dataesr/scientific-paragraphs-categorization huggingface dataset:
Validation of the PCR array data by additional RT-qPCR, immunoblotting and ELISA Additional RT-qPCR tests were then performed to confirm the data obtained with the PCR arrays for several genes which expression was found to be unchanged (DDX58, IFI16, IRF1, IRF7 and STAT2), significantly up-regulated with fold change >2 (CXCL10, IFIT1, ISG15, MX2, OAS2 and RSAD2) or between 1. 9 and 2 (IFIH1 and STAT1) (Figure  5A ). Similar results were obtained by RT-qPCR except for the mRNA level of DDX58 that was found to be significantly up-regulated by RT-qPCR but unchanged in the PCR array (fold change 1. 4) after HEV-3f infection. This upregulation of DDX58 expression was confirmed by immunoblot at the protein level (Figure  5B ). Immunoblot analysis also showed that the expression of IFIH1 is increased and the one of IRF1 unchanged after HEV infection, corroborating the results obtained by PCR array and RT-qPCR.
Funding The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was funded by the  Agence Nationale de la Recherche  ( HEVISTAR  project, grant  ANR-16-CE35-0002-01 ) and the  Agence Nationale de la Recherche sur le SIDA et les heṕatites virales -Maladies Infectieuses Emergentes ( ANRS-MIE ) ( AAP2022-1 , grant  ECTZ187893 ). SG is supported by a post-doctoral research grant from  ANRS-MIE  ( AAP2022-1 , grant  ECTZ187635 ).
Study population In total, 25,745 pigs from the Pietrain NN Français paternal line (Pie NN) of the AXIOM company, i. e. that do not carry the halothane sensitivity allele, were used in this study. Individuals from the Pie NN line were born on three farms that are integrated into the AXIOM breeding scheme and that comply with AXIOM's biosafety and health requirements. Some of the males were selected before weaning and raised on the boar test station of the breeding company AXIOM Genetics (Azay-sur-Indre, France). The animals considered in the present dataset included 6885 entire males and 13,012 females that were raised and individually tested on their farm of birth from April 2014 to April 2022, and 5848 entire males that were raised from September 2015 to April 2022 on the boar test station.
8% of the records) and FI it (0. 8% of the records) were each imputed using the local regression model "proc loess" implemented in SAS  [24]. Data recorded on day of entry into the fattening room were excluded from the dataset due to calibration of the AFS and animal adaptation. Animals that were evaluated for at least 20 consecutive days were kept, even if they died during the test period or weighed less than 70 kg on the day of testing. After processing of the data, the file included 405,983 daily records for the 5848 males that were fattened on the test station, representing 91. 9% of all animals in the original data.
(  2 ) that describes changes in α it (an unobserved where v it is a random observation error for animal i ; σ 2 iv is the observational variance for animal i ; w it are the ran- dom and unpredictable changes in level between time t -1 and t ; and σ 2 iw is the system variance. Compared to daily weight gain and intake, the use of cumulative weight gain and intake has the advantage of being less impacted by the presence of noise in measurements and by the effects linked to the dates of measurements, and of allowing easier representation of individual trajectories  [11, 30]. This model was built using the R package dlm  [31]. Estimation of the unobserved state variable at time t was carried out using the Kalman smoother, a recursive algorithm that uses all available information  [29]. The values of α it were estimated independently for each animal.
Estimated allocation coefficients and associated robustness indicator The descriptive statistics for the dataset used in this study are in Table  1. The averages of the estimates of residual variances of the DLM were 113. 2 ± 77. 5 kg 2 for σ 2 iv in the observation equation and 0. 00031 ± 0. 00045 kg 2 /MJ NE 2 for σ 2 iw in the system equation. The means of α t and LSR were 0. 099 ± 0. 027 kg/MJ NE and -12. 62 ± 2. 50, respectively. The phenotypic correlations, estimated with the cor. test function in R  [22] , for trait α t were positive with e t (0. 241 ± 0. 002) and with LSR (0. 23 ± 0. 002), which means that a higher energy allocation rate to growth was associated with greater variability, i. e. , lower robustness. The phenotypic coefficients of variation were greater than 20% for IBW, α t , and LSR, and between 10 and 20% for TBW, ADG, DFI, and BF100, which indicate large phenotypic variations for all these traits.
