dataesr/scientific-paragraphs-categorization huggingface dataset:
2021). Each farmer replied in his or her native language. The scientists who conducted the interviews wrote down or recorded the answers and transcribed them afterwards. The written answers were translated into English by the scientist who conducted the interview. The collected data were transferred to a database. The present paper focuses on work satisfaction and combines a qualitative and quantitative approach. In this context, farmers were asked to rate their overall work satisfaction and their satisfaction with income using a scale from 1 (not satisfied) to 4 (highly satisfied), the mental complexity and the physical severity of their work using a scale from 1 (simple/easy) to 4 (often complex/very hard), and the work intensity of 24 half-month periods in a year using a scale from 1 (not work intense) to 4 (very work intensive).
Analyses of quantitative data From the quantitative data, we selected five variables, which reflect aspects of work satisfaction (i. e. , overall work satisfaction, satisfaction with income, mental complexity, physical severity, and proportion of work peaks per year) as outcome measures for statistical analyses. We aimed to identify contributing factors for these aspects of work satisfaction. As the distribution of answers for overall work satisfaction was skewed and did not allow statistical analyses, we included only the four remaining outcome measures in further analyses. Not all farmers responded to all questions, and the sample size thereby differs between quantitative variables used as outcome measures (see Table  2 ). A more detailed description of the sample can be found in  Ulukan et al.
(2021). Based on assumptions for associations with the four remaining outcome measures, i. e. , satisfaction with income, proportion of work peaks, mental complexity, and physical severity, we selected quantitative variables originating from data on other aspects of the farm (e. g. , the number of free days would be lower with a high proportion of work peaks, and a high number of different tasks could contribute to a high mental complexity). A total of 40 quantitative variables with a possible connection to farmer work satisfaction as measured by the four outcome measures were selected, including 23 continuous and 17 categorical variables, to analyze their influence on the outcome measures. Each outcome measure was also included as a possible explanatory variable for the other outcome measures.
5 quantitatively and graphically (functions: influence and cooks. distance), and no data were removed. Pairwise t tests and visual inspection were used as post hoc tests to ascertain direction of effects. All statistical analyses were performed and all graphs created using the statistical programming language R (R version 4. 0. 3 (R Core Team, 2020), RStudio version 1. 4. 1103 (RStudio Team, 2021)). Graphs were created with the package ggplot2  (Wickham 2016 ) and regression models conducted with the package ordinal (Christensen 2019).
This may have introduced a bias in our dataset towards farmers who were inclined to share information. Therefore, our results need to be interpreted cautiously and to be verified in a larger and possibly more representative sample of OMLF. In addition, our results are specific to organic farms and are not transferable to work satisfaction on conventional mixed livestock farms. A comparison between organic single-and mixed livestock farmers could yield further insights into possible reasons for work satisfaction on farms with multiple livestock species. Future studies should involve life partners and farm workers to access new perspectives on farmers' work satisfaction. Additionally, here we demonstrated that autonomy, environmental mastery, and personal growth were dimensions linked to farmers' high work satisfaction.
The mapped reads were then submitted to edgeR  (Robinson et al. , 2010)  for further analysis. Low-abundance transcripts were removed to reduce false discovery during differential transcriptional analysis, using the edgeR function' filterByExpr', which retained genes with counts per million (CPM) more than 0. 37 across the smallest group sample size (equating to at least 10 counts per median library size) (Supplementary Fig. 1A ). Libraries were normalized by Trimmed Mean of M-values (TMM) normalization (Supplementary Fig. 1B ), followed by unsupervised clustering by limma  (Ritchie et al. , 2015)  according to leading log 2 fold change (log 2 -FC) dimensions (Supplementary Fig. 1C ). Normalized counts were further adjusted by voom to remove identifiable technical variation (Supplementary Fig.
