dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We are grateful to the genotoul bioinformatics platform Toulouse Occitanie (Bioinfo Genotoul, doi: 10. 15454/1. 5572369328961167E12) and Sigenae group for providing help and/or computing and/or storage ressources thanks to Galaxy instance http://sigenae-workbench. toulouse. inra. fr. We thank  Frédéric Terrier ,  Franck Sandres  and  Anthony Lanuque  for the fish rearing in the Donzacq (France) INRAE experimental fish farm. Figure 1 created with BioRender. com.
Availability of data and material All sequence data are available at the NCBI sequence read archive under accession numbers PRJNA827991, https://www. ncbi. nlm. nih. gov/bioproject/827991. Data are presented as mean ± SD (n=12 fish). statistical differences are calculated by one-ways ANOVA (P<0. 05). NS not significant. P<0. 05 *. P<0. 01 **. P<0. 001 ***. glut2a and glut2b: glucose transporter 2 paralogs. gcka and gckb: glucokinase paralogs. pfkla and pfklb: 6-phosphofructokinase. pk : pyruvate.
Voice 200 Ultra SIFT-MS (SYFT Technologies, Christchurch, New Zealand) equipped with a dual source generating positive soft ionizing reagent ions (H3O+, O2+, NO+) with the nitrogen carrier gas (Air Liquid, Alphagaz 2) was used in this study. Full-scan mass spectra were recorded for each positive precursor ion (H3O+, O2+, NO+) in a range from 15 to 250 with an integration time of 60 s and accumulated during 16h identification was based on specific ion-molecule reactions patterns of target analytes with the three positive precursor ions described in the literature and in the database of the LabSyft software (LabSyft 1. 6. 2, SYFT Technologies). Product ions from ion-molecule reactions of SCFA are summarized in Supplementary table 1. Quantification of short-chain fatty acids was performed using the NO+ precursor ion.
The 10 most abundant common OTUs are Ralstonia pickettii (OTU 1), Mycoplasma sp. (OTU 2), Sphingomonas sp. (OTU 4), Cutibacterium sp. (OTU 6), Sphingomonas sp. (OTU 7), Lactobacillus sp. (OTU 20), Ligilactobacillus sp. (OTU 11), Bacillus sp. (OTU 18), Weissella sp. (OTU 13), and Enhydrobacter sp. (OTU 10) (Figure3B). After performing taxonomic affiliations on OTUs, removing OTUs found in less than 4 samples, and having a minimum abundance of 0. 005 %, 7 phyla, 54 families, and 85 genera were observed in the overall dataset. Results are presented at phylum (Figure4A, 4B), family (Figure4D, 4E) and genera (Figure4G, 4H) levels. Proteobacteria and Firmicutes were the most abundant phyla regardless the sample origin. Independently of the diet, we observed significant difference between mucosa associated microbiota and digesta associated microbiota.
The excitation and emission wavelengths used were set at λ Ex = 633 nm and λ Em = 635-690 nm for Fast Green, and λ Ex = 488 nm and λ Em = 495-550 nm for 5-DTAF. The colloidal stability of the precursors (both solutions and hydrogels) was evaluated using a LUMiSizer (LUM GmbH, Berlin, Germany). The Instability Index (II) served as a measure for assessing phase separation phenomena, protein precipitation in the solution, and resistance to serum exudation in hydrogel precursors. Measurements were taken at 2300 g for 10 min with 5 s intervals between each. The experiment was conducted at a temperature of 25 • C, and light transmittance was measured at λ = 865 nm. Data analysis was carried out using the SEP-View software (LUM GmbH, Berlin, Germany).
, 2006). Phylogenetic trees based on the complete mitochondrial genome evidence that C. pyrenaica and C. ibex are close to each other  (Manceau et al. , 1999b; Pidancier et al. , 2006; Kazanskaya et al. , 2007). Ureña et al. (2018)  suggested a monophyletic origin of the Alpine ibex and the Iberian ibex, and highlighted the distinctiveness of the bucardo from the remaining Iberian ibex. These authors considered C. p. pyrenaica as one of the major clades of wild Capra species in western Europe. We have obtained a maximum likelihood tree  (Saitou and Nei, 1987 ) using the complete mitochondrial sequences from different Capra species available in the GenBank® and from Ammotragus lervia as outgroup (Fig. 3 ). The best-fit nucleotide substitution model with the lowest BIC (Bayesian Information Criterion) value was chosen using MEGA version 10 (  Kumar et al.
