dataesr/scientific-paragraphs-categorization huggingface dataset:
Gene family clusters and single-copy ortholog sequences within the protein set were identified using Orthofinder2 (ref. 69 ) (v. 2. 3. 3). Evaluation of divergence time. Twenty single-copy orthologous protein sequences were used to construct the phylogenetic tree using MCMCtree 70 (v. 4. 9i) with two calibration points (Fig. 1c , red diamond represents Rutaceae fossil record  26  ; the blue diamond represents S. lycopersicum 66 divergence time  27  ). We adopted the Bayesian relaxed molecular clock approach to estimate species divergence time using the independent molecular clock and Empirical+F models with default 'wag. dat' amino acid substitution rate in the program MCMCtree  70  , with other parameters set to defaults. Calculation of K a and K s. Paralogous and orthologous gene pairs were identified in syntenic blocks using the MCScanX 71 software, while K a and K s were calculated in TBtools 68 using coding and protein sequences.
0. 7. 17). The mapped reads were then sorted according to genomic coordinates using Samtools  39. Sequence data generated from different Illumina lanes were combined using 'samtools merge'. After merging, duplicates were removed using Picard 77 (v. 2. 5. 0), and then HaplotypeCaller from the GATK  40 (v. 3. 8)  was used to call individual-specific gvcf files. Finally, the GenotypeGVCFs was used for joint calling of SNPs. After quality control of the SNPs using bcftools  78  , the SNPs were hard filtered using GATK VariantFiltration (DP < 300 || DP > 3000 || QD < 2. 0 || FS > 60. 0 || MQ < 40. 0 || MQRankSum < -12. 5 || ReadPosRankSum < -8. 0), and only biallelic SNPs were selected for further analysis. Admixture. We carried out analysis of admixture using the ADMIXTURE 9 software (v. 1. 3.
0). Before analysis, SNPs in LD were dropped out in Plink 79 using a sliding window of 50 SNPs with 10 SNP step size, SNPs with R 2 >0. 1 were filtered out, these settings were the same as recommended in the ADMIXTURE user manual (-indep-pairwise 50 10 0. 1). The optimal number of populations (K) was selected using tenfold cross-validation. SNP tree. The data filtered for ADMIXTURE analysis were also used for estimating the population SNP tree with SNPhylo  80 (v. 20180901 ). The SNPs were filtered by >10% minor allele frequency (-m 0. 1) and missing rate <40% (-M 0. 4) and R 2 <0. 4 (-l 0. 4). The resulting fasta file was used as input to RAxML 81 (v. 8. 2. 12), where the NATuRe GeNeTiCS | www. nature. com/naturegenetics Articles Nature GeNetics tree was run using the GTR-GAMMA model and 1,000 bootstrap replicates, and thereafter rooted by the outgroup species.
Population split time and the presence of subsequent bottlenecks was assessed with Fastsimcoal 87 (v. 2. 6. 0. 3). The two-dimensional derived allele site frequency spectrum was obtained with easySFS (https://github. com/isaacovercast/easySFS), using a. vcf file filtered for repeat regions and sites with no ancestral allele call. A projection to 22 samples was found optimal. In fastsimcoal  87  (v. 2. 6. 0. 3), three models were compared: (1) a model with constant population sizes and a single split event fitted to data; (2) a model with constant population sizes, with bottlenecks in each of the populations and split time estimated from data; and (3) a model with population growth rates, bottlenecks and split time estimated from data. For each of the models, 100 parameter files were simulated.
Effect of inbreeding on population trajectories. To correct the differences in population trajectories due to higher level of inbreeding Yunnan population, the generation time was adjusted to three times the generation time in the Hainan population (upplementary Note II). To avoid overfitting, matching was made in pairwise sequentially Markovian coalescent plots and the effect was then confirmed in Stairway plots and SMC++ analyses. To estimate new split times, independent SMC++ models for Yunnan and Hainan populations were fitted and the generation times were adjusted according to the method described in Supplementary Note II. The split time was then estimated manually from the matched Ne trajectories. Kinship inference. The KING 29 software (kinship-based inference for GWAS, v. 2. 2. 4) was used to estimate kinship among individuals.
