dataesr/scientific-paragraphs-categorization huggingface dataset:
Details on metabolite identification are described in our previous work  24, 25. Biochemical assay data production. Sera were assayed for total bilirubin, conjugated bilirubin, total protein, creatinine, creatine kinase, β-hydroxybutyrate, and aspartate transaminase (ASAT), γ-glutamyltransferase and serum amyloid A levels on an RX Imola analyzer (Randox, Crumlin, UK). Blood acylcarnitine profiling. As a proxy for mitochondrial β-oxidation, the serum acylcarnitine profiles were produced and analyzed as described elsewhere  6. In the positive mode, free carnitine and 27 acylcarnitines were analyzed for their butyl ester derivatives by electrospray tandem mass spectrometry (ESI-MS-MS) on a triple quadrupole mass spectrometer (Xevo TQ-S Waters, Milford, MA, USA) using deuterated water. Fecal measurements: SCFA, DNA extraction, and microorganism concentrations.
A negative control sample alongside biological samples at the DNA extraction and PCR steps was considered in attempts to control DNA contamination before and after sequencing. In addition, contamination was minimized through laboratory techniques such as UV irradiation of material, ultrapure water, the DNA-free Taq DNA polymerase, and the separation of pre-and post-PCR areas. The Divisive Amplicon Denoising Algorithm (DADA) was implemented using the DADA2 plug-in for QIIME 2 (v. 2021. 2) to perform quality filtering and chimera removal and to construct a feature table consisting of read abundance per amplicon sequence variant (ASV) by sample  80. Taxonomic assignments were given to ASVs by importing Greengenes 16S rRNA Database (release 13. 8) to QIIME 2 and classifying representative ASVs using the naive Bayes classifier plug-in  81.
The function isContaminnat() was used to determine the distribution of the frequency of each contaminant feature as a function of the input DNA concentration. Only 6 ASV were statistically classified (p < 0. 05) as contaminants, although their frequency plots showed they were non-contaminants (Supplementary Fig. 11 ). Fecal metagenome: Shotgun sequencing data production and analysis. Metagenomic sequencing was performed using the same DNA extractions. For each individual, a paired-end metagenomic library was prepared from 100 ng of DNA using the DNA PCR-free Library Prep Kit (Illumina, San Diego, CA, USA). The size was selected at about 400 bp. The pooled indexed library was sequenced in an Illumina HiSeq3000 using a paired-end read length of 2 × 150 pb with the Illumina HiSeq3000 Reagent Kits at the PLaGe facility (INRAe, Toulouse).
MAG assembly and annotation. Raw metagenomic reads were quality-trimmed, assembled, binned, and annotated using the ATLAS pipeline, v. 2. 4. 4  84. In short, using tools from the BBmap suite v. 37. 99  85  , reads were quality trimmed with ATLAS parameters: preprocess_minimum_base_quality = 10, preprocess_ minimum_passing_read_length = 51, preprocess_minimum_base_frequency = 0. 05, preprocess_adapter_min_k = 8, preprocess_allowable_kmer_mismatches = 1, and the preprocess_reference_kmer_match_length = 27. The contamination from the horse genome (available at NCBI sequence archive with the accession number GCA_002863925. 1; Equus_caballus. EquCab3. 0) was filtered out using the following settings: contaminant_max_indel = 20, contaminant_min_ratio = 0. 65, contaminant_kmer_length=13, contaminant_minimum_hits = 1, and con-taminant_ambiguous=best.
Contig predictions were combined using DAS Tool v. 1. 1. 2-1  90  with diamond engine and score_threshold set to 0. 5. ATLAS configuration file, summaries of individual samples quality control, contigs from the individuals, and detected bins are available at the INRAE data repository (https://doi. org/10. 15454/NGBSPC)  91  and are contained in the files "ATLAS_config. yalm", "ATLAS_dag. pdf", "notebook. html", "ATLAS_QC_report. html", and "ATLAS_bin_report_DASTool. html". Assembly statistics for the predicted MAGs such as completeness, redundancy, size, number of contigs, contig N50, length of the longest contig, average GC content, and the number of predicted genes were computed using the lineage workflow from CheckM v. 1. 1. 3  92. MAGs were designated as near-complete drafts if they had completeness ≥90%, redundancy <5%, transfer RNA gene sequences for at least 18 unique amino acids, or medium-quality drafts if they had completeness ≥50% and a redundancy <10%.