, 2020; Granados et al. , 2022; Barros et al. , 2022). More than 30 haplotypes have been described (Table  2 ) and the SNNS population harbouring 50 % of them shows the highest diversity. Recently,  Barros et al. (2022)  found up to 14 cyt-b haplotypes in several ibex populations from Cataluña (northeastern Spain). Nevertheless, as they used longer sequences (1140 bp) it is not easy to relate these haplotypes with those described in former studies (Table  2 ). Diverse cytb sequences from C. pyrenaica and other wild Capra species and Ammotragus lervia were obtained from the GenBank® and used to construct a phylogenetic tree (Fig. 4 ). Curiously, the Alpine ibex, C. ibex, was closer to C. p. hispanica than to the bucardo, but, overall, sequences from both C. pyrenaica "subspecies" were clearly separated.
Statistical Analysis Grazing characteristics such as PrGHY, PrGSH, PoGSH, density, herbage DM, weekly growth rate, and sward WC content were analyzed using PROC MIXED in SAS (SAS 9. 4, SAS Institute Inc. , Cary, NC). Terms included in the model were year, block, rotation, WC treatment, fertilizer rate, and their interactions. Individual paddock was the experimental unit, with paddock included as a random factor and rotation as a repeated measure. A compound symmetry covariance structure among records within paddock was used. Sward nutritive value was analyzed using PROC GLM, with year, time point, WC treatment, and fertilizer rate included in the model. Concentrate and silage supplementation was analyzed by PROC MIXED at group level measurements expressed by cow, taking into account the effects of year, fertilizer rate, WC treatment, parity, and their subsequent interactions.
Tukey's test was used to determine differences between treatment means. Significance was declared at P < 0. 05 and a tendency at P > 0. 05 and P < 0. 10. Data for 360 lactation records from 212 cows were available for analysis. Milk data such as daily milk yield, milk fat, protein, and lactose content, daily MSo, cumulative milk and MSo yield, BW, and BCS were also analyzed using PROC MIXED. Terms included in the model were year, WC treatment, fertilizer rate treatment, parity, breed, and their interactions. The experimental unit was the individual cow. Tukey's test was used to determine differences between treatment means. Significance was declared at P < 0. 05 and a tendency at P > 0. 05 and P < 0. 10. Paddock was the experimental unit for milk, fat, protein, lactose, MSo yield, and GD per hectare, and variables were analyzed individually using PROC MIXED, with the effect of year, block, fertilizer rate, WC treatment, number of silage cuts, and their interactions included in the model.
ACKNOWLEDGMENTS The authors gratefully acknowledge the work and the invaluable assistance of the technical staff based at  Teagasc Moorepark (Fermoy, Co. Cork, Ireland)  and the farm staff  Andrew ,  Caroline ,  Kieran, Finnbar , and  John  for their help and care of the experimental animals at  Clonakilty Agricultural College (Clonakilty, Co. Cork, Ireland ). The authors also thank the numerous placement students who helped in the data collection over the duration of the study. The authors have not stated any conflicts of interest.
Aboveground and belowground plant biomass Aboveground and belowground plant biomass was collected from 1 to 6 June 2015, at the onset of flowering, inside a sampling area of 52. 5 cm x 50 cm, containing 3 sowing lines (see Appendix A: Fig. 3 ), by uprooting whole plants and by separating shoots from roots. This sampling area was located inside an area of 90 cm by 90 cm where 15 N was added (see below). Two soil cores (8 cm diameter x 15 cm depth) were collected within the same sampling area of 52. 5 cm x 50 cm, one within the sowing line and one in between, and fine roots were extracted from these soil cores. Roots were washed, and shoot and root materials were dried at 65 • C for 72 h and weighed. Data were expressed in g m -2. Total root biomass was extrapolated to the sampling area based on the roots collected within and between the sowing lines.
Analysis of the effect of the number of varieties The effect of varietal diversity was tested using analysis of variance (ANOVA, package Car -3. 0-11) (4 levels; number of varieties: 1, 2, 4, 8). Prior to analyses, and as some soil characteristics showed a non-random distribution along the experimental field with spatial gradients, a model integrating coordinates and their square values (X, X 2 , Y, Y 2 ) of each experimental plot was conducted on each of the response variables to test for potential spatial gradients. It included a stepwise selection based on AIC criteria (package MASS 7. 3-54) to retain the most relevant model. When significant, an analysis was conducted on the residuals of this model to correct for a geographical bias (see Appendix B: Fig. 1 ). In total, 15 among the 21 response variables showed a significant geographical signal (total plant biomass, shoot biomass, plant N content, shoot N content, root N content, soil N content, root 15 N recovery, and shoot Ca, Cu, Fe, Mg, Mn, P, K and Zn contents).
