dataesr/scientific-paragraphs-categorization huggingface dataset:
Growth performance Animals were weighed at the beginning (at age 331±27 days) and at the end (at age 421±27 days) of the evaluation period. Average daily gain (ADG) over this period was calculated. Behavioural data was cross-compared against the key weight values for genetic selection rather than using actual weights  (France Génétique Elevage, 2009). The key weights i. e. the 120-day weight and 400-day weight were calculated using the following formula. ℎ = -2 2 -1 2 -1 + 2 where A is the reference age in days (120 or 400), A1 is real age at the first weighing, A2 is real age at the second weighing, and W is weight at the first (W1) and the second weighing (W2).
Statistical analyses Data were analysed using R software version 3. 5. 0 (R Core Team, 2018). Wilcoxon-Mann-Whitney or Kruskal-Wallis tests were used to study the influence of dam's presence (absence/presence), housing conditions (pasture/indoor) or weaning status on BeF and the technician identity effect on docility score. The farm or technician effects were not tested for BeF because the number of bulls evaluated per farm or by each technician was too low. ADF test-retest consistency was assessed by calculating the intraclass correlation coefficient (ICC) of ordinal logistic regressions with random effects. The fixed effect was the test number (one to three) and the random effect was the animal identifier. On-station freestalled bull groups and age were also tested but showed no significant effects on ADF and were not considered in the final model.
The model was run using the 'ordinal' package  (Christensen, 2019) , and ICC was calculated with the 'performance' package  (Lüdecke et al. , 2020). Relationships between ADF and routinely collected behavioural data were evaluated using ordinal logistic regressions with random effects run using the ordinal package. The fixed effect was test number and behavioural test (BeF, Do or BeS) and the random effect was animal identifier. We checked for normality of the residuals using a quantile-quantile plot, and we checked the homogeneity of the variance graphically (residuals vs. fitted values plot and square root of the residuals vs. fitted values plot). Multiple linear regressions were performed to evaluate the relationships between performance data and mean ADF score (mADF). Age at the first ADF test was added to these regressions as a fixed effect.
Discussion Our study shows that the ADF test is discriminant among young Limousin bulls at the testing station and is moderately consistent over at least a three-week period. ADF test data also shows degrees of consistency with other data routinely collected on-station on behavioural reactions that involve human interaction with free-to-move animals. ADF test data also appears slightly but positively related to indicators of higher growth performances classically used in the genetic selection process. Consistency and scientific validity are two important aspects to consider when developing a test to evaluate animal reactions to humans  (Waiblinger et al. , 2006). The levels of consistency are moderate in our study but similar to those observed in other studies in dairy cattle or fattening bulls  (Ebinghaus et al.
Limitations of this study This observational study is based on correlations, with about one hundred animals coming from a large number of farms and large number of sires. On the basis of age and body weight at animals' entry in station these last four years, our sampling appears reasonably representative of Limousin bulls tested at the Lanaud station. The Lanaud station is purposedesigned for evaluating bulls in standardised conditions. The farms that provide the bulls to the station differ in herd size, housing, and human proximity from many other countries around the world. In addition, some bulls had to be eliminated (essentially due to overaggressivity in the docility test) before the whole on-station dataset was compiled. The ADF test would be particularly interesting if it could also discriminate the most dangerous animals.
Acknowledgements This research was made possible by funding from the  French Ministry for Agriculture  for the PhD support, and  CASDAR   BEBOP  project. RMT and  CNR BEA  supported the project. We thank the staff at  France Limousin Selection , the Lanaud station, and the  Limousin Herd Book  for their cooperative and proactive efforts on helping to collect the data. This work received financial support from the  French government's IDEX-ISITE  initiative  16-IDEX-0001  ( CAP 20-25 ). We thank  Mohammed El Jabri  from  Idele  for valuable help on statistical data processing, and  Metaform Langues  for English-language proof-editing support.
Animals and experimental design Pigs used in this study were part of a larger experiment described by  Godinho et al. (2018)  and originated from a threeway cross, i. e. Synthetic boar Â (Large White Â Landrace) sow. Part of the data used in the present study has been described previously and was used to predict faecal digestibility values based on faecal microbiota composition  (Verschuren et al. , 2020). Phenotypic data were available for one hundred and sixty pigs, eighty intact boars and eighty gilts, originating from twenty one litters. Due to death (six animals) and insufficient faecal sample volume (fourty nine animals), data of one hundred and five pigs were used for the present study. All pigs were kept under commercial conditions at the experimental facilities of Schothorst Feed Research B.
