dataesr/scientific-paragraphs-categorization huggingface dataset:
The joint model (Model 3) provides effect estimates of G and GEnv, their robust standard errors (SE) and robust covariance matrices, and a joint P value from a 2 degree-of-freedom (df) Wald test. Robust estimates of SEs and covariance matrices were used. All analyses were adjusted for age, sex, PCs of population stratification and intracranial volume (ICV). Adjustment for ICV was not performed for studies using visual grading of WMH burden, as visual grades are inherently normalized for brain size  17  (Supplementary Data 1, Supplementary Methods 1). Genome-wide association meta-analyses. A custom harmonization script along with the R package EasyQC  73  was used to perform the QC of cohort-specific GWAS results. SNPs with minor allele frequency (MAF) lower than 1%, poor imputation quality (R 2 < 0.
80), or product of MAF, R 2 and sample size less than 10, or 15 for the 2-df interaction analysis were excluded. Inverse-variance weighted meta-analysis was conducted using METAL  74  , first within each ancestry group followed by a meta-analysis of the ancestry-specific results. A patch implemented in the METAL  75  software was used to perform a 2-df joint meta-analysis (JMA) with inverse-variance weighting. For cohort-specific GWAS results with genomic inflation factors (λ) exceeding 1, genomic control (GC) correction was applied to correct for any residual population stratification. After meta-analysis only SNPs represented in more than half of participating studies and/or more than half of sample size and with no evidence of betweenstudy heterogeneity (PHet > 1 × 10 -4 ) were considered.
Briefly, white matter tracts were skeletonized with Tract-Based Spatial Statistics (TBSS) and a diffusion histogram analysis was performed, as described in the supplementary information (Supplementary Table  1 ), to derive DTI metrics measuring the integrity of the white matter microstructure, including fractional anisotropy (FA) and mean, radial and axial diffusivity (MD, RD, A x D), as well as peak width of skeletonized mean diffusivity (PSMD). PSMD was calculated using a fully automated method via a shell script (www. psmd-marker. com) (Supplementary Table  1 ). A mixed linear model (MLM) was used to test the association of individual SNPs with each DTI trait, accounting for any sample substructure (admixture) and possible relatedness in the sample by using a genetic relationship matrix (GRM) as a random effect.
The GRM was computed by implementing the MLMA-LOCO scheme in GCTA, where the SNP marker tested for association with a given outcome was excluded at each instance. MLMA-LOCO has been shown to better control false-positives over the standard mixed models especially in the presence of geographic population structure and cryptic relatedness  78. The model was additionally adjusted for age, sex, ICV and the first four PCs of population stratification. The effect allele for each risk variant was defined as the allele associated with larger WMH volume. For associations with individual SNPs the significance threshold was set at P < 2 × 10 -3 (0. 05/25). The aggregate effect of 25 WMH risk variants with DTI metrics was estimated by using the GTX package in R  79. The association of FA, MD, RD, A x D, and PSMD with reaction time on the Stroop test, reflecting information processing speed, was examined using linear regression in i-Share participants who underwent both MRI and cognitive testing (N = 1401).
Mendelian randomization (MR). For each vascular trait genetic variant (instrument) details were retrieved from the latest largest GWAS (Supplementary Table  2 ). Only independent SNPs (r 2 < 0. 10, based on 1000 G EUR panel) reaching genome-wide significance were included as recommended  82. Similarly, 25 independent WMH risk variants from the SNP-main effects were used as instruments to test the association of genetically predicted WMH volume with neurological traits. The putative causal effect (β IVW ) of an exposure on the outcome was estimated, using the inverse-variance weighting (IVW) method, by the weighted sum of the ratios of beta-coefficients from the SNP-outcome associations for each variant (j) over corresponding beta-coefficients from the SNP-exposure associations (β j ). The ratio estimates from each genetic variant were averaged after weighting on the inverse variance (W j ) of β j across L uncorrelated SNPs, implemented as an R package RadialMR (available through CRAN repositories)  40.