Relatedness was estimated using the '-related' option and default settings. The method calculates an estimator of the kinship coefficient that is independent of sample composition or population structure based on the difference between shared heterozygosity and shared homozygosity  29. The order of relationship was obtained using the ranges recommended in KING user manual, and compared with identity-by-state values calculated with plink. The detected relationships were then illustrated using Apple Keynote. Linkage disequilibrium. For LD analysis, the. vcf file was filtered by dropping out the outgroup species and by selecting SNPs present in all individuals and having minor allele count of greater than four. LD was calculated for a 500 kb window, and all R 2 values >0. 01 were reported. In R, a linear model was applied to the data using the 'lm' function with log 10 transformed distance versus R 2 value data, and the point where R 2 dropped to 50% of the initial value was reported as the measure of decay.
LD levels in wild and cultivated population groups were calculated by PopLDdecay  88  (3. 40) software using the correlation coefficient (r 2 ) of alleles. Introgression statistics. F3 statistics were calculated using the Admixtools 10 package (v. 4. 1). The Plink formatted file was first converted into eigenstrat format using the 'convertf ' function, and then qp3Pop was used to estimate the F3 introgression statistics. The wild individuals were grouped into populations according to sampling location, whereas the cultivars were analyzed separately. Visualization and false discovery rate adjustment of the Z-score statistics were done using R, as in Salojarvi et al. 83. F st estimates. F st estimation was carried out using vcftools  89  (v. 0. 1. 15) by grouping the wild populations according to sampling site, and cultivars according to maturation time.
The positions with nonsynonymous changes inside high-quality gene models were then selected based on 'high' or 'moderate' SNP effect annotation using SnpEff  91 (v. 4. 3t ). The nucleotide diversities in the identified positions were then summed together using a custom script in R with a parallel implementation for higher speed. In the presence of missing data, the mean nucleotide diversity estimate is heavily affected by the number of called SNP positions. To obtain this number, GATK  40 (v. 3. 8) GenotypeGVCFs was run by also calling monomorphic positions, and the resulting. vcf files were filtered using the Phred quality thresholds used for filtering the. vcf file containing only variant positions in vcftools. For the π n estimate the number of called positions inside high-quality gene model regions was then calculated.
We operationally assumed that every third position in the exons caused a synonymous mutation and therefore multiplied the number of nucleotide calls inside high-quality gene models by two-thirds. For π s estimation, all regions with predicted gene models were filtered out from the ANGSD  82  (v. 0. 933) file containing sitewise nucleotide diversities. To obtain the number of called positions, GATK  40 (v. 3. 8) GenotypeGVCFs was run by also calling monomorphic alleles and the resulting. vcf file was filtered using the Phred quality thresholds used for filtering the. vcf file containing only variant positions. Subsequently all regions with predicted gene models were filtered out. The sums of diversities outside of gene models were then calculated using a script in R with parallel implementation for higher speed.
and the sums were then divided by the number of called positions. To verify that the values obtained from the pipeline are correct, they were compared with results from several species reported in Chen et al. 92. All values are comparable with other plant species as well as the π n /π s ratio. The draft pipeline containing the R codes is given in Zenodo  93. In addition to nucleotide diversity calculation, also the actual numbers of SNPs with different functional impact were quantified. The impacts of SNPs were first predicted using SnpEff 91 (v. 4. 3t), and processed with custom made bash and R scripts. The significance of the difference between the counts for different populations was assessed in R using ANOVA and TukeyHSD post hoc analysis; ANOVA was used since the normality assumption of the residuals could not be rejected (P > 0.
Acknowledgements This work was supported by the  China Litchi and Longan Industry Technology Research System  ( CARS-32 ),  National Key Research and Development Program of China  ( 2018YFD1000104 ,  2019YFD1000904 ), the  National Nature Science Foundation of China  ( 30900980 ,  31272135 ,  3127213 ,  31401829 ,  31872063 ,  31872066 ,  32072547 ), the  Outstanding Talent Program of the Ministry of Agriculture , the  Lingnan Fruit Innovation Team , the  Litchi Breeding Post of Guangdong Modern Agricultural Industry and Technology System  ( LNSG2012-03 ,  LNSG2012-04 ), the  Special Support Program of Guangdong Province  ( 2019TX05N193 ), the  Key-Area Research and Development Program of Guangdong Province  ( 2018B020202011 ), the  '211 Project' Construction Fund for Key Subjects of the College of Horticulture, South China Agricultural University , the  Bagui Young Scholars Supporting Projects  ( BGQN201979-1 ), the discipline team building projects of  Guangdong Academy of Agricultural Sciences  in the 13th Five-Year Period, the  Academy of Finland  ( 318288 ) and  NTU  start-up grant to J.
