dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical Analysis Continuous variables were summarized by their means and SD across all included patients. Categorical variables were summarized by the corresponding counts and percentages. The risk of adverse events among patients undergoing PCI in the LAD/LM versus no-LAD/LM was evaluated by using a multivariable Cox regression model adjusted for clinically relevant variables, including age, sex, smoking, hypertension, hyperlipidemia, diabetes, previous MI, previous PCI, previous coronary artery bypass grafting, clinical presentation, and multivessel disease. All outcomes were analyzed using time-to-event analysis and according to the intention-to-treat principle (ie, patients were analyzed according to the allocated treatment to DES or BMS). We first summarized the data using unadjusted Kaplan-Meier estimates at the longest available follow-up.
Assembled Dataset IPD were provided for the following variables, where available: demographic data and risk factors (age, sex, smoking status, hypertension, dyslipidemia, diabetes, insulin-treated diabetes), history of coronary artery disease (previous myocardial infarction, previous PCI, previous coronary artery bypass grafting), clinical presentation at the time of the index procedure (chronic coronary syndrome, unstable angina, non-ST-segment elevation acute coronary syndrome, ST-segment elevation myocardial infarction), pharmacological therapy during the index procedure (glycoprotein IIb/IIIa receptor inhibitors), angiographic and procedural data (number  Heterogeneity is reported at maximum follow-up and was calculated with a two-stage meta-analysis.
Statistical analysis. Values are presented as the mean ± standard deviation (SD) or median (25th; 75th percentile), according to data distribution. Normality was verified by Shapiro-Wilk test. Qualitative data are expressed as crude values and/or percentages. Difference between global parameters were analyzed by nonpaired t-test (if necessary, with Welch's correction) for normally distributed data or Mann-Whitney's test for nonnormally distributed data. Differences between sectorial and segmental groups were analyzed using 2-way analysis of variance (ANOVA). Multiple comparisons within each group were corrected via Holm-Sidak's method. To reduce the number of comparisons among segments in the intragroup analysis, we first computed all the possible pairs and chose as control the most repeated segment with lowest p-values.
Acknowledgements This study was funded by the  Medical University of Silesia  statutory funds ( KNW-1-135/N/6/K ),  ReGenHeart  ( H2020 , grant no. 731532 ),  SCIENCE ( H2020 , grant no. 643478 ) and Circulate ( STRATEGMED II , grant no. Strategmed2/265761/10/NCBR/2015 ) trials. The authors would like to thank  Dr. Hanspeter Fischer  ( Biologics Delivery Systems ) for advanced NOGA data analysis and HR  Mag. Klaus Demetz  for his important mathematical insights. This study was funded by the  Medical University of Silesia  statutory funds ( KNW-1-135/N/6/K ).
Here, we present methods and resources that help elucidate how genetic variants associated with gene expression (cis-eQTLs) or splicing (cis-sQTLs) contribute to, or mediate, the functional mechanisms underlying a wide array of complex diseases and quantitative traits. Since splicing QTLs have largely been understudied, we perform a comprehensive integrative study of this class of QTLs, in a broad collection of tissues, and disease associations. We provide predictions of functional mechanisms for 74 distinct complex traits from 87 GWA study results and demonstrate independent validation and evaluation of findings using likely causal gene-disease relationships in the Online Mendelian Inheritance of Man (OMIM) database. Notably, we find widespread dose-dependent effects of cis-QTLs on traits through multiple lines of evidence.
Mapping the regulatory landscape of complex traits The final GTEx data release (v8) included 54 primary human tissues, 49 of which included at least 70 samples with both whole genome sequencing (WGS) and tissue-specific RNAseq data. A total of 15,253 samples from 838 individuals were used for cis-QTL mapping (Fig. 1 )  [9]. In addition to the expression quantitative trait loci (eQTL) mapping, we also evaluated genetic variation associated with alternative splicing (sQTL) and their impact on complex traits. Fig. 1  Overview of workflow for mapping complex trait-associated QTLs. Full variant association summary statistics results from 114 GWAS were downloaded, standardized, and imputed to the GTEx v8 WGS variant calls (maf > 0. 01) for analyses. A total of 8. 87 million imputed and genotyped variants were investigated to identify trait-associated QTLs.