37. 99  85  (pairlen = 100, minid=0. 9, mdtag=t, xstag=fs, nmtag=t, sam=1. 3, ambiguous=best, secondary=t, saa=f, maxsites=10). The sample-specific median coverage of each MAG was then computed using pileup within BBMap with default parameters. For the taxonomic annotation, ATLAS predicted the genes of each MAG sequence using Prodigal v. 2. 6. 3  94  with single-mode and closed-end parameters. The taxonomy of the predicted MAGs was inferred using the genome taxonomy database (GTDB-Tk)  43  (v. 5. 0, release 95 (July 17, 2020)). As such, GTDB-Tk taxonomy names were used throughout this paper. In addition, domain-specific trees incorporating the predicted MAGs were inferred by constructing a maximum-likelihood tree using the de novo workflow in GTDB-Tk v. 5. 0 with the following parameters: --bacteria | --archaea, min_perc_aa=50, prot_model=WAG.
Trees were visualized using ggtree (v. 3. 0. 2) in the R package. To assess the contribution of the constructed MAGs to the functional potential of the gut microbiome, the predicted gene and proteins extracted by Prodigal during the CheckM pipeline were compared to the EggNOG database 5. 0 using eggnog-mapper (v2. 0. 1). KEGG annotation (Kyoto Encyclopedia of Genes and Genomes) and CAZymes annotation (Carbohydrate-active Enzyme) were extracted from this output. Since the detection of KOs and CAZymes families is likely influenced by sequencing depth, their relative abundance was normalized 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.
Genes were predicted by Prodigal v. 2. 6. 3 and then clustered using linclust  95  to generate a non-redundant gene catalog. Redundant genes were removed with linclust using the following parameters: minlength_nt = 100, minid = 0. 95, coverag = 0. 9, and subsetsize = 500,000. The quantification of genes per sample was done through the combine_gene_coverages() function in the ATLAS workflow, which aligned the high-quality clean reads to the gene catalog using the BBmap suite v. 37. 99  85  (minid = 0. 95, mdtag = t, xstag = fs, nmtag = t, sam= 1. 3, ambiguous = all, secondary = t, saa = f, maxsites = 4). Taxonomic and function annotations were done based on the EggNOG database 5. 0 using eggnog-mapper (v. 2. 0. 1) (emapper. py-annotate_hits_table {input. seed}-no_file_comments). The eggNOG numbers corresponding to CAZymes based on homology searches to the CAZyme database were retrieved from these.
We used the derived eggNOG abundance matrix to obtain a CAZyme profile per sample. Similarly, KEGG annotation was recovered from the EggNOG output. KEGG gene IDs were mapped to KEGG KOs and used to get the KEGG functional pathway hierarchy. Furthermore, using mmseqs2 (v. 13. 45111) to find genes at a 95% similarity threshold and 80% overlap, we compared our gene catalog with a previously published gene catalog containing ~4 million genes  30. The parameters used were the following: easy-search --search-type 3 --min-seq-id 0. 95 --cov-mode 0 -c 0. 8 --threads 16 --alignment-mode 3 --max-seq-len 100000. The annotated gene catalog fasta file is deposited at DDBJ/ENA/GenBank Whole Genome Shotgun under the BioProject ID PRJNA438436 and is also available at https://doi. org/10. 15454/NGBSPC  91  as "Genecatalog_withnote.
fna. gz". The KO and CAZymes derived from the gene catalog are available in the same INRAE data repository and are in the "Genecatalog_KO. tab" and "Genecatalog_CAZy. tab" files, respectively. Annotation of metagenomes using Kaiju. The kmer-based kaiju v. 1. 8. 0 (https:// github. com/bioinformatics-centre/kaiju)  31  approach was used for microbial taxonomic profiling of the trimmed shotgun metagenomes and the microbial gene catalog. The microbial gene catalog fasta, core group genes fasta, and paired reads after quality trimmed and decontamination from the horse genome were used and annotated against the NCBI nr_euk reference database (released on May 25, 2020) containing all proteins belonging to archaea, bacteria, fungi, microbial eukaryotes, and viruses for classification in Greedy run mode with -a greedy -e 3 allowing for maximum three mismatches.
org/ 10. 15454/NGBSPC as "Ecaomic_dominant_phylotypes_nonrariefied. rds". 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. It contains many genes with numerous highly similar alleles (e. g. , 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)  96  and a reference sequence was attributed to each cluster. Two successive mappings were done: (i) the first mapping with standard parameters (bowtie2 --end-to-end --no-discordant --no-overlap --no-dovetail -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.
