dataesr/scientific-paragraphs-categorization huggingface dataset:
They also found that ComBat-GAM, compared to other approaches, consistently led to improved prediction accuracy for each dataset in a leave-one-site-out validation experiment. However,  Pomponio et al. (2020)  only investigated data from healthy participants, which did not involve case-control comparisons, nor formal comparisons to LME methods. Consequently, the goals of the present study were to investigate (1) the performance of ComBat-GAM for comparing clinical cases to controls, (2) how performance is influenced by age, and (3) how well per-formance characteristics compare to LME INT , LME INT + SLP , and ComBat. Although the random-effects meta-analysis (RE-Meta) has been widely used by ENIGMA projects  ( Zugman et al. , 2022 ) , we did not include RE-Meta in this study because several studies showed that LME and ComBat produce results with greater statistical power than RE-Meta  ( Boedhoe et al.
Participants Data were obtained for secondary analysis from the ENIGMA-PGC PTSD Working Group. The dataset originated from 29 sites located on five continents (PTSD, N = 1340; Trauma-Exposed Controls, N = 2057) from a broad age group (6. 2-85. 2 years old). Three sites were the source of all children and adolescents (Duke De Bellis 9. 9 ± 2. 5; Leiden University 16. 0 ± 1. 9; University of Washington 13. 2 ± 2. 9) and one site was the source of older participants (ADNI-DoD 67. 9 ± 3. 6), with minimal overlap between the 3 sites with participants under 20 years and sites with participants over 20 years. Only one site contributed both children (Duke University-De Bellis) and adults (Duke University-Morey). Demographic information is summarized in Table  1. Clinical measures and assessment of PTSD are explained in the Supplementary Table  S1.
Imaging data preprocessing Anatomical brain images were preprocessed at Duke University through a standardized neuroimaging and QC pipeline developed by the ENIGMA Consortium ( http://enigma. ini. usc. edu/protocols/imagingprotocols/ )  ( Logue et al. , 2018 ). Cortical thickness measurements were generated using the FreeSurfer software ( https://surfer. nmr. mgh. harvard. edu ) based on the Destrieux atlas  ( Destrieux et al. , 2010 )  that contains 74 regions per hemisphere. All sites used FreeSurfer 5. 3 for parcellation except ADNI-DoD, Minneapolis VA, and the Waco VA, which used FreeSurfer 6. 0, as well as Amsterdam Medical Center and University of South Dakota, which used FreeSurfer 7. 1. 1 ( Supplementary Table  S2  ). Briefly, white matter surfaces were deformed toward the gray matter boundary at each surface vertex.
ComBat harmonization ComBat removes the effects of site while preserving inherent biological variance in the data  ( Fortin et al. , 2018 ). In the present study, PTSD diagnosis, age, and sex were designated as biological variables. The ComBat approach was implemented using R scripts ( https://github. com/Jfortin1/ComBatHarmonization ) running on  RStudio (ver. 1. 3. 1073) and R (ver. 4. 0. 2). Unlike implementations of LME models that merge data harmonization and statistical analyses, ComBat and ComBat-GAM perform only harmonization and make harmonized data available to the user.
ComBat-GAM harmonization PTSD diagnosis, age, and sex were designated as biological variables, and age was specified as the only smooth term in the model. We employed the default setting so that the empirical Bayes estimates were used for site effects, and there were no custom boundaries for the smoothing terms. The ComBat-GAM approach was implemented using Python (ver. 3. 8. 5) scripts ( https://github. com/rpomponio/neuroHarmonize ).
Distribution of non-harmonized, ComBat harmonized, and ComBat-GAM harmonized data Pairwise comparisons of non-harmonized, ComBat harmonized, and ComBat-GAM harmonized data using the function pairs() (from the R package emmeans ) were applied to the absolute differences between the site-specific mean values and the mean value averaged across sites. The absolute, but not signed values, of the differences were investigated in order to test whether ComBat and ComBat-GAM harmonization led to more consistent distributions Specifically, smaller differences between the site-specific mean values and the mean across sites). The pairwise comparisons were also applied to site-specific standard deviations for cortical thickness across cortical regions. The p -values were adjusted using Bonferroni correction for three pairwise comparisons (i.
