dataesr/scientific-paragraphs-categorization huggingface dataset:
2. 1. 4. 2. Step 2: Pathway enrichment. Identification of pathways enriched in miRNA-TF associations was carried out next. Target matrices of pathways were built using the MSIGdb pathway database as described for miRNA and TF matrices in Step 1. For each miRNA-TF association identified in Step 1, pathways significantly enriched in the miRNA and/or in the associated TF regulatory networks were searched separately. A pathway was considered as significantly enriched in the miRNA-TF association if it passed the Fisher's exact test with an adjusted p-value lower than 0. 1 in both regulatory programs. When miRNAs were associated with more than 1 TF, we selected the TF that had the lowest pathway enrichment p-value. This resulted in miRNA-pathway associations. This approach was chosen over calculating enrichment in common miRNA-TF targets, since this strategy strongly reduced gene num-bers and compromised statistical analyses.
Functional validation with TCGA datasets Breast Cancer (BRCA), Prostate Adenocarcinoma (PRAD), Lung Adenocarcinoma (LUAD), Kidney renal clear cell carcinoma (KIRC), and Colon adenocarcinoma (COAD) small RNA and mRNA sequencing datasets were retrieved from The Cancer Genome Atlas (TCGA) via the Genomic Data Commons Data Portal (https://portal. gdc. cancer. gov/repository). Correlations of expression between the 368 miRNAs and each of their predicted targets in miRSTATION, Targetscan, miRDIP high or miRDIP low were carried out using a test for association between samples (cor. test function) in R (version 3) on log 2 transformed expression data for each cancer, separately. The estimate of the association and significance of the correlations for each cancer types were calculated using the default cor.
Functional miRNA targets are enriched for co-regulations by transcription factor Early in silico analyses of gene network co-regulation by miRNA-TF pairs have previously suggested a functional overlap between select miRNA targets and TF targets  [23]. This indicates that certain miRNAs more specifically modulate TF activities, consistent with their effect in buffering protein expression from transcriptional bursts  [24, 25]. Since the previously identified miRNA-TF interactions exclusively relied on predicted miRNA targets and TF targets, we initially decided to revisit the frequency of miRNA-TF interactions relying on datasets of experimentally validated miRNA targets (miRTarBase V7  [17] ) and TF targets (ENCODE  [18] ). miRTarBase compiles a vast array of experimentally derived miRNA-target interactions (MTIs), originating from both lowthroughput assays (e.
Above each bar, the rank of each TF based on its amount of its total targets is indicated. (C) Violin plot of the number of enriched TF per miRNA for each of the 660 miRNAs and their targets, predicted by each approach: miRDIP high (Predicted MTIs high confidence), miRDIP low (Predicted MTIs low confidence) and miRTarBase (Validated MTIs) (the lines in the violin plots represent the median -data based on Supplementary Table  S4  for miRDIP high). Wilcoxon ranking tests between datasets are shown. **** p < 0. 0001, ns: non-significant. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article. ) binding sites of 124 TFs from ENCODE (Supplementary Table  S2 ). The targets of 348 miRNAs were significantly associated with at least one TF, when compared to our ''genome background" set (i.
Enrichment for co-regulations by transcription factor is more frequent for high-ranking predicted and validated miRNA targets To define the biological relevance of TF enrichment in miRNA targets, we next tested whether it was influenced by the functional nature of the MTIs, i. e. whether the MTIs were true targets or not. For this purpose, we investigated how relying on in silico predicted MTIs (which have a higher proportion of non-functional MTIs than miRTarBase, as they strictly rely on predictions) would impact TF enrichment. To obtain more comprehensive analyses, we relied on validated binding sites of 1249 TFs from the TFtargets database encompassing ENCODE TFs (Supplementary Table  S3 ), affording a larger dataset of TF-target sites than the ENCODE dataset itself (86,070 target sites with TFtargets, versus 59,306 with ENCODE).
miRSTATION predicted targets are negatively correlated with miRNA levels in several cancers To validate further the capacity of our approach to identify biologically relevant MTIs in an unbiased manner, we next assessed the correlation between the expression levels of miRNAs and their MTIs, in datasets from The Cancer Genome Atlas (TCGA). Since miRNAs predominantly impact mRNA translation by decreasing mRNA levels  [27] , we were interested to test whether correlations between miRNA levels and those of the MTIs predicted by miRSTA-TION were more often negative correlations than positive ones. First, we created a correlation analysis of miRNA and mRNA expression levels for each patient with Breast Cancer (BRCA), Prostate Adenocarcinoma (PRAD), Lung Adenocarcinoma (LUAD), Kidney renal clear cell carcinoma (KIRC), and Colon adenocarcinoma (COAD), in the selected datasets (having concurrent small and mRNA sequencing data available), restricted to the 368 miRNAs and the 187,503 MTIs identified by miRSTATION.
