dataesr/scientific-paragraphs-categorization huggingface dataset:
Data upload and file input The application starts with the About page, which displays the general and background information of the application. The sidebar provides a simple demonstration dataset, including virtual data from 2 groups with 5 samples each, to explore the features of the web application. Alongside, user can upload and analyse its own dataset, appropriately formatted. While FaDA can process files with thousands of values, analysis of such large datasets should be performed using a local version to improve interactivity thanks to the source code available through GitHub. Data are uploaded in a text format (tabular-delimited '. txt' or '. csv' file), with a point or a coma as a decimal separator, with a file limit size of 5 Mo. To allow for flexible use of the application with minimal preparation time, sample identification is in columns or rows.
Statistical summary FaDA initially formulates a descriptive statistical summary after the upload of a dataset. The statistical analysis table presents mean and standard deviation or median and interquartile interval (IQR) per group as parametric or nonparametric options, respectively. The p-value of the Shapiro-Wilk normality test indicates whether the distribution of the dataset differs from a Gaussian distribution guiding the users toward parametric or nonparametric tests. The data can be log2 or log10 transformed, which is notably useful for gene expression datasets. Group comparisons are performed using parametric t. tests or ANOVA with Tukey's 'honest significant difference' method for multiple group comparisons. Welch's t. test is proposed in case of unequal variance. Alternatively, the Mann-Whitney test or the Kruskal-Wallis test with Dunn's test of multiple comparisons using the FSA package are available as nonparametric group comparisons  [8].
Graph visualization plots Shiny allows for built-in support of interactive graph plots of data using R's graph representative and graph plot packages gplots and ggplots2  [10]. The available graph plots include boxand-whiskers, points, individual bars, grouped bars and violins plots and users can define the Y axis origin to 0 instead of the automatic level. Using the plotly package  [11] , interactive features are displayed, including zooming, panning, selecting, and downloading plots as png image files. Heatmap data representation is available as either static or interactive. A static heatmap, may be customized by adding sample hierarchical clustering and color schemes using the ComplexHeatmap package  [12]. PCA allows the display of a covariance matrix and PCA plots to identify potential outliers or sample clustering.
Correlation analysis To assess the correlations among parameters, the correlation coefficients are summarized in a correlogram thanks to the corrplot package  [15]. Individual correlation graphs display scatter plots of two selected parameters with the correlation of these two parameters given. Correlation coefficients (r) and statistical significance tests are calculated either with the parametric Pearson correlation or the Spearman nonparametric methods. Since complete observations are used to calculate the correlations, the ten nearest neighbour averaging method is used to impute missing values  [13]. Association (r) or significative values can be highlighted in bold and yellow for more readability using the sliding bars in the sidebar.
Case study 1 -gene expression data We used a 20-gene expression dataset from peripheral blood from two groups of renal transplant patients: 46 operational tolerant patients who stopped their immunosuppressive regimen while maintaining a stable renal function and 266 renal transplant patients with stable function under immunosuppression  [17]. This matrix was already normalized (mean-centered log-intensity values divided by the standard deviation), so no transformation, e. g. log2 transformation, was applied. Given the gene expression matrix, FaDA allows clear discrimination of the two populations of patients using heatmap and PCA visualization (Fig 2A  and 2B ). The first component of the PCA (PC1) explained 52% of the observed variance. ROC curves analysis highlighted individual genes able to discriminate both populations with AUCs above 0.
7, such as the AKR1C3 gene, which reached an AUC of 0. 796 (Fig  2C ). The correlogram allows the identification of correlated genes MS4A1, CD22, CD79B, FCRL2, BLK and TCL1A (Fig 2D ), in accordance with the previous signature found for operational tolerance and the implication of B cells  [18]. In addition, FaDA provides same values for means of STA and TOL, raw p-values of standard t. tests and AUCs from ROC curves comparing STA and TOL than commercial GraphPad Prism (v. 9. 1. 0) or Microsoft Excel softwares with less time-consuming data manipulation (S2 Table ).
