dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements These results would not have been possible without the ongoing dedication of PTEA staff to animal care, measurement, and sampling. We would like to thanks  Médhy Jolo ,  Célia Benmouffok  and  Alexia Denimal  for their participation in building this dataset. Finally we thanks ' C3I'  and ' Exocet' HPC facility of the Université des Antilles in Guadeloupe  for providing valuable resources. This work was funded by the departement of animal genetic, including the project suiRAVI. It was also supported by  Region Guadeloupe  and the  European Union Fund (FEDER)  in the framework of the  AgroEcoDiv  project. This work was also supported by the  French National Research Agency , under the project  HealtHavior   ANR-22-CE32-0005-01.
Body condition, defined by backfat thickness (BF), muscle depth (MD) and body weight (BW), influences sow performances. The objective of this study was to assess the relationship between a sow's body condition and performance over several reproductive cycles. A total of 653 sows were monitored on a French farm over 6 years. Sow BF, MD and BW were measured at 110 days of gestation and 21 days of lactation. Data on prolificity (i. e. total born, live born, stillborn) and lactation performance (i. e. piglet weight, number of piglets) were collected. Four phenotypes of sows were defined: ''fat" (high BF:MD ratio), ''muscular" (low BF:MD ratio), ''light" (average BF:MD ratio and lower BW) and ''heavy" (average BF:MD ratio and higher BW). Differences in body mobilization and lactation performance among the phenotypes were analysed using variance analysis.
Perspectives Other new antifungals are awaited  [172]. Ibrexafungerp, a BDG-synthesis inhibitor, is currently being tried in combination with voriconazole against voriconazole monotherapy in the setting of IA (NCT03672292; https://clinicaltrials. gov/, accessed on 22 January 2022) after promising results in vitro and mouse models in combination with isavuconazole  [173, 174]. Olorofim, an inhibitor of dihydro-oroate dehydrogenase, is currently being tried against L-amB (NCT05101187; https://clinicaltrials. gov/ accessed on 22 January 2022) in the setting of IA after promising results in vitro and in mouse models of neutropenia  [175, 176]. Fosmanogepix (APX001), an inhibitor of the fungal enzyme Gwt1, is currently in phase 2 trial (NCT04240886; https://clinicaltrials. gov/ accessed on 22 January 2022) after promising preclinical results  [177].
Analysis of the PCR Products The products of PCR were separated by electrophoresis on 1% agarose gel (Applichem, Germany, GmbH) in 1x TBE buffer at room temperature using gradients of 5 V/cm. For gel analysis, 20 µL of the PCR products were loaded in each gel slot. Gene ruler 100 bp DNA ladder (Fermentas, Sigma) was used to determine the fragment sizes. The gel was photographed by a gel documentation system (Alpha Innotech, Biometra) and the data were analyzed through computer software.
Detection of HEV by RT-qPCR HEV, Mengo virus, and MNV-1 were detected using a method described previously  (Trudel-Ferland et al. , 2021). Genomes were amplified using an iTaq Universal Probe One-step kit (Bio-Rad, Hercules, CA, USA). The primers and probes used are listed in Table  1. The expected size of the HEV amplicon is 72 pb. To quantify HEV, a standard curve was produced in triplicate from 10 5 genome copies/µl to 10 1 genome copies/µl. The MiniGene plasmid pIDTSMART-AMP+ generated by IDT (Integrated DNA Technologies, Coralville, IA, USA) was used (GenBank number MP690161. 1). RT-qPCR reactions were performed in 96-well clear PCR microplates (Applied Biosystems, Thermo Fisher Scientific, Waltham, MA, USA) sealed with an optical adhesive film (Applied Biosystems, USA). The ABI7500 real-time PCR thermal cycler (Applied Biosystems, Thermo Fisher Scientific, Waltham, MA, USA) was used as follows: 50 • C for 10 min, 95 • C for 3 min and 45 cycles of 95 • C for 15 s, and 60 • C for 30 s.
ROX dye was used as the reference dye. Each sample was analyzed in duplicate. All RT-qPCR runs included a negative control (RNase-free water, VWR, Radnor, PA, USA). All qPCR standard curves had an R² over 0. 985 and efficiency between 90 and 110%. Viral RNA recovery was calculated for all samples using the following formula: Percent recovery of viral RNA = [RNA] sample [RNA] positive control × 100, where  [RNA]  is expressed in genome copies per sample. All results were analyzed using SDS Software v1. 5. 1 (Applied Biosystems, Thermo Fisher Scientific, Waltham, MA, USA).
