dataesr/scientific-paragraphs-categorization huggingface dataset:
MLVA is a convenient first line assay for outbreak investigations, fast quality check of strain identity in a collection, and identification of outliers, i. e. , strains which should be sequenced in priority as they may represent new lineages. With more than 5,000 strains in the current version of the online database, it is likely that most of the existing genotype diversity has now been uncovered and that the discovery of new MLVA11 genotypes will be limited. From a practical point of view, MLVA can be run with a variety of equipment including regular agarose gels  (Le Flèche et al. , 2006) , monoplex capillary electrophoresis  (De Santis et al. , 2013) , or multiplex PCR multicolor capillary electrophoresis  (Garofolo et al. , 2013). Target loci can be selected to suit a specific epidemiological background.
Acknowledgements The authors are grateful to the staff of the  Institut Français du Cheval et de l'Equitation (IFCE) experimental  farm ( Domaine de la Valade, Chamberet, France ) for care and management of animals and assistance during sampling, metabolic tests and foalings. The authors would also like to thank  Catherine Rey from ProfileXpert (Service de Génomique & Microgénomique, Université Lyon 1 ,  SFR santé Lyon-EST ,  UCBL-INSERM US 7-CNRS UMS 3453 , 8 avenue Rockfeller  69373 Lyon Cedex 08 ) for RNA extraction and RT-qPCR approach. Finally, this study was funded by the  Institut Français du Cheval et de l'équitation (IFCE).
Breeding sheep for enhanced resistance to gastrointestinal parasites is a promising strategy to limit the use of anthelmintics due to the now widespread resistance of parasites to these molecules. This paper reports the genetic parameters estimated for parasite resistance and resilience traits in the Blond-faced Manech dairy sheep breed and the putative impacts of the selection for resistance to gastrointestinal nematodes (GIN) on farms. Two datasets were used. First, the rams of the selection scheme were artificially infected twice with L3 Haemonchus contortus larvae. Faecal egg counts (FEC) and packed cell volume (PCV) loss were measured 30 days after each infection. Secondly, the FEC, PCV and body condition score (BCS) (1-6 measures per ewe) of naturally infected ewes on farms were measured in the spring, summer and autumn over a two-year period.
Genetic parameters were estimated for each dataset independently but also globally based on the pedigree connections between the two datasets. For the experimentally infected sires, the FEC following the second infection was moderately heritable (heritability: 0. 35) and strongly correlated with FEC after the first infection (genetic correlation: 0. 92). For the naturally infected ewes, FEC was also heritable (0. 18). Using the two datasets together, a genetic correlation of 0. 56-0. 71 was estimated between the FEC values of the experimentally infected rams and naturally infected ewes. Consequently, the genetic variability of parasite resistance is similar whatever the physiological status (males or milking/pregnant ewes) and the infection conditions (experimental infection with one parasite or natural infection with several parasites).
, 2004). Faecal and blood samples were collected to measure FEC and PCV. The body condition score (BCS) was assessed by the same operator at each sampling date using a scale from 0 to 5 described by  Russel et al. (1969). In the course of the 6 visits to each farm, the ewes were phenotyped 1 to 6 times. Around 1300 measured values were available for each trait. Ewes were treated by farmers one to three times a year using one of these molecules: eprinomectin, ivermectin, oxfendazole, doramectin or moxidectin. The type of treatment and the interval between the date of FEC measurement and the date of treatment prior to FEC measurement were recorded. The relationship between the dataset of the experimentally infected rams and the dataset of the naturally infected dairy ewes is shown in Fig. 2.
Data analysis Data distributions and descriptive statistics were obtained using the univariate procedure of SAS (SAS Institute, Cary, NC, USA). After testing several transformations, a fourth root transformation was applied to correct FEC data for asymmetric distribution. The transformed variables, thereafter referred to as root_FEC_inf1 and root_FEC_inf2 for rams and root_FEC for ewes, were used for the subsequent analyses. Linear mixed models were used to identify the environmental, genetic and residual effects affecting FEC, PCV and BCS. For the 603 experimentally infected rams, data were analysed using a linear mixed model including the contemporary group (7 groups described above) and age (2yr, 3yr, 4yr and older) as fixed effects, a random additive genetic effect u ij of the animal i for the trait j following a normal distribution with mean 0 and variance σ u 2 , and a random residual effect ε ij following a normal distribution with mean 0 and var- iance σ e 2.