To assess the effect of age on energy allocation to growth, future work should analyse temporal dynamics over a longer period, ideally the entire lifespan. In this study, we used a DLM regression to model the relation between CNEA it-1 and CW it over time. The DLM approach is a powerful tool for analysing timeseries variables and enabled allocation coefficient dynamics to be characterized by a stochastic process, without requiring strong deterministic assumptions. With this method, it is possible to determine whether the allocation coefficient increased, decreased, or stagnated, without assuming that it followed any given analytical trend, such as a linear, quadratic, or cubic trend  [44]. Our approach takes advantage of the dlm package in R  [31] , which enables processing of the full data with short computation times (around 35 min for the 405,104 measurements).
In addition, our simple DLM approach can be expanded to the development of multivariate models or the implementation of fixed (batch, herd, etc. ) and random effects  [45]. The two-step modelling approach (DLM followed by RR model analysis of the allocation coefficient estimates) allowed interesting results associated with robustness to be identified in a large dataset with daily measurements. Future work should assess the amount and frequency of the data required to apply the developed approach.
If the end of the measurement period corresponded to a weight closer to mature weight, quadratic random regression may be more suitable  [28]. The trait α t , which describes the allocation of net energy to growth during the fattening period, had moderate heritabilities, in the same range as those estimated for FCR or RFI. In a previous study  [48] , we considered the average estimate of α t during the evaluated period and not the daily estimates, and obtained a lower heritability (0. 16 ± 0. 05) but this was based on a different dataset. For RFI, David et al. [49]  reported heritabilities ranging from 0. 19 ± 0. 06 to 0. 28 ± 0. 06, using an RR model for weekly estimates of RFI over 10 weeks in pigs.
Each measurement was carried out for a duration of 30 s, which guaranteed the stabilization of the cycle. In a second step, the pressure peaks (i. e. , maximum pressure values) in the pudendal nerve territory, as well as the location of the center of pressure (CoP), were extracted using the TexiMonitor ® software. Three riding situations are investigated for each of the 12 participants. One is static (simulator halted), and the other two are dynamic for canter and gallop, at simulator frequencies of 0. 8 Hz ("slow dynamic") and 2 Hz ("fast dynamic"), respectively. Pressure distributions are recorded at a sampling rate of 10 Hz, which ensures proper acquisition, given the operating frequencies. Statistical analysis is performed using a t-test on all 12 values obtained in the different situations (stationary, slow dynamic, fast dynamic, and anterior and posterior phases).
This group continued to be naltrexone administered each day until the extinction of the CPP. The time spent in each compartment during a 15 min session was measured. Data were presented as means of individual differences between times spent in the drug-paired compartment during the pretest phase and the test day. Positive values reflected a CPP. To mention that the CPP experimenter was unaware of the treatment given to the animals. Software from the CPP apparatus gave information's on the number of entries on each compartment, the movement made at the back, front, and back and forth in each compartment. Locomotors activity performance (actimetry) represents the sum of movements performed in each compartment for each animal (mean ± SD) in 15 min. The input number is the sum of entries in the compartments made for each animal (mean ± SD) in 15 min.
The specificity of the immunostaining was assessed by omitting the primary antibody or protein pre-adsorbed antibody, which resulted in the absence of a signal. Photomicrographs of immuno-stained sections were captured and image intensity analysis was completed using ImageJ software (ImageJ v1. 41, National Institute of Health, USA). The number of Δ-FosB positive cells or the intensity of the labeling were counted using ImageJ software as previousely reported  [42] [43] [44]. For each animal group, data were the total number of cells or the labeling intensity per identical used square of the sections (n = 12 per animal).
Statistics Data analysis and statistical comparisons were performed using GraphPad Prism 8 (GraphPad Software Inc. ). The results are expressed as mean ± SEM. Level of significance was set as P ≤ 0. 05. Brown Forsythe ANOVA one way and Bartlett's test were used to compare between three and more groups. Otherwise, Student's t-test was used for comparing pretest and test results (Fig. 3 ).
Histology and Stereology Placental samples were embedded in paraffin. Sections (7 µm) were stained with hematoxylin/eosin using an automat (Varistain, Thermofisher, Waltham, MA, USA) for stereological analysis and scanned using NanoZoomer Digital Pathology ® (Hamamatsu Photonics, Hamamatsu, Japan). Surface densities (Sv) and Volume fractions (Vv) of the different components of the allantochorion, including microcotyledons and allantois, were quantified using One stop stereology with the Mercator ® software (ExploraNova, version 7. 9. 8, La Rochelle, France  [47] ). The measured components included haemotrophic and histiotrophic trophoblasts, microcotyledonary and allantoic vessels, microcotyledonary and allantoic connective tissues, and microcotyledons as the sum of all microcotyledonary components.