Specific primer sequences targeting the HEV ORF2 region were used for the amplification: forward primer GTYATGYTYTGCATACATGGCT and reverse primer AGCCGACGAAATYAATTCTGTC  (Mykytczuk et al. , 2017). The amplicons with an expected size of 348 bp were stored at -80 • C until processing. An agarose gel (2%) in tris acetate EDTA buffer 1× with 0. 01% SYBR R Safe (Invitrogen, Thermo Fisher Scientific, Waltham, MA, USA) was used. Each well was loaded with 3 µl of sample and 2 µl of TriTrack DNA loading dye (Thermo Scientific, Thermo Fisher Scientific, Waltham, MA, USA). The amplicon size was compared to a 100 bp DNA ladder (Thermo Scientific, Thermo Fisher Scientific, Waltham, MA, USA) after 45 min of migration at 120 V. A UV-light photo of the gel was taken with the ChemiDocTM MP imaging system (Bio-Rad, Hercules, CA, USA) and processed using Quantity One R software (Bio-Rad, Hercules, CA, USA).
HEV sequence analysis A large proportion of the samples found positive by RT-qPCR then transcribed into cDNA and amplified did not show up as positive on an electrophoresis gel. Only one sample showed a PCR product of the expected size (348 nt) and was sent for Sanger sequencing (P22 * in Table  3 ). The sequence was found to match HEV-3 from that in Tokyo, Japan with a sequence similarity of 95% (accession number: LC699232. 1). The sequence of the HEV detected in our study appears to be HEV-3 and has been deposited in GenBank (accession number: OQ032868).
Ethical Statement The methods and procedures of this study were in accordance with Italian legislation (D. L. vo n. 26 of 04th March 2014) on the protection of animals used for scientific purposes (Authorization n°1055/2015-PR). Owners of horses gave written consent to the enrolment of their animals in the study. Moreover, parents of participants signed informed written consent for the involvement of their children in riding sessions, and all data about their children were processed in compliance with privacy and data protection law. [34]. Children were selected among those who attended the riding centers participating in the study and they were divided into two groups according to their experience in EAA: i) less than 1 month of experience (n ASD=9; n TD=7); ii) more than 1 month of experience in EAA (n ASD=10; n TD=12).
Acknowledgments: The authors of this study would like to acknowledge all individuals and organizations that contributed to its realization, including the riding centers:  Centro di Riabilitazione Equestre "Vittorio di Capua" ,  Azienda Ospedaliera Niguarda Ca'Granda, Milano ;  Il Grande Ranch ,  San Francesco al Campo , Torino;  Centro Ippico Meisino, Torino ;  A. S. D Equitazione  per tutti Onlus, Fiumicino. Special acknowledgements to  Laura Contalbrigo ,  Emanuela Valle  and  Adele Tuozzi  for helping setting up methodologically the original study.
Proteins were trypsinized (Thermo Fisher Scientific, Waltham, MA) overnight at a 1:10 enzyme-to-substrate ratio. For tandem mass tag (TMT) 6-plex labeling (TMTsixplex ™ isobaric label reagent set, Thermo Fisher Scientific, Waltham, MA), 0. 8 mg of TMT reagent was resuspended in 41 ml of acetonitrile and samples were spun down quickly at 2,000 rpm (380g) for 10 s and incubated at room temperature for 1 h. A total of four biological replicates per group (n = 4 calves per group) were labeled with TMT reagents and one TMT tag (131) contained the pooled samples [i. e. , equal amounts of peptides (20 µg) of each sample per group] and served as internal standards for normalizing the data across the groups. For labeling, peptides were incubated with TMT reagents (room temperature, 1 h), and the reaction was quenched by adding 8 ml of 5% hydroxylamine and incubated for 15 min at 25°C.
Dynamic exclusion was enabled for 45 s to avoid the acquisition of same precursor ions having a similar m/z ( ± 10 ppm). For bioinformatic analysis, spectral data were matched to peptide sequences against a bovine and C. parvum UniProt protein database using the Andromeda algorithm  (Cox et al. , 2011)  as implemented in the MaxQuant  (Cox and Mann, 2008)  software package v. 1. 6. 0. 1, at a peptide-spectrum match false discovery rate (FDR) of < 0. 01. Search parameters included a mass tolerance of 20 p. p. m. for the parent ion, 0. 5 Da for the fragment ion, carbamidomethylation of cysteine residues (+ 57. 021464 Da), variable N-terminal modification by acetylation (+ 42. 010565 Da), and variable methionine oxidation (+ 15. 994915 Da). TMT 6-plex labels 126-131 were defined as labels for relative quantification.
