dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions The aim of the present paper was to define indicators extracted from IMUs' data that could reveal locomotion changes associated with fatigue in horses. For this, various locomotion indicators were considered to assess for changes in the kinematics following a training session supposed to provoke fatigue in the horses. If the training session did not seem to induce an important physiological fatigue, some locomotor indicators were statistically different after vs before the training session, indicating that IMUs seem appropriate to track locomotion pattern modifications due to training. Future research should be conducted to be able to fully attribute the modifications of these indicators to fatigue.
22 , where A 22 is the pedigree-based numerator relationship matrix for the genotyped animals and ω = (1 -0. 05) * + 0. 05A 22. Matrix * was obtained by scaling the genomic relationship matrix  [33] , to make the means of the diagonal and offdiagonal elements of * and A 22 matrices equal to each other. The A matrix was obtained for all animals in the pedi- gree plus ancestors (grandparents), and comprised 3095 individuals over 10 generations. The H matrix was obtained and formatted using the PreGSf90 software  [34]  and modified with R to be supplied to ASReml as a user defined relationship matrix.
, u i10 ) ). Fixed effects included in the model ( x ij β ) were the combination week by generation (10 × 8 levels), the combination batch by sex (66 × 3 levels), birth herd (2 levels), age at start of the test (covariate), and pen (16 levels). Variance components for this full dataset were estimated using the REML approach in ASReml 4. 0  [35]  and were considered as known in all subsequent analyses. Estimates of heritability of each SBV l , obtained by the Eigen decomposition of the genetic covariance matrix G , were computed as h 2 SBV l = L ′ G,l GL G,l L ′ G,l G+ P+ D L G,l. The LR method applied to the SBV of a group of focal individuals (i. e. individuals of interest) consists in comparing the estimated SBV (ESBV) of these focal individuals based on less information to their ESBV based on more information, considering that more information provides a reasonable reference.
The additional information considered were the phenotypes of their descendants or their genomic information. To do so, five cut-offs were considered, corresponding to the number of generations with phenotypes in the dataset (from 3 generations (G0, G1, G2, cut-1) to 7 generations (cut-5)), as described in Fig. 1. Based on these cut-offs, five groups of focal individuals were defined (group_FIi , i = 1,…,5), which corresponded to the dams of the first generation of animals without phenotypes. For instance, the first group of focal individuals (group_FI1) consisted of the dams of the first generation without phenotypes in cut-1, i. e. the dams of the phenotyped animals in G3. For each group of focal individuals, the three LR statistics were computed to compare predictions obtained with less information to those obtained with more information.
Thus, the following comparisons made were: ESBV p -ESBV w L ′ G,l (1) SBV predicted for the group of focal individuals (group_FIi ) obtained with phenotypes in cut_i compared to SBV predicted for the group of focal individuals obtained with phenotypes in cut_(i+1), cut_(i+1) corresponding to the full dataset if ( i = 5) using (a) pedigree information only (i. e. A matrix) or (b) genomic informa- tion (i. e. the A matrix is then replaced by the H matrix for the distribution of genetic effects in the RR model); (2) SBV predicted for the group of focal individuals (group_FIi ) obtained with phenotypes in cut_i compared to SBV predicted for the group of focal individuals obtained with phenotypes of the full dataset using (a) pedigree information only (i. e. A matrix)) or (b) genomic information (i.
e. H matrix); (3) SBV predicted for the group of focal individuals using pedigree information (i. e. A matrix) compared to SBV obtained with phenotypes of the same dataset using genomic information (i. e. H matrix). SBV were obtained using phenotypes in (a) cut_i or (b) phenotypes of the full dataset. The first two comparisons allowed us to evaluate the quality of the model to predict SBV based on phenotypic information on ascendants and collateral relatives of the focal individuals. For these two comparisons, two "whole" datasets were considered as the gold standard (cut-(i+1) or full data) to account for random errors in the estimates of the LR statistics  [36]. The third comparison allowed us to evaluate the benefit of genomic information for predictions. The comparisons were performed separately for each line and for the three first SBV.