The random effects of the model for the ewe i and the trait j are the random additive genetic effect u ij and the residual effect ε ij and a permanent environment effect pe i. These random effects follow a normal distribution with mean 0 and variance σ σ and σ , u e p e 2 2 2 respectively. Then, the total phenotypic variance is a sum of the variances between ewes (due to genetic effect σ u 2 and unknown environmental effects σ e 2 ) and the variance between the measures of a given ewe (due to unknown environmental effects affecting the repeated measures of a given ewe σ pe 2 ). The pedigree information used to estimate the genetic parameters included three-generation pedigrees (2054 animals). The correlations between the FEC values for the 603 experimentally Fig. 2. Relationship between the dataset of the 603 experimentally infected rams and the dataset of the 400 naturally infected dairy ewes.
infected rams and the naturally infected dairy ewes were estimated by combining the two linear mixed models described above. The pedigree file included three generations for a total of 3723 animals. For both datasets, the fixed and random effects, genetic variances, permanent environment and residual variances (σ u 2 , σ pe 2 , σ e 2 ) and covariances were estimated by average information restricted maximum likelihood using AIREMLF90  (Misztal, 1999)   2 for the naturally infected ewes), and the genetic and phenotypic correlations. The estimates of genetic, permanent environment and residual variances were checked by expectationmaximization REML using REMLF90 software  (Misztal, 1999). The standard errors of genetic parameters were estimated using the method of  Houle and Meyer (2015). A significant value of genetic parameters was assumed when it was higher than its standard error.
The proportion of variance due to permanent environment effects was low (3% of the total variance of FEC). The heritability and repeatability were not significantly different which demonstrates that the similarities between the measurements for an animal are due to its genetic background rather than its immune history. The genetic correlations between FEC after the first and second infections (FEC_inf1 and FEC_inf2) for the 603 rams and FEC measured in the 400 naturally infected dairy ewes were high and positive (0. 71 and 0. 56, respectively) (Table  5 ). The correlation between the sires' genetic values based on their own FEC values after the second infection and the sires' genetic values based on the FEC of the dairy ewes was also estimated and was 0. 87 (Supplementary data -Fig. S1 ). After dividing the 103 rams with offspring measured for FEC into two groups, 178 ewes out of the 400 naturally infected ewes were born to susceptible sires and 222 were born to resistant sires.
Acknowledgements The authors would like to thank  CDEO  (breeders' association for Blond-faced Manech sheep) and the breeders from  French Basque  country who participated in this study. This work was supported by  Aquitaine region (PSDR-INGEDICO program );  France Génétique Elevage (FENOPAR program ); and  FranceAgriMer and CASDAR (PARASEL program  n°220164581). S. Aguerre receives a grant from  Midi-Pyrénées region  and from the  INRA Animal Health and Animal Genetics departments.
A second round of sequencing was performed with 75 bp paired-end reads to increase the coverage of 43 samples (Supporting Fig. 15 ). After sequencing, two samples were identified to be heavily contaminated by kraken-0. 10. 6-a2d113dc8f  58  were discarded. In total, 18 sequencing lanes consisting of 4,152,170,256 reads were sequenced, the raw data of which are archived under the ENA study accession PRJEB9837.
Sequencing data processing Read mapping to both the mitochondrial and nuclear genomes (v3. 0, available at ftp://ngs. sanger. ac. uk/production/pathogens/Haemonchus_contortus) was performed using SMALT (http://www. sanger. ac. uk/science/tools/smalt-0) with a median insert size of 500 bp, k-mer length of 13 bp, and a stringency of 90%. For samples that had two or more BAM files (when split across multiple sequencing lanes), the BAM files were merged using samtools v. 0. 1. 19-44428  59 , and duplicated reads removed using Picard v. 2_14_0 (https://github. com/broadinstitute/picard) before performing realignment around indels using Genome Analysis Toolkit (GATK v3. 6)  60  RealignerTargetCreator. Mean coverage of the mitochondrial and genomic genomes were estimated using GATK DepthOfCoverage, revealing a lower than the estimated target coverage (original 8x) for most samples.
