dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding This work was funded by Institut Carnot Santé Animal 2015, by  Eurolap, Hycole , and Hypharm breeding companies, and by the French inter professional rabbit meat committee CLIPP. The research leading to these results also received funding from the  People Programme  ( Marie Curie Actions ) of the European Union's  Seventh Framework Programme  ( FP7/2007-2013)  under  REA  grant agreement n°  PCOFUND-GA-2013-609102 , through the  PRESTIGE program  coordinated by  Campus France.
3. 0  [47]. Semilandmarks were slid while minimizing the bending energy using the R package 'Morpho'  [48]. We used a generalized Procrustes analysis  [49]  to rotate, translate and scale the landmark configurations to obtain a new set of shape variables (Procrustes coordinates) and the centroid size (CS) of the calcaneus using 'Morpho'. CS is the square root of the sum of squared distances of the landmarks from their centroid.
Muscle force estimates To measure the covariation between calcaneus shape and the functional properties of the muscles attached to the calcaneus, we dissected the lateral gastrocnemius (LG), the medial gastrocnemius (MG) and the soleus (S) of 22 experimental captive-reared wild boars, four specimens from the control population, and eight wild boars from French populations (table 1, electronic supplementary material, SI 1). Muscle data were not available for other specimens. The muscles were weighed to the nearest gram and muscle fascicle length was measured with calipers. Based on the known density of mammalian muscle (1. 06 g cm -3 )  [52] , we calculated the physiological cross-sectional area (PCSA) as a proxy for muscle force by dividing the muscle volume by the fibre length  [53].
Comparing plastic size and shape response to captivity with changes induced by artificial selection Size differences between wild boars and domestic pig groups were tested using an ANOVA with the pairwise comparison tests (Bonferroni correction) and visualized with a box plot. The shape differences were tested with a Procrustes ANOVA and visualized using a Canonical Variate Analysis (CVA); both were performed on a shape-reduced dataset after a principal component analysis (PCA) performed on the Procrustes coordinates to keep 95% of the variance  [56]. To visualize shape deformations along the canonical axes, we calculated the theoretical minimum and maximum shapes for each axis, associated with a heatmap on landmarks corresponding to the distance between the minimum and maximum of the axis.
We also visualized the deformation along two vectors using the mean shape of wild-caught wild boars as a reference, towards the mean shape of captive-reared wild boars as the first vector and the mean shape of domestic pigs as the second vector. To test the shape difference depending on the main categories (GP2: wild boars/traditional pig breeds/improved pig breeds) and their sex, including the interaction of these two factors, we used a factorial MANOVA with a 1000 permutations procedure. All the statistics were performed using R  [57]. Factorial MANOVA, Procrustes ANOVA and PLS were performed using the R package 'geomorph'  [58]. ANOVA, PCA were performed using the package 'stats'  [57] , CVA using the package 'Morpho'  [48]  and visualizations were performed by using 'Morpho', 'stat' and 'plotrix'  [59].
Acknowledgements. We are most grateful to the director ( Roland Simon ) of the  Réserve Zoologique de la Haute-Touche  and its staff ( Christophe Audureau ,  Jérémy Bernard ,  Jérémy Coignet ,  Christophe Jubert ,  Fabien Kurek ,  Sandrine Laloux ,  Emmanuel Marechal ,  Régis Rabier ,  Patrick Roux ,  Colin Vion ) for their help during the set-up of the experimental structures and during the data acquisition, and the care they provided to the experimental specimens. Without their dedication and expertise, this research would not have been possible. We thank the CIRE platform at the  Institut National de la Recherche Agronomique (INRA) in Nouzilly  for the CT data acquisition ( François Lecompte ,  Hans Adriensen ) and their technical team ( Frédéric Elbout ,  Christian Moussu  and  Luc Perrigouard ) for their support during the CT acquisition and the handling of animals.
ACKNOWLEDGMENTS We thank  Fabrice Reigner  and his team for allowing us to use the animals and facilities and for their assistance in taking care of the horses. We also thank  Jean-Marie Yvon ,  Céline Parias ,  Julie Lemarchand , and  Ségolène Le Pivain  for their help during data collection. Finally, we want to thank the editor,  Dr. David Leavens  and the two reviewers for their constructive and helpful comments.