Mirroring the SBV, fixed effects and variance component estimates obtained for the full dataset and the pedigree relationship matrix were used to compute the following three summarized phenotypes y SBV l , l = 1, 2, 3 : y SBV l = L ′ G,l FI -X β -β 1 MBW -β 2 ADG -β 3 BFT. In order to obtain summarized phenotypes for all animals for which one of the longitudinal phenotype (FI, MBW, ADG or BFT) was missing (4. 2% of the data corresponding to 20% of the animals with at least one missing weekly phenotype), missing values for FI -X β -β 1 MBW- the applicability of longitudinal models for routine genetic evaluation, we then compared the EBV of y SBV l u ySBV l to those obtained for SBV l , assuming that the eigenvec- tors do not have to be re-computed for each new evaluation. β 2 ADG -β 3 BFT.
Results The genetic parameters were estimated based on the full dataset and the pedigree relationship matrix. The three first eigenvalues from the eigen decomposition of G represented 59, 26 and 15% of the total genetic vari- ance, respectively. Heritability estimates of the corresponding first three SBV were 0. S1 ). The ratio of accuracy between SBV obtained with more or less phenotypic information was significantly lower for SBV3 than for SBV2 ( ρ w,p = 0. 56 [0. 51,0. 61] versus 0. 67 [0. 61,0. 72]). Considering the SBV obtained using pedigree and genomic information as the gold standard, on average, SBV obtained with pedigree information only were biased downwards ( �µ wp =-0. 08 [-0. 09,-0. 07]). The bias dif- fered between SBV: underestimated for SBV 1 and SBV 2 and overestimated for SBV 3 (see Additional file 2: Fig.
S2 ). Finally, the SBV predicted using longitudinal phenotypes and EBV predicted using summarized phenotypes ( u ySBV p ) for genotyped animals in the full dataset are summarized in Table  2 : the EBV obtained from summarized phenotypes were unbiased, neither overnor under-dispersed, and were as accurate as the SBV predicted using longitudinal phenotypes, when computed with the pedigree information only ( A matrix) or combining pedigree and genotypes ( H matrix).
In addition, the BV of interest for selection in the longitudinal case, i. e. SBV, are a linear combination of EBV that accumulates those aforementioned difficulties for each time point, making the computation of the accuracy of the resulting SBV with traditional approaches quite complex when information is heterogeneous across time points and candidates. To be relevant, the LR method should be applied to a set of focal individuals that is sufficiently large and diverse (i. e. animals from several families), that should represent the population of interest (selection candidates in our case), for which the quantity of "information" used to estimate SBV should be similar for the different focal individuals in the partial dataset as well as in the whole dataset and for which the reference EBV should be reasonably accurate.
[36] , the estimation of LR indicators in successive focal groups. In order to meet the requirement for the focal individuals to have the same quantity of information to predict their EBV, only genotyped dams were considered as focal individuals, and not genotyped sires. Indeed, the quantity of information to predict the SBV of sires and dams would have been similar in the partial dataset (phenotypes from ascendants), but not in the whole dataset, because sires had on average six times more phenotyped progeny than females. Finally, the theoretical accuracy of the reference EBV (whole population) for the focal individuals was around 0. 6 for SBV1 and 0. 5 for SBV2 and SBV3, and thus fulfilled the requested reasonable accuracy (Andres Legarra, personal communication). In this study, we were not interested in estimating the accuracy of SBV of the focal animals themselves, as the ratio of accuracies was sufficient to judge the quality of the SBV at the time of selection and to evaluate the gain from genomic information for selection.
Consequently, too little of the genetic variance could be captured by the genomic information in this dataset to have a significant impact on ESBV. In addition, there were no animals with both genotype and phenotype, and the number of phenotyped progeny for each focal genotyped animal was small. It should be noted that a single set of scaling factors to construct the H matrix has been tested, although their values may impact bias and dispersion of GEBV  [42]. Breeding values that summarized the trajectory of RFI over time using eigenvectors of the estimated variancecovariance structure of the longitudinal data were obtained using two approaches: SBV l , which were extracted from the genetic analysis of the longitudinal phenotypes (considered as the reference), and u SBV l , which corresponds to the breed- ing values in the analysis of summarized phenotypes.