, 2005; Apourvari and Arns, 2004; Scheibe et al. , 2015; Soulaine et al. , 2016; Soulaine et al. , 2019). In this study, we developed a comprehensive open-source simulator to model coupled hydro-geochemical processes at continuum-, pore-and hybrid-scales. This unique multi-scale framework relies on the microcontinuum model and its ability to tend asymptotically toward continuum-scale models if grid block contains solid content (0 < φ < 1) or towards pore-scale models otherwise (φ = 1)  (Soulaine and Tchelepi, 2016). The resulting advanced RTM allows the treatment of complex reactions network as a function of flow conditions, water composition and minerals distribution within the rock including the complex porosity feedback between flow and chemistry. It is part of porousMe-dia4Foam, an open-source package developed by the authors to solve flow and transport in porous media within the popular simulation platform OpenFOAM®.
Because of its versatility and advanced features such as three-dimensional unstructured grids, dynamic meshes, and high-performance computing, there is a growing interest in the community to develop mathematical models for solving flow and transport in porous media within OpenFOAM®  (Horgue et al. , 2015; Maes and Geiger, 2018; Orgogozo et al. , 2014; Soulaine et al. , 2017). We developed a generic interface to combine flow and transport models with existing geochemical packages. We illustrate the versatility of our coupling interface by combining flow models with geochemical models using PHREEQC  (Parkhurst and Wissmeier, 2015) , an open-source and popular geochemistry package that is used in many continuum-scale RTM  (Rolle et al. , 2018; Healy et al. , 2018; Muniruzzaman and Rolle, 2019; Muniruzzaman et al.
The porousMedia4Foam package The multi-scale solver for simulating hydro-geochemical problems is part of porousMedia4Foam (https://github. com/csoulain/porousMedia 4Foam), a generic platform for solving flow and transport in porous media at various scales of interest. porousMedia4Foam is an open-source platform developed by the authors using the C++ library OpenFOAM (http://www. openfoam. org). Hence, the package benefits from all the features of OpenFOAM including the solution of partial differential equations using the finite-volume method on three-dimensional unstructured grids as well as High Performance Computing. Although porousMedia4Foam has capabilities for solving two-phase flow (liquidliquid and liquid-gas) in porous systems, the geochemistry coupling that is introduced in this paper only considers single-phase flow.
Geochemical packages In porousMedia4Foam, complex reaction networks are handled by geochemical packages. The aqueous components are transported using the velocity profile, v f , computed by the flow solver (see section 2. 2) and surface reactions rely on the reactive surface area, A e , calculated with the porous media models (see section 2. 4. 2). The code architecture of porousMedia4Foam is generic so that a wide variety of third-party geochemical packages can be coupled with our platform for solving hydro-geochemical processes at the pore-scale and at the continuumscale. In this paper, we illustrate the potential of the coupled simulation framework using the popular geochemistry package PHREEQC  (Parkhurst and Appelo, 2013; Parkhurst and Wissmeier, 2015). Models currently implemented in porousMedia4Foam to account for geochemistry are summarized in Table  2.
phreeqcRM The phreeqcRM class calls the general-purpose geochemical reaction model PHREEQC through the PhreeqcRM module. It carries out the transport of the aqueous solution composition, C j (in mol/kg water ), along with equilibrium and kinetic reactions with the solid minerals described by Y s,i. phreeqcRM is set with components, i. e. SOL-UTION_MASTER_SPECIES total concentration. The geochemistry setup is carried out using an input file that follows PHREEQC format. Hence, the aqueous composition is defined in the block SOLUTION (0 for the composition of the injected fluid at the inlet boundary, 1 for the initial aqueous composition in the bulk). The EQUILIBRIUM_PHASES and KINETICS blocks are generated automatically within the code and the user only has to assign before the calculation which mode of reactions is used for each mineral.