We used two case studies from previously published datasets  [17, 19]  to demonstrate the usefulness of FaDA for analysing data commonly generated by biological and medical researchers, such as microarray and flow cytometry datasets, two major methods used in biology research. Nevertheless, as designed for a general purpose and for noncomputational biologist scientists, advanced analyses such as time series or time-dependant analyses, will require other tools. The R Shiny library has been specifically built to implement web-based applications from R language, a powerful tool for data analysis and especially statistical analyses  [7]. However, interactivity is limited with R and other programming languages, such as Python or Perl, would also have been well-suited for creating interactive visualizations.
In particular, complete customization of figures would require other tools than FaDA which is not its primary utility. Despites, previous tools have been successfully implemented in R Shiny for large datasets and complex analyses, such as for RNAseq or scRNAseq analyses  [3, 5, 20, 21] , the purpose of FaDA is not to perform heavy-lifting bioinformatic analysis that would results in substantial time to analysis. In such cases, advanced users will either use a local version thanks to the source code available through GitHub or dedicated software along with their bioinformatic and statistical skills. Although FaDA supports various statistical options and help text is provided, either in the Tutorial page or as mouseover texts, it cannot replace recommendations from statisticians that users may need for particular cases, as for any type of statistical analysis software.
This study was performed with the support of the French government financial support managed by the  National Research Agency  in the form of the following funding:  ANR-10-IBHU-005  ( IHU-Cesti project );  ANR-11-LABX-0070_TRANSPLANTEX  ( Labex TRANSPLANTEX );  ANR-11-LABX-0016-01  ( LabEX IGO );  ANR-18-CE17-0019  (project  PRELUD );  ANR-17-CE17-0008  (project  BIKET );  ANR-17-RHUS-0010  (project that no competing interests exist. , Area Under the Curve; FaDA, ; ROC,  Receiver Operating Curve.
Background Opal is part of the Veneris framework. Although in this work we focus on its particular features, we first provide a brief overview of the overall framework to provide context. Parts of the description are taken from  [8]  where additional details can be found. Veneris is made of a set of tools that provide different functionality and can interact with each other. The main components, shown in Fig 1  , are:  • Veneris simulator. A set of Unity  [9]  components that provide a realistic microscopic road network simulation in an interactive 3D environment. The components have been grouped in functional modules. Builder components are used to generate the scenario elements: roads, intersections, traffic lights or buildings. Vehicle components include a model of the dynamics of the vehicle and components which model the behavior of the vehicles on roads, intersections and the interaction with other vehicles.
Communication components implement the communication with simulation modules. Managers handle different aspects of the simulation globally. • Opal. The main focus of this paper, it is a ray-launching based, deterministic radio-frequency propagation simulator, implemented in C++ with NVIDIA OptiX  [12] , a library and application framework for high performance ray tracing on the GPU. • Veneris OMNET++ modules. A set of OMNET++ and INET  [10]  modules which enable bidirectional coupling between the communication network and the traffic simulation. These tools can be used independently or combined in different ways to perform different types of simulations. The main combinations are: • Traffic simulation: a scenario from real-world map data is built and run with Veneris to obtain traffic-related metrics.
Of course, not all the simulation types can be run independently: a n-order reflection-diffraction simulation would require a specific simulation class, depending on the implementation. Simulations also include the set of CUDA files with the required OptiX programs such as ray generation, closest-hit, miss and so on. In general, the user program associated to the closest hit for meshes performs the operations required when a ray hits the geometry, such as computing the reflected ray, the reflection and/or diffraction coefficient so on. The closest hit program for receivers usually filter duplicates, depending on the type of simulation, and adds the ray contributions to the electric field. In this sense, let us remark, that in our implementation most of the required computations are done directly on the GPU when possible, including for instance, duplicate ray filtering, which is done using the Thrust library provided by CUDA  [13].