Data availability All raw sequencing data of DNA resequencing, 10x Genomic, Hi-C and RNA-seq are available at the NCBI database with a project ID of PRJNA747875. The monoploid reference and two haplotype assemblies are also deposited in NCBI with accession nos. JAHYJY000000000, JAIUGD00000000, and JAIUGE000000000, respectively. The assembly and annotation of monoploid and haplotype genomes were also uploaded to the Mendeley database (https://data. mendeley. com/datasets/ kggzfwpdr9/1). VCF files that contain all clean SNPs were also uploaded to the Mendeley database (https://data. mendeley. com/datasets/v37bv5jt6g/1). Accession number or websites for public genomic data or sequencing data are listed in Supplementary Table  34. Source data are provided with this paper.
RNA purity and quantification were assessed using Small RNA chips (Agilent Technologies, Santa Clara, CA, USA). Total RNA was then analyzed using Affymetrix miRNA 4. 0 chips (GENOM'IC platform, Institut Cochin, Paris, France) as previously described  (20). DCMU-dysregulated mature miRNA target genes were selected using results from three tools: miRNAtap and multimir R packages (R3. 4. 2) and online software mirDIP  (21). In details, each tool questioned retrieved a prediction score for specific miRNA-mRNA interaction. At this stage of the analysis, the only inclusion criterion was that the target should be significantly predicted to be the miRNA target by all three tools used (using aggregate mean score retrieved from each tool for each miRNA-mRNA interaction). Common genes were then incremented into R package PathfindR and Enrichr software (RRID : SCR_001575)  (22, 23)  to visualize if DCMU-dysregulated mature miRNA target genes were enriched significantly in signaling pathways.
For genes analysis, 3′ sequencing RNA profiling was performed by the GenoBird plateform (IRS-UN, Nantes, France) using a NovaSeq 6000 (Illumina Inc. , San Diego, CA, USA). The raw sequence reads were filtered based on quality using FastQC (RRID : SCR_014583). Adapter sequences were trimmed off the raw sequence reads using Cutadapt (RRID : SCR_011841). Reads were then aligned to the reference genome using BWA (RRID : SCR_010910). Differential expressions are detected with the DESeq2 Bioconductor package (RRID : SCR_000154 and RRID : SCR_006442)  (24). Significantly dysregulated genes were compared to predicted targets obtained for miRNA analysis. All original microarray and sequencing data were deposited in the NCBI's Gene-Expression Omnibus database under the references GSE189440 and GSE189443.
DCMU Toxicity on Human CD8+ T Cells To set DCMU doses used in the following experiments, we first tested toxicity of DCMU in CD8+ T cells by evaluating cell viability in a cancer-specific CD8+ T-cell clone (CTL03. 1), directed against the melanoma antigen Melan-A. DCMU concentrations were selected according to two criteria. First is consistency with the literature. We used data from studies investigating blood concentrations of various pesticides following acute intoxications  (25, 26). We could not apply such an analysis only on DCMU, since information available on blood concentration is sparse for that herbicide  (27). This analysis revealed that blood concentrations are very variable, and the expected correlation between concentration and death is not that obvious. The second criterion was moderate toxicity on CD8 + T cells.
DCMU Impairs CD8+ T-Cell Functions by Dysregulating Their miRNAs We selected next, candidate miRNAs by two approaches (Figure  3A ). First, we tried to establish DEmiRNA-DEmRNA regulatory networks. Targets of DEmiRNA were first predicted using three databases implemented in R. Identified genes were next crossed with DEmRNAs obtained from our sequencing data. Common genes both belonging to DEmiRNA targets and DEmRNAs that have inverse expression relationship with DEmiRNA were selected and plotted in Figures  3B, C. Second, since miRNAs also regulate gene expression by translational repression without alteration of the mRNA amount (3), we selected other candidate DEmiRNAs based this time on an FC above 2 or -2 and with at least one target gene in T-cell function/ activation pathways (Table  1 ).
