dataesr/scientific-paragraphs-categorization huggingface dataset:
All behavioral tests were recorded by a video camera connected to a video recorder. Videos were subsequently analyzed by a trained observer. At the end of the behavioral recording, rats were released and cared for at the BUMS animal house. The experimenter was aware of the rat group, but a researcher blinded to the group allocation carried out statistical analysis and outcome assessment. Collected data were analyzed using SPSS software 16. The normality of quantitative variables was checked using Shapiro-Wilk tests. Kruskal-Wallis, Dunn-Bonferroni post hoc, and multivariate regression tests were used to compare quantitative parameters and chi-square tests to compare qualitative parameters. The generalized estimating equation (GEE) model was employed to analyze the longitudinal variables measured at different time points.
Thermoregulation is essential to piglets' neonatal survival. This study used infrared thermography (IRT) to assess thermoregulation abilities of piglets from two lines divergent for residual feed intake (RFI). At birth, morphology (weight, length, width and circumference), vigour (respiration, mobility and vocalisation), and rectal temperature were recorded from piglets of the 11th generation of the low RFI (LRFI, more efficient; n = 34) and the high RFI (HRFI, less efficient; n = 28) lines. Infrared thermography images were taken at 8, 15, 30 and 60 min post partum. Temperatures of the ear base and tip, and of the back (i. e. shoulders to rumps) were extracted (Thermacam Researcher Pro 2. 0) and analysed with linear mixed models (SAS 9. 4). Piglets had different average hourly weight gain (HRFI = 7.
, 2012; Kammersgaard et al. , 2013). As still being a novel technique, IRT studies should consider factors of influence on the data and control or account for external factors as much as possible. Llamas  Moya et al. (2006)  found that the skin temperature was not correlated with birth weight but was influenced by the huddling behaviour of the piglets and their location relative to a heat source. The authors further recommended drying piglets before taking the thermal images as the emissivity of wet skin is different from emissivity of dry skin. Tabuarici et al. (2012)  used IRT images of neonatal piglets' ear base, ear tip and back to assess the potential use of IRT to detect piglets at risk of hypothermia. They found that the IRT data were not predictive of mortality risk but they could be used to predict shivering  (Tabuarici et al.
Neonatal thermoregulation The thermoregulation of piglets was measured by collecting thermal data at 8 min (i. e. birth), 15, 30 and 60 min post partum. Piglets were placed in a transparent plastic box (55. 4× 38. 0× 31. 7 cm; Carrefour®) and images of their back were acquired from an upside view by a single experimenter who was standing next to the box and held the camera at 1 m distance from the piglet's back, with an angle of 75°(Fig. 1a ). The emissivity of the camera was set at 0. 98 (as validated by  Soerensen et al. (2014) ), the ambient temperature was set during image analysis according to the records (average 23. 2 ± 0. 07 °C), and the reflected temperature was set according to the measure done before data collection (average 22. 8 ± 0. 1 °C). Between two image acquisitions, piglets were returned to their pen with the sow.
At the time of each image acquisition, piglets' behaviour was scanned and scored as present or absent: suckling (i. e. having a teat in the mouth), huddling (i. e. resting in close contact with at least one other piglet) and locomotor (i. e. being in motion) behaviours were observed, as well as their proximity (i. e. being at less than 10 cm) with the sow and the heat lamp. Behaviours were scored as present (score 1) or absent (score 0) and were not analysed as such but used as covariates in the analyses of thermal images. Thermal images were then analysed using the software Thermacam Researcher Pro 2. 0. Measures of temperature were acquired from points of interest: base of the ear (maximum ear temperature), tip of the ear (minimum ear temperature) and 'back area' minimum, mean and maximum temperatures, which was drawn from the shoulders to the rumps and along the ribs (rectangle-like shape) (Fig.
