dataesr/scientific-paragraphs-categorization huggingface dataset:
Central quality control of study-level data Quality control of all study-level GWAS summary statistics was performed centrally using the EasyQC R package (version 9. 2)  93. In brief, variants were aligned to either the 1000 G or HRC reference panel and allele frequency plots for each study were compared against the reference. Quantilequantile (QQ) and P-value -Z-score statistics (P-Z) plots were visually inspected. Variants with invalid beta estimates, standard errors or P-values were removed. Per-study summary statistics were generated including standard error and beta estimate ranges. Genomic-control inflation factors (lambdas) were calculated to identify systematic inflation of test statistics, which can result from a variety of factors, including population stratification, and lead to a large number of true positive findings  94.
GWAS Meta-Analysis The meta-analysis workflow is summarized in Fig. 2. The primary analyses were pre-specified to be the multi-ancestry rank-based inverse normal transformed meta-analysis for each ECG trait, to avoid unstable normal approximation test statistics for low-frequency variants or outlier trait values. Ancestry-specific secondary meta-analyses were also performed for European, African, and Hispanic ancestries. Due to the lack of suitably sized replication datasets, we undertook a one-stage, single-discovery design. Additionally, to enable estimation of clinically recognizable effect sizes (inverse normal transformation produces results on a standard deviation scale), a meta-analysis for each trait and ancestry was also performed using the raw phenotype on the millisecond (ms) scale.
Conditional and heritability analyses Conditional analyses were performed using Genome-wide Complex Trait Analysis (GCTA, v1. 26. 0), for loci that achieved genome-wide significance in the European-ancestry analysis with a reference sample of 52,230 individuals of European ancestry from UK Biobank  18. Related pairs up to the 2nd-degree (kinship coefficient< 0. 0884) were excluded. Due to insufficient reference sample size and an inability to effectively reproduce the ancestral mix, conditional analyses were not performed for other ancestries or the multi-ancestry meta-analyses. Stringent thresholds of P Joint < 5 × 10 -08 and minimal correlation (r 2 < 0. 1) with the lead variant were used to declare a variant "conditionally independent". Using the same dataset, heritability estimates for each trait in European samples were obtained using BOLT-Restricted Maximum Likelihood (REML, v2.
Gene-based testing meta-analysis Gene-based meta-analysis was performed using the R package rar-eMETALs (v. 7. 1)  99. Analyses were restricted to up to 192,780 individuals of European descent (from 37 studies) due to potential differences in the allele frequency of rare (MAF≤ 0. 01) variants between populations. QC of study-level data was performed as described above for the single-variant meta-analysis. Variants from all studies, however, were subsequently filtered to only include those predicted by VEP (Ensembl release 99) to have high or moderate impact (and thus be proteinaltering). Score and covariance files used as input for gene-based metaanalyses in rareMETALS were generated using Rvtests as described above  92. Gene-based meta-analysis was subsequently performed for inverse-normal transformed QT (N = 192,780), JT (N = 192,501) and QRS (N = 192,495) using Sequence Kernel Association Testing (SKAT)  27  , which considers the joint effects of multiple variants on the phenotype, while taking into account the effect size and direction of effect of each variant.
Reg-ulomeDB annotates variants with known and predicted regulatory elements in intergenic regions including regions of Dnase hypersensitivity, transcription factors binding sites, and promotor regions utilizing publicly available datasets  103. Association with tissue-specific gene expression. To evaluate correlations between GWAS variants and tissue-specific gene expression, data from the Genotype-Tissue Expression project (GTEx, v8)  33, 104, 105  were extracted for tissues relevant to cardiac electrophysiology including cardiac, vascular (coronary artery and aorta) and brain (as autonomic regulation influence ECG traits). First, lead and conditionally independent variants and their proxies (r 2 > 0. 8), were checked to determine whether they overlapped with the lead variant at an expression quantitative trait locus (eQTL) for each tissue.
Additionally, to determine whether the same variant may be causal in both our GWAS meta-analysis and the original eQTL study, colocalization analyses were performed using the R package COLOC (v5. 1. 0), which uses Bayesian methods to determine the correlation between variants from the two datasets  106. A posterior probability of >75% was used to determine significance. Tissue-and cell-type specific regulatory elements. To identify tissue-specific enrichment of variants in DnaseI hypersensitivity sites, we used GWAS Analysis of Regulatory and Functional Information Enrichment with LD correction (GARFIELD, v2)  37. GARFIELD performs greedy pruning of GWAS SNPs (r 2 > 0. 1) and annotates them based on overlapping functional information to assess the enrichment of association signals with features extracted from the ENCODE, GENCODE and Roadmap Epigenomics projects  37.
