dataesr/scientific-paragraphs-categorization huggingface dataset:
Timescale Interactions The SF-MT model generates multiple individual signals, in which each signal (i. e. , timescale cluster) has a unique timescale relation between the wave forcing and shoreline signal. A certain selection of all these generated signals make up the total shoreline signal (Section 3. 3, Equation (  4 )). To visualize these unique timescale relations of all chosen modelled signals, a grid of timescale interactions is presented (Figure  8 ). In those grids, the percentage to the total shoreline signal per individual modelled signal (i. e. , timescale cluster) is plotted, revealing the most important timescale interactions per dataset. The axes can be used to check which timescales are considered and the diagonal, upper left corner and lower right corner correspond to signals generated with the direct forcing, upscaling, and downscaling approach, respectively.
Acknowledgments:  Rob Schepper  would like to thank  LEGOS , the  Delft University of Technology  and  IMDC  for providing the opportunity to write this paper and, secondly, he is grateful to LEGOS, the  University of Plymouth , the  Water Research Laboratory of the University of New South Wales  and the  University of Auckland  for providing the datasets used in this study. He is also grateful to  Caroline Katsman  of the  Delft University of Technology  for her contribution to this paper. Erwin Bergsma  was funded through a post-doctoral fellowship of the  French National Space Agency (CNES-Centre National d'Etudes Spatiales ).
The G-res Tool closely follows the conceptual approach outlined in  Prairie et al. (2018) , which ultimately aims at predicting the reservoir-induced change in GHG fluxes to the atmosphere of the flooded landscape. The G-res Tool is applicable globally  (Harrison et al. , 2021)  and can be used with an Earth Engine functionality  (Prairie et al. , 2017)  so that it can be used dynamically on existing reservoirs as well as on potential or planned reservoir locations. The core of the G-res Tool relies on a series of empirical models developed from a synthesis of published literature on reservoir emissions. These models are based on the influence of local to regional environmental controls on GHG emission and on the characteristics of the individual reservoirs and their catchments. In this paper, we report on the development of the underlying models predicting the magnitude of each emission pathway, their linkages with global databases as well as their integration into a comprehensive and publicly available platform.
Database To develop the GHG emissions models, we undertook an extensive review of the pre-2016 scientific literature and collected data from globally distributed reservoirs with CO 2 and CH ). This database is largely overlapping with the one developed by  Deemer et al. (2016). Because the assembled dataset of GHG emissions depended entirely on the availability of published data, we compared the size and climate distributions of the sampled reservoirs with that of a more exhaustive and larger set of reservoirs worldwide (GRanD database;  Lehner et al. , 2011). In general, our database essentially covered the full range of reservoir surface areas. Our dataset also covered all climate zones, although boreal (and to a lower extent sub-tropical and tropical) reservoirs were somewhat over-represented relative to the GRanD (See Supplementary material Table  S2 ).
In addition to GHG flux data, we also collated information on climatic, geographic, edaphic and hydrologic conditions of each reservoir and its catchment. These variables were obtained from a variety of open sources including the literature, worldwide GIS layers (see Table  1 ) and information contained in the GRanD database  (Lehner et al. , 2011). The complete list of potential predictor variables from both reservoirs and catchments used in the models is listed in Table  1. Geographical information systems (GIS) were used to acquire two sets of data, pertaining either to the reservoir themselves or their catchments. We used the GIS polygons provided in the GRanD database (156 reservoirs) when available and added 67 reservoirs that were delineated using contemporary satellite imagery. Zonal statistics tools applied to global raster layers were then used to estimate the variables of interests for each reservoir (e.
Prioritization of data sources For any given reservoir, several estimates of the same variables can be extracted from various sources. When such cases occurred, data gleaned directly from the scientific literature were prioritized for inclusion in the database, followed by the data from the GRanD database  (Lehner et al. , 2011). If values were unavailable from the peer-reviewed literature, we extracted the relevant values from global GIS layers (see Table  1 ) or estimated them from general models found in the literature (see  Prairie et al. , 2017 for details).