29 (https://gatkforums. broadinstitute. org/gatk/discussion/39/variant-quality-scorerecalibration-vqsr), which first uses a reference (truth) SNP set to estimate the covariance between called SNP quality score annotations and SNP probabilities, followed by application of these probabilities to the raw SNPs of interest. The reference "truth" SNP database was generated from the intersection of variants called from samples with at least a mean of 10× coverage (n = 13) using three independent SNP callers: (i) samtools mpileup (-q20 -Q20 -C50 -uD), (ii) Freebayes 61 v. 9. 9. 2-13-gad718f9-dirty (--min-mapping-quality 20 --min-alternatecount 5 --no-indels --min-alternate-qsum 40 --pvar 0. 0001 --use-mapping-quality -posterior-integration-limits 1,3 --genotype-variant-threshold 4 --use-mapping-quality --site-selection-max-iterations 3 --genotyping-max-iterations 25 --max-complex-gap 3), and (iii) GATK HaplotypeCaller followed by hard filtering (--QD<2, --DP>10000, --FS>60, --MQ<40, --MQRS <-12.
5, --RPRS<-8). The three variant call sets were merged (GATK CombineVariants with --genotypeMergeOptions UNIQUIFY), resulting in an intersecting set of 794,606 SNPs (extracted with GATK SelectVariants). The GATK VariantRecalibrator model was trained with this reference SNP database with a 90% prior likelihood, before being subsequently applied to the raw set of SNPs (n = 30,040,159). The estimation was run for several truth sensitivity threshold values ranging from 90 to 99. 9%. After visual inspection of the additional number of SNPs by using sensitivity tranche curves (Supplementary Fig. 16a ), a 97% sensitivity threshold was applied to the raw SNP set with GATK ApplyRecalibration, resulting in a total set of 23,868,644 SNPs spanning the five autosomes. Variant depth of coverage (DP) and strand bias (FS) were the main drivers of SNP removal (Supplementary Fig.
16b ). Called SNPs were used for particular analyses (Ne trajectory through time, crosspopulation composite likelihood-ratio) that could not be performed under the probabilistic framework that relied on genotype likelihoods as implemented in ANGSD 62 v. 0. 919-20-gb988fab (Supplementary note). These data were also used to estimate average differentiation between populations. However, within population diversity and admixture were analysed using ANGSD  62  (Supplementary note).
Mitochondrial DNA data processing The mitochondrial genome exhibited an average coverage depth of 322× (ranging from 24x to 5,868x) per sample (Supplementary Table  1 ). Mitochondrial reads were extracted and filtered from poorly mapped reads using samtools view (-q 20 -f 0x0002 -F 0x0004 -F 0x0008) and from duplicated reads using Picard v. 2_14_0 MarkDuplicates (https://github. com/broadinstitute/picard). Realignment around indels was applied with GATK  63  and SNP were subsequently called using samtools  64  mpileup using only reads that acheived a mapping quality of 30 and base quality of 30. The occurrence of heterozygous sites in mtDNA, known as heteroplasmy, has been described across vertebrate species  65  and in other nematode species, including C. briggsae  66  and C. elegans  67.
http://dx. doi. org/10. 1101/450692 doi: bioRxiv preprint first posted online Oct. 24, 2018;  of the putative SNP sites (3,052 out of 4,234 SNPs). Nucleotide diversity and Tajima's D were computed by sliding-windows of 100 bp using vcftools v. 0. 1. 15  69. A principal component analysis (PCA) was performed on genotypes using the SNPrelate package  70  in R version 3. 5  71. A consensus fasta sequence was subsequently generated with GATK FastaAlternateReferenceMaker for each sample using the filtered variant set, which was used for the phylogenetic analyses.
24, 2018;  population cross-coverage (Pearson's r (136) = -0. 05, P = 0. 55; Supplementary Fig. 18 ). The pairwise sequence divergence between individual samples was calculated using the Hamming distance, i. e. 1-IBS, using PLINK  74  , considering the only individuals with a mean coverage above 2. 5× as failure to do so yielded biased estimates (Supplementary note, Supplementary Fig. 19 ). A neighbour-joining tree of Hamming distances calculated from the nuclear DNA genotypes was built using the R package ape  75. PCA on genotypes inferred from genotype likelihoods of the 223 samples was generated using ANGSD ngsCovar, filtering for sites with base and mapping quality phred scores less than 30, minimum depth of 5 reads, and a SNP p-value (as computed by ANGSD) below 10 -3. Clustering was robust to coverage variation and closely matched the PCA from the VQSR-called SNP genotypes (Supplementary Fig.
Phylogenetic inference To determine the phylogenetic structure of the cohort, the 223 consensus mitochondrial fasta sequences, including the mtDNA genomes of Haemonchus placei (accession number AP017687. 1) and Teladorsagia circumcincta (accession number AP017699. 1) as outgroup sequences, were first aligned using Muscle v3. 8. 31  76  , followed by stringent trimming of sequence alignments using Gblocks  77. The most likely evolutionary model, GTR substitution model with rate heterogeneity modelled by a gamma distribution with invariable sites, was determined using modelgenerator v. 0. 851  78. A maximum-likelihood tree was subsequently generated using PhyML 79 v. 20120412, with branch supports computed using 100 bootstraps.
