dataesr/scientific-paragraphs-categorization huggingface dataset:
| D ISCUSS I ON Our findings show that ontogenetic allometry between horn length and body mass varies across bovid species and is influenced by the adult body size of the species. In small species, horns grow at a much faster rate than body mass, but stop growing early compared to body mass. In large species, the ontogenetic allometry remains steeper than isometry, but horns grow much slower relative to body mass than in the small species, and this growth carries on for a longer period, sometimes as long as body mass increases. These differences in ontogenetic allometry generate a nonlinear evolutionary allometry similar to the one previously reported  (Tidière et al. , 2017). It is noteworthy that the evolutionary allometries in both sexes obtained with the current dataset are very similar to those reported by Tidière et al.
Transcriptomic profiling of H. contortus communities from resistant and susceptible sheep Pools of 50 male worms were collected from four susceptible and four resistant G1 sheep and snap-frozen in liquid nitrogen. DNA and RNA were simultaneously extracted using the AllPrep DNA/RNA/miRNA Universal Kit Ⓡ (Qiagen, UK) following manufacturer's instructions on two pools of 10 worms and an additional pool of 30 worms, yielding three replicates per sheep. RNAs were sequenced on two lanes of HiSeq v4 using 75-bp paired-end reads. Adapter-trimmed reads were mapped onto the H. contortus genome v4  (Doyle et al. , 2020)  using STAR v2. 5  (Dobin et al. , 2013). Read counts were subsequently normalized for length and GC content biases using the cqn package v1. 28. Differentially expressed genes between worms from resistant and susceptible sheep were identified by DESeq2 v1.
22. 1  (Love, Huber, & Anders, 2014)  and VOOM  (Law, Chen, Shi, & Smyth, 2014)  as implemented in limma v3. 38. 3. Genes with absolute foldchange higher than 1 and with Bonferroni-Hochberg  (Benjamini & Hochberg, 1995)  adjusted Pvalues below 5% were regarded as significantly differentially expressed. To avoid relying on a single read mapping pipeline, pseudo-alignment was also implemented with Salmon v0. 11. 3  (Patro, Duggal, Love, Irizarry, & Kingsford, 2017)  correcting for GC content, position-specific and sequence-specific biases using dedicated options (--gcBias, --seqBias, --posBias respectively). Quantification files were imported using the tximport package v1. 10  (Soneson, Love, & Robinson, 2015)  before identification of differentially expressed genes as described above. The intersecting set of differentially expressed genes between the three pipelines were retained as significant.
Fold-changes and associated P-values estimated from STAR counts with the VOOM frameworks were reported, as estimates in this case were in general more conservative. Genomic positions, protein domains and predicted orthologs of differentially expressed genes in Caenorhabditis elegans were retrieved from WormBaseParasite v13. Validation of the differentially expressed genes was run on independent pools of worms collected from three resistant and three susceptible G2 lambs. For each sheep genetic background, RNA was extracted from 6 pools of 50 worms using an RNeasy ® kit (Macherey-Nagel). Total RNA was used for oligo(dT) cDNA synthesis (SuperScript ® III First-Strand Synthesis System, ThermoFisher, France, 18080051). The resulting cDNA was diluted 1:250 for quantitative PCR and 1 µl added to each reaction.
qRT-PCR was carried out following the iQ SYBR GREEN supermix ® protocol (Biorad, France, 1708882) and was run in triplicate. Primers were designed using the Primer3Plus website and blasted against H. contortus genome  (Doyle et al. , 2020)  using the WormBase Parasite interface  (Howe, Bolt, Shafie, Kersey, & Berriman, 2017). Designed primers have been listed in supplementary Table  1. To identify genes with statistically different expression levels, measured Ct cycles were regressed upon gene, treatment group and their interaction, fitting house-keeping gene average Ct as a covariate and considering each biological replicate (n = 6 per gene) as a random effect. This mixed model was implemented with the R nlme package v. 3. 1-142 (Pinheiro, Bates, Debroy, & Sarkar, 2019). One sample (S-1) was an outlier and was removed from the analysis.