1b ). Reliability of the method was assessed with the intraclass correlation coefficients on SPSS Statistics 24 (IBM Corp. , Armonk, NY, USA). Several images of the same piglet were rated by the same experimenter to obtain the interimage reliability: coefficients were 0. 47 for minimum, 0. 95 for maximum, 0. 96 for average, 0. 89 for ear base and 0. 82 for ear tip. The same experimenter rated the same images several times to obtain intraobserver reliability: coefficients were 0. 44 for minimum, 1. 00 for maximum, 1. 00 for average, 0. 99 for ear base and 0. 86 for ear tip. Given the low reliability of the minimum temperature of the back, this measure was not retained for further analysis.
Statistical analyses To determine the contribution of each variable to the line differences observed between piglets, data were first processed in a principal component analysis (PCA) using the factoextra_1. 0. 5, ggplot2_3. 2. 0, FactoMineR_1. 42 (Lê et al. , 2008)  and missMDA_1. 14 packages of R version 3. 5. 3. Following the recommendation included in the FactoMiner package, missing data were imputed using the imputePCA function of the missMDA package  (Josse and Husson, 2016). When plotting the individual piglets against three first principal components, an ellipse representing the 75% confidence range for each line was drawn to show differences between the two genetic lines. The other analyses were performed using the software SAS 9. 4. A Pearson rank correlation test was used to investigate the correlations between birth rectal temperature and temperature data obtained from thermal images at birth.
The models to analyse the standardised weight and average weight gain included the birth weight as a covariate. Models for the analysis of temperature data included genetic line, time post partum and their interaction as fixed effects, time within animal as a repeated effect, and sow nested within genetic line as a random effect. Behavioural measures (binary variables: the presence/ absence of behaviour) were kept in the models as fixed effects if their effect on temperatures was significant: only the proximity to the lamp was a covariate in the model for ear tip, maximum and average back temperature. Birth weight was also included as a covariate in all models except for the ear tip temperature, as it was not significant. Finally, temperature changes at the different locations between times of measure were also calculated from the thermal data and analysed.
Principal component analysis The PCA computed the following morphological and thermal variables: Morphological variables: crown-to-rump length, circumference, width, BMI, PI, weight at birth, official weight (at approximately 24 h post partum), average hourly weight gain between weight at birth and official weight. Temperature variables: rectal temperature, minimum/maximum/ average back temperature at birth/15/30/60 min post partum, temperature of the ear tip and base at birth/15/30/60 min post partum. Three dimensions (or principal components) were retained from the analysis, as they explained 35. 7% (dimension 1), 14. 2% (dimension 2) and 12. 9% (dimension 3) of the total variance of the data (Supplementary Figure  S1 ). Dimension 1 was mostly characterised by positive loadings of temperature collected at 15, 30, and 60 min post partum at the back (maximum and average) and at the ear base (Fig.
Immediately after washing, the slides were scanned on Agilent G2505C Microarray Scanner using Agilent Scan Control A. 8. 5. 1 software and fluorescence signal extracted using Agilent Feature Extraction software v10. 10. 1. 1 with default parameters. Microarray data and experimental details are available in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE131763 (https://www. ncbi. nlm. nih. gov/ geo/query/acc. cgi?acc=GSE131763).
Statistical analysis of results Data relating to health, performance and behaviour were analysed using SAS Analytics pro 9. 0. All repeated data on lambs were analysed for variance with a mixed model (proc MIXED, or proc. GLIMMIX SAS according to the type of variable). As animals were gathered in different sex groups after weaning, separate analyses were performed before weaning, around weaning and during the postweaning period. We checked the group effect as a random factor for all the analyses but as it was never significant, we removed it to simplify models. The fixed effects are treatment, sex, age and two-way interactions with treatment. For the analysis of QBA, factor scores for PC1 and PC2, phase in the video editing and observer were added as fixed effects. For the analysis of dirtiness score, we combined the scores 0 and 1 and the observer was added as a fixed effect.
