(Garbuzov, Alton & Ratnieks, 2017;</ns0:ref><ns0:ref type='bibr' target='#b22'>Erickson et al., 2019)</ns0:ref>, all of the native milkweed cultivars we evaluated were bee-attractive. Moreover, with the possible exception of A. tuberosa 'Blonde Bombshell' which attracted an inordinately high number of Lasioglossum sp., bee assemblages of the milkweed cultivars were generally similar to those of their respective parental species.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions</ns0:head><ns0:p>Restoration ecologists, conservation groups, and U.S. federal and state agencies are promoting increased use of native plants in landscaping and gardening to help support biodiversity in urbanized areas. A major challenge to that goal is availability of native plants that satisfy Manuscript to be reviewed requirements for ecological function, cost-effective production, and desirable ornamental characteristics with consumer appeal. Breeding, marketing, and use of native plant cultivars is widespread and growing in the horticulture industry. This study suggests that, at least in small gardens, native milkweed cultivars can be as suitable as their parental species for attracting and supporting monarch butterflies and native bees. Although probably not appropriate for use in natural areas where maintaining a reservoir of genetic variability is important for plant population resilience, use of native milkweed cultivars in pollinator gardens can help support the urban public's contribution to monarch and native bee conservation. For urban gardens, planting several species of native milkweeds, regardless of whether they are wild types or native cultivars, plus a variety other plants to provide nectar and pollen throughout the growing season, is probably the best strategy for helping to support monarchs, bees, and other pollinators. </ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ reviewing PDF \| (2020:06:49796:1:1:NEW 27 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ reviewing PDF \| (2020:06:49796:1:1:NEW 27 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>Figure legends Figure 1 .</ns0:head><ns0:label>legends1</ns0:label><ns0:figDesc>Figure legends Figure 1. Native milkweed straight species and cultivars as they appeared in the field in 2019. Left column, Asclepias incarnata: A. wild type, B. 'Cinderella', C. 'Ice Ballet', D. 'Soulmate'.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head>Figure 2 .</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2. Relative proportions of bee families (A-D) and genera (E-H) collected from A. incarnata wild type and cultivars.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head>Figure 3 .</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Figure 3. Relative proportions of bee families (A-D) and genera (E-H) collected from A. tuberosa wild type and cultivars.</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='24,42.52,199.12,525.00,354.00' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='25,42.52,199.12,525.00,375.00' type='bitmap' /></ns0:figure> <ns0:note place='foot'>PeerJ reviewing PDF \| (2020:06:49796:1:1:NEW 27 Jul 2020)</ns0:note> </ns0:body> "	"Dear Dr. Colla, Thank you for your prompt, efficient handling of our manuscript and for your recommendation of 'minor revisions'. Thanks, too, to both reviewers for their helpful comments and suggestions. We have addressed all of their questions and incorporated nearly all of their suggestions in the revision. The reviewers' comments are copied below. Under each comment, we indicate in bold what changes were incorporated into the revised paper and indicate in brackets, e.g. [pg. xx], where they appear in the revision. Additional explanation is given in italics, if needed. Thank you for your important service as Editor. We trust that our revision is satisfactory and look forward to your decision - Adam & Dan Reviewer 1 (Anonymous) Basic reporting • Happy to see this work. Cultivars, especially nativars, are a big question in the field of pollinator conservation and pollinator-friendly gardening. There has been little work to date on it. • Raw data was not shared for field and greenhouse counts of larvae, eggs, instar development, or for bee visitors to flowers, so analyses could not be checked; the authors do say in the paper they will share it with others upon request We are archiving the raw data at UKnowledge, the University of Kentucky’s open-access data repository https://uknowledge.uky.edu/entomology data. This information has been added to the Data Availability Statement near the end of the paper [lines 431-433] • Line 403/reference section: Access dates are commonly provided with website references but are not in this paper Access dates were added to all appropriate citations • Figures look pixelated (photos, text, and diagrams); if it’s not due to the proof creation itself, you may want to reconsider quality High quality images will be uploaded with final submission • Consider the appearance and contrast of the colours of your figures – perhaps add hatching or similar to distinguish; even in colour Megachile & Hylaeus, for instance, look similar, and if anyone were to view these in black & white, the colours could be even harder to distinguish We acknowledge that (in Figs 2 & 3) some of the colors of genera comprising very small proportions of the total may be difficult to distinguish from one another. With 17 genera, many of them represented by very small wedges in the genus-level pie charts, the same would be true even if the wedges were hatched. We did not intend for the pie charts to be used for that purpose. Rather, our intent for the upper rows of pie charts (Figs 2 & 3) is to visually convey the general similarity of the family-level bee assemblages visiting the wild types and their cultivars, which is easy to see because only five families are represented. The lower series of pie charts are intended to visually convey dominance of apid bees (Bombus, Apis, and Xylocopa) visiting the A. incarnata milkweeds, and the relatively greater proportion of non-apid bees associated with A. tuberosa. The breakdown of bee families and genera in samples from each milkweed species and cultivar is given in Table 3, so a reader interested in the exact percentages can easily calculate them. • Table 3: include information in the title as to what these numbers are; counts of each species of bee over all sampling times and treatment blocks combined? Similarly, explain what diversity index you are using here too. We modified the title to improve clarity, and added a footnote indicating the diversity index we used (Simpson Index of Diversity 1-D) • Table S1: it is currently hard to tell what text belongs to which species/cultivar; if the format for PeerJ does not allow for horizontal lines, add more space in between the lines (this may be corrected in the final type-set paper but should be checked before publication) Table S1 has been reformatted to improve clarity Experimental design o The paper does describe original research that is relevant and meaningful. I do have some comments and questions, primarily for clarification, although some additional analyses would be welcome. o Line 135, 148, etc.: why, in addition to monarchs, are you focusing on bees and not other taxa? Discuss. We focused the study on monarch butterflies and bees because those are the taxa that most gardeners who plant milkweeds are interested in supporting. Bees were by far the most abundant pollinators on our milkweeds. Quantifying visitation/usage by beetles, flies, milkweed bugs, aphids, etc. was beyond the scope of the study. We feel our focus on monarch and bees is adequately justified in the introduction. o Line 156: grassland (i.e. tall grasses, flowers, etc.) or turf? What is in the surrounding landscape? Information about the garden sites and surrounding landscape was added [lines 161-162] o Line 163/Table S1: you indicate that the cultivar ‘Gay Butterflies’ can have three different bloom colours; are these different on a single plant, or within a flower? What did the plants in your experiment look like? The colour could affect your results vs that of others in the future/what gardeners would find. We added a footnote to table S1 regarding bloom color of ‘Gay Butterflies’. A photo of that cultivar in Figure 1 depicts how those plants appeared in our gardens. It shows 2 bloom colors on the same plant, but not within the same umbel. We did not record the proportions of colors for individual plants. o Table S1: Clarify that controlled vs open pollination means, and where this data came from if not from your observations We added footnotes to Table S1 to clarify those points o Line 166: started in your own greenhouse after purchase and prior to planting, vs the vendor’s greenhouse? We clarified that the plants were started in our greenhouse [lines 173] o Line 171: presumably all observations occurred one the same day for all plots? Yes, this was already stated [line 181; i.e. “At each visit all plants were inspected...”] o Line 172: what times of the day did you do the investigations? Did you consider weather conditions? To clarify, we added: “All observations took place between 1000 to 1400 h, on clear warm days.” [lines 183-184] o Line 172ish: did you do any observations on adult monarchs in the area (counts & observations of behaviours)? No, we did not record any observations of adult monarchs in the area or their behaviors. That was not an objective of this study. • Line 174/Table 1: why are you combining the number of eggs and larvae together? I would be interested in seeing the differences between these numbers. E.g. are more eggs laid but don’t survive on one species/nativar than another? We combined eggs and larvae as a metric for colonization. The aim of the study was to assess colonization on wild-type milkweeds compared to their cultivars. It would be difficult to infer mortality rates without tracking stage specific mortality, and given the size and number of plants (8 milkweed types x 4 plants of each type per garden x 6 gardens), doing so would have been prohibitively consuming and well beyond the scope of this study. o Line 198: repetition/duplication of the word adaxial Sorry for that typo. We've corrected the second mention [line 208] to abaxial o Line 199: why cut the tips off vs e.g. the edges of the leaf? Caterpillars do not solely feed on tips. While is true that monarch caterpillars feed on various parts of milkweed leaves, to standardize our measurements we followed protocols published by experts on milkweed defense (e.g., Agrawal & Fishbein 2006). Leaf discs were cut 2 cm from tips, which on A. incarnata and A. tuberosa is likely to be about a third of the way from leaf tip to base. o Line 209: Where did the instars come from for your greenhouse experiments? How did you confirm that they were all the same age (i.e. is newly molted recognizable and thus all the instars were within a few hours of each other vs a day/days)? We've added more information about where the larvae came from (our greenhouse colony), how their initial age and size was standardized, and how they were confined on the intact plants [lines 219-222] o Line 211: did you change the position of where you put the instars on the plants or you changed the plant locations? If you changed the plants, how were they arranged so that you could move them vertically (i.e. on shelving?)? The wording was modified slightly to clarify that plants (not larvae) were rotated daily to minimize any potential positional bias on the greenhouse bench [lines 222-224]. o Line 213: suggest adding in ‘stage’ or ‘level’ for instar attained Done. Wording is changed to 'instar level attained' [line 225] o Line 216: did you sample the first 50 bees seen then to avoid collection bias? Did you change the order of approach to the species in the beds? Information was added to clarify both of these points [lines 231-239] o Line 226: you do not discuss chi-square analyses here but do present the results later, for bee diversity We've added a statement that Chi-square analysis was used to compare proportionate representation of different bee taxa in samples from different milkweeds to the Data analyses section [line 253-254] Validity of the findings • Line 249/Table 2: you don’t present the Dunnet’s results for the stats, just stated they are significant We added 'P < 0.05' to the Table 2 footnote to clarify meaning of the asterisks denoting significant Dunnett's tests for preplanned comparisons between cultivars versus wild type. Given that the Table also gives the overall ANOVA results for each comparison, presenting an individual Dunnett's test statistic for every comparison seems overkill, just as it would be a less conservative mean separation test such as LSD been used. • Line 257/Table S2: you don’t present the ANOVA results for the between or within group comparisons ANOVA F and P values were added to the footnote of Table S2 • Line 270: why don’t you compare visitors between the two groups of milkweed? i.e. number of visitors, species, etc? are there significant differences between cultivars (within group) or between groups – you list richness and diversity but don’t statistically compare? Figure 2 clearly shows that there are differences, e.g. Ice Ballet vs Soul mate for incarnata, wild type vs Hello yellow for tuberosa. Rebuttal: Throughout the paper our focus has been on comparisons between cultivars and the wild type within each species, testing the null hypothesis of no difference, which is the overarching question of the study (see final sentence of the Introduction where that central hypothesis is stated). We are less concerned with between species or between-cultivar comparisons, which is why we used Dunnett's test (cultivars versus wild-type) rather than a mean separation test (e.g., Tukey's or LSD) comparing cultivars against one another. We did statistically compare genus richness and diversity within each species. For the revision, rather than reporting Genus Richness as totals summed across all replicates, we've replaced those totals with means and SE (Table 3) and added the ANOVA results [lines 291-293; lines 305-307] • Line 280: do you mean Table 3 not table 1? Text has been corrected to Table 3 [line 302] • Table 3: shouldn’t genus richness just be the total number of different genera observed per plant? Because the numbers don’t add up that way (e.g. for A. incarnata wild type, 7 genera were observed but the richness is 5). Also, how do you have a mean and se for diversity index per cultivar? Are you doing it by plot or something vs overall? Genus richness totals have been corrected. The diversity index data are mean (SE) per cultivar calculated across all six gardens (replicates). We modified the footnote to clarify that. • Line 297: this is the first time you mention insects of conservation concern; if this is a reason for your research, you should mention it in your intro and perhaps try to identify species of concern that would be relevant besides monarchs The Abstract contained the sentence 'There is particular need for information on whether native cultivars can be as effective as true or 'wild-type' native species for supporting specialist native insects of conservation concern'. We've added a similar sentence to the Introduction [lines 117-119] to emphasize that need, which was the rationale for our study. Because the monarch is the only specialist native insect of conservation concern considered in this study, we feel that mentioning other herbivores in other systems would be off topic and distracting • Line 354: but there were significant differences found in insect visitation, diversity, between cultivars and species. Why is this? Any hypotheses? You reference Baker & Potter 2018 for wild type studies – did they discuss why there is a difference? We added several sentences and three supporting citations (Heinrich 1976, Kephart 1983, Ivey et al. 2003) discussing some possible reasons why the bee assemblages of A. incarnata and A. tuberosa differ [lines 371-374, 379-382]. We added a qualifier (the exception that A. tuberosa 'Blonde Bombshell' was inordinately attractive to Lasioglossum spp.) to the sentence stating that cultivars attracted generally similar bee assemblages as their respective parental species [line 385-388]. • Line 354: discuss bee (& other pollinator) vision and search patterns and how different coloured flowers may effect their visitation rates (esp. white flowers of Ice Ballet vs pink of wild type; maybe no difference seen for bees but would other groups be affected?) We prefer not to speculate on these points which are well beyond the scope of the study. Although 'Ice Ballet' has white flowers, its colonization by monarchs, suitability for monarch larvae, and bee assemblage were very similar to the other cultivars and the wild-type. Indeed, the overarching conclusion of the study is the similarity between cultivars and their respective wild types. • Did you notice any predation happening of the larvae/eggs? Did this seem to differ between species? We did not observe any direct predation while conducting our counts. Numerous studies have documented that predation of monarch eggs and larvae is generally quite high (> 90%) regardless of host species. The higher counts of monarch eggs and larvae on A. incarnata compared to A. tuberosa are probably due to the larger stature and visual apparency of the former [discussed in lines 346-353]. • Why was egg/instar development faster on tuberosa than incarnata? Numerous interacting factors influence monarch development (chemical and physical defenses, plant nutrient content, temperature, pressure from predators, etc.) and there is no consensus on their relative importance in driving growth and development. We prefer not to speculate on this, particularly given that nutritional ecology was not the focus of the study. Comments for the Author • Some additional comments, primarily for clarification: • Line 30: I suggest including a brief description of the nativars used in your abstract (e.g. were they similar in appearance to the natives) We added a brief statement that the cultivars varied in stature, floral display, and foliage color [line 31]. • Line 100-ish: Annie White has also been doing some work in this area; see e.g. her thesis or website (https://pollinatorgardens.org/2013/02/08/my-research/) White's work appears to not be published, at least not in refereed journals [based on our Web of Science search] but we added a citation to her dissertation [line 99, and in reference list; lines 659-660] o Line 119/throughout: be sure to include the taxonomic authority for every species you list, whether it be plant or insect (just need to do this for the first use of the species name) We've added the taxonomic authority at first mention of every Latin binomial species [lines 29,30,121,165,166] o Line 135: milkweeds are visited by many insect taxa besides monarchs and bees (e.g. beetles, wasps, moths, other butterflies, flies, other arthropods); I suggest mentioning them or at least acknowledging this point in your introduction We've added a statement to emphasize this [Lines 138-140] o Line 163/Table S1: why do you use the precise color descriptor of “kelly green” but then less precise color descriptors like light green, dark green? Line 163/Table S1: provide further clarification on additional features; i.e. larger flower clusters – larger in number of flowers per cluster, larger in flower size, how much larger? Line 260/Table S2: I suggest including actual dates of bloom, and/or number of days/weeks in bloom for each species, as “June-July” could be 2 weeks or 8 weeks long and you’re just presenting results for one year; and/or discuss period when e.g. 10% bloom, 50% bloom, 75% bloom, 100% bloom These supplemental tables are intended to provide general cultivar characteristic information for applied readers. Descriptors other than plant size (which was measured) are from our own observations. We changed foliage descriptors in Table S1 to light green, green, and dark green, and added a footnote to Table S1 addressing additional features. We did not record specific data on percentage bloom over time. All cultivars bloomed in late June and July (removed from the text) for varying durations, and there was also some variation from plot to plot. We also removed the bloom date column from Table S2. o Line 310: should be a period and not a comma between A. incarnata Error is fixed [line 334] o Line 364: add the word “in” in at the end of the line OK - done! [line 398] o Line 542: You may wish to investigate and then replace the website blog reference you used for Keith Nevison with his actual MSc thesis, which is available online at http://udspace.udel.edu/handle/19716/21442 OK - done! [lines 595-596] • Discuss general preferred soil conditions for each species, and their general persistence elsewhere & overtime (e.g. incarnata prefers wet soils but can grow in dry, but sometimes not as well over time) Rebuttal: Both A. incarnata and A. tuberosa are widely marketed and available. They are almost certainly the most commonly-planted milkweeds in urban gardens in the eastern USA. Given that our study's focus is planted milkweeds in urban gardens, and the study was done as a 2-year 'common garden' experiment, we do not feel that it is relevant to discuss milkweed ecology or persistence in areas outside of garden setting. Reviewer 2 (Anonymous) Basic reporting The manuscript is extremely well-written throughout. The overall issue/research question is clearly and concisely presented and the authors provide sufficient background/context about this important issue. The overall manuscript structure is well throughout out and again easy to follow. The associated figures/tables are clear. Thus the manuscript meets all the criteria of this reporting section. Thank you! Experimental design The overall research presented, in my opinion, fits within the journal's Aims and Scope appropriately. The authors present a clearly defined research question, which is an emerging area of interest, particularly as it pertains to the Green Industry, green marketing, monarch and native pollinator conservation, and urban biodiversity conservation/green networks. It will also have significant value to extension agents, APGA professional, etc. The results evaluating native type vs cultivars indeed fills a needed, identified niche knowledge gap. I feel that the methods/research design are appropriate, adequately rigorous, and clearly presented (appropriate replication, well-designed, and described such that another researcher could replicate from the information provided- and as such meet the appropriate standards for publication. I also feel that the depth of design is well conceived - evaluating organism attraction, use, plant chemical and mechanical defense, plant performance, etc. All the key variables essential to measure and that are relevant to help address the initial research question. Thank you! Validity of the findings As in # 2 above, I feel that the results are novel and important as they pertain to the Green Industry, green marketing, monarch and native pollinator conservation, and urban biodiversity conservation/green networks. It will also have significant value to extension agents, APGA professional, etc. Such pollinator attraction, plant variety trials need to be expanded to better elucidate key questions for urban biodiversity conservation impact. Data are sufficient, analyses is appropriate and statistically sound in my opinion. The overall conclusions are well stated, appropriately linked to the original research question and supported by the results. Thank you! Comments for the Author Please see my comments and questions on the manuscript PDF itself. Overall, this is a very well crafted study and manuscript that has significant benefits to urban biodiversity/monarch conservation and the Green Industry as a whole. I wonder though if it would be worth mentioning in the conclusion some cautions regarding nursery purchased plants such as the use of systemic insecticides that may have lethal or sublethal impacts to insects. Such may be the case with cultivars offered at larger retailers that often do not carry true native types - with those being more commonly sold from specialty or native plant nurseries. While we agree that nursery-purchased milkweeds that have been treated with systemic insecticides could be a hazard to monarchs and bees. That, however, is outside the scope of this study. We are unaware of a survey showing that milkweed cultivars are any more or less likely than wild-type milkweeds to be insecticide treated by producers, and we do not wish to suggest that they might be. Comments within pdf Line 168 Was regular irrigation provided? Nutrients? We added a statement that plants were watered twice per week for the first three weeks to aid establishment and during a drought period in 2019 [lines 175-176]. No nutrients were applied once the plants were in the field. Line 201 Were plant pests (aphids and Oncopeltus spp.) recorded? No, plant pests were not recorded (although that would make for a good follow-up study!) Line 220 how was damage to plants minimized using hand-netting? Or was there any plant damage resulting from this technique? We mostly used the technique of knocking bees into a container with EtOH, which minimized any plant damage. To emphasize that, we've switched the order in which the two sampling techniques are mentioned in the Methods. Nets were sometimes used to collect small bee taxa that regularly feed from the bottom of the bloom and are hard to see from the top. In such cases, we gently sweeping over flowers or holding the net over an umbel, disturbing the flowers so that the bees fly up into the net without damaging the plant. We also experimented with a bee vac, but it was not as efficient as the other methods. We added more detail about the bee sampling [lines 231-239] Line 257 To what extent do you feel that this contributed to selection by ovipositing females due to overall apparency/detectability in the landscape. This comment has already been addressed [lines 346-353] "	Here is a paper. Please give your review comments after reading it.
501	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>For many fish taxa, trophic position and relative fecundity increase with body size, yet fisheries remove the largest individuals, altering food webs and reducing population productivity. Marine reserves and other forms of spatial protection can help mitigate this problem, but the effectiveness of these management tools may vary interspecifically and spatially. Using visual survey data collected on the Central Coast of British Columbia, for 12 species of exploited rockfish we found that body size responses to spatial fishery closures depended on interspecific variation in growth parameter k (the rate at which the asymptotic body size is approached) and on location. For two closures, relative body sizes were larger at protected than at adjacent fished sites, and these differences were greater for species with lower k values. Reduced fishery mortality likely drove these results, as an unfished species did not respond to spatial protection. For three closures, however, body sizes did not differ between protected and adjacent fished sites, and for another closure species with higher k values were larger at fished than at protected sites while species with lower k values had similar sizes in both treatments. Variation in the age of closures is unlikely to have influenced results, as most data were collected when closures were 13 to15-years-old. Rather, the lack of larger fish inside four of six spatial fishery closures potentially reflects a combination of smaller size of the area protected, poor fisher compliance, and lower oceanographic productivity. Interspecific differences in movement behavior did not affect body size responses to spatial protection. To improve understanding, additional research should be conducted at deeper depths encompassing the distribution of older, larger fish. Our study-which was conceptualized and executed by an alliance of Indigenous peoples seeking to restore rockfishes-illustrates how life history and behavioral theory provide a useful lens for framing and interpreting species differences in responses to spatial protection.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>For centuries, many Indigenous cultures have recognized that protecting parts of the ocean from exploitation mitigates human impacts on biodiversity and contributes to fishery sustainability <ns0:ref type='bibr' target='#b24'>(Johannes, 1978;</ns0:ref><ns0:ref type='bibr' target='#b25'>Jones, Rigg & Lee, 2010;</ns0:ref><ns0:ref type='bibr' target='#b1'>Ban et al., 2018)</ns0:ref>. Consistent with the knowledge and practices of these cultures, there is growing evidence that marine reserves or other forms of spatial protection promote recovery from exploitation <ns0:ref type='bibr' target='#b4'>(Baskett & Barnett, 2015)</ns0:ref>, and that spillover of adults and larvae from protected areas can enhance adjacent fisheries (Di <ns0:ref type='bibr' target='#b30'>Lorenzo, Claudet & Guidetti, 2016;</ns0:ref><ns0:ref type='bibr' target='#b1'>Baetscher et al., 2019)</ns0:ref>.</ns0:p><ns0:p>The benefits of spatial protection often are quantified as increases in the density, biomass, and body sizes of exploited species <ns0:ref type='bibr' target='#b4'>(Baskett & Barnett, 2015)</ns0:ref>. Understanding factors affecting body size distributions is particularly important because fisheries remove the largest individuals, often at great ecological cost <ns0:ref type='bibr' target='#b43'>(Strong & Frank, 2010;</ns0:ref><ns0:ref type='bibr' target='#b22'>Hixon, Johnson & Sogard, 2014)</ns0:ref>. Larger individuals tend to occupy higher trophic positions (e.g. <ns0:ref type='bibr' target='#b44'>Trebilco et al., 2016;</ns0:ref><ns0:ref type='bibr' target='#b36'>Olson et al., 2020)</ns0:ref>, and their removal may alter food web structure <ns0:ref type='bibr' target='#b41'>(Shackell et al., 2010;</ns0:ref><ns0:ref type='bibr' target='#b47'>Zgliczynski & Sandin, 2017)</ns0:ref>. Further, for many fish taxa the relationship between fecundity and body size is hyperallometric (a power function with exponents >1), such that larger females contribute disproportionately more offspring (per unit of body size) than smaller females <ns0:ref type='bibr' target='#b11'>(Dick et al., 2017a;</ns0:ref><ns0:ref type='bibr' target='#b3'>Barneche et al., 2018)</ns0:ref>. Size differences between protected and fished areas, therefore, can signal the extent to which spatial protection promotes species recoveries and restores food webs.</ns0:p><ns0:p>The benefits of spatial protection, however, can differ across species, and understanding this variation may help predict reserve performance <ns0:ref type='bibr' target='#b23'>(Jennings, 2000;</ns0:ref><ns0:ref type='bibr' target='#b8'>Claudet et al., 2010;</ns0:ref><ns0:ref type='bibr' target='#b26'>Kaplan et al., 2019)</ns0:ref>. Species with longer lifespans often have slower growth rates, later maturity, and other traits which increase their vulnerability to exploitation and reduce their recovery rates during fishery reprieves <ns0:ref type='bibr' target='#b23'>(Jennings, 2000)</ns0:ref>. Consequently, the benefits of spatial protection may require more time to manifest in longer-lived species <ns0:ref type='bibr' target='#b42'>(Starr et al., 2015;</ns0:ref><ns0:ref type='bibr' target='#b26'>Kaplan et al., 2019)</ns0:ref>.</ns0:p><ns0:p>Variation in parameter k of the von Bertalanffy growth function, which depicts the rate at which the asymptotic body size is approached, should influence the time required by different species to restore a larger size structure after the implementation of spatial protection <ns0:ref type='bibr' target='#b23'>(Jennings, 2000;</ns0:ref><ns0:ref type='bibr' target='#b26'>Kaplan et al., 2019)</ns0:ref>. Additionally, species characterized by smaller movements and greater site fidelity are more likely to remain within a protected area <ns0:ref type='bibr' target='#b21'>(Hannah & Rankin, 2011)</ns0:ref> and may benefit more from spatial protection than more mobile species <ns0:ref type='bibr' target='#b28'>(Kramer & Chapman, 1999;</ns0:ref><ns0:ref type='bibr' target='#b34'>Moffitt et al., 2009)</ns0:ref>. These predictions, however, do not apply to unfished species, for which PeerJ reviewing PDF \| (2020:05:48810:1:1:NEW 14 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed environmental variation is a primary driver of population characteristics <ns0:ref type='bibr' target='#b8'>(Claudet et al., 2010;</ns0:ref><ns0:ref type='bibr' target='#b7'>Caselle et al., 2015)</ns0:ref>. Rockfish (Sebastes spp.), a genus of marine fish with diverse life history traits, allow tests of these ideas. In the northeast Pacific, maximum lifespans range across rockfish species from two decades to two centuries, and interspecific variation in body size and growth parameters are similarly wide <ns0:ref type='bibr' target='#b31'>(Love, Yoklavich & Thorsteinson, 2002)</ns0:ref>. While most rockfishes associate with rocky reefs, their behavior ranges from sedentary to very mobile and from demersal to benthopelagic. Most rockfishes are exploited yet some small planktivores are unfished <ns0:ref type='bibr' target='#b31'>(Love, Yoklavich & Thorsteinson, 2002)</ns0:ref>.</ns0:p><ns0:p>In British Columbia, Canada, commercial and recreational sectors overfished rockfishes during the latter part of the 20 th century, diminishing their abundance and body sizes <ns0:ref type='bibr' target='#b46'>(Yamanaka & Logan, 2010;</ns0:ref><ns0:ref type='bibr' target='#b14'>Eckert et al., 2017)</ns0:ref>. During the 2000s federal legislators implemented more conservative management and established a network of spatial fishery closures known as Rockfish Conservation Areas (RCAs) where commercial and recreational fishers cannot use bottom trawls, groundlines, or hook-and-line gear <ns0:ref type='bibr' target='#b46'>(Yamanaka & Logan, 2010;</ns0:ref><ns0:ref type='bibr'>Appendix S1)</ns0:ref>. For some species, however, declines in size and age structure have continued and their biomass remains depressed <ns0:ref type='bibr'>(McGreer & Frid, 2017 and references within)</ns0:ref>. RCAs can help reverse these trends, yet prior studies in British Columbia, conducted only 5-7 years after RCA implementation, found no evidence of larger or more abundant rockfishes inside protected sites <ns0:ref type='bibr' target='#b19'>(Haggarty, Shurin & Yamanaka, 2016;</ns0:ref><ns0:ref type='bibr' target='#b37'>Olson, Trebilco & Salomon, 2019)</ns0:ref>. As time since RCA implementation progresses, size and biomass increases are more likely to be detected <ns0:ref type='bibr' target='#b42'>(Starr et al., 2015;</ns0:ref><ns0:ref type='bibr' target='#b27'>Keller et al., 2019)</ns0:ref>, particularly for shorter-lived species with greater k values <ns0:ref type='bibr' target='#b26'>(Kaplan et al., 2019)</ns0:ref>.</ns0:p><ns0:p>Analyses of RCA benefits must account for variation in habitat characteristics which, independently of exploitation, affect the distribution and sizes of rockfish. For some demersal species, relative abundance may increase with topographic structural complexity and larger or older individuals tend to use greater depths than smaller or younger individuals <ns0:ref type='bibr' target='#b31'>(Love, Yoklavich & Thorsteinson, 2002)</ns0:ref>. Benthopelagic species, however, have a wider range of vertical movements into the water column <ns0:ref type='bibr' target='#b31'>(Love, Yoklavich & Thorsteinson, 2002;</ns0:ref><ns0:ref type='bibr' target='#b21'>Hannah & Rankin, 2011)</ns0:ref> and their relationships to depth or topographic structural complexity might be more variable.</ns0:p><ns0:p>Canada is among the countries where Indigenous people are using their traditional knowledge and science to improve marine conservation <ns0:ref type='bibr' target='#b25'>(Jones, Rigg & Lee, 2010;</ns0:ref><ns0:ref type='bibr' target='#b1'>Ban et al., 2018;</ns0:ref><ns0:ref type='bibr' target='#b2'>Ban, Wilson & Neasloss, 2020)</ns0:ref>. Since 2013, the Central Coast Indigenous Resource Alliance (CCIRA)-comprised of the Wuikinuxv, Nuxalk, Heiltsuk and Kitasoo/Xai'xais First Nations-has been surveying rockfish populations and their habitats <ns0:ref type='bibr' target='#b16'>(Frid et al., 2018)</ns0:ref> inside and outside RCAs of British Columbia's Central Coast (Fig. <ns0:ref type='figure' target='#fig_0'>1</ns0:ref>; Appendix S2). Data collected to date represent RCAs that were 8 to 15-years-old and encompass the earliest stage expected for the benefits of spatial protection to manifest for rockfishes <ns0:ref type='bibr' target='#b27'>(Keller et al., 2019;</ns0:ref><ns0:ref type='bibr' target='#b26'>Kaplan et al., 2019)</ns0:ref>.</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:05:48810:1:1:NEW 14 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed Within this temporal context, we used CCIRA's visual survey data to test the hypothesis that body size responses to spatial protection vary according to species life history traits, movement behavior, and susceptibility to fishing, while controlling for depth and topographic structural complexity. We predicted that the body sizes of exploited species would be larger inside RCAs than in adjacent fished areas, but the strength of this effect would decrease for species which require more time to reach their asymptotic body size (i.e. have lower k values) <ns0:ref type='bibr' target='#b23'>(Jennings, 2000;</ns0:ref><ns0:ref type='bibr' target='#b26'>Kaplan et al., 2019)</ns0:ref> or that are more mobile <ns0:ref type='bibr' target='#b28'>(Kramer & Chapman, 1999;</ns0:ref><ns0:ref type='bibr' target='#b34'>Moffitt et al., 2009)</ns0:ref>. We also predicted that the body sizes of Puget Sound rockfish (S. emphaeus), a small planktivore unlikely to be caught by fishing gear <ns0:ref type='bibr' target='#b31'>(Love, Yoklavich & Thorsteinson, 2002)</ns0:ref>, would not differ between protected and fished sites <ns0:ref type='bibr' target='#b8'>(Claudet et al., 2010;</ns0:ref><ns0:ref type='bibr' target='#b7'>Caselle et al., 2015)</ns0:ref>.</ns0:p><ns0:p>In addition to testing a priori predictions, we examined differences in conservation effectiveness between RCA locations. Though exploratory, this aspect of our research was motivated by the need to understand whether some RCAs are performing poorly and the potential ways to improve their management or design <ns0:ref type='bibr' target='#b18'>(Haggarty, Martell & Shurin, 2016;</ns0:ref><ns0:ref type='bibr'>DFO, 2019a,b)</ns0:ref>. .</ns0:p></ns0:div> <ns0:div><ns0:head>Materials & Methods</ns0:head></ns0:div> <ns0:div><ns0:head>Ethics statement</ns0:head><ns0:p>Data collection was observational and did not require permits from federal agencies. The Wuikinuxv, Kitasoo/Xai'xais, Heiltsuk and Nuxalk First Nations hold Indigenous rights to their own territories, where all data were collected. Scientific staff conducting field research-who are members of these Nations or work directly for them-had the approvals required by Indigenous rights holders.</ns0:p></ns0:div> <ns0:div><ns0:head>Surveys by SCUBA divers</ns0:head><ns0:p>During March-April or July-October of 2013 and 2015-2019, we sampled rocky reefs, which were located through local Indigenous knowledge or a bathymetric model <ns0:ref type='bibr' target='#b20'>(Haggarty & Yamanaka, 2018)</ns0:ref>. Within the constraints of weather, we attempted to balance sample sizes between paired sites inside RCAs (protected treatment) or outside the same RCA but ≤10 km from its nearest boundary (control treatment) (Fig. <ns0:ref type='figure' target='#fig_0'>1</ns0:ref>).</ns0:p><ns0:p>SCUBA divers collected visual data on rockfish and their habitats along depth contours within transects that were 30 m-long by 4 m-wide by 4 m-high (480 m 3 ). Dives generally included three transects stratified by depth: 25-35 m, 15-18 m, and ≤6 m. Observations included estimates of topographic structural complexity (variation in vertical relief, size, and abundance of rocky structures: Appendix S3), relative density by species (count/480 m 3 ), and total lengths of individual fish. Video was taken during most transects to corroborate habitat descriptions and species identifications.</ns0:p><ns0:p>Total lengths were estimated visually with the aid of a 1-metre-long ruler attached to a pole. Early in their training, the 3 divers who collected these data tested their accuracy measuring fish models attached to a line suspended in the water column. Comparisons of estimated and actual sizes showed low measurement errors that did not vary with model size, and relatively low variation between divers (Appendix S4).</ns0:p><ns0:p>Most fish were encountered alone or in small groups (<20 fish) and sized individually. Benthopelagic species, however, sometimes formed larger schools. In these cases, divers sized fish in clusters, recording a length and a multiplier for the corresponding fish count. This approach allowed divers to avoid long pauses, reducing their influence on attraction or avoidance behaviors that bias counts <ns0:ref type='bibr' target='#b15'>(Emslie et al., 2018)</ns0:ref>. A tradeoff is that size estimates that may have spanned 2-3 cm (had fish been sized individually) were collapsed into a single measurement inflating peaks in the frequency of that measurement (see Analyses). For further details on dive surveys, see <ns0:ref type='bibr' target='#b16'>Frid et al. (2018)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Species characteristics</ns0:head><ns0:p>We obtained literature values for maximum lifespan, maximum total length, total length at 50% maturity, and von Bertalanffy growth parameter k (Table <ns0:ref type='table'>1</ns0:ref>). The latter two parameters vary with latitude <ns0:ref type='bibr' target='#b31'>(Love, Yoklavich & Thorsteinson, 2002)</ns0:ref>, so we prioritized estimates from British Columbia <ns0:ref type='bibr' target='#b0'>(Anderson, Keppel & Edwards, 2019)</ns0:ref>; if unavailable, we used estimates from Oregon and Washington. Species lacking sufficient observations or key information were not analyzed (Appendix S5). The exception was Tiger rockfish (S. nigrocinctus), a long-lived species for which we had a large sample size but which lacked data on growth rates; k for this species was estimated as a function of maximum lifespan (Appendices S6, S7). We used the literature or consulted three experts to class species behaviors according to a combination of relative mobility (high or low) and habitat preference (benthopelagic or demersal) (Table <ns0:ref type='table'>1</ns0:ref>; Appendix S8). No species qualified as high mobility-demersal and the one species classed as low mobility-benthopelagic had insufficient observations (Appendix S5). Analyses, therefore, included only 2 behavioral categories: high mobility-benthopelagic and low mobility-demersal (Table <ns0:ref type='table'>1</ns0:ref>).</ns0:p><ns0:p>Species were classed as 'exploited' or 'unfished' based on fishery data compiled by <ns0:ref type='bibr' target='#b0'>Anderson et al. (2019)</ns0:ref> or descriptions by <ns0:ref type='bibr' target='#b31'>Love et al. (2002)</ns0:ref>. Only Puget Sound rockfish qualified as unfished. With the caveat that Puget Sound rockfish have the smallest maximum size in our sample, and therefore shows less body size variation than other rockfishes, we assumed that size distributions for this species are driven primarily by environmental variability, and therefore are an adequate control for the effects of exploitation and protection. Should this assumption be wrong, body size increases under spatial protection are likely to be detected, as Puget Sound rockfish have the highest k value in our sample (Table <ns0:ref type='table'>1</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>Analyses</ns0:head><ns0:p>We limited analyses to individuals with total lengths of ≥10 cm. Smaller individuals are unlikely to be caught by fishing gear and their abundance is influenced by environmental variability <ns0:ref type='bibr' target='#b32'>(Markel, Lotterhos & Robinson, 2017)</ns0:ref> outside the scope of analyses.</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:05:48810:1:1:NEW 14 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed Total lengths were standardized as length anomalies (LA) which, pooling data from all locations and all years, were calculated for each species as:</ns0:p><ns0:formula xml:id='formula_0'>𝐿𝐴 𝑠 = 𝐿𝑠 𝑖 -𝐿𝑠 𝑥̅ 𝐿𝑠 𝑠𝑡𝑑</ns0:formula><ns0:p>where Ls i is the observed length for individual i belonging to species s and Ls x̅ and Ls std are, respectively, the mean and standard deviations of all lengths observed for species s. The use of length anomalies, rather than actual lengths, allowed us to include multiple species that differ in maximum body size into a single comprehensive analysis that tested for the effects of species traits on responses to spatial protection. This approach also increased sample sizes, allowing us to include all predictors of interest without overfitting the model.</ns0:p><ns0:p>Length anomaly was the response variable in two generalized least squares models (GLSMs) implemented in R <ns0:ref type='bibr'>(Pinheiro et al., 2020)</ns0:ref>. The first included all exploited species. The predictors were RCA treatment (protected or control) and its two-way interaction with location (i.e., name of RCA associated with protected and control treatments), k and its interaction with RCA treatment, behavioral class and its interaction with RCA treatment, topographic structural complexity and its interaction with behavioral class, and depth and its interaction with behavioral class. Parameter k values (Table <ns0:ref type='table'>1</ns0:ref>) were averaged for males and females, which divers cannot distinguish visually. Because each site contained multiple transects, a Gaussian correlation structure derived from the latitude and longitude of each sampling site (projected into the Albers coordinate system) accounted for the spatial autocorrelation of residuals <ns0:ref type='bibr' target='#b38'>(Pinheiro & Bates, 2000)</ns0:ref>. The second GLSM was specific to the unfished Puget Sound rockfish; its predictors were depth, topographic structural complexity, and RCA treatment and its interaction with location. This analysis also included the Gaussian correlation structure derived from the coordinates of each sampling site.</ns0:p><ns0:p>Although surveys spanned 6 years, sampling across RCA age-location-treatment combinations was unbalanced (Appendix S9), precluding GLSMs from testing RCA age by treatment interactions. Consequently, analyses pooled data for RCA ages of 8-15 years, with ages 13-15 being best represented (Appendices S9, S10).</ns0:p><ns0:p>To reduce skew in size frequency distributions, multipliers assigned to similarly sized schooling fish were truncated to the 95 th percentile of their distribution and incorporated into GLSMs as weights. Competing models were compared with AICc model selection procedures <ns0:ref type='bibr'>(Burnham & Anderson 2002)</ns0:ref>. We expected the independent effects of topographic structural complexity and depth to affect responses <ns0:ref type='bibr' target='#b31'>(Love, Yoklavich & Thorsteinson, 2002)</ns0:ref>; these predictors did not undergo model selection, reducing the number of competing models. Results are presented in terms of the best GLSMs. Quantile-quantile plots, residuals vs fitted plots, and correlation values between variables were examined to verify model assumptions.</ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head><ns0:p>PeerJ reviewing PDF \| (2020:05:48810:1:1:NEW 14 Jul 2020)</ns0:p></ns0:div> <ns0:div><ns0:head>Manuscript to be reviewed</ns0:head><ns0:p>Of 13224 observations for exploited species (Appendix S11), 73% were from high mobilitybenthopelagic species, and 27% from low mobility-demersal species. The proportion of individuals that had reached or exceeded length at 50% maturity was 47% for low mobilitydemersal species and 5% for high mobility-benthopelagic species (Appendix S12).</ns0:p><ns0:p>The effects of spatial protection on the body sizes of exploited species depended on growth parameter k and RCA location, but not on behavioral class (Fig. <ns0:ref type='figure' target='#fig_1'>2</ns0:ref>; Appendices S13a, S14a). At two RCAs (West Aristazabal, Goose Island), body sizes were larger at protected than control sites, but these differences were greater for slower-than for faster-growing species (Fig. <ns0:ref type='figure' target='#fig_2'>3</ns0:ref>). At three RCAs (Fish Egg Inlet, McMullin Group, Smith Sound), body sizes did not differ between protected and control sites, regardless of k values. At the remaining RCA (Kitasu Bay), species with greater k values were larger at control than at protected sites while species with lower k values had similar sizes in both treatments (Fig. <ns0:ref type='figure' target='#fig_2'>3</ns0:ref>). Independently of RCA treatment, the body sizes of high mobility-benthopelagic species increased with depth and decreased with greater topographic structural complexity. In contrast, the body sizes of low mobility-demersal species increased with both depth and topographic structural complexity (Fig. <ns0:ref type='figure' target='#fig_3'>4</ns0:ref>).</ns0:p><ns0:p>For the unfished Puget sound rockfish, body sizes increased with depth and topographic structural complexity but did not differ between RCA treatments or locations (Fig. <ns0:ref type='figure' target='#fig_1'>2b</ns0:ref>, Appendices S13b, S14b, S15).</ns0:p></ns0:div> <ns0:div><ns0:head>Discussion</ns0:head><ns0:p>We predicted that the body sizes of exploited species would be larger inside RCAs than in adjacent fished areas, but the strength of this effect would decrease for species which require more time to reach their asymptotic body size (i.e. have lower values for growth parameter k) or that are more mobile. Our results were inconsistent with these predictions. At two RCAs (Goose Island, West Aristazabal) the body sizes of exploited rockfishes were larger at protected than at adjacent fished sites, but these differences increased with lower k values. One potential explanation for this result is that body size differences between protected and exploited sites could be influenced by an interaction between parameter k and the intensity of exploitation (see <ns0:ref type='bibr' target='#b23'>Jennings, 2000;</ns0:ref><ns0:ref type='bibr' target='#b26'>Kaplan et al., 2019)</ns0:ref>; exploitation rates at control sites may have not been high enough to generate strong RCA effects for species with high k values. Also, most rockfishes shift ontogenetically to deeper depths <ns0:ref type='bibr' target='#b31'>(Love, Yoklavich & Thorsteinson, 2002)</ns0:ref>, and the depths that we sampled (≤35 m) encompassed primarily immature fish. Data collected at deeper depths via remotely operated vehicles (e.g. <ns0:ref type='bibr' target='#b19'>Haggarty, Shurin & Yamanaka, 2016)</ns0:ref> are required to more fully understand how variation in growth parameters affect responses to spatial protection. Also contrary to our predictions, interspecific differences in movement behavior did not affect responses to spatial protection by exploited species. One potential explanation is that the proportion of immature fish was greater for high mobility-benthopelagic species (95%) than for low mobility-demersal species (53%). Most mature individuals from high mobilitybenthopelagic species may have already undergone ontogenetic depth shifts below the reach of our dive surveys <ns0:ref type='bibr' target='#b31'>(Love, Yoklavich & Thorsteinson, 2002)</ns0:ref>, reinforcing the need to collect data at PeerJ reviewing PDF \| (2020:05:48810:1:1:NEW 14 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed deeper depths to strengthen inferences. Additionally, high mobility-benthopelagic species tended to have higher k values (Table <ns0:ref type='table'>1</ns0:ref>), which might have confounded mobility effects.</ns0:p><ns0:p>The relative body sizes of high mobility-benthopelagic species increased with depth and decreased with greater topographic structural complexity, while the body sizes of low mobilitydemersal species had the opposite relationship to these habitat variables. Perhaps the different responses reflect the distribution of resources being tracked (high mobility-benthopelagic species feeding more on pelagic prey near the surface: <ns0:ref type='bibr' target='#b31'>Love et al. 2002)</ns0:ref> and different antipredator strategies (low mobility-demersal species relying more on crypsis and refuges in complex reefs).</ns0:p><ns0:p>The larger body sizes for exploited species within the Goose Island and West Aristazabal RCAs likely reflect reduced fishery mortality rather than environmental variation, as body sizes for the unfished Puget Sound rockfish did not differ between protected and fished sites. Given that fecundity increases disproportionately with body size <ns0:ref type='bibr' target='#b13'>(Dick et al., 2017b)</ns0:ref> and that larvae exports can occur from protected to fished areas <ns0:ref type='bibr' target='#b1'>(Baetscher et al., 2019)</ns0:ref>, these RCAs may already be contributing to fishery sustainability; such benefits should increase over time <ns0:ref type='bibr' target='#b42'>(Starr et al., 2015;</ns0:ref><ns0:ref type='bibr' target='#b26'>Kaplan et al., 2019)</ns0:ref>.</ns0:p><ns0:p>At four of six RCAs that we examined, however, rockfish body sizes at protected sites were similar to or smaller than those at adjacent fished sites. There are at least three potential, nonmutually exclusive explanations that might explain this result. First, the effectiveness of a protected area generally increases with its size and age <ns0:ref type='bibr' target='#b35'>(Molloy, McLean & Côté, 2009)</ns0:ref>. In our sample, RCA age varied little, but RCA size ranged widely. The two RCAs that included larger fish within their boundaries (relative to adjacent areas) also were the two largest RCAs that we examined (Appendix S2). While more RCAs of varying sizes need to be examined, it is plausible that RCA size influenced our results.</ns0:p><ns0:p>Second, despite analyses controlling for depth and topographic structural complexity , some of the variation in RCA performance might reflect oceanographic differences between control and protected sites (see <ns0:ref type='bibr' target='#b7'>Caselle et al., 2015)</ns0:ref>. Within a rockfish species, growth rates are faster in oceanic areas-which have lower temperatures and higher salinity-than in inland coastal waters <ns0:ref type='bibr' target='#b45'>(West, Helser & O'neill, 2014)</ns0:ref>. Oceanographic context, therefore, may partly explain why we found no protection benefits at the Fish Egg Inlet RCA; this RCA encompasses inland coastal waters but local geography constrained control sites to more oceanic conditions (Fig. <ns0:ref type='figure' target='#fig_1'>2</ns0:ref>), where growth rates likely are faster <ns0:ref type='bibr' target='#b45'>(West, Helser & O'neill, 2014)</ns0:ref>. Perhaps also reflecting oceanographic factors, at the Kitasu Bay RCA a control site just outside its boundary encompassed a reef with outstanding rockfish productivity, which might explain why individuals from faster-growing species were larger outside this RCA. Similarly, the Goose Island and McMullin Group RCAs share a boundary, yet only the former had larger fish in protected than in control sites; the control sites for the McMullin Group include several locations where conditions are more oceanic than at the control sites for Goose Island, which may have influenced this result (Fig. <ns0:ref type='figure' target='#fig_1'>2</ns0:ref>). Third, while analyses are lacking for the extent to which legal and illegal fisheries occur within RCAs of the Central Coast, the intensity of these activities might have varied between PeerJ reviewing PDF \| (2020:05:48810:1:1:NEW 14 Jul 2020) locations. Studies in southern British Columbia documented a high incidence of illegal recreational fisheries within RCAs <ns0:ref type='bibr' target='#b29'>(Lancaster, Dearden & Ban, 2015;</ns0:ref><ns0:ref type='bibr' target='#b18'>Haggarty, Martell & Shurin, 2016)</ns0:ref>. Fisher compliance tended to decline with fishing effort adjacent to the RCA and with the RCA's proximity to fishing lodges. Further, lower compliance was associated with fewer resources for enforcement and smaller RCA sizes <ns0:ref type='bibr' target='#b18'>(Haggarty, Martell & Shurin, 2016)</ns0:ref>. Importantly, RCAs were created to protect rockfish from the highest-risk commercial and recreational fisheries, yet trap fisheries for invertebrates and mid-water trawls for groundfish remain legal within them (Appendix S1), likely causing rockfish mortalities <ns0:ref type='bibr' target='#b10'>(DFO, 2019b)</ns0:ref>. The extent to which illegal and legal fisheries may have contributed to the lack of larger fish sizes at two thirds of the RCAs that we examined requires further investigation. Further, responses to spatial protection might be more likely to be detected if areas adjacent to reserves are fished more intensely (see <ns0:ref type='bibr' target='#b23'>Jennings, 2000;</ns0:ref><ns0:ref type='bibr' target='#b26'>Kaplan et al., 2019)</ns0:ref>, and future analyses should consider these effects.</ns0:p><ns0:p>Indigenous fisheries for traditional foods are allowed in RCAs, yet these fisheries are regulated by traditional laws derived from the long-term knowledge and stewardship principles that have allowed sustainable harvests over centuries <ns0:ref type='bibr' target='#b25'>(Jones, Rigg & Lee, 2010;</ns0:ref><ns0:ref type='bibr' target='#b1'>Ban et al., 2018;</ns0:ref><ns0:ref type='bibr' target='#b2'>Ban, Wilson & Neasloss, 2020)</ns0:ref>. Hereditary chiefs remain the traditional stewards of their territories who, with technical staff, develop food fishing policies consistent with traditional laws. Such policies are operationalized via Coastal Guardian Watchmen who communicate with food fishers and patrol the waters for compliance, mitigating impacts from traditional fisheries in protected areas. Additionally, Central Coast First Nations run their own catch-monitoring programs, which they use to inform the governance and management of their traditional fisheries <ns0:ref type='bibr' target='#b1'>(Ban et al., 2018;</ns0:ref><ns0:ref type='bibr' target='#b2'>Ban, Wilson & Neasloss, 2020)</ns0:ref>. Thus, it is unlikely that Indigenous traditional fisheries contributed to the lower performance of some RCAs. Consistent with this notion, other studies in British Columbia found that the relative abundance of Dungeness crab (Cancer magister) increased over time after spatial fishery closures for commercial and recreational fishers where implemented, despite traditional Indigenous fisheries continuing within these closures <ns0:ref type='bibr' target='#b17'>(Frid, McGreer & Stevenson, 2016;</ns0:ref><ns0:ref type='bibr' target='#b6'>Burns et al., 2020)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions</ns0:head><ns0:p>At two spatial fishery closures that were only 13 to 15 years-old when most data were collected, we found that the body sizes of 12 species of exploited rockfishes were larger at protected than at fished sites. Differences between protected and fished sites were greater for species with lower values for growth parameter k, which was the opposite of what we expected. Also contrary to expectation, interspecific differences in movement behavior did not affect body size responses to spatial protection. Nonetheless, life history and behavioral frameworks guided the prediction and interpretation of species differences in responses to spatial protection, which is essential for the adaptive management of protected areas <ns0:ref type='bibr' target='#b23'>(Jennings, 2000;</ns0:ref><ns0:ref type='bibr' target='#b8'>Claudet et al., 2010;</ns0:ref><ns0:ref type='bibr' target='#b26'>Kaplan et al., 2019)</ns0:ref>.</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:05:48810:1:1:NEW 14 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed Canada's federal fishery management agency is reviewing the effectiveness of RCAs (DFO, 2019a,b) and our results can inform this process. For four of six RCAs we found no evidence that fish were larger in protected than in adjacent fished sites. Perhaps these deficiencies in conservation effectiveness could be mitigated by increasing resources for compliance monitoring and enforcement, and potentially by modifying some RCA boundaries to increase the proportion of more productive, oceanic habitats within such boundaries.</ns0:p><ns0:p>Critically, the trophic position of rockfishes increases with their individual body sizes <ns0:ref type='bibr' target='#b44'>(Trebilco et al., 2016;</ns0:ref><ns0:ref type='bibr' target='#b36'>Olson et al., 2020)</ns0:ref>. Larger size classes of yelloweye rockfish (S. ruberrimus)-a long-lived demersal species inherent to Indigenous diets and undergoing size declines <ns0:ref type='bibr' target='#b14'>(Eckert et al., 2017;</ns0:ref><ns0:ref type='bibr'>McGreer & Frid, 2017</ns0:ref>)-occupy a particularly high trophic position in rocky reefs <ns0:ref type='bibr' target='#b36'>(Olson et al., 2020)</ns0:ref>. Ensuring that RCAs effectively restore large size structures for exploited species, therefore, is essential for ecosystem-based fishery management. Manuscript to be reviewed </ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head>Figure 1</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head>Figure 2</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>Figure 3</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Figure 3</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head>Figure 4</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Figure 4</ns0:figDesc></ns0:figure> <ns0:note place='foot'>PeerJ reviewing PDF \| (2020:05:48810:1:1:NEW 14 Jul 2020)</ns0:note> </ns0:body> "	"We thank the reviewers for their constructive criticism. Below we first provide a summary of major changes to the manuscript. This is followed by specific responses to each reviewer. (Blue font indicates our writing while black font quotes the reviewers). Major changes 1. Issues with RCA age Reviewers 1 and 2 pointed out that we did not analyze effects of time/RCA age. Reviewer 2 also implied that Fig 1 (our graphical hypothesis) had set the expectation for testing an interaction between RCA age and RCA treatment, which we did not do. Although we intended to test that interaction, the analysis was not possible because of unbalanced sampling across time-location-treatment combinations (see Figure below). Consequently: 1. We removed the graphical hypothesis (the original Figure 1) and revised the text accordingly. This removes the expectation that we are testing an interaction between RCA age and RCA treatment. 2. We added a statement in methods/analysis section explaining how the unbalanced sampling precluded us from testing that interaction (revision lines 216-219). 3. The above statement refers to the figure below, which we added to supplementary material. 2. Restructuring of the introduction All reviewers found problems with the introduction. To address their collective comments (and adding some improvements of our own), we restructured the introduction as follows. 1. The 1st paragraph is a general statement about spatial protection as a management tool to protect biodiversity and promote recovery from exploitation. 2. The 2nd paragraph elaborates on body size responses to exploitation and to spatial protection. 3. The 3rd paragraph introduces the notion that life history and behavioral traits might affect how different species respond to spatial protection. 4. The 4th paragraph introduces general predictions derived from #3 based on growth parameter k and movement behavior. 5. The 5th paragraph introduces rockfish and why they are suitable for testing the predictions in #4. 6. The 6th paragraph describes the conservation status of rockfish in British Columbia and the potential role of RCAs in reversing size and abundance declines. 7. The 7th paragraph describes why analyses of RCA effects must account for the potentially confounding effects of depth and habitat complexity. Importantly, the paragraph avoids an expectation that we are addressing theory/predictions about habitat effects (i.e., these are outside the scope of our objectives, and we just want to control for habitat). 8. The 8th paragraph introduces the data set of the Central Coast Indigenous Resource Alliance. Importantly, it states that “Data collected to date represent RCAs that were 8 to 15-years-old and encompass the earliest stage expected for the benefits of spatial protection to manifest for rockfishes (Kaplan et al. 2019; Keller et al. 2019).” 9. The 9th paragraph specifies our predictions and reiterates the above temporal context. 10. The 10th paragraph explains that that much of our findings had to do with location effects, but we had no a priori predictions about such effects. Still, those findings are relevant to improving the RCA network. Independent of reviewer comments, we also did some minor revisions to our use of references. 3. Terminology 1. In response to a comment by reviewer 1, we have modified some key terminology. Throughout the manuscript, including the title, we have dropped the term “somatic growth rates”. We now refer only to “growth parameter k”, which is defined at first introduction in the text. 2. Independently of reviewer comments we realized our mistake in stating that fecundity increases exponentially with body size. The relationship is hyperallometric (a power function with exponents >1), and the revised text uses the proper terminology. Reviewer 1 (Scott Hamilton) Basic reporting In this study, the authors examine the effects of marine protection on body size of 12 species of rockfish, inside and outside rockfish conservation areas (RCAs) closed for up to 15 years, and the effects of life history and behavioral differences on the strength of that response. The authors find that fish are larger in body size in 2 of the 6 RCAs examined and that growth parameters may explain some of the differences in the strength of the response. They also report effects of topographic complexity and depth on body size, which varies in different ways based on mobility. The paper was short and concise and generally well written. Raw data were shared. Thank you for the summary and for your comments that helped us improve the manuscript. Experimental design I’m not particularly in love with the length anomaly metric as the sole means of providing information on the response of fish body size to protection. I think I would like it better if this approach complimented more traditional analyses using mean lengths, comparisons of size distributions, calculations of differences in lengths in vs. out, or response ratios. I understand why the authors used this standardization approach, because it allows them to compare across species that differ greatly in body size in the same analysis, but this was not articulated clearly in the manuscript. The following statement has been added to methods (revision lines 197-201): “The use of length anomalies, rather than actual lengths, allowed us to include multiple species that differ in maximum body size into a single comprehensive analysis that tested for the effects of species traits on responses to spatial protection. This approach also increased sample sizes, allowing us to include all predictors of interest without overfitting the model.” The methods section needs to better describe how the length anomalies were calculated and then averaged and used in subsequent analyses. Did you calculate the mean anomaly for each species in each location pooled over all years and use that in the models? The following text has been added to methods (revision lines 193-197): “Total lengths were standardized as length anomalies (LA) which, pooling data from all locations and all years, were calculated for each species as: where Lsi is the observed length for individual i belonging to species s and Lsx̅ and Lsstd are, respectively, the mean and standard deviations of all lengths observed for species s.” If you have sizes of each RCA, how about conducting an analysis to test explicitly whether reserve size is associated with the strength of the length response measured? We agree that, given adequate sample sizes, it would be good to test for the effect of RCA size. Our sample of 6 RCAs, however, would generate only weak insights and fail to account for potentially confounding covariates for which we lack data (e.g. spatial variation in fishing pressure). Given our current data, the best we can do is acknowledge the potential importance of RCA size, as already done in the discussion. The same can be done with estimates of ocean productivity or fishing pressure. While we agree that formal tests of the effects of ocean productivity and fishing pressure are important, these are beyond the scope of our study. Also, those data are unavailable at a spatiotemporal scale that is meaningful to our objectives. There is also a good chance that adding more variables (and their requisite interaction with RCA treatment) would overfit the models. Our original discussion already acknowledges ocean productivity and fishing pressure, framing them as potential objectives for future work (and bigger data sets). With 6 RCAs, the statistical power may be low, unless the effects are strong, but it would be interesting to see if the patterns match the qualitative descriptions of the likely drivers that the authors describe in the discussion. We agree that there is low statistical power in having only 6 RCAs. See above comments for additional rationale on why we think this analysis would not be informative. Are there differences in habitat complexity between any of the RCAs and their control sites that may help to explain why reserve effects were detected in some locations but not in others? Topographic structural complexity is a fixed effect in the model. Any model inferences on the effects of RCA location X RCA treatment would explicitly account for spatial variation in this predictor. Validity of the findings The growth parameter k is not necessarily a good measure of somatic growth. Instead, it reflects how quickly the asymptotic size Lmax is attained by a species, which may or may not correspond well with somatic growth. In the VBGF model, Lmax and k are often inversely related (higher Lmax results in a smaller k). However, species with a larger Lmax often attain larger sizes and can be bigger in size at the same age compared to species with a lower Lmax and higher k. Since they are larger at the same age, this means that actual somatic growth can be higher in cases of a lower k. Thank you for helping us use terminology more carefully. Throughout the manuscript, including the title, we have dropped the term “somatic growth rates”. We now refer only to “growth parameter k”, defining it when first introduced in the text. Goose Island and McMullin appear to share an RCA boundary (or they are very close), yet the responses to protection are very different. Is this just an artifact of habitat and which sites are the paired control sites? Some discussion is warranted. We have added the following statement to the discussion (revision lines 303-305): “…the Goose Island and McMullin Group RCAs share a boundary, yet only the former had larger fish in protected than in control sites; the control sites for the McMullin Group include several locations where conditions are more oceanic than at the control sites for Goose Island, which may have influenced this result (Fig. 2).” Table 2 includes the parameter values for the GLSM, however nowhere in the manuscript were there reports of the test statistics, significance levels and p-values for the different factors included in the model. This is much more important than the parameter values and should be what is reported, especially because there were lots of factors and interactions included in the models. Most readers care much more about the significance of the model factors than the parameters that can used to create a predictive model of the expected length anomaly at any given location (which is what Table 2 is good for). We used an AIC model selection approach, which is common in modern ecological research and which does not rely on traditional significance testing. Readers can evaluate parameters from top models via the confidence intervals presented in figures. We have also been very transparent showing competing models and diagnostic plots in the appendices. The body size anomaly effects of mobility class and topographic complexity need to be described in more detail and interpreted more in the discussion. The results are interesting that high mobility species are large in low complexity habitats while demersal species are larger in high complexity habitats. It is also interesting how the shifts in body size with depth are different between the two mobility classes. The following statement has been added to the discussion (revision lines 272-277). “The body sizes of high mobility-benthopelagic species increased with depth and decreased with greater topographic structural complexity, while the body sizes of low mobility-demersal species had the opposite relationship to these habitat variables. Perhaps the different responses reflect the distribution of resources being tracked (high mobility-benthopelagic species feeding more on pelagic prey near the surface: Love et al. 2002) and different antipredator strategies (low mobility-demersal species relying more on crypsis and refuges in complex reefs).” Show size frequency distributions of each species, but plotted by samples inside RCAs vs outside RCAs. These can be overlapping and shaded with transparency to allow the reader to see whether fish are bigger inside MPAs in general for each species. Appendix S10 shows this for each species overall, but not broken down into inside vs. outside. It would also be good to test whether the distributions differ significantly for any species or locations. The size frequency distributions in revision Appendix S12 are now stratified by RCA treatment. We did not test for differences in these distributions because our inferences are based on length anomalies and GLSMs that account for multiple predictors and some key interactions. The species by species, single predictor approach suggested by the reviewer, given our limited data set, would yield weak inferences. The actual data points should be shown on the plots in Fig. 3 and Fig. 4, not just the model fit and 95% CI, so the reader can see the spread of the length anomaly data as a function of growth parameter k or topographic complexity or depth in each panel. Reviewer 3 made the same comment and this response is meant for both reviewers. We too prefer to see actual data as much as possible, yet this suggestion is difficult to implement for our analysis because the trends we are testing can be visualized only after accounting for other variables that are continuous. Fig 3 is specific to demersal low-mobility species at depth = 30 m and structural complexity=3. We attempted to subset the data in a way that would approximate those depth and complexity values but our data are too few to do so meaningfully (after also subsetting by RCA location, RCA treatment, and behavioral class). Similarly, Fig. 4 is specific to species with k = 0.12. Because k is continuous, this creates similar problems. In the context of these issues, Appendix S17 now provides descriptive scatterplots of length anomalies of exploited species in relation to (a) parameter k, RCA treatment, RCA location and behavioral class (all depths and complexity values pooled), and (b) in relation to topographic structural complexity or depth, by behavioral class and RCA treatment (all locations and k values pooled). Our view is that the story told by those plots is not strong enough to include in the main text. It would also be nice to see the mean anomaly +/- SE for each point (which gives different information than the overall model fit and 95% CI). As explained above, this would provide only weak inferences, not adding anything to the statistical fits. Would it be possible use a different symbol for each species, so readers can see which points correspond to the different species? The different symbols would not be discernable because of point overlap; see Appendix S17. Why not also include the fish density data from the surveys to test whether abundances differ inside and outside the RCAs? Or the density and size data can be combined to calculate biomass and that could be used to test for MPA effects. We are working on a separate analysis of relative abundance (count per transect) using similar predictors. This is a complex analysis that requires negative binomial zero inflated models that we are still refining and will be the subject of a different paper. Are there any effects of time on the size differences detected inside and outside MPAs? The data from the first year could be compared to data collected in the last year or two of the surveys to calculate a change in size distributions and to test whether there is evidence that the size responses have increased over time inside the RCAs relative to the fished areas. As stated earlier under “Major changes,” our current analysis precludes rigorous testing of time/RCA age effects. See also revision lines 216-219 of methods. Reviewer 2 (Anonymous) Basic reporting Overall, the content of this manuscript is valuable to both the scientific community and ecosystem managers. The paper addresses the differences in how commercially and recreationally important fish species may differ in their response to spatial protection because of different life history strategies. However, there are some issues with the writing, analysis, and reported information that should be addressed prior to acceptance. The introduction seemed very choppy and is not constructed in logical progression. One simple change I would recommend is to combine the first and third paragraphs. The first paragraph discusses protected areas and how they may be beneficial, the third paragraph discusses how protection benefits are measured. The fourth paragraph (line 62) is situated in the Introduction as if it was a summary of the literature, but in reality this paragraph contains a hypothesis that the authors are trying to test. The Introduction should be re-written to identify the hypothesis that “interspecific variation in somatic growth rates and the age of spatial fishery closures should influence body size responses by exploited species to spatial protection (Fig. 1).” This is an interesting hypothesis, but is not sufficiently accepted or proven to be discussed as if it was fact. With respect to Fig. 1, I would like to see the data generated by this project placed into this construct, it might lead credence to, or allow the rejection of, this unstated hypothesis. Similarly, the authors correctly identify in the Introduction that habitat type affects life history characteristics of rockfishes, but don’t clearly describe how habitat fits into their conceptual model. These are all good points. We have addressed them with major revisions to the introduction and by eliminating the graphical hypothesis, as detailed above under “Major changes” Experimental design The work presented in this manuscript is appropriate for PeerJ. The research question is relevant and meaningful and the authors show how the research addresses an identified knowledge gap. The problem I saw, however, is that the authors mixed variables together in a way that makes it difficult to address their primary hypothesis. They said they wanted to know if species life history parameters, habitats, and age of protected areas influenced body size responses (growth rates) of different species. It appears to me, however, that they lumped all fished species together, didn’t include habitats specifically, and lumped different ages of protected areas. These points have been addressed in the revised introduction (See above). The comment about lumping all fished species is covered by our response to Reviewer 1 on the justification for using length anomalies; see revision lines 197-201. Also, I am concerned about the use of S. emphaeus as a control fish between the protected and fished sites. That species is small and probably shows less variation in body size compared to other rockfishes. In response to this comment, we have added the following paragraph to the methods section (revision lines 180-187): “Species were classed as “exploited” or “unfished” based on fishery data compiled by Anderson et al. (2019) or descriptions by Love et al. (2002). Only Puget Sound rockfish qualified as unfished. With the caveat that Puget Sound rockfish have the smallest maximum size in our sample, and therefore shows less body size variation than other rockfishes, we assumed that size distributions for this species are driven primarily by environmental variability, and therefore are an adequate control for the effects of exploitation and protection. Should this assumption be wrong, body size increases under spatial protection are likely to be detected, as Puget Sound rockfish have the highest k value in our sample (Table 1).” Also, I would like to see a more thorough explanation of the use of length anomalies. It is not clear why the authors chose to use length anomalies when they are trying to show that there is a difference in response of fast growing (larger) fishes vs slow growing (smaller) fishes. The justification for length anomalies is clarified in revisions lines 197-201. One major concern I have is the statement that, “Total lengths were estimated visually with the aid of a 1-metre-long ruler attached to a pole; although these estimates have limited precision, their measurement error likely is consistent across treatments and species, not biasing results.” I would argue that this type of visual length estimate is very likely to be problematic unless the various scuba divers have trained together and evaluated their individual accuracy and precision biases. In many places around the world, researchers using underwater visual census techniques have tested their accuracy and precision by estimating the lengths of targets of known size (e.g., plastic fish). In one study I know of, observers systematically underestimated the lengths of large fishes and overestimated the lengths of small fishes, but not every observer was biased in the same direction. I would like to see the authors address this issue more carefully. Apologies not being clearer on these matters during the initial submission. Revision lines 151-155 and Appendix S4 now describe our use of fish models for diver training in size estimates. Validity of the findings I liked the creativity and analytical approach taken by the authors. I would like to see more of the author’s work in the results section rather than in the supplementary material. Thank you for this comment. We have curated our choice of figures and tables for the main text in a way that focuses on our objectives and inferences derived from generalized least square models. The supplementary material is meant to support that story with some descriptive plots and additional information for readers who want to dig deeper. We prefer to leave the balance between the main text and supplements as it stands. A major portion of the results discuss the percentages of fish captured at an RCA since implementation. However, time since RCA implementation was not a factor in any of the analyses presented and is confounded by the factor of location. We have removed this material from results. The methods (revision lines 216-219) now state that “Although surveys spanned 6 years, sampling across RCA age-location-treatment combinations was unbalanced (Appendix S9), precluding GLSMs from testing RCA age by treatment interactions. Consequently, analyses pooled data for RCA ages of 8-15 years, with ages 13-15 being best represented (Appendices S9, S10).” I think conclusions are well stated along with possible factors that could be influencing the results. The authors provided good recommendations about modifying RCA’s . One question I have is that while the authors go into detail about illegal fishing activity within the RCA and how that could be affecting their results, I am curious about the amount of fishing outside of the RCA’s in your reference sites. Could some reference sites be experiencing low to moderate fishing while others are experiencing high fishing? How could that affect the differences that you are seeing, or not seeing, between the protected sites and the adjacent fishes sites. While we lack the data to test this, revision lines 318-320 of the discussion now state that: “…responses to spatial protection might be more likely to be detected if areas adjacent to reserves are fished more intensely (see Jennings 2000; Kaplan et al. 2019), and future analyses should consider these effects.” Comments for the Author I like the questions you are asking about the differences in response of fishes with different life history characteristics to protections offered by MPAs. Thank you for helping us improve the manuscript. Reviewer 3 (Rowan Trebilco) Basic reporting This article is well written and generally well-presented. My main suggestions for improvements are: (1) I think it is important to visualise the data (not just statistical fits) and some information presented in tables could be more usefully presented in figure form; (2) Consider adding some nuance to the expectations for responses to exploitation/protection (3) There are a few places where additional references could be added to better situate this work with relation to previously published work; I expand on these 3 points and provide additional specific suggestions below. Please also refer to my comments in the 'comments for the author' section for context. Thank you for these suggestions, which we address below. 1) visualising the data - As a general principle, in studies based on primary data, in my view the data should always be visualised (ideally in a way that directly relates to the statistical models being fit) so that the reader can visually assess the patterns. Just showing the statistical fits can be a bit misleading. I acknowledge that you do present the diagnostic plots in the supplementary materials, and that interested readers can grab the data and make plots themselves, but I still think it is preferable to show the data in the main manuscript. In this case, ideally points would be added to figs 3 and 4 showing the raw data that the models are fit to. Please see response to reviewer 1 on the same comment, which explains why the data could not be superimposed on the model fits. Appendix S17 now provide descriptive scatterplots. - The information presented in table 2 could be more usefully presented in the form of a 'parameter plot'. Most importantly, parameter plots allow for easy comparison of the magnitude of parameter estimates and whether CIs around estimates include 0. The table has been replaced by a new Fig. 2 (parameter plot). 2) expectations for responses to exploitation/protection - it struck me that the expectations explained from lines 62-72 and in figure 1 might be a bit of a simplification, as the expectations for responses of fast growing species in particular will be dependent upon the intensity of exploitation (along with how fast growing the species is). For the fastest-growing species, it might be expected that there would not be any appreciable impact on body size distribution until exploitation reaches a very high intensity. It would be good to add this nuance to the explanation. It would also be helpful to make it clearer in lines 62-71 that the explanation is in terms of relative (not absolute) body size. This is apparent in figure 1 and later in the main text, but not clear at lines 62-71. The context of this comments has changed, as we have revised the introduction extensively and eliminated Figure 1(see “major changes” described earlier). Based on this comment, however, the discussion now states (revision lines 256-258): “…body size differences between protected and exploited sites could be influenced by an interaction between parameter k and the intensity of exploitation (see Jennings 2000; Kaplan et al. 2019)…” 3) framing with respect to the literature: - line 59: consider adding papers that directly coniser impacts of exploitation on size structure of reef fish e.g. Zgliczynski, B.J., Sandin, S.A., 2017. Size-structural shifts reveal intensity of exploitation in coral reef fisheries. Ecological Indicators 73, 411–421. https://doi.org/10.1016/j.ecolind.2016.09.045 Dulvy, N.K., Polunin, N.V., Mill, A.C., Graham, N.A., 2004. Size structural change in lightly exploited coral reef fish communities: evidence for weak indirect effects. Can. J. Fish. Aquat. Sci. 61, 466–475. https://doi.org/10.1139/f03-169 The first of these papers is now cited in the introduction. - line 88: consider adding papers led by indigenous authors at line 88. e.g. Jones, R., Rigg, C., Lee, L., 2010. Haida Marine Planning: First Nations as a Partner in Marine Conservation. E&S 15, art12. https://doi.org/10.5751/ES-03225-150112 Jones, R., Rigg, C., Pinkerton, E., 2017. Strategies for assertion of conservation and local management rights: A Haida Gwaii herring story. Marine Policy 80, 154–167. https://doi.org/10.1016/j.marpol.2016.09.031 The first of these papers is now cited in the introduction and discussion - lines 100 -- 111: It would be good to provide some context for expectations around the effects of habitat on size structure given that this features prominently in analyses. e.g. Trebilco, R., Dulvy, N., Stewart, H., Salomon, A., 2015. The role of habitat complexity in shaping the size structure of a temperate reef fish community. Mar. Ecol. Prog. Ser. 532, 197–211. https://doi.org/10.3354/meps11330 We appreciate this comment, but the request is outside the scope of our objectives. As clarified in the 7th paragraph of the revised introduction, we addressed key habitat variables only as necessary controls that would allow us to focus on our main predictors of interest (k, RCA treatment, and behavioral class). - line 301: Trebilco et al 2016 demonstrated increasing trophic position with body size among rockfish prior to the article by Olson et al. cited here. Trebilco, R., Dulvy, N.K., Anderson, S.C., Salomon, A.K., 2016. The paradox of inverted biomass pyramids in kelp forest fish communities. Proc. R. Soc. B 283, 20160816. https://doi.org/10.1098/rspb.2016.0816 We now cite this in the introduction and discussion # additional specific suggestions and corrections Abstract - line 26: consider rewording the sentence beginning 'For 12 species' to 'Here, using a visual survey data collected from inside and outside Rockfish Conservation Areas on the Central Coast of British Columbia'. This could also note that these data were collected by the Central Coast Indigenous Research Alliance, in collaboration with First Nations. This is a really outstanding aspect of this work. The statement about visual surveys was incorporated into the abstract as described above (slightly modified). The statement about First Nations was incorporated into the last sentence of the abstract. - line 40: consider rewording 'frameworks can predict and help interpret...' to 'theory provide a useful lens for framing and interpreting...' Done Discussion: - line 222: consider adding 'better' with 'more fully' Done - line 228: add 'mature' after 'most' Done (good catch!) - line 242: 'exclusive' missing after 'mutually' - Done (another good catch) line 248: consider replacing 'controlling for key habitat variables' with 'accounting for habitat structural complexity' Done - line 252--257: consider adding some mention of potential importance of algal cover and kelp canopy structure. We appreciate this comment, but this is outside the scope of our objectives. That is why we are so explicit about structural complexity being “topographic.” Tables: - consider adding RVI (relative variable importance) values to table 2 to give some indication of how consistently variables were included in the best-supported models, without having to refer to the supplementary materials. Also see my comment above --- this would be better as a figure. We added RVIs to the parameter plot Figure 1 - consider adding 'with respect to growth rate' to main title. - add definition of k parameter to the caption - consider making labelling on the plot axes more readily interporable (e.g. change 'length anomaly' to 'length anomaly (observed size relative to species average) and 'lower (smaller)' and 'higher (larger)' instead of just 'lower' and 'higher') As described earlier, we have eliminated this figure (see “Major Changes”) Figure 2 - are points not shown for site locations for a particular reason? If not, I'd suggest showing them. Open circles could still work if points overlap. As now stated in the revised caption, individual sites are not identified to protect spatial data considered to be sensitive by First Nations. Figures 3 & 4 - Invoke Ram Myers' 3 golden rules of data analysis: Plot the data, Plot the data, Plot the data. See earlier response to this suggestion. Throughout - be consistent with a convention for presenting common names followed by latin names in parentheses or vice versa. At present it's a bit haphazard. We revised the main text accordingly. Experimental design The experimental design is appropriate and sample sizes are impressive given the logistical challenges of data collection. Validity of the findings Findings are valid and conclusions are well stated, with clear links to supporting results. The statistical analysis is rigorous and intelligent. Interpretations and speculation are articulated appropriately. Comments for the Author I enjoyed reading this manuscript and think that it will be a valuable addition to the literature. It is well-conceived and well-written, the statistical analyses are rigorous and appropriate, and the authors do an exemplary job of making the analyses transparent and repeatable. I think the manuscript is at a publishable standard in its current form (with a few minor typographical corrections), but I also offer some suggestions for improvement that I would encourage the authors to consider under 'basic reporting'. Thank you for helping us improve the manuscript. "	Here is a paper. Please give your review comments after reading it.
502	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Background. Jatropha curcas L., a perennial oilseed plant, is considered as a promising feedstock for biodiesel production. Genetic modification of flowering characteristics is critical for Jatropha breeding. However, analysis floral-specific promoters in Jatropha is limited.</ns0:p><ns0:p>Methods. In this study, we isolated the Jatropha ortholog of TM6 (JcTM6) gene from Jatropha flower cDNA library and detect the expression pattern of JcTM6 gene by quantitative reverse transcriptionpolymerase chain reaction (qRT-PCR). We isolated a 1.8-kb fragment from the 5' region of the JcTM6 gene and evaluated its spatiotemporal expression pattern in Arabidopsis using the β-glucuronidase (GUS) reporter gene and Arabidopsis ATP/ADP isopentenyltransferase 4 (AtIPT4) gene, respectively.</ns0:p><ns0:p>Results. JcTM6 was identified as a flower-specific gene in Jatropha. As expected, JcTM6 promoter was only active in transgenic Arabidopsis flowers with the strongest activity in stamens. Moreover, JcTM6:AtIPT4 transgenic Arabidopsis showed a phenotype of large flowers without any alterations in other organs. Furthermore, deletion of the region from -1,717 to -876 bp resulted in the disappearance of promoter activity in stamens but an increase in promoter activity in young leaves, sepals, and petals. Deletion analysis suggests that the -1,717-to -876-bp promoter fragment contains regulatory elements that confer promoter activity in stamens and inhibit activity in young leaves, sepals, and petals.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>Promoter plays a significant role in gene expression regulation. Three types of promoters are currently employed in plant genetic engineering, constitutive, tissue-specific, and inducible promoters <ns0:ref type='bibr' target='#b25'>(Muthusamy et al. 2017;</ns0:ref><ns0:ref type='bibr' target='#b29'>Potenza et al. 2004</ns0:ref>). Tissue-specific promoters drive transgene expression in a specific spatiotemporal pattern, which is effective in the modification of agronomic traits of crop plants. For example, the rice (Oryza sativa L.) gene OsGA2ox1 encodes a gibberellin (GA) catabolic enzyme, GA 2-oxidase <ns0:ref type='bibr' target='#b18'>(Lester et al. 1999;</ns0:ref><ns0:ref type='bibr' target='#b20'>Martin et al. 1999;</ns0:ref><ns0:ref type='bibr' target='#b41'>Thomas et al. 1999)</ns0:ref>. When the expression of OsGA2ox1 was driven by the constitutive Actin promoter, transgenic rice plants failed to set grains. To prevent sterility, the promoter of a GA biosynthesis gene, OsGA3ox2, which encodes GA 3-oxidase and is specifically active in shoots, was used to control the expression of OsGA2ox1. As expected, transgenic rice exhibited a semi-dwarf phenotype with normal yield <ns0:ref type='bibr' target='#b35'>(Sakamoto et al. 2003)</ns0:ref>. GA 20-oxidase is a GA biosynthetic enzyme in plants <ns0:ref type='bibr' target='#b5'>(Coles et al. 1999)</ns0:ref>. In poplar (Populus spp.), overexpression of the Pinus densiflora GA 20-oxidase gene (PdGA20ox) under the control of the constitutive 35S promoter increased GA levels, thereby accelerating stem growth and plant biomass; however, transgenic poplar plants showed poor leaf development and root growth. When the PdGA20ox gene was driven by a xylem-specific promoter DX15 from poplar, the undesirable phenotypes were reduced <ns0:ref type='bibr' target='#b15'>(Jeon et al. 2016)</ns0:ref>.</ns0:p><ns0:p>Physic nut (Jatropha curcas L.) is an oilseed plant belonging to the Euphorbiaceae family. The seed oil of Jatropha is a promising feedstock for biodiesel production <ns0:ref type='bibr' target='#b16'>(Kumar & Sharma 2008)</ns0:ref>. However, low seed yield, which is mainly caused by low female: male ratio, is a longstanding problem in Jatropha <ns0:ref type='bibr' target='#b30'>(Raju & Ezradanam 2002;</ns0:ref><ns0:ref type='bibr' target='#b31'>Rao et al. 2008)</ns0:ref>. Jatropha is a monoecious plant species with male and female flowers on the same inflorescence, and the average ratio of female to male flowers is 1:13-1:29 <ns0:ref type='bibr' target='#b30'>(Raju & Ezradanam 2002;</ns0:ref><ns0:ref type='bibr' target='#b39'>Tewari et al. 2007</ns0:ref>). There are 100-300 flowers in each inflorescence of Jatropha, which only produce approximately 10 fruits <ns0:ref type='bibr' target='#b16'>(Kumar & Sharma 2008;</ns0:ref><ns0:ref type='bibr' target='#b27'>Pan & Xu 2011)</ns0:ref>. Hence, genetic modification of flowering characteristics is critical for Jatropha breeding. Floral-specific promoters play crucial roles in this modification because they can drive efficient expression of functional genes in flowers without affecting the vegetative growth of plants. In pea (Pisum sativum), the PsEND1 promoter exhibits anther-specific activity. Expression of the ribonuclease gene barnase <ns0:ref type='bibr' target='#b9'>(Gardner et al. 2009)</ns0:ref> in Arabidopsis and Brassica napus under the control of the PsEND1 promoter causes anther ablation at an early developmental stage, leading to male sterility <ns0:ref type='bibr' target='#b34'>(Roque et al. 2007)</ns0:ref>. Arabidopsis APETALA3 (AP3) promoter was identified as a floral-specific promoter in petunia (Petunia x hybrida). Expression of the Agrobacterium tumefaciens isopentenyltransferase (ipt) gene under the control of the AtAP3 promoter in petunia increased the flower size, without affecting vegetative development <ns0:ref type='bibr' target='#b44'>(Verdonk et al. 2008)</ns0:ref>. However, analysis of promoters, especially floral-specific promoters, in Jatropha is limited. Although the Jatropha APETALA1 (JcAP1) promoter was recently identified as a reproductive tissue-specific promoter showing high activity in inflorescence buds and seeds <ns0:ref type='bibr' target='#b38'>(Tao et al. 2016)</ns0:ref>, it is not sufficient to address transgene expression analysis in Jatropha.</ns0:p><ns0:p>In this study, we isolated the promoter of the Jatropha ortholog of TOMATO MADS-BOX GENE 6 (JcTM6), a floral-specific gene. The activity of JcTM6 promoter was evaluated in Arabidopsis using the β-glucuronidase (GUS) reporter gene. The results of GUS staining showed that the JcTM6 promoter was active only in flowers, with the highest activity in stamens. By using this promoter directed a cytokinin biosynthesis gene, Arabidopsis ATP/ADP isopentenyltransferase 4 (AtIPT4) gene <ns0:ref type='bibr' target='#b19'>(Li et al. 2010)</ns0:ref>, only flower phenotype was changed in transgenic Arabidopsis. Furthermore, deletion analysis showed that an approximately 0.85-kb fragment of the JcTM6 promoter (-1717 to -876 bp) is critical for maintaining its floral-specific expression pattern.</ns0:p></ns0:div> <ns0:div><ns0:head>Materials & Methods</ns0:head></ns0:div> <ns0:div><ns0:head>Plant materials</ns0:head><ns0:p>Plants of Jatropha curcas and Arabidopsis thaliana ecotype Columbia (Col-0) were used in this study. Jatropha plants were cultivated in Xishuangbanna, Yunnan Province, China, as described previously <ns0:ref type='bibr' target='#b27'>(Pan & Xu 2011)</ns0:ref>. Arabidopsis plants were grown in an environmentally controlled room at 22°C under 16-h light/8-h dark photoperiod.</ns0:p></ns0:div> <ns0:div><ns0:head>JcTM6 expression analysis</ns0:head><ns0:p>The JcTM6 gene (GenBank accession no. MN820724) was identified in the Jatropha flower cDNA library <ns0:ref type='bibr' target='#b3'>(Chen et al. 2014)</ns0:ref>. Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was performed to examine the expression level of JcTM6 in different organs of Jatropha (roots, stems, young leaves, mature leaves, inflorescence buds, female flowers, male flowers, pericarps and seeds at 42 days after pollination (DAP), male sepals and petals, stamens, female sepals and petals, and pistils) and Arabidopsis (leaves and flowers). Total RNA from each organ was isolated using the silica particle extraction method <ns0:ref type='bibr' target='#b7'>(Ding et al. 2008)</ns0:ref>. Then, qRT-PCR was performed as previously described in <ns0:ref type='bibr' target='#b37'>Tao (2015)</ns0:ref>. The JcGAPDH and Atactin were used as an internal control for data normalization. Primers used for qRT-PCR are listed in Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref>. The results of qRT-PCR were obtained from three biological replicates and three technical replicates.</ns0:p></ns0:div> <ns0:div><ns0:head>Cloning of the upstream region of JcTM6</ns0:head><ns0:p>The 5' region of JcTM6 was isolated from Jatropha genomic DNA by genome walking <ns0:ref type='bibr' target='#b36'>(Siebert et al. 1995)</ns0:ref> according to the Genome Walker TM Kit Universal User Manual (Clontech). Then, the full-length JcTM6 promoter was amplified using the primers, XT405 and XT408. The PCR product was cloned into the pGEM-T Easy vector. Putative cis-acting elements in the JcTM6 promoter were analyzed using the PLACE database <ns0:ref type='bibr' target='#b12'>(Higo et al. 1999)</ns0:ref>. The transcriptional start site of JcTM6was identified as previously described in <ns0:ref type='bibr' target='#b38'>Tao (2016)</ns0:ref>. Primers employed for genome walking and 5'-RACE are listed in Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Construction of JcTM6 promoter-GUS fusion and Arabidopsis transformation</ns0:head><ns0:p>PeerJ reviewing <ns0:ref type='table' target='#tab_0'>PDF \| (2020:01:44947:1:1:NEW 14 Jun 2020)</ns0:ref> Manuscript to be reviewed</ns0:p><ns0:p>To generate the JcTM6:GUS plasmid, XbaI and BamHI were used to digested pBI101 <ns0:ref type='bibr' target='#b14'>(Jefferson et al. 1987)</ns0:ref>, and the pGEM -T Easy vector containing the JcTM6 promoter, respectively. The resulting fragments were ligated using the T4 DNA Ligase (Promega) to generate the JcTM6:GUS fusion construct. Then, the JcTM6:GUS plasmid was introduced into Agrobacterium tumefaciens EHA105 by electroporation (GenePulser Xcell; Bio-Rad), and the transformed A. tumefaciens cells were used to transform Arabidopsis plants by the floral dip method <ns0:ref type='bibr' target='#b4'>(Clough & Bent 1998)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Histochemical GUS staining assay</ns0:head><ns0:p>To perform GUS staining, various tissues of transgenic Arabidopsis were submerged in the GUS assay buffer (50 mM sodium phosphate [pH 7.0], 0.5 mM K 3 Fe (CN) 6 , 0.5 mM K 4 Fe (CN) 6 •3H 2 O, 0.5% Triton X-100, and 1 mM X-Gluc) and vacuum-infiltrated for 15 min. Then, tissues were incubated overnight at 37°C, cleared in 70% ethanol <ns0:ref type='bibr' target='#b14'>(Jefferson et al. 1987)</ns0:ref>, and examined under a stereomicroscope (Leica M80). The results of GUS staining were obtained from five biological replicates and three technical replicates.</ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head></ns0:div> <ns0:div><ns0:head>JcTM6 expression in Jatropha</ns0:head><ns0:p>We identified the JcTM6 cDNA (GenBank accession no. MN820724) from our Jatropha flower cDNA library constructed previously <ns0:ref type='bibr' target='#b3'>(Chen et al. 2014)</ns0:ref>. JcTM6 encodes a 230-amino acid protein, which shows high similarity to TM6 homologs from other plant species (Fig. <ns0:ref type='figure' target='#fig_0'>1A</ns0:ref>). Phylogenetic analyses showed that JcTM6, which contains the paleoAP3 motif, belongs to the TM6 group, rather than the euAP3 group (Fig. <ns0:ref type='figure' target='#fig_0'>1B</ns0:ref>).</ns0:p><ns0:p>To analyze the expression pattern of JcTM6 in Jatropha, qRT-PCR was performed using total RNA extracted from various tissues including roots, stems, leaves, inflorescences, female and male flowers, and pericarps and seeds at 42 DAP. The JcTM6 gene was predominantly expressed in female and male flowers (Fig. <ns0:ref type='figure' target='#fig_1'>2</ns0:ref>), indicating that JcTM6 is a flower-specific gene. Furthermore, JcTM6 showed high expression in the stamens of male flowers and petals of male and female flowers but low expression in sepals and pistils (Fig. <ns0:ref type='figure' target='#fig_1'>2</ns0:ref>). Thus, the expression pattern of JcTM6 in floral organs is consistent with that of class B genes <ns0:ref type='bibr' target='#b45'>(Weigel & Meyerowitz 1994)</ns0:ref>.</ns0:p><ns0:p>Isolation and sequence analysis of JcTM6 promoter A 1.8-kb fragment of the JcTM6 promoter (Fig. <ns0:ref type='figure' target='#fig_2'>3A</ns0:ref>, -1717 to +103 bp; GenBank accession no. MN044579) was isolated from Jatropha genomic DNA by genome walking <ns0:ref type='bibr' target='#b36'>(Siebert et al. 1995)</ns0:ref>. The transcription start site of JcTM6 was located 103 nt upstream of the translation start codon (Fig. <ns0:ref type='figure' target='#fig_2'>3A</ns0:ref>). Analysis of the JcTM6 promoter using the PLACE database <ns0:ref type='bibr' target='#b12'>(Higo et al. 1999</ns0:ref>) revealed various putative cis-elements in the 1.8-kb JcTM6 promoter fragment (Fig. <ns0:ref type='figure' target='#fig_2'>3A</ns0:ref>) including two CArG boxes, which act as binding sites for MADS-box transcription factors <ns0:ref type='bibr' target='#b13'>(Irish & Yamamoto 1995)</ns0:ref>, some pollen-specific elements, including five GTGANTG10 motifs (GTGA) and eight POLLEN1LELAT52 motifs (AGAAA) <ns0:ref type='bibr' target='#b24'>(Muschietti et al. 1994;</ns0:ref><ns0:ref type='bibr' target='#b33'>Rogers et</ns0:ref> al.</ns0:p></ns0:div> <ns0:div><ns0:head>Discussion</ns0:head><ns0:p>TM6 is a member of the MADS-box gene family, which belongs to the paleoAP3 lineage <ns0:ref type='bibr' target='#b28'>(Pnueli et al. 1991;</ns0:ref><ns0:ref type='bibr' target='#b32'>Rijpkema et al. 2006;</ns0:ref><ns0:ref type='bibr' target='#b46'>Wu et al. 2011)</ns0:ref>. In tomato (Solanum lycopersicum) and petunia, TM6 functions as a class B gene that play an essential role in stamen development, although it is mainly expressed in whorls 3 and 4, similar to a class C gene (de <ns0:ref type='bibr' target='#b22'>Martino et al. 2006;</ns0:ref><ns0:ref type='bibr' target='#b32'>Rijpkema et al. 2006)</ns0:ref>. In trioecious papaya (Carica papaya) plants, which produce male, female, and hermaphrodite flowers, two TM6 genes were isolated previously (CpTM6-1 and CpTM6-2). Both genes are predominantly expressed in the petals of all sex types and stamens of hermaphrodite and male flowers, although CpTM6-2 is also expressed in leaves <ns0:ref type='bibr' target='#b1'>(Ackerman et al. 2008)</ns0:ref>. In this study, we identified JcTM6 as a flower-specific gene in Jatropha, with high expression in female and male flowers (Fig. <ns0:ref type='figure' target='#fig_1'>2</ns0:ref>). Similar to CpTM6-1, the JcTM6 gene showed high expression in the petals of female and male flowers and stamens of male flowers. Because JcTM6 showed flower-specific expression, we isolated its upstream region from Jatropha genomic DNA and analyzed its activity in Arabidopsis by GUS staining.</ns0:p><ns0:p>In transgenic Arabidopsis, GUS staining showed that the JcTM6 promoter was active only in flowers (Fig. <ns0:ref type='figure'>4</ns0:ref>), suggesting that the JcTM6 promoter is a flower-specific promoter. AtIPT4 is a cytokinin biosynthesis gene encoding ATP/ADP isopentenyltransferase. The expression of this gene under the control of AP1 promoter results in the alterations in flower number and organs <ns0:ref type='bibr' target='#b19'>(Li et al. 2010</ns0:ref>). However, the AtIPT4 driven by JcTM6 promoter only gave rise to the changes in flower organs (Fig. <ns0:ref type='figure'>6</ns0:ref>), indicating that JcTM6 promoter is active at the late stage of flower development rather than floral meristem. This activity is consistent with the expression pattern of the JcTM6 gene in Jatropha. Recently, <ns0:ref type='bibr' target='#b23'>Ming et al. (2020)</ns0:ref> showed that JcTM6 promoter has a high activity in female flowers of Jatropha, suggesting that JcTM6 promoter can drive flowerspecific expression of transgenes in different plant species.</ns0:p><ns0:p>When the 842-bp fragment of the JcTM6 promoter (-1,717 to -876 bp) was deleted, the promoter was not only active in flowers but also in young leaves (Fig. <ns0:ref type='figure' target='#fig_3'>5B</ns0:ref>). We found that the deleted region contained one of the two CArG box motifs, which are very important for mediating the regulatory effect of MADS-box transcription factors <ns0:ref type='bibr' target='#b8'>(Dolan & Fields 1991;</ns0:ref><ns0:ref type='bibr'>RichardTreisman 1992)</ns0:ref>. In Jatropha, a fragment of the JcAP1 promoter (from -1,313 to -1,057 bp), which contains a CArG box motif, is required for promoter activity in inflorescence buds <ns0:ref type='bibr' target='#b38'>(Tao et al. 2016)</ns0:ref>. The Arabidopsis AP3 promoter contains three CArG boxes: CArG1 is essential for AP3 promoter activity at all stages of flowering; CArG2 is critical for AP3 expression in petals, and CArG3 represents the binding site of a transcription factor that represses the activity of AP3 promoter during early floral stages <ns0:ref type='bibr' target='#b42'>(Tilly et al. 1998)</ns0:ref>. Therefore, we propose that the CArG box motif in JcTM6 promoter plays an important role in conferring floral-specific activity in transgenic plants.</ns0:p><ns0:p>Among the floral organs, stamens exhibited the highest activity of JcTM6 promoter (Fig. <ns0:ref type='figure'>4F</ns0:ref>). This expression pattern could be regulated by pollen-specific elements contained in this promoter, including five GTGA and eight AGAAA motifs. The GTGA motif is critical for the expression of g10 promoter in tobacco pollen because mutation of the GTGA motif reduced g10 promoter activity in pollen <ns0:ref type='bibr' target='#b33'>(Rogers et al. 2001</ns0:ref>). The AGAAA motif, which was identified in the tomato late-stage pollen-specific LAT52 promoter, is necessary for promoter activity during pollen maturation <ns0:ref type='bibr' target='#b2'>(Bate & Twell 1998)</ns0:ref>. In potato (Solanum tuberosum L.), the GTGA and AGAAA motifs present in the promoter of SBgLR, a pollen-specific gene, are critical for highlevel gene expression in pollen <ns0:ref type='bibr' target='#b17'>(Lang et al. 2008</ns0:ref>). In the current study, deletion of an 84283-bp fragment of the JcTM6 promoter, containing four GTGA and two AGAAA motifs, abolished promoter activity in stamens (Fig. <ns0:ref type='figure' target='#fig_3'>5D</ns0:ref>). We assumed that these motifs are essential for the activity of the JcTM6 promoter in stamens. Given the importance of CArG box motifs, it is possible that the GTGA and AGAAA motifs cooperate with the CArG box to regulate JcTM6 promoter activity in stamens. In addition, although the deleted region contained six AGAAA motifs, these motifs do not seem to be required for JcTM6 promoter activity in stamens. Furthermore, the deleted region also contained a 6-bp quantitative element (Q-element), which plays an enhancer-like role <ns0:ref type='bibr' target='#b11'>(Hamilton et al. 1998)</ns0:ref>. In maize, deletion of the Q-element from the pollen-specific ZM13 promoter reduced the promoter activity by 10-fold <ns0:ref type='bibr' target='#b10'>(Hamilton et al. 2000)</ns0:ref>. Deletion of the Q-element probably also contributed to the loss of JcTM6 promoter activity in stamens in this study (Fig. <ns0:ref type='figure' target='#fig_3'>5D</ns0:ref>). In addition, the deletion variant of the JcTM6 promoter exhibited increased activity in sepals and petals (Fig. <ns0:ref type='figure' target='#fig_3'>5C and D</ns0:ref>), indicating the presence of potential negative elements in the deleted region, which inhibit promoter activity in sepals and petals. By the deletion analysis of the JcTM6 promoter, we demonstrate these elements are of great importance to the promoter activity in the flowers, and researches on the functions of these elements will be conducted in the future.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions</ns0:head><ns0:p>Floral-specific promoters play crucial roles in genetic modification of flowering characteristics. In this study, a 1.8-kb JcTM6 promoter fragment was isolated from Jatropha and characterized as a flower-specific promoter in transgenic Arabidopsis plants. When the region from -1,717 to -876 bp in the JcTM6 promoter was deleted, the promoter lost its flower-specific activity and gained activity in young leaves. Our results suggest that the JcTM6 promoter could be used to drive flower-specific expression of transgenes in plants. Manuscript to be reviewed</ns0:p><ns0:note type='other'>Figure 6</ns0:note><ns0:p>Flower size is increased in transgenic JcTM6:AtIPT4 Arabidopsis. </ns0:p><ns0:note type='other'>Figure 7</ns0:note><ns0:p>The expression analysis of AtIPT4, AHK2 and ARR5 in JcTM6:AtIPT4 transgenic Arabidopsis. </ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head>Figure 1 A</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head>Figure 2</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>Figure 3 JcTM6</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Figure 3</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head>Figure 5 Histochemical</ns0:head><ns0:label>5</ns0:label><ns0:figDesc>Figure 5</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head /><ns0:label /><ns0:figDesc>Inflorescences of wild-type (A) and transgenic L1 (B) and L22 (C) lines. Flowers of wild-type and transgenic L1 and L22 lines (D). Dissected flowers of WT and transgenic L1 and L22 lines (E). Se, sepals; Pe, petals; St, stamens; Ca, carpels; WT, wild-type. White bars = 3 mm, yellow bar = 2mm.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head>(</ns0:head><ns0:label /><ns0:figDesc>A) The expression levels of AtIPT4 in the leaves and flowers of wild type (WT) plants and transgenic lines (L1 and L22). (B) The expression levels of AHK2 and ARR5 in the flowers of wild type (WT) plants and transgenic lines (L1 and L22). The values represent the means ± standard deviation (n =3). Student's t-test was used to determine significant differences. * p ≤0.05, ** p ≤0.01.</ns0:figDesc></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head>Table 1 .</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Sequences of the primers used in this study</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Name</ns0:cell><ns0:cell>Sequence (5' to 3')</ns0:cell><ns0:cell>Feature</ns0:cell></ns0:row><ns0:row><ns0:cell>GSP1</ns0:cell><ns0:cell cols='2'>CTCTTGGAATAAGTAACCTGTCTGTTGG JcTM6 gene-specific primer for</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>genome walking</ns0:cell></ns0:row><ns0:row><ns0:cell>GSP2</ns0:cell><ns0:cell cols='2'>CAAAACCCACTACTACAAAACCGAAGA JcTM6 gene-specific primer for</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>genome walking</ns0:cell></ns0:row><ns0:row><ns0:cell>XT95</ns0:cell><ns0:cell>GCTGCTAAGGCTGTTGGGAA</ns0:cell><ns0:cell>JcGAPDH gene primer for qRT-</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>PCR</ns0:cell></ns0:row><ns0:row><ns0:cell>XT96</ns0:cell><ns0:cell>GACATAGCCCAATATTCCCTTCAG</ns0:cell><ns0:cell>JcGAPDH gene primer for qRT-</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>PCR</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>XK712 TATCTCTTCGGTTTTGTAGTAGTGGG</ns0:cell><ns0:cell>JcTM6 gene primer for qRT-PCR</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>XK713 TCTCTTGGAATAAGTAACCTGTCTGT</ns0:cell><ns0:cell>JcTM6 gene primer for qRT-PCR</ns0:cell></ns0:row><ns0:row><ns0:cell cols='3'>XT405 TGCTCTAGAAATAGCTATAAAATCAATT For cloning the full-length</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>promoter and construction of</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>JcTM6:GUS</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>XT408 CGCGGATCCTTTTCCTTTCTTCTTGATA</ns0:cell><ns0:cell>For cloning the full-length</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>promoter and construction of</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>JcTM6:GUS</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>XD548 GCTCTAGACGCTTACAGAATTTGCGA</ns0:cell><ns0:cell>For construction of D:GUS</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>XB994 CAATCTTTCCACGACCCATTTTTCCTT</ns0:cell><ns0:cell>JcTM6 gene-specific primer for 5'-</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>RACE</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>XK718 TGTGCCAATCTACGAGGGTTT</ns0:cell><ns0:cell>Atactin gene primer for qRT-PCR</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>XK719 TTTCCCGCTCTGCTGTTGT</ns0:cell><ns0:cell>Atactin gene primer for qRT-PCR</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>XK984 TCGCTGAGTTCCACCGCTCTAAG</ns0:cell><ns0:cell>AtIPT4 gene primer for qRT-PCR</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>XK985 AGGGTCCCATTTATCCATGTCATTG</ns0:cell><ns0:cell>AtIPT4 gene primer for qRT-PCR</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>XE815 CCTTGTCAATGGCAAGAAGAGGCAA</ns0:cell><ns0:cell>AHK2 gene primer for qRT-PCR</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>(Nishimura et al, 2004)</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>XE816 CACCTTCTGCAACTCGTCTGTT</ns0:cell><ns0:cell>AHK2 gene primer for qRT-PCR</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>XE819 TCAGAGAACATCTTGCCTCGT</ns0:cell><ns0:cell>ARR5 gene primer for qRT-PCR</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>XE820 AGCTGCGAGTAGATATCATTAGCTT</ns0:cell><ns0:cell>ARR5 gene primer for qRT-PCR</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:note place='foot'>PeerJ reviewing PDF \| (2020:01:44947:1:1:NEW 14 Jun 2020)</ns0:note> </ns0:body> "	"Response to the reviewers’ comments --- Manuscript ID 44947 June 3, 2020 Dear Editor， Thank you for your e-mail of Match 12, 2020 and for the reviewers' comments on our manuscript entitled ‘Jatropha curcas ortholog of tomato MADS-box gene 6 (JcTM6) promoter exhibits floral-specific activity in Arabidopsis thaliana’ by Wang et al. (Manuscript ID 44947). The reviewers’ comments and suggestions greatly helped us to improve the manuscript. We have addressed the reviewers' concerns and suggestions in a revised version of the manuscript, which we are resubmitting for your consideration. As suggested by the reviewers, new Fig. 6E (dissection of flowers in WT and transgenic plants) and new Fig. 7 (Expression analysis of AtIPT4, AHK2 and ARR5 in JcTM6:AtIPT4 transgenic Arabidopsis) were included in the revised manuscript. All other changes made in the revised manuscript are highlighted in a red font. Our point by point responses to the reviewers' comments are as follows: Reviewer #1 Physic nut (Jatropha curcas L.) is an important biodiesel resource, although its low seed yield significantly limits its potential. The authors of this study sought to identify a Jatropha curcas promoter that would likely be involved in flower yield improvement, it’s an interesting project. The experimental design is good, but there are still some experiments and results should be provided. This study isolates a JcTM6 promoter in Jatropha curcas, which might be useful for the Jatropha breeding in the future studies. However, there are still some comments for the authors to substantively revise the manuscript. 1. One of the major issue is the approach adopted to analyze the role of JcTM6 promoter in Arabidopsis. The authors expressed JcTM6 promoter and introduce this construct into Wild-type Arabidopsis plants. Then all the comparison made between wild-type Arabidopsis plants and wild-type plants that over expressed JcTM6 promoter. As we all know, the Arabidopsis thaliana is a model plant, and a frequent but inappropriate or inconclusive approach to isolate the function of the heterologous gene. Answer: Thanks to the referee's comments. It is also feasible to verify the promoter activity in heterologous plant species. For example, SRD1 is mainly expressed in the storage roots in sweet potato, and the SRD1 promoter confers underground organs-specific expression in transgenic Arabidopsis, carrot and potato (Noh et al. 2012. A sweetpotato SRD1 promoter confers strong root-, taproot-, and tuber-specific expression in Arabidopsis, carrot, and potato. Transgenic Research 21:265-278). In this study, the floral-specific activity of the JcTM6 promoter in Arabidopsis (Fig. 4) is consistent with the expression pattern of the JcTM6 gene in Jatropha (Fig. 2). 2. The function of JcTM6 is not clarified in this study. Only the expression pattern of JcTM6 is not enough to illustrate its roles. Answer: Thanks to the referee's comments. The main purpose of this study is to identify the activity of JcTM6 promoter rather than the function of JcTM6. 3. Based on the manuscript, the fragment of the JcTM6 promoter is about 1.8-kb, from–1717 to +103 bp. The rough result is that why only the 842-bp fragment of the JcTM6 promoter (–1,717 to –876 bp) was deleted to identify the function of JcTM6. The author shows that this deleted region contained one of the two CArG box motifs, but what about the other one. I noticed the Arabidopsis AP3 promoter contains three CarG boxes with different functions (Line 215-218). Generally, a series of deletions of the JcTM6 promoter should be generated to analyze the regulatory effect of different regions of the promoter, and then the vital region could be accurately confirmed. I think more interesting and useful results would be obtained. Answer: We agree with the referee's comments. A series of deletions of JcTM6 promoter should help us better understand the vital regions. In this study, by deleting the region from –1,717 to –876 bp of the JcTM6 promoter, which contains elements including CArG box, GTGA motif, AGAAA motifs and Q-element, we have demonstrated these elements are of great importance to the promoter activity in the flowers. Detailed researches on the functions of these elements will be conducted in the future. 4. I noticed that JcTM6 promoter was used to drive the AtIPT4 gene. I think the Figure 6 is rough to present the function of JcTM6 promoter. It’s not clear that the different morphological results were caused by the JcTM6 or AtIPT4 in Arabidopsis thaliana. Moreover, the sole morphological results provided do not exclude a possible silencing of the endogenous transcript. So, I suggest that, at least, the cytokinin contents must be investigated to confirm the results, and not only the morphological observations. The characters of floral organs and the expression abundance of the genes associated with cytokinin synthesis and transduction pathways should also be observed between the wild type and transgenic plants. Answer: Thanks to the referee’s comments. We think the morphological changes in flowers of JcTM6:AtIPT4 transgenic plants were caused by expression of the transgene AtIPT4, because JcTM6:GUS transgenic plants did not exhibit similar morphological alterations in flowers. As per the referee's suggestion, a new Fig. 7 has been included in the revised manuscript to show the expression levels of the transgene AtIPT4, a cytokinin biosynthesis gene, and the cytokinin signaling genes AHK2 and ARR5 in wild type and JcTM6:AtIPT4 transgenic plants. The result show that the expression levels of these genes were remarkably increased in the flowers of JcTM6:AtIPT4 transgenic plants, whereas the AtIPT4 expression in the leaves of transgenic plants was similar to that in leaves of wile-type plants (new Fig. 7). These results indicate that the morphological alterations in flowers of JcTM6:AtIPT4 transgenic plants were caused by the flower-specific expression of the transgene driven by the JcTM6 promoter. 5. I did not check whether the references were all mentioned. I would suggest the authors should make substantial and in-depth revisions to the manuscript. The conclusions in the manuscript are based on more data than the data presented in the current manuscript, and then the authors should restate their discussion and conclusions. Answer: Thanks to the referee’s comments. We have checked the references and added new results (new Fig. 6E and new Fig. 7) in the revised manuscript. Reviewer #2 1. The article titled ‘Jatropha curcas ortholog of tomato MADS-box gene 6 (JcTM6) promoter exhibits floral-specific activity in Arabidopsis thaliana’ is well written and clear. English is correct and easy to understand. I have only a small correction on line 190 ‘TM6 functions as a class B gene that play an essential role in stamen development’ and on line 193 ‘Both genes are predominantly …’ Answer: Thanks to the referee for this suggestion. We have revised the two sentences on lines 212 and 216 in the revised manuscript. 2. The literature references are adequate. Nevertheless, there is a typing mistake on line 145 and 359 ‘Meyerowitz’ instead of ‘Meyerowltzt’ Answer: Thanks to the referee for this suggestion. We have corrected the errors on lines 157 and 394 in the revised manuscript. 3. The context is clearly explained in the Introduction. However, I suggest you add some information about the flowering and reproduction of Jatropha (lined 60-61). For example, mention that Jatropha is a monoecious plant species. It would help the reader to understand the importance of male to female flower ratio. It would also clearly state why you investigate the gene expression in male and female flowers. Answer: Thanks to the referee for this suggestion. We have added more information about the flowering and reproduction of Jatropha in the introduction (lines 61‒65) in the revised manuscript. 4. The proposed article corresponds to the scopes of the journal. The research questions are well defined in the Introduction. The experimental design is relevant to answer the research questions. The methods are clearly described but the number of replicates is sometimes missing. Please provide the number of biological replicates and repetitions that were used for the qRT-PCR analysis (line 104) and the GUS staining assays (line 130). Answer: Thanks to the referee for this suggestion. The number of biological replicates and repetitions that were used for the qRT-PCR analysis (lines 112‒113) and GUS staining assays (lines 141‒142) have been included in the revised manuscript. 5. Regarding the deletion analysis, it is not clearly stated why you choose to remove this part of the promoter and not another neither why you analyze only one deletion and not several to understand more clearly the role of the different parts of the promoter. Please justify your strategy either in the 'material and methods' or the 'result' sections. Answer: Thanks to the referee's comments. A series of deletions of JcTM6 promoter should help us better understand the vital regions. In this study, by deleting the region from –1,717 to –876 bp of the JcTM6 promoter, which contains elements including CArG box, GTGA motif, AGAAA motifs and Q-element, we demonstrate these elements are of great importance to the promoter activity in the flowers. Detailed researches on the functions of these elements will be conducted in the future, which were mentioned in the discussion section (lines 267‒270) in the revised manuscript. 6. The results are well presented and raw data are available. The conclusions are well stated and answer the research question. Regarding the phenotype of the JcTM6::AtIPT4 transgenic Arabidopsis flowers, were all floral organs affected in the same way? Are the stamens and petals more affected than the other floral organs? Indeed, you show that the promoter is mainly active in the stamens and that the gene expression is higher in petals and stamens. Do you have some numerical data for flower comparison? It would help to be more precise on the observed phenotype. On the same way, you conclude from your results that the JcTM6 promoter is a flower-specific promoter. However, it is not active in all floral organ. Could we consider that it is more a stamen-specific promoter? Answer: Thanks to the referee's comments. We have dissected the flowers of wild type and JcTM6:AtIPT4 transgenic plants and showed the alterations in four whorls of floral organs (new Fig. 6E) in the revised manuscript. We found all the floral organs become larger, especially the petals. The results indicate that JcTM6 promoter is a flower-specific promoter rather than a stamen-specific promoter. We thank the reviewers for their thoughtful and constructive criticisms. Yours sincerely, Zeng-Fu Xu Zeng-Fu Xu, Ph.D., Professor CAS Key Laboratory of Tropical Plant Resource and Sustainable Use, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Menglun 666303, Yunnan, China Tel: +86-158 8718 2981; Fax: +86-691-8715070 E-mail: zfxu@xtbg.ac.cn; zengfu.xu@gmail.com Website: http://groups.xtbg.cas.cn/mbep/ "	Here is a paper. Please give your review comments after reading it.
503	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Background. Jatropha curcas L., a perennial oilseed plant, is considered as a promising feedstock for biodiesel production. Genetic modification of flowering characteristics is critical for Jatropha breeding. However, analysis floral-specific promoters in Jatropha is limited.</ns0:p><ns0:p>Methods. In this study, we isolated the Jatropha ortholog of TM6 (JcTM6) gene from Jatropha flower cDNA library and detect the expression pattern of JcTM6 gene by quantitative reverse transcriptionpolymerase chain reaction (qRT-PCR). We isolated a 1.8-kb fragment from the 5' region of the JcTM6 gene and evaluated its spatiotemporal expression pattern in Arabidopsis using the β-glucuronidase (GUS) reporter gene and Arabidopsis ATP/ADP isopentenyltransferase 4 (AtIPT4) gene, respectively.</ns0:p><ns0:p>Results. JcTM6 was identified as a flower-specific gene in Jatropha. As expected, JcTM6 promoter was only active in transgenic Arabidopsis flowers with the strongest activity in stamens. Moreover, JcTM6:AtIPT4 transgenic Arabidopsis showed a phenotype of large flowers without any alterations in other organs. Furthermore, deletion of the region from -1,717 to -876 bp resulted in the disappearance of promoter activity in stamens but an increase in promoter activity in young leaves, sepals, and petals. Deletion analysis suggests that the -1,717-to -876-bp promoter fragment contains regulatory elements that confer promoter activity in stamens and inhibit activity in young leaves, sepals, and petals.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>Promoter plays a significant role in gene expression regulation. Three types of promoters are currently employed in plant genetic engineering, constitutive, tissue-specific, and inducible promoters <ns0:ref type='bibr' target='#b25'>(Muthusamy et al. 2017;</ns0:ref><ns0:ref type='bibr' target='#b29'>Potenza et al. 2004</ns0:ref>). Tissue-specific promoters drive transgene expression in a specific spatiotemporal pattern, which is effective in the modification of agronomic traits of crop plants. For example, the rice (Oryza sativa L.) gene OsGA2ox1 encodes a gibberellin (GA) catabolic enzyme, GA 2-oxidase <ns0:ref type='bibr' target='#b18'>(Lester et al. 1999;</ns0:ref><ns0:ref type='bibr' target='#b20'>Martin et al. 1999;</ns0:ref><ns0:ref type='bibr' target='#b41'>Thomas et al. 1999)</ns0:ref>. When the expression of OsGA2ox1 was driven by the constitutive Actin promoter, transgenic rice plants failed to set grains. To prevent sterility, the promoter of a GA biosynthesis gene, OsGA3ox2, which encodes GA 3-oxidase and is specifically active in shoots, was used to control the expression of OsGA2ox1. As expected, transgenic rice exhibited a semi-dwarf phenotype with normal yield <ns0:ref type='bibr' target='#b35'>(Sakamoto et al. 2003)</ns0:ref>. GA 20-oxidase is a GA biosynthetic enzyme in plants <ns0:ref type='bibr' target='#b5'>(Coles et al. 1999)</ns0:ref>. In poplar (Populus spp.), overexpression of the Pinus densiflora GA 20-oxidase gene (PdGA20ox) under the control of the constitutive 35S promoter increased GA levels, thereby accelerating stem growth and plant biomass; however, transgenic poplar plants showed poor leaf development and root growth. When the PdGA20ox gene was driven by a xylem-specific promoter DX15 from poplar, the undesirable phenotypes were reduced <ns0:ref type='bibr' target='#b15'>(Jeon et al. 2016)</ns0:ref>.</ns0:p><ns0:p>Physic nut (Jatropha curcas L.) is an oilseed plant belonging to the Euphorbiaceae family. The seed oil of Jatropha is a promising feedstock for biodiesel production <ns0:ref type='bibr' target='#b16'>(Kumar & Sharma 2008)</ns0:ref>. However, low seed yield, which is mainly caused by low female: male ratio, is a longstanding problem in Jatropha <ns0:ref type='bibr' target='#b30'>(Raju & Ezradanam 2002;</ns0:ref><ns0:ref type='bibr' target='#b31'>Rao et al. 2008)</ns0:ref>. Jatropha is a monoecious plant species with male and female flowers on the same inflorescence, and the average ratio of female to male flowers is 1:13-1:29 <ns0:ref type='bibr' target='#b30'>(Raju & Ezradanam 2002;</ns0:ref><ns0:ref type='bibr' target='#b39'>Tewari et al. 2007</ns0:ref>). There are 100-300 flowers in each inflorescence of Jatropha, which only produce approximately 10 fruits <ns0:ref type='bibr' target='#b16'>(Kumar & Sharma 2008;</ns0:ref><ns0:ref type='bibr' target='#b27'>Pan & Xu 2011)</ns0:ref>. Hence, genetic modification of flowering characteristics is critical for Jatropha breeding. Floral-specific promoters play crucial roles in this modification because they can drive efficient expression of functional genes in flowers without affecting the vegetative growth of plants. In pea (Pisum sativum), the PsEND1 promoter exhibits anther-specific activity. Expression of the ribonuclease gene barnase <ns0:ref type='bibr' target='#b9'>(Gardner et al. 2009)</ns0:ref> in Arabidopsis and Brassica napus under the control of the PsEND1 promoter causes anther ablation at an early developmental stage, leading to male sterility <ns0:ref type='bibr' target='#b34'>(Roque et al. 2007)</ns0:ref>. Arabidopsis APETALA3 (AP3) promoter was identified as a floral-specific promoter in petunia (Petunia x hybrida). Expression of the Agrobacterium tumefaciens isopentenyltransferase (ipt) gene under the control of the AtAP3 promoter in petunia increased the flower size, without affecting vegetative development <ns0:ref type='bibr' target='#b44'>(Verdonk et al. 2008)</ns0:ref>. However, analysis of promoters, especially floral-specific promoters, in Jatropha is limited. Although the Jatropha APETALA1 (JcAP1) promoter was recently identified as a reproductive tissue-specific promoter showing high activity in inflorescence buds and seeds <ns0:ref type='bibr' target='#b38'>(Tao et al. 2016)</ns0:ref>, it is not sufficient to address transgene expression analysis in Jatropha.</ns0:p><ns0:p>In this study, we isolated the promoter of the Jatropha ortholog of TOMATO MADS-BOX GENE 6 (JcTM6), a floral-specific gene. The activity of JcTM6 promoter was evaluated in Arabidopsis using the β-glucuronidase (GUS) reporter gene. The results of GUS staining showed that the JcTM6 promoter was active only in flowers, with the highest activity in stamens. By using this promoter directed a cytokinin biosynthesis gene, Arabidopsis ATP/ADP isopentenyltransferase 4 (AtIPT4) gene <ns0:ref type='bibr' target='#b19'>(Li et al. 2010)</ns0:ref>, only flower phenotype was changed in transgenic Arabidopsis. Furthermore, deletion analysis showed that an approximately 0.85-kb fragment of the JcTM6 promoter (-1717 to -876 bp) is critical for maintaining its floral-specific expression pattern.</ns0:p></ns0:div> <ns0:div><ns0:head>Materials & Methods</ns0:head></ns0:div> <ns0:div><ns0:head>Plant materials</ns0:head><ns0:p>Plants of Jatropha curcas and Arabidopsis thaliana ecotype Columbia (Col-0) were used in this study. Jatropha plants were cultivated in Xishuangbanna, Yunnan Province, China, as described previously <ns0:ref type='bibr' target='#b27'>(Pan & Xu 2011)</ns0:ref>. Arabidopsis plants were grown in an environmentally controlled room at 22°C under 16-h light/8-h dark photoperiod.</ns0:p></ns0:div> <ns0:div><ns0:head>JcTM6 expression analysis</ns0:head><ns0:p>The JcTM6 gene (GenBank accession no. MN820724) was identified in the Jatropha flower cDNA library <ns0:ref type='bibr' target='#b3'>(Chen et al. 2014)</ns0:ref>. Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was performed to examine the expression level of JcTM6 in different organs of Jatropha (roots, stems, young leaves, mature leaves, inflorescence buds, female flowers, male flowers, pericarps and seeds at 42 days after pollination (DAP), male sepals and petals, stamens, female sepals and petals, and pistils) and Arabidopsis (leaves and flowers). Total RNA from each organ was isolated using the silica particle extraction method <ns0:ref type='bibr' target='#b7'>(Ding et al. 2008)</ns0:ref>. Then, qRT-PCR was performed as previously described in <ns0:ref type='bibr' target='#b37'>Tao (2015)</ns0:ref>. The JcGAPDH and AtActin were used as an internal control for data normalization. Primers used for qRT-PCR are listed in Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref>. The results of qRT-PCR were obtained from three biological replicates and three technical replicates.</ns0:p></ns0:div> <ns0:div><ns0:head>Cloning of the upstream region of JcTM6</ns0:head><ns0:p>The 5' region of JcTM6 was isolated from Jatropha genomic DNA by genome walking <ns0:ref type='bibr' target='#b36'>(Siebert et al. 1995)</ns0:ref> according to the Genome Walker TM Kit Universal User Manual (Clontech). Then, the full-length JcTM6 promoter was amplified using the primers, XT405 and XT408. The PCR product was cloned into the pGEM-T Easy vector. Putative cis-acting elements in the JcTM6 promoter were analyzed using the PLACE database <ns0:ref type='bibr' target='#b12'>(Higo et al. 1999)</ns0:ref>. The transcriptional start site of JcTM6was identified as previously described in <ns0:ref type='bibr' target='#b38'>Tao (2016)</ns0:ref>. Primers employed for genome walking and 5'-RACE are listed in Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Construction of JcTM6 promoter-GUS fusion and Arabidopsis transformation</ns0:head><ns0:p>PeerJ reviewing <ns0:ref type='table' target='#tab_0'>PDF \| (2020:01:44947:2:0:NEW 16 Jul 2020)</ns0:ref> Manuscript to be reviewed</ns0:p><ns0:p>To generate the JcTM6:GUS plasmid, XbaI and BamHI were used to digested pBI101 <ns0:ref type='bibr' target='#b14'>(Jefferson et al. 1987)</ns0:ref>, and the pGEM -T Easy vector containing the JcTM6 promoter, respectively. The resulting fragments were ligated using the T4 DNA Ligase (Promega) to generate the JcTM6:GUS fusion construct. Then, the JcTM6:GUS plasmid was introduced into Agrobacterium tumefaciens EHA105 by electroporation (GenePulser Xcell; Bio-Rad), and the transformed A. tumefaciens cells were used to transform Arabidopsis plants by the floral dip method <ns0:ref type='bibr' target='#b4'>(Clough & Bent 1998)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Histochemical GUS staining assay</ns0:head><ns0:p>To perform GUS staining, various tissues of transgenic Arabidopsis were submerged in the GUS assay buffer (50 mM sodium phosphate [pH 7.0], 0.5 mM K 3 Fe (CN) 6 , 0.5 mM K 4 Fe (CN) 6 •3H 2 O, 0.5% Triton X-100, and 1 mM X-Gluc) and vacuum-infiltrated for 15 min. Then, tissues were incubated overnight at 37°C, cleared in 70% ethanol <ns0:ref type='bibr' target='#b14'>(Jefferson et al. 1987)</ns0:ref>, and examined under a stereomicroscope (Leica M80). The results of GUS staining were obtained from five biological replicates and three technical replicates.</ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head></ns0:div> <ns0:div><ns0:head>JcTM6 expression in Jatropha</ns0:head><ns0:p>We identified the JcTM6 cDNA (GenBank accession no. MN820724) from our Jatropha flower cDNA library constructed previously <ns0:ref type='bibr' target='#b3'>(Chen et al. 2014)</ns0:ref>. JcTM6 encodes a 230-amino acid protein, which shows high similarity to TM6 homologs from other plant species (Fig. <ns0:ref type='figure' target='#fig_0'>1A</ns0:ref>). Phylogenetic analyses showed that JcTM6, which contains the paleoAP3 motif, belongs to the TM6 group, rather than the euAP3 group (Fig. <ns0:ref type='figure' target='#fig_0'>1B</ns0:ref>).</ns0:p><ns0:p>To analyze the expression pattern of JcTM6 in Jatropha, qRT-PCR was performed using total RNA extracted from various tissues including roots, stems, leaves, inflorescences, female and male flowers, and pericarps and seeds at 42 DAP. The JcTM6 gene was predominantly expressed in female and male flowers (Fig. <ns0:ref type='figure' target='#fig_1'>2</ns0:ref>), indicating that JcTM6 is a flower-specific gene. Furthermore, JcTM6 showed high expression in the stamens of male flowers and petals of male and female flowers but low expression in sepals and pistils (Fig. <ns0:ref type='figure' target='#fig_1'>2</ns0:ref>). Thus, the expression pattern of JcTM6 in floral organs is consistent with that of class B genes <ns0:ref type='bibr' target='#b45'>(Weigel & Meyerowitz 1994)</ns0:ref>.</ns0:p><ns0:p>Isolation and sequence analysis of JcTM6 promoter A 1.8-kb fragment of the JcTM6 promoter (Fig. <ns0:ref type='figure'>3A</ns0:ref>, -1717 to +103 bp; GenBank accession no. MN044579) was isolated from Jatropha genomic DNA by genome walking <ns0:ref type='bibr' target='#b36'>(Siebert et al. 1995)</ns0:ref>. The transcription start site of JcTM6 was located 103 nt upstream of the translation start codon (Fig. <ns0:ref type='figure'>3A</ns0:ref>). Analysis of the JcTM6 promoter using the PLACE database <ns0:ref type='bibr' target='#b12'>(Higo et al. 1999</ns0:ref>) revealed various putative cis-elements in the 1.8-kb JcTM6 promoter fragment (Fig. <ns0:ref type='figure'>3A</ns0:ref>) including two CArG boxes, which act as binding sites for MADS-box transcription factors <ns0:ref type='bibr' target='#b13'>(Irish & Yamamoto 1995)</ns0:ref>, some pollen-specific elements, including five GTGANTG10 motifs (GTGA) and eight POLLEN1LELAT52 motifs (AGAAA) <ns0:ref type='bibr' target='#b24'>(Muschietti et al. 1994;</ns0:ref><ns0:ref type='bibr' target='#b33'>Rogers et al. 2001)</ns0:ref>, and a Q element (TGACCT), which shows enhancer-like activity for the pollen-specific expression of maize (Zea mays L.) ZM13 gene <ns0:ref type='bibr' target='#b11'>(Hamilton et al. 1998)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Activity of the JcTM6 promoter in Arabidopsis</ns0:head><ns0:p>To detect the activity of JcTM6 promoter, a JcTM6 promoter-GUS fusion construct (Fig. <ns0:ref type='figure'>3B</ns0:ref>) was expressed in Arabidopsis, and GUS staining was monitored in homozygous T3 plants (Fig. <ns0:ref type='figure'>4</ns0:ref>). No GUS staining was observed in 10-day-old Arabidopsis seedlings (Fig. <ns0:ref type='figure'>4A</ns0:ref>). Among the five tissues of adult plants examined (including roots, stems, leaves, flowers, and green siliques) GUS staining was detected only in flowers (Fig. <ns0:ref type='figure'>4B-G</ns0:ref>). Among all floral organs, GUS staining intensity was the strongest in stamens, followed by sepals and petals, with faint staining in carpels (Fig. <ns0:ref type='figure' target='#fig_0'>S1</ns0:ref>). Based on the results of GUS staining, we conclude that the JcTM6 promoter functions as a flower-specific promoter in Arabidopsis.</ns0:p></ns0:div> <ns0:div><ns0:head>Deletion analysis of the JcTM6 promoter</ns0:head><ns0:p>To analyze the region essential for flower-specific activity of the JcTM6 promoter, we carried out a deletion analysis. A deletion variant of the JcTM6 promoter lacking the region from -1,717 to -876 bp was fused to the GUS gene and transformed into Arabidopsis (Fig. <ns0:ref type='figure' target='#fig_3'>5A</ns0:ref>). Compared with the full-length JcTM6 promoter, the deletion was not only active in flowers but also in young leaves (Fig. <ns0:ref type='figure' target='#fig_3'>5B</ns0:ref>). Moreover, the deletion showed no promoter activity in stamens but increased activity in sepals and petals (Fig. <ns0:ref type='figure' target='#fig_3'>5C and D</ns0:ref>). These results indicate that the region from -1,717 to -876 bp is critical for JcTM6 promoter activity in stamens and inhibition of promoter activity in young leaves, sepals, and petals.</ns0:p></ns0:div> <ns0:div><ns0:head>JcTM6:AtIPT4 transgenic Arabidopsis produced large flowers</ns0:head><ns0:p>To further verify the floral specificity of JcTM6 promoter, a cytokinin biosynthetic gene (AtIPT4) was expressed under the control of JcTM6 promoter in Arabidopsis. JcTM6:AtIPT4 vector was constructed and was transformed into Arabidopsis plants. A total of 25 independent JcTM6:AtIPT4 lines were obtained. As expected, all transgenic lines showed no vegetative difference from the wild type and most of them produced larger flowers (Fig. <ns0:ref type='figure'>6</ns0:ref>). Furthermore, the development of siliques was also unaffected. To verify the morphological alteration in flowers that is caused by the transgene, we examined the expression levels of AtIPT4 and the cytokinin signaling genes Arabidopsis histidine kinase 2 (AHK2) <ns0:ref type='bibr' target='#b26'>(Nishimura et al, 2004)</ns0:ref> and Arabidopsis response regulator 5 (ARR5) <ns0:ref type='bibr' target='#b6'>(D'Agostino et al, 2000)</ns0:ref> in wild type and JcTM6:AtIPT4 transgenic plants. The expression level of AtIPT4 in flowers of transgenic lines is significantly higher than that in wild type, whereas the AtIPT4 expression in the leaves of transgenic plants was not different from that in leaves of wile-type plants (Fig. <ns0:ref type='figure'>7A</ns0:ref>). As expected, higher expression levels of AHK2 and ARR5 were detected in the flowers of transgenic lines (Fig. <ns0:ref type='figure'>7B</ns0:ref>). These results indicate that the morphological alteration in flowers of JcTM6:AtIPT4 transgenic plants is caused by the flower-specific expression of the transgene driven by the JcTM6 promoter. JcTM6 promoter is indeed a flower-specific promoter.</ns0:p></ns0:div> <ns0:div><ns0:head>Discussion</ns0:head><ns0:p>TM6 is a member of the MADS-box gene family, which belongs to the paleoAP3 lineage <ns0:ref type='bibr' target='#b28'>(Pnueli et al. 1991;</ns0:ref><ns0:ref type='bibr' target='#b32'>Rijpkema et al. 2006;</ns0:ref><ns0:ref type='bibr' target='#b46'>Wu et al. 2011)</ns0:ref>. In tomato (Solanum lycopersicum) and petunia, TM6 functions as a class B gene that play an essential role in stamen development, although it is mainly expressed in whorls 3 and 4, similar to a class C gene (de <ns0:ref type='bibr' target='#b22'>Martino et al. 2006;</ns0:ref><ns0:ref type='bibr' target='#b32'>Rijpkema et al. 2006)</ns0:ref>. In trioecious papaya (Carica papaya) plants, which produce male, female, and hermaphrodite flowers, two TM6 genes were isolated previously (CpTM6-1 and CpTM6-2). Both genes are predominantly expressed in the petals of all sex types and stamens of hermaphrodite and male flowers, although CpTM6-2 is also expressed in leaves <ns0:ref type='bibr' target='#b1'>(Ackerman et al. 2008)</ns0:ref>. In this study, we identified JcTM6 as a flower-specific gene in Jatropha, with high expression in female and male flowers (Fig. <ns0:ref type='figure' target='#fig_1'>2</ns0:ref>). Similar to CpTM6-1, the JcTM6 gene showed high expression in the petals of female and male flowers and stamens of male flowers. Because JcTM6 showed flower-specific expression, we isolated its upstream region from Jatropha genomic DNA and analyzed its activity in Arabidopsis by GUS staining.</ns0:p><ns0:p>In transgenic Arabidopsis, GUS staining showed that the JcTM6 promoter was active only in flowers (Fig. <ns0:ref type='figure'>4</ns0:ref>), suggesting that the JcTM6 promoter is a flower-specific promoter. AtIPT4 is a cytokinin biosynthesis gene encoding ATP/ADP isopentenyltransferase. The expression of this gene under the control of AP1 promoter results in the alterations in flower number and organs <ns0:ref type='bibr' target='#b19'>(Li et al. 2010)</ns0:ref>. However, the AtIPT4 driven by JcTM6 promoter only gave rise to the changes in flower organs (Fig. <ns0:ref type='figure'>6</ns0:ref>), indicating that JcTM6 promoter is active at the late stage of flower development rather than floral meristem. This activity is consistent with the expression pattern of the JcTM6 gene in Jatropha. Recently, <ns0:ref type='bibr' target='#b23'>Ming et al. (2020)</ns0:ref> showed that JcTM6 promoter has a high activity in female flowers of Jatropha, suggesting that JcTM6 promoter can drive flowerspecific expression of transgenes in different plant species.</ns0:p><ns0:p>When the 842-bp fragment of the JcTM6 promoter (-1,717 to -876 bp) was deleted, the promoter was not only active in flowers but also in young leaves (Fig. <ns0:ref type='figure' target='#fig_3'>5B</ns0:ref>). We found that the deleted region contained one of the two CArG box motifs, which are very important for mediating the regulatory effect of MADS-box transcription factors <ns0:ref type='bibr' target='#b8'>(Dolan & Fields 1991;</ns0:ref><ns0:ref type='bibr'>RichardTreisman 1992)</ns0:ref>. In Jatropha, a fragment of the JcAP1 promoter (from -1,313 to -1,057 bp), which contains a CArG box motif, is required for promoter activity in inflorescence buds <ns0:ref type='bibr' target='#b38'>(Tao et al. 2016)</ns0:ref>. The Arabidopsis AP3 promoter contains three CArG boxes: CArG1 is essential for AP3 promoter activity at all stages of flowering; CArG2 is critical for AP3 expression in petals, and CArG3 represents the binding site of a transcription factor that represses the activity of AP3 promoter during early floral stages <ns0:ref type='bibr' target='#b42'>(Tilly et al. 1998)</ns0:ref>. Therefore, we propose that the CArG box motif in JcTM6 promoter plays an important role in conferring floral-specific activity in transgenic plants.</ns0:p><ns0:p>Among the floral organs, stamens exhibited the highest activity of JcTM6 promoter (Fig. <ns0:ref type='figure'>4F</ns0:ref>). This expression pattern could be regulated by pollen-specific elements contained in this promoter, including five GTGA and eight AGAAA motifs. The GTGA motif is critical for the expression of g10 promoter in tobacco pollen because mutation of the GTGA motif reduced g10 promoter activity in pollen <ns0:ref type='bibr' target='#b33'>(Rogers et al. 2001</ns0:ref>). The AGAAA motif, which was identified in the tomato late-stage pollen-specific LAT52 promoter, is necessary for promoter activity during pollen maturation <ns0:ref type='bibr' target='#b2'>(Bate & Twell 1998)</ns0:ref>. In potato (Solanum tuberosum L.), the GTGA and AGAAA motifs present in the promoter of SBgLR, a pollen-specific gene, are critical for highlevel gene expression in pollen <ns0:ref type='bibr' target='#b17'>(Lang et al. 2008</ns0:ref>). In the current study, deletion of an 84283-bp fragment of the JcTM6 promoter, containing four GTGA and two AGAAA motifs, abolished promoter activity in stamens (Fig. <ns0:ref type='figure' target='#fig_3'>5D</ns0:ref>). We assumed that these motifs are essential for the activity of the JcTM6 promoter in stamens. Given the importance of CArG box motifs, it is possible that the GTGA and AGAAA motifs cooperate with the CArG box to regulate JcTM6 promoter activity in stamens. In addition, although the deleted region contained six AGAAA motifs, these motifs do not seem to be required for JcTM6 promoter activity in stamens. Furthermore, the deleted region also contained a 6-bp quantitative element (Q-element), which plays an enhancer-like role <ns0:ref type='bibr' target='#b11'>(Hamilton et al. 1998)</ns0:ref>. In maize, deletion of the Q-element from the pollen-specific ZM13 promoter reduced the promoter activity by 10-fold <ns0:ref type='bibr' target='#b10'>(Hamilton et al. 2000)</ns0:ref>. Deletion of the Q-element probably also contributed to the loss of JcTM6 promoter activity in stamens in this study (Fig. <ns0:ref type='figure' target='#fig_3'>5D</ns0:ref>). In addition, the deletion variant of the JcTM6 promoter exhibited increased activity in sepals and petals (Fig. <ns0:ref type='figure' target='#fig_3'>5C and D</ns0:ref>), indicating the presence of potential negative elements in the deleted region, which inhibit promoter activity in sepals and petals. By the deletion analysis of the JcTM6 promoter, we demonstrate the combination of these elements are of great importance to the promoter activity in the flowers, and detailed studies of the functions of these elements will be conducted in the future.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions</ns0:head><ns0:p>Floral-specific promoters play crucial roles in genetic modification of flowering characteristics. In this study, a 1.8-kb JcTM6 promoter fragment was isolated from Jatropha and characterized as a flower-specific promoter in transgenic Arabidopsis plants. When the region from -1,717 to -876 bp in the JcTM6 promoter was deleted, the promoter lost its flower-specific activity and gained activity in young leaves. Our results suggest that the JcTM6 promoter could be used to drive flower-specific expression of transgenes in plants. Manuscript to be reviewed</ns0:p><ns0:note type='other'>Figure 3</ns0:note><ns0:p>JcTM6 promoter sequence and promoter-reporter gene construct. Manuscript to be reviewed</ns0:p><ns0:note type='other'>Figure 6</ns0:note><ns0:p>Flower size is increased in transgenic JcTM6:AtIPT4 Arabidopsis. </ns0:p><ns0:note type='other'>Figure 7</ns0:note><ns0:p>The expression analysis of AtIPT4, AHK2 and ARR5 in JcTM6:AtIPT4 transgenic Arabidopsis. </ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head>Figure 1 A</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head>Figure 2</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>(</ns0:head><ns0:label /><ns0:figDesc>A) The nucleotide sequence of the JcTM6 promoter. The transcription start site (+1) is in red. The start codon ATG is in bold and boxed. Putative regulatory elements on both strands are shown in bold and underlined. (B) A schematic of the T-DNA regions of the JcTM6:GUS binary vector used for transformation.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head>Figure 5 Histochemical</ns0:head><ns0:label>5</ns0:label><ns0:figDesc>Figure 5</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head /><ns0:label /><ns0:figDesc>Inflorescences of wild-type (A) and transgenic L1 (B) and L22 (C) lines. Flowers of wild-type and transgenic L1 and L22 lines (D). Dissected flowers of WT and transgenic L1 and L22 lines (E). Se, sepals; Pe, petals; St, stamens; Ca, carpels; WT, wild-type. White bars = 3 mm, yellow bar = 2mm.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head>(</ns0:head><ns0:label /><ns0:figDesc>A) The expression levels of AtIPT4 in the leaves and flowers of wild type (WT) plants and transgenic lines (L1 and L22). (B) The expression levels of AHK2 and ARR5 in the flowers of wild type (WT) plants and transgenic lines (L1 and L22). The values represent the means ± standard deviation (n =3). Student's t-test was used to determine significant differences. * p ≤0.05, ** p ≤0.01.</ns0:figDesc></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head>Table 1 .</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Sequences of the primers used in this study</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Name</ns0:cell><ns0:cell>Sequence (5' to 3')</ns0:cell><ns0:cell>Feature</ns0:cell></ns0:row><ns0:row><ns0:cell>GSP1</ns0:cell><ns0:cell cols='2'>CTCTTGGAATAAGTAACCTGTCTGTTGG JcTM6 gene-specific primer for</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>genome walking</ns0:cell></ns0:row><ns0:row><ns0:cell>GSP2</ns0:cell><ns0:cell cols='2'>CAAAACCCACTACTACAAAACCGAAGA JcTM6 gene-specific primer for</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>genome walking</ns0:cell></ns0:row><ns0:row><ns0:cell>XT95</ns0:cell><ns0:cell>GCTGCTAAGGCTGTTGGGAA</ns0:cell><ns0:cell>JcGAPDH gene primer for qRT-</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>PCR</ns0:cell></ns0:row><ns0:row><ns0:cell>XT96</ns0:cell><ns0:cell>GACATAGCCCAATATTCCCTTCAG</ns0:cell><ns0:cell>JcGAPDH gene primer for qRT-</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>PCR</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>XK712 TATCTCTTCGGTTTTGTAGTAGTGGG</ns0:cell><ns0:cell>JcTM6 gene primer for qRT-PCR</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>XK713 TCTCTTGGAATAAGTAACCTGTCTGT</ns0:cell><ns0:cell>JcTM6 gene primer for qRT-PCR</ns0:cell></ns0:row><ns0:row><ns0:cell cols='3'>XT405 TGCTCTAGAAATAGCTATAAAATCAATT For cloning the full-length</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>promoter and construction of</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>JcTM6:GUS</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>XT408 CGCGGATCCTTTTCCTTTCTTCTTGATA</ns0:cell><ns0:cell>For cloning the full-length</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>promoter and construction of</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>JcTM6:GUS</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>XD548 GCTCTAGACGCTTACAGAATTTGCGA</ns0:cell><ns0:cell>For construction of D:GUS</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>XB994 CAATCTTTCCACGACCCATTTTTCCTT</ns0:cell><ns0:cell>JcTM6 gene-specific primer for 5'-</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>RACE</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>XK718 TGTGCCAATCTACGAGGGTTT</ns0:cell><ns0:cell>Atactin gene primer for qRT-PCR</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>XK719 TTTCCCGCTCTGCTGTTGT</ns0:cell><ns0:cell>Atactin gene primer for qRT-PCR</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>XK984 TCGCTGAGTTCCACCGCTCTAAG</ns0:cell><ns0:cell>AtIPT4 gene primer for qRT-PCR</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>XK985 AGGGTCCCATTTATCCATGTCATTG</ns0:cell><ns0:cell>AtIPT4 gene primer for qRT-PCR</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>XE815 CCTTGTCAATGGCAAGAAGAGGCAA</ns0:cell><ns0:cell>AHK2 gene primer for qRT-PCR</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>(Nishimura et al, 2004)</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>XE816 CACCTTCTGCAACTCGTCTGTT</ns0:cell><ns0:cell>AHK2 gene primer for qRT-PCR</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>XE819 TCAGAGAACATCTTGCCTCGT</ns0:cell><ns0:cell>ARR5 gene primer for qRT-PCR</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>XE820 AGCTGCGAGTAGATATCATTAGCTT</ns0:cell><ns0:cell>ARR5 gene primer for qRT-PCR</ns0:cell></ns0:row></ns0:table></ns0:figure> </ns0:body> "	"Response to the reviewers’ comments --- Manuscript ID 44947 July 16, 2020 Dear Editor， Thank you for your e-mail of June 29, 2020 and for the reviewer’s comments on our manuscript entitled ‘Jatropha curcas ortholog of tomato MADS-box gene 6 (JcTM6) promoter exhibits floral-specific activity in Arabidopsis thaliana’ by Wang et al. (Manuscript ID 44947). The reviewer’s comments and suggestions greatly helped us to improve the manuscript. We have addressed the reviewer’s concerns and suggestions in a revised version of the manuscript, which we are resubmitting for your consideration. As suggested by the reviewer, a new Figure S1 (Histochemical GUS staining in different floral organs of transgenic Arabidopsis harboring the JcTM6:GUS fusion) was included in the revised manuscript. All other changes made in the revised manuscript are highlighted in a red font. Our point by point responses to the reviewer’s comments are as follows: Reviewer #3 1. An aim of this study is isolation of the promoter that is specifically active in the flower. The author’s experiments are all necessary to reveal the flower-specific promoter activity of JcTM6 in Arabidopsis and the results are support their hypothesis. However, a part of the experiments is not enough. My concern on the experimental design is the deletion analysis of the JcTM6 promoter (Figure 5). Authors searched the putative cis elements in the JcTM6 promoter using PLACE database and found that many putative sequences of the four type of cis elements were located in the promoter (Figure 3). Then, they analyzed a deletion JcTM6 promoter using Arabidopsis transgenic plants, but it is thought that this deletion analysis is very rough. Authors discussed cis elements that is required for the stamen (and petal) specific activity of the JcTM6 promoter region and proposed that a CArG motif, that is located around -1,060-bp region of the promoter, is important for the specific manner (Line 233 to 235). However, because another two motifs, GTGA and AGAAA, are also included in the deletion region between -1,717 bp and -875 bp (Figure 3 and 5), it is also possible that the GUS activity of the deletion promoter may result from the combined effect of deletion of CArG, GTGA and AGAAA. Therefore, authors should carry out the deletion promoter analyses using several types of the deletion JcTM6 promoter that have different length of the deletion regions. Alternatively, authors should analyze the promoter that include mutations or deletion in the CArG motif that is located around -1,060 bp region. These results from mentioned above analyses will be help to understand the promoter activity and regulatory mechanism of the JcTM6 promoter that is shown a flower-specific activity. Answer: We agree with the referee's comments. A series of deletions of JcTM6 promoter should help us better understand the effect of each motif on the promoter activity. In this study, by deleting the region from –1,717 to –876 bp of the JcTM6 promoter, which contains elements including CArG box, GTGA motif, AGAAA motifs and Q-element, we have demonstrated the combination of these elements are of great importance to the promoter activity in the flowers. Detailed studies of the functions of these elements will be conducted in the future. 2. Another my concern is the JcTM6 promoter activity in petals. The qRT-PCR analysis of JcTM6 in Jatropha (Figure 2) indicated that highly amount of its transcript is detected in the petals of male and female Jatropha flowers like as in stamens of male flower. Ming et al. (2020) reported that the GUS activity of the JcTM6 promoter:GUS fusion gene was observed in the petals of female flowers in Jatropha. The JcTM6 promoter:AtIPT4 transgenic plants (Figure 6) have large petals in its flowers compared to that in wild type. These results suggested that the JcTM6 promoter is active in either Arabidopsis petals or Jatropha petals. In contrast, In the JcTM6 promoter:GUS analysis (Figure 4), authors mentioned that faintly GUS staining was observed in the Arabidopsis petals (Line 168). This result is not enough to support the possibility that the promoter is active in the petals. Considering these matters, I recommend authors that they should carry out the histochemical analysis of the promoter:GUS fusion gene in the different stages during flower development to confirm whether strong promoter activity is observed in the Arabidopsis petals. Answer: Thanks to the referee’s comments. A new Figure S1 showing the GUS staining in different floral organs of JcTM6:GUS transgenic Arabidopsis was included in the revised manuscript, , in which weak GUS staining was observed in the petals. The result indicates a weak JcTM6 promoter activity in the petals of transgenic Arabidopsis. We thank the reviewer for the thoughtful and constructive criticisms and suggestions. Yours sincerely, Zeng-Fu Xu Zeng-Fu Xu, Ph.D., Professor CAS Key Laboratory of Tropical Plant Resource and Sustainable Use, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Menglun 666303, Yunnan, China Tel: +86-158 8718 2981; Fax: +86-691-8715070 E-mail: zfxu@xtbg.ac.cn; zengfu.xu@gmail.com Website: http://groups.xtbg.cas.cn/mbep/ "	Here is a paper. Please give your review comments after reading it.
504	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Soybean stem necrosis is caused by Cowpea mild mottle virus (CPMMV), transmitted by the whitefly Bemisia tabaci. CPMMV has already been recorded in all major soybeanproducing areas of Brazil. The impacts caused by CPMMV to the current Brazilian soybean production are unknown, thus the main objective of this study was to evaluate the effects of CPMMV infection on the main important soybean cultivars grown in the Southern and Midwestern regions of Brazil. Although asymptomatic in some of the tested cultivars, CPMMV infection significantly reduced the plant height, the number of pods per plant and the 1,000-grain weight. In addition, estimated yield losses ranged from 174 to 638 kg ha -1 , depending on the cultivar. Evidence of seed transmission of CPMMV was observed in the BMX POTENCIA RR cultivar. These results suggest that CPMMV could have an important role in the reduction of soybean productivity in Brazil, but symptomless infections might be hiding the actual impact of this pathogen in commercial fields and infected seeds could be the primary inoculum source of the virus in the field.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>INTRODUCTION</ns0:head><ns0:p>Soybean [Glycine max (L.) Merril] is an important crop worldwide as a source of oilseed and protein. Brazil is the second largest producer of soybean in the world, producing 114.8 million tons in a cultivated area of 35.8 million hectares in the 2018/2019 growing season. In Brazil, soybean is the most important economic crop, generating approximately 675 million US$ to the internal market and 31 billion US$ to exportation <ns0:ref type='bibr' target='#b20'>(Hirakuri & Lazzarotto, 2014;</ns0:ref><ns0:ref type='bibr' target='#b13'>Conab, 2018)</ns0:ref>. In the last decade, the soybean cultivated area in Brazil increased 64.9%, while the productivity increased from 2,800 to 3,400 kg ha -1 over the same time <ns0:ref type='bibr' target='#b13'>(Conab, 2018)</ns0:ref>.</ns0:p><ns0:p>Soybean can be affected by several pests regardless of growth stage. The occurrence of at least 15 viral diseases have been reported in Brazil <ns0:ref type='bibr' target='#b0'>(Almeida, 2008;</ns0:ref><ns0:ref type='bibr' target='#b29'>De Marchi et al., 2018)</ns0:ref>. The Cowpea mild mottle virus (CPMMV; family Betaflexiviridae, genus Carlavirus), which is the agent of soybean 'stem necrosis disease', is a single-stranded positive sense PeerJ reviewing PDF \| (2020:03:46655:1:1:NEW 8 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed RNA virus with flexuous filamentous particles (approx. 650 nm in length). The genome of 8200 nucleotides, a cap structure [m7GpppG] linked to the 5' terminus and a polyadenylated tail at the 3'end <ns0:ref type='bibr' target='#b32'>(Menzel, Winter & Vetten, 2010;</ns0:ref><ns0:ref type='bibr' target='#b25'>King et al., 2011;</ns0:ref><ns0:ref type='bibr' target='#b48'>Zanardo et al., 2014a)</ns0:ref> and has genomic organization with six open reading frames (ORFs), typical of the genus Carlavirus. ORF1 encode a replicase protein containing four conserved motifs: methyltransferase, C23 peptidase, RNA helicase and an RNAdependent RNA polymerase. ORF2, ORF3 and ORF4 encode proteins of the triple gene block (TGB). ORF5 encodes the coat protein (CP), and ORF6 encodes a nucleic acid binding protein <ns0:ref type='bibr' target='#b32'>(Menzel, Winter & Vetten, 2010)</ns0:ref>. CPMMV was reported infecting soybean in the 2000/2001 season in the state of Goiás, having been subsequently identified in soybean fields across Brazil in the states of Bahia, Mato Grosso, Maranhão, Paraná <ns0:ref type='bibr' target='#b1'>(Almeida et al., 2003</ns0:ref><ns0:ref type='bibr' target='#b2'>(Almeida et al., , 2005;;</ns0:ref><ns0:ref type='bibr' target='#b0'>Almeida, 2008)</ns0:ref> and in 2008, in the Minas Gerais and Tocantins states <ns0:ref type='bibr' target='#b0'>(Almeida, 2008)</ns0:ref>. Although steam necrosis is the common name of this disease, in the last years, mild mottle and mosaic were the most common symptoms observed for this virus infection <ns0:ref type='bibr'>(Zanardo et al. 2014;</ns0:ref><ns0:ref type='bibr'>Zanardo e Carvalho 2017)</ns0:ref>. The use of resistant cultivars is the most important method to reduce losses caused by virus disease. Resistant soybean cultivars to CPMMV have been reported in India (cv. F4C7-32 and JS335) <ns0:ref type='bibr' target='#b12'>(Cheruku et al., 2017)</ns0:ref>, in Puerto Rico (cv. IA3023) <ns0:ref type='bibr' target='#b5'>(Brace, Fehr & Graham, 2012)</ns0:ref>, Indonesia (cv. MLG0120) <ns0:ref type='bibr' target='#b41'>(Suryanto et al., 2014)</ns0:ref> and in Brazil (cv. BRS 133) a former and obsolete cultivar <ns0:ref type='bibr' target='#b3'>(Arias et al., 2015;</ns0:ref><ns0:ref type='bibr' target='#b36'>De Oliveira et al., 2018)</ns0:ref>. In general, carlaviruses are transmitted by aphids <ns0:ref type='bibr' target='#b25'>(King et al., 2011)</ns0:ref>, however CPMMV is one of the two exceptions of the genus that are transmitted in a non-persistent manner by the whitefly Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) <ns0:ref type='bibr' target='#b0'>(Almeida, 2008;</ns0:ref><ns0:ref type='bibr' /> PeerJ reviewing PDF \| (2020:03:46655:1:1:NEW 8 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed <ns0:ref type='bibr' target='#b31'>Marubayashi, Yuki & Wutke, 2010)</ns0:ref>. This pest itself can reduce soybean productivity <ns0:ref type='bibr'>(Lourenção et al.,1999;</ns0:ref><ns0:ref type='bibr'>Tamai et al., 2006)</ns0:ref> and was listed between the most important pest affecting this crop in Brazil <ns0:ref type='bibr' target='#b7'>(Brasil, 2019)</ns0:ref>. Bemisia tabaci is also an excellent vectors of viruses <ns0:ref type='bibr' target='#b34'>(Navas-Castillo, Fiallo-Olivé & Sánchez-Campos, 2011;</ns0:ref><ns0:ref type='bibr' target='#b19'>Gilbertson et al., 2015)</ns0:ref>, affecting several crops such as vegetables, fibres and ornamentals (De <ns0:ref type='bibr' target='#b4'>Barro et al., 2011;</ns0:ref><ns0:ref type='bibr' target='#b26'>Lapidot et al., 2014)</ns0:ref>. Bemisia tabaci is widely spread in Brazil, and the Middle East Asia Minor 1 (MEAM1, known as B biotype) is the prevalent species on major crops across the country <ns0:ref type='bibr' target='#b33'>(Moraes et al., 2018)</ns0:ref>. The species Mediterranean (MED, also known as biotype Q), which was reported in the south region of our country <ns0:ref type='bibr'>(Barbosa et al. 2015)</ns0:ref> was recently also detected in different states of Brazil but on ornamental plants collected from flower shops <ns0:ref type='bibr' target='#b33'>(Moraes et al., 2018)</ns0:ref>.</ns0:p><ns0:p>CPMMV is also easily transmitted by sap <ns0:ref type='bibr' target='#b9'>(Brunt & Kenten, 1973)</ns0:ref>, one characteristic that is very helpful to study the virus. The seed transmission of CPMMV has also been reported in different plant species such as soybean, cowpea (Vigna unguiculata) and common bean (Phaseolus vulgaris) in Africa <ns0:ref type='bibr' target='#b9'>(Brunt and Kenten 1973)</ns0:ref>, yardlong bean (Vigna unguiculata subsp. sesquipedalis) in Venezuela <ns0:ref type='bibr' target='#b8'>(Brito et al., 2012)</ns0:ref>, and by some soybean cultivars in India <ns0:ref type='bibr' target='#b45'>(Yadav et al., 2013)</ns0:ref>. There is no information about soybean seed transmission of Brazilian CPMMV isolates <ns0:ref type='bibr' target='#b2'>(Almeida et al., 2005)</ns0:ref>.</ns0:p><ns0:p>The effects caused by CPMMV to Brazilian soybean production have never been estimated <ns0:ref type='bibr' target='#b47'>(Zanardo and Carvalho 2017)</ns0:ref>, and the ability of Brazilian CPMMV to be transmitted by seed is still unknown. Thus, the goal of this study was to evaluate the damage of CPMMV on the major soybean varieties used in the main growing areas of the </ns0:p></ns0:div> <ns0:div><ns0:head>MATERIALS & METHODS</ns0:head></ns0:div> <ns0:div><ns0:head>Obtaining CPMMV isolate, complete genome characterisation and Bayesian phylogenetic analysis</ns0:head><ns0:p>The CPMMV isolate was collected from soybeans in Casa Branca County, São Paulo <ns0:ref type='bibr'>State, Brazil (2016</ns0:ref><ns0:ref type='bibr'>/2017)</ns0:ref>. Total RNA was extracted from the leaf tissue of symptomatic soybean plants using the PureLink Viral RNA/DNA Mini Kit (Thermo Fisher Scientific) following the manufacturer instructions. A transcription-polymerase chain reaction (RT-PCR) One Step using AMV reverse transcriptase (Promega, Brazil) was performed using the specifical primers CPMMV 1280-F (5'-GGC GTT CCA AAA GCT GCC GAT-3') and CPMMV 1696-R (5'-GGA GCC ACC TTT CCA ATC AA-3') <ns0:ref type='bibr' target='#b30'>(De Marchi et al., 2017)</ns0:ref>. All amplifications consisted of an initial step of 42°C for 30 min, a second step of 94°C for 2 min, 30 cycles of 94°C for 54 sec, annealing at 54°C for 50 sec and elongation at 72°C for 50 sec, followed by a final extension step at 72°C for 10 min. In order to obtain the complete genome characterisation of the CPMMV isolate from Casa Branca -SP (called CPMMV Casa Branca_BR), the RNA was used for construction of a cDNA library using the Complete ScriptSeq Kit (Epicenter, Illumina) and transcriptome sequencing with Illumina HiSeq2500 platform <ns0:ref type='bibr' target='#b39'>(Roossinck, Martin & Roumagnac, 2015)</ns0:ref> at the Center of Functional Genomics (ESALQ/USP, Piracicaba, Brazil). Adapter sequence removal and quality trimming were performed with CLC Genomics Workbench software version 9.0.3. Manuscript to be reviewed</ns0:p><ns0:p>The sequence obtained was analysed using the software Geneious v11.1.5. <ns0:ref type='bibr' target='#b23'>(Kearse et al., 2012)</ns0:ref> and compared to a dataset composed of 8 CPMMV complete genome isolates from Ghana <ns0:ref type='bibr' target='#b32'>(Menzel, Winter & Vetten, 2010)</ns0:ref>, Florida <ns0:ref type='bibr' target='#b40'>(Rosario et al., 2014)</ns0:ref> different Brazilian CPMMV isolates described by <ns0:ref type='bibr'>Zanardo et al., (2014)</ns0:ref>. and a sequence from India retrieved from GenBank. The sequences were compared using MAFFT v7.222 within the Geneious v.11.1.5 software, and phylogenetic analysis was performed using MRBAYERS 3.2.2. <ns0:ref type='bibr' target='#b38'>(Ronquist & Huelsenbeck, 2003)</ns0:ref>. Two independent runs were conducted simultaneously using 10 million generations and excluding 25% from the resulting tree as burnin. Phylogenetic tree was visualized, edited and rooted using FigTree v1.4.4. (tree.bio.ed.ac.uk/software/figtree/). Pairwise comparison between the sequences were performed with the program SDT v.1.2 <ns0:ref type='bibr'>(Muhire, Varsani & Martin, 2014)</ns0:ref> using the MUSCLE alignment option <ns0:ref type='bibr'>(Edgar, 2004)</ns0:ref>. The CPMMV CP (coat protein) nt sequence obtained in this study were also compared with 33 sequences of CPMMV CP retrieved from GenBank, the phylogenetic analysis of the CP can be found in Supplementary Figure <ns0:ref type='figure' target='#fig_7'>1</ns0:ref>. After virus identification and characterisation, the isolate was maintained in common bean (Phaseolus vulgaris L.) cv. Jalo by whitefly transmission.</ns0:p><ns0:p>Virus transmission was performed by transferring whitefly specimens (MEAM1) in cages containing infected soybean leaves for a viral acquisition access period (AAP) of 24 h. Following virus acquisition, whiteflies were transferred to cages containing healthy bean plants at the VC (cotyledon leaves) growth stage, for a 24-h inoculation access period (IAP) under controlled conditions at 30°C. After inoculation, insecticides (Oberon and Cartap) were sprayed on plants to eliminate all the whitefly adults, nymphs and eggs.</ns0:p><ns0:p>Thirty days after the IAP, plants were analysed for the virus presence. After virus PeerJ reviewing <ns0:ref type='table'>PDF \| (2020:03:46655:1:1:NEW 8 Jul 2020)</ns0:ref> Manuscript to be reviewed confirmation, the plants were used as source of inoculum for the field inoculation experiments. Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Experimental areas</ns0:head></ns0:div> <ns0:div><ns0:head>Field</ns0:head></ns0:div> <ns0:div><ns0:head>Experimental design and field inoculation</ns0:head><ns0:p>The experimental design was a randomised block, with two treatments (healthy and infected plants with CPMMV) with five replications. Each plot was comprised of six rows (5m), 0.45 m between rows and an average of 14 plants per meter (around 200 soybean plants, totalizing around 1,000 plants/treatment).</ns0:p><ns0:p>Soybean plants 30 days after sowing were inoculated with CPMMV using as inoculum source leaves of common bean cv Jalo infected with CPMMV. Leaves were ground in phosphate buffer 0.01 M, pH 7 containing the abrasive carborundum (600 mesh). The presence of the virus was detected by RT-PCR using specific primers for CPMMV as described previously.</ns0:p><ns0:p>The susceptibility of all cultivar was also tested under the same open field conditions.</ns0:p><ns0:p>Approximately 100 seeds per cultivar were sowed in the field in Botucatu during the 2019/2020 growing season, and the seedlings were inoculated ten days after emergence.</ns0:p><ns0:p>Inoculation and virus detection were performed as described previously.</ns0:p></ns0:div> <ns0:div><ns0:head>Field sampling and evaluation of agronomic traits</ns0:head><ns0:p>A total of 100 soybean samples collected for each treatment were evaluated for CPMMV infection 30 days after inoculation. When the plants were at physiological maturity (R8), the plant height was evaluated by the distance from the soil to the apex of the plant (cm) and the number of pods per plants was obtained by counting the total number of pods per plant. At the harvest, all plants of the plot were hand harvested and run through a thresher.</ns0:p><ns0:p>For each plot, the 1,000-grain weight (g) was determined, which was obtained by weighing 1,000 grains from the plants in the plot, and adjusting to 13% moisture in Manuscript to be reviewed addition to grain productivity, which was obtained by weighing the grains produced, and adjusting to 13% moisture, then converting into kg ha -1 .</ns0:p></ns0:div> <ns0:div><ns0:head>CPMMV transmission by soybean seeds</ns0:head><ns0:p>To study the seed-borne capacity of CPMMV, a random sample of seeds were collected from the CPMMV-infected BMX POTENCIA RR plot, harvested in the Botucatu field.</ns0:p><ns0:p>These soybean seeds were planted in Styrofoam seedling trays containing Tropstrato HA Hortaliças (Vida Verde Tecnologia em Substratos, Mogi Mirim, SP). The seedling trays were kept in an insect-proof cage. Germination was greater than 90%, and the seedlings did not show any typical disease symptoms. For virus detection, leaf samples were tested using RT-PCR. To compose a sample, leaves of ten plants were collected and combined in 80 samples tested, totalling 800 plants analysed. Once the presence of CPMMV was detected in a sample, the ten individually plants were tested for the presence of CPMMV.</ns0:p><ns0:p>The positive plants were kept in an insect-proof cage for 60 days in order to observe the appearance of symptoms.</ns0:p></ns0:div> <ns0:div><ns0:head>Data Analysis</ns0:head><ns0:p>Because of the interdependency and interrelationship of agronomic traits, principal component analysis (PCA) was performed to investigate the data collected in the current study. PCA was performed using Minitab 17 Statistical Software (2010). Data were also submitted to analysis of variance (ANOVA) using Statview software <ns0:ref type='bibr' target='#b14'>(Concepts and StatView, 1987)</ns0:ref> to determine whether significant differences (p < 0.05) occurred between treatments. Then means were compared using Tukey's test (α = 5%).</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:03:46655:1:1:NEW 8 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>RESULTS</ns0:head></ns0:div> <ns0:div><ns0:head>Virus characterisation and phylogenetic analysis</ns0:head><ns0:p>Based on pairwise sequence comparision, the complete genome of the CPMMV isolate Casa Banca_BR GenBank accession number MT473963) obtained in this study showed 99% of nucleotide identity with the KC774020 -Bean (FL_USA), KC884245 -Soybean (Brazil_MG), KC884244 -Soybean (Brazil_MG) and KC884246 -Soybean (Brazil_MT).</ns0:p><ns0:p>According to the classification used by <ns0:ref type='bibr'>Zanardo et al. (2014)</ns0:ref>, the CPMMV Casa Branca_BR isolate belongs to the BR2 group, which encompasses the most common CPMMV strains found in soybean in Brazil. This isolate was used as inoculum source for virus infection in all field experiments.</ns0:p><ns0:p>The CPMMV phylogenetic tree of the complete nucleotide genome sequence analysis grouped the CPMMV Casa Branca_BR Isolate with five isolates from Brazil and one from</ns0:p><ns0:p>The USA (Figure <ns0:ref type='figure' target='#fig_7'>1</ns0:ref>). Although there are few CPMMV complete sequences published in GenBank, the analysis showed that the isolate used in this study is representative to the Brazilian isolates.</ns0:p><ns0:p>(INSERT FIGURE <ns0:ref type='figure' target='#fig_7'>1</ns0:ref>)</ns0:p><ns0:p>Virus incidence and symptoms on inoculated plotsIn the field assays, where the cultivars were planted according to their regions, the symptoms observed were variable Manuscript to be reviewed the estimate of virus infection was carried out by sampling 100 soybean leaves from the plots followed by molecular analysis. For all the six sap-inoculated soybean cultivars the incidence of CPMMV was greater than 70% (Table <ns0:ref type='table'>1</ns0:ref>). showed mottle symptoms and the cultivar M 9144 RR showed weak mosaic.</ns0:p><ns0:p>(INSERT FIGURE <ns0:ref type='figure' target='#fig_9'>3</ns0:ref>)</ns0:p></ns0:div> <ns0:div><ns0:head>Field plot experiments and agronomic performance of the cultivars</ns0:head><ns0:p>The PCA of soybean cultivars comparing CPMMV-infected and healthy plants showed that the proportion of the variance retained by the first principal component (PC1) was 67.5% and for the second principal component (PC2) corresponded to 22.3% of the original remaining variance (Figure <ns0:ref type='figure' target='#fig_10'>4</ns0:ref>).</ns0:p><ns0:p>The exploratory analysis allowed the evaluation of the virus influence on the groups of cultivars and cultivated areas (Figures <ns0:ref type='figure' target='#fig_11'>4 and 5</ns0:ref>). Among the cultivars, the cv. M 9144 RR healthy plants had the greatest plant height and were among the varieties that have the highest number of pods per plants, which set this cultivar and treatment apart from the rest. Regarding the 1,000-grain weight, the cultivar that showed the best performance As expected, each cultivar had distinct performance once they have particular characteristics and they were cultivated in areas with contrasting environmental conditions (Figure <ns0:ref type='figure' target='#fig_11'>5</ns0:ref>). The exploration of the data also demonstrated that the cultivars M 6410 IPRO and TMG 7062 IPRO cultivated in Mogi Mirim -SP had a close performance to all evaluated traits as well as the cultivars M 7739 IPRO and M 8372 IPRO which were cultivated in Pedra Preta -MT. The cv. M 9144 RR tested in Planaltina -DF showed the most distant performance data comparing to the other cultivars (Figure <ns0:ref type='figure'>6</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>(INSERT FIGURE 6)</ns0:head><ns0:p>Analysis of variance showed that cultivar tested in this work presented different response to the virus infection, showing a significant (p<0.05) or non-significant reduction in the traits evaluated (Table <ns0:ref type='table'>1</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>(INSERT TABLE 1)</ns0:head><ns0:p>There was a significant effect (p<0.05) of CPMMV-infection in all agronomic traits evaluated in the cv. BMX POTÊNCIA RR. CPMMV-infected plants have reduction in plant height (p<0.01, F=467.76), number of pods per plant (p<0.01, F=36.53) and 1,000-grain weight (p<0.01, F=11.64) that reflected directly in the productivity (p=0.01, F=11.20),</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:03:46655:1:1:NEW 8 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed which had a loss of 638 kg ha -1 , this cultivar being the one that had the greater yield loss, approximately 16%.</ns0:p><ns0:p>Regarding the cultivar M 6410 IPRO, there was also a significant reduction (p < 0.05) in all traits evaluated, and only the plant height (p=0.74, F=0.1) did not differ to the cv. TMG 7062 IPRO. The cultivars tested in Pedra Preta -MT differed significantly only in one parameter each, plant height (p<0.01, F=17.92) and number of pods per plant (p<0.01, F=8.97), for cv. M 7739 IPRO and M 8372 IPRO, respectively. Although no significant effect of treatment in the yield occurred in both cultivars, there was a reduction of 370 kg ha -1 for cv. M 7739 IPRO (p=0.07, F=4.08), and 174 kg ha -1 for cv. M 8372 IPRO (p=0.33, F=1.07).</ns0:p><ns0:p>All traits in the cv. M 9144 RR, except for plant height, were affected significantly (p<0.05) due to CPMMV-infection. Although the height was significantly the same for the treatments (p=0.51, F=0.47), the reduction caused in number of pods per plant (p=0.25, F=7.57) and 1000-grain weight (p<0.01, F=182328.14) in the diseased plants directly affected the in productivity (p=0.01, F=9.85) with a reduction of 316 kg ha -1 , or approximately 14%.</ns0:p></ns0:div> <ns0:div><ns0:head>CPMMV transmission by soybean seeds</ns0:head><ns0:p>From 800 seedlings obtained from seeds harvested in the cv. BMX POTÊNCIA RR CPMMV-infected field plot, three plants were found to be infected by CPMMV, confirmed by RT-PCR. Not a single plant developed typical disease symptoms after emergence until their senescence. The observed percentage of plants infected with the virus was 0.375%, PeerJ reviewing PDF \| (2020:03:46655:1:1:NEW 8 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed but it is important to mention that the incidence of the virus in these plots was around 85%</ns0:p><ns0:p>and seeds from healthy soybean plants were part of the sample.</ns0:p></ns0:div> <ns0:div><ns0:head>DISCUSSION</ns0:head><ns0:p>The data obtained in this study revealed that CPMMV causes reduction of productivity, plant height, 1000-grain weight and pods per plant in the main soybean cultivars used in Brazil, suggesting that this virus may be responsible for economic losses for soybean crop in our country. Additionally, the CPMMV-seed transmission data for a Brazilian isolate highlights the seed importance as a primary inoculum source in the field, especially in areas with low whitefly population, such as the southern states of the country.</ns0:p><ns0:p>CPMMV was first recorded in Brazil in common bean in the 1980s <ns0:ref type='bibr'>(Costa et al.,1983)</ns0:ref>, and reported as a threat to soybean production in 2002 <ns0:ref type='bibr' target='#b1'>(Almeida et al., 2003)</ns0:ref>. The losses reported at that time were higher than 85%, since the cultivars used developed the stem necrosis symptom that affected the whole plant <ns0:ref type='bibr' target='#b1'>(Almeida et al., 2003)</ns0:ref>. The new cultivars has reduced the impact of this disease in soybean since there is no longer the development of systemic necrosis but infected plants show symptoms of mottling and mosaic <ns0:ref type='bibr' target='#b3'>(Arias et al., 2015)</ns0:ref>. Although some soybean cultivars are symptomless when infected by CPMMV, our results provide evidence that the productivity can be affected.</ns0:p><ns0:p>In our study, the highest reduction in productivity was observed for cv. BMX POTENCIA RR (638 kg ha -1 ), a cultivar that did not show any visual symptoms of CPMMV infection (Table <ns0:ref type='table'>1</ns0:ref>, Figure <ns0:ref type='figure' target='#fig_9'>3</ns0:ref>). The soybean genotype M 9144 RR, also asymptomatic for CPMMV infection, showed a reduction in productivity around 316 kg ha -1 (Table <ns0:ref type='table'>1</ns0:ref>). It is also Manuscript to be reviewed important to highlight that even a reduction in productivity of 174 kg ha -1 observed for cv.</ns0:p><ns0:p>M 8372 IPRO (asymptomatic for CPMMV infection) may cause an economic impact considering that an infected soybean field can show a reduction of productivity around 3 bags ha -1 , the bag (60 kg) price is, on average, 20.00 US$ (CEPEA, 2019). As Brazil is the largest soybean oilseed exporter in the world (USDA. 2019), the amount of bags reduction impacts directly not only for the farmers, but also the Brazilian economy.</ns0:p><ns0:p>The seed-borne virus transmission can also be an important component of the epidemiology of the disease in the field. It has already been reported that different isolates of CPMMV can be seed transmitted, as observed for cowpea, soybean and common bean seeds in Ghana <ns0:ref type='bibr' target='#b9'>(Brunt & Kenten, 1973)</ns0:ref> and yardlong bean seeds in Venezuela <ns0:ref type='bibr' target='#b8'>(Brito et al., 2012)</ns0:ref>. In Thailand, the virus was observed to be transmitted by soybean seeds at a frequency lower than 1% <ns0:ref type='bibr'>(Iwaki et al., 1982)</ns0:ref> but in India, the seed-borne nature of the virus was detected in several soybean cultivars with higher rates of transmission, ranging from 0.62% to 14.2% <ns0:ref type='bibr' target='#b45'>(Yadav et al., 2013)</ns0:ref>. Here we provide evidence that Brazilian CPMMV soybean isolates can be seed transmitted. In the world scale, phylogenetic analysis of the CP amino acids sequences demonstrate that CPMMV isolate from Casa Branca clusters together with CPMMV isolate from Argentina (KP402890) and Florida (KC774020), indicating a common origin <ns0:ref type='bibr' target='#b47'>(Zanardo & Carvalho, 2017)</ns0:ref>. Our data reinforce that the CPMMV capacity to be transmitted by seeds might have contribute for virus dissemination through different countries, highlighting the importance of studying the transmission capacity of this virus by infected seeds.</ns0:p><ns0:p>In addition, a few infected seeds can provide enough CPMMV inoculum to be disseminated by the efficient vector B. tabaci that is considered one of the main important Manuscript to be reviewed pest for soybean, common bean, melon and tomatoes in Brazil <ns0:ref type='bibr' target='#b6'>(Brasil, 2018)</ns0:ref> and a excelent vector of begomovirus, carlavirus and crinivirus <ns0:ref type='bibr' target='#b34'>(Navas-Castillo et al., 2011;</ns0:ref><ns0:ref type='bibr' target='#b19'>Gilbertson et al., 2015)</ns0:ref>. Bemisia tabaci MEAM1 (biotype B) is the predominant species in soybean in our country <ns0:ref type='bibr' target='#b33'>(Moraes et al., 2018)</ns0:ref> and is a highly polyphagous insect that can feed on more than 600 species of plants <ns0:ref type='bibr' target='#b37'>(Polston, De Barro & Boykin, 2014)</ns0:ref>. The Brazilian middle-western region is the largest soybean and common bean producer, and both crops are cultivated near to each other. It is already known that whiteflies can colonize soybean and common bean, as well the CPMMV can infect both crops <ns0:ref type='bibr' target='#b31'>(Marubayashi, Yuki & Wutke, 2010;</ns0:ref><ns0:ref type='bibr' target='#b22'>Inoue-Nagata et al., 2016)</ns0:ref>. The combination of these conditions may contribute for CPMMV transmission, since the common beans can serve as inoculum source of whiteflies and CPMMV to the soybean crop and vice versa.</ns0:p><ns0:p>In summary, we conclude that even asymptomatic for some important soybean genotypes currently planted in Brazil, the CPMMV infection can affect the soybean yield. Seed transmission of the virus can also be an important component for CPMMV dissemination in the field. Soybean breeding programs need to take into account CPMMV infection in order to provide genotypes that are resistant/tolerant to the virus. Manuscript to be reviewed Manuscript to be reviewed Manuscript to be reviewed </ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ reviewing PDF \| (2020:03:46655:1:1:NEW 8 Jul 2020) Manuscript to be reviewed Southern and Midwestern regions of Brazil. Additionally, the seed transmission ability of one CPMMV isolate from the São Paulo State was assessed.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ reviewing PDF \| (2020:03:46655:1:1:NEW 8 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ reviewing PDF \| (2020:03:46655:1:1:NEW 8 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head /><ns0:label /><ns0:figDesc>among the cultivars. BMX POTÊNCIA RR, M 7739 IPRO and M 8372 IPRO cultivars were symptomless to CPMMV infection. In contrast, the M 6410 IPRO, TMG 7062 IPRO and M 9144 RR cultivars showed the most severe symptoms. The most common symptoms were chlorosis, mottling and mild symptoms (Figure 2). Due to the variation in symptoms, PeerJ reviewing PDF \| (2020:03:46655:1:1:NEW 8 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ reviewing PDF \| (2020:03:46655:1:1:NEW 8 Jul 2020)Manuscript to be reviewed was TMG 7062 IPRO followed by M 7739 IPRO, M 6410 IPRO and BMX POTÊNCIA IPRO. The greatest productivity was reached by the cultivar BMX POTÊNCIA IPRO, followed by M 7739 IPRO and M 8372 IPRO, being the cv. BMX POTÊNCIA the most affected by the presence of the virus.(INSERT FIGURES 4 AND 5) </ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ reviewing PDF \| (2020:03:46655:1:1:NEW 8 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ reviewing PDF \| (2020:03:46655:1:1:NEW 8 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_7'><ns0:head>Figure 1 CPMMV</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_8'><ns0:head /><ns0:label /><ns0:figDesc>Figure 2</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_9'><ns0:head>Figure 3</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Figure 3</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_10'><ns0:head>Figure 4</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Figure 4</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_11'><ns0:head>Figure 5</ns0:head><ns0:label>5</ns0:label><ns0:figDesc>Figure 5</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='28,42.52,220.38,525.00,372.00' type='bitmap' /></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head /><ns0:label /><ns0:figDesc>experiments were conducted during the 2017/2018 growing season with six cultivars distributed in four different growing areas: cv. BMX POTENCIA RR in Botucatu, State of São Paulo (coordinates: 22°48'25.4'S, 48°25'46.4'W, elevation: 739 m, sowing date 01/11/2017), cv. M 6410 IPRO and TMG 7062 IPRO in Mogi Mirim, State of São Paulo (coordinates: 22°26'42.8'S 47°04'10.8'W, elevation: 687 m, sowing date 15/12/2017) , cv. M 7739 IPRO and M 8372 IPRO in Pedra Preta, State of Mato Grosso (coordinates:</ns0:figDesc><ns0:table><ns0:row><ns0:cell cols='2'>16°50'30.3'S 54º02'39.8'W, elevation: 744 m, sowing date 23/11/2017), and cv. M 9144</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>RR in Planaltina, Federal District (coordinates: 15°39'51.7'S 47°20'02.0'W, elevation:</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>887 m, sowing date 27/11/2017). The maximum, minimum and average temperatures</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>and rainfall that occurred during the experimental periods in the four areas were collected</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>from meteorological stations located next to the experimental fields and are available as</ns0:cell></ns0:row><ns0:row><ns0:cell>a climograph in Supplementary Table 2.</ns0:cell><ns0:cell>There is no technical information about</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>susceptibility/resistance to CPMMV available for all tested cultivar.</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>The four sites represented some important soybean producing areas in Brazil. They have</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>contrasting environmental conditions, and the cultivars were selected according to the</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>frequency that they were planted in each region. Parameters such as the occurrence of</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>weeds, diseases, and insect pressure, especially B. tabaci, were monitored throughout</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>the season. Field experiments were approved by the Universidade Estadual Paulista Julio</ns0:cell></ns0:row></ns0:table><ns0:note>de Mesquita Filho (UNESP) and Fundação de Estudos Agrícolas e Florestais (FEPAF) Processo 1259 Dow 01 Renate Krause-Sakate.PeerJ reviewing PDF \| (2020:03:46655:1:1:NEW 8 Jul 2020)</ns0:note></ns0:figure> <ns0:note place='foot' n='2'>PeerJ reviewing PDF \| (2020:03:46655:1:1:NEW 8 Jul 2020)</ns0:note> </ns0:body> "	"Faculdade de Ciências Agronômicas Universidade Estadual Paulista “Júlio de Mesquita Filho” 3780, Avenida Universitária Botucatu SP 18610-034 Tel: +55-014-3880-7487 https://www.fca.unesp.br/ renate.krause@unesp.br June 26th, 2020 Dear Editors We appreciate the reviewers for the carefully attention on our manuscript. We thank their generous comments and concerns which have helped us to improve our manuscript. All sentences commented were corrected and the questions were better explained. We believe that the manuscript is now appropriated for publication in PeerJ. Dr. Renate Krause Sakate Associate Professor of Plant Virology On behalf of all authors. Departamento de Proteção Vegetal \| DPV \| Faculdade de Ciências Agronômicas Av. Universitária, 3780 - Altos do Paraíso\| CEP 18610-034 \| Botucatu \| SP Tel (14) 38807487 \| e-mail: renate.krause@unesp.br \| www.fca.unesp.br Reviewer 1 (Anonymous) Basic reporting Authors addressed the effect of CPMMV isolate from Brazil on few soybean cultivars in different geographical locations. The result showed that CPMMV infection reduced crop yield and affected other agronomical traits, especially when a cultivar doesn’t show clear symptoms of infection. The introduction need more details in terms of the following: 1) I suggest authors to add more description about the Cowpea mild mottle virus: ssRNA(+), genome length, virion types, ORFs encoded.. etc. More information about de Cowpea mild mottle virus were added in the introduction as ssRNA(+), genome length, virion types, ORFs encoded. I have included these description in the introduction on page 1, lines 59 – 69. 2) Also, adding literature about soybean cultivars that have been reported to resist CPMMV would enrich the introduction. Some of the examples I found are listed below, and pretty sure authors can enrich the introduction with more information. • https://doi.org/10.1111/pbr.12455 • https://doi.org/10.2135/cropsci2012.01.0042 I have added literature about reported soybean cultivars resistant to CPMMV. We agree that this addition enriches the manuscript. We also added more references besides those ones sent by the Reviewer, I appreciate your attention in suggesting some references. Also adding some discription (in terms of virus resistance) of the tested cultivars would be of good addition. Datasheet of all cultivar tested in this work were consulted and there is no information about susceptibility or resistance to CPMMV. I added an observation on our manuscript on page 7, line 174. Experimental design On the experimental level, the manuscript can be significantly improved with a few extra works to carry out: 1) Partial sequence may not give a sufficient idea about the distance to other isolates. I encourage authors to fully sequence the CPMMV isolate and generate a phylogenetic tree with other known isolates of CPMMV strains. For example, some ORFs of few soybean mosaic virus strains share 100% identity, such as those in G5H and G7H strain, but their strains’ pathogenicity is different in few cultivars. I encourage authors to fully sequence the isolate they have. We agree that partial sequence is not sufficient to compare with other isolates. In order to improve our data in this way, the isolate studied in this work was completely sequenced (we had already sequenced it before). I have submitted the sequence to GenBank (accession number MT473963). It’s worth to mention that there are few complete sequences of CPMMV isolates available on GenBank, as far as we searched, there are only eight fully sequences available. We are also providing a phylogenetic tree of the CPMMV CP (coat protein) nt sequence obtained in this study compared with more 33 sequences of CPMMV CP retrieved from GenBank. The phylogenetic analysis of the CP will be available in Supplementary Figure 1. 2) Test the susceptibility of those cultivars in lab conditions where temperature, light, humidity and other factors are fixed. We have tested these cultivars in field conditions of Botucatu, Sao Paulo, Brazil. It is not on lab conditions, but in a same field condition (the cultivars were raised in the same day and were stayed under same field conditions). It was possible to observe the differences of symptoms caused by CPMMV on these cultivars. We add this test on manuscript on Material and Methods page 8, lines 197 – 201, and the results on page 11, lines 255 – 260. Validity of the findings 1) It will be of valuable addition if authors provide information about the climatic and geographical differences among the regions used in this study. We have added information about weather (temperature and reinfall), this information is available as Supplementary Figure 2, and an observation were added on page 7, lines 170 – 173. Information about geographical differences as coordinates and elevation of these region are described in the item “Experimental Areas” 2) Compare the susceptibility of those cultivars depending on the region. Each cultivar was tested on a specific region to see yield losses caused by CPMMV in the regions they are indicated (climatic zones of soybean production in Brazil). We have an experiment in Botucatu were we tested the susceptibility of all the cultivars. We saw that all were susceptible, but we did not test yield parameters since they are not indicated for Botucatu region and we cannot estimate yield parameters if the cultivar is not indicated for this region. 3) Please clarify the difference between symptomatic vs asymptomatic infection in terms of agronomic traits including crop yield.**** According to Hull (2014) a virus can influence plants in a variety of more subtle ways (reduction in growth, reduction in vigor, crop failure, size, shape). The types of response by plants to inoculation with a virus when the host is infectable are: Resistant (extreme hypersensitivity and hypersensitivity) and Susceptible (sensitive or tolerant). So, foliar symptom is not the only parameter to say if a cultivar is susceptible or not. In our experiment we can say that all cultivars tested were susceptible to the virus, since the virus was detected in the soybeans tested by RT-PCR. Even symptomless, we cannot assume that the plants are tolerant because we have significant effect of the virus in plant height, number of pods per plant, 1,000-grain weight causing a impact in the yield ranging 174 to 638 kg ha-1, in terms of bags per hectare, the losses reached 10 bags, which is very expressive, once that the national production average is 60 bags per hectare. In our case we assume that all cultivars are susceptible, and yield was the more affected agronomic trait by CPMMV. Comments for the Author I hope that authors address the experimental points in the revision so the manuscript can have better structure and results that are interesting for wider number of audience. Reviewer 2 (Valerien Zinsou) Basic reporting The english language is clear and unambiguous. The manuscript structure is conform to Peer J. The summary is clear in succint. The number of tables and figures is appropriate and their quality adequate. The caption/legends contain sufficient information. The references are appropriate. Thank you! We have cared in proofreading our manuscript. Experimental design The introduction state the scientific problem clearly. The materials and methods used are adequate for the problem under investigation and sufficiently well documented. The statistical treatment is adequate. Validity of the findings The manuscript provide important new information on the effects of CPMMV to Brazilian soybean production and the abilty the Brazilian CPMMV to be transmitted by seed. The experimental procedures are competently conducted even if field trials were not repeated. Conclusions are well stated. Comments for the Author I consider the manuscript worthy for publication subject to the minor corrections and suggestions provided below. The title sound like a review and need to be improved. Suggestions Title : Effects of Cowpea mild mottle virus on soybean cultivars in Brazil Lamas et al (Plant disease 101, 2017, Vol 10) shown that CPMMV was also detected in Sida sp (Malvaceae) and for the first time CPMMV was found in uncultivated plants belonging to several botanical families. Please include it in Lines 68-69 and complete your references. We agree about the title and we believe that de title suggested by the reviewer suits better for our manuscript. The lines pointed by the reviewer refers to plants (crops) affected by Bemisia tabaci. We have not specified the species of plant affected by CPMMV. I thank you for this observation and I have also added uncultivated plants as known crops affected by CPMMV on page 4, Lines 90. Add one column to the table for the yield loss I agree. I have added a column for the field loss to the table. "	Here is a paper. Please give your review comments after reading it.
