dataesr/scientific-paragraphs-categorization huggingface dataset:
Direct behavioural observations were analysed as frequencies per pig per minute, and the interactions from the video scans were analysed as frequencies per pig per scan. For the analysis of the consumption of loose materials, week, treatment and the interaction between treatment and material were included. For the direct observations, sex was included as a fixed effect to investigate differences between male and female pigs. The behaviour data were averaged with week, which was included in the model as a repeated effect. All behavioural data were analysed using pen as the experimental unit (n = 8), except when comparing sex differences (n = 16). The growth data such as weight and feed intake were also analysed at the pen level (n = 8), as well as the weight of the loose materials (n = 8). All physical scores (alive and postmortem) were analysed using the GLIMMIX procedure to fit the Generalised Linear Mixed Models, including week (or stage), treatment, and sex as fixed effects.
Acknowledgments: We would like to thank the farm staff in  Moorepark Pig Research Facility  for their help in conducting the study and their care for the pigs, especially the farm manager  Tomas Ryan 's support in the purchase of most enrichment materials. We would also like to acknowledge the people and organisations that generously helped in providing free materials as enrichment items to facilitate this experiment, especially  Michelle Liddane  (Teagasc),  John Finnan  (Teagasc), and  Milltech Digital Ltd.
DNA extraction, 16S ribosomal RNA amplicon and sequencing Microbial genomic DNA was extracted from all samples using a HiPure Stool DNA Kit (Magen, Guangzhou, China) following the manufacturer's instructions. The final DNA concentration and purification were determined using a NanoDrop 2000 UV-vis spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA), and DNA quality was checked using 1% agarose gel electrophoresis. Illumina MiSeq sequencing and general data analyses were performed by a commercial company (Magigene, Guangzhou, China). The V4 hypervariable regions of 16S rDNA were amplified by PCR using 515F/806R primers and the following protocol  (Bates et al. 2010). One pre-denaturation cycle at 94 °C for 5 min, followed by 25 cycles of denaturation at 94 °C for 30 s, annealing at 52 °C for 30 s, elongation at 72 °C for 45 s, and one post-elongation cycle at 72 °C for 10 min.
Bioinformatic analysis High-quality sequences were uploaded to QIIME (version 1. 7. 0) for further study. Raw data was mass filtered using Trimmomatic software to obtain qualitycontrolled clean data. Each pair of PE reads was then spliced using Mothur (version 1. 3) and FLASH (version 1. 2. 1) software, to obtain the original spliced sequence (Raw contigs). The quality of the spliced sequence was controlled and filtered by Mothur to obtain effective splicing fragments (clean contigs). The spliced sequences were then assigned to the corresponding samples by Qiime according to the barcode and primer information. Preprocessed sequence reads were clustered into operational taxonomic units (OTUs) with similarity thresholds of 97% using Usearch software (version 7. 1), after which the chimera and singletons were also eliminated by the Usearch software (version 7.
1). A representative sequence from each OTU was selected using QIIME. The representative sequences were then classified by species to better clarify the source of all sequence species using QIIME. QIIME was used for representative sequence species annotation. RDP Classifier was used for species annotation, and the database used was Green genes. The taxonomy results of species annotation were divided into 6 levels, namely phylum (L2), class (L3), order (L4), family (L5), and genus (L6). The analysis of the alpha diversity indexes, including Chao1, Shannon, and Simpson were calculated using QIIME. Beta-diversity analysis, including an unweighted UniFrac principal coordinate analysis (PCoA) was performed using QIIME. Least discriminant analysis effect size (LEfSe) and LDA effect size were applied to identify differences in the dominant bacterial community.
Statistical analysis Data represent the means ± SE. For metabolites (including SCFAs and bioamines), a general linear model (GLM) and least significant difference (LSD) test were used to analyze data among more than two groups using SPSS 22. 0 (IBM, USA). Differences with P < 0. 05 were considered statistically significant, while a tendency was considered to exist at 0. 05 ≤ P < 0. 10.
