dataesr/scientific-paragraphs-categorization huggingface dataset:
MPRA identifies SNPs altering β cell transcriptional activity. To identify SNPs that altered transcriptional activation in β cells, we compared MPRA activity of each allele under each experimental condition (standard culture, DMSO, Tg). Combining all three categories (caQTL, T2D, and non-caQTL), 879 SNPs exhibited allelic effects on MPRA activity (FDR < 10%) in one or more experimental condition (Fig. 2a , Supplementary Fig. 5a-c , Supplementary Data 3 and 4). For 98. 2% (n = 332/338; Binomial test p = 7. 2 × 10 -90 ) of SNPs that altered MPRA activity in multiple conditions, the direction of allelic effects was concordant (Supplementary Fig. 5a-c ). To assess if MPRA properly captured in vivo allelic effects, we assessed their concordance with caQTL effects. Strikingly, 82. 8% (n = 246/297; p = 8.
These SNP-containing elements exhibited distinct patterns of transcriptional activity in Tg-vs. DMSO-treated β cells, with caQTL and T2D SNP-containing elements losing or gaining activity under ER stress, respectively. MPRA nominates functional SNPs at T2D GWAS signals. We next sought to nominate T2D causal variants within each locus from the set of 2512 SNPs tested by MPRA. We identified allelic effects for 220 T2D-associated SNPs in 104 distinct association signals (Fig. 3a ; Supplementary Data 5-7 and Supplementary Fig. 5d-f ), only 17 of which were reported index SNPs. The Fig. 1  Massively parallel reporter assay (MPRA) identifies steady state and ER stress-responsive transcription activating sequences in β cells. a Schematic of MPRA study workflow to identify β cell transcription activating sequences.
Because oligo design and synthesis for this study occurred prior to the report of credible SNP sets for T2D-associated loci  4  , our LD-based approach to select SNPs for testing did not exhaustively and comprehensively test the functionality of all T2D credible set SNPs. However, for this and other GWAS signals, MPRA has nominated multiple high-priority candidate causal SNPs for targeted and mechanistic investigation in the future (Supplementary Data 5-7). Integrating MPRA data with QTL maps refines T2D causal alleles/mechanisms. Finally, we sought to understand the in vivo consequences of the putative functional T2D SNPs nominated by MPRA. Integration with islet caQTL revealed striking correlations between SNP effects on in vitro MPRA activity and in vivo islet chromatin accessibility (Fig.
4c, e ). Importantly, allelic effects on MPRA activity and chromatin accessibility were concordant, i. e. , the rs4630391-C allele was associated with both higher in vitro MPRA activity in MIN6 and increased in vivo chromatin accessibility in human islets (Fig. 4b, e ). EMSA revealed that decreased transcriptional activity of the rs4630391-T allele was accompanied by increased T allele-specific binding in MIN6 nuclear extracts (Fig. 4f , arrow). While this does not definitively rule out that the lead caQTL SNP (rs34584161) or other SNPs in high LD are also functional, these data provide compelling functional support for rs4630391 as a putative causal variant despite its lower reported genetic posterior probability. MPRA also refined the relative functional contributions of two T2D-associated SNPs in high LD.
These data suggest that SINEs, and SNPs within them, may play underappreciated roles in modulating β cell transcriptional programs in response to stress or other stimuli. Recent studies have contributed to an emerging appreciation of the importance of these elements in epigenetic and transcriptional regulation, demonstrating repetitive elementmediated oncogene activation and modulation of chromatin structure  [81] [82] [83] [84] [85] [86] [87] [88] [89]. Alu/SINEs have been shown to be transcriptionally induced by cellular stressors  63, 90  and are emerging as pervasive transcriptional modulators of cellular functions and stress  62, 64, 81, 85, 91. Three of four SNPs tested by EMSA overlapped SINEs (rs113350503 and rs11630895 in the LARP6 locus, rs4630391 in the RNF6 locus) and showed specific binding for the allele with lower MPRA activity, suggesting that the activating alleles disrupt binding of a transcriptional repressor.
Acknowledgements We thank members of the  Stitzel and Ucar labs  for helpful discussion and critiques during study design and execution and  Francis S. Collins ,  D. Leland Taylor ,  Cassandra Spracklen ,  Christine Beck , and  Taneli Helenius  for helpful comments on the manuscript. This work was supported by  The Jackson Laboratory Director's Innovation Fund  (to M. L. S. and R. T. ),  W81XWH-18-0401  (to M. L. S. and D. U. ),  R01DK118011  (to M. L. S. ), and  R00HG008179  (to R. T. ).
