dataesr/scientific-paragraphs-categorization huggingface dataset:
Quality parameters of beef at 3, 14, and 28 days post-mortem According to Table  5 , there was a significant (P < 0. 05) impact of pHu and ageing time on the L*, b* parameters, specifically on chroma, oxymyoglobin, and deoxymyoglobin. Only the ageing time had an effect on the a*, hue, surface colour stability, metmyoglobin, and waterholding capacity; without interaction between the two variables (pHu x ageing time). However, only in the pHu ranges was a difference seen for shear force. Compared to meat with a normal pHu, meat with an intermediate pHu had a lower shear force value. Lastly, data collected for both intermediate and normal pHu samples during the ageing period was subjected to Principal Component Analysis (PCA). 93. 21 % of the variation in the total could be explained by the first two principal components, according to this analysis.
Main The Weismann theory (1) states that hereditary traits are transmitted exclusively from the germline. The theory is valid in most animals  (2)  where germline cells are set aside early in development  (1). In plants, germline segregation is generally assumed to occur late in development  (3) (4) (5) , which leads to several predictions on the fate of somatic mutations occurring in plant tissues: mutations have generally low frequency in plant tissues  (6) ; mutations at high frequency have a higher chance of intergenerational transmission; branching topology of the tree dictates mutation distribution  (7) ; and, exposure to UV radiation increases mutagenesis  (8). We produced a unique plant dataset of 60 high-coverage whole-genome sequences of two tropical tree species and identified 18,274 de novo somatic mutations, almost all at low frequency in tissues.
guianensis can be explained by an enrichment in low fraction mutations in the D. guianensis tree detected through deeper sequencing (Supplementary Fig. E1 ), because increasing the number of reads of a genomic region increases the chances of finding a mutation present in only a few cells of a sample. We generalised the result of the predominance of low fraction mutations in two pedunculate oaks  (4, 10) , and an unpublished dataset from one tortuous beech Fagus sylvatica using the same methodology (Fig. 3c ). We then considered mutations at a high allelic fraction (>0. 25), a category of mutations for which methodological differences are expected to have a limited impact. The two tropical trees had 3 and 6 somatic mutations with allelic fraction>0. 25, as compared to 56-421 somatic mutations for the reanalysed oaks and beech trees from temperate regions (Fig.
Material and methods Choice of species and individuals. The study was conducted in the Amazon forest, in the coastal forests of French Guiana. A database of 710 tree species containing available information on the presence of tree rings, maximum diameter at breast height, architectural type, reproductive phenology and ecological and economic importance was constructed. A set of 15 candidate species was selected, and their genome size was estimated by flow cytometric analyses. On this basis, we chose to work on Dicorynia guianensis (Amshoff) and Sextonia rubra (Mez) van der Werff, which are common in French Guiana, and are ecologically and economically important species. We selected large-stature trees above 40 metres without signs of senescence to maximise the potential for mutations with an increased number of cell divisions.
923 gaps), while 609 contigs remained unanchored with a total length of 53. 067Mb representing 5. 08% of the genome. We constructed an automated genome annotation workflow that performs: (i) de novo and known transposable element (TE) detection, (ii) de novo and known gene models detection, and (ii) functional gene annotation. De novo TE detection uses RepeatModeler2 (v2. 0. 3) followed by classification using RepeatClassifier (v2. 0. 3) and  TEclass (v2. 1. 3). The de novo TEs obtained are merged with the known TE accessions for Viridiplantae from RepBase (v27. 07). This consolidated database is used for TE detection in each genome prior to soft masking using RepeatMasker (v2. 0. 3). Detection of de novo and known gene models is based on BRAKER2 and its dependencies. Finally, functional annotation of candidate genes is based on the Trinotate (v3.
2. 1) pipeline using TransDecoder (v5. 5. 0), TMHMM, HMMER, BLAST (v2. 13. 0), RNAmmer (v1. 2), and SignalP (v4. 1) with UniProt and Pfam databases (Sup. Note B). Sampling. On 13 October 2020, we sampled the tree S. rubra: three cambium tissues at the base of the trunk, about 1. 3 m above the ground and equidistant around the perimeter of the trunk; three leaves from the same twig per branch out of eight branches were sampled, the branches were selected in three pairs, each from a different bough, plus two independent branches from two other boughs; and fruits from 5 different branches where leaves had been collected. On 22 April 2021, we sampled the D. guianensis tree: three cambium tissues at the base of the trunk, about 1. 3 m above the ground and equidistant from the perimeter of the trunk; three leaves from a single twig per branch of ten branches were sampled, the branches were selected in five pairs of branches each from a different bough; and fruits from 5 different branches where leaves were collected.