Microbiota diversity Several alpha diversity measures were calculated for all samples to assess the diversity, richness, and phylogenetic diversity of the bacterial community. The results clarified unambiguously that the alpha diversity values (including Observed species, Chao 1, Dominance, PD whole tree, Shannon, and Simpson indices) exhibited no significant differences between the CN and any XOS addition groups (data not shown), while significant differences in the Shannon index were observed between the GFP 250 group and AB group (P < 0. 05) (Fig. 3a ). The PCoA based on the unweighted UniFrac distance matrix clustered all replicate intestinal content samples together within the same treatment plot and they displayed stronger dissimilarities to those from other treatments (Fig. 3b ). In the GFP groups, although sample overlapping occurred among the three dose groups, clustering tendencies were observed, which exhibited a greater similarity in the gut microbiota structure between the GFP 100 group and GFP 250 group.
Statistical analyses For behavioral data recorded through the behavior assessment scheme, we established behavioral categories (called 'item' in Table  1 ) that comprised several behavioral criteria (e. g. the 'facial expression' item comprised 'Upper-lip curl' and 'Nose wrinkling'). For each sheep in each period (P1, P2, P3) and for each criterion, its frequency of observation was multiplied by its initial score to give a weighted score. In each behavioral item, we then first summed all the weighted scores for each sheep at each phase and then divided the total by the maximum reachable weighted score. The final composite score for each behavioral item was thus expressed on a 0 to 100 scale. For all indicators (haptoglobin, SAA, cortisol, NEFAs, GSH/ GSSG ratio, behavioral item score, RMSSD, SDNN, LF/HF ratio, body temperature, vitamins A, E and NO), the mean value was calculated for each phase and each sheep, and that value was then used for further analyses.
The performance of the multiparametric analysis was assessed based on its κ coefficient, sensitivity (percentage of sheep correctly predicted as belonging to a treatment; Se = true positive/true positive + false negative) and specificity (percentage of sheep correctly predicted as not belonging to a treatment; Sp = true negative/true negative + false positive). Sensitivity and Sp were calculated after a leave-one-out analysis performed after the discriminant analysis. Statistical analyses were performed using R 3. 3. 2 with the ade4 package for discriminant analysis (R Development Core  Team, 2008). Threshold for significance was set at P = 0. 05. R codes of statistical models are detailed in Supplementary Material S2.
Acknowledgements The authors thank  Denis Roux ,  Pascal Faure ,  Maxime Navech ,  Yoan Thomas ,  André Guittard ,  Sebastien Alcouffe ,  Bernard Mallet ,  Simon Collange  and  Julien Mongiat  for providing technical assistance during the experiments,  Agnès Thomas ,  Anne-Sophie Bage ,  Hervé Chandèze ,  Eric Delval ,  Stéphane Andanson  and  Christine Ravel  for providing assistance with the laboratory analyses,  Dr Caroline Prouillac  and  Dr Junot  for pharmacological expertise, and  A. T. T. for providing editorial support. This work was funded by  INRA  (Crédits incitatifs du département PHASE) and by  VetAgro Sup.
Due to the large number of null values, oral and locomotion-related stereotypies as well as aggressive behaviours were treated as binary data (Yes/No). "Withdrawn" and alert postures were retained as continuous variables, allowing the frequency of expression of these two indicators to be studied. A total of 20 iterations were carried out to produce the NMDS ordination matrix. The NMDS axis solution were selected as the best portrayal of the data when the final stress value was below 0. 1. Subsequently, covariates of behavioural variation were identified by calculating the associations between the 12 factors studied and the behavioural ordination matrix with the envfit function in the vegan R library, with 10,000 permutations and Benjamin Hochberg multiple testing correction. The factors included were the gender, the age, the presence of an opening toward the external environment, the presence of a grilled window between boxes, the bedding material, the mean quantity of concentrated feed offered and the number of meal per day, the discipline along with the level of performance, the number of competing events performed during the study, and the time spent being ridden and being trained on the lunge/using an automatic walker.