Data availability All datasets generated and analyzed during the current study are publicly available in GEO under Accession GSE145643 (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi? acc = GSE145643), which includes fastq files containing barcode sequences in the 3′UTR of gfp in the plasmid library and MIN6 RNA samples and processed files containing the sum of all barcode counts for each test construct in the plasmid DNA and MIN6 RNA samples. Human islet ATAC-seq data were obtained from NCBI Sequence Read Archive Accession SRP117935. Summary InsPIRE Consortium73 islet eQTL statistics were obtained from https://zenodo. org/record/3408356 and SNP2TFBS predictions for transcription factor motifs altered by MPRA-modulating SNP alleles were obtained from https://ccg. epfl. ch/snp2tfbs/.
CDRs have an unmatched potential to measure population movements, and have been shown to be a key tool for retrospective analysis  20. Over the past decade, CDRs have increasingly been used in health research  [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31]  ; particularly to model the geographic spread of infectious diseases  32  , most recently SARS-CoV-2  22, 28. In these studies, population mobility networks are modeled by using algorithms to aggregate large datasets of CDRs into Origin-Destination (OD) matrices. The CDRs that we use have previously been used by Ruktanonchai and colleagues to evaluate the impact of mobility on malaria transmission in Namibia  29  , and to design novel malaria elimination strategies  30, 33. We use the OD matrix that they constructed  34  ; it contains aggregated CDRs from 9 billion calls/texts made from 1.
Given that Namibia had a population of 1. 34 million individuals (15 or older)  35  and a very high percentage of the population were using mobile phones (with some owning more than one SIM card  36  ), relatively few of the 1. 19 million SIM cards in the database would have been owned by non-Namibians. Consequently, the movements of non-residents would have had little effect on the mobility network. We use an approach based on modeling spatial networks to determine the effect that mobility had on the risk of acquiring, transmitting, and dispersing HIV in the time before treatment was made widely available. We conceptualize HIV epidemics as spatial networks consisting of communities that import and/or export risk; communities are interconnected by groups of individuals moving, on a temporary basis, between them.
6 months per year outside their home constituency between October 2010 and September 2011, although there was considerable geographic variation, ranging from approximately 1-6 months (Fig. 2a  and Supplementary Fig. 1 ). As a result of the specific travel patterns, many of the constituencies were highly connected (Fig. 1b ). In 2010, there were 107 constituencies in Namibia; 11 of these are not represented in the mobility network because, due to a low population density, they lacked cell phone towers. To conduct our analyses, we needed to estimate the prevalence of HIV in women and men at the approximate time that the CDRs were collected. However, the first estimate of HIV prevalence in Namibia, based on a representative sample of the population, was based on data collected in 2013  37. At that stage of the epidemic, HIV prevalence was relatively stable  38.
UNAIDS' goals for 2030 are to have 95% of HIV-infected individuals diagnosed, 95% of the diagnosed on treatment, and 95% of treated patients with viral suppression  43. Our new understanding of the generalized HIV epidemic in Namibia suggests that, in order to eliminate HIV, control strategies will need to take into account the riskiness, and vulnerability, of constituencies. To design effective strategies, it will be necessary to identify the current mobility network; therefore more recent data on CDRs in Namibia need to be collected. Then, by expanding the methodology that we have presented in this study to include treatment, the current risk flow networks and the risk hubs can be identified. We recommend developing strategies that preferentially target the most important in-flow and out-flow risk hubs in the country.
The survey also collects data on ownership and usage of mobile phones. The 2013 NDHS utilized a two-stage cluster design to collect a representative sample of Namibia's population  37  ; clusters were georeferenced. Demographic and behavioral data were collected from 14,499 individuals in 9849 households. Participants who were 15 or older were tested for HIV infection. Each individual's test results were linked to their demographic and behavioral data. The overall response rate was high: 92% for women, 85% for men. Participation in HIV-testing (in 15-49 year olds) was also high: 83% for women, 74% for men. HIV prevalence mapping. We used data from the 7731 individuals (aged 15-49 years old) who were tested for HIV in the 2013 NDHS  37  to construct the genderspecific prevalence maps for Namibia: Fig.
