dataesr/scientific-paragraphs-categorization huggingface dataset:
trichocarpa) roots (Supplementary Dataset 3A1). Of 401 genes, 275 were assigned to the known functions, including the genes that regulate lipid transportation (29 genes), amino acid transportation (24 genes), carbohydrate transport (e. g. , monocarboxylate, hexose, sucrose, 19 genes), inorganic ion transport (Ca, Na, P, K, Zn, Cu, 14 genes) and other transporters. This result suggests the potential molecular mechanisms of Suillus and Rhizopogon involved in fungal nutrient uptake and fungal-Populus nutrient exchange while Pinus grown nearby. In contrast, different molecular mechanisms were identified for non-EMF in response to heterospecific plants. For example, a high proportion of genes (FunGene_P. tri) for transporters (7%), amino acid transporters (4%), transcriptional co-activators (3%) and chitinases (2%) were enriched, when saprotrophs and endophytes interacted with Pinus compared to all other plant host combinations (Supplementary Dataset 4).
taeda was grown with P. trichocarpa compared to when grown with P. taeda. Data of loading gene factors were generated using coordinate scales on the left (log10 of expression rate) and the bottom (mean of log2-fold changes). Cross-comparative expression of the genes was analyzed using t-test to compare the fungal genes of the roots from a plant species grown with conspecific vs. heterospecific neighbors (n ≥ 3; Wilcox. Test P < 0. 01; FDR < 0. 05; fold changes ≥ 2). The total counts of genes detected and significant changes in gene counts in response to neighbor effects are listed in Supplementary Dataset 3.
Statistical Analyses Mann-Whitney's test was done to compare the mean number of broad bean weevils collected in each cultivar, and the differences in body fat between males and females. Linear regression was used to follow the physiological ranks of weevils during the infestation (i. e. , the ovarian development, the number of filled spermatheca, the number of mature oocytes, and the body fat reserve). Correlations between the rate of full mated females and the ovarian development rank were done, using a Spearman test. The test was carried out using GraphPad Instat 3. 10 software (GraphPad Software, San Diego California USA). Finally, nonlinear curves were fitted to predict the relationship between previous physiological parameters over time. NLFit module in ORIGIN 2016 software (Origin Lab, Northampton, MA) was used.
Data Collection and Sample The people's consumption intentions for hay milk were studied using a structured online survey that was developed for this purpose. In keeping with some studies  [45] [46] [47]  about consumer behavior, people were recruited through invitations to participate in the online survey (using the Google Drive platform) via social networks. Moreover, following some authors  [25]  a snowball sampling recruitment was also adopted, using the emails of the authors' interpersonal relations to reach a large number of respondents. Given the recruitment method used, the sample cannot be considered representative of the entire Italian population as happens in many studies about consumer behavior  [25, 33, 47, 48]. Moreover, a pre-test was conducted on 50 consumers. Respondents were recruited in Central and Southern Italy.
Materials and Methods This paper presents a methodology to build a scale-based classification of biorefineries processing lignocellulosic biomass driven by available data. The approach is bottom-up and unsupervised, in the sense that no preconceived levels of scale are assumed when establishing the classification system. The methodology focuses on characterizing basic strategic choices for various biorefinery projects, starting from disclosed information of heterogeneous quality. A multivariate analysis identifies clusters of biorefineries with similar strategies to reveal how they can be assigned a level of scale. Figure  1  represents the four-step methodology: (1) information about existing biorefineries is collated in a table of information; (2) the table is cleaned, pretreated and encoded to obtain a workable dataset for statistical analysis; (3) a classification of biorefineries is obtained through multivariate analysis combined with unsupervised clustering (no predefined classes or properties are used as inputs); (4) a stability analysis of the classification is carried out by running the clustering method after generating perturbations in the dataset (see Section 2.