Other approaches, such as  [6] , trace the rays and record the interactions and then post-process the logged trace to compute the field. While this procedure has its own advantages, it requires a huge amount of GPU memory, unlike ours, which also remarkably speeds up the simulations. Finally the generation program is responsible for creating the rays to be traced. In SBR methods, the full 3D unit sphere is discretized by some method and a ray per point is created. The number of rays generated per solid angle, that is, rays per stereorad (rays/sr), is the ray density of the simulation. Opal provides several methods described below. As propagation mechanisms, Opal currently implements n-order reflections for flat, curved or combined flat-curved scenario elements and single-order diffraction as well as two alternative methods.
Let us note that this procedure is the minimum general way to construct automatically generated scenarios, but it depends on the quality of the OSM data, that can vary widely depending on the geographical area. Although for many purposes, such as vehicular simulation, the quality is enough, for more accurate simulations, it is usually necessary to enhance the base scenario. Therefore, additional data pipelines can be created. As an example, for our research, we have processed very accurate building data for the city of Cartagena, acquired from the Spanish Land Registry and we have also added terrain elevation data from the LiDAR database from the Spanish Instituto Geografico Nacional. A screenshot of the generated scenario is shown in Fig 5. The details depend on the particular spatial data source used, that should be processed and adapted to Veneris format.
In the second case, a custom scenario can be designed and built using the capabilities of the Unity engine. In addition to the basic 3D elements available, procedural generation can be implemented using the full power of the C# language provided by Unity, as well as its support for extending the Unity Editor with custom scripts. As an example, in Fig 6  we  show the Anvers tunnel, where we have reproduced a real, slightly curved, underground road tunnel close to the city of Anvers. The tunnel curvature and dimensions can be accurately recreated with a C# script which can be added for future generation as a custom menu item in the Editor. In summary, these examples show the flexibility of Opal for scenario generation that can be achieved by combining both the provided scenario generation tool as well as the programmability capabilities of the Unity engine.
To validate our results we have simulated a rectangular tunnel, for which approximate analytical results exist  [16]. Simulations were done by placing all the receivers at the cross section, that is, the EM field is computed in parallel for 255 receivers, either with a single launch for isotropic generation (actually, only the forward hemisphere has been traced, since backward rays outside the tunnel do not contribute) or in multiple sequential launches with sectorized generation, each angular sector spans 10-degree in elevation by 10-degree in azimuth. In the latter case, the ray density is kept constant in all launches by setting the number of rays accordingly to the solid angle spanned by each sector. The analytical image-based  [2]  multi-modal solution has been computed with Matlab and plotted in Fig 7a  together  with the results computed by Opal with different simulation types: RDN and LPR.
Our approach is more general. The most similar work to Opal is found in  [6] , where authors also use NVIDIA OptiX to perform the ray tracing, but their implementation is quite different as they log the ray traces and post-process the results to perform the EM computations. In addition, unlike Opal, the code is not freely available. Propagation simulation by ray launching has usually been used for EM characterization of static environments, where the transmitter is kept fixed at a few positions  [2] , mainly because of a lack of tools for proper representation of dynamical 3D environments and low performance of the tracing process. On the contrary, ray tracing engines such as OptiX have been primarily designed to obtain high performance and quality rendering for interactive video games and, as such, they are optimized for real-time changing scenes.
This work was supported in part by the  AEI/FEDER/UE [Agencia Estatal de Investigacion (AEI) ,  Fondo Europeo de Desarrollo Regional (FEDER) , and  Union Europea (UE) ] under Grant  PID2020-112675RB-C41  ( ONOFRE-3 ) and Grant  PID2019-107885GB-C33. Part of this work was supported through the  RITMEA  project co-financed by the  European Union  with the  European Regional dynamic Development Fund , the  French state and the Hauts de France Region. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Methods: In an effort to study sex-specific genetic factors associated with PD, we explored 2 large genetic datasets from the International Parkinson's Disease Genomics Consortium and the UK Biobank consisting of 13,020 male PD cases, 7,936 paternal proxy cases, 89,660 male controls, 7,947 female PD cases, 5,473 maternal proxy cases, and 90,662 female controls. We performed GWAS meta-analyses to identify distinct patterns of genetic risk contributing to disease in male versus female PD cases. Results: In total, 19 genomewide significant regions were identified and no sex-specific effects were observed. A high genetic correlation between the male and female PD GWAS were identified (rg = 0. 877) and heritability estimates were identical between male and female PD cases (~20%). Interpretation: We did not detect any significant genetic differences between male or female PD cases.
