dataesr/scientific-paragraphs-categorization huggingface dataset:
Four hundred eighty-eight of our reference set of 519 NLRs could be identified. Summary statistics S and Fis were computed using the Python package scikit-allel v. 1. 3. 3 60. On average, inbreeding coefficient in the 3KRG indica reference datasets was Fis=0. 62 (standard deviation: 0. 02) and number of segregating sites was S=14707 (standard deviation: 1059). The closest of the 2592 Reads2snp datasets was dataset 286 (Fis=0. 63, S=15016), obtained with the following parameters: min=10, th1=0. 95,     par=1, th2=0. 001, aeb=False, Fis=0. 99, bqt=40, rqt=20 (Supplementary Figure 16. Rice is a selfing species and accessions were subjected to single seed descent before sequencing, so the number of heterozygous calls per SNP was expected to be much lower than postulated by Hardy-Weinberg equilibrium.
Although the -par option in Reads2snp removed most SNP calls caused by hidden paralogy, SNPs with excess heterozygosity within populations remained in the dataset. In particular, plotting observed heterozygosity (Hobs) against the minor allele frequency (p) revealed SNPs with no or very few homozygous alternate calls, distributed along the Hobs=2p line, likely caused by gene duplications present in certain accessions (Supplementary Figure  17 ). SNPs with excess heterozygosity were removed using the same criterion as in ref. 58  , by filtering out, in each landrace and rice subspecies, sites where observed heterozygosity was more than ten times the most likely value for a given frequency and inbreeding rate (Supplementary Figure  17 ). After filtering, summary statistics computed across the six 3KRG indica reference datasets (average [standard deviation]: Fis=1 [0], S=10200 [898]) remained very close to those computed on READS2SNP dataset 286 (Fis=1, S=10051).
Haplotype richness, in contrast, reached 90% of its maximum value with 39 accessions (Supplementary Figure  18 ), suggesting that the molecular diversification of NLRs occurs not only by mutation but also by recombination and gene conversion 66. Rarefaction analysis of GBS data revealed that our dataset is sufficient to reliably characterize genomewide levels of polymorphism (Supplementary Figure  18 ). Demographic modeling. To determine if patterns of variation at NLRs departed from selective neutrality, we performed coalescent simulations to correct for deviation from demographic equilibrium (i. e. constant population size). We used an approximate Bayesian computation (ABC) framework 22 to identify the historical demographic model accounting for most coefficients (γ), and the constraint (or non-synonymous) effects, which reflects mutational constraint (1-f, f being the proportion of non-synonymous mutations that are not lethal).
( O. sativa indica ASM465v1 version 43, O. sativa japonica ASM465v1 version 45) using the BioMart utility to filter gene IDs with Interpro entry IPR002182 (NBARC domain). To avoid redundancy among sequences caused by orthology between O. sativa indica and O. sativa japonica, and because 36/38 of the landraces included in our dataset are of indica type, we determined orthology relationships between O. sativa indica and O. sativa japonica sequences, and retained a final set combining all O. sativa indica sequences with O. sativa japonica sequences having no ortholog in O. sativa indica (Supplementary Table.
Microarray analysis Microarray analysis was conducted following the protocol outlined in  Hachiya et al. (2021). RNA quality was evaluated using an Agilent 2100 bioanalyzer (Agilent Technologies). RNA amplification, labeling, hybridization, and scanning were performed using the 3¢ IVT Express Kit (Affymetrix) and GeneChip Arabidopsis Genome ATH1 Array (Affymetrix) as per the manufacturer's instructions. Data from the microarray chips were normalized using the Microarray Suite 5. 0 (MAS5) method (Affymetrix). Transcripts labeled as "absent" or "marginal" were excluded from subsequent quantitative analysis. The raw microarray data used in this study are accessible in the ArrayExpress database at EMBL-EBI under accession number E-MTAB-13395.
Targeted GWAS subsets reveal 16 potentially novel resistance-associated loci Utilizing targeted subsets, we discovered 16 peaks on ten chromosomes that are most likely novel (Table  2 ). GWAS are expected to be more powerful when conducted on large datasets where individuals are drawn randomly from the population (139). However, SNPs important at a small regional scale might yet be diluted in species-wide panels and not detected by GWAS, which are typically lacking the power to detect associations with rare alleles (140). Pending sufficient phenotypic and genetic variation, GWAS in local panels have proven to be very effective in Arabidopsis thaliana to characterize the genetic architecture of defensive metabolite diversity (141). For this reason, resistance genes which may have been selected at small regional scales might be more efficiently detected in subsets of accessions from the same geographical origin.