Overall, CD8+ Tcell immune capacities are impaired by DCMU exposure, and this might participate to the establishment of a suitable environment for the development of cancers. This work provides strong basis to study environmental pollutant effects on immune cells by combining transcriptomic, functional, and in vivo analysis. Further investigation will be required to evaluate whether or not our observations could, to some extent, be shared by other pattern of exposure (mimicking chronic exposure for instance) or to assess the impact that such exposures (acute or chronic) might have on other cells types. Altogether, our result describes for the first time the deleterious effects of DCMU acute exposure to human CD8+ T-cell functions. and France Genomique, and to the Bioinformatics Core Facility BiRD, member of Biogenouest and Institut Francais de Bioinformatique (IFB) (ANR-11-INBS-0013), for the use of their resources and their technical support.
ACKNOWLEDGMENTS We thank the  Flow Cytometry and FACS core facility "Cytocell" (BioCore, SFR Bonamy, Nantes, France)  for their expert technical assistance. the  genomic facility "GENOM'IC" (Institut Cochin Paris)  for their expert technical assistance. We acknowledge the  IBISA MicroPICell facility (Biogenouest) , member of the national infrastructure  France-Bioimaging  supported by the  French National Research Agency  ( ANR-10-INBS-04 ) for its expert technical assistance. We are most grateful to the  Genomics Core Facility GenoA , member of Biogenouest.
FUNDING Our research was funded by  INSERM, CNRS , and grants from  "Ligue contre le Cancer , Comiteś  16 ,  22 ,  29 ,  35 ,  44 ,  56. "This work was realized in the context of the  LabEX IGO program  supported by the  National Research Agency  via the investment of the future program  ANR-11-LABX-0016-01. This work was realized in the context of the  SIRIC ILIAD program  supported by the  French National Cancer Institute national (INCa) , the  Ministry of Health , and the  Institute for Health and Medical Research (Inserm)  ( SIRIC ILIAD, INCa-DGOS Inserm-12558 ). PA was supported by a fellowship from  EpiSAVMEN  (" Dynamique scientifique" program  of  Reǵion des Pays de la Loire ).
BAL Catalogs The twelfth data release of SDSS  (Pâris et al. 2017 ) included measurements of both AI and BI for every quasar in the catalog. BALs were identified both through visual inspection and through an automated search. The automated search looked only at sightlines with  ≥ 1. 57, where the entire C spectral region could be detected by SDSS. The search classified 16,422 quasars as  > 0. After visual inspection, a total of 29,580 quasars, or 7. 3% of the catalog, were identified as BALs. A second automated search for AI BALs found 21. 7% of the catalog to have  > 0. With the large catalog size of DR14  (Pâris et al. 2018) , BALs were not identified by visual inspection. Instead, detection and BI measurements were done automatically for quasars with  > 1. 57. About 7. 4% of quasars in this range had  > 0.
Another result of these stacking studies was that the strongest BALs of DR12 studied in  Hamann et al. (2019)  appear to have significant continuum differences in the Ly forest region relative to non-BAL QSOs. If there are significant differences between the unabsorbed continuum of BALs and non-BALs, then this could lead to systematic differences in the Ly analysis. This is because the analysis software applied for BOSS and eBOSS fits a single, mean continuum shape multiplied by a first-order polynomial to each QSO  (Busca et al. 2013; Delubac et al. 2015; Bautista et al. 2017 ). If the mean continuum based on all quasars is not a good match to BALs, then the measured Ly absorption for the BALs would have larger systematic errors. To investigate potential continuum differences, as well as the broader impact of BALs on cosmological analysis, we mask the expected locations of all potential BAL features associated with Ly, N , C *, S , and P.
Our procedure is to take the velocity limits of the absorption trough(s) based on the C region and mask out the corresponding velocity range associated with each of these other features. We mask these regions regardless of whether or not absorption is apparent through visual inspection, as in most cases we expect the features to be too weak to separate from the IGM absorption in the forest region. While this may mask out some pathlength that is unaffected by BALs, for most BALs it is a relatively small fraction of the total pathlength, and in any case there is still a net increase in pathlength by including these BALs in the analysis. We used the picca 1 package to develop and test the BAL mask. This software was developed for the SDSS Ly forest studies of the auto-and cross-correlation (e.
2019 ), we separate this 2D grid into four wedges with the baoutils package 2  and calculate the correlation function and uncertainty in each wedge as a function of r. Each wedge covers a range  = r/ ⊥ = () that range from mostly transverse to mostly parallel to the line of sight. Bins that fall along the boundaries of the wedges are subsampled onto a 7 × 7 grid. We calculate the uncertainties in each bin by propagating the relevant bins in the covariance matrix.
