dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion In this large-scale longitudinal study, we used data from 295,189 alcohol drinkers and provided insight into the interplay between genetic factors, alcohol consumption, and urinary sodium on blood pressure and cardiovascular outcomes. We found that the genetic factors underpinning alcohol consumption are linked to higher blood pressure and stroke. Alcohol consumption lowers urinary sodium and the effect of genetic factors on stage 2 hypertension and the risk of CVD is greater among participants with high urinary sodium excretion. Observational studies have demonstrated an association of alcohol consumption (drinks per day) with blood pressure  [38]  and CVD  [39]. Similarly genetic epidemiological studies  [40, 41]  focusing on genetic variants (rs671/rs1229984) within the aldehyde/alcohol dehydrogenase (ALDH2/ADH1B) gene demonstrated that individuals carrying the risk allele for alcohol consumption had a higher risk of hypertension  [40, 41]  and CVD  [40]  compared to non-carriers.
The use of individual-level data allowed us to investigate the effect of potential confounders and perform subgroup analyses. Additionally, our GRS for alcohol consumption was created based on a total of 105 SNPs identified from the two largest-scale GWAS meta-analysis studies for alcohol consumption thus far  [12, 13]. This is the largest number of SNPs identified for alcohol consumption so far and allowed us to capture more variation in alcohol consumption compared to previous studies that used a limited number of SNPs in their analyses. Yet, these SNPs (although the largest currently available) capture less than 1% of the total variation in alcohol consumption  [13]. Future large-scale GWASs would be helpful to increase the number of SNPs identified for alcohol consumption. One of our limitations is that UKB is a recently established study and thus has a relatively short follow-up time to study outcomes and this might have affected the statistical power for investigation of the survival data.
It should be considered that alcohol consumption is assessed using a self-reported questionnaire in the UKB and thus bias due to underreporting or over-reporting may be present. It has been shown that self-reported alcohol data can be more reliable if collected within a shorter time span prior to the completion of questionnaires  [47]. The UKB has self-reported data available on the average monthly intake and average weekly intake of alcohol. To reduce recall bias related to self-reported alcohol data, we used the weekly intake of alcohol instead of monthly intake. Another limitation of our study was the fact that we used spot urine samples as this is the only available source to quantify sodium excretion within the UKB while the gold standard for sodium intake estimation is a 24 h collection or timed overnight collections.
and the National Institute of Environmental Health Sciences (NIEHS) under award T32 ES007091 (JBG). Acquisition of the replication cohort from the University of Tours with the independent replication population was supported by the IRTH (Institut pour la Recherche sur la Thrombose et l'Hémostase) and by a PHRC grant (PHRN09-YG/FRIGTIH). The study was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) Project Number 374031971-TRR240 (AG). Dataset(s) obtained from Vanderbilt University Medical Center's BioVU are supported by numerous sources: institutional funding, private agencies, and federal grants. These include the NIH funded Shared Instrumentation Grant S10RR025141; and CTSA grants UL1TR002243, UL1TR000445, and UL1RR024975. Genomic data are also supported by investigator-led projects that include U01HG004798, R01NS032830, RC2GM092618, P50GM115305, U01HG006378, U19HL065962, R01HD074711; and additional funding sources listed at https://victr.
Discovery Cohort An observational cohort of patients who underwent laboratory testing for PF4/heparin antibodies and HIPA at the platelet laboratory at Greifswald University Medicine (Greifswald, Germany) constituted the discovery cohort. Greifswald University Medicine is utilized as a central testing facility for HIT diagnostic assays across multiple institutions and uses standardized polyclonal enzyme-linked immunosorbent assay (ELISA) for PF4/heparin antibodies, including IgG, IgA, and IgM quantification, and HIPA for functional assay confirmation of HIT. HIPA is a washed platelet assay with very similar characteristics as the serotonin release assay used in North America  16. The HIPA assay has been extensively validated in comparison with the serotonin release assay (SRA) and by analysis of prospective clinical study data  [16] [17] [18] [19].
