dataesr/scientific-paragraphs-categorization huggingface dataset:
0 using eggnog-mapper (v2. 0. 1). From this output, KEGG annotation (Kyoto Encyclopedia of Genes and Genomes) and CAZymes annotation (Carbohydrate-active Enzyme) were extracted. Since the detection of KOs and CAZymes families are likely to be influenced by sequencing depth, we first normalized their abundance relative to the abundance of the MAG they derived from. Pathways attributed to each KO were annotated from the KEGG Database (downloaded 23-October-2021; https://www. genome. jp/brite/ko00001). The uniqueness of our predicted MAG catalog was confirmed by dereplicating them with the 121 MAGs produced by (Gilroy et al. , 2021) and 3 reported by (Youngblut et al. , 2020) using dRep v. 3. 2. 0  79. dRep performed pairwise genomic comparisons by sequentially applying an estimation of genome distance and an accurate measure of average nucleotide identity.
Construction of the integrated gene catalog The establishment and assessment of the quality and representation of the microbiome gene catalog was performed through the metagenomic ATLAS pipeline (v. 2. 4. 4)  71. As described above, we first assembled the clean reads into longer contigs. Genes were predicted by Prodigal v. 2. 6. 3 and then clustered using linclust  82  to generate a non-redundant gene catalog. Redundant genes were removed (≥ 95% identity and ≥ 90% overlap) with linclust. The quantification of genes per sample was done through the "combine_gene_coverages" function in the ATLAS workflow, which aligned the highquality clean reads to the gene catalog. Taxonomic and function annotations were done based on the EggNOG database 5. 0 using eggnog-mapper (v. 2. 0. 1). From these, the eggNOG numbers corresponding to CAZymes based on homology searches to the CAZyme database were retrieved.
Annotation of metagenome using Kaiju The k-mer-based kaiju v. 1. 8. 0. (https://github. com/bioinformatics-centre/kaiju)  20  approach was used for microbial taxonomic profiling of the shotgun metagenomes. Paired reads after quality trimmed and decontamination from the horse genome were used and annotated against the NCBI nr reference database (released on May 25 th 2020) containing all proteins belonging to archaea, bacteria, eukaryota and virus for classification in Greedy run mode with -a greedy -e 3 allowing for maximum three mismatches. By default, Kaiju returned a "NA" if it could not find a taxonomic classification at certain ranks.
Resistome The high-quality clean paired reads were aligned to the ResFinder database (accessed March 2018, v. 4. 0) using bowtie2 (v. 2. 3. 5). ResFinder is a manually curated database of horizontally acquired antimicrobial resistance (AMR) genes and contains many genes with numerous highly similar alleles (i. e. , β-lactamases). To avoid random assignment of read pairs on these high-identity alleles, the database was clustered at 95% of identity level, over 200 bp using CDHIT-EST (options -G 0 -A 200 -d 0 -c 0. 95 -T 6 -g 1)  83  and a reference sequence was attributed to each cluster. Two successive mappings were done: (i) a first mapping with standard parameters (bowtie2 --end-to-end --no-discordant --no-overlap --nodovetail --no-unal) on the complete ResFinder database and (ii) a second mapping on the clustered database using the reads from the first mapping, with less stringent parameters (bowtie2 --local --score-min L,10,0.
Biodiversity and richness analysis: αand β-diversity The microbiome R package allowed us to study global indicators of the gut ecosystem state, including measures of evenness, dominance, divergences and abundance. Comparison of the gut α-diversity indices between groups was performed by two-tailed Wilcoxon test (pairwise comparison). Benjamini-Hochberg multiple testing correction p < 0. 05 was set as the significance threshold for the comparisons between groups. To estimate β-diversity, Bray-Curtis dissimilarity was calculated using the phyloseq R package. All samples were normalized using the "rarefy_even_depth" function in the phyloseq R package, which is implemented as an ad hoc means to normalize features that have resulted from libraries of widely differing sizes. The PerMANOVA test (a nonparametric method of multivariate analysis of variance based on pairwise distances) implemented in the "adonis2" function from the vegan R package allowed testing the global association between ecological or functional community structure and groups.
easi" function of the SpiecEasi package (v. 1. 1. 1). The features were clustered using the method = mb, lambda. min. ratio = 1e -5 , nlambda = 100, pulsar. params=list (thresh = 0. 001). Regression coefficients from the SPIEC-EASI output were extracted and used as edge weights to generate a feature co-occurrence network R igraph package (v. 1. 2. 6) and Cytoscape (v. 3. 8. 2).
