dataesr/scientific-paragraphs-categorization huggingface dataset:
However, it is still an important horizontal transmission route that warrants special attention for emerging ilarviruses. For instance is PMoV, the closest phylogenetically-related virus to SnIV1, was shown to be pollen-transmitted and thus poses threat for tomato production, at least in some European countries  (Aramburu et al. 2010; Aparicio et al. 2018; Parrella et al. 2020). Based on the results of this study, it is thus hypothesized that contamination by SnIV1infected pollen might be one of the reasons (if not the most probable) for the identification of SnIV1 in different sequencing datasets. Detection of SnIV1 sequences in 39 datasets of leaf surface or epiphyte RNA sequencing from two Poaceae species (Panicum and Miscanthus)  (Howe et al. 2022)  fits well with such a hypothesis of surface contamination by SnIV1.
Rudgers and M82). This suggests that association of SnIV1 with tomato in the  Ma et al. (2020)  study might be a result of surface contamination or, alternatively, differences in host plant or viral genotype. Nevertheless, the infectivity of SnIV1 in a genotype of pepper raises the possibility that SnIV1 could be adapting to cultivated solanaceous hosts. It is worthwhile to note that both S. villosum and S. nigrum overwinter in at least some parts of Europe and may serve as reservoir of the virus during the winter months. Overwintering of SnIV1 is likely also possible through infected seeds, since vertical transmission through seeds of these two wild species was demonstrated here. Summary and future perspectives. Overall, viromic surveys, extensive sequence database exploration, and literature searches conducted in this study resulted to the expansion of knowledge on the geographic distribution, associated hosts, and diversity of SnlV1.
In parallel, classical plant virology techniques facilitated the characterization of its pathobiology and possible modes of transmission. The properties of SnIV1 appear to be similar to those of other ilarviruses, but it is interestingly (and quite unexpectedly) associated much more frequently with very diverse plant samples and even with animal species mostly from Apidae. This raises further questions on unexplored properties of SnIV1 leading to its propensity to come up in unexpected HTS datasets. As discussed, many other ilarviruses are pollen-borne, including some infecting solanaceous species, although none have been reported in association with grapevines, despite the heavy HTS sequencing efforts performed in many laboratories around the world on this species. Although the present work facilitated progress in understanding aspects of the biology and epidemiology of SnIV1, there are still more information to be uncovered in order to fully understand this intriguing virus.
b SRA datasets where SnIV1 sequences are detected and assembled, and/or GenBank accessions of SnIV1 genomes with number superscripts in parentheses correspond to each SnIV1 RNA 3 segment that were used in the phylogenetic analyses with the resulting tree shown in Fig. 1  and Supplementary Fig. 2C. c The three genome segments from this study were used as query search for BLASTn searches in GenBank databases. d The virus was deposited under the name 'surrounding legume associated ilarvirus (sLaIV)', and identified from a composite sample of pooled RNAs from three different Trifolium species and one Vicia species. (see  Gaafar et al. 2020  for details). e The virus was deposited under the name 'grapevine associated ilarvirus (GaIV)' and identified from grapevines infected with the downy mildew fungi Plasmopara viticola (kingdom Chromista / family Peronosporaceae).
f The virus was deposited under the name 'Erysiphe necator associated ilar-like virus 1 (EnaIV1)', and identified from grapevines infected with the powdery mildew fungi Erysiphe necator (kingdom Fungi / family Erysiphaceae). g SnIV1 sequences are not available in GenBank r. v. 250. SnIV1 genomes were either provided upon request from the authors ahead of release or were assembled from the SRA dataset of the study. e Accession numbers deposited in GenBank as TPA with number superscripts in parentheses corresponding to SnIV1 RNA 3 genome segment that were used in the phylogenetic analyses with the resulting tree shown in Fig. 1  Supplementary Fig. 2C. f Genomes assembled from the two SRA datasets (SRR9995129, SRR11364885) and another dataset (SRR9995131 was used to assemble SnIV1 isolate DMG 25 (MN520742-MN520744, deposited with the name 'grapevine assoc ilarvirus (GaIV)') came from the same study  (Chiapello et al.