Genetic diversity of H. contortus communities from resistant and susceptible sheep Genomic data were mapped onto the latest H. contortus reference genome using smalt v 0. 7. 4 (ftp://ftp. sanger. ac. uk/pub/resources/software/smalt/smalt-manual-0. 7. 4. pdf) with 90% identity for alignment and a maximum insert size of 2000 bp (-y 0. 9 -i 2000 options respectively) and default k-mer length of 13. Bam files corresponding to worms collected from the same sheep were merged using the picard v2. 14. 0 MergeSamFiles tool. The resulting bam file was filtered further with samtools v0. 1. 19-44428cd to remove reads of poor quality (-q 20), with poor mapping quality (-Q 30), or unmapped (-F 0x4 -F 0x8) and to only retain properly mapped pairs (-f 0x2). Duplicate reads were removed using the Picard v2.
Response to selection To estimate phenotypic divergence in FEC between sheep lines, individual H. contortus infection data were pre-corrected for fixed environmental effects, i. e. sex, litter size, and generation (accounting for year effect), and an individual random effect, using the nlme package v3. 1-140  (Pinheiro et al. , 2019). Individual random effects were standardized to unselected G0 mean and standard deviation. Responses to selection for FEC at first, second infection or across infections were evaluated within each generation by regressing individual random effects from H. contortus infected offspring upon their respective midparent values, computed as the average value of each lamb's sire and dam  (Falconer & Mackay, 1996). This regression coefficient provides an estimate for realized heritability  (Falconer & Mackay, 1996)  and was used to establish the asymmetry of response between resistant and susceptible lines.
To estimate the expected genetic gain across infection, we computed the mean genetic gain between first and second infection. We considered pedigree-based breeding values (eBV) as they were available across generations (geBVs were only available for G0 and G1 individuals) and were strongly correlated with geBV. eBVs were estimated from recorded phenotypes in G0, G1 and G2 individuals using a mixed model including fixed environmental effects and a random individual effect estimated from the pedigree relationship matrix (encompassing 1559 individuals) as implemented in the AIReml software (http://nce. ads. uga. edu/wiki/doku. php?id=readme. aireml). Genetic gain was expressed in genetic standard deviation (-g) and was estimated within each line as the cross-product between the overall selection intensity (average of selection intensities in males and females) and the selection accuracy.
Genetic gains from both lines were summed to yield the expected genetic divergence. Of note, the genetic gain in G2 was obtained by summing the gain from the mating between G1 sires and G1 dams on one hand, and that from the matings between G1 sires and G0 dams on the other hand. To estimate putative trade-offs between resistance to GIN and lamb weight, measured body weights were modelled using a mixed model with repeated measures, including fixed effects (litter size, sex, generation, day post-infection, and an effect aggregating line and corresponding generation), and a random effect accounting for inter-individual variations. To account for differences in body weight at the beginning of the trial, weight data measured before any infection took place was fitted as a covariate.
91 and 0. 98). Physical contact records were however skewed toward 0 and were thus binary encoded, i. e. 0 or 1 in absence or presence of contact with their mates or with the operator, and modelled using a logistic regression framework. To test for the effect of behaviour treatment, recorded behaviour data were regressed upon sheep line, sex and day and their interactions, and a random effect accounting for inter-individual variation. Regression models were built following stepwise variable selection procedure that aims to find the model with minimal Akaike Information Criterion (AIC). Bleating records in phase 1 of the test were also corrected for their initial value before chronic stress treatment took place to correct for the increased bleating in susceptible lambs from the stress group.
2 ). Of note, T. colubriformis infection yielded fewer eggs (average FEC of 411 eggs/g across conditions, Another putative issue faced by the breeding of more resistant sheep, is the ability of parasites to adapt and circumvent the response of their host. To identify key parasite genes mediating survival of the host immune response, we compared the transcriptomic profiles of surviving H. contortus collected in both sheep backgrounds. On average 16 M (standard deviation of 5. 66 M) reads were available for each library. One replicate collected from sheep 229 behaved as an outlier (supplementary Fig. 4 ). This difference was driving 38% of total variation in normalized RNA-seq read counts as shown in a principal components analysis (supplementary Fig. 4 ). This replicate was subsequently discarded from the dataset.
Acknowledgements This project was funded by the  ANR   Institut Carnot Santé Animale program  ( GEMANEMA  project  N° 11 CARN 016-01 ). GS received a  Marie-Curie Agreenskills+ fellowship  (grant agreement  n°609398 ). NH and JAC were funded by the  Wellcome Sanger Institute  via their core funding of the  Wellcome Sanger Institute  (grant  206194 ).
