dataesr/scientific-paragraphs-categorization huggingface dataset:
Data analysis Line comparison for egg metrics, yolk sex hormone and IgY levels and albumen lysozyme concentrations was performed separately within each selection experiment. All data were examined for fit to a normal distribution by the Kolmogorov-Smirnov test. The concentration and content of yolk sex hormones in all three selection experiments, the concentration and content of albumen lysozyme in the selection for TI duration and the concentration of yolk IgY in the selection for yolk T showed a deviation from normality and thus these data were logarithmically transformed. One-way analysis of variance (ANOVA) was used to compare the concentrations and total contents of yolk steroids in lines selected for TI duration and SR behaviour. Hierarchical ANOVA (with a fixed factor of line and a random factor of female nested within line) was used to compare external egg quality parameters, yolk IgY and albumen lysozyme in all three selection experiments and yolk T levels in the lines selected for yolk T concentration.
We demonstrated significant line differences in yolk IgY levels, but no consistent inter-line pattern between yolk IgY and T was found across three selection experiments. On the other hand, we recorded a consistent inverse inter-line pattern between yolk IgY and P 4 levels in both selections for behavioural traits. In addition, selections for the duration of TI and SR behaviour related to changes in albumen lysozyme concentrations, and a negative inter-line pattern between the deposition of yolk IgY and albumen lysozyme was found in lines selected for contrasting social motivation. Thus, our data support a mutually adjusted maternal deposition of sex hormones and immune-competent molecules, although there are probably more complex interrelationships between these parameters that can by shaped not only by genetic factors.
g. Gaillard et al. , 2000; Poissant et al. , 2008) , adult body mass was not correlated with annual age-specific survival. We measured body mass only once and the fact that body mass does not seem to be a relevant predictor of annual survival may reflect the inter-and intra-annual variability of this variable. Concerning horn size, the model with most support in the data showed a delayed cost of growing large horns: old females with longer horns tended to have a lower survival than old females with shorter horns. The absence of a highly significant effect may be due to a lack of power because of the sample size and/or because of the large annual variation in survival between years. Indeed, trade-offs are more easily detected in years with overall low survival. Further studies will be needed to better understand the influence of annual variations on horn growth and on the trade-off with survival.
ACKNOWLEDGEMENTS We thank the  Office National de la Chasse et de la Faune Sauvage (ONCFS) , and in particular  Jean-Michel Jullien  and  Thibaut Amblard , for collecting and managing data with the help of many volunteers. We are also grateful to  Mathieu Garel ,  Marco Festa-Bianchet  and two anonymous reviewers for their helpful comments. Funding sources:  Ministère de l'Enseignement Supérieur et de la Recherche ,  ONCFS  and  European Research Council.
Statistical analysis Data are expressed as mean 6 standard deviation. Time course of body weight was analyzed by a linear mixed effects model incorporating diet, time and diet by time interaction as fixed effects, cats within diet and cats by time interaction as random effects with a software (R Development Core Team (2009). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www. R-project. org). For other variables, standard repeated measures analyses were performed with another software package (Systat version 8. 0, SPSS Inc, Chicago, IL, USA) by use of the following generalized linear model: Y i,j,k ~mzdiet i zperiod j zdiet Ã period i,j zcat(diet) j,k z" i,j,k With: Y i,j,k being the value of variable Y for Cat k with diet i in Period j m being the general mean effect diet i being the effect of diet (i = HSD or CD) period j being the effect of period (j = 0, 3, 6, 12 or 24 months) diet*period i, j being the diet by period interaction term of the model cat(diet) j, k being the effect of cat nested in its diet group e i, j , k being the error of the model.
All these data led us to measure BP in all animals throughout the study period at Day 0 and then at 3, 6, 12, and 24 months in the present study, in order to assess the cardiovascular safety of one of the HS diets commercially available for cats with lower urinary tract diseases. No systemic arterial hypertension was found in any cat throughout the 24-month study period. No significant effect of the diet was detected either. The positive above-mentioned relationship between salt intake and the slope of the rise in BP with age reported in humans  [27]  was hence not found in these aged cats, which do not seem therefore to be sensitive to the deleterious vascular effects of excess dietary salt intake as observed in elderly people. These results are in accordance with those reported in this species in short-and medium-term feeding trials, showing that high salt feeding (2.
