dataesr/scientific-paragraphs-categorization huggingface dataset:
The reciprocals (i. e. , one over 3, 10, and 30) would be respectively taken as corresponding evidence for H 0 over H 1. Finally, 1/3 < BF 10 < 3 is taken as inconclusive evidence for either H 1 or H o. All our Bayesian analyses were conducted using the JASP software package  [30] [31] [32]. Bayesian analysis requires us to make assumptions about the prior distribution of the parameter in which we are interested, which affects the BF 10 we get. However, we found no literature on priors to use in Bayesian statistical tests for RSG. We therefore opted to use JASP's default prior: the Cauchy distribution with a scale/ parameter of 0. 707. First, selecting a prior in advance guards against cherry picking a prior that supports gaining evidence for one's favored hypothesis. Second, we often use Bayesian inference here to seek evidence for H 0.
Interestingly, those biases were found in the DLPFC, which has also been implicated in RSG. It would therefore be interesting to investigate the extent to which biases in decision making and RSG are supported by the same neural mechanisms, which may involve the DLPFC. Several potential limitations of our study should be mentioned. One stems from its gender imbalance. We found no evidence for gender differences in RSG generation in the literature. And we found evidence that there are no differences in RSG ability between males and females in our dataset. Nevertheless, our participants were 73% female and this roughly 3:1 gender imbalance might still have some effect on our results. Future research with a large-enough dataset that is roughly gender balanced might dispel this concern. Another limitation of our study was our inability to account for the great majority of the variance in the LZC measure using other factors that we measured; less than 1/3 of the LZC variance was explained by the run-length scores.
, 2004; Heizmann et al. , 2002; Cristóvão and Gomes, 2019). S100 proteins do not have enzymatic activity  17-56 (PDB ID: 6T58)  (Ecsédi et al. , 2020)  complexes show the characteristics of clamp-like fuzzy interactions where the TAD is anchored at several binding points, while the linking regions remain flexible and appear in numerous conformations. Finally, (E) p300-p53 TAD 1-39 (PDB ID: 2K8F)  (Feng et al. , 2009) , (F) TFIIH-p53 TAD 1-55 (PDB ID: 2GS0)  (Di Lello et al. , 2006) , and (G) HMG box-p53 TAD 1-93 (PDB ID: 2LY4)  (Rowell et al. , 2012)  complexes are good examples of flanking fuzzy interactions. Here, only a small portion of the TAD is binding the partner, while the sequences of the N-and C-termini remain disordered without any confinable structure. but instead control the functions of numerous proteins via PPIs.
In such a case, the bound partner partially dissociates, but the resulting electrostatic encounter complex remains and re-isomerizes into a bound conformation again, where this time the partner utilizes the opposite binding pocket of the S100 dimer. This way, a dynamic exchange could occur   and 5CSI )  (Gógl et al. , 2016) , NM2A (light blue, PDB: 3ZWH)  (Kiss et al. , 2012) , ANXA2 (bright orange, PDB: 5LPU and 4HRE)  (Oh et al. , 2013; Ecsédi et al. , 2017) , SIP (black, PDB: 2JTT)  (Lee et al. , 2008) , AHNAK (forest green, PDB: 4FTG)  (Ozorowski et al. , 2013) , and p53 (deep purple, PDB: 6T58)  (Ecsédi et al. , 2020). Small pictograms show the nature of each interaction, i. e. , whether it is symmetric or asymmetric. The black asterisk marks the structure of the RSK1-S100B complex determined in part using SAXS data.
In one structure, the visible parts of RSK1 adopted only coiled conformations, while in others, the peptide also formed helices, but the location of these structural elements varied in the different crystal forms (Figure  2 ). Circular dichroism (CD) spectroscopy was used to define the structure in solution. Analysis of the CD data, which were processed using the quantitative secondary structure prediction program BeStSel  (Micsonai et al. , 2015; Micsonai et al. , 2018) , showed the presence of helices in the complex but also ruled out the possibility of the whole peptide adopting a helical structure upon binding to S100B. Thus, the observed helical elements, appearing in the structures at different locations, cannot exist as helices at the same time  (Gógl et al. , 2016). CD spectroscopy was also successfully applied in the case of the p53-S100A4 interaction, where the data indicated more helical regions in solution than appearing in the crystal structure (Figure  2 ), suggesting that the transiently bound sections of p53 are also in helical conformation  (Ecsédi et al.