A covariate was integrated into the model: for vaccinal antibody response (pre-vaccination antibody titres), and the level on D3 for GSH/GSSG or for TAS. Possible non-normality and heterogeneity of variance were checked. Pathogen infection rates, antibody titres and oxidative stress data needed to be log-transformed. Comparisons between three treatments of lambs displaying signs of diarrhoea were performed using the exact Fisher test. P < 0. 05 was considered significant and 0. 05 < P < 0. 1 was considered as a tendency. Microarray data were analysed using R and Bioconductor packages (www. r-project. org, R v. 3. 1. 2, Gentleman et al. , 2004)  as described in GEO accession GSE131763. Briefly, raw data (median signal intensity) were filtered, log2 transformed, corrected for bath effects (washing and labelling serials) and normalised using the quantile method  (Bolstad et al.
, 2003). A model was fitted using the limma lmFit function  (Smyth, 2004)  considering sex as a blocking factor. A correction for multiple testing was then applied using Benjamini-Hochberg procedure  (Benjamini and Hochberg, 1995)  for the false discovery rate (FDR). Probes with FDR ≤ 0. 05 were considered to be differentially expressed between conditions. For bioinformatic analyses, we selected probes that displayed a non-adjusted P-value of P < 0. 005 as no probes were found with an FDR ≤ 0. 05 and a fold change in expression >20%. Enrichr software  (Chen et al. , 2013; Kuleshov et al. , 2016)  was used for gene list enrichment analysis. Transcription factors with an adjusted P-value< 0. 10 were retained. Student t-test was used to compare PCR results of gene expression between two groups with P < 0.
Acknowledgements We would like to express our gratitude to the staff of the experimental farm ( UE 0332 , Bourges), and to the technicians of the UMR 1213 who performed behavioural tests, samples collections and assays, and to  Anne Ferlay  for fatty acids information. We also thank  Claire Naylies  and  Yannick Lippi  for their contribution to microarray fingerprints acquisition and microarray data analysis carried out at  GeT-TRiX Genopole Toulouse Midi-Pyrénées facility. Special thanks to  Scott Love  for reading the final version of the manuscript and correcting the quality of the English.
Statistical Analysis The mortality curves were generated by Kaplan-Meier method and compared by the log-rank test. For determination of LD 90 (inoculum concentration that gave a 90% mortality at day 7), the percentage of mortality at day 7 post infection as a function of the log of the inoculums was analyzed. A regression curve, according to a sigmoid dose-response model, was obtained by nonlinear regression, by setting the minimum and maximum mortality values to 0 and 100%, respectively. The correlation coefficient (R 2 ) was calculated. From the curve regression, the inoculums values corresponding to LD 90 were determined. All analyzes were performed using GraphPad Prism V. 3. 0 software for Windows (GraphPad Software, San Diego, CA, USA). A value of p < 0. 05 was considered to be significant.
[5]. Repeated measurements over time have a high potential for the quantification of an animal's ability to cope with environmental challenges  [5]. Today, highly frequently recorded data are becoming increasingly available due to high-frequency recording systems such as automated monitoring technology or robot milking. The availability of a large volume of data enables to follow the whole process during which occurs the response of an animal to and the recovery from perturbations  [5] , but it also sets a challenge that involves dealing with a large amount of records. In general, the information recorded is related to productive performance (e. g. weights, milk production, feed intake, etc. ) and, in some cases, also to climatic factors. However, little information (if any) is available for variables that can help identifying other environmental challenges such as problems related to health (e.
g. pathogen load), nutritional conditions, the constraints imposed by the farming system, management, etc. However, the effect of environmental challenges on animal performance can be observed indirectly, through changes in variability patterns of repeated records of performance over time as shown by Nguyen-Ba et al. [7]  for voluntary feed intake in pigs. In small ruminants, Colditz and Hine  [1]  and Friggens et al. [8]  reported significant variation between animals in their response profiles to thermal and nutritional challenges, respectively. They observed that increased variation was related to the occurrence of an environmental challenge. Several approaches have been proposed to identify variation in high-frequency datasets that may be related to the occurrence of environmental challenges.