The mixed procedure (SAS 9. 3; SAS Inst. Inc. , Cary, NC) was used to obtain the RFI. One day before slaughter, individual faecal grab samples were collected, stored at 4 °C, freeze-dried and milled over a 1 mm sieve prior to chemical analyses. Faecal samples were analysed from one hundred and five out of one hundred and sixty pigs, related to death of a few pigs (six animals) or availability of insufficient sample material (forty nine animal). Diets and faecal samples were analysed in duplicate for moisture, ash, CP, crude fat (CFat), crude fibre (CF), and titanium oxide as marker using the following methods, respectively: ISO 6496, NEN 3329, ISO/CD 15670, ISO/FDIS 6492 method B,  ISO-6865:2001, and EEG 26-11-1992    1 An additional premix was added to the finisher diets containing titanium dioxide as inert marker (2.
The three statistical models were as follows: housing pen assumed to be normally distributed N 0; Ir 2 pen , and e ijklm , e ijklm , and ijklm are random residual terms assumed to be nor- X ijklm ¼ l þ sex i þ diet j þ sex i Á diet j þ group k þ b a BW l þ pen m þ e ijklm ð1Þ Y ijklm ¼ l þ sex i þ diet j þ sex i Á diet j þ group k þ b b FIs jl ð Þ þ pen m þ e ijklm ð2Þ X ijklm ¼ l þ sex i þ diet j þ sex i Á diet j þ group k þ b a BW l þ b c CFD l þ pen m þ ijklm ð3Þ mally distributed N 0; Ir 2 e À Á. The mixed procedure (SAS 9. 3; SAS Inst. Inc. , Cary, NC) was used to fit the models. Hereafter, variance will be used as the statistical term for variation. Total variance in X ijklm and Y ijklm, i. e. Var X ijklm À Á and Var Y ijklm À Á , were calculated and consecutively explained variance by the models was calculated as: Var X ijklm À Á À Var e ijklm À Á Var X ijklm À Á Â 100%; Var Y ijklm À Á À Var e ijklm À Á Var Y ijklm À Á Â 100%: Least squares means of model 1 and 2 were calculated per factor, and statistical differences between least square means for diet, sex, and diet by sex combinations were calculated using a posthoc Tukey test.
Feed efficiency traits Pigs on different diets and pigs from different sexes showed some differences in ADG, ADFI, FCR, BF and RFI during both phases and the overall phase (Table  2 ). Mean BW at start of the first phase Fig. 1. Experimental design and measurements according to the experimental phase and diet fed to the grower-finisher pigs. At onset of the experiment (d0) body weight (BW) was recorded and individual feed intake registration (FI) started. One day before slaughter (end), BW and backfat thickness (BF) were recorded and faecal samples were collected. Feed intake at sampling (FIs) was calculated as feed intake over the last 3 days of the experiment. was 22. 6 kg, at start of the second phase 70. 2 kg, and at the end of the experiment 121. 1 kg (data not shown). The BW covariates in model 1 were significant for all traits except for RFI.
Literature search and inclusion criteria Studies were gathered from the Medline database (https://pubmed. ncbi. nlm. nih. gov/) using "strongyl* horse" as keywords, yielding a database of 1,032 articles. Article titles and abstracts were subsequently screened for particular terms including all possible combinations of the following: "intensity", "Intensity", "Abundance", "abundance", "Prevalence", "prevalence", "communit",.
5 "Communit", "Helminth" or "helminth". To identify studies reporting strongylid burden, we also included the main genera ("Cylicocyclus", "Cyathostomum", "Cylicostephanus") as keywords. This filtering step retained 300 papers. For these, titles and abstracts were read to determine whether findings of strongylid species prevalence and abundance would be available. To broaden our database, we also run manual search on google (https://scholar. google. com/) using the keywords "prevalence abundance Cyathostominae horses" and "prevalence abundance Strongylidae horse". This query identified six additional studies. Additional studies were identified after inspection of the references cited in three papers  (Gawor, 1995; Ogbourne, 1976)  that yielded prevalence data from 13 additional studies. To be included in the meta-analysis, reported articles had to meet several criteria.