A total of 49 tissues, 87 studies (74 distinct traits), and 23,268 protein-coding genes and lncRNAs remained after stringent quality assurance protocols and selection criteria. A wide array of complex trait classes, including cardiometabolic, anthropometric, and psychiatric traits, were included We downloaded and processed 114 publicly available GWAS datasets with genomewide variant association summary statistics (here onwards, summary statistics). After data harmonization, format standardization, missing data imputation, and other quality assurance steps (Additional file 1: Fig. S1 , Fig. S2 , and Fig. S3 ), we retained 87 datasets representing 74 distinct complex traits including cardiometabolic, hematologic, neuropsychiatric, and anthropometric traits (Additional file 1: Fig. S4 ). We provide the full list of datasets used in our study and all processing scripts as a resource to the community (Additional file 2: Table  S1  and Additional file 1: Table  S2 ).
Causal gene prediction and prioritization In addition to genome-wide analyses that shed light on the molecular architecture of complex traits, QTL analysis of GWAS data can identify potential causal genes and molecular changes in individual GWAS loci. Towards this end, we performed association analysis with genetically predicted regulation and colocalization (Fig. 4a ). After evaluating the performance of coloc and enloc  [16, 17] , we chose enloc as our primary approach, due to its use of hierarchical models to estimate colocalization priors  [16]  and its ability to account for multiple causal variants. The coloc assumption of a single causal variant drastically reduces performance especially in large QTL datasets such as GTEx with widespread allelic heterogeneity. For a more extensive discussion on the benefits of Bayesian colocalization methods and comparison of enloc to other colocalization approaches including SMR-HEIDI, see  [25].
We estimated the posterior regional colocalization probability (rcp), using enloc, for 12,072,964 tissue-gene-GWAS locus-trait tuples and 67,943,800 tissue-splicing event-GWAS locus-trait tuples. For the tally of colocalized genes, we used rcp > 0. 5 as a stringent cutoff as demonstrated below with the low colocalization probabilities of height loci using two different datasets. In total, we identified 3477 (15% of 23,963) unique genes colocalizing with GWAS hits (rcp > 0. 5) across all traits and tissues analyzed. Similarly, 3157 splicing events (1% out of 310,042) colocalized with GWAS hits, corresponding to 1226 genes with at least one colocalized splicing event (5% of  23, 963). Colocalization of e/sQTLs with GWAS variants provides important causal support for molecular traits. However, we found their estimates to be overly conservative.
To replicate the PrediXcan expression associations in an independent dataset, BioVU, which is a large-scale biobank tied to Electronic Health Records  [30, 31] , we selected seven traits with predicted high statistical power. Out of 947 gene-tissue-trait discoveries tested, 458 unique gene-tissue-trait triplets (48%) showed replication in this independent biobank (PrediXcan association p < 0. 05; see Supplementary Text). Further confirming this statistical replication in BioVU, we used the PheWAS  [32]  catalog as the silver standard and found an AUC curve of 0. 62. [33]. Altogether, these results provide abundant links between gene regulation and GWAS loci. To further quantify this, we split the genome into approximately LD-independent blocks  [34]  and identified blocks with a significant GWAS variant for each trait (at Bonferroni threshold adjusted for number of variants 0.
Differences may arise with our analysis from the fact that (1) GTEx v8 doubles the number of samples with both genotype and RNA-seq relative to v7, (2) we include links based on splicing in addition to expression, (3) a variant may act through both regulation of expression levels and other undetected mechanisms (pleiotropy), and (4) attenuation bias may reduce the estimates given the error in eQTL effect sizes. Of note, two members of the sterolin family, ABCG5 and ABCG8, showed highly significant predicted causal associations using both PrediXcan and enloc for LDL-C levels and self-reported high cholesterol levels. ABCG8 showed more significant associations in both datasets (chr2: 43838964-43878466; UKB self-reported high cholesterol: -log10(p PrediXcan ) = 38. 43, rcp = 0. 985; GLGC LDL-C: -log10(p PrediXcan ) = 71.