RNAseq DI, HK, and liver samples from CD and LAN6 groups (4 fish per diet from duplicate tanks) were used to perform RNAseq at the Norwegian Sequencing Center (UiO, Norway) using an Illumina NovaSeq 6000 System (150 bp paired-end RNA sequencing). For this, total RNA was extracted from each sample using the RNeasy Mini Kit (Qiagen) following the supplier's instructions. Then, RNA samples were quantified using a NanoDrop™ 8000 spectrophotometer and RNA integrity was measured by an Agilent Bioanalyzer 2100. All samples showed an RNA integrity number ≥ 7). Following what we reported recently  (Morales-Lange et al. , 2022; Rocha et al. , 2023) , RNAseq data analysis was performed using Salmo salar genome SSAL_v3. 1 (GenBank assembly accession: GCA_905237065. 2) and nf-core rnaseq v3. 3 with default settings  (Ewels et al.
, 2020). Among the samples, the average trimmed-reads and the overall alignment rate were ~ 2. 5% and ~ 95%, respectively. Fragment mapping was counted using featureCounts (subread v1. 5. 1). On average > 20 million of reads were assigned. Differentially expressed genes (DEGs) were estimated using SARTools R package (v1. 7. 3) comparing LAN6 vs. CD data and considered significant when the adjusted p-value (padj) was <0. 05. For DEGs enrichment analysis and functional.
No one tissue satisfactorily discriminated the four treatment groups, with ≤75% of lambs correctly classified. However, visible spectroscopy discriminated L0 from L21 + L42 + L63 lambs with an accuracy of 92. 8%, 92. 0%, and 85. 3% lambs correctly classified on PF, DF and muscle, respectively, and discriminated L0 + L21 from L42 + L63 lambs with an accuracy of 90. 1%, 76. 5% and 92. 3% on PF, DF and muscle, respectively. The pasture fingerprint or signature on the spectrum appeared in most lambs between 0 and 21d in PF and DF and between 0 and 42d in muscle. Pasture signature gradually stabilized with increasing time on pasture but was not entirely stabilized in any tissue within the range of grazing durations explored. These promising results need to be confirmed on larger datasets with different breeds and grazing conditions.
Visible reflectance spectrum (400-700 nm) of fat and muscle The reflectance spectrum of PF, DF and LTL muscle was measured on all lambs at 24 h post-mortem at 10 nm intervals across the range of wavelengths from 400 nm to 700 nm, using a MINOLTA CM-700d spectrophotometer (Minolta France S. A. , Carrières-sur-Seine, France) with D65 illuminant and an observer angle of 10 •. For each tissue, the reflectance spectrum was averaged from 5 replicates, transformed into absorbance values [log(1/reflectance values)], and exported into WinISI III software version 1. 63 (Infrasoft International, Port Matilda, PA, USA) for analysis.
Animal performances and carcass characteristics Data on animal performances and carcass characteristics were analyzed by ANOVA followed by a Tukey test for pairwise comparisons with treatment as a fixed factor and year as a random factor, if applicable. If the distribution of the variables lacked normality and/or homoscedasticity, variables were log-transformed. These statistical analyses were performed using R software version 4. 2. 2 (R Foundation for Statistical Computing, Vienna, Austria).
Partial least squares-discriminant analysis Partial least squares-discriminant analysis (PLS-DA) was run on the absorbance spectrum of each tissue to classify the lambs into the four treatment groups, via a 4-fold cross-validation with 10 iterations procedure as described in  Huang et al. (2015b). The PLS-DA was performed on raw spectra and on spectra subjected to standard normal variate transformation followed by detrend correction (SNV-D;  Barnes et al. , 1989)  and the first derivative (1 st D). The preprocessing of the spectra was made with the WinISI III software version 1. 63 (Infrasoft International, Port Matilda, PA, USA). Beforehand, a principal component analysis was performed to study, for each tissue and treatment group, the Mahalanobis distance (H) between each spectrum and the mean spectrum, in order to identify outliers  (H > 3, Garrido-Varo et al.
, 2019). No outliers were identified in the raw spectra while one pre-processed DF and LTL muscle spectrum and two pre-processed PF spectra were identified as outliers and removed. PLS-DA models were optimized using cross-validation on the dataset: the process was repeated until the addition of latent variables (LV) to the model produced no increase in cross-validation accuracy. These statistical procedures were also applied to discriminate between two treatment groups, i. e. L0 vs L21 lambs, L0 vs L42 lambs, L0 vs 63 lambs, L21 vs L42 lambs, L21 vs L63 lambs, and L42 vs L63 lambs. Pairwise discriminations showed that L0 and L21 lambs were not well discriminated using LTL spectra, unlike PF and DF. These first results prompted us to test i) discrimination between L0 + L21 lambs and L42 + L63 lambs, and ii) discrimination between L0 lambs vs L21 + L42 + L63 lambs to get advantage of this difference between fat and muscle tissue.