1D ; Table  S2 ). Differentially transcribed genes (DTGs) between the ALB-S and ALB-R conditions were identified using the emBayes test module at a false discovery rate (FDR) of 0. 05 and a minimum absolute log 2 -FC of more than 1. SNP calling of TUB1 was performed by "exactSNP" in Rsubread by default threshold (base quality score at 13; q-value at 12). Hypothetical proteins identified among the DTGs that were less than 750 amino acids were queried for functional annotations based on structural homology modelling for G. duodenalis  (Ansell et al. , 2019). Larger hypothetical proteins not included in this previous study  (Ansell et al. , 2019)  were analyzed by Phyre2. DAVID  (Huang et al. , 2009)  was used for domain and molecular function enrichment analysis for DTGs. Romer  (Wu et al. , 2010)  was used to test the enrichment of Gene Ontology (GO) terms and KEGG pathways throughout the whole transcriptome (here termed as "functional trend analysis") in susceptible and resistant lines (significance threshold: adjusted P value less than 0.
Major VSPs turnover with ALB-R Variant-specific surface proteins (VSPs) featured prominently among Q. Su et al. up-and down-regulated transcripts. Nine of the ten most differentially transcribed genes (five up-and four down-regulated) are VSPs (Fig. 1A ). Functional enrichment analyses identified VSPs and VSP-associated conserved protein domains (IPR009030, IPR6212 and IPR000742) as statistically significantly enriched among up-and down-regulated genes (Fig. 1B ). Gene-set enrichment analysis also revealed a bimodal transcriptional pattern among G. duodenalis WB-1B's VSPs (Fig. 1C ). These data appear consistent with a major switching (some genes up-and others down-regulated) of transcribed VSPs within ALB-R, as has been observed previously  (Argüello-García et al. , 2009). Such switching events have been described previously in G.
However, it is unclear how much a single-celled organism, such as G. duodenalis, might rely on vesicle trafficking for glucose uptake. Therefore, we thought to look at the behaviour of glucose and other sugar transporters in our dataset. However, these transporters are not well annotated in G. duodenalis. To overcome this, we used homolog-based matching against a panel of prokaryotic and eukaryotic sugar transporters  (Landfear, 2010; Holman, 2020; Jeckelmann and Erni, 2020)  to better annotate these genes in G. duodenalis. We identified two glucose transporter (GLUT) orthologs in G. duodenalis WB-1B (GL_5164 and GL_11540) alongside 22 additional genes with putative orthology to sugar transporters of various bacteria (Table  S8 ). None of these was identified as statistically significantly up-regulated with ALB-R.
Acknowledgments Q. Su 's PhD scholarship was funded by the  Melbourne Research Scholarship, University of Melbourne ,  Australia. AR Jex  was funded by the  Australian National Health and Medical Research Foundation   Leadership Fellowship program  ( APP1194330 ),  Victorian State Government Operational Infrastructure Support, Australia  and the  Australian Government National Health and Medical Research Council Independent Research Institute Infrastructure Support Scheme. SJ Emery-Corbin  was funded by a  Jack Brockhoff Foundation (Australia)   Early Career Grant  ( ID JBF 4184 ,  2016 ). We gratefully acknowledge the Walter and  Eliza Hall Research Institute, Australia , for topping up the scholarship and providing Edith Moffatt with student travel scholarships.
Locomotion The Equimetrix system® is associated with software that allows data processing. We calculated locomotor variables from the data using the software as speed, stride frequency, stride length and total activity. These parameters are described in Table  3. We used de nitions from previous research on horse locomotion 63. Figure 4 Median values and interquartile ranges of the speed in meters per second (n=14), stride frequency in cycles per second (n=19) and total activity in watts per kilogram (n=19) during the P+ and N-conditions, *P < 0. 05; ***P< 0. 001. Figure 5 Schematic of the experimental procedures. The horse was allowed to walk to the food bucket in the P+ condition (black arrow) and then was guided away from the food bucket in the N-condition (gray arrow). All measurements were taken between the departure and arrival lines.
Acknowledgements This project was funded by the  French Horse and Riding Institute (IFCE). This funding source has no role in the study design, data collection and analyses or in the preparation and submission of the manuscript. We want to thank the  IFCE  staff for giving us access to the horses and helping for this study and the members of the  INRAE  for their collaboration. For the purpose of Open Access, a CC-BY public copyright license has been applied by the authors to the present document and will be applied to all subsequent versions up to the Author Accepted Manuscript arising from this submission. The license CC-BY 4. 0 is a xed to this document, see details in https://creativecommons. org/licenses/by/4. 0/.
