dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analyses Traits defined across the entire recording period were normalized using the "bestNormalize" package  [27]  of the R software  [28]  to avoid overestimation of the residual variance. They were then scaled to a mean of 0 and variance of 1 to facilitate model convergence (except the binary trait NAL). Variance components were estimated using the restricted maximum-likelihood method applied to a nine-trait animal model, separately for each line, with the hen's hatch date as fixed. Known pedigrees of seven generations for RIR (2705 birds) and five generations for WL (2323 birds) were used to determine genetic relationships. Variance components for the 28-day laying duration traits were estimated using the same animal model as that used for the entire recording period but separately for each 28-day period, using three-trait models.
Identification and quantification of metabolites. Automatic identification and relative quantification of metabolites from NMR spectra were performed with the R package ASICS  12, 13. From a library of pure spectra of 190 metabolites, 72 metabolites were quantified including 15 AA in common with those quantified by UPLC. Compared to the AA analyzed by UPLC, four AA (histidine, lysine, threonine, and tryptophan) were not quantified by ASICS while they were present in the library of pure spectra. 1  H-NMR is often considered as a less sensitive technology for metabolomics than mass spectrometry  14  ; some metabolites cannot be detected, especially when they are in low concentration. This could be the case of tryptophan with low mean values when measured by UPLC (Supplementary Table  S1 ). Histidine, lysine, and threonine were between 5 and 15 times more concentrated in the plasma than tryptophan, and further investigations are needed to understand why they were not detected by ASICS.
Similarly, we did not detect histidine, lysine, and tryptophan in plasma of 90-and 110-day-old pig fetuses  12. Moreover, cysteine was detected by UPLC while the 1 H-NMR method detected the cystine, a form composed of two cysteine molecules and described as more stable than cysteine. Correlations between AA concentrations obtained by UPLC and those obtained by 1H-NMR/ASICS are presented in Table  1. For the 14 proteinogenic AA that could be assayed by both methods, significant correlations were observed consistently with our previous findings  12. The complete dataset included 83 metabolites, i. e. , 27 AA and 56 metabolites from 1 H-NMR/ASICS. In order to have a specific focus on AA and avoid redundancy in the subsequent analyses, the AA dataset was kept from the UPLC results and AA were discarded from the NMR dataset except for cystine and aspartate (identified only in the NMR dataset).
Enrichment analysis was performed on all the 25 most important metabolites (the 18 found influential and differential in the ASICS dataset and the 7 found influential in the AA dataset), with the online MetaboAnalyst 5. 0. The top enriched pathways are presented in Table  3. Among metabolites, 2-oxoglutarate, arginine, aspartate, and ornithine were significantly more concentrated in piglets from the HRFI line and involved in the urea cycle, proline synthesis, and glutamine-oxoglutarate metabolism. These three pathways were manually joined, which mainly highlighted the urea cycle as differentiating the two lines (Fig. 2 ). Multivariate modeling of biometric phenotypes, mortality, and rectal temperature. The seven traits (Weight, Length, Width, Circ, Temp, BMI, and PI) were analyzed by OPLS modeling as numeric variables (Supplementary Tables  S2  and S3 ) and the mortality within 5 days after birth was explored by OPLS-DA as a binary trait.
Multivariate analyses to identify covariations between metabolites and phenotypes. Multivariate analyses (sPLS, sparse Partial Least Squares) were performed to identify covariations between each dataset of metabolites and phenotypes, which are presented as an interaction graph (Fig. 3 ). These results highlighted that pantothenic acid and hydroxyproline had the highest VIP values for rectal temperature (Supplementary Table  S2 ) and showed, respectively, negative and positive covariations with the rectal temperature. Similarly, BMI and PI were positively connected to 4-amino-hippurate, and BMI was negatively connected to carnosine. Many positive covariations were observed with birth weight, width between the two shoulders, chest circumference, and crown-to-rump length. In contrast, myo-inositol was negatively connected to birth weight and chest circumference.