To deal with the class imbalance in the discrimination between stall-fed (L0 lambs) and alfalfa pasture finished lambs (L21 + L42 + L63 lambs), the down sampling method of caret R package was used prior to PLS-DA. It randomly samples the alfalfa pasture finished lambs group to be the same size as the stall-fed lambs group. For all discriminant analyses, the performance of the classification was expressed in terms of accuracy, i. e. the proportion of correctlyclassified lambs in the total population. When the classification considered all the four treatment groups, the results were also expressed as proportion of misclassified lambs in each treatment group. When the classification considered only two groups of lambs (e. g. L0 vs L21 + L42 + L63 lambs, or L0 + L21 vs L42 + L63 lambs), we calculated four other outcomes, i.
The study demonstrated that for most lambs, the pasture signature appeared in the visible spectrum of fat tissues between 0 and 21 days of alfalfa grazing and in the visible spectrum of LTL muscle between 0 and 42 days of alfalfa grazing. We also found that the pasture signature in the visible spectrum was not completely stabilized in any of the three tissues within the range of alfalfa grazing durations explored, but was acquired in a growing proportion of lambs with increasing duration of pasture finishing. This study shows that visible spectroscopy on PF, DF and LTL muscle  combined with PLS-DA can inform on dietary changes of lambs made shortly before slaughter. Further research is now needed, with larger datasets involving more breeds and more diverse grazing conditions, in order to test the extent to which these results can be generalized.
Acknowledgements The authors would like to thank  L. Lavelle ,  C. Coustet  and the staff of the  INRAE Herbipôle Experimental Unit ,  S. Faure  and  C. Labonne  from  UMR Herbivores  for their technical support at the slaughterhouse. We also thank  V. Bellet  from  Institut de l'Elevage  and  C. Experton  and  S. Rouger  from  Institut Technique de l'Agriculture Biologique  for the implementation and animation of the REVABIO project.
Data generation We selected 49 Asian rice (Oryza sativa) accessions for RenSeq analysis, representing seven indica landraces (36 accessions), two japonica landraces (two accessions), and eleven modern varieties of indica, japonica, and aus (11 accessions). Landrace accessions were sampled in 2014 and 2015 in the fields of traditional rice farmers in three villages from the Yuanyang rice terrace region in Yunnan. RenSeq baits were designed to hybridize with 761 NLR-coding sequences from japonica and indica rice. To generate a baseline against which to identify features of polymorphism in genes of interest, 68 accessions were also characterized using genotyping-by-sequencing (GBS), representing nine landraces and 28 modern varieties (Supplementary Table  1 ). After standardizing the number of reads, read-mapping, SNP calling, and masking of paralogous calls or other SNPs with excess heterozygosity, the RenSeq dataset included 40,530 biallelic SNPs (reference sequence: 596 genes, 2.
To test for balancing selection at NLRs as a group, we corrected for the deviation from the standard demographic equilibrium by simulating datasets of similar number of sequences and loci as the observed NLR datasets according to the best supported demographic models estimated from GBS data. Nucleotide diversity π observed in RenSeq data was higher than expected in all populations (p<0. 0001) and Tajima's D was higher than expected in all populations (p<0. 0001) except Xiaogu and Hongjiao (p=0. 115 and p=0. 293, respectively; Supplementary Figure  8 ). Higher nucleotide diversity at NLRs was also supported by comparisons between GBS sequences from NLR and non-NLR genes (Supplementary Table  5 ). Assuming that the mutation rate is not elevated at NLRs, these findings indicate that in both modern varieties and landraces, balancing selection has maintained molecular variation at NLRs.
Search for NLRs under balancing selection To identify NLRs under balancing selection, we mapped the observed values of nucleotide diversity π and Tajima's D of each NLR on the joint density of (π, D) expected under selective neutrality while accounting for the demographic history of each group. NLRs were identified as under balancing selection if falling in the top 5% of π and D values calculated on datasets simulated under the best supported demographic models. Five and 19 NLRs were identified as being under balancing selection in modern indica and japonica, respectively (Supplementary Table  6 ). Across all individual landraces, on average 35. 3 NLRs were identified to be under balancing selection in 30 resamplings of one accession per landrace (standard deviation: 3. 3; range: 31 -41; Figure  4A ; Supplementary Table  6 ).