Specifically, the method allows microorganisms and functions to interact differently, from bidirectional competition to mutualism or not interacting at all. The statistical method SPIEC-EASI comprises two steps: a transformation for compositionality correction of the feature matrices and estimation of the interaction graph from the transformed data using sparse inverse covariance selection. The sparse graphical modeling framework was constructed using the spiec. 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). Integrative statistical analysis. Data integration was carried out using several approaches and different combinations of datasets. Before the integration, we applied some additional pre-processing steps to our exploratory datasets. In particular, to eliminate intra-individual variability and focus on the differential signals between T1 and T0, we considered Δ values (T1-T0) for each of these datasets, namely biochemical assay data and metabolome acylcarnitine profiles, and gene expression data. For the transcriptome, we constructed a matrix of log-transformed expression values between T1 and T0 (e. g. , the difference in log 2 -normalized expression between T1 and T0). The integration of data was then performed using complementary methods and working with different datasets available, namely: 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.
Stress values were calculated to determine the number of dimensions for each NMDS. The explanatory datasets were then fit to the ordination plots using the envfit() function in the vegan R package  98  with 10,000 permutations. Each covariate's effect size and significance were determined, and all p-values derived from the envfit() function were adjusted Benjamini-Hochberg. Variation partitioning was performed using the varpart() function in vegan in R. The N-integration algorithm DIABLO of the mixOmics R package (http:// mixomics. org/, v6. 12. 2) was used as a second integrative approach. It is to be noted that, in the case of the N-integration algorithm DIABLO, the variables of all the datasets were also centered and scaled to unit variance before integration. In this case, the relationships among all datasets were studied by adding a different categorical variable, e.
g. , the cardiovascular fitness of horses. Horses with poor cardiovascular fitness (n = 8) were compared to horses with enhanced cardiovascular fitness (n = 3). DIABLO seeks to estimate latent components by modeling and maximizing the correlation between pairs of pre-specified datasets to unravel similar functional relationships 99. To predict the number of latent components and the number of discriminants, the block. splsda() function was used. The model was first fine-tuned using 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. Finally, the DESeq2 (v. 1. 32. 0) 100 R package was used to test differential abundances analysis between groups for the dominant phylotypes, MAGs, and the genetic functionalities derived from KOs and CAZymes at the basal time.
DESeq2 assumes counts can be modeled as a negative binomial distribution with a mean parameter, allowing for size factors and a dispersion parameter. The p-values were adjusted for multiple testing using the Benjamini-Hochberg procedure. DESeq2 comparisons were run with the parameters fitType="parametric" and sfType="Wald". The validation cohort. The validation set consisted of 22 pure-breed or half-breed Arabian horses (12 females, three males, and seven geldings; age: 9. 2 ± 1. 27) not included in the experimental set to ensure that the observed effects were reproducible in a broader context (Supplementary Data 20). Five animals were enrolled in a 160 km endurance competition among the horses in the validation set, while 17 were in a 120 km race. The management practices throughout the endurance ride and the International Equestrian Federation (FEI) compulsory examinations and the weather conditions, terrain difficulty, and altitude were that of the experimental set.
Acknowledgements We are grateful to  Marine Beinat ,  Julie Rivière ,  Emmanuelle Rebours ,  Jordi Estellé ,  Caroline Morgenthaler , and  Arni Janssen  for participating in the sample collection and organization during the project. We particularly thank  Valentine Ballan  and  Fanny Blanc , who helped isolate the blood RNA. We also thank  Catherine Philippe  for the SCFA analysis in feces, and  Diane Esquerré , who prepared the libraries and performed the MiSeq sequencing at the  GeT-PlaGe genomics core facility (INRAE, Toulouse , https://get. genotoul. fr/) and  Estel Blasi , who created the picture of horses. Last, we are grateful to all the horse owners, riders, and endurance race organizers who participated in the study. This work was funded by grants from the  Fonds Eperon , the  Institut Français du Cheval et de l'Equitation (IFCE) , the  Association du Cheval Arabe (ACA) , and the  Animal Genetic Department at Institute National de la Recherche pour l'Agriculture, l'alimentation et l'environnement (INRAE).