Additionally, it highlights enriched pathways and tissues/cell types where genes from associated loci are highly expressed. DEPICT uses a clumping method (r 2 = 0. 1, window size = 250 kb, P = 5 × 10 -08 ) to identify uncorrelated variants from each meta-analysis using 1000 G reference data after excluding the major histocompatibility complex region on chromosome 6. Gene-set enrichment analysis was conducted based on 14,461 predefined reconstituted gene sets from various databases and data types, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), REACTOME, phenotypic gene sets derived from the Mouse genetics initiative, and molecular pathways derived from protein-protein interactions. Finally, tissue and cell type enrichment analyses were performed based on expression information in any of the 209 Medical Subject Heading (MeSH) annotations for the 37,427 human Affymetrix HGU133a2.
In addition to DEPICT gene prioritization results, the list for each locus was supplemented with candidate genes highlighted by these bioinformatic analyses. A literature review was performed which also included a look up of genes using the Online Mendelian Inheritance in Man (OMIM, https://www. omim. org/) and Mouse Genome Informatics (MGI, http://www. informatics. jax. org/) databases. Druggability analyses. To identify potential novel drug targets from our GWAS findings, we interrogated a previously published druggable gene set database developed by Finan et al. which includes detailed information on methods used to assemble and annotate the dataset  40. In brief, this reference set contains predominantly protein-coding genes (as annotated from the Ensembl v. 73). Genes were subsequently assembled into three tiers: Tier 1 includes targets of approved drugs and drugs in clinical development including targets of small molecule and biotherapeutic drugs.
Tier 2 incorporates proteins closely related to drug targets or associated with drug-like compounds. Genes where one or more Ensembl peptide sequence shared ≥50% identity (over ≥75% of the sequence) with an approved drug target were included. Tier 3 incorporated extracellular proteins and members of key drugtarget families (including G protein-coupled receptors, kinases, ion channels, nuclear hormone receptors, and phosphodiesterases). Using the list of "most likely candidate genes" at unreported GWAS loci and previously reported candidate genes for QT, JT and QRS, a look up was performed in the database. Genes which are existing drug targets for anti-arrhythmic drugs (annotated using KEGG drug (https://www. genome. jp/) were excluded. As genes which were significant findings in cardiac tissue-specific eQTL co-localization and Hi-C analyses may be favored for prioritization, a look up was performed in our data to highlight these.
Acknowledgements All study acknowledgements can be found in Supplementary Note 3, and study funding information in Supplementary Note 4. William J Young  was supported by an  MRC  grant  MR/R017468/1. Alvaro Alonso is supported by an  NHLBI  award  K24HL148521. Antonio Luiz P Ribeiro  is supported in part by  CNPq  ( 310679/2016-8  and  465518/2014-1 ) and by  FAPEMIG  ( PPM-00428-17  and  RED-00081-16 ). Eduardo Tarazona-Santos ,  Maria Fernanda Lima-Costa  and  Thiago P Leal  are supported by  Brazilian Conselho Nacional de Desenvolvimento Científico e Tecnologico (CNPq). Eduardo Tarazona-Santos  and  Maria Fernanda Lima-Costa  are also supported by the  Brazilian Ministry of Health  ( DECIT/MS, EPIGEN-Brazil Project ),  Brazilian Ministry of Science and Technology (Financiadora de Estudos e Projetos (FINEP)  and  Fundação de Amparo a Pesquisa do Estado de Minas Gerais (FAPEMIG).