505	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction.</ns0:head><ns0:p>In contrast to other countries, Taiwan's National Health Insurance (NHI) program allows patients to freely select the specialists and tiers of medical care facility without a referral. Some medical centers in Taiwan receive over 10,000 outpatients per day. In the NHI program, the co-payment was increased for high-tier facilities for outpatient visits in 2002, 2005, and 2017. However, the policies only mildly reduced the use of high-tier medical care facilities. The main purpose of this study was to evaluate the factors contributing to the patients' selection of the outpatient clinic of medical centers without a referral.</ns0:p><ns0:p>Methods. An online anonymous survey was conducted by using Google Form platform utilizing selfconstructed questionnaire from September to October 2018. A nationwide sample in Taiwan was recruited using convenience sampling through social media. Based on a literature review and a focus group, 20 factors that may affect the choice of the outpatient institution were constructed. The associations between items that affect the patients' choice of outpatient clinics were assessed using exploratory factor analysis. Principal axis factoring was performed to identify the major factors. Hierarchical logistic regression was conducted to determine which factors satisfactory explained 'visiting the outpatient clinic of the medical center for an illness without a referral.'</ns0:p><ns0:p>Results. During the survey period, 5060 people browsed the online survey, and 1003 responded and completed the online questionnaire. The response rate was 19.8%. A total of 987 valid responses was collected. In univariate analysis, 'physicians are highly reputable', 'physicians have a good medical practice', ' the institution has advanced equipment' had the largest effect on patients' selection of an outpatient institution. Exploratory factor analysis revealed that three main factors, namely 'physician factor,' 'image and reputation factor,' and ' facility and medication factor,' affected the outpatient choice. Multiple logistic regression indicated patients who reported that hospital facilities, high-quality drugs, and diverse specialties were very important were more likely to select the outpatient clinic of a medical center (OR 2.218,. Patients who reported that the physician factors were very important were less likely to select a medical center (OR 0.717, 95% CI: 0.523-0.984). Patients who were previously satisfied with their experience of the primary clinics or had a regular family doctor were less likely to choose a medical center (OR 0.509, 95% CI: 0.435-0.595 and OR 0.676, 95% CI: 0.471-0.969).</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusion.</ns0:head><ns0:p>In Taiwan, patients with good primary medical experience and regular family physicians had significantly lower rates by selecting the outpatient clinic of a medical center. The results of this study support that the key to establishing graded medical care is to prioritize the strengthening of the primary medical system.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>affect the choice of the outpatient institution were constructed. The associations between items that affect the patients' choice of outpatient clinics were assessed using exploratory factor analysis. Principal axis factoring was performed to identify the major factors. Hierarchical logistic regression was conducted to determine which factors satisfactory explained 'visiting the outpatient clinic of the medical center for an illness without a referral.' Results. During the survey period, 5060 people browsed the online survey, and 1003 responded and completed the online questionnaire. The response rate was 19.8%. A total of 987 valid responses was collected. In univariate analysis, 'physicians are highly reputable', 'physicians have a good medical practice', ' the institution has advanced equipment' had the largest effect on patients' selection of an outpatient institution. Exploratory factor analysis revealed that three main factors, namely 'physician factor,' 'image and reputation factor,' and ' facility and medication factor,' affected the outpatient choice. Multiple logistic regression indicated patients who reported that hospital facilities, high-quality drugs, and diverse specialties were very important were more likely to select the outpatient clinic of a medical center <ns0:ref type='bibr'>(OR 2.218,</ns0:ref>. Patients who reported that the physician factors were very important were less likely to select a medical center (OR 0.717, 95% CI:</ns0:p></ns0:div> <ns0:div><ns0:head n='1.'>Introduction</ns0:head><ns0:p>The National Health Insurance (NHI) program in Taiwan is a single-payer system founded in 1995. The NHI program comprises a hierarchy of medical care facilities consisting of four tiers: medical centers, regional hospitals, local community hospitals, and primary clinics.</ns0:p><ns0:p>However, referral systems have not yet been successfully implemented.</ns0:p><ns0:p>Hierarchical medical care means that medical resources can be used the most efficiently through professional division in the medical system. In most countries, primary care physicians act as healthcare 'gatekeepers' by providing initial medical interventions and referring patients to additional specialists (Yan, <ns0:ref type='bibr'>Kung & Lu, 2019)</ns0:ref>. Excluding situations of major illnesses and the urgent need for treatment at a medical center, people who are ill should first go to a family doctor or a nearby primary clinic. After doctors diagnose and treat patients, they could be referred to other specialty clinics or hospitals if indicated.</ns0:p><ns0:p>In contrast with other countries, patients in Taiwan have full and unrestricted access to all medical care facilities. Patients in Taiwan's NHI program can freely select specialists and the tier of medical care facility directly without a referral <ns0:ref type='bibr'>(Lynn et al., 2015)</ns0:ref> . The design of global budget payments and the fee for services result in patients favoring treatment at large hospitals, even for mild diseases, and medical centers are more likely to use advanced instruments and pharmaceuticals <ns0:ref type='bibr'>(Kuo, Chen & Lin, 2019;</ns0:ref><ns0:ref type='bibr'>Lee et al., 2018)</ns0:ref>.. Many patients in Taiwan not only consulted several physicians of different specialties and at different healthcare facilities, but also switched the physicians and facilities quickly <ns0:ref type='bibr'>(Wang & Lin, 2010)</ns0:ref>. This phenomenon has been suggested as a source of inefficiency in healthcare use and has resulted in high medical expenditures and costs of outpatient visits.</ns0:p><ns0:p>Studies have reported that people in developed countries visit a doctor 5-6 times a year, whereas in Taiwan, the average frequency of visits is 13 . More than 30,000 insured residents in Taiwan seek hospital inpatient and outpatient services over 100 times a year <ns0:ref type='bibr'>(Lynn et al., 2015)</ns0:ref>.</ns0:p><ns0:p>In certain large medical centers in northern Taiwan, the number of outpatients per day often exceeds 10,000. Furthermore, physicians frequently see over 50 patients in a morning, spending only 5 minutes or less for each consultation <ns0:ref type='bibr'>(Wu, Majeed & Kuo, 2010)</ns0:ref>. These short consultations can cause misinformation and misunderstanding between healthcare providers and patients because of the time to build rapport. The freedom to have multiple hospital return visits results in high use of outpatient hospital visits, drug prescriptions, and other health services <ns0:ref type='bibr'>Wang & Lin, 2010;</ns0:ref><ns0:ref type='bibr'>Yip et al., 2019)</ns0:ref> . Excessive use of health services is a critical and persistent problem in Taiwan. To moderate these rising costs, a graded medical system was implemented in the NHI program and increased the copayment for high-tier facilities for outpatient visits in <ns0:ref type='bibr'>2002, 2005, and 2017</ns0:ref>. Patients without a referral are charged an additional copayment ranging from 240 to 420 NTD (approximately 8 to 14 USD) for every visit to a high-tier medical facility. Although changes to the NHI copayment policies have mildly reduced the use of high-tier medical care facilities, studies have indicated that the effect of medical prices on people's medical behavior is very limited <ns0:ref type='bibr'>(Lee et al., 2018)</ns0:ref>. The increment in the copayment had little effect on the population, making them more willing to visit primary clinics first <ns0:ref type='bibr'>(Yang, Tsai & Tien, 2019)</ns0:ref>. . Factors affecting patients' selection of high-tier medical care facilities have not been fully identified. Cheng et al. reported that patients tend to base their judgment of hospital quality on medical equipment <ns0:ref type='bibr'>(Cheng, 2015)</ns0:ref> . The main purpose of this study was to evaluate the factors contributing to the patients' selection of the outpatient clinic of medical centers without a referral. Only when we clearly understand the motives underlying the public's choice, then we could establish a successful graded medical system in Taiwan.</ns0:p><ns0:formula xml:id='formula_0'>(</ns0:formula></ns0:div> <ns0:div><ns0:head n='2.'>Materials and Methods</ns0:head></ns0:div> <ns0:div><ns0:head n='2.1.'>Study design</ns0:head><ns0:p>The present study was a web-based cross-sectional online survey. The development and reporting of the survey followed the Checklist for Reporting Results of Internet E-survey (CHERRIES) guidelines <ns0:ref type='bibr'>(Eysenbach, 2004)</ns0:ref>. The checklist is available in the supplementary data. The questionnaire was developed in Google forms (https://www.google.com/forms/about/). After the initial tests and revision of the questionnaire were completed, and a nationwide sample in Taiwan was recruited using convenience sampling through an online anonymous survey from September 3 to October 31, 2018. By using the snowball sampling method, the questionnaire was introduced to a variety of community groups. To maximize public outreach, the survey was promoted in different social media such as Facebook, Line and the most popular bulletin board system (https://facebook.com/; https://linecorp.com/; and https://www.ptt.cc/index.bbs.html) with interested citizens being invited to complete the questionnaire and the respondents who took the survey being asked to continue inviting their friends to participate in the survey and fill out the questionnaire.</ns0:p><ns0:p>The link to the survey was available for a period of 8 weeks. All participants were invited to complete an anonymous self-administered online questionnaire, which required approximately 10 minutes to complete. Informed consent was requested from all participants on the first page of the questionnaire. Only participants who were at least 20 years old and were able to read Chinese fluently were given access. No rewards were provided to participants. A deduplication protocol was applied to identify multiple submissions and preserve data integrity, including crossvalidation of the eligibility criteria of key variables and discrepancies in key data <ns0:ref type='bibr' target='#b0'>(Bowen et al., 2008)</ns0:ref>. This study was approved by the Institutional Review Board of Taipei Veterans General Hospital (2017-07-009AC) , and the study was conducted in accordance with the guideline of Helsinki declaration 2013.</ns0:p></ns0:div> <ns0:div><ns0:head n='2.2.'>Questionnaire design</ns0:head><ns0:p>Because there was no similar questionnaire related to the selection of outpatient clinics, we developed our own questionnaire, finalized after experts were invited to review and revise. A literature search was performed for publications that discuss the factors affecting the outpatient choice. Search terms used were ' health care seeking behavior ', ' hospital outpatient clinics' Manuscript to be reviewed levels, attitudes towards copayment, and whether they have a regular family physician. Five experts with expertise in subject content were invited to modify the questionnaire for ensuring content validity. Questions were refined after feedback and finalized into the online survey.</ns0:p></ns0:div> <ns0:div><ns0:head n='2.3'>Reliability and validity analysis</ns0:head><ns0:p>The content of this questionnaire was obtained through the literature review and a focus group. Five senior researchers, who were expert in research, were invited to perform repeated questionnaire testing and discuss the entire instrument for content validity. The content was rated by five experts, resulting in a mean content validity index (CVI) of 86.0%.</ns0:p><ns0:p>At the beginning of the study, the questionnaire was pretested in 20 patients to determine if the content was appropriate and to ascertain whether the content was understandable. The internal consistency reliability test was used for reliability analysis. Cronbach's alpha of the questionnaire was 0.895, which is satisfactory.</ns0:p></ns0:div> <ns0:div><ns0:head n='2.4'>Statistical analysis</ns0:head><ns0:p>Descriptive statistics were used to present the results for patient hospital choices.</ns0:p><ns0:p>Independent samples t-tests and Chi-square tests were adopted to examine the association between respondents' demographic characteristics and their outpatient preference. The normality of the collected data was analyzed by the Kolmogorov-Smirnov test. As the data follow the normal distribution, comparisons between three groups were conducted by an analysis of variance (ANOVA).A p value of <0.05 (two-tailed) was considered statistically significant.</ns0:p><ns0:p>The associations between items that affect the patients' choice of outpatient clinics were assessed using exploratory factor analysis. Measures of sample adequacy such as Kaiser-Meyer-Olkin (0.868) and Bartlett's Test of Sphericity (significance <0.001) show that factor analysis can be applied. Principal axis factoring was performed to identify the major factors by using a correlation matrix and oblimin rotation. The number of principal components to be extracted was determined by examining the eigenvalues (>1). Loadings over 0.5 were used to interpret components in the study was set at 0.5. Finally, the number of domains was reduced to three and Manuscript to be reviewed</ns0:p><ns0:p>Hierarchical logistic regression was conducted to determine which factors satisfactorily explained the dependent variable 'visiting the outpatient clinic of the medical center for an illness without a referral.' The adjusted odds ratios (ORs) with 95% confidence intervals (CIs)</ns0:p><ns0:p>for predicting 'visit to an outpatient clinic of the medical center for an illness' were computed.</ns0:p><ns0:p>In model 1, the association of age, gender and personal experience of primary clinics were tested. </ns0:p></ns0:div> <ns0:div><ns0:head n='3.'>Results</ns0:head><ns0:p>During the survey period, 5060 people browsed the online survey, and 1003 responded and completed the online questionnaires. Therefore, the response rate was 19.8%. We excluded 16 participants because of duplication (the same age, occupation, and answer options). Manuscript to be reviewed family doctors. Significantly more patients who favor primary clinics for outpatient visits had had regular family doctors than patients who prefer medical centers (61.9% vs 41.2%, p < 0.001). Approximately 67.6% of the respondents were satisfied with their previous medical experience in primary care. Furthermore, patients who favored primary clinics for outpatient visits exhibited significantly higher satisfaction rates than patients who favored medical centers (75.2% vs 52.9%, p < 0.001).</ns0:p><ns0:p>Table <ns0:ref type='table' target='#tab_2'>2</ns0:ref> summarizes the average rating of respondents to each factor when selecting an outpatient facility and their preferred outpatient institution. 'Physicians were highly reputable' , 'physicians explained in detail', and 'physicians have a good medical practice' were the most important factors to consider when choosing the outpatient institution. Low copayment is the least important factor for outpatient medical choice among all patients ( the average rating of Likert scale : 3.08 ± 1.16).</ns0:p><ns0:p>In univariate analysis, the importance of six factors was significantly higher among the respondents who chose to visit a medical center (p < 0.001). These factors were 'physicians are highly reputable', 'physicians have a good medical practice', ' the institution has advanced equipment', 'the institution has high-quality drugs', 'the institution has diverse specialties',and 'the institutions with a good reputation'. In this study, we conducted exploratory factor analysis to understand the potential common characteristics among factors and clarify the influencing factors. We used principal component analysis to extract data using a correlation matrix and oblimin rotation method. We removed six items because of cross-loading or because the factor load was too low (< 0.4). Factors with eigenvalues greater than 1, cumulative percentages of variance explained above 71.2%, KMO value reaching of 0.868, and p value less then 0.001 were excluded. Three main factors were retained in the final extraction (Table <ns0:ref type='table' target='#tab_4'>3</ns0:ref>), namely 'physician factor,' 'image and reputation factor,' and 'facility and medication factor.' We subsequently converted the scores to three factors into a multivariable analysis model. Table <ns0:ref type='table' target='#tab_5'>4</ns0:ref> illustrates three models of logistic regression for predicting 'visits to the outpatient clinic of the medical center for an illness.' The multiple logistic regression revealed no significant correlations between gender, education, income, and residence regions in the selection of outpatient institutions. Age, past medical experience in primary clinics, copayment, regular family physician, equipment of the institution, drug-quality of the institution, and diversity of the institution specialties were the most valuable factors for prediction.</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:04:47877:1:1:CHECK 26 Jun 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p><ns0:p>Model 2 indicated that the likelihood of choosing to visit a medical center when ill increased by 2.8% for every additional year of age (95% CI: 1.6%-4.1%) when other variables were controlled for. Patients who were previously satisfied with the medical experience of primary clinics had a 0.509 lower likelihood of choosing a medical center to visit when ill (95% CI: 0.435-0.595). Patients who rated copayment as important were 0.525 times as likely to select a medical center to visit when ill (95% CI: 0.354-0.781). People with a regular family doctor were 0.676 times less likely to select a medical center (95% CI: 0.471-0.969). Patients who rated the physician factor as very important were less likely to select an outpatient clinic in a medical center when they were ill (OR 0.717, 95% CI: 0.523-0.984). Patients who reported that hospital facilities, high-quality drugs, and diverse specialties as very important had increased likelihood of selecting the outpatient clinic of the medical center (OR 2.218, 95% CI: 1.514-3.249).</ns0:p></ns0:div> <ns0:div><ns0:head n='4.'>Discussion</ns0:head><ns0:p>Several factors significantly affected the selection of the medical center, including older age, the physician factor, advanced equipment, high-quality drugs, good reputation and visibility, and diversity of specialists. In Taiwan, more subjects agreed to the hierarchical medical system and medical referral system, but many people still disagreed with changes to their healthcare seeking choices due to policy promotion (Yan, <ns0:ref type='bibr'>Kung & Lu, 2019)</ns0:ref>. Previous survey found that age, gender, residence, education and monthly family income are significantly related to inpatient hospital choice <ns0:ref type='bibr'>(Kamra, Singh & De, 2016)</ns0:ref>. Some results are consisted to ours.</ns0:p><ns0:p>However, in our study, income did not have obvious impact on outpatient choice. The may due to the exemption for low-income people in Taiwan's health insurance. When they visit the medical center without a referral, they don't have to pay any component <ns0:ref type='bibr'>(Yang, Tsai & Tien, 2019)</ns0:ref>.</ns0:p><ns0:p>It has been more than 20 years since the introduction of the family physician in Taiwan, but only 51.5% of the respondents have regular family doctors. In this study, patients with regular family doctors, who were satisfied with the past medical experience in primary care , and who rated the physician factor as important, and who rated copayment as important, were less likely to choose a medical center when ill. Such results show that the implementation of the family physician system, so that the public generally has a trusted family doctor will help reduce the number of patients directly to the medical center without a referral.</ns0:p><ns0:p>Gender, marital status, and education level did not affect the choice of outpatient visits. In univariate analysis, the choice of the outpatient institution was only slightly related to the income level, and the income level was no related to the outpatient choice when other variables were controlled for in regression analyses. Low copayment is the least important factor for outpatient medical choice among all patients. This result may be caused by the low copayment amount in Taiwan's NHI system. Furthermore, in the NHI program, most of the cost of medical treatment is waived for low-income households and catastrophic illness patients in Taiwan. Thus the financial burden is rarely a consideration in the patients' choice of outpatient institution <ns0:ref type='bibr'>(Chen & Fan, 2015)</ns0:ref>. The insurance system is fee-for-service in Taiwan. People who visit the medical center may have more blood tests or radiologic examinations ordered by their physician because no copayment is charged for the inspection. Furthermore, the current copayment of outpatient medicines is a fixed fee, the out of pocket maximum is only NTD$200 (approximately USD$6.7). Although the NHI copayment reforms had mildly reduced the probability that patients with minor ailments would choose to visit high-tier medical facilities, several studies have indicated that the effect of medical prices on people's medical behavior is limited.</ns0:p><ns0:p>In the present research, a similar phenomenon was also observed. Low copayment has the lowest average rating of Likert scale when considering the importance of outpatient medical choice among all patients. Changing the health insurance system, such as changing the copayment to a fixed-rate coinsurance, appears to be the only method to eliminate unnecessary testing and medical waste <ns0:ref type='bibr'>(Victor et al., 2018)</ns0:ref>.</ns0:p><ns0:p>Ideally, every older adult should have trusted primary care physicians who can provide outpatient services. However, in this study, older people had a greater likelihood to visit the medical center for outpatient visits. Liu's research in 2012 pointed out that different health profiles of elderly people on the likelihood of utilization and expenditure on health care services were significant. The high comorbidity group tended to utilize more services in the ambulatory care and the frail group had higher health care expenditures <ns0:ref type='bibr'>(Liu, Tian & Yao, 2012)</ns0:ref> . Our research results</ns0:p><ns0:p>could not be found to be related to such findings. Requires the design of further studies to understand whether the primary clinics in Taiwan meet the needs of the elderly. This study has several limitations which impact its findings.. First, given the web-based survey design, participants were recruited over the internet, the low response rate deserved further exploration. Although the online survey represents a wide age range and geographic distribution, Manuscript to be reviewed this sample is generally younger and more highly educated <ns0:ref type='bibr'>(Tengilimoglu et al., 2017)</ns0:ref>. Hsieh found that Internet use in Taiwan was significantly associated with more outpatient clinic visits for those with chronic diseases <ns0:ref type='bibr'>(Hsieh et al., 2016)</ns0:ref>; thus, the results should be generalized with caution. Second, the variance explained by the logistic regression model suggests that other significant factors may determine outpatient clinic decisions <ns0:ref type='bibr'>(Cheng, 2015;</ns0:ref><ns0:ref type='bibr'>Yip et al., 2019)</ns0:ref>. Despite these limitations, this study is the first to investigate how the public chooses outpatient institutions in Taiwan. Further research should explore the influencing factors among the older group.</ns0:p></ns0:div> <ns0:div><ns0:head n='5.'>Conclusions</ns0:head><ns0:p>A good primary medical experience and a regular family physician significantly reduces people's likelihood of visiting the medical center without a referral. The results of this study support that the key to establishing graded medical care is prioritizing the strengthening of the primary medical system.</ns0:p><ns0:p>1 Manuscript to be reviewed Manuscript to be reviewed Table <ns0:ref type='table' target='#tab_2'>2</ns0:ref>. Association between the average rating of respondents to each factor when selecting an outpatient facility and their preferred outpatient institution Six factors were removed because the factor load was too low (< 0.4) or because of crossloading. The removed factors were 'consider the severity of the disease,' 'institution has convenient transportation,' 'reasonable waiting time,' 'institution was recommended by friends or relatives,' 'willing to prescribe for chronic diseases,' and 'low copayment.'</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:04:47877:1:1:CHECK 26 Jun 2020)</ns0:p><ns0:p>Manuscript to be reviewed </ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>and a combination thereof. Based on the factors identified by the literature search, we invited two family physicians, three outpatient nurses and five volunteers to participate in the focus group. The main topic was 'What are the important factors in one's selection of an outpatient clinic when patients were ill?' The opinions provided by the experts are used as reference for the questionnaire.Based on a literature review and the focus group, factors that related to the outpatient choice were proposed and included in the questionnaire. The main dependent variable of this study was 'preferred choice of outpatient clinics when you are ill,' and the independent variables were assessed using the following question: 'Please indicate the importance of each of the following factors in your selection of an outpatient clinic when you were ill?' A total of 20 factors affecting the choice of the outpatient institution was included. The survey questions wereformatted as short answer, single choice, or Likert rating scale questions.All respondents were asked to rate the importance of the 20 factors in the selection of an outpatient institution when they were ill on a 5-point Likert scale ranging from 1 = not at all important to 5 = very important. At the end of the questionnaire, respondents were asked to provide demographic information and information on past experiences during outpatient visits at different hospital PeerJ reviewing PDF \| (2020:04:47877:1:1:CHECK 26 Jun 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>named 'physician factor' , 'image & reputation factor'and 'facility & medication factor'. Internal consistency was demonstrated, with the Cronbach's α coefficient ranging from 0.792 to 0.905 for the factors. These three factors accounted for 61.7% of the total variance of the variables. PeerJ reviewing PDF \| (2020:04:47877:1:1:CHECK 26 Jun 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head /><ns0:label /><ns0:figDesc>The physician factor, image and reputation factor, and facility and medication factor were added in model 2 to test the associations beyond the personal factors. The other variables were added in model 3 to test the associations of sociodemographic factors beyond above factors.To ensure the security of the data, all data were stored on a secure server, and were backed up on a local hard disk. Only the researcher could access these materials. Data were primarily evaluated by Dr. Lin, Ming-Hwai. The survey data were extracted into Excel (Microsoft Corp) and the statistical analysis was performed with the Statistical Package for the Social Sciences (SPSS, version 20.0; SPSS Inc., Chicago, IL, USA).</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ reviewing PDF \| (2020:04:47877:1:1:CHECK 26 Jun 2020)</ns0:figDesc></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head>Table 1</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>provides a comparison of the demographic characteristics of the patients who favor different institutions for outpatient visits.The mean age of the respondents was 43.6 (SD 10.6, minimum age 19, maximum 85 years).</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Men accounted for 43.8% and women accounted for 56.2% of the 987 respondents included; 509</ns0:cell></ns0:row><ns0:row><ns0:cell>(51.6%) respondents favored visiting a primary clinic, 308 (31.2%) favored visiting the general</ns0:cell></ns0:row><ns0:row><ns0:cell>hospital, and 170 (17.2%) favored visiting the medical center without a referral. Table 1 provides</ns0:cell></ns0:row><ns0:row><ns0:cell>a comparison of demographic characteristics and preferred institutions for outpatient visits.</ns0:cell></ns0:row><ns0:row><ns0:cell>Gender, marital status, and education level were not statistically related to the choice of</ns0:cell></ns0:row><ns0:row><ns0:cell>outpatient visits. In univariate analysis, the choice of medical treatment facility was statistically</ns0:cell></ns0:row><ns0:row><ns0:cell>related to income (p = 0.026). Patients with a monthly income of NTD 50,001-70,000 favored</ns0:cell></ns0:row></ns0:table><ns0:note>outpatient clinics of medical centers. People living in urban areas accounted for 65.8% of respondents. A larger number of people living in urban areas favored medical centers than patients living in other areas (p < 0.001). Approximately 51.5% of the respondents had regular PeerJ reviewing PDF \| (2020:04:47877:1:1:CHECK 26 Jun 2020)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_1'><ns0:head>Table 1 .</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Demographic characteristics and preferred institution for outpatient visits (N = 987)</ns0:figDesc><ns0:table><ns0:row><ns0:cell /><ns0:cell /><ns0:cell cols='3'>preferred institution for outpatient visit</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>total</ns0:cell><ns0:cell>primary clinic</ns0:cell><ns0:cell>general hospital</ns0:cell><ns0:cell>medical center</ns0:cell><ns0:cell>p value</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>n = 987</ns0:cell><ns0:cell>n = 509</ns0:cell><ns0:cell>n = 308</ns0:cell><ns0:cell>n = 170</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>n (%)</ns0:cell><ns0:cell>n (%)</ns0:cell><ns0:cell>n (%)</ns0:cell><ns0:cell>n (%)</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>age (mean, SD)</ns0:cell><ns0:cell>43.6 (10.6)</ns0:cell><ns0:cell cols='2'>41.7 (10.7) 43.6 (10.3)</ns0:cell><ns0:cell>49.6 (8.8)</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>sex: male</ns0:cell><ns0:cell>432 (43.8)</ns0:cell><ns0:cell>221 (43.4)</ns0:cell><ns0:cell>138 (44.8)</ns0:cell><ns0:cell>73 (42.9)</ns0:cell><ns0:cell>0.902</ns0:cell></ns0:row><ns0:row><ns0:cell>educational level</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>0.927</ns0:cell></ns0:row><ns0:row><ns0:cell>tertiary or below</ns0:cell><ns0:cell>149 (15.1)</ns0:cell><ns0:cell>76 (14.9)</ns0:cell><ns0:cell>48 (15.6)</ns0:cell><ns0:cell>25 (14.7)</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>university</ns0:cell><ns0:cell>647 (65.6)</ns0:cell><ns0:cell>338 (66.4)</ns0:cell><ns0:cell>201 (65.3)</ns0:cell><ns0:cell>108 (63.5)</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>postgraduate</ns0:cell><ns0:cell>191 (19.4)</ns0:cell><ns0:cell>95 (18.7)</ns0:cell><ns0:cell>59 (19.2)</ns0:cell><ns0:cell>37 (21.8)</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>marriage</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>0.193</ns0:cell></ns0:row><ns0:row><ns0:cell>married</ns0:cell><ns0:cell>644 (65.2)</ns0:cell><ns0:cell>328 (64.4)</ns0:cell><ns0:cell>195 (63.3)</ns0:cell><ns0:cell>121 (71.2)</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>others</ns0:cell><ns0:cell>343 (34.8)</ns0:cell><ns0:cell>181 (35.6)</ns0:cell><ns0:cell>113 (36.7)</ns0:cell><ns0:cell>49 (28.8)</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>income</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>0.026</ns0:cell></ns0:row><ns0:row><ns0:cell>NTD < 15000</ns0:cell><ns0:cell>168 (17.0)</ns0:cell><ns0:cell>90 (17.7)</ns0:cell><ns0:cell>50 (16.2)</ns0:cell><ns0:cell>28 (16.5)</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell cols='2'>NTD 15001-30000 130 (13.2)</ns0:cell><ns0:cell>70 (13.8)</ns0:cell><ns0:cell>37 (12.0)</ns0:cell><ns0:cell>23 (13.5)</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell cols='2'>NTD 30001-50000 346 (35.1)</ns0:cell><ns0:cell>180 (35.4)</ns0:cell><ns0:cell>120 (39.0)</ns0:cell><ns0:cell>46 (27.1)</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell cols='2'>NTD 50001-70000 176 (17.8)</ns0:cell><ns0:cell>74 (14.6)</ns0:cell><ns0:cell>57 (18.5)</ns0:cell><ns0:cell>45 (26.5)</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>NTD > 70000</ns0:cell><ns0:cell>167 (16.9)</ns0:cell><ns0:cell>95 (18.7)</ns0:cell><ns0:cell>44 (14.3)</ns0:cell><ns0:cell>28 (16.5)</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>area</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>< 0.001</ns0:cell></ns0:row><ns0:row><ns0:cell>urban</ns0:cell><ns0:cell>649 (65.8)</ns0:cell><ns0:cell>337 (66.2)</ns0:cell><ns0:cell>179 (58.1)</ns0:cell><ns0:cell>133 (78.2)</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>suburban/rural</ns0:cell><ns0:cell>338 (34.2)</ns0:cell><ns0:cell>172 (33.8)</ns0:cell><ns0:cell>129 (41.9)</ns0:cell><ns0:cell>37 (21.8)</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>residency</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>0.059</ns0:cell></ns0:row><ns0:row><ns0:cell>northern</ns0:cell><ns0:cell>662 (67.1)</ns0:cell><ns0:cell>335 (65.8)</ns0:cell><ns0:cell>199 (64.6)</ns0:cell><ns0:cell>128 (75.3)</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>middle</ns0:cell><ns0:cell>115 (11.7)</ns0:cell><ns0:cell>59 (11.6)</ns0:cell><ns0:cell>40 (13.0)</ns0:cell><ns0:cell>16 (9.4)</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>southern</ns0:cell><ns0:cell>163 (16.5)</ns0:cell><ns0:cell>96 (18.9)</ns0:cell><ns0:cell>48 (15.6)</ns0:cell><ns0:cell>19 (11.2)</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>east/archipelagos</ns0:cell><ns0:cell>47 (4.8)</ns0:cell><ns0:cell>19 (3.7)</ns0:cell><ns0:cell>21 (6.8)</ns0:cell><ns0:cell>7 (4.1)</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>have a regular family physician</ns0:cell><ns0:cell>508 (51.5)</ns0:cell><ns0:cell>315 (61.9)</ns0:cell><ns0:cell>123 (39.9)</ns0:cell><ns0:cell>70 (41.2)</ns0:cell><ns0:cell>< 0.001</ns0:cell></ns0:row><ns0:row><ns0:cell>satisfied with the experience of the</ns0:cell><ns0:cell>667 (67.6)</ns0:cell><ns0:cell>383 (75.2)</ns0:cell><ns0:cell>194 (63.0)</ns0:cell><ns0:cell>90 (52.9)</ns0:cell><ns0:cell>< 0.001</ns0:cell></ns0:row></ns0:table><ns0:note>PeerJ reviewing PDF \| (2020:04:47877:1:1:CHECK 26 Jun 2020)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_2'><ns0:head>Table 2 (on next page)</ns0:head><ns0:label>2</ns0:label><ns0:figDesc /><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_3'><ns0:head>Table 2 .</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Association between the average rating of respondents to each factor when selecting an outpatient facility and their preferred outpatient institution preferred institution for outpatient visit</ns0:figDesc><ns0:table><ns0:row><ns0:cell /><ns0:cell>Total</ns0:cell><ns0:cell>primary clinic</ns0:cell><ns0:cell>general hospital</ns0:cell><ns0:cell>medical center</ns0:cell><ns0:cell>p value</ns0:cell></ns0:row><ns0:row><ns0:cell>factors considered when</ns0:cell><ns0:cell>n = 987</ns0:cell><ns0:cell>n = 509</ns0:cell><ns0:cell>n = 308</ns0:cell><ns0:cell>n = 170</ns0:cell></ns0:row><ns0:row><ns0:cell>selecting an outpatient facility</ns0:cell><ns0:cell /><ns0:cell cols='2'>average rating of respondents</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>physicians are highly reputable</ns0:cell><ns0:cell>4.65 ± 0.71</ns0:cell><ns0:cell cols='3'>4.66 ± 0.69 4.55 ± 0.78 4.81 ± 0.58</ns0:cell><ns0:cell>0.001**</ns0:cell></ns0:row><ns0:row><ns0:cell>physicians explained in detail</ns0:cell><ns0:cell>4.57 ± 0.75</ns0:cell><ns0:cell cols='3'>4.58 ± 0.74 4.49 ± 0.80 4.68 ± 0.69</ns0:cell><ns0:cell>0.027</ns0:cell></ns0:row><ns0:row><ns0:cell>physicians have a good medical practice</ns0:cell><ns0:cell>4.47 ± 0.80</ns0:cell><ns0:cell cols='3'>4.40 ± 0.82 4.46 ± 0.77 4.66 ± 0.72</ns0:cell><ns0:cell>0.001*</ns0:cell></ns0:row><ns0:row><ns0:cell>consider the severity of the disease</ns0:cell><ns0:cell>4.37 ± 0.91</ns0:cell><ns0:cell cols='3'>4.34 ± 0.94 4.36 ± 0.85 4.48 ± 0.94</ns0:cell><ns0:cell>0.235</ns0:cell></ns0:row><ns0:row><ns0:cell>the institution has advanced equipment</ns0:cell><ns0:cell>4.35 ± 0.86</ns0:cell><ns0:cell cols='3'>4.25 ± 0.86 4.34 ± 0.85 4.65 ± 0.79</ns0:cell><ns0:cell><0.001</ns0:cell></ns0:row><ns0:row><ns0:cell>the institution has high-quality drugs</ns0:cell><ns0:cell>4.34 ± 0.92</ns0:cell><ns0:cell cols='3'>4.28 ± 0.93 4.28 ± 0.95 4.62 ± 0.75</ns0:cell><ns0:cell><0.001</ns0:cell></ns0:row><ns0:row><ns0:cell>physicians are not in a hurry</ns0:cell><ns0:cell>4.30 ± 0.87</ns0:cell><ns0:cell cols='3'>4.32 ± 0.88 4.22 ± 0.89 4.40 ± 0.81</ns0:cell><ns0:cell>0.071</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>physicians are gracious and kind 4.25 ± 0.85</ns0:cell><ns0:cell cols='3'>4.25 ± 0.85 4.22 ± 0.85 4.30 ± 0.86</ns0:cell><ns0:cell>0.645</ns0:cell></ns0:row><ns0:row><ns0:cell>have good medical experience</ns0:cell><ns0:cell>4.24 ± 0.79</ns0:cell><ns0:cell cols='3'>4.25 ± 0.79 4.20 ± 0.79 4.29 ± 0.80</ns0:cell><ns0:cell>0.414</ns0:cell></ns0:row><ns0:row><ns0:cell>the institution has friendly staff</ns0:cell><ns0:cell>4.15 ± 0.96</ns0:cell><ns0:cell cols='3'>4.12 ± 1.00 4.14 ± 0.91 4.22 ± 0.94</ns0:cell><ns0:cell>0.500</ns0:cell></ns0:row><ns0:row><ns0:cell>the institution has convenient transportation</ns0:cell><ns0:cell>4.13 ± 0.96</ns0:cell><ns0:cell cols='3'>4.11 ± 0.95 4.13 ± 0.94 4.18 ± 1.03</ns0:cell><ns0:cell>0.722</ns0:cell></ns0:row><ns0:row><ns0:cell>the institution has diverse specialties</ns0:cell><ns0:cell>4.09 ± 0.99</ns0:cell><ns0:cell cols='3'>3.97 ± 1.06 4.12 ± 0.90 4.39 ± 0.87</ns0:cell><ns0:cell><0.001</ns0:cell></ns0:row><ns0:row><ns0:cell>waiting time is not too long</ns0:cell><ns0:cell>3.90 ± 0.93</ns0:cell><ns0:cell cols='3'>3.91 ± 0.93 3.94 ± 0.86 3.78 ± 1.02</ns0:cell><ns0:cell>0.171</ns0:cell></ns0:row><ns0:row><ns0:cell>the institution was recommended by friends or relatives</ns0:cell><ns0:cell>3.55 ± 0.99</ns0:cell><ns0:cell cols='3'>3.54 ± 1.01 3.50 ± 0.89 3.71 ± 1.05</ns0:cell><ns0:cell>0.074</ns0:cell></ns0:row><ns0:row><ns0:cell>institutions with a good reputation</ns0:cell><ns0:cell>3.53 ± 1.03</ns0:cell><ns0:cell cols='3'>3.46 ± 1.03 3.45 ± 0.99 3.88 ± 1.05</ns0:cell><ns0:cell><0.001*</ns0:cell></ns0:row><ns0:row><ns0:cell>the visibility of medical institutions is high</ns0:cell><ns0:cell>3.43 ± 1.06</ns0:cell><ns0:cell cols='3'>3.39 ± 1.07 3.41 ± 0.97 3.62 ± 1.18</ns0:cell><ns0:cell>0.042</ns0:cell></ns0:row><ns0:row><ns0:cell>willing to prescribe for chronic diseases</ns0:cell><ns0:cell>3.40 ± 1.16</ns0:cell><ns0:cell cols='3'>3.36 ± 1.16 3.40 ± 1.16 3.50 ± 1.16</ns0:cell><ns0:cell>0.381</ns0:cell></ns0:row><ns0:row><ns0:cell>physicians are famous</ns0:cell><ns0:cell>3.32 ± 0.98</ns0:cell><ns0:cell cols='3'>3.25 ± 0.97 3.31 ± 0.94 3.52 ± 1.03</ns0:cell><ns0:cell>0.007</ns0:cell></ns0:row><ns0:row><ns0:cell>physicians with a good reputation</ns0:cell><ns0:cell>3.29 ± 0.91</ns0:cell><ns0:cell cols='3'>3.23 ± 0.90 3.26 ± 0.91 3.50 ± 0.89</ns0:cell><ns0:cell>0.003</ns0:cell></ns0:row><ns0:row><ns0:cell>low copayment</ns0:cell><ns0:cell>3.08 ± 1.16</ns0:cell><ns0:cell cols='3'>3.07 ± 1.14 3.15 ± 1.17 3.00 ± 1.19</ns0:cell><ns0:cell>0.394</ns0:cell></ns0:row><ns0:row><ns0:cell>* p ≤ 0.001, p ≤0.01,</ns0:cell><ns0:cell>* p ≤0.05</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_4'><ns0:head>Table 3 .</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Exploratory factor analysis loads and variance percentages for factors considered when selecting an outpatient facility -Olkin (KMO): 0.868 Bartlett sphericity tests (P<0.001).</ns0:figDesc><ns0:table><ns0:row><ns0:cell>factors loads</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_5'><ns0:head>Table 4 (on next page)</ns0:head><ns0:label>4</ns0:label><ns0:figDesc /><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_6'><ns0:head>Table 4 .</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Results of the logistic regression for predicting 'visit to an outpatient clinic of the medical center for an illness' PeerJ reviewing PDF \| (2020:04:47877:1:1:CHECK 26 Jun 2020)</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Manuscript to be reviewed</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_7'><ns0:head>Table 4 .</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Results of the logistic regression for predicting 'visit to an outpatient clinic of the medical center for an illness'</ns0:figDesc><ns0:table><ns0:row><ns0:cell>MODEL 1</ns0:cell><ns0:cell>MODEL 2</ns0:cell><ns0:cell>MODEL 3</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:note place='foot' n='2'>* p < 0.001, p < 0.01, * p < 0.05 PeerJ reviewing PDF \| (2020:04:47877:1:1:CHECK 26 Jun 2020)</ns0:note> </ns0:body> "	"We sincerely thank the editor and the reviewers for their response and constructive comments. We have addedressed the concerns raised by further editing the manuscript and believe that due to this process, the manuscript has been significantly improved. We are very grateful for the opportunity to submit this revised manuscript and hope it is now suitable for publication in PEERJ. Reply to editor comments 1. The questionnaire development method needs explanation in methods. This includes a description of the focus group and which previous studies were used to develop the questionnaire. As suggested by the editor, we have added the questionnaire development method in the method section 2.2. Questionnaire design on page 5, line 129-138: Because there was no similar questionnaire related to the selection of outpatient clinics, we developed our own questionnaire, finalized after experts were invited to review and revise. A literature search was performed for publications that discuss the factors affecting the outpatient choice. Search terms used were ' health care seeking behavior ', ' hospital outpatient clinics' and a combination thereof. Based on the factors identified by the literature search, we invited two family physicians, three outpatient nurses and five volunteers to participate in the focus group. The main topic was 'What are the important factors in one's selection of an outpatient clinic when patients were ill?' The opinions provided by the experts are used as reference for the questionnaire. 2. I did not understand the rationale behind the use of 3 models in results. They are not explained In methods, they include a mix of the variables derived from the PCA and the individual questions And some background variables are included in one model and the others in another model. This Part needs revision. As suggested by the editor, we have added explanation for hierarchical logictic regression applied to this study in method section 2.4 Statistical analysis on page 7,line 181-188: Hierarchical logistic regression was conducted to determine which factors satisfactorily explained the dependent variable “visiting the outpatient clinic of the medical center for an illness without a referral.' The adjusted odds ratios (ORs) with 95% confidence intervals (CIs) for predicting “visit to an outpatient clinic of the medical center for an illness” were computed. In model 1, the association of age, gender and personal experience of primary clinics were tested. The physician factor, image and reputation factor, and facility and medication factor were added in model 2 to test the associations beyond the personal factors. The other variables were added in model 3 to test the associations of sociodemographic factors beyond above factors. 3. The dichotomization of the 5-point likert scale responses to important vs not leads to loss of Details. An alternative is to treat the scores as quantitative variables and report their means then Compare the means among the 3 groups in table 2 using anova/ similar tests. As suggested by the editor, we have changed to treat the scores as quantitative variables to avoid the loss of information by this dichotomization of scale. We then modified the table 2, and compared the means among the 3 groups by using ANOVA tests in the table 2. Please see the modified table 2 and revised description in method 2.4 Statistical analysis on page 6,line 167-170 . The normality of the collected data was analyzed by the Kolmogorov – Smirnov test. As the data follow the normal distribution, comparisons between three groups were conducted by an analysis of variance (ANOVA). 4. In various sections (discussion and otherwise), some parts do not fit and should be shifted to the Sections where they belong or removed altogether. Details are included in my comments. As suggested by the editor, we have made the following adjustment: 1) added the reason for reference quoting on page 9-10, line 260-265. Previous survey found that age, gender, residence, education and monthly family income are significantly related to inpatient hospital choice (Kamra et al. 2016). Some results are consisted to ours. However, in our study, income did not have obvious impact on outpatient choice. The may due to the exemption for low-income people in Taiwan's health insurance. When they visit the medical center without referral, they don't have to pay any component. 2) shift this paragraph of line 217-223 to introduction section page 3, line 59-65. 3) and add the reference for supporting the statement on page10, line 279-280. Thus the financial burden is rarely a consideration in the patients’ choice of outpatient institution (Chen & Fan 2015). 4) moved the paragraph of line 252-254 to discussion section( page 10, line 285-287) . 5. The discussion should include comparison to previous studies. Line 238:Pls link this to the study findings reported in results as opposed to the general idea of the study (that patients are abusing the system) As suggested by the editor, we have compare the study findings to previous studie in line 298-300. In the present research, a similar phenomenon was also observed. Low copayment has the lowest average rating of Likert scale when considering the importance of outpatient medical choice among all patients. 6. Some references need to be updated- they are very old. Pls check the attached pdf. We have removed reference Chen et al. 2006 and Huang et al. 2003, and updated new references. 7. An annotated manuscript with additional comments provided by reviewers. The set of comments and the author's reply are as follows: Line 247: Pls check grammar We have corrected the grammar mistake in line 309. Table 1: align p values to vars: for example, the first p value is supposed to be of age but is placed next to sex We have updated the table 1 and align p values to vars: Line 52: Pls use MeSH terms for keywords We have changed to use mesh terms for keywords in line 52-53:. Keywords:health care seeking behavior; national health programs; hospital outpatient clinic; healthcare survey; single-payer system line 29:Pls add a brief description of the analysis method We have added a brief description of the analysis method in abstract line 23-32:. However, due to the word limit of the abstract, only partial information can be provided. Line 120: pls cite previous studies on which the questionnaire was based and explain how it was developd. We have added the description in method section 2.2 questionnaire design:line 129-147. Line 162: there is no low or high significance. pls remove this description Agree. ”low” was deleted. Line182-184: Pls shift lines 182-184 to Methods and edit unless this info is already there. In that case, pls remove altogether Line 186-187 lease provide a reference for these values/ cutoff points and shift the part that you based your exclusion on this to Methods Line 193-194 explain in Methods what these models included and how their factors were selected? As suggested by the reviewer, the structure of the result section has been re-organized by moving relevant sentences in line 172-174 to method section. In addition, some description on line 175-177 and line 183-184 have been moved to the method section. What these models, included and how their factors were selected, and reference to these values and cutoff points have been supplemented. All the changes had been modifeied in method 2.4 Statistical analysis on page 7, line 171-180. The associations between items that affect the patients’ choice of outpatient clinics were assessed using exploratory factor analysis. Measures of sample adequacy such as Kaiser-Meyer-Olkin (0.868) and Bartlett’s Test of Sphericity (significance <0.0001) show that factor analysis can be applied. Principal axis factoring was performed to identify the major factors by using a correlation matrix and oblimin rotation. The number of principal components to be extracted was determined by examining the eigenvalues (>1). Loadings over 0.5 were used to interpret components in the study was set at 0.5. Finally, the number of domains was reduced to three and named ‘physician factor’ , ‘image & reputation factor’and ‘facility & medication factor’. Internal consistency was demonstrated, with the Cronbach's α coefficient ranging from 0.792 to 0.905 for the factors. These three factors accounted for 61.7% of the total variance of the variables. Line 240 what does this ref support? It is a recommendation based on a finding.... Does it agree with the findings? Pls clarify As suggested by the reviewer, we have added description on page 11, line 293-300: Ideally, every older adult should have trusted primary care physicians who can provide outpatient services. However, in this study, older people had a greater likelihood to visit the medical center for outpatient visits. Liu’s research in 2012 pointed out that different health profiles of elderly people on the likelihood of utilization and expenditure on health care services were significant. The high comorbidity group tended to utilize more services in the ambulatory care and the frail group had higher health care expenditures(Liu et al. 2012). Our research results could not be found to be related to such findings. Requires the design of further studies to understand whether the primary clinics in Taiwan meet the needs of the elderly. Line 136 Pls use the Cherries checklist for online surveys and include it filled as an appendix We have completed the Cherries checklist for online survey and uploaded as a supplementary data. Reply to reviewer 1 In basic reporting, the reviewer suggest: The reference style needs to be checked and matched with the journal's style. We have reviewed and corrected the references follow the APA style. In experimental design, the reviewer suggest: Methods is not described with sufficient detail & information to replicate We have modified the methods section 2.1-2.4 with sufficient detail and information to replicate the study. Please see page 4-7 , line 103-193. In validity of the findings, the reviewer suggest: The low response rate inherent in internet-based survey could affect the validity of the findings. As suggested by the reviewer, we have added the description of low response rate in the limitation:page 11 , line 301-303： This study has several limitations which impact its findings.. First, given the web-based survey design, participants were recruited over the internet, the low response rate deserved further exploration. In comments for the author, the reviewer suggests a number of clarifications need to be made, particularly regarding the study methodology. 1. Line 103: please provide more details on how the survey was advertised on the three social media platforms. Is there a link to a study survey website, such as google form? As suggested by the reviewer, we have added more details in the methodology section 2.1. study design on page 4-5, line 107, 119-116: The questionnaire was developed in Google forms (https://www.google.com/forms/about/). …… By using the snowball sampling method, the questionnaire was introduced to a variety of community groups. To maximize public outreach, the survey was promoted in different social media such as Facebook, Line and the most popular BBS (https://facebook.com/; https://linecorp.com/; and https://www.ptt.cc/ index.bbs.html) with interested citizens being invited to complete the questionnaire and the respondents who took the survey being asked to continue inviting their friends to participate in the survey and fill out the questionnaire. 2. Line 109: was the deduplication protocol based on only identical age, occupation, and answer options? Were ips checked for duplication? Is there a survey report generated by the survey website that allows one to roughly identify the geographical distribution of the survey respondents? If so, this will add support to the “nationwide” coverage of the survey. IP addresses were not collected from participants because of technical difficulties. 3. Line 119: please provide more information on the focus group, including the demographic characteristics of its member and the main questions used. As suggested by the reviewer, we have added more information about the focus group in the method section 2.2. Questionnaire design on page 5-6, line 130-147: Based on the factors identified by the literature search, we invited two family physicians, three outpatient nurses and five volunteers to participate in the focus group. The main topic was 'What are the important factors in one's selection of an outpatient clinic when patients were ill?' The opinions provided by the experts are used as reference for the questionnaire……. 4. Line 120: “expert validity” should be referred to as “content validity”. Was content validity index (cvi) measured? If so, this should be used to replace the sentence (line 129) “it exhibited a satisfactory level of content validity”. Agreed. We have corrected the description in line 150-152.: 'Five experts were invited to modify the questionnaire for ensuring “content” validity'. We have modified the description in line 155-158: Five senior researchers , who were expert in survey research , were invited to perform repeated questionnaire testing and discuss the entire instrument for content validity. The content was rated by five experts, resulting in a mean content validity index (CVI) of 86.0%. 5. Line 142 and 187: a p-value of less than .001 should be presented as < .001 rather than .000. Agreed. We have corrected the presentation of p-value as <0.001. in line 173 and line 217 . 6. Line 143: while the results may not be different, principal axis factoring rather than principal components method should be used for the factor analysis because it appears that the aim of the authors were trying to assess a set of latent variables that cannot be directly measured with a single variable. Agree. Principal axis factoring rather than principal component method should be used for the factor analysis. We have redone the analysis, and have updated the results in table 3 although the results just a little different. 7. Line 145 and 190: “multiple logistic regression” rather than “multivariate logistic regression” is the correct term to use here. Agree. It is more correct to use 'multiple logistic regression' here. We have corrected in line 43 and 238. 8. Line 156: the age distribution should be described. In addition to the mean and sd, please provide the minimum and maximum age of the participants. As suggested by the reviewer, we have added the minimum and maximum age of the participants in line 201: The mean age of the respondents was 43.6 (SD 10.6, minimum age 19, maximum 85 years). 9. Line 191 and table 4: the authors used hierarchical (nested) regression models, which should be first described in the methods section. Please explain why this approach rather than stepwise regression approach was used. As suggested by the reviewer, we have added the description in the methodology section 2.3. on page 7, line 181-188: Hierarchical logistic regression was conducted to determine which factors satisfactorily explained the dependent variable “visiting the outpatient clinic of the medical center for an illness without a referral.' The adjusted odds ratios (ORs) with 95% confidence intervals (CIs) for predicting “visit to an outpatient clinic of the medical center for an illness” were computed. In model 1, the association of age, gender and personal experience of primary clinics were tested. The physician factor, image and reputation factor, and facility and medication factor were added in model 2 to test the associations beyond the personal factors. The other variables were added in model 3 to test the associations of sociodemographic factors beyond above factors. 10. Line 241: the low response rate should also be mentioned in the limitations. As suggested by the reviewer, we have added the description of low response rate in the limitation:page 11 , line 301-303： This study has several limitations which impact its findings.. First, given the web-based survey design, participants were recruited over the internet, the low response rate deserved further exploration. 11. References: they are a number of formatting errors, such as missing page number (line 287), and the use abbreviated journal title (peerj style is journal title in full form) (line 273, 275, 277, etc.). Thanks for the reviewer’s comment. All the references have been checked, the formatting errors have been corrected fully. 12. Table 4: the columns for beta and standard error of beta should be deleted and replaced by columns showing p values and 95% confidence intervals for the odds ratios. As suggested by the reviewer, we have modified the presentation of table 4. The columns for beta and standard error of beta have been replaced by columns showing p values and 95% confidence intervals for the odds ratios. 13. Table 4: income was represented by a categorical variable with five levels in table 1. However, it was represented by a single variable “income degree”. Are the five levels treated as a continuous variable? Unless there are special reasons for this, it should be analyzed as a categorical variable. Agree. The income variable should be analyzed as a categorical variable instead of a continuous variable. We have made a correction and finally removed the income variable in regression analysis due to no significant effect in all models. 14. Table 4: the models should be compared formally with likelihood-ratio chi-square test. It is not clear whether model 3 is significantly “better” than model 2. Agree. We have added the comparision of ikelihood-ratio chi-square test in the regression analysis (table 4). The model 3 is not significantly better than model 2. Reply to reviewer 2 In basic reporting, the reviewer suggest: 1. Bibliographic references, according to the instructions for the journal's authors. However, errors have been detected in the citation in the text. We have reviewed and checked the formatting errors in both main text and bibliographic references. All the references have been corrected to follow the APA style. 2. The introduction needs more bibliographic support. Suggest that you quote other authors on lines 61-62, 72-74, and 86-92 to provide more justification for your study. More bibliographic support have been added in the introduction section, the correction includes： In lines 60-62, we quote 'in most countries, primary care physicians act as 'gatekeepers' to health care by providing initialmedical interventions and referring patients to additional specialists.' we have added the bibliographic reference (Yan, Kung & Lu, 2019). In lines 76-78, we quote 'studies have reported that people in developed countries visit a doctor 5 to 6 times a year, while in taiwan, the average frequency of visits is 13.' we have added the bibliographic reference (Lynn et al., 2015). In lines 99-103, we quote 'although changes in nhi co-pay policies have slightly reduced the use of high-level healthcare facilities, studies have indicated that the effect of medical prices on people's medical behavior is very limited (Lee et al., 2018). 3. We suggest that in the materials and methods part the authors elaborate a sub-section on ethical and legal aspects for lines 112-113. In addition, we recommend that you expand the information on the legal guidelines that the authors followed to protect the confidentiality of the data. We have elaborate a sub-section on ethical and legal aspects, and expand the information on the legal guidelines that the authors followed to protect the confidentiality of the data in line 125-127,189-190. This study was approved by the Institutional Review Board of Taipei Veterans General Hospital (2017-07-009AC), and the study was conducted in accordance with the guideline of Helsinki declaration 2013. To ensure the security of the data, all data were stored on a secure server, and were backed up on a local hard disk. Only the researcher could access these materials. In experimental design, the reviewer suggest: 1. Bibliographic references, according to the instructions for the journal's authors. However, errors have been detected in the citation in the text. We have corrected minor spelling mistakes and grammatical mistakes, and all the references have been corrected to follow the APA style. 2. We suggest that the authors restate the objective of the abstract study and recommend that the authors change the verb “explore” to “identify or evaluate”., since they use a likert scale questionnaire and not interviews or open questions. Agree. We have restated and modified the objective of the abstract following the reviewer’s suggestion in line 20-22: 'The main purpose of this study was to evaluate the factors contributing to the patients' selection of the outpatient clinic of medical centers without a referral.” 3. The authors must write a short justification of the study together with the objective at the end of the introduction. We have added the justification of the study at the end of the introductory section in line 97-100: The main purpose of this study was to evaluate the factors contributing to the patients’ selection of the outpatient clinic of medical centers without a referral. Only when we clearly understand the motives underlying the public’s choice, then we could establish a successful graded medical system in Taiwan. 4. The authors should briefly identify the knowledge gap being investigated and statements should be made about how the study contributes to filling that gap. We have added the knowledge gap and how the study contributes to filling that gap in introduction section line 98-100,127-131,139-140. 5. We suggest that the authors expand the information in relation to the elaboration of the questionnaire that allows its correct interpretation, as well as its reproducibility. -Which criteria the group of experts were selected and how they were recruited for the identification and review of the factors expressed in the questionnaire. -The methods must be described with enough information so that another researcher can reproduce them. We have modified the methods section 2.1-2.4 with sufficient detail and information in questionnire design including the focus group and experts selection. Please see page 5-6 , line 129-194. 2.2. Questionnaire design Because there was no similar questionnaire related to the selection of outpatient clinics, we developed our own questionnaire, finalized after experts were invited to review and revise. A literature search was performed for publications that discuss the factors affecting the outpatient choice. Search terms used were ' health care seeking behavior ', ' hospital outpatient clinics' and a combination thereof. Based on the factors identified by the literature search, we invited two family physicians, three outpatient nurses and five volunteers to participate in the focus group. The main topic was 'What are the important factors in one's selection of an outpatient clinic when patients were ill?' The opinions provided by the experts are used as reference for the questionnaire. Based on a literature review and the focus group, factors that related to the outpatient choice were proposed and included in the questionnaire. The main dependent variable of this study was 'preferred choice of outpatient clinics when you are ill,' and the independent variables were assessed using the following question: 'Please indicate the importance of each of the following factors in your selection of an outpatient clinic when you were ill?” A total of 20 factors affecting the choice of the outpatient institution was included. The survey questions were formatted as short answer, single choice, or Likert rating scale questions. All respondents were asked to rate the importance of the 20 factors in the selection of an outpatient institution when they were ill on a 5-point Likert scale ranging from 1 = not at all important to 5 = very important. At the end of the questionnaire, respondents were asked to provide demographic information and information on past experiences during outpatient visits at different hospital levels, attitudes towards copayment, and whether they have a regular family physician. Five experts with expertise in subject content were invited to modify the questionnaire for ensuring content validity. Questions were refined after feedback and finalized into the online survey. In validity of the findings, the reviewer suggest: 1. Despite the fact that the data seems reliable and statistically sound, the authors are encouraged to provide information on who was responsible for managing and performing the analysis and interpretation of the data, and how this procedure was carried out. We have added the details of the data analysis and interpretation in the method 2.4 data analysis, line 189-190. 2. We recommend the authors to move the first paragraph of the conclusions to the discussion section, since due to its content it is more appropriate. The conclusions must respond to the stated objective and should be limited only to those supported by the results expressed in the manuscript. Agree. . We have moveed the first paragraph of the conclusions to the discussion section to ensure the appropriateness of the conclusion. In comments for the author, the reviewer suggest: 1. Despite the importance of the topic covered and the aspects previously discussed, some aspects of the manuscript require review by the authors for clarity and reproducibility. Thanks for the reviewer’s comment. We have added more details in methodology to make the research purpose and methods of this study clearer and reassured its reproducibility. 2. We recommend that the authors add the study design in the abstract. Thanks for the reviewer’s comment. We have added the study design in the abstract section(line 23-32 ). However, due to the word limit of the abstract, only partial information can be provided. Methods. An online anonymous survey was conducted by using Google Form platform utilizing self-constructed questionnaire from September to October 2018. A nationwide sample in Taiwan was recruited using convenience sampling through social media. Based on a literature review and a focus group, 20 factors that may affect the choice of the outpatient institution were constructed. The associations between items that affect the patients’ choice of outpatient clinics were assessed using exploratory factor analysis. Principal axis factoring was performed to identify the major factors. Hierarchical logistic regression was conducted to determine which factors satisfactory explained “visiting the outpatient clinic of the medical center for an illness without a referral.' 3. Regarding the keywords, we suggest that they change 'national health insurance' to 'national health programs' and 'survey' to 'health care surveys' to improve the presence of the manuscript in the databases. In addition, we recommend reviewing the rest of the keywords and, if possible, replacing them with appropriate mesh terms according to the topic of the study. Thanks for the reviewer’s comment. We have changed 'national health insurance' to 'national health programs' and 'survey' to 'healthcare surveys' and 'outpatient clinic' to 'hospital outpatient clinic” in the keywords and adding 'health care seeking behavior ' and ' ' single-payer system' to ensure that the article better meets the needs of MESH search. (Line 52-53) Keywords: health care seeking behavior; national health programs; hospital outpatient clinic; healthcare survey; single-payer system 5. Finally, it is important that the authors mention in the manuscript the reporting guidelines they have followed according to their type of study, with the aim of improving the quality and transparency of their research. If you have questions, you can consult the instructions for the authors of the magazine or https://www.equator-network.org/ Thanks for the reviewer’s comment. We have added the reporting guidelines that we have followed in line 104-106. The present study was a web-based cross-sectional online survey. The development and reporting of the survey followed the Checklist for Reporting Results of Internet E-survey (CHERRIES) guidelines (Eysenbach, 2004). The checklist is available in the supplementary data. "	Here is a paper. Please give your review comments after reading it.
506	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction.</ns0:head><ns0:p>In contrast to other countries, Taiwan's National Health Insurance (NHI) program allows patients to freely select the specialists and tiers of medical care facility without a referral. Some medical centers in Taiwan receive over 10,000 outpatients per day. In the NHI program, the co-payment was increased for high-tier facilities for outpatient visits in 2002, 2005, and 2017. However, the policies only mildly reduced the use of high-tier medical care facilities. The main purpose of this study was to evaluate the factors contributing to the patients' selection of the outpatient clinic of medical centers without a referral.</ns0:p><ns0:p>Methods. An online anonymous survey was conducted by using Google Form platform utilizing selfconstructed questionnaire from September to October 2018. A nationwide sample in Taiwan was recruited using convenience sampling through social media. Based on a literature review and a focus group, 20 factors that may affect the choice of the outpatient institution were constructed. The associations between items that affect the patients selection of outpatient clinics were assessed using exploratory factor analysis. Principal axis factoring was performed to identify the major factors affecting the decision. Multiple logistic regression was performed to determine which factors satisfactorily explained 'visiting the outpatient clinic of the medical center for an illness without a referral.'</ns0:p><ns0:p>Results. During the survey period, 5060 people browsed the online survey, and 1003 responded and completed the online questionnaire. Therefore, the response rate was 19.8%. A total of 987 valid responses was collected. Exploratory factor analysis revealed that three main factors, namely the 'physician factor,' 'image and reputation factor,' and ' facility and medication factor,' affected the selection of outpatient clinics. A series of logistic regressions indicated that patients who reported that hospital facilities, high-quality drugs, and diverse specialties were very important were more likely to select the outpatient clinic of a medical center (OR = 2.218, 95% CI = 1.514-3.249). Patients who reported that physician factors were very important were less likely to select a medical center (OR = 0.717, 95% CI = 0.523-0.984). Patients who were previously satisfied with their experience of the primary clinics or had a regular family doctor were less likely to choose a medical center (OR = 0.509, 95% CI = 0.435-0.595 and OR = 0.676, 95% CI = 0.471-0.969).</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusion.</ns0:head><ns0:p>In Taiwan, patients with good primary medical experience and regular family physicians had significantly lower rates by selecting the outpatient clinic of a medical center. The results of this study support that the key to establishing graded medical care is to prioritize the strengthening of the primary medical system.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head n='1.'>Introduction</ns0:head><ns0:p>The National Health Insurance (NHI) program in Taiwan is a single-payer system founded in 1995. The NHI program comprises a hierarchy of medical care facilities consisting of four tiers: medical centers, regional hospitals, local community hospitals, and primary clinics.</ns0:p><ns0:p>However, referral systems have not yet been successfully implemented.</ns0:p><ns0:p>Hierarchical medical care means that medical resources can be used the most efficiently through professional division in the medical system. In most countries, primary care physicians act as healthcare 'gatekeepers' by providing initial medical interventions and referring patients to additional specialists (Yan, <ns0:ref type='bibr'>Kung & Lu, 2019)</ns0:ref>. Excluding situations of major illnesses and the urgent requirement of treatment at a medical center, people should first consult a family doctor or a nearby primary clinic regarding an illness. After diagnosis and treatment, patients can be referred to other specialty clinics or hospitals.</ns0:p><ns0:p>In contrast with other countries, patients in Taiwan have full and unrestricted access to all medical care facilities. Patients in Taiwan's NHI program can freely select specialists and the tier of medical care facility directly without a referral <ns0:ref type='bibr'>(Lynn et al., 2015)</ns0:ref>. The design of global budget payments and the fee for services result in patients favoring treatment at large hospitals, even for mild diseases, and medical centers are more likely to use advanced instruments and pharmaceuticals (Kuo, <ns0:ref type='bibr'>Chen & Lin, 2019;</ns0:ref><ns0:ref type='bibr'>Lee et al., 2018)</ns0:ref>. Numerous patients in Taiwan consult several physicians with different specialties and at different health care facilities and switch physicians and facilities rapidly <ns0:ref type='bibr'>(Wang & Lin, 2010)</ns0:ref>. This phenomenon has been suggested as a source of inefficiency in healthcare use and has resulted in high medical expenditures and costs of outpatient visits.</ns0:p><ns0:p>Studies have reported that people in developed countries visit a doctor 5-6 times a year, whereas in Taiwan, the average frequency of visits is 13. More than 30,000 insured residents in Taiwan seek hospital inpatient and outpatient services over 100 times a year <ns0:ref type='bibr'>(Lynn et al., 2015)</ns0:ref>.</ns0:p><ns0:p>In certain large medical centers in northern Taiwan, the number of outpatients per day often exceeds 10,000. Furthermore, physicians frequently see over 50 patients in a morning, spending only 5 minutes or less for each consultation <ns0:ref type='bibr'>(Wu, Majeed & Kuo, 2010)</ns0:ref>. These short consultations can cause misinformation and misunderstanding between healthcare providers and patients because of the time to build rapport. The freedom to have multiple hospital return visits results in high use of outpatient hospital visits, drug prescriptions, and other health services <ns0:ref type='bibr'>Wang & Lin, 2010;</ns0:ref><ns0:ref type='bibr'>Yip et al., 2019)</ns0:ref>. Excessive use of health services is a critical and persistent problem in Taiwan. To moderate these rising costs, a graded medical system was implemented in the NHI program and increased the copayment for high-tier facilities for outpatient visits in <ns0:ref type='bibr'>2002, 2005, and</ns0:ref> Manuscript to be reviewed (approximately 8 to 14 USD) for every visit to a high-tier medical facility. Although changes to the NHI copayment policies have mildly reduced the use of high-tier medical care facilities, studies have indicated that the effect of medical prices on people's medical behavior is very limited <ns0:ref type='bibr'>(Lee et al., 2018)</ns0:ref>. The implementation of the copayment system exerted little effect on encouraging the population visit primary clinics first <ns0:ref type='bibr'>(Yang, Tsai & Tien, 2019)</ns0:ref>.</ns0:p><ns0:formula xml:id='formula_0'>(</ns0:formula><ns0:p>Factors affecting patients' selection of high-tier medical care facilities have not been fully identified. Cheng et al. reported that patients tend to base their judgment of hospital quality on medical equipment (Cheng, 2015 . The main purpose of this study was to evaluate the factors contributing to the patients' selection of the outpatient clinic of medical centers without a referral. Understanding motivations underlying the public's choices would enable the implementation of a successful graded medical system in Taiwan.</ns0:p></ns0:div> <ns0:div><ns0:head n='2.'>Materials and Methods</ns0:head></ns0:div> <ns0:div><ns0:head n='2.1.'>Study design</ns0:head><ns0:p>The present study was a web-based cross-sectional online survey. The development and reporting of the survey were performed following the guidelines of the Checklist for Reporting</ns0:p><ns0:p>Results of Internet E-survey (CHERRIES) <ns0:ref type='bibr'>(Eysenbach, 2004)</ns0:ref>. The checklist is available in supplementary data. The questionnaire was developed in Google forms (https://www.google.com/forms/about/). After the initial tests and revision of the questionnaire were completed, and a nationwide sample in Taiwan was recruited using convenience sampling through an online anonymous survey from September 3 to October 31, 2018. The questionnaire was administered to various community groups by using the snowball sampling method. To maximize public outreach, the survey was promoted on various social media platforms, such as Facebook, Line, and the most popular bulletin board system (https://facebook.com/; https://linecorp.com/; and https://www.ptt.cc/index.bbs.html). Interested citizens were invited to complete the questionnaire and respondents were asked to invite their friends to participate in the survey and fill out the questionnaire.</ns0:p><ns0:p>The link to the survey was available for 8 weeks. All participants were invited to complete an anonymous self-administered online questionnaire, which required approximately 10 minutes to complete. Informed consent was requested from all participants on the first page of the PeerJ reviewing PDF \| (2020:04:47877:2:0:NEW 4 Aug 2020)</ns0:p><ns0:p>Manuscript to be reviewed questionnaire. Only participants who were at least 20 years old and were able to read Chinese fluently were given access. No rewards were provided to participants. A deduplication protocol was applied to identify multiple submissions and preserve data integrity, including crossvalidation of the eligibility criteria of key variables and discrepancies in key data <ns0:ref type='bibr' target='#b0'>(Bowen et al., 2008)</ns0:ref>. This study was approved by the Institutional Review Board of Taipei Veterans General Hospital (2017-07-009AC), and the study was conducted following the guidelines of the Helsinki declaration of 2013. Manuscript to be reviewed questionnaire was developed based on a literature review and the opinions of the focus group to ensure content validity. Five senior researchers with subject matter expertise were invited to revise the questionnaire and perform repeated testing of the questionnaire. The content was rated by five experts with an average content validity index of 86.0%. The questions were refined after feedback and finalized into an online survey.</ns0:p></ns0:div> <ns0:div><ns0:head n='2.2.'>Questionnaire design</ns0:head><ns0:p>At the beginning of the study, the questionnaire was pretested in 20 patients to determine if the content was appropriate and to ascertain whether the content was understandable. The internal consistency reliability test was used for reliability analysis. Cronbach's alpha of the questionnaire was 0.895, which is satisfactory. Independent samples t-tests and Chi-square tests were adopted to examine the association between respondents' demographic characteristics and their outpatient preference. The normality of the collected data was analyzed using the Kolmogorov-Smirnov test. The data followed a normal distribution; thus, comparisons among the three groups were performed using analysis of variance (ANOVA).A p value of <0.05 (two-tailed) was considered statistically significant.</ns0:p></ns0:div> <ns0:div><ns0:head n='2.3'>Statistical analysis</ns0:head><ns0:p>The associations between items that affect the patients' choice of outpatient clinics were assessed using exploratory factor analysis. Measures of sample adequacy, such as Kaiser-Meyer-Olkin (0.868) and Bartlett's Test of Sphericity (significance <0.001), indicated that factor analysis could be applied. Principal axis factoring was performed to identify the major factors by using a correlation matrix and oblimin rotation.</ns0:p><ns0:p>The number of principal components to be extracted was determined by examining the eigenvalues (>1). Loadings of over 0.5 were used to interpret components in the study. The number of domains was reduced to three and named 'physician factor, ' 'image and reputation factor,' and 'facility and medication factor.'. Internal</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:04:47877:2:0:NEW 4 Aug 2020)</ns0:p><ns0:p>Manuscript to be reviewed consistency was demonstrated, with the factors' Cronbach's α coefficients ranging from 0.792 to 0.905. These three factors accounted for 61.7% of the total variance of the variables. Multiple logistic regressions were performed to determine factors that satisfactorily explained the dependent variable 'visiting the outpatient clinic of the medical center for an illness without a referral.' The adjusted odds ratios (ORs) with 95% confidence intervals (CIs)</ns0:p><ns0:p>for predicting 'visit to the outpatient clinic of a medical center for an illness' were computed. In model 1, the association of age, gender and personal experience of primary clinics was tested.</ns0:p><ns0:p>The physician factor, image and reputation, and facility and medication factors were included in model 2 to test the associations beyond the personal factors. The other variables were included in model 3 to test the association of sociodemographic factors, in addition to the aforementioned factors.</ns0:p></ns0:div> <ns0:div><ns0:head n='3.'>Results</ns0:head><ns0:p>During the survey period, 5060 people browsed the online survey, and 1003 responded and completed the online questionnaires. Therefore, the response rate was 19.8%. We excluded 16 participants because of duplication (the same age, occupation, and answer options). Approximately 67.6% of the respondents were satisfied with their previous medical experience in primary care. Furthermore, patients who favored primary clinics for outpatient visits exhibited significantly higher satisfaction rates than patients who favored medical centers (75.2% vs 52.9%, p < 0.001).</ns0:p><ns0:p>Table <ns0:ref type='table' target='#tab_2'>2</ns0:ref> summarizes the average rating of respondents on the importance of each factor when selecting an outpatient facility and their preferred outpatient institution. 'Physicians were highly reputable,' 'physicians explained in detail,' and 'physicians have a good medical practice' were the rated most important factors to consider when selecting the outpatient institution. The low copayment was the least important factor for outpatient medical choice among all patients (Likert scale rating of 3.08 ± 1.16).</ns0:p><ns0:p>In univariate analysis, six factors were significantly more important among the respondents who chose to visit a medical center (p < 0.001). These factors were 'physicians are highly reputable,' 'physicians have a good medical practice,' ' the institution has advanced equipment,' 'the institution has high-quality drugs,' 'the institution has diverse specialties, ' and 'the institutions has a good reputation'. In this study, we conducted exploratory factor analysis to understand the potential common characteristics among factors and clarify the influencing factors. We used principal component analysis to extract data using a correlation matrix and oblimin rotation method. We removed six items because of cross-loading or because the factor load was too low (< 0.4). Factors with eigenvalues greater than 1, cumulative percentages of variance explained above 71.2%, KMO value reaching of 0.868, and p value less then 0.001 were excluded. Three main factors were retained in the final extraction (Table <ns0:ref type='table' target='#tab_4'>3</ns0:ref>), namely 'physician factor,' 'image and reputation factor,' and 'facility and medication factor.' We subsequently converted the scores to three factors into a multivariable analysis model. Table <ns0:ref type='table' target='#tab_5'>4</ns0:ref> illustrates three models of logistic regression for predicting 'visits to the outpatient clinic of the medical center for an illness.' Age was a crucial predictor in all the models. The likelihood of choosing to visit a medical center when ill increased by 2.7%-3.1% for every additional year of age (95% CI = 1.4%-4.3%) when other variables were controlled.</ns0:p><ns0:p>In Model 1, when age, gender, 'have a regular family physician,' and 'consider that copayment is important' were adjusted, patients who were previously satisfied with the medical experience of primary clinics had a 0.5 lower likelihood of visiting the outpatient clinic of a medical center for an illness (95% CI =0.429-0.584).</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:04:47877:2:0:NEW 4 Aug 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p><ns0:p>Model 2 was then also adjusted for the extracted factors I to III, which revealed that patients who reported that hospital facilities, high-quality drugs, and diverse specialties were very important had a 2.218 higher likelihood of selecting the outpatient clinic of the medical center (OR = 2.218, 95% CI = 1.514-3.249). Patients who were previously satisfied with the medical experience of primary clinics had a 0.509 lower likelihood of choosing a medical center to visit when ill (95% CI = 0.435-0.595). Patients who rated copayment as important were 0.525 times as likely to select a medical center to visit when ill (95% CI = 0.354-0.781). People with a regular family doctor were 0.676 times less likely to select a medical center (95% CI = 0.471-0.969). Patients who rated physician factors as very important were less likely to select an outpatient clinic in a medical center (OR = 0.717, 95% CI = 0.523-0.984). The gender of the patient and the image and reputation of the hospital and physicians were not significantly related to inpatient hospital choice.</ns0:p><ns0:p>In Model 3, when the possible sociodemographic confounding variables were added, the step Wald chi-square statistic was insignificant (Wald chi-square difference = 13.581, df = 8, p = 0.093). The residential area, income, and education level did not appear to be related to the selection of an outpatient clinic. Therefore, we decided to adopt model 2 as the result of our analysis.</ns0:p></ns0:div> <ns0:div><ns0:head n='4.'>Discussion</ns0:head><ns0:p>Several factors significantly affected the selection of a medical center, including older age, the physician factors, advanced equipment, high-quality drugs, past experience in primary clinics and the copayment. Most of the Taiwanese population agree with the principle of a hierarchical medical system and a medical referral system. However, many people still disagree with changes to their health care-seeking choices because of policy promotion (Yan, <ns0:ref type='bibr'>Kung & Lu, 2019)</ns0:ref>. A survey determined that age, residence, education, and monthly family income were significantly related to inpatient hospital choice <ns0:ref type='bibr'>(Kamra, Singh & De, 2016)</ns0:ref>. Some results were consistent with ours. However, in our study, income did not have an obvious effect on outpatient choice. This may be because of the exemption for low-income people in Taiwan's health insurance. Low-income residents do not pay any component when visiting a medical center without a referral <ns0:ref type='bibr'>(Yang, Tsai & Tien, 2019)</ns0:ref>.</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:04:47877:2:0:NEW 4 Aug 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p><ns0:p>Family physicians were introduced over 20 years ago in Taiwan. However, only 51.5% of the respondents had regular family doctors. In this study, patients with regular family doctors, who were satisfied with their medical experience in primary care, who rated the physician factor as important, and who rated copayment as important, were less likely to select a medical center when ill. These results indicated that the implementing a family physician system, whereby the public generally has a trusted family doctor, would help reduce the number of patients electing to go directly to the medical centers without a referral. Gender, marital status, and education level did not affect the choice of outpatient visits. The univariate analysis indicated that the choice of the outpatient institution was only slightly related to income levels, and income levels were not related to the outpatient choice, after controlling for other variables in regression analysis. Low copayment was the least important factor for outpatient medical choice among all patients. This result may be caused by the low copayment amount in Taiwan's NHI system. Furthermore, in the NHI program, most of the cost of medical treatment is waived for low-income households and catastrophic illness patients in Taiwan. Thus, the financial burden is rarely a consideration in the patients' choice of outpatient institution <ns0:ref type='bibr' target='#b1'>(Chen & Fan, 2015)</ns0:ref>. The current copayment of outpatient medicines is a fixed fee, and the out of pocket maximum is only NTD$200 (approximately USD$6.7). Although the NHI copayment reforms had mildly reduced the probability that patients with minor ailments would choose to visit high-tier medical facilities, several studies have indicated that the effect of medical prices on people's medical behavior is limited.</ns0:p><ns0:p>In the present research, a similar phenomenon was also observed. Low copayment had the lowest average rating on the Likert scale when considering the importance of outpatient medical choices among all patients. Changes to the health insurance system (e.g., changing the copayment to a fixed-rate coinsurance) may be the only method to eliminate unnecessary testing and medical waste <ns0:ref type='bibr'>(Victor et al., 2018)</ns0:ref>.</ns0:p><ns0:p>Ideally, every older adult should have trusted primary care physicians who can provide outpatient services. However, in this study, older people had a greater likelihood to visit the medical center for outpatient visits. Manuscript to be reviewed group had higher health care expenditures <ns0:ref type='bibr'>(Liu, Tian & Yao, 2012)</ns0:ref>. Our research results did not accord with these findings. Further research is needed to understand whether the primary clinics in Taiwan satisfy the needs of elderly people. This study has several limitations that may affect the findings. First, participants were recruited over the Internet because of the web-based survey design, thus the low response rate warrants further exploration. Although the online survey represents a wide age range and geographic distribution, the sample was younger and more highly educated than the general public <ns0:ref type='bibr'>(Tengilimoglu et al., 2017)</ns0:ref>. Hsieh et al. determined that Internet use in Taiwan was significantly associated with more outpatient clinic visits among people with chronic diseases in <ns0:ref type='bibr'>Taiwan (Hsieh et al., 2016)</ns0:ref>; therefore , caution should be exercised when generalizing these results. Second, the variance explained by the logistic regression model suggests that other significant factors may determine outpatient clinic decisions <ns0:ref type='bibr'>(Cheng, 2015;</ns0:ref><ns0:ref type='bibr'>Yip et al., 2019)</ns0:ref>. Despite these limitations, this study is the first to investigate how the public chooses outpatient institutions in Taiwan. Further research should explore the influencing factors among the older group.</ns0:p></ns0:div> <ns0:div><ns0:head n='5.'>Conclusions</ns0:head><ns0:p>A good primary medical experience and a regular family physician significantly reduces people's likelihood of visiting the medical center without a referral. The results of this study support that the key to establishing graded medical care is prioritizing the strengthening of the primary medical system.</ns0:p><ns0:p>1 Manuscript to be reviewed Manuscript to be reviewed Table <ns0:ref type='table' target='#tab_2'>2</ns0:ref>. Association between the average rating of respondents to each factor when selecting an outpatient facility and their preferred outpatient institution Six factors were removed because the factor load was too low (< 0.4) or because of crossloading. The removed factors were 'consider the severity of the disease,' 'institution has convenient transportation,' 'reasonable waiting time,' 'institution was recommended by friends or relatives,' 'willing to prescribe for chronic diseases,' and 'low copayment.'</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:04:47877:2:0:NEW 4 Aug 2020)</ns0:p><ns0:p>Manuscript to be reviewed </ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>2017. Patients without a referral are charged an additional copayment ranging from 240 to 420 NTD PeerJ reviewing PDF \| (2020:04:47877:2:0:NEW 4 Aug 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>A questionnaire was developed because no similar questionnaires related to the selection of outpatient clinics are available. The questionnaire was finalized after experts were invited to review and revise. A literature search was performed for publications that discussed the factors affecting the selection of outpatient clinics. Search terms used were ' health care seeking behavior,' ' hospital outpatient clinics,' and a combination of the two. Based on factors identified in the literature search, two family physicians, three outpatient nurses, and five volunteers were invited to participate in the focus group. The main topic was 'What are the important factors in the selection of an outpatient clinic by a patient.' Opinions provided by the experts were used as a reference for the questionnaire.Based on a literature review and the opinions of the focus group, factors that related to the selection of outpatient clinic were proposed and included in the questionnaire. The main dependent variable of this study was 'preferred choice of outpatient clinics when you are ill,'and the independent variables were assessed using the following question: 'Please indicate the importance of each of the following factors in your selection of an outpatient clinic when you were ill?' A total of 20 factors affecting the choice of the outpatient institution was included. All respondents were asked to rate the importance of the 20 factors in the selection of an outpatient institution when they were ill on a 5-point Likert scale ranging from 1 = not at all important to 5 = very important. At the end of the questionnaire, respondents were asked to provide demographic information and information on past experiences during outpatient visits at different hospital levels, attitudes towards copayment, and whether they have a regular family physician. The PeerJ reviewing PDF \| (2020:04:47877:2:0:NEW 4 Aug 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head /><ns0:label /><ns0:figDesc>All data were stored on a secure server and backed up on a local hard disk to ensure the security of the data. Only the researcher could access these materials. The data were primarily evaluated by Dr. Lin, Ming-Hwai. The survey data were extracted into Excel (Microsoft Corp), and statistical analyses were performed using the Statistical Package for the Social Sciences (SPSS, version 20.0; SPSS Inc., Chicago, IL, USA).Descriptive statistics were used to present the results for patient hospital choices.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head /><ns0:label /><ns0:figDesc>In 2012, Liu et al. indicated that the different health profiles of elderly people significantly affected the likelihood of use and expenditure on health care services. The high comorbidity group tended to use more ambulatory care services, and the frail PeerJ reviewing PDF \| (2020:04:47877:2:0:NEW 4 Aug 2020)</ns0:figDesc></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head>Table 1</ns0:head><ns0:label>1</ns0:label><ns0:figDesc /><ns0:table><ns0:row><ns0:cell>provides a comparison of the demographic characteristics of the patients who favor different</ns0:cell></ns0:row><ns0:row><ns0:cell>institutions for outpatient visits.</ns0:cell></ns0:row><ns0:row><ns0:cell>The mean age of the respondents was 43.6 years (SD, minimum, and maximum were 10.6,</ns0:cell></ns0:row><ns0:row><ns0:cell>19, and 85 years, respectively). Men accounted for 43.8% and women accounted for 56.2% of</ns0:cell></ns0:row><ns0:row><ns0:cell>the 987 respondents included; 509 (51.6%) respondents favored visiting a primary clinic, 308</ns0:cell></ns0:row><ns0:row><ns0:cell>(31.2%) favored visiting the general hospital, and 170 (17.2%) favored visiting the medical</ns0:cell></ns0:row><ns0:row><ns0:cell>center without a referral. Table 1 provides a comparison of demographic characteristics and</ns0:cell></ns0:row><ns0:row><ns0:cell>preferred institutions for outpatient visits. Gender, marital status, and education level were not</ns0:cell></ns0:row><ns0:row><ns0:cell>statistically related to the choice of outpatient visits. In univariate analysis, the choice of medical</ns0:cell></ns0:row><ns0:row><ns0:cell>treatment facility was statistically related to income (p = 0.026). Patients with a monthly income</ns0:cell></ns0:row><ns0:row><ns0:cell>of NTD 50,001-70,000 favored outpatient clinics of medical centers. People living in urban</ns0:cell></ns0:row><ns0:row><ns0:cell>areas accounted for 65.8% of respondents. A larger number of people living in urban areas</ns0:cell></ns0:row></ns0:table><ns0:note>favored medical centers than patients living in other areas (p < 0.001). Approximately 51.5% of the respondents had regular family doctors. Significantly more patients who favor primary clinics for outpatient visits had regular family doctors than patients who prefer medical centers (61.9% vs 41.2%, p < 0.001).</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_1'><ns0:head>Table 1 .</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Demographic characteristics and preferred institution for outpatient visits (N = 987)</ns0:figDesc><ns0:table><ns0:row><ns0:cell /><ns0:cell /><ns0:cell cols='3'>preferred institution for outpatient visit</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>total</ns0:cell><ns0:cell>primary clinic</ns0:cell><ns0:cell>general hospital</ns0:cell><ns0:cell>medical center</ns0:cell><ns0:cell>p value</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>n = 987</ns0:cell><ns0:cell>n = 509</ns0:cell><ns0:cell>n = 308</ns0:cell><ns0:cell>n = 170</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>n (%)</ns0:cell><ns0:cell>n (%)</ns0:cell><ns0:cell>n (%)</ns0:cell><ns0:cell>n (%)</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>age (mean, SD)</ns0:cell><ns0:cell>43.6 (10.6)</ns0:cell><ns0:cell cols='2'>41.7 (10.7) 43.6 (10.3)</ns0:cell><ns0:cell>49.6 (8.8)</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>sex: male</ns0:cell><ns0:cell>432 (43.8)</ns0:cell><ns0:cell>221 (43.4)</ns0:cell><ns0:cell>138 (44.8)</ns0:cell><ns0:cell>73 (42.9)</ns0:cell><ns0:cell>0.902</ns0:cell></ns0:row><ns0:row><ns0:cell>educational level</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>0.927</ns0:cell></ns0:row><ns0:row><ns0:cell>tertiary or below</ns0:cell><ns0:cell>149 (15.1)</ns0:cell><ns0:cell>76 (14.9)</ns0:cell><ns0:cell>48 (15.6)</ns0:cell><ns0:cell>25 (14.7)</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>university</ns0:cell><ns0:cell>647 (65.6)</ns0:cell><ns0:cell>338 (66.4)</ns0:cell><ns0:cell>201 (65.3)</ns0:cell><ns0:cell>108 (63.5)</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>postgraduate</ns0:cell><ns0:cell>191 (19.4)</ns0:cell><ns0:cell>95 (18.7)</ns0:cell><ns0:cell>59 (19.2)</ns0:cell><ns0:cell>37 (21.8)</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>marriage</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>0.193</ns0:cell></ns0:row><ns0:row><ns0:cell>married</ns0:cell><ns0:cell>644 (65.2)</ns0:cell><ns0:cell>328 (64.4)</ns0:cell><ns0:cell>195 (63.3)</ns0:cell><ns0:cell>121 (71.2)</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>others</ns0:cell><ns0:cell>343 (34.8)</ns0:cell><ns0:cell>181 (35.6)</ns0:cell><ns0:cell>113 (36.7)</ns0:cell><ns0:cell>49 (28.8)</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>income</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>0.026</ns0:cell></ns0:row><ns0:row><ns0:cell>NTD < 15000</ns0:cell><ns0:cell>168 (17.0)</ns0:cell><ns0:cell>90 (17.7)</ns0:cell><ns0:cell>50 (16.2)</ns0:cell><ns0:cell>28 (16.5)</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell cols='2'>NTD 15001-30000 130 (13.2)</ns0:cell><ns0:cell>70 (13.8)</ns0:cell><ns0:cell>37 (12.0)</ns0:cell><ns0:cell>23 (13.5)</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell cols='2'>NTD 30001-50000 346 (35.1)</ns0:cell><ns0:cell>180 (35.4)</ns0:cell><ns0:cell>120 (39.0)</ns0:cell><ns0:cell>46 (27.1)</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell cols='2'>NTD 50001-70000 176 (17.8)</ns0:cell><ns0:cell>74 (14.6)</ns0:cell><ns0:cell>57 (18.5)</ns0:cell><ns0:cell>45 (26.5)</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>NTD > 70000</ns0:cell><ns0:cell>167 (16.9)</ns0:cell><ns0:cell>95 (18.7)</ns0:cell><ns0:cell>44 (14.3)</ns0:cell><ns0:cell>28 (16.5)</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>area</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>< 0.001</ns0:cell></ns0:row><ns0:row><ns0:cell>urban</ns0:cell><ns0:cell>649 (65.8)</ns0:cell><ns0:cell>337 (66.2)</ns0:cell><ns0:cell>179 (58.1)</ns0:cell><ns0:cell>133 (78.2)</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>suburban/rural</ns0:cell><ns0:cell>338 (34.2)</ns0:cell><ns0:cell>172 (33.8)</ns0:cell><ns0:cell>129 (41.9)</ns0:cell><ns0:cell>37 (21.8)</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>residency</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>0.059</ns0:cell></ns0:row><ns0:row><ns0:cell>northern</ns0:cell><ns0:cell>662 (67.1)</ns0:cell><ns0:cell>335 (65.8)</ns0:cell><ns0:cell>199 (64.6)</ns0:cell><ns0:cell>128 (75.3)</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>middle</ns0:cell><ns0:cell>115 (11.7)</ns0:cell><ns0:cell>59 (11.6)</ns0:cell><ns0:cell>40 (13.0)</ns0:cell><ns0:cell>16 (9.4)</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>southern</ns0:cell><ns0:cell>163 (16.5)</ns0:cell><ns0:cell>96 (18.9)</ns0:cell><ns0:cell>48 (15.6)</ns0:cell><ns0:cell>19 (11.2)</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>east/archipelagos</ns0:cell><ns0:cell>47 (4.8)</ns0:cell><ns0:cell>19 (3.7)</ns0:cell><ns0:cell>21 (6.8)</ns0:cell><ns0:cell>7 (4.1)</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>have a regular family physician</ns0:cell><ns0:cell>508 (51.5)</ns0:cell><ns0:cell>315 (61.9)</ns0:cell><ns0:cell>123 (39.9)</ns0:cell><ns0:cell>70 (41.2)</ns0:cell><ns0:cell>< 0.001</ns0:cell></ns0:row><ns0:row><ns0:cell>satisfied with the experience of the</ns0:cell><ns0:cell>667 (67.6)</ns0:cell><ns0:cell>383 (75.2)</ns0:cell><ns0:cell>194 (63.0)</ns0:cell><ns0:cell>90 (52.9)</ns0:cell><ns0:cell>< 0.001</ns0:cell></ns0:row></ns0:table><ns0:note>PeerJ reviewing PDF \| (2020:04:47877:2:0:NEW 4 Aug 2020)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_2'><ns0:head>Table 2 (on next page)</ns0:head><ns0:label>2</ns0:label><ns0:figDesc /><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_3'><ns0:head>Table 2 .</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Association between the average rating of respondents to each factor when selecting an outpatient facility and their preferred outpatient institution preferred institution for outpatient visit</ns0:figDesc><ns0:table><ns0:row><ns0:cell /><ns0:cell>Total</ns0:cell><ns0:cell>primary clinic</ns0:cell><ns0:cell>general hospital</ns0:cell><ns0:cell>medical center</ns0:cell><ns0:cell>p value</ns0:cell></ns0:row><ns0:row><ns0:cell>factors considered when</ns0:cell><ns0:cell>n = 987</ns0:cell><ns0:cell>n = 509</ns0:cell><ns0:cell>n = 308</ns0:cell><ns0:cell>n = 170</ns0:cell></ns0:row><ns0:row><ns0:cell>selecting an outpatient facility</ns0:cell><ns0:cell /><ns0:cell cols='2'>average rating of respondents</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>physicians are highly reputable</ns0:cell><ns0:cell>4.65 ± 0.71</ns0:cell><ns0:cell cols='3'>4.66 ± 0.69 4.55 ± 0.78 4.81 ± 0.58</ns0:cell><ns0:cell>0.001**</ns0:cell></ns0:row><ns0:row><ns0:cell>physicians explained in detail</ns0:cell><ns0:cell>4.57 ± 0.75</ns0:cell><ns0:cell cols='3'>4.58 ± 0.74 4.49 ± 0.80 4.68 ± 0.69</ns0:cell><ns0:cell>0.027</ns0:cell></ns0:row><ns0:row><ns0:cell>physicians have a good medical practice</ns0:cell><ns0:cell>4.47 ± 0.80</ns0:cell><ns0:cell cols='3'>4.40 ± 0.82 4.46 ± 0.77 4.66 ± 0.72</ns0:cell><ns0:cell>0.001*</ns0:cell></ns0:row><ns0:row><ns0:cell>consider the severity of the disease</ns0:cell><ns0:cell>4.37 ± 0.91</ns0:cell><ns0:cell cols='3'>4.34 ± 0.94 4.36 ± 0.85 4.48 ± 0.94</ns0:cell><ns0:cell>0.235</ns0:cell></ns0:row><ns0:row><ns0:cell>the institution has advanced equipment</ns0:cell><ns0:cell>4.35 ± 0.86</ns0:cell><ns0:cell cols='3'>4.25 ± 0.86 4.34 ± 0.85 4.65 ± 0.79</ns0:cell><ns0:cell><0.001</ns0:cell></ns0:row><ns0:row><ns0:cell>the institution has high-quality drugs</ns0:cell><ns0:cell>4.34 ± 0.92</ns0:cell><ns0:cell cols='3'>4.28 ± 0.93 4.28 ± 0.95 4.62 ± 0.75</ns0:cell><ns0:cell><0.001</ns0:cell></ns0:row><ns0:row><ns0:cell>physicians are not in a hurry</ns0:cell><ns0:cell>4.30 ± 0.87</ns0:cell><ns0:cell cols='3'>4.32 ± 0.88 4.22 ± 0.89 4.40 ± 0.81</ns0:cell><ns0:cell>0.071</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>physicians are gracious and kind 4.25 ± 0.85</ns0:cell><ns0:cell cols='3'>4.25 ± 0.85 4.22 ± 0.85 4.30 ± 0.86</ns0:cell><ns0:cell>0.645</ns0:cell></ns0:row><ns0:row><ns0:cell>have good medical experience</ns0:cell><ns0:cell>4.24 ± 0.79</ns0:cell><ns0:cell cols='3'>4.25 ± 0.79 4.20 ± 0.79 4.29 ± 0.80</ns0:cell><ns0:cell>0.414</ns0:cell></ns0:row><ns0:row><ns0:cell>the institution has friendly staff</ns0:cell><ns0:cell>4.15 ± 0.96</ns0:cell><ns0:cell cols='3'>4.12 ± 1.00 4.14 ± 0.91 4.22 ± 0.94</ns0:cell><ns0:cell>0.500</ns0:cell></ns0:row><ns0:row><ns0:cell>the institution has convenient transportation</ns0:cell><ns0:cell>4.13 ± 0.96</ns0:cell><ns0:cell cols='3'>4.11 ± 0.95 4.13 ± 0.94 4.18 ± 1.03</ns0:cell><ns0:cell>0.722</ns0:cell></ns0:row><ns0:row><ns0:cell>the institution has diverse specialties</ns0:cell><ns0:cell>4.09 ± 0.99</ns0:cell><ns0:cell cols='3'>3.97 ± 1.06 4.12 ± 0.90 4.39 ± 0.87</ns0:cell><ns0:cell><0.001</ns0:cell></ns0:row><ns0:row><ns0:cell>waiting time is not too long</ns0:cell><ns0:cell>3.90 ± 0.93</ns0:cell><ns0:cell cols='3'>3.91 ± 0.93 3.94 ± 0.86 3.78 ± 1.02</ns0:cell><ns0:cell>0.171</ns0:cell></ns0:row><ns0:row><ns0:cell>the institution was recommended by friends or relatives</ns0:cell><ns0:cell>3.55 ± 0.99</ns0:cell><ns0:cell cols='3'>3.54 ± 1.01 3.50 ± 0.89 3.71 ± 1.05</ns0:cell><ns0:cell>0.074</ns0:cell></ns0:row><ns0:row><ns0:cell>institutions with a good reputation</ns0:cell><ns0:cell>3.53 ± 1.03</ns0:cell><ns0:cell cols='3'>3.46 ± 1.03 3.45 ± 0.99 3.88 ± 1.05</ns0:cell><ns0:cell><0.001*</ns0:cell></ns0:row><ns0:row><ns0:cell>the visibility of medical institutions is high</ns0:cell><ns0:cell>3.43 ± 1.06</ns0:cell><ns0:cell cols='3'>3.39 ± 1.07 3.41 ± 0.97 3.62 ± 1.18</ns0:cell><ns0:cell>0.042</ns0:cell></ns0:row><ns0:row><ns0:cell>willing to prescribe for chronic diseases</ns0:cell><ns0:cell>3.40 ± 1.16</ns0:cell><ns0:cell cols='3'>3.36 ± 1.16 3.40 ± 1.16 3.50 ± 1.16</ns0:cell><ns0:cell>0.381</ns0:cell></ns0:row><ns0:row><ns0:cell>physicians are famous</ns0:cell><ns0:cell>3.32 ± 0.98</ns0:cell><ns0:cell cols='3'>3.25 ± 0.97 3.31 ± 0.94 3.52 ± 1.03</ns0:cell><ns0:cell>0.007</ns0:cell></ns0:row><ns0:row><ns0:cell>physicians with a good reputation</ns0:cell><ns0:cell>3.29 ± 0.91</ns0:cell><ns0:cell cols='3'>3.23 ± 0.90 3.26 ± 0.91 3.50 ± 0.89</ns0:cell><ns0:cell>0.003</ns0:cell></ns0:row><ns0:row><ns0:cell>low copayment</ns0:cell><ns0:cell>3.08 ± 1.16</ns0:cell><ns0:cell cols='3'>3.07 ± 1.14 3.15 ± 1.17 3.00 ± 1.19</ns0:cell><ns0:cell>0.394</ns0:cell></ns0:row><ns0:row><ns0:cell>* p ≤ 0.001, p ≤0.01,</ns0:cell><ns0:cell>* p ≤0.05</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_4'><ns0:head>Table 3 .</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Exploratory factor analysis loads and variance percentages for factors considered when selecting an outpatient facility -Olkin (KMO): 0.868 Bartlett sphericity tests (P<0.001).</ns0:figDesc><ns0:table><ns0:row><ns0:cell>factors loads</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_5'><ns0:head>Table 4 (on next page)</ns0:head><ns0:label>4</ns0:label><ns0:figDesc /><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_6'><ns0:head>Table 4 .</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Results of the logistic regression for predicting 'visit to an outpatient clinic of the medical center for an illness' PeerJ reviewing PDF \| (2020:04:47877:2:0:NEW 4 Aug 2020)</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Manuscript to be reviewed</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_7'><ns0:head>Table 4 .</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Results of the logistic regression for predicting 'visit to an outpatient clinic of the medical center for an illness'</ns0:figDesc><ns0:table><ns0:row><ns0:cell>MODEL 1</ns0:cell><ns0:cell>MODEL 2</ns0:cell><ns0:cell>MODEL 3</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:note place='foot' n='2'>* p < 0.001, p < 0.01, * p < 0.05 PeerJ reviewing PDF \| (2020:04:47877:2:0:NEW 4 Aug 2020)</ns0:note> </ns0:body> "	"We sincerely thank the Editor and the reviewers for their response and constructive comments. Following the editor's comments, we have revised the manuscript and believe that the manuscript has been significantly improved. The Editor has identified that our manuscript required English editing. The article has been carefully revised by a professional language editing service to improve the grammar and readability. We are very grateful for the opportunity to submit this revised manuscript and hope it is now suitable for publication in PEERJ. Reply to editor comments 1. This is not correct. Pls check the meaning of hierarchical logistic regression here (https://amstat.tandfonline.com/doi/abs/10.1080/01621459.1985.10478148#:~:text=A%20hierarchical%20logistic%20regression%20model,at%20both%20of%20these%20levels.). The authors used a series of logistic regression models not a hierarchical logistic regression model. Pls correct this As suggested by the editor, we have modified the description in abstract line 30-31 as: Multiple logistic regression was performed to determine which factors satisfactorily explained “visiting the outpatient clinic of the medical center for an illness without a referral.'. 2. Pls correct this as explained Agree. We have corrected the description in abstract as the editor’s comment. 3. Pls remove this sentence Agree. We've deleted the sentence on page 5, line 144. 4. Pls read these sections in yellow on this page and edit to avoid repeating the description of the validation procedure. If needed, remove unneeded subheadings As suggested by the editor, we have rewritten this paragraph to avoid repeating the description of the validation procedure and remove unneeded subheadings to make it clearer and easier to read (page 5-6, line 150-157). At the end of the questionnaire, respondents were asked to provide demographic information and information on past experiences during outpatient visits at different hospital levels, attitudes towards copayment, and whether they have a regular family physician. The questionnaire was developed based on a literature review and the opinions of the focus group to ensure content validity. Five senior researchers with subject matter expertise were invited to revise the questionnaire and perform repeated testing of the questionnaire. The content was rated by five experts with an average content validity index of 86.0%. The questions were refined after feedback and finalized into an online survey. 5. Pls edit the sentence to avoid repeating “0.5” twice. Agree. We have corrected the English in line 178-179 as： Loadings over 0.5 were used to interpret components in the study. 6. Pls correct this as previously explained As suggested by the editor, we have modified the description in page,line 183-185 as: Multiple logistic regressions were performed to determine factors that satisfactorily explained the dependent variable “visiting the outpatient clinic of the medical center for an illness without a referral.' 7. This part should be shifted to the beginning of the analysis section 8. This part, too, should be shifted to the beginning of the analysis section Agree. We have moved these two sections to the beginning of the analysis section. Please see page 6, line 163-168. 9. Pls remove the repeated word Agree. Agree. We have corrected the error in page 7, line 209 10. There are no estimates for education in model I. these are in model II 11. Not seen in table. Where is this variable? 12. No estimates in model I. these are in model III 13. No estimates in model 1 at all. These are in model II and III 14. Where s the interpretation of factor II: image and reputation? Report on the improved first from model I to III As suggested by the reviewer, we have rewritten this section about model selection and interpretation, please see page 8-9, line 237-261: Table 4 illustrates three models of logistic regression for predicting “visits to the outpatient clinic of the medical center for an illness.” Age was a crucial predictor in all the models. The likelihood of choosing to visit a medical center when ill increased by 2.7%–3.1% for every additional year of age (95% CI = 1.4%–4.3%) when other variables were controlled. In Model 1, when age, gender, “have a regular family physician,” and “consider that copayment is important” were adjusted, patients who were previously satisfied with the medical experience of primary clinics had a 0.5 lower likelihood of visiting the outpatient clinic of a medical center for an illness (95% CI =0.429–0.584). Model 2 was then also adjusted for the extracted factors I to III, which revealed that patients who reported that hospital facilities, high-quality drugs, and diverse specialties were very important had a 2.218 higher likelihood of selecting the outpatient clinic of the medical center (OR = 2.218, 95% CI = 1.514–3.249). Patients who were previously satisfied with the medical experience of primary clinics had a 0.509 lower likelihood of choosing a medical center to visit when ill (95% CI = 0.435–0.595). Patients who rated copayment as important were 0.525 times as likely to select a medical center to visit when ill (95% CI = 0.354–0.781). People with a regular family doctor were 0.676 times less likely to select a medical center (95% CI = 0.471–0.969). Patients who rated physician factors as very important were less likely to select an outpatient clinic in a medical center (OR = 0.717, 95% CI = 0.523-0.984). The gender of the patient and the image and reputation of the hospital and physicians were not significantly related to inpatient hospital choice. In Model 3, when the possible sociodemographic confounding variables were added, the step Wald chi-square statistic was insignificant (Wald chi-square difference = 13.581, df = 8, p = 0.093). The residential area, income, and education level did not appear to be related to the selection of an outpatient clinic. Therefore, we decided to adopt model 2 as the result of our analysis. 15. Not n the full model: model III ! Pls remove 16. Not sig in any model. Pls correct this Agreed. We have corrected the presentation according to the final result in page 9, line 264-266 as: Several factors significantly affected the selection of a medical center, including older age, the physician factors, advanced equipment, high-quality drugs, past experience in primary clinics and the copayment. 17. Pls correct the English Agree. We have corrected the error in page 9, line 271 Some results are consistent with ours. 18. It s not included in the models, is it? As previously described, we have added the income variable and re-do the analysis. In model 3, the income variable did not have a significant correlation to outpatient choice, so we kept the original narrative. 19. Not in the full model. Not correct. Pls address this We finally adopted model 2. The physician factor is statistically significant in model 2, so we kept the original narrative. 20.Pls remove this part and edit to ensure flow As suggested by the reviewer, we have removed this part in page 10, line 286.: 21.Pls check and correct the En Agree. We have corrected the error in page 10, line 306-308. Our research results did not accord with these findings. Further research is needed to understand whether the primary clinics in Taiwan satisfy the needs of elderly people. 22.Than what/ who? Pls clarify Agree. We've added additional explanations to make the sentence clearer in page 11 , line 311–313. Although the online survey represents a wide age range and geographic distribution, the sample was younger and more highly educated than the general public "	Here is a paper. Please give your review comments after reading it.
507	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Osteocytes and blood vessels are the main cellular and tissue components of the bone tissue of vertebrates. Evidence of these soft-tissue microstructures has been widely documented in the fossil record of Mesozoic and Cenozoic turtles. However, all these studies have characterized morphologically and elementally these microstructures via isolation from the fossilized bone matrix where they were preserved or in ground sections, which could raise skepticism about the results due to potential cross-contamination or reagents effects. Fossil turtle bones from three different localities with distinct preservation environments and geological settings, including Mongolemys elegans from the Late Cretaceous of Mongolia, Allaeochelys crassesculpta from the Eocene of Germany, and a podocnemidid indet. from the Miocene of Colombia are studied here. Bone from two extant turtle species, Lepidochelys olivacea, and Podocnemis lewyana, as well as a commercial chicken Gallus gallus were used for comparisons. Scanning Electron Microscopy-Energy Dispersive Spectroscopy analyses performed directly on untreated fresh surfaces show that osteocytes-like in the fossil turtle bone are mostly composed of iron and manganese. In contrast, the in situ blood vessels-like of the fossil turtles, as well as those from the extant taxa are rich in elements typically organic in origin (carbon and nitrogen), which are absent to minimally present in the surrounding bone or rock matrix; this suggests a possible endogenous composition for these fossil structures. Also, the results presented here show that although originally both (osteocytes and blood vessels) are organic soft components of bone as evidenced in the extant turtles and chicken, they can experience completely different preservational pathways only microns away from each other in the same fossil bone.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>In situ SEM/EDS compositional characterization of osteocytes-and blood vessels-like in fossil and extant turtles on untreated bone surfaces; different preservational pathways microns away.</ns0:p></ns0:div> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>Bone is a complex biological tissue that characterizes extant and fossil vertebrates, and consists of a mineralized (calcium, phosphorus) and a non-mineralized (collagen and non-collagenous proteins) extracellular matrix, plus water and some lipids <ns0:ref type='bibr' target='#b4'>(Boskey & Gehron 2013;</ns0:ref><ns0:ref type='bibr' target='#b23'>Rey et al. 2009)</ns0:ref>. Cells involved in bone tissue are osteoclasts, osteoblasts, and the most abundant of them osteocytes <ns0:ref type='bibr' target='#b3'>(Bonewald 2011)</ns0:ref>. Osteocytes are embedded within the hard-mineralized component of bone throughout life (exceptions being when released by fracture or during remodeling) <ns0:ref type='bibr' target='#b24'>(Robling & Bonewald 2020)</ns0:ref>, providing them high preservation potential within fossil bones, which has been extensively documented in different clades of vertebrates (e.g., <ns0:ref type='bibr' target='#b1'>Bailleul et al. 2019;</ns0:ref><ns0:ref type='bibr' target='#b12'>Enlow & Brown 1956;</ns0:ref><ns0:ref type='bibr' target='#b22'>Pawlicki & Nowogrodzka-Zagorska 1998;</ns0:ref><ns0:ref type='bibr' target='#b27'>Schweitzer 2011;</ns0:ref><ns0:ref type='bibr' target='#b30'>Schweitzer et al. 2013;</ns0:ref><ns0:ref type='bibr' target='#b32'>Surmik et al. 2019)</ns0:ref>. Similar preservation of osteocytes-and blood vessels-like has also been documented in fossil turtles, showing that their preservation is independent of geologic time, paleoenvironment, lithology, lineages, and latitude <ns0:ref type='bibr' target='#b6'>(Cadena 2016;</ns0:ref><ns0:ref type='bibr' target='#b7'>Cadena et al. 2013;</ns0:ref><ns0:ref type='bibr' target='#b8'>Cadena & Schweitzer 2012</ns0:ref><ns0:ref type='bibr'>, 2014)</ns0:ref> Something in common to all aforementioned studies are the analytical tools used to study and characterize these fossil bone microstructures, which include principally: 1) ground sections and observation under transmitted and polarized microscopy <ns0:ref type='bibr' target='#b8'>(Cadena & Schweitzer 2012;</ns0:ref><ns0:ref type='bibr' target='#b32'>Surmik et al. 2019)</ns0:ref>; 2) bone demineralization using ethylenediaminetetraacetic acid (EDTA) as a chelating agent (0.5 M, pH 8.0), facilitating release the osteocytes-, blood vessels-, and any other cells-or soft-tissue fibers-like from the bone matrix for their posterior study by transmitted and/or polarized light, scanning and/or transmission electron microscopy and any coupled elemental analyzer, Raman spectroscopy, Fourier-transform infrared spectroscopy (FTIR), immunological and antibody studies (e.g., Alfonso-Rojas & Cadena 2020; <ns0:ref type='bibr' target='#b1'>Bailleul et al. 2019</ns0:ref><ns0:ref type='bibr' target='#b2'>Bailleul et al. , 2020;;</ns0:ref><ns0:ref type='bibr' target='#b6'>Cadena 2016;</ns0:ref><ns0:ref type='bibr' target='#b25'>Saitta et al. 2019;</ns0:ref><ns0:ref type='bibr' target='#b30'>Schweitzer et al. 2013;</ns0:ref><ns0:ref type='bibr' target='#b32'>Surmik et al. 2019;</ns0:ref><ns0:ref type='bibr' target='#b35'>Wiemann et al. 2018)</ns0:ref> The preservation of these soft-tissue microstructures (osteocytes and blood vessels) and their potential original constituents (proteins and DNA) has been questioned and considered a consequence of microbial interactions within fossil bone and its microenvironment or even as a result of cross-contamination in the laboratory <ns0:ref type='bibr' target='#b5'>(Buckley et al. 2017;</ns0:ref><ns0:ref type='bibr' target='#b16'>Kaye et al. 2008;</ns0:ref><ns0:ref type='bibr' target='#b25'>Saitta et al. 2019)</ns0:ref>. The 'biofilm hypothesis' as a source for soft-tissue preservation in dinosaur bones has been rigorously tested, which identified fundamental morphological, chemical and textural differences between the resultant biofilm structures and those derived from dinosaur bone, demonstrating that the recovered microstructures in the reports cited above are endogenous in origin and that the 'biofilm hypothesis' should therefore be rejected <ns0:ref type='bibr' target='#b28'>(Schweitzer et al. 2016)</ns0:ref>. Issues concerning cross-contamination and replications, timing of sample collections, and reagents have also been addressed by <ns0:ref type='bibr' target='#b29'>Schweitzer et al (2019)</ns0:ref>.</ns0:p><ns0:p>Compositionally, the osteocytes-and blood vessels-like from different clades of fossil vertebrates have been shown to commonly be enriched in iron <ns0:ref type='bibr' target='#b6'>(Cadena 2016;</ns0:ref><ns0:ref type='bibr' target='#b31'>Schweitzer et al. 2014;</ns0:ref><ns0:ref type='bibr' target='#b32'>Surmik et al. 2019;</ns0:ref><ns0:ref type='bibr' target='#b34'>Ullmann et al. 2019</ns0:ref>), an element that has been suggested to play a key role in preserving and even masking identification of proteins in fossil tissues via Fenton reactions <ns0:ref type='bibr' target='#b31'>(Schweitzer et al. 2014)</ns0:ref>. Other elements typically found in these fossil bone microstructures are carbon, calcium, and silicon <ns0:ref type='bibr' target='#b6'>(Cadena 2016;</ns0:ref><ns0:ref type='bibr' target='#b34'>Ullmann et al. 2019)</ns0:ref>. At present, all these studies of elemental characterization have been conducted using SEM/EDS on isolated (post-demineralization) osteocytes-and blood vessels-like, or from polished ground sections, which implies some degree of manipulation or contact with reagents or preparation tools, potentially raising skepticism on the elemental results.</ns0:p><ns0:p>Here, I explore the in situ (directly on fresh and untreated surfaces) preservation and elemental composition of bone microstructural elements (cells and blood vessels) of fossil turtle bones from three localities which have completely different geological settings (lithological, taphonomic, and fossil diagenesis), including: 1) Gobi Desert, Mongolia, from the Late Cretaceous (late Campanian-early Maastrichtian); 2) Messel Pit, Germany, from the Eocene; and 3) La Venta fauna, Colombia, from the Miocene. Comparison samples include bone from two extant turtles and a domesticated chicken. I discuss herein the results of these analyses and the advantages of using in situ SEM/EDS for understanding preservation of cells/tissues in fossils.</ns0:p></ns0:div> <ns0:div><ns0:head>Materials & Methods</ns0:head><ns0:p>Fossil and extant samples. All the fossil and extant samples analyzed here were free of any resin, glue, or stabilizing additives since field collection. Two small pieces donated by Dr. M. Norell (American Museum of Natural History, AMNH) from a partially-articulated shell (carapace and plastron) of Mongolemys elegans (IGM-90/42) were used for this study. Specimen IGM-90/42 has been previously figured, including ground sections that show excellent preservation of osteocytes-like under transmitted light microscopy <ns0:ref type='bibr'>(Cadena et al. 2013, figs. 7, 9)</ns0:ref>. This fossil material was collected by the AMNH and the Mongolian Academy of Sciences joint field expeditions at the Bugin Tsav locality, Gobi Desert, Mongolia, from fine-grained sandstones representing ponds deposits within the Nemegt Formation, considered to be late Campanian-early Maastrichtian (~ 80 Ma) in age <ns0:ref type='bibr'>(Jerzykiewicz 2000, references therein)</ns0:ref>.</ns0:p><ns0:p>Small isolated fragments from the carapace of an Allaeochelys crassesculpta (SMF ME 2449) were donated by Dr. K. Smith (Senckenberg Naturmuseum Frankfurt, SMF); these were collected from the well-known locality of Messel Pit, which represents volcanically-influenced lake deposits from the early-middle Eocene (~ 48 Ma) <ns0:ref type='bibr' target='#b17'>(Lenz et al. 2015)</ns0:ref>. Osteocytes-, blood vessels-, and collagen fibers-like from this specimen were previously described and elementally characterized by <ns0:ref type='bibr'>Cadena (2016, figs, 4-7)</ns0:ref>.</ns0:p><ns0:p>Carapace fragments from a podocnemidid indet. specimen, (UR-CP-0043), as well as the surrounding rock matrix, were collected in 2018 directly from an excavation site (approximately 1.5 m from the surface) using strict aseptic techniques (nitrile gloves, face mask, wrapped in sterilized aluminum foil and kept in glass containers with silica gel for moisture control until analyses were performed). This fossil material was collected from the Repartidora locality, La Victoria Formation, middle Miocene (13.6 ± 0.2 Ma), Tatacoa Desert, Colombia, from what are interpreted as fluvial deposits <ns0:ref type='bibr' target='#b10'>(Cadena et al. 2020)</ns0:ref>. Permits for collecting and study of the samples were granted by the Colombian Geological Survey (Radicado Nº 20193800017321).</ns0:p><ns0:p>For comparisons, two extant turtle carcasses were sampled directly in the field following the same aseptic protocols used for specimen UR-CP-0043. The first corresponds to carapace fragments from an individual of the sea turtle Lepidochelys olivacea (uncatalogued specimen) collected in January 2017 at the Pacific coast, Santa Elena Province, Ecuador, permit granted by Yachay Tech University. The second (uncatalogued specimen) sampled corresponds to a carcass of the side-necked turtle Podocnemis lewyana found in a sand bed of the Magdalena River, close to La Victoria village, Huila Department, Colombia, under a permit granted by the ethics committee of Universidad del Rosario (Resolución DVO005 672-CV1066) and the Colombian Autoridad Nacional de Licencias Ambientales (Technical concept Nº 02263, 2019). A third sample corresponds to a femur fragment from a commercial chicken Gallus gallus obtained directly from a local market. Muscle tissue was removed and small bone fragments were cut using a sterilized scalpel and dried out at room temperature for several days.</ns0:p><ns0:p>Institutional abbreviations. AMNH, American Museum of Natural History, New York, USA; IGM, Geological Institute of the Mongolian Academy of Sciences, Ulaan Baatar, Mongolia ; SMF ME, Senckenberg Naturmuseum Frankfurt, Germany; UR-CP, paleontological collection, Facultad de Ciencias Naturales, Universidad del Rosario, Bogotá, Colombia.</ns0:p></ns0:div> <ns0:div><ns0:head>Scanning electron microscopy and elemental analysis (SEM/EDS).</ns0:head><ns0:p>Each of the fossils, rock matrix and extant bone samples were placed between two disposable sterilized lab-weighing boats and gently hit with a rock hammer to break them into smaller pieces. Using tweezers (sterilized before every mounting process) one of the smaller pieces of broken bone was transferred to an SEM holder with adjustable screws and secured. To prevent any potential particles or dust from entering the SEM chamber, each sample was gently air cleaned before placing it in the SEM carousel. Elemental analysis was performed in combination with high resolution imaging of the bone surfaces, as well as (in some cases) the rock matrix attached to it using a scanning electron microscope coupled with an energy-dispersive X-ray spectroscopy analyzer (Phenom ProX, at the Paleontological Lab of Yachay Tech University (YTU), San Miguel de Urcuqui, Ecuador). Imaging was performed at 5 kV using different magnification settings, and point-and-map analyses of elemental composition of selected regions or features were performed at 15 kV. At least five or more points were explored for each osteocyte-or blood vessel-like, as well as the surrounding bone matrix or rock. Quality of EDS analyses was evaluated considering only those with one million counts or higher. Full raw data is presented in Data S1.</ns0:p><ns0:p>Bone demineralization. In order to test for the occurrence and preservation of osteocytes-and blood vessels-like in some of the samples, small bone pieces of Mongolemys elegans (IGM-90/42) and the podocnemidid indet. specimen (UR-CP-0043) were demineralized using disodium ethylenediaminetetraacetic acid (EDTA) (0.5 M, pH 8.0 filter-sterilized using a 0.22 μm filter) as previously described <ns0:ref type='bibr' target='#b6'>(Cadena 2016;</ns0:ref><ns0:ref type='bibr' target='#b11'>Cleland et al. 2015)</ns0:ref> for a period of five days to two weeks, or until osteocytes-and blood vessels-like were detected. Photographs of the recovered osteocytes-like were taken using a transmitted light microscope (Olympus BX-63) and a polarized light microscope (Olympus BX-53) at the paleontological lab of YTU. Some of the isolated osteocytes-like from IGM-90/42 were collected with a tip in a 1.5 ml tube, rinsed three times with E-pure water to get rid of EDTA, being centrifuged at 1500 RPM for 2 minutes between step. A drop of the supernatant was mounted in a stub, dried out at room temperature inside in a sealed small SEM-stub box to avoid any air or dust particles interact with the sample, and analyzed following the same protocol and SEM/EDS machine aforementioned.</ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head><ns0:p>Mongolemys elegans, Late Cretaceous of Mongolia. The in situ osteocytes-like of Mongolemys elegans (IGM-90/42) under SEM exhibit a distinct contrast with the surrounding bone matrix, which is exclusive of their three-dimensional volume, and it is also different from the empty osteocytes-lacuna, which exhibits the same contrast as the bone matrix (Figs. <ns0:ref type='figure' target='#fig_0'>1A-B</ns0:ref>). Compositionally, they are predominantly composed of iron, calcium, carbon, manganese, and minor amounts of barium and nitrogen (Figs. <ns0:ref type='figure' target='#fig_0'>1C-K</ns0:ref>; 2A; Data S2; and Fig. <ns0:ref type='figure' target='#fig_0'>S1</ns0:ref>). There is no evidence of any of these elements in empty osteocyte-lacunae walls, which are composed of calcium and phosphorus, like the bone matrix (Figs. <ns0:ref type='figure' target='#fig_0'>1L-N</ns0:ref>). The isolated osteocytes-like show that iron is concentrated on their external surface and the manganese in the internal, this is clearly evident in elemental maps and a cross-line elemental profile (Figs. <ns0:ref type='figure' target='#fig_0'>1O-P; S1</ns0:ref>). Observation of some of the isolated (post-demineralization) osteocytes-like under transmitted and polarized light revealed excellent morphological preservation, with some of them emitting low-degree birefringence colors under polarized light (Fig. <ns0:ref type='figure'>3</ns0:ref>).</ns0:p><ns0:p>Allaeochelys crassesculpta, Eocene of Germany. The most abundant bone microstructures preserved in this sample are blood vessels-like and the walls that formed the Haversian-Volkmann (H-V) canals; also, in some, there is evidence of very small (2.5 µm diameter) structures with a striated margin which resemble the morphology of osteoblast cells (Figs. <ns0:ref type='figure' target='#fig_4'>4A-D</ns0:ref>; S2). The blood vessels-like exhibit a width of 1-3 µm, with an average wall thickness of 0.2 µm (Fig. <ns0:ref type='figure' target='#fig_4'>4D</ns0:ref>). Compositionally, the blood vessels-like are mainly composed of carbon and nitrogen, with minor amounts of calcium, phosphorus and iron (Figs. 2B; 4E-G; 4J-N; S2; Data S2). The bone matrix surrounding them lacks nitrogen and carbon, and it is exclusively characterized by calcium, phosphorus, and iron (Figs. <ns0:ref type='figure' target='#fig_4'>4E,I</ns0:ref>). A bone sample with rock matrix attached shows that the bone is composed of calcium, phosphorus, iron, and nitrogen, and, in contrast, the rock matrix is rich in aluminum and silicon (Figs. <ns0:ref type='figure' target='#fig_1'>2C-D</ns0:ref></ns0:p><ns0:formula xml:id='formula_0'>; 4O-Q; Data S2).</ns0:formula><ns0:p>Podocnemidid indet, Miocene of Colombia. The sample of the side-necked turtle from La Venta, Colombia, shows on the bone external cortex preservation of walls that formed the H-V canals, blood vessels-and osteocytes-like tightly embedded in the very homogenous bone matrix (Figs. <ns0:ref type='figure'>5A-B</ns0:ref>; 5F-G). Elementally, the blood vessels-like and H-V canal walls are rich in carbon, nitrogen, and calcium, with minor amounts of phosphorous and silicon (Figs. 2B; 5C-D; 5H-I; Data S2). In contrast, the osteocytes-like are composed of iron, calcium, aluminum, manganese, phosphorus, and minor amounts of silicon (Figs. 2B; 5H-J; Data S2). The bone matrix lacks carbon and nitrogen, and it is constituted by calcium and phosphorus mainly (Figs. <ns0:ref type='figure' target='#fig_1'>2C; 5C, E, H</ns0:ref>). An isolated bone fragment (post-demineralization) shows some of the osteocytes-like still embedded in the matrix, varying in color from orange to black, the darker ones located closer to black, dendritic mats (Figs. <ns0:ref type='figure'>5K-M</ns0:ref>).</ns0:p><ns0:p>In situ extant turtle and chicken bone microstructures. The carapace bone fragment of the extant side-necked turtle Podocnemis lewyana shows osteocytes within lacunae (Figs. <ns0:ref type='figure'>6A-B</ns0:ref>). Their composition is rich in carbon, nitrogen, calcium, and phosphorus (Figs. 2A; 6C-D; Data S2). The bone matrix is relatively richer in calcium (Figs. <ns0:ref type='figure' target='#fig_1'>2C; 6C, E</ns0:ref>). The H-V canals exhibit a distinct wall and a high concentration of blood vessels and red blood cells, which are rich in carbon and nitrogen (Figs. 2B; 6F-G; S3; S4; Data S2). Similar spatial patterns and composition are shared by the bone of the extant marine turtle Lepidochelys olivacea (Figs. 2; 6H-M; Data S2), and the bone of Gallus gallus (chicken) (Figs. 2; 6N-P; Data S2).</ns0:p></ns0:div> <ns0:div><ns0:head>Discussion</ns0:head><ns0:p>As previously shown <ns0:ref type='bibr' target='#b6'>(Cadena 2016;</ns0:ref><ns0:ref type='bibr' target='#b31'>Schweitzer et al. 2014;</ns0:ref><ns0:ref type='bibr' target='#b32'>Surmik et al. 2019;</ns0:ref><ns0:ref type='bibr' target='#b34'>Ullmann et al. 2019)</ns0:ref>, the in situ analyses presented here, concur with that iron is a very common constituent of fossil osteocytes-like, such as those found in the Late Cretaceous Mongolemys elegans and the Miocene podocnemidid indet. bone samples studied herein (Figs. <ns0:ref type='figure' target='#fig_0'>1,5</ns0:ref>). However, this composition is not always homogenous and may vary between the external and the internal layer of osteocytes-like, as shown in a broken and folded osteocyte-like from M. elegans, which exhibits richer content of manganese internally and iron externally (Figs. <ns0:ref type='figure' target='#fig_0'>1O-P</ns0:ref>). High levels of manganese were also detected in osteocytes-like from the Miocene side-necked turtle from Colombia, indicating that besides iron as initially suggested by <ns0:ref type='bibr' target='#b31'>Schweitzer et al. (2014)</ns0:ref>, manganese may also be involved in the preservation of these bone microstructures in deep time. The source for this rich content of manganese seems to be from manganese oxides such as pyrolusite penetrating bone microfractures, which were found herein in some fragments of the Miocene podocnemidid indet. from Colombia (Figs. <ns0:ref type='figure'>5K-M</ns0:ref>), and also has been characterized to occur in dinosaur fossil bones from the same Nemegt Formation, from which the M. elegans studied herein was collected <ns0:ref type='bibr' target='#b21'>(Owocki et al. 2016)</ns0:ref>. The color variation exhibited by the fossil osteocytes-like of M. elegans and podocnemidid indet. seems to be related to enrichment of manganese, higher their manganese content darker their color. In contrast to the osteocytes of the extant turtle and chicken bone, which are rich in carbon and nitrogen (Figs. <ns0:ref type='figure' target='#fig_1'>2, 6</ns0:ref>), these elements only appear in minor amounts in fossil osteocytes-like. However, these cells exhibited a very distinct composition when compared to the surrounding bone matrix and even the wall surfaces of their osteocytes-lacunae, indicating that their mineralized preservation occurred at micro-scale inside the bone, a hypothesis that should be tested by future studies using additional tools (e.g, Raman and FTIR spectroscopy).</ns0:p><ns0:p>The blood vessels-l and H-V canal walls-like preserved in the Eocene Allaeochelys crassesculpta from the Messel Pit and the Miocene podocnemidid indet. specimen from the La Venta not only exhibited a similar morphology, but also exhibited the same elemental composition as their corresponding tissues in extant turtle and chicken bone. (Figs. <ns0:ref type='figure' target='#fig_4'>2, 4</ns0:ref>, 5, S2, S3, S4; Data S2). In both cases (extant and fossils) being rich in carbon and nitrogen, and differing from the surrounding bone matrix which is richer in calcium and phosphorus, or the rock matrix which is rich in silicon and aluminum (without any traces of carbon, calcium, or nitrogen) which suggests that carbonates or nitrates were in the surrounding microenvironment. The in situ measurements performed on some of the preserved blood vessels-like from A. crassesculpta, exhibiting uniform fabric and thin walls of 0.2 µm thickness (Fig. <ns0:ref type='figure' target='#fig_4'>4D</ns0:ref>) suggest that they are not consistent with the characteristics of biofilms, which tend to be amorphous and larger in diameter <ns0:ref type='bibr' target='#b28'>(Schweitzer et al. 2016)</ns0:ref>. Blood vessels constitute one of the most promising microstructures preserved in fossil turtles for molecular paleontology studies, and future studies should focus on their molecular in situ characterization using ToF-SIMS mass spectrometry, similarly as it has been used in dinosaurs and other fossil vertebrates (Alfonso-Rojas & Cadena 2020; <ns0:ref type='bibr' target='#b14'>Henss et al. 2013;</ns0:ref><ns0:ref type='bibr' target='#b18'>Lindgren et al. 2018;</ns0:ref><ns0:ref type='bibr' target='#b29'>Schweitzer et al. 2019)</ns0:ref>.</ns0:p><ns0:p>For the first time, I herein report the preservation of osteoblasts-like in fossil vertebrates, particularly in the Messel Pit turtle A. crassesculpta. They occur as oval objects with striated margins that are attached to the H-V canals (Figs. <ns0:ref type='figure'>5B-D</ns0:ref>; S2), and thus resemble the morphology and size of osteblasts observed in electron micrographs of human bone <ns0:ref type='bibr' target='#b20'>(Nakamura 2007;</ns0:ref><ns0:ref type='bibr' target='#b26'>Schmidt et al. 2002)</ns0:ref>. At the same time, evidence here provided from the Miocene podocnemidid indet. turtle from Colombia (Figs. <ns0:ref type='figure'>5F-J</ns0:ref>) shows that, in the same bone specimen, osteocytes-and blood vessels-like that are only 20 microns away from each other are compositionally different. This indicates that each microstructure went through a different preservational pathway. Osteocytes-like seem to be more mineralized than blood vessels-like in these fossil samples, with high amount of iron and manganese, and less organic components than blood vessels-like (Fig. <ns0:ref type='figure' target='#fig_1'>2, 3</ns0:ref>). In the extant bone of turtles and chicken, osteocytes and blood vessels exhibit similar elemental composition under SEM/EDS, both being rich in carbon and nitrogen, which are typically present in abundance within proteins <ns0:ref type='bibr'>(Torabizadeh 2011) (Figs. 2, 6)</ns0:ref>. A similar composition was detected herein in fossil blood vessels from A. crassesculpta and the podocnemidid indet., from Colombia (Figs. <ns0:ref type='figure' target='#fig_4'>4, 5</ns0:ref>).</ns0:p><ns0:p>Traditionally, it has been suggested that SEM/EDS has to be performed on homogenous or polished surfaces to avoid topographic effects on EDS analyses <ns0:ref type='bibr' target='#b13'>(Goldstein et al. 2003)</ns0:ref>. However, as I showed here, such effects were negligible for the analyzed sample with composition and signal intensities being very similar in both the fossil and extant samples (Fig. <ns0:ref type='figure' target='#fig_1'>2</ns0:ref>). I therefore suggest that a more critical condition for EDS analysis on untreated samples is acquire the highest maximum count rate possible; above 1 million counts is ideal.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusion</ns0:head><ns0:p>This study provided evidence that in situ analyses using a conventional technique, SEM/EDS, on untreated fresh surfaces of fossil and extant bones constitutes a protocol that should be added to the rigorous plethora of proxies and tools (e.g, those recently reviewed and summarized by <ns0:ref type='bibr' target='#b29'>Schweitzer et al., (2019)</ns0:ref>) to support and demonstrate the preservation of cells, soft-tissues and their original constituents in deep time. Furthermore, in situ analyses of fossil and extant bone samples may also help eliminate any potential skepticism of results obtained by molecular paleontology studies, because, as demonstrate here, it requires minimal sample preparation/manipulation, use of reagents, or contact with lab tools that could cause possible contamination. Manuscript to be reviewed </ns0:p><ns0:note type='other'>Figure 1</ns0:note><ns0:note type='other'>Figure 2</ns0:note></ns0:div><ns0:figure xml:id='fig_0'><ns0:head>Figure 1 .</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1. SEM/EDS analyses of Mongolemys elegans (IGM-90/42) bone.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head>Figure 2 .</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2. %Wt of elements for fossil and extant turtle bone</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>Figure 3 Figure 3 .</ns0:head><ns0:label>33</ns0:label><ns0:figDesc>Figure 3</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head>Figure 4</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Figure 4</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head>Figure 4 .</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Figure 4. SEM/EDS analyses of Allaeochelys crassesculpta (SMF ME 2449) bone</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head>Figure 5 Figure 5 .</ns0:head><ns0:label>55</ns0:label><ns0:figDesc>Figure 5</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head>Figure 6 Figure 6 .</ns0:head><ns0:label>66</ns0:label><ns0:figDesc>Figure 6</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='14,42.52,70.87,525.00,348.75' type='bitmap' /></ns0:figure> <ns0:note place='foot'>PeerJ reviewing PDF \| (2020:07:50649:1:0:NEW 26 Jul 2020)Manuscript to be reviewed</ns0:note> </ns0:body> "	"Bogotá, Colombia, July 26 2020 Dear Editor PeerJ I hope you and all your relatives are well under the current circumstances. I attached in here the revised version de the manuscript entitled “In situ SEM/EDS compositional characterization of osteocytes and blood vessels in fossil and extant turtles on untreated bone surfaces; different preservational pathways microns away”, PeerJ ID-50649. I have addressed and responded carefully all the comments and suggestions pointed out by the three reviewers and yours. They are listed one by one below. Please do not hesitate in contact me if additional actions or information is required and I hope with these improvements the manuscript can be push forward to the next step. Sincerely yours, The author Reviewer 01. Dr. Paul Ullmann Comment 01: Some sentences could use rephrasing to improve their ease of readability and to provide better clarity on some of the methods, results, or intended meaning. I’ve noted where changes would be most helpful with comments, and have provided small in-text suggestions that could augment the text’s readability. Answer to comment 01: Accepted, all the annotated suggestions marked in the attached file were carefully considered, modified or corrected accordingly. Comment 02: ‘Groups’ should instead be referred to as ‘clades’. Answer to comment 02: Corrected as suggested. Comment 03: Do the grouped citations each need to be rearranged into chronological order?. Answer to comment 03: I am using a EndNote template that organizes them accordingly to journal guidelines. Comment 04: The author might consider adding a couple citations to a published doctoral thesis (Boles 2016) that also explores preservation and composition of cells and tissues within fossil turtle bones via demineralization and EDX. Answer to comment 04: I avoid including references that have not been peer-reviewed, I consider the ones provided are the most relevant and appropriate to support the statements. Comment 05: Many of the figure captions could use clarification/further details concerning labeling, explanation of color uses and symbols in the figures, and further explanation of how to interpret them. I may know how to read/interpret most of them visually, but not every reader will, so adding a few details would be beneficial. I’ve attempted to identify questions that can be answered through such (short) additions, and to provide suggested edits to improve reader comprehension of them. Answer to comment 05: Many of the suggestions to improve figure captions clarity were accepted and corrected. Comment 06: Experimental design. A bit more detail on the treatment, transfer, and imaging protocols used with demineralization-isolated cells would be helpful to less-informed readers looking to try this type of study themselves. See my Comment #27 in the attached PDF for details. Answer to comment 06: Expanded details on the protocol execution were added on this matter as follow: “Some of the isolated osteocytes-like from IGM-90/42 were collected with a tip in a 1.5 ml tube, rinsed three times with E-pure water to get rid of EDTA, being centrifuged at 1500 RPM for 2 minutes between step. A drop of the supernatant was mounted in a stub, dried out at room temperature inside in a sealed small SEM-stub box to avoid any air or dust particles interact with the sample, and analyzed following the same protocol and SEM/EDS machine aforementioned”. Comment07: Validity of the findings. Since no biochemical methods have been used to rigorously test the endogeneity of the recovered microstructures and identification was guided instead by morphology and gross elemental composition, osteocytes, osteoblasts, and blood vessels should all technically be referred to with either single or double quotation marks around them, or with the addition of a ‘-like’ term after the word (e.g., ‘osteocytes’, or osteocyte-like microstructures). This is standard practice for this topic, and is a requirement until they are shown via more advanced biochemical methods to truly be endogenous in molecular structure/composition. Answer to comment 07: Thanks for the remaining, I already have you this is previous publications. “-like” added accordingly along the entire manuscript. Comment 08: In a couple spots in the Discussion the statements seem to be written in a bit too strong of language. It’s not over-interpretation of results, but rather how the verbs are used in a few sentences. It would be appropriate to simply dial back the statements applying toward other microstructures recovered by previous and/or future studies, such as using “may vary” instead of saying it simply “varies” (line 256), or using “may also be involved” rather than “is involved” (line 261). Those statements might be true with the samples studied here, but might not be universally true concerning similar microstructures identified in other previous and future reports. So, I think it would be better to use more conservative language. My Comments #41, 51, and 56 in the attached review PDF note similar instances. Answer to comment 08: Many of the suggested changes were incorporated to the document. (See file with Tracked-Changes). Reviewer 02. Comment 01: For all specimens, especially the Mongolian ones, were all permits obtained? Answer to comment 01: As clearly mentioned in the Methods, some specimens (Mongolia and Messel pit were donated by curators of the museums who authorized the use for the studies). For other specimens from Colombia and Ecuador the number of permits/ethics committee and institutions who granted them were already clearly mentioned in the methods, as well as acknowledgments given. Comment 02: Please make sure that all figures are colorblind accessible. For example, Figure S2 has multiple areas with both red and green figures. If there are questions of accessibility, https://www.color-blindness.com/coblis-color-blindness-simulator/ works quite well. Answer to comment 02: Figures checked for colorblind access as suggested. FigureS2 green color indicating osteoblasts changed to blue. Editor. Comment 01: The two reviews received suggest minor revisions. I would like you to name osteocytes, osteoblasts, and blood vessels with quotation marks around them or add a ‘-like’ term after the word, to highlight that more advanced biochemical procedures should be used to confirm their identities. It is also needed to make the statements suggested by our first reviewer less intense. In this first review an annotated pdf was attached, please pay attention to all the suggestion included in it. Please clarify the point of the permits for the use of the specimens. Answer to comment 01: The ending word “-like” was added to the bone microstructures. Statements and recommendations given by Reviewer 1 accepted and most of them incorporated to the manuscript (see ManuscriptTrackedChanges). Reviewer 2: permits and authorizations for using the specimens were already clearly mentioned in the Methods, including number of the permits when apply or names and institutions that granted the authorization. All this was also already detailed in the section Declarations/Required statements of PeerJ system. "	Here is a paper. Please give your review comments after reading it.
508	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Allergic rhinitis (AR) is a common disease affecting 400 million of the population worldwide. Nasal epithelial cells form a barrier against the invasion of environmentalpathogens. These nasal epithelial cells are connected together by tight junction (TJ) proteins including zonula occludens-1 (ZO-1), ZO-2 and ZO-3. Impairment of ZO proteins are observed in AR patients whereby dysfunction of ZOs allows allergens to pass the nasal passage into the subepithelial causing AR development. In this review, we discuss on ZO proteins and their impairment leading to AR, regulation of their expression by Th1 cytokines (i.e. IL-2, TNF-α and IFN-γ), Th2 cytokines (i.e. IL-4 and IL-13) and histone deacetylases (i.e. HDAC1 and HDAC2). These findings are pivotal for future developments of targeted therapies by restoring ZO proteins expression and improving nasal epithelial barrier integrity in AR patients.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>INTRODUCTION</ns0:head><ns0:p>Tight junction (TJ) proteins are required to form the nasal epithelial barrier and maintain its integrity. Breakdown of TJ function or expression deregulation is associated with derailed nasal epithelial barrier, leading to infiltration by allergens and subsequent development of allergic rhinitis (AR) <ns0:ref type='bibr'>(Fukuoka & Yoshimoto, 2018;</ns0:ref><ns0:ref type='bibr' target='#b33'>Steelant et al., 2016)</ns0:ref>. Moreover, growing evidence has implicated regulation of the nasal epithelial barrier integrity by histone deacetylases (HDACs), Th1 and Th2 cytokines in AR. Thus, an overall assessment and compilation of this accumulating evidence is desirable. In this review, we present and discuss the mechanisms leading to breakdown of TJs specifically on zonula occludens (ZOs), a group of important TJ proteins, as well as regulation of their expression by HDACs, Th1 and Th2 cytokines that would be informative for clinicians and researchers alike in this field.</ns0:p></ns0:div> <ns0:div><ns0:head>SURVEY METHODOLOGY</ns0:head><ns0:p>This review focuses on ZOs and their regulators i.e. HDACs, Th1 and Th2 cytokines in AR research. All articles were searched and screened by two investigators (COSS, KKW) using the electronic databases PubMed and Google Scholar. References described in this review were obtained from the databases up to year 2019. The following keywords were used: 'allergic rhinitis', 'AR', 'nasal epithelial barrier integrity', 'zonula occludens', 'ZO', 'histone deacetylases', 'HDACs', 'Th1' and 'Th2'.</ns0:p></ns0:div> <ns0:div><ns0:head>ALLERGIC RHINITIS (AR)</ns0:head><ns0:p>Allergy is a hypersensitivity reaction that occurs when an individual is sensitized by allergens such as grass, tree pollen, house dust mites (HDMs), foods, insect venoms or medicines <ns0:ref type='bibr' target='#b3'>(Azid et al., 2019;</ns0:ref><ns0:ref type='bibr' target='#b23'>Sani et al., 2019;</ns0:ref><ns0:ref type='bibr' target='#b39'>Tanno et al., 2016)</ns0:ref>. AR is a global health issue affecting approximately 10-25% of the population worldwide <ns0:ref type='bibr' target='#b16'>(Elango, 2005)</ns0:ref>. AR can be characterized by events of sneezing, rhinorrhea, nasal obstruction, nasal itching and postnasal drip. It is also associated with itching of the eyes, ears and throat <ns0:ref type='bibr' target='#b16'>(Elango, 2005;</ns0:ref><ns0:ref type='bibr' target='#b19'>Pang et al., 2017)</ns0:ref>.</ns0:p><ns0:p>Onset of AR consists of two phases of reaction where the first phase involves allergen infiltration that induces the production of immunoglobulin E (IgE) and triggers the humoral immune response mediated by mast cells. The second phase is a clinical phase where the patients present with symptoms of AR as a response to subsequent antigen exposure. This involves mediators release such as multiple cytokines and chemokines. Nasal symptoms can be observed within minutes due to the release of neuroactive and vasoactive agents including histamine, cysteinyl leukotrienes and prostaglandin D 2 <ns0:ref type='bibr' target='#b49'>(Wheatley & Togias, 2015)</ns0:ref>. The mucosa is rendered more reactive to allergens and nasal symptoms can persist for days after exposure to allergens <ns0:ref type='bibr' target='#b25'>(Sarin et al., 2006;</ns0:ref><ns0:ref type='bibr' target='#b49'>Wheatley & Togias, 2015)</ns0:ref>.</ns0:p><ns0:p>AR is also defined immunologically as IgE-mediated inflammation reaction in the nasal airways. This is primarily due to the exposure of environmental pathogens, allergens or any foreign agents that induce inflammation reaction <ns0:ref type='bibr' target='#b7'>(Bayrak Degirmenci et al., 2018)</ns0:ref>. These allergens contain proteases that contribute to the disruption of the airway epithelial barrier <ns0:ref type='bibr' target='#b22'>(Runswick et al., 2007;</ns0:ref><ns0:ref type='bibr' target='#b26'>Schleimer & Berdnikovs, 2017;</ns0:ref><ns0:ref type='bibr' target='#b44'>Wan et al., 1999)</ns0:ref>. The interaction between IgE and dendritic cells (DCs) increases allergen uptake and its subsequent processing and presentation to naive T cells <ns0:ref type='bibr' target='#b30'>(Sin & Togias, 2011)</ns0:ref>. Hence, higher allergen infiltration into the nasal airway increases the production of IgE in the blood. Perennial AR patients present with higher total IgE levels <ns0:ref type='bibr'>(Lee et al., 2016;</ns0:ref><ns0:ref type='bibr' target='#b28'>Shirasaki et al., 2011)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>NASAL EPITHELIAL BARRIER INTEGRITY IN AR</ns0:head><ns0:p>Nasal epithelial barrier plays an important role in sealing the nasal passage and underlying tissues from foreign pathogens by connecting the epithelial cells to each other <ns0:ref type='bibr'>(London & Ramanathan, 2017;</ns0:ref><ns0:ref type='bibr' target='#b33'>Steelant et al., 2016)</ns0:ref>. Any intrusion from foreign particles can stimulate the production of antimicrobial host defence molecules, pro-inflammatory cytokines and chemokines by nasal epithelial cells through the activation of recognition receptors. In addition, T cells are also recruited to epithelial cells to enhance adaptive immunity.</ns0:p><ns0:p>Dysfunction of these TJ barriers can increase exposure of nasal tissues to environmental antigens. It can lead to the infusion of inflammatory cells into the lumen which contributes to tissue damage or inflammation <ns0:ref type='bibr' target='#b31'>(Soyka et al., 2012)</ns0:ref>. The disruption of mucosal epithelial barrier has also been observed in AR animal models <ns0:ref type='bibr' target='#b50'>(Zhang et al., 2016)</ns0:ref>.</ns0:p><ns0:p>Nasal epithelial barrier is primarily formed by cell-to-cell TJs which consist of integral membrane proteins such as claudins, occludin, junctional adhesion molecules (JAMs), as well as scaffold adaptor proteins consisting of ZO-1, ZO-2 and ZO-3 <ns0:ref type='bibr' target='#b8'>(Beutel et al., 2019;</ns0:ref><ns0:ref type='bibr'>London & Ramanathan, 2017)</ns0:ref>. These proteins form the intracellular connection between the cells that regulate the passage of foreign pathogens <ns0:ref type='bibr' target='#b33'>(Steelant et al., 2016)</ns0:ref>. These proteins connect together to form a complex structure that protects the epithelial barrier from inhaled pathogens (Figure <ns0:ref type='figure' target='#fig_0'>1</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>ZONULA OCCLUDENS (ZO) PROTEINS</ns0:head><ns0:p>ZO proteins are a group of key proteins associated with TJ molecules that connect transmembrane proteins to the actin cytoskeleton <ns0:ref type='bibr' target='#b33'>(Steelant et al., 2016)</ns0:ref>. ZO proteins form an anchor directly to the underlying cytoskeleton with other TJ proteins including occludin, claudin, JAMs and tricellulin <ns0:ref type='bibr' target='#b6'>(Bauer et al., 2010;</ns0:ref><ns0:ref type='bibr'>Furuse et al., 1994)</ns0:ref>. ZO proteins belong to the family of membrane-associated guanylate kinase (MAGUK)-like proteins. MAGUKs are scaffolding proteins that form and maintain multimolecular complexes at distinct subcellular sites such as the cytoplasmic surface of the plasma membrane <ns0:ref type='bibr' target='#b6'>(Bauer et al., 2010)</ns0:ref>.</ns0:p><ns0:p>ZO-1, ZO-2 and ZO-3 form a belt-like region at the outer end of intercellular space between the epithelial cells that separate the apical from the lateral plasma membrane. The proteins also play vital roles in regulating the passage of ions and molecules through the membrane <ns0:ref type='bibr'>(Gonzalez-Mariscal et al., 2000)</ns0:ref>. ZO proteins consist of multidomain structure including SRC homology 3 (SH3), guanylate kinase-like (GUK) and multiple PDZ domains <ns0:ref type='bibr' target='#b2'>(Anderson, 1996)</ns0:ref>.</ns0:p><ns0:p>ZO-1 and ZO-2 have been detected in human nasal mucosa where ZO-1 is found in the uppermost layer of epithelium <ns0:ref type='bibr'>(Kojima et al., 2013)</ns0:ref>. ZO-1 is expressed by DCs to form epithelial barrier <ns0:ref type='bibr' target='#b20'>(Rescigno et al., 2001;</ns0:ref><ns0:ref type='bibr' target='#b37'>Sung et al., 2006)</ns0:ref>. ZO-1 protein contains N-terminal PDZ domain that can recognize specific C-terminal or other peptide motifs to assemble with other TJ molecules such as claudins to form a TJ barrier at gaps between epithelial cells <ns0:ref type='bibr'>(Heinemann & Schuetz, 2019;</ns0:ref><ns0:ref type='bibr'>Herve et al., 2014;</ns0:ref><ns0:ref type='bibr' target='#b42'>Umeda et al., 2006)</ns0:ref>. The TJ barrier controls the diffusion of molecules by acting as semipermeable diffusion barriers through the paracellular pathway. It has been reported that transmembrane proteins such as claudin and occludin are essential for the regulation of paracellular permeability <ns0:ref type='bibr' target='#b4'>(Balda & Matter, 2000;</ns0:ref><ns0:ref type='bibr'>Lee, 2015;</ns0:ref><ns0:ref type='bibr' target='#b21'>Roehlen et al., 2020)</ns0:ref>. ZO-1 is also responsible in the regulation of paracellular permeability (i.e. permeability for the passage of molecules between adjacent epithelial cells) via TJ complexes as it binds directly to transmembrane proteins <ns0:ref type='bibr' target='#b4'>(Balda & Matter, 2000;</ns0:ref><ns0:ref type='bibr'>Lee, 2015;</ns0:ref><ns0:ref type='bibr' target='#b21'>Roehlen et al., 2020)</ns0:ref>. Loss of ZO-1 can retard the formation of the TJ complexes, and further breakdown of ZO-1 may result in severe disruption of paracellular barrier in epithelial cells <ns0:ref type='bibr' target='#b21'>(Roehlen et al., 2020)</ns0:ref>. Hence, ZO-1 plays important roles in maintaining the epithelial barrier by connecting TJ molecules to seal the epithelial cells from infiltration of environmental allergens.</ns0:p></ns0:div> <ns0:div><ns0:head>Disruption of ZO proteins in AR</ns0:head><ns0:p>The disruption of ZO proteins affects the interaction of TJ molecules, allowing the passage of allergens into the host. Decreased expression of ZO-1 in AR patients has been reported by gene expression studies <ns0:ref type='bibr'>(Lee et al., 2016;</ns0:ref><ns0:ref type='bibr'>London & Ramanathan, 2017)</ns0:ref>. Study by Steelant and colleagues showed decreased levels of ZO-1 through immunofluorescent staining on AR biopsy specimens <ns0:ref type='bibr' target='#b33'>(Steelant et al., 2016)</ns0:ref>. Furthermore, nasal epithelial cells isolated from inferior turbinate of HDM-induced AR patients demonstrated reduced ZO-1 mRNA expression <ns0:ref type='bibr' target='#b34'>(Steelant et al., 2018)</ns0:ref>. Likewise, the expression of ZO-1 in asthma and chronic rhinosinusitis patients was also decreased compared with healthy controls <ns0:ref type='bibr' target='#b14'>(de Boer et al., 2008;</ns0:ref><ns0:ref type='bibr' target='#b31'>Soyka et al., 2012)</ns0:ref>.</ns0:p><ns0:p>Immunofluorescence analysis of RPMI 2650, a human nasal epithelial cell line, showed a decreased of ZO-1 expression after being exposed to diesel exhaust particles <ns0:ref type='bibr'>(Fukuoka et al., 2016)</ns0:ref>. Transepithelial electric resistance (TER) measurement, a procedure that assessed the integrity of TJ in cell culture of epithelial monolayers, of the RPMI 2650 was reduced in the study, and the decreased ZO-1 expression was associated with severity of AR <ns0:ref type='bibr'>(Fukuoka et al., 2016)</ns0:ref>. Moreover, HDM cysteine proteinase antigen from Dermatophagoids pteryonysinus caused the mislocalization of ZO-1 from TJ <ns0:ref type='bibr' target='#b44'>(Wan et al., 1999)</ns0:ref>. Hence, patients with AR demonstrate lower integrity of nasal epithelial barrier that is associated with decreased expression or disruption of ZO-1 protein.</ns0:p><ns0:p>Accumulating evidence has shown that reduced expression of ZO-1 or ZO-2 occurs in patients with chronic rhinosinusitis (CRS) without nasal polyps <ns0:ref type='bibr' target='#b31'>(Soyka et al., 2012)</ns0:ref> or eosinophilic esophagitis (EoE) <ns0:ref type='bibr'>(Katzka et al., 2014)</ns0:ref>, respectively. CRS is characterized by mucosal inflammation involving both the nasal cavity and paranasal sinuses <ns0:ref type='bibr' target='#b31'>(Soyka et al., 2012)</ns0:ref>, while EoE represents inflammation of the oesophagus when food antigens interact with oesophageal mucosa <ns0:ref type='bibr'>(Katzka et al., 2014)</ns0:ref>. Both CRS and EoE are caused by the penetration of antigens through the gap between nasal epithelial cells. The expression of ZOs in these allergic diseases in both patients and animal models are summarized in Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>HISTONE DEACETYLASES (HDACs) IN AR</ns0:head><ns0:p>HDACs are enzymes responsible for removing acetyl group from lysine residues of target proteins. HDACs prevent gene transcription by allowing DNA to be wrapped by histones <ns0:ref type='bibr'>(Jiang et al., 2015)</ns0:ref>. HDACs also promote the condensation of chromation <ns0:ref type='bibr' target='#b27'>(Shakespear et al., 2011)</ns0:ref>. HDACs have been implicated in several inflammatory and allergic conditions including AR <ns0:ref type='bibr' target='#b5'>(Barnes, 2013;</ns0:ref><ns0:ref type='bibr' target='#b38'>Sweet et al., 2012;</ns0:ref><ns0:ref type='bibr' target='#b43'>Vendetti & Rudin, 2013)</ns0:ref>. Upregulation of HDAC activity occurs in nasal epithelial cells of AR patients <ns0:ref type='bibr' target='#b35'>(Steelant et al., 2019)</ns0:ref>.</ns0:p><ns0:p>It has been shown that expression of TJs can be increased by inhibiting the activity of HDAC1 and simultaneously decreasing the defect of epithelial barriers <ns0:ref type='bibr' target='#b48'>(Wawrzyniak et al., 2017)</ns0:ref>. In animal models, HDAC1 protein levels in rats AR model were higher than naive rats <ns0:ref type='bibr'>(Jiang et al., 2015)</ns0:ref>. Immunohistochemical results also demonstrated higher expression of HDAC1 protein in nasal epithelium of patients with sinusitis and nasal polyps contributing to the disruption of TJs <ns0:ref type='bibr'>(Kaneko et al., 2017)</ns0:ref>. Furthermore, HDAC1 could supress the activity of TWIK-related potassium channel-1 (Trek-1), and Trek-1 is pivotal in the maintenance of epithelial cells barrier function <ns0:ref type='bibr' target='#b10'>(Bittner et al., 2013)</ns0:ref>. Higher mRNA expression of HDAC1 together with lower mRNA expression of Trek-1 was found in nasal epithelial from patients with AR compared with healthy subjects <ns0:ref type='bibr' target='#b47'>(Wang et al., 2015)</ns0:ref>.</ns0:p><ns0:p>ZO-1 expression was previously shown to be decreased in the presence of HDAC1.Lower levels of ZO-1 mRNA expression were observed in AML-12 murine hepatocyte cells that overexpressed HDAC1 <ns0:ref type='bibr'>(Lei et al., 2010)</ns0:ref>. Studies on epithelial-mesenchymal transition (EMT), an oncogenic process that induces epithelial cells to transform into anchorage-independent mesenchyme-like cells for increased metastatic capabilities of cancer cells, also showed an association with HDAC1 and ZO-1 <ns0:ref type='bibr' target='#b53'>(Zhou et al., 2015)</ns0:ref>. ZO-1 is involved in EMT where loss of ZO-1 expression can induce invasion of cancer cells. Higher HDAC1 mRNA and protein expression levels were found in hepatocellular carcinoma (HCC) cell lines (HepG2, Hep3B, Huh7, PLC/PRF/5, SK-Hep-1) compared with normal human epithelial cell line (THLE-3) <ns0:ref type='bibr' target='#b53'>(Zhou et al., 2015)</ns0:ref>. Inhibition of HDAC1 in these HCC cells showed an increase of ZO-1 mRNA and protein expression, leading to decreased invasion capabilities of HCC cells <ns0:ref type='bibr' target='#b53'>(Zhou et al., 2015)</ns0:ref>. Thus, ZO-1 expression can be inhibited by HDAC1 leading to breakdown of epithelial cells' anchorage, and it remains unknown if similar effects might also occur in nasal epithelial cells.</ns0:p><ns0:p>In contrast with HDAC1, evidence has shown that HDAC2 expression is required to prevent breakdown of nasal epithelial barrier integrity in AR. Decreased levels of HDAC2 were observed in patients with asthma and asthmatic smoking patients, as in patients with chronic obstructive pulmonary disease <ns0:ref type='bibr' target='#b9'>(Bhavsar et al., 2008)</ns0:ref>. Higher levels of HDAC2 can restore steroid sensitivity in asthmatic patients <ns0:ref type='bibr' target='#b9'>(Bhavsar et al., 2008)</ns0:ref>, and nasal scrape samples of patients with persistent AR showed weak expression of HDAC2 <ns0:ref type='bibr' target='#b24'>(Sankaran et al., 2014)</ns0:ref>. Moreover, deficiency of HDAC2 in intestinal epithelial cells (IEC) of mice was associated with chronic basal inflammation <ns0:ref type='bibr' target='#b41'>(Turgeon et al., 2013)</ns0:ref>. Deletion of HDAC2 from IEC displayed an increased permeability to fluorescein isothiocyanate-dextran 4kDa (FD4; a fluorochrome for investigation of cell permeability) by assessing the intensity of fluorescence in the mice blood <ns0:ref type='bibr' target='#b41'>(Turgeon et al., 2013)</ns0:ref>, and increased penetration by FD4 indicated increased leakiness that may be due to disruption of epithelial barrier.</ns0:p><ns0:p>However, downregulation of HDAC2 with the treatment of Trichostation-A (TSA), an HDAC inhibitor (HDACi), increased the expression of ZO-1 mRNA in fetal human lens epithelial cells <ns0:ref type='bibr'>(Ganatra et al., 2018)</ns0:ref>. The effect of HDAC2 inhibitor CAY10683 was investigated on the expression on ZO-1 at the intestinal mucosal barrier of lipopolysaccharide (LPS)-stimulated NCM460 cells (a normal human colon mucosal epithelial cell line) <ns0:ref type='bibr' target='#b46'>(Wang et al., 2018)</ns0:ref>. LPS was used to induce damage to the mucosal barrier of NCM460 cells. The NCM460 cells treated with the HDAC2 inhibitor (CAY10683) increased mRNA and protein levels of ZO-1 <ns0:ref type='bibr' target='#b46'>(Wang et al., 2018)</ns0:ref>.</ns0:p><ns0:p>Inhibiting HDAC activities with HDACi (JNJ-26481585) may be able to restore the structure of ZO molecules in nasal epithelial cells <ns0:ref type='bibr' target='#b35'>(Steelant et al., 2019)</ns0:ref>. In the same study, immunofluorescent staining showed that ZO-1 expression was significantly weaker in AR patients compared with healthy controls, and further treatment with JNJ-26481585 increased the expression of ZO-1 protein.</ns0:p><ns0:p>The HDACi sodium butyrate (SoB) is a short chain fatty-acid produced by the microbial fermentation of dietary fibre in colonic lumen <ns0:ref type='bibr' target='#b11'>(Bordin et al., 2004)</ns0:ref>. The Rat-1 fibroblasts cell line expresses ZO-1 and ZO-2 proteins <ns0:ref type='bibr' target='#b11'>(Bordin et al., 2004)</ns0:ref>. When the cells lysates were cultured in the presence of SoB, densitometric analysis of immunoblots showed that ZO-1 and ZO-2 levels were upregulated <ns0:ref type='bibr' target='#b11'>(Bordin et al., 2004)</ns0:ref>. Collectively, HDAC1 and HDAC2 suppress the expression of ZO proteins leading to breakdown of epithelial cells barrier integrity as demonstrated by these studies either in AR or non-AR epithelial cells.</ns0:p></ns0:div> <ns0:div><ns0:head>TH1 CYTOKINES IN AR</ns0:head><ns0:p>Cytokines play an important role in mediating allergic inflammation. The roles of Th2 cytokines in AR have been well-documented <ns0:ref type='bibr' target='#b33'>(Steelant et al., 2016;</ns0:ref><ns0:ref type='bibr' target='#b36'>Sun et al., 2020;</ns0:ref><ns0:ref type='bibr' target='#b52'>Zhao et al., 2017)</ns0:ref>. Imbalance of Th1 and Th2 cytokines appears to be involved in the AR inflammatory pathway <ns0:ref type='bibr' target='#b52'>(Zhao et al., 2017)</ns0:ref>. However, there is a lack of review on Th1 cytokines and their roles in the breakdown of nasal epithelial barrier integrity. Moreover, dysfunctional Th1 responses have been proposed to be responsible for the exaggerated Th2 responses that occur in AR patients <ns0:ref type='bibr' target='#b15'>(Eifan & Durham, 2016)</ns0:ref>. Th1 cells produce IL-2, IFN-γ and TNF-α in response to allergic inflammation <ns0:ref type='bibr' target='#b0'>(Ackaert et al., 2014)</ns0:ref>. Th1 cytokines can cause disruption of TJ molecules including ZO proteins in nasal epithelial barrier, leading to allergic inflammation.</ns0:p><ns0:p>Th1 response is characterized by IFN-γ production which stimulates bactericidal activities of macrophages and boosts the immunity against intracellular pathogens and virus infection <ns0:ref type='bibr' target='#b18'>(Marshall et al., 2018)</ns0:ref>. IFN-γ plays a key role in bridging the innate and adaptive immune systems <ns0:ref type='bibr' target='#b7'>(Bayrak Degirmenci et al., 2018)</ns0:ref>. It is also essential in the regulation of local leukocyteendothelial interaction <ns0:ref type='bibr' target='#b1'>(Akkoc et al., 2008)</ns0:ref>. IFN-γ increases the permeability of primary bronchial epithelial cells and T84 colonic epithelial cells by disassembling TJ structures <ns0:ref type='bibr' target='#b12'>(Bruewer et al., 2005)</ns0:ref>. Accordingly, the level of IFN-γ in plasma sample of AR patients was significantly lower compared with healthy controls <ns0:ref type='bibr' target='#b7'>(Bayrak Degirmenci et al., 2018)</ns0:ref>. The same study showed that downregulated levels of Th1 cytokines were associated with higher severity of AR symptoms. Furthermore, the levels of IFN-γ were inversely correlated with higher nasal symptoms scores as measured by evaluating the severity of sneezing, nasal itching, nasal obstruction and watery nasal discharge <ns0:ref type='bibr' target='#b7'>(Bayrak Degirmenci et al., 2018)</ns0:ref>.</ns0:p><ns0:p>In order to observe the expression of ZO-2 in CRS patients, human epithelial cells were treated on air-liquid interface (ALI) culture with IFN-γ. The results showed that opening of TJs between the neighbouring cells occurred in patients compared with healthy controls <ns0:ref type='bibr' target='#b31'>(Soyka et al., 2012)</ns0:ref>. However, no significant decrease of ZO-1 expression in AR patients was observed when the epithelial cells were treated with IFN-γ and TNF-α cytokines <ns0:ref type='bibr'>(Lee et al., 2016)</ns0:ref>. Additionally, cultured primary nasal epithelial cells in ALI stimulated with TNF-α and IFN-γ showed a decrease of epithelial barrier integrity in vitro <ns0:ref type='bibr' target='#b34'>(Steelant et al., 2018)</ns0:ref>.</ns0:p><ns0:p>Furthermore, expression of ZO-1 protein in primary airway cells from cystic fibrosis patients was reduced in the presence of IFN-γ and TNF-α cytokines <ns0:ref type='bibr' target='#b13'>(Coyne et al., 2002)</ns0:ref>. Prolonged exposure of IFN-γ and TNF-α to the cell culture led to a significant damage to ZO-1 molecules <ns0:ref type='bibr' target='#b13'>(Coyne et al., 2002)</ns0:ref>. This damage caused an increase of cell permeability to external solutes and a decrease in transepithelial resistance. Further investigation of wild type BALB/c mice endonasally instilled with IFN-γ and TNF-α increased the FD4 mucosal barrier permeability associated with decreased ZO-1 expression in vivo <ns0:ref type='bibr' target='#b34'>(Steelant et al., 2018)</ns0:ref>. Blocking TNF-α cytokines activity with anti-TNF-α partially restored the ZO-1 expression in HDM-induced mice <ns0:ref type='bibr' target='#b34'>(Steelant et al., 2018)</ns0:ref>.</ns0:p><ns0:p>IL-2, also produced by Th1 cells, plays a vital role in inflammatory reaction. Lower levels of Th1 cytokines, IL-2 and IFN-γ were detected in the serum sample from OVA-sensitized mice with AR compared with controls <ns0:ref type='bibr' target='#b45'>(Wang et al., 2016)</ns0:ref>. When the OVA-sensitized mice were treated with SoB, IL-2 and IFN-γ levels were increased, leading to increased expression of TJ molecules <ns0:ref type='bibr' target='#b45'>(Wang et al., 2016)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>TH2 CYTOKINES IN AR</ns0:head><ns0:p>The involvement of Th2 cytokines in AR has been widely investigated. The serum levels of Th2 cytokines including IL-4 and IL-13 are elevated in AR patients <ns0:ref type='bibr'>(Jordakieva & Jensen-Jarolim, 2018)</ns0:ref>. Increased expression of IL-4 in nasal epithelial cells of HDM-induced AR patients reduced ZO-1 mRNA expression <ns0:ref type='bibr' target='#b33'>(Steelant et al., 2016)</ns0:ref>. Breakdown of epithelial barrier was observed after stimulation of nasal epithelial cells with IL-4 and significantly increased the permeability of FD4 <ns0:ref type='bibr' target='#b33'>(Steelant et al., 2016)</ns0:ref>.</ns0:p><ns0:p>Both IL-4 and IL-13 play critical roles in promoting B cells to produce IgE <ns0:ref type='bibr' target='#b29'>(Shirkani et al., 2019;</ns0:ref><ns0:ref type='bibr' target='#b52'>Zhao et al., 2017)</ns0:ref>. Protein levels of IL-4 and IL-13 in nasal mucosa of guinea pig of ARsensitized pig were higher compared with controls <ns0:ref type='bibr' target='#b52'>(Zhao et al., 2017)</ns0:ref>. This was supported by findings where higher serum levels of IL-4 and IL-13 were found in AR-sensitized pigs compared with controls <ns0:ref type='bibr' target='#b52'>(Zhao et al., 2017)</ns0:ref>. In addition, treatment of lung cancer cells (Calu-3) with IL-4 and IL-13 reduced the protein expression of ZO-1 protein <ns0:ref type='bibr'>(Fukuoka & Yoshimoto, 2018)</ns0:ref>.</ns0:p><ns0:p>Immunofluorescent staining of human bronchial epithelial cells of asthmatic patients demonstrated that disruption of TJs in the ALI cultures occurred and weak expression of ZO-1 was observed <ns0:ref type='bibr' target='#b48'>(Wawrzyniak et al., 2017)</ns0:ref>. Blocking IL-4 and IL-13 in asthma patients did not show difference in TER measurement <ns0:ref type='bibr' target='#b32'>(Srinivasan et al., 2015;</ns0:ref><ns0:ref type='bibr' target='#b48'>Wawrzyniak et al., 2017)</ns0:ref>. However, nullification the effects of IL-4 and IL-13 using anti-IL4 and anti-IL-13 supplemented to the ALI culture of control bronchial epithelial cells in vitro enhanced the TER measurement <ns0:ref type='bibr' target='#b48'>(Wawrzyniak et al., 2017)</ns0:ref>. Moreover, IL-4 and IL-13 mRNA expression levels were increased together with downregulated ZO-1 mRNA expression in the jejunum of OVA-sensitized rats <ns0:ref type='bibr' target='#b40'>(Tulyeu et al., 2019)</ns0:ref>. Downregulation of ZO-1 mRNA expression potentially through regulation by Th2 cytokines was also observed in vivo. Endonasal stimulation of wild-type BALB/c mice with IL-4 and IL-13 demonstrated increased FD4 permeability associated with reduced ZO-1 mRNA expression compared with saline-instilled mice <ns0:ref type='bibr' target='#b34'>(Steelant et al., 2018)</ns0:ref>. Taken together, these studies indicate that IL-4 and IL-13 contribute to the breakdown of nasal epithelial barrier by reducing the expression of ZO-1.</ns0:p></ns0:div> <ns0:div><ns0:head>CONCLUSION</ns0:head><ns0:p>In conclusion, HDAC1 and HDAC2 play pathogenic roles in the breakdown of nasal epithelial barrier integrity via suppression of ZO proteins expression. This is potentially regulated by Th1 and Th2 cytokines signaling pathways as higher levels of Th1 and Th2 cytokines in AR patients are accompanied with decreased epithelial barrier integrity and ZO-1 expression. Future research should investigate and compare which specific HDACi or blocking antibodies of Th1 and Th2 cytokines that demonstrate potent restoration of ZO proteins expression in nasal epithelial cells of AR animal models, as well as ameliorating their symptoms. Targeting these pathogenic pathways might be effective in AR therapy to maintain the expression and structure of ZOs at the nasal epithelial barrier. Manuscript to be reviewed </ns0:p><ns0:note type='other'>Figure Legends</ns0:note><ns0:note type='other'>Figure 1</ns0:note></ns0:div><ns0:figure xml:id='fig_0'><ns0:head>Figure 1 :</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1: Pathophysiology of allergic rhinitis (AR) involving the disruption of nasal epithelial barrier and regulation by HDACs, Th1 and Th2 cytokines.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head>Figure 1 :</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1: Pathophysiology of allergic rhinitis (AR) from the disruption of nasal epithelial barrier and the involvement of HDACs, Th1 and Th2 cytokines.</ns0:figDesc><ns0:graphic coords='19,42.52,199.12,525.00,518.25' type='bitmap' /></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head>Table 1 (on next page)</ns0:head><ns0:label>1</ns0:label><ns0:figDesc /><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_1'><ns0:head>Table 1 :</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>The expression of Zonula Occludens (ZOs) in human and animal models</ns0:figDesc><ns0:table /><ns0:note>PeerJ reviewing PDF \| (2020:01:45164:1:1:NEW 22 May 2020)Manuscript to be reviewed 1</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_2'><ns0:head>Table 1 : The expression of Zonula Occludens (ZOs) in human and animal models ZOs Samples Treatment Change in expression References Treatment with Th1 cytokines:</ns0:head><ns0:label>1</ns0:label><ns0:figDesc /><ns0:table><ns0:row><ns0:cell>ZO-1</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>Nasal mucosa from</ns0:cell><ns0:cell>TNF-α, IFN-γ</ns0:cell><ns0:cell>Downregulated</ns0:cell><ns0:cell>(Steelant et</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>normal wild type</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell>al., 2018)</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>BALB/c mice</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>Nasal epithelial cells</ns0:cell><ns0:cell>IFN-γ</ns0:cell><ns0:cell>Downregulated</ns0:cell><ns0:cell>(Steelant et</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>from HDM-induced</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell>al., 2016)</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>AR patients</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='2'>Treatment with Th2 cytokines:</ns0:cell><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>Nasal mucosa from</ns0:cell><ns0:cell>IL-4</ns0:cell><ns0:cell>Downregulated</ns0:cell><ns0:cell>(Steelant et</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>normal wild type</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell>al., 2018)</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>BALB/c mice</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>Nasal epithelial cells</ns0:cell><ns0:cell>IL-4</ns0:cell><ns0:cell>Downregulated</ns0:cell><ns0:cell>(Steelant et</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>from HDM-induced</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell>al., 2016)</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>AR patients</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>Calu-3 cells (human</ns0:cell><ns0:cell>IL-4 and IL-13</ns0:cell><ns0:cell>Downregulated</ns0:cell><ns0:cell>(Fukuoka &</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>epithelial nasal cell</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell>Yoshimoto,</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>lines)</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell>2018)</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>Other treatment:</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>Human nasal epithelial</ns0:cell><ns0:cell>Cultured with diesel</ns0:cell><ns0:cell>Downregulated</ns0:cell><ns0:cell>(Fukuoka et</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>cell line, RPMI 2650</ns0:cell><ns0:cell>exhaust particle</ns0:cell><ns0:cell /><ns0:cell>al., 2016)</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>(DEP)</ns0:cell><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>Nasal epithelial cells</ns0:cell><ns0:cell>Intranasal</ns0:cell><ns0:cell>Downregulated</ns0:cell><ns0:cell>(Fukuoka et</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>in AR mice</ns0:cell><ns0:cell>administration with</ns0:cell><ns0:cell /><ns0:cell>al., 2016)</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>DEP</ns0:cell><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>Nasal biopsy</ns0:cell><ns0:cell>No treatment</ns0:cell><ns0:cell>Downregulated</ns0:cell><ns0:cell>(Steelant et</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>specimens from HDM-</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell>al., 2016)</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>induced AR patients</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>Bronchial epithelium</ns0:cell><ns0:cell>No treatment</ns0:cell><ns0:cell>Downregulated</ns0:cell><ns0:cell>(de Boer et</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>biopsy from asthmatic</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell>al., 2008)</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>patients</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>Nasal epithelial cells</ns0:cell><ns0:cell>Treated with</ns0:cell><ns0:cell>Downregulated</ns0:cell><ns0:cell>(Shin et al.,</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>from septal surgery</ns0:cell><ns0:cell>Alternaria alternate</ns0:cell><ns0:cell /><ns0:cell>2019)</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>patients</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>ZO-2</ns0:cell><ns0:cell>Nasal biopsy</ns0:cell><ns0:cell>No treatment</ns0:cell><ns0:cell>Downregulated</ns0:cell><ns0:cell>(Soyka et al.,</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>specimens from</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell>2012)</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>chronic rhinosinusitis</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row></ns0:table><ns0:note>PeerJ reviewing PDF \| (2020:01:45164:1:1:NEW 22 May 2020)Manuscript to be reviewed</ns0:note></ns0:figure> <ns0:note place='foot' n='2'>PeerJ reviewing PDF \| (2020:01:45164:1:1:NEW 22 May 2020)Manuscript to be reviewed</ns0:note> </ns0:body> "	" 9 May 2020 Dear Prof. Dr. Andrew Herr, I would like to thank the reviewers for the constructive comments that have helped to clarify and improve several important points of the manuscript. Below you will find the actions taken in response to the reviewers’ comments. Please do not hesitate to contact me if you require any further information, and it is hoped that you will find the revised manuscript to be suitable for publication in PeerJ. Thank you. Yours sincerely, Assoc. Prof. Dr. Kah Keng Wong (BSc, Mal; DPhil, Oxon) Department of Immunology, School of Medical Sciences, Health Campus, Universiti Sains Malaysia, 16150 Kubang Kerian, Kelantan, Malaysia Tel: +6017-6273843 / +609-7676229 Fax: +609-7653370 E-mail: kahkeng@usm.my ; kahkeng3@gmail.com Web: http://www.medic.usm.my/immunology Two expert reviewers have read your manuscript and provided feedback to me. While both agree that there is potential in this review article, they have pointed out that it needs major improvement in terms of the clarity of the writing and the transitions between sections, and that it would greatly benefit from editing by a native English speaker before being ready for publication. Thank you for the comments. The manuscript has been edited multiple times to improve the clarity of the writing, transitions between sections as well as to fix any remaining grammatical errors and to improve the article’s readability. Reviewer 1 (Anonymous) Basic reporting The review has good references, is cross disciplinary and introduces the subject clearly. However, the field has been reviewed recently: https://www.sciencedirect.com/science/article/pii/S1323893017301594?via%3Dihub https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5752131/ However, this review focuses more on HDAC inhibitors that the other reviews. Thank you for the comments. Our review has indeed focused more on the pathogenic involvement of HDACs, their inhibitors, as well as regulation by Th1 and Th2 cytokines. In particular, few reviews have emphasized on the roles of Th1 cytokines in AR pathogenesis, and we have also highlighted this issue in the manuscript as follows (page 8 of the Word file with track changes): However, there is a lack of review on Th1 cytokines and their roles in the breakdown of nasal epithelial barrier integrity. Moreover, dysfunctional Th1 responses have been proposed to be responsible for the exaggerated Th2 responses that occur in AR patients (Eifan & Durham, 2016). Experimental design The review has good organization, but it needs to be edited to flow better. Presently all of the data is there, but it is more of a list of study findings organized by topic. Thank you for the comments. The manuscript has been edited multiple times for a better flow, please refer to the Word file containing the track changes. Validity of the findings The conclusions are well stated, but introductory sentences are not always present and more work is needed to identify unresolved questions and future directions. Also the concept that ZO-1 is a scaffolding protein for the tight junction and how it may regulate the paracellular pathway should be brought up in more detail. For example, how might it translate to barrier function? Thank you for the comments. The concluding paragraph has been revised as follows (page 10 of the Word file with track changes): In conclusion, HDAC1 and HDAC2 play pathogenic roles in the breakdown of nasal epithelial barrier integrity via suppression of ZO proteins expression. This is potentially regulated by Th1 and Th2 cytokines signaling pathways as higher levels of Th1 and Th2 cytokines in AR patients are accompanied with decreased epithelial barrier integrity and ZO-1 expression. Future research should investigate and compare which specific HDACi or blocking antibodies of Th1 and Th2 cytokines that demonstrate potent restoration of ZO proteins expression in nasal epithelial cells of AR animal models, as well as ameliorating their symptoms. Targeting these pathogenic pathways might be effective in AR therapy to maintain the expression and structure of ZOs at the nasal epithelial barrier. Regarding the concept of ZO-1 as a scaffolding protein and how it may regulate the paracellular pathway, we have expanded the descriptions for better clarity as follows (pages 4-5 of the Word file with track changes): ZO-1 protein contains N-terminal PDZ domain that can recognize specific C-terminal or other peptide motifs to assemble with other TJ molecules such as claudins to form a TJ barrier at gaps between epithelial cells (Heinemann & Schuetz, 2019; Herve et al., 2014; Umeda et al., 2006). The TJ barrier controls the diffusion of molecules by acting as semipermeable diffusion barriers through the paracellular pathway. It has been reported that transmembrane proteins such as claudin and occludin are essential for the regulation of paracellular permeability (Balda & Matter, 2000; Lee, 2015; Roehlen et al., 2020). ZO-1 is also responsible in the regulation of paracellular permeability (i.e. permeability for the passage of molecules between adjacent epithelial cells) via TJ complexes as it binds directly to transmembrane proteins (Balda & Matter, 2000; Lee, 2015; Roehlen et al., 2020). Loss of ZO-1 can retard the formation of the TJ complexes, and further breakdown of ZO-1 may result in severe disruption of paracellular barrier in epithelial cells (Roehlen et al., 2020). Hence, ZO-1 plays important roles in maintaining the epithelial barrier by connecting TJ molecules to seal the epithelial cells from infiltration of environmental allergens. I have attached some minor comments in the pdf as well. How does the epithelial barrier limit intercellular passage? Via gap junctions? We go from talking about tight junctions to cell-cell communication and this could be confusing. Thank you for the comments. The sentence has now been revised as follows (page 3 of the Word file with track changes): Nasal epithelial barrier plays an important role in sealing the nasal passage and underlying tissues from foreign pathogens by connecting the epithelial cells to each other (London & Ramanathan, 2017; Steelant et al., 2016). Tricellulin is a transmembrane protein unlike ZO proteins. I am not shore what is meant by peripheral membrane proteins. Thank you for the comments. The sentence has now been revised as follows (page 4 of the Word file with track changes): Nasal epithelial barrier is primarily formed by cell-to-cell TJs which consist of integral membrane proteins such as claudins, occludin, junctional adhesion molecules (JAMs), as well as scaffold adaptor proteins consisting of ZO-1, ZO-2 and ZO-3 (Beutel et al., 2019; London & Ramanathan, 2017). 'That is associated with' might be better phrase Thank you for the comment. It has been rephrased accordingly as follows (page 5 of the Word file with track changes): Hence, patients with AR demonstrate lower integrity of nasal epithelial barrier that is associated with decreased expression or disruption of ZO-1 protein. Should this be HDAC1 or HDACs? Thank you for the comment. It should be HDAC1 and it has now been corrected (page 6 of the Word file with track changes). I think this paragraph could have a better topic sentence. Thank you for the comment. A new topic sentence has been added as follows (page 6 of the Word file with track changes): ZO-1 expression was previously shown to be decreased in the presence of HDAC1. I feel these two sentences could be made clearer. I don't really understand Thank you for the comments. The sentence has been revised for better clarity as follows (page 6 of the Word file with track changes): Lower levels of ZO-1 mRNA expression were observed in AML-12 murine hepatocyte cells that overexpressed HDAC1 (Lei et al., 2010). This doesn't specifically suggest a similar effect could occur in nasal epithelial cells. Maybe rephrase to remove the word suggest. Thank you for the comments. The sentence has been revised as follows (page 6 of the Word file with track changes): Thus, ZO-1 expression can be inhibited by HDAC1 leading to breakdown of epithelial cells’ anchorage, and it remains unknown if similar effects might also occur in nasal epithelial cells. Based on the data you listed, I'm not sure this shows structure differences. Just increased leakiness...may be tight junction may be gross structure Thank you for the comments. The sentence has been revised as follows (page 7 of the Word file with track changes): Deletion of HDAC2 from IEC displayed an increased permeability to fluorescein isothiocyanate-dextran 4kDa (FD4; a fluorochrome for investigation of cell permeability) by assessing the intensity of fluorescence in the mice blood (Turgeon et al., 2013), and increased penetration by FD4 indicated increased leakiness that may be due to disruption of epithelial barrier. Finally, all language and minor editing present in the PDF have been implemented in the revised manuscript. -End of page- "	Here is a paper. Please give your review comments after reading it.
509	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Allergic rhinitis (AR) is a common disease affecting 400 million of the population worldwide. Nasal epithelial cells form a barrier against the invasion of environmental pathogens. These nasal epithelial cells are connected together by tight junction (TJ) proteins including zonula occludens-1 (ZO-1), ZO-2 and ZO-3. Impairment of ZO proteins are observed in AR patients whereby dysfunction of ZOs allows allergens to pass the nasal passage into the subepithelium causing AR development. In this review, we discuss on ZO proteins and their impairment leading to AR, regulation of their expression by Th1 cytokines (i.e. IL-2, TNF-α and IFN-γ), Th2 cytokines (i.e. IL-4 and IL-13) and histone deacetylases (i.e. HDAC1 and HDAC2). These findings are pivotal for future developments of targeted therapies by restoring ZO protein expression and improving nasal epithelial barrier integrity in AR patients.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>INTRODUCTION</ns0:head><ns0:p>Tight junction (TJ) proteins are required to form the nasal epithelial barrier and maintain its integrity. Breakdown of TJ function or expression deregulation is associated with derailed nasal epithelial barrier, leading to infiltration by allergens and subsequent development of allergic rhinitis (AR) <ns0:ref type='bibr' target='#b19'>(Fukuoka & Yoshimoto, 2018;</ns0:ref><ns0:ref type='bibr' target='#b49'>Steelant et al., 2016)</ns0:ref>. Moreover, growing evidence has implicated regulation of the nasal epithelial barrier integrity by histone deacetylases (HDACs), Th1 and Th2 cytokines in AR. Thus, an overall assessment and compilation of this accumulating evidence is desirable. In this review, we present and discuss the mechanisms leading to breakdown of TJs specifically on zonula occludens (ZOs), a group of important TJ proteins, as well as regulation of their expression by HDACs, Th1 and Th2 cytokines that would be informative for clinicians and researchers alike in this field.</ns0:p></ns0:div> <ns0:div><ns0:head>SURVEY METHODOLOGY</ns0:head><ns0:p>This review focuses on ZOs and their regulators i.e. HDACs, Th1 and Th2 cytokines in AR research. All articles were searched and screened by two investigators (COSS, KKW) using the electronic databases PubMed and Google Scholar. References described in this review were obtained from the databases up to year 2019. The following keywords were used: 'allergic rhinitis', 'AR', 'nasal epithelial barrier integrity', 'zonula occludens', 'ZO', 'histone deacetylases', 'HDACs', 'Th1' and 'Th2'.</ns0:p></ns0:div> <ns0:div><ns0:head>ALLERGIC RHINITIS (AR)</ns0:head><ns0:p>Allergy is a hypersensitivity reaction that occurs when an individual is sensitized by allergens such as grass, tree pollen, house dust mites (HDMs), foods, insect venoms or medicines <ns0:ref type='bibr' target='#b3'>(Azid et al., 2019;</ns0:ref><ns0:ref type='bibr' target='#b39'>Sani et al., 2019;</ns0:ref><ns0:ref type='bibr' target='#b55'>Tanno et al., 2016)</ns0:ref>. AR is a global health issue affecting approximately 10-25% of the population worldwide <ns0:ref type='bibr' target='#b16'>(Elango, 2005)</ns0:ref>. AR can be characterized by events of sneezing, rhinorrhea, nasal obstruction, nasal itching and postnasal drip. It is also associated with itching of the eyes, ears and throat <ns0:ref type='bibr' target='#b16'>(Elango, 2005;</ns0:ref><ns0:ref type='bibr' target='#b35'>Pang et al., 2017)</ns0:ref>.</ns0:p><ns0:p>Onset of AR consists of two phases of reaction where the first phase involves allergen infiltration that induces the production of immunoglobulin E (IgE) and triggers the humoral immune response mediated by mast cells. The second phase is a clinical phase where the patients present with symptoms of AR as a response to subsequent antigen exposure. This involves the release of mediators such as multiple cytokines and chemokines. Nasal symptoms can be observed within minutes due to the release of neuroactive and vasoactive agents including histamine, cysteinyl leukotrienes and prostaglandin D 2 <ns0:ref type='bibr' target='#b65'>(Wheatley & Togias, 2015)</ns0:ref>. The mucosa is rendered more reactive to allergens and nasal symptoms can persist for days after exposure to allergens <ns0:ref type='bibr' target='#b41'>(Sarin et al., 2006;</ns0:ref><ns0:ref type='bibr' target='#b65'>Wheatley & Togias, 2015)</ns0:ref>.</ns0:p><ns0:p>AR is also defined immunologically as an IgE-mediated inflammation reaction in the nasal airways. This is primarily due to exposure to environmental pathogens, allergens or any foreign agents that induce an inflammation reaction <ns0:ref type='bibr' target='#b7'>(Bayrak Degirmenci et al., 2018)</ns0:ref>. These allergens contain proteases that contribute to the disruption of the airway epithelial barrier <ns0:ref type='bibr' target='#b38'>(Runswick et al., 2007;</ns0:ref><ns0:ref type='bibr' target='#b42'>Schleimer & Berdnikovs, 2017;</ns0:ref><ns0:ref type='bibr' target='#b60'>Wan et al., 1999)</ns0:ref>. The interaction between IgE and dendritic cells (DCs) increases allergen uptake and its subsequent processing and presentation to naive T cells <ns0:ref type='bibr' target='#b46'>(Sin & Togias, 2011)</ns0:ref>. Hence, higher allergen infiltration into the nasal airway increases the production of IgE in the blood. Perennial AR patients present with higher total IgE levels <ns0:ref type='bibr' target='#b30'>(Lee et al., 2016;</ns0:ref><ns0:ref type='bibr' target='#b44'>Shirasaki et al., 2011)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>NASAL EPITHELIAL BARRIER INTEGRITY IN AR</ns0:head><ns0:p>The nasal epithelial barrier plays an important role in sealing the nasal passage and underlying tissues from foreign pathogens by connecting the epithelial cells to each other <ns0:ref type='bibr' target='#b33'>(London & Ramanathan, 2017;</ns0:ref><ns0:ref type='bibr' target='#b49'>Steelant et al., 2016)</ns0:ref>. Any intrusion from foreign particles can stimulate the production of antimicrobial host defence molecules, pro-inflammatory cytokines and chemokines by nasal epithelial cells through the activation of recognition receptors. In addition, T cells are also recruited to epithelial cells to enhance adaptive immunity.</ns0:p><ns0:p>Dysfunction of these TJ barriers can increase exposure of nasal tissues to environmental antigens. It can lead to the infusion of inflammatory cells into the lumen which contributes to tissue damage or inflammation <ns0:ref type='bibr' target='#b47'>(Soyka et al., 2012)</ns0:ref>. The disruption of the mucosal epithelial barrier has also been observed in AR animal models <ns0:ref type='bibr' target='#b66'>(Zhang et al., 2016)</ns0:ref>.</ns0:p><ns0:p>The nasal epithelial barrier is primarily formed by cell-to-cell TJs which consist of integral membrane proteins such as claudins, occludin, junctional adhesion molecules (JAMs), as well as scaffold adaptor proteins consisting of ZO-1, ZO-2 and ZO-3 <ns0:ref type='bibr' target='#b8'>(Beutel et al., 2019;</ns0:ref><ns0:ref type='bibr' target='#b33'>London & Ramanathan, 2017)</ns0:ref>. These proteins form the intercellular connection between the cells that regulate the passage of foreign pathogens <ns0:ref type='bibr' target='#b49'>(Steelant et al., 2016)</ns0:ref>. These proteins connect together to form a complex structure that protects the epithelial barrier from inhaled pathogens (Figure <ns0:ref type='figure' target='#fig_1'>1</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>ZONULA OCCLUDENS (ZO) PROTEINS</ns0:head><ns0:p>ZO proteins are a group of key proteins associated with TJ molecules that connect transmembrane proteins to the actin cytoskeleton <ns0:ref type='bibr' target='#b49'>(Steelant et al., 2016)</ns0:ref>. ZO proteins form an anchor directly to the underlying cytoskeleton with other T J proteins including occludin, claudin, JAMs and tricellulin <ns0:ref type='bibr' target='#b6'>(Bauer et al., 2010;</ns0:ref><ns0:ref type='bibr' target='#b20'>Furuse et al., 1994)</ns0:ref>. ZO proteins belong to the family of membrane-associated guanylate kinase (MAGUK)like proteins. MAGUKs are scaffolding proteins that form and maintain multimolecular complexes at distinct subcellular sites such as the cytoplasmic surface of the plasma membrane <ns0:ref type='bibr' target='#b6'>(Bauer et al., 2010)</ns0:ref>.</ns0:p><ns0:p>ZO-1, ZO-2 and ZO-3 form a belt-like region at the outer end of intercellular space between the epithelial cells that separates the apical from the lateral plasma membrane. The proteins also play vital roles in regulating the passage of ions and molecules through the membrane <ns0:ref type='bibr' target='#b22'>(Gonzalez-Mariscal et al., 2000)</ns0:ref>. ZO proteins consist of a multidomain structure including SRC homology 3 (SH3), guanylate kinase-like (GUK) and multiple PDZ domains <ns0:ref type='bibr' target='#b2'>(Anderson, 1996)</ns0:ref>.</ns0:p><ns0:p>ZO-1 and ZO-2 have been detected in human nasal mucosa where ZO-1 is found in the uppermost layer of epithelium <ns0:ref type='bibr' target='#b29'>(Kojima et al., 2013)</ns0:ref>. ZO-1 protein contains an N-terminal PDZ domain that can recognize specific C-terminal or other peptide motifs to assemble with other TJ molecules such as claudins to form a TJ barrier at gaps between epithelial cells <ns0:ref type='bibr' target='#b23'>(Heinemann & Schuetz, 2019;</ns0:ref><ns0:ref type='bibr' target='#b24'>Herve et al., 2014;</ns0:ref><ns0:ref type='bibr' target='#b58'>Umeda et al., 2006)</ns0:ref>. The TJ barrier controls the diffusion of molecules by acting as semipermeable diffusion barriers through the paracellular pathway. It has been reported that transmembrane proteins such as claudin and occludin are essential for the regulation of paracellular permeability <ns0:ref type='bibr' target='#b4'>(Balda & Matter, 2000;</ns0:ref><ns0:ref type='bibr' target='#b31'>Lee, 2015;</ns0:ref><ns0:ref type='bibr' target='#b37'>Roehlen et al., 2020)</ns0:ref>. ZO-1 is also responsible for the regulation of paracellular permeability (i.e. permeability for the passage of molecules between adjacent epithelial cells) via TJ complexes as it binds directly to transmembrane proteins <ns0:ref type='bibr' target='#b4'>(Balda & Matter, 2000;</ns0:ref><ns0:ref type='bibr' target='#b31'>Lee, 2015;</ns0:ref><ns0:ref type='bibr' target='#b37'>Roehlen et al., 2020)</ns0:ref>. Loss of ZO-1 can retard the formation of the TJ complexes, and further breakdown of ZO-1 may result in severe disruption of the paracellular barrier in epithelial cells <ns0:ref type='bibr' target='#b37'>(Roehlen et al., 2020)</ns0:ref>. Hence, ZO-1 plays important roles in maintaining the epithelial barrier by connecting TJ molecules to seal the epithelial cells from infiltration of environmental allergens.</ns0:p></ns0:div> <ns0:div><ns0:head>Disruption of ZO proteins in AR</ns0:head><ns0:p>The disruption of ZO proteins affects the interaction of TJ molecules, allowing the passage of allergens into the host. Decreased expression of ZO-1 in AR patients has been reported by gene expression studies <ns0:ref type='bibr' target='#b30'>(Lee et al., 2016;</ns0:ref><ns0:ref type='bibr' target='#b33'>London & Ramanathan, 2017)</ns0:ref>. A study by Steelant and colleagues showed decreased levels of ZO-1 through immunofluorescent staining on AR biopsy specimens <ns0:ref type='bibr' target='#b49'>(Steelant et al., 2016)</ns0:ref>. Furthermore, nasal epithelial cells isolated from inferior turbinate of HDM-induced AR patients demonstrated reduced ZO-1 mRNA expression <ns0:ref type='bibr' target='#b50'>(Steelant et al., 2018)</ns0:ref>. Likewise, the expression of ZO-1 in asthma and chronic rhinosinusitis patients was also decreased compared with healthy controls <ns0:ref type='bibr' target='#b14'>(de Boer et al., 2008;</ns0:ref><ns0:ref type='bibr' target='#b47'>Soyka et al., 2012)</ns0:ref>.</ns0:p><ns0:p>Immunofluorescence analysis of RPMI 2650, a human nasal epithelial cell line, showed a decreased of ZO-1 expression after being exposed to diesel exhaust particles <ns0:ref type='bibr' target='#b17'>(Fukuoka et al., 2016)</ns0:ref>. Transepithelial electric resistance (TER) measurement, a procedure that assessed the integrity of TJ in cell culture of epithelial monolayers, of the RPMI 2650 was reduced in the study, and the decreased ZO-1 expression was associated with severity of AR <ns0:ref type='bibr' target='#b17'>(Fukuoka et al., 2016)</ns0:ref>. Moreover, HDM cysteine proteinase antigen from Dermatophagoids pteryonysinus caused the mislocalization of ZO-1 from TJ <ns0:ref type='bibr' target='#b60'>(Wan et al., 1999)</ns0:ref>. Hence, patients with AR demonstrate lower integrity of nasal epithelial barrier that is associated with decreased expression or disruption of ZO-1 protein.</ns0:p><ns0:p>Accumulating evidence has shown that reduced expression of ZO-1 or ZO-2 occurs in patients with chronic rhinosinusitis (CRS) without nasal polyps <ns0:ref type='bibr' target='#b47'>(Soyka et al., 2012)</ns0:ref> or eosinophilic esophagitis (EoE) <ns0:ref type='bibr' target='#b28'>(Katzka et al., 2014)</ns0:ref>, respectively. CRS is characterized by mucosal inflammation involving both the nasal cavity and paranasal sinuses <ns0:ref type='bibr' target='#b47'>(Soyka et al., 2012)</ns0:ref>, while EoE represents inflammation of the oesophagus when food antigens interact with oesophageal mucosa <ns0:ref type='bibr' target='#b28'>(Katzka et al., 2014)</ns0:ref>. Both CRS and EoE are caused by the penetration of antigens through the gap between nasal epithelial cells <ns0:ref type='bibr' target='#b28'>(Katzka et al., 2014;</ns0:ref><ns0:ref type='bibr' target='#b47'>Soyka et al., 2012)</ns0:ref>. The expression of ZOs in these allergic diseases in both patients and animal models are summarized in Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>HISTONE DEACETYLASES (HDACs) IN AR</ns0:head><ns0:p>HDACs are enzymes responsible for removing acetyl group from lysine residues of target proteins. HDACs prevent gene transcription by allowing DNA to be wrapped by histones <ns0:ref type='bibr' target='#b25'>(Jiang et al., 2015)</ns0:ref>. HDACs also promote the condensation of chromation <ns0:ref type='bibr' target='#b43'>(Shakespear et al., 2011)</ns0:ref>. HDACs have been implicated in several inflammatory and allergic conditions including AR <ns0:ref type='bibr' target='#b5'>(Barnes, 2013;</ns0:ref><ns0:ref type='bibr' target='#b54'>Sweet et al., 2012;</ns0:ref><ns0:ref type='bibr' target='#b59'>Vendetti & Rudin, 2013)</ns0:ref>. Upregulation of HDAC activity occurs in nasal epithelial cells of AR patients <ns0:ref type='bibr' target='#b51'>(Steelant et al., 2019)</ns0:ref>.</ns0:p><ns0:p>It has been shown that expression of TJs can be increased by inhibiting the activity of HDAC1 and simultaneously decreasing the defect of epithelial barriers <ns0:ref type='bibr' target='#b64'>(Wawrzyniak et al., 2017)</ns0:ref>. In animal models, HDAC1 protein levels in rats AR model were higher than naive rats <ns0:ref type='bibr' target='#b25'>(Jiang et al., 2015)</ns0:ref>. Immunohistochemical results also demonstrated higher expression of HDAC1 protein in nasal epithelium of patients with sinusitis and nasal polyps contributing to the disruption of TJs <ns0:ref type='bibr' target='#b27'>(Kaneko et al., 2017)</ns0:ref>. Furthermore, HDAC1 could supress the activity of TWIK-related potassium channel-1 (Trek-1), and Trek-1 is pivotal in the maintenance of epithelial cell barrier function <ns0:ref type='bibr' target='#b10'>(Bittner et al., 2013)</ns0:ref>. Higher mRNA expression of HDAC1 together with lower mRNA expression of Trek-1 was found in nasal epithelial cells from patients with AR compared with healthy subjects <ns0:ref type='bibr' target='#b63'>(Wang et al., 2015)</ns0:ref>.</ns0:p><ns0:p>ZO-1 expression was previously shown to be decreased in the presence of HDAC1.Lower levels of ZO-1 mRNA expression were observed in AML-12 murine hepatocyte cells that overexpressed HDAC1 <ns0:ref type='bibr' target='#b32'>(Lei et al., 2010)</ns0:ref>. Studies on epithelial-mesenchymal transition (EMT), an oncogenic process that induces epithelial cells to transform into anchorage-independent mesenchyme-like cells for increased metastatic capabilities of cancer cells, also showed an association with HDAC1 and ZO-1 <ns0:ref type='bibr' target='#b63'>(Zhou et al., 2015)</ns0:ref>. ZO-1 is involved in EMT where loss of ZO-1 expression can induce invasion of cancer cells. Higher HDAC1 mRNA and protein expression levels were found in hepatocellular carcinoma (HCC) cell lines (HepG2, Hep3B, Huh7, PLC/PRF/5, SK-Hep-1) compared with normal human epithelial cell line (THLE-3) <ns0:ref type='bibr' target='#b63'>(Zhou et al., 2015)</ns0:ref>. Inhibition of HDAC1 in these HCC cells showed an increase of ZO-1 mRNA and protein expression, leading to decreased invasion capabilities of HCC cells <ns0:ref type='bibr' target='#b63'>(Zhou et al., 2015)</ns0:ref>. Thus, ZO-1 expression can be inhibited by HDAC1 leading to breakdown of epithelial cells' anchorage, and it remains unknown if similar effects might also occur in nasal epithelial cells.</ns0:p><ns0:p>In contrast with HDAC1, evidence has shown that HDAC2 expression is required to prevent breakdown of nasal epithelial barrier integrity in AR. Decreased levels of HDAC2 were observed in patients with asthma and asthmatic smoking patients, as in patients with chronic obstructive pulmonary disease <ns0:ref type='bibr' target='#b9'>(Bhavsar et al., 2008)</ns0:ref>. Higher levels of HDAC2 can restore steroid sensitivity in asthmatic patients <ns0:ref type='bibr' target='#b9'>(Bhavsar et al., 2008)</ns0:ref>, and nasal scrape samples of patients with persistent AR showed weak expression of HDAC2 <ns0:ref type='bibr' target='#b40'>(Sankaran et al., 2014)</ns0:ref>. Moreover, deficiency of HDAC2 in intestinal epithelial cells (IEC) of mice was associated with chronic basal inflammation <ns0:ref type='bibr' target='#b57'>(Turgeon et al., 2013)</ns0:ref>. Deletion of HDAC2 from IEC displayed an increased permeability to fluorescein isothiocyanate-dextran 4kDa (FD4; a fluorochrome for investigation of cell permeability) by assessing the intensity of fluorescence in the mice blood <ns0:ref type='bibr' target='#b57'>(Turgeon et al., 2013)</ns0:ref>, and increased penetration by FD4 indicated increased leakiness that may be due to disruption of epithelial barrier.</ns0:p><ns0:p>However, downregulation of HDAC2 with the treatment of Trichostation-A (TSA), an HDAC inhibitor (HDACi), increased the expression of ZO-1 mRNA in fetal human lens epithelial cells <ns0:ref type='bibr' target='#b21'>(Ganatra et al., 2018)</ns0:ref>. TSA treatment in this study decreased the association between HDAC2 with the promoter region of ZO-1 as demonstrated by chromatin immunoprecipitation assay <ns0:ref type='bibr' target='#b21'>(Ganatra et al., 2018)</ns0:ref>. The effect of HDAC2 inhibitor CAY10683 was investigated on the expression on ZO-1 at the intestinal mucosal barrier of lipopolysaccharide (LPS)-stimulated NCM460 cells (a normal human colon mucosal epithelial cell line) <ns0:ref type='bibr' target='#b62'>(Wang et al., 2018)</ns0:ref>. LPS was used to induce damage to the mucosal barrier of NCM460 cells. The NCM460 cells treated with the HDAC2 inhibitor (CAY10683) increased mRNA and protein levels of ZO-1 <ns0:ref type='bibr' target='#b62'>(Wang et al., 2018)</ns0:ref>. Collectively, this suggests that HDAC2 plays differential roles in the increase or reduction of epithelial barrier integrity depending on the site of the human epithelial cells. HDAC2 prevents the breakdown of nasal epithelial barrier but it may promote the opposite effect in human lens or colon mucosal epithelial cells via downregulation of ZO-1 expression.</ns0:p><ns0:p>Inhibiting HDAC activities with HDACi (JNJ-26481585) may be able to restore the structure of ZO molecules in nasal epithelial cells <ns0:ref type='bibr' target='#b51'>(Steelant et al., 2019)</ns0:ref>. In the same study, immunofluorescent staining showed that ZO-1 expression was significantly weaker in AR patients compared with healthy controls, and further treatment with JNJ-26481585 increased the expression of ZO-1 protein.</ns0:p><ns0:p>The HDACi sodium butyrate (SoB) is a short chain fatty-acid produced by the microbial fermentation of dietary fibre in colonic lumen <ns0:ref type='bibr' target='#b11'>(Bordin et al., 2004)</ns0:ref>. The Rat-1 fibroblasts cell line expresses ZO-1 and ZO-2 proteins <ns0:ref type='bibr' target='#b11'>(Bordin et al., 2004)</ns0:ref>. When the cells lysates were cultured in the presence of SoB, densitometric analysis of immunoblots showed that ZO-1 and ZO-2 levels were upregulated <ns0:ref type='bibr' target='#b11'>(Bordin et al., 2004)</ns0:ref>. Collectively, HDAC1 and HDAC2 suppress the expression of ZO proteins leading to breakdown of epithelial cells barrier integrity as demonstrated by these studies either in AR or non-AR epithelial cells.</ns0:p></ns0:div> <ns0:div><ns0:head>TH1 CYTOKINES IN AR</ns0:head><ns0:p>Cytokines play an important role in mediating allergic inflammation. The roles of Th2 cytokines in AR have been well-documented <ns0:ref type='bibr' target='#b49'>(Steelant et al., 2016;</ns0:ref><ns0:ref type='bibr' target='#b52'>Sun et al., 2020;</ns0:ref><ns0:ref type='bibr' target='#b67'>Zhao et al., 2017)</ns0:ref>. Imbalance of Th1 and Th2 cytokines appears to be involved in the AR inflammatory pathway <ns0:ref type='bibr' target='#b67'>(Zhao et al., 2017)</ns0:ref>. However, there is a lack of review on Th1 cytokines and their roles in the breakdown of nasal epithelial barrier integrity. Moreover, dysfunctional Th1 responses have been proposed to be responsible for the exaggerated Th2 responses that occur in AR patients <ns0:ref type='bibr' target='#b15'>(Eifan & Durham, 2016)</ns0:ref>. Th1 cells produce IL-2, IFN-γ and TNF-α in response to allergic inflammation <ns0:ref type='bibr' target='#b0'>(Ackaert et al., 2014)</ns0:ref>. Th1 cytokines can cause disruption of TJ molecules including ZO proteins in nasal epithelial barrier, leading to allergic inflammation.</ns0:p><ns0:p>Th1 response is characterized by IFN-γ production which stimulates bactericidal activities of macrophages and boosts immunity against intracellular pathogens and virus infection <ns0:ref type='bibr' target='#b34'>(Marshall et al., 2018)</ns0:ref>. IFN-γ plays a key role in bridging the innate and adaptive immune systems <ns0:ref type='bibr' target='#b7'>(Bayrak Degirmenci et al., 2018)</ns0:ref>. It is also essential in the regulation of local leukocyte-endothelial interaction <ns0:ref type='bibr' target='#b1'>(Akkoc et al., 2008)</ns0:ref>.</ns0:p><ns0:p>IFN-γ increases the permeability of primary bronchial epithelial cells and T84 colonic epithelial cells by disassembling TJ structures <ns0:ref type='bibr' target='#b12'>(Bruewer et al., 2005)</ns0:ref>. In order to observe the expression of ZO-2 in CRS patients, human epithelial cells were treated on air-liquid interface (ALI) culture with IFN-γ. The results showed that opening of TJs between the neighbouring cells occurred in patients compared with healthy controls <ns0:ref type='bibr' target='#b47'>(Soyka et al., 2012)</ns0:ref>. However, no significant decrease of ZO-1 expression in AR patients was observed when the epithelial cells were treated with IFN-γ and TNF-α cytokines <ns0:ref type='bibr' target='#b30'>(Lee et al., 2016)</ns0:ref>. Additionally, cultured primary nasal epithelial cells in ALI stimulated with TNF-α and IFN-γ showed a decrease of epithelial barrier integrity in vitro <ns0:ref type='bibr' target='#b50'>(Steelant et al., 2018)</ns0:ref>.</ns0:p><ns0:p>Furthermore, expression of ZO-1 protein in primary airway cells from cystic fibrosis patients was reduced in the presence of IFN-γ and TNF-α cytokines <ns0:ref type='bibr' target='#b13'>(Coyne et al., 2002)</ns0:ref>. Prolonged exposure of IFN-γ and TNF-α to the cell culture led to a significant damage to ZO-1 molecules <ns0:ref type='bibr' target='#b13'>(Coyne et al., 2002)</ns0:ref>. This damage caused an increase of cell permeability to external solutes and a decrease in transepithelial resistance. Further investigation of wild type BALB/c mice endonasally instilled with IFN-γ and TNF-α increased the FD4 mucosal barrier permeability associated with decreased ZO-1 expression in vivo <ns0:ref type='bibr' target='#b50'>(Steelant et al., 2018)</ns0:ref>.</ns0:p><ns0:p>However, in AR mice model and AR patients, Th1 cytokines have been associated with increased expression of TJ molecules and decreased AR severity, respectively. Lower levels of Th1 cytokines, IL-2 and IFN-γ were detected in the serum sample from OVA-sensitized mice with AR compared with controls <ns0:ref type='bibr' target='#b61'>(Wang et al., 2016)</ns0:ref>. When the OVA-sensitized mice were treated with SoB, IL-2 and IFN-γ levels were increased, leading to increased expression of TJ molecules <ns0:ref type='bibr' target='#b61'>(Wang et al., 2016)</ns0:ref>. The levels of IFN-γ in plasma sample of AR patients was significantly lower compared with healthy controls <ns0:ref type='bibr' target='#b7'>(Bayrak Degirmenci et al., 2018)</ns0:ref>. The same study showed that downregulated levels of Th1 cytokines were associated with higher severity of AR symptoms. Furthermore, the levels of IFN-γ were inversely correlated with higher nasal symptoms scores as measured by evaluating the severity of sneezing, nasal itching, nasal obstruction and watery nasal discharge <ns0:ref type='bibr' target='#b7'>(Bayrak Degirmenci et al., 2018)</ns0:ref>. Further mechanistic studies are recommended to elucidate whether Th1 cytokines exert their protective effects on nasal epithelial barrier integrity via increased TJ molecules expression in human AR cells.</ns0:p></ns0:div> <ns0:div><ns0:head>TH2 CYTOKINES IN AR</ns0:head><ns0:p>The involvement of Th2 cytokines in AR has been widely investigated. The serum levels of Th2 cytokines including IL-4 and IL-13 are elevated in AR patients <ns0:ref type='bibr' target='#b26'>(Jordakieva & Jensen-Jarolim, 2018)</ns0:ref>. Increased expression of IL-4 in nasal epithelial cells of HDM-induced AR patients reduced ZO-1 mRNA expression <ns0:ref type='bibr' target='#b49'>(Steelant et al., 2016)</ns0:ref>. Breakdown of the epithelial barrier was observed after stimulation of nasal epithelial cells with IL-4 and significantly increased the permeability of FD4 <ns0:ref type='bibr' target='#b49'>(Steelant et al., 2016)</ns0:ref>.</ns0:p><ns0:p>Both IL-4 and IL-13 play critical roles in promoting B cells to produce IgE <ns0:ref type='bibr' target='#b45'>(Shirkani et al., 2019;</ns0:ref><ns0:ref type='bibr' target='#b67'>Zhao et al., 2017)</ns0:ref>. Protein levels of IL-4 and IL-13 in nasal mucosa of guinea pig of ARsensitized pig were higher compared with controls <ns0:ref type='bibr' target='#b67'>(Zhao et al., 2017)</ns0:ref>. This was supported by findings where higher serum levels of IL-4 and IL-13 were found in AR-sensitized pigs compared with controls <ns0:ref type='bibr' target='#b67'>(Zhao et al., 2017)</ns0:ref>. In addition, treatment of lung cancer cells (Calu-3) with IL-4 and IL-13 reduced the protein expression of ZO-1 protein <ns0:ref type='bibr' target='#b19'>(Fukuoka & Yoshimoto, 2018)</ns0:ref>.</ns0:p><ns0:p>Immunofluorescent staining of human bronchial epithelial cells of asthmatic patients demonstrated that disruption of TJs in the ALI cultures occurred and weak expression of ZO-1 was observed <ns0:ref type='bibr' target='#b64'>(Wawrzyniak et al., 2017)</ns0:ref>. Blocking IL-4 and IL-13 in asthma patients did not show difference in TER measurement <ns0:ref type='bibr' target='#b48'>(Srinivasan et al., 2015;</ns0:ref><ns0:ref type='bibr' target='#b64'>Wawrzyniak et al., 2017)</ns0:ref>. However, nullifying the effects of IL-4 and IL-13 using anti-IL4 and anti-IL-13 supplemented to the ALI culture of control bronchial epithelial cells in vitro enhanced the TER measurement <ns0:ref type='bibr' target='#b64'>(Wawrzyniak et al., 2017)</ns0:ref>. Moreover, IL-4 and IL-13 mRNA expression levels were increased together with downregulated ZO-1 mRNA expression in the jejunum of OVA-sensitized rats <ns0:ref type='bibr' target='#b56'>(Tulyeu et al., 2019)</ns0:ref>. Downregulation of ZO-1 mRNA expression potentially through regulation by Th2 cytokine was also observed in vivo. Endonasal stimulation of wild-type BALB/c mice with IL-4 and IL-13 demonstrated increased FD4 permeability associated with reduced ZO-1 mRNA expression compared with saline-instilled mice <ns0:ref type='bibr' target='#b50'>(Steelant et al., 2018)</ns0:ref>. Taken together, these studies indicate that IL-4 and IL-13 contribute to the breakdown of nasal epithelial barrier by reducing the expression of ZO-1.</ns0:p></ns0:div> <ns0:div><ns0:head>CONCLUSION</ns0:head><ns0:p>In conclusion, HDAC1 and HDAC2 play pathogenic roles in the breakdown of nasal epithelial barrier integrity via suppression of ZO proteins expression. This is potentially regulated by Th2 cytokine signaling pathways as higher levels of Th2 cytokines in AR patients are accompanied with decreased epithelial barrier integrity and ZO-1 expression. In contrast, higher levels of Th1 cytokines appear to preserve the nasal epithelial barrier integrity of AR patients. Future research should investigate and compare which specific HDACi or blocking antibodies of Th2 cytokines that demonstrate potent restoration of ZO proteins expression in nasal epithelial cells of AR animal models, as well as ameliorating their symptoms. Targeting these pathogenic pathways might be effective in AR therapy to maintain the expression and structure of ZOs at the nasal epithelial barrier. Manuscript to be reviewed </ns0:p><ns0:note type='other'>Figure Legends</ns0:note><ns0:note type='other'>Figure 1</ns0:note></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>Zhou H, Wang J, Peng G, Song Y, Zhang C. 2015. A novel treatment strategy in hepatocellular carcinoma by down-regulation of histone deacetylase 1 expression using a shRNA lentiviral system. Int J Clin Exp Med 8:17721-17729.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head>Figure 1 :</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1: Pathophysiology of allergic rhinitis (AR) involving the disruption of nasal epithelial barrier and regulation by HDACs, Th1 and Th2 cytokines.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>Figure 1 :</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1: Pathophysiology of allergic rhinitis (AR) from the disruption of nasal epithelial barrier and the involvement of HDACs, Th1 and Th2 cytokines.</ns0:figDesc><ns0:graphic coords='19,42.52,199.12,525.00,485.25' type='bitmap' /></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head>Table 1 (on next page)</ns0:head><ns0:label>1</ns0:label><ns0:figDesc /><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_1'><ns0:head>Table 1 :</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>The expression of Zonula Occludens (ZOs) in human and animal models</ns0:figDesc><ns0:table /><ns0:note>PeerJ reviewing PDF \| (2020:01:45164:2:0:NEW 29 Jul 2020)Manuscript to be reviewed 1</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_2'><ns0:head>Table 1 : The expression of Zonula Occludens (ZOs) in human and animal models ZOs Samples Treatment Change in expression References Treatment with Th1 cytokines:</ns0:head><ns0:label>1</ns0:label><ns0:figDesc /><ns0:table><ns0:row><ns0:cell>ZO-1</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>Nasal mucosa from</ns0:cell><ns0:cell>TNF-α, IFN-γ</ns0:cell><ns0:cell>Downregulated</ns0:cell><ns0:cell>(Steelant et</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>normal wild type</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell>al., 2018)</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>BALB/c mice</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>Nasal epithelial cells</ns0:cell><ns0:cell>IFN-γ</ns0:cell><ns0:cell>Downregulated</ns0:cell><ns0:cell>(Steelant et</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>from HDM-induced</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell>al., 2016)</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>AR patients</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='2'>Treatment with Th2 cytokines:</ns0:cell><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>Nasal mucosa from</ns0:cell><ns0:cell>IL-4</ns0:cell><ns0:cell>Downregulated</ns0:cell><ns0:cell>(Steelant et</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>normal wild type</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell>al., 2018)</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>BALB/c mice</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>Nasal epithelial cells</ns0:cell><ns0:cell>IL-4</ns0:cell><ns0:cell>Downregulated</ns0:cell><ns0:cell>(Steelant et</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>from HDM-induced</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell>al., 2016)</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>AR patients</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>Calu-3 cells (human</ns0:cell><ns0:cell>IL-4 and IL-13</ns0:cell><ns0:cell>Downregulated</ns0:cell><ns0:cell>(Fukuoka &</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>epithelial nasal cell</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell>Yoshimoto,</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>lines)</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell>2018)</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>Other treatment:</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>Human nasal epithelial</ns0:cell><ns0:cell>Cultured with diesel</ns0:cell><ns0:cell>Downregulated</ns0:cell><ns0:cell>(Fukuoka et</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>cell line, RPMI 2650</ns0:cell><ns0:cell>exhaust particle</ns0:cell><ns0:cell /><ns0:cell>al., 2016)</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>(DEP)</ns0:cell><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>Nasal epithelial cells</ns0:cell><ns0:cell>Intranasal</ns0:cell><ns0:cell>Downregulated</ns0:cell><ns0:cell>(Fukuoka et</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>in AR mice</ns0:cell><ns0:cell>administration with</ns0:cell><ns0:cell /><ns0:cell>al., 2016)</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>DEP</ns0:cell><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>Nasal biopsy</ns0:cell><ns0:cell>No treatment</ns0:cell><ns0:cell>Downregulated</ns0:cell><ns0:cell>(Steelant et</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>specimens from HDM-</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell>al., 2016)</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>induced AR patients</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>Bronchial epithelium</ns0:cell><ns0:cell>No treatment</ns0:cell><ns0:cell>Downregulated</ns0:cell><ns0:cell>(de Boer et</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>biopsy from asthmatic</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell>al., 2008)</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>patients</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>Nasal epithelial cells</ns0:cell><ns0:cell>Treated with</ns0:cell><ns0:cell>Downregulated</ns0:cell><ns0:cell>(Shin et al.,</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>from septal surgery</ns0:cell><ns0:cell>Alternaria alternate</ns0:cell><ns0:cell /><ns0:cell>2019)</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>patients</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>ZO-2</ns0:cell><ns0:cell>Nasal biopsy</ns0:cell><ns0:cell>No treatment</ns0:cell><ns0:cell>Downregulated</ns0:cell><ns0:cell>(Soyka et al.,</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>specimens from</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell>2012)</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>chronic rhinosinusitis</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row></ns0:table><ns0:note>PeerJ reviewing PDF \| (2020:01:45164:2:0:NEW 29 Jul 2020)Manuscript to be reviewed</ns0:note></ns0:figure> <ns0:note place='foot'>PeerJ reviewing PDF \| (2020:01:45164:2:0:NEW 29 Jul 2020)Manuscript to be reviewed</ns0:note> <ns0:note place='foot' n='2'>PeerJ reviewing PDF \| (2020:01:45164:2:0:NEW 29 Jul 2020)Manuscript to be reviewed</ns0:note> </ns0:body> "	" 28 July 2020 Dear Prof. Dr. Andrew Herr, I would like to thank you for the constructive comments and for taking the liberty to directly edit the manuscript, they have been incredibly helpful. Below you will find the actions taken accordingly. Please do not hesitate to contact me if you require any further information, and it is hoped that you will find the revised manuscript to be suitable for publication in PeerJ. Thank you. Yours sincerely, Assoc. Prof. Dr. Kah Keng Wong (BSc, Mal; DPhil, Oxon) Department of Immunology, School of Medical Sciences, Health Campus, Universiti Sains Malaysia, 16150 Kubang Kerian, Kelantan, Malaysia Tel: +6017-6273843 / +609-7676229 Fax: +609-7653370 E-mail: kahkeng@usm.my ; kahkeng3@gmail.com Web: http://www.medic.usm.my/immunology Do you mean the subepithelium? Thank you for the comment. That is correct, and it has now been rectified accordingly. Is this statement a hypothesis proposed by the authors, or are you stating that this is known and established from the literature? If the latter is the case, you should include references to support this. If there are no references, you should state this more carefully. Thank you for the comments. The statement is established from literature, and the required references have been added as follows: “Both CRS and EoE are caused by the penetration of antigens through the gap between nasal epithelial cells (Katzka et al., 2014; Soyka et al., 2012).” Epithelial tissue? Epithelial cells? Or do you mean “nasal epithelium”? Thank you for the comments. It was “epithelial cells” and corrected accordingly as follows: “Higher mRNA expression of HDAC1 together with lower mRNA expression of Trek-1 was found in nasal epithelial cells from patients with AR compared with healthy subjects (Wang et al., 2015).” How is this discrepancy explained? In the previous paragraph you discuss how HDAC2 expression prevents breakdown of the nasal epithelial barrier. Do the authors of these papers suggest any explanations? Do you have any ideas? Thank you for the comments. The following sentences have been added at the end of the above mentioned paragraph: “Collectively, this suggests that HDAC2 plays differential roles in the increase or reduction of epithelial barrier integrity depending on the site of the human epithelial cells. HDAC2 prevents the breakdown of nasal epithelial barrier but it may promote the opposite effect in human lens or colon mucosal epithelial cells via downregulation of ZO-1 expression.” In addition, the following sentence has been added to improve the clarity of the first sentence of this paragraph: “TSA treatment in this study decreased the association between HDAC2 with the promoter region of ZO-1 as demonstrated by chromatin immunoprecipitation assay (Ganatra et al., 2018).” These two sentences contradict what you’ve just stated above (and below), that Th1 cytokines like IFNγ disrupt TJ molecules and lead to allergic inflammation. Again, these sentences contradict most of the rest of this entire section. Are you intending to show that Th1 cytokines are associated with decreased TJ molecules and increased allergic inflammation, or the opposite? It is extremely confusing to switch back and forth without any discussion about why these findings are completely contradictory. Thank you for the two comments above. In non-allergic rhinitis (AR) samples including human colonic epithelial cells, chronic rhinosinusitis (CRS) patients and airway cells from cystic fibrosis patients, Th1 cytokines seem to exert opposite effects compared with those observed in AR samples i.e. protective of nasal epithelial barrier integrity in AR but not protective of epithelial barrier integrity in non-AR conditions. For better clarity and to avoid confusion, the paragraphs have been re-arranged in this section where elaborations on Th1 non-protective effects on epithelial barrier integrity in non-AR samples were first described before Th1 protective effects on nasal epithelial barrier integrity in AR samples were elaborated as follows: “Th1 response is characterized by IFN-γ production which stimulates bactericidal activities of macrophages and boosts immunity against intracellular pathogens and virus infection (Marshall et al., 2018). IFN-γ plays a key role in bridging the innate and adaptive immune systems (Bayrak Degirmenci et al., 2018). It is also essential in the regulation of local leukocyte-endothelial interaction (Akkoc et al., 2008). IFN-γ increases the permeability of primary bronchial epithelial cells and T84 colonic epithelial cells by disassembling TJ structures (Bruewer et al., 2005). In order to observe the expression of ZO-2 in CRS patients, human epithelial cells were treated on air-liquid interface (ALI) culture with IFN-γ. The results showed that opening of TJs between the neighbouring cells occurred in patients compared with healthy controls (Soyka et al., 2012). However, no significant decrease of ZO-1 expression in AR patients was observed when the epithelial cells were treated with IFN-γ and TNF-α cytokines (Lee et al., 2016). Additionally, cultured primary nasal epithelial cells in ALI stimulated with TNF-α and IFN-γ showed a decrease of epithelial barrier integrity in vitro (Steelant et al., 2018). Furthermore, expression of ZO-1 protein in primary airway cells from cystic fibrosis patients was reduced in the presence of IFN-γ and TNF-α cytokines (Coyne et al., 2002). Prolonged exposure of IFN-γ and TNF-α to the cell culture led to a significant damage to ZO-1 molecules (Coyne et al., 2002). This damage caused an increase of cell permeability to external solutes and a decrease in transepithelial resistance. Further investigation of wild type BALB/c mice endonasally instilled with IFN-γ and TNF-α increased the FD4 mucosal barrier permeability associated with decreased ZO-1 expression in vivo (Steelant et al., 2018). Blocking TNF-α cytokine activity with anti-TNF-α partially restored the ZO-1 expression in HDM-induced mice (Steelant et al., 2018). However, in AR mice model and AR patients, Th1 cytokines have been associated with increased expression of TJ molecules and decreased AR severity, respectively. Lower levels of Th1 cytokines, IL-2 and IFN-γ were detected in the serum sample from OVA-sensitized mice with AR compared with controls (Wang et al., 2016). When the OVA-sensitized mice were treated with SoB, IL-2 and IFN-γ levels were increased, leading to increased expression of TJ molecules (Wang et al., 2016). The levels of IFN-γ in plasma sample of AR patients was significantly lower compared with healthy controls (Bayrak Degirmenci et al., 2018). The same study showed that downregulated levels of Th1 cytokines were associated with higher severity of AR symptoms. Furthermore, the levels of IFN-γ were inversely correlated with higher nasal symptoms scores as measured by evaluating the severity of sneezing, nasal itching, nasal obstruction and watery nasal discharge (Bayrak Degirmenci et al., 2018). Further mechanistic studies are recommended to elucidate whether Th1 cytokines exert their protective effects on nasal epithelial barrier integrity via increased TJ molecules expression in human AR cells”. The conclusion has also been revised as follows: “In conclusion, HDAC1 and HDAC2 play pathogenic roles in the breakdown of nasal epithelial barrier integrity via suppression of ZO proteins expression. This is potentially regulated by Th2 cytokine signaling pathways as higher levels of Th2 cytokines in AR patients are accompanied with decreased epithelial barrier integrity and ZO-1 expression. In contrast, higher levels of Th1 cytokines appear to preserve the nasal epithelial barrier integrity of AR patients. Future research should investigate and compare which specific HDACi or blocking antibodies of Th2 cytokines that demonstrate potent restoration of ZO proteins expression in nasal epithelial cells of AR animal models, as well as ameliorating their symptoms. Targeting these pathogenic pathways might be effective in AR therapy to maintain the expression and structure of ZOs at the nasal epithelial barrier.” Finally, the following sentences which you had kindly highlighted have been excluded from the manuscript: 1) ZO-1 is expressed by DCs to form an epithelial barrier (Rescigno et al., 2001; Sung et al., 2006). 2) The disruption of ZO proteins affects the interaction of TJ molecules, allowing the passage of allergens into the host. All direct edits have been implemented in the manuscript. Thank you very much. "	Here is a paper. Please give your review comments after reading it.
