dataesr/scientific-paragraphs-categorization huggingface dataset:
The DNA extracted from pure culture of Fibrobacter succinogenes was diluted in serial decimal dilution and further quantified by real-time PCR using the primer set targeting F. succinogenes species. The standard curve was generated by plotting the observed threshold cycle values v. bacterial concentration (log 10 cells/ml). The standard curve for the absolute quantification of total bacteria was generated using the same procedure with the primer set targeting total bacteria. Standard curves were linear over the range of cell concentrations from 2. 4 log 10 to 6. 4 log 10 cells/ml (data not shown). DNA extracted from faecal samples was diluted 100-fold in sterile redistilled water before quantitative PCR analysis to reduce inhibition. DNA amplifications were performed in duplicate using a BioRad iCycler iQ5 (BioRad, Marne-La-Coquette, France) with fluorescence detection of SYBR green dye.
ACKNOWLEDGEMENTS This work was supported by an ' Investissement d'Avenir ' grant managed by the  Agence Nationale de la Recherche  ( Centre d'Étude de la Biodiversité Amazonienne , reference  ANR-10-LABX-25-01 ) through its integrative research program  BIOHOPSYS (Biodiversity and dynamic interactions in multiple hostparasite systems) on Biodiversity and Infectious Diseases. MC  was funded by a post-doctoral fellowship from the  Institut de Recherche pour le Développement (IRD)  and CJV was supported by a PhD studentship from The  Norwegian State Educational Loan Fund. JFG, BR and REG received support from both  IRD ,.
py command in QIIME. We applied a default base call Phred threshold of 20, allowing maximum three low-quality base calls before truncating a read, including only reads with >75% consecutive high-quality base calls, and excluding reads with ambiguous (N) base calls  (Navas-Molina et al. , 2013). Subsequently, the sequences were clustered into OTUs against the GreenGenes database (release 2013-08: gg_13_8_otus)  (DeSantis et al. , 2006 ) by using the uclust  (Edgar, 2010)  method at a 97% similarity cutoff. The filtering of chimeric OTUs was performed by using Usearch version 6. 1  (Edgar et al. , 2011)  against the GreenGenes reference alignment  (DeSantis et al. , 2006). A phylogenic tree was generated from the filtered alignment using FastTree  (Price et al. , 2010). Singletons were discarded from the dataset to minimize the effect of spurious, low abundance sequences using the 'filter_otus_from_otu_table.
py' script in QIIME. To confirm the annotation, the resulting OTU representative sequences were then searched against the Ribosomal Database Project naïve Bayesian classifier (RDP 10 database, version 6  (Cole et al. , 2009)  database, using the online program SEQMATCH (http://rdp. cme. msu. edu/seqmatch/seqmatch_intro. jsp). Finally, consensus taxonomy was provided for each OTU based on the taxonomic assignment of individual reads using GreenGenes and RDP databases. Using OTU abundance and the corresponding taxonomic classifications, feature abundance matrices were calculated at different taxonomic levels, representing OTUs and taxa abundance per sample. The "Phyloseq"  (Mcmurdie and Holmes, 2012)  and "Vegan"  (Dixon, 2003 ) R package were used for the detailed downstream analysis on abundance matrix.
In the end, a total of 8,010,052 paired-end 250 bp reads were obtained, 6,428,315 of which were retained as high-quality sequences (Table  S1 ). On average, a total of 58,015 sequences per sample were obtained in the study, with a mean length of 441 ± 15 bp. These sequences were clustered into 15,784 OTUs using the reference-based OTU-picking process (Table  S2 ). Among them, 12,069 were classified taxonomically down to the genus level (Table  S2 ). OTU counts per sample and OTU taxonomical assignments are available in Table  S2. We filtered out unclassified taxa from the analysis because the main goal of the current study was to identify specific taxa related to host susceptibility to strongyle infection. The α-diversity indexes (observed species richness, Chao1  (Chao, 1984)  and Shannon  (Shannon, 1997 )) were calculated using the "Phyloseq" R package  (Mcmurdie and Holmes, 2012 ).
Shannon's diversity index is a composite measure of richness (number of OTUs present) and evenness (relative abundance of OTUs). The nonparametric Wilcoxon rank-sum test was used to compare α-diversity indexes between groups. Relative abundance normalization was applied, which divides raw counts from a particular sample by the total number of reads in each sample. To estimate β-diversity, un-weighted and weighted UniFrac distances were calculated from the OTU and genera abundance tables, and used in principal coordinates analysis (PCoA), correspondence analysis (CA), and non-parametric multidimensional scaling (NMDS) with the "Phyloseq" R package. The Permutational Multivariate Analysis of Variance (PERMANOVA), on un-weighted and weighted UniFrac distance matrices were applied through the Adonis function from "Vegan" R package to test for groups effect.