Conclusions Using the LR method, we evaluated, the quality of prediction of breeding values for candidates without own phenotypes in the study of longitudinal data, by using two different approaches (analysis of longitudinal or summarized phenotypes), and evaluated the benefit of adding genomic information for prediction. Predictions were of similar quality with the two approaches, meaning that, in this population design, the use of summarized phenotypes would be of interest for routine evaluation of longitudinal traits. We did not highlight any benefit of genomic information for prediction in this study, which is certainly due to the number of genotyped animals with accurate estimation being too small in this dataset, contrary to the usual expectation.
ESBV p was evaluated by the regression of ESBV w on ESBV p : b w,p = cov(ESBV p ,ESBV w ) var(ESBV p). Its expected value is 1, if there is no over-/under-dispersion. Finally, the ratio of the prediction accuracy (i. e. the correlation between true and estimated breeding values) of ESBV p to the predic- tion accuracy of ESBV w was evaluated by the correlation cov(ESBV p ,ESBV w ) √ between these ESBV: ρ w,p = var(ESBV p) var(ESBV w ) GL G,l, where ESBV p and ESBV w are the mean of ESBV p and ESBV w , respectively. Its expected value is 0 if the evalua- tion with the partial dataset is unbiased. Dispersion of.
Morphology of the body of closely related monogeneans The silhouettes of the body of several diclidophorids from parasitic isopods are shown in Figure  6. Data are in Table  2. Our comparison includes 13 species: 4 of these species were collected from isopods parasite in the mouth of fish; one species from the buccal cavity; one from the buccal cavity and parasitic Cymothoa; the remaining 7 species of diclidophorids infect gills of fishes. The anterior extension, the stem, was observed only in  diclidophorids which dwell on parasitic isopods (Figs. 6A-6M ) but never in specimens that infect gills (Figs. 6P-6V ). A comparison of silhouettes of two diclidophorids collected in the present study, Cy. bellones and Choricotyle cf. chrysophryi shows that the stem was found only in the former species (Fig.
Experimental design In the current study, we used the same animals as those described in Déru et al. [3]. In total, 1942 purebred Large White (LW) male pigs were reared in 35 consecutive batches between 2017 and 2018 at the INRAE UE3P France Génétique Porc phenotyping station under two dietary conditions. The study was designed to genetically connect the datasets obtained with the two diets, i. e. to facilitate the estimation of genetic covariances between diets in a dataset of limited size, from each pair of full sibs of homogeneous weights, one was fed one diet and the other the alternative diet. All pigs were obtained from 171 sires that were representative of those used in the French Large White collective breeding scheme, and each pair of full-sibs came from a different dam. Housing conditions and management of pigs were as described in Déru et al.
Recorded traits and sampling For each animal, ADG and DFI were measured between 35 and 115 kg. ADG was computed as the ratio between body weight gain and number of days on test. The FE traits were FCR and RFI. FCR was calculated as the ratio between DFI and ADG and was expressed in kg/kg. For the two diets, RFI was determined using a single multiple linear regression  [19]  of DFI on ADG, lean meat percentage and carcass yield recorded at slaughter, and average metabolic body weight as described in Déru et al. [3]. The proportion of pigs that experienced health problems or injury during the test period was the same in the two diet groups, and these were discarded from the analysis. Among the 1942 pigs in the experiment, 1663 had data available for FCR, ADG and DFI, and only 1595 pigs had data for RFI.
Microbiota DNA preparation and sequencing Since it has been shown that the microbiota composition of feces is similar to that of the large intestine  [21] , fecal samples were collected and analyzed to approximate the gut microbiota composition based on sequencing data of the ribosomal 16S DNA gene. Details of the microbial DNA preparation and sequencing are in Déru et al. [9] , and briefly summarized in the next paragraph. After sequencing of the V3-V4 region of the 16S rRNA, high quality reads were filtered and trimmed using the DADA2 package in the R software  [22]. Chimera were removed and no further clustering was applied, so in this study OTU were equivalent to amplicon sequence variants (ASV). Subsequently, the final OTU abundance table was obtained, followed by taxonomic annotation using the assignTaxonomy function of DADA2 with the Silva Dataset v132  [23].