The animals were bedded on straw in individual box stalls and were turned out onto a dry lot for approximately 5 hours a day. The project was approved by the Ethics Committee for Animal Right Protection of the Leipzig District Government (No. TVV 32/15) in accordance with German legislation for animal rights and welfare. The experimental study was conducted over two years. During an adaption period, ponies and horses received meadow grass hay and a commercial mineral supplement to meet or exceed energy and nutrient requirements during maintenance according to the Society of Nutrition Physiology (GfE 2014)  [40]. The basal health status of the animals was assessed at the beginning of the trial (t0). Following this first data collection animals underwent a feeding period receiving 180% of their individual maintenance metabolizable energy requirements according to GfE (2014)  [40].
Scaling, Body Condition Score (BCS) and Cresty Neck Score (CNS) Body weight, BCS and CNS were measured weekly. An electronic large animal scale (scale system Iconix FX 1, Texas Trading, scale precision: 0. 5 kg) was used to determine BW. BCS was assessed on a scale from 0 to 5 as described by  Carrol and Huntington (1988)   [42]. The CNS was evaluated on a scale from 0 to 5 according to  Carter et al (2009)   [43]. Two trained evaluators performed both scores and the mean of their evaluation was calculated for data analyses.
Data analysis The sequencing data was processed on the Galaxy signenae workbench provided by INRA Toulouse using the Find Rapidly OTUs with Galaxy Solutions (FROGS) pipeline. First the sequences were cleaned through elimination of sequences shorter than 380 base pairs or longer than 490 base pairs, and those containing an unidentified base or not containing one of the primer sequences. Chimeras were identified using VSEARCH  [48]  and removed from further analysis. The remaining sequences were aligned in clusters with SWARM  [49]. Clusters that contained only one sequence were excluded from further analysis and the remaining clusters were grouped in operation taxonomic units (OTUs). The taxonomic assignment of the OTUs was performed with the Basic Local Alignment Search Tool (BLAST) provided by the National Centre for Biotechnology Information database.
The Shannon index and observed richness were calculated on the Galaxy signenae workbench. The further grouping of the data and the calculation of the relative abundance of OTUs were conducted with Excel (Office 365, Microsoft, Redmond, US). The Excel software programme was also used for the calculation of the coefficient of variation (CV) for crude protein (CP), crude lipid (CL), crude fibre (CF), neutral detergent fibre (aNDFom), sugar and starch intake to determine variations in nutrition intake. The CV was also calculated for the DM intake of hay during the study period. For the analysis of the CV the data were grouped in months for horses and for ponies. The CV was calculated by the mean nutrition intake for the last two months prior to sampling for horses and ponies at each sampling point and between the different sampling points.
(2018)   We also demonstrated that BW gain was associated with an increase of the phylum Actinobacteria in both breeds and a decrease of Fibrobacteres in the horse´s microbiota. These findings are in accordance with results obtained by  Morrison et al. (2018)   [9]. Between t1 and t2 the phylum Proteobacteria rose significantly in ponies and showed a significant drop between t2 and t3 while it remained unchanged in horses. From studies investigating the influence of a high fat ration (8. 3% fat) on the faecal microbiota of horses, it is known that a high fat ration may lead to an increase of the phylum Proteobacteria  [8]. Since we recorded a 22. 2% increase in crude lipid intake between t1 and t2 a dietary influence on the phylum Proteobacteria cannot be excluded. From our data the reasons for the changes in the phylum Proteobacteria in ponies but not in horses are not fully understood.
The Protocol The framework of the protocol  [23]  was the Welfare Quality ® concept of "5 freedoms" that have given rise to four principles and 12 criteria: good feeding (i. e. , absence of thirst and hunger); good housing (comfort around resting, thermal comfort and ease of movement), good health (absence of injuries, disease and pain) and appropriate behavior (expression of social behavior, other behaviors, good human-animal relationship and positive emotional state) (see  [46] ). The AWIN work started with an examination of the database on animal-based welfare indicators since 1980, including peer-reviewed publications, proceedings and abstracts in English, and to place them within the Welfare Quality ® frame. Experts examined this database, with the aim of identifying which indicators were valid (in accordance with what they were supposed to measure), reliable (repeatability in time, within and between assessors) and feasible (practical likelihood of using this indicator during on-farm inspection).