Statistical models In all models, we included sex, age, and PTSD diagnosis as fixed factors to estimate their effects on regional cortical thickness, and as covariates for testing interaction effects of interest. Either age by diagnosis interaction, or sex by diagnosis interaction was included in the models as a fixed factor when the corresponding interaction was of interest. The supplementary materials report on the influence of age 2 as a fixed-factor to estimate effects on regional cortical thickness, and for testing interaction effects. were used to calculate the unstandardized regression coefficients for lineal models and the random effects models, respectively. The R package lmerTest was utilized to extract the statistical significance of models. The fitted curves in this manuscript were made using default settings (i.
e. loess) of the R ggplot2 function geom_smooth(). The number of regions with significant findings and the magnitude of effect size was compared separately between the 4 harmonization methods. A chi-squared test based on the function chisq. test() (from the native stats package in R) was used to compare the number of cortical regions showing significant effects. The region-specific regression coefficients were compared using repeated-measures ANOVA based on the function aov_ez() (from the R package afex ). If the omnibus ANOVA results were statistically significant, then post-hoc pairwise comparisons of the 4 harmonization methods were conducted using the function pairs() (from the R package emmeans ). The p -values were adjusted using the Bonferroni method for the 6 pairwise comparisons made with the outputs of the 4 harmonization methods.
The upper whisker extends from the hinge to the largest value no further than 1. 5 * IQR from the hinge (where IQR is the inter-quartile range, or distance between the first and third quartiles). The lower whisker extends from the hinge to the smallest value at most 1. 5 * IQR of the hinge. Data beyond the end of the whiskers are called "outlying" points and are plotted individually. teractions were detected by ComBat, LME INT , and LME INT + SLP. ComBat-GAM outperformed the other methods in detecting this interaction effect ( X 2 (3) = 12. 082, p = 0. 007), see Table  2. As shown in Fig. 9  , age-related declines in cortical thickness were slower in cases than controls for 4 regions: right posterior-dorsal part of the cingulate gyrus, right marginal branch of the cingulate sulcus, right inferior temporal gyrus, and right fusiform gyrus.
The simulated data could be generated by adding characteristic noise, covariance, and bias profiles for each scanner manufacturer and each MRI facility. The simulated data could then be harmonized with several tools of interest to determine the method that produces data that most closely resembles the pre-noised data. Along the same lines, the post-harmonization data and the pre-noised data could be modeled for case-control effects, age effects, and interaction effects. The results of statistical modeling on post-harmonization datasets could be compared to the results from modeling the pre-noised dataset. The harmonization method that leads to results that most closely resemble the results obtained from modeling the pre-noised data would be deemed most faithful to the ground truth. Scanning an appropriate phantom may add value to ascertaining the ground truth, but is unlikely to add value to characterizing the role of age, sex, and diagnosis on harmonization methods.
Limitations There are four major limitations in the present study. Firstly, we investigated age-related changes in cortical thickness. However, only cross-sectional data was available. New approaches have been developed to harmonize data across scanners and sites as well as longitudinal visits  ( Beer et al. , 2020 ; Dewey et al. , 2019 ). Age-related cortical thinning estimated by one longitudinal study design was 3 times greater than cortical thinning from a cross-sectional study  ( Rast et al. , 2018 ). Secondly, we only investigated cortical thickness, which is one of many brain measures that is disturbed in neuropsychiatric disorders. Further studies should investigate the performance of harmonization methods on multi-modal neuroimaging data with various anatomical, diffusion, functional, and clinical/behavioral measures.
Acknowledgments DoD  W81XWH-10-1-0925 ;  Center for Brain and Behavior Research Pilot Grant ;  South Dakota Governor's Research Center Grant ;  CX001600   VA CDA  ;  NHMRC Program Grant  #  1073041  ;  R01 MH111671  ;  VISN6   MIRECC  ;  German Research Foundation  grant to  J. K. Daniels  (  DA 1222/4-1  and  WA 1539/8-2  );  VA RR&D 1IK2RX000709  ;  NIMH R01-MH043454  ;  NIMH K01-MH122774  ;  NIMH K01 MH118428-01  ( Suarez-Jimenez );  5U01AA021681-08 ;  K24MH71434 ;  K24 DA028773 ;  R01 MH63407 ;  K99NS096116 ;  VA RR&D 1K1RX002325 ;  VA RR&D 1K2RX002922 ;  MH101380 ;  ZonMw , the  Netherlands organization for Health Research and Development  grant to  Miranda Olff  (  40-00812-98-10041  );  Academic Medical Center Research Council  grant to  Miranda Olff  (  110614  ); VA CSR&D 1IK2CX001680; VISN17 Center of Excellence pilot funding;  NIMH   R01MH105535  ;  NIMH   1R21MH102634  ;  German Federal Ministry of Education and Research  ( BMBF RELEASE 01KR1303A  );  German Research Society (Deutsche Forschungsgemeinschaft, DFG ;  SFB/TRR 58 :  C06 ,  C07  );  R01MH117601  ;  R01AG059874  ;  MJFF 14848  ;  MH098212  ;  MH071537  ;  M01RR00039  ;  UL1TR000454  ;  HD071982  ;  HD085850  ;  R21MH112956  ;  Anonymous Women's Health Fund; Kasparian Fund; Trauma Scholars Fund; Barlow Family Fund;  NIMH K01 MH118467  ;  W81XWH-08-2-0159  ;  Department of Veterans Affairs  via support for the  National Center for PTSD; NI-AAA  via its support for (P50) Center for the Translational Neuroscience of Alcohol;  NCATS  via its support of  (CTSA) Yale Center for Clinical Investigation  ;  NIH R01 MH106574  ;  F32MH109274  ;  NIMH   1R21MH102634  ;  R01MH113574  ;  R01-MH103291  ;  BOF 2-4  year project to  Sven C.