Concentrations of six AA (histidine, lysine, asparagine, aspartate, arginine, and ornithine) and one AA derivate (taurine) were Table  3. Result of the metabolomics pathway enrichment analysis. Top enrichment analysis with (A) all differential metabolites, (B) influential metabolites more abundant in more efficient LRFI piglets, and (C) influential metabolites more abundant in less efficient HRFI piglets. a: Total number of metabolites referenced in the pathway in the database, b: number of influential metabolites in this study, c: False Discovery Rate (adjusted p-value for the control of the FDR).
Bipartite graph between metabolites and biometric phenotypes of piglets. This graph was obtained after two separate sPLS (canonical mode, cutoff 0. 45) analyses of each of the two metabolite datasets (AA and NMR, blue circles) with the phenotypic dataset (red circles). The two graphs were joined using Gephi. Connections between metabolites and phenotypes are in red for negative covariations and in green for positive covariations. Temp rectal temperature, Circ chest circumference, BMI Body Mass Index, PI Ponderal Index. colostrum. A greater ability for gluconeogenesis might be an advantage for post-natal performance. Differences in energy metabolism were found between the lines before, more systematically than the protein related pathways  16. Piglets from the two lines also differed by their plasma concentrations of betaine and cadaverine, which were greater in HRFI piglets than in LRFI piglets (VIP scores > 2).
All 1 H NMR spectra were acquired on a Bruker Avance III HD NMR spectrometer (Bruker Biospin, Rheinstetten, Germany) operating at 600. 13 MHz for 1H resonance frequency and at 300 K, using the Carr-Purcell-Meiboom-Gill (CPMG) spin-echo pulse sequence. Spectrum preprocessing (group delay correction, solvent suppression, apodization, Fourier transformation, zero-order phase correction, internal referencing, baseline correction, and window selection) was performed using the package PepsNMR 39 (version 1. 2. 1) with the TSP peak for internal reference. Finally, spectra were aligned with each other using the method implemented in the ASICS package  12, 13. The metabolites in the NMR spectra were quantified using the ASICS package with default procedure and parameters, except for the threshold under which the signal is considered as noise, which was set to 0.
01, and the multiplicative and additive noise standard deviations, which were set to 0. 07 and 0. 09, respectively. The NMR raw spectra are available in the Metabolights database under accession number MTBLS2137. A raw complex spectrum with the spectrum reconstructed by ASICS from its deconvolution method is presented in see Supplementary Figure  S1  and was manually annotated for ten metabolites identified by ASICS. This plot shows that the original spectrum is well reconstructed by ASICS, which is a good indication of the quality of the quantification. UPLC protocol. Amino acid concentrations of the same plasma samples were determined by Ultra Performance Liquid Chromatography (UPLC). Plasma AA concentrations were obtained using an ultra HPLC system (Waters Acquity UPLC system, Waters, Guyancourt, France) coupled to an Acquity tunable UV detector and a mass detector (SQD detector) to identify the few coeluting chromatographic peaks.
These missing values were imputed with a PCA based method (independently for the two datasets) using the R package missMDA  41  prior PLS analyses. For the other analyses (correlation computation and statistical tests), missing values were ignored (dropped from data prior computation). Principal Component Analysis were performed in each dataset (PHENO, AA, ASICS) independently to search for outliers or batch effects. Only the AA dataset showed two outliers (in HRFI piglets) and were removed prior multivariate analyses, following the ropls package recommendation. Correlation analysis. Spearman correlations were computed between metabolites quantification with NMR and UPLC dosage of the same metabolites. Multivariate analyses of association with phenotypes. First, multivariate analyses were performed to assess the predictive power of groups of metabolites on phenotypes, and to find groups of metabolites and phenotypes co-varying.
First, the predictive power of metabolites on each of the phenotypes was assessed using orthogonal partial least squares (OPLS) as implemented in the R package ropls  42  (version 1. 20. 0). More precisely, each of the two metabolite datasets (AA and ASICS) was, in turn, used to predict the genetic line as well as every phenotype (independently). Binary phenotypes (genetic line or survival) were predicted using OPLS-DA, and numeric phenotypes (birth weight-weight0, crown-to-rump length-Length, shoulder width-Width, chest circumference-Circ, and rectal temperature-Temp) were predicted using OPLS. Influential metabolites were chosen as those with a VIP (Variable Importance in Projection) value larger than 1 (commonly chosen arbitrary value). In addition, the model relevance was assessed by testing the optimal number of predictive (predI) and orthogonal components (orthoI) together, using permutation tests at the 0.
