dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank the staff from the  Herbipôle experimental unit  and the farmers for allowing us to use the data from their logbooks. We warmly thank  GEA Farm Technologies  for providing us with the CowView data and giving us access to the datasets from commercial farms. We also thank  Metaform  for valuable assistance with Englishlanguage proofing. This collaborative work was made possible by funding from the  French government research agency Agence Nationale de la Recherche  through the  ''Investissements d'Avenir'' programme  ( 16-IDEX-0001 CAP 20-25 ,  2017 ). Nicolas Wagner  had PhD grant support from  INRAE  ( Phase division ) and  Université Clermont Auvergne  ( SPI doctorate school ). The experiment on LPS injections (in Dataset 1) was funded by the  INRAE GISA programme  ( LongHealth Project, coord.
2 Detected visually by caretakers or from milk progesterone profile3LPS injected in the mammary gland4Detected from ruminal pH5Interventions, e. g. vaccination, oestrus synchronization, anthelmintic treatment, claw trimming, relocation Tab. 2. Overall performance of FBAT on four datasets. The threshold τ used to differentiate normal vs. abnormal time series was computed on training sets (30% of each dataset). , and , are for the percentage of normal series detected as normal and the percentage of abnormal series detected as abnormal (i. e. with a modified circadian rhythm) on test sets (70% of each dataset). Training time is the time to compute threshold τ. Test time is the time to detect any changes in activity rhythm. Tab. 3. Performance of FBAT expressed in terms of detection of at least one day with a modified circadian rhythm within a sequence surrounding an event, stratified by type of events (with a fixed threshold τ = 2000).
Detected visually by caretakers or from milk progesterone profile3LPS injected in the mammary gland4Detected from ruminal pH5Interventions, e. g. vaccination, oestrus synchronization, anthelmintic treatment, claw trimming, relocation na: not applicable Events 1 2 Datasets 3 4 Accidental event 1 no. events % detected 0 na 0 na 0 na 10 100 Calving no. events % detected 9 100 0 na 0 na 171 99. 4 Dataset Oestrus 2 No. Training time no. events % detected 41 95. 1 Test time 7 85. 7 29 69. 2 : ;<= 257 91. 4 ;<= cow*days Lameness (s) no. events % detected 4 100 (s) 16 93. 8 0 na 114 98. 2 1 Mastitis 5124 2810 no. events % detected 9 100 25. 8 3 0 0 na 2216 75. 8 32 87. 5 29. 3 2 3 Other disease 2562 1220 4 109800 LPS injection 3 Ruminal acidosis 4 1220 781 no. events % detected 69300 no. events % detected no.
Dairy goat data set In this study, we used data from 181 goats (94 Alpine and 87 Saanen) born between 2009 and 2017. Data concerned 319 lactations (126 primiparous and 193 multiparous; parity ranging from 1 to 7) including 80 773 milk records from the dairy goat herd of the INRAE-AgroParisTech Systemic Modelling Applied to Ruminants research unit (Paris, France) between 2015 and 2018. Records are shown in Fig. S3  by breed and parity. All lactations considered had at least one record in the first 5 days of lactation and a last record between 150 and 358 days of lactation (no extended lactation included).
Fitting perturbed lactation model to lactation data Beyond the original concepts behind PLM, a key methodological issue was the development of the fitting algorithm. The number of parameters to be determined is important, including the Wood parameters of the unperturbed curve (3 parameters), and PLM parameters (4 parameters for each perturbation). To overcome the difficulty of estimating a high number of parameters, a 2-step algorithm was implemented. The first step of the procedure was to determine Wood parameters and the times when the perturbations start. The second step of the procedure was to determine PLM parameters. This 2-step algorithm was selected for three main reasons. The first one was related to the visual quality of the fitting results itself. Indeed, the obtained fitted curve is always very close to what would have been drawn after simply looking at the raw data and wondering what the lactation curve would be without perturbations.
Perturbed lactation model limitations A major limitation of PLM resides in its dependency on the quality of data. Indeed, if data are recorded with a low accuracy (due to technical problems of measurements), the outputs of PLM do not have consistency as detected perturbations have nothing to do with perturbations of the lactation curve, but are related to accuracy problem as intuitively identified when evaluating the curve shape. In addition, PLM has been developed with daily records. It will be necessary to evaluate if PLM can operate correctly with less frequent data.