To detect leaf mutations, we compared each leaf library to the first cambium library as a reference sample. We filtered out candidate leaf mutations from previously identified heterozygous sites and all candidate mutations from all cambium comparisons using BEDTools subtract (v2. 29. 2). We also filtered mutations using the following criteria: (i) no copies of the mutated allele in the reference sample, in this case the cambium sample; (ii) a read depth for both samples between the 5th quantile and the 95th quantile of the corresponding library coverage; and (iii) the presence of the mutation in at least two biological replicates (at least 2 leaves of the crown) We used the same pipeline and compared mutations detected in two pedunculate oaks Quercus robur  (4, 10) , and in an unpublished dataset from a twisted beech Fagus sylvatica (J.
-Genome size: we aimed to work on species with smaller genomes, which would reduce the amount of sequencing needed to identify low-frequency mutations. The study was conducted in the Amazon forest, in the coastal forests of French Guiana. A database of 710 species of trees from French Guiana containing information on the functional group, maximum diameter at breast height, architectural model, presence of wood rings, reproductive phenology, and ecological and economical importance was constructed. A set of 15 candidate species was thus selected based on these criteria.
, 2009). The obtained de novo TE are merged with known TE accessions for Viridiplantae from RepBase (v27. 07;  Kapitonov and Jurka 2008). This consolidated database is used for TE detection in each genome before soft masking using  (v2. 0. 3; Flynn et al. , 2020). De novo and known gene models detection rely on BRAKER2 and its dependencies  (Brůna et al. , 2020). Finally, functional annotation of candidate genes is based on the Trinotate pipeline (v3. 2. 1; Bryant et al. , 2017)  using TransDecoder (v5. 5. 0; https://github. com/TransDecoder/TransDecoder), TMHMM  (Krogh et al. , 2001) , HMMER  (Finn et al. , 2011) ,  BLAST (v2. 13. 0; Altschul et al. , 1990), RNAmmer (v1. 2; Lagesen et al. , 2007), and SignalP (v4. 1; Petersen et al. , 2011)  with UniProt  (Bateman et al. , 2015)  and Pfam databases  (Punta et al.
We produced two datasets with the filtered mutations: (i) all filtered mutations; and (ii) a more stringent dataset that passed the empirical variant score (EVS) filtering of Strelka2 (v2. 9. 10,  Kim et al. , 2018). We used the same pipeline and compared detected mutations on two pedunculate oaks Quercus robur  (Schmitt et al. , 2022)  named Napoleon  (Schmid-Siegert et al. 2017 ) and 3P  (Plomion et al. 2018) , and on an unpublished dataset from one tortuous beech Fagus sylvatica (C. Plomion pers. com. ). We detected a total of 15,066 unique somatic mutations in Angela and 3,208 in Sixto by comparison with the cambium reference samples. Similarly, we found 2,356 and 13,976 unique somatic mutations in Napoleon and 3P, respectively, and 6,560 unique somatic mutations in the tortuous beech.
Acknowledgments We are grateful to  Valentine Alt ,  Emeline Houël ,  Laetitia Brechet ,  Saint-Omer Cazal , and  Hadrien Lalagüe  for their help with tree climbing and sampling. We thank  Olivier Brunaux  and  Caroline Bedeau  for their help in accessing the site and the forest office data. We are grateful to  Nicolas Barbier ,  Ilona Clocher , and  Jean-Louis Smock  for their help with lidar acquisition. PacBio HiFi reads were produced at Gentyane platform and Lausane platform for S. rubra and  D. guianensis , respectively. Bionano Saphir  reads and genome de-novo assembly were produced at CNRGV. Whole genome sequencing was performed at  Genoscope  with the help of  Eric Mahieu ,  Corinne Cruaud , and  Pedro H. Oliveira. Sequence-based genotyping was performed at PGTB (doi:10. 15454/1. 5572396583599417E12) with the help of  Zoé Delporte.
We are grateful to the genotoul bioinformatics platform Toulouse Occitanie (doi:10. 15454/1. 5572369328961167E12) for providing help and/or computing and/or storage resources The climate data were provided by  Météo-France  to the  Unité Mixte de Recherche EcoFoG  for research purposes within the framework of a MétéoFrance- INRAE AgroClim  agreement. This study was funded by an  ANR   Investissement d'Avenir grant :  CEBA  ( ANR-10-LABEX-0025 ).