Gene identification and Gene Ontology enrichment analysis Annotation of the revised H. contortus genome is ongoing. Therefore, genes underlying major differentiation signals were inferred by blasting the region of interest (10 Kbp window for F ST analyses, 2000 bp region around XP-CLR hits) against the published V1 H. contortus genome assembly  13. Any genes falling within most probable blast hit coordinates were retrieved from the H. contortus published assembly available from WormBase Parasite  87. This database was also used to retrieve C. elegans orthologs of positional candidate genes and H. contortus Gene Ontology (GO) terms. GO term enrichment analysis was run with the R topGO  95  package, considering nodes with at least 10 annotated genes. The "weight01" algorithm was used to account for existing topology between GO terms.
Acknowledgements JAC, MB, NH, and SD are supported by the  Wellcome Trust  via their core funding of the  Wellcome Trust Sanger Institute  (grant  206194 ). GS has received the support of the  EU  in the framework of the  Marie-Curie FP7 COFUND People Programme , through the award of an  AgreenSkills  (grant agreement n°  267196 ) and  AgreenSkills+ fellowships  (grant agreement  n°609398 ).
Data Analysis All the collected data: age, gender, and parasitological results, were reported on an Excel sheet. Prevalence of infection (percentage of infected schoolchildren among those examined) was estimated for each parasite, for each parasite group: schistosomiasis and STH and for the combined schistosomiasis and STH, at the overall, regional, departmental, school, school category (public/private), school location (urban/rural), and gender levels. The 95% confidence intervals (CI) for prevalence were calculated using the exact method in the software R version 3. 2. 2. Arithmetic mean intensities of infection (number of egg per infested schoolchild) with standard deviations (SD) for each parasite species were estimated, including only the positives schoolchildren  [23]. The Chi squared or Fisher exact tests were used to compare the prevalence differences in relation to the region, gender, school category (private/public), and school area (urban/rural), while the non-parametric Wilcoxon or Mann-Whitney rank sum test were used to compare differences in mean intensities of infection using R version 3.
Acknowledgments: The authors are grateful to the both Regional Directors of Health:  Dr. Guikombi Jean Réné  in the North and  Ms. Mbeng Mba Félicité  in the East for planning and communication work in the field. We want to thank all the participants in this study, particularly the schoolchildren and their parents, the directors and teachers of the schools surveyed. We are very grateful to  Lilody Ikoutsi Gertrude ,  Ollomo Nziengui Fabrice ,  Oniane Nicole ,  Emane Alain Georges ,  Nzamba Boulingui Antoine Chyder , Minko mi  Engone Gui , and  Inguimba Elvis  for the technical supports and assistance.
Acknowledgements This research was supported by grants from  FONACIT  No. 2004000400 RIDMOH,  Misión Ciencia2007001425 , Ecos NordPI-2008002104,  GID21-USB ,  Instituto Venezolano de Investigaciones Científicas (IVIC)  No. 1365. We thank to  Dr. Howard Takiff  from  Instituto Venezolano de Investigaciones Científicas  for critically reading this manuscript.
We analyzed its whole-genome sequence in order to describe its genomic content and help develop molecular identification tools. Nocardia sp. strain S-137 was subcultured on Colombia agar incorporating 5% sheep's blood (bioMérieux, Marcy l'Etoile, France) at 37°C. DNA extracted using an EZ1 biorobot and an EZ1 DNA tissue kit (Qiagen, Courtaboeuf, France) for Ͻ50 L volume, was quantified by a Qubit assay with a high-sensitivity kit (Life Technologies, Carlsbad, CA, USA) to 23. 1 ng/L. DNA was then sequenced using MiSeq technology (Illumina, Inc. , San Diego, CA, USA) with paired-end and mate pair applications. The index representation for strain S-137 was 6. 98%. A total of 866,229 paired-end reads were filtered per read qualities and assembled using SPAdes software  (5). Contigs were combined by using SSPACE (6), GapFiller  (7) , and manual finishing using similarity searches and synteny block detection.