, 2017). This targeted locus study project has been deposited at DDBJ/EMBL/GenBank under the accession KBTQ01000000. The version described in this paper is the first version, KBTQ01000000. The bioproject described in this paper belongs to the BioProject PRJNA413884. The corresponding BioSamples accession numbers were SAMN07773451 to SAMN07773550.
11. Functional metagenomic predictions The functional prediction for the 16S rRNA marker gene sequences was done using the phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt)  (Langille et al. , 2013). After excluding the unknown OTUs from the GreenGenes reference database and normalizing by 16S rRNA gene copy number, functional metagenomes for each sample were predicted from the Kyoto Encyclopedia of Genes and Genomes (KEGG) catalogue and collapsed to a specified KEGG level. We used Wilcoxon rank-sum test with Benjamini-Hochberg multiple test correction to evaluate pathway-level enrichments between groups. A q < 0. 05 was considered as significant.
Further details are depicted in  Ramayo-Caldas et al. (2016). The genera with < 0. 1% mean relative abundances were excluded to acquire the results for the taxa that met the statistical conditions for correlation estimations. Nodes in the network represent the genera and edges that connect these nodes represent correlations between genera. Based on correlation coefficient and pvalues for correlation, we constructed co-occurrence networks. The cutoff of p-values was 0. 05. The cutoff of correlation coefficients was determined as r≥|0. 35|. Network properties were calculated with the NetworkAnalyzer plugin in Cytoscape. We used the "iGraph" R package to visualize the network. Strong and significant correlation between nodes (r≥|0. 60|) were represented with larger edge width in the network.
Indeed, a heat wave took place over the first 43 days of the trial resulting in almost no rainfall (0. 2 mm) and high temperatures peaking at 37. 7 ºC (Figure  S3A ). This situation ultimately led to grass senescence and lowered pasture quality (Figure  S3B , Table  S3 ). Mild dehydration also occurred in ponies as supported by a constant rise in measured hematocrits from 32. 33 to 39. 63 % during the first 43 gd (Figure  2C ). To account for this, blood cell counts were corrected by hematocrit through time. Recorded hematological data showed that ponies were neither anemic nor thrombocytopenic throughout the experiment (Figure  S4 ). Concomitantly to these challenging conditions, increased levels of some white blood cell populations were observed, namely eosinophils (2. 75-fold increase, p = 5.
Acknowledgements We are grateful to  Jean Marie Yvon ,  Yvan Gaude ,  Thierry Blard ,  Thierry Gascogne ,  Philippe Barriere ,  Francois Stieau  and  Adelaide Touchard  for the animal sampling and management, and to  Marine Guego ,  Marine Beinat , and  Julie Rivière  for participating to the sample collection during the project. We also thank  Diane Esquerré  for preparing the libraries and performed the MiSeq sequencing. Lastly, we are grateful to the  INRA MIGALE bioinformatics platform  (http://migale. jouy. inra. fr)for providing computational resources.
Molecular analyses. In order to confirm the assignment of the African bullfrog strains to the genus and to refine their position within the known Brucella species a series of molecular typing methods with increasing resolution were applied. Bcsp31, IS711, and Bruce-ladder multiplex PCR. The presence of the Brucella-specific IS711 element and amplification of bcsp31 using Brucella specific primers in all strains demonstrated their affiliation with the genus Brucella. Strains were identical in the Bruce-ladder PCR, a multiplex assay for the differentiation of Brucella species, displaying a previously known banding pattern with amplicon sizes of 152, 272, 450, 587, and 794 bp. IS711 was also detected by Southern blot analysis. The IS711 copy number varied from one copy to more than 14 copies depending on the genotype determined with other molecular analyses (data not shown).
i2bc. paris-saclay. fr/]) that have not yet been previously identified in more than 3,500 other Brucella strains. On the basis of the MLVA-11 data set, seven individual genotypes could be identified, numbered 196 to 202. Phylogenetic and comparative analyses. Phylogeny. Whole genome sequencing of nine isolates, at least two each from the three major clades and the two strains found in separate lineages, confirmed their affiliation with the genus Brucella (Fig. 3 ). Phylogenetically, the African bullfrog strains are basal to the classical Brucella species and are intermixed in the tree with isolates from Australian rodents and the unusual BO1 and BO2 isolates associated with human clinical disease but of unknown transmission route to humans. Four distinct clades of bullfrog isolates are apparent but limited bootstrap support for these basal groups and a high level of homoplasy (homoplasy index = 0.