Only blast hits with an e-value <0. 05 were kept as potential homologs. We identified tardigrade specific genes for each species based on two criteria. First, we ran a homology search using Diamond (v2. 1. 6. 160)  (Buchfink, Reuter, and Drost 2021)  on nr database (Downloaded Apr 12 11:17:28 2023) for each transcript from A. antarcticus and P. fairbanksi (by blastx) or for each protein sequence for H. exemplaris (by blastp). Gene sequences with no hit in all three non-tardigrade proteomes (reciprocal hit -blastx and tblastn -with the same gene for the transcripts) and no-hit on the nr database (diamond blastx or blastp -e 0. 001 --taxon-exclude 42241 --ultrasensitive) were considered as Tardigrade specific and noted "TardiSpe" in Supp Tables and data  (Mapalo et al. 2020; Hara et al. 2021; Kamilari et al.
2019 ). We identified tardigrade specific genes for each species based on two criteria. First, we ran a homology search using Diamond (v2. 1. 6. 160)  (Buchfink, Reuter, and Drost 2021)  on nr database (2023-04-12) for each transcript from A. antarcticus and P. fairbanksi (by blastx) or for each protein sequence for H. exemplaris (by Blastp). Gene sequences with at least one hit (parameters: -e 0. 001 --taxon-exclude 42241 --ultra-sensitive) were discarded. In parallel, we ran a search against well annotated proteomes of three model organisms (Drosophila melanogaster, GCF_000001215. 4; Caenorhabditis elegans,GCF_000002985. 6; Homo sapiens, GCF_000001405. 40). For A. antarcticus and P. fairbanksi, we conducted the search using the transcripts as query (blastx), for H. exemplaris, we conducted the search using the proteins as query (blastp).
The MS and MS/MS data were acquired by Xcalibur (version 2. 9, Thermo Fisher Scientific). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (Perez-Riverol 2022) partner repository with the dataset identifier PXD043897. Peptides and proteins were identified and quantified using MaxQuant (version 1. 6. 17 Trypsin was chosen as the enzyme and 2 missed cleavages were allowed. Peptide modifications allowed during the search were: C6H11NO (C, fixed), acetyl (Protein N-ter, variable) and oxidation (M, variable). The minimum peptide length and minimum number of unique peptides were respectively set to seven amino acids and one. Maximum false discovery rates -calculated by employing a reverse database strategy -were set to 0. 01 at peptide and protein levels.
Statistical analysis of MS-based quantitative proteomic data was performed using the ProStaR software  (Wieczorek et al. 2017). Proteins identified in the reverse and contaminant databases, proteins identified only in the Chlorella database, proteins only identified by site, and proteins quantified in less than three replicates of one condition were discarded. After log2 transformation, extracted corrected reporter abundance values were normalized by Variance Stabilizing Normalization (vsn) method. Statistical testing for comparison of two conditions was conducted with limma, whereby differentially expressed proteins were sorted out using a Log2(Fold Change) cut-off of 0. 3 and a limma p-value cut-off of 0. 01, leading to a FDR inferior to 3 % according to the Benjamini-Hochberg estimator.
The numbers of tardigrade specific DE proteins are also indicated. 9 tardigrade specific DE proteins were common to the 3 conditions, the corresponding list is provided in Supp Table  7. Tardigrade specific proteins are defined as proteins that have no homolog in C. elegans, D. melanogaster or in humans and don't have significant sequence homology to any sequence in the NCBI protein database (excluding tardigrade sequences) (see Methods section). A heatmap of the 50 upregulated genes common to all conditions is given in Supp Figure  8.
ACKNOWLEDGEMENTS We thank  Dr V. Gross  ( University of Kassel ) for advice on whole mount immunolabeling experiments,  Gabriel Ramasamy  ( RADEXP facility, Institut Curie ) for help with irradiation experiments and  Nawel Cherkaoui  (joint service unit  CNRS UAR  3750 at  Institute Pierre Gilles de Gennes   (IPGG))  for manufacturing the brass mold for the PDMS injection chamber. Funding.
for the project was from Sorbonne Université (Projet Emergence, projet TardiGRaDe), INSERM, CNRS, MNHN and ANR TEFOR (ANRII-INSB-0014). M. A. was funded by a doctoral fellowship from Ministère de de l'Enseignement Supérieur et de la Recherche (France) and from Fondation pour la Recherche Médicale. The proteomic experiments were partially supported by Agence Nationale de la Recherche under projects ProFI (Proteomics French Infrastructure, ANR-10-INBS-08) and GRAL, a program from the Chemistry Biology Health (CBH) Graduate School of University Grenoble Alpes (ANR-17-EURE-0003).