This is because the SNR of the spectra of these quartiles is greater than that of non-BALs, and therefore the BAL forest pixels have greater weight in the autocorrelation function calculation. One caveat is that the greater SNR of the BAL sample is because it is easier to detect BALs in higher SNR spectra, so the non-BAL sample will consequently be impacted by unidentified and therefore unmasked BAL troughs. The gain with the addition of the third and fourth quartiles is much less. This is likely because it is easier to identify stronger BALs in lower SNR data. It may also be the case that the continuum fit is lower accuracy because more of the continuum of each BAL is masked. The addition of BAL quasars will benefit both the re-analysis of current datasets and the analysis of future experiments such as the Dark Energy Spectroscopic Instrument  (DESI Collaboration et al.
ACKNOWLEDGEMENTS LE and PM acknowledge support from the  United States Department of Energy, Office of High Energy Physics  under Award Number  DE-SC-0011726 , and PM is grateful for support from the  Radcliffe Institute for Advanced Study at Harvard University  while some of this work was completed. We also acknowledge helpful discussions with the DESI Ly forest working group, and with  Victoria de Sante Agathe  and  Julien Guy  at the start of this project. AFR is supported by  MICINN  with a Ramón y Cajal contract ( RYC-2018-025210 ). IFAE is partially funded by the  CERCA program  of the  Generalitat de Catalunya. This material is based upon work supported by the  National Science Foundation Graduate Research Fellowship Program  under Grant No. DGE-1343012. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the  National Science Foundation.
After staining, slides were mounted with Prolong Gold Antifade Reagent with DAPI (Invitrogen, Carlsbad, CA) before analysis with confocal microscopy. The percentage of positive staining was analyzed using ImageJ software. Cell isolation. Rat spleen was digested by collagenase D (Roche) for 30 min at 37°C; the reaction was stopped by adding 0. 01 mM EDTA. Mice spleen and rats thymic cells and lymph nodes were isolated by crushing with PBS. Red blood cells were lysed using a lysis solution (8. 29 g NH4Cl, 1 g KHCO3, 37. 2 mg EDTA/1 l deionized water [pH 7. 2-7. 4]). Rat TCRαβ - SIRPα + , TCRαβ + CD4 + CD25 -, TCRαβ + CD4 + CD45RC high , TCRαβ + CD4 -CD45RC high , TCRαβ + CD4 + CD25 + CD127 low and TCRαβ + CD4 -CD45RC low/-cells were sorted using a FACS ARIA II (BD Biosciences, Mountain View, CA).
Acknowledgments. We thank  Séverine Battaglia  ( Inserm UMR1238, Nantes, France ) for performing the Micro-CT analysis and  Ms Maire Pihlap  for the LIPS analysis. We thank the biological resource center for biobanking of  Nantes Hotel-Dieu University hospital  ( CRB Nantes , F-44093, France -BRIF:  BB-0033-00040 ) for their help in the preparation of the samples. We thank  Laure-Hélène Ouisse  ( ITUN CRTI UMR1064, Nantes, France ) for the Ig quantification. This work was partially funded by  Labex IGO  project ( n°ANR-11-LABX-0016-01 ). Labex IGO  is part of the  « Investissements d'Avenir » French Government program  managed by the  ANR. This work was funded by the  Agence Nationale de la Recherche   ANR-17-CE18-0008 , the  Fondation du rein "Don de Soi -Don de Vie   2017 FdR-Trans-Forme/FRM " and the  Agence de la Biomedecine.
It is not known whether these enhanced biomarkers are able to improve risk-stratification, beyond what is possible with routine biomarkers. To address these questions, we analyzed data from the STOPhepatitis C virus (HCV) cirrhosis study, a prospective cohort of patients with HCV-related liver cirrhosis recruited from UK liver clinics. The main objective of this study was to evaluate the performance of validated biomarkers (APRI, FIB-4, computer tomography perfusion, MELD, MELD-sodium, ALB, and ALBI-FIB-4) for prognostic risk-stratification in a cohort of HCV-related cirrhosis followed for 4 years on average. A secondary objective was to explore the prognostic performance of factors that examine a wider breadth of information (serum markers of fibrogenesis, alcohol intake, comorbidity, and genetic risk polymorphisms) both in isolation and when added to existing validated biomarkers.