G-res tool user interface To make the predictive models described above widely available, we developed a web interface, hereafter called the G-res Tool. This online tool (www. hydropower. org/gres-tool) allows users to use reservoirspecific input data to calculate net GHG footprint estimates. The G-res Tool also provides auxiliary modules to estimate emissions for the construction phase as well as to allocate GHG footprint to the different services associated with a particular reservoir (Hydroelectricity, Water supply, Flood control, Irrigation, Fisheries, Recreation, Navigation and Environmental flow). The methods used in these modules are described in more detail in a technical document  (Prairie et al. , 2017). In this Fig. 4. G-res predicted Annual CO 2 emission values (mg C m -2 d -1 ; Full black line) with model 95% confidence interval (dotted black line) compared to annualized field measurements diffusive CO 2 emissions (Grey points) with associated 95% confidence interval on the means (Grey bars) for reservoir measurements (12 yrs for Eastmain-1(a), 20 yrs for Petit Saut (c)) and Cumulative CO 2 diffusive emissions from G-res predicted values (Black) and Field measurement (Grey) for the 12 yrs measurements for Eastmain-1 (c) and 20 yrs for Petit Saut (d).
Comparison to previous models It is important to emphasize that there is currently no other modelling platform that can be used to compute, in a comprehensive and globally applicable framework, all four GHG emission pathways. The Gres integration of several components and flux estimates due to individual GHG emissions pathways moves beyond past efforts to quantify GHG emission from reservoirs  (Barros et al. , 2011; Bastviken et al. , 2011; Deemer et al. , 2016; Hertwich, 2013; St-Louis et al. , 2000). Similarly, the ability to distinguish between natural and anthropogenic CO 2 emissions is unique to G-res as well as the estimation of the net GHG footprint through the estimation and accounting of the landscape GHG balance prior to flooding. Thus, the comparison between G-res and previously published models revolves around the driver variables identified, the extensiveness of the database used and, consequently, the robustness of the individual empirical models.
Barros et al. (2011)  highlighted the influence of age and temperature (using latitude) on reservoir GHG emissions using 85 reservoirs. Similarly, the more recently published study from  Deemer et al. (2016)  has improved the estimation of GHG emissions from reservoirs by using a much bigger database (267 reservoir-years, largely overlapping with ours), as well as showing that reservoir productivity plays an important role in GHG emissions, along with age, temperature and hydrology. The G-res model developed here builds on these studies and has confirmed many of these drivers previously identified while integrating several new ones to develop a globally consistent modelling platform for each component of reservoir GHG emission based on a much larger number of potential predictor variables (>40; see Table  2  for the variables retained).
The incorporation of the more recently available GHG measurements into empirical models has improved predictive power and, in particular, the robustness of the estimated model coefficients. For models where the age of the reservoir was deemed a significant predictor (diffusive CO 2 and CH 4 emissions), the larger dataset helped better define the temporal evolution of emissions and therefore the integrated lifetime (100 years) GHG footprint, also a unique feature of G-res. While their predictive abilities are far from perfect, regression-based models are also less prone to introduce biases than a simple application of average per-area rates, particularly in the case of GHG pathways (mostly for CH 4 ) known to have a highly skewed distribution. For example, regional or global estimates of GHG emissions from reservoirs derived from simply applying an average value inherently assumes that the sampled systems are representative of the population distribution.
Partitioning among emission pathways The heterogeneity of the modelling database precluded the direct comparison of the relative importance of the various GHG components because very few reservoirs had concurrent measurements of all emission pathways. However, the modelled emission rates to the same dataset shows that, excluding the CH 4 degassing component, the overall GHG footprint is dominated by the CO 2 diffusion pathway in about 73% of the cases while CH 4 diffusion and bubbling is the main pathway in 10 and 16% of the reservoirs, respectively (See Supplementary material Figure  S3 ). In this analysis, CH 4 degassing was omitted because its contribution to the overall GHG footprint was relatively small (mean: 14%, median: 4%) and it would assume that all reservoirs have the required configuration for significant degassing to occur (i.
Limitations of the models While the G-res model predictions carry large numerical uncertainty, the G-res Tool is, to our knowledge, the most complete and the only globally consistent framework to predict the GHG footprint of reservoirs. However, proper usage of G-res also requires an understanding of its current limitations. For example, because the models are regressionbased, one of the inherent limits of application is the observation range in the predictor variables of the assembled model dataset. While the observational ranges in our dataset captures most the variability of the global database provided in the GRanD database  (Lehner et al. , 2011)  (Table  S2 ), we recommend applying G-res only to reservoirs that fall within the limits of the current data. Furthermore, the G-res development has helped identify a number of knowledge gaps that deserve additional attention and research.