511	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Background. Simulating vegetation distribution is an effective method for identifying vegetation distribution patterns and trends. The primary goal of this study was to determine the best simulation method for improving the vegetation in an area heavily affected by human disturbance.</ns0:p><ns0:p>Methods. We used climate, topographic, and spectral data as the input variables for four machine learning models (random forest (RF), decision tree (DT), support vector machine (SVM), and maximum likelihood classification (MLC)) on three vegetation classification units (vegetation group (I), vegetation type (II), and formation and subformation (III)) in Jing-Jin-Ji, one of China's most developed regions. We used a total of 2,789 vegetation points for model training and 974 vegetation points for model assessment.</ns0:p></ns0:div> <ns0:div><ns0:head>Results.</ns0:head><ns0:p>Our results showed that the RF method was the best of the four models, as it could effectively simulate vegetation distribution in all three classification units. The DT method could only simulate vegetation distribution in units I and II, while the other two models could not simulate vegetation distribution in any of the units. Kappa coefficients indicated that the DT and RF methods had more accurate predictions for units I and II than for unit III. The three vegetation classification units were most affected by six variables: three climate variables (annual mean temperature, mean diurnal range, and annual precipitation), one geospatial variable (slope), and two spectral variables (Mid-infrared ratio of winter vegetation index and brightness index of summer vegetation index). Variables Combination 7 produced the highest simulation accuracy.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions.</ns0:head><ns0:p>We determined that the RF model was the most effective for simulating vegetation distribution in all classification units present in the Jing-Jin-Ji region. The RF model produced high accuracy vegetation distributions in classification units I and II, but relatively low accuracy in classification unit III. Four climate variables were sufficient for vegetation distribution simulation in such region.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>Vegetation is an essential component of terrestrial ecosystems and landscapes (Editorial Committee of Vegetation Map of China, Chinese Academy of <ns0:ref type='bibr'>Science, 2007)</ns0:ref>. Environmental research, resource management, and conservation planning require vegetation distribution maps <ns0:ref type='bibr' target='#b21'>(Franklin, 2010)</ns0:ref> to better understand, use, and monitor vegetation. Vegetation patterns and distributions are affected by the climate <ns0:ref type='bibr' target='#b8'>(Chen et al., 2015;</ns0:ref><ns0:ref type='bibr' target='#b62'>Zhang et al., 2018)</ns0:ref> and other disturbances, particularly those caused by changes in land use <ns0:ref type='bibr' target='#b27'>(Hansen et al., 2013;</ns0:ref><ns0:ref type='bibr' target='#b58'>Wehkamp et al., 2018)</ns0:ref>. Industrialization strongly influences the environment by greatly altering vegetation patterns, making exact mapping a significant challenge <ns0:ref type='bibr' target='#b61'>(Xie, Sha, & Yu, 2008;</ns0:ref><ns0:ref type='bibr' target='#b68'>Zhou et al., 2016)</ns0:ref>.</ns0:p><ns0:p>Field surveys, the traditional method used to map vegetation, are costly and labor-intensive <ns0:ref type='bibr' target='#b41'>(Newell & Leathwick, 2005;</ns0:ref><ns0:ref type='bibr' target='#b68'>Zhou et al., 2016)</ns0:ref>. Mapping using remote sensing data is also a popular method that has been used over the last 30 years <ns0:ref type='bibr' target='#b61'>(Xie, Sha, & Yu, 2008)</ns0:ref>. This method makes it possible to obtain a wide range of reliable data from remote sensing images, and it updates vegetation boundaries by visually interpreting images and field surveys <ns0:ref type='bibr' target='#b64'>(Zhang et al., 2008)</ns0:ref>. However, determining vegetation units and their boundaries by visual interpretation can produce inaccurate results. Researchers may get different results from the same images for the same study areas <ns0:ref type='bibr' target='#b2'>(Bie & Beckett, 1973;</ns0:ref><ns0:ref type='bibr' target='#b45'>Pfeffer, Pebesma, & Burrough, 2003)</ns0:ref>. Furthermore, field survey and remote sensing methods manually draw vegetation unit boundaries based on climate, elevation, and soil type information, which can be inaccurate in transition areas <ns0:ref type='bibr' target='#b64'>(Zhang et al., 2008)</ns0:ref>. Using simulation models in conjunction with field and remote sensing data may be an effective alternative for mapping vegetation.</ns0:p><ns0:p>Changes in the environment can affect vegetation composition, structure, function, and spatial distribution. Environmental variables have been used to simulate the global distribution of vegetation <ns0:ref type='bibr' target='#b13'>(Dilts et al., 2015;</ns0:ref><ns0:ref type='bibr' target='#b40'>Mod et al., 2016)</ns0:ref>. Simulation models are usually developed to test how environmental variables control vegetation distribution <ns0:ref type='bibr' target='#b24'>(Guisan & Zimmermann, 2000)</ns0:ref>. Modern remote sensing data and software make it more convenient than ever before to produce predictive vegetation maps <ns0:ref type='bibr' target='#b20'>(Franklin, 1995)</ns0:ref>.</ns0:p><ns0:p>Predictive vegetation mapping uses environmental variables and various models based on niche theory and gradient analysis to visualize communities in geographic space <ns0:ref type='bibr' target='#b13'>(Dilts et al., 2015</ns0:ref><ns0:ref type='bibr' target='#b35'>, Lany et al., 2019)</ns0:ref>. Other methods based on statistics and machine learning have also been used to simulate vegetation distribution. Predictive vegetation mapping includes various statistical methods such as the generalized linear model, the generalized additive model, and multivariate statistical approaches <ns0:ref type='bibr' target='#b35'>(Lany et al., 2019;</ns0:ref><ns0:ref type='bibr' target='#b48'>Prasad, Iverson, & Liaw, 2006)</ns0:ref>. Recently, machine learning modeling methods have been used to map the distribution of both vegetation communities and individual species. These methods include the support vector machine (SVM), decision tree (DT), and artificial neural network <ns0:ref type='bibr' target='#b24'>(Guisan & Zimmermann, 2000;</ns0:ref><ns0:ref type='bibr' target='#b26'>Hastie, Tibshirani, & Friedman, 2009;</ns0:ref><ns0:ref type='bibr' target='#b68'>Zhou et al., 2016)</ns0:ref>. These machine learning models have fewer limitations and can produce more reliable results than traditional vegetation modeling methods <ns0:ref type='bibr' target='#b26'>(Hastie, Tibshirani, & Friedman, 2009)</ns0:ref>. Advanced machine learning techniques can integrate spectral and spatial predictors and improve classification accuracy by retaining important information about vegetation composition and structural differences <ns0:ref type='bibr' target='#b50'>(Sesnie et al., 2010)</ns0:ref>. Machine learning models efficiently and cost-effectively produce vegetation maps without the general inaccuracies caused by visual interpretation <ns0:ref type='bibr' target='#b21'>(Franklin, 2010)</ns0:ref>.</ns0:p><ns0:p>The Jing-Jin-Ji region, also known as the Beijing-Tianjin-Hebei urban agglomeration, is the center of northern Chinese politics, culture, and economy. Because of its magnitude, it faces significant problems such as unbalanced regional development and the struggle between economic growth and limited resources. The region's larger cities, including Beijing and Tianjin, have large populations, developed economies, and abundant educational resources. However, these big cities face issues of limited natural resources and serious ecological and environmental pollution. In particular, Beijing's large population requires more limited resources such as water, land, and vegetation <ns0:ref type='bibr' target='#b55'>(Wang & Gong, 2018)</ns0:ref>. Breaking up administrative divisions may be the best method to coordinate regional development <ns0:ref type='bibr' target='#b56'>(Wang et al., 2019)</ns0:ref>. The new Xiong'an area located in Hebei province is being constructed to relocate some of Beijing's population. The development of areas like Xiong'an is affected by the surrounding natural environment. To better integrate the environmental carrying capacity and socioeconomic development of the Jing-Jin-Ji region, including the new Xiong'an area, accurate vegetation maps with temporal resolution are necessary. The most updated vegetation map of the Jing-Jin-Ji region is the Vegetation Map of the People's Republic of China (VMC), with a scale of 1:1,000,000 (Editorial Committee of Vegetation Map of China, Chinese Academy of <ns0:ref type='bibr'>Science, 2007)</ns0:ref>. Most of its data come from a field survey conducted between 1980 and 1990, meaning its temporal and spatial scales are both outdated.</ns0:p><ns0:p>In this study, we integrated geospatial, climate, and spectral data to simulate vegetation distribution through four different models across three vegetation classification units. Our primary objectives were to: (1) determine the best modeling method for vegetation affected by high socioeconomic disturbance, (2) create an improved vegetation map of the Jing-Jin-Ji region, (3) determine the predictive abilities of different models across different vegetation classification units, and (4) determine which variables enhanced the classification accuracy for vegetation mapping.</ns0:p></ns0:div> <ns0:div><ns0:head>Materials & Methods</ns0:head></ns0:div> <ns0:div><ns0:head>Study area</ns0:head><ns0:p>The Jing-Jin-Ji region is located in the northern part of the North China Plain. Its location ranges from 113°04′ to 119°53′E and 36°01′ to 42°37′N and is bordered by Taihang Mountain in the west, Yanshan Mountain in the north, and the Bohai Sea in the east. The region includes the Beijing, Tianjin, and Hebei provinces (Fig. <ns0:ref type='figure'>1</ns0:ref>). Jing-Jin-Ji has a population of approximately 110 million people and covers an area of approximately 216,000 km 2 <ns0:ref type='bibr' target='#b56'>(Wang et al., 2019)</ns0:ref>. The region is a temperate monsoon climate zone with an elevation range of -14 to 2,837 m (Fig. <ns0:ref type='figure'>1</ns0:ref>). The annual precipitation ranges from 305 to 711 mm, with increased precipitation at lower altitudes. The annual mean temperature ranges from -3 to 14°C, with colder averages at higher elevations. The amount of precipitation in the region gradually decreases moving from the southeast to the northwest, while temperature changes show the reverse pattern.</ns0:p></ns0:div> <ns0:div><ns0:head>Vegetation and training data</ns0:head><ns0:p>The VMC, completed in 2007 based on field survey data, included eight vegetation groups (I), 15 vegetation types (II), and 75 formations and subformations (III) from the Jing-Jin-Ji region. However, some of the map's vegetation unit areas are very small and difficult to distinguish. Therefore, we selected eight units I, 12 units II, and 39 units III from the study area (Table <ns0:ref type='table'>1</ns0:ref>). Cultivated vegetation are mainly distributed in low areas with an altitude range of -14 to 254 m and an annual mean temperature range of 7 to 14℃. Major cultivated plants include winter wheat and coarse grains. Scrub and grass-forb communities are mainly distributed in the north, in elevations ranging from 254 to 1,440 m.</ns0:p><ns0:p>We obtained model training and assessment data on vegetation composition from field surveys and other publications. We collected a total of 3,763 vegetation points, with 2,789 of those used for model training and 974 used for model assessment. Each unit III had at least 80 vegetation points, with at least 60 of those used for model training and 20 used for model assessment. The model training and assessment data were randomly selected for each unit III. Additionally, we increased the credibility of the model assessment by first rasterizing the vector VMC onto the same grid as the modeled data, and then assessing the data using the Kappa coefficient.</ns0:p></ns0:div> <ns0:div><ns0:head>Geospatial, climate, and spectral data</ns0:head><ns0:p>We derived geospatial variables, including elevation, slope, and aspect, from the 30 m resolution Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) <ns0:ref type='bibr' target='#b66'>(Zhao et al., 2018)</ns0:ref>. We then resampled these data to a 500×500 m grid cell size using the cubic technique in ArcGIS 10.3 <ns0:ref type='bibr' target='#b60'>(Wu et al., 2019)</ns0:ref>.</ns0:p><ns0:p>We downloaded the climate data, including 19 bioclimatic variables, at 1 km resolution from WorldClim <ns0:ref type='bibr' target='#b19'>(Fick & Hijmans, 2017)</ns0:ref> at http://worldclim.org/. These climate data were also resampled to a 500×500 m grid cell size using the cubic technique in ArcGIS 10.3 <ns0:ref type='bibr' target='#b60'>(Wu et al., 2019)</ns0:ref>. Climatic variables are important for plant ecophysiology <ns0:ref type='bibr' target='#b40'>(Mod et al., 2016)</ns0:ref> and are commonly used as bioclimatic limits in vegetation models <ns0:ref type='bibr' target='#b52'>(Sitch et al., 2003)</ns0:ref>.</ns0:p><ns0:p>We acquired the MYD09A1500M product data (sinusoidal projection, path 4 and row 26, path 4 and row 27, path 5 and row 26, path 5 and row 27) from summer (July 20, 2013) and winter <ns0:ref type='bibr'>(January 17, 2013)</ns0:ref> as Modis images from the Geospatial Data Cloud at http://www.gscloud.cn/. Our image pre-processing included image subset mosaicking and clipping in ENVI 5.2 <ns0:ref type='bibr' target='#b11'>(Deng, 2010)</ns0:ref>. We obtained the land surface albedo in bands 1-7 directly from the MYD09A1500M product, and calculated the indices' effectiveness at reflecting vegetation information <ns0:ref type='bibr' target='#b47'>(Price, Guo, & Stiles, 2002;</ns0:ref><ns0:ref type='bibr' target='#b68'>Zhou et al., 2016)</ns0:ref>.</ns0:p><ns0:p>Since vegetation indices can provide information on both vegetation and environment <ns0:ref type='bibr' target='#b0'>(Bannari, Morin, & Bonn, 1995)</ns0:ref>, these indices are more sensitive than single spectral bands at detecting green vegetation <ns0:ref type='bibr' target='#b0'>(Bannari, Morin, & Bonn, 1995;</ns0:ref><ns0:ref type='bibr' target='#b9'>Cohen & Goward, 2004)</ns0:ref>. Therefore, vegetation indices can be used for image interpretation, vegetation discrimination and prediction, and land use change detection <ns0:ref type='bibr' target='#b0'>(Bannari, Morin, & Bonn, 1995;</ns0:ref><ns0:ref type='bibr' target='#b9'>Cohen & Goward, 2004;</ns0:ref><ns0:ref type='bibr' target='#b68'>Zhou et al., 2016)</ns0:ref>. We tested the vegetation discrimination of 14 vegetation indices (Table <ns0:ref type='table' target='#tab_1'>2</ns0:ref>).</ns0:p><ns0:p>To determine the distribution predictive ability of different variables, we grouped the variables into different combinations based on the results of the Pearson correlation. We only used less correlated variables (R <\|0.7\|) <ns0:ref type='bibr' target='#b7'>(Chala et al., 2017)</ns0:ref> in Combinations 1-9 (Table <ns0:ref type='table'>3</ns0:ref>), then used variable combinations as input predictor variables to simulate vegetation distribution. Combination 1 included the less correlated variables of the summer land surface albedos from bands 1 to 7. Combination 2 included the less correlated variables of the winter land surface albedos from bands 1 to 7. Combination 3 included the less correlated variables in Combinations <ns0:ref type='table' target='#tab_2'>1 and 2</ns0:ref> <ns0:ref type='table'>3</ns0:ref>). The SVM and maximum likelihood classification (MLC) methods only output the simulation results of variable Combinations 1 to 6, likely due to the training samples' weak separability <ns0:ref type='bibr' target='#b11'>(Deng, 2010)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Vegetation distribution models</ns0:head><ns0:p>We used DT, RF, MLC, and SVM vegetation distribution models in this study. The DT model is a divisive, monothetic, and supervised classifier often used for species distribution modeling and related applications <ns0:ref type='bibr' target='#b21'>(Franklin, 2010)</ns0:ref>. It is computationally fast and easy to understand and implement. It uses classification or regression algorithms to generate classification rules, and then visualizes those rules into simple tree graphics <ns0:ref type='bibr' target='#b26'>(Hastie, Tibshirani, & Friedman, 2009;</ns0:ref><ns0:ref type='bibr' target='#b68'>Zhou et al., 2016)</ns0:ref>. The DT model calculates the most significant variables contributing to the model <ns0:ref type='bibr' target='#b11'>(Deng, 2010)</ns0:ref>. We used a DT with five layers, 40 samples in the smallest parent node, and 10 samples in the smallest child node.</ns0:p><ns0:p>The RF model is an ensemble method that has been applied in risk assessment and species distribution modeling studies <ns0:ref type='bibr' target='#b10'>(Cutler et al., 2007;</ns0:ref><ns0:ref type='bibr' target='#b63'>Zhang & Dong, 2017)</ns0:ref>. The RF model creates and combines different DTs to produce considerably more accurate classifications that are unaffected by noise or overtraining <ns0:ref type='bibr' target='#b3'>(Burai et al., 2015;</ns0:ref><ns0:ref type='bibr' target='#b10'>Cutler et al., 2007;</ns0:ref><ns0:ref type='bibr' target='#b22'>Gislason, Benediktsson, & Sveinsson, 2006)</ns0:ref>. The RF model also calculates the most significant variables that contribute to the model <ns0:ref type='bibr' target='#b10'>(Cutler et al., 2007)</ns0:ref>. Running an RF model requires defined parameters, including tree number, number of randomly selected features, and node impurity function. We generated the RF model using the default settings in EnMAPBox with 100 trees. The number of randomly selected features was equal to the square root of the number of all features, and we used a Gini coefficient for the node impurity function <ns0:ref type='bibr' target='#b28'>(Jakimow et al., 2014;</ns0:ref><ns0:ref type='bibr' target='#b38'>Ma, Gao, & Gu, 2019;</ns0:ref><ns0:ref type='bibr' target='#b54'>van der Linden et al., 2015;</ns0:ref><ns0:ref type='bibr' target='#b68'>Zhou et al., 2016)</ns0:ref>.</ns0:p><ns0:p>The MLC model is one of the most commonly used supervised image classification methods. MLC's classification rules use the statistics of the Gaussian probability density function to assign each pixel to the class with the highest probability. Although the MLC method usually generates similar or more accurate classifications than other methods, it is not applicable when there are fewer training samples than input predictors <ns0:ref type='bibr' target='#b3'>(Burai et al., 2015;</ns0:ref><ns0:ref type='bibr' target='#b68'>Zhou et al., 2016)</ns0:ref>.</ns0:p><ns0:p>The SVM model is a supervised machine learning model used for classification and regression. It is a complex and widely used method that can output more accurate predictions <ns0:ref type='bibr' target='#b3'>(Burai et al., 2015)</ns0:ref> than other methods. The SVM model searches for an optimal plane in a multidimensional space to divide all sample elements into two categories, making the distance between the closest points in the two classes as large as possible <ns0:ref type='bibr' target='#b32'>(Kabacoff, 2016)</ns0:ref>. Running an SVM model requires a defined kernel parameter g and regularization parameter c. In this study, we applied the default settings in EnMAP-Box to the SVM model, where the parameter g was 0.01 to 1,000, and the parameter c was 0.1 to 1,000. Parameters g and c were tested using a grid search with internal performance estimation, and we used those with the best performance for data training <ns0:ref type='bibr' target='#b37'>(Lin et al., 2014;</ns0:ref><ns0:ref type='bibr' target='#b54'>van der Linden et al., 2014;</ns0:ref><ns0:ref type='bibr' target='#b54'>van der Linden et al., 2015)</ns0:ref>.</ns0:p><ns0:p>We generated the predicted vegetation maps of the three classification units using the DT, RF, MLC, and SVM methods with a resolution of 500 m. We selected all 11 variable combinations as the input variables for each method. The DT and RF method results indicated which variables were most important for vegetation discrimination.</ns0:p></ns0:div> <ns0:div><ns0:head>Model assessment</ns0:head><ns0:p>We used the VMC and a total of 974 vegetation points to assess the overall accuracy and Kappa coefficient of every predicted vegetation map. Kappa coefficient values ranging from 0.4 to 0.55 indicated moderate agreement, from 0.56 to 0.8 indicated substantial agreement, and from 0.81 to 1 indicated almost perfect agreement <ns0:ref type='bibr' target='#b33'>(Landis & Koch, 1977;</ns0:ref><ns0:ref type='bibr' target='#b59'>Weng & Zhou, 2006;</ns0:ref><ns0:ref type='bibr' target='#b68'>Zhou et al., 2016)</ns0:ref>. When the Kappa coefficient value was greater than 0.4, the assessed predicted map was considered acceptable.</ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head></ns0:div> <ns0:div><ns0:head>Unit I modeling and assessment</ns0:head><ns0:p>The RF model's results were better than the results of the DT, MLC, and SVM models (Table <ns0:ref type='table' target='#tab_3'>4</ns0:ref>). The RF model had a Kappa coefficient larger than 0.4 when using variable Combinations 6 to 11 assessed by field point data, with an overall accuracy of 50% to 72%. The RF model had a Kappa coefficient larger than 0.56 when using variable Combinations 7 to 11 assessed by field data, with an overall accuracy of 68% to 72%. The RF model with the highest Kappa coefficient of 0.66 and the highest overall accuracy of 72% used variable Combination 7. The DT model had a Kappa coefficient larger than 0.4 when using variable Combinations 7 to 11 assessed by field point data, with an overall accuracy of 54% to 56%. The DT model had no Kappa coefficient larger than 0.56 when using all variable combinations. After VMC assessment, we found the highest Kappa coefficient was 0.38 with an overall accuracy of 57% in the RF model using variable Combinations 9 to 11 (Table <ns0:ref type='table' target='#tab_3'>4</ns0:ref>; Fig. <ns0:ref type='figure'>2</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>Unit II modeling and assessment</ns0:head><ns0:p>The RF model results were better than the results of the other three models. The RF model using variable Combinations 7 to 11 had a Kappa coefficient larger than 0.4, with overall accuracies of 66%-70% and 54%-55% for field point data and VMC assessments, respectively. The RF model using Combinations 7 to 11 had a Kappa coefficient larger than 0.56 and an overall accuracy of 66%-70% when assessed by field point data. The RF model with the highest Kappa coefficient of 0.65 and the highest overall accuracy of 70% used variable Combination 7. The DT model using variable Combinations 7 to 11 had a Kappa coefficient larger than 0.4, with overall accuracies of 53%-55% and 65%-72% for field point data and VMC assessments, respectively. The DT model with the highest Kappa coefficient of 0.54 and overall accuracy of 72% used variable Combination 7. The DT model had a larger Kappa coefficient and greater overall accuracy when assessed by VMC rather than the RF model (Table <ns0:ref type='table' target='#tab_5'>5</ns0:ref>; Fig. <ns0:ref type='figure'>3</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>Unit III modeling and assessment</ns0:head><ns0:p>Only the RF model could simulate vegetation distribution in unit III. The RF model using variable Combinations 7 to 11 had a Kappa coefficient larger than 0.4 and an overall accuracy of 55%-58% assessed by field point data. The RF model using variable Combination 7 had the highest Kappa coefficient of 0.57 (the only model with a Kappa coefficient larger than 0.56) and the highest overall accuracy of 58% assessed by field point data. The Kappa coefficients in all models were less than 0.4 when assessed by the VMC (Table <ns0:ref type='table' target='#tab_7'>6</ns0:ref>; Fig. <ns0:ref type='figure'>4</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>Important variables</ns0:head><ns0:p>For the RF model, eight of the top 10 most important variables were the same across the different vegetation units: three climate variables (annual mean temperature, mean diurnal range, and annual precipitation), one geospatial variable (slope), and four spectral variables (Mid-infrared ratio and NDVI of winter vegetation index, brightness index and NDVI of summer vegetation index). For the DT model, nine of the top 10 most important variables were the same across the different vegetation units: four climate variables (annual mean temperature, mean diurnal range, precipitation of the driest month, and annual precipitation), one geospatial variable (slope), and 4 spectral variables (Mid-infrared ratio of winter vegetation index, brightness index of summer vegetation index, summer surface albedo of band 1, winter surface albedo of band 6) (Table <ns0:ref type='table' target='#tab_9'>7</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>Discussion</ns0:head></ns0:div> <ns0:div><ns0:head>Vegetation classification units</ns0:head><ns0:p>PeerJ reviewing PDF \| (2020:01:44790:1:0:REVIEW 8 Jun 2020)</ns0:p><ns0:p>Manuscript to be reviewed Vegetation classification is an important and complex system with multiple levels. Higher level classification methods not only accurately classify vegetation, but they can also describe ecosystem diversity, even during global changes <ns0:ref type='bibr' target='#b18'>(Faber-Langendoen et al., 2014)</ns0:ref>. Plants in different vegetation classification units have different spectral characteristics and climatic conditions that are the basis for vegetation distribution simulation. Thus, models using the same variables to simulate the vegetation distribution of different classification units may produce different classification accuracies <ns0:ref type='bibr' target='#b14'>(Dobrowski et al., 2008;</ns0:ref><ns0:ref type='bibr' target='#b48'>Prasad, Iverson, & Liaw, 2006)</ns0:ref>. Map accuracy has been found to be a function of which classification system and categories are used <ns0:ref type='bibr' target='#b39'>(Muchoney et al., 2000)</ns0:ref>.</ns0:p><ns0:p>Previous studies have explored vegetation distribution simulation using different vegetation classification systems. Plant functional types (PFTs), defined as plant sets sharing similar perturbation response effects on dominant ecosystem processes, have been used to simulate vegetation distribution, as seen in the Biome and Box system models <ns0:ref type='bibr' target='#b4'>(Box, 1981;</ns0:ref><ns0:ref type='bibr' target='#b5'>Box, 1996;</ns0:ref><ns0:ref type='bibr' target='#b15'>Dormann & Woodin, 2002)</ns0:ref> with positive simulation results <ns0:ref type='bibr' target='#b4'>(Box, 1981;</ns0:ref><ns0:ref type='bibr' target='#b53'>Song, Zhou & Ouyang, 2005;</ns0:ref><ns0:ref type='bibr' target='#b59'>Weng & Zhou, 2006)</ns0:ref>. The Mapped Atmosphere-Plant-Soil System (MAPSS) model was also used to simulate vegetation distribution using vegetation life forms, leaf area index, leaf morphology, and leaf longevity <ns0:ref type='bibr' target='#b65'>(Zhao et al., 2002)</ns0:ref>. Other researchers studied potential vegetation distribution using the Holdridge life zone model, with positive vegetation pattern results <ns0:ref type='bibr' target='#b67'>(Zheng et al., 2006)</ns0:ref>. When the IGBP classification system was applied to simulate vegetation distribution at a regional scale, the map estimate accuracy was upwards of 80% <ns0:ref type='bibr' target='#b39'>(Muchoney et al., 2000)</ns0:ref>. In this study, we used machine learning models and a hierarchical classification system from the VMC to determine the best modeling method for vegetation affected by high socioeconomic disturbance at various classification levels. In the VMC, unit I was the highest classification level, mainly based upon community appearance; unit II was the second highest level, mainly based upon community and climate appearance; and unit III was the medium classification level, based upon the dominant species. The accuracy of the vegetation distribution simulations in units I and II was similar to each other and higher than unit III's simulation (Tables <ns0:ref type='table' target='#tab_8'>4-6</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>Different model performances</ns0:head><ns0:p>We were interested in vegetation distribution modeling's ability to forecast and respond to environmental changes and vegetation pattern management at local to global scales. Vegetation distribution predictions can help explain the relationship between plants and their abiotic and biotic environments <ns0:ref type='bibr' target='#b21'>(Franklin, 2010)</ns0:ref>. To benefit from ecosystem service functions, people can design vegetation distributions according to distribution and abundance patterns and trends <ns0:ref type='bibr' target='#b26'>(Hastie, Tibshirani, & Friedman, 2009)</ns0:ref>. Vegetation classification has become a widely used ecological method due to a number of new statistical and machine learning methods used alongside mapped biological and environmental data to model vegetation distributions over large spatial scales at higher resolutions <ns0:ref type='bibr' target='#b10'>(Cutler et al., 2007)</ns0:ref>. Different image classification methods are rarely used together in the same classification research, especially when combined with environmental variables <ns0:ref type='bibr' target='#b36'>(Li et al., 2014)</ns0:ref>.</ns0:p><ns0:p>In this study, the RF model performed better than the DT, SVM, and MLC models across the three classification levels. This finding was consistent with the results of other studies that found that the RF method modeled vegetation distribution better than other methods <ns0:ref type='bibr' target='#b48'>(Prasad, Iverson, & Liaw, 2006)</ns0:ref>. The DT model divided the data into homogenous subgroups according to the range of predictor variable values. The DT model was generally able to handle a large number of independent variables and could build a tree model faster than the other methods. However, the DT model was somewhat unstable for vegetation distribution modeling and had lower classification accuracy <ns0:ref type='bibr' target='#b68'>(Zhou et al., 2016)</ns0:ref>. The RF model generated a large number of independent trees through data subsets and developed a split in every tree model using a random subset of predictor variables. Therefore, we concluded that the RF model was generally better than the DT model. The SVM model was developed from statistical learning methods and discriminated class samples by locating potentially nonlinear or multiple linear boundaries between individual training points <ns0:ref type='bibr' target='#b3'>(Burai et al., 2015)</ns0:ref>. The aim of the MLC model was to maximize the overall probability that a pixel is correctly assigned to a class. However, the MLC model requires a large number of training samples that limits its application <ns0:ref type='bibr' target='#b50'>(Sesnie et al., 2010)</ns0:ref>. Previous research has shown that classification accuracies when using the SVM classifier were higher than the MLC model <ns0:ref type='bibr' target='#b43'>(Pal & Mather, 2005;</ns0:ref><ns0:ref type='bibr' target='#b6'>Boyd, Sanchez-Hernandez, & Foody, 2006;</ns0:ref><ns0:ref type='bibr' target='#b49'>Sanchez-Hernandez, Boyd, & Foody, 2007;</ns0:ref><ns0:ref type='bibr' target='#b50'>Sesnie et al., 2010)</ns0:ref>. Because the model had fewer requirements, the DT method provided significantly more accurate classifications than those of the MLC model <ns0:ref type='bibr' target='#b6'>(Boyd, Sanchez-Hernandez, & Foody, 2006)</ns0:ref>. Other studies found that the RF and SVM models were similarly accurate (65.3% and 66.6%, respectively) <ns0:ref type='bibr' target='#b50'>(Sesnie et al., 2010)</ns0:ref>, and that the RF, MLC, DT, and SVM models performed similarly and reasonably well when simulating land use classification <ns0:ref type='bibr' target='#b36'>(Li et al., 2014)</ns0:ref>. In addition to the methods mentioned above, an artificial neural network implemented at a regional scale produced classification accuracies of 60%-80% <ns0:ref type='bibr' target='#b39'>(Muchoney et al., 2000;</ns0:ref><ns0:ref type='bibr' target='#b25'>Haslem et al., 2010)</ns0:ref>. In the Arctic, this method provided the most accurate vegetation mapping <ns0:ref type='bibr' target='#b34'>(Langford et al., 2019)</ns0:ref>. The reasons for the similarly positive results of these models may be due to the relatively large differences between classification objects, and their use of sufficiently representative training samples and appropriate input variables. In our study, only the SVM and MLC models' output simulated the results of variable Combinations 1 to 6. This may be due to the poor separability of the training samples, as the models could not recognize the training points or their vegetation categories <ns0:ref type='bibr' target='#b29'>(Jarnevich et al., 2015)</ns0:ref>. The Jing-Jin-Ji region has many types of vegetation with very small distribution areas, so the selected training points may have been insufficient. Future training points for these vegetation types should be selected using field surveys, and more suitable models for modeling global vegetation distribution should be developed and tested <ns0:ref type='bibr' target='#b30'>(Jiang et al., 2012)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Important variables</ns0:head><ns0:p>PeerJ reviewing PDF \| (2020:01:44790:1:0:REVIEW 8 Jun 2020)</ns0:p></ns0:div> <ns0:div><ns0:head>Manuscript to be reviewed</ns0:head><ns0:p>Variable selection is directly related to the vegetation distribution model's ability to capture important environmental factors <ns0:ref type='bibr' target='#b40'>(Mod et al., 2016)</ns0:ref>. Models predict the important variables that drive the distribution of vegetation <ns0:ref type='bibr' target='#b48'>(Prasad, Iverson, & Liaw, 2006)</ns0:ref>. Vegetation distribution is predominantly driven by temperature, precipitation, and topographical variables <ns0:ref type='bibr' target='#b20'>(Franklin, 1995;</ns0:ref><ns0:ref type='bibr' target='#b40'>Mod et al., 2016;</ns0:ref><ns0:ref type='bibr' target='#b48'>Prasad, Iverson, & Liaw, 2006)</ns0:ref>, specifically those related to physiological tolerance, site energy, and moisture balance <ns0:ref type='bibr' target='#b20'>(Franklin, 1995)</ns0:ref>. In addition to environmental variables, some spectral variables are used as input variables. However, the overuse of spectral variables can actually decrease discrimination accuracy, meaning that only spectral variables reflecting vegetation information should be selected, such as those related to the visible spectrum, infrared spectrum, and vegetation indices <ns0:ref type='bibr' target='#b47'>(Price, Guo, & Stiles, 2002</ns0:ref><ns0:ref type='bibr' target='#b68'>, Zhou et al., 2016)</ns0:ref>. Different variables respond to different information. Spectral variables directly reflect land surface object information, while geospatial and climatic variables reveal information about the vegetative environment.</ns0:p><ns0:p>Terrain, an important variable in vegetation distribution models, has long been used to improve map accuracy, especially for regions with large elevation differences <ns0:ref type='bibr' target='#b14'>(Dobrowski et al. 2008;</ns0:ref><ns0:ref type='bibr' target='#b42'>Oke & Thompson, 2015)</ns0:ref>. <ns0:ref type='bibr' target='#b50'>Sesnie et al. (2010)</ns0:ref> found that adding elevation as a predictive variable dramatically improved the accuracies of the SVM and RF models >80% for most forest types. Slopes with similar elevations but different aspects have very different soil and vegetation temperatures <ns0:ref type='bibr' target='#b23'>(Gunton, Polce, & Kunin, 2015;</ns0:ref><ns0:ref type='bibr' target='#b40'>Mod et al., 2016)</ns0:ref>. <ns0:ref type='bibr' target='#b14'>Dobrowski et al. (2008)</ns0:ref> highlighted the importance of slope and aspect when mapping vegetation communities in the Sierra Nevada. Slope was also an important variable in this study (Table <ns0:ref type='table' target='#tab_9'>7</ns0:ref>) since different types of vegetation require different precipitation and temperature levels and have different tolerances to extreme heat and cold. The significance of these climate variables (annual mean temperature, temperature range, and annual precipitation) has been validated in other studies <ns0:ref type='bibr' target='#b48'>(Prasad, Iverson & Liaw, 2006;</ns0:ref><ns0:ref type='bibr' target='#b51'>Sesnie et al., 2008)</ns0:ref>. We looked at two surface albedo indices (the summer surface albedo of band 1 and the winter surface albedo of band 6). <ns0:ref type='bibr' target='#b50'>Sesnie et al. (2010)</ns0:ref> combined elevation and spectral band data to increase the classification accuracy to a satisfactory level for most forest types. De <ns0:ref type='bibr' target='#b11'>Colstoun et al. (2003)</ns0:ref> obtained high accuracies (80%) when classifying coniferous, temperate broad-leaf, and mixed forest types using Landsat ETM+ bands. Other studies have used different vegetation index variables <ns0:ref type='bibr' target='#b47'>(Price, Guo & Stiles, 2002;</ns0:ref><ns0:ref type='bibr' target='#b68'>Zhou et al., 2016)</ns0:ref> specific to their study areas and data.</ns0:p><ns0:p>The input variables used in our vegetation distribution model are not exhaustive. Ecophysiologically meaningful predictors such as soil moisture, pH, and nutrients, should be considered. Other factors, such as actual light, disturbance, biotic interactions, land use, and bioclimatic information could also be incorporated into vegetation distribution models <ns0:ref type='bibr' target='#b14'>(Dobrowski et al., 2008;</ns0:ref><ns0:ref type='bibr' target='#b40'>Mod et al., 2016;</ns0:ref><ns0:ref type='bibr' target='#b48'>Prasad, Iverson, & Liaw, 2006;</ns0:ref><ns0:ref type='bibr' target='#b50'>Sesnie et al., 2010)</ns0:ref>. We suggest building more ecophysiologically sound vegetation distribution models that require a collaborative effort across the ecological, geographical, and environmental sciences <ns0:ref type='bibr' target='#b40'>(Mod et al., 2016)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Other factors affecting classification accuracy</ns0:head><ns0:p>In addition to classification units, models, and input variables, classification accuracy is affected by other factors, including algorithm error and image data <ns0:ref type='bibr' target='#b36'>(Li et al., 2014)</ns0:ref>. We must acknowledge the existence of errors in random sample selection, modeling, and data preprocessing algorithms. Remote sensing data sources, as well as the date and processing of selected images, vary, resulting in different simulated values and accuracies <ns0:ref type='bibr' target='#b47'>(Price, Guo, & Stiles, 2002)</ns0:ref>. Remote sensing images with high spectral and spatial resolutions provide rich spectral and ground information, moderately improving the predictive ability of the vegetation distribution model <ns0:ref type='bibr' target='#b44'>(Peng et al., 2002)</ns0:ref>. However, the use of high spectral and spatial resolution images creates a greater demand for data access, larger computer storage capacities, and faster data processors <ns0:ref type='bibr' target='#b47'>(Price, Guo, & Stiles, 2002)</ns0:ref>, which is why we did not use high spectral and spatial resolution images in this study. Moreover, some cultivated vegetation and shelter forests in the Jing-Jin-Ji region are greatly affected by human disturbance, which affects their water-heat conditions and soil nutrition. Urbanization reduces vegetation, transforming some areas into industrial, commercial, and residential land. This has led to the direct or indirect pollution of the water, soil, and air, and the reduced predictive ability of vegetation distribution models. The VMC we used for model assessment was published in 2007, and no updated study has been published over the past 10 years. The current state of the Jing-Jin-Ji region's vegetation no longer coincides with the VMC's assessment.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions</ns0:head><ns0:p>Our main objective was to determine the best simulation method for vegetation affected by high socioeconomic disturbance. The RF model was the most capable at simulating vegetation distribution across all three units. The DT model could simulate the vegetation distribution in units I and II. The SVM and MLC models could not simulate the distribution in any of the three units. Based on the Kappa coefficient, the RF model was generally better than the DT model and the most suitable model for simulating vegetation distribution in the Jing-Jin-Ji region. The most important variables affecting vegetation classification accuracy were three climate variables (annual mean temperature, mean diurnal range, and annual precipitation), one geospatial variable (slope), and two spectral variables (Mid-infrared ratio of winter vegetation index and brightness index of summer vegetation index). We recommend using the RF model to produce or improve the vegetation maps in areas of high human disturbance. Manuscript to be reviewed The vegetation indices Manuscript to be reviewed Manuscript to be reviewed Manuscript to be reviewed Top ten most important variables of models in the different vegetation classification units.</ns0:p></ns0:div> <ns0:div><ns0:head n='1'>Table 1: Classification units of the vegetation of China</ns0:head><ns0:p>The abbreviations of indices were shown in Table <ns0:ref type='table' target='#tab_1'>2</ns0:ref>.</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:01:44790:1:0:REVIEW 8 Jun 2020)</ns0:p><ns0:p>Manuscript to be reviewed </ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>. Combination 4 included the less correlated variables of the summer vegetation indices. Combination 5 included the less correlated variables of the winter vegetation indices. Combination 6 included the less correlated variables in Combinations 4 and 5. Combination 7 included the less correlated variables from the 19 bioclimatic variables. Combination 8 included the less correlated variables from the 19 bioclimatic variables and three geospatial variables. Combination 9 included the less correlated variables in Combinations 3, 6, and 8. Combinations 10 and 11 represented the top 10 most important variables of the DT and RF methods, with Combination 9 in vegetation unit I, respectively (Table</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='37,42.52,275.62,525.00,371.25' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='38,42.52,275.62,525.00,371.25' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='39,42.52,275.62,525.00,371.25' type='bitmap' /></ns0:figure> <ns0:figure type='table' xml:id='tab_1'><ns0:head>Table 2 (on next page)</ns0:head><ns0:label>2</ns0:label><ns0:figDesc /><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_2'><ns0:head>Table 2 : The vegetation indices</ns0:head><ns0:label>2</ns0:label><ns0:figDesc /><ns0:table><ns0:row><ns0:cell>Indices</ns0:cell><ns0:cell cols='2'>Abbreviation Formula</ns0:cell></ns0:row><ns0:row><ns0:cell>Ratio vegetation index</ns0:cell><ns0:cell>RVI</ns0:cell><ns0:cell>NIR/Red</ns0:cell></ns0:row><ns0:row><ns0:cell>Brightness index</ns0:cell><ns0:cell>BI</ns0:cell><ns0:cell>0.2909Blue + 0.2493Green + 0.4806Red +</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>0.5568NIR + 0.4438SWIR1 + 0.1706SWIR2</ns0:cell></ns0:row><ns0:row><ns0:cell>Green vegetation index</ns0:cell><ns0:cell>GI</ns0:cell><ns0:cell>-0.2728Blue -0.2174Green-0.5508Red +</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>0.7221NIR + 0.0733SWIR1 -0.1648SWIR2</ns0:cell></ns0:row><ns0:row><ns0:cell>Wetness index</ns0:cell><ns0:cell>WI</ns0:cell><ns0:cell>0.1446Blue + 0.1761Green + 0.3322Red +</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>0.3396NIR -0.6210SWIR1 -0.4186SWIR2</ns0:cell></ns0:row><ns0:row><ns0:cell>Differenced vegetation index</ns0:cell><ns0:cell>DVI</ns0:cell><ns0:cell>NIR -Red</ns0:cell></ns0:row><ns0:row><ns0:cell>Green ratio</ns0:cell><ns0:cell>GR</ns0:cell><ns0:cell>NIR/Green</ns0:cell></ns0:row><ns0:row><ns0:cell>Mid-infrared ratio</ns0:cell><ns0:cell>MR</ns0:cell><ns0:cell>NIR/SWIR1</ns0:cell></ns0:row><ns0:row><ns0:cell>Soil-adjusted vegetation index</ns0:cell><ns0:cell>SAVI</ns0:cell><ns0:cell>(1.5(NIR -Red))/((NIR + Red + 0.5))</ns0:cell></ns0:row><ns0:row><ns0:cell>Optimization of soil-adjusted</ns0:cell><ns0:cell>OSAVI</ns0:cell><ns0:cell>(1.16(NIR -Red))/((NIR + Red + 0.16))</ns0:cell></ns0:row><ns0:row><ns0:cell>vegetation index</ns0:cell><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>Atmospherically resistant vegetation</ns0:cell><ns0:cell>ARVI</ns0:cell><ns0:cell>(NIR -(2Red -Blue))/(NIR + (2Red -Blue))</ns0:cell></ns0:row><ns0:row><ns0:cell>index</ns0:cell><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell cols='2'>Normalized difference vegetation index NDVI</ns0:cell><ns0:cell>(NIR -Red)/(NIR + Red)</ns0:cell></ns0:row><ns0:row><ns0:cell>Enhanced vegetation index</ns0:cell><ns0:cell>EVI</ns0:cell><ns0:cell>2.5[(NIR -Red)/(NIR + 6Red -7.5Blue + 1)]</ns0:cell></ns0:row><ns0:row><ns0:cell>Normalized difference tillage index</ns0:cell><ns0:cell>NDTI</ns0:cell><ns0:cell>(SWIR1-SWIR2)/(SWIR1 + SWIR2)</ns0:cell></ns0:row><ns0:row><ns0:cell>Normalized difference senescent</ns0:cell><ns0:cell>NDSVI</ns0:cell><ns0:cell>(SWIR1-Red)/(SWIR1 + Red)</ns0:cell></ns0:row><ns0:row><ns0:cell>vegetation index</ns0:cell><ns0:cell /><ns0:cell /></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_3'><ns0:head>Table 4 (on next page)</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Model assessment of vegetation groups by field point data and VMC.</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Variable combinations were shown in Table 3. VMC, the Vegetation Map of the People's</ns0:cell></ns0:row><ns0:row><ns0:cell>Republic of China. , the kappa coefficient lager than 0.56; , the kappa coefficient larger</ns0:cell></ns0:row><ns0:row><ns0:cell>than 0.4 and less than 0.56. OA, Overall accuracy; KC, Kappa coefficient.</ns0:cell></ns0:row></ns0:table><ns0:note>PeerJ reviewing PDF \| (2020:01:44790:1:0:REVIEW 8 Jun 2020)Manuscript to be reviewed 1</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_4'><ns0:head>Table 4 : Model assessment of vegetation groups by field point data and VMC.</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Variable combinations were shown in Table3. VMC, the Vegetation Map of the People's Republic of China. *, the kappa coefficient 3 lager than 0.56; , the kappa coefficient larger than 0.4 and less than 0.56. OA, Overall accuracy; KC, Kappa coefficient.</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Variable</ns0:cell><ns0:cell cols='2'>Decision tree</ns0:cell><ns0:cell /><ns0:cell cols='2'>Random forest</ns0:cell><ns0:cell cols='2'>Support vector machine</ns0:cell><ns0:cell cols='2'>Maximum likelihood classification</ns0:cell></ns0:row><ns0:row><ns0:cell>combinations</ns0:cell><ns0:cell>Point data</ns0:cell><ns0:cell>VMC</ns0:cell><ns0:cell cols='2'>Point data</ns0:cell><ns0:cell>VMC</ns0:cell><ns0:cell>Point data</ns0:cell><ns0:cell>VMC</ns0:cell><ns0:cell>Point data</ns0:cell><ns0:cell>VMC</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='3'>OA KC OA KC OA</ns0:cell><ns0:cell>KC</ns0:cell><ns0:cell cols='5'>OA KC OA KC OA KC OA KC OA KC</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='9'>34% 0.18 55% 0.22 37% 0.24 32% 0.09 36% 0.21 53% 0.21 23% 0.08 11% 0.02</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='9'>38% 0.20 52% 0.23 39% 0.27 37% 0.13 35% 0.20 55% 0.24 18% 0.07 9% 0.03</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='9'>45% 0.31 54% 0.26 47% 0.36 45% 0.21 41% 0.27 54% 0.27 24% 0.12 15% 0.05</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='9'>32% 0.16 46% 0.16 42% 0.30 42% 0.17 37% 0.22 57% 0.26 11% 0.04 3% 0.01</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='9'>31% 0.11 59% 0.14 44% 0.32 44% 0.19 36% 0.22 51% 0.22 9% 0.04 4% 0.02</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='9'>41% 0.26 44% 0.18 50% 0.40* 52% 0.27 42% 0.29 54% 0.27 13% 0.08 4% 0.03</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='5'>54% 0.45* 57% 0.34 72% 0.66** 55% 0.35</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='5'>55% 0.46* 56% 0.35 69% 0.63** 56% 0.37</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='5'>55% 0.46* 53% 0.34 68% 0.61** 57% 0.38</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>10</ns0:cell><ns0:cell cols='5'>55% 0.46* 53% 0.33 69% 0.63** 57% 0.38</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>11</ns0:cell><ns0:cell cols='5'>56% 0.46* 56% 0.36 68% 0.62** 57% 0.38</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>4</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>5</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>6</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row></ns0:table><ns0:note>2 PeerJ reviewing PDF \| (2020:01:44790:1:0:REVIEW 8 Jun 2020)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_5'><ns0:head>Table 5 (on next page)</ns0:head><ns0:label>5</ns0:label><ns0:figDesc>Model assessment of vegetation types by field point data and VMC.</ns0:figDesc><ns0:table /><ns0:note>The Abbreviations were same with Table4.1</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_6'><ns0:head>Table 5 : Model assessment of vegetation types by field point data and VMC.</ns0:head><ns0:label>5</ns0:label><ns0:figDesc /><ns0:table><ns0:row><ns0:cell>Variable</ns0:cell><ns0:cell /><ns0:cell cols='2'>Decision tree</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell cols='2'>Random forest</ns0:cell><ns0:cell /><ns0:cell cols='4'>Support vector machine</ns0:cell><ns0:cell cols='4'>Maximum likelihood classification</ns0:cell></ns0:row><ns0:row><ns0:cell>combinations</ns0:cell><ns0:cell cols='2'>Point data</ns0:cell><ns0:cell cols='2'>VMC</ns0:cell><ns0:cell cols='2'>Point data</ns0:cell><ns0:cell cols='2'>VMC</ns0:cell><ns0:cell cols='2'>Point data</ns0:cell><ns0:cell cols='2'>VMC</ns0:cell><ns0:cell cols='2'>Point data</ns0:cell><ns0:cell cols='2'>VMC</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>OA</ns0:cell><ns0:cell>KC</ns0:cell><ns0:cell>OA</ns0:cell><ns0:cell>KC</ns0:cell><ns0:cell>OA</ns0:cell><ns0:cell>KC</ns0:cell><ns0:cell>OA</ns0:cell><ns0:cell>KC</ns0:cell><ns0:cell>OA</ns0:cell><ns0:cell>KC</ns0:cell><ns0:cell>OA</ns0:cell><ns0:cell>KC</ns0:cell><ns0:cell>OA</ns0:cell><ns0:cell>KC</ns0:cell><ns0:cell>OA</ns0:cell><ns0:cell>KC</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='12'>42% 0.24 63% 0.33 32% 0.22 23% 0.09 32% 0.18 40% 0.18</ns0:cell><ns0:cell>6%</ns0:cell><ns0:cell>0.02</ns0:cell><ns0:cell>7%</ns0:cell><ns0:cell>0.00</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='12'>44% 0.27 58% 0.31 34% 0.23 30% 0.14 31% 0.18 44% 0.24</ns0:cell><ns0:cell>5%</ns0:cell><ns0:cell>0.02</ns0:cell><ns0:cell cols='2'>14% 0.00</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='12'>43% 0.30 58% 0.35 44% 0.34 38% 0.22 37% 0.26 43% 0.25</ns0:cell><ns0:cell>9%</ns0:cell><ns0:cell>0.05</ns0:cell><ns0:cell cols='2'>13% 0.00</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='14'>36% 0.20 47% 0.20 39% 0.29 31% 0.15 32% 0.19 43% 0.21 13% 0.07</ns0:cell><ns0:cell>6%</ns0:cell><ns0:cell>0.02</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='12'>32% 0.14 59% 0.23 41% 0.31 36% 0.19 34% 0.22 43% 0.22</ns0:cell><ns0:cell>6%</ns0:cell><ns0:cell>0.03</ns0:cell><ns0:cell>6%</ns0:cell><ns0:cell>0.03</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='14'>36% 0.23 45% 0.24 47% 0.38 44% 0.27 40% 0.29 43% 0.25 14% 0.09</ns0:cell><ns0:cell cols='2'>21% 0.06</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='8'>55% 0.46* 72% 0.54* 70% 0.65** 54% 0.41</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='8'>53% 0.44 68% 0.52* 68% 0.63** 55% 0.43</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='8'>54% 0.45 65% 0.49* 66% 0.60** 55% 0.43</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>10</ns0:cell><ns0:cell cols='8'>54% 0.45 65% 0.49* 68% 0.63** 55% 0.43</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>11</ns0:cell><ns0:cell cols='8'>53% 0.44 68% 0.52* 67% 0.62** 55% 0.43</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>3</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>4</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row></ns0:table><ns0:note>2 The Abbreviations were same with Table4.PeerJ reviewing PDF \| (2020:01:44790:1:0:REVIEW 8 Jun 2020)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_7'><ns0:head>Table 6 (on next page)</ns0:head><ns0:label>6</ns0:label><ns0:figDesc>Model assessment of formations and subformations by field point data and VMC.</ns0:figDesc><ns0:table><ns0:row><ns0:cell>The Abbreviations were same with Table 4.</ns0:cell></ns0:row></ns0:table><ns0:note>1</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_8'><ns0:head>Table 6 : Model assessment of formations and subformations by field point data and VMC.</ns0:head><ns0:label>6</ns0:label><ns0:figDesc>The Abbreviations were same with Table4.</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Variable combinations</ns0:cell><ns0:cell cols='4'>Decision tree Point data VMC OA KC OA KC</ns0:cell><ns0:cell cols='4'>Random forest Point data VMC OA KC OA KC</ns0:cell><ns0:cell cols='4'>Support vector machine Point data VMC OA KC OA KC</ns0:cell><ns0:cell cols='4'>Maximum likelihood classification Point data VMC OA KC OA KC</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>23%</ns0:cell><ns0:cell>0.14</ns0:cell><ns0:cell>19%</ns0:cell><ns0:cell>0.08</ns0:cell><ns0:cell>20%</ns0:cell><ns0:cell>0.18</ns0:cell><ns0:cell>5%</ns0:cell><ns0:cell>0.02</ns0:cell><ns0:cell>11%</ns0:cell><ns0:cell>0.09</ns0:cell><ns0:cell>6%</ns0:cell><ns0:cell>0.03</ns0:cell><ns0:cell>8%</ns0:cell><ns0:cell>0.06</ns0:cell><ns0:cell>8%</ns0:cell><ns0:cell>0.04</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>22%</ns0:cell><ns0:cell>-0.04</ns0:cell><ns0:cell>49%</ns0:cell><ns0:cell>0.04</ns0:cell><ns0:cell>19%</ns0:cell><ns0:cell>0.17</ns0:cell><ns0:cell>6%</ns0:cell><ns0:cell>0.03</ns0:cell><ns0:cell>13%</ns0:cell><ns0:cell>0.11</ns0:cell><ns0:cell>7%</ns0:cell><ns0:cell>0.04</ns0:cell><ns0:cell>8%</ns0:cell><ns0:cell>0.06</ns0:cell><ns0:cell>13%</ns0:cell><ns0:cell>0.05</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>26%</ns0:cell><ns0:cell>0.14</ns0:cell><ns0:cell>45%</ns0:cell><ns0:cell>0.23</ns0:cell><ns0:cell>29%</ns0:cell><ns0:cell>0.27</ns0:cell><ns0:cell>9%</ns0:cell><ns0:cell>0.07</ns0:cell><ns0:cell>21%</ns0:cell><ns0:cell>0.19</ns0:cell><ns0:cell>10%</ns0:cell><ns0:cell>0.07</ns0:cell><ns0:cell>12%</ns0:cell><ns0:cell>0.09</ns0:cell><ns0:cell>13%</ns0:cell><ns0:cell>0.07</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>30%</ns0:cell><ns0:cell>0.20</ns0:cell><ns0:cell>30%</ns0:cell><ns0:cell>0.04</ns0:cell><ns0:cell>22%</ns0:cell><ns0:cell>0.20</ns0:cell><ns0:cell>7%</ns0:cell><ns0:cell>0.04</ns0:cell><ns0:cell>16%</ns0:cell><ns0:cell>0.14</ns0:cell><ns0:cell>6%</ns0:cell><ns0:cell>0.03</ns0:cell><ns0:cell>9%</ns0:cell><ns0:cell>0.07</ns0:cell><ns0:cell>8%</ns0:cell><ns0:cell>0.04</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>33%</ns0:cell><ns0:cell>0.01</ns0:cell><ns0:cell>67%</ns0:cell><ns0:cell>0.00</ns0:cell><ns0:cell>22%</ns0:cell><ns0:cell>0.20</ns0:cell><ns0:cell>7%</ns0:cell><ns0:cell>0.04</ns0:cell><ns0:cell>15%</ns0:cell><ns0:cell>0.13</ns0:cell><ns0:cell>5%</ns0:cell><ns0:cell>0.03</ns0:cell><ns0:cell>11%</ns0:cell><ns0:cell>0.09</ns0:cell><ns0:cell>10%</ns0:cell><ns0:cell>0.04</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>26%</ns0:cell><ns0:cell>0.15</ns0:cell><ns0:cell>22%</ns0:cell><ns0:cell>0.02</ns0:cell><ns0:cell>31%</ns0:cell><ns0:cell>0.30</ns0:cell><ns0:cell>11%</ns0:cell><ns0:cell>0.08</ns0:cell><ns0:cell>21%</ns0:cell><ns0:cell>0.19</ns0:cell><ns0:cell>8%</ns0:cell><ns0:cell>0.06</ns0:cell><ns0:cell>12%</ns0:cell><ns0:cell>0.09</ns0:cell><ns0:cell>15%</ns0:cell><ns0:cell>0.08</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>33%</ns0:cell><ns0:cell>0.20</ns0:cell><ns0:cell>52%</ns0:cell><ns0:cell>0.27</ns0:cell><ns0:cell cols='3'>58% 0.57* 23%</ns0:cell><ns0:cell>0.20</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>27%</ns0:cell><ns0:cell>0.17</ns0:cell><ns0:cell>34%</ns0:cell><ns0:cell>0.18</ns0:cell><ns0:cell>55%</ns0:cell><ns0:cell>0.54</ns0:cell><ns0:cell>23%</ns0:cell><ns0:cell>0.20</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>25%</ns0:cell><ns0:cell>0.15</ns0:cell><ns0:cell>22%</ns0:cell><ns0:cell>0.15</ns0:cell><ns0:cell>55%</ns0:cell><ns0:cell>0.53</ns0:cell><ns0:cell>22%</ns0:cell><ns0:cell>0.20</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>10</ns0:cell><ns0:cell>30%</ns0:cell><ns0:cell>0.17</ns0:cell><ns0:cell>41%</ns0:cell><ns0:cell>0.22</ns0:cell><ns0:cell>56%</ns0:cell><ns0:cell>0.55</ns0:cell><ns0:cell>23%</ns0:cell><ns0:cell>0.21</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>11</ns0:cell><ns0:cell>31%</ns0:cell><ns0:cell>0.20</ns0:cell><ns0:cell>41%</ns0:cell><ns0:cell>0.22</ns0:cell><ns0:cell>56%</ns0:cell><ns0:cell>0.55</ns0:cell><ns0:cell>23%</ns0:cell><ns0:cell>0.20</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>3</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>4</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row></ns0:table><ns0:note>2 PeerJ reviewing PDF \| (2020:01:44790:1:0:REVIEW 8 Jun 2020)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_9'><ns0:head>Table 7 (on next page)</ns0:head><ns0:label>7</ns0:label><ns0:figDesc /><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_10'><ns0:head>Table 7 : Top ten most important variables of models in the different vegetation classification units. 2</ns0:head><ns0:label>7</ns0:label><ns0:figDesc>The abbreviations of indices were shown in Table2.</ns0:figDesc><ns0:table><ns0:row><ns0:cell cols='2'>1 Vegetation groups</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell cols='2'>Vegetation types</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell cols='3'>Formations and sub-formations</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>Decision tree</ns0:cell><ns0:cell /><ns0:cell>Random forest</ns0:cell><ns0:cell /><ns0:cell>Decision tree</ns0:cell><ns0:cell /><ns0:cell cols='2'>Random forest</ns0:cell><ns0:cell>Decision tree</ns0:cell><ns0:cell /><ns0:cell cols='2'>Random forest</ns0:cell></ns0:row><ns0:row><ns0:cell>Important</ns0:cell><ns0:cell>Standardized</ns0:cell><ns0:cell>Important</ns0:cell><ns0:cell>Normalized</ns0:cell><ns0:cell>Important</ns0:cell><ns0:cell>Standardized</ns0:cell><ns0:cell>Important</ns0:cell><ns0:cell>Normalized</ns0:cell><ns0:cell>Important</ns0:cell><ns0:cell>Standardized</ns0:cell><ns0:cell>Important</ns0:cell><ns0:cell>Normalized</ns0:cell></ns0:row><ns0:row><ns0:cell>variables</ns0:cell><ns0:cell>Importance</ns0:cell><ns0:cell>variables</ns0:cell><ns0:cell>importance</ns0:cell><ns0:cell>variables</ns0:cell><ns0:cell>Importance</ns0:cell><ns0:cell>variables</ns0:cell><ns0:cell>importance</ns0:cell><ns0:cell>variables</ns0:cell><ns0:cell>Importance</ns0:cell><ns0:cell>variables</ns0:cell><ns0:cell>importance</ns0:cell></ns0:row><ns0:row><ns0:cell>1 Annual mean</ns0:cell><ns0:cell>1.00</ns0:cell><ns0:cell>Annual mean</ns0:cell><ns0:cell>3.68</ns0:cell><ns0:cell>Annual mean</ns0:cell><ns0:cell>1.00</ns0:cell><ns0:cell>Annual mean</ns0:cell><ns0:cell>3.51</ns0:cell><ns0:cell>Annual mean</ns0:cell><ns0:cell>1.00</ns0:cell><ns0:cell>Annual mean</ns0:cell><ns0:cell>4.16</ns0:cell></ns0:row><ns0:row><ns0:cell>temperature</ns0:cell><ns0:cell /><ns0:cell>temperature</ns0:cell><ns0:cell /><ns0:cell>temperature</ns0:cell><ns0:cell /><ns0:cell>temperature</ns0:cell><ns0:cell /><ns0:cell>temperature</ns0:cell><ns0:cell /><ns0:cell>temperature</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>2 Annual</ns0:cell><ns0:cell>0.88</ns0:cell><ns0:cell>Slope</ns0:cell><ns0:cell>2.94</ns0:cell><ns0:cell>Slope</ns0:cell><ns0:cell>0.83</ns0:cell><ns0:cell>Slope</ns0:cell><ns0:cell>3.35</ns0:cell><ns0:cell>Annual</ns0:cell><ns0:cell>0.86</ns0:cell><ns0:cell>Annual</ns0:cell><ns0:cell>3.28</ns0:cell></ns0:row><ns0:row><ns0:cell>precipitation</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>precipitation</ns0:cell><ns0:cell /><ns0:cell>precipitation</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>3 Slope</ns0:cell><ns0:cell>0.80</ns0:cell><ns0:cell>Mean diurnal</ns0:cell><ns0:cell>2.60</ns0:cell><ns0:cell>Annual</ns0:cell><ns0:cell>0.51</ns0:cell><ns0:cell>Mean diurnal</ns0:cell><ns0:cell>3.06</ns0:cell><ns0:cell>Slope</ns0:cell><ns0:cell>0.63</ns0:cell><ns0:cell>Mean diurnal</ns0:cell><ns0:cell>3.25</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>range</ns0:cell><ns0:cell /><ns0:cell>precipitation</ns0:cell><ns0:cell /><ns0:cell>range</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>range</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>4 Winter</ns0:cell><ns0:cell>0.36</ns0:cell><ns0:cell>Annual</ns0:cell><ns0:cell>2.38</ns0:cell><ns0:cell>Winter</ns0:cell><ns0:cell>0.30</ns0:cell><ns0:cell>Annual</ns0:cell><ns0:cell>2.8</ns0:cell><ns0:cell>Mean diurnal</ns0:cell><ns0:cell>0.52</ns0:cell><ns0:cell>Slope</ns0:cell><ns0:cell>2.24</ns0:cell></ns0:row><ns0:row><ns0:cell>vegetation</ns0:cell><ns0:cell /><ns0:cell>precipitation</ns0:cell><ns0:cell /><ns0:cell>vegetation</ns0:cell><ns0:cell /><ns0:cell>precipitation</ns0:cell><ns0:cell /><ns0:cell>range</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>index MR</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>index MR</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>5 Mean diurnal</ns0:cell><ns0:cell>0.33</ns0:cell><ns0:cell>Summer</ns0:cell><ns0:cell>1.88</ns0:cell><ns0:cell>Mean diurnal</ns0:cell><ns0:cell>0.28</ns0:cell><ns0:cell>Summer</ns0:cell><ns0:cell>1.84</ns0:cell><ns0:cell>Precipitation</ns0:cell><ns0:cell>0.52</ns0:cell><ns0:cell>Precipitation</ns0:cell><ns0:cell>2.16</ns0:cell></ns0:row><ns0:row><ns0:cell>range</ns0:cell><ns0:cell /><ns0:cell>vegetation</ns0:cell><ns0:cell /><ns0:cell>range</ns0:cell><ns0:cell /><ns0:cell>vegetation</ns0:cell><ns0:cell /><ns0:cell>of driest</ns0:cell><ns0:cell /><ns0:cell>of driest</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>index BI</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>index BI</ns0:cell><ns0:cell /><ns0:cell>month</ns0:cell><ns0:cell /><ns0:cell>month</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>6 Summer</ns0:cell><ns0:cell>0.29</ns0:cell><ns0:cell>Winter</ns0:cell><ns0:cell>1.37</ns0:cell><ns0:cell>Summer</ns0:cell><ns0:cell>0.22</ns0:cell><ns0:cell>Winter</ns0:cell><ns0:cell>1.61</ns0:cell><ns0:cell>Winter</ns0:cell><ns0:cell>0.4</ns0:cell><ns0:cell>Summer</ns0:cell><ns0:cell>1.83</ns0:cell></ns0:row><ns0:row><ns0:cell>surface</ns0:cell><ns0:cell /><ns0:cell>vegetation</ns0:cell><ns0:cell /><ns0:cell>vegetation</ns0:cell><ns0:cell /><ns0:cell>vegetation</ns0:cell><ns0:cell /><ns0:cell>vegetation</ns0:cell><ns0:cell /><ns0:cell>vegetation</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>albedo of</ns0:cell><ns0:cell /><ns0:cell>index NDVI</ns0:cell><ns0:cell /><ns0:cell>index EVI</ns0:cell><ns0:cell /><ns0:cell>index NDVI</ns0:cell><ns0:cell /><ns0:cell>index MR</ns0:cell><ns0:cell /><ns0:cell>index BI</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>band 1</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>7 Summer</ns0:cell><ns0:cell>0.28</ns0:cell><ns0:cell>Summer</ns0:cell><ns0:cell>1.36</ns0:cell><ns0:cell>Precipitation</ns0:cell><ns0:cell>0.21</ns0:cell><ns0:cell>Winter</ns0:cell><ns0:cell>1.45</ns0:cell><ns0:cell>Summer</ns0:cell><ns0:cell>0.32</ns0:cell><ns0:cell>Summer</ns0:cell><ns0:cell>1.7</ns0:cell></ns0:row><ns0:row><ns0:cell>vegetation</ns0:cell><ns0:cell /><ns0:cell>vegetation</ns0:cell><ns0:cell /><ns0:cell>of driest</ns0:cell><ns0:cell /><ns0:cell>vegetation</ns0:cell><ns0:cell /><ns0:cell>surface albedo</ns0:cell><ns0:cell /><ns0:cell>vegetation</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>index BI</ns0:cell><ns0:cell /><ns0:cell>index EVI</ns0:cell><ns0:cell /><ns0:cell>month</ns0:cell><ns0:cell /><ns0:cell>index MR</ns0:cell><ns0:cell /><ns0:cell>of band 1</ns0:cell><ns0:cell /><ns0:cell>index NDVI</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>8 Precipitation</ns0:cell><ns0:cell>0.25</ns0:cell><ns0:cell>Winter</ns0:cell><ns0:cell>1.30</ns0:cell><ns0:cell>Summer</ns0:cell><ns0:cell>0.20</ns0:cell><ns0:cell>Summer</ns0:cell><ns0:cell>1.31</ns0:cell><ns0:cell>Summer</ns0:cell><ns0:cell>0.32</ns0:cell><ns0:cell>Winter</ns0:cell><ns0:cell>1.61</ns0:cell></ns0:row><ns0:row><ns0:cell>of driest</ns0:cell><ns0:cell /><ns0:cell>vegetation</ns0:cell><ns0:cell /><ns0:cell>vegetation</ns0:cell><ns0:cell /><ns0:cell>vegetation</ns0:cell><ns0:cell /><ns0:cell>vegetation</ns0:cell><ns0:cell /><ns0:cell>vegetation</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>month</ns0:cell><ns0:cell /><ns0:cell>index MR</ns0:cell><ns0:cell /><ns0:cell>index BI</ns0:cell><ns0:cell /><ns0:cell>index WI</ns0:cell><ns0:cell /><ns0:cell>index BI</ns0:cell><ns0:cell /><ns0:cell>index NDVI</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>9 Summer</ns0:cell><ns0:cell>0.23</ns0:cell><ns0:cell>Summer</ns0:cell><ns0:cell>1.22</ns0:cell><ns0:cell>Summer</ns0:cell><ns0:cell>0.19</ns0:cell><ns0:cell>Precipitation</ns0:cell><ns0:cell>1.24</ns0:cell><ns0:cell>Summer</ns0:cell><ns0:cell>0.31</ns0:cell><ns0:cell>Winter</ns0:cell><ns0:cell>1.47</ns0:cell></ns0:row><ns0:row><ns0:cell>vegetation</ns0:cell><ns0:cell /><ns0:cell>vegetation</ns0:cell><ns0:cell /><ns0:cell>surface</ns0:cell><ns0:cell /><ns0:cell>of driest</ns0:cell><ns0:cell /><ns0:cell>vegetation</ns0:cell><ns0:cell /><ns0:cell>vegetation</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>index EVI</ns0:cell><ns0:cell /><ns0:cell>index NDVI</ns0:cell><ns0:cell /><ns0:cell>albedo of</ns0:cell><ns0:cell /><ns0:cell>month</ns0:cell><ns0:cell /><ns0:cell>index WI</ns0:cell><ns0:cell /><ns0:cell>index MR</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>band 1</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>10 Winter</ns0:cell><ns0:cell>0.19</ns0:cell><ns0:cell>Summer</ns0:cell><ns0:cell>1.12</ns0:cell><ns0:cell>Winter</ns0:cell><ns0:cell>0.14</ns0:cell><ns0:cell>Summer</ns0:cell><ns0:cell>1.22</ns0:cell><ns0:cell cols='2'>Winter surface 0.28</ns0:cell><ns0:cell>Summer</ns0:cell><ns0:cell>1.32</ns0:cell></ns0:row><ns0:row><ns0:cell>surface</ns0:cell><ns0:cell /><ns0:cell>vegetation</ns0:cell><ns0:cell /><ns0:cell>surface</ns0:cell><ns0:cell /><ns0:cell>vegetation</ns0:cell><ns0:cell /><ns0:cell cols='2'>albedo of band</ns0:cell><ns0:cell>vegetation</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>albedo of</ns0:cell><ns0:cell /><ns0:cell>index MR</ns0:cell><ns0:cell /><ns0:cell>albedo of</ns0:cell><ns0:cell /><ns0:cell>index NDVI</ns0:cell><ns0:cell /><ns0:cell>6</ns0:cell><ns0:cell /><ns0:cell>indices EVI</ns0:cell><ns0:cell /></ns0:row></ns0:table></ns0:figure> <ns0:note place='foot'>PeerJ reviewing PDF \| (2020:01:44790:1:0:REVIEW 8 Jun 2020)</ns0:note> </ns0:body> "	"Dear Prof. Ghermandi: Thanks much for handling our manuscript, entitled “Simulating highly disturbed vegetation distribution: the case of China’s Jing-Jin-Ji region” with Manuscript ID of 44790. The manuscript was revised with tracked changes based on all insightful comments. We are pleased to revise it further in case of necessary. Thank you very much for your considerations and the best regards. Yours sincerely, Yuanrun Zheng June 8, 2020 Report for manuscript revision: In responses to the editor (Prof. Ghermandi) Comments: Though both reviewers acknowledge that the study is potentially valuable, they raise substantial concerns that should be thoroughly addressed before the paper can be further considered for publication. Both note the need for a substantial proofreading of the paper. More importantly, they both raise a number of issues regarding the methodology of the study (see especially section 'Experimental design' for reviewer #1 and the 'Comments for the author' section for reviewer #2): I think these are important points that should all be clearly and adequately addressed in the revised version, should you decide to submit one. Response: We thank reviewers and editors for insightful comments, the manuscript was carefully revised based on all comments. The manuscript was carefully edited by an editor of PeerJ. In responses to reviewer 1 1. Comments: Basic reporting The reviewed manuscript is comprehended only with great difficulty. The text is on many occasions ambiguous and often lacking strong arguments. The text needs major improvements in English language, consistency and clarification. Both the introduction and the discussion are lacking a coherent structure. Response: Thank you for the comments. The manuscript including the introduction and the discussion was carefully revised. The English has been revised by an editor of PeerJ in this revision. 2. Comments: Long difficult-to-understand sentences: line 48-52; line 213-217; lines 321-328 Comma problems: line73 … vague language: line 78-86; line 189, line 238-239 out of context: line 98-99, rephrasing required: line 59 “smartly use vegetation”; line 69 “using information based on information..”; line 189 “through different model methods, we used different combinations…”; line 299 “a complex, multi-level, non-linear system, is one of the complex problems…; repetition of methods: line 359-362 language problems: line 122; line 345 Response: Original line 48-52 was changed as “Environmental research, resource management, and conservation planning require vegetation distribution maps (Franklin, 2010) to better understand, use, and monitor vegetation”. Original line 213-217 was changed as ' The RF model creates and combines different DTs to produce considerably more accurate classifications that are unaffected by noise or overtraining'. Original line 321-328, long sentence was revised into several short sentences for better understanding. Original line 73 was changed as “Changes in the environment can affect vegetation composition, structure, function, and spatial distribution”. Original line 78-86 was changed as “Modern remote sensing data and software make it more convenient than ever before to produce predictive vegetation maps”. Original line 189 was changed as “To determine the distribution predictive ability of different variables, we grouped the variables into different combinations based on the results of the Pearson correlation”. Original line 238-239 was changed as “We selected all 11 variable combinations as the input variables for each method”. Original line 98-99 was deleted. Original line 59 was changed as “Industrialization strongly influences the environment by greatly altering vegetation patterns, making exact mapping a significant challenge”. Original line 69 was changed as “Furthermore, field survey and remote sensing methods manually draw vegetation unit boundaries based on climate, elevation, and soil type information, which can be inaccurate in transition areas”. Original line 299 was changed as “Vegetation classification is an important and complex system with multiple levels”. Original line 359-362 was changed as “The SVM model was developed from statistical learning methods and discriminated class samples by locating potentially nonlinear or multiple linear boundaries between individual training points”. Original line 122 was changed as “Most of its data come from a field survey conducted between 1980 and 1990, meaning its temporal and spatial scales are both outdated”. Original line 345 was changed as “Different image classification methods are rarely used together in the same classification research, especially when combined with environmental variables”. 3. Comments: Manuscript uses broad and sufficiently varied literature, however it often relies on literature from a single book (Franklin 2010) without mentioning page numbers within this book (all instances when the book is cited) and often lacking reasoning (line 359). Statement unsupported by reference: line 64; line 354 statement without reference: line 102-103 @Franklin, J. (2010). Mapping species distributions: spatial inference and prediction, Cambridge University Press. Response: Thank you for the comments. The in-text citations in original line 52, 205, 338 were quoted in page 11-17, 166-167 and 12 of the book, respectively. The citation in other places were removed, because there were several literatures for same statement. Original line 359, the quoted sentence was removed. Original line 64, a reference was added. Original line 354, a reference was added. Original line 102-103, a reference was added. 4. Comments: Figures are generally in low resolution (and no hi-resolution/original resolution GeoTiff is provided). Color schemes used are difficult to distinguish and not color-blind friendly (example in Figure 4, where 39 different colors are used – impossible for human eye to distinguish). Figure captions are cut-off (Figure 2,3,4). Figure 1 is missing an overview-localization map (or marked large cities), for the readers who are not familiar with geography of China. Response: Thank you for the comment. All figures were revised based on the comment. Hi-resolution figures were provided. Big cities including Beijing, Tianjin and Shijiazhuang, were marked on Figure 1. 5. Comments: Raw data is partially shared, but point files are in uncommon format that is difficult to open using common computer programs (and not specified which program should be used). Additionally, the provided shapefile data for Vegetation Map (VMC) contains descriptions and encoding in non-latin and non-English characters, thus could not be explored further. Response: Raw data were reorganized and resubmitted accordingly. 6. Comments: Experimental design This research is within the scopes of PeerJ journal, however it seems very repetitive and little original with regard to the previous article from the authors Zhou et al. from the year 2016. There is no significant new findings or methodological advancements (default settings of an existing software) other than the produced vegetation map predictions (which should at least be provided in full resolution) @Zhou, J., et al. (2016). 'Comparison modeling for alpine vegetation distribution in an arid area.' Environmental monitoring and assessment 188(7): 408. Response: Thank you for the comment. This research is different from research of Zhou et al. Firstly, the research area of this research is the Jing-Jin-Ji region located in the North China Plain and affected by high social-economic disturbance. Qilian Mountain in the research of Zhou et al. is characterized by complex terrain, but no high social-economic disturbance. Secondly, the predictive variables as well as the combinations of these variables were different from research of Zhou et al. Thirdly, we compared four model methods for simulating distribution of vegetation in three vegetation classification levels, while only three models were used for simulation in two vegetation classification levels in research of Zhou et al. 7. Comments: The main flaw is in the fact that the authors are using relatively old vegetation survey data (that they claim is from year 1980 – line 121), which is then put through the modelling procedures using recent predictor variables (including vegetation indices from 2013). This could be causing a problematic temporal mismatch, especially since the study area is reported to be are heavily influenced by socio-economic development. Response: Thank you for the comments. Although most field data used in the vegetation map were collected in 1980-1990, however, this map was published in 2007. During vegetation mapping, some new field data (in regions where the vegetation changed much) were referenced to produce the map. Therefore, using predictor variables from 2013 is acceptable. Further, the quality of remote sensing data (such as less affected by cloud) in 2013 are better than in other periods from 1980-2013 in this region. 8. Comments: The software that was used to carry out the entire study is very little described (EnMAP-Box), and only mentioned at line 220. Describe the modelling platform early in this paragraph (line 202). The default settings are used, but it would be useful to elaborate further on what are the settings. Why was it left on default? Other studies that only use the default settings? Response: Thank you for the comments. The suggested information was added in the section of “Materials & Methods” in the revised manuscript. The SVM and RF models were implemented in EnMAP-Box software in this study. When SVM model was used for classification, it requires the definition of the kernel parameter g and the regularization parameter c. In this study, the default settings in EnMAP-Box were applied to the SVM model, where g was from 0.01 to 1000, c was from 0.1 to 1000. Parameters of g and c are tested using grid search with internal performance estimation and those with the best performance are used for the data training (van der Linden et al., 2014; van der Linden et al., 2015). Because the testing of a larger range of parameters can optimize the parameters with a high probability, default settings already lead to high accuracies for SVM methods in most cases (van der Linden et al., 2014), and the optimal parameters of some studies were also in this range (Lin et al., 2014). When RF models were used for classification, it requires the definition of parameters, including tree number, the number of randomly selected features and the node impurity function. In this study, classification rules of the RF models were using the default settings in EnMAP-Box, where 100 trees existed, the number of randomly selected features was equal to the square root of the number of all features and the node impurity function was with Gini coefficient (Jakimow et al., 2014; van der Linden et al., 2015). In general, default settings also lead to high accuracies for RF methods (Jakimow et al., 2014), which has been adopted by some researchers (Ma, Gao & Gu, 2019; Zhou et al., 2016). 9. Comments: Lacking description of how data between training and assessment points were split (Random subset?). Response: Thank you for the comment. Model training and assessment data in each unit III were randomly selected. This information was added in the section of “Materials & Methods”. 10. Comments: Not described how the vector vegetation map of China (VMC) was used as assessment. I must only assume that the vector map was first rasterized onto the same grid as the modelled data, and then tested with Kappa. Please explain further Response: Thank you for the comments. We did as you assumed, the information was added in the section of “Materials & Methods”. 11. Comments: Kappa was used as the only assessment method. It is known that Kappa is a threshold dependent metric, see Guisan 2017 page 242 for additional assessment methods@Guisan, A., et al. (2017). Habitat suitability and distribution models: with applications in R, Cambridge University Press. Response: Kappa and overall accuracy were used in this research. Kappa and overall accuracy are widely used in classification mission as assessment methods and some researchers were also using them in recent years (Chala et al., 2017; Burai et al., 2015; Jiang et al., 2012; Li et al., 2014; Zhou et al., 2016). There are additional assessment methods suitable for this research, such as Hanssen-kuipers (also known as TSS or HKSS) in page 242 from authors Guisan et al. The tendency of assessment results from different models by other method are basically consistent with ones by Hanssen-kuipers (Shabani, Kumar & Ahmadi, 2016), therefore, we used Kappa coefficient and overall accuracy in this research. 12. Comments: Validity of the findings It is common in the Distribution Modelling (DM) research to provide all input data on a repository for possibility of future replication. It is highly advisable to do so in this study. See Araújo et al. (2019) for standards for DM @Araújo, M. B., et al. (2019). 'Standards for distribution models in biodiversity assessments.' Science Advances 5(1): 1-11. Response: Thank you for the comment. All input data were downloaded freely from public website, and the websites were indicated in the section of “Materials & Methods”, therefore, these data were not provided on a repository. 3763 vegetation points data used for training and assessment were provided on a repository, 4TU.Centre for Research Data, at https://doi.org/10.4121/uuid:1b27dc6b-b77e-4f18-b035-e8a249f595c0. 13. Comments: Conclusion paragraph both in the abstract and discussion is a word-by-word repetition of the results. Line 329 – there is no explanation to support your statement. Response: The conclusion section was revised. The sentence in original line 329 was deleted. 14. Comments: Comments for the Author I appreciate the last few sentences in discussion (line447-451) taking up the problem of human disturbance to the study area. This should be elaborated on in much greater extent. Response: The human disturbances in the study area were revised in original line 447-451. 15. Comments: The title should be reconsidered: “region that has been highly disturbed by social-economic development” it is not the core topic of this study, and the authors have avoided explaining/discussing or performing analysis on the human disturbances – rather they are modelling the potential vegetation Response: The title was changed as “Simulating highly disturbed vegetation distribution: the case of China’s Jing-Jin-Ji region” based on the comments of Reviewer 2. 16. Comments: Shortcuts are not used consistently throughout the article (DT, RF, SVM and MLC sometimes written out, sometimes used with shortcut) Response: Shortcuts were used consistently throughout in the revised manuscript. 17. Comments: Use table to present the variable combinations (starting on line 187). Variable combination 11 and 12 are not sufficiently explained (give explicit examples – create a overview table). Response: All variable combinations were shown in Table 3, and variable combinations were explained in detail in the revised manuscript. 18. Comments: How can predictor variables be used for assessment? Line 189 Response: The “assessment” in original line 189 was deleted. 19. Comments: When listing out the three classification units, try to delimit the units with numbers (I. vegetation groups; II. vegetation types; and III. formations and subformations) to avoid double “and” Response: Thank you for the comment. Units were delimited with numbers (I. vegetation groups; II. vegetation types; and III. formations and subformations) in the revised manuscript. In responses to reviewer 2 1. Comments: Basic reporting I see many text errors throughout the manuscript and some of the statements can definitely be written in a better way. I suggest that the co-authors whose first language is English carefully peruse the text. Most sentences are long and superfluous. Here are some of the few examples: Response: Thank you for the comments. The manuscript was edited by an editor of PeerJ. The sentences were revised based on your comments. 2. Comments: Line 19 - Vegetation distribution simulations could help to understand… may be better to write as: Vegetation distribution simulation is important to understand …. Response: The original sentence was changed as “Simulating vegetation distribution is an effective method for identifying vegetation distribution patterns and trends”. 3. Comments: lines 48 - 52: Very long sentence and difficult to understand. It can benefit from paraphrasing and breaking into two or three sentences. Response: The original sentence was changed as “Environmental research, resource management, and conservation planning require vegetation distribution maps”. 4. Comments: Caption of figure 3 – “label e” is lacking … the Vegetation Map of the People's Republic of China in (e) Response: Label for e was added for Fig. 3. 5. Comments: Caption of figure 4 – “label e” is lacking … the Vegetation Map of the People's Republic of China in (e) Response: Label for e was added for Fig. 4. 6. Comments: Please supplement the 3763 observation points with corresponding vegetation groups, types, and formations and sub-formations. Response: Thank you for the comment. Because 3763 vegetation points data could make a big Table, these data were provided on a repository, 4TU.Centre for Research Data, at https://doi.org/10.4121/uuid:1b27dc6b-b77e-4f18-b035-e8a249f595c0. 7. Comments: Experimental design The experimental design is good. But I have some concerns on resampling techniques and variable selections. Below I am also suggesting an alternative way of achieving the same objective. Response: The concerns were responded in the following sections. 8. Comments: Validity of the findings The findings are valid but the objective is not clear. The paper will benefit by setting a clear objective - whether the primary goal of the paper is to improve the vegetation map of the study area or to explore and answer methodological questions. This should be clearly mentioned in the results and conclusion sections too. Response: Thank you for the comment. The primary goal of this paper was mentioned in the section of Introduction, Results and Conclusion in the revised manuscript. 9. Comments: Comments for the Author The resampling techniques used: nearest-neighbor method is used to resample both the DEM and the climatic variables. This approach is commonly used for discrete variables such as land-cover. For continuous variables, bilinear or cubic techniques are commonly used. The authors should justify either how their approach may not affect their data and consequently their results or should use the right approach. Response: Thank you for the insightful comments. The data were resampled by cubic technique in the revised manuscript. 10. Comments: Variable selections: what is the criteria to select the four bioclimatic variables? Is there any objective way to determine the number of bioclimatic variables to be used as predictor variables? It is better to consider all the 19 bioclimatic variables, stack them together with the other predictor variables, extract their values at the 3763 observation point, compute pair wise correlation and consider only less correlated variables (R < /0.7/ is usually used as threshold; see Chala et al., 2017). This way, it is possible to be objective and avoid redundantly using correlated variables. It is also interesting to see the correlation among the topographic, spectral and climate variables. Response: Thank you for the comments. The related parts were revised based on your comments, the variables were decided after Pearson correlation analysis. The revision was described in detail in the section of “Materials and Methods”. 11. Comments: Elevation is a proxy variable and does not have direct impact on vegetation. Thus I don’t recommend using elevation as a predictor variable. It is also expected to be highly correlated with temperature related bioclimatic variables. Either the use of altitude as predictor variable should be justified or its correlation with the bioclimatic variables should be checked. Response: Thank you for the comments. The elevation was excluded after correlation analysis in the revised manuscript. 12. Comments: Probablity of using alternative approaches: cultural vegetation groups and the vegetation types included in this category are mainly shaped by anthropogenic impacts. Including this group in the model will definitely deteriorate model quality. I am very curious what if you just use satellite images with higher spatial resolution such as sentinel and spot images and check how they perform in capturing the vegetation groups, types, formations and sub formations. That asks less energy and probably more reliable. That will also allow detecting anthropogenic driven changes and monitoring the land-use land-cover of the study area. Specially of the main objective of the paper is to explore the method that perfoms well, this vegetation types should be excluded from the model. Response: Thank you for the comments. In this study, two of aims were to explore a suitable modeling method for vegetation affected by high social- economic disturbance, and to determine which variables enhanced the accuracy of classification for vegetation mapping. If some good variables such as climatic factors can be found, we can predict the future vegetation by these variables, so we used different kinds of variables. For cultural vegetation, although they mainly shaped by anthropogenic impacts, these vegetations were planted in the suitable environments. Therefore, they had some characteristics like those of natural communities, they also can reflect the relationship between vegetations and the environments. Considering this point, we included them in the study. 13. Comments: Checking the homogeneity of classes: It is also good to check the elevation ranges of each vegetation groups and types. If they cover wider elevation ranges, by dividing them into upper and lower elevation classes, it is possible to improve the performances of the algorithms (See Chala et al., 2017). Response: Thank you for the comments. The elevation was excluded based on your comments in the revised manuscript. We checked it, most of them cover relatively small elevation ranges. 14. Comments: Figure 1 – it is great that the elevation range of the study area is provided. But it will be more informative if it is classified in to at least five reasonable elevation range classes. Can you please consider that? Response: The Figure 1 was revised based on the comment. 15. Comments: The title can be kept short and precise – for example: Simulation of highly disturbed vegetation types: the case of Jing-Jin-Ji region, China Response: The title was changed as “Simulating highly disturbed vegetation distribution: the case of China’s Jing-Jin-Ji region”. 16. Comments: Last and finally: Is it possible to include the specific objective of the paper in the abstract – whether it is more interested in improving the vegetation map of the study area or exploring methodological issues. Just a sentence or two? Reference: Chala, D., et al. (2017). 'Migration corridors for alpine plants among the ‘sky islands’ of eastern Africa: do they, or did they exist?' Alpine Botany 127(2): 133-144. Response: Thank you for the comments. The specific objective was added in the Abstract. "	Here is a paper. Please give your review comments after reading it.
512	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Background. Simulating vegetation distribution is an effective method for identifying vegetation distribution patterns and trends. The primary goal of this study was to determine the best simulation method for a vegetation in an area that is heavily affected by human disturbance.</ns0:p><ns0:p>Methods. We used climate, topographic, and spectral data as the input variables for four machine learning models (random forest (RF), decision tree (DT), support vector machine (SVM), and maximum likelihood classification (MLC)) on three vegetation classification units (vegetation group (I), vegetation type (II), and formation and subformation (III)) in Jing-Jin-Ji, one of China's most developed regions. We used a total of 2,789 vegetation points for model training and 974 vegetation points for model assessment.</ns0:p></ns0:div> <ns0:div><ns0:head>Results.</ns0:head><ns0:p>Our results showed that the RF method was the best of the four models, as it could effectively simulate vegetation distribution in all three classification units. The DT method could only simulate vegetation distribution in units I and II, while the other two models could not simulate vegetation distribution in any of the units. Kappa coefficients indicated that the DT and RF methods had more accurate predictions for units I and II than for unit III. The three vegetation classification units were most affected by six variables: three climate variables (annual mean temperature, mean diurnal range, and annual precipitation), one geospatial variable (slope), and two spectral variables (Mid-infrared ratio of winter vegetation index and brightness index of summer vegetation index). Variables Combination 7, including annual mean temperature, annual precipitation, mean diurnal range and precipitation of driest month, produced the highest simulation accuracy.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions.</ns0:head><ns0:p>We determined that the RF model was the most effective for simulating vegetation distribution in all classification units present in the Jing-Jin-Ji region. The RF model produced high accuracy vegetation distributions in classification units I and II, but relatively low accuracy in classification unit III. Four climate variables were sufficient for vegetation distribution simulation in such region.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>Vegetation is an essential component of terrestrial ecosystems and landscapes (Editorial Committee of Vegetation Map of China, Chinese Academy of <ns0:ref type='bibr'>Science, 2007)</ns0:ref>. Environmental research, resource management, and conservation planning require vegetation distribution maps <ns0:ref type='bibr' target='#b20'>(Franklin, 2010)</ns0:ref> to better understand, use, and monitor vegetation. Vegetation patterns and distributions are affected by the climate <ns0:ref type='bibr' target='#b7'>(Chen et al., 2015;</ns0:ref><ns0:ref type='bibr' target='#b62'>Zhang et al., 2018)</ns0:ref> and other disturbances, particularly those caused by changes in land use <ns0:ref type='bibr' target='#b27'>(Hansen et al., 2013;</ns0:ref><ns0:ref type='bibr' target='#b58'>Wehkamp et al., 2018)</ns0:ref>. Human disturbances, such as industrialization, urbanization, population growth, land use change for agricultural use, etc., strongly influence the environment by greatly altering vegetation patterns, making exact mapping a significant challenge <ns0:ref type='bibr' target='#b61'>(Xie, Sha, & Yu, 2008;</ns0:ref><ns0:ref type='bibr'>Zhou et al., 2016)</ns0:ref>.</ns0:p><ns0:p>Field surveys, the traditional method used to map vegetation, are costly and labor-intensive <ns0:ref type='bibr' target='#b41'>(Newell & Leathwick, 2005;</ns0:ref><ns0:ref type='bibr'>Zhou et al., 2016)</ns0:ref>. Mapping using remote sensing data is also a popular method that has been used over the last 30 years <ns0:ref type='bibr' target='#b61'>(Xie, Sha, & Yu, 2008)</ns0:ref>. This method makes it possible to obtain a wide range of reliable data from remote sensing images, and it updates vegetation boundaries by visually interpreting images and field surveys <ns0:ref type='bibr' target='#b64'>(Zhang et al., 2008)</ns0:ref>. However, determining vegetation units and their boundaries by visual interpretation can produce inaccurate results. Researchers may get different results from the same images for the same study areas <ns0:ref type='bibr' target='#b1'>(Bie & Beckett, 1973;</ns0:ref><ns0:ref type='bibr' target='#b45'>Pfeffer, Pebesma, & Burrough, 2003)</ns0:ref>. Furthermore, field survey and remote sensing methods manually draw vegetation unit boundaries based on climate, elevation, and soil type information, which can be inaccurate in transition areas <ns0:ref type='bibr' target='#b64'>(Zhang et al., 2008)</ns0:ref>. Using simulation models in combination with field and remote sensing data may be an effective alternative for mapping vegetation.</ns0:p><ns0:p>Changes in the environment can affect vegetation composition, structure, function, and spatial distribution. Environmental variables have been used to simulate the global distribution of vegetation <ns0:ref type='bibr' target='#b13'>(Dilts et al., 2015;</ns0:ref><ns0:ref type='bibr' target='#b40'>Mod et al., 2016)</ns0:ref>. Simulation models are usually developed to test how environmental variables control vegetation distribution <ns0:ref type='bibr' target='#b23'>(Guisan & Zimmermann, 2000)</ns0:ref>. Modern remote sensing data and software make it more convenient than ever before to produce predictive vegetation maps <ns0:ref type='bibr' target='#b19'>(Franklin, 1995)</ns0:ref>.</ns0:p><ns0:p>Predictive vegetation mapping uses environmental variables and various models based on niche theory and gradient analysis to visualize communities in geographic space <ns0:ref type='bibr' target='#b13'>(Dilts et al., 2015</ns0:ref><ns0:ref type='bibr' target='#b34'>, Lany et al., 2019)</ns0:ref>. Other methods based on statistics and machine learning have also been used to simulate vegetation distribution. Predictive vegetation mapping includes various statistical methods such as the generalized linear model, the generalized additive model, and multivariate statistical approaches <ns0:ref type='bibr' target='#b34'>(Lany et al., 2019;</ns0:ref><ns0:ref type='bibr' target='#b47'>Prasad, Iverson, & Liaw, 2006)</ns0:ref>. Recently, machine learning modeling methods have been used to map the distribution of both vegetation communities and individual species. These methods include the support vector machine (SVM), decision tree (DT), and artificial neural network <ns0:ref type='bibr' target='#b23'>(Guisan & Zimmermann, 2000;</ns0:ref><ns0:ref type='bibr' target='#b26'>Hastie, Tibshirani, & Friedman, 2009;</ns0:ref><ns0:ref type='bibr'>Zhou et al., 2016)</ns0:ref>. These machine learning models have fewer limitations and can produce more reliable results than traditional vegetation modeling methods <ns0:ref type='bibr' target='#b26'>(Hastie, Tibshirani, & Friedman, 2009)</ns0:ref>. Advanced machine learning techniques can integrate spectral and spatial predictors and improve classification accuracy by retaining important information about vegetation composition and structural differences <ns0:ref type='bibr' target='#b50'>(Sesnie et al., 2010)</ns0:ref>. Machine learning models efficiently and cost-effectively produce vegetation maps without the general inaccuracies caused by visual interpretation <ns0:ref type='bibr' target='#b20'>(Franklin, 2010)</ns0:ref>.</ns0:p><ns0:p>The Jing-Jin-Ji region, also known as the Beijing-Tianjin-Hebei urban agglomeration, is the center of northern Chinese politics, culture, and economy. Because of its extension, it faces significant problems such as unbalanced regional development and the struggle between economic growth and limited resources. The region's larger cities, including Beijing and Tianjin, have large populations, developed economies, and abundant educational resources. However, these big cities face issues of limited natural resources and serious ecological and environmental pollution. In particular, Beijing's large population requires limited resources such as water, land, and vegetation <ns0:ref type='bibr' target='#b56'>(Wang & Gong, 2018)</ns0:ref>. Breaking up administrative divisions may be the best method to coordinate regional development <ns0:ref type='bibr' target='#b57'>(Wang et al., 2019)</ns0:ref>. The new Xiong'an area located in Hebei province is being constructed to relocate some of Beijing's population. The development of areas like Xiong'an is affected by the surrounding natural environment. To better integrate the environmental carrying capacity and socioeconomic development of the Jing-Jin-Ji region, including the new Xiong'an area, accurate vegetation maps with temporal resolution are necessary. The most updated vegetation map of the Jing-Jin-Ji region is the Vegetation Map of the People's Republic of China (VMC), with a scale of 1:1,000,000 (Editorial Committee of Vegetation Map of China, Chinese Academy of <ns0:ref type='bibr'>Science, 2007)</ns0:ref>. Most of its data come from a field survey conducted between 1980 and 1990, meaning its temporal and spatial scales are both outdated.</ns0:p><ns0:p>In this study, we integrated geospatial, climate, and spectral data to simulate vegetation distribution through four different models across three vegetation classification units. This research was different from the research of <ns0:ref type='bibr'>Zhou et al. (2016)</ns0:ref>. Firstly, the research area of this research was the Jing-Jin-Ji region located in the North China Plain and affected by high socialeconomic disturbance, while the Qilian Mountain in the research of Zhou et al. is characterized by complex terrain, but without high social-economic disturbance. Secondly, the predictive variables as well as the combinations of these variables were different from the research of <ns0:ref type='bibr'>Zhou et al. (2016)</ns0:ref>. Thirdly, we compared four model methods for simulating distribution of vegetation in three vegetation classification levels, while only three models were used for simulation in two vegetation classification levels in the research of <ns0:ref type='bibr'>Zhou et al. (2016)</ns0:ref>. Our primary objectives were to: (1) determine the best modeling method for vegetation affected by high socioeconomic disturbance, (2) create an improved vegetation map of the Jing-Jin-Ji region, (3) determine the predictive abilities of different models across different vegetation classification units, and (4) determine which variables enhanced the classification accuracy for vegetation mapping.</ns0:p></ns0:div> <ns0:div><ns0:head>Materials & Methods</ns0:head></ns0:div> <ns0:div><ns0:head>Study area</ns0:head><ns0:p>The Jing-Jin-Ji region is located in the northern part of the North China Plain. Its location ranges from 113°04′ to 119°53′E and 36°01′ to 42°37′N and is bordered by Taihang Mountain in the west, Yanshan Mountain in the north, and the Bohai Sea in the east. The region includes the Beijing, Tianjin, and Hebei provinces (Fig. <ns0:ref type='figure'>1</ns0:ref>). Jing-Jin-Ji has a population of approximately 110 million people and covers an area of approximately 216,000 km 2 <ns0:ref type='bibr' target='#b57'>(Wang et al., 2019)</ns0:ref>. The region is a temperate monsoon climate zone with an elevation range of -14 to 2,837 m (Fig. <ns0:ref type='figure'>1</ns0:ref>). The annual precipitation ranges from 305 to 711 mm, with increased precipitation at lower altitudes. The annual mean temperature ranges from -3 to 14°C, with colder averages at higher elevations. The amount of precipitation in the region gradually decreases moving from the southeast to the northwest, while temperature changes show the reverse pattern.</ns0:p></ns0:div> <ns0:div><ns0:head>Vegetation and training data</ns0:head><ns0:p>The VMC, completed in 2007 based on field survey data, included eight vegetation groups (I), 15 vegetation types (II), and 75 formations and subformations (III) from the Jing-Jin-Ji region. However, some of the map's vegetation unit areas are very small and difficult to distinguish. To ensure that enough training and assessment point data can be randomly selected in units II and III, we selected eight units I, 12 units II, and 39 units III from the study area (Table <ns0:ref type='table'>1</ns0:ref>). Cultivated vegetation are mainly distributed in low areas with an altitude range of -14 to 254 m and an annual mean temperature range of 7 to 14℃. Major cultivated plants include winter wheat and coarse grains. Scrub and grass-forb communities are mainly distributed in the north, in elevations ranging from 254 to 1,440 m.</ns0:p><ns0:p>We obtained model training and assessment data on vegetation composition from field surveys and other publications. We collected a total of 3,763 vegetation points, with 2,789 of those used for model training and 974 used for model assessment. Each unit III had at least 80 vegetation points, with at least 60 of those used for model training and 20 used for model assessment. The model training and assessment data were randomly selected for each unit III. Additionally, we increased the credibility of the model assessment by first rasterizing the vector VMC onto the same grid as the modeled data, and then assessing the data using the Kappa coefficient <ns0:ref type='bibr' target='#b32'>(Landis & Koch, 1977;</ns0:ref><ns0:ref type='bibr' target='#b59'>Weng & Zhou, 2006;</ns0:ref><ns0:ref type='bibr'>Zhou et al., 2016)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Geospatial, climate, and spectral data</ns0:head><ns0:p>We derived geospatial variables, including elevation, slope, and aspect, from the 30 m resolution Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM; <ns0:ref type='bibr'>Zhao et al., 2018)</ns0:ref>. We then resampled these data to a 500×500 m grid cell size using the cubic technique in ArcGIS 10.3 <ns0:ref type='bibr' target='#b60'>(Wu et al., 2019)</ns0:ref>.</ns0:p><ns0:p>We downloaded the climate data, including 19 bioclimatic variables, at ~1 km resolution from WorldClim <ns0:ref type='bibr' target='#b18'>(Fick & Hijmans, 2017)</ns0:ref> at http://worldclim.org/. These climate data were also resampled to a 500×500 m grid cell size using the cubic technique in ArcGIS 10.3 <ns0:ref type='bibr' target='#b60'>(Wu et al., 2019)</ns0:ref>. Climatic variables are important for plant ecophysiology <ns0:ref type='bibr' target='#b40'>(Mod et al., 2016)</ns0:ref> and are commonly used as bioclimatic limits in vegetation models <ns0:ref type='bibr' target='#b52'>(Sitch et al., 2003)</ns0:ref>.</ns0:p><ns0:p>We acquired the MYD09A1500M product data (sinusoidal projection, path 4 and row 26, path 4 and row 27, path 5 and row 26, path 5 and row 27) from summer (July 20, 2013) and winter <ns0:ref type='bibr'>(January 17, 2013)</ns0:ref> as Modis images from the Geospatial Data Cloud at http://www.gscloud.cn/. Our image pre-processing included image subset mosaicking and clipping in ENVI 5.2 <ns0:ref type='bibr' target='#b10'>(Deng, 2010)</ns0:ref>. We obtained the land surface albedo in bands 1-7 directly from the MYD09A1500M product, and calculated the indices' effectiveness at reflecting vegetation information <ns0:ref type='bibr' target='#b46'>(Price, Guo, & Stiles, 2002;</ns0:ref><ns0:ref type='bibr'>Zhou et al., 2016)</ns0:ref>.</ns0:p><ns0:p>Since vegetation indices can provide information on both vegetation and environment <ns0:ref type='bibr' target='#b0'>(Bannari, Morin, & Bonn, 1995)</ns0:ref>, these indices are more sensitive than single spectral bands at detecting green vegetation <ns0:ref type='bibr' target='#b0'>(Bannari, Morin, & Bonn, 1995;</ns0:ref><ns0:ref type='bibr' target='#b8'>Cohen & Goward, 2004)</ns0:ref>. Therefore, vegetation indices can be used for image interpretation, vegetation discrimination and prediction, and land use change detection <ns0:ref type='bibr' target='#b0'>(Bannari, Morin, & Bonn, 1995;</ns0:ref><ns0:ref type='bibr' target='#b8'>Cohen & Goward, 2004;</ns0:ref><ns0:ref type='bibr'>Zhou et al., 2016)</ns0:ref>. We tested the vegetation discrimination of 14 vegetation indices (Table <ns0:ref type='table' target='#tab_1'>2</ns0:ref>).</ns0:p><ns0:p>To determine the distribution predictive ability of different variables, we grouped the variables into different combinations based on the results of the Pearson correlation. We only used less correlated variables (R <\|0.7\|, Pearson correlation) <ns0:ref type='bibr' target='#b6'>(Chala et al., 2017)</ns0:ref> in Combinations 1-9 (Table <ns0:ref type='table'>3</ns0:ref>), then used variable combinations as input predictor variables to simulate vegetation distribution. Combination 1 included the less correlated variables of the summer land surface albedos from bands 1 to 7. Combination 2 included the less correlated variables of the winter land surface albedos from bands 1 to 7. Combination 3 included the less correlated variables in Combinations <ns0:ref type='table' target='#tab_2'>1 and 2</ns0:ref> <ns0:ref type='table'>3</ns0:ref>). The SVM and maximum likelihood classification (MLC) methods only output the simulation results of variable Combinations 1 to 6, likely due to the training samples' weak separability <ns0:ref type='bibr' target='#b10'>(Deng, 2010)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Vegetation distribution models</ns0:head><ns0:p>We used DT, RF, MLC, and SVM vegetation distribution models in this study. The DT model is a divisive, monothetic, and supervised classifier often used for species distribution modeling and related applications <ns0:ref type='bibr' target='#b20'>(Franklin, 2010)</ns0:ref>. It is computationally fast and easy to understand and implement. It uses classification or regression algorithms to generate classification rules, and then visualizes those rules into simple tree graphics <ns0:ref type='bibr' target='#b26'>(Hastie, Tibshirani, & Friedman, 2009;</ns0:ref><ns0:ref type='bibr'>Zhou et al., 2016)</ns0:ref>. The DT model calculates the most significant variables contributing to the model <ns0:ref type='bibr' target='#b10'>(Deng, 2010)</ns0:ref>. We used a DT with five layers, 40 samples in the smallest parent node, and 10 samples in the smallest child node.</ns0:p><ns0:p>The RF model is an ensemble method that has been applied in risk assessment and species distribution modeling studies <ns0:ref type='bibr' target='#b9'>(Cutler et al., 2007;</ns0:ref><ns0:ref type='bibr' target='#b63'>Zhang & Dong, 2017)</ns0:ref>. The RF model creates and combines different DTs to produce considerably more accurate classifications that are unaffected by noise or overtraining <ns0:ref type='bibr' target='#b2'>(Burai et al., 2015;</ns0:ref><ns0:ref type='bibr' target='#b9'>Cutler et al., 2007;</ns0:ref><ns0:ref type='bibr' target='#b21'>Gislason, Benediktsson, & Sveinsson, 2006)</ns0:ref>. The RF model also calculates the most significant variables that contribute to the model <ns0:ref type='bibr' target='#b9'>(Cutler et al., 2007)</ns0:ref>. Running an RF model requires defined parameters, including tree number, number of randomly selected features, and node impurity function. We generated the RF model in EnMAP-Box, a license-free and platform-independent software interface designed to process hyperspectral remote sensing data, which was developed by the Humboldt University of Berlin. There are in-built applications aimed at the processing of hyperspectral data, such as SVM and RF for classification of image data in the EnMAP-Box <ns0:ref type='bibr' target='#b48'>(Held et al., 2014)</ns0:ref>. We used the default settings in EnMAP-Box with 100 trees. The number of randomly selected features was equal to the square root of the number of all features, and we used a Gini coefficient for the node impurity function <ns0:ref type='bibr' target='#b28'>(Jakimow et al., 2014;</ns0:ref><ns0:ref type='bibr' target='#b37'>Ma, Gao, & Gu, 2019;</ns0:ref><ns0:ref type='bibr' target='#b55'>van der Linden et al., 2015;</ns0:ref><ns0:ref type='bibr'>Zhou et al., 2016)</ns0:ref>.</ns0:p><ns0:p>The MLC model is one of the most commonly used supervised image classification methods. MLC's classification rules use the statistics of the Gaussian probability density function to assign each pixel to the class with the highest probability. Although the MLC method usually generates similar or more accurate classifications than other methods, it is not applicable when there are fewer training samples than input predictors <ns0:ref type='bibr' target='#b2'>(Burai et al., 2015;</ns0:ref><ns0:ref type='bibr'>Zhou et al., 2016)</ns0:ref>.</ns0:p><ns0:p>The SVM model is a supervised machine learning model used for classification and regression. It is a complex and widely used method that can output more accurate predictions <ns0:ref type='bibr' target='#b2'>(Burai et al., 2015)</ns0:ref> than other methods. The SVM model searches for an optimal plane in a multidimensional space to divide all sample elements into two categories, making the distance between the closest points in the two classes as large as possible <ns0:ref type='bibr' target='#b31'>(Kabacoff, 2016)</ns0:ref>. Running an SVM model requires a defined kernel parameter g and regularization parameter c. In this study, we generated the SVM model in the EnMAP-Box. The default settings in EnMAP-Box to the SVM model was applied, where the parameter g was 0.01 to 1,000, and the parameter c was 0.1 to 1,000. Parameters g and c were tested using a grid search with internal performance estimation, and we used those with the best performance for data training <ns0:ref type='bibr' target='#b36'>(Lin et al., 2014;</ns0:ref><ns0:ref type='bibr' target='#b55'>van der Linden et al., 2014;</ns0:ref><ns0:ref type='bibr' target='#b55'>van der Linden et al., 2015)</ns0:ref>.</ns0:p><ns0:p>We generated the predicted vegetation maps of the three classification units using the DT, RF, MLC, and SVM methods with a resolution of 500 m. We selected all 11 variable combinations as the input variables for each method. The DT and RF method results indicated which variables were most important for vegetation discrimination.</ns0:p></ns0:div> <ns0:div><ns0:head>Model assessment</ns0:head><ns0:p>PeerJ reviewing PDF \| (2020:01:44790:2:0:NEW 5 Aug 2020)</ns0:p></ns0:div> <ns0:div><ns0:head>Manuscript to be reviewed</ns0:head><ns0:p>We used the VMC and a total of 974 vegetation points to assess the overall accuracy and Kappa coefficient of every predicted vegetation map. Kappa coefficient values ranging from 0.4 to 0.55 indicated moderate agreement, from 0.56 to 0.8 indicated substantial agreement, and from 0.81 to 1 indicated almost perfect agreement <ns0:ref type='bibr' target='#b32'>(Landis & Koch, 1977;</ns0:ref><ns0:ref type='bibr' target='#b59'>Weng & Zhou, 2006;</ns0:ref><ns0:ref type='bibr'>Zhou et al., 2016)</ns0:ref>. When the Kappa coefficient value was greater than 0.4, the assessed predicted map was considered acceptable.</ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head></ns0:div> <ns0:div><ns0:head>Unit I modeling and assessment</ns0:head><ns0:p>The RF model's results were better than the results of the DT, MLC, and SVM models (Table <ns0:ref type='table' target='#tab_3'>4</ns0:ref>). The RF model had a Kappa coefficient larger than 0.4 when using variable Combinations 6 to 11 assessed by field point data, with an overall accuracy of 50% to 72%. The RF model had a Kappa coefficient larger than 0.56 when using variable Combinations 7 to 11 assessed by field data, with an overall accuracy of 68% to 72%. The RF model had the highest Kappa coefficient of 0.66 and the highest overall accuracy of 72% when using variable Combination 7. The DT model had a Kappa coefficient larger than 0.4 when using variable Combinations 7 to 11 assessed by field point data, with an overall accuracy of 54% to 56%. The DT model had no Kappa coefficient larger than 0.56 when using all variable combinations. After VMC assessment, we found the highest Kappa coefficient was 0.38 with an overall accuracy of 57% in the RF model using variable Combinations 9 to 11 (Table <ns0:ref type='table' target='#tab_3'>4</ns0:ref>; Fig. <ns0:ref type='figure'>2</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>Unit II modeling and assessment</ns0:head><ns0:p>The RF model results were better than the results of the other three models. The RF model using variable Combinations 7 to 11 had a Kappa coefficient larger than 0.4, with overall accuracies of 66%-70% and 54%-55% for field point data and VMC assessments, respectively. The RF model using Combinations 7 to 11 had a Kappa coefficient larger than 0.56 and an overall accuracy of 66%-70% when assessed by field point data. The RF model had the highest Kappa coefficient of 0.65 and the highest overall accuracy of 70% when using variable Combination 7. The DT model using variable Combinations 7 to 11 had a Kappa coefficient larger than 0.4, with overall accuracies of 53%-55% and 65%-72% for field point data and VMC assessments, respectively. The DT model had the highest Kappa coefficient of 0.54 and overall accuracy of 72% when using variable Combination 7. The DT model had a larger Kappa coefficient and greater overall accuracy when assessed by VMC rather than the RF model (Table <ns0:ref type='table' target='#tab_5'>5</ns0:ref>; Fig. <ns0:ref type='figure'>3</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>Unit III modeling and assessment</ns0:head><ns0:p>Only the RF model could simulate vegetation distribution in unit III. The RF model using variable Combinations 7 to 11 had a Kappa coefficient larger than 0.4 and an overall accuracy of 55%-58% assessed by field point data. The RF model using variable Combination 7 had the highest Kappa coefficient of 0.57 (the only model with a Kappa coefficient larger than 0.56) and the highest overall accuracy of 58% assessed by field point data. The Kappa coefficients in all models were less than 0.4 when assessed by the VMC (Table <ns0:ref type='table' target='#tab_7'>6</ns0:ref>; Fig. <ns0:ref type='figure'>4</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>Important variables</ns0:head><ns0:p>For the RF model, eight of the top 10 most important variables were the same across the different vegetation units: three climate variables (annual mean temperature, mean diurnal range, and annual precipitation), one geospatial variable (slope), and four spectral variables (Mid-infrared ratio and NDVI of winter vegetation index, brightness index and NDVI of summer vegetation index). For the DT model, nine of the top 10 most important variables were the same across the different vegetation units: four climate variables (annual mean temperature, mean diurnal range, precipitation of the driest month, and annual precipitation), one geospatial variable (slope), and 4 spectral variables (Mid-infrared ratio of winter vegetation index, brightness index of summer vegetation index, summer surface albedo of band 1, winter surface albedo of band 6) (Table <ns0:ref type='table' target='#tab_9'>7</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>Discussion</ns0:head></ns0:div> <ns0:div><ns0:head>Vegetation classification units</ns0:head><ns0:p>Vegetation classification is an important and complex system with multiple levels. Higher level classification methods not only accurately classify vegetation, but they can also describe ecosystem diversity, even during global changes <ns0:ref type='bibr' target='#b17'>(Faber-Langendoen et al., 2014)</ns0:ref>. Plants in different vegetation classification units have different spectral characteristics and climatic conditions that are the basis for vegetation distribution simulation. Thus, models using the same variables to simulate the vegetation distribution of different classification units may produce different classification accuracies <ns0:ref type='bibr' target='#b14'>(Dobrowski et al., 2008;</ns0:ref><ns0:ref type='bibr' target='#b47'>Prasad, Iverson, & Liaw, 2006)</ns0:ref>. Map accuracy has been found to be a function of which classification system and categories are used <ns0:ref type='bibr' target='#b38'>(Muchoney et al., 2000)</ns0:ref>.</ns0:p><ns0:p>Previous studies have explored vegetation distribution simulation using different vegetation classification systems. Plant functional types (PFTs), defined as plant sets sharing similar perturbation response effects on dominant ecosystem processes, have been used to simulate vegetation distribution, as seen in the Biome and Box system models <ns0:ref type='bibr' target='#b3'>(Box, 1981;</ns0:ref><ns0:ref type='bibr' target='#b4'>Box, 1996;</ns0:ref><ns0:ref type='bibr' target='#b15'>Dormann & Woodin, 2002)</ns0:ref> with positive simulation results <ns0:ref type='bibr' target='#b3'>(Box, 1981;</ns0:ref><ns0:ref type='bibr' target='#b54'>Song, Zhou & Ouyang, 2005;</ns0:ref><ns0:ref type='bibr' target='#b59'>Weng & Zhou, 2006)</ns0:ref>. The Mapped Atmosphere-Plant-Soil System (MAPSS) model was also used to simulate vegetation distribution using vegetation life forms, leaf area index, leaf morphology, and leaf longevity <ns0:ref type='bibr' target='#b65'>(Zhao et al., 2002)</ns0:ref>. Other researchers studied potential vegetation distribution using the Holdridge life zone model, with positive vegetation pattern results <ns0:ref type='bibr'>(Zheng et al., 2006)</ns0:ref>. When the IGBP classification system was applied to simulate vegetation distribution at a regional scale, the map estimate accuracy was upwards of 80% <ns0:ref type='bibr' target='#b38'>(Muchoney et al., 2000)</ns0:ref>. In this study, we used machine learning models and a hierarchical classification system from the VMC to determine the best modeling method for vegetation affected by high socioeconomic disturbance at various classification levels. In the VMC, unit I was the highest classification level, mainly based upon community appearance; unit II was the second highest level, mainly based upon community and climate appearance; and unit III was the medium classification level, based upon the dominant species. The accuracy of the vegetation distribution simulations in units I and II was similar to each other and higher than unit III's simulation (Tables <ns0:ref type='table' target='#tab_8'>4-6</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>Different model performances</ns0:head><ns0:p>We were interested in vegetation distribution modeling's ability to forecast and respond to environmental changes and vegetation pattern management at local to global scales. Vegetation distribution predictions can help explain the relationship between plants and their abiotic and biotic environments <ns0:ref type='bibr' target='#b20'>(Franklin, 2010)</ns0:ref>. To benefit from ecosystem service functions, people can design vegetation distributions according to distribution and abundance patterns and trends <ns0:ref type='bibr' target='#b26'>(Hastie, Tibshirani, & Friedman, 2009)</ns0:ref>. Vegetation classification has become a widely used ecological method due to a number of new statistical and machine learning methods used alongside mapped biological and environmental data to model vegetation distributions over large spatial scales at higher resolutions <ns0:ref type='bibr' target='#b9'>(Cutler et al., 2007)</ns0:ref>. Different image classification methods are rarely used together in the same classification research, especially when combined with environmental variables <ns0:ref type='bibr' target='#b35'>(Li et al., 2014)</ns0:ref>.</ns0:p><ns0:p>In this study, the RF model performed better than the DT, SVM, and MLC models across the three classification levels. This finding was consistent with the results of other studies that found that the RF method modeled vegetation distribution better than other methods <ns0:ref type='bibr' target='#b47'>(Prasad, Iverson, & Liaw, 2006)</ns0:ref>. The DT model divided the data into homogenous subgroups according to the range of predictor variable values. The DT model was generally able to handle a large number of independent variables and could build a tree model faster than the other methods. However, the DT model was somewhat unstable for vegetation distribution modeling and had lower classification accuracy <ns0:ref type='bibr'>(Zhou et al., 2016)</ns0:ref>. The RF model generated a large number of independent trees through data subsets and developed a split in every tree model using a random subset of predictor variables. Therefore, we concluded that the RF model was generally better than the DT model. The SVM model was developed from statistical learning methods and discriminated class samples by locating potentially nonlinear or multiple linear boundaries between individual training points <ns0:ref type='bibr' target='#b2'>(Burai et al., 2015)</ns0:ref>. The aim of the MLC model was to maximize the overall probability that a pixel is correctly assigned to a class. However, the MLC model requires a large number of training samples that limits its application <ns0:ref type='bibr' target='#b50'>(Sesnie et al., 2010)</ns0:ref>. Previous research has shown that classification accuracies when using the SVM classifier were higher than the MLC model <ns0:ref type='bibr' target='#b43'>(Pal & Mather, 2005;</ns0:ref><ns0:ref type='bibr' target='#b5'>Boyd, Sanchez-Hernandez, & Foody, 2006;</ns0:ref><ns0:ref type='bibr' target='#b49'>Sanchez-Hernandez, Boyd, & Foody, 2007;</ns0:ref><ns0:ref type='bibr' target='#b50'>Sesnie et al., 2010)</ns0:ref>. Because the model had fewer requirements, the DT method provided significantly more accurate classifications than those of the MLC model <ns0:ref type='bibr' target='#b5'>(Boyd, Sanchez-Hernandez, & Foody, 2006)</ns0:ref>. Other studies found that the RF and SVM models were similarly accurate (65.3% and 66.6%, respectively) <ns0:ref type='bibr' target='#b50'>(Sesnie et al., 2010)</ns0:ref>, and that the RF, MLC, DT, and SVM models performed similarly and reasonably well when simulating land use classification <ns0:ref type='bibr' target='#b35'>(Li et al., 2014)</ns0:ref>. In addition to the methods mentioned above, an artificial neural network implemented at a regional scale produced classification accuracies of 60%-80% <ns0:ref type='bibr' target='#b38'>(Muchoney et al., 2000;</ns0:ref><ns0:ref type='bibr' target='#b24'>Haslem et al., 2010)</ns0:ref>. In the Arctic, this method provided the most accurate vegetation mapping <ns0:ref type='bibr' target='#b33'>(Langford et al., 2019)</ns0:ref>. The reasons for the similarly positive results of these models may be due to the relatively large differences between classification objects, and their use of sufficiently representative training samples and appropriate input variables. In our study, only the SVM and MLC models' output simulated the results of variable Combinations 1 to 6. This may be due to the poor separability of the training samples, as the models could not recognize the training points or their vegetation categories <ns0:ref type='bibr' target='#b29'>(Jarnevich et al., 2015)</ns0:ref>. The Jing-Jin-Ji region has many types of vegetation with very small distribution areas, so the selected training points may have been insufficient. Future training points for these vegetation types should be selected using field surveys, and more suitable models for modeling global vegetation distribution should be developed and tested <ns0:ref type='bibr' target='#b30'>(Jiang et al., 2012)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Important variables in vegetation classification models</ns0:head><ns0:p>Variable selection is directly related to the vegetation distribution model's ability to capture important environmental factors <ns0:ref type='bibr' target='#b40'>(Mod et al., 2016)</ns0:ref>. Models predict the important variables that drive the distribution of vegetation <ns0:ref type='bibr' target='#b47'>(Prasad, Iverson, & Liaw, 2006)</ns0:ref>. Vegetation distribution is predominantly driven by temperature, precipitation, and topographical variables <ns0:ref type='bibr' target='#b19'>(Franklin, 1995;</ns0:ref><ns0:ref type='bibr' target='#b40'>Mod et al., 2016;</ns0:ref><ns0:ref type='bibr' target='#b47'>Prasad, Iverson, & Liaw, 2006)</ns0:ref>, specifically those related to physiological tolerance, site energy, and moisture balance <ns0:ref type='bibr' target='#b19'>(Franklin, 1995)</ns0:ref>. In addition to environmental variables, some spectral variables are used as input variables. However, the overuse of spectral variables can actually decrease discrimination accuracy, meaning that only spectral variables reflecting vegetation information should be selected, such as those related to the visible spectrum, infrared spectrum, and vegetation indices <ns0:ref type='bibr' target='#b46'>(Price, Guo, & Stiles, 2002</ns0:ref><ns0:ref type='bibr'>, Zhou et al., 2016)</ns0:ref>. Different variables respond to different information. Spectral variables directly reflect land surface object information, while geospatial and climatic variables reveal information about the vegetative environment.</ns0:p><ns0:p>Terrain, an important variable in vegetation distribution models, has long been used to improve map accuracy, especially for regions with large elevation differences <ns0:ref type='bibr' target='#b14'>(Dobrowski et al. 2008;</ns0:ref><ns0:ref type='bibr' target='#b42'>Oke & Thompson, 2015)</ns0:ref>. <ns0:ref type='bibr' target='#b50'>Sesnie et al. (2010)</ns0:ref> found that adding elevation as a predictive variable dramatically improved the accuracies of the SVM and RF models >80% for most forest types. Slopes with similar elevations but different aspects have very different soil and vegetation temperatures <ns0:ref type='bibr' target='#b22'>(Gunton, Polce, & Kunin, 2015;</ns0:ref><ns0:ref type='bibr' target='#b40'>Mod et al., 2016)</ns0:ref>. <ns0:ref type='bibr' target='#b14'>Dobrowski et al. (2008)</ns0:ref> highlighted the importance of slope and aspect when mapping vegetation communities in the Sierra Nevada. Slope was also an important variable in this study (Table <ns0:ref type='table' target='#tab_9'>7</ns0:ref>) since different types of vegetation require different precipitation and temperature levels and have different tolerances to extreme heat and cold. The significance of these climate variables (annual mean temperature, temperature range, and annual precipitation) has been validated in other studies <ns0:ref type='bibr' target='#b47'>(Prasad, Iverson & Liaw, 2006;</ns0:ref><ns0:ref type='bibr' target='#b51'>Sesnie et al., 2008)</ns0:ref>. We looked at two surface albedo indices (the summer surface albedo of band 1 and the winter surface albedo of band 6). <ns0:ref type='bibr' target='#b50'>Sesnie et al. (2010)</ns0:ref> combined elevation and spectral band data to increase the classification accuracy to a satisfactory level for most forest types. De <ns0:ref type='bibr' target='#b11'>Colstoun et al. (2003)</ns0:ref> obtained high accuracies (80%) when classifying coniferous, temperate broad-leaf, and mixed forest types using Landsat ETM+ bands. Other studies have used different vegetation index variables <ns0:ref type='bibr' target='#b46'>(Price, Guo & Stiles, 2002;</ns0:ref><ns0:ref type='bibr'>Zhou et al., 2016)</ns0:ref> specific to their study areas and data.</ns0:p><ns0:p>The input variables used in our vegetation distribution model are not exhaustive. Ecophysiologically meaningful predictors such as soil moisture, pH, and nutrients, should be considered. Other factors, such as actual light, disturbance, biotic interactions, land use, and bioclimatic information could also be incorporated into vegetation distribution models <ns0:ref type='bibr' target='#b14'>(Dobrowski et al., 2008;</ns0:ref><ns0:ref type='bibr' target='#b40'>Mod et al., 2016;</ns0:ref><ns0:ref type='bibr' target='#b47'>Prasad, Iverson, & Liaw, 2006;</ns0:ref><ns0:ref type='bibr' target='#b50'>Sesnie et al., 2010)</ns0:ref>. We suggest building more ecophysiologically sound vegetation distribution models that require a collaborative effort across the ecological, geographical, and environmental sciences <ns0:ref type='bibr' target='#b40'>(Mod et al., 2016)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Other factors affecting classification accuracy</ns0:head><ns0:p>In addition to classification units, models, and input variables, classification accuracy is affected by other factors, including algorithm error and image data <ns0:ref type='bibr' target='#b35'>(Li et al., 2014)</ns0:ref>. We must acknowledge the existence of errors in random sample selection, modeling, and data preprocessing algorithms. Remote sensing data sources, as well as the date and processing of selected images, vary, resulting in different simulated values and accuracies <ns0:ref type='bibr' target='#b46'>(Price, Guo, & Stiles, 2002)</ns0:ref>. Remote sensing images with high spectral and spatial resolutions provide rich spectral and ground information, moderately improving the predictive ability of the vegetation distribution model <ns0:ref type='bibr' target='#b44'>(Peng et al., 2002)</ns0:ref>. However, the use of high spectral and spatial resolution images creates a greater demand for data access, larger computer storage capacities, and faster data processors <ns0:ref type='bibr' target='#b46'>(Price, Guo, & Stiles, 2002)</ns0:ref>, which is why we did not use high spectral and spatial resolution images in this study. Moreover, some cultivated vegetation and shelter forests in the Jing-Jin-Ji region are greatly affected by human disturbance, which affects their water-heat conditions and soil nutrition. Urbanization reduces vegetation, transforming some areas into industrial, commercial, and residential land. This has led to the direct or indirect pollution of the water, soil, and air, and the reduced predictive ability of vegetation distribution models. The VMC we used for model assessment was published in 2007, and no updated study has been published over the past 10 years. The current state of the Jing-Jin-Ji region's vegetation no longer coincides with the VMC's assessment.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions</ns0:head><ns0:p>Our main objective was to determine the best simulation method for vegetation affected by high socioeconomic disturbance in the Jing-Jin-Ji region. The RF model was the most capable at simulating vegetation distribution across all three units. The DT model could simulate the vegetation distribution in units I and II. The SVM and MLC models could not simulate the distribution in any of the three units. Based on the Kappa coefficient, the RF model was generally better than the DT model and the most suitable model for simulating vegetation distribution in the Jing-Jin-Ji region. The most important variables affecting vegetation classification accuracy were three climate variables (annual mean temperature, mean diurnal range, and annual precipitation), one geospatial variable (slope), and two spectral variables (Midinfrared ratio of winter vegetation index and brightness index of summer vegetation index). We recommend using the RF model to produce or improve the vegetation maps in areas of high human disturbance. Manuscript to be reviewed The vegetation indices Manuscript to be reviewed Manuscript to be reviewed Manuscript to be reviewed Top ten most important variables of models in the different vegetation classification units.</ns0:p><ns0:p>The abbreviations of indices were shown in Table <ns0:ref type='table' target='#tab_1'>2</ns0:ref>.</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:01:44790:2:0:NEW 5 Aug 2020)</ns0:p><ns0:p>Manuscript to be reviewed </ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>. Combination 4 included the less correlated variables of the summer vegetation indices. Combination 5 included the less correlated variables of the winter vegetation indices. Combination 6 included the less correlated variables in Combinations 4 and 5. Combination 7 included the less correlated variables from the 19 bioclimatic variables. Combination 8 included the less correlated variables from the 19 bioclimatic variables and three geospatial variables. Combination 9 included the less correlated variables in Combinations 3, 6, and 8. Combinations 10 and 11 represented the top 10 most important variables of the DT and RF methods, with Combination 9 in vegetation unit I, respectively (Table</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='38,42.52,275.62,525.00,371.25' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='39,42.52,275.62,525.00,371.25' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='40,42.52,275.62,525.00,371.25' type='bitmap' /></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head>1 Table 1 : Classification units of the vegetation of China</ns0:head><ns0:label>11</ns0:label><ns0:figDesc /><ns0:table><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>Manuscript to be reviewed</ns0:cell></ns0:row><ns0:row><ns0:cell>8. Cultural</ns0:cell><ns0:cell>10 One crop</ns0:cell><ns0:cell>31 Spring wheat, naked oats, buckwheat, potatoes; flux</ns0:cell></ns0:row><ns0:row><ns0:cell>vegetation</ns0:cell><ns0:cell>annually and</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell cols='2'>Vegetation groups (I) Vegetation types cold-resistant</ns0:cell><ns0:cell>Formations and sub-formations (III)</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>(II) economic crops</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>0. No vegetation</ns0:cell><ns0:cell>0 No vegetation 11 One crop</ns0:cell><ns0:cell>0 No vegetation 32 Coarse grains</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>1. Needleleaf forest 1 Temperate annually, cold-resistant needleleaf forest economic crops 2. Broadleaf forest 2 Temperate and deciduous broadleaf orchards deciduous forest 12 Three crops two</ns0:cell><ns0:cell>1 Pinus tabulaeformis forest 2 Quercus mongolica forest 3 Quercus liaotungensis forest 4 Quercus variabilis forest 33 Winter wheat, coarse grains</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>years and two crops annually non irrigation, deciduous</ns0:cell><ns0:cell>5 Robinia pseudoacacia forest 6 Salix matsudana forest 7 Populus davidiana forest 34 Coarse grains 35 Rice 36 Winter wheat, corn, cotton</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>orchards</ns0:cell><ns0:cell>8 Betula platyphylla forest 37 Apple, pear orchard</ns0:cell></ns0:row><ns0:row><ns0:cell>3. Scrub</ns0:cell><ns0:cell>3 Temperate</ns0:cell><ns0:cell>9 Corylus heterophylla scrub 38 Winter wheat, corn, Chinese sorghum, sweet</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>broadleaf deciduous scrub</ns0:cell><ns0:cell>potatoes; cotton, tabacco, peanut, sesame; apple, 10 Lespedeza bicolor scrub pear, hauthorn, persimmon, walnut, pomegranat, 11 Prunus armeniaca var. ansa scrub grape 12 Vitex negundo var. heterophylla, Zizyphus jujuba var. spinosa scrub 39 Winter wheat, coarse grains (loamy soil)</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>13 Cotinus coggygria var. cinerea scrub</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>14 Spiraea spp. scrub</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>15 Ostryopsis davidiana scrub</ns0:cell></ns0:row><ns0:row><ns0:cell>4. Steppe</ns0:cell><ns0:cell>4 Temperate grass-</ns0:cell><ns0:cell>16 Stipa baicalensis, forb meadow steppe</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>forb meadow steppe</ns0:cell><ns0:cell>17 Filifolium sibiricum, grass-forb meadow steppe</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>5 Temperate</ns0:cell><ns0:cell>18 Aneurolepidium chinense, needlegrass steppe</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>needlegrass arid steppe</ns0:cell><ns0:cell>19 Stipa krylovii steppe 20 Stipa bungiana steppe</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>21 Thymus mongolicus, needlegrass steppe</ns0:cell></ns0:row><ns0:row><ns0:cell>5. Grass-forb</ns0:cell><ns0:cell>6 Temperate grass-</ns0:cell><ns0:cell>22 Bothriochloa ischaemum community</ns0:cell></ns0:row><ns0:row><ns0:cell>community</ns0:cell><ns0:cell>forb community</ns0:cell><ns0:cell>23 Bothriochloa ischaemum community</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>24 Vitex negundo var. heterophylla，Zizyphus jujuba</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>var. spinosus, Bothriochloa ischaemum scrub and</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>grass community</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>25 Vitex negundo var. heterophylla，Zizyphus jujuba</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>var. spinosus, Themeda triandra var. japonica scrub</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>and grass community</ns0:cell></ns0:row><ns0:row><ns0:cell>6. Meadow</ns0:cell><ns0:cell>7 Temperate grass</ns0:cell><ns0:cell>26 Arundinella hirta, Spodiopogon sibiricus, forb</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>and forb</ns0:cell><ns0:cell>meadow</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>meadow</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>27 Carex spp., forb meadow</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>8 Temperate grass</ns0:cell><ns0:cell>28 Achnatherum splendens holophytic meadow</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>and forb holophytic</ns0:cell><ns0:cell>29 Suaeda glauca holophytic meadow</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>meadow</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>7. Swamp</ns0:cell><ns0:cell>9 Cold-temperate</ns0:cell><ns0:cell>30 Phragmites communis swamp</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>and temperate</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>swamp</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell cols='3'>PeerJ reviewing PDF \| (2020:01:44790:2:0:NEW 5 Aug 2020)</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_1'><ns0:head>Table 2 (on next page)</ns0:head><ns0:label>2</ns0:label><ns0:figDesc /><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_2'><ns0:head>Table 2 : The vegetation indices</ns0:head><ns0:label>2</ns0:label><ns0:figDesc /><ns0:table><ns0:row><ns0:cell>Indices</ns0:cell><ns0:cell cols='2'>Abbreviation Formula</ns0:cell></ns0:row><ns0:row><ns0:cell>Ratio vegetation index</ns0:cell><ns0:cell>RVI</ns0:cell><ns0:cell>NIR/Red</ns0:cell></ns0:row><ns0:row><ns0:cell>Brightness index</ns0:cell><ns0:cell>BI</ns0:cell><ns0:cell>0.2909Blue + 0.2493Green + 0.4806Red +</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>0.5568NIR + 0.4438SWIR1 + 0.1706SWIR2</ns0:cell></ns0:row><ns0:row><ns0:cell>Green vegetation index</ns0:cell><ns0:cell>GI</ns0:cell><ns0:cell>-0.2728Blue -0.2174Green-0.5508Red +</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>0.7221NIR + 0.0733SWIR1 -0.1648SWIR2</ns0:cell></ns0:row><ns0:row><ns0:cell>Wetness index</ns0:cell><ns0:cell>WI</ns0:cell><ns0:cell>0.1446Blue + 0.1761Green + 0.3322Red +</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>0.3396NIR -0.6210SWIR1 -0.4186SWIR2</ns0:cell></ns0:row><ns0:row><ns0:cell>Differenced vegetation index</ns0:cell><ns0:cell>DVI</ns0:cell><ns0:cell>NIR -Red</ns0:cell></ns0:row><ns0:row><ns0:cell>Green ratio</ns0:cell><ns0:cell>GR</ns0:cell><ns0:cell>NIR/Green</ns0:cell></ns0:row><ns0:row><ns0:cell>Mid-infrared ratio</ns0:cell><ns0:cell>MR</ns0:cell><ns0:cell>NIR/SWIR1</ns0:cell></ns0:row><ns0:row><ns0:cell>Soil-adjusted vegetation index</ns0:cell><ns0:cell>SAVI</ns0:cell><ns0:cell>(1.5(NIR -Red))/((NIR + Red + 0.5))</ns0:cell></ns0:row><ns0:row><ns0:cell>Optimization of soil-adjusted</ns0:cell><ns0:cell>OSAVI</ns0:cell><ns0:cell>(1.16(NIR -Red))/((NIR + Red + 0.16))</ns0:cell></ns0:row><ns0:row><ns0:cell>vegetation index</ns0:cell><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>Atmospherically resistant vegetation</ns0:cell><ns0:cell>ARVI</ns0:cell><ns0:cell>(NIR -(2Red -Blue))/(NIR + (2Red -Blue))</ns0:cell></ns0:row><ns0:row><ns0:cell>index</ns0:cell><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell cols='2'>Normalized difference vegetation index NDVI</ns0:cell><ns0:cell>(NIR -Red)/(NIR + Red)</ns0:cell></ns0:row><ns0:row><ns0:cell>Enhanced vegetation index</ns0:cell><ns0:cell>EVI</ns0:cell><ns0:cell>2.5[(NIR -Red)/(NIR + 6Red -7.5Blue + 1)]</ns0:cell></ns0:row><ns0:row><ns0:cell>Normalized difference tillage index</ns0:cell><ns0:cell>NDTI</ns0:cell><ns0:cell>(SWIR1-SWIR2)/(SWIR1 + SWIR2)</ns0:cell></ns0:row><ns0:row><ns0:cell>Normalized difference senescent</ns0:cell><ns0:cell>NDSVI</ns0:cell><ns0:cell>(SWIR1-Red)/(SWIR1 + Red)</ns0:cell></ns0:row><ns0:row><ns0:cell>vegetation index</ns0:cell><ns0:cell /><ns0:cell /></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_3'><ns0:head>Table 4 (on next page)</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Model assessment of vegetation groups by field point data and VMC.</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Variable combinations were shown in Table 3. VMC, the Vegetation Map of the People's</ns0:cell></ns0:row><ns0:row><ns0:cell>Republic of China. , the kappa coefficient lager than 0.56; , the kappa coefficient larger</ns0:cell></ns0:row><ns0:row><ns0:cell>than 0.4 and less than 0.56. OA, Overall accuracy; KC, Kappa coefficient.</ns0:cell></ns0:row></ns0:table><ns0:note>PeerJ reviewing PDF \| (2020:01:44790:2:0:NEW 5 Aug 2020)Manuscript to be reviewed 1</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_4'><ns0:head>Table 4 : Model assessment of vegetation groups by field point data and VMC.</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Variable combinations were shown in Table3. VMC, the Vegetation Map of the People's Republic of China. *, the kappa coefficient 3 lager than 0.56; , the kappa coefficient larger than 0.4 and less than 0.56. OA, Overall accuracy; KC, Kappa coefficient.</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Variable</ns0:cell><ns0:cell cols='2'>Decision tree</ns0:cell><ns0:cell /><ns0:cell cols='2'>Random forest</ns0:cell><ns0:cell cols='2'>Support vector machine</ns0:cell><ns0:cell cols='2'>Maximum likelihood classification</ns0:cell></ns0:row><ns0:row><ns0:cell>combinations</ns0:cell><ns0:cell>Point data</ns0:cell><ns0:cell>VMC</ns0:cell><ns0:cell cols='2'>Point data</ns0:cell><ns0:cell>VMC</ns0:cell><ns0:cell>Point data</ns0:cell><ns0:cell>VMC</ns0:cell><ns0:cell>Point data</ns0:cell><ns0:cell>VMC</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='3'>OA KC OA KC OA</ns0:cell><ns0:cell>KC</ns0:cell><ns0:cell cols='5'>OA KC OA KC OA KC OA KC OA KC</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='9'>34% 0.18 55% 0.22 37% 0.24 32% 0.09 36% 0.21 53% 0.21 23% 0.08 11% 0.02</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='9'>38% 0.20 52% 0.23 39% 0.27 37% 0.13 35% 0.20 55% 0.24 18% 0.07 9% 0.03</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='9'>45% 0.31 54% 0.26 47% 0.36 45% 0.21 41% 0.27 54% 0.27 24% 0.12 15% 0.05</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='9'>32% 0.16 46% 0.16 42% 0.30 42% 0.17 37% 0.22 57% 0.26 11% 0.04 3% 0.01</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='9'>31% 0.11 59% 0.14 44% 0.32 44% 0.19 36% 0.22 51% 0.22 9% 0.04 4% 0.02</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='9'>41% 0.26 44% 0.18 50% 0.40* 52% 0.27 42% 0.29 54% 0.27 13% 0.08 4% 0.03</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='5'>54% 0.45* 57% 0.34 72% 0.66** 55% 0.35</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='5'>55% 0.46* 56% 0.35 69% 0.63** 56% 0.37</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='5'>55% 0.46* 53% 0.34 68% 0.61** 57% 0.38</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>10</ns0:cell><ns0:cell cols='5'>55% 0.46* 53% 0.33 69% 0.63** 57% 0.38</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>11</ns0:cell><ns0:cell cols='5'>56% 0.46* 56% 0.36 68% 0.62** 57% 0.38</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>4</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>5</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>6</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row></ns0:table><ns0:note>2 PeerJ reviewing PDF \| (2020:01:44790:2:0:NEW 5 Aug 2020)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_5'><ns0:head>Table 5 (on next page)</ns0:head><ns0:label>5</ns0:label><ns0:figDesc>Model assessment of vegetation types by field point data and VMC.</ns0:figDesc><ns0:table /><ns0:note>The Abbreviations were same with Table4.1</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_6'><ns0:head>Table 5 : Model assessment of vegetation types by field point data and VMC.</ns0:head><ns0:label>5</ns0:label><ns0:figDesc /><ns0:table><ns0:row><ns0:cell>Variable</ns0:cell><ns0:cell /><ns0:cell cols='2'>Decision tree</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell cols='2'>Random forest</ns0:cell><ns0:cell /><ns0:cell cols='4'>Support vector machine</ns0:cell><ns0:cell cols='4'>Maximum likelihood classification</ns0:cell></ns0:row><ns0:row><ns0:cell>combinations</ns0:cell><ns0:cell cols='2'>Point data</ns0:cell><ns0:cell cols='2'>VMC</ns0:cell><ns0:cell cols='2'>Point data</ns0:cell><ns0:cell cols='2'>VMC</ns0:cell><ns0:cell cols='2'>Point data</ns0:cell><ns0:cell cols='2'>VMC</ns0:cell><ns0:cell cols='2'>Point data</ns0:cell><ns0:cell cols='2'>VMC</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>OA</ns0:cell><ns0:cell>KC</ns0:cell><ns0:cell>OA</ns0:cell><ns0:cell>KC</ns0:cell><ns0:cell>OA</ns0:cell><ns0:cell>KC</ns0:cell><ns0:cell>OA</ns0:cell><ns0:cell>KC</ns0:cell><ns0:cell>OA</ns0:cell><ns0:cell>KC</ns0:cell><ns0:cell>OA</ns0:cell><ns0:cell>KC</ns0:cell><ns0:cell>OA</ns0:cell><ns0:cell>KC</ns0:cell><ns0:cell>OA</ns0:cell><ns0:cell>KC</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='12'>42% 0.24 63% 0.33 32% 0.22 23% 0.09 32% 0.18 40% 0.18</ns0:cell><ns0:cell>6%</ns0:cell><ns0:cell>0.02</ns0:cell><ns0:cell>7%</ns0:cell><ns0:cell>0.00</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='12'>44% 0.27 58% 0.31 34% 0.23 30% 0.14 31% 0.18 44% 0.24</ns0:cell><ns0:cell>5%</ns0:cell><ns0:cell>0.02</ns0:cell><ns0:cell cols='2'>14% 0.00</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='12'>43% 0.30 58% 0.35 44% 0.34 38% 0.22 37% 0.26 43% 0.25</ns0:cell><ns0:cell>9%</ns0:cell><ns0:cell>0.05</ns0:cell><ns0:cell cols='2'>13% 0.00</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='14'>36% 0.20 47% 0.20 39% 0.29 31% 0.15 32% 0.19 43% 0.21 13% 0.07</ns0:cell><ns0:cell>6%</ns0:cell><ns0:cell>0.02</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='12'>32% 0.14 59% 0.23 41% 0.31 36% 0.19 34% 0.22 43% 0.22</ns0:cell><ns0:cell>6%</ns0:cell><ns0:cell>0.03</ns0:cell><ns0:cell>6%</ns0:cell><ns0:cell>0.03</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='14'>36% 0.23 45% 0.24 47% 0.38 44% 0.27 40% 0.29 43% 0.25 14% 0.09</ns0:cell><ns0:cell cols='2'>21% 0.06</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='8'>55% 0.46* 72% 0.54* 70% 0.65** 54% 0.41</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='8'>53% 0.44 68% 0.52* 68% 0.63** 55% 0.43</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='8'>54% 0.45 65% 0.49* 66% 0.60** 55% 0.43</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>10</ns0:cell><ns0:cell cols='8'>54% 0.45 65% 0.49* 68% 0.63** 55% 0.43</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>11</ns0:cell><ns0:cell cols='8'>53% 0.44 68% 0.52* 67% 0.62** 55% 0.43</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>3</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>4</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row></ns0:table><ns0:note>2 The Abbreviations were same with Table4.PeerJ reviewing PDF \| (2020:01:44790:2:0:NEW 5 Aug 2020)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_7'><ns0:head>Table 6 (on next page)</ns0:head><ns0:label>6</ns0:label><ns0:figDesc>Model assessment of formations and subformations by field point data and VMC.</ns0:figDesc><ns0:table><ns0:row><ns0:cell>The Abbreviations were same with Table 4.</ns0:cell></ns0:row></ns0:table><ns0:note>1</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_8'><ns0:head>Table 6 : Model assessment of formations and subformations by field point data and VMC.</ns0:head><ns0:label>6</ns0:label><ns0:figDesc>The Abbreviations were same with Table4.</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Variable combinations</ns0:cell><ns0:cell cols='4'>Decision tree Point data VMC OA KC OA KC</ns0:cell><ns0:cell cols='4'>Random forest Point data VMC OA KC OA KC</ns0:cell><ns0:cell cols='4'>Support vector machine Point data VMC OA KC OA KC</ns0:cell><ns0:cell cols='4'>Maximum likelihood classification Point data VMC OA KC OA KC</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>23%</ns0:cell><ns0:cell>0.14</ns0:cell><ns0:cell>19%</ns0:cell><ns0:cell>0.08</ns0:cell><ns0:cell>20%</ns0:cell><ns0:cell>0.18</ns0:cell><ns0:cell>5%</ns0:cell><ns0:cell>0.02</ns0:cell><ns0:cell>11%</ns0:cell><ns0:cell>0.09</ns0:cell><ns0:cell>6%</ns0:cell><ns0:cell>0.03</ns0:cell><ns0:cell>8%</ns0:cell><ns0:cell>0.06</ns0:cell><ns0:cell>8%</ns0:cell><ns0:cell>0.04</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>22%</ns0:cell><ns0:cell>-0.04</ns0:cell><ns0:cell>49%</ns0:cell><ns0:cell>0.04</ns0:cell><ns0:cell>19%</ns0:cell><ns0:cell>0.17</ns0:cell><ns0:cell>6%</ns0:cell><ns0:cell>0.03</ns0:cell><ns0:cell>13%</ns0:cell><ns0:cell>0.11</ns0:cell><ns0:cell>7%</ns0:cell><ns0:cell>0.04</ns0:cell><ns0:cell>8%</ns0:cell><ns0:cell>0.06</ns0:cell><ns0:cell>13%</ns0:cell><ns0:cell>0.05</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>26%</ns0:cell><ns0:cell>0.14</ns0:cell><ns0:cell>45%</ns0:cell><ns0:cell>0.23</ns0:cell><ns0:cell>29%</ns0:cell><ns0:cell>0.27</ns0:cell><ns0:cell>9%</ns0:cell><ns0:cell>0.07</ns0:cell><ns0:cell>21%</ns0:cell><ns0:cell>0.19</ns0:cell><ns0:cell>10%</ns0:cell><ns0:cell>0.07</ns0:cell><ns0:cell>12%</ns0:cell><ns0:cell>0.09</ns0:cell><ns0:cell>13%</ns0:cell><ns0:cell>0.07</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>30%</ns0:cell><ns0:cell>0.20</ns0:cell><ns0:cell>30%</ns0:cell><ns0:cell>0.04</ns0:cell><ns0:cell>22%</ns0:cell><ns0:cell>0.20</ns0:cell><ns0:cell>7%</ns0:cell><ns0:cell>0.04</ns0:cell><ns0:cell>16%</ns0:cell><ns0:cell>0.14</ns0:cell><ns0:cell>6%</ns0:cell><ns0:cell>0.03</ns0:cell><ns0:cell>9%</ns0:cell><ns0:cell>0.07</ns0:cell><ns0:cell>8%</ns0:cell><ns0:cell>0.04</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>33%</ns0:cell><ns0:cell>0.01</ns0:cell><ns0:cell>67%</ns0:cell><ns0:cell>0.00</ns0:cell><ns0:cell>22%</ns0:cell><ns0:cell>0.20</ns0:cell><ns0:cell>7%</ns0:cell><ns0:cell>0.04</ns0:cell><ns0:cell>15%</ns0:cell><ns0:cell>0.13</ns0:cell><ns0:cell>5%</ns0:cell><ns0:cell>0.03</ns0:cell><ns0:cell>11%</ns0:cell><ns0:cell>0.09</ns0:cell><ns0:cell>10%</ns0:cell><ns0:cell>0.04</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>26%</ns0:cell><ns0:cell>0.15</ns0:cell><ns0:cell>22%</ns0:cell><ns0:cell>0.02</ns0:cell><ns0:cell>31%</ns0:cell><ns0:cell>0.30</ns0:cell><ns0:cell>11%</ns0:cell><ns0:cell>0.08</ns0:cell><ns0:cell>21%</ns0:cell><ns0:cell>0.19</ns0:cell><ns0:cell>8%</ns0:cell><ns0:cell>0.06</ns0:cell><ns0:cell>12%</ns0:cell><ns0:cell>0.09</ns0:cell><ns0:cell>15%</ns0:cell><ns0:cell>0.08</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>33%</ns0:cell><ns0:cell>0.20</ns0:cell><ns0:cell>52%</ns0:cell><ns0:cell>0.27</ns0:cell><ns0:cell cols='3'>58% 0.57* 23%</ns0:cell><ns0:cell>0.20</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>27%</ns0:cell><ns0:cell>0.17</ns0:cell><ns0:cell>34%</ns0:cell><ns0:cell>0.18</ns0:cell><ns0:cell>55%</ns0:cell><ns0:cell>0.54</ns0:cell><ns0:cell>23%</ns0:cell><ns0:cell>0.20</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>25%</ns0:cell><ns0:cell>0.15</ns0:cell><ns0:cell>22%</ns0:cell><ns0:cell>0.15</ns0:cell><ns0:cell>55%</ns0:cell><ns0:cell>0.53</ns0:cell><ns0:cell>22%</ns0:cell><ns0:cell>0.20</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>10</ns0:cell><ns0:cell>30%</ns0:cell><ns0:cell>0.17</ns0:cell><ns0:cell>41%</ns0:cell><ns0:cell>0.22</ns0:cell><ns0:cell>56%</ns0:cell><ns0:cell>0.55</ns0:cell><ns0:cell>23%</ns0:cell><ns0:cell>0.21</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>11</ns0:cell><ns0:cell>31%</ns0:cell><ns0:cell>0.20</ns0:cell><ns0:cell>41%</ns0:cell><ns0:cell>0.22</ns0:cell><ns0:cell>56%</ns0:cell><ns0:cell>0.55</ns0:cell><ns0:cell>23%</ns0:cell><ns0:cell>0.20</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>3</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>4</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row></ns0:table><ns0:note>2 PeerJ reviewing PDF \| (2020:01:44790:2:0:NEW 5 Aug 2020)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_9'><ns0:head>Table 7 (on next page)</ns0:head><ns0:label>7</ns0:label><ns0:figDesc /><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_10'><ns0:head>Table 7 : Top ten most important variables of models in the different vegetation classification units. 2</ns0:head><ns0:label>7</ns0:label><ns0:figDesc>The abbreviations of indices were shown in Table2.</ns0:figDesc><ns0:table><ns0:row><ns0:cell cols='2'>1 Vegetation groups</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell cols='2'>Vegetation types</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell cols='3'>Formations and sub-formations</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>Decision tree</ns0:cell><ns0:cell /><ns0:cell>Random forest</ns0:cell><ns0:cell /><ns0:cell>Decision tree</ns0:cell><ns0:cell /><ns0:cell cols='2'>Random forest</ns0:cell><ns0:cell>Decision tree</ns0:cell><ns0:cell /><ns0:cell cols='2'>Random forest</ns0:cell></ns0:row><ns0:row><ns0:cell>Important</ns0:cell><ns0:cell>Standardized</ns0:cell><ns0:cell>Important</ns0:cell><ns0:cell>Normalized</ns0:cell><ns0:cell>Important</ns0:cell><ns0:cell>Standardized</ns0:cell><ns0:cell>Important</ns0:cell><ns0:cell>Normalized</ns0:cell><ns0:cell>Important</ns0:cell><ns0:cell>Standardized</ns0:cell><ns0:cell>Important</ns0:cell><ns0:cell>Normalized</ns0:cell></ns0:row><ns0:row><ns0:cell>variables</ns0:cell><ns0:cell>Importance</ns0:cell><ns0:cell>variables</ns0:cell><ns0:cell>importance</ns0:cell><ns0:cell>variables</ns0:cell><ns0:cell>Importance</ns0:cell><ns0:cell>variables</ns0:cell><ns0:cell>importance</ns0:cell><ns0:cell>variables</ns0:cell><ns0:cell>Importance</ns0:cell><ns0:cell>variables</ns0:cell><ns0:cell>importance</ns0:cell></ns0:row><ns0:row><ns0:cell>1 Annual mean</ns0:cell><ns0:cell>1.00</ns0:cell><ns0:cell>Annual mean</ns0:cell><ns0:cell>3.68</ns0:cell><ns0:cell>Annual mean</ns0:cell><ns0:cell>1.00</ns0:cell><ns0:cell>Annual mean</ns0:cell><ns0:cell>3.51</ns0:cell><ns0:cell>Annual mean</ns0:cell><ns0:cell>1.00</ns0:cell><ns0:cell>Annual mean</ns0:cell><ns0:cell>4.16</ns0:cell></ns0:row><ns0:row><ns0:cell>temperature</ns0:cell><ns0:cell /><ns0:cell>temperature</ns0:cell><ns0:cell /><ns0:cell>temperature</ns0:cell><ns0:cell /><ns0:cell>temperature</ns0:cell><ns0:cell /><ns0:cell>temperature</ns0:cell><ns0:cell /><ns0:cell>temperature</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>2 Annual</ns0:cell><ns0:cell>0.88</ns0:cell><ns0:cell>Slope</ns0:cell><ns0:cell>2.94</ns0:cell><ns0:cell>Slope</ns0:cell><ns0:cell>0.83</ns0:cell><ns0:cell>Slope</ns0:cell><ns0:cell>3.35</ns0:cell><ns0:cell>Annual</ns0:cell><ns0:cell>0.86</ns0:cell><ns0:cell>Annual</ns0:cell><ns0:cell>3.28</ns0:cell></ns0:row><ns0:row><ns0:cell>precipitation</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>precipitation</ns0:cell><ns0:cell /><ns0:cell>precipitation</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>3 Slope</ns0:cell><ns0:cell>0.80</ns0:cell><ns0:cell>Mean diurnal</ns0:cell><ns0:cell>2.60</ns0:cell><ns0:cell>Annual</ns0:cell><ns0:cell>0.51</ns0:cell><ns0:cell>Mean diurnal</ns0:cell><ns0:cell>3.06</ns0:cell><ns0:cell>Slope</ns0:cell><ns0:cell>0.63</ns0:cell><ns0:cell>Mean diurnal</ns0:cell><ns0:cell>3.25</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>range</ns0:cell><ns0:cell /><ns0:cell>precipitation</ns0:cell><ns0:cell /><ns0:cell>range</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>range</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>4 Winter</ns0:cell><ns0:cell>0.36</ns0:cell><ns0:cell>Annual</ns0:cell><ns0:cell>2.38</ns0:cell><ns0:cell>Winter</ns0:cell><ns0:cell>0.30</ns0:cell><ns0:cell>Annual</ns0:cell><ns0:cell>2.8</ns0:cell><ns0:cell>Mean diurnal</ns0:cell><ns0:cell>0.52</ns0:cell><ns0:cell>Slope</ns0:cell><ns0:cell>2.24</ns0:cell></ns0:row><ns0:row><ns0:cell>vegetation</ns0:cell><ns0:cell /><ns0:cell>precipitation</ns0:cell><ns0:cell /><ns0:cell>vegetation</ns0:cell><ns0:cell /><ns0:cell>precipitation</ns0:cell><ns0:cell /><ns0:cell>range</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>index MR</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>index MR</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>5 Mean diurnal</ns0:cell><ns0:cell>0.33</ns0:cell><ns0:cell>Summer</ns0:cell><ns0:cell>1.88</ns0:cell><ns0:cell>Mean diurnal</ns0:cell><ns0:cell>0.28</ns0:cell><ns0:cell>Summer</ns0:cell><ns0:cell>1.84</ns0:cell><ns0:cell>Precipitation</ns0:cell><ns0:cell>0.52</ns0:cell><ns0:cell>Precipitation</ns0:cell><ns0:cell>2.16</ns0:cell></ns0:row><ns0:row><ns0:cell>range</ns0:cell><ns0:cell /><ns0:cell>vegetation</ns0:cell><ns0:cell /><ns0:cell>range</ns0:cell><ns0:cell /><ns0:cell>vegetation</ns0:cell><ns0:cell /><ns0:cell>of driest</ns0:cell><ns0:cell /><ns0:cell>of driest</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>index BI</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>index BI</ns0:cell><ns0:cell /><ns0:cell>month</ns0:cell><ns0:cell /><ns0:cell>month</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>6 Summer</ns0:cell><ns0:cell>0.29</ns0:cell><ns0:cell>Winter</ns0:cell><ns0:cell>1.37</ns0:cell><ns0:cell>Summer</ns0:cell><ns0:cell>0.22</ns0:cell><ns0:cell>Winter</ns0:cell><ns0:cell>1.61</ns0:cell><ns0:cell>Winter</ns0:cell><ns0:cell>0.4</ns0:cell><ns0:cell>Summer</ns0:cell><ns0:cell>1.83</ns0:cell></ns0:row><ns0:row><ns0:cell>surface</ns0:cell><ns0:cell /><ns0:cell>vegetation</ns0:cell><ns0:cell /><ns0:cell>vegetation</ns0:cell><ns0:cell /><ns0:cell>vegetation</ns0:cell><ns0:cell /><ns0:cell>vegetation</ns0:cell><ns0:cell /><ns0:cell>vegetation</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>albedo of</ns0:cell><ns0:cell /><ns0:cell>index NDVI</ns0:cell><ns0:cell /><ns0:cell>index EVI</ns0:cell><ns0:cell /><ns0:cell>index NDVI</ns0:cell><ns0:cell /><ns0:cell>index MR</ns0:cell><ns0:cell /><ns0:cell>index BI</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>band 1</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>7 Summer</ns0:cell><ns0:cell>0.28</ns0:cell><ns0:cell>Summer</ns0:cell><ns0:cell>1.36</ns0:cell><ns0:cell>Precipitation</ns0:cell><ns0:cell>0.21</ns0:cell><ns0:cell>Winter</ns0:cell><ns0:cell>1.45</ns0:cell><ns0:cell>Summer</ns0:cell><ns0:cell>0.32</ns0:cell><ns0:cell>Summer</ns0:cell><ns0:cell>1.7</ns0:cell></ns0:row><ns0:row><ns0:cell>vegetation</ns0:cell><ns0:cell /><ns0:cell>vegetation</ns0:cell><ns0:cell /><ns0:cell>of driest</ns0:cell><ns0:cell /><ns0:cell>vegetation</ns0:cell><ns0:cell /><ns0:cell>surface albedo</ns0:cell><ns0:cell /><ns0:cell>vegetation</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>index BI</ns0:cell><ns0:cell /><ns0:cell>index EVI</ns0:cell><ns0:cell /><ns0:cell>month</ns0:cell><ns0:cell /><ns0:cell>index MR</ns0:cell><ns0:cell /><ns0:cell>of band 1</ns0:cell><ns0:cell /><ns0:cell>index NDVI</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>8 Precipitation</ns0:cell><ns0:cell>0.25</ns0:cell><ns0:cell>Winter</ns0:cell><ns0:cell>1.30</ns0:cell><ns0:cell>Summer</ns0:cell><ns0:cell>0.20</ns0:cell><ns0:cell>Summer</ns0:cell><ns0:cell>1.31</ns0:cell><ns0:cell>Summer</ns0:cell><ns0:cell>0.32</ns0:cell><ns0:cell>Winter</ns0:cell><ns0:cell>1.61</ns0:cell></ns0:row><ns0:row><ns0:cell>of driest</ns0:cell><ns0:cell /><ns0:cell>vegetation</ns0:cell><ns0:cell /><ns0:cell>vegetation</ns0:cell><ns0:cell /><ns0:cell>vegetation</ns0:cell><ns0:cell /><ns0:cell>vegetation</ns0:cell><ns0:cell /><ns0:cell>vegetation</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>month</ns0:cell><ns0:cell /><ns0:cell>index MR</ns0:cell><ns0:cell /><ns0:cell>index BI</ns0:cell><ns0:cell /><ns0:cell>index WI</ns0:cell><ns0:cell /><ns0:cell>index BI</ns0:cell><ns0:cell /><ns0:cell>index NDVI</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>9 Summer</ns0:cell><ns0:cell>0.23</ns0:cell><ns0:cell>Summer</ns0:cell><ns0:cell>1.22</ns0:cell><ns0:cell>Summer</ns0:cell><ns0:cell>0.19</ns0:cell><ns0:cell>Precipitation</ns0:cell><ns0:cell>1.24</ns0:cell><ns0:cell>Summer</ns0:cell><ns0:cell>0.31</ns0:cell><ns0:cell>Winter</ns0:cell><ns0:cell>1.47</ns0:cell></ns0:row><ns0:row><ns0:cell>vegetation</ns0:cell><ns0:cell /><ns0:cell>vegetation</ns0:cell><ns0:cell /><ns0:cell>surface</ns0:cell><ns0:cell /><ns0:cell>of driest</ns0:cell><ns0:cell /><ns0:cell>vegetation</ns0:cell><ns0:cell /><ns0:cell>vegetation</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>index EVI</ns0:cell><ns0:cell /><ns0:cell>index NDVI</ns0:cell><ns0:cell /><ns0:cell>albedo of</ns0:cell><ns0:cell /><ns0:cell>month</ns0:cell><ns0:cell /><ns0:cell>index WI</ns0:cell><ns0:cell /><ns0:cell>index MR</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>band 1</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>10 Winter</ns0:cell><ns0:cell>0.19</ns0:cell><ns0:cell>Summer</ns0:cell><ns0:cell>1.12</ns0:cell><ns0:cell>Winter</ns0:cell><ns0:cell>0.14</ns0:cell><ns0:cell>Summer</ns0:cell><ns0:cell>1.22</ns0:cell><ns0:cell cols='2'>Winter surface 0.28</ns0:cell><ns0:cell>Summer</ns0:cell><ns0:cell>1.32</ns0:cell></ns0:row><ns0:row><ns0:cell>surface</ns0:cell><ns0:cell /><ns0:cell>vegetation</ns0:cell><ns0:cell /><ns0:cell>surface</ns0:cell><ns0:cell /><ns0:cell>vegetation</ns0:cell><ns0:cell /><ns0:cell cols='2'>albedo of band</ns0:cell><ns0:cell>vegetation</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>albedo of</ns0:cell><ns0:cell /><ns0:cell>index MR</ns0:cell><ns0:cell /><ns0:cell>albedo of</ns0:cell><ns0:cell /><ns0:cell>index NDVI</ns0:cell><ns0:cell /><ns0:cell>6</ns0:cell><ns0:cell /><ns0:cell>indices EVI</ns0:cell><ns0:cell /></ns0:row></ns0:table></ns0:figure> <ns0:note place='foot'>PeerJ reviewing PDF \| (2020:01:44790:2:0:NEW 5 Aug 2020)</ns0:note> </ns0:body> "	"Dear Prof. Ghermandi: Thanks much for handling our manuscript, entitled “Simulating highly disturbed vegetation distribution: the case of China’s Jing-Jin-Ji region” with Manuscript ID of 44790. The manuscript was revised with tracked changes based on all insightful comments. We are pleased to revise it further in case of necessary. Thank you very much for your considerations and the best regards. Yours sincerely, Yuanrun Zheng Aug. 5, 2020 Report for manuscript revision: In responses to the editor (Prof. Ghermandi) 1. Comments: Having carefully examined the revised manuscript myself, I agree with the reviewer's assessment that the submission has substantially improved with respect to the original version. Reviewer #2 raises some additional points that I kindly ask you to address in a revised version. In addition, I kindly ask you to consider also the following points when revising and resubmitting your manuscript. Response: We thank the editor and reviewers for insightful comments, the manuscript was carefully revised based on all comments. 2. Comments: Main comments The novelty of the work could be better highlighted in the paper. I make here direct reference to comment 6 by reviewer #1 in the original submission. I am satisfied with the answer you give to this comment in the rebuttal letter, but this should be included in the manuscript as well. In particular, you should acknowledge the link with Zhou et al (2016), while explaining that the present work is sufficiently distinct. I think the end of the Introduction (L105) could be a good place for this. Response: Thank you for the comment. The information was added in the end of the Introduction (The original L 105) in the revised manuscript based on the comment. 3. Comments: with reference to comment 8 by reviewer #1 in the original submission, while you have satisfactorily improved the description of methods in the revised submission, I think the role of the 'EnMAP-Box software' still remains rather obscure and should be better clarified. Response: Thank you for the comment. The suggested information was added in the section of “Materials & Methods” in the revised manuscript. The EnMAP-Box, developed by the Humboldt University of Berlin, is a license-free and platform-independent software interface designed to process hyperspectral remote sensing data. There are in-built applications aimed at the processing of hyperspectral data, such as Support Vector Machines and Random Forests for classification or regression of image data (Held et al., 2014). 4. Comments: Minor comments L19: a word seems to be missing here after 'improving the vegetation...' Response: Thank you for the comment. The sentence was changed to 'The primary goal of this study was to determine the best simulation method for a vegetation in an area that is heavily affected by human disturbance' based on reviewers 1 and 2. 5. Comments: L50: 'Industrialization' is not the only source of alterations in vegetation patterns (e.g., urbanization, population growth, land use change for agricultural use, etc.). Please revise to acknowledge other pressures that are relevant in the study region in addition to industrialization. Response: Thank you for the comment. The suggested information was added in original line 50-51. 6. Comments: L88: I don't think 'magnitude' is the correct word here. Maybe 'extension'? Response: Thank you for the comment. The “magnitude” was changed to “extension” in original line 88. 7. Comments: L93: remove 'more' before 'limited resources' Response: Thank you for the comment. The “more” before “limited resources” was removed in original line 93. 8. Comments: L130: please add a sentence to clarify what were the criteria for selecting 8, 12 and 39 units from the three different groups. Response: Thank you for the comment. The suggested information was added in original line 130. 9. Comments: L141: a reference is needed for the 'Kappa coefficient'. Response: Thank you for the comment. The references were added in original line 141. 10. Comments: L168: please clarify what type of correlation the 'R' presented here refers to (e.g., Pearson, Spearman, etc.) Response: Thank you for the comment. The type of correlation of the “R” presented here refers to Pearson. It was revised accordingly in original line 168. 11. Comments: L357: I think the title of this revised section could be more descriptive. Perhaps 'Important variables in vegetation classification models'? Response: Thank you for the comment. The title of this section was changed to “Important variables in vegetation classification models”. 12. Comments: L420: please add 'in the Jing-Jin-Ji region' at the end of the sentence currently ending with 'socioeconomic disturbance'. Response: Thank you for the comment. “in the Jing-Jin-Ji region” was added at the end of the sentence currently ending with 'socioeconomic disturbance'. In responses to reviewer 2 1. Comments: Basic reporting The article is much improved now except there are still some minor editorial mistakes. Response: Thank you for the comment. The manuscript was revised carefully based on comments. 2. Comments: Comments for the author The authors included (justified) most of the comments which were given on the earlier version. But still there are few minor editorial mistakes. They are presented as follows: 18 - May be it can improved as: The primary goal of this study was to determine the best simulation method for a vegetation in an area that is heavily affected by human disturbance. Response: Thank you for the comment. The original line 18 was changed to “The primary goal of this study was to determine the best simulation method for a vegetation in an area that is heavily affected by human disturbance” 3. Comments: 35 - 'Variables Combination 7 produced the highest simulation accuracy'. I think it is better to list the variables by name. Response: Thank you for the comment. The variables Combination 7 was listed by name in original line 35. 4. Comments: 63 - 'in conjunction': may be better to use in combination? Response: Thank you for the comment. “in conjunction” was changed to “in combination” in original line 63. 5. Comments: 145 - 'digital elevation model (DEM) (Zhao et al., 2018)' - may better to write it as - digital elevation model (DEM; Zhao et al., 2018). Response: Thank you for the comment. 'digital elevation model (DEM) (Zhao et al., 2018)' was changed to “digital elevation model (DEM; Zhao et al., 2018)” in original line 145. 6. Comments: 148 - '1 km resolution' - may be better to put it as - at ~ 1 km resolution? I don't think the resolution is exactly 1 km. Please cross check this. Response: Thank you for the comment. “at 1 km resolution' was changed to “at ~ 1 km resolution” in original line 148. "	Here is a paper. Please give your review comments after reading it.
513	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>The BioBrick standard makes possible iterated pairwise assembly of cloned parts without any depletion of unique restriction sites. Every part that conforms to the standard is compatible with every other part, thereby fostering a worldwide user community. The assembly methods, however, are labor intensive or inefficient compared to some newer ones so the standard may be falling out of favor. An easier way to assemble BioBricks is described herein. Plasmids encoding BioBrick parts are purified from Escherichia coli cells that express a foreign site-specific DNA methyltransferase, so that each is subsequently protected in vitro from the activity of a particular restriction endonuclease. Each plasmid is double-digested and all resulting restriction fragments are ligated together without gel purification. The ligation products are subsequently double-digested with another pair of restriction endonucleases so only the desired insert-recipient vector construct retains the capacity to transform E. coli. This 4R/2M BioBrick assembly protocol is more efficient and accurate than established workflows including 3A assembly. It is also much easier than gel purification to miniaturize, automate and perform at high throughput. As such, it should streamline DNA assembly for the existing community of BioBrick users, and possibly encourage others to join.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>A bottleneck in many synthetic biology projects is the physical linkage of cloned synthetic genes ('parts') to each other to form longer functional assemblies ('devices'). The costs of gene synthesis, cloning and DNA sequencing have decreased significantly but syntheses are still limited in length (≤ 3 kb), nucleotide composition, accuracy and yield <ns0:ref type='bibr' target='#b12'>(Kosuri & Church 2014;</ns0:ref><ns0:ref type='bibr' target='#b13'>Kuhn et al. 2017</ns0:ref>). Many DNA assembly methods have been invented <ns0:ref type='bibr' target='#b3'>(Casini et al. 2015;</ns0:ref><ns0:ref type='bibr' target='#b4'>Chao et al. 2015;</ns0:ref><ns0:ref type='bibr' target='#b25'>Sands & Brent 2016;</ns0:ref><ns0:ref type='bibr' target='#b29'>Vazquez-Vilar et al. 2018;</ns0:ref><ns0:ref type='bibr' target='#b30'>Watson & Garcia-Nafria 2019)</ns0:ref>, which suggests that none work well for every user. The challenges of assembling cloned parts are not identical to those of ligating PCR products into plasmids <ns0:ref type='bibr' target='#b1'>(Bryksin & Matsumura 2010</ns0:ref>) so different solutions are demanded.</ns0:p><ns0:p>Many synthetic biologists have adopted cloning standards that stipulate particular type II or type IIS restriction sites at the ends of each DNA 'part.' The BioBrick RCF <ns0:ref type='bibr'>[10]</ns0:ref> standard <ns0:ref type='bibr' target='#b11'>(Knight 2003</ns0:ref>) is most established (Figure <ns0:ref type='figure' target='#fig_14'>1</ns0:ref>). All BioBrick-compliant plasmids contain a characteristic pattern of sites recognized by type II restriction endonucleases (EcoRI-NotI-XbaIinsert-SpeI-NotI-PstI). Two such inserts can be combined by digesting one plasmid <ns0:ref type='bibr'>(recipient)</ns0:ref> with SpeI and PstI, and the other (donor) with XbaI and PstI. Alternatively, one plasmid (recipient) can be cut with EcoRI and XbaI, and the other with EcoRI and SpeI (donor). The overhangs of XbaI and SpeI digests products are compatible but anneal to form a 'scar' not recognized by either restriction endonuclease. The ligation of the desired insert to the desired recipient plasmid thus creates a new BioBrick-compatible plasmid. The virtue of this approach compared to ad hoc subcloning strategies is that an infinite number of inserts can be combined, two at a time, without running out of unique restriction sites. The problem, and focus of this PeerJ reviewing PDF \| (2020:04:48149:1:0:NEW 7 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed study, is that conventional subcloning <ns0:ref type='bibr' target='#b19'>(Matsumura 2015)</ns0:ref>, particularly the gel purification step, remains labor-intensive and recalcitrant to automation.</ns0:p><ns0:p>Golden Gate assembly <ns0:ref type='bibr' target='#b9'>(Engler et al. 2009</ns0:ref>) was invented in part to circumvent gel purifications, though not without some cost. Type IIS restriction endonucleases recognize asymmetric sequences but cut outside of them. BsaI, for example, recognizes the sequence GGTCTC and introduces staggered cuts in both strands downstream regardless of sequence, creating 5' overhangs that are four nucleotides long. This capacity to create up to 256 different sticky ends with a single enzyme enables concurrent restriction digests and ligations in a single pot. Such simultaneous reactions will hereafter be called 'continuous' to distinguish them from 'discontinuous' sequential digestions and ligations. Unlike BioBrick assembly, the Golden Gate method can be used to combine multiple parts in a single reaction. It does not leave the characteristic XbaI/SpeI scar of BioBrick assembly so it is better suited for the fusion of open reading frames.</ns0:p><ns0:p>Golden Gate assembly is not, however, without drawbacks. Any BioBrick part can be adjoined to any other part using standard protocols, including those described here. In contrast, the sticky ends produced by BsaI and other Type IIS restriction enzymes are only compatible with others designed to be complementary. Cloning standards for Type IIS restriction endonucleases, such as MoClo <ns0:ref type='bibr' target='#b31'>(Weber et al. 2011)</ns0:ref>, Phytobricks <ns0:ref type='bibr' target='#b21'>(Patron et al. 2015)</ns0:ref>, Golden Braid <ns0:ref type='bibr' target='#b26'>(Sarrion-Perdigones et al. 2011)</ns0:ref> or Loop assembly <ns0:ref type='bibr' target='#b22'>(Pollak et al. 2019)</ns0:ref>, facilitate some repurposing of parts for other devices. The MoClo standard, for example, employs nearly three dozen intermediate vectors, each with a unique pair of restriction sites and overhangs, each Manuscript to be reviewed dedicated to a separate category of parts (e.g. promoters, 5' upstream untranslated regions, open reading frames, terminators etc.) <ns0:ref type='bibr' target='#b31'>(Weber et al. 2011)</ns0:ref>. The BioBrick standard employs a single type of vector <ns0:ref type='bibr' target='#b11'>(Knight 2003</ns0:ref>) and a single overhang, created by Type II restriction enzymes XbaI or SpeI, to connect parts. BioBrick assembly experiments are thus relatively easy to plan. I value the simplicity and universal part compatibility of BioBricks, so I invented a less labor intensive and automation-friendly way to assemble them. The concept that underlies my approach is straightforward and easy to implement. In nature every restriction endonuclease is paired with a corresponding site specific DNA modifying enzyme, most often a methyltransferase <ns0:ref type='bibr' target='#b16'>(Loenen & Raleigh 2014)</ns0:ref>. Previous reports have described the use of methyltransferases <ns0:ref type='bibr' target='#b15'>(Lin & O'Callaghan 2018)</ns0:ref> or methylated primers <ns0:ref type='bibr' target='#b5'>(Chen et al. 2013)</ns0:ref> to enable Golden Gate assemblies that would otherwise have been impossible. The 2ab assembly method is most relevant to the current study. It utilizes in vivo plasmid methylation and recombination of selectable markers to effect one pot, discontinuous ligations of BglBrick parts using Type II restriction enzymes BglII and BamHI <ns0:ref type='bibr' target='#b14'>(Leguia et al. 2013)</ns0:ref>. It is efficient, requires little labor and amenable to automation. Unfortunately, the BglBrick and BioBrick standards are incompatible. Moreover 2ab assembly requires specialized plasmids encoding pairs of selectable markers. It is nevertheless an important precedent for easier ways to combine BioBrick parts, preferably in existing plasmids.</ns0:p><ns0:p>Here I describe the cloning of relevant methylases and their expression in a laboratory E. coli strains. Cells co-transformed with BioBrick-compatible plasmids thus add methyl groups to DNA at specific sites (Figure <ns0:ref type='figure' target='#fig_9'>2</ns0:ref>). The methylated plasmids are prepared and double digested in Manuscript to be reviewed accordance with traditional cloning protocols, except that smaller quantities of DNA are required. The restriction fragments are not gel purified but rather combined and reacted with T4 DNA ligase. The undesired ligation products, including the original parental plasmids, are subsequently cut by another pair of restriction enzymes. The desired ligation product (insertrecipient plasmid) is protected from both restriction enzymes, so it alone retains the capacity to transform E. coli.</ns0:p></ns0:div> <ns0:div><ns0:head>Materials and Methods</ns0:head></ns0:div> <ns0:div><ns0:head>Materials</ns0:head><ns0:p>The synthetic methylase genes used in this study (M.EcoRI, M.XbaI, M.Ocy1ORF8430P, M.PstI, and M.AvaIII) were purchased from IDT (Coralville, IA) as gBlocks. Seakem LE agarose was from Lonza Rockland (Rockland, ME) using lambda HindIII, 100 bp (New England BioLabs, Ipswich, MA) and 10 bp (Thermo Fisher) ladders as molecular size markers.</ns0:p><ns0:p>Restriction enzymes, T4 DNA ligase and pure bacteriophage lambda DNA were from NEB (Ipswich, MA). TempliPhi rolling circle amplification kits were from Cytiva (Marlborough, MA). MinElute PCR purification and GeneRead Size Selection kits were from Qiagen (Valencia, CA), as was the QIAcube and the custom protocol (vide infra). E. coli OmniMax2 cells were from Invitrogen. Ethylenediaminetetraacetic acid (EDTA), L-arabinose and L-rhamnose were from Sigma Chemicals (St. Louis, MO); isopropyl β-D-1-thiogalactopyranoside (IPTG) was from Gold Biotechnology (St. Louis, MO). LB broth (Miller) was from EMD Millipore (Billerica, MA) and Bacto-agar was from BD Difco (Franklin Lakes, NJ).</ns0:p></ns0:div> <ns0:div><ns0:head>Methods</ns0:head><ns0:p>PeerJ reviewing PDF \| (2020:04:48149:1:0:NEW 7 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed Subcloning via gel purification Two plasmids were purified in triplicate (from cultures seeded with different colonies) via the Qiagen QIAprep spin miniprep kit. Recipient tagRFP-pUC (1 µg) was digested in 1x NEB CutSmart buffer (80 µL total reaction volume) by EcoRI-HF and XbaI (20 units each), thus releasing a short 15 base pair stuffer fragment ('snippet'); lacI-Ptac-lacO-pUC was similarly digested with EcoRI-HF and SpeI-HF in the same buffer, thereby releasing the lacI-Ptac-lacO insert and pUC donor plasmid. All restriction digests in this study were incubated overnight at 37° C unless otherwise stated. The desired fragments were separated from the undesired ones in 0.8% LE agarose gels; the bands corresponding to the recipient plasmid tagRFP-pUC and insert tagRFP were excised with a razor blade. The desired DNA was purified from the agarose slices via the QiaQuick gel extraction protocol. The fragments (20 fmol ~ 50 ng tagRFP-pUC or 25 ng lacI-Ptac-lacO), alone or in combination, were reacted to T4 DNA ligase (3 Weiss units) in 1x NEB buffer containing 1 mM ATP (20 µL total reaction volume) overnight in temperature cycled reactions (30° C x 30 sec, 10° C x 30 sec) <ns0:ref type='bibr' target='#b18'>(Lund et al. 1996)</ns0:ref>. The ligase was heat killed (10 min at 65° C), and the reactions (1 ng) were used to transform chemically competent OmniMax 2 cells (20 µL). All experiments employed the same batch of cells made competent by the classical method of Inoue et al. <ns0:ref type='bibr' target='#b10'>(Inoue et al. 1990</ns0:ref>). Transformation efficiency was 3 x 10 7 /µg, as determined by counting colonies after transformation with 10 pg of pUC19.</ns0:p></ns0:div> <ns0:div><ns0:head>Tip Snip subcloning</ns0:head><ns0:p>The lacI-Ptac-lacO-pUC donor plasmid (1µg) in 1x NEB CutSmart buffer (80 µL total reaction volume) was shortened slightly by an extra restriction enzyme (20 units PstI-HF) that recognizes a site adjacent to those used to release the insert (20 units each of EcoRI-HF and PeerJ reviewing PDF \| (2020:04:48149:1:0:NEW 7 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed SpeI-HF) <ns0:ref type='bibr' target='#b20'>(Matsumura 2017)</ns0:ref>. The tagRFP-pUC recipient plasmid (1µg) was cut as usual (20 units each of EcoRI-HF and XbaI in 1x NEB CutSmart buffer, 80 µL total reaction volume). The small restriction fragments ('snippets') in both digests are denatured, annealed to exogenously added anti-snippet oligonucleotides (100 nM BioBrick suffix in the donor digestion, 100 nM BioBrick prefix in the recipient digestion), thereby inactivating their sticky ends, and eliminated via Qiagen GeneRead size selection silica spin column chromatography. The purified restriction fragments were ligated (20 fmol ~ 60 ng tagRFP-pUC, 90 ng lacI-Ptac-lacO + pUC, 50 nM PstI 'unlinker') in temperature cycled NEB T4 DNA ligase buffer (20 µL total reaction volume) prior to heat killing and transformation of E. coli as described above.</ns0:p></ns0:div> <ns0:div><ns0:head>3A assembly</ns0:head><ns0:p>A BioBrick-compatible plasmid that encodes chloramphenicol acetyltransferase, RP4 oriT-pUC57-mini-cat (2 µg) in 1x NEB CutSmart buffer (80 µL total reaction volume) by 20 units each of EcoRI-HF, PstI-HF and NotI-HF (so as to eliminate the sticky ends of its stuffer fragment), dephosphorylated in reactions with NEB Calf Intestinal Phosphatase. The lacI-Ptac-lacO-pUC donor plasmid (300 ng) in 1x NEB 2. 1 buffer (15 µL total reaction volume) was digested with 6 units each of EcoRI-HF and SpeI; the tagRFP-pUC donor plasmid was similarly digested with XbaI and PstI. The digests containing pUC-mini-cat recipient vector (60 ng), the lacI-Ptac-lacO and tagRFP-pUC inserts (50 ng each) were reacted in a thermocycler with 3 Weiss units of T4 DNA ligase in 1x NEB T4 DNA ligase buffer (10 µL total reaction volume).</ns0:p></ns0:div> <ns0:div><ns0:head>Construction of DNA methyltransferase expression vectors</ns0:head><ns0:p>PeerJ reviewing <ns0:ref type='table'>PDF \| (2020:04:48149:1:0:NEW 7 Jul 2020)</ns0:ref> Manuscript to be reviewed The intergenic region between rhaS and rhaB, which includes promoters and operators for both genes, was previously described <ns0:ref type='bibr' target='#b20'>(Matsumura 2017)</ns0:ref>.</ns0:p><ns0:p>The p15A, aadA, Prham and methylase genes were assembled by a combination of traditional and Tip Snip BioBrick assembly. Leaky expression of M.XbaI or M.Ocy1ORF8430P from BioBricks containing these parts prevented efficient digests of the plasmids with XbaI or SpeI-HF. Those plasmids were amplified in vitro by utilizing the TempliPhi rolling circle protocol. The resulting unmethylated amplification product was subsequently digested, and the desired part was gel purified and ligated to other parts. The BioBrick restriction enzymes (EcoRI, XbaI, SpeI and PstI) were eliminated by digesting the plasmids (or amplified versions of them) with XbaI and SpeI-HF, self-ligating the p15A-aadA-Prahm-methylase and using ligation reaction products to transform E. coli OmniMax2. All five methylase expression vectors, plus Prham-tagRFP-pUC, which was used to optimize the optimal concentration of glucose for autoinduction, have been deposited in the Addgene repository (RRID:Addgene_149338 -149343).</ns0:p></ns0:div> <ns0:div><ns0:head>2RM assembly</ns0:head><ns0:p>PeerJ reviewing <ns0:ref type='table'>PDF \| (2020:04:48149:1:0:NEW 7 Jul 2020)</ns0:ref> Manuscript to be reviewed Methylated, uncut lacI-Ptac-lacO-pUC and tagRFP-pUC plasmids (240 ng each) were reacted with XbaI, SpeI (6 units each) and T4 DNA ligase (3 Weiss units) in 1x NEB CutSmart buffer supplemented with 1 mM ATP (25 µL total reaction volume) in a single pot reaction analogous to that of Golden Gate assembly (72 cycles of 5 min. at 37° C, followed by a nested 10 cycles of 30 sec at 10° C and 30 sec at 30° C). The reaction was incubated for another hour at 37° C, then heat killed for 10 min at 65° C; 1 ng of total DNA was used to transform 20 µL competent E. coli OmniMax 2 cells. 4R/2M (PstI) M.EcoRI-protected lacI-Ptac-lacO-pUC (500 ng) was digested overnight at 37° C by 6 units of SpeI and 8 units of PstI in 1x NEB 2.1 buffer (25 µL total reaction volume). M.Ocy1protected tagRFP-pUC was similarly digested by 8 units of XbaI and 12 units of PstI. Note that PstI-HF cannot be heat-killed, nor is SpeI-HF fully active in NEB 2.1 buffer, so PstI and SpeI were utilized instead. The restriction enzymes were heat-killed (20 min at 80° C), and the restriction fragments (45 ng tagRFP-pUC, 10 ng tagRFP + pUC) were reacted to T4 DNA ligase (2.4 Weiss units in 1x NEB 2.1 buffer supplemented with 1 mM ATP, 20 µL total reaction volume) overnight in a thermocycler (600 cycles of 30 sec at 30° C, 30 sec at 10°C). The ligase was heat killed by incubation at 65° C for 10 min. A 2 µL aliquot of each ligation was diluted into a 26 µL 1x NEB 2.1 buffer containing 8 units each of EcoRI-HF and SpeI. The post-ligation digest was incubated for 3 hours at 37° C, and 1 µL of the reaction was used to transform 20 µL of competent E. coli OmniMax 2 cells.</ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head><ns0:p>PeerJ reviewing PDF \| (2020:04:48149:1:0:NEW 7 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed Subcloning via gel purification as a gold standard Established subcloning methods <ns0:ref type='bibr' target='#b19'>(Matsumura 2015)</ns0:ref> were initially applied to set quantitative benchmarks for efficiency (number of correctly assembled clones per ng ligated DNA) and accuracy (fraction of correctly assembled clones among total). Efficiency is important because it is an indirect measure of reliability when optimal conditions cannot be achieved. Two plasmids, lacI-Ptac-lacO-IMBB2.4-pUC57-mini and tagRFP-IMBB2.4-pUC57-mini (hereafter abbreviated lacI-Ptac-lacO-pUC and tagRFP-pUC respectively) were selected as models for this study (Figure <ns0:ref type='figure' target='#fig_10'>3</ns0:ref>). Both comply with requirements for established BioBrick RFC[10] assembly protocols. Colonies of cells transformed with the desired assembly product, lacI-Ptac-lacO-tagRFP-pUC, turn pink due to leaky expression of the fluorescent marker protein. Throughout this study, the same E. coli strains, DNA purification techniques, restriction enzymes, ligases and reaction buffers were used, generally in accordance with manufacturer's instructions except as noted. Differences in outcome can thus be attributed solely to differences in assembly protocols.</ns0:p><ns0:p>Each cloning step was carried out in triplicate, starting with individual isolated bacterial colonies; standard errors are reported as a measure of variation between experimental replicates.</ns0:p><ns0:p>The most labor-intensive steps of a traditional subcloning experiment are the separation of restriction fragments via agarose gel electrophoresis, excision of bands corresponding the desired fragments and the extraction of DNA from the agarose slice. Overnight incubations of transformed bacteria, restriction digests and temperature cycled ligation reactions were ratelimiting. The aim here was not to accelerate the workflow, but rather to decrease labor input and increase throughput without compromising efficiency or accuracy. After restriction digests, gel purification and ligation, transformation of E. coli with the ligation products led to the growth of PeerJ reviewing PDF \| (2020:04:48149:1:0:NEW 7 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed 126 ± 44 pink colonies per ng; a minority of white colonies (11 ± 4 = 8%) grew on those LBampicillin plates (Table <ns0:ref type='table'>1</ns0:ref>). The background on control plates spread with cells transformed with vector only ligations was low (7 ± 2 cfu/ng), which suggested that restriction digests were nearly complete. The insert only ligation controls produced greater background (61 ± 27 cfu/ng), which suggests that the insert was not effectively separated from the donor plasmid in this experiment.</ns0:p><ns0:p>Two other established subcloning techniques, tip snip <ns0:ref type='bibr' target='#b20'>(Matsumura 2017</ns0:ref>) and 3A <ns0:ref type='bibr' target='#b27'>(Shetty et al. 2011)</ns0:ref>, were also used to provide standards of comparison (Supplemental Material, Table <ns0:ref type='table'>1</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>Methylase expression vectors</ns0:head><ns0:p>The overarching strategy of this study is to replace the gel purification step of subcloning by a combination of site-specific DNA methylation and post-ligation restriction digestion <ns0:ref type='bibr' target='#b28'>(Spear 2000;</ns0:ref><ns0:ref type='bibr' target='#b32'>Zeng et al. 1997</ns0:ref>). To realize this strategy, BioBrick compliant genes encoding the DNA methyltransferases of the EcoRI, XbaI and PstI restriction modification systems were synthesized, cloned into compatible plasmids and sequenced. The complete sequence of SpeI methylase (M.SpeI) is not available on REbase <ns0:ref type='bibr' target='#b23'>(Roberts et al. 2010)</ns0:ref>, so a putative ortholog M.Ocy1ORF8430P (hereafter abbreviated M.Ocy1) was synthesized instead. Each DNA methyltransferase gene was subcloned via traditional techniques downstream of the T5 <ns0:ref type='bibr' target='#b2'>(Bujard et al. 1987</ns0:ref><ns0:ref type='bibr'>), tac (de Boer et al. 1983</ns0:ref>) and rhamnose operon <ns0:ref type='bibr' target='#b8'>(Egan & Schleif 1993)</ns0:ref> promoters and a strong ribosome binding site.</ns0:p><ns0:p>The promoter-methylase expression cassettes were subcloned into a simple plasmid consisting only of the p15A replication origin, which is low in copy number and compatible with more common plasmids that encode the pUC origin, and streptomycin 3''-adenylyltransferase PeerJ reviewing PDF \| (2020:04:48149:1:0:NEW 7 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed (aadA) selectable marker (Figure <ns0:ref type='figure' target='#fig_10'>3</ns0:ref>). The new expression plasmids (promoter-methylase-p15A-aadA) confer resistance to streptomycin and spectinomycin. They don't contain any of the restriction sites normally used for BioBrick assembly (e.g. EcoRI, XbaI, SpeI or PstI) so they won't release any restriction fragments that would interfere with any downstream subcloning steps.</ns0:p><ns0:p>The in vivo methylase activities produced by these expression vectors was tested as follows. E. coli strain OmniMax 2 was co-transformed with each vector and another BioBrick compatible plasmid, propagated to mid-log culture and induced (either with IPTG or Lrhamnose) for three hours. The plasmids were purified and reacted with restriction endonucleases including the one normally associated with each DNA methyltransferase in wildtype bacteria. The degree of protection was assessed by comparing the mobilities in agarose gels of plasmids that were uncut, completely cut by a restriction endonuclease unrelated to the methylase or protected at least in part by in vivo methylation. For example, agarose gel electrophoresis showed that lacI-Ptac-lacO-pUC purified from E. coli carrying Prham-M.XbaI-p15A-aadA was digested by SpeI but mostly resistant to XbaI. Conversely, tagRFP-pUC protected by Prham-M.Ocy1-p15A-aadA was digested with XbaI but mostly resistant to SpeI (Figure <ns0:ref type='figure' target='#fig_11'>4</ns0:ref>).</ns0:p><ns0:p>The rhamnose promoter, reputedly the weakest of the three tested, proved most reliable for consistent and complete in vivo methylation. I speculate that DNA methyltransferases that are site-specific at moderate concentrations become toxic to host cells when over-expressed <ns0:ref type='bibr' target='#b0'>(Bandaru et al. 1996)</ns0:ref>. Extended over-expression could thus favor the accumulation of mutations Manuscript to be reviewed beneficial to transformed cells but unwanted by human scientists. Induction of transformants at mid-log phase is itself labor-intensive, as cultures propagated in parallel don't always grow at the same rate, so an auto-induction protocol was developed. The rhamnose promoter is regulated by catabolite repression as well as by L-rhamnose. The plasmid Prham-tagRFP-pUC <ns0:ref type='bibr' target='#b20'>(Matsumura 2017</ns0:ref>) was used to transform E. coli OmniMax 2. Limiting amounts of glucose were added to saturating concentrations of L-rhamnose (0.1%) in LB medium supplemented with ampicillin.</ns0:p><ns0:p>Commercial LB contains varying quantities of glucose, but for the addition of 0.001% glucose to 0.1% L-rhamnose led to maximum tagRFP expression as measured in a microtiter plate spectrofluorimeter. Autoinduction under those growth conditions led complete in vivo methylation when the methylase expression vectors were used instead.</ns0:p><ns0:p>The other lesson inferred from the in vivo methylation experiments was that M.PstI is rarely able to methylate plasmids within E. coli cells as completely as M.EcoRI, M.XbaI or M.Ocy1. Each of these methylases evolved in a different bacterial species so it isn't surprising that one of the four proved less active than the others in the alien environment of the E. coli cytoplasm. Most of our plasmids include an NsiI site adjacent to the PstI site. The sequence of M.NsiI was not available on REbase <ns0:ref type='bibr' target='#b23'>(Roberts et al. 2010</ns0:ref>) so the gene encoding the M.AvaIII ortholog was synthesized, cloned, sequenced and subcloned downstream of the rhamnose promoter. M.AvaIII proved much more adept at methylating plasmids in the E. coli cytoplasm than did M.PstI.</ns0:p></ns0:div> <ns0:div><ns0:head>2RM assembly</ns0:head><ns0:p>PeerJ reviewing PDF \| (2020:04:48149:1:0:NEW 7 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p><ns0:p>The potential utility of the methylase expression vectors was demonstrated in a series of assembly experiments. The 3A BioBrick assembly protocol <ns0:ref type='bibr' target='#b27'>(Shetty et al. 2011)</ns0:ref> was so named because it employs three plasmids, each with a distinct antibiotic selection marker. For similar reasons, 2RM assembly utilizes the components of two restriction modification systems: restriction endonucleases XbaI and SpeI-HF, and DNA methyltransferases M.XbaI and M.SpeI homologue M.Ocy1 (Figure <ns0:ref type='figure' target='#fig_12'>5</ns0:ref>). In this embodiment, the lacI-Ptac-lacO-pUC was purified from triplicate cultures of auto-induced cells containing Prham-M.XbaI-p15A-aadA, while tagRFP-pUC was purified from cultures co-transformed with Prham-M.Ocy1-p15A-aadA. The purified plasmids were mixed and reacted with XbaI and SpeI. Each plasmid, lacI-Ptac-lacO-pUC and tagRFP-pUC, was cut with one of the two restriction enzymes and protected by methylation from the other. The linearized plasmids (Figures <ns0:ref type='figure' target='#fig_14'>5 and S1</ns0:ref>) react with T4 DNA ligase to form three sets of products. Most common, presumably, are the two original parental plasmids. Each of the linearized plasmids can also be ligated to other copies of themselves in one of two orientations to form homodimers (Figures <ns0:ref type='figure' target='#fig_15'>5 and S2</ns0:ref>). All contain unmethylated XbaI or SpeI sites, so they are susceptible to re-digestion by the restriction enymes in the reaction vessel. The linearized plasmids can also ligation to each other to form heterodimers (Figures <ns0:ref type='figure' target='#fig_12'>5 and S3</ns0:ref>). These products are resistant to both restriction endonucleases so they should accumulate over the course of the digestion/ligation reaction.</ns0:p><ns0:p>When E. coli were transformed with one nanogram of each ligation reaction, 118 ± 13 pink cfu/ng and 260 ± 25 white cfu/ng were observed on each plate (Table <ns0:ref type='table'>1</ns0:ref>). Colony numbers on plates corresponding to control ligations with only one plasmid (20 ± 4 cfu/ng) or the other (4 ± 1 cfu/ng) were relatively low, suggesting that both methylation and restriction digestion was Manuscript to be reviewed nearly complete. These results in combination show that restriction digestion of the parental plasmids and homodimeric ligation products was efficient, and that ligation to form heterodimeric products was also efficient. In principle, the ratio of pink to white colonies should be 1:1, but the 1:2.2 ratio observed here could mean that the ligation product with the undesired orientation conferred greater fitness upon the host cell. The desired product contains two copies of the selectable marker and origin of replication (Figures <ns0:ref type='figure' target='#fig_12'>5 and S3</ns0:ref>), which could complicate subsequent assembly reactions. Double digests of existing BioBrick-compatible plasmids enable directional cloning, which is more practical.</ns0:p></ns0:div> <ns0:div><ns0:head>4R/2M (PstI) assembly</ns0:head><ns0:p>In 4R/2M assembly, the two parental plasmids are sequentially reacted with two DNA methyltransferases, three restriction endonucleases, T4 DNA ligase and a fourth restriction enzyme (Figure <ns0:ref type='figure' target='#fig_13'>6</ns0:ref>). In its 4R/2M (PstI) embodiment, the recipient encodes the part that will end up on the 5' end of the desired ligation product. Its EcoRI site is methylated in vivo and subsequently digested by SpeI and PstI (Figures <ns0:ref type='figure' target='#fig_13'>6 and S4</ns0:ref>). The donor plasmid that encodes the insert destined for the 3' end of the desired ligation product; it is protected from SpeI by M.Ocy1 Manuscript to be reviewed product is the only viable construct that remains intact. Homodimeric constructs are produced in any ligation of fragments produced by type II restriction endonucleases (Figure <ns0:ref type='figure' target='#fig_14'>1</ns0:ref>), but none are viable in vivo because plasmids are destabilized by large inverted repeats. Competent E. coli were transformed with the 4R/2M (PstI) assembly reactions, leading to the formation 177 ± 4 pink cfu/ng and only 2 ± 1 white cfu/ng (Table <ns0:ref type='table'>1</ns0:ref>). Background colony counts on the control plates representing vector only (1 ± 0.2 cfu/ng) and insert only (1 ± 0.2 cfu/ng) ligations were very low. The 4R/2M (PstI) assembly is thus well suited for routine high throughput BioBrick assembly. I have subsequently used it to assemble 65 more pairs of BioBricks in batches of up to 18.</ns0:p></ns0:div> <ns0:div><ns0:head>4R/2M (EcoRI) assembly</ns0:head><ns0:p>The logic of 4R/2M (EcoRI) BioBrick assembly is identical to that of 4R/2M (PstI), except that the recipient and donor plasmids are switched. The BioBrick part that ends up on the 5' end of the assembled product is the insert rather than part of the recipient plasmid. The recipient tagRFP-pUC was methylated in vivo by M.PstI; 600 ng was double digested by EcoRI-HF and XbaI (12 units each in 30 µL NEB CutSmart buffer). Donor lacI-Ptac-lacO-pUC was protected by M.XbaI prior to purification; 600 ng was similarly digested with EcoRI-HF and SpeI-HF (Figure <ns0:ref type='figure'>S7</ns0:ref>). The restriction enzymes in both digests were heat-killed (20 min. at 80° C) and the restriction fragments (50 ng tagRFP-pUC, 90 ng lacI-Ptac-lacO) were mixed and reacted overnight in a thermocycler with T4 DNA ligase (3 Weiss units in 25 µL NEB CutSmart buffer supplemented with 1 mM ATP). The enzyme was heat-killed (10 min. at 65° C), and the ligation product (1 ng/µL) digested with 8 units each PstI-HF and XbaI in NEB CutSmart buffer (Figures <ns0:ref type='figure'>S8 and S9</ns0:ref>). The transformation of competent E. coli cells produced only 19 ± 7 pink colonies, PeerJ reviewing PDF \| (2020:04:48149:1:0:NEW 7 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed significantly less than the 4R/2M (PstI) experiment with the same plasmids, and 8 ± 6 white colonies per ng (Table <ns0:ref type='table'>1</ns0:ref>). As previously noted, M.PstI does not methylate in vivo as reliably as our other DNA methyltransferases.</ns0:p><ns0:p>The assembly was repeated, except that the tagRFP-pUC plasmid was reacted in vivo with M.AvaIII instead of M.PstI. M.AvaIII catalyzes the methylation of NsiI sites, which exist in most BioBrick compatible plasmids in my lab <ns0:ref type='bibr' target='#b20'>(Matsumura 2017)</ns0:ref>. NsiI produces sticky ends compatible with those of PstI so it offers a good comparison. This assembly, after digestion with NsiI and XbaI, produced 299 ± 91 pink colonies and only 12 ± 3 white colonies per ng (Table <ns0:ref type='table'>1</ns0:ref>). This improved result in consistent with the hypothesis that 4R/2M assembly can be limited by the degree to which the populations of plasmids purified from E. coli are methylated.</ns0:p></ns0:div> <ns0:div><ns0:head>Discussion</ns0:head><ns0:p>The assembly protocols described here could be further improved in several ways. The 4R/2M (EcoRI) is more efficient when M.AvaIII expression vectors were employed instead of those that produce M.PstI. Not all BioBrick compatible plasmids contain NsiI sites, so in vivo M.PstI activity could be enhanced, either by optimizing expression via directed evolution (using in vitro PstI activity as a selection), co-expression with the PstI restriction endonuclease (as in the wild-type operon) or by identifying an M.PstI ortholog that is more active in the E. coli cytoplasm. Another alternative is to clone and express another site-specific DNA methyltransferase that protects some other site that is common in plasmid backbones but very rare within inserts. The tactic of using pairs of methylases to protect desired insert-recipient plasmids from double digests following ligation need not be restricted to BioBrick assembly. It Manuscript to be reviewed could potentially be generalized to streamline other kinds of subcloning experiments if the relevant DNA methyltransferase expression vectors were available.</ns0:p><ns0:p>The 2RM assembly method is a single pot continuous reaction for the restriction digestion and ligation of BioBrick parts, analogous to Golden Gate assembly except that half or more of the recombinant plasmids are ligated in the undesired orientation. The utility of the existing protocol is limited, but it offers some evidence that continuous assembly of correctly oriented ligation products is possible. Such a process would probably require a more elaborate variant of the BioBrick standard and plasmids methylated at more than one restriction site. If four Type II restriction endonucleases and T4 DNA ligase work together efficiently, two mixing steps (heat killing restriction enzymes, ligation reaction setup) of the 4R/2M protocol would be obviated. This hypothetical assembly process would retain the simplicity of the BioBrick standard but emulate the ease of use of Golden Gate.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions</ns0:head><ns0:p>The 4R/2M (PstI) BioBrick assembly described above is less labor-intensive and easier to scale up than is the traditional gel purification approach. It is more efficient and accurate than is 3A assembly and requires less reagents than does Tip Snip subcloning. The value of the labor savings is proportional to the number of assemblies that can be conducted in parallel. The 4R/2M procedure was not designed to match the convenience of single pot, continuous Golden Gate Manuscript to be reviewed any of the restriction sites employed in BioBrick assembly protocols (EcoRI, XbaI, SpeI or PstI), so they will not produce restriction fragments that ligate to those that are desired. Model plasmids lacI-Ptac-lacO-pUC and tagRFP-pUC were purified from triplicate cultures of E. coli OmniMax 2 co-transformed with Prham-M.XbaI-p15A-aadA or Prham-M.Ocy1ORF8430P-p15A-aadA (Figure <ns0:ref type='figure' target='#fig_10'>3</ns0:ref>) respectively. Each purified enzyme was reacted in vitro with XbaI or SpeI-HF, and the extent to which each was cut was assessed by agarose gel electrophoresis. Each of the DNA methyltransferases appears to protect co-transformed plasmid from its corresponding restriction endonuclease, and that protection is sequence specific. Model plasmids used in this study <ns0:ref type='bibr'>(Top left)</ns0:ref> The lacI-Ptac-lacO insert includes a promoter that is somewhat leaky at high copy number. The IMBB2.4-pUC57-mini backbone, hereafter abbreviated pUC, is BioBrickcompatible and also includes an NsiI site downstream of PstI <ns0:ref type='bibr' target='#b20'>(Matsumura 2017)</ns0:ref> . (Top right)</ns0:p><ns0:p>The tagRFP reporter protein can cause colonies to turn visibly pink, but only when the gene encoding it is subcloned downstream of a leaky or constitutive promoter. (Bottom) RP4 oriT-pUC-cat is a BioBrick compatible plasmid that confers resistance to chloramphenicol instead of ampicillin. RP4 oriT serves as a small stuffer in these experiments. In this study this latter plasmid is used only as a recipient plasmid (destination vector) for 3A assembly. The six restriction fragments produced during 3A assembly can ligate to each other to produce a variety of products (not shown). Only those that are circular and contain the right selectable marker (chloramphenicol acetyltransferase in this case) are viable. Still, most E. coli colonies will carry undesired ligation products (top and bottom left) so colony screening is required to identify the desired construct (bottom right).</ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ reviewing PDF \| (2020:04:48149:1:0:NEW 7 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ reviewing PDF \| (2020:04:48149:1:0:NEW 7 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head /><ns0:label /><ns0:figDesc>The methylase expression vectors (Prham-M.EcoRI-p15A-aadA, Prham-M.XbaI-p15A-aadA, Prham-M.Ocy1-p15A-aadA, Prham-M.PstI-p15A-aadA, and Prham-M.AvaIII-p15A-aadA) were constructed as follows. BioBrick compatible DNA methyltransferase genes were synthesized without internal BioBrick restriction sites (EcoRI, NotI, XbaI, SpeI or PstI), cloned into IMBB2.4-pUC57-mini using restriction enzymes EcoRI and PstI, and sequenced. The p15A plasmid origin and spectinomycin resistance marker (aadA) were subcloned from pACYC Duet and pCDF Duet (EMD Millipore, Novagen) respectively into a BioBrick compatible plasmid.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ reviewing PDF \| (2020:04:48149:1:0:NEW 7 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ reviewing PDF \| (2020:04:48149:1:0:NEW 7 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head /><ns0:label /><ns0:figDesc>and separately double digested by XbaI and PstI. The restriction endonucleases in both digestion reactions are subsequently heat-killed (20 min. at 80° C); the four digestion products are combined and reacted with T4 DNA ligase and ATP. The ligase is then heat-killed, and the ligation products (Figures 6, S5 and S6) are diluted and further digested with EcoRI and SpeI. EcoRI linearizes the undesired donor plasmid and any ligation product that includes it. SpeI linearizes the other parental plasmid, so that the desired insert-recipient plasmid ligation PeerJ reviewing PDF \| (2020:04:48149:1:0:NEW 7 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ reviewing PDF \| (2020:04:48149:1:0:NEW 7 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_7'><ns0:head /><ns0:label /><ns0:figDesc>assembly, but BioBrick assembly experiments are arguably easier to design and debug. The BioBrick standard thus remains well suited for the high school and undergraduate students who participate in iGEM competitions. The throughput of 4R/2M BioBrick assembly is mostly PeerJ reviewing PDF \| (2020:04:48149:1:0:NEW 7 Jul 2020) Manuscript to be reviewed limited by the numbers of plasmid minipreps that users can perform in parallel. The quantity of plasmid required is relatively low (≤ 400 ng/digest, as opposed to 1-2 µg for gel purification or Tip Snip) because none is lost during subsequent spin column chromatography. This methodological advance should thus accelerate the work of the BioBricks user community and encourage others to join.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_8'><ns0:head>Figure Legends Figure 1 .</ns0:head><ns0:label>Legends1</ns0:label><ns0:figDesc>Figure Legends</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_9'><ns0:head>Figure 2 .</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2. Subcloning of a methylated insert into a methylated recipient plasmid.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_10'><ns0:head>Figure 3 .</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Figure 3. Model plasmids used in this study</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_11'><ns0:head>Figure 4 .</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Figure 4. M.XbaI and M.Ocy1ORF8430P protect plasmids from XbaI and SpeI</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_12'><ns0:head>Figure 5 .</ns0:head><ns0:label>5</ns0:label><ns0:figDesc>Figure 5. 2RM BioBrick assembly</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_13'><ns0:head>Figure 6 .</ns0:head><ns0:label>6</ns0:label><ns0:figDesc>Figure 6. 4R/2M (PstI) BioBrick assembly</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_14'><ns0:head>Figure 1</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_15'><ns0:head>Figure 2</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_16'><ns0:head /><ns0:label /><ns0:figDesc>Figure 3</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_17'><ns0:head /><ns0:label /><ns0:figDesc>Figure 4</ns0:figDesc></ns0:figure> <ns0:note place='foot'>PeerJ reviewing PDF \| (2020:04:48149:1:0:NEW 7 Jul 2020)Manuscript to be reviewed</ns0:note> </ns0:body> "	"July 7, 2020 Dear Dr. Gillespie, Thank you for managing the review of the manuscript I submitted to PeerJ ('Methylase-assisted subcloning for high throughput BioBrick assembly'). The comments of both reviewers (in blue Calibri font below) were thoughtful and constructive so I have addressed them (in black Times New Roman font) to the best of my ability. Both Reviewers had difficulty following my narrative as originally written, so I also moved my detailed old figures, along with two non-essential subsections describing older methods, to a new Supplemental Materials section. The changes in the tracked version of the manuscript are manually underlined. It is also possible to view the changes in Microsoft Word 2016 (Review/Tracking/All Markup). I hope that the Reviewers will agree that the revised document is easier to understand, and that they will be better able to evaluate the work on its technical merits. Yours Truly, Ichiro Matsumura Emory University Reviewer 1, John Anderson: There is a prior study from my own lab: '2ab assembly: a methodology for automatable, high-throughput assembly of standard biological parts' PMID: 23305072 that describes a similar method to the subject of this paper, and is worth comparing. I am sorry that I was unaware of this important and very relevant precedent. It is now described as such in the introduction (page 5, lines 104-109) I was a little unclear about the claims about one-pot reactions. The statement 'The 2RM assembly technique shows how site-specific methylation could potentially enable one pot digestion/ligation reactions.' sounds like you are saying that one-pot doesn't work yet. But in other places it seems like you are saying it does work, and I don't see any description of what goes wrong if that does not work. There are protocols talking about stepwise digestions, and others as one-pot. I got a little lost on those details. I realized when I read the 2ab paper that I was previously using the term 'one-pot reaction' narrowly to describe only Golden Gate-like assembly processes like 2RM in which restriction digestion and ligation occur at the same time. In 2ab assembly, enzymes are added and heat killed sequentially to plasmids in a single tube. So I have defined my terms more precisely to distinguish between one pot reactions that are 'continuous' (e.g. Golden Gate) and others that are 'discontinuous' (e.g. 2ab). Reviewer 2, Anonymous: This manuscript presents a new method for assembling BioBricks using differential methylation and related non-gel-based DNA ligation approaches. The idea seems sound, the implementation is detailed in general though missing some key analyses. The presentation, however, limits the impact of this work. Both Reviewers criticized my presentation so I strove to simplify the entire manuscript in an effort to clarify my key points. The manuscript presents colony counts as the main evidence to support its claims on correct plasmid assembly. This is indirect evidence and as such I would suggest that a sample of the resulting plasmids are sequenced to directly confirm that the plasmid was assembled as expected and to screen for any systematic sequence artifacts that may arise. My goal was to develop a method that is efficient (e.g. create large quantities of desired recombinant ligation product), and therefore reliable under suboptimal conditions, and accurate (low quantities of undesired ligation products). The colony counts are 'direct evidence' that the methods described work as advertised. Only colonies that carried correctly assembled recombinant ligation products turned red, and those were scored separately from the white colonies carrying either parental plasmids or undesired ligation products. No variation in red color between or within colonies was observed, so genetic stability was not an issue within the short time span of these experiments. Some ligation products were purified from red or white colonies and restriction mapped, albeit not in a systematic or statistically significant way and observed no surprises. While restriction mapping cannot rule out the occurrence of point mutations or small indels, I have no reason to suppose that mutation frequencies will vary between subcloning protocols. Indeed, we and others have used the same type II restriction enzymes and T4 DNA ligase to assemble BioBrick parts for nearly two decades. PCR can introduce systematic sequence variants, but the method described here employs neither PCR nor any other in vitro amplification reactions. I therefore don't think that DNA sequencing would be informative. Major presentation issues: 1. Thank you for a thorough description of your technique and thought process. However, your message is lost amidst the figures. In general, they would benefit from being more schematic and displaying only the information necessary to get the point across. Figure 1 does a better job at this by only labeling the relevant sites and color coding the general segments of the plasmids. However, although Figure 1 summarizes the technique successfully, it would be better to organize the method in step-wise columns so the figure can convey the same information quicker. In particular, a large figure side-by-side comparisons of the different cloning techniques utilized in the manuscript would be extremely useful (gold standard, trip snip, 3A, 2RM and 4R/2M). I created three new schematic Figures to illustrate the traditional/gold standard, 2RM and 4R/2M assembly methods. The tip snip and 3A methods are well described elsewhere, so the descriptions of these experiments has been moved into a new Supplementary Materials section. I was unable to place the various plasmid forms into columns, or to create a single large figure to represent all of the techniques, without shrinking the images and letters to the point of near invisibility. 2. Figures 3,4,7-15 share enough context to consolidate into a fewer, maybe a single large figure that summarizes all the cloning comparisons. Figures 2 and 5 can also be combined. I have moved all of the old highly detailed figures into a Supplemental Materials section. 3. Figure 5 is not mentioned in the main text. The old Figure 5 has been added to the new Figure 3, which is now mentioned in the text. 4. Why are none of the students acknowledged authors of the manuscript? I am generally inclined to include students, including those in high school, as co-authors. In this case, I led them through the 4R/2M protocol. It didn't work very well in their hands, but I subsequently discovered that the PstI methylase expression vector was unreliable. None of the data they produced was included in the manuscript. If, however, the Reviewers and/or Editor believe that the contribution of these students merits co-authorship, I would be glad to credit them as such. 5. Some examples where the language could be improved and or concepts could be expanded upon include: a. Line 51. Can you expand and provide references on what you mean by improvements in ‘miniturization and automation’? I have deleted this phrase because it is not central to my argument. Many recent technical advances remain trade secrets so appropriate references are unavailable. The cost of gene synthesis has decreased, but service providers either don't offer to synthesize, clone and sequence genes longer than 3 kb, or charge much more for them. Demand for gene assembly techniques thus continues to increase. b. Line 61. The current BioBrick RCF paradigm could use a Figure. c. Line 71. For the sake of clarity, it would be useful to have a Figure that shows the gold standard process and highlight where the pain point is (i.e. gel purification step). I have created a new Figure 1 to describe the traditional subcloning. d. Lines 93 and 99. Can you provide evidence about BioBrick technology being ‘easier to comprehend’ and your method being ‘easier to understand’? I argued that the BioBrick standard, which employs a single kind of overhang and a single kind of plasmid, is easier to comprehend than any Golden Gate assembly standard since those include up to 256 possible overhangs and 65,536 plasmids (each with a unique pair of overhangs). I never wrote that my method, which was introduced later in the manuscript, was easier to understand. I nevertheless changed the wording to BioBrick 'assembly experiments are relatively easy to plan' (lines 93-95) to clarify my meaning. The virtues and drawbacks of Golden Gate assembly are rooted in the Type IIS restriction enzymes. They recognize non-palindromic sites, so the orientation of the site in one fragment must be the opposite of that in the fragment to which it will be ligated. If multiple fragments are to be ligated in a Golden Gate assembly, users must make sure that each overhang complements only one other in the reaction. Some low fidelity overhang sequences anneal to non-complementary overhangs, thereby creating unexpected and undesired ligation products. In contrast, established subcloning protocols that rely on Type II restriction enzymes, including every variation of BioBrick assembly, remain free of these complications. e. Line 102. Can you be more specific with the phrasings ‘protect sites within inserts’ [from what?] and ‘mark sites at their ends’? I can be more specific by summarizing previous methylation assisted subcloning methods below. I mentioned these methods because readers should know that I was not the first to use DNA methyltransferases for gene assembly. That said, I am trying to simplify my narrative so I have eschewed lengthy explanations of previous methods in the revised manuscript, other than 2ab, since it is the most relevant (lines 100-108). The MetClo method invented by Lin and O'Callahan enables hierarchical Golden Gate assemblies using a single Type IIS restriction enzyme. They identify sequence specific DNA methyltransferases that modify sequences that overlap those of a Type IIS recognition site. They created cloning vectors that contain four sites normally recognized by the same Type IIS enzyme; the two on the outside were designed to be methylated and thus protected from that restriction enzyme, while two inner sites were left susceptible to cleavage. The two inner sites are eliminated during Golden Gate assembly, and the two outer sites are not methylated by host E. coli so they can be used in the following round of assembly. The MASTER ligation method developed by Chen et al. employs the methylation-dependent Type IIS restriction endonuclease, MspJI. They use methylated primers to create PCR products that are cut by MspJI only at the ends. The virtue of this method is that unmethylated MspJI sites within the PCR product remain uncut, so it is not necessary to exclude them as is normally the case for either BioBrick or canonical Golden Gate assembly. f. Line 104. Use a different term than ‘decorate DNA’ to describe the addition of methyl groups to DNA. The word 'decorate' has been replaced with 'add.' g. Line 106. What do you mean by ‘smaller scale’? The phrase 'smaller scale' has been replaced with 'smaller quantities of DNA are required.' Gel extraction yields are sometimes low so traditional subcloning generally begins with digestions of 1-2 micrograms of each plasmid. In the 2RM and 4R/2M assemblies I digested 400 ng of plasmid but ended up using 80 ng per digest. h. Line 144. Include how did you determine transformation efficiency in methods. The sentence now reads, 'Transformation efficiency was 3 x 107/µg, as determined by counting colonies after transformation with 10 pg of pUC19. i. Line 268. Can you provide further evidence about the 3A method not ‘working well’ in people’s hands? Also, can you be more specific about what you mean by ‘working well’? The tip snip and 3A assembly results are not essential to the narrative so I have moved them to the Supplemental Materials section. The term 'worked well' was replaced by 'never been efficient.' The results shown in Table 1 were actually the best of four different attempts to get 3A assembly to work. The other three attempts are not shown. j. Line 258. Grammar, ‘ligation’ should be ‘ligate’. This word must have been in the tip snip description (now in Supplemental Material) but I was unable to find it. k. Line 443. Grammar? What do you mean by ‘multiply methylated’? I meant 'methylated at more than one site.' The wording has been changed accordingly. 6. I recommend using RRIDs (Research Resource Identifiers) to better document the materials used in the experiments. The DNA methyltransferase expression vectors constructed for these studies were deposited in the Addgene collection. They have already been assigned RRID numbers (RRID:Addgene_149338 - RRID:Addgene_149343). They will be available to the scientific public after this manuscript has been accepted for publication and should appear in the RRID database at that time. "	Here is a paper. Please give your review comments after reading it.