Methods We present a method that consists first, in inferring the existence of highly variable days (where high variation among individuals in phenotype is a putative indicator of environmental challenge) via mixture models applied to frequently recorded phenotypic measures, and second, in using the inferred probabilities of the occurrence of an environmental challenge in a reaction norm model to evaluate the genetic determinism of resilience to these events. We illustrate the method by using an ovine dataset with daily feed intake (DFI) records.
g. [1, 7, 8] ). In fact, Scheffer et al. [14]  refer to phenotypic variance as a "dynamic indicator of resilience", since it can be useful to dynamically monitor changes in a system. Thus, we expect a dataset to contain a (at least) two-component normal mixture model: one for "normal" days, and another for "stressful" days with this high variability being related to the occurrence of an environmental challenge. However, the number of components is unclear and should be inferred from the data. Following Chen et al. [15] , the trait values related to the presence or absence of a disturbance are distributed as N (µ 1 , σ 2 1 ) and N (µ 2 , σ 2 2 ) , respectively, and thus the quantitative trait under study in the population has a two-component normal mixture distribution: where α i are the mixing proportions (non-negative and summing to 1) and N (µ i , σ 2 i ) are the component densities.
This approach is simple and practical because it enables analysis of all the phenotypic data together from all individuals, and estimation of the probability that, at a given date, an unrecorded environmental challenge occurred based on the variability observed for each day. There is no need for an initial step to estimate, first, an observed or a target curve (e. g. a growth or milk yield curve), with all the parameters that this implies, and, second, the deviations from the curve as indicators of environmental Var y = 2 i=1 α i N (µ i , σ 2 i ), challenges. Thus, instead of measuring deviations within individuals across time, we measure deviations across individuals within time. Moreover, this approach is sufficiently general to be applied to various types of datasets coming from different species and with records for different kinds of traits, as it only focuses on daily (or other time frame) variability.
Animals and phenotypes The present study was carried out in agreement with the French National Regulations for humane care and use of animals for research purposes (Décret 2013/118). Animals were bred at the experimental INRAE Farm (La Sapinière, Osmoy, France) which has the experimental approval C18-174-01. Data are fully described in  [17, 18] , and here, we only summarize the main aspects. Data for the analysis were from 951 Romane male lambs that are evaluated as part of the national Romane breeding scheme. Over an 8-year period (from 2009 to 2016), each year, a cohort of 119 lambs on average (ranging from 92 to 149 depending on the year) was continuously phenotyped during eight weeks for feeding behavior. They were housed in the same experimental barn during the 8-week period. Although animals were reared indoors, environmental variables such as temperature or humidity were neither recorded nor regulated, and rearing conditions were similar across years.
In total, 775,580 visit records were available for all eight periods, with an average of 14. 56 visits per day per animal. All visit records within each day for each animal were summed up to obtain the daily feed intake (DFI), resulting in 51,832 DFI records available for all the animals during the eight years. As the data were recorded in growing animals, DFI (and its variance due to a scale effect) tends to increase as the animal gets larger over time. In order to take this into account, we used the (natural logarithm of ) daily coefficient of variation (CV) instead of the daily variance to assess variability. This represents a total of 438 days across the eight years, i. e. 55 days per year on average. For the genetic analysis, the pedigree consisted of 5114 animals, i. e. the 951 tested lambs and their ancestors.
Estimation of variance components and comparison of models (Co)variance components were estimated using Gibbs sampling and REML with the GIBBSF90 and AIREMLF90 software (available at http://nce. ads. uga. edu/wiki/), respectively  [22]. For Gibbs sampling, 200,000 iterations were run, with a burn-in of 10,000 initial iterations and a sample interval of 10. Posterior means and posterior standard deviations (SD) were calculated. For REML, the asymptotic standard error for the genetic correlation was computed following Houle and Meyer  [23] , as implemented in AIREMLF90. For this purpose, first we ran EM-REML for all the initial iterations and then switched to AI in the final iteration because the EM-REML algorithm is much more stable than the AI algorithm and is very robust to poor initial estimates and can thus provide a good starting point for the AI algorithm  [24].