Acknowledgments The authors would like to sincerely thank  Samuel Ninet , Therese Fregier and  Philippe Bleis  (Oniris),  Thomas Daste  and  Amelie Dutech  ( National Veterinary School of Toulouse ) for their technical assistance during the animal phase,  Claude Germain  ( National Veterinary School of Toulouse ) and  Joe ¨lle Ribaut  ( National Veterinary School of Toulouse ) for administrative and logistical support.
coli strain, IPSID-1. Whole-genome shotgun sequencing was performed using an Illumina HiSeq 2000 sequencer. Libraries were constructed using Nextera technology and sequenced using a 2 ϫ 100 nucleotide paired-end strategy, leading to ~10,585,000 paired-read sequences. All reads were preprocessed to remove low-quality or artifactual bases. Library adapters, as well as base pairs occurring at 5= and 3= ends and supported by a Phred quality score Ͻ20, were trimmed off using AlienTrimmer  (6). Reads of length Ͻ95 bp after the above cleaning steps were discarded, as well as those containing Ͼ20% bp with a Phred score of Ͻ20. Finally, the program fqduplicate (ftp://ftp. pasteur. fr/pub/gensoft/projects /fqtools) was used to discard every duplicate paired-end read. The remaining reads (~6,682,000 paired-end and ~2,176,000 singleend) were assembled using clc_assembler (version 4.
10. 86742) from the CLC Genomics Workbench analysis package (http: //www. clcbio. com/products/clc-genomics-workbench/), with contig sequences of Ͻ500 nucleotides being discarded and with a de Bruijn graph k-mer parameter value of 57, which maximized the N 50 , N 75 , and N 90 values (i. e. , 302,476, 162,145, and 45,795 bp, respectively). A total of 64 contigs organized into 38 scaffolds were obtained, with a total length of 1,683,384 bp. An average coverage depth of ~920ϫ was obtained. The GϩC content of the genome sequence is 31. 26%. The sequences were submitted to the RAST server  (7)  for gene prediction and annotation, which led to 1,766 proteincoding sequences, 40 detected tRNA genes, and 3 rRNA regions. Extraction of gene sequences corresponding to the Campylobacter multilocus sequence typing scheme  (8)  showed that IPSID-1 belongs to sequence type 4956 (ST-4956), which is associated with clonal complex 828.
The genome comprises genes homologous to the cdtABC cluster, coding for cytolethal distending toxin  (9, 10). The availability of the draft genome sequence of C. coli IPSID-1, the first Campylobacter isolate from a case of IPSID, will contribute to a better understanding of the pathophysiological mechanisms of this disease. Nucleotide sequence accession numbers. This whole-genome shotgun project has been deposited at DDBJ/EMBL/GenBank under the accession no. CBXC000000000. The version described in this paper is the first version, CBXC01000000.
Fragment sizes converted to tandem repeat unit (U) numbers were imported into BioNumerics v6. 6 (Applied Maths NV) as a character data set. The cluster analysis was performed using the UPGMA algorithm (Unweighted Pair Group Method Algorithm) with Euclidean distance matrices. All loci do not evolve at the same evolution rate. Genetic diversity (Hunter-Gaston diversity index [HGDI]) was therefore calculated (http://www. hpa-bioinformatics. org. uk/cgi-bin/DICI/ DICI. pl). Values of the HGDI can range from 0. 0 (no diversity) to 1. 0 (complete diversity). A Minimum Spanning Tree (MST) was constructed with a categorical coefficient (with 1/HGDI weight) to determine the evolution minimum path from one strain to all others on the network. The obtained MLVA patterns were compared into the database Brucella2012, hosted by University Paris Sud (Orsay, France): http://mlva.