514	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Uncanny valley research has shown that human likeness is an important consideration when designing artificial agents. It has separately been shown that artificial agents exhibiting human-like kinematics can elicit positive perceptual responses. However the kinematic characteristics underlying that perception have not been elucidated. This paper proposes kinematic jerk amplitude as a candidate metric for kinematic human likeness, and aims to determine whether a perceptual optimum exists over a range of jerk values.</ns0:p><ns0:p>We created minimum-jerk two-digit grasp kinematics in a prosthetic hand model, then added different amplitudes of temporally smooth noise to yield a variety of animations involving different total jerk levels, ranging from maximally smooth to highly jerky. Subjects indicated their perceptual affinity for these animations by simultaneously viewing two different animations side-by-side, first using a laptop, then separately within a virtual reality (VR) environment. Results suggest that (a) subjects generally preferred smoother kinematics, (b) subjects exhibited a small preference for rougher-than minimum jerk kinematics in the laptop experiment, and that (c) the preference for rougher-than minimum-jerk kinematics was amplified in the VR experiment. These results suggest that non-maximally smooth kinematics may be perceptually optimal in robots and other artificial agents.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>INTRODUCTION</ns0:head><ns0:p>The uncanny valley is a phenomenon introduced in the robotics literature by Masahiro Mori, explaining that our affinity toward objects generally increases as the object becomes more human-like, however at a certain human-likeness our affinity drops drastically and we feel a sense of eeriness <ns0:ref type='bibr' target='#b18'>[18]</ns0:ref>. With appearance being quite human but being incompletely so, the viewer senses that something is unnatural and the object is seen as odd, creepy, and/or terrifying.</ns0:p><ns0:p>Kinematics may be important to consider in conjunction with the uncanny valley. Mori pointed out that moving objects can amplify affinity relative to non-moving objects, both in positive and negative senses (such as a corpse being animated becomes much more eerie) <ns0:ref type='bibr' target='#b18'>[18]</ns0:ref>. From this idea, one could surmise that different levels of human likeness in the objects' kinematics may modulate uncanny valley responses.</ns0:p><ns0:p>The uncanny valley has arguably been overcome in many applications involving static, lifelike imagery such as computer-generated humans <ns0:ref type='bibr' target='#b0'>[1,</ns0:ref><ns0:ref type='bibr' target='#b22'>22]</ns0:ref>, but animated human-like objects (mainly robots) are arguably far less human-like. A variety of research has shown the importance of kinematics in human-robot PeerJ reviewing PDF \| (2020:02:46215:1:3:NEW 2 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed interactions. For example, it has been shown that both humanoid features and human movements are important factors for facilitating human-robot social interactions <ns0:ref type='bibr' target='#b13'>[13,</ns0:ref><ns0:ref type='bibr' target='#b19'>19,</ns0:ref><ns0:ref type='bibr' target='#b2'>3,</ns0:ref><ns0:ref type='bibr' target='#b6'>6]</ns0:ref>. Whereas the biological movement in the latter study <ns0:ref type='bibr' target='#b2'>[3]</ns0:ref> was from a motion captured trajectory, a separate but similar study <ns0:ref type='bibr' target='#b10'>[10]</ns0:ref> showed that human-robot interaction improved when the robot moved with minimum-jerk trajectories as approximations to human kinematics <ns0:ref type='bibr' target='#b4'>[4]</ns0:ref>, instead of with mechanical trapezoid trajectories. This research suggests that kinematics, and potentially jerk specifically, may interact with the uncanny valley to form an overall affinity for moving artificial agents.</ns0:p><ns0:p>Jerk is the third time derivative of position, and applies to both linear and angular motion. It can be interpreted as the rate of change of acceleration, or perhaps more clearly as proportional to the rate of change of force. Jerk has been shown to be relevant to human-robot interaction <ns0:ref type='bibr' target='#b9'>[9]</ns0:ref>, and theoretical predictions of movement based on a minimum jerk criterion have been shown to qualitatively reproduce many features of human movement <ns0:ref type='bibr' target='#b4'>[4,</ns0:ref><ns0:ref type='bibr' target='#b5'>5,</ns0:ref><ns0:ref type='bibr' target='#b28'>28]</ns0:ref>. Minimizing jerk is potentially useful movement strategy because high jerk implies rapid force changes and thus both wasted muscular energy and compromised mechanical control. Jerky motion is additionally characteristic of movement disorders <ns0:ref type='bibr' target='#b15'>[15,</ns0:ref><ns0:ref type='bibr' target='#b12'>12]</ns0:ref>, implying that lower jerk, if perceivable by humans, may also be perceived as healthier and/or more natural than jerky movements.</ns0:p><ns0:p>The purpose of this study was to quantify the changes in affinity for a moving artificial agent as a function of kinematic jerk. We postulate that jerk may be an important affinity-relevant variable based on previous studies which hypothesize that humans follow minimum jerk trajectories <ns0:ref type='bibr' target='#b4'>[4]</ns0:ref> and that smooth trajectory endpoints are associated with greater affinity <ns0:ref type='bibr' target='#b10'>[10]</ns0:ref>. Through this quantification a secondary goal was to find the optimal smoothness, if any, for artificial agent kinematics.</ns0:p><ns0:p>This study used the hand as an artificial agent model because the hand is more recognizable as human-like than a limb, and because using a hand avoids problems associated with uncanny valley-like responses to heads and faces. We aimed to examine our affinity toward robot hand prostheses by measuring affinity as a function of smoothnesses from very jerky kinematics, jerkier than a robot, to minimum-jerk kinematics.</ns0:p><ns0:p>To secure simplicity for this experiment design, and provide functional findings that can be implemented into robotic designs, we aimed for a simple 1-DoF grasp motion. Specifically, a motion that all subjects are familiar with, able to reenact, and have a general sense of its naturalness; to which we chose to examine an index-thumb two-digit motion for this study.</ns0:p></ns0:div> <ns0:div><ns0:head>EXPERIMENT 1: HUMAN AND ROBOT KINEMATICS</ns0:head><ns0:p>The purpose of this experiment was to roughly quantify the expected range of kinematic jerk values for robot and human finger grasping motions. Specifically the human jerk magnitude that was used for kinematic references and basic kinematic motion robot hand device HACKberry was used to generate the minimum-jerk value and trajectory.</ns0:p></ns0:div> <ns0:div><ns0:head>Methods</ns0:head><ns0:p>A sample of 21 human subjects was tested. All subjects (including experiment 2 and 3) were requited from Shinshu University and provided written informed consent following the procedures of Shinshu University's ethical review board (approval number: 216).</ns0:p><ns0:p>Two main devices were used for this experiment. The first was a six-camera motion capture system (VENUS 3D Flex13, Tokyo, Japan) which recorded the positions of reflective markers at a sampling rate of 120 Hz. The second device was an open-source robotic hand (HACKberry, exiii Inc., Tokyo, Japan) (figure <ns0:ref type='figure' target='#fig_0'>1</ns0:ref>). The individual hand segments were 3D-printed using polylactic acid filament and, unlike the original open-source design, only the wrist segments and more distal segments were used (i.e., the forearm segments were not used). Also unlike the original open-source design, which was battery-powered, the device was modified to accept power using a standard wall adapter. Switches on the back of the hand were used to close the fingers into a grasp-like posture, and then return the fingers to the extended posture depicted in figure <ns0:ref type='figure' target='#fig_0'>1</ns0:ref>.</ns0:p><ns0:p>Reflective markers were placed on the medial side of the human at the following locations: proximal interphalangeal joint (PIP joint), metacarpophalangeal joint (MP joint), and metacarpal bone (MP bone) (figure <ns0:ref type='figure' target='#fig_2'>2</ns0:ref>). The motion capture system's six cameras were placed around the table, yielding a total capture volume of approximately 50 cm 3 .</ns0:p></ns0:div> <ns0:div><ns0:head>2/16</ns0:head><ns0:p>PeerJ reviewing PDF \| (2020:02:46215:1:3:NEW 2 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed A small eraser (1.0 × 1.6× 4.5 cm) was placed on the table with its wide face down, and was used both as a reference point and as a guide for the subjects in order to generate a two finger grasp motion. Each participant held their thumb tip close to the eraser, and keeping an 'L' shape with their two fingers, they extended their index finger while facing their hand's dorsal surface upwards. Marker positions were recorded for a minimum of 60 s at 120 Hz. Subjects were informed to grasp the eraser as they normally do and maintain the grasp until they are instructed to release it and return to their initial position. The interval between movements were 5 s and the grasp-release motions were repeated six times.</ns0:p><ns0:p>A similar motion recording was done on the HACKberry device to measure the grasp range and its time of movement. Like the human hand measurement, markers were placed onto the corresponding locations of the HACKberry device and its thumb and index finger will grasp the same object. The grasp motion was measured five times.</ns0:p></ns0:div> <ns0:div><ns0:head>Analysis</ns0:head><ns0:p>Data were processed in Python 3 (Python Software Foundation). From the six grasp-release motions that the human subjects each performed, five grasp trials with the minimal marker swapping were selected for analysis. Each start and end point of the motion were manually determined and the average absolute jerk being calculated as:</ns0:p><ns0:formula xml:id='formula_0'>Average absolute jerk = 1 T T ∑ t=t 0 d 3 θ (t) dt 3 (<ns0:label>1</ns0:label></ns0:formula><ns0:formula xml:id='formula_1'>)</ns0:formula><ns0:p>where T is the total time of the finger motion, t 0 is the time at which the finger starts to move, θ is the angle of the MP joint, and θ 's third derivative was estimated numerically using adjacent time samples. All joint angle data were processed with a low-pass fileter using a fifth-order, zero delay Butterworth filer with a cutoff frequency of 10 Hz. The HACKberry device was measured for its grasp time length and its angular range by determining the initial and final MP joint angle from the average values. Just like the human subject data, the HACKberry's initial and final point of its motion were manually determined. Lastly, based on HACKberry's measured data, we created a minimum-jerk trajectory. Flash and Hogan (1985) <ns0:ref type='bibr' target='#b4'>[4]</ns0:ref> describe a general minimum-jerk trajectory (x minjerk ), where an object travels from x i to x f in time t = d seconds, as:</ns0:p><ns0:formula xml:id='formula_2'>x minjerk (t) = x i + (x f − x i ) 10 t d 3 − 15 t d 4 + 6 t d 5 (<ns0:label>2</ns0:label></ns0:formula><ns0:formula xml:id='formula_3'>)</ns0:formula><ns0:p>and the measured initial and final points, and the average grasp duration was applied accordingly, and the absolute average jerk magnitude is calculated using equation 1.</ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head><ns0:p>As figure <ns0:ref type='figure' target='#fig_3'>3</ns0:ref> shows, the average absolute human jerk for the task used in this paper was 0.50 × 10 5 deg/s 3 .</ns0:p><ns0:p>The HACKberry performed its two-digit 1-degree of freedom (DoF) grasp motion from 161.4 to 96.9 deg in 0. </ns0:p></ns0:div> <ns0:div><ns0:head>Brief discussion</ns0:head><ns0:p>Among the results, a subject produced a mean value (0.190 × 10 5 deg/s 3 ) that is just under the minimal jerk value (0.20 × 10 5 deg/s 3 ). This is because the subject moved its finger slower and/or in smaller pinch angle than the time and/or the travel angle of the minimum-jerk trajectory. Also note that the parameters for the minimum-jerk trajectory was based on the kinematics of the HACKberry robot hand.</ns0:p><ns0:p>Note that this experiment does not seek to identify differences between artificial and human kinematics.</ns0:p><ns0:p>An artificial agent was used simply because jerk cannot be controlled precisely in real human motion.</ns0:p><ns0:p>The human jerk data from Experiment 1 had just one purpose: to quantify an approximate range of jerk values that can be expected in a 1-DoF two finger grasp motion.</ns0:p></ns0:div> <ns0:div><ns0:head>4/16</ns0:head><ns0:p>PeerJ reviewing PDF \| (2020:02:46215:1:3:NEW 2 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>EXPERIMENT 2: LAPTOP-BASED MOTION PERCEPTION Methods</ns0:head><ns0:p>The purpose of this experiment was to quantify subjects' affinity for particular kinematics as a function of kinematic noise amplitude by a one-paired comparison of multiple sample animations on a laptop screen.</ns0:p><ns0:p>The animations showed a robot hand (HACKberry device) performing a two-digit 1-DoF grasp motion with different degrees of jerk. We chose to show a robotic-looking hand model instead of a human-looking one because the primary motivation for this study was improving the kinematic quality of artificial agents and not humans. The first part of this Methods section describes how the kinematic samples were created and the second part explains how the software was prepared and used in the experiment.</ns0:p></ns0:div> <ns0:div><ns0:head>Generating random kinematics with controlled jerk</ns0:head><ns0:p>Based on the minimum-jerk trajectory generated from Experiment 1, the kinematic trajectory is depicted in figure <ns0:ref type='figure' target='#fig_5'>4 (c,d</ns0:ref>).</ns0:p><ns0:p>To this minimum-jerk trajectory we added temporally smooth Gaussian noise using the open-source Python package spm1d <ns0:ref type='bibr' target='#b20'>[20]</ns0:ref>. As compared with temporally rough noise, which is non-physiological, this temporally smooth noise increased jerk in a more natural manner (figure <ns0:ref type='figure' target='#fig_5'>4</ns0:ref>). We subsequently refer to the amplitude of this smooth, Gaussian noise as 'standard deviation' (SD) (Table <ns0:ref type='table'>1</ns0:ref>). Since the constant-weighted Gaussian noise depicted in figure <ns0:ref type='figure' target='#fig_5'>4 (a,b</ns0:ref>) fails to control the initial and final postures, the Gaussian noise was multiplied by a weighting trajectory w(t), which tapered exponentially to zeros at its endpoints, resulting in endpoint-constrained random noise. The final trajectories (Fig. <ns0:ref type='figure' target='#fig_5'>4a,b</ns0:ref>) were defined as:</ns0:p><ns0:formula xml:id='formula_4'>x(t) = x minjerk (t) + SD(t)w(t) (3)</ns0:formula><ns0:p>Table <ns0:ref type='table'>1</ns0:ref>. Key variable definitions. We created nine random trajectories in total: one minimum-jerk trajectory (SD=0) and eight noiseadded trajectories with SD values: 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, where SD = 0.5 corresponds to the human SD value measured in Experiment 1. As in Experiment 2 the animations were created using Blender 2.77. Only the index finger was animated, according to the nine trajectories.</ns0:p></ns0:div> <ns0:div><ns0:head>Variable</ns0:head><ns0:p>Since the aforementioned SD value describes only noise amplitude, which is only indirectly related to kinematic jerk, we also calculated the average absolute jerk following equation 1.</ns0:p></ns0:div> <ns0:div><ns0:head>Laptop-based game set up and experiment protocol</ns0:head><ns0:p>The main device used in this experiment was a laptop (MacBook Air, Apple, USA) on which the same animations were displayed but in 2D view. All 22 subjects who participated in this experiment were familiar with laptop use.</ns0:p><ns0:p>Using the Blender Game Engine (Blender Foundation, Amsterdam, www.blender.org), we first developed nine animation videos each based on the generated kinematic samples. Each video showed a</ns0:p><ns0:p>HACKberry device an animation of a digital model of the device performing a two-digit grasp: from its starting position (index finger extended) to its movement where its MP joint moved to perform a pinch motion. The animation total length was 2.0 s long (0.8 s of static when extending + 0.4 s motion + 0.8 s of static when grasping). Using those nine animation videos, a game where two of the nine videos with different jerk trajectories were shown side-by-side (figure <ns0:ref type='figure' target='#fig_7'>5</ns0:ref>), played in loop simultaneously, and players were instructed to 'choose the video you find to be most natural' by pressing the left or right key on the laptop's keyboard. After choosing, a new pair of videos was presented and the selection task was repeated until 72 selections were completed (all combinations of the 9 videos, with each left-right pair repeated as right-left). The video pairs were presented in a random order. One game took 15 to 20 minutes to complete.</ns0:p></ns0:div> <ns0:div><ns0:head>Analysis</ns0:head><ns0:p>As n Experiment 1, data were processed in Python 3.</ns0:p></ns0:div> <ns0:div><ns0:head>Bradley-Terry paired comparison analysis</ns0:head><ns0:p>The goal of this analysis was to quantify preference (p). For this purpose we used the Bradley-Terry maximum likelihood estimate of binary preference. The preference for animation i with respect to animation j (p i j ) was calculated as.</ns0:p><ns0:formula xml:id='formula_5'>p i j = p i p i + p j (<ns0:label>4</ns0:label></ns0:formula><ns0:formula xml:id='formula_6'>)</ns0:formula><ns0:p>where p i and p j are the relative selection frequencies of animations i and j, respectively, and where p i</ns0:p></ns0:div> <ns0:div><ns0:head>6/16</ns0:head><ns0:p>PeerJ reviewing PDF \| (2020:02:46215:1:3:NEW 2 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed The animations were randomly selected so that the jerkier animation did not always appear on the same side.</ns0:p><ns0:p>and p j are subject to the constraints:</ns0:p><ns0:formula xml:id='formula_7'>   T i p i = n ∑ j =i 1 p i + p j ∑ k i=1 p i = 1<ns0:label>(5)</ns0:label></ns0:formula><ns0:p>where T i is the number of times that animation i was chosen over animation j. Manuscript to be reviewed </ns0:p></ns0:div> <ns0:div><ns0:head>Bayesian comparison of preference models</ns0:head><ns0:p>The goal of this analysis was to probabilistically compare different models of the jerk-preference relation.</ns0:p><ns0:p>We used the software package PyMC <ns0:ref type='bibr' target='#b21'>[21]</ns0:ref> to construct three competing data models (figure <ns0:ref type='figure' target='#fig_10'>6</ns0:ref>): <ns0:ref type='figure' target='#fig_10'>6</ns0:ref>). Finally, the three models will be analyzed in a appropriate range that of SD < 3.0.</ns0:p><ns0:formula xml:id='formula_8'>Model A : f (x) = a 0 + a 1 x Model B : f (x) = b 0 + b 1 x, if x < b 2 b 0 + b 1 b 2 , otherwise Model C : f (x) = c 0 + c 1 x, if x < c 2 c 0 − c 1 x + 2c 1 c 2 , otherwise<ns0:label>(6)</ns0:label></ns0:formula><ns0:p>We separately fit each model to the data using Bayesian inference, with one million iterations, a burn-in of 20,000 iterations, and a thinning rate of six. Relatively weak prior normal distributions were placed on all model parameters. The relative strengths-of-fit were assessed using Bayes factor, with one Bayes factor value computed for each model pair. Bayes factors were interpreted using previously published guidelines <ns0:ref type='bibr' target='#b11'>[11]</ns0:ref>. Last, sensitivity to the selected prior distributions was assessed by systematically changing the center and breadth of the prior distributions. Results were found to be qualitatively robust to prior distribution adjustments, so sensitivity results are not reported here in interest of space.</ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head><ns0:p>An overall negative correlation between preference and jerk was observed (figure <ns0:ref type='figure' target='#fig_11'>7</ns0:ref>). An approximately linear trend was observed for absolute average jerk greater than SD = 0.5 (jerk = 0.50 × 10 5 deg/s 3 ). The minimum jerk trajectory was the second most preferred after SD = 0.5, which embodied slighter jerkier kinematics.</ns0:p><ns0:p>Bayesian model comparison found that Models B and C were better fit to the data than Models A (table <ns0:ref type='table' target='#tab_0'>2</ns0:ref>). According to published interpretation guidelines <ns0:ref type='bibr' target='#b11'>[11]</ns0:ref>, Models B and C were 'very strong' fits, and differences between Models B and C were 'barely worth mentioning'.</ns0:p></ns0:div> <ns0:div><ns0:head>Brief discussion</ns0:head><ns0:p>The Bradley-Terry paired comparison analysis shows that SD = 0.5, the smoothest noise amplitude next to the minimum jerk kinematics and the corresponding to the human kinematic smoothness, was selected as most natural by the subjects. However, whereas the Bayesian analysis shows that both minimum jerk trajectory and SD = 0.5 noise amplitude are the suitable candidates for the HACKberry's grasp motion, significance between the two kinematics was not found. Based on this, we can understand that subjects prefer smoother kinematics, however, it is not clear if subjects find smoother kinematics more natural or human smoothness to be the most natural. Also, even smoother kinematics were preferred more then the jerkier ones, the linear increase of the Bradley-Terry preference (p) in figure <ns0:ref type='figure' target='#fig_11'>7</ns0:ref> suggests that there were subjects that preferred high noise amplitude such as SD = 3.0 or 3.5 above all. From what the subjects were instructed with, it is possible that the observed hand model was not necessarily human-like or related and found it more natural for it to operate in a non-human kinematics (e.g., robotic).</ns0:p></ns0:div> <ns0:div><ns0:head>EXPERIMENT 3: VR-BASED MOTION PERCEPTION</ns0:head><ns0:p>The purpose of this experiment was to examine the same kinematic perception in Experiment 2, but in a virtual reality with increased realism and reduced restraint on movement observation . The idea was that Experiment 2 involved observing grasping motions from a fixed perspective, and that perspective itself could affect motion perceptions. We therefore conducted an additional experiment to test whether freer, interactive observation of the movements in a VR observation would alter the perceptual results we observed in Experiment 2. Subjects wore a VR head mount display with 3D view and 6-DoF head tracking (three translational and three rotational DoF) to be able to observe the HACKberry models with high realism. Subjects also used two 6-DoF controllers (one for each hand) to control the model postures in the virtual world.</ns0:p></ns0:div> <ns0:div><ns0:head>Methods</ns0:head><ns0:p>Two main devices were used in this experiment: a VR system and a motion controller. The VR system (Oculus VR, Menlo Park, USA) through which custom animations of 3D models were presented in 3D view to subjects as described below. The motion controller (Leap Motion, San Francisco, USA) that Identical to Experiment 2, the VR game presented two side-by-side 3D animations of the HACKberry prosthesis, each repeating a two-digit grasping motion in a loop. Also identical to Experiment 2, the 72 total combinations of the nine grasp trajectories created in Experiment 2 were presented in a random order to each subject. The locations of the animated hands in VR space were translated and rotated accordingly.</ns0:p><ns0:p>Thus in the VR world, subjects could rotate the VR hands interactively, freely, and realistically, as if they were the subject's own hands (figure <ns0:ref type='figure' target='#fig_12'>8</ns0:ref>). They were able to position and rotate the hand models freely.</ns0:p><ns0:p>Note that the subjects' finger motion was not tracked, and the HACKberry 3D model's index finger was animated based on the same synthetic kinematics from Experiment 2. Instructions, the video selection task, and game flow proceeded identically to Experiment 2.</ns0:p></ns0:div> <ns0:div><ns0:head>Analysis</ns0:head><ns0:p>As in Experiment 1 and 2, data were processed in Python 3. Also like in Experiment 2, the preference is measured using Bradley-Terry paired comparison analysis, then the Bayesian comparison was done to compare different models of the jerk-preference relation. </ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head><ns0:p>A similar preference shift (a negative trend between preference and jerk) was observed with more of a sigmoidal or s-shaped pattern in the VR experiment. A negative trend in the preference is observed from the SD = 1.0 kinematic and reaches little to no preference from SD = 2.5 and higher jerk amplitudes.</ns0:p><ns0:p>Bayesian model comparison found that Models B and C were better fits to the data than Model A (table <ns0:ref type='table' target='#tab_0'>2</ns0:ref>).</ns0:p><ns0:p>According to published interpretation guidelines <ns0:ref type='bibr' target='#b11'>[11]</ns0:ref>, Models C was a 'decisively' better fit than Model B, and differences between Models B and A were 'barely worth mentioning'.</ns0:p></ns0:div> <ns0:div><ns0:head>Brief discussion</ns0:head><ns0:p>Both Bradley-Terry paired comparison analysis and Bayesian analysis show that the minimum jerk trajectory is not the optimal smoothness for the HACKberry hand model observed in the VR environment and, the less smoother kinematics are a preferable choice in terms of naturalness. SD = 1.0 is ranked the highest for according to the Bradley-Terry analysis, however, the significance between its neighboring kinematic smoothness cannot be found.</ns0:p><ns0:p>From the preference shown in figure <ns0:ref type='figure' target='#fig_13'>9</ns0:ref>, subjects overwhelmingly find the minimum jerk trajectory and SD = 0.5 to 2.0 more natural than the kinematic smoothness of SD = 2.5 to 4.0. This can be from the experiment environment where the compared kinematic hand models are moved and observed as if it is the subjects' biological extension, thus the subjects prefer a naturalness relatively more human-like.</ns0:p><ns0:p>However, the possibility still remains that an perceptibly optimal smoothness (i.e., noise amplitude) exists that is not the human kinematic smoothness, but specifically for the robotic hand design. </ns0:p></ns0:div> <ns0:div><ns0:head>DISCUSSION</ns0:head></ns0:div> <ns0:div><ns0:head>Main implications</ns0:head><ns0:p>In Experiment 1, we motion captured two-digit 1-DoF grasp motion of human hands and a robot hand device (HACKberry). Human and HACKberry jerk magnitude were calculated and used to formulate an approximate range of jerk values that could be expected in humans and robots. For Experiment 2, we generated computer animations of HACKberry index finger motion in nine steps of jerk over the jerk range calculated in Experiment 1, and we presented these nine animations in pairs to subjects who evaluated their naturalness. Experiment 3 repeated Experiment 2 with new subjects, and in a virtual reality environment where subjects could manipulate the HACKberry animation's 3D positions and rotations using hand-held 6-DoF motion controllers.</ns0:p><ns0:p>This study found that (a) subjects generally preferred smoother kinematics to rough kinematics (figure <ns0:ref type='figure' target='#fig_11'>7</ns0:ref>), but that (b) subjects did not necessarily prefer the maximally smooth, minimum jerk trajectory to slightly rougher kinematics (figure <ns0:ref type='figure' target='#fig_13'>9</ns0:ref>). That is, the general trend for preferring smooth vs. rough kinematics did not necessarily apply to the minimum jerk trajectory. Whereas the laptop experiment shows that slightly rougher kinematics (SD = 0.5 (0.50 × 10 5 deg/s 3 )) were preferred either equally or more, the VR experiment showed more clearly that subjects preferred slightly rougher kinematics. Both experiments showed qualitatively similar peak preference and also similar preference decline.</ns0:p><ns0:p>Bayesian analyses support these results quantitatively, by suggesting that Models B and C (preference flattening toward maximal smoothness, and preference reduction toward maximal smoothness) both modeled the data better than Model A (linearly increasing preference) (table <ns0:ref type='table' target='#tab_0'>2</ns0:ref>). This result was amplified in the VR experiment, which also showed that Model C was a considerably better model of the data than Model B. If true, these results suggest that, when designing robots from the perspective of kinematic aesthetics, minimum jerk may not be the optimum solution. This study's results pertain directly only to simple grasping kinematics, but it is possible that these results generalize to broader categories of robotic movements, including general prosthetic hand kinematics, and even general humanoid robot kinematics.</ns0:p></ns0:div> <ns0:div><ns0:head>Qualitative comparisons of the laptop and VR experiments</ns0:head><ns0:p>The laptop and VR experiments yielded similar trends: an overall negative correlation between preference and jerk, but the VR experiment showed that minimum jerk was not preferred to slightly jerkier, humanlike kinematics (figures 7 and 9).</ns0:p><ns0:p>First, whereas the preference pattern appeared to be approximately linear, or v-shaped for the laptop experiment (figure <ns0:ref type='figure' target='#fig_11'>7</ns0:ref>), the pattern was sigmoidal, or s-shaped in the VR experiment (figure <ns0:ref type='figure' target='#fig_13'>9</ns0:ref>). Second, the overall preference weight was different between the two experiments, where slightly smaller preferences were observed in VR experiment. These results suggest that a broader range of low-jerk values may appear natural in VR as opposed to on a 2D computer screen. A study found that subjects wearing a VR head mount display felt more present and immersive than those who were on a 2D monitor <ns0:ref type='bibr' target='#b7'>[7]</ns0:ref>.</ns0:p><ns0:p>Another reason that could have contributed to the weight difference is based on the first-person embodiment perspective nature of the VR experiment; it is possible that the subjects' choice of naturalness became more specific in the VR experiment under a more first-person environment. Both experiments compared the samples under the instruction: 'choose the video/hand you find to be most natural', however, to avoid unnecessary biases, no further instructions were given. Each subject therefore judged 'naturalness' on unspecified criteria. When witnessing the pairs of grasp animations on the laptop screen (without an attached arm or body), it is possible that some subjects regarded these as non-real, contrived movements, and therefore that it was confusing for them to judge realness. However, in the VR experiment, since subjects could move both themselves and the hands in virtual 3D space, it is possible that they perceived both the hands and the motions as more real, which could have shifted all subjects toward a uniform agreement of their definition of naturalness. Unfortunately, after the experiment subjects were not asked about the criteria they used to judge naturalness. We speculate that the VR environment shifted subjects toward more consistent perceptions do the increased realism of the viewing context.</ns0:p><ns0:p>Finally, it is important to note that the two experiments were conducted independently, with different subjects, and their data were not combined. Nevertheless, it is possible that the two experiments involved similar perceptual mechanisms, so in this Discussion we speculate that (a) some of the perceptual mechanisms may have been common across the two experiments, and that (b) the VR experiment may have enhanced particular perceptual constructs, and that this resulted in reduced inter-subject perceptual variability. We have no direct proof of these speculations; these interpretations are instead hypotheses that are consistent with our data, and that require further testing.</ns0:p></ns0:div> <ns0:div><ns0:head>Relation to uncanny valley literature</ns0:head><ns0:p>Mori hypothesized that the uncanny valley amplifies when objects are in motion <ns0:ref type='bibr' target='#b18'>[18]</ns0:ref>. Subsequent studies have shown the negative affinity does not become negatively enhanced as predicted, instead affinity increases in the positive direction <ns0:ref type='bibr' target='#b23'>[23,</ns0:ref><ns0:ref type='bibr' target='#b27'>27]</ns0:ref>. Other studies similarly found that subjects interacting with humanoid robots with minimum jerk trajectories led to cognitive improvements <ns0:ref type='bibr' target='#b2'>[3,</ns0:ref><ns0:ref type='bibr' target='#b10'>10]</ns0:ref>, showing that kinematically natural movement, in this case the minimum jerk trajectory, improves the overall affinity and task performances in human-robot interactions. This study's findings agree with the fundamental finding that minimum jerk induces greater affinity, but unlike previous studies this study also explored intermediary jerk values. Results suggest that adding slight kinematic noise to robot's minimal jerk movements mimicking, or close to human jerk magnitude may be preferable to the minimum jerk trajectory.</ns0:p><ns0:p>Last, although Mori's uncanny valley findings are generally repeatable, it is notable that uncanny valley effects can depend on the nature of the objects being viewed, and also that uncanny valley existence is not supported in some previous human face viewing experiments <ns0:ref type='bibr' target='#b25'>[25,</ns0:ref><ns0:ref type='bibr' target='#b17'>17,</ns0:ref><ns0:ref type='bibr' target='#b16'>16]</ns0:ref>. Although these studies cast doubt on the universality of the uncanny valley, a separate study found an uncanny valley for hand appearance <ns0:ref type='bibr' target='#b24'>[24]</ns0:ref>. In the experiment, subjects rated prosthetic hands with human skin texture as more eerie than mechanically exposed robot hands and actual human hands. This suggests that, although the uncanny valley response can apparently be suppressed in certain circumstances, it can still emerge for non-face body parts. This finding, coupled with the results of the current study, suggest that both object appearance and its kinematics may conspire to alter the uncanny valley response, or more generally, that human likeness and kinematics may interact to form an affinity landscape.</ns0:p></ns0:div> <ns0:div><ns0:head>13/16</ns0:head><ns0:p>PeerJ reviewing PDF \| (2020:02:46215:1:3:NEW 2 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head>Figure 1 .</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1. Assembled HACKberry system</ns0:figDesc><ns0:graphic coords='4,249.31,63.78,198.42,264.78' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head>Protocol</ns0:head><ns0:label /><ns0:figDesc /></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>Figure 2 .</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2. Marker placements on a human subject hand.</ns0:figDesc><ns0:graphic coords='5,249.31,63.81,198.36,148.60' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head>Figure 3 .</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Figure 3. Absolute human jerk values relative to the minimum-jerk trajectory (MJT). Each point represents one subject's mean (averaged across five trials). The dashed-line represents the grand mean (across subjects). The shaded area represents standard deviation (SD).</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head /><ns0:label /><ns0:figDesc>noise value [deg] (a) (a) (a) (a) (a) Gaussian noise SD = 0(b) (b) (b) (b) Gaussian noise SD = 3(d) (d) (d)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head>Figure 4 .</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Figure 4. Example of Gaussian noise added to the minimum jerk trajectory. Temporally smooth Gaussian noise with two different amplitudes (a) and (b), weighed noise trajectories added to the robot hand's minimum jerk trajectory (fat line). Note that the created trajectories on the bottom graphs are starting and ending at the same points.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head /><ns0:label /><ns0:figDesc>Symbol Description Average absolute jerk (None) The average value of third differentiation of the third time derivative of the recorded joint angle trajectory. Due to the nature that jerk value is in constant fluctuation between positive and negative, its value has to be absolute. Gaussian noise SD Standard deviation (SD) of the Gaussian noise added to the minimum jerk trajectory. SD = 0 is the minimum jerk trajectory. Artificially generated trajectories with variance in jerk. Bradley-Terry preference p Preference strength, estimated using maximum likelihood, as defined by the Bradley-Terry paired comparison. Human kinematics (None) The kinematic jerk of the human. In this paper, they are mainly the measured by the average absolute jerk of the index finger of human hand.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_7'><ns0:head>Figure 5 .</ns0:head><ns0:label>5</ns0:label><ns0:figDesc>Figure 5. Screenshots of the game animation. Each subject selected one of the hand animations with a left or right arrow key (and one of the two cubes on the bottom of the screen will light up when the key is pressed). The number on the top of the screen indicates the number of remaining pairs to compare. The animations move from an initial open posture (a) to a closed grasp posture (b). Subjects simultaneously observed two animations which had different Gaussian noise 'SD' (and thus different jerk levels). The animations were randomly selected so that the jerkier animation did not always appear on the same side.</ns0:figDesc><ns0:graphic coords='8,141.73,63.78,413.58,467.84' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_8'><ns0:head>169 7 / 16</ns0:head><ns0:label>716</ns0:label><ns0:figDesc>PeerJ reviewing PDF \| (2020:02:46215:1:3:NEW 2 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_9'><ns0:head /><ns0:label /><ns0:figDesc>where x is absolute average jerk, a 0 , b 0 , and c 0 are intercepts, and a 1 , b 1 , and c 1 are slopes. At the transition points x = b 2 and x = c 2 the modeled behavior changes. The a, b and c coefficients are unrelated to variables mentioned in previous sections, and the transition points are restricted into the area of SD < 1.5. Models A, B and C represent linear, flattened and reversed preference profiles, respectively (figure</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_10'><ns0:head>Figure 6 .</ns0:head><ns0:label>6</ns0:label><ns0:figDesc>Figure 6. Examples of the three employed preference models: Model A represents a linear increase in preference with smoothness, Model B represents a flattened preference, and Model C represents a reversed preference.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_11'><ns0:head>Figure 7 .</ns0:head><ns0:label>7</ns0:label><ns0:figDesc>Figure 7. Bradley-Terry preference (p) for the laptop experiment. Greater p values represent higher preference rankings. The smallest jerk value corresponds to the minimum-jerk trajectory (MJT).</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_12'><ns0:head>Figure 8 .</ns0:head><ns0:label>8</ns0:label><ns0:figDesc>Figure 8. VR experiment: Demonstration of a subject observing the robot hand models. The computer monitor in front of the subject displayed what the subject saw in the VR goggles as reference for the examiner.</ns0:figDesc><ns0:graphic coords='12,141.73,63.77,413.55,310.16' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_13'><ns0:head>Figure 9 .</ns0:head><ns0:label>9</ns0:label><ns0:figDesc>Figure9. The Bradley-Terry preference (p) for the VR experiment. p represents the strength of the preference of being suitable for the robot hand, for each kinematic jerk. The greater the value, the higher in the preference ranking. All nine subjects' result were used.</ns0:figDesc></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head>Table 2 .</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Bayes factor values comparing preference models within the laptop and VR experiments.</ns0:figDesc><ns0:table><ns0:row><ns0:cell cols='2'>Models Laptop</ns0:cell><ns0:cell>VR</ns0:cell></ns0:row><ns0:row><ns0:cell>A vs. B</ns0:cell><ns0:cell cols='2'>8.3 >1e6</ns0:cell></ns0:row><ns0:row><ns0:cell>A vs. C</ns0:cell><ns0:cell cols='2'>5.6 >1e6</ns0:cell></ns0:row><ns0:row><ns0:cell>B vs. C</ns0:cell><ns0:cell cols='2'>0.7 78726</ns0:cell></ns0:row><ns0:row><ns0:cell>B vs. A</ns0:cell><ns0:cell>0.1</ns0:cell><ns0:cell>0.0</ns0:cell></ns0:row><ns0:row><ns0:cell>C vs. A</ns0:cell><ns0:cell>0.2</ns0:cell><ns0:cell>0.0</ns0:cell></ns0:row><ns0:row><ns0:cell>C vs. B</ns0:cell><ns0:cell>1.5</ns0:cell><ns0:cell>0.0</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:note place='foot' n='3'>/16 PeerJ reviewing PDF \| (2020:02:46215:1:3:NEW 2 Jul 2020)</ns0:note> <ns0:note place='foot' n='5'>/16 PeerJ reviewing PDF \| (2020:02:46215:1:3:NEW 2 Jul 2020)Manuscript to be reviewed</ns0:note> <ns0:note place='foot' n='10'>/16 PeerJ reviewing PDF \| (2020:02:46215:1:3:NEW 2 Jul 2020)Manuscript to be reviewed</ns0:note> <ns0:note place='foot' n='11'>/16 PeerJ reviewing PDF \| (2020:02:46215:1:3:NEW 2 Jul 2020)</ns0:note> <ns0:note place='foot' n='12'>/16 PeerJ reviewing PDF \| (2020:02:46215:1:3:NEW 2 Jul 2020)</ns0:note> </ns0:body> "	"June 24, 2020 Dear Editors and Referees, We thank the Editors and Referees for your time. Please find that we have reproduced your comments below in red, and that our responses immediately follow in black. Additionally, we have numbered all comments and sub-comments for cross-referencing purposes. The updated tracked manuscript is processed in latexdiff, however we have also uploaded alternative tracked PDF under supplemental files where we have manually highlighted most manuscript revisions in yellow and labeled its page location. In the manually tracked manuscript, we have highlighted neither our major manuscript reorganization changes (i.e., reordering of the Methods and Results sections) nor minor typos, but all other revisions are highlighted for your convenience. Thank you for re-considering our work, James Hirose, on behalf of all Co-Authors Reviewer 1 (Anonymous) Basic reporting 1-1. This manuscript describes preference in the perception of motion rendered on a robotic hand. The scientific approach is valid but the quality falls short of usual standards, and is not sufficient to support the claims of the title and abstract. Thank you for your feedback. Through the following responses, we hope you can find that the title’s and abstract’s claims are more sufficiently justified in the revised manuscript. Experimental design 1-2. The two experiments, screen and VR, don’t test the same context, and the authors didn’t take into consideration that they shouldn’t be compared directly without discussing these differences. In particular, the n is different but the authors claim it is not an issue (as small n yield false negative and here we have only positive results) yet don’t mention it when making claims about the differences between real screen and VR (p12, l48). This aspect (how is the motion presented?) is not informative for the main argument of the paper regarding motion preference and almost invalidates the other results. We agree with many aspects of this comment, but we also feel that some of these criticisms are over-stated and/or scientifically minor, and thus that these issues should not preclude publication. Please allow us to address each of your points individually. 1-2-1. ”The two experiments, screen and VR, don’t test the same context…” We agree that the two experiments do not test the same context. Indeed, this was precisely the primary purpose of these two experiments: to quantify the behavior of the proposed perception-relevant variable (jerk) in different contexts. Since many scientific papers report the results of multiple experiments, and since those experiments often have different purposes and even completely different dependent variables, we would argue that the issue of different contexts, by itself, does not preclude publication. Please find that we have attempted to clarify the purpose of conducting the two different experiments as follows: P9: “The purpose of this experiment was to examine the same kinematic perception in Experiment 2, but in a virtual reality with increased realism and reduced restraint on movement observation . The idea was that Experiment 2 involved observing grasping motions from a fixed perspective, and that perspective itself could affect motion perceptions. We therefore conducted an additional experiment to test whether freer, interactive observation of the movements in a VR observation would alter the perceptual results we observed in Experiment 2. Subjects wore a VR head mount display with 3D view and 6-DoF head tracking (three translational and three rotational DoF) to be able to observe the HACKberry models with high realism. Subjects also used two 6-DoF controllers (one for each hand) to control the model postures in the virtual world.” 1-2-2. “… and the authors didn’t take into consideration that they shouldn’t be compared directly without discussing these differences” We would like to disagree with this comment. Although we used “Comparing” in the section title, our intent was not to state that one method was better than the other. Our intent was rather to test whether the basic result was repeatable in a VR context. Importantly, we have not compared the results of the two experiments directly. We conducted the experiments separately, and we conducted analyses separately. Our paper discusses the similarities and differences between the experimental methods, and also qualitatively discusses the similarity and differences between the experiments’ results. At no point were data from the two experiments pooled into a single analysis. Please find that we have attempted to clarify in the manuscript as follows: P12: “Another reason that could have contributed to the weight difference is based on the first-person embodiment perspective nature of the VR experiment; it is possible that the subjects’ choice of naturalness became more specific in the VR experiment under a more first-person environment. Both experiments compared the samples under the instruction: ``choose the video/hand you find to be most natural'', however, to avoid unnecessary biases, no further instructions were given. Each subject therefore judged “naturalness” on unspecified criteria. When witnessing the pairs of grasp animations on the laptop screen (without an attached arm or body), it is possible that some subjects regarded these as non-real, contrived movements, and therefore that it was confusing for them to judge realness. However, in the VR experiment, since subjects could move both themselves and the hands in virtual 3D space, it is possible that they perceived both the hands and the motions as more real, which could have shifted all subjects toward a uniform agreement of their definition of naturalness. Unfortunately, after the experiment subjects were not asked about the criteria they used to judge naturalness. We speculate that the VR environment shifted subjects toward more consistent perceptions do the increased realism of the viewing context.” 1-2-3. “In particular, the n is different but the authors claim it is not an issue…” We apologize for our lack of clarity. We agree that n is an issue in general, but we beg to disagree that n is a problem in this study. Please allow us to consider the purpose of n to justify our position… When choosing a sample size n, the primary goals are: (a) To adopt an n that is sufficiently large to avoid false negatives (i.e., to avoid an under-powered study), and (b) To adopt an n that is sufficiently small to avoid false positives (i.e., to avoid an over-powered study). This balance between over- and under-powering in a study is fundamental to all experimentation. Since different experiments involve different variance characteristics, and different signal-to-noise properties, different experiments generally require different n to achieve equivalent power. Since the two experiments in this study, as Reviewer 1 has correctly pointed out in the comment above, and their variances are different, they can have different n. In attempts to clarify, please find that we have revised our comment as follows: P10: “Since the two experiments involved completely different viewing environments, and since it is possible that perceptual metrics had different inter-subject variance in these two experiments, the required numbers of subjects to elucidate a specific effect are not necessarily the same in the two experiments. Additionally, since the perceptual criteria used to judge naturalness may be different between the two experiments, we chose to conduct the results independently, with different subjects and also different numbers of subjects, and to only compare the results qualitatively. Experiments 2 & 3 used exploratory, convenience samples of students from Shinshu University. Experiment 2 aimed to identify potential perceptual trends, so the sample size was maximized within convenience. Experiment 3 was conducted after Experiment 2 was completed, again maximizing sample size within convenience, while requiring individuals who did not participate in Experiment 2. 1-2-4. “… (as small n yield false negative.and here we have only positive results) …” We beg to disagree with this comment. We have not claimed that small n yields false negatives, as this statement is statistically nonsensical. Small n, by itself, cannot yield false positives or false negatives. Smaller n implies only lower power (i.e., the risk of observing false negatives in an infinite number of experiments), and is not directly related to a particular experiment’s result. 1-2-5. “...yet don’t mention it when making claims about the differences between real screen and VR (p12, l48). We apologize for our lack of clarity. Please see changes on “Qualitative comparisons of the laptop and VR experiments” in page 11 (as mentioned above). 1-2-6. …This aspect (how is the motion presented?) is not informative for the main argument of the paper regarding motion preference and almost invalidates the other results. We beg to disagree with the idea that results from the two observation contexts (i.e., laptop and VR) invalidate each other. As stated above, we have not combined the data. We have instead conducted two separate experiments, and have qualitatively considered how the results from the two experiments relate to one another. Thus, if the two experiments are independently valid, which we believe Reviewer #1 has stated, then their results are also independently valid because at no point were the data directly combined. Our interpretations of the perceptual mechanisms response for the results do indeed consider the possibility that similar perceptual mechanisms underlie the results of both experiments, but this is clearly an interpretation and Discussion issue and is unrelated to the validity of the Results section. If Reviewer #1 disagrees with our interpretation, we will gladly alter it. However, we cannot accept the criticism that the results are invalid without an explanation of why conducting and reporting the results of independent experiments is scientifically or statistically invalid. We nevertheless appreciate that other readers may also not see the distinction between (a) independent experiments, and (b) integrative interpretation, please find that we have attempted to clarify these points in the manuscript as follows: P13: “Finally, it is important to note that the two experiments were conducted independently, with different subjects, and their data were not combined. Nevertheless, it is possible that the two experiments involved similar perceptual mechanisms, so in this Discussion we speculate that (a) some of the perceptual mechanisms may have been common across the two experiments, and that (b) the VR experiment may have enhanced particular perceptual constructs, and that this resulted in reduced inter-subject perceptual variability. We have no direct proof of these speculations; these interpretations are instead hypotheses that are consistent with our data, and that require further testing.” Validity of the findings 1-3. Most important, claiming a preferences in kinematics for artificial agents on the basis of a single experiment with a very simple action (grasp, but mostly transport cf point 1), on one robotic hand with a sample of 22 subjects and 9 kinematics is simply insufficient nowadays, where, in particular for behavioral studies, tens of participants undergo a large number of conditions in order to support the conclusion. In the present case, for example, are particularly missing a larger number of different kinematics in a second experiment, a larger sample to adequately assess inter individual variance, a replication with other robots and actions, a direct comparison with the same actions performed by a human hand with the kinematics of a human hand manipulated along the same dimensions to demonstrate the effect is specific to the robotic hand or simply a feature of the normal action perception system that generalizes to artificial agent. These are just a few examples of the comparisons that would be required to support convincingly the main argument of the manuscript. We agree that the sample number is not high. However, we fail to understand the scientific basis for this comment. Reviewer #1 has invoked literature precedent to state that many participants and many more conditions are required. However, we would argue that this criticism is scientifically nonsensical because literature precedent can only be invoked to support the use of specific methods and cannot be invoked as a counter-argument to specific methods. If literature precedent could be used to block methods, then no new methods would ever be introduced! We therefore request a clarification of the scientific or statistical reasons why our methods are inadequate. Reviewer #1 has suggested that we need to test more robots, more actions, and human hands. We would agree that this is necessary, but only if the purpose of this study was to identify the perceptual variables that are unique to artificial agents. This was not the purpose of our study. Our study instead introduces a new variable (integrated jerk) that is potentially relevant to motion perception, and tests whether perceptions change when this variable changes. Our study does not attempt to consider the perceptual differences amongst different artificial agents, nor to consider the perceptual differences associated with humans vs. artificial agents. We agree that these are useful issues to consider, but we also believe that these issues are peripheral to the main purpose of the study. Note that our use of human jerk data in this study has only one specific purpose: to quantify an approximate range of jerk values that can be expected in various humans and various artificial agents. For example, please consider our purpose statement in the Introduction: (P2) “The purpose of this study was to quantify the changes in affinity for a moving artificial agent as a function of kinematic jerk”. This statement does not imply that we are interested in differences between artificial agents and humans. It instead implies only that affinity will be tested using an artificial agent (and not a human, because we cannot precisely control jerk in real human movement). Our manuscript argues only that integrated jerk is a perception-relevant variable, and that this variable might be relevant to artificial design. It does not argue that this perception is unique to artificial agents. If Reviewer #1 believe that either (a) our manuscript claims to distinguish between artificial agents and humans, or (b) our manuscript claims to distinguish amongst different artificial agents, then please cite the relevant passages from our manuscript so that we may reconsider this comment. To clarify our point, we hope you find the following revised passage expressing our points:: P4: ”Note that this experiment does not seek to identify differences between artificial and human kinematics. An artificial agent was used simply because jerk cannot be controlled precisely in real human motion. The human jerk data from Experiment 1 had just one purpose: to quantify an approximate range of jerk values that can be expected in a 1-DoF two finger grasp motion.” Comments for the Author 1-4. This manuscript describes preference in the perception of motion rendered on a robotic hand. The scientific approach is valid but the quality falls short of usual standards, and is not sufficient to support the claims of the title and abstract. We beg to disagree with this comment, as we believe that usual standards are insufficient as scientific counter-arguments to specific methods. Please see our response to Comment 1-3 above for more details. 1-5. - The article talks about kinematics of grasp, but the three markers are on the index finger and none on the thumb, so it is already a degraded recording of a grasp (transport? index flexion?), and not a two-digit grasping as claimed in the title. We agree that only index finger markers were used, and we also agree that this makes the title unclear. The title was meant to convey only that this was a two-digit grasp task, and not necessarily that the tested kinematics involved all kinematics associated with that task. In attempts to clarify, please find that we have altered the title as follows: Title: Integrated jerk as an indicator of affinity for artificial agent kinematics: laptop and virtual reality experiments involving index finger motion during two-digit grasping 1-6. - The two experiments, screen and VR, don’t test the same context, and the authors didn’t take into consideration that they shouldn’t be compared directly without discussing these differences. In particular, the n is different but the authors claim it is not an issue (as small n yield false negative and here we have only positive results) yet don’t mention it when making claims about the differences between real screen and VR (p12, l48). This aspect (how is the motion presented?) is not informative for the main argument of the paper regarding motion preference and almost invalidates the other results. Please refer to our reply to Comment 1-2 (: 1-2-1 to 1-2-6) above, where we partially address this issue. We agree that this is an important issue, and we hope that you will find our reply and corresponding manuscript revisions to be satisfactory. 1-7. - The explanations of the differences in results in Table 2 are not convincing. Actually, the RESULTS of Table 2 are not the same as in the previous version of the manuscript I reviewed, which is VERY puzzling: Current: Bvs.A 0.7 78726 Bvs.C 0.1 0.0 Previous: C vs. B 0.7 78726 A vs. B 0.1 0 We apologize for the confusion. The values were mislabeled in the previous version of the manuscript. Regardless, please note that these mislabeled items affect neither the Discussion interpretation nor the overall conclusions. The text in Table 2 and the manuscript is now corrected. P9, Table 2: A vs. B 8.3 >1e6 A vs. C 5.6 >1e6 B vs. C 0.7 78726 B vs. A 0.1 0.0 C vs. A 0.2 0.0 C vs. B 1.5 0.0 P10: “Models C was a ``decisively'' a better fit than Model B, and differences between Models B and A were ``barely worth mentioning''.” 1-8. - Most important, claiming a preferences in kinematics for artificial agents on the basis of a single experiment with a very simple action (grasp, but mostly transport cf point 1), on one robotic hand with a sample of 22 subjects and 9 kinematics is simply insufficient nowadays, where, in particular for behavioral studies, tens of participants undergo a large number of conditions in order to support the conclusion. In the present case, for example, are particularly missing a larger number of different kinematics in a second experiment, a larger sample to adequately assess inter individual variance, a replication with other robots and actions, a direct comparison with the same actions performed by a human hand with the kinematics of a human hand manipulated along the same dimensions to demonstrate the effect is specific to the robotic hand or simply a feature of the normal action perception system that generalizes to artificial agent. These are just a few examples of the comparisons that would be required to support convincingly the main argument of the manuscript. Please refer to our reply to Comment 1-3 above where we address this issue. To summarize that response, we disagree that we have made conclusions that are specific to artificial agents. Both the purpose of this study and our manuscript’s conclusions are limited to the idea that integrated jerk may be a perception-relevant variable, and that this variable appears to generate reasonably consistent perceptual responses. We agree that these perceptions may also apply to humans, but please note that our manuscript does not claim that these conclusions are specific to artificial agents. We agree that this is an important issue, and we hope that you will find our reply and corresponding manuscript to be satisfactory Reviewer 2 (Emma Gowen) Basic reporting Introduction 2-1. The introduction is generally clear and concise. I think there should be more background information about what jerk is and how it is related to human movement (e.g. what is the normal profile for humans compared to robots). Thank you for your input. We agree that more information would be helpful, so in response to this comment we have expanded the definition of jerk and cited additional research as suggested.. P2: “The uncanny valley has arguably been overcome in many applications involving static, lifelike imagery such as computer-generated humans [1, 18], but animated human-like objects (mainly robots) are arguably far less human-like. A variety of research has shown the importance of kinematics in human-robot interactions. For example, it has been shown that both humanoid features and human movements are important factors for facilitating human-robot social interactions [11, 15, 3, 5]. Whereas the biological movement in the latter study [3] was from a motion captured trajectory, a separate but similar study [9] showed that human-robot interaction improved when the robot moved with minimum-jerk trajectories [4], instead of with mechanical trapezoid trajectories. This research suggests that kinematics, and potentially jerk specifically, may interact with the uncanny valley to form an overall affinity for moving artificial agents. Jerk is the third time derivative of position, and applies to both linear and angular motion. It can be interpreted as the rate of change of acceleration, or perhaps more clearly as proportional to the rate of change of force. Jerk has been shown to be relevant to human-robot interaction [9], and theoretical predictions of movement based on a minimum jerk criterion have been shown to qualitatively reproduce many features of human movement [4, Furuna & Nagasaki, 1993; Viviani & Flash 1995]. Minimizing jerk is potentially useful movement strategy because high jerk implies rapid force changes and thus both wasted muscular energy and compromised mechanical control. Jerky motion is additionally characteristic of movement disorders (Latash 2012; Kavanagh et al. 2016), implying that lower jerk, if perceivable by humans, may also be perceived as healthier and/or more natural than jerky movements.“ Additional references [5] Furuna, T. and Nagasaki, H. (1993). Trajectory formation of vertical arm movements through a via-point: a limit of validity of the minimum-jerk model. Perceptual and motor skills, 76(3 Pt 1):875–84. [12] Kavanagh, J. J., Wedderburn-Bisshop, J., and Keogh, J. W. (2016). Resistance training reduces force tremor and improves manual dexterity in older individuals with essential tremor. Journal of Motor Behavior, 48(1):20–30. [15] Latash, M. L. (2012). Fundamentals of Motor Control. Academic Press. [28] Viviani, P. and Flash, T. (1995). Minimum-Jerk, Two-Thirds Power Law, and Isochrony: Converging Approaches to Movement Planning. Journal of Experimental Psychology: Human Perception and Performance, 21(1):32–53. 2-2. Towards the end of the experiment, please provide an overview of all the experiments (e.g. that you first collect kinematic information, add this to an animation based on a recorded robot model…..etc). We agree that an experiment overview would be a useful addition to help readers understand the context of the various results. In response to this comment, please find that we have added an overview that summarizes Experiment 1, 2, and 3. With the major section reorder, we hope you are satisfied with the overview located at the beginning of the Discussion section. P11: “In Experiment 1, we motion captured two-digit 1-DoF grasp motion of human hands and a robot hand device (HACKberry). Human and HACKberry jerk magnitude were calculated and used to formulate an approximate range of jerk values that could be expected in humans and robots. For Experiment 2, we generated computer animations of HACKberry index finger motion in nine steps of jerk over the jerk range calculated in Experiment 1, and we presented these nine animations in pairs to subjects who evaluated their naturalness. Experiment 3 repeated Experiment 2 with new subjects, and in a virtual reality environment where subjects could manipulate the HACKberry animation’s 3D positions and rotations using hand-held 6-DoF motion controllers.” 2-3. Also, more rationale needs to be added for experiment 3, which seems to cover perspective and embodiment. This will enable the reader to more easily process the methods section. We agree with your statement. We hope the updated manuscript clarifies Experiment 3’s brief introduction. P9: “The purpose of this experiment was to examine the same kinematic perception in Experiment 2, but in a virtual reality with increased realism and reduced restraint on movement observation. The idea was that Experiment 2 involved observing grasping motions from a fixed perspective, and that perspective itself could affect motion perceptions. We therefore conducted an additional experiment to test whether freer, interactive observation of the movements in a VR observation would alter the perceptual results we observed in Experiment 2. Subjects wore a VR head mount display with 3D view and 6-DoF head tracking (three translational and three rotational DoF) to be able to observe the HACKberry models with high realism. Subjects also used two 6-DoF controllers (one for each hand) to control the model postures in the virtual world.” 2-4. References to some materials are missing (states (see §)) and some of the figures are not referenced in the text We agree and apologize for our error. The three missing subsection and sub-subsection references are changed as follows: P6: “Using the Blender Game Engine (Blender Foundation, Amsterdam, www.blender.org), we first developed nine animation videos each based on the generated kinematic samples.” P6: “…a game where two of the nine videos with different jerk trajectories were shown (figure 5), played in…” P10: “The VR system (Oculus VR, Menlo Park, USA) through which custom animations of 3D models were presented in 3D view to subjects as described below.” P10: “Also identical to Experiment 2, the 72 total combinations of the nine grasp trajectories created in Experiment 2 were presented…” and figure 3 and 4 are now referenced in the text as follow (note: due to manuscript update, figure 3 and 4 are labeled figure 5 and 8 respectively): P10: “Thus in the VR world, subjects could rotate the VR hands interactively, freely, and realistically, as if they were the subject's own hands (figure 8).” Experimental design 2-5. The study is within the scope of the journal. The research question is well defined and relevant - this is an unexplored area and i like the quantitative and detailed approach the authors use. Thank you very much for your positive feedback. Methods 2-6. The layout of the methods and analysis is unclear. My questions below could probably be answered if the methods were structured so that each experiment is presented separately, followed by a general discussion. For example: Experiment 1: methods, analysis, results, brief discussion Experiment 2: methods (making use of exp 1 data), analysis, results, brief discussion. Make it clearer that kinematics were added to an animation of the hackberry robot Experiment 3: methods, analysis, results, brief discussion We apologize for the unclearness in the method and analysis structure. We agree with your suggestion and Experiment 1, 2, and 3 are now structured as suggested. Please find that we have reorganized the Methods and Results as recommended. Since this reorganization has affected 9 manuscript pages, we have not highlighted all changes. Please note that we have made some minor text changes to accommodate the reorganization, and that these minor changes have also not been highlighted. 2-7. Equipment – this states that there were four main devices, but there are actually five We agree. We have updated to five, however, due to changes in the flow of the manuscript, the statement is removed from the manuscript. 2-8. Experiment 1 – how is the data used? The rationale for this needs to be clearer and the use of the kinematics could be added to experiment 2 methods (developing the stimuli). We apologize for the unclearness. We agree with your statement and hope the updated introduction for Experiment 1 will make the rationale clearer. P2: “The purpose of this experiment was to roughly quantify the expected range of kinematic jerk values for robot and human finger grasping motions. Specifically the human jerk magnitude that was used for kinematic references and basic kinematic motion robot hand device HACKberry was used to generate the minimum-jerk value and trajectory.” 2-9. How were those specific kinematics chosen? We agree that this was unclear. We hope the updated Introduction clarifies on why we chose a two-digit 1-DoF grasp motion. P2: “To secure simplicity for this experiment design, and provide functional findings that can be implemented into robotic designs, we aimed for a simple 1-DoF grasp motion. Specifically, a motion that all subjects are familiar with, able to reenact, and have a general sense of its naturalness; to which we chose to examine an index-thumb two-digit motion for this study.” 2-10. Experiment 2 – indicate that the stimulus was robotic looking (based on the hackberry model) We agree that this was unclear. Please see the following update to clarify the stimulus generation: P5: “The purpose of this experiment was to quantify subjects' affinity for particular kinematics as a function of kinematic noise amplitude by a one-paired comparison of multiple sample animations on a laptop screen. The animations showed a robot hand (HACKberry device) performing a two-digit 1-DoF grasp motion with different degrees of jerk. We chose to show a robotic-looking hand model instead of a human-looking one because the primary motivation for this study was improving the kinematic quality of artificial agents and not humans. The first part of this Methods section describes how the kinematic samples were created and the second part explains how the software was prepared and used in the experiment.” 2-11. Experiment 3 – it is not clear when or why the participants would move the hand stimuli. How are they stopped from moving them during each trial? Overall, there needs to be more description of the procedure and rationale for this experiment. We agree that this was unclear. The participants were not instructed to move the hand models nor to keep them stationed in the VR environment, nevertheless, they all moved the hand models. Although, we are uncertain why participants would move the stimuli, we believe implies that the ability to move them was somehow beneficial to the task of perceiving naturalness. Another potential reason is that the physical laboratory space was relatively small, so participants could not walk around stationary stimuli, and thus moving the stimuli was the only useful option for changing the viewing perspective. Alternatively, it may have simply been more enjoyable to move the hand stimuli, and thus a boredom-avoidance strategy, and not directly related to the perceptual task. Regardless, the primary goal of Experiment 3 was to provide a more realistic environment in which participants could more realistically interact with the artificial agents. Due to the narrow laboratory space which constrained the participants’ ability to change their own position with respect to the stimuli, we gave the participants the option of changing the viewing perspective through stimulus position control. The participants were not stopped from moving the stimuli; they were free to move the stimuli until they entered their choice. We have clarified by revising the purpose statement for Experiment 3 and its brief introduction: P9: “The purpose of this experiment was to examine the same kinematic perception in Experiment 2, but in a virtual reality with increased realism and reduced restraint on movement observation. The idea was that Experiment 2 involved observing grasping motions from a fixed perspective, and that perspective itself could affect motion perceptions. We therefore conducted an additional experiment to test whether freer, interactive observation of the movements in a VR observation would alter the perceptual results we observed in Experiment 2. Subjects wore a VR head mount display with 3D view and 6-DoF head tracking (three translational and three rotational DoF) to be able to observe the HACKberry models with high realism. Subjects also used two 6-DoF controllers (one for each hand) to control the model postures in the virtual world.” P10: “The locations of the animated hands in VR space were translated and rotated accordingly. Thus in the VR world, subjects could rotate the VR hands interactively, freely, and realistically, as if they were the subject's own hands. They were able to position and rotate the hand models freely. Note that the subjects' finger motion was not tracked, and the HACKberry 3D model's index finger was animated based on the same synthetic kinematics from Experiment 2.” Validity of the findings Results 2-12. Experiment 2 and 3 – please add statistics for correlations (r/p values) We agree with this statement and have added the r and p values for linear regression to the updated figures (fig 7 and 9). For the Bayesian inference, please note two things: first, Bayes factor values (in Table 2, P9) encompass regression probabilities for the Bayesian analysis and are more meaningful than model-specific p values. Secondly, while we could use the Bayesian posterior distributions to calculate r and p values for each of the fitted models, we would prefer to not present these because this would be somewhat inconsistent with the analysis purpose, which pertains to model comparisons (i.e., Table 2), and not to individual models' goodness-of-fit. Discussion 2-13. Comparing laptop and VR experiments. The following lines need to be backed up in the results section. The linear trend/correlation is mentioned for the laptop experiment but not for the VR experiment . Please document these differences statistically in the results (and /or descriptively). “but the VR experiment showed that minimum jerk was not preferred to slightly jerkier, human like kinematics (figures 8 and 9). First, whereas the preference pattern appeared to be approximately linear, or v-shaped for the laptop experiment (figure 8), the pattern was sigmoidal, or s-shaped in the VR experiment (figure 9). “ We agree with this statement and apologize for the lack of explanation. Please see the updated statements in the manuscript: P10: “A similar preference shift (a negative trend between preference and jerk) was observed with more of a sigmoidal or s-shaped pattern in the VR experiment. A negative trend in the preference is observed from the SD = 1.0 kinematic and reaches little to no preference from SD = 2.5 and higher jerk amplitudes.” 2-14. This sentence does not make sense: “In the experiment, subjects rated prosthetic hands with human skin texture as more eery than all of: mechanically exposed robot hands, prosthetic hands with human skin texture, and actual human hands.” We agree and apologize for the confusion. The correct phrasing is updated in the manuscript as: P13: “In the experiment, subjects rated prosthetic hands with human skin texture as more eerie than mechanically exposed robot hands and actual human hands.” 2-15. Please provide a little more discussion around the potential use of the findings (for robotics/prosthetic or neuroscience research). The authors briefly mention previous findings that have used different appearances, but a more detailed discussion on how appearance and kinematics might interact would be valuable We agree with this statement and have added a new section in Discussion: “Further studies and potential applications” P14: “These results could potentially be used as a guideline for tuning the jerk amplitude of a robotic movement. Many robotic trajectory planning applications employ minimum jerk trajectories [ Kyriakopoulos & Sardis 1988], but this study’s results suggest that minimum jerk may not be optimal if the goal is to maximize the perception of naturalness. Here, as we only used one robot hand in animations, we are unable to make conclusions regarding human-like vs. robot-like appearance. If the goal is to maximize perceptions of naturalness across multiple appearances, future work should consider multiple levels of human-likeliness and the relation amongst appearance, kinematics and affinity. Another factor that requires further study is integrated jerk control in kinematic chains, including multi-joint fingers, and whether kinematics need to be planned at the joint level in order to achieve optimal affinity. Since it has been reported that human-robot interactions can be improved by implementing the minimum-jerk profile [8], it is conceivable that further improvement could be achieved by affinity-maximized kinematics as opposed to jerk-minimized kinematics.” Additional reference [14] Kyriakopoulos, K. J. and Saridis, G. N. (1988). Minimum Jerk Path Generation. In Proceedings. 1988 IEEE International Conference on Robotics and Automation, pages 364–369. IEEE Comput. Soc. Press. "	Here is a paper. Please give your review comments after reading it.
515	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Background</ns0:head><ns0:p>In European and North American cities geese are among the most common and most visible large herbivores. As such, their presence and behaviour often conflict with the desires of the human residents. Fouling, noise, aggression and health concerns are all cited as reasons that there are 'too many'. Lethal control is often used for population management, however, this raises questions about whether this is a sustainable strategy to resolve the conflict between humans and geese, when paradoxically, it is humans that are responsible for creating the habitat and often providing the food and protection of geese at other times. We hypothesise that the landscaping of suburban parks can be improved to decrease its attractiveness to geese and to reduce the opportunity for conflict between geese and humans. Methods Using observations collected over five years from a botanic garden situated in suburban Belgium and data from the whole of Flanders in Belgium, we examined landscape features that attract geese. These included the presence of islands in lakes, the distance from water, barriers to level flight and the size of exploited areas. The birds studied were the tadornine goose Alopochen aegyptiaca (L. 1766) (Egyptian goose) and the anserine geese, Branta canadensis (L. 1758) (Canada goose), Anser anser (L. 1758) (greylag goose) and Branta leucopsis (Bechstein, 1803) (barnacle goose). Landscape modification is a known method for altering goose behaviour, but there is little information on the power of such methods with which to inform managers and planners.</ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head><ns0:p>Our results demonstrate that lakes with islands attract more than twice as many anserine geese than lakes without islands, but make little difference to Egyptian geese. Furthermore, flight barriers between grazing areas and lakes are an effective deterrent to geese using an area for feeding. Keeping grazing areas small and surrounded by trees reduces their attractiveness to geese.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusion</ns0:head><ns0:p>The results suggest that landscape design can be used successfully to reduce the number of geese and their conflict with humans. However, this approach has its limitations and would require humans to compromise on what they expect from their landscaped parks, such as open vistas, lakes, islands and closely cropped lawns.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Background</ns0:head><ns0:p>In European and North American cities geese are among the most common and most visible large herbivores. As such, their presence and behaviour often conflict with the desires of the human residents. Fouling, noise, aggression and health concerns are all cited as reasons that there are 'too many'. Lethal control is often used for population management, however, this raises questions about whether this is a sustainable strategy to resolve the conflict between humans and geese, when paradoxically, it is humans that are responsible for creating the habitat and often providing the food and protection of geese at other times. We hypothesise that the landscaping of suburban parks can be improved to decrease its attractiveness to geese and to reduce the opportunity for conflict between geese and humans.</ns0:p></ns0:div> <ns0:div><ns0:head>Methods</ns0:head><ns0:p>Using observations collected over five years from a botanic garden situated in suburban Belgium and data from the whole of Flanders in Belgium, we examined landscape features that attract geese. These included the presence of islands in lakes, the distance from water, barriers to level flight and the size of exploited areas. The birds studied were the tadornine goose Alopochen aegyptiaca (L. 1766) (Egyptian goose) and the anserine geese, Branta canadensis (L. 1758) (Canada goose), Anser anser (L. 1758) (greylag goose) and Branta leucopsis (Bechstein, 1803) (barnacle goose). Landscape modification is a known method for altering goose behaviour, but there is little information on the power of such methods with which to inform managers and planners.</ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head><ns0:p>Our results demonstrate that lakes with islands attract more than twice as many anserine geese than lakes without islands, but make little difference to Egyptian geese. Furthermore, flight barriers between grazing areas and lakes are an effective deterrent to geese using an area for feeding. Keeping grazing areas small and surrounded by trees reduces their attractiveness to geese.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusion</ns0:head><ns0:p>The results suggest that landscape design can be used successfully to reduce the number of geese and their conflict with humans. However, this approach has its limitations and would require humans to compromise on what they expect from their landscaped parks, such as open vistas, lakes, islands and closely cropped lawns.</ns0:p></ns0:div> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>In Europe and North America wild and feral geese frequently inhabit artificial lakes and their surrounding parks in urban and suburban areas. These parks are appreciated by people for their recreational and aesthetic value. However, this often brings geese in conflict with people <ns0:ref type='bibr'>(Conover & Chasko, 1985;</ns0:ref><ns0:ref type='bibr' target='#b45'>Hughes et al., 1999;</ns0:ref><ns0:ref type='bibr' target='#b66'>Smith, Craven & Curtis, 1999;</ns0:ref><ns0:ref type='bibr' target='#b31'>Fox, 2019)</ns0:ref>. While people often enjoy seeing small numbers of geese, when there are large flocks the soil becomes fouled and people are intimidated by the geese's threatening behaviour <ns0:ref type='bibr' target='#b54'>(Miller et al., 2001)</ns0:ref>.</ns0:p><ns0:p>Geese are also known to exert pressure on small water bodies such as ponds, reducing water quality through eutrophication <ns0:ref type='bibr' target='#b6'>(Allan et al., 1995;</ns0:ref><ns0:ref type='bibr' target='#b33'>Gosser et al., 1997;</ns0:ref><ns0:ref type='bibr'>Smith et al., 2000;</ns0:ref><ns0:ref type='bibr' /> Kumschick & Nentwig 2010). They have also been suggested to be a disease risk, though the evidence is circumstantial and other domestic and wild animals pose a greater known risk <ns0:ref type='bibr' target='#b27'>(Fleming & Fraser, 2001;</ns0:ref><ns0:ref type='bibr'>Clark, 2003;</ns0:ref><ns0:ref type='bibr'>Bönner et al. 2004</ns0:ref>). Throughout Europe and the western Palearctic, native as well as non-native geese are increasing in numbers and distribution <ns0:ref type='bibr' target='#b6'>(Allan, Kirby & Feare, 1995;</ns0:ref><ns0:ref type='bibr'>Fox et al. 2010</ns0:ref>). Several populations have developed a resident component and their year-round presence increases human-wildlife conflicts and impacts on biodiversity <ns0:ref type='bibr'>(Buij et al. 2017)</ns0:ref>. A variety of strategies are needed to reduce these impacts <ns0:ref type='bibr' target='#b10'>(Austin et al., 2007;</ns0:ref><ns0:ref type='bibr' target='#b40'>Gyimesi & Lensink, 2012)</ns0:ref>.</ns0:p><ns0:p>In Europe, from the 18 th century onwards, it has been traditional to create landscaped parks reflecting an idealised vision of the countryside. Lakes with islands, open vistas, lawns and PeerJ reviewing PDF \| (2020:01:45121:1:1:NEW 12 Apr 2020)</ns0:p><ns0:p>Manuscript to be reviewed patches of woodland are typical <ns0:ref type='bibr' target='#b68'>(Turner, 1985)</ns0:ref>. Lake-side vegetation and lawns are cut regularly and the canopies of trees are kept high to ensure unimpeded views. For those goose species that are habituated to the presence of people, such landscapes are very suitable, they have abundant grazing; proximity to water and islands for undisturbed nesting sites. In addition, people often provide supplementary feeding.</ns0:p><ns0:p>In north-western Europe four species of 'geese' are the main inhabitants of urban and suburban parks, non-native Egyptian geese (Alopochen aegyptiaca), Canada geese (Branta canadensis), mixed populations of wild and feral greylag geese (Anser anser) and barnacle geese (Branta leucopsis). All are members of the family Anatidae, but Egyptian geese are members of the subfamily Tadorninae, which are referred to as tadornine geese, whereas the others are members of subfamily Anserinae, which are referred to as anserine geese. Egyptian geese are similar in several aspects to anserine geese, such as their large size, long neck and feeding behaviour, but they do differ in other important aspects. Anserine geese, such as Canada geese, barnacle geese, greylag geese and their hybrids, usually nest on the ground close to bodies of water and are also likely to form large flocks <ns0:ref type='bibr' target='#b47'>(Adriaens et al. 2020</ns0:ref>). Egyptian geese are also water birds, but their biology shows many characteristics of a duck, including larger clutch sizes. Although they nest on the ground, their nest site selection is highly variable and they also nest in large tree holes, on buildings, on top of willow trees or in nest boxes <ns0:ref type='bibr' target='#b40'>(Gyimesi & Lensink 2012;</ns0:ref><ns0:ref type='bibr' target='#b47'>Huysentruyt et al. 2020</ns0:ref>). They also differ in their social behaviour. Paired Egyptian geese defend territories near their nest site before and during nesting. Large flocks of Egyptian geese only occur after breeding during moulting <ns0:ref type='bibr' target='#b39'>(Gyimesi & Lensink 2010)</ns0:ref>.</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:01:45121:1:1:NEW 12 Apr 2020)</ns0:p></ns0:div> <ns0:div><ns0:head>Manuscript to be reviewed</ns0:head><ns0:p>The site selection criteria of geese are important, because their sites can bring them into conflict with people. The proximity of water, food and breeding sites are relevant to goose site selection, but there are likely to be additional influences. These features may be related to predator avoidance <ns0:ref type='bibr' target='#b14'>(Conover & Kania, 1991)</ns0:ref>, accessibility of feeding grounds for adults and families with chicks, nutritional quality of feed <ns0:ref type='bibr' target='#b59'>(Owen, Nugent & Davies, 1977;</ns0:ref><ns0:ref type='bibr' target='#b28'>Fox & Kahlert, 2005)</ns0:ref>, sward length <ns0:ref type='bibr' target='#b42'>(Hassall, Riddington & Helden, 2001;</ns0:ref><ns0:ref type='bibr' target='#b26'>Feige et al., 2008;</ns0:ref><ns0:ref type='bibr' target='#b14'>Conover, 1991;</ns0:ref><ns0:ref type='bibr' target='#b72'>Van Gils et al., 2009;</ns0:ref><ns0:ref type='bibr'>Huysentruyt & Casaer, 2010)</ns0:ref> and competition with other grazers such as other geese, livestock and rabbits ( <ns0:ref type='bibr' target='#b69'>Van der Wal, Kunst & Drent, 1998)</ns0:ref>. Given this, it may be possible to identify management strategies and landscape features that alter the site selection of geese and these might be used to control the geese in such a way to reduce conflict between geese and people <ns0:ref type='bibr' target='#b15'>(Conover, 1992;</ns0:ref><ns0:ref type='bibr' target='#b58'>Owen, 1975)</ns0:ref>.</ns0:p><ns0:p>Culling is often used to reduce the impact of geese <ns0:ref type='bibr'>(Reyns et al. 2018</ns0:ref>), but several other strategies have been used to discourage and redistribute geese, including birds scarers and chemical antifeedants <ns0:ref type='bibr'>(Conover, 1985)</ns0:ref>, fencing of feeding grounds or landscape modification including altered mowing regimes or landscaping solutions <ns0:ref type='bibr' target='#b16'>(Cooper 1998;</ns0:ref><ns0:ref type='bibr' target='#b71'>Van Daele et al. 2012</ns0:ref>). In the context of a landscaped park with large numbers of visitors, culling risks losing public support for a public garden and bird scaring might disturb people too. At the same time, the context of a botanic garden urges careful consideration of grazing and fouling impacts of geese on plantings, lawns and vegetations without losing the recreational opportunities for wildlife watching provided by the presence of these attractive birds. Therefore, habitat PeerJ reviewing PDF \| (2020:01:45121:1:1:NEW 12 Apr 2020)</ns0:p><ns0:p>Manuscript to be reviewed modification is considered as a cost effective, sustainable solution to reduce numbers of geese on sites and to mitigate the impact. Previous studies on site occupancy of geese have concentrated on wild geese in more or less rural settings. These studies have concentrated on ways to discourage geese from feeding on crop plants (e.g. <ns0:ref type='bibr' target='#b56'>Olsson et al., 2017;</ns0:ref><ns0:ref type='bibr' target='#b65'>Si et al., 2011)</ns0:ref>.</ns0:p><ns0:p>In the case of Canada geese most studies have occurred in North America (e.g. <ns0:ref type='bibr' target='#b15'>Conover, 1992)</ns0:ref>.</ns0:p><ns0:p>The aim of this study is to quantify the site selection of the different species of geese within Meise Botanic Garden (Belgium) and create models to predict their behaviour based upon the landscape of the park. These models can then be used to suggest strategies to reduce conflict between the geese and the visitors to the park without losing the opportunities they represent for wildlife watching.</ns0:p></ns0:div> <ns0:div><ns0:head>Materials & Methods</ns0:head><ns0:p>Most of the research was conducted at Meise Botanic Garden <ns0:ref type='bibr'>(Flanders, Belgium)</ns0:ref>, situated just north of Brussels, Belgium (50°55'42.4'N 4°19'37.6'E). The exception was the study on the effect of islands and those data are described below. The 92 ha Garden is a landscaped park like many such parks in northern and western Europe. It has extensive lawns, woodlands, two large lakes and one small one (Fig. <ns0:ref type='figure' target='#fig_4'>1</ns0:ref>). The Garden is subdivided into different numbered areas, divided by paths, which join various historic buildings and greenhouses with formal gardens, with approximately half the area covered by woodland. Most of the grassland is mown between two and four times a month during the growing season, though small areas are PeerJ reviewing <ns0:ref type='table'>PDF \| (2020:01:45121:1:1:NEW 12 Apr 2020)</ns0:ref> Manuscript to be reviewed maintained as wildflower meadows and are cut once or twice a year. All geese in the Garden are considered either non-native or feral. All species breed in the park, though the breeding of Canada geese is, in part, controlled by egg-shaking. The birds using the park are part of a larger population of geese that inhabit the greater Brussels area, and birds move in and out of the park to the many other lakes and waterways in the neighbourhood. None of these populations are truly migratory, except for local movements <ns0:ref type='bibr' target='#b7'>(Anselin & Cooleman, 2007)</ns0:ref>. Canada goose is under management in the region and flocks of geese are regularly moult captured on water bodies in neighbouring municipalities since 2010 <ns0:ref type='bibr'>(Reyns et al. 2018</ns0:ref>). The park is in almost constant use by geese except for on the rare occasions when the lakes freeze over for long periods in the winter. Geese feed on all the lawns and grasslands within the park, but the extent to which these areas are used varies considerably from area to area and from species to species.</ns0:p></ns0:div> <ns0:div><ns0:head>The preference for grazing areas</ns0:head><ns0:p>The usage by geese of the different areas of the Botanic Garden was assessed by fixed transect counts <ns0:ref type='bibr' target='#b35'>(Groom, 2019a;</ns0:ref><ns0:ref type='bibr' target='#b36'>Groom, 2019b)</ns0:ref>. A total of four routes around the garden were used, each route took approximately 40 minutes to walk and was always walked in a clockwise direction. Almost all of the grassland areas of the garden were counted on at least two of these routes, woodland sectors were only counted when they were on the route between grassland areas.</ns0:p><ns0:p>Transect counts were conducted between 12pm and 2pm Central European Time. Geese were counted on an average of 2.7 days per week spread throughout the survey period that lasted Manuscript to be reviewed nearly 6 years, between 11 Oct 2011 and 10 July 2017. Counts were conducted only on Monday to Friday at the convenience of the surveyors, but irrespective of weather conditions. The only consistent period of the year when surveying was not conducted was between 25th December and 1st January. On a few occasions, two routes were walked simultaneously to give an approximate number for the total number of geese in the park for that day. Routes 1 and 2 gave the best coverage for all the main areas used by geese in the park. On other days routes 1 to 4 were chosen at random <ns0:ref type='bibr' target='#b41'>(Haahr 2019)</ns0:ref>. All the observation data are available on the Global Biodiversity Information Facility <ns0:ref type='bibr' target='#b38'>(Groom, 2019c)</ns0:ref>.</ns0:p><ns0:p>It has been well argued, with good justification, that detectability is an important consideration in site occupancy modelling of animals <ns0:ref type='bibr' target='#b51'>(Kéry & Schmidt, 2008)</ns0:ref>. Nevertheless, geese are large, noisy and bold and easy to recognize apart from the occasional hybrid. The areas where they feed in the Garden are small and open. Therefore, counts of the geese are expected to be reliable. We have not considered detectability in our analysis as we have no reason to think that this would make a difference to the results.</ns0:p><ns0:p>In one year, four hybrids were observed, two between greylag and Canada geese and two between barnacle and Canada geese. Furthermore, many of the greylag geese were either escapes from captivity or hybrids with farmed birds. Nevertheless, such distinctions were not made during counting and hybrids were counted along with the species they consorted with.</ns0:p><ns0:p>Three landscape parameters were examined for their importance for geese in site selection.</ns0:p><ns0:p>The size of the survey area, the distance from the site to the nearest lake and the presence of physical barriers preventing direct flight to the nearest lake. Details of each survey sector are available in <ns0:ref type='bibr' target='#b36'>Groom (2019b)</ns0:ref>. For the physical barriers, each area was evaluated as to whether it PeerJ reviewing PDF \| (2020:01:45121:1:1:NEW 12 Apr 2020) was surrounded by barriers, such as tall trees and buildings that prevented easy flight access either to or from the lakes to the sector (Fig. <ns0:ref type='figure' target='#fig_4'>1</ns0:ref>). These data have several issues which need to be addressed in statistical models, these are seasonal variations in behaviour, temporal autocorrelation and potentially spatial autocorrelation. Various statistical modelling approaches were considered including generalized linear models, mixed effects models and time series models. However, although these techniques might be useful to extract other valuable information from these data, we determined that, for the questions we wanted to answer, we would fit linear models to the mean individual count per sector. By averaging site occupancy across time, we eliminate the issue of temporal autocorrelation. Model selection was achieved by stepwise simplification of the model as described in <ns0:ref type='bibr' target='#b21'>Crawley (2012)</ns0:ref>, using the step and lm functions of R <ns0:ref type='bibr' target='#b73'>(Venables & Ripley, 2002)</ns0:ref>. Independent variables were the area of the sector; the closest distance from the sector to the nearest lake; whether the sector was woodland (1) or grassland (0) and the presence or absence of flight barriers out of the sector towards the lakes. The log of the mean individual count per sector was our dependent variable. Evaluation of our initial models using residuals versus leverage plots showed that the sectors containing lakes (13, 18 & 21) had a disproportionate influence on the models as judged by the Cook's Distance. This is not surprising as the behaviour of geese and their relation to these areas is very different to grassland areas they visit to graze. For this reason, the lake sectors of the garden were excluded from our models. This reduced the number of sectors used for the model to 29, but no sector had a disproportionate influence on the models. R version 3.4.1 was used in all modelling and data manipulations. Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Edge effects between grassland and woodland</ns0:head><ns0:p>Where goose grazing lawns are bordered by woodland it is reasonable to expect an edge effect, whereby the difference in usage by geese at a woodland-lawn boundary is gradual rather than abrupt. These might be the result of decreased forage quality in the partial shade of trees, or perhaps the avoidance of areas that give cover to potential predators. The use by geese of different areas of lawn was estimated by the amount of droppings on the lawn. Geese defecate frequently and seemingly indiscriminately. Counting dropping is a well-known method for estimating relative intensity of goose grazing on areas of land <ns0:ref type='bibr' target='#b57'>(Owen, 1971;</ns0:ref><ns0:ref type='bibr'>Van Gils et al. 2010</ns0:ref>). However, we found it difficult to distinguish individual defecation events, because the droppings tend to break apart as they are released. Therefore, we preferred to measure the total length of droppings in a unit area. We considered this measure more reliable than trying to count the number of defecation events.</ns0:p><ns0:p>The presence of edge effects was investigated with 10 m wide rectangular plots laid out on the lawns perpendicular to the woodland-lawn boundary. The first set of four plots were 12m long and were surveyed in July 2014. The second set were 15m long and surveyed in March and April 2015. These plots are detailed in table <ns0:ref type='table'>S1</ns0:ref>. The sites for these plots were chosen because they were on sections of the Garden frequently used by all goose species; well separated from each other; were away from other trees and faced different directions. The plots were marked out using bamboo canes and a tape measure. Then either 20 or 30 randomly chosen 1 m 2 square quadrats were surveyed within the rectangular plot. The cumulative length of dropping in a quadrat was measured to the nearest centimetre with a ruler.</ns0:p><ns0:p>Analysis of these data was conducted using non-linear mixed effects models using the plot PeerJ reviewing PDF \| (2020:01:45121:1:1:NEW 12 Apr 2020)</ns0:p><ns0:p>Manuscript to be reviewed number as a random factor <ns0:ref type='bibr' target='#b21'>(Crawley, 2012)</ns0:ref>. Calculations were performed using the 'nlme' package in R <ns0:ref type='bibr' target='#b61'>(Pinheiro et al., 2016)</ns0:ref>. Two possible models were compared, a 3-parameter asymptotic exponential model and a 3-parameter logistic sigmoidal function, both with a positive intercept. Model comparisons were made using the Akaike information criterion. Models were conducted using distances perpendicular to the woodland -lawn boundary and for a control modelling was repeated with distances parallel to the woodland -lawn boundary.</ns0:p></ns0:div> <ns0:div><ns0:head>Summer goose count data to investigate the influence of islands</ns0:head><ns0:p>Only one of the three lakes in the Botanic Garden has an island and this is the primary nesting site of greylag, Canada and barnacle geese. Nevertheless, with only one island it is impossible to draw conclusions about the importance of islands on habitat choice. Therefore, we used a dataset of summering goose counts from Flanders, that includes the Botanic Garden <ns0:ref type='bibr' target='#b22'>(Devisscher et al., 2016)</ns0:ref>. These annual counts of geese are collected by volunteers from bird working groups at set sites across Flanders, Belgium. They are conducted simultaneously over one weekend in mid-July, to avoid double counts and when most species have completed their moult but are still found aggregated in larger groups on water bodies <ns0:ref type='bibr' target='#b3'>(Adriaens et al. 2010</ns0:ref><ns0:ref type='bibr' target='#b4'>(Adriaens et al. , 2011))</ns0:ref>. These data are provided with the geographic centroid of the lake. The area of the lake was calculated by tracing it on a GIS system and the area of the lake included the area of any island in the lake. The presence of an island in the lake was determined from visual inspection of aerial photographs from Google Maps.</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:01:45121:1:1:NEW 12 Apr 2020)</ns0:p></ns0:div> <ns0:div><ns0:head>Manuscript to be reviewed</ns0:head></ns0:div> <ns0:div><ns0:head>Results</ns0:head></ns0:div> <ns0:div><ns0:head>Do geese avoid proximity to trees?</ns0:head><ns0:p>During the study geese were rarely ever observed in woodland. Egyptian geese are occasionally found perched in trees where they nest, but rarely on the ground in woodland. It was hypothesised that this negative association with woodland would extend beyond the boundary between the woodland and lawns and be the cause of an edge effects on grazing.</ns0:p><ns0:p>Quantification of the length of geese droppings showed a clear edge effect at the border to woodland (Fig. <ns0:ref type='figure'>2</ns0:ref>). A shorter length of droppings was found close to the woodland, but this effect only extended 5-10 m from the boundary. Modelling was also performed in parallel to the woodland boundary as a control, but models either failed to converge or showed no directional trend.</ns0:p></ns0:div> <ns0:div><ns0:head>Which habitat features attract geese?</ns0:head><ns0:p>Here we model the site selection of geese based upon habitat features we suspect might be important to geese. The area of the sector, barriers to flight, presence of woodland and proximity to lakes all appear relevant from observations of geese and the literature cited in the introduction. The mean individual counts of geese in the different sectors of the Garden are mapped in figure <ns0:ref type='figure'>3</ns0:ref>. From these maps it is clear that all species had a high affinity to the sectors containing lakes, though there are clear differences between species. The greylag geese in particular are far more wide-ranging than other species notably in the large western sectors.</ns0:p><ns0:p>The models of sector usage were evaluated with various means. The Cook's distance was used to evaluate if particular sectors had an exaggerated influence on the model outcomes, but this PeerJ reviewing PDF \| (2020:01:45121:1:1:NEW 12 Apr 2020)</ns0:p><ns0:p>Manuscript to be reviewed does not appear to be the case (Fig. <ns0:ref type='figure' target='#fig_4'>S1</ns0:ref>). Variograms of the residuals did not show evidence for spatial autocorrelation that was not accounted for in the model parameters (Figs S2-S5). A plot of residuals versus fitted values indicates that there may be some non-linearity between the predictors and the abundance of geese, but this was not clear (Fig. <ns0:ref type='figure'>S6</ns0:ref>). The Q-Q plot shows that the residuals were quite normally distributed for all models (Fig. <ns0:ref type='figure'>S7</ns0:ref>). The Scale-Location plot was used to test for homoscedasticity. Some amount of heteroscedasticity was evident in all models, however we consider that only the model for Branta leucopsis was so heteroscedastic that it might impact our interpretation of the results. Given that no real-world model will perfectly match our assumptions and some of the reasons for deviation from these assumptions are suggested in the discussion.</ns0:p><ns0:p>A summary of the minimum adequate models is given in table 1. The simplest minimum adequate model selected was for Anser anser. Only the area of the sector and the presence of woodland were significantly correlated to their distribution in the Garden, when away from the sectors containing a lake. For B. canadensis the area was also positively correlated with the number of geese, but not significantly in the model. However, in contrast to Anser anser, distance from a lake was a significant factor for B. canadensis, but also barriers to direct flight and their interacting term. For Alopochen aegyptiaca, area and barriers are significant as single factors, and they reoccur in interacting terms. Distance from the lake was not a significant term, but it did occur in an interaction term with area. In the case of B. leucopsis, area was a significant correlate, the other terms are more difficult to interpret, but both distance from a lake and the presence of barriers remained in the model due to their interactions and their interaction with area.</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:01:45121:1:1:NEW 12 Apr 2020)</ns0:p><ns0:p>Manuscript to be reviewed Goose abundance was negatively correlated with woodland for all except B. leucopsis, but this variable is not ideal as all those areas of woodland are also surrounded by trees as barriers to flight, So, there are no areas of woodland without barriers. Therefore, some of the variance stemming from the presence of woodland may be being accounted for in the barrier variable.</ns0:p><ns0:p>Therefore, for all species the area of the sector was positively correlated with goose abundance and the area was part of the significant interactions included in the models for Alopochen aegyptiaca and Branta leucopsis. The distance from the lake remained in models for all species, except Anser anser. This is also evident in figure <ns0:ref type='figure'>3</ns0:ref>, where A. anser can be seen to range more widely than other geese. All other predicted habitat determinants were included in one or more of the models.</ns0:p><ns0:p>For Canada and greylag geese there was a negative influence of barriers on site usage, particularly for Canada geese. In the case of Egyptian and barnacle geese, barriers were not a clear determinant of site selection, but did remain in minimum adequate models as interactions with distance and area.</ns0:p></ns0:div> <ns0:div><ns0:head>Do islands in lakes attract geese?</ns0:head><ns0:p>Lakes with islands house more Canada, greylag and barnacle geese in the summer (Fig. <ns0:ref type='figure'>4</ns0:ref>).</ns0:p><ns0:p>These results indicate that a lake without an island had 35%-60% fewer anserine geese than a lake of an equivalent size with an island. However, islands made no difference to the number of Egyptian geese. All goose numbers showed a positive relationship with lake size, although this is PeerJ reviewing PDF \| (2020:01:45121:1:1:NEW 12 Apr 2020)</ns0:p><ns0:p>Manuscript to be reviewed not significant in the case of barnacle geese.</ns0:p></ns0:div> <ns0:div><ns0:head>Discussion</ns0:head><ns0:p>The results demonstrated the complicated relationship between habitat choice and the landscape for suburban geese. A casual observer could assume that there is a rather passive relationship between geese and their landscape, but as with any other animal, geese are clearly actively selecting and using particular landscapes and landscape features suited to their preferences.</ns0:p><ns0:p>Edge effects are relevant to the usage of geese on lawns because they reduce the active area of use for the geese. Our methodology did not distinguish whether there are species differences, however, the effect was so distinct that we speculate that all species are influenced. While there may be many potential causes of an edge effect, an area of lawn less than 20 m in diameter is likely to be undesirable to geese. However, with increasing size the relevance of this effect will diminish. In ornamental parks individual specimen trees might extend the influence of this edge effect.</ns0:p><ns0:p>Sector area was the most consistent predictor of goose abundance (Table <ns0:ref type='table'>1</ns0:ref>). This was anticipated, as more space can contain more geese. Yet in addition to the edge effects there are reasons to expect a more sophisticated relationship between goose number and area. Manuscript to be reviewed the flight angle needed to enter and leave it from the air becomes progressively steeper the smaller the area becomes. Mature trees stand 15-20m tall, but average vertical and horizontal airspeeds of geese are approximately 0.5 m s -1 and 16 m s -1 respectively <ns0:ref type='bibr' target='#b43'>(Hedenström & Alerstam, 1992)</ns0:ref>. Therefore, to enter and escape a small area surrounded by trees they must either considerably steepen their descent or climb rate, or circle while gaining or losing height.</ns0:p><ns0:p>Both of these strategies would be more energetically expensive <ns0:ref type='bibr' target='#b55'>(Norberg 1996)</ns0:ref>. For these reasons, it is not surprising that the area of the sector also appears in interacting terms in the models with barriers. Barriers particularly restrict movement of geese when flight is not an option, such as, when raising young or moulting. However, the negative influence of barriers was barely significant for Alopochen aegyptiaca. This may be a result of their behaviour of nesting in tree holes. Though they do not inhabit densely forested areas, their preferred habitat is open grassland with some trees in proximity to freshwater <ns0:ref type='bibr'>(Cramp et al., 1984;</ns0:ref><ns0:ref type='bibr' target='#b17'>Carboneras, 1992;</ns0:ref><ns0:ref type='bibr' target='#b40'>Gyimesi and Lensink, 2012)</ns0:ref>. They defend territories around nest sites and therefore must be in proximity to trees <ns0:ref type='bibr' target='#b67'>(Sutherland & Allport, 1991)</ns0:ref>.</ns0:p><ns0:p>Distance from lakes was not as important to site selection as had been assumed, and the interactions with area and the presence of barriers suggests that the ease of access to grazing is more important to site selection than the linear distance. This perhaps indicates that careful usage of landscape features could guide geese to use particular feeding sites, irrespective of their distance from the lake.</ns0:p><ns0:p>The results show a strong preference of anserine geese for lakes with islands during the PeerJ reviewing PDF \| (2020:01:45121:1:1:NEW 12 Apr 2020)</ns0:p><ns0:p>Manuscript to be reviewed summer (Fig. <ns0:ref type='figure'>4</ns0:ref>). Islands are used by geese year-round, as they provide protection from disturbance where geese can rest and nest. The lack of a similar preference for Egyptian geese is consistent with the territorial breeding behaviour of Egyptian geese and their use of nest holes in trees. Although anserine geese prefer lakes with islands in the summer, the reasons are probably many and this preference may not be true in winter. Island breeders are presumably more protected from predators, particularly foxes <ns0:ref type='bibr' target='#b74'>(Wright & Giles, 1988)</ns0:ref>, stone marten (Martes foina), brown rat (Rattus norvegicus) and carrion crow (Corvus corone) <ns0:ref type='bibr' target='#b47'>(Huysentruyt et al. 2020</ns0:ref>). However, when breeding success on islands has been examined it is not always better than on the mainland <ns0:ref type='bibr' target='#b32'>(Gosser & Conover, 1999;</ns0:ref><ns0:ref type='bibr' target='#b60'>Petersen, 1990)</ns0:ref>. Other studies on the influence of islands on goose nest site selection vary. <ns0:ref type='bibr'>Fox et al. (1989)</ns0:ref> showed no influence for greylag goose, whereas others report an effect for Canada Goose <ns0:ref type='bibr' target='#b53'>(Lokemoen & Woodward, 1992;</ns0:ref><ns0:ref type='bibr'>Bromley & Hood, 2013)</ns0:ref>. <ns0:ref type='bibr' target='#b47'>Huysentruyt et al. (2020)</ns0:ref>, in their study of 200 breeding pairs of barnacle goose in Flanders, also note that barnacle goose mainly breeds on small islands in lakes and ponds in the region.</ns0:p><ns0:p>Based on the results of this study we suggest that landscape adaptations could indeed reduce the number of geese in suburban parks, which could be an alternative to lethal control and prevent conflict with people. Unfortunately, many of the landscape adaptations that would reduce the presence of geese are in opposition to popular landscape design features, such as ponds and lakes, islands, open vistas and extensive lawns. Other sorts of landscape and garden design with more enclosed and higher vegetation are more suitable where geese are a problem. Woodlands, shrubberies, coppice, hedges, tall grass meadows, prairie planting, hard PeerJ reviewing PDF \| (2020:01:45121:1:1:NEW 12 Apr 2020) landscaping features, shallow water and moving-water features would all deter geese from using an area <ns0:ref type='bibr' target='#b6'>(Allan, Kirby & Feare, 1995;</ns0:ref><ns0:ref type='bibr' target='#b33'>Gosser, Conover & Messmer, 1997;</ns0:ref><ns0:ref type='bibr' target='#b5'>Allan, 1999;</ns0:ref><ns0:ref type='bibr' target='#b11'>Baxter, Hart & Hutton, 2010)</ns0:ref>.</ns0:p><ns0:p>If artificial islands were eliminated from suburban lakes it might be argued that native birds would also suffer from the lack of island breeding sites, however, islands in suburban parks are mostly unsuitable for island nesters of conservation concern, such as common terns (Sterna hirundo) which do breed well on artificial rafts in bigger lakes and lagoons <ns0:ref type='bibr'>(Coccon et al., 2018;</ns0:ref><ns0:ref type='bibr' target='#b25'>Dunlop et al., 1991)</ns0:ref>. Islands could perhaps be made less attractive if they were connected to the mainland by constructing bridges or an isthmus. They can also be modified with banks that deter access from the water, rather than from the air. However, making feeding areas inaccessible is controversial as chicks can then starve <ns0:ref type='bibr' target='#b5'>(Allan 1999)</ns0:ref>. Modifications or removal of islands should however consider the trade-off with ongoing management. For example, when practicing egg shaking or egg oiling for fertility reduction, the success of this measure depends on sustained effort and a high percentage of treated nests <ns0:ref type='bibr' target='#b52'>(Klok et al., 2010;</ns0:ref><ns0:ref type='bibr' target='#b12'>Beston et al., 2016)</ns0:ref>. Hence, having all geese nest on the same island is practical to perform this management.</ns0:p><ns0:p>There is also a need to educate the public to the benefits of geese. In the Botanic Garden their selective grazing of grasses has created an exceptional species rich grassland that is unlikely to be maintained with mowing alone yet can only be maintained under current grazing intensity <ns0:ref type='bibr' target='#b64'>(Ronse 2011</ns0:ref>). An adaptive management approach, whereby vegetations as well as goose Manuscript to be reviewed numbers in the Garden are thoroughly monitored and objectives are clearly stipulated, could be a good way to learn more about the behaviour and impacts of geese.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions</ns0:head><ns0:p>Landscape features have a powerful influence on the distribution of geese, though these influences differ between species. Landscape modifications cannot completely remove geese from a suburban landscape and an integrated management strategy may be necessary <ns0:ref type='bibr' target='#b6'>(Allan, Kirby & Feare, 1995)</ns0:ref>. Retroactively modifying landscapes to reduce their attractiveness to geese is difficult, so designing landscapes for wildlife usage should be among the primary design criteria. Manuscript to be reviewed</ns0:p><ns0:note type='other'>Figure 2</ns0:note><ns0:p>Geese land usage measure by the droppings deposited at varying distances from the boundary between woodland and lawn.</ns0:p><ns0:p>The total length of geese droppings deposited at varying distances from the boundary between woodland and lawn. Geese dropping were the sum length of all dropping from all species of geese. The numbers on each graph refer to the original plot number. See the methods for details of the model applied to the data.</ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ reviewing PDF \| (2020:01:45121:1:1:NEW 12 Apr 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ reviewing PDF \| (2020:01:45121:1:1:NEW 12 Apr 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>Firstly</ns0:head><ns0:label /><ns0:figDesc>, anserine geese are social species forming large flocks and they may only select areas with sufficient capacity to hold the whole flock. Secondly, if an area is surrounded by tall trees PeerJ reviewing PDF \| (2020:01:45121:1:1:NEW 12 Apr 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ reviewing PDF \| (2020:01:45121:1:1:NEW 12 Apr 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head>Figure 1 A</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='32,42.52,70.87,298.54,672.95' type='bitmap' /></ns0:figure> </ns0:body> "	"Responses to the reviewer’s comments and corrections Our thanks to the reviewers for their comments. We are sure they will result in a better written paper. All spelling and grammar changes suggested have been addressed as suggested by the reviewer. More specific comments are address below. Responses to Reviewer: Liviu Parau Commented [LP1]: Please remove this (North America). Your study is focused on Europe This is part of the introductory information it is relevant to the reader that although the research was conducted in Europe the problem exists elsewhere. Capitalization of vernacular names It is convention to capitalise proper nouns, which is why Canada goose has a capital and greylag goose does not. Nonetheless, it is a widespread point of disagreement and is, to some extent a matter of style. I stick to what I believe is the consensus opinion for British English grammar. See: Potter, E. F. (1984). On capitalization of vernacular names of species. The Auk, 101(4), 895-896. Also https://www.englishgrammar.org/rules-capitalization/ Commented [LP7]: You mean 12pm, i.e. noon /midday? Indeed, it has been corrected. Commented [LP11]: Here I miss a more detailed section on the island characteristics that could attract geese, as well as woodland. You focus a lot in these two objectives in the first side of the paper, but not so much in the Discussion. The introduction and discussion have been reduced as requested by the other reviewers. As we do not address island characteristics we have not expanded on this issue. However, we now cite several papers that have dealt with this subject. Secondly, what about presence of humans during the counting times in the garden? Do you think this might have an influence on the presence and abundance of geese? Did you count humans as well during your geese monitoring? We’ve no doubt that the presence of humans could have an influence on the distribution of geese. It is however, extremely difficult to study without a much more sophisticated and expensive experimental design. Geese were monitored on weekdays because that is when the experimenters worked in the Garden. We freely admit this is opportunistic, rather than by design. Visitors to the Garden are mostly at the weekend when the geese were not surveyed. This may have influenced the results, but we do not think it invalidates them. You have a strong focus on islands and tree barriers in the first sections on the paper, but you somehow loose this in the Discussion. Although you mention something about the edges of the islands and its accessibility, nothing is written on the dimensions of the islands, vegetation, type of soil and substrate. For the tree barriers, again I believe some more emphasis should be delivered. Which tree species would bring more benefit for reducing geese numbers. At which height from the ground should the canopy start? Which density do you recommend? Or these factors have no importance at all? These are all interesting questions, but we do not have suitable data to address them. Perhaps island size could be addressed from aerial photography, but even this is hard to evaluate, because trees obscure the edges of many islands. The Botanic Garden’s woodland are largely of Fagus sylvatica and Quercus robur. The variation in species is insufficient to address questions related to the tree species. When counting the geese in the garden, did you also take into account human presence? You mention the counts were done on Monday and Friday. I guess Monday is not the busiest day in the garden, but I expect Friday you have quite a lot of visitors. It is possible that the admission / entry office keeps a record on the number of visitors. If related to the number of geese, you think there might be a correlation? See above Finally, throughout the article the English is sometimes tricky. Several times I've got myself lost in long sentences, where the verb was sometimes missing. I've corrected some of these mistakes, but please take a thorough inspection of the whole text. And I absolutely do not understand why 'Egyptian' and 'Canada geese' start with capital, but 'greylag' and 'barnacle geese' do not have capital. You use this system in the entire manuscript and after a few sentences I stopped correcting it. Thank you for your grammatical corrections. We’ve addressed the issue of capitalization above. The introduction and discussion have been reviewed, reconfigured and reduced in length. Responses to Reviewer 2 In many northern European and North American cities geese are one of the most common and most visible It is suggested to add “many” before the word northern, because geese are not found in every city. However, this seems superfluous as we do not suggest that. Rabbits are also common, but not so visible. Omit “the desires of the human residents”, because it is “unscientific”. We disagree, the opinion of the general public is fundamental to applied wildlife management. Ignoring public opinion is unlikely to resolve conflicts and result in positive conservation outcomes. Admittedly writing “too many” geese without citation is for stylistic effect to get the reader’s attention, but this is what the abstract is for. To illustrate my point, try googling “Too Many Geese”, “te veel ganzen” or 'trop d'oies' and you will see how often this term is used. Lethal control is often used for population management, however, this raises questions about whether this is a sustainable strategy to resolve the conflict between humans and geese, when paradoxically, it is humans that are responsible for creating the habitat and often providing the food and protection of geese at other times. If we understand the comment correctly, the reviewer suggests that the aspect of lethal control is not relevant to the sustainability of geese management, whereas cost is. However, we disagree, animal welfare considerations are relevant, also the local people who feed the wildfowl do not like to see their birds killed. Furthermore, lethal controls are much more difficult to implement in a built-up environment where firearms cannot be used. There are many aspects to sustainability and cost is only one of them. The reviewer mentions “costly … non-lethal measures”, but we are not clear what these might be. Perhaps bird scaring, though this does not control goose numbers, but redistributes them. Responses to Reviewer: Johanne Martens The review requested addition of references, an improvement in the structure and a reduction in speculative phrases. The paper has been thoroughly revised, adding many new references and restricting all sections. Results section: Contains sections that belong into the other sections, e.g. lines 276 – 279 belong in Material & Methods, 289 – 293 are speculative and sound more like a discussion. These sections have been revised The results need a lot more proof. E.g. if you state something is “significant”, give at least the p-value. Don’t expect the reader to go to the supplementary tables to check every result. Sadly, a critical table was accidently omitted in the submission, which is why the reviewers could not see all the details of the results. Figures and tables: Axes are often unclear – what are you showing? Captions are missing! Large parts of the body of the text can probably moves into figure/table captions. We’re not clear what happened here, as all figures had legends. Nevertheless, they have been reviewed. References: you need to support each statement you make. If you give references, please don’t dump them all together at the end of a long sentence (e.g. lines 105-106), but put each reference right after the statement it belongs too. Make it easy for the reader! These have been changed. Discussion: Needs a lot more structure. Please start by summarizing your main findings briefly in the first paragraph. Then start each paragraph with a finding, which you then discuss. Go from the most important to the less important results, and keep it short and concise. The text has been revised along the lines suggested by the reviewer. "	Here is a paper. Please give your review comments after reading it.
516	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Background</ns0:head><ns0:p>In European and North American cities geese are among the most common and most visible large herbivores. As such, their presence and behaviour often conflict with the desires of the human residents. Fouling, noise, aggression and health concerns are all cited as reasons that there are 'too many'. Lethal control is often used for population management, however, this raises questions about whether this is a sustainable strategy to resolve the conflict between humans and geese, when paradoxically, it is humans that are responsible for creating the habitat and often providing the food and protection of geese at other times. We hypothesise that the landscaping of suburban parks can be improved to decrease its attractiveness to geese and to reduce the opportunity for conflict between geese and humans. Methods Using observations collected over five years from a botanic garden situated in suburban Belgium and data from the whole of Flanders in Belgium, we examined landscape features that attract geese. These included the presence of islands in lakes, the distance from water, barriers to level flight and the size of exploited areas. The birds studied were the tadornine goose Alopochen aegyptiaca (L. 1766) (Egyptian goose) and the anserine geese, Branta canadensis (L. 1758) (Canada goose), Anser anser (L. 1758) (greylag goose) and Branta leucopsis (Bechstein, 1803) (barnacle goose). Landscape modification is a known method for altering goose behaviour, but there is little information on the power of such methods with which to inform managers and planners.</ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head><ns0:p>Our results demonstrate that lakes with islands attract more than twice as many anserine geese than lakes without islands, but make little difference to Egyptian geese. Furthermore, flight barriers between grazing areas and lakes are an effective deterrent to geese using an area for feeding. Keeping grazing areas small and surrounded by trees reduces their attractiveness to geese.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusion</ns0:head><ns0:p>The results suggest that landscape design can be used successfully to reduce the number of geese and their conflict with humans. However, this approach has its limitations and would require humans to compromise on what they expect from their landscaped parks, such as open vistas, lakes, islands and closely cropped lawns.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Background</ns0:head><ns0:p>In European and North American cities geese are among the most common and most visible large herbivores. As such, their presence and behaviour often conflict with the desires of the human residents. Fouling, noise, aggression and health concerns are all cited as reasons that there are 'too many'. Lethal control is often used for population management, however, this raises questions about whether this is a sustainable strategy to resolve the conflict between humans and geese, when paradoxically, it is humans that are responsible for creating the habitat and often providing the food and protection of geese at other times. We hypothesise that the landscaping of suburban parks can be improved to decrease its attractiveness to geese and to reduce the opportunity for conflict between geese and humans.</ns0:p></ns0:div> <ns0:div><ns0:head>Methods</ns0:head><ns0:p>Using observations collected over five years from a botanic garden situated in suburban Belgium and data from the whole of Flanders in Belgium, we examined landscape features that attract geese. These included the presence of islands in lakes, the distance from water, barriers to level flight and the size of exploited areas. The birds studied were the tadornine goose Alopochen aegyptiaca (L. 1766) (Egyptian goose) and the anserine geese, Branta canadensis (L. 1758) (Canada goose), Anser anser (L. 1758) (greylag goose) and Branta leucopsis (Bechstein, 1803) (barnacle goose). Landscape modification is a known method for altering goose behaviour, but there is little information on the power of such methods with which to inform managers and planners.</ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head><ns0:p>Our results demonstrate that lakes with islands attract more than twice as many anserine geese than lakes without islands, but make little difference to Egyptian geese. Furthermore, flight barriers between grazing areas and lakes are an effective deterrent to geese using an area for feeding. Keeping grazing areas small and surrounded by trees reduces their attractiveness to geese.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusion</ns0:head><ns0:p>The results suggest that landscape design can be used successfully to reduce the number of geese and their conflict with humans. However, this approach has its limitations and would require humans to compromise on what they expect from their landscaped parks, such as open vistas, lakes, islands and closely cropped lawns.</ns0:p></ns0:div> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>In Europe and North America wild and feral geese frequently inhabit artificial lakes and their surrounding parks in urban and suburban areas. These parks are appreciated by people for their recreational and aesthetic value. However, this often brings geese in conflict with people <ns0:ref type='bibr' target='#b21'>(Conover & Chasko, 1985;</ns0:ref><ns0:ref type='bibr' target='#b51'>Hughes et al., 1999;</ns0:ref><ns0:ref type='bibr' target='#b75'>Smith, Craven & Curtis, 1999;</ns0:ref><ns0:ref type='bibr' target='#b38'>Fox, 2019)</ns0:ref>. While people often enjoy seeing small numbers of geese, when there are large flocks the soil becomes fouled and people are intimidated by the geese's threatening behaviour <ns0:ref type='bibr' target='#b62'>(Miller et al., 2001)</ns0:ref>.</ns0:p><ns0:p>Geese are also known to exert pressure on small water bodies such as ponds, reducing water quality through eutrophication <ns0:ref type='bibr' target='#b6'>(Allan et al., 1995;</ns0:ref><ns0:ref type='bibr' target='#b40'>Gosser et al., 1997;</ns0:ref><ns0:ref type='bibr'>Smith et al., 2000;</ns0:ref><ns0:ref type='bibr'>Kumschick & Nentwig 2010)</ns0:ref>. They have also been suggested to be a disease risk, though the evidence is circumstantial and other domestic and wild animals pose a greater known risk <ns0:ref type='bibr' target='#b34'>(Fleming & Fraser, 2001;</ns0:ref><ns0:ref type='bibr' target='#b16'>Clark, 2003;</ns0:ref><ns0:ref type='bibr' target='#b13'>Bönner et al. 2004</ns0:ref>). Throughout Europe and the western Palearctic, native as well as non-native geese are increasing in numbers and distribution <ns0:ref type='bibr' target='#b6'>(Allan, Kirby & Feare, 1995;</ns0:ref><ns0:ref type='bibr'>Fox et al. 2010)</ns0:ref>. Several populations have developed a resident component and their year-round presence increases human-wildlife conflicts and impacts on biodiversity <ns0:ref type='bibr' target='#b15'>(Buij et al. 2017)</ns0:ref>. A variety of strategies are needed to reduce these impacts <ns0:ref type='bibr' target='#b10'>(Austin et al., 2007;</ns0:ref><ns0:ref type='bibr' target='#b46'>Gyimesi & Lensink, 2012)</ns0:ref>.</ns0:p><ns0:p>In Europe, from the 18 th century onwards, it has been traditional to create landscaped parks reflecting an idealised vision of the countryside. Lakes with islands, open vistas, lawns and Manuscript to be reviewed patches of woodland are typical <ns0:ref type='bibr' target='#b77'>(Turner, 1985)</ns0:ref>. Lake-side vegetation and lawns are cut regularly and the canopies of trees are kept high to ensure unimpeded views. For those goose species that are habituated to the presence of people, such landscapes are very suitable, they have abundant grazing; proximity to water and islands for undisturbed nesting sites. In addition, people often provide supplementary feeding.</ns0:p><ns0:p>In north-western Europe four species of 'geese' are the main inhabitants of urban and suburban parks, non-native Egyptian geese (Alopochen aegyptiaca), Canada geese (Branta canadensis), mixed populations of wild and feral greylag geese (Anser anser) and barnacle geese (Branta leucopsis) <ns0:ref type='bibr'>(Fox et al. 2010)</ns0:ref>. All are members of the family Anatidae, but Egyptian geese are members of the subfamily Tadorninae, which are referred to as tadornine geese, whereas the others are members of subfamily Anserinae, which are referred to as anserine geese <ns0:ref type='bibr' target='#b60'>(Livezey 1996)</ns0:ref>. Egyptian geese are similar in several aspects to anserine geese, such as their large size, long neck and feeding behaviour, but they do differ in other important aspects.</ns0:p><ns0:p>Anserine geese, such as Canada geese, barnacle geese, greylag geese and their hybrids, usually nest on the ground close to bodies of water and are also likely to form large flocks <ns0:ref type='bibr' target='#b53'>(Adriaens et al. 2020)</ns0:ref>. Egyptian geese are also water birds, but their biology shows many characteristics of a duck, including larger clutch sizes. Although they nest on the ground, their nest site selection is highly variable and they also nest in large tree holes, on buildings, on top of willow trees or in nest boxes <ns0:ref type='bibr' target='#b46'>(Gyimesi & Lensink 2012;</ns0:ref><ns0:ref type='bibr' target='#b53'>Huysentruyt et al. 2020)</ns0:ref>. They also differ in their social behaviour. Paired Egyptian geese defend territories near their nest site before and during nesting. Large flocks of Egyptian geese only occur after breeding during moulting <ns0:ref type='bibr'>(Gyimesi &</ns0:ref> PeerJ reviewing PDF \| (2020:01:45121:2:0:NEW 21 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed Lensink 2010).</ns0:p><ns0:p>The site selection criteria of geese are important, because their sites can bring them into conflict with people. The proximity of water, food and breeding sites are relevant to goose site selection, but there are likely to be additional influences. These habitat features may be related to predator avoidance <ns0:ref type='bibr' target='#b22'>(Conover & Kania, 1991)</ns0:ref>, accessibility of feeding grounds for adults and families with chicks, nutritional quality of feed <ns0:ref type='bibr' target='#b68'>(Owen, Nugent & Davies, 1977;</ns0:ref><ns0:ref type='bibr' target='#b35'>Fox & Kahlert, 2005)</ns0:ref>, sward length <ns0:ref type='bibr' target='#b49'>(Hassall, Riddington & Helden, 2001;</ns0:ref><ns0:ref type='bibr' target='#b33'>Feige et al., 2008;</ns0:ref><ns0:ref type='bibr' target='#b20'>Conover, 1991;</ns0:ref><ns0:ref type='bibr' target='#b80'>Van Gils et al., 2009;</ns0:ref><ns0:ref type='bibr'>Huysentruyt & Casaer, 2010)</ns0:ref> and competition with other grazers such as other geese, livestock and rabbits ( <ns0:ref type='bibr' target='#b78'>Van der Wal, Kunst & Drent, 1998)</ns0:ref>. Given this, it may be possible to identify management strategies and landscape features that alter the site selection of geese and these might be used to control the geese in such a way to reduce conflict between geese and people <ns0:ref type='bibr' target='#b23'>(Conover, 1992;</ns0:ref><ns0:ref type='bibr' target='#b67'>Owen, 1975)</ns0:ref>.</ns0:p><ns0:p>Culling is often used to reduce the impact of geese <ns0:ref type='bibr'>(Reyns et al. 2018)</ns0:ref>, but several other strategies have been used to discourage and redistribute geese, including birds scarers and chemical antifeedants <ns0:ref type='bibr' target='#b19'>(Conover, 1985)</ns0:ref>, fencing of feeding grounds or landscape modification, including altered mowing regimes or landscaping solutions <ns0:ref type='bibr' target='#b24'>(Cooper 1998;</ns0:ref><ns0:ref type='bibr' target='#b79'>Van Daele et al. 2012</ns0:ref>). In the context of a landscaped park with large numbers of visitors, culling risks losing public support for a public garden and bird scaring might disturb people too. At the same time, a botanic garden needs to consider the impact of grazing and fouling on plantings, lawns and vegetation, without losing the recreational opportunities for wildlife watching provided by the Manuscript to be reviewed presence of these attractive birds. Therefore, habitat modification is considered as a cost effective, sustainable solution to reduce numbers of geese on sites and to mitigate the impact <ns0:ref type='bibr' target='#b23'>(Conover, 1992)</ns0:ref>. Previous studies on site occupancy of geese have concentrated on wild geese in more or less rural settings. These studies have concentrated on ways to discourage geese from feeding on crop plants (e.g. <ns0:ref type='bibr' target='#b64'>Olsson et al., 2017;</ns0:ref><ns0:ref type='bibr' target='#b74'>Si et al., 2011)</ns0:ref>. In the case of Canada geese most studies have occurred in North America (e.g. <ns0:ref type='bibr' target='#b23'>Conover, 1992)</ns0:ref>.</ns0:p><ns0:p>The aim of this study is to quantify the site selection of the different species of geese within Meise Botanic Garden (Belgium) and create models to predict their behaviour based upon the landscape of the Garden. These models can then be used to suggest strategies to reduce conflict between the geese and the visitors to the Garden without losing the opportunities they represent for wildlife watching.</ns0:p></ns0:div> <ns0:div><ns0:head>Materials & Methods</ns0:head><ns0:p>Most of the research was conducted at Meise Botanic Garden <ns0:ref type='bibr'>(Flanders, Belgium)</ns0:ref>, situated just north of Brussels, Belgium (50°55'42.4'N 4°19'37.6'E). The exception was the study on the effect of islands and those data are described below. The 92 ha Garden is a landscaped park like many such parks in northern and western Europe. It has extensive lawns, woodlands, two large lakes and one small one (Fig. <ns0:ref type='figure' target='#fig_11'>1</ns0:ref>). The Garden is subdivided into different numbered areas, divided by paths, which join various historic buildings and greenhouses with formal gardens, with approximately half the area covered by woodland. Most of the grassland is mown PeerJ reviewing <ns0:ref type='table'>PDF \| (2020:01:45121:2:0:NEW 21 Jul 2020)</ns0:ref> Manuscript to be reviewed between two and four times a month during the growing season, though small areas are maintained as wildflower meadows and are cut once or twice a year. All geese in the Garden are considered either non-native or feral. All species breed in the Garden, though the breeding of Canada geese is, in part, controlled by egg-shaking. The birds using the Garden are part of a larger population of geese that inhabit the greater Brussels area, and birds move in and out of the Garden to the many other lakes and waterways in the neighbourhood. None of these populations are truly migratory, except for local movements <ns0:ref type='bibr' target='#b7'>(Anselin & Cooleman, 2007)</ns0:ref>.</ns0:p><ns0:p>Canada goose is under management in the region and flocks of geese are regularly moult captured on water bodies in neighbouring municipalities since 2010 <ns0:ref type='bibr'>(Reyns et al. 2018)</ns0:ref>. The Garden is in almost constant use by geese except for on the rare occasions when the lakes freeze over for long periods in the winter. Geese feed on all the lawns and grasslands within the Garden, but the extent to which these areas are used varies considerably from area to area and from species to species.</ns0:p></ns0:div> <ns0:div><ns0:head>The preference for grazing areas</ns0:head><ns0:p>The usage by geese of the different areas of the Botanic Garden was assessed by fixed transect counts <ns0:ref type='bibr' target='#b41'>(Groom, 2019a;</ns0:ref><ns0:ref type='bibr' target='#b42'>Groom, 2019b)</ns0:ref>. A total of four routes around the Garden were used, each route took approximately 40 minutes to walk and was always walked in a clockwise direction. Almost all of the grassland areas of the Garden were counted on at least two of these routes, woodland sectors were only counted when they were on the route between grassland areas.</ns0:p><ns0:p>Transect counts were conducted between 12pm and 2pm Central European Time. Geese were PeerJ reviewing <ns0:ref type='table'>PDF \| (2020:01:45121:2:0:NEW 21 Jul 2020)</ns0:ref> Manuscript to be reviewed counted on an average of 2.7 days per week spread throughout the survey period that lasted nearly 6 years, between 11 Oct 2011 and 10 July 2017. Counts were conducted only on Monday to Friday at the convenience of the surveyors, but irrespective of weather conditions. The only consistent period of the year when surveying was not conducted was between 25th December and 1st January. On a few occasions, two routes were walked simultaneously to give an approximate number for the total number of geese in the Garden for that day. Routes 1 and 2 gave the best coverage for all the main areas used by geese in the Garden. On other days routes 1 to 4 were chosen at random <ns0:ref type='bibr' target='#b48'>(Haahr 2019)</ns0:ref>. All the observation data are available on the Global Biodiversity Information Facility <ns0:ref type='bibr' target='#b44'>(Groom, 2019c)</ns0:ref>.</ns0:p><ns0:p>It has been well argued, with good justification, that detectability is an important consideration in site occupancy modelling of animals <ns0:ref type='bibr' target='#b58'>(Kéry & Schmidt, 2008)</ns0:ref>. Nevertheless, geese are large, noisy and bold and easy to recognize apart from the occasional hybrid. The areas where they feed in the Garden are small and open. Therefore, counts of the geese are expected to be reliable. We have not considered detectability in our analysis as we have no reason to think that this would make a difference to the results.</ns0:p><ns0:p>In one year, four hybrids were observed, two between greylag and Canada geese and two between barnacle and Canada geese. Furthermore, many of the greylag geese were either escapes from captivity or hybrids with farmed birds. Nevertheless, such distinctions were not made during counting and hybrids were counted along with the species they consorted with. Three landscape parameters were examined for their importance for geese in site selection: the size of the survey area, the distance from the site to the nearest lake and the presence of Manuscript to be reviewed available in <ns0:ref type='bibr' target='#b42'>Groom (2019b)</ns0:ref>. For the physical barriers, each area was evaluated as to whether it was surrounded by barriers, such as tall trees and buildings that prevented easy flight access either to or from the lakes to the sector (Fig. <ns0:ref type='figure' target='#fig_11'>1</ns0:ref>). These data have several issues which need to be addressed in statistical models/ These are seasonal variations in behaviour, temporal autocorrelation and potentially spatial autocorrelation. Various statistical modelling approaches were considered including generalized linear models, mixed effects models and time series models. However, although these techniques might be useful to extract other valuable information from these data, we determined that, for the questions we wanted to answer, we would fit linear models to the mean individual count per sector. By averaging site occupancy across time, we eliminate the issue of temporal autocorrelation. Model selection was achieved by stepwise simplification of the model as described in <ns0:ref type='bibr' target='#b29'>Crawley (2012)</ns0:ref>, using the step and lm functions of R <ns0:ref type='bibr' target='#b81'>(Venables & Ripley, 2002)</ns0:ref>. Independent variables were the area of the sector; the closest distance from the sector to the nearest lake; whether the sector was woodland (1) or grassland (0) and the presence or absence of flight barriers out of the sector towards the lakes. The log of the mean individual count per sector was our dependent variable. Evaluation of our initial models using residuals versus leverage plots showed that the sectors containing lakes (13, 18 & 21) had a disproportionate influence on the models as judged by the Cook's Distance. This is not surprising as the behaviour of geese and their relation to these areas is very different to grassland areas they visit to graze. For this reason, the lake sectors of the Garden were excluded from our models. This reduced the number of sectors used for the model to 29, but no sector had a disproportionate influence on the models. Residuals verses fitted Q-Q plots Manuscript to be reviewed were used to test whether residuals were normally distributed. A scale-location plot was used to test for homoscedasticity, meaning that the variance of the residual is homogenous across the range of the model. R version 3.4.1 was used in all modelling and data manipulations.</ns0:p></ns0:div> <ns0:div><ns0:head>Edge effects between grassland and woodland</ns0:head><ns0:p>Where goose grazing lawns are bordered by woodland it is reasonable to expect an edge effect, whereby the difference in usage by geese at a woodland-lawn boundary is gradual rather than abrupt. These might be the result of decreased forage quality in the partial shade of trees, or perhaps the avoidance of areas that give cover to potential predators. The use by geese of different areas of lawn was estimated by the amount of droppings on the lawn. Geese defecate frequently and seemingly indiscriminately. Counting dropping is a well-known method for estimating relative intensity of goose grazing on areas of land <ns0:ref type='bibr' target='#b66'>(Owen, 1971;</ns0:ref><ns0:ref type='bibr'>Van Gils et al. 2010</ns0:ref>). However, we found it difficult to distinguish individual defecation events, because the droppings tend to break apart as they are released. Therefore, we preferred to measure the total length of droppings in a unit area. We considered this measure more reliable than trying to count the number of defecation events.</ns0:p><ns0:p>The presence of edge effects was investigated with 10 m wide rectangular plots laid out on the lawns perpendicular to the woodland-lawn boundary. The first set of four plots were 12m long and were surveyed in July 2014. The second set were 15m long and surveyed in March and April 2015. These plots are detailed in table <ns0:ref type='table'>S1</ns0:ref>. The sites for these plots were chosen because they were on sections of the Garden frequently used by all goose species; well separated from each other; were away from other trees and faced different directions. The plots were marked out PeerJ reviewing PDF \| (2020:01:45121:2:0:NEW 21 Jul 2020) using bamboo canes and a tape measure. Then either 20 or 30 randomly chosen 1 m 2 square quadrats were surveyed within the rectangular plot. The cumulative length of dropping in a quadrat was measured to the nearest centimetre with a ruler.</ns0:p><ns0:p>Analysis of these data was conducted using non-linear mixed effects models using the plot number as a random factor <ns0:ref type='bibr' target='#b29'>(Crawley, 2012)</ns0:ref>. Calculations were performed using the 'nlme' package in R <ns0:ref type='bibr' target='#b70'>(Pinheiro et al., 2016)</ns0:ref>. Two possible models were compared, a 3-parameter asymptotic exponential model and a 3-parameter logistic sigmoidal function, both with a positive intercept. Model comparisons were made using the Akaike information criterion. Models were conducted using distances perpendicular to the woodland -lawn boundary and for a control modelling was repeated with distances parallel to the woodland -lawn boundary.</ns0:p></ns0:div> <ns0:div><ns0:head>Summer goose count data to investigate the influence of islands</ns0:head><ns0:p>Only one of the three lakes in the Botanic Garden has an island and this is the primary nesting site of greylag, Canada and barnacle geese. Nevertheless, with only one island it is impossible to draw conclusions about the importance of islands on habitat choice. Therefore, we used a dataset of summering goose counts from Flanders, that includes the Botanic Garden <ns0:ref type='bibr' target='#b30'>(Devisscher et al., 2016)</ns0:ref>. These annual counts of geese are collected by volunteers from bird working groups at set sites across Flanders, Belgium. They are conducted simultaneously over one weekend in mid-July, to avoid double counts and when most species have completed their moult but are still found aggregated in larger groups on water bodies <ns0:ref type='bibr' target='#b3'>(Adriaens et al., 2010</ns0:ref><ns0:ref type='bibr' target='#b4'>(Adriaens et al., , 2011))</ns0:ref>. These data are provided with the geographic centroid of the lake. The area of the lake was calculated by tracing it on a GIS system and the area of the lake included the area of any Manuscript to be reviewed island in the lake. The presence of an island in the lake was determined from visual inspection of aerial photographs from Google Maps.</ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head></ns0:div> <ns0:div><ns0:head>Do geese avoid proximity to trees?</ns0:head><ns0:p>During the study geese were rarely ever observed in woodland. Egyptian geese are occasionally found perched in trees where they nest, but rarely on the ground in woodland. It was hypothesised that this negative association with woodland would extend beyond the boundary between the woodland and lawns and be the cause of an edge effects on grazing.</ns0:p><ns0:p>Quantification of the length of geese droppings showed a clear edge effect at the border to woodland (Fig. <ns0:ref type='figure'>2</ns0:ref>). A shorter length of droppings was found close to the woodland, but this effect only extended 5-10 m from the boundary.</ns0:p><ns0:p>As a control modelling was also performed in parallel to the woodland boundary, but models either failed to converge or showed no directional trend.</ns0:p></ns0:div> <ns0:div><ns0:head>Which habitat features attract geese?</ns0:head><ns0:p>Here we model the site selection of geese based upon habitat features we suspect might be important to geese. The area of the sector, barriers to flight, presence of woodland and proximity to lakes all appear relevant from observations of geese and the literature cited in the introduction. The mean individual counts of geese in the different sectors of the Garden are mapped in figure <ns0:ref type='figure'>3</ns0:ref>. From these maps it is clear that all species had a high affinity to the sectors containing lakes, though there are clear differences between species. The greylag geese in particular are far more wide-ranging than other species notably in the large western sectors.</ns0:p><ns0:p>The models of sector usage were evaluated with various means. The Cook's distance was used to evaluate if particular sectors had an exaggerated influence on the model outcomes, but this does not appear to be the case (Fig. <ns0:ref type='figure' target='#fig_11'>S1</ns0:ref>). Variograms of the residuals did not show evidence for spatial autocorrelation that was not accounted for in the model parameters . A plot of residuals versus fitted values indicates that there may be some non-linearity between the predictors and the abundance of geese, but this was not clear (Fig. <ns0:ref type='figure'>S6</ns0:ref>). The Q-Q plot shows that the residuals were quite normally distributed for all models (Fig. <ns0:ref type='figure'>S7</ns0:ref>). The Scale-Location plot showed that some heteroscedasticity was evident in all models, however we consider that only the model for B. leucopsis was so heteroscedastic that it might impact our interpretation of the results. Given that no real-world model will perfectly match our assumptions and some of the reasons for deviation from these assumptions are suggested in the discussion.</ns0:p><ns0:p>A summary of the minimum adequate models is given in table 1. The simplest minimum adequate model selected was for Anser anser. Only the area of the sector and the presence of woodland were significantly correlated to their distribution in the Garden. Note, that these models do not include areas of the Garden containing a lake. For B. canadensis the area was also positively correlated with the number of geese, but not significantly in the model. However, in contrast to Anser anser, distance from a lake was a significant factor for B.</ns0:p><ns0:p>canadensis, but also barriers to direct flight and their interacting term. For Alopochen aegyptiaca, area and barriers are significant as single factors, and they reoccur in interacting terms. Distance from the lake was not a significant term, but it did occur in an interaction term with area. In the case of B. leucopsis, area was a significant correlate, the other terms are more Manuscript to be reviewed difficult to interpret, but both distance from a lake and the presence of barriers remained in the model due to their interactions and their interaction with area.</ns0:p><ns0:p>Goose abundance was negatively correlated with woodland for all except B. leucopsis, but this variable is not ideal as all those areas of woodland are also surrounded by trees as barriers to flight, So, there are no areas of woodland without barriers. Therefore, some of the variance stemming from the presence of woodland may be being accounted for in the barrier variable.</ns0:p><ns0:p>Therefore, for all species the area of the sector was positively correlated with goose abundance and the area was part of the significant interactions included in the models for Alopochen aegyptiaca and Branta leucopsis. The distance from the lake remained in models for all species, except Anser anser. This is also evident in figure <ns0:ref type='figure'>3</ns0:ref>, where Anser anser can be seen to range more widely than other geese. All other predicted habitat determinants were included in one or more of the models.</ns0:p><ns0:p>For Canada and greylag geese there was a negative influence of barriers on site usage, particularly for Canada geese. In the case of Egyptian and barnacle geese, barriers were not a clear determinant of site selection, but did remain in minimum adequate models as interactions with distance and area.</ns0:p></ns0:div> <ns0:div><ns0:head>Do islands in lakes attract geese?</ns0:head><ns0:p>Lakes with islands attract more Canada, greylag and barnacle geese in the summer (Fig. <ns0:ref type='figure'>4</ns0:ref>).</ns0:p><ns0:p>These results indicate that a lake without an island had 35%-60% fewer anserine geese than a PeerJ reviewing PDF \| (2020:01:45121:2:0:NEW 21 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed lake of an equivalent size with an island (p<.05). However, islands made no difference to the number of Egyptian geese. All goose numbers showed a positive relationship with lake size (p<0.05), although this is not significant in the case of barnacle geese.</ns0:p></ns0:div> <ns0:div><ns0:head>Discussion</ns0:head><ns0:p>The modelling results, edge effects and impact of islands demonstrated the complicated relationship between habitat choice and the landscape for suburban geese (Fig. <ns0:ref type='figure' target='#fig_11'>1</ns0:ref>, Fig. <ns0:ref type='figure'>2</ns0:ref>, Table <ns0:ref type='table'>1</ns0:ref>). A casual observer could assume that there is a rather passive relationship between geese and their landscape, but as with any other animal, geese are clearly actively selecting and using particular landscapes and landscape features suited to their preferences.</ns0:p><ns0:p>Edge effects are relevant to the usage of geese on lawns because they reduce the active area of use for the geese. Our methodology did not distinguish whether there are species differences, however, the effect was so distinct that we speculate that all species are influenced. While there may be many potential causes of an edge effect, such as predator avoidance and poorer grazing, an area of lawn less than 20 m in diameter is likely to be undesirable to geese.</ns0:p><ns0:p>However, with increasing ratio of area to circumference means that the relevance of this effect will diminish with increasing area. In ornamental parks individual specimen trees might extend the influence of this edge effect.</ns0:p><ns0:p>Sector area was the most consistent predictor of goose abundance (Table <ns0:ref type='table'>1</ns0:ref>). This was anticipated, as more space can contain more geese. Yet in addition to the edge effects there are reasons to expect a more sophisticated relationship between goose number and area. Manuscript to be reviewed Firstly, anserine geese are social species forming large flocks and they may only select areas with sufficient capacity to hold the whole flock. Secondly, if an area is surrounded by tall trees the flight angle needed to enter and leave it from the air becomes progressively steeper the smaller the area becomes. Mature trees stand 15-20m tall, but average vertical and horizontal airspeeds of geese are approximately 0.5 m s -1 and 16 m s -1 respectively <ns0:ref type='bibr' target='#b50'>(Hedenström & Alerstam, 1992)</ns0:ref>. Therefore, to enter and escape a small area surrounded by trees they must either considerably steepen their descent or climb rate, or circle while gaining or losing height.</ns0:p><ns0:p>Both of these strategies would be more energetically expensive <ns0:ref type='bibr' target='#b63'>(Norberg 1996)</ns0:ref>. For these reasons, it is not surprising that the area of the sector also appears in interacting terms in the models with barriers. Barriers particularly restrict movement of geese when flight is not an option, such as, when raising young or moulting. However, the negative influence of barriers was scarcely significant for Alopochen aegyptiaca. This may be a result of their behaviour of nesting in tree holes. Though they do not inhabit densely forested areas, their preferred habitat is open grassland with some trees in proximity to freshwater <ns0:ref type='bibr'>(Cramp et al., 1984;</ns0:ref><ns0:ref type='bibr' target='#b25'>Carboneras, 1992;</ns0:ref><ns0:ref type='bibr' target='#b46'>Gyimesi and Lensink, 2012)</ns0:ref>. They defend territories around nest sites and therefore must be in proximity to trees <ns0:ref type='bibr' target='#b76'>(Sutherland & Allport, 1991)</ns0:ref>.</ns0:p><ns0:p>Distance from lakes was not as important to site selection as had been assumed, and the interactions with area and the presence of barriers suggests that the ease of access to grazing is more important to site selection than the linear distance. This perhaps indicates that careful usage of landscape features could guide geese to use particular feeding sites, irrespective of their distance from the lake. </ns0:p></ns0:div> <ns0:div><ns0:head>Manuscript to be reviewed</ns0:head><ns0:p>The results show a strong preference of anserine geese for lakes with islands during the summer (Fig. <ns0:ref type='figure'>4</ns0:ref>). Islands are used by geese year-round, as they provide protection from disturbance where geese can rest and nest. The lack of a similar preference for Egyptian geese is consistent with the territorial breeding behaviour of Egyptian geese and their use of nest holes in trees. Although anserine geese prefer lakes with islands in the summer, the reasons are probably many and this preference may not be true in winter. Island breeders are presumably more protected from predators, particularly foxes (Vulpes vulpes) <ns0:ref type='bibr' target='#b82'>(Wright & Giles, 1988)</ns0:ref>, stone marten (Martes foina), brown rat (Rattus norvegicus) and carrion crow (Corvus corone) <ns0:ref type='bibr' target='#b53'>(Huysentruyt et al. 2020</ns0:ref>). However, when breeding success on islands has been examined it is not always better than on the mainland <ns0:ref type='bibr' target='#b39'>(Gosser & Conover, 1999;</ns0:ref><ns0:ref type='bibr' target='#b69'>Petersen, 1990)</ns0:ref>. Other studies on the influence of islands on goose nest site selection vary. <ns0:ref type='bibr'>Fox et al. (1989)</ns0:ref> showed no influence for greylag goose, whereas others report an effect for Canada Goose <ns0:ref type='bibr' target='#b61'>(Lokemoen & Woodward, 1992;</ns0:ref><ns0:ref type='bibr' target='#b14'>Bromley & Hood, 2013)</ns0:ref>. <ns0:ref type='bibr' target='#b53'>Huysentruyt et al. (2020)</ns0:ref>, in their study of 200 breeding pairs of barnacle goose in Flanders, also note that barnacle goose mainly breeds on small islands in lakes and ponds in the region.</ns0:p><ns0:p>Based on the results of this study we suggest that landscape adaptations could indeed reduce the number of geese in suburban parks, which could be an alternative to lethal control and prevent conflict with people. Unfortunately, many of the landscape adaptations that would reduce the presence of geese are in opposition to popular landscape design features, such as Manuscript to be reviewed design with more enclosed and higher vegetation are more suitable where geese are a problem. Woodlands, shrubberies, coppice, hedges, tall grass meadows, prairie planting, hard landscaping features, shallow water and moving-water features would all deter geese from using an area <ns0:ref type='bibr' target='#b6'>(Allan, Kirby & Feare, 1995;</ns0:ref><ns0:ref type='bibr' target='#b40'>Gosser, Conover & Messmer, 1997;</ns0:ref><ns0:ref type='bibr' target='#b5'>Allan, 1999;</ns0:ref><ns0:ref type='bibr' target='#b11'>Baxter, Hart & Hutton, 2010)</ns0:ref>.</ns0:p><ns0:p>If artificial islands were eliminated from suburban lakes it might be argued that native birds would also suffer from the lack of island breeding sites, however, islands in suburban parks are mostly unsuitable for island nesters of conservation concern, such as common terns (Sterna hirundo) which do breed well on artificial rafts in bigger lakes and lagoons <ns0:ref type='bibr' target='#b17'>(Coccon et al., 2018;</ns0:ref><ns0:ref type='bibr' target='#b32'>Dunlop et al., 1991)</ns0:ref>. Islands could perhaps be made less attractive if they were connected to the mainland by constructing bridges or an isthmus. They can also be modified with banks that deter access from the water, rather than from the air. However, making feeding areas inaccessible is controversial as chicks can then starve <ns0:ref type='bibr' target='#b5'>(Allan 1999)</ns0:ref>. Modifications or removal of islands should however consider the trade-off with ongoing management. For example, when practicing egg shaking or egg oiling for fertility reduction, the success of this measure depends on sustained effort and a high percentage of treated nests <ns0:ref type='bibr' target='#b59'>(Klok et al., 2010;</ns0:ref><ns0:ref type='bibr' target='#b12'>Beston et al., 2016)</ns0:ref>. Hence, having all geese nest on the same island makes it easier to perform this management.</ns0:p><ns0:p>There is also a need to educate the public to the benefits of geese. In the Botanic Garden their selective grazing of grasses has created an exceptional species rich grassland that is unlikely to Manuscript to be reviewed be maintained with mowing alone <ns0:ref type='bibr' target='#b73'>(Ronse 2011</ns0:ref>). An adaptive management approach, whereby vegetation and goose numbers in the Garden are thoroughly monitored and objectives are clearly stipulated, could be a good way to learn more about the behaviour and impacts of geese.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions</ns0:head><ns0:p>Landscape features have a powerful influence on the distribution of geese, though these influences differ between species. For example, we show that…  Lakes with islands attract more than twice as many anserine geese  Flight barriers between grazing areas and lakes deter geese  Small grazing areas surrounded by trees reduces their attractiveness to geese  Proximity of a lake is most important to Canada geese, and least to greylag geese Landscape modifications cannot completely remove geese from a suburban landscape and an integrated management strategy may be necessary <ns0:ref type='bibr' target='#b6'>(Allan, Kirby & Feare, 1995)</ns0:ref>. Retroactively modifying landscapes to reduce their attractiveness to geese is difficult, so designing landscapes for wildlife usage should be among the primary design criteria. Manuscript to be reviewed</ns0:p><ns0:note type='other'>Figure 2</ns0:note><ns0:p>Geese land usage measure by the droppings deposited at varying distances from the boundary between woodland and lawn.</ns0:p><ns0:p>The total length of geese droppings deposited at varying distances from the boundary between woodland and lawn. Geese dropping were the sum length of all dropping from all species of geese. The numbers on each graph refer to the original plot number. See the methods for details of the model applied to the data.</ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ reviewing PDF \| (2020:01:45121:2:0:NEW 21 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ reviewing PDF \| (2020:01:45121:2:0:NEW 21 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head /><ns0:label /><ns0:figDesc>physical barriers preventing direct flight to the nearest lake. Details of each survey sector are PeerJ reviewing PDF \| (2020:01:45121:2:0:NEW 21 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ reviewing PDF \| (2020:01:45121:2:0:NEW 21 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ reviewing PDF \| (2020:01:45121:2:0:NEW 21 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ reviewing PDF \| (2020:01:45121:2:0:NEW 21 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ reviewing PDF \| (2020:01:45121:2:0:NEW 21 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_7'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ reviewing PDF \| (2020:01:45121:2:0:NEW 21 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_8'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ reviewing PDF \| (2020:01:45121:2:0:NEW 21 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_9'><ns0:head /><ns0:label /><ns0:figDesc>ponds and lakes, islands, open vistas and extensive lawns. Other sorts of landscape and garden PeerJ reviewing PDF \| (2020:01:45121:2:0:NEW 21 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_10'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ reviewing PDF \| (2020:01:45121:2:0:NEW 21 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_11'><ns0:head>Figure 1 A</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='33,42.52,70.87,298.54,672.95' type='bitmap' /></ns0:figure> </ns0:body> "	"Responses to the reviewer’s comments and corrections Responses to Reviewer: Liviu Parau As in my first review, I mention again that the English should be improved. Long, unstructured phrases make the reading difficult. The text has been read and revised again for clarity and simplicity. Many Oxford commas are missing. I have never used Oxford commas and there is no consensus in English as to whether they are useful. There is no requirement in the author guidelines and I do not see any reason to add this superfluous punctuation. Furthermore, throughout the text, you are inconsistent with abbreviations. For example, between lines 390 and 411, all scientific names are given in full, except for Branta. However, sometimes you used “B.” and sometimes “Branta”. The published author guidelines for species formatting have been reviewed. “Branta” and “B.” are still used, but are compliant with the guidelines. Another example, at line 410: you write: “A. anser can be seen to range more widely than other geese”. I changed this to “A. anser can be seen to have a wider range than any other geese”. Your suggestion has another meaning. We use range as a verb. Species’ range (noun) has a different meaning. Line 419: “Lakes with islands house more Canada, greylag and barnacle geese in the summer”. Changed to “Lakes with islands attract more Canada, greylag and barnacle geese in the summer” Changed as suggested. Line 495: “the negative influence of barriers was barely significant”. Changed to “hardly significant”. We are not clear why the word ‘barely’ was not suitable as it is a synonym for ‘hardly’. However, ‘hardly’ is a less suitable word due to its multiple meanings. Therefore, we have changed the word to ‘scarcely’. We hope this is considered an improvement. Responses to Reviewer: Johanne Martens Lines 79 – 84 provide reference We have added Livezey (1996) as a reference to the taxonomic position of these geese and Fox et al. 2010 as a reference to the distribution of these geese in Europe. 84 – 86 does not lead anywhere – omit We have retained this sentence. For people who do not know these species it is important to know why we included them all in the same study and excluded other waterfowl. It is also useful introductory information when later one is comparing the results from different species and the anserine geese behave differently to the tadorine goose. 98 which features? Added the word “habitat” to make it clear which features are being referred to. 117 considered by whom? Added the reference to Conover (1992) to make give an example of someone who considered habitat modification a cost effective, sustainable solution. Line 143 change to ‘Canada geese are’ The sentence is referring to the “breeding of Canada geese”, this is singular, so “is” is the correct conjugation. 130 – 149 you change keep switching between the terms ‘Garden’ and ‘park’, which I find confusing. If you’re talking about the same area, please keep the terminology consistent. I have changed all specific references to the Botanic Garden Meise from “park”, to “Garden”. Note the capitalization. The Botanic Garden is not all cultivated as a garden, but as a whole can be considered a park. 178 – 179 Please connect this sentence better with the previous one, e.g. ‘Three landscape parameters were examined for their importance for geese in site selection: the size of the survey area, the distance from the site to the nearest lake and the presence of physical barriers preventing direct flight to the nearest lake.’ Changed as suggested 183 change to ‘….be addressed in statistical models. These are…’ Changed as suggested 204 – 206 reference missing, and please define the term ‘edge effect’. We are not sure what to cite here. There are definitions ‘edge effect’ in most ecology textbooks but adding such a citation to a textbook is clearly padding. 256 – 259 is not a result. Please move or omit. The results from the control are import and are a result, even if the modelling algorithms failed to converge. They should not be omitted and are not methods. This sentence has been reworded to make it clear that this is a result. 269 – 274 Define terms ‘homoscedasticity’ and ‘heteroscedastic’ in Methods. Make sure that you don’t mix methodology with results. The text has been amended to explain these words. These tests are outputs of the modelling process and as such are clearly results. Figure 1 and 3 As I already pointed out in the first round of revisions, please explain the numbering/labels in your figures more thoroughly. Readers which are not too familiar with maps like the ones you are using will not be able to follow. The legends have been amended to give more information. Figure 4 Please move the statistics (p-values etc) from the caption to the results text. It makes it easier for the reader to follow the story you’re telling. We have added p values to the text, but not removed them from the legend. Moving them completely from the legend would make the text harder to read and separate the statistics from the graph that they are intended to explain. In general, as mentioned in my earlier review, please improve clarity. What are your main findings? Discussion 310 – 314 As I pointed out during the first revision, please give a brief but precise summary of your main results at the start of the Discussion. We have added a bullet point list to the conclusions. You mentioned a complicated relationship. Tell the reader why? What are you basing that on? The sentence has been amended to make it clear we are referring to the all the results. You state that ‘geese are clearly actively selecting’ particular landscapes. Tell us instead what brings you to this conclusion? E.g. ‘In this study, we found that a) geese avoided woodland areas, b)…………etc etc. At the moment, you are losing the reader in the results section and don’t give them a chance to get back on track in the discussion. It is unclear how to address this comment. The results and discussion are clearly divided into the three avenues of investigation, edge effects, landscape and islands. The sentence mentioned is introductory to sections explaining exactly why we are brought to those conclusions. 316 – 322 Please provide references. We have not found any other papers measuring edge effects for geese or determining their causes. E.g. line 319 which potential causes? Two potential causes have been given as examples. Line 320 why do you expect this effect to diminish? The sentence has been amended to make it clearer. "	Here is a paper. Please give your review comments after reading it.
517	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Colorectal cancer (CRC) is one of the most common and deadly malignancies. Novel biomarkers for the diagnosis and prognosis of this disease must be identified. Besides, metabolism plays an essential role in the occurrence and development of CRC. This article aims to identify some critical prognosis-related metabolic genes (PRMGs) and construct a prognosis model of CRC patients for clinical use. We obtained the expression profiles of CRC from The Cancer Genome Atlas database (TCGA), then identified differentially expressed PRMGs by R and Perl software. Hub genes were filtered out by univariate Cox analysis and least absolute shrinkage and selection operator Cox analysis. We used functional enrichment analysis methods, such as Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis, to identify involved signaling pathways of PRMGs. The nomogram predicted overall survival (OS) Calibration traces were used to evaluate the consistency between the actual and the predicted survival rate.</ns0:p><ns0:p>Finally, a prognostic model was constructed based on six metabolic genes (NAT2, XDH, GPX3, AKR1C4, SPHK1, and ADCY5), and the risk score was an independent prognostic prognosticator. Genetic expression and risk score were significantly correlated with clinicopathologic characteristics of CRC. A nomogram based on the clinicopathological feature of CRC and risk score accurately predicted the OS of individual CRC cancer patients. We also validated the results in the independent colorectal cancer cohorts GSE39582 and GSE87211. Our study demonstrates that the risk score is an independent prognostic biomarker and is closely correlated with the malignant clinicopathological characteristics of CRC patients. We also determined some metabolic genes associated with the survival and clinical stage of CRC as potential biomarkers for CRC diagnosis and treatment.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>Cancer is associated with multiple genes, and the accumulation of molecular modifications in the genome of somatic cells is the basis of cancer progression <ns0:ref type='bibr' target='#b54'>(Vogelstein & Kinzler 2004)</ns0:ref>. Cancers of all types are the primary cause of death globally and one of the most significant obstacles to increased life expectancy <ns0:ref type='bibr'>(World Health Organization, 2018)</ns0:ref>. Colorectal cancer (CRC) is the fourth most fatal carcinoma, accounting for almost 900,000 deaths annually <ns0:ref type='bibr' target='#b4'>(Dekker et al. 2019)</ns0:ref>. It is the second most frequently diagnosed cancer in women and the third most frequently diagnosed in men <ns0:ref type='bibr'>(Christopher J, 2018)</ns0:ref>. The development of CRC is a complicated biological process requiring a constellation of factors, including lifestyle, obesity, and environmental influences that may be associated with CRC, and which involves profound shifts at various molecular levels, including in the genome, transcriptome, methylation, and epigenome. The application of second-generation DNA sequencing techniques through whole-genome, whole-exome, and wholetranscriptome approaches leads to significant advances in cancer genomics <ns0:ref type='bibr' target='#b34'>(Meyerson et al. 2010</ns0:ref>). The Cancer Genome Atlas (TCGA) has created an unprecedented opportunity to investigate cancer biology with clinical significance <ns0:ref type='bibr' target='#b29'>(Liu et al. 2018)</ns0:ref>.</ns0:p><ns0:p>Cellular metabolism is the foundation of all biological activities and participates in the regulation of cell growth and proliferation <ns0:ref type='bibr'>(Zhu & Thompson 2019)</ns0:ref>. Cancer cells reprogram their metabolism to utilize nitrogen and carbon to synthesize the macromolecules necessary for the growth and proliferation of tumors <ns0:ref type='bibr' target='#b21'>(Keshet et al. 2018)</ns0:ref>. As mentioned before, CRC is a multi-step development in which several genetic events drive the initiation and progression of CRC. Specific metabolic pathways can also affect the occurrence and progression of CRC <ns0:ref type='bibr' target='#b22'>(La Vecchia & Sebastián 2020)</ns0:ref>. Remarkably, some genetic drivers of CRC are well-known regulators of cancer metabolism, such as p53 <ns0:ref type='bibr' target='#b23'>(Labuschagne et al. 2018)</ns0:ref>, KRas <ns0:ref type='bibr' target='#b19'>(Kawada et al. 2017)</ns0:ref>, and Wnt <ns0:ref type='bibr' target='#b52'>(Thompson 2014)</ns0:ref>. A recent report also manifested that knockdown of MYC in CRC cells can reset the altered metabolism and suppressed cell growth <ns0:ref type='bibr' target='#b46'>(Satoh et al. 2017)</ns0:ref>. Oncogene signals drive the progression of CRC associated with the control of specific metabolic pathways in CRC <ns0:ref type='bibr' target='#b14'>(Hutton et al. 2016;</ns0:ref><ns0:ref type='bibr' target='#b38'>Pate et al. 2014;</ns0:ref><ns0:ref type='bibr' target='#b46'>Satoh et al. 2017)</ns0:ref>. Metastatic CRC cells participate in a selective metabolic adaptation to efficiently form liver metastasis <ns0:ref type='bibr' target='#b31'>(Loo et al. 2015)</ns0:ref>. Metabolic genes also play essential role in the epithelialmesenchymal transition <ns0:ref type='bibr' target='#b47'>(Shaul et al. 2014</ns0:ref>). Previous research in metabolic genes related to CRC has garnered significant attention, and metabolic gene variants promote colorectal carcinogenesis <ns0:ref type='bibr' target='#b10'>(Hlavata et al. 2010;</ns0:ref><ns0:ref type='bibr' target='#b11'>Hong et al. 2018;</ns0:ref><ns0:ref type='bibr' target='#b41'>Pommier et al. 2016)</ns0:ref>. Transcription and genomic data from many tumor samples have been used to research tumor metabolism and explore the role of genes to promote metabolic reprogramming in tumors <ns0:ref type='bibr' target='#b0'>(Arif et al. 2017;</ns0:ref><ns0:ref type='bibr' target='#b3'>Dejure & Eilers 2017;</ns0:ref><ns0:ref type='bibr' target='#b61'>Ying et al. 2012)</ns0:ref>. However, few studies have focused on constructing a prognostic model based on differential metabolic genes combined with the prognostic prediction model in CRC.</ns0:p><ns0:p>We attempted to construct a prognostic model of prognosis-related metabolic genes (PRMGs) and explore the prognostic value of these PRMGs in CRC. The colorectal cancer-specific prognostic model was used to determine some pivotal PRMGs for the diagnosis and treatment of CRC and to identify novel potential targets. We also validated the PRMG signature in an independent colorectal cancer cohort. We identified a new essential marker (risk score) to predict the prognosis of CRC patients. Our study developed a new risk model as an independent prognostic biomarker in risk stratification for CRC patients. The flow chart of the study design and analysis is shown in Figure <ns0:ref type='figure' target='#fig_5'>1</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Materials and Methods</ns0:head></ns0:div> <ns0:div><ns0:head>Data sources</ns0:head><ns0:p>Both the RNA-sequencing datasets and clinical data of CRC were obtained from the TCGA database (Data Release 22.0 -January 16, 2020, https://tcga-data.nci.nih.gov/tcga/). The two main filter criteria for our data were as follows: (1) The keywords of cases are 'colon, rectosigmoid junction, and rectum 'HTSeq -FPKM [Workflow Type]'') , 'Clinical [Data Category],' 'BCR XML [Data Format]''). The matrix files of RNA-sequencing for different samples were collated and annotated onto the genome. The expression of mRNA was extracted from the matrix file obtained from the RNA-sequencing data. The data access used GSE39582 and GSE87211 as the validation data obtained from Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/ ). Raw data of gene chips are normalized using the RMA algorithm provided by 'limma' <ns0:ref type='bibr' target='#b44'>(Ritchie et al. 2015)</ns0:ref>. Perl and R-package 'sva' were used to merge microarray data and reduce heterogeneity between the three studies. Metabolism-related genes were obtained from KEGG symbols of the Gene Set Enrichment Analysis (GSEA) website (https://www.gseamsigdb.org ). R software (version 3.6.2) was used for data annotation and the extraction of metabolic gene expression for the TCGA and GEO data.</ns0:p></ns0:div> <ns0:div><ns0:head>Identification of prognosis-associated metabolism-related genes</ns0:head><ns0:p>Data extraction and integration were performed using Perl software. We screened differentially expressed metabolism-related genes using the Wilcox Test with R-package 'edgeR' <ns0:ref type='bibr'>(Robinson et al. 2010)</ns0:ref>, and 'limma'. \|Log Fold Change \| >1.5 and False Discovery Rate (FDR) < 0.05 were set as the cutoff.</ns0:p><ns0:p>Bidirectional hierarchical clustering was analyzed and we drew a heatmap using R-package 'pheatmap'(https://cran.r-project.org/web/packages/pheatmap/). R-package 'ggplot2' was used to draw a volcano map. Prognosis-associated metabolism-related genes were identified by univariate Cox regression analysis.</ns0:p></ns0:div> <ns0:div><ns0:head>Construction prognosis model of metabolism-related genes and survival analysis</ns0:head><ns0:p>We screened out the metabolism-related genes that had a significant correlation with overall survival (OS) of colorectal cancer cohorts using univariate Cox regression analysis (p < 0.01). The least absolute shrinkage and selection operator (Lasso) Cox regression analysis constructed the optimal model of metabolism-related genes using the R-package 'glmnet' <ns0:ref type='bibr' target='#b7'>(Friedman et al. 2010)</ns0:ref>. Genes expression and survival analysis were evaluated using the Kaplan-Meier method and the log-rank test (p<0.05). The risk score for each patient calculated as: Risk score= , denotes the coefficient and</ns0:p><ns0:formula xml:id='formula_0'>∑ 𝑛 𝑗 = 1 𝐶𝑜𝑒𝑓𝑗 * 𝑋𝑗 𝐶𝑜𝑒𝑓𝑗 𝑋𝑗</ns0:formula><ns0:p>denotes the expression levels of each metabolism-related gene <ns0:ref type='bibr' target='#b30'>(Liu et al. 2019)</ns0:ref>. Survival data were selected from each sample obtained from the clinical data downloaded from the TCGA and combined with the previously acquired expression profiling data. The median risk score was selected as a cutoff value to create colorectal cancer cohorts. The survival curve was drawn according to the high and lowrisk value by R-package 'survival' 'survminer'. The R-package 'survival ROC' drew the Receiver Operating Characteristic (ROC) curve, which was used to investigate the sensitivity and specificity of the survival prediction by the gene marker risk score <ns0:ref type='bibr' target='#b25'>(Le et al. 2019)</ns0:ref>. Area Under Curve (AUC) served as an indicator of prognostic veracity <ns0:ref type='bibr' target='#b24'>(Le 2019;</ns0:ref><ns0:ref type='bibr' target='#b45'>Sachs 2017)</ns0:ref>. The risk curve, survival state diagram, and heatmap were drawn based on the different risk scores of the patients. Independent prognostic metabolic genes were recognized using univariate and multivariate Cox proportional risk regression analysis. We</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:03:47137:1:1:NEW 27 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed conducted clinical correlation an analysis using the R-package 'beeswarm'. The prognostic metabolismrelated gene was verified in the independent colorectal cancer cohort of GEO (GSE39582, GSE87211) <ns0:ref type='bibr' target='#b12'>(Hu et al. 2018;</ns0:ref><ns0:ref type='bibr' target='#b33'>Marisa et al. 2013)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Functional enrichment analysis of metabolism-related genes</ns0:head><ns0:p>Functional enrichment analysis of metabolic genes was conducted based on the DAVID database (https://david.ncifcrf.gov/home.jsp) <ns0:ref type='bibr' target='#b13'>(Huang et al. 2009)</ns0:ref>, which identified Gene Ontology (GO) categories in the biological processes (BP), cellular components (CC), or molecular functions (MF). The DAVID database was also used to determine the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways.</ns0:p><ns0:p>FDR < 0.05 was the cutoff value. R-package 'GOplot' and 'ggplot2' were used to integrate expression data and functional and pathway enrichment analysis. Gene Set Enrichment Analysis (GSEA) was also used to uncover the signaling pathways and biological processes of differentially expressed genes between the high and low-risk subgroups. The number of permutations was set to 1,000, and FDR <0.25 was considered to be statistically significant.</ns0:p></ns0:div> <ns0:div><ns0:head>Exploitation of the nomogram</ns0:head><ns0:p>Age, gender, stage, TNM stage, and risk score were used to draw a nomogram using the R-packages 'Hmisc,' 'lattice,' 'Formula,' 'ggplot2,' 'foreign' and 'rms'. Calibration traces were used to assess the consistency between the actual and predicted survival rates. The Consistency index (C-index), ranging from 0.5 to 1.0, was calculated to evaluate the model's capability for predicting an accurate prognosis.</ns0:p><ns0:p>Measurements of 0.5 and 1.0 from the model represent a random probability or an excellent performance for predicting survival, respectively.</ns0:p><ns0:p>Statistical analysis was performed using the R software (Version 3.6.2; https://www.R-project.org). Pvalues for all of the analyses were: less than 0.05 (statistically significant), 0.01 (more statistically significant), and 0.001 (most statistically significant).</ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head></ns0:div> <ns0:div><ns0:head>Characteristics of patients</ns0:head><ns0:p>The TCGA CRC cohort consisted of 624 patients. Patients who have not survival time and TNM stage were excluded, resulting in a total of 596 patients. Patients were further screened, and those whose survival time was less than 90 days were also excluded, resulting in a total of 545 patients. A total of 692 mRNA expression profiles of CRC were downloaded from TCGA. Among them, 51 (7.4%) derive from healthy samples, and 641 (92.6%) come from tumor samples.</ns0:p><ns0:p>The Marisa cohort consisted of 585 patients (GSE39582), including 566 with stage I-IV colon adenocarcinoma and 19 colon mucosa samples <ns0:ref type='bibr' target='#b33'>(Marisa et al. 2013</ns0:ref>). The Hu cohort included 363 patients (GSE87211), consisting of 203 rectal tumors and 160 rectal mucosa samples <ns0:ref type='bibr' target='#b12'>(Hu et al. 2018)</ns0:ref>. A total of 948 patients were obtained after merging the two sets of data. Patients with incomplete information were excluded, resulting in a total of 720 tumor patients. Patients were further screened and those whose survival time was less than 90 days were also excluded, resulting in a total of 701 tumor patients. The demographic and clinical characteristics of patients are listed in Supplementary Table <ns0:ref type='table'>1</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Construction prognosis model for TCGA colorectal cancer cohort</ns0:head><ns0:p>A total of 102 differentially expressed metabolism-associated genes were screened containing 40 upregulated and 62 down-regulated genes. Gene expression profiles of 102 genes were displayed separately for colorectal cancer cohorts in normal and tumor tissues in Supplementary Figure <ns0:ref type='figure' target='#fig_5'>1</ns0:ref>.</ns0:p><ns0:p>Univariate Cox regression analysis revealed seven metabolism-related genes that were significantly related to the OS of CRC. NAT2 and XDH were low-risk PRMGs (Hazard Ratio (HR)<1, p<0.05);</ns0:p><ns0:p>GPX3, AOC3, AKR1C4, SPHK1, and ADCY5 were high-risk PRMGs (HR>1, p<0.05) (Figure <ns0:ref type='figure' target='#fig_6'>2A</ns0:ref>).</ns0:p><ns0:p>Six PRMGs were filtered out by LASSO COX regression analysis and the coefficient was calculated. The model provided the best figure when six genes were included (Figure <ns0:ref type='figure' target='#fig_6'>2B, C</ns0:ref>). The coefficient of each CRC gene is shown in Figure <ns0:ref type='figure' target='#fig_6'>2D</ns0:ref>. The prognosis model was constructed based on these six genes (NAT2, XDH, GPX3, AKR1C4, SPHK1, ADCY5). The full names, pathways, and coefficients of these genes are shown in Table <ns0:ref type='table'>1</ns0:ref>.</ns0:p><ns0:p>We investigated genetic alterations in the role of CRC risk-related metabolic genes in CRC using Gene Set Cancer Analysis (GSCA) Lite (http://bioinfo.life.hust.edu.cn/web/GSCALite/). The genes of interest in CRC were changed in 38 of 38 queried samples (100%) (Figure <ns0:ref type='figure' target='#fig_6'>2E</ns0:ref>). The frequent genetic changes indicated the crucial roles of these genes in the development of CRC. Patients were divided into a high-risk group and a low-risk group with the median risk score as the cut-off value. A heatmap was developed to show the gene expression profiles of the high-and low-risk TCGA-CRC groups (Figure <ns0:ref type='figure'>3A</ns0:ref>) and the GEO-CRC group (Figure <ns0:ref type='figure'>3B</ns0:ref>). The risk score had a significant correlation with age, T, N, M, and clinical stage in TCGA-CRC, and age, gender, T, N, and clinical stage in GEO-CRC. The high-risk genes (GPX3, AKR1C4, SPHK1, ADCY5) were more likely to be expressed in the high-risk group patients, while low-risk genes (NAT2 and XDH) were expressed in the low-risk group patients of TCGA-CRC (Figure <ns0:ref type='figure'>3A</ns0:ref>) and GEO-CRC (Figure <ns0:ref type='figure'>3B</ns0:ref>). The association between survival time and risk score is shown in Figure <ns0:ref type='figure'>3C</ns0:ref> (TCGA-CRC), 3D (GEO-CRC). The survival time decreased as risk score increased and the higher the risk score, the more deaths in TCGA-CRC (Figure <ns0:ref type='figure'>3E</ns0:ref>) and GEO-CRC (Figure <ns0:ref type='figure'>3F</ns0:ref>). Risk scores both were significantly related to OS in the univariate independent prognostic analysis of TCGA-CRC (HR=3.324, 95% CI=1.956-5.650, P<0.001) (Figure <ns0:ref type='figure'>3G</ns0:ref>) and GEO-CRC (HR=2.096, 95% CI=1.383-3.177, P<0.001) (Figure <ns0:ref type='figure'>3H</ns0:ref>).</ns0:p><ns0:p>The multivariate independent prognostic analysis showed that the risk score was an independent prognostic predictor in the TCGA-CRC group (HR=2.639, 95% CI=1.413-4.928, P=0.002) (Figure <ns0:ref type='figure'>4A</ns0:ref>)</ns0:p><ns0:p>and the GEO-CRC group (HR=1.658, 95% CI=1.059-2.598, P=0.027) (Figure <ns0:ref type='figure'>4B</ns0:ref>). Kaplan-Meier cumulative curves indicated that the OS rate between the high-risk group and the low-risk group was significantly different. The survival time of patients with high-risk score was significantly shorter than that of patients with low-risk score in the TCGA-CRC group (P<0.001) (Figure <ns0:ref type='figure'>4C</ns0:ref>) and GEO-CRC group (P<0.001) (Figure <ns0:ref type='figure'>4D</ns0:ref>). The 1-, 3-, and 5-year AUC values of the risk score were 0.672, 0.608, and 0.648, respectively, and the prognostic accuracy of the stage was higher than other clinical characteristics in TCGA-CRC (Figure <ns0:ref type='figure'>4E</ns0:ref>, 4G, 4I). Similarly, the 1-, 3-, and 5-year AUC values of the risk score were 0.612, 0.566, and 0.573, respectively, and the prognostic accuracy of the M-stage was higher than other clinical characteristics in GEO-CRC (Figure <ns0:ref type='figure'>4F</ns0:ref>, 4H, 4J). These results confirm that six metabolic gene signatures can also predict survival in the independently validated GEO colorectal cohort.</ns0:p></ns0:div> <ns0:div><ns0:head>The risk score and metabolic genes related to the clinicopathological features of CRC</ns0:head><ns0:p>The expressions of NAT2, ADCY5, SPHK1, GPX3, and the risk score were significantly correlated with the clinicopathological features of TCGA-CRC (Supplementary Table <ns0:ref type='table'>2</ns0:ref>, Figure <ns0:ref type='figure'>5A-5K</ns0:ref>). NAT2 was highly expressed in stages I, II, N 0 , and M 0 , but had low expression in stages III, IV, N 1-2 , and M 1 (Figure <ns0:ref type='figure'>5A</ns0:ref>-5C) (p< 0.05). However, the expression level of ADCY5 was higher in stages III, IV, and N 1-2 than in stages I, II, and N 0 (Figure <ns0:ref type='figure'>5D, 5E</ns0:ref>). SPHK1 and GPX3 were also highly expressed in T 3-4 but had low expressions in T 1-2 (Figure <ns0:ref type='figure'>5F</ns0:ref> Manuscript to be reviewed which was highly expressed in stages III, IV, T 3-4 , N 1-2 , and M 1 , but had low expression in stages I, II, T 1-2 , N 0 , and M 0 , (Figure <ns0:ref type='figure'>5H</ns0:ref>-5K) (p< 0.05). These results showed that NAT2 was a low-risk metabolic gene, and ADCY5, SPHK1, GPX3, were high-risk metabolic genes, which is consistent with our previous results. More importantly, the risk score closely correlated with the malignant clinicopathological characteristics of CRC and is an independent prognostic factor.</ns0:p><ns0:p>Similar results were seen in the validation group (Supplementary Table <ns0:ref type='table'>3, Figure</ns0:ref> </ns0:p></ns0:div> <ns0:div><ns0:head>Identification involved signaling pathways of PRMGs</ns0:head><ns0:p>GO enrichment analysis of the seven metabolic genes showed that there were two GO terms in BP, one GO term in MF, and one GO term in CC, which was significant (FDR <0.05). GO enrichment analysis showed that these seven genes could be classified into several essential processes, including oxidationreduction, the xenobiotic metabolic process, electron carrier activity, and cytosol (Figure <ns0:ref type='figure'>6A</ns0:ref>). KEGG pathway enrichment shown that PRMGs were mainly enriched in caffeine metabolism, metabolic pathways, and drug metabolism by other enzymes (Figure <ns0:ref type='figure'>6B</ns0:ref>) (p< 0.05). GSEA analysis showed that changed genes were observably enriched in several common pathways. 98 of 178 gene sets were upregulated in the high-risk phenotype group, and 95 gene sets were remarkable at FDR < 25%. The topfive gene sets of the high-risk group were significantly related to basal cell carcinoma (NES=2.31, P=0.000), dilated cardiomyopathy (NES=2.28, P=0.000), vascular smooth muscle contraction (NES=2.25, P=0.000), glycosaminoglycan biosynthesis chondroitin sulfate (NES=2.24, P=0.000), and axon guidance (NES=2.23, P=0.000). 80 of 178 gene sets were upregulated in the low-risk phenotype group. 74 gene sets were markedly enriched at FDR < 25%, and the five most common gene sets of the low-risk group were negatively associated with peroxisome (NES=-2.37, P=0.000), fatty acid metabolism (NES=-2.36, P=0.000), butanoate metabolism (NES=-2.31, P=0.000), valine, leucine, and isoleucine degradation (NES=-2.26, P=0.000), and propanoate metabolism (NES=-2.26, P=0.000) (Figure <ns0:ref type='figure'>6C</ns0:ref>). The integration of the 5 most prevalent phenotypes of the high-risk and the low-risk groups was visualized in Figure <ns0:ref type='figure'>6D</ns0:ref>.</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:03:47137:1:1:NEW 27 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>A characterized prognostic prediction model</ns0:head><ns0:p>A nomogram is a powerful tool quantifying an individual's risk in a clinical setting by integrating multiple risk factors <ns0:ref type='bibr' target='#b27'>(Liang et al. 2015;</ns0:ref><ns0:ref type='bibr' target='#b56'>Won et al. 2015)</ns0:ref>. We used the nomogram to predict the probabilities of 1-, 3-and 5-year OS by incorporating the age, gender, TNM stage, and risk score of TCGA-CRC (Figure <ns0:ref type='figure' target='#fig_12'>7A</ns0:ref>). The results were verified in the GEO-CRC. Each factor was assigned a score in proportion to its contribution to the risk of survival. The calibration curve showed that the actual survival time is in agreement with the predicted survival time and the C-index is 0.8. (Figure <ns0:ref type='figure' target='#fig_12'>7B</ns0:ref>, 7D, 7F). A 75-year old (60 points) female patients (6 points) would acquire a total of 224 points if she had stage III (41points), T3</ns0:p><ns0:p>(32 points), N1 (0 points), and M0 (0 points), with a risk score (85 points). Her 1-, 3-, and 5-year survival rate was approximately 67%, 45%, and 28%, respectively. The nomogram in the GEO colorectal cancer cohorts and the 1-, 3-and 5-year calibration curves are shown in Figure <ns0:ref type='figure' target='#fig_12'>7C</ns0:ref>, 7E, and 7G, respectively.</ns0:p></ns0:div> <ns0:div><ns0:head>Discussion</ns0:head><ns0:p>Specific metabolic activities can directly influence the transformational process or support the biological processes that make tumors grow <ns0:ref type='bibr' target='#b53'>(Vander Heiden & DeBerardinis 2017)</ns0:ref>. Recent data have shown that microbial metabolites, such as secondary bile acids, promote carcinogenesis; metabolic links between gut microbes are associated with cancer and a diet rich in fat and meat; and extracellular metabolic energetics can promote cancer progression, especially in colorectal cancer <ns0:ref type='bibr' target='#b16'>(Jia et al. 2018;</ns0:ref><ns0:ref type='bibr' target='#b32'>Louis et al. 2014;</ns0:ref><ns0:ref type='bibr' target='#b55'>Wirbel et al. 2019;</ns0:ref><ns0:ref type='bibr' target='#b57'>Wong & Yu 2019)</ns0:ref>. Prognostic predictions are crucial to the selection of clinical treatment regimens for cancer patients. Several studies have explored prognostic biomarkers and found that gene expression profiles play a vital role in the prognosis of cancer <ns0:ref type='bibr' target='#b17'>(Jiang et al. 2019;</ns0:ref><ns0:ref type='bibr' target='#b48'>Shen et al. 2019a;</ns0:ref><ns0:ref type='bibr' target='#b63'>Zhou et al. 2018)</ns0:ref>.</ns0:p><ns0:p>We attempted to construct a prognostic model for CRC patients based on six PRMGs filtered out by LASSO COX regression analysis and determined the risk score. The prognostic model was accurate and gave a precise predictive value. Our results showed that the risk score is an independent prognostic factor. The expression of NAT2, ADCY5, SPHK1, GPX3, and risk score also was significantly associated with the clinicopathological features of CRC. The prognostic metabolism-associated gene signature was validated in an independent GEO colorectal cancer cohort. We also explored the mechanism of the seven PRMGs through GO term, KEGG pathway enrichment, and GSEA analysis. The results showed that these genes correlated with some metabolic processes and metabolic pathways. Moreover, we constructed a nomogram to predict 1-, 3-and 5-year OS probabilities by integrating six-metabolic gene signatures and clinicopathological features and the results were validated within the independent cohort of GEO databases.</ns0:p><ns0:p>Univariate Cox regression analysis demonstrated that seven metabolism-related genes were significantly related to the OS of CRC, including five high-risk PRMGs and two low-risk PRMGs. Previous reports have shown that Glutathione peroxidase 3 (GPX3) methylation may play a crucial role in predicting the platinum sensitivity of CRC <ns0:ref type='bibr' target='#b39'>(Pelosof et al. 2017</ns0:ref>). SPHK1 has an essential role in the development of multifunctional NF-κB-regulated cytokine IL-6 and is continuously activated by the transcription factor STAT3. It also plays a role in colitisassociated cancer <ns0:ref type='bibr' target='#b26'>(Liang et al. 2013)</ns0:ref>. There are additional reports that AKR1C4 and ADCY5 are hub genes that may be independent prognosis biomarkers and therapeutic targets for CRC patients <ns0:ref type='bibr' target='#b8'>(Gylfe et al. 2013;</ns0:ref><ns0:ref type='bibr'>Yang et al. 2019)</ns0:ref>, which was consistent with our results.</ns0:p><ns0:p>Whether the expression level of metabolism-related genes can be used as a prognostic maker is a vital topic of research. Our colorectal cancer prognostic model based on six metabolism-related genes was found to be of value and the 1-, 3-, and 5-year risk scores and the AUC values of ROC were consistent with previous reports <ns0:ref type='bibr' target='#b15'>(Jeun et al. 2019;</ns0:ref><ns0:ref type='bibr' target='#b37'>Park et al. 2009;</ns0:ref><ns0:ref type='bibr' target='#b40'>Peng et al. 2019;</ns0:ref><ns0:ref type='bibr' target='#b51'>Sun et al. 2019)</ns0:ref>. Prognoses were classified as high-and low-risk and the six metabolic genes we screened may be ideal prognostic markers. 5-year OS was about 50% in the high-risk group and 75% in the low-risk group, which was consistent with previous reports <ns0:ref type='bibr' target='#b5'>(Dueland et al. 2018)</ns0:ref>. High-risk genes in the model, including GPX3, AKR1C4, SPHK1and ADCY5, have been reported to promote hypermethylation, rare mutations, cancer progression, poor prognosis, and the developmental process of malignant cells in CRC patient samples or cell lines <ns0:ref type='bibr' target='#b8'>(Gylfe et al. 2013;</ns0:ref><ns0:ref type='bibr' target='#b20'>Kawamori et al. 2009;</ns0:ref><ns0:ref type='bibr' target='#b26'>Liang et al. 2013;</ns0:ref><ns0:ref type='bibr' target='#b36'>Pan et al. 2019;</ns0:ref><ns0:ref type='bibr' target='#b62'>Zhou et al. 2019</ns0:ref>).</ns0:p><ns0:p>SPHK1 promotes the phosphorylation and activation of p65, thus promoting the occurrence of CRC <ns0:ref type='bibr' target='#b49'>(Shen et al. 2019b</ns0:ref>). The expressions of NAT2, ADCY5, SPHK1, GPX3, and risk score were significantly correlated with the clinicopathological features of CRC, and the risk score was closely correlated with the malignant clinicopathological characteristics of CRC and is an independent prognostic factor. NAT2 is associated with a high risk of colorectal cancer, mainly due to its involvement in the metabolism of aromatic and heterocyclic aromatic amines in cooked red meat <ns0:ref type='bibr' target='#b28'>(Lilla et al. 2006</ns0:ref>). We also identified six metabolically-related genes that were significantly correlated with gene expression and prognosis in CRC patients in the GEO database (a separate cohort of 720 CRC patients). GPX3, AKR1C4, and SPHK1 are reported to have involvement in the pathogenesis of CRC and in predicting overall survival, reinforcing the prognostic value of our TCGA and GEO cohort analysis. The remaining ADCY5 gene has not been associated with CRC prognosis and may be used as a potential biomarker for CRC.</ns0:p><ns0:p>Our study identified the metabolic genes associated with the GO and signaling pathways of CRC. Several crucial processes and signaling pathways have been identified by GO enrichment analysis, caffeine metabolism, metabolic pathways, and KEGG pathway analysis including oxidation-reduction and the xenobiotic metabolic process. Previous research demonstrated that the oxidation-reduction process and xenobiotic metabolic process play a crucial role in the development of CRC or colorectal cancer cells <ns0:ref type='bibr' target='#b1'>(Bensard et al. 2020;</ns0:ref><ns0:ref type='bibr' target='#b9'>Han et al. 2016)</ns0:ref>.</ns0:p><ns0:p>We constructed a nomogram to predict individual clinical outcomes. The nomogram is a stable and reliable tool for quantifying individual risk by combining and describing risk factors. It has been used for tumor prognosis, including for CRC <ns0:ref type='bibr' target='#b43'>(Renfro et al. 2017;</ns0:ref><ns0:ref type='bibr' target='#b50'>Sjoquist et al. 2018)</ns0:ref>. The nomogram generates a graphical statistical prediction model that assigns scores to each factor, including age, sex, and clinical stage. The model summarizes all clinical points to provide numerical possibilities for clinical outcomes such as OS, relapse, and drug nonadherence. In addition to traditional clinicopathologic features such as TNM staging, tumor size, and histological subtypes, risk scores based on genetic markers can also be incorporated into a predictive nomogram model to predict clinical outcomes <ns0:ref type='bibr' target='#b42'>(Reichling et al. 2019;</ns0:ref><ns0:ref type='bibr' target='#b50'>Sjoquist et al. 2018)</ns0:ref>. A nomogram predicted 3-and 5-year recurrence-free survival rates for non-small cell lung cancer and gave a prognostic score calculated by the autophagy gene signature <ns0:ref type='bibr' target='#b30'>(Liu et al. 2019)</ns0:ref>. The combination of autophagy gene characteristics and prognostic factors has a better prognostic value than the single application <ns0:ref type='bibr' target='#b35'>(Mo et al. 2019)</ns0:ref>. Calibration curves showed that the nomogram, which included RNA signals and conventional prognostic factors, accurately predicted 3 -and 5-year survival probabilities <ns0:ref type='bibr' target='#b59'>(Xiong et al. 2017)</ns0:ref>. Our nomogram, which includes risk scores and clinicopathologic features, is a good predictor of survival in 1 -, 3 -and 5-year CRC patients.</ns0:p><ns0:p>We constructed a six metabolic gene model for colorectal cancer patients based on TCGA to predict the prognosis of colorectal cancer patients. A risk score based on six genes may be a promising independent Manuscript to be reviewed Manuscript to be reviewed TCGA, the cancer genome atlas database; GEO, gene expression omnibus; CRC, colorectal cancer. OS, overall survival.</ns0:p><ns0:note type='other'>Figure 1</ns0:note><ns0:note type='other'>Figure 2</ns0:note></ns0:div><ns0:figure xml:id='fig_0'><ns0:head>[</ns0:head><ns0:label /><ns0:figDesc>Primary Site],' ''TCGA[Program],'' 'TCGA-COAD, TCGA-READ [Project],'' 'Adenomas and Adenocarcinomas [Disease Type].'(2) The keywords of files are 'Transcriptome Profiling [Data Category],' ''Gene Expression Quantification [Data Category],' 'RNA-Seq [Experimental Strategy],''</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>Each patient's risk score was calculated based on the mRNA expression level and risk coefficient of each gene. The calculation of the risk score was: risk score = 0.0283 × expression of AKR1C4 + 0.0075 × expression of SPHK1 + 0.0110 × expression of GPX3 + (-0.0631) × expression of NAT2 + (-0.0293) × expression of XDH + 0.1465 × expression of ADCY5. The risk score was used to predict prognosis.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head /><ns0:label /><ns0:figDesc>,5G). The risk score was significantly correlated with the clinical stage, PeerJ reviewing PDF \| (2020:03:47137:1:1:NEW 27 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head /><ns0:label /><ns0:figDesc>5L-5AA). NAT2 and XDH were highly expressed in the <65-year age group, M 0 , and had low expression in the >65-year age group, M 1 (Figure5L-5O) (p< 0.05). The expression level of ADCY5 was the opposite of NAT2 and XDH (Figure5P, 5Q). SPHK1 and GPX3 were highly expressed in stage III, IV, T 3-4 , and N 1-3 , and had low expressions in stages I, II, T 1-2 , and N 0 (Figure5R-5W). The expression of AKR1C4 was higher in stages III, IV, and N 1-3 , than in stages I, II, and N 0 (Figure5X-5Y). The risk score was higher in the group of >65 years old and M 1 , lower in the group of <65 years old and M 0 (Figure5Z, 5AA) (p< 0.05).</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head /><ns0:label /><ns0:figDesc>prognostic biomarker and is closely correlated with the malignant clinicopathological characteristics for CRC patients. A nomogram based on genetic characteristics and clinicopathologic characteristics may accurately predict the survival probability of individual CRC patients at 1, 3, and 5 years. Some of the programmatic improvements to the model can be made at other levels to represent scheduling activities in more detail. Further research on these genes may improve their clinical application and may provide a new perspective into the pathogenesis of CRC. Some of the antecedently overlooked genes may be additional biomarkers for CRC and require further study. Our study improved our understanding of the interactions between CRC cells and the tumor metabolism microenvironment and may identify novel therapeutic targets.PeerJ reviewing PDF \| (2020:03:47137:1:1:NEW 27 Jul 2020)Manuscript to be reviewedConclusionWe comprehensively identified PRMGs, constructed a six-metabolic gene model, and analyzed their molecular function in CRC. Our study also highlighted the crucial role of the risk score as an independent prognostic biomarker that is closely correlated with the malignant clinicopathological characteristics for CRC patients. Our research identified several crucial processes and signaling pathways of the metabolic genes in CRC. These findings provide a comprehensive outlook for further studies into the roles of metabolic genes in the pathogenesis of CRC and as potential biomarkers for CRC diagnosis and therapeutics.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head>Figure 1 .</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1. A flow chart of the study design and analysis.</ns0:figDesc><ns0:graphic coords='22,42.52,178.87,525.00,447.00' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head>Figure 2 .</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2. Establishment of prognostic metabolic gene signature by univariate and LASSO Cox regression analysis.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_7'><ns0:head>Figure 3 Figure 3 .</ns0:head><ns0:label>33</ns0:label><ns0:figDesc>Figure 3</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_8'><ns0:head>Figure 4 Figure 4 .</ns0:head><ns0:label>44</ns0:label><ns0:figDesc>Figure 4</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_9'><ns0:head>Figure 5 Figure 5 .</ns0:head><ns0:label>55</ns0:label><ns0:figDesc>Figure 5</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_10'><ns0:head>Figure 6 Figure 6 .</ns0:head><ns0:label>66</ns0:label><ns0:figDesc>Figure 6</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_11'><ns0:head>Figure 7</ns0:head><ns0:label>7</ns0:label><ns0:figDesc>Figure 7</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_12'><ns0:head>Figure 7 .</ns0:head><ns0:label>7</ns0:label><ns0:figDesc>Figure 7. The nomogram to anticipate prognostic probabilities in CRC.</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='24,42.52,70.87,525.00,327.00' type='bitmap' /></ns0:figure> </ns0:body> "	"Rebuttal letter Dear editor and Reviewers: Thank you for your letter and the reviewers’ comments concerning our manuscript entitled “Prognostic implications of metabolism-associated gene signatures in colorectal cancer” (Manuscript number: #2020:03:47137).Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made correction which we hope meet with approval. We used track changes mode to revise part of the manuscript. We have major revised and resubmit the revised manuscript. The main corrections in the paper and the responds to the reviewer’s comments are as flowing: Responds to the reviewer’s comments: Editor comments (Jeffrey Stuart) I find this to be a well-written manuscript that should be suitable for publication once all of the reviewers' comments have been adequately addressed. Author response and action taken: we have adequately addressed all of the reviewers' comments. Reviewer #1: The resolution of figures 1, 2, 3, 5 and 6 are very poor. It is very hard for this reviewer to read the contents of figures. Please expand all the figure legends and appropriately describe X and Y axis in the figures. Author response and action taken: We have redrawn all the figures and improved the resolution of them. We have expanded all the figure legends and appropriately described X and Y axis in the figures. Special thanks to you for your good comments. Reviewer #2 1. Basic reporting - There still has some grammatical errors and typos. The authors should re-check and revise carefully. - Quality of figures needs to be improved. - Background and literature review are weak, need to add more information. - The authors should provide source codes for reproducing the results. It is very important. Author response and action taken: 1) We have re-checked and revised the manuscript, we used track changes mode to revise part of the manuscript. 2) We have redrawn all the figures and improved the resolution of them. 3) We have added related background information in Introduction. Line:60-71. 4) We packaged and uploaded the source code to supplementary materials. 2. Experimental design - The flowchart (Fig. 1) looks unclear. For example, did the clinicopathology data come directly to DEG analysis? As I known, it is maybe only gene data. - ROC curve or AUC has been used in previously biomedical works such as PMID: 31277574 and PMID: 31362508. Therefore, it is suggested to add more references in this description. Author response and action taken: 1) We have redrawn the flowchart. As you said that gene data comes from the DEG analysis. The clinicopathology data obtained from the TCGA and GEO datasets, we use a series of software to sort out the raw data and eliminate the cases with incomplete information. 2) We have added these references in this description of ROC and AUC. 3. Validity of the findings - It is easy to see that the performance results are not quite significant (i.e., very low AUC). Therefore, it's hard to convince that this gene set is significant. - Also, ROC curves showed that the model is not so good. Author response and action taken: It is really true as Reviewer suggested that our AUC value of the ROC curve not very high, maybe it's a bit inappropriate to use the word significant but our prognosis model still has certain prognostic value, there are several reasons. First, 0.5 < AUC < 1 has predictive value. Second, the AUC values of several other colorectal cancer prognostic models previously reported are basically consistent with ours, such as Jeun et al. reported that colon cancer secreted protein-2 (CCSP-2) as a promising blood marker for CRC, the AUC value is 0.67 (PMID: 31179210, Advanced science (IF:15.84), 2019; 6 (11): 1802115. doi: 10.1002/advs.201802115); Park et al reported that the AUC was 0.61 (95% CI, 0.60 to 0.62) for men and 0.61 (95% CI, 0.59 to 0.62) for women during a colorectal cancer risk prediction model among white patients age 50 years and older (PMID: 19114700, Journal of clinical oncology (IF:32.956), 2009; 27 (5): 694-698. doi: 10.1200/jco.2008.17.4813); Peng et al reported that the AUCs ranged from 0.58 to 0.65 in KolosSal and from 0.57 to 0.61 in BliTz for all risk scores (PMID: 31464746, Am J Gastroenterol (IF:10.171). 2019;114(9):1520-1530. doi:10.14309/ajg.0000000000000370). Special thanks to you for your good comments. Reviewer: 3 1. Abstract of the manuscript is verbose and lacks specificity. Abstract should be written in a clear and consise way, explaining the research question and results/conclusions. Author response and action taken: We have re-written part of abstract according to the Reviewer’s suggestion. line numbers: line 20-32. 2. Authors have not mentioned how prognosis associated metabolism related genes were identified? Was comparison pf CRC patients was done with normal? Line 86 -> Define what is data extraction and integration here? What pathways are enriched in Differentially expressed PRMGs? Does these metabolism associated genes are also differentially expressed in different subtypes of CRC? Author response and action taken: 1) We are very sorry for our negligence that we have not mentioned how prognosis associated metabolism related genes were identified, we have added the method in the article (line numbers: 115-116). 2) We done the comparison between normal people and colorectal cancer patients. 3) Line 86 -> Define what is data extraction and integration here? Because a large amount of raw data obtained from TCGA needs to be extracted and integrated through perl scripts into data that can be analyzed. These data mainly include the raw RNA‐sequencing and clinical data of CRC. 4) In this article we have described in detail the pathway of differentially expressed PRMGs is enriched. (line 257-271). 5) This paper mainly analyzes the whole of colorectal cancer, the differential expression in different subtypes is not involved, but this is really a good idea, maybe we can continue to explore in depth in the follow-up study. 3. What is the significance of using Gene Set Cancer Analysis when mutation patterns in genes can be easily mapped using TCGA mutation information of CRCs? A simple bargraph representing the mutation frequency can also show the importance of picked genes. Cite appropriate references to discuss the importance of selected genes and there known role in CRC if any. Author response and action taken: 1) Enrichment analysis based on hypergeometric distribution often focuses on comparing gene expression differences between the two groups, mainly focusing on a few genes that are significantly up-regulated or down-regulated, which is easy to miss some genes that are not significantly differentially expressed but have important biological significance. Ignore valuable information such as the biological characteristics of some genes, the relationship between gene regulatory networks, and gene function and meaning. However, GSEA uses the gene set contained in each pathway, sorts the genes according to Fold Change in the two types of samples, and then checks whether the preset gene set is enriched at the top or bottom of this sorting table. Therefore, GSEA analysis does not detect changes in the expression of individual genes, but includes changes in the expression of those subtle genes, thereby obtaining more ideal results. 2) we have descripted the importance of selected genes and role in CRC. Line 326-334. 4. Line 65-> which genes? Explain why there is a need of a new prognostic model? Define PRMGs in introduction section of the manuscript. What is rationale of using metabolism related genes? Author response and action taken: 1) Line 65-> which genes? genes means prognosis-related metabolic genes. line 79 2) There are several reasons to construct a new prognostic model. First of all, in the current clinical application, there is no good risk model for the prognosis of colorectal cancer. Meanwhile, metabolism plays an important role in the occurrence of colorectal cancer, so it is very meaningful to construct a metabolic gene prognostic model. Secondly, the prediction model can provide an objective prediction of the risk of final events, which can be used as a supplement to the subjective judgment of clinicians and clinical diagnosis and treatment guidelines. At the same time, accurate prediction results can improve the clinical decision-making ability of doctors and thus improve the prognosis of patients. 3) PRMGs: line 78. 4) In this article we have described in detail the rationale of using metabolism related genes. line 64-66。 5. What is advantage of using risk score over patients clinical characteristics when five year survival statistics is similar with either (Figure 3)? Author response and action taken: First, the risk score is an independent prognostic factor and single clinicopathological features are not all independent prognostic factor. Second, precisely because the risk value is an independent prognostic factor, a single indicator can evaluate the prognosis, which is convenient and practical, and the clinicopathological features are not all independent prognostic factors. Therefore, it is relatively cumbersome to analyze multiple indicators together. Of course, if the risk value is combined with the clinicopathological features with independent prognostic ability, the predictive ability of the model can be improved. Minor comments: 6. What level of TCGA was used for the analysis? Does authors perform expression normalization? Author response and action taken: 1) In this article, the transcriptome and clinical data of TCGA were used for the analysis, the transcriptome data mainly include RNA‐sequencing. We have added the two main filter criteria of our data from TCGA. line numbers: line 91-97. 2) We are very sorry for our negligence that we have not described the batch correction of data. We have carried out batch correction on the data from different platforms to make expression normalization. line numbers: line 102-104. 7. Table1: Mainly pathway -> Pathway Author response and action taken: According to the Reviewer’s suggestion we have revise these English mistakes. 8 Figure quality is very poor and text is unreadable. Author response and action taken: All the pixels we uploaded are 600bpi. Perhaps the format is not clearly displayed. We apologize for the Figure quality. All Figures have been redrawn and the Figure quality has been further improved. The legend has also been modified to make it easier to understand. 9. Figure1: Figure quality is very poor. 1E what does X and Y axis indicates? Author response and action taken: According to the Reviewer’s suggestion we have improved Figure quality and detailed description X and Y axis of Figure 1E. 10. How patients were divided into low-high risk groups? Are these six prognostic genes are known prognostic genes for other tumor types? Author response and action taken: 1) In this paper, line 204-205, we have described “How patients were divided into low-high risk groups”. 2) There are some sporadic reports of AKR1C4 is prognostic for liver cancer, SPHK1 for breast cancer, GPX3 for High-Grade Bladder Urothelial Carcinoma, XDH for gastric cancer, and ADCY5 for triple negative breast cancer. GPX3 are unknown for other tumor types 11. Why flowchart of study design is Figure 7 of the paper? It should be the first figure explaining the schematic of study design. Author response and action taken: According to the Reviewer’s suggestion we have changed Figure 7 to Figure 1, and adjust the order of other Figures. Thank you very much for your careful and meticulous work. Other changes: 1. We redraw the Figure and improve the quality of the Figure. 2. We rewritten part of Figure legends and main manuscript. 3. We have added cities and provinces to the author’s affiliate. We tried our best to improve the manuscript and made some changes in the manuscript. These changes will not influence the content and framework of the paper. And here we did not list all the changes but marked in red by track changes mode in revised paper. We appreciate for Editors/Reviewers’warm work earnestly, and hope that the correction will meet with approval. Once again, thank you very much for your comments and suggestions. Yours sincerely, Denghai Mi mi.dh@outlook.com "	Here is a paper. Please give your review comments after reading it.
518	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Biominerals are crucial to the fitness of many organism and studies of the mechanisms of biomineralization are driving research into novel materials. Biomineralization is generally controlled by a matrix of organic molecules including proteins, so proteomic studies of biominerals are important for understanding biomineralization mechanisms. Many such studies identify large numbers of proteins of unknown function, which are often of low sequence complexity and biased in their amino acid composition. A lack of user-friendly tools to find patterns in such sequences and robustly analyse their statistical properties relative to the background proteome means that they are often neglected in follow-up studies. Here we present ProminTools, a user-friendly package for comparison of two sets of protein sequences in terms of their global properties and motif content. Outputs include data tables, graphical summaries in an html file and an R-script as a starting point for data-set specific visualizations. We demonstrate the utility of ProminTools using a previously published shell matrix proteome of the giant limpet Lottia gigantea.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>Mineralized structures are formed by many organisms across the tree of life including bacteria, metazoans, plants and algae <ns0:ref type='bibr' target='#b29'>(Skinner & Jahren 2007)</ns0:ref>. These biominerals are critical for fitness, playing roles in support, defence, buoyancy, regulation of ion budgets and orientation among others. Proteins have been found to be associated with many biominerals, and are hypothesised to have a key role in mineral synthesis <ns0:ref type='bibr' target='#b3'>(Evans 2019a;</ns0:ref><ns0:ref type='bibr' target='#b4'>Evans 2019b;</ns0:ref><ns0:ref type='bibr' target='#b35'>Wang & Nilsen-Hamilton 2012)</ns0:ref>. In some cases the roles of such proteins is relatively well understood, and some of the best studies examples come from molluscs <ns0:ref type='bibr' target='#b30'>(Song et al. 2019)</ns0:ref>. For example, the proteolytic products of the Pif protein in molluscs have been shown to bind CaCO 3 crystals and induce formation of the aragonite polymorph of CaCO3 in vitro <ns0:ref type='bibr' target='#b31'>(Suzuki et al. 2009)</ns0:ref>. Knock-down of the Pif gene results in disordered growth of the aragonite crystals in the nacreous layer of the shell. In other systems, well studies examples include Amelogenin from tooth enamel, Silicatein from sponge spicules and Mms6 from magnetosome synthesising bacteria <ns0:ref type='bibr' target='#b35'>(Wang & Nilsen-Hamilton 2012)</ns0:ref>. However in the majority of cases the function of biomineral associated proteins remains elusive.</ns0:p><ns0:p>A common workflow in biomineralization research is to first clean a mineral preparation using detergents or oxidizing agents to remove loosely associated organic matter, and subsequently to dissolve the mineral, releasing tightly mineral-associate proteins into solution that can then be analysed using proteomic methods <ns0:ref type='bibr' target='#b19'>(Marie et al. 2013b)</ns0:ref>. It is generally hypothesised that these proteins are likely to be involved in mineralization, and that proximity to the site of mineralization results in their incorporation into the mineral as it grows. Some of the proteins identified may have homology to proteins of known function or recognisable domains strongly suggestive of a certain function. For example, carbonic anhydrases have been found associated PeerJ reviewing PDF \| (2020:04:47682:1:1:NEW 18 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed with calcium carbonate minerals in several organisms (Le <ns0:ref type='bibr' target='#b13'>Roy et al. 2014</ns0:ref>) and may aid generation of bicarbonate as a substrate for calcification. However there are generally many proteins in such data sets which lack similarity to proteins of known function (e.g. <ns0:ref type='bibr' target='#b10'>Jackson et al. 2015;</ns0:ref><ns0:ref type='bibr' target='#b11'>Kotzsch et al. 2016;</ns0:ref><ns0:ref type='bibr' target='#b17'>Mann et al. 2006)</ns0:ref>. Intriguingly, these proteins of unknown function often display unusual primary sequence characteristics, such as low complexity, biased composition and a high degree of predicted intrinsic disorder.</ns0:p><ns0:p>Informatic tools which allow biologists to easily investigate the global features of groups of proteins of unknown function relative to the background proteome are currently lacking. Thus many studies restrict their analysis of these proteins to noting the compositional biases or motifs which are obvious from manual inspection of the protein sequences. This method has the risk that important patterns in the data are missed and that rules are not applied consistently in identifying these patterns. Ideally the context of the proteome as a whole should also be taken into account. The more specific a feature is to the proteins of interest (POIs) the more likely it is to be involved in the specific function of those proteins. This notion is based on the wellestablished biological principle that the primary sequence of a protein is a strong determinant of molecular function, and that proteins with similar functions tend to share regions of sequence similarity. Thus, sequence motifs shared by a group of biomineral associated proteins are more likely to be involved in the specific function of those proteins if they are rare in the background proteome than if they are commonly found motifs. This principle is already used in various sequence analysis tools, including those seeking to identify important motifs <ns0:ref type='bibr' target='#b34'>(Wagih et al. 2016)</ns0:ref>.</ns0:p><ns0:p>Although there are many tools available that allow researchers to investigate the properties of protein sequences in silico, including analysis of compositional bias, sequence complexity, intrinsic disorder and sequence motifs, such tools are not always easy to use. Some require PeerJ reviewing PDF \| (2020:04:47682:1:1:NEW 18 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed command line use, data input formats differ, some can only run on one protein at a time and most require post-processing of the output to format the data for statistical and graphical analyses in commonly used environments such as Microsoft® Excel® or R. These tools also rarely allow researchers to compare two sets of sequences.</ns0:p><ns0:p>Here we present ProminTools, a set of easy-to-use tools for the statistical comparison of two sets of protein sequences, available as apps in the CyVerse Discovery Environment (https://de.cyverse.org/) <ns0:ref type='bibr' target='#b23'>(Merchant et al. 2016)</ns0:ref> or to run locally from a Docker™ container. The inputs are simply two fasta files containing the proteins of interest (POIs) and the background proteome respectively, while the outputs include data tables and an html document containing graphical summaries of the data and interactive tables for data exploration. To demonstrate the utility of these tools, we reanalyse a published data set of shell matrix proteins (SMPs) from the giant limit Lottia gigantea <ns0:ref type='bibr' target='#b16'>(Mann & Edsinger 2014)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Materials and Methods</ns0:head></ns0:div> <ns0:div><ns0:head>ProminTools structure</ns0:head><ns0:p>The inputs for ProminTools are two fasta formatted files: the first containing the protein set of interest (POI set), and the second the reference or background proteins (typically the predicted proteome of the organism of interest). The background proteins are used for statistical comparisons with the POI sets, allowing the user the answer the following question: 'Are the features observe in the POI common in the background sequences or are they unusual?'.</ns0:p><ns0:p>ProminTools has two component programmes: 'Protein Motif Finder' and 'Sequence Properties Analyzer'. Both are written in Perl and R and bundled with all dependencies in Docker TM (www.docker.com) containers. They can be run from Apps within the CyVerse Discovery Environment <ns0:ref type='bibr' target='#b23'>(Merchant et al. 2016)</ns0:ref> or on a personal computer via Docker™ Desktop. The PeerJ reviewing PDF \| (2020:04:47682:1:1:NEW 18 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed primary outputs of the tools are data tables summarising key information from the comparison of the two sequences sets. The tools use these tables to generate an html file with a graphical summary of the information along with explanations, statistical analyses and interactive versions of certain data tables. A publication ready SVG (Scalable Vector Graphic) formatted figure is also generated by Protein Motif Finder. The R script that generates the html file from the data tables is also an output of the tools, allowing the user to reproduce the figures in the html report and to provide a starting point for further analyses specific to the data set. For licence information for all components of ProminTools the reader is referred to Data S1.</ns0:p></ns0:div> <ns0:div><ns0:head>Analyses performed by 'Protein Motif Finder'</ns0:head></ns0:div> <ns0:div><ns0:head>Motif finding with motif-x</ns0:head><ns0:p>Protein Motif Finder uses the motif-x engine <ns0:ref type='bibr' target='#b28'>(Schwartz & Gygi 2005;</ns0:ref><ns0:ref type='bibr' target='#b34'>Wagih et al. 2016</ns0:ref>) for motif finding. This engine was chosen because it breaks sequences down into their constituent motifs, by an iterative procedure that avoids oversimplification of motifs and prioritises motifs that are most enriched relative to a background sequence set. It is exhaustive for a given p-value and generates definite motifs rather than a position weight matrix, which simplifies downstream analyses and is more useful to molecular biologists. In this work it was always run with the recommended, conservative, binomial p-value of 10 -6 , but this parameter is user customisable in Protein Motif Finder. The motif width is also user customisable, while the minimum occurrences parameter is hard coded at a value of five. Motif-x is run via the R module rmotif-x, centred on each amino acid sequentially and the results are combined. This procedure means that some motifs are likely to be redundant. For example, if the central residue is 'S' and the motif width 7 then the motifs '…S.S..' and '..S.S… (where dot represents any amino acid) may both be identified, but these would be collapsed to the single motif 'S.S'. Note that this procedure is PeerJ reviewing PDF \| (2020:04:47682:1:1:NEW 18 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed conservative with respect to the original p-value calculated by motif-x. Significant motifs are then enumerated in the POI and the background sequence sets, and motif counts and enrichments reported in the output tables. Downstream analyses do not rely on the motif-x p-value, but only on calculated enrichment values for the motifs.</ns0:p></ns0:div> <ns0:div><ns0:head>Graphical representations of motif data</ns0:head><ns0:p>To provide a visual summary of the motif data, the motifs are represented in three wordclouds in the Protein Motif Finder output, which take into account two distinct measures of 'importance'.</ns0:p><ns0:p>The first is the number of proteins in which that motif is found. The more proteins containing the motif, the more likely it is to have general importance in the function of the group of proteins.</ns0:p><ns0:p>The second measure is the enrichment of the motif. The more enriched the motif the more unusual it is and thus is more likely to be involved in the specific function of these sets of proteins. A third measure attempts to combine the previous two by scaling them equivalently and then taking the product of the scaled values (PS-value). This measure prioritises motifs that are both highly enriched and found in a high proportion of the proteins.</ns0:p><ns0:p>In the output, proteins that are biased in sequence composition are also clustered based on their motif number and motif enrichment. The distance measure for clustering was calculated as one minus the Distance Correlation <ns0:ref type='bibr' target='#b33'>(Szekely et al. 2013</ns0:ref>) for all pairwise combinations of proteins or motifs, since this method is especially robust to outliers and produces reasonable results across a variety of datasets. Hierarchical clustering was performed using the Ward.D method. In Heatmap 1, the following filters are applied to select proteins and motifs for clustering: 1) Select proteins that contain a biased region (fLPS p-value < 10 -20 ; user adjustable), 2) Remove infinitely enriched motifs. 3) Remove motifs not present in at least 3 POI proteins (if >10 proteins in POI set). 4) Select the 70 overall most enriched motifs. 5) Select proteins with a good motif based PeerJ reviewing <ns0:ref type='table' target='#tab_0'>PDF \| (2020:04:47682:1:1:NEW 18 Jul 2020)</ns0:ref> Manuscript to be reviewed correlation to at least one other protein (dcor > 0. 65, user adjustable). The filters for Heatmap 2 are the same except that filter 3 is not applied. The same protein and motif set is displayed in Heatmap 3 as in Heatmap 2 except that motif count is displayed instead of motif enrichment.</ns0:p></ns0:div> <ns0:div><ns0:head>Analyses performed by 'Sequence Properties Analyzer'</ns0:head><ns0:p>Sequence Properties Analyzer performs the following analyses:</ns0:p></ns0:div> <ns0:div><ns0:head>Amino acid enrichment</ns0:head><ns0:p>Compositional bias is analysed using fLPS <ns0:ref type='bibr' target='#b5'>(Harrison 2017)</ns0:ref> and the results collated to several files described in the html output of the program.</ns0:p></ns0:div> <ns0:div><ns0:head>Significance of sequence bias</ns0:head><ns0:p>To estimate the probability of obtaining the observed bias in amino acid composition in the POI set by random sampling of the background proteome, the following procedure was implemented.</ns0:p><ns0:p>First the degree of bias was quantified by calculating a bias index (BI):</ns0:p><ns0:formula xml:id='formula_0'>𝐵𝐼 = ∑ 𝐴𝑚𝑖𝑛𝑜 𝑎𝑐𝑖𝑑𝑠 (𝑃𝑂𝐼 𝑓𝑟𝑒𝑞. -𝑃𝑟𝑜𝑡𝑒𝑜𝑚𝑒 𝑓𝑟𝑒𝑞.) 2</ns0:formula><ns0:p>Where POI freq. is the frequency of the amino acid in the POI proteins, while Proteome freq. is the frequency of the amino acid in the background sequence set. The BI is calculated for 1000 random samples of the background sequence set, each containing the same number of sequences as the POI set. A kernel density estimate of the distribution of BI is calculated, and a function approximating this distribution is generated. The area under the curve greater than the BI value of the POI set is used as an estimate of the probability of obtaining a sequence set of this degree of bias by chance, given this particular background proteome.</ns0:p><ns0:p>The only program we are aware of that makes a similar calculation is Composition Profiler <ns0:ref type='bibr'>(Vacic et al. 2007)</ns0:ref>. However this makes the assumption that all POI sequences come from the same underlying distribution of amino acid frequencies and tests whether this distribution is PeerJ reviewing <ns0:ref type='table' target='#tab_0'>PDF \| (2020:04:47682:1:1:NEW 18 Jul 2020)</ns0:ref> Manuscript to be reviewed significantly different from the background. ProminTools does not make this assumption, but accepts that the POI set my contain proteins with different types of bias, and thus analyses bias per se without reference to the type of bias.</ns0:p></ns0:div> <ns0:div><ns0:head>Sequence complexity</ns0:head><ns0:p>The program SEG <ns0:ref type='bibr' target='#b38'>(Wootton & Federhen 1993</ns0:ref>) is used to identify low complexity regions in the datasets using default parameters, although these are customisable by the user in Sequence Properties Analyzer. For each protein, the percentage of the sequence identified as low complexity is calculated, and a Wilcoxon rank sum test with continuity correction is used to test whether there is a significant difference in the distribution of this percentage length between the POI and the background sequence set.</ns0:p></ns0:div> <ns0:div><ns0:head>Intrinsic disorder</ns0:head><ns0:p>Predicted intrinsic disorder was calculated using the VSL2 predictor <ns0:ref type='bibr' target='#b26'>(Peng et al. 2006)</ns0:ref>, due to its speed and good accuracy <ns0:ref type='bibr' target='#b25'>(Nielsen & Mulder 2019)</ns0:ref>. This is the most time consuming step of Protein Sequence Analyzer and is thus parallelized in the implementation. For each protein, the percentage of the sequence identified as intrinsically disordered is calculated and a Wilcoxon rank sum test with continuity correction is used to test whether there is a significant difference in the distribution of this percentage length between the POI and the background sequence set.</ns0:p></ns0:div> <ns0:div><ns0:head>Charged clusters</ns0:head><ns0:p>Clusters of charged amino acids are identified using the SAPS software <ns0:ref type='bibr' target='#b2'>(Brendel et al. 1992)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Data and methods for validation of ProminTools</ns0:head><ns0:p>Representative CxxC Zn finger proteins were chosen from the Wingender database <ns0:ref type='bibr' target='#b36'>(Wingender et al. 2013</ns0:ref>) and compared to the human proteome Swissprot database accessed on the 28/05/20.</ns0:p><ns0:p>For the analysis of human low complexity proteins, all models were downloaded from Ensemble PeerJ reviewing PDF \| (2020:04:47682:1:1:NEW 18 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed version 100. Models shorter than 100 amino acids were removed, as were models with internal stop codons, resulting in 89562 proteins that were used as the background sequence in the analysis. The foreground sequence set was the 500 most biased proteins identified using fLPS <ns0:ref type='bibr' target='#b5'>(Harrison et al. 2017</ns0:ref>). These proteins were annotated using eggNOG mapper <ns0:ref type='bibr' target='#b6'>(Huerta-Cepas et al. 2017</ns0:ref><ns0:ref type='bibr'>, 2019)</ns0:ref> with parameters 'taxonomic scope hominidae, -target_orthologs all --seed_ortholog_evalue 0.001 --seed_ortholog_score 60 --query-cover 20'.</ns0:p></ns0:div> <ns0:div><ns0:head>L. gigantea shell matrix proteome data</ns0:head><ns0:p>To illustrate the utility of the ProminTools package, we used the shell matrix proteome of the giant limpet, L. gigantea as published by <ns0:ref type='bibr' target='#b16'>Mann and Edsinger (2014)</ns0:ref> which is a reanalysis of their original data <ns0:ref type='bibr' target='#b15'>(Mann et al. 2012)</ns0:ref>. The protein identifiers were extracted from table S1 of <ns0:ref type='bibr' target='#b16'>(Mann & Edsinger 2014)</ns0:ref> and the protein sequences extracted from files Lotgi1_GeneModels_AllModels_20070424_aa.fasta and Lotgi1_GeneModels_FilteredModels1_aa.fasta which were downloaded from the JGI (https://mycocosm.jgi.doe.gov/Lotgi1/Lotgi1.home.html) on the 5/02/2020. The final set of proteins consisted of 381 sequences, and are available in Data S2. This is one less than the number of accepted identifications in <ns0:ref type='bibr' target='#b16'>(Mann & Edsinger 2014)</ns0:ref> since the protein Lotgi\|172500 was not available in any database.</ns0:p></ns0:div> <ns0:div><ns0:head>Analyses of L. gigantea data using ProminTools</ns0:head><ns0:p>ProminTools was run locally using the shell matrix proteins as the foreground sequences and the 'Lotgi1_GeneModels_FilteredModels1_aa.fasta' file as the background proteome. The filtered models were chosen as they were considered likely to be a closer representation of the true proteome of L. gigantea than the 'All Models' set, and thus the more appropriate set for statistical comparisons.</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:04:47682:1:1:NEW 18 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Additional analyses</ns0:head><ns0:p>Proteins were clustered based on motif content as an output of the Protein Motif Finder tool. To determine an optimal cluster number, manual inspection of a plot of the cindex <ns0:ref type='bibr' target='#b9'>(Hubert & Levin 1976)</ns0:ref> for cluster sizes 2 -50 was carried out. A cluster number of 8 seemed appropriate for the present work, since it captured the major patterns in the data without becoming too granular.</ns0:p><ns0:p>These clusters were the input for further runs of Protein Motif Finder.</ns0:p><ns0:p>Sequence similarity was quantified using and all vs all pairwise BLASTp analysis, reporting the percentage identity of the top scoring high scoring pair (HSP), after applying an e-value cut-off of 0.01 and a cut-off specifying that the HSP alignment length must be at least 20% of the query length.</ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head></ns0:div> <ns0:div><ns0:head>ProminTools provides a user-friendly method to analyse biomineralization proteomes</ns0:head><ns0:p>The Docker TM image containing ProminTools can be run via a GUI on the CyVerse Discovery Environment without any need for use of the command line. Runtimes on CyVerse are variable due to variable resource availability, but a typical analysis with either Protein Motif Finder or Sequence Properties Analyzer takes between 30 and 120 minutes to complete. Although ProminTools is designed to run in a Unix environment, it can also be run on a windows PC via Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Validation of ProminTools</ns0:head></ns0:div> <ns0:div><ns0:head>Suitability for a range of data inputs</ns0:head><ns0:p>To ensure stability and good performance, we have tested ProminTools on a number of published and unpublished biomineralization datasets and used synthetic data to ensure that the program deals sensibly with unusual situations, such as small numbers of POI sequences or no motifs being found. An example analysis of a second data set, of proteins from freshwater mussel shells <ns0:ref type='bibr' target='#b20'>(Marie et al. 2017)</ns0:ref>, is provided in Data S3.</ns0:p></ns0:div> <ns0:div><ns0:head>Validation with negative control protein sets</ns0:head><ns0:p>Five sets of 100 proteins were drawn at random from the L. gigantea proteome and each used as the POI set to run ProminTools in five separate analyses. No enriched motifs were reported in any of the analyses. In all analysis, there were no significant differences in the degree of sequence bias, sequence complexity or intrinsic disorder between the random 'POI' set and the background proteome. Representative analyses are provided in Data S4.</ns0:p></ns0:div> <ns0:div><ns0:head>Validating motif retrieval in Protein Motif Finder</ns0:head><ns0:p>Motif finding in ProminTools relies on the motif-x motif finding engine, which has already been well validated <ns0:ref type='bibr' target='#b28'>(Schwartz and Gygi 2005)</ns0:ref>. However to ensure that there were no bugs in our post-processing of the output we spiked motifs at known frequencies into a set of protein sequences and ran Protein Motif Finder with these sequences as foreground, and the un-spiked sequences as background. The spiked motifs were recovered at the expected frequencies.</ns0:p><ns0:p>We also validated Protein Motif Finder on a groups of sequences containing motifs that have already been established as important for protein function. Manuscript to be reviewed <ns0:ref type='bibr' target='#b36'>(Wingender et al. 2013)</ns0:ref>. Using a set of CXXC zinc fingers factors as the POI set and the human proteome as the background set, Protein Motif Finder correctly identified CG.C..C as the most important motif, and found it to be 194 fold enriched in these proteins relative to the background proteome (Data S5).</ns0:p><ns0:p>It should be noted that not all motifs important for a protein's function will be enriched relative to the background. For example the L..LL motif is important in protein-protein interactions that regulate transcription <ns0:ref type='bibr' target='#b27'>(Plevin et al. 2005)</ns0:ref>. Using the 55 Swissprot proteins annotated as possessing an L..LL motif as the POI set and the Swissprot human proteome as background, Protein Motif Finder does not recover the L..LL motif (Data S6). This is because L..LL is relatively common in other contexts, and so is not significantly enriched in the POI set.</ns0:p></ns0:div> <ns0:div><ns0:head>Validating the biological meaning of clustering by motif enrichment</ns0:head><ns0:p>A key output of Protein Motif Finder is clustering of the POIs based on their motif enrichment. The usefulness of this clustering is based on the assumption that proteins within a cluster are likely to be involved in similar molecular processes. To test this assumption on a well annotated proteome, but focusing on biased sequences similar to those expected from biomineral associated proteins, we analysed the 500 most biased sequences from the human proteome. Of these, 303 could be annotated by eggNOG mapper (Huerta-Cepas et al. 2017) and they fell into 120 clusters when analysed with Protein Motif Finder (Data S7). Remarkably, proteins within a cluster all shared the same eggNOG functional annotation in 117 of the clusters, even when the proteins diverged significantly in primary sequence similarity as assessed by global alignments (Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref>). Of the input proteins, 53 were collagens, and 11 different types of collagen were successfully separated into separate clusters. The members of three clusters mapping to more than one annotation were clearly related within a cluster: two clusters contained two different types of collagen, while one cluster contained two types of epidermal growth factor-like domains.</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:04:47682:1:1:NEW 18 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Global properties of L. gigantea shell matrix proteome</ns0:head><ns0:p>Previous analyses of the L. gigantea shell matrix proteome <ns0:ref type='bibr' target='#b15'>(Mann et al. 2012;</ns0:ref><ns0:ref type='bibr' target='#b16'>Mann & Edsinger 2014;</ns0:ref><ns0:ref type='bibr' target='#b18'>Marie et al. 2013a</ns0:ref>) had noted a tendency for the proteins to be low complexity and disordered and that some proteins were enriched in particular residues. Here, ProminTools was used to put these observations on a more quantitative footing (Data S8, Data S9) and to discover enriched sequenced motifs in the data set from <ns0:ref type='bibr' target='#b16'>Mann et al. (2014)</ns0:ref>, which contained 381 proteins.</ns0:p><ns0:p>G and P rich motifs were found to be enriched most frequently among the proteins (Fig. <ns0:ref type='figure' target='#fig_3'>2A</ns0:ref>).</ns0:p><ns0:p>Given that we are seeking to find the motifs that are shared within a group of proteins, Protein Motif Finder excludes motifs found in fewer than four proteins from certain plots to prevent the picture being dominated by a highly enriched motif found in very few proteins. The result can be seen in Fig. <ns0:ref type='figure' target='#fig_3'>2B</ns0:ref>, where Q containing motifs displayed the greatest enrichments relative to the background proteome. For example QQP was enriched 7.5 fold while Q.N.Q was enriched 6.1 fold (see data tables in Data S8 for these numbers). In general there is often a negative correlation between the number of proteins in a set containing motifs and the enrichment of those motifs. These two measures are combined (see Materials and Methods) in Fig. <ns0:ref type='figure' target='#fig_3'>2C</ns0:ref>, which emphasises motifs found in a high number of proteins with high enrichment (a high PS-value).</ns0:p><ns0:p>For example, GG is found in 270 proteins and is 1.8 fold enriched, G..D in 234 proteins at 1.4 fold enrichment and NG in 249 proteins at 1.53 fold enrichment (Data S8).</ns0:p><ns0:p>The analysis of sequence bias and amino composition bias with Sequence Properties Analyzer was concordant with the motif finding results, in that G, P, Q and A were the most enriched amino acids (Fig. <ns0:ref type='figure' target='#fig_3'>2D</ns0:ref>). H, I, K, L, W, E and F were found to be the most depleted relative to the background proteome. The most commonly enriched amino acids were P, G, A and C (enriched in 87, 82, 74 and 69 proteins respectively, Fig. <ns0:ref type='figure' target='#fig_3'>2E</ns0:ref>). Amino acid residues C and A are not found PeerJ reviewing PDF \| (2020:04:47682:1:1:NEW 18 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed among the most enriched motifs or the motifs with the highest PS-value, indicating that the proteins are sometimes enriched in an amino acid without that amino acid being embedded in a particular primary sequence context.</ns0:p><ns0:p>The shell matrix proteins showed a clear tendency to contain more low complexity sequence than the background proteome (Wilcoxon rank sum test, p = 7.6 × 10 -6 , Fig. <ns0:ref type='figure' target='#fig_3'>2F</ns0:ref>) but there was no significant tendency for the sequences to contain a greater proportion of predicted disordered sequence than the background proteome (p = 0.1). The shell matrix protein set contained a similar proportion of proteins with negative and positive clusters of amino acids to the background proteome (Fig. <ns0:ref type='figure' target='#fig_3'>2H</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>Clustering of proteins based on motif content reveals relationships not found by blast searches</ns0:head><ns0:p>The Sequence Properties Analyzer carries out three hierarchical clustering analyses (Data S8, Materials and Methods). Eight protein clusters were identified (Fig. <ns0:ref type='figure' target='#fig_5'>3A</ns0:ref>), six of which contained more than two proteins. To investigate the nature of each cluster, Protein Motif Finder was rerun on each of the six main clusters (Data S10). Clusters 1 and 2 were riche in G containing motifs, especially NG.GG in cluster 1. Cluster 3 contained proteins rich in D containing motifs (especially D.NDD); cluster 4 in a variety of Q containing motifs; cluster 5 in C.I.P.D and C..YC..G and cluster 6 in various T and P containing motifs. By analysing the specific set of proteins in each cluster, the motifs identified are more specific to those proteins, and thus differ from the most enriched motifs in the data set as a whole displayed in Fig. <ns0:ref type='figure' target='#fig_5'>3A</ns0:ref>. For example D.NDD is the most prominent motif from the reanalysis of cluster 3, but is not among the most PeerJ reviewing PDF \| (2020:04:47682:1:1:NEW 18 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed enriched motifs in the global analysis of the entire data set, demonstrating the value of this iterative approach.</ns0:p><ns0:p>Given the results of our validation analysis with human low complexity proteins, it can be hypothesised that proteins found within the same cluster have related function. Only one of the proteins (Lotgi1\|143247, cluster 5) has an annotation: a 'four disulphide core domain protein' (Pfam PF00095), suggesting that it may function as a protease inhibitor. Given the lack of annotations, it was not possible to further test the relationship between cluster membership and function using the L. gigantea data.</ns0:p><ns0:p>We next asked whether the motif clusters reflected larger scale sequence similarity between the proteins within a cluster. To this end, protein sequences in each cluster were subject to an all-vsall pairwise BLASTp analysis, which is summarised in the matrices in Fig. <ns0:ref type='figure' target='#fig_5'>3C</ns0:ref> for six of the clusters. In general larger scale sequence similarity was low within clusters, with only three protein pairs from the five clusters displaying identity above 50% for the highest scoring HSP. This demonstrates that the clustering method can be used to find similarities that are not obvious from BLAST searches.</ns0:p></ns0:div> <ns0:div><ns0:head>Discussion</ns0:head><ns0:p>Proteins are a prominent part of the organic matrices of many biominerals and are thought to have a number of roles including catalysis, templating, and control of nucleation and crystal growth. Studies of biomineral associated proteins understandably often emphasise proteins with conserved domains, which lend themselves to discussions of their possible molecular functions.</ns0:p><ns0:p>However most studies also identify many proteins of unknown function, many of which appear to be low complexity in nature, with biased compositions and a high proportion of intrinsic disorder. Although authors often carefully inspect their protein sequences and note sequences PeerJ reviewing PDF \| (2020:04:47682:1:1:NEW 18 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed that appear particularly rich in certain residues or motifs, and note the degree of disorder, this information is rarely put in the context of the predicted proteome as a whole.</ns0:p><ns0:p>Here we introduce ProminTools, a user friendly package that allows researchers to glean more information from primary sequences of proteins of unknown function and put this in the context of the background proteome. Importantly, ProminTools allows users with minimal bioinformatic skills to run a suite of analyses and produce visualization that would otherwise require a lot of scripting. The giant limpet L. gigantea has a complex shell matrix proteome for which two different data sets exist. The data analysed in the present study derives from all shell layers <ns0:ref type='bibr' target='#b15'>(Mann et al. 2012</ns0:ref><ns0:ref type='bibr' target='#b16'>, Mann & Edsinger 2014)</ns0:ref>, and is thus more complex than the second data set that is derived from the aragonite shell layers only, excluding the calcitic layers <ns0:ref type='bibr' target='#b18'>(Marie et al. 2013a</ns0:ref>).</ns0:p><ns0:p>ProminTools revealed a complex array of strongly enriched motifs in the <ns0:ref type='bibr' target='#b16'>Mann et al. (2014)</ns0:ref> data set, which were not uncovered in the original study. Q, P and G rich motifs were particularly prominent and the proteins could be clustered based on their motif content even when they shared little larger scale sequence similarity. Re-running Protein Motif Finder on each of these clusters revealed unique motif profiles that could be hypothesised to be important for the molecular function of proteins in the group. For example, one group was enriched in acidic (D rich) motifs, another in Q and P rich motifs and other in G rich motifs. Interestingly, the Marie et al. study also identify a group of low complexity proteins rich in Q, suggesting that the functions of these proteins could be important for formation of all shell layers or just the aragonite layers, but that they are unlikely to be specific to the calcite layers.</ns0:p><ns0:p>We hypothesise that clustering protein sequences with biased composition based on their motif enrichment patterns can be used to group proteins of related function. Although this hypothesis PeerJ reviewing PDF \| (2020:04:47682:1:1:NEW 18 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed has yet to be confirmed on biomineral associated protein data sets, we show that this procedure can group functionally related proteins of biased composition from humans. Additional support for the idea comes from a previous study in which accurate predictors of enzyme function were built using the motif content of protein sequences <ns0:ref type='bibr' target='#b1'>(Ben-Hur et al. 2006)</ns0:ref>.</ns0:p><ns0:p>We found that the shell matrix proteins as a group were significantly lower in complexity than the background proteome, providing a statistical underpinning for this observation, and supporting the conclusion of <ns0:ref type='bibr' target='#b18'>Marie et al. (2013a)</ns0:ref> who noted the high proportion of low complexity sequences in their data set. The <ns0:ref type='bibr'>Mann et al. studies (Mann et al. 2012;</ns0:ref><ns0:ref type='bibr' target='#b16'>Mann & Edsinger 2014)</ns0:ref> highlight several proteins in their data which have high degrees of intrinsic disorder. Here, using the Sequence Properties Analyzer we were able to demonstrate that this is not a general feature of the data set, which is not predicted to be significantly more disordered than the background proteome. This highlights the importance of the proteome context when assigning significance to protein features, and demonstrates that the generally observed correlation between protein disorder and low complexity <ns0:ref type='bibr' target='#b24'>(Mier et al. 2019)</ns0:ref> does not hold in every data set.</ns0:p><ns0:p>The role of low complexity regions in biomineralization has only been determined in a very few cases. For example, the enamel protein Amelogenin has a central block of hydrophobic sequence rich in P, H and Q. Intramolecular hydrophobic interactions involving this regions are thought to be critical for self-assembly of Amelogenin into nanospheres and higher order structures that regulated crystal growth <ns0:ref type='bibr' target='#b35'>(Wang & Nilsen-Hamilton 2012)</ns0:ref>. It is possible that the Q and P rich regions in the L. gigantea shell matrix proteins might have a similar role in driving self-assembly processes.</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:04:47682:1:1:NEW 18 Jul 2020)</ns0:p></ns0:div> <ns0:div><ns0:head>Manuscript to be reviewed</ns0:head><ns0:p>Although at present we can only speculate on the role of low complexity proteins in biomineralization, it is clear that low complexity sequences are not unique to biomineralization related proteins. Depending on the species, 22 -36 % of residues in eukaryotic proteins fall into low complexity regions <ns0:ref type='bibr' target='#b37'>(Wootton 1994)</ns0:ref>. It remains to be investigated whether the low complexity regions of biomineralization related proteins have features that set them apart from other low complexity regions in proteomes, and ProminTools could be used to investigate such questions.</ns0:p><ns0:p>ProminTools allows researchers to easily find patterns in their data, but it has limitations and judgement should be applied in interpreting the output. For example, patterns found by ProminTools can reflect technical biases as well as biological signals. Post-translational modifications of particular residues could affect peptide detectability and thus protein inference, leading to biases in the input data. It should also be remembered that ProminTools is primarily a tool for hypothesis generation. For example, proteins which share similar motifs can be hypothesised to perform similar molecular functions, but this may or may not be the case for a particular biological system, and experimental validation is required. ProminTools will be at its most useful when combined with other methods for spotting repeating patterns in sequences (e.g. HhpreID <ns0:ref type='bibr' target='#b39'>(Zimmermann et al. 2018)</ns0:ref>, Meme <ns0:ref type='bibr' target='#b0'>(Bailey et al. 2009)</ns0:ref> or simply inspecting dot-plots) and when put in the context of additional information such as known domain content, posttranslational modifications, phylogenetic distributions and expression patterns.</ns0:p><ns0:p>We would like to point out that ProminTools can be used for any pairwise comparison of sets of protein sequences. For examples, protein sets associated with different part of a biomineral or different developmental stages could also be compared, and if carefully carried out, cross-species comparisons could also be made. The latter could be particularly useful, since the fast evolving PeerJ reviewing PDF \| (2020:04:47682:1:1:NEW 18 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed nature of low complexity sequences <ns0:ref type='bibr' target='#b21'>(McDougall et al. 2013</ns0:ref>) can make it difficult to detect homology. It could also be applied to other protein sets rich in low complexity sequences, such as proteins found in pathological amyloids associated with diseases such as Alzheimer's and Parkinson's <ns0:ref type='bibr' target='#b12'>(Kumari et al. 2018)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions</ns0:head><ns0:p>ProminTools will help researchers generate new hypotheses about the important of particular motifs and protein chemistries in their system of interest and provide new directions for experimental work. Putting the patterns identified into the context of the rest of the proteome ensures that features that are genuinely overrepresented in the POIs are prioritised for further study.</ns0:p></ns0:div> <ns0:div><ns0:head>Table 1(on next page)</ns0:head><ns0:p>Annotations of the 10 largest clusters identified by ProminTools in an analysis of human proteins of biased sequence composition PeerJ reviewing PDF \| (2020:04:47682:1:1:NEW 18 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed Manuscript to be reviewed </ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>Docker TM Desktop with simple commands in Windows® Power Shell™ (for details see https://github.com/skeffington/Promin-tools). On a Window® 10 machine with an Intel® Core™ i7-2600 3.4 GHz processer and 16 GB RAM, Protein Motif Finder completed analysing the L. gigantea data set in 11 min 30 s provided with 1 core and 2.5 GB RAM, while the Sequence Properties Analyser completed in 32 min 2 s provided with 5 cores and 5 GB RAM. The ProminTools workflow is summarised in Fig. 1. PeerJ reviewing PDF \| (2020:04:47682:1:1:NEW 18 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>For example CXXC class zinc finger factors are transcription factors and histone methyltransferases that bind to CpG elements via zinc fingers. The Zn binding residues consist of cysteines arranged in CGxCxxC motifs PeerJ reviewing PDF \| (2020:04:47682:1:1:NEW 18 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head /><ns0:label /><ns0:figDesc>Figure 1</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head>Figure 2</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head>Figure 3 L</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Figure 3</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head>Figure 3 :</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Figure 3: L. gigantea shell matrix proteins with biased composition can be clustered based on motif content despite low sequence identity. The heatmap displays (A) motif enrichment in the SMP set relative to the background proteome. Proteins are clustered by their motif enrichment pattern and motifs are clustered by their distribution amongst the proteins. Each motif is a row in the heatmap and each protein is a vertical column. For clusters1 -6, a wordcloud (B) representing the PS-value of the enriched motifs are displayed in addition to a heatmap (C) representing the percentage identity between all pairs of proteins in the cluster in an all-vs-all blastp analysis (see Materials and Methods).</ns0:figDesc></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head>Table 1 : Annotations of the 10 largest clusters identified by ProminTools in an analysis of human proteins of biased sequence composition. Cluster ID Cluster size Identity (%) in all- vs-all alignments (min ; mean ; max)</ns0:head><ns0:label>1</ns0:label><ns0:figDesc /><ns0:table><ns0:row><ns0:cell>eggNog annotation</ns0:cell><ns0:cell>Proteins in cluster</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>carrying this</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>annotation (%)</ns0:cell></ns0:row></ns0:table></ns0:figure> </ns0:body> "	" Dear Prof. Gillespie and PeerJ Editors, We would like to thank you for editing our manuscript and to all three reviewers for their constructive comments. All the reviewers comments have been addressed, which we think has improved the manuscript greatly, and we believe that it is now suitable for publication in PeerJ. The major revisions include: • Validation with negative control data • Validating motif retrieval by Protein Motif Finder • Validating the biological meaning of motif-based clustering by a new analysis of human proteins • Inclusion of an example of ProminTools run on another data set • A new output table to allow users to judge the confidence of motif-based clusters • A new output table summarizing, protein-wise, the results of Sequence Properties Analyzer • Explicit calculation of p-values and q-values for motif enrichment • Expanded discussion of the relationship between ProminTools and existing software In the remainder of this document you will find the reviewers comment in black, an explanation of our response in blue, and changes to the manuscript are copied in red. We look forward to hearing your decision, Alastair Skeffington (on behalf of all authors) Reviewer 1: l.15 – molluscs are definitely metazoans We have corrected this sentence to avoid giving the impression that molluscs are not metazoans. It now reads: “Mineralized structures are formed by many organisms across the tree of life including bacteria, molluscs, metazoans, plants and algae” l.33 – general references for proteins involved in biomineralization, as the backbones of present report assessments, may come from works prior to 2019, even 2012 Given the large literature on the subject of proteins in biomineralization, and that at this point in the introduction we are not focused on any particular experimental system, we feel it is most appropriate to refer to recent reviews of the subject, which would provide a starting point for interested readers to explore further. l.35 – Pif protein in molluscs should be referred to with Suzuki et al. 2009 (instead of Song et al. 2019). The whole sentence containing this reference should be reworked to be tightly reframed with better focus. We have included the Suzuki 2009 reference as suggested by the reviewer and have expanded the section be more informative: “In some cases the roles of such proteins is relatively well understood, and some of the best studies examples come from molluscs (Song et al. 2019). For example, the proteolytic products of the Pif protein in molluscs have been shown to bind CaCO3 crystals, induce formation of the aragonite polymorph of CaCO3 in vitro (Suzuki et al. 2009). Knock-down of the Pif gene results in disordered growth of the aragonite crystals in the nacreous layer of the shell In other systems, well studies examples include Amelogenin from tooth enamel, Silicatein from sponge spicules and Mms6 from magnetosome synthesising bacteria (Wang & Nilsen-Hamilton 2012).” l.39 onwards – Proteomics method (the highway to recover the dedicated protein sequences) is introduced with kind of workflow and upstream cleaning procedures (required for cautiousness) mix up. Irrelevant to the present work purpose, with regard to the latter. On the contrary, critical limits for sequence identification, e.g. amino acid composition, presence of multiple repeats and low-complexity domains, solubility properties, glycosylations / phosphorylations sites, cleavage sites availability, … or fragmentation techniques have been overlooked even though several issues have driven some packages of the tool. We are slightly confused by the first half of this comment. The aim of this sentence was to illustrate (particularly to reader who do not work on biomineralization) a typical workflow which would generate data that we envisage being a suitable input for ProminTools. A cleaning procedure is normally included in such a workflow, but the reviewer is right that this is not essential for the data to be analysed with ProminTools. Indeed data does not have to original from analysis of a biomineral at all. We have left this sentence as it is, but would welcome further clarification of the reviewers point. In the second half of the comment, the reviewer quite rightly points out that we have neglected to discuss the effects of sequence properties and protein modifications on protein detectability. It is important to note these effects, because they mean that patterns identified by ProminTools may reflect technical biases as well as biological signals. The discussion has been updated with the following sentence to incorporate this point: “in their data, but it has limitations and researchers should use their judgement in interpreting the output. For example the hierarchical clustering method will always generate some clusters whether or not there are interesting, patterns found by ProminTools can reflect technical biases as well as biological signals. Post-translational modifications of particular residues could affect peptide detectability and thus protein inference, leading to biases in the input data, and users must inspect the output to decide if the results are meaningful...” Then in the ‘analysis’ (Motif Finder) section - What is the guideline for providing two different ‘sets’ of protein sequences to compare the sequences of interest with? Do the authors take each set apart for the specific package application. This point should be clarified. ProminTools does not compare the proteins of interest with two sets of sequences, but with one other set of sequences. Thus the inputs for protein tools are two files containing 1) the proteins of interest and 2) the background sequence set for comparison. From line 83 of the original manuscript, we state this clearly: “For both tools, the inputs are two fasta formatted files: one containing the protein set of interest (POI set), and the other the reference or background proteins, typically the predicted proteome of the organism of interest.” To further improve clarity, we have moved a modified version of this sentence to the beginning of the section: “The inputs for ProminTools are two fasta formatted files: the first containing the protein set of interest (POI set), and the second the reference or background proteins (typically the predicted proteome of the organism of interest).” We have added the following sentence, and provide further guidance for the nature of the input files: “The background proteins are used for statistical comparisons with the POI sets, allowing the user the answer the following question: ‘Are the features observe in the POI common in the background sequences or are they unusual?’” We are unclear about what the reviewer means by “take each set apart for the specific package application”, but would be happy to address any concerns on receipt of further clarification. Careful attention should be paid by the author when writing : - ‘Although the human eye is good at detecting patterns, this method has the risk that important patterns in the data are missed … .’(l.57) - ‘Protein Motif Finder can facilitate researchers in detecting the important patterns in their data’ (l. 267) Oversimplification or statement driven deduction are both compelling reasons to ask for a step further discussion of these issues. Point 1: We thank the reviewer for pointing out that this statement could be misinterpreted. We have changed the sentence to: “This method has the risk that important patterns in the data are missed and that rules are not applied consistently in identifying these patterns. ” Point 2: The relevant section has been removed from the manuscript (see responses to reviewers 2 and 3). Minor issues : A few spelling errors (e.g. l. 75, 195, 217, 329) indicate at least thorough editing is needed. An additional colleague has proof-read the manuscript. Reviewer 2: “However, it is assumed that the tool will be developed/ improved further in the future to gain useful functional information.” The reviewer is correct – the tool in its present form is a starting point. We intend to work with users to further develop the tool and make it as useful to the community as possible. This initial publication is important for developing the initial user base. 1. One general comment is that no validation of ProminTools using well known proteins and annotated database (ex. Human) was carried out and demonstrate its usefulness to get functional information of proteins of interest. We accept the reviewers comment that more validation would be useful to demonstrate the usefulness of ProminTools, and have added a section to the results: “Validation of ProminTools” to remedy this as well as point 5 from reviewer 2 and point 1 from reviewer 3. The key analyses that have been added are: • Validating motif retrieval by Protein Motif Finder using synthetic test data where sequences are spiked with motifs at low frequencies. • Validating that Protein Motif Finder can identify motifs that have previously been identified as important for protein function • Validating that clustering proteins based on their motif enrichment patters can generate biologically meaningful groupings. An analysis of low complexity proteins from the human proteome yielded clusters with near perfect overlap with functional annotations. 2. Lines 59-62: The authors should include references or illustration to strengthen this statement. We thank the reviewer for the suggestion that this statement should be strengthened. We have changed the emphasis of this section to be more general and to better express the principle behind ProminTools: “Ideally the context of the proteome as a whole should also be taken into account. The more specific a feature is to the proteins of interest (POIs) the more likely it is to be involved in the specific function of those proteins. This notion is based on the well-established biological principle that the primary sequence of a protein is a strong determinant of molecular function, and that proteins with similar functions tend to share regions of sequence similarity. Thus, sequence motifs shared by a group of biomineral associated proteins are more likely to be involved in the specific function of those proteins if they are rare in the background proteome than if they are commonly found motifs. This principle is already used in various sequence analysis tools, including those seeking to identify important motifs (Wagih et al. 2016).” 3. Information about the shell matrix protein dataset used (Marie et al.) should be indicated in Material and methods section. The Marie et al. data is not analysed with ProminTools in this study. Instead it is the data from Mann et al 2014 that are analysed. This is clearly described in the methods section “Analyses of L. gigantea data using ProminTools” and further stated in the results section (line 204 of the original manuscript). The Marie et al. study is mentioned several times in the discussion since it provides important context for the biological interpretation of the Mann et al. 2014 data. 4. Lines 217 -220: According to the Figure 2D, not only G,P and Q are enriched but also A. Similarly, along with H, I, K and L, the amino acid W is found to be depleted. Here we were pointing out the major patterns in the data, hence the phrases ‘most enriched’ and ‘most depleted’ in the text. We accept that A is almost as enriched as Q and that W (and E and F) are nearly as depleted as I. Thus we have modified the sentence to read: “The analysis of sequence bias and amino composition bias with Sequence Properties Analyzer was concordant with the motif finding results, in that G, P, Q and A were the most enriched amino acids (Fig. 2D). H, I, K, L, W, E and F were found to be the most depleted relative to the background proteome.” 5. Lines 239 - 241. Statement “It can be hypothesised that proteins found within the same cluster have a similar function” needs to corroborated in the discussion section with examples. We have included an analysis of compositionally biased protein sequences from humans in the section “Validating the biological meaning of clustering by motif enrichment”. This confirmed that clustering based on enriched motif content can provide functional information. We have additionally added the following paragraph to the discussion, including a reference to a study using motifs to classify enzyme function: “We hypothesise that clustering protein sequences with biased composition based on their motif enrichment patterns can be used to group proteins of related function. Although this hypothesis has yet to be confirmed on biomineral associated protein data sets, we show that this procedure can group functionally related proteins of biased composition from humans. Additional support for the idea comes from a previous study in which accurate predictors of enzyme function were built using the motif content of protein sequences (Ben-Hur et al. 2006).” 6. Lines 283-295: Although the protein groups are enriched in different motifs (e.g., M..M and others mentioned in the text) the authors should use specific examples of the proteins found in these cluster to indicate if they contain same molecular/biological functions, domains etc. The main aim of clustering by motifs is to try and provide some insight into possible relationships between proteins in biomineralization data sets that are low in sequence complexity / biased in amino acid composition, because very often these proteins are not annotated. The underlying hypothesis is that, in these sequences, the chemistry of the amino acids and their local sequence environment might be important for interacting directly with the mineral and directing the mineralization process. Alternatively, short motifs might be important as post translational modification sites, again endowing the sequence with chemical properties that are important for mineralization. ProminTools is designed to provide researchers with the tools to test these ideas using their data. On reflection, we have decided that automatically clustering all proteins in the Protein Motif Finder input is best avoided. This is because it will always generate clusters whether or not they are biologically meaningful, and thus can easily be misleading. Instead we have decided to restrict the default clustering to proteins displaying strong biases in sequence composition with respect to the background proteome. This has two main advantages: 1) It more directly addresses the hypotheses that ProminTools is designed to test (see above). 2) Our validation shows that clustering by motif enrichment on biased sequences can produce biologically meaningful clusters. Advanced users can adjust the fLPS p-value cut-off used to decide whether a protein is sufficiently biased in composition to be included. This change of the scope of the clustering has resulted in a new versions of Fig 3 . As is often the case in compositionally biased sequences very few (one) of the sequences have a homology based annotation. This means that a discussion of molecular functions of the clusters based on the annotation is not possible. Since none of the clusters in the new Fig 3. particularly lend themselves to further investigation (they are farily homogeneous in terms of motif content), we have removed Fig. 4 and associated text from the manuscript. 7. Lines 324-326: Is it possible to give any indicators (such as false identification rate or e-values etc.) for the users to understand/estimate the validity of the generated results? The sentence to which the reviewer refers reads: “For example, proteins which share similar motifs can be hypothesised to perform similar molecular functions, but this may or may not be the case in reality and experimental validation is required.” The reviewer is asking whether it is possible to assess the likelihood that proteins in a cluster share a similar molecular function: for example, by giving a false discovery rate for the identification of meaningful clusters vs clusters of proteins with unrelated functions. Although I appreciate that this would be extremely useful, I do not think this is possible at the moment for two reasons. Firstly, this is very much a property of the particular data set and biological system, meaning any relationships between cluster / protein properties and false discovery rates will be hard to generalize. Secondly, we lack enough well characterised systems in which to test any such relationships at present. We have however made changes to the tool and the manuscript which we think address the reviewer’s point: 1) As discussed above, we have shown that in human low complexity proteins there is a strong relationship between cluster membership and biological function. 2) We have introduced a new table in the outputs ( “_prot_correlations.txt” ) which gives the distance correlation between each pair of proteins in the data set based on their motif enrichment pattern. This allows users to assess the degree of motif-based similarity between the proteins and thus the strength of evidence underlying any clusters. 3) As described in response to point 6, we introduce a distance correlation cut-off filter. This is user adjustable, so users can choose to cluster only the proteins with the highest degree of motif based similarity, or allow more relaxed similarity as they explore their data. Reviewer 3: 1. Are there any other useful tools which can be compared with your tools? And what are the performances of these tools and what are the advantages of ProminTools? You should provide strong evidence to show that ProminTools are in high accuracy and efficiency. ProminTools is, to a large extent, an agglomeration of existing, well validated tools, but the reviewer is right to point out that comparisons with existing software should be made wherever possible in order to demonstrate that ProminTools is accurate and efficient. To our knowledge, there is no tool available that performs a similar set of analyses to ProminTools, although there are tools that are similar to components of ProminTools. A general advantage of all the components of ProminTools is minimal informatic skills are required to run a whole series of analyses, which would otherwise involve running multiple command-line only programs and scripting to process the inputs and outputs and visualise the results. This point is already make in the introduction, but we have added an extra sentence in the discussion to further emphasis it: “Importantly, ProminTools allows users with minimal bioinformatic skills to run a suite of analyses and produce visualization that would otherwise require a lot of scripting. ” The second general advantages is that ProminTools performs comparisons between two sets of sequences, which is not supported by many existing tools. To avoid overgeneralisation, I will address each of the component analyses of ProminTools in turn: Sequence Properties Analyzer 1) Amino acid composition bias Overall bias: This component uses a custom program, and is unique in the use of a bootstrapping procedure that allows the user to empirically estimate the probability of achieving a similar level of bias as in the POIs by random sampling of the background proteome. We are not aware of another tool that performs a similar procedure, which has the advantage that no assumptions are made about the structure of the underlying probability distributions. The most similar program is “Composition Profiler” (Vacic et al. BMC Bioinformatics 8, 211 (2007); http://www.cprofiler.org/cgi-bin/profiler.cgi). We have added the following text to the manuscript to clarify the difference between the two programs: “The only program we are aware of that makes a similar calculation is Composition Profiler (Vacic et al. 2007). However this makes the assumption that all POI sequences come from the same underlying distribution of amino acid frequencies and tests whether this distribution is significantly different from the background. ProminTools doesn’t make this assumption, but accepts that the POI set my contain proteins with different types of bias, and thus analyses bias per se without reference to the type of bias.” Biased regions: These are discovered using fLPS (Harrison et al. 2017), which has been fully validated and is extremely fast (it processes a typical proteome in seconds). 4) Intrinsic disorder: We use the VSL2 classifier for predicting intrinsic disorder, and on line 151 of the original manuscript we justify this choice with reference to a recent benchmarking publication. 5) Sequence complexity: This is calculated through the widely used program SEG, and is justified on line 144 of the original manuscript. 6) Charged clusters: This makes use of the long standing software SAPS. We are not aware of an alternative. Protein Motif Finder Protein motif finder makes use of a well validated motif-finding engine, motif-x. Another well-known tools for protein motif finding is the meme suite. Given that both these tools have been validated in the past, we do not feel the need to validate one against the other. We felt motif-x was more useful because 1) It generates definite motifs (e.g. KxxK) rather than a position weight matrix. This hugely simplifies follow-up analysis and is useful for molecular biologists who may want to search other proteins for the same motif as identified in the initial analysis. 2) Motif-x automatically produces an exhaustive motif search, whereas this is harder to achieve with meme. In the discussion (line 328 of original manuscript) we suggest that researchers also analyse their data with meme, to reap the benefits of both systems. We have updated the relevant sentence of the methods section to make these points clearer: “This engine was chosen because it breaks sequences down into their constituent motifs, by an iterative procedure that avoids oversimplification of motifs and prioritises motifs that are most enriched relative to a background sequence set. It is exhaustive for a given p-value and generates definite motifs rather than a position weight matrix, which simplifies downstream analyses and is more useful to molecular biologists.” Protein Motif finder is, to our knowledge, the first tool that clusters proteins by their motif enrichment. 2. Another questions are about the test sets. (1) Why do you use the proteome of the giant limpet? Given that there are two different results based on the same raw sets, how could you illustrate the superiority of ProminTools The giant limpet data was chosen because of all the data sets tested (see response to 2. (2)), it provided the best illustration of the utility of ProminTools, as well as because of the importance of mollusc shell models in biomineralization research. The authors did not pick up on various patterns in the data in their original manuscript, and this provides the opportunity to offer some useful new analysis as well as simply presenting ProminTools. It is also surprisingly difficult to find data sets in the literature where all necessary information is present. Very often no fasta file of the sequences identified is provided, and sometimes the identifiers provided do not match current databases, or only match an ‘in-house’ database which is not provided. For consistency I prefer to use the same database as that used for protein identification as the background proteome, but this is often not available. The initial (2012) study of Mann et al. clearly has some shortcomings. In particular, many of the numbers of proteins in different classes reported in the text do not tally with the tables containing the protein identifications. The protein identification pipeline was also changed for the 2014 study, which will also change the list of identifications to some extent. We do not see that there are particularly opportunities to be had in comparing the two data sets for the purpose of demonstrating the superiority of ProminTools. To be quite clear, our tool is positioned downstream of the protein identification step. (2) Have you ever used any other data sets to prove the utility of ProminTools? We have run ProminTools on a wide variety of datasets. It has been used to analyse our own proteomics data derived from algal biominerals, and that work is still being written up. We have also analysed a number of other biomineralization datasets from the literature, including bivalve shell proteins (Arivalagan et al. Mol Biol Evol. 2017 Jan; 34(1): 66–77.), sea star skeleton proteins (Flores et al. BMC Evolutionary Biology volume 17, Article number: 125, 2017), sea urchin test proteomes (Mann et al. Proteome Science volume 8, Article number: 6, 2010) and coral skeletal proteins (Takeuchi et al. 2016 https://doi.org/10.1371/journal.pone.0156424) among others. We have run it on artificial datasets to make sure it behaves sensibly when, for example, the number of foreground sequences is low, or when no motifs are found. The following statement has been added to the results to reassure the reader on the above point: “To ensure stability and good performance, we have tested ProminTools on a number of published and unpublished biomineralization datasets and used synthetic data to ensure that the program deals sensibly with unusual situation, such as small numbers of POI sequences or no motifs being found.” In addition, the output of a second analysis, for mussel shell associated proteins, has been added to the supplements to further reassure the reader that the tools work on a variety of data. This is referenced in the main text: “An example analysis of a second data set, of proteins from freshwater mussel shells (Marie et al. 2017) is provided in Supplementary Data S2.” (3) How many shell matrix proteins do you select as the foreground sequences? I couldn't find the exact number only if I look back to the data in the paper of Mann 2014. The number of foreground proteins (381) is already stated in the methods on line 167 of the original manuscript, and the sequences are available in Data S2. We accept that the number could be given greater prominence, so have additionally mentioned the number in the results: “Here, ProminTools was used to put these observations on a more quantitative footing and to discover enriched sequenced motifs in the data set from Mann et al. (2014), which contained 381 proteins.” (4) Have you ever set negative control ground in the case of the giant limpet? A group of randomly selected proteins is strongly suggested. We have added analyses of groups of randomly selected proteins to the new results section “Validation of ProminTools”. 3. As for the methods: (1) In terms of the runtime, have you ever tried to run the scripts in parallel on the GPU? It may be helpful to decrease the runtimes in future. It’s a very interesting idea to use GPU parallelization to increase performance, and it might well be possible in the future. We have several reasons for not exploring this option at present: 1) We want ProminTools to continue to be available as Cyverse apps with a GUI, to increase availability of the tools to biologists who find command line daunting. Cyverse does not have GPU hardware. 2) Intrinsic disorder prediction in ProminTools is carried out using the VLS2 java binary. Without dissecting / modifying the source code we do not see an obvious way to run it on GPU. Running this binary is by far the most time-intensive step in ProminTools – the other steps are already fast. 3) Good GPU processing is not available on all PCs, and we want the tools to be available to as many researchers as possible, regardless of their hardware. For example the PCs distributed as standard in out institute have no suitable GPUs available, and neither does our shared Linux machine or our computing cluster. (2) The function of low complexity proteins is surely a hard question. Have you ever machine learning algorithm to solve this problem? This is an interesting point and I very much hope the field will move in this direction in the future. The limitation here at the moment (particularly for biomineralization research) is a lack of sufficient training data. The functional characteristics of more biomineralization related proteins must be established experimentally for this approach to become useful. Validity of the findings ProminTools are designed to deal with the biomineral associated proteins, and the author also mentioned that the tool can also be used in many other cases. It seems to be a powerful tools, but I have one major questions: The tool did provide the motif and sequence properties, but could the tool give a scoring report for POI set and potential shell matrix protein lists? It will be more useful for the investigators. The reviewer is right to point out the investigators are generally interested in focusing down on a few particular proteins, and that ways to score or rank them are especially helpful in this. Although the existing output tables / html report already allow researchers to rank their data in various ways, we have modified the tools to make this even easier: 1) We have added a new output table to Sequence Properties Analyzer that summarizes, protein-wise, the main outputs of the program. 2) We have expanded the Protein Motif Finder _motifsummary.txt output file to incorporate p-hypergeometric p-values and q-values for the motifs identified, allowing the motifs to be easily ranked. 3) The new _port_correlations.txt output files gives the distance correlations for each pair of proteins, which can be easily sorted to find the most similar proteins for a particular protein of interest. "	Here is a paper. Please give your review comments after reading it.
