dataesr/scientific-paragraphs-categorization huggingface dataset:
The simulated historical yields were compared with observed yield data from the FAO dataset. The FAO dataset is provided at the country level for each crop without variety differentiation. Then, simulated yields were aggregated across varieties at the country level for comparison to the FAO dataset following the method of Porwollik et al. [38]. The correlation between observed and predicted crop yield time series was obtained after removing a linear trend in both time series. Indeed, FAO yield time series over the 1979-2013 period show increasing yields over time (Burkina-Faso, Senegal), while others show yield losses (Chad, Niger). Although climate variability may influence these trends, non-climatic factors such as land degradation, management and economic crises are likely to be the main drivers.
Since these non-climatic factors are not included in the crop model, it is necessary to remove trends in crop yields. Thus, we removed a linear trend in both observed and simulated yields, as suggested by Sultan et al. [36]. In this study, a set of crop-specific masks were used for yield analysis. This type of mask is often used to identify pixels where specific land crops are present. The resulting land use datasets represent the harvested area of several different crops in the world around the year 2000  [39]. Three different crops in West Africa (maize, millet, and sorghum) were selected for use in this study. In order to avoid extreme values, we have set a threshold for the harvested area and we took when the average number of hectares harvested per land area of a grid was above 100 per hectare.
Evaluation of the crop model The comparison between simulated crop yields and FAO time series data for maize, millet and sorghum on average over West Africa shows that the performance of the SIMPLACE model varies from crop to crop (Fig  1 ). As suggested by Sultan et al. [6] , we removed the data in Nigeria when calculating average yield anomalies over West Africa, as FAO data in this country appear to be problematic. Simulations of maize yields show the best correlation with observed yields (R = 0. 47), but the mean yield is overestimated (2. 1 t/ha in SIMPLACE simulations versus 1. 2 t/ha in the FAO dataset). The variability of maize yield is also largely underestimated in the simulations with a coefficient of variation of 0. 09 in SIMPLACE versus 0. 23 in the observations. Mean yield of millet and sorghum are better estimated by the crop model although still overestimated and the coefficient of variation is quite close between FAO data and SIMPLACE simulations for these two crops.
Similarly, pseudo r-squared values for mutation data showed 18% and 38% increase when using only the shared layer and full model compared to a null model (Figures  S2A  and S2B ). The investigation of the ''importance'' of the features showed majority of the latent features resulting in similar increase in error profile when permuted (Figure  S3 ). The identified groups resulted in distinct differences in the distribution of MN subtypes. HC cases were mainly distributed in three clusters: C-13 (41% of cases), C-2 (40%), and C-7 (16%). C-4 counted only 3% of HC. Myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML) cases populated distinct clusters with AML mostly enriching six clusters: C-10 (100%), C-9 (96%), C-5 (91%), C-0 (88%), C-6 (80%), and C-4 (78%) and patients with MDS grouped in three: C-12 (88%), C-1 (81%), and C-3 (76%) (Figures  2A  and 2B ).
In general, SF3B1 mutations were enriched in C-1, C-3, C-7, and C-12, whereas SRSF2 mutations dominated C-14 and C-8 together with TET2. However, non-SF  MT  were equally distributed in different clusters. For example, carriers of DNMT3A, TET2, and FLT3 hits clustered in C-0, C-5, and C-6, while DNMT3A and FLT3 mutants were equally distributed in C-10. C-2, C-7, and C-13 showed similar PSI features as HC. C-2 and C-13 contained mainly TET2 and ASXL1 mutations, patients with myeloid neoplasia and 63 healthy controls. Mutational data and copy number variants were obtained from whole-genome sequencing performed on the same cohort. CIBERSORT analysis was performed to quantify the cellular composition of the samples. In total, 54% of the patients carried at least one mutation in a common splicing factor (SF) gene.