Over the past couple of decades, the number of wildfires and area of land burned around the world has been steadily increasing, partly due to climatic changes and global warming. Therefore, there is a high probability that more people will be exposed to and endangered by forest fires. Hence there is an urgent need to design pervasive systems that effectively assist people and guide them to safety during wildfires. This paper presents EscapeWildFire, a mobile application connected to a backend system which models and predicts wildfire geographical progression, assisting citizens to escape wildfires in real-time. A small pilot indicates the correctness of the system. The code is open-source; fire authorities around the world are encouraged to adopt this approach.
Destination here is defined by a fixed spatial increment in the direction at the tangent to the perimeter of the burned area. Real-time wind information is provided by the Windy 6  online service. The land type distribution field comes from CORINE Land Cover data  [19]. The elevation field is extracted from the NASA SRTM dataset, which originally has a 30 m resolution. Fuel parametrization is performed to assign reference fuel parameters to each type of vegetation in the land use data for the ROS computations. Data includes 2D fields of wind speed vectors, to account for the influence of the elevation field. 3) Web server: The Web server is responsible for linking the mobile apps of the end users with the backend (i. e. FMT and FBM). The Web server uses the HERE HYZ database and its Maps API in order to properly model fire propagation as polygons, in a language understood by the map provider software used by the mobile app.
HERE HYZ 7  is a location data management cloud service, built around standards like GeoJSON. Each fire is modelled as a GeoJSON geometry object. When accessing the service through HERE Maps API, communication is through GeoJSON files. 4) Fire management tool: This software is intended to be used by fire departments to manage wildfire incidences, i. e. add/remove, ignite/stop and configure wildfires in real-time. New fires can be added via the "Add new wildfire" button, shown in Figure  3  (left). When the fire officer clicks this button, relevant details can be added (Figure  3 , right) such as the exact location where the wildfire started, time of ignition and other important information to be shared with stakeholders (i. e. fire fighters and citizens).
In a Likert scale of 1 (very unlikely) to 5 (very likely), participants gave a score of 4. 1 (likely) to the question of whether they would use the app in a real-life wildfire situation. It was observed that older people (51+) found it hard to trust mobile apps, especially in emergency situations. Concerning ease of use, participants scored 3. 9 for the question whether the app was easy to understand (Likert scale, 1: very difficult to 5: very easy). Participants gave a score of 3. 6 for the question of how easy it was to follow the advice given by the app. This perhaps reveals some difficulty of navigation outside of areas with designated roads. The system and pilot presented in this paper had some limitations, which will be addressed in future work. More detailed land-use datasets and more specific fire velocity models need to be included, while pilots should engage more participants in a range of different landscapes and modes of transport (e.
Background. Uveal melanoma (UM), a rare malignant tumor of the eye, is predominantly observed in populations of European ancestry. UMs carrying a monosomy 3 (M3) frequently relapse mainly in the liver, whereas UMs with disomy 3 (D3) are associated with more favorable outcome. Here, we explored the UM genetic predisposition factors in a large genome-wide association study (GWAS) of 1,142 European UM patients and 882 healthy controls. Methods. We combined two independent datasets (GSA array) with the dataset described in a previously published GWAS in UM (Omni5 array), which were imputed separately and subsequently merged. Patients were stratified according to their chromosome 3 status and identified UM risk loci were tested for differential association with M3 or D3 subgroups. All statistical tests were two-sided.