05 level (permI = 1000). Second, the R package mixOmics (http:// mixom ics. org/) was used to identify groups of metabolites covarying with groups of phenotypes. More precisely, two sparse PLS (sPLS, mode "canonical") were performed with, respectively, AA and ASICS as the first dataset and with the morphometric values measured on the same neonates as the second dataset. The most interesting covariations identified by the method were further selected using a correlation cutoff of 0. 45. They were used to build two networks of highly covarying variables, one for each analysis (with AA or ASICS as the first dataset). In these networks, links between variables corresponded to a common selection on the same axis of the sPLS. Finally, the two networks were joined with the Gephi application (version 0.
9. 2, https:// gephi. org/). Differential analysis. To confirm the statistical significance of found influential variables, several tests were performed to identify metabolites whose dosages or ASICS quantifications were significantly different between the two genetic lines or phenotypes significantly different between the two lines. First, non-parametric Wilcoxon tests were performed for each variable independently and then, a mixed-effect model with a fixed line effect and the random sow effect nested in the line effect (R packages nlme version 3. 1-149 and multcomp version 1. 4-14) was fitted. P-values were adjusted with the Benjamini-Hochberg 43 procedure to control the false discovery rate (FDR) in each of the three datasets independently. Variables were considered significant at the threshold FDR < 0.
05. Note that we could not expect much from the mixed-effect model with the random sow effect since the genetic line effect is strongly related to this effect. It was nevertheless performed to assess if any of the found effect could be strong enough to be visible even after the sow effect has been accounted for. Finally, all metabolites from the ASICS datasets that were found significant were manually confirmed by the identification of their corresponding peaks in the spectrum (see Supplementary Fig. S1 ). In addition, the peaks leading to the identification of all metabolites from the ASICS datasets that were found significant or that had a VIP > 1 in one of the PLS/OPLS-DA analyses (results provided in Supplementary Table  S1  and Supplementary Table  S4 ) were systematically positioned on the spectrum (Supplementary Table  S4 ).
Metabolic pathway analyses. The HMDB (Human Metabolome Database, https:// hmdb. ca/) and KEGG  44  (Kyoto Encyclopedia of Genes and Genomes, www. genome. jp/ kegg/) databases were used to recover molecular information for each metabolite. The KEGG mapper module was used to identify metabolic pathways including identified metabolites. To ensure the strength of our analysis, we performed the enrichment only with the most confirmed metabolites (i. e. , the 18 metabolites that were found both differential and influential in the ASICS dataset and have all been confirmed on the spectrum-see Supplementary Fig. S1 -together with the 7 metabolites found influential in the AA dataset). Metabolite Set Enrichment Analysis was performed with MetaboAnalyst 5. 0 exploring SMPDB  45  database with the Human genome as a reference.
Acknowledgements All the analyses were performed in the context of the SuBPig "Enhancing survival at birth in piglets" project funded by  INRAE   GISA 2018-2019 , Grant n°  DI2017. The PhD fellowship of Gaelle Lefort work was supported by the  French National Research Agency  under the Investments for the Future Program, referred as  ANR-16-CONV-0004 , by  INRAE Mathematics and Computer Science Division , by  INRAE Animal Genetics Division  and by  INRAE Animal Health Divison. The funders had no role in the study design, analyses, results interpretation and decision to publish. The authors are very grateful to the staff of experimental pig facilities (INRAE, 2018. Pig phenotyping and Innovative breeding facility, https:// doi. org/ 10. 15454/1. 55724 15481 18584 7E12 ). They also thank all the participants from  GenPhySE ,  PEGASE  and  GenESI laboratories 707 (INRAE)  and  MetaToul-AXIOM plateform  ( Cécile Canlet  and  Roselyne Gautier ) for sample and data collection, and also  Nadine Mézières  and  Colette Mustière  ( INRAE PEGASE ) for performing all UPLC dosages.