2b  (women) and Fig. 2c  (men). Prevalence was estimated, and maps constructed, using R v. 3. 6. 3 (packages "survey" and "sp"). Calculating prevalence-connectivity matrices. We combined the OD matrix with the prevalence data to construct the gender-specific prevalence-connectivity matrices. These matrices are shown in Fig. 2d  (men) and Supplementary Fig. 2  (women). Each matrix was calculated by using the data in the OD matrix to determine the time spent in each constituency. The prevalence data for women were used to construct the prevalence-connectivity matrix for men; the prevalence data for men were used to construct the prevalence-connectivity matrix for women. Modeling spatial networks of risk flows. To model these networks, we used three sources of data: (i) the OD matrix, (ii) HIV prevalence data  37  , and (iii) Census data  35.
(  8 )). It is the opposite direction for t, which flows from constituency i to constituency j through individuals going from constituency j to constituency i (see the presence of the term A ji in Eq. (  7 )). In this latter case, risk in constituency j increases as its residents travel to other constituencies and risk getting infected there. Risk was calculated using Python 3 and standard libraries (numpy v1. 19, pandas v1. 2). Calculating risk importation and exportation. The in-strength of a node in a weighted, directed network is the sum over the weights of its incoming links. We define risk importation as the in-strength of a node in the networks. Therefore, the risk importation into each node is: t IN i ¼ ∑ j≠i t ji ; ð12Þ v IN i ¼ ∑ j≠i v ji : ð13Þ The out-strength of a node in a weighted, directed network is the sum over the weights of its outgoing links.
Data availability The 2010-2011 Namibian mobility data in the form of a geographic origin-destination adjacency matrix are freely available at: https://doi. org/10. 1371/journal. pcbi. 1004846. s002  34. The 2013 Namibian Demographic and Health Survey (NDHS) data (Individual recode and HIV test results recode) are available for non-commercial use to registered users at: https://dhsprogram. com/data/dataset/Namibia_Standard-DHS_2013. cfm. The 2011 Namibia Population and Housing census data is freely available for non-commercial use to registered users at: https://nsa. org. na/microdata1/index. php/catalog/19.
Sample Size and Data Collection Patients eligible according to aforementioned inclusion criteria will be selected at the cardiology consultation level or during cardiology or emergency hospitalization. As much as 250 cardiologists (from both public and liberal sectors) will participate in patient selection. Patient inclusion will occur consecutively until the end of the inclusion period. Suitable patients will be selected and requested to sign an informed consent form, confirming their agreement to participate in the study. A detailed questionnaire will then be administered to eligible patients and clinical, biological, and exploratory data will be collected. The inclusion began on October 2, 2017 (duration 1 month). A regular follow-up was done, wherein patients must visit the cardiology clinic 1, 3, and 12 months after inclusion into the study.
This follow-up is provided by the investigator. Given the observational nature of the NATURE-HF study, no specific treatment or intervention is planned for the management of HF. Patients should be cared for according to the usual medical procedures. All data are collected via the DACIMA Clinical Suite web interface  [8]. The platform complies with international standards FDA 21 CFR part 11 (Food and Drug Administration 21 Code of Federal Regulations part 11), HIPPA (Health Insurance Portability and Accountability Act), ICH (International Conference on Harmonisation), MedDRA (Medical Dictionary for Regulatory Activities), Health Canada, and Tunisian regulations.
Statistical Analysis The DACIMA Clinical Suite platform enables the collection of online data and their extraction in SAS or SPSS format. Statistical analysis will be performed with SAS software (University Edition; SAS Institute). The statistical analysis is exploratory, involving the calculation of 95% CI. The data will be described for the whole analyzed population. Continuous quantitative variables (eg, age, weight, left ventricular ejection fraction, biological parameters) will be described by the number of patients documented, mean, median, SD, and extreme values. Categorical variables will be described by the number and percentage of each category. The normality of the continuous quantitative variables will be verified with the Shapiro-Wilk test. If the hypothesis of normality is rejected, then a simple transformation will be made on the variable to normalize it.