, 2020). Besides CD spectroscopy, small-angle X-ray scattering (SAXS)  (Bernadó and Svergun, 2012)  measurements together with molecular dynamics (MD) simulations  (Berendsen et al. , 1995; Abraham et al. , 2015)  can be a useful tool to complement X-ray data in the case of fuzzy complexes  (Gógl et al. , 2016; Ecsédi et al. , 2017; Dudás et al. , 2020; Manalastas-Cantos et al. , 2021). In SAXS   (Gógl et al. , 2016). S100 proteins are displayed as gray surface representations and their partners as deep purple (p53 TAD 17-56 ) and red (RSK1 683-735 ) ribbons. The green meshes around the partner peptides are simulated annealing Fo-Fc omit maps (contoured at 2σ). Red asterisks show the tracks of flexible regions not appearing in the X-ray structures of these complexes. experiments, X-rays are transmitted through the sample and the scattered portion is collected using a 2D detector.
Finally, (E) p300-p53 TAD 1-39 (PDB ID: 2K8F) (Feng et al. , 2009) , (F) TFIIH-p53 TAD 1-55 (PDB ID: 2GS0) (Di Lello et al. , 2006) , and (G) HMG box-p53 TAD 1-93 (PDB ID: 2LY4) (Rowell et al. , 2012)  complexes are good examples of flanking fuzzy interactions. Here, only a small portion of the TAD is binding the partner, while the sequences of the N-and C-termini remain disordered without any confinable structure.
Towards this end, we analyzed 4175 PAH cases from an international consortium with ES or GS. The National Biological Sample and Data Repository for PAH (aka PAH Biobank) was comprised of 2570 PAH cases (1110 IPAH and 1239 APAH) and the UK NIHR BioResource -Rare Diseases Study was comprised of 1144 cases, almost entirely IPAH. Thus, the increased power of the combined cohort was a 2-fold increase in the number of IPAH cases, and we focused our association analyses on this PAH subclass. The cohort size precluded testing of the oligogenicity hypothesis suggested by the incomplete penetrance of known PAH risk genes. Non-inherited de novo mutations could also contribute to genetically unexplained non-familial cases but require access to parental sequencing data. We previously showed that pediatric-onset PAH cases were enriched with damaging de novo variants.
Patient cohorts and control datasets A total of 4175 PAH cases from the National Biological Sample and Data Repository for PAH (PAH Biobank, n = 2570 exomes)  [7] , UK NIHR BioResource -Rare Diseases Study (UK NIHR BioResource, n = 1144 genomes)  [8] , and the Columbia University Irving Medical Center (CUIMC, n = 461 exomes)  [17, 18, 27]  were included in a combined analysis of rare inherited variants. The subset of 124 affected child-unaffected parents trios (n = 111 CUIMC, n = 8 UK NIHR BioResource, n = 5 PAH Biobank) were included in an analysis of de novo variants. An additional 65 BMPR2 mutation-positive cases from CUIMC without exome sequencing data were previously reported  [17, 18]  and included in the overall cohort counts (total of 4241 cases). As previously described, cases were diagnosed by medical record review including right heart catheterization and all were classified as World Symposium on Pulmonary Hypertension (WSPH) Group I  [32].
ES/GS data analysis PAH Biobank, CUIMC, and SPARK cohort samples were all sequenced in collaboration with the Regeneron Genetics Center as previously described  [7, 8, 17, 18, 27] ; the UK NIHR BioResource sequence data were also previously described  [8]. For case and SPARK control data, we used a previously established bioinformatics procedure  [34]  to process and analyze exome and genome sequence data. For the UK NIHR BioResource data, we extracted reads from GS data by the following procedure: (1) obtained all reads that were mapped to the human genome regions that overlapped with the target regions of xGEN exome capture intervals (Exome Research panel 1. 0); (2) the mate pairs of these reads. We then processed the extracted GS data using the same pipeline as the ES data. Specifically, we used BWA-MEM  [35]  to map and align paired-end reads to the human reference genome (version GRCh38/hg38, accession GCA 000001405.
15), Picard v1. 93  MarkDuplicates to identify and flag PCR duplicates, and GATK v4. 1  [36, 37]  HaplotypeCaller in Reference Confidence Model mode to generate individual-level gVCF files from the aligned sequence data. We then performed joint calling of variants from all three datasets using GLnexus  [38]. We used the following inclusion rules to select variants for downstream analysis: AF < 0. 05% in the cohort, < 0. 01% in gnomAD exome_ALL (all ancestries); > 90% target region with dp ≥ 10; mappability = 1; and allele balance ≥ 0. 25. We also ran DeepVariants  [39, 40] , a new tool based on machine learning, for all cases and SPARK controls. We used the ES mode for ES data and GS mode for GS data, and then filtered by "PASS" DeepVariants. Inclusion criteria for variants observed in multiple carriers was ≥ 50% of all calls PASS DeepVariants.
