dataesr/scientific-paragraphs-categorization huggingface dataset:
Behavioral traits Arena and corridor tests were video recorded. Locomotor activity, vigilance postures, and behaviors to the flockmates or the human, were measured afterwards using the video software The Observer 6. 0 (Noldus). Locomotor activity was assessed by measuring the number of zones crossed during arena test phases 1 and 2 (AT1/2-LOCOM) and corridor test phase 1 (CT1-LOCOM). The overall degree of agitation during the isolation box test was measured objectively using an electronic 'agitation meter' (IBT-LOCOM). Vigilance postures (i. e. animal motionless, head in an upright position and ears perpendicular to the head) were measured during arena test phase 1 (AT1-VIGIL). The proximity to flock-mates and the human during arena test phase 2 was calculated using the following formula: AT2-PROX = (time spent in zone 1 × 0/6) + (time spent in zone 2 × 1/6) + … + (time spent in zone 7 × 6/6).
Statistical handling Deviations from normality of row data were tested using the Kolmogorov-Smirnov test (Univariate procedure of SAS, SAS Institute Inc. ). Several raw measures were transformed in order to minimize major deviations from the normal distribution. Square root transformation was applied to AT1/2-HBLEAT, CT1-HBLEAT, IBT-HBLEAT and IBT-LOCOM. Logarithmic transformation was applied to AT1/2-LBLEAT, CT1-LBLEAT and CORT. A multivariate analysis was performed to take into account the multidimensional aspect of behavioral responses. Principal component analysis (PCA), followed by orthogonal rotation was carried out using SAS® software (FACTOR procedure, version 8. 1, SAS® Institute Inc. , Cary, NC, USA). Factor scores were then calculated for each lamb and used in subsequent analyses.
In addition, we constructed four synthetic variables using PCA (PRINCOMP procedure in the SAS® software). Each PCA was performed for a set of similar behavioral variables across the three behavioral tests. The first component of each PCA, explaining the largest part of total variance, was defined as a synthetic variable. Three synthetic variables were specific to the reactivity to social isolation: high bleats (ISO_HBLEAT), locomotion (ISO_LOCOM) and low bleats (ISO_LBLEAT). One synthetic variable was specific to the reactivity to a human: the tolerance to being approached when the lamb was free to flee (HUMAPPRO). The correlations between original measurements and the 4 synthetic variables ranged between 0. 64 and 0. 93. The synthetic variables accounted for 46% to 87% of the total variability.
Analyses of variance using the GLM procedure of the SAS® software were performed on the original and synthetic variables to assess differences between the lambs for different rearing year, sex, litter size born and litter size reared, and the age of the mother. Phenotypes were then corrected for identified fixed effects prior to subsequent genetic analyses (Additional file 1). Heritability estimates were generated for each trait by restricted maximum likelihood (REML) methodology using univariate analyses with VCE 4. 2. 5 software (Neumaier and Groeneveld, 1998). Random effects in the mixed models included a direct genetic effect (animal), a maternal permanent environment effect and a litter permanent environment effect.
SNP genotypes 1038 lambs (out of the 1099 lambs phenotyped) were genotyped as well as their nine respective sires using the Illumina OvineSNP50 BeadChip (comprising 54,241 SNPs). Individuals with a call rate (i. e. number of called SNPs per sample over the total number of SNPs in the data set) below 98% and with Mendelian inconsistencies (i. e. no alleles shared between it's a sire and its progeny for a given SNP) were discarded (104 and 9 lambs, respectively). SNP quality was also checked as described by Sallé et al. [44]. 5448 SNPs with a call rate lower than 97%, a minor allele frequency below 1% or inconsistent Hardy-Weinberg disequilibrium were discarded. Furthermore, 3095 SNPs were discarded due to a too high recombination rate or because more than 50% of heterozygous sire's offspring were heterozygous.
Linkage analysis Data were analyzed using linkage analyses (LA) by interval mapping within each sire family. The presence of a QTL was tested against the null hypothesis (absence of a QTL) at every 0. 1-cM interval by likelihood computation. Chromosome-wise significance levels were computed for each chromosome and trait by testing with 3000 to 10,000 permutations  [51]. Genome-wise thresholds were obtained using the Bonferroni correction (1-P genome wise ) = (1-P chromosome wise ) n where n is the number of chromosomes (i. e. 26 autosomous chromosomes in sheep)  [52]. Confidence intervals were determined using the "2 lod drop off" criterion and assuming 1 LOD = 4. 61 LRT  [53].