Moreover, porousMedia4Foam can load any customized database using PHREEQC format. The coupling between transport and reactions relies on an operatorsplitting approach based on the Strang's algorithm  (Strang, 1968). First, all species concentration fields, C j , are transported sequentially using the advection-dispersion equations, ∂φC j ∂t + ∇⋅ ( v f C j ) -∇⋅ ( φD * j. ∇C j ) = 0, (8) where v f is the fluid velocity computed with the flow solver (see Section 2. 2) and D * j is an effective diffusion tensor that accounts for tortuosity and hydrodynamic dispersion effects (see Section 2. 4. 3). The transport equation is discretized on the computational domain using the finitevolume method and solved implicitly using OpenFOAM's engines. Then, the volume fractions of solid minerals, Y s,i , are updated according to phase equilibrium and/or kinetic reaction calculations provided by PHREEQC.
Verification of the hydro-geochemical simulation platform In this section, the multi-scale hydro-geochemical simulation package porousMedia4Foam introduced in Section 2 is used along with PHREEQC to investigate several scenarios for which reference solutions exist. The verification of the results is achieved by comparison against benchmarks published in literature both at the continuum-scale and at the pore-scale. Essential files required to run all the test cases presented in this section are available as examples within the package. All simulations were run on Intel Xeon with 2. 60 GHz.
Verification at the continuum-scale We verify the ability of porousMedia4Foam to simulate coupled hydro-geochemical processes that include porosity feedback on the transport properties at the continuum-scale. We also verify that the multi-scale solver dbsFoam tends asymptotically towards Darcy's law in porous domains. The case is based on the Benchmark 1 described in  Xie et al. (2015). It consists of a 2 meters long 1D column initially filled with 35% of inert mineral and 30% of calcite. An acid (pH = 3) is continuously injected at the inlet to initiate the dissolution of calcite according to, CaCO 3 + H + ↔ Ca 2+ + HCO - 3 , HCO - 3 ↔ H + + CO 2- 3. ( 13 ) Table  6  provides the initial and boundary conditions data specific to the primary components. A difference of 0. 007 m in hydraulic head is applied between the inlet and outlet  (Xie et al.
Verification at the pore-scale In this section, we highlight the capabilities of our OpenFOAM package to model hydro-geochemical interactions occurring at the porescale using PHREEQC. In porousMedia4Foam, pore-scale simulations are run using the micro-continuum approach through the flow solver dbsFoam. At the pore-scale, the reaction rates are directly applied at the fluid-mineral interface that is described explicitly in the computational grid using the mineral volume fraction. The micro-continuum approach has been used to simulate the dissolution of a calcite crystal at the pore-scale and compared successfully with microfluidic experiments  (Soulaine et al. , 2017). In  Molins et al. (2020) , the approach is compared with state-of-the-art RTM at the pore-scale using various numerical techniques including Chombo-Cruch with Level-Set  (Molins et al.
This reaction rate may not be fully representative of the underlying geochemical processes. It has been chosen in  Molins et al. (2020)  to demonstraste the ability of various approaches to move the fluid-mineral interface according to geochemical processes. All the numerical methods were able to capture accurately the shape evolution of the calcite crystal, giving confidence in pore-scale simulators for moving fluid-mineral interfaces along with geochemical processes. Actually, in  Soulaine et al. (2017)  and  Molins et al. (2020) , the micro-continuum approach, dbsFoam, was combined with the  C. Soulaine et al. geochemical package simpleFirstOrderKineticsMole (see Table  2 ) that solves Eq. (  14 ) using OpenFOAM's internal engines. This limits drastically the applicability of the approach to comprehensive reaction networks.
Conclusion We developed an integrated open-source simulator to model hydrogeochemical processes at various scales of interest including pore-scale and reservoir-scale. The simulation platform is part of porousMedia4-Foam, a package that solves flow and transport in porous media using the open-source library OpenFOAM. The modelling framework handles complex reactions network as a function of flow conditions, water composition and minerals distribution within the rock including the complex porosity feedback between flow and chemistry. Moreover, porousMedia4Foam benefits from all features of OpenFOAM libraries. Hence, the code is fully parallel and handles structured as well as unstructured grids in one, two and three dimensions. The interface between the flow simulator and the geochemistry is generic and can be used to couple a large variety of geochemical packages.