Statistical analysis Data for lamb performance (BW at birth, weaning and slaughter; average daily gain between birth and weaning (ADG bw ); average daily gain between birth and slaughter (ADG bs ); age at slaughter), carcass characteristics (carcass weight, conformation and degree of fatness; dorsal fat thickness; kidney fat weight), carcass sensory quality attributes (colour coordinates and firmness of dorsal fat), meat quality attributes (LTL pHu and colour coordinates) were all analysed using ANOVA with system as fixed factor, following test for normality (Shapiro-Wilk test) and homogeneity of variances (F-test). As the variance for dorsal fat lightness and LTL muscle redness, yellowness, chroma and hue angle significantly differed between systems and was not stabilised using log-transformation, these variables were analysed using nonparametric statistics (Wilcoxon W test).
Acknowledgements The authors thank  J. Ballet ,  S. Vallette  and  M. Barbet  from the  INRAE Herbipôle experimental  unit for performing the experimental procedures, measurements and samplings. We also thank  S. Faure  from the  INRAE Herbivores Research Unit  and  C. Coustet ,  S. Collange  and  J. Mongiat  from the  INRAE Herbipôle experimental  unit for performing measurements at the abattoir, and  W. Gros  from the  INRAE UMR PEGASE  for analysing the fat skatole and indole concentrations.
Data analysis Descriptive analysis of both the environmental and animal-based welfare measures and the behavioral observations was carried out. The results report the proportion of horses in each category for individual measures and the mean proportion of observations spent exhibiting a behavior for behavioral observations. A horse was classified as displaying abnormal or stereotypic behavior, if it performed these behaviors on two or more occasions during data collection. Missing data were removed for analysis and all analyses only include horses who were present during both visits. Each of the welfare measures were modeled as response variables using the potential risk factors as explanatory variables to identify good or poor welfare in racehorses. Welfare measures were modeled using binomial Generalized Linear Mixed Models (GLMMs) and linear mixedeffects models for binomial and continuous measures, respectively.
The models were fitted and analyzed using the lme4 package  (32)  in R  (33). To account for non-independent and potentially clustered data as a result of repeated measures on the same horse and multiple horses at the same yard, individual horse ID and yard ID were modeled as random effects  (34). The response welfare indicator variables and potential explanatory risk factor variables can be seen in Table  4  along with the data type for statistical analysis. Response variables which were recorded in categorical or scale format were aggregated to binary variables for statistical analysis. From the Behavioral observation data, the mean proportion of observations in each Behavior category was treated as a response variable in the multivariable models. A backwards elimination process was used to select variables for each model.
A Wald test provided the p values in the model summary output. p values of greater than 0. 05 were used to guide the removal of explanatory variables for model comparison. Lowered Akaike information criterion (AIC) following the removal of variables showed an improved model fit and simplicity and determined the final model. Variance Inflation Factors were calculated using the Car package  (35)  to assess multicollinearity between explanatory variables. Models were also validated by visual inspection of the residuals and by using the DHARMa package  (36)  for residual diagnostics for mixed regression models (see Supplementary material). In each of the final models, determined by AIC model selection, risk factors were considered significant if they had a p-value < 0. 05  (37, 38). Odd ratios (OR), a measure of the association between an explanatory and response variable  (39) , or Estimated Marginal Mean (emmean), and Confidence Intervals (CI) were produced for each of the significant risk factor variables.
Results Data was collected for 353 horses (231 NH and 122 Flat) from eight NH and five Flat training yards in England, at both the beginning (visit 1) and at the peak (visit 2) of each racing season. Initially 496 horses were assessed during visit 1 and 353 of these horses were also present during the second visit and were considered for the study. The 143 horses which had been assessed on the initial visit which were not present during the second visit were documented as absent along with the reason they were not present, given by a senior staff member or the trainer. Reasons for their absence included change of trainer or sold (n = 35, 7. 1%), training break (n = 25, 5. 1%), injury (n = 24, 4. 8%), retirement (n = 14, 2. 5%) and the reason for absence was unknown for 32 horses (6. 5%). Thirteen horses (2.