Furthermore, our approach can be applied to various types of datasets from different species and with records on different kinds of traits because it focuses only on daily (or other time frame) variability, without the need to assume any kind of curve or function for the trait or traits analyzed. Consequently, the method is widely applicable. However, the method requires frequently recorded (daily or similar) data and homogeneity of animals within groups -for instance, lambs and adults should be analyzed separately. Daily recording is not frequent but there are more and more automatic measuring devices such as milking robots that provide records that can be used. As described by Friggens et al. [5] , technology for the measurement of milk yield and body weight at each milking is commercially available and used routinely in some milking systems.
Systems that automatically weigh animals, either directly or using image analysis of body shape, have been developed. Imaging technology has recently been commercialized for measuring body condition  [30]. There are also a number of monitoring technologies for measuring animal behavior (accelerometers, position tracking, video), and an increasing number of sensors for measuring biomarkers of different aspects  of health status in milk, in exhaled breath, and in situ with under-skin sensors or boluses in the rumen  [5]. Today, dealing with large amounts of records produced by all these monitoring technologies is a challenge. For very large datasets (e. g. as in dairy cattle national evaluations), genetic connections (i. e. through sires) may reveal classes of variances as in our study. In this paper, we illustrate the analysis of data and the modeling procedure proposed by using a dataset with DFI records from Romane lambs across eight years.
Our results indicate that this approach is promising and appears to be viable to identify unrecorded environmental challenge events, which is useful when selecting resilient animals and only productive data are available. Although our illustration uses an ovine dataset with DFI records, the proposed approach can be applied to a wide variety of phenotypic records from different species and, given its simplicity, is useful when dealing with large quantities of data coming, for example, from high frequency recording systems such as automated monitoring technology. To our knowledge, there are no similar approaches proposed in the literature.
Conclusions Today, very frequently recorded data are becoming increasingly available, due to the availability of high-frequency recording systems such as automated monitoring technologies. The method that we propose consists, first, in inferring the existence of highly variable days via mixture models applied to frequent phenotypic records and second, in using the inferred probabilities of a mixture in a norm reaction model. The approach is simple and practical and can be widely used for different species and traits. It enables the estimation of the probability of the occurrence of an environmental challenge for each day, and this variable proved to be informative and useful to be included in genetic analyses as an environmental descriptor to select resilient animals. We estimated breeding values for environmental sensitivity of the genetic potential for DFI through the use of reaction norm analysis.
Acknowledgements We are grateful to the  Genotoul bioinformatics platform Toulouse Occitanie  ( Bioinfo Genotoul , https ://doi. org/10. 15454/1. 55723 69328 96116 7E12 ) for providing computing resources. The authors would like to thank the staff on the  INRAE experimental farm of La Sapinière  for taking care of the animals, maintaining automatic feeders and helping in a posteriori identification of stressful events.
Animals, materials and methods This observational study was performed between February and June 2018 at the Lanaud station (Boisseuil, France) for the Limousin beef breed. Every year, this national breed station evaluates about 750 candidate young bulls pre-selected early based on morphological criteria. These candidate animals come from a network of a thousand private farms across all of France and Luxembourg. Data were obtained on a subsample of 115 bulls present during the whole observation period and evaluated via routine practices already performed in the Limousin breed selection process. Our study did not specifically impose stressful situations for the animals, and so, institutional animal care and use committee approval was not required under European regulations. The bulls were originally born within 83 different farms and were the products of 104 different sires.
The technicians performed this test for all bulls entering the evaluation station. One technician tested 6 to 8 animals before switching for another technician to take over. Tests were stopped if there was a clear risk of injury to a technician or the bull if a bull attacked or tried to violently escape from the testing area. Seven animals that scored '4' were eliminated from the controls at this step and returned to their farms, and therefore were ruled out of inclusion in the analysed dataset.