The 96-well Micronaut ® BfR Brucella assay tests for 29 aminopeptidases, two phosphatases, four glucosidases, one esterase, and the metabolism of eleven monosaccharides, three disaccharides, seven sugar derivates, 15 amino acids, eleven organic acids, one salt, one amino acid derivate, one peptide, one base, and six classical reactions (nitrite and nitrate reduction, pyrazinamidase, Voges-Proskauer medium, urease, and H 2 S production). Biotyping experiments were carried out in triplicates. Hierarchical cluster analysis was conducted by Ward's linkage algorithm using binary coded data based on empirically set cutoffs (Bionumerics v. 7. 5, Applied Maths, Sint-Martens-Latem, Belgium). All characters were considered equal within the data set. Motility of the bacteria was tested using a 0. 3% semisolid agar including TTC (2,3,5 triphenyltetrazolium chloride) to visualize dynamics of bacterial swarming.
Phylogenetic analysis. Comparative genomic analyses were conducted following previously described methodology  53, 54  to discover single nucleotide polymorphisms (SNPs). Representative strains clustering in different groups were selected according to molecular typing data. Genome sequencing was carried out by the sequencing services provider GATC Biotech, Konstanz, Germany using either MiSeq (Illumina Inc. , San Diego, CA, USA) or PacBio (Pacific Biosciences, Menlo Park, CA, USA). De novo assembly of the Illumina and PacBio sequencing reads was done with CLC Genomics Workbench 9. 0 (CLC bio, Aarhus, Denmark) and SMRT Analysis Software v. 2. 3 (Pacific Biosciences), respectively. Illumina reads were assembled using standard parameters. For the assembly of PacBio reads, the HGAP3 algorithm with a read length minimum of 2,500 bp was used.
SNP analysis was conducted with the NASP v. 1. 0. 0 pipeline (https://github. com/TGenNorth/NASP) using default settings for the programs implemented in the pipeline. Briefly, assemblies of the amphibian Brucella sp. in FASTA format were aligned to the B. abortus 2308 reference genome (GenBank accession numbers NC_007618, NC_007624) and analyzed for SNPs with MUMmer 3. 23  55  ; SNP discovery using sequence reads gave nearly identical results (data not shown). Reads were aligned to the reference with BWA v. 0. 3. 7  56  , and SNPs discovered in the alignments with the Genome Analysis Toolkit v. 2. 5 Unified Genotyper  57. We required SNP loci had have a base call in all samples, i. e. , no missing data. SNP loci in duplicated regions, determined by an alignment of the reference to itself with MUMmer, were excluded from analyses.
Phylogenetic trees were built using maximum parsimony in PAUP* with 100 bootstrap replicates to indicate the amount of support for various branches  58. Trees were visualized with FigTree v. 1. 4. 0 (http://tree. bio. ed. ac. uk/software/figtree/). Eight of the classical Brucella species were included in the tree for phylogenetic context and to contrast the amounts of genetic diversity among the various clades. Ochrobactrum anthropi ATCC 49188 T (GenBank accession numbers CP000758-CP000763) was used as an outgroup. Comparative analysis. The comparative gene-based analysis included the genomes of strains 10RB9213, 10RB9215, 09RB8910, 09RB8913, and 09RB8471. An initial examination using 16S rRNA revealed that some bullfrog isolates were closely related to B. inopinata BO1  19  , so these genomes were compared to B.
inopinata BO1. Brucella sp. BO2 and B. microti CCM 4915 T were also chosen for the initial comparison. All genomes were annotated with RASTtk to provide consistency across the results. A list of all genomes used, as well as their annotation statistics, is provided (Table  3 ). Several methodologies were used to compare the genomes, all of which are available in PATRIC  53. The Protein Family Sorter 54 was used to look for unique regions in individual genomes. Regions determined to be unique to each bullfrog strain, or shared across them, were verified by BLAST against the representative genome database at NCBI. In addition, these unique regions were examined by BLAST against both the complete plasmid and complete bacteriophage databases. A detailed comparison of specific genomes (10RB9215, B13-0095, 10RB9213, BO2, 09RB8910, 09RB8913, 09RB8471, B.