Step 1: Collecting Information about Biorefineries First, general information about biorefineries processing lignocellulosic biomass was collected to build a knowledge/reference base with several case studies. Here, the focus was on biorefinery projects self-defining as small, farm-scale or preprocessing facilities. However, energy and fuel-producing biorefineries, usually regarded as large-scale, were added to the database to increase the diversity of case studies. The information was collected from dedicated web sources such as Biorrefineria Blogspot  [19] , or from the biorefinery projects' websites. For each project, the following fields were filled when possible:.
• Process information (biomass, process type, process scheme, complexity of the process, products, raw material processed, production capacity, flexibility) A significant part of this information was not publicly disclosed or was missing. Therefore, some project managers were contacted directly to complete the missing information and to further describe their project. Scientific literature was explored as well, to collect additional case studies. The literature search was conducted in the Web of Knowledge database using the following search terms: "small scale" and "biorefinery" or "farm scale" and "biorefinery". Nine articles were selected but it turned out, in the following steps of the work, that none of them was able to provide factual information about operational case studies, which is practically mandatory to establish a realistic classification.
Step 2: Building Dataset and Data Pretreatment The available information about biorefinery projects obtained from step 1 was heterogeneous: it contained figures, texts, expert assessments and so on. Furthermore, certain fields were incomplete, which made the raw information unfit for statistical analysis. A first workable dataset was obtained by selecting the suitable information and by converting it into computable factors, with either continuous, ordinal or nominal values. This selection was performed by dismissing the fields or the biorefinery projects with too much missing information; information unlikely to be used to specify the concept of scale, such as the country of the project, was dismissed as well. The second step aimed to harmonize the data that described distinct biorefinery configurations with specific conversion pathways, feedstock types, end-products and so on.
Step 3: Establishing a Classification through Clustering Analysis A clustering analysis was performed by combining a multifactorial data analysis and a hierarchical clustering analysis, according to the methodology reported by Husson et al. [20]. The multifactorial analysis generates the scores for the individuals biorefineries, so as to apply the clustering procedure on the scores scaled to the corresponding eigenvalue. Factorial analysis of mixed data (FAMD) was the data analysis method used in this work, more precisely, the function FAMD( ) of the FactoMineR R package  [21]. FAMD is a multifactorial data analysis method for exploring data with both quantitative and qualitative variables. It is generally described as a mix between principal component analysis for quantitative variables and multiple correspondence analysis for qualitative variables.
FAMD is used to visualize the similarities between individual subjects and to study their proximities in Euclidean space  [22]. In our case, this method is especially useful: (i) to analyze the interrelations between the factors of the dataset and (ii) to apply the clustering on the scores of the FAMD. The first step of the analysis reviews the interplay between the factors; if one factor (or more) exhibits no linear relations with the others, it is usually better to rule this factor out of the clustering. This is performed using a leave-one-out strategy: at each run, one factor is removed from the dataset and an FAMD is computed to obtain the percentage of explained variance over the principal components. If removing a factor brings about a large increase of the explained variance percentage, then this factor variance is largely unexplained by the FAMD and should be dismissed from the analysis.
In a second step, a hierarchical clustering was performed on the principal components of the FAMD with the HCPC( ) function of the FactoMineR R package  [20]. The hierarchical clustering is a well-known unsupervised learning method that does not make use of pre-existing output categories. We used hierarchical agglomerative clustering (HAC), a classification method that builds a hierarchy of clusters by merging pairs of clusters as the hierarchy moves up. HAC uses measures of dissimilarity between objects to perform clustering following a linkage method (or aggregation index). The distance chosen in this work is the Euclidean distance, and the aggregation index is the unweighted pair-group arithmetic average method. The classification is obtained by cutting the hierarchy of clusters (usually visualized as a dendrogram) at a selected height to obtain a partitioning of the biorefineries project.
Step 4: Testing HAC Stability over Perturbations in the Dataset The interpretation of the classification resulting from the clustering analysis can be uncertain, as HAC is a method that is quite sensitive to perturbations in the dataset  [23]. To assess the robustness of the classification, we have implemented a simple data perturbation procedure that allows the identification of clusters that are stable, i. e. , less affected by perturbations. A common stability test consists of running clustering after removing one factor at a time and then in taking the average of the results  [24]. Here the number of factors is not adequate to use this procedure; therefore, we performed an adaptation by excluding a combination of factors from the dataset at a time, instead of only one. The general idea of the procedure is to run the pair FAMD+HAC for every perturbation in order to compute the average level from which two individuals get separated, called the separation level.