First, studies were included in the dataset if they reported parasite prevalence (proportion of infected host), abundance (the number of parasite individuals per host, including uninfected individuals) or relative abundance. Second, published data were only included if the number of horses sampled was reported. Finally, 39 papers met the aforementioned selection criteria (Supplementary Table  1 ). Among these, 16 papers presented both abundance and prevalence data, while others either only reported abundance (n = 5) or prevalence (n = 18) data. Papers from Africa (n = 2) and Australia (n = 3) were included to determine average relative abundance and prevalence across region-based studies but were not considered in any further analyses as too few replicates were available to test for environmental effects.
Parasite, environmental and host variables When available, metadata related to environmental factors were collected. Horse age was not considered for the continent-wide study as only scarce and inaccurate data were available. The same applied for horse breed or management type (wild or managed horses) that were reported in three 6 studies only. Following edition, information regarding continent, country, sampling effort (number of horses sampled), the recovery method used (necropsy or deworming) were included. Climate conditions were determined using the Köppen-Geiger climate classification (http://hanschen. org/koppen/#data) matching the sampling area. The Köppen-Geiger climate classification is encoded by a three-or two-letter system corresponding to the type of climate, rainfall pattern and temperature range respectively.
In addition, because count data usually follow a negative binomial distribution, reported arithmetic mean values can be poor descriptors of the parasite population. This also speaks against sound estimation. Because of the data structure, geoclimatic conditions (aggregated continent and climate zone) and the recovery method (deworming or necropsy) were confounded. In addition, some conditions were underrepresented (less than five community sets) and not considered further. This included the community sets from Asia (n = 1), Australia (n = 3), or sets from temperate Europe collected after deworming (n = 3). As the deworming strategy was only applied in Europe, a continental European subset was considered (n = 33 communities) to evaluate how the recovery method could affect the strongylid diversity.
Impact of horse age on strongylid community diversity To study the interactions between strongylid species within their hosts, we re-analyzed individual necropsy report data collected in Normandy  (Collobert-Laugier et al. , 2002). Abundances of 20 strongylid species and other non-strongylid species (Anoplocephala sp. , Oxyuris sp. ) were quantified within the caecum, ventral-and dorsal-colon of 46 horses. The data from 36 horses that had their age registered and were infected by at least one parasite species were kept. To avoid spurious signals associated with rare observations, we focused on the 15 species achieving at least 10% prevalence. To capture the strongylid community assembly pattern across anatomical niches and age group, we applied the hierarchical Bayesian joint species distribution model  (Ovaskainen et al.
, 2020). A hurdle-type model was considered to account for the overdispersed nature of the count data, modeling log-transformed positive species counts as normal and the binary presence or absence status using a probit-link function  (Abrego et al. , 2020)  To study the interactions between strongylid species within their hosts, we re-analyzed individual necropsy report data collected in Normandy  (Collobert-Laugier et al. , 2002). Abundances of 20 strongylid species and other non-strongylid species (Anoplocephala sp. , Oxyuris sp. ) were quantified within the caecum, ventral-and dorsal-colon of 46 horses. The data from 36 horses that had their age registered and were infected by at least one parasite species were kept. To avoid spurious signals associated with rare observations, we focused on the 15 species achieving at least 10% prevalence.
To capture the strongylid community assembly pattern across anatomical niches and age group, we applied the hierarchical Bayesian joint species distribution model  (Ovaskainen et al. , 2017; Tikhonov et al. , 2017)  implemented in the HMSC R package (v. 3. 0-10)  (Tikhonov et al. , 2020). This model offers an integrated framework to explicitly model species-species interactions while simultaneously accounting for environmental covariates, species traits or their phylogenetic relationships  (Ovaskainen et al. , 2017; Tikhonov et al. , 2017). We used the same approach as described elsewhere  (Abrego et al. , 2020) , fitting two models that either accounted for the worm burden measured at the horse level or for additional fixed effects including the anatomical niche and horse age. In both cases, horse and horse x anatomical niche were fitted as random effects to account for inter-horse variation in species pairs co-occurrences and for sample level variation not accounted for otherwise  (Abrego et al.