Shotgun metagenomic sequencing in feces Fecal samples for each calf collected at 0 and 6 dpc were sequenced using a KAPA Dual-indexed PCR-free library in a 6000 NovaSeq. The raw reads were trimmed using Trimmomatic  (Bolger et al. , 2014)  and filtered to remove adapters and Bowtie  (Langmead et al. , 2009)  with the latest Bos Taurus RefSeq assembly (GCF_002263795. 1) to remove cow reads. Filtered reads were mapped to the virulence factor database (mgc. ac. cn/VFs/) and a customized Metagenome Assembled Genomes database using KMA  (Clausen et al. , 2018). Data integration for pathway and Gene Ontology (GO) enrichment was performed with Metascape  (Zhou et al. , 2019)  and STRING-db  (Szklarczyk et al. , 2019). For virulence feature characterization, read counts were transformed by converting counts to proportions, dividing gene counts by gene length, and correcting them by dividing by the total counts of VFdb features for each sample.
Virulence gene counts were converted to abundance tables and imported in R-studio for further analysis. For microbiota characterization, MAG (Metagenome Assembled Genomes) catalogs (database) representative of the predicted bacterial genomes in the samples were used. The MAGs were built using MEGAHIT assembler  (Li et al. , 2015)  and autometa  (Miller et al. , 2019)  and filtered using CheckM to remove the ones below 85% completeness and > 5% contamination  (Parks et al. , 2015). The newly built MAGs and the ones from a previous study  (Teseo et al. , 2022)  using dRep  (Olm et al. , 2017)  were compared, thus creating a non-redundant dataset of representative genomes for the samples. For abundance matrices, the read counts per sample were estimated using KMA and corrected by converting counts to proportions [(MAG contig counts/ contig length)/average genome size] calculated using MicrobeCensus  (Nayfach and Pollard, 2015).
Statistical analysis Statistical analysis of clinical and histological data was performed with Prism (v9. 0) (Graph Pad San Diego, CA, USA). The descriptive analysis was performed on all variables, and normality of the data was tested, when possible, by Kolmogorov-Smirnov test. Values were presented as mean and standard deviation (SD) or median and interquartile range (IQR), as appropriate based on the distribution. For consistency and comparisons among experimental groups, the average daily physical examination results were analyzed. Clinical parameters (i. e. , heart rate, respiratory rate, temperature, hydration, attitude, appetite, fecal consistency, and ability to rise) were evaluated by repeated measures two-way analysis of variance (ANOVA), while histopathology scoring results were compared by one-way ANOVA.
A Geisser-Greenhouse correction was applied when sphericity was not assumed due to the small sample size, followed by Tukey test for multiple comparisons. Clinical and histopathology scoring results are displayed as summary graphs of the means with bars representing SD or standard errors of the mean (SEM) as indicated in the figure legends. The number of OES treatments was compared by Chi-square or Fisher's exact test, as appropriate. For proteomic bioinformatic analysis, GO annotation, proteins basic functions, domain functional descriptions, and annotation of biological processes were studied by the UniProt-GOA database (http://www. ebi. ac. uk/GOA/), InterPro domain database (http:// www. ebi. ac. uk/interpro/), and the Kyoto Encyclopedia of Genes and Genomes (KEGG) data-base (http://www.
genome. jp/kaas-bin/ kaas_main; http://www. kegg. jp/kegg/mapper. html). A two-tailed Fisher's exact test was employed to test the enrichment of the differentially expressed proteins (DEPs) against all identified proteins. All the categories obtained after enrichment were collected and filtered for those categories that were at least enriched in one of the clusters with a P-value < 0. 05. This filtered P-value matrix was transformed and clustered using one-way hierarchical clustering in Genesis. Clusters were visualized using the R Package pheatmap (https://cran. r-project. org/web/packages/ cluster/). Pathway enrichment and protein networking were analyzed by an online meta-analysis tool (metascape. org) (Zhou et al. , 2019). All DEPs' database accession numbers or sequences were searched against STRING database (v11) for protein-protein interaction (PPI) analysis and interaction networks visualized in Cytoscape  (Shannon et al.