The integration of data was then performed using complementary methods and working with different data sets available, namely: (1) Δ values of mitochondrial-related genes; (2) Δ values of 1 H NMR metabolites; (3) Δ values of the biochemical assay metabolites; (4) Δ values of plasmatic acylcarnitines; (5) the fecal SCFAs at T0; (6) the bacterial, ciliate protozoal and fungal loads at T0; (7) the dominant gut phylotypes at T0; (8) the CAZymes profiles at T0; (7) the KOs at T0 and the (8) athletic performance data. As a first integration approach, a global non-metric multidimensional scaling (NMDS) ordination was used to extract and summarize the variation in microbiome composition using the "metaMDS" function in the vegan R package. To determine the number of dimensions for each NMDS, stress values were calculated.
The explanatory data sets were then fit to the ordination plots using the "envfit" function in the vegan R package 89 with 10,000 permutations. The effect size and significance of each covariate were determined and all of the p-values derived from the "envfit" function were Benjamini-Hochberg adjusted. Variation partitioning was performed using the "varpart" function in vegan in R. The "varpart" function uses linear constrained ordination to assess the shared and independent (partialling out the others) contributions (adjusted R 2 ) of several covariates on microbiome composition variation. As a second integrative approach, the N-integration algorithm DIABLO of the mixOmics R package (http://mixomics. org/, v6. 12. 2) was used. It is to be noted that, in the case of the Nintegration algorithm DIABLO, the variables of all the data sets were also centered and scaled to unit variance prior to integration.
In this case, the relationships existing among all data sets were studied by adding a further categorical variable, i. e. , the cardiovascular fitness of horses. Horses that had poor cardiovascular fitness (n = 8) were compared to horses that had enhanced cardiovascular fitness (n = 3). DIABLO seeks to estimate latent components by modelling and maximizing the correlation between pairs of pre-specified datasets to unravel similar functional relationships between them  90. A full weighted design was considered. To predict the number of latent components and the number of discriminants, the "block. splsda" function was used. In both cases, the model was first fine-tuned using the leave-one-out cross-validation by splitting the data into training and testing. Then, classification error rates were calculated using balanced error rates (BERs) between the predicted latent variables with the centroid of the class labels using the "max.
dist" function. Additionally, the DESeq2 (v. 1. 32. 0)  91  R package was used to test for differential abundances analysis between groups for each independent omic dataset. DESeq2 assumes that counts can be modeled as a negative binomial distribution with a mean parameter, allowing for size factors and a dispersion parameter. Next to the group, the horse dependency was included in the generalized linear model. The p-values were adjusted for multiple testing using the Benjamini-Hochberg procedure. DESeq2 comparisons were run with the parameters fitType = "parametric" and sfType = "poscounts".
FINANCIAL SUPPORT This work was funded by grants from the Animal Genetic Department at Institute National de la Recherche pour l'Agriculture, l'alimentation et l'environnement (INRAE) and the Institut Français du Cheval et de l'Equitation (IFCE). FIGURES Figure 1 -Description of the first horse gut gene catalog: core microbiome and taxonomic annotation (a) Contribution of different sample sources to gene content of the horse gut catalog. Vertical purple and blue bars represent the number of genes present in only one sample or shared between pairs of samples, respectively. Horizontal orange bars in the lower panel indicate the total number of genes contained in each sample; (b)Visualization of the taxonomic assignment of Illumina reads in a Krona plot using the software tool Kaiju; (c) Lollipop plot showing the read counts identified by the Kaiju resolved at the phylum level.
