dataesr/scientific-paragraphs-categorization huggingface dataset:
Next-Generation sequencing (NGs) Briefly, 225 ng of gDNA was digested and hybridized with a HaloPlex biotinylated probe library in presence of an indexing primer cassette for enrichment. After capturing and ligating the circularized target DNA-probe hybrids with streptavidin beads, amplification of targeted fragments was done by PCR. Sample barcodes were introduced during amplification for precise tracking. After elution, PCR-amplification and pooling of equimolar amounts of indexed targeted-samples were prepared for multiplexed sequencing on the Illumina MiSeq platform. Having run samples of different quantity and quality, we averaged 99% of the bases in the exons covered with a depth of at least 38-fold. The sequences were aligned to the human genome using the Agilent SureCall TM software.
statistical Analysis We performed a cross-sectional study with prospective data collection to illustrate the operative characteristics by receiver operating characteristic (ROC) analysis of our flow cytometrybased test for the diagnosis of LRBA deficiency. LRBA sequencing results were used as gold standard. Optimal MFI ratio cut-point of LRBA to distinguish between "mutated (definitive) LRBA patients" and "wild-type (non) LRBA patients" was selected among all potential cut-points values, considering a minimum of 90% sensitivity and maximal specificity. ROC statistical analysis was performed using StataCorp. 2015 (Stata Statistical Software: Release 14. College station, TX: StataCorp LP). Statistical significances were calculated with a non-parametric two-tailed Mann-Whitney test using GraphPad Prism 6.
g. , by readthrough mechanisms. Indeed, LRBA-deficient patients with residual LRBA expression have been also reported by others  (10, 16). We found 10 healthy donors, including 2 family members with LRBA MFI ratios below 2. 6, despite optimal cell activation (measured by CD69). Nevertheless, their LRBA expression was higher than their paired LRBA patient sample when the difference of the LRBA MFI ratios (LRBA MFI ratio from healthy donor -LRBA MFI ratio from patient) was calculated (data not shown). Finally, nine patients with decreased expression of LRBA have a wild-type LRBA sequence. This reduced expression might be a consequence of mutations in other genes belonging to the functional pathway of LRBA. Further analysis of the CTLA-4 shuttling and trans-endocytosis assays should be considered as functional tests in patients with possible residual LRBA expression, as it has been recently shown that LRBA affects CTLA-4 trafficking and lysosomal degradation through direct protein-protein interaction  (9, 20).
The assessment of LRBA expression should be performed by calculating geometric MFI ratios, since the fluorescence intensity can vary between experiments, particularly after repeated thawing and freezing cycles of the anti-LRBA antibody (data not shown). In addition, the LRBA-MFI ratio of stimulated vs. unstimulated PBMCs of any given individual must always be compared to a MFI ratio of a matched healthy/travel control. In addition, a difference of LRBA MFI ratio of the healthy donor and its paired patient sample should be calculated when both individuals present LRBA MFI ratios below 2. 6. Moreover, the expression of a cell activation marker, such as CD69, for example, should always be included in the staining controls in order to confirm the efficiency of the cell stimulation step. The reported sensitivity and specificity of this flow cytometry test has to be taken with care as they are based on a selected sample collection.
ACKNoWLedGMeNts We are very grateful to all the patients and their families for participating in this study. In addition, we want to thank all physicians that have referred their patient's samples to our center. In addition, we want to thank  Dr. Natalie Frede ,  Jessica Rojas ,  Katrin Hübscher , and  Dr. Alla Bulashevska , members of  CCI-Genetics Unit  for the sequencing analysis. Finally, we also thank to  Prof. Hermann Eibel  for providing us with IL-21 and anti-IgM. This study was supported by the  Bundesministerium für Bildung und Forschung (BMBF)  grant numbers: IFB/CCI:  01E01303 , E-med SysINFLAME:  012X1306F ,  01GM1517C  and  BMBF PID-NET :  01GM1111B , by the  Deutsches Zentrum für Infektionsforschung  (DZIF:  8000805-3 ), and by the  Jeffrey Modell Foundation.
| Cell metabolism: glucose and lactate levels Indicators of cell metabolism were used to monitor cell growth and the establishment of a functional BBB in vitro. Depletion of the carbohydrate component of the growth medium (glucose levels) and accumulation of metabolically produced lactic acid levels are used as indicators of cell growth. 21 The lumen and extracapillary spaces (ECSs) were sampled at 0, 7, and 14 days (n = 4 donors in triplicate per condition). Data obtained show glucose and lactate levels in grams per liter.