Petri dishes with detached leaf segments were incubated for six to nine days at 20°C, 80% relative humidity with a 16 h light/8 h dark cycle and 50 μmol m -2 s -1 photon flux density. Disease levels were assessed seven to nine days after inoculation using a discrete quantitative scale with a score from 0 to 100 for the percentage of leaf area covered by sporulating mildew colonies, as described earlier by  Kaur (157). When necessary to define resistance categories, i. e. , for visualizations and defining overlapping or unique resistance, the following thresholds apply: 0-30% = resistant, >   (28) , we first cleaned the genotyping data of the combined datasets provided by INRAe in the same way as we did for the LandracePLUS panel, resulting in 30,021 polymorphic SNPs. Because no creation of a Hapmap or VCF file was necessary for further downstream analysis, this dataset could directly be used for PCA using the prcomp function.
Acknowledgments We would like to thank  Nikolaos Minadakis ,  Zoe Bernasconi ,  Alexandros Sotiropoulos ,  Thomas Wicker  and  Bruno Studer  for their input and guidance in acquiring necessary techniques for this study. This project was financially supported by the  University of Zurich ,  Swiss National Science Foundation  grants  310030_204165  and  310030B_182833  to B. K. JSM is recipient of the grant " Ramon y Cajal " Fellowship  RYC2021-032699-I  funded by  MCIN/AEI/10. 13039/501100011033  and by the  "European Union.
In silico genotyping of TaBW35K array SNP variants in high-quality sequenced wheat genomes For the comparison of genetic diversity, Fielder (40) , Renan (41)  and the 10+ wheat reference genomes ArinaLrFor, Chinese Spring, Claire, Cadenza, Jagger, Julius, Landmark, Lancer, Mace, Paragon, Norin61, Robigus, Stanley, SYMattis and Weebill (25, 36)  were added. Flanking sequences of SNP array markers were queried using BLASTN searches against the publicly available wheat genomes. Blast results were filtered for hits with at least 60bp alignment and 96% shared identity, thereby allowing no more than three mismatched nucleotides. Positions of the SNPs were then extracted from BLASTN alignments on the respective genomes. Merging the resulting dataset with the 29,556 previously used markers resulted in an overlap of 27,016 SNPs that were used for PCAs and hierarchical cluster analysis.
1- 9 (163). Wheat Pm gene sequence assembly and alignment was done with CLC Genomics Workbench version 20. 0. 4 (Qiagen Bioinformatics, https://digitalinsights. qiagen. com/). Pathogen Avr gene sequence alignment was done using IGV version 2. 15. 4 (164). DNA extraction and genotyping DNA extraction of plant material was performed as previously described (165). DNA quality was assessed via agarose gels and genotyped using the TaBW35K SNP array (166). The SNP call dataset included marker 30-60% = positions and flanking sequences based on RefSeq v1. 0 of Chinese Spring (36). We filtered for intermediate and > 60% = susceptible. We used mildew susceptible accession Kanzler as a control for a "PolyHighResolution" or OTVs and markers with known chromosomal position in the reference genome proper mildew infection for all infection tests and Chancellor and Federation as additional susceptible Chinese Spring.
gouv. fr dataverse repository (https://doi. org/10. 57745/NZNMZH). Liquid Chromatography Quadrupole Time-of-Flight Mass Spectrometry (LC-QTOF-MS) ngerprinting of semi-polar extracts was performed as in  Lamari et al. [76]  with the modi cations described below. Brie y, lyophilized leaf powder samples were extracted with a methanol/water solution (70:30, v/v) containing 0. 1% formic acid and 1. 37 mmol. L - 1 methyl vanillate (used for injection veri cation). Blank extracts, i. e. without leaf powder, were also prepared. Supernatants were ltered. A quality control (QC) sample was prepared using a mixture of the same volume of each extract, except blank extracts. Methanolic extracts were analyzed in a single batch by LC-QTOF-MS. The extracts were injected randomly. A QC injection was performed after the analysis of every 10 samples to correct for mass spectrometer intensity drift.
Enzyme activity measurements Soluble enzymes extracted from fresh weight samples (20 ± 0. assessed by searching a decoy database and estimated at 0. 34% for peptides and 0. 04% for proteins. Peptide ions were quanti ed based on the extracted ion chromatograms (XIC), using MassChroQ software version 2. 2  [90]. Their intensity was normalized as described previously  [91]. Proteins represented by at least two reproducible and consistent peptides were quanti ed by summing their intensities, as in Balliau et al. [92] , in order to measure their relative abundance. Protein annotations and GO terms were extracted from maizegdb (https://www. maizegdb. org/, RRID:SCR_006600,  [93] ) and complemented using Mercator V4.
To visualize the relationships between the variables selected by the multiblock sparse PLS-DA and contributing to separate the two N fertilization treatments, Spearman correlations were calculated between the variables with a loading value higher than 0. 1 on the rst latent variable (LV1). The corresponding correlation network was reconstructed using Cytoscape  [99]  with a r > 0. 75 threshold for Spearman correlations. Only subnetworks comprising variables of the three datasets were considered. To study the variability of adaptation of the genotypes to the N treatment, for each yield-related phenotypic trait and for metabolome and proteome variables signi cantly affected by the N treatment according to the two-way ANOVAs, the "susceptibility" value per genotype was calculated as follows: susceptibility = (LSmeans (HN) -LSmeans (LN)) / LSmeans (HN).
75 are shown in a network built with Cytoscape. Only the subnetwork comprising variables of the three datasets is shown. Nodes are colored according to their block: eco-physiological, developmental and yield-related data as green octagons, metabolites as red hexagons and proteins as blue triangles. Node size is proportional to the number of connections. For edges, a solid line means a positive correlation and a dashed line means a negative correlation. Node symbols are represented by green octagons for eco-physiological, developmental and yield-related traits and red hexagons for metabolites. For proteins, different symbols and colors are used according to Mercator categories.
Acknowledgements We thank the GCIE team for plant culture and sample harvest, the  Saint-Martin de Hinx INRAE Experimental Unit  for providing the seeds, and  Emilie Cooke-Martageix  for copyediting the manuscript. The IJPB bene ted from the support of  Saclay Plant Sciences -SPS. Proteomics analyses were performed on the PAPPSO platform (http://pappso. inra. fr), which is funded by  INRAE  (http://www. inrae. fr), the Ile-de-France regional council (https://www. iledefrance. fr/education-recherche), IBiSA (https://www. ibisa. net) and  Saclay Plant Sciences-SPS  ( ANR-17-EUR-0007 ).
Availability of data and materials The proteomics data have been deposited in the ProteomeXchange Consortium via the PRIDE partner repository with dataset identi ers PXD034145. The NMR and LC-QTOF-MS data have been deposited in the data. gouv. fr dataverse repository (https://doi. org/10. 57745/NZNMZH and https://doi. org/10. 57745/UPAO0X).
Annotation with putative name assignments of the LC-QTOF-MS-based biomarkers highlighted by statistical analyses was performed by calculating chemical formulae using SmartFormula software (Bruker, Bremen, Germany) and comparing them with those available in the Dictionary of Natural Products (https://dnp. chemnetbase. com/). The LC-MS data and metadata were deposited in the data. gouv. fr dataverse repository (https://doi. org/10. 57745/UPAO0X). Raw data and LSmeans for the identi ed metabolites and metabolite signatures in HN and LN fertilization conditions are presented in Additional Tables3a and 3c, respectively. Annotations of the LC-MS-QTOF metabolite signatures are presented in Additional Table3d.
[86], except for the column used (same reference but 25 cm in length), the linear gradient was shortened to 40  min and the mass range for full MS scan was extended to 350-1,400 m/z. Raw data les were transformed into mzXML open source format, using msconvert software in the ProteoWizard 3. 0. 3706 package [87]. During conversion, MS and MS/MS data were centroided. Peptide and protein identi cation was performed with X!Tandem software ([88], version Piledriver 2015. 04. 01. 1), using the maize genome (http://www. maizesequence. org/version 5a) and a home-made contaminant database. Protein inference was performed using X!TandemPipeline v3. 4. 1 [89] with the following parameters: at least two peptides per protein, peptide e-value < 0. 01, and protein e-value < 10 - 5. The false discovery rate (FDR) was 5 mg).
The mass spectrometry proteomics data were deposited in the ProteomeXchange Consortium via the PRIDE [94]  partner repository with dataset identi ers PXD034145. Raw data and LSmeans for the identi ed proteins in HN and LN are presented in Additional Table 5a and 5c, respectively. 0 (https://doi. org/10. 1016/j. molp. 2019. 01. 003 or https://www. plabipd. de/portal/mercator4) and enzymatic data included in CornCyc v8. 0 (https://www. plantcyc. org/databases/corncyc/8. 0).
Acknowledgments The authors would like to  Cécile Canlet ,  Patrice Dehais ,  Katia Fève ,  Jérôme Fleury ,  Laure Gress ,  Nathalie Iannuccelli ,  Yolande Jaguelin ,  Yann Labrune , and  Yannick Lippi  for their technical support in carrying out the animal measurements, laboratory, and bioinformatic analyses.
quantiles function from the preprocessCore library. Agilent Technology) used in this experiment consisted of 43,603 probes derived from the 44 K Agilent-026440 porcine speci c microarray (V2, for 71% of total probes), 3773 probes from the immune system, 9532 probes from adipose tissue and 3768 probes from muscle tissue, as already reported[68]. After quality control and quantile normalization steps [69], log2 transformation, data from 26 to 36 microarrays per tissue were available with 40,649 probes expressed in muscle, 37,276 probes in SCAT, 42,051 probes in liver, 39,072 probes in blood, 45,667 probes in pituitary, 46,097 probes in thyroid, 45,348 in adrenal glands for further statistical analysis. Normalized expression data (log2 transformed), raw data and sample information are available under NCBI GEO accession number GSE240257 as SuperSeries record including GSE240251 for total blood, GSE240253 for adrenal, GSE240254 for thyroid, GSE240255 for SCAT, GSE240345 for liver, GSE240347 for muscle and GSE240433 for pituitary.
Annotation was based on Voillet et al. (2014) data [68] and was updated (nblast/Ensembl November 2018, Sscrofa11. 1). Moreover, annotation for some differentially expressed probes (DEPs) was manually checked by a sequence alignment with blast against NCBI or Ensembl databases. 90. 2% of DEPs were annotated. Transcriptome Data. Raw Total RNA was extracted and quanti ed from individual samples with the Nucleospin RNA kit (Macherey data, normalized data and metadata are available under https://entrepot. recherche. data. gouv. fr/dataverse/omics with the DOI accession https://doi. org/10. 57745/HCFXBY. RNA Isolation and Microarray Hybridization Nagel) for all tissues (muscle, liver, adipose tissue, adrenal gland, pituitary, thyroid) except blood. Brie y, 60 mg to 100 mg of tissue was disrupted 2 min at 30 Hz in Trizol with an inox bead with the mixer Mill MM 400 (Retsch).
Estimating the speed, accuracy, precision and repeatability of the sleeve method To further assess the performance of the sleeve method, we used the dataset with 600 observations to estimate the speed, accuracy, precision and repeatability. We estimated the accuracy (bias relative to the exact number of larvae) and precision (magnitude of the standard deviation among observer) of each larval count. For each sample, and separately for specialist and non-specialist observers and for the first and second replicate counts, we computed the bias of each count as the difference between the mean of larval counts and the exact number of larvae. Similarly, we computed for each sample, separately for specialist and non-specialist observers and for the first and second replicate counts, the standard deviation among larval counts (2 types of observers x 2 replicates x 30 samples = 120 bias/standard deviation estimates in total).
Acknowledgements We are grateful to the volunteers for their participation in the study and to  Frédéric Soula  for providing strawberries for the experiment. We also thank  Joël Escaich ,  Isaack Wanko  and  Antoine Escamez  for their help. L. T. B. and P. P. were supported by funding from the  Agence Nationale de la Recherche  ( ANR-21-ECOM-0002 ).
Search strategy and study selection This systematic review was reported in line with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement guidelines  (Moher et al. , 2009; Page et al. , 2021). The systematic literature search was conducted using PubMed, Scopus, and the Web of Science databases in April 2022, using the syntaxes reported in Supplementary Table  S1. Temporal or spatial filters were applied to the search. Reports were included in this review provided they met the following criteria (i) they were human studies investigating the ADME of HCAs, (ii) volunteers consumed single or repeated (multiple) dose(s) of HCAs through a dietary source, an extract or a pure compound, (iii) they provided a quantitative characterization of the total content of ingested precursor compounds, (iv) native HCAs and their derived metabolites were quantified in plasma, serum, and/or urine samples without applying a hydrolysis step to remove phase-II conjugating sulfate and glucuronide moieties (this approach avoided the possible distortion of data for the phase-II glucuronide and sulfate conjugates of HCA metabolites), and (v) at least one pharmacokinetic parameter is reported, namely peak plasma concentration (C max ), area under the curve (AUC), total cumulative urinary excretion, or urinary excretion (expressed as % of intake), for native HCAs and their circulating metabolites.
Study Selection The study selection process is shown in Supplementary Figure  S1. A total of 8383 records were identified through database searches. After removing 2260 duplicates, up to 6123 studies were screened, of which 5908 were excluded based on the title or abstract. A total of 198 eligible records went under the full-text screening process, after which 151 records were excluded. Forty-seven publications met eligibility criteria and were included in the data analysis.
Antioxidants and Redox Signaling A systematic review and comprehensive evaluation of human intervention studies to unravel the bioavailability of hydroxycinnamic acids  (DOI: 10. 1089 (DOI: 10. /ars. 2023. 0254). 0254)  This paper has been peer-reviewed and accepted for publication, but has yet to undergo copyediting and proof correction. The final published version may differ from this proof. feruloylquinic acid, 4′-hydroxy-3′-methoxycinnamic acid, 3′-hydroxy-4′-methoxycinnamic acid, 3′-methoxy-4′-hydroxycinnamoyl-glycine and 3′ and 4′ sulfate conjugate of 3′,4′dihydroxycinnamic acid (Table  1 ). More chemical data for each metabolite described in Table  1  are reported in the PhytoHub database (www. phytohub. eu). Circulating metabolites were grouped based on their metabolic pathway and chemical structure in up to 16 classes, namely unchanged acylquinic acids and C 6 -C 3 cinnamic acids, aglycones, and phase-II conjugates of acyl-quinic acids (n=3 classes) [i.
Analysis of 16S rRNA sequence of isolated bacteria The 16S rRNA gene was amplified in a PCR reaction and visualized on agarose gel. PCR products sequencing of the isolated bacteria revealed two types of Vibrio spp. , namely V. owensii and V. parahaemolyticus (GenBank accession numbers: MT192348. 1 and MT672780. 1, respectively). The phylogenetic tree showed that these two isolates were in different clades of the V. harveyi complex (Fig. 8 ).
Phylogenetic analysis of the isolated fungi The sequence of isolated fungi beta-tubulin gene from black gill shrimp showed the highest identity with A. sydowii strain based upon the 478 bp PCR products. In the phylogenetic analyze, it was clustered with the same clade of Aspergillus genus. The neighbor-joining tree showed the position and dissimilarity of A. sydowii (Genbank accession No. MT199421. 1) to other A. flavus and A. awamori strains. Moreover, there was a close relationship between the isolated fungi with A. tabacinus and A. protaberus with the evidence of phylogenetic tree (Fig. 9 ).
Data availability statement Partial sequences of bacterial 16S rRNA and beta-tubulin mRNA were submitted to the database in NCBI. The GenBank accession numbers for the 16S rRNA gene of V. owensii and V. parahaemolyticus were MT192348. 1 and MT672780. 1, respectively. The accession numbers for the beta-tubulin mRNA of Aspergillus sydowii were MT199421. 1.
thaliana GT-3a TF in planta. GT-3a is important for M. incognita parasitism GT-3a has been reported to be predominantly expressed in floral buds and roots, especially at the onset of secondary root development  (Ayadi et al. , 2004). RNA-sequencing data for M. incognita-infected A. thaliana galls at 3, 5, and 7 dpi and for non-infected roots showed that GT-3a was significantly upregulated by nematode infection at these early time points  (Yamaguchi et al. , 2017). For analysis of GT-3a expression in galls, we cloned a fragment of the GT-3a promoter (À2023 to 0) and transformed A. thaliana plants with a ProGT-3a:GUS fusion. We then inoculated the transformed plants with M. incognita and performed histochemical assays. We observed a strong GUS signal in uninfected root vascular tissues and lateral root initials and in developing galls at 3, 5, and 7 dpi (Figure  4A ).
Data represent the average length ± SD (n = 10) and the average number ± SD (n = 10). Different letters indicate significant differences (P < 0. 05, one-way ANOVA). Scale bar, 1 cm. See also Supplemental Figure  4H. (C) Lateral root density calculated as the number of emerged lateral roots divided by the total primary root length. Different letters indicate significant differences (P < 0. 05, one-way ANOVA). (D) Overexpression of GT-3a in A. thaliana increased susceptibility to M. incognita. Two independent T3 lines ectopically expressing GT-3a were inoculated with M. incognita pre-J2s. Total numbers of galls and egg masses were counted at 35 dpi. M. incognita inoculated WT A. thaliana inoculated with M. incognita was used as a control. Data are the average number ± SD (n = 18). Different letters indicate significant differences (P < 0.