Accumulating studies have shown that long non-coding RNAs (lncRNAs) are involved in many biological processes and play important roles in a variety of complex human diseases. Developing effective computational models to identify potential relationships between lncRNAs and diseases can not only help us understand disease mechanisms at the lncRNA molecular level, but also promote the diagnosis, treatment, prognosis, and prevention of human diseases. For this paper, a network-based model called NBLDA was proposed to discover potential lncRNA–disease associations, in which two novel lncRNA–disease weighted networks were constructed. They were first based on known lncRNA–disease associations and topological similarity of the lncRNA–disease association network, and then an lncRNA–lncRNA weighted matrix and a disease–disease weighted matrix were obtained based on a resource allocation strategy of unequal allocation and unbiased consistence. Finally, a label propagation algorithm was applied to predict associated lncRNAs for the investigated diseases. Moreover, in order to estimate the prediction performance of NBLDA, the framework of leave-one-out cross validation (LOOCV) was implemented on NBLDA, and simulation results showed that NBLDA can achieve reliable areas under the ROC curve (AUCs) of 0.8846, 0.8273, and 0.8075 in three known lncRNA–disease association datasets downloaded from the lncRNADisease database, respectively. Furthermore, in case studies of lung cancer, leukemia, and colorectal cancer, simulation results demonstrated that NBLDA can be a powerful tool for identifying potential lncRNA–disease associations as well. In recent years, accumulating evidence studies have shown that non-coding RNAs (ncRNAs) are involved in various biological processes in the human body ,2,3, andCurrently, with the rapid development of bioinformatics, some lncRNA–disease association databases such as LncRNADisease  and Lnc2βk to each node in the network, where k is the degree of the node and β is a freely adjustable parameter. Moreover, considering that traditional mass diffusion-based algorithms focused on unidirectional mass diffusion only, we further applied a consistence-based mass diffusion algorithm via bidirectional diffusion on NBLDA to predict potential lncRNA–disease associations by adopting a label propagation algorithm. Finally, in order to estimate the prediction performance of NBLDA, the framework of leave-one-out cross validation (LOOCV) was implemented, and simulation results show that NBLDA can achieve reliable AUCs of 0.8846, 0.8273, and 0.8075 in LOOCV based on three versions of known lncRNA–disease association datasets downloaded from the lncRNADisease database, respectively, which demonstrates the excellent prediction performance of NBLDA. In addition, in case studies of lung cancer, leukemia, and colorectal cancer, simulation results show that there are 9, 10, and 7 out of the top 10 predicted disease-related lncRNAs of these three kinds of diseases having been validated by evidence from studies in the PubMed literature and Lnc2Cancer database, respectively, which further indicates NBLDA has a satisfactory prediction performance in discovering potential lncRNA–disease associations as well.Inspired by the above-mentioned state-of-the-art methods, a network-based computational model NBLDA was proposed for this paper to predict potential lncRNA–disease associations based on the assumption that functionally similar lncRNAs show similar interaction patterns with similar diseases. In NBLDA, two new networks were constructed first based on known lncRNA–disease associations and Gaussian interaction profile kernel similarity for lncRNAs and diseases, and then we assigned an attraction that is proportional to In order to estimate the prediction performance of NBLDA, and described in this section, we implemented LOOCV on NBLDA based on known lncRNA–disease associations downloaded from the LncRNADisease database. While implementing LOOCV, each known lncRNA–disease association was left out in turn as a test sample and the other remaining known lncRNA–disease associations were taken as training samples. Moreover, all lncRNA–disease pairs without known relevance evidences were considered as candidate samples. Thereafter, we obtained the ranking of each test sample within all candidate samples according to their scores predicted by NBLDA, and then, the test sample was regarded as successfully predicted if its ranking exceeded a given threshold. Furthermore, the receiver operating characteristic (ROC) curves were drawn based on true positive rate  and false positive rate  obtained at different thresholds. Here, the sensitivity represents the proportion of test samples with a ranking higher than the given threshold to all positive samples, whereas 1-specifcity indicates the ratio between candidate samples with a ranking above a given threshold and all candidate samples. Then, the areas under the ROC curve (AUCs) were further calculated to evaluate the predictive performance of our model NBLDA, and it is obvious that the larger the value of AUC, the better the prediction performance of NBLDA will be.DS1, respectively  is one of the most common types of cancer in the United States and the second leading cause of cancer death . The aveAccumulating evidence studies have shown that lncRNAs are closely related to a variety of biological processes. Identifying potential lncRNA–disease association not only helps us understand the pathogenesis of disease at the molecular level of lncRNA, but also contributes to the diagnosis, treatment, prognosis, and prevention of diseases. In this paper, we presented a computational model NBLDA to reveal potential lncRNA–disease associations based on known lncRNA–disease associations and Gaussian interaction profile kernel similarity for lncRNAs and diseases. We improved the baseline algorithm of bipartite network recommendation based on the network topological similarity of the lncRNA–disease association network and resource allocation strategy of unequal allocation and unbiased consistence. A label propagation algorithm was then used to predict potential lncRNA–disease associations. NBLDA achieved AUCs of 0.8846, 0.8273, and 0.8075 in the validation framework of LOOCV based on three versions of known lncRNA–disease association datasets, which significantly improved the previous classic models. Furthermore, we conducted case studies of lung cancer, leukemia, and colorectal cancer, and simulation results show that there are 9, 10, and 7 out of the top 10 predicted candidate lncRNAs having been confirmed by previous studies in the literature respectively. As a result, both cross validation and case studies have shown that NBLDA has a good performance in potential lncRNA–disease association prediction.D (or a given lncRNA L) may also contribute resources to D (or L), we then constructed novel networks based on known lncRNA–disease associations and the Gaussian interaction profile kernel similarity for diseases and lncRNAs. Third, we adopted a resource allocation strategy of unequal allocation and unbiased consistence. Certainly, there are still some limitations in NBLDA which must be improved in the future. First of all, the similarity measures for diseases and lncRNAs are relatively simple, and more effective similarity measures such as disease semantic similarity, disease phenotypic similarity, and lncRNA functional similarity can improve the performance of our model. Moreover, although the numbers of lncRNA–disease associations data have increased compared to before, the known lncRNA–disease associations in our dataset are still too sparse, and the performance of NBLDA can be further improved when more lncRNA–disease associations datasets are available and more reliable types of biological datasets are integrated. Last but not least, increasing lncRNA–disease association data can be used as training samples for model learning with the development of biological experimental techniques.The novel and reliable performance of NBLDA is mainly attributed to the following aspects. First, the method proposed by us is based on a classical approach that has already achieved excellent performance in predicting associations in other biological networks. Second, considering that the lncRNAs (or diseases) which are not associated with a given disease http://www.cuilab.cn/lncrnadisease), respectively (see DS1) from the LncRNADisease database, and after removing duplicated records and associations that do not belong to human beings, we finally obtained 1695 known lncRNA–disease associations involving 314 diseases and 828 lncRNAs. Next, we downloaded the 2015 version of the dataset (denoted as DS2) from the LncRNADisease database, and after removing duplicated data, we finally obtained 621 known lncRNA–disease associations including 226 diseases and 285 lncRNAs. Finally, we downloaded the 2012 version of the dataset (denoted as DS3) from the LncRNADisease database, and after removing duplicated data, we finally obtained 293 known lncRNA–disease associations including 167 diseases and 118 lncRNAs. Thereafter, we adopted an adjacency matrix Y to indicate known associations between lncRNAs and diseases. In the adjacency matrix Y, if there is a known association between lncRNA il and disease jd, then there is Y = 1; otherwise, there is Y = 0. Moreover, for convenience, we further introduced DN and LN to denote the number of diseases and lncRNAs collected above, respectively.Three versions of the datasets were downloaded from the LncRNADisease database  is the ith row of the adjacency matrix Y and represents the interaction profile of lncRNA il with all diseases. The parameter lS based on these lncRNAs collected above.Based on the hypothesis that functionally similar lncRNAs are always associated with similar diseases , for anyous work . Obviousid and jd, we can obtain the Gaussian interaction profile kernel similarity between id and jd according to Equation (3) as follows:IP(id) is the ith column of the adjacency matrix Y and represents the interaction profile of disease id with all lncRNAs. The parameter dS based on these diseases collected above.In a similar way, for any given diseases set to 1 . ObviouslS, dS, and Y as follows:As illustrated in SL, we can construct a bipartite network first, and then, for a randomly given node ψ in the newly constructed bipartite network, supposing that ψ has been assigned an attraction such as βk(ψ), where k(ψ) represents the degree of node ψ in the bipartite network and β is a freely adjustable parameter, it is obvious that β = 0 means the average allocation of resources, β < 0 means that nodes with lower degrees are more attractive and will obtain more resources, and β > 0 indicates that nodes with higher degrees have greater attraction and will be allocated more resources  and jDN].From the above descriptions, it is easy to see that