Common features across clusters To define the most important AS changes informative for the clusters, we first filtered for genes with high mean GE and high correlation with the latent space across the dataset. To further characterize our clusters (C-0 through C-14, termed observation-clusters hereinafter) and AS events, we extracted the top 15 AS ). Of note is that AS events in feature-clusters F-8, F-10, and F-11 exhibited characteristics differentiating observation-clusters in the first major group. For instance, observation-clusters C-6 and C-13 separated from the rest by increased PSI levels for AS events in feature-cluster F-8, which included SUZ12, RBM5, and USP24 as well as decreased PSI levels for AS events in feature-cluster F-11, which included instead MAPK14, NDUFV2, and STK4. Furthermore, AS changes in feature-cluster F-10 differentiated observation-clusters C-6 and C-13, with observation-cluster C-6 showing increased PSI levels for AS events mapping to SF3B3, KDM6A (UTX), and ATP5F (refer to Table  S2  Feature-clusters of top alternative splicing events in observation-clusters for a list of AS changes).
In the recent era, the availability of large omics data along with ML-based methods has opened the possibility to help the diagnosis or inform on the prognosis and therapeutic vulnerabilities of patients with cancer, opening a new era of personalized medicine. 27 However, the utilization of multiple omics datasets is generally performed in a post hoc fashion, where more formal approaches such as canonical correlation analysis can benefit from increasingly large datasets and be more suitable for diseases showing complex interactions among different layers of geno-transcriptomic information. 25] 30  The importance of such models is also demonstrated by the efforts of the GENOMED4ALL project (NCT04889729), which is currently recruiting patient cohorts from multiple institutions with the aim of developing artificial intelligence-based tools to improve MDS classification and prognostication.
STAR+METHODS Detailed methods are provided in the online version of this paper and include the following: SNV and indels were called using Strelka2 software (version 2. 4. 7; Illumina, San Diego, CA), followed by Ensembl Variant Effect Predictor annotation. CNV imputation was performed using GATK4 (Broad Institute, Cambridge, MA). CNV and karyotype of chromosomes 5, 7, 17 and X were used to assess deletions encompassing DDX41, LUC7L2, PRPF8, and ZRSR2. To capture robustness of our features in relation to high mutational burden, we filtered out mutations with a variant allele frequency (VAF) < 15% in downstream analysis. Germline mutations were not the purpose of this study and were not retained. Results from a panel of myeloid related genes (Table  S1 ) were used for our analyses. d KEY RESOURCES RNA-Seq was performed as previously described by our group.
Analysis of alternative splicing Analysis of alternative splicing (AS) was conducted as previously described. 9 rMATS (python rMATS. 4. 0. 1) was used to annotate junction counts for AS events. We removed AS events in which the total skipped and included read counts were <10. Constitutive splice events were defined as events for which there were no samples with that had a percent spliced in (PSI) between 10 and 90%. Such events were excluded from analysis. Skipped exon (SE) junctions labeled as alternative 5' splice site (A5SS) and 3 0 splice site (A3SS) as well as unannotated/novel SEs, A3SS, and A5SS containing a UCSC-annotated intron were also removed. The length normalized percent spliced in values were filtered for total count of reads mapping to splicing event greater than 10 in 95% of cases.
Feature similarity and bioinformatic analysis Pairwise feature similarities for gene expression, mutations and PSI were quantified based on cosine similarity of the decoder network weights. Since we utilized a single layer of linear mapping from latent features to observed features, cosine similarity of the network weights was used as a direct proxy for feature correlations. Differential gene expression was analyzed using limma package and cyclicLoess normalization on log2 scaled gene expression values. 50 Specifically, we normalized log2 scaled expression values via cyclic loess normalization and estimated FDR adjusted p values across 2 major groups via linear-models using limma package and empirical Bayes shrinkage. Gene Set Enrichment Analysis was done using the fgsea R package with Reactome pathways gene-set obtained from MsigDB database  51, 52  and using default parameters.
Survival analysis Survival estimation was assessed using Kaplan-Meier estimator for each cluster using R package survival and pairwise comparisons were performed using the log-rank test. 54 No corrections were applied for multi-testing in pairwise comparisons. Cox Proportional-Hazards model was applied by integrating the latent dimensions to clinical factors (age, gender) and treatment information. Treatment with disease modifying agents such as hypomethylating agents, supportive therapy and induction chemotherapy or chemotherapy were included. OS was defined from diagnosis of myeloid neoplasia to death or last follow-up. Proportionality assumption for latent features with significant deviation is corrected by adding a time-dependent interaction term with time cutoff selected at median follow-up of 35 months.