As eutrophication may often be linked to UAS loading (see 2. 1. 1), improving the incorporation of the relationship between GHG emissions and trophic state will also be useful for identifying the impact of UAS. Lastly, the net GHG footprint, i. e. , the difference in the GHG balance before and after impoundment, currently relies on generic CH 4 and CO 2 emissions factors from the IPCC for different land cover types in different climate zones. Application of our current approach to our modelling dataset suggests that, while significant in areas where the flooded terrestrial landscape is rich in highly organic soils or if the flooded land has special characteristics leading to large fluxes of GHG, the importance of the GHG balance of the pre-flooding landscape is generally modest, altering the median footprint by only 4%.
Conclusion The G-res model framework proposes a novel integrative approach to the prediction of Net GHG footprint from reservoirs whereby predictions include the local environmental conditions and physical configuration of each reservoir in a globally consistent predictive framework. It allows for a quantification of relative contribution of various emission pathways of CH 4 and CO 2 and how emissions of these gases change over time after impoundment. Accounting for temporal trends provides a means to assess its GHG footprint over the lifetime of reservoirs and also the estimation of the share of CO 2 emissions that are sustained by external organic inputs that would have occurred even in the absence of the reservoir, the so-called displaced emissions  (Prairie et al. , 2018). The G-res Tool and associated models are freely available in a cloudbased modelling portal that will allow the scientific community to further probe the past and future geography of carbon emissions from reservoirs by applying the framework to larger datasets than the one used to develop it.
Density*Annual Wind Speed at 10m 2 9. 80665*(Bottom Water Density -Surface Water Density). Temperature Correction Coefficient CH 4 (To do for Each Month) Temperature Correction Coefficient CH 4 = 10 (Temperature per Month*0. 052) ** 0. 052 is the slope of the temperature vs CH 4 flux function in our database. ** If Temperature per month lower then 4 • C, use 4 • C. A. 13. Temperature Correction Coefficient CO 2 (To do for Each Month) Temperature Correction Coefficient CO 2 = 10 (Temperature per Month*0. 05) ** 0. 05 is the slope of the temperature vs CO 2 flux function in our database. ** If Temperature per month lower then 4 • C, use 4 • C. A. 14. Effective Temperature CH 4 ( • C) Effective Temperature CH 4 = log10(Average (12 Month Temperature Correction Coefficient CH 4 )) 0. 052 ** 0. 052 is the slope of the temperature vs CH 4 flux function in our database.
A. 15. Effective Temperature CO 2 ( • C) Effective Temperature CO 2 = log10(Average (12 Month Temperature Correction Coefficient CO 2 )) 0. 05 ** 0. 05 is the slope of the temperature vs CO 2 flux function in our database. A. 16. k600 k600 = 0. 24 * ( 2. 51 + 1. 48 * Annual Wind Speed at 10m 2 + 0. 39 * Annual Wind Speed at 10m 2 * log ( Reservoir Area) ) ** Vachon and Prairie (2013). A. 17. kh kh = exp ( ( -115. 6477 -6. 1698 * (Effective Temperature CH 4 + 273. 15) / 100 ) + (155. 5756 / ((Effective Temperature CH 4 + 273. 15) / 100) ) + ( 65. 2553 * ln ((Effective Temperature CH 4 + 273. 15) / 100) )) * ( 1000 / 18. 0153 ) ** (Lide, 1994). A. 18. pCH 4 pCH 4 = 10 ( ( 1. 46 + 0. 03 * Effective Temperature CH4) -0. 29 * log (Reservoir Area) ) ** Rasilo et al. (2014). A. 19. Surface Water CH 4 Concentration Surface Water CH 4 Concentration = kh * pCH 4.
Acknowledgements The G-res Tool was developed with the financial support of the  International Hydropower Association  and from the  WorldBank. Special thanks to  Rikard Liden ,  Richard Taylor  and  Mathis Rogner  for many discussions. Further funding to YTP was provided by the  NSERC   Discovery grant. This is a contribution to the  UNESCO Chair in Global Environmental Change. Funding to JAH was provided by an  NSF   INFEWS grant  ( NSF EAR1639458 ), a  GRIL Fellowship grant , the Cox visiting professorship fund at  Stanford University, a U. S. Army Corps  of.