A lot of studies indicated that aberrant expression of long non-coding RNA genes (lncRNAs) is closely related to human diseases. Identifying disease-related lncRNAs (disease lncRNAs) is critical for understanding the pathogenesis and etiology of diseases. Most of the previous methods focus on prioritizing the potential disease lncRNAs based on shallow learning methods. The methods fail to extract the deep and complex feature representations of lncRNA-disease associations. Furthermore, nearly all the methods ignore the discriminative contributions of the similarity, association, and interaction relationships among lncRNAs, disease, and miRNAs for the association prediction. A dual convolutional neural networks with attention mechanisms based method is presented for predicting the candidate disease lncRNAs, and it is referred to as CNNLDA. CNNLDA deeply integrates the multiple source data like the lncRNA similarities, the disease similarities, the lncRNA-disease associations, the lncRNA-miRNA interactions, and the miRNA-disease associations. The diverse biological premises about lncRNAs, miRNAs, and diseases are combined to construct the feature matrix from the biological perspectives. A novel framework based on the dual convolutional neural networks is developed to learn the global and attention representations of the lncRNA-disease associations. The left part of the framework exploits the various information contained by the feature matrix to learn the global representation of lncRNA-disease associations. The different connection relationships among the lncRNA, miRNA, and disease nodes and the different features of these nodes have the discriminative contributions for the association prediction. Hence we present the attention mechanisms from the relationship level and the feature level respectively, and the right part of the framework learns the attention representation of associations. The experimental results based on the cross validation indicate that CNNLDA yields superior performance than several state-of-the-art methods. Case studies on stomach cancer, lung cancer, and colon cancer further demonstrate CNNLDA's ability to discover the potential disease lncRNAs. Long non-coding RNA genes (lncRNAs) are transcripts longer than 200 nucleotides which are not translated into proteins Reik, . AccumulPredicting disease-related lncRNAs (disease lncRNAs) can screen the potential candidates for the biologists to discover the real lncRNA-disease associations with the wet-lab experiments  that includes all the disease terms related to the disease. If two diseases have more common disease terms, they are more similar, which is the basic idea for semantic similarity between Gene Ontology terms . LS is defined as,As the lncRNAs associated with the similar diseases are generally possible to have more similar functions, Chen et al. measured the similarity of two lncRNAs based on their associated diseases  is the semantic similarity of disease of dai and dbj which belong to DTa and DTb respectively. m and n are the numbers of diseases that are included by DTa and DTb. The lncRNA similarities are denoted by matrix Lij is the similarity of two lncRNAs li and lj   .l1 and d2, P, is input to the convolutional module on the left to learn a global deep representation for l1 and d2. The convolutional module includes two convolutional layers and two pooling layers  is the element at the ith row and the jth column of P′, and P′k,i,j represents a region within the filter when the kth filter slides to the position P′. The formal definitions of P′k,i,j and Zconv1,k are as follows,For the first convolutional layer, the length of a filter is set as bconv1 is the bias vector, f is a relu function  is the element at the ith row and jth column of the kth feature map Zconv1,k.where Zconv1. ng and np are the length and width of a filter of pooling layer, respectively. The pooling outputs of all the feature maps are Zconvpool1,We apply the max pooling to extract the robust features from the feature maps Zconvpool1,k is the kth feature map, and Zconvpool1,k is the element at its' ith row and jth column.where l1 and disease d2. Thus, the module consists of the attention mechanism at the feature level and the one at relationship level , and it contains the probability that a lncRNA and a disease is determined to have an association relationship and the probability that they have no association.p is defined as In our model, the cross-entropy loss between the ground truth distribution of lncRNA-disease association and the prediction probability z ∈ ℝ2 is the classification label vector and T is a set of training samples. If l1 is associated with d2, the second dimension of the vector z is 1 and the first one is 0. On the contrary, if l1 is not associated with d2, the first dimension of z is 1 and the second one is 0.where We denote all neural network parameters by θ. The objective function in our learning process is defined as follows,where λ is a trade-off parameter between the training loss and regularization term. We use Adam optimization algorithm to optimize the objective function  and the false positive rates (FPRs) to get a receiver operating characteristic (ROC) curve by changing threshold θ. TPR and FPR are defined as follows,The samples are ranked by their association scores after the association probabilities of the testing samples are estimated. The higher the node pairs of the positive samples are ranked, the better CNNLDA performs. If an observed association exists in lncRNA-disease node pair samples, and its association score is greater than a threshold θ, it is a successfully determined positive sample. If the prediction score of a negative sample is smaller than θ, it is a determined correctly negative sample. We calculate the true positive rates   and the unobserved ones (the negative samples) form the serious imbalance. In such case, we also use the precision-recall (PR) curve and its area (AUPR) to assess the performance of a prediction method  for each of the 5 folds when used as a test set, and the 5 performances are averaged to give the final performance.k among the total positive samples.In addition, the biologists usually select lncRNA candidates from the top part of the ranking list, and then further validate their associations with diseases. Therefore, the recall values of top 30, 60, …, 240, are calculated, and they represent the fraction of the successfully recovered positive samples in the top list To evaluate the performance of CNNLDA, we compare it with several state-of-the-art methods including SIMCLDA (Lu et al., Wilcoxon-test to evaluate whether CNNLDA's AUCs and AUPRs across all of the tested diseases are significantly higher than those of another method. CNNLDA achieves significantly higher performance than the other methods in terms of both AUCs and AUPRs as the corresponding P-values are smaller than 0.05 (We perform a paired han 0.05 .k ranked lncRNA-disease associations is, the more genuine associations are determined correctly. Under different k cutoffs, the performance of CNNLDA consistently outperforms other methods (The higher the recall rate on the top  methods , and ranIn addition, to validate the effectiveness of exploiting the information related to the miRNAs, we construct another instance of CNNLDA that is trained without this kind of information, and the instance is referred to as CNNLDA-nM. The instance of CNNLDA that is trained by using the miRNA-related information is still named as CNNLDA. CNNLDA's AUC and AUPR are 0.2%and 0.94% greater than CNNLDA-nM, which confirms the importance of integrating the information for improving CNNLDA's prediction performance.stomach cancer, lung cancer, and colon cancer and analyze the top 15 candidates respectively related to these cancers (To demonstrate CNNLDA's ability to discover potential candidate disease lncRNAs, we execute the case studies on  cancers .First, a database named Lnc2Cancer curates the lncRNAs that have different expression in the disease tissues compared to the normal ones. Lnc2Cancer contains lncRNAs related to cancers that have been identified by analyzing the results of northern blot experiments, microarray experiments, and quantitative real-time polymerase chain reaction experiments (Gao et al., 1” are supported by several published literature. These lncRNAs are confirmed to have dysregulations in the cancers when compared with the normal tissues (Bahari et al., Next, 2 candidates of stomach cancer, 1 candidate of lung cancer and 2 candidates of colon cancer labeled with “literature2,” and “miRCancer, StarBase” are related to the important factors affecting the development of the corresponding cancers. In the metabolic network, lncRNA HCP5 is regulated by three miRNAs, and the miRNAs are downregulated in stomach cancer. It indicates that the expression of HCP5 is more likely to associate with stomach cancer (Mo et al., Finally, 5 candidates labeled with “literatureAfter evaluating its prediction performance through the cross-validation process and case studies, CNNLDA is applied to all 402 diseases. All the positive samples and the negative ones are used to train CNNLDA to predict the novel disease-associated lncRNAs. The potential candidate lncRNAs for these diseases are listed in A novel method based on dual convolutional neural networks, CNNLDA, is developed for predicting the potential disease-related lncRNAs. We respectively construct the attention mechanism at feature and relationship levels to discriminate the different contributions of features and learn the more informative representation of lncRNA-disease associations. The new framework based on dual convolutional neural networks is developed for learning the global representation and the attention of lncRNA-disease associations. The experimental results indicate that CNNLDA is superior to the compared other methods in terms of both AUCs and AUPRs. The case studies on 3 diseases demonstrate CNNLDA's ability for discovering potential disease-associated lncRNAs.All datasets analyzed for this study are cited in the manuscript and the PX and YC conceived the prediction method, and they wrote the paper. YC and ZZ developed computer programs. TZ and RK analyzed the results and revised the paper.