International Parkinson's Disease Genomics Consortium Data Genotyping data, all derived from Illumina platform-based genotyping, was obtained from members of the International Parkinson's Disease Genomics Consortium (IPDGC), collaborators, and publicly available datasets (Supplementary Table  S2 ). All PD cases were diagnosed using standard UK Brain bank or the Movement Disorder Society (MDS) criteria. 12, 13 Control participants were excluded if they had any known neurological disease. All datasets underwent quality control separately, both on individuallevel data and variant-level data, before imputation was performed, as previously described. 11, 14 Quality control steps included: relatedness filtering (PIHAT > 0. 125, at the level of first cousin one random individual was removed from each pair), removal of genetic ancestry outliers departing 6 standard deviations from the European CEU/TSI HapMap3 populations, removal of samples with call rates < 95% and whose genetically determined sex from X-chromosome did not match that from clinical data, as well as samples exhibiting excess heterozygosity estimated by an F-statistic > AE 0.
15. The quality control process and underlying scripts for filtering can be found at https://github. com/neurogenetics/GWAS-pipeline. Filtered genotype data was imputed using the Michigan imputation server with the Haplotype Reference Consortium reference panel r1. 1 2016 under default settings with phasing using the EAGLE option. 15, 16 For GWAS analyses, variants passing the post-imputation quality criteria of R 2 > 0. 3 and minor allele frequency (MAF) > 1% were included. Data were split into male and female datasets based on genetic sex. Sex specific case-control GWAS were performed using RVTESTS (version 20190205) under default settings  17  using logistic regression on genotype dosages adjusted for the following covariates: age at onset for cases and age of last examination for controls (for a small subset age was not available and missing values were imputed with the mean value using -imputeCov), principal components (PCs) 1 to 5 to account for population stratification, and dataset origin.
Age was not included in 3 datasets due to missing data (MF, VANCE) or co-linearity (FINLAND). PCs were calculated from non-imputed genotype data using FlashPCA (version 2. 0). 18 K Biobank Data Imputed UK Biobank (UKB) genotype data (version 3) was downloaded (April 2018) under application number 33601. 19, 20  PD cases were identified using data fields 42032 and 42033. "Proxy" PD cases were included as part of the analyses, considering individuals who reported a parent affected with PD (paternal PD = data field 20107 and maternal PD = data field 20110) because it has been previously shown to share genetic risk with PD cases. 11  Controls were set as people with no report of PD, and no parent affected with PD, and with an age of recruitment over 60 (data field 21022). Covariates were obtained from the data fields: age of recruitment (data field 21022) and Townsend index (data field 189).
Proxy conversion was performed as previously described. 23  Additional Analyses Post GWAS quality control was applied to remove variants with an MAF < 1%, unrealistic beta values for GWAS (> 5 or < 5), and multi-allelic variants. Sex-specific meta-analyses were performed using METAL version 2018-08-28 under default settings. 24 Post GWAS meta-analysis, the following filtering steps were further applied: variants present in at least 13 out of the 19 datasets and displaying an I 2 heterogeneity value of < 80 were included. Mirror Manhattan plot generated with both filtered datasets using the Hudson package (https://github. com/anastasia-lucas/hudson). Linkage Disequilibrium Score regression (LDSC) was performed to calculate the genetic correlation between summary statistics. 25  To assess differences in the magnitude of associations between men's and women's heterogeneity tests were performed.
Additionally, genetic heritability was calculated excluding the UKB proxies for men and women separately. All figures and statistical calculations were created and performed using R (version 4. 0. 3) or Python (version 3. 7). The genetic risk score was estimated using IPDGC data, and  11  as the reference dataset to define risk-weighted alleles. Locus numbering was obtained as previously described. 26  Large effect size variants from the GBA and LRRK2 regions were excluded (rs76763715, rs35749011, rs34637584, and rs114138760).