The strain S-137 draft genome was assembled into 25 scaffolds composed of 142 contigs, with a total size of 9,465,473 bp and a GC content of 67. 1%. Automatic annotation was performed with Prokka version 1. 12 (8). The genome presents two repeat regions and is predicted to encode 8,658 genes, including 8,582 protein-coding genes and 76 RNA genes, including 66 tRNAs, 1 transfer-messenger RNA, 3 5S rRNAs, 3 23S rRNAs, and 3 16S rRNAs. The three 16S rRNA gene copies exhibit a total of 17 nucleotide differences and were compared with the reference N. brasiliensis genome (GenBank accession number CP022088), resulting in a similarity score of 98% for one copy, while the two other copies match with 99% similarity. Based on the 16S rRNA gene sequence proximity, genomes were selected and incorporated into in silico DNA-DNA hybridization (DDH)  (9).
Determination of prevalences of oocyst shedding at different time points We used a subset of data obtained from experimental infections of neonatal piglets (n = 134 piglets) from previous studies with known infection days, onset and duration of excretion as determined by daily sampling and qualitative and quantitative oocyst excretion  [8]. Over a period of 14 days (4th to 17th day after infection, i. e. during the patent period) the course of oocyst excretion and faecal consistency was determined for 117 piglets. Assuming that piglets can become infected for the first time from the first day of life until weaning we modelled 23 subgroups (n = 5-8 randomly allocated animals/group with a total of 134 modelled individuals) with primo infections on the 1st to 23rd day of life. This time span was chosen because the minimum pre-patent period for C.
Owners were asked to check the warmth and liquid Two types of assessment methods were used in the study: daily diary entries (from D-7 to D42) and the weekly Oakland Feline Social Interaction Scale (OFSIS) questionnaire (see Appendix 2 in the supplementary material). Daily dairy entries consisted of answering two questions pertaining to the occurrence of aggressive events or fights between the cats in the household, whether they had aggressive encounters or fought that day, and, if so, how many times aggressive behaviors or fighting were observed. Owners were instructed to have the same person complete the diary throughout the study, though input and observations from all family members were allowed. Owners were asked to fax or email diaries weekly to the investigator. In addition to the daily diary, a weekly questionnaire was used to gain more specific data on the types of aggressive interactions in the home.
OFSIS assessments and the daily diary were returned to the investigator on D0, D7, D14, D21 and D28. Any concerns were clarified during the telephone interview. On D28, owners were also asked 'Generally, do you find your cats are getting along better?'. A trained technician conducted the phone interviews and recorded a yes/no polar conclusion. Upon conclusion of the D28 telephone interview, owners were instructed to remove the diffusers. To assess any potential post-treatment effect of the pheromone, daily diaries were maintained and two additional weekly OFSIS telephone interviews were conducted on D35 and D42. Statistical analysis SAS 9. 3 software (SAS Institute) was used for the statistical analyses. Results are expressed as mean ± SD or n (%). Tests were two-sided and as this was a pilot study the significance threshold was set at 0.
Amplification was performed in a 30 cycle PCR using the HotStarTaq Plus Master Mix Kit (Qiagen, Valencia, CA, USA) under the following conditions: 94 °C for 3 min followed by 28 cycles of 94 °C for 30 s, 53 °C for 40 s and 72 °C for 1 min, after which a final elongation step at 72 °C for 5 min was performed. Pooled and purified PCR products were used to prepare DNA library following Illumina TruSeq DNA library preparation protocol. Sequencing was performed on a MiSeq following the manufacturer's guidelines. The sequencing data are uploaded into the Sequence Read Archive (SRA) of NCBI (http://www. ncbi. nlm. nih. gov/sra) and can be accessed through accession number SRR5515885. Metadata file used for processing the sequencing data and conducting statistical analyses can be found in Supplementary Table  S3.
Bioinformatics analyses. The FLASH assembler (v. 1. 2. 11)  45  was used to merge overlapping paired-end Illumina fastq files. The output fastq file was then analyzed using QIIME v. 1. 8  46. Chimeric reads were filtered using UCHIME  47  and sequences were assigned to Operational Taxonomic Units (OTUs) using the QIIME implementation of UCLUST 48 at 97% pairwise identity threshold. An even depth of 20,000 sequences per sample was used for calculation of richness (observed OTUs and Chao1 estimates of species richness) or diversity (Shannon's index of diversity) indices for cecum and proximal colon samples. Within-sample true diversity (effective counts of Shannon's index) was calculated according to Jost  49. β-diversity of microbial communities was calculated based on weighted UniFrac distances  50  and principal coordinate analysis (PCoA) was applied on resulting distance matrices to generate two-dimensional plots using the online package MicrobiomeAnalyst  51.