Data availability The datasets presented in this study can be found in different online repositories. Microarray expression data (MIAME compliant) are available in Gene Expression Omnibus (GEO) repository under the accession number GSE163767. Metabolomic data are available in the NIH Common Fund's Data Repository and Coordinating Center UrqK1489 (http://dev. metabolomicsworkbench. org:22222/data/DRCCMetadata. php? Mode=Study&StudyID=ST000945). The gut metagenome 16S rRNA targeted locus data from the experimental and validation sets are available in the DDBJ/EMBL/GenBank under the BioProject PRJNA438436. The accession numbers of the BioSamples included in the experimental set are SAMN08715729, SAMN08715728, SAMN08715727, SAMN08715725, SAMN08715723, SAMN08715721, SAMN08715719, SAMN08715718, SAMN08715714, SAMN08715713, SAMN08715710.
Apports thérapeutiques Les connaissances acquises sur l'oncogenèse dépendante de la SDH permettent d'envisager une médecine de précision via l'utilisation de thérapies ciblées. Ainsi, les inhibiteurs de tyrosine kinase, dont le sunitinib qui cible les récepteurs du VEGF, du PDGF (platelet-derived growth factor) et RET 3 , qui a une action antiangiogénique, semble particulièrement intéressant dans les PPGL métastatiques. Les résultats d'une étude de phase II (FIRSTMAPP, NCT01371201) seront connus prochainement. Conséquence directe du phénotype hyperméthylateur, le témozolomide, un agent alkylant, a une effi-cacité plus importante sur les tumeurs mutées SDHB, du fait de l'inhibition épigénétique de la méthylguanine méthyltransférase MGMT qui répare les cassures de l'ADN induites par ce traitement  [55].
Measurements and photographs of the floral parts were made with Keyence VHX-7000/7020 digital microscope. Floral bud shapes terminology is based on  Marinho (2018). The conservation status was evaluated according to the IUCN Red List criteria (IUCN 2012); the extent of occurrence (EOO) and the area of occupancy (AOO) were calculated by using the online "GeoCAT" software (http://geocat. kew. org) with a cell width of 2 km  (Bachman et al. 2011).
Georeferencing was inferred from the localities reported by Franchet, or from the herbarium labels, and all gatherings were georeferenced according to our own knowledge of the localities in the region. Therefore, all data gathered for all 1387 specimens were transcribed, interpreted and reported in Appendix 1. Geographic coordinates were imported into ArcGIS 10. 0 to create plants distribution maps. A WGS84 Geographic coordinate system was used as a reference datum.
Acknowledgements This study was supported by the " Grid mapping of the flora of Uzbekistan ". The authors are grateful to the curators of P; the The herbarium collections by Guillaume Capus ADANSONIA, sér. 3 •  2022 • 44 (22 )  MNHN (Paris, France ) gives access to the collections in the framework of the  RECOLNAT national Research Infrastructure. We thank  Natalia Shchegolova  ( Department of Botany, Institute of Biology, Tomsk State University, Tomsk, Russia ), and  Iraj Mehregan  ( Islamic Azad University Tehran Science and Research Branch, Tehran, Iran ) for their help in the identification of Cousinia and Ranunculus species. We thank the editor-in-chief,  Dr Thierry Deroin , an anonymous reviewer and the desk editor  Emmanuel Côtez  for their help in improving this manuscript.
Remerciements Je remercie les virologues  Bruno Canard et Nolwenn Jouvenet , qui ont bien voulu me faire part de leurs commentaires sur ce texte, ainsi que les évaluateurs anonymes de NSS et  François Bontems , pour leurs précieuses remarques. Ma reconnaissance va aussi à l'équipe de la Maison française d'Oxford, qui m'a accueilli au mois de juin 2021 et permis de démarrer ce travail dans des conditions idéales (malgré les nécessaires restrictions dues à la circulation du virus dont il est question ici).