Acknowledgements The authors would like to thank the  INRA Divisions both of Animal Genetics and of Animal Physiology and Livestock Systems  for their financial support over the years in providing salaries, experimental costs and computational facilities. The authors are indebted to  Paul Autran  and all the staff of the experimental farm  La Fage  for managing the experimental flock as well as for their active role in collecting experimental data. The authors also thank  Stéphane Andanson  and  Christine Ravel  of the UMRH for their investment in blood collection and the cortisol assay. Genotyping was funded by  ANR  and  APIS-GENE organizations as part of the  project  ANR-08-GENM-039 SheepSNPQTL.
Figure. Phylogenetic analysis of partial open reading frame (ORF) 2 and ORF1 sequences of hepatitis E virus (HEV). Phylogenetic trees were constructed in MEGA6 software (http://www. megasoftware. net) by using the neighbor-joining method from a Kimura 2-parameter distance matrix based on partial nucleotide sequences of ORF2 (A) and ORF1 (B). Bootstrap values obtained from 500 resamplings are shown. Sequences were retrieved from the serum of a 45-year-old woman in France in whom hepatitis E was diagnosed in December 2013 and from frozen leftovers of the figatellu she had eaten in October 2013. Sequences obtained from the figatellu (GenBank accession nos. KJ603858 and KJ603860) were 100% nt identical to sequences obtained from the index case-patient (GenBank accession nos. KJ603859 and KJ603861).
Figure. Figure. Phylogenetic analysis of partial open reading frame (ORF) 2 and ORF1 sequences of hepatitis E virus (HEV). Phylogenetic trees were constructed in MEGA6 software (http://www. megasoftware. net) by using the neighbor-joining method from a Kimura 2-parameter distance matrix based on partial nucleotide sequences of ORF2 (A) and ORF1 (B). Bootstrap values obtained from 500 resamplings are shown. Sequences were retrieved from the serum of a 45-year-old woman in France in whom hepatitis E was diagnosed in December 2013 and from frozen leftovers of the figatellu she had eaten in October 2013. Sequences obtained from the figatellu (GenBank accession nos. KJ603858 and KJ603860) were 100% nt identical to sequences obtained from the index case-patient (GenBank accession nos. KJ603859 and KJ603861).
Distribution of Belostomatidae and Naucoridae aquatic insect families Data were collected as described in  [15]  hereafter referred to as the SME dataset. In brief, 36 sample sites in Cameroon were visited monthly from September 2012 to February 2013 (Figure  1 ), a period including both wet and dry seasons. Dip net sampling was conducted at all sites. Due to limitations of current taxonomic keys, the aquatic insects of interest were only identifiable to the phylogenetic division of Family. A second dataset was used in model validation, using data collected separately by A. Garchitorena (Figure  1 ). This dataset was collected as described in  [16] , and is hereafter referred to as the AG dataset. Niche models of Belostomatidae and Naucoridae are constructed using the SME dataset, and then tested on their ability to reproduce the independent AG dataset.
Environmental parameters used in ecological niche modelling Five ecological parameters were used to describe the distribution of suitable habitats: rainfall, flow accumulation, wetness index, land cover at the sample site, and land cover within the flight range of adult insects. These variables were selected on the basis of their likelihood to influence the distribution and condition of water, and are summarised in Table  1. Rainfall was highly seasonal, so we divide models by the season of collection. Models constructed using species distribution data from the dry season used the precipitation in the driest season; models constructed using species distribution data from the wet season used the precipitation in the wettest season. These two variables were taken from the Worldclim database, as BIO13 Precipitation of Wettest Month and BIO14 Precipitation of Driest Month  [17].
Data preparation, niche modelling, and prediction of spatial distribution of suitable habitat Insect distribution data from the 36 sample sites (Figure  1 ) were explored to identify normality, homogeneity of variance and correlation in the five environmental parameters used. Two sites were excluded from analysis as apparent outliers in flow accumulation, otherwise the data were normally distributed and homogenous. Correlation was observed between flow accumulation and wetness index and between precipitation in the driest and in the wettest seasons, however this was not significant (p > 0. 05) using a Spearman's correlation test. All data were resampled to a spatial resolution of 0. 004 decimal degrees (~300 m 2 ) for modelling using resample() in the library 'raster' of the software R  [24].
