dataesr/scientific-paragraphs-categorization huggingface dataset:
Estimation of durations related to HEV infection dynamics The latent and infectious period durations and the duration of the period required to produce anti-HEV antibodies were estimated using survival data analyses. For each inoculated animal, the latent period was determined as the time elapsed between the inoculation day and the date of the first positive fecal sample for HEV RNA. The latent period after inoculation was fitted to a gamma distribution, from which the shape and scale parameters were estimated by the maximum log-likelihood method. A nonparametric bootstrap procedure was used to determine the 95% confidence interval of the parameter estimates. A parametric model for survival data was built to estimate the duration of the infectious period, using the RT-PCR performed on livers after euthanasia as the last observation date.
Two parametric models were tested (log-normal and Weibull distributions of survival times) and compared using the Akaike Information Criterion (AIC). The impact of PRRSV co-infection on the time to HEV seroconversion was also studied with a parametric survival model applied to the data from the co-infection trial and the only HEV infection experiment  [25]. The link between the earliness of the HEV antibody response and the duration of the infectious period was studied with a Cox model. The immune response was considered as absent or late if the delay before seroconversion was longer than 25 dpi, and as early if it was shorter than 25 dpi  [39]. All analyses were performed using the R software (survreg and coxph functions)  [40].
Quantification of HEV shedding, environmental accumulation and transmission The distributions of HEV shed viral loads with time (with and without co-infection) are represented with box plot series. A linear mixed model (proc Mixed, SAS 9. 3,  [41] ) which took into account repeated measurements with time was built to study the difference in the quantity of HEV shed particles between co-infected and non co-infected pigs. The environmental load corresponds to the accumulation of viral particles in the environment through fecal shedding by infected animals, which is partially compensated by the clearance rate hereafter denoted δ. The clearance rate takes into account feces elimination through the metallic flat deck and HEV intrinsic mortality in the environment. As described in Andraud et al.
An IFC controller was used to prime the fluidics array (chip) with control line fluid and then with samples and assay mixes in the appropriate inlets. After loading, the chip was placed in the BioMark Instrument for a first step of thermal mixing at 50°C for two minutes, 70°C for 30 minutes and 25°C for 10 minutes, then UNG & HotStart step at 50°C for two minutes followed by 95°C for two minutes. PCR was performed by 35 cycles at 95°C for 15 seconds and 60°C for one minute. Finally, melting curve was performed by increasing temperature from 60 to 95°C. The data were analysed with Real-Time PCR Analysis Software in the BIOMARK instrument (Fluidigm Corporation, California, USA). RNA level quantification was assessed by calculating 2 -ΔΔCT  (Schmittgen and Livak 2008). In the previous study, we identified 2553 genes significantly upregulated or downregulated between the OC group and the control group  (Serteyn and others 2010).
Acknowledgements We thank the  Languedoc-Roussillon  facilities (1) Montpellier GenomiX (MGX) and the Q-PCR facility, and (2 )  HPC@LR Center for high-performance computing and the 'Institut de Biologie Computationnelle' (IBC, Montpellier, France)  for fruitful discussions. We also thank  B. Deliege  and  P. Lejeune  for their excellent technical assistance. Contributors Pascal Lejeune  and  Brigitte Deliege  provided technical help for X-ray and blood samples. Funding  Ministère de l'agriculture, Wallonie. Competing interests Two coauthors (RB and FN) are working in a private company and performing the statistical analysis.
Then they were preserved in 90°ethanol for molecular analysis (Table  1 ). After rinsing larvae in distilled water, then drying in an oven at 37°C, DNA extraction was performed with a Qiamp DNA mini kit (Qiagen, Hilden, Germany) according to the manufacturer's instructions. Sequencing of the D2 domain of the 28S was used for the identification of nematodes as described previously  [41, 58]  (Table  2 ). PCR products were directly sequenced in both directions with the primers used for DNA amplification (Genoscreen, France). Sequence alignment was performed using the ClustalW routine included in the MEGA version 5 software and checked by eye. Haplotypes of the larvae were compared to sequences of Protostrongylinae available in GenBank: P. pulmonalis (EU595590), P. pulmonalis (KJ450993-1018), P.