| Immunocytochemistry Immunocytochemistry was performed on the epileptic human brain ECs that were (1) either transfected for GR silencing (EPI-EC GR siRNA) or not (EPI-EC non-siRNA) and compared with control HBMECs (n = 4 donors in triplicate per condition); (2) EPI-ECs exposed to MF and compared with EPI-EC (no inhibitor) (n = 4 donors in triplicate per condition). Cells were stained with antibodies against GR, PXR, CYP3A4, UGT1A4, and MDR1 and analyzed by fluorescence microscopy. Specifications on the antibodies, concentration, imaging, and quantification are provided in the Appendix S1. Images were processed using ImageJ (National Institutes of Health, Bethesda, Maryland) and Q-Capture-Pro software (QImaging, Inc. , Surrey, Canada). All images were converted to an 8-bit format; brightness/contrast was adjusted using constant parameters.
| CYP function and resorufin formation We performed an ethoxyresorufin-O-deethylase assay, determining the extent of 7-ethoxyresorufin metabolism into resorufin by CYP enzymes. 24 7-Ethoxyresorufin (20 μg/mL, dissolved in dimethyl sulfoxide) was added to the DIV-BBB containing EPI-EC GR siRNA, EPI-EC non-siRNA, or HBMECs (n = 4 donors in triplicate per condition). A similar approach was adopted for the MF-exposed DIV-BBB (n = 4 donors in triplicate per condition). 5] [26] [27]  A standard plot for resorufin (5-200 nmol/L) is shown in Figure  S3. Samples were analyzed by a multimode reader using Gen5 software (BioTek, Winooski, Vermont; fluorescent reader) at excitation-emission of 530/25 and 645/40, respectively. 24.
ACKNOWLEDGMENTS This work was supported in part by grants  R01NS078307  (to CG and NM) and  R01NS095825  (to CG) from the  National Institute of Neurological Disorders and Stroke/ National Institutes of Health  and by grants awarded from the  Brain and Behavior Research Foundation (formerly NARSAD) , the  American Heart Association  ( 13SDG13 950015 ), and  Alternatives Research & Development Foundation  (to CG).
Neutron Spectrum Characteristics of SFR The use of sodium for cooling a FR provides a neutron energy spectrum in which the fission neutrons' flux maximum is shifted to lower energies comparing to other fast reactors spectra  [45] , while the neutron population distribution   Figure  4 : Total, elastic, and inelastic microscopic cross sections of 23 Na as produced by using the JEFF-3. 1. 1 Library [38] and the JANIS software  [39]. around the maximum is broader (see Figure  5 (a)). The softening of the spectrum is caused by both inelastic and elastic scattering of sodium. As stated in  [46]  sodium degrades the spectrum at high end as a result of inelastic scattering and builds up the low end of the spectrum by elastic scattering. The total, elastic, and inelastic microscopic cross sections of 23 Na are plotted in Figure  4 , as produced by using the JEFF-3.
1. 1 Library [38] and the JANIS software  [39]. In this work a reference SFR spectrum has been utilized, provided by the neutronic data of the prototype SFR reactor Phenix  [47, 48]. Phenix was a 565 MWth SFR; description of the main Phenix characteristics can be found in  [49]. 1E-8 1E-7 1E-6 1E-5 1E-4 1E-3 1E-2 1E-1 1E+0 1E-11 1E-9 1E-7 1E-5 1E-3 1E-1 1E+1 Energy (MeV) JHR SFR Flux per Lethargy (n/cm  /s) (a) <0. 01 eV 0. 1eV - 0. 01e V 1eV- 0. 1eV 10eV -1eV 100e V- 10eV 1keV - 100e V 10ke V- 1keV 100k eV- 10ke V 1Me V- 100k eV >1Me V JHR 4.
Simulation Procedure The JHR reactor was here simulated with TRIPOLI-4 code, version 8. 1  [44]. TRIPOLI-4 is dedicated to nuclear reactor physics and nuclear processes simulation. The code is composed of six software libraries: a geometry library, a cross section reading library derived from NJOY  [50]  I/O Fortran routines, a memory management library, a simulation library, and two special libraries enabling parallel calculations  [51]. TRIPOLI-4 is used essentially for four major classes of applications: shielding studies, criticality studies, core physics studies, and instrumentation studies. The neutron energy domain of TRIPOLI-4 ranges from 10 -5 eV to 20MeV, while, for photons, electrons, and positrons it ranges from 1 keV to 20 MeV  [52]. The cross sections used by the code can be either continuous (pointwise) in ENDF-format or multigroup calculated by the APOLLO2 code  [53].