Genotypes and Clusters The complete MLVA-16 (16 loci) was performed on nearly one third of the isolates of the study, i. e. 25 field strains. The unique MLVA-11 pattern (panel 1 and panel 2A) obtained (panel 1 with for bruce06, bruce08, bruce11, bruce12, bruce42, bruce43, bruce45 and bruce55: 3 U, 5 U, 3 U, 13 U, 1 U, 1 U, 3 U and 3 U respectively and panel 2A with for bruce18, bruce19 and bruce21: 7 U, 42 U and 8 U respectively) was compared in the database Brucella2012. It belonged to the West Mediterranean group (genotype 96), with the MLVA-8 (only panel 1) genotype 51. MLVA-panel2B assay allowed clustering the 77 investigated strains (excluding the reference strains) into 61 genotypes (Fig. 2 ). With a similarity cut-off of 98%, the strains were distributed into 7 main clusters (Fig. 2 ).
Acknowledgments The authors would like thank  Moulay-Ali Cherfa  and  Antoine Drapeau  for their technical support,  Gabriela Vecchio  for her logistic expertise,  Jean Hars  and  Jean-Marie Le Horgne  for their expertise in wildlife diseases. We are grateful to the  Anonymous Reviewer of PLoS ONE ,  Karine Laroucau ,  Sylviane Derzelle  and  Marı ´a Laura Boschiroli  for critically reviewing the manuscript and for their supportive comments to improve the paper.
Statistical analysis Estimation of the genetic parameters was performed by considering the back-cross population as a single breed population. The breed proportions are taken into account by the genetic relationship matrix in the model including SNP information but could not be taken into account in the model including pedigree information only due to convergence failure. The heritability of each trait and both the genetic correlation and phenotypic correlation between each pair of traits were estimated by bivariate animal mixed models, which were solved by the AIREML procedure implemented in AIRemlF90 software  [39]. This software features by default the correction for the change in the definition of the base population so that the estimates obtained when using pedigree information only were comparable to those obtained when including molecular information  [40, 41].
Significance tests The parameters under study are the ratio of two normally distributed variables (heritability) and the ratio between a gaussian variable over the square root of the product of two gaussian variables (correlations). The sampling distribution of heritability can be approximated to a gaussian distribution under certain specific conditions only  [45]. When these conditions are filled, the significance test for gaussian variables can be applied. However, the significance tests for the correlation coefficient can be developed by deriving its confidence interval according to Fisher's Z-transformation  [46]. Otherwise, both parameters can be tested by using a re-sampling procedure such as delete-d jackknife: where d is the number of observations randomly discarded from the dataset and n 1/2 < d < n (n is the total number of observations in the dataset)  [47].
One thousand sub-samples of the whole dataset were created by randomly discarding 20% of the observations. Each parameter computed above was reestimated from each sub-sample in order to build its empirical distribution. The empirical distribution of each parameter was used to compute the confidence interval of each parameter by taking its 2. 5% quantile as the lower bound and its 97. 5% quantile as the upper bound of each estimate. The null hypothesis "the estimate is not different from 0" was tested as follows: if the confidence interval of the estimate included 0, then the null hypothesis was not rejected, otherwise the alternative hypothesis "the estimate is different from 0" was accepted. In order to test whether the SNPED and SNP models converged to the same estimate, the distribution of the difference between the PED and SNPED estimates was built for each estimate as follows: ½d i ¼½Ped i -Snped i ; where Ped i is the vector containing the n realizations of the estimate obtained from the PED model, Snped i is the vector containing the n realizations of the estimate obtained from the SNPED model, d i the vector of the differences between and each element of Ped i and Snped i.
The increase in precision can be explained by the fact that SNP information allows to compute more precisely than pedigree information what proportion of genome two individuals actually have in common. The pedigree structure in the data available for this study was not ideal to estimate genetic parameters by pedigree information only because all observations are recorded on a population of animals composed of four groups of half-sibs. Such a structure causes the pedigree-based relationship matrix to predict that within each group of half-sibs all animals share one quarter of the sire's genome. Whereas, the marker-based relationship matrix allows capturing the segregation variance, which means capturing the random deviation of the proportion of genes shared by two individuals around the expected proportion of shared genes according to the pedigree  [35].