If this transformation succeeds in normalizing the variable, then the analysis will be performed on the transformed variable. Otherwise nonparametric tests will be used or possibly an analysis on the quartiles or quintiles will be performed. Statistical tests will be bilateral with a statistically significant threshold of 5%. An analysis of variance will be performed for the quantitative variables (parametric test for the variables that follow a normal distribution, and nonparametric test in other cases) and a chi-square test will be realized for the categorical variables. The Yates correction or Fisher exact test will be used if the validity conditions for the chi-square test are not met (theoretical number <5). The sample size (both power and sample) is estimated by the SPSS 2008 software (IBM).
To estimate the number of patients to be included in this study, a range of 95% CI (5% significance threshold) is set as the primary endpoint. The impact of this criterion is estimated at 35%, based on the US registry OPTIMIZE-HF  [9] , which found a hospital mortality ranging from 2. 2% to 4. 1%, a global mortality ranging between 6. 5% and 9. 1%, and an overall incidence of all events evaluated (rehospitalization or death) ranging from 35. 3% to 36. 6%. Considering these data, the number of patients to be included in the study would be 1436. However, by predicting a 5% missing data rate and a 10% loss to follow-up, the sample size to be selected will be 1700 patients. The intermediate analyses will be carried out after formulating the statistical analysis plan.
In the last few years, many drugs have been evaluated to treat HF; unfortunately, results were disappointing in terms of efficacy and safety  [15] [16] [17] [18] [19] [20] [21] [22]. This large, contemporary longitudinal study of Tunisian patients with HF will provide a unique opportunity to answer many clinical questions. The NATURE-HF study is important in several respects. First, systematic observational and outcomes data can be generated from this registry study, which is especially valuable given that previous data for Tunisian patients with HF are limited. Second, treatment of HF changes dramatically, and thus HF management needs to be evaluated in real-world studies. Third, the NATURE-HF study provides a good opportunity to compare treatment and response variation among HF populations in Africa for comparison with different countries and evaluate adherence to recent guidelines.
Sheppard and Trujillo OSSOS DES wide survey DES supernova fields As a quantitative example of the effectiveness of FastSSim, Figure  2  shows a comparison with the CFEPS/OSSOS simulator. For this test each simulator uses the population model defined in §3. Using Kuiper's test, we find that the distributions of the 1,615 data points calculated by FastSSim are statistically indistinguishable from those computed using the CFEPS/OSSOS survey simulator. Thus in order to distinguish the two simulators one would need > 1, 615 ETNOs-well above the quantity discovered by OSSOS. We achieve similar results in quantitative comparisons against the distributions computed using the DES survey simulators (see Figure  5. 1 in  Hamilton (2019)  and Figure  2  in  Bernardinelli et al. (2020a) ). In Figure  3  we plot a Gaussian kernel density estimate of each simulator's detections in the (x, y) and (p, q) spaces (x, y, p, and q are defined in §4).
We first perform 10 6 iterations sampling from our simulated detections a set of objects whose cardinality is equal to that of the set of real ETNOs detected by the given survey. We then take the average {x, y, p, q} position of each sample, and use these values to construct a 4-dimensional histogram. We display a Gaussian kernel density estimation of these data in the xy and pq-planes in Figure  5. We perform a Gaussian kernel density estimation on our mean-sampled histograms to obtain a probability distribution function (PDF). Next we draw N samples from our simulated data (where N is the number of ETNOs actually detected by the survey), find the mean {x, y, p, q} position, and evaluate our PDF at that position. We repeat this 10 5 times to construct a likelihood function. Next we compute this value for the ETNOs actually discovered by the survey.
ACKNOWLEDGMENTS This material is based upon work supported by the  National Aeronautics and Space Administration  under Grant No. NNX17AF21G  issued through the  SSO Planetary Astronomy Program  and by the  National Science Foundation  under Grant No. AST-2009096. Funding for the DES Projects has been provided by the  U. S. Department of Energy , the  U. S. National Science Foundation , the  Ministry of Science and Education of Spain , the  Science and Technology Facilities Council of the United Kingdom , the  Higher Education Funding Council for England , the  National Center for Supercomputing Applications  at the  University of Illinois at Urbana-Champaign , the  Kavli Institute of Cosmological Physics at the University of Chicago , the  Center for Cosmology and Astro-Particle Physics at the Ohio State University , the  Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University ,  Financiadora de Estudos e Projetos, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro ,  Conselho Nacional de Desenvolvimento Científico e Tecnológico  and the  Ministério da Ciência, Tecnologia e Inovação , the  Deutsche Forschungsgemeinschaft  and the  Collaborating Institutions in the Dark Energy Survey.