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Evidences have increasingly indicated that lncRNAs (long non-coding RNAs) are deeply involved in important biological regulation processes leading to various human complex diseases. Experimental investigations of these disease associated lncRNAs are slow with high costs. Computational methods to infer potential associations between lncRNAs and diseases have become an effective prior-pinpointing approach to the experimental verification.In this study, we develop a novel method for the prediction of lncRNA-disease associations using bi-random walks on a network merging the similarities of lncRNAs and diseases. Particularly, this method applies a Laplacian technique to normalize the lncRNA similarity matrix and the disease similarity matrix before the construction of the lncRNA similarity network and disease similarity network. The two networks are then connected via existing lncRNA-disease associations. After that, bi-random walks are applied on the heterogeneous network to predict the potential associations between the lncRNAs and the diseases. Experimental results demonstrate that the performance of our method is highly comparable to or better than the state-of-the-art methods for predicting lncRNA-disease associations. Our analyses on three cancer data sets  also indicate the usefulness of our method in practical applications.Our proposed method, including the construction of the lncRNA similarity network and disease similarity network and the bi-random walks algorithm on the heterogeneous network, could be used for prediction of potential associations between the lncRNAs and the diseases. Long non-coding RNAs (lncRNAs) form a new class of important ncRNAs, with length longer than 200nt –3. AccumComputational models have been developed to predict potential associations between lncRNAs and diseases. Chen et al.  had an aBased on the fact that non-coding genes are often cooperated in human diseases to predict potential lncRNA-disease association, Peng et al.  proposedSome models predict novel associations without referring to known associations between lncRNAs and diseases. Chen  proposedSome computational models have been applied to predict lncRNA-disease associations based on random walk on networks. Chen et al.  considerIn this study, we propose a novel computational model of Laplacian normalization and bi-random walks on heterogeneous networks for predicting lncRNA-disease associations (Lap-BiRWRHLDA). Firstly, the method calculates the Gaussian interaction profile kernel similarity of lncRNAs and diseases by known lncRNA-disease associations. Next, we integrate the two sources of similarity to construct an lncRNA-lncRNA similarity network. The disease-disease similarity network can be constructed by the profile kernel similarity of diseases. Subsequently we perform Laplacian normalization on the similarity matrices of lncRNAs and diseases as the transpose matrices. Furthermore, we apply random walks on the lncRNA similarity network and the disease similarity network, respectively. Finally, we use a weighted average of random walks on both networks as a predictor of lncRNA disease associations. We believe that the higher scores of lncRNA-disease associations will have greater possibility for further verification. To evaluate our proposed method, we utilize leave-one-out cross-validation experiments to demonstrate its superior performance compared with existing approaches. Furthermore, the analyses of three cancers  effectively support the practical application of our method. We then use Lap-BiRWRHLDA to infer potential lncRNA-disease associations. Some high-score results are successfully verified by the LncRNADisease and Lnc2Cancer databases.To assess the performance of our proposed method, we use the leave-one-out cross-validation to perform the assessment. We leave out each known lncRNA-disease association in turn as test sample, while other known relationships are used as training samples and all unknown relationships are taken as candidate samples. Since disease similarity and lncRNA similarity depend on the Gaussian interaction profile kernel similarity of the known lncRNA-disease association, the disease similarity and lncRNA similarity will change when we delete a known lncRNA-disease association, so we will get different similarities.A receiver-operating characteristics (ROC) curve is applied to determine the predictive performance, which plots the correlation between true-positive rate (TPR) indicating sensitivity and false-positive rate (FPR) indicating specificity at different thresholds. Sensitivity represents the percentage of the left-out associations achieving the ranking higher than a given threshold; specificity means the percentage of candidate associations achieving the ranking lower than this given threshold. When we vary thresholds, we will obtain the corresponding different TPRs and FPRs. In this way, ROC is drawn and AUC is calculated. As a result, Lap-BiRWRHLDA achieved the AUC of 0.8409, 0.8527 and 0.8429 for three datasets used, respectively.α controls the probability of the random walk restart. To optimize the parameter α, we increased α from 0.1 to 1 with step size 0.1, and then calculated the corresponding AUC value by LOOCV. After experimental verification, we chose α=0.9, and we achieved the AUC values of 0.8409. The experimental results indicate that Lap-BiRWRHLDA offers better performance on the LncRNADisease dataset on October 2012, when α=0.9 is selected. Similarly we achieved the AUC values of 0.8527 (α=0.2) and 0.8429 (α=0.8) based on Lnc2Cancer dataset on July 2016, and the LncRNADisease dataset on April 2016.Parameter We compared Lap-BiRWRHLDA with previous published methods in LOOCV based on the LncRNADisease dataset on October 2012. (1) LRLSLDA  computesWe also compared the LncRNADisease dataset on April 2018 with the LncRNADisease dataset on October 2012, then selected 50 lncRNA-disease associations which were unverified in the LncRNADisease dataset on October 2012 but were verified in the LncRNADisease dataset on April 2018. We compared our method with LRLSLDA, GrwLDA by independently testing the ranking of the 50 relationships. Through experimental tests, our method has 30 rankings higher than the LRLSLDA method, and 41 rankings higher than the GrwLDA method.To further highlight the performance of Lap-BiRWRHLDA, we studied the predictive performance of three cancers: breast cancer, lung cancer, and liver cancer. For each type of cancer, we take the top 10 most probable lncRNAs as candidates associated with this cancer. Next, we manually checked these lncRNAs by mining biomedical literature from the LncRNADisease dataset and the Lnc2Cancer dataset.Breast cancer is the second leading cause of female cancer deaths, comprising 22% of all cancers in women , 17. LapAccumulated experimental evidences have shown that lncRNAs play an important role in the human complex disease mechanism, and mutations or disorders of lncRNAs are associated with various complex diseases. More and more evidences show that it is crucial to propose an effective computational model to infer potential lncRNA-disease associations. In this article, we proposed a novel computational model of Laplacian normalization and bi-random walks on heterogeneous networks for predicting lncRNA-disease associations. Our method shows better performance in LOOCV experiments by comparison with previous methods. In 50 unverified lncRNA-disease associations experiments, We compared our method with LRLSLDA, GrwLDA. The results indicated that our method has the higher ranking. Furthermore, the study of the cases of breast cancer, lung cancer, and liver cancer shows that our method improves the performance of predicting potential relationships.Although our method can improve the prediction accuracy, it still has some limitations. For example, construction of the disease-disease similarity matrix relies on the Gaussian interaction profile kernel similarity matrix for diseases from the known disease-lncRNA associations. In further work, we will improve our method in the following aspects: Firstly, Lap-BiRWRHLDA relies on the calculation of similarity matrix when constructing an lncRNA similarity network, and so the incompleteness of data may affect the final performance. Therefore, the integration of gene disease correlation data or the addition of more bioinformatics data may improve the performance of our method. These aspects have been considered in previous methods such as TPGLDA  and BRWLIn this study, we proposed a method called Lap-BiRWRHLDA to predict the relationship between lncRNA and diseases. This model utilizes the Laplacian normalization of the lncRNA similarity matrix and the disease similarity matrix. Then constructs a heterogeneous network based on lncRNA similarity network, disease similarity network and available lncRNA-disease associations. Next, it applies bi-random walks on the heterogeneous network to predict potential associations between lncRNAs and diseases. Our method can be used to better identify potential associations between lncRNAs and diseases.The reason why our method has good results is mainly due to two factors. On the one hand, we exploit the similarity of lncRNAs by integrating Gaussian interaction profile kernel similarity of lncRNA and lncRNA expression similarity, and then apply Laplacian normalization. We also rely on lncRNA similarity matrices to construct an lncRNA similarity network. On the other hand, the bi-random walk algorithm simulates random walk restarts on the lncRNA similarity network and disease similarity network; we then infer the relationship between lncRNAs and diseases by weighted averaging. We believe that the higher the score of potential lncRNA-disease relationship is, the higher the probability of association is.A, where the value A of row i and column j is 1 if disease d(i) is related to lncRNA l(j), otherwise it is 0. Let L={l(1),l(2),⋯,l(nl)} denote the set of lncRNAs, and D={d(1),d(2),⋯,d(nd)} denote the set of diseases.We downloaded three data sets of lncRNA-disease associations from the supplementary files of published articles , 8, whicL1, where L1 is composed of lncRNAs with lncRNA expression profiles (L1⊆L). According to the previous approaches [l(i), l(j)∈L1, we calculated the Spearman correlation coefficient of l(i) and l(j) as the lncRNA expression similarity. The lncRNA expression similarity matrix is represented by matrix SPC, where SPC(l(i),l(j)) is the expression similarity between l(i) and l(j) if they belongs to L1, otherwise 0.We also downloaded lncRNA expressions and the gene expression levels from the supplementary files of the published articles , 8, whicproaches , if l(i)M=M,i,j=1,2,⋯,N, is a symmetric matrix, D is a diagonal matrix of which D is the sum of row i of M and D=0 for i≠j. M is normalized by Suppose that M. It is often used to normalize a weighted matrix of a network [This process is called Laplacian normalization of  network , 20, 21.IP(l(i)) is a binary vector which is 1 if lncRNA l(i) is related to the disease, 0 otherwise, defined as the i-th column of the adjacency matrix A of the known lncRNA-disease association network constructed above. Then we can calculate the Gaussian interaction profile kernel similarity of lncRNA l(i) and lncRNA l(j) from their interaction profiles as Based on the