, 2003). P-values of < 0. 05 were considered statistically significant for all analyses [MaxQuant software (v. 1. 6. 0. 1) using a peptide FDR of 0. 01]. For the metagenomic analysis, barplots were plotted using ggplot2 to visualize features overview. Differential representation analysis was performed using the indicspecies package. Alpha diversity was calculated using the absolute number of observed features (Sobs), and the Shannon index and groups were compared using the non-parametric Kruskal-Wallis tests. Beta-diversity ordination was calculated by either Bray-Curtis distances and non-metric dimensional scale (NMDS) or, alternatively, by imputing the 0s in the abundance matrices, CLR-transform them, and running principal component analysis. Significant contrasts were evaluated using the Adonis package in R.
Intestinal pathobiome Genomic material from Bacillus sp. (n = 3), haemolytic E. coli (n = 7), Enterobacter cloacae (n = 1), Enterococcus spp. (n = 10), Klebsiella pneumoniae (n = 7), Lactobacillus spp. (n = 11), and Streptococcus spp. (n = 9) was detected in feces from all calves (n = 25) regardless of whether calves were challenged with C. parvum or supplemented with colostrum (P > 0. 05, Table  4 ). All calves were PCR negative for E. coli K99, Salmonella spp. , and bovine rotaand coronavirus. By shotgun metagenomic sequencing assembled with previously reconstructed genomes  (Teseo et al. , 2022) , and newly assembled MAGs with the data from this study, the entire microbial community and predicted virulence features were analyzed. From the metagenomic reads, 1,338 MAGs were mapped, and their abundance examined across fecal samples of 0 and 6 dpc.
Acknowledgments The authors thank  Dr. Greg Muench  and  Greg Boorman  from  Veterinary Science Research Station  for their assistance and guidance. during the studies with calves, the team of students who assisted extensively with data collection, particularly  Benjamin Caddey  and  Cecilia Lucio Rodriguez. Deborah Schaefer  ( Research Specialist, School of Animal and Comparative Biomedical Sciences, University of Arizona ) for providing resources and guidance manipulating C. parvum oocytes. Immunofluorescence studies were conducted in the Live Cell Imaging Facility, The Calvin, Phoebe and  Joan Snyder Institute for Chronic Diseases, University of Calgary. Proteomic, liquid chromatography and mass spectrometry experiments were carried out by the  Southern Alberta Mass Spectrometry (SAMS) core facility, University of Calgary.
Stimuli The videos and voices were extracted from the GEMEP Core Set database (faces: 02sad, 02joy, 07sad, 07joy, 09sad and 09joy; voices: 02joy, 02sad, 06joy, 07joy, 07sad and 10joy -  (Bänziger et al. 2012 ), a validated psychological database for experimental research on emotion perception. The horses were therefore not familiar with either the faces or the voices. Pairs of videos of the same woman expressing sadness and joy were projected simultaneously, one on each projection screen, while the voice of another woman expressing sadness or joy was played over a loudspeaker placed between the projection screens (Fig. 1 ). The faces were projected at a height of approximately 160 cm and were approximately twice the size of a real human face (as in  Trösch et al. 2019; Jardat et al. 2022b ).
Behavioral and physiological analyses The videos of the test phase were analyzed with BORIS software (Friard and Gamba 2016) by a rst coder. The videos did not contain sound and the screens were not visible in the videos, so the coder was blind to the side on which each emotion appeared and the location of the congruent video. A horse was considered to look at the right screen when her left eye was visible from the camera placed under the right screen, and vice versa. She was considered to be attentive to the right screen when her right eye could also be seen from the camera placed under the right screen (i. e. , both eyes visible from this camera), with both of her ears oriented forward; and vice versa. Thirty percent of the videos (9 individuals) were analyzed again by a second coder to evaluate scoring reliability.
Interclass correlation coe cients (ICC) and their 95% con dence intervals were calculated; they were equal to 0. 822[0. 75,0. 88] for the looking duration and 0. 91[0. 86,0. 93] for the duration of being attentive to the videos, which are considered good to excellent reliabilities, respectively  (Koo and Li 2016). From the analysis of the rst coder, the following variables were quanti ed: for each section, the total looking duration and duration of being attentive to each video, the number of looks and the duration of the rst look toward each video, and the latency between the start of a section and the rst look toward each video. RR data were extracted from the Polar recordings. A visual correction was applied to eliminate artifactual beats (as recommended by von Borell et al. 2007, see Supplementary Information (SI) -Figure  S1 ).