Across the scale of West Africa we assume that absence data is not reliable, as it is more likely to indicate failure of detection rather than evidence of absence. For this reason, we chose to conduct presence-only modelling, and the specific method selected was Maximum Entropy  [25]. We used the software Maxent (Maxent 3. 3. 3 k,  [25] ) to construct these models. Maxent has been used several times in the past to model the ecological niche of disease vectors  [26]. Maximum entropy.
Herein, we generated background points in every raster cell within the extent of our study (that is, every environment was described). The models were replicated 100 times and averaged. In order to select an appropriate extent to the area considered for modelling we chose to confine the spatial extent of our study to the region ecologically interpolated by the sample sites (Figure  2 ). The sample sites of the SME dataset describe a particular range of environmental conditions for the five environmental parameters (Table  1 ). We excluded conditions that were not studied by the SME regime (much larger or lower values of rainfall, land cover categories not sampled, flow accumulation not observed at the study sites) to avoid extrapolating beyond the range of ecological parameter values studied.
Evaluating model performance We evaluated the performance of these models according to their ability to correctly predict the distribution of the insects (with a separate dataset, the AG dataset) as efficiently as possible (avoiding overfitting). We used two methods to evaluate the models. First, the Akaike information criterion, corrected for small sample size (AICc), was used. AICc was generated as in  [28] , and penalises complex models to avoid over-fitting the data, low AICc scores indicate the model is not over-fitting. Second, we tested the models ability to predict the AG dataset. The Area Under Curve (AUC) is often used to evaluate Maxent models, and has been criticized previously  [29]. We used a modified version of the AUC for model evaluation, termed here AG AUC. The model predictions were compared to the known values as collected by AG.
AG AUC values range from 0 to 1, values close to 1 indicate good performance, 0. 5 is no better than random. Use of the AG dataset allowed us to use true absence data for model validation, avoiding the problem of pseudo-absence data in Maxent. The purpose of these two different metrics is to consider different aspects of performance, neither were without Buruli ulcer was absent from certain villages (grey dots) where habitat suitability for the insects is high. Because this can skew any correlation between habitat suitability and prevalence, we explored the effect of including (thin lines) or excluding (thick lines) these villages. We note that in either case Spearman's rank correlation coefficient was significant. For Belostomatidae with Buruli ulcer absent villages p = 0. 08, without BU absent villages p = 0.
Identifying the relationship between habitat suitability and Buruli ulcer prevalence Buruli ulcer prevalence data was collected for two endemic regions in Cameroon, Akonolinga  [30]  and Bankim  [31] , as shown in Figure  3. Around the centre of each village a buffer was created, and average habitat suitability in this buffer was correlated to the village Buruli ulcer prevalence using Spearman's rank correlation coefficient. Seven buffers were used to explore the effect of buffer size and shape. Around the centre of the village circular buffers of 1, 2, 3, 4, 5 and 10 km were selected, and average habitat suitability recorded. We also used a buffer defined by the borders of the village (Figure  3 ). This buffer changes in size for each village, and represents the approximate extent of the village area.
Model performance AICc for Naucoridae adults (dry was 14. 6, and 14. 2 in the wet season, as in Table  2. For Belostomatidae adults (dry season) the AICc was 12. 5, and 12. 2 in the wet season. Scores of overfitting are relative; these scores indicate the Belostomatidae model was less prone to overfitting than the Naucoridae model. The AG data set was also used in model validation. In the dry season Naucoridae adults had an AG AUC of 0. 83, and 0. 80 in the wet season. Belostomatidae adults had an AG AUC of 0. 80 in the dry season, and 0. 86 in the wet season. These scores indicate that the models are able to describe the distribution of the insects with good accuracy; the model based on SME dataset is able to accurately replicate the independently collected AG dataset.
Acknowledgements An initial draft of this paper was reviewed by two anonymous referees, whom we sincerely thank for their insightful discussions on the subject. This work has benefited from funding by the  Laboratoire d'Excellence Centre d'Etude de la Biodiversité Amazonienne  (grant  ANR-10-LABX-25-01 ), by the  Young International Research Team JEAI ATOMyc (AIRD/IRD ), and by a grant from the  French National Research Agency  ( ANR 11 CEPL 007 04 EXTRA-MU ). Our grateful thanks are also extended to the staff of the  Institut de Recherche pour le Développement (IRD) ,  le Centre de coopération internationale en recherche agronomique pour le développement (CIRAD) , and  Institut Pasteur for discussion  and support in the research. K. Carolan was supported by the  ANR   EXTRA-MU  project , LabEx CEBA  and  JEAI ATOMyc.