Acknowledgements The reviewers of this article are acknowledged for their constructive comments and helpful suggestions. Dr Stephen Bishop  is also acknowledged for contributing to the language review. This study was funded by a  Marie Curie  grant within the  "NEMATODE SYSTEM HEALTH " project ( FP7-PEOPLE-2010-ITN-ID:264639 ). The data set was available thanks to the technical staff at the farm  La Sapinière  and at the  Nouzilly experimental unit, LABOGENA , the  French Region Centre , the  Animal Genetics department division of INRA ,  ANR , and  APISGENE  ( SheepSNPQTL  project, Toulouse, France) and  EU  grants ( FEOGA 024/m/1873 , Osmoy, France).
Statistical Analysis All results were reported as means 6 se. Kolmogorov-Smirnov tests determined whether data were normally distributed or not. Behaviours related to reactivity to human, novel object and novel environment as well as food preferences assessed by the choice tests were analysed using non-parametric statistics. URNS effect, genetic effect, and URNS effect within each line were tested with Mann-Whitney tests. The relative preference for the hypercaloric diet, measured as a proportion of the total intake during preference tests, was compared to random (50%) using a Wilcoxon test to determine whether a preference existed. Data related to feed intake (on the short term and daily), growth, and behavioural data that were normally distributed (reactivity to novel food and restraint in a crush cage) were analysed with ANOVAs.
Background MicroRNAs (miRNAs) are an abundant class of small single-stranded non-coding RNA molecules of 18 to 24 nucleotides generated by a sequential processing of long RNA transcripts by two specific RNAse III proteins: Dicer and Drosha  [1]. Studies performed during the past decade have led to the discovery of many miRNAs in almost all organisms  [2]. Over 8000 predicted miRNAs from plants, animals and viruses are currently described in miRBase, the reference database. It has been shown that miRNAs negatively regulate gene expression at the post-transcriptional level by targeting specific mRNAs for cleavage or translational repression trough the RNAinduced silencing complex (RISC)  [3]. They play key roles in many biological processes, including development, cell proliferation, differentiation as well as apoptosis and have been shown to be involved in a number physiopathological processes  [4].
miRNAs annotation Equine mature miRNAs annotations were extracted from the miRBase database (http://www. mirbase. org/). In order to extend annotations, human, bovine, dog, pig and mouse miRNAs precursor sequences were aligned to the horse genome by BLAST (version2. 2. 25). Hits with a bit score higher than 90 were conserved and IntersectBed (tool of BedTools) was used to annotate each candidate miRNA. Manual cleaning and curation was performed to correct or improve nomenclature. For putative new miRNAs, we applied the follow filtering criteria: a size between 17 and 29 bases, a count greater than or equal to 1000 in at least one experimental library and a free energy of the secondary structure calculated by RNAfold lower than -20 kcal/mol, which is characteristic of known miRNAs RNAfold scores.
Statistical analysis Statistical analysis for determining differential expression was performed with R v2. 15. 0  [25]  using the Bioconductor package DESeq version 1. 0. 0  [26]. DESeq utilizes a negative binomial distribution for modeling read counts per miRNA and implements a method for normalizing the counts. This normalization procedure uses the library median of the ratios between the read count and the geometric mean of each gene as a scaling factor for each library. The p-values were adjusted for multiple testing using the Benjamini and Hochberg method  [27] ). A comprehensive description of miRNA expression patterns was performed by principal component analysis (PCA) and between-class analysis (PCA of the table of the group means, BCA). The R software (version 1. 4. 17) was used with the R package ade4 (Version 1.
Experimental mechanical loading Two explants from each horse were subjected to compressive stress using a Flexercell FX-4000C system, whereas two other explants were left in unloaded conditions to serve as controls. Prostaglandin E2 (PGE2) release in the media (pg/mg/ml of explant) was measured to evaluate compression efficiency. In agreement with previous data obtained on mouse costal cartilage explants  [31] , a significant two-fold induction was observed, on average, between compressed and uncompressed samples (Supplementary data). No significant differences were observed between compressed predisposed and healthy samples.