Observations were performed when the stables were quiet, outside feeding and working times. The experimenter walked slowly and regularly (1 step/second) in the middle of the corridor, or 2 m away from the boxes in stables with one line of boxes. She approached each box slowly, in order to be able to see the ear positions through the trough opening or box door, remaining at a distance. The instantaneous ear position of the feeding horse was silently noted (only if the horse kept feeding and paid no attention to the observer). The observer then resumed her walk along the midline up to the next stall. These samplings were made every day, for three consecutive days, and distributed all along the day until 10 and 20 ear positions were obtained per horse. The percentage of scans in each position was calculated for each horse, and we analyzed the animals' ranking in relation to the number of data obtained: the results show that 10 measures were as representative as 20 for the ears positions: backward (rs = 0.
8 log 10 cfu/g of sample, confidence level of 95%, and power of 95%. The POWER procedure of SAS (version 9. 0, SAS Institute Inc. , Cary, NC) was used to perform the statistical analysis. Birth BW was recorded before moving calves to individual and disinfected indoor pens (150 × 125 cm) bedded with a consistent amount of fresh straw to facilitate nesting. Newborns were randomly assigned according to their date of birth and initial BW to a unique pen referring to 1 of the 2 dietary treatments for 1 wk. All calves were then dried with clean towels for 10 min and navel-dipped using 7% iodine before the start of any procedures. Animals included in this study presented healthy at birth based on general appearance, rectal temperature, umbilical appearance, and nasal discharge; none were vaccinated or treated with therapeutic fluids during the trial.
The real-time (RT)-qPCR analysis was performed to evaluate gene expression of PIGR encoding for the Polymeric Immunoglobulin Receptor and of CD79A encoding for B-cell antigen receptor complex-associated protein α chain using primers reported in Table  1. We chose ACTB and GAPDH as the housekeeping genes out of 4 genes tested with all of the mucosal tissue samples (ACTB, GAPDH, ribosomal protein large P0, and 18S rRNA) using geNorm analysis  (Vandesompele et al. , 2002). The cDNA was synthesized by using iScript reverse transcription Supermix from Bio-Rad (Bio-Rad Laboratories, Hercules, CA) and used for RT-qPCR analysis. The RT-qPCR was performed using 1 µL of 50 ng/ µL of amplified RNA as the template and the following program: 95°C for 15 s, followed by 40 cycles of 95°C for 3 s and 60°C for 30 s in ViiA 7 Real-Time PCR (Applied Biosystems, Foster City, CA).
Statistical Analysis To determine the effect of SCB supplementation, all data were analyzed using the MIXED procedure of SAS software. For serum total protein, IgG, and IgA concentrations, repeated measures of time were used with the model, including the fixed effects of dietary treatment, time, and treatment by time interaction. The fixed effects of parity and the mode of colostrum feeding (tubing or bottle-fed) were tested but were not included in the model because they were not significant. For the bacterial targets (Bifidobacterium, Lactobacillus, E. coli, F. prausnitzii, and total bacteria), the sIgA concentration, and the number of IgA+ plasma cells, data were analyzed with gut location as the repeated measure and calf as the experimental unit using compound symmetry covariance structure, which was selected as the best fit by the Bayesian information criterion.
ACKNOWLEDGMENTS We thank the team, including  André Luis Nagatani Rigueiro , for their dedicated time during this newborn experiment and for their contributions to this research. We also thank the staff of the  Dairy Research and Technology Centre (University of Alberta, Edmonton, AB)  for their assistance with the animal experiment,  Amanda Fischer  for the revision of the manuscript, as well as  Jan Shivers  from  University of Minnesota (Minneapolis, MN ) for her technical assistance. This study was supported by funding from  Lallemand Health Solution (Mirabel, QC) ,  Alberta Milk (Edmonton, AB) , the  Saskatoon Colostrum Co. Ltd. (Saskatoon, SK)  and the  Natural Sciences and Engineering Research Council of Canada (Ottawa, ON). Clothilde Villot  was supported by a  Mitacs Accelerate Program from Mitacs Canada (Toronto, ON)  and  Lallemand (Blagnac, France).