Dots are colored by kingdom; (d) Heatmap depicting the core phylome and their prevalence at different detection thresholds (relative abundance). The percentage of shared items and the proportion of shared samples are represented on the y-and x-axis, respectively; (e) Heatmap showing the normalized counts of antimicrobial resistance (AMR) genes for each individual based on ResFinder database Figure 2 -Gut microbiome composition and structure in endurance horses (a) NMDS ordination analysis (Bray-Curtis distance) of dominant phylotypes composition. Points denote individual samples which are colored according to the clustering group. The shape of the dots indicates the competition level of horses; (b) Biplot values of the dominant phylotypes driving the NMDS ordination. The phylotypes contributing to the distinction between groups on at least one axis are depicted.
Dots represent samples, which are colored according to the type of community: the community type 1 (red color) and community type 2 (blue color); (g) Effect sizes of the main variables affecting the NMDS ordination. The length of the horizontal bars shows the amount of variance (r 2 ) explained by each covariate in the model. Covariates are colored according to the type of dataset: athletic performance are in green and mitochondrial related genes in blue; (h) Violin plot representing the cardiovascular fitness, which was calculated as a composite of postexercise heart rate, cardiac recovery time and average speed during the race. Colors indicate community classification, the community type 1 (red color) and community type 2 (blue color) and boxplots show median, 25 th and 75 th percentile, the whiskers indicate the minima and maxima and the points lying outside the whiskers of boxplots represent the outliers.
In all cases, colors indicate community classification, the community type 1 (red color) and community type 2 (blue color). Column in the left depict the CAZy class; (d) Cooccurrence network analysis of dominant phylotypes and carbohydrate-active enzymes (CAZy) classes datasets using sparse inverse covariance estimation for ecological association inference (SPIEC-EASI). Louvain clustering was able to generate 12 feature co-occurrence modules. The two extreme assortative modules are depicted in detail using Cytoscape. A positive correlation between nodes is indicated by red connecting lines, negative correlation by blue. Species and CAZymes features are denoted by a circle or triangle, respectively. Nodes are colored by phyla. Features with higher text size are those revealed as discriminant along with component 1 by the MixOmics approach.
In contrast, OntoStrength's goal is to contribute to the development of digital solutions dedicated to bio-motor strength ability analysis. OntoStrength considers other bio-motor skills like speed, endurance, or flexibility, as well as other activities like muscle analysis, movement patterns, or training load management, to support sport, professional and daily-life activities. OntoStrength enables the personalization of strength development programs in the Selfit ITS by providing a comprehensive data layer for its student, domain, and tutoring models.
They build a tutor for learning Java programming language using Protégé for building the ontology and Python for the integration with the knowledge repository. An ontology-driven method for an ITS was developed by Dermeval et al. [14]. The goal was to develop a gamification module in an ITS by connecting concepts in a standard and formalized manner. The knowledge base was structured in ontologies and covered concepts such as gamification, domain, student, and tutoring. The link between ITS concepts and gamification facilitated the automated reasoning of all the information handled by these systems, enabling their interoperability and defining good practices for creating a gamified ITS. The connection between the ontologies described by the authors relied on technologies such as RDF/OWL vocabularies (e.
g. , FOAF), and modeling in Protégé. Methontology  [15] , a methodology for building ontologies based on the IEEE standard criteria, was considered throughout the entire ontology lifecycle process. Panagiotopoulos et al. [16]  designed a student model for an ITS and enhanced it with semantics using an ontology. Their Student model integrated student characteristics and progress, while their ITS provided personalized educational content based on student learning and academic particularities. The study targeted facilitating the communication between intelligent agents through a domain-independent vocabulary while modeling student profiles. The ontology was developed in Protégé using Ontology Development 101 (OD101) methodology  [10]  and adopted OWL as a formal representation language. In addition, the authors developed inference mechanisms as a set of rules using Semantic Web Rule Language (SWRL) for the automated classification of the students into stereotypic profiles.