Identification and characterization of miRNAs in equine bone and cartilage tissues Total RNA and small RNA were extracted in parallel. RIN between 7. 5 and 8 were observed for cartilage samples, whereas RIN in the range 8. 5 to 9 were obtained for bone samples. Thirty libraries were built and sequenced using the SOLID™ technology, generating 9 to 15 million reads for each library. All sequence data can be retrieved from the ArrayExpress database under accession number E-MTAB-2736. After adaptor trimming and size selection, about 70% of these reads were mapped to the EquCab2 horse genome for each sample, defining about 53998 to 97964 regions. Among these regions, 327 and 329 in cartilage and bone, respectively, overlapped with previously known mammalian miRNA (Additional file 2: Table  S1 -A, and Additional file 3: Table  S1 -B).
C-Annotated miRNAs differentially expressed between healthy and predisposed cartilage and bone. In cartilage, 5 annotated miRNAs were down-regulated whereas in bone, 8 annotated miRNAs were upregulated. Those data suggest a role of miRNAs in OC physiopathology. contrast to bone where 40 miRNAs were up-regulated and only a single miRNA species was found to be downregulated (Figure  B, C ). Six differentially expressed miRNAs (three in each tissue) were also studied by qPCR on a panel of 5 OC-affected and 5 healthy foals to confirm RNA-seq results. Expression and differential trends were confirmed in all cases, but 2 miRNAs failed to reach statistical significance (supplementary data). We also investigated changes in miRNA expression induced by mechanical stress. Nineteen differentially expressed miRNAs were identified in cartilage (2 annotated) and 21 in bone (2 annotated).
The purpose of this study was to perform a functional validation of this QTL region. Based on the within-family QTL detection results, some BC sheep were selected to produce BCxBC progeny that would carry two alleles associated with resistance or two alleles associated with susceptibility. BCxBC progenies were subsequently selected based on their genotypes for the investigated QTL and submitted to an exhaustive parasitological and haematological data collection. A gene expression study on the top ten differentially expressed genes between resistant MBB and susceptible RMN sheep highlighted in a previous microarray experiment  [12]  and on a candidate gene lying within the QTL region was also performed.
Production of the R and N sheep To investigate the biological properties of the identified QTL region, a marker-assisted mating of BC sheep was performed to produce lambs carrying particular combination of QTL alleles, i. e. RR, RN or NN. BC sheep were selected according to the QTL allele they carried. Chromosomes of every BC sheep were reconstructed using their 50 K SNP genotypes (described in  [19] ) and the LinkPHASE software  [28] , so that the QTL region could be traced from pure breed grand-parents to BC lambs. Two BC sires with RN genotype and one BC sire with NN genotype were selected for mating with 73 BC ewes (45 NN, 26 RN and two RR ewes). Because of their low frequency, the S alleles were not segregating in the remaining BC population. To randomize as much as possible the distribution of the other QTL in the BCxBC progenies, the three sires were mated to RN and NN ewes.
Sorting R and N sheep according to the association analysis BCxBC sheep were genotyped with the 50 K ovine SNP chip (Illumina Inc, San Diego, CA, USA) and the same workflow as applied for their parents SNP data (described in  [19] ) was followed to select genotypes of interest. After data processing, 85 NN, 32 RN and 13 RR BCxBC progenies were counted. We retained the 54 sheep (22 NN, 20 RN and 12 RR) for which the chance of having inherited the QTL fragment from their ancestors was the highest. We finally compared the NN sheep (N group) against carriers of the R allele (R group, composed of the RN and RR sheep).
Statistical analyses Strong departures from normality were found for FEC data (using the UNIVARIATE procedure implemented in the SAS software, SAS 2001, Cary NC) that were corrected by a 4 th root transformation. Resistance to nematodes is known to be polygenic and other QTL did segregate in this population  [19]. Therefore, a genomic value (gEBV) evaluating the effect of the genomic background on FEC at first infection was estimated for each BCxBC sheep. FEC was considered as a proxy for resistance to nematodes in order to maximize the data available for estimating the gEBV by using the whole set of BC and BCxBC data (n = 1200 records). The computation was performed with the Bayes C genomic selection method  [35]  implemented in the GS3 software  [36]. FEC was modelled as the sum of a mean, the already identified environmental fixed effects  [19] , i.