Molecular Analyses Genomic DNA was extracted using the commercially available QIAGEN QIAamp DNA minikit (QIAGEN, Germany) following the manufacturer's instructions. The Real-Time PCR (RT-PCR)  [35] , Multiple Locus Variable-number Tandem Repeat Analysis (MLVA)-16  [36]  and High-Resolution Melting (HRM) PCR  [37]  analyses were performed as previously described. The individual DNA samples of 2 isolates were typed with the MLVA-16 panel single-plex PCR and agarose gel method. Clustering and congruence analyses were conducted with BioNumerics 7. 6. 3 (BioMérieux), using data as a character dataset via the categorical distance coefficient and MST (Minimum Spanning Tree) method. A total of 561 MLVA-16 profiles originating from different countries (29 Belgium, 1 Bulgaria, 4 Croatia, 2 Czech Republic, 9 Denmark, 214 France, 36 Germany, 69 Hungary, 10 Italy, 2 Poland, 96 Portugal, 77 Spain, 2 Switzerland, 5 United Kingdom and 5 not reported) and hosts (8 bovine, 1 caprine, 2 dog, 92 hare, 5 human, 4 ovine, 212 swine and 227 wild boar) available in MLVA database of our strain collection (https://microbesgenotyping.
A phylogenetic tree using the maximum parsimony algorithm was generated from the SNP matrix, yielding 8849 SNPs after filtering (Figure  2 ). The two strains were clustered with all BSB2 isolates, which were available from public databases. The two analyzed strains are classified within a subclade from France, Spain and Belgium. Finally, to better correlate the two isolates from dogs compared to BSB2 strains circulating in French pigs and wildlife, a wgSNP analysis of 33 sequences available in our laboratory (returning 9500 SNPs after filtering) highlighted the presence of a subclade encompassing another 15 strains isolated from pigs, wild boars and hares (Figure  3 ). algorithm was generated from the SNP matrix, yielding 8849 SNPs after filtering (Figure  2 ). The two strains were clustered with all BSB2 isolates, which were available from public databases.
Phylogenetic tree was visualized with EMBL online toll "Interactive Tree of Life" (iTOL v6) and annotated with four concatenated datasets separated based on underscores (strain name, year of isolation, isolation country and host) colored in black and blue for all species and two isolates from dogs, respectively. Not reported data are marked with "NR". All species are color-coded for branches: pink for B. suis, red for B. canis RM6/66 and magenta for B. melitensis 16M.
However, no Brucella sp. are characterised by the development of specific clinical symptoms, either in humans or in animal hosts. At the same time, the emergence of new reservoirs for Brucella sp. could be linked to a new consumption   B. canis reference strains RM6/66 as two outliers. Phylogenetic tree was annotated with four concatenated datasets separated based on underscores (strain name, year of isolation, isolation country and host). Not reported data are marked with "NR". B. suis isolates are represented in pink, with two canine isolates represented by blue arrows. B. canis RM6/66 strain is represented in red, and referent B. melitensis 16M strain is represented in magenta.
Figure2. Maximum parsimony tree of B. suis isolates from various geographical regions of world (Africa, Asia, Europe, North America and South America). Parsimony tree was generated with Bi-oNumerics using maximum parsimony algorithm from 8849 SNPs identified from complete genomes of 140 B. suis strains, using reference strain B. melitensis biovar 1 16M and B. canis reference strains RM6/66, as two outliers. Phylogenetic tree was visualized with EMBL online toll "Interactive Tree of Life" (iTOL v6) and annotated with four concatenated datasets separated based on underscores (strain name, year of isolation, isolation country and host) colored in black and blue for all species and two isolates from dogs, respectively. Not reported data are marked with "NR". All species are color-coded for branches: pink for B.
Figure3. Maximum parsimony tree of B. suis biovar 2 isolates circulating in France. Parsimony tree was generated with BioNumerics using maximum parsimony algorithm from 9500 SNPs identified from complete genomes of 31 French B. suis strains, using reference strain B. melitensis biovar 1 16 M and B. canis reference strains RM6/66 as two outliers. Phylogenetic tree was annotated with four concatenated datasets separated based on underscores (strain name, year of isolation, isolation country and host). Not reported data are marked with "NR". B. suis isolates are represented in pink, with two canine isolates represented by blue arrows. B. canis RM6/66 strain is represented in red, and referent B. melitensis 16M strain is represented in magenta.