Behavioral analysis The durations of looking at and of being attentive to each video, the number of looks, and the duration of the rst look toward each video were analyzed according to the congruence of the video with generalized linear mixed models (GLMMs) from the package glmmTMB (Brooks et al. 2017), using linear, Tweedie and Poisson distributions as appropriate for the given variable (see Table  1 ). The latency to look at the video was investigated according to the congruence of the video with the sound, and to the emotion displayed in the video, using survival analyses from the package coxme  (Therneau 2022). This allowed us to include in the analysis horses that did not look at one of the videos at all during a section. For each of the ve response variables, an initial model was constructed to assess the interaction effect of video-sound congruence with the emotion displayed in the video.
The side of the watched video was added to control for potential side biases. Horse identity was added as a random effect to account for individual variation in paired data, as each horse was tested six times. Distributions, within-group variance and homoscedasticity of the residuals for the GLMM were checked using the package DHARMa (Hartig 2021). For the survival analysis, outliers, the linear assumption and the proportionality assumption were evaluated graphically. If a horse did not look at one of the videos during the test, the latency to look at this video was set as censored (this happened 56 times). The best version of each model was selected according to model comparison via two-tailed ANOVA with the null model and simpler models (without the interaction, then without each variable of interest).
Heart-rate analysis Heart-rate variation was investigated with GLMMs from the package glmmTMB  (Brooks et al. 2017 ), using a generalized Poisson distribution (family=genpois). A model was constructed to assess the effect of the voice heard. Horse identity was added as a random effect to account for individual variation in paired data, as each horse was tested six times. Distributions, within-group variance and homoscedasticity of the residuals were checked using the package DHARMa  (Hartig 2021 ). The model was selected according to model comparison via two-tailed ANOVA with the null model (Table  1 , see SI -Table  S4  for the detailed results of the ANOVA).
Acknowledgements We would like to thank the staff from the  UEPAO (Unité Expérimentale de Physiologie Animale de l'Orfrasière)  for technical help; and the  CISA (Centre Interfacultaire en Sciences Affectives)  for allowing us to use the GEMEP Core Set. We also thank  Amanda Empeyte  and  Delphine Soulet  whose pictures were taken to make up Fig 2 ; and  Julie Lemarchand  for the second behavioral coding of 30% of our videos.
Behaviours, facial expressions and physiological parameters recorded during training Training sessions were recorded on video using a camera on tripods. Horses were recorded each time they traveled down a straightaway on the racetrack. The videos were analyzed with BORIS software  (Friard and Gamba, 2016)  to register behaviours and facial expressions. The ethogram used was adapted from  Dyson et al. (2018)  and is described in Table  2. The variables are presented as proportions of duration (100 × duration of behaviour/total duration of the video), except for head con ict behaviours, which are expressed as a frequency (number of occurrences/total duration of the video). Eye movements were not recorded because some horses wore blinkers that partially or totally concealed their eyes. The amount of sweat and salivation over time and [La], measured after each training session (in mmol/L; see 2.
Molecular Modeling of Norovirus P Domain Interaction with Methylated A Antigens In order to determine the potential influence of methyl groups decorating the terminal A antigen trisaccharide on the interactions between oyster saccharides and human noroviruses from different genogroups, the interaction maps of a GI. 1 (Norwalk virus) and GII. 3 (TV24) with non-methylated Human A-antigen were compared with those of monoand dimethylated epitopes identified in C. gigas and O. edulis. The X-ray crystallographic structures in the P domain of GI. 1 (PDB code 3D26) and GII. 3 (PDB code 6IS5) viruses in complex with A-trisaccharide or A-tetrasaccharide, respectively, were used as patterns. The glucose in the A-tetrasaccharide was removed and hydroxyls at position 3 of GalNAc and 4 of Fuc in the trisaccharides were substituted with a methyl group to model saccharides exposed on the surface of the oyster species Crassostrea gigas and Ostrea edulis.
Acknowledgments: We are grateful to  UAR 2014-US 41-Plateformes Lilloises en Biologie et Santé and PAGés-P3M  for providing the scientific and technical environment conducive to achieving this work. We also thank the  Academia Sinica Common Mass Spectrometry Facilities for Proteomics and Protein Modification Analysis  ( AS-CFII-108-107 ) for nanoLC-MS/MS data acquisition.
Funding: This research was funded and supported by the  French National Research Agency   ANR-ANR-19-CE35-0014  (GOyAVE) to YG, JLP and SLG;  Academia Sinica   AS-IR-111-01  to K-H. K. ; JSPS Core-to-Core Program Grant No. JRMJCR18H2  to H A. ; and the  International Research Network UGSF-iGCORE Glyco-Network (CNRS)  to YG.