Performance for identifying "ground truth" genes To compare the ability of different approaches to identify the causal gene that mediates the association between GWAS loci and the traits, we sought to curate sets of "ground truth" genes using information that is independent of GWAS results (Additional file 1: Fig. S9 ). We call these sets "silver standards" as a reminder of their imperfect nature. The first silver standard was based on the OMIM (Online Mendelian Inheritance in Man) database  [45] , and the second one was based on publicly available rare variant tests from exome-wide association studies  [46] [47] [48] , resulting in 1592 OMIM gene-trait pairs and 101 rare variant-based gene-trait pairs (Additional file 3: Table  S4 , Additional file 4: Table  S5 ). The rationale behind the choice of the OMIM database is the comorbidity among Mendelian and complex diseases suggesting that genes whose loss of function cause Mendelian diseases also manifest in milder phenotypic variation when modified to a lesser degree by regulatory variation  [49, 50].
Colocalization approaches can be used to weed out LD contamination, but given the lack of LD references from source studies, they can also be overtly conservative. General purpose reference LD from publicly available sources are not sufficient for fine-mapping and colocalization approaches, which can be highly sensitive to LD misspecification when only summary results are used  [56]. The GWAS community has made great progress in recognizing the need to share summary results, but to take full advantage of these data, improved sharing of LD information from the source study as well as from large sequencing reference datasets is also required. Finally, we generated several resources that can open the door for addressing key questions in complex trait genomics. We present a catalog of gene-level associations, including potential target genes for nearly half of the GWAS loci investigated here that provides a rich basis for studies on the functional mechanisms of complex diseases and traits.
We provide a database of optimal gene expression imputation models that were built on the fine-mapping probabilities for feature selection and that leverage the global patterns of tissue sharing of regulation to improve the weights. These imputation models of expression and splicing, which to date has been challenging to study, provide a foundation for transcriptome-wide association studies of the human phenome-the collection of all human diseases and traits-to further accelerate discovery of trait-associated genes. Collectively, these data thus represent a valuable resource, enabling novel biological insights and facilitating follow-up studies of causal mechanisms.
Acknowledgements We thank the donors and their families for their generous gifts of organ donation for transplantation, and tissue donations for the GTEx research project;  Mariya Khan  and  Christopher Stolte  for the illustration in Fig. 1; and  Laura Vairus  for the illustrations in Figs. 2 and3. We thank the  International Genomics of Alzheimer's Project (IGAP)  for providing summary results data for these analyses. The investigators within IGAP contributed to the design and implementation of IGAP and/or provided data but did not participate in analysis or writing of this report. IGAP was made possible by the generous participation of the control subjects, the patients, and their families. http://web. pasteur-lille. fr/en/recherche/u744/igap/igap_download. php.
Availability of data and materials Genotype-Tissue Expression (GTEx) project's raw whole transcriptome and genome sequencing data are available via dbGaP accession number phs000424. v8. p2  [57]. All processed GTEx data are available via GTEx portal (http://gtexportal. org/). All the code used for the reproducible analysis is available, under MIT license, on Zenodo with the access code DOI https://doi. org/10. 5281/zenodo. 4321149 [58]  and GitHub https://github. com/hakyimlab/gtex-gwas-analysis [59]. The softwares for imputed summary results, enloc, coloc, PrediXcan, MultiXcan, dap-g, prediction models are available at links there in. 1000 Genomes Project Reference for LDSC, https://data. broadinstitute. org/alkesgroup/LDSCORE/1000G_ Phase3_plinkfiles. tgz; 1000 Genomes Project Reference with regression weights for LDSC, https://data.
broadinstitute. org/alkesgroup/LDSCORE/1000G_Phase3_weights_hm3_no_MHC. tgz; BioVU, https://victr. vanderbilt. edu/pub/biovu/? sid=194; eCAVIAR, https://github. com/fhormoz/caviar; QTLEnrich, https://github. com/segrelabgenomics/eQTLEnrich; flashr, https://gaow. github. io/mnm-gtex-v8/analysis/mashr_flashr_workflow. html#flashr-prior-covariances; Gencode, https://www. gencodegenes. org/releases/26. html; GTEx GWAS subgroup repository, https://github. com/broadinstitute/ gtex-v8; GTEx portal, http://gtexportal. org; Hail, https://github. com/hail-is/hail; HapMap Reference for LDSC, https://data. broadinstitute. org/alkesgroup/LDSCORE/w_hm3. snplist. bz2; LD score regression (LDSD regression), https://github. com/ bulik/ldsc; MetaXcan, https://github. com/hakyimlab/MetaXcan; Mouse Phenotype Ontology, http://www.