A Q R close to 1 indicates that MR-Egger is not a better fit to the data and therefore offers no benefit over IVW  40. Nonsignificant MR-Egger intercept was used as an indicator to formally rule out horizontal pleiotropy. Cell and tissue type enrichment analysis. Association statistics from the WMH SNP-main effects (European-only) were used to test cell/tissue-specific enrichment. First we used the EPIGWAS software  44  and histone marks for promoters (H3K4me3) and enhancers (H3K4me1) from publicly available information on tissue-specific histone regulatory marks (Supplementary Methods 2). EPIGWAS calculates specificity scores for the lead WMH risk variant and its proxies (r 2 ≥ 0. 80, 1000 G EUR) based on the distance to the strongest ChIP-seq peak signal, and estimates enrichment significance by comparing the relative proximity and specificity of the test set with 10,000 sets of matched background (using permutation).
Bonferroni correction for the number of histone marks tested was applied (P < 2. 5 × 10 -2 ). Second, we used MAGMA  28  (gene-property analysis) and differentially expressed gene sets from the single-cell transcriptomic (scRNA) data in mouse brain from the Karolinska Institute. MAGMA generates gene-level association statistics by combining SNP p-values in a specified window (10 kb upstream and 1. 5 kb downstream of each gene) accounting for LD (1000 G EUR) and under a linear regression framework performs a one-sided test between the association of genes with WMH volume and cell specificity. Using the MAGMA. celltyping R package  45  , scRNA expression values were obtained from five different mouse brain regions (neocortex, hippocampus, hypothalamus, striatum, and midbrain)  84. A gene-expression specificity metric for each cell-type was calculated by dividing the expression level in a given cell type by the sum of the expression levels from all cell types (i.
Data availability Summary statistics for the GWAS meta-analysis of the CHARGE cohorts and the UK Biobank on WMH burden main-effects generated and analyzed in the downstream analyses are deposited in a public repository (dbGAP: https://www. ncbi. nlm. nih. gov/gap/) under the accession number: phs002227. v1. p1. All other data supporting the findings of this study are available either within the article, the supplementary information and supplementary data files, or from the authors upon reasonable request. Publicly available resources used for this study were the UK Biobank [http://www. ukbiobank. ac. uk/]; Geneexpression weights for TWAS [http://gusevlab. org/projects/fusion/]; Magma. Celltyping [https://github. com/NathanSkene/MAGMA_Celltyping]; Histone regulatory marks [http://egg2. wustl.
Management of infections The scarce serious infections reported in some studies were both viral and bacterial infections. Although these adverse events in patients with SpA are not very frequent, specific recommendations for vaccination for patients with rheumatic disease have been published. 47 In these patients, vaccination should be preferably administered prior to planned immunosuppression, with seasonal influenza and pneumococcal vaccination strongly recommended. Despite these recommendations, the COMOSPA study demonstrated a non-optimal rate of vaccination in these patients, showing that only 17. 3% received a pneumococcal vaccination within the past 5 years, and 30% received an influenza vaccination within the past 12 months. 3 A recent clinical trial (COMEDSPA, NCT02374749) demonstrated that a nurse-led programme can improve the percentage of patients receiving vaccination; for patients who visited a nurse checking for comorbidities, 28.
Statistical Analysis The data are presented as the graph or the scattered dot plot with the median and interquartile range (IQR) for at least 4 mice. Each point is an average of values that were obtained from different regions of the specimen (n = 2-4). The differences between two groups were analyzed with the Mann-Whitney U test using GraphPad Prism 7 software (GraphPad Software, San Diego, CA). A p < 0. 05 was considered statistically significant.
ACKNOWLEDGMENTS The authors thank  Prof. Michel C. Nussenzweig  ( The Rockefeller University, New York, NY ) for providing CD11c-EYFP C57BL/6 mice. The authors also thank  Prof. Sven Krappmann  ( University Hospital Erlangen and FUA Erlangen-Nürnberg, Germany ) for providing the Aspergillus fumigatus conidia strain AfS150.
One month after the initial data extraction, a subgroup of children from Nuuk eligible for the first MMR-vaccination was reviewed manually. Short text messages (SMS) were sent to the parents of children appearing to have missed the vaccine, reminding them about the vaccine and informing them of the opening hours of the Queen Ingrid Health Care Center in Nuuk, where the vaccines in the CVP are administered. After 4 weeks, this group was reviewed again to assess the success of the SMS-reminder. The ages of the children in each cohort can be seen in Table  1.