Description and Treatment of the Collected Information (Step 1 and Step 2) A file with information about a total of 24 biorefinery projects collected from websites, surveys and literature is provided in the Supplementary Data. Distinct processes or facilities belonging to the same project are distinguished: GRASSA 1&2 and NETTENERGY1&2. The file was refined first by removing unnecessary and incomplete information. Information in several fields was missing for too many projects-crop area, runtime. Some information was difficult to process as a single factor-process scheme, process flexibility-, and other information was unlikely to be discriminant given the inconsistency of the answersfacility ownership, direct stakeholders. Sorting and synthetizing the information further allowed the obtention of a first consistent dataset with 15 operational case studies and six factors-processing capacity, investment cost, biorefinery mobility, biomass type, added value and process complexity-with the following characteristics: or during monitoring operations such as tree pruning (e.
Medium, when the process is mainly mechanical and chemical or chemical and biotech, with more processing steps than for low-complexity process. High, when many processes (mechanical, chemical and biotech, etc. ) are involved, which requires significant engineering resources to run sophisticated facilities. Codification of the data. To restrict the downside of handling heterogeneous variables for statistical analysis, a codification of P. C, I. C, A. V and P. Co onto an ordinal scale of a maximum of five levels (from very low to very high) was performed, based on domain experts' interpretations, and on the necessity to ensure all of the levels are represented in the dataset. The ordinal data were coded as 0 (very low), 1 (low), 2 (medium), 3 (high) or 4 (very high) for the sake of statistical analysis.
Removing B. T from the dataset brings about a significant increase of the percentage of variance (80. 9%). Removing any other factors would only give a negligible decrease or increase of the percentage of variance, except for B. M (74. 3%), which remains acceptable. This result shows that B. T is weakly related to the other factors in this dataset. For this reason we chose to rule B. T out of the main clustering analysis; however, we kept B. T to assess the stability of the clusters.
Describing the Relations between the Factors The relations between the five factors (I. C, P. C, B. M, A. V, P. Co) given by the FAMD are represented in the biplot, Figure  3. Even though the modalities of B. M (Mobile and Centralized in blue) and the quantitative factors (in black) are not represented in the same space, the directions of the arrows still indicate the linear relations between the factors, regardless of their nature. Thus, the graph shows that a centralized plant is related to high I. C and a mobile plant is related to a low I. C. Finally, in our dataset, P. Co is negatively correlated to A. V, while surprisingly, P. C is not strongly correlated to A. V, as one would expect: a negative relation epitomized by the classical production of biofuel (i. e. , low A. V, high P. C).
C (0. 9 against 1. 5 for overall mean), while the cluster on the right is composed of only centralized facilities with high I. C (2. 6 against 1. 5) and high P. C (2. 0 against 1. 5). Going down on the left, the cluster NETTENERGY1&2 (in red) stands out with very low P. C (0 against 1. 5). The next cluster on the right, in gray, has high A. V (3. 2 against 1. 9), low I. C (0. 2 against 1. 5) and low P. Co (1. 0 against 1. 7). Further, it splits into the mobile biorefineries-GLAS, GRASSA1, DADTCO-and the centralized ones-GRASSA2, BIOFABRIK. Continuing on the right, the yellow cluster-MINIDEST, AGRIMAX, ARBIOM-does not appear to have distinctive features. It includes centralized facilities of limited processing capacities, which represent an intermediary profile in this dataset. This is confirmed by the central position of this cluster's individuals in the FAMD (Figure  S1 ).