Replication Cohort The replication cohort was made up of two previously described cohorts recruited prospectively at the University Hospital of Tours (Tours, France)  15, 16. The replication cohort was similarly divided into three groups: (1) SRA-confirmed HIT cases; (2) antibody-positive (functional assay [SRA] negative) controls with positive PF4/heparin antibodies; and (3) PF4/heparin antibody-negative controls with both negative functional assay (SRA) and PF4/heparin antibody results. Both PF4-specific ELISA and SRA were positive in every HIT case. More detailed clinical data were available on these prospectively collected patients, including age, sex, UFH versus LMWH treatment, platelet counts prior to and following heparin treatment, and whether patients underwent cardio-pulmonary bypass surgery.
This research received ethical approval from both the University Hospital of Tours ethics committee and the Ministry of Research. Additional details regarding the discovery and replication populations are described in the Supplemental Materials. Genotyping, Imputation, and Quality Control GWAS genotyping was performed at the Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Sciences (IMS). All samples in the discovery and replication cohorts were genotyped using the lllumina ® Infinium HumanOmniExpressExome BeadChip, which contains 958,497 markers, including 273,000 functional exonic markers. Quality control was accomplished using sample and marker call rate ≥98%, sex mismatch, duplicate concordance, and removal of related samples through identity by descent. Genomic imputation was performed for samples using Minimac4 and the phase3 v5 reference panel data from the 1000 genomes project  20.
Sequencing and Fine-Mapping of GWAS Association: In order to investigate functional markers in genome-wide associated regions, multiplex PCR-based target sequencing was performed at RIKEN IMS, as previously described  24. Sequencing was performed in all discovery and replication cohort samples to acquire full coverage of the ABO gene +/-5000 base pairs (kbps). Fine-mapping of causal variants from the association with functional assay positive status summary statistics was carried out using variants derived from ABO sequencing in PAINTOR V3. 0, a Bayesian probabilistic framework algorithm that integrates association strength with functional genomic annotation data to improve accuracy in selecting functional SNPs  25. Genomic regions for significant markers from genome wide meta-analysis were plotted and visually inspected alongside PAINTOR results.
A meta-analysis was performed with adjustment for PRS in addition to age, sex, and PCAs 1-3. We then tested for association of known thrombosis risk SNPs with functional assay-positive case status. ABO blood groups (A, B, AB, and O) were then derived based on haplotypes of rs8176719 and rs8176746, as previously described, and tested for association with functional assay positive status in both cohorts combined  31, 32. All statistical analyses were performed using R v4. 04, PLINK v1. 90  33  , and SAS v9. 4 (SAS, Cary, NC). Additional details related to statistical analysis, genotyping, imputation, quality control, sequencing, fine-mapping analysis, and PRS are described in the Supplemental Materials. Genome-wide data and phenotype data used in this analysis have been provided on database of Genotypes and Phenotypes (dbGaP), at accession # phs002863.
Fine-Mapping of ABO association Sequencing was performed for the ABO gene +/-5kbp, constituting a 30,068 bp region. At least 20X coverage was achieved in 99% of the target region. A total of 956 polymorphic sites were identified, including 335 variants with MAF greater than 0. 01. PAINTOR identified rs8176719 as the SNP with the highest posterior probability (0. 994) in the ABO sequence dataset of being the functional polymorphism responsible for the association (Figure  2  and Supplementary Table  S3 ). The aggregated results of FUMA also indicated that rs8176719 was the most likely causal SNP, based on functional annotations and deleterious score (Supplementary Table  S4 ). FUMA-based ANNOVAR  35  results ranked rs8176719 as the most likely causal variant based on LD (r 2 ) and functional consequences while FUMA-based Combined Annotation-Dependent Depletion (CADD)  36.