The chorionic mesoderm and allantoic connective tissue were combined as allantoic connective tissue. Vv and Sv were then multiplied by the total volume of the placenta to estimate the absolute volume (cm 3 ) and surface area (cm 2 ) of the components of the allantochorion, as previously described  [45]. For each placenta, 12 allantoic arterioles were randomly selected. The surface area of the lumen (calculated using transverse diameters) and the thickness of the vascular wall were measured using the ImageJ ® software (version 1. 8. 0_345, National Institute of Health, Bethesda, MD, USA).
Candidate Gene Accession Number Reverse and forward Primers Reference  GADPH NM_001163856. 1 F 5 ′ -AGTTGGGTGCCAAAACTTGTG-3 ′ R 5 ′ -TGAAGGGGTCATTGATGGCG-3 ′ SCAMP3 Brosnahan et al. 2012 F 5 ′ -CTGTGCTGGGAATTGTGATG-3 ′ R 5 ′ -ATTCTTGCTGGGCCTTCTG-3 ′ RPL32 ENSECAG00000007201 F 5 ′ -TGAAGTGCTGCTCATGTGCA-3 ′ R 5 ′ -GGGATTGGTGATTCTGATGGC-3 ′ Vascularization ENG XM_0011500078. 5 F 5 ′ -ACAGTCGAACAGCGACTTCA-3 ′ R 5 ′ -TTCTTCCCCAAATTCGATTCA-3 ′ Flt1 XM_003363176. 1 F 5 ′ -AGTGTGAGCGGCTCCCTTATG-3 ′ R 5 ′ -ATGCCAAATGCAGATGCTTG-3 ′ KDR XM_001916946. 2 F 5 ′ -CAGTGGGCTGATGACCAAGA-3 ′ R 5 ′ -TCCACCGAAGATTCCATGCC-3 ′ Nutrient transport SCL2A1 NM_001163971. 1 F 5 ′ -TGTGCTCATGACCATCGCC-3 ′ R 5 ′ -AAGCCAAAGATGGCCACGAT-3 ′ SLC2A3 XM_001498757. 2 F 5 ′ -CCGTTGGTGGTATGATTGGC-3 ′ R 5 ′ -CAGAACCCCATAAGGCAGCC-3 ′ SlC38A2 ENSECAT00000016020 F 5 ′ -ACAGCTCGAACAGCGACTTCA-3 ′ R 5 ′ -TTCTTCCCCAAATTCGATTCA-3 ′ CD36 ENSECAG00000015229 F 5 ′ -CCGTGCAGAAGCAGTGGTTA-3 ′ R 5 ′ -CCGTGCAGAAGCAGTGGTTA-3 ′ LPL XM_001489577.
, Templemars, France), as described by Rousseau et al. [52]. Identification of fatty acids was made in reference to known fatty acid profiles obtained from the injection of a standard FAME (fatty acid methyl esters) mix (Supelco 37-component FAME mix, ref Finally, 20 fatty acids were measured in milk and plasma from mares and plasma from foals, as presented in Table  2. Table  2. Common and systematic name, symbol formula and category of fatty acids measured and presented (lipid maps database). Other fatty acids, such as C22:5ω3 and C24:1ω9, were also assayed but not displayed because, for most of the samples, their concentration was below the detection limits.
Statistical Analyses Results are expressed as median [quartile 1-quartile 3] and presented as curves (median and interquartile range IQR) or boxplots (minimum to maximum). Statistical analyses and figures were performed using the Rstudio software (version 1. 1. 463. , R version 3. 5. 1). For all analyses, effects were considered significant when p-values < 0. 05 (or adjusted p-values < 0. 05). The results of placental biometry, stereology, and gene expression were analysed with a non-parametric permutation ANOVA (package lmPerm, function aovp) considering the group (N or O), the age, and wither's height of the mare. For reasons of reproducibility, the seed was set at 1234. The sex of the foal had no effect on the measured parameters and was therefore not included in the model. Due to multiple testing for stereology and gene expression analyses, p-values were corrected using the fdr method.
2. Non-parametric permutations ANOVA (package lmPerm, function aovp) considering the group (N or O), the age of the mare for plasma of mares and milk, and the sex of the foal for plasma of foals. The seed was set at 1234, and p-values were corrected using the fdr method. A volcano plot was drawn using the log2(fold-change) and the -10log(adjusted p-value) to summarise the linear model results. Boxplots and volcano plots were drawn using the ggplot2 package. Missing data (between 4% and 39% depending on the sample type and time of sampling) were imputed using the missMDA package before analysis. The function impute-Multilevel was used to specify the data structure for the imputation. PCA is a factorial method that enables the simultaneous study of several variables defined for the same set of individuals.