Acknowledgements We gratefully acknowledge the team at the  INRAE UMR 791 Modélisation Systémique Appliquée aux Ruminants (Paris, France) experimental installation  for the care of animals and their work to provide robust performance data. thanks to  Dr. R. Muñoz-Tamayo  for his conscientious reading and meticulous corrections of the manuscript. This work was carried out with the financial support of the  ANR -Agence Nationale de la Recherche  -The  French National Research Agency  under the " Deffilait project "  (ANR;  project:  ANR-15-CE20-0014 ). This article was deposited as a pre-print has been reviewed and recommended by  Peer Community In Animal Science  (https://animsci. peercommunityin. org; https://doi. org/10. 24072/pci. animsci. 100001).
Identification of hypothalamic differentially expressed genes by RNA-seq resulting from the embryonic and D35 treatments The RNA-sequencing of the 48 hypothalamic samples obtained from the four experimental conditions (i. e. CRT, CHC, TMRT and TMHC; Fig. 1 ) in both sexes generated in average 30. 7 +/-7 million reads per sample (Additional file 1). An average of 90. 4 % +/-2. 4 of the reads were uniquely aligned to the reference quail genome, version 2. 0 (Additional file 2). Reads were counted to genes using the reference quail annotation and differentially expressed genes (DEG) were identified by pairwise comparisons of the four experimental conditions from female and male datasets separately (Fig. 2 , Additional file 3 and deposited data). In males, 76, 27 and 47 genes were determined as DEG in CHC vs.
Analysis of common DEG between treatments and sexes The comparison of the DEG lists between experimental conditions from female and male datasets revealed little overlap (Fig. 4 ). In the male datasets, the IL1R1 gene coding for interleukin 1 receptor was the only one found in common between the three DEG lists (CHC vs. CRT, CRT vs. TMRT and TMHC vs. TMRT; Fig. 4a ). Eleven genes (PER3, PER2, CIART, PYCR1, CA14, C2H6orf62, ARNTL, GANC, NPAS2, NFIL3 and LOC107325652) were impacted by the HC irrespectively of the embryonic treatment as they were found in both CHC vs. CRT and TMHC vs. TMRT comparisons. Additionally, four genes (SIK1, TBX2, LOC107321951 and DHH) were found in both CHC vs. CRT and CRT vs. TMRT comparisons. Two genes (COL18A1 and CD93) were found in both CRT vs. TMRT and TMHC vs.
TMRT comparisons. In the female datasets, given the low number of DEG identified except for the TMHC vs. TMRT comparison, only the comparison CHC vs. CRT and TMHC vs. TMRT had nine common DEG (PER3, PER2, CIART, NR1D2, CIRBP, HSPA5, CA14, ARNTL and NPAS2; Fig. 4b ). Interestingly, some DEG were shared by both female and male datasets (Additional file 6). For both CHC vs. CRT and TMHC vs. TMRT comparisons, PER3, PER2, CIART, CA14, ARNTL and NPAS2 were found differential for both sexes. Three other DEG were found in both sexes for each comparison: ACAN, NR1D2 and NFIL3 for the CHC vs. CRT comparison; SLC13A5, CIRBP and LOC107313685 (coding for a putative melatonin receptor type 1 A according to NCBI database) for the TMHC vs. TMRT comparison. Finally, eight other genes (CD320, LOC107310113, TEF, PMM1, MED11, LOC107312385, Fig.
Gene Ontology functional analysis of DEG lists In order to reveal biological processes impacted by both embryonic and D35 treatments, lists of DEG from male and female datasets (except for the comparison CHC vs. TMHC in females with only 1 DEG) were analysed with the ViSEAGO R package  [27]  by hierarchical clustering enriched Gene Ontology (GO) terms (Fig. 6  and Additional file 7). In this analysis, four and five broad functions were identified in males and females, respectively. In males, 20 GO terms were found significantly enriched in the analysis. The analysis in non-challenging conditions (CRT vs. TMRT) led to the identification of two specifically enriched GO terms related to the DNA repair-related processes in males. GO terms related to the membrane transport and the cellular responses to stimuli (in particular, rhythmic processes) were significantly enriched in both CHC vs.
Library construction, RNA sequencing and bioinformatics analysis Six biological replicates per experimental condition and sex were included in the RNA-seq experiment. Library construction and sequencing were performed the Geno-mEast genomic platform (IGBMC, Illkirch, France). Libraries were created from 500 ng of total RNA using the TruSeq stranded mRNA library prep kit and TruSeq RNA single indexes kits A and B (Illumina). Libraries were sequenced with an Illumina Hi-Seq 4000 sequencer using a multiplexing strategy (50 bp single-end reads). Image analysis and base calling were performed using RTA 2. 7. 3 and bcl2fastq 2. 17. 1. 14. Sequenced reads were mapped to Coturnix japonica 2. 0 genome obtained from the NCBI database (Coturnix japonica 2. 0, GCA_ 001577835. 1) using STAR version 2. 7.