Our study also included only patients that underwent functional assay and antibody testing due to clinically suspected HIT in the context of heparin/LMWH exposure. We acknowledge that our control populations may be enriched for conditions leading to thrombocytopenia, which may confound our genetic association. We attempted to adjust for a confounding effect due to genetic predisposition to thromboembolism and our analyses suggest that enrichment of SNPs associated with thromboembolism was not responsible for our observed ABO association. Furthermore, our association between the O blood group and HIT is in the opposite direction of what would be expected with an overabundance of thrombosis in HIT cases, since thromboembolism is most strongly associated with A blood group  27. Due to the lack of ABO serotyping data in our two cohorts, we were also unable to confirm the existence of rare or novel ABO variation that resulted in the O blood group.
Acknowledgements We would like to thank the staff of the  Laboratory for Genotyping Development, RIKEN Center for the Integrative Medical Sciences. We are grateful to  Ulrike Strobel ,  Carmen Freyer ,  Katrin Stein ,  Ines Warnig ,  Ricarda Raschke ,  Jessica Fuhrmann  for excellent technical support in performing all HIT assays in the discovery cohort.
Control Sample Ascertainment A cancer-free cohort from the Technical University of Munich served as the first control group. Of 27 501 controls, 95% were recruited in Western Europe. Variants were called with a pipeline described previously  (38). The second control cohort was extracted from the Genome Aggregation Database (gnomAD) noncancer set version 3. 1. 1 (May 1 and 2, 2021)  (39). This data set included 74 023 samples; 32 411 samples were from European individuals (gnomAD European).
Validation Analysis To validate the results of the main analysis, we used NGS data from 1664 patients analyzed at the Hopp Children's Cancer Center (Heidelberg, Germany). The variant calling pipeline used for these patients has been described previously  (42). Whole genome and whole exome sequencing data for the entire set of 1664 patients  (22, 42)  were available from the International Cancer Genome Consortium-Pedbrain Tumor and molecular mechanisms of malignant lymphoma (MMML)seq (http://www. icgc. org), the German Cancer Consortium (https://dktk. dkfz. de/en), the Pediatric Cancer Genome Project (http://explore. pediatriccancergenomeproject. org/), the Heidelberg Institute for Personalized Oncology (http://www. dkfz. de/en/hipo), the Medulloblastoma Advanced Genomics International Consortium  (43) , the international case-control study on mobile phone use and brain tumour risk in children and adolescents (CEFALO) series  (44) , a series from France  (45)   were performed with written informed consent and in accordance with institutional review board guidelines.
The annual incidence of rabies deaths has been estimated in Cambodia at nearly 5. 8/100,000 person-years. The cost of post exposure prophylaxis (PEP) and travel is potentially a significant barrier for exposed patients and their families, although safety nets are in place to provide the prophylaxis at no cost for low-income families. A decision-tree model was built to estimate changes in the costs from the patients' perspective and the survival outcomes of the Institut Pasteur du Cambodge (IPC) rabies PEP regimen after the switch from the Thai Red Cross (TRC) rabies PEP regimen in patients exposed to WHO category II or III bites by dogs. Derived from the IPC database, data included the trajectory of 203,497 patients, 1412 called-back patients and economic data on 201 patients. Uncertainty was addressed using one-way and probabilistic sensitivity analyses.
Methods We developed a decision-tree to model the trajectory, costs and clinical outcomes for patients with category II bites and category III bites. A decision tree presents the disease outcomes under different treatment assumptions; the likelihood of each outcome and the associated costs can be represented on the tree-like diagram. We compared the IPC regimen (new strategy) vs. TRC regimen (reference strategy) (Fig. 1 ). For each comparison, we calculated the effectiveness (probability of survival without rabies) and the costs. All inputs for the model were derived from patient-level data collected prospectively by the IPC.