The obtained file was rarefied to 10,000 counts per sample using the Phyloseq package  [24]  and had information on 1564 pigs. In total, 14,366 OTU were retained in the abundance tables for 812 pigs fed the CO diet and 752 pigs fed the HF diet. Sequence information obtained in the current study was deposited in the Short-Read Archive with accession number PRJNA741111.
Genotyping In total, 1691 animals were genotyped with the 70K single nucleotide polymorphism (SNP) GeneSeek GGP Porcine HD chip. The following quality control (QC) criteria were applied: a call rate per individual (the percentage of genotypes present per individual) of 95%, and a SNP call rate (the percentage of genotypes by SNP) of 95%. SNPs with a minor allele frequency lower than 5%, or that showed a significant deviation from Hardy-Weinberg equilibrium (P < 0. 000001), and those on the sex chromosomes or not mapped were deleted. This QC was performed with the PLINK 1. 09 software  [25]. After QC, 1687 animals and 48,919 SNPs were available for further analyses.
Models We performed a preliminary analysis by ignoring the additive genomic and microbiota effects to determine the fixed and random effects to be included in subsequent analyses using a linear mixed model implemented with the "lme4" and "lmerTest" R packages  [28, 29]. Only the effects that were significant at a threshold of 5% were retained. To estimate the contributions of host genetics and microbiota to the phenotypic variation of FE and DE traits, and the GEBV and EMV, we used three Bayesian M = SS T n , linear regression models fitted with the BGLR package in R  [30] : model Gen that included a genomic effect, model Micro that included a microbiota effect, and model Micro+Gen with both effects. Each model was fitted separately for the CO and HF diets and all were univariate linear mixed models.
The third model that jointly fitted genomic and microbiota information, with no covariance between the two random variables, was: where the effects are the same as in models (Gen) and (Micro), but jointly estimated. A Bayesian reproducing kernel Hilbert space (RKHS) model was used. For all the models, the residual, microbiota, and genetic variances were assigned scaled-inverse Chi-square densities as prior density, with hyperparameters of 5 degrees of freedom and a scale parameter based on the sample variance of the phenotypes, as proposed by default in the BGLR package  [30]. A Gaussian prior with mean zero and variance equal to 10 10 was assigned to the fixed effects. A single chain of 120,000 iterations was run for all the models, with 20,000 rounds discarded as burn-in and a thinning of 20.
An R script for the computation of the G × D interaction for ADG is provided in Additional file 2: Script S1. A univariate linear mixed model was applied to quantify the G × D interaction: with b 0 ∼ N (0, Iσ 2 b0 ) and b j ∼ N (0, Iσ 2 bj ) being the main SNP effects and the specific SNP effects in each environment, respectively. Z i is the block of the full Z matrix cor- responding to the animals raised in diet i (I ∈ {CO, HF }). The model was implemented as follows: where u 0 ∼ N 0, Gσ 2 u0 is the animal main genetic effects, and u 1 = u CO u HF ∼ N (0, G 1 ) the environment specific genetic effects, with G 1 = σ 2 uCO Z CO Z CO ′ 0 0 σ 2 uHF Z HF Z HF ′ d, where d is the number of SNPs. The genetic-by-diet interaction was calculated as follows: Similarly, a univariate linear mixed model was applied to quantify the M × D interaction as: where m 0 ∼ N 0, Mσ 2 m0 is the vector of the animal main microbiota effects, and m 1 ∼ N (0, M 1 ) the vector of the environment-specific microbiota effects, with M 1 = σ 2 mCO W CO W CO ′ 0 0 σ 2 mHF W HF W HF ′ n, with n the number of OTU.
Item Based on fecal microbiota samples that were collected from 160 growing pigs from a three-way cross, Verschuren et al. [15]  estimated high m 2 values for DC of dry matter and organic matter (0. 59 ± 0. 19 and 0. 58 ± 0. 19, respectively), and even higher values for DC of crude protein (0. 93 ± 0. 10). Our results confirmed these high m 2 values for DE traits on a larger dataset. Microbiota is closely related to the digestion process. Due to its fermentation activity, the gut microbiota enables the production of primary and secondary metabolites (such as short-chain fatty acids, vitamins, metabolites, etc. ), which are then absorbed at the level of the intestinal barrier  [6, 7]. This biological interaction between microbiota composition and digestion could explain the high m 2 esti- mates found for the DE traits.