3), 1. 5 mM MgCl 2 , 50 mM KCl, 0. 01% Triton X 100, 200 μM dNTP each base, and 1. 25 units of Taq polymerase (Eppendorf, Germany). Our sequences were compared with sequences of terrestrial snails available in GenBank for all these domains. Our sequences were  Phylogenetic trees were constructed using the Neighbor Joining (NJ), the Maximum Likelihood (ML), and Minimum Evolution (ME) methods using the MEGA 5 software  [61]. For all NJ, ML, and ME analyses, the most appropriate nucleotide substitution model was determined, gaps were treated as missing data and internal node support was assessed by bootstrapping over 500 replicates.
Identification of larvae Pre-identification of larvae of Protostrongylids was based on simple morphological criteria. The third stage (infective) larva of this group is generally covered by a double sheath. The upper sheath is thin, transparent, and the lower sheath is compact and rugose with rough traverse folds of brownish color. The larvae along with its sheath usually coil into a ring. (Figure  2 )  [1, 12]. Larvae were then analyzed using molecular markers (D2) in order to identify them at the specific level. For seventeen positive snails, sequences of the larvae are 100% homologous with the haplotype of P. oryctolagi, whereas one snail was positive for larvae whose haplotype corresponds with P. pulmonalis deposited in GenBank.
AT10 and ET3; C: seq. EA1 and ET1), except for the Helicella group where tests failed. We did not take into account this snail for our next results. An initial study of the combined dataset of 18S, ITS-1, COI and 16S sequences show the membership of all our positive snails to the Hygromiidae family (Figure  3 ) and the separation in three different clades corresponding to the different morphotypes. A second study, by analysing the specific 16S and COI allows us to identify some of the species involved in the different groups (Figure  4 ). Groupe A is only composed of the species Xeropicta.
It is important to note that the collection method used here is not suitable for small snail species approx a millimeter in size, living in ground litter and moving very little and very slowly, and already identified Figure  3  Phylogenetic tree based on a combined dataset of COI, 16S, 18S and ITS-1 sequences with a total of 1677 nucleotide sites constructed using the Maximum Likelihood method and the general time reversible model (GTR + I + Γ). The tree has been rooted using Albinaria sp. (AY546262/AY546342/AY546382/AY546302). The asterisk indicates bootstrap values of >95 for NJ, ML and ME. A = Hygromiidae; B = Helicidae. as an intermediate host of Protostrongylidae, e. g: Euconulus fulvus or Vertigo alpestris  [28]. Hence we cannot exclude the possibility that we missed some small snail species infested by Protostrongylids, meaning that the number of intermediate host species could be higher than five.
Acknowledgments Financial support was provided by  ONCFS  ( Office National de la Chasse et de la Faune Sauvage : National hunting and wildlife agency). The authors would like to thank the members of the network SAGIR : The staff of hunters, hunting Federations, environmental officers of ONCFS and the local administrative Laboratories of veterinary analyses of  Ardèche, Hérault, Tarn, Vaucluse  and their General council,  Fabrice Etienne ,  Cédric Arnal ,  Pascal Arnaud ,  Lise Guennegan ,  Guillaume Robert ,  Philippe Aubry ,  Clément Calenge  and  Philippe Geniez. We are grateful to  Scott Harris  for having kindly reviewed the English.
Hybridization protocol Blood samples from 30 female pigs from only 2 batches were used. Each of the 15 arrays used contained 8 microarrays which corresponded to the 4 observations of two individuals, each from one batch. This design secured the kinetics of the response for each individual and prevented confounding effects between batch and array. After quality control and filtering, 35,419 probes were kept and log 2 transformed. Technical biases were handled by aligning the within-array medians for all genes and by a quantile normalization within animal (function normalize. quantiles in the R package preprocessCore  [16] ). No missing data were reported. Normalized data were submitted to GEO/NCBI with the GSE71207 accession number.