Results Distributions of baseline patient characteristics selected in the three databases and inputs are presented in Table  2. Out of 1711 patients who received three or four PEP sessions after a bite by a dog with confirmed or suspected rabies and were included in the call-back procedure 6 months or more after the bite, 318 received the IPC regimen and 1393 received the TRC regimen. For each of the two regimens, the number of patients lost to follow-up was 78 (24. 6%) and 221 (15. 9%) respectively. The comparison of patient characteristics between lost to follow-up and followed-up is presented in Table  3. The model parameters for trajectory, clinical outcomes and costs are presented in Table  4.
For the 15% of patients who actually had one fewer session with the IPC regimen, the out-of-pocket costs were reduced by $9. 0 (a significant savings relative to the mean net monthly income of $162 for individuals over 17 years of age). The main strength of our study is the robustness of the results, which was possible due to the use of large databases, including the trajectory of 203,497 patients, 1412 follow-up patients and economic data in 201 patients. We have identified the following limitations. Firstly, our study investigated association, not causation. All three fatal cases had initiated PEP with ERIG within the first 24 h after the exposure. One rabies case in 2011 suffered several bites and massive inoculum to the highly innervated areas of the head and upper extremities. The two other rabies deaths which occurred throughout this 11-year period are highly suspected of having received inadequate PEP as they were clinically managed on the same day in 2008.
1172). As IPC switched to the three-session "IPC regimen" in May 2018 (no reported deaths since full implementation), a large patient dataset will soon be available for further analysis. Thirdly, the economic analysis in the 201 patients may have been limited by the lack of exhaustiveness of economic data. Indeed, it was not possible to estimate costs by bite category since only ten (5. 0%) interviewed patients had sustained a category III bite. However, we can assume that the most distant residents will have greater interest to seek PEP after a severe bite, thereby incurring higher out-of-pocket costs. We may therefore have underestimated the cost to patients with a category III bite. Fourthly, as the incubation period of rabies varies from three months to one year, it is possible that some rabies deaths may have gone undetected.
However, 87% and 74% of patients bitten by sick-looking and untested dogs or by confirmed rabid dogs, respectively, were successfully called back after at least one year after receiving the IPC regimen  [8]. Fifthly, among 318 and 1393 patients who received three or four PEP sessions after suffering a category II or III bite by rabid confirmed dogs or sick-looking but untested dogs and vaccinated with Verorab®, only 240 (75%) and 1172 (84%) was successfully called-back. However, we performed a sensitivity analysis assuming that all patients lost to follow-Table  3  Comparison between patients lost to follow-up and those followed-up included in the call-back procedure 6 months or more after the bite -Distribution of patient characteristics*. up in database 2 were deceased. IPC being identified as the expert center for rabies in Cambodia, we can assume that the experts would have been informed of an additional death.
g. , see  [29] [30] [31]. A method capable of recovering all six parameters of the JCAL model from impedance tube measurements is introduced in  [32]. On the other side, the microscale geometry of a given porous material can be obtained, e. g. , by means of computer tomography (CT) scans, as shown in  [33, 34]. Additionally, CT scans can support the identification of porous material model parameters when combined with a pore-resolved flow simulation, as shown in  [35]. Therefore, a flow simulation of the fluid flow in the pores is conducted using the Lattice-Boltzmann method allowing for a direct computation of flow resistivity, tortuosity and the characteristic lengths. In summation, several approaches exist that allow us to obtain a dataset of the microscale geometry and acoustic model parameters of porous materials, i.
In total, a dataset comprising 500 pairs of geometry values and JCA model parameters is obtained. However, it should be noted that these parameters only represent a best fit, and therefore are not necessarily physically correct. In order to investigate the general validity of the resulting parameters, a plausibility check for the porosity is shown in the preceding study  [36]. For the investigated generic porous specimens, the porosity can be calculated approximately based on the bar geometry by Φ ≈ s s+d. These results are compared to the parameter values resulting from the optimization process, and are in reasonable agreement with the analytic prediction. Thus, it is assumed that the parameter values resulting from the optimization process are a reasonable estimate of the correct physical values.