A Classification of Scale-Based Designs The clustering results (Figure  4 ) distinguish various biorefinery profiles. We selected four clusters, whose interpretable descriptions define four classes of biorefineries with different scales. The four profiles of scale-based designs are described below and in Figure  5:  1. Smallest-Scale Biorefinery. NETTENERGY1&2 characterize the smallest scale biorefinery cluster in our dataset. It is outlined by a low processing capacity (2-10 t/day), a small investment cost (≤1 M€), a mobile facility that can move close to feedstock depots to process it on site. The process converts low energy density and low-value product (e. g. , wood residues) into high-energy-density products (bio-oil, biochar), that have low added value. The compactness of the process is crucial to fit in a mobile plant.
Stability of the Clusters (Step 4) To provide an assessment of the stability of the clustering presented in Figure  4 , we applied perturbations to the dataset following the procedure described in Section 2. 4. A total of 63 perturbations applied onto the complete dataset (i. e. , Table  1 ) produced 63 distinct clusterings of the biorefineries. Each of the 63 perturbations takes out from zero to five factors from the dataset. Roughly, at each run the procedure keeps track of the separation level, which indicates the branching from which a pair of individuals get separated, starting from separation level = 1 (where a unique cluster contains all the individuals) to a separation level equal to the maximum number of clusters (one individual per cluster). Similar individuals are assigned arbitrarily the maximum separation level value.
Focus on Investment Cost and Processing Capacity in This Work Usually, the scale of a biorefinery is associated with the plant size or the processing capacity. Classical early-stage cost-estimation methods also relate capital cost with processing capacity through a power function  [30] , such as: Capital Cost = Constant * (Processing Capacity) α (1) If the power factor α is less than 1, the capital cost increases less than proportionately with the plant capacity (economy of scale), and vice-versa if α is more than 1 (diseconomy of scale). The power function is obviously not applicable here as the dataset include distinct kinds of biorefineries. However, the pair-processing capacity and investment cost-might still be sufficient to discriminate the levels of scale. To assess this assumption, the Figure  7  shows the distribution of the 15 biorefineries over these two factors.
Limitations of the Work The amount and quality of data collected from literature search on the web and from exchanges with biorefinery societies is a limitation to be discussed. Given the exploratory nature of data analysis performed in this work, the dataset had to present sufficient variability, as well as meaningful information. In our experience the existing scientific publications on this topic cannot provide this kind of data, as they are focused on technical aspects and include little information about concrete facilities. Our method also favored an extensive refinement of the data to obtain a usable dataset, making it small. The resulting dataset (Table  1 ) is coherent and does not contain any outlier that would indicate suspicious data. The discretization step allows working with intervals of values and groups of information instead of specific data, which mitigates greatly the potential impact of approximations and increases confidence in the data analysis.
Furthermore, it introduces human expertise into the data analysis process, which can be beneficial in approaching the design of sustainable biorefinery  [9]. Obviously, larger datasets are generally better for statistical analysis, however the application of the two techniques (FAMD and HAC) used in this paper are legitimate for a dataset of this size. Two main concerns with small datasets are detecting effects and the stability of the results. Two analyses were performed in that regard: (i) Section 3. 2. 1 presents the results of the test on the projection of the factors on the FAMD, resulting in the exclusion of biomass type, (ii) Section 3. 3 presents the test of clustering stability. Both tests were useful to improve and test the results constancy. They also confirmed the need to test and select the factors prior to the clustering.
Overall, the method succeeded in capturing core aspects of the logic underlying the design of the biorefineries. In further studies, appending new data to the dataset, either additional biorefineries or factors, will improve the classification by adjusting the boundaries of the four classes or revealing new sub-classes of scale-based designs. This can be supported by the creation of a global database of biorefinery facilities suitable for regional bioeconomies, as has been done for bioenergy facilities (see for example, https://www. ieabioenergy. com/installations/ (accessed on 9 June 2021)).
The classification exhibits two structuring trade-offs that account for the differences between the facilities' production models: (i) the mobility of the facility versus the investment cost and processing capacity, (ii) the process complexity versus the added value. These trade-offs account for the strategies adopted by the biorefineries with regard to the economy of scale and should be studied further with additional data. Along with the growing development of biorefineries, more data about biorefinery facilities will be available to update and enrich the present classification. The systematic compilation of this kind of data in an open database would greatly favor analogous initiatives and open perspectives to gain insight into biorefinery strategies and into the conditions for viability.