Victor Nauffal is funded by a T32 training grant from the  National Institutes of Health  ( 5T32HL007604-35 ). Nona Sotoodehnia is supported by grants  AHA19SFRN348300063 ,  R01HL141989 ,  Medic One Foundation, Laughlin Family. Ulrich Schotten  received funding from the  Netherlands Heart Foundation  ( CVON2014-09 ,  RACE V Reappraisal of Atrial Fibrillation: Interaction between hypercoagulability , Electrical remodelling, and  Vascular Destabilization in the Progression of AF) , the  European Union  ( ITN Network Personalize AF: Personalized Therapies for Atrial Fibrillation: a translational network , grant number  860974 ;  MAESTRIA: Machine Learning Artificial Intelligence Early Detection Stroke Atrial Fibrillation , grant number  965286 ). Sébastien Thériault is supported by a  Junior 1 Clinical Research Scholar award  from the  Fonds de Recherche du Québec-Santé (FRQS).
James F Wilson  and  Pau Navarro  acknowledge support from the  MRC Human Genetics Unit programme  grant, "Quantitative traits in health and disease" ( U. MC_UU_00007/10 ). Linda Repetto is funded by a  University of Edinburgh studentship. Xia Shen  was in receipt of a  Starting Grant  ( 2017-02543 ) from the  Swedish Research Council (Vetenskaprådet). Lars Lind is funded by  Uppsala University Hospital, Uppsala, Sweden. Niek Verweij  was supported by  NWO VENI  ( 016. 186. 125 ). Jan-Walter Benjamins  is funded by the  Research Project CVON-AI  ( 2018B017 ) financed by the  PPP Allowance made available by Top Sector Life Sciences & Health  to the  Dutch Heart Foundation  to stimulate publicprivate partnerships. J. Wouter Jukema  is an Established Clinical Investigator of the  Netherlands Heart Foundation  (grant  2001 D 032 ).
Oliver Hines is supported by a  Medical Research Council   DTP Studentship. Massimo Mangino  is supported by the  National Institute for Health Research (NIHR)-funded BioResource, Clinical Research Facility and Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust  in partnership with King's  College London. Patricia B Munroe ,  Pier D Lambiase ,  Andrew Tinker  and  Michele Orini  were supported by  Medical Research Council  grant  MR/N025083/1. Julia Ramirez  is funded by  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No. 786833. Pier D Lambiase  is supported by  UCL/UCLH Biomedicine NIHR, Barts Heart Centre Biomedical Research Centre. Patricia B Munroe ,  Pier D Lambiase ,  Andrew Tinker  and  Helen Warren  acknowledge the  NIHR Cardiovascular Biomedical Centre at Barts  and  The London, Queen Mary University of London.
5x10 -6 ) one rare variant • CondiƟon on most significant variant PRS analyses • Significance if P < 0. 05/no. genes Druggability analyses • Look up of mulƟ-ancestry UK-Biobank SCD PRS candidate genes • Druggable genome dataset (Finan et al. ) • 357,971 individuals • EA ancestry • PRS: Lead EA variants meta-analyses • 3981 cases, 12,077 controls, EA ancestry • Outcomes: • 4 cohorts: ARIC, CABS, AF, Stroke, CAD, CD, FinGesture, NFBC1966 HF, NICM, VA • PRS: Lead EA variants.
| SEEG signal analysis All signal analyses were performed in a bipolar montage using the open source AnyWave software  32  available at https://meg. univ-amu. fr/wiki/AnyWave. The epileptogenic regions were defined by two expert clinicians (F. B. and J. M. ), based on visual and quantitative SEEG signal analysis using the EI method. 13 A normalized EI value, ranging from 0 (no epileptogenicity) to 1 (maximal epileptogenicity) was used for each considered structure (for detailed methodology see Bartolomei et al. 13  ). In each patient, maximal EI values from at least three representative seizures (when available) were computed for all bipolar SEEG contacts. The same SEEG datasets of each respective patient were used for the data fitting within the VEP pipeline.
| VEP workflow The VEP workflow  31  methodological details are provided in Appendix S1 and Figure  S1. In brief, the virtual brain model defines the brain as a network of 162 regions, each representing a node in the network. These regions are delineated by the VEP atlas, a cortical and subcortical parcellation of the brain developed specifically for use in epileptology and functional neurosurgery (available at https://ins-amu. fr/vep-atlas). 33 Brain regions are automatically labeled, based on the patients' 3D-MPRAGE images (further referred to as T1-MRI). Connections between regions are estimated through streamline tractography from DW-MRI. The MRtrix3 software package was used to process the DW-MRI, by employing constrained spherical deconvolution to derive the fiber orientation distribution functions and a probabilistic algorithm to sample 15 million tracts.