Acknowledgments The research leading to these results has received funding from the  French Agency for Research (Agence Nationale de la Recherche, ANR)  through the labex Voltaire  ANR-10-LABX-100-01 , the grant  CATCH ANR-18-CE05-0035 , and through the  FraMatI project  under contract  ANR-19-CE05-0002. It has also received financial support from the  CNRS  through the  MITI interdisciplinary programs. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreements No  847593  ( WP DONUT ) and No  850626  ( REFLECT  Project). SP postdoctoral fellowship was funded by  BRGM  through the  PORE-REACTIF  project from the  Alliance Nationale de Coordination de la Recherche pour l'Energie (ANCRE). The authors benefitted from the use of the cluster at the  Centre de Calcul Scientifique en région Centre-Val de Loire.
HYPE source code and full documentation are freely available (SMHI 2020b). HYPE simulates major water pathways and fluxes in a catchment with mass conservation at each time step using precipitation and temperature as forcing data. Model parameters linked to the catchments' physiographic properties determine the storages and fluxes of water and water quality constituents among the model components. Specific routines account for snow, glacier, reservoir regulations, floodplains, and deep aquifer processes, although the deep aquifer routine was not implemented in the specific applications used in this study due to a lack of large-scale information. Floodplain routine was implemented only in applied worldwide (WW-HYPE;  Arheimer et al. 2020) , where temporary inundation plays a significant role, e.
g. , in inland deltas  (Andersson et al. 2017). A number of potential evapotranspiration algorithms are available. WW-HYPE uses three evapotranspiration algorithms: Jensen-Haise  (Jensen and Haise 1963)  in temperate areas (same as E-HYPE and S-HYPE), modified Hargreaves  (Hargreaves and Samani 1982)  in arid and equatorial areas, and Priestley-Taylor  (Priestley and Taylor 1972)  in polar and snow-/ice-dominated areas. The algorithms were selected based on their applicability in different climate conditions, which was tested prior to the model calibration. Evapotranspiration parameters were calibrated using the moderate resolution imaging spectroradiometer (MODIS) global evapotranspiration product (MOD16) by  Mu et al. (2011). There are two erosion modules in HYPE. The default option used in S-HYPE is based on the Morgan-Morgan-Finney erosion model  (Morgan et al.
Results from the individual RCMs were evaluated in order to assess the impact from regional downscaling via assessing the variability among the RCMs. Results from other GCMs/RCMs and RCPs have also been calculated but are not presented here to simplify the analyses and to limit them to the same GCMs for both models, but the full set of results is available elsewhere (SMHI 2020a). The forcing data were bias-adjusted to a reference data set, HydroGFD v2  (Berg et al. 2018 ), using a distribution-based scaling (DBS) method  (Yang et al. 2010). The DBS method matches observed and simulated frequency distributions by assuming variabledependent theoretical distributions. The reference period for the calibration of the bias-adjustment parameters was set to 1971-2000. The same bias-adjustment method was used for both WW-HYPE and E-HYPE forcing data.
6 , solid lines), although the closest match was found for E-HYPE at the Swedish domain. The difference in results is rather interesting becacuse both E-HYPE and WW-HYPE use the same erosion module and have very similar model performance indicators for 2001-2010 at their respective domains (Table  1 ). It is also of interest to note that although the overall model performance is similar for the two models, median and average REs for S-HYPE and WW-HYPE are negative, whereas E-HYPE has negative median RE but positive average RE. A thorough investigation of the average simulated and observed values across stations for the nested models show (1) a severe lack of low sediment concentrations among observed data for both E-HYPE and WW-HYPE (less than 1% compared with 18% for S-HYPE), (2) a decreasing correlation between the simulated and observed values across stations with increasing model scale, and (3) decreasing correlation between the simulated and observed values across stations for WW-HYPE with decreasing size of the nested domains (Figs.
Data Availability Statement The Hydrological Predictions for Environment (HYPE) model is available as open-source code under the Lesser GNU Public License. WW-HYPE and E-HYPE simulation results are available for viewing at https://hypeweb. smhi. se/ and shared under license the Creative Commons Attribution-ShareAlike 4. 0 International (CC BY-SA 4. 0). Data for the WW-HYPE, E-HYPE, and S-HYPE model development and calibration were provided by many third parties (full information available at https://hypeweb. smhi. se/ for each model).