519	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Background : Minimal change disease (MCD) is a common form of nephrotic syndrome in adults. However, the molecular mechanism underlying the pathogenesis of MCD remains incompletely understood. In this study we aimed to investigate the role of the cytokines expression of Th1/Th2/Th17 and urinary CD80 excretion in adult-onset MCD patients.</ns0:p></ns0:div> <ns0:div><ns0:head>Methods:</ns0:head><ns0:p>The lymphocyte subsets, 34 cytokine levels of Th1/Th2/Th17, serum and urine concentrations of CD80, and expression of CD80 in glomeruli were analyzed in 28 cases (15 males and 13 females; average age: 34.1 years, age range: 18-56 years), including 10 patients with MCD in relapse, 9 patients with MCD in remission and 9 healthy controls.</ns0:p><ns0:p>Results: There was no significant difference of CD3 + CD4 + cells proportion among patients with MCD in relapse, MCD in remission and healthy controls (P=0.802). The cytokine levels of GM-CSF and tumor necrosis factor (TNF)-related activation-induced cytokine (TRANCE) in patients with MCD in relapse increased 1.5 times higher than those in remission. An evident increase in the excretion of urinary CD80 was found in patients with relapsed MCD compared with those in remission (598.4±115.8 vs 81.78±7.04 ng/g creatinine, P<0.001) and healthy controls (598.4±115.8 vs 67.44±8.94 ng/g creatinine, P<0.001). CD80 expression was observed in podocyte of MCD patient in relapse by immunofluorescence technique.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions:</ns0:head><ns0:p>The cytokines GM-CSF and TRANCE are increased and the urinary CD80 levels are elevated in adult-onset MCD patients in relapse, indicating a disorder of Th1/Th2/Th17 balance and that the elevated excretion of CD80 may underlie the pathogenesis and development of adult-onset MCD.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>Minimal change disease (MCD) is commonly regarded as due to T cell subsets disorder and certain circulating cytokines that trigger dysfunction of podocytes and resulting in proteinuria <ns0:ref type='bibr' target='#b1'>(Boumediene et al., 2018;</ns0:ref><ns0:ref type='bibr' target='#b2'>Kaneko et al., 2015;</ns0:ref><ns0:ref type='bibr' target='#b3'>Iwabuchi et al., 2018;</ns0:ref><ns0:ref type='bibr' target='#b4'>Wang & Greenbaum, 2019)</ns0:ref>. However, not much is known about which type of T cell subset and cytokine play a critical role in the pathogenesis of MCD. Previous studies revealed that patients with MCD were characterized by downregulation of Th1 cytokines and predominance of Th2 and Th17, which might be harmful factors of glomerulus, leading to the occurrence and development of MCD (Le <ns0:ref type='bibr' target='#b5'>Berre et al., 2005;</ns0:ref><ns0:ref type='bibr' target='#b6'>Salcido-Ochoa et al., 2017)</ns0:ref>. But <ns0:ref type='bibr' target='#b7'>(Kaneko et al., 2002)</ns0:ref> reported that the percentages of Th1, Th2 and the ratio of Th1/Th2 showed no significant differences between nephrotic relapse, remission and healthy controls in childhood. Thus, the cytokines expression of Th1/Th2/Th17 still needs to be characterized and evaluated in MCD patients. CD80, also known as B7-1, is a costimulatory molecule of T cells and is also involved in T cell activation and termination <ns0:ref type='bibr' target='#b8'>(Novelli, Benigni & Remuzzi, 2018)</ns0:ref>. <ns0:ref type='bibr'>(Reiser et al., 2004)</ns0:ref> demonstrated that induction of CD80 expression on podocyte led to reorganization of actin cytoskeleton that modified glomerular permselectivity and caused proteinuria in mice. Moreover, it has been proposed that MCD is a 'two-hit' podocyte immune disorder ( <ns0:ref type='bibr' target='#b11'>(Shimada et al., 2011)</ns0:ref>.</ns0:p><ns0:p>The first hit is the induction of CD80 in podocyte by various stimuli and the second hit is the ineffective censoring of podocyte CD80 due to a defective autoregulation by podocyte itself.</ns0:p><ns0:p>Besides, urinary CD80 is elevated in MCD in children and measuring urinary CD80 concentrations could distinguish MCD from focal segmental glomerulosclerosis (FSGS) <ns0:ref type='bibr' target='#b12'>(Ling et al., 2015;</ns0:ref><ns0:ref type='bibr' target='#b13'>Garin et al., 2010;</ns0:ref><ns0:ref type='bibr' target='#b14'>Garin et al., 2009)</ns0:ref>. Recently, a CD80 inhibitor, abatacept (cytotoxic T-lymphocyte-associated antigen 4-immunoglobulin fusion protein, CTLA-4-Ig), was used as a therapeutic drug in 'CD80-positive kidney disease' in MCD patients even though in only a limited number of cases <ns0:ref type='bibr' target='#b15'>(Garin et al., 2015)</ns0:ref>. However, few studies have tried to explain the possible reasons why elevated urinary levels of CD80 excretion are encountered in adult-onset MCD.</ns0:p><ns0:p>Therefore, in this study, we aimed to characterize the serum cytokines expression of Th1/Th2/Th17 and urinary CD80 excretion in adult-onset patients with MCD and healthy controls, as well as their role in the pathogenesis and development of MCD.</ns0:p></ns0:div> <ns0:div><ns0:head>Materials & Methods</ns0:head></ns0:div> <ns0:div><ns0:head>Patient selection</ns0:head><ns0:p>Adult patients with biopsy proven MCD and healthy volunteers were studied (Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref>). None of the healthy control subjects involved in the study had any underlying immunologic disease in the study. Patients with renal dysfunction were excluded (glomerular filtration rate<60 ml/min or serum creatinine>1.5 mg/dl). MCD was defined according to the established pathology criteria <ns0:ref type='bibr' target='#b16'>(Vivarelli et al., 2017)</ns0:ref>. Relapse of MCD was defined as proteinuria (≥3+ using the tetrabromophenol-citrate buffer colorimetric qualitative dipstick test or urinary protein/creatinine ratio >3.0 mg/mg), edema and hypoalbuminemia (of<30 g/l). Remission of MCD was defined as a urinary protein/creatinine ratio<0.3 mg/mg or no proteinuria using the colorimetric qualitative test. Our study was approved by the Medical Ethics Committee of Ningbo First Hospital (No.</ns0:p></ns0:div> <ns0:div><ns0:head>2017-R033</ns0:head><ns0:p>). A written informed consent was obtained from all participants.</ns0:p></ns0:div> <ns0:div><ns0:head>Clinical Samples Collection and Cytokine antibody array measure</ns0:head><ns0:p>We randomly selected six patients with MCD in relapse, three patients with MCD in remission and three healthy controls. From January 2017 to January 2018, 10ml peripheral blood samples were collected from patients with MCD and healthy controls in Ningbo First Hospital, Medical College of Ningbo University. The total 34 cytokines of Th1/Th2/Th17 in MCD patients and healthy controls were detected by cytokine antibody array using a RayBio® human cytokine antibody array (RayBiotech, Inc, Norcross, GA, USA, and AAH-TH17-G1). Membranes were incubated with diluted antibodies at room temperature for 2 h. The detections were accomplished according to the manufacturer's manual and previous research <ns0:ref type='bibr' target='#b17'>(Huang, 2001)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Lymphocyte subsets assessment</ns0:head><ns0:p>We used flow cytometry analysis to detect lymphocyte subsets in whole peripheral blood in ten patients with MCD in relapse, nine patients with MCD in remission and nine healthy controls.</ns0:p><ns0:p>Briefly, peripheral blood was incubated with marked monoclonal antibodies for 20 minutes at room temperature in the dark. Then lysing reagent was added and incubated for 15 minutes at room temperature in the dark. The lymphocyte subsets analysis was performed by using a BD flow cytometer (BD Bioscience).</ns0:p></ns0:div> <ns0:div><ns0:head>CD80 and CTLA-4 measurements</ns0:head><ns0:p>A commercially available ELISA kit (Bender Med-Systems, Burlingame, CA, USA) was used for measuring CD80 in blood and urine. We detected CTLA-4 in blood and urine according to previous study <ns0:ref type='bibr' target='#b18'>(Oaks & Hallett, 2000)</ns0:ref>. We adjusted the results of CD80 and CTLA-4 with urinary creatinine excretion. Urinary creatinine and protein and serum albumin were detected by an auto-analyzer.</ns0:p></ns0:div> <ns0:div><ns0:head>Immunohistochemistry</ns0:head><ns0:p>We used immunofluorescence technique to test the expression of CD80 in glomeruli of MCD patient in relapse. Snap-frozen renal specimens were incubated with monoclonal synaptopodin or WT-1 antibody (1:50; Santa Cruz, CA) for 2 h at room temperature to reveal podocyte. Then, we washed the sections three times with PBS and incubated specimens with anti-CD80 antibody (1:100) (goat; R&D Systems, Minneapolis, MN) 2 h at room temperature. After washing three times with PBS, we incubated sections with anti-goat 488 and chicken anti-mouse 594 Alexa Fluor antibodies (1:1500, Invitrogen, Carlsbad, CA) for 1 h at room temperature.</ns0:p></ns0:div> <ns0:div><ns0:head>Statistical analysis</ns0:head><ns0:p>Graph Pad Instat version 5.0 was used to perform statistical analysis and data graphics. Nonparametric ANOVA (Kruskal-Wallis test) was conducted for statistical analysis. We used Mann-Whitney U test or Wilcoxon signed rank test (when applicable) to determine the differences between means. Spearman correlation coefficient was used to calculate the correlation between urinary CD80 and proteinuria. Values were expressed as means ± standard error of mean (SEM).</ns0:p><ns0:p>Results were considered statistically significant if P< 0.05.</ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head></ns0:div> <ns0:div><ns0:head>Clinical characteristics</ns0:head><ns0:p>The clinical characteristics and laboratory results of patients with MCD in relapse (n=10), MCD in remission (n=9) and healthy controls (n=9) are summarized in Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref>. A total of 28 cases were reported, 15 males and 13 females, with an average age of 34.1 ± 2.1 years (age range: 18 -56 years). Patients with MCD in relapse were analyzed at onset of illness. Eight of the relapsed patients were on a tapering dose of immunosuppressive treatment while the remaining two were after drug withdrawal. All patients with MCD in remission were getting immunosuppressive treatment at the time of measuring.</ns0:p></ns0:div> <ns0:div><ns0:head>Lymphocyte subsets in the peripheral blood of MCD patients and healthy controls</ns0:head><ns0:p>We analyzed lymphocyte subsets, including CD3 + T cells, CD3 + CD4 + T cells, CD3 + CD8 + T cells, CD3 -CD19 + B cells and CD3 -CD16 + /CD56 + NK cells. The distribution of lymphocyte subsets in the peripheral blood of MCD patients and healthy controls are showed in Table <ns0:ref type='table' target='#tab_4'>2</ns0:ref>. There were no statistically significant differences in the proportions of CD3 + and CD3 + CD4 + cells among patients with MCD in relapse, MCD in remission and healthy controls (P=0.445 and P=0.802). MCD patients in relapse had significant higher proportions of CD3 + CD8 + cells compared to MCD in remission (42.7±2.29% vs. 27.42±1.51%, P=0.008) and healthy controls (42.7±2.29% vs. 27.97±2.34%, P<0.001). Furthermore, CD4 + /CD8 + ratio was lower in MCD in relapse compared with MCD in remission (0.84±0.09 vs. 1.45±0.14, P=0.014) and healthy controls (0.84±0.09 vs. 1.4±0.12, P=0.005). The CD3 -CD16 + /CD56 + NK cell and CD3 -CD19 + B cell populations revealed no significant differences among patients with MCD in relapse, MCD in remission and healthy controls (P=0.199 and P=0.445). <ns0:ref type='table' target='#tab_12'>2020:05:48577:1:2:NEW 28 Jul 2020)</ns0:ref> Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>PeerJ reviewing PDF \| (</ns0:head></ns0:div> <ns0:div><ns0:head>Cytokine profiles in serum of MCD patients and controls</ns0:head><ns0:p>We investigated Th1/Th2/Th17 cytokines in the serum of the randomly selected MCD patients and controls by cytokine antibody array (Fig. <ns0:ref type='table' target='#tab_9'>1A-1C and Table 3</ns0:ref>). The characteristics of the randomly selected MCD patients and healthy controls are presented in Table <ns0:ref type='table' target='#tab_10'>4</ns0:ref>. Each cytokine antibody array included 34 cytokines (Fig. <ns0:ref type='figure' target='#fig_2'>1D</ns0:ref>). The concentrations that increased by ≥1.5-fold or decreased by ≤0.65-fold were considered as significant. Our results in Table <ns0:ref type='table' target='#tab_8'>3</ns0:ref> indicate that the levels of GM-CSF and tumor necrosis factor (TNF)-related activation-induced cytokine (TRANCE) in patients with MCD in relapse increased 1.5 times higher than in patients in remission. GM-CSF, IL-10, IL-22 and TNF beta levels increased significantly in patients with MCD in relapse compared to healthy controls. The expression of CD40 was found to have decreased in MCD in relapse compared to healthy controls.</ns0:p></ns0:div> <ns0:div><ns0:head>Measurement of CD80 and CTLA-4 expression</ns0:head><ns0:p>We explored urinary CD80 expression adjusted by urinary creatinine in patients with MCD in relapse (n=10), MCD in remission (n=9) and healthy controls (n=9) (Fig. <ns0:ref type='figure'>2A</ns0:ref>). A significant increase in the excretion of urinary CD80 was observed in MCD patients in relapse when compared with patients in remission (598.4±115.8 ng/g vs. 81.78±7.04 ng/g creatinine, P<0.001) and healthy controls (598.4±115.8 ng/g vs. 67.44±8.94 ng/g creatinine, P<0.001). The excretion of urinary CD80 showed no significant difference between MCD patients in remission and healthy controls (P=0.269, Fig. <ns0:ref type='figure'>2A</ns0:ref>). There was no correlation between urinary CD80 and proteinuria in MCD patients in relapse (r=-0.32, P=0.366, Fig. <ns0:ref type='figure'>2B</ns0:ref>).</ns0:p><ns0:p>In contrast to the urinary results, no significant differences were found in serum CD80 concentrations among patients with MCD in relapse, MCD in remission, and healthy control subjects (379.9±32.3 vs. 287.6±19.48 vs. 342.4±28.43 pg/ml, P=0.081). Urinary CTLA-4 levels were not significantly increased in MCD patients in relapse when compared with patients in remission (173.7±8.73 vs. 155.0±8.54 ng/g creatinine, P=0.098) and healthy control subjects (173.7±8.73 vs. 160.0±10.85 ng/g creatinine, P=0.253, Fig. <ns0:ref type='figure'>2C</ns0:ref>). There was no correlation between the concentrations of urinary CTLA-4 and proteinuria in MCD patients in relapse (r=0.18, P=0.632, Fig. <ns0:ref type='figure'>2D</ns0:ref>). There was also no inverse correlation between urinary CTLA-4 and urinary CD80 in MCD patients in relapse (r=0.15, P=0.682, Fig. <ns0:ref type='figure'>2E</ns0:ref>) nor in remission (r=0.3, P=0.437, Fig. <ns0:ref type='figure'>2F</ns0:ref>).</ns0:p><ns0:p>Compared with MCD patients in remission, the urinary CD80 to CTLA-4 ratio was elevated significantly in MCD in relapse (P=0.004, Fig. <ns0:ref type='figure' target='#fig_5'>3A</ns0:ref>). No significant differences of serum CD80/CTLA-4 ratio (P=0.278, Fig. <ns0:ref type='figure' target='#fig_5'>3B</ns0:ref>) and CTLA-4 concentrations (P=0.949, Fig. <ns0:ref type='figure' target='#fig_5'>3C</ns0:ref>) were found among patients with MCD in relapse, MCD in remission and healthy controls.</ns0:p></ns0:div> <ns0:div><ns0:head>The expression of CD80 in podocyte in patient with relapsed MCD</ns0:head><ns0:p>We used immunofluorescence technique to test the expression of CD80 in glomeruli of patient 5 as listed in Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref>. Two glomeruli from patient 5 with relapsed MCD were stained for CD80 in green (Fig. <ns0:ref type='figure' target='#fig_7'>4A and 4D</ns0:ref>), synaptopodin in red (Fig. <ns0:ref type='figure' target='#fig_7'>4B</ns0:ref>) and WT-1 in red (Fig. <ns0:ref type='figure' target='#fig_7'>4E</ns0:ref>). The double immunostaining for CD80 and synaptopodin in the glomerulus of MCD patient in relapse showed colocalization (Fig. <ns0:ref type='figure' target='#fig_7'>4C</ns0:ref>). CD80 and WT-1 co-localized in the glomerulus of MCD patient in relapse (Fig. <ns0:ref type='figure' target='#fig_7'>4F</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>Discussion</ns0:head><ns0:p>In the present study, we investigated the lymphocyte subsets, 34 cytokines expression of Th1/Th2/Th17 and the CD80 expression in adult-onset MCD. Our results showed that there was no significant difference in the proportion of CD3 + CD4 + cells among patients with MCD in relapse, MCD in remission and healthy controls, but the cytokines GM-CSF and TNFSF11 were increased and the urinary CD80 levels were elevated in adult-onset MCD patients in relapse, indicating a disorder of Th1/Th2/Th17 balance and that the elevated excretion of CD80 may underlie the pathogenesis and development of adult-onset MCD.</ns0:p><ns0:p>Even though the proportion of CD3 + CD4 + cells showed no significant difference between patients with MCD and healthy controls, the cytokines of GM-CSF and TRANCE increased significantly in patients with MCD in relapse, which might be due to the changes in CD3 + CD4 + cell function. Interestingly, we found the proportions of CD8 + counts were elevated in MCD in relapse compared with controls, implicating CD8+ T cells might also participate in the course of MCD. GM-CSF, a representative cytokine of Th1 and Th17, is increased in the urine of patients with FSGS and is related to glomerulosclerosis <ns0:ref type='bibr' target='#b19'>(Stangou et al., 2017)</ns0:ref>. Moreover, GM-CSF is PeerJ reviewing PDF \| (2020:05:48577:1:2:NEW 28 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed secreted by renal parenchymal cells and inflammatory cells, which could mediate crescent formation, renal tubule injure and proteinuria, and ultimately lead to renal dysfunction in murine crescentic nephritis <ns0:ref type='bibr' target='#b20'>(Timoshanko et al., 2005)</ns0:ref>. TRANCE, also known as RANKL, is a cytokine secreted by Th1 and its expression can be promoted by IL-17 which is a cytokine secreted by Th17 <ns0:ref type='bibr' target='#b21'>(Hienz, Paliwal & Ivanovski, 2015)</ns0:ref>. However, the role of GM-CSF and TRANCE in patients with MCD is rarely reported. By analyzing the 34 cytokines secreted by Th1/Th2/Th17, we found that GM-CSF and TRANCE in the serum of patients with MCD in relapse were significantly higher than that in patients in remission. To our knowledge, we did not find the same results that there was increased expression of GM-CSF and TRANCE in the serum of patients with recurrent MCD. Accordingly, we speculate that the imbalance of Th1/Th2/Th17 in patients with MCD in relapse leads to the increase of GM-CSF and TRANCE secretion by Th1 and Th17 cells, which further induces podocyte damage and leads to proteinuria.</ns0:p><ns0:p>It has been proposed that CD80 plays a vital role in the 'two-hit' podocyte immune disorder of MCD <ns0:ref type='bibr' target='#b11'>(Shimada, Araya, Rivard, Ishimoto, Johnson & Garin, 2011)</ns0:ref>. Our study showed that the excretion of urinary CD80 increased in adult-onset MCD patients in relapse when compared with MCD in remission and healthy controls. Similar results have been reported in other studies of childhood and adult-onset MCD patients <ns0:ref type='bibr' target='#b14'>(Garin, Diaz, Mu, Wasserfall, Araya, Segal & Johnson, 2009;</ns0:ref><ns0:ref type='bibr' target='#b22'>Zhao et al., 2018)</ns0:ref>. However, we found there was no correlation between urinary CD80 and proteinuria in adult-onset MCD patients in relapse, demonstrating the elevated CD80 was not simply a reflection of proteinuria. Besides, the serum CD80 in patients with MCD in remission was not different from those in relapse, implicating that the elevated urinary levels could not be explained by higher serum concentrations. Synaptopodin is a highly expressed actin binding protein in podocyte <ns0:ref type='bibr'>(Yu et al., 2018)</ns0:ref>. WT-1 is known to be expressed on podocyte in kidney <ns0:ref type='bibr'>(Funk et al., 2016)</ns0:ref>. The double immunostaining for CD80 and synaptopodin, and CD80 and WT-1 in the glomerulus of MCD patient in relapse showed colocalization, confirming the source of the urinary CD80 in MCD patients in relapse was the podocyte. In general, CD80 cannot be expressed on podocytes, but in some glomerulopathies, its expression on the surface of podocyte is increased. One of the possible reasons is that podocytes are switched to an antigen presenting cell phenotype <ns0:ref type='bibr'>(Trimarchi, 2015)</ns0:ref>. Although the staining in Fig. <ns0:ref type='figure' target='#fig_6'>4</ns0:ref> was convincing, immunohistochemistry was only performed on a single biopsy and further studies are needed to verify these findings.</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:05:48577:1:2:NEW 28 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed Moreover, CD80 may interact with CTLA-4, which plays a crucial role in cellular and humoral immunity <ns0:ref type='bibr'>(Greenwald, Freeman & Sharpe, 2005)</ns0:ref>. We found that urinary or serum CTLA-4 levels were not significantly increased in MCD patients in relapse compared with those in remission. However, the urinary CD80 to CTLA-4 ratio was higher in MCD in relapse versus in remission, which could be due to the defective response of Treg from patients in relapse to produce CTLA-4. <ns0:ref type='bibr' target='#b15'>(Garin, Reiser, Cara-Fuentes, Wei, Matar, Wang, Alachkar & Johnson, 2015)</ns0:ref> reported that abatacept was useful in the treatment of one patient with MCD, but not in FSGS.</ns0:p><ns0:p>Thus, the CD80-CTLA-4 axis seems to play an important part in the mechanism of proteinuria in recurrent MCD. Drugs targeting the CD80-CTLA-4 axis may be expected to treat MCD in relapse in the future.</ns0:p><ns0:p>There are some limitations in our study. Firstly, the sample size is small, so more studies are needed to verify the expression of GM-CSF and TRANCE in patients with MCD in relapse.</ns0:p><ns0:p>Secondly, further studies are needed to explore the mechanism of GM-CSF and TRANCE in podocyte injury of patients with MCD. Thirdly, the interaction between GM-CSF, TRANCE and CD80 pathway in podocyte injury needs to be elucidated.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions</ns0:head><ns0:p>In our study, the cytokines GM-CSF and TRANCE were increased in the serum and the urinary CD80 levels were elevated in adult-onset MCD, indicating a disorder of Th1/Th2/Th17 balance and that the elevated excretion of CD80 may underlie the pathogenesis and development of adult-onset MCD. Further studies are warranted to investigate the precise mechanism of the interaction between Th1/Th2/Th17 balance and CD80 during the course of MCD. Manuscript to be reviewed Manuscript to be reviewed Manuscript to be reviewed Manuscript to be reviewed Table <ns0:ref type='table' target='#tab_8'>3</ns0:ref> The results of 34 human Th1-, Th2-and Th17-related cytokines in patients with MCD in relapse, MCD in remission and healthy controls (median).</ns0:p><ns0:p>Table <ns0:ref type='table' target='#tab_8'>3</ns0:ref>. The results of 34 human Th1-, Th2-and Th17-related cytokines in patients with MCD in relapse, MCD in remission and healthy controls (median). Manuscript to be reviewed 5 ※ P<0.05 compared remission with healthy controls 6</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:05:48577:1:2:NEW 28 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed Manuscript to be reviewed Manuscript to be reviewed Manuscript to be reviewed </ns0:p><ns0:note type='other'>Figure 1</ns0:note><ns0:note type='other'>Figure 4</ns0:note></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>markers in adult-onset minimal change disease: a retrospective study. PeerJ 6:e5400 doi 10.7717/peerj.5400 Yu SM, Nissaisorakarn P, Husain I, Jim B. 2018. Proteinuric Kidney Diseases: A Podocyte's Slit Diaphragm and Cytoskeleton Approach. Front Med (Lausanne) 5:221 doi 10.3389/fmed.2018.00221 Funk J, Ott V, Herrmann A, Rapp W, Raab S, Riboulet W, Vandjour A, Hainaut E, Benardeau A, Singer T, Jacobsen B. 2016. Semiautomated quantitative image analysis of glomerular immunohistochemistry markers desmin, vimentin, podocin, synaptopodin and WT-1 in acute and chronic rat kidney disease models. Histochem Cell Biol 145 (3):315-326 doi 10.1007/s00418-015-1391-6 Trimarchi H. 2015. Abatacept and Glomerular Diseases: The Open Road for the Second Signal as a New Target is Settled Down. Recent Pat Endocr Metab Immune Drug Discov 9 (1):2-14 doi 10.2174/1872214809666150302104542 Greenwald RJ, Freeman GJ, Sharpe AH. 2005. The B7 family revisited. Annu Rev Immunol 23:515-548 doi 10.1146/annurev.immunol.23.021704.115611 PeerJ reviewing PDF \| (2020:05:48577:1:2:NEW 28 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head>Figure 1</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1 Cytokine profiles of patients with MCD and normal control.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>Figure 1</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1 Cytokine profiles of patients with MCD and normal control. Cytokine Ab array images from a patient with MCD in relapse (A), a patient in remission (B), and a healthy control (C) were shown. The levels of cytokine in serum were represented by the spot density. The four spots in the upper left and two in the lower right corners of the membranes indicate positive controls. The position of 34 human Th1-, Th2-and Th17--related cytokines in the antibody based microarray (D).</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head>Figure 2 Figure 2 Figure 2</ns0:head><ns0:label>222</ns0:label><ns0:figDesc>Figure 2</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head>Figure 3 Figure 3</ns0:head><ns0:label>33</ns0:label><ns0:figDesc>Figure 3</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head>Figure 3</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Figure 3 CD80 and CTLA-4 in patients with MCD in relapse, MCD in remission and healthy controls. (A) The ratio of CD80 (ng/g creatinine) to CTLA-4 (ng/g creatinine) in urine in patients with MCD in relapse, MCD in remission and healthy controls. (B) Serum CD80/CTLA-4 ratio between patients with MCD in relapse, MCD in remission and healthy controls. (C) Serum CTLA-4 concentrations in patients with MCD in relapse, MCD in remission and healthy controls.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head>Figure 4</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Figure 4 The expression of CD80 in glomeruli of MCD patient in relapse.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_7'><ns0:head>Figure 4</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Figure4The expression of CD80 in glomeruli of MCD patient in relapse. CD80 is expressed (green stain) in the glomeruli of MCD patient in relapse (A and D). Synaptopodin is expressed (red stain) in glomeruli of MCD patients in relapse (B). WT-1 is expressed (red stain) in glomeruli of MCD patients in relapse (E). CD80 and synaptopodin colocalized at the glomeruli (C). CD80 and WT-1 co-localized at the glomeruli (F).</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='24,42.52,70.87,525.00,422.25' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='30,42.52,70.87,525.00,447.00' type='bitmap' /></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head>Table 1 (on next page)</ns0:head><ns0:label>1</ns0:label><ns0:figDesc /><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_1'><ns0:head>Table 1</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Characteristic of patients with MCD in relapse, MCD in remission and healthy controls</ns0:figDesc><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_2'><ns0:head>Table 1</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Characteristic of patients with MCD in relapse, MCD in remission and healthy controls</ns0:figDesc><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_3'><ns0:head>Table 1 .</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Characteristic of patients with MCD in relapse, MCD in remission and healthy controls</ns0:figDesc><ns0:table><ns0:row><ns0:cell>No. of patients</ns0:cell><ns0:cell>Age</ns0:cell><ns0:cell>Gender</ns0:cell><ns0:cell>Serum Alb (g/l)</ns0:cell><ns0:cell>eGFR (ml/min/1.73 m2)</ns0:cell><ns0:cell>Up/Uc Ratio</ns0:cell><ns0:cell>Urinary CD80 (ng/g Cre)</ns0:cell><ns0:cell>Urinary CTLA-4 (ng/g Cre)</ns0:cell><ns0:cell>Treatment</ns0:cell></ns0:row><ns0:row><ns0:cell>MCD in relapse</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>1</ns0:cell><ns0:cell /><ns0:cell>M</ns0:cell><ns0:cell>18.7</ns0:cell><ns0:cell>117.4</ns0:cell><ns0:cell>10.5</ns0:cell><ns0:cell>271.00</ns0:cell><ns0:cell>158.00</ns0:cell><ns0:cell>None</ns0:cell></ns0:row><ns0:row><ns0:cell>2</ns0:cell><ns0:cell /><ns0:cell>M</ns0:cell><ns0:cell>29</ns0:cell><ns0:cell>108.5</ns0:cell><ns0:cell>3.7</ns0:cell><ns0:cell>737.00</ns0:cell><ns0:cell>209.00</ns0:cell><ns0:cell>Pre10 mg/d</ns0:cell></ns0:row><ns0:row><ns0:cell>3</ns0:cell><ns0:cell /><ns0:cell>M</ns0:cell><ns0:cell>28</ns0:cell><ns0:cell>99.9</ns0:cell><ns0:cell>7</ns0:cell><ns0:cell>199.00</ns0:cell><ns0:cell>147.00</ns0:cell><ns0:cell>Pre 5 mg/d</ns0:cell></ns0:row><ns0:row><ns0:cell>4</ns0:cell><ns0:cell /><ns0:cell>F</ns0:cell><ns0:cell>24</ns0:cell><ns0:cell>131.6</ns0:cell><ns0:cell>15</ns0:cell><ns0:cell>1050.00</ns0:cell><ns0:cell>200.00</ns0:cell><ns0:cell>Pre 20mg QD</ns0:cell></ns0:row><ns0:row><ns0:cell>5</ns0:cell><ns0:cell /><ns0:cell>M</ns0:cell><ns0:cell>26.5</ns0:cell><ns0:cell>122.9</ns0:cell><ns0:cell>3.5</ns0:cell><ns0:cell>864.00</ns0:cell><ns0:cell>157.00</ns0:cell><ns0:cell>Met 12 mg QD, Tac 1 mg BID</ns0:cell></ns0:row><ns0:row><ns0:cell>6</ns0:cell><ns0:cell /><ns0:cell>F</ns0:cell><ns0:cell>12.5</ns0:cell><ns0:cell>97.7</ns0:cell><ns0:cell>22.6</ns0:cell><ns0:cell>503.00</ns0:cell><ns0:cell>178.00</ns0:cell><ns0:cell>Pre 15 mg every other day</ns0:cell></ns0:row><ns0:row><ns0:cell>7</ns0:cell><ns0:cell /><ns0:cell>F</ns0:cell><ns0:cell>25.5</ns0:cell><ns0:cell>110.8</ns0:cell><ns0:cell>11</ns0:cell><ns0:cell>560.00</ns0:cell><ns0:cell>196.00</ns0:cell><ns0:cell>Met 8 mg QD</ns0:cell></ns0:row><ns0:row><ns0:cell>8</ns0:cell><ns0:cell /><ns0:cell>M</ns0:cell><ns0:cell>24.7</ns0:cell><ns0:cell>120.5</ns0:cell><ns0:cell>8.5</ns0:cell><ns0:cell>1250.00</ns0:cell><ns0:cell>150.00</ns0:cell><ns0:cell>Pre 5 mg/d</ns0:cell></ns0:row><ns0:row><ns0:cell>9</ns0:cell><ns0:cell /><ns0:cell>F</ns0:cell><ns0:cell>19.8</ns0:cell><ns0:cell>100.1</ns0:cell><ns0:cell>25.9</ns0:cell><ns0:cell>240.00</ns0:cell><ns0:cell>207.00</ns0:cell><ns0:cell>None</ns0:cell></ns0:row><ns0:row><ns0:cell>10</ns0:cell><ns0:cell /><ns0:cell>M</ns0:cell><ns0:cell>23.4</ns0:cell><ns0:cell>130.8</ns0:cell><ns0:cell>18</ns0:cell><ns0:cell>310.00</ns0:cell><ns0:cell>135.00</ns0:cell><ns0:cell>Pre 10 mg every other day</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>mean±SEM 33.9±3.42</ns0:cell><ns0:cell /><ns0:cell>23.21±1.57</ns0:cell><ns0:cell>114±3.98</ns0:cell><ns0:cell>12.48±2.47</ns0:cell><ns0:cell>598.4±115.8</ns0:cell><ns0:cell>173.7±8.73</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell cols='2'>MCD in remission</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>11</ns0:cell><ns0:cell /><ns0:cell>M</ns0:cell><ns0:cell>35</ns0:cell><ns0:cell>125.9</ns0:cell><ns0:cell>0.12</ns0:cell><ns0:cell>90.00</ns0:cell><ns0:cell>142.00</ns0:cell><ns0:cell>Pre 30 mg/d</ns0:cell></ns0:row><ns0:row><ns0:cell>12</ns0:cell><ns0:cell /><ns0:cell>M</ns0:cell><ns0:cell>40</ns0:cell><ns0:cell>138</ns0:cell><ns0:cell>Neg</ns0:cell><ns0:cell>79.00</ns0:cell><ns0:cell>139.00</ns0:cell><ns0:cell>Pre 20 mg/d</ns0:cell></ns0:row><ns0:row><ns0:cell>13</ns0:cell><ns0:cell /><ns0:cell>F</ns0:cell><ns0:cell>32</ns0:cell><ns0:cell>125.6</ns0:cell><ns0:cell>0.18</ns0:cell><ns0:cell>60.00</ns0:cell><ns0:cell>127.00</ns0:cell><ns0:cell>Met 16 mg QD, Tac 1 mg BID</ns0:cell></ns0:row><ns0:row><ns0:cell>14</ns0:cell><ns0:cell /><ns0:cell>M</ns0:cell><ns0:cell>34</ns0:cell><ns0:cell>115</ns0:cell><ns0:cell>0.11</ns0:cell><ns0:cell>110.00</ns0:cell><ns0:cell>189.00</ns0:cell><ns0:cell>Pre 60 mg/d</ns0:cell></ns0:row><ns0:row><ns0:cell>15</ns0:cell><ns0:cell /><ns0:cell>F</ns0:cell><ns0:cell>39</ns0:cell><ns0:cell>118</ns0:cell><ns0:cell>0.06</ns0:cell><ns0:cell>120.00</ns0:cell><ns0:cell>199.00</ns0:cell><ns0:cell>Met 12mg/d</ns0:cell></ns0:row><ns0:row><ns0:cell>16</ns0:cell><ns0:cell /><ns0:cell>F</ns0:cell><ns0:cell>40</ns0:cell><ns0:cell>140</ns0:cell><ns0:cell>0.05</ns0:cell><ns0:cell>54.00</ns0:cell><ns0:cell>165.00</ns0:cell><ns0:cell>Met 12mg every other day</ns0:cell></ns0:row><ns0:row><ns0:cell>17</ns0:cell><ns0:cell /><ns0:cell>M</ns0:cell><ns0:cell>45</ns0:cell><ns0:cell>130</ns0:cell><ns0:cell>0.01</ns0:cell><ns0:cell>68.00</ns0:cell><ns0:cell>155.00</ns0:cell><ns0:cell>Pre 20 mg/d</ns0:cell></ns0:row><ns0:row><ns0:cell>18</ns0:cell><ns0:cell /><ns0:cell>F</ns0:cell><ns0:cell>47</ns0:cell><ns0:cell>99</ns0:cell><ns0:cell>Neg</ns0:cell><ns0:cell>73.00</ns0:cell><ns0:cell>153.00</ns0:cell><ns0:cell>Pre15g/d</ns0:cell></ns0:row><ns0:row><ns0:cell>19</ns0:cell><ns0:cell /><ns0:cell>M</ns0:cell><ns0:cell>55</ns0:cell><ns0:cell>111</ns0:cell><ns0:cell>Neg</ns0:cell><ns0:cell>82.00</ns0:cell><ns0:cell>126.00</ns0:cell><ns0:cell>Pre 10 mg/d</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>mean±SEM 32.44±3.408</ns0:cell><ns0:cell /><ns0:cell>40.78±2.4</ns0:cell><ns0:cell>122.5±4.386</ns0:cell><ns0:cell>0.05±0.002</ns0:cell><ns0:cell>81.78±7.038</ns0:cell><ns0:cell>155±8.54</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>healthy controls</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>20</ns0:cell><ns0:cell /><ns0:cell>F</ns0:cell><ns0:cell>45</ns0:cell><ns0:cell>120</ns0:cell><ns0:cell>Neg</ns0:cell><ns0:cell>100.00</ns0:cell><ns0:cell>200.00</ns0:cell><ns0:cell>N/A</ns0:cell></ns0:row><ns0:row><ns0:cell>21</ns0:cell><ns0:cell /><ns0:cell>F</ns0:cell><ns0:cell>55</ns0:cell><ns0:cell>119</ns0:cell><ns0:cell>Neg</ns0:cell><ns0:cell>44.00</ns0:cell><ns0:cell>210.00</ns0:cell><ns0:cell>N/A</ns0:cell></ns0:row></ns0:table><ns0:note>PeerJ reviewing PDF \| (2020:05:48577:1:2:NEW 28 Jul 2020) Manuscript to be reviewed PeerJ reviewing PDF \| (2020:05:48577:1:2:NEW 28 Jul 2020)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_4'><ns0:head>Table 2 (on next page)</ns0:head><ns0:label>2</ns0:label><ns0:figDesc /><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_5'><ns0:head>Table 2</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Lymphocyte subsets in patients with MCD in relapse, MCD in remission and healthy controls</ns0:figDesc><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_6'><ns0:head>Table 2</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Lymphocyte subsets in patients with MCD in relapse, MCD in remission and healthy</ns0:figDesc><ns0:table /><ns0:note>controlsPeerJ reviewing PDF \| (2020:05:48577:1:2:NEW 28 Jul 2020)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_7'><ns0:head>Table 2 .</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Lymphocyte subsets in patients with MCD in relapse, MCD in remission and healthy controls</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Variables</ns0:cell><ns0:cell>MCD in relapse (n=10)</ns0:cell><ns0:cell>MCD in remission (n=9)</ns0:cell><ns0:cell>Healthy controls (n=9)</ns0:cell></ns0:row><ns0:row><ns0:cell>CD3+%</ns0:cell><ns0:cell>75.74±2.36</ns0:cell><ns0:cell>72.59±2.15</ns0:cell><ns0:cell>70.98±2.85</ns0:cell></ns0:row><ns0:row><ns0:cell>CD3+CD4+%</ns0:cell><ns0:cell>35.07±2.02</ns0:cell><ns0:cell>37.45±1.94</ns0:cell><ns0:cell>35.23±2.4</ns0:cell></ns0:row><ns0:row><ns0:cell>CD3+CD8+%</ns0:cell><ns0:cell>42.7±2.29* #</ns0:cell><ns0:cell>27.42±1.51</ns0:cell><ns0:cell>27.97±2.34</ns0:cell></ns0:row><ns0:row><ns0:cell>CD4+/CD8+</ns0:cell><ns0:cell>0.84±0.09 # </ns0:cell><ns0:cell>1.45±0.14</ns0:cell><ns0:cell>1.4±0.12</ns0:cell></ns0:row><ns0:row><ns0:cell>CD3 -CD16 + / CD56 + NK%</ns0:cell><ns0:cell>11.44±1.54</ns0:cell><ns0:cell>14.44±1.4</ns0:cell><ns0:cell>11.52±1.7</ns0:cell></ns0:row><ns0:row><ns0:cell>CD3 -CD19 + B cells%</ns0:cell><ns0:cell>10.85±1.14</ns0:cell><ns0:cell>15.10±2.56</ns0:cell><ns0:cell>12.05±1.12</ns0:cell></ns0:row><ns0:row><ns0:cell cols='3'>p<0.05 compared MCD in relapse with healthy controls</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell cols='3'># P<0.05 compared MCD in relapse with MCD in remission</ns0:cell><ns0:cell /></ns0:row></ns0:table><ns0:note>PeerJ reviewing PDF \| (2020:05:48577:1:2:NEW 28 Jul 2020)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_8'><ns0:head>Table 3 (on next page)</ns0:head><ns0:label>3</ns0:label><ns0:figDesc /><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_9'><ns0:head>Table 3 .</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>The results of 34 human Th1-, Th2-and Th17-related cytokines in patients with MCD in relapse, MCD in remission and healthy controls (median).</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Variables</ns0:cell><ns0:cell>MCD in relapse</ns0:cell><ns0:cell>MCD in remission</ns0:cell><ns0:cell>healthy controls</ns0:cell></ns0:row><ns0:row><ns0:cell>CD30</ns0:cell><ns0:cell>0.05 (0.02-0.07)</ns0:cell><ns0:cell>0.07 (0.04-0.07)</ns0:cell><ns0:cell>0.07 (0.03-0.29)</ns0:cell></ns0:row><ns0:row><ns0:cell>CD40 Ligand</ns0:cell><ns0:cell>0.43 (0.3-0.62)</ns0:cell><ns0:cell>0.42 (0.29-0.53)</ns0:cell><ns0:cell>0.51 (0.35-0.74)</ns0:cell></ns0:row><ns0:row><ns0:cell>CD40</ns0:cell><ns0:cell>0.14 (0.07-0.24) #</ns0:cell><ns0:cell>0.20 (0.06-0.34)</ns0:cell><ns0:cell>0.21 (0.08-0.30)</ns0:cell></ns0:row><ns0:row><ns0:cell>GCSF</ns0:cell><ns0:cell>0.04 (0.02-0.09)</ns0:cell><ns0:cell>0.05 (0.02-0.09) ※</ns0:cell><ns0:cell>0.03 (0.01-0.05)</ns0:cell></ns0:row><ns0:row><ns0:cell>GITR (TNFRSF18)</ns0:cell><ns0:cell>0.61 (0.44-0.75)</ns0:cell><ns0:cell>0.54 (0.40-0.61)</ns0:cell><ns0:cell>0.57 (0.47-0.64)</ns0:cell></ns0:row><ns0:row><ns0:cell>GM-CSF</ns0:cell><ns0:cell>0.10 (0.02-0.26)* #</ns0:cell><ns0:cell>0.05 (0.02-0.06)</ns0:cell><ns0:cell>0.06 (0.06-0.1)</ns0:cell></ns0:row><ns0:row><ns0:cell>IFN-gamma</ns0:cell><ns0:cell>0.54 (0.44-0.65)</ns0:cell><ns0:cell>0.52 (0.49-0.56)</ns0:cell><ns0:cell>0.49 (0.41-0.57)</ns0:cell></ns0:row><ns0:row><ns0:cell>IL-1 R1</ns0:cell><ns0:cell>0.34 (0.20-0.58)</ns0:cell><ns0:cell>0.37 (0.34-0.44) ※</ns0:cell><ns0:cell>0.23 (0.13-0.32)</ns0:cell></ns0:row><ns0:row><ns0:cell>IL-1 R2</ns0:cell><ns0:cell>0.71 (0.62-0.78)</ns0:cell><ns0:cell>0.77 (0.73-0.82)</ns0:cell><ns0:cell>0.71 (0.61-0.81)</ns0:cell></ns0:row><ns0:row><ns0:cell>IL-10</ns0:cell><ns0:cell>0.18 (0.05-0.33) #</ns0:cell><ns0:cell>0.16 (0.11-0.20)</ns0:cell><ns0:cell>0.11 (0.10-0.12)</ns0:cell></ns0:row><ns0:row><ns0:cell>IL-12 p40</ns0:cell><ns0:cell>0.68 (0.60-0.76)</ns0:cell><ns0:cell>0.62 (0.61-0.64)</ns0:cell><ns0:cell>0.70 (0.61-0.64)</ns0:cell></ns0:row><ns0:row><ns0:cell>IL-12 p70</ns0:cell><ns0:cell>0.46 (0.33-0.57)</ns0:cell><ns0:cell>0.38 (0.25-0.47)</ns0:cell><ns0:cell>0.49 (0.42-0.62)</ns0:cell></ns0:row><ns0:row><ns0:cell>IL-13</ns0:cell><ns0:cell>0.20 (0.13-0.28)</ns0:cell><ns0:cell>0.16 (0.06-0.23)</ns0:cell><ns0:cell>0.20 (0.13-0.29)</ns0:cell></ns0:row><ns0:row><ns0:cell>IL-17A</ns0:cell><ns0:cell>0.16 (0.10-0.25)</ns0:cell><ns0:cell>0.16 (0.07-0.21)</ns0:cell><ns0:cell>0.18 (0.14-0.26)</ns0:cell></ns0:row><ns0:row><ns0:cell>IL-17F</ns0:cell><ns0:cell>0.16 (0.10-0.23)</ns0:cell><ns0:cell>0.16 (0.08-0.23)</ns0:cell><ns0:cell>0.23 (0.14-0.30)</ns0:cell></ns0:row><ns0:row><ns0:cell>IL-17 RA</ns0:cell><ns0:cell>0.03 (0.01-0.05)</ns0:cell><ns0:cell>0.04 (0.03-0.06)</ns0:cell><ns0:cell>0.03 (0.03-0.06)</ns0:cell></ns0:row><ns0:row><ns0:cell>IL-1 beta (IL-1 F2)</ns0:cell><ns0:cell>0.44 (0.30-0.62)</ns0:cell><ns0:cell>0.38 (0.27-0.45)</ns0:cell><ns0:cell>0.45 (0.26-0.65)</ns0:cell></ns0:row><ns0:row><ns0:cell>IL-2</ns0:cell><ns0:cell>0.58 (0.45-0.68)</ns0:cell><ns0:cell>0.54 (0.47-0.58)</ns0:cell><ns0:cell>0.59 (0.46-0.65)</ns0:cell></ns0:row><ns0:row><ns0:cell>IL-21</ns0:cell><ns0:cell>0.54 (0.41-0.66)</ns0:cell><ns0:cell>0.52 (0.48-0.57)</ns0:cell><ns0:cell>0.50 (0.40-0.58)</ns0:cell></ns0:row><ns0:row><ns0:cell>IL-21 R</ns0:cell><ns0:cell>0.49 (0.35-0.67)</ns0:cell><ns0:cell>0.49 (0.44-0.54)</ns0:cell><ns0:cell>0.41 (0.34-0.50)</ns0:cell></ns0:row><ns0:row><ns0:cell>IL-22</ns0:cell><ns0:cell>0.29 (0.07-0.39) #</ns0:cell><ns0:cell>0.27 (0.20-0.37) ※</ns0:cell><ns0:cell>0.11 (0.10-0.12)</ns0:cell></ns0:row><ns0:row><ns0:cell>IL-23</ns0:cell><ns0:cell>0.56 (0.44-0.71)</ns0:cell><ns0:cell>0.65 (0.60-0.68)</ns0:cell><ns0:cell>0.55 (0.47-0.62)</ns0:cell></ns0:row><ns0:row><ns0:cell>IL-28A</ns0:cell><ns0:cell>0.22 (0.17-0.28)</ns0:cell><ns0:cell>0.20 (0.17-0.22)</ns0:cell><ns0:cell>0.25 (0.20-0.28)</ns0:cell></ns0:row><ns0:row><ns0:cell>IL-4</ns0:cell><ns0:cell>0.21 (0.13-0.30)</ns0:cell><ns0:cell>0.16 (0.08-0.22)</ns0:cell><ns0:cell>0.23 (0.08-0.22)</ns0:cell></ns0:row><ns0:row><ns0:cell>IL-5</ns0:cell><ns0:cell>0.07 (0.05-0.13)</ns0:cell><ns0:cell>0.05 (0.03-0.09) ※</ns0:cell><ns0:cell>0.09 (0.07-0.12)</ns0:cell></ns0:row><ns0:row><ns0:cell>IL-6</ns0:cell><ns0:cell>0.08 (0.04-0.11)</ns0:cell><ns0:cell>0.07 (0.05-0.11)</ns0:cell><ns0:cell>0.08 (0.06-0.12)</ns0:cell></ns0:row><ns0:row><ns0:cell>IL-6 R</ns0:cell><ns0:cell>0.95 (0.89-0.98)</ns0:cell><ns0:cell>0.94 (0.88-0.98)</ns0:cell><ns0:cell>0.96 (0.93-0.99)</ns0:cell></ns0:row><ns0:row><ns0:cell>MIP-3α</ns0:cell><ns0:cell>0.35 (0.24-0.45)</ns0:cell><ns0:cell>0.32 (0.19-0.41)</ns0:cell><ns0:cell>0.30 (0.20-0.44)</ns0:cell></ns0:row><ns0:row><ns0:cell>gp130</ns0:cell><ns0:cell>0.86 (0.82-0.90)</ns0:cell><ns0:cell>0.87 (0.83-0.90)</ns0:cell><ns0:cell>0.88 (0.85-0.92)</ns0:cell></ns0:row><ns0:row><ns0:cell>TGF beta 1</ns0:cell><ns0:cell>0.45 (0.34-0.58)</ns0:cell><ns0:cell>0.46 (0.38-0.51)</ns0:cell><ns0:cell>0.48 (0.43-0.51)</ns0:cell></ns0:row><ns0:row><ns0:cell>TGF beta 3</ns0:cell><ns0:cell>0.52 (0.40-0.68)</ns0:cell><ns0:cell>0.51 (0.48-0.56)</ns0:cell><ns0:cell>0.52 (0.50-0.57)</ns0:cell></ns0:row><ns0:row><ns0:cell>TNF alpha</ns0:cell><ns0:cell>0.37 (0.24-0.65)</ns0:cell><ns0:cell>0.32 (0.30-0.34)</ns0:cell><ns0:cell>0.29 (0.23-0.40)</ns0:cell></ns0:row><ns0:row><ns0:cell>TNF beta</ns0:cell><ns0:cell>0.45 (0.30-0.64) #</ns0:cell><ns0:cell>0.33 (0.29-0.35)</ns0:cell><ns0:cell>0.29 (0.24-0.37)</ns0:cell></ns0:row><ns0:row><ns0:cell>TRANCE (TNFSF11)</ns0:cell><ns0:cell>0.22 (0.13-0.36)</ns0:cell><ns0:cell>0.15 (0.07-0.20)</ns0:cell><ns0:cell>0.18 (0.12-0.21)</ns0:cell></ns0:row></ns0:table><ns0:note>P<0.05 compared relapse with remission # P<0.05 compared relapse with healthy controls PeerJ reviewing PDF \| (2020:05:48577:1:2:NEW 28 Jul 2020)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_10'><ns0:head>Table 4 (on next page)</ns0:head><ns0:label>4</ns0:label><ns0:figDesc /><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_11'><ns0:head>Table 4</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Characteristic of randomly selected patients with MCD in relapse, MCD in remission and healthy controls</ns0:figDesc><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_12'><ns0:head>Table 4 .</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Characteristic of randomly selected patients with MCD in relapse, MCD in remission</ns0:figDesc><ns0:table><ns0:row><ns0:cell>and healthy controls</ns0:cell></ns0:row></ns0:table><ns0:note>PeerJ reviewing PDF \| (2020:05:48577:1:2:NEW 28 Jul 2020)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_13'><ns0:head>1</ns0:head><ns0:label /><ns0:figDesc>Table 4. Characteristic of randomly selected patients with MCD in relapse, MCD in remission and healthy controls 2 Abbreviation: MCD, minimal change disease; eGFR, estimated glomerular filtration rate; Up/Uc, urinary protein/urinary creatinine; M, male; F, female; 3 Pre, Prednisone; Met, Methylprednisolone; Tac, Tacrolimus; Cre, Creatinine; Alb, Albumin; NA, not available</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Patient</ns0:cell><ns0:cell>Age</ns0:cell><ns0:cell>Gender</ns0:cell><ns0:cell>Serum Alb (g/l)</ns0:cell><ns0:cell>eGFR (ml/min/1.73 m2)</ns0:cell><ns0:cell>Up/Uc Ratio</ns0:cell><ns0:cell>Urinary CD80 (ng/g Cre)</ns0:cell><ns0:cell>Urinary CTLA-4 (ng/g Cre)</ns0:cell><ns0:cell>Treatment</ns0:cell></ns0:row><ns0:row><ns0:cell>MCD in relapse</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>2</ns0:cell><ns0:cell /><ns0:cell>M</ns0:cell><ns0:cell>29</ns0:cell><ns0:cell>108.5</ns0:cell><ns0:cell>3.7</ns0:cell><ns0:cell>737.00</ns0:cell><ns0:cell>209.00</ns0:cell><ns0:cell>Pre10 mg/d</ns0:cell></ns0:row><ns0:row><ns0:cell>4</ns0:cell><ns0:cell /><ns0:cell>F</ns0:cell><ns0:cell>24</ns0:cell><ns0:cell>131.6</ns0:cell><ns0:cell>15</ns0:cell><ns0:cell>1050.00</ns0:cell><ns0:cell>200.00</ns0:cell><ns0:cell>Pre 20mg QD</ns0:cell></ns0:row><ns0:row><ns0:cell>5</ns0:cell><ns0:cell /><ns0:cell>M</ns0:cell><ns0:cell>26.5</ns0:cell><ns0:cell>122.9</ns0:cell><ns0:cell>3.5</ns0:cell><ns0:cell>864.00</ns0:cell><ns0:cell>157.00</ns0:cell><ns0:cell>Met 12 mg QD, Tac 1 mg BID</ns0:cell></ns0:row><ns0:row><ns0:cell>6</ns0:cell><ns0:cell /><ns0:cell>F</ns0:cell><ns0:cell>12.5</ns0:cell><ns0:cell>97.7</ns0:cell><ns0:cell>22.6</ns0:cell><ns0:cell>503.00</ns0:cell><ns0:cell>178.00</ns0:cell><ns0:cell>Pre 15 mg every other day</ns0:cell></ns0:row><ns0:row><ns0:cell>7</ns0:cell><ns0:cell /><ns0:cell>F</ns0:cell><ns0:cell>25.5</ns0:cell><ns0:cell>110.8</ns0:cell><ns0:cell>11</ns0:cell><ns0:cell>560.00</ns0:cell><ns0:cell>196.00</ns0:cell><ns0:cell>Met 8 mg QD</ns0:cell></ns0:row><ns0:row><ns0:cell>10</ns0:cell><ns0:cell /><ns0:cell>M</ns0:cell><ns0:cell>23.4</ns0:cell><ns0:cell>130.8</ns0:cell><ns0:cell>18</ns0:cell><ns0:cell>310.00</ns0:cell><ns0:cell>135.00</ns0:cell><ns0:cell>Pre 10 mg every other day</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>mean±SEM 35±5.05</ns0:cell><ns0:cell /><ns0:cell>23.48±2.34</ns0:cell><ns0:cell>117±5.54</ns0:cell><ns0:cell>12.3±3.15</ns0:cell><ns0:cell>670.7±109</ns0:cell><ns0:cell>179.2±11.6</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>MCD in remission</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>14</ns0:cell><ns0:cell /><ns0:cell>M</ns0:cell><ns0:cell>34</ns0:cell><ns0:cell>115</ns0:cell><ns0:cell>0.11</ns0:cell><ns0:cell>110.00</ns0:cell><ns0:cell>189.00</ns0:cell><ns0:cell>Pre 60 mg/d</ns0:cell></ns0:row><ns0:row><ns0:cell>15</ns0:cell><ns0:cell /><ns0:cell>F</ns0:cell><ns0:cell>39</ns0:cell><ns0:cell>118</ns0:cell><ns0:cell>0.06</ns0:cell><ns0:cell>120.00</ns0:cell><ns0:cell>199.00</ns0:cell><ns0:cell>Met 12mg/d</ns0:cell></ns0:row><ns0:row><ns0:cell>19</ns0:cell><ns0:cell /><ns0:cell>M</ns0:cell><ns0:cell>55</ns0:cell><ns0:cell>111</ns0:cell><ns0:cell>Neg</ns0:cell><ns0:cell>82.00</ns0:cell><ns0:cell>126.00</ns0:cell><ns0:cell>Pre 10 mg/d</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>mean±SEM 28.67±3.75</ns0:cell><ns0:cell /><ns0:cell>42.67±6.33</ns0:cell><ns0:cell>114.7±2.02</ns0:cell><ns0:cell>0.06±0.03</ns0:cell><ns0:cell>104±11.37</ns0:cell><ns0:cell>171.3±22.85</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>healthy controls</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>22</ns0:cell><ns0:cell /><ns0:cell>F</ns0:cell><ns0:cell>50</ns0:cell><ns0:cell>110</ns0:cell><ns0:cell>Neg</ns0:cell><ns0:cell>32.00</ns0:cell><ns0:cell>168.00</ns0:cell><ns0:cell>N/A</ns0:cell></ns0:row><ns0:row><ns0:cell>26</ns0:cell><ns0:cell /><ns0:cell>M</ns0:cell><ns0:cell>52</ns0:cell><ns0:cell>118</ns0:cell><ns0:cell>0.01</ns0:cell><ns0:cell>110.00</ns0:cell><ns0:cell>132.00</ns0:cell><ns0:cell>N/A</ns0:cell></ns0:row><ns0:row><ns0:cell>28</ns0:cell><ns0:cell /><ns0:cell>M</ns0:cell><ns0:cell>44</ns0:cell><ns0:cell>112</ns0:cell><ns0:cell>Neg</ns0:cell><ns0:cell>40.00</ns0:cell><ns0:cell>127.00</ns0:cell><ns0:cell>N/A</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>mean±SEM 44±6.36</ns0:cell><ns0:cell /><ns0:cell>48.67±2.4</ns0:cell><ns0:cell>113.3±2.4</ns0:cell><ns0:cell cols='2'>0.003±0.003 60.67±24.77</ns0:cell><ns0:cell>142.3±12.91</ns0:cell><ns0:cell /></ns0:row></ns0:table><ns0:note>PeerJ reviewing PDF \| (2020:05:48577:1:2:NEW 28 Jul 2020)</ns0:note></ns0:figure> </ns0:body> "	"Dear Editors We thank the reviewers for their generous comments on the manuscript and have edited the manuscript to address their concerns as follows. Best wishes Ping Chen Reviewer 1 Basic reporting No comment Experimental design No comment Validity of the findings No comment Comments for the author Data are relatively novel, but of limited clinical impact. Whether this evaluation can be applied in clinical practice, whether it can be handled by all labs and how expensive it is remain unaddressed issues. Moreover, this paper suffers from some methodological limitations and results do not support any definite conclusion. Thanks very much for your comments. In this paper, the cytokines expression of Th1/Th2/Th17 and urinary CD80 excretion in adult-onset MCD were described in our results. We must acknowledge that the low number of included cases is the main shortcoming of this paper. We explained this limitation in our discussion. We tried to find out the candidate cytokines, which might play an important role in the mechanism of MCD in relapse, from these limited cases. The price of cytokine antibody array measurement is expensive as you said. However, this is a research program and we have found some significant differences between patients with MCD in relapse, MCD in remission and healthy controls. If our results were confirmed by large case-series in the future and demonstrated to be useful in guide clinical practice, then, I think it would be worth it. Overall, in our study, we found that the cytokines GM-CSF and TRANCE are increased and the urinary CD80 excretion is elevated in adult-onset MCD patients in relapse, indicating a disorder of Th1/Th2/Th17 balance and that the elevated excretion of CD80 may underlie the pathogenesis and development of adult-onset MCD. Given the low number of patients, it is difficult to draw a definite conclusion. Therefore, further studies are warranted to investigate the precise mechanism of the interaction between Th1/Th2/Th17 balance and CD80 during the course of MCD. Major concerns: - The number of patients is very limited. Thanks for your comments. The low number of patients is our main limitation, and we have highlighted this drawback in the limitations of the study. More studies are needed to verify our results in patients with MCD in relapse. - More data should be provided about patient clinical history (proteinuria, previous treatment) Thanks for your suggestions. In our results, the urinary protein/urinary creatinine is showed in Table 1, which usually referred to as proteinuria. The initial treatment modalities were also provided for patients with MCD. Moreover, when patients were diagnosed with MCD in relapse and MCD in remission, the lymphocyte subsets, 34 cytokine levels of Th1/Th2/Th17, CD80 and CTLA-4 were analyzed in the blood, and the CD80 and CTLA-4 levels were monitored in the urine. We also provided a supplementary table to show the patients clinical history, including previous treatment modalities (initial therapies). Abbreviation: MCD, minimal change disease; eGFR, estimated glomerular filtration rate; M, male; F, female; Pre, Prednisone; Met, Methylprednisolone; Tac, Tacrolimus; Cre, Creatinine; Alb, Albumin; NA, not available; Up/Uc, urinary protein/urinary creatinine Reviewer 2 Basic reporting The article is written in professional English, technically correct and unambiguous. The literature references are of sufficient support as background. Structure of the manuscript is professionally designed and the paper is self-contained. Experimental design The primary research is within the aims and scope of the journal. The research question is well defined relevant and meaningful, and the whole study has a role to fill a knowledge gap. Investigation is performed to a high technical and ethical standard. Methods are described with details and give sufficient information to replicate. Validity of the findings The data are provided, results are clear and novel, some negative results are discussed and explained. Conclusions are well stated and are limited to supporting results. Comments for the author The paper is very well written, no comments for methods, results, discussion. Few typing errors, such as in line 160 word TRNACE must change to TRANCE. Thank you very much for your comments and suggestions. Sorry for my mistake. I have changed “TRNACE” to “TRANCE” in the results. Reviewer: Hernan Trimarchi Basic reporting The use of the English language requires major polishing. Too many grammatical mistakes. Needs to be corrected by a native English speaking person. The references are adequate, despite some suggestions are given ahead. Professional article structure is adequate. There is coherence between hypothesis, findings and conclusions. Experimental design The research has been undertaken appropriately. Validity of the findings This article offers new data to the literature. The number of patients is low, a drawback that has been highlighted in the limitations of the study. Conclusions are correctly addressed. Comments for the author The present article written by Chen Ping et al is about some T cell sub-population cytokines in a small cohort of patients with minimal change disease (MCD), some in relapse and some in remission. Concerns: English language is poor, and requires major polishing. The article is sometimes hard to follow or interpret. The number of cases is low to draw firm conclusions. Thanks very much for your comments and it’s of great importance to our article. And I have asked a friend of mine, a native English speaker, who works in Loyal free hospital ( London, UK), to edit the language of this manuscript. He has finished revising the language of the paper. Thanks again for your comments and suggestions. Abstract: Background, authors state that MCD is a common form of nephritic syndrome. This is completely wrong. MCD is a nephrotic entity, not nephritic, please. Agreed, thanks very much for your correction. I have changed it to nephrotic. Methods: The age and gender of patients must be outlined in the abstract. Thanks a lot and I have added the patients’ age and gender in the methods. Abbreviations must be explained what they stand for, as TRANCE, etc. OK, thank you. I have added the full name of TRANCE in the abstract. At the end of the Introduction, authors comment that '...few studies have reported the role of urinary CD80 excretion in adult-onset CD80..'. This is wrong. Please, replace by: few studies have tried to explain the possible reasons why elevated urinary levels of CD80 excretion are encountered in adult-onset MCD'. Thanks a lot for your suggestion and I have replaced it as you suggested. How was MCD defined?. Was electron microscopy performed?. They cite Vivarelli et al 2017, a review paper based both for children and adults. The definition of MCD given by this review is broad and ample, and of course describes the findings in OM, IF and EM. So, authors must address the way MCD was defined: OM?. Was EM performed?. Thank you for your suggestion. All renal biopsies were processed according to standard methods for light microscopy, immunofluorescence, and electron microscopy. By light microscopy, no glomerular lesions or only mild focal mesangial prominence not exceeding three or four cells per segment are seen. Immunofluorescence is usually negative. By electron microscopy, foot process effacement is the only morphologic feature of MCD. Other pathologic features automatically exclude the diagnosis of MCD, which is therefore a diagnosis of exclusion. Methods: Delete the words 'on an empty stomach'. OK, thanks. I have deleted these words. Authors must hypothesize why CD80 levels are elevated in the urine. The source appears to be podocytes. The why?. One of the possible reasons is that podocytes are switched to an antigen presenting cell phenotype. Please read and cite Trimarchi H: Recent Pat Endocr Metab Immune Drug Discov 2015;9(1):2-14. doi: 10.2174/1872214809666150302104542. Thanks so much for your suggestions and I have downloaded this article and read it very carefully. It is of great clinical significance to elucidate the relationship and mechanism between abatacept and glomerular diseases. I have cited this paper in my article. "	Here is a paper. Please give your review comments after reading it.
520	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Background. The present pandemic COVID-19 is caused by SARS-CoV-2, a Single Stranded positive sense RNA virus from Coronaviridae family. Due to lack of antiviral drugs, vaccine against the virus is urgently required. Methods. In this study, validated computational approaches were used to identify peptide based epitopes from six structural proteins having antigenic properties. The Net-CTL 1.2 tool was used for the prediction of CD8 + T-cell epitopes, while the robust tools Bepi-Pred 2 and LBtope was employed for the identification of linear B-cell epitopes. Docking studies of the identified epitopes were performed using HADDOCK 2.4 and the structures were visualized by Discovery Studio and LigPlot + . Antigenicity, immunogenicity, conservancy,population coverage and allergenicity Computational perspectives revealed prospective vaccine candidates from five structural proteins of Novel SARS Corona Virus 2019 (SARS-CoV-2</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>of the predicted epitopes were determined by the bioinformatics tools like VaxiJen v2.0 server, the Immune Epitope Database (IEDB) tools and AllerTOP v.2.0,AllergenFP 1.0 and ElliPro. Results. The predicted T cell and linear B-cell epitopes were considered as prime vaccine targets in case they passed the requisite parameters like antigenicity, immunogenicity, conservancy, non-allergenicity and broad range of population coverage.</ns0:p><ns0:p>Among the predicted CD8+ T cell epitopes, potential vaccine targets from surface glycoprotein were; YQPYRVVVL, PYRVVVLSF, GVYFASTEK, QLTPTWRVY, and those from ORF3a protein were LKKRWQLAL, HVTFFIYNK. Similarly RFLYIIKLI, LTWICLLQF from membrane protein and three epitopes viz; SPRWYFYYL, TWLTYTGAI, KTFPPTEPK from nucleocapsid phosphoprotein were the superior vaccine targets observed in our study. The negative values of HADDOCK and Z scores obtained for the best cluster indicated the potential of the epitopes as suitable vaccine candidates. Analysis of the 3D and 2D interaction diagrams of best cluster produced by HADDOCK 2.4 displayed the binding interaction of leading T cell epitopes within the MHC-1 peptide binding clefts. On the other hand, among linear B cell epitopes majority of potential vaccine targets were from nucleocapsid protein, viz;</ns0:p></ns0:div> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>Globally the present dreaded pandemic of corona virus disease 2019 has resulted in deaths of more than 445000 humans [World Health Organization (WHO) COVID-2019-situation report-150]. The causative agent of the disease has been severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) [Corona virus disease 2019-WHO]. The family Coronaviridae consists of a large group of viruses known as Corona viruses (CoVs). The corona viruses were thought to be harmless respiratory human pathogens due to (i) harmless mild infections and (ii) limited number of the circulating viruses in humans <ns0:ref type='bibr' target='#b69'>[Song et al., 2019]</ns0:ref>. However, the emergence of series of three severe and fatal diseases caused by corona virus changed the concept. The first instance was severe acute respiratory syndrome (SARS) in <ns0:ref type='bibr'>November 2002</ns0:ref><ns0:ref type='bibr'>to Feb. 2003</ns0:ref> in China and the second was Middle East Respiratory Syndrome (MERS) in June 2012 in Saudi Arabia <ns0:ref type='bibr' target='#b13'>[de Wit et al., 2016)</ns0:ref>]. The most recent cases of fatal disease outbreaks caused by corona virus occurred in December 2019, in Wuhan, Hubei, China. These consecutive viral outbreaks also indicate the threat of cross-species transmission of these viruses leading to severe infectious outbreak in humans that should be considered seriously <ns0:ref type='bibr'>[Menachery, V. D. et al., 2015]</ns0:ref>. Therefore, the threats of CoVs should not be undermined and the research on the life cycle and host-virus interactions should be advanced in order to develop treatments and vaccines against these viruses. The scientific and clinical investigations demonstrated that SARS-CoV and MERS-CoV share remarkable features that lead to preferential viral replication in the lower respiratory tract and viral immunopathology. The recent investigations on the clinical, laboratory, radiological and epidemiological characteristics and outcomes of treatments in patients demonstrated that the severe respiratory illness similar to SARS-CoV was due to SARS-CoV-2 (COVID-19) <ns0:ref type='bibr'>[Huang et al., 2020]</ns0:ref>. Although the early investigations patterns suggested that the COVID-19 virus can cause severe illness in some patients, with limited transmission among people, up to date epidemiological data strongly favours the statement that the new virus has evolved/ adapted more efficiently for transmission among humans. The genome sequences of COVID-19 viruses obtained from patients indicated that they share 79.5% sequence identity to SARS-CoV <ns0:ref type='bibr'>[Zhou et al., 2020]</ns0:ref> and 96% identity to bat corona virus at the whole genome level. The phylogenetic studies of corona viruses obtained from different organisms indicated that COVID-19 could have originated from Chinese horseshoe bats, however, the vehicle which led to the transmission to host has not yet been identified <ns0:ref type='bibr' target='#b17'>[Dong et al., 2020]</ns0:ref>. COVID-19 virus was bannered as a novel type of corona virus from bat due to a high degree of variation from the human SARS virus <ns0:ref type='bibr' target='#b66'>[Shereen et al., 2020;</ns0:ref><ns0:ref type='bibr' target='#b1'>Andersen et al., 2020]</ns0:ref>. Altogether seven member of the family of CoVs infect humans, the COVID-19 is the newest of all. Both SARS-Corona Virus and COVID-19 virus enters host cells through an endosomal pathway and the cells through the entry receptor, angiotensin-converting enzyme II (ACE2) <ns0:ref type='bibr'>[Zhou et al., 2020;</ns0:ref><ns0:ref type='bibr' target='#b36'>Letko et al., 2020]</ns0:ref>. The SARS-CoV and hCoV-NL63 utilizes human angiotensin converting enzyme 2 (ACE2) for virus entry <ns0:ref type='bibr' target='#b24'>[Hofmann et al., 2005;</ns0:ref><ns0:ref type='bibr'>Li et al., 2003;</ns0:ref><ns0:ref type='bibr' target='#b83'>Wu et al., 2009]</ns0:ref>. Recently scientists have determined that SARSCoV-2 also interacts with ACE2 for cell entry <ns0:ref type='bibr'>[Zhou et al., 2020]</ns0:ref>. The entry process of corona viruses is facilitated by the surface-located spike glycoprotein <ns0:ref type='bibr' target='#b41'>[Lu et al., 2015]</ns0:ref>. Spike protein can be divided into the S1 and S2 subunits, which are utilized as receptor recognition and membrane fusion molecules, respectively <ns0:ref type='bibr' target='#b32'>[Lai et al., 2007]</ns0:ref>. S1 Both the Nterminal domain (NTD) and a C-terminal domain (CTD) of S1 unit can function as a receptorbinding entity or receptor binding domain (RBD) <ns0:ref type='bibr' target='#b37'>[Li et al., 2005;</ns0:ref><ns0:ref type='bibr' target='#b40'>Lu et al., 2013;</ns0:ref><ns0:ref type='bibr' target='#b72'>Taguchi and Hirai-Yuki, 2012]</ns0:ref>. Recently, the S1 CTD (SARS-CoV-2-CTD) has been identified as the prime region in SARS-CoV-2 that interacts with the ACE2 receptor <ns0:ref type='bibr'>[Wang et al., 2020]</ns0:ref>. The crystal structure of SARS-CoV2-CTD in complex with human ACE2, exhibited bindings similar to that observed for the SARS-CoV-RBD. It has been further identified that SARS-CoV-2-CTD forms more atomic interactions with human ACE2 than SARS-RBD, resulting in higher affinity for receptor binding <ns0:ref type='bibr' target='#b64'>[Shang et al., 2020;</ns0:ref><ns0:ref type='bibr'>Wang et al., 2020]</ns0:ref>. On the basis of the genetic properties, Coronaviridae family can be divided in to four genera, including genus Alpha corona virus, genus Beta corona virus, genus Gamma corona virus, and genus Delta corona virus. Among the RNA viruses, the corona virus has the largest genome (ranging from 26 to 32 kb) with particle size of viruses about 125 nm in diameter <ns0:ref type='bibr' target='#b31'>[Ji et al., 2020]</ns0:ref>. CoVs possess a composite genome expression strategy as numerous CoV proteins expressed in the infected cell contribute to the corona virus-host interactions. These strategies include (i) associations with the host cell to create a favorable environment for CoV replication, (ii) modification of the host gene expression and nullifying the antiviral defences of host. The CoV-host interplay is thus key to pathogenesis of virus <ns0:ref type='bibr' target='#b11'>[de Wilde et al., 2018]</ns0:ref>. Two-thirds of the CoV genome belongs to genes for non-structural proteins. Amid the structural proteins, spike <ns0:ref type='bibr'>(S)</ns0:ref>, envelope (E), membrane (M), and nucleocapsid (N) can be considered important in terms of vaccine potential. The viral membrane has S, E, and M proteins. The Spike protein is a surfacelocated trimeric glycoprotein and actively plays a role in viral ingress into host cells, viral infection, and pathogenesis and was contemplated as a prime vaccine and therapeutic target against SARS-CoV and MERS-CoV. The membrane and envelope proteins are required in viral assemblage, whereas the nucleocapsid protein is involved for assembly of RNA genome <ns0:ref type='bibr' target='#b69'>[Song et al., 2019]</ns0:ref>. Although CoVs share numerous resemblances, they also have genetically evolved significantly and finding the potential targets for vaccines and antiviral drugs against COVID-19 should exploit the structural similarities between SARS-CoV and COVID-19 virus and focus on proteins that are highly conserved across multiple CoVs. In this work all the structural proteins were selected for finding the epitopes for designing the vaccine against CoVID-19 using validated in silico approaches.</ns0:p></ns0:div> <ns0:div><ns0:head>Materials & Methods</ns0:head><ns0:p>The overall procedures used in the present study for epitope-based vaccine design and physicochemical property prediction have been depicted in the form of flow chart (Fig. <ns0:ref type='figure' target='#fig_2'>1</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>Retrieval of the Protein Sequence:</ns0:head><ns0:p>The protein sequences of SARS-CoV-2 were retrieved from the Virus Pathogen Database and Analysis Resource (ViPR) (http://www.viprbrc.org/) in FASTA format. ViPR database helps in fetching the sequence from both GenBank and UniProtKB in the FASTA format <ns0:ref type='bibr' target='#b56'>[Pickett et al., 2012]</ns0:ref>. The nonstructural proteins of SARS-CoV-2 were removed from the complete proteome of SARS-CoV-2. Similarity search and selection of protein for epitope prediction: The sequences of potential structural proteins (surface glycoprotein, orf3a protein, envelope protein, membrane glycoprotein, nucleocapsid phosphoprotein, orf6 protein) were searched for the similarity using the BLAST tool (https://blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE=Proteins). Only those hits were selected for comparison of identities which showed 100 % query cover. The BLAST results of surface glycoprotein (Accession No. QHQ82464) exhibited more than 99.84 % identity (100 hits) and those of orf3a (Accession No.QHQ82465) showed identities of more than 99.27 % (74 hits). Next the envelope protein (Accession No.QHQ82466) resulted in 14 hits with the identity range between 100 to 94.67% while the BLAST using the membrane glyco-protein (Accession No. QHQ82467) resulted in about 50 hits with the range of identity between 100 to 93.24 %. Similarly the nucleocapsid phosphoprotein (Accession No.QHQ82471) resulted in 100 hits with identity in the range from 100 to 99.28 %. The last selected protein from SARS-CoV-2, orf6 (Accession No. QHQ82468) exhibited an identity range between 100 to 95 % in more than 40 hits. Since all the BLAST searches performed using the above structural proteins demonstrated the identity between 93 to 100%, a conclusion was drawn that any one representative protein from the six structural proteins could be used for further study. Thus we selected all the six complete sequences of proteins with the above accession numbers for further studies.</ns0:p></ns0:div> <ns0:div><ns0:head>Determination of antigenicity of the SARS-CoV-2 MHC I epitopes:</ns0:head><ns0:p>The term antigenicity is the capacity of a molecule to be specifically recognized by the antibodies generated as a result of immune response to the given substance. Proteins generated by divergent or convergent evolution may lack apparent sequence similarity, although they may share structural similarity and biological characteristics <ns0:ref type='bibr' target='#b55'>[Petsko & Ringe, 2004]</ns0:ref>. Antigenicity may be encoded in a sequence in a fine and obscure manner not feasible to direct recognition by sequence alignment. Similarly, the search of novel antigens will be circumvented by their lack of similarity to antigens of known origin <ns0:ref type='bibr' target='#b18'>[Doytchinova and Flower 2007]</ns0:ref>. A novel alignment-free approach for antigen prediction,VaxiJen, based on auto cross covariance (ACC) transformation of protein sequences into uniform vectors of principal amino acid properties was developed to control the limitations of alignment based strategies <ns0:ref type='bibr' target='#b18'>[Doytchinova and Flower 2007]</ns0:ref>. All the structural proteins of SARS-CoV-2 were submitted to the VaxiJen v2.0 server (http://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html) <ns0:ref type='bibr' target='#b18'>[Doytchinova and Flower, 2007]</ns0:ref> in FASTA format for the determination of antigenicity. A threshold value of 0.4 was considered for determination of antigenicity.</ns0:p></ns0:div> <ns0:div><ns0:head>Prediction and identification of T Cell Epitopes</ns0:head><ns0:p>The T-cell epitopes are usually small peptide fragments of 8-11 amino acids and can elicit specific immune responses. These are important for epitope-based peptide vaccine design <ns0:ref type='bibr' target='#b51'>[Patronov and Doytchinova, 2013]</ns0:ref>. The NetCTL 1.2 server (http://www.cbs.dtu.dk/services/NetCTL/), can be utilized for the prediction of the T cell epitopes in any specified protein. The server can anticipate the epitope for 12 MHC-I super types A1, A2, A3, A24, A26, B7, B8, B27, B39, B44, B58, B62 present on CD8 + T Cells. The prediction of epitope of CD8 + T cell is on the basis of interpretations obtained from proteasomal C terminal cleavage, MHC class I binding, and TAP transport efficiency. The artificial neural network (ANN) is used for the prediction of MHC class I binding, proteasomal C terminal cleavage, while weight matrix is employed for the estimation of TAP transport efficiency <ns0:ref type='bibr' target='#b34'>[Larsen et al., 2005;</ns0:ref><ns0:ref type='bibr' target='#b35'>Larsen et al., 2007]</ns0:ref>. In this study the server was used for the prediction of epitopes of all the 12 super types of MHC I and a higher threshold value of 1.25 for epitope prediction was fixed, which has a better sensitivity and specificity of 0.54 and 0.993, respectively. The default parameters set by server for the weight matrix determination for proteasomal C terminal cleavage (0.15) and TAP transport efficiency (0.05) were used.</ns0:p></ns0:div> <ns0:div><ns0:head>Prediction of Antigenicity</ns0:head><ns0:p>For the recognition of both frequently and non-frequently occurring MHC-I-binding alleles, the T cell epitopes of SARS-CoV-2 were analyzed by the stabilized matrix base method (SMM) of the IEDB analysis tool (http://tools.iedb.org/mhci/) as described earlier <ns0:ref type='bibr'>[Peters et al., 2005;</ns0:ref><ns0:ref type='bibr' target='#b42'>Lundegaard et al, 2008]</ns0:ref>. The recognition of MHC-I binding alleles were performed on the parameters (i) the peptide length of epitope was restricted to 9 and (ii) the IC50 of less than 250 nM was selected on the server. Lower IC50 value signifies higher binding. The HLA-binding affinity of the epitopes is quantitatively described in the IC50 nM units. In general, for similar ligands, higher binding affinity of the epitopes with the MHC class I molecule is reflected by the lower IC50 value. Therefore, IC50 values less than 250nM (IC50 < 250) was selected for ensuring higher binding affinity of the epitopes. The IC50 of the epitopes were determined by the IEDB tool. IEDB being a resourceful server, can also be used for the estimation of processing score, TAP score, proteasomal cleavage, and the MHC-I binding score of the specified epitopes and their respective alleles using the stabilized matrix based method <ns0:ref type='bibr' target='#b54'>[Peters et al.,2003;</ns0:ref><ns0:ref type='bibr'>Tenzer et al., 2005]</ns0:ref>. Epitopes were selected based on the highest combined score, but the final selection for further study was made after the prediction of antigenicity by VaxiJen v2.0 server and that of immunogenicity by IEDB server. The combined score is the derived from median percentile rank of seven alleles and immunogenicity score using following equation Manuscript to be reviewed Percentile rank of seven alleles), where alpha is optimized to 0.4 as described earlier <ns0:ref type='bibr' target='#b14'>[Dhanda et al., 2018]</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Epitope Immunogenicity and Conservancy Prediction</ns0:head><ns0:p>Immunogenicity is defined as the ability of a substance/molecule to instigate cellular and humoral immune response <ns0:ref type='bibr' target='#b28'>[Ilinskaya and Dobrovolskaia, 2016]</ns0:ref>. Conservancy may be defined as the fragment of protein sequences that carry the epitope which is considered at or above a specified level of identity <ns0:ref type='bibr' target='#b2'>[Bui et al., 2007]</ns0:ref>. The effective T-cell epitopes are more immunogenic and are considered better than the less immunogenic peptides <ns0:ref type='bibr' target='#b0'>[Adhikari et al., 2018]</ns0:ref>. Therefore, the epitope with better immunogenicity was selected for further evaluation. The immunogenicity prediction tool available on the server http://tools.iedb.org/immunogenicity/ was utilized for the identification of immunogenicity while conservancy was predicted by the tool available on iedb (http://tools.iedb.org/conservancy/) <ns0:ref type='bibr' target='#b49'>[Nielsen et al., 2007;</ns0:ref><ns0:ref type='bibr' target='#b3'>Calis et al., 2013]</ns0:ref>. All the epitopes having positive immunogenicity scores (given by IEDB tool) were considered a potential immunogen. Determination of population coverage MHC molecules are exceptionally polymorphic and more than a thousand divergent human MHC (HLA) alleles are recognized. To determine the population coverage a tool is required that can optimally calculate the distribution of humans which will respond to a given group of epitope on the basis of HLA genotypic prevalence and MHC binding and/or T cell restriction data <ns0:ref type='bibr' target='#b2'>[Bui et al., 2007]</ns0:ref>. Population coverage for each identified epitope and their corresponding MHC HLA-binding alleles was determined by the population coverage tool available on IEDB server (http://tools.iedb.org/population/). Here we used the allelic frequency of the interacting HLA alleles for the prediction of the population coverage for the corresponding epitope. In a recent report population coverage of about 64% was reported for an epitope <ns0:ref type='bibr' target='#b50'>[Oany et al., 2014]</ns0:ref>.In this study a population coverage of 65 % or more was selected.</ns0:p></ns0:div> <ns0:div><ns0:head>Allergenicity and Toxicity assessment</ns0:head><ns0:p>The web-based AllerTOP v.2.0 (http://www.ddg-pharmfac.net/AllerTOP/) <ns0:ref type='bibr' target='#b15'>[Dimitrov et al., 2014]</ns0:ref> and AllergenFP 1.0 (http://www.ddg-pharmfac.net/AllergenFP/) <ns0:ref type='bibr' target='#b16'>[Dimitrov et al., 2014a]</ns0:ref> was used to check the allergenicity of our proposed epitope for vaccine development. AllergenFP 1.0 has been established on a novel alignment-free descriptor-based fingerprint technique. An accuracy of 87.9% is observed in the identification of both allergens and nonallergens by AllergenFP 1.0. In contrast, to classify allergens and nonallergens, AllerTOP v. 2.0 has been established on the basis of k-nearest neighbours (kNN) method. The web server ToxinPred (http://crdd.osdd.net/raghava/toxinpred/) was implemented to predict toxicity of the peptides <ns0:ref type='bibr' target='#b21'>[Gupta et al., 2013]</ns0:ref>. This strategy was developed the basis of machine learning technique and quantitative matrix utilizing distinctive properties of peptides.</ns0:p></ns0:div> <ns0:div><ns0:head>Prediction of MHC II epitopes:</ns0:head><ns0:p>The disadvantage of many bioinformatics methods including Gibbs samplers, Ant colony, Artificial neural networks, Support vector machines, hidden Markov models, and motif search algorithms for predicting MHC class II epitopes is owing to training and evaluation on very limited data sets covering a single or a few different MHC class II alleles <ns0:ref type='bibr' target='#b49'>[Nielsen et al., 2007]</ns0:ref>. On the IEDB database, a large group of quantitative MHC class II peptide-binding data is available <ns0:ref type='bibr'>[Toseland et al., 2005]</ns0:ref>. The data includes the peptide with binding affinities (IC50) for more than 14 HLA/MHC. A novel stabilized matrix method (SMM)-align method (NetMHCII) for quantitative predictions of MHC class II binding was developed which utilizes the IEDB MHC class II peptide binding database <ns0:ref type='bibr' target='#b49'>[Nielsen et al., 2007]</ns0:ref>. The SMM-align method attempts to recognize a weight matrix that ideally emulates the measured IC50 values for each peptide in the training group <ns0:ref type='bibr' target='#b49'>[Nielsen et al., 2007]</ns0:ref>. The MHC I epitopes derived from structural proteins were selected for the prediction of MHC-II-binding alleles using the SMM-align method. As per the instruction of the tool, an IC 50 value up to 3000 nM was considered significant.</ns0:p></ns0:div> <ns0:div><ns0:head>Design of the three-dimensional (3D) structure of epitope</ns0:head><ns0:p>In order to be considered as proper vaccine candidate, an epitope need to fulfill all the criteria like antigenicity, immunogenicity, conservancy of epitopes, non-toxicity and it should be nonallergen. Epitope candidates were evaluated on the basis of above parameters and were subjected to the determination of three-dimensional structure using the PEP-FOLD peptide prediction server (http://bioserv.rpbs.univ-paris-diderot.fr/services/PEP-FOLD/) <ns0:ref type='bibr' target='#b75'>[Thévenet et al., 2012;</ns0:ref><ns0:ref type='bibr' target='#b65'>Shen et al., 2014]</ns0:ref>. Thus the potential epitopes fulfilling all the above criteria were used for the structure determination. The best model obtained using the server was taken forward for docking analysis.</ns0:p></ns0:div> <ns0:div><ns0:head>Docking analysis</ns0:head><ns0:p>To know the binding interactions between HLA molecules and the predicted epitope, molecular docking simulation was executed using High Ambiguity Driven protein-protein DOCKing (HADDOCK) version: 2.4 (https://bianca.science.uu.nl/haddock2.4/). HADDOCK is an information-driven flexible docking approach for the modeling of biomolecular complexes. Despite continuous advances in the field, the accuracy of ab initio docking-without using any experimental restraints-remains generally low <ns0:ref type='bibr' target='#b27'>[Huang, 2015]</ns0:ref>. Data-driven approaches such as HADDOCK <ns0:ref type='bibr'>[Van Zundert et al., 2015]</ns0:ref>, which integrate information derived from biochemical, biophysical or bioinformatics methods to enhance sampling, scoring or both <ns0:ref type='bibr' target='#b60'>[Rodrigues and Bonvin, 2014]</ns0:ref>, perform remarkably better. The main attribute of HADDOCK is the Ambiguous Interaction Restraints or AIRs. These permit the conversion of raw data including mutagenesis experiments or NMR chemical shift perturbation into distance restraints which are integrated in the energy functions. These energy functions are used in calculations. In the docking protocol of HADDOCK, molecules pass through varying degrees of flexibility and distinct chemical surroundings <ns0:ref type='bibr'>[Van Zundert et al., 2015]</ns0:ref>. The performance of HADDOCK protocol depends on the number of models generated at each step. The grading of the clusters is based on the average score of the top 4 members of each cluster. The score is calculated as: HADDOCK score = 1.0 * Evdw + 0.2 * Eelec + 1.0 * Edesol + 0.1 * Eair Where, 'Evdw' represents the intermolecular van der Waals energy, 'Eelec' is the intermolecular electrostatic energy, where as 'Edesol' is an empirical desolvation energy <ns0:ref type='bibr'>[Fernandez-Recio et al., 2004]</ns0:ref>, and Eair represents the AIR energy. Numbering of cluster in the results indicates the magnitude of the cluster. The diverse elements of the HADDOCK score are also described for each cluster on the results web page. The top cluster is the most reliable according to HADDOCK. The more negative results of HADDOCK score and Z score signifies better structures and interaction [HADDOCK 2.4 basic proteinprotein docking tutorial]. For the HADDOCK inputs, the crystal structure of the HLA-C07:02 (PDB id: 5VGE) HLA-A30:01 (6J1W), HLA-B58:01, (5VWH), HLA-B08:01, (3X13) was retrieved from the RCSB Protein Data Bank (PDB) in the PDB format <ns0:ref type='bibr' target='#b20'>[Gras et al., 2010]</ns0:ref>. PyMol (Version-2.3.4) was used to remove water and for the retrieval of different chains of HLA allele from the crystal structure, which was in a complex form with protein and a peptide <ns0:ref type='bibr'>[PyMOL]</ns0:ref>. The structure of chain A having the peptide binding cleft was then directly submitted on the HADDOCK 2.4 as protein molecule while PEPFOLD derived structures of predicted epitopes were used as ligands. After registration 'easy interface' was selected for docking. In the docking parameter section default parameter was selected. The default parameters can be found on the haddock server website https://wenmr.science.uu.nl/haddock2.4/settings. Similarly for MHC II epitopes, the crystal structures of HLA-DRB101:01 (PDB id:2FSE), HLA-DRB101:01 (2FSE) HLA-DRB104:01 (5LAX) were retrieved from PDB. PyMOL was used for removing water and the structures of chain A and B were derived to be submitted at HADDOCK as protein molecule. Other procedures similar to MHC I were also followed for docking of MHC II alleles and predicted epitopes. The 3D structures of the best cluster obtained from the HADDOCK results were visualized using PyMOL (Version-2.3.4). For 2D interaction studies the Discovery studio visualizer (Version: v20.1.0.19295) was used for the MHC I epitopes [ <ns0:ref type='bibr' target='#b9'>Dassault Systèmes, 2020]</ns0:ref>, on the other hand LigPlot + (Version: Ligplot+ v.1.4.5) was used for the MHC II epitopes <ns0:ref type='bibr' target='#b82'>[Wallace et al., 1995]</ns0:ref>. LigPlot was used for MHC II epitopes as the discovery studio visualize has a limit of 1000 atoms for epitope. Re-docking and Validation of the Docking Methods: For validating docking methodologies the crystal structure of HLA molecules and the corresponding epitope as available in the PDB were selected for re-docking. The crystal structures of the following PDB IDs (i) 5VGE (ii) 6J1W (iii) 5VWH (iv) 3X13, (v) 3C9N (vi) 2FSE and (vii) 5LAX were taken. Then the structures of the HLA molecules and the corresponding peptide were retrieved by using PYMOL. The chain 'A' for MHC I allele and chains 'A' and 'B' from MHC II allele were submitted as protein molecules as done for the predicted epitope dockings above. In the redocking, however, the peptides derived from the above crystal structures were used as ligands. In the next steps all the above procedures used for the predicted MHC I and MHC II epitope docking (HADDOCK 2.4 protocol) were followed for re-docking and the best cluster structures were visualized using PYMOL and Discovery Studio/ LIGPLOT+.</ns0:p></ns0:div> <ns0:div><ns0:head>Identification of the B cell epitope</ns0:head><ns0:p>The optimum B-cell epitope identification is the crucial step for epitope-based vaccine design. The B-cell epitopes were recognized from the SARS-CoV-2 proteins utilizing the web based server BepiPred-2.0 (http://www.cbs.dtu.dk/services/BepiPred/) <ns0:ref type='bibr' target='#b30'>[Jespersen et al., 2017]</ns0:ref> and LBtope methods (http://crdd.osdd.net/raghava//lbtope/) <ns0:ref type='bibr' target='#b67'>[Singh et al., 2013]</ns0:ref>. BepiPred-2.0 might be viewed as the prime and up-to-date B-cell epitope prediction strategy as it exhibit remarkable solution on both epitope data obtained from a vast number of linear epitopes taken from the IEDB database and on structural data of epitope derived from crystallography studies. LBtope is other robust tool for linear B-cell epitope prediction. It was developed on the basis of experimentally proven non B-cell epitopes derived from IEDB database. The ElliPro (http://tools.iedb.org/ellipro/) tool was used for the prediction of conformational or discontinuous B-cell epitopes <ns0:ref type='bibr' target='#b57'>[Ponomarenko et al., 2008]</ns0:ref>. ElliPro is considered as most comprehensive method that can identify both the conformational and linear epitopes on the basis of 3-dimensional structure and provides the result score as a protrusion index (PI) <ns0:ref type='bibr' target='#b57'>[Ponomarenko et al., 2008]</ns0:ref>. The specifications for conformational epitope prediction were fixed at 0.8 for minimum score and 7 Angstrom (Å) for maximum distance.</ns0:p></ns0:div> <ns0:div><ns0:head>Assessment of Physicochemical properties</ns0:head><ns0:p>ExPASy ProtParam tools (https://web.expasy.org/protparam/) was used for the assessment of various physiochemical properties of SARS-CoV-2 proteins and the potential vaccine candidates. Properties like amino acid composition, molecular weight, extinction coefficient, isoelectric point (pI), instability index, aliphatic index, stability (in bacterial, yeast, and mammalian system) grand average hydropathicity (GRAVY) value was identified by ExPASy ProtParam <ns0:ref type='bibr' target='#b19'>[Gasteiger et al., 2005]</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head></ns0:div> <ns0:div><ns0:head>Retrieval of SARS-CoV-2 proteins and determination of antigenicity of structural proteins</ns0:head><ns0:p>A total of ten protein sequences of SARS-CoV-2 (orf1ab polyprotein, surface glycoprotein or spike protein, orf3a protein, envelope protein, membrane glycoprotein, orf6, orf7a, orf8 proteins and nucleocapsid phosphoprotein) was retrieved from viPR database, out of which six confirmed structural protein (surface glycoprotein, orf3a protein, envelope protein, membrane glycoprotein, orf6 protein, nucleocapsid phosphoprotein) was selected for the epitope-based vaccine designing. Antigenicity analysis of all the six structural proteins was performed by Vaxijen server. The Vaxijen score of all these proteins were above threshold level, ≥ 0.4 (Table <ns0:ref type='table' target='#tab_1'>-S1</ns0:ref>), thus all six selected proteins were antigenic in nature. Highest Vaxijen score was observed for Orf 6 protein (0.6131) and minimum was found in case of surface glycoprotein (0.4646).</ns0:p></ns0:div> <ns0:div><ns0:head>T-Cell Epitope Prediction</ns0:head><ns0:p>The web based server NetCTL 1.2 was used for the identification of CD8 + T-cell epitopes and the combined score was considered for the selection of epitopes. From the protein sequences of S, ORF3a, E, M, ORF6, and N proteins, the server predicted a total of <ns0:ref type='bibr'>83,</ns0:ref><ns0:ref type='bibr'>33,</ns0:ref><ns0:ref type='bibr'>10,</ns0:ref><ns0:ref type='bibr'>31,</ns0:ref><ns0:ref type='bibr'>4 and 26 epitopes,</ns0:ref>.</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:04:47510:1:1:NEW 24 Jun 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Analysis of antigenicity and immunogenicity:</ns0:head><ns0:p>All the 187 identified T-cell epitopes were then evaluated for antigenicity by the VaxiJen server and then for immunogenicity by IEDB server. However, only 97 epitopes from all the six structural proteins were regarded as antigenic based on VaxiJen scores. Similarly, 106 epitopes had immunogenicity values more than the threshold value when analyzed by IEDB tool ( <ns0:ref type='table' target='#tab_3'>Table-S2</ns0:ref>). Altogether 82 epitopes were selected on the basis of positive scores for both antigenicity and immunogenicity (Table <ns0:ref type='table'>-S3</ns0:ref>). The HLA-binding affinity of the epitopes is described by the IC50 nM unit. Higher binding affinity of the epitopes with the MHC class I molecule is reflected by the lower IC50 value. Therefore, IC50 values less than 250nM (IC50 < 250) were fixed for securing higher binding affinity. The IC50 values of the epitopes were determined by the IEDB tool (Table <ns0:ref type='table'>-S3</ns0:ref>). These selected epitopes were then subjected to evaluation of conservancies. We eventually selected 38 epitopes from all the six structural proteins that had a conservancy scores greater than 65 % (Table-1).</ns0:p></ns0:div> <ns0:div><ns0:head>Evaluation of allergenicity and toxicity</ns0:head><ns0:p>The allergenicity determination of the potential epitopes is a critical step in vaccine design. Therefore, Allergen FP 1.0 server and AllerTOP v. 2.0 were used for identifying the allergens in the T cell epitopes. About one third of the epitopes were non-allergenic, while remaining two third were allergic, when the tool, Allergen FP 1.0 was used for evaluation. However, when AllerTOP v. 2.0 was used for the identification of allergenicity, only ten epitopes were found to be allergenic in nature. The server recognized thirteen epitopes from the proteins as non-allergen (Table-1). All the predicted epitopes of MHC-1 from structural proteins of SARS-CoV-2 were indicated as non-toxic, when ToxinPred was used for the toxicity assessment (Table-1).</ns0:p></ns0:div> <ns0:div><ns0:head>Selection of potential MHC-I epitopes for vaccine design:</ns0:head><ns0:p>The potential 38 CD8 + T cell epitopes from six structural proteins were finally evaluated for all the above parameters simultaneously for determination of most suitable vaccine candidates. Among the S protein epitopes, 'YQPYRVVVL' exhibited high binding affinity for seven MHC- <ns0:ref type='table'>-S3</ns0:ref>). It had the VaxiJen score of 0.5964 and the immunogenicity score of 0.14090, which were well above the respective threshold values. The conservancy score of the epitope was 100.00% and both the allergenicity prediction tools identified this epitope as non allergenic. Furthermore, it was nontoxic as determined by the toxicity analysis tool used in this study. Similarly, three more epitopes from spike protein viz; 'QLTPTWRVY', 'PYRVVVLSF' and 'GVYFASTEK' exhibited desired values for the above parameters (highlighted with yellow color in Table-1). Next, the epitopes from ORF3a was selected on the basis of outcomes of the parameters evaluated in the study. The epitope 'HVTFFIYNK' also showed binding capabilities with seven MHC class I alleles. The VaxiJen score of 0.9862 and immunogenicity score of 0.36278 was found for the epitope 'HVTFFIYNK'. Further, with the conservancy value of 66.67 % and being non-allergenic and nontoxic this epitope can be regarded as best vaccine candidate from ORF3a protein. Likewise one more epitope 'LKKRWQLAL' was marked as potential vaccine candidates based on their scores gathered during the analysis by the computational tools (Table <ns0:ref type='table' target='#tab_1'>-1 & Table-S3</ns0:ref>). In contrast to epitopes from spike and ORF3a proteins, although E protein epitope 'LLFLAFVVF' exhibited binding affinity with seven MHC I alleles and high antigenicity, immunogenicity, it could not pass the allergenicity evaluation. Thus, no epitope from envelope protein could be regarded as potential vaccine candidates. Among epitopes from M protein, 'LTWICLLQF' had high antigenicity score of 1.1393 and immunogenicity score of 0.06584. 'LTWICLLQF' exhibited binding to (i) HLA-C14:02 (129.6), (ii) HLA-B58:01, (141.02), (iii) HLA-C12:03 (166.42), (iv) HLA-A32:01 (245.43) molecules and had a conservancy score of 77.78%. Furthermore, the epitope was non-allergenic and had no toxicity, thus can be regarded as one of the best vaccine candidates from M protein. One more epitope 'RFLYIIKLI' had better scores in the computational analysis performed for the evaluation of vaccine potential (Table <ns0:ref type='table' target='#tab_1'>-1 & Table-S3</ns0:ref>). Similar to envelope protein, ORF6 epitope also did not show any promising vaccine candidate that could fulfill all the criteria evaluated in our study (Table-1). Last of the selected structural protein, nucleocapsid protein showed three promising epitopes, when evaluated by the computational tools. One of three epitopes; 'KTFPPTEPK' displayed significant binding affinities with nine MHC-1 molecules. It had VaxiJen score of 0.7571 and immunogenicity score of 0.13060. The conservancy was 100.00% for this epitope and was categorized as non-allergen and non-toxic by the computational tools. Two other epitopes 'SPRWYFYYL' and 'TWLTYTGAI' also fulfilled all the criteria analyzed in the study for the determination of vaccine potential (Table <ns0:ref type='table' target='#tab_1'>-1 & Table S3</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>Analysis of Population Coverage</ns0:head><ns0:p>The distribution of MHC HLA alleles varies across various geographic territories and ethnic classes throughout the world. Consequently, consideration of population coverage is essential prerequisite for designing an effective vaccine. IEDB population coverage tool was thus used to predict the population coverage of all the shortlisted T-cell epitopes (Table-1) and their respective MHC-I-binding alleles. Remarkable population coverage was identified for the epitopes in different geographic regions of the world (Figure <ns0:ref type='figure' target='#fig_4'>-S1 and Table-S4</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>Prediction of MHC II epitopes:</ns0:head><ns0:p>The MHC II epitopes of 15-mer length were derived from the sequences of CD8 + T cell epitopes and were evaluated on the basis of IC50 scores. The promising CD8 + T cell epitopes 'YQPYRVVVL' and 'QLTPTWRVY' were evaluated first. The analysis revealed that the epitope sequence 'YQPYRVVVL' was present as the core sequence in more than fifty predicted MHC II epitopes, whereas the epitope 'QLTPTWRVY' was found as the core sequence of a single CD4+ T-cell epitope (Table <ns0:ref type='table'>-S5</ns0:ref>). The MHC II epitopes containing the core peptide 'LKKRWQLAL' from ORF3a was found to be present as the core sequence in 44 predicted MHC II epitopes (Table-S5).</ns0:p><ns0:p>More than forty CD4 + T cell epitopes having the core sequence 'LTWICLLQF' derived from M protein had a range of binding affinities with IC50 values between 61-2801 nM (Table <ns0:ref type='table'>-S5</ns0:ref>). As the computational analysis of ORF6 and envelope protein did not result in any potential CD8 + T cell epitope, the MHC II epitopes derived from these proteins were not considered further. Lastly, the search for MHC II binding epitopes using the core peptide 'KTFPPTEPK' could not result in the potential epitopes in the acceptable range of the IC50 value 1-3000 nM.</ns0:p></ns0:div> <ns0:div><ns0:head>Docking Simulation Analysis:</ns0:head><ns0:p>The CD4 + T cell epitopes which were considered to be potential vaccine candidates based on appropriate values obtained during the analysis by the computational tools were used in the docking simulation studies. The binding models of epitopes and their respective HLA molecules (both class I and class II) were generated by taking advantage ofHADDOCK 2.4. The tool generated clusters and the numbering of cluster reflected the size of the cluster. The various components of the HADDOCK results like HADDOCK score, Cluster size, Root mean square deviation (RMSD) from the overall lowest-energy structure, Van der Waals energy, Electrostatic energy, Desolvation energy, Restraints violation energy, Buried Surface Area, and Z-Score were reported for each cluster on the results web page. Irrespective of the number of cluster, the top cluster is considered as most reliable according to HADDOCK. Therefore, the first cluster from the result displayed by HADDOCK server was selected for visualization of structures. The more negative results of HADDOCK score and Z score signifies better structures and interaction. At the first instance two promising MHC I epitopes viz; 'PYRVVVLSF', 'QLTPTWRVY' from surface glycoprotein was used for the docking with HLA-C07:02. The Haddock score of the first cluster was -30.4 +/-7.5 and the Z score was -1.2 indicating the proper docking solution for HLA-C07:02 and epitope PYRVVVLSF (Table <ns0:ref type='table' target='#tab_3'>-2</ns0:ref>). Similarly the second epitope 'QLTPTWRVY' from S protein also had negative values for both the HADDOCK score and Z score indicating the cluster as a good docking solution. In the next HADDOCK analysis epitope 'HVTFFIYNK' from ORF3a was used along with the HLA-A30:01. The best cluster had the HADDOCK score of -65.5 +/-7.7 and the Z score of -1.8, suggesting proper docking results. Another round of docking studies was performed with the MHC I peptide 'LTWICLLQF' obtained from the membrane protein and the structure of HLA-B58:01 molecule derived from PDB. HADDOCK and Z score of the best cluster of this pair were also promising and could be used for the structure visualization. Finally epitope 'SPRWYFYYL' from nucleocapsid protein was selected for docking with the HLA-B08:01 molecule, which resulted in the Haddock score of -29.3 +/-3.2 and the Z score of -2.1 (Table <ns0:ref type='table' target='#tab_3'>-2</ns0:ref>). In the next step model structures of all the above best clusters obtained in the HADDOCK results were downloaded. Then 3D model of the clusters were visualized by PYMOL and Discovery Studio was utilized for getting the 2D interaction map (Figure <ns0:ref type='figure'>-2</ns0:ref>). All the 3D and 2D interaction map indicated binding in the antigen binding groove thus providing proper docking solutions by HADDOCK. Altogether three promising MHC II epitopes were used for HADDOCK docking analysis. First docking analysis was performed using HLA-DRB101:01 structure obtained from PDB and the structure of MHC class II epitope 'TNGVGYQPYRVVVLS' (from S protein) predicted using PEPFOLD tool. The HADDOCK protocol produced the best cluster with the HADDOCK score of -38.1 +/-9.9 and the Z score of -2.2, which indicated optimum solution of docking. The second docking analysis between the MHC-II allele (HLA-DRB101:01) and epitope 'ITLKKRWQLALSKGV' from ORF3a protein also resulted in desired negative values of HADDOCK and Z scores The last HADDOCK docking examination was performed with the MHC II allele (HLA-DRB104:01) and the epitope 'LFLTWICLLQFAYAN' from membrane glycoprotein of SARS-CoV-2, which revealed a HADDOCK score of -78.4 +/-10.7 and the Z score of -1.6 (Table <ns0:ref type='table' target='#tab_3'>-2</ns0:ref>). Similar to MHC I, the model structures of all the above three best clusters obtained in the HADDOCK results were downloaded from the HADDOCK result page. 3D models of the clusters were visualized by PYMOL and Discovery Studio was implied for getting the 2D interaction maps of all three docking solutions (Figure <ns0:ref type='figure'>-3</ns0:ref>). All three 3D and 2D interaction images exhibited proper MHC II allele and epitope binding suggesting appropriate docking solutions by HADDOCK (Fig. <ns0:ref type='figure'>-3</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>Re-docking and Validation of docking methods:</ns0:head><ns0:p>For the validation of docking methodologies crystal structures of seven HLA alleles and the corresponding epitopes as described in method section were selected and all the procedures described for HADDOCK 2.4 were followed for re-docking. The HADDOCK scores were in the range of -29.6 +/-7.8 to -65.0 +/-2.1 fo the MHC I epitopes while it was -92.1 +/-5.8 and -96.7 +/-5.6 for the two MHC II epitopes. The Z scores were also in the range of -1.0 to -2.3 for MHC I structures while it was 0.0 and -1.5 for MHC II alleles (Table <ns0:ref type='table'>-S6</ns0:ref>). Then 3D and 2D structures of the best clusters were visualized using PYMOL and Discovery studio/ LigPLOT+. The results indicated proper docking solutions provided by HADDOCK and the structures were similar as available in the PDB (Figure <ns0:ref type='figure'>-S2</ns0:ref>). The desired findings with the known epitopes and HLA alleles suggested the appropriate and acceptable approach of HADDOCK protocol implied in the HADDOCK 2.4.</ns0:p></ns0:div> <ns0:div><ns0:head>Analysis of Linear and Conformational B-Cell Epitopes</ns0:head><ns0:p>B-cell epitope is a segment of an antigen recognized in a humoral immune response by either a specific B-cell receptor or by the evoked antibody <ns0:ref type='bibr'>[Peters et al., 2005;</ns0:ref><ns0:ref type='bibr' target='#b71'>Sun et al., 2013]</ns0:ref>. The Bcell epitopes are categorized into two distinct groups as (i) continuous or linear and (ii) discontinuous or conformational B-cell epitopes. One of the significant steps of epitope-based vaccine design is the identification of B-cell epitopes from the antigenic proteins of pathogens. Consequently, the web server based computational tools, BepiPred-2.0 and LBtope were used to find out B-cell vaccine candidates in the different proteins of SARS-CoV-2. The BepiPred-2.0 generated fair number of linear B-cell epitopes from the S protein of SARS-CoV-2. Among these linear epitopes eleven were non-antigenic as predicted by the VaxiJen v2.0 server and had conservancy level between 56.25 to 97.50% (Table-S7). Accordingly, these epitopes could not be considered as prospective vaccine candidates. Conversely, six epitopes; (i) GQSKRVDFC, (ii) VEAEVQI, (iii) SCCKFDEDDSEPVLKGVKL,(iv) GDEVRQIAPGQTGKIADYNYK, (v) YQTSNFRVQP and (vi) NSASFSTFKCYGVSPTKLND LCFTNV can be regarded as vaccine candidates due to their antigenicity and high conservancy scores (Table <ns0:ref type='table'>-3</ns0:ref>). Nevertheless, the epitope 'SCCKFDEDDSEPVLKGVKL' being toxic in nature could not be considered as the potential vaccine candidate. Based on the results of allergenicity (AllerTOP 2.0 and AllergenFP v. 1.0) and toxicity, epitope 'NSASFSTFKCYGVSPTKLNDLCFTNV' could be considered as best potential linear B-cell epitope for vaccine design (highlighted in <ns0:ref type='table'>Table-3</ns0:ref>). Over twenty linear B-cell epitopes were recognized from the S protein using LBtope (Table <ns0:ref type='table'>-S7</ns0:ref>). Altogether seven epitopes were nonantigenic and cannot be considered as good vaccine candidates. The epitope 'AGAAAYYVGYLQPRT' had high antigenicity and high conservancy scores and were not classified as allergen by the tools, hence can be considered as potential vaccine candidate. Compared to the spike protein, only five linear B-cell epitopes from ORF3a protein were identified by BepiPred-2.0 (Table-S7). Out of these epitopes, three were non-antigenic as discerned by the VaxiJen v2.0 server and their conservancy scores varied between 57.69 to 74.29 %. On the other hand, two epitopes; (i) 'QGEIKDATPSDF' and (ii) 'KIITLKKRWQL' can be considered as vaccine candidates due to their antigenicity and conservancy score (Table <ns0:ref type='table'>-3</ns0:ref>). After due consideration of all the factors like allergenicity (AllerTOP 2.0 and AllergenFP v. 1.0) and toxicity, none of the epitope predicted by BepiPred-2.0 could be safely recommended as potential linear B-cell epitopes. Another tool for linear epitope discovery, LBtope, led to the identification of only three linear B-cell epitopes from the ORF3a protein. Although, two epitopes; 'EIKDATPSDF and WKCRSKNPLL had fair values for antigenicity and high conservancy score, but owing to its toxicity, it cannot be projected as potential vaccine candidates. The evaluation of antigenicity, conservancy, toxicity, and allergenicity of B-cell epitopes suggested that none of the linear B-cell epitopes from ORF3a could be considered as candidates for peptide-based vaccine design. When the E protein was investigated using BepiPred-2.0 server, only one epitope; 'YVYSRVKNLNSSRVP' was identified as linear B-cell epitope (Table-S7). It showed good antigenicity with non-allergenic and non-toxic property and a conservancy score of 80.00%. Similarly, LBtope also showed only one epitope, YVYSRVKNLNSSRVPDLL that too was antigenic, non allergenic and non toxic with conservancy score of 72.22%. Consequently, YVYSRVKNLNSSRVP can be regarded as most potential B-cell epitope candidate from E protein for peptide-based vaccine (Table <ns0:ref type='table'>-3</ns0:ref>). The search for potential linear B-cell epitopes from M and ORF6 protein by BepiPred-2.0 and LBtope could not be successful as none of the predicted epitope could satisfy all the criteria evaluated in the present study. The search of linear B-cell epitopes in N protein by BepiPred-2.0 resulted in identification of eight epitopes (Table-S7). Among the predicted epitopes four were reported as non-antigenic by the VaxiJen v2.0 server. Out of the remaining four epitope only two epitopes viz; 'HGKEDLKFPRGQGVPINTNSSPDDQIGYYRRATRRIRGGDGKMKDLS', and 'LNQLESKMSGKGQQQ QGQTVTKKSAAEASKKPRQKRTATK' could be considered as the potential linear B-cell epitopes for vaccine development. On the other hand only 4 linear Bcell epitopes were predicted by LBtope (Table <ns0:ref type='table'>-S7</ns0:ref>). The analysis of antigenicity, conservancy, toxicity, and allergenicity of B-cell epitopes identified by LBtope revealed that epitopes, DNGPQNQRNAPRITFGGP, GERSGARSKQRRPQGL could be regarded as the most potential B-cell linear epitope (Table <ns0:ref type='table'>-3</ns0:ref>). For identifying conformational B-cell epitopes, the ElliPro tool of IEDB was utilized in this study, and a total of eleven discontinuous peptides were identified when the structural proteins of SARS-CoV-2 were used as targets. The ElliPro tool evaluates results based on the protrusion index (PI) score, and the PI score above 0.8 are considered significant. The PI value of the 11 predicted epitopes ranged from 0.809 to 0.911 and the epitopes with higher scores indicated greater solvent accessibility. Conformational epitopes and their associated parameters and scores revealed that epitopes with highest number of residues (110) were present in conformational epitopes from S protein and the minimum number of residues (03) was predicted from M protein (Table <ns0:ref type='table'>-S8</ns0:ref>). The three dimensional structure and location of the conformational epitopes were displayed by ElliPro (Figure <ns0:ref type='figure' target='#fig_4'>-4</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>Analysis of Physicochemical properties</ns0:head><ns0:p>Physicochemical properties of the SARS-CoV-2 structural proteins are described in Table-S9. The values revealed that the S, ORF3a and ORF6 proteins were naturally acidic whereas the E, M, N protein were naturally basic. All the six structural proteins from SARS-CoV-2 used in the study had estimated half-life of 30 hours in mammalian reticulocytes under in vitro conditions, whereas in yeast the estimated half life was more than 20 hours. The least survival time of more than 10 hours was estimated in Escherichia coli. Unlike the proteins, proposed MHC I epitopes had different half life. An estimated half life of less than an hour in mammalian reticulocytes was associated with QLTPTWRVY epitope derived from S protein, whereas maximum half life of thirty hours was estimated for GVYFASTEK. On the other hand least estimated half life in yeast system was predicted for RFLYIIKLI and however maximum estimated half life of more than 20 hours was found in case of four epitopes (Table <ns0:ref type='table'>-S9</ns0:ref>). Lastly in the most commonly used protein expression system i.e., E. coli five epitopes had a life of more than ten hours. Similarly, out of the four potential MHC II epitopes two had the maximum estimated half life of 20 hours in mammalian reticulocytes, while, three epitopes had an estimated half life of more than 10 h in E. coli. In contrast, no MHC II epitope had an estimated half life of more than 30 minutes in yeast system (Table <ns0:ref type='table'>-S9</ns0:ref>). These estimated half lives of MHC I and MHC II peptide epitopes suggested that most of the promising vaccine candidates could safely be produced in one or the other protein expression systems mentioned above.</ns0:p></ns0:div> <ns0:div><ns0:head>Discussion</ns0:head><ns0:p>The advancing pandemic of corona virus disease 2019 (COVID-19) has resulted in death of more than 445000 human population globally <ns0:ref type='bibr'>[COVID-2019-situation report-150]</ns0:ref>. The disease is generated by severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) [Corona virus disease 2019-WHO]. Keeping the SARS-CoV-2 (RNA-virus) mutability in mind <ns0:ref type='bibr' target='#b78'>[Twiddy et al., 2003;</ns0:ref><ns0:ref type='bibr' target='#b45'>Manzin et al., 1998</ns0:ref>], a comprehensive vaccine needs to be designed to overcome the adverse effects of this viral infection. However, an efficacious vaccine development and mass production are expensive and can take several years to be completed. Therefore, an attempt was PeerJ reviewing PDF \| (2020:04:47510:1:1:NEW 24 Jun 2020)</ns0:p><ns0:p>Manuscript to be reviewed made to design a peptide-based vaccine using the immuno-informatics approaches to minimize the time required for searching a potent vaccine candidate for SARS-CoV-2 At present, distinct Bioinformatics approaches are available for the design and development of successful and safe new-generation vaccines <ns0:ref type='bibr' target='#b46'>[María et al., 2017;</ns0:ref><ns0:ref type='bibr' target='#b63'>Seib et al., 2012]</ns0:ref>. The advancement in computational immunology and newer immuno-informatics tools have created a broader way in developing the vaccine or vaccine candidates by the adequate understanding of the human immune response against a pathogen within a short period of time <ns0:ref type='bibr' target='#b10'>[de Groot and Rappuoli, 2004;</ns0:ref><ns0:ref type='bibr'>Korbe et al., 2006;</ns0:ref><ns0:ref type='bibr' target='#b58'>Purcell et al. 2007]</ns0:ref>. The scheme of an epitope-based vaccine against rhinovirus, <ns0:ref type='bibr' target='#b33'>[Lapelosa et al., 2009]</ns0:ref> dengue virus, <ns0:ref type='bibr'>[Chakraborty et al., 2010]</ns0:ref> chikungunya virus, <ns0:ref type='bibr' target='#b29'>[Islam et al., 2012]</ns0:ref> Saint Louis encephalitis virus, <ns0:ref type='bibr' target='#b23'>[Hasan et al., 2013]</ns0:ref> etc. has already been proposed.</ns0:p><ns0:p>In the present study, we first attempted to identify the potential vaccine candidates based on the T cell peptide epitope. In contrast to earlier vaccines, which are predominantly based on B cell immunity, vaccine based on T cell epitope has also been recommended as the host can induce a strong immune response by CD8 + T cell against the infected cell [ <ns0:ref type='bibr' target='#b79'>Van Regenmortel, 2001]</ns0:ref>. Due to antigenic drift, any foreign particle can escape the antibody memory response mounted by B cells; however, the immune response generated by T cells usually provides long-lasting immunity. There are various specifications that need to be fulfilled by a peptide vaccine candidate. The potential epitopes proposed in our study satisfied all the criteria evaluated using computational tools. The T-cell epitope was identified based on high threshold values (1.25) obtained in the output of NetCTL 1.2 tool. Primarily, more than one hundred fifty epitopes from six structural proteins were identified by selecting twelve super types of MHC-1 alleles. The antigenicity, immunogenicity and conservancy of the epitopes are considered as important determinants. Therefore, by maintaining critical thresholds of the antigenicity, immunogenicity and conservancy of the epitopes, we picked thirty eight epitopes from structural proteins of SARS-CoV-2 (Table <ns0:ref type='table' target='#tab_1'>-1</ns0:ref>). These selected T-cell epitopes had a higher conservancy between 65.0 to 100.0%, which further support the feasibility of these predicted epitopes and indicate them as a potential vaccine candidate. Most of the present day vaccines activate the immune system into allergic state <ns0:ref type='bibr' target='#b44'>[McKeever et al., 2004]</ns0:ref> by inducing type 2 T helper T (Th2) cells and immunoglobulin E (IgE). Consequently, allergenic property is one of the major hurdles in vaccine development. Hence, all the selected Tcell epitopes were screened for allergenicity by two computational tools; AllerTOP v.2.0 and AllergenFP 1.0. Altogether only eleven epitopes were classified by both the tools as nonallergens. Those eleven epitopes with all the characteristics of good vaccine candidates may be considered most important epitope in comparison with the other epitopes. Another important factor in the selection of a potential vaccine is population coverage. The human leukocyte antigen alleles are remarkably polymorphic in diverse ethnic populations. Consequently, allele specificity of T-cell epitopes is considered as the initial criterion for the induction of proper immune responses in numerous ethnic human populations <ns0:ref type='bibr' target='#b70'>[Stern and Wiley, 1994]</ns0:ref>. For all the eleven promising T cell vaccine candidates, the cumulative percentage of population coverage was measured. Overall the recommended epitopes from surface glycoprotein showed world population coverage of 80.37% followed by nucleocapsid phosphoprotein and ORF3a epitopes showing 68.10% and 54.43% of world population coverage, respectively (Table <ns0:ref type='table'>-S4</ns0:ref>). The SARS-CoV-2 outbreak has resulted pandemic in which cases have been reported in almost all the countries of world [WHO situation report-150], so a vaccine candidate which can protect the majority of world's population is required. However, the epitopes from membrane protein could cover only 31.04% population of World. Notably, the epitopes from the surface protein had population coverage of 89.08% for China, where the virus originated <ns0:ref type='bibr'>[Zhou et al., 2020]</ns0:ref> and 88.99% for Southeast Asia. In the list of badly hit countries, majority is from Europe [WHO situation report-150] and the epitope from S protein had coverage of 80.69% of Europe's population. The population coverage of 77.72% was obtained for USA where the highest number of cases has been reported . The nucleoprotein epitopes covered 76.28% of Europe population followed by 69.53% and 63.26% of Italy and United States populations; respectively. Next, the ORF3a epitopes had 64.57% coverage of China's population followed by 63.85%, and 57.98% of Hong Kong and Europe's population, respectively. Taken together all the suggested epitopes having higher population coverage may be considered as strong vaccine candidates. The proper binding of the T cell epitope to the MHC I antigen binding cleft is essential for the induction of desired immune response <ns0:ref type='bibr' target='#b70'>[Stern and Wiley, 1994]</ns0:ref>. The legitimate binding should result in a negative HADDOCK and Z scores. Thus the 3D structure of the proposed vaccine candidate was designed using PEP-FOLD and the crystal structure of the selected MHC allele was obtained from Protein Data Bank (PDB). Thereafter, removal of water and retrieval of chain wise structure of MHC alleles were performed using PyMol. In the next step molecular docking simulation was executed with selected chain of the MHC as protein molecule and the proposed vaccine candidate as ligand using HADDOCK 2.4. The HADDOCK and Z scores, the two most significant parameters in the results of HADDOCK indicated the predicted epitopes to be reasonable. The 3D and 2D interaction maps were derived using the HADDOCK best cluster model generated in result page by applying appropriate bio-informatics resources like PYMOL and Discovery studio. These structures exhibited appropriate binding of predicted epitopes in MHC I peptide binding cleft suggesting the pertinent selection of bio-informatics approach for epitope identifications. The re-docking and validation of docking method was carried out by using the seven crystal structures of MHC I and MHC II alleles and their corresponding peptide epitope obtained from PDB. The structures of both HLA allele and corresponding peptides were obtained using PyMol and re-docking was performed using HADDOCK 2.4. The docking procedures were same as that for the SARS-CoV-2 predicted epitopes. HADDOCK and Z scores were in the acceptable range (negative values) and the 3D and 2D interaction results were similar to the corresponding PDB structures. Furthermore the results of HADDOCK re-docking were similar to those achieved by dockings performed using the predicted SARS-CoV-2 MHC I and MHC II epitopes, which reflected valid docking methodologies adopted in the present study. The physicochemical properties of proposed epitopes indicated that these can be produced in any three of the systems used for the expression of peptides, viz, mammalian cells, Yeast cell or E. coli (Table-S9) Most of the current day vaccines are based on the B cell epitopes <ns0:ref type='bibr' target='#b61'>[Sarkander et al., 2016]</ns0:ref>. BepiPred-2.0 might be viewed as the prime and most up-to-date B-cell epitope prediction computational tool as it exhibits notably good performance on both epitope data obtained from a vast number of linear epitopes taken from the IEDB database and on structural data of epitope derived from crystallography studies. LBtope is other robust tool for linear B-cell epitope prediction. It has been generated based on the experimentally proven non B-cell epitopes derived from the IEDB database. Antigenicity, allergenicity, toxicity and conservancy of the predicted B cell linear epitopes are prime determinants for identifying potential vaccine candidates. Therefore, all the four criteria were evaluated using different standard bioinformatics tools and potential epitopes were selected on the basis of high threshold values as fixed for T cell epitopes. Thus, on the basis of above criteria and conservancy altogether seven B cell epitopes from structural proteins were proposed as potential B cell vaccine candidates. The majority of the Bcell epitopes are discontinuous or conformational epitopes, and the quantum of this epitope is more than 90% <ns0:ref type='bibr' target='#b79'>[Van Regenmortel 2001]</ns0:ref>. Therefore, discontinuous B-cell epitopes were identified using ElliPro, a strong tool for the identification of conformational B cell epitopes. The tool identified 4 epitopes from surface glycoprotein followed by 2 epitopes each from the orf3a protein, membrane protein and nucleocapsid phosphoprotein. The extensive range of these conformational epitopes drawn on different proteins of SARS-CoV-2 indicated their potential as conformational B cell vaccine candidates. An earlier study has reported a single epitope from spike protein having the conservancy of about 64 % <ns0:ref type='bibr' target='#b50'>[Oany et al., 2014</ns0:ref>]. Here we have reported several epitopes as potential vaccine candidates from five structural proteins of SARS-CoV-2. As all the vaccine candidates need to be verified in clinical trials, the normal path of vaccine development, we propose the identified potential vaccine candidates should be pursued in clinical trials.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions</ns0:head><ns0:p>In view of the present COVID-19 pandemic, for development of vaccine efficiently and within minimal time, vaccine candidates need to be identified at the early. Based on advanced computational approaches, we have altogether identified eleven potential T-cell epitopes, seven B cell linear epitopes and ten B cell conformational epitopes from the six structural proteins of SARS-CoV-2. Taken together these numerous potential vaccine candidates may provide important timely avenues for effective vaccine development against SARS-CoV-2. The future efforts may focus on the clinical trials of the multi-epitope vaccine candidates based on the present study. Manuscript to be reviewed Manuscript to be reviewed Results of docking studies </ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>Combined percentile rank = (alpha Immunogenicity model score) + ((1 -alpha) * Median PeerJ reviewing PDF \| (2020:04:47510:1:1:NEW 24 Jun 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>I molecules viz; (i) HLA-C12:03 (37.77) (ii) HLA-A02:06 (68.16), (iii) HLA-B39:01 (75.92), (iv) HLA-B15:02 (92.94), (v) HLA-B15:01 (181.97), (vi) HLA-C14:02 (198.89), (vii) HLA-C03:03 (199.8) (Table</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>Figure 1</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head>Figure 2 Figure 2 :Figure 3 :</ns0:head><ns0:label>223</ns0:label><ns0:figDesc>Figure 2</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head>Figure 4</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Figure 4</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head>FLHVTYVPA</ns0:head><ns0:label /><ns0:figDesc>14:02,HLA-C12:03,HLA-C03:03,HLA-A23:01,HLA-AHLA-C03:03,HLA-A02:01,HLA-B15:02,HLA-C14:02,HLA-14:02,HLA-C12:03,HLA-A24:02,HLA-A23:01,HLA-30:01,HLA-C12:03,HLA-C14:02,HLA-A03:01,HLA-A31:01,HLA-A68:01,HLA-C03:03,HLA-A32:01,HLA-A</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='30,42.52,70.87,475.43,672.95' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='32,42.52,344.62,525.00,386.25' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='37,42.52,70.87,525.00,519.75' type='bitmap' /></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head>Table 1 (on next page)</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Potential T cell epitopes from different structural proteins from SARS-CoV-2</ns0:figDesc><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_1'><ns0:head>Table 1 : The potential T-cell epitopes with interacting MHC-I alleles and antigenicity, immunogenicity and conservancy scores derived from Structural proteins of SARS-CoV-2. Most promising proposed vaccine epitopes are highlighted.</ns0:head><ns0:label>1</ns0:label><ns0:figDesc /><ns0:table><ns0:row><ns0:cell>Surface glycoprotein (S)</ns0:cell></ns0:row><ns0:row><ns0:cell>Allergenicity</ns0:cell></ns0:row></ns0:table><ns0:note>PeerJ reviewing PDF \| (2020:04:47510:1:1:NEW 24 Jun 2020)Manuscript to be reviewed 1 Table-1</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_2'><ns0:head>Table 2 (on next page)</ns0:head><ns0:label>2</ns0:label><ns0:figDesc /><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_3'><ns0:head>Table 2 :</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Results of docking studies performed using HADDOCK 2.4 with selected T cell epitopes and corresponding HLA molecules</ns0:figDesc><ns0:table /><ns0:note>PeerJ reviewing PDF \| (2020:04:47510:1:1:NEW 24 Jun 2020)</ns0:note></ns0:figure> <ns0:note place='foot'>PeerJ reviewing PDF \| (2020:04:47510:1:1:NEW 24 Jun 2020)</ns0:note> <ns0:note place='foot'>PeerJ reviewing PDF \| (2020:04:47510:1:1:NEW 24 Jun 2020)Manuscript to be reviewed</ns0:note> </ns0:body> "	"Walter de Azevedo Jr. Academic Editor, PeerJ Dear Editor, We thank all three reviewers for their generous comments on the manuscript entitled “Computational perspectives revealed prospective vaccine candidates from five structural proteins of Novel SARS Corona Virus 2019 (SARS-CoV-2)” [ID=47510] and have edited the manuscript to address their concerns. In particular we have used HADDOCK 2.4 for docking and re-docking for validation as suggested by reviewer. We believe that the manuscript is now suitable for publication in Peer J. With kind regards Dr. Rajesh Anand Assistant Professor Dept. of Biotechnology Guru Ghasidas Vishwavidyalaya Bilaspur, C.G.495009 On behalf of all authors. Reviewer 1 (Anonymous) Basic reporting The main idea of the work is to propose epitopes from the SARS-CoV-2 which can act as vaccines, using viral proteins, using computational methods. The English language is well written and clear in all the manuscript. The authors addressed the main problem and ideas in the introduction section, as well as used appropriate references. On the other hand, I have some questioning described below: We appreciate the reviewer for their constructive and well directed comments on our manuscript. • Lines 49 – 51: Please update WHO numbers. We have updated the WHO numbers throughout the manuscript. • Lines: 79 – 80: Do you have any information about what is this variation? Please include some paper talking about it. Two papers having the related information have been included [Lines 81] • Lines 87 – 91: Could you better describe ACE2/RBD importance in SARS infection here? I have been reading many papers targeting its importance for viral pathogenicity, as well as drug development. Dong et al. (2020) just hypothesize about ACE2 role in viral infection, and I think you need more examples to confirm this sentence. Please complete your sentence with other references. Thanks for bringing our attention to this important information that was lacking in our manuscript. Accordingly we have included the updated information [Lines-88-98] Figures 1, 5 and tables are well-representatives in most of them, except in the cases below: -Figures 2 and 3: should be completed with 2D interaction maps and re-docking experiments. Thanks for the comment and we have included the 2D interaction maps and re-docking using HADDOCK 2.4. -Figure 4: due to bad resolution I can't read coverage results. The figure has been shifted to supplementary (Figure S2) with better resolution. Computational predictions of possible epitope activity were validated by docking studies with crystallographic structures from PDB. In this case, I found some flaws that are described in the experimental design. I will recommend the paper publication only with major revisions, that will be described in the next sections. Thanks for the constructive comment. We have accordingly revised the manuscript. Experimental design Comments about experimental design are described below: • Lines 126 - 128: VaxiJen server uses physicochemical proprieties in its analysis, instead of peptide alignment. Did you consider using another tool with alignment mode? Why VaxiJen is a better choice? Please, include an advantage to use it. Additionally, indicate here in methods the threshold that you considered. Thanks for the constructive comment. The VaxiJen related advantage and threshold has been included [Lines 149-161] • Lines 143 – 144: Please include the default parameters you used. We thank reviewer for the positive comment. Accordingly we have included the parameters [Lines:177-178] • Lines 162 – 163: Please include a reference for this statement. The reference has been included [Lines: 206] • Lines 168 – 173: Too simple description of the determination of population coverage. Please, include more details about this part of the methods. Thanks for the positive comments. We have included more details regarding population coverage [Lines: 214-221] • Lines 185 -188: Too simple method description. More description on methods have been included [237-249] • Lines 189 – 195: What criteria did you use to select the best epitopes for modeling? What comes first in those characteristics that you considered (antigenicity, conservancy, etc.). Please, be clearer on what was more important to select your best epitopes. Thanks for the comment. In order to be considered as proper vaccine candidate, an epitope need to fulfill all the criteria. Accordingly we have revised the manuscript [Lines:251-253]The information has also been included at other place in manuscript. • Lines 196 – 202: Is the Autodock Vina the best option for peptide docking? I think there is a problem considering this program instead of other peptide docking programs (Cluspro, Haddock, DockThor, etc.) and/or Autodock 4 as well, giving better evaluation results. In the case of Autodock Vina, its algorithm is more restricted for huge molecule evaluation, as well as its results, could not reflect several characteristics in peptide interaction, such as protonation states of the amino acids, charges and solvent accessibility for example. In this case, this could be a problem to represent the better pose of the molecule inside the protein of interest. I suggest you choose another program that uses a peptide docking approach, in order to validate this part of your work. Thanks for the constructive comment and positive direction for the improvement of quality of the manuscript. We have used HADDOCK 2.4 for the docking and accordingly the Methods, Results and Discussion sections have been revised. [Lines: 261-306, 444-490, 657-675] • Line 202: What Pymol version? Cite Pymol. The Pymol version and citations has been included.[288-290] • Line 205: in Autodock Vina the program considers Affinity energy, which is a little different from binding energy. Change the term. The sentence has been completely removed due to use of HADDOCK tool in place of Autodoc. • Line 207: Setting up the grid box as default is not the best choice. Depending on the type of binding site that you have, it will require smaller or bigger boxes. The sentence has been completely removed due to use of HADDOCK tool. • Lines: 227 – 230: Wrong terminology. I think the authors wanted to say physicochemical instead. Thanks for bringing the attention to this typo error. We have revised the term [Lines:335-340]. I would like to do some considerations/questioning about the methods section: 1) The authors used complete or partial protein sequences? Please include this information in the methods section. Thanks for the comment. The information was really lacking and we have included that information in the method section [Line:146] 2) As you are proposing a vaccine using protein sequences for computational predictions, I strongly recommend that you include variability analysis using sequences from different viral strains. From what region of the world the sequences that you used came from? They are consensus sequences from different countries or from just one country? This important information is really lacking in the manuscript and we thank reviewer for this suggestion. Accordingly the related information has been included in the manuscript [Lines:130-146] 3) I recommend you include a sequence alignment for each protein group, or you can align just the region that you used as epitopes for each protein type. Thanks for the recommendation. We have included this information [Lines:130-146] Validity of the findings This work presented important findings of new peptide sequences that can be used for vaccine development. On the other hand, there are computational validations and other explanations that must be included. The study didn't say how many sequences for each protein type they used, and from where these sequences come from so is very hard to accept that a global vaccine could be developed using these findings. Furthermore, the results and consequently discussion sections lack some information about best epitope selection, as well as docking results and validation. I suggest some changes below. Lines 256 – 257: what criteria the authors used to put a 250 nM IC50 cutoff? Please, explain this in methods or in results/discussion sections. The criteria has been explained in the method section [Lines:186-190] Line 260: What is this score of 65%? I didn’t find this in methods. Include for what reason you used this. Thanks for the comment. The details have been included. [Lines:221-223] Lines 329 – 354: Docking results section needs to be re-written with more details. There is no validation for docking results, and there is no 2D interaction maps peptide-protein. Please include the 2D interaction maps for each docking calculation. I recommend including an epitope re-docking for validation: 3C9N, 5IEK and 5VGD crystals are protein-peptide complexes. I should use some of them for re-docking and validation of your docking methods. Thanks for the constructive comments. We have included the re-docking for validation using 7 HLA alleles and their corresponding peptide as found in the PDB. [Lines: Methods:307-317, Results: 491-501, Discussion:668-676] Lines 434 – 435: in Physicochemical properties analysis what you consider as stable structures? We appreciate reviewer for bringing our attention towards this lacking information. We have revised this section to include the data of proposed epitopes. [Lines: Methods:336-341, Results:571-588, Discussion:676-679] Reviewer 2 (Anonymous) Basic reporting The manuscript has a clear and professional use of English. Although the COVID-19 literature increases by the day, and there are many preprints, the references used are adequate for the methodology applied. We appreciate and thank reviewers for the positive comments. The paper has good structure the protein models are properly drawn and colored, although figure 4 is impossible to read. Maybe they can be included at full size as supplemental material and just make a reference to the important alleles in results and discussion. Thanks for the suggestion. We have shifted the figure 4 to Supplementary Figure-S2 with good resolution. In figure 5, it would be nice if some sequence numbering is included. Also, it is not clear if the images correspond to different proteins or are views of the same protein. Please explain in more detail. Figure 5 has been revised as directed and the residue numbers of epitope have been included. In line 225. and 227, it should be “physicochemical” instead of “Physiological”, probably a typo. In the same section please also include the prediction of stability if expressed on E. coli We thank reviewer for this constructive comments and suggestion. The typo error has been Revised and the section has been revised to include the stability in E. coli. [Lines: Methods:336-341, Results:571-588, Discussion:676-679] Maybe design the figure using a “surface glycoprotein” label, and then include figures a-d. Then a label “ORF3a protein” and the corresponding g-h images and “membrane glycoprotein”, and a fourth label “nucleocapsid phosphoprotein” for i-j images. We are thankful to the reviewer for this particular suggestion for the improvement of presentation of Figure. We have revised the Figure accordingly. Experimental design The methods are described in detail and the parameters used in each algorithm are included. I could not find details of the program used to display the structures, please include the reference and the PDB access for each structure (if taken from there) or if these are your predictions, how they were modeled. Thanks for the positive comments on the manuscript. We have included the desired information in the manuscript in method section. Validity of the findings Novel work that predicts potential targets towards a COVID-19 vaccine. The pipeline is clear and it helps the non-specialist to understand the procedures. Table 2, although detailed, is great because it resumes all the scores that point out towards the target peptides that are worth further investigation. We thank the reviewer for the positive comment on the manuscript. I consider important, since physicochemical properties are reported, that the author proposes the challenges of expressing these peptides as vaccines, Which ones would be more suitable for bacterial expression, pros, and contras, and also if there are issues with glycosylation. What are the more adequate systems for expressing and purifying the peptides? The information has been included in the physicochemical section [Lines: Methods:336-341, Results:571-588, Discussion:676-679] Labeling in figure 2 could be done with 1-letter amino acid symbols to reduce the crowding of the stick figures. Letters in figure 2c are too small, in 2F hard to read, in 2i too big. In all of them, a one-letter amino acid code would be better. Thanks for this particular suggestion. We have revised the figure 2 accordingly. Finally, I suggest that the author takes a final review of the current literature since there are new reports by the day, of the current status of vaccine development. Thanks for this suggestion, we have updated the literature to include ACE related information in introduction section [Lines: 88-98]. Comments for the Author The paper is novel and understandable, please keep it updated so it stays as an important resource in the avalanche of COVID-19 publications. Thanks for the comment, we have updated the information. Reviewer 3 (Jonathan Mullins) Basic reporting Comments for the author on English. Figure 4 - text in figure is far too small, impossible to read, it might need a key. Thanks for the comments. Figure-4 has been shifted to Supplementary as Figure-S1 with good resolution. Experimental design No comment. Validity of the findings No comment. Comments for the Author The introduction is highly informative and the literature is well referenced and appropriate. It provides good background and context to the reporting of the study. There are numerous minor grammatical errors, in addition to those I have identified in my comments, so I would recommend careful and thorough proofreading. The research question is well defined and very timely. The knowledge gap is identified clearly, as is the approach to the intended filling of the gap. The study addresses the important question of identifying effective epitopes for SARS-CoV-2 by the coherent application of a number of well established bioinformatics methods that predict antigenicity, immune protein epitopes, immunogenicity, allergenicity and toxicity, in this case applied to viral protein sequences, which when taken together allow cross-referencing of their results to address the specific question concerning epitope-based vaccine design. The methods are appropriate to the problem and the authors should be commended on a thorough and well-directed study. However, some clarifications of the core analytical approach and extensive grammatical corrections are required. We thank Respected Dr. Jonathan Mullins for the positive and constructive comments. We have done proof reading to correct the grammatical errors. Line by line comments: 'anticipation' should be replaced by 'prediction'. This word is frequently misused and should be Revised throughout. Thanks for the suggestion. We have replaced the word anticipation with prediction throughout the manuscript. Line 146 - missing 'For' the recognition... Revised [Line:183] Line 147 - was - were. This is also a frequent error Revised [Line:184]. Line 150 - 250? The units need to be included, nM? Revised [Lines:187-191]. Line 151 - 'Lower IC50 value signifies higher binding' - this should be qualified or prefaced by the term 'In general, for similar ligands,' as the maximal bound levels of different ligands can vary considerably even at the same site, meaning that comparison of IC50 values for different ligands in bringing about 50% inhibition only relates to the required concentration of that ligand relative to that for the highest attainable inhibition for that ligand. It doesn't allow for comparison of the level of binding of different ligands, or actual concentration of ligand at a given site. Thanks for the comment and clarifications. We have revised the sentence accordingly [Lines 189-190]. Line 151 - ingenious - not clear what is meant here. Thanks for the comment. We have replaced the word with resourceful [Line: 193] Line 155 - combined score - how is this derived? Thanks for the question? This part was missing in our manuscript and it has been included [Lines: 198-202] Line 156 - performed – made Revised [Line: 197] Line 183 - created – developed Revised [Line: 235]. Line 186 - was – were Revised [Line: 250]. Line 187 - The – An Revised [Line: 251]. Line 190 - was – were Revised [Line: 255]. Line 194 - implied - implicated, or better still, taken forward Revised [Line: 258]. Line 207 - Eventually - remove this Thanks for the comment. Due to the introduction of HADDOCK related protocol in the manuscript the sentence was completely removed. Line 217 - generated – developed Revised [Line: 327]. Line 222 - demonstrate – provides Revised [Line: 332]. Line 226 - upper case F in For, should be lower case Revised [Line: 336]. Line 240 - the - not needed Revised [Line: 3450]. Line 249 - the (antigenicity) - not needed Revised [Line: 359]. Line 251 - Surprisingly? Why is this a surprise? Thanks for bringing our attention towards this particular mistake. We have replaced the word. [Line:360] Line 251 - Similarly? - the values are very different Thanks for the comment. Actually there was a typo error in the previous line and the number was 97 instead of 49. So we have kept the word Similarly here with your due permission [Line:361] Line 252 - immunogenicity value - immunogenicity values Revised [Line: 362]. Line 253/254 - Table S2 details 82 epitopes rather than 83. The combined scores are shown in the table. However, it is not clear how these scores are derived. Further, the selection criteria for the 82 epitopes for further study is not described, this requires clarification, particularly if VaxiJen indicated only 49 epitopes to be antigenic. This contradicts the assertion in line 155/156 - 'the final selection for further study was performed after the (prediction) of antigenicity by VaxiJen v2.0 server....', and suggests that a proportion of the negative results obtained using VaxiJen, which represent an important observation, are being overlooked. Thanks for bringing our attention to this disparity in text and table S2. There were 97 epitope in place of 49 as asserted above as well. We have revised the text and included the calculation of combined score in method section. [Lines: 198-202] Line 257 - was - were; IC50 values Revised [Line: 367]. Line 267 - allergic - allergenic; allergen – allergenic Revised [Line: 376]. Line 268 - was – were Revised [Line: 377]. Line 289 - fetched – gathered Revised [Line: 399]. Line 291 - should read - although E protein epitope “LLFLAFVVF” exhibited binding.... Revised [Line: 401-402]. Line 298 - non-allergen - non-allergenic Revised [Line: 408]. Line 299 – candidates Revised [Line: 409]. Line 308 - two other epitopes... Revised [Line: 418]. Line 318-320 - ....ORF3a was found to be present.... (the two sentences could be condensed into one) Thanks for the suggestions. We have made one sentence out of the two sentences [Lines: 434-436] Line 321 - sentence is not clear at all, should be rephrased Thanks for this particular comment. We have rephrased the sentence [Line: 437-438]. Line 324 - resulted – result Revised [Line: 442]. Line 330 - considered to be potential... Revised [Line: 444]. Line 347 - As a representative of what? Thanks for the comment. The sentence has been revised in light of HADDOCK result. Line 356 - amid – across Revised [Line: 422]. Line 369 - was – were Revised [Line: 508]. Line 384 – candidates Revised [Line: 523]. Line 385 - adjective missing before 'antigenicity' Revised [Line: 524]. Line 387 - the spike protein Revised [Line: 526]. Line 396 – values Revised [Line: 535]. Line 400 – candidates Revised [Line: 539]. Line 415 - remove 'only' Revised [Line: 554]. Line 430 - exhibited – displayed Revised [Line: 569]. Line 444 - design a peptide-based Revised [Line: 597]. Line 460 - Cells - cells (lower case) Revised [Line: 613]. Line 468 – thresholds Revised [Line: 621]. Line 470/471 - further ensure the acceptance - further supports the feasibility Revised [Line: 624]. Line 472 - incomplete sentence - it looks like it should be part of the previous sentence? Revised [Line: 626]. Line 478 - considered as the most important epitopes, no need for 'rest of the' Revised [Line: 632]. Line 482 – criterion Revised [Line: 635]. Line 485-488 - reference should be made to Table-S4 Reference to S4 included [Line: 641] Line 488 - The SARS-CoV-2 outbreak has resulted... Revised [Line: 641]. Line 505 - the selected Revised [Line: 658]. Line 506 - simplified? Thanks for bringing the attention towards the word. We have included the work performed by PYMOL in our study. [Lines: 659-660] Line 507 - no need for 'investigation' Revised [Lines: 661] Line 514 - most up-to-date Revised [Line: 681] Line 515 - exhibits notably good performance Revised [Line: 682] Line 519-521 - incomplete sentence Revised [Lines: 686-689] Line 525 - The majority Revised [Line: 691] Line 527 - the ElliPro - 'the' not needed Revised [Line: 694] Line 532 - An earlier study Revised [Line: 699] Line 535-536 - we propose the identified potential vaccine candidates should be pursued in clinical trials. Thanks for the comment and improvement. We have revised [Line: 703] Line 539 - at the earliest – early Revised [Line: 706] Line 542 - vaccines – vaccine Revised [Line: 709] Line 543 - directives - directions / avenues; no need for 'the' Revised [Line: 710] Figure 1 legend – proteins Revised Throughout Figure 2 and Figure 3 legends - odd use of 'as' Legends have been revised as suggested. Figure 4 legend – proteins Revised Figure 4 - text in figure is far too small, impossible to read, it might need a key. Otherwise, the figures are very good, relevant and the legends are informative. Thanks for the important suggestion. We have shifted the better resolution figure 4 to Supplementary as Figure-S1. The tables are fine. Table 4 should perhaps be moved to the supplementary. Thanks for the suggestion. We have moved Table 4 to Supplementary. There are no legends provided for the supplementary tables. In all, this is a good manuscript, in my view very worthy of publication, which successfully applies and integrates a number of bioinformatics tools and resources in a coherent, meaningful way to address the challenge of the day. The basis of some scores and selection or filtering processes requires clarification and the manuscript needs busy checking of the English to ensure the widest possible understanding of the findings. Thanks for the positive and constructive comments. Supplementary Table legend was submitted separately as did for main text table. Now we have added the legends of supplementary table in the tables now for perusal. We have made clarifications as suggested. Hope it will be acceptable for the publication in Peer J. "	Here is a paper. Please give your review comments after reading it.
521	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Background Aquatic ecosystems are greatly altered by urban development, including the complete loss of natural habitat due to water diversions or channel burial. However, novel freshwater habitats also are created in cities, such as effluent-dependent streams that rely on treated wastewater for flow. However, it is unclear how diverse these novel ecosystems are, or how quickly aquatic species are able to colonize them. In this study, we (1) quantify odonate (Insecta, Odonata) colonization of a novel effluentdependent river reach, (2) examine how drying events affect odonates in these novel habitats, and (3) explore whether effluent-dependent streams can support diverse odonate assemblages.</ns0:p></ns0:div> <ns0:div><ns0:head>Methods</ns0:head><ns0:p>We conducted monthly odonate surveys at three sites along the Santa Cruz River (Tucson, Arizona, USA) between June 2019 and May 2020. One site was in a long-established effluent-dependent reach (flowing since the 1970s) that served as a reference site and two sites were in a newly-established reach that began flowing on June 24, 2019 (it was previously dry). We compared odonate species richness, assemblage composition, and colonization patterns across these reaches, and examined how these factors responded to flow cessation events in the new reach.</ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head><ns0:p>Seven odonate species were observed at the study sites in the new reach within hours of flow initiation, and species rapidly continued to arrive thereafter. Within three months, species richness and assemblage composition of adult odonates were indistinguishable in the new and reference reaches. However, drying events resulted in short-term and chronic reductions in species richness at one of the sites. Across all three sites, we found over 50 odonate species, which represent nearly 40% of species known from the state of Arizona.</ns0:p></ns0:div> <ns0:div><ns0:head>Discussion</ns0:head><ns0:p>Odonates were surprisingly diverse in the effluent-dependent Santa Cruz River and rapidly colonized a newly established reach. Richness levels remained high at study sites that did not experience drying events. These results suggest that consistent discharge of high-quality effluent into dry streambeds can be an important tool for promoting urban biodiversity. However, it remains to be seen how quickly and effectively less vagile taxa (e.g. mayflies, caddisflies) can colonize novel reaches. Effluent-dependent urban streams will always be highly managed systems, but collaboration between ecologists and urban planners could help to maximize aquatic biodiversity while still achieving goals of public safety and urban development.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>Urbanization is generally associated with dramatic alterations of natural ecosystems and subsequent losses of biodiversity <ns0:ref type='bibr' target='#b25'>(Grimm et al., 2008)</ns0:ref>. Urban ecosystems frequently have reduced species richness and are more homogeneous when compared to natural systems (e.g. <ns0:ref type='bibr' target='#b41'>McKinney, 2006;</ns0:ref><ns0:ref type='bibr' target='#b1'>Ball-Damerow et al., 2014;</ns0:ref><ns0:ref type='bibr' target='#b60'>Villalobos-Jimenez et al., 2016)</ns0:ref>. Freshwater ecosystems are particularly affected by urbanization. For example, the physical structure of water bodies is often modified to allow development close to water while trying to reduce flood risk <ns0:ref type='bibr' target='#b54'>(Stein et al., 2013;</ns0:ref><ns0:ref type='bibr' target='#b52'>Steele & Heffernan, 2014)</ns0:ref>. Additionally, runoff from impervious surfaces alters flow regimes and hydroperiods, and often delivers a complex combination of excess nutrients and contaminants <ns0:ref type='bibr' target='#b62'>(Walsh et al., 2005)</ns0:ref>. Furthermore, many urban water bodies simply disappear when they are hidden under concrete <ns0:ref type='bibr' target='#b46'>(Napieralski & Carvalhaes, 2016)</ns0:ref> or completely dry up due to water withdrawals <ns0:ref type='bibr' target='#b63'>(Webb et al., 2014)</ns0:ref>.</ns0:p><ns0:p>However, urbanization also can lead to the creation of novel anthropogenic water bodies, such as canals, stormwater runoff basins, and ponds in urban parks. In some cases, these novel habitats may become important reservoirs of biodiversity, supporting unique communities or species of conservation concern <ns0:ref type='bibr' target='#b14'>(Chester & Robson, 2013;</ns0:ref><ns0:ref type='bibr' target='#b34'>Lambert & Donihue, 2020)</ns0:ref>. Aquatic species must be able to colonize these novel habitats via flow connections with adjacent water bodies or overland dispersal. But how quickly do species find these habitats and successfully colonize them? And are the communities that develop therein unique, or are they similar to those from natural habitats or older urban habitats? Novel urban waters could provide an intriguing window into community assembly processes, but these systems were mostly ignored by ecologists for much of the 20 th century <ns0:ref type='bibr' target='#b25'>(Grimm et al., 2008)</ns0:ref>. One challenge is that rigorous study of these systems requires coordination between urban planners and ecologists, which is not common practice <ns0:ref type='bibr' target='#b32'>(Hunter & Hunter, 2008;</ns0:ref><ns0:ref type='bibr' target='#b34'>Lambert & Donihue, 2020)</ns0:ref>. Effluent-dependent streams, which rely on discharge from wastewater treatment plants for their baseflow, are increasingly common in urban areas <ns0:ref type='bibr' target='#b27'>(Hamdhani et al., 2020)</ns0:ref>. In fact, effluent discharge in arid regions has restored flow to some urban streams that were dry for decades because of groundwater overdraft and upstream diversions <ns0:ref type='bibr' target='#b63'>(Webb et al., 2014)</ns0:ref>. Despite decades of research regarding water quality in these effluent-dependent streams <ns0:ref type='bibr' target='#b8'>(Brooks et al., 2006)</ns0:ref>, only recently have we begun to study their potential biodiversity value <ns0:ref type='bibr'>(Bischel et al., 2013;</ns0:ref><ns0:ref type='bibr' target='#b36'>Luthy et al., 2015;</ns0:ref><ns0:ref type='bibr' target='#b47'>Peschke et al., 2019)</ns0:ref>. Wastewater treatment plants often discharge effluent into riverbeds simply because there is nowhere else to put it-and it is not customary to notify ecologists when discharge begins or ends <ns0:ref type='bibr' target='#b8'>(Brooks et al., 2006;</ns0:ref><ns0:ref type='bibr' target='#b27'>Hamdhani et al., 2020)</ns0:ref>.</ns0:p><ns0:p>In this study, we document colonization and community assembly of dragonflies and damselflies (hereafter, odonates) in a new effluent-dependent reach of the Santa Cruz River in Tucson, AZ (USA). We compare community structure in this new reach with that from a nearby effluent-dependent reach that had been flowing for decades. This rare study opportunity arose because urban planners publicly announced they would begin discharging effluent into the new reach several months prior to flow initiation. We asked three primary research questions: (1) how quickly do dragonflies and damselflies (odonates) colonize novel habitat?, (2) can effluentdependent streams support diverse odonate assemblages?, and (3) how do drying events in these novel habitats affect odonates? The third question arose because occasional periods of infrastructure or channel maintenance can lead to cessation of discharge in effluent-dependent streams.</ns0:p><ns0:p>We focused our surveys on odonates for several reasons. First, they tend to be strong dispersers and are known to colonize novel anthropogenic habitats <ns0:ref type='bibr' target='#b17'>(Corbet, 1999;</ns0:ref><ns0:ref type='bibr' target='#b48'>Prescott & Eason, 2018;</ns0:ref><ns0:ref type='bibr' target='#b11'>Cerini et al., 2019)</ns0:ref>. Second, diverse assemblages of odonates have been found in urban water bodies in many cities <ns0:ref type='bibr' target='#b64'>(Willigalla & Fartmann, 2012;</ns0:ref><ns0:ref type='bibr' target='#b23'>Goertzen & Suhling, 2015;</ns0:ref><ns0:ref type='bibr' target='#b29'>Holtmann et al., 2018)</ns0:ref>. Third, odonates can be visually surveyed and easily identified to species. Fourth, odonates can be used for biotic indices that assess environmental conditions and ecological integrity in streams (e.g. <ns0:ref type='bibr' target='#b16'>Chovanec et al., 2015;</ns0:ref><ns0:ref type='bibr' target='#b21'>Golfieri et al., 2016;</ns0:ref><ns0:ref type='bibr' target='#b61'>Vorster et al., 2020)</ns0:ref>. Finally, they are conspicuous, colorful, and charismatic, which are useful traits for environmental education and ecotourism <ns0:ref type='bibr' target='#b35'>(Lemelin, 2007;</ns0:ref><ns0:ref type='bibr' target='#b15'>Clausnitzer et al., 2017)</ns0:ref>. These potentially important cultural and economic links to urban residents could inspire more collaborations between planners and ecologists to study novel water bodies.</ns0:p></ns0:div> <ns0:div><ns0:head>Materials & Methods</ns0:head></ns0:div> <ns0:div><ns0:head>Study system</ns0:head><ns0:p>Historically, the Santa Cruz River had alternating sections of perennial, intermittent, and ephemeral flow throughout its course, from its headwaters in southern Arizona and northern Sonora (Mexico) to its confluence with the Gila River near Phoenix, Arizona <ns0:ref type='bibr' target='#b63'>(Webb et al., 2014)</ns0:ref>. However, diversions and groundwater pumping caused 99% of the river to become ephemeral by the 1940s; groundwater levels have fallen as far as 80 meters below the riverbed in Tucson <ns0:ref type='bibr' target='#b9'>(Carlson et al., 2011)</ns0:ref>. Discharge of effluent (treated municipal wastewater) into the dry riverbed has occurred in two reaches of the lower Santa Cruz River since at least the 1970s, restoring perennial surface flow in those sections (Figure <ns0:ref type='figure' target='#fig_0'>1</ns0:ref>). Although water quality in these effluent-dependent reaches was initially very poor, treatment plants were upgraded in 2013 and have produced high quality tertiary-treated effluent ever since (Sonoran Institute, 2017).</ns0:p><ns0:p>In 2019, a third effluent-dependent reach of the river was created in downtown Tucson as part of the Santa Cruz River Heritage Project (Figure <ns0:ref type='figure' target='#fig_0'>1</ns0:ref>). The purpose of this new reach is to enhance recharge of the local aquifer and create a new recreational, ecological, and economic feature in the city <ns0:ref type='bibr' target='#b58'>(Tucson Water, 2020)</ns0:ref>. For this new reach, effluent is piped from treatment plants in the north to an outfall location 10 km south of the two established reaches (Figure <ns0:ref type='figure' target='#fig_0'>1</ns0:ref>). Flow began in this new reach on 24 June 2019, but occasional flow reduction or cessation events occurred in the following months as operational issues arose or infrastructure upgrades were needed (Supplemental Figure <ns0:ref type='figure' target='#fig_0'>1</ns0:ref>). In May 2020, effluent releases ceased in the new reach to allow for sediment removal from the flood control channel, but there are plans to resume flow later in the year <ns0:ref type='bibr' target='#b58'>(Tucson Water, 2020)</ns0:ref>. The nearest naturally perennial stream to all three effluentdependent reaches is Sabino Canyon, a canyon-bound headwater stream 23 km to the east. The nearest low gradient naturally perennial rivers are the San Pedro and Gila Rivers, >70 km to the east and northeast, respectively.</ns0:p></ns0:div> <ns0:div><ns0:head>Sampling design</ns0:head><ns0:p>We selected three study sites on the Santa Cruz River to survey for adult odonates: (1) a reference site on one of the long-established effluent-dependent reaches, (2) a site near the effluent outfall of the new reach (Starr Pass Blvd), and (3) a site 2km downstream of the outfall (Cushing St) near the end of the new reach (Figure <ns0:ref type='figure' target='#fig_0'>1</ns0:ref>). The reference site was selected because it supports the highest diversity of aquatic invertebrates (including larval odonates) known from various biomonitoring sites along the established reaches (Sonoran <ns0:ref type='bibr' target='#b50'>Institute, 2017;</ns0:ref><ns0:ref type='bibr'>Eppehimer et al., 2020)</ns0:ref>.</ns0:p><ns0:p>At each of the three survey sites, we established 400m long transects to survey for adult odonates. This length of transect was chosen to incorporate multiple distinct habitat units in one transect to maximize the potential for odonate species richness. In each site, there was at least one riffle, one pool, one run, and one backwater (i.e. off-channel isolated pool), and usually at least two of each, but we did not quantify the exact proportion of each habitat unit within sites. Each of the three sites was located within flood control levees, so while there was a small amount of sinuosity in the actively flowing channel at each site, meandering was limited by levees (Figure <ns0:ref type='figure' target='#fig_0'>1</ns0:ref>).</ns0:p><ns0:p>From 1 June 2019 to 10 May 2020, we conducted monthly 90-minute adult odonate surveys along each transect, always between 11:00 and 15:00 hours, under partly sunny to clear skies, with minimal winds and warmer temperatures (>15ºC). Although ≥80% of odonate species in a site can be detected with shorter surveys (e.g. 30 minutes), additional survey time allows greater detection of rare species <ns0:ref type='bibr' target='#b7'>(Bried et al., 2012)</ns0:ref>. During each survey, the same observer (MTB) slowly walked along and in the water through the 400m reach while identifying adult odonates by sight and occasionally using a hand-net. Voucher photos for each species observed at each site were taken using a digital camera (Canon Powershot SX60 with 65x optical zoom) and shared with regional odonate experts for taxonomic confirmation <ns0:ref type='bibr' target='#b0'>(Bailowitz et al., 2015)</ns0:ref>. Additionally, during each survey, each odonate species observed was scored into one of three abundance categories: (1) rare: <10 individuals seen and no breeding activity observed; (2) uncommon: 10 to 100 individuals and breeding activity observed; (3) common: >100 individuals with breeding activity observed. There were no instances where less than 10 individuals of a species were seen but breeding was observed among those few individuals. During each survey across the 10 months, larval exuviae and teneral adults were also sought to confirm that a successful reproduction event for a given species observed as adults at a given site.</ns0:p><ns0:p>Surveys at each site generally were conducted on consecutive days within a month, but occasionally occurred as far as 4 days apart so that weather conditions were optimal for each survey. In addition to monthly surveys at all sites, biweekly surveys were conducted at one site (Starr Pass Blvd) on the Heritage reach. These additional surveys were intended to detect new or rare species as soon as possible when they colonized this newly flowing reach, because biweekly surveys can reveal species that monthly surveys fail to capture <ns0:ref type='bibr' target='#b6'>(Bried et al., 2007)</ns0:ref>. During each survey, we recorded weather conditions and measured air temperature and a suite of physiochemical water quality factors (e.g. temperature, conductivity, pH, dissolved oxygen) during each monthly survey.</ns0:p><ns0:p>Finally, to create a list of all odonate species currently known from the lower Santa Cruz River in the metro Tucson region, and provide context for the results of our surveys, we compiled records from regional experts (Rich Bailowitz, Doug Danforth, and Pierre Deviche, pers. comm.), a regional field guide <ns0:ref type='bibr' target='#b0'>(Bailowitz et al., 2015)</ns0:ref>, and online databases [Arizona Dragonflies (azdragonfly.org), iNaturalist (inaturalist.org), and Odonata Central (odonatacentral.org)].</ns0:p></ns0:div> <ns0:div><ns0:head>Data analyses</ns0:head><ns0:p>Survey data were used to calculate species richness values for each site by month. These data were plotted to examine changes in species richness over time at the reference and newly flowing sites, and to see how richness values responded to occasional stream drying events. We also use the Hill numbers approach <ns0:ref type='bibr' target='#b13'>(Chao et al., 2014)</ns0:ref> to extrapolate the full species richness of each site if additional sampling events were to occur. Estimates of the first Hill number (q = 0 for species richness) were made with the package iNEXT (version 2.0.19) in R Version 3.5.3 <ns0:ref type='bibr' target='#b31'>(Hsieh et al., 2016)</ns0:ref>. To ensure fair comparisons, we used only monthly survey data to generate richness estimates, excluding the additional biweekly survey data from the Starr Pass site.</ns0:p><ns0:p>Differences in adult odonate assemblage composition across all sites and survey dates were visualized with non-metric multidimensional scaling (NMS) in PC-ORD Version 5 (MJM Software, Gleneden Beach, Oregon) using Sorensen distance as the measure of community dissimilarity <ns0:ref type='bibr' target='#b40'>(McCune et al., 2002)</ns0:ref>. For ordination analyses, we used survey abundance codes (0 = undetected, 1 = rare, 2 = uncommon, 3 = common) so that species with higher abundances would have more influence on the ensuing ordination than rare species. To assess which species were most influential in the observed ordination patterns, we calculated linear correlation values between species abundances and ordination axes. We also calculated linear correlation coefficients between measured environmental variables and ordination axes. Finally, we examined temporal trends in abundance for each species in the newly flowing reach to visualize colonization patterns in this novel habitat.</ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head><ns0:p>Within 6 hours of flow initiation in the newly flowing sites (on 24 June 2019), 7 odonate species (Enallagma civile, Erythrodiplax basifusca, Ischnura demorsa, Orthemis ferruginea, Pachydiplax longipennis, Sympetrum corruptum, and Tramea onusta) were observed mating and ovipositing (Figure <ns0:ref type='figure' target='#fig_1'>2</ns0:ref>). Additional species quickly arrived in the following weeks, and species richness at the Starr Pass site reached or exceeded that of the reference site within 3 months (Figure <ns0:ref type='figure' target='#fig_2'>3</ns0:ref>). Species richness values at the Cushing site, which experienced several drying events during the course of the study, remained lower than Starr Pass but exhibited similar seasonal trajectories (i.e. lower in winter months, higher in spring and summer months). Species richness in both of the newly flowing sites plummeted when flow ceased in May 2020 (Figure <ns0:ref type='figure' target='#fig_2'>3</ns0:ref>). The Hill numbers estimation of species richness found almost complete overlap in 95% confidence intervals for the Starr Pass and reference sites, but values at Cushing were predicted to remain lower even with numerous additional monthly sampling events (Figure <ns0:ref type='figure' target='#fig_3'>4</ns0:ref>). Across all sites and dates, we observed 50 of the 53 odonate species currently known from the entire lower Santa Cruz River, including 44 species at the reference site, 43 species at Starr Pass, and 28 species at Cushing (Table <ns0:ref type='table'>1</ns0:ref>). We confirmed successful recruitment via larval exuviae and/or teneral adults for 35 species at the reference site (80%), 28 species at Starr Pass (65%), and 17 species at Cushing (61%).</ns0:p><ns0:p>Assemblage composition between Star Pass and the reference site converged within 3 months (Figure <ns0:ref type='figure' target='#fig_4'>5</ns0:ref>; NMS results: stress = 0.14, final instability > 0.00001, cumulative R 2 = 0.84, p = 0.004). Surveys from the reference site generally occupied the lowest region of NMS axis 2, remained stable through summer 2019, and were characterized by high abundances of several damselflies (e.g. Argia sedula, Hetaerina americana, Argia pallens, Telebasis salva) and dragonflies (e.g. Anax junius, Libellula saturata) (Table <ns0:ref type='table'>2</ns0:ref>). Assemblages at all three sites moved to right along NMS axis 1 for the winter months, exhibiting lower abundances of monsoonal or summer dragonflies (e.g. Orthemis ferruginea, Pantala flavescens, Tramea lacerata) and two species of damselflies (Ischnura demorsa, Enallagma civile) (Table <ns0:ref type='table'>2</ns0:ref>). Overall, the reference site exhibited less variation in assemblage composition through time than the two newly flowing sites (Figure <ns0:ref type='figure' target='#fig_4'>5</ns0:ref>). Both water and air temperatures were strongly negatively correlated with NMS axis 1 (r = -0.77 and -0.73, respectively); all other measured variables were only weakly correlated with ordination axes (i.e. -0.5 < r < 0.5). Of all species known to have breeding populations in the reference site, only 6 failed to become established at the new sites within 10 months (Table <ns0:ref type='table'>1</ns0:ref>). We also found 7 species in the newly flowing sites that were absent from the reference site; however, 6 of these were rare and were never observed breeding. Only one species of damselfly (Argia extranea) became established at the new sites but was never observed at the reference site. Several different colonization modes were observed among the species documented in the newly flowing sites (Figure <ns0:ref type='figure' target='#fig_5'>6</ns0:ref>). Some species colonized on the first day of flow initiation, quickly established robust breeding populations, and remained abundant through the course of the study, while other species colonized rapidly but exhibited seasonal variation in abundances thereafter. Yet other species took a few months to colonize and remained at relatively low abundances, which varied seasonally. Finally, some species were vagrants that only appeared once or twice and did not appear to have breeding populations in any of the sites (e.g. Libellula luctosa).</ns0:p></ns0:div> <ns0:div><ns0:head>Discussion</ns0:head><ns0:p>Dragonflies and damselflies colonized a new effluent-dependent reach of the Santa Cruz River incredibly fast, with 7 species arriving on the first day of flow, each of which successfully established breeding populations. This was especially surprising because flow began during the hottest and driest part of the year, and many aquatic insects in the region only disperse aerially after summer rains begin <ns0:ref type='bibr'>(Bogan & Boermsa, 2012)</ns0:ref>. Within three months, species richness values of adult odonates were equal in the new reach and the long-established reach more than 10 km away. Although previous studies have found that odonates can disperse tens to hundreds of kilometers across arid landscapes <ns0:ref type='bibr' target='#b57'>(Suhling et al., 2017)</ns0:ref>, the speed at which species arrived here was still impressive. It often takes >2-3 months for odonates to find novel water bodies, with lower colonization rates usually observed at more isolated waters <ns0:ref type='bibr' target='#b39'>(McCauley, 2006;</ns0:ref><ns0:ref type='bibr' target='#b4'>Bogan & Boersma, 2012;</ns0:ref><ns0:ref type='bibr' target='#b26'>Groover, 2017)</ns0:ref>.</ns0:p><ns0:p>Both the rapid colonization rates and the high overall species richness values observed were surprising. Across all dates, we found 50 odonate species in our three effluent-dependent study sites, which is >35% of the species known from the entire state of Arizona <ns0:ref type='bibr' target='#b0'>(Bailowitz et al., 2015)</ns0:ref>. For comparison, only 58 species were found in the entire Grand Canyon <ns0:ref type='bibr' target='#b55'>(Stevens & Bailowitz, 2009)</ns0:ref>, an area is many times larger than our study area and encompasses many desert and montane springs, streams, and rivers. Odonates thrive in warm environments <ns0:ref type='bibr' target='#b17'>(Corbet, 1999)</ns0:ref>, so the warm air temperatures and mild winters of southern Arizona may be partly responsible for the high odonate diversity we observed. Although nearly 100 odonate species are known from Pima County, where the lower Santa Cruz River is located, the county hosts aquatic habitats in areas ranging from hot deserts (<275 m asl) to cold montane forests (>2,700 m asl) <ns0:ref type='bibr' target='#b0'>(Bailowitz et al., 2015)</ns0:ref>. So, the fact that over half of the species known in the county were found in a single, effluent-dependent urban river-an artificial ecosystem-is remarkable.</ns0:p><ns0:p>We could not compare our findings to those from naturally perennial rural reaches of the river outside of Tucson because none remain <ns0:ref type='bibr' target='#b63'>(Webb et al., 2014)</ns0:ref>. However, nearly 70% of published studies report some reduction in odonate species richness in urban cores, with species losses often being related to pollution issues <ns0:ref type='bibr'>(Villalobos-Jimemez et al., 2016)</ns0:ref>. Interestingly, many of these studies reported reductions in odonate richness in ponds rather than rivers. For example, a study from Kentucky (USA) found no difference in odonate diversity between urban and rural streams, but urban ponds were less diverse than rural ponds <ns0:ref type='bibr' target='#b48'>(Prescott & Eason, 2018)</ns0:ref>. The high diversity we observed in the Santa Cruz River also could be due in part to the relative lack of industrial pollution (e.g. factories) and high-density development in Tucson. Furthermore, the warm water temperature regimes of effluent-dependent streams <ns0:ref type='bibr'>(Bischel et al., 2013)</ns0:ref> may be ideal for the growth and development of odonates. In fact, some odonates we observed, such as the Neotropical bluet (Enallagma novaehispaniae), are tropical species that have only colonized Arizona in the past decade <ns0:ref type='bibr' target='#b0'>(Bailowitz et al., 2015)</ns0:ref>. To date, they have only been found in effluent-dependent streams, whose warm waters may mimic their tropical home streams <ns0:ref type='bibr'>(Bailowitz & Deviche, unpubl. data)</ns0:ref>. Similar patterns have been observed in central European cities, where southern Mediterranean odonate species have been documented expanding their range northward <ns0:ref type='bibr' target='#b64'>(Willigalla & Fartmann, 2012)</ns0:ref>.</ns0:p><ns0:p>Although we found diverse odonate assemblages in the tertiary-treated wastewater of the Santa Cruz River, this does not mean that wastewater is always beneficial to odonates. Untreated wastewater (i.e. raw sewage) generally reduces the abundance and diversity of odonates (Henriques- <ns0:ref type='bibr' target='#b28'>de-Oliveira et al., 2007)</ns0:ref>. In fact, within wastewater treatment facilities, odonate species richness and abundance increase dramatically from initial wastewater lagoons to subsequent treatment ponds with better water quality <ns0:ref type='bibr' target='#b10'>(Catling, 2005)</ns0:ref>. Given these sensitivities to organic pollution, odonates are often used as bioindicators <ns0:ref type='bibr' target='#b33'>(Júnior et al., 2015;</ns0:ref><ns0:ref type='bibr' target='#b43'>Mendes et al., 2017;</ns0:ref><ns0:ref type='bibr' target='#b45'>Miguel et al., 2017)</ns0:ref>. Some species are known to tolerate inputs of raw or poorly treated sewage (e.g. Anax junius, Enallagma civile: <ns0:ref type='bibr' target='#b10'>Catling, 2005)</ns0:ref>. Unfortunately, there are no historical odonate data available from our reference site on the Santa Cruz River when it was receiving lower quality effluent (1970s to 2013). However, studies from the upper Santa Cruz River, 60 km to the south of our study sites, may be informative. <ns0:ref type='bibr' target='#b5'>Boyle & Fraleigh (2003)</ns0:ref> found only six odonate genera in the upper Santa Cruz River when it was fed by low quality effluent in the 1990s. In contrast, we found 27 genera in the lower Santa Cruz. These findings suggest that wastewater treatment plant upgrades are at least partly responsible for the high odonate diversity we observed.</ns0:p><ns0:p>One risk of living in effluent-dependent streams is that drying events can occur when infrastructure fails (e.g. pipes break) or discharge is paused to allow for channel maintenance <ns0:ref type='bibr' target='#b58'>(Tucson Water, 2020)</ns0:ref>. In naturally flowing streams, increased frequency or duration of drying events usually causes reductions in the diversity of aquatic invertebrates <ns0:ref type='bibr' target='#b18'>(Datry et al., 2014;</ns0:ref><ns0:ref type='bibr' target='#b56'>Stubbington et al., 2017)</ns0:ref>. For odonates, drying events eliminate larvae of most species, and these losses will have cascading impacts on adult populations <ns0:ref type='bibr' target='#b42'>(McPeek, 2008)</ns0:ref>. In our study, we observed much higher richness of adult odonates in the site that dried infrequently versus the one that dried multiple times (Figure <ns0:ref type='figure' target='#fig_2'>3</ns0:ref>). However, short periods of flow cessation (e.g. hours to a couple days) did not seem to have a dramatic effect, as odonate larvae likely found refuge in damp algal mats or remnant pools <ns0:ref type='bibr' target='#b56'>(Stubbington et al., 2017)</ns0:ref>. To maximize odonate diversity in effluent-dependent streams, managers should minimize the duration and frequency of shutoff events that result in stream drying, and avoid shutting off flow during the hottest, driest times of the year, when in-stream refuges would quickly disappear.</ns0:p><ns0:p>Although recurring drying events are likely the primary cause of reduced species richness observed at the Cushing site, that site's species richness trajectory was lower than that of the nearby Starr Pass even before drying began (Figure <ns0:ref type='figure' target='#fig_2'>3</ns0:ref>). One potentially important factor that we did not measure is the structural complexity of riparian vegetation. Neither site had welldeveloped mesophilic riparian vegetation; however, the Starr Pass site included the effluent outfall, where discharge flowed for ~60 meters across a vegetated terrace (Figure <ns0:ref type='figure' target='#fig_1'>2</ns0:ref>) before dropping into the active river channel. This area may have provided an enhanced number of perches or structural complexity that were 'attractive' to odonates (e.g. <ns0:ref type='bibr' target='#b49'>Samways & Steytler, 1996)</ns0:ref>. After several months of flow, however, the complexity of riparian vegetation seemed to increase at both sites as wetland plants colonized the river (e.g. Typha sp.: Figure <ns0:ref type='figure' target='#fig_0'>1</ns0:ref>). Future studies of new effluent-dependent stream reaches should quantify riparian vegetation complexity before and after flow begins.</ns0:p><ns0:p>One major limitation of our study is that there are no historical data available from when our study reaches were naturally perennial in the early 1900s. We do not know what the diversity or composition of odonates was in the natural Santa Cruz River. However, studies from other regions suggest that the lower Santa Cruz River, as diverse as it is today, may still lack species which were present historically. For example, the odonate fauna of California today is more homogeneous than it was 100 years ago, with sharp losses of habitat specialist species in urbanized areas <ns0:ref type='bibr' target='#b1'>(Ball-Damerow et al., 2014)</ns0:ref>. Additionally, we know that vegetation along the Santa Cruz River has changed dramatically in the last century, with the elimination of floodplain forests and the extirpation of many native plant species <ns0:ref type='bibr' target='#b63'>(Webb et al., 2014)</ns0:ref>. Odonate diversity generally increases with the diversity and complexity of riparian vegetation in both urban and rural habitats <ns0:ref type='bibr' target='#b49'>(Samways & Steytler, 1996;</ns0:ref><ns0:ref type='bibr' target='#b22'>Goertzen & Suhling, 2013;</ns0:ref><ns0:ref type='bibr' target='#b19'>Dutra & De Marco, 2015)</ns0:ref>. Thus, historic vegetation losses along the Santa Cruz River may have led to the extirpation of some odonate species that were historically present.</ns0:p><ns0:p>Finally, it is likely that odonate recolonization of the Santa Cruz River is still happening. The river was dry for many decades in all reaches, even in the long-established effluentdependent reaches, water quality has only been high since 2013. So, the river has only been 'palatable' to many odonate colonizers for a few years. Further, the nearest naturally perennial stream is a small headwater stream over 20 km away, and the nearest perennial rivers of similar size to the Santa Cruz are over 70 km away. Rare and stochastic colonization events from these distant source streams and rivers may take time and likely are still occurring. For example, the dragonfly Stylurus plagiatus is known from natural rivers in Arizona <ns0:ref type='bibr' target='#b0'>(Bailowitz et al., 2015)</ns0:ref>, but it was not known from the effluent-dependent Santa Cruz River until we found larvae and adults in two reaches in 2019 <ns0:ref type='bibr'>(Bogan, unpubl. data)</ns0:ref>. Even over the relatively short duration of the current study, we observed that colonization rate and success varied greatly among odonate species (Figure <ns0:ref type='figure' target='#fig_5'>6</ns0:ref>). Repeat surveys and long-term studies along the Santa Cruz River will be invaluable for documenting colonization processes in this novel urban ecosystem.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions</ns0:head><ns0:p>Odonates were surprisingly diverse in the effluent-dependent Santa Cruz River, supporting nearly 40% of all species known in the state of Arizona. Additionally, numerous odonate species rapidly colonized a newly-established reach of the river. In the absence of prolonged drying events, assemblage composition in the new sites was indistinguishable from the reference site within three months. These results suggest that consistent discharge of highquality effluent into dry streambeds can be an important tool for promoting urban biodiversity, especially in arid and semi-arid regions <ns0:ref type='bibr'>(Bischel et al., 2013;</ns0:ref><ns0:ref type='bibr' target='#b36'>Luthy et al., 2015)</ns0:ref>. However, it remains to be seen how quickly and effectively less vagile taxa (e.g. mayflies, caddisflies) will colonize novel reaches, and this topic deserves further study. Furthermore, our study ended because flow ceased in the newly-established reach to allow for sediment removal and flood risk mitigation <ns0:ref type='bibr' target='#b58'>(Tucson Water, 2020)</ns0:ref>. This dramatic ending highlights the fact that urban streams will always be highly managed systems. But with collaboration between ecologists and urban planners, these management activities can be modified to maximize aquatic biodiversity while still achieving public safety goals <ns0:ref type='bibr' target='#b32'>(Hunter & Hunter, 2008)</ns0:ref>. Collaborations between ecologists and planners also would enhance ecotourism opportunities and better connect urban residents with their local ecosystems <ns0:ref type='bibr' target='#b35'>(Lemelin, 2007;</ns0:ref><ns0:ref type='bibr' target='#b15'>Clausnitzer et al., 2017)</ns0:ref>. The 20 th century was a difficult time for urban streams and the species that resided in them, but there is hope for better ecological outcomes by the end of the 21 st century. Manuscript to be reviewed Enallagma civile) or were at least abundant during warmer weather (e.g. Sympetrum corruptum, Orthemis ferruginea). Other species took longer to colonize and had limited seasons of adult flight activity (e.g. Hetaerina americana, Perithemis intensa). Other species were vagrants that appeared once or twice but failed to establish populations (e.g. Libellula luctosa).</ns0:p></ns0:div> <ns0:div><ns0:head>Table 1(on next page)</ns0:head><ns0:p>Odonate species encountered at three sites along the effluent-dependent Santa Cruz River Each species was scored into one of three abundance categories for each site: (1) rare: <10 individuals seen; (2) uncommon: 10 to 100 individuals; (3) common: >100 individuals. We observee evidence of reproduction success at a given site (e.g. teneral adults) for species classified as common or uncommon, but not for those classified as rare. Species marked with an asterisk were not found during the surveys for this study, but have been documented from adjacent reaches of the Santa Cruz River in existing databases (e.g. iNaturalist, Odonata Central).</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:05:49345:1:1:NEW 28 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head>Figure 1</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head>Figure 2 First</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>Figure 3</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Figure 3</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head>Figure 4</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Figure 4</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head>Figure 5</ns0:head><ns0:label>5</ns0:label><ns0:figDesc>Figure 5</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head>Figure 6</ns0:head><ns0:label>6</ns0:label><ns0:figDesc>Figure 6</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='18,42.52,70.87,525.00,405.00' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='25,42.52,70.87,525.00,400.50' type='bitmap' /></ns0:figure> </ns0:body> "	"Editor comments (Donald Baird) MINOR REVISIONS All three reviewers have indicated minor revisions to your paper and I concur - one specific area requiring attention is the rate of recolonization, and whether the presence of adults at a site is really sufficient evidence for recolonization without also establishing the presence of larvae / exuviae. MTB: Thank you—we have addressed these issues in the revisions described below. Reviewer 1 (Anonymous) Basic reporting The manuscript describes the adult odonate fauna of a stretch of Santa Cruz River in southern Arizona. The authors took advantage of the fact that they could compare odonate diversity (and changes thereof over time) in a section of river with established perennial water flow with that in two sections of the same river where this flow, likewise consisting of treated wastewater, was recently restored and underwent large fluctuations during the study period. Studies such as this have the potential to improve our understanding of how insect communities adjust to urbanization, colonize new habitats, and respond to short- and longer-term human-related environmental manipulations, and they are, therefore, welcome and warranted. Experimental design I found the manuscript to be well-written and organized, and informative. The data were analyzed using appropriate statistical methods. The results are novel and contribute to our knowledge of odonate population dynamics and ecology, as well as to our understanding of urban aquatic ecosystems. Validity of the findings See above and below. Comments for the Author L252: The rate of colonization (i.e., species showing up within a few hours of water flow restoration) was, indeed, rather fast, but why the authors conclude that species richness (50 species) at the study sites during the survey period was higher than expected is unclear. Approximately 100 odonate species are recorded in Pima Co., where the work was conducted, and so 50% of the species present in the county were cumulatively found during surveys. Survey sites encompass diverse types of micro-habitats and one stretch of river that was investigated has had water consistently running for several years. Given overall regional richness (approx. 100 species) and the diversity of micro-habitats that were surveyed, one would predict a relatively large number of species to show up at least occasionally and this was, indeed, the case. Thus, and in view of the demonstrated dispersal ability of odonates in general, what is the rationale for stating that species richness was higher than anticipated? The authors compare their findings with those in much larger regions of the state (Stevens & Bailowitz 2008, 2009) and where similar numbers of species (32 and 58, respectively) as recorded here were found. That a much smaller survey area had comparable richness is not particularly surprising because the Santa Cruz river is located well south of and at lower altitude than the regions surveyed by Stevens and Bailowitz, and so is expectedly to hold considerably more species to begin with. MTB: We thank the reviewer for these perspectives! We have removed the comparisons with Stevens & Bailowitz 2008 because their study was out-of-state and in a different desert (Mojave). However, we have retained the reference to Stevens and Bailowitz 2009; we think it’s still relevant given that the lower part of the Grand Canyon has a climate and floral and faunal elements similar to southern Arizona. Finally, in lines 273-277, we’ve also clarified why we think it was surprising to observe so many species in the Santa Cruz River despite the high richness of Pima County, where the river is located. Briefly, the county has a large elevation range (~270-2750m) and a very wide range of aquatic habitats across that elevation range, from springs to marshes to lakes to small and large streams. So, to observe in one effluent-dependent urban stream over 50% of the species that are observed across that whole range of habitats seems remarkable to us. L275: The Neotropical Bluet was first found in Arizona in 2010 and so has been present in the state, at least sporadically, for more than 5 years – although it is unknown when breeding populations became established. Too, this bluet is common/abundant in the southern half of Texas and so presumably colonized the United States (L275) quite a while ago. MTB: Thank you for this correction and opportunity for clarification! The Neotropical Bluet was indeed first found in Arizona in 2010, and then only single or a few males were found until 2016, when the species was first documented to have a breeding population (Tres Rios effluent facility in Phoenix). We have changed the text to say “have only colonized Arizona in the past decade”, and we apologize for forgetting about the Texas populations, which have been known for many decades. L301: The authors write “We observed much higher richness of adult odonates in the site that dried infrequently versus the one that dried multiple times”. This is correct based on data shown in Fig. 3. However, this figure also shows that this difference was already present in July 2019, i.e., before the first drying event. Thus, drying does by itself not seem to fully explain the observed site differences in odonate richness, indicating that other factors are likely involved. MTB: This is a pattern we also observed, but unfortunately none of our measured data could account for that difference. Our best guess is that it may have something to do with the complexity of riparian vegetation in a small portion of the Starr Pass site versus the Cushing site. Although it is purely speculation since we did not collect quantitative data to support this hypothesis, we have added the paragraph in lines 324-335 to offer a potential explanation. If the reviewer or editor feels it is too speculative, we are happy to remove this new text as well. I am wondering whether the authors could comment on the possibility that the effects of drying on odonate populations may be season dependent. For example, a short period of drying in early summer, before the summer monsoon and when temperatures are usually elevated, would be expected to have a more pronounced and faster effect than a same period of drying during winter, when ambient temperatures are much lower. If so, water resource managers may want to consider limiting drying to the winter, if possible, in order to minimize negative impacts on aquatic biodiversity. MTB: Excellent point! We have added the text suggesting that managers should “avoid shutting off flow during the hottest, driest times of the year, when in-stream refuges would quickly disappear” [lines 321-323] Minor issues: L101 & L259: Corbet, 1999 MTB: corrected L224: were strongly negatively correlated… MTB: corrected L238: Libellula MTB: corrected L248: Suhling et al., 2017 is not in the list of references. MTB: We list this reference on lines 568-569. Fig. 2: Why include two pictures of Enallagma civile pairs (one in wheel, the other in tandem with ovipositing female)? Readers would probably prefer to see pictures of different species here. MTB: We replaced the Enallagma civile photo with lower resolution with a photo of a different colonizer from the first day, the Blue Dasher. Fig. 5. Label X and Y axes. MTB: corrected Supplemental data table(s): Red-tailed Pennant = Brachymesia furcaTa; Neotropical Bluet = Enallagma novaehispanIae; Western Pondhawk = Erythemis collOcata; FilIgree Skimmer. MTB: thanks for catching these—corrected! Reviewer 2 (Michael Patten) Basic reporting line 189 – I suggest “undetected” in place of “absent.” MTB: Good point! We made the suggested replacement. lines 252–253 – The authors say that “Not only were colonization rates faster than expected, but overall species richness values were higher than we expected as well.” I do not mean to pedantic, but when I see “expect” in a scientific study I construe it to mean a null expectation (i.e., from a null model) or some statistical expectation. In this case, though, I think the authors mean that the results surprised them, which is not the same thing. MTB: We edited the text to state that these patterns were ‘surprising’ rather than ‘higher than expected’ lines 255–258 – I do not find the comparisons to Ash Meadows or the Grand Canyon compelling. They are in different biogeographic provinces and ecoregions, ones that have lower richness across the broad, not just with regard to Odonata. MTB: We have removed the Ash Meadows comparison and citation because, as the reviewer correctly points out, it’s from a cooler desert (Mojave Desert) than the study area. However, we would like to retain the Grand Canyon comparison because the flora and fauna of the western (lower) Grand Canyon shares many floral and faunistic similarities with the Sonoran Desert, and has a fairly similar climate as well. We note this in a modified version of the text in these lines (269-271). lines 267–269 – This sentence needs to be reworded. As it stands, it says, “diversity did not differ even though diversity was lower.” MTB: In the cited study, which compared rural and urban ponds and streams, odonate diversity did not differ between urban and rural streams-- however it was lower in urban ponds compared to rural ponds. So it’s just a difference in patterns between lotic vs lentic habitats—we hope that’s clear now! line 270 – I am unsure what is meant by “more robust tolerances of lotic versus lentic odonates.” It is axiomatic that more lotic than lentic species would occur along a river, especially a shallow one with steady flow. MTB: This was in reference to the previous two sentences (and cited studies therein) showing that odonate diversity often is reduced in urban ponds more than urban rivers when compared to their rural counterparts. Through this, one could infer that odonates in urban rivers are more tolerant of urbanization than odonates in urban ponds. But we don’t want the phrase to confuse readers, and the sentence stands on its own without it, so we have removed it. lines 274–275 – What the authors mean here is that Enallagma novaehispaniae has colonized southeastern Arizona in the past five years (or so). It has been known from and well established in the United States for many decades. MTB: We corrected this based on feedback from Reviewer 1 and apologize for forgetting about the Texas populations. Experimental design If anything gave me pause in this study it revolves around two research questions that intrigued me: “[H]ow quickly do dragonflies and damselflies (odonates) colonize novel habitat?” and “[C]an effluent-dependent streams support diverse odonate assemblages?” Colonization is tricky to establish in Odonata, precisely because odonates are so vagile, the upshot being that just because one can find a suite of odonate species at a site it does not follow that those species have colonized the site. The survey methods perhaps suggest a way around this conundrum, but I cannot be certain. The authors state that they “selected three study sites on the Santa Cruz River to survey for adult odonates” [emphasis added] yet later in the subsequent paragraph they imply that they surveyed for “larval emergence” [=tenerals?]. If the latter is true, then I recommend that it is clearer earlier that surveys were not confined to adults. If, however, principally adult surveys alone were conducted, then I suggest the authors adopt some sort of indicator model (e.g., Bried et al. 2015, Freshwater Science; Patten et al. 2019, Ecological Indicators) to support the idea that a breeding population was established. MTB: We have modified the text in lines 164-170 and hopefully clarified our approach. We did search for emergence and tenerals as part of the 90-minute survey. Our hope there was to, at least once over the 11 months of surveys, document that a given species has successfully reproduced there. We have also added that information into the Results—we now present the total number of species observed by site and also the total number of species with confirmed reproduction at a site. And that information is available in Table 1 as well—species marked as “rare” at a site were not confirmed to be successfully breeding there. We have added that clarifying information to the Table 1 caption as well. Unfortunately, I was not aware of the Patten et al. 2019 paper when we started this study or we certainly would have used that approach! In our continuing work at these sites, we are definitely going to record the numbers of males and females of each species and use the excellent threshold approach described in Patten et al. 2019. Thanks for this excellent publication. Validity of the findings Only in the sense that findings hinge on species having in fact colonized. Comments for the Author no comment Reviewer 3 (Anonymous) Basic reporting This is an interesting and well-researched paper. However, I have some issues mainly with sampling design and the description of the study system. Detailed comments Line 100 to 108 (Introduction) I would recommend to add another significant reason for focusing on odonate surveys, i.e. that the collected data of odonate assemblages can be summarized in biotic indices. There are valid examples of dragonfly-based biotic indices developed mainly, but not exclusively, for river assessment in different geographical contexts and continents: e.g. central Europe (Chovanec et al., 2015), Mediterranean Europe (Berquier et al., 2016; Golfieri et al., 2016) and Africa (Vorster et al., 2020). MTB: Thank you for this important point—we have added a sentence to this part of the introduction with some of these references (lines 105-107) Line 515 (References) The alphabetical order of this reference is not correct. MTB: We have corrected the alphabetical order for this reference. Experimental design Detailed comments Line 123 to 133 (Study system) The authors need to better characterize the channel morphology of the study reaches, possibly according to the reach-scale classifications of Rinaldi et al. 2016 (Aquatic sciences) (e.g. sinuous, meandering, wandering, braided etc.) and Brierley and Fryirs, 2005. Moreover, they should also describe channel substrate material and the presence/percentages of aquatic and riparian vegetation in the three sampling sites. Lastly, they could include data about hydromorphological assessment of the study reaches, if available, as morphological conditions significantly influence odonate assemblages at the reach scale (Golfieri et al., 2018). MTB: Unfortunately, we did not collect extensive channel morphology data as part of this study. We have added more description of the habitat characteristics and the limited channel sinuosity in lines 148-154 of the Methods. And we strongly agree that riparian and aquatic vegetation could play an important role, especially in explaining some of the differences between the two sites that were newly flowing. Unfortunately, we do not have quantitative data for this factor either, but we added speculation about the role of vegetation in lines 324-335, in response to another reviewer’s comments. In continuing studies on the Santa Cruz River, we definitely plan to quantify the extent and structure of riparian vegetation to help address this factor. Line 132 to 133 (Study system) The authors state that “The nearest naturally perennial stream to all three effluent dependent reaches is Sabino Canyon, 23 km to the east.”, but later on, at lines 324-325 in the Discussion section, they state that “the nearest perennial river source populations are over 100 km away (e.g. the San Pedro and Gila Rivers)”. These statements seem to be contrasting and the authors should clarify this element. MTB: Thank you for pointing out this apparent contradiction! We’ve added clarifying text to the Methods and the Discussion to explain that Sabino Canyon is the nearest perennial stream, but it is a canyon-bound headwater stream, not a valley river like the Santa Cruz (lines 135-137). While there is some species overlap, the habitat is so different that many riverine species are not found at Sabino Canyon. So, apart from this nearest perennial stream of any size, the nearest similar-sized natural perennial stream (i.e. a valley-bottom river) is the distant San Pedro and Gila Rivers. Line 136 (Sampling design) Samplings focused on adult odonates, but why the collection of larvae and/or exuviae was not considered, at least during the second half of the research period? Are larval stages of all the resident species described and identifiable? Several studies underline the importance of sampling larvae and/or exuviae to characterize odonate assemblages (e.g. Raebel et al., 2010). Moreover, later on, at lines 157 to 159, the authors introduce the issue of “larval emergence” at the site without giving any explanation about how these data were collected and treated. This element has to be clarified. MTB: In addressing the previous reviewer’s concerns, we hopefully have addressed this issue. We did indeed look for emerging larvae and teneral adults during each month’s survey at each site, and used those observations to confirm at least one successful breeding event for that species over the 11 month study period. We do not use larvae from the river for a couple of reasons—(1) not all of the local species are known or easily identified from larvae and (2) in benthic sampling from other parts of the Santa Cruz River, including the reference site used in this study, we have noticed that our standard benthic sampling approach (similar to that used by biomonitoring agencies like the EPA) fails to detect the larvae of species/genera that we know are abundant as tenerals and adults in the same reach. So, we would have to do some preliminary studies to figure out how to maximize detection of odonate larvae in the microhabitats they must occupy (and that are missed in standard benthic sampling efforts). Line 144 (Sampling design) Each study transect incorporate multiple distinct habitat units: is it possible to summarize the habitats units sampled in each transect with a table? Could this element (i.e. a difference in the habitat units sampled) influence the different results of the study sites? Line 155 to 159 (Sampling design) The authors used three abundance categories, including also elements of breeding behaviour. I would suggest dividing abundance categories from behaviour description, as it also possible to observe less than 10 individuals showing breeding activity. In addition, a clear explanation of the criteria used to consider a species as breeding is needed. MTB: We have added clarifying text to the Methods about how we documented breeding behavior. And it is definitely possible to have less than 10 individuals that still show breeding behavior at a site—so we added text explaining that just during the course of this study, we did not observe any species with less than 10 individuals exhibiting breeding behavior (even though technically all it takes is 2 individuals or a even a single female with fertilized eggs to establish a new population). Validity of the findings Detailed comments Line 266 to 270 (Discussion) The authors partly explain the high diversity of Santa Cruz River as a consequence of the “more robust tolerances of lotic odonates versus lentic odonates”. This can be true in some specific contexts, but studies carried out in other countries revealed a significant impact of hydromorphological alterations and related urbanization on riverine odonate assemblages (e.g. Golfieri et al., 2016 and 2018). MTB: Based on comments from another reviewer and these comments here, we have removed this sentence. Line 288 (Discussion) The word “junius” has to be written in italics. MTB: We have made this correction. "	Here is a paper. Please give your review comments after reading it.