After an extensive analysis throughout the periodic table, using also ENDF library and JANIS software, it was found that only a few isotopes exhibit an intense peak of scattering cross section at the desired energy range (Table  2 ). In all cases this peak is observed above 2MeV and mainly around 3MeV. At this point it should be stressed that the above elements present also important inelastic cross section. Although a possible requirement of isotope separation would be very expensive and even nonfeasible in many cases, only for the sake of research purposes the examined screening materials are assumed to be composed of the selected isotopes in 100% isotopic abundance. Nevertheless this isotopic study could be helpful in case that the enrichment of a selected element with respect to a particular isotope would be deemed as technoeconomically worthwhile.
Data management and statistical analyses A specific case report form was created for the study and all data were collected in a dedicated database. The Chi-square test and Fisher's exact test were used as appropriate to compare categorical variables. Continuous variables were compared between subgroups using the Mann-Whitney test. All p-values were considered significant at p < 0. 05. The statistical analyses were performed using IBM SPSS and R software. The performance of the diagnostic methods was tested using two gold standards:  (Rakotonirina et al. , 2016)  proven cases, and (World Health Organization, 2014a) proven and probable cases grouped together. Exact binomial confidence limits were calculated for test sensitivity, specificity, and positive and negative predictive values (PPV and NPV).
Acknowledgements The study was funded by grants from  Geneva University Hospitals (Humanitarian Commission ,  CGR 75 388 ),  Bordeaux University Hospital , and the  Aquitaine Region. We thank all of the technicians at the  Mycobacteria Laboratory, Institut Pasteur de Madagascar. Conflict of interest: RR, JA, VR, NR, MR, RA, RR, MT, JR, AA, MR, FB, AC: none to declare.
Clinical trial setting and design The Age-Well observational, cross-sectional study is a monocentric trial with 30 autonomous and motivated cognitively normal older expert meditators aged 65 years. The clinical trial protocol (EudraCT: 2016-002441-36; IDRCB: 2016-A01767-44, ClinicalTrials. gov Identifier: NCT02977819) adheres to Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) guidelines for clinical trial protocols  [32].
Analysis of the endpoints Paired sample t-tests or Wilcoxon tests (for non-normally distributed parameters) will be performed to compare the behavioral and neuroimaging measures collected during MM and LKCM. Two sample t-tests or Mann-Whitney tests (for non-normally distributed parameters) will be used to compare the other biological, neuroimaging, and behavioral measures in the expert meditators versus the nonmeditator older controls from the Age-Well RCT. If the distribution of baseline confounding factors (age, education, gender, Mini-Mental State Examination) is unbalanced between groups, the main and sensitivity comparative analyses will be performed with adjustment on those factors. An exploratory analysis of the effect of the exposure to meditation (number of hours of practice in life) will be performed with available data using appropriate models.
Analyses with neuroimaging data will include both voxelwise and region-of-interest-based analyses. For fMRI data analysis, we will be using several statistical packages: SPM software (Wellcome Trust Centre for Neuroimaging, London), PLS  [36, 37] , and CONN toolbox (www. nitrc. org/projects/conn, RRID:SCR_009550,  [38] ) on Matlab (MathWorks, Natick, MA). For other statistical analyses, we will use R statistical software  [39] , SAS (https://sas. com), IBM SPSS Statistics for Windows (Armonk, NY; IBM Corp) and Statistica (StatSoft, Dell Software).
Partner perception of the participant's well-being, willingness to help, social interactions, and memory capacity. Fasting sampling performed in the morning and after one day of diet excluding serotonin-rich food (tomatoes, avocados, pineapple, chocolate, bananas, etc. ). 18 tubes (68 mL) of blood collected at V1 and 16 tubes (62 mL) at V3. -Global health: blood count, glucose, cholesterol/lipid profile, urea, creatinine, g-glutamyltransferase, glutamic oxaloacetic transaminase, Glutamic pyruvic transaminase, brain natriuretic peptide, thyroidstimulating hormone -Stress/inflammation: high-sensible C-reactive protein, cytokines, cortisol, superoxide dismutase -Aging/AD (telomere length, telomerase activity, b-amyloid (Ab) 1-40/42, total tau, phospho-tau, tissue plasminogen activator, plasminogen activator inhibitor-1, brain-derived neurotrophic factor, insulin, insulin growth factor-1, lymphocyte immunophenotyping, repressor element 1-silencing transcription factor, neurofilament -Mood: serotonin, -Sex/gender: bioavailable testosterone, estradiol, sex hormone binding globulin, dehydroepiandrosterone sulfate -Genetic: Apolipoprotein E