Acknowledgments: We thank the technical support team for medium preparation and lab management, as well as the scientists working at the  Bacterial Zoonoses Unit  for critical and supportive comments. We would also like to thank the  IdentyPath Platform-Fabien Vorimore and Patrick Fach (Anses, Maisons-Alfort, France)  for their help in the sequencing of the Brucella strains, as well as the two local veterinary diagnostic laboratories (" Inovie Vet"-Laboratoire d'Analyses et Biologie Vétérinaires de Montpellier and  " Cerba Vet"-Laboratoire vétérinaire ZAC du Moulin de Massy)  for their contributions to the isolation of the two strains.
However, these factors were not sufficient to provide a satisfying predictive model. In France, the price paid to pig farmers depends in part on their ability to deliver pigs at an optimal weight and lean meat content (LMC). To maximise the price paid, farmers can adapt the departure strategy of their pigs. Tattooing pigs with their week of birth gives the opportunity to better characterise pigs according to their departure order and age at slaughter as an additional tool for managing pig farms. The objective of this study was to analyse the departure strategies of farmers to obtain references that would help identify ways to improve farm performance. Carcass quality and breeding data from 2,320,319 pigs from 740 breeding sites, tattooed with their week of birth, were analysed over one year.
, 2015). Firstly for site A, a list of horse behaviors was established and different options on the timing of the observations were tested in order to find the best compromise between the total number of bites recorded per horse and the number of horses observed. A list of potential bites was established for both sites based on the plants and plant parts ingested by the horses. The periods of the day when the horses grazed most were determined, as well as the affinities between individuals. The young horses usually grazed by pairs in both sites. It was thus decided that each observer would follow a pair. The second week was devoted to experimental observations and data collection. On Sunday evening, two young horses (one per pair) were fitted with GPS collars designed by SELMET research unit (CIRAD, INRAE, Montpellier, France).
An additional commercial GPS collar fitted on a friendly horse in the group continuously transmitted its position and was used to locate the horses in the morning and in the afternoon. The other two GPS collars were programed to record the horses' positions seven times in a row, every five minutes. The data was not transmitted but stored in the GPS memory so that its integrity did not depend on the communication network. For each five-minute record, the seven successive points were averaged in order to reduce the impact of outliers.
Statistical analysis Plant bite classification based on NIR spectra Mathematical pretreatment was applied to the spectra (2nd derivative, normalization and de-trending) of plant samples in order to minimize non-informative content. Hierarchical ascendant classification of samples (Ward aggregation criterion) was performed on the mathematically pretreated spectra with XLSTAT software (Addinsoft, Paris, France). The chemical composition of each class was then determined and ANOVA with Tukey's honestly significant difference (HSD) was used to compare classes.
Preliminary observations of the spatial distribution of horses as a function of shrub and tree cover GPS data were collected continuously over 115 hours in site A and 111 hours in site B. In site A, as the electronic circuit of one of the two GPS devices was damaged, the distance traveled and the spatial occupation were calculated for only one horse (GL, Suppl. Mat. SI). For site B, the two GPS devices worked, which made it possible to describe the spatial behavior for two horses (HL and HM). In both sites, the GPS tracks obtained had no generic value but could still provide interesting insight into spatial behavior since the horses generally moved in a group. Based on such limited GPS data, the estimated distance traveled differed between sites and between horses and ranged from 6 km. day -1 (site A) to 9 km.
Acknowledgments This work was funded by the  Provence-Alpes-Côte-d'Azur administrative region (France)  and the  European Agricultural Fund for Rural Development (GO PEI  funding  FEADER ,  Actions 16. 1 / 16. 2 - Emergence and/or realization of projects of  PEI-AGRI operational group ). We are grateful to the two farmers who kindly accepted to participate in the research by providing the horses and the rangeland paddocks. Our thanks also go to the  Centre d'études et de réalisations pastorales Alpes-Méditerranée (Cerpam)  that provided expertise for the characterization of topo-facies, to  Olivier Bonnet  and  Julio Azambuja , who shared their expertise and practice of the continuous bite monitoring method, and to  Gail Wagman  for proofreading.