Genotyping, imputation and merge Genotypes from the previously published GWAS (dataset1)  (12) , and for the two new sets (dataset2 and dataset3) were filtered (Supplementary Methods), and independently imputed on the Michigan Imputation Server using Eagle for the phasing and Haplotype Reference Consortium r1. 1 as the reference dataset. Imputed datasets were merged together, and another quality control was performed (Supplementary Table  1 ). Manual genotyping was also performed on selected SNPs and individuals (Supplementary Methods). Cases and controls of European ancestry were stringently selected for further analyses (Supplementary Methods, Supplementary Figures  1  and 2 ).
Genome-wide association study in UM We combined two independent datasets (dataset2: 369 UM and 496 controls; dataset3: 577 UM, GSA array) with that of our previous UM GWAS (dataset1 of 271 UM and 429 controls; Omni5 array)  (12). The data were quality filtered (Figure  1  and Supplementary Table  1 ). The three datasets were imputed separately using the Haplotype Reference Consortium on the Michigan server and subsequently merged. Quality of the genotyping and imputation was further assessed by TaqMan genotyping on rs421284, rs12203592 and rs12913832 SNPs on 972 selected samples, with 95. 2%, 99. 1% and 99. 6% of good match, respectively (Supplementary Table  2 ). Data from individuals of European ancestry were stringently selected from Principal Component Analyses (PCA) using plink2, in which the first two principal components were used.
Outliers were then excluded from those selected samples using SmartPCA with 10 iterative PCAs (Supplementary Figures  1 2 3 ). The final dataset for the UM GWAS analysis consisted of 7,488,175 SNPs in 1,142 cases and 882 controls (Figure  1 ). The GWAS Manhattan plot showed three distinct loci reaching genome-wide significance (Firth logistic regression P<5. 00×10 -8 ) (chr5, CLPTM1L/TERT locus; chr6, IRF4 locus; and chr15, HERC2/OCA2 locus) (Figure  2 , Supplementary significance. The most statistically significant SNPs at this locus were rs1129038 and rs12913832 (OR = 0. 56, 95% CI = 0. 48-0. 66, P =5. 97×10 -12 ; and OR=0. 57, 95% CI = 0. 48-0. 67, P=1. 88×10 -11 ; respectively), located in HERC2. A single SNP located in IRF4 was found to be well above the genome-wide significance: rs12203592 (OR=1.
IRF4 expression was found to be strongly associated with rs12203592 alleles, with a decreased expression in tumors carrying the risk TT genotype (linear regression P =2. 00×10 -6 , Supplementary Figure  5A ). Looking at eQTLs in the Genotype-Tissue Expression (GTEx) database, rs12203592 is linked to IRF4 expression in most tissues, but the directionality of the association varies. As in UM, sunexposed skin had a lower IRF4 expression linked to the T allele, whereas a lower expression of IRF4 is associated with the C allele in all other tissues, suggesting a tissue-specific regulation for this gene (Supplementary Figure  5B ). At the HERC2 locus, no correlation was found between rs12913832 alleles and expression of this gene in UM (Supplementary Figure  6A ), in contrast to whole blood, where there is a statistically significant decrease in HERC2 expression associated with the G allele (Supplementary Figure  6B ).
Pigmentation risk loci and UM epidemiology The higher prevalence of UM among individuals of European ancestry strongly supports the existence of inherited risk alleles for the disease. The TERT/CLPTM1L risk locus does not account for this population bias, as the risk haplotype is more frequent in African American populations than those of European ancestry (rs421284, VAF=0. 597 vs 0. 429, respectively) (Supplementary Table  5 , Genome Aggregation Database, GnomAD v2. 1). However, the risk haplotypes of both IRF4 and HERC2 are found at statistically significantly higher frequencies in populations of Non-Finish European ancestry (NFE) than in those of African/ African-American (AFR) and East-Asian (EAS) origins (populations defined by gnomAD) (IRF4 rs12203592, VAF=0. 144, 0. 034 and 0. 000, respectively; HERC2 rs12913832, VAF=0.