S. Meyin À Ebong  was supported by a PhD studentship from  AIRD/IRD  and the  Centre Pasteur du Cameroun ,  J. Landier  was supported by a PhD studentship from  Institut Pasteur, Unité d'Epidemiologie de Maladies Emergentes , and  D. Sanhueza , was supported by a PhD studentship from  Becas Agencia de Cooperación Internacional de Chile (AGCI)  and the  BIOHOPSYS  programme from the  LabEx-CEBA ,  A. Garchitorena  was supported by a PhD studentship from the  École des hautes études en santé publique (EHESP).
Eosinophil granules induce the specific overexpression of Hco-pgp-3 and Hco-pgp-16 genes in exsheathed L3 larvae The Rh123 transport assay data suggest that eosinophil granule products may interact directly with Pgp transporters in H. contortus eggs. Previous studies of bacteria, mammals and nematodes report that Pgp substrates can specifically induce overexpression of their corresponding Pgp gene  [26] [27] [28] [29]. Taking advantage of this property, we examined which H. contortus Pgp genes respond specifically to host granule products in vivo. Expression of Hco-pgp-3, Hco-pgp-9. 2, Hco-pgp-11 and Hco-pgp-16 was monitored by qRT-PCR in artificially exsheathed L3 H. contortus larvae that mimic the first parasitic stage in contact with eosinophil granules in vivo. Larvae were exposed for 24 h to increasing concentrations of granule products ranging from 300 to 2 000 mg/ml.
contortus Pgp expression analysis  [37]. In the present work, H. contortus Pgp transcripts have been further investigated in the adult stage using an RT-PCR approach. We have also extended transcript sequence information including 3'UTR regions in order optimize transcript specific primer design for subsequent quantitative PCR experiments. In the present study we found corresponding partial or complete cDNA sequence for 8 of them (i. e. Hco-pgp-3, Hco-pgp-9. 1, Hco-pgp-9. 2, Hco-pgp-9. 3, Hco-pgp-10, Hco-pgp-11, Hco-pgp-14 and Hco-pgp-16) and further confirmed the expression of the previously reported Hco-pgp-2  [36]  in H. contortus adult males. Analyses of the H. contortus Pgp sequences found a high degree of conservation with their respective homologs in C. elegans, with the striking exception of Hco-pgp-16, for which the closest homolog found in Genbank corresponds to the recently reported pgp-16 sequence from the distantly related horse parasite Parascaris equorum.
The closest homolog of Hco-pgp-3 identified in databank is C. elegans pgp-3, which is involved in defense against natural toxins produced by plants and bacteria  [42, 43]. Interestingly, our phylogenetic analysis revealed that pgp-16 orthologs of H. contortus, Ascaris suum and Parascaris equorum are clustered within the same group as Cel-pgp-3, highlighting the importance of this Pgp group for future studies. It is tempting to speculate that the specific over expression of Hco-pgp-3, Hco-pgp-9. 2, Hco-pgp-11 and Hco-pgp-16 in parasitic stages might reflect their potential activation during the host/ parasite interaction. It has been reported that Pgp substrates can induce over expression of their cognate Pgp genes in nematodes  [28, 29]  and we were able to show that, Hco-pgp-3 and Hco-pgp-16, but not Hco-pgp-9.
Eggs were kept in the dark for 60 min before analyses. Specific fluorescence was measured using a Quanta Master spectrofluorometer (PTI, NJ, USA) equipped with a 75 W xenon lamp (l excitation = 495 nm, l emission = 525 nm). Data were given in arbitrary units of green fluorescence corrected for the native green fluorescence of eggs and expressed as the percentage of fluorescence in control eggs. Three replicates per condition were analyzed. The Rh123 efflux without eosinophil granules was normalized to 0. The Hill equation was used to describe the dose-response relationships  [47]  by calculating the following equation: Y = Bottom + (Top -Bottom)/(1+10 ' ((logEC50-X))) where Bottom is the baseline and Top is the maximum effect, EC50 the dose giving half the maximum effect, X the granule protein concentration (log).