Statistical analysis To identify novel risk genes for IPAH, we performed a rare variant association test in unrelated participants of European ancestry. Genetic ancestry and relatedness of cases and SPARK controls were checked using Peddy  [46] , and only unrelated cases (n = 2789) and controls  (18,819: 11 ,101 SPARK parents and 7718 gnomAD individuals) were included in the association test. The gno-mAD controls were confined to non-Finnish Europeans (NFE). We performed a gene-based case-control test comparing the frequency of rare deleterious variants in PAH cases with unaffected controls. To reduce batch effects in combined datasets from different sources  [47] , we limited the analysis to regions targeted by xGen and with at least 10× coverage in 90% of samples. We then tested for similarity of the rare synonymous variant rate among cases and controls, assuming that most rare synonymous variants do not have discernible effects on disease risk.
Protein modeling Homology structures of conserved protein domains in FBLN2 and PDGFD were built using EasyModeller 4. 0  [54]. Template structures were downloaded from the protein database (PDB) for endothelial growth factor (EGF, PDB ID 5UK5) and CUB (PDB ID 3KQ4) domains. The template structure for platelet-derived growth factor (PDGF)/vascular EGF (VEGF) was downloaded directly from PrePPI  [55, 56].
Gene expression Single-cell RNA-seq data of aorta, lung, and heart tissues were obtained from Tabula Muris, a transcriptome compendium containing RNA-seq data from ~100,000 single cells from 20 adult-staged mouse organs  [57]. We chose 14 tissue/cell types including endothelial, cardiac muscle, and stromal cells from the three tissues, restricting the analysis to tissues/cell types for which there was RNA-seq data from at least 70 individual cells (Additional file 1, Supplementary Figure  1 ). Relative gene expression was based on the fraction of cells with > 0 reads in each cell type. PCA of cell type-specific gene expression profiles was performed using a script available through GitHub  [58].
Cohort characteristics Demographic data and mean hemodynamic parameters of the combined US/UK cohort are shown in Table  1. The cohort includes 4241 cases: 54. 6% IPAH, 34. 8% APAH, 5. 9% FPAH, and 4. 6% other PAH. Most of the APAH and other PAH cases came from the PAH Biobank and have been described previously  [7]. The majority of cases were adult-onset (92. 6%) with a mean ageof-diagnosis (by right heart catheterization) of 45. 9 ± 20 years (mean ± SD). As expected for adult-onset PAH cohorts  [7, 8, 59] , the majority of cases were female (75. 1%). The genetically determined ancestries were European (74. 5%), Hispanic (8. 6%), African (8. 7%), East Asian (2. 5%), and South Asian (2. 8%). Hemodynamic data were collected at the time of PAH diagnosis. Diagnostic criteria for PAH is mean pulmonary arterial pressure (mPAP) > 20-25 mmHg  [32].
In addition, the dataset utilized in this study was based on adult-staged murine cells and is not well-suited for developmental genes such as TBX4 and other genes likely to contribute to pediatric-onset disease. Thus, additional datasets from different time points are needed. Rare deleterious variants in established PAH genes are clearly pathogenic based on segregation analyses, enrichment of rare deleterious variants in PAH cases compared to controls with replication over time, and demonstrated loss of function or aberrant function in vitro, in vivo (model organisms), or ex vivo  [6]. However, none of the PAH genes are fully dominant and many carriers are never diagnosed with PAH. BMPR2 variants exhibit variable penetrance with ~14% penetrance in male carriers and 42% females, suggesting sex as an important modifier of penetrance  [6].
Acknowledgements Samples and/or data from the  National Biological Sample and Data Repository  for PAH (aka PAH Biobank) were used in this study. We thank contributors, including the  Pulmonary Hypertension Centers  who collected samples used in this study, as well as patients and their families, whose help and participation made this work possible. We appreciate the contribution of the research coordinators across the clinical sites and  Patricia Lanzano  for coordinating the  Columbia biorepository. Exome sequencing and genotyping data were generated by the  Regeneron Genetics Center. We thank  NIHR BioResource  volunteers for their participation, and gratefully acknowledge  NIHR BioResource centers, NHS Trusts , and staff for their contribution. We thank the  National Institute for Health Research  and  NHS Blood and Transplant.