-x 1. ,. , x p i. -x p. x k i. = 1 4 t∈{0,+1,+4,+24} x k it. X w = X -X i. is the withinanimal deviation matrix of size (N × p) with X i. the matrix defined by concatenating the matrices 1 4 x T i. for every animal into X i. , with x T i. = x 1 i. ,. , x p i. By splitting the different parts of the variation in the data while taking into account the repeated measurements on the subjects, the multilevel step allows us to study the effect of different conditions within a subject separately from the variation between subjects. This method is especially relevant when a high between-subject variability is observed in repeated data: multivariate approaches (such as principal components analysis, PCA  [19]  and partial least square regressions, PLS  [20] ) can then be performed on X w to highlight the most relevant correlations between variables in the dataset, independently from individual variations.
Sequence annotation Each cDNA sequence was compared to Refseq_rna mammalian database using the NCBI blastn program (http://blast. ncbi. nlm. nih. gov/Blast. cgi). Resulting hits were sorted out according to their closeness to the pig genome, their coverage and sequence identity. The selected cDNA sequences were submitted to the HUGO (Human Genome Organization) gene nomenclature committee, using their RefSeq IDs (http://genenames. org). Then, HUGO gene symbols or official gene symbol were used as gene names.
Functional enrichment and pathway analysis Functional enrichment was performed on each list of clustered genes identified by HAC and on the list of genes of which expression is significatively explained by cell population changes in blood (according to the mixed model described in Section "Integration"). Functional annotation of genes was provided by the BioMart database  [22]. To set the statistical enrichment of a particular biological function, a Fisher's exact test was performed, using the list of genes expressed on the microarray as the reference list of genes. Resulting P-values were adjusted for multiple tests using a BH approach. A minimum of 3 genes per gene ontology and a FDR < 0. 05 were necessary to consider a biological function to be enriched. A pathway is an interconnected arrangement of processes, representing the functional roles of genes in the genome.
Functional integration of gene expression, i. e. , identification of gene networks, was performed using the 'Gene Ontology' database AmiGO (http:// amigo. geneontology. org). The significantly up-or downregulated genes could be assembled into networks using 'Ingenuity Pathway Analysis' (http://www. ingenuity. com) under licence. This application provides computational algorithms to identify the enriched biological pathways, functions and biological mechanisms of selected genes and proposes also enriched regulators as transcription factors. A regulatory network could be constructed with the information provided by the option 'Upstream Regulator'. This option proposes a list of regulators known to have a significant effect on some of the targeted genes in the input list. Ingenuity also provides computational algorithms to identify and to dynamically generate significant biological networks.
2e -16 ) and at t = +24 (FDR = 1. 39e -10 ), as can be expected from the feedback of cortisol on its own secretion. Plasma glucose levels showed a slight increase at t = +4 after ACTH injection, but FFA levels showed a large increase at t = +1 (×3. 21, FDR < 2. 2e -16 ), with a strong variation across animals, and were almost back to basal levels at t = +4. The values measured at t = +1 were correlated with basal values (r 2 = 0. 42, FDR = 2. 30e -06 ). Cortisol induces a weak increase in circulating glucose and also potentiates the effect of other counter-regulatory hormones  [24, 25]  and increases FFA levels via acute lipolysis  [26]. The data obtained here for clinical hematology measures are in the range of published values in pigs. These values show large variations with age and breed among other sources  [27] [28] [29].
Between-subject variability at t = 0 Results of the PCA at t = 0 on variables responding to ACTH and birth and weaning weights are shown in Fig. 2. As red cells count (RC), hematocrit (Hct) and hemoglobin (Hgb) are redundant variables, decision was made to keep only RC for the PCA. No outliers are identified on the projection of the individuals on the two first dimensions of the PCA. On this PCA, sex was not found to be related to the main variability (i. e. , to the first axes of the PCA) in the dataset. The first dimension shows an opposition between the proportion of granulocytes (positively correlated with this axis) and the proportion of lymphocytes (negatively correlated with this axis). The second axis shows an opposition between cortisol (positively correlated with this axis) and  birth and (to a lesser extent) weaning weights (negatively correlated with this axis) with a strong opposition between these variables on the whole plan formed by the first and second axis.