Comparison of WW-HYPE, E-HYPE, and S-HYPE Note: Hydro-GFD = hydrological global forcing data; PTHBV = precipitation and temperature dataset for HBV; RE = relative error; NSE = Nash-Sutcliff efficiency; and KGE = Kling-Gupta efficiency. Correlation coefficient for sediment is determined from the average observed and simulated sediment concentrations.
Acknowledgments This work was partially funded by several projects and efforts. For instance, sediment modeling was developed by Bilateral Cooperation in South Africa funded by  Swedish Environmental Protection Agency  as well as  EU projects SPACE-O  ( H2020 GA 730005 ), DIRT-X (funded by  JPI-AXIS , Grant No. 776608 ), and  HYPOS  ( H2020 GA 870504 ). The S-HYPE model is a national tool financed by the  Swedish Government. The hydrological research unit (FoUh) at  SMHI  works as a team with continuous model development and applications, so the authors would like to acknowledge everybody's contribution over the decades for making this analysis possible. The authors also extend gratitude to other organizations and individuals that have made the development of their models possible by sharing their data.
Based on angiographic examination of renal arteries in kidney donor cohorts the estimated prevalence of FMD is ~3%  11. In the current study, we report findings from a meta-analysis of six genome-wide association studies (GWAS) to investigate the genetic basis of FMD. We report four loci (PHACTR1, LRP1, ATP2B1, and LIMA1) that are associated with FMD, in addition to SLC24A3 identified through transcriptome-wide association studies of arterial transcriptomes from the GTEx database. Through the integration of open chromatin maps generated by ATAC-Seq in vascular cells, combined with publicly available data for arteries, we prioritize variants in associated loci. Using colocalization with expression quantitative trait loci (eQTLs) and transcriptome-wide association studies in arteries, we narrow down potential target genes.
We queried the GTEx database for eQTL association (v8 release)  15  and found the lead SNPs in FMD loci to be top eQTLs of at least one gene in one or more of the three arterial tissues available (Fig. 2a , Supplementary Table  S4 ). Top eQTL associations linked lead variants to the closest gene at PHACTR1, LRP1 and ATP2B1 loci (Fig. 2b, d, h ), with a high probability (100%, 100% and 98. 3%, respectively) of colocalization between FMD and tibial artery eQTL association signals (Fig. 2c, e, i ). FMD risk alleles were associated with higher expression of PHACTR1 and LRP1 and lower expression of ATP2B1 in tibial artery tissue (Fig. 2b, d, f ). We note that the expression of ATP2B1 antisense transcript (ATP2B1-AS) was also regulated by lead SNPs at the ATP2B1 locus in tibial artery samples, also with a high probability (96.
S5e, f ). We annotated the credible sets of variants, those representing >95% of cumulated posterior probability to cause the association at each locus, with overlapping open chromatin regions from these ATAC-Seq datasets (Table  3 ). At the PHACTR1 locus, the lead variant (rs9349379) was located in a chromatin region open in arterial tissue but not accessible in VSMCs, fibroblasts, or ECs. This variant also overlapped with well-defined enhancer marks in arterial tissue and was strongly supported as the causal variant in this locus (Fig. 3a, b ). The lead variant (rs11172113) in LRP1 overlapped with open chromatin peaks in arterial tissue, primary VSMCs and fibroblasts, but not primary ECs (Fig. 3c, d ). Strong enhancer and promoter marks were also present in arterial tissue around this variant, which mapped 5 kb downstream of LRP1 promoter and is the most likely causal variant.
At the LIMA1 locus, the variant with the highest posterior probability of being causal, rs7301566, overlapped a region active in arteries, VSMCs, and fibroblasts and also strong enhancer marks in arteries (Fig. 3f ). Three other variants (rs4459386, rs10783342, rs12815871) also overlapped marks indicating potential regulatory properties (Fig. 3f , Table  3 ). Finally, the ATP2B1 locus included three highly associated and potentially causal variants (rs11105352, rs11105353 and rs11105354, Fig. 3g ), which all overlapped a small open chromatin peak specifically observed in arterial tissue, and an enhancer-specific H3K4me1 histone mark (Fig. 3h ). FMD potential target genes are expressed in VSMCs and fibroblasts. We assessed the expression of FMD potential target genes (Table  2 ) by leveraging results from pre-existing transcriptomics datasets.