assumption that similar diseases tend to show a similar interaction or non-interaction with the lncRNAs, the Gaussian interaction profile kernel similarity of lncRNAs can be calculated from known lncRNA-disease associations . The lncγl controls the kernel bandwith, which is calculated based on the new kernel bandwidth parameter where the parameter nl denotes the number of lncRNAs. For simplicity we set where us works , 22.SL the lncRNA similarity matrix, where the element SL defines the similarity between lncRNA l(i) and lncRNA l(j) as Following previous approaches , we consSPC(l(i),l(j)) represents the expression profile similarity of lncRNA l(i) and lncRNA l(j), and its value is the Spearman correlation coefficient of lncRNA l(i) and lncRNA l(j), so the matrix SPC is a symmetric matrix. KL(l(i),l(j)) represents the Gaussian interaction profile kernel similarity of lncRNA l(i) and lncRNA l(j), so the matrix KL is also a symmetric matrix. Therefore the lncRNA similarity matrix SL is a symmetric matrix. In Eq.  is calculated through two steps: Next, using Laplacian normalization, the element Similar to lncRNAs, the Gaussian interaction profile kernel similarity of diseases can be constructed as IP(d(i)) is defined as the i-th row of the adjacency matrix A of the known lncRNA-disease association. It is a binary vector representing the relationship between disease d(i) and each gene. The Gaussian interaction profile kernel similarity matrix KD is a symmetric matrix. The parameter γd is calculated as Here nd denotes the number of diseases; for simplicity we set where us works , 22.From relevant research , 19, to c and d are two parameters, for which we adopt the same parameter selection as in the previous studies [c=−15,d= log(9999). The disease similarity matrix SD is a symmetric matrix. Next, using Laplacian normalization, the element SD is calculated through two steps: where  studies , 19, i.eLD, LL to construct two networks, namely a disease similarity network, and an lncRNA similarity network. In the lncRNA similarity network, the edge between l(i) and l(j) is weighted by the similarity value of these two lncRNAs.We first use the two matrices d(i) and d(j) is weighted by the similarity value of these two diseases.Likewise, in the disease similarity network, the edge between d(i) and lncRNA l(j), the weight of the edge is 1; otherwise it is 0. We divide the nodes of the heterogeneous network into two types. Those nodes connecting the lncRNA similarity network with the disease similarity network are called bridging nodes, and the other nodes are named internal nodes [Besides, the lncRNA-disease association network can be modeled as a bipartite graph. In this graph, the heterogeneous nodes correspond to either lncRNA or disease, and edges denote the presence or absence of the associations between them. If there is a known association between disease al nodes .The heterogeneous network can be constructed by connecting the lncRNA similarity network and the disease similarity network via the known lncRNA-disease associations. A simple example of a heterogeneous network is illustrated in Fig. In this study, we develop a novel computational method called Lap-BiRWHLDA to predict human lncRNA-disease associations. Figure d(1) to l(1) and then to l(2). We can take d(1) as the starting node for the random walk. To simulate this process, we apply a random walk on the lncRNA similarity network. The iterative process can be described as Suppose a random walker can jump from Similarly, we can also apply a random walk on the disease similarity network as follows: α is a parameter to control the restart probability for the random walker, l and disease d in the t-th iteration, d and lncRNA l in the t-th iteration, with where t-th step, Lap-BiRWHLDA further combines RTt as follows: After the bi -random walks in the disease similarity network and in the lncRNA similarity network in the RTt+1 and RTt is less than 10−10, we obtain the steady prediction score matrix RT, where RT is the probability of potential association disease d(i) and lncRNA l(j).After several steps, when the change between


Ecological carrying capacity is an important factor of sustainable development for cities, and a critical part of achieving the coordinated development of the social economic and ecological environment for urban agglomerations. In order to evaluate the regional ecological carrying capacity and provide a basis for decision-making for regional sustainable development, this paper constructs an ecological carrying capacity model for the urban agglomeration from two dimensions: ecological carrying elastic force and ecological carrying pressure. The analytic hierarchy process is utilized to determine the weights of nine indices in these two dimensions. For the Yangtze River urban agglomeration, the comprehensive index of its ecological carrying capacity is investigated quantitatively, and the spatial distribution map of its comprehensive index measuring ecological carrying capacity is computed. The results show that Nanjing, Yangzhou, Taizhou, and Changzhou are in the stage of high load carrying; Suzhou, Wuxi, Nantong, and Zhenjiang are in the stage of low load carrying. In addition, the environmental protection investment has the greatest impact on ecological carrying elastic force, followed by the proportion of the tertiary industry; wastewater discharge has the greatest impact on ecological carrying pressure. The level of ecological carrying capacity varies within the region. It is necessary to take measures to increase the ecological carrying elastic force and reduce the ecological carrying pressure according to the actual conditions in each region. Meanwhile, exchanges and cooperation between different regions should be strengthened to stimulate the coordinated and sustainable development. In recent years, China’s economy has rapidly developed, and the level of urbanization has continuously upgraded. By the end of 2017, the urbanization rate of China’s permanent residents was 58.52%, increasing 40.6 percentage points from 1978, with an annual average increase of one percent  , 11, 20]  cIn summary, domestic and foreign scholars pay more attention to the study of ecological carrying capacity, but there are two shortcomings: (1) The systematic nature of the evaluation index system for regional ecological carrying capacity has not been well studied in depth. The selection of evaluation indicators and the methods for determining the weight of indicators vary also. In particular, since the selection of indicators needs to be based on the actual socio-economic development of urban agglomerations, the scientific nature of the indicator system for urban agglomerations needs to be explored. (2) In terms of the evaluation method of ecological carrying capacity, most evaluation methods are not comprehensive, and there is a lack of integrated evaluation from the perspectives of the ecology, resources, environment, society, and economy.Therefore, this paper focuses on constructing a comprehensive evaluation index system for the ecological carrying capacity for the Yangtze River urban agglomeration from two dimensions: ecological carrying elastic force and ecological carrying pressure, and utilizes the analytic hierarchy process to determine the index weight according to the connection between the indicators. Moreover, the spatial distribution of the ecological carrying capacity for Yangtze River urban agglomeration is analyzed. The research aims at exploring the comprehensive evaluation method of regional ecological carrying capacity, so as to provide effective suggestions for realizing regional sustainable development. In the remainder of the paper, the content is structured as follows. With the issuance of “Opinions on accelerating the construction of Yangtze river urban agglomeration” by the Jiangsu provincial party committee and provincial government and the implementation of the “Development plan of Yangtze river urban agglomeration”, the Yangtze River urban agglomeration was born.The Yangtze River urban agglomeration is located along the Yangtze River in Jiangsu Province, China, covering Nanjing, Zhenjiang, Changzhou, Wuxi, Suzhou, Yangzhou, Taizhou, and Nantong . With anHowever, its traditional industry occupies a dominant position in industrial structure, and its economic development and environmental protection are not coordinated due to its large population density, large resource occupation and irregular agricultural development. Assessing the ecological carrying capacity of this region has become an important issue that cannot be ignored for sustainable development.We selected some relevant indicators from two aspects: the ecological carrying elastic force and ecological carrying pressure, which was motivated by the literature research and the findings of Li and Ma  (2013), This index system covers several aspects of the development of the Yangtze River urban agglomeration, including the social economy, population, industrial structure, environmental pollution, resource consumption, etc., which reflects the concept of sustainable development and the comprehensive evaluation of the whole index system.The specific evaluation index system is shown in Constructing a reasonable evaluation model of ecological carrying capacity is the premise and basis for the evaluation of ecological carrying capacity. In this paper, the ecological carrying capacity system is divided into the ecological carrying elastic system and the ecological carrying pressure system, so as to construct a comprehensive index model for the ecological carrying elastic force and ecological carrying pressure. Among them, we define ecological carrying capacity as the comprehensive reflection of ecosystem service capacity and social and economic development pressure; ecological carrying elastic force as the self-repairing ability of the ecological environment system; ecological carrying pressure as the direct negative impact of human activities on the natural ecological environment.The urban ecological carrying elastic index is : (1)S=∑jThe urban ecological carrying pressure index is :(2)P=∑j=The comprehensive index of urban ecological carrying capacity is :(3)D=P/SThe combination of the ecological carrying pressure index with the ecological carrying elastic index can reflect the ecological carrying status of a city. Among them, the value of the ecological carrying elastic index reflects the level of urban ecological carrying capacity; the value of the ecological carrying pressure index reflects the level of urban ecological environment carrying pressure. The higher the pressure, the higher the comprehensive index of ecological carrying capacity. If the comprehensive index of ecological carrying capacity of a city is high, indicating that the urban ecosystem is in a stage of high load, the lower the ecological carrying capacity. The comprehensive index of urban ecological carrying capacity reflects the overall carrying status of a city.According to the research of Li et al.  (2017) aAt present, various methods are used to determine factor weights, including the analytic hierarchy process, principal component analysis, and the entropy method. Considering the fuzzy connection between the selected indicators and the complexity of the selected indicators in our study, we used the analytic hierarchy process to determine the index weights. The main steps are as follows :CI is the consistency index, RI the random consistency index, n the number of characteristic roots of the judgment matrix. when CR < 1.0, the judgment matrix is considered to be consistent, and the smaller the value of CR, the better the consistency of the judgment matrix. When CR ≥ 1.0, it is considered that the judgment matrix does not conform to the consistency and needs to be revised again. Firstly, the hierarchical model is established; secondly, a judgment matrix is established, in which the scale method of 1–9 is utilized, and the value is assigned according to its importance; thirdly, we calculate the weight vector, perform the consistency test, and calculate the weight value. In order to avoid the interference of other factors on the judgment matrix, the judgment matrix is required to meet the general consistency in practice, and the consistency test should be conducted according to Formula (4).The data in this paper were selected from the 2017 statistical yearbook of Jiangsu province, the Environmental Statistical Bulletin of Jiangsu province, and the Statistical Yearbook and Environmental Statistical Bulletin of all cities in the Yangtze River urban agglomeration in 2017. Due to the differences in the measurement units, the nature and order of magnitude among the indicators in the indicator system, a unified dimensionless treatment was needed to eliminate the differences among the indicators. The specific method is as follows:Standardized treatment formula of the benefit index:Standardized treatment formula of the cost index:Through the judgment matrix of the analytic hierarchy process, Ten experts from government agencies, universities, and enterprises engaged in ecological and environmental research were invited to score the relative importance of the indices in the criterion Layer B and the indicator Layer C to calculate the index weights of the indicator layer. Then, the weighted average was utilized to summarize the scores of ten experts, so as to get the judgment matrix. The final results are shown in It can be seen from In the ecological carrying pressure system, C6 accounted for the largest proportion, indicating that industrial wastewater discharge had the greatest impact on the ecological carrying pressure, followed by C9, industrial tailpipe emission. With the development of the city, the population has been expanding, and the discharge of wastewater and waste gas has also been increasing, which has caused tremendous pressure on the ecological environment. Based on the ecological carrying elastic index model, the ecological carrying elastic indices by city were calculated. Then, the histogram of the ecological carrying elastic indices  can be oIt follows from (1) Suzhou has the highest ecological carrying elastic index, which belongs to the medium carrying level. Due to the high level of urbanization in Suzhou, the proportion of the tertiary industry is relatively high, and the industrial structure is relatively perfect. At the same time, the environmental protection investment in Suzhou is higher, and the environmental protection is also stronger.(2) Wuxi’s ecological carrying elastic index ranks the second, which belongs to a low carrying level. It has a relatively developed economy and a complete industrial structure, but its environmental investment is relatively lower when compared with Suzhou.(3) The ecological carrying elastic indices for Nanjing, Nantong, and Changzhou are similar, at a weak carrying level. However, the reasons are slightly different. Nanjing and Changzhou have higher economic development levels and better industrial structures, so their ecological carrying elastic indices are relatively higher. Nantong has more investment in environmental protection, which partly compensates for the impact of the low economic development level and industrial structure imbalance on the ecological carrying elastic index. The ecological carrying capacities of Zhenjiang, Yangzhou, and Taizhou are also weaker and lag behind. Zhenjiang city, due to its low investment in environmental protection, has a lower ecological carrying elastic force; The economic development levels of Yangzhou and Taizhou are lower, and their industrial structures are not balanced, resulting in low levels of ecological carrying elastic force. In general, the ecological carrying elastic force of most cities in Yangtze River urban agglomeration is not high, at a weak carrying level, implying that the ecological carrying capacities of most cities are not high, and the environmental conditions are not optimistic. It is necessary to improve their ecological carrying capacities according to the specific conditions of each city.Through the ecological carrying pressure index model, the ecological carrying pressure indices of each city were calculated. Combined with the urban ecological carrying capacity grading evaluation criteria , a histoFrom (1) Suzhou has the highest ecological carrying pressure index, which belongs to the medium pressure level. Suzhou’s industry has developed. Meanwhile, the population density is high, and hence, the exhaust emissions are large, while the occupancy of resources is large, and hence, the pressure on the ecological environment is relatively large.(2) Nanjing, Changzhou, Yangzhou, and Wuxi are at low pressure levels, but the pressures are still high in Yangtze River urban agglomeration. The reason lies in their high population density and wastewater discharge. Among them, Yangzhou and Changzhou have increased their ecological carrying capacities to some extent due to the large amount of chemical fertilizers and pesticides applied.(3) Taizhou, Zhenjiang, and Nantong are under weak pressure. Primarily due to the low discharge of wastewater and the small population pressure, the ecological carrying pressure is relatively small.On the whole, the higher the level of economic development, the higher the corresponding ecological carrying pressure. At the same time, most of the cities in the Yangtze River urban agglomeration have low ecological carrying pressure, and most of them are in a stage of low carrying pressure or weak carrying pressure. However, with the further development of the Yangtze River urban agglomeration, the ecological carrying pressure will increase, and measures still need to be taken to decrease the ecological carrying pressure of each city. Especially in Suzhou, the economic development and population density lead to the large amount of resources and the maximum ecological carrying pressure.Through the comprehensive index model of urban ecological carrying capacity, with the ecological carrying elastic indices  and ecolFrom (1) Nanjing, Changzhou, Taizhou, and Yangzhou are under high load. The economic development levels of Nanjing, Changzhou, Suzhou, and Wuxi are higher and the population density higher, and so are the waste and pollutant emissions of production and living. Therefore, the ecological carrying elastic indices and ecological carrying pressure indices of these four cities are higher than other cities. Among them, the ecological carrying elastic indices of Nanjing and Changzhou are lower than those of their ecological carrying pressure indices because the environmental protection investment cannot maintain their huge population and economic development. Taizhou and Yangzhou have a relatively low level of economic development, an underdeveloped industrial structure, a high use of chemical fertilizers and pesticides in agricultural development, and a low investment in environmental protection, resulting in huge ecological carrying pressure. Therefore, the comprehensive indices of their ecological carrying capacities are high, and they are in a stage of high load.(2) Zhenjiang, Wuxi, Suzhou, and Nantong are under low load. Among them, Zhenjiang has a low comprehensive index of ecological carrying capacity due to low population density and low production and living emissions. Nantong has more environmental protection investment, and the ecological carrying elastic index is higher than the ecological carrying pressure index, so the comprehensive index of ecological carrying capacity is low. Although Suzhou and Wuxi have large emissions from production and living, they have a large investment in environmental protection. Therefore, they are in a low-load stage. However, the ecological carrying pressure indices of Suzhou and Wuxi are slightly lower than the ecological carrying elastic indices, and the comprehensive indices of ecological carrying capacity are close to one, which means close to ecological balance. In short, the comprehensive indices of the ecological carrying capacity of Yangzhou, Taizhou, Changzhou, and Nanjing are higher in the Yangtze River urban agglomeration and are in the stage of high load carrying, indicating that the development of these cities has exceeded the capacity of the regional ecological environment and is in an unsustainable development state. These cities should pay more attention to environmental inputs while developing their economies. For Wuxi, Suzhou, Nantong, and Zhenjiang, although they are in a low-load stage, they still need to pay attention to environmental protection and reduce the comprehensive indices of ecological carrying capacity, especially Wuxi and Suzhou: their comprehensive indices of ecological carrying capacities were close to one.According to the ecological carrying elastic indices , the ecoThrough further analysis, we found that environmental protection investment and industrial structure had the greatest impact on ecological carrying elastic force. The proportion of tertiary industry and the investment in environmental protection in Nanjing, Suzhou, Nantong, and Wuxi were higher than other cities, making their ecological carrying elastic indices higher than other cities. It can be seen that a reasonable industrial structure can enhance the ecological carrying capacity of the region; the environmental protection investment can strengthen the cycle mechanism of the ecosystem, thereby improving the ecological carrying elastic force. Suzhou’s environmental protection investment had a larger proportion, which improved its ecological carrying elastic force to a certain extent, therefore improving its comprehensive carrying