Initial Data Overview After quality control, we included 13,020 male PD cases, 7,936 paternal proxies, 89,660 male controls, 7,947 female PD cases, 5,473 maternal proxies, and 90,662 female controls totaling 214,698 individuals (Supplementary Table  S2 ). The odds ratio for PD in men versus women (excluding proxies) was 1. 72 (95% confidence interval [CI] = 1. 66-1. 78). Metaanalyses at MAF > 1%, variant present at least 13 out of the 19 datasets and displaying an I2 heterogeneity value of < 80 resulted in 7,153,507 variants passing quality control for men and 7,141,404 variants for women. No evidence of genomic inflation was observed, with Lambda 1000 and LDSC intercept values of 1. 0013 and 0. 9548 (SE = 0. 0077) for male PD GWAS and 1. 0008 and 0. 9516 (SE = 0. 0077) for female PD GWAS.
11 when comparing IPDGC (clinically diagnosed) summary statistics with 23andMe data (mostly self-reported; rg = 0. 85) or UK Biobank (rg = 0. 84; family history defined cases). Heritability estimates (excluding UKB proxies) were similar between males and females (h2_male = 0. 2077, SE = 0. 0238, and h2_female = 0. 1857, SE = 0. 0304) as expected based on previous estimates. Next, we tested whether there was a difference of cumulative genetic risk score, based on known GWAS hits and using weights from ref. 11 between male and female PD cases using the IPDGC data. Each cohort was analyzed separately and results were meta-analyzed. Meta-analysis showed that there was no difference between the cumulative genetic risk score in men and women with PD, p = 0. 572 (Fig  2 ). Subsequently, we investigated whether any of the known GWAS risk signals were associated with PD differently between male and female cases.
Discussion Men are ~1. 5 times more likely to develop PD compared to women in European ancestry. This is similar to dementia with Lewy bodies (DLB), which is also more common in men, but in contrast with Alzheimer's disease (AD) in which women are almost 1. 5 to 2 times more likely to develop disease. 27 Where on the phenotype level slight differences are observed between men and women in PD, DLB, and AD and overall men have a faster progression/ cognitive decline and shorter lifespan after diagnosis. 6, 27 ex-dependent autosomal effects on clinical progression of AD have been observed  28  and data on sex-dependent autosomal effects for other neurodegenerative diseases is lacking. Here, we assessed whether an autosomal genetic difference explains these differences by performing GWAS using several large case-control datasets and separating these by men and women.
Acknowledgments The authors would like to thank all of the subjects who donated their time and biological samples to be a part of this study. We also would like to thank all members of the  International Parkinson Disease Genomics Consortium (IPDGC). See for a complete overview of members, acknowledgments, and funding http://pdgenetics. org/partners. This work was supported in part by the  Intramural Research Programs of the National Institute of Neurological Disorders and Stroke (NINDS) , the  National Institute on Aging (NIA) , and the  National Institute of Environmental Health Sciences  both part of the  National Institutes of Health, Department of Health and Human Services ; project numbers  1ZIA-NS003154 ,  Z01-AG000949-02 , and  Z01-ES101986. In addition, this work was supported by the  Department of Defense  (award  W81XWH-09-2-0128 ), and  The Michael J.
Fox Foundation for Parkinson's Research. This work was supported by  National Institutes of Health  grants  R01NS037167 ,  R01CA141668 , and  P50NS071674 ,  American Parkinson Disease Association (APDA) ;  Barnes Jewish Hospital Foundation ;  Greater St Louis Chapter  of the APDA. The  KORA (Cooperative Research in the Region of Augsburg ) research platform was started and financed by the  Forschungszentrum für Umwelt und Gesundheit , which is funded by the  German Federal Ministry of Education, Science, Research, and Technology  and by the  State of Bavaria. This study was also funded by the  German Federal Ministry of Education and Research (BMBF)  under the funding code  031A430A , the  EU Joint Programme -Neurodegenerative Diseases Research (JPND)  project under the aegis of JPND -www.