Residuals were checked graphically for normal distribution and homoscedasticity. The proportion of variance explained by the fixed factors was also quantified using the marginal R 2 (R 2 GLMM(m) %), as well as by both the fixed and random factors using the conditional R 2 (R 2 GLMM(c) %) (r. squaredGLMM function, MuMln R library). Oral, locomotion-related stereotypies, and aggressive behaviours presented too positively skewed distribution due to a large number of null values. These behavioural indicators were thus analysed as binary data (Yes/No) using generalized linear mixed models (GLMMs) (glmer function, lme4 R library) fitted with binomial error distributions and the Laplace approximation for parameters estimation. Wald χ 2 tests (wald. test function, aod R library) were carried out to test the fixed effects after checking for overdispersion  [62].
The models were built in the same manner as LMMs. The goodness-of-fit of the final models was assessed using the Hosmer-Lemeshow χ 2 test (hoslem_gof function, sjstats R library). For all models, Tukey post-hoc tests (glht function, multcomp R library) were carried out to further investigate the effects of significant parameters. All statistical analyses were performed using R software (version 3. 6. 0, R Development Core Team, Vienna, Austria, 2019) with a significance level of (p) ≤ 0. 05.
Acknowledgments: The authors are very grateful to the staff of the  French Horse and Riding Institute (IFCE)  and  INRA (Nouzilly, France) , and particularly  Patrick Galloux ,  Xavier Goupil ,  Isabelle Burgaud ,  Sophie Biau ,  Colonel Patrick Teisserenc ,  Jean-Marie Yvon ,  Milena Trosh , and all the animal keepers for their collaboration during the experiment. We would like to thank the  Springer Nature Author Services team  for correcting the English manuscript.
experience and to promote positive ones. Emotions can affect vocalizations directly or indirectly through the 28 brain, lungs, larynx or vocal tract. As a result, vocal expression of emotions has been observed across 29 species (Briefer 2012), and could serve as a non-invasive and potentially very reliable tool to assess animal 30 emotions. In pigs (Sus scrofa), vocal expression of emotions has been relatively well studied (e. g. Leliveld et 31 al. 2016; Briefer et al. 2019). However, it is not known if the vocal indicators revealed in previous studies are 32 valid across call types and contexts. To find this out, we conducted an analysis of the effect of emotional 33 valence on a large database of pig vocalizations, including calls recorded in the most common emotional 34 situations encountered by pigs throughout their lives, from birth to slaughter.
We analyzed bacteriological results from vaccinated animals according to different "innocuousness criteria" accounting for Rev. 1 dissemination and the individual ability to contain the live-vaccine: (i) an indicator of Rev. 1 strain dissemination among vaccinated individuals, by considering the proportion of organs positive to culture, (ii) an indicator of vaccine strain shedding potential among vaccinated individuals, by considering the proportion of culture-positive organs from the pelvic area; and (iii) the bacterial burden among infected organs or swabs. We analyzed hematological results from vaccinated or non-vaccinated animals according to different "innocuousness criteria" after Rev. 1 vaccination in function of time, sex and genders, using GraphPad Prism software version 5. 0 (non-parametric tests: Kruskall-Wallis, Mann-Whitney tests, GraphPad ® , San Diego, Ca, USA).
This analysis focused on necropsy samples only (i. e. days 45 or 90 pv). The number of plates was variable among organs (from 1 to 4), mainly due to varying organ/swab nature or size; the number of plates was thus introduced as a model offset. The fixed effects accounted for were species, gender, time of necropsy, and type of organ (Figure  2 ). Model selection was based on the Akaike Information Criterion (AIC,  [43] ). Statistical analyses were performed using R software version 3. 4. 2 (the R foundation for statistical computing, 2017), and the lme4 [44] and MuMIn packages  [45].
Acknowledgements This study was supported and funded by the  French Directorate General for Food (DGAL) ,  Ministry of Agriculture and Food  (Grant No. 020602002001 ,  2017-104 , France) and the  Ministry for an Ecological and Solidary Transition  (Grant No. 2102054258 , France). The authors want to thank the technicians and researchers from the  Zoological Park of Haute Touche, INRA ,  ANSES , the technicians and scientists of  Laboratoire de Touraine  (in particular José Delaval (Director) and the technical microbiology staff ) and the  French Wildlife and Hunting Agency (ONCFS)  for their contribution to this study. Many thanks to the directors of the  Experimental Infectiology Platform , the " Centre international des ressources microbiennes: bactéries pathogènes" (CIRM-BP ) and to the zootechnical staff of the  PFIE (Nouzilly, France) and of RZHT (Obterre, France ).