0a with default arguments  [45]. Reads were counted using Feature-Counts version 1. 6. 3 with arguments -T 6 -s 2 -t exon -g gene  [46]. The heat map was drawn using the pheatmap R function version 1. 0. 12 (https://CRAN. R-project. org/ package=pheatmap), using log2 counts-per-million with normalized library sizes, Euclidean distance between genes and complete clustering method. The datasets supporting the conclusions of this article are available at the GEO repository GSE161976.
Gene expression analysis by RT-qPCR RT-qPCR was performed as previously described  [47]. Nine to 10 animals per conditions and sex (including the one used for the RNA-seq experiment) were used. The cDNAs were synthesized from 500 ng of total RNAs using the Superscript II enzyme (Invitrogen) and hexamer random primers (Promega), following the manufacturer's instructions. Quantitative real-time PCR (qPCR) was carried out with Takyon qPCR Kits (Eurogentec) and using a LightCycler® 480 Instrument II system (Roche) with 384-well plates (4TI-0382, 4titude). Primer sequences were designed with NCBI Primer blast (https://www. ncbi. nlm. nih. gov/tools/primer-blast/ index. cgi?LINK_LOC=BlastHome) software. Primer sequences are available in the Additional file 8. Three technical replicates were done for each sample and a standard curve protocol was used to evaluate gene expression.
To assess the specificity of the amplification, every PCR product size was checked on a 2 % agarose gel and sequenced by Sanger sequencing (Genewiz). Relative expression was normalized to the expression of five or six reference genes (selected among 9 reference genes) according to the sex (GAPDH, PGK1, RPS7, RPS8 and YWHAZ for males and ACTB, GAPDH, PGK1, SDHA, TBP and YWHAZ for females; Additional file 8) using the qbase + software (version 3. 2, Biogazelle, Gent, Belgium).
Statistical analyses All statistical analyses were performed with R software, version 3. 6. 1  [48]. Statistical analyses of the RNA-seq data were performed using the Bioconductor edgeR package (version 3. 24. 3) [49, 50]  to identify the differentially expressed genes (DEG) between the four experimental conditions CRT, CHC, TMRT and TMHC. Samples from females and males were analysed independently in two datasets. TMM normalization (trimmed mean of M values) was applied to each dataset to account for compositional difference between the libraries. A generalized linear model was fitted to test the pairwise comparisons between all four conditions with likelihood ratio tests. P-values were adjusted by controlling the false positive rate below 0. 05 with a Benjamini-Hochberg correction  [51]. Lists of DEG between all four experimental conditions from females and males datasets were compared using Venn diagram in jvenn web application  [52].
Differentially expressed genes identified by RNA-seq were also investigated in RT-qPCR. Gene expression data obtained by RT-qPCR was transformed with a logarithmic function (base = 2) and differential expression between two biological conditions was analysed with Student's test or Welch's test upon assumption of equal variances. The difference in gene expression by RT-qPCR between conditions was considered significant for p-values ≤ 0. 05.
Gene Ontology functional analysis Gene annotation was performed with the EntrezGene NCBI database for the Japanese quail (organism id: 93, 934, version 2019-10-18)  [53]  to perform functional analysis at the gene level. The Gene Ontology enrichment was explored with the R package ViSEAGO  [27]  with the whole quail genome as background. Enrichment tests were performed on each list of DEG from females and males datasets using exact Fisher's test. Enriched GO terms are provided in the Additional file 7. All enriched GO terms (p ≤ 0. 01) were grouped into functional clusters using hierarchical clustering based on Wang's semantic similarity between GO terms respecting GO graph topology and Ward's criterion.
Acknowledgements We are grateful to all the members of the MOQA team at  INRAE  for assistance with sample collection and to the staff of the  INRAE  PEAT (2018, https://doi. org/10. 15454/1. 5572326250887292E12 ) Poultry Experimental Facility  for animal care and management. The authors thank the SRISE platform of the  UMR PRC (INRAe-CNRS-Université de Tours, Nouzilly, France) , and in particular to  Benoît Piégu , for providing the bioinformatics tools and support for the analysis. RNA sequencing was performed by the IGBMC.