Acknowledgments We thank  François Leulier ,  Sandrine Hughes  and  Maxime Veites  for their help with microbiota sequencing. We thank  Emilie Avazéri  and  Gladys Gazelle  for their technical help. This work was supported by  CNRS ,  ANR BACNEURODRO  ( ANR-17-CE16-0023-01 ),  Equipe Fondation pour la Recherche Médicale  ( EQU201603007783 ) et l' Institut Universitaire de France  to J. R. and the  ANR Pepneuron  ( ANR-21-CE16-0027 ) to J. R. and Y. G. Research in Y. G. 's laboratory is supported by the  CNRS , the  "Université de Bourgogne Franche-Comté" , the  Conseil Régional Bourgogne Franche-Comté  ( PARI grant ), the  FEDER (European Funding for Regional Economical Development) , and the  European Council  ( ERC starting grant ,  GliSFCo-311403 ).
The reads were analysed with the dedicated EPI2ME 16S pipeline (v2022. 01. 07) using the following parameters: minimum score > Q10, minimum length 1000, maximum length 2200, minimum coverage 50%, 5 max target sequence and two different BLAST identity thresholds were tested (85 or 90%). This version used 22,162 sequences of reference coming from the microbial 16S rRNA NCBI RefSeq database. 36.
Statistical analysis All analyses were performed using XLSTAT Statistical Software (Addinsoft, Paris, France), with significance accepted at P ≤ 0. 05 and tendencies (0. 05 < P < 0. 1) not discussed. Anderson-Darling tests and QQ-plots were used to assess the normality of the data and their residuals. Bartlett tests were performed to test variance homogeneity. The two groups were balanced for breed, home pen, and body weight at the beginning of the experiment so the data were pooled for statistical analyses. Four days (Familiar, Novel 1, Novel 2a and Novel 2b) were considered for the statistical analyses of feed intake and behavioural data (time spent in the social zone, approach index, intake rate and number of vocalisations). There was one data point per animal and per day. As the data and their residuals were not normally distributed and variances were unequal, non-parametric statistics were performed.
The outliers were not removed from the analyses as the individuals representing these outliers varied between days. The breed effect was tested with Mann-Whitney tests considering each feed type independently. For each variable, when there was a significant difference between the breeds with regard to the familiar feed, the difference between the measurement for the first day with each novel feed and the measurement for the familiar feed was calculated per animal and then, breeds were compared on this difference with Mann-Whitney tests. Regarding the results of the breed effect (which was mainly found during the habituation period), it was decided to pool the data from both breeds in order to focus on the analysis of the behavioural responses of all goats to feed novelty. Friedman tests were used to analyse the effect of feed type (Familiar, Novel 1, Novel 2a and Novel 2b) on goats' behaviours.
Introduction In the last ten years, many methodological improvements have been put forward, in order to generate informative metagenomics datasets for the viral fraction (or virome) of any type of environmental or biological sample  (1) (2) (3) (4) (5). A particular attention has to be paid to the fact that viromes are mixtures of single-stranded (ss) and double-stranded (ds) DNA viruses. Having a simple method to include the sequencing of ssDNA phages in any type of environmental shotgun sequencing would also be relevant in the future, as more groups are now analysing the viral fraction of environmental or biological samples directly from whole metagenomics shotgun sequences, rather than first preparing a virome filtrate  (6, 7). To do so, they rely on the sorting of viral contigs among all assembled contigs, based on their characteristics and gene contents  (8, 9).
This result still held when outgroups were included in the analysis (Supplementary Fig. S4 ). Species morphology measured by PC1 explained well the genome-wide estimate of inbreeding coefficient, F, calculated on the whole transcriptome dataset (R 2 =0. 83, p-value=1. 686e-05, Fig. 1C ), suggesting that our phenotypic data represented a good proxy of the mating system. Thus, in the following analyses, we used PC1 to describe the selfing syndrome, which avoids using genomic data for both characterizing mating systems and their genomic consequences. These findings are in agreement with previous knowledge  (Dvořák et al. , 1998; Escobar et al. , 2010)  and allowed us to characterize the mating system for the species that lacked outcrossing rate estimations. They also showed that the effects of selfing are gradual, with species exhibiting mixed mating strategies described by intermediate F values and phenotypic selfing syndrome.