Acknowledgments The authors would like to thank the  Sequencing Platform of Toulouse (GeT-PlaGe)  for Illumina sequencing and for  Marie Lefebvre  for assisting in the processing of sequencing data. The authors are grateful for the assistance provided by  Thierry Mauduit ,  Christophe Higelin  and  Maryam Khalili  in the field, greenhouse, and sample preparation work. They are also grateful to  Laure Beven  for assisting in the use of dark field microscope, and  Mickael Maucourt  for assisting in tissue lyophilization. The authors would like to thank  Varvara Maglioka  and  Reza M. Hajimorad  for providing their SnIV1 genomic sequences ahead of release in GenBank and the publication of their papers. Training and Sequencing of Virome (INEXTVIR)" (GA 813542) under the management of the  European Commission-Research Executive Agency.
Illumina short read sequencing of rRNAdepleted total RNA from two grapevine (cv. Ugni Blanc) plants yielded near complete SnIV1 genomes with average r. m. d. of 16x-60x. This detection was later confirmed by RT-PCR tests for both individual samples. Illumina sequencing of VANAs from a pool of S. melongena and S. tuberosum samples collected in Belgium yielded partial genome of SnIV1, with only a near complete RNA 3 segment assembled from the S. melongena dataset. The detection of SnIV1 in pooled samples of both species was later confirmed with a positive RT-PCR test. The amplicon from S. tuberosum was Sanger sequenced and confirmed to be SnIV1. RT-PCR detection of SnIV1 in different plant tissues. The presence of SnIV1 was further investigated in different tissues of SnIV1-positive S. villosum and grapevines (cv.
a High-throughput sequencing detections confirmed by RT-PCR in individual plants are marked by an asterisk (*) for SnIV1 and hashtag ( # ) for TBRV2 b Number of valid reads after quality screening and trimming of barcodes. The number in parentheses represent the range of read length in number of nucleotidesc One of the SnIV1 genomes in GenBank (a. n. OL472060-OL472062) and TBRV2 genome (a. n. OL472116) were used in reference-based genome assembly in CLC-GWB, with at least 90% identity and coverage threshold. This genome was also used to determine the percentage of the SnIV1 genome segment covered by the mapping d On average, number of times each loci in a reference genome is covered by the mapped reads e Accession numbers deposited in GenBank as third-party annotations (TPA), with number superscripts in parentheses corresponding to each SnIV1 RNA3 genome segment that were used in the phylogenetic tree construction for Fig.
1017b Number of reads after quality screening and trimming of barcodes. The number in parentheses represent the average 1018 read length in number of nucleotides. 1019 c One of the SnIV1 genomes from GenBank (a. n. OL472060-OL472062) was used in reference-based genome assembly 1020 in CLC-GWB, with at least 90% identity and coverage threshold for reads mapping. 1021 d On average, number of times each locus in a reference genome is covered by the mapped reads.
Participants Twenty healthy volunteers (9 males, 25. 4 ± 1. 4, right-handed) participated in the study. All of them fitted the criteria for MRI and TMS experimentation  ( Rossini et al. , 2015 )  and gave their informed written consent to participate in the study. Neither of them had a history of psychiatric, neurologic disorders nor alcohol or substance abuse. All of them were free of pharmaceutical impacting cortical excitability. This study was approved by the ethical committee of Grenoble University Hospital (ID RCB: 2013-A01734-41) and registered on ClinicalTrials. gov (number NCT02168413).
Data and code availability The data recorded for this study are not publicly available due to absence of public data sharing statement in the informed consent form signed by the participant. The data may be available on request from the corresponding author [SH], on the condition that a data sharing agreement between the academic buyer and Grenoble University Hospital is established. The EEG data analysis function used in this work (using Matlab and Fieldtrip toolbox) can be found here: https://gricadgitlab. univ-grenoble-alpes. fr/harquels/public_codes.
Protocol design MRI and TMS-EEG acquisition took place at the IRMaGe facility of University Grenoble Alpes. Prior to the experiment, each participant underwent a T1 MRI required for the neuronavigation software (Achieva 3. 0T TX, Philips, Netherlands; T1TF2, TR = 25 ms, TE = 4 ms, voxel size = 0. 95 mm 3 anisotropic). Following the EEG cap set-up, we performed a standard hotspot hunting procedure on the primary motor cortex (M1) for the first dorsal interosseous (FDI). We positioned targets on the posterior and anterior neighboring BA of M1 on BA 4 (i. e. primary sensory cortex S1 on BA 1-2&3, and premotor cortex PM on BA 6 respectively), aligned with the motor hotspot. Lateral and medial targets were placed at least one centimeter from the other targets, leading to a square grid of 9 targets ( Figure  1  ).