522	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Background: Acute-on-chronic liver failure (ACLF), which is characterized by rapid deterioration of liver function and multiorgan failure, has high mortality. This study was designed to identify prognostic scores to predict short-term and long-term outcome in patients with ACLF to facilitate early treatment and thereby improve patient survival.</ns0:p></ns0:div> <ns0:div><ns0:head>Materials and Methods:</ns0:head><ns0:p>We retrospectively analyzed 102 ACLF patients who were hospitalized in the gastroenterology department. The EASL-CLIF criteria were used to define the ACLF. The demographic characteristics and biochemical examination results of the patients were acquired, and seven scores (CTP score, MELD score, MELD-Na, CLIF ACLF score, CLIF-C OF score, CLIF SOFA score ) were calculated 24 hours after admission. All patients were observed until loss to follow-up, death, or specific follow-up times (28 days, 3 months , 6 months), which were calculated after the initial hospital admission. The receiver operating characteristic (ROC) curve was employed to estimate the power of six scores to forecast ACLF patients´ outcome. Results: All scores were distinctly higher in nonsurviving patients than in surviving patients and had predictive value for outcome in patients with ACLF at all time points (P<0.050). The areas under the ROC curve (AUROCs) of the CLIF-SOFA score were higher than those of other scores at all time points. The comparison of the AUROC of the CLIF-SOFA score with other scores was statistically significant at 28 days (P<0.050), which was the only time point at which it was greater than 0.800. Conclusion: Patients with ACLF have high mortality. These six scores are effective tools for assessing the prognosis of ACLF patients. The CLIF-SOFA score is especially effective for evaluating 28-day mortality.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>Acute-on-chronic liver failure (ACLF) is a clinical syndrome characterized by the rapid deterioration of liver function due to acute injury. Patients diagnosed with ACLF often have multiple organ failures and high short-term mortality <ns0:ref type='bibr' target='#b0'>(1)</ns0:ref> . Patients with chronic liver disease may progress to liver failure induced by enhanced viral replication, combined with bacterial or fungal infection and liver injury due to drug abuse or alcoholism <ns0:ref type='bibr' target='#b1'>(2)</ns0:ref> . The basic etiology of ACLF is mainly alcoholism in European and American countries; however, hepatitis virus infection is the leading etiology of ACLF in Asian countries, especially in China <ns0:ref type='bibr' target='#b2'>(3)</ns0:ref> . Although treatments such as liver transplantation and hemodialysis markedly improve survival in the short term, they are not extensively obtainable in clinical practice because of their high costs, the limited availability of liver resources, and the need for hospitalization. ACLF causes a heavy economic burden on patients. ACLF patients perform obvious differences in accordance with morbidity and survival. So, it is essential to develop an applicable prognostic score to estimate the outcomes in ACLF patients and help guide doctors in determining the treatment options according to the predicted outcomes. Manuscript to be reviewed was first established as a widely utilized liver-specific score nearly 50 years ago <ns0:ref type='bibr' target='#b3'>(4)</ns0:ref> the MELD score is superior to the CTP score with regard to the prediction of 3-month mortality in patients with chronic end-stage liver disease <ns0:ref type='bibr' target='#b4'>(5)</ns0:ref> . The MELD combined with serum sodium concentration (MELD-Na) score is related to the MELD score and has improved prognostic efficacy in cirrhotic patients awaiting liver transplantation <ns0:ref type='bibr' target='#b5'>(6)</ns0:ref> . In the EASL-CLIF acute-onchronic liver failure in cirrhosis (CANONIC) study, ACLF was defined using a novel scoring system called the CLIF-sequential organ failure assessment score (CLIF-C SOFA), which is a modification of the original SOFA score. The EASL-CLIF consortium also developed the CLIF consortium organ failure score (CLIF-C OF), which simplified the original CLIF-SOFA.</ns0:p><ns0:note type='other'>.</ns0:note><ns0:p>Through further studies, Jalan et al found that age and white blood cell count were independent risk factors for mortality and established the CLIF-C ACLF score <ns0:ref type='bibr' target='#b7'>(7)</ns0:ref> . The CLIF-C ACLF score not only assesses the effects of extrahepatic organ injury, coagulation and circulatory failure but also includes age and inflammatory indicators; the CLIF-C ACLF score has high clinical value for evaluating the prognosis of ACLF. Up to now, there are less study on comparing all methods for the evaluation and prediction of prognosis in ACLF patients with a variety of etiologies, especially among Asians. Our study was designed to assess the short-term and long-term discriminative power of all of the above scores in ACLF patients to direct clinical practice.</ns0:p></ns0:div> <ns0:div><ns0:head>Material and methods</ns0:head></ns0:div> <ns0:div><ns0:head>Study patients</ns0:head><ns0:p>Our study was a single-center retrospective study that was completed in acute-on-chronic liver failure patients hospitalized in our institute between January 2015 and December 2018. Patients were included when they fulfilled these criteria: (a) ≥18 years old and (b) diagnosed with cirrhosis and ACLF (defined by the EASL-CLIF Consortium). Exclusion criteria included (1) hepatocellular carcinoma, (2) previous liver transplantation, (3) </ns0:p></ns0:div> <ns0:div><ns0:head>Definitions</ns0:head><ns0:p>Cirrhosis was defined by laboratory tests, radiologic imaging, endoscopy or liver biopsy. The ACLF criteria and organ failures were defined based on the CLIF-SOFA score according to the EASL-CLIF Consortium. The ACLF grading system classifies patients with ACLF in one of 3 grades according to the number of organ failures as per the CLIF-SOFA score as follows: Grade 1 if (1) single kidney failure (serum creatinine level ≥2.0 mg/dl) or (2) another organ failure (respiration, circulation, coagulation, or liver) is accompanied by grade I-II (West Haven criteria) hepatic encephalopathy (HE) and/or a serum creatinine level of 1.5-1.9 mg/dl, or (3) single cerebral failure (grade III-IV HE) is present with a serum creatinine level of 1.5-1.9 mg/dl; grade 2 if 2 organ failures are identified; or grade 3 if 3 or more organ failures have been diagnosed.</ns0:p><ns0:p>The Child-Pugh score was computed based on albumin, ascites, hepatic encephalopathy, prothrombin time (PT), and serum bilirubin <ns0:ref type='bibr' target='#b3'>(4)</ns0:ref> . The MELD: 3.8×log (bilirubin) +9.6×log(creatinine) +11.2×log (INR)+6.43 <ns0:ref type='bibr' target='#b8'>(8)</ns0:ref> . The MELD-Na score was calculated as bellow: MELD-Na=[0.025×MELD×(140-Na)] +140 <ns0:ref type='bibr' target='#b5'>(6)</ns0:ref> . The CLIF-SOFA score was computed as the sum of the scores for six organ systems, including the cardiovascular, hepatic, coagulation, respiratory, nervous, and renal systems <ns0:ref type='bibr' target='#b10'>(9)</ns0:ref> . The CLIF-C OF score includes the revised six organ systems of the CLIF-SOFA score. The CLIF-C ACLF score was revised according to the CLIF-SOFA score and was computed with the formula: 10× [0.63×log (white-cell count) + 0.33×CLIF-C OF + 0.04×age-2] <ns0:ref type='bibr' target='#b7'>(7)</ns0:ref> .</ns0:p></ns0:div> <ns0:div><ns0:head>Study protocols</ns0:head><ns0:p>Patients with ACLF were included in the study. During hospitalization, data were collected regarding medical records, demographics, the presence of other comorbidities, clinical features, the number of complications and type of decompensation, the etiology of cirrhosis, and blood haematological index at admission (such as blood platelet count, white blood cell count, the INR, Manuscript to be reviewed renal function test, liver function test). The patients were followed up for 6 months to obtain survival information. Patients with incomplete follow-up at 28 days, 3 months, and 6 months were not included in the final analysis of the corresponding time.</ns0:p></ns0:div> <ns0:div><ns0:head>Statistical analysis</ns0:head><ns0:p>The statistical analyses were performed using SPSS software version 20.0 (SPSS Inc., Chicago, IL). Continuous variables were expressed as the mean±standard deviation (SD) or medians (interquartile range [IQR])，and categorical data were expressed as percentages. Differences in variables were analyzed using Student t-tests or the Mann-Whitney U test. Categorical variables are described as the frequencies (percentages [%]) and were compared with chi-squared or Fisher's exact tests. Receiver operating characteristic (ROC) curves were used to measure the performance of the score for the prediction of 28-day, 3-month, and 6-month mortality in patients with ACLF. The specificity, sensitivity, negative likelihood ratio (NLV) and positive likelihood ratio (PLV) were computed for each cut-off value. The cut-off point was obtained by Youden's index with greatest Sensitivity and Specificity. The comparing of the areas under the ROC curve (AUROCs) was performed by Delong-test. 0.050 of two-tailed was significant meaning.</ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head></ns0:div> <ns0:div><ns0:head>Characteristics of ACLF patients</ns0:head><ns0:p>There are 102 patients in this study. During the study period, 92 patients were enrolled in the analysis of the outcomes at 28 days; subsequently, 3 patients were lost to follow-up, and 89 patients were finally enrolled at both 3 and 6 months. The flowchart is shown in Figure <ns0:ref type='figure' target='#fig_8'>1</ns0:ref>, and the demographic and biochemical characteristics of the study population are summarized in Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref>. The mean (±standard deviation) age of the 102 patients was 56.96 (±12.18) years. The leading cause of decompensation events responsible for hospitalization was variceal bleeding (70/102, 68.6%). The ACLF patient distribution was grade 1 (31/102 30.4%), grade 2 (45/102, 44.1%), and grade 3 (26/102 25.5%). The most common degree of ascites was moderate (28/102 27.5%), followed by severe (25/102 24.5%) and mild (13/102 12.7%). Forty-nine (48%) patients had undergone endoscopic hemostasis, 41 (40.2%) patients had undergone mechanical ventilation, and 66 (64.7%) patients had used vasopressors. In the 28-day and 3-month analyses, the mean age was 57.5 (±12) years and 57.8 (±12) years, respectively, and 62 (67.4%) and 59 (66.3%) patients were male. The leading cause of liver cirrhosis is Hepatitis virus infection and variceal bleeding accounts for the majority of hospitalizations. The distributions of patients who were included in the complete follow-up within 28 days and were included in the complete follow-up within 3 months were similar to that of all 102 patients in terms of ascites grade, ACLF grade, and treatment strategy. A total of 47 (46.1%), 58 (56.9%), and 61 (59.8%) patients died within 28 days, 3 months, and 6 months, respectively. The causes of death at 6 months were as follows: 3 (4.9%) patients had cardiogenic shock, 6 (9.8%) patients had infectious shock, 12 (19.7%) patients had respiratory failure, 18 (29.5%) patients had hemorrhagic shock, 19 (31.1%) patients had liver-related complications (4 patients had liver failure, 15 patients had hepatic encephalopathy) and 3 (4.9%) patients had an uncertain cause of death. The causes of death at 28 days, 3 months, and 6 months are outlined in Supplement Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Comparison of prognostic scores between the nonsurviving group and the surviving patients</ns0:head><ns0:p>The comparison of the six scores of patients with ACLF were shown in Table <ns0:ref type='table' target='#tab_3'>2</ns0:ref>. ACLF patients were grouped into surviving and nonsurviving groups based on their 28-day, 3-month, and 6month outcomes. The non-surviving patients had a higher CTP score, MELD score, CLIF-C OF score, CLIF-SOFA score and CLIF-ACLF score, compared with surviving patients (P<0.050).</ns0:p><ns0:p>Although the comparison of the MELD-Na score was not statistically significant (P=0.081), it was still higher in the nonsurviving group. Statistically significant differences were found for the CTP score, MELD-Na score, MELD score, CLIF-SOFA score, CLIF-ACLF score, CLIF-C OF score at 3 months and 6 months (P<0.050).</ns0:p></ns0:div> <ns0:div><ns0:head>Predictive ability for 28-day, 3-month and 6-month outcome in ACLF patients.</ns0:head><ns0:p>The discriminative ability of the CTP score, MELD score, MELD-Na score, CLIF-C OF score, and CLIF-ACLF score calculated for 28-day, 3-month, and 6-month survival is summarized in Table <ns0:ref type='table' target='#tab_6'>3</ns0:ref>. At 28 days, the CLIF SOFA score had the highest AUROC (0.805, 95%CI:0.715-0. Manuscript to be reviewed 0.560-0.787), and MELD-Na score (0.606, 95%CI: 0.487 to 0.724). When predicting 3-month and 6-month mortality, the CLIF-C SOFA score both had the highest AUROC (0.751, 95%CI: 0.646-0.857; 0.742, 95%CI: 0.633-0.852, respectively), by contrast, CTP score both had the lowest AUROC (0.641, 95%CI: 0.521-0.760; 0.640, 95%CI: 0.518-0.762, respectively). The ROC curves for the prognostic scores are shown in Figure <ns0:ref type='figure' target='#fig_9'>2</ns0:ref>. All prognostic scores were able to predict mortality at 28 days, 3 months, and 6 months (P<0.050).</ns0:p></ns0:div> <ns0:div><ns0:head>Comparing the predictive performance of all scores</ns0:head><ns0:p>As shown in Table <ns0:ref type='table' target='#tab_6'>3</ns0:ref>, the AUROC of the CLIF-SOFA score is superior to those of the other five scores with regard to 28-day, 3-month, and 6-month mortality. The CLIF-SOFA has the highest predicting value in 28-day mortality with the AUROC of 0.805. The predicting performer of CLIF-SOFA is significantly higher than CTP score, MELD-Na score, MELD score, CLIF-C OF score, and CLIF-ACLF score (P<0.050). At 3 months and 6 months, the comparison of AUROCs between the CTP score and the CLIF-SOFA score was statistically significant (P<0.050); however, the comparisons of AUROCs between the CLIF-C OF score, CLIF-ACLF score, MELD-Na score and MELD score were not significant (P>0.050). At 28 days, the AUROC of MELD-Na was lower than other five scores.</ns0:p></ns0:div> <ns0:div><ns0:head>Discussion</ns0:head><ns0:p>It is important to develop predictive scores that can identify patients who are at high risk of mortality, enabling the early provision of effective treatment to reduce mortality, especially in diseases with high mortality rates. ACLF is a clinical syndrome with a high mortality rate that is characterized by the development of acute decompensation (encephalopathy, ascites, gastrointestinal hemorrhage) and organ failure (such as kidney, renal, hepatic, coagulation, respiration and circulation), so prognostic assessment is an indispensable for ACLF patients <ns0:ref type='bibr' target='#b10'>(9)</ns0:ref> . However, in the clinical setting, the prognosis is often hard to predict for certain patients because of different factors, such as etiology, disease stage, and complications. Previous studies have shown that many different scores have predictive value for mortality in ACLF patients. It is very important to choose the most efficient score for predicting mortality in Asian patients in clinical treatment. The clinical characteristics of ACLF patients in Asian is completely different form patients in Europe and America. In this study, the leading etiology of liver cirrhosis was hepatitis virus infection (58.8%), followed by alcohol-related cirrhosis (34.1%), which was similar to the primary etiologies of liver disease in most Asian countries.</ns0:p><ns0:p>It is not surprising that the mortality of ACLF patients was high in this study, as that it consistent with previous research <ns0:ref type='bibr' target='#b11'>(10)</ns0:ref><ns0:ref type='bibr' target='#b12'>(11)</ns0:ref><ns0:ref type='bibr' target='#b14'>(12)</ns0:ref> . The mortality rate was 46.1% in the short term (28 days), and the mortality rate was 59.8% in the long term (6 months). The high mortality rate, which we find appalling, has spurred us to meaningfully contribute. Effective and inexpensive treatment strategies for patients with low socioeconomic status are limited because of the high costs associated with liver transplant and hemodialysis, partially in developing countries. The economical load produced by ACLF is still severe. Predicting the prognosis of patients with ACLF may be more important than treatment from the perspective of health economics for lowincome families.</ns0:p><ns0:p>Recently, the CLIF-ACLF score, CLIF-C OF score, CLIF-SOFA score have been used to evaluate prognosis in ACLF patients <ns0:ref type='bibr' target='#b15'>(13,</ns0:ref><ns0:ref type='bibr' target='#b16'>14)</ns0:ref> . To the best of our knowledge, although the discriminative ability of these scores for predicting outcomes in ACLF patients has been illustrated, different conclusions have been drawn regarding the relative predictive value of these scores because of differences in study populations or observation durations.</ns0:p><ns0:p>The predictive value of the six scores (CTP score, MELD score, MELD-Na, CLIF-ACLF score, CLIF-C OF score, and CLIF-SOFA score) was compared at 28 days, 3 months, and 6 months.</ns0:p><ns0:p>The AUROC of CLIF-SOFA is higher than other prognostic scores at 28 days, 3 months, and 6 months in our cohort, especially at 28 days. The CLIF-SOFA score provides a comprehensive and effective assessment of the severity of organ failure in ACLF patients and takes into account multiple systems, including the hepatic, renal, coagulation, respiratory, circulatory and nervous systems; it was established by the European Liver Disease Collaboration Group for Liver Failure Manuscript to be reviewed in 2013. Sy E's study indicated that the predictive value of the CLIF-SOFA score is better than those of the CTP score and MELD score for short-term outcomes <ns0:ref type='bibr' target='#b17'>(15)</ns0:ref> . Any score has its advantages and disadvantages. Although the predictive value of the CLIF-SOFA score is high, the calculation of the CLIF-SOFA score is complicated due to the inclusion of more indicators.</ns0:p><ns0:p>The Child-Pugh score is computed based on the prothrombin time, ascites, serum bilirubin, albumin, and hepatic encephalopathy <ns0:ref type='bibr' target='#b3'>(4)</ns0:ref> . The presence or absence of hepatic encephalopathy and ascites, which forms part of the CTP score, is subjective and has no clear cut-off value. The MELD score contains contains three indicators: the INR, creatinine and bilirubin; it is vulnerable to confounding by hemorrhaging, ascites and the use of diuretics, with the absence of clearly defined cutoff values for categorizing cirrhotic patients <ns0:ref type='bibr' target='#b19'>(16)</ns0:ref> . The occurrence of hyponatremia is closely related to the prognosis of patients with cirrhosis, particularly patients with ascites; therefore, the MELD-Na score has been created based on the MELD score <ns0:ref type='bibr' target='#b20'>(17)</ns0:ref> . However, the MELD score had a lower AUROC than the other five scores at all time points in this study. This may be due to the main complications of patients in this study. The patients were mainly enrolled from the Department of Gastroenterology and needed endoscopic treatment for bleeding esophageal gastric varices (70/102, 68.6%). The number of cirrhosis patients with ascites as the primary reason for hospitalization was very small (6/102 5.9%), Previous study have confirmed the ascites is the main complication of liver cirrhosis <ns0:ref type='bibr' target='#b21'>(18)</ns0:ref> , and ascites is associated with a high risk of developing further complications of cirrhosis such as dilutional hyponatremia <ns0:ref type='bibr' target='#b22'>(19)</ns0:ref> , Because of the number of patients with ascites are small, so the MELD-Na score may not play an important role in predicting patients'mortality. which may explain why the discriminative power of the MELD-Na score is lower than other five scores. The predicting value of the CTP, MELD-Na, and MELD scores in ACLF is not complete prefect because indicators reflecting systemic inflammation and organ failure is lacking. The CANONIC study had shown the advantage of the CLIF-ACLF, CLIF-SOFA, and CLIF-C OF scores over the CTP, MELD-Na, and MELD scores for the prediction of mortality in ACLF patients, which is according with the results in our study <ns0:ref type='bibr' target='#b7'>(7)</ns0:ref> . Jalan Manuscript to be reviewed CLIF-C OF score is equivalent to that of the CLIF-SOFA score for the prediction of mortality <ns0:ref type='bibr' target='#b7'>(7)</ns0:ref> .</ns0:p><ns0:p>Considering the effects of white blood cell (WBC) count and age on prognosis, Jalan et al established the CLIF-ACLF score based on the CLIF-C OF score <ns0:ref type='bibr' target='#b23'>(20)</ns0:ref> . The CLIF-ACLF score not only considers the effects of extrahepatic organ damage, coagulation and circulatory system failure on the prognosis but also includes the WBC count, which reflects the severity of inflammation; the CLIF-ACLF score was superior to the CTP, MELD-Na, and MELD scores <ns0:ref type='bibr' target='#b23'>(20)</ns0:ref> .</ns0:p><ns0:p>Despite the high predictive value of the CLIF-ACLF score and CLIF-C OF, these scores were established based on patients from European countries and the US with alcohol-related liver disease, and further researches are needed to explore whether they are applicable to Asian populations. Our research results have indicated that the scores also apply to Asian populations.</ns0:p><ns0:p>Several limitations existed in this study. First, this was a retrospective study, the number of patients included in our study was still not large, and some patients were lost to follow-up, which may have resulted in selection bias. Second, the scores were evaluated when admission to hospital and did not reflect the dynamic changes. Finally, the leading etiologies in patients in our study were hepatitis B virus infection, but most of the patients were diagnosed according to the EASL-ACLF criteria, leading to etiological bias.</ns0:p><ns0:p>In conclusion, our data reveal that the CTP score, MELD score, MELD-Na, CLIF-C OF score, CLIF-SOFA score, CLIF-ACLF score are effective tools for predicting the prognosis in ACLF patients. The CLIF-SOFA score has better discriminative power for the evaluation of short-term mortality, and may help improve the management of ACLF patients. Manuscript to be reviewed Manuscript to be reviewed Manuscript to be reviewed Manuscript to be reviewed </ns0:p><ns0:note type='other'>Figure 2</ns0:note></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>Some prognostic scores have been established previously. The Child-Turcotte-Pugh (CTP) score PeerJ reviewing PDF \| (2020:04:47745:1:1:NEW 15 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>Wiesner's research analyzed data and established the Model for End-Stage Liver Disease (MELD) score;</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head /><ns0:label /><ns0:figDesc>complications with other severe chronic extrahepatic diseases and (4) infection with human immunodeficiency virus. Our study PeerJ reviewing PDF \| (2020:04:47745:1:1:NEW 15 Jul 2020) Manuscript to be reviewed was approved by the Ethics Committee of the First Affiliated Hospital of Nanchang University (No. 2015-1203). All the patients signed the informed consent.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ reviewing PDF \| (2020:04:47745:1:1:NEW 15 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head /><ns0:label /><ns0:figDesc>896), followed by the CLIF-ACLF score (0.741, 95%CI: 0.640-0.843), CLIF-C OF score (0.712, 95%CI: 0.676 to 0.869), CTP score (0.707, 95%CI: 0.600-0.813), MELD score (0.673, 95% CI: PeerJ reviewing PDF \| (2020:04:47745:1:1:NEW 15 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ reviewing PDF \| (2020:04:47745:1:1:NEW 15 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head /><ns0:label /><ns0:figDesc>et al. first proposed the CLIF-C OF score in 2014 and proved that the value of the PeerJ reviewing PDF \| (2020:04:47745:1:1:NEW 15 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_7'><ns0:head>Figure 1 Figure 2</ns0:head><ns0:label>12</ns0:label><ns0:figDesc>Figure 1The flowchart in our study</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_8'><ns0:head>Figure 1 figure</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_9'><ns0:head>Figure 2 Receiver</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2</ns0:figDesc></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head>Table 1 (on next page)</ns0:head><ns0:label>1</ns0:label><ns0:figDesc /><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_1'><ns0:head>Table 1</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Characteristics of Patients in the ACLF cohortPeerJ reviewing PDF \| (2020:04:47745:1:1:NEW 15 Jul 2020)</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Manuscript to be reviewed</ns0:cell></ns0:row></ns0:table><ns0:note>PeerJ reviewing PDF \| (2020:04:47745:1:1:NEW 15 Jul 2020)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_2'><ns0:head>Table 1 .</ns0:head><ns0:label>1</ns0:label><ns0:figDesc /><ns0:table><ns0:row><ns0:cell cols='2'>Characteristic</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>s of Patients</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>in the ACLF</ns0:cell></ns0:row><ns0:row><ns0:cell>cohort</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell cols='2'>ACLF: Acute-</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>on-chronic liver</ns0:cell></ns0:row><ns0:row><ns0:cell>failure;</ns0:cell><ns0:cell>SD;</ns0:cell></ns0:row><ns0:row><ns0:cell>Standard</ns0:cell><ns0:cell /></ns0:row></ns0:table><ns0:note>PeerJ reviewing PDF \| (2020:04:47745:1:1:NEW 15 Jul 2020)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_3'><ns0:head>Table 2 (on next page)</ns0:head><ns0:label>2</ns0:label><ns0:figDesc /><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_4'><ns0:head>Table 2</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>The comparison of prognostic scores</ns0:figDesc><ns0:table /><ns0:note>PeerJ reviewing PDF \| (2020:04:47745:1:1:NEW 15 Jul 2020)Manuscript to be reviewed </ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_5'><ns0:head>Table 2 .</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>The comparison of prognostic scores</ns0:figDesc><ns0:table><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>28-days</ns0:cell><ns0:cell>3-months</ns0:cell><ns0:cell /><ns0:cell>6-months</ns0:cell></ns0:row><ns0:row><ns0:cell>Prognostic score</ns0:cell><ns0:cell>All Patients(n=92)</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>survivors(n=45)</ns0:cell><ns0:cell>non-survivors(n=47) P-value</ns0:cell><ns0:cell>survivors(n=31) non-survivors(n=58) P-value</ns0:cell><ns0:cell>survivors(n=28)</ns0:cell><ns0:cell>non-survivors(n=61)</ns0:cell><ns0:cell>P-value</ns0:cell></ns0:row></ns0:table><ns0:note>CTP: Child-Turcotte-Pugh; MELD: model for end-stage liver disease; MELD-Na: model for end-stage liver disease-sodium; CLIF-C OF: chronic liver failure consortium organ function; CLIF-SOFA: chronic liver failure-sequential organ failure assessment; CLIF-C ACLF: chronic liver failure consortium acute-on-chronic liver failure</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_6'><ns0:head>Table 3 .</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>The efficacy and performance comparison of the prognostic scores for predicting mortality in 28-day,3-month and 6-monthCLIF-SOFA score vs MELD-Na 0.089 (-0.023-0.207) 0.126 CLIF-SOFA score vs CLIF-C ACLF 0.043 (-0.019-0.106)</ns0:figDesc><ns0:table><ns0:row><ns0:cell>0.109</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:note place='foot'>PeerJ reviewing PDF \| (2020:04:47745:1:1:NEW 15 Jul 2020)</ns0:note> </ns0:body> "	"Dear editors, Thanks for your work and for the reviewer’s comments concerning our manuscript entitled Assessing the prognostic scores for the prediction of the mortality of patients with acute-on-chronic liver failure: a retrospective study. Those comments are all valuable and very helpful for revising and improving our paper, as well as guiding significance to our researches, we have read the comments carefully and have made correction which we hope meet with approval. The main corrections in the paper and the responds to the reviewer’s comments are as following. Reviewer 1 Basic reporting The article is well-written in English language, although there are some minor erratum along the text that should be corrected in a further revised version. Response: Thank you very much for your advises, I had revised my manuscript carefully according to your requirement. Experimental design The study is well designed and address the hypothesis. However, I think it would be interesting to perform an additional analysis to find the optimal cut-off point of each ROC curve. In table 3 authors already present a cut-off point, but it is not mentioned along the manuscript if this is the optimal cut-off point based on the Youden's index. If so, authors should mention it in the methodology section. If not, I would recommend to calculate the 'optimal cut-off point' where both sensitivity and specificity maximize using the above mentioned Youden index. Response: Thank you very much for your advice, I agree with your opinion. The cut-off point had been described in table 3. The cut-off point was calculated based on Youden's index, which made both sensitivity and specificity maximize. I have added the content in the methods section (Statistical analysis). Please check it at line 115. Validity of the findings No comment. Comments for the author This is an interesting study that compares the predictive and discriminative power of several scores in acute-on-chronic liver failure patients. In addition to my previous comments (correct some minor erratum throughout the manuscript and calculate the optimal cut-off point for each ROC curve), I would recommend authors to summarize the main findings in the section 'Predictive ability for 28-day, 3-month and 6-month mortality in ACLF patients.' As authors state, these findings are shown in Table 3, so I recommend to write only the main findings in order to facilitate the reading. Despite these minor issues, I think this is a great and interesting work. Response: Thank you very much for your evaluation and advice, I have simplified my description and summarized the main findings in the section 'Predictive ability for 28-day, 3-month and 6-month mortality in ACLF patients.' Please check it at line 158 Reviewer 2 Basic reporting No comment Experimental design line 179,180 and table, liver failure, and encephalopathy were mentioned separately as causes of death at 6 months. You can possibly combine them as liver-related mortality and subgroup causes. Do you have reasons for loss of follow up? Response 1: Thank you very much for your advice. Liver failure and encephalopathy are combined as liver-related complications in my manuscript and supplement table 1. Please check it at my manuscript and supplement table 1. Nevertheless, I still have some doubts. Although hemorrhagic shock and septic shock are not directly related to liver function, complications of liver cirrhosis are related, such as ascites, variceal bleeding. Response 2: Thank you very much for your advice, We retrospectively reviewed the ACLF patients at specific follow-up times (28 days, 3 months, 6 months) during the period of discharge, some patients lost to follow up because of we could not connect with them, this may be caused by the wrong telephone number or contact address. Validity of the findings line 272, you mentioned patients required treatment for bleeding oesophageal and gastric varices. Do you have data on the type of variceal bleed and treatment modality? For a single-centre study, the conclusion is too strong to generalize results to whole China. Response 1: Thank you very much for your advice. The most patients enrolled in this study needed endoscopic treatment due to bleeding esophageal gastric varices. As for the type of variceal bleed is all upper gastrointestinal bleeding. Treatment of patients with variceal bleed includes drug therapy (such as vasopressin, somatostatin, proton pump inhibitor, blood products), endoscopic treatment (such as endoscopic variceal ligation, endoscopic injection sclerotherapy) and Transjugular intrahepatic portosystemic shunt. In this study, most patients with variceal bleed had received general medicines and endoscopic treatment. The measures of endoscopic treatment are mostly endoscopic variceal ligation, endoscopic injection sclerotherapy. In my view, the impact of treatment strategies on the research conclusions is limited. Of course, the detailed data about treatment in this study. In future in-depth research, we will further improve the data. Response 2: Thank you very much for your advice. As you said, single-centre study is a limit in this manuscript. Although this study had this limitation, it gave us more awareness about the role of these ACLF-related prognostic scores, we need a large sample and multi-centre study to identify them in the future. As for the conclusion is too strong to generalize results to whole China， it does not apply to all Chinese people in the conclusion, but only represents some of them. Sincerely get your understanding. Reviewer 3 Basic reporting No comment Experimental design No comment Validity of the findings No comment Comments for the author The authors present a retrospective study for assessing the prognostic scores for the prediction of the mortality of patients with acute-on-chronic liver failure. There are major points that need to be addressed: Question 1：It is important to have a validation cohort to validate the performance. Response 1: Thank you very much for your advice, as reviewer suggested that it was important to get a validation cohort, but our study did not collect large sample patients, it was difficult to have a validation cohort, so it was necessary to collect more patients with ACLF in the foreseeable future. And the predictive value of these scores in ACLF patients has been reported. Our study was designed to assess the short-term and long-term discriminative power of all of the above scores in ACLF patients to direct clinical practice. This is just to assess the level of prediction. Sincerely get your understanding. Question 2：The results are written unclear. For example, the authors don't need to write any AUC values for every scores. They could be explained by only a table or figure and with some text description. Response 2: Thank you very much for your advice, I agree with you and I have simplified my description about the AUC values for scores and summarize the main findings in this section. Please check it at the section of “Predictive ability for 28-day, 3-month and 6-month mortality in ACLF patients” in my manuscript. Please check it. Question 3：How did the authors define normal distribution and abnormal distribution? Response 3: Thank you very much for your attention. If the random variable (X) follows a probability distribution with position parameter μ and scale parameter σ, and its probability density function is , so, this random variable is called a normal random variable, and the random variable follows the distribution Normal distribution. There are several comparison methods for the normal distribution test, such as X2 Goodness-of-fit test, Kolmogorov-Smirnov test (K-S test), Shapiro-Wilk test ((BMJ. 1995; Feb 4;310(6975):298). In this study, the Kolmogorov-Smirnov test and Shapiro-Wilk are used to determine whether it conforms to the normal distribution, as shown below. Question 4：ROC curve and AUC had been used in previous works in biomedical such as PMID:31921391, PMID:31277574, and https://doi.org/10.1016/j.neucom.2019.09.070. Therefore, the authors are suggested to refer more works in this description. Response 4: Thank you very much for your advice, ROC curve and AUC had been described in these studies that you recommend. According your professional advises, I have revised the description of the AUC and ROC, and make sure that the description of the AUC and ROC is accurate and clear. Please check it at the section of “Predictive ability for 28-day, 3-month and 6-month mortality in ACLF patients” in my manuscript. Question 5：It is important to have some comparisons to the previous works on the same problem. Response 5: Thank you very much for your advice, as reviewer suggests that it is important to compare this study with the previous works, I have cited the study in this manuscript, such as CANONIC and Sy E’s study. Their results had shown that the predicting value of CLIF-SOFA score is better than CTP score and MELD score for short-term outcomes. The conclusion of previous studies is similar to our study, but the study population is completely different from previous studies. Question 6：The ROC curves should be presented together with AUC values (i.e. Fig. 2) Response 6: Thank you very much for your advice, I agree with your comments, I have revised my figure and make sure that the AUROC values had been shown in figure 2. We appreciate for Editors/Reviewers’ warm work earnestly, and hope that the correction will meet with approval. Once again, thank you very much for your comments and suggestions. Considering the severe COVID-19 outbreak in world, Best wishes for your healthy. Best regards! Dr Zhang, Dr Nie, PhD Zhu "	Here is a paper. Please give your review comments after reading it.
523	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Background: Acute-on-chronic liver failure (ACLF), which is characterized by rapid deterioration of liver function and multiorgan failure, has high mortality. This study was designed to identify prognostic scores to predict short-term and long-term outcome in patients with ACLF to facilitate early treatment and thereby improve patient survival.</ns0:p></ns0:div> <ns0:div><ns0:head>Materials and Methods:</ns0:head><ns0:p>We retrospectively analyzed 102 ACLF patients who were hospitalized in the gastroenterology department. The EASL-CLIF criteria were used to define the ACLF. The demographic characteristics and biochemical examination results of the patients were acquired, and seven scores (CTP score, MELD score, MELD-Na, CLIF ACLF score, CLIF-C OF score, CLIF SOFA score ) were calculated 24 hours after admission. All patients were observed until loss to follow-up, death, or specific follow-up times (28 days, 3 months , 6 months), which were calculated after the initial hospital admission. The receiver operating characteristic (ROC) curve was employed to estimate the power of six scores to forecast ACLF patients´ outcome. Results: All scores were distinctly higher in nonsurviving patients than in surviving patients and had predictive value for outcome in patients with ACLF at all time points (P<0.050). The areas under the ROC curve (AUROCs) of the CLIF-SOFA score were higher than those of other scores at all time points. The comparison of the AUROC of the CLIF-SOFA score with other scores was statistically significant at 28 days (P<0.050), which was the only time point at which it was greater than 0.800. Conclusion: Patients with ACLF have high mortality. These six scores are effective tools for assessing the prognosis of ACLF patients. The CLIF-SOFA score is especially effective for evaluating 28-day mortality.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>Acute-on-chronic liver failure (ACLF) is a clinical syndrome characterized by the rapid deterioration of liver function due to acute injury. Patients diagnosed with ACLF often have multiple organ failures and high short-term mortality <ns0:ref type='bibr' target='#b0'>(1)</ns0:ref> . Patients with chronic liver disease may progress to liver failure induced by enhanced viral replication, combined with bacterial or fungal infection and liver injury due to drug abuse or alcoholism <ns0:ref type='bibr' target='#b1'>(2)</ns0:ref> . The basic etiology of ACLF is mainly alcoholism in European and American countries; however, hepatitis virus infection is the leading etiology of ACLF in Asian countries, especially in China <ns0:ref type='bibr' target='#b2'>(3)</ns0:ref> . Although treatments such as liver transplantation and hemodialysis markedly improve survival in the short term, they are not extensively obtainable in clinical practice because of their high costs, the limited availability of liver resources, and the need for hospitalization. ACLF causes a heavy economic burden on patients. ACLF patients perform obvious differences in accordance with morbidity and survival. So, it is essential to develop an applicable prognostic score to estimate the outcomes in ACLF patients and help guide doctors in determining the treatment options according to the predicted outcomes. Manuscript to be reviewed was first established as a widely utilized liver-specific score nearly 50 years ago <ns0:ref type='bibr' target='#b3'>(4)</ns0:ref> the MELD score is superior to the CTP score with regard to the prediction of 3-month mortality in patients with chronic end-stage liver disease <ns0:ref type='bibr' target='#b4'>(5)</ns0:ref> . The MELD combined with serum sodium concentration (MELD-Na) score is related to the MELD score and has improved prognostic efficacy in cirrhotic patients awaiting liver transplantation <ns0:ref type='bibr' target='#b5'>(6)</ns0:ref> . In the EASL-CLIF acute-onchronic liver failure in cirrhosis (CANONIC) study, ACLF was defined using a novel scoring system called the CLIF-sequential organ failure assessment score (CLIF-C SOFA), which is a modification of the original SOFA score. The EASL-CLIF consortium also developed the CLIF consortium organ failure score (CLIF-C OF), which simplified the original CLIF-SOFA.</ns0:p><ns0:note type='other'>.</ns0:note><ns0:p>Through further studies, Jalan et al found that age and white blood cell count were independent risk factors for mortality and established the CLIF-C ACLF score <ns0:ref type='bibr' target='#b7'>(7)</ns0:ref> . The CLIF-C ACLF score not only assesses the effects of extrahepatic organ injury, coagulation and circulatory failure but also includes age and inflammatory indicators; the CLIF-C ACLF score has high clinical value for evaluating the prognosis of ACLF. Up to now, there are less study on comparing all methods for the evaluation and prediction of prognosis in ACLF patients with a variety of etiologies, especially among Asians. Our study was designed to assess the short-term and long-term discriminative power of all of the above scores in ACLF patients to direct clinical practice.</ns0:p></ns0:div> <ns0:div><ns0:head>Material and methods</ns0:head></ns0:div> <ns0:div><ns0:head>Study patients</ns0:head><ns0:p>Our study was a single-center retrospective study that was completed in acute-on-chronic liver failure patients hospitalized in our institute between January 2015 and December 2018. Patients were included when they fulfilled these criteria: (a) ≥18 years old and (b) diagnosed with cirrhosis and ACLF (defined by the EASL-CLIF Consortium). Exclusion criteria included (1) hepatocellular carcinoma, (2) previous liver transplantation, (3) </ns0:p></ns0:div> <ns0:div><ns0:head>Definitions</ns0:head><ns0:p>Cirrhosis was defined by laboratory tests, radiologic imaging, endoscopy or liver biopsy. The ACLF criteria and organ failures were defined based on the CLIF-SOFA score according to the EASL-CLIF Consortium. The ACLF grading system classifies patients with ACLF in one of 3 grades according to the number of organ failures as per the CLIF-SOFA score as follows: Grade 1 if (1) single kidney failure (serum creatinine level ≥2.0 mg/dl) or (2) another organ failure (respiration, circulation, coagulation, or liver) is accompanied by grade I-II (West Haven criteria) hepatic encephalopathy (HE) and/or a serum creatinine level of 1.5-1.9 mg/dl, or (3) single cerebral failure (grade III-IV HE) is present with a serum creatinine level of 1.5-1.9 mg/dl; grade 2 if 2 organ failures are identified; or grade 3 if 3 or more organ failures have been diagnosed.</ns0:p><ns0:p>The Child-Pugh score was computed based on albumin, ascites, hepatic encephalopathy, prothrombin time (PT), and serum bilirubin <ns0:ref type='bibr' target='#b3'>(4)</ns0:ref> . The MELD formula was: 3.8×log (bilirubin) +9.6×log(creatinine) +11.2×log (INR)+6.43 <ns0:ref type='bibr' target='#b8'>(8)</ns0:ref> . The MELD-Na score was calculated as below: MELD-Na=[0.025×MELD×(140-Na)] +140 <ns0:ref type='bibr' target='#b5'>(6)</ns0:ref> . The CLIF-SOFA score was computed as the sum of the scores for six organ systems, including the cardiovascular, hepatic, coagulation, respiratory, nervous, and renal systems <ns0:ref type='bibr' target='#b10'>(9)</ns0:ref> . The CLIF-C OF score includes the revised six organ systems of the CLIF-SOFA score. The CLIF-C ACLF score was revised according to the CLIF-SOFA score and was computed with the formula: 10× [0.63×log (white-cell count) + 0.33×CLIF-C OF + 0.04×age-2] <ns0:ref type='bibr' target='#b7'>(7)</ns0:ref> .</ns0:p></ns0:div> <ns0:div><ns0:head>Study protocols</ns0:head><ns0:p>Patients with ACLF were included in the study. During hospitalization, data were collected regarding medical records, demographics, the presence of other comorbidities, clinical features, the number of complications and type of decompensation, the etiology of cirrhosis, and blood haematological index at admission (such as blood platelet count, white blood cell count, the INR, Manuscript to be reviewed renal function test, liver function test). The patients were followed up for 6 months to obtain survival information. Patients with incomplete follow-up at 28 days, 3 months, and 6 months were not included in the final analysis of the corresponding time.</ns0:p></ns0:div> <ns0:div><ns0:head>Statistical analysis</ns0:head><ns0:p>The statistical analyses were performed using SPSS software version 20.0 (SPSS Inc., Chicago, IL). Continuous variables were expressed as the mean±standard deviation (SD) or medians (interquartile range [IQR])，and categorical data were expressed as percentages. Differences in variables were analyzed using Student t-tests or the Mann-Whitney U test. Categorical variables are described as the frequencies (percentages [%]) and were compared with chi-squared or Fisher's exact tests. Receiver operating characteristic (ROC) curves were used to measure the performance of the score for the prediction of 28-day, 3-month, and 6-month mortality in patients with ACLF. The specificity, sensitivity, negative likelihood ratio (NLV) and positive likelihood ratio (PLV) were computed for each cut-off value. The cut-off point was obtained by Youden's index with greatest Sensitivity and Specificity <ns0:ref type='bibr' target='#b11'>(10)</ns0:ref> . The comparing of the areas under the ROC curve (AUROCs) was performed by Delong-test. 0.050 of two-tailed was significant meaning.</ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head></ns0:div> <ns0:div><ns0:head>Characteristics of ACLF patients</ns0:head><ns0:p>There were 102 patients in this study. During the study period, 92 patients were enrolled in the analysis of the outcomes at 28 days; subsequently, 3 patients were lost to follow-up, and 89 patients were finally enrolled at both 3 and 6 months. The flowchart is shown in Figure <ns0:ref type='figure' target='#fig_8'>1</ns0:ref>, and the demographic and biochemical characteristics of the study population are summarized in Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref>. The mean (±standard deviation) age of the 102 patients was 56.96 (±12.18) years. The leading cause of decompensation events responsible for hospitalization was variceal bleeding (70/102, 68.6%). The ACLF patient distribution was grade 1 (31/102 30.4%), grade 2 (45/102, 44.1%), and grade 3 (26/102 25.5%). The most common degree of ascites was moderate (28/102 27.5%), followed by severe (25/102 24.5%) and mild (13/102 12.7%). Forty-nine (48%) patients had undergone endoscopic hemostasis, 41 (40.2%) patients had undergone mechanical ventilation, and 66 (64.7%) patients had used vasopressors. In the 28-day and 3-month analyses, the mean age was 57.5 (±12) years and 57.8 (±12) years, respectively, and 62 (67.4%) and 59 (66.3%) patients were male. The leading cause of liver cirrhosis is Hepatitis virus infection and variceal bleeding accounts for the majority of hospitalizations. The distributions of patients who were included in the complete follow-up within 28 days and were included in the complete follow-up within 3 months were similar to that of all 102 patients in terms of ascites grade, ACLF grade, and treatment strategy. A total of 47 (46.1%), 58 (56.9%), and 61 (59.8%) patients died within 28 days, 3 months, and 6 months, respectively. The causes of death at 6 months were as follows: 3 (4.9%) patients had cardiogenic shock, 6 (9.8%) patients had infectious shock, 12 (19.7%) patients had respiratory failure, 18 (29.5%) patients had hemorrhagic shock, 19 (31.1%) patients had liver-related complications (4 patients had liver failure, 15 patients had hepatic encephalopathy) and 3 (4.9%) patients had an uncertain cause of death. The causes of death at 28 days, 3 months, and 6 months are outlined in Supplement Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Comparison of prognostic scores between the nonsurviving group and the surviving patients</ns0:head><ns0:p>The comparison of the six scores of patients with ACLF were shown in Table <ns0:ref type='table' target='#tab_3'>2</ns0:ref>. ACLF patients were grouped into surviving and nonsurviving groups based on their 28-day, 3-month, and 6month outcomes. The non-surviving patients had a higher CTP score, MELD score, CLIF-C OF score, CLIF-SOFA score and CLIF-ACLF score, compared with surviving patients (P<0.050).</ns0:p><ns0:p>Although the comparison of the MELD-Na score was not statistically significant (P=0.081), it was still higher in the nonsurviving group. Statistically significant differences were found for the CTP score, MELD-Na score, MELD score, CLIF-SOFA score, CLIF-ACLF score, CLIF-C OF score at 3 months and 6 months (P<0.050).</ns0:p></ns0:div> <ns0:div><ns0:head>Predictive ability for 28-day, 3-month and 6-month outcome in ACLF patients.</ns0:head><ns0:p>The discriminative ability of the CTP score, MELD score, MELD-Na score, CLIF-C OF score, and CLIF-ACLF score calculated for 28-day, 3-month, and 6-month survival is summarized in Table <ns0:ref type='table' target='#tab_6'>3</ns0:ref>. At 28 days, the CLIF SOFA score had the highest AUROC (0.805, 95%CI:0.715-0. Manuscript to be reviewed 0.560-0.787), and MELD-Na score (0.606, 95%CI: 0.487 to 0.724). When predicting 3-month and 6-month mortality, the CLIF-C SOFA score both had the highest AUROC (0.751, 95%CI: 0.646-0.857; 0.742, 95%CI: 0.633-0.852, respectively), by contrast, CTP score both had the lowest AUROC (0.641, 95%CI: 0.521-0.760; 0.640, 95%CI: 0.518-0.762, respectively). The ROC curves for the prognostic scores are shown in Figure <ns0:ref type='figure' target='#fig_9'>2</ns0:ref>. All prognostic scores were able to predict mortality at 28 days, 3 months, and 6 months (P<0.050).</ns0:p></ns0:div> <ns0:div><ns0:head>Comparing the predictive performance of all scores</ns0:head><ns0:p>As shown in Table <ns0:ref type='table' target='#tab_6'>3</ns0:ref>, the AUROC of the CLIF-SOFA score is superior to those of the other five scores with regard to 28-day, 3-month, and 6-month mortality. The CLIF-SOFA has the highest predicting value in 28-day mortality with the AUROC of 0.805. The predicting performer of CLIF-SOFA is significantly higher than CTP score, MELD-Na score, MELD score, CLIF-C OF score, and CLIF-ACLF score (P<0.050). At 3 months and 6 months, the comparison of AUROCs between the CTP score and the CLIF-SOFA score was statistically significant (P<0.050); however, the comparisons of AUROCs between the CLIF-C OF score, CLIF-ACLF score, MELD-Na score and MELD score were not significant (P>0.050). At 28 days, the AUROC of MELD-Na was lower than other five scores.</ns0:p></ns0:div> <ns0:div><ns0:head>Discussion</ns0:head><ns0:p>It is important to develop predictive scores that can identify patients who are at high risk of mortality, enabling the early provision of effective treatment to reduce mortality, especially in diseases with high mortality rates. ACLF is a clinical syndrome with a high mortality rate that is characterized by the development of acute decompensation (encephalopathy, ascites, gastrointestinal hemorrhage) and organ failure (such as kidney, renal, hepatic, coagulation, respiration and circulation), so prognostic assessment is an indispensable for ACLF patients <ns0:ref type='bibr' target='#b10'>(9)</ns0:ref> . However, in the clinical setting, the prognosis is often hard to predict for certain patients because of different factors, such as etiology, disease stage, and complications. Previous studies have shown that many different scores have predictive value for mortality in ACLF patients. It is very important to choose the most efficient score for predicting mortality in Asian patients in clinical treatment. The clinical characteristics of ACLF patients in Asian is completely different form patients in Europe and America. In this study, the leading etiology of liver cirrhosis was hepatitis virus infection (58.8%), followed by alcohol-related cirrhosis (34.1%), which was similar to the primary etiologies of liver disease in most Asian countries.</ns0:p><ns0:p>It is not surprising that the mortality of ACLF patients was high in this study, as that it consistent with previous research <ns0:ref type='bibr' target='#b12'>(11)</ns0:ref><ns0:ref type='bibr' target='#b13'>(12)</ns0:ref><ns0:ref type='bibr' target='#b15'>(13)</ns0:ref> . The mortality rate was 46.1% in the short term (28 days), and the mortality rate was 59.8% in the long term (6 months). The high mortality rate, which we find appalling, has spurred us to meaningfully contribute. Effective and inexpensive treatment strategies for patients with low socioeconomic status are limited because of the high costs associated with liver transplant and hemodialysis, partially in developing countries. The economical load produced by ACLF is still severe. Predicting the prognosis of patients with ACLF may be more important than treatment from the perspective of health economics for lowincome families.</ns0:p><ns0:p>Recently, the CLIF-ACLF score, CLIF-C OF score, CLIF-SOFA score have been used to evaluate prognosis in ACLF patients <ns0:ref type='bibr' target='#b16'>(14,</ns0:ref><ns0:ref type='bibr' target='#b17'>15)</ns0:ref> . To the best of our knowledge, although the discriminative ability of these scores for predicting outcomes in ACLF patients has been illustrated, different conclusions have been drawn regarding the relative predictive value of these scores because of differences in study populations or observation durations.</ns0:p><ns0:p>The predictive value of the six scores (CTP score, MELD score, MELD-Na, CLIF-ACLF score, CLIF-C OF score, and CLIF-SOFA score) was compared at 28 days, 3 months, and 6 months.</ns0:p><ns0:p>The AUROC of CLIF-SOFA is higher than other prognostic scores at 28 days, 3 months, and 6 months in our cohort, especially at 28 days. The CLIF-SOFA score provides a comprehensive and effective assessment of the severity of organ failure in ACLF patients and takes into account multiple systems, including the hepatic, renal, coagulation, respiratory, circulatory and nervous systems; it was established by the European Liver Disease Collaboration Group for Liver Failure Manuscript to be reviewed in 2013. Sy E's study indicated that the predictive value of the CLIF-SOFA score is better than those of the CTP score and MELD score for short-term outcomes <ns0:ref type='bibr' target='#b18'>(16)</ns0:ref> . Any score has its advantages and disadvantages. Although the predictive value of the CLIF-SOFA score is high, the calculation of the CLIF-SOFA score is complicated due to the inclusion of more indicators.</ns0:p><ns0:p>The Child-Pugh score is computed based on the prothrombin time, ascites, serum bilirubin, albumin, and hepatic encephalopathy <ns0:ref type='bibr' target='#b3'>(4)</ns0:ref> . The presence or absence of hepatic encephalopathy and ascites, which forms part of the CTP score, is subjective and has no clear cut-off value. The MELD score contains contains three indicators: the INR, creatinine and bilirubin; it is vulnerable to confounding by hemorrhaging, ascites and the use of diuretics, with the absence of clearly defined cutoff values for categorizing cirrhotic patients <ns0:ref type='bibr' target='#b19'>(17)</ns0:ref> . The occurrence of hyponatremia is closely related to the prognosis of patients with cirrhosis, particularly patients with ascites; therefore, the MELD-Na score has been created based on the MELD score <ns0:ref type='bibr' target='#b20'>(18)</ns0:ref> . However, the MELD score had a lower AUROC than the other five scores at all time points in this study. This may be due to the main complications of patients in this study. The patients were mainly enrolled from the Department of Gastroenterology and needed endoscopic treatment for bleeding esophageal gastric varices (70/102, 68.6%). The number of cirrhosis patients with ascites as the primary reason for hospitalization was very small (6/102 5.9%), Previous study have confirmed the ascites is the main complication of liver cirrhosis <ns0:ref type='bibr' target='#b21'>(19)</ns0:ref> , and ascites is associated with a high risk of developing further complications of cirrhosis such as dilutional hyponatremia <ns0:ref type='bibr' target='#b22'>(20)</ns0:ref> , Because of the number of patients with ascites are small, so the MELD-Na score may not play an important role in predicting patients'mortality. which may explain why the discriminative power of the MELD-Na score is lower than other five scores. The predicting value of the CTP, MELD-Na, and MELD scores in ACLF is not completely prefect because indicators reflecting systemic inflammation and organ failure is lacking. The CANONIC study had shown the advantage of the CLIF-ACLF, CLIF-SOFA, and CLIF-C OF scores over the CTP, MELD-Na, and MELD scores for the prediction of mortality in ACLF patients, which is according with the results in our study <ns0:ref type='bibr' target='#b7'>(7)</ns0:ref> . Jalan Manuscript to be reviewed CLIF-C OF score is equivalent to that of the CLIF-SOFA score for the prediction of mortality <ns0:ref type='bibr' target='#b7'>(7)</ns0:ref> .</ns0:p><ns0:p>Considering the effects of white blood cell (WBC) count and age on prognosis, Jalan et al established the CLIF-ACLF score based on the CLIF-C OF score <ns0:ref type='bibr' target='#b23'>(21)</ns0:ref> . The CLIF-ACLF score not only considers the effects of extrahepatic organ damage, coagulation and circulatory system failure on the prognosis but also includes the WBC count, which reflects the severity of inflammation; the CLIF-ACLF score was superior to the CTP, MELD-Na, and MELD scores <ns0:ref type='bibr' target='#b23'>(21)</ns0:ref> .</ns0:p><ns0:p>Despite the high predictive value of the CLIF-ACLF score and CLIF-C OF, these scores were established based on patients from European countries and the US with alcohol-related liver disease, and further researches are needed to explore whether they are applicable to Asian populations. Our research results have indicated that the scores also apply to Asian populations.</ns0:p><ns0:p>Several limitations existed in this study. First, this was a retrospective study, the number of patients included in our study was still not large, and some patients were lost to follow-up, which may have resulted in selection bias. Second, the scores were evaluated when admission to hospital and did not reflect the dynamic changes. Finally, the leading etiologies in patients in our study were hepatitis B virus infection, but most of the patients were diagnosed according to the EASL-ACLF criteria, leading to etiological bias.</ns0:p><ns0:p>In conclusion, our data reveal that the CTP score, MELD score, MELD-Na, CLIF-C OF score, CLIF-SOFA score, CLIF-ACLF score are effective tools for predicting the prognosis in ACLF patients. The CLIF-SOFA score has better discriminative power for the evaluation of short-term mortality, and may help improve the management of ACLF patients. Manuscript to be reviewed Manuscript to be reviewed Manuscript to be reviewed Manuscript to be reviewed </ns0:p><ns0:note type='other'>Figure 2</ns0:note></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>Some prognostic scores have been established previously. The Child-Turcotte-Pugh (CTP) score PeerJ reviewing PDF \| (2020:04:47745:2:0:NEW 2 Aug 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>Wiesner's research analyzed data and established the Model for End-Stage Liver Disease (MELD) score;</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head /><ns0:label /><ns0:figDesc>complications with other severe chronic extrahepatic diseases and (4) infection with human immunodeficiency virus. Our study PeerJ reviewing PDF \| (2020:04:47745:2:0:NEW 2 Aug 2020) Manuscript to be reviewed was approved by the Ethics Committee of the First Affiliated Hospital of Nanchang University (No. 2015-1203). All the patients signed the informed consent.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ reviewing PDF \| (2020:04:47745:2:0:NEW 2 Aug 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head /><ns0:label /><ns0:figDesc>896), followed by the CLIF-ACLF score (0.741, 95%CI: 0.640-0.843), CLIF-C OF score (0.712, 95%CI: 0.676 to 0.869), CTP score (0.707, 95%CI: 0.600-0.813), MELD score (0.673, 95% CI: PeerJ reviewing PDF \| (2020:04:47745:2:0:NEW 2 Aug 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ reviewing PDF \| (2020:04:47745:2:0:NEW 2 Aug 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head /><ns0:label /><ns0:figDesc>et al. first proposed the CLIF-C OF score in 2014 and proved that the value of the PeerJ reviewing PDF \| (2020:04:47745:2:0:NEW 2 Aug 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_7'><ns0:head>Figure 1 Figure 2</ns0:head><ns0:label>12</ns0:label><ns0:figDesc>Figure 1The flowchart in our study</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_8'><ns0:head>Figure 1 figure</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_9'><ns0:head>Figure 2 Receiver</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2</ns0:figDesc></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head>Table 1 (on next page)</ns0:head><ns0:label>1</ns0:label><ns0:figDesc /><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_1'><ns0:head>Table 1</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Characteristics of Patients in the ACLF cohort</ns0:figDesc><ns0:table><ns0:row><ns0:cell>PeerJ reviewing PDF \| (2020:04:47745:2:0:NEW 2 Aug 2020)</ns0:cell></ns0:row></ns0:table><ns0:note>PeerJ reviewing PDF \| (2020:04:47745:2:0:NEW 2 Aug 2020)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_2'><ns0:head>Table 1 .</ns0:head><ns0:label>1</ns0:label><ns0:figDesc /><ns0:table><ns0:row><ns0:cell cols='2'>Characteristic</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>s of Patients</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>in the ACLF</ns0:cell></ns0:row><ns0:row><ns0:cell>cohort</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell cols='2'>ACLF: Acute-</ns0:cell></ns0:row><ns0:row><ns0:cell cols='2'>on-chronic liver</ns0:cell></ns0:row><ns0:row><ns0:cell>failure;</ns0:cell><ns0:cell>SD;</ns0:cell></ns0:row><ns0:row><ns0:cell>Standard</ns0:cell><ns0:cell /></ns0:row></ns0:table><ns0:note>PeerJ reviewing PDF \| (2020:04:47745:2:0:NEW 2 Aug 2020)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_3'><ns0:head>Table 2 (on next page)</ns0:head><ns0:label>2</ns0:label><ns0:figDesc /><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_4'><ns0:head>Table 2</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>The comparison of prognostic scores</ns0:figDesc><ns0:table /><ns0:note>PeerJ reviewing PDF \| (2020:04:47745:2:0:NEW 2 Aug 2020)Manuscript to be reviewed </ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_5'><ns0:head>Table 2 .</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>The comparison of prognostic scores</ns0:figDesc><ns0:table><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>28-days</ns0:cell><ns0:cell>3-months</ns0:cell><ns0:cell /><ns0:cell>6-months</ns0:cell></ns0:row><ns0:row><ns0:cell>Prognostic score</ns0:cell><ns0:cell>All Patients(n=92)</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>survivors(n=45)</ns0:cell><ns0:cell>non-survivors(n=47) P-value</ns0:cell><ns0:cell>survivors(n=31) non-survivors(n=58) P-value</ns0:cell><ns0:cell>survivors(n=28)</ns0:cell><ns0:cell>non-survivors(n=61)</ns0:cell><ns0:cell>P-value</ns0:cell></ns0:row></ns0:table><ns0:note>CTP: Child-Turcotte-Pugh; MELD: model for end-stage liver disease; MELD-Na: model for end-stage liver disease-sodium; CLIF-C OF: chronic liver failure consortium organ function; CLIF-SOFA: chronic liver failure-sequential organ failure assessment; CLIF-C ACLF: chronic liver failure consortium acute-on-chronic liver failure</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_6'><ns0:head>Table 3 .</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>The efficacy and performance comparison of the prognostic scores for predicting mortality in 28-day,3-month and 6-monthCLIF-SOFA score vs MELD-Na 0.089 (-0.023-0.207) 0.126 CLIF-SOFA score vs CLIF-C ACLF 0.043 (-0.019-0.106)</ns0:figDesc><ns0:table><ns0:row><ns0:cell>0.109</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:note place='foot'>PeerJ reviewing PDF \| (2020:04:47745:2:0:NEW 2 Aug 2020)</ns0:note> </ns0:body> "	"Dear editors, Thanks for your work and for the reviewer’s comments concerning our manuscript entitled Assessing the prognostic scores for the prediction of the mortality of patients with acute-on-chronic liver failure: a retrospective study. Editor comments (Minjun Chen) We appreciated your efforts to significantly improve the manuscript; however before we consider the acceptance these issues proposed by the reviewers should be solved point-by-point. We are looking forward to your revision. [# PeerJ Staff Note: It is PeerJ policy that additional references suggested during the peer-review process should only be included if the authors are in agreement that they are relevant and useful #] Response: Thank you very much for your advises, I had revised my manuscript carefully according to your requirement. I have responded to expert opinions point-by-point. Please check it. Reviewer 1 (Ismael Alvarez Alvarez) Basic reporting Question 1：The article is well-written, and authors have made an effort and corrected the erratum detected in the prior version. However, there are some minor issues to be corrected: Line 110: 'The MELD: 3.8×log (bilirubin) +9.6×log(creatinine) +11.2×log (INR)+6.43 (reference 8).' Response 1: Thank you very much for your advice. The manuscript has been modified. Please check it at line 92 Question 2：Line 111: please, correct 'below' instead of 'bellow' Response 2: Thank you very much for your advice. The manuscript has been modified. Please check it at line 93. Question 3：Line 147: There are 102 patients in this study. I think this sentence should be in past tense: 'There were 102 patients in this study. Response 3: Thank you very much for your advice. The manuscript has been modified. Please check it at line 123. Question 4：Line 271: 'The predicting value of the CTP, MELD-Na, and MELD scores in ACLF is not complete prefect because indicators reflecting systemic inflammation and organ failure is lacking.' I would suggest: 'The predicting value of the CTP, MELD-Na, and MELD scores in ACLF is not completely perfect because indicators reflecting systemic inflammation and organ failure is lacking.' Response 4: Thank you very much for your advice. The manuscript has been modified. Please check it at line 238. Experimental design Question 1：The authors have included the information about the Youden index in the methods section, as requested. However, I would suggest to reference this work: Youden, W.J. (1950). «Index for rating diagnostic tests». Cancer 3: 32-35 (PMID:15405679). Apart from this suggestion, I have no further comments. The study question is well defined and addressed, and methodology is sufficiently explained. Response 1: Thank you very much for your advice. The manuscript has been modified. Please check it at line 119. Validity of the findings Question 1：I have no suggestions nor comments regarding the validity of findings. Conclusions are well defined and answer the research question based on the findings, and the clinical relevance of these findings is notable. Response 1: Thank you very much for your advice. We appreciate for Editors/Reviewers’ warm work earnestly, and hope that the correction will meet with approval. Once again, thank you very much for your comments and suggestions. Considering the severe COVID-19 outbreak in world, Best wishes for your healthy. Best regards! Dr Zhang, Dr Nie, PhD Zhu "	Here is a paper. Please give your review comments after reading it.
524	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Collaborative hunting by complex social groups is a hallmark of large dogs (Mammalia: Carnivora: Canidae), whose teeth also tend to be hypercarnivorous, specialized toward increased cutting edges for meat consumption and robust p4-m1 complex for cracking bone. The deep history of canid pack hunting is, however, obscure because behavioral evidence is rarely preserved in fossils. Dated to the early Pleistocene (~1.2 Ma), Canis chihliensis from the Nihewan Basin of northern China is one of the earliest canines to feature a large body size and hypercarnivorous dentition. We present the first known record of dental infection in C. chihliensis, likely inflicted by processing hard food, such as bone. Another individual also suffered a displaced fracture of its tibia and, despite such an incapacitating injury, survived the trauma to heal. The long period required for healing the compound fracture is consistent with social hunting and family care (food-sharing) although alternative explanations exist. Comparison with abundant paleopathological records of the putatively pack-hunting late Pleistocene dire wolf, Canis dirus, at the Rancho La Brea asphalt seeps in southern California, U.S.A., suggests similarity in feeding behavior and sociality between Chinese and American Canis across space and time. Pack hunting in Canis may be traced back to the early Pleistocene, well before the appearance of modern wolves, but additional evidence is needed for confirmation.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>Large, hypercarnivorous dogs (family Canidae)-such as gray wolves (Canis lupus), African hunting dogs (Lycaon pictus), and Asian dholes (Cuon alpinus)-are known to be highly social because of their need for collaborative hunting <ns0:ref type='bibr' target='#b76'>(Van Valkenburgh 1991)</ns0:ref>. In all three species, energetic requirements necessitate that they pursue prey species that are larger than themselves <ns0:ref type='bibr' target='#b12'>(Carbone et al. 1999)</ns0:ref>. But, unlike their felid (cat family) counterparts, canids lack retractile claws and are usually unable to bring down their prey single-handedly <ns0:ref type='bibr' target='#b89'>(Wang et al. 2008)</ns0:ref>, making collaborative (pack) hunting a useful compensatory strategy. Despite the importance of pack hunting as a key biological indicator for social interactions, trophic relationship, and diets, however, fossil records rarely preserve direct information on behavior.</ns0:p><ns0:p>Discovery of an injured and healed skeleton and jaws of a large ancestral wolf, Canis chihliensis, from the early Pleistocene hominin site of Nihewan Basin, northern China, is of interest in inferring their social behavior. Evidence of healing raises the possibility that individuals survived incapacitating injuries by sharing food with family members <ns0:ref type='bibr' target='#b45'>(Palmqvist et al. 1999)</ns0:ref>, a question to be explored in this paper.</ns0:p></ns0:div> <ns0:div><ns0:head>Materials & Methods</ns0:head><ns0:p>The methods employed in this study include morphological observations, CT scanning, and Xray examination. CT slicing intervals followed that of <ns0:ref type='bibr' target='#b51'>Rothschild et al. (1994)</ns0:ref>. The osteological terms are from <ns0:ref type='bibr' target='#b43'>Mescher (2018)</ns0:ref>. The stages of fracture healing follow <ns0:ref type='bibr' target='#b21'>Edge-Hughes & Nicholson (2007)</ns0:ref>. Age determination follows <ns0:ref type='bibr'>Sumner-Smith (1966)</ns0:ref> for epiphyseal fusion and <ns0:ref type='bibr' target='#b24'>Gipson et al. (2000)</ns0:ref> for tooth wear. Body-mass estimates were calculated using regressions on canid femur shaft diameter by <ns0:ref type='bibr' target='#b3'>Anyonge & Roman (2006)</ns0:ref> and m1 length by <ns0:ref type='bibr' target='#b75'>Van Valkenburgh (1990)</ns0:ref>. Permission for excavation was granted by the State Administration of Cultural Heritage with a permit number of 2018-090. Locality and Fauna. The present large sample of early Pleistocene wolf, Canis chihliensis, comprises more than 200 specimens including excellently preserved pathological conditions. A left dentary (IVPP V17755.11), a right dentary (IVPP V17755.12), and a right tibia (IVPP V18139.20) of Canis chihliensis are all from the Shanshenmiaozui (SSMZ) Site in Nihewan Basin. C. chihliensis from SSMZ is dominated by older individuals as inferred from wear on teeth <ns0:ref type='bibr' target='#b16'>(Chen 2018;</ns0:ref><ns0:ref type='bibr'>Chen & Tong 2015)</ns0:ref>. The SSMZ locality (40˚13' 08'N, 114˚ 39' 54'E) lies at the southern bank of the Sangganhe River, and at the edge of the Haojiatai fluviolacustrine platform in Yangyuan County, Hebei Province (Fig. <ns0:ref type='figure' target='#fig_3'>S1</ns0:ref>). The fossiliferous layer was dated to ca. 1.2 Ma by magnetostratigraphy and associated fauna <ns0:ref type='bibr' target='#b36'>(Liu et al. 2016;</ns0:ref><ns0:ref type='bibr' target='#b69'>Tong et al. 2011)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Institution and Locality</ns0:head><ns0:p>Canids are the most abundant carnivorans in the Early Pleistocene Nihewan Fauna <ns0:ref type='bibr' target='#b46'>(Qiu 2000;</ns0:ref><ns0:ref type='bibr' target='#b63'>Teilhard de Chardin & Piveteau 1930)</ns0:ref>, as also confirmed by our recent excavations at SSMZ (Fig. <ns0:ref type='figure' target='#fig_4'>S2</ns0:ref>). The dominant taxon of the canid guild in the SSMZ Fauna is Canis chihliensis <ns0:ref type='bibr' target='#b69'>(Tong et al. 2011;</ns0:ref><ns0:ref type='bibr' target='#b70'>Tong et al. 2012)</ns0:ref> <ns0:ref type='bibr'>(Tong & Chen 2015;</ns0:ref><ns0:ref type='bibr' target='#b67'>Tong et al. 2017;</ns0:ref><ns0:ref type='bibr' target='#b68'>Tong et al. 2018;</ns0:ref><ns0:ref type='bibr' target='#b69'>Tong et al. 2011;</ns0:ref><ns0:ref type='bibr' target='#b70'>Tong et al. 2012;</ns0:ref><ns0:ref type='bibr' target='#b71'>Tong & Wang 2014;</ns0:ref><ns0:ref type='bibr' target='#b72'>Tong & Zhang 2019)</ns0:ref>.</ns0:p><ns0:p>Rancho La Brea Canis dirus. The best records of paleopathology in extinct canids are from the world's largest collection of late Pleistocene dire wolves, Canis dirus, from the Rancho La Brea asphalt seeps in Los Angeles, California, U.S.A. The Rancho La Brea paleopathology collection comprises about 3,200 specimens of dire wolves assembled from over 200,000 specimens representing a minimum of 3,500 individuals (dire wolves represent greater than 50% of all mammal specimens from the Rancho La Brea) <ns0:ref type='bibr' target='#b57'>(Shaw & Ware 2018)</ns0:ref>. As the largest Canis that ever lived and presumably preferring larger prey, dire wolves are widely considered a social predator <ns0:ref type='bibr' target='#b3'>(Anyonge & Roman 2006;</ns0:ref><ns0:ref type='bibr' target='#b14'>Carbone et al. 2009;</ns0:ref><ns0:ref type='bibr' target='#b28'>Hemmer 1978;</ns0:ref><ns0:ref type='bibr' target='#b42'>Merriam 1912;</ns0:ref><ns0:ref type='bibr' target='#b59'>Stock 1930;</ns0:ref><ns0:ref type='bibr' target='#b81'>Van Valkenburgh & Hertel 1998;</ns0:ref><ns0:ref type='bibr' target='#b83'>Van Valkenburgh & Sacco 2002)</ns0:ref>. The Rancho La Brea dire wolf collection preserves a range of pathological conditions throughout the skeleton <ns0:ref type='bibr' target='#b25'>(Hartstone-Rose et al. 2015;</ns0:ref><ns0:ref type='bibr' target='#b33'>Lawler et al. 2017;</ns0:ref><ns0:ref type='bibr' target='#b44'>Moodie 1918;</ns0:ref><ns0:ref type='bibr' target='#b56'>Shaw & Howard 2015;</ns0:ref><ns0:ref type='bibr' target='#b59'>Stock 1930;</ns0:ref><ns0:ref type='bibr' target='#b93'>Ware 2005)</ns0:ref>, with particularly debilitating examples offering evidence that strong social bonds existed to allow weakened or disabled individuals to survive for extended periods of time <ns0:ref type='bibr' target='#b56'>(Shaw & Howard 2015;</ns0:ref><ns0:ref type='bibr' target='#b57'>Shaw & Ware 2018)</ns0:ref>.</ns0:p><ns0:p>Focusing on Canis dirus from a single deposit (Pit 61/67) at Rancho La Brea, <ns0:ref type='bibr' target='#b9'>Brown et al. (2017)</ns0:ref> quantified patterns of traumatic pathology-injuries that likely resulted from hunting, including healed fractures and evidence of severe or chronic muscle strain as well as osteoarthritis-and predicted skull injuries to be common because of the probability of being kicked while chasing prey. Contrary to expectation, the cranium showed a low incidence of traumatic injury (1.6%) and the dentary even less so (0.18%) <ns0:ref type='bibr' target='#b9'>(Brown et al. 2017</ns0:ref>). This study, however, excluded dental injuries likely incurred from feeding-such as abscesses and alveolar resorption stemming from infection-which were also sustained by and preserved in C. dirus from Rancho La Brea. In the current study, we quantify these dental injuries, as well as traumatic damage to the dire wolf tibia, for comparison with dental and tibial injuries in C. chihliensis.</ns0:p></ns0:div> <ns0:div><ns0:head>Taxonomic and Phylogenetic Remarks</ns0:head><ns0:p>As far as we are aware, there are few reports of debilitating injuries to large hypercarnivorous canines in the fossil record, including early Pleistocene Canis falconeri from Venta Micena of Spain <ns0:ref type='bibr' target='#b45'>(Palmqvist et al. 1999)</ns0:ref>, Cuon from late Pleistocene of Italy <ns0:ref type='bibr' target='#b29'>(Iurino & Sardella 2014)</ns0:ref>, and the latest Pleistocene occurrences of Canis dirus in the Rancho La Brea asphalt seeps <ns0:ref type='bibr' target='#b56'>(Shaw & Howard 2015)</ns0:ref>. This is despite a generally excellent fossil record for large canids in the late Cenozoic because of canids' preference for mid-latitude open habitats, where terrestrial fossil records are best preserved and most extensively explored <ns0:ref type='bibr' target='#b62'>(Tedford et al. 2009;</ns0:ref><ns0:ref type='bibr' target='#b87'>Wang 1994;</ns0:ref><ns0:ref type='bibr' target='#b89'>Wang et al. 2008;</ns0:ref><ns0:ref type='bibr' target='#b90'>Wang et al. 1999)</ns0:ref>.</ns0:p><ns0:p>The holotype of Canis chihliensis was originally described based on a maxillary fragment with P3-M2 from Feng-Wo at Huang-Lu village (Locality 64) in Huailai County, Hebei (Chihli) Province by <ns0:ref type='bibr' target='#b96'>Zdansky (1924)</ns0:ref>. Teilhard de <ns0:ref type='bibr' target='#b63'>Chardin & Piveteau (1930)</ns0:ref> referred additional specimens to this species from Nihewan Basin. <ns0:ref type='bibr' target='#b47'>Rook (1994)</ns0:ref> synonymized C. chihliensis with C. antonii <ns0:ref type='bibr' target='#b96'>Zdansky, 1924</ns0:ref><ns0:ref type='bibr' target='#b62'>, but Tedford et al. (2009)</ns0:ref> returned to C. chihliensis by restricting the concept to large Nihewan Canis. The systematics of C. chihliensis from SSMZ has been treated by <ns0:ref type='bibr'>Tong et al. (</ns0:ref> <ns0:ref type='formula'>2012</ns0:ref>) <ns0:ref type='bibr' target='#b47'>Rook (1994)</ns0:ref> and <ns0:ref type='bibr' target='#b58'>Sotnikova (2001)</ns0:ref> referred the Pliocene-Early Pleistocene species Canis falconeri from Europe, C. antonii from Asia and C. africanus from Africa to the supraspecific group Canis (Xenocyon) ex gr. falconeri. All of them readily fall into the category of hypercarnivores based on dentition and C. falconeri has also been hypothesized to be a hypercarnivore similar to modern gray wolves <ns0:ref type='bibr' target='#b45'>(Palmqvist et al. 1999)</ns0:ref>. Canis chihliensis shares some similarities with Sinicuon dubius <ns0:ref type='bibr' target='#b70'>(Tong et al. 2012)</ns0:ref>. Furthermore, C. chihliensis is among the largest Canis species of Eurasia in the early Pleistocene.</ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head><ns0:p>Dental Fracture and Inflammations as Related to Bone-crushing and Hypercarnivory. The left dentary (IVPP V17755.11) and right dentary (IVPP V17755.12) belong to the same individual. The left dentary (Fig. <ns0:ref type='figure' target='#fig_3'>1</ns0:ref>.A-D) has c, p1-3 and m2-3 intact, while the crown of p4, trigonid of m1, and mesial root of m1 are fractured and lost, apparently due to injuries suffered during life. Both root fragments of p4 are retained. On m1 only the talonid is preserved. Note on Fig 1 <ns0:ref type='figure'>.</ns0:ref>A that the alveolar bone in the region of the missing mesial root of m1 shows no residual socket, which indicates antemortem bone remodeling. This is consistent with the radiographic evidence of periapical bone resorption associated with the apices of the retained roots of p4 and the distal root of m1 (described below). There is also partial loss of the enamel on c and m1 and fracturing of the crowns of p2, p3, and root of m1. The pulp cavities of p4 and m1 are exposed. The dentin of all teeth is stained brown. All remaining cusps are moderately worn.</ns0:p><ns0:p>There are multiple fractures of the buccal and lingual cortical surfaces of the dentary, primarily in the regions of p2-p3, m1-m2, and the posterior surface of the mandibular ramus including the condylar process. All fractures appear to be postmortem as suggested by the absence of any repair.</ns0:p><ns0:p>There is loss of the cortical bone on the alveolar ridge in the regions of p3, p4, and m1. This was most likely caused by periodontitis in vivo although there may have also been some postmortem fracturing of the alveolar bone around m1.</ns0:p><ns0:p>The right dentary (Fig. <ns0:ref type='figure' target='#fig_3'>1</ns0:ref>.E-H) preserves i2-3, c, p1-4, and m1-2 in situ; the crown of m3 is missing, but one root tip remains deep in the alveolus. The crown of m1 is brownish due to loss of most of the enamel cap, and with the pulp cavity exposed; m2 was broken during excavation; and other teeth are moderately worn. There are multiple fractures of the buccal and lingual cortical bone, predominantly in the regions of p1 and m2, that are postmortem defects.</ns0:p><ns0:p>The right dentary also suffered serious injury. The bone surrounding the m1 root is perforate on the buccal cortex (purple arrow, fpp, on Fig. <ns0:ref type='figure' target='#fig_3'>1</ns0:ref>.H) by an apparent fistula and there is extensive loss of alveolar bone over the buccal aspect of the mesial root of m1 (red arrow, pp, on Interpretation and Implications for Dental Injury. IVPP V17755 suffered from repeated dental injuries in similar locations on both left and right sides. Although both lever models and in vivo experimentation <ns0:ref type='bibr' target='#b22'>(Ellis et al. 2008)</ns0:ref> show that biting forces are greatest on the posterior-most molars, patterns of tooth wear suggest that the lower p4-m1 are used more frequently than more posterior molars <ns0:ref type='bibr' target='#b73'>(Tseng & Wang 2010;</ns0:ref><ns0:ref type='bibr' target='#b89'>Wang et al. 2008;</ns0:ref><ns0:ref type='bibr' target='#b94'>Werdelin 1989)</ns0:ref>, although in the case of the most hypercarnivorous canid, Lycaon, bone consumption may be at a more posterior location <ns0:ref type='bibr' target='#b77'>(Van Valkenburgh 1996)</ns0:ref>. Dental modifications for bone consumption in fossil borophagine canids are most apparent in the p4-m1 region, indicating that this was the location of most bone-cracking behavior <ns0:ref type='bibr' target='#b90'>(Wang et al. 1999)</ns0:ref>. We interpret the loss of the left p4-m1 in IVPP V17755 as owing to bone-cracking-the p4 and m1 are the largest lower cheek teeth in Canis and their loss must have been inflicted by a strong biting force. Preservation of the roots of both the p4 and the m1 trigonid (Fig. <ns0:ref type='figure' target='#fig_3'>1</ns0:ref>.D) suggests tooth fracture from a strong bite and/or encountering hard objects. The alveolar bone in the region of the missing m1 mesial root eventually healed, but the periapical infections associated with both retained root fragments of p4 and the distal root of m1 still show active lesions.</ns0:p><ns0:p>The need for bone-crushing in IVPP V17755 would have continued during and after the healing of the wounds on the left side. Accordingly, the right p4-m1 suffered excessive wear, likely to compensate for the loss of the same function on the left side. Again, we infer that the heavy wear is due to chewing on bones. The wear on the crown of m1 led to exposure of the pulp chamber through two pulp horns in the mesial cusp and directly to the periapical lesions (abscess) (blue arrows, pi, in Figs. 1.D and 1.H). This lesion grew sufficiently that it created a fistula to the buccal surface of the dentary to allow drainage of pus. It is also likely that excessive use on the right side led to bone splinters (shards, fragments) being imbedded into the gum tissue between p4 and m1, causing a periodontal pocket.</ns0:p><ns0:p>The above scenario suggests prolonged and possibly repeated injuries and infections, first to the left p4-m1 (possibly broken in a single bite), and then to the right jaw perhaps after the left side had partially healed. Such a scenario is consistent with a hypercarnivorous dentition in C. chihliensis frequently used for bone consumption, as also seen in late Pleistocene European Cuon <ns0:ref type='bibr' target='#b29'>(Iurino & Sardella 2014)</ns0:ref>. Bone-crushing behavior in canids has been linked to collaborative hunting and competitive consumption of carcasses within the same family group of predators <ns0:ref type='bibr' target='#b89'>(Wang et al. 2008;</ns0:ref><ns0:ref type='bibr' target='#b91'>Wang et al. 2018)</ns0:ref>. Such a behavior is especially prevalent among large, hypercarnivorous canids, and <ns0:ref type='bibr' target='#b82'>Van Valkenburgh et al. (2019)</ns0:ref> recently linked high tooth fractures in extant gray wolves to limited prey availability.</ns0:p><ns0:p>Comparison to Rancho La Brea Canis dirus. In Pit 61/67 alone, 35 dentaries of adult age (14 left, 21 right)-out of 64 pathological adult dentaries (25 left, 39 right; 55%) and 617 dentaries total (both pathological and non-pathological; 5.7%)-exhibit dental injuries similar to those in the Nihewan C. chihliensis dentaries examined in this current study (Fig. <ns0:ref type='figure' target='#fig_5'>S3</ns0:ref>). Across Rancho La Brea deposits, abscesses and alveolar resorption likely due to infection were preserved in 43% (Pit 16) to 77% (Pit 3) of pathological dentaries (Fig. <ns0:ref type='figure' target='#fig_4'>2</ns0:ref>.A). Most of the remaining pathological dentaries also preserved dental anomalies, predominantly supernumerary teeth (particularly in the first and second premolars) or a missing lower first premolar (p1) and/or third molar (m3). Because both the p1 and m3 <ns0:ref type='bibr' target='#b7'>(Balisi et al. 2018;</ns0:ref><ns0:ref type='bibr' target='#b10'>Buchalczyk et al. 1981;</ns0:ref><ns0:ref type='bibr' target='#b87'>Wang 1994</ns0:ref>) vary in their presence among canids, we excluded anomalies in these teeth from our comparison with Nihewan C. chihliensis. Across 200 C. dirus jaws (both left and right) bearing abscesses and alveolar infections, the lower first molar or carnassial showed the highest frequency of injury (87 total specimens with m1-associated injuries), likely inflicted by bone-crushing during the consumption of prey, followed by the second premolar (79 total specimens with p2-associated injuries), likely the result of biting and killing while chasing prey or in fighting with conspecifics or competitors of other species (Fig. <ns0:ref type='figure' target='#fig_4'>2</ns0:ref>.B). The fourth premolar was the third most frequently injured tooth (57 specimens); often, it was injured in conjunction with the lower first molar (34 specimens), as in the case of C. chihliensis. As C. dirus is a predator widely recognized to have had a forceful bite capable of processing bone <ns0:ref type='bibr' target='#b2'>(Anyonge & Baker 2006;</ns0:ref><ns0:ref type='bibr' target='#b8'>Brannick et al. 2015;</ns0:ref><ns0:ref type='bibr' target='#b80'>Van Valkenburgh & Hertel 1993)</ns0:ref>, the high frequency of injury in its p4-m1 complex-similar to that found in the specimens of C. chihliensis examined here-supports the inference that C. chihliensis also processed bone using p4 and m1. Tibia Fracture. A normal left tibia (IVPP V18139.21) and pathologic right tibia (IVPP V18139.20) of Canis are present in the collection from Shanshenmiaozui (SSMZ). The pathologic tibia has healed fractures at the lower one-third of the shaft. Compared with the normal tibia on the left side (Fig. <ns0:ref type='figure' target='#fig_5'>3</ns0:ref>), the pathologic tibia is stouter; it is much broader distally, especially at the fracture site, and is shorter, the maximum length for the normal tibia being 181.6 mm, in contrast to the pathologic one at 166.5 mm (Table <ns0:ref type='table'>1</ns0:ref>). In addition, the nutrient foramen is much more enlarged in the pathologic tibia. The partially healed bone has a rough and porous surface (callus).</ns0:p><ns0:p>The porous bone surface indicates that the periosteal vessels also took part in the repair of the fracture, which penetrated into the hard callus. Because the woven/primary bone is not replaced with secondary lamellar bone, this individual did not survive to the stage of lamellar bone formation, i.e. the fracture healing stage 6 by <ns0:ref type='bibr' target='#b21'>Edge-Hughes & Nicholson (2007)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Foreshortening of tibia.</ns0:head><ns0:p>The pathologic tibia has fused overlapping components with remodeling starting 4 cm from the proximal surface and extending throughout the length. Accentuation (irregularities) of the entheseal region at the lateral margin of the tibial plateau suggests increased stress at the proximal tibial-fibular joint. The tibia widens abnormally starting 6 cm distal to proximal surface, with concurrent alteration of surface color and texture, continuing on to the fused distal component of the tibial fracture, where surface filigree reaction (characteristic of infection) is more prominent. There are increased vascular markings at the junction of proximal and middle third (related to current length) of the tibia. A shallow groove identifies the original demarcation of the fracture components now fused. The fibula was also fractured, and residual components are noted at the distal 6 cm. A linear defect is noted at the mid-portion of the tibia, slightly medial to the sagittal line. It appears to be perforated in a manner more suggestive of vasculature than of draining sinuses. It may be the residue of the fracture. If so, it would mean that the injury not only caused fracture, separation and overlap of components, but also caused a 'splintering' or at least slight separation of the distal portion of the proximal component. Increased vascularity is noted 2 cm from the distal end of the tibia.</ns0:p><ns0:p>X-ray Examination. Increased density of the medial tibial plateau is noted. If not related to an artifact (e.g., glued component), this is suggestive of a healed, minimally displaced fracture. There clearly is a displaced distal fracture, fused incompletely with overlap. The curvature of the distal portion of the proximal component suggests torsion of the components related to each other. Several layers of periosteal reaction are noted, with partial disruption of subjacent cortex. The distal fibula is fused to the tibia, with focal loss of margin definition. Irregular cavities are noted in the distal portion of the proximal component of the fracture and adjacent to the distal junction of the tibia and fibula. Both contain radio-dense material. This suggests that this was a compound fracture, with skin breach and exposure to environmental contamination. The fracture was incompletely stabilized during the healing process, with continued movement of the components.</ns0:p><ns0:p>CT Scan. The CT images show clearly that it was a comminuted fracture, and all three pieces of the fractures are displaced, which resulted in the division of the medullary cavity into three chambers whose broken ends were enclosed by callus or woven bones (Figs. 4.A-D).</ns0:p></ns0:div> <ns0:div><ns0:head>CT longitudinal sections slice 1 (Figs. 4.A-B</ns0:head><ns0:p>) -There is a focal area of trabecular loss just distal to the proximal epiphyseal plate. It is irregularly ellipsoid in shape and contains slightly thickened bone 'fragments' of apparently increased density. Increased density is noted in the subsequent proximal fracture component. Periosteal reaction is noted with multiple focal areas of trabecular loss, bounded by sclerotic margins, characteristic of abscesses. There is massive loss of cortical bone in the region of fragment fusion. Fibular fusion with a distal radio-dense inclusion is noted. Presence of foreign bodies is consistent with the diagnosis of a compound fracture.</ns0:p><ns0:p>CT longitudinal sections slice 2 (Figs. 4.C-D) -There is an area of increased density at the median tibial plateau noted on the x-ray. The CT shows this area to be separated by a fracture line from subjacent bone. The trabecular pattern is denser. The lateral portion of the proximal epiphyseal plate is partially preserved, in contrast to the medial portion, which cannot be distinguished from the epiphysis. This appears to be a non-displaced fracture through the epiphyseal plate, only affecting a portion of that plate.</ns0:p><ns0:p>There is a linear focal disruption (partially occluded at the surface) of the medial aspect at the midpoint of the current length and a U-shaped defect (also seen in CT slice 1) with thickened margins at the distal fifth. The latter could represent a draining abscess, although the former suggests the possibility of a penetrating injury. Radio-dense inclusions are noted, perhaps representing environmental exposure at time of injury. The surface imperfection seen on the reconstructed tibial image (Fig. <ns0:ref type='figure' target='#fig_6'>4</ns0:ref>.E) may be a CT averaging artifact. A series of 8 cross sections (Figs.</ns0:p></ns0:div> <ns0:div><ns0:head>4.F-M) allows comparisons of healthy cancellous (F), healthy cortical (G-H), and injured and healed bones (I-M).</ns0:head><ns0:p>Interpretation, Comparison, and Implications for Limb Injury. That the injury, plus the subsequent infections, suffered by IVPP V18139 must have been devastating seems not in doubt. The displacement of the right hindlimb and the pain associated with a compound fracture with skin breach and exposure to environmental contamination all but rule out hunting activities. For modern domestic dogs of more than 1 year of age, fracture healing can take 7 weeks to 1 year <ns0:ref type='bibr' target='#b21'>(Edge-Hughes & Nicholson 2007)</ns0:ref>. Therefore, it is safe to assume that healing of the open fractures in IVPP V18139 without medical intervention (broken bones not re-aligned nor cast to immobilize wounds) would take a considerable amount of time, much longer than its metabolic reserve can sustain. Such a long-term survival by an injured wolf requiring a high degree of meat consumption thus suggests collaborative hunting and potentially family care.</ns0:p><ns0:p>In addition to abnormalities in the jaws and dentition, the Rancho La Brea dire wolf collection has numerous healed fractures in the limb bones <ns0:ref type='bibr' target='#b44'>(Moodie 1918;</ns0:ref><ns0:ref type='bibr' target='#b56'>Shaw & Howard 2015;</ns0:ref><ns0:ref type='bibr' target='#b59'>Stock 1930;</ns0:ref><ns0:ref type='bibr' target='#b93'>Ware 2005)</ns0:ref>. Again focusing on Pit 61/67, which has a minimum number of 371 dire wolf individuals, <ns0:ref type='bibr' target='#b9'>Brown et al. (2017)</ns0:ref> showed that frequencies of traumatic injuryincluding healed fractures-were higher than expected for most limb bones, especially the tibia. Surveying dire wolf tibiae across all Rancho La Brea deposits, we found 11 specimens (5 left, 6 right) of 251 total pathologic tibiae (4.38%) to have suffered an oblique fracture with foreshortening similar to that in IVPP V18139 (Fig. <ns0:ref type='figure' target='#fig_6'>S4</ns0:ref>). In studies of modern Saskatchewan gray wolves and sympatric coyotes, such bone fractures-which likely resulted from conflicts with large prey-were found to be more common in wolves than in coyotes, a difference thought to result from wolves' tendency to prey on larger animals like moose <ns0:ref type='bibr' target='#b95'>(Wobeser 1992)</ns0:ref>. Similarly, Rancho La Brea preserves no fractured and healed tibiae belonging to the coyote-which is also found abundantly in the Pleistocene to Holocene-age asphalt seeps-though this lack may be confounded by a coyote sample size an order of magnitude smaller than that of the dire wolf.</ns0:p></ns0:div> <ns0:div><ns0:head>Discussions</ns0:head><ns0:p>Life is not easy for large predators. In modern canids, hypercarnivory is almost always associated with social hunting, such as in the gray wolves (Canis lupus), African hunting dogs (Lycaon pictus), and Asiatic dholes (Cuon alpinus). Of these, the latter two most hypercarnivorous species almost invariably hunt cooperatively, whereas gray wolves regularly, but not exclusively, hunt together for large prey <ns0:ref type='bibr' target='#b38'>(Macdonald 1983)</ns0:ref>. Group hunting by these highly social canids offers apparent advantages that are otherwise unavailable to individual hunters, such as the ability to bring down prey much larger than the predators themselves, plus coordinated attacks that seal off escape routes as well as relaying strategies that lessen the burden of individual hunters. These strategies are especially critical to canids because, unlike felids, PeerJ reviewing PDF \| (2020:05:48717:1:1:NEW 6 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed canids never evolved fully retractile claws that are effective weapons for grappling and subduing prey <ns0:ref type='bibr' target='#b86'>(Wang 1993)</ns0:ref>. Therefore, for canids, group hunting is not optional, as it is for large cats (only the lions are social hunters, as are occasionally the cheetahs), once canids have crossed the critical body mass threshold of about 21 kg above which energetic costs necessitate feeding on large prey <ns0:ref type='bibr' target='#b12'>(Carbone et al. 1999)</ns0:ref>. For canids, it is possible that this body size threshold may even be substantially lowered as in the case of the Asiatic dholes (10-13 kg) that have the most extremely hypercarnivorous dentitions among living canids <ns0:ref type='bibr' target='#b18'>(Cohen 1978)</ns0:ref>. The Nihewan Canis chihliensis is larger than the dholes (13.7-16.8 kg based on femur shaft diameter; ~21.2 kg based on the mean of m1 length).</ns0:p><ns0:p>Social hunting is characteristic of large canids, hyaenids, and some felids, and depending on how such behavior is described, may even be quite common in carnivorans <ns0:ref type='bibr' target='#b6'>(Bailey et al. 2013)</ns0:ref>. Such behavior has important implications not only in the social organizations of large carnivorans but also in their trophic relationships and diet. Among large, hypercarnivorous living canids, the gray wolf (Canis lupus) is the best studied in its pack hunting behavior. The basic social unit is the mated pair; prey size is a factor in pack sizes, which range from a few up to 20 individuals, with the largest packs preying on bison and moose and smaller packs preying on deer <ns0:ref type='bibr' target='#b41'>(Mech & Boitani 2003)</ns0:ref>. Social hunting, however, may not always be the most efficient in terms of food intake per wolf because the packs must share their proceeds <ns0:ref type='bibr' target='#b65'>(Thurber & Peterson 1993)</ns0:ref>. The formation of packs, therefore, offers the opportunity to kill prey too large to tackle by one individual alone, as well as the opportunity both to better defend kills against carcass theft and to steal carcasses from larger predators <ns0:ref type='bibr' target='#b11'>(Carbone et al. 1997;</ns0:ref><ns0:ref type='bibr' target='#b20'>Eaton 1979;</ns0:ref><ns0:ref type='bibr' target='#b78'>Van Valkenburgh 2001;</ns0:ref><ns0:ref type='bibr' target='#b85'>Vucetich et al. 2004</ns0:ref>).</ns0:p><ns0:p>It has been long known that large Canis from the Nihewan Basin includes individuals with highly trenchant lower molars (Teilhard de Chardin & Piveteau 1930). Hypercarnivorous characteristics (dominance of cutting edge of m1 trigonid and enlargement of hypoconid at the expense of entoconid, along with reductions of posterior molars) in C. chihliensis are variable <ns0:ref type='bibr' target='#b70'>(Tong et al. 2012</ns0:ref>) but strongly converge on the morphology of living African hunting dogs and Asiatic dholes (Fig. <ns0:ref type='figure' target='#fig_7'>5</ns0:ref>). Such a dental morphology is commonly associated with emphasis in slicing meat using the sharp carnassial blades. Trenchant molars thus correlate well with hypercarnivory (Crusafont-Pairó & Truyols-Santonja 1956), i.e., tendency to consume meat exclusively, which also drives the evolution of larger body size as a macroevolutionary ratchet <ns0:ref type='bibr' target='#b84'>(Van Valkenburgh et al. 2004</ns0:ref>).</ns0:p><ns0:p>Wolves have a dangerous life as long-distance pursuit predators. The traumas and infections inflicted on Canis chihliensis likely are related to hunting behavior, feeding strategies, and predator-prey interactions, as have also been suggested for other extinct carnivores <ns0:ref type='bibr' target='#b57'>(Shaw & Ware 2018)</ns0:ref>. Healing from such devastating injuries is also a testimony to its survival for long periods of time during which the ability to hunt must have been seriously limited or nonexistent, suggesting that assisted living was a possibility. Debilitating bone diseases in the Pleistocene apex predator Smilodon, which were even more hypercarnivorous than canids, have also been used to argue for social or gregarious behaviors <ns0:ref type='bibr' target='#b0'>(Akersten 1985;</ns0:ref><ns0:ref type='bibr' target='#b27'>Heald 1989;</ns0:ref><ns0:ref type='bibr' target='#b54'>Shaw 1992a;</ns0:ref><ns0:ref type='bibr' target='#b55'>Shaw 1992b;</ns0:ref><ns0:ref type='bibr' target='#b79'>Van Valkenburgh 2009;</ns0:ref><ns0:ref type='bibr' target='#b83'>Van Valkenburgh & Sacco 2002)</ns0:ref> although the pathologysociality link has been challenged <ns0:ref type='bibr' target='#b40'>(McCall et al. 2003)</ns0:ref>. <ns0:ref type='bibr' target='#b53'>Schleidt & Shalter (2004)</ns0:ref> also noted that social predators should have more healed injuries than solitary predators. Often infirm animals are allowed to feed on group kills, as observed in spotted hyaenas and African wild dogs.</ns0:p><ns0:p>Whereas sociality in sabertooth cats has been questioned given its rarity among extant large felids, all of which are capable of killing on their own, pack hunting in dog-like carnivorans (wolves, hunting dogs, dholes, hyenas) is the dominant mode of predation and may partly be driven by the necessity of overcoming larger prey <ns0:ref type='bibr' target='#b41'>(Mech & Boitani 2003)</ns0:ref>. Dental morphology and pathology in our Nihewan Canis chihliensis strongly suggest processing of hard food (bone cracking), which is commonly associated with hypercarnivory and pack hunting in large canids. While herbivores, too, suffer from crippling injuries, comparisons to herbivores are irrelevant in this case because injured herbivores can continue eating plant matter, foraging on food items that do not move, while recovering from injuries. However, critical carnivore injuries, such as to the running hindlimbs, blunt active predators' ability to hunt and chase animal prey. Although the massive, healed tibial fracture may not be a definitive indication of social care, a predator's recovery from such a devastating injury is suggestive of food provisioning that only social groups can offer. This has been similarly proposed from an early Pleistocene Spanish record of C. falconeri <ns0:ref type='bibr' target='#b45'>(Palmqvist et al. 1999)</ns0:ref>, although temporary shift to a more omnivorous diet is also possible. With this new record from Nihewan, we extend the history of Canis sociality to the early Pleistocene, and likely to the Pliocene as well if the even larger Canis antonii from Fugu area in Shanxi Province is taken into consideration <ns0:ref type='bibr'>(Tedford et al. 2009:appendix I)</ns0:ref>.</ns0:p><ns0:p>Arguably the most definitive (though still correlative) pathological evidence to support sociality in Canis chihliensis would be a significant prevalence of similar injuries not only in the extinct Canis dirus but in the three extant hypercarnivorous canines whose pack-hunting behavior can be observed directly, in contrast to a low prevalence of similar injuries in non-packhunting carnivoran species. However, one common challenge in predator paleopathology is the lack of sufficient samples of large-predator post-crania relative to crania in museum collections of living mammals. Survival with just the leg or just the dental damage does have isolated representation, but not the combination. This limitation-and the corresponding lack of published systematic pathological surveys across large sample sizes within and among extant species-prevents statistically robust inferences of injury prevalence in extant wild animals. When isolated cases are available, lack of field documentation on behaviors related to pathological specimens also hampers interpretations. Such deficiencies make it difficult to ground-truth inferences of extinct behaviors based on extant relatives, even where large samples of extinct predators are available <ns0:ref type='bibr' target='#b9'>(Brown et al. 2017</ns0:ref>). While such a systematic comparative survey exceeds the scope of the current paper, future studies that calculate injury prevalence across large museum and zoo collections of extant species of known behavior (e.g., <ns0:ref type='bibr' target='#b50'>Rothschild et al. 1998)</ns0:ref> would bolster inferences of extinct behavior based on skeletal injuries.</ns0:p><ns0:p>As knowledge of the fossil history of hypercarnivorous canids in the Plio-Pleistocene of Eurasia increases, more complexity than has been previously assumed is now emerging, both in its chronology and its morphologic diversity. Recent molecular studies placed Cuon and Lycaon, two of the most hypercarnivorous living canids, near the base of the Canis clade <ns0:ref type='bibr' target='#b15'>(Chavez et al. 2019;</ns0:ref><ns0:ref type='bibr'>Koepfli et al. 2015;</ns0:ref><ns0:ref type='bibr'>Lindblad-Toh et al. 2005)</ns0:ref>, in contrast to morphological analysis suggesting that hypercarnivorous forms are at the terminal end of the canine phylogeny <ns0:ref type='bibr' target='#b61'>(Tedford et al. 1995;</ns0:ref><ns0:ref type='bibr' target='#b62'>Tedford et al. 2009</ns0:ref>). If the molecular relationship is correct, then records of Cuon and Lycaon are expected to be at least as old, if not older, than that of many species of Canis. This new record pushes back the first occurrence of pack hunting likely accompanied by social care by about 1.7 million years to when early Homo erectus was first recorded in Asia <ns0:ref type='bibr' target='#b4'>(Ao et al. 2013;</ns0:ref><ns0:ref type='bibr' target='#b97'>Zhu et al. 2004</ns0:ref>). This record is important because it coincides with the initial diversification of the large canids (such as Canis and Lycaon), also known as the Wolf Event in Eurasia <ns0:ref type='bibr' target='#b5'>(Azzaroli 1983;</ns0:ref><ns0:ref type='bibr' target='#b52'>Sardella & Palombo 2007)</ns0:ref>, and Lycaon's arrival in Africa (Hartstone-Rose et al. 2010).</ns0:p><ns0:p>Although records of early wolves have been pushed back slightly <ns0:ref type='bibr' target='#b39'>(Martínez-Navarro et al. 2009;</ns0:ref><ns0:ref type='bibr' target='#b48'>Rook & Martínez-Navarro 2010;</ns0:ref><ns0:ref type='bibr' target='#b52'>Sardella & Palombo 2007)</ns0:ref>, the wolf event is essentially confined to the Early Pleistocene, i.e., Late Pliocene before recent redefinition <ns0:ref type='bibr' target='#b23'>(Gibbard et al. 2010)</ns0:ref>. A recent new Tibetan record in the Middle Pliocene, Sinicuon cf. S. dubius, seems to suggest that hypercarnivorous canines may have predated the genus Canis <ns0:ref type='bibr' target='#b88'>(Wang et al. 2014)</ns0:ref>. Whatever the detailed relationships of these records, it seems clear that hyper-predators, such as large wolves and hunting dogs, were associated with the increasingly open habitats in Eurasia during the onset of the Pleistocene. In this background of large-canine radiation at the beginning of the Ice Age, our new record of a pathological wolf from the Early Pleistocene of Nihewan hints at pack hunting as a major step toward social collaboration while procuring food and, as such, signals a major step in the evolution of large canids.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions</ns0:head><ns0:p>We document dental injuries and infections and a healed tibia fracture in Canis chihliensis from the early Pleistocene (~1.2 Ma) Nihewan Basin of northern China. This early species of wolf-like Canis begins to have large body size and hypercarnivorous dentition. The dental injuries and infections likely occurred while processing hard food, such as bones, whereas the tibia fractures severely limited locomotion during recuperation. Dental injuries and healing of compound fracture suggests social hunting and family care (food-sharing) although alternative explanations exist. We made comparisons with abundant paleopathological records of the putatively packhunting late Pleistocene dire wolf, Canis dirus, at the Rancho La Brea in southern California, and demonstrate similarity in feeding behavior and sociality between Chinese and American Canis across space and time. </ns0:p></ns0:div> <ns0:div><ns0:head>Captions of Figures</ns0:head></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>Abbreviations. HPICR, Hebei Province Institute of Cultural Relics; IVPP, Institute of Vertebrate Paleontology and Paleoanthropology; MNHN, Muséum national d'Histoire naturelle; NM, Nihewan Museum; NNNRM, Nihewan National Nature Reserve Management; SSMZ, Shanshenmiaozui; TNHM, Tianjin Natural History Museum; V, Prefix in the catalog numbers for vertebrate fossils in IVPP. Morphological Abbreviations: DAP: anteroposterior diameter; DT: transverse diameter; MC: medullar cavity; NF: nutrient foramen.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>. The mammalian fauna associated with C. chihliensis at the SSMZ site are as follows: Lepus sp., Ochotona sp., Pantherinae gen. et sp. indet., Pachycrocuta sp., Mammuthus trogontherii, Coelodonta nihowanensis, Elasmotherium peii, Proboscidipparion sp., Equus sanmeniensis, Sus sp., Eucladoceros boulei, Spirocerus wongi, Bison palaeosinensis, and Gazella sinensis. Our fieldwork between 2015-2018 recovered additional taxa, e.g. Alactaga sp. (represented by metacarpal), Acinonyx sp. (radius), Panthera sp. (partial mandible and manus bones), Lynx sp. (partial mandible with m1, mandible), Paracamelus sp. (partial metatarsal), Pseudodama sp. (partial antler and metacarpal), and Gazella subgutturosa (metatarsal)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head /><ns0:label /><ns0:figDesc>Fig. 1.H). The buccal cortical surface is porous adjacent to p4 and m1 (white arrows, vab, on Fig. 1.F). This is most likely the result of increased number and size of vascular canals associated with inflammation in this region. Radiographic Observation. The radiographic images of the right and left dentaries reveal periapical bone loss (rarefying osteitis) (blue arrows, pi, on Figs. 1.D and 1.H) associated with exposed pulp cavities, a periodontal pocket between the right p4 and m1 (red arrow, pp, on Fig. 1.H), and an apparent fistula from the periodontal pocket to the surface (purple arrows, fpp, on Figs. 1.F and 1.H).</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head>Figure 1</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1 Two dentaries of the same individual of Canis chihliensis. (A-D) left dentary (IVPP V17755.11); (E-H) right dentary (IVPP V17755.12). (A, E) occlusal views; (B, F) buccal views; (C, G) lingual views; (D, H) X-ray images. White arrows (labeled vab) indicate areas of increased vascularity of alveolar bone; red arrows (labeled pp) mark periodontal pocket, purple arrows (labeled fpp) indicate probable fistula from periodontal pocket, and blue arrows (labeled pi) mark periapical infections associated with exposed pulp chambers.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head>Figure 2</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2 Frequencies of dental injury in the mandible of Rancho La Brea dire wolves, C. dirus. (A) numbers of specimens of adult age bearing injuries similar to those in C. chihliensis (orange) compared with other dental injuries (gray). Most dental injuries in C. dirus involve abscesses and alveolar resorption stemming from infection. (B) categorization of dental injuries by tooth position. The m1 shows the highest frequency of infection or injury, followed by p2 and p4.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head>Figure 3</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Figure 3 Tibias of the same individual of Canis chihliensis from SSMZ, Nihewan. (A-D) normal tibia of left side (IVPP V 18139.21). (E-H) pathologic tibia of right side (IVPP V 18139.20). (A, E) anterior views; (B, F) posterior views; (C, G) medial views; (D, H) lateral views. NF: nutrient foramen.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head>Figure 4</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Figure 4 CT scan images of the pathologic right tibia of Canis chihliensis (V18139-20) from SSMZ, Nihewan. (A-B) anteroposterior longitudinal sections; (C-D) mediolateral longitudinal sections; (E) 3-D reconstruction of the pathologic tibia; (F-M) cross sections; (F-J) the upper part of the tibia; (K) the upper and middle parts of the fracture; (L) the middle and lower parts of the fracture; (M) lower part of the fracture, infection with subtle cortical loss. MC1-MC3, represent the medullar cavities of the three fractions of the fractured tibia; NP, nutrient foramen.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_7'><ns0:head>Figure 5</ns0:head><ns0:label>5</ns0:label><ns0:figDesc>Figure 5 Lower molars from SSMZ as compared to living hypercarnivorous taxa. Occlusal views of lower molars, m1-3, of Canis chihliensis (A-C) from SSMZ in Nihewan, as compared</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='23,42.52,331.87,525.00,396.00' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='26,42.52,255.37,525.00,301.50' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='27,42.52,326.62,525.00,328.50' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='28,42.52,331.87,525.00,259.50' type='bitmap' /></ns0:figure> <ns0:note place='foot'>PeerJ reviewing PDF \| (2020:05:48717:1:1:NEW 6 Jul 2020)</ns0:note> </ns0:body> "	"Dear Dr. Abdala: We thank you and the reviewers for your time, efforts and critical comments. Since reviewer 2 is the most critical, our response mainly addresses their concerns. First, our documentation of the pathologies in teeth and limb bones is not in question (it seems that even Reviewer 2 would accept that). It is thus our behavioral interpretations that is objectionable by Reviewer 2. We are cognizant to the difficulties in interpreting pathological specimens in fossil records, and we have tried to frame our interpretations with plenty of caveats of uncertainties. As you have suggested in your comments, we actually used the word “suggest” in our title. We stand by our original assessment of dental pathology and bone-crushing. Not only do we consider our interpretation (biting of hard objects) the most likely explanation of the multiple pathological conditions documented in this paper, all of them consistent with injuries and loss of teeth and subsequent infections, but also chewing on bones is a behavior that is well known in living wolves (Esteban-Nadal et al. 2010) as well as borophagine canids (Wang et al. 2018), an extinct subfamily of diverse bone-crushing and non-bone crushing carnivorans from North America (Wang et al. 1999). Despite their strong objections, Reviewer 2 seems to have no problem that our fossil Canis did consume bones. Reviewer 2 was clearly offended by our linkage between a broken tibia and social hunting. Actually, our own arguments are more subtle. We began our argument by the observation that large living canids are more likely pack hunters (e.g., Mech & Boitani 2003) and that they also tend to evolve more hypercarnivorous dentitions (Van Valkenburgh et al. 2004), which are shown in Figure 5. All living canids that have achieved the body size and hypercarnivory of our Canis chihliensis are social hunters, including African hunting dogs, Asian dholes, and grey wolves. In other words, based on the body size and dental specialization alone, it is actually a good assumption that C. chihliensis was a pack hunter without limb injuries. Furthermore, elsewhere we have argued that bone-crushing in canids is more likely the result of competitive consumptions during social feeding (Wang et al. 2008; Wang et al. 2018). Therefore, our conclusion about pack hunting is built on multiple layers of arguments backed by evidence, in addition to the crippling tibia injury. Regarding Reviewer 2’s argument that herbivores, too, suffer from crippling injuries: while it is certainly true that all vertebrates can sustain bad injuries, in our case, survival by herbivores is irrelevant because injured herbivores can continue eating plant matter, foraging on food items that do not move, while recovering from the injuries. However, critical carnivore injuries, such as to the running hindlimbs, blunt active predators’ ability to hunt and chase prey, such that they either have to change diet (to become more of an omnivore if the digestive system can handle it) or share food with a group. We have clarified the herbivore connection (or lack thereof) in the text. We are disappointed that reviewer 2 did not provide citations that might support his/her contentious contentions. In surveys of tens of thousands of animals (Rothschild & Martin 1993; Rothschild & Martin 2006), one of the coauthors (BMR) of the present study has been impressed by the scarcity of such-trauma-related damage. Nevertheless, in light of the strong reactions of Reviewer 2 and your own suggestion, we feel that our language may potentially mislead. We thus have toned down our conclusion to give greater emphasis on inherent difficulties and alternative interpretations. And, where appropriate, we highlight the multiple lines of evidence. Along similar lines, we would like to point out that in our previous submission, we have devoted considerable text to outline the challenges and limitations in paleopathology studies such as ours—ample caveats were already given (line 398-413 in first submission, and 436-452 of revised ms). We have accepted all of the editorial suggestions by Julie Meachen except one editorial change (page 5, line 163, MJ2 comment about “a run-on sentence”; some of her suggestions were over a previous submission, which differs from the current version) and one suggested reference (Landon et al 1998, which is not applicable to our fossils). We have also accepted nearly all of the suggested changes by Josh Samuels, including moving Taxonomic and Phylogenetic Remarks to be closer to Locality and Faunas, and adding new citations and references by Bailey et al (2013), Vucetich et al. (2004), Chavez et al (2019), Koepfli et al (2015), and Hartstone-Rose et al (2010). All of our revisions are highlighted by Track Changes, except citations and references, which are handled by EndNote software (new citations are not highlighted by EndNote). We also provide a point-by-point response to specific comments/suggestions by reviewers below. Best wishes, Xiaoming Wang and Haowen Tong Literatures Cited: Esteban-Nadal M, Cáceres I, and Fosse P. 2010. Characterization of a current coprogenic sample originated by Canis lupus as a tool for identifying a taphonomic agent. Journal of Archaeological Science 37:2959-2970. http://dx.doi.org/10.1016/j.jas.2010.06.033 Mech LD, and Boitani L. 2003. Wolf social ecology. In: Mech LD, and Boitani L, eds. Wolves, Behavior, Ecology, and Conservation. Chicago: University of Chicago Press, 1-34. Rothschild BM, and Martin LD. 1993. Paleopathology: Disease in the Fossil Record. Boca Raton: CRC-Press. Rothschild BM, and Martin LD. 2006. Skeletal impact of disease. New Mexico Museum of Natural History and Science Bulletin 33:1-226. Van Valkenburgh B, Wang X, and Damuth J. 2004. Cope's rule, hypercarnivory, and extinction in North American canids. Science 306:101-104. Wang X, Tedford RH, and Antón M. 2008. Dogs: Their Fossil Relatives & Evolutionary History. New York: Columbia University Press. Wang X, Tedford RH, and Taylor BE. 1999. Phylogenetic systematics of the Borophaginae (Carnivora: Canidae). Bulletin of the American Museum of Natural History 243:1-391. Wang X, White SC, Balisi M, Biewer J, Sankey J, Garber D, and Tseng ZJ. 2018. First bone-cracking dog coprolites provide new insight into bone consumption in Borophagus and their unique ecological niche. eLife 7:e34773. 10.7554/eLife.34773 The following is a point-by-point response to reviewer comments (our response is in red): Reviewer 1 (Julie Meachen) Basic reporting I though everything looked good Experimental design I especially liked the comparison to the La Brea Tar Pits to better understand the ecology of this extinct canid. Validity of the findings No comment Comments for the Author I reviewed this manuscript previously, and I thought it was great then. I don't quite understand all of the issues the other reviewers had. I also wasn't able to upload my minor changes in my previous review, so I am uploading them here. Annotated manuscript The reviewer has also provided an annotated manuscript as part of their review: Download Annotated Manuscript Reviewer 2 (Anonymous) Basic reporting Very professional article (with a clearly disprovable central premise) Experimental design The flawed central premise (see below) calls into question the experimental design: the 'question [(i.e., is this taxon a durophage and group hunter) is NOT] relevant [or] meaningful' given that it can't be answered from these fossils. Furthermore, because of this flawed logic, the 'investigation [is NOT] performed to a high technical ... standard'. The methods are detailed and replicable. Validity of the findings Absolutely NOT! See below. Our findings are based on well-established observations founded on well-preserved fossils. This reviewer did not dispute the validity of our observations. The reviewer must have meant “interpretation” that he/she did not agree. Comments for the Author This is a well written paper by Tong, Wang and colleagues about some interesting pathological specimens of an ancient large dog. Unfortunately, the central claim of their paper is entirely indefensible: they claim that these extensive pathologies indicate not only the “processing hard food, such as bone” but also that they are evidence of “social hunting and family care (food-sharing)”. The claims are so strong that they lead to the conclusion that “Pack hunting in Canis can thus be traced back to the early Pleistocene, well before the appearance of modern wolves.” Parsimoniously, we would assume that this taxon is a hypercarnivorous pack hunter that likely ate bones – like most large canids. However, it is completely impossible to infer this merely from these pathologies – and especially offensive for them to do so to such an extent as this definitive claim. As the authors must know, it is quite easy to find similar pathologies in both fossil and modern collections in taxa that neither crunch bones nor are communal hunters. Would the authors acknowledge that these type of dental pathologies are common in many species of animals (including herbivores) in individuals that live to very old ages? Would they also confirm that they have seen similarly healed fractures in taxa that couldn’t possibly food provision (e.g., herbivores or solitary carnivores or rodents, etc.)? These are certainly rare, but still fairly easy to find in collections. If these comparative specimens exist, then it is absolutely false to claim – especially with the outlandish certitude of the language in this paper – that they MUST equate to bone crunching and food provisioning. It is hard to review the details of this paper when its central claim is so disprovable, though there is clearly good paleontological description in this piece. See rebuttal letter above. Reviewer 3 (Joshua Samuels) Basic reporting The Methods indicate 'The osteological terms are from Mescher (2018)', but that source is not included in the references. Mescher (2018) was in our original submission (line 535), apparently missed by reviewer. The Results starts with a section entitled 'Taxonomic and Phylogenetic Remarks', lines 127-148, which really represents background information on Canis chihliensis and other similar canids. There is no new analysis presented in that section, but rather a review of the current understanding of large, hypercarnivorous canines. I would recommend relocating this section in its entirety to be part of the (rather short) introduction. We agree it does not belong to here and it was placed here because of our attempt to adhere to the structure of PeerJ. But it does not quite belong to Introduction either. We now create a new level one heading just before the Result section, but we are open to suggestions by PeerJ. Figure 1 includes a number of color coded arrows indicating key pieces of information. Those may be difficult to interpret for color-blind individuals, and I would recommend adding some simple labels (i.e. abbreviations like those in Figures 3 and 4) to make this figure accessible to all readers. This is a good idea. We retain the color arrows for those not impaired by color-blindness and added a short label for those who are. Experimental design No Comment Validity of the findings No Comment Comments for the Author Overall, I enjoyed reading this manuscript and found it to be very well-written. In addition to my comments related to basic reporting, I have added some additional comments to an annotated pdf file. Most of my suggestions are relatively minor and should be very easily addressed. Annotated manuscript The reviewer has also provided an annotated manuscript as part of their review: "	Here is a paper. Please give your review comments after reading it.
525	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Collaborative hunting by complex social groups is a hallmark of large dogs (Mammalia: Carnivora: Canidae), whose teeth also tend to be hypercarnivorous, specialized toward increased cutting edges for meat consumption and robust p4-m1 complex for cracking bone. The deep history of canid pack hunting is, however, obscure because behavioral evidence is rarely preserved in fossils. Dated to the early Pleistocene (~1.2 Ma), Canis chihliensis from the Nihewan Basin of northern China is one of the earliest canines to feature a large body size and hypercarnivorous dentition. We present the first known record of dental infection in C. chihliensis, likely inflicted by processing hard food, such as bone. Another individual also suffered a displaced fracture of its tibia and, despite such an incapacitating injury, survived the trauma to heal. The long period required for healing the compound fracture is consistent with social hunting and family care (food-sharing) although alternative explanations exist. Comparison with abundant paleopathological records of the putatively pack-hunting late Pleistocene dire wolf, Canis dirus, at the Rancho La Brea asphalt seeps in southern California, U.S.A., suggests similarity in feeding behavior and sociality between Chinese and American Canis across space and time. Pack hunting in Canis may be traced back to the early Pleistocene, well before the appearance of modern wolves, but additional evidence is needed for confirmation.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>Large, hypercarnivorous dogs (family Canidae)-such as gray wolves (Canis lupus), African hunting dogs (Lycaon pictus), and Asian dholes (Cuon alpinus)-are known to be highly social because of their need for collaborative hunting <ns0:ref type='bibr' target='#b76'>(Van Valkenburgh 1991)</ns0:ref>. In all three species, energetic requirements necessitate that they pursue prey species that are larger than themselves <ns0:ref type='bibr' target='#b13'>(Carbone et al. 1999)</ns0:ref>. But, unlike their felid (cat family) counterparts, canids lack retractile claws and are usually unable to bring down their prey single-handedly <ns0:ref type='bibr' target='#b89'>(Wang et al. 2008)</ns0:ref>, making collaborative (pack) hunting a useful compensatory strategy. Despite the importance of pack hunting as a key biological indicator for social interactions, trophic relationship, and diets, however, fossil records rarely preserve direct information on behavior.</ns0:p><ns0:p>Discovery of an injured and healed skeleton and jaws of a large ancestral wolf, Canis chihliensis, from the early Pleistocene hominin site of Nihewan Basin, northern China, is of interest in inferring their social behavior. Evidence of healing raises the possibility that individuals survived incapacitating injuries by sharing food with family members <ns0:ref type='bibr' target='#b45'>(Palmqvist et al. 1999)</ns0:ref>, a question to be explored in this paper.</ns0:p></ns0:div> <ns0:div><ns0:head>Materials & Methods</ns0:head><ns0:p>The methods employed in this study include morphological observations, CT scanning, and Xray examination. CT slicing intervals followed that of <ns0:ref type='bibr' target='#b51'>Rothschild et al. (1994)</ns0:ref>. The osteological terms are from <ns0:ref type='bibr' target='#b43'>Mescher (2018)</ns0:ref>. The stages of fracture healing follow <ns0:ref type='bibr' target='#b21'>Edge-Hughes & Nicholson (2007)</ns0:ref>. Age determination follows <ns0:ref type='bibr'>Sumner-Smith (1966)</ns0:ref> for epiphyseal fusion and <ns0:ref type='bibr' target='#b24'>Gipson et al. (2000)</ns0:ref> for tooth wear. Body-mass estimates were calculated using regressions on canid femur shaft diameter by <ns0:ref type='bibr' target='#b3'>Anyonge & Roman (2006)</ns0:ref> and m1 length by <ns0:ref type='bibr' target='#b75'>Van Valkenburgh (1990)</ns0:ref>. Permission for excavation was granted by the State Administration of Cultural Heritage with a permit number of 2018-090. Locality and Fauna. The present large sample of early Pleistocene wolf, Canis chihliensis, comprises more than 200 specimens including excellently preserved pathological conditions. A left dentary (IVPP V17755.11), a right dentary (IVPP V17755.12), and a right tibia (IVPP V18139.20) of Canis chihliensis are all from the Shanshenmiaozui (SSMZ) Site in Nihewan Basin. C. chihliensis from SSMZ is dominated by older individuals as inferred from wear on teeth <ns0:ref type='bibr' target='#b16'>(Chen 2018;</ns0:ref><ns0:ref type='bibr'>Chen & Tong 2015)</ns0:ref>. The SSMZ locality (40˚13' 08'N, 114˚ 39' 54'E) lies at the southern bank of the Sangganhe River, and at the edge of the Haojiatai fluviolacustrine platform in Yangyuan County, Hebei Province (Fig. <ns0:ref type='figure' target='#fig_3'>S1</ns0:ref>). The fossiliferous layer was dated to ca. 1.2 Ma by magnetostratigraphy and associated fauna <ns0:ref type='bibr' target='#b36'>(Liu et al. 2016;</ns0:ref><ns0:ref type='bibr' target='#b69'>Tong et al. 2011)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Institution and Locality</ns0:head><ns0:p>Canids are the most abundant carnivorans in the Early Pleistocene Nihewan Fauna <ns0:ref type='bibr' target='#b46'>(Qiu 2000;</ns0:ref><ns0:ref type='bibr' target='#b63'>Teilhard de Chardin & Piveteau 1930)</ns0:ref>, as also confirmed by our recent excavations at SSMZ (Fig. <ns0:ref type='figure' target='#fig_4'>S2</ns0:ref>). The dominant taxon of the canid guild in the SSMZ Fauna is Canis chihliensis <ns0:ref type='bibr' target='#b69'>(Tong et al. 2011;</ns0:ref><ns0:ref type='bibr' target='#b70'>Tong et al. 2012)</ns0:ref> <ns0:ref type='bibr'>(Tong & Chen 2015;</ns0:ref><ns0:ref type='bibr' target='#b67'>Tong et al. 2017;</ns0:ref><ns0:ref type='bibr' target='#b68'>Tong et al. 2018;</ns0:ref><ns0:ref type='bibr' target='#b69'>Tong et al. 2011;</ns0:ref><ns0:ref type='bibr' target='#b70'>Tong et al. 2012;</ns0:ref><ns0:ref type='bibr' target='#b71'>Tong & Wang 2014;</ns0:ref><ns0:ref type='bibr' target='#b72'>Tong & Zhang 2019)</ns0:ref>.</ns0:p><ns0:p>Rancho La Brea Canis dirus. The best records of paleopathology in extinct canids are from the world's largest collection of late Pleistocene dire wolves, Canis dirus, from the Rancho La Brea asphalt seeps in Los Angeles, California, U.S.A. The Rancho La Brea paleopathology collection comprises about 3,200 specimens of dire wolves assembled from over 200,000 specimens representing a minimum of 3,500 individuals (dire wolves represent greater than 50% of all mammal specimens from Rancho La Brea) <ns0:ref type='bibr' target='#b57'>(Shaw & Ware 2018)</ns0:ref>. As the largest Canis that ever lived and presumably preferring larger prey, dire wolves are widely considered a social predator <ns0:ref type='bibr' target='#b3'>(Anyonge & Roman 2006;</ns0:ref><ns0:ref type='bibr' target='#b14'>Carbone et al. 2009;</ns0:ref><ns0:ref type='bibr' target='#b28'>Hemmer 1978;</ns0:ref><ns0:ref type='bibr' target='#b42'>Merriam 1912;</ns0:ref><ns0:ref type='bibr' target='#b59'>Stock 1930;</ns0:ref><ns0:ref type='bibr' target='#b81'>Van Valkenburgh & Hertel 1998;</ns0:ref><ns0:ref type='bibr' target='#b83'>Van Valkenburgh & Sacco 2002)</ns0:ref>. The Rancho La Brea dire wolf collection preserves a range of pathological conditions throughout the skeleton <ns0:ref type='bibr' target='#b25'>(Hartstone-Rose et al. 2015;</ns0:ref><ns0:ref type='bibr' target='#b33'>Lawler et al. 2017;</ns0:ref><ns0:ref type='bibr' target='#b44'>Moodie 1918;</ns0:ref><ns0:ref type='bibr' target='#b56'>Shaw & Howard 2015;</ns0:ref><ns0:ref type='bibr' target='#b59'>Stock 1930;</ns0:ref><ns0:ref type='bibr' target='#b93'>Ware 2005)</ns0:ref>, with particularly debilitating examples offering evidence that strong social bonds existed to allow weakened or disabled individuals to survive for extended periods of time <ns0:ref type='bibr' target='#b56'>(Shaw & Howard 2015;</ns0:ref><ns0:ref type='bibr' target='#b57'>Shaw & Ware 2018)</ns0:ref>.</ns0:p><ns0:p>Focusing on Canis dirus from a single deposit (Pit 61/67) at Rancho La Brea, <ns0:ref type='bibr' target='#b9'>Brown et al. (2017)</ns0:ref> quantified patterns of traumatic pathology-injuries that likely resulted from hunting, including healed fractures and evidence of severe or chronic muscle strain as well as osteoarthritis-and predicted skull injuries to be common because of the probability of being kicked while chasing prey. Contrary to expectation, the cranium showed a low incidence of traumatic injury (1.6%) and the dentary even less so (0.18%) <ns0:ref type='bibr' target='#b9'>(Brown et al. 2017</ns0:ref>). This study, however, excluded dental injuries likely incurred from feeding-such as abscesses and alveolar resorption stemming from infection-which were also sustained by and preserved in C. dirus from Rancho La Brea. In the current study, we quantify these dental injuries, as well as traumatic damage to the dire wolf tibia, for comparison with dental and tibial injuries in C. chihliensis.</ns0:p></ns0:div> <ns0:div><ns0:head>Taxonomic and Phylogenetic Remarks</ns0:head><ns0:p>As far as we are aware, there are few reports of debilitating injuries to large hypercarnivorous canines in the fossil record, including early Pleistocene Canis falconeri from Venta Micena of Spain <ns0:ref type='bibr' target='#b45'>(Palmqvist et al. 1999)</ns0:ref>, Cuon from late Pleistocene of Italy <ns0:ref type='bibr' target='#b29'>(Iurino & Sardella 2014)</ns0:ref>, and the latest Pleistocene occurrences of Canis dirus in the Rancho La Brea asphalt seeps <ns0:ref type='bibr' target='#b56'>(Shaw & Howard 2015)</ns0:ref>. This is despite a generally excellent fossil record for large canids in the late Cenozoic because of canids' preference for mid-latitude open habitats, where terrestrial fossil records are best preserved and most extensively explored <ns0:ref type='bibr' target='#b62'>(Tedford et al. 2009;</ns0:ref><ns0:ref type='bibr' target='#b87'>Wang 1994;</ns0:ref><ns0:ref type='bibr' target='#b89'>Wang et al. 2008;</ns0:ref><ns0:ref type='bibr' target='#b90'>Wang et al. 1999)</ns0:ref>.</ns0:p><ns0:p>The holotype of Canis chihliensis was originally described based on a maxillary fragment with P3-M2 from Feng-Wo at Huang-Lu village (Locality 64) in Huailai County, Hebei (Chihli) Province by <ns0:ref type='bibr' target='#b96'>Zdansky (1924)</ns0:ref>. Teilhard de <ns0:ref type='bibr' target='#b63'>Chardin & Piveteau (1930)</ns0:ref> referred additional specimens to this species from Nihewan Basin. <ns0:ref type='bibr' target='#b47'>Rook (1994)</ns0:ref> synonymized C. chihliensis with C. antonii <ns0:ref type='bibr' target='#b96'>Zdansky, 1924</ns0:ref><ns0:ref type='bibr' target='#b62'>, but Tedford et al. (2009)</ns0:ref> returned to C. chihliensis by restricting the concept to large Nihewan Canis. The systematics of C. chihliensis from SSMZ has been treated by <ns0:ref type='bibr'>Tong et al. (</ns0:ref> <ns0:ref type='formula'>2012</ns0:ref>) <ns0:ref type='bibr' target='#b47'>Rook (1994)</ns0:ref> and <ns0:ref type='bibr' target='#b58'>Sotnikova (2001)</ns0:ref> referred the Pliocene-Early Pleistocene species Canis falconeri from Europe, C. antonii from Asia and C. africanus from Africa to the supraspecific group Canis (Xenocyon) ex gr. falconeri. All of them readily fall into the category of hypercarnivores based on dentition and C. falconeri has also been hypothesized to be a hypercarnivore similar to modern gray wolves <ns0:ref type='bibr' target='#b45'>(Palmqvist et al. 1999)</ns0:ref>. Canis chihliensis shares some similarities with Sinicuon dubius <ns0:ref type='bibr' target='#b70'>(Tong et al. 2012)</ns0:ref>. Furthermore, C. chihliensis is among the largest Canis species of Eurasia in the early Pleistocene.</ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head><ns0:p>Dental Fracture and Inflammations as Related to Bone-crushing and Hypercarnivory. The left dentary (IVPP V17755.11) and right dentary (IVPP V17755.12) belong to the same individual. The left dentary (Fig. <ns0:ref type='figure' target='#fig_3'>1</ns0:ref>.A-D) has c, p1-3 and m2-3 intact, while the crown of p4, trigonid of m1, and mesial root of m1 are fractured and lost, apparently due to injuries suffered during life. Both root fragments of p4 are retained. On m1 only the talonid is preserved. Note on Fig 1 <ns0:ref type='figure'>.</ns0:ref>A that the alveolar bone in the region of the missing mesial root of m1 shows no residual socket, which indicates antemortem bone remodeling. This is consistent with the radiographic evidence of periapical bone resorption associated with the apices of the retained roots of p4 and the distal root of m1 (described below). There is also partial loss of the enamel on c and m1 and fracturing of the crowns of p2, p3, and root of m1. The pulp cavities of p4 and m1 are exposed. The dentin of all teeth is stained brown. All remaining cusps are moderately worn.</ns0:p><ns0:p>There are multiple fractures of the buccal and lingual cortical surfaces of the dentary, primarily in the regions of p2-p3, m1-m2, and the posterior surface of the mandibular ramus including the condylar process. All fractures appear to be postmortem as suggested by the absence of any repair.</ns0:p><ns0:p>There is loss of the cortical bone on the alveolar ridge in the regions of p3, p4, and m1. This was most likely caused by periodontitis in vivo although there may have also been some postmortem fracturing of the alveolar bone around m1.</ns0:p><ns0:p>The right dentary (Fig. <ns0:ref type='figure' target='#fig_3'>1</ns0:ref>.E-H) preserves i2-3, c, p1-4, and m1-2 in situ; the crown of m3 is missing, but one root tip remains deep in the alveolus. The crown of m1 is brownish due to loss of most of the enamel cap, and with the pulp cavity exposed; m2 was broken during excavation; and other teeth are moderately worn. There are multiple fractures of the buccal and lingual cortical bone, predominantly in the regions of p1 and m2, that are postmortem defects.</ns0:p><ns0:p>The right dentary also suffered serious injury. The bone surrounding the m1 root is perforate on the buccal cortex (purple arrow, fpp, on Fig. <ns0:ref type='figure' target='#fig_3'>1</ns0:ref>.H) by an apparent fistula and there is extensive loss of alveolar bone over the buccal aspect of the mesial root of m1 (red arrow, pp, on Interpretation and Implications for Dental Injury. IVPP V17755 suffered from repeated dental injuries in similar locations on both left and right sides. Although both lever models and in vivo experimentation <ns0:ref type='bibr' target='#b22'>(Ellis et al. 2008)</ns0:ref> show that biting forces are greatest on the posterior-most molars, patterns of tooth wear suggest that the lower p4-m1 are used more frequently than more posterior molars <ns0:ref type='bibr' target='#b74'>(Tseng & Wang 2010;</ns0:ref><ns0:ref type='bibr' target='#b89'>Wang et al. 2008;</ns0:ref><ns0:ref type='bibr' target='#b94'>Werdelin 1989)</ns0:ref>, although in the case of the most hypercarnivorous canid, Lycaon, bone consumption may be at a more posterior location <ns0:ref type='bibr' target='#b77'>(Van Valkenburgh 1996)</ns0:ref>. Dental modifications for bone consumption in fossil borophagine canids are most apparent in the p4-m1 region, indicating that this was the location of most bone-cracking behavior <ns0:ref type='bibr' target='#b90'>(Wang et al. 1999)</ns0:ref>. We interpret the loss of the left p4-m1 in IVPP V17755 as owing to bone-cracking-the p4 and m1 are the largest lower cheek teeth in Canis and their loss must have been inflicted by a strong biting force. Preservation of the roots of both the p4 and the m1 trigonid (Fig. <ns0:ref type='figure' target='#fig_3'>1</ns0:ref>.D) suggests tooth fracture from a strong bite and/or encountering hard objects. The alveolar bone in the region of the missing m1 mesial root eventually healed, but the periapical infections associated with both retained root fragments of p4 and the distal root of m1 still show active lesions.</ns0:p><ns0:p>The need for bone-crushing in IVPP V17755 would have continued during and after the healing of the wounds on the left side. Accordingly, the right p4-m1 suffered excessive wear, likely to compensate for the loss of the same function on the left side. Again, we infer that the heavy wear is due to chewing on bones. The wear on the crown of m1 led to exposure of the pulp chamber through two pulp horns in the mesial cusp and directly to the periapical lesions (abscess) (blue arrows, pi, in Figs. 1.D and 1.H). This lesion grew sufficiently that it created a fistula to the buccal surface of the dentary to allow drainage of pus. It is also likely that excessive use on the right side led to bone splinters (shards, fragments) being imbedded into the gum tissue between p4 and m1, causing a periodontal pocket.</ns0:p><ns0:p>The above scenario suggests prolonged and possibly repeated injuries and infections, first to the left p4-m1 (possibly broken in a single bite), and then to the right jaw perhaps after the left side had partially healed. Such a scenario is consistent with a hypercarnivorous dentition in C. chihliensis frequently used for bone consumption, as also seen in late Pleistocene European Cuon <ns0:ref type='bibr' target='#b29'>(Iurino & Sardella 2014)</ns0:ref>. Bone-crushing behavior in canids has been linked to collaborative hunting and competitive consumption of carcasses within the same family group of predators <ns0:ref type='bibr' target='#b89'>(Wang et al. 2008;</ns0:ref><ns0:ref type='bibr' target='#b92'>Wang et al. 2018)</ns0:ref>. Such a behavior is especially prevalent among large, hypercarnivorous canids, and <ns0:ref type='bibr' target='#b82'>Van Valkenburgh et al. (2019)</ns0:ref> recently linked high tooth fractures in extant gray wolves to limited prey availability.</ns0:p><ns0:p>Comparison to Rancho La Brea Canis dirus. In Pit 61/67 alone, 35 dentaries of adult age (14 left, 21 right)-out of 64 pathological adult dentaries (25 left, 39 right; 55%) and 617 dentaries total (both pathological and non-pathological; 5.7%)-exhibit dental injuries similar to those in the Nihewan C. chihliensis dentaries examined in this current study (Fig. <ns0:ref type='figure' target='#fig_5'>S3</ns0:ref>). Across Rancho La Brea deposits, abscesses and alveolar resorption likely due to infection were preserved in 43% (Pit 16) to 77% (Pit 3) of pathological dentaries (Fig. <ns0:ref type='figure' target='#fig_4'>2</ns0:ref>.A). Most of the remaining pathological dentaries also preserved dental anomalies, predominantly supernumerary teeth (particularly in the first and second premolars) or a missing lower first premolar (p1) and/or third molar (m3). Because both the p1 and m3 <ns0:ref type='bibr' target='#b7'>(Balisi et al. 2018;</ns0:ref><ns0:ref type='bibr' target='#b10'>Buchalczyk et al. 1981;</ns0:ref><ns0:ref type='bibr' target='#b87'>Wang 1994</ns0:ref>) vary in their presence among canids, we excluded anomalies in these teeth from our comparison with Nihewan C. chihliensis. Across 200 C. dirus jaws (both left and right) bearing abscesses and alveolar infections, the lower first molar or carnassial showed the highest frequency of injury (87 total specimens with m1-associated injuries), likely inflicted by bone-crushing during the consumption of prey, followed by the second premolar (79 total specimens with p2-associated injuries), likely the result of biting and killing while chasing prey or in fighting with conspecifics or competitors of other species (Fig. <ns0:ref type='figure' target='#fig_4'>2</ns0:ref>.B). The fourth premolar was the third most frequently injured tooth (57 specimens); often, it was injured in conjunction with the lower first molar (34 specimens), as in the case of C. chihliensis. As C. dirus is a predator widely recognized to have had a forceful bite capable of processing bone <ns0:ref type='bibr' target='#b2'>(Anyonge & Baker 2006;</ns0:ref><ns0:ref type='bibr' target='#b8'>Brannick et al. 2015;</ns0:ref><ns0:ref type='bibr' target='#b80'>Van Valkenburgh & Hertel 1993)</ns0:ref>, the high frequency of injury in its p4-m1 complex-similar to that found in the specimens of C. chihliensis examined here-supports the inference that C. chihliensis also processed bone using p4 and m1. Tibia Fracture. A normal left tibia (IVPP V18139.21) and pathologic right tibia (IVPP V18139.20) of Canis are present in the collection from Shanshenmiaozui (SSMZ). The pathologic tibia has healed fractures at the lower one-third of the shaft. Compared with the normal tibia on the left side (Fig. <ns0:ref type='figure' target='#fig_5'>3</ns0:ref>), the pathologic tibia is stouter; it is much broader distally, especially at the fracture site, and is shorter, the maximum length for the normal tibia being 181.6 mm, in contrast to the pathologic one at 166.5 mm (Table <ns0:ref type='table'>1</ns0:ref>). In addition, the nutrient foramen is much more enlarged in the pathologic tibia. The partially healed bone has a rough and porous surface (callus).</ns0:p><ns0:p>The porous bone surface indicates that the periosteal vessels also took part in the repair of the fracture, which penetrated into the hard callus. Because the woven/primary bone is not replaced with secondary lamellar bone, this individual did not survive to the stage of lamellar bone formation, i.e. the fracture healing stage 6 by <ns0:ref type='bibr' target='#b21'>Edge-Hughes & Nicholson (2007)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Foreshortening of tibia.</ns0:head><ns0:p>The pathologic tibia has fused overlapping components with remodeling starting 4 cm from the proximal surface and extending throughout the length. Accentuation (irregularities) of the entheseal region at the lateral margin of the tibial plateau suggests increased stress at the proximal tibial-fibular joint. The tibia widens abnormally starting 6 cm distal to proximal surface, with concurrent alteration of surface color and texture, continuing on to the fused distal component of the tibial fracture, where surface filigree reaction (characteristic of infection) is more prominent. There are increased vascular markings at the junction of the proximal and middle third (related to current length) of the tibia. A shallow groove identifies the original demarcation of the fracture components now fused. The fibula was also fractured, and residual components are noted at the distal 6 cm. A linear defect is noted at the mid-portion of the tibia, slightly medial to the sagittal line. It appears to be perforated in a manner more suggestive of vasculature than of draining sinuses. It may be the residue of the fracture. If so, it would mean that the injury not only caused fracture, separation and overlap of components, but also caused a 'splintering' or at least slight separation of the distal portion of the proximal component. Increased vascularity is noted 2 cm from the distal end of the tibia.</ns0:p><ns0:p>X-ray Examination. Increased density of the medial tibial plateau is noted. If not related to an artifact (e.g., glued component), this is suggestive of a healed, minimally displaced fracture. There clearly is a displaced distal fracture, fused incompletely with overlap. The curvature of the distal portion of the proximal component suggests torsion of the components related to each other. Several layers of periosteal reaction are noted, with partial disruption of subjacent cortex. The distal fibula is fused to the tibia, with focal loss of margin definition. Irregular cavities are noted in the distal portion of the proximal component of the fracture and adjacent to the distal junction of the tibia and fibula. Both contain radio-dense material. This suggests that this was a compound fracture, with skin breach and exposure to environmental contamination. The fracture was incompletely stabilized during the healing process, with continued movement of the components.</ns0:p><ns0:p>CT Scan. The CT images show clearly that it was a comminuted fracture, and all three pieces of the fractures are displaced, which resulted in the division of the medullary cavity into three chambers whose broken ends were enclosed by callus or woven bones (Figs. 4.A-D).</ns0:p></ns0:div> <ns0:div><ns0:head>CT longitudinal sections slice 1 (Figs. 4.A-B</ns0:head><ns0:p>) -There is a focal area of trabecular loss just distal to the proximal epiphyseal plate. It is irregularly ellipsoid in shape and contains slightly thickened bone 'fragments' of apparently increased density. Increased density is noted in the subsequent proximal fracture component. Periosteal reaction is noted with multiple focal areas of trabecular loss, bounded by sclerotic margins, characteristic of abscesses. There is massive loss of cortical bone in the region of fragment fusion. Fibular fusion with a distal radio-dense inclusion is noted. Presence of foreign bodies is consistent with the diagnosis of a compound fracture.</ns0:p><ns0:p>CT longitudinal sections slice 2 (Figs. 4.C-D) -There is an area of increased density at the median tibial plateau noted on the x-ray. The CT shows this area to be separated by a fracture line from subjacent bone. The trabecular pattern is denser. The lateral portion of the proximal epiphyseal plate is partially preserved, in contrast to the medial portion, which cannot be distinguished from the epiphysis. This appears to be a non-displaced fracture through the epiphyseal plate, only affecting a portion of that plate.</ns0:p><ns0:p>There is a linear focal disruption (partially occluded at the surface) of the medial aspect at the midpoint of the current length and a U-shaped defect (also seen in CT slice 1) with thickened margins at the distal fifth. The latter could represent a draining abscess, although the former suggests the possibility of a penetrating injury. Radio-dense inclusions are noted, perhaps representing environmental exposure at time of injury. The surface imperfection seen on the reconstructed tibial image (Fig. <ns0:ref type='figure' target='#fig_6'>4</ns0:ref>.E) may be a CT averaging artifact. A series of 8 cross sections (Figs.</ns0:p></ns0:div> <ns0:div><ns0:head>4.F-M) allows comparisons of healthy cancellous (F), healthy cortical (G-H), and injured and healed bones (I-M).</ns0:head><ns0:p>Interpretation, Comparison, and Implications for Limb Injury. That the injury, plus the subsequent infections, suffered by IVPP V18139 must have been devastating seems not in doubt. The displacement of the right hindlimb and the pain associated with a compound fracture with skin breach and exposure to environmental contamination all but rule out hunting activities. For modern domestic dogs of more than 1 year of age, fracture healing can take 7 weeks to 1 year <ns0:ref type='bibr' target='#b21'>(Edge-Hughes & Nicholson 2007)</ns0:ref>. Therefore, it is safe to assume that healing of the open fractures in IVPP V18139 without medical intervention (broken bones not re-aligned nor cast to immobilize wounds) would take a considerable amount of time, much longer than its metabolic reserve can sustain. Such a long-term survival by an injured wolf requiring a high degree of meat consumption thus suggests collaborative hunting and potentially family care.</ns0:p><ns0:p>In addition to abnormalities in the jaws and dentition, the Rancho La Brea dire wolf collection has numerous healed fractures in the limb bones <ns0:ref type='bibr' target='#b44'>(Moodie 1918;</ns0:ref><ns0:ref type='bibr' target='#b56'>Shaw & Howard 2015;</ns0:ref><ns0:ref type='bibr' target='#b59'>Stock 1930;</ns0:ref><ns0:ref type='bibr' target='#b93'>Ware 2005)</ns0:ref>. Again focusing on Pit 61/67, which has a minimum number of 371 dire wolf individuals, <ns0:ref type='bibr' target='#b9'>Brown et al. (2017)</ns0:ref> showed that frequencies of traumatic injuryincluding healed fractures-were higher than expected for most limb bones, especially the tibia. Surveying dire wolf tibiae across all Rancho La Brea deposits, we found 11 specimens (5 left, 6 right) of 251 total pathologic tibiae (4.38%) to have suffered an oblique fracture with foreshortening similar to that in IVPP V18139 (Fig. <ns0:ref type='figure' target='#fig_6'>S4</ns0:ref>). In studies of modern Saskatchewan gray wolves and sympatric coyotes, such bone fractures-which likely resulted from conflicts with large prey-were found to be more common in wolves than in coyotes, a difference thought to result from wolves' tendency to prey on larger animals like moose <ns0:ref type='bibr' target='#b95'>(Wobeser 1992)</ns0:ref>. Similarly, Rancho La Brea preserves no fractured and healed tibiae belonging to the coyote-which is also found abundantly in the Pleistocene to Holocene-age asphalt seeps-though this lack may be confounded by a coyote sample size an order of magnitude smaller than that of the dire wolf.</ns0:p></ns0:div> <ns0:div><ns0:head>Discussions</ns0:head><ns0:p>Life is not easy for large predators. In modern canids, hypercarnivory is almost always associated with social hunting, such as in the gray wolves (Canis lupus), African hunting dogs (Lycaon pictus), and Asiatic dholes (Cuon alpinus). Of these, the latter two most hypercarnivorous species almost invariably hunt cooperatively, whereas gray wolves regularly, but not exclusively, hunt together for large prey <ns0:ref type='bibr' target='#b38'>(Macdonald 1983)</ns0:ref>. Group hunting by these highly social canids offers apparent advantages that are otherwise unavailable to individual hunters, such as the ability to bring down prey much larger than the predators themselves, plus coordinated attacks that seal off escape routes as well as relaying strategies that lessen the burden of individual hunters. These strategies are especially critical to canids because, unlike felids, canids never evolved fully retractile claws that are effective weapons for grappling with and subduing prey <ns0:ref type='bibr' target='#b86'>(Wang 1993)</ns0:ref>. Therefore, for canids, group hunting is not optional, as it is for large cats (only the lions are social hunters, as are occasionally the cheetahs), once canids have crossed the critical body mass threshold of about 21 kg above which energetic costs necessitate feeding on large prey <ns0:ref type='bibr' target='#b13'>(Carbone et al. 1999)</ns0:ref>. For canids, it is possible that this body size threshold may even be substantially lowered as in the case of the Asiatic dholes (10-13 kg) that have the most extremely hypercarnivorous dentitions among living canids <ns0:ref type='bibr' target='#b18'>(Cohen 1978)</ns0:ref>. The Nihewan Canis chihliensis is larger than the dholes (13.7-16.8 kg based on femur shaft diameter; ~21.2 kg based on the mean of m1 length).</ns0:p><ns0:p>Social hunting is characteristic of large canids, hyaenids, and some felids, and depending on how such behavior is described, may even be quite common in carnivorans <ns0:ref type='bibr' target='#b6'>(Bailey et al. 2013)</ns0:ref>. Such behavior has important implications not only in the social organizations of large carnivorans but also in their trophic relationships and diet. Among large, hypercarnivorous living canids, the gray wolf (Canis lupus) is the best studied in its pack hunting behavior. The basic social unit is the mated pair; prey size is a factor in pack sizes, which range from a few up to 20 individuals, with the largest packs preying on bison and moose and smaller packs preying on deer <ns0:ref type='bibr' target='#b41'>(Mech & Boitani 2003)</ns0:ref>. Social hunting, however, may not always be the most efficient in terms of food intake per wolf because the packs must share their proceeds <ns0:ref type='bibr' target='#b65'>(Thurber & Peterson 1993)</ns0:ref>. The formation of packs, therefore, offers the opportunity to kill prey too large to tackle by one individual alone, as well as the opportunity both to better defend kills against carcass theft and to steal carcasses from larger predators <ns0:ref type='bibr' target='#b12'>(Carbone et al. 1997;</ns0:ref><ns0:ref type='bibr' target='#b20'>Eaton 1979;</ns0:ref><ns0:ref type='bibr' target='#b78'>Van Valkenburgh 2001;</ns0:ref><ns0:ref type='bibr' target='#b85'>Vucetich et al. 2004</ns0:ref>).</ns0:p><ns0:p>It has been long known that large Canis from the Nihewan Basin includes individuals with highly trenchant lower molars (Teilhard de Chardin & Piveteau 1930). Hypercarnivorous characteristics (dominance of cutting edge of m1 trigonid and enlargement of hypoconid at the expense of entoconid, along with reductions of posterior molars) in C. chihliensis are variable <ns0:ref type='bibr' target='#b70'>(Tong et al. 2012</ns0:ref>) but strongly converge on the morphology of living African hunting dogs and Asiatic dholes (Fig. <ns0:ref type='figure' target='#fig_7'>5</ns0:ref>). Such a dental morphology is commonly associated with emphasis in slicing meat using the sharp carnassial blades. Trenchant molars thus correlate well with hypercarnivory (Crusafont-Pairó & Truyols-Santonja 1956), i.e., tendency to consume meat exclusively, which also drives the evolution of larger body size as a macroevolutionary ratchet <ns0:ref type='bibr' target='#b84'>(Van Valkenburgh et al. 2004</ns0:ref>).</ns0:p><ns0:p>Wolves have a dangerous life as long-distance pursuit predators. The traumas and infections inflicted on Canis chihliensis likely are related to hunting behavior, feeding strategies, and predator-prey interactions, as have also been suggested for other extinct carnivores <ns0:ref type='bibr' target='#b57'>(Shaw & Ware 2018)</ns0:ref>. Healing from such devastating injuries is also a testimony to its survival for long periods of time during which the ability to hunt must have been seriously limited or nonexistent, suggesting that assisted living was a possibility. Debilitating bone diseases in the Pleistocene apex predator Smilodon, which were even more hypercarnivorous than canids, have also been used to argue for social or gregarious behaviors <ns0:ref type='bibr' target='#b0'>(Akersten 1985;</ns0:ref><ns0:ref type='bibr' target='#b27'>Heald 1989;</ns0:ref><ns0:ref type='bibr' target='#b55'>Shaw 1992a;</ns0:ref><ns0:ref type='bibr' target='#b55'>Shaw 1992b;</ns0:ref><ns0:ref type='bibr' target='#b79'>Van Valkenburgh 2009;</ns0:ref><ns0:ref type='bibr' target='#b83'>Van Valkenburgh & Sacco 2002)</ns0:ref> although the pathologysociality link has been challenged <ns0:ref type='bibr' target='#b40'>(McCall et al. 2003)</ns0:ref>. <ns0:ref type='bibr' target='#b54'>Schleidt & Shalter (2004)</ns0:ref> also noted that social predators should have more healed injuries than solitary predators. Often infirm animals are allowed to feed on group kills, as observed in spotted hyaenas and African wild dogs.</ns0:p><ns0:p>Whereas sociality in sabertooth cats has been questioned given its rarity among extant large felids, all of which are capable of killing on their own, pack hunting in dog-like carnivorans (wolves, hunting dogs, dholes, hyenas) is the dominant mode of predation and may partly be driven by the necessity of overcoming larger prey <ns0:ref type='bibr' target='#b41'>(Mech & Boitani 2003)</ns0:ref>. Dental morphology and pathology in our Nihewan Canis chihliensis strongly suggest processing of hard food (bone cracking), which is commonly associated with hypercarnivory and pack hunting in large canids. While herbivores, too, suffer from crippling injuries, comparisons to herbivores are irrelevant in this case because injured herbivores can continue eating plant matter, foraging on food items that do not move, while recovering from injuries. However, critical carnivore injuries, such as to the running hindlimbs, blunt active predators' ability to hunt and chase animal prey. Although the massive, healed tibial fracture may not be a definitive indication of social care, a predator's recovery from such a devastating injury is suggestive of food provisioning that only social groups can offer. This has been similarly proposed from an early Pleistocene Spanish record of C. falconeri <ns0:ref type='bibr' target='#b45'>(Palmqvist et al. 1999)</ns0:ref>, although temporary shift to a more omnivorous diet is also possible. With this new record from Nihewan, we extend the history of Canis sociality to the early Pleistocene, and likely to the Pliocene as well if the even larger Canis antonii from Fugu area in Shanxi Province is taken into consideration <ns0:ref type='bibr'>(Tedford et al. 2009:appendix I)</ns0:ref>.</ns0:p><ns0:p>Arguably the most definitive (though still correlative) pathological evidence to support sociality in Canis chihliensis would be a significant prevalence of similar injuries not only in the extinct Canis dirus but in the three extant hypercarnivorous canines whose pack-hunting behavior can be observed directly, in contrast to a low prevalence of similar injuries in non-packhunting carnivoran species. However, one common challenge in predator paleopathology is the lack of sufficient samples of large-predator post-crania relative to crania in museum collections of living mammals. Survival with just the leg or just the dental damage does have isolated representation, but not the combination. Museum records of similar injuries and survivals undoubtedly exist for non-bone-crunching and non-social species as well (but published documentation is often lacking) and a definitive inference is not possible without more detailed records, both extant and extinct. This limitation-and the corresponding lack of published systematic pathological surveys across large sample sizes within and among extant speciesprevents statistically robust inferences of injury prevalence in extant wild animals. When isolated cases are available, lack of field documentation on behaviors related to pathological specimens also hampers interpretations. Such deficiencies make it difficult to ground-truth inferences of extinct behaviors based on extant relatives, even where large samples of extinct predators are available <ns0:ref type='bibr' target='#b9'>(Brown et al. 2017</ns0:ref>). While such a systematic comparative survey exceeds the scope of the current paper, future studies that calculate injury prevalence across large museum and zoo collections of extant species of known behavior (e.g., <ns0:ref type='bibr' target='#b50'>Rothschild et al. 1998</ns0:ref>) would bolster inferences of extinct behavior based on skeletal injuries.</ns0:p><ns0:p>As knowledge of the fossil history of hypercarnivorous canids in the Plio-Pleistocene of Eurasia increases, more complexity than has been previously assumed is now emerging, both in its chronology and its morphologic diversity. Recent molecular studies placed Cuon and Lycaon, two of the most hypercarnivorous living canids, near the base of the Canis clade <ns0:ref type='bibr' target='#b15'>(Chavez et al. 2019;</ns0:ref><ns0:ref type='bibr'>Koepfli et al. 2015;</ns0:ref><ns0:ref type='bibr'>Lindblad-Toh et al. 2005)</ns0:ref>, in contrast to morphological analysis suggesting that hypercarnivorous forms are at the terminal end of the canine phylogeny <ns0:ref type='bibr' target='#b61'>(Tedford et al. 1995;</ns0:ref><ns0:ref type='bibr' target='#b62'>Tedford et al. 2009</ns0:ref>). If the molecular relationship is correct, then records of Cuon and Lycaon are expected to be at least as old, if not older, than that of many species of Canis. This new record pushes back the first occurrence of pack hunting likely accompanied by social care by about 1.7 million years to when early Homo erectus was first recorded in Asia <ns0:ref type='bibr' target='#b4'>(Ao et al. 2013;</ns0:ref><ns0:ref type='bibr' target='#b97'>Zhu et al. 2004</ns0:ref>). This record is important because it coincides with the initial diversification of the large canids (such as Canis and Lycaon), also known as the Wolf Event in Eurasia <ns0:ref type='bibr' target='#b5'>(Azzaroli 1983;</ns0:ref><ns0:ref type='bibr' target='#b53'>Sardella & Palombo 2007)</ns0:ref>, and Lycaon's arrival in Africa <ns0:ref type='bibr' target='#b26'>(Hartstone-Rose et al. 2010)</ns0:ref>.</ns0:p><ns0:p>Although records of early wolves have been pushed back slightly <ns0:ref type='bibr' target='#b39'>(Martínez-Navarro et al. 2009;</ns0:ref><ns0:ref type='bibr' target='#b48'>Rook & Martínez-Navarro 2010;</ns0:ref><ns0:ref type='bibr' target='#b53'>Sardella & Palombo 2007)</ns0:ref>, the wolf event is essentially confined to the Early Pleistocene, i.e., Late Pliocene before recent redefinition <ns0:ref type='bibr' target='#b23'>(Gibbard et al. 2010)</ns0:ref>. A recent new Tibetan record in the Middle Pliocene, Sinicuon cf. S. dubius, seems to suggest that hypercarnivorous canines may have predated the genus Canis <ns0:ref type='bibr' target='#b88'>(Wang et al. 2014)</ns0:ref>. Whatever the detailed relationships of these records, it seems clear that hyper-predators, such as large wolves and hunting dogs, were associated with the increasingly open habitats in Eurasia during the onset of the Pleistocene. In this background of large-canine radiation at the beginning of the Ice Age, our new record of a pathological wolf from the Early Pleistocene of Nihewan hints at pack hunting as a major step toward social collaboration while procuring food and, as such, signals a major step in the evolution of large canids.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions</ns0:head><ns0:p>We document dental injuries and infections and a healed tibia fracture in Canis chihliensis from the early Pleistocene (~1.2 Ma) Nihewan Basin of northern China. This early species of wolf-like Canis signals the evolution of large body size and hypercarnivorous dentition in the genus. The dental injuries and infections likely occurred while processing hard food, such as bones, whereas the tibia fractures would have severely limited locomotion during recuperation. Dental injuries and healing of compound fracture supports social hunting and family care (food-sharing) although alternative explanations exist because similar injuries likely appear in non-bone crunching and non-social species as well. Comparisons with abundant paleopathological records of the putatively pack-hunting late Pleistocene dire wolf, Canis dirus, at Rancho La Brea in southern California demonstrates similarity in feeding behavior and sociality between Chinese and American Canis across space and time.</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:05:48717:2:0:NEW 5 Aug 2020)</ns0:p><ns0:p>Manuscript to be reviewed </ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>Abbreviations. HPICR, Hebei Province Institute of Cultural Relics; IVPP, Institute of Vertebrate Paleontology and Paleoanthropology; MNHN, Muséum national d'Histoire naturelle; NM, Nihewan Museum; NNNRM, Nihewan National Nature Reserve Management; SSMZ, Shanshenmiaozui; TNHM, Tianjin Natural History Museum; V, Prefix in the catalog numbers for vertebrate fossils in IVPP. Morphological Abbreviations: DAP: anteroposterior diameter; DT: transverse diameter; MC: medullar cavity; NF: nutrient foramen.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>. The mammalian fauna associated with C. chihliensis at the SSMZ site are as follows: Lepus sp., Ochotona sp., Pantherinae gen. et sp. indet., Pachycrocuta sp., Mammuthus trogontherii, Coelodonta nihowanensis, Elasmotherium peii, Proboscidipparion sp., Equus sanmeniensis, Sus sp., Eucladoceros boulei, Spirocerus wongi, Bison palaeosinensis, and Gazella sinensis. Our fieldwork between 2015-2018 recovered additional taxa, e.g. Alactaga sp. (represented by metacarpal), Acinonyx sp. (radius), Panthera sp. (partial mandible and manus bones), Lynx sp. (partial mandible with m1, mandible), Paracamelus sp. (partial metatarsal), Pseudodama sp. (partial antler and metacarpal), and Gazella subgutturosa (metatarsal)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head /><ns0:label /><ns0:figDesc>Fig. 1.H). The buccal cortical surface is porous adjacent to p4 and m1 (white arrows, vab, on Fig. 1.F). This is most likely the result of increased number and size of vascular canals associated with inflammation in this region. Radiographic Observation. The radiographic images of the right and left dentaries reveal periapical bone loss (rarefying osteitis) (blue arrows, pi, on Figs. 1.D and 1.H) associated with exposed pulp cavities, a periodontal pocket between the right p4 and m1 (red arrow, pp, on Fig. 1.H), and an apparent fistula from the periodontal pocket to the surface (purple arrows, fpp, on Figs. 1.F and 1.H).</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head>Figure 1</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1 Two dentaries of the same individual of Canis chihliensis. (A-D) left dentary (IVPP V17755.11); (E-H) right dentary (IVPP V17755.12). (A, E) occlusal views; (B, F) buccal views; (C, G) lingual views; (D, H) X-ray images. White arrows (labeled vab) indicate areas of increased vascularity of alveolar bone; red arrows (labeled pp) mark periodontal pocket, purple arrows (labeled fpp) indicate probable fistula from periodontal pocket, and blue arrows (labeled pi) mark periapical infections associated with exposed pulp chambers.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head>Figure 2</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2 Frequencies of dental injury in the mandible of Rancho La Brea dire wolves, C. dirus. (A) numbers of specimens of adult age bearing injuries similar to those in C. chihliensis (orange) compared with other dental injuries (gray). Most dental injuries in C. dirus involve abscesses and alveolar resorption stemming from infection. (B) categorization of dental injuries by tooth position. The m1 shows the highest frequency of infection or injury, followed by p2 and p4.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head>Figure 3</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Figure 3 Tibias of the same individual of Canis chihliensis from SSMZ, Nihewan. (A-D) normal tibia of left side (IVPP V 18139.21). (E-H) pathologic tibia of right side (IVPP V 18139.20). (A, E) anterior views; (B, F) posterior views; (C, G) medial views; (D, H) lateral views. NF: nutrient foramen.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head>Figure 4</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Figure 4 CT scan images of the pathologic right tibia of Canis chihliensis (V18139-20) from SSMZ, Nihewan. (A-B) anteroposterior longitudinal sections; (C-D) mediolateral longitudinal sections; (E) 3-D reconstruction of the pathologic tibia; (F-M) cross sections; (F-J) the upper part of the tibia; (K) the upper and middle parts of the fracture; (L) the middle and lower parts of the fracture; (M) lower part of the fracture, infection with subtle cortical loss. MC1-MC3, represent the medullar cavities of the three fractions of the fractured tibia; NP, nutrient foramen.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_7'><ns0:head>Figure 5</ns0:head><ns0:label>5</ns0:label><ns0:figDesc>Figure 5 Lower molars from SSMZ as compared to living hypercarnivorous taxa. Occlusal views of lower molars, m1-3, of Canis chihliensis (A-C) from SSMZ in Nihewan, as compared with those of C. lupus (D), Cuon alpinus (E) and Lycaon pictus (F). (A) right m1-3 (IVPP V17755.6); (B) right m1-3 (IVPP V17755.4); (C) left (inverted) m1-2 (IVPP V17755.5); (D) right m1-3 (IOZ no number, extant, China); (E) right m1-2 (IOZ 26747, extant, China); (F) right m1-3 (T.M. No. 5560 and BPI/C 223, extant, South Africa). Modified from Tong et al. (2012).</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='23,42.52,331.87,525.00,396.00' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='26,42.52,255.37,525.00,301.50' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='27,42.52,326.62,525.00,328.50' type='bitmap' /></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='28,42.52,331.87,525.00,259.50' type='bitmap' /></ns0:figure> </ns0:body> "	"Dear Dr. Abdala: We thank you and the reviewers, again, for the second round of review. We have adopted all of your (and reviewer) suggested changes including the wording “support” in the title and Conclusion, as well as additional languages of caveats in Discussion and Conclusion, on top of what we have added in the first round of revision (see, for example, languages immediately following the added sentences in Discussion). We hope this has adequately addressed the concerns by the reviewer. We look forward to your decision. Xiaoming Wang and Hawwen Tong "	Here is a paper. Please give your review comments after reading it.
526	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Light plays an important role in the growth and differentiation of Lentinula edodes mycelia, and mycelial morphology is influenced by light wavelengths. The blue light-induced formation of brown film on the vegetative mycelial tissues of L. edodes is an important process. However, the mechanisms of L. edodes' brown film formation, as induced by blue light, are still unclear. Using a high-resolution liquid chromatography-tandem mass spectrometry integrated with a highly sensitive immune-affinity antibody method, phosphoproteomes of L. edodes mycelia under red-and blue-light conditions were analyzed. A total of 11,224 phosphorylation sites were identified on 2,786 proteins, of which 9,243 sites on 2,579 proteins contained quantitative information. In total, 475 sites were up-regulated and 349 sites were down-regulated in the blue vs red group. To characterize the differentially phosphorylated proteins, systematic bioinformatics analyses, including gene ontology annotations, domain annotations, subcellular localizations, and Kyoto Encyclopedia of Genes and Genomes pathway annotations, were performed. These differentially phosphorylated proteins were correlated with light signal transduction, cell wall degradation, and melanogenesis, suggesting that these processes are involved in the formation of the brown film. Our study provides new insights into the molecular mechanisms of the blue light-induced brown film formation at the post-translational modification level.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>INTRODUCTION</ns0:head><ns0:p>Lentinula edodes, also known as shiitake mushroom, belonging to Lentinus, is a valuable medicinal and edible fungus <ns0:ref type='bibr' target='#b12'>(Ozcelik & Peksen 2007)</ns0:ref>. It is a popular edible mushroom and the third most cultivated mushroom in the world <ns0:ref type='bibr'>(Philippoussis et al. 2000)</ns0:ref>. During cultivation, there PeerJ reviewing PDF \| (2020:03:47263:1:1:NEW 22 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed are at least four growth stages: vegetative mycelial growth with growth substrate colonization, the light-induced brown film formation, primordial formation, and fruiting body development <ns0:ref type='bibr' target='#b1'>(Aleksandrova et al. 1998)</ns0:ref>. The brown film formation on the surface of mature mycelia usually appears on the fruiting body primordia and may represent a speciation step <ns0:ref type='bibr' target='#b1'>(Aleksandrova et al. 1998;</ns0:ref><ns0:ref type='bibr'>Chum et al. 2008;</ns0:ref><ns0:ref type='bibr' target='#b21'>Tsivileva et al. 2005)</ns0:ref>. In addition, the mycelial surface does not form a brown film, which is easily occupied by pathogenic organisms, such as bacteria, green molds and fungi <ns0:ref type='bibr'>(Koo et al.)</ns0:ref>. Light signals are essential factors in the formation of brown films <ns0:ref type='bibr'>(Tang et al. 2013;</ns0:ref><ns0:ref type='bibr' target='#b37'>Yin et al. 2017;</ns0:ref><ns0:ref type='bibr' target='#b44'>Zhang et al. 2015)</ns0:ref>. The basic genetic regulatory mechanisms of brown film formation and the influence of environmental factors, especially light, remain unclear. Comparative transcriptome studies revealed that the mechanisms of lightinduced brown film formation are related to photosensitivity, signal transduction pathways, and melanin deposition <ns0:ref type='bibr'>(Tang et al. 2013)</ns0:ref>. Several gene ontology (GO) classifications related to brown film formation were revealed by two-dimensional electrophoresis combined with the matrix-assisted laser desorption/ionization tandem time-of-flight mass spectrometry approach and included small molecule metabolic processes, response to oxidative stress, and organic substance catabolic processes <ns0:ref type='bibr'>(Tang et al. 2016)</ns0:ref>. Jim et al. compared the morphological changes and gene expression of Lentinus edodes under blue light and continuous dark conditions. Their results indicated that the differential genes were involved in the morphological development of primordia and embryonic muscle, cell adhesion and the structure of cellulose and non-cellulose cell walls that affect the development of fruiting bodies, as well as photoreceptors of blue light signals for fruiting body development and pigment formation <ns0:ref type='bibr'>(Kim et al,2020)</ns0:ref>.Blue light is an important environmental factor in inducing primordial differentiation and the fruiting body development of mushrooms, such as Hypsizygus marmoreus, Pleurotus ostreatus, and Coprinus cinereus <ns0:ref type='bibr'>(Kues et al. 1998;</ns0:ref><ns0:ref type='bibr'>Terashima et al. 2005;</ns0:ref><ns0:ref type='bibr' target='#b33'>Xie et al. 2018)</ns0:ref>.</ns0:p><ns0:p>During the growth and development of fungi, the influence of light is very important, and it is also necessary for their growth and development <ns0:ref type='bibr'>(Crosson et al. 2003)</ns0:ref>. As an external signal, light regulates mycelial growth, primordial differentiation, fruiting body formation, gene expression, and metabolite and enzyme activities through complex light-sensing systems <ns0:ref type='bibr'>(Cohen et al. 2013;</ns0:ref><ns0:ref type='bibr' target='#b11'>Miyake et al. 2005;</ns0:ref><ns0:ref type='bibr' target='#b32'>Wu et al. 2013;</ns0:ref><ns0:ref type='bibr' target='#b46'>Zhang et al. 2013)</ns0:ref>. At least 100 kinds of fungi have light-perception systems, including red, blue, green, and near-violet <ns0:ref type='bibr'>(Casas-Flores et al. 2006)</ns0:ref>. Photoreceptors are proteins that harvest light and produce signals that are then transported to the nucleus to activate the transcription of light-responsive genes <ns0:ref type='bibr'>(Hurley et al. 2012</ns0:ref>). The white collar-1/white collar-2 (WC-1/WC-2) complex is the main blue-light sensor in Neurospora</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:03:47263:1:1:NEW 22 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed crassa, a model organism for studying photoperiod <ns0:ref type='bibr'>(Dunlap 2006;</ns0:ref><ns0:ref type='bibr' target='#b6'>Linden & H.)</ns0:ref>. Other blue-light receptors have been successfully identified and cloned, such as the dst1 and dst2 genes in C.</ns0:p><ns0:p>cinereus, phrA and phrB in L. edodes, Cmwc-1 in different strains of Cordyceps militaris, and Slwc-1 from Sparassis latifolia <ns0:ref type='bibr'>(Kuratani et al. 2010;</ns0:ref><ns0:ref type='bibr' target='#b15'>Sano et al. 2009;</ns0:ref><ns0:ref type='bibr' target='#b16'>Sano et al. 2007;</ns0:ref><ns0:ref type='bibr'>Terashima et al. 2005;</ns0:ref><ns0:ref type='bibr'>Yang et al. ;</ns0:ref><ns0:ref type='bibr' target='#b35'>Yang et al. 2012)</ns0:ref>. However, the molecular mechanisms of blue light-induced brown film formation are still unknown.</ns0:p><ns0:p>With the determinations and in-depth analyses of genome and transcriptome sequences of model organisms, such as Arabidopsis thaliana, researchers have realized that it is impossible to understand the functions of organisms from only a gene-based perspective <ns0:ref type='bibr' target='#b0'>(Abbott 2001)</ns0:ref>.</ns0:p><ns0:p>Proteomics studies the compositions, expressions, structures, functions, interactions between proteins and their activities <ns0:ref type='bibr'>(Graves & Haystead 2002)</ns0:ref>. Isobaric tags for relative and absolute quantification/tandem mass tag (iTRAQ/TMT)-labeling combined with tandem mass spectrometry is a high-throughput quantitative proteomics application technology developed in recent years <ns0:ref type='bibr' target='#b41'>(Zhan et al. 2019)</ns0:ref>. Compared with relatively stable genomes, proteins are diverse and changeable. In addition, the presence of post-translational modifications (PTMs) and protein processing, such as phosphorylation, glycosylation, and acetylation, are not comparable at the genome or RNA level <ns0:ref type='bibr' target='#b14'>(Piehler 2005)</ns0:ref>. Proteomics research is a cutting-edge technique in the edible fungi industry. With the effects of abiotic stresses on protein expression levels have been studied the most <ns0:ref type='bibr'>(Hernandez-Macedo et al. 2002;</ns0:ref><ns0:ref type='bibr'>Liang et al. 2007)</ns0:ref>.</ns0:p><ns0:p>In this study, an immunoaffinity analysis combined with high-resolution liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to study the global phosphorylated proteome of brown films induced by blue light. This study provides new insights into the molecular mechanisms of blue light-induced brown film formation at the PTM level.</ns0:p></ns0:div> <ns0:div><ns0:head>Materials and Methods</ns0:head></ns0:div> <ns0:div><ns0:head>Materials treatment and protein extraction</ns0:head><ns0:p>The L. edodes strain L901 which is a new hybrid strain was obtained from the Zhejiang Manuscript to be reviewed agar media. Samplings were taken after mycelial changed colour under blue light conditions. The determination of total polysaccharides was performed according to Zhang's description <ns0:ref type='bibr' target='#b43'>(Zhang et al. 2018)</ns0:ref>. For protein extraction, a proper amount of sample was ground in liquid nitrogen into a cellular powder and then transferred to a 5-mL centrifuge tube. The samples were treated with four volumes of lysis buffer (10 mM dithiothreotol, 1% protease inhibitor, and 1% phosphatase inhibitor) and then sonicated three times. The supernatant was centrifuged for 10 min at 4°C and 5,500 g with an equal volume of Tris equilibrium phenol. The supernatant was taken and precipitated overnight with a fivefold volume of 0.1 M ammonium acetate/methanol. The protein precipitation was washed sequentially with methanol and acetone. The protein was redissolved in 8 M urea, and the protein concentration was determined using a bicinchoninic acid assay kit (P0012, Beyotime, Shanghai, China) according to the manufacturer's instructions.</ns0:p></ns0:div> <ns0:div><ns0:head>Trypsin digestion, TMT labeling, and HPLC fractionation</ns0:head><ns0:p>For digestion, the final concentration of dithiothreotol in the protein solution was 5 mM and was reduced at 56°C for 30 min. The 11-m final concentration of iodoacetamide was incubated at room temperature for 15 min. Finally, the urea concentration of the sample was diluted to less than 2 M. Trypsin was added at 1:50 trypsin-to-protein mass ratio for the first digestion overnight and 1:100 trypsin-to-protein mass ratio for a second 4 h-digestion.The trypsinasehydrolyzed peptide segments were desalted using a Strata X C18 (Phenomenex) and then freezedried in a vacuum. The peptide segment was dissolved in 0.5 M Triethylammonium bicarbonate and labeled according to the instructions of the TMT kit(90066,Thermo-Scientific, Rockford, IL, USA). The simple operation was as follows: the labeled reagent was dissolved in acetonitrile after thawing, incubated at room temperature for 2 h after mixing with the peptide segment, desalinated after mixing with the labeled peptide segment, and freeze-dried in a vacuum.</ns0:p><ns0:p>The tryptic peptides were fractionated using high pH reverse-phase HPLC on an Agilent 300Extend C18 column (5-μm particles, 4.6-mm ID, 250-mm length). Briefly, peptides were first separated using a gradient of 8% to 32% acetonitrile (pH 9.0) over 60 min into 60 fractions.</ns0:p><ns0:p>Then, the peptides were combined into six fractions and dried by vacuum centrifugation.</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:03:47263:1:1:NEW 22 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Affinity enrichment</ns0:head><ns0:p>Peptide mixtures were first incubated with an immobilized metal ion affinity chromatography (IMAC) microsphere suspension and vibrated in loading buffer (50% acetonitrile and 6% trifluoroacetic acid). IMAC microspheres were used Ti. The IMAC microspheres enriched with phosphopeptides were collected by centrifugation, and the supernatant was removed. To remove nonspecifically adsorbed peptides, the IMAC microspheres were washed with loading buffer and 30% acetonitrile plus 0.1% trifluoroacetic acid, sequentially. To elute the enriched phosphopeptides from the IMAC microspheres, elution buffer containing 10% NH 4 OH was added, and the enriched phosphopeptides were eluted with vibration. The resulting peptides were desalted with C18 ZipTips (Millipore) and lyophilized for the LC-MS/MS analysis.</ns0:p></ns0:div> <ns0:div><ns0:head>LC-MS/MS analysis</ns0:head><ns0:p>The tryptic peptides were dissolved in 0.1% formic acid and directly loaded onto a home-made reversed-phase analytical column (15-cm length and 75-μm i.d.). The gradient increased from 6% to 23% solvent B (0.1% formic acid in 98% acetonitrile) over 26 min, 23% to 35% in 8 min and to 80% in 3 min. It was then held at 80% for the last 3 min, at a constant flow rate of 400 nL/min on an EASY-nLC 1000 UPLC system. The peptides were subjected to an NSI source followed by MS/MS in Q ExactiveTM Plus (Thermo) coupled online to the UPLC. The electrospray voltage applied was 2.0 kV. The m/z scan range was 350 to 1,800 for a full scan, and intact peptides were detected in the Orbitrap at a resolution of 70,000. Peptides were then selected for MS/MS using normalized collision energy (NCE) set as 28, and the fragments were detected in the Orbitrap at a resolution of 17,500. The data-dependent procedure alternated between one MS scan and 20 MS/MS scans with a 15.0-s dynamic exclusion. The automatic gain control was set at 5E4. The fixed first mass was set as 100 m/z.</ns0:p></ns0:div> <ns0:div><ns0:head>Database search</ns0:head><ns0:p>The MS data were retrieved using Maxquant (v1.5.2.8) using the following search parameter settings: the database was Lentinula_edodes_uniprot (https://www.uniprot.org/proteomes/?query=organism:5353&sort=score); an anti-database was</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:03:47263:1:1:NEW 22 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed added to calculate the false positive rate (FDR) caused by random matching and a common contamination library was added to eliminate the contamination proteins from the results.</ns0:p><ns0:p>Trypsin/P was specified as the cleavage enzyme, allowing up to four missing cleavage events.</ns0:p><ns0:p>The mass tolerance for precursor ions was set as 20 ppm in the first search and 5 ppm in the main search, and the mass tolerance for fragment ions was set as 0.02 Da. Carbamidomethyl on Cys was specified as a fixed modification, and acetylation and oxidation of Met were specified as variable modifications. The FDR was adjusted to < 1%, and the minimum score for modified peptides was set > 40.</ns0:p></ns0:div> <ns0:div><ns0:head>Annotation methods and functional enrichment</ns0:head><ns0:p>The GO annotation on the proteomics level was derived from the UniProt-GOA database (http://www.ebi.ac.uk/GOA/). First, the system converted the protein ID to UniProt ID, matched the GO ID with the UniProt ID, and then extracted the corresponding information from the UniProt-GOA database based on the GO ID. If there was no protein information queried in the UniProt-GOA database, then algorithm software based on the protein sequence, InterProScan, was used to predict the GO function of the protein.</ns0:p><ns0:p>The KEGG database was used to annotate protein pathways. First, the KEGG online service tool KAAS was used to annotate the submitted proteins, and then KEGG mapper was used to place the annotated proteins into the corresponding pathways in the database. WoLF PSORT, a software for predicting subcellular localization, was used to annotate the submitted proteins for subcellular localization. Fisher's exact test was used to detect differentially modified proteins against the background of identified proteins. A P-value of less than 0.05 was considered significant. The softwares motif-x and MoMo were used to analyze the models of sequences that contained the amino acids in specific positions of modified 13-mers (six amino acids upstream and downstream of the site) in all the protein sequences.</ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head></ns0:div> <ns0:div><ns0:head>Characteristics of quantitative phosphoproteomic data in L. edodes mycelia</ns0:head><ns0:p>Using affinity enrichment followed by LC-MS/MS, the phosphoproteomic changes in L. edodes mycelia grown in red or blue light were investigated. A flow chart of our experiment is exhibited</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:03:47263:1:1:NEW 22 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed in Fig. <ns0:ref type='figure'>1A</ns0:ref>. Pearson's correlation coefficient between the two groups showed sufficient reproducibility (Fig. <ns0:ref type='figure'>1B</ns0:ref>). In this study, 160,949 secondary spectra were obtained by MS analyses. After searching the theoretical protein data, the effective number of spectra was 22,857 and the utilization rate of the spectra was 14.2%. In total, 8,830 peptides and 7,777 phosphorylated peptides were identified. There were 11,224 phosphorylation modification sites on 2,786 proteins, of which 9,243 sites on 2,579 proteins provided quantitative information (Fig. <ns0:ref type='figure'>1C</ns0:ref>). The first-order mass errors of most spectra are less than 10 ppm, which is in accordance with the high accuracy of the MS (Fig. <ns0:ref type='figure'>1D</ns0:ref>). Most of the peptides were distributed in 7-20 amino acids, which was in accordance with the general rules of trypsin-based enzymatic hydrolysis and high energy collision dissociation (HCD) fragmentation, indicating that the sample preparation and the quality accuracy of the mass spectrometer reached the standard required(Fig. <ns0:ref type='figure'>1E</ns0:ref>). The detailed information regarding the identified peptides are listed in Table <ns0:ref type='table' target='#tab_0'>S1</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Analysis of phosphorylation sites</ns0:head><ns0:p>In L. edodes mycelia, 977 (35.07%) phosphoproteins were modified at a single site, 519 (18.63%) at two sites, and 1,290 (46.3%) at three or more phosphosites (Fig. <ns0:ref type='figure'>2A</ns0:ref>). Interestingly, some proteins contained a large number of phosphosites. For example, there are 34 phosphosites in a non-specific serine/threonine protein kinase (A0A1Q3E061), 45 phosphosites in a regulatory transcript from a polymerase II promoter-related protein (A0A1Q3ERS8) and 53 phosphosites in a SRC Homology 3(Sh3) domain-containing protein (A0A1Q3ENM7)( Table <ns0:ref type='table' target='#tab_0'>S1</ns0:ref>).</ns0:p><ns0:p>To analyze the density levels of the phosphorylation sites in each protein, the phosphorylated proteome of L. edodes was compared with those of other species. The average number of phosphorylation sites per protein in L. edodes is 3.22, which is similar to the numbers in Bombyx </ns0:p></ns0:div> <ns0:div><ns0:head>Characteristics of the identified phosphoproteins in L. edodes</ns0:head><ns0:p>To predict the possible functions of the identified phosphoproteins, a GO classification analysis was performed. Most of the proteins were classified into three GO categories(Fig. <ns0:ref type='figure' target='#fig_6'>3A</ns0:ref>).</ns0:p><ns0:p>Specifically, 594 proteins were annotated as 'metabolic process', 519 proteins were annotated as 'cellular process', and 361 proteins were annotated as 'single-organism process'. In the cellular</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:03:47263:1:1:NEW 22 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed component category, the largest terms were 'cell' (289 proteins), 'organelle' (186 proteins), and 'macromolecular complex' (154 proteins). In the molecular function category, 'binding' ( <ns0:ref type='formula'>846</ns0:ref>proteins), 'catalytic activity' (627 proteins), and 'transporter activity' (51 proteins) were the three top dominant terms. The euKaryotic Ortholog Groups annotation clustered all the phosphoproteins into four major categories. The 'cellular processes and signaling' category contained the largest number of proteins(Fig. <ns0:ref type='figure' target='#fig_6'>3B</ns0:ref>). Most identified phosphoproteins were grouped into 13 subcellular component categories predicted by WoLF PSORT software, including 783 nuclear, 380 cytoplasmic, and 275 mitochondrial proteins(Fig. <ns0:ref type='figure' target='#fig_6'>3C</ns0:ref>). The detailed annotation information for all the identified phosphoproteins are listed in Table <ns0:ref type='table' target='#tab_1'>S2</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Protein motifs associated with phosphorylation</ns0:head><ns0:p>Among the identified phosphosites in L. edodes, 8,645 sites occurred at serine residues, 2239 sites at threonine residues, and 340 sites at tyrosine residues (Fig. <ns0:ref type='figure'>4A</ns0:ref>). To understand the upstream pathway of the identified phosphorylated proteins, a motif analysis was carried out using MOMO and Motif-X software. A number of conserved phosphorylation motifs were enriched in the phosphorylated proteins of L. edodes (Table <ns0:ref type='table'>S3</ns0:ref>). A total of 7,741 distinct sequences containing 13 residues were obtained, with 6 upstream and 6 downstream residues around each phosphosite (Table <ns0:ref type='table'>S4</ns0:ref>). The five S-based motifs containing the largest numbers of sequences were 'sP', 'RxxsP','PxsP''Gs', and 'RRxS', and the five top T-based motifs were 'tP','tPP','RxxtP', RxtP', and 'Rxxt'. A Y-based motif, 'Rxxxxxy', was identified. Two position-specific heat maps of upstream and downstream amino acids at all the identified phosphorylated serine or threonine sites. For the S-based motifs, strong preferences for glutamic acid, lysine, and arginine upstream, and aspartic acid, glutamic acid, and proline downstream, of the phosphorylation sites were observed. For the T-based motifs, preferences for lysine, proline, and arginine upstream, and aspartic acid and proline downstream, of the phosphorylation sites were observed (Fig. <ns0:ref type='figure'>4C</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>Differentially phosphorylated proteins (DPPs) in response to a blue-light treatment</ns0:head><ns0:p>To compare the DPPs between red-and blue-light treated samples, expression profiles of the proteins generated by MeV software are shown in a heatmap (Fig. <ns0:ref type='figure'>5A</ns0:ref>). The screening of DPPs followed the following criteria: change threshold ≥1.5 times and t-test p-value < 0.05. Among</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:03:47263:1:1:NEW 22 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed these DPPs, 475 sites in 317 phosphorylated proteins were up-regulated and 349 sites in 243 phosphorylated proteins were down-regulated (Fig. <ns0:ref type='figure'>5B</ns0:ref> and Table <ns0:ref type='table'>S5</ns0:ref>). Based on the subcellular localizations predicted by WoLF PSORT software, all the DPPs were classified into 10 subcellular components. There were 204 nuclear localized DPPs, 82 cytoplasmic localized DPPs, and 51 plasma membrane localized DPPs (Fig. <ns0:ref type='figure'>5C</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>Functional enrichment analysis of the DPPs</ns0:head><ns0:p>To understand the biological functions of these phosphorylated proteins, GO, KEGG and protein domain enrichment analyses of DPPs were carried out. For biological process, cellular component, and molecular function categories, the DPPs were mostly enriched in 'DNA conformation change' (Fig. <ns0:ref type='figure' target='#fig_8'>6A</ns0:ref>); 'nucleosome' (Fig. <ns0:ref type='figure' target='#fig_8'>6B</ns0:ref>), and 'transporter activity' (Fig. <ns0:ref type='figure' target='#fig_8'>6C</ns0:ref>), respectively.</ns0:p><ns0:p>To reveal the metabolic pathways involved in the formation of brown films induced by blue light, the DPPs were further analyzed using the KEGG database. For the up-regulated DPPs, two KEGG pathways, 'Ribosome biogenesis in eukaryotes', and 'ABC transporters', were significantly enriched' (Fig. <ns0:ref type='figure' target='#fig_9'>7A</ns0:ref>). For the down-regulated DPPs, four enriched KEGG pathways were identified, 'Valine, leucine and isoleucine degradation', 'Phenylalanine metabolism', 'Galactose metabolism', and 'Fructose and mannose metabolism' (Fig. <ns0:ref type='figure' target='#fig_9'>7B</ns0:ref>). We also found that the total polysaccharides of blue light treatment was significantly lower than that of red light treatment (Fig. <ns0:ref type='figure'>S1</ns0:ref>). A protein domain enrichment analysis revealed that the up-regulated DPPs were enriched in 19 protein domains, with 'ABC transporter-like', 'P-type ATPase', and 'HADlike domain' being the most highly enriched(Fig. <ns0:ref type='figure' target='#fig_9'>7C</ns0:ref>). The down-regulated DPPs were most strongly associated with 'Glutathione S-transferase, C-terminal-like', 'YTH domain' 'VPS9 domain', 'Domain of unknown function DUF1708', and 'High mobility group box domain'(Fig. <ns0:ref type='figure' target='#fig_9'>7D</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>Identification of DPPs related to signal transduction mechanisms and carbohydrate-active enzymes (CAZymes)</ns0:head><ns0:p>To better understand the DPPs related to blue light-induced mycelial brown film formation, a functional classification of DPPs was conducted using euKaryotic Ortholog Groups. A total of PeerJ reviewing PDF \| (2020:03:47263:1:1:NEW 22 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed 319 DDPs were grouped into 23 subcategories (Fig. <ns0:ref type='figure'>S2</ns0:ref>). For the 'signal transduction mechanisms' subcategory, 50 phosphosites in 29 phosphorylated proteins were identified (Table <ns0:ref type='table' target='#tab_0'>1</ns0:ref>). Among these, 30 phosphosites were up-regulated and 20 were down-regulated. CAZymes, including auxiliary activity (AA), carbohydrate-binding modules (CBM), carbohydrate esterase (CE), glycoside hydrolase (GH), glycosyl transferase (GT), and polysaccharide lyase (PL), were involved in the hydrolysis of plant cell wall polysaccharides and play an important role in the degradation of substrates(Davies & Williams 2016). In the present study, 13 DPPs were identified as CAZymes, including 11 phosphosites in three CBMs, two phosphosites in two CEs, four phosphosites in three GHs, and six phosphosites in five GTs(Table <ns0:ref type='table' target='#tab_1'>2</ns0:ref>). Interestingly, the GHs were up-regulated, while the CBMs were down-regulated.</ns0:p></ns0:div> <ns0:div><ns0:head>Discussion</ns0:head><ns0:p>With the completion of various biological genome sequences, proteomics has become an increasingly important analysis of important proteins based on the differential recognition of their expression levels. Protein phosphorylation is an important PTM, which can rapidly control enzyme activity, subcellular localization, and protein stability, and involves the regulation of metabolism, transcription, and translation, as well as protein degradation, homeostasis, cell signaling, and communication <ns0:ref type='bibr' target='#b10'>(Lv et al. 2014;</ns0:ref><ns0:ref type='bibr'>Thingholm et al. 2009;</ns0:ref><ns0:ref type='bibr'>Yu et al. 2019)</ns0:ref>. Recently, large-scale quantitative phosphoproteomics analyses were performed in many plants to elucidate the growth, development, and diverse response mechanisms, but the technology has rarely been applied to L. edodes <ns0:ref type='bibr' target='#b10'>(Lv et al. 2014</ns0:ref>). Here, we report a comprehensive analysis of phosphoproteomic responses to blue light-induced mycelial brown film formation of L. edodes through a combination of affinity enrichment and LC-MS/MS. Protein phosphorylation is a common PTM, but the level of phosphorylation varies with species., The number of phosphorylation sites in each protein is 3.22, which is higher than most published phosphorylation proteomes, indicating that the degree of phosphorylation in the L.</ns0:p><ns0:p>edodes proteome is very high. The large number of identified phosphoproteins provide an opportunity to comprehensively analyze the mechanism of blue light-induced mycelial brown film formation. The 'sP' motif most frequently occurred in many species, including L.</ns0:p><ns0:p>edodes <ns0:ref type='bibr' target='#b22'>(van Wijk et al. 2014;</ns0:ref><ns0:ref type='bibr' target='#b26'>Wang et al. 2014;</ns0:ref><ns0:ref type='bibr' target='#b45'>Zhang et al. 2014)</ns0:ref>. 'sP' is a target of the following kinases: cyclin-dependent kinase, mitogen-activated protein kinase (MAPK), and</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:03:47263:1:1:NEW 22 Jul 2020)</ns0:p><ns0:p>Manuscript to be reviewed sucrose non-fermenting1-related protein kinase 2 <ns0:ref type='bibr' target='#b22'>(van Wijk et al. 2014;</ns0:ref><ns0:ref type='bibr' target='#b45'>Zhang et al. 2014</ns0:ref>). The 'tP'motif also provides a target for MAPKs <ns0:ref type='bibr' target='#b27'>(Wang et al. 2013)</ns0:ref>.</ns0:p><ns0:p>In Basidiomycetes, light is a crucial environmental factor that affects fruiting body induction and development <ns0:ref type='bibr'>(Kues 2000;</ns0:ref><ns0:ref type='bibr'>Kues & Liu 2000)</ns0:ref>. In recent years, in fungi, the effects of different light wavelengths on mycelial morphology, metabolites, and enzymatic activities have been studied. In Monascus, red and blue light can affect the formation of mycelia and spores, as well as the production of secondary metabolites <ns0:ref type='bibr' target='#b11'>(Miyake et al. 2005)</ns0:ref>. In this study, we found that blue light can promote the formation of a brown film associated with L. edodes mycelia, but no correlation was found with a red-light treatment. The effects of blue light on the expression levels of phosphorylated proteins during brown film formation were studied. Phosphorylation proteomics revealed that 560 phosphorylated proteins were differentially expressed during a blue-light treatment.</ns0:p><ns0:p>Brown film formation at the transcriptional level is correlated with photoreceptor activity, light signaling pathways, and pigment formation <ns0:ref type='bibr'>(Tang et al. 2013)</ns0:ref>. Most fungi perceive blue light through homologues of the white collar complex, which is a complex of photoreceptors and transcription factors that was first found in Neurospora crassa <ns0:ref type='bibr' target='#b20'>(Tagua et al. 2015)</ns0:ref>. The Nterminus of WC-1 is a lov domain, which is a special Per-Arnt-Sim (PAS) domain that can bind to flavin adenine dinucleotide <ns0:ref type='bibr'>(Crosson et al. 2003)</ns0:ref>. Light sensing via photoreceptors such as FMN-and FAD-bindings and signal transduction by kinases and G protein-coupled receptors were identificated as differential expression genes specific to the light-induced brown film phenotypes <ns0:ref type='bibr'>(Yoo et al,2019 )</ns0:ref>. In the present study, three flavin adenine dinucleotide-binding domains and an FMN-binding domain differentially accumulated, indicating that the L. edodes mycelia could have perceived blue light when the brown film was formed. The MAPK cascade is an important signal transduction pathway connecting light responses and the biological clock(de <ns0:ref type='bibr'>et al. 2008)</ns0:ref>. MAPK also regulates various secondary metabolic activities in Aspergillus nidulans and Colletotrihum lagenarium, and it controls light-influenced melanin biosynthesis in B. cinerea <ns0:ref type='bibr' target='#b2'>(Atoui et al. 2008;</ns0:ref><ns0:ref type='bibr' target='#b4'>Bayram & Braus 2012;</ns0:ref><ns0:ref type='bibr' target='#b8'>Liu et al. 2011;</ns0:ref><ns0:ref type='bibr'>Takano et al. 2000)</ns0:ref>. The that are attached to the catalytic enzyme modules by linkers <ns0:ref type='bibr' target='#b23'>(Varnai et al. 2014)</ns0:ref>. Some CE1 enzymes may contain a CBM48 family protein, which is associated with starch binding <ns0:ref type='bibr' target='#b29'>(Wilkens et al. 2017;</ns0:ref><ns0:ref type='bibr' target='#b30'>Wong et al. 2017)</ns0:ref>. Our research showed that these CAZymes play important roles in the degradation of lignocellulose and provide sufficient nutrition for the formation of the brown film of mushroom mycelia.</ns0:p><ns0:formula xml:id='formula_0'>Paula</ns0:formula><ns0:p>To survive, fungi have evolved the ability to adapt to different environmental conditions, and various metabolic pathways secrete different metabolites <ns0:ref type='bibr' target='#b39'>(Yu & Keller 2005)</ns0:ref>. The regulation of these metabolites is not only related to fungal growth and development, but also to light stimulation and responses. The shorter the light wavelength, the more polysaccharides accumulated in the cells of Pleurotus eryngii <ns0:ref type='bibr'>(Jang et al. 2011)</ns0:ref>. Blue-light treatments significantly improved the synthesis of ergosterol and polyphenols in the fruiting body of Pleurotus eryngii, and the scavenging ability of the free radicals was the greatest compared with other light treatments <ns0:ref type='bibr'>(Jang et al. 2011</ns0:ref>). In our study, the KEGG-enrichment analysis showed that four DPPs belonged to 'Galactose metabolism' and 'Fructose and mannose metabolism', suggesting that the blue light affected the sugar metabolism of L. edodes. Phenolic compounds were correlated with pigment formation <ns0:ref type='bibr' target='#b28'>(Weijn et al. 2013)</ns0:ref>. Phenylalanine ammonia-lyase and tyrosinase-encoding genes were significantly up-regulated in P. eryngii under blue-light conditions <ns0:ref type='bibr'>(Du et al. 2019)</ns0:ref>. Two 'Phenylalanine metabolism' pathway phosphoproteins, amidase (A0A1Q3E9W2) and aspartate aminotransferase (A0A1Q3EG41), were down-regulated in mycelia under blue-light conditions. These results suggested that blue light may promote the formation of melanin and inhibit the formation of other phenolic compounds. Polyketide synthase (PKS) is an essential enzyme in the biosynthesis of fungal secondary metabolites <ns0:ref type='bibr' target='#b3'>(Austin & Noel 2003;</ns0:ref><ns0:ref type='bibr' target='#b7'>Linnemannstons et al. 2002)</ns0:ref>. PKSs modify the polyketide backbone with other enzymes, such as Cytochrome P450 monooxygenases, oxidoreductase, and omethyltransferase <ns0:ref type='bibr' target='#b3'>(Austin & Noel 2003)</ns0:ref>. P450-linked monooxygenases mediate oxidationreduction steps in aflatoxin biosynthesis, and omethyltransferase was involved in yellow pigment biosynthesis through an aflatoxigenic Aspergillus strain <ns0:ref type='bibr' target='#b5'>(Bhatnagar et al. 2003)</ns0:ref>. In our study, the Manuscript to be reviewed phosphorylation levels of PKS, O-methyltransferase, P450 monooxygenase, and oxidoreductase changed in brown film formation, indicating that they may play roles in pigment production.</ns0:p><ns0:p>The ABC transport family is widely distributed in all living species, including several subfamilies, which are responsible for different types of material transport <ns0:ref type='bibr'>(Higgins.2001;</ns0:ref><ns0:ref type='bibr'>Holland and Blight. 1999)</ns0:ref>. ATPase is the largest ATP dependent ion transporter in organisms, transporting many different ions, metals and other substrates <ns0:ref type='bibr' target='#b13'>(Palmgren & Nissen, 2011)</ns0:ref>. Two VPS9 domain containing proteins, Rab5 GDP/GTP exchange factor, were downregulated under blue light treatment. Studies have shown that the transport of endocytic vesicles is partially regulated by Rab protein <ns0:ref type='bibr' target='#b48'>(Zhu et al, 2018)</ns0:ref>.Rab protein needs to be activated by guanine nucleotide exchange factor, which transforms Rab from a GDP binding state to a GTP binding state <ns0:ref type='bibr'>(Zerial,2001)</ns0:ref>.The changed in these proteins suggest that blue light altered the transport of certain substances. In mushrooms, blue light can promote growth, which is considered to be an important environmental factor affecting the growth of fruiting bodies <ns0:ref type='bibr' target='#b38'>(Yoo et al, 2019)</ns0:ref>.In this study, ribosome biogenesis related proteins were observed to be up-regulated under blue light treatment.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions</ns0:head><ns0:p>Using a high-resolution LC-MS/MS integrated with a highly sensitive immune-affinity antibody method, phosphoproteomes of L. edodes mycelia under red-and blue-light conditions were analyzed. In this study, 11,224 phosphorylation sites were identified on 2,786 proteins, of which 9,243 sites on 2,579 proteins contained quantitative information. In total, 475 sites were upregulated and 349 sites were down-regulated in the blue vs red group. Then, we carried out a systematic bioinformatics analyses of proteins containing quantitative information sites, including protein annotations, functional classifications, and functional enrichments. Our study provides new insights into the molecular mechanisms of the blue light-induced brown film formation at the PTM level Manuscript to be reviewed Manuscript to be reviewed Manuscript to be reviewed Manuscript to be reviewed Manuscript to be reviewed Manuscript to be reviewed </ns0:p><ns0:note type='other'>Figure 3</ns0:note><ns0:note type='other'>Figure 4</ns0:note><ns0:note type='other'>Figure 6</ns0:note><ns0:note type='other'>Figure 7</ns0:note></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>Academy of Agricultural Sciences. Fungal mycelia were grown at 22°C under red-and blue-light conditions (LED light sources) for 22 d. The light intensity approximately 100 lux and the incubator illuminated all day. Fungal mycelia were grown were grown in the potato dextrose PeerJ reviewing PDF \| (2020:03:47263:1:1:NEW 22 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>mori (3.07), Nicotiana tabacum (3.05), and Physcomitrella patens (3.44) (Fig.2B)(Fang et al. 2016;<ns0:ref type='bibr' target='#b9'>Lu et al. 2019;</ns0:ref><ns0:ref type='bibr' target='#b18'>Shobahah et al. 2017</ns0:ref>).</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head /><ns0:label /><ns0:figDesc>MAPK signal transduction pathways may be directly involved in brown film formation(Tang et al. 2013). Several MAPK signal transduction pathways related to DPPs were identified in this study, suggesting that these signal pathways are involved in the formation of brown films.The differential expression of CAZymes were observed in L. edodes mycelia under two light conditions. GHs mainly hydrolyze glycosidic bonds between carbohydrates or betweenPeerJ reviewing PDF \| (2020:03:47263:1:1:NEW 22 Jul 2020) Manuscript to be reviewed carbohydrates and non-carbohydrates(Sathya & Khan 2014). The GH61 family contains copperdependent lytic polysaccharide monooxygenase(Langston et al. 2011). CEs catalyze the deacylation of esters or amides, in which sugar plays the role of alcohol and amine(Biely 2012; Vidal-Melgosa et al. 2015). They are currently divided into 16 different families, which have a great diversity in substrate specificity and structure(Vidal-Melgosa et al. 2015). CE10 (two DPPs) were down-regulated by blue light. CBMs are noncatalytic, individually folded domains</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ reviewing PDF \| (2020:03:47263:1:1:NEW 22 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head>Figure 1 Figure 1</ns0:head><ns0:label>11</ns0:label><ns0:figDesc>Figure 1</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head>Figure 2 Figure 2</ns0:head><ns0:label>22</ns0:label><ns0:figDesc>Figure 2</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head>Figure 3</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Figure 3 Annotation and classification of all identified phosphorylated proteins.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_7'><ns0:head>Figure 4 Figure 5 Figure 5</ns0:head><ns0:label>455</ns0:label><ns0:figDesc>Figure 4 Phosphosite types and peptide motifs associated with phosphorylation.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_8'><ns0:head>Figure 6</ns0:head><ns0:label>6</ns0:label><ns0:figDesc>Figure 6 GO enrichment analysis of DPPs based on biological process (A), cellular component (B) and molecular function (C).</ns0:figDesc><ns0:graphic coords='33,42.52,199.12,525.00,393.75' type='bitmap' /></ns0:figure> <ns0:figure xml:id='fig_9'><ns0:head>Figure 7</ns0:head><ns0:label>7</ns0:label><ns0:figDesc>Figure 7 KEGG and domain enrichment analysis of the DPPs in fungal mycelium between two different illumination treatments.</ns0:figDesc></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head>Table 1 (on next page)</ns0:head><ns0:label>1</ns0:label><ns0:figDesc /><ns0:table><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>Manuscript to be reviewed</ns0:cell></ns0:row><ns0:row><ns0:cell>Protein</ns0:cell><ns0:cell /><ns0:cell>Regulated</ns0:cell><ns0:cell /><ns0:cell>Amino</ns0:cell></ns0:row><ns0:row><ns0:cell>accession</ns0:cell><ns0:cell>Position Ratio</ns0:cell><ns0:cell>Type</ns0:cell><ns0:cell>P value</ns0:cell><ns0:cell cols='2'>acid Protein description</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>Actin cytoskeleton-regulatory complex</ns0:cell></ns0:row><ns0:row><ns0:cell>A0A1Q3DXT2</ns0:cell><ns0:cell cols='2'>2.041Up</ns0:cell><ns0:cell cols='2'>0.000701S</ns0:cell><ns0:cell>protein pan1</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>Actin cytoskeleton-regulatory complex</ns0:cell></ns0:row><ns0:row><ns0:cell>A0A1Q3DXT2</ns0:cell><ns0:cell cols='2'>0.661Down</ns0:cell><ns0:cell cols='2'>0.0212S</ns0:cell><ns0:cell>protein pan1</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>Actin cytoskeleton-regulatory complex</ns0:cell></ns0:row><ns0:row><ns0:cell>A0A1Q3DXT2</ns0:cell><ns0:cell cols='2'>1.566Up</ns0:cell><ns0:cell cols='2'>0.00104S</ns0:cell><ns0:cell>protein pan1</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>Actin cytoskeleton-regulatory complex</ns0:cell></ns0:row><ns0:row><ns0:cell>A0A1Q3DXT2</ns0:cell><ns0:cell cols='2'>1.503Up</ns0:cell><ns0:cell cols='2'>0.000622S</ns0:cell><ns0:cell>protein pan1</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>Actin cytoskeleton-regulatory complex</ns0:cell></ns0:row><ns0:row><ns0:cell>A0A1Q3DXT2</ns0:cell><ns0:cell cols='2'>2.041Up</ns0:cell><ns0:cell cols='2'>0.000701S</ns0:cell><ns0:cell>protein pan1</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>Actin cytoskeleton-regulatory complex</ns0:cell></ns0:row><ns0:row><ns0:cell>A0A1Q3DXT2</ns0:cell><ns0:cell cols='2'>1.573Up</ns0:cell><ns0:cell cols='2'>0.00602S</ns0:cell><ns0:cell>protein pan1</ns0:cell></ns0:row><ns0:row><ns0:cell>A0A1Q3EQA0</ns0:cell><ns0:cell cols='2'>0.666Down</ns0:cell><ns0:cell cols='2'>0.0269S</ns0:cell><ns0:cell>Arf gtpase activator</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>Carbohydrate-binding module family 21</ns0:cell></ns0:row><ns0:row><ns0:cell>A0A1Q3EH65</ns0:cell><ns0:cell cols='2'>0.457Down</ns0:cell><ns0:cell cols='2'>0.00398S</ns0:cell><ns0:cell>protein</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>Carbohydrate-binding module family 21</ns0:cell></ns0:row><ns0:row><ns0:cell>A0A1Q3EH65</ns0:cell><ns0:cell cols='2'>0.46Down</ns0:cell><ns0:cell cols='2'>0.000337S</ns0:cell><ns0:cell>protein</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>Carbohydrate-binding module family 21</ns0:cell></ns0:row><ns0:row><ns0:cell>A0A1Q3EH65</ns0:cell><ns0:cell cols='2'>0.634Down</ns0:cell><ns0:cell cols='2'>0.00106S</ns0:cell><ns0:cell>protein</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>Carbohydrate-binding module family 21</ns0:cell></ns0:row><ns0:row><ns0:cell>A0A1Q3EH65</ns0:cell><ns0:cell cols='2'>0.512Down</ns0:cell><ns0:cell cols='2'>0.0000778S</ns0:cell><ns0:cell>protein</ns0:cell></ns0:row><ns0:row><ns0:cell>A0A1Q3EIZ3</ns0:cell><ns0:cell cols='2'>0.542Down</ns0:cell><ns0:cell cols='2'>0.0000823S</ns0:cell><ns0:cell>Casein kinase II subunit beta</ns0:cell></ns0:row><ns0:row><ns0:cell>A0A1Q3EIZ3</ns0:cell><ns0:cell cols='2'>0.472Down</ns0:cell><ns0:cell cols='2'>0.000779S</ns0:cell><ns0:cell>Casein kinase II subunit beta</ns0:cell></ns0:row><ns0:row><ns0:cell>A0A1Q3EHC7</ns0:cell><ns0:cell cols='2'>1.787Up</ns0:cell><ns0:cell cols='2'>0.0024S</ns0:cell><ns0:cell>Ck1 ck1 ck1-d protein kinase</ns0:cell></ns0:row><ns0:row><ns0:cell>A0A1Q3EML6</ns0:cell><ns0:cell cols='2'>0.596Down</ns0:cell><ns0:cell cols='2'>0.00757S</ns0:cell><ns0:cell>Gtpase-activating protein gyp7</ns0:cell></ns0:row></ns0:table><ns0:note>Table1.List of differentially expressed signal transduction mechanisms related phosphosites PeerJ reviewing PDF \| (2020:03:47263:1:1:NEW 22 Jul 2020)Manuscript to be reviewed1 Table1.List of differentially expressed signal transduction mechanisms related phosphosites PeerJ reviewing PDF \| (2020:03:47263:1:1:NEW 22 Jul 2020) PeerJ reviewing PDF \| (2020:03:47263:1:1:NEW 22 Jul 2020) 2 PeerJ reviewing PDF \| (2020:03:47263:1:1:NEW 22 Jul 2020)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_1'><ns0:head>Table 2 (on next page)</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Table2. List of differentially expressed carbohydrateactive enzymes family phosphosites</ns0:figDesc><ns0:table /><ns0:note>PeerJ reviewing PDF \| (2020:03:47263:1:1:NEW 22 Jul 2020)Manuscript to be reviewed</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_2'><ns0:head>Protein accession Position Ratio Regulated Type P value Amino acid Protein description</ns0:head><ns0:label /><ns0:figDesc /><ns0:table><ns0:row><ns0:cell>glycosyl transferase</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>A0A1Q3DXW9</ns0:cell><ns0:cell>1240</ns0:cell><ns0:cell>1.663</ns0:cell><ns0:cell>Up</ns0:cell><ns0:cell>0.000342</ns0:cell><ns0:cell>S</ns0:cell><ns0:cell>Glycosyltransferase family 20 protein</ns0:cell></ns0:row><ns0:row><ns0:cell>A0A1Q3E591</ns0:cell><ns0:cell>146</ns0:cell><ns0:cell>0.655</ns0:cell><ns0:cell>Down</ns0:cell><ns0:cell>0.00272</ns0:cell><ns0:cell>T</ns0:cell><ns0:cell>Glycosyltransferase family protein</ns0:cell></ns0:row><ns0:row><ns0:cell>glycoside hydrolase</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>A0A1Q3E591</ns0:cell><ns0:cell>24</ns0:cell><ns0:cell>2.532</ns0:cell><ns0:cell>Up</ns0:cell><ns0:cell>0.0291</ns0:cell><ns0:cell>S</ns0:cell><ns0:cell>Glycosyltransferase family protein</ns0:cell></ns0:row><ns0:row><ns0:cell>A0A1Q3DVW4 A0A1Q3EH60</ns0:cell><ns0:cell>888 1581</ns0:cell><ns0:cell>1.522 1.585</ns0:cell><ns0:cell>Up Up</ns0:cell><ns0:cell>0.00618 0.000836</ns0:cell><ns0:cell>S S</ns0:cell><ns0:cell>Glycoside hydrolase family 105 protein Glycosyltransferase family protein</ns0:cell></ns0:row><ns0:row><ns0:cell>A0A1Q3DVY0 A0A1Q3EI36</ns0:cell><ns0:cell>489 235</ns0:cell><ns0:cell>1.774 0.64</ns0:cell><ns0:cell>Up Down</ns0:cell><ns0:cell>0.000241 0.009</ns0:cell><ns0:cell>S S</ns0:cell><ns0:cell>Glycoside hydrolase family 1 protein Glycosyltransferase Family 22 protein</ns0:cell></ns0:row><ns0:row><ns0:cell>A0A1Q3DVY0 A0A1Q3ERC2</ns0:cell><ns0:cell>481 75</ns0:cell><ns0:cell>1.508 0.622</ns0:cell><ns0:cell>Up Down</ns0:cell><ns0:cell>0.0398 0.00812</ns0:cell><ns0:cell>S T</ns0:cell><ns0:cell>Glycoside hydrolase family 1 protein Glycosyltransferase family protein</ns0:cell></ns0:row><ns0:row><ns0:cell>A0A1Q3EE19</ns0:cell><ns0:cell>429</ns0:cell><ns0:cell>1.931</ns0:cell><ns0:cell>Up</ns0:cell><ns0:cell>0.0000401</ns0:cell><ns0:cell>S</ns0:cell><ns0:cell>Glycoside hydrolase family 61 protein</ns0:cell></ns0:row><ns0:row><ns0:cell cols='7'>carbohydrate-binding 1 Table2. List of differentially expressed carbohydrateactive enzymes family phosphosites</ns0:cell></ns0:row><ns0:row><ns0:cell>module</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>A0A1Q3DXJ6</ns0:cell><ns0:cell>394</ns0:cell><ns0:cell>0.555</ns0:cell><ns0:cell cols='2'>Down 0.0000212</ns0:cell><ns0:cell>T</ns0:cell><ns0:cell>Carbohydrate-binding module family 48</ns0:cell></ns0:row><ns0:row><ns0:cell>A0A1Q3DXJ6</ns0:cell><ns0:cell>396</ns0:cell><ns0:cell>0.556</ns0:cell><ns0:cell>Down</ns0:cell><ns0:cell>0.000962</ns0:cell><ns0:cell>T</ns0:cell><ns0:cell>Carbohydrate-binding module family 48</ns0:cell></ns0:row><ns0:row><ns0:cell>A0A1Q3DXJ6</ns0:cell><ns0:cell>377</ns0:cell><ns0:cell>0.483</ns0:cell><ns0:cell cols='2'>Down 0.00000158</ns0:cell><ns0:cell>S</ns0:cell><ns0:cell>Carbohydrate-binding module family 48</ns0:cell></ns0:row><ns0:row><ns0:cell>A0A1Q3DXJ6</ns0:cell><ns0:cell>409</ns0:cell><ns0:cell>0.503</ns0:cell><ns0:cell cols='2'>Down 0.0000161</ns0:cell><ns0:cell>S</ns0:cell><ns0:cell>Carbohydrate-binding module family 48</ns0:cell></ns0:row><ns0:row><ns0:cell>A0A1Q3DXJ6</ns0:cell><ns0:cell>380</ns0:cell><ns0:cell>0.508</ns0:cell><ns0:cell cols='2'>Down 0.0000025</ns0:cell><ns0:cell>S</ns0:cell><ns0:cell>Carbohydrate-binding module family 48</ns0:cell></ns0:row><ns0:row><ns0:cell>A0A1Q3DXJ6</ns0:cell><ns0:cell>388</ns0:cell><ns0:cell>0.451</ns0:cell><ns0:cell cols='2'>Down 0.0000568</ns0:cell><ns0:cell>S</ns0:cell><ns0:cell>Carbohydrate-binding module family 48</ns0:cell></ns0:row><ns0:row><ns0:cell>A0A1Q3E7W8</ns0:cell><ns0:cell>134</ns0:cell><ns0:cell>0.561</ns0:cell><ns0:cell cols='2'>Down 0.0000969</ns0:cell><ns0:cell>S</ns0:cell><ns0:cell>Carbohydrate-binding module family 12</ns0:cell></ns0:row><ns0:row><ns0:cell>A0A1Q3EH65</ns0:cell><ns0:cell>1114</ns0:cell><ns0:cell>0.634</ns0:cell><ns0:cell>Down</ns0:cell><ns0:cell>0.00106</ns0:cell><ns0:cell>S</ns0:cell><ns0:cell>Carbohydrate-binding module family 21</ns0:cell></ns0:row><ns0:row><ns0:cell>A0A1Q3EH65</ns0:cell><ns0:cell>391</ns0:cell><ns0:cell>0.512</ns0:cell><ns0:cell cols='2'>Down 0.0000778</ns0:cell><ns0:cell>S</ns0:cell><ns0:cell>Carbohydrate-binding module family 21</ns0:cell></ns0:row><ns0:row><ns0:cell>A0A1Q3EH65</ns0:cell><ns0:cell>184</ns0:cell><ns0:cell>0.46</ns0:cell><ns0:cell>Down</ns0:cell><ns0:cell>0.000337</ns0:cell><ns0:cell>S</ns0:cell><ns0:cell>Carbohydrate-binding module family 21</ns0:cell></ns0:row><ns0:row><ns0:cell>A0A1Q3EH65</ns0:cell><ns0:cell>144</ns0:cell><ns0:cell>0.457</ns0:cell><ns0:cell>Down</ns0:cell><ns0:cell>0.00398</ns0:cell><ns0:cell>S</ns0:cell><ns0:cell>Carbohydrate-binding module family 21</ns0:cell></ns0:row><ns0:row><ns0:cell>carbohydrate esterase</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>A0A1Q3E195</ns0:cell><ns0:cell>171</ns0:cell><ns0:cell>0.312</ns0:cell><ns0:cell>Down</ns0:cell><ns0:cell>0.000639</ns0:cell><ns0:cell>S</ns0:cell><ns0:cell>Lipase from carbohydrate esterase family ce10</ns0:cell></ns0:row><ns0:row><ns0:cell>A0A1Q3EGY1</ns0:cell><ns0:cell>39</ns0:cell><ns0:cell>0.605</ns0:cell><ns0:cell>Down</ns0:cell><ns0:cell>0.000116</ns0:cell><ns0:cell>T</ns0:cell><ns0:cell>Lipase from carbohydrate esterase family ce10</ns0:cell></ns0:row></ns0:table><ns0:note>PeerJ reviewing PDF \| (2020:03:47263:1:1:NEW 22 Jul 2020) Manuscript to be reviewed PeerJ reviewing PDF \| (2020:03:47263:1:1:NEW 22 Jul 2020)</ns0:note></ns0:figure> </ns0:body> "	"Dear Editors and Reviewers: Thank you for your letter and for the reviewers’comments concerning our manuscript entitled “Comparative phosphoproteome analysis to identify candidate phosphoproteins involved in blue light-induced brown film formation in Lentinula edodes” (Manuscript ID peerj-47263). Those comments are not only highly valuable and helpful for revising and improving our paper, but also have significant implications to our researches. We have read comments carefully and have made the corresponding correction which we hope to meet with the approval. We prepared two versions of revised manuscript using a Word based program with track changes. The main corrections in the paper and the response to the reviewer’s comments are as follows: Reviewer 1 (Takehito Nakazawa) Basic reporting Song et al. performed phosphoproteome analysis in L. edodes. This manuscript lacks many important descriptions, which may cause misunderstanding. Also, I could not find any importance in fungal biology. So, I do not recommend to publish this manuscript. Specific comments Comment 1. No importance in phosphoproteome is stated in the current manuscript, although I also suppose it may be important. Why was comparative/differential phosphoproteome analysis conducted in the two samples? Especially, relationship between blue light and protein phosphorylation was not described. Just performing experiment and analysis using new samples? Response 1 Thank you for that excellent and insightful series of remarks. After a certain period of modification, we added a lot of descriptions and reduced many errors. Comment 2. The authors state that many DPPs were identified. But, I suppose most of them may be proteins differentially expressed between the two samples, but not those of which phosphorylation levels differ. Did the author analyze simple proteome analysis using total proteins from the two samples to normalize? I could not find any descriptions about this in the current manuscript. If not, the manuscript should be rewritten to avoid misunderstanding. Response 2 We are very sorry for our incorrect writing. We changed some of the descriptions. Comment 3. The culture condition is not well described. What medium was used in this study? What is the sources of blue and red light? Was the incubator illuminated all day or under cycles such as 12 h ON/ 12 h OFF? Response 3 Fungal mycelia were grown were grown in the potato dextrose agar media. The incubator illuminated all day. We have added the relevant description. Comment 4. Discussion must be rewritten because it does not contain “discussion” based on the results, but many descriptions that could be used for Introduction of the other manuscripts. Almost of the descriptions are not relate to this study and the results. The most serious description is lines 351-354, which is not new finding in this manuscript. Response 4 We have deleted lines 351-354. Discussion was rewritten and relevant contents have been added. Comment 5. I suppose many proteins that exhibit affinity to metal ion are listed, suggesting enrichment of phosphorylated proteins by IMAC may not be enough or background is high. Response 5 The IMAC spheres were used TiO2. Ti4+-IMAC material allow excellent and robust enrichment of phosphopeptides. References: Zhou H, Ye M, Dong J, et al. Robust phosphoproteome enrichment using monodisperse microsphere-based immobilized titanium (IV) ion affinity chromatography. Nat Protoc. 2013;8(3):461-480. doi:10.1038/nprot.2013.010 Comment 6. Lines 232-243: Is this description based on the MS analysis or genome database? Response 6 The description was based on the MS analysis. We modified the description in materials and methods sections. Reviewer 2 (Christian Kubicek) Comment 1. The resulting MS/MS data were processed using the Maxquant search engine (v.1.5.2.8), and queried against a human Uniprot database concatenated with a reverse decoy database. This is a standard procedure in most studies. However, protein sequences of Human orthologs that can be found in Lentinus edodes are usually poorly conserved, and one would have to be very lucky to find peptides that bear the similarity that the authors claim. The genome of Lentinus edodes has been sequenced (Chen et al. PLoS One. 2016;11(8):e0160336) and it would therefore be possible to retrieve the sequences for the whole putative proteins of all the identified peptides. This would put the putative identification of the phosphopeptides on a much more reliable level. A (supplementary) table that lists these peptides, the respective proteins deduced from the genome sequence, and their accession numbers needs to be presented. Response 1: Sorry, we made a mistake. We have changed “The resulting MS/MS data were processed using the Maxquant search engine (v.1.5.2.8). The MS/MS spectra were used as query against a human uniprot database concatenated with a reverse decoy database” to “The MS data were retrieved using Maxquant (v1.5.2.8) using the following search parameter settings: the database was Lentinula_edodes_uniprot (https://www.uniprot.org/proteomes/?query=organism:5353&sort=score); an anti-database was added to calculate the false positive rate (FDR) caused by random matching and a common contamination library was added to eliminate the contamination proteins from the results”. The detail information of identified peptides pertinent to detected proteins are listed in Table S1. Their accession numbers (Gene name) were also presented in TableS1 Comment 2. I have a similar problem with GO and KEGG analysis: GO is very popular because it enables an enrichment analysis of proteins belonging to the same category. However, GO has not been optimized for multicellular fungi, but is strongly based on yeast, plant and bacterial databases. Consequently, GO analysis always introduces an identification error of 5-10 % with fungal proteins, and I am afraid that this would be particularly the case if only peptides are used. This problem could be solved in a similar way as above: don’t use the peptides but the full length proteins (extracted from the genome sequence), and compare the resulting identity with the results of a blast search and conserved domain analysis. Such a (supplementary) table, including the best hit and its statistics, needs to be presented. Response 2: The annotation of GO on proteomic level comes from UniProt Goa database(http://www.ebi.ac.uk/GOA/). First, the system converts protein ID to UniProt ID, then uses UniProt ID to match GO ID, and extracts corresponding information from UniProt-GOA database based on GO ID. If there is no protein information in UniProt Goa database, interprocan, an algorithm software based on protein sequence, will be used to predict the go function of the protein. The proteins were then classified according to Cellular component, Molecular function or Biological process. The full length proteins were used to blast search and conserved domain analysis. The detail information of all identified proteins are listed in Table S2. Comment 3. As for the KEGG analysis, I request that the authors name the proteins that they claim to be enriched rather than only giving the KEGG group, because some of the groups provide no information. Examples: “fructose metabolism”. Fructose can be metabolized by the glycolytic pathway without any additional enzyme, and I therefore wonder which protein gave rise to this claim of enrichment. Or “galactose metabolism”: I looked up the respective KEGG pathway sheet and found that it lacks the enzymes of the alternative, reducing galactose catabolic pathway that has been identified in fungi. So do the authors mean the Leloir enzymes? I believe this attribution of phosphoproteins to KEGG groups needs to be included in the Supplementary Table I requested above. Response3 The detail information of all identified proteins are listed in Table S2. The bioinformatic analyses of protein annotation, functional classification, KEGG KO No., KEGG Gene name, and subcellular localization were listed in Table S2. Comment 4 I believe these the above three points are essential to present the results of this study in a way that it provides a useful and reliable information to the readers. Yet there are additional points that should be considered too: Comments for the Author The authors detect phosphorylation in glycoside hydrolases and their polysaccharide binding domains. This is new. Phoshorylation has been detected in the carbohydrates of the attached glycan chains of these glycoproteins but never on the proteins themselves. The authors should discuss the potential significance of this finding. Phosphorylation of a protein alters its isoelectric point and consequently the solubility. Would the phosphorylation improve solubility or binding to the substrate? This is a point where a rather simple experiment could have contributed significantly to the paper. One limitation of the paper is its descriptive nature, and the use of broad categorizations in the present manuscript does not allow any clear insights. It would significantly improve the manuscript if the authors would be able to provide some additional experiments that prove the importance of some of the identified peptides in the response to blue light. In Table1 (List of differentially expressed signalling proteins) the authors use a cut-off of 1.5-fold. This appears very low to me. In transcriptome analyses, a log2 >2 is the current. Response: In this study, based on the number of identified phosphorylated proteins, the number of differential modified phosphosites screened by the 1.5 (0.667) fold change was suitable for subsequent bioinformatic analysis. We agree that 2 fold-change was more popular used in proteomic analyses. However, due to the ratio compression of TMT (Reference1), the 1.5 fold change cut off was also accepted in some previous reports (References2-4). References: 1. Savitski M M, Mathieson T, Zinn N, et al. Measuring and managing ratio compression for accurate iTRAQ/TMT quantification[J]. Journal of proteome research, 2013, 12(8): 3586-3598. 2. Yabing, Cao, Guoqiang, et al. Phytoplasma-induced changes in the acetylome and succinylome of Paulownia tomentosa provide evidence for involvement of acetylated proteins in witches' broom disease.[J]. Molecular & cellular proteomics : MCP, 2019. 3. Liu, Zekun, Zhang, et al. Quantitative Dynamics of Proteome, Acetylome, and Succinylome during Stem-Cell Differentiation into Hepatocyte-like Cells[J]. Journal of Proteome Research, 2018. 4. Galván-Peña, Silvia, Carroll R G , Newman C , et al. Malonylation of GAPDH is an inflammatory signal in macrophages[J]. Nature Communications, 2019, 10(1). Reviewer 3 (Anonymous) Basic reporting Comment Overall there is comprehensive information provided, but still clarity is lacking in few aspects. Some figures are mislabeled, and also resolution for figures is poor. A couple of them are hard to see/read (details below). In the introduction, authors very briefly touch through the blue light photoreceptors. However, in the context of this manuscript it will be nice to have some more background on their biological roles specifically in context of Lentinula edodes. Also, authors are missing some very recent important references such as Yoo Seung-il et al, BMC Genomics 20, 121 (2019) though on transcriptional study but is highly in context of this particular study. One another reference is Kim J Y et al, PLoS ONE 15(3): e0230680 (2020). These should be briefly discussed. Raw data is provided in the supplementary tables. Language is mostly clear. Response: We have added “Jim et al. compared the morphological changes and gene expression of Lentinus edodes under blue light and continuous dark conditions. Their results indicated that the differential genes were involved in the morphological development of primordia and embryonic muscle, cell adhesion and the structure of cellulose and non-cellulose cell walls that affect the development of fruiting bodies, as well as photoreceptors of blue light signals for fruiting body development and pigment formation” in introduction section and we have added “Light sensing via photoreceptors such as FMN- and FAD-bindings and signal transduction by kinases and G protein-coupled receptors were identificated as differential expression genes specific to the light-induced brown film phenotypes(Yoo et al，2019 ) .” in Discussion section. Experimental design Experimental: Information on experimental replicates is not clear. It seems three biological replicates are taken. Are they take in parallel, or on three different times? Authors should clarify this in text. Also, methodology section needs more clarification. Points mentioned under general comments. Validity of the findings No Comment Comments for the Author This is a novel study elucidating differential phosphoproteome involved in mycelial browning of basidiomycete fungi Lentinula edodes. Overall there is comprehensive information provided, but still clarity is lacking in few aspects. More expansion in introduction in needed to strengthen the foundation of study, and in discussion section to justify the key experimental inferences. Comment 1. A better picture with higher resolution will be good for the red vs blue light treatment of the L. edodes. (Fig 1A) Response 1. A better picture with higher resolution was provided. Comment 2. Instead of just stating blue or red light, authors should also provide lumen intensity of the lights used in this study. Response 2:The light intensity approximately 100 lux and the incubator illuminated all day Comment 3 In the material and method section, trypsinization strategy is confusing. Were there two rounds of trypsinization one after another (Lines 147-149). Response3:Yes, there were two rounds of trypsinization one after another. We have changed Trypsin was added at a mass ratio of 1:50 (trypsin:protein), and enzymatic hydrolysis was carried out overnight at 37°C. The trypsin was added at a mass ratio of 1:100, and the enzymatic hydrolysis continued for 4 h”to “Trypsin was added at 1:50 trypsin-to-protein mass ratio for the first digestion overnight and 1:100 trypsin-to-protein mass ratio for a second 4 h-digestion.”. Comment 4. Which TMT kit?, What IMAC spheres were used Fe, Ti,..?? Provide more detailed and accurate description. Response 4:TMT kit purchased from Thermo-Scientific (code:90066). IMAC spheres were used Ti.We've added the relevant information. Comment 5. Include more description on how the phosphosites were identified, and how is the quantification done. Response 5:MaxQuant output file Phospho (STY)Sites. txt was used to analysis of phosphorylation sites quantification；In this study, the quantitative values of each sample in three replicates were obtained by three full-quantitative quantitative experiments. The first step is to calculate the differentially modified phosphosites between the two samples. Firstly, calculate the average value of the quantitative values of each sample in three replicates, and then calculate the ratio of the average values between the two samples. The ratio is used as the final quantitation. The second step is to calculate the significant p value of differential expression between two samples. Firstly, the relative quantitative values of each sample were taken as log2 transform (so that the data conforms to the normal distribution), and p value was calculated by the two-sample two-tailed T-test method. When p value<0.05 and protein ratio > 1.5 was regarded as up-regulation. When p value<0.05 and protein ratio < 0.667 was regarded as down-regulation. Comment 6 Figure 1C, hard to read the axis of the graph Response 6: We have changed to a clearer chart Comment 7. Figure 2B, Label axis Response 7: We have added label axis Comment 8. Figure 3B is mislabeled in the legend, also not clear how is B different from A Response 8: Sorry, we made a mistake. We have changed “ Subcellular locations of DPPs” to “ The euKaryotic Ortholog Groups annotation clustered all the phosphoproteins into four major categories: Information storage and processing, Cellular processes and signaling, Metabolism and Poorly characterized. ” Comment9. Figure 4C, provide more description on the intensity map (to clarify the understanding, distance and position are cleat, what does red and green signify in fig 4C, numbers or something else) Response 9: We have added “Red indicated that this amino acid was significantly enriched near the modification site, and green indicated that this amino acid was significantly reduced near the modification site. Letters represent abbreviations for amino acids” Comment 10. Legend not accurate. 5C? Also, resolution of figures very poor, hence sometimes difficult to read no's Response 10: we have added “(c) Subcellular locations of differentially phosphorylated proteins”. And a higher resolution of figure was provided. Comment11. Line 297, correct Fig7B to 7A Response 11: We have changed “Fig.7B” to “Fig.7A” Comment 12. Did authors see/analyze any change in localization of proteins in red vs blue light treatments? That will be an interesting information which can be easily fished from the given data. Response 12: Protein phosphorylation changes the subcellular localization. This is an interesting phenomenon and a good research direction.however, we did not analyze any change in localization of proteins in red vs blue light treatments in this study. Comment 13 Authors, should expand the discussion section adding some key points. A. For DPP, KEGG enrichment analysis (Fig7) up-regulated phosphorylated proteins show ABC transporters, and ribosome biogenesis phosphorylated proteins. authors should put this in discussion, as why they think these are upregulated and possible relevance to brown film formation. B. Expanding on the similar lines, for top upregulated domains, ABC transporter like, P-type ATPase, etc. Similarly, discussion should be expanded more for downregulated phosphorylated proteins and domains. Response 13: We have added “The ABC transport family is widely distributed in all living species, including several subfamilies, which are responsible for different types of material transport(Higgins.2001;Holland & Blight. 1999). ATPase is the largest ATP dependent ion transporter in organisms, transporting many different ions, metals and other substrates (Palmgren & Nissen, 2011). Two VPS9 domain containing proteins, Rab5 GDP/GTP exchange factor, were down-regulated under blue light treatment. Studies have shown that the transport of endocytic vesicles is partially regulated by Rab protein(Zhu et al, 2018).Rab protein needs to be activated by guanine nucleotide exchange factor, which transforms Rab from a GDP binding state to a GTP binding state(Zerial, 2001).The changed in these proteins suggest that blue light altered the transport of certain substances. In mushrooms, blue light can promote growth, which is considered to be an important environmental factor affecting the growth of fruiting bodies(Yoo et al, 2019). In this study, ribosome biogenesis related proteins were observed to be up-regulated under blue light treatment.” in Discussion section We tried our best to improve the manuscript and made some changes in the manuscript. These changes will not influence the content and framework of the paper. We appreciate for Editors/Reviewers’ warm work earnestly, and hope that the correction will meet with approval. Once again, thank you very much for your comments and suggestions. With kind personal regards, Sincerely yours, Weiming Cai "	Here is a paper. Please give your review comments after reading it.