capacity.Secondly, the wastewater discharge had the greatest impact on ecological carrying pressure, higher for Suzhou, Wuxi, Nanjing, and Yangzhou than other cities, making their ecological carrying pressure indices higher. The use of chemical fertilizers and pesticides in agricultural production also exerted a certain pressure on the ecological environment, most significant for Changzhou and Yangzhou. The use of large amounts of chemical fertilizers and pesticides aggravated the regional ecological carrying pressure and had a large negative impact on the ecological environment.By utilizing the comprehensive indices and ranking table of the ecological carrying capacity for the Yangtze River urban agglomeration  and the (1) Nanjing, Yangzhou, Taizhou, and Changzhou are under high load of carrying conditions. First of all, these cities are farther away from the estuary of the Yangtze River, and their own ecosystem circulation is weaker. Among them, Nanjing’s industrial structure is relatively complete, but the environmental protection investment is relatively low; meanwhile, as a political and cultural center of Jiangsu province, Nanjing is densely populated, resulting in high wastewater and gas emissions. For the economically-developed cities like Nanjing, we should pay more attention to environmental protection investments, optimize the population structure, and reduce the environmental pollution and resource waste. Yangzhou and Taizhou have lower economic development levels, imperfect industrial structure, and insufficient investment in environmental protection, and their comprehensive indices of ecological carrying capacity are higher due to the higher discharge of wastewater and waste gas and the heavier use of chemical fertilizers and pesticides. For such cities, local resources should be fully utilized to develop the economy and improve the industrial structure; at the same time, increasing investment in environmental protection, raising public awareness of environmental protection, and improving resource utilization. Changzhou’s industrial structure is improper; the agricultural production behavior is not standardized; and environmental protection investment is insufficient. For such cities, we should first focus on the transformation and upgrading of the industrial structure while developing the economy, and at the same time increase investment in environmental protection; second, increasing investment in science and technology, stimulating the transformation of the agricultural production mode, and reducing the use of fertilizers and pesticides.(2) Suzhou, Wuxi, Nantong, and Zhenjiang are under a lower load of carrying conditions. Except Zhenjiang, other cities are close to the estuary of the Yangtze River, and their ecosystems have stronger circulation capacities. Like Suzhou, it is densely populated and has a large amount of resources, but the industrial structure is good and the environmental protection investment higher. Therefore, although having a higher load of carrying and higher pressure state, it has only a lower load. For such cities, it is still necessary to optimize the industrial structure and strengthen the formulation of relevant environmental policies. On the contrary, Zhenjiang has a better environmental condition; at the same time, wastewater discharge is lower, and it is a lowlier loaded, lower pressure state. For such cities, we should vigorously encourage the tertiary industry such as the service industry and tourism on the basis of making full use of the advantages of local natural resources and continuously increase environmental protection investments in the later stage of development. The economic development level of Nantong is a little lower; the proportion of tertiary industry is low; but its environmental investment is high, and population density is low. For such cities, it is necessary to develop the tertiary industry on the basis of geographical advantages and accelerate the transformation and upgrading of the industrial structure. Wuxi has a relatively high level of economic development and a relatively complete industrial structure. At the same time, due to the higher investment in environmental protection and less pollution in agricultural development, its ecological environment is good. For such cities, the industrial structure should be continuously optimized during the development process, and investment in environmental protection should be increased. On the other hand, we should also increase the investment in science and technology, improve resource utilization, and formulate relevant policies such as sewage tax to reduce pollutant emissions, thereby maintaining the low-load carrying stage.The comprehensive evaluation index system of urban ecological carrying capacity is the basis for urban carrying capacity assessment and the focus of sustainable development. The assessment of the ecological carrying capacity for urban agglomerations is conducive to guiding the industrial structure and development direction for each city and has positive significance for promoting urbanization. We have established such a system for the Yangtze River urban agglomeration from two dimensions, ecological carrying elastic force and ecological carrying pressure, and analyzed the spatial difference of its ecological carrying capacity. The results revealed that:(1) The scientific evaluation of ecological carrying capacity requires comprehensive evaluation from two dimensions: ecological carrying elastic force and ecological carrying pressure. The more developed areas in the region tended to have larger resource occupations and thus higher ecological carrying pressure; moreover, due to the relatively complete industrial structure and high technical level, the ecological carrying elastic force of these areas was also higher. In areas where the ecological carrying elastic index was lower than the ecological carrying pressure index, the ecosystem was in a higher load stage because it could not maintain the ecological pressure brought about by its production and life; the areas where the ecological carrying elastic index was higher than the ecological carrying pressure index had lower ecological pressure and were in the stage of lower load.(2) Environmental protection investment had the greatest impact on ecological carrying elastic force, followed by the proportion of the tertiary industry; wastewater discharge had the greatest impact on ecological carrying pressure. For example, the proportion of tertiary industry and environmental protection investment in Nanjing, Suzhou, Nantong, and Wuxi were higher than other cities, and so were their ecological carrying elastic indices. Such densely-populated and economically-developed cities as Suzhou and Nanjing had larger relatively complete systems treating wastewater and waste gas, and therefore, the pressure on the ecological environment was naturally heavier.(3) The ecological conditions of cities in the Yangtze River urban agglomeration were different. It is urgent to build an exchange and cooperation mechanism for urban agglomerations to improve the quality of the overall ecological environment. First of all, municipal governments should take measures to improve the ecological carrying elastic force and reduce the ecological carrying pressure according to the actual conditions of each city. For example, for cities with large problems in industrial structures and an insufficient proportion of tertiary industry like Yangzhou, Taizhou, Zhenjiang, and Nantong, the key is to stimulate the transformation and upgrading of their industrial structures. Secondly is strengthening communication and cooperation among cities in the development process and highlighting the leading role of such central cities as Suzhou, sharing green development experiences with cities for decision-making. Thirdly is setting up special supervision departments to strengthen the supervision and inspection of the ecological environment in urban agglomerations, such as strict control of wastewater and waste gas emissions, strengthening the supervision and guidance of agricultural practices. Finally is coordinating regional development and establishing a complete urban ecological economic system. The mutually beneficial urban ecological network is used to facilitate the coordinated development of ecology and economy among cities and between urban and rural areas.The innovations of this paper are as follows: (i) most of the existing research is on typical provinces, cities, or regions, but there is a lack of research from the perspective of the urban agglomeration. The construction of the index system and the evaluation of ecological carrying capacity for the urban agglomeration in this paper enrich the research of ecological carrying capacity for urban agglomerations. (ii) Different from previous studies evaluating ecological carrying capacity from a single perspective, we established a comprehensive evaluation index system for the Yangtze River urban agglomeration from the two dimensions of ecological carrying elastic force and ecological carrying pressure, which is a combination of ecological and social economic perspectives. Furthermore, the advantage of this paper is that it is not limited to specific research conclusions. Through the comprehensive evaluation of ecological carrying capacity for the Yangtze River urban agglomeration, it points out the general standard for evaluating regional ecological carrying capacity, which is more universal. Meanwhile, the indicator system established in this study lays a foundation for the establishment of a more scientific indicator system in the future and also provides a decision-making basis for the sustainable development of urban agglomerations. However, due to the large amount of data required for the evaluation of the ecological carrying capacity of the ecological composite system in this study, the complicated process of obtaining the data, the limitation of creating indicators, and the subjectivity in determining the weight of the index, subsequent research for other areas with significant conditions needs to be conducted to further optimize the indicator system, the method of determining the index weight can also choose a more scientific method combining subjective and objective weighting, especially for the creation of indicators; for example, tailpipe emissions are derived from the conversion of various types of exhaust gases into standard conditions. However, some air pollutants may not be suitable for conversion to a standard state, or simply adding them together has little impact on research problems; follow-up research will carefully consider these issues, and the creation of indicators will be more rigorous. Then, the improved ecological carrying capacity evaluation model will have better scientific and universal value.