Sensitivity analysis Parameter values were calibrated in Lambert et al. [33]. However, given the remaining uncertainty in the three parameters fitted by ABC, and the additional uncertainty around new parameters related to management interventions, we performed a sensitivity analysis of model outputs to a selected subset of 11 parameters (Table  1 ). Using a fractional factorial plan, with three levels per parameter: the median, minimum and maximum of their range values, we performed a global sensitivity analysis on simulated outputs, implemented in the multisensi R package  [38, 39]. For each parameter, generalized sensitivity indexes GSI were calculated and were represented by Pareto plots. Details of the sensitivity analysis can be found in Additional file 2.
Acknowledgements This work was performed using the computing facilities of the  CC LBBE/PRABI  and of the  CC IN2P3 , and within the framework of the  LABEX ECOFECT  ( ANR-11-LABX-0048 ) of  Université de Lyon  and of the program " Investissements d' Avenir"  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR). This study was coordinated by the  French Biodiversity Agency (Office Français de la Biodiversité ,  OFB, formerly Hunting and Wildlife Agency ,  Office National de la Chasse et de la Faune Sauvage, ONCFS). The authors warmly thank all the field staff who performed monitoring and management of the Alpine ibex population. They also thank the scientific experts of the ANSES working groups on brucellosis of Alpine ibex in the Bargy massif, as well as  Charlotte Dunoyer  ( ANSES; UERSABA's chief unit ) and  Jean Hars.
Plasma kinetic data of TZ and TZ-SO 2 following oral application to piglets is limited  [37, 38]. Fast absorption and extensive metabolization of TZ to toltrazuril sulfoxide and TZ-SO 2 were reported following single oral application to piglets  [38]. The main metabolite TZ-SO 2 is slowly eliminated, mainly in the feces  [7]. Data were reported as part of registration dossiers for the 5% suspension product for oral administration  [7]. Information for different tissues (muscle, fat, skin, liver, and kidney) are available and were obtained as part of residue depletion studies for establishing the withdrawal period of specific products. The characterization of the plasma concentration profiles and results of residue depletion studies of the TZ and TZ-SO 2 provide understanding of the pharmacological and toxicological features of this anticoccidial compound; however, concentration profiles reached in the tissues of parasite location greatly contributes to improved understanding of its antiparasitic effect in vitro and in vivo.
This was implemented only for ponies on which data was collected three times a year, over at least two years. Using these estimates, the ten most susceptible (S) and ten most resistant (R) ponies were retained for this study. Median FEC over the past 5 years were 800 and 0 eggs/g for S and R ponies respectively  34. These two groups of female Welsh ponies were matched for age (mean group ages of 3. 63 years ± 1. 33 and 4. 44 years ± 1. 30 in the R and S groups respectively). Ponies were housed in November 2014. While indoors they received hay ad libitum and 600 g concentrate per animal per day (Tellus Thivat Nutrition Animale Propriétaire, Saint Germain de Salles, France) as outlined previously  34. This concentrate contained oats and oat bran, wheat straw, barley, alfalfa, sugar beet pulp, molasses, salt and calcium carbonate and was enriched with vitamins and minerals  34.
For further analysis, an albumin to globulin ratio (AGR) was also performed. In addition, serum biochemical parameters were quantified using Select-6 V rings with the M-Scan II Biochemical analyser (Melet Schloesing Laboratories, France). These parameters included albumin, cholesterol, globulin, glucose, alkaline phosphatase (ALP), total proteins (TP) and urea concentrations. Normality of haematological and biochemistry parameters was tested with the Shapiro-Wilk's test (shapiro. test() function in R) and variables showing test value below 0. 90, i. e. FEC, glucose, ALP and AGR concentrations, and eosinophil count were log-transformed (Supplementary Table  4 ). Validation of monocyte and neutrophil counts were obtained from a group of 25 ponies in October 2020 using the same setting. Proton nuclear magnetic resonance ( 1 H NMR) data acquisition and processing.