64 2. 22E-02 -0. 75 0. 89 III 31,491,433 - Nematode cuticle collagen and - 31,496,242 Collagen triple helix repeat domain containing protein HCON_00090510 3. 97 2. 91E-03 -0. 74 4. 41 III 36,604,400 - Secreted clade V protein (IPR035126) - 36,605,347 HCON_00090580 2. 62 1. 48E-02 -0. 85 1. 12 III 36,632,125 - Secreted clade V protein (IPR035126) - 36,633,161 HCON_00132830 2. 77 6. 49E-04 -0. 87 1. 12 V 2,187,669 - - aqp-6 2,196,836 HCON_00182130 3. 12 3. 50E-04 -0. 88 3. 50 X 31,665,074 - Collagen protein col-160, col-166, col-167, col-168, 31,666,599 col-169, col-170, col-180.
, Fullerton, CA, USA), the libraries were normalized and pooled for sequencing on the MiSeq platform. Paired-end sequencing and automated cluster generation with dual indexed 2× 251-bp reads were performed. A total of 14. 5 Gb of information was obtained from a 1,168 k/mm2 cluster density, with a cluster quality control filter of 69. 1%. Subsequently, the reads of each sample were assembled using SPAdes software  [24]. Then, the highest number of contigs obtained from each sample was identified individually via a BLASTn-specific script against the NCBI database for nearly 5 hours. Finally, a simple-format Excel output containing the related species of each contig was generated. All positive reads identified in feces from wild T. swinderianus grasscutters were mapped with the M. ulcerans sequence (NC_008611.
ulcerans. We further investigated sediment specimens collected around the plant roots to specify the association between M. ulcerans and plant roots. Although limits in the specificity for detecting M. ulcerans DNA of the molecular detection method we used, have been previously reported  [20] , these limits may not prevent from comparing results from one sample to others. Accordingly, data showed that the prevalence of M. ulcerans DNA in roots was significantly higher than the prevalence in surrounding sediments. Accordingly, all previous studies indicated an absence of detection or the detection of low amounts of M. ulcerans DNA in soil in the Ivory Coast and Benin  [29]. Additionally, these field observations agreed with our previous observation regarding the rapid death of M. ulcerans in experimentally inoculated soil  [30].
This work was supported by  Agence Nationale de la Recherche  ( ANR-17-CE35-0006-01 PRIME , http://www. agencenationale-recherche. fr/) and by the  French Government  under the  « Investissements d'Avenir » (Investments for the Future) program  managed by the  ANR  (reference: Mediterranee Infection  10-IAHU-03 ), as well as the  Agence Nationale de la Recherche  ( ANR-17-CE35 -Author summary This work was supported by  Region Provence Alpes Cote d'Azur and European funding FEDER IHUBIOTK. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Ltd. (Shanghai, China). Raw fastq files were quality-filtered by Trimmomatic and merged by FLASH. Operational taxonomic units (OTU) were clustered with 0. 97 similarity cutoff using UPARSE (version 7. 1 http://drive5. com/uparse/). The taxonomy of each 16S rRNA gene sequence was analyzed by Ribosomal Database Project (RDP) Classifier algorithm (http://rdp. cme. msu. edu/) against the Silva (SSU123) 16S rRNA database using confidence threshold of 0. 70.
Calculations The apparent total tract digestibility (ATTD) of nutrients, total heat production (THP), retained energy (RE), and NE values in diets were calculated using the following equations  [12, 20] : ATTD = [(Fi -Ff)/Fi] × 100%, where Fi was total intake of energy (kJ) or nutrients (g), and Ff was the total fecal output of energy (kJ) or nutrients (g); THP = 16. 18 × O 2 + 5. 02 × CO 2 -2. 17 × CH 4 -5. 99 × urinary nitrogen excretion, where THP was in kilojoule, O 2 was oxygen consumption in liters, CO 2 was carbon dioxide production in liters, and CH 4 was methane production in liters. Urinary nitrogen excretion was in grams. The data collection for O 2 , CO 2 , and CH 4 used the standard procedure as described by Lyu et al. [11]. In addition, to eliminate the effects of activity of pigs, FHP was calculated using the equation for THP with gas data obtained from 22:30 h (d 19) to 06:30 h (d 20).