Acknowledgements This project received financial support from the  French National Agency of Research  ( SUSoSTRESS ,  ANR-12-ADAP-0008 ) which also funds the PhD thesis of VS together with the  Région Midi-Pyrénées. We thank the team of the Magneraud center which took care of animal breeding,  GeT-TRiX  platform (Toulouse) for expression data production,  GenPhySE INRA Toulouse (more specificaly Nathalie Iannuccelli ,  Katia Fève  and  Juliette Riquet ) for experimental support,  ANEXPLO platform (Toulouse)  for biological dosages,  PEGASE INRA Saint-Gilles (more specifically Rafael Comte ) for cortisol assay,  Aurélie Ducan  for hematology analysis,  Magali San Cristobal  and  Pascal Martin  for helpful discussions on the analyses and the  French National Research Agency  for funding the research project.
, 2007) , yielding aberrant RER. Based on the significant relationship that exists between heart rate (HR) and VO 2 , heart-rate monitoring would be an alternative way to evaluate EE of exercises performed in genuine field conditions. A prediction model of VO 2 from HR has been recently developed from literature data in exercising horses  (Coenen, 2010). However, in humans, individual variations including gender, age and training status, as well as variance in co-variables that affect HR such as emotion, have been demonstrated to impact the HR-VO 2 relationship in terms of slope, intercept and curve characteristics  (Levine, 2005). Establishing the HR-VO 2 regression for each individual horse would be an essential pre-requisite for estimating EE from HR measurements during exercise. Therefore, the objective of this study was to obtain field cardio-respiratory measurements in Arabian endurance horses using the equine K4b 2 in order to assess EE.
Measurements After 4 months of endurance training, measurements were carried out at rest and during an exercise test, between 1400 and 1600 h systematically. Measurements at rest were carried out the day before the exercise test. Data were recorded over a period of 10 min on horses maintained calm and standing, with no access to food or water. Measurements at exercise were carried out on a 1150-m flat sandy track. Horses were equipped with the K4b 2 system and HR monitor (RS800; Polar Electro Yo, Kempele, Finland) after a 40-min warm-up (at walk, trot and canter). The exercise consisted of the following four consecutive steps at increasing speeds, chosen to range within the usual paces in endurance (Goachet and Julliand, 2011): 5-min walk at 1. 6 m/s, 5-min trot at 3. 7 m/s, 5-min canter at 5.
Data analysis Once downloaded to a PC using the K4b 2 management data software, the captured data were filtered and averaged over 15 s, and then transferred onto Excel files to convert VO 2 and VCO 2 in ml/min per kg BW and to calculate RER as VCO 2 (l/min)/VO 2 (l/min). EE (kJ/min) was calculated by multiplying total oxygen consumption by 20. 1. The mean was calculated for each variable (RF, VE, VT, HR, VO 2 , VCO 2 , RER and EE) at rest, trot and canter from the last 3 min of each step. The relationship between HR and VO 2 (ml/min per kg BW) was determined for each horse from all the data captured at rest and during exercise, as well as from the average values obtained at rest and during each step of exercise. The EE of the exercise sessionthat is, the 20-min measurement phasewas calculated from direct VO 2 measurements, individual HR-VO 2 regression and Coenen's prediction model (y = 0.
002816 × 1. 9955 )  (Coenen, 2010). Correlations (R s ) between speed and RF, VE, VT, HR, VO 2 , VCO 2 , RER and EE were determined using Pearson's correlations analysis. Statistical analysis was carried out following the GLM procedure of Statistical Analysis Systems Institute SASv8 (SAS Institute Inc. , Cary, NC, USA). The effects of the 'speed' and 'horse' were tested on the cardio-respiratory variables and EE variables, according to the following statistical model: X ij = μ + α i + ε ij , where X ij is the studied variable; μ the overall mean; α i the fixed effect of ith 'speed'; C j the effect of jth 'horse'; and ε ij the normally distributed error. Means were compared using linear contrast (PDIFF option of SAS). Differences were considered statistically significant at P < 0. 05.
Acknowledgments This study was funded by  Le Conseil Régional de Bourgogne and AgroSup Dijon. Authors are very grateful to  Astrid Loriers , who managed the measurements phase, to the students who helped with training horses, to the people of AgroSup for the care of the horses and to  Marcel Mézy  and  Jean-Claude Guillaume , the horses' owners. The authors also thank  Justine Fortier  for the review of this manuscript.