Results Here, we report using PacBio long read and OpGen and 10X Genomics long-molecule methods to generate a highly contiguous 283. 4 Mbp chromosome-scale genome assembly including a resolved sex chromosome. We show a remarkable pattern of almost complete conservation of chromosome content (synteny) with Caenorhabditis elegans , but almost no conservation of gene order. Long-read transcriptome sequence data has allowed us to define coordinated transcriptional regulation throughout the life cycle of the parasite, and refine our understanding of cisand trans -splicing relative to that observed in C. elegans. Finally, we use this assembly to give a comprehensive picture of chromosome-wide genetic diversity both within a single isolate and globally.
Using a hybrid assembly approach incorporating short and long-read sequencing followed by manual finishing, we demonstrate how a highly contiguous assembly overcomes some of the critical limitations imposed by draft assemblies in interpreting large-scale genetic and functional genomics datasets. We provide insight into patterns of transcriptional change and putative co-regulation using a significantly improved genome annotation, derived de novo from both short-and long-read cDNA sequencing, and describe within-and between-population genetic diversity that shapes the genome. This manually refined chromosome-scale assembly now offers insight into genome evolution among a broad group of important parasite species and offers a robust scaffold for genome-wide analyses of important parasite traits such as anthelmintic resistance.
Data is visualised using the R package circulize. D. Haplotype switching between chromosome and additional haplotypes present in the genome assembly. Whole-genome sequencing was performed on individual worms (n = 11; MHco3(ISE). N1 strain, used in the genome assembly), after which reads were mapped competitively against the chromosomes and haplotype sequences. Normalised read depth coverage is presented, which was calculated per 100 kbp relative to the "maximum genome-wide coverage" set at the 95% quantile to prevent high-coverage outlier regions from skewing coverage estimates. Blue = ~100% of maximum coverage; Yellow = ~50% of maximum coverage; Red = ~ 0% of maximum coverage.
Transcriptional dynamics throughout development and between sexes Both 2013 publications describing draft assemblies of H. contortus provide extensive descriptions of the key developmental transitions between sequential pairs of life stages throughout the life cycle  [21, 22]. We revisited this in the current annotation, and for visual comparison, we present the top 1,000 most variable transcripts across all life stage samples in the MHco3(ISE). N1 datasets ( Figure  4  A; see Additional file 2: Table  S8  for all differentially expressed genes from pairwise comparisons of the life stages throughout the life cycle). Most striking was the transcriptional transition between the juvenile free-living life stages and the more mature parasitic stages, reflecting not only the development toward reproductive maturity but also significant changes in the parasite's environment.
N1 individuals, only a proportion of the total diversity will be captured. Long-read sequencing data (e. g. PacBio or Oxford Nanopore) from Haemonchus populations and bioinformatics approaches designed to capture more complex variation should help reveal the full extent of genetic variation in this species and shed light on the role of structural variation in shaping this genetic variation. Our high-quality annotation allowed us to extend previous pairwise transcriptomic comparisons and focus on the trajectories of genes with correlated expression throughout the life cycle. These data identified gene sets that are putatively co-regulated, providing greater insight into coordinated developmental programs than has been described previously. The broad-scale coordinated gene expression associated with ion transport, channel activity, and transmembrane signalling receptor activity throughout the lifecycle was of interest, particularly as ion channels and receptors are the primary targets of a number of anthelmintic drugs used to control H.
Optical mapping was performed following methods described previously  [14]  with the following exceptions: high molecular weight DNA was prepared by proteinase K lysis of pools of ~500 H. contortus L3 embedded in agarose plugs, after which one of three restriction enzymes (KpnI, AflII and KpnI) were used to generate three separate restriction map datasets. Initial attempts to generate a de novo assembly using optical molecules alone was unsuccessful, and therefore, optical contigs were generated using DNA sequence seeds from the genome assembly using GenomeBuilder (OpGen) and visualised and manually curated using AssemblyViewer (OpGen). Although this approach was successful, it was limited by the quality and integrity of the gap-dense scaffolds and arbitrary nature of the haplotype scaffolding.