Listeria monocytogenes through a microbial challenge test. Sliced cured pork loins can be stored at 6 ± 1 °C for 105 days while maintaining a consumer acceptance of more than 75%. The freshness loss was associated mainly with a decrease in aromatic notes (particularly the smoke and cured aroma), and with the appearance of spoiled characteristics, specifically a sour/vinegar aroma and acidic taste that were detected by a reduced proportion of participants. The freshness evaluation was positively influenced by the typical characteristics of cured products, such as color and a garlic and wine aroma. Sour/vinegar aroma and acidic taste were the attributes most associated with higher freshness penalization. During the period of the test, Listeria monocytogenes inoculated onto the cured loin slices did not grow.Cured pork loins are sausages with a production tradition in several regions worldwide. They are made from one of the noblest cuts of pork, and for this reason cured loins are one of the most expensive pork meat products. Establishing the correct shelf life allows products to be accepted by the consumer, and to avoid the costs associated with shorter shelf lives. The aim of this study is: (1) to establish proper shelf life by evaluating the willingness of participants to consume and the sensory modifications that occur during prolonged storage via Check All That Apply (CATA) questions; and (2) to study the behavior of  Cured pork loins are different according to the production region. Variations in seasoning, size, cut  and smoking determine the differences. All of these products are cured using salt and/or nitrate, and are dried to achieve a sliceable texture and an activity of water (aw) reduced enough to contribute to the safety and preservation capacity of the product ,2,3,4,5.Clostridium botulinum and Listeria monocytogenes, among others [Salmonella or enteropathogenic Escherichia coli is commonly overcome by the use of high microbial quality raw materials and application of mild heat treatments at the beginning of the drying [Salmonella population resulting from the processing. The hurdles used to achieve that reduction might include chemical preservatives, mild heat treatments, and drying [Cured loins are made from one of the noblest cuts of pork and are, therefore, one of the most expensive pork meat products . They usg others ,9. The pe drying ,11. Whene drying ,13,14. Rd drying . Increasd drying  has beend drying . Chemicad drying . The stad drying . Anotherd drying ,21. In td drying ,23.L. monocytogenes or other psychotropic pathogens and if the product has intrinsic parameters, namely aw and pH, that allows their multiplication [Despite the very high hygiene measures used in slicing, any accidental contamination with foodborne pathogens might result in a lack of food safety. It is of particular concern if the contamination occurs with lication ,25. Takilication . When salication . There alication ,29. For lication .L. monocytogenes through a microbial challenge test.The aim of the present study was: (1) to establish proper shelf life by evaluating the willingness of consumers to consume and the sensory modifications the occur during prolonged storage via a Check All That Apply (CATA) test; and (2) to study the behavior of 2, water vapor transmission < 3 g/m2.d, and oxygen permeability < 25 cm3/m2.d.bar; lower part—0.12 mm, 118.00 g/m2, water vapor transmission < 3 g/m2·d, and oxygen permeability < 25 cm3/m2.d.bar. The upper film was 0.17 mm, 167.50 g/m2, water vapor transmission < 2 g/m2·d, and oxygen permeability ≤ 30 cm3/m2.d.bar.Cured pork loins, locally named salpição, were obtained from an industrial unit on the week of their production. The cured loins were produced using the normal industrial manufacturing process. White commercial crossbreed pork carcasses (80 to 90 kg) of both sexes were obtained two days after slaughter. The loins were excised during the carcass dressing. Cured pork loins were prepared by cutting the loin transversely in single pieces about 5-cm thick and weighing about 400 g, then seasoning with red wine, salt, dextrose, garlic, and spice extracts. Non-meat ingredients were also included, specifically soya protein concentrate, milk powder, pork hemoglobin, and the food additives sodium nitrite, trisodium citrate, and pentasodium triphosphate, according to the limits of addition previewed in the Regulation UE 1129 . The mixThe sampling procedure used in the present experiment is presented in For the consumer test, 90 packages were transported to the laboratory under refrigeration, then divided into seven groups and stored at 6 ± 1 °C for a total of 126 days, corresponding to seven storage periods with expirations spaced 21 days apart . Upon expiration of the stipulated period, the samples were frozen to −18 °C until sensory evaluation, to allow a reverse storage approach. This approach enabled us to analyze samples with different storage durations simultaneously . Before The challenge test was made using samples from the two batches. Once they arrived at the laboratory with one week of difference, we kept the samples from the first batch at 2 °C for one week. The packages were opened in aseptic conditions, and the slices were distributed for new packages, with five slices per package .Focus groups (FGs) were used to evaluate the effect of freezing on the sensory characteristics of the cured loins and validate the usefulness of the reverse storage strategy used, as well as to identify vocabulary with the potential to describe the freshness and spoilage of cured pork loins . Three fConsumer test was performed with total of 81 consumers. They were recruited from the professional and personal relationships of the researchers. Less than 20% of the participants were students. The group was composed of 58% women, and the mean age was 39 ± 15 years (21–65). Of these participants, 90% were regular consumers of dry-cured meat products, while the reaming 10% were sporadic consumers. Those not liking cured sliced loins did not accept the invitation. It was not possible to conduct this work with a randomly selected group of consumers that might represent a more appropriate approach. Nonetheless, the group of consumers represented several socio-demographic groups and were consumers of the product. On the other hand, none of the participants had any relationship with the manufacturer or the meat products industry, and most of the respondents were not connected to food science, aspects that are usually pointed out as being potential biases of a group ,36.The test was divided into two sessions with the same participants to avoid the overload of analyzing seven samples in the same session. Storage durations were distributed for the two sessions using alternate times . The preFor each sample, participants were asked to rate its freshness using a five-point scale (where 1 corresponded to “not fresh” and 5 corresponded to “very fresh”) and to indicate their consuming and purchasing intentions using a binary yes/no question. They were also asked to fill in the CATA part of the form, which consisted of a list of 24 attributes . Water activity was measured in a Rotronic Hygroscope DT apparatus with a WA40 probe . Color was measured directly in the slices 30 min after opening the packages via a tristimulus color analyzer Minolta CR 310  with a standard illuminant DEnterobacteriaceae and Pseudomonas spp. LAB counts were determined on De Man, Rogosa and Sharpe Agar and incubated under at 30 °C for 2–3 days; mold and yeasts were determined on Rose-Bengal Chloramphenicol Agar Base and incubated at 25 °C for 5 days; Enterobacteriaceae were determined on Violet Red Bile Glucose Agar and incubated at 37 °C for 24 h; and Pseudomonas spp. were determined on Pseudomonads Agar Base with Cetrimide, Fucidin and Cephalosporin supplements and incubated at 25 °C for 48 h. All culture media were from Biokar . Results are expressed as log cfu/g. For statistical purposes, when the microorganism count was below the detection limit, it was considered to be zero. Estimated counts were considered for data analysis when countable colonies were present but below the countable range.Microbiological analysis was performed at days 0, 42, 63, 84, and 126 of storage. After opening each of the three packages considered for each storage duration, 10 g was randomly cut from at least three slices then weighed and suspended in 90 mL of peptone water, as described in the reference , and anaL. monocytogenes during the storage of sliced cured pork loins, as described in the literature [L. monocytogenes was performed immediately after packaging, then again at 42, 63, 84, and 126 days. For each storage duration, three packages were reviewed to perform