Acknowledgments: Acknowledgements are due to the project managers of NETTENERGY, BIOFAB-RIK, AGRIMAX, ARBIOM and NEWFOSS, who accepted and responded to our questions, replied to interviews and provided us with the necessary information to carry out this study. This work was carried out in the framework of the European Project  FLEXIBI from the EraNet JPI FACCE SURPLUS second call: Experimental evaluation of small-scale flexi-feed biorefineries and development of a decision support tool devoted  to urban and peri-urban wastes. A project gathering collaborators from  Hamburg University, Hamburg University of Technology, Ecole Supérieure du Bois, University of Helsinki ,  KU Leuven  and  INRAE  as project coordinator.
Acknowledgements The collection of specimens in caves of northeastern China has been possible thanks to  Prof. Wu Donghui  ( NEIGAE, Changchun ). We are grateful to  Wu Donghui ,  Anne Bedos  and  Louis Deharveng  ( Muséum national d'histoire naturelle, Paris ) who provided us with the cave specimens from Liaoning and  Jilin Provinces (China)  for study. Special thanks also to  Sergei Golovatch  ( Russian Academy of Sciences, Moscow ) for making accessible the type material of  ZMMU  and the kind translation of the descriptions of B. P. Chevrizov. We are grateful to  Fauna & Flora Myanmar  for providing material from Myanmar. Many thanks to the  SEM  facility of the  Universitat de València , especially to  Enrique Navarro ,  Pilar Gómez  and  Rafael Benito , as well as to  Lucia Maltez  and  Katie Marzen  for the English revision of the text.
g. in cancer prevention, immunomodulation, and fat or sugar regulation. With respect to sugars, the (C) free sugar compositions of cow, ewe and goat raw milks, raw egg white or yolk, and mature soy, pea, lupin or chickpea whole seeds, in g/100g of each considered fraction. Fig. 3(B ) also indicates the saturated fatty acid/unsaturated fatty acid (SFA/UFA) ratio, in g/g. The PD-CAAS of plant proteins concern protein isolates, which include lupin protein isolate that mainly contains globulin-type protein. This leads to a lower PD-CAAS than expected if all lupin proteins had been present. Data were collected from the ANSES CIQUAL and the USDA Food Data Central databases for (A) and (B) and from the academic literature for (C). great majority in milk is lactose (Fig. 3C ), with only traces of other sugars such as oligosaccharides.
Acknowledgements Valérie Gagnaire  and  Fanny Guyomarc 'h would like to thank  Eric Beaucher ,  Valérie Briard-Bion ,  Hélène Falentin ,  Gwenaëlle Henry ,  Julien Jardin ,  Linda Le Roux ,  Marie-Noëlle Madec ,  Marie-Bernadette Maillard ,  Olivia Ménard ,  Jordane Ossemond  and  Florence Rousseau  for their commitment to the STLO workgroup on animal/plant mixes. The authors also thank the  STLO management committee  for its support. The authors would like to thank  Victoria Hawken  for the English corrections of the manuscript.
Toknai. Note that an offcut was taken at one of the corners of this wood sample for anatomical research in the 20th century. Taxus cuspidata Sieb. et Zucc. (Fig. 12 , sample Kb 216), "a yew species up to 10 feet high" (in reality this species can grow up to 20 m) was used by the Ainu people for making bows in archery; a very common traditional use also of other Taxus species in Europe and North America of this highly elastic and tough wood. Salix sp. (Fig. 13 , sample Kb 197). This willow species was identified by  Yamaguchi and Kato (1998)  as S. rorida Laksch. On the reverse side of the sample, a label has been glued with an illegible Dutch text, which can partly be deciphered when consulting the description in Nippon. The drawing refers to a religious utensil called Inaw (or Inao): whittled willow prayer sticks with pendent, curl-like shavings used to mark graves and houses  (Siebold 1832).