In addition, local TEPs and single trial activity were computed using a common electrodes cluster gathering all ROIs. This larger cluster was used as a control for the effect of ROI definition, which could significantly influence results regarding spatial resolution. Lastly, to investigate the influence of peripheral muscle activation on the results, a second dataset containing only trials without MEPs (when possible) or the lowest MEPs was drawn from the original database and analyzed. For that purpose, 50 trials were selected based on the median split of MEPs amplitude distribution for each target and subject. When the median was equal to zero (majority of MEP negative trials), 50 trials were randomly selected. Over the entire database, most of the trials were MEP negative or induced MEPs inferior to 100 μV (51 and 62% respectively), the latter being of 47% and 69% on M1 and non-M1 targets respectively.
Statistical analysis Statistical analyses were conducted with Fieldtrip  ( Oostenveld et al. , 2011 ) ,  Rstudio and Jamovi ( RStudio Team (2019). RStudio: Integrated Development for R. RStudio, Inc. , Boston, MA URL http://www. rstudio. com/. , n. d. ; The jamovi project  (2020). jamovi (Version 1. 2) [Computer Software]. Retrieved from https://www. jamovi. org, n. d. ), and are detailed thereafter. For repeated measure ANOVA (rmANOVA), whenever the assumption of sphericity was violated (Mauchly's test, p < 0. 05), a Huynh-Feldt correction was applied, and the subsequent F values were reported as F HF together with the corrected degrees of freedom. All post-hoc tests subsequent to ANOVAlike models, as well as multiple comparison or correlation tests, were Bonferroni corrected and reported as p bonf.
05. Local regressions (pRQS) were analyzed using rmANOVA across subjects, with two fixed effects: Brodmann area (S1, M1 and PM) and mediolateral position of the targets (a, b and c). Local regressions were compared to two other metrics (P30 component amplitude and MEP amplitude) using Spearman correlation. To account for the spatial cor-relation in the analysis of remote regressions (uRQS), we used the spam package ( https://cran. r-project. org/web/packages/spaMM/ ) that implement spatial Generalized Mixed Models (spaGLMMs,  ( Rousset and Ferdy, 2014 ) ). Spatial autocorrelations were fitted by Gaussian process as well as random term and fixed effects were evaluated using a model comparison procedure, i. e. removing a factor from a model and comparing the fit and residual errors. Since RQS distributions were left skewed, we used a square root transformation on data and checked for normality prior to the modeling of fixed and random effects.
The difference in muscle implantation underneath the coil from the most medial to the most lateral targets of our grid may not be sufficient to significantly elicit different PEPs (Figure  S1 ). Finally, one experimental way to control for this aspect would have been to quantify the perception of stimulation on subjective scales for each subject, and test whether or not it was different between targets  ( Conde et al. , 2018 ). Furthermore, PEPs also cover the reafferent somatosensory activity from muscle movements. First, despite covering latencies potentially affected by this reafference ( > 40 ms), local regressions (pRQS) seem to be insensitive to this contamination since the resulting functional maps were not correlated with motor maps using EMG as readouts ( Figure  4. B). Furthermore, the influence of sensorimotor activations on the main result of this work appears to be marginal, since the results on remote regression (uRQS) were not modulated by the presence or absence of MEPs, or the presence of high or low MEPs amplitude within the trials dataset.
We chose to apply the two round ICA method, that has been extensively validated in the past  ( Bertazzoli et al. , 2021 ; Rogasch et al. , 2017 ) , but a generalization of this results to other preprocessing methods would be yet to be done. Overall, the intra-subject variability of the preprocessing efficiency within the stimulation grid had little influence on our results, given the fact that the raw datasets noise level was uniformly distributed and equally corrected across targets (Figure  S1 ). Another limitation of this study lies on the stimulation intensity chosen. We stimulated at a fixed intensity corrected for scalp-to-cortex distances. However, other mapping approach could have been considered. In motor mapping setting, one could use a mapping of rMT at different targets to assess the muscle specificity of the target  ( Meincke et al.