Model validation: IPSS-R score and stochasticity of clusters B Feature similarity and bioinformatic analysis B Survival analysis only variants with a minimum depth of 10 reads and 4 reads supporting the alternate allele were retained, b) synonymous, polymorphisms with a population frequency >1% and potential germline and benign variants were not included, c) nonsynonymous variants (missense, stop codon, frameshift, indels) were included and filtered according to sequenced control databases [gnomAD (https://gnomad. broadinstitute. org/) (%0. 1%), dbSNP13850, 1000Genomes], and databases of mutations including COSMIC, ClinVar, and HePPy (Hematological Predictor of Pathogenicity) for pathogenicity confirmation. Due to the lack of paired normal tissues, a mixture of genomic DNA for multiple unidentified healthy subjects was used as control.
ACKNOWLEDGMENTS This work was supported by the  Henry and Marilyn Taub Foundation , grants  R01HL118281  (to J. P. M. ),  R01HL123904  (to J. P. M. and R. A. P. ),  R01HL132071  (to J. P. M. and R. A. P. ),  R35HL135795  (to J. P. M. ), The  Leukemia & Lymphoma Society TRP  Award  6645-22  (to J. P. M. ),  AA&MDSIF  (to V. V. , S. P. , and J. P. M. ),  VeloSano 9 Pilot Award ,  Vera and Joseph Dresner Foundation-MDS  (to V. V. ),  Vera and Joseph Dresner Foundation-MDS  (to R. A. P. ),  Fondation ARC pour la Recherche sur le Cancer and Italian society of Hematology  (to S. P. ),  Tito Bastianello MDS Young Investigator Awards  (to C. G. , S. P. , and V. A. ) and  F31HL131140  (to C. E. H. ). This work was supported by a grant from the  Edward P. Evans Foundation  (to C. G. ). This work made use of the  High Performance Computing Resource in the Core Facility for Advanced Research Computing at Case Western Reserve University.
Software and algorithms Tensorflow (Apache License) v2. 0 https://www. tensorflow. org/ Strelka2 v2. 4. 7 https://github. com/Illumina/strelka GATK4 v4. 0. 1 https://github. com/broadinstitute/gatk Annovar https://annovar. openbioinformatics. org/en/ latest/ STAR & Cufflinks v2. 5. 0 & v2. 2. 1 https://github. com/Illumina/Isaac3 Isaac3 aligner v03. 16 Limma v3. 54 https://bioconductor. org/packages/release/ bioc/html/limma. html umap-learn v0. 5. 3 https://pypi. org/project/umap-learn/ randomForestSRC v3. 1 https://cran. r-project. org/web/packages/ randomForestSRC/index. html rMATS v4. 0. 1 https://rnaseq-mats. sourceforge. net/ Fgsea v1. 24 https://bioconductor. org/packages/release/ bioc/html/fgsea. html.
We first note that SWOT discharge is not monolithic-open satellite data will allow for many "SWOT Discharge" products created by hydrologists across the scientific community. This paper is therefore primarily concerned with the SWOT discharge to be archived and distributed by the US and French space agencies (referred to as the "Agency" discharge estimates). We first describe the philosophy behind the SWOT discharge (Section 2), and data sets used to produce SWOT discharge (Section 3), including SWOT observations and ancillary measurements. We then describe how SWOT discharge will be produced (Section 4) and expected accuracy (Section 5), relating expected SWOT discharge accuracy with that achievable from in situ measurements. Our aim is to describe SWOT discharge characteristics before their availability, thus maximizing hydrologic science returns from SWOT.
SWOT Discharge Philosophy To understand the SWOT discharge products, it is helpful to begin with an appreciation of the challenges that must be overcome to estimate river discharge globally. These challenges have led to data product decisions that together constitute a philosophy for SWOT discharge. Whereas previous papers on SWOT discharge and related efforts have predominantly described methodological advances, here we bring together these challenges and the resulting philosophy in a single place. Discharge is a critical part of the SWOT mission, but not all the information needed to compute discharge is directly available from the SWOT measurements. Discharge is specified as a required product to be produced and distributed by the space agencies in the SWOT science requirements document, the foundational mission document that specifies what SWOT products must be produced and with what accuracy  (JPL Internal Document, 2018).