Statistical analysis All data of the experiment were averaged per pig and were analyzed using the MIXED procedure of SAS (SAS Institute Inc. , Cary, NC, USA). The experimental unit was the individual pig. Diet, enzyme and their interactions were treated as the fixed effects and collection, period and chamber as random effects. The LSMEANS statement with Tukey's adjustment was used to separate mean values. To eliminate the effect of ME intake, the THP and RE data were adjusted for each collection period by covariance analysis for ME intake of 2322 kJ/ (kg BW 0. 60 •d) (mean value for the experiment). A mixture of two fecal samples was used to analyze bacterial communities. Bacterial diversity was analyzed using standardized OTU reads with the help of R software. Relative abundance of bacteria at phylum and family levels were showed as bar plots.
Heatmap was used to analyze bacterial communities at genus level. The linear discriminant analysis (LDA) effect size (LEfSe) algorithm was adopted to classify abundances of differential bacteria from phylum to genus. The logarithmic LDA value of 2. 0 was used to be the criterion. The comparative analysis between OB-based diets (n = 12) and WB-based (n = 12) diets or between high-fiber diets (36% OB diets and 27% WB diets; n = 12) and low-fiber diets (15% OB diets and 12% WB diets; n = 12), was conducted based on the method of Welch's t-test. If P < 0. 05, it was considered significant difference. The contrast for data obtained in the enzyme-supplemented group to those obtained in enzyme-free group was also calculated using same method to validate the enzyme effect.
| Statistical analyses Power calculations were performed in G*Power freeware and based on historic RSS results assuming 80% power and two-sided significance using p <. 05 divided by the eventual number of comparisons (six comparisons between four diets creates a p <. 008). Historic average (measured between 2004 and 2014) of CaOx RSS mean ± SD on low sodium diets (~0. 3% sodium) was approximately three times as high as CaOx RSS on high sodium diets (~1. 3% sodium). This determined a group size of a minimum of eight animals. Because of animal availability, it was decided to use a total of 13 cats and eight dogs. SAS software was used for the remainder of the statistical analyses. A linear mixed model was used to assess the effect of diet (fixed effect in four levels) including cat or dog as a random term as each animal was its own control.
0 g/1,000 kcal. In cats, a retrospective study has also found lower CaOx RSS with diets containing >2. 8 g sodium/1,000 kcal  (Hawthorne & Markwell, 2004). However, a more recent prospective study failed to show an effect of dietary NaCl on CaOx RSS  (Passlack et al. , 2014). The four diets were formulated to contain sodium levels of 0. 9, 1. 6, 2. 8 and 3. 5 g/1,000 kcal, close to the levels in the present study. The methodology was similar except for longer adaptation (21 days) and collection (7 days) periods. Urinary ions were assayed by ion chromatography, and Supersat software was used to calculate RSS. This is an important factor to consider as other software solutions can over-or underestimate RSS  (Robertson et al. , 2002). Differences between the diet profiles might however explain the discrepancy between the results of this and our current study.
Statistical Analyses All statistical analyses were performed using the Statistical Analysis System (SAS) 9. 2 software (Cary, NC, USA). Piglet bodyweight was analyzed with the MIXED repeated procedure of SAS, using time as repeated factor, sow as random factor and sex, and parity and maternal diet as fixed factors. Milk composition was analyzed with the repeated MIXED procedure of SAS, including time (week) as repeated effect, sow as random effect and diet and parity as fixed factor. For the high-throughput qPCR, results were analyzed with the one-way ANOVA considering the maternal treatment as fixed factor. All averages are shown in mean ± SEM. Next, q-values were obtained using a false discovery rate correction with the linear method of Benjamini and Hochberg. Significance was obtained with q < 0.
Acknowledgments: The authors would like to thank the  Research Foundation for Industry and Agriculture, National Scientific Research Foundation (FRIA-FNRS)  and the  University of Liège  for funding this study. We thank the  feed company Dumoulin  for supplying feed. We also thank the  COST action SALAAM  ( BM1308 ) for funding the learning of the ex vivo technique. CRAw and the lab team are also acknowledged for their help during the animal experiment and laboratory analyses.
Recording of accelerometric data was managed in a regular breeding show in France over 3 years, in parallel with traditional scoring by judges. This allows for the genetic study of this trait and judgement scores, both of which can be used as selection criteria for jumping horses. The aim of this study was to evaluate the genetic component of the locomotor jumping ability measured by accelerometry in breeding shows and to estimate the genetic correlation with performances in competitions. This trait will be compared to judgement scores, traditionally used for selection in these breeding shows, to introduce such criteria in selection schema.