La plateforme se situe dans le domaine des outils d'analyse qualitative de données dits CAQDAS (Computer-Assisted Qualitative Data Analysis Software) comme Transana et Nvivo, qui proposent des fonctionnalités similaires à l'outil que nous présentons ici en ce qui concerne l'aide à la transcription de corpus ainsi que la définition et la visualisation du schéma d'annotation. Néanmoins ces deux outils sont payants et leurs formats d'import et d'export permettent peu d'interopérabilité avec d'autres outils existants utilisés par la communauté linguistique.
Spécificités de Sarramanka Il s'agit d'une application en ligne, hébergée sur les serveurs de la Très Grande Infrastructure de Recherche Huma-Num. Elle repose sur un développement en PHP, HTML5, CSS3 et JQUERY avec une base de données relationnelle. L'interface se veut facile à utiliser et ergonomique pour faire la part belle au corpus. Il suffit de surligner une chaîne de caractères pour lui assigner ce que l'utilisateur veut : une annotation, une catégorie ou encore un commentaire. L'étiquetage comme la catégorisation sont libres, et peuvent se faire à la volée. Ces phénomènes sont repérables par la suite grâce à une coloration liée à l'annotation réalisée. Ces éléments peuvent ensuite être regroupés sous la forme d'un sous-corpus via un moteur de recherche, afin d'être analysés ou exportés.
, 1990) , and sequenced these amplicons with the Sanger method at Eurofins Genomics (Germany). The obtained sequences were aligned with sequences of orchids from La Réunion retrieved on GenBank using MAFTT  (Katoh et al. , 2002) , and a phylogenetic inference was performed by ML using IQTREE 2. 1. 3 (Minh et al. , 2020)  for phylogenetic comparison, using default parameters.
| Bioinformatics and OTU filtering Paired reads from 2 × 250 bp Illumina sequencing were processed using a customed pipeline based on VSEARCH  (Rognes et al. , 2016)  as in Perez-Lamarque,  Krehenwinkel, et al. (2022). Briefly, pairedend reads were assembled using the fastq_mergepairs function with default parameters and assembled reads with more than two errors in the alignment were discarded. The resulting assembled reads were assigned to samples according to their tag+primer sequences using CUTADAPT  (Martin, 2011)  allowing zero discrepancies. Tag jumps  (Zinger et al. , 2019 ) was estimated at this step and represented 0. 04% of the reads. Operational taxonomic units (OTUs) were clustered using a 97% similarity threshold with VSEARCH algorithm. Chimeras were detected at this step using the uchime_denovo option of VSEARCH and removed from the dataset.
OTUs were taxonomically assigned using the usearch_global option of VSEARCH with default parameters on the UNITE V8. 3 (2021-05-10) eukaryotes reference database  (Nilsson et al. , 2015). Singletons and short OTUs (< 200 bp) were removed before OTU table building. Nonfungal OTUs and OTUs abnormally detected in the positive and negative controls were also discarded from the dataset, as well as OTUs identified as contaminants by the decontam r-package with frequency method  (Davis et al. , 2018). Finally, based on the previously estimated tag-jumps rate, OTUs that represented <0. 5% of the sample reads (hereafter referenced as the bioinformatics threshold) were set to 0, hence minimizing the impact of artefactual sequences and tag jumps. Samples with <1000 reads or without OMF were not considered in subsequent analyses.
| Diversity analyses The following beta diversity analyses were conducted with the vegan r-package  (Oksanen et al. , 2013) , using the Jaccard index as implemented in the vegdist function. To assess differences in OMF composition between substrates (i. e. root vs. bark), orchid species and tree individuals and species in the overall dataset, we used a nonmetric multidimensional scaling (NMDS) approach using the meta-MDS function. The differences were tested using a PERMANOVA, with 999 permutations, using the adonis function. As the studied epiphytic orchids grew on different trunks in the same forest area, the permutations were constrained to each tree individual using the strata option in adonis. To test the effect of tree individuals on fungal communities within orchid species growing on several trees we conducted a PERMANOVA using the tree identity (A-F) as explanatory variable and orchid species as strata.