Based in part on observations at  Cerro Tololo Inter-American Observatory at NSF's NOIRLab (NOIRLab Prop. ID  2012B-0001 ; PI:  J. Frieman ), which is managed by the  Association of Universities for Research in Astronomy (AURA)  under a cooperative agreement with the  National Science Foundation. The DES data management system is supported by the  National Science Foundation  under Grant Numbers  AST-1138766  and  AST-1536171. The DES participants from Spanish institutions are partially supported by  MICINN  under grants  ESP2017-89838 ,  PGC2018-094773 ,  PGC2018-102021 ,  SEV-2016-0588 ,  SEV-2016-0597 , and  MDM-2015-0509 , some of which include ERDF funds from the  European Union. IFAE is partially funded by the  CERCA program  of the  Generalitat de Catalunya. Research leading to these results has received funding from the  European Research Council  under the European Union's  Seventh Framework Program  (FP7/2007-2013) including  ERC  grant agreements  240672 ,  291329 , and  306478.
The goal-attainment method with two objective functions is illustrated in Figure  11. By altering w vector, we searched for the Pareto-optimal solutions. Therefore, our solving process included two phases: 1. Phase 1: Find one Pareto-optimal solution of the configuration matrix with the goalattainment method; 2. Phase 2: Check the robustness index of the chosen solution in Phase 1. The optimization toolbox in the MATLAB environment was used to solve our problem. Note that our problem was formulated as a multi-objective optimization problem. One objective has one desired value excluding the robustness index, which is as a constraint, and therefore, the desired vector is set up. The goal-attainment method was used to solve the problem. An attainment vector was chosen as a trade-off between the underachievement and overachievement of the objective functions.
Acknowledgements This work has been supported by the  EIPHI Graduate School  (contract " ANR-17-EURE-0002 ") and by the  Agence Nationale de la Recherche  and the  FEDER  ( INSOMNIA  project, contract " ANR-18-CE09-0003 "). Financial support of  NanoMat  (www. nanomat. eu)by the  "Ministère de l'enseignement supérieur et de la recherche ," the " Conseil régional Champagne-Ardenne ," the  "Fonds Européen de Développement Régional (FEDER)  fund", and the  "Conseil général de l'Aube"  is also acknowledged. The authors also thanks the M2P IRT support for funding the COMODO and COMBINE RESEM projects.
Baseline comorbidities including hypertension, diabetes and obesity, increase SARS-CoV-2 susceptibility and severity  1, 10, [16] [17] [18] [19]. In addition, chronic lung disease (CLD) has been identified as a risk factor for hospitalization and mortality in patients with COVID-19  [20] [21] [22] [23] [24] [25] [26] [27]. Patients with chronic obstructive pulmonary disease (COPD) and interstitial lung disease (ILDs), especially Idiopathic Pulmonary Fibrosis (IPF), have a significantly higher COVID-19 mortality rate compared to patients without chronic lung disease  28. However, the molecular mechanisms underlying the increased risk of SARS-CoV-2 severity and mortality in patients with pre-existing lung diseases are not well understood. In this work, we performed an integrated analysis of four lung single cell RNA-sequencing (scRNA-seq) datasets  [29] [30] [31] [32]  in addition to unpublished data, together including 78 control and 132 CLD samples (n = 31 COPD, 82 IPF and 19 other ILDs), to investigate the molecular basis of SARS-CoV-2 severity and mortality risk in CLD patients.
Results Integrated analysis of lung single cell RNA sequencing datasets. To determine why COVID-19 patients with CLD have a higher risk of severe infection and poorer outcomes, we performed an integrated analysis on four published scRNA-seq lung datasets: Northwestern (biopsy deemed representative of explanted lung), Pittsburgh and VUMC/TGen (biopsy of apical and basal region of explanted lung) and Yale/BWH (longitudinal biopsy through explanted lung) (Supplementary Table  1a ), in addition to previously unpublished samples (VUMC/TGen). We analyzed the transcriptomes from 611,398 single cells derived from healthy donors (78 samples), COPD (31 samples), IPF (82 samples) and Non-IPF ILD (Other ILD, 19 samples) (Supplementary Table  1b , 2). Using published cell type specific markers  31, 32  , we identified 32 distinct cell types in the dataset (Supplementary Fig.