34 The structural connectome was constructed by assigning and counting the streamlines to and from each VEP brain region. In this way, a patient-specific structural connectivity matrix is obtained, which defines the links and the connection strength between the nodes of the network. The dynamics of each node are defined by an NMM, which is a system of nonlinear differential equations, the Epileptor,  35  that represents the neural activity in that brain region. The source signals generated by the Epileptor at the brain region level are mapped upon the sensor signals measured by the SEEG electrodes. The electromagnetic forward problem is solved by the gain matrix, which is a function of the distance between the sources and sensors. 36 It is obtained by localizing the SEEG electrodes in patient-specific MRI space using the open source software GARDEL (Graphical user interface for Automatic Registration and Depth Electrodes Localization),  37  available at https://meg.
Machine learning and artificial intelligence are used to estimate the patient-specific parameters of the model. The model inversion module allows inferring each region's epileptogenicity by fitting the SEEG data features. The inputs of the model inversion module are the data feature and a prior. The envelope of signal power (high-pass filtered signal followed by envelope smoothing) from empirical SEEG recordings serves as the data feature. The data features are extracted during the periods of interictal to ictal transition and may extend until the seizure end, whereas the interictal activity is regarded as a baseline. The prior is computed using signal processing of raw SEEG data based on the channels' spectral content across 52 different frequency bands (10-110 Hz), weighted by the seizure onset delay in each channel.
It maps a weighted sum of the prior epileptogenicity values of all sensors to each source, based on their distance. The output of the model inversion module is a distribution of epileptogenicity values (EV), which are determined by the key parameters from the model inversion and the patient's structural connectivity. An overview of the most relevant brain regions is presented in the VEP report table, including the information on highlighted brain regions with the distribution of the estimated EV and the EZ definition (yes/no) based on EV. The regions are defined as epileptogenic with a threshold of EV >. 5. They are displayed in red in the three classical planes of the patient's 3D T1-MRI, called a heatmap.
| Resection extent estimation For the operated patients, the extent of resection of different brain regions was defined using GARDEL software. The coregistration of the postimplantation CT with electrodes with the postoperative MRI, and that of the post-and preoperative MRI were performed. The resected contacts were identified by visual inspection and assigned to the respective VEP atlas regions as defined by the preoperative MRI (see above). The regions resected but not explored by depth electrodes were then identified visually on the pre-/postoperative MRI coregistered images (S. M. V. , J. M. ). The completeness of resection of each region was estimated visually by two trained clinicians (F. B. , J. M. ). A VEP region was defined as resected if at least 50% of it was resected, with respect to its major axis length.
| Statistical analysis Statistical analyses were performed using MATLAB statistics toolbox (15. 0 and 18. 0). Precision recall was used as a performance measure to compare the epileptogenic regions predicted by VEP (EZ VEP ) against the clinical analysis incorporating the EI method (EZ C ), considered as standard. We assessed VEP model performances (1)  in the whole cohort of 53 patients, (2) according to surgical outcome (seizure-free vs. not seizure-free patients), and (3) according to brain region topography: mesial versus lateral, temporal versus extratemporal. The latter was assessed for 28 regions, each sampled in at least 10 patients. Two-sided Wilcoxon test was used to assess group differences in the extent of resection of EZ C and EZ VEP , the number of nonresected epileptogenic regions, the number of EZ VEP regions not identified as epileptogenic by the clinical approach, and the number of EZ VEP regions not sampled by SEEG, between the seizure-free and non-seizure-free patients.
| Clinical features Patients' clinical data are summarized in Table  1  (for detailed characteristics, see Table  S1 ). Fifty-three patients (25 males, 28 females) were included. Mean age at epilepsy onset was 16. 2 years (range =. 1-55), mean duration of epilepsy was 17. 2 years (range = 3-45), and mean age at evaluation was 33. 4 years (range = 15-60). An MRI-visible structural abnormality was present in 64% of cases, whereas 36% had unremarkable MRI findings. The EZ organization according to the clinical hypothesis incorporating EI was unilateral in 39, bilateral in 11, and unilateral multifocal in two cases. The EZ was topographically limited to the temporal lobe in 20 cases (37. 7%), whereas it was extratemporal or temporal Plus 39 in 33 patients (62. 3%). Within the temporal Plus subgroup, two thirds were temporofrontal and temporoinsular epilepsies, followed by temporoparietal, temporo-occipital, and more complex, temporoperisylvian EZ organization.