Other welfare indicators A set of basic physical indicators have been tested in our study and most of them showed a lack of inter-yard and inter-horse variability since the vast majority of horses were observed with good scores. For instance, body condition scores and signs of hoof neglect revealed no problem, which was expected for high performance horses. However, thin racehorses and those with poor hoof condition are occasionally reported to the BHA as welfare concerns. It is therefore useful for jurisdictions to have data to know whether these are isolated cases or more widespread when seeking to provide societal assurance on reported welfare issues. The low prevalence in our study of some physical welfare issues highlights the need to have additional types of welfare parameters for such equine populations, as we investigated here, to assess racehorse welfare (e.
Results: We established two pig lines contrasted for abundances of the genera pairs specifying each enterotype at 60 days of age and assessed them for fecal microbiota composition and growth throughout three consecutive generations. Response to selection across three generations revealed, per line, an increase in the prevalence of the selected enterotype and in the average relative abundances of directly and indirectly selected bacterial genera. The PM enterotype was found less diverse than the RT enterotype but more e cient for piglet growth during the post-weaning period. Shotgun metagenomics revealed differentially abundant bacterial species between the two enterotypes. By using the KEGG Orthology database, we show that functions related to starch degradation and polysaccharide metabolism are enriched in the PM enterotype, whereas functions related to general nucleoside transport and peptide/nickel transport are enriched in the RT enterotype.
FastQ les were generated after the run was completed (MiSeq Reporter software, Illumina, USA). We used DADA2  [19]  to process reads into an amplicon sequence variant (ASV) table, with an approach that follows the author's recommendations in their "Big Data" work ow. In brief, for each MiSeq run, we performed separately the quality ltering, denoising pair-end merging, and amplicon variant calling steps. Brie y, for each run, primers were trimmed from read pairs by using cutadapt  [20]  and a quality ltering was performed on reads by using DADA2's lterAndTrim function with options truncF=235, truncR=225 and truncQ=5. ASV unique sequences and corresponding counts per sample were inferred for each run with the DADA2 algorithm after learning the error distribution with the learnErrors function.
This step included the merging of both read pairs and the removal of chimeras. Finally, the ASV tables from each run were merged based on ASV sequences, and the annotation of each ASV assigned in DADA2 by using the SILVA database (version 138)  [21]. Based on the sequencing depth and the rarefaction curves, we rare ed counts for subsequent analyses at 7000 counts per sample. Alpha diversity was measured through microbial richness (that is, the number of taxa present in each sample after rarefaction) and the Shannon index estimated in vegan R package  [22]. Enterotype categorization was done in R and was based on Jensen-Shannon divergence (JSD) distance measured from the rare ed genus abundance table as recommended by  [23]. In brief, we rst determined the optimal number of distinct clusters after partitioning the microbiota around medoids with all samples in each generation, and then con rmed if, as previously reported in weaned pigs, the fecal microbiota was also divided into two enterotypes.
Shotgun metagenomics and analysis DNA extraction was performed by the SAMBO platform located at INRAE MetaGenoPolis. Fecal DNA was extracted following the SOP 07 V2 H from  [26] [27]. The DNA preparation was subjected to quality control using Qubit Fluorometric (ThermoFisher Scienti c, Waltham, US) and quali ed using DNA size pro ling on a Fragment Analyzer instrument (Agilent Technologies, Santa Clara, US). Sequencing was performed by the MetaQuant platform located at INRAE MetaGenoPolis. Three µg of high molecular weight DNA (>10 kbp) were used to build sequencing libraries. Shearing of DNA into fragments of approximately 150 bp was performed using an ultrasonicator (Covaris, Woburn, US) and DNA fragment library construction was performed using the Ion Plus Fragment Library and Ion Xpress Barcode Adapters Kits (ThermoFisher Scienti c, Waltham, US).
Puri ed and ampli ed DNA fragment libraries were sequenced using the Ion Proton Sequencer (ThermoFisher Scienti c, Waltham, US). The raw sequences are available through the project PRJEB60032 on the EMBL-EBI's European Nucleotide Archive (ENA accessions ERS14678539 to ERS14678568). Quality control was performed with AlienTrimmer [28]: (1) sequencing adapters were removed; (2) lowquality reads were trimmed or discarded; and (3) reads that were too short (< 60 bp) were discarded. Then, reads mapped to the pig reference genome (Sscrofa11. 1 GCA_000003025. 6) with bowtie2  [29]  were removed. Finally, 18M high-quality reads were randomly selected in each sample with fastq-sample  [30]. The gene abundance table was generated with the METEOR software suite  [31]. First, selected highquality reads were mapped with bowtie2  [29]  to a gene catalog representative of the pig gut microbiota  [32] , comprising 9.