Briefly, this method allows description, summarization, and reduction of the dimensionality of a dataset; it simplifies a multidimensional dataset into less numerous new variables called principal components. These principal components are therefore used to visualise the data graphically: - The individual factor map is a plot of the Principal Component Scores for individuals on the first two principal components. Confidence ellipses (0. 95, around the barycenter) were added to the figure. A permutational multivariate analysis of variance (PERMANOVA, package vegan, function adonis2) was used to evaluate the difference between the barycenters of both groups, which was considered significant when p < 0. 05. - The variable factor map presents a view of the projection of the observed variables projected into the plane spanned by the first two principal components.
Also named a Correlation circle, it can help to visualise the most correlated variables (i. e. , variables that group together) and to show the structural relationship between the variables and the components. - The projection of a variable vector onto the component axis allows us to directly read the correlation between the variable and the component. To simplify the reading of the figures, we merged the individuals and the variable factor maps into one biplot using the factoextra package (function fviz_pca_biplot). Fatty acid concentrations were considered variables, while group and sampling type were considered qualitative factors. Total SFA: saturated fatty acids, MUFA: monounsaturated fatty acids, PUFA: polyunsaturated fatty acids, medium chain SFA (C10-14), medium chain MUFA (C10-C14), long chain SFA (C15-C20), long chain MUFA (C15-C20), ω-7 fatty acids, ω-9 fatty acids, ω-6 PUFA, ω-3 PUFA, ω3/ω6 PUFA ratio were added as supplementary variables.
Rats born to insulin-resistant dams have an increased hepatic activity of SCD1, resulting in an increased proportion of C16:1ω7 lipids in the liver  [105]. In humans, mesenchymal stem cells derived from the umbilical cord of infants born to obese mothers have an increased lipid content and altered fatty oxidation compared to normal mesenchymal stem cells [106]. These alterations in lipid metabolism are often associated with insulin resistance  [105]. In the present article, the metabolism of foals was evaluated at 6, 12, and 18 months of age, and foals born to Obese mares had a reduced insulin sensitivity at 6 and 18 months of age compared to foals born to Normal mares. No conclusion can be drawn from the data available, and more research is needed to understand the biological mechanisms behind these differences.
The full ethogram is described in Supp. Data 1. TB (%) = 100× (Time spent on a state behaviour in seconds)/(30×6×3=540) For a 6-day-period of behavioural observation, each individual was observed for 3 days. We recorded 6 times per day the activities of the animals for 30 s (9 min in total). Each of the two experimenters recorded the activities of 50 out of the 100 studied male broilers. To limit experimenter bias, we allowed a preliminary period of training to the ethogram before beginning this study. Inter-observer agreement was optimal for all of the state behaviours as Cohen's kappa coefficient was 0. 80-1. 00 (Supp. Data 2).
Statistical analysis Our objectives were to understand how genetic variability affects the broiler's personality traits, to identify early-life behavioural indicators of range use and to assess the stability of early-life indicators of range use among four broiler strains. We performed the statistical analysis in three steps using R version 4. 1. 2. Step 1: Effect of strain on early-life behaviours (before range access). In this first step, we analysed the differences in early-life behaviours measured by focal sampling and individual tests before range access between the four studied strains with the Kruskal-Wallis test (because the data were not normally distributed). We considered differences between groups as significant when the p-value was < 0. 05. When the pvalue was significant, we performed a pairwise Wilcoxon test to evaluate the variation between strains.
Range use For each strain independently, we measured the evolution of range use over time by considering the individual DI by day of observation until the FDI. At the level of the strain including the FDI for 100 individuals per strain, range use increased with time but we observed a relatively poor quality of adjustment (adjusted R 2 0. 16-0. 49) in the linear regression between the FDI and the age of measurement (Supp. Data 3). This means that due to inter-individual variability in range use, we could not draw a conclusion regarding the time consistency of range use at the strain level. By contrast, we found a strong linear relationship between the FDI and the age at the level of the individual, indicating that within-individual range use is consistent over time. Indeed, the adjusted R 2 coefficient of the linear regression was > 0.