Data validation The national EMR is directly linked to the national civil registry (CPR) in Greenland, meaning that every person with a permanent address in Greenland is automatically registered in the EMR. Synchronisation of data between the two systems is controlled by a built-in lookup function in the EMR to ensure that any recent changes to personal information are imported and stored correctly in the EMR. The consistency between the CPR and the EMR is validated every 24 h which is the maximum latency period for changes to the patients' personal information. The extracted data was validated through manual revision of the EMR. A sub-sample of the extracted data for 500 individuals was compared to their medical records to check for the degree of accordance between the extracted data and the EMR, including vaccination status, vaccination date, vaccination type and current address.
Patients and data collection for development of the prognostic model Patients older than 18 years were included in the study if they had a diagnosis of WM and a concurrent or sequential histological diagnosis of DLBCL. The diagnosis of WM was based on criteria established in the Second International Workshop on WM. 8  Patients with a diagnosis of indolent lymphoma other than WM were excluded. Histological assessment of transformation was mandatory for being considered in this study. We retrospectively identified 133 patients diagnosed with HT between January 1995 and December 2016 from French FILO (French Innovative Leukemia Organization) centers, Dana-Farber Cancer Institute (Boston), UCLH (University College London Hospitals) and Nieuwegein (Netherlands) (detail of the centers in Supplementary Table  1 ).
The prognostic score was then defined as the sum of single-risk parameters. Risk subgroups were pooled according to the number of patients within each category and the relative risk of death. The discriminatory value of the prognostic model and the score was assessed using the Concordance Probability Estimates (CPE) by Harrell's concordance index (C-index). 10 Calibration was assessed using the May and Hosmer test for goodness-of-fit. An internal validation of both model and score was performed using the bootstrap resampling method  11  (replication on 2000 different samples drawn with replacement). External validation was performed in a second dataset of subjects. All tests of statistical significance were two-sided, and a P-value < 0. 05 was considered statistically significant. Statistical analyses were performed using SAS 9.
Acknowledgments The authors would like to acknowledge the following people who participated in the study:  Fatiha Merabet  (Versailles),  Eric Van Den Neste (Bruxelles) ,  Sarah Ivanoff  (Amiens),  Xavier Roussel (Besançon) ,  Jean-Marc Zini  ( Paris, St-Louis ),  Caroline Regny (Grenoble) ,  Richard Lemal  ( Clermont-Ferrand ),  Laurent Sutton (Argenteuil), Aurore Perrot (Nancy)  and  Katell Le Dû  ( Le Mans ).
Over a period of 8 years, the Global aHUS Registry has been built up to become the world's largest dataset of patients with aHUS, engaging with such crucial issues as the natural course of the disease and real-world information on drug safety. As of July 2019, a total of 1811 patients, both children and adults, had been included in the registry. Among its noteworthy achievements, the registry has yielded reassuring results with respect to the safety profile of eculizumab in both the adult and pediatric populations. 3 typical hemolytic uremic syndrome has a great impact on health-related quality of life (HRQoL) not only because of direct clinical consequences (renal, extrarenal complications such as anemia, neurological sequelae, and diabetes in children) but also because of management requirements (such as plasma exchanges, dialysis, or repeated hospitalizations).
Next to highlighting the drug's satisfactory safety profile, the Global aHUS Registry thus adds another milestone by substantiating the claim that anti-C5 therapy translates into an improved quality of life. In particular, this result is likely to address a core concern of any patient (and attending caregiver) on the verge of starting a new treatment. From the wealth of data provided by this report, 2 observations merit singling out: (i) patients in both the "never on eculizumab" and "on eculizumab" groups yielded unchanged FACIT-F scores throughout the study period, and furthermore, (ii) these scores were roughly similar to those from the general population, as the reviewers candidly admit. Inferences derived from the combination of these results are bound to remain speculative in the absence of statistical analysis.