The obtained sequenced amplicon data was evaluated for quality and trimmed. Upon unsuccessful attempts to split heterozygous indels, consensus sequences were derived from forward and reverse reads (where possible) and aligned using the CodonCode Aligner software (v. 9. 0. 1; CodonCode Corporation, Massachusetts, USA). Subsequent evaluation was performed by manually examining the alignments. The sequenced amplicon data has been deposited to GenBank (accession numbers: MT679733-MT679792). The assembled sequences were used to manually create a new pair of primers to be used in the ddPCR assay -ACR8F1 (5 ′ CTCCATATTC GAGTTGTGTCTT3 ′ ) and ACR8R1 (5 ′ GTATCCAACATTGAATTAAAG GC3 ′ ), which create amplicons of either 182 bp (full-length sequence) and/or 119 bp (containing the 63 bp deletion). The optimal annealing temperature was determined via a gradient PCR (Supplementary figure  1 ).
After optimization, the amplification steps for using the pair of primers were as follows: 1 cycle of 95 • C for 5 min, 40 cycles of 95 • C for 45 s, 56 • C for 30 s, 72 • C for 1 min, and a single cycle of 72 • C for 10 min. The products were visualized using GelRed® dye on a 2% agarose gel. The primers for the reference amplicon in the exon 1 of the Hco-acr-8 gene were developed in silico using the available sequence information for the hco-acr-8 (HCON_00151270) on the WormBase ParaSite database.
Amplification and sequencing To design primers and probes to assay the 63 bp deletion site in the hco-acr-8, we first amplified and sequenced the second intron region containing the 63 bp deletion site in the hco-acr-8 from 90 samples of individual adult H. contortus (42 of Swedish origin, 48 -reference isolates) using the previously described PCR protocol by  Barrère et al. (2014). Among these, 56 individual samples (21 of Swedish origin, 35reference isolates) produced high-quality, full-sequence length sequences and were retained (Supplementary figure  3 ), whereas 34 samples yielded either only partially resolved or were low-quality, i. e. noisy (especially surrounding the area of the deletion), and were removed from further analysis. No heterozygous individuals were found upon attempting to 'split heterozygotes indels' within the CodonCode Aligner software and, therefore, only the major allele was recovered for each sample.
Acknowledgements This work was supported by the  Swedish Foundation of Agricultural Research  grant  O-16-20 -742and Formas 2016-1744as well as it was part of the  COST Action  CA16230 Combatting anthelmintic resistance in ruminants (COMBAR). Claude Charvet is a grateful recipient of the program TOR from the  French Institute in Sweden. The authors are also grateful for the valuable inputs and critical evaluation of the manuscript by  Dr. Stephen Doyle.
The LBW pigs had a BiW of 1. 30 kg or lower, and the HBW pigs had a BiW of 1. 55 kg or higher, similar to those used by  Van der Peet-Schwering et al. (2020). Within 24 h after birth, a tissue sample was taken from the ear of the piglets while inserting an identity tag (Eartag FlexoPlus P Geno) and put into a test tube marked with a bar code for identification. The EBV of individual pigs was derived from genotyping the individual pigs with a 50K single-nucleotide polymorphism chip and genomic prediction of the protein deposition capacity by Topigs Norsvin, Beuningen, the Netherlands. For calculating EBVs, data of about 1. 9 * 10 6 purebred and 0. 6 * 10 6 (F2) crossbred animals collected over the past 6 yr were available. For all animals, at least live time daily BW gain was available. The trait with the lowest number of observations, average daily feed intake, still contained data on 170,000 purebred and 19,000 crossbred animals.
The pedigree consisted of about 2. 8 * 10 6 animals of which 340,000 were genotyped (320,000 purebreds and 20,000 crossbreds). The genotyped animals were used to create a genomic relationship matrix applying the so-called algorithm for proven and young animals  (Misztal, 2016). Both the traditional relationship matrix based on the pedigree and the genomic relationship matrix were blended to create a joint relationship matrix, the so-called H-1. The latter is used for breeding value estimations in pigs. Breeding values were estimated using MiXBLUP  (Ten Napel et al. , 2018). The genotyped pigs in the present study were added to the pedigree database so that EBV could be calculated for these pigs. Piglets were weaned at an age of 28 d (range 26 to 30 d) and moved to the rooms for growing and finishing (GF) pigs at an age of 63 d.