Data and code availability The data recorded for this study are not publicly available due to absence of public data sharing statement in the informed consent form signed by the participant. The data may be available on request from the corresponding author [BP], on the condition that a data sharing agreement between the academic buyer and Grenoble University Hospital is established. The EEG data analysis function used in this work (using Matlab and Fieldtrip toolbox) can be found here: https://gricadgitlab. univ-grenoble-alpes. fr/harquels/public_codes.
Acknowledgments This work was funded by the  Agence Nationale pour la Recherche  grant " ANR-15-CE37-0015-1  " and by  NeuroCoG IDEX UGA  in the framework of the " Investissements d'avenir " program  ( ANR-15-IDEX-02 ). Data were acquired on a platform of  France Life Imaging Network partly funded by the grant  " ANR-11-INBS-0006. ".
, 2009; Cammarato et al. , 2015). Fly cardiac function parameters are highly influenced by sex  (Wessells et al. , 2004). Due to the experimental burden of analyzing individual cardiac phenotypes, we focused on female flies only and designed our experiment in the following way: we randomly selected 14 lines out of 167 that were replicated twice. The remaining 153 lines were replicated once. Each replicate was composed of 12 individuals. No block effect was observed due to the replicates in the 14 selected lines (see Supplementary file 1a). This allowed us to perform our final analysis on one replicate of each of the 167 lines. A total sample of 1956 individuals was analyzed. Seven cardiac traits were analyzed across the whole population (Figure  1 -source data 1 and Table  1 ). As illustrated in Figure  1A , we analyzed phenotypes related to the rhythmicity of cardiac function: the systolic interval (SI) is the time elapsed between the beginning and the end of one contraction, the diastolic interval (DI) is the time elapsed between two contractions and the heart period (HP) is the duration of a total cycle (contraction+relaxation (DI+SI)).
Variants not mapped to genes (located at >1 kb from TSS or TES) were slightly depleted in our set. In GWAS analyses, loci associated with a complex trait collectively account for only a small proportion of the observed genetic variation  (Manolio et al. , 2009)  and part of this 'missing heritability' is thought to come from interactions between variants  (Flint and Mackay, 2009; Manolio et al. , 2009; Huang et al. , 2012; Shorter et al. , 2015). As a first step toward identifying such interactions, we used FastEpistasis  (Schüpbach et al. , 2010). SNP identified by GWAS were used as focal SNPs and were tested for interactions with all other SNPs in the DGRP. FastEpistasis reports best ranked interacting SNP for each starting focal SNP, thus extending the network of variants and genes associated to natural variation of cardiac performance, which were used for hypothesis generation and functional validations; 288 unique SNPs were identified, which were mapped to 261 genes (Figure  1 -source data 3, Supplementary file 1e).
Of the 562 identified genes, 419 were mapped to the fly interactome that includes both physical (protein-protein) and genetic interactions from both DroID  (Murali et al. , 2011)  and flybi databases (see Materials and methods and Figure  2 -source data 1). Remarkably, a high proportion (148) of the GWAS identified genes were directly connected within the fly interactome and formed a large network of interacting genes/proteins (Supplementary file 2c and d, Figure  2B ), suggesting that they participate in common biological processes. This network encompasses several TFs and ion channel complex genes, consistent with their potential role in the genetic architecture of natural variation of heart performance. Several components of signaling pathways are also present in the network, including members of the FGF and TGFβ pathways (see below).
However, oligo analysis  (van Helden et al. , 1998)  of the 150 bp sequences around these non-coding variants detected significant over-represented k-mers (Evalue ≤ 1e-4). These were further assembled and converted into positions-specific scoring matrices (PSSMs) and compared with PSSM databases of known fly TFs binding motifs were used for annotations (see Materials and methods, Figure  3 -source data 1 and Figure  3A ). Variants associated with quantitative traits linked to heart size and contractility (ESD, EDD, and FS; 367 variants) and those related to heart rhythmicity (AI, HP, DI, SI; 435 variants) were analyzed separately. We identified enrichment of potential DNA binding motifs for both sets of traits (Figure  3B ). Although this approach predicted motifs that were specific for each group of variants, a significant proportion of potential DNA binding motifs were predicted by both groups, suggesting commonalities in the genetic architecture of the corresponding traits.