Data collection A total of 19 mothers agreed to participate. Participants were grouped into one of three FGDs by age of the child: 0-6-months, 7-14-months, and 15-24-months. Thereafter, 12 mothers (4 from each FGD) were selected for inclusion in the IDIs based on their participation in the FGDs (i. e. those who gave input during the FGDs were selected to participate). All mothers completed a short demographic questionnaire prior to commencement of the FGDs with a trained member of research staff. Both the FGDs and IDIs were initially conducted in Englishwith flexibility for the participants and facilitator to use vernacular languagesand were audio recorded. Each FGD and IDI was conducted by one of two trained facilitators, while comprehensive field notes were compiled by an observer to supplement the audio files.
The first stage implies deriving a clinical model based on data collected through retrospective chart review only. In the second stage, the predictors comprising the medical record model will be translated into International Classification of Diseases, 10th revision discharge diagnosis codes in order to obtain a claim data-based risk model. The study sample consists of 1150 hospital stays with a diagnosis of CAP. 30-day index hospital readmission rate is 17. 5%. Ethics and dissemination The protocol was reviewed by the Comité de Protection des Personnes Sud Est V (IRB#6705). Efforts will be made to release the primary study results within 6 months of data collection completion. Trial registration number ClinicalTrials. gov Registry (NCT02833259).
Administrative claims data As part of the French diagnosis-related group-based prospective payment system, computerised hospital discharge data include patient and hospital stay identifiers, admission and discharge dates, age, gender, length of stay, discharge location, primary and secondary ICD-10 discharge diagnosis codes for both index admission and readmission. ICD-10 coding complies with national guidelines and is done by trained technicians or physicians, depending on the hospital. Coders usually abstract diagnoses from physician notes, admission notes, daily The following variables are recorded for the first hospital readmission within 1 year of discharge: patient and hospital stay identifiers, time from discharge to readmission, length of stay, physical examination and laboratory findings on readmission, X-ray or CT-scan findings within 48 hours of readmission, hospital readmission course (ICU admission, pneumonia-related and pneumoniaunrelated complications, in-hospital mortality), and primary and secondary reasons for readmissions.
Positive predictive value of ICD-10 discharge diagnosis codes for CAP The discharge diagnosis codes used in claims databases do not distinguish between community-acquired and hospital-acquired pneumonia, two distinct clinical entities. 44 Consistent with previous studies, the positive predictive value of ICD-10 discharge diagnosis codes will be assessed using three reference methods: Medical record and/or discharge letter notation of CAP diagnosis. Medical record notation of ≥1 respiratory symptom (cough, sputum production, dyspnoea, tachypnoea or pleuritic pain), and ≥1 auscultation finding (rales or crepitations), and ≥1 sign of infection (temperature >38°C, shivering, or white cell count >10 x10 9 /L or <4 x10 9 /L), and a new infiltrate on chest radiography or CT scan performed within 48 hours of admission.
Model development overview The model development sample will consist of eligible patients who have been readmitted within 30 days of discharge. A flow chart will present graphically patient flow throughout the study. We will use a two-stage approach to develop an administrative claims-based model intended to identify potentially avoidable readmissions. The first stage implies deriving a medical record-based model with the use of data collected through retrospective chart review only. In the second stage, the predictors included in the resulting medical record-based model will be translated into ICD-10 discharge diagnosis codes in order to obtain an administrative claims-based model. Medical record-based model development Derivation and internal validation will be conducted according to current standards.
Ethics and dissemination The protocol for this study was approved by the Comité de Protection des Personnes Sud-Est V, Grenoble, France (IRB#6705). The consent for data collection through chart review and the use of corresponding administrative claims data is sought under a regime of 'non-opposition' (opt-out): after appropriate written information is delivered by regular mail, data are collected unless the patient opposes. Computerised study data will be processed at GUH, in compliance with French data protection regulations. Efforts will be made to reduce the interval between the completion of data collection and the release of the primary study results. It is expected that 6 months will be necessary for the writing committee to compile the primary study results before manuscript submission to an appropriate journal.
e. , ≥1-stage improvement in fibrosis and no worsening of NASH; 20% versus 10%; odds ratio [OR], 2. 2; 95% confidence interval [CI], 1. 11-4. 35; P = 0. 02). (16) Moreover, CVC treatment was associated with reduced biomarkers of systemic inflammation, including C-reactive protein (CRP), interleukin (IL)-6, IL-1β, and fibrinogen, supporting its antifibrotic and anti-inflammatory mechanism of action. Herein, we report data from the final year of the 2-year study.