The advantage of this analysis is that the variance is separated in a common component between the two diets that corresponds to the covariance between diets, and in specific components due to the diets. However, these results need to be confirmed on a larger dataset. In addition, since the microbiability estimate was high for the DE traits, selection for microbiota genera that are favorably associated with digestive efficiency could also be considered and thus improve feed efficiency and growth performances in pigs as suggested in Déru et al. [9]. Additional work is needed to investigate these dynamics. Furthermore, in this work we used fecal samples to collect gut microbiota information, which is a non-invasive method, justifying its potential use for breeding purposes. Finally, to select DE traits, especially in a context of diversification of feed resources, collecting microbiota information to better estimate GEBV for these traits is probably relevant.
Because of their intense metabolism, lactating sows are highly sensitive to high ambient temperature which induces a reduction in their voluntary feed intake and milk production, which decreases piglet weaning weight. This also results in an increase in mobilisation of body reserves that may impair reproduction after weaning. The aim of the study was to quantify, on the basis of a quantitative analysis of the literature data, the effect of ambient temperature on the performance and physiology of lactating sows, with the perspective of integrating this knowledge in sow nutrition decision support tools. A literature database with 38 publications and a total of 227 observations was built in order to adjust prediction equations according to temperature, using a Mixed linear or quadratic model with random effect of publication, for different criteria such as feed intake, litter and piglet growth rate, milk production, maternal body reserve mobilisation, respiratory rate (RR) and core body temperature.
Database A database was compiled from 39 published papers in scientific journals indexed in PubMed, Science direct, and from proceedings of scientific meetings. To be included in the final database, studies had to meet the following criteria: research methods (housing, feeding management) were adequately described, sows had free access to feed and water, feed composition was adequately described, and at least two temperature treatments were compared in the experiment. Moreover selected publications should report data on at least some of the following criteria on sows: average daily feed intake (ADFI), metabolisable energy (ME) intake, losses of BW or backfat thickness (BF) during the lactation period, respiration rate (RR), body rectal temperature (RT), and on litters: litter growth rate (LGR), milk consumption.
Statistical analysis Data were analysed using the MIXED procedure (SAS Inst. Inc. , Cary, NC) according to a Mixed model adapted from  Renaudeau et al. (2011)  with the linear and quadratic effects of temperature, the random effect of publication and its interaction with temperature: Y ij ¼ b 0 þ b 1 T ij þ b 2 T 2 ij þ p i þ a i T ij þ e ij ð1Þ where Y ij is the value for variable Y from publication i at temperature T ij , b 0 is the intercept, b 1 and b 2 are the slopes for the fixed linear and quadratic effects of temperature, p i is the random effect of the publication i, a i is the slope of the random linear effect of temperature for publication i, and e ij is the residual error. For criteria with a sufficient amount of data available, the effect of sow parity (primiparous vs multiparous) was added to the model (d 1p ) as well as its interaction with temperature d 2p T ij as follows: Y ij ¼ b 0 þ b 1 T ij þ b 2 T 2 ij þ p i þ a i T ij þ d 1p þ d 2p T ij þ e ij ð2Þ Modelling and simulations The prediction equations of ADFI and LGR (f FI and f LGR, respectively) obtained from the meta-analysis were integrated in the InraPorc model, which predicts nutrient utilisation and requirement of lactating sows  (Dourmad et al.
Meta-Analysis The literature database is presented in Table  1 , with the number of publications and the number of observations for all recorded criteria, and their mean, standard deviation, and minimum and maximum values. On average, the ambient temperature was 25. 3 °C with values varying between 18 and 38 °C, according to experimental treatments. Average lactation length was 25 ± 4. 1 d. Most of the studies (70 %) were performed on multiparous sows or on sows from different parities, the remaining (30 %) being performed on first-parity sows. Sows' ADFI was 4. 78 ± 1. 19 kg/d on average, with values varying between 2. 54 and 7. 97 kg/d, according to the temperature treatments (Table  1 ). According to the linear model (Table  3 ), ADFI and metabolisable energy (ME) intake decreased (P < 0.