Polymorphism correlate with mating system but not with species geographic range Genetic diversity was estimated for each species from whole transcriptome sequencing data. Sequences of 48 accessions were generated and de novo assembled in this study and were added to the datasets of  (Glémin, Scornavacca, et al. , 2019)  and  (Clément et al. , 2017)  ( Supplementary Table  S1 ). Between 19,518 and 28,834 coding sequences were obtained per species. After filtering, genotype calling was performed on a number of contigs varying from 7,083 (Ae. searsii) to 21,706 (T. caput-medusae) (Supplementary Table  S4 ). Selfing is expected to reduce neutral genetic diversity  (Ingvarsson, 2002; Jarne, 1995; Nordborg, 2000; Pollak, 1987; Schoen & Brown, 1991) , here estimated as synonymous polymorphism, 𝜋S.
In agreement with this prediction, the efficacy of purifying selection estimated by the ratio of non-synonymous to synonymous polymorphism 𝜋N/𝜋S  (Kimura, 1983)  was lower for selfing species (max 𝜋N/𝜋S value 0. 21 for T. urartu) than for outcrossing ones (0. 09 for Ae. speltoides). Similarly to neutral diversity, the efficacy of selection was also significantly explained by the selfing syndrome (R 2 =0. 48, p-value=0. 0083; with phylogenetic control R 2 =0. 35, p-value=0. 041; Fig. 2B ). We further verified that both polymorphism statistics, 𝜋S and 𝜋N/𝜋S, also correlated with F estimates (Supplementary Fig. S5 ). If species range also affects genetic diversity, we expect widespread species to be more polymorphic than species with restricted geographic range. Using occurrence data from the GBIF database, we estimated the geographic range of each species.
For this analysis, we restricted the dataset to the genes for which we identified an orthologue in the barley genome (see Supplementary methods). In all species, synonymous polymorphism was strongly correlated with recombination and presented a U-shaped pattern along chromosomes more or less mirroring the recombination pattern (see Fig. 3A  and Supplementary Fig. S13 ). However, the higher the selfing rate, the flatter the relationship (Fig. 3B , Supplementary Fig. S7 ), suggesting a strong effect of the mating system on the relationship between polymorphism and recombination, which cannot be explained by a mutagenic effect of recombination alone (Supplementary Fig. S7 ). To quantify this relationship, we fitted a simple phenomenological model using a logistic function (Fig. 3B  for the rationale).
Transcriptome assembly, mapping and genotype calling Reads cleaning and assembly were performed following Sarah et al. (Sarah et al. , 2017). CDS per contig was predicted with prot4est  (Wasmuth & Blaxter, 2004)  and three gene datasets: the output of a Rapsearch  (Ye et al. , 2011)  similarity analysis, Oryza matrix model for de-novo-based predictions and the codon usage bias observed in T. monococcum. We run Rapsearch in either plant species of Uniprot swissprot (http://www. uniprot. org) or in the Monocotyledon species of greenphyl (http://www. greenphyl. org/cgi-bin/index. cgi). For each species, mapping was done following Sarah et al. (Sarah et al. , 2017) , except for the use of the bwa (H. Li & Durbin, 2009)  option -mem. Reads were mapped on the sequences of the individual with the highest coverage or with the highest number of annotated contigs and only on predicted CDS longer than 250 bp ( Supplementary Table  S4 ).
Phenomenological analysis of the effect of linked selection We regressed π S against recombination rate, after grouping genes into 50 quantiles of recombination, using a logistic function of the form: π S = a 1+b e -cr From this, we estimated i) the slope at the inflexion point, given by ac 4 , which measures the rate at which polymorphism increases with recombination, so lower slopes correspond to stronger effect of linked selection, and ii) a, which is the maximum π S that can be reached, assuming free recombination so no effect of linked selection. We fitted this non-linear model with the nls function of the R software using the inverse of the variance of π S estimates as weights, that is n π S ( 1-π S ). We reported mean estimates and confidence intervals obtained with 1000 bootstrapped datasets, after discarding nonconvergent or aberrant runs.