Feed allowance during balance period 2 was based on the BW of each individual pig on day 114 and the expected daily gain from day 114 to 125. Feed refusals were removed and weighed 30 min after each feeding. To calculate feed intake, it was assumed that the feed to water ratio in feed refusals was 1:2. The data of pigs refusing 15% or more of their daily feed allowance during a balance period were excluded from the results obtained in that balance period. In the remaining pigs, feed refusals were very low. The measured feed intake of the remaining pigs on the R regime was 96. 0% of that of pigs receiving the A regime. All pigs had free access to drinking water. 1. 6 1. 2 AID isoleucine 4 6. 7 5. 0.
Metabolomics In both balance periods, at day 4 between 0900 and 1000 hours, three representative 1. 5 mL urine samples per pig were taken from the buckets with urine collected from 0800 hours that morning. The samples were stored at -80 °C pending analyses. Nontargeted metabolomics analyses were performed in the urine and plasma samples. The preparation of the urine and plasma samples before data processing and metabolite identification is described in Van der  Peet-Schwering et al. (2020). As quality controls (QCs), a blank sample (5% acetonitrile) was injected after each four samples to evaluate potential cross-contamination from samples and four pooled urine and plasma samples were injected four times during the chromatographic run to evaluate the analytical system performance, loss of sensitivity, and system reproducibility during the run.
Data processing and metabolite identification Mass spectra were calibrated and converted into the mzXML file format. Mass features of the urine and plasma samples were extracted using the R-based XCMS package  (Smith et al. , 2006)  where peak picking was performed using the "centWave" method and retention time (RT) was aligned using "Obiwarp. " Missing values were substituted using the "fillpeaks" method and isotopes were annotated using CAMERA  (Kuhl et al. , 2012). Exported data were filtered to eliminate peaks present in blanks, and RT was truncated to contain only portions with chromatographic peaks and masses higher than 700 m/z were discarded. The datasets were normalized using the van der Kloet procedure (van der  Kloet et al. , 2009)  based on the QC samples. Initial PCA was performed to check the quality of the data sets and eliminate potential outliers.
Compounds in both urine and plasma samples were identified based on queries in the METLIN (http://metlin. scripps. edu/), Human Metabolome Database (http://www. hmdb. ca/), and LIPID MAPS (http://www. lipidmaps. org/) online databases for obtaining possible chemical structures using accurate mass and mass spectrometric fragmentation patterns. The identification of annotated compounds was confirmed with standards, when available, on the same analytical system under the same conditions.
Statistical analyses The experimental data were analyzed by analysis of variance (ANOVA) with balance period as a random factor; pig as an experimental unit; and BiW, EBV, dietary protein regime, sequence of offering the experimental diets, and the interaction between BiW and EBV, BiW and dietary protein regime, and EBV and dietary protein regime as explanatory factors using GenStat statistical software (GenStat 19th edition). The same statistical model, using the lme4 package in R  (Bates et al. , 2015) , was used to analyze the metabolite ion intensities in urine and plasma samples that were found discriminating between EBV and dietary protein regime via multivariate analyses. The R package "car"  (Fox and Weisberg, 2011)  was used for estimating the P-values. Least squares means were computed using the R package "lsmeans"  (Lenth, 2016).
Results One HBW-LBV pig died during the N balance study. In the first balance period data of two LBW-HBV pigs and one HBW-HBV pig and, in the second balance period data of two LBW-LBV pigs were excluded from the results because these pigs refused more than 15% of their daily feed allowance. This means that during balance period 1, the data of 18 LBW (9 on the A regime and 9 on the R regime), 18 HBW (8 on the A regime and 10 on the R regime), 19 LBV (9 on the A regime and 10 on the R regime), and 17 HBV (8 on the A regime and 9 on the R regime) pigs were included in the results. During balance period 2, the data of 18 LBW (9 on the A regime and 9 on the R regime), 19 HBW (10 on the A regime and 9 on the R regime), 17 LBV (9 on the A regime and 8 on the R regime), and 20 HBV (10 on the A regime and 10 on the R regime) pigs were included in the results.
Identification marks were randomized across treatments within a pen. The assessment of the behavior posttreatment was carried out off-line, after all the studies had been completed, by an operator blinded to treatment and active. This operator was trained by an experienced animal behavior assessor, and a randomized subset (10%) of lambs were separately assessed by the trainer to ensure concurrence between operators (96% concurrence, data not presented). For each lamb, the video footage was assessed continuously for the first hour after treatment. The number of times the lamb expressed active pain avoidance behaviors in every 10-min block after the lamb had been returned to the pen was counted, until 60 min had elapsed. The active pain behaviors counted were according to a pre-determined ethogram adapted from Paull et al.