π, π). To ensure a positive speed value, the analysis uses the logarithm of the speed value. ⎛ ⎜ ⎜ ⎝ log ( R t 1 ) ⋮ log ( R t m ) ⎞ ⎟ ⎟ ⎠ = ⎛ ⎜ ⎜ ⎝ log ( R t-1 1 ) ⋯ log ( R t-n 1 ) ⋮ ⋮ log ( R t-1 1 ) ⋯ log ( R t-n 1 ) ⎞ ⎟ ⎟ ⎠ ⎛ ⎝ α 1 ⋮ α n ⎞ ⎠ The motion is then estimated as: R t i = ∏ t-1 s=1 ( R t-s σ -(t-s+1),-t (i) ) αs (21) ΔΘ t i = ∑ t-1 s=1 β s ΔΘ t-s σ -(t-s+1),-t (i) (22) ΔΘ t- 1 i = Θ t i -Θ t-1 i ( 23 ) Vt+1 σ t+1 (i) = Rt+1 σ t+1 (i) ⎛ ⎝ cos ( Θ t i + Δ Θt+1 σ t+1 (i) ) sin ( Θ t i + Δ Θt+1 σ t+1 (i) ) ⎞ ⎠ (24) Parameters in (  21 ) and (  22 ) are also estimated with least squares method on the reference database. As said previously, a non detected object would be considered to be at the same position as its last measure, since its motion is set as null. It means, a null speed but also an undetermined orientation of motion.
Because undetermined is a number difficult to handle with, we arbitrary set it to zero. Least squares under constraints In the previous motion estimation there is no dependence between speed values and variations of orientation. Thanks to the tracking database created manually (all objects are detected, there are no false positive and no tracking error), it is possible to check the relationship between the speed and the variation of orientation. Variations of orientation are centered around zero. For each Fig. 5. Relation between the speed and the variation of orientation.
Parameters estimations Two data sets are associated to each video. The first data set is the reference one. A surrounding bounding box has been drawn manually around all broilers in order to have a good localization, without any miss detection nor false positive detection. The second data set are data generated by a convolutional neural network. These data are used to test the tracking motion estimation methods. Tracking has been run over the reference data set and then over the test data set for the six following tracking methods which differs by the way the motion is estimated: • Nearest Neighbor (Method 1) • Regular motion (Method 2) • Weighted Average (Method 3) • Cartesian Least Squares (Method 4) • Polar Least Squares (Method 5) • Least Squares Under Constraints (Method 6) The objective is to have common coefficients for all data sets.
Analyze of performance for each method Method 1 Nearest neighbor As expected Nearest Neighbor method is one of the worsts as predict high speed motions. Even with the reference data (perfect detection), it is one of the last to reach a minimum of errors. Its best performance is found for high density video in test situation. Method 2 Regular motion based on  [11] , it was expected to have good performances for straight movements and good quality of detection. Straight movements are mainly presents in the video of 21 days of age broilers as speeds are the higher. In practice its best performance is effectively find the 21 days of age broilers data test where it reaches quickly the minimum of errors. However performances decrease for low broiler activities both in reference and test data sets.
Method 5 & 6 Polar least squares & least squares under constraints Even though their coefficients have been tuned with reference data set, the Polar method remain one of the worst one both for reference and test data sets. However these methods did present the lowest standard deviation in Table  3. It means they are too sensitive to movements due to imprecision of detection. Nevertheless, the limit of outliers by the Hybrid method seems really efficient when the two methods are compared together. Even though the Hybrid method is quite slow to reach the minimum for low densities video (21 and 37 days of age), it is among those which reach the lowest number of tracking errors and seems to reach the best performance for high density video. A successful method of motion prediction has to be robust to: the density of the broilers, to their activities, to motions derived from the detection imprecision, to the occlusions and miss detections.
COI and ITS-2 primer design We aimed to define a 450-bp amplicon within the 650-bp fragment of the cytochrome c oxidase subunit I (COI) locus previously described  (Bredtmann et al. , 2019; Louro et al. , 2021). This would leave at most 50 bp overlap, thereby allowing sequencing error correction and better amplicon resolution  (Edgar & Flyvbjerg, 2015). For this, 18 mitochondrial sequences of 17 strongylid species with complete mitochondrial genomes available at that time (October 9, 2020) were retrieved from GenBank using the PrimerMiner package v4. 0. 5  (Elbrecht & Leese, 2017b)   (Edgar, 2004). Subsequently, this alignment was used to quantify sequence heterozygosity for 450-bp sliding windows using a custom python script (File S1). The consensus sequence of the region with the highest diversity, i.