OntoStrength Design The OntoStrength ontology aims to support the development of the Selfit ITS dedicated to enhancing psychomotor skills and associated bio-motor abilities, such as strength. A multidisciplinary team composed of computer scientists and sports scientists applied the seven steps of OD101  [10]  to formalize the subject domain. The first step consists in determining the domain and scope of the ontology by formalizing competency questions. The team characterized competency questions by considering trainee requirements, as well as the domain and tutoring modules from the Selfit system. The first set of competency questions raises the need for the ontology to cover the diversity of strength skills. Then, a second set considered the complexity of strength development. Finally, the third set of questions raised the need to model variables supporting the personalization of development processes for each trainee.
OntoStrength Structure Strength development is the domain covered by OntoStrength. Strength is defined as the maximal force or torque (rotational force) that a muscle or muscle group can generate or as the ability of the neuromuscular system to produce force against an external resistance  [24]. Strength development includes a set of essential tasks, such as the assessment of trainee strength skills, the definition of a development program, and the monitoring of its application. The ontology provides knowledge to support the Selfit ITS functionalities. First, OntoStrength supports the Selfit domain module by providing classes that describe the diversity of strength skills. Second, classes on strength development processes support the tutoring module. Finally, OntoStrength supports the Selfit student module with knowledge about the different individual characteristics to consider for the personalization of strength program tasks.
Subdomains The Strength Skill. The first OntoStrength sub-domain aims to describe strength skills. The domain module uses strength skills to provide development objectives, the student module uses it to design a trainee strength fingerprint, while the tutoring module generates and monitors training workouts as an input. When defining strength, Bompa  [24]  makes the distinction between general and specific strength. The first refers to the strength of the entire muscular system, whereas the second is the motor patterns of muscle groups that are essential to a given activity. He also defines three strength properties: power, maximum strength, and strength endurance. Power is the ability to develop force rapidly and at high velocities. Maximum strength refers to the highest force the neuromuscular system can generate during a maximum voluntary contraction.
Strength development program. The second OntoStrength sub-domain supports the description of a strength development program. The domain module for Selfit uses this knowledge to provide relevant content to generate and schedule training workouts. The student module uses this knowledge to update trainee training components when performing workouts. Finally, the classes structure the behavior of the tutoring module. Strength development consists of a well-organized process, where the body and mind are constantly exposed to varying volumes and intensity of stressors  [24]. The implementation of a strength development program starts by defining the temporal units that structure the different phases of the strength development process. Depending on its duration, macro-cycles (six to twelve weeks), mesocycles (two to six weeks), microcycles (five to ten days), and workouts (thirty minutes to two hours) are used to generate the development program.
OntoStrength content The previously described major class hierarchies support the instantiation of strength development programs, from the macro-cycle level to the exercise level. In addition, SPARQL queries were implemented to solve specific training tasks -for example, to obtain exercises associated with a specific body part (wide triceps push-up, side to side pull-up, feet elevated pike push-up), or to obtain generic training templates for a training objective, based on trainee characteristics. GraphDB was used to test the queries and interact with the ontology. Through SPARQL, new data can also be added to the ontology. OntoStrength presents the instantiation a Macrocycle entity, which has a Mesocycle entity as an object property, called hasMesoCycle. The Mesocycle entity has a Microcycle entity, an object property, called hasMicroCycle.
List of movements The strength individualization signature consists of a level ranging between 1-4 to match the level at which exercises can be safely performed, for all strength movement skills. The evaluation of these parameters is performed through an initial calibration workout session, which has an incremental complexity, until failure. For example, "Side to Side Push Up" for upper body area level 1 complexity or "Self-Assisted One Arm Push Up" for level 2. This Selfit calibration feature uses the RESTful API to obtain the exercises for a given strength movement skill and level, to get the actual trainee level for a selected strength movement skill, and to update the student profile after each exercise. Once an initial student calibration is performed, a strength development training program is generated and is continuously updated after each workout.