Three accessions correspond to the Hongyang2 variety, a true breeding line bred from landrace germplasm and widely cultivated in the Yuanyang terraces in recent years. Three accessions are glutinous rice: Zinuo (indica), Huangpinuo (japonica) and Nuogu (japonica). Japonica rice is cultivated on limited surfaces in the Yuanyang terraces, ca. 5% of total rice acreage. The eleven modern varieties were selected in a collection 44 representative of the world-wide rice phenotypic and genetic diversity (temperate japonica: four varieties, tropical japonica: three varieties, indica: four varieties, aus: one variety). We designed a bait library capable of hybridizing to a wide variety of Asian rice NLRs. We characterized the NLR complements in the genomes of the japonica rice reference variety Nipponbare (MSU Rice Genome Annotation Project Release 7 45 ) and the indica rice reference variety 93-11 46 by three different approaches: (1) searching NBARC domaincoding sequences (containing Pfam|PF00931 motif) in the CDS of both genomes with PFAMSCAN 47 ; (2) identifying NLRs among CDS of both genomes with NLR PARSER v1 48 using default parameters followed by removing those with an NBARC domain coding sequence shorter than 500 or longer than 1100 nucleotides; (3) recovering the NLR repertoires identified by  Luo et al.
49  in the Nipponbare and 93-11 genomes and filtering them for presence the NBARC domain (Pfam|PF00931). Redundancy within the japonica and the indica NLR gene sets was removed by positional information of the corresponding genes. In addition, to further remove redundancy in the NLR repertoire, NLRs whose NBARC -coding sequences were more than 95% identical between japonica and indica NLRs or among indica NLRs were removed by keeping the homolog with the longest NBARC domain. From the resulting set of 761 NLR sequences, 21,000 baits of 120 nucleotides and with 20 bp overlap were designed using a proprietary script from Arbor Bioscience (https://arborbiosci. com/). These oligos were aligned to the Nipponbare and 93-11 genomes with BLASTN and oligonucleotides with more than 10 perfect matches per genome were excluded.
Genomic DNA was extracted from two weeks-old rice seedling using a CTAB method 50. Enrichment and library preparation were carried out as described in  Witek et al. 18, 51. Post-enrichment samples were sequenced using Illumina HiSeq 2500. We mapped RenSeq reads against a reference set of NLR sequences identified in Ensembl Plants Genes database   8 ). Blast-n analysis revealed that 593 of the 596 reference sequences had a minimum identity of 69% with sequences used as baits (Supplementary Table  8 ). Raw Illumina paired-end reads from 49 rice accessions were aligned to the FASTA file of 596 NLR gene sequences using BOWTIE v2. 3. 5 52 with the option -very-sensitive and the rest all defaults to produce 49 BAM files that were then sorted using SAMTOOLS (v1. 9) tool  53, 54. Illumina sequencing of captured RenSeq sequences provided 3,702,690 pairs of paired-end reads per accession on average (standard deviation: 4,462,281; min: 937,145; max:     27,528;626; Supplementary Table  9 ; Supplementary Figure  12 ).
Variability in the number of reads, in particular between landraces and domesticates, had an impact on the proportion of sites covered and the coverage (Supplementary Figure  13 ; Supplementary Figure  14 ). In order to reduce the impact of the heterogeneity in the number of reads in our analysis of presence/absence and nucleotide diversity, we standardized our dataset to the same level of average sequencing depth, by randomly subsampling 937,145 pairs of reads for landraces and 2,342,863 pairs of reads for domesticates (937,145 is the number of pairs of reads of the less deeply sequenced accession V11; 2,342,863 is 937,145*2. 5). This procedure reduced coupling between the number of reads and sequencing depth statistics (Supplementary Figure  13 ; Supplementary Figure  14 ), as observed by computing the standard deviation of sequencing depth across NLRs, which decreased from 219.
To select the best combination of SNP calling parameters, we computed the number of segregating sites (S) and inbreeding coefficient (Fis) at NLRs for the group of four indica varieties in each of the 2592 SNP sets and compared with the values obtained for six random samples of four indica accessions from the three thousand rice genome dataset, referred to as 3KRG indica reference datasets  58. We computed the Euclidian distance between (Fis,S) estimated for the indica group in the 2592 SNP sets and for the three thousand rice genomes (3KRG) dataset, and by minimizing this distance we selected the "best" (or most comparable) SNP dataset with the lowest deviation from the 3KRG dataset. SNPs occurring in NLRs in Ref. 58  were identified by mapping protein sequences of our reference set of NLR against their older version of the 93-11 genomic sequence using Exonerate 59.