One of the few published studies related to this is in cattle (Bos taurus), where vocalisation was quantified in repeated open field tests  [45]. Furthermore, a study in lambs of domestic sheep (Ovis aries aries) showed that individual differences in the emission of high pitched bleats after separation were positively associated with lambs' sociability  [50]. Here, we present data from two altricial mammals with rather different life histories, the domestic cat (Felis silvestris catus) and the mound-building mouse (Mus spicilegus). Domestic cats can be readily kept under semi-natural free-ranging conditions and mothers allow observation and handling of their new born young by familiar care givers without apparent protest or negative effects on the kittens' growth or survival  [29, 51, 52].
(b) Mound-building mouse We collected data from 77 pups (60 males and 17 females) of 23 litters, each stemming from a different parental pair (further details under Experimental procedure below). Studies on mound-building mice (Mus spicilegus) were carried out in the Laboratoire d'Ethologie Expérimentale et Comparée at the Université Paris 13 in France. The animals of the breeding stock maintained in this laboratory were descendants (16 th, 17 th and 18 th generation) of animals caught from the wild at different sites in Hungary in 1999. We ensured the genetic variation of the breeding stock by adding some new individuals every 2-4 years, captured at the same Hungarian collection sites. Animals were kept on a 14:10 h light/dark cycle (lights off and red light on at 12:00 am) in standard polycarbonate cages (26 × 14 × 16 cm, Iffa Credo, Lyon, France), containing wood shavings as bedding.
Statistical analysis Statistical analyses were carried out using the program R, version 3. 1. 1 [83]. First, we tested for changes across age with respect to the number of emitted calls and locomotor activity. This was done by using linear mixed-effects models (LMM) to analyse locomotor activity (dependent variable), and generalized linear mixed-effects models (GLMM) for count (i. e. Poisson distributed) data for the analysis of the number of emitted calls (dependent variable). Vocalizations of kittens corresponded well to a normal distribution and thus we used an identity link. For data analysis of young mice, we used GLMM with a squareroot link in order to adjust for the right-skewed distribution of the data. GLMMs and LMMs were calculated with the R package lme4  [84]. Models included multiple independent variables.
In the case of kittens we tested for changes across the 4 ages (factor with 4 levels), and in the case of young mound-building mice we ran 2 different models as we had 2 different data sets, each with 2 ages (postnatal days 13 vs. 14 and 14 vs. 16; factors with 2 levels). In addition, we tested for potential differences among males and females (factor with 2 levels) and models also included individual body mass (covariate; measured directly after each test). The latter variable was considered as previous reports on pre-weaned small mammals highlight the effects of individual body mass on behavioural responses in different test situations  [22, 85]. Statistical models on kittens included random factors coding for litter identity and maternal identity, as several subjects were litter siblings (in total 8 different litters) or originated from the same mothers (in total 5 different mothers).
To this end we applied intra-class correlations calculated as the proportion of phenotypic variation that can be attributed to between-subject variation  [90]. For kittens, this was done using repeated measurements of n = 33 individuals across 4 tests conducted during the first 4 postnatal weeks (once per week). In young mice this was done separately in two different samples: n = 18 individuals were tested on postnatal days 13 and 14, and another n = 55 individuals were tested on days 14 and 16. We used (G)LMM-based calculations of repeatability with the aid of the R package rptR  [91]. For testing the repeatability of the frequency of vocalization we applied an intra-class correlation based on GLMM for count (Poisson distributed) data. For data on frequencies of vocalization of kittens we used GLMM with an identity link, according to the distribution of the model residuals.
Repeatabilities of locomotor activities (% time, for kittens and young mice) were calculated using an intraclass correlation based on a LMM with restricted maximum likelihoods. In mound-building mice we used an intra-class correlation based on a GLMM with a square-root link in order to adjust for the right-skewed distribution of the vocalization data base obtained. Within-litter deviation in vocalisation as well as in locomotor activity was also analysed using a LMM with restricted maximum likelihoods. For all intra-class correlations we assessed 95% confidence intervals by 1000 bootstrap steps. Individual identity was used as a random factor. P-values were calculated by 1000 permutations.