KEGG Orthologs (KOs) were assigned to genes in the catalog with KofamScan (14) relying on KEGG 102. KO abundances were computed by combining the abundance of genes assigned to the same KO. Differential abundance analyses were performed by using the Wilcoxon-Mann-Whitney tests as described above. The pathway enrichment analyses were performed on the lists of differentially abundant KO by using clusterPro ler R package  [36]. In this package, we used the enrichKEGG function and used the list of KO present in the differential analyses as background genes in the tests. All statistical analyses were performed with the R software suite v3. 6. 0 using mainly packages of the tidyverse collection. Differentially abundant MGS or functional modules were searched for by using Wilcoxon-Mann-Whitney tests.
False Discovery Rate (FDR) was controlled by correcting p-values for multiple testing with the Benjamini Hochberg Procedure. Effect size was estimated using the Cliff's Delta statistic (CD) with the package effsize v0. 7. 4. Unless stated otherwise, features with corrected p-values (q-values) below 0. 1 and a magnitude of the effect size |CD| > 0. 7 were reported in Supplementary Tables (Table_S1, Table_S2, Table_S3).
0% ± 2. 3%; PM: 1. 8% ± 1. 1%), and consisted of two unknown species with temporary names (Cryptobacteroides sp000433355 and Onthomorpha sp004551865) (Fig. 2C , gure_S3 and Table_S1). Consistent with the average relative abundance of genera in each enterotype and the coabundance network, this analysis suggested that combined with Treponema, the Rikenellaceae RC9 group might better specify the RT enterotype than the Ruminococcus group (Fig. 2B ). Using the KEGG Orthology database, we searched for differentially-abundant functional orthologs (KOs) between the two enterotypes. Strikingly, KOs related to starch degradation and polysaccharide metabolism were among the most enriched features in the PM enterotype. In the RT enterotype, the enriched KOs were different and included general nucleoside transport and peptide/nickel transport.
Acknowledgments We thank all members of the  Genetics, Microbiota & Health team of the GABI unit  for their help at all steps of the project, including animal sampling in the experimental farm. This work has bene ted from the facilities and expertise of the @ BRIDGe core facility (GABI ,  INRAE, AgroParisTech, Paris-Saclay University ); we thank  Benjamin Houel  for helping in biobanking and DNA extraction, and  Marie-Noëlle Rossignol  for her contribution to the sequencing. We also thank the pig team of the  GenESI unit  for animal housing and sampling all along the experiment. We are grateful to the  INRAE MIGALE bioinformatics facility  (MIGALE, INRAE, 2020. Migale bioinformatics Facility, doi: 10. 15454/1. 5572390655343293E12) for providing help in computing, and storage resources. We warmly thank  Joan Lunney  and  Hélène Hayes  for comments and suggestions to improve the article.
Faecal glucocorticoid metabolites (FGMs) Baseline adrenocortical activity was estimated through FGMs  (Palme 2019). We collected faecal matter rectally at capture from 2010 to 2021. Faeces were frozen at -20 °C within 24 hours prior to 2013 in CH and 2017 in TF, and immediately frozen at -80 °C after collection since then. The time between faecal sampling and freezing, as well as freezing temperature, may impact FGM values, with longer delays and higher temperature likely to result in lower FGM levels due to bacterial activity  (Lexen et al. 2008 , Hadinger et al. 2015 , Carbillet et al. 2023b). In CH, the first FGM values available in our dataset are post-2013, so all samples were immediately frozen at -80 °C. However, in TF, FGM values are effectively higher when samples were immediately frozen (0.
FGM repeatability Individual FGM repeatability was calculated for all individuals, and separately for each population, using the 'rptR' package  (Stoffel et al. 2017). Repeatability analysis included individuals sampled only once to improve estimates of the within-individual variance  (Martin et al. 2011). Within-individual FGM repeatability was detectable but weak in both CH (r = 0. 14, 95% CI = [0. 03, 0. 26]) and TF (r = 0. 15 [0. 01, 0. 29]).