the L. monocytogenes count. For each sample, slices were unpacked, weighed, and homogenized with the necessary volume of NaCl 0.85% to achieve an initial dilution of 0.1 g/mL. Serial decimal dilutions in NaCl 0.85% were prepared, and 0.1 mL of appropriate dilutions was spread onto selective Oxford Agar  followed by incubation at 30 °C for 24 to 48 h. When low counts were expected, 0.5 mL of the initial dilution was inoculated on two Petri dishes (0.25 mL each), dried in the laminar flow (to avoid biofilm formation), and counted as the total colonies in both dishes. Results are expressed in log cfu/g.A challenge test was performed to study the behavior of terature . FreshlyL. monocytogenes counts—were made using ANOVA for the three packages analyzed for each storage duration (p < 0.05). An evaluation of freshness was compared between the consumption and purchasing intentions (yes/no using a t-test). The Pearson’s correlation between the LAB counts (expressed in log cfu/g) and pH was computed. The CATA results were recorded as 1 when a participant checked an attribute and 0 when they did not. The data were analyzed by factor analysis. Each attribute was compared between formulations using a Cochran test. The mean impact of each CATA attribute in the freshness evaluation was computed. The impact of each CATA attribute on the freshness evaluation was calculated only for the longest storage duration, when the spoilage notes were more easily detected. All the statistics were calculated using XLStat . Raw data might be accessed at http://www.mdpi.com/2304-8158/9/5/621/s1  than those not accepted by the participants (2.51 ± 1.73). A similar trend was observed for purchasing intention . Still, in the period of high acceptance, the freshness evaluation was statistically different (p < 0.05) only between the first day of storage and all the other storage durations. After the initial more accentuated slope, the freshness assessment was similar from the 42nd day of storage until the end. The median values . Accordn values  also refTo understand what modifications in the product were associated with a reduced consumption intention/freshness evaluation, participants were asked to identify those attributes of the cured loins (from a list provided) that they considered relevant at different storage durations. The results are illustrated in From the initial list of 24 attributes, 11  were withdrawn from the analysis if they were used by less than 20% of participants for all the storage durations . That stp < 0.05) between storage durations (p = 0.097).Even though the attributes “sour/vinegar” and “acid” had a frequency of use lower than 20% for most of the storage durations, they had frequencies or 20% or more at the longer durations, and so were kept. The first factor (F1) in urations . Still, p > 0.05) between any storage durations. The cured color, which seems to be discriminative in the factor analysis (p = 0.097) to be noted by fewer participants, from 79% at the beginning of the experiment to 61% at 126 days of storage. In the same sense, the color parameters a* and b* did not have differences among the tested storage durations , which is a common occurrence since the production of lactic acid, and eventually acetic acid, is the main factor responsible for pH reductions in meat products that support the growth of LAB and incorporate carbohydrates into their formulation [Brochothrix thermosphacta, which can dominate the microbiota of packaged thermal processed meat products [Enterobacteriaceae and Pseudomonas are frequently associated with the occurrence of pungent spoilage odors due to strong amino acid catabolic activity [The formation of aromatic ketones due to lipid oxidation is further associated with the characteristic aroma of cured meat products , and in mulation . Cured lmulation ). Duringmulation ,65. In cproducts . In the activity . In the activity ,68,69,70activity . In the activity ,73)—but activity , and itsCured pork loins usually have a low amount of fat. Combining that reduced fat with vacuum packaging and the presence of food additives that are not classified as antioxidants but have a role in preventing lipid oxidation , it was not assumed that the sliced cured loins experienced lipid oxidation. However, this attribute was maintained for the CATA test, as the initiation of oxidation might occur in some products before packaging, and the further modification can occur, albeit slowly, during long storage periods ,75. As oThe CATA analysis allowed us to understand the loss of freshness of sliced cured loins during storage. The impact each attribute had on the freshness evaluation was assessed only using the data of the longest storage duration (126 days) .In L. monocytogenes during the storage of sliced cured loins. The main inference from this test is that the pathogen did not grow during storage. L. monocytogenes has a reputation for high resistance under adverse environmental conditions [L. monocytogenes did not grow, probably due to the presence of nitrite. For the longest storage durations, the LAB could also contribute to pathogen inhibition Additionally, L. monocytogenes couts showed a slight decrease for the longest storage duration, and determined significant differences (p < 0.05) between 0 days (3.21 ± 0.09 log cfu/g) and all other storage durations. At 126 days, the count of the pathogen was 1.61 ± 0.25 log cfu/g, the lowest of the experiment. On average, the count of the pathogen decreased by around 1.5 log cfu/g. This behavior was probably associated with the start of the LAB growth. The first shift with a more pronounced slope—between zero and 42 days—matched the major reduction in pH observed  (L. monocytogenes has been observed previously in cured products [nditions . In the 6 units) . The secproducts .L. monocytogenes. Testing should be performed to guarantee that, if the product leaves the industry with L. monocytogenes, it does not grow [L. monocytogenes. According to that legal framework, only meat products with a pH ≤ 4.4 or an aw ≤ 0.92, or products with a pH ≤ 5.0 or aw ≤ 0.94, are exempted from the need to perform challenge tests with L. monocytogenes if the manufacturer chooses to allow the presence of L. monocytogenes in the finished product. The results of the present work allow the industry to be unconcerned with this seriously concerning biological hazard. The sliced cured loins used in the present work meet all the conditions necessary to be L. monocytogenes-free. Besides the high level of hygiene in the industry and the qualification of suppliers, , the hot-smoking process and presence of nitrite contribute to the elimination of potential contaminations [L. monocytogenes, knowing how pathogens behave in cold chains allows the industry to ponder the risks associated with sliced meat products if one accidental or unpredicted contamination occurs.The sliced cured loins had an average aw of 0.93 and a pH of 5.8, which, according to Regulation EC 2073 , might snot grow . The indinations . The mosinations . The indinations . DespiteSliced cured loins can be stored at 6 ± 1 °C for 105 days and maintain a consumer acceptance of more than 75%. Freshness loss is due to two concomitant mechanisms. On one hand, sliced cured loins lose intrinsic aromatic notes, particularly the smoke and cured aromas, probably due to scalping as a result of the packaging material. On the other hand, the product develops spoiled notes putatively related to the fermentative activity of LAB. A sour/vinegar aroma and acidic taste are the main sensory characteristics associated with spoilage. Any sensory trait related to rancidity or non-protein-nitrogen catabolic activity of the microbiota was detected in this study. Although lightness (L*) decreased during the storage, participants did not detect any difference in the “bright” or “dull: aspects, nor in the “moisten” or “dry” aspect.The freshness evaluation was influenced mainly by the typical characteristics of cured products with a positive impact. The spoilage notes of a sour/vinegar aroma and acidic taste were responsible for the major penalizations in the mean evaluation of freshness.L. monocytogenes inoculated onto the surface of the cured loin slices did not grow. A slight reduction of about 1.5 log cfu/g was observed, and thus shelf life extension of the vacuum packaged sliced cured loins did not compromise the safety of the product.During the period of the test, This study had the limitation of using a convenience consumer group. As pointed out in the materials and methods section, the group was considered acceptable due to its demographic profile, consumption habits, lack of connections with the manufacturer, and small proportion of food science-related professionals.