Sequence comparison and Phylogenetic analyses Database searches were performed using the Haemonchus contortus blast server (http://www. sanger. ac. uk/cgi-bin/blast/submitblast/ h_contortus) and the BLAST Network Service (National Center for Biotechnology Information), using the BLASTX algorithm  [48]. The nucleotide sequences (Genbank-accession numbers are provided in Table  S3 ) were translated and amino acids sequences were aligned using MUSCLE  [49]. The corresponding nucleotide alignments were obtained by concatenating codons using the REVTRANS server (http://www. cbs. dtu. dk/services/RevTrans/) [50]. The model of nucleotide substitution that best fitted the data was determined using jModeltest version 2. 0  [51]  on the Phylemon 2 server  [52]. Phylogenetic relationships between Pgp sequences were defined for all the full length nucleotide sequences using the splitdecomposition method implemented in SPLITSTREE version 4.
Primer sequences used for quantitative RT-PCR experiments are provided in table  S2. PCR amplification products were run on an electrophoresis gel and sequenced to ensure the absence of non-specific amplification products and that the primers were suitable for qRT-PCR. Real-time PCR experiments were performed by monitoring the increase of fluorescence of IQ SYBRH Green in real-time (Bio-Rad, Hercules, CA, USA) with the Rotor-Gene 3000 (Corbett Research, Sydney Australia). The relative gene expression fold changes were calculated by the Gene Expression Analysis software (Bio-Rad, Hercules, CA, USA) using H. contortus gapdh, actin, and btubulin as reference genes. Pgp mRNA expression of eggs or xL3 incubated in the absence of eosinophil granules was normalized to 1. The data are presented as fold changes in mean 6 SEM of mRNA expression.
For the analysis of H. contortus P-glycoprotein mRNA expression in different nematode developmental stages, statistically significant differences between eggs and other stages were analyzed with one-way ANOVA test followed by Bonferroni's Multiple Comparison Test. Differences were considered significant when P,0. 05. Statistically significant differences between xL3 in the presence and absence of eosinophil granules were analyzed with the non-parametric Man-Whitney test using Prism software (v5. 02, GraphPad Software, San Diego, Ca, USA).
ACKNOWLEDGMENTS This work was funded by  FONACIT  project  G2005000393  to  H. T. ,  LOCTI  project " Nuevos fármacos contra la tuberculosis " to H. T. , " Vaincre La Mucroviscidose " grant  RF2011 06000446  to A. B. , the visiting investigator program of the Université Montpellier 2 to H. T. , and  NIH   AI26170  and  Center for AIDS Research  award  A151519  to W. R. J. , Jr. We declare no conflicts of interest.
Participating laboratories and training Six laboratories participated in Phase 1 and nine laboratories in Phase 2 (Table  2 ). The laboratories had been recruited via a call for expression of interest launched by the study coordinator or had expressed their willingness to participate in the study to a member of the VMG. The laboratories were trained in the use of the ZFET SOP and reporting templates by testing 3,4-dichloroaniline (3,4-DCA) in a single run as a first step and in three independent runs as a second step (Phases 1a and 2a). The 3,4-DCA data of the participating laboratories in Phase 1a were used to determine the concentration of the positive control.
A simple criterion was used to determine inclusion in further analysis: the prediction model had to achieve a response rate of at least 50% at the maximum dose tested. All calculations were performed using the statistical package R (R Development Core  Team, 2011). In order to facilitate intra-and inter-laboratory comparisons, coefficients of variation (CVs) were computed from data produced by each individual laboratory and each chemical tested as a measure of intra-laboratory reproducibility at 48 and 96 h of exposure. Mean LC50 values for each chemical per laboratory were then used to also develop CVs as a measure of inter-laboratory reproducibility for that chemical. In general, the VMG considered CVs less than 30% to be of ''high quality'' in that these would be judged to have a small or reasonable level of intra-or inter-laboratory variability.
Training of the laboratories -testing of 3,4-DCA The feedback from the laboratories during the training and the in-life evaluation of the toxicity of 3,4-DCA to fish embryos improved the comprehensiveness of the SOP. For example, standardisation of the description of heartbeat observations was refined, and the time and minimum levels of magnification required to make observations were increased. The 3,4-DCA LC50 values generated in eleven laboratories as well as the CVs as a measure for intra-and inter-laboratory reproducibility are given in Tables  6 7 8. The 3,4-DCA data set was the single most-studied chemical in the entire validation as a consequence of its use as a demonstration of laboratory competence and as a subsequent permanent positive control during all other tests at a set concentration of 4 mg/L.