Acknowledgements We are grateful to  Nelly Bernardet ,  Cornelia Göllner ,  Anna-Louisa Hauffe ,  Robert Schneider ,  Astrid Thomas , and  Phillip Vette  for excellent technical assistance and  Miguel Angel Navarro  for animal care. We also thank  Philippe Le Flèche  for his assistance in MLVA typing,  Mark S. Koylass  and  Jakub Muchowski  for MLST analysis,  Charles H. D. Williamson  for genome assembling, and  Jochen Reetz  for electron microscopy. Thanks to  Risa Y. Dickerman  for her help in creating the flagellum figure. A. R. Wattam is supported in part by federal funds from the  National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services , under contract no. HHSN272201400027C. The work of  M. P. Jiménez de Bagüés  was supported by the  Spanish  grant  INIA-RTA2013-00065-C02-01.
The work of  S. Köhler  and  A. Occialini  was funded by the  Federal Institute for Risk Assessment, Germany  ( BfR  grant no. 1329-485 ). Brucella research work at  APHA  is supported by the  Department of Environment, Food and Rural Affairs  (projects  SE0314  and  SE0316 ), at the  German Federal Institute for Risk Assessment  by the  Federal Ministry of Food and Agriculture  ( BfR  project no. 47-003 ,  1322-503 , and  1322-619 ), and at the  Hessian State Laboratory  by the  Hessian Ministry of Environment ,  Climate Change, Agriculture and Consumer Protection. The development of genotyping methods for strain identification of dangerous pathogens as part of microbial forensics is supported by the  French DGA (Direction Générale de l' Armement)  via the  MicroType  project  ANR-14-ASMA-0002-02.
NF2653 PRJNA41859 113 3104330 3250 Rodent inopinata BO1 PRJNA41855 55 3366774 3362 Human inopinata-like BO2 PRJNA41857 174 3305941 3317 Human sp. B13-0095 PRJNA326393 37 3360799 3325 Pacman frog sp. 10RB9213 62 3510722 3486 African bullfrog sp. 10RB9215 ERA672254 2 3562813 3514 African bullfrog sp. 09RB8910 PRJNA347474 2 3592978 3615 African bullfrog sp. 09RB8913 110 3336969 3320 African bullfrog sp. 09RB8471 PRJNA347475 2 3434749 3491 African bullfrog.
NIRS analysis and calibration procedure Ground faecal samples were packed in ringcups sample cells and were then scanned from 1,100 to 2,500 nm using a NIRSystems 5000 scanning monochromator (FOSS, Hillerød, Denmark). Reflectance was recorded at 2 nm intervals as log (1/R), where R represents the reflected energy, resulting in 692 data points for each sample. The analysis was carried out in duplicate and all measurements were performed by the same operator. The WinISI III (v. 1. 6) software program was employed for spectral data analysis and development of chemometric models. Prior to calibration, log (1/R) spectra were corrected for the effects of scatter using the standard normal variate (SNV) and detrend (DT) algorithms and by multiplicative scatter correction (MSC) to reduce the effects of the particle size.
Analysis of NIR spectrum The NIR spectra of faecal samples averaged for each animal species are shown in Fig  1  (see S1 Dataset for raw data). The log (1/R) reflectance spectra of faecal samples were similar to those reported for domestic ruminants  [18, 52, 53]  and rabbits  [54]. The following characteristic bands were identified: 1450 nm (1st overtone of the O-H stretch, characteristic of water content); the region 1700-1762 nm (1st overtone of C-H stretch of CH 2 and CH 3 groups, associated with fat content); 1930 nm (a combination of the O-H bend and the stretching band of water) and the bands 2106, 2312, and 2350 nm (combination bands of C-O stretching and bending vibrations associated with the absorption of starch and protein)  [55, 56]. The FN content was visible in the 2100-2350 nm region.
Relationship between NIRS predictions and laboratory FN estimates among species The best model to explain the observed variability of laboratory FN included the effects of NIRS predictions and the herbivore species (R 2 = 0. 92, Wi = 0. 89; Table  6 ). It indicates that the slope between laboratory FN and NIRS calibrations was similar for all species varying only in the intercept (Fig  2 , see S1 Dataset for raw data). Thus, we can assume a single relationship between NIRS FN predictions and laboratory FN in all species considered. Moreover, our Bland-Altman analysis showed that the mean of differences (d) between monospecies and multispecies predictions (i. e. , bias) was -0. 0122, and the limits of agreement were d + 1. 96sd = 0. 2167 and d -1. 96sd = -0. 2411 (Fig  3 ). Also, the confidence intervals (95%) were calculated for both the mean of differences and the limits of agreement.