SWOT measurements of rivers include WSE, river width, and slope, each of which is invaluable in estimating river discharge (for further information on SWOT measurements; see Section 3. 2). However, these measurements together do not have a unique relationship to river discharge. Thus, the SWOT Science Team will develop and deploy methods to estimate the additional properties of global rivers needed to produce the Agency discharge estimate. (Note that SWOT, like many large satellite missions, has a "Science Team" comprised of researchers from around the globe to support the mission. ) The Science Team will likely create and distribute additional discharge data products: see Section 4. 7 for details. The Agency discharge estimates are thus a partnership between the Agencies and the Science Team.
Second, once the rating curve is established, discharge is predicted from the rating curve via continuous observations of river stage, typically measured by a pressure transducer. SWOT discharge will also be estimated by a two-step process that is an analog to gages: In the first step, we establish a relationship between SWOT observations and river discharge, and in the second step, SWOT observations are used along with the relationship to estimate discharge on each SWOT overpass. However, the methodological details for the first step differ significantly from the rating curve calibration approach due to the lack of in situ discharge data for most of the world. As noted earlier, this article focuses on the SWOT discharge produced by the space agencies (JPL Internal Document, 2020), which follows this two-step methodology; see Section 4.
These unobserved terms in the flow laws are referred to as "flow law parameters" (FLPs) hereafter. FLP estimates will be computed by the Science Team after SWOT data is available using algorithms described in Section 4. 3. After FLPs are estimated, SWOT discharge will be produced automatically for each SWOT pass. These two steps are referred to as "flow law parameter estimation" (FLPE) and "discharge production. " Third, SWOT discharge will be produced reaching approximately 10 km in length. The selection of 10 km as the reach length was driven by precision of reach averaged WSE, width, and slope measurements. SWOT WSE measurements will be noisy at the scale of individual radar pixels (JPL Internal Document, 2017). Rodriguez et al. (2020)  showed that averaging to reaches of approximately 10 km is necessary to resolve river features.
Thus, the Agency discharge products will be produced at reach scale; reach averaging necessitates adaptation of flow laws, as shown by  Rodriguez et al. (2020) , and discussed in Section 4. 2. We control for changes in discharge within the reach by choosing reaches to avoid major confluences: see Section 3. 1. Reach definition takes into account low-head dams and other river obstructions (X. Yang et al. , 2022). Possible Science Team discharge estimates at higher spatial resolution are discussed in Section 4. 7. Fourth, two branches of SWOT discharge will be produced: one where in situ data are used to constrain SWOT discharge, and one where in situ data are not used to constrain discharge, referred to as "gage constrained" and "unconstrained," respectively. Philosophically, these two branches are driven by the fact that SWOT discharge estimates will be used in both gaged and ungaged basins, with different sets of expectations and requirements regarding discharge accuracy.
1029/2021WR031614 5 of 31 of mean annual flow or river geomorphology against information derived from inverse algorithms, based on their respective uncertainties  (Hagemann et al. , 2017). In contrast, the "gage constrained" FLPs will be chosen assuming the availability of suitable in situ discharge data and informed by global models calibrated at specific gage sites. Gage discharge will be used only during the calculation of the FLPs, not during the operational discharge calculation by space agencies. Additionally, some discharge gages will be reserved for validation purposes (i. e. , not used to constrain either prior models or SWOT discharge) to assess discharge accuracy and precision of both the gage-constrained and unconstrained products (see Section 4. 5, below). Fifth, Agency products will include an ensemble of discharge estimates, produced using several different flow laws and FLPE algorithms described in Section 4.
SWOT Mission River Database SWORD archives both spatial data and reaches attributes for SWOT reaches  (Altenau et al. , 2021)  and is critical to the creation of SWOT river data products. The primary spatial attributes of SWOT reaches are SWORD river centerlines, which are specified based on the Global River Widths from the Landsat data set  (Allen & Pavelsky, 2018)  at ∼30 m spatial resolution, using Landsat data and the RivWidth algorithm  (Pavelsky & Smith, 2008). SWORD also defines spatial data and attributes for river nodes, a series of points at approximately 200 m increments along river longitudinal profiles defined by the SWORD centerline. SWORD reaches and nodes are used in several stages of SWOT processing: for example, SWOT radar pixels are mapped onto SWORD node locations using the RiverObs software (https://github.
com/SWOTAlgorithms/RiverObs), translating two-dimensional imagery to one-dimensional measurements of WSE, width, and slope. SWORD archives river ice climatology (derived following the methods of X. Yang et al. (2020) ) used for SWOT ice flagging. SWORD distance from river outlet (also called "chainage") and SWOT WSE at the node scale are combined to compute SWOT reach averaged river slope. SWORD also archives drainage area, extracted from data sets such as MERIT Hydro  (Yamazaki et al. , 2019) , river topology, and river obstructions data from the Global River Obstruction Database  (Whittemore et al. , 2020). Once FLPs have been computed by the Science Team, they will be attached to SWORD for the Agencies to use in producing discharge estimates. See  Altenau et al. (2021)  for further details.