(7, 25) e such combination treatment with tropifexor (an FXR agonist) and CVC is currently under way in a phase 2 study of adult patients with NASH and liver fibrosis (TANDEM study, NCT03517540). The key limitation of our study was a higher attrition rate than was anticipated over the 2 years of treatment (23% versus 15%), which might be associated with the protocol requirement of three serial biopsies over a 2-year period. While currently the gold standard in assessing clinical benefit and disease severity, liver biopsies are otherwise painful and invasive and may be associated with complications. (26, 27) Moreover, the missing liver biopsy data predominantly affected the CVC group (arm A), which limited the evaluation of the year 2 data, particularly among the year 1 responders without evaluable biopsies at year 2, who thus were considered nonresponders at year 2.
This is an important reminder of the need to identify diagnostic and prognostic biomarkers independent of histological sampling. Another limitation of the study was the limited sample size of patients with severe disease; nonetheless, our post hoc analysis on ≥2-stage fibrosis improvement without impact on underlying steatohepatitis suggests that patients with stage 3 fibrosis may potentially be the ideal target population for CVC. Based on these limitations and our overall observations from the phase 2b CENTAUR study, the ongoing AURORA phase 3 study (NCT03028740) was designed to evaluate and confirm the efficacy and safety of CVC for the treatment of liver fibrosis in adults with NASH. The AURORA study differs from CENTAUR in two main aspects: (1) the phase 3 study population specifically includes patients who are likely to benefit from treatment with CVC (i.
e. , those with stage 2 or 3 fibrosis) and excludes patients with stage 1 fibrosis who comprised 34% of the CENTAUR population and (2) the primary outcomes focus on improvement of liver fibrosis (i. e. , improvement in fibrosis by ≥1 stage and no worsening of steatohepatitis). Nevertheless, data from CENTAUR support the timing of liver biopsy for the AURORA study, which will be collected at baseline, year 1 (primary endpoint analysis), and year 5 (end of study). Notably, previous phase 2 trials were designed for a single, short-term evaluation of histological endpoints. In contrast, CENTAUR is a unique attempt to assess the durability of the antifibrotic response beyond the initial, 1-year evaluation 904 together with the variability of the placebo response in the long term. In current phase 3 trials, follow-up data beyond the interim analysis will only be available several years from now.
Acknowledgment: The sponsor and authors would like to thank study participants and their families, study investigators, research coordinators, and study staff. The sponsor and authors express their gratitude to  Dr. Eric Lefebvre  (formerly of Allergan plc) and  Dr. Scott L. Friedman  ( Icahn School of Medicine at Mount Sinai, New York, NY ) for their guidance and contributions to this study. The sponsor and authors thank  Mette Juul Fisker Nielsen  and  Nordic Bioscience Biomarkers & Research A/S  for their assistance in biomarker data analyses.
Participants: Inclusion criteria: All stroke cases met the inclusion criteria. The diagnosis of stroke was based on the World Health Organization (WHO) criteria  [9]  or on brain CT-scan results. Stroke was defined as "rapidly developing signs of focal (or global) disturbance of cerebral function, leading to death or lasting longer than 24 hours, with no apparent cause other than vascular"  [9]. The patients with transient ischemic attack, those who died prior to evaluation and others with subarachnoid hemorrhage were excluded. Sampling: All stroke cases were included and were recruited systematically. Intervention: Sociodemographic data, vascular risk factors, and clinical data were recorded upon admission. The vascular risk factors were defined according to WHO stepwise approach  [10]  The initial neurologic impairment assessment was based on the National Institute of Health Stroke Scale (NIHSS).
Statistical methods: The data were analyzed using Statistical Package for Social Sciences (SPSS) version 21. 0 software (IBM) and the usual parameters were used to describe the variables. We compared the different types of stroke according to other variables and the associated variables for in-hospital death. We used the chi-2 test (or Fisher test) for the percentages and the Student t-test to compare the quantitative variables. A p value of 0. 05 was considered to be significant.