[63] , where databases with a diversity of plant species were used to predict digestibility in sheep. Nuñez et al. [54]  also used different diets as a calibration set to predict digestibility parameters, as well as the crude protein in faeces, of domestic rabbits. However, to the best of our knowledge, no previous study has developed a multispecies equation to predict the FN of animal species. The fact that the slope of the relationship between NIRS FN predictions and laboratory FN was similar for each species, supports the possibility of using a single global equation to predict the faecal nitrogen in a broad range of herbivores. We have shown that a calibration built with samples from a wide diversity of herbivores (large range of FN) can predict with accuracy the FN values in samples from other herbivores when the FN values are within the FN range of the data set used in the multispecies calibration.
Samples Blood and tissue biopsy samples from dogs were collected by a network of veterinarians through the Cani-DNA biobank (http://dog-genetics. genouest. org), which is part of the CRB-Anim infrastructure. The work with dog samples was approved by the CNRS ethical board, France (35-238-13) for UMR6290 and dog owners consented to the use of data for research purposes anonymously. We selected 3 distinct cancers (lymphomas, sarcomas and gliomas) representing different tissues (neurological, subcutis, hematological) and different embryonic origins, with known fusions in the human counterpart. The set of samples included 3 lymphomas (2 DLBCLs and 1 T lymphoma), 2 gliomas and 1 dermatofibrosarcoma protuberans-like.
Identification of fusion transcripts Candidate gene fusion transcripts were identified with CRAC and chimCT softwares (http://cractools. gforge. inria. fr/) using the CanFam3 canine genome reference. Fusion transcripts were manually filtered by discarding transcripts with less than five reads spanning the fusion junction and three reads encompassing the two fusion transcripts, as well as transcripts involving paralogous genes or pseudogenes, transcripts identified as read-through, transcripts with no Ensembl annotation (Supplementary Table  1 ).
Acknowledgments We thank  Olivier Albaric  and  Frédérique Nguyen  ( AMaROC research unit, Oniris, Ecole Nationale Vétérinaire de Nantes, France ) as well as  Caroline Laprie  ( Histopathology laboratory, Amboise, France ),  Marie Lagadic  ( Idexx laboratory, Alfort, France ),  Frédérique Degorce-Rubiale  ( LAPVSO, Toulouse, France ) who provided anatomopathologic diagnoses. We thank the  French veterinarians Anne-Sophie Guillory  ( IGDR, Rennes, France), Marie-Anne Colle (Oniris, Nantes, France ),  Valérie Gerard  ( Les Mages, France ),  Laurie Anne Dulière-Hyrien (Le Plessis-Belleville, France) ,  Arnaud and Claude Muller (Lomme, France) ,  Laurence Armando (Tavaux, France) ,  Catherine Escriou  ( VetAgro Sup, Lyon, France ) and  Patrick Devauchelle  ( MICEN VET, Creteil, France ) for providing us with clinical data and samples, as well as dog owners and breeders.
Grant support: For this study,  C Andre  had been award grants by  CNRS ,  INCa PLBio "Canine rare tumours" funding  ( N° 2012-103 ;  2012-2016 ), Aviesan/INSERM MTS  2012-06 , in the framework of Plan Cancer 2009-2013 and by the  CRB-Anim infrastructure ,  ANR-11-INBS-0003 , in the framework of the ' Investing for the Future' program  (PIA) and  T. Guillaudeux  has been awarded by the  ANR  with the LabCom Oncotrial project  2014-2017. Ronan Ulvé, PhD  was funded through a  CIFRE contract between CNRS  and the  Biotrial company  ( 2013-2016 ). Mélanie Rault, PhD  was funded through a  French "Ministère de la recherche " funding ( 2013-2016 ).
Environmental sources of substrates metabolized by all tested M. ulcerans strains In order to find the potential environmental origin of the carbon substrates metabolized by M. ulcerans, we used the PubMed database to obtain information on the environmental sources for each of the 190 carbon substrates present in the PM1and PM2 plates. The environmental sources were organized in 10 categories (plants, fruits and vegetables, bacteria, algae, fungi, nematodes, mollusks, mammals, insects and the inanimate environment). The Chi-square test was used to compare the proportion of each category for substrates not metabolized by M. ulcerans versus substrates metabolized by all tested M. ulcerans strains; a P value < 0. 05 was used as the criterion for statistical significance. We then used the PubMed database to match each substrate, used as a key-word, with all environmental sources significantly associated with substrates metabolized by all tested M.