Data Availability Dataset2 and 3 genotyping data used in the analysis are have been deposited and are available on the European Genome-Phenome Archive (EGA) (https://ega-archive. org/) under accession number EGAS00001005200. Previously published genotyping of dataset1 cases and controls are found on EGA under Accession number EGAS00001002334 and on the database for Genotypes and Phenotypes (dbGaP) under accession number phs001271. v1. p1. , respectively. Previously published expression data (RNA-seq data) of 73 UM tumors are available at EGA under accession no. EGAS00001002932). PCAs were performed using HapMap3 (ftp://ftp. ncbi. nlm. nih. gov/hapmap/genotypes/hapmap3_r3). For expression quantitative trait loci (eQTL) analyses, data was obtained from the Genotype-Tissue Expression (GTEx) public database (https://www.
gtexportal. org/home/). Allele frequency of SNPs of interest in different populations was obtained from the Genome Aggregation Database (GnomAD v2. 1. 1, https://gnomad. broadinstitute. org). Code availability: The following web-based resources were used in the GWAS analysis: PLINK 1. 9 and 2. 0 (https://www. cog-genomics. org/plink/1. 9/ , https://www. coggenomics. org/plink/2. 0/), Michigan Imputation Server (https://imputationserver. sph. umich. edu/index. html), GitHub (https://github. com/DReichLab/EIG), and HIrisPlex (https://hirisplex. erasmusmc. nl/). The code generated during this study is available at https://gitlab. curie. fr/ahouy/gwas/-/blob/master/GWAS_analysis. html.
TableTable 1. Main risk loci in uveal melanoma according to their chromosome 3 status. 1 ID refers to chromosome number : chromosomal genomic position : reference allele > alternative allele, based on genome build GRCh37 (hg19). CI = confidence interval; OR = odds ratio. b SNP = single nucleotide polymorphism, according to The Single Nucleotide Polymorphism Database (dbSNP). ID a SNP b Symbol Alternative allele Total No. (cases/controls) Monosomy 3 OR (95% CI) P c Total No. (cases/controls) Disomy 3 OR (95% CI) P c 5:1325590:T>C rs421284 CLPTM1L C 1126 (244/882) 1. 55 (1. 18-2. 03) 0. 001 1018 (137/881) 2. 26 (1. 61-3. 17) 2. 64x10 -6 6:396321:C>T rs12203592 IRF4 T 1126 (244/882) 1. 01 (0. 70-1. 47) 0. 95 1018 (137/881) 2. 73 (1. 87-3. 97) 1. 78x10 -7 15:28365618:A>G rs12913832 HERC2 G 1126 (244/882) 2.
The Python code of our implementation (analyzer. py), which accepts FTSs in the format. dot as input, is publicly available  [4]. In this paper, we present FTS4VMC, a front-end for the research tool VMC  [7, 9, 11] , developed to make VMC amenable to FTSs. VMC (http://fmt. isti. cnr. it/vmc) is a tool for the analysis of behavioral models with variability during a system's early design phase. It accepts as input a Modal Transition System (MTS) with a set of logical variability constraints (MTSυ), akin to an FTS' feature expressions. An MTS  [25]  is an LTS that distinguishes admissible ('may'), necessary ('must'), and optional (may but not must) transitions which are such that by definition all necessary and optional transitions are also admissible. MTSs were introduced to capture the refinement of a partial description into a more detailed one, reflecting increased knowledge on the admissible (but not necessary) behavior.
py; (ii) to remove ambiguities and thus turn an ambiguous FTS into an unambiguous one, by calling disambiguator. py; (iii) to transform an FTS into an MTS, by calling translator. py; and (iv) to perform an efficient kind of family-based model checking of an MTS obtained as described in iii from an FTS without hidden deadlock states, by calling VMC via vmc_controller. py. The FTS4VMC implementation, including the Python code of disambiguator. py and translator. py, is publicly available from https://github. com/fts4vmc/FTS4VMC. We use benchmarks from the literature to demonstrate the novelties offered by the resulting toolchain (cf. also the tutorial  [10] ).
TOOLCHAIN AT WORK The methodology outlined in the Introduction has been fully automated. With the newly developed front-end FTS4VMC, the toolchain constituted by (i) FTS4VMC, (ii) the Python code analyzer. py from  [4]  (implementing the static analysis algorithm from  [5] , where it was shown to be more efficient than the one presented in  [3] ) and (iii) VMC, can assist an end user (modeler) in the following steps of the engineering and verification methodology envisioned in Fig. 1  (in which all green blocks have been automated by the toolchain): • specify or upload an FTS in FTS4VMC or an MTSυ in VMC; • use FTS4VMC to check whether the FTS is ambiguous (by calling analyzer. py); • use FTS4VMC to transform any ambiguous FTS into an unambiguous one (or to remove only some particular kinds of ambiguities detected, by calling disambiguator.