The COVID-19 pandemic and subsequent lockdowns modified work environments, lifestyles, and food consumption. Eating habits and mood changes in a French population during the first lockdown were examined using an online self-reported questionnaire with REDCap software through the COVISTRESS. ORG website. In 671 French participants, the main changes during lockdown were increased stress levels (64 [23; 86]  vs. 3 [0; 18]) and sedentary behavior (7 [4; 9] vs. 5 [3; 8] hours per day), a deterioration in sleep quality (50 [27; 83] vs. 70 [48; 94]) and mood (50 [30; 76] vs. 78 [50; 92]), and less physical activity (2. 0 [0. 5; 5. 0] vs. 3. 5 [2. 0; 6. 0]). Mood was modified, with more anger (56 [39; 76] vs. 31 [16; 50]), more sadness (50 [34; 72] vs. 28 [16; 50]), more agitation (50 [25; 66] vs.
Participants All participants were volunteers and were informed of the objective of the survey and that their data would be processed anonymously and be used for research purposes only. Participants could withdraw from the study at any time without having to give reasons. Participants included in the analysis were French adults who answered questions about their weight before and during the first lockdown and who answered all questions about their consumption in the different food categories before and during the first lockdown. The study was reviewed and approved by the human ethics committees Sud-Est VI, France (2020/CE 11-clinicaltrials. gov NCT 04308187). Selection of participants, recruitment, and advertising were carried out through radio, television, fliers, posters, newspapers, and online media, including social media such as Facebook, Twitter, etc.
Study Design and Setting We implemented an open epidemiological, observational, descriptive study by administering a self-reported questionnaire proposed to volunteers using the REDCap software available through the COVISTRESS. ORG website. The REDCap questionnaire was hosted by the University Hospital of Clermont-Ferrand. The questions analyzed in this work were therefore specific questions included in a large questionnaire with various thematic sections. The thematic sections were presented in random order after the demographic questions. The online questionnaire was distributed several times through mailing lists held by institutions and French social groups. The data analyzed were obtained between 26 June 2020 and 2 March 2021.
For this analysis, the variables were chosen according to univariate results, clinical relevance, and statistical distribution (characteristics always present or always absent were not considered). Only individuals without missing data were included in the MCA, and the sample of excluded subjects was compared to the sample of included subjects, with the chi-squared test or Fisher's exact test for categorical data, and the Student's t-test or Mann-Whitney test for continuous data. Statistical analysis was performed using Stata (version 15; StataCorp, College Station, TX, USA) and R 3. 5. 1 (http://cran. r-project. org/, accessed on 1 March 2022) software. All tests were two-sided, with an alpha level set at 0. 05. No missing data were observed for variables related to the primary objective (consumption in the different food categories).
Multiple-Correspondence Analysis For the MCA, 113 out of 671 (16. 8%) subjects were removed because of missing data and 558 were retained. These two samples were similar in age, sex, occupational category, weight, and body mass index. The MCA highlighted four groups of subjects, whose characteristics appear in Table  3  and Figures  3  and 4. Group 1 (n = 253) included subjects who did not change their eating habits or their number of hours of sleep during the lockdown,.
Multiple-Correspondence Analysis For the MCA, 113 out of 671 (16. 8%) subjects were removed because of missing data and 558 were retained. These two samples were similar in age, sex, occupational category, weight, and body mass index. The MCA highlighted four groups of subjects, whose characteristics appear in Table  3  and Figures  3  and 4. Group 1 (n = 253) included subjects who did not change their eating habits or their number of hours of sleep during the lockdown, who did not have mood changes or variations in their emotions (anger, sadness, agitation, and boredom), and who were more often men than in the other groups. Group 2 (n = 155) included nearly all women (90%), whose mood (anger, sadness, agitation, and boredom) deteriorated during the lockdown, and of whom approximately 40% had decreased their sleep time but did not change their eating habits.