informatics. jax. org/vocab/mp_ontology; NHGRI-EBI GWAS catalog, https://www. ebi. ac. uk/gwas/; picard, http://picard. sourceforge. net/; PLINK 1. 90, https://www. cog-genomics. org/plink2; PrediXcan, https://github. com/hakyim/PrediXcan; pyliftover, https:// pypi. org/project/pyliftover/; Storeyś qvalue R package, https://github. com/StoreyLab/qvalue; Summary GWAS imputation, https://github. com/hakyimlab/summary-gwas-imputation; TORUS, https://github. com/xqwen/torus; UK Biobank GWAS, http://www. nealelab. is/uk-biobank/; UK Biobank, http://www. ukbiobank. ac. uk/.
Systolic aortic valve area (AVA) was also measured by planimetry at mid-systole in the same manner using the multiplanar reformatting mode. Receiver operating characteristic curves were used to determine the best threshold values for sensitivity and specificity for each measurement. Statistical analysis was performed using GraphPad Prism 9 (GraphPad Software, San Diego, CA). Sixty patients were included. The median age was 9. 5 months old (15 days-133 months). The median 3D image resolution was 33 MHz (28-36 MHz). Ventricular septal defect was muscular in 20 patients (33. 3%) and membranous in 40 patients (66. 7%). Characteristics of the whole study population and of subgroups according to the shunt severity are reported in Supplemental Table  1. There were 24 patients (40%) with small shunt, 21 patients (35%) with moderate shunt, and 15 patients (25%) with large shunt.
Abstract. Without properly accounting for both fault kinematics and observations of a faulted surface, it is challenging to create 3D geological models of faulted geological units. Geometries where multiple faults interact, where the faulted surface geometry significantly deviate from a flat plane and where the geological interfaces are poorly characterised by sparse datasets are particular challenges. There are two existing approaches for incorporating faults into geological surface modelling. One approach incorporates the fault displacement into the surface description but does not incorporate fault kinematics and in most cases will produce geologically unexpected results such as shrinking intrusions, fold hinges without offset and layer thickness growth in flat oblique faults. The second approach builds a continuous surface without faulting and then applies a kinematic fault operator to the continuous surface to create the displacement.
Both approaches have their strengths; however, neither approach can capture the interaction of faults within complicated fault networks, e. g. fault duplexes, flower structures and listric faults because they either (1) impose an incorrect (not defined by data) fault slip direction or (2) require an over-sampled dataset that describes the faulted surface location. In this study, we integrate the fault kinematics into the implicit surface, by using the fault kinematics to restore observations, and the model domain prior to interpolating the faulted surface. This new approach can build models that are consistent with observations of the faulted surface and fault kinematics. Integrating fault kinematics directly into the implicit surface description allows for complexly faulted stratigraphy and fault-fault interactions to be modelled.
A fault can then be applied to this geological feature which produces a faulted geological feature. The fault restores the observations attached to the faulted geological feature to their location using the fault kinematics from the fault operator. The faulted geological feature can then be interpolated using the restored locations. To evaluate the scalar  field of the faulted geological feature the fault transformation must be added to the implicit surface representation. This is done by applying the fault restoration kinematics to the data points prior to evaluating the scalar field values. φ faulted (x, y, z) = φ base f disp (x, y, z) (3) Using this workflow, it is possible to stack multiple fault operators and apply, backwards in time, the appropriate displacements to restore the location to its position prior to faulting.
Implementation The generalised workflow presented above does not depend on the specifics of the interpolation scheme. We present an overview of how to build the fault frame using dis- crete interpolation algorithms using LoopStructural  (Grose et al. , 2021a) , an open-source 3D modelling library (https:// github. com/Loop3D/LoopStructural, last access: 29 September 2021). Using discrete interpolation algorithms, the complexity of the interpolation problem is defined by the resolution of the support and not by the number of constraints.
Case studies In the following sections we present three synthetic case studies that demonstrate the application of our approach to both extensional and contractional systems. All of the case studies have been implemented in the open-source 3D structural modelling library, LoopStructural 1. 2. 0 (https://doi. org/ 10. 5281/zenodo. 5234619, Grose et al. , 2021b, a). The case studies are provided as interactive Jupyter notebooks in https: //doi. org/10. 5281/zenodo. 5234634 (Grose, 2021).