Introduction Buruli ulcer (BU), caused by Mycobacterium ulcerans, was only treated by surgery until 2004. The first medical treatment recommended by the World Health Organization (WHO). was a daily eight-week treatment based on an association of two antibiotics, rifampin (RIF), an oral ansamycin, and streptomycin (STR) an injectable aminoglycoside  [1]. Currently a promising fully oral regimen combining RIF and clarithromycin (CLR), a macrolide compound  [2, 3]  is tested clinically at a large scale (NCT01659437, clinicaltrials. gov). The oral RIF-CLR combination has been promoted to suppress toxic effects and injections with aminoglycosides, resulting in better patients adherence and safety. Nevertheless, this combination is given daily during eight weeks. Shorter or intermittent treatment would facilitate adherence and the supervision by healthcare workers.
Infection of mice with M. ulcerans Respectively 190 and 390 4 weeks-old female balb/c/j mice were used in the 1 st and the 2 nd experiment (Janvier Labs, Le Genest Saint-Isle, France). Mice were inoculated according to the Shepard method  [13]  in the left hind footpad with 0. 03 ml of a bacterial suspension containing around 5 log 10 Colony Forming Unit (CFU) of the M. ulcerans strain Cu001 (5. 02 and 4. 6 log 10 in the 1 st and 2 nd experiment, respectively). This strain, isolated in 1996 from a Buruli ulcer patient in Adzopé, Ivory Coast  [14] , was kindly provided by the local laboratory without any identification data regarding the patient. The strain is susceptible to all drugs used in BU treatment and was maintained in our lab by regular passage into mice footpad.
Data extraction from videotaped trials. Observer and control coyotes were video recorded using digital cameras (Sony Digital 8 and DCR-SR68 Handycams, and Panasonic SDR-H85) at the transparent section of the fence during each demonstration. All puzzlebox trials (i. e. , demonstration and testing trials) were also video recorded. A single observer counted the number of times observer and control coyotes observed their respective demonstrator. The same person extracted detailed behavioral data from all trials of each observer and control coyote using Noldus Observer XT software. Box height (17 cm) was used to determine the subject's proximity to the puzzle box by markings on a transparency sheet overlaying the monitor screen during video review. Data recorded included whether individuals solved the puzzle box, trial day(s) the puzzle box was solved, latency to eat from the puzzle box (named hereafter latency to solve), and persistence (Table  1 ).
For all three studies, distributions of the response variables were tested prior to running the models using the "fitdistrplus" R package  [55]  and model assumptions (i. e. normality of the residuals, over-dispersion and heteroscedasticity) were verified after performing model selection. All GLMMs were run using the lme4 package, version 1. 1-18. 1 in R (version 3. 5. 2, R Development Core Team, 2011). Thus, all results presented assume that model assumptions were respected (i. e. residuals normally distributed, no over-dispersion or heteroscedasticity) or otherwise are noted. Parameter estimates ± standard errors (SE) are provided for all selected models. All analyses were performed with R (version 3. 5. 2, R Development Core Team, 2011). Results. All observer coyotes (n = 5) witnessed at least four of the five demonstrations and all control coyotes (n = 5) witnessed at least two, but usually four, of the five control demonstrations before their trials.
Trials for the control coyotes were conducted immediately after the observer's trials using an identical apparatus and following the same sequence. For all observer and control pairs, a successful individual was defined as one that moved the board and dug to access food in at least one trial. An unsuccessful individual failed to perform the task in all trials. All coyote pairs that did not perform the task received their daily food rations about 5 hours after a trial ended each day of testing. A single investigator extracted detailed behavioral data from each trial (i. e. demonstrator, control and observer coyotes) through live observations from an observation vehicle parked approximately 15-m away from the testing enclosure in order to minimize human disturbance effects. Data collected included attentiveness, neophobia, persistence, and latency to solve the task (Table  1 ).