The final structure of the network is found in a trial-anderror approach. Since the resulting network is very similar to the one presented in  [36] , the reader is referred to that study for more details on the network. The network has a total of 1,111,721 parameters and is trained using the Adam algorithm and the "mean squared error" error measure. The results are reported using the coefficient of determination R 2. The model can be seen schematically in Figure  3. The available dataset is split into three parts, namely the training set, the validation set and the test set. The test dataset amounts to 20 % of the data, and is not used for the training and model generation. The remaining 80 % of the data are used in a threefold cross-validation. Thus, the model is trained using two-thirds of the data (the training dataset) and is tested against the remaining one-third (validation dataset).
This training procedure is repeated using different model structures. The best results have been obtained with the model shown in Figure  3. Hereafter, a short overview of the model performance is given. Since no general changes are made and only slight accuracy improvements are achieved compared to the preceding study, the results of the performance evaluation are shown in this section. The training score of this model is R 2 = 0. 83 ± 0. 12, whereas the reported uncertainty accounts for twice the standard deviation (≈95 % confidence interval) and is computed over the three cross-validation runs. The model is finally evaluated using the (yet unseen) test dataset, resulting in a test score of R 2 = 0. 93. The increase in the test score relative to the training score here results from an increase in available training data, as the model is retrained using the entire training and validation dataset before computing the test score.
It should be noted that generally ∑ i S T i ≥ 1, since here, the interaction effects of all inputs are counted multiple times, i. e. , in all corresponding interactions. An efficient method to compute the aforementioned sensitivity indices is presented e. g. , in  [56]. Within this study, the SALib implementation in python is used  [57]  for the necessary computations with respect to the GSA. The underlying Monte Carlo procedure employs a Quasi Monte Carlo approach with a Sobol sequence  [58]  for equal sampling of the parameter space. A total of 1 • 10 6 Monte Carlo runs is performed. Since the used python framework assumes a scalar valued model output, the analysis is performed for each output separately. However, since all inputs are processed in parallel, this is assumed to not limit the applied method.
We take a step forward in the current analysis of segregation in mobility by asking the following question: How do socioeconomic attributes and geographic constraints affect the spatiotemporal process of individuals moving in urban spaces? To answer this question, we analyse individual check-in trajectories in the twenty largest cities of the United States, coupled with detailed socioeconomic maps indicating the economic status of people and places they visit. After a short data description, in the following we will introduce stratification matrix measures and individual-and class-level mobility bias scores to quantify patterns of mobility segregation, visiting biases, and their variation across cities with wide-ranging socioeconomic and ethnics segregation profiles.
Data description In order to simultaneously capture the mobility patterns and socioeconomic status of people, we concentrate on two independent sources (mobility and socioeconomic data, described below) and combine them using spatial information. Mobility data: To construct individual mobility trajectories, we analyse a large, open Foursquare dataset  [48] , which records how people move from one place to another. Data comes as a sequence of user check-ins to places, or points of interest (POIs), thus providing information on mobility trajectories of individuals and visiting frequencies of places. This dataset is not collected directly through the Foursquare open API, but from Foursquare check-ins via Twitter. The crawling method corresponds to 18 months of observations between April 2012 and September 2013 for users with Foursquare-tagged tweets.
In order to estimate the economic status of a place, we follow a similar strategy and assign local socioeconomic status indicators to POIs based on their locations. Although the socioeconomic status of venues could arguably be better estimated from their pricing, this information is at present not available to us. Thus, we assume that the socioeconomic status (SES) of people living at a location is well correlated with the pricing of venues in the same neighbourhood and offered services around (for a summary of our data construction pipeline see Figure  1 ). In order to obtain a proper representation of socioeconomic status in the context of segregation, we consider 78 features from the ACS data. Although such a large number of dimensions in principle provides a rich way of quantifying the socioeconomic status of locations (and people living there), it turns out these variables have high redundancy.