Funding This study was funded in part by  NIH  grants  R24HL105333  (WCN, MWP),  RO1HL060056  (WKC),  UO1HL125218  ( WKC, ER ), and  RO1GM1200609  (YS). The  UK National Cohort of Idiopathic and Heritable PAH  is supported by the  NIHR Cambridge Biomedical Research Centre (BRC)  and  Cambridge University Hospitals NHS Foundation Trust (CUH)  ( BRC 2012-2017 ), the  British Heart Foundation (BHF)  ( SP/12/12/29836  and  SP/18/10/33975 ), the  BHF Cambridge Centre of Cardiovascular Research Excellence (CRE) , the  UK Medical Research Council  ( MR/K020919/1 ), and the  Dinosaur Trust, BHF  Programme grants to RCT ( RG/08/006/25302 ) and  NWM  ( RG/13/4/30107 ). NWM is a BHF Professor and  NIHR Senior Investigator. AL is supported by a  BHF Senior Basic Science Research Fellowship  ( FS/13/48/30453 ).
Availability of data and materials The datasets used and/or analyzed during the current study are available via contact with the senior authors. For PAH Biobank data, a Confidentiality Agreement with the collaborating Regeneron Sequencing Center grants to Dr. Nichols a nonexclusive, worldwide, irrevocable, perpetual, royalty-free sublicensable license to access and use the genomic data for any and all purposes. Therefore, while the PAH Biobank data are not uploaded to a publicly available database, direct access to the data are granted by the corresponding author on reasonable request who has full administrative access to all of the data. The data from the NIHRBR-RD study have been deposited in the European Genome-Phenome Archive  [100]. Data from most of the affected participants in the US/UK combined cohort were included in previous publications from our group  [7, 8, 17, 18, 27, 61, 100].
Disopyramide and its phosphate salt are intravenously and orally administrated for clinical use  [13, 14]. There is very limited research on the crystal engineering of DPA available in the literature  [15] , and there are only limited DPA phosphate salt  [16]  crystal structures of solid forms of DPA to be found in the Cambridge Structural Database (CSD). DPA has a basic nature and is likely to form salts/cocrystal adduct with different cofomers that have acid functionality. Moreover, DPA has a flexible molecular framework, with a hydrogen bond donor and acceptor site, and can adopt different orientations or conformations in the novel solid form; hence, from a crystal engineering viewpoint, DPA could be a molecules that has the potential for the exploration of different solid forms by the use of crystal engineering principles.
Slurry Method Reproducing the salt crystal was found to be difficult, so another method for crystallization was used. Luckily, similar solid salt is easily obtained by the slurry method. One mmole each of DPA and TA were weighed at a molar ratio of 1:1 and suspended in 50 mL acetonitrile in a 250 mL conical flask. The resulting suspension was stirred in a magnetic stirrer at 25 °C, 500 rpm for about 36 h until a white solid material precipitated out. The resulting suspension was filtered to isolate the white solid, which was then air dried for 5-6 days at ambient conditions before being used for further analysis. Powder X-ray diffraction (PXRD) patterns of the isolated solids matched the simulated PXRD pattern obtained from single crystal data DPA:TA, suggesting that both were the same solid form.
Slurry Method Reproducing the salt crystal was found to be difficult, so another method for crystallization was used. Luckily, similar solid salt is easily obtained by the slurry method. One mmole each of DPA and TA were weighed at a molar ratio of 1:1 and suspended in 50 mL acetonitrile in a 250 mL conical flask. The resulting suspension was stirred in a magnetic stirrer at 25 • C, 500 rpm for about 36 h until a white solid material precipitated out. The resulting suspension was filtered to isolate the white solid, which was then air dried for 5-6 days at ambient conditions before being used for further analysis. Powder X-ray diffraction (PXRD) patterns of the isolated solids matched the simulated PXRD pattern obtained from single crystal data DPA:TA, suggesting that both were the same solid form.
Single-Crystal X-ray Diffraction The single-crystal X-ray diffraction data for DPA:TA salt was collected at 93 K. The measurements were carried out in ω-scan mode with an R-AXIS RAPID II (Rigaku Co. , Tokyo, Japan) with the Cu-Kα X-ray obtained from rotating the anode source with a graphite monochromator. The integrated and scaled data were empirically corrected for absorption effects using ABSCOR  [17, 18]. The structures were solved by direct methods using SHELXS and refinement was carried out by full-matrix least-squares technique using SHELXL  [19, 20]. All non-hydrogen atoms were refined anisotropically. The hydrogen atom attached to the nitrogen N2, N3, and O4 atoms in the DPA:TA salt were located using the differential Fourier map and refined isotropically. All other hydrogen atom positions were calculated geometrically and included in the calculation using the riding atom model.