, 2005) Global Climate database (Hijmans Average for the period 1950-2000 Mean monthly and annual wind m s -1 et al. , 2005) NOAA GLOBE Task Team (Hastings speed et al. , 1999) Reservoir mean global horizontal radiance Phosphorus concentration kWh m -2 d -1 μg L -1 SSE (NASA, 2008) Literature, Estimated See Appendix A. To convert to Cumulative global horizontal radiance (kWh m -2 period -1 ). Catchment land cover %, Catchment area, Water residence time and Soil carbon content of the kgC m -2 SoilGrids -global gridded soil Annual runoff used in order to estimate this value if unavailable Surface layer of the soil only (30 cm) Catchment variables inundated reservoir area Catchment area Mean annual runoff km 2 mm yr -1 information (Hengl et al. , 2017) Literature, GRanD DB, GIS Fekete et al. (2000) Population density Annual discharge person km -1 m 3 s -1 CIESIN (2005) Literature, Estimated Land coverage % (ESA-CCI, 2014) 9 categories: Croplands, Forest, Grassland/Shrubland, Wetlands, Settlements, Bare Areas, Water Bodies, Permanent Snow/Ice, No Data a Literature: Data from scientific publications, See Supplementary material Figure.
A comprehensive analysis of immune signatures in TCGA identified features of the immune response that predict survival across many tumor types  (Thorsson et al. , 2018). Here, we used TCGA to perform a pan-cancer evaluation of the contribution of germline variation to anti-tumor immune response. First, we calculated genomewide heritability of immune traits from common variants. Next, we performed GWAS for immune traits to identify the loci with the strongest effects. Finally, we examined the contribution of rare germline variants in known cancer susceptibility genes to the cancer immune microenvironment. Our findings provide evidence for the impact of the host's genetic background on the composition and functional orientation of the tumor immune microenvironment. Furthermore, the curated datasets and results herein generated are presented through the CRI iAtlas portal for interactive exploration and visualization (http://www.
, 2013)  (2) leukocyte subset proportion (%), which estimates the leukocyte subset proportion within infiltrating leukocytes using CIBERSORT deconvolution  (Gentles et al. , 2015)  (3) overall proportion, which include measures of leukocyte infiltration and stromal contents; (4) adaptive receptor, quantifying T cell receptor (TCR) and B cell receptor (BCR) diversity (TCR and BCR Shannon entropy and richness); (5) expression signatures, consisting of a collection of annotated functional signatures summarizing different immune-related biological processes (e. g. , wound healing, Interferon  (IFN)  and Tumor growth factor beta (TGF-β signaling, antigen-presenting machinery etc. ); and (6) attractor metagenes, which include co-expression signatures (metagene attractors) derived from the whole TCGA dataset  (Cheng et al.
5 all plausibly colocalize in the Genotype-Tissue Expression (GTEx) data with traits of the IFN response module (Table  S5 ). In the expanded region analyses (within +/-1MB for eQTL and +/-500 KB for the sQTL, see Methods) we did not find counterevidence for colocalization for any of these genes (i. e. no SNPs with higher eQTL/sQTL and lower GWAS signal; expanded region plots are available at https://figshare. com see Methods). Therefore, we considered the evidence of colocalization as strong. Of these three genes, we found that TMEM108 (colocalization shown in Figure  S5D-E ) is also significantly associated with the top SNPs in the Dataset of Immune Cell Expression (DICE)  (Schmiedel et al. , 2018) , particularly CD8 T cell and regulatory T cell (Treg cell) subsets (Figure  4F ), suggesting it is the most likely causal gene at this locus.
, 2020)  and IFN-β Zhang et al. , production following stimuli. The causal association between this variant and impaired IFN response has been corroborated by molecular simulations suggesting that the ligand-binding loops were more rigid for R232 compared to H232  (Kennedy et al. , 2020). We performed an in-silico evaluation of the effect of this amino acid substitution on the protein by superimposing H232 and R232 STING structures, and found that H232 interacts less closely with its ligand cGAMP (Figure  4H ), in agreement with its lower activity. Based on known protein-protein interactions (PPI), as annotated in String database (minimum interaction score confidence > 0. 7), we found that IFIH1 and TMEM173 formed a highly connected PPI network with the majority of genes that define the traits in the IFN response module (Figure  4B ).