SWOT Observations: Spatial and Temporal Sampling Characteristics, and Precision SWOT WSE, width, and slope resolution and precision are relevant to methods used to calculate discharge, and so are briefly reviewed here; for more details, see the SWOT River Single Pass Product Description Document (JPL Internal Document, 2020) example data products (https://podaac. jpl. nasa. gov/swot?tab=datasets), Science Requirements Document  (JPL Internal Document, 2018)  and Mission Performance and Error Budget (JPL Internal Document, 2017). SWOT WSE is measured interferometrically, and is defined relative to the Earth Gravitational Model 2008 (EGM2008) geoid  (Pavlis et al. , 2012) , where the geoid is the vertical distance above the World Geodetic System (WGS84) ellipsoid model of the Earth surface. SWOT width is computed as a reach average, by summing the inundated area of each SWOT radar pixel associated with a particular river reach and dividing by reach length  (Frasson et al.
Confluence: A Computational Engine for SWOT Discharge and FLPE The Confluence computational software engine (https://github. com/swot-confluence/) has been developed to enable FLPE in a timely manner from SWOT observations for multiple flow laws across global reaches. All Confluence code is currently publicly available, save for individual Mass-Conserved Flow Law Inversion (McFLI) algorithms which are maintained and made public by their original authors. To support the Agency discharge products, the Science Team will be required to produce FLP estimates rapidly at the global scale. This means we must ingest SWOT observations, reference many data fields within the SWORD database, and run computationally expensive discharge algorithms for on the order of 10 5 reaches, all on a short timeline.
Nonetheless, care must be taken not to apply the modified Manning's equation in parts of the river such as riffles or low-head dams where there is a significant elevation drop across a very short distance where flow is expected to be supercritical. This is handled for SWOT discharge by using a database of such structures within SWORD to define reach boundaries that exclude such structures. The length of river that includes the hydraulic structure is defined as a "dam reach"  (Altenau et al. , 2021) , a special class of reach for which WSE, width, slope, and discharge are not computed. Similarly, lakes on SWOT rivers are expected to have a surface slope too low to resolve; discharge is not computed for lakes  (Altenau et al. , 2021).
assess the degree to which genetic risk is shared in GDM vs. T2D and whether any variants and biological pathways are specific to, or have differential effects in, GDM. Thus, to elucidate the genetic underpinnings of GDM we conducted a genome-wide association study  16  of GDM in 12,332 cases and 131,109 parous female controls. Participants were of Finnish ancestry from the FinnGen study. The association analysis was performed as described in Kurki et al  17. Cases were identified using Finnish health and population registry sources including registry data from inpatient hospitalizations, outpatient specialty clinics, and birth registry. Cases were confirmed to have a diagnosis within a pregnancy window and those with diagnoses of diabetes prior to the index pregnancy were excluded (Online Methods, Supplementary Note).
Interestingly the GDM-associated allele identified in our study is associated with lower BMI (β = -0. 02, p = 5. 3e-11), lower weight (β = -0. 02, p = 5. 3e-11), and lower height (β = -0. 01, p = 3. 9e-6). Finally, to gain further insight into potential functional differences between GDM and T2D we examined the cell type specific expression patterns associated with the GWAS summary statistics  25  (Figure  3 , Online Methods, Supplementary Tables  26 27 28 29 , Supplementary Figures  26 27 28 ). We evaluate cell-type specific enrichment despite the lack of significant tissue-level enrichment because pregnancy induces major adaptive changes to specific cell populations within maternal tissues that might not be reflected in bulk tissue expression. Analyses integrating multiple large single cell RNA expression datasets indicated that pancreatic β cells are significantly associated both with GDM and T2D.