Since all control coyotes failed to problem solve, the next steps of the analyses focused on the observer individuals. First, we used GLMMs (binomial family) to test whether attentiveness, neophobia and/or persistence was/were the best predictor(s) of problem solving success within this treatment group. All variables were tested individually in the models, along with all possible combinations. Coyote ID was always included as a random effect to account for repeated measures. Pair ID was also included as a random effect to account for the non-independence of the behavior displayed within dyads (e. g. , an individual preventing its mate to access to the apparatus). All GLMMs were run using the lme4 package (version 1. 1-18. 1) in R. Then, we investigated the effect of intrinsic factors, represented by the sex and the social rank of the individuals, on the variable(s) retained as best predictor(s) of problem solving success.
All coyotes who did not perform the task were fed about five hours after the trial. Unsuccessful observer and control coyotes were subject to an additional trial at the end of the five testing days in which 650 g of fresh food was placed within the hoop to determine if object neophobia may have caused failure. A single investigator extracted detailed behavioral data from each trial through direct observations from within the enclosure. Data recorded were identical to study 2, with the addition of latency to eat food from the hoop in the post-trial test of unsuccessful pairs of coyotes. Statistical analyses. Considering the high rate of unsuccessful individuals (92. 9%), we focused statistical analysis on the effects of social learning opportunities and individual characteristics on persistence and neophobia.
Thus, after we ensured that model assumptions were met as in study 1, we used GLMMs (lme4 package in R) to assess the effects of treatment group and trial number on these two variables. Coyote and pair IDs were included as random effects to account for repeated measures (i. e. , trials) and non-independence of the measures between members of a pair. Trials during which individuals failed to approach the experimental apparatus were not included in the analyses. Latency to approach and time spent working on the problem were both log-transformed to achieve normality. We then focused the analyses on the observer treatment group to determine whether individual characteristics captured by sex and social rank influenced both neophobia and persistence. Age class was included as a random effect to account for a potential age effect.
Acknowledgments This research was supported by the intramural research program of the  U. S. Department of Agriculture, National Wildlife Research Center (NWRC)  and approved by the  NWRC's Institutional Animal Care and Use Committee  ( QA-1724  and  QA-2581 ). The findings and conclusions in this publication have not been formally disseminated by the  U. S. Department of Agriculture  and should not be construed to represent any Agency determination or policy. The datasets generated during the current study are available as supplementary material ( S4-S7  Tables). They are also archived and available through the  USDA-National Wildlife Research Center 's archival desk or from the corresponding author. We thank  S. Breck  for early discussions on study design and review of the manuscript. D.
and reproducibility a b s t r a c t In this paper, we present data from measurements made in the textured fibers bobbins in two different conditions, presenting critical quality characteristics such as diameter, mass and density. In order to obtain a significant amount of information, in each of the two conditions, 270 measurements were obtained for each of the quality characteristics. Three different equipments (Automatic Package Analyzer -APA) were used in ten different parts, replicated three times for each of them. Considering the two measurement data collection, an amount of 540 bobbins measurements were obtained. Almeida et al. , (2019)  applied these measurement data in his study. Taking into account the multicorrelated nature of the information, we also have the representation of the principal components' scores for these measurements, besides the eigenvalues and eigenvectors of the data.
Data These data present the measurements of a real textured fibers bobbins process of type Partially Oriented Yarns (POY), randomly and automatically collected from specific measurements devices called Automatic Package Analyzer (APA). Data were used in the study by Almeida et al. [1]. The data present a great amount of information, describing three of the main critical characteristics of the quality, such as: diameter (cm), mass (g) and density (g/cm 3 ). In manufacturing processes that have many quality characteristics, one must also verify the data's multi-correlation  [2] , so the principal components' scores of these measurements are also presented, along with their eigenvalues and eigenvectors. The information based on principal components was used due to the great applicability of this strategy, as in works of  [3, 4].