Future studies of these loci in larger datasets of homogeneous cell types and in studies using single cell sequencing may identify more candidate genes, and mechanistic experiments might further elucidate their function. Flowchart showing analytic workflows, source of genetic germline data, quality control filtering, and immune traits used in the analysis. See also Figure  S1  and Tables  S1-S2. Figure  2. Genome-wide Heritability of Immune Traits. GCTA GREML estimates of the percentage of phenotypic variance explained by all common variants. (A) Twenty-eight of 139 immune traits analyzed in the European ancestry group (N=7,813) showed nominally significant level of genome-wide heritability (V(Genotype)/Vp) (LRT p < 0. 05), 10 traits (FDR p < 0. 05) and 15 traits (FDR p < 0. 1) showed significant heritability after correction for multiple hypothesis testing.
(B) Colocalization analysis in GTEx and TCGA for the SNPs associated with T cell ES (max CLPP > 0. 01). Plot of the maximum CLPP (left) either assuming one causal SNP, two causal SNPs, or the regional CLPP in GTEx and TCGA. For each eQTL, the gene-SNP pair with the highest CLPP is represented. For each sQTL, the SNP-splicing event pair with the highest CLPP is represented. For GTEx, results from the tissue with the highest max CLPP. Plot of the eQTL and sQTL -log10 p (right). For each SNP-gene expression or SNP-gene splicing category pair, 200 SNPs (+/-100 SNPs) around the index SNP were included in the analysis. Evidence for the expanded range analysis (+/-1MB for eQTL and +/-500KB for sQTL) is labeled  (A) Suggestive associations (p < 0. 005 and FDR p. between germline pathogenic or likely pathogenic cancer predisposition variants (rare variants) extracted from whole-exome data  (Huang et al.
EXPERIMENTAL MODEL AND SUBJECT DETAILS Human Subjects: A total of 11,521 genotype files from participants across 33 different cancer types included in TCGA were downloaded (Downloaded May 30, 2018 from https://portal. gdc. cancer. gov/legacy-archive). The TCGA dataset has previously been described  (Liu et al. , 2018; Thorsson et al. , 2018). We excluded all participants with hematological malignancies (diffuse large B cell lymphoma, and acute myeloid leukemia) and thymoma since these could not be characterized for immune cell infiltration based on gene expression analyses. We included all participants who had genotype data from Affymetrix array on at least one normal sample (peripheral blood or matched normal tissue). After data cleaning (see below), the dataset for imputation included 10,128 individuals (Table  S1 ).
Further removal of samples based genetic relatedness, availability of immune traits and covariate data resulted in a dataset of 9,603 individuals. Of these individuals, there were a total of 4,585 men and 5,018 women based on selfreported sex. The participants' age ranged from yr to y with a median age of yr. Selfreported race and ethnicity were available from 8,510 samples, of whom 7,073, 825, 583, 20, and 9 samples were reported to be White/Caucasian, Black/African American, Asian, American Indian or Alaska Native, and Native Hawaiian/Pacific Islander, respectively. Self-reported ethnicity was reported on 7,351 samples, of whom 295 and 7056 samples were reported to be Hispanic/Latino, or Not Hispanic/Latino, respectively. Of these samples, 8,204 were typed on blood-derived normal, 1,397 on solid tissue normal and 2 on buccal cell normal tissue.
Institutional review boards at each of the sites that provided samples and data reviewed the consent forms and approved the use of samples. Germline genotype data: Germline genotype data for common variants used in heritability analysis and GWAS were obtained from Affymetrix Genome Wide SNP 6. 0 arrays (TCGA legacy archive https://portal. gdc. cancer. gov/legacy-archive). Birdseed genotyping files representing 905,600 variants for 11,521 samples were downloaded. Whole exome sequencing data: Germline genotype data for rare variants were based on whole exome sequencing data (TCGA archive https://portal. gdc. cancer. gov/). Processed whole exome sequencing data for pathogenic or likely-pathogenic variants from  Huang et al. , 2018  were considered for rare variant analysis representing 10,389 individuals of which 9,138 had all of the phenotype and covariate information for analysis (Table  S1 ).