527	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Background. Anthropized landscapes play a crucial role in biodiversity conservation, as they encompass about 90% of the remaining tropical forest. Effective conservation strategies require a deep understanding of how anthropic disturbances determine diversity patterns across these landscapes. Here, we evaluated how attributes and assembly mechanisms of dung beetle communities vary across the Selva El Ocote Biosphere Reserve (REBISO) landscape.</ns0:p><ns0:p>Methods. Community attributes (species diversity, abundance, and biomass) were assessed at the landscape scale, using spatial windows and vegetation classes. Windows were categorized as intact, variegated, or fragmented based on their percent cover of tropical forest. The vegetation classes analyzed were tropical forest, second-growth forest, and pastures.</ns0:p><ns0:p>Results. We collected 15,457 individuals and 55 species. Variegated windows, tropical forests, and second-growth forests showed the highest diversity values, while the lowest values were found in intact windows and pastures. Landscape fragmentation was positively and strongly related to dung beetle diversity and negatively related to their abundance; biomass was positively associated with forest cover. Beta diversity was the primary driver of the high dung beetle diversity in the landscape analyzed.</ns0:p><ns0:p>Discussion. The landscape heterogeneity and its biodiversity-friendly matrix facilitate the complementarity of dung beetle assemblages in the Selva El Ocote Biosphere Reserve. Random processes govern beta-diversity patterns in intact and variegated windows. Therefore, vegetation cover in the region is sufficient to maintain a continuous flow of dung beetles between forested landscape segments. However, intense anthropic disturbances acted as deterministic environmental filters in fragmented windows and pastures sites, leading to biotic homogenization processes. Our results suggest that increasing habitat variegation in highly fragmented sites is an effective strategy to prevent or buffer homogenization processes in the REBISO landscape.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>Anthropized neotropical landscapes encompass a complex combination of natural and seminatural habitats, where some species can thrive while others may go locally extinct <ns0:ref type='bibr' target='#b9'>(de Castro Solar et al., 2015)</ns0:ref>. Today, almost 90% of remaining tropical forests are located within anthropized landscapes <ns0:ref type='bibr' target='#b14'>(Chazdon et al., 2009)</ns0:ref>. These landscapes now play a crucial role in biodiversity conservation agendas <ns0:ref type='bibr' target='#b17'>(DeClerck et al., 2010)</ns0:ref>. Therefore, it is imperative to understand how species diversity responds to anthropized landscapes in order to implement suitable management actions <ns0:ref type='bibr' target='#b29'>(Gardner et al., 2009;</ns0:ref><ns0:ref type='bibr' target='#b75'>Socolar et al., 2016)</ns0:ref>, especially given the multiple successional pathways and disturbance states that these modified landscapes can follow <ns0:ref type='bibr' target='#b25'>(Fischer & Lindenmayer, 2007;</ns0:ref><ns0:ref type='bibr' target='#b5'>Arroyo-Rodríguez et al., 2017)</ns0:ref>.</ns0:p><ns0:p>Traditionally, researchers have assessed the effect of anthropic disturbance on biotic communities by comparing one or more community attributes (e.g., species diversity, abundance, biomass) across different sampling units at a local level (i.e., vegetation cover types or land-use types). However, the composition and configuration of the habitats that surround the sampling units are also important drivers of ecological processes in biotic communities <ns0:ref type='bibr' target='#b27'>(Franklin & Lindenmayer, 2009)</ns0:ref>. A landscape-level approach provides the necessary context to understand better how communities respond to anthropic disturbances by incorporating the effects of the multiple landscape components <ns0:ref type='bibr' target='#b29'>(Gardner et al., 2009;</ns0:ref><ns0:ref type='bibr' target='#b36'>Hodder et al., 2014)</ns0:ref>. Besides, landscape studies provide useful information for effective natural resource management since many anthropogenic drivers of biodiversity loss, e.g., land-use change or habitat destruction, operate at the landscape level <ns0:ref type='bibr' target='#b36'>(Hodder et al., 2014)</ns0:ref>.</ns0:p><ns0:p>McIntyre and <ns0:ref type='bibr' target='#b49'>Barret (1992)</ns0:ref> coined the variegation concept for anthropized landscapes exhibiting disturbance and vegetation cover gradients. <ns0:ref type='bibr' target='#b50'>McIntyre and Hobbs (1999)</ns0:ref> then added the fragmentation concept to the variegation model. These authors classified the landscape into four categories based on the percentage of remaining original vegetation (OV) and the intensity of habitat transformation: a) intact landscapes (>90% OV): sites with little or no modification; b) variegated landscapes (60-90% OV), showing either gradual or abrupt limits between their component units; c) fragmented landscapes (10-60% OV), characterized by a high degree of modification; and d) relict landscapes (<10% OV), showing severe modification and almost no forest cover remnants. <ns0:ref type='bibr' target='#b35'>Halffter and Rös (2013)</ns0:ref> proposed studying landscape diversity through sampling windows in the geographical space analyzed. These windows are based on the landscape model proposed by <ns0:ref type='bibr'>McIntyre and Hobbs (1992)</ns0:ref> and consist of equally-sized sampling spaces that are semi-randomly located to maximize the representation of the vegetation heterogeneity and land-use types in the landscape.</ns0:p><ns0:p>The Selva El Ocote Biosphere Reserve (REBISO, hereafter) harbors some of the most heterogeneous, although highly disturbed, remnants of tropical forest in Mexico <ns0:ref type='bibr' target='#b26'>(Flamenco-Sandoval, Martínez Ramos & Masera, 2007)</ns0:ref>. Frequent forest fires, in addition to the complex geological nature, climate features, and socio-economic dynamics (livestock and agricultural activities) in the REBISO have led to a complex landscape comprising a mosaic of tropical forests, second-growth forests, pastures, and croplands <ns0:ref type='bibr' target='#b59'>(Ochoa, 1996;</ns0:ref><ns0:ref type='bibr' target='#b73'>SEMARNAT/CONANP, 2001;</ns0:ref><ns0:ref type='bibr' target='#b26'>Flamenco-Sandoval, Martínez Ramos & Masera, 2007;</ns0:ref><ns0:ref type='bibr' target='#b67'>Ramírez-Marcial et al., 2017)</ns0:ref>. Thus, a landscape-level approach seems most appropriate for examining how species respond to anthropogenic disturbance in the REBISO, given its complex and heterogeneous landscape.</ns0:p><ns0:p>Dung beetles (Scarabaeidae: Scarabaeinae) are globally distributed insects that feed on decomposing organic matter such as mammal feces, carrion, rotting fruit, or fungi <ns0:ref type='bibr' target='#b34'>(Halffter & Matthews, 1966)</ns0:ref>. Due to their sensitivity to environmental disturbances, dung beetles are ideal bioindicators to assess the effects of landscape changes on diversity <ns0:ref type='bibr' target='#b23'>(Favila & Halffter, 1997;</ns0:ref><ns0:ref type='bibr' target='#b55'>Nichols et al., 2007)</ns0:ref>. Previous studies have shown how habitat loss leads to abrupt changes in the composition and structure of dung beetle communities <ns0:ref type='bibr' target='#b43'>(Klein, 1989;</ns0:ref><ns0:ref type='bibr' target='#b66'>Quintero & Roslin, 2005;</ns0:ref><ns0:ref type='bibr' target='#b55'>Nichols et al., 2007;</ns0:ref><ns0:ref type='bibr' target='#b53'>Navarrete & Halffter, 2008;</ns0:ref><ns0:ref type='bibr' target='#b18'>Díaz, Galante & Favila, 2010;</ns0:ref><ns0:ref type='bibr' target='#b8'>Cajaiba et al., 2017)</ns0:ref>. However, few studies have evaluated the response of dung beetle communities to disturbances at the landscape level <ns0:ref type='bibr' target='#b57'>(Numa et al., 2009;</ns0:ref><ns0:ref type='bibr' target='#b69'>Rös, Escobar & Halffter, 2012;</ns0:ref><ns0:ref type='bibr'>Sánchezde-Jesús et al., 2016;</ns0:ref><ns0:ref type='bibr' target='#b1'>Alvarado et al., 2018</ns0:ref><ns0:ref type='bibr' target='#b2'>Alvarado et al., , 2020))</ns0:ref>, or whether the observed diversity patterns are stochastic or determined by environmental filters or competitive exclusion between species <ns0:ref type='bibr' target='#b60'>(Ortega-Martínez et al., 2020)</ns0:ref>. Assessing dung beetle diversity at the landscape level, using multiple but complementary metrics, can provide a more comprehensive view of how diversity is maintained and what community assembly mechanisms operate in anthropized landscapes.</ns0:p><ns0:p>In this study, we evaluate the assemblage structure and diversity patterns of dung beetle communities in the heterogeneous tropical landscape of the Selva El Ocote Biosphere Reserve. We address the following questions: (1) How do the diversity and structure of dung beetle assemblages vary across the REBISO landscape and its vegetation classes? (2) How do the composition and configuration of the REBISO landscape influence the diversity and structure of dung beetle assemblages? (3) How does beta diversity change and is maintained across the landscape and between different vegetation classes? The information obtained in this study will be useful for designing conservation strategies in complex tropical landscapes with different heterogeneity levels.</ns0:p></ns0:div> <ns0:div><ns0:head>Materials & Methods</ns0:head></ns0:div> <ns0:div><ns0:head>Study Area</ns0:head><ns0:p>The study was carried out at the REBISO, located in the municipalities of Ocozocoautla de Espinosa and Cintalapa, Chiapas, Mexico (16°45'42' -17°09'00' N and 93°54'19' -93°21'20' W, Fig. <ns0:ref type='figure' target='#fig_2'>1</ns0:ref>). The area is mostly underlain by dolomite rocks and limestone, with a dominance of water-soluble sedimentary rocks <ns0:ref type='bibr' target='#b19'>(Domenici, 2016)</ns0:ref>. The predominant climate types are warm, humid (climate type Am) and warm, subhumid (climate type Am(f)), with a mean annual temperature of 22 °C and heavy rainfall throughout the year (SEMARNAT/CONANP, 2001).</ns0:p><ns0:p>We produced a vegetation map of REBISO from a multispectral SPOT6 image acquired in 2014, using a supervised classification method in QGIS v2.12.3 <ns0:ref type='bibr'>(QGIS Development Team, 2016)</ns0:ref>. The vegetation classes considered were tropical forest, second-growth forest, and pastures (Fig. <ns0:ref type='figure' target='#fig_2'>1</ns0:ref>, Table <ns0:ref type='table'>1</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>Sampling Design</ns0:head><ns0:p>We established eight 1-km 2 (100 ha) sampling windows to capture the landscape heterogeneity in the REBISO (Sánchez- <ns0:ref type='bibr' target='#b70'>de-Jesús et al., 2016)</ns0:ref>. Each window was separated from each other by at least 2 km to ensure spatial independence between them (Sánchez- <ns0:ref type='bibr' target='#b70'>de-Jesús et al., 2016)</ns0:ref>. The landscape composition in each window was described by estimating the percent coverage of each vegetation class and evaluating the evenness of their distribution (landscape compositional diversity -Shannon diversity). The spatial configuration of the vegetation classes in each window was assessed with the splitting index and edge density metric <ns0:ref type='bibr' target='#b48'>(McGarigal, Cushman & Ene, 2012)</ns0:ref>. Edge density is computed as the length (m) of the edges of each vegetation class divided by the window area (ha). The splitting index describes the degree of fragmentation of a landscape and is equivalent to the effective number of patches. Thus, as a landscape becomes increasingly sub-divided, the splitting index increases <ns0:ref type='bibr' target='#b39'>(Jaeger, 2000;</ns0:ref><ns0:ref type='bibr' target='#b21'>Fahrig, 2017)</ns0:ref>. The landscape composition, edge density, and splitting index metrics (Table <ns0:ref type='table'>S1</ns0:ref>) were obtained with FRAGSTAT v4.2.1 <ns0:ref type='bibr' target='#b48'>(McGarigal et al., 2012)</ns0:ref>. Based on the percent cover of tropical forest (F), windows were classified as intact (W1, W2; F > 90%), variegated (W3, W4, and W5; 60% < F < 90%), or fragmented (W6, W7, and W8; 10% < F < 60%).</ns0:p><ns0:p>We sampled dung beetles (Scarabaeidae: Scarabaeinae) during the dry (March to May) and the rainy (July-August) seasons of 2016 using pitfall traps. This sampling scheme allowed us to integrate the seasonal activities of dung beetles <ns0:ref type='bibr' target='#b8'>(Cajaiba et al., 2017)</ns0:ref>. Each trap consisted of a 1 L cylindrical plastic container with 300 mL of ethylene glycol as preservative, buried at ground level, and covered with a plastic lid to protect the bait from rain and sun radiation. Pitfall traps were baited with 70 g of either an 80:20 mixture of pig and human feces (copro-traps) or squid flesh (necro-traps) in order to obtain a representative sample of the dung beetle assemblages in the area.</ns0:p><ns0:p>Seven sampling sites were established in each window, separated 250-360 meters from each other, to proportionally adjust the number of pitfall traps per vegetation class according to the vegetation class composition of each window (Table <ns0:ref type='table' target='#tab_1'>S2</ns0:ref>). Proportional sampling is a suitable method for detecting changes in beta diversity in heterogeneous landscapes <ns0:ref type='bibr' target='#b72'>(Schoereder et al., 2004)</ns0:ref>. In each sampling site, three copro-traps and three necro-traps (42 traps/window), were placed in a rectangular area separated 50 m from each other to minimize interference between them <ns0:ref type='bibr' target='#b44'>(Larsen & Forsyth, 2005)</ns0:ref>. The rectangular layout of some trap sets was modified in some cases due to the topographic characteristics of the sites. The pitfall traps were left active for 48 hours.</ns0:p><ns0:p>The specimens collected were counted and identified to species. To estimate the dung beetle biomass, we randomly selected ten specimens of each species and dried them at 70 °C for 72 hours. We weighed each specimen to the nearest 0.1mg with an analytical balance (Explorer Pro) and calculated the average biomass for each species. Finally, we multiplied the mean biomass of each species by its abundance in each window and vegetation class. The dung beetle specimens were deposited in the entomological collection of El Colegio de la Frontera Sur, San Cristóbal de Las Casas. Field sampling in the REBISO was carried out under permit SGPA/DGS/14214/15 issued by the Secretaria de Medio Ambiente y Recursos Naturales, Mexico.</ns0:p></ns0:div> <ns0:div><ns0:head>Data Analysis</ns0:head><ns0:p>We followed a spatial and structural approach (sensu <ns0:ref type='bibr' target='#b69'>Rös, Escobar & Halffter, 2012)</ns0:ref> to analyze the data. Windows were the sampling units for the spatial approach (n = 8), while vegetation classes within windows were the sampling units for the structural approach (n = 21). The sampling completeness of each window and vegetation class was determined using the coverage estimator of <ns0:ref type='bibr' target='#b11'>Chao and Jost (2012)</ns0:ref>, which allows comparing species diversity across multiple sites.</ns0:p><ns0:p>Alpha diversity in each sampling unit (window or vegetation class) was evaluated using the 0 D and 1 D diversity numbers. 0 D is equivalent to species richness and is insensitive to the species abundance <ns0:ref type='bibr' target='#b40'>(Jost, 2006</ns0:ref>); 1 D is equivalent to the exponential of Shannon diversity index and accounts for the most abundant species in a community <ns0:ref type='bibr' target='#b40'>(Jost, 2006)</ns0:ref>.</ns0:p><ns0:p>We examined differences in species richness between windows by constructing and comparing their 95% bootstrap confidence intervals. Non-overlapping confidence intervals denote significantly different species richness <ns0:ref type='bibr' target='#b31'>(Gotelli & Colwell, 2011;</ns0:ref><ns0:ref type='bibr' target='#b10'>Chao et al., 2014)</ns0:ref>. Differences in species richness between vegetation classes were determined using interpolationextrapolation curves <ns0:ref type='bibr' target='#b10'>(Chao et al., 2014)</ns0:ref>. The sampling coverage, 0 D and 1 D diversity numbers, confidence intervals, and the interpolation-extrapolation curves were obtained with the software iNEXT v2.0.11 <ns0:ref type='bibr' target='#b38'>(Hsieh, Ma & Chao, 2016)</ns0:ref>.</ns0:p><ns0:p>Generalized linear models (GLM) were used to assess differences in abundance and biomass between windows and vegetation classes. The abundance and biomass data approached a normal distribution after logarithmic transformation and were analyzed assuming a Gaussian error distribution <ns0:ref type='bibr' target='#b16'>(Crawley, 2013)</ns0:ref>. Pairwise comparisons using Tukey's test were carried out, with the multcomp package <ns0:ref type='bibr' target='#b37'>(Hothorn et al., 2016</ns0:ref>) whenever significant differences were detected.</ns0:p><ns0:p>GLMs were also used to assess the effect of the landscape composition and configuration on the species richness ( 0 D), exponential of the Shannon diversity ( 1 D), abundance, and biomass of the dung beetle assemblages. These data were first tested for normality and were then analyzed assuming a Gaussian error distribution. Since only eight observations were available to fit these models, separate models containing only one predictor variable were constructed to avoid overfitting <ns0:ref type='bibr' target='#b42'>(Kelley & Maxwell, 2003)</ns0:ref>. The best-fit models were selected based on the Akaike′s information criterion corrected for small samples (AICc) and the deviance explained (D 2 ). The model with the smallest AICc (∆AICc >2) and the largest D 2 values was selected as the best-fit model <ns0:ref type='bibr' target='#b7'>(Burnham & Anderson, 2002)</ns0:ref>. Based on the results from the Moran I test (as implemented in the package LetsR), no significant spatial structure was detected in the response variables (Table <ns0:ref type='table' target='#tab_3'>S3</ns0:ref>) <ns0:ref type='bibr' target='#b78'>(Vilela & Villalobos, 2015)</ns0:ref>.</ns0:p><ns0:p>True beta diversity (i.e., the effective number of distinct communities) was estimated for species richness ( 0 β) and Shannon diversity ( 1 β) using the multiplicative partitioning method <ns0:ref type='bibr' target='#b41'>(Jost, 2007)</ns0:ref>. The multiple-site Sørensen dissimilarity was partitioned as β Sor = β Sim + β Sne using the package Betapart v1.3 <ns0:ref type='bibr' target='#b6'>(Baselga & Orme, 2012)</ns0:ref> to determine whether the ecological differences between sampling units resulted from species turnover (β Sim ) or nestedness (β Sne ). Turnover measures the replacement of species between sites caused by environmental differences, disturbance, or competition. Nestedness is a loss of species between sites, usually due to differences in local conditions or ecological niches, where the species-poorer site contains a subset of the species present in the species-richer site <ns0:ref type='bibr' target='#b45'>(Legendre, 2014)</ns0:ref>.</ns0:p><ns0:p>Null models were used to determine whether beta-diversity patterns resulted from either random changes in alpha and gamma diversity, or from underlying deterministic mechanisms in communities or the landscape <ns0:ref type='bibr' target='#b13'>(Chase et al., 2011)</ns0:ref>. We constructed null models for the beta Raup-Crick index (β R-C ) using the algorithm developed by <ns0:ref type='bibr' target='#b13'>Chase et al. (2011)</ns0:ref> with 9999 randomizations. β R-C compares the observed versus expected beta diversity under the null model, scaling the results to a range between -1 and 1. This value indicates whether the beta diversity observed between windows, or vegetation classes, is more similar (values close to -1), equal (values close to 0), or less similar (values close to 1) than the one expected by chance (β R-C null model). We built a dendrogram and a nonmetric multidimensional scaling (NMDS) plot based on β R-C values for windows and vegetation classes, respectively <ns0:ref type='bibr' target='#b13'>(Chase et al., 2011)</ns0:ref>. The dendrogram was constructed using the complete linkage method, as it produces clusters with ecological discontinuities <ns0:ref type='bibr' target='#b46'>(Legendre & Legendre, 2003)</ns0:ref>. We compared the dendrogram and NMDS plot based on β R-C with homologous plots based on Sørensen dissimilarity to examine whether deterministic mechanisms are underlying the observed beta diversity across the landscape <ns0:ref type='bibr' target='#b13'>(Chase et al., 2011)</ns0:ref>. All statistical analyses and models were carried out using R v.3.3.1 (R Development Core Team, 2015).</ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head><ns0:p>We collected a total of 15,457 specimens belonging to 55 species in the eight windows at REBISO (Table <ns0:ref type='table'>S4a</ns0:ref>). The most abundant species was Deltochilum mexicanum (15% of total abundance), followed by Onthophagus corrosus (13%), Eurysternus maya (12%), Canthon vazquezae (11%), and Onthophagus batesi (8%). Sampling coverage on each window was 99% (Table <ns0:ref type='table'>S4a</ns0:ref>). However, the sampling coverage of vegetation classes varied between windows: for forest vegetation it ranged from 91% (W6) to 100% (W8), it was over 98% for second-growth forests, and between 95% (W3) and 99% (W6) for pastures (Table <ns0:ref type='table'>S4a</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>Diversity, Abundance, and Biomass Patterns in Windows</ns0:head><ns0:p>Species richness ( 0 D) in the windows sampled ranged from 22 (W1, intact window) to 37 (W4, variegated window), whereas the exponential Shannon diversity index ( 1 D) ranged from 4.9 (W2, intact window) to 17.6 (W5, variegated window) species (Table <ns0:ref type='table' target='#tab_1'>2</ns0:ref>). Species richness in W4 and W5 was significantly higher than in the other windows (Fig. <ns0:ref type='figure' target='#fig_5'>2a</ns0:ref>).</ns0:p><ns0:p>Deltochilum mexicanum, E.maya, and C. vazquezae were the most abundant species in intact windows W1 and W2 (Fig <ns0:ref type='figure' target='#fig_5'>S1a</ns0:ref> The highest abundance values (44-52 individuals per trap) were recorded in the intact windows W1 and W2 (Table <ns0:ref type='table' target='#tab_3'>3</ns0:ref>), followed by fragmented windows W6, W7, and W8 (30-36 individuals per trap), and variegated windows W3, W4, and W5 (15-30 individuals per trap). However, these differences were not statistically significant (χ 2 = 8.923; df = 7; P = 0.26; Table <ns0:ref type='table' target='#tab_3'>3</ns0:ref>). By contrast, there were significant differences in mean biomass between windows (χ 2 = 45.143; df = 7; P = >0.001; Table <ns0:ref type='table' target='#tab_3'>3</ns0:ref>). Mean biomass per trap was significantly higher in windows W1 and W2 (8.2-9.1 grams per trap, Table <ns0:ref type='table' target='#tab_3'>3</ns0:ref>), but no significant differences were found between fragmented (2.2-3.7 grams per trap, Table <ns0:ref type='table' target='#tab_3'>3</ns0:ref>) and variegated windows (2.4-3.9 grams per trap, Table <ns0:ref type='table' target='#tab_3'>3</ns0:ref>).</ns0:p><ns0:p>All the landscape variables had a significant positive effect on the species richness ( 0 D) in each window. However, the splitting index was the variable that best explained variations in species richness (Table <ns0:ref type='table'>4</ns0:ref>). Although the exponential Shannon diversity ( 1 D) values were positively related to the splitting index and edge density, the splitting index was the best predictor for variations in Shannon diversity between windows. Edge density and forest cover were the best predictor variables for dung beetle abundance and biomass, respectively; edge density was negatively correlated with abundance, and forest cover positively correlated with biomass (Table <ns0:ref type='table'>4</ns0:ref>).</ns0:p><ns0:p>True beta diversity of orders 0 and 1 indicated two effective communities between windows, 0 β being slightly smaller than 1 β (Table <ns0:ref type='table' target='#tab_1'>2</ns0:ref>). The multiple-site Sørensen value calculated for all windows was 0.65 (Fig. <ns0:ref type='figure' target='#fig_5'>3a</ns0:ref> 'W Total'); 85% of this dissimilarity was due to species turnover (β Sim ) and 15% to nested processes (β Sne ). Sørensen dissimilarity for intact windows W1 and W2 was lower than 0.4, mainly due to nested processes (β Sne ) (Fig. <ns0:ref type='figure' target='#fig_5'>3a</ns0:ref>). Dissimilarity ranged from 0.3 to 0.45 in variegated windows (W3, W4, W5), and from 0.3 to 0.58 in fragmented windows (W6, W7, W8). In most cases (except for W8), the observed Sørensen dissimilarity values were primarily due to species turnover (Fig. <ns0:ref type='figure' target='#fig_5'>3a</ns0:ref>).</ns0:p><ns0:p>The dendrogram based on the Sørensen distance revealed two main groups (Fig. <ns0:ref type='figure' target='#fig_4'>3b</ns0:ref>). The first group includes the fragmented windows (W6, W7, W8), while the second group reveals a gradient of increasing similarity ranging from the intact (W1, W2) to the variegated (W3, W4, W5) windows. The null-model analysis showed that the difference between fragmented windows with respect to the variegated and intact windows was higher than expected by chance (β RC Value: 1.0, Fig. <ns0:ref type='figure' target='#fig_4'>3c</ns0:ref>). However, the dissimilarity between variegated and intact windows did not exceed the null expectation of beta diversity (0< β RC < 0.3, Fig. <ns0:ref type='figure' target='#fig_4'>3c</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>Diversity, Abundance, and Biomass in Vegetation Classes</ns0:head><ns0:p>The dung beetle species richness ( 0 D = 34 species) in pastures was significantly lower than in the other vegetation classes, but there were no significant differences between second-growth and tropical forests (44 and 45 species, respectively) (Fig. <ns0:ref type='figure' target='#fig_3'>2b</ns0:ref>). We recorded the lowest exponential Shannon diversity values ( 1 D) in the tropical forests (7.75) and the highest in second-growth forests (15.54) (Table <ns0:ref type='table' target='#tab_1'>2</ns0:ref>). No significant differences in abundance were observed between vegetation classes (χ 2 = 3.701; df = 2; P = 0.16, Table <ns0:ref type='table' target='#tab_3'>3</ns0:ref>). The average number of individuals per trap was 37.8 in secondgrowth forests, followed by tropical forests (30.2) and pasture sites (17.7) (Table <ns0:ref type='table' target='#tab_3'>3</ns0:ref>). By contrast, there were significant differences in mean biomass between vegetation classes (χ 2 = 10.829; df = 2; P= 0.004). Pasture sites had a significantly lower mean biomass per trap (2.09 g) than tropical forest (5.05 g) and second-growth forest (4.53) sites, which showed no significant differences between them (Table <ns0:ref type='table'>.</ns0:ref> 3).</ns0:p><ns0:p>According to the multiplicative partition of diversity, there were 2.9 effective communities for 0 β and 2.5 communities for 1 β in the three vegetation classes combined. Two effective communities were estimated for both the tropical forest and second-growth forest classes, with 1 β higher than 0 β in both cases. Only one effective community was estimated for the pasture class, with 0 β higher than 1 β (Table <ns0:ref type='table' target='#tab_1'>2</ns0:ref>). The Sørensen dissimilarity between vegetation classes was 0.85, with 88% of this value accounted for by species turnover (β Sim ) and 12% by nestedness processes (β Sne ) (Fig. <ns0:ref type='figure'>4b</ns0:ref>).</ns0:p><ns0:p>Tropical forests and second-growth forests showed higher Sørensen values (0.71 and 0.70, respectively) than pastures (0.54) (Fig. <ns0:ref type='figure' target='#fig_5'>4a</ns0:ref>). The NMDS plot based on the Sørensen distance formed a compact cluster of pasture sites, whereas most of the tropical forest and second-growth forest sites overlapped between themselves and with the pasture sites. (Fig. <ns0:ref type='figure'>4b</ns0:ref>). The NMDS plot based on the beta Raup-Crick null model index (β R-C ) separated the tropical forest sites from pastures, whereas second-growth forest sites overlapped with tropical forest and pasture classes (Fig. <ns0:ref type='figure'>4c</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>Discussion</ns0:head><ns0:p>Our results identify the REBISO as one of the regions with highest diversity of Scarabaeinae in Mexican tropical forests, with 55 species, along with the Chimalapas, Oaxaca, with 74 species (Peralta Moctezuma, 2019); the Lacandon forest, Chiapas, with 49 species <ns0:ref type='bibr' target='#b53'>(Navarrete & Halffter, 2008)</ns0:ref>; and the Tuxtlas forest, Veracruz, with 44 species <ns0:ref type='bibr' target='#b22'>(Favila, 2005)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Local Patterns of Species Richness and Assemblage Structure</ns0:head><ns0:p>Dung beetle communities in variegated windows showed the highest richness values in the REBISO. <ns0:ref type='bibr' target='#b69'>Rös, Escobar & Halffter (2012)</ns0:ref> and <ns0:ref type='bibr' target='#b15'>Costa et al. (2017)</ns0:ref> also found a higher richness of dung beetle species in variegated landscapes. Landscape variegation can be a significant environmental driver of local diversity as it increases the range of habitats available for species by creating a complex composition and configuration <ns0:ref type='bibr' target='#b77'>(Tscharntke et al., 2012;</ns0:ref><ns0:ref type='bibr' target='#b68'>Ramírez-Ponce et al., 2019)</ns0:ref>. The high species richness and diversity found in variegated windows in the REBISO can be attributed to the convergence of multiple dung beetle assemblages including forest specialists (e.g., Eurysternus caribaeus, Sulcophanaeus chryseicollis, and Uroxys boneti), forestpasture edge specialists (O. landolti, Canthon cyanellus), open habitat specialists (D. annae, C. lugubris, O. corrosus), and generalist beetles (O.batesi) <ns0:ref type='bibr' target='#b22'>(Favila, 2005;</ns0:ref><ns0:ref type='bibr' target='#b53'>Navarrete & Halffter, 2008)</ns0:ref>.</ns0:p><ns0:p>Intact and fragmented windows showed lower diversity values than variegated windows. This diversity pattern is consistent with the intermediate disturbance theory <ns0:ref type='bibr' target='#b32'>(Grime, 1973)</ns0:ref>. Sites with little or no disturbance favor the predominance of highly competitive forest specialists such as D. mexicanum, C. vazquezae, and E. maya, which accounted for 85% of the total abundance and 90% of the total biomass in intact windows, thus preventing a higher local diversity. On the other hand, the intense landscape changes caused by livestock production in fragmented windows reduce the local species richness of dung beetles since many native-forest species are unable to adapt to the new open habitat conditions <ns0:ref type='bibr' target='#b33'>(Halffter, Favila & Halffter, 1992;</ns0:ref><ns0:ref type='bibr' target='#b74'>Silva, Storck-Tonon & Vaz-de-Mello, 2016;</ns0:ref><ns0:ref type='bibr' target='#b1'>Alvarado et al., 2018)</ns0:ref>.</ns0:p><ns0:p>The presence of the exotic African species Digitonthophagus gazella (Montes de Oca & Halffter, 1998) in the REBISO is worth mentioning. Although D. gazella was only recorded in pastures of fragmented windows (W6, W7), and contributed with only a small fraction of the community abundance and biomass (six and four percent, respectively), they may pose competitive pressure on native species inhabiting open areas <ns0:ref type='bibr' target='#b47'>(Lobo & Montes de Oca, 1994)</ns0:ref>. Further studies are needed to assess how this invasive beetle might affect native species in the REBISO.</ns0:p><ns0:p>Dung beetles are involved, among other ecological processes, in the recycling of organic matter, soil bioturbation, and secondary seed dispersal <ns0:ref type='bibr' target='#b56'>(Nichols et al., 2008)</ns0:ref>. The amounts of soil removed, dung buried, and seed dispersed are significantly and positively influenced by the species richness and biomass of dung beetle assemblages <ns0:ref type='bibr' target='#b58'>(Nunes et al., 2018;</ns0:ref><ns0:ref type='bibr' target='#b0'>Alvarado, Dáttilo & Escobar, 2019)</ns0:ref>. The tropical forest sites showed the highest dung beetle species richness and biomass values. Besides, forest coverage was positively related to dung beetle biomass in the REBISO. Both results indicate that the tropical forest sites likely contain the most functionally efficient dung beetle assemblages, thus emphasizing the importance of forest conservation in the REBISO.</ns0:p></ns0:div> <ns0:div><ns0:head>Effects of Landscape Composition and Configuration on Dung Beetle Assemblages</ns0:head><ns0:p>Previous studies conducted in tropical ecosystems have identified landscape composition as the main predictor of the diversity of dung beetle assemblages (Sánchez- <ns0:ref type='bibr' target='#b70'>de-Jesús et al., 2016;</ns0:ref><ns0:ref type='bibr' target='#b1'>Alvarado et al., 2018)</ns0:ref>. However, in our study, landscape fragmentation was the primary explanatory variable of variations in species richness and diversity. These findings are likely due to the variegated structure of the REBISO landscape and its 'biodiversity-friendly' matrix of second-growth forest (see <ns0:ref type='bibr' target='#b62'>Perfecto & Vandermeer, 2008;</ns0:ref><ns0:ref type='bibr' target='#b51'>Melo et al., 2013)</ns0:ref>. First, second-growth forests in the REBISO are structurally similar to forest habitats <ns0:ref type='bibr' target='#b67'>(Ramírez-Marcial et al., 2017)</ns0:ref>. Therefore, while many dung beetle species are restricted to forest patches, others may persist and use the second-growth forest matrix to move between forest patches <ns0:ref type='bibr' target='#b18'>(Díaz, Galante & Favila, 2010)</ns0:ref>. Second, fragmentation in variegated environments creates conditions that allow the coexistence of species from different habitat types (e.g., forest species, pasture species, edge specialist species), thereby increasing the diversity of dung beetles at the landscape scale <ns0:ref type='bibr' target='#b79'>(Villada-Bedoya et al., 2016;</ns0:ref><ns0:ref type='bibr' target='#b21'>Fahrig, 2017)</ns0:ref>.</ns0:p><ns0:p>Not all fragmentation effects are beneficial since a higher edge density can have adverse effects on the abundance, biomass, and even the physiological condition of tropical dung beetles (Portela <ns0:ref type='bibr' target='#b63'>Salomão et al., 2018)</ns0:ref>. We found the lowest abundance of dung beetles in variegated windows, where the highest edge density occurs. A higher edge density is coupled with less habitat area, limiting the capacity of the landscape to support medium-and large-sized mammal species. Pozo-Montuy et al. ( <ns0:ref type='formula'>2019</ns0:ref>) observed that medium-and large-sized mammals are significantly less abundant and diverse in the REBISO buffer zone (i.e., where the variegated windows are located). Such reduction in mammal density can cause a marked decrease in dung quantity and availability, thus limiting the growth of dung beetle populations <ns0:ref type='bibr' target='#b54'>(Nichols et al., 2009)</ns0:ref>. Also, microclimatic conditions such as temperature and relative humidity are more variable in forest edges, which might negatively affect the reproduction and survival of dung beetles <ns0:ref type='bibr' target='#b43'>(Klein, 1989;</ns0:ref><ns0:ref type='bibr' target='#b24'>Feer, 2013)</ns0:ref>. Our findings suggest that fragmentation processes in variegated windows foster a high dung beetle diversity, but might also limit their population growth due to insufficient resources, reduced habitat area, or sub-optimal microclimatic conditions. Future studies should assess the strength and extent of this trade-off between dung beetle diversity and abundance, and its functional consequences across the REBISO.</ns0:p></ns0:div> <ns0:div><ns0:head>Beta Diversity Patterns and Mechanisms of Diversity Maintenance</ns0:head><ns0:p>Species turnover is the primary driver of the high diversity and complementarity of the dung beetle communities found in the REBISO. There were between 3 and 27 species not shared between windows, and from 4 to 35 species not shared between vegetation classes. Each window and vegetation class contributed two or three unique species to the overall diversity. The largest turnover values were found between the fragmented windows (W6, W7, and W8) vs. the variegated and intact windows (W1 to W5), and between the forested vegetation classes (tropical forest, second-growth forest) vs. the pasture sites. The anthropic disturbances and the heterogeneous landscape of REBISO favor this high beta diversity since dung beetles are especially susceptible to environmental variability <ns0:ref type='bibr' target='#b3'>(Arellano, Leon-Cortes & Halffter, 2008;</ns0:ref><ns0:ref type='bibr' target='#b15'>Costa et al., 2017)</ns0:ref>.</ns0:p><ns0:p>The differences observed in the species assemblages of fragmented windows (W6, W7, and W8) and those in the other windows (W1 to W5) are not random. Likewise, the differences between tropical forest and pasture assemblages are not random. Significant deviations from random expectations of beta diversity indicate niche-structured assemblages in which environmental filters determine species membership in a community <ns0:ref type='bibr' target='#b13'>(Chase et al., 2011;</ns0:ref><ns0:ref type='bibr' target='#b65'>Püttker et al., 2015)</ns0:ref>. Intensive anthropic disturbances such as deforestation can act as an environmental filter in fragmented windows and pastures, selecting stress-tolerant dung beetle species able to survive in open habitats <ns0:ref type='bibr' target='#b33'>(Halffter, Favila & Halffter, 1992;</ns0:ref><ns0:ref type='bibr' target='#b76'>Spector & Ayzama, 2003;</ns0:ref><ns0:ref type='bibr' target='#b30'>Gardner et al., 2008)</ns0:ref>. Deltochilum mexicanum, C. vazquezae, S. chryseicollis, Canthon femoralis, and E. maya probably are the species most sensitive to the environmental filters caused by anthropic disturbance. Although these forest species are widely distributed in the biosphere reserve <ns0:ref type='bibr' target='#b71'>(Sánchez-Hernández et al., 2018)</ns0:ref>, their abundance was drastically reduced in fragmented windows.</ns0:p><ns0:p>We found signs of biotic homogenization in the pasture sites. For instance, the lowest alfa and beta diversity values were recorded in pastures, and their species assemblages were more similar to each other than expected by chance, regardless of the windows where they were located, indicating shared environmental filtering processes <ns0:ref type='bibr' target='#b12'>(Chase, 2010)</ns0:ref>. Anthropogenic environmental filters are one of the main drivers of biotic homogenization, eroding alfa and beta diversity and diminishing ecosystem resilience and viability <ns0:ref type='bibr' target='#b28'>(Gámez-Virués et al., 2015)</ns0:ref>. Hence, the advance of the agricultural frontier in the REBISO landscape should be monitored closely to prevent further biotic homogenization processes among the dung beetle species assemblages.</ns0:p><ns0:p>In our study, 1 β between the intact and variegated windows (W1 to W5), as well as between tropical forests and second-growth forests, was higher than 0 β. Thus, the true beta diversity is mainly due to differences in the abundance of shared species rather than to differences in richness <ns0:ref type='bibr' target='#b41'>(Jost, 2007)</ns0:ref>. Besides, the overall beta diversity between these windows and vegetation classes was not different from that expected by chance. Most species in neutral communities are considered ecologically equivalent since, in the absence of any factor limiting their dispersal, they can appear at random in any of the null assemblages <ns0:ref type='bibr' target='#b65'>(Püttker et al., 2015;</ns0:ref><ns0:ref type='bibr' target='#b60'>Ortega-Martínez et al., 2020)</ns0:ref>. Both results suggest that the REBISO still holds sufficient vegetation cover to maintain a continuous flow of dung beetles between forested landscape sections (W1 to W5).</ns0:p><ns0:p>Given the significant stochasticity of beta diversity between intact and variegated windows, and between tropical forests and second-growth forests, we can conclude that the landscape variegation in the REBISO does not affect dung beetle diversity negatively. However, it is essential to conserve the forested patches to maintain a high dispersal between sites, thereby increasing the resilience of dung beetle populations to habitat loss and isolation <ns0:ref type='bibr' target='#b9'>(de Castro Solar et al., 2015;</ns0:ref><ns0:ref type='bibr' target='#b75'>Socolar et al., 2016)</ns0:ref>. Landscape variegation can be an effective strategy to buffer the impact of intense anthropic disturbances <ns0:ref type='bibr' target='#b69'>(Rös, Escobar & Halffter, 2012;</ns0:ref><ns0:ref type='bibr' target='#b15'>Costa et al., 2017)</ns0:ref>. Variegation can be achieved by maintaining the forest cover and incorporating more biodiversity-friendly production systems, such as agroforestry practices, in the landscape matrix <ns0:ref type='bibr' target='#b62'>(Perfecto & Vandermeer, 2008)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions</ns0:head><ns0:p>This research contributes to better understand how diversity is distributed in variegated landscapes, and the role of heterogeneous landscapes in the conservation and management of tropical biodiversity. Tropical forests and second-growth forests contributed significantly to maintaining the diversity and biomass of dung beetle assemblages. The variegated structure of the landscape fosters a high dung beetle diversity. The heterogeneity of the REBISO landscape favors the formation of complementary dung beetle communities. Both deterministic and stochastic processes drive the beta-diversity patterns in the landscape. Intense anthropic disturbances in fragmented windows and pastures act as non-stochastic filters upon dung beetle species, eroding the alpha and beta diversity of these sites. By contrast, random processes govern the less disturbed sites of the REBISO: fragmented tropical forests and second-growth forests. Increasing habitat variegation in highly disturbed sites can be an effective strategy to buffer and prevent further biotic homogenization processes. Manuscript to be reviewed Manuscript to be reviewed Manuscript to be reviewed Mean abundance and biomass (g) per trap (± sd) in each window and vegetation class. Manuscript to be reviewed </ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>); C. vazqueze, E. maya, and Eurysternus angustulus were the most abundant ones in variegated windows W3, W4, and W5; and O. batesi, O. corrosus, and Copris lugubris were the most abundant species in fragmented windows W6, W7, and W8 (Fig S1a). Biomass patterns in the dung beetle communities differed from those observed in their abundance values. Deltochilum mexicanum, E. maya, and Ontherus mexicanus were the dominant species, in terms of biomass, in intact windows W1 and W2 (Fig S1a); D. mexicanum, E. maya, and Dichotomius amplicollis were the dominant species in W3; D. mexicanum, D. amplicollis, and Dichotomius annae in W4; and Coprophanaeus corythus, Deltochilum sublaeve, and D. amplicollis in W5. Coprophanaeus corythus, C. lugubris, and D. amplicollis were the species with the highest biomass in fragmented windows W6, W7, and W8 (Fig S1a).</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>Deltochilum mexicanum, C. vazquezae, and E. maya were the most abundant species in tropical forest sites (Fig S1b); O. corrosus, D. mexicanum, and E. maya in the second-growth forest; and O. batesi, O. corrosus, and C. lugubris in pasture sites (Fig S1b). Deltochilum mexicanum, E. maya, and O. mexicanus contributed with the highest biomass in tropical forests; D. mexicanum, C. corythus, and E. maya in the second-growth forests (Fig S1b); and C. corythus, C. lugubris, and D. amplicollis in pasture sites (Fig S1b).</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>Figure 1</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head>Figure 2 (</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head>Figure 3 (</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Figure 3</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head>( A )</ns0:head><ns0:label>A</ns0:label><ns0:figDesc>Black dots: Sørensen beta diversity (βSor) between windows. White bars: Percentage contribution of species turnover (βSim) to beta diversity (βSim/βSor); Black bars: Percentage contribution of species-nestedness (βSne) to beta diversity (βSne/βSor).Forest in a mature successional stage with a dense canopy cover. The most common tree species are Pseudolmedia spuria, Louteridium donnell-smithii, Manilkara sapota, Swietenia macrophylla and Quararibea funebris<ns0:ref type='bibr' target='#b73'>(SEMARNAT/CONANP 2001;</ns0:ref><ns0:ref type='bibr' target='#b67'>Ramírez- Marcial et al. 2017)</ns0:ref>. Mean canopy cover, 82.32% (±1.35 s.e.); mean basal area, 912.24 cm 2 (±163.88 s.e.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head>2</ns0:head><ns0:label /><ns0:figDesc>PeerJ reviewing PDF \| (2020:03:46967:1:0:NEW 1 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_7'><ns0:head>F</ns0:head><ns0:label /><ns0:figDesc>: Forest; SF: Second-growth forest; P: Pasture PeerJ reviewing PDF \| (2020:03:46967:1:0:NEW 1 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_8'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ reviewing PDF \| (2020:03:46967:1:0:NEW 1 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_9'><ns0:head>F</ns0:head><ns0:label /><ns0:figDesc>: Forest; SF: Second-growth forest; P: Pasture. Pairwise comparison results are shown in TableS5. Different letters indicate statistically significant differences between windows and between vegetation classes (P<0.05).PeerJ reviewing PDF \| (2020:03:46967:1:0:NEW 1 Jul 2020)</ns0:figDesc></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head /><ns0:label /><ns0:figDesc>). Forest in intermediate successional stage, recovering after 1998 fire; the canopy is less dense than in the tropical forest. Dominated by Heliocarpus appendiculatus and Eugenia acapulcensis (SEMARNAT/CONANP, 2001; Ramírez-Marcial et al. 2017). Mean canopy cover, 56.76% (± 3.22 s.e.); mean basal area, 577.65 cm 2 (± 105.14 s.e.). Pastures are at least ten years old (SEMARNAT/CONANP, 2001). The few trees present are used mainly as shade for cattle. Mean basal area, 874.29 cm 2 (± s.e. 94.60); canopy cover ranges from 2% to 53% ( 22.11%, ± s.e 3.03). 𝑥 1 a Mean coverage over the eight windows.</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Second-growth forest</ns0:cell></ns0:row><ns0:row><ns0:cell>(n=8): a 30%</ns0:cell></ns0:row><ns0:row><ns0:cell>W1, W2, W3, W4,</ns0:cell></ns0:row><ns0:row><ns0:cell>W5, W6, W7, W8</ns0:cell></ns0:row><ns0:row><ns0:cell>Pasture (n=5): a 32%</ns0:cell></ns0:row><ns0:row><ns0:cell>W3, W4, W5, W6,</ns0:cell></ns0:row><ns0:row><ns0:cell>W7, W8</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_1'><ns0:head>Table 2 (on next page)</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>values in each window and vegetation class at Reserva de la Biosfera Selva El Ocote, Mexico.</ns0:figDesc><ns0:table /><ns0:note>0 D and 1 D</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_2'><ns0:head /><ns0:label /><ns0:figDesc>/15.84 b 𝜶 18.85 19.5 18.66 27.37 a /18.9 b 2.93 6.57 7.34 7.09 a /6.15 b 𝜷 2.38 2.25 1.87 2.01 a /2.9 b 2.64 2.36 1.21 2.23 a /2.57 b 1 a Overall window diversity, b Overall vegetation class diversity.</ns0:figDesc><ns0:table><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>0 D</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>1 D</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>F</ns0:cell><ns0:cell>SF</ns0:cell><ns0:cell>P</ns0:cell><ns0:cell>Species richness</ns0:cell><ns0:cell>F</ns0:cell><ns0:cell>SF</ns0:cell><ns0:cell>P</ns0:cell><ns0:cell>Exp (Shannon diversity)</ns0:cell></ns0:row><ns0:row><ns0:cell>W1</ns0:cell><ns0:cell /><ns0:cell>11</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>22</ns0:cell><ns0:cell>5.23</ns0:cell><ns0:cell>4.8</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>5.36</ns0:cell></ns0:row><ns0:row><ns0:cell>W2</ns0:cell><ns0:cell /><ns0:cell>12</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>24</ns0:cell><ns0:cell cols='2'>5.05 4.27</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>4.97</ns0:cell></ns0:row><ns0:row><ns0:cell>W3</ns0:cell><ns0:cell /><ns0:cell>21</ns0:cell><ns0:cell>11</ns0:cell><ns0:cell>28</ns0:cell><ns0:cell cols='3'>7.71 7.22 5.38</ns0:cell><ns0:cell>8.69</ns0:cell></ns0:row><ns0:row><ns0:cell>W4</ns0:cell><ns0:cell /><ns0:cell>29</ns0:cell><ns0:cell>20</ns0:cell><ns0:cell>37</ns0:cell><ns0:cell cols='3'>12.39 13.51 13.91</ns0:cell><ns0:cell>16.49</ns0:cell></ns0:row><ns0:row><ns0:cell>W5</ns0:cell><ns0:cell /><ns0:cell>32</ns0:cell><ns0:cell>19</ns0:cell><ns0:cell>35</ns0:cell><ns0:cell cols='3'>9.88 16.37 9.55</ns0:cell><ns0:cell>17.59</ns0:cell></ns0:row><ns0:row><ns0:cell>W6</ns0:cell><ns0:cell>5</ns0:cell><ns0:cell>14</ns0:cell><ns0:cell>19</ns0:cell><ns0:cell>23</ns0:cell><ns0:cell cols='3'>3.50 5.51 6.40</ns0:cell><ns0:cell>6.57</ns0:cell></ns0:row><ns0:row><ns0:cell>W7</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>17</ns0:cell><ns0:cell>19</ns0:cell><ns0:cell>24</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell cols='2'>8.08 7.38</ns0:cell><ns0:cell>7.61</ns0:cell></ns0:row><ns0:row><ns0:cell>W8</ns0:cell><ns0:cell>2</ns0:cell><ns0:cell>20</ns0:cell><ns0:cell>21</ns0:cell><ns0:cell>26</ns0:cell><ns0:cell cols='3'>1.50 3.46 9.51</ns0:cell><ns0:cell>5.49</ns0:cell></ns0:row><ns0:row><ns0:cell>𝜸</ns0:cell><ns0:cell /><ns0:cell>44</ns0:cell><ns0:cell>34</ns0:cell><ns0:cell>55 a /55 b</ns0:cell><ns0:cell cols='3'>7.75 15.54 8.93</ns0:cell><ns0:cell>15.84 a</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_3'><ns0:head>Table 3 (on next page)</ns0:head><ns0:label>3</ns0:label><ns0:figDesc /><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_4'><ns0:head>Mean abundance/trap (±sd) P < 0.05 Mean biomass/trap (±sd) P < 0.05 W1</ns0:head><ns0:label /><ns0:figDesc /><ns0:table><ns0:row><ns0:cell /><ns0:cell>52.94 (11.13)</ns0:cell><ns0:cell>a</ns0:cell><ns0:cell>9.07 (1.01)</ns0:cell><ns0:cell>a</ns0:cell></ns0:row><ns0:row><ns0:cell>W2</ns0:cell><ns0:cell>44.95 (0.11)</ns0:cell><ns0:cell>a</ns0:cell><ns0:cell>8.20 (0.93)</ns0:cell><ns0:cell>a, b</ns0:cell></ns0:row><ns0:row><ns0:cell>W3</ns0:cell><ns0:cell>20.83 (13.94)</ns0:cell><ns0:cell>a</ns0:cell><ns0:cell>2.37 (0.97)</ns0:cell><ns0:cell>c</ns0:cell></ns0:row><ns0:row><ns0:cell>W4</ns0:cell><ns0:cell>15.72 (7.88)</ns0:cell><ns0:cell>a</ns0:cell><ns0:cell>2.53 (0.72)</ns0:cell><ns0:cell>c</ns0:cell></ns0:row><ns0:row><ns0:cell>W5</ns0:cell><ns0:cell>29.55 (10.88)</ns0:cell><ns0:cell>a</ns0:cell><ns0:cell>3.89 (1.70)</ns0:cell><ns0:cell>b, c</ns0:cell></ns0:row><ns0:row><ns0:cell>W6</ns0:cell><ns0:cell>15.82 (5.12)</ns0:cell><ns0:cell>a</ns0:cell><ns0:cell>2.20 (0.49)</ns0:cell><ns0:cell>c</ns0:cell></ns0:row><ns0:row><ns0:cell>W7</ns0:cell><ns0:cell>35.26 (18.29)</ns0:cell><ns0:cell>a</ns0:cell><ns0:cell>2.75 (0.05)</ns0:cell><ns0:cell>c</ns0:cell></ns0:row><ns0:row><ns0:cell>W8</ns0:cell><ns0:cell>36.51 (46.98)</ns0:cell><ns0:cell>a</ns0:cell><ns0:cell>3.71 (1.22)</ns0:cell><ns0:cell>b, c</ns0:cell></ns0:row><ns0:row><ns0:cell>F</ns0:cell><ns0:cell>30.28 (13.91)</ns0:cell><ns0:cell>a</ns0:cell><ns0:cell>5.05 (2.21)</ns0:cell><ns0:cell>a</ns0:cell></ns0:row><ns0:row><ns0:cell>SF</ns0:cell><ns0:cell>37.89 (26.86)</ns0:cell><ns0:cell>a</ns0:cell><ns0:cell>4.53 (3.04)</ns0:cell><ns0:cell>a</ns0:cell></ns0:row><ns0:row><ns0:cell>P</ns0:cell><ns0:cell>17.74 (15.56)</ns0:cell><ns0:cell>a</ns0:cell><ns0:cell>2.09 (0.62)</ns0:cell><ns0:cell>b</ns0:cell></ns0:row><ns0:row><ns0:cell>1</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>2</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row></ns0:table></ns0:figure> <ns0:note place='foot'>PeerJ reviewing PDF \| (2020:03:46967:1:0:NEW 1 Jul 2020)</ns0:note> <ns0:note place='foot'>PeerJ reviewing PDF \| (2020:03:46967:1:0:NEW 1 Jul 2020)Manuscript to be reviewed</ns0:note> <ns0:note place='foot' n='2'>PeerJ reviewing PDF \| (2020:03:46967:1:0:NEW 1 Jul 2020)Manuscript to be reviewed</ns0:note> </ns0:body> "	"July 1, 2020 Dear Editor, Dr. Nigel Andrew Academic Editor, Peer J This is to respectfully submit to your kind consideration the revised version of our manuscript entitled 'Mechanisms of diversity maintenance of dung beetle assemblages in a heterogeneous tropical landscape'. First, we want to thank the reviewers for their valuable comments on a previous version of our manuscript. In this revised version, we accepted all the changes suggested by the reviewers and, as described below, addressed all their concerns about the text. In particular, we corrected the figures and tables, included a second analysis modeling the relationship between landscape and the richness, diversity, abundance, and biomass of dung beetles, and modified the discussion to make it clearer. We hope you find this updated version suitable for publication in PeerJ. Thanks in advance for your attention and hoping to hear from you soon, José D. Rivera Duarte Ph.D. student, Instituto de Ecología, A.C. Xalapa, Veracruz, Mexico. Reviewer 1: Basic reporting English: English is good and the technical/scientific terms are employed properly. Intro & Background: It is adequate for a wide audience. The introduction is well structured and brings a relevant background about the main problem addressed in this study. Structure: The study is well structured, with primary and secondary sections presented properly. Figures: Some figures could be improved. Below, I present a detailed description of my suggestions (see Minor issues). R/ We improved the figures following the suggestions made by Reviewer 1. Raw data: I appreciate the authors provided raw data. However, supporting information has no titles and it is difficult to read apart. R/ Correct, thanks for the observation. Titles have been added. Experimental design Original primary research: This type of research is not totally new but needed because there are not many studies evaluating dung beetles under these approaches. Research question: The central question is relevant; also, the specific questions are clear and adequate. Technical & Ethical standards: Samplings were carried out using standard methods to collect dung beetles and permitted by legal institutions. Besides, voucher material was deposited in a public institution. Methods: In general, the methods are well presented. However, I would like to see a more detailed presentation of the samplings at each site. For instance, the authors have sites, within vegetation classes, within landscape windows. The analytical approach takes into account vegetation classes and landscape windows but they are not totally independent because some vegetation classes only occur in more fragmented landscapes. How do the authors evaluate this kind of dependence? R/ We added more details of the sampling sites on the map. The main philosophy of our sampling system, formally proposed by Halffter & Rös (2013), is to adequately represent the heterogeneity of complex and heterogeneous landscapes, taking into consideration the proportional effects of the elements that make them up (e.g., land-uses or vegetation classes). The spatial approach allows disentangling how diversity is distributed across the landscape, whereas the structural approach provides a “semi-random” sample of the dung beetle diversity within each vegetation-class present in the landscape. In addition, the structural approach takes into account local land-use dynamics, where one can find pastures in some locations but these might be absent from other localities due to management decisions. While we followed the structural and spatial approaches in parallel, they were not crossed. Rather, they were used in a complementary fashion to get a better idea of how the landscape heterogeneity influences dung beetle diversity (e.g., Rös, Escobar & Halffer, 2012). On the other hand, the results of Moran I showed no spatial dependency in any of our response variables, either in the spatial or structural approaches, thus ensuring independence between sampling units. Novelty: The results are good and novel. But I suggest the authors considering a better presentation of both approaches used. Some results are confusing, especially those related to species turnover. R/ Agree, we modified the results and discussion sections to improve their quality and clarity. Data: The raw data were provided. They are robust and statistically sound. The authors based their line of argumentation on their results. Speculation: The authors based their line of argumentation on their results. Conclusions: They are ok, but a reinterpretation of results based on a better presentation of both analytical approaches used to evaluate the central questions of this study is needed. R/ Agree, the results and discussion sections have been rewritten and reinterpreted. Comments for the Author Assessment of the manuscript entitled “Mechanisms of diversity maintenance of dung beetle assemblages in a heterogeneous tropical landscape” for PeerJ (peerj-46967) Main Comments: In this paper, the authors aimed to evaluate how the diversity (species richness and Shannon diversity), abundance, and biomass of dung beetles vary across the heterogeneous tropical landscape of Selva El Ocote Biosphere Reserve, Mexico. This study has a good sample design and data to investigate the effects of fragmentation on dung beetles. However, I think that the analytical issues and interpretation of the results should be separated by the two approaches used: spatial approach (windows) and structural approach (classes of vegetation within the windows), as they are not as independent since certain classes of vegetation will be more represented or will occur only in certain classes of fragmentation (e.g. pasture). I raise this issue because the species turnover is said to be random or non-random, depending on the approach used, and this result causes some confusion in the reader. How can the same ecological process be random or non-random? Authors need to improve the presentation and discussion of both approaches so that the reader can better understand the results. I would also like to have a more in-depth discussion of the two approaches and the conservation implications of each. After addressing these issues, the paper will be a good contribution to fragmentation ecology. As follows I present a detailed evaluation. R/ We concur with the view that our results needed reinterpretation as they were confusing. We respond to the main issues pointed out by the reviewer in the following section. We also improved the presentation and discussion of our results, showing that both neutral and random processes maintain dung beetle diversity in the REBISO. We used both the spatial and structural approaches to complement our discussion, responding how diversity is distributed through the landscape, while also discussing the effects and importance of the vegetation classes to diversity maintenance. Finally, we discussed the implications of our results for conservation. Reviewer detailed evaluation: Abstract: 1. L 25-26 I am not sure about vegetation classes. Wouldn't “secondary vegetation” be a type of “tropical forest”? The difference is the successional stage only. R/ Agree. However, we retained the second-growth forest as a distinct vegetation class for analysis purposes, similar to Nichols et al., (2007), Gardner et al.,(2009) and Braga et al., (2013). 2. L 27-28 Perhaps providing average and standard deviation (or error) values could be a better option here because the values sometimes overlap each other when the authors talk about higher and lower species richness: “The highest species richness was recorded in variegated windows (28-37 species) and tropical forest (45 species), while the lowest values were found in intact windows (22-24 species) and pasture (34 species).” R/ Thanks for the suggestion. However, we modified the entire abstract and wanted it to be more succinct, without going into too much detail on specific figures. 3. L 31 Why would it be a “random turnover”? Do authors think that intact forests would not have species restricted to this type of habitat? R/ Our null models showed that the beta diversity between the tropical and second-growth forest communities, as well as between the intact and variegated window communities, was random. These results led us to conclude that species turnover between those sites is random. We do not reject the possibility of some beetle species being restricted to one type of habitat. However, our data showed that species restricted to one single habitat or site are singletons (e.g., Ateuchus candezei, Cyptocanthon lobatus, Onthophagus maya, Uroxys deavilai), from which it is difficult to draw any conclusions. 4. L 32 I also disagree with the following statement as an explanation to the high beta diversity: “this suggests a high level of connectivity in the landscape.” If there is a high beta diversity between landscape components or habitats, the connectivity among them, measured by compositional changes of dung beetles, is not high if beta diversity dominates. If turnover dominates beta diversity it means some species are restricted spatially, which consequently decreases connectivity. R/ Agree. We re-interpreted our results in the discussion section and rewrote the abstract accordingly. Introduction: 5. L 66 I suggest the authors be more specific about what “secondary vegetation” does mean. It could be a secondary tropical forest, right? Or is it a different thing? R/ Thanks for the observation, by “secondary vegetation” we mean secondary tropical forest (which we now describe as “second-growth forest” in the text). We changed this term throughout the text to avoid confusion. Materials and Methods: 6. L 97 The authors can remove spaces between degrees. There are some coordinates with and without spaces between degrees. R/ Done 7. L 113 Add a comma after ‘et al.’ R/ Done 8. Rev. 1~ L 126 See also da Silva and Hernández (2015, Plos One, https://doi.org/10.1371/journal.pone.0126112) about trapping distance. R/ We were fully aware of the trapping distance proposed by da Silva & Hernández (2015). However, the topography and area of each window did not allow us to keep the 100 m sampling separation as we intended. We had to use, instead, the 50-meter distance recommended by Larsen & Forsyth, (2005), which also enabled us to have a higher sampling density in each window. 9. L 146-154 Why do authors use two different methods for the same purpose? Anne Chao’s approach is a more robust approach than using Estimates, which is no longer being updated (see http://viceroy.eeb.uconn.edu/estimates/). R/ We agree. We dropped the analysis with Estimates. We now used Inext to calculate species richness (0D) for windows and vegetation classes. We constructed bootstrap confidence intervals of species richness to compare 0D between windows (Gotelli & Colwell, 2011). We did not rarefy the windows’ species richness to a base abundance or base coverage, since each window sampling coverage was 0.99. 10. L 159 Change “allow” to “account” R/ Done 11. L 163 Ok, but do you use species richness and/or Shannon diversity? It is not totally clear. R/ We calculated true beta diversity using both species richness and Shannon diversity. The text has now been modified as “ True beta diversity (i.e., effective number of distinct communities) was estimated for species richness (0β) and Shannon diversity (1β) using the multiplicative partitioning method (Jost, 2007).” 12. L 164-166 Do you mean “betapart R package”? Besides, the nestedness accounted for this metric does not take into account richness differences per se. Therefore, if you have strong richness differences (lack of species shared) and not nestedness, the turnover component estimated by Baselga’s approach is overestimated. See Legendre (2014, Global Ecology and Biogeography, https://doi.org/10.1111/geb.12207) for options, if needed. R/ Yes, the text refers to the betapart R package. We concur with your statement about Baselga’s metrics independence from differences in richness. However, we respectfully disagree with the statement that the properties of the Baselga approach pose a problem to our analysis since, contrary to the Podani family (the alternative family of metrics available to calculate species replacement), species replacement and nestedness are not dependent or constrained by differences in richness. Instead, as expressed by Baselga & Leprieur (2015): 'even in the absence of shared species (i.e., a = 0), the replacement component in the POD framework decreases with increasing richness difference... while it remains constant at its maximum in the BAS framework '. Besides, we prefer to use the Baselga family of indexes since, according to Baselga (2012), the Podani family indexes could underestimate species replacement (the main component of beta diversity in our study) due to its dependence on richness differences. On the other hand, we believe that the Baselga’s framework provides complementary information to the other beta diversity analyses that we used (as seen with the diversity partitions and Sørensen's dissimilarity results). Results: 13. L 190 You see, here the authors use “secondary forest” not “secondary vegetation” as a vegetation class. R/ Agree. We homogeneized the name used for this landscape category throughout the manuscript as second-growth forest. 14. L 199 Add a space here “E.maya” R/ Done 15. L 205 “mexicanus” is italicized. R/ Done 16. L 214 Add a space here “df=” R/ Done 17. L 216 Add a space here “df=” R/ Done 18. L 239 Add a space here “C.vazquezae” R/ Done 19. L 243 Add a space here “C.corythus” R/ Done 20. L 247 Add a space here “df=” R/ Done 21. L 250 Add a space here “df=” and here “P=” R/ Done 22. L 251 “secondary vegetation” or “secondary forest”? R/ Second-growth forest, text was corrected 23. L 258 Correct to “nestedness processes” R/ Done 24. L 259“secondary vegetation” or “secondary forest”? R/ Text is now corrected and homogenized to second-growth forest. Discussion: 25. L 270 Why do authors think there is a “stochastic distribution of species” here? R/ The results of our null models showed that the distribution of species is stochastic in some parts of the studied landscape. Nevertheless, we modified much of the discussion to make it more accurate. 26. L 280 Well, the intermediate disturbance theory is not Connell's. See Wilkinson’s work about “The Disturbing History of Intermediate Disturbance” (Oikos, 84(1): 145-147; https://www.jstor.org/stable/3546874?seq=1). R/ Thanks for the clarification. We corrected the text using the proper citation. 27. L 292 Add a space here “E.maya” R/ Done 28. L 296 Correct “Digitonthotophagus” to “Digitonthophagus” R/ Done 29. L 310 Using both “Beta (β)” is redundant. R/ Agree, we removed (β) from the title 30. L 313 My comment about how nestedness was calculated has its foundation here. Is there at least one species shared between windows and habitats? If not, the turnover would be overestimated since richness differences are not accounted for. R/ Thanks for the criticism and suggestions. We modified the text of this section to improve its clarity. Nonetheless, as stated previously (comment # 12), Baselga & Lepeour (2016) showed that replacement (turnover) component of the Baselga index family remains constant, regardless of the richness difference between sites. 31. Rev. 1~ L 321-322 Well, earlier the authors stated that species distributions are random or stochastic! E.g. line 31-32: “random turnover of species between intact and variegated windows, and between tropical forests and secondary vegetation” R/ We agree. Our results required a better interpretation for a more precise discussion. We discuss now how both deterministic and stochastic processes drive beta-diversity patterns in the REBISO landscape. For instance, beta diversity between W1 to W5 (composed of 80% to 90% tropical forest and second-growth forest) was not different from that expected by chance. Here we argue that most species in neutral communities are considered ecologically equivalent since, in the absence of any limiting factor to their dispersal, they can appear at random within any of the null assemblages (Püttker et al., 2015; Ortega-Martínez et al., 2020). We also showed that differences between the species assemblages of the fragmented windows (W6, W7, and W8) vs. the other windows (W1 to W5) are not random. Likewise, the differences between the tropical forest’s and pasture’s assemblages are not random. Here, we argue that significant deviations from random expectations of beta diversity indicate niche-structured assemblages, in which environmental filters determine species membership (Chase & Myers, 2011). Our results do not present contradictions, regardless of the analysis approach used. Rather, we believe that using both methods improves the discussion. For example, the null model analysis using the structural approach showed that the species assemblages in pasture sites shared environmental filtering processes, regardless of which window they were located, since they were more similar to each other than expected by chance. 32. L 349-352 I got confused! Previously, you stated “Species turnover between fragmented (W6, W7, and W8) and other windows (W1 to W5) is not a random process; likewise, the differences in diversity between tropical forest and pasture sites are not random.” (Lines 320-322). R/ Both deterministic and stochastic processes drive beta-diversity patterns in the REBISO landscape. The fragmented windows and pastures act as non-stochastic filters on the dung beetle species, eroding alpha and beta diversity in these sites due to the intense anthropic disturbances. However, random processes govern the less disturbed sites of the REBISO, which are mainly composed of tropical forest and second-growth forest. We changed the discussion section to improve its clarity and quality. Figures: 33. Fig. 3 Fig. 3a is not good to read. I suggest separating it into two: one for abundance, one for biomass. Fig. 3n – title: “between windows” or within windows? R/ We appreciate the suggestions. Figure 3a was transferred to Supplementary figures. However, we decided to keep the plots together so that the reader can easily compare both curves. To make them easier to read, we assigned a different color to each curve. Also, we improved the image resolution and added a table with the Id of each species. Additionally, “between the windows” was replaced with “within the windows.” 34. Rev. 1~ Fig. 4 Fig. 4a is not good to read. They overlap each other. I suggest separating it into two: one for abundance, one for biomass. R/ Thanks for the suggestions. The rank-abundance and rank-biomass curves (Figure 4a) were transferred to supplementary figures and “between the windows” was replaced with “within the windows.” Tables: 35. Rev. 1~ Table 2 Remove “alpha, beta and gamma” from “Overall alpha, beta and gamma diversity over all the windows” and “Overall alpha, beta and gamma diversity over all the vegetation classes”; it is implicit in the table. Do not forget to specify what “F”, “SV” and “P” mean. R/ Done 36. Rev. 1~ Table 3 Do not forget to specify what “F”, “SV” and “P” mean. R/ Done References: Alvarado F, Salomão RP, Hernandez-Rivera Á, de Araujo Lira AF. 2020. Different responses of dung beetle diversity and feeding guilds from natural and disturbed habitats across a subtropical elevational gradient. Acta Oecologica 104:103533. DOI: 10.1016/j.actao.2020.103533. Baselga A. 2012. The relationship between species replacement, dissimilarity derived from nestedness, and nestedness. Global Ecology and Biogeography 21:1223–1232. DOI: 10.1111/j.1466-8238.2011.00756.x. Baselga A, Leprieur F. 2015. Comparing methods to separate components of beta diversity. Methods in Ecology and Evolution 6:1069–1079. DOI: 10.1111/2041-210X.12388. Braga RF, Korasaki V, Andresen E, Louzada J. 2013. Dung Beetle Community and Functions along a Habitat-Disturbance Gradient in the Amazon: A Rapid Assessment of Ecological Functions Associated to Biodiversity. PLOS ONE 8. DOI: 10.1371/journal.pone.0057786. Chase JM, Myers JA. 2011. Disentangling the importance of ecological niches from stochastic processes across scales. Philosophical Transactions of the Royal Society B: Biological Sciences 366:2351–2363. DOI: 10.1098/rstb.2011.0063. Filgueiras BKC, Tabarelli M, Leal IR, Vaz-de-Mello FZ, Peres CA, Iannuzzi L. 2016. Spatial replacement of dung beetles in edge-affected habitats: biotic homogenization or divergence in fragmented tropical forest landscapes? DIVERSITY AND DISTRIBUTIONS 22:400–409. DOI: 10.1111/ddi.12410. Gardner TA, Barlow J, Chazdon R, Ewers RM, Harvey CA, Peres CA, Sodhi NS. 2009. Prospects for tropical forest biodiversity in a human-modified world. Ecology Letters 12:561–582. DOI: 10.1111/j.1461-0248.2009.01294.x. Gotelli N, Colwell R. 2011. Estimating species richness. In: Biological Diversity. Frontiers in Measurement and Assessment. 39–54. DOI: 10.2307/3547060. Halffter G, Rös M. 2013. A Strategy for Measuring Biodiversity. Acta Zoológica Mexicana 29:400–411. Jost L. 2007. Partitioning diversity into independent alpha and beta components. Ecology 88:2427–2439. DOI: 10.1890/06-1736.1. Larsen TH, Forsyth A. 2005. Trap spacing and transect design for dung beetle biodiversity studies. Biotropica 37:322–325. DOI: 10.1111/j.1744-7429.2005.00042.x. Marsh C, Feitosa R, Louzada J, Ewers R. 2018. Is diversity of Amazonian ant and dung beetles communities elevated at rainforest edges? JOURNAL OF BIOGEOGRAPHY 45:1966–1979. DOI: 10.1111/jbi.13357. Nichols E, Larsen T, Spector S, Davis a. L, Escobar F, Favila M, Vulinec K. 2007. Global dung beetle response to tropical forest modification and fragmentation: A quantitative literature review and meta-analysis. Biological Conservation 137:1–19. DOI: 10.1016/j.biocon.2007.01.023. Ortega-Martínez IJ, Moreno CE, Rios-Díaz CL, Arellano L, Rosas F, Castellanos I. 2020. Assembly mechanisms of dung beetles in temperate forests and grazing pastures. Scientific Reports 10:1–10. DOI: 10.1038/s41598-019-57278-x. Püttker T, de Arruda Bueno A, Prado PI, Pardini R. 2015. Ecological filtering or random extinction? Beta-diversity patterns and the importance of niche-based and neutral processes following habitat loss. Oikos 124:206–215. DOI: 10.1111/oik.01018. Salomão RP, Alvarado F, Baena-Díaz F, Favila ME, Iannuzzi L, Liberal CN, Santos BA, Vaz-de-Mello FZ, González-Tokman D. 2019. Urbanization effects on dung beetle assemblages in a tropical city. Ecological Indicators 103:665–675. DOI: 10.1016/j.ecolind.2019.04.045. da Silva PG, Hernández MIM. 2015. Spatial Patterns of Movement of Dung Beetle Species in a Tropical Forest Suggest a New Trap Spacing for Dung Beetle Biodiversity Studies. Plos One 10:e0126112. DOI: 10.1371/journal.pone.0126112. Reviewer 2 Basic reporting Some moderate revision of the written English is necessary. There are a few issues with verb tenses or run on sentences throughout the manuscript that affects the clarity of the text. R/ Agree, the language was thoroughly revised and corrected as appropriate. They do have good references, but other references might be added to contrast or support their discussion. R/ The references suggested were added to the text. The structure, figures and tables are ok, just minimal editing will be needed. R/ We improved the figures following the reviewers' suggestions. Very relevant results, but the general discussion lacks of substance, they contrast their results with other publications, but more information and discussion about their results will be better to improve the manuscript. R/ Agree, we added a more in-depth discussion of the results. Experimental design The authors follow the common and used methodology for this kind of studies and group. Some minimal details that need to be explained in detail are suggested. This research is done in a Biosphere Reserve, and their results should be used to improve the conservation efforts. They have a lot of interesting findings but not addressed them in a circular discussion or conclusion. Methodology is good, some clarification is suggested in specific parts of the text. R/ We rewrote the results and discussion sections following the reviewer’s comments and suggestions. Validity of the Findings Interesting results for a very well study group. Authors should take advantage of the natural history and biology of the group, that some of the authors have, to describe and discuss with more detail their results. Discussion and conclusion can be improved. Comments for the Authors The authors studied dung beetles’ diversity patterns and their maintenance mechanisms in a Biosphere reserve at the south of Mexico. Their study has a good methodological approach, from the fieldwork protocol through the diversity analyses. Their results are very interesting and can be better exploited. A broader discussion will be better, there is a lot of potential in them and you can go further with your findings. Especially when talking about the higher diversity in variegated windows in a Biosphere Reserve, they can take advantage of their information, and discuss more conscientiously their results in terms of conservation efforts. Overall, the manuscript is very good and with valuable information. Below are some remarks, comments and questions that I believe must be addressed prior to consider publication to improve the manuscript. • First, some moderate revision of the written English is necessary. There are a few issues with verb tenses or run on sentences throughout the manuscript that affects the clarity of the text. R/ Done • Be consistent with the use of the entire genus name along the text, you change the way you use it, even in the same paragraph. R/ For the sake of grammatical formalism, we do not use initials at the beginning of a sentence (i.e., after a period). For this reason, the full name of the genus or just its initial may appear in the same paragraph. • Review again the biology and preferences of some of the species, there are some inconsistencies between your discussion and what other authors found. R/ We expanded the discussion, including a more in-depth interpretation of our results and taking into account the biology of these beetles. • Did you try to make your analysis between seasons or differentiating the traps? There were species strictly necrophagous? It might be good to take the functionality; there are two different functional guilds and can react differentially to heterogeneity R/ We appreciate the suggestion. However, our work did not aim at comparing different methods for dung beetle capture or assessing the effect of seasons on the beetle communities in REBISO but to complement the information by using different bait types and sampling seasons, such as other researchers have done, including: Halffter & Arellano (2002), Andresen (2008), Díaz, Galante & Favila (2010), Villada-Bedoya et al. (2016), Cajaiba et al. (2017), among others. Nevertheless, we agree that it would be interesting to examine the effect of seasons or the type of bait; this could be addressed in future studies.. • D. gazella was just collected in W7 and W8, just in pastures, what does that implies for the other sites and for an introduced species like this one. This species might compete with native species inhabited open areas. Read: Lobo, J. M., & Montes, D. O. (1994). Local distribution and coexistence of Digitonthophagus gazella (Fabricius, 1787) and Onthophagus batesi Howden & Cartwright, 1963 (Coleoptera: Scarabaeidae). Elytron, 8, 117-127. R/ We developed this idea further in the discussion section. Extra literature to read and include in your work and discussion: Amézquita, S., & Favila, M. E. (2011). Carrion removal rates and diel activity of necrophagous beetles (Coleoptera: Scarabaeinae) in a fragmented tropical rain forest. Environmental entomology, 40(2), 239-246. Arellano, L., Leon-Cortes, J. L., & Halffter, G. (2008). Response of dung beetle assemblages to landscape structure in remnant natural and modified habitats in southern Mexico. Insect Conservation and Diversity, 1(4), 253-262. Moctezuma, V., Halffter, G., & Arriaga-Jiménez, A. (2018). Archipelago reserves, a new option to protect montane entomofauna and beta-diverse ecosystems. Revista Mexicana de Biodiversidad, 89(3), 927-937. Morón, M. A. (1987). The necrophagous Scarabaeinae beetles (Coleoptera: Scarabaeidae) from a coffee plantation in Chiapas, Mexico: habits and phenology. The Coleopterists' Bulletin, 225-232. Silva, R. J., Storck-Tonon, D., & Vaz-de-Mello, F. Z. (2016). Dung beetle (Coleoptera: Scarabaeinae) persistence in Amazonian forest fragments and adjacent pastures: biogeographic implications for alpha and beta diversity. Journal of insect conservation, 20(4), 549-564. R/ We added most of the references suggested, except for Moron 1987 and Montezuma et al. 2018, as we believe that their subject themes are not entirely relevant for our work. Reviewer detailed evaluation: Abstract: 1. Rev. L 78 Read and include this reference here and in some other parts of the text: Alvarado, F., Salomão, R. P., Hernandez-Rivera, Á., & de Araujo Lira, A. F. (2020). Different responses of dung beetle diversity and feeding guilds from natural and disturbed habitats across a subtropical elevational gradient. Acta Oecologica, 104, 103533. R/ Done. 2. L 79 In the introduction, you talk about how this landscape analyses can contribute to conservation and diversity maintenance, but later in the discussion, you do not detail this and do not give any suggestions. If you will maintain this phrase in the introduction (use some references too), a larger discussion is expected. R/ Agree, we provided a more in-depth discussion of our results, in particular their implications for the conservation and maintenance of dung beetle diversity in the REBISO landscape. Materials and Methods: 3. L 116 Since the title you refer to dung beetles, but you use carrion traps too, why? Scarabaeidae that feed on carrion did not fall in the pitfall traps baited with dung? If you sample and analyse carrion beetles too, maybe mention that since the title? R/ As now specified in the text, we used carrion and dung baits to obtain a more representative sample of the Scarabaeinae assemblages in the region, as did Halffter & Arellano (2002), Andresen (2008) and Díaz, Galante & Favila (2010). However, most of the species collected did fall on both types of traps. To avoid confusion, we included a sentence in the introduction clarifying that, although Scarabaeinae are commonly called “dung beetles”, they do not feed exclusively on feces. 4. L 128 Carrion traps were also in place 24 hours; did you leave the squid fermenting before setting the traps? Explain more, usually carrion traps are for longer periods. R/ Carrion and dung baits were left on the field for 48 hours. We believe that using fresh carrion bait for 48 hours is an excellent method for catching primary scavenger beetles, as shown by Favila & Díaz (1996). On the other hand, the sampling coverage results show that we obtained a complete sample of the species in the region. 5. L 129 You collect a lot of individuals, you might want to deposit some of the material in other national collections, or to the different coauthors institutions. R/ Thanks for the suggestion, we are working on that. 6. L 137 Repetitive phrase, rewrite it. R/ Done 7. L 140 The cite is already in the phrase, no need to cite it again at the end. R/ Done 8. L 150 Why Ros et al cite is there? R/ We removed the citation. Results: 9. L 198 Why do you think that happen? Maybe the sampling was not enough? How would that affect your results interpretation? R/ Since we used a different process to analyze the data, the text was re-interpreted. Discussion: 10. L 315 Are there any endemic or rare species? R/ We did not capture any endemic or rare species 11. L 326 Explain what might happens the other way around, with the species from open spaces that cannot go into the forests. R/ While it would be interesting to address it from that perspective, we believe that, from a conservation standpoint, it is more important to focus the discussion on how disturbance creates environmental filters for forest species. Forest fragmentation in the REBISO is relatively recent (Flamenco-Sandoval, Martínez Ramos & Masera, 2007). Therefore, it is the native forest beetles that are likely the most affected ones by these disturbances rather than the generalist or heliophile species. On the other hand, although environmental filters could potentially isolate the beetle populations inside the forest patches, we argue in the discussion that this does not necessarily happen in REBISO, possibly thanks to its secondary forest matrix of variegated landscapes. 12. L 346 It seem that C. leechi and O. landolti are not typical from open sites but rather forest indicators? (see Alvarado et al 2020). If you collected them in open areas, what would that mean? R/ We agree. We have cataloged O. landolti as a forest-pasture edge specialist. On the other hand, since the discussion was rewritten, we removed C.leechi from the text. Nonetheless, it is worth mentioning that earlier references describe C. leechi as a pasture specialist (e.g., Reyes Novelo, Delfín González & Ángel Morón, 2007; Navarrete & Halffter, 2008; Basto-Estrella et al., 2012). Also, our data support the habitat preference of C.leechi (90% of individuals were captured in pastures sites, mainly in pastures in fragmented windows). 13. L 348 Species might be just rare in this vegetation, or they are typical in other place. Rephrase avoiding the use of “still minimal”. R/ Agree, we removed the paragraph form the text. Figures: 14. Fig. 1 It is not clear the trap location in each window, it might not be necessary to include that on the map, but is marked in the symbology and is not obvious in the figure. Colors of the vegetation classes are not quite the same in the symbology that in the map, homogenize that. In the rest of the text you refer to the Windows with W and a number, use the same labels for the figure to make it easier to identify them instead of a,b,c,d… R/ Done, Figure 1 was changed according to the reviewer’s suggestions. 15. Fig. 3 It is not clear what the * means aside the numbers (species identity). I do not have the Figure legends, and this information should be clearly explain there, as well as the meaning of every abbreviation and letters used in the figures. R/ We appreciate the suggestion; however, Figure 3a was transferred to Supplementary figures. Nonetheless, we explain what does * means in the figure legend. We also added a table with the species ID for the rank-abundance and rank-biomass curves. Finally, we assigned a different color to each curve to make them easier to read. Tables: 16. Tables The table legends are not in the text, some explanation and detail is needed in order to understand them correctly and easily. Details in the supplementary materials are there, I recommend making it too for the other tables. R/ We corrected the issue by adding more detail to the table legends. 17. Table 1 In the vegetation class there are some Windows that repeat, do you mean you separate the traps of every vegetation class to do the analyses? I assume that but is not very clear near in the text, nor the table. R/ Correct, we added additional information in the table and text to clarify how the analysis was done: “Windows were the sampling units for the spatial approach (n=8), while vegetation classes within windows were the sampling units for the structural approach (n=21).” References Andresen E. 2008. Dung beetle assemblages in primary forest and disturbed habitats in a tropical dry forest landscape in western Mexico. Journal of Insect Conservation 12:639–650. DOI: 10.1007/s10841-007-9100-y. Basto-Estrella G, Rodríguez-Vivas RI, Delfín-González H, Reyes-Novelo E. 2012. Escarabajos estercoleros (Coleoptera: Scarabaeidae: Scarabaeinae) de ranchos ganaderos de Yucatán, México. Revista Mexicana de Biodiversidad 83:380–386. Cajaiba R, Périco E, Schmidt Dalzochio M, Barreto da Silva W, Bastos R, Alexandre Cabral J, Santos M. 2017. Does the composition of Scarabaeidae ( Coleoptera ) communities reflect the extent of land use changes in the Brazilian Amazon? Ecological Indicators 74:285–294. DOI: 10.1016/j.ecolind.2016.11.018. Díaz A, Galante E, Favila ME. 2010. The Effect of the Landscape Matrix on the Distribution of Dung and Carrion Beetles in a Fragmented Tropical Rain Forest. Journal of Insect Science 10:1–16. DOI: 10.1673/031.010.8101. Favila ME, Díaz A. 1996. Canthon cyanellus cyanellus LeConte (Coleoptera: Scarabaeidae) makes a nest in the field with several brood balls. Coleopterists Bulletin 50:52–60. Flamenco-Sandoval A, Martínez Ramos M, Masera OR. 2007. Assessing implications of land-use and land-cover change dynamics for conservation of a highly diverse tropical rain forest. Biological Conservation 138:131–145. DOI: 10.1016/j.biocon.2007.04.022. Halffter G, Arellano L. 2002. Response of dung beetle diversity to human-induced changes in a tropical landscape. BIOTROPICA 34:144–154. DOI: 10.1646/0006-3606(2002)034{[}0144:RODBDT]2.0.CO;2. Lobo JM, Montes de Oca E. 1994. Distribución local y coexistencia de Digitonthophagus gazella (Fabricius, 1787) y Onthophagus batesi Howden & Cartwright, 1963 (Coleoptera: Scarabaeidae). Elytron 8:117–127. Montes de Oca E, Halffter G. 1998. Invasion of Mexico By Two Dung Beetles Previously Introduced Into the United States. Studies on Neotropical Fauna and Environment 33:37–45. DOI: 10.1076/snfe.33.1.37.2174. Navarrete D, Halffter G. 2008. Dung beetle (Coleoptera : Scarabaeidae : Scarabaeinae) diversity in continuous forest, forest fragments and cattle pastures in a landscape of Chiapas, Mexico: the effects of anthropogenic changes. BIODIVERSITY AND CONSERVATION 17:2869–2898. DOI: 10.1007/s10531-008-9402-8. Reyes Novelo E, Delfín González H, Ángel Morón M. 2007. Copro-necrophagous beetle (Coleoptera: Scarabaeidae) diversity in an agroecosystem in Yucatan, Mexico. Revista de Biología Tropical 55. DOI: 10.15517/rbt.v55i1.6059. Villada-Bedoya S, Cultid-Medina CA, Escobar F, Guevara R, Zurita G. 2016. Edge effects on dung beetle assemblages in an Andean mosaic of forest and coffee plantations. Biotropica 0:1–11. DOI: 10.1111/btp.12373. Reviewer 3 Basic reporting Overall, this paper was well written, with sufficient references. However, I feel that a bit more context and clarity are needed to make the paper suitable for publication. The abstract needs work. A sentence or two of background at the start would help to set the stage before jumping into details of results. See the following link for some guidelines: https://www.wiley.com/network/researchers/preparing-your-article/how-to-write-a-scientific-abstract. R/ Agree, we rewrote the abstract and provided more background information. I also feel that the Introduction needs to include a bit more background. I think it would be good to include more context about habitat vs landscape - your paper is about looking at biodiversity at the landscape level, but most biodiversity research is looked at from a habitat level so elaborate more on how your study is different. Can you clarify your third research question i.e. how is it different from question 2? R/ Done, we provided more background information in the introduction section. We agree, although the first two questions were similar, the second question focused on the vegetation classes rather than on the type of windows. However, since we were allowed to improve the manuscript, we decided to combine questions 1 and 2 and add a new second question. The third question remained the same. Could you clarify the windows concept and how it links with landscapes? Windows are an important part of the project yet they are not mentioned in the introduction. In the introduction, you talk about landscapes and vegetation but there is no mention of windows. Not all of your readers will have a solid background in all the ecological terms so it would be helpful to add a bit more clarity and background. R/ We appreciate the suggestion, the concept of 'window' is now explained in the introduction section. The results are well presented, but Figure 1 needs some work. Why do a and b have smaller circles than the others. Figure design is confusing - could you use black instead of white for letters OR perhaps use lines to connect to the location instead of letters? Why not highlight the circle with black so its is easier to see. I think more explanation is needed in the figure legend. You could explain what category each window represents. Is window centre important? From the map legend: trap location is not used so should not be included in legend. Can you create more contrast between the orange and the yellow boxes (secondary and pasture) to match better with map image? R/ We modified figure 1 according to the reviewers’ suggestion. Window centers were just a reference point. The circles were smaller in a and b because the sites´ topographic characteristics impeded a rectangular layout of traps. However, the proportional sampling was maintained. Thank you for supplemental data tables, however, each table need a title with a number. While legends are optional, I think a brief explanation of what the table is showing would be very helpful. R/ We added tittles to the supplementary tables, as well as additional explanation where needed. You also mention communities (line 253) but these are not explained anywhere. R/ We added more details on what “effective communities” mean in the Materials and Methods section. You mentioned that dung beetles were sampled in both rainy season and dry season, but didn't mention how this may have affected the results. You sampled using two different baits but didn't mention how this may have affected the results either. R/ We concur that it would be quite interesting to examine the effect of seasons or bait type on our results and this can explored in future studies. However, our work was not aimed at comparing different methods of beetle capture or the effect of different seasons on the beetle communities in REBISO but to complement the information by using different bait types and sampling seasons, similar to other studies (e.g., Halffter & Arellano, 2002; Andresen, 2008; Díaz, Galante & Favila, 2010; Villada-Bedoya et al., 2016; Cajaiba et al., 2017) In the discussion, species turnover is mentioned as an important biodiversity measure, but it’s not mentioned in introduction or methods. Could this be explained further? R/ Agree, we added additional information about species turnover and nestedness in the materials and methods section. Experimental design I feel that the research is relevant and meaningful. Dung beetles make excellent bioindicators and can be very useful for increasing our knowledge for conservation management. The research questions were well defined. The sampling efforts were commendable and combined with extensive statistical analysis. Methods were described sufficiently with the exception of how biomass was determined. R/ Done, we added information on how biomass was measured. Validity of the Findings The data appear to be sound. Null models were discussed. The conclusions are stated clearly but don’t quite match with research questions. However, this is simply a matter of clarity, not a problem. R/ Done, the conclusion was rewritten in a more circular form. Comments for the Author Overall this is a good paper. A lot of work went into sampling and statistical analysis. The text just needs a bit more clarity to effectively communicate the results and implications of this research. Reviewer detailed evaluation: Abstract: 1. L 2 Begin abstract with a bit more background information - see notes R/ Done, thanks for the suggestion Introduction: 2. L 39 Is this in the neotropics or in the world? R/ We specifically refer to the neotropics. We clarified this in the text. 3. L 87 Add an ‘it’ to this sentence R/ Done 4. L 89 Might be?? how about ‘will be’ R/ Definitively will be, thanks. Materials and Methods: 5. L 97 should add the country as well; it’s an international journal R/ Done 6. L 122 Are you sampling vegetation here? proportional sampling? this paragraph is about dung beetle sampling; should vegetation sampling be mentioned separately? R/ Thanks for pointing out this confusion. We corrected the text to make it clear that we proportionally adjusted the number of pitfall traps according to the vegetation composition of each window (sensu Rös, Escobar & Halffter, 2012). Results: Rev. 3~ L 253 Explain these communities; this is the first mention of them. are these dung beetle communities? R/ These communities are the 'effective number of distinct communities' that resulted from partitioning diversity into independent components (Jost, 2007). We now described these 'effective communities' in the Materials and Methods section. Discussion: 7. L 312 This concept is not clearly defined. How is species turnover measured? How did you determine it? R/ Agree, we added a more in-depth explanation of what species turnover is and how we measured it in the Materials and Methods section. 8. L 323 Could you give some examples of ecological filters here? R/ Done, we added the following: “Intensive anthropic disturbance like deforestation can act as environmental filters within the fragmented windows and pastures, selecting dung beetle species with stress-tolerance traits, capable of surviving to open habitats conditions (Spector & Ayzama, 2003; Gardner et al., 2008)”. Figures 9. Fig. 1 Figure 1 needs some work. Why do a and b have smaller circles than the others. Figure design is confusing - could you use black instead of white for letters OR perhaps use lines to connect to the location instead of letters? Why not highlight the circle with black so its is easier to see. I think more explanation is needed in the figure legend. You could explain what category each window represents. Is window centre important? From the map legend: trap location is not used so should not be included in legend. Can you create more contrast between the orange and the yellow boxes (secondary and pasture) to match better with map image? R/ Figure 1 was modified according to the reviewer’s suggestions. The circles were smaller in these windows because the sites´ topographic characteristics impeded a rectangular layout of traps. However, the proportional sampling was maintained. References: Andresen E. 2008. Dung beetle assemblages in primary forest and disturbed habitats in a tropical dry forest landscape in western Mexico. Journal of Insect Conservation 12:639–650. DOI: 10.1007/s10841-007-9100-y. Cajaiba R, Périco E, Schmidt Dalzochio M, Barreto da Silva W, Bastos R, Alexandre Cabral J, Santos M. 2017. Does the composition of Scarabaeidae ( Coleoptera ) communities reflect the extent of land use changes in the Brazilian Amazon? Ecological Indicators 74:285–294. DOI: 10.1016/j.ecolind.2016.11.018. Díaz A, Galante E, Favila ME. 2010. The Effect of the Landscape Matrix on the Distribution of Dung and Carrion Beetles in a Fragmented Tropical Rain Forest. Journal of Insect Science 10:1–16. DOI: 10.1673/031.010.8101. Gardner TA, Hernandez MIM, Barlow J, Peres CA. 2008. Understanding the biodiversity consequences of habitat change: the value of secondary and plantation forests for neotropical dung beetles. Journal of Applied Ecology 45:883–893. DOI: 10.1111/j.1365-2664.2008.01454.x. Halffter G, Arellano L. 2002. Response of Dung Beetle Diversity to Human-induced Changes in a Tropical Landscape. Biotropica 34:144–154. DOI: 10.1111/j.1744-7429.2002.tb00250.x. Jost L. 2007. Partitioning diversity into independent alpha and beta components. Ecology 88:2427–2439. DOI: 10.1890/06-1736.1. Rös M, Escobar F, Halffter G. 2012. How dung beetles respond to a human-modified variegated landscape in Mexican cloud forest: A study of biodiversity integrating ecological and biogeographical perspectives. Diversity and Distributions 18:377–389. DOI: 10.1111/j.1472-4642.2011.00834.x. Spector S, Ayzama S. 2003. Rapid turnover and edge effects in dung beetle assemblages (Scarabaeidae) at a Bolivian Neotropical forest-savanna ecotone. BIOTROPICA 35:394–404. Villada-Bedoya S, Cultid-Medina CA, Escobar F, Guevara R, Zurita G. 2016. Edge effects on dung beetle assemblages in an Andean mosaic of forest and coffee plantations. Biotropica 0:1–11. DOI: 10.1111/btp.12373. "	Here is a paper. Please give your review comments after reading it.
528	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Background. Anthropized landscapes play a crucial role in biodiversity conservation, as they encompass about 90% of the remaining tropical forest. Effective conservation strategies require a deep understanding of how anthropic disturbances determine diversity patterns across these landscapes. Here, we evaluated how attributes and assembly mechanisms of dung beetle communities vary across the Selva El Ocote Biosphere Reserve (REBISO) landscape.</ns0:p><ns0:p>Methods. Community attributes (species diversity, abundance, and biomass) were assessed at the landscape scale, using spatial windows and vegetation classes. Windows were categorized as intact, variegated, or fragmented based on their percent cover of tropical forest. The vegetation classes analyzed were tropical forest, second-growth forest, and pastures.</ns0:p><ns0:p>Results. We collected 15,457 individuals and 55 species. Variegated windows, tropical forests, and second-growth forests showed the highest diversity values, while the lowest values were found in intact windows and pastures. Landscape fragmentation was positively and strongly related to dung beetle diversity and negatively related to their abundance; biomass was positively associated with forest cover. Beta diversity was the primary driver of the high dung beetle diversity in the landscape analyzed.</ns0:p><ns0:p>Discussion. The landscape heterogeneity and its biodiversity-friendly matrix facilitate the complementarity of dung beetle assemblages in the Selva El Ocote Biosphere Reserve. Random processes govern beta diversity patterns in intact and variegated windows. Therefore, vegetation cover in the region is sufficient to maintain a continuous flow of dung beetles between forested landscape segments. However, intense anthropic disturbances acted as deterministic environmental filters in fragmented windows and pastures sites, leading to biotic homogenization processes. Our results suggest that increasing habitat variegation in highly fragmented sites is an effective strategy to prevent or buffer homogenization processes in the REBISO landscape.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>Anthropized neotropical landscapes encompass a complex combination of natural and seminatural habitats, where some species can thrive while others may go locally extinct <ns0:ref type='bibr' target='#b9'>(de Castro Solar et al., 2015)</ns0:ref>. Today, almost 90% of remaining tropical forests are located within anthropized landscapes <ns0:ref type='bibr' target='#b14'>(Chazdon et al., 2009)</ns0:ref>. These landscapes now play a crucial role in biodiversity conservation agendas <ns0:ref type='bibr' target='#b17'>(DeClerck et al., 2010)</ns0:ref>. Therefore, it is imperative to understand how species diversity responds to anthropized landscapes in order to implement suitable management actions <ns0:ref type='bibr' target='#b29'>(Gardner et al., 2009;</ns0:ref><ns0:ref type='bibr' target='#b75'>Socolar et al., 2016)</ns0:ref>, especially given the multiple successional pathways and disturbance states that these modified landscapes can follow <ns0:ref type='bibr' target='#b25'>(Fischer & Lindenmayer, 2007;</ns0:ref><ns0:ref type='bibr' target='#b5'>Arroyo-Rodríguez et al., 2017)</ns0:ref>.</ns0:p><ns0:p>Traditionally, researchers have assessed the effect of anthropic disturbance on biotic communities by comparing one or more community attributes (e.g., species diversity, abundance, biomass) across different sampling units at a local level (i.e., vegetation cover types or land-use types). However, the composition and configuration of the habitats that surround the sampling units are also important drivers of ecological processes in biotic communities <ns0:ref type='bibr' target='#b27'>(Franklin & Lindenmayer, 2009)</ns0:ref>. A landscape-level approach provides the necessary context to understand better how communities respond to anthropic disturbances by incorporating the effects of the multiple landscape components <ns0:ref type='bibr' target='#b29'>(Gardner et al., 2009;</ns0:ref><ns0:ref type='bibr' target='#b36'>Hodder et al., 2014)</ns0:ref>. Besides, landscape studies provide useful information for effective natural resource management since many anthropogenic drivers of biodiversity loss, e.g., land-use change or habitat destruction, operate at the landscape level <ns0:ref type='bibr' target='#b36'>(Hodder et al., 2014)</ns0:ref>.</ns0:p><ns0:p>McIntyre and <ns0:ref type='bibr' target='#b49'>Barret (1992)</ns0:ref> coined the variegation concept for anthropized landscapes exhibiting disturbance and vegetation cover gradients. <ns0:ref type='bibr' target='#b50'>McIntyre and Hobbs (1999)</ns0:ref> then added the fragmentation concept to the variegation model. These authors classified the landscape into four categories based on the percentage of remaining original vegetation (OV) and the intensity of habitat transformation: a) intact landscapes (>90% OV): sites with little or no modification; b) variegated landscapes (60-90% OV), showing either gradual or abrupt limits between their component units; c) fragmented landscapes (10-60% OV), characterized by a high degree of modification; and d) relict landscapes (<10% OV), showing severe modification and almost no forest cover remnants. <ns0:ref type='bibr' target='#b35'>Halffter and Rös (2013)</ns0:ref> proposed studying landscape diversity through sampling windows in the geographical space analyzed. These windows are based on the landscape model proposed by <ns0:ref type='bibr'>McIntyre and Hobbs (1992)</ns0:ref> and consist of equally-sized sampling spaces that are semi-randomly located to maximize the representation of the vegetation heterogeneity and land-use types in the landscape.</ns0:p><ns0:p>The Selva El Ocote Biosphere Reserve (REBISO, hereafter) harbors some of the most heterogeneous, although highly disturbed, remnants of tropical forest in Mexico <ns0:ref type='bibr' target='#b26'>(Flamenco-Sandoval, Martínez Ramos & Masera, 2007)</ns0:ref>. Frequent forest fires, in addition to the complex geological nature, climate features, and socio-economic dynamics (livestock and agricultural activities) in the REBISO have led to a complex landscape comprising a mosaic of tropical forests, second-growth forests, pastures, and croplands <ns0:ref type='bibr' target='#b59'>(Ochoa, 1996;</ns0:ref><ns0:ref type='bibr' target='#b73'>SEMARNAT/CONANP, 2001;</ns0:ref><ns0:ref type='bibr' target='#b26'>Flamenco-Sandoval, Martínez Ramos & Masera, 2007;</ns0:ref><ns0:ref type='bibr' target='#b67'>Ramírez-Marcial et al., 2017)</ns0:ref>. Thus, a landscape-level approach seems most appropriate for examining how species respond to anthropogenic disturbance in the REBISO, given its complex and heterogeneous landscape.</ns0:p><ns0:p>Dung beetles (Scarabaeidae: Scarabaeinae) are globally distributed insects that feed on decomposing organic matter such as mammal feces, carrion, rotting fruit, or fungi <ns0:ref type='bibr' target='#b34'>(Halffter & Matthews, 1966)</ns0:ref>. Due to their sensitivity to environmental disturbances, dung beetles are ideal bioindicators to assess the effects of landscape changes on diversity <ns0:ref type='bibr' target='#b23'>(Favila & Halffter, 1997;</ns0:ref><ns0:ref type='bibr' target='#b55'>Nichols et al., 2007)</ns0:ref>. Previous studies have shown how habitat loss leads to abrupt changes in the composition and structure of dung beetle communities <ns0:ref type='bibr' target='#b43'>(Klein, 1989;</ns0:ref><ns0:ref type='bibr' target='#b66'>Quintero & Roslin, 2005;</ns0:ref><ns0:ref type='bibr' target='#b55'>Nichols et al., 2007;</ns0:ref><ns0:ref type='bibr' target='#b53'>Navarrete & Halffter, 2008;</ns0:ref><ns0:ref type='bibr' target='#b18'>Díaz, Galante & Favila, 2010;</ns0:ref><ns0:ref type='bibr' target='#b8'>Cajaiba et al., 2017)</ns0:ref>. However, few studies have evaluated the response of dung beetle communities to disturbances at the landscape level <ns0:ref type='bibr' target='#b57'>(Numa et al., 2009;</ns0:ref><ns0:ref type='bibr' target='#b69'>Rös, Escobar & Halffter, 2012;</ns0:ref><ns0:ref type='bibr'>Sánchezde-Jesús et al., 2016;</ns0:ref><ns0:ref type='bibr' target='#b1'>Alvarado et al., 2018</ns0:ref><ns0:ref type='bibr' target='#b2'>Alvarado et al., , 2020))</ns0:ref>, or whether the observed diversity patterns are stochastic or determined by environmental filters or competitive exclusion between species <ns0:ref type='bibr' target='#b60'>(Ortega-Martínez et al., 2020)</ns0:ref>. Assessing dung beetle diversity at the landscape level, using multiple but complementary metrics, can provide a more comprehensive view of how diversity is maintained and what community assembly mechanisms operate in anthropized landscapes.</ns0:p><ns0:p>In this study, we evaluate the assemblage structure and diversity patterns of dung beetle communities in the heterogeneous tropical landscape of the Selva El Ocote Biosphere Reserve. We address the following questions: (1) How do the diversity and structure of dung beetle assemblages vary across the REBISO landscape and its vegetation classes? (2) How do the composition and configuration of the REBISO landscape influence the diversity and structure of dung beetle assemblages? (3) How does beta diversity change and is maintained across the landscape and between different vegetation classes? The information obtained in this study will be useful for designing conservation strategies in complex tropical landscapes with different heterogeneity levels.</ns0:p></ns0:div> <ns0:div><ns0:head>Materials & Methods</ns0:head></ns0:div> <ns0:div><ns0:head>Study Area</ns0:head><ns0:p>The study was carried out at the REBISO, located in the municipalities of Ocozocoautla de Espinosa and Cintalapa, Chiapas, Mexico (16°45'42' -17°09'00' N and 93°54'19' -93°21'20' W, Fig. <ns0:ref type='figure' target='#fig_2'>1</ns0:ref>). The area is mostly underlain by dolomite rocks and limestone, with a dominance of water-soluble sedimentary rocks <ns0:ref type='bibr' target='#b19'>(Domenici, 2016)</ns0:ref>. The predominant climate types are warm, humid (climate type Am) and warm, subhumid (climate type Am(f)), with a mean annual temperature of 22 °C and heavy rainfall throughout the year (SEMARNAT/CONANP, 2001).</ns0:p><ns0:p>We produced a vegetation map of REBISO from a multispectral SPOT6 image acquired in 2014, using a supervised classification method in QGIS v2.12.3 <ns0:ref type='bibr'>(QGIS Development Team, 2016)</ns0:ref>. The vegetation classes considered were tropical forest, second-growth forest, and pastures (Fig. <ns0:ref type='figure' target='#fig_2'>1</ns0:ref>, Table <ns0:ref type='table'>1</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>Sampling Design</ns0:head><ns0:p>We established eight 1-km 2 (100 ha) sampling windows to capture the landscape heterogeneity in the REBISO (Sánchez- <ns0:ref type='bibr' target='#b70'>de-Jesús et al., 2016)</ns0:ref>. Each window was separated from each other by at least 2 km to ensure spatial independence between them (Sánchez- <ns0:ref type='bibr' target='#b70'>de-Jesús et al., 2016)</ns0:ref>. The landscape composition in each window was described by estimating the percent coverage of each vegetation class and evaluating the evenness of their distribution (landscape compositional diversity -Shannon diversity). The spatial configuration of the vegetation classes in each window was assessed with the splitting index and edge density metric <ns0:ref type='bibr' target='#b48'>(McGarigal, Cushman & Ene, 2012)</ns0:ref>. Edge density is computed as the length (m) of the edges of each vegetation class divided by the window area (ha). The splitting index describes the degree of fragmentation of a landscape and is equivalent to the effective number of patches. Thus, as a landscape becomes increasingly sub-divided, the splitting index increases <ns0:ref type='bibr' target='#b39'>(Jaeger, 2000;</ns0:ref><ns0:ref type='bibr' target='#b21'>Fahrig, 2017)</ns0:ref>. The landscape composition, edge density, and splitting index metrics (Table <ns0:ref type='table'>S1</ns0:ref>) were obtained with FRAGSTAT v4.2.1 <ns0:ref type='bibr' target='#b48'>(McGarigal et al., 2012)</ns0:ref>. Based on the percent cover of tropical forest (F), windows were classified as intact (W1, W2; F > 90%), variegated (W3, W4, and W5; 60% < F < 90%), or fragmented (W6, W7, and W8; 10% < F < 60%).</ns0:p><ns0:p>We sampled dung beetles (Scarabaeidae: Scarabaeinae) during the dry (March to May) and the rainy (July-August) seasons of 2016 using pitfall traps. This sampling scheme allowed us to integrate the seasonal activities of dung beetles <ns0:ref type='bibr' target='#b8'>(Cajaiba et al., 2017)</ns0:ref>. Each trap consisted of a 1 L cylindrical plastic container with 300 mL of ethylene glycol as preservative, buried at ground level, and covered with a plastic lid to protect the bait from rain and sun radiation. Pitfall traps were baited with 70 g of either an 80:20 mixture of pig and human feces (copro-traps) or squid flesh (necro-traps) in order to obtain a representative sample of the dung beetle assemblages in the area.</ns0:p><ns0:p>Seven sampling sites were established in each window, separated 250-360 meters from each other, to proportionally adjust the number of pitfall traps per vegetation class according to the vegetation class composition of each window (Table <ns0:ref type='table' target='#tab_1'>S2</ns0:ref>). Proportional sampling is a suitable method for detecting changes in beta diversity in heterogeneous landscapes <ns0:ref type='bibr' target='#b72'>(Schoereder et al., 2004)</ns0:ref>. In each sampling site, three copro-traps and three necro-traps (42 traps/window), were placed in a rectangular area separated 50 m from each other to minimize interference between them <ns0:ref type='bibr' target='#b44'>(Larsen & Forsyth, 2005)</ns0:ref>. The rectangular layout of some trap sets was modified in some cases due to the topographic characteristics of the sites. The pitfall traps were left active for 48 hours.</ns0:p><ns0:p>The specimens collected were counted and identified to species. To estimate the dung beetle biomass, we randomly selected ten specimens of each species and dried them at 70 °C for 72 hours. We weighed each specimen to the nearest 0.1mg with an analytical balance (Explorer Pro) and calculated the average biomass for each species. Finally, we multiplied the mean biomass of each species by its abundance in each window and vegetation class. The dung beetle specimens were deposited in the entomological collection of El Colegio de la Frontera Sur, San Cristóbal de Las Casas. Field sampling in the REBISO was carried out under permit SGPA/DGS/14214/15 issued by the Secretaria de Medio Ambiente y Recursos Naturales, Mexico.</ns0:p></ns0:div> <ns0:div><ns0:head>Data Analysis</ns0:head><ns0:p>We followed a spatial and structural approach (sensu <ns0:ref type='bibr' target='#b69'>Rös, Escobar & Halffter, 2012)</ns0:ref> to analyze the data. Windows were the sampling units for the spatial approach (n = 8), while vegetation classes within windows were the sampling units for the structural approach (n = 21). The sampling completeness of each window and vegetation class was determined using the coverage estimator of <ns0:ref type='bibr' target='#b11'>Chao and Jost (2012)</ns0:ref>, which allows comparing species diversity across multiple sites.</ns0:p><ns0:p>Alpha diversity in each sampling unit (window or vegetation class) was evaluated using the 0 D and 1 D diversity numbers. 0 D is equivalent to species richness and is insensitive to the species abundance <ns0:ref type='bibr' target='#b40'>(Jost, 2006</ns0:ref>); 1 D is equivalent to the exponential of Shannon diversity index and accounts for the most abundant species in a community <ns0:ref type='bibr' target='#b40'>(Jost, 2006)</ns0:ref>.</ns0:p><ns0:p>We examined differences in species richness between windows by constructing and comparing their 95% bootstrap confidence intervals. Non-overlapping confidence intervals denote significantly different species richness <ns0:ref type='bibr' target='#b31'>(Gotelli & Colwell, 2011;</ns0:ref><ns0:ref type='bibr' target='#b10'>Chao et al., 2014)</ns0:ref>. Differences in species richness between vegetation classes were determined using interpolationextrapolation curves <ns0:ref type='bibr' target='#b10'>(Chao et al., 2014)</ns0:ref>. The sampling coverage, 0 D and 1 D diversity numbers, confidence intervals, and the interpolation-extrapolation curves were obtained with the software iNEXT v2.0.11 <ns0:ref type='bibr' target='#b38'>(Hsieh, Ma & Chao, 2016)</ns0:ref>.</ns0:p><ns0:p>Generalized linear models (GLM) were used to assess differences in abundance and biomass between windows and vegetation classes. The abundance and biomass data approached a normal distribution after logarithmic transformation and were analyzed assuming a Gaussian error distribution <ns0:ref type='bibr' target='#b16'>(Crawley, 2013)</ns0:ref>. Pairwise comparisons using Tukey's test were carried out, with the multcomp package <ns0:ref type='bibr' target='#b37'>(Hothorn et al., 2016</ns0:ref>) whenever significant differences were detected.</ns0:p><ns0:p>GLMs were also used to assess the effect of the landscape composition and configuration on the species richness ( 0 D), exponential of the Shannon diversity ( 1 D), abundance, and biomass of the dung beetle assemblages. These data were first tested for normality and were then analyzed assuming a Gaussian error distribution. Since only eight observations were available to fit these models, separate models containing only one predictor variable were constructed to avoid overfitting <ns0:ref type='bibr' target='#b42'>(Kelley & Maxwell, 2003)</ns0:ref>. The best-fit models were selected based on the Akaike′s information criterion corrected for small samples (AICc) and the deviance explained (D 2 ). The model with the smallest AICc (∆AICc >2) and the largest D 2 values was selected as the best-fit model <ns0:ref type='bibr' target='#b7'>(Burnham & Anderson, 2002)</ns0:ref>. Based on the results from the Moran I test (as implemented in the package LetsR), no significant spatial structure was detected in the response variables (Table <ns0:ref type='table' target='#tab_3'>S3</ns0:ref>) <ns0:ref type='bibr' target='#b78'>(Vilela & Villalobos, 2015)</ns0:ref>.</ns0:p><ns0:p>True beta diversity (i.e., the effective number of distinct communities) was estimated for species richness ( 0 β) and Shannon diversity ( 1 β) using the multiplicative partitioning method <ns0:ref type='bibr' target='#b41'>(Jost, 2007)</ns0:ref>. The multiple-site Sørensen dissimilarity was partitioned as β Sor = β Sim + β Sne using the package Betapart v1.3 <ns0:ref type='bibr' target='#b6'>(Baselga & Orme, 2012)</ns0:ref> to determine whether the ecological differences between sampling units resulted from species turnover (β Sim ) or nestedness (β Sne ). Turnover measures the replacement of species between sites caused by environmental differences, disturbance, or competition. Nestedness is a loss of species between sites, usually due to differences in local conditions or ecological niches, where the species-poorer site contains a subset of the species present in the species-richer site <ns0:ref type='bibr' target='#b45'>(Legendre, 2014)</ns0:ref>.</ns0:p><ns0:p>Null models were used to determine whether beta diversity patterns resulted from either random changes in alpha and gamma diversity, or from underlying deterministic mechanisms in communities or the landscape <ns0:ref type='bibr' target='#b13'>(Chase et al., 2011)</ns0:ref>. We constructed null models for the beta Raup-Crick index (β R-C ) using the algorithm developed by <ns0:ref type='bibr' target='#b13'>Chase et al. (2011)</ns0:ref> with 9999 randomizations. β R-C compares the observed versus expected beta diversity under the null model, scaling the results to a range between -1 and 1. This value indicates whether the beta diversity observed between windows, or vegetation classes, is more similar (values close to -1), equal (values close to 0), or less similar (values close to 1) than the one expected by chance (β R-C null model). We built a dendrogram and a nonmetric multidimensional scaling (NMDS) plot based on β R-C values for windows and vegetation classes, respectively <ns0:ref type='bibr' target='#b13'>(Chase et al., 2011)</ns0:ref>. The dendrogram was constructed using the complete linkage method, as it produces clusters with ecological discontinuities <ns0:ref type='bibr' target='#b46'>(Legendre & Legendre, 2003)</ns0:ref>. We compared the dendrogram and NMDS plot based on β R-C with homologous plots based on Sørensen dissimilarity to examine whether deterministic mechanisms are underlying the observed beta diversity across the landscape <ns0:ref type='bibr' target='#b13'>(Chase et al., 2011)</ns0:ref>. All statistical analyses and models were carried out using R v.3.3.1 (R Development Core Team, 2015).</ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head><ns0:p>We collected a total of 15,457 specimens belonging to 55 species in the eight windows at REBISO (Table <ns0:ref type='table'>S4a</ns0:ref>). The most abundant species was Deltochilum mexicanum (15% of total abundance), followed by Onthophagus corrosus (13%), Eurysternus maya (12%), Canthon vazquezae (11%), and Onthophagus batesi (8%). Sampling coverage on each window was 99% (Table <ns0:ref type='table'>S4a</ns0:ref>). However, the sampling coverage of vegetation classes varied between windows: for forest vegetation it ranged from 91% (W6) to 100% (W8), it was over 98% for second-growth forests, and between 95% (W3) and 99% (W6) for pastures (Table <ns0:ref type='table'>S4a</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>Diversity, Abundance, and Biomass Patterns in Windows</ns0:head><ns0:p>Species richness ( 0 D) in the windows sampled ranged from 22 (W1, intact window) to 37 (W4, variegated window), whereas the exponential Shannon diversity index ( 1 D) ranged from 4.9 (W2, intact window) to 17.6 (W5, variegated window) species (Table <ns0:ref type='table' target='#tab_1'>2</ns0:ref>). Species richness in W4 and W5 was significantly higher than in the other windows (Fig. <ns0:ref type='figure' target='#fig_5'>2a</ns0:ref>).</ns0:p><ns0:p>Deltochilum mexicanum, E. maya, and C. vazquezae were the most abundant species in intact windows W1 and W2 (Fig <ns0:ref type='figure' target='#fig_5'>S1a</ns0:ref> The highest abundance values (44-52 individuals per trap) were recorded in the intact windows W1 and W2 (Table <ns0:ref type='table' target='#tab_3'>3</ns0:ref>), followed by fragmented windows W6, W7, and W8 (30-36 individuals per trap), and variegated windows W3, W4, and W5 (15-30 individuals per trap). However, these differences were not statistically significant (χ 2 = 8.923; df = 7; P = 0.26; Table <ns0:ref type='table' target='#tab_3'>3</ns0:ref>). By contrast, there were significant differences in mean biomass between windows (χ 2 = 45.143; df = 7; P = >0.001; Table <ns0:ref type='table' target='#tab_3'>3</ns0:ref>). Mean biomass per trap was significantly higher in windows W1 and W2 (8.2-9.1 grams per trap, Table <ns0:ref type='table' target='#tab_3'>3</ns0:ref>), but no significant differences were found between fragmented (2.2-3.7 grams per trap, Table <ns0:ref type='table' target='#tab_3'>3</ns0:ref>) and variegated windows (2.4-3.9 grams per trap, Table <ns0:ref type='table' target='#tab_3'>3</ns0:ref>).</ns0:p><ns0:p>All the landscape variables had a significant positive effect on the species richness ( 0 D) in each window. However, the splitting index was the variable that best explained variations in species richness (Table <ns0:ref type='table'>4</ns0:ref>). Although the exponential Shannon diversity ( 1 D) values were positively related to the splitting index and edge density, the splitting index was the best predictor for variations in Shannon diversity between windows. Edge density and forest cover were the best predictor variables for dung beetle abundance and biomass, respectively; edge density was negatively correlated with abundance, and forest cover positively correlated with biomass (Table <ns0:ref type='table'>4</ns0:ref>).</ns0:p><ns0:p>True beta diversity of orders 0 and 1 indicated two effective communities between windows, 0 β being slightly smaller than 1 β (Table <ns0:ref type='table' target='#tab_1'>2</ns0:ref>). The multiple-site Sørensen value calculated for all windows was 0.65 (Fig. <ns0:ref type='figure' target='#fig_5'>3a</ns0:ref> 'W Total'); 85% of this dissimilarity was due to species turnover (β Sim ) and 15% to nestedness-resultant component (β Sne ). Sørensen dissimilarity for intact windows W1 and W2 was lower than 0.4, mainly due to nestedness (β Sne ) (Fig. <ns0:ref type='figure' target='#fig_5'>3a</ns0:ref>). Dissimilarity ranged from 0.3 to 0.45 in variegated windows (W3, W4, W5), and from 0.3 to 0.58 in fragmented windows (W6, W7, W8). In most cases (except for W8), the observed Sørensen dissimilarity values were primarily due to species turnover (Fig. <ns0:ref type='figure' target='#fig_5'>3a</ns0:ref>).</ns0:p><ns0:p>The dendrogram based on the Sørensen distance revealed two main groups (Fig. <ns0:ref type='figure' target='#fig_4'>3b</ns0:ref>). The first group includes the fragmented windows (W6, W7, W8), while the second group reveals a gradient of increasing similarity ranging from the intact (W1, W2) to the variegated (W3, W4, W5) windows. The null-model analysis showed that the difference between fragmented windows with respect to the variegated and intact windows was higher than expected by chance (β RC Value: 1.0, Fig. <ns0:ref type='figure' target='#fig_4'>3c</ns0:ref>). However, the dissimilarity between variegated and intact windows did not exceed the null expectation of beta diversity (0< β RC < 0.3, Fig. <ns0:ref type='figure' target='#fig_4'>3c</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>Diversity, Abundance, and Biomass in Vegetation Classes</ns0:head><ns0:p>The dung beetle species richness ( 0 D = 34 species) in pastures was significantly lower than in the other vegetation classes, but there were no significant differences between second-growth and tropical forests (44 and 45 species, respectively) (Fig. <ns0:ref type='figure' target='#fig_3'>2b</ns0:ref>). We recorded the lowest exponential Shannon diversity values ( 1 D) in the tropical forests (7.75) and the highest in second-growth forests (15.54) (Table <ns0:ref type='table' target='#tab_1'>2</ns0:ref>). No significant differences in abundance were observed between vegetation classes (χ 2 = 3.701; df = 2; P = 0.16, Table <ns0:ref type='table' target='#tab_3'>3</ns0:ref>). The average number of individuals per trap was 37.8 in secondgrowth forests, followed by tropical forests (30.2) and pasture sites (17.7) (Table <ns0:ref type='table' target='#tab_3'>3</ns0:ref>). By contrast, there were significant differences in mean biomass between vegetation classes (χ 2 = 10.829; df = 2; P= 0.004). Pasture sites had a significantly lower mean biomass per trap (2.09 g) than tropical forest (5.05 g) and second-growth forest (4.53) sites, which showed no significant differences between them (Table <ns0:ref type='table'>.</ns0:ref> 3).</ns0:p><ns0:p>According to the multiplicative partition of diversity, there were 2.9 effective communities for 0 β and 2.5 communities for 1 β in the three vegetation classes combined. Two effective communities were estimated for both the tropical forest and second-growth forest classes, with 1 β higher than 0 β in both cases. Only one effective community was estimated for the pasture class, with 0 β higher than 1 β (Table <ns0:ref type='table' target='#tab_1'>2</ns0:ref>). The Sørensen dissimilarity between vegetation classes was 0.85, with 88% of this value accounted for by species turnover (β Sim ) and 12% by nestedness processes (β Sne ) (Fig. <ns0:ref type='figure'>4b</ns0:ref>).</ns0:p><ns0:p>Tropical forests and second-growth forests showed higher Sørensen values (0.71 and 0.70, respectively) than pastures (0.54) (Fig. <ns0:ref type='figure' target='#fig_5'>4a</ns0:ref>). The NMDS plot based on the Sørensen distance formed a compact cluster of pasture sites, whereas most of the tropical forest and second-growth forest sites overlapped between themselves and with the pasture sites. (Fig. <ns0:ref type='figure'>4b</ns0:ref>). The NMDS plot based on the beta Raup-Crick null model index (β R-C ) separated the tropical forest sites from pastures, whereas second-growth forest sites overlapped with tropical forest and pasture classes (Fig. <ns0:ref type='figure'>4c</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>Discussion</ns0:head><ns0:p>Our results identify the REBISO as one of the regions with highest diversity of Scarabaeinae in Mexican tropical forests, with 55 species, along with the Chimalapas, Oaxaca, with 74 species (Peralta Moctezuma, 2019); the Lacandon forest, Chiapas, with 49 species <ns0:ref type='bibr' target='#b53'>(Navarrete & Halffter, 2008)</ns0:ref>; and the Tuxtlas forest, Veracruz, with 44 species <ns0:ref type='bibr' target='#b22'>(Favila, 2005)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Local Patterns of Species Richness and Assemblage Structure</ns0:head><ns0:p>Dung beetle communities in variegated windows showed the highest richness values in the REBISO. <ns0:ref type='bibr' target='#b69'>Rös, Escobar & Halffter (2012)</ns0:ref> and <ns0:ref type='bibr' target='#b15'>Costa et al. (2017)</ns0:ref> also found a higher richness of dung beetle species in variegated landscapes. Landscape variegation can be a significant environmental driver of local diversity as it increases the range of habitats available for species by creating a complex composition and configuration <ns0:ref type='bibr' target='#b77'>(Tscharntke et al., 2012;</ns0:ref><ns0:ref type='bibr' target='#b68'>Ramírez-Ponce et al., 2019)</ns0:ref>. The high species richness and diversity found in variegated windows in the REBISO can be attributed to the convergence of multiple dung beetle assemblages including forest specialists (e.g., Eurysternus caribaeus, Sulcophanaeus chryseicollis, and Uroxys boneti), forestpasture edge specialists (O. landolti, Canthon cyanellus), open habitat specialists (D. annae, C. lugubris, O. corrosus), and generalist beetles (O. batesi) <ns0:ref type='bibr' target='#b22'>(Favila, 2005;</ns0:ref><ns0:ref type='bibr' target='#b53'>Navarrete & Halffter, 2008)</ns0:ref>.</ns0:p><ns0:p>Intact and fragmented windows showed lower diversity values than variegated windows. This diversity pattern is consistent with the intermediate disturbance theory <ns0:ref type='bibr' target='#b32'>(Grime, 1973)</ns0:ref>. Sites with little or no disturbance favor the predominance of highly competitive forest specialists such as D. mexicanum, C. vazquezae, and E. maya, which accounted for 85% of the total abundance and 90% of the total biomass in intact windows, thus preventing a higher local diversity. On the other hand, the intense landscape changes caused by livestock production in fragmented windows reduce the local species richness of dung beetles since many native-forest species are unable to adapt to the new open habitat conditions <ns0:ref type='bibr' target='#b33'>(Halffter, Favila & Halffter, 1992;</ns0:ref><ns0:ref type='bibr' target='#b74'>Silva, Storck-Tonon & Vaz-de-Mello, 2016;</ns0:ref><ns0:ref type='bibr' target='#b1'>Alvarado et al., 2018)</ns0:ref>.</ns0:p><ns0:p>The presence of the exotic African species Digitonthophagus gazella (Montes de Oca & Halffter, 1998) in the REBISO is worth mentioning. Although D. gazella was only recorded in pastures of fragmented windows (W6, W7), and contributed with only a small fraction of the community abundance and biomass (six and four percent, respectively), they may pose competitive pressure on native species inhabiting open areas <ns0:ref type='bibr' target='#b47'>(Lobo & Montes de Oca, 1994)</ns0:ref>. Further studies are needed to assess how this invasive beetle might affect native species in the REBISO.</ns0:p><ns0:p>Dung beetles are involved, among other ecological processes, in the recycling of organic matter, soil bioturbation, and secondary seed dispersal <ns0:ref type='bibr' target='#b56'>(Nichols et al., 2008)</ns0:ref>. The amounts of soil removed, dung buried, and seed dispersed are significantly and positively influenced by the species richness and biomass of dung beetle assemblages <ns0:ref type='bibr' target='#b58'>(Nunes et al., 2018;</ns0:ref><ns0:ref type='bibr' target='#b0'>Alvarado, Dáttilo & Escobar, 2019)</ns0:ref>. The tropical forest sites showed the highest dung beetle species richness and biomass values. Besides, forest coverage was positively related to dung beetle biomass in the REBISO. Both results indicate that the tropical forest sites likely contain the most functionally efficient dung beetle assemblages, thus emphasizing the importance of forest conservation in the REBISO.</ns0:p></ns0:div> <ns0:div><ns0:head>Effects of Landscape Composition and Configuration on Dung Beetle Assemblages</ns0:head><ns0:p>Previous studies conducted in tropical ecosystems have identified landscape composition as the main predictor of the diversity of dung beetle assemblages (Sánchez- <ns0:ref type='bibr' target='#b70'>de-Jesús et al., 2016;</ns0:ref><ns0:ref type='bibr' target='#b1'>Alvarado et al., 2018)</ns0:ref>. However, in our study, landscape fragmentation was the primary explanatory variable of variations in species richness and diversity. These findings are likely due to the variegated structure of the REBISO landscape and its 'biodiversity-friendly' matrix of second-growth forest (see <ns0:ref type='bibr' target='#b62'>Perfecto & Vandermeer, 2008;</ns0:ref><ns0:ref type='bibr' target='#b51'>Melo et al., 2013)</ns0:ref>. First, second-growth forests in the REBISO are structurally similar to forest habitats <ns0:ref type='bibr' target='#b67'>(Ramírez-Marcial et al., 2017)</ns0:ref>. Therefore, while many dung beetle species are restricted to forest patches, others may persist and use the second-growth forest matrix to move between forest patches <ns0:ref type='bibr' target='#b18'>(Díaz, Galante & Favila, 2010)</ns0:ref>. Second, fragmentation in variegated environments creates conditions that allow the coexistence of species from different habitat types (e.g., forest species, pasture species, edge specialist species), thereby increasing the diversity of dung beetles at the landscape scale <ns0:ref type='bibr' target='#b79'>(Villada-Bedoya et al., 2016;</ns0:ref><ns0:ref type='bibr' target='#b21'>Fahrig, 2017)</ns0:ref>.</ns0:p><ns0:p>Not all fragmentation effects are beneficial since a higher edge density can have adverse effects on the abundance, biomass, and even the physiological condition of tropical dung beetles (Portela <ns0:ref type='bibr' target='#b63'>Salomão et al., 2018)</ns0:ref>. We found the lowest abundance of dung beetles in variegated windows, where the highest edge density occurs. A higher edge density is coupled with less habitat area, limiting the capacity of the landscape to support medium-and large-sized mammal species. Pozo-Montuy et al. ( <ns0:ref type='formula'>2019</ns0:ref>) observed that medium-and large-sized mammals are significantly less abundant and diverse in the REBISO buffer zone (i.e., where the variegated windows are located). Such reduction in mammal density can cause a marked decrease in dung quantity and availability, thus limiting the growth of dung beetle populations <ns0:ref type='bibr' target='#b54'>(Nichols et al., 2009)</ns0:ref>. Also, microclimatic conditions such as temperature and relative humidity are more variable in forest edges, which might negatively affect the reproduction and survival of dung beetles <ns0:ref type='bibr' target='#b43'>(Klein, 1989;</ns0:ref><ns0:ref type='bibr' target='#b24'>Feer, 2013)</ns0:ref>. Our findings suggest that fragmentation processes in variegated windows foster a high dung beetle diversity, but might also limit their population growth due to insufficient resources, reduced habitat area, or sub-optimal microclimatic conditions. Future studies should assess the strength and extent of this trade-off between dung beetle diversity and abundance, and its functional consequences across the REBISO.</ns0:p></ns0:div> <ns0:div><ns0:head>Beta Diversity Patterns and Mechanisms of Diversity Maintenance</ns0:head><ns0:p>Species turnover is the primary driver of the high diversity and complementarity of the dung beetle communities found in the REBISO. There were between 3 and 27 species not shared between windows, and from 4 to 35 species not shared between vegetation classes. Each window and vegetation class contributed two or three unique species to the overall diversity. The largest turnover values were found between the fragmented windows (W6, W7, and W8) vs. the variegated and intact windows (W1 to W5), and between the forested vegetation classes (tropical forest, second-growth forest) vs. the pasture sites. The anthropic disturbances and the heterogeneous landscape of REBISO favor this high beta diversity since dung beetles are especially susceptible to environmental variability <ns0:ref type='bibr' target='#b3'>(Arellano, Leon-Cortes & Halffter, 2008;</ns0:ref><ns0:ref type='bibr' target='#b15'>Costa et al., 2017)</ns0:ref>.</ns0:p><ns0:p>The differences observed in the species assemblages of fragmented windows (W6, W7, and W8) and those in the other windows (W1 to W5) are not random. Likewise, the differences between tropical forest and pasture assemblages are not random. Significant deviations from random expectations of beta diversity indicate niche-structured assemblages in which environmental filters determine species membership in a community <ns0:ref type='bibr' target='#b13'>(Chase et al., 2011;</ns0:ref><ns0:ref type='bibr' target='#b65'>Püttker et al., 2015)</ns0:ref>. Intensive anthropic disturbances such as deforestation can act as an environmental filter in fragmented windows and pastures, selecting stress-tolerant dung beetle species able to survive in open habitats <ns0:ref type='bibr' target='#b33'>(Halffter, Favila & Halffter, 1992;</ns0:ref><ns0:ref type='bibr' target='#b76'>Spector & Ayzama, 2003;</ns0:ref><ns0:ref type='bibr' target='#b30'>Gardner et al., 2008)</ns0:ref>. Deltochilum mexicanum, C. vazquezae, S. chryseicollis, Canthon femoralis, and E. maya probably are the species most sensitive to the environmental filters caused by anthropic disturbance. Although these forest species are widely distributed in the biosphere reserve <ns0:ref type='bibr' target='#b71'>(Sánchez-Hernández et al., 2018)</ns0:ref>, their abundance was drastically reduced in fragmented windows.</ns0:p><ns0:p>We found signs of biotic homogenization in the pasture sites. For instance, the lowest alfa and beta diversity values were recorded in pastures, and their species assemblages were more similar to each other than expected by chance, regardless of the windows where they were located, indicating shared environmental filtering processes <ns0:ref type='bibr' target='#b12'>(Chase, 2010)</ns0:ref>. Anthropogenic environmental filters are one of the main drivers of biotic homogenization, eroding alfa and beta diversity and diminishing ecosystem resilience and viability <ns0:ref type='bibr' target='#b28'>(Gámez-Virués et al., 2015)</ns0:ref>. Hence, the advance of the agricultural frontier in the REBISO landscape should be monitored closely to prevent further biotic homogenization processes among the dung beetle species assemblages.</ns0:p><ns0:p>In our study, 1 β between the intact and variegated windows (W1 to W5), as well as between tropical forests and second-growth forests, was higher than 0 β. Thus, the true beta diversity is mainly due to differences in the abundance of shared species rather than to differences in richness <ns0:ref type='bibr' target='#b41'>(Jost, 2007)</ns0:ref>. Besides, the overall beta diversity between these windows and vegetation classes was not different from that expected by chance. Most species in neutral communities are considered ecologically equivalent since, in the absence of any factor limiting their dispersal, they can appear at random in any of the null assemblages <ns0:ref type='bibr' target='#b65'>(Püttker et al., 2015;</ns0:ref><ns0:ref type='bibr' target='#b60'>Ortega-Martínez et al., 2020)</ns0:ref>. Both results suggest that the REBISO still holds sufficient vegetation cover to maintain a continuous flow of dung beetles between forested landscape sections (W1 to W5).</ns0:p><ns0:p>Given the significant stochasticity of beta diversity between intact and variegated windows, and between tropical forests and second-growth forests, we can conclude that the landscape variegation in the REBISO does not affect dung beetle diversity negatively. However, it is essential to conserve the forested patches to maintain a high dispersal between sites, thereby increasing the resilience of dung beetle populations to habitat loss and isolation <ns0:ref type='bibr' target='#b9'>(de Castro Solar et al., 2015;</ns0:ref><ns0:ref type='bibr' target='#b75'>Socolar et al., 2016)</ns0:ref>. Landscape variegation can be an effective strategy to buffer the impact of intense anthropic disturbances <ns0:ref type='bibr' target='#b69'>(Rös, Escobar & Halffter, 2012;</ns0:ref><ns0:ref type='bibr' target='#b15'>Costa et al., 2017)</ns0:ref>. Variegation can be achieved by maintaining the forest cover and incorporating more biodiversity-friendly production systems, such as agroforestry practices, in the landscape matrix <ns0:ref type='bibr' target='#b62'>(Perfecto & Vandermeer, 2008)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions</ns0:head><ns0:p>This research contributes to better understand how diversity is distributed in variegated landscapes, and the role of heterogeneous landscapes in the conservation and management of tropical biodiversity. Tropical forests and second-growth forests contributed significantly to maintaining the diversity and biomass of dung beetle assemblages. The variegated structure of the landscape fosters a high dung beetle diversity. The heterogeneity of the REBISO landscape favors the formation of complementary dung beetle communities. Both deterministic and stochastic processes drive the beta diversity patterns in the landscape. Intense anthropic disturbances in fragmented windows and pastures act as non-stochastic filters upon dung beetle species, eroding the alpha and beta diversity of these sites. By contrast, random processes govern the less disturbed sites of the REBISO: fragmented tropical forests and second-growth forests. Increasing habitat variegation in highly disturbed sites can be an effective strategy to buffer and prevent further biotic homogenization processes. Manuscript to be reviewed Manuscript to be reviewed Manuscript to be reviewed Mean abundance and biomass (g) per trap (± sd) in each window and vegetation class. Manuscript to be reviewed </ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>); C. vazqueze, E. maya, and Eurysternus angustulus were the most abundant ones in variegated windows W3, W4, and W5; and O. batesi, O. corrosus, and Copris lugubris were the most abundant species in fragmented windows W6, W7, and W8 (Fig S1a). Biomass patterns in the dung beetle communities differed from those observed in their abundance values. Deltochilum mexicanum, E. maya, and Ontherus mexicanus were the dominant species, in terms of biomass, in intact windows W1 and W2 (Fig S1a); D. mexicanum, E. maya, and Dichotomius amplicollis were the dominant species in W3; D. mexicanum, D. amplicollis, and Dichotomius annae in W4; and Coprophanaeus corythus, Deltochilum sublaeve, and D. amplicollis in W5. Coprophanaeus corythus, C. lugubris, and D. amplicollis were the species with the highest biomass in fragmented windows W6, W7, and W8 (Fig S1a).</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>Deltochilum mexicanum, C. vazquezae, and E. maya were the most abundant species in tropical forest sites (Fig S1b); O. corrosus, D. mexicanum, and E. maya in the second-growth forest; and O. batesi, O. corrosus, and C. lugubris in pasture sites (Fig S1b). Deltochilum mexicanum, E. maya, and O. mexicanus contributed with the highest biomass in tropical forests; D. mexicanum, C. corythus, and E. maya in the second-growth forests (Fig S1b); and C. corythus, C. lugubris, and D. amplicollis in pasture sites (Fig S1b).</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>Figure 1</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head>Figure 2 (</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head>Figure 3 (</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Figure 3</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head>( A )</ns0:head><ns0:label>A</ns0:label><ns0:figDesc>Black dots: Sørensen beta diversity (βSor) between windows. White bars: Percentage contribution of species turnover (βSim) to beta diversity (βSim/βSor); Black bars: Percentage contribution of species-nestedness (βSne) to beta diversity (βSne/βSor).Forest in a mature successional stage with a dense canopy cover. The most common tree species are Pseudolmedia spuria, Louteridium donnell-smithii, Manilkara sapota, Swietenia macrophylla and Quararibea funebris<ns0:ref type='bibr' target='#b73'>(SEMARNAT/CONANP 2001;</ns0:ref><ns0:ref type='bibr' target='#b67'>Ramírez- Marcial et al. 2017)</ns0:ref>. Mean canopy cover, 82.32% (±1.35 s.e.); mean basal area, 912.24 cm 2 (±163.88 s.e.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head>2</ns0:head><ns0:label /><ns0:figDesc>PeerJ reviewing PDF \| (2020:03:46967:2:0:NEW 5 Aug 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_7'><ns0:head>F</ns0:head><ns0:label /><ns0:figDesc>: Forest; SF: Second-growth forest; P: Pasture PeerJ reviewing PDF \| (2020:03:46967:2:0:NEW 5 Aug 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_8'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ reviewing PDF \| (2020:03:46967:2:0:NEW 5 Aug 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_9'><ns0:head>F</ns0:head><ns0:label /><ns0:figDesc>: Forest; SF: Second-growth forest; P: Pasture. Pairwise comparison results are shown in TableS5. Different letters indicate statistically significant differences between windows and between vegetation classes (P<0.05).PeerJ reviewing PDF \| (2020:03:46967:2:0:NEW 5 Aug 2020)</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='25,42.52,70.87,525.00,435.75' type='bitmap' /></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head /><ns0:label /><ns0:figDesc>). Forest in intermediate successional stage, recovering after 1998 fire; the canopy is less dense than in the tropical forest. Dominated by Heliocarpus appendiculatus and Eugenia acapulcensis (SEMARNAT/CONANP, 2001; Ramírez-Marcial et al. 2017). Mean canopy cover, 56.76% (± 3.22 s.e.); mean basal area, 577.65 cm 2 (± 105.14 s.e.). Pastures are at least ten years old (SEMARNAT/CONANP, 2001). The few trees present are used mainly as shade for cattle. Mean basal area, 874.29 cm 2 (± s.e. 94.60); canopy cover ranges from 2% to 53% ( 22.11%, ± s.e 3.03). 𝑥 1 a Mean coverage over the eight windows.</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Second-growth forest</ns0:cell></ns0:row><ns0:row><ns0:cell>(n=8): a 30%</ns0:cell></ns0:row><ns0:row><ns0:cell>W1, W2, W3, W4,</ns0:cell></ns0:row><ns0:row><ns0:cell>W5, W6, W7, W8</ns0:cell></ns0:row><ns0:row><ns0:cell>Pasture (n=5): a 32%</ns0:cell></ns0:row><ns0:row><ns0:cell>W3, W4, W5, W6,</ns0:cell></ns0:row><ns0:row><ns0:cell>W7, W8</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_1'><ns0:head>Table 2 (on next page)</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>values in each window and vegetation class at Reserva de la Biosfera Selva El Ocote, Mexico.</ns0:figDesc><ns0:table /><ns0:note>0 D and 1 D</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_2'><ns0:head /><ns0:label /><ns0:figDesc>/15.84 b 𝜶 18.85 19.5 18.66 27.37 a /18.9 b 2.93 6.57 7.34 7.09 a /6.15 b 𝜷 2.38 2.25 1.87 2.01 a /2.9 b 2.64 2.36 1.21 2.23 a /2.57 b 1 a Overall window diversity, b Overall vegetation class diversity.</ns0:figDesc><ns0:table><ns0:row><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>0 D</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell>1 D</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>F</ns0:cell><ns0:cell>SF</ns0:cell><ns0:cell>P</ns0:cell><ns0:cell>Species richness</ns0:cell><ns0:cell>F</ns0:cell><ns0:cell>SF</ns0:cell><ns0:cell>P</ns0:cell><ns0:cell>Exp (Shannon diversity)</ns0:cell></ns0:row><ns0:row><ns0:cell>W1</ns0:cell><ns0:cell /><ns0:cell>11</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>22</ns0:cell><ns0:cell>5.23</ns0:cell><ns0:cell>4.8</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>5.36</ns0:cell></ns0:row><ns0:row><ns0:cell>W2</ns0:cell><ns0:cell /><ns0:cell>12</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>24</ns0:cell><ns0:cell cols='2'>5.05 4.27</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>4.97</ns0:cell></ns0:row><ns0:row><ns0:cell>W3</ns0:cell><ns0:cell /><ns0:cell>21</ns0:cell><ns0:cell>11</ns0:cell><ns0:cell>28</ns0:cell><ns0:cell cols='3'>7.71 7.22 5.38</ns0:cell><ns0:cell>8.69</ns0:cell></ns0:row><ns0:row><ns0:cell>W4</ns0:cell><ns0:cell /><ns0:cell>29</ns0:cell><ns0:cell>20</ns0:cell><ns0:cell>37</ns0:cell><ns0:cell cols='3'>12.39 13.51 13.91</ns0:cell><ns0:cell>16.49</ns0:cell></ns0:row><ns0:row><ns0:cell>W5</ns0:cell><ns0:cell /><ns0:cell>32</ns0:cell><ns0:cell>19</ns0:cell><ns0:cell>35</ns0:cell><ns0:cell cols='3'>9.88 16.37 9.55</ns0:cell><ns0:cell>17.59</ns0:cell></ns0:row><ns0:row><ns0:cell>W6</ns0:cell><ns0:cell>5</ns0:cell><ns0:cell>14</ns0:cell><ns0:cell>19</ns0:cell><ns0:cell>23</ns0:cell><ns0:cell cols='3'>3.50 5.51 6.40</ns0:cell><ns0:cell>6.57</ns0:cell></ns0:row><ns0:row><ns0:cell>W7</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>17</ns0:cell><ns0:cell>19</ns0:cell><ns0:cell>24</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell cols='2'>8.08 7.38</ns0:cell><ns0:cell>7.61</ns0:cell></ns0:row><ns0:row><ns0:cell>W8</ns0:cell><ns0:cell>2</ns0:cell><ns0:cell>20</ns0:cell><ns0:cell>21</ns0:cell><ns0:cell>26</ns0:cell><ns0:cell cols='3'>1.50 3.46 9.51</ns0:cell><ns0:cell>5.49</ns0:cell></ns0:row><ns0:row><ns0:cell>𝜸</ns0:cell><ns0:cell /><ns0:cell>44</ns0:cell><ns0:cell>34</ns0:cell><ns0:cell>55 a /55 b</ns0:cell><ns0:cell cols='3'>7.75 15.54 8.93</ns0:cell><ns0:cell>15.84 a</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_3'><ns0:head>Table 3 (on next page)</ns0:head><ns0:label>3</ns0:label><ns0:figDesc /><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_4'><ns0:head>Mean abundance/trap (±sd) P < 0.05 Mean biomass/trap (±sd) P < 0.05 W1</ns0:head><ns0:label /><ns0:figDesc /><ns0:table><ns0:row><ns0:cell /><ns0:cell>52.94 (11.13)</ns0:cell><ns0:cell>a</ns0:cell><ns0:cell>9.07 (1.01)</ns0:cell><ns0:cell>a</ns0:cell></ns0:row><ns0:row><ns0:cell>W2</ns0:cell><ns0:cell>44.95 (0.11)</ns0:cell><ns0:cell>a</ns0:cell><ns0:cell>8.20 (0.93)</ns0:cell><ns0:cell>a, b</ns0:cell></ns0:row><ns0:row><ns0:cell>W3</ns0:cell><ns0:cell>20.83 (13.94)</ns0:cell><ns0:cell>a</ns0:cell><ns0:cell>2.37 (0.97)</ns0:cell><ns0:cell>c</ns0:cell></ns0:row><ns0:row><ns0:cell>W4</ns0:cell><ns0:cell>15.72 (7.88)</ns0:cell><ns0:cell>a</ns0:cell><ns0:cell>2.53 (0.72)</ns0:cell><ns0:cell>c</ns0:cell></ns0:row><ns0:row><ns0:cell>W5</ns0:cell><ns0:cell>29.55 (10.88)</ns0:cell><ns0:cell>a</ns0:cell><ns0:cell>3.89 (1.70)</ns0:cell><ns0:cell>b, c</ns0:cell></ns0:row><ns0:row><ns0:cell>W6</ns0:cell><ns0:cell>15.82 (5.12)</ns0:cell><ns0:cell>a</ns0:cell><ns0:cell>2.20 (0.49)</ns0:cell><ns0:cell>c</ns0:cell></ns0:row><ns0:row><ns0:cell>W7</ns0:cell><ns0:cell>35.26 (18.29)</ns0:cell><ns0:cell>a</ns0:cell><ns0:cell>2.75 (0.05)</ns0:cell><ns0:cell>c</ns0:cell></ns0:row><ns0:row><ns0:cell>W8</ns0:cell><ns0:cell>36.51 (46.98)</ns0:cell><ns0:cell>a</ns0:cell><ns0:cell>3.71 (1.22)</ns0:cell><ns0:cell>b, c</ns0:cell></ns0:row><ns0:row><ns0:cell>F</ns0:cell><ns0:cell>30.28 (13.91)</ns0:cell><ns0:cell>a</ns0:cell><ns0:cell>5.05 (2.21)</ns0:cell><ns0:cell>a</ns0:cell></ns0:row><ns0:row><ns0:cell>SF</ns0:cell><ns0:cell>37.89 (26.86)</ns0:cell><ns0:cell>a</ns0:cell><ns0:cell>4.53 (3.04)</ns0:cell><ns0:cell>a</ns0:cell></ns0:row><ns0:row><ns0:cell>P</ns0:cell><ns0:cell>17.74 (15.56)</ns0:cell><ns0:cell>a</ns0:cell><ns0:cell>2.09 (0.62)</ns0:cell><ns0:cell>b</ns0:cell></ns0:row><ns0:row><ns0:cell>1</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row><ns0:row><ns0:cell>2</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row></ns0:table></ns0:figure> <ns0:note place='foot'>PeerJ reviewing PDF \| (2020:03:46967:2:0:NEW 5 Aug 2020)</ns0:note> <ns0:note place='foot'>PeerJ reviewing PDF \| (2020:03:46967:2:0:NEW 5 Aug 2020)Manuscript to be reviewed</ns0:note> <ns0:note place='foot' n='2'>PeerJ reviewing PDF \| (2020:03:46967:2:0:NEW 5 Aug 2020)Manuscript to be reviewed</ns0:note> </ns0:body> "	"August 5, 2020 Dear Editor, Dr. Nigel Andrew Academic Editor, Peer J This is to respectfully submit to your kind consideration the revised version of our manuscript entitled 'Mechanisms of diversity maintenance of dung beetle assemblages in a heterogeneous tropical landscape'. Again, we want to thank the reviewers for their valuable comments and feedback on a previous version of our manuscript. In this revised version, we accepted all the changes suggested by the reviewer. We hope you find this updated version suitable for publication in PeerJ. Thanks in advance for your attention and hoping to hear from you soon, José D. Rivera Duarte Ph.D. student, Instituto de Ecología, A.C. Xalapa, Veracruz, Mexico. Reviewer 1: Basic reporting no comment Experimental design no comment Validity of the findings no comment Comments for the author After my second reading of the paper, I was able to notice the great improvement of the study in theoretical and analytical terms. The authors addressed my major and minor suggestions. As in my previous assessment, I reaffirm the quality of the study in terms of all the necessary aspects to be assessed (basic report, experimental design, and validity of the findings). However, a few issues of writing still remain. After these corrections, I think the paper will be a good contribution to the understanding of fragmentation ecology in variegated landscapes. Minor issues 1. L. 40 Change “beta-diversity” to “beta diversity” R/ Done 2. L. 219 Change “beta-diversity” to “beta diversity” R/ Done 3. L. 252 Lack of space “E.maya” R/ Done 4. L. 283. Change “nested processes” to “nestedness-resultant component” R/ Done 5. L 349 Lack of space “O.batesi” R/ Done 6. L 352 Remove a space here “is consistent” R/ Done 7. L 471 Change “beta-diversity” to “beta diversity” R/ Done "	Here is a paper. Please give your review comments after reading it.