Subsequent integration of PacBio long-read data alongside the optical mapping data resulted in major increases in contiguity. PacBio sequencing libraries were prepared and sequenced using the PacBio RSII system. A total of 32. 3 Gbp raw subreads (n = 4,085,541, N50 = 10,299 bp) were generated from 33 flow cells, representing approximately 133. 8× coverage (based on the estimated genome size of 283 Mbp). A de novo PacBio assembly was generated using Sprai (v0. 9. 9. 18; http://zombie. cb. k. u-tokyo. ac. jp/sprai/index. html ), which was mapped to the assembly and used to manually resolve many gaps and resolve some of the phasing between haplotypes. Using the Sprai PacBio de novo assembly, we were also able to incorporate contigs that were previously missing from the working assembly. The increase in contiguity of the PacBio assemblies, further improved using canu v1.
Haplotype analyses The identification and discrimination of the major and minor haplotypes in our assembly offered an opportunity to characterise the distribution of haplotypes throughout the genome, and the relative diversity between them. To visualise the broad-scale variation between the chromosome and haplotypes, we generated an approximate genome graph using minigraph ( https://github. com/lh3/minigraph ; parameters: -xggs). The output GFA file was visualised using Bandage  [80]  ; a representative set of haplotypes were chosen for further comparison, which were compared using nucmer and visualised using Genome Ribbon  [81]. To quantify the distribution and diversity of haplotypes, we used minimap to align haplotypes per chromosome. Haplotype density was calculated in 1 Mbp windows throughout the genome using bedtools coverage  [82]  and visualised using the R package circlize  [83].
Gene model prediction and genome annotation A schematic of the strategy used for genome annotation is presented in Additional file 1: Figure  S6. Initial attempts to transfer genome annotations from the V1 assembly to the intermediate V3 assembly using tools such as Ratt  [87]  were unsuccessful, and with the generation of new Iso-Seq data, a new strategy was designed and implemented. Existing RNAseq data were individually mapped to the V4 genome using STAR v2. 5. 2  [88]  , before combined into a single bam using samtools merge. The merged bam was as input to Braker (version 2. 0;  [35, 89]  , which can exploit mapped RNAseq information as hints to train the gene prediction tools GeneMark-EX and Augustus , substantially improving genome annotation. The use of exon, intron, and other hints derived from RNAseq or proteins mapped to the genome can be used to train Augustus directly; initial attempts to incorporate mapped Iso-Seq transcripts as an input to Braker , or by training Augustus using exon and intron hints, were successful and produced superior gene models compared with Braker using RNASeq alone.
However, as Iso-Seq data encode full-length transcripts that include UTRs in a single sequence, and that in this data there was little discrimination between the depth of coverage between UTR and exon, many UTRs were incorrectly annotated as 5' or 3' exons. As such, only short-read RNAseq data were used as input to Braker , resulting in a primary annotation (GFF 1 ; Additional file 1: Figure  S6  ). To incorporate Iso-Seq data into the genome annotation, we used the PASA (v2. 2. 0) genome annotation tool  [90]  to map the high-quality full-length transcripts to the genome using both blat and gmap , akin to the use of a de novo transcriptome assembly produced from, for example, trinity , to generate a transcript database ( https://github. com/PASApipeline/PASApipeline/wiki/PASA_comprehensive_db ).
The Braker GFF 1 was subsequently loaded into PASA and integrated with the mapped Iso-Seq transcripts to produce the first round merged annotation (GFF 2 ; Additional file 1: Figure  S6  ). To incorporate further coding evidence into the annotation, we used EvidenceModeller v1. 1. 1  [36]  to apply a differential weighting schema to multiple evidence sources, before merging them into a single genome annotation. Evidence used included the GFF ABINITIO_PREDICTION (GFF 1 from Braker) = 2; TRANSCRIPT (GFF 2 from PASA) = 5. The merged output of EvidenceModeller (GFF 3 ) was imported back into PASA to update gene models with the full-length transcript information, including updates to UTRs and evidence for alternative splicing. Consistent with the author's recommendations, two rounds of iterative update and comparison were performed to maximise the use of the full-length transcript, i.
Annotation statistics were determined using Genome Annotation Generator (GAG)  [92]. Annotation comparisons to a curated gene set to determine the sensitivity and specificity of the approaches used were performed using gffcompare v0. 10. 1 ( http://ccb. jhu. edu/software/stringtie/gffcompare. shtml ). The repetitive content of the genome was analysed using RepeatModeller (v1. 0. 11) followed by RepeatMasker (v4. 0. 7). Further analysis of LTRs was performed using LTRharvest and LTRdigest  [93]  from the GenomeTools v1. 5. 9  [94]  package.