55% in non-seizure-free, p <. 01), independently from its concordance with clinical hypothesis. However, in one third of seizure-free patients with completely removed EZ VEP , resections were extended beyond its limits and included the regions not detected by VEP but considered epileptogenic by clinical hypothesis. The interpretation of such discordant results in the frame of a retrospective study is not univocal. Either these regions were not epileptogenic and could have been spared, or they were epileptogenic and here the VEP lacks sensitivity. Nonetheless, when applied prospectively in complex situations, such as illustrated in the Use Case 2, VEP might be helpful in confirming the extent of the EZ beyond the MRI-visible lesion. These results suggest the importance of the ongoing prospective French multicenter trial EPINOV (NCT03643016), The EZ VEP included the same structures as EI, and additionally, the left inferior frontal sulcus.
ACKNOWLEDGMENTS This work is funded through the  European Union 's  Horizon 2020 Framework Program for Research and Innovation  under the  Specific Grant  Agreement No. 945539  ( Human Brain  Project  SGA3 ) and the  French National Research Agency  as part of the  second Investissements d'Avenir program  ( ANR-17-RHUS-0004 , EPINOV). We thank  Dr Cristina Filipescu  for her help with data collection;  Dr Elodie Garnier  for her help with statistical analysis;  Pr Didier Scavarda  and  Dr Romain Carron  for the surgical management of included patients; and  Pr Agnès Trebuchon ,  Dr Aileen McGonigal , and  Dr Nathalie Villeneuve  for the clinical management of some included patients.
Introduction Today, the availability of satellite image time series (SITS) data is rapidly increasing. For instance, the twin Sentinel-2 satellites provide imagery of the entire Earth every two to five days  [1]. A frequent acquisition of images is crucial for vegetation-related remote sensing applications such as crop type classification  [2, 3]. Multi-temporal data enables capturing the phenological development of crops (i. e. , the progressions of crop growth), a key dimension to discriminate each crop type  [4]. Recently, the increasing availability of SITS along with advances in deep learning has led to crop classifiers with temporal neural architectures using convolutions  [5, 6] , recurrent units  [7] [8] [9] [10] , self-attention  [11, 12] , or combinations thereof  [13, 14]. These crop classification models achieve impressive performance by capturing the temporal structure of the problem but rely on the existence of a large amount of labeled training data.
While unlabeled SITS are plenty, access to labels in the region of interest (the target domain) is often either costly or otherwise unavailable. A possible solution is to train a model in a region with labels available (the source domain) and apply it to the unlabeled target region. However, when the two regions are geographically different, the dissimilarity between the source and target data distributions can cause a source-trained model to perform poorly when applied to the target region  [15] [16] [17]. Solving the distributional shift problem to adapt a sourcetrained model to an unlabeled target domain is in ma-  chine learning known as unsupervised domain adaptation (UDA)  [15, 18, 19]. Here, we consider the cross-region UDA problem for SITS  [20] , where we are provided with labeled data from a source region and unlabeled data from a target region.
Then, we adapt the crop classifier to the target domain using the pseudo-labeled target data along with the available labels for temporally shifted source data, resulting in an accurate crop classifier for the target region. Lastly, we present the TimeMatch dataset, a challenging new open-access dataset for training and evaluating crossregion models on SITS with over 300. 000 annotated parcels from four different regions in Europe. Evaluated on this dataset, our approach outperforms all competing methods by 11% in F1-score on average across five different crossregion UDA experiments. In summary, our contributions are as follows: • We propose a method for estimating the temporal shift between a labeled source region and an unlabeled target region to reduce their temporal discrepancy. • We propose TimeMatch, a novel UDA method designed for the cross-region problem of SITS, where crop classification models are adapted to an unlabeled target region by semi-supervised learning on temporally shifted data for improved performance compared to existing UDA methods.
• We release the TimeMatch dataset  [29] , a new dataset for training and evaluating cross-region UDA models on SITS from four different European regions. This paper is organized as follows. Section 2 describes the existing literature related to our work. Section 3 describes the proposed method for temporal shift estimation and the TimeMatch learning algorithm. Section 4 presents our dataset and the experimental setup, and Section 5 the experimental results. Lastly, Section 6 concludes this work.