Thermal Properties The thermal behavior of the DPA:TA was measured by DSG and TG. DPA:TA salt, exhibits a sharp, single endothermic peak at around 181 • C, corresponding to melting point of salt in DSC (Figure  9 ). It has been reported that there are two melting points of DPA; a low-melting type crystal (85-87 • C) and a high-melting type crystal (95-98 • C)  [12] , and TA melted with sublimation at around 350 • C  [23] [24] [25]. DPA:TA showed a different melting point from each starting materials, suggesting that DPA:TA is a novel salt. TG data of DPA:TA revealed that there was no weight loss before melting, which confirmed the absence of any solvent or hydrate in the crystal lattice, as per the single crystal data.
The local comparison of the MARINE outputs with the SMOSMANIA measure-ments, as well as the comparison at the basin scale of the MARINE outputs with the gridded LDAS-Monde and CGLS data, lead to the following conclusion: both the dynamics and the amplitudes of the soil saturation degree simulated with the SSF and SSF-DWF models are better correlated with both the SMOSMANIA measurements and the LDAS-Monde data than the outputs of the base model. Finally, the soil saturation degree simulated by the two-layers model for the deep layer is compared to the soil saturation degree provided by the LDAS-Monde product at corresponding depths. In conclusion, the developments presented for the representation of subsurface flow in the MARINE model enhance the soil saturation degree simulation during flash floods with respect to both gridded data and local soil moisture measurements.
, 2017; Calvet et al. , 1998)  (this work uses the version of LDAS-Monde at the 2. 5 km × 2. 5 km spatial resolution); (iii) the hourly soil water content measurements taken from the SMOSMANIA observation network  (Calvet et al. , 2007) ; and (iv) the Soil Water Index provided by the Copernicus Global Land Service (CGLS), available at the 1 km × 1 km resolution and derived from Sentinel-1 Cband SAR and ASCAT satellite data  (Bauer-Marschallinger et al. , 2018a). The comparison between the MARINE output for SSD dynamics and these three sources of data is performed both at the local point measurement scale and at the catchment scale. These products represent valuable indicators of the spatiotemporal dynamics of soil moisture at various scales. In Sect. 2, the MARINE model, its new developments for the soil model, and also the study cases considered for this work are described.
Base model (BM) The MARINE model is a distributed, physically based hydrological model  (Roux et al. , 2011). MARINE consists of three main modules. First, precipitation is separated between surface runoff and infiltration using the Green and Ampt model. Then, the subsurface flows are represented using an approximation of Darcy's law. Finally, the overland and river fluxes are simulated using the Saint-Venant equations simplified with kinematic wave approximation. The connections between the model components are extensively described in  Roux et al. (2011). Based on sensitivity analyses of the model, five parameters are calibrated in MARINE for the representation of the soil and the surface: the multiplier coefficient for soil depth maps (C z ), the multiplier coefficient for the spatialized saturated hydraulic conductivity used in lateral flow modeling (C kss ), the multiplier coefficient for the spatialized hydraulic conductivity at saturation that is used in infiltration modeling (C kga ), and two friction coefficients for low-and high-water channels  (Garambois, 2012).
In this DWF model, the depth of the upper layer is equal to the soil depth provided by the soil database and the deep layer has an uniform depth over the catchment. The deep layer depth is calibrated for each catchment. The two developments made for the SSF and DWF models can be merged to create the SSF-DWF model for the subsurface flow representation in MARINE. In the SSF-DWF model, the soil column is separated into two layers. Vertical and lateral transfers in the upper soil layer are described as a function of the soil saturation degree. In the SSF-DWF model, the flows in the deep layer is defined as a function of the water height in the deep layer. The integration of the.
The pedological information is taken from the French National Institute for Agronomic Research (INRA) soil database for the Ardeche and Languedoc-Roussillon regions (Robbez-  Masson et al. , 2000). The land cover information is taken from the Corine Land Cover 2006 database  (Aune-Lundberg and Strand, 2010). The Ardeche catchment (622 km 2 ; 193 to 1347 m a. s. l. ) is located in the Cevennes region and exposed to intense precipitation events due to the convection of humid sea air masses over the Cevennes mountain slopes. The Orbieu catchment (236 km 2 ; 135 to 807 m a. s. l. ) is also exposed to Mediterranean extreme events, for example, the dramatic flood event of October 2018. The Ardeche catchment presents a mixed geology, with metamorphic rocks and schists on the upper part of the catchment and sedimentary plains downstream (source: http://infoterre.