1 H NMR spectra were acquired using a Carr-Purcell-Meiboom-Gill (CPMG) spin echo pulse sequence with a 2-s relaxation delay. The spectral width was set to 20 ppm and 128 scans were collected with 32 k points. Free induction decays were multiplied by an exponential window function (LB = 0. 3 Hz) before Fourier Transform. Spectra were manually phase and baseline corrected using Topspin 3. 2 software (Bruker, Karlsruhe, Germany). All spectra were referenced to TSP (d 0 ppm). The spectral data were imported in the Amix software (version 3. 9, Bruker, Rheinstetten, Germany) to perform data reduction in the region between 9. 0 and 0. 5 ppm with a bucket width of 0. 01 ppm. The region between 5. 1 and 4. 5 ppm corresponding to water signal was excluded and data were normalized to the total intensity of the spectra.
1 H-NMR data were filtered from noise-related signals and finally consisted in 791 buckets ranging from 8. 585 to 0. 505 ppm (Supplementary Table  1 ). Buckets were annotated based on similarity of chemical shifts and coupling constants between plasma samples and reference compounds. The comparison was performed between one dimensional analytical data and reference compounds acquired under the same analytical conditions in our internal database, as well as from public databases like the Human Metabolome Database (HMDB, http:// hmdb. ca/) and the Biological Magnetic Resonance Data Bank (http:// www. bmrb. wisc. edu/). Manually curation was performed to isolate buckets corresponding to the same metabolite. Intensities of these metabolite-specific buckets were summed together to define metabolite-associated signals (hereafter referred to as "signals"), yielding 119 signals for further analysis.
ASVs were subsequently assigned taxonomy using a naive Bayes classifier trained on the green genes reference database (gg_13_8) clustered at 99% similarity. This workflow left 6208 ASVs that were aggregated to 91 genera with phyloseq (v. 1. 32) for subsequent analysis. Total sum scaling normalization was applied to each taxon abundances for subsequent data integration analysis. The raw sequences of the gut metagenome 16S rRNA targeted locus are available in NCBI under the Sequence Read Archive (SRA), with the BioProject number PRJNA413884 and SRA accession numbers from SAMN07773451 to SAMN07773550. Statistical analysis and data integration. Our experiment focused on resistant and susceptible ponies monitored during a pasture season. Resistance in this context was defined as the ability to limit parasite egg excretion.
Using this experimental design, we interrogated their respective metabolomes (1) to characterize metabolite trajectories throughout a single pasture season (June 2015 to Octobre 2015), (2) to identify biomarkers that would predict the pony intrinsic resistance to strongyle infection, (3) to identify biomarkers that could be used to differentiate between ponies reaching FEC cut-off for anthelmintic treatment (200 eggs/g) at the end of pasture season. We restricted this analysis to ponies whose predicted resistance status matched the observed FEC value at the end of pasture season, leaving nine true resistant (TR) and eight true susceptible (TS) ponies respectively. Statistical analyses applied for each of these three objectives are presented below. Multivariate analysis of longitudinal metabolomic data in TR and TS ponies.
Differential analysis of signal metabolite intensities between TR and TS ponies and between ponies in need of treatment. Second, we aimed to identify biomarkers that would either be predictive of the intrinsic resistance potential of an individual (TR vs. TS comparison at day 0) or would distinguish between individuals in need of treatment (TR vs. TS comparison at day 132). To fulfill these two aims, we respectively performed Student's t tests on the 319 retained metabolite signals between TR and TS pony baselines or between individuals showing FEC below or above 200 eggs/g at day 132. To account for multiple testing, nominal P-values were adjusted using the Benjamini-Hochberg correction (FDR) as implemented in the p. adjust function (stats package v. 4. 0. 2). Spearman's correlations were estimated using the rcorr function from the Hmisc package v.
4. 4-1. Data integration to identify biomarkers associated with parasite resistance or need of treatment. As a complementary approach for biomarker identification, clinical (including blood cell population profile, blood biochemistry and average daily weight gain), metabolomic (319 signals) and faecal microbiota data were integrated to identify correlated signals associated with intrinsic resistance potential or need of treatment. This approach also has the potential to uncover biological signals that would be missed when considering each dataset independently  42. We ran two analyses to extract the features from each dataset with best discriminant ability between TR and TS ponies either before the pasture season on one hand, or between TR and TS ponies in need of treatment at day 132 on the other hand.