Horses and Jumping Data Collection Conditions In the breeding program of the Selle Français breed (SF), young horses aged 3 are routinely inspected for their conformation, gait, and jumping ability (free and under saddle) during breeding shows. For 3 years, from 2015 to 2017, the regional breeding shows with the largest number of horses were retained for measurements (four shows in 2015, three in 2016, and three in 2017). The data included 1,056 horses performing 3,125 jumps. The horses were all 3 years old. There were 58% females, 16% geldings, and 25% males. They were all SF except for 28 with only one SF parent, and 12 crossed foreign sport horses. During regular free jumping tests, all horses were equipped with a gait analysis device (Equimetrix R ) fixed with a leather pocket over the caudal part of the sternum at the level of the girth place by means of surcingle  (Barrey and Galloux, 1997; Barrey, 2014a).
Jump Analysis by Accelerometry The body acceleration close to the center of gravity of the horse was recorded with a 3-dimensional accelerometer connected to a microcomputer including large random accessory memory to record the data for up to 7 h (Equimetrix R ). All these components are installed in a small aluminum box: 50 × 25 × 110 mm, with a total weight of 240 g. The moment of the jump was analyzed using the jumping tool analysis of the software Equimetrix R on the dorsoventral acceleration visualization and verified with a video tape recording. Jumps with faults were discarded. Raw data included acceleration recorded at 100 Hz per axis in the dorsoventral, longitudinal and lateral axis of the horse from the forelimbs' last impact in the approach stride, to the FIGURE 1 | Definition of jump variables used in the analysis from an example of curve of dorso-ventral acceleration during jump with time.
Judgment Data During the free jump test, judgements of (1) balance, (2) strength, (3) style, and (4) reactivity were scored on a scale from 1 (poor) to 20 (excellent), but in practice from 10 to 19. Of the horses recorded for accelerometry, 1,045 obtained judgment scores. Similar scores were recorded during the jumping test under saddle. During the jumping test under saddle, the horse performed on a line with combinations of one obstacle up to 0. 50 m, followed by 13. 5 m, and a second obstacle, an oxer of 0. 90 m for the front pole and 1. 00 for back pole with a spread of 1. 00 m. The entrance was a trotting gait controlled by the ridder.
Competition Data To estimate genetic correlation between accelerometric variables and judgment in breeding shows, with the results in an official competition in show jumping, all horses that performed in competitions and were born from 1997 were added to the data. There were 160,257 horses and 649,491 annual performances. Performance in a competition was measured and normalized using the logarithm of the annual sum of points earned in each competition. Points allocated to the horse in a competition depended on the technical difficulty of the event and the rank of the horse. Technical difficulty of the event was assessed by the height of the obstacle, the level of the championship (departmental, regional, national), the category of the rider (amateur/professional), and the table used to score the horse (rules of the table of penalties A or C of the International Equestrian Federation).
Pedigree Data Pedigree was provided by the Institut Français du Cheval et de l'Equitation (IFCE), who are in charge of zootechnical recording of equids for the count of Stud Book of Selle Français (SBSF). Pedigrees ranging across four generations were added for a genetic analysis. Pedigrees were complete up to the second generation and completeness was 98. 3% at the fourth generation. The number of horses in the pedigree data was 353,236 when competition performance is included in the analysis and only 6,900 horses when analyzing only accelerometric data and judgment scores. The 1,056 horses with accelerometric data were issued from 274 sires (mean progeny per sire 3. 9, from 1 to 39; 63 sires had a progeny more than 5). There were 896 mares (maximum size of progeny 4), 386 maternal grand sires (mean grand progeny 2.
male and geldings, two levels), effect of month of birth (four levels: until March, April, May, June, and after June), effect of successive number of jumps (first, second, and third monitored jump, three levels). -For judgment scores: effect of jumping test location combined with date (10 breeding shows with three being over 2 days, so 13 levels), effect of gender (female vs. male and geldings, two levels), effect of month of birth (four levels). -For competition: effect of year of competition combined with age (125 levels), effect of gender (female vs. male and geldings, two levels) Wald F statistics tests for fixed effects were obtained using ASREML software  (Gilmour et al. , 2014)  in a preliminary univariate analysis to determine which fixed effects remained. Then WOMBAT software  (Meyer, 2007)  was used in a multivariate analysis.