529	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Dimethylsulfoniopropionate (DMSP), an osmolyte produced by oceanic phytoplankton and bacteria, is primarily degraded by bacteria belonging to the Roseobacter lineage and other marine Alphaproteobacteria via DMSP-dependent demethylase A protein (DmdA). To date, the evolutionary history of DmdA gene family is unclear. Some studies indicate a common ancestry between DmdA and GcvT gene families and a co-evolution between Roseobacter and the DMSP-producing-phytoplankton around 250 million years ago (Mya). In this work, we analyzed the evolution of DmdA under three possible evolutionary scenarios: 1) a recent common ancestor of DmdA and GcvT, 2) a coevolution between Roseobacter and the DMSP-producing-phytoplankton, and 3) an enzymatic adaptation for utilizing DMSP in marine bacteria prior to Roseobacter origin. Our analyses indicate that DmdA is a new gene family originated from GcvT genes by duplication and functional divergence driven by positive selection before a coevolution between Roseobacter and phytoplankton. Our data suggest that Roseobacter acquired dmdA by horizontal gene transfer prior to an environment with higher DMSP. Here, we propose that the ancestor that carried the DMSP demethylation pathway genes evolved in the Archean, and was exposed to a higher concentration of DMSP in a sulfur-rich atmosphere and anoxic ocean, compared to recent Roseobacter eco-orthologs (orthologs performing the same function under different conditions), which should be adapted to lower concentrations of DMSP.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>Dimethylsulfoniopropionate (DMSP) is an osmolyte synthesized by oceanic phytoplankton and bacteria <ns0:ref type='bibr' target='#b23'>(Galinski, 1995;</ns0:ref><ns0:ref type='bibr' target='#b96'>Yoch, 2002;</ns0:ref><ns0:ref type='bibr' target='#b13'>Curson et al., 2017)</ns0:ref>. This molecule became abundant in the oceans approximately 250 million years ago (Mya), coinciding with the expansion and diversification of dinoflagellates <ns0:ref type='bibr' target='#b7'>(Bullock et al., 2017)</ns0:ref>. Since then, it has played an important role in the biogeochemistry of sulfur cycle on Earth <ns0:ref type='bibr' target='#b50'>(Lovelock, 1983)</ns0:ref>. DMSP is the main precursor of the climate-relevant gas dimethylsulfide (DMS; <ns0:ref type='bibr'>Reisch et al., 2011)</ns0:ref>. In marine ecosystems, DMSP is rapidly degraded by different bacterial communities <ns0:ref type='bibr' target='#b26'>(González et al., 1999)</ns0:ref>, and some strains seem to be very efficient and even become dependent on its degradation <ns0:ref type='bibr' target='#b87'>(Tripp et al., 2008)</ns0:ref>. In fact, DMSP supports up to 13% of the bacterial carbon demand in surface waters, making it one of the most significant substrates for bacterioplankton <ns0:ref type='bibr' target='#b38'>(Kiene et al., 1999;</ns0:ref><ns0:ref type='bibr' target='#b26'>González et al., 1999)</ns0:ref>. Candidatus Pelagibacter ubique (SAR11), dominant in the bacterioplankton and especially in surface waters, can only use sulfur atoms derived organic molecules, such as DMSP <ns0:ref type='bibr' target='#b87'>(Tripp et al., 2008)</ns0:ref>. In the case of Ruegeria pomeroyi DSS-3, a model organism for DMSP studies, the turnover rate of DMSP transformation depends on salinity conditions <ns0:ref type='bibr' target='#b75'>(Salgado et al., 2014)</ns0:ref>.</ns0:p><ns0:p>The first step in the degradation of DMSP involves two competing pathways, cleavage and demethylation. The DMSP cleavage pathway metabolizes DMSP with the release of DMS <ns0:ref type='bibr' target='#b38'>(Kiene et al., 1999)</ns0:ref>, a step catalyzed by a number of enzymes <ns0:ref type='bibr' target='#b12'>(Curson et al., 2011)</ns0:ref>. In the alternative pathway, DMSP is first demethylated by a DMSP-dependent demethylase A protein (DmdA; <ns0:ref type='bibr' target='#b32'>Howard et al., 2006)</ns0:ref>. Compared to genes in the DMS-releasing pathway, dmdA is more frequently found in the genomes of oceanic bacteria <ns0:ref type='bibr' target='#b59'>(Newton et al., 2010;</ns0:ref><ns0:ref type='bibr'>Todd et al., 1999)</ns0:ref>. The DmdA enzyme was originally annotated as a glycine cleavage T-protein (GcvT) in the model bacteria R. pomeroyi <ns0:ref type='bibr' target='#b71'>(Reisch et al., 2011a)</ns0:ref>, although it forms a separate clade from the known GcvTs (gcvT and Unchar. AMT) <ns0:ref type='bibr' target='#b84'>(Sun et al., 2011;</ns0:ref><ns0:ref type='bibr' target='#b7'>Bullock et al., 2017)</ns0:ref>. Despite their structural similarity which might indicate a common ancestry, DmdA and GcvT are mechanistically distinct <ns0:ref type='bibr' target='#b78'>(Schuller et al., 2012)</ns0:ref>. DmdA produces 5-methyl-THF from DMSP as the result of a redoxneutral methyl transfer, while GcvT produces glycine to 5,10-methylene-THF from glycine <ns0:ref type='bibr' target='#b70'>(Reisch et al., 2008)</ns0:ref>.</ns0:p><ns0:p>Nearly all known DMSP-catabolizing bacteria belong to the phylum Proteobacteria with DmdA orthologs found in most of the sequenced members of the Rhodobacteraceae family, as well as bacterioplankton strains of SAR11, SAR324, SAR116 and in marine Gammaproteobacteria <ns0:ref type='bibr' target='#b26'>(González et al., 1999;</ns0:ref><ns0:ref type='bibr' target='#b27'>González, 2003;</ns0:ref><ns0:ref type='bibr' target='#b32'>Howard et al., 2006;</ns0:ref><ns0:ref type='bibr' target='#b8'>Bürgmann et al., 2007;</ns0:ref><ns0:ref type='bibr' target='#b70'>Reisch et al., 2008)</ns0:ref> like Chromatiales which could have gotten DmdA gene by HGT as some studies suggest <ns0:ref type='bibr' target='#b32'>(Howard et al., 2006;</ns0:ref><ns0:ref type='bibr' target='#b28'>González et al., 2019 )</ns0:ref>. This phylogenetic distribution suggests an expansion of dmdA through HGT events between different lineages of bacteria, presumably through viruses <ns0:ref type='bibr' target='#b65'>(Raina et al., 2010)</ns0:ref>. Since an episode of genome expansion of Roseobacter, predicted early in its genome evolution, coincides with the diversification of the dinoflagellates and coccolithophores around 250 Mya <ns0:ref type='bibr' target='#b52'>(Luo et al., 2013;</ns0:ref><ns0:ref type='bibr' target='#b53'>Luo & Moran, 2014)</ns0:ref>, it has been suggested a co-evolutionary event between Roseobacter and the DMSP-producing-phytoplankton <ns0:ref type='bibr' target='#b52'>(Luo et al., 2013;</ns0:ref><ns0:ref type='bibr' target='#b53'>Luo & Moran, 2014;</ns0:ref><ns0:ref type='bibr' target='#b7'>Bullock et al., 2017)</ns0:ref>. Under this scenario, the enzymes of the DMSP demethylation pathway could have evolved within the last 250 Mya, as phytoplankton responded to the marine catastrophe at the end of the Permian, with the diversification of dinoflagellates that produce DMSP and the Roseobacter clade expanding by using DMSP as its main sulfur source. Despite this hypothesis, there is a lack of knowledge about the main evolutionary events that lead the adaptation to DMSP in Roseobacter.</ns0:p><ns0:p>The biosynthesis of DMSP has been reported in marine heterotrophic bacteria, such as the Alphaproteobacteria, i.e. Labrenzia aggregata <ns0:ref type='bibr' target='#b13'>(Curson et al., 2017)</ns0:ref>, Gammaproteobacteria and Actinobacteria <ns0:ref type='bibr' target='#b88'>(Williams et al., 2019)</ns0:ref>. Moreover, bacteria seem to be important producers of DMSP and DMS in coastal and marine sediments <ns0:ref type='bibr' target='#b88'>(Williams et al., 2019)</ns0:ref>. Since the common ancestor of heterotrophic bacteria and Roseobacter originated in the Archean, more than 2 billion years ago <ns0:ref type='bibr' target='#b44'>(Kumar et al., 2017)</ns0:ref>, the Roseobacter and other Alphaproteobacteria might have been exposed to DMSP early <ns0:ref type='bibr'>(Reisch et al. 2011a,b)</ns0:ref>. According to this hypothesis, the DMSP demethylation and the cleavage pathways arose by the evolution of enzymes that were already present in bacterial genomes and adapted in response to the wide availability of DMSP. As mentioned earlier, Alphaproteobacteria in the SAR11 group seems to thrive at the expense of organic sulfur compounds, such as DMSP, and had a common ancestor that lived ca. 826 Mya, at the end of the Precambrian <ns0:ref type='bibr' target='#b52'>(Luo et al., 2013)</ns0:ref>. We would then expect a common ancestor of the DmdA gene family during the early Proterozoic and that the functional divergence between DmdA and GcvT gene families was driven by both functional constraints and widespread HGT, probably during the Huronian snowball Earth, a period of planetary crisis where the greatest microbial diversity took refuge in the shallow seas close to the equator <ns0:ref type='bibr'>(Tang, Thomas, & Xia, n.d.)</ns0:ref>.</ns0:p><ns0:p>Here, we analyzed the evolutionary history of the DmdA gene family in marine Proteobacteria by considering three evolutionary scenarios: 1) a recent common ancestry of DmdA and GcvT, 2) a coevolution between Roseobacter and the DMSP-producing-phytoplankton, and 3) an enzymatic adaptation for utilizing DMSP in marine bacteria prior to Roseobacter origin. We first analyzed if convergent, independent or HGT-based evolution can explain the presence of dmdA genes in different bacterial lineages SAR11, SAR116 and Rhodobacteraceae. Then, we inferred the most recent common ancestor (MRCA) of the DmdA gene family, the timing of its origin and any duplication events. We also reconstructed the ancestral forms of DmdA enzymes to infer the most likely ecological conditions where DmdA thrive. We provide insights into their function by analyzing DmdA structural evolution. Finally, we examined how natural selection could have driven the divergence of the DmdA gene family. Our results indicate that dmdA appeared before the origin of the Roseobacter clade and the conditions of the late Permian created by eukaryotic phytoplankton. Therefore, DmdA is an adapted version of enzyme that evolved in response to the availability of DMSP.</ns0:p></ns0:div> <ns0:div><ns0:head>Materials & Methods</ns0:head></ns0:div> <ns0:div><ns0:head>Data mining</ns0:head><ns0:p>Peptides and genes from DmdA gene family were collected from a set of 771 genomes manually curated and hosted in the MarRef database <ns0:ref type='bibr' target='#b42'>(Klemetsen et al., 2018)</ns0:ref>. The DmdA orthologs and homologs sequences were obtained as described by <ns0:ref type='bibr' target='#b28'>González et al. (2019)</ns0:ref>. The DmdA homologs included were obtained using a HMM designed for DmdA orthologs <ns0:ref type='bibr' target='#b28'>(González et al., 2019)</ns0:ref>, with a relaxed maximum e-value (e-50). A total of 204 sequences from 184 genomes were used to infer the evolutionary history of DmdA gene family (Table <ns0:ref type='table'>S1</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>Phylogenetic tree reconstruction and topology tests</ns0:head><ns0:p>The phylogenetic tree of the DmdA protein sequences included DmdA orthologs and DmdA homologs (non-DmdA as in <ns0:ref type='bibr' target='#b28'>González et al (2019)</ns0:ref>). The sequences were aligned using MUSCLE <ns0:ref type='bibr' target='#b20'>(Edgar, 2004)</ns0:ref>. Regions poorly aligned or with gaps were removed using TrimAl <ns0:ref type='bibr' target='#b9'>(Capella-Gutiérrez et al., 2009)</ns0:ref> with parameters set to a minimum overlap of 0.55 and a percent of good positions to 60. Best-fit evolutionary model was selected based on the results of the package ProtTest 3 <ns0:ref type='bibr' target='#b15'>(Darriba et al., 2011)</ns0:ref> to determine the best-fit model for maximum likelihood (ML) and Bayesian inference (BI).</ns0:p><ns0:p>For the maximum likelihood analysis (ML), PhyML v3.0 <ns0:ref type='bibr' target='#b29'>(Guindon et al., 2010)</ns0:ref> or RAxML v7.2.6 <ns0:ref type='bibr' target='#b83'>(Stamatakis, 2006)</ns0:ref> were used to generate 100 ML bootstrap trees, using the Le Gascuel (LG; <ns0:ref type='bibr' target='#b49'>Le & Gascuel, 2008)</ns0:ref> model with a discrete gamma distribution (+G) with four rate categories, as this was the model with the lowest Akaike information criterion and Bayesian information criterion score. For the Bayesian analysis (BI), trees were constructed using the PhyloBayes program <ns0:ref type='bibr' target='#b46'>(Lartillot & Philippe, 2004</ns0:ref><ns0:ref type='bibr' target='#b83'>, 2006;</ns0:ref><ns0:ref type='bibr' target='#b48'>Lartillot et al., 2007)</ns0:ref> with the CAT model that integrates heterogeneity of amino acid composition across sites of a protein alignment. In this case, two chains were run in parallel and checked for convergence using the tracecomp and bpcomp scripts provided in PhyloBayes. As an alternative, we computed a phylogenetic tree using a BI implemented in BEAST2 program which was run with relaxed clock model and Birth Death tree prior <ns0:ref type='bibr' target='#b6'>(Bouckaert et al., 2014)</ns0:ref>. Finally, we used R v3.6.1 (R Core Team, 2017) with phangorn v2.5.5 <ns0:ref type='bibr' target='#b77'>(Schliep, 2011)</ns0:ref> to perform consensus unrooted trees.</ns0:p><ns0:p>We ran several topology tests to establish whether the trees generated using the ML and BI methods provided an equivalent explanation for the two main groups, i.e., the non-DmdA and DmdA clades. For this analysis, the topologies were compared with the TOPD/FMTS software v4.6 <ns0:ref type='bibr' target='#b63'>(Puigbo et al., 2007)</ns0:ref>. A random average split distance of 100 trees was also created to check if the differences observed were more likely to have been generated by chance.</ns0:p></ns0:div> <ns0:div><ns0:head>Horizontal gene transfer (HGT) test and GC content analysis</ns0:head><ns0:p>Two approaches were used to detect HGT. First, a phylogenetic incongruence analysis <ns0:ref type='bibr' target='#b69'>(Ravenhall et al., 2015)</ns0:ref> through three topology tests, the Kishino-Hasegawa (KH) <ns0:ref type='bibr' target='#b41'>(Kishino & Hasegawa, 1989)</ns0:ref>, the Shimodaira-Hasewaga (SH) <ns0:ref type='bibr' target='#b79'>(Shimodaira & Hasegawa, 1999)</ns0:ref> and the approximately unbiased (AU) <ns0:ref type='bibr' target='#b80'>(Shimodaira, 2002)</ns0:ref>, implemented in the IQ-TREE software v1.5.5 <ns0:ref type='bibr' target='#b60'>(Nguyen et al., 2015)</ns0:ref>. Two topologies were tested, the ML topology obtained for the species tree of the genomes here analyzed, and the ML phylogeny of DmdA. To construct the species tree, ribosomal protein 16 small subunit (RPS16) sequences were collected from the MarRef database <ns0:ref type='bibr' target='#b42'>(Klemetsen et al., 2018)</ns0:ref>, one for each genome (Table <ns0:ref type='table'>S1</ns0:ref>).</ns0:p><ns0:p>The GC content variation was studied to identify genes that have a different percentage of GC content at the third position of codons with respect to the neighboring genomic regions. The EPIC-CoGe browser <ns0:ref type='bibr' target='#b58'>(Nelson et al., 2018)</ns0:ref> was used to visualize the genomes and sequences and look for genes that use different codons with respect to the rest of the genomic dataset (data are available under permission as 'ULL-microevolution' on https://genomevolution.org/).</ns0:p></ns0:div> <ns0:div><ns0:head>Molecular dating</ns0:head><ns0:p>We first tested for heterogeneities in the substitution rates of the genes using a likelihood ratio test (LRT) <ns0:ref type='bibr' target='#b21'>(Felsenstein, 1981)</ns0:ref> with the ML-inferred tree. Likelihoods' values were estimated using baseml in PAML v4.8 <ns0:ref type='bibr' target='#b93'>(Yang, 2007)</ns0:ref> under rate constant and rate variable models and used to compute the likelihood ratio test (LRT) statistic according to the following equation:</ns0:p><ns0:p>LRT=-2(logL 1 -logL 0 )</ns0:p><ns0:p>where L 1 is the unconstrained (nonclock) likelihood value, and L 0 is the likelihood value obtained under the rate constancy assumption. LRT is distributed approximately as a chi-square random variable with (m-2) degrees of freedom (df), m being the number of branches/parameters.</ns0:p><ns0:p>To conduct a molecular dating analysis with BEAST 2 <ns0:ref type='bibr' target='#b6'>(Bouckaert et al., 2014)</ns0:ref>, two independent MCMC tree searches were run for 50 million generations, with a sampling frequency of 1000 generations over codon alignment obtained, as we explain in the next section. The GTR substitution model with a gamma shape parameter and a proportion of invariants (GTR + G + I), was selected with PartitionFinder software v2.1.1 <ns0:ref type='bibr' target='#b45'>(Lanfear et al., 2016)</ns0:ref> based on the Bayesian Information Criterion <ns0:ref type='bibr' target='#b17'>(Darriba et al., 2012)</ns0:ref>, applied with a Birth Death tree prior <ns0:ref type='bibr' target='#b25'>(Gernhard, 2008)</ns0:ref> and an uncorrelated relaxed clock log-normal. The molecular clock was calibrated using information from the TimeTree database <ns0:ref type='bibr' target='#b30'>(Hedges et al., 2006</ns0:ref><ns0:ref type='bibr' target='#b31'>(Hedges et al., , 2015;;</ns0:ref><ns0:ref type='bibr' target='#b44'>Kumar et al., 2017)</ns0:ref>. We used the proposed dates of the most recent common ancestor of (1) the Alpha-and Gammaproteobacteria (2480 Mya), (2) the Halobacteriales (455 Mya) (Fig. <ns0:ref type='figure' target='#fig_2'>S1-S3</ns0:ref>) <ns0:ref type='bibr' target='#b30'>(Hedges et al., 2006</ns0:ref><ns0:ref type='bibr' target='#b31'>(Hedges et al., , 2015;;</ns0:ref><ns0:ref type='bibr' target='#b44'>Kumar et al., 2017)</ns0:ref>, and (3) the SAR11 (826 Mya) <ns0:ref type='bibr' target='#b52'>(Luo et al., 2013)</ns0:ref>. A lognormal prior distribution on the calibrated nodes centered at the values mentioned above was specified with 20 standard deviations and constrained to be monophyletic. Convergence of the stationary distribution was checked by visual inspection of plotted posterior estimates in Tracer v1.6 <ns0:ref type='bibr' target='#b68'>(Rambaut, & Drummond, 2013)</ns0:ref> to ensure effective sample sizes (ESSs) of parameters were >> 200, as recommended by the authors. After discarding the first 15% trees as burn-in, the samples were summarized in the maximum clade credibility tree using TreeAnnotator v1.6.1 <ns0:ref type='bibr' target='#b66'>(Rambaut, & Drummond, 2002)</ns0:ref> with a PP limit of 0.5 and summarizing mean node heights.</ns0:p><ns0:p>Means and 95 % higher posterior densities (HPDs) of age estimates are obtained from the combined outputs using Tracer v1.6. The results were visualized using FigTree v.1.4.3 <ns0:ref type='bibr' target='#b67'>(Rambaut, 2009)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Maximum likelihood tests of positive selection</ns0:head><ns0:p>To measure the strength and mode of natural selection during the evolution of DmdA gene family, the ratio of non-synonymous (dN) to synonymous substitutions (dS) (ω=dN/dS) was calculated in CodeML implemented in the suite Phylogenetic Analysis by Maximum Likelihood (PAML package v4.8) <ns0:ref type='bibr' target='#b93'>(Yang, 2007)</ns0:ref>.</ns0:p><ns0:p>CodeML requires an alignment of coding sequences, and a phylogenetic tree. DNA alignment was achieved by MUSCLE <ns0:ref type='bibr' target='#b20'>(Edgar, 2004)</ns0:ref> implemented in MEGA-CC v7.0.26 <ns0:ref type='bibr' target='#b43'>(Kumar et al., 2016)</ns0:ref> and poorly aligned segments were eliminated with Gblocks under defaults parameters <ns0:ref type='bibr' target='#b10'>(Castresana, 2000)</ns0:ref>. The phylogenetic tree was built using ML with PhyML v3.0 <ns0:ref type='bibr' target='#b29'>(Guindon et al., 2010)</ns0:ref> as described above and a nucleotide substitution model selected by jModelTest <ns0:ref type='bibr' target='#b17'>(Darriba et al., 2012)</ns0:ref>. DAMBE <ns0:ref type='bibr' target='#b90'>(Xia, 2001)</ns0:ref> was also used to check for saturation of nucleotide substitutions using a plot of the number of transitions and transversions for each pairwise comparison against the genetic distance calculated with the F84 model of nucleotide substitution <ns0:ref type='bibr' target='#b35'>(Huelsenbeck & Rannala, 1997)</ns0:ref>, which allows different equilibrium nucleotide frequencies and a transition ratetransversion rate bias. Multiple sequence alignments with similar characteristics (i.e., showing saturation of nucleotide substitutions) were then analyzed with CodeML <ns0:ref type='bibr' target='#b93'>(Yang, 2007)</ns0:ref>.</ns0:p><ns0:p>Three sets of models were used (site-specific, branch-specific and branch-site models) to detect pervasive and episodic selection during the evolution of dmdA orthologs. Likelihood-ratio tests (LRTs) were used to compare models, and significant results (p-value<0.05) were determined contrasting with a chi-square distribution (chisq) <ns0:ref type='bibr' target='#b0'>(Anisimova et al., 2001)</ns0:ref>.</ns0:p><ns0:p>In the site-specific analysis, we tested for variability of selection (type and magnitude) across the codons of the gene using three pairs of nested models. The first pair includes M0 (just one dN/dS ratio) and M3 ('K' discrete categories of dN/dS) and has four degrees of freedom (df). The second pair of models considers M1a (just two classes of sites, purifying [dN/dS<1] and neutral selection [dN/dS=1]) and M2a (the same as M1a adding a third class of sites dedicated to positive selection [dN/dS>1]), this has two df. Finally, the third pair of models comprised M7 (a beta distribution that allows dN/dS to vary among the interval [0,1]) and M8 (adds an extra discrete category to M7 with dN/dS>1), with two df. Whereas M0 vs M3 tests for evidence of dN/dS variation across sites, M1a vs M2a and M7 vs M8 tests for the presence of sites under positive selection (dN/dS > 1).</ns0:p><ns0:p>Using three branch models <ns0:ref type='bibr' target='#b91'>(Yang, 1998)</ns0:ref>, we tested for variation of selection over evolutionary time. The null model (M0) assumes that all branches evolve at the same rate, therefore, there is only one value of dN/dS for all the branches of the tree. The two-ratio model allows two dN/dS values, one value for all the Roseobacter lineage (we called this group A) and another for the rest of branches (group B). The free-ratio model, allows one dN/dS value for each branch. Null and two-ratio model are compared by LRT with one df but null and free-ratio model are compared with 36 df.</ns0:p><ns0:p>For the last set of models, we identified sites that have been under positive selection at a particular point of evolution using branch-site models, in which dN/dS can vary among sites and among branches <ns0:ref type='bibr'>(Zhang, 2005)</ns0:ref>. We computed two models: a null model, in which the 'foreground branch' may have different proportions of sites under neutral selection to the 'background branches', and an alternative model in which the 'foreground branch' may have a proportion of sites under positive selection. We compare these models for each terminal branch with a LRT of one df. For each branch-site analysis, we applied the Bonferroni correction for multiple testing.</ns0:p><ns0:p>In site and branch-site tests, we identified sites under positive selection as those with Bayes Empirical Bayes (BEB) posterior probability above 0.95 <ns0:ref type='bibr' target='#b92'>(Yang, 2005)</ns0:ref>. We also checked for convergence of the parameter estimates in PAML by carrying out at least two runs for each tree and starting the analysis with different ω (0.2, 1, 1.2 and 2). In addition, to test for convergent selection in several lineages, we ran at branch-site analysis selecting as 'foreground branches' all those under positive selection in a previous analysis.</ns0:p></ns0:div> <ns0:div><ns0:head>Analysis of functional divergence</ns0:head><ns0:p>Divergent selection is indicated by different ω values among paralogous clades. We tested whether selective pressures diverged following duplication that led to dmdA and non-dmdA genes <ns0:ref type='bibr' target='#b5'>(Bielawski & Yang, 2004)</ns0:ref>. We compared the M3 model, which accounts for ω variation among sites but not among branches or clades, with a model allowing a fraction of sites to have different ω between two clades of a phylogeny (clade model D). We also tested M0 and M3 models and we used a posterior BEB probability above 0.95 to identify sites evolving under divergent selective pressures. We checked for convergence of the parameter estimates in PAML by carrying out at least two runs for the tree and starting the analysis with different ω (0.1, 0.25, 2, 3 and 4).</ns0:p><ns0:p>Finally, we applied two branch-site models (as described above) to test dN/dS differences on the branches representing the ancestral lineages of the DmdA and non-DmdA clades (see results). We considered the ancestral sequences from DmdA and non-DmdA clades as foreground branches in two different models.</ns0:p></ns0:div> <ns0:div><ns0:head>Reconstruction of ancestral DmdA sequence</ns0:head><ns0:p>To reconstruct the ancient conditions where dmdA gene prospered, we inferred the ancestral sequences of the DmdA node using the FastML web server <ns0:ref type='bibr' target='#b2'>(Ashkenazy et al., 2012)</ns0:ref> and then computed estimated physico-chemical properties on predecessor sequence using Compute ProtParam tool from Expasy -SIB Bioinformatics Resource Portal <ns0:ref type='bibr' target='#b24'>(Gasteiger et al., 2005)</ns0:ref>. Moreover, we also reconstructed the ancestral sequence of the non-DmdA node, as well as the ancestral sequence of both the DmdA, and the non-DmdA families. FastML was run considering the alignment of proteins and the ML phylogenetic tree for those DmdA orthologs or homologs inferred as we explained above. Posterior amino acid probabilities at each site were calculated using the LG matrix and Gamma distribution. Both marginal and joint probability reconstructions were performed. Protein sequences resulting from marginal reconstructions were used to predict tertiary structure (see below) as well as to identify family domains using Pfam v32 <ns0:ref type='bibr' target='#b22'>(Finn et al., 2010)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Protein tertiary structure analysis</ns0:head><ns0:p>Predicted three-dimensional structures of protein sequences were examined by Iterative Threading ASSEmbly Refinement (I-TASSER) <ns0:ref type='bibr' target='#b74'>(Roy et al., 2010;</ns0:ref><ns0:ref type='bibr' target='#b95'>Yang et al., 2015)</ns0:ref>. First, I-TASSER uses local meta-threading-server (LOMETS) <ns0:ref type='bibr' target='#b89'>(Wu & Zhang, 2007)</ns0:ref> to identify templates for the query sequence in a non-redundant Protein Data Bank (PDB) structure library. Then, the top-ranked template hits obtained are selected for the 3D model simulations. To evaluate positively the global accuracy of the predicted model, a C-score should return between -5 and 2. At the end, the top 10 structural analogs of the predicted model close to the target in the PDB <ns0:ref type='bibr'>(Berman et al., 2000)</ns0:ref> are generated using TM-align <ns0:ref type='bibr'>(Zhang, 2005)</ns0:ref>. The TM-score value scales the structural similarity between two proteins and should return 1 if a perfect match between two structures is found. A TM-score value higher than 0.5 suggests that the proteins belong to the same fold family.</ns0:p><ns0:p>We used PyMol v1.7.4 (DeLano, 2002) to visualize the 3D structure of the proteins and to map the positively selected sites onto the 3D structure of DmdA (pdb: 3tfh).</ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head></ns0:div> <ns0:div><ns0:head>Phylogenetic tree for DmdA family</ns0:head><ns0:p>We identified a total of 204 DmdA protein sequences out of 150 curated genomes (see Table <ns0:ref type='table'>S1</ns0:ref>: Genomes and genomic diversity sheets), and reconstructed their evolutionary relationships using BI (Fig. <ns0:ref type='figure' target='#fig_0'>1</ns0:ref>) and ML (Fig. <ns0:ref type='figure' target='#fig_3'>S4</ns0:ref>). Unrooted trees in TOPD-FMTS showed that split distances did not exceed 0.19, indicating that the phylogenetic reconstruction is robust, with minor variations in alignment filtering and methods for inferring topologies (Table <ns0:ref type='table'>S2</ns0:ref>).</ns0:p><ns0:p>The BI tree (Fig. <ns0:ref type='figure' target='#fig_0'>1</ns0:ref>) shows a main duplication between two lineages. The larger phylogenetic group comprises genes mainly from Bacteroidetes, while the smaller group includes genes mainly from Alphaproteobacteria. We focused on this smaller group as it includes the DmdA sequences (Fig. <ns0:ref type='figure' target='#fig_0'>1</ns0:ref>; green color) and the closest homologs to DmdA (Fig. <ns0:ref type='figure' target='#fig_0'>1</ns0:ref>; brown color).</ns0:p><ns0:p>Using phylogenetic analyses including DmdA orthologs and DmdA homologs close to those (the limit to select the closer homologs was set to a maximum e-value of e-80), we resolve the position of the first DmdA sequences isolated from two marine bacterial species, R. pomeroyi (AAV95190.1) and Ca. P. ubique (AAZ21068.1). In addition, the inclusion of DmdA homologs allowed to resolve a robust phylogenetic relationship of the DmdA gene family (Fig. <ns0:ref type='figure'>2</ns0:ref>). We detected a clear separation between DmdA and putative non-DmdA families. Indeed, the four DmdA family trees constructed using different methods compared in TOPD-FMTS using split distances (Table <ns0:ref type='table'>S3</ns0:ref>) and unrooted trees (Fig. <ns0:ref type='figure' target='#fig_4'>S5</ns0:ref>) agreed with this result. The average split distance was 0.60, indicating that the trees were neither identical (split difference=0) nor completely different (1). A random split distance was calculated to analyze whether the split distances were significantly different. Because the random split distance resulted in a value close to 1 (0.988), our observations are unlikely to be given by chance.</ns0:p><ns0:p>To identify HGT and duplication events, we constructed a proxy for the species tree of the genomes considered here by using a set of small subunit ribosomal protein (see Material and Methods). Given this (proxy) species tree (Fig. <ns0:ref type='figure' target='#fig_5'>S6</ns0:ref>), the positions of many sequences on the DmdA tree are better explained as cases of HGT (Fig. <ns0:ref type='figure' target='#fig_5'>S6</ns0:ref>; Fig. <ns0:ref type='figure' target='#fig_2'>3</ns0:ref>) with high statistical support.</ns0:p><ns0:p>Then we tested whether the topology for a common set of taxa within the DmdA family (Fig. <ns0:ref type='figure' target='#fig_6'>S7</ns0:ref>) similar to that of the species tree (Fig. <ns0:ref type='figure'>S8</ns0:ref>). We found significant differences (at an alpha of 0.01) between the topology of DmdA group and that of the proxy species tree (Table <ns0:ref type='table'>S4</ns0:ref>); this incongruence between phylogenies is conserved irrespective of the test used (KH, SH and unbiased tests). From these results, we concluded that the phylogenetic relationships within each DmdA group were different to those of the species tree, strongly supporting a HGT-based evolution of DmdA family (Fig. <ns0:ref type='figure'>S8</ns0:ref>). Moreover, we found many genes that use different codons from the neighboring genomic regions. These genes are inferred as having been horizontally transferred given their (G+C) wobble content (Table <ns0:ref type='table'>S1</ns0:ref>), supporting HGT as a plausible mechanism of genomic variability which introduces more variation than vertical gene transfer (VGT) and that contribute to DmdA evolution (Fig. <ns0:ref type='figure'>S8</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>Structural modeling</ns0:head><ns0:p>The structure for DmdA orthologs inferred on the protein sequences by Iterative Threading ASSEmbly Refinement (I-TASSER) were threaded onto the known structure of DMSPdependent demethylase A protein (PDB accession: 3tfhA) with a C-score<= 2 (Table <ns0:ref type='table'>S5</ns0:ref>). However, the predicted models for DmdA homologs were threaded onto two types of known structure; DmdA orthologs, and the structure of the mature form of rat dimethylglycine dehydrogenase (DmgdH) (PDB accession, 4ps9sA) with a C-score < 2 except for the sequence with accession number AEM59334.1, which showed a C-score > 2 (Fig. <ns0:ref type='figure'>S9a</ns0:ref>, Fig. <ns0:ref type='figure'>S9b</ns0:ref>, Data S1).</ns0:p><ns0:p>We clustered sequences with a putative DmgdH structure in a separate group using principal component analysis (Fig. <ns0:ref type='figure'>S9c</ns0:ref>). There is a clear 3D-structure coincidence between DmdA clade (green color in Fig. <ns0:ref type='figure'>S9a</ns0:ref>) and the majority of lineages from non-DmdA clade (brown color in Fig. <ns0:ref type='figure'>S9a</ns0:ref>), as well as a conserved folate-binding domain (Fig. <ns0:ref type='figure'>S9b</ns0:ref>: 99S, 178E and 180Y). However, in the alignment we found a pattern of conserved residues coherent with the phylogenetic results (Fig. <ns0:ref type='figure'>S9a</ns0:ref>, Fig. <ns0:ref type='figure'>S9b</ns0:ref>), where non-DmdA clade is formed by three subclades, one of them with DmgdH tertiary structure. Indeed, a key residue for DMSP specific interaction is shown in clades</ns0:p><ns0:p>PeerJ reviewing PDF \| (2019:12:44271:1:1:NEW 6 Aug 2020)</ns0:p><ns0:p>Manuscript to be reviewed with DmdA tertiary structure (Fig. <ns0:ref type='figure'>S9b</ns0:ref>: W171), but not in a clade with DmgdH tertiary structure (Fig. <ns0:ref type='figure' target='#fig_6'>S9b: F171</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>Molecular dating</ns0:head><ns0:p>The log likelihood test (LRT) detected heterogeneity in the substitution rates of dmdA orthologs and dmdA homologs genes (Fig. <ns0:ref type='figure'>2</ns0:ref>) (log L 0 =-29,827.108; log L 1 = -29,546.053; degrees of freedom = 46; chisq = 562.11; P<0.001), thus rejecting the hypothesis of a strict molecular clock. This finding validates the use of a relaxed molecular clock approach to estimate the node ages through Bayesian analysis (see Methods for details). We observed that the marginal densities for each run of the divergence time estimate analysis were nearly identical, pointing that the runs converged on the same stationary distributions. In all runs the marginal densities for the standard deviation hyperparameter of the uncorrelated log-normal relaxed clock model were quite different from the prior, with no significant density at zero, and with a coefficient of variation around 0.2. Analyses using three different calibrated prior dates showed no discrepancies in the final divergence time estimates (Table <ns0:ref type='table'>S6</ns0:ref>).</ns0:p><ns0:p>The time estimates for the MRCA of each gene family (Table <ns0:ref type='table'>S6</ns0:ref> and Fig. <ns0:ref type='figure' target='#fig_3'>4</ns0:ref>) indicate that the most recent common ancestor of DmdA gene family occurred in the late Archean, around 2,400 Mya, after a gene duplication event. Also, a duplication within the DmdA lineage generated a separated SAR11 and Roseobacter DmdA lineage in the early Precambrian ca. 1,894 Mya (Fig. <ns0:ref type='figure' target='#fig_3'>4</ns0:ref>: red arrow). Ca. P. ubique HTCC1062 within the SAR11 cluster and R. pomeroyi DSS-3 within the Roseobacter cluster, resulted from a duplication around 300 Mya (Fig. <ns0:ref type='figure' target='#fig_3'>4</ns0:ref>: blue arrow). However, a higher number of duplication events took place in the second cluster. (Fig. <ns0:ref type='figure' target='#fig_3'>4</ns0:ref>: green color).</ns0:p><ns0:p>We detected two duplication events within the putative non-DmdA clade (Fig. <ns0:ref type='figure' target='#fig_3'>4</ns0:ref>; brown color); showing that the gene families were originated through old duplication events. One duplication involving the DmgdH family (Fig. <ns0:ref type='figure' target='#fig_3'>4</ns0:ref>: light yellow color; Table <ns0:ref type='table'>S5</ns0:ref>) occurred ca. 1,480 Mya and another duplication ca. 1,000 Mya (Fig. <ns0:ref type='figure' target='#fig_3'>4</ns0:ref>: green arrow), involving a gene family with tertiary structure similar to Ca. P. ubique DmdA The other duplication event took place during the Huronian glaciation, around 2100 Mya (Fig. <ns0:ref type='figure' target='#fig_3'>4</ns0:ref>: violet arrow).</ns0:p></ns0:div> <ns0:div><ns0:head>Reconstruction of ancestral DmdA sequence</ns0:head><ns0:p>Our analysis was focused on the reconstruction of the ancestral sequences of the DmdA clade, the non-DmdA clade as well as the ancestral sequence of both the DmdA and non-DmdA clades. FastML inferred the 100 most likely ancestral sequences of the DmdA family. We observed that the same sequences were always inferred. Indeed, the difference in log-likelihood between the most likely ancestral sequence at this node (N1; Fig. <ns0:ref type='figure' target='#fig_0'>S10</ns0:ref>) and the 100th most likely sequence was only 0.105, indicating that both sequences were almost as likely to reflect the 'true' ancestral sequence. That ancestral protein contains both PF01571 (GCV_T) and PF08669 (GCV_T_C) domains, found in the DmdA orthologs and it is nearly identical to Ca. P. ubique HTCC1062 DmdA sequence. Moreover, PSI-BLAST search confirmed that the ancestral sequence in node 1 close to DmdA genes hosted in EMBL-EBI databases (Fig. <ns0:ref type='figure' target='#fig_0'>S11</ns0:ref>) and the structure for Ca. P. ubique apoenzyme DmdA was the closest analog to our predicted models (Table <ns0:ref type='table'>S5</ns0:ref>; Data S1).</ns0:p><ns0:p>Inferred physico-chemical properties are identical between Ca. P. ubique and the DmdA ancestral sequence (Table <ns0:ref type='table'>S7</ns0:ref>).</ns0:p><ns0:p>On the other hand, the ancestral sequence inferred for non-DmdA family (N1; Fig. <ns0:ref type='figure' target='#fig_0'>S12</ns0:ref>) and the ancestral sequence previous to functional divergence (N1; Fig. <ns0:ref type='figure' target='#fig_2'>S13</ns0:ref>) contains only the PF01571 domain. That domain was located onto the known structure of T-protein of the Glycine Cleavage System (PDB accession: 1wooA) with a C-score= 1.25 (Table <ns0:ref type='table'>S5</ns0:ref>; Data S1) in the case of the ancestral DmdA and non-DmdA sequence. However, the ancestral sequence for non-DmdA was better threaded onto the known structure of mature form of rat DmgdH (PDB accession: 4p9sA) with a C-score= 0.76 (Table <ns0:ref type='table'>S5</ns0:ref>; Data S1).</ns0:p></ns0:div> <ns0:div><ns0:head>Detection of positive selection on dmdA sequences</ns0:head><ns0:p>To infer how natural selection has influenced the evolutionary history of DmdA gene family, we used an alignment of the 20 sequences clustered as dmdA orthologs (Fig. <ns0:ref type='figure' target='#fig_3'>S14</ns0:ref>). The phylogenetic tree for these sequences was constructed by ML using the symmetrical model (SYM) with a discrete gamma distribution.</ns0:p><ns0:p>The average dN/dS value for the dmdA gene was 0.085, suggesting that this gene evolved under strong negative (purifying) selection. Then, we analyzed dN/dS variation across the codons in the gene, comparing M0 and M3 models through a LRT. The M3 model fits the data better than the M0 model (chisq= 775.387, p-value< 0.01). All codons in the gene are under strong purifying selection with dN/dS <1 (Fig. <ns0:ref type='figure' target='#fig_4'>5</ns0:ref>), which indicates that this sulfur pathway is important for the cells. In accordance with this, the LTRs designed to detect codons under positive selection were not significant (M1 vs M2, chisq= 0 and p-value = 1, and M7 vs M8, chisq = 1.459 and p-value = 0.482). Hence, we did not detect sites in dmdA subjected to positive selection (Fig. <ns0:ref type='figure' target='#fig_4'>S15</ns0:ref>).</ns0:p><ns0:p>We tested the variation in the intensity of selection over evolutionary time. A two-ratio model comparing the Roseobacter with the rest of lineages (Fig. <ns0:ref type='figure' target='#fig_5'>S16</ns0:ref>) fits the data, as the LRT was 23.777 and p-value < 0.01 (Table <ns0:ref type='table'>S8</ns0:ref>). dN/dS value in Roseobacter (ω 1 : 0.0767) was significantly lower than in the remaining branches (ω 2 : 0.1494), suggesting stronger purifying selection on dmdA in Roseobacter. When we tested the intensity of selection over evolutionary time using the free-ratio model (Table <ns0:ref type='table'>S8</ns0:ref>), we found changes in the selection pressure from the branches which defines the separation of SAR11 from Roseobacter DmdA gene families (Fig. <ns0:ref type='figure' target='#fig_6'>S17</ns0:ref>: branches from nodes 21 to 23). In particular, we observed a dN/dS value > 1 in the branch connecting nodes 21-23. We also identified some more recent branches (connecting nodes 25-26 and 28-29) for which dN/dS >> 1 was estimated (Fig. <ns0:ref type='figure' target='#fig_6'>S17</ns0:ref>).</ns0:p><ns0:p>Finally, we applied the two branch-site models to test for sites under selection on the individual lineages associated with dmdA (Fig. <ns0:ref type='figure' target='#fig_0'>S18</ns0:ref>). Four sequences (WP_047029467, AHM05061.1, ABV94056.1, AFS48343.1) had a significant LRT after correcting for multiple testing (Table <ns0:ref type='table'>S9</ns0:ref>), corresponding to episodic positive selection on these lineages (Fig. <ns0:ref type='figure' target='#fig_0'>S18</ns0:ref>). It should be highlighted that three selected sites are shared by at least two lineages (Table <ns0:ref type='table'>S9</ns0:ref>; Fig. <ns0:ref type='figure' target='#fig_5'>6</ns0:ref>). One shared site is located next to the GcvT domain (152 K; Fig. <ns0:ref type='figure' target='#fig_0'>S19</ns0:ref>), and two shared sites are close to conserved positions (17E; 87Y; Fig. <ns0:ref type='figure' target='#fig_0'>S19</ns0:ref>). The residue 87Y is adjacent to the conserved interaction site with THF (88Y; Fig. <ns0:ref type='figure' target='#fig_0'>S19</ns0:ref>). Interestingly, since the selected lineages are separated in the tree, the adaptive mutations seem to have occurred through three parallel independent changes (Fig. <ns0:ref type='figure'>S20</ns0:ref>).</ns0:p></ns0:div> <ns0:div><ns0:head>Functional divergence during the molecular evolution of DmdA sequences</ns0:head><ns0:p>We tested whether DmdA and non-DmdA gene families were subject to different functional constrains after gene duplication (Fig. <ns0:ref type='figure' target='#fig_4'>S5</ns0:ref>). We estimated the one-ratio model (M0) that yielded a value ω = 0.053 (Table <ns0:ref type='table'>S10</ns0:ref>), indicating that purifying selection dominated the evolution of these proteins. The discrete model (M3) was applied to these sequences (Table <ns0:ref type='table'>S10</ns0:ref>) and the LRTs comparing M0 and M3 indicated significant variation in selective pressure among sites (Table <ns0:ref type='table'>S10</ns0:ref>; Fig. <ns0:ref type='figure' target='#fig_0'>S21</ns0:ref>).</ns0:p><ns0:p>The M3 model was compared with Model D, which accommodates both heterogeneity among sites and divergent selective pressures. The LRT was significant and supported the model D (Table <ns0:ref type='table'>S10</ns0:ref>), implying statistical evidence for functional divergence between DmdA and non-DmdA. Parameter estimates under Model D with k=3 site classes suggested that 23.6% of sites were evolving under strong purifying selection (ω = 0.006), while 26.7% of sites were evolving under weaker selective pressure (ω = 0.04). Interestingly, a large set of sites (49.6%) were evolving under divergent selective pressures, with weaker purifying selection in the DmdA-clade (ω = 0.169) than non-DmdA-clade (ω = 0.100). We identified 77 sites evolving under divergent selective pressures between DmdA and non-DmdA (Table <ns0:ref type='table'>S10</ns0:ref>). Nineteen sites were located within the alpha helix (red tube in Fig. <ns0:ref type='figure'>S22</ns0:ref>) of the secondary structure prediction and sixteen were located in the beta sheet (green arrows in Fig. <ns0:ref type='figure'>S22</ns0:ref>). According to the global dN/dS estimates, for all divergent positions, dmdA sequences seem to be more conserved than non-dmdA sequences. Moreover, this data were only compatible with recombination breaking linkage disequilibrium within the gene set that we observed with the HGT analysis.</ns0:p><ns0:p>Finally, we were interested in finding out if adaptive evolution has occurred in the lineages immediately following the main duplication event (Fig. <ns0:ref type='figure' target='#fig_2'>S23</ns0:ref>). We applied two branch-site models to test for sites under selection on the ancestor associated with the DmdA and non-DmdA clades (Table <ns0:ref type='table'>S9</ns0:ref>). The LRT was significant for both ancestral branches (LRT > 7 and p-value < 0.05). Nonetheless, the foreground ω for class 2 sites tended to infinity (ω=999) in both cases, indicating lack of synonymous substitutions (dS=0) in these sites. We also performed two-ratio models to estimate global ω on these branches, but both estimates tended to infinity (Table <ns0:ref type='table'>S11</ns0:ref>), suggesting lack of synonymous substitution in the divergence of DmdA and non-DmdA ancestors. Therefore, although the fixation of only non-synonymous substitutions following gene duplication might indicate strong positive selection driving functional divergence of DmdA and non-DmdA families, we cannot confirm it with the applied tests.</ns0:p></ns0:div> <ns0:div><ns0:head>Discussion</ns0:head><ns0:p>In this study we evaluated three scenarios for the evolutionary history of the DmdA gene family in marine bacteria. The results for each one are discussed separately.</ns0:p></ns0:div> <ns0:div><ns0:head>First scenario: a recent common ancestry between DmdA and GcvT</ns0:head><ns0:p>In relation to the first scenario, we found that contrary to our initial expectations, DmdA and GcvT do not seem to have a recent common ancestry, but DmdA and non-DmdA. The clear separation between DmdA and putative non-DmdA gene families that originated in the Archean ca. 2,400 Mya after a gene duplication, supports a common recent ancestry for DmdA (Fig. <ns0:ref type='figure' target='#fig_6'>7</ns0:ref>.A) and non-DmdA (Fig. <ns0:ref type='figure' target='#fig_6'>7</ns0:ref>.B). Our tertiary structure analyses indicate that they share a putative GcvT protein as their ancestor sequence (EC 2.1.2.10). Indeed, our results agree with other studies in the case of DmdA <ns0:ref type='bibr' target='#b70'>(Reisch et al., 2008)</ns0:ref>. Then, this clade seems to have been originally a GcvT (Fig. <ns0:ref type='figure' target='#fig_6'>7</ns0:ref>), as <ns0:ref type='bibr' target='#b7'>Bullock et al. (2017)</ns0:ref> suggested.</ns0:p><ns0:p>The DmdA clade is a member of aminomethyltransferase (AMT/GCV_T) family with DMSPdependent demethylase tertiary structure, while non-DmdA clade includes an ancestor with a tertiary structure that better matches the dimethylglycine dehydrogenase oxidorreductase (DmgdH, EC 1.5.99.2) (Fig. <ns0:ref type='figure' target='#fig_6'>7</ns0:ref>.B) and members with DmdA tertiary structure. To establish structural convergence as the reason of this DmdA structure coincidence between DmdA and non-DmdA members, we used a phylogenetic approach based on reconstructing ancestral sequences of the two clades, and then to model the ancestral proteins. We determined different structural features between ancestral sequence reconstructed from DmdA and non-DmdA families. In the first case, the ancestral sequence reconstructed coincides with a DmdA tertiary structure (Fig. <ns0:ref type='figure' target='#fig_6'>7</ns0:ref>.A), as well as with a DmdA sequence with physico-chemical properties inferred in this study (Table <ns0:ref type='table'>S7</ns0:ref>) and agree with previous ones <ns0:ref type='bibr' target='#b70'>(Reisch et al., 2008)</ns0:ref>. However, the non-DmdA ancestral sequence reconstructed is a DmgdH that seems to be kept in the clade called DmgdH (Fig. <ns0:ref type='figure' target='#fig_6'>7</ns0:ref>. B), as well as in some members of DmdA clades (DmdA_1 and DmdA_2 within non-DmdA clade) where the majority of sequence gained DmdA structure (Fig. <ns0:ref type='figure' target='#fig_6'>7</ns0:ref>.B). Therefore, DmdA structural features seem to have emerged independently in both clades: DmdA and non-DmdA. This finding is interesting, since known cases of structural convergence of proteins are rare <ns0:ref type='bibr' target='#b97'>(Zakon, 2002)</ns0:ref>. Experimental assays expressing and screening the activity of the ancestral proteins at different conditions will be required to corroborate the structural convergence.</ns0:p><ns0:p>Since GcvT does not share the most recent common ancestry with DmdA (as we observe in Fig. <ns0:ref type='figure' target='#fig_6'>7</ns0:ref>), we examined the functional divergence between DmdA and non-DmdA clades to explain how natural selection could have driven the divergence of the DmdA gene family. We found 77 codon sites evolving under divergent selective pressures between DmdA and non-DmdA gene families.</ns0:p><ns0:p>Structural divergence seemed to be imposed on the protein during sequence divergence, since 19 sites were located within the alpha helix of 2D structure and 16 in the beta sheet (Fig. <ns0:ref type='figure'>S22</ns0:ref>). Nonetheless, essential regions of the enzymes as active sites seem to be under strong purifying selection, suggesting preservation of the ancestral function. The observation that DmdA sequences have more divergent sites than non-DmdA sequences suggest that non-DmdA conserves the ancestral function, whereas DmdA evolved to acquire new functions in different environments, probably as a response to the Huronia ice ball Earth <ns0:ref type='bibr' target='#b98'>(Zhang, 2003)</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Second scenario: coevolution between Roseobacter and DMSP-producing-phytoplankton</ns0:head><ns0:p>In the second scenario, our results do not support the hypothesis of a co-evolution scenario between Roseobacter and DMSP-producing-phytoplankton <ns0:ref type='bibr' target='#b52'>(Luo et al., 2013)</ns0:ref>. On the contrary, we found an ancestor sequence of DmdA cluster similar to DmdA from a strain of Ca. P. ubique that diverged after a more recent duplication event (Fig. <ns0:ref type='figure' target='#fig_6'>7</ns0:ref>.A and Fig. <ns0:ref type='figure' target='#fig_0'>S10</ns0:ref>), before the dinoflagellate radiation in the late Permian (Fig. <ns0:ref type='figure' target='#fig_3'>4</ns0:ref>). This finding indicates that the enzyme activity has not changed in the course of DmdA evolution and is in Roseobacter because their genome expansion (250 mya) provided a new trait to use DMSP produced by phytoplankton during its diversification. Indeed, we found that most of the codons in DmdA clade are under purifying selection, probably due to the importance of this pathway for sulfur acquisition. Nonetheless, we also detected episodic positive selection in four sequences affecting a few sites, suggesting that adaptive evolution fine-tuned the function of DmdA in Roseobacter and other types of Alphaproteobacteria (like HIMB59 and Hoeflea). Furthermore, positively selected residues were located around the GcvT domain and close to the residue involved in conserved interaction with THF (Fig. <ns0:ref type='figure' target='#fig_5'>6</ns0:ref>), reinforcing the idea of adaptive evolution in response to the external environment.</ns0:p><ns0:p>During the study of this scenario, we suspected that dmdA was acquired by HGT in Roseobacter and SAR11 (Fig. <ns0:ref type='figure'>S8</ns0:ref>). This agrees with <ns0:ref type='bibr' target='#b52'>Luo et al., (2013)</ns0:ref> and <ns0:ref type='bibr' target='#b85'>Tang et al. (2010)</ns0:ref> which found that the expansion of dmdA resulted from HGT events. According to our phylogeny, the ancestral dmdA sequence originated as a results of HGT (in individuals not connected by inheritance that acquired the dmdA ancestral sequence) from other marine heterotrophic bacteria, that during the Archean adapted to the presence of DMSP. However, after the HGT events, some dmdA sequences have acquired similar residue changes by independent (parallel) evolution, reinforcing the idea of functional/ecological constrains. Therefore, Rhodobacteraceae can live in an environment where DMSP is the main source of sulfur because they acquired the dmdA ancestor sequence by HGT, prior to having been exposed to the environment in which the DmdA protein proved useful, as <ns0:ref type='bibr' target='#b53'>Luo & Moran (2014)</ns0:ref> suggested. We did not find any signal of positive selection in the Roseobacter group, but in contrast we found episodic evolution between SAR11 sequences. Yet, as we already mentioned, DMSP is part of an ancient pathway in Alphaproteobacteria <ns0:ref type='bibr' target='#b7'>(Bullock et al., 2017)</ns0:ref> and it could explain the ancient origin of DmdA.</ns0:p><ns0:p>On the other hand, Roseobacter orthologs analyzed in this study were functionally annotated as DmdA <ns0:ref type='bibr' target='#b28'>(González et al., 2019)</ns0:ref>, as they were predicted to originate from the same DmdA ancestor. However, we identified orthologs within DmdA gene family as <ns0:ref type='bibr' target='#b76'>Sánchez-Pérez et al (2008)</ns0:ref> proposed in their study regarding related genes that perform the same cellular function, but apparently under different ecological conditions, as we found differences in predicted isoelectric point values (pI) (Table <ns0:ref type='table'>S7</ns0:ref>). <ns0:ref type='bibr' target='#b57'>Nandi et al. (2005)</ns0:ref> results also support that orthologs with very variable pI values may be taken as markers to predict the organism's ecological niche. We suggest the name 'eco-orthologs', similar to the ecoparalogs describe by Sánchez-Pérez et al <ns0:ref type='bibr'>(2008)</ns0:ref> in their study of the halophilic species Salinibacter ruber. The pI values of a protein provide an indication of its acidic nature on the surface, corresponding to its optimal activity and stability at high salinity <ns0:ref type='bibr' target='#b61'>(Oren et al., 2005;</ns0:ref><ns0:ref type='bibr' target='#b76'>Sánchez-Pérez et al, 2008)</ns0:ref>. Therefore, proteins that differ in their acid residue content on their surface, and consequently in their predicted pI values and halophilicities are considered eco-orthologs <ns0:ref type='bibr' target='#b57'>(Nandi et al., 2005;</ns0:ref><ns0:ref type='bibr' target='#b61'>Oren et al., 2005;</ns0:ref><ns0:ref type='bibr' target='#b76'>Sánchez-Pérez et al, 2008)</ns0:ref>. We observed the highest pI values in the DmdA ancestor sequences, as well as in Ca. P. ubique DmdA (Fig. <ns0:ref type='figure' target='#fig_6'>7</ns0:ref>.A; red color). Therefore, we deduce that the DmdA ancestor was adapted to a higher salinity, which could have modulated the selection of the DMSP enzymatic degradation routes as in bacteria such as the model organism R. pomeroyi DSS-3 <ns0:ref type='bibr' target='#b75'>(Salgado et al., 2014)</ns0:ref>. Interestingly, R. pomeroyi degrades more DMSP by the demethylation pathway under high salinity conditions, releasing a higher amount of MeSH <ns0:ref type='bibr' target='#b34'>(Howard et al., 2008;</ns0:ref><ns0:ref type='bibr' target='#b54'>Magalhães et al., 2012;</ns0:ref><ns0:ref type='bibr' target='#b75'>Salgado et al., 2014)</ns0:ref>. The success of the dmdA gene could be explained if we consider that the environment evolved from higher to lower salinity conditions. Under this environment, dmdA would have been kept without important changes in its structure, sequence, function and K m value and even would be essential for the degradation of the large amounts of DMSP produced by phytoplankton. Indeed, it would be interesting to evaluate K m values among ancestral proteins of DmdA and their descendants to support the key role of K m during DmdA evolution. In addition, since dmdA seems to be part of a conserved operon <ns0:ref type='bibr' target='#b28'>(González et al., 2019)</ns0:ref>, its evolution might be linked to genes such as dmdB, dmdC and dmdD that encode part of the enzymes for the rest of the pathway.</ns0:p><ns0:p>Given our data, we propose that the ancestor of the pathway that evolved during the Archean was exposed to a higher concentration of DMSP in a sulfur-rich atmosphere and in an anoxic ocean, compared to recent eco-ortologs which should adapt to lower concentration of DMSP (Fig 7 <ns0:ref type='figure'>.</ns0:ref>A: blue color). Indeed, the ancestral eco-orthologs from which recent eco-orthologs derived (Candidatus Puniceispirilum marinum IMCC1322, ADE38317.1 and the Roseobacter clade) could have undergone episodes of adaptation (the branch showed positive selection in branchmodels) which would explain the change in protein stability <ns0:ref type='bibr' target='#b62'>(Pál et al., 2006)</ns0:ref>. As consequence, the protein could have experienced slight reductions or loss of function.</ns0:p><ns0:p>Third scenario: pre-adapted enzymes to DMSP prior to Roseobacter origin In this evolutionary scenario, the Roseobacter clade was pre-adapted to the conditions created by eukaryotic phytoplankton of the late Permian, including dinoflagellates that released vast amounts of DMSP <ns0:ref type='bibr' target='#b7'>(Bullock et al., 2017;</ns0:ref><ns0:ref type='bibr' target='#b53'>Luo & Moran, 2014)</ns0:ref>. Our analyses indicate that the Roseobacter ancestor was already adapted to a high DMSP before the Roseobacter clade arose <ns0:ref type='bibr' target='#b52'>(Luo et al., 2013)</ns0:ref>. Therefore, we support <ns0:ref type='bibr'>Reisch et al. (2011 a,b)</ns0:ref> hypothesis that DMSP demethylation pathway enzymes are an adapted versions of enzymes that were already in bacterial genomes and that evolved in response to the availability of DMSP. Since the first step in DMSP demethylation is a reaction catalyzed by DMSP demethylase encoded by dmdA gene <ns0:ref type='bibr' target='#b19'>(Dickschat et al., 2015)</ns0:ref>, DMSP adaptation could have been evolved in this gene that originated in the Archean, a time where several lineages of bacteria produced DMSP as an osmolyte or antioxidant in the presence of the early cyanobacteria, or as a cryoprotectant in the Huronian glaciation. In bacteria, a methyltransferase gene, dysB, is up-regulated during increased salinity, nitrogen limitation, and at low temperatures <ns0:ref type='bibr' target='#b13'>(Curson et al., 2017)</ns0:ref>, conditions already predicted to stimulate DMSP production in phytoplankton and algae <ns0:ref type='bibr' target='#b7'>(Bullock, et al., 2017;</ns0:ref><ns0:ref type='bibr' target='#b37'>Ito, et al., 2011)</ns0:ref>. Afterward, those roles may have helped to drive the fine adaptation of existing enzymes for DMSP metabolism, and those adaptations came handy in the late Precambrian glaciations that allowed the radiation of algae and animals.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions</ns0:head><ns0:p>In conclusion, we found that Roseobacter adaptation to DMSP occurred via functional diversification after duplication events of the dmdA gene and adaptations to environmental variations via eco-orthologs of intermediate divergence. Our findings suggest that the DmdA ancestor evolved to play a key role in the ocean sulfur cycle due to a shift in salinity concentration, which involved a change in DMSP synthesis. Manuscript to be reviewed</ns0:p><ns0:note type='other'>Figure 2</ns0:note><ns0:p>RAxML phylogenetic tree built with 20 DmdA ortholog protein sequences and 28 DmdA homologs (more information in Table <ns0:ref type='table'>S1</ns0:ref>). Manuscript to be reviewed Manuscript to be reviewed </ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head>Figure 1</ns0:head><ns0:label>1</ns0:label><ns0:figDesc>Figure 1</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>Non-parametric bootstrap values are shown to establish the support for the clades. DmdA sequences are indicated with blue branches. Tip labels show color according to their taxonomy classification and the asterisk indicates the first gene identified experimentally. Tip labels include a maximum e-value < e-80. PeerJ reviewing PDF \| (2019:12:44271:1:1:NEW 6 Aug 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>Figure 3 BEAST2</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Figure 3</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head>Figure 4 BEAST2</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Figure 4</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head>Figure 5</ns0:head><ns0:label>5</ns0:label><ns0:figDesc>Figure 5</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head>Figure 6</ns0:head><ns0:label>6</ns0:label><ns0:figDesc>Figure 6</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head>Figure 7</ns0:head><ns0:label>7</ns0:label><ns0:figDesc>Figure 7</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='32,42.52,70.87,525.00,428.25' type='bitmap' /></ns0:figure> </ns0:body> "	"Dear Editors, We thank the reviewers for their generous comments and have edited the manuscript to address their concerns. In particular many of the figures and their captions have been modified for better understanding. The tense in the manuscript has been revised and corrected as well as wrong terms, complicated and controversial concepts. In addition, we made a huge effort to explain better the discussion and conclusions. We believe that the manuscript is now suitable for publication in PeerJ. Dr. Laura Hernández Javier On behalf of all authors. 1 Reviewer 1 Basic reporting Text mostly well presented, though some confusions with the tense. The Tables and Figures could be better explained with improved and more substantive legends. Experimental design My expertise is not suited to make accurate judgments on the approaches taken. Validity of the findings Fine as far as I can see. This is, however, has a strong theoretical flavour and so, even if their conclusions are not 100% correct, they recognise this and in any event, this work will form the basis for interesting discussions in the field. Comments for the author Comments on PeerJ MS on DmdA - see also annotated Pdf On reading this MS, I feel a little like the chap who wanders into the Prado muttering “I don’t know much about art, but I know what I like”. So, the good news is that I liked this paper, with its full-on bioinformatic attack on a single enzyme/gene system. We appreciate your kind words, thank you. But the bad news is that my abilities to make meaningful judgments on the fine details of these bioinformatic analyses would make Donald Trump blush. So, with that in mind, here goes. Most of my comments (167 of them) can be seen in situ in the attached Pdf. Many are minor – the English and written presentation are mostly very good, but there are some glitches and these are indicated. One more important problem though, is the need to decide if they are writing in the present or past tense. They start in the former, then switch. There are also some more substantive comments (prefaced with a *** in the Pdf) and these need attention. Agreed. The past tense has been chosen for the manuscript. 1) Is it certain that this pathway is predominant compared to the overall contributions of other pathways, found both in bacteria and in the phytoplankton? And, if so, what are relative percentages? (line 30). Todd et al. (2009; doi:10.1111/j.1462-2920.2009.01864.x) quantifies the contribution of a set of DMSP transforming enzymes in the GOS dataset. Table 2 in their publication shows that, compared 2 to recA, a single copy gene, dmdA is in about half of the cells, while dddD, dddL or dddP are not detected or their contribution is much lower than dmdA. This citation has been included in line 70. 2) As written, the term “Roseobacter” specifically and explicitly applies to members of the genus of that name. The word “Roseobacter” or “Roseobacters” (note that these are not italicized) is a colloquial, though widely term to describe a group of bacteria withing the family Rhodobacteraceae. It has no formal recognition as a taxonomic group -other than in the genus mentioned above. Note that this is indicated in the titles of several papers that are cited -including some in which Gonzalez is the author. The term Roseobacter is shown, incorrectly, in many places in this MS and should be corrected thoughout. Agreed. We corrected all the mansucript to “Roseobacter”. 3) I am not sure what the term “pre-adapted” means (line 37). Indeed, “pre-adapted” is a complicated and controversial concept, and thus we have modified the mansucript to give a more comprehensive explanation. The DMSP demethylation pathway enzymes are hypothesized to be adapted versions of enzymes that were already contained within bacterial genomes and developed in response to the availability of this substrate. Therefore, we propose that kind of the evolutionary scenario as the third alternative to explain the evolution of DmdA. We modified line 37 and added “an enzymatic adaptation for utilizing DMSP in marine bacteria” 4) I do think that you should refer to the recent evidence that bacteria also make a major contribution to global DMSP production (line 49). We agree, and we included that bacteria as DMSP producers (lines 98 and 100: “ Gammaproteobacteria and Actinobacteria (Williams et al., 2019). Moreover, bacteria seem to be important producers of DMSP and DMS in coastal and marine sediments (Williams et al., 2019)”) and we added more information in point 11, results recently published (during the review process of this MS). 5) Yes, the relevant section in the Bullock et al paper is “The red plastid lineage phytoplankton, including coccolithophores, diatoms, and most dinoflagellates, first began to increase in abundance after the end-Permian extinction about 250 mya (Falkowski et al., 2004ab). By implication, this supports the concepts of a common ancestor at this time. But, in light of the identification of the dsyb gene and its distribution (Fig 1b in curson et al., 2018). I think that you should also consider the possibility of Horizontal Gene Transfer among different phytplankton taxa and also the 3 likelihood that DMSP synthesis originated in bacteria, most likely alpha-proteobacteria in the Rhodobacterales lineage (line 51). Indeed, we know that DMSP can be produced by alpha-proteobacteria, as we say in lines 97 and 100. In this new version, we proposed that the functional divergence and HGT could explain the origin of the gene involved in the initial step to transform DMSP, as we have included (lines 108 to 110: “we would then expect a common ancestor of the DmdA gene family during the early Proterozoic and that the functional divergence between DmdA and GcvT gene families was driven by both functional constraints and widespread HGT”). Moreover, we focus our attention on dmdA although we also consider that dsyb might have been the reason that explains that DMSP production could have been taken place before the abundance of phytoplankton at the end of Permian extinction, probably due to a change in ocean chemistry. However, we cannot include phytoplankton in our evolutionary study because we should use only orthologs. 6) I am not sure that Tripp et al actually say this (line56). Trip et al 2008 (DOI: 10.1038/nature/06776) said in pag 743 “The elimination of assimilatory sulphate reduction genes from SAR11 makes them dependent on reduced sulphur compounds found in the environment”. 7) I am really not clear what these mean. I searched Bullock et al and could not find any mention of gcfH or gcvP (71 and 72). We are sorry, you are right, as a mistake was done in lines 71 and 72 because we meant “GcvT” instead of “Gcv”. The first case defines the glycine system cleavage and includes: glycine catabolism genes like glycine system cleavage T-protein (gcvT), glycine cleavage H-protein (gcvH), glycine cleavage P-protein (gcvP) and glycine cleavage T-protein C-terminal barrel domain (gcvTC). Therefore, “(gcvT, gcvH, gcvP and gcvT-C)” is changed to “(gcvT and Unchar. AMT)” which are shown in figure 4 in Reisch et al. (2011; DOI:10.3389/fmicb.2011.00172) as a phylogeny of AMT (aminomethyltransferase) proteins. In this new version we added in line 73 the reference “Sun et al., 2011” as they showed the phylogeny of AMT. “Bullock et al., 2017” was not removed because they argued about DmdA and GcvT as being differentiated clades. The reference of Sun et al. was added to the list of references. 7) Useful if you could say explicitly that these are (mostly) marine and that they are not a million miles away form the Rhodobacterales in terms of their relatedness (line79). 4 Agreed. I have corrected this. In this new version, “SAR11, SAR324, SAR116” is changed to “bacterioplankton strains of SAR11, SAR324, SAR116” (line 81). 8) Maybe you could expand on these, which are indeed likely to have arisen via HGT. The clustering of DmdA within the other taxa is more coherent, so may not demand HGT (line 79). Agreed. Our last study (González et al., 2019: DOI:10.1038/s41396-019-0347-6) suggest that some species of Gammaproteobacteria could have gotten the DmdA gene by HGT. Therefore, we have included a more comprehensive explanation in lines 81 – 84: “ in marine Gammaproteobacteria (González et al., 1999; González, 2003; Howard et al., 2006; Bürgmann et al., 2007; Reisch et al., 2008) like Chromatiales which could have gotten DmdA gene by HGT as some studies suggest (Howard et al., 2006; González et al., 2019 )”. 9),10) This term sort of comes out of nowhere. Maybe some introduction is needed? (line 83) “Genome expansion” means that a genome has more genes than the ancestor genome, and is commonly used in evolutionary genomics literature. According to Luo et al. (2013) Roseobacter clade first experienced a net genome reduction from a large common ancestral genome, that was followed by two episodes of genome innovation and expansion through HGT. In the MS we mention “genome expansion” as an event in the evolution of Roseobacter genomes that coincides with the rise of two red-plastid-lineage eukaryotic phytoplankton groups, the dinofagellates and coccolithophorids, around 250 mya. That coincidence supports adaptive evolution. Therefore, we included some extra information to provide a better explanation of the concept (lines 86-87: “Since an episode of genome expansion of Roseobacter, predicted early in its genome evolution,...”). 11) Ok, we now see the reference to the bacterial contribution to DMSP production. But, as shown by Williams et al 2019, this may be even more pronounced and widespread, involving a wider range of bacterial types. Thanks a lot for your contribution at this point. We were not aware of Williams et al. study because our MS was finished before it was published. We highlighted in lines 98 - 100 the remarkable contribution of bacteria in DMPS synthesis and added Williams et al. 2019 to the list of references. Indeed, their results further support our study. 12) Surely these should be DmdA in both cases, since (I assume) they are referring to the DmdA gene product, and not the dmdA gene (line 126). 5 We made a mistake in line 126, as DmdA peptides and dmdA genes were searched for a set of sequence database from the MAR database. On the other hand, they were classified as homologs or orthologs, following the protocols published in González et al. 2019. Therefore, in the new version, “DmdA orthologs and dmdA homologs” (line 133) was changed to “Peptides and genes from DmdA gene family”. Moreover, we added in line 134 “DmdA orthologs and homologs”. 13) Where is the red colour mentioned at the foot of this table? (line 131) You can see the red colour in some values cited in column called “(G+C) wobble content” of the supplemental table 1. 14) I wonder if this term could be improved. In a sense any other polypeptide could be defined as “non-DmdA”. Iwas Ok-ish with the term when I first came across it, but as I progressed through the MS, I found it less and less useful. How about “DmdA-like” or “quasi-DmdA”, or something else along thos lines? (line 136) We understand the doubts of the reviewer, but we used “non-DmdA” to maintain the nomenclature that we used in a previous article (González et al., 2019). Our DmdA homologs and orthologs classes are based on the protocol published there, and non-DmdA is defined based on phylogenetic analysis, confirmed by structural analysis of protein, sequence similarity network analysis and gene arrangement analysis. Therefore, we added in line in 143 “as in González et al (2019)” to guide the reader”. 15) Maybe you should circumscribe these dates with a note of some caution- “proposed” or “calculated”,etc (line 198). We completely agree, and we modified the term “dates” by “proposed dates” in line 206. 16) Do you mean “dmdA”? (line 213) No, because if the genes of a gene family encode proteins, the term protein family is often used in an analogous manner to gene family. Therefore, in this MS we only modified gene nomenclature, based on rules published by Demerec et al. 1966 (PMCID:PMC1211113), to designate protein (DmdA) or gene (dmdA). 17) But isn’t this a GcvT tree? After all, that is what you show in the heading to the figure – see below- GcvT phylogenetic tree based on 20 DmdA orthologs protein sequences and 184 DmdA homologs using Beast and the same parameters set for molecular dating but with 100 million generations (lines 323 - 324). 6 You are right. All sequences belong to GcvT superfamily or AMT superfamily. Many sequences of this superfamily are included to decide what the value of the e-value parameter could be used to select the closer DmdA homologs. When we did this, we observed a major real phylogenetic relationship between GcvT and DmdA proteins because the bootstrap gives more support to the tree. The result is shown in figures 2, 3 and 4. Sometimes, the term “GcvT family” is confusing, because they are not only including gcvT genes. Indeed, in uniprot the GcvT family is also called AMT family. Both are aminomethyltransferases proteins (EC 2.1.2.10) and include glycine catabolism genes (gcvT, gcvH, gcvP, gcvT-C) and the DMSP demethylation gene (dmdA) as independent families. Therefore, for a clearer explanation, we have decided to delete the term “GcvT” from the heading of the figure 1 and supplementary figure 4. 18) I thought at first that I was either dozy or blind when I read this. But then peered into the branches of the tree and saw that, indeed, the Bacterioidetes was subtended by a biggish triangle. Yes, but so were several other taxa and there was no highlighting of the numbers of different species in each of these. Furthermore, I think that it would have been useful to use some colour coding or other methods to show: a) Those names that correspond to those that are mentioned in the text. Notably, where is the gamma species mentioned on line 79? b) Given that Rhodobacterales are the focus of this paper, maybe show these in a special colour? Thank you for the suggestions. In figure 1, we highlighted, with green and brown the sequences which were used to study the DmdA evolution. Next, the taxonomic names are colored in figure 2. Also, in supplementary table 1, we show in “genomic diversity” sheet a summary of all taxa included in the phylogenetic tree of figure 1 and in “Genomes” sheet we included the species names of DmdA homologs selected and not selected to study the DmdA evolution. We added in lines 331 - 332 the text “(see Table S1: Genomes and genomic diversity sheets)” for a clearer and more comprehensive explanation of the results. 19) It might be useful if they were to state explicitly why the cut-off of the greens was located as shown. Why for example, did they not include the group immediately below it, which contained the Thioglobus sequences? Actually, I can think of a good reason why they can indeed exclude at least one member of this branch, namely the AAV94935 of R. pomeroyi DSS-3. This is because the Georgia group showed that a mutation in the “real” DmdA AAV95190 of this strain was wholly defective in the DMSP demethylation – therefore AV94935 lacked activity (or, I suppose, strictly speaking, may not have been expressed) (line 332). 7 In the section Material and methods (lines 134-135 from the new version) we explain that DmdA orthologs were predicted following González et al. 2019. Then, all taxa with green color belong to DmdA orthologs while the taxa with brown color are close homolog selected, as we explained in lines 135 - 137 and lines 339 - 343, meaning that green taxa have DmdA protein and brown taxa have proteins similar to DmdA. To sum up, the green clade was located as shown due to a bioinformatic method of annotation: figure 1 in González et al., 2019, ISME Journal: doi:10.1038/s41396-019-0347-6. 20) Should it not be BEAST in this table? (Supplementary table 3) Completely agreed. I have corrected it. 21) I think that it would be very helpful to give an indication of just how far did the gene have to jump. It is one thing if there is a lack of congruence at the genus level and quite a different one if the HGT required a massive leap from one phylum to another. Where, for example, does the Gamma-proteobacterium (above) stand. In fact, looking at the figures, the “leaps” within the Rhodobacterales look to be quite small. Is this true? (lines 353 – 358). You are right. In supplementary figure 9 (in the new version is supplementary figure 8) is shown a quite small jump within Roseobacter and from SAR11 to Roseobacter. 22) Sorry, but I don’t get this. Does this relate to the red material, which I could not find (see above) (line 362). That is right: in Supplementary Table 1 you can see in column “(G+C) wobble content” the values in red color which indicate different percentage of GC content at the third position with respect to the neighboring genomic regions. The value corresponds to the sequence of the column “GcvT superfamily sequences (ncbi)” in the same sense that they are written and separate by “;”. 23) The point I make is relevant here, but also elsewhere. I query the usefulness of the “either or” way of thinking. Surely this gene, and many, many others can evolve using BOTH vertical and horizontal trajectories. I am sure that the author know this, but it is still a point worth making (line 362-363). In biological systems, evolutionary innovations can spread not only from parent to offspring (i.e. vertical transmission) which gives rise to a model of evolution represented as a bifurcation, but also “horizontally” between lineages, which may or may not be related. Nowhere is this more apparent than in bacteria, where HGT can become a major driving force leading to genomic variability that possibly contribute to its evolution via adaptation and survival with the newly transferred genes. Indeed, as high as 47% of the culturable natural microbial community have been confirmed to be 8 gene recipients (McDaniel et al., 2010, DOI: 10.1126/science.1192243). Therefore, HGT is a plausible mechanism by which marine bacteria acquire novel traits. As you can see HGT introduces more variation than vertical heredity, simply because the acquired trait differs from the rest of the genome context. The acquired genes, once trimmed and adjusted for GC content and codon bias, represent in evolutionary terms, a single-step acquisition of complex metabolic pathways and can stand in contrast with historical formulations of evolution that imply gradual change. According to this logic, we do not pay any remarkable attention to normal vertical descent, but in lines 367 – 369, we added a mention to that process “supporting HGT as a plausible mechanism of genomic variability which introduces more variation than vertical gene transfer (VGT) and that contribute to DmdA evolution (Fig. S8).” 24) Is this the right word? Do you mean, “moreover”? (line 370) No, “However” is correct. In the first case we speak about orthologs (proteins annotated as DmdA using the protocol shown in González et al. 2019) and in the second case we talk about homologs (proteins pretty similar to DmdA but classified as non-DmdA). 25) Sorry, but I could not open Supp Fig 11 (line 377). Sorry, we originially checked the format and it was right. However, the figure will be submitted as pdf format. 26) Fig Supp 10b is hard to follow. Very difficult to figure out which sequences correspond to DmgH or DmdA – real or apparent (lines 384-385). Agreed. A modification has been made in supplementary figure 10b (in the new version, this named supplementary figure 9b) to show with a more comprehensive view the information related to DmdA and DmgdH. More information is available in table 2 (table S5 in the new version) and supplementary figure 10a (supplementary figure 9a in the new version). Indeed, the supplementary figures 10a (9a in the new version) and 10b (9b in the new version) show strains and ID in the same order. 27) I just notice – you are now in past tense! Be consistent (I am not going to check back to see when the change occurred!) (line 410) Agreed. We paid more attention this time and modified the text where necessary. 28) Very precise. What sort of standard errors do you estimate? (line 412) As we explained in Material and methods, we estimated mean values with a standard deviation of the mean, which considers the effective sample size (ESS), so a small ESS will give a large stdev. In this analysis, we used effective sample sizes of parameters >> 200, as recommended by the authors of BEAST (lines 212 - 213). 9 Moreover, the posterior probability for each node is shown in the figure 4 and it can be loosely thought of as a Bayesian analogue to a confidence interval. 29) Does this make sense? Can a “duplication” have a “structure”? (lines 413 - 414) You are right. We added “involving a gene family”, in line 417, to “with tertiary structure similar to the DmdA from Ca. P. ubique” for giving a more comprehensive sense. 30) Meaning? (line 528) Here, we wanted to say that DmdA clade is shown in the lower part of the figure and non-DmdA clade is in the higher part. However, we added in line 528 “Fig. 7.A” for DmdA clade and in line 529 “Fig. 7.B” for non-DmdA clade. 31) But aren’t two of the corresponding bacteria (HIMB59 and Hoeflea) NOT members of the Roseobacters? Please mention this and take this into consideration (lines 572 -574). You are right. We added a mention to HIMB59 and Hoeflea in lines 577 - 578:”and other types of Alphaproteobacteria (like HIMB59 and Hoeflea)”. 32) Should be lineage (line 583) It may be the same or different lineage. We corrected it in the lines 586 - 587: “in individuals not connected by inheritance that acquired the dmdA ancestral sequence”. 33) Their study on what? (line 598) We added more information in lines 601 – 602 (“ regarding related genes that perform the same cellular function, but apparently under different ecological conditions ”) to give a more comprehensive explanation about the study of Sánchez Perez et al. 2008. 34) That is a bit of a jump – please justify. Do we know what is the internal osmoticum in the Roseobacters? (line 600) This is a general claim based on Oren et al., 2005 (DOI: 10.1007/s00792-005-0442-7), which studied halophiles and non-halophiles that belong to alpha proteobacteria. Therefore, in this MS we focused on pI values differences inferred in our study to explain how members of the DmdA clade can evolve: we compared pI of proteins from Roseobacters and some members of alpha proteobacteria with dmdA orthologous, as you can see in figure 7 (they have to be orthologous to be eco-orthologs). The internal osmoticum in Roseobacters is not a key information here, because as Sanchez et al 2008 explained, the pI provides an indication of the acidic nature of proteins and, as halophilic proteins tend to accumulate acidic residues on their surface, with pI values we observe that those proteins can be eco-orthologs when they differed in their predicted pI values. However, R. pomeroyi is capable of accumulating DMSP to high intracellular concentrations for osmoprotection (70 mM; Reisch et al., 2008). To explain this point better, we added information for a more comprehensive explanation in lines 606 – 611 “The pI values of a protein provide an indication of its acidic nature on the surface, 10 corresponding to its optimal activity and stability at high salinity (Oren et al., 2005; Sánchez-Pérez et al, 2008). Therefore, proteins that differ in their acid residue content on their surface, and consequently in their predicted pI values and halophilicities are considered eco-orthologs (Nandi et al., 2005; Oren et al., 2005; Sánchez-Pérez et al, 2008)”. 35) This is a very interesting - and provocative – line of thinking. It is, however, easily addressed by comparing the pI values of other enzymes that occur in both marine and (closely related) aquatic cousins (lines 602 - 604). We are sorry but we believe you are wrong in this case. In order to explore this hypothesis, we would have to compare enzymes with the same function, orthologs. For instance, comparing pI from DmdA and DmdB does not give any information because they are involved in a different step of the pathway. Moreover, in this study, we use all DmdA single copy of orthologs described until the year 2019. Therefore, in lines 598 – 606 we explained that important point: “On the other hand, Roseobacter orthologs analyzed in this study were functionally annotated as DmdA (González et al., 2019), as they were predicted to originate from the same DmdA ancestor. However, we identified orthologs within DmdA gene family as Sánchez-Pérez et al (2008) proposed in their study regarding related genes that perform the same cellular function, but apparently under different ecological conditions, as we found differences in predicted isoelectric point values (pI) (Table S7). Nandi et al. (2005) results also support that orthologs with very variable pI values may be taken as markers to predict the organism’s ecological niche. We suggest the name “eco-orthologs”, similar to the ecoparalogs describe by Sánchez-Pérez et al (2008) in their study of the halophilic species Salinibacter ruber.”. 36) But this world be an unfortunate outcome for a process in which sulfur acquisition is an important function. Comment? (line 606) Not really, because if the concentration of salinity in ocean environment have been high enough, DmdA probably would have disappeared, because it would not be advantageous as an osmoprotectant. On the contrary, if the ocean environment had had lowered salinity, as we propose in this MS, DmdA would have been kept without important changes in its structure, sequences and function and even would be key for the degradation of the high production of DMSP by phytoplankton. We added information in lines 617 – 621 to mention this hypothesis in the new version of MS: “ The success of the dmdA gene could be explained if we consider that the environment evolved from higher to lower salinity conditions. Under this environment, dmdA would have been kept without important changes in its structure, sequence, function and Km value and even would be essential for the degradation of the large amounts of DMSP produced by phytoplankton. ”. 11 37) Do you mean “experienced”? (line 616) Yes, you are right. “Experienced” is a suitable option 38) This came across as a rather weak final sentence (lines 642 – 643). Agreed. We improved the lines with the sentence added in lines 660 -662 of the new version: “the DmdA ancestor evolved to play a key role in the ocean sulfur cycle due to a shift in salinity concentration, which involved a change in DMSP synthesis.”. 39) Note some inconsistencies in type size for nouns in titles – some UPPER CASE and some lower. These are highlighted. It should be the latter (references). Agreed. We corrected this problem in references section. 40) This is from HIMB59, a strain of unresolved taxonomy – see Viklund et al – https://doi.org/10.1371/journal.pone.0078858 Why not mention this? (table 5) Agreed. We mention this information in table 5 (table S9 in the new version). 41) Colours of the different species correspond to the taxa as indicated. Note that all those bone finde DmdA sequences are located on the blue branch. Actually, I also think that it would be clearer if you showed DSS-3 and HTCC1062 in their “proper” taxonomic colours but mark them with (EG) an *, and say that these two were the first to be demonstrated experimentally. (figure 2) Agreed. We corrected figure 2. 42) Sorry, but I am not sure how this differs from a slighytly blown up version of figure 1. Having said that, why (for example) have the two thioglobus strains moved compared to figure 1 and what has happened to the second DmdA homologue in R. pomeroyi DSS-3? (figure 3). We are sorry, first of all, we made a mistake in figure 3, where the Thioglobus strain name PS1 should be GG2. We have corrected that mistake. In addition, we used 204 homologs for the phylogenetic reconstruction in figure 1 and 48 in figure 3 as well as different length of the MCMC (Markov chain Monte Carlo) chain in BEAST2 to average over tree space, so that each tree is weighted proportional to its posterior probability. A 100M chain model was used in figure 1 and 20M chain in figure 3. This is the reason why the topologies might differ if they are not well supported. On the other hand, the tree shown in figure 1, is a first approach to show how DmdA homologs are related. Indeed, the bootstrap of the tree improved when only closer homologsare considered (figure 3). Therefore, our study developed from this set of sequences. For instance, the bootstrap of the clade where Thioglobus singularis is located (WP_053819980 in figure 1) improves when only closer DmdA homologs are included in the phylogenetic analysis, as in figure 3. In the case of R. pomeroyi, to analyzed DmdA evolution, we used single copy DmdA orthologs because it is the standard protocol for selection analyses. 43) Why not say what are the meanings of the different background colours? (figure 4) 12 We have added information to the figure 4 legend: “The predicted non-DmdA clade is shown in brown, DmgdH gene family is in light yellow and the DmdA clade in green color ”. 44) Maybe better if all the arrows were in same orientation- either vertical or horizontal (figure 4). Agreed. We have corrected it. 45) Sorry, but to my untutored eye, the only yellows I could see looked to be part (sulfurous) of some cofactor (and not THF) that also included blue and red atoms. Clearly, I am mis-reading the signs. In any event, I did not think that the figure was as informative as it might be. Why not indicate the actual residues and shown in relation to the DMSP- and DHF- binding sites? Also, would it not help to name the species (P.u) used for this structure? (figure 6) Agreed. Some modifications were made in figure 6. In particular the signs were improved in the figure and headline, the residues are shown in relation to the binding sites and P.u was named as the specie used for this structure. Here, I indicate my wider concerns. I think that my overall worries relate to the fact that the DmdA/dmdA polypeptide/gene (by the way, there are several places where the nomenclature for these is mixed up and wrong) are considered in isolation, addressing them purely in terms of their sequences and their known and proposed structures. Given the terms of reference that they set themselves, that may be fair enough, but, no gene is an island, so maybe they need to bring in other factors. The nomenclature was carefully revised. On the other hand, the target of this study is not a single gene but a gene family, which includes at least the same function, domain and 3D structure and the same answer under DMSP condition in the environment. Indeed, in this MS we reconstructed the ancient conditions where dmdA gene prospered and we defined eco-orthologs for orthologs performing the same function under different conditions. Moreover, dmdA is part of a conserved operon where the association of dmdA, dmdB and dmdC is observed in the same transformation pathway (González et al., 2019). The annotation method used in this MS for classifying orthologs and homologs includes the gene neighborhood and/or position of the gene along the genome to confirm the annotation (protocol of González et al., 2019). A clearer mention that dmdA is not considered in isolation we added in lines 623 – 625 new information: “ since dmdA seems to be part of a conserved operon (González et al., 2019), its evolution might be linked to genes such as dmdB, dmdC and dmdD that encode part of the enzymes for the rest of the pathway”. So, a few things. Of the elephants in the room, the one that gives me most cause for concern is the lack of any mention (I checked) of the worryingly (for some) high Km (~10 mM) values of those 13 DmdA enzymes that have been studied. If the family has had such a long time to evolve, this almost implies that a high Km is an adaptive advantage, perhaps in the context of the high intracellular DMSP concentrations. I think that this point needs to be addressed somewhere. [And, yes, I know that some (though not all) of the Ddd lyases are even less efficient!] It is an interesting idea that we had not thought, but we are going to keep it in mind for the future. Indeed, to evaluate if a high Km is an adaptive advantage, we should do not only analyzed ancestral sequence reconstruction by bioinformatics tools as we did but also proteins resurrection in the laboratory using molecular biology. In fact, we were able to infer the salinity role in DmdA evolution because some previous experiments report more DMSP degradation under high salinity concentrations and pI values was identified to relate tosalinity. Unfortunately, there are no data on Km values in ancestral proteins and this cannot be calculated by bioinformatics tools. Therefore, we cannot say much more about the existing information for Km in DmdA proteins. However, it would be a really good project for the future if Km and other biochemical parameters were determined in ancestral proteins of DmdA. We mention Km in lines 618 – 620: “Under this environment, dmdA would have been kept without important changes in its structure, sequence, function and Km value” and in lines 621 -623: “Indeed, it would be interesting to evaluate Km values among ancestral proteins of DmdA and their descendants to support the key role of Km during DmdA evolution”. Although they certainly do mention the recent observations that much (maybe most?) of the world’s DMSP is of bacterial origin, I actually feel that this may be a game changer in this field, especially in light of the findings by Williams et al (https://www.nature.com/articles/s41564-019-0527-1) of the range of different bacteria and the pathways that can accomplish this synthesis. This finding may not substantially change the overall line of thinking and the conclusions of this MS, but I do think that it changes the backdrop to the DMSP story and needs to be considered. (I assure you that this is not a case of “point-scoring”.) As you can see, we mentioned the findings by Williams et al in points 1, 4 and 11. I would like to have seen some consideration of what they think are the features of the Roseobacter lineage (not Roseobacter in italics as repeatedly written in the MS) that makes them so DMSPphilic. If the dmdA functional gene has been around for such a long time, why did it not find its way into a much wider range of marine bacteria? Actually, we did this in our “Second scenario section” (line 566). In this study the dmda gene seemed to be acquired by a Roseobacter ancestor. We know that during Roseobacter evolution, gain 14 and loss of traits has occurred. This is consistent with the scenario that Roseobacter have continuously explored new ecological habitats. One of these ecologically relevant genetic traits, was acquired coinciding with Roseobacter genome expansion and eukaryotic phytoplankton diversification which provided new ecological habitats for ancestral roseobacters. In our study, we did not rule out this possibility, but we propose that dmdA was useful for the Roseobacter group a few times after the origin of Roseobacters (when both genome expansion and eukaryotic phytoplankton diversification took place) because dmdA was in the ancestor’s genome. The information that shows that idea, can be found in lines 572 – 574: “is in Roseobacter because their genome expansion (250 mya) provided a new trait to use DMSP produced by phytoplankton during its diversification ”. After all, there are very close homologues of the downstream DmdB, C and D polypeptides in a whole host of other bacteria, so these would only require the acquisition of the single dmdA gene for a functional pathway? And that is not all. Emphasising the importance of DMSP in the life and times of the Roseobacters, these bacteria are home to many of the Ddd lyases, and there are links between the two pathways, most obviously that in several Roseobacters, the expression of the dmdA is under the control of the key product (acrylate) of the lyase mediated cleavage. I am not sure what this means in relation to the evolution of the DmdA enzyme, but I suspect that it means something. DMSP is transformed to MMPA by marine organisms abundant in marine surface waters (Roseobacters) where DMSP is abundant. That MMPA thus can be catalyzed by marine organisms with the rest of proteins involved in DMSP demethylation, which are not only in the Roseobacter clade. However, it was complicated to explore in this study all interactions between genes involved in DMSP metabolism. Iinformation about that was included in lines 623 – 625: “In addition, since dmdA seems to be part of a conserved operon (González et al., 2019), its evolution might be linked to genes such as dmdB, dmdC and dmdD that encode part of the enzymes for the rest of the pathway ”. Another study should look to understand better what is the evolutionary role of this association. I thought that the figure legends could be generally improved, with more information and written more clearly. See my in situ comments on Fig 1 line 329 for example. Agreed. We have corrected this figure and made an tried to improve all the figure legends. Overall, though, I think that this paper will be a useful addition to the DMSP literature and at the very least will set off some productive and interesting discussions. Thank you for all you detailed and thoughtful comments. 15 Reviewer 2 Basic reporting Comments on clarity/english in text: Line 68-69: Consider rewording to make this sentence more accurate to your point, such as: “Compared to genes in the DMS-releasing pathways, dmdA is more frequently found in the genomes of oceanic bacteria.” We agree. We corrected it. Lines 360-363: This doesn’t need to be a separate paragraph. You could add it onto the previous paragraph. Agreed. We corrected it. Lns 405-407: I would suggest changing the terms ‘first cluster’ and ‘second cluster’ since these are hard to interpret for finding things where things are on the tree. Agreed. We corrected it. “First cluster” was replaced by “the SAR11 cluster” in line 409 and “second cluster” by “the Roseobacter cluster” in line 410. Ln528 : What does Fig. 7; “down and up” mean? A meaning was included. We added in line 528 “Fig. 7.A” for DmdA clade and “Fig. 7.B” for nonDmdA clade in line 529. Ln 525-528: contradictory phrases “have not a recent common ancestry” and “supports a common recent ancestry” in same paragraph. We disagree since this applies to DmdA vs. GcvT and DmdA vs. non-DmdA. Their ancestries could well be different. Non-DmdA includes only a group of genes which are related to DmdA and studied in this project. Ln 568-569: Are you referring to Figure 4? It would be helpful here and throughout the Discussion to reference the results to which you refer. We apologizes. Yes, we are referring to figure 4 where we inferred the most recent common ancestor of the DmdA gene family and the timing of its origin and any duplication events. But also to the reconstruction of ancestral DmdA sequence which was found similar to the DmdA from Ca P ubique which is a recent descendant. In the Discussion, we refer to this figure in line 571 to make it more clear. Ln 581-584: I cannot understand this sentence. We apologize again. We have rewritten the sentence (lines 585 - 588): “According to our phylogeny, the ancestral dmdA sequence originated as a results of HGT (in individuals not connected by 16 inheritance that acquired the dmdA ancestral sequence) from other marine heterotrophic bacteria, that during the Archean adapted to the presence of DMSP”. In other words, the logics is as follows: the enzyme activity has not changed in the course of DmdA evolution, because we found that in the ancestor and descendant (i.e. in Ca. P. ubique) the sequences are similar. Therefore, DmdA was probably pre-adapted to DMSP in a bacteria lineage, before DMSP became abundant in the environment and was more recently transferred through HGT. Ln 600-602: please consider rewording “as well as Ca. P. ubique sequence and this last one has a pI similar to the first” I don’t know what first and last one means. Last one is referring to DmdA from Ca. P. ubique which is the last cited in the sentence for not repeating the name of bacteria, and the first is referred to DmdA ancestor which is written between parenthesis after “the first”. However, we modified the sentence for a more comprehensive explanation in lines 611 - 612: “We observed the highest pI values in the DmdA ancestor sequences, as well as in Ca. P. ubique DmdA (Fig. 7.A; red color)”. Ln 605: “degradates” to “degrades” The answer is degrades Ln 616: “experimented” to “experienced The answer is experienced Ln 444 should say “…selection has influenced the evolutionary history…” Agreed. We corrected it in line 447. Ln 460 should say “…fits the data better…” Agreed, We have corrected it in line 454. Ln 587 should day “…prior to having been…” Agreed, We have corrected it in line 592. Comments on Figures, Tables, and Supplemental Material: All Tables (1-6) can be moved to supplemental material. Agreed. We moved all tables to supplemental material. Therefore, table names have been changed as we show next: Supplementary table 1 is replaced by table S1, supplementary table 2 by table S2, supplementary table 3 by table S3, table 1 by table S4, table 2 by table S5, table 3 by table S6, supplementary table 4 by table S7, table 4 by table S8, table 5 by table S9, table 6 by table S10 and supplementary table 5 by table S11. 17 Supplemental Table 1: I do not understand how to interpret the G+C wobble values, some cells have multiple values, some have decimals and others have commas. Please revise. (G+C) wobble includes values for each sequence shown in column “GcvT superfamily sequences (ncbi)” in the same order that the sequence appeared in this column and separate by “;” in (G+C) wobble content column. We have included a new explanation in this supplemental table for a more comprehensive. Legends for the Supplemental figures and tables are only found on the PeerJ website; final versions of the supplemental materials need to have these legends within the article documents. Sorry, but manuscript template in docx format for Peerj does not add legends. Indeed, Peerj does not allow to submit manuscript with the legends for figures and tables in the MS as odt or docx format. Figure 1: In the legend, please define what is meant by ‘closer homologs’ for those with yellow color. We included new information in the legend for a more comprehensive explanation. In particular, all sequences in the tree are homologs, however green colour show homologs which are orthologs because they share the same genes in different species. In brown are shown only homologs. Moreover, “closer homologs” are sequences with a phylogenetic relationship closer than other sequences, as you can see and infer with the figure 1, and they were selected with a maximum e-value of e-80 as we indicated in figure 2. I’m not convinced that Supplemental Figure 6 is necessary. It’s hard to directly compare with Figure 3 because you’re using a different set/number of sequences. You then go on to do the same analysis/comparison with a smaller subset of sequences in Supplemental Figures 7-9 that accomplish your point about HGT. Each figure includes new information, and in the case of supplemental figure 6 we consider that it is necessary because it is a species tree created with ribosomal protein 16 small subunit (RPS16) from all species annotated with DmdA orthologs. Moreover, suppl fig 7 or DmdA tree and suppl fig 8 or specie tree show topologies with common taxa for a statistic comparation which concludes that the phylogenetic relationships within each DmdA group are different to those of the species tree, strongly supporting a HGT-based evolution of DmdA family (Suppl Fig 9). Legends for the supplemental figures are sometimes too vague. For example, Supplemental Figures 8 and 9 have the exact same title. 18 It is because they are the same figure but with new information in case of figure 9. However, we eliminated supplemental figure 8 since it is not needed anymore. There is a fair amount of color switching between figures. DmdA sequences are labeled in blue, red, green, depending on which tree it is – and there are many trees. This leads to reader fatigue. I would suggest trying to make your coloring schemes as consistent as possible. We modified figure 3 and supplemental 10a (replaced by supplemental figure 9a in the new version) for showing DmdA orthologs with green colour as in figure 1. Moreover, supplemental figure 4 was modified to show DmdA orthologs as blue branches which is consistent with the rest of the figures. Supplemental Figure 10b is divided into 4 clades, which are supposed to correspond to Figure 4 and Supplemental Figure 10a, but I only see 3 clades. It would also help to include taxonomic labels on Supp. Fig 10b so it’s easier to compare between it and the trees. Agreed. We have modified the figures for a more comprehensive explanation. Supplemental Figure 10b: what’s the difference between darker and lighter blue? The intensity of the color depend on the percent of the residues in each column that agree with the consensus sequence. Supplemental Figure 11: All I see is a black box with squares and a yellow “L” line. Should there be labels somewhere? Are the points corresponding to the red and yellow sequences from 10a? Again, keeping consistent color coding would be really helpful. Each square is a sequence from the alignment that you see in supplemental figure 10b (replaced by supplemental figure 9b in the new version) projected along vectors which are shown like lines. On the other hand, grey colour are sequences with a putative dmgdH structure which are separated from the rest of the sequences in this principal component analysis. That means that those sequences with dmgdH structure, form an independent clade of protein from the rest. We modified this figure for a more comprehensive view. Figure 4: Please offset the node numbers and bars so that it’s easier to read. Move the node numbers off of the node labels. Also provide a label for the time scale and increase the font size of the scale numbers. Define violet arrow in legend. Agreed. We have corrected it. Supplemental Figure 20: I don’t think this figure is necessary or provides any information beyond the text. What’s written in the legend is all the info that is needed. Applying the two branch-site models to test for sites under selection on the individual lineages associated with dmdA is a complicated process which need to define with precision the branches of 19 interest that are going to be selected during the analysis. Therefore, the figure shows the particular branch selected for the analysis as Zhang, et al., 2005 explain in their study ( http://www.ncbi.nlm.nih.gov/pubmed/16107592). Figure 6: what do the colors other than blue represent? Some modifications were made in figure 6 for a more comprehensive explanation. Figure 7: define 'pI' Agreed. We corrected it and defined like “predicted isoelectric point value”. Overall comment: My list of figure corrections is probably not exhaustive, I would suggest going back through everything to find other missing information, mismatched colors, labels that need definitions, etc. Yes, the figures were revised as well as their legends. Experimental design See comments on conclusions that may relate to experimental design. Validity of the findings Comments on interpretation/conclusions: I admit that I am only partly familiar with some of the techniques used in the manuscript. An overarching question that I have is, How are your results/interpretations influenced by the fact that you are comparing DmdA vs a potentially “mixed bag” of other proteins (i.e. DmgdH + DmdA-like paralogs) that 1) may have multiple different functions and 2) represent different taxonomic groups? For example, how is your comparison of purifying selection in DmdA versus non-DmdA clades (Lns 491-504) influenced by analysis of so many different taxa/potential functions? We used non-DmdA sequences as a reference homolog group that allows to analyze the evolution of DmdA family since its origin, and with functional divergence analysis we seek to identify sites that have evolved with different rate after a duplication event. Differences in functional properties and taxonomic distribution among sequences within a homology group can be accounted for by using a large number of sequences representing the different taxa, and that is what we did. This allows to infer ancestral sequences with higher confidence and in turn to analyze divergence between DmdA and non-DmdA sequences with higher power. Lns 407-408: If I understand correctly, by ‘second cluster’ this is the one in green (please clarify this in the text). You conclude that the number of paralogous genes is greater for Roseobacters versus SAR11. Does having greater representation of Roseobacter sequences on the tree influence this? What about the greater phylogenetic distance among members in the Roseobacters? I may be 20 confused because I was under the impression that the green block included DmdA orthologs only. Green colour shows DmdA orthologs. The first cluster is SAR11 and the second cluster is Roseobacter. The number of Roseobacter paralogous are greater than those in SAR11, since only three genomes represent SAR11 in our study (figure 4). Here, considered paralogs as pairs of genes that derived from the same ancestral gene for Roseobacter and SAR11 genes after a duplication event 1,984 mya. However, we deleted the sentence “Thus the number of paralogous genes comprising the Roseobacter DmdA family is larger than in SAR11” in the new version to avoid confusing. Ln 546-548: I’m not sure I understand/agree with this conclusion. The authors say that this phenomenon would be rare, but could your interpretation potentially be wrong? We were very careful to show this convergence and we think it is properly cited (Zakon, 2002). Our conclusions are based on the tertiary structure models predicted from I-TASSER, which agreed with the DmdA 3D structure in the model species R. pomeroyi, and the reconstruction of ancestral DmdA sequences. Both indicate that non-DmdA and DmdA clades have different ancestral sequences and 3D structure (a summary is shown in figure 7). You can see a better explanation in lines 423 to 444. Firstly, is it true that both the dmdA_1 and dmdA_2 clades on the tree are all true DmdA sequences? (You reference a paper, Gonzalez, 2019, but this is based purely on computational analysis, not experimental verification of function) which gives me concern. As reported in Gonzalez, 2019, computational analysis is used to classify orthologs starting from proteins with experimental verification of function as seed of all bioinformatic analysis. On the other hand, dmdA_1 and dmdA_2 are not classified as DmdA orthologs using this protocol, although they seem to have the same dmdA 3D structure. Moreover, we agree that experimental assays are necessary to corroborate the structural convergence and then the way of evolution in nonDmdA and DmdA clades as you can see in lines 550-551. For the tertiary structure analysis, you are feeding in known structures of DmdA and DmgdH, but you don’t have an accurate structure of whatever makes up the dmdA_1 and dmdA_2 clades. Could it be that you are seeing an effect of not having a good predicted model for dmdA_1/2, and those sequences are just matching up with the best hit, which is the 'true' DmdA model? Yes, you are right. As you can see in lines 312 – 323 (protein tertiary structure analysis), we evaluated a model with an 3D structure based on the C-score, which is a confidence score for estimating the quality of predicted models as you can see in table 1. The C- score is typically in the 21 range of [-5,2], where a C-score of higher value means a model with a high confidence and viceversa. In our results (table 1) the C-score were really high for the majority of sequences. Moreover, dmgdh is identified thanks to the predicted model using I-TASSER, alignment analysis (supplemental figure 9b which agreed with the classification of figure 7) and principal component analysis from Jalview (supplemental figure 11 replaced by supplemental figure 9c). This goes back to my concerns about interpreting the results when you have these ‘mixed bags’ of protein sequences that could represent multiple, different functions. Alternatively, what if the ancestor of the protein famil looked more like DmdA; you are only two yellow nodes away from a different conclusion (Fig 7)? Yes, you are right. This is a bioinformatic analysis and these are only predictions although we used as templates proteins with experimental verification. However, the percent error is not producing remarkable changes in our results. Additionally, bioinformatics is the only tool available to reconstruct the evolutionary pathways of genes and microorganisms since there are no other methods. Experimental validation of the predicted functions would be possibly but only for a small set of genes since the diversity of microorganisms is immense. The only method to track evolutionary pathways is through sequence analysis. Comments for the author The authors use a suite of phylogenetic and computational approaches to reconstruct the evolutionary timeline and history of DmdA in marine bacteria. The topic is timely and interesting overall, and there has been recent literature that questions when/how DMSP degradation genes evolved (with some controversy). So, I applaud the authors for tackling this question. We thank the reviewer for the kind words. My main issue with the manuscript has to do with clarity. Many figures need better descriptions/definitions in the legends; at times, it was challenging as the reader to interpret results because of this. There are also many figures and tables, and the paper would benefit from more consistency in colors/labels and possibly removing/consolidating some figures. An important effort was made to improve the descriptions of the figures, tables, legends and manuscript. I also question some of the conclusions that the authors draw, but these concerns may be addressed through clearer language and explanation in the text. My specific comments are listed herein. Thank you. An effort was made to explain clearer the conclusions and all the reviewer's comments. 22 "	Here is a paper. Please give your review comments after reading it.
530	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Background. This study aimed to analyze the clinical outcomes associated with patients with recurrent / metastatic head and neck squamous cell carcinoma (RM HNSCC) who received cetuximab-based chemotherapy in a real-world clinical setting. Methods. The clinical data were extracted from RM HNSCC patients diagnosed between 2016 and 2019. Kaplan-Meier survival estimates and Cox proportional hazards model were used for survival analyses. Results. Out of 106 RM HNSCC patients (mean age = 55.1 years),</ns0:p><ns0:p>38.7% exhibited recurrent disease and 61.3% had metastatic disease. The majority of patients showed a habit of addictive substance use, including alcohol (67.0%), betel nuts (71.7%), or tobacco (74.5%). The primary tumor sites included oral cavity (64.1%), hypopharynx (19.8%), and oropharynx (16.0%). The median cetuximab cycle of 106 patients was 11(2-24). The disease control rate (DCR) was 48.1%, and the overall response rate (ORR) was 28.3%. The median progression-free survival (PFS) and overall survival (OS) were 5.0 and 9.23 months, respectively. Patients treated with more than 11 cycles of cetuximab exhibited longer median PFS and median OS than patients treated with less than 11 cycles (median PFS: 7.0 vs. 3.0 months, p < 0.001; OS: 12.43 vs. 4.46 months, p = 0.001). Patients without previous concurrent chemoradiotherapy (CRT) may be associated with better median PFS than with previous CRT (6.0 vs. 4.0 months, p = 0.046). Multi-variate analysis revealed perineural invasion and less cycles of cetuximab (<11 cycles) were two independent risk factors associated with disease progression. In</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>Head and neck squamous cell carcinoma (HNSCC) is the sixth most common malignancy in the world, and recurrent and/or metastatic head and neck squamous cell carcinoma (RM-HNSCC) harbors lethal clinical features and dismal medical outcomes <ns0:ref type='bibr' target='#b30'>(Parkin et al. 2005)</ns0:ref>. Over 90% of head and neck cancers are squamous cell carcinomas, which develop from the mucosa of the oral cavity, oropharynx, larynx, or hypopharynx <ns0:ref type='bibr'>(Warnakulasuriya 2009)</ns0:ref>. In western countries, oropharyngeal SCC accounts for the largest group of HNSCC, with a minority of the patients related to human papillomavirus (HPV) infection <ns0:ref type='bibr' target='#b13'>(Gatta et al. 2015</ns0:ref><ns0:ref type='bibr' target='#b14'>, Gillison et al. 2000)</ns0:ref> HNSCC, with a minority of the patients related to human papillomavirus (HPV) infection <ns0:ref type='bibr' target='#b13'>(Gatta et al. 2015</ns0:ref><ns0:ref type='bibr' target='#b14'>, Gillison et al. 2000)</ns0:ref>. However, oral cavity SCC is the most predominant site of head and neck cancer in Taiwan due to high oral betel nut consumption <ns0:ref type='bibr' target='#b3'>(Belcher et al. 2014;</ns0:ref><ns0:ref type='bibr' target='#b7'>Chang et al. 2017)</ns0:ref>. Virus-induced HNSCC in western countries is different from its Taiwanese counterpart in that the mechanism of tumorigenesis of HNSCC in Taiwan is mainly related to carcinogens and addictive substances, including alcohol, betel nuts, or tobacco (Cancer 2012). These carcinogen-related HNSCCs harbor higher Ras oncogene mutations and increased chromosome instability, which implies that the genetic background and clinical features may be unique in these patients <ns0:ref type='bibr' target='#b8'>(Chang et al. 1991;</ns0:ref><ns0:ref type='bibr' target='#b26'>Kuo et al. 1994;</ns0:ref><ns0:ref type='bibr' target='#b31'>Riaz et al. 2014)</ns0:ref>.</ns0:p><ns0:p>Epidermal growth factor receptor (EGFR) is usually upregulated with increased levels of its ligand transforming growth factor alpha (TGF-α) noted in most HNSCCs, with both proteins contributing to the carcinogenesis of HNSCC <ns0:ref type='bibr' target='#b16'>(Grandis 2007)</ns0:ref>. Upregulation of EGFR is an independent poor prognostic factor in HNSCCs <ns0:ref type='bibr' target='#b1'>(Ang et al. 2004;</ns0:ref><ns0:ref type='bibr' target='#b9'>Dassonville et al. 1993)</ns0:ref>.</ns0:p><ns0:p>Cetuximab, an IgG1 chimeric monoclonal antibody targeting EGFR, has been the one of the first-line treatments for RM HNSCC patients with low programmed death ligand 1 (PD-L1) expression <ns0:ref type='bibr' target='#b5'>(Burtness et al. 2019;</ns0:ref><ns0:ref type='bibr'>Vermorken et al. 2008)</ns0:ref>.The addition of cetuximab to platinumbased chemotherapy with fluorouracil (platinum-fluorouracil) improved the overall response rates, median progression-free survival (PFS), and overall survival (OS) compared with chemotherapy alone. Another combination of cetuximab with chemotherapy agents like taxane also demonstrated substantial benefits <ns0:ref type='bibr' target='#b0'>(Adkins et al. 2018;</ns0:ref><ns0:ref type='bibr' target='#b12'>Friesland et al. 2018;</ns0:ref><ns0:ref type='bibr' target='#b19'>Guigay et al. 2019)</ns0:ref>. However, most of these clinical trials are conducted in western countries with less patients of primary oral cavity cancer and the data regarding the effect of carcinogens like betel nuts on outcome is very limited. In addition, the percentage of HPV infection status is quite different between Asian and western countries, indicating distinct tumor microenvironments <ns0:ref type='bibr'>(Wang et al. 2019)</ns0:ref>.</ns0:p><ns0:p>In Taiwan, cetuximab combined with systemic chemotherapy has been proved as the first line treatment in patients with RM HNSCC by the National Health Insurance since 2016. After approval of application, the patients can receive cetuximab-containing treatment without copayment. Due to limited financial resource, cetuximab can only be administered to a total of eighteen cycles if no progression was noted. Different from clinical trials which can achieve therapeutic efficacy with cetuximab maintenance, patients in real life were not affordable continuous maintenance with high-cost cetuximab to control their diseases. Therefore, making modification of treatment protocol a possible strategy <ns0:ref type='bibr' target='#b21'>(Hsu & Lu 2016;</ns0:ref><ns0:ref type='bibr' target='#b33'>Shih et al. 2015)</ns0:ref>.</ns0:p><ns0:p>However, the impact of above modification like limited cetuximab treatment cycle on patient outcome is still an open-ended question. Moreover, the real-world data of cetuximab in RM HNSCC patients with a high percentage of exposure to different carcinogen remains is also very limited. To answer above questions, we conducted this retrospective and single-arm study to analyze clinical data, hoping to elucidate the clinical outcome and prognostic factors in this subset of RM HNSCC patients.</ns0:p></ns0:div> <ns0:div><ns0:head>Materials and methods</ns0:head></ns0:div> <ns0:div><ns0:head>Patient Characteristics</ns0:head><ns0:p>Clinicopathological data of patients with HNSCC were confirmed by pathological examination of the specimens from biopsy or surgery, and the positive samples were collected and analyzed. A total of 106 cases of RM HNSCC were identified with metastasis or recurrence and were deemed unsuitable for locoregional curative treatment in the Kaohsiung Medical University Hospital. The inclusion criteria included: age at diagnosis (20 years or older), tumor histology of squamous cell carcinoma (grade 1 to grade 3), ICD-9 site code-specific for the oral cavity (OC), hypopharynx (HPC), oropharynx (OPC), and larynx, and patients treated with cetuximab during January 2016-April 2019. The exclusion criteria included patients with secondary malignancy; tumor histology of carcinoma in situ, and SCC from the nasopharynx and salivary glands.</ns0:p></ns0:div> <ns0:div><ns0:head>Study design</ns0:head><ns0:p>This was an observational, retrospective, single-center, single-arm study and the treatment schema was showed in Fig. <ns0:ref type='figure'>1</ns0:ref>. The collected medical and demographic data included age, gender, alcohol, betel nut usage, tobacco habits, and other clinical parameters from the medical records or interviews with patients. The clinicopathological factors included types and grade of histology, size of tumor, lymph node status, surgical margin, perineural invasion, lymphovascular invasion, and extranodal extension. We defined CRT (chemoradiation)refractory patients as patients with disease progression during CRT or within three months after the end of CRT. We evaluated the results of a retrospective and single-arm study with the primary endpoint of assessing outcomes in a southern Taiwan comprehensive cancer institution.</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:01:45342:1:1:NEW 20 Jun 2020)</ns0:p></ns0:div> <ns0:div><ns0:head>Manuscript to be reviewed</ns0:head><ns0:p>We analyzed the median OS and PFS (defined as the time from registration to objective disease progression or death from any cause) after the addition of cetuximab to chemotherapy. The secondary endpoints of this study included the assessment of treatment response and disease control. This study was approved by the Institutional Review Board and Ethics Committee of Kaohsiung Medical University Hospital (KMUHIRB-E(II)-20190357). The data were analyzed anonymously, and therefore, no additional informed consent was required. All the methods were performed in accordance with the approved guidelines and regulations.</ns0:p></ns0:div> <ns0:div><ns0:head>Treatment</ns0:head><ns0:p>All the patients received cetuximab (250 mg/m 2 ) weekly with a loading dose of 400 mg/m 2 till disease progression existed. The regimen of chemotherapy included PF 75/1000 (cisplatin at 75 mg/m 2 or carboplatin at AUC=5 every 3 weeks plus fluorouracil at 1,000 mg/m 2 /d for 4 days every 3 weeks), PF 60/800 (cisplatin at 60 mg/m 2 or carboplatin at AUC5 every 3 weeks plus fluorouracil at 800 mg/m 2 /d for 4 days every 3 weeks), taxane-based chemotherapy (docetaxel and cisplatin 75 mg/m 2 both at day 1 and every 3 weeks for four courses of paclitaxel 80 mg/m 2 weekly), and MTX (methotrexate 40 mg/m 2 weekly). Patients could receive chemotherapy or concurrent chemoradiotherapy with weekly cisplatin administration previously before recruitment.</ns0:p></ns0:div> <ns0:div><ns0:head>Treatment Response and Safety Assessment</ns0:head><ns0:p>All our patients followed regularly at outpatient department of medical oncology and department (OPD) of otorhinolaryngology. During cetuximab treatment period, the patients visited OPD of medical oncology weekly and otorhinolaryngology monthly. The evaluation of disease status included tumor site inspection, laboratory text, and imagine studies. Treatment response was assessed and determined by computed tomography (CT) or magnetic resonance imaging (MRI) at baseline (before cetuximab) and at 3-month intervals after treatment was started. Imaging study within 4 weeks before cetuximab was acceptable, and imaging study could be performed whenever clinical physicians suspected disease progression. RECIST version 1.1were used to determine disease progression and tumor response.</ns0:p><ns0:p>The treatment response of patients was classified into four categories: complete response (CR, disappearance of all target lesions), partial response (PR, decrease in target lesion diameter sum > 30%), progression disease (PD, increase in target lesion diameter sum > 20%), and stable disease (SD, does not meet other criteria). The calculation of overall response rate (ORR) was based on the best objective response achieved during cetuximab treatment. After disease progression, further treatments and survival status were documented every 3 months. Regarding safety assessment, treatment-related adverse events were monitored weekly throughout the study PeerJ reviewing <ns0:ref type='table' target='#tab_9'>PDF \| (2020:01:45342:1:1:NEW 20 Jun 2020)</ns0:ref> Manuscript to be reviewed and evaluated using Common Terminology Criteria for Adverse Events version 4.0.</ns0:p></ns0:div> <ns0:div><ns0:head>Statistical Analysis</ns0:head><ns0:p>The primary goal of the study was to analyze the outcome of cetuximab-based chemotherapy in recurrent or metastatic settings, including a comparison between median PFS and OS among patients receiving different cycles of cetuximab and different regimens of chemotherapy. The location of primary sites (OC, OPC, or HPC), histological grade (Grade 1, 2, 3), tumor size and status (T1, T2, T3, T4), lymph node status (N0, N1, N2, N3), stage at initial diagnosis (I, II, III, or IV), surgery status (with or without previous surgery), CRT (with or without previous CRT), and chemotherapy before cetuximab therapy (with or without prior chemotherapy) were all included for analysis. Between-group comparisons were analyzed by using Fisher's exact test and Pearson's chi-square test for different categorical variables. We estimated median PFS and OS with Kaplan−Meier analysis, and we analyzed differences between the curves by using the log-rank test. We defined the median PFS as the time between the start of disease progression and treatment, including disease progression or death. Patients </ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head></ns0:div> <ns0:div><ns0:head>Baseline characteristics of patients</ns0:head><ns0:p>The clinical data of 106 patients (including 99 males and 7 females) with a median age of 55.1 years were collected for this study. Among these patients, 65 patients (61.3%) had metastatic disease and 41 patients (38.4%) had recurrent disease while initiation of cetuximab. Almost all patients had addiction of alcohol or betel nuts, or history smoking, including 61 patients (57.5%) with all carcinogen exposure. Only 5 patients (4.7%) have no previous exposure to above risk factors. Regarding the tumor site, most of the primary sites had origins in the oral cavity (64.1%), sequentially hypopharynx (19.8%), and oropharynx (16.0%). The majority of patients were in advanced disease, such as T3-4, N2-3, or clinical stage 4. The detail basic information of study population was listed in Table <ns0:ref type='table'>1</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Treatment modality</ns0:head><ns0:p>With respect to prior treatment before cetuximab treatment, most patients experience various HNSCC treatment including surgery (78.3%), chemotherapy (81.1%) and CRT (80.2%).</ns0:p><ns0:p>In addition, there were 34 CRT-refractory patients who suffered from disease progression during CRT or within three months after the end of CRT.</ns0:p><ns0:p>The major reason for cetuximab treatment is recurrent disease with metastatic tumors. The median cycles of cetuximab were 11 cycles (2-24), with 60 patients receiving >11 cycles of cetuximab, and 46 patients receiving ≤11 cycles of cetuximab. Among these patients, 76 patients received chemotherapy with EXTREME regimen (cisplatin and fluorouracil) and 17 patients received taxane-based chemotherapy. The median cetuximab administration cycles in these 76 patients with a PF regimen was 11 (range: 2-24) while the median cetuximab cycles in 17 patients using taxane-based regimen was 12 (range: 4-23). There was no significant difference in the number of cetuximab cycles between these two groups (p = 0.427). The details of the treatment modalities are shown in Table <ns0:ref type='table'>2</ns0:ref>. The demographic data of different cetuximab cycles (≥11 and <11) were shown in Supplementary Table <ns0:ref type='table'>S1</ns0:ref> and Table <ns0:ref type='table'>S2</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Treatment outcomes</ns0:head><ns0:p>After cetuximab treatment, clinical response was observed in 20 patients including 1 complete response and 29 partial response, with ORR of 28.3%. When the patients with stable disease (n=21, 19.8%) were included into analysis, the disease control rate was 48.1%. The median PFS and OS were 5 months and 9.23 months, respectively. As of data cut-off, only one patient did not progress, and 38 patients survived eventually. The median PFS was 5 months (95% CI 3.0-6.0 months) and the median OS was 9.23 months (95% CI 7.03-13.84 months).</ns0:p><ns0:p>The treatment responses according to different stages were shown in Supplementary Table <ns0:ref type='table' target='#tab_4'>S3</ns0:ref>.</ns0:p><ns0:p>The median PFS in different sub-groups stratified by treatment modalities was shown in Fig. <ns0:ref type='figure' target='#fig_2'>2</ns0:ref>. Notably, the patients who received more cetuximab treatment (≥ 11 cycles) had better median PFS than patients received less cetuximab (7 months vs 3 months, p< 0.001).</ns0:p><ns0:p>Additionally, the median PFS was longer in patients without prior CRT (6 months vs 4 months, p = 0.046). Other factors including chemotherapy regimen (PF or taxane-based), chemotherapy dose (PF dose), or CRT refraction status didn't lead to significant effect on PFS. When it comes to analysis of OS, the patients who received more cetuximab treatment (≥ 11 cycles) had better median OS than patients received less cetuximab (12.43 months vs 4.46 months, p< 0.001).</ns0:p><ns0:p>Other factors including chemotherapy regimen, chemotherapy dose, or didn't lead to significant effect on PFS. The OS curves were shown in Fig. <ns0:ref type='figure'>3</ns0:ref>.</ns0:p><ns0:p>Next, we applied landmark method for further validation. Since the response could observes within the first 3 months following cetuximab exposure, a 3-months landmark was used. After excluding the patients who progressed or died within the three months, the patients with more cycles of cetuximab (≥ 11 cycles) still showed better median PFS (8 months vs 2 months, p = 0.057) and OS (13.9 months vs 5.07 months, p=0.0002) than the patients treated with less cycles of cetuximab.</ns0:p><ns0:p>To clarify the effects of CRT-refraction on the survival, we evaluated median PFS and OS in patients with or without C RT-refraction. In non-CRT-refractory cohort (n=72), the median PFS and OS were 5.00 months (95% CI = 3.00-7.00) and 10.43 months (95% CI = 7.03-14.64), respectively. The 3-year OS was 28.72% (95% CI = 17.25-41.24). Further evaluation of these 72 subjects, 27 patients with < 11 cetuximab cycles obtained a 3-year PFS rate of 3.70% (95% CI =0.27-15.90), and a 3-year OS rate of 2.22% (95% CI = 0.18-10.15). Additionally, 45 patients with ≥ 11 cetuximab cycles obtained a 3-year PFS rate of 11.57% (95% CI =1.04-36.08), and a 3-year OS rate of 37.07% (95% CI = 21.60-52.59). The patients treated with more cetuximab cycle also showed better median PFS and OS then the patients treated with less cetuximab cycles, shown in Fig. <ns0:ref type='figure' target='#fig_4'>4</ns0:ref>.</ns0:p><ns0:p>In the CRT-refractory patients, the median PFS and OS were 3.00 months (95% CI =3.00-6.00) and 7.8 months, respectively. The 3-year OS rate was 25.30% (95% CI = 10.32-43.53). Six CRT-refractory patients who used taxane-based regimen obtained a median PFS and OS of 3.00 months (95% CI = 2.00-8.00) and 5.62 months (95% CI = 2.03-NA), respectively. The 3-year OS was 16.67% (95% CI = 0.77-51.68).</ns0:p></ns0:div> <ns0:div><ns0:head>Risk factor investigation for disease progression</ns0:head><ns0:p>Risks of disease progression were analyzed by univariate regression consisting of parameters as age, alcohol, betel nuts, tobacco consumption, tumor site, margin positivity, histologic features (including LVI, PNI, and ENE), tumor size, lymph node status, stage, previous treatment modality (including surgery, chemotherapy, and CRT), treatment status, cetuximab cycles, dose, and regimens of chemotherapy. In addition, a subsequent multivariate regression analysis was performed to evaluate the significant progression factors in univariate analysis.</ns0:p><ns0:p>As shown in Table <ns0:ref type='table' target='#tab_1'>3</ns0:ref>, positive PNI was the independent factor related with shorter median PFS. Besides, N3 disease showed a trend toward poorer PFS (p = 0.055, univariate analysis).</ns0:p><ns0:p>After adjustment for other different variables in the multivariate analysis, this difference became significant (HR = 2.57; p = 0.043). Significantly, treatment with more cetuximab cycles (≥ 11 cycles) was the favorite factor associated with a better median PFS (HR = 0.19; p < 0.001, and HR = 0.18; p < 0.001 during both, univariate and multivariate analysis, respectively).</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:01:45342:1:1:NEW 20 Jun 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Determining the risk factor for poorer overall survival</ns0:head><ns0:p>Similar clinicopathological factors were analyzed for overall survival. N2 disease showed a significantly negative impact on OS (HR = 2.09; p = 0.022 and HR = 4.79; p = 0.006 in univariate and multivariate analyses, respectively). Treatment with more cetuximab cycles showed a significant, positive effect on OS (HR = 0.46; p =0.002 and HR = 0.48; p = 0.010 in both univariate and multivariate analyses, respectively). Other factors with a trend toward shorter OS include N3 disease (p = 0.170). After adjustment for other variables, this difference became significant in the multivariate analysis (HR = 7.34; p = 0.005). These results are shown in Table <ns0:ref type='table' target='#tab_5'>4</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Safety and tolerability</ns0:head><ns0:p>All grade and the worst grade 3 and grade 4 treatment related adverse events (AEs) in patients received cetuximab therapy are listed in Table <ns0:ref type='table'>5</ns0:ref>. Among the patients treated with the platinum/5FU and cetuximab regimen, the most commonly AEs were skin rash (2.6%), anemia (2.6%), neutropenia (1.3%), vomiting (1.3%) and febrile (1.3%). Among patients treated with the taxane-based regimen, only one patient suffered from grade 3 febrile (5.9%). There was no grade 3 or grade 4 AE in others groups. In general, skin rash was the most frequent cetuximab-related AE, but most of patients were tolerable. There was no interstitial lung disease observed in our patients.</ns0:p></ns0:div> <ns0:div><ns0:head>Discussion</ns0:head><ns0:p>The treatment options for HNSCC are sophisticated and take multidisciplinary specialists to tailor personalized treatment for individual patients. Since 2008, the addition of cetuximab to chemotherapy has become the first-line treatment of RM HNSCC regarding the advancement in response and survival <ns0:ref type='bibr'>(Vermorken et al. 2008)</ns0:ref>. However, HNSCC is a heterogenous disease and considerable effects of carcinogens have been reported especially in the Asian population <ns0:ref type='bibr' target='#b29'>(Network 2015)</ns0:ref>. Besides, drug accessibility of expensive drugs and the restrictions of the reimbursement policy also has an impact on the responses and outcomes of treatment in many countries, including Taiwan <ns0:ref type='bibr' target='#b10'>(Davidoff et al. 2018;</ns0:ref><ns0:ref type='bibr' target='#b23'>Hsu et al. 2019;</ns0:ref><ns0:ref type='bibr' target='#b28'>Morgan & Kennedy 2010)</ns0:ref>. This retrospective study points out the important role of cetuximab cycles in RM HNSCC, especially in an endemic carcinogen exposure area, such as Taiwan.</ns0:p><ns0:p>In this study, 106 patients treated with cetuximab-based regimens were assessed; most patients had the habit of using an addictive substance and over half the patients had concurrent exposure to all the three addictive substances. However, our outcomes were not inferior when indirectly compared to the other clinical trials, such as the EXTREME regimen conducted by European cancer institutes (De <ns0:ref type='bibr' target='#b11'>Mello et al. 2014) and</ns0:ref><ns0:ref type='bibr'>EXTREME trial (Vermorken et al. 2008)</ns0:ref>.</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:01:45342:1:1:NEW 20 Jun 2020)</ns0:p></ns0:div> <ns0:div><ns0:head>Manuscript to be reviewed</ns0:head><ns0:p>The possible reasons may contribute to regular and frequent follow-up, laboratory and imagine study to detect disease progression and guide subsequent treatment plan when progression was noted. As compared to the aforementioned Asian trial, including Japanese <ns0:ref type='bibr'>(Tahara et al. 2016)</ns0:ref> and Chinese trial <ns0:ref type='bibr'>(Guo Y et al. 2014)</ns0:ref>, the ORR of our study is slightly lower, which may relate to usage of cetuximab maintenance, different regimens of chemotherapy, and patient population with distinct endemic carcinogen exposure. The patients of Japanese trial received cetuximab maintenance and chemotherapy with carboplatin and paclitaxel. However, there was nearly no effect of betel nuts in the Japanese population. The effects of carcinogen were also not mentioned in the Chinese and Korean population. The results of above studies were summarized in Table 6 <ns0:ref type='bibr' target='#b0'>(Adkins et al. 2018;</ns0:ref><ns0:ref type='bibr' target='#b4'>Bossi et al. 2017;</ns0:ref><ns0:ref type='bibr' target='#b11'>De Mello et al. 2014;</ns0:ref><ns0:ref type='bibr' target='#b12'>Friesland et al. 2018;</ns0:ref><ns0:ref type='bibr' target='#b17'>Guigay et al. 2016;</ns0:ref><ns0:ref type='bibr'>Guigay et al.2012;</ns0:ref><ns0:ref type='bibr' target='#b19'>Guigay et al. 2019;</ns0:ref><ns0:ref type='bibr'>Guo Y et al. 2014;</ns0:ref><ns0:ref type='bibr'>Tahara et al. 2016;</ns0:ref><ns0:ref type='bibr'>Vermorken et al. 2008)</ns0:ref>.</ns0:p><ns0:p>Importantly, the median PFS and OS of our study are compatible with another retrospective study <ns0:ref type='bibr' target='#b11'>(De Mello et al. 2014)</ns0:ref>. Moreover, our real-world results were also comparable with other clinical trials. As we just mentioned, these may contribute to every diagnosed patient receiving frequent physical and imaging examinations, taking care form multidisciplinary team (including nurse case management, integrating expertise of medical oncologist, surgeon, radiologists, case managers, nurses, nutritionists, and pharmacists), and meeting periodically to discuss treatment direction, evaluate therapeutic effects, and provide further recommendations. As noted in breast cancer care, earlier detection from more aggressive monitoring could lead to improved treatment strategies and possibly improved survival <ns0:ref type='bibr' target='#b15'>(Graham et al. 2014)</ns0:ref>.</ns0:p><ns0:p>Although our study was conducted retrospectively in a single medical center, our study reflects the observation of the real-world setting in an endemic carcinogen exposure area.</ns0:p><ns0:p>However, our study still had limitations in terms of relatively smaller sample size and immortal time bias. To address the immortal time bias and reverse causality, we applied landmark analysis, which suggested more cycles of cetuximab may bring survival benefit in HNSCC patients. The heterogeneous study population is also an issue. Unlike the EXTREME or TPEX studies which excluded CRT-refractory patients, we included CRT-refractory patients for analysis. Besides, patients who received non-platinum chemotherapy regimens, including taxane and MTX, were also included. Heterogeneity of study population may confound the analysis. However, our findings revealed the real-world condition in term of financial burden of novel treatment, which lead to absence of cetuximab maintenance. In addition, our study included and evaluated the Taiwanese population with high incidence of oral cavity cancer which may be related to strong carcinogen exposure, including alcohol, betel nuts, and tobacco. Previous studies had revealed lower expression of tumor suppressor gene p53 alterations, higher percentage of MDM2 protein expression, as well as higher rate of Ras oncogene mutation after long-term exposure to betel nuts <ns0:ref type='bibr' target='#b24'>(Huang et al. 2001;</ns0:ref><ns0:ref type='bibr' target='#b26'>Kuo et al. 1994;</ns0:ref><ns0:ref type='bibr' target='#b27'>Kuo et al. 1999)</ns0:ref>. Besides, the upregulation of EGFR has been confirmed in betel-nuts-associated cancer of the oral cavity associated with poor prognosis <ns0:ref type='bibr' target='#b32'>(Sheu et al. 2009</ns0:ref>). Three top amplicons, including KRAS, MAPK1, and CCND1, have been observed in cancer of oral cavity from Taiwanese patients, and hence, all could possibly contribute to activation of the EGFR signaling <ns0:ref type='bibr' target='#b32'>(Sheu et al. 2009)</ns0:ref>.</ns0:p><ns0:p>EGFR protein upregulation, excluding the effect of EGFR gene copy number on protein overexpression, was related to poor differentiation of the tumor cells and lymph node metastasis, especially ENE <ns0:ref type='bibr' target='#b25'>(Huang et al. 2017)</ns0:ref>. Taking together, cetuximab targeting EGFR on HNSCC cells can induce potent antibody dependent cell-mediated cytotoxicity, which can further augment anti-tumor effect when combined with chemotherapy <ns0:ref type='bibr' target='#b34'>(Specenier & Vermorken 2013)</ns0:ref>.</ns0:p><ns0:p>The restrictions in targeted therapy-related reimbursement policies defer patients' benefits in RM HNSCC. The limitation of the total 18 cycles of cetuximab without maintenance has been executed since 2016 in Taiwan. In other countries, cetuximab maintenance plays an important role in improving survival and outcomes with tolerable adverse events <ns0:ref type='bibr'>(Wakasugi et al. 2015)</ns0:ref>.</ns0:p><ns0:p>The median duration of maintenance was 11 weeks in the EXTREME trial, 16 weeks in the realworld study in France, and 17 weeks in the real-world study of Portugal. Broadening the duration of the eligible patient population to the targeted therapies may be an effective way to improve the clinical outcomes of treatments.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions</ns0:head><ns0:p>Consistent administration of cetuximab provides potential clinical benefits in HNSCC patients at endemic carcinogen exposure area in the Asian population and hence, longer cetuximab maintenance is urgent and warranted in these patients with poor prognostics. Manuscript to be reviewed Manuscript to be reviewed Manuscript to be reviewed ORR: overall response rate; OS: overall survival; Q3W: every three weeks; AUC: area under the curve. Manuscript to be reviewed Manuscript to be reviewed </ns0:p><ns0:note type='other'>Figure 2</ns0:note><ns0:note type='other'>Figure 4</ns0:note></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>alive and without disease progression by the last follow-up visit were considered as potential right censoring subjects, and the follow-up interval were truncated at the end of study. Univariate and multivariable analyses by using the Cox proportional hazard model was preformed to analyze prognostic factors in cetuximab treatment. The factors for above analysis included age at initial diagnosis, location of primary sites, histological grade, pathological feature (margin, LVI, PNI, and ENE), tumor size, lymph node status, stage at initial diagnosis, previous treatment before cetuximab (surgery, chemotherapy, or CRT), combined regimen and dosage of chemotherapy. All p-values were considered significant if p < 0.05 and were two-sided. Statistical analyses were performed using STATA version 11 (STATA Corp., TX, USA).</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head>Figure 1 Figure 1 .</ns0:head><ns0:label>11</ns0:label><ns0:figDesc>Figure 1</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>Figure 2 .</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2. Progression-free survival curve.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head>Figure 3 Figure 3 .</ns0:head><ns0:label>33</ns0:label><ns0:figDesc>Figure 3</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head>Figure 4 .</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Figure 4. Subgroups analysis in CRT-refractory patients.</ns0:figDesc></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head /><ns0:label /><ns0:figDesc>Table 1. Baseline characteristics in the entire cohort (N=106).</ns0:figDesc><ns0:table><ns0:row><ns0:cell>1 2 Table 2. Treatment modality. Variables Age, years (mean ± SD) Alcohol Betel nuts Smoking Primary sites HPC OC OPC Grade 1 2 3 Unknown Margin positivity LVI, positive PNI, positive ENE, positive Tumor size T0 T1 T2 T3 T4 Lymph node status N0 N1 N2 N3 Stage at initial diagnosis I II III IV Variables Previous treatment Surgery Chemotherapy CRT CRT-refractory Erbitux applied reason Metastasis Recurrence Erbitux cycle, median (range) < 11 ≥ 11 Regimen of chemotherapy PF Taxane-based Others Platinum Cisplatin Carboplatin Chemotherapy dose 60/800 75/1000 Disease progressed ORR DCR Median PFS (months, 95% CI) All-cause mortality Median OS (months, 95% CI) 3 CRT: concurrent chemoradiotherapy; PF: cisplatin and fluorouracil; ORR: overall response rate; n (%) 55.1 ± 9.9 71 (67.0%) 76 (71.7%) 79 (74.5%) 21 (19.8%) 68 (64.1%) 17 (16.0%) 28 (26.4%) 57 (53.8%) 16 (15.1%) 5 (4.7%) 11 (10.4%) 4 (3.8%) 9 (8.5%) 5 (4.7%) 2 (1.9%) 14 (13.2%) 24 (22.6%) 16 (15.1%) 50 (47.2%) 27 (25.5%) 12 (11.3%) 56 (52.8%) 11 (10.4%) 9 (8.5%) 6 (5.7%) 11 (10.4%) 80 (75.5%) n (%) 83 (78.3%) 86 (81.1%) 85 (80.2%) 34 (32.1%) 65 (61.3%) 41 (38.7%) 11 (2-24) 46 (43.4%) 60 (56.6%) 76 (71.7%) 17 (16.0%) 13 (12.3%) 85 (80.2%) 5 (4.7%) 36 (34.0%) 57 (53.8%) 105 (99.1%) 30 (28.3%) 51 (48.1%) 5.00 (3.00-6.00) 68 (64.2%) 9.23 (7.03-13.84) 4 DCR: disease control rate; PFS: progression-free survival; OS: overall survival; 95% CI: 95% 2 1 5 confidence intervals.</ns0:cell></ns0:row></ns0:table><ns0:note>PeerJ reviewing PDF \| (2020:01:45342:1:1:NEW 20 Jun 2020)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_1'><ns0:head>Table 3 (on next page)</ns0:head><ns0:label>3</ns0:label><ns0:figDesc /><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_2'><ns0:head>Table 3 .</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Cox regression for disease progression.</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Manuscript to be reviewed</ns0:cell></ns0:row><ns0:row><ns0:cell>HPC: hypophyngeal cancer; OC: oral cavity cancer; OPC: oropharyngeal cancer; LVI:</ns0:cell></ns0:row><ns0:row><ns0:cell>lymphovascular invasion; PNI: perineural invasion; ENE: extranodal extension; CRT:</ns0:cell></ns0:row><ns0:row><ns0:cell>concurrent chemoradiotherapy; PF: cisplatin and fluorouracil; HR: hazard ratio; 95% CI: 95%</ns0:cell></ns0:row><ns0:row><ns0:cell>confidence intervals. Variables with p-value less than 0.2 in univariate analysis were</ns0:cell></ns0:row><ns0:row><ns0:cell>included in multivariate model.</ns0:cell></ns0:row></ns0:table><ns0:note>PeerJ reviewing PDF \| (2020:01:45342:1:1:NEW 20 Jun 2020)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_3'><ns0:head>.57 (1.03-6.43) 0.043</ns0:head><ns0:label /><ns0:figDesc /><ns0:table><ns0:row><ns0:cell>Chemotherapy dose</ns0:cell><ns0:cell>75/1000 vs. 60/800</ns0:cell><ns0:cell>0.90 (0.56-1.43)</ns0:cell><ns0:cell>0.644</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>Stage</ns0:cell><ns0:cell>II vs. I</ns0:cell><ns0:cell>1.66 (0.59-4.69)</ns0:cell><ns0:cell>0.339</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>III vs. I</ns0:cell><ns0:cell>1.76 (0.72-4.28)</ns0:cell><ns0:cell>0.214</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>IV vs. I</ns0:cell><ns0:cell>1.50 (0.75-3.02)</ns0:cell><ns0:cell>0.252</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>Surgery</ns0:cell><ns0:cell>With vs. without</ns0:cell><ns0:cell>0.80 (0.50-1.28)</ns0:cell><ns0:cell>0.354</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>Chemotherapy before target therapy</ns0:cell><ns0:cell>With vs. without</ns0:cell><ns0:cell>0.87 (0.53-1.42)</ns0:cell><ns0:cell>0.585</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>CRT-refractory</ns0:cell><ns0:cell>Yes vs. no</ns0:cell><ns0:cell>1.32 (0.87-1.99)</ns0:cell><ns0:cell>0.191</ns0:cell><ns0:cell>1.18 (0.72-1.91)</ns0:cell><ns0:cell>0.511</ns0:cell></ns0:row><ns0:row><ns0:cell>Erbitux applied reason</ns0:cell><ns0:cell>Metastasis vs. recurrence</ns0:cell><ns0:cell>1.002 (0.68-1.49)</ns0:cell><ns0:cell>0.992</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>Erbitux cycle, median (range)</ns0:cell><ns0:cell>≥ 11 vs. <11</ns0:cell><ns0:cell>0.19 (0.11-0.30)</ns0:cell><ns0:cell cols='2'><0.001 0.18 (0.09-0.33)</ns0:cell><ns0:cell><0.001</ns0:cell></ns0:row><ns0:row><ns0:cell>Regimen of chemotherapy</ns0:cell><ns0:cell>Taxane-based vs. PF</ns0:cell><ns0:cell>0.75 (0.44-1.29)</ns0:cell><ns0:cell>0.297</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>Others vs. PF</ns0:cell><ns0:cell>0.85 (0.47-1.54)</ns0:cell><ns0:cell>0.591</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>Platinum</ns0:cell><ns0:cell>Carboplatin vs. Cisplatin</ns0:cell><ns0:cell>0.55 (0.22-1.39)</ns0:cell><ns0:cell>0.206</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell /></ns0:row></ns0:table><ns0:note>PeerJ reviewing PDF \| (2020:01:45342:1:1:NEW 20 Jun 2020)Manuscript to be reviewed</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_4'><ns0:head>Table 3 .</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Cox regression for disease progression. HPC: hypophyngeal cancer; OC: oral cavity cancer; OPC: oropharyngeal cancer;</ns0:figDesc><ns0:table><ns0:row><ns0:cell>cisplatin and fluorouracil; HR: hazard ratio; 95% CI: 95% confidence intervals.</ns0:cell></ns0:row><ns0:row><ns0:cell>Variables with p-value less than 0.2 in univariate analysis were included in multivariate model.</ns0:cell></ns0:row></ns0:table><ns0:note>LVI: lymphovascular invasion; PNI: perineural invasion; ENE: extranodal extension; CRT: concurrent chemoradiotherapy; PF: PeerJ reviewing PDF \| (2020:01:45342:1:1:NEW 20 Jun 2020)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_5'><ns0:head>Table 4 (on next page)</ns0:head><ns0:label>4</ns0:label><ns0:figDesc /><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_6'><ns0:head>Table 4 .</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Cox regression for overall mortality.</ns0:figDesc><ns0:table><ns0:row><ns0:cell>HPC: hypophyngeal cancer; OC: oral cavity cancer; OPC: oropharyngeal cancer; LVI:</ns0:cell></ns0:row><ns0:row><ns0:cell>lymphovascular invasion; PNI: perineural invasion; ENE: extranodal extension; CRT:</ns0:cell></ns0:row><ns0:row><ns0:cell>concurrent chemoradiotherapy; PF: cisplatin and fluorouracil; HR: hazard ratio; 95% CI: 95%</ns0:cell></ns0:row><ns0:row><ns0:cell>confidence intervals. Variables with p-value less than 0.2 in univariate analysis were</ns0:cell></ns0:row><ns0:row><ns0:cell>included in multivariate model.</ns0:cell></ns0:row></ns0:table><ns0:note>PeerJ reviewing PDF \| (2020:01:45342:1:1:NEW 20 Jun 2020)Manuscript to be reviewed</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_7'><ns0:head>.09 (1.11-3.92) 0.022 4.79 (1.55-14.77) 0.006 N3</ns0:head><ns0:label /><ns0:figDesc /><ns0:table><ns0:row><ns0:cell>vs. N0</ns0:cell><ns0:cell>1.92 (0.76-4.88)</ns0:cell><ns0:cell>0.170</ns0:cell><ns0:cell>7</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_8'><ns0:head>.34 (1.85-29.16) 0.005</ns0:head><ns0:label /><ns0:figDesc /><ns0:table><ns0:row><ns0:cell>Chemotherapy dose</ns0:cell><ns0:cell>75/1000 vs. 60/800</ns0:cell><ns0:cell>1.19 (0. 66-2.17)</ns0:cell><ns0:cell>0.564</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>Stage</ns0:cell><ns0:cell>II vs. I</ns0:cell><ns0:cell>2.75 (0.79-9.51)</ns0:cell><ns0:cell>0.110</ns0:cell><ns0:cell>1.69 (0.19-15.31)</ns0:cell><ns0:cell>0.640</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>III vs. I</ns0:cell><ns0:cell>0.85 (0.23-3.18)</ns0:cell><ns0:cell>0.812</ns0:cell><ns0:cell>0.15 (0.02-1.42)</ns0:cell><ns0:cell>0.098</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>IV vs. I</ns0:cell><ns0:cell>1.56 (0.62-3.91)</ns0:cell><ns0:cell>0.341</ns0:cell><ns0:cell>0.14 (0.02-1.08)</ns0:cell><ns0:cell>0.060</ns0:cell></ns0:row><ns0:row><ns0:cell>Surgery</ns0:cell><ns0:cell>With vs. without</ns0:cell><ns0:cell>0.66 (0.38-1.13)</ns0:cell><ns0:cell>0.127</ns0:cell><ns0:cell>0.83 (0.46-1.51)</ns0:cell><ns0:cell>0.541</ns0:cell></ns0:row><ns0:row><ns0:cell>Chemotherapy before target therapy</ns0:cell><ns0:cell>With vs. without</ns0:cell><ns0:cell>1.25 (0.64-2.46)</ns0:cell><ns0:cell>0.517</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>CRT-refractory</ns0:cell><ns0:cell>Yes vs. no</ns0:cell><ns0:cell>1.20 (0.73-1.98)</ns0:cell><ns0:cell>0.479</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>Erbitux applied reason</ns0:cell><ns0:cell>Metastasis vs. recurrence</ns0:cell><ns0:cell>1.16 (0.70-1.91)</ns0:cell><ns0:cell>0.561</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>Erbitux cycle, median (range)</ns0:cell><ns0:cell>≥ 11 vs. <11</ns0:cell><ns0:cell>0.46 (0.28-0.75)</ns0:cell><ns0:cell>0.002</ns0:cell><ns0:cell>0.48 (0.27-0.84)</ns0:cell><ns0:cell>0.010</ns0:cell></ns0:row><ns0:row><ns0:cell>Regimen of chemotherapy</ns0:cell><ns0:cell>Taxane-based vs. PF</ns0:cell><ns0:cell>0.75 (0.38-1.49)</ns0:cell><ns0:cell>0.417</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>Others vs. PF</ns0:cell><ns0:cell>0.90 (0.43-1.89)</ns0:cell><ns0:cell>0.777</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>Platinum</ns0:cell><ns0:cell>Carboplatin vs. Cisplatin</ns0:cell><ns0:cell>0.51 (0.16-1.64)</ns0:cell><ns0:cell>0.260</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell /></ns0:row></ns0:table><ns0:note>PeerJ reviewing PDF \| (2020:01:45342:1:1:NEW 20 Jun 2020)Manuscript to be reviewed</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_9'><ns0:head>Table 4 .</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Cox regression for overall mortality. HPC: hypophyngeal cancer; OC: oral cavity cancer; OPC: oropharyngeal cancer; LVI: lymphovascular invasion; PNI: perineural invasion; ENE: extranodal extension; CRT: concurrent chemoradiotherapy; PF: cisplatin and fluorouracil; HR: hazard ratio; 95% CI: 95% confidence intervals.Variables with p-value less than 0.2 in univariate analysis were included in multivariate model.</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Manuscript to be reviewed</ns0:cell></ns0:row></ns0:table><ns0:note>PeerJ reviewing PDF \| (2020:01:45342:1:1:NEW 20 Jun 2020) PeerJ reviewing PDF \| (2020:01:45342:1:1:NEW 20 Jun 2020)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_10'><ns0:head>Table 6 (on next page)</ns0:head><ns0:label>6</ns0:label><ns0:figDesc /><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_11'><ns0:head>Table 6 .</ns0:head><ns0:label>6</ns0:label><ns0:figDesc>Comparisons between different trials of cetuximab-based chemotherapy.</ns0:figDesc><ns0:table /></ns0:figure> </ns0:body> "	"Rebuttal letters Reviewer 1 (Anonymous) Comments Overall, this is a well written article that clearly explains the important real-world data of cetuximab-containing treatment in RM HNSCC. Authors should consider providing high resolution images for all figures. Reply Thank you for your generous comments, we have revised all figures with higher resolution images. Reviewer 2 (Anonymous) In their manuscript the authors retrospectively analyze the efficacy of cetuximab based therapy in 106 Taiwanese patients suffering from R/M HNSCC in a real-world setting. Since the outcome parameters were comparable to what has been published in the literature, the authors conclude that cetuximab is effective in this population as well. Although it is a strength of this manuscript provide real-world data in a population with a high percentage of carcinogen exposure, a couple of questions arose, when reviewing the manuscript: Comments The introduction section states that “oropharyngeal SCC accounts for the largest group of head and neck cancers related to HPV” (line 72) and that the most predominant site in Taiwan is OPC. However, it has to be noted that the most predominant HNSCC in western countries is OPC as well, which is usually HPV negative (see Cramer et al. Nature Rev.Cancer or for epidemiology data extraction from https://gco.iarc.fr/; or Gatta et al. EJC 2015 for Europe). Only a minority of the patients in the EXTREME trial were HPV positive (Vermorken, Annals of Oncology 2014). Thus, the introduction should be revised accordingly. Reply Thank you for the suggestions. First, we have rewritten the description in the Induction section as follow (line 70-72). “In western countries, oropharyngeal SCC accounts for the largest group of head and neck cancers, with a minority of the patients related to human papillomavirus (HPV) infection” Second, we have added on related references as follow. Gatta G, Botta L, Sánchez MJ, Anderson LA, Pierannunzio D, Licitra L; EUROCARE Working Group. 2015. Prognoses and improvement for head and neck cancers diagnosed in Europe in early 2000s: The EUROCARE-5 population-based study. European Journal of Cancer 51:2130-2143. Comments The inclusion/exclusion criteria are poorly defined in the method section: Were patients included, which were platinum resistant? This seems to be likely, since patients resistant to CRT were included. Why were those patients not excluded? The EXTREME or TPEX trials excluded those patients. Why were patients included, who did not receive platinum based first line therapy such as MTX (line 145). This decision results in a heterogeneous population and a potential bias. The results are hardly comparable to the Extreme trial or the trials listed in table 5. All of them including the European real-world trial evaluated a well-defined homogenous population. Reply Thank you for your comments. We included platinum resistant patients for analysis because this population account for a significant portion of RM-HNSCC patients in real-world daily practice. Since our study aimed to show real-world data and clinical status, this subgroup was also analyzed. Related discussion was also made. We have added some explanations in Discussion section as follow(line 324-329). The heterogeneous study population is also an issue. Unlike the EXTREME or TPEX studies which excluded CRT-refractory patients, we included CRT-refractory patients for analysis. Besides, patients who received non-platinum chemotherapy regimens, including taxane and MTX, were also included. Heterogeneity of study population may confound the analysis. However, our findings revealed the real-world condition in term of financial burden of novel treatment, which lead to absence of cetuximab maintenance. Comments Likewise the ORR is considerably lower in this study compared to the aforementioned trials and especially to the Japanese trial. This should be stated and discussed accordingly. Reply Thank you for your suggestions. After checking the data, the ORR is 28.3%. We have corrected it in the revised manuscript. The reason why our ORR is lower than other studies may contribute to high percentage of carcinogen exposure, especially betel nuts (which is not shown in other studies). We have related statement in Discussion section of revised manuscript as follow (line 301-307). As compared to the aforementioned Asian trial, including Japanese (Tahara et al. 2016) and Chinese trial (Guo Y et al. 2014), the ORR of our study is slightly lower, which may relate to usage of cetuximab maintenance, different regimens of chemotherapy, and patient population with distinct endemic carcinogen exposure. The patients of Japanese trial received cetuximab maintenance and chemotherapy with carboplatin and paclitaxel. However, there was nearly no effect of betel nuts in the Japanese population. The effects of carcinogen were also not mentioned in the Chinese and Korean population. Comments It stated in the manuscript that cetuximab exposure <11 cycles is an independent risk factor for progression. However, the reason for cetuximab discontinuation is not given. It is very likely that cetuximab was stopped because of disease progression. Therefore, disease progression might be the reason for cetuximab exposure <11 cycles or at least the very same endpoint and not the other way round?? What would figure 2A look like, when plotting the PFS curve in patients with at least SD vs. patients with PD? Reply Thank you for your comments. We agreed that some patients failed to continue cetuximab due to disease progression. To address the immortal time bias and reverse causality, we performed a landmark study. The result supported more cetuximab cycles was associated with better outcome. We have added related description in revised manuscript. The related description is as follow (line 232-236). Next, we applied landmark method for further validation. Since the response could observes within the first 3 months following cetuximab exposure, a 3-months landmark was used. After excluding the patients who progressed or died within the three months, the patients with more cycles of cetuximab (≥ 11 cycles) still showed better median PFS (8 months vs 2 months, p = 0.057) and OS (13.9 months vs 5.07 months, p=0.0002) than the patients treated with less cycles of cetuximab. 2. We analyzed the PFS of patients with at least SD vs. patients with PD. The median PFS of patients with PD was 3 months. We have attached this figure as follow. However, there were few impacts on our conclusion. Comments The adverse event rate should be included in a real-world report and compared to the literature. There are studies in HNSCC that show that cetuximab can cause severe lung injuries in Asian patients (Nakano et al, Head and neck 2019). Was ILD observed in this population? Reply Thank you for your suggestion. We have added a paragraph for Safety and Tolerability in revised manuscript (line 276-283), a Table 5 summarized AEs was also added. Besides, we also add the description with “Adverse events were monitored weekly throughout the study and evaluated using Common Terminology Criteria for Adverse Events version 4.0.” in the revised manuscript (line 164-166). Comments Additional ref. should be included and discussed such as Guo et al. Head and Neck 2014, who performed a cetuximab/platinum R/M HNSCC first line trial in a Chinese population. Reply Thank you for your suggestions. We have added this study in Discussion section and revised Table 6. Comments Revision with respect to English grammar is advised (i.e. line 322-324 is hardly comprehensible) Reply Thank you for your suggestions. We have rewritten the description in the revised manuscript as follow (line 339-341). '..., cetuximab targeting EGFR on HNSCC cells can induce potent antibody dependent cell-mediated cytotoxicity, which can further augment anti-tumor effect when combined with chemotherapy (Specenier & Vermorken 2013).' Reviewer 3 (Vancheswaran Gopalakrishnan) Basic reporting Comments The first sentence of the ‘results’ section in the abstract does not add much value. It would rather be more interesting for the reader to get a sense of what proportion of total HNSCC patients developed recurrent or metastatic disease within the timeframe of the study. Reply Thank you for your suggestions. We have revised this part in revised manuscript as follow (line 191-193). The clinical data of 106 patients (including 99 males and 7 females) with a median age of 55.1 years were collected for this study. Among these patients, 65 patients (61.3%) had metastatic disease and 41 patients (38.4%) had recurrent disease while initiation of cetuximab. Comments Lines 85 – 87: It is misleading to suggest that Cetuximab is first-line therapy for RM HNSS across the board. The authors should clarify the rationale for choosing Cetuximab therapy when CPS <0 Reply Thank you for your suggestions. We have revised this part in the revised manuscript as follow(line 85-87). Cetuximab, an IgG1 chimeric monoclonal antibody targeting EGFR, has been the one of the first-line treatments in RM HNSCC patients with low programmed death ligand 1 (PD-L1) expression (Burtness et al. 2019; Vermorken et al. 2008). We also added related reference as follow: Reference: Burtness B, Harrington KJ, Greil R, Soulières D, Tahara M, de Castro G Jr, Psyrri A, Basté N, Neupane P, Bratland Å, Fuereder T, Hughes BGM, Mesía R, Ngamphaiboon N, Rordorf T, Wan Ishak WZ, Hong RL, González Mendoza R, Roy A, Zhang Y, Gumuscu B, Cheng JD, Jin F, Rischin D; KEYNOTE-048 Investigators. 2019. Pembrolizumab alone or with chemotherapy versus cetuximab with chemotherapy for recurrent or metastatic squamous cell carcinoma of the head and neck (KEYNOTE-048): a randomised, open-label, phase 3 study. Lancet 394:1915-1928. Comments Line 99-102: Please fix grammar Reply Thank you for your suggestions. We have revised this part in the revised manuscript as follow (line 101-104). “Different from clinical trials which can achieve therapeutic efficacy with cetuximab maintenance, patients in real life were not affordable continuous maintenance with high-cost cetuximab to control their diseases. Therefore, making modification of treatment protocol became a possible strategy (Hsu & Lu 2016; Shih et al. 2015).” Comments Line 171 – multivariable Reply Thank you for your comment. We have corrected this error. Comments Line 197-198 – Please add percentage Reply Thank you for your suggestion. We have revised this part in the revised manuscript as follow(line 201-202). With respect to prior treatment before cetuximab treatment, most patients experience various HNSCC treatment including surgery (78.3%), chemotherapy (81.1%) and CRT (80.2%). Comments Line 214: Table 2 does not summarize ORR. Have the authors missed including a table Reply Thank you for your comment. We summarized the ORR and DCR in Table 2. Comments Line 222: The ‘median’ PFS was calculated as 6 months. This has been overlooked in multiple places Reply Thank you for your comment. We revised the manuscripts in multiple places and marked them in red. Comments Line 300: Not sure how the conclusion of more frequent visits can be drawn from this dataset Reply Thank you for your comment. During out follow-up period, the more frequent visits may help the physicians to detect progression of disease with endoscopy or physical examination, and arrangement of image examination. Therefore, we assumed more frequent visits would provide more detailed and timely detection Experimental design Comments 1) Line 124: Were interviews conducted in all patients in the cohort? If not, this could lead to information bias, and preferentially higher recall in these patients compared to those who were recruited on the basis of EMR alone. Reply Thank you for your comments. We have added related description in the revised manuscript as follow(line 151-156). All our patients followed regularly at outpatient department of medical oncology and department (OPD) of otorhinolaryngology. During cetuximab treatment period, the patients visited OPD of medical oncology weekly and otorhinolaryngology monthly. The evaluation of disease status included tumor site inspection, laboratory text, and imagine studies. Treatment response was assessed and determined by computed tomography (CT) or magnetic resonance imaging(MRI) at baseline (before cetuximab) and at 3-month intervals after the start of treatment. Comments 2) Besides OS, and PFS, methods to assess categorical responses (CR, PR ,SD,PD) have not been detailed in the methods. Why was the RECIST methodology not used (PMC2785927, PMC5373019)? Additionally, was response determined at the time of first restaging or was this best objective response? Reply Thank you for your suggestions. We apologized for ambiguity for response criteria. We provided the criteria for assessment of categorical responses in our manuscript. We modified to” RECIST version 1.1 was used to determine tumor response and disease progression (line 157-158). We also added related description as follow (line 159-162). The treatment response of patients was classified into four categories: complete response (CR, disappearance of all target lesions), partial response (PR, decrease in target lesion diameter sum > 30%), progression disease (PD, increase in target lesion diameter sum > 20%), and stable disease (SD, does not meet other criteria).” Previous studies have shown that the RECIST criteria are comparable to the WHO criteria in evaluating the response, so we supposed RECIST version 1.1 was optimal for our study (Choi JH et al. 2005; Subbiah et al. 2017). The response was evaluated based on best objective response during cetuximab. We have added related description in the revised manuscript (line 162-163). The calculation of overall response rate (ORR) was based on the best objective response achieved during cetuximab treatment. References: Choi JH, Ahn MJ, Rhim HC, Kim JW, Lee GH, Lee YY, Kim IS. 2005. Comparison of WHO and RECIST criteria for response in metastatic colorectal carcinoma. Cancer Research and Treatment 37:290-293. Subbiah V, Chuang HH, Gambhire D, Kairemo K. 2017. Defining Clinical Response Criteria and Early Response Criteria for Precision Oncology: Current State-of-the-Art and Future Perspectives. Diagnostics (Basel) 7: pii: E10. Comments 3) Line 160: Unclear to the reviewer which patients represent the control group? Please refrain from using ambiguous terminology - this is a retrospective cohort study Reply Thank you for your suggestion. We apologized for the ambiguous terminology. There is no control group in our study. Therefore, we revised our manuscript to “To answer above questions, we conducted this retrospective and single-arm study…” in line 107-108 of “Introduction” section, and ” This was an observational, retrospective, single-center, single-arm study” in line 124 of “Study Design” section. Comments 4) What was the median time interval between time of start of treatment and determination of response in all patients? What was the median number of cycles in all patients after which response was determined? Reply The median time interval between time of start of treatment and determination of response is three months. When the response was determined as progression (after the first three months), there was no longer use of cetuximab. When the response was determined as SD, PR, and CR, the median number of cycles was 8.3. Comments 5) Line 169-170. Unclear what the rationale is for excluding patients who were alive and without disease progression at the time of last follow-up – this is not a typical censoring strategy. In fact, it would lead to overestimation of the true hazard ratio Reply Thank you for your suggestion. We apologized for the ambiguity. We have added related description in the revised manuscript as follow (line 179-181). Patients alive and without disease progression by the last follow-up visit were considered as potential right censoring subjects, and the follow-up interval were truncated at the end of study. Comments 6) Model building strategy is confusing. How were variables selected for inclusion in the final models? Additionally, multivariable models for both PFS and OS seem to be overfit, as there are likely more variables in the model than the number of events would allow. This reviewer recommends a best subsets or stepwise regression for more prudent model building Reply The variables obtained a p-value less than 0.2 in univariate Cox regression analysis were included in in the multivariate model. The statistical criteria for multivariate model inclusion using p-value less than 0.2 has been widely used in various studies (Chiu et al. 2019, Wei et al. 2018). This is because a p value that is adjusted upwards can reduce the risk of incorrectly declaring a statistical significance and thus avoid type 2 error (Chen et al. 2017, Althouse 2016). References: Chiu CH, Wang CY, Moi SH, Wu CH, Yang CH, and Chen JB. 2019. Comparison of tunneled central venous catheters and native arteriovenous fistulae by evaluating the mortality and morbidity of patients with prevalent hemodialysis. Journal of the Formosan Medical Association 118:807-814. Wei S, Gonzalez Rodriguez E, Chang R, Holcomb JB, Kao LS, Wade CE; PROPPR Study Group. 2018. Elevated Syndecan-1 After Trauma and Risk of Sepsis: A Secondary Analysis of Patients From the Pragmatic, Randomized Optimal Platelet and Plasma Ratios (PROPPR) Trial. Journal of the American College of Surgeons 227:587-595. Chen SY, Feng Z, Yi X. 2017. A general introduction to adjustment for multiple comparisons. Journal of Thoracic Disease 9:1725-1729. Althouse AD. 2016. Adjust for Multiple Comparisons? It's Not That Simple. The Annals of Thoracic Surgery 101:1644-1645. Validity of the findings Comments 7) The results section is poorly written and is merely a line-by-line recapitulation of thee tables themselves. As such, the leader struggles with a lack of perspective. Along the same lines Tables 1 and 2 are largely uninformative. Since the authors seem to pivot around number of cycles of cetuximab CRT refraction, it would be interesting to see for instance what the distribution is of demographic characteristics is between >11 and <11 treatment cycles. Reply Thank you for your suggestion. We have rewritten the Result section. We analyzed the distribution of demographic characteristics between >11 and <11 cetuximab treatment cycles. We provided the analysis as Supplemental Table S2 and Table S3. We also added “The distributions of demographic characteristics between >11 and <11 treatment cycles were shown in Supplemental Table S1 and Table S2.“ in line 212-213 of “Treatment Modality” section. Comments 8) The use of a binary variable for CRT use, as well as a separate variable for CRT refraction seems redundant. This reviewer recommends using CRT refraction only. Additionally, a large majority of patients were Stage IV at initial diagnosis. Were the response rates better in patients with stage I – III at initial diagnosis? Why were earlier stage patients included in the analysis? Reply Thank you for your suggestion. We have done analysis based on your suggestion. The results are shown as Table 3 and Table 4 in the revised manuscript. We have analyzed the ORR and DCR between stage I – III and stage IV and provided as Supplemental Table S3. We also added” The treatment responses according to different stages were shown in Supplemental Table S3.” in line 221-222 of “Treatment Outcomes” section. Comments for the Author Comments In their work entitled, ‘ Consistent administration of cetuximab is associated with favorable outcomes in recurrent/metastatic head and neck squamous cell carcinoma at endemic carcinogen exposure area: a retrospective observational study’, Wang et al. have studied factors associated with survival in a cohort of RM HNSCC patients treated with Cetuximab in a region of high endemic carcinogen exposure. The authors have specifically reported an association between CRT-refraction and number of Cetuximab cycles on treatment response. While the analysis itself is not novel, the choice of study population with endemic carcinogenic habits, and lack of drug accessibility is potentially interesting. That being said none of the endemic factors seem to impact survival in the cohort – in fact smoking seems to have a protective effect which is odd. Furthermore, several issues exist as have been detailed Reply Thank you for your comments. Our study provides the different views and observations of real-world data from the endemic carcinogen exposure area. In addition, our study included a significant portion of patients with oral cavity cancer, advanced disease status and carcinogen exposure. Besides, we also demonstrate the significantly differences between >11 and <11 cetuximab treatment cycles, which also emphasize that the prolongation of cetuximab plays important role in RM HNSCC treatments. Although minor part of our analysis didn’t show the results as we expected, our data reflected the different perspectives from different ethics and backgrounds of lifestyles. However, studies from more populations and case numbers are warranted in the future. "	Here is a paper. Please give your review comments after reading it.
531	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Background. This study aimed to analyze the clinical outcomes associated with patients with recurrent/metastatic head and neck squamous cell carcinoma (RM HNSCC) who received cetuximab-based chemotherapy in a real-world clinical setting. Methods. Clinical data were extracted from RM HNSCC patients diagnosed between 2016 and 2019.</ns0:p><ns0:p>Kaplan-Meier survival estimates and Cox proportional hazards model were used for survival analyses. Results. Of 106 RM HNSCC patients (mean age = 55.1 years), 38.7% exhibited recurrent disease and 61.3% had metastatic disease. The majority of patients showed a habit of addictive substance use, including alcohol (67.0%), betel nuts (71.7%), or tobacco (74.5%). The primary tumor sites included the oral cavity (64.1%), hypopharynx (<19.8%), and oropharynx (16.0%). The median number of cetuximab cycles for the 106 patients was 11 (2-24). The disease control rate (DCR) was 48.1%, and the overall response rate (ORR) was 28.3%. The median progression-free survival (PFS) and overall survival (OS) were 5.0 and 9.23 months, respectively. Patients treated with more than 11 cycles of cetuximab exhibited a longer median PFS and median OS than did patients treated with less than 11 cycles (median PFS: 7.0 vs. 3.0 months, p < 0.001; OS: 12.43 vs. 4.46 months, p = 0.001). Patients without previous concurrent chemoradiotherapy (CRT) had a better median PFS than did those with previous CRT (6.0 vs. 4.0 months, p = 0.046).</ns0:p><ns0:p>Multivariable analysis revealed that perineural invasion and fewer cycles of cetuximab (<11 cycles) were independent risk factors associated with disease progression. In</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>Introduction</ns0:head><ns0:p>Head and neck squamous cell carcinoma (HNSCC) is the sixth most common malignancy in the world; recurrent and/or metastatic head and neck squamous cell carcinoma (RM-HNSCC) harbors lethal clinical features and dismal medical outcomes <ns0:ref type='bibr' target='#b30'>(Parkin et al. 2005)</ns0:ref>. Over 90% of head and neck cancers are squamous cell carcinomas that develop from the mucosa of the oral cavity, oropharynx, larynx, or hypopharynx <ns0:ref type='bibr' target='#b39'>(Warnakulasuriya 2009)</ns0:ref>. In Western countries, a subgroup of oropharyngeal SCC is related to human papillomavirus (HPV) infection <ns0:ref type='bibr' target='#b14'>(Gatta et al. 2015</ns0:ref><ns0:ref type='bibr' target='#b15'>, Gillison et al. 2000)</ns0:ref>. However, oral cavity SCC is the most predominant site of head and neck cancer in Taiwan due to high prevalence of betel nut consumption <ns0:ref type='bibr' target='#b3'>(Belcher et al. 2014;</ns0:ref><ns0:ref type='bibr' target='#b8'>Chang et al. 2017)</ns0:ref>. Virus-induced HNSCC in Western countries is different from its Taiwanese counterpart in that the mechanism of tumorigenesis of HNSCC in Taiwan is mainly related to carcinogens and addictive substances, including alcohol, betel nuts, and tobacco (Cancer 2012).</ns0:p><ns0:p>These carcinogen-related HNSCCs harbor higher Ras oncogene mutations and increased chromosome instability, suggesting that the genetic background and clinical features may be unique to these patients <ns0:ref type='bibr' target='#b9'>(Chang et al. 1991;</ns0:ref><ns0:ref type='bibr' target='#b26'>Kuo et al. 1994;</ns0:ref><ns0:ref type='bibr' target='#b31'>Riaz et al. 2014)</ns0:ref>.</ns0:p><ns0:p>Epidermal growth factor receptor (EGFR) is usually upregulated with increased levels of its ligand transforming growth factor alpha (TGF-α) in most HNSCCs, with both proteins contributing to the carcinogenesis of HNSCC <ns0:ref type='bibr' target='#b17'>(Grandis 2007)</ns0:ref>. Upregulation of EGFR is an independent poor prognostic factor in HNSCCs <ns0:ref type='bibr' target='#b2'>(Ang et al. 2004;</ns0:ref><ns0:ref type='bibr' target='#b10'>Dassonville et al. 1993)</ns0:ref>.</ns0:p><ns0:p>Cetuximab, an IgG1 chimeric monoclonal antibody targeting EGFR, was one of the first-line treatments for RM HNSCC patients with low programmed death ligand 1 (PD-L1) expression <ns0:ref type='bibr'>(Burtness et al. 2019;</ns0:ref><ns0:ref type='bibr' target='#b36'>Vermorken et al. 2008)</ns0:ref>.The addition of cetuximab to platinum-based chemotherapy with fluorouracil (platinum-fluorouracil) improved the overall response rates, median progression-free survival (PFS), and overall survival (OS) compared with chemotherapy alone. Another combination of cetuximab with chemotherapy agents such as taxane also demonstrated substantial benefits <ns0:ref type='bibr' target='#b0'>(Adkins et al. 2018;</ns0:ref><ns0:ref type='bibr' target='#b13'>Friesland et al. 2018;</ns0:ref><ns0:ref type='bibr' target='#b20'>Guigay et al. 2019)</ns0:ref>.</ns0:p><ns0:p>However, most of these clinical trials were conducted in Western countries with fewer patients with primary oral cavity cancer; data regarding the effect of carcinogens such as betel nuts on outcome are very limited. In addition, the percentage of HPV infection status is quite different between Asian and Western countries, suggesting distinct tumor microenvironments <ns0:ref type='bibr' target='#b38'>(Wang et al. 2019)</ns0:ref>.</ns0:p><ns0:p>In Taiwan, cetuximab combined with systemic chemotherapy has been indicated as first line treatment in patients with RM HNSCC by the National Health Insurance since 2016. After receiving approval for application, the patients can receive cetuximab-containing treatment without copayment. Because of limited financial resources, cetuximab can only be administered Manuscript to be reviewed in a total of eighteen cycles if no progression is noted. Unlike clinical trials that provide subjects with maintenance cetuximab, patients in real life cannot afford continuous maintenance with high-cost cetuximab to control their disease. Therefore, modifying the treatment protocol wound be a possible strategy <ns0:ref type='bibr' target='#b22'>(Hsu & Lu 2016;</ns0:ref><ns0:ref type='bibr' target='#b33'>Shih et al. 2015)</ns0:ref>. Nevertheless, the impact of modifications such as limiting cetuximab treatment cycle on patient outcome remains unknown.</ns0:p><ns0:p>Moreover, real-world data on cetuximab in RM HNSCC patients with high percentages of exposure to various carcinogen remains are also very limited. To answer these questions, we conducted this retrospective and single-arm study to analyze clinical data, hoping to determine the clinical outcomes and prognostic factors in this subset of RM HNSCC patients.</ns0:p></ns0:div> <ns0:div><ns0:head>Materials and methods</ns0:head></ns0:div> <ns0:div><ns0:head>Patient characteristics</ns0:head><ns0:p>Clinicopathological data of patients with HNSCC were confirmed by pathological examination of specimens from biopsy or surgery, and the positive samples were collected and analyzed. A total of 106 cases of RM HNSCC were identified with metastasis or recurrence and were deemed unsuitable for locoregional curative treatment at the Kaohsiung Medical University Hospital. The inclusion criteria included: age at diagnosis 20 years or older; tumor histology of squamous cell carcinoma (grade 1 to grade 3); ICD-9 site code-specific for the oral cavity (OC), hypopharynx (HPC), oropharynx (OPC), and larynx; and treatment with cetuximab from January 2016 to April 2019. The exclusion criteria included secondary malignancy; tumor histology of carcinoma in situ; and SCC of the nasopharynx or salivary glands.</ns0:p></ns0:div> <ns0:div><ns0:head>Study design</ns0:head><ns0:p>This was an observational, retrospective, single-center, single-arm study, and the treatment schema is shown in Fig. <ns0:ref type='figure'>1</ns0:ref>. The collected medical and demographic data included age, gender, alcohol, betel nut usage, tobacco habits, and other clinical parameters obtained from the medical records or interviews with patients. The clinicopathological factors included types and grade of histology, size of tumor, lymph node status, surgical margin, perineural invasion, lymphovascular invasion, and extranodal extension. We defined CRT (chemoradiotherapy)refractory patients as patients with disease progression during CRT or within three months of the end of CRT. The primary endpoints were median OS and PFS. Specifically, the median OS and PFS (defined as the time from registration to objective disease progression or death from any cause) were determined after the addition of cetuximab to chemotherapy. Other endpoints included the assessment of treatment response and disease control. This study was approved by the Institutional Review Board and Ethics Committee of Kaohsiung Medical University Hospital (KMUHIRB-E(II)-20190357). The data were analyzed anonymously, and therefore, no Manuscript to be reviewed additional informed consent was required. All methods were performed in accordance with approved guidelines and regulations.</ns0:p></ns0:div> <ns0:div><ns0:head>Treatment</ns0:head><ns0:p>All patients received cetuximab (250 mg/m 2 ) weekly with a loading dose of 400 mg/m 2 until disease progression was noted. The regimen of chemotherapy included PF 75/1000 (cisplatin at 75 mg/m 2 or carboplatin at AUC=5 every 3 weeks plus fluorouracil at 1,000 mg/m 2 /d for 4 days every 3 weeks), PF 60/800 (cisplatin at 60 mg/m 2 or carboplatin at AUC5 every 3 weeks plus fluorouracil at 800 mg/m 2 /d for 4 days every 3 weeks), taxane-based chemotherapy (docetaxel and cisplatin 75 mg/m 2 both at day 1 and every 3 weeks for four courses of paclitaxel 80 mg/m 2 weekly), and MTX (methotrexate 40 mg/m 2 weekly). The patients could receive chemotherapy or concurrent chemoradiotherapy with weekly cisplatin administration previously before recruitment.</ns0:p></ns0:div> <ns0:div><ns0:head>Treatment Response and Safety Assessment</ns0:head><ns0:p>All patients were followed regularly as outpatients at the medical oncology and department (OPD) of otorhinolaryngology. During the cetuximab treatment period, the patients visited the OPD of medical oncology weekly and that of otorhinolaryngology monthly. The evaluation of disease status included tumor site inspection, laboratory text, and imaging studies. Treatment response was assessed and determined using computed tomography (CT) or magnetic resonance imaging (MRI) at baseline (before cetuximab) and at 3-month intervals after treatment was started. Imaging within 4 weeks before cetuximab was acceptable, and imaging could be performed whenever clinical physicians suspected disease progression. RECIST version 1.1 was used to determine disease progression and tumor response.</ns0:p><ns0:p>The treatment response of patients was classified into four categories: complete response (CR, disappearance of all target lesions), partial response (PR, decrease in target lesion diameter sum > 30%), progression disease (PD, increase in target lesion diameter sum > 20%), and stable disease (SD, does not meet other criteria). The calculation of overall response rate (ORR), including patients classified as having complete and partial responses, was based on the best objective response achieved during cetuximab treatment. The calculation of disease control rate (DCR) included patients classified as having complete response, partial response, and stable disease. After disease progression, further treatments and survival status were documented every 3 months. Regarding safety assessment, treatment-related adverse events were monitored weekly throughout the study and were evaluated using Common Terminology Criteria for Adverse Events version 4.0.</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:01:45342:2:1:NEW 12 Aug 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Statistical Analysis</ns0:head><ns0:p>The primary goal of the study was to analyze the outcome of cetuximab-based chemotherapy in recurrent or metastatic settings, including a comparison between median PFS and OS among patients receiving various cycles of cetuximab and regimens of chemotherapy.</ns0:p><ns0:p>The location of primary sites (OC, OPC, or HPC), histological grade (Grades 1, 2, 3), tumor size and status (T1, T2, T3, T4), lymph node status (N0, N1, N2, N3), stage at initial diagnosis (I, II, III, or IV), surgery status (with or without previous surgery), CRT (with or without previous CRT), and chemotherapy before cetuximab therapy (with or without prior chemotherapy) were all included for analysis. Between-group comparisons were analyzed using Fisher's exact test and Pearson's chi-square test for various categorical variables. We calculated median PFS and OS using Kaplan−Meier analysis, and we analyzed differences between the curves using the logrank test. We defined the median PFS as the time between the start of disease progression and treatment, including disease progression or death. Patients alive and without disease progression by the final follow-up visit were considered potential right censoring subjects, and the follow-up interval was truncated at the end of study. Univariate and multivariable analyses using the Cox </ns0:p></ns0:div> <ns0:div><ns0:head>Results</ns0:head></ns0:div> <ns0:div><ns0:head>Baseline characteristics of patients</ns0:head><ns0:p>Clinical data from 106 patients (99 males and 7 females) with a median age of 55.1 years were collected. Among these patients, 65 patients (61.3%) had metastatic disease and 41 patients (38.4%) had recurrent disease with initiation of cetuximab. Almost all patients had addictions to alcohol or betel nuts or history of smoking, including 61 patients (57.5%) with exposure to all three carcinogens. Only 5 patients (4.7%) had no previous exposure to these risk factors.</ns0:p><ns0:p>Regarding the tumor site, most of the primary sites had origins in the oral cavity (64.1%), followed by the hypopharynx (19.8%), and oropharynx (16.0%). The majority of patients had advanced disease, including T3-4, N2-3, or clinical stage 4. The details of basic information of the study population are listed in Table <ns0:ref type='table'>1</ns0:ref>. <ns0:ref type='table' target='#tab_9'>PDF \| (2020:01:45342:2:1:NEW 12 Aug 2020)</ns0:ref> Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>PeerJ reviewing</ns0:head></ns0:div> <ns0:div><ns0:head>Treatment modality</ns0:head><ns0:p>With respect to prior treatment before cetuximab treatment, most patients had undergone various HNSCC treatments, including surgery (78.3%), chemotherapy (81.1%) and CRT (80.2%). In addition, there were 34 CRT-refractory patients who suffered from disease progression during CRT or within three months of the end of CRT.</ns0:p><ns0:p>The major reason for cetuximab treatment was recurrent disease with metastatic tumors. The median number of cycles of cetuximab was 11 (2-24), with 60 patients receiving ≥11 cycles of cetuximab, and 46 patients receiving <11 cycles of cetuximab. Among these patients, 76 patients received chemotherapy with the EXTREME regimen (cisplatin and fluorouracil) and 17 patients received taxane-based chemotherapy. The median number of cetuximab administration cycles in these 76 patients with a PF regimen was 11 (range: 2-24) while the median number of cetuximab cycles in 17 patients using taxane-based regimen was 12 (range: 4-23). There was no significant difference in the number of cetuximab cycles between the two groups (p = 0.427).</ns0:p><ns0:p>The details of the treatment modalities are shown in Table <ns0:ref type='table'>2</ns0:ref>. The demographic data of various cetuximab cycles (≥11 and <11) are shown in Supplementary Tables <ns0:ref type='table'>S1 and S2</ns0:ref>. Interestingly, there was no difference in terms of previous treatments, including surgery, chemotherapy, and CRT, between patients who received <11 cycles of cetuximab and those who received ≥11 cycles of cetuximab.</ns0:p></ns0:div> <ns0:div><ns0:head>Treatment outcomes</ns0:head><ns0:p>After cetuximab treatment, clinical responses were observed in 30 patients including 1 complete response and 29 partial responses, with ORR of 28.3%. When the patients with stable disease (n=21, 19.8%) were included in the analysis, the disease control rate was 48.1%. The median PFS and OS were 5 months and 9.23 months, respectively. As of the cut-off date, only one patient did not progress, and 38 patients survived. The median PFS was 5 months (95% CI 3.0-6.0 months) and the median OS was 9.23 months (95% CI 7.03-13.84 months). The treatment responses according to various stages are shown in Supplementary Table <ns0:ref type='table' target='#tab_4'>S3</ns0:ref>.</ns0:p><ns0:p>The median PFS in various subgroups stratified by treatment modalities is shown in Fig. <ns0:ref type='figure' target='#fig_5'>2</ns0:ref>. Notably, the patients who received more cetuximab treatment (≥ 11 cycles) had a better median PFS than did patients who received less cetuximab (7 months vs 3 months, p< 0.001). The median PFS was longer in patients without prior CRT (6 months vs 4 months, p = 0.046). Other factors including chemotherapy regimen (PF or taxane-based), chemotherapy dose (PF dose), or CRT refraction status did not lead to significant effect on PFS. In regard to analysis of OS, the patients who received more cetuximab treatment (≥ 11 cycles) had a better median OS than those who received less cetuximab (12.43 months vs 4.46 months, p< 0.001). Other factors, including chemotherapy regimen and dose, did not lead to significant effects on PFS. The OS curves are Manuscript to be reviewed shown in Fig. <ns0:ref type='figure'>3</ns0:ref>.</ns0:p><ns0:p>Next, we applied a landmark method for further validation. Because responses could be observed within the first 3 months following cetuximab exposure, a 3-month landmark was used.</ns0:p><ns0:p>After excluding patients who progressed or died within the three months, the patients with more cycles of cetuximab (≥ 11 cycles) still showed better median PFS (8 months vs 2 months, p = 0.057) and OS (13.9 months vs 5.07 months, p=0.0002) than the patients treated with fewer cycles of cetuximab.</ns0:p><ns0:p>To clarify the effects of CRT-refraction on survival, we evaluated median PFS and OS in patients with or without CRT-refraction. In the non-CRT-refractory cohort (n=72), the median PFS and OS were 5.00 months (95% CI = 3.00-7.00) and 10.43 months (95% CI = 7.03-14.64), respectively. The 3-year OS was 28.72% (95% CI = 17.25-41.24). On further evaluation of these 72 subjects, 27 patients with < 11 cetuximab cycles obtained a 3-year PFS rate of 3.70% (95% CI =0.27-15.90), and a 3-year OS rate of 2.22% (95% CI = 0.18-10.15). Additionally, 45 patients with ≥ 11 cetuximab cycles obtained a 3-year PFS rate of 11.57% (95% CI =1.04-36.08), and a 3-year OS rate of 37.07% (95% CI = 21.60-52.59). The patients treated with more cetuximab cycles also showed a better median PFS and OS then did the patients treated with fewer cetuximab cycles, shown in Fig. <ns0:ref type='figure' target='#fig_7'>4</ns0:ref>.</ns0:p><ns0:p>In the CRT-refractory patients, the median PFS and OS were 3.00 months (95% CI =3.00-6.00) and 7.8 months, respectively. The 3-year OS rate was 25.30% (95% CI = 10.32-43.53). Six CRT-refractory patients who used taxane-based regimens obtained a median PFS and OS of 3.00 months (95% CI = 2.00-8.00) and 5.62 months (95% CI = 2.03-NA), respectively. The 3-year OS was 16.67% (95% CI = 0.77-51.68).</ns0:p></ns0:div> <ns0:div><ns0:head>Risk factor investigation for disease progression</ns0:head><ns0:p>Risks of disease progression were analyzed using univariate regression consisting of parameters as age, alcohol, betel nuts, tobacco consumption, tumor site, margin positivity, histologic features (including lymphovascular invasion, perineural invasion, and extranodal extension), tumor size, lymph node status, stage, previous treatment modality (including surgery, chemotherapy, and CRT), treatment status, cetuximab cycles, dose, and regimens of chemotherapy. In addition, a subsequent multivariable regression analysis was performed to evaluate the significant progression factors in univariate analysis.</ns0:p><ns0:p>As shown in Table <ns0:ref type='table' target='#tab_1'>3</ns0:ref>, positive perineural invasion was the independent factor related with shorter median PFS. N3 disease showed a trend toward poorer PFS (p = 0.055, univariate analysis). After adjustment for other different variables in the multivariable analysis, this difference became significant (HR = 2.57; p = 0.043). Significantly, treatment with more cetuximab cycles (≥ 11 cycles) was a favorable factor associated with better median PFS (HR = PeerJ reviewing PDF \| (2020:01:45342:2:1:NEW 12 Aug 2020)</ns0:p><ns0:p>Manuscript to be reviewed 0.19; p < 0.001, and HR = 0.18; p < 0.001 in univariate and multivariable analysis, respectively).</ns0:p></ns0:div> <ns0:div><ns0:head>Determining the risk factor for poorer overall survival</ns0:head><ns0:p>Similar clinicopathological factors were analyzed for overall survival. N2 disease had a significantly negative impact on OS (HR = 2.09; p = 0.022 and HR = 4.79; p = 0.006 in univariate and multivariable analyses, respectively). Treatment with more cetuximab cycles showed a significant, positive effect on OS (HR = 0.46; p =0.002 and HR = 0.48; p = 0.010 in both univariate and multivariable analyses, respectively). Other factors with trends toward shorter OS included N3 disease (p = 0.170). After adjustment for other variables, this difference became significant in the multivariable analysis (HR = 7.34; p = 0.005). These results are shown in Table <ns0:ref type='table' target='#tab_5'>4</ns0:ref>.</ns0:p><ns0:p>Although endemic habits showed no significant impact on PFS and OS, multiple endemic habits might increase risk in PFS and OS compared to single or double endemic habits. The impact of multiple endemic habits on PFS and OS are summarized in Supplementary Table <ns0:ref type='table' target='#tab_9'>S4</ns0:ref>.</ns0:p></ns0:div> <ns0:div><ns0:head>Safety and tolerability</ns0:head><ns0:p>All grades and the worst grade 3 and grade 4 treatment-related adverse events (AEs) in patients receiving cetuximab therapy are listed in Table <ns0:ref type='table'>5</ns0:ref>. Among the patients treated with the platinum/5FU and cetuximab regimen, the most common AEs were skin rash (2.6%), anemia (2.6%), neutropenia (1.3%), vomiting (1.3%) and fever (1.3%). Among patients treated with taxane-based regimens, only one patient suffered from grade 3 fever (5.9%). There were no grade 3 or grade 4 AEs in other groups. In general, skin rash was the most frequent cetuximabrelated AE; however, most of patients tolerated it. There was no interstitial lung disease observed in our patients.</ns0:p></ns0:div> <ns0:div><ns0:head>Discussion</ns0:head><ns0:p>The treatment options for HNSCC are sophisticated and require multidisciplinary groups to tailor personalized treatment. Since 2008, the addition of cetuximab to chemotherapy has become the first-line treatment of RM HNSCC regarding advancements in response and survival <ns0:ref type='bibr' target='#b36'>(Vermorken et al. 2008)</ns0:ref>. However, HNSCC is a heterogenous disease and considerable effects of carcinogens have been reported, especially in the Asian population <ns0:ref type='bibr' target='#b29'>(Network 2015)</ns0:ref>.</ns0:p><ns0:p>Accessibility to expensive drugs and restrictions on reimbursement policies also have impacts on the responses and outcomes of treatment in many countries, including Taiwan <ns0:ref type='bibr' target='#b11'>(Davidoff et al. 2018;</ns0:ref><ns0:ref type='bibr' target='#b23'>Hsu et al. 2019;</ns0:ref><ns0:ref type='bibr' target='#b28'>Morgan & Kennedy 2010)</ns0:ref>. This retrospective study highlights the important role of cetuximab cycles in RM HNSCC, especially in an endemic carcinogen exposure area such as Taiwan.</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:01:45342:2:1:NEW 12 Aug 2020)</ns0:p></ns0:div> <ns0:div><ns0:head>Manuscript to be reviewed</ns0:head><ns0:p>In this study, 106 patients treated with cetuximab-based regimens were assessed; most patients had the habit of using an addictive substance and over half the patients had concurrent exposure to all three addictive substances. However, our outcomes were not inferior when indirectly compared to those of other clinical trials, including the EXTREME regimen conducted by European cancer institutes (De <ns0:ref type='bibr' target='#b12'>Mello et al. 2014</ns0:ref>) and the EXTREME trial <ns0:ref type='bibr' target='#b36'>(Vermorken et al. 2008</ns0:ref>). The possible reasons may relate to regular and frequent follow-up, laboratory, and imaging studies to detect disease progression and guide subsequent treatment plan when progression was noted. Compared to the aforementioned Asian trial, including Japanese <ns0:ref type='bibr' target='#b35'>(Tahara et al. 2016</ns0:ref>) and Chinese trials <ns0:ref type='bibr'>(Guo et al. 2014)</ns0:ref>, the ORR of our study was slightly lower, which may be related to usage of cetuximab maintenance, different regimens of chemotherapy, and a patient population with distinct endemic carcinogen exposures. The patients in the Japanese trial received cetuximab maintenance and chemotherapy with carboplatin and paclitaxel. However, there was nearly no effect of betel nuts in the Japanese population. The effects of carcinogen were also not mentioned in the Chinese and Korean population. The results of these studies are summarized in Table 6 <ns0:ref type='bibr' target='#b0'>(Adkins et al. 2018;</ns0:ref><ns0:ref type='bibr' target='#b4'>Bossi et al. 2017;</ns0:ref><ns0:ref type='bibr' target='#b12'>De Mello et al. 2014;</ns0:ref><ns0:ref type='bibr' target='#b13'>Friesland et al. 2018;</ns0:ref><ns0:ref type='bibr' target='#b18'>Guigay et al. 2016;</ns0:ref><ns0:ref type='bibr' target='#b19'>Guigay et al. 2012;</ns0:ref><ns0:ref type='bibr' target='#b20'>Guigay et al. 2019;</ns0:ref><ns0:ref type='bibr'>Guo et al. 2014;</ns0:ref><ns0:ref type='bibr' target='#b35'>Tahara et al. 2016;</ns0:ref><ns0:ref type='bibr' target='#b36'>Vermorken et al. 2008)</ns0:ref>.</ns0:p><ns0:p>Importantly, the median PFS and OS of our study are compatible with those of another retrospective study <ns0:ref type='bibr' target='#b12'>(De Mello et al. 2014)</ns0:ref>. Our real-world results were also comparable with those of other clinical trials. As we mentioned, these may be related to every diagnosed patient receiving frequent physical and imaging examinations, receiving care from a multidisciplinary team (including nurse case management, integrating expertise of medical oncologist, surgeon, radiologists, case managers, nurses, nutritionists, and pharmacists), and meeting periodically to discuss treatment direction, evaluating therapeutic effects, and providing further recommendations. As noted in breast cancer care, earlier detection from more aggressive monitoring could lead to improved treatment strategies and possibly improved survival <ns0:ref type='bibr' target='#b16'>(Graham et al. 2014)</ns0:ref>.</ns0:p><ns0:p>Although our study was conducted retrospectively in a single medical center, our study reflects the observation of the real-world setting in an endemic carcinogen exposure area.</ns0:p><ns0:p>However, our study still had limitations in terms of relatively smaller sample size and inevitable time bias. To address the immortal time bias and reverse causality, we applied landmark analysis, which suggested more cycles of cetuximab may bring survival benefit to HNSCC patients. The heterogeneous study population is also an issue. Unlike the EXTREME or TPEX studies that excluded CRT-refractory patients, we included CRT-refractory patients.</ns0:p><ns0:p>Furthermore, patients who received nonplatinum chemotherapy regimens, including taxane and MTX, were also included. Heterogeneity of the study population may confound the analysis.</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:01:45342:2:1:NEW 12 Aug 2020)</ns0:p><ns0:p>Manuscript to be reviewed However, our findings revealed real-world conditions in term of financial burden of novel treatment, which lead to absence of cetuximab maintenance. In addition, our study included a Taiwanese population with high incidence of oral cavity cancer that may be related to strong carcinogen exposure, including alcohol, betel nuts, and tobacco. Previous studies had revealed lower expression of tumor suppressor gene p53 alterations, higher percentage of MDM2 protein expression, as well as higher rate of Ras oncogene mutation after long-term exposure to betel nuts <ns0:ref type='bibr' target='#b24'>(Huang et al. 2001;</ns0:ref><ns0:ref type='bibr' target='#b26'>Kuo et al. 1994;</ns0:ref><ns0:ref type='bibr' target='#b27'>Kuo et al. 1999)</ns0:ref>. The upregulation of EGFR has been confirmed in betel-nut-associated cancer of the oral cavity associated with poor prognosis <ns0:ref type='bibr' target='#b32'>(Sheu et al. 2009)</ns0:ref>. Three amplicons (KRAS, MAPK1, and CCND1) have been observed in cancer of oral cavity from Taiwanese patients, and therefore, all could possibly contribute to activation of EGFR signaling <ns0:ref type='bibr' target='#b32'>(Sheu et al. 2009)</ns0:ref>. EGFR protein upregulation, excluding the effect of EGFR gene copy number on protein overexpression, was related to poor differentiation of tumor cells and lymph node metastasis, especially extranodal extension <ns0:ref type='bibr' target='#b25'>(Huang et al. 2017)</ns0:ref>. Taken together, cetuximab targeting EGFR on HNSCC cells induces potent antibody-dependent cell-mediated cytotoxicity that further augments anti-tumor effect when combined with chemotherapy <ns0:ref type='bibr' target='#b34'>(Specenier & Vermorken 2013)</ns0:ref>.</ns0:p><ns0:p>The restrictions in targeted therapy-related reimbursement policies defer patients' benefits related to RM HNSCC. The limitation of a total 18 cycles of cetuximab without maintenance has been in place since 2016 in Taiwan. In other countries, cetuximab maintenance plays an important role in improving survival and outcomes with tolerable adverse events <ns0:ref type='bibr' target='#b37'>(Wakasugi et al. 2015)</ns0:ref>. The median duration of maintenance was 11 weeks in the EXTREME trial, 16 weeks in a real-world study in France, and 17 weeks in a real-world study in Portugal. Broadening the duration of the eligible patient population to targeted therapies may be an effective way to improve clinical outcomes of treatments.</ns0:p></ns0:div> <ns0:div><ns0:head>Conclusions</ns0:head><ns0:p>Consistent administration of cetuximab provides potential clinical benefits in HNSCC patients in endemic carcinogen exposure areas in an Asian population; therefore, longer cetuximab maintenance therapy is urgently warranted in these patients with poor prognoses. Manuscript to be reviewed Manuscript to be reviewed Manuscript to be reviewed Manuscript to be reviewed ORR: overall response rate; OS: overall survival; Q3W: every three weeks; AUC: area under the curve. Manuscript to be reviewed Manuscript to be reviewed </ns0:p><ns0:note type='other'>Figure 2</ns0:note><ns0:note type='other'>Figure 4</ns0:note></ns0:div><ns0:figure xml:id='fig_0'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ reviewing PDF \| (2020:01:45342:2:1:NEW 12 Aug 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ reviewing PDF \| (2020:01:45342:2:1:NEW 12 Aug 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head /><ns0:label /><ns0:figDesc>proportional hazard model were preformed to analyze prognostic factors associated with cetuximab treatment. The factors for this analysis included age at initial diagnosis, location of primary sites, histological grade, pathological features (margin, lymphovascular invasion, perineural invasion, and extranodal extension), tumor size, lymph node status, stage at initial diagnosis, previous treatment before cetuximab (surgery, chemotherapy, or CRT), combined regimen and dosage of chemotherapy. All p-values were considered significant if p < 0.05 and were two-sided. Statistical analyses were performed using STATA version 11 (STATA Corp., TX, USA).</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head /><ns0:label /><ns0:figDesc>PeerJ reviewing PDF \| (2020:01:45342:2:1:NEW 12 Aug 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head>Figure 1 Figure 1 .</ns0:head><ns0:label>11</ns0:label><ns0:figDesc>Figure 1</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_5'><ns0:head>Figure 2 .</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2. Progression-free survival curve.</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_6'><ns0:head>Figure 3 Figure 3 .</ns0:head><ns0:label>33</ns0:label><ns0:figDesc>Figure 3</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_7'><ns0:head>Figure 4 .</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Figure 4. Subgroups analysis in CRT-refractory patients.</ns0:figDesc></ns0:figure> <ns0:figure type='table' xml:id='tab_0'><ns0:head /><ns0:label /><ns0:figDesc>Table 1. Baseline characteristics in the entire cohort (N=106).</ns0:figDesc><ns0:table><ns0:row><ns0:cell>1 2 Table 2. Treatment modality. Variables Age, years (mean ± SD) Alcohol Betel nuts Smoking Primary sites HPC OC OPC Grade 1 2 3 Unknown Margin positivity LVI, positive PNI, positive ENE, positive Tumor size T0 T1 T2 T3 T4 Lymph node status N0 N1 N2 N3 Stage at initial diagnosis I II III IV Variables Previous treatment Surgery Chemotherapy CRT CRT-refractory Cetuximab applied reason Metastasis Recurrence Cetuximab cycle, median (range) < 11 ≥ 11 Regimen of chemotherapy PF Taxane-based Others Platinum Cisplatin Carboplatin Chemotherapy dose 60/800 75/1000 Disease progressed ORR DCR Median PFS (months, 95% CI) All-cause mortality Median OS (months, 95% CI) 3 CRT: concurrent chemoradiotherapy; PF: cisplatin and fluorouracil; ORR: overall response rate; n (%) 55.1 ± 9.9 71 (67.0%) 76 (71.7%) 79 (74.5%) 21 (19.8%) 68 (64.1%) 17 (16.0%) 28 (26.4%) 57 (53.8%) 16 (15.1%) 5 (4.7%) 11 (10.4%) 4 (3.8%) 9 (8.5%) 5 (4.7%) 2 (1.9%) 14 (13.2%) 24 (22.6%) 16 (15.1%) 50 (47.2%) 27 (25.5%) 12 (11.3%) 56 (52.8%) 11 (10.4%) 9 (8.5%) 6 (5.7%) 11 (10.4%) 80 (75.5%) n (%) 83 (78.3%) 86 (81.1%) 85 (80.2%) 34 (32.1%) 65 (61.3%) 41 (38.7%) 11 (2-24) 46 (43.4%) 60 (56.6%) 76 (71.7%) 17 (16.0%) 13 (12.3%) 85 (80.2%) 5 (4.7%) 36 (34.0%) 57 (53.8%) 105 (99.1%) 30 (28.3%) 51 (48.1%) 5.00 (3.00-6.00) 68 (64.2%) 9.23 (7.03-13.84) 4 DCR: disease control rate; PFS: progression-free survival; OS: overall survival; 95% CI: 95% 2 1 5 confidence intervals.</ns0:cell></ns0:row></ns0:table><ns0:note>PeerJ reviewing PDF \| (2020:01:45342:2:1:NEW 12 Aug 2020)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_1'><ns0:head>Table 3 (on next page)</ns0:head><ns0:label>3</ns0:label><ns0:figDesc /><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_2'><ns0:head>Table 3 .</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Cox regression for disease progression.</ns0:figDesc><ns0:table><ns0:row><ns0:cell>Manuscript to be reviewed</ns0:cell></ns0:row><ns0:row><ns0:cell>HPC: hypopharyngeal cancer; OC: oral cavity cancer; OPC: oropharyngeal cancer; LVI:</ns0:cell></ns0:row><ns0:row><ns0:cell>lymphovascular invasion; PNI: perineural invasion; ENE: extranodal extension; CRT:</ns0:cell></ns0:row><ns0:row><ns0:cell>concurrent chemoradiotherapy; PF: cisplatin and fluorouracil; HR: hazard ratio; 95% CI: 95%</ns0:cell></ns0:row><ns0:row><ns0:cell>confidence intervals. Variables with p-value less than 0.2 in univariate analysis were</ns0:cell></ns0:row><ns0:row><ns0:cell>included in multivariable model.</ns0:cell></ns0:row></ns0:table><ns0:note>PeerJ reviewing PDF \| (2020:01:45342:2:1:NEW 12 Aug 2020)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_3'><ns0:head>.57 (1.03-6.43) 0.043</ns0:head><ns0:label /><ns0:figDesc /><ns0:table><ns0:row><ns0:cell>Chemotherapy dose</ns0:cell><ns0:cell>75/1000 vs. 60/800</ns0:cell><ns0:cell>0.90 (0.56-1.43)</ns0:cell><ns0:cell>0.644</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>Stage</ns0:cell><ns0:cell>II vs. I</ns0:cell><ns0:cell>1.66 (0.59-4.69)</ns0:cell><ns0:cell>0.339</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>III vs. I</ns0:cell><ns0:cell>1.76 (0.72-4.28)</ns0:cell><ns0:cell>0.214</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>IV vs. I</ns0:cell><ns0:cell>1.50 (0.75-3.02)</ns0:cell><ns0:cell>0.252</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>Surgery</ns0:cell><ns0:cell>With vs. without</ns0:cell><ns0:cell>0.80 (0.50-1.28)</ns0:cell><ns0:cell>0.354</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>Chemotherapy before target therapy</ns0:cell><ns0:cell>With vs. without</ns0:cell><ns0:cell>0.87 (0.53-1.42)</ns0:cell><ns0:cell>0.585</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>CRT-refractory</ns0:cell><ns0:cell>Yes vs. no</ns0:cell><ns0:cell>1.32 (0.87-1.99)</ns0:cell><ns0:cell>0.191</ns0:cell><ns0:cell>1.18 (0.72-1.91)</ns0:cell><ns0:cell>0.511</ns0:cell></ns0:row><ns0:row><ns0:cell>Cetuximab applied reason</ns0:cell><ns0:cell>Metastasis vs. recurrence</ns0:cell><ns0:cell>1.002 (0.68-1.49)</ns0:cell><ns0:cell>0.992</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>Cetuximab cycle, median (range)</ns0:cell><ns0:cell>≥ 11 vs. <11</ns0:cell><ns0:cell>0.19 (0.11-0.30)</ns0:cell><ns0:cell cols='2'><0.001 0.18 (0.09-0.33)</ns0:cell><ns0:cell><0.001</ns0:cell></ns0:row><ns0:row><ns0:cell>Regimen of chemotherapy</ns0:cell><ns0:cell>Taxane-based vs. PF</ns0:cell><ns0:cell>0.75 (0.44-1.29)</ns0:cell><ns0:cell>0.297</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>Others vs. PF</ns0:cell><ns0:cell>0.85 (0.47-1.54)</ns0:cell><ns0:cell>0.591</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>Platinum</ns0:cell><ns0:cell>Carboplatin vs. Cisplatin</ns0:cell><ns0:cell>0.55 (0.22-1.39)</ns0:cell><ns0:cell>0.206</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell /></ns0:row></ns0:table><ns0:note>PeerJ reviewing PDF \| (2020:01:45342:2:1:NEW 12 Aug 2020)Manuscript to be reviewed</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_4'><ns0:head>Table 3 .</ns0:head><ns0:label>3</ns0:label><ns0:figDesc>Cox regression for disease progression. HPC: hypopharyngeal cancer; OC: oral cavity cancer; OPC: oropharyngeal cancer;</ns0:figDesc><ns0:table><ns0:row><ns0:cell>cisplatin and fluorouracil; HR: hazard ratio; 95% CI: 95% confidence intervals.</ns0:cell></ns0:row><ns0:row><ns0:cell>Variables with p-value less than 0.2 in univariate analysis were included in multivariable model.</ns0:cell></ns0:row></ns0:table><ns0:note>LVI: lymphovascular invasion; PNI: perineural invasion; ENE: extranodal extension; CRT: concurrent chemoradiotherapy; PF: PeerJ reviewing PDF \| (2020:01:45342:2:1:NEW 12 Aug 2020)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_5'><ns0:head>Table 4 (on next page)</ns0:head><ns0:label>4</ns0:label><ns0:figDesc /><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_6'><ns0:head>Table 4 .</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Cox regression for overall mortality.</ns0:figDesc><ns0:table><ns0:row><ns0:cell>HPC: hypopharyngeal cancer; OC: oral cavity cancer; OPC: oropharyngeal cancer; LVI:</ns0:cell></ns0:row><ns0:row><ns0:cell>lymphovascular invasion; PNI: perineural invasion; ENE: extranodal extension; CRT:</ns0:cell></ns0:row><ns0:row><ns0:cell>concurrent chemoradiotherapy; PF: cisplatin and fluorouracil; HR: hazard ratio; 95% CI: 95%</ns0:cell></ns0:row><ns0:row><ns0:cell>confidence intervals. Variables with p-value less than 0.2 in univariate analysis were</ns0:cell></ns0:row><ns0:row><ns0:cell>included in multivariable model.</ns0:cell></ns0:row></ns0:table><ns0:note>PeerJ reviewing PDF \| (2020:01:45342:2:1:NEW 12 Aug 2020)Manuscript to be reviewed</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_7'><ns0:head>.09 (1.11-3.92) 0.022 4.79 (1.55-14.77) 0.006 N3</ns0:head><ns0:label /><ns0:figDesc /><ns0:table><ns0:row><ns0:cell>vs. N0</ns0:cell><ns0:cell>1.92 (0.76-4.88)</ns0:cell><ns0:cell>0.170</ns0:cell><ns0:cell>7</ns0:cell></ns0:row></ns0:table></ns0:figure> <ns0:figure type='table' xml:id='tab_8'><ns0:head>.34 (1.85-29.16) 0.005</ns0:head><ns0:label /><ns0:figDesc /><ns0:table><ns0:row><ns0:cell>Chemotherapy dose</ns0:cell><ns0:cell>75/1000 vs. 60/800</ns0:cell><ns0:cell>1.19 (0. 66-2.17)</ns0:cell><ns0:cell>0.564</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>Stage</ns0:cell><ns0:cell>II vs. I</ns0:cell><ns0:cell>2.75 (0.79-9.51)</ns0:cell><ns0:cell>0.110</ns0:cell><ns0:cell>1.69 (0.19-15.31)</ns0:cell><ns0:cell>0.640</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>III vs. I</ns0:cell><ns0:cell>0.85 (0.23-3.18)</ns0:cell><ns0:cell>0.812</ns0:cell><ns0:cell>0.15 (0.02-1.42)</ns0:cell><ns0:cell>0.098</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>IV vs. I</ns0:cell><ns0:cell>1.56 (0.62-3.91)</ns0:cell><ns0:cell>0.341</ns0:cell><ns0:cell>0.14 (0.02-1.08)</ns0:cell><ns0:cell>0.060</ns0:cell></ns0:row><ns0:row><ns0:cell>Surgery</ns0:cell><ns0:cell>With vs. without</ns0:cell><ns0:cell>0.66 (0.38-1.13)</ns0:cell><ns0:cell>0.127</ns0:cell><ns0:cell>0.83 (0.46-1.51)</ns0:cell><ns0:cell>0.541</ns0:cell></ns0:row><ns0:row><ns0:cell>Chemotherapy before target therapy</ns0:cell><ns0:cell>With vs. without</ns0:cell><ns0:cell>1.25 (0.64-2.46)</ns0:cell><ns0:cell>0.517</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>CRT-refractory</ns0:cell><ns0:cell>Yes vs. no</ns0:cell><ns0:cell>1.20 (0.73-1.98)</ns0:cell><ns0:cell>0.479</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>Cetuximab applied reason</ns0:cell><ns0:cell>Metastasis vs. recurrence</ns0:cell><ns0:cell>1.16 (0.70-1.91)</ns0:cell><ns0:cell>0.561</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>Cetuximab cycle, median (range)</ns0:cell><ns0:cell>≥ 11 vs. <11</ns0:cell><ns0:cell>0.46 (0.28-0.75)</ns0:cell><ns0:cell>0.002</ns0:cell><ns0:cell>0.48 (0.27-0.84)</ns0:cell><ns0:cell>0.010</ns0:cell></ns0:row><ns0:row><ns0:cell>Regimen of chemotherapy</ns0:cell><ns0:cell>Taxane-based vs. PF</ns0:cell><ns0:cell>0.75 (0.38-1.49)</ns0:cell><ns0:cell>0.417</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell>Others vs. PF</ns0:cell><ns0:cell>0.90 (0.43-1.89)</ns0:cell><ns0:cell>0.777</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell>Platinum</ns0:cell><ns0:cell>Carboplatin vs. Cisplatin</ns0:cell><ns0:cell>0.51 (0.16-1.64)</ns0:cell><ns0:cell>0.260</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell /></ns0:row></ns0:table><ns0:note>PeerJ reviewing PDF \| (2020:01:45342:2:1:NEW 12 Aug 2020)Manuscript to be reviewed</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_9'><ns0:head>Table 4 .</ns0:head><ns0:label>4</ns0:label><ns0:figDesc>Cox regression for overall mortality. HPC: hypopharyngeal cancer; OC: oral cavity cancer; OPC: oropharyngeal cancer; LVI: lymphovascular invasion; PNI: perineural invasion; ENE: extranodal extension; CRT: concurrent chemoradiotherapy; PF: cisplatin and fluorouracil; HR: hazard ratio; 95% CI: 95% confidence intervals.Variables with p-value less than 0.2 in univariate analysis were included in multivariable model.</ns0:figDesc><ns0:table /><ns0:note>PeerJ reviewing PDF \| (2020:01:45342:2:1:NEW 12 Aug 2020)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_10'><ns0:head>1</ns0:head><ns0:label /><ns0:figDesc>Table 5. adverse effects observed according to CTCAE version 4.0.</ns0:figDesc><ns0:table><ns0:row><ns0:cell /><ns0:cell /><ns0:cell>PF</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell cols='2'>Taxane-based</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell>Others</ns0:cell><ns0:cell /></ns0:row><ns0:row><ns0:cell /><ns0:cell cols='2'>All grades</ns0:cell><ns0:cell cols='2'>Grade 3-4</ns0:cell><ns0:cell cols='2'>All grades</ns0:cell><ns0:cell cols='2'>Grade 3-4</ns0:cell><ns0:cell cols='2'>All grades</ns0:cell><ns0:cell cols='2'>Grade 3-4</ns0:cell></ns0:row><ns0:row><ns0:cell /><ns0:cell>No.</ns0:cell><ns0:cell>%</ns0:cell><ns0:cell>No.</ns0:cell><ns0:cell>%</ns0:cell><ns0:cell>No.</ns0:cell><ns0:cell>%</ns0:cell><ns0:cell>No.</ns0:cell><ns0:cell>%</ns0:cell><ns0:cell>No.</ns0:cell><ns0:cell>%</ns0:cell><ns0:cell>No.</ns0:cell><ns0:cell>%</ns0:cell></ns0:row><ns0:row><ns0:cell>Febrile</ns0:cell><ns0:cell>7</ns0:cell><ns0:cell>9.2</ns0:cell><ns0:cell>1</ns0:cell><ns0:cell>1.3</ns0:cell><ns0:cell>4</ns0:cell><ns0:cell>23.5</ns0:cell><ns0:cell>1</ns0:cell><ns0:cell>5.9</ns0:cell><ns0:cell>2</ns0:cell><ns0:cell>15.4</ns0:cell><ns0:cell /><ns0:cell>-</ns0:cell></ns0:row><ns0:row><ns0:cell>Neutropenia</ns0:cell><ns0:cell>24</ns0:cell><ns0:cell>31.6</ns0:cell><ns0:cell>1</ns0:cell><ns0:cell>1.3</ns0:cell><ns0:cell>6</ns0:cell><ns0:cell>35.3</ns0:cell><ns0:cell>0</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>2</ns0:cell><ns0:cell>15.4</ns0:cell><ns0:cell /><ns0:cell>-</ns0:cell></ns0:row><ns0:row><ns0:cell>Skin rash</ns0:cell><ns0:cell>46</ns0:cell><ns0:cell>60.5</ns0:cell><ns0:cell>2</ns0:cell><ns0:cell>2.6</ns0:cell><ns0:cell>9</ns0:cell><ns0:cell>52.9</ns0:cell><ns0:cell>0</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>5</ns0:cell><ns0:cell>38.5</ns0:cell><ns0:cell /><ns0:cell>-</ns0:cell></ns0:row><ns0:row><ns0:cell>Anemia</ns0:cell><ns0:cell>51</ns0:cell><ns0:cell>67.1</ns0:cell><ns0:cell>2</ns0:cell><ns0:cell>2.6</ns0:cell><ns0:cell>14</ns0:cell><ns0:cell>82.4</ns0:cell><ns0:cell>0</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>4</ns0:cell><ns0:cell>30.8</ns0:cell><ns0:cell /><ns0:cell>-</ns0:cell></ns0:row><ns0:row><ns0:cell>Hypomagnesemi a</ns0:cell><ns0:cell>31</ns0:cell><ns0:cell>40.8</ns0:cell><ns0:cell>0</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>11</ns0:cell><ns0:cell>64.7</ns0:cell><ns0:cell>0</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>4</ns0:cell><ns0:cell>30.8</ns0:cell><ns0:cell /><ns0:cell>-</ns0:cell></ns0:row><ns0:row><ns0:cell>Pneumonia</ns0:cell><ns0:cell>7</ns0:cell><ns0:cell>9.2</ns0:cell><ns0:cell>0</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>2</ns0:cell><ns0:cell>11.8</ns0:cell><ns0:cell>0</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>1</ns0:cell><ns0:cell>7.7</ns0:cell><ns0:cell /><ns0:cell>-</ns0:cell></ns0:row><ns0:row><ns0:cell>Infusion reaction</ns0:cell><ns0:cell>5</ns0:cell><ns0:cell>6.6</ns0:cell><ns0:cell>0</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>0</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>0</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>0</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell /><ns0:cell>-</ns0:cell></ns0:row><ns0:row><ns0:cell>Vomiting</ns0:cell><ns0:cell>28</ns0:cell><ns0:cell>36.8</ns0:cell><ns0:cell>1</ns0:cell><ns0:cell>1.3</ns0:cell><ns0:cell>5</ns0:cell><ns0:cell>29.4</ns0:cell><ns0:cell>0</ns0:cell><ns0:cell>-</ns0:cell><ns0:cell>8</ns0:cell><ns0:cell>61.5</ns0:cell><ns0:cell /><ns0:cell>-</ns0:cell></ns0:row><ns0:row><ns0:cell>2</ns0:cell><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /><ns0:cell /></ns0:row></ns0:table><ns0:note>PeerJ reviewing PDF \| (2020:01:45342:2:1:NEW 12 Aug 2020)</ns0:note></ns0:figure> <ns0:figure type='table' xml:id='tab_11'><ns0:head>Table 6 (on next page)</ns0:head><ns0:label>6</ns0:label><ns0:figDesc /><ns0:table /></ns0:figure> <ns0:figure type='table' xml:id='tab_12'><ns0:head>Table 6 .</ns0:head><ns0:label>6</ns0:label><ns0:figDesc>Comparisons between different trials of cetuximab-based chemotherapy.</ns0:figDesc><ns0:table /></ns0:figure> <ns0:note place='foot'>PeerJ reviewing PDF \| (2020:01:45342:2:1:NEW 12 Aug 2020)</ns0:note> </ns0:body> "	"Rebuttal letters Reviewer 2 (Anonymous) Although the authors addressed the majority of the issues stated, there are still a few issues pending: Comments Abstract: A “conclusion section” is missing. Reply We have added the “Conclusion” in the abstract section. Comments it is still incorrect that “In western countries, oropharyngeal SCC accounts for the largest group of HNSCC”, since oral cavity is the largest group. Reply Thank you for your suggestion. We revised to “In Western countries, a subgroup of oropharyngeal SCC is related to human papillomavirus (HPV) infection.“ in the “Introduction” section. Comments Revision with respect to English grammar is still advised for the whole manuscript. Reply Thank you for your suggestion. This manuscript has been thoroughly checked and revised by professional langue editing service. The certification of English editing was also uploaded. Reviewer 3 (Vancheswaran Gopalakrishnan) Comments for the Author Comments This reviewer appreciates the authors rebuttal and most issues that were raised have been adequately addressed. The flow of the manuscript is also much better. A few outstanding issues still remain that requires their attention, though missing line numbers made it difficult to recommend specific changes. Reply Thank you for your comments. We would mark the revision more correctly and outline the specific change. Comments • Several grammatical errors still exist, and a thorough proof-read is imminently warranted Reply Thank you for your suggestion. This manuscript has been thoroughly checked and revised by professional langue editing service. The certification of English editing was also uploaded. Comments • Please replace multivariate with multivariable throughout the manuscript. The difference is key. Multivariate refers to multiple outcome which is not the case for this analysis Reply Thank you for your suggestions. We replaced multivariate with multivariable throughout the manuscript and in all figures. Comments Per the letter – line 99-102 is still grossly incorrect – “Different from clinical trials…”. Please fix Reply Thank you for your suggestion. We revised to “Unlike clinical trials that provide subjects with maintenance cetuximab, patients in real life cannot afford continuous maintenance with high-cost cetuximab to control their disease.” in the “Introduction” section. Comments Median time to determination of response should be included in the manuscript Reply Thank you for your suggestions. We had mentioned “Treatment response was assessed and determined using computed tomography (CT) or magnetic resonance imaging (MRI) at baseline (before cetuximab) and at 3-month intervals after treatment was started.” in “Treatment Response and Safety Assessment” section of our manuscript. Comments Regarding the impact of endemic habits, what is the impact of OS in patients who have more than 1 endemic habit compared to those who have multiple endemic habits? For ex: smoking alone vs smoke + alcohol use? Along the same line, the authors belief in the novelty of this work lies in the population being ‘endemic’. Perhaps a paragraph is warranted in the discussion section to go over how they are impacting treatment responses. Reply Although the endemic habits showed no significant impact on PFS and OS, the multiple endemic habits might increase risk in PFS and OS compared with single or double endemic habits. The impact of multiple endemic habits on PFS and OS were summarized in Supplementary tables S4. Comments Study design: It would be important to clarify that the primary endpoint was assessed after treatment with cetuximab Reply Thank you for your suggestions. We revised to “The primary endpoints were median OS and PFS. Specifically, the median OS and PFS (defined as the time from registration to objective disease progression or death from any cause) were determined after the addition of cetuximab to chemotherapy. Other endpoints included the assessment of treatment response and disease control.” in the “Study design” section of our manuscripts. Comments • Table 2: Were SD patients also included in the calculation of ORR? If yes, how long were they classified as SD What was the median time to disease progression? Since 99.1% of patients progressed, how was DCR defined? No description of this estimation has been provided in the methods Reply Thank you for your suggestions. The ORR includes complete response and partial response. SD patients are not included in the calculation of ORR. The median time to disease progression was 5.00 (3.00-6.00) months. To clarify the definition of DCR and ORR, we revised to “The calculation of overall response rate (ORR), including patients classified as having complete and partial responses, was based on the best objective response achieved during cetuximab treatment. The calculation of disease control rate (DCR) included patients classified as having complete response, partial response, and stable disease.” in the “Treatment Response and Safety Assessment” section. Comments • Interesting that there are no differences in concurrent chemotherapy use between patients who received <11 vs >11 cycles. This would be an important fact to highlight Reply Thank you for your suggestion. We add “Interestingly, there was no difference in terms of previous treatments, including surgery, chemotherapy, and CRT, between patients who received <11 cycles of cetuximab and those who received ≥11 cycles of cetuximab.” In “Treatment modality” of “Result” section. Comments • Please refrain from using the word ‘favorite’ in describing the predictor variable after multivariable adjustment. Reply Thank you for your suggestion. We revised the word “favorite” to “favorable” in our manuscript. Comments Table 3: Why suddenly change the annotation from ‘Cetuximab’ to ‘Erbitux’. Please be consistent. Reply Thank you for your suggestion. We replaced all the “Erbitux” to “Cetuximab” in all the Tables. "	Here is a paper. Please give your review comments after reading it.
532	"<ns0:abstract xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:p>Background: Serological assays that detect antibodies to SARS-CoV-2 are critical for determining past infection and investigating immune responses in the COVID-19 pandemic. We established ELISA-based immunoassays using locally produced antigens when NewZealand went into a nationwide lockdown and the supply chain of diagnostic reagents was a widely held domestic concern. The relationship between serum antibody binding measured by ELISA and neutralising capacity was investigated using a surrogate viral neutralisation test (sVNT).</ns0:p><ns0:p>Methods: A pre-pandemic sera panel (n=113), including respiratory infections with symptom overlap with COVID-19, was used to establish assay specificity. Sera from PCR-confirmed SARS-CoV-2 patients (n=21), and PCR-negative patients with respiratory symptoms suggestive of COVID-19 (n=82) that presented to the two largest hospitals in Auckland during the lockdown period were included. A two-step IgG ELISA based on the receptor binding domain (RBD) and Spike protein was adapted to determine seropositivity, and neutralising antibodies that block the RBD/hACE-2 interaction were quantified by sVNT.</ns0:p></ns0:div> <ns0:div><ns0:head>Results:</ns0:head><ns0:p>The calculated cut-off (> 0.2) in the two-step ELISA maximised specificity by classifying all prepandemic samples as negative. Sera from all PCR-confirmed COVID-19 patients were classified as seropositive by ELISA ≥7 days after symptom onset. There was 100% concordance between the two-step ELISA and the sVNT with all 7+ day sera from PCR-confirmed COVID-19 patients also classified as positive with respect to neutralising antibodies. Of the symptomatic PCR-negative cohort, one individual with notable travel history was classified as positive by two-step ELISA and sVNT, demonstrating the value of serology in detecting prior infection.</ns0:p></ns0:div> </ns0:abstract> <ns0:body xmlns:ns0='http://www.tei-c.org/ns/1.0'> <ns0:div><ns0:head>INTRODUCTION</ns0:head><ns0:p>Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of the Coronavirus Disease 2019 (COVID-19) global pandemic, is typically detected in acutely infected individuals via nucleic acid-based polymerase chain reaction (PCR) tests <ns0:ref type='bibr' target='#b20'>(Sethuraman, Jeremiah & Ryo, 2020)</ns0:ref>. The first evidence of community transmission in New Zealand was reported on 23 rd March 2020, the country went into an intense 'Alert Level 4' lockdown (the highest level of a 4-level response system) three days later and remained at this Alert Level for the following five weeks <ns0:ref type='bibr' target='#b3'>(Baker, Kvalsvig & Verrall, 2020)</ns0:ref>. During this time there was notable increase in national nucleic acid testing capacity and this remains the cornerstone of SARS-CoV-2 diagnosis.</ns0:p><ns0:p>However, there is also a need for reliable serological assays that measure antibody responses to the virus. While serological assays are not suited to the diagnosis of acute infections due to the days required to generate an antibody response, they are critical for determining past exposure and investigating immune responses <ns0:ref type='bibr' target='#b0'>(Abbasi, 2020;</ns0:ref><ns0:ref type='bibr' target='#b20'>Sethuraman, Jeremiah & Ryo, 2020;</ns0:ref><ns0:ref type='bibr'>Krammer & Simon, 2020)</ns0:ref>.</ns0:p><ns0:p>Numerous laboratory-based serological assays for SARS-CoV-2 are being developed worldwide including enzyme-linked immunosorbent assays (ELISAs) and bead-based immunoassays for measurement of SARS-CoV-2 antibodies <ns0:ref type='bibr' target='#b19'>(Petherick, 2020;</ns0:ref><ns0:ref type='bibr' target='#b12'>Garcia-Basteiro et al., 2020;</ns0:ref><ns0:ref type='bibr'>Krammer & Simon, 2020)</ns0:ref>, as well as virus neutralisation assays for quantification of neutralising antibodies <ns0:ref type='bibr' target='#b22'>(Tan et al., 2020;</ns0:ref><ns0:ref type='bibr' target='#b2'>Anderson et al., 2020)</ns0:ref>. The spike protein (S protein) expressed by SARS-CoV-2 contains a receptor binding domain (RBD), which interacts with host cells via the human angiotensin-converting enzyme 2 (hACE2) <ns0:ref type='bibr' target='#b9'>(Diamond & Pierson, 2020)</ns0:ref>. The S protein is highly immunogenic and, given the integral role of the S protein and RBD in facilitating viral entry, these antigens form the basis of many immunoassays described to date <ns0:ref type='bibr' target='#b10'>(Duan et al., 2020;</ns0:ref><ns0:ref type='bibr' target='#b12'>Garcia-Basteiro et al., 2020;</ns0:ref><ns0:ref type='bibr' target='#b17'>Long et al., 2020;</ns0:ref><ns0:ref type='bibr' target='#b1'>Amanat et al., 2020)</ns0:ref>. Recent data from immunoassays based on the SARS-CoV-2 nucleocapsid protein also show high sensitivity <ns0:ref type='bibr' target='#b20'>(Sethuraman, Jeremiah & Ryo, 2020;</ns0:ref><ns0:ref type='bibr' target='#b6'>Bryan et al., 2020)</ns0:ref>. However, the higher sequence conservation of the SARS-CoV-2 nucleocapsid with other coronaviruses, compared to the S protein, increases the possibility of antibody cross-reactivity against the nucleoprotein in those infected with related viruses <ns0:ref type='bibr'>(Krammer & Simon, 2020;</ns0:ref><ns0:ref type='bibr' target='#b2'>Anderson et al., 2020)</ns0:ref>.</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:07:50726:1:1:NEW 10 Aug 2020)</ns0:p></ns0:div> <ns0:div><ns0:head>Manuscript to be reviewed</ns0:head><ns0:p>As New Zealand entered Alert Level 4 Lockdown, and the supply chain of diagnostic reagents for managing COVID-19 testing was a widely held domestic concern, we sought to establish a serologic ELISA assay based on locally-produced SARS-CoV-2 antigens. We adapted the two-step ELISA protocols developed at The Icahn School of Medicine at Mount Sinai (New York City) based on the S protein and RBD, which has FDA Emergency Use Authorization <ns0:ref type='bibr' target='#b1'>(Amanat et al., 2020;</ns0:ref><ns0:ref type='bibr' target='#b21'>Stadlbauer et al., 2020)</ns0:ref>. A panel of sera/plasma from PCR-confirmed COVID-19 patients was compared with pre-pandemic sera to determine assay parameters. The relationship between antibody binding to the S protein and RBD, and neutralising ability was explored using the surrogate viral neutralisation test (sVNT) recently developed at Duke-NUS (Singapore) <ns0:ref type='bibr' target='#b22'>(Tan et al., 2020)</ns0:ref>. Finally, the utility of both the ELISA and sVNT to identify prior SARS-CoV-2 infection was investigated in a cohort of PCR-negative patients that presented to hospital with respiratory symptoms during the lockdown period.</ns0:p></ns0:div> <ns0:div><ns0:head>METHODS</ns0:head></ns0:div> <ns0:div><ns0:head>Human samples</ns0:head><ns0:p>Human plasma and sera were obtained from several different sources, all of which were granted ethical approval by the University of Auckland Human Ethics Committee or the Health and Disability Ethics Committee in New Zealand. A total of 113 samples collected before 2020 were used as negative controls and all participants (or their parents or legal 109 guardians) provided written informed consent. These included healthy adult volunteers Manuscript to be reviewed sera/plasma stored following completion of all routine testing for validation of antibody diagnostics (ethics HDEC 20NTB76) (Table <ns0:ref type='table'>1</ns0:ref>). All serum and plasma samples were heated at 56°C for 30 min before use to inactivate any residual virus, as published <ns0:ref type='bibr' target='#b1'>(Amanat et al., 2020)</ns0:ref>.</ns0:p><ns0:p>Pooled human intravenous immunoglobulin (IVIG) were produced before 2020 from > 1500 donors in New Zealand (Intragram P) and Europe and North America (Privigen) (CSL Behring).</ns0:p></ns0:div> <ns0:div><ns0:head>Indirect ELISA</ns0:head><ns0:p>The S protein and RBD antigens were expressed and purified from pCAGGS-RBD and pCAGGS-solSpike vectors respectively, kindly provided by Florian Krammer (The Icahn School of Medicine at Mt Sinai, New York City) using Expi293F or Freestyle293 human embryonic kidney (HEK) cells and published protocols <ns0:ref type='bibr' target='#b21'>(Stadlbauer et al., 2020)</ns0:ref>, but with a modified transient transfection protocol using polyethyleneimine (PEI). Plasmid DNA was added at 3.5 g/mL with PEI 7.0 at g/mL for 24hrs, after which culture volumes were doubled and supplemented with 2.2 mM valproic acid. Cultures were incubated with shaking for a further 72 hours before protein purification was performed.</ns0:p><ns0:p>The two-step ELISA protocol that includes a single point screen against the RBD, followed by a confirmatory titration against the S protein <ns0:ref type='bibr' target='#b21'>(Stadlbauer et al., 2020)</ns0:ref>, was utilised with minor modifications. In step one immunoplates were coated with RBD (5 g/ml) overnight at 4°C and blocked with phosphate-buffered saline supplemented with 0.1% Tween 20 (PBST) and 3% skim milk powder at 20°C for 1 hr. Serum or plasma diluted 1:100 in diluent buffer (PBST + 1% skim milk powder) was added for 1 hr at 20°C. Following washing (3x PBST), peroxidase-labelled anti-human IgG (Abcam, 97221) diluted 1:10,000 was added for 1 hr at 20°C. The reaction was developed with 3,3′,5,5′-Tetramethylbenzidine (TMB) and stopped with 1M HCl. The optical density (OD) at 450-570nm was measured using an EnSight absorbance reader. In step two, immunoplates were coated with S protein (5 g/ml) overnight at 4°C and the ELISA was performed using the same protocol as in step one, except that 3-fold serial dilutions of samples starting at 1:100, were prepared. Samples were classified as seropositive if they had an OD above the calculated cut-off (> 0.2) in the single point RBD ELISA and in at least two consecutive wells in the S protein titration ELISA. Positive and negative quality controls were included on each plate, with the assay meeting acceptance criteria if the OD was > 0.75 and < 0.03 for the positive and negative control, respectively.</ns0:p><ns0:p>To assess healthy control IgG reactivity to human coronaviruses (HCoV) ELISA were performed as described above with S1 antigens from the HKU1, NL63 and 229E strain (Sino Biological) coated at 5 g/ml and a 1:300 sera dilution. Samples from PCR-confirmed COVID-19 patients were also subject to isotype-specific titration ELISA using peroxidase-labelled anti-human IgG (Abcam, 97221), anti-human IgM (Abcam, 97205) and anti-human IgA (Abcam, 97215) at 1:10,000 dilution. The area under the curve (AUC) was used to compare isotype-specific antibody titres and was calculated by subtracting background AUC of pooled negative control sera for each isotype in Prism 8 (GraphPad).</ns0:p></ns0:div> <ns0:div><ns0:head>Surrogate Viral Neutralisation Test (sVNT)</ns0:head><ns0:p>Surrogate neutralization assays were carried out using a SARS-CoV-2 sVNT Kit supplied pre-launch by GenScript as described <ns0:ref type='bibr' target='#b22'>(Tan et al., 2020)</ns0:ref>. Briefly, serum or plasma was diluted 1:20 before incubation with an equivalent volume of peroxidase-conjugated RBD for 30 minutes at 37°C. This was added to wells pre-coated with human ACE-2 receptor protein and incubated for a further 15 minutes at 37°C. Following washing and TMB development the OD 450 nm was measured using an EnSight absorbance reader. Inhibition was calculated as (1 -OD sample / OD of negative control) x 100. Samples with a percentage inhibition ≥ 20% were deemed to have neutralizing antibodies.</ns0:p></ns0:div> <ns0:div><ns0:head>Statistical analysis</ns0:head><ns0:p>The two-step ELISA and sVNT analysis utilized a Kruskal-Wallis test for comparison between three groups with Dunn's multiple comparisons test. AUC data were log10 transformed to achieve a Gaussian distribution and analysed by one-way ANOVA followed by Tukey's multiple comparisons test. Correlations were calculated using Pearson's correlation coefficient. Data were analysed using Prism 8 (GraphPad) or R (version 3.6.3) within R Studio (version 1.2.5033) and a P-value of ≤ 0.05 was considered statistically significant.</ns0:p></ns0:div> <ns0:div><ns0:head>RESULTS</ns0:head><ns0:p>PeerJ reviewing PDF \| (2020:07:50726:1:1:NEW 10 Aug 2020)</ns0:p><ns0:p>Manuscript to be reviewed</ns0:p></ns0:div> <ns0:div><ns0:head>Indirect ELISA with the RDB and S protein</ns0:head><ns0:p>The RBD and S proteins used as antigens in the ELISA were shown to be >95% pure by SDS-PAGE following expression and purification from mammalian (HEK derived) cells. In line with previous reports the yield of RBD was approximately 10-fold higher per litre of culture than the S protein <ns0:ref type='bibr' target='#b1'>(Amanat et al., 2020)</ns0:ref>. To establish ELISA cut-off values a panel of 113 sera collected prior to 2020 were tested, with the cut-off defined as mean plus three-standard deviations. Importantly, this panel included samples from participants with bacterial pneumonia and common respiratory viruses that have symptom overlap with SARS-CoV-2 infections (Table <ns0:ref type='table'>1</ns0:ref>). The healthy control sera (n=31) within the panel showed broad reactivity with S protein antigens from HCoV (HKU1, NL63, 229E), but not for SARS-CoV-2 (Supplementary Figure <ns0:ref type='figure'>1</ns0:ref>).</ns0:p><ns0:p>As shown in Figure <ns0:ref type='figure'>1A</ns0:ref>, all serum samples from PCR-confirmed COVID-19 patients collected ≥7 days from symptom onset had IgG above the determined cut-off in the RBD screening ELISA (P < 0.0001), while the pooled IVIG preparations (representing >1,500 donors each) were negative.</ns0:p><ns0:p>To illustrate the utility of the second confirmatory ELISA, samples with absorbance close to the cut-off in the RBD screen were titrated against the S protein. As shown in Figure <ns0:ref type='figure'>1B</ns0:ref>, the anti-S protein IgG titration clearly separated the true positives from the false positives, as only the PCR-confirmed COVID-19 samples met the seropositive criteria (OD > cut-off in at least two consecutive dilutions). Indeed, this second ELISA resulted in all pre-pandemic samples being classified as negative and a calculated specificity of 100%, albeit in a moderately sized sample panel. The use of IgM and IgA in the two-step ELISA protocol were also explored, however IgM was found to have lower sensitivity with only 4/18 of the 7+ day COVID-19 samples being seropositive, compared with 18/18 (100%) for IgG. IgA was deemed unsuitable as the assay window was inferior to that of IgG (OD range 0.01-0.82 compared with 0.01-1.42), and recent reports highlighted reduced sensitivity and specificity for IgA based SARS-CoV-2 ELISA <ns0:ref type='bibr'>(Meyer et al., 2020;</ns0:ref><ns0:ref type='bibr' target='#b4'>Beavis et al., 2020)</ns0:ref>.</ns0:p><ns0:p>Isotyping ELISA performed with the PCR-confirmed COVID-19 samples showed significantly higher titres for IgG compared with IgM and IgA for both the RBD and S protein (Figure <ns0:ref type='figure'>1C</ns0:ref> and D, P < 0.01). Of note, only one sample had IgM levels higher than IgG, with the remaining 20 samples having higher IgG than IgM, despite ~40% of the samples being collected within twoweeks of symptom onset.</ns0:p></ns0:div> <ns0:div><ns0:head>Neutralising anti-SARS-CoV-2 antibodies</ns0:head><ns0:p>The presence of neutralising antibodies (NAbs) capable of blocking the interaction between the SARS-CoV-2 RBD and the hACE-2 receptor was assessed using sVNT (Figure <ns0:ref type='figure' target='#fig_4'>2A</ns0:ref>) <ns0:ref type='bibr' target='#b22'>(Tan et al., 2020)</ns0:ref>. To validate performance, the pre-pandemic panel (n=113) was compared with the PCRconfirmed COVID-19 samples. Using the cut-off value of 20% inhibition recommended by the manufacturer, all control samples tested were negative, resulting in a 100% specificity. Similarly, all sera from PCR-confirmed COVID-19 patients collected ≥7 days from symptom onset were positive for a sensitivity of 100%. An experimentally calculated cut-off of 19.59% (mean + 3SD of controls) also gave 100% sensitivity and there was a significant increase in the level of NAbs (% inhibition) in the 7+ day COVID-19 samples compared with controls (P < 0.001).</ns0:p><ns0:p>A correlation analysis of the sVNT with ELISA titre data for IgG, IgM and IgA against the RBD and S protein found the highest correlation between the sVNT and anti-RBD IgG (r 0.91, P < 0.0001), suggesting anti-RBD IgG is the best predictor of neutralisation (Figure <ns0:ref type='figure' target='#fig_4'>2B</ns0:ref>). Temporal samples were available for three of the PCR-confirmed COVID-19 patients, with increasing NAbs detected in patient one between days 6 and 9, and higher levels of NAbs in patients two and three between days 11 and 31 and days 15 and 31, respectively (Figure <ns0:ref type='figure' target='#fig_4'>2C</ns0:ref>). In keeping, there was a significant, positive correlation between days from symptom onset and the level of NAbs (% inhibition) in the PCR-confirmed COVID-19 patient group up to 40 days (Figure <ns0:ref type='figure' target='#fig_4'>2D</ns0:ref>; r 0.54, P < 0.05).</ns0:p></ns0:div> <ns0:div><ns0:head>Patients with COVID-19-like symptoms</ns0:head><ns0:p>To assess the utility of the two-step ELISA protocol and the sVNT in detecting prior SARS-CoV-2 infection, 82 patients who presented to hospital during the lockdown period with respiratory symptoms fitting the case definition for COVID-19 testing, but negative for PCR, were analysed. A single patient was classified as seropositive in the two-step ELISA (RBD screen, OD 0.87; S protein titration OD > 0.2 in four consecutive dilution wells, Supplementary data). Similarly, only one patient was classified as positive in the sVNT with 65.5% inhibition PeerJ reviewing PDF \| (2020:07:50726:1:1:NEW 10 Aug 2020)</ns0:p><ns0:p>Manuscript to be reviewed (Figure <ns0:ref type='figure' target='#fig_4'>2A</ns0:ref>). This was the same patient identified using the two-step ELISA, indicating a prior undetected SARS-CoV-2 infection. Although the patient was PCR negative at presentation, they had notable travel history as a risk factor.</ns0:p></ns0:div> <ns0:div><ns0:head>DISCUSSION</ns0:head><ns0:p>At the time of writing, New Zealand has eliminated community transmission of SARS-CoV-2. This is a very different scenario to that during Alert Level 4 Lockdown, when human activity was severely restricted, and the perceived need for rapid establishment of serological assays was significant. Through the generous sharing of reagents and technical expertise by the international scientific community, and highly collaborative efforts of scientists and clinicians across the country we were able to establish and assess the described serological assays in a matter of weeks. These assays detect SARS-CoV-2 IgG and the presence of neutralising antibodies, in persons who have been infected with SARS-CoV-2 at least 7 days after symptom onset, with repeat sampling recommended in those where samples are obtained <7 days from symptom onset. While the lack of community transmission in New Zealand has limited the scale of serological investigations thus far, maintaining our current state requires near-perfect management of returning travellers, as new cases are imported. Serological assays could have a key role in our border setting, identifying persons who have previously had a SARS-CoV-2 infection. Furthermore, the application of serological assays that enable accurate measurement and further understanding of SARS-CoV-2 immune responses are crucial to any 'exit strategy' reliant on effective vaccines and/or therapeutics <ns0:ref type='bibr' target='#b3'>(Baker, Kvalsvig & Verrall, 2020)</ns0:ref>.</ns0:p><ns0:p>The two-step ELISA protocol is based on published protocols <ns0:ref type='bibr' target='#b21'>(Stadlbauer et al., 2020)</ns0:ref>, with the seropositive cut-off established using a panel of pre-pandemic sera that showed bacterial pneumonia and other common respiratory infections such as rhinovirus, influenza and respiratory syncytial virus did not cross-react with the SARS-CoV-2 RBD and S proteins. Although the prepandemic panel does not include samples from known human coronavirus infections, the healthy control sera in the panel had broad reactivity with HCoV antigens. This is consistent with other studies have reported that the majority of banked pre-pandemic sera react with human coronavirus antigens given the ubiquitous nature of these infections <ns0:ref type='bibr' target='#b1'>(Amanat et al., 2020;</ns0:ref><ns0:ref type='bibr' target='#b15'>Juno et al., 2020)</ns0:ref>. Furthermore, neither of the pooled IVIG preparations derived from > 1500 donors PeerJ reviewing PDF \| (2020:07:50726:1:1:NEW 10 Aug 2020)</ns0:p><ns0:p>Manuscript to be reviewed tested in this study reacted with SARS-CoV-2 RBD or S proteins, consistent with the negligible cross-reactivity of human coronavirus sera reported by the assay developers <ns0:ref type='bibr' target='#b1'>(Amanat et al., 2020;</ns0:ref><ns0:ref type='bibr' target='#b22'>Tan et al., 2020)</ns0:ref>.</ns0:p><ns0:p>All PCR-confirmed COVID-19 patients showed strong seroconversion a week after symptom onset, with mean ELISA AUC titre for anti-RBD IgG of 1:1,500. Although our sample size was limited, the antibody responses follow trends observed in larger COVID-19 cohorts in settings with higher case numbers. This includes a concurrent rise in IgM and IgG <ns0:ref type='bibr' target='#b13'>(Huang et al., 2020;</ns0:ref><ns0:ref type='bibr'>To et al., 2020)</ns0:ref>, and robust antibody responses to the full-length S protein, as well as RBD <ns0:ref type='bibr' target='#b8'>(Chen et al., 2020;</ns0:ref><ns0:ref type='bibr' target='#b1'>Amanat et al., 2020;</ns0:ref><ns0:ref type='bibr' target='#b15'>Juno et al., 2020)</ns0:ref>. The level of NAbs, which block the interaction of the RBD with the hACE-2 receptor was highly correlated with anti-RBD IgG in this study. This is in keeping with observations that most SARS-CoV-2 neutralising epitopes are localised in the RBD <ns0:ref type='bibr' target='#b14'>(Ju et al., 2020;</ns0:ref><ns0:ref type='bibr' target='#b8'>Chen et al., 2020)</ns0:ref> and that neutralisation measured by conventional, viral neutralisation assays is reported to correlate with the sVNT <ns0:ref type='bibr' target='#b22'>(Tan et al., 2020)</ns0:ref>.</ns0:p><ns0:p>While more extensive comparison of the sVNT with gold standard viral neutralisation assays is required, the ability of the assay to measure total NAbs in less than 2 hours, without the need to handle live SARS-CoV-2 virus, provides strong rationale to consider these types of assays in pandemic management, particularly in settings where BSL3 laboratory infrastructure is limited.</ns0:p><ns0:p>Serological tests are traditionally used to support clinical diagnosis by determining recent or prior infection when swab results are negative <ns0:ref type='bibr' target='#b7'>(Bryant et al., 2020)</ns0:ref>. In the context of SARS-CoV-2 infections, serology could confirm diagnosis in individuals who are PCR-negative due to late presentation or technical limitations of swab-based tests. Proof of principle was demonstrated in this study by the identification of a seropositive individual in the two-step ELISA and the sVNT from a cohort of 82 patients with respiratory symptoms that presented to hospital during the Alert level 4 lockdown period. The positive serology results, combined with travel history, suggest a prior undetected SARS-CoV-2 infection and highlight the value of accurate serology in clinical diagnosis. Extending the application of serology to understand SARS-CoV-2 transmission within clusters was recently demonstrated in Singapore, where detection of seropositive individuals enabled three small clusters to be connected <ns0:ref type='bibr' target='#b24'>(Yong et al., 2020)</ns0:ref>. Beyond transmission studies, serological testing in managed isolation facilities could provide a more complete picture of previous SARS-CoV-2 infections in returning citizens in settings like New Zealand where strict border restrictions remain.</ns0:p><ns0:p>Large scale serosurveys require careful consideration of assay sensitivity and specificity. While the serological assays described in this study show very high sensitivity and specificity, markedly larger cohorts would need to be tested before robust assessments of accuracy can be performed. Indeed, the need for rigorously validated assays is arguably greater in low prevalence settings like New Zealand before wide-spread serosurveys are considered. Even an assay with near perfect specificity of 99.9% would identify 100 false positives in every 100,000 individuals, and with <0.1% of the New Zealand population having been infected, positive predictive value is extremely limited. In contrast, targeted studies of high-risk individuals such as health care workers, those linked with clusters and in managed isolation would provide an opportunity to assess serological assay performance, and generate critical local data on levels of antibodies in those with symptomatic versus asymptomatic infection <ns0:ref type='bibr'>(Krammer & Simon, 2020;</ns0:ref><ns0:ref type='bibr' target='#b7'>Bryant et al., 2020)</ns0:ref>. Studies that incorporate RBD-based assay such as those described here, as well as the nucleoprotein as per the high-throughput systems recently launched by Roche and Abbott <ns0:ref type='bibr' target='#b6'>(Bryan et al., 2020)</ns0:ref>, would provide insight into SARS-CoV-2 exposure together with antibody neutralisation, and lay the foundation for future studies aimed at understanding long-term antibody persistence and vaccine efficacy.</ns0:p></ns0:div> <ns0:div><ns0:head>CONCLUSION</ns0:head><ns0:p>In summary, the collective support of international colleagues combined with a collaborative domestic network of scientists and clinicians enabled serological assays for COVID-19 to be established during a nationwide lockdown. The success of the open approach we have taken may offer considerable advantages in other settings where access to reagents and resources is currently constrained. Importantly, these assays showed that hospitalised patients infected with SARS-CoV-2 develop high levels of neutralising antibodies. While the low prevalence of COVID-19 infections in New Zealand currently limits the use of serological assays in population level serosurveys, they have immediate utility in clinical diagnostics, studies to understand transmission in high-risk cohorts and underpinning longer-term 'exit' strategies based on effective vaccines and/or therapeutics.</ns0:p><ns0:p>PeerJ reviewing PDF \| (2020:07:50726:1:1:NEW 10 Aug 2020)</ns0:p><ns0:p>Manuscript to be reviewed </ns0:p></ns0:div><ns0:figure xml:id='fig_0'><ns0:head>(</ns0:head><ns0:label /><ns0:figDesc>n=31) (ethics UOA021200), hospitalised adults with bacteraemia or bacterial pneumonia (n=25) (ethics HDEC 17/STH/233), together with children infected with various respiratory viruses (n=57) (ethics HDEC 17/NTA/262) (Bennett et al., 2019). The patients with bacterial pneumonia had signs, symptoms and radiological imaging diagnostic of pneumonia and Streptococcus pneumoniae identified. The COVID-19 panel comprised serum and plasma (n=21) obtained from 17 patients with PCR-confirmed SARS-CoV-2 infection and those with respiratory symptoms fitting the case definition for COVID-19 testing that were subsequently found negative by PCR (n=82). The PCR-confirmed and PCR-negative patients were admitted to Middlemore or Auckland City hospitals in Auckland, New Zealand between March and May 2020 with residual PeerJ reviewing PDF \| (2020:07:50726:1:1:NEW 10 Aug 2020)</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_1'><ns0:head>FIGURE LEGENDS Figure 1 .Figure 2 .</ns0:head><ns0:label>LEGENDS12</ns0:label><ns0:figDesc>FIGURE LEGENDS</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_2'><ns0:head>Figure 1 Figure 1 .</ns0:head><ns0:label>11</ns0:label><ns0:figDesc>Figure 1</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_3'><ns0:head>Figure 2</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2</ns0:figDesc></ns0:figure> <ns0:figure xml:id='fig_4'><ns0:head>Figure 2 .</ns0:head><ns0:label>2</ns0:label><ns0:figDesc>Figure 2. Surrogate viral neutralisation test (sVNT).</ns0:figDesc></ns0:figure> <ns0:figure><ns0:head /><ns0:label /><ns0:figDesc /><ns0:graphic coords='22,42.52,70.87,525.00,371.25' type='bitmap' /></ns0:figure> <ns0:note place='foot'>PeerJ reviewing PDF \| (2020:07:50726:1:1:NEW 10 Aug 2020)</ns0:note> </ns0:body> "	"Collaborative networks enable the rapid establishment of Serological Assays for SARS-CoV-2 during Nationwide Lockdown in New Zealand We thank the Editor and the three reviewers for their positive comments and feedback. We have addressed the points in turn as detailed here. Editor: Did the authors perform within- and between-run ELISA analyses of reference samples to see how reliable and reproducible the result is? Yes we did perform these analyses, and in order to ensure that reliability and reproducibility is maintained a positive and negative control is included on every plate, and there is acceptance criteria that must be met for the results to be valid. This is detailed in the methods as follows: Line 154-156 Positive and negative quality controls were included on each plate, with the assay meeting acceptance criteria if the OD was > 0.75 and < 0.03 for the positive and negative control, respectively. Fig 1C and 1D, I wonder if adding the levels of each Ig subclass from the control samples will be useful and informative, so readers can see the differences of these values between the case and control groups. Fig 1C and 1D, please check if the Y-axis represents AUC, not OD nor titer? If the Y-axis represents AUC, how AUC was calculated? Fig1C and 1D show the levels of Ig subclass for each of the PCR-positive COVID-19 cases analysed. The goal of this figure is to compare the levels of Ig subclasses rather than the differences between cases and controls, since, as shown in Figure 1A, controls had negligible reactivity to the SARS-CoV-2 antigens. The subclass levels are presented as the Area Under the Curve (AUC), which was calculated from the titration curve for each sample with the background AUC of pooled negative control sera subtracted. We have added further details of this in the methods section for clarity: Line 163-165 The area under the curve (AUC) was used to compare isotype-specific antibody titres and was calculated by subtracting background AUC of pooled negative control sera for each isotype in Prism 8 (GraphPad). How was the ELISA cut-off value (i.e., 0.2) calculated? When the authors perform ELISA at different time points (i.e., on different days), do the authors use the same ELISA cut-off value (i.e., 0.2)? If so, how the authors can be sure that the between-run results are comparable and the cut-off value is still valid each time (giving that ELISA OD of the same serum sample could fluctuate each run)? The ELISA cut-off was determined as the mean plus three standard deviations of the pre-pandemic sera panel (n=113 samples). This is now defined clearly in the results section of the manuscript (see below). The same cut-off is used between runs as the ELISA is highly reproducible when our standard operating procedures are followed. This includes timed incubations, an incubator to ensure consistent temperatures and positive and negative controls (with pre-defined acceptance criteria as mentioned above) on every plate. These controls provide further evidence of assay stability. Line 192-194 To establish ELISA cut-off values a panel of 113 sera collected prior to 2020 were tested, with the cut-off defined as mean plus three-standard deviations. In the method part, the protein expression protocol is worth mentioned briefly since the authors modified the original method of Stadlbauer et al 2020. We have included an extra details explaining how the transfections procedures differed from Stadlbauer et al 2020. Line 136-139 Plasmid DNA was added at 3.5 g/mL with PEI 7.0 at g/mL for 24hrs, after which culture volumes were doubled and supplemented with 2.2 mM valproic acid. Cultures were incubated with shaking for a further 72 hours before protein purification was performed. Reviewer 1: McGregor et al. showed the collaborative work in New Zealand for the rapid establishment of serological assays for SARS-CoV-2. Although the manuscript nicely described the effort to produce the in-house test kit quickly, the work did not provide the novelty to the field. The antigens were produced from the provided vectors, and the assays were performed using ELISA. Another major point is that the authors compared the in-house test to the surrogate viral neutralization test (sVNT). The preprint by Tan et al. analyzed only 13 samples to demonstrate the correlation between sVNT and the standard neutralization test. If the BSL-3 facility is not available, and the authors would like to demonstrate the correlation of serological assays with the viral neutralization, the comparison should be better performed using validated pseudotype virus. We acknowledge that the antigens are produced from provided vectors and the ELISA follow protocols recently published by the Krammer laboratory (Amanat et al., Nat Medicine. 2020; Stadlbauer et al., Curr Protocol Microbiol, 2020). However, we respectfully disagree that our manuscript is lacking novelty, and rather is of value to the field. During the current pandemic it is vital that each country generates data with respect to serological assays, and we describe the first serological data produced from New Zealand during our Alert Level 4 lockdown. Furthermore, independent validation of new assays is crucial prior to wider use and roll-out. Given that both the 2-step ELISA developed by the Krammer laboratory, and the sVNT developed by Duke-NUS (Tan et al., Nat Biotechnology, 2020) are very recently published, our manuscript contributes to these important validation activities. Indeed, as far as we are aware, there are no other peer reviewed papers describing the validation of the sVNT, particularly as a means of identifying an undiagnosed PCR negative case as we have (as highlighted by reviewer 3). With regards to the correlations, the peer-reviewed paper describing the sVNT was published 2 weeks ago in Nature Biotechnology (https://doi.org/10.1038/s41587-020-0631-z), and now includes an analysis of 60 samples in the correlation of sVNT with conventional VNT and pseudovirus-based VNT. Reviewer 2. This is a well-structured and conducted study that is clearly laid out and written. It is a preliminary evaluation of the testing but adds solid information to the usefulness of these antigens, specific Ab isotypes and platforms for serology to support retrospective case finding, evaluating the tests using human samples. My only suggestion is that perhaps a mention could be made of the impact of repeated testing (using a new blood sample) on improving issues around sensitivity. I wonder if the authors could note whether any of their negative sera were from patients with respiratory virus infections known to have been due to an HCoV and if there was anything notable about those results compared to non-HCoV positives. Thank you for the suggestions. With respect to repeat blood samples, we agree that timing of sampling impacts on the sensitivity of serology. This is shown in our data with samples obtained <7 days having significantly lower antibody levels that the 7+ day group. For clarity we have added mention of the value of repeat sampling in the discussion. Line 258-261: These assays detect SARS CoV 2 IgG and the presence of neutralising antibodies, in persons who have been infected with SARS-CoV-2 at least 7 days after symptom onset, with repeat sampling recommended in those where samples are obtained <7 days from symptom onset. In terms of the pre-pandemic sera, a number had PCR confirmed respiratory infections, but their history of infection with HCoV was not documented. To explore antibody responses to HCoV we have now performed additional ELISA with the healthy adults (n=31) from the pre-pandemic panel against HCoV S1 antigens from HKU1, NL63 and 229E. We observed broad reactivity to these antigens in line with other recent reports of seropositivity to HCoV in adults. However, none of these samples were seropositive for SARS-CoV-2. These data are now included in the manuscript in the results (line 195-198) and the discussion (line 273-274), and as a supplementary figure as shown below. Reviewer 3 The authors had done a commendable effort in difficult time of COVID-19 pandemic when medical testing were lacking in the situation of many countries locking down. The article validated the in-house ELISA for IgG, IgM, IgA to SAR-CoV-2 RBD and spike1 using 3 sera or plasma from 3 groups; a. COVID-19 PCR positive patients, b. PCR negative COVID-19-like presentation, and c. sera obtained before the pandemic. Showing the best performance, the IgG ELISA was utilized in a 2-step process; screening with anti-RBD followed by anti-spike confirmation. Outstandingly, the detection of a PCR-negative case demonstrated the usefulness and necessity of serology for complimenting the PCR. The results were also compared with a commercial surrogate neutralization antibody test (sVNT), of which the format is a total anti-RBD detection using blocking assay. As expected, the IgG anti-RBD showed the most correlation with sVNT. This article also revealed the high specificity of sVNT. Specific items for the authors: 1. “Alert 4” (line 64, 231), in the New Zealand COVID-19 Alert Levels, is needed to be clarified for reader outside New Zealand. This has been clarified with the modified sentence as follows: Line 66-68: The first evidence of community transmission in New Zealand was reported on 23rd March 2020, the country went into an intense “Alert Level 4” lockdown (the highest level of a 4-level response system) three days later and remained at this Alert Level for the following five weeks (Baker, Kvalsvig & Verrall, 2020) 2. Please discuss more of the reason why false-positive results in anti-RBD screening became negative in anti-spike test. This is due to the 2-step approach, with the first step (anti-RBD screen) designed for high-throughput sample analysis and maximum sensitivity. This is a single point ELISA at a 1:100 dilution with a potentially seropositive sample defined as being above the 0.2 OD cut-off. In contrast the second step is designed for maximum specificity and has stricter criteria to confirm seropositive samples. In this second step a titration is performed with a positive sample defined as one with an OD >0.2 in 2 consecutive dilutions (eg an OD above 0.2 at a 1:300 dilution). Thus, the reason potential false-positives become negative is likely due to the different nature of the ELISA in each step (high-throughput single point screen vs titration) rather than an antigen driven response. 3. The authors did not find good performance of IgA testing. Considering lower level of IgA than IgG, should a higher concentration of anti-IgA peroxidase be used? Please also discuss regarding the finding of a commercial IgA anti-spike being better than IgG. (Kathleen G. Beavis, Scott M., et. al. Evaluation of the EUROIMMUN Anti-SARS-CoV-2 ELISA Assay for detection of IgA and IgG antibodies. Journal of Clinical Virology. 2020;129:104468, ISSN 1386-6532, https://doi.org/10.1016/j.jcv.2020.104468). Thank you for this suggestion – we had actually experimented with different IgA secondary concentrations and found this did not improve the performance of the IgA ELISA in our hands. It consistently had a narrower assay window than IgG. We have now added details of our experience with IgA into the results for clarity, and referenced the Beavis paper. We note that Beavis et al., showed reduced specificity and sensitivity of IgA compared with IgG. It was several pre-prints that showed similar reduced performance of IgA, available at the time we were developing our assays, that further informed our decision to proceed with IgG rather than IgA. Line 212-214: The use of IgM and IgA in the two-step ELISA protocol were also explored, however IgM was found to have lower sensitivity with only 4/18 of the 7+ day COVID-19 samples being seropositive, compared with 18/18 (100%) for IgG. IgA was deemed unsuitable as the assay window was inferior to that of IgG (OD range 0.01-0.82 compared with 0.01-1.42), and recent reports highlighted both reduced sensitivity and specificity for IgA based SARS-CoV-2 ELISA (Meyer et al., 2020; Beavis et al., 2020). 4. New reference showing sVNT correlation with conventional VNT was available and should be added (Tan, C.W., Chia, W.N., Qin, X. et al. A SARS-CoV-2 surrogate virus neutralization test based on antibody-mediated blockage of ACE2–spike protein–protein interaction. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0631-z) We have updated this reference as well as the Juno et al., reference that is now published in Nature Medicine. "	Here is a paper. Please give your review comments after reading it.