, 2016). The fault is added to the fold frame meaning that the interpolation of the fold frame occurs after the observations have been restored. The scalar fields for the fold frame can be seen in Fig. 12a  and b. Within the fold frame, the intersection lineation is calculated by finding the cross product between the normal to observations of bedding and the gradient of the interpolated axial foliation. The fold axis rotation angle is calculated by finding the angle between the intersection lineation and the fold axis direction field. The fold axis rotation angle is interpolated throughout the model by fitting a Fourier series to the calculated fold rotation angles. We use the Levenberg-Marquardt algorithm for fitting the non-linear least-squares  problem implemented in the curvefit function found in the scipy optimise module.
Acknowledgements. The authors would like to thank  Gabriel Godefroy  for fruitful discussions and reading an early draft of this manuscript. This research has been supported by  LP170100985 :  Loop -Enabling Stochastic 3D Geological Modelling  is a OneGeology initiative funded by the  Australian Research Council  and supported by  Monash University ; the  University of Western Australia ;  Geoscience Australia ; the  Geological Surveys of Western Australia, Northern Territory ,  South Australia  and New South Wales; Research for  Integrative Numerical Geology ;  Université de Lorraine ;  RWTH Aachen , the  Geological Survey of Canada ; the  British Geological Survey ;  Bureau de Recherches Géòlogiques et Minières ; and Auscope. Financial support. This research has been supported by the  Australian Research Council  (grant no.
Code and data availability. All of the 3D modelling examples presented in this paper have been generated using the open-source 3D modelling package LoopStructural 1. 2. 0 (https://doi. org/10. 5281/zenodo. 5234619, Grose et al. , 2021b). LoopStructural can be downloaded from GitHub https://www. github. com/Loop3d/LoopStructural (last access: 29 September 2021, Grose et al. , 2021b)  or installed using pip install LoopStructural. The examples presented in this paper are included on Jupyter notebooks including datasets, are provided at https://doi. org/10. 5281/zenodo. 5234634 (Grose, 2021)  and can be run using the provided Dockerfile or Google Colab.
The current analysis concerns 2019 Polish patients. Full data on ECG recordings were available for 1611 patients, 1460 patients had available data on the primary endpoint and were included in the final analysis. The prevalence of the major ECG changes was analyzed on a standard resting 12-lead ECG. ECG containing at least one of the following parameters was considered a major abnormality: abnormal rhythm; tachycardia (>100 bpm); duration of QRS complex ≥120 ms; QTc interval ≥450 ms (Bazett correction); pathological Q-wave; left ventricle hypertrophy; LBBB. Patients were divided into two groups according to the presence of ECG abnormalities and compared with regard to baseline clinical characteristics, type of HF (HFrEF, HFmrEF, and HFpEF), and one-year outcomes. The type of HF was defined by the authors based on baseline LVEF measurement.
The primary endpoint was all-cause death at one year. The secondary endpoint was a composite of all-cause death and hospitalization for HF worsening at one year (follow-up available for 1326 participants). The New York Heart Association (NYHA) functional class with regard to the presence of ECG changes at baseline and 12-month was also evaluated. Data regarding participants' status were collected via telephone follow-up from patients or their close relatives. If the contact was not possible, then the primary endpoint was ascertained from the data of the Polish National Health Fund. Additionally, we sought to determine whether major ECG abnormalities were independent predictors of the primary and secondary endpoints in the study cohort.
Statistical analysis The results were presented as median and quartiles for continuous variables and as frequencies and percentages for ordinal variables. Fisher's exact test was used for comparison of categorical variables and a Mann-Whitney U test for continuous and ordinal variables. Cox proportional hazards regression models were used to identify predictors of the primary and secondary endpoints. All variables found to be statistically significant in univariable analyses (p <0. 05) were included in multivariable analyses. Kaplan-Meier survival curves were plotted for both study endpoints. A P-value below 0. 05 was considered significant for all tests. All tests were two-tailed. Statistical analyses were performed using SPSS software, version 22 (IBM SPSS Statistics 22, New York, NY, USA).