064, SI: 0. 09, ESD: 0. 08) (Figure  4 -figure  supplement 1C ). This suggested that distinct loci may affect natural variations of CVe and means of cardiac performance parameters. We performed GWAS analysis to identify candidate variants and genes associated with variation of CVe of cardiac performance using both single marker and epistasis GWAS analyses. Similar to trait means we retained the 100 top ranked variants for reporting single marker associations. These variants were further used as focal variants for epistasis detection and the best ranked interacting SNP for each starting focal SNP was identified. Overall, this led to the identification of 887 variants and 566 genes associated with natural variations of CVe of cardiac performance (Figure  4 -source data 1, Supplementary file 3a), which were used for hypothesis generation and functional annotations.
Conserved features of natural variations of heart performance between flies humans Next, we compared our GWAS results for cardiac performance in flies with similar data in humans. A literature survey identified a comprehensive set of human genes associated with cardiac disorders and coronary artery diseases (CAD) in human populations (Supplementary file 3f and h). Using DIOPT  (Hu et al. , 2011) , we identified human orthologues of the genes associated with cardiac trait means and CVe in flies (Supplementary file 4a and d). Compared to the whole fly genome gene set (17,500 genes), both sets of GWAS-associated genes were significantly enriched for genes that have a human orthologue (FC = 1. 4, pval = 4. 3 • 10 -21 for genes associated with trait means and FC = 1. 25; pval = 7 • 10 -11 for genes associated with traits CVe, Table  2 ), indicating that, overall, the loci associated with natural variations in cardiac traits have evolutionary conserved functions.
Yet, GWAS on human populations are not designed to test the variants involved in intra-genotypic variations. One possible explanation may be that GWAS in humans can pick up variants that do not necessarily affect the mean of cardiac traits, but that cause extreme phenotypes in some individuals by affecting genes that function as buffers. The high proportion of orthologous genes identified in GWAS in both flies and humans -for cardiac traits in Drosophila and for cardiac disorders in humans -suggested that our results in flies may accelerate discovery of causal genes in humans. We suggest that our study may be used as an aid in prioritizing genes at loci identified by GWAS for cardiac pathologies in humans. Indeed, we show that our data allow identification of new actors that have a conserved role in cardiac function from flies to humans.
Genotype-phenotype associations: GWAS and epistasis study We used mean and CV summary statistics from our GWAS analysis to study the leverage effect of extreme values. We identified strong leverage effects in some phenotypes and treated them for outliers. We decided to remove lines with large leverage effect from GWAS analysis of the phenotype, since they imply a strong bias in the linear model. Hence, mean/CV values outside the range of [Q1 -1. 5*IQR; Q3+1. 5*IQR] were discarded from GWAS analysis of the corresponding phenotype, thus removing all leverage effects. GWAS analysis was performed using FastLMM  (Lippert et al. , 2011). Linear mixed model was built using Wolbachia infection and common polymorphic inversions (data provided by the DGRP Consortium) as covariates. A GitHub project containing the instructions and material to reproduce the analyses reported is available at: https://doi.
org/10. 5281/zenodo. 5582846. A QQ plot was generated to display the quantile distribution of observed marker-phenotype association p-values vs. the distribution of expected p-values by chance. The QQ plot was plotted to check if some polymorphisms are more associated with the phenotype under a null hypothesis, which would be represented by a uniform distribution of p-values. For each phenotype, we selected the 100 variants with the lowest GWAS p-value. These variants were mapped to genes using the following rules: (i) if the variant was located in a gene (INTRON, EXON, 3'or 5' UTR), the SNP was then associated with this gene, (ii) if not, the variant was associated with a gene if its distance from the boundary of the gene (TSS and TES was less than 1 kb); (iii) otherwise the variant was not mapped to a gene.
Interaction network analysis Drosophila interactome: A network generated from genetic and physical interaction databases has been built. Briefly, known protein-protein interactions, identified using Y2H and AP-MS techniques, and genetic interactions, are curated in the DroID database (http://www. droidb. org/)  (Murali et al. , 2011) , to which the Y2H FlyBi dataset (https://flybi. hms. harvard. edu/results. php) has been added. Overall, this Drosophila interaction network contains 79,698 interactions between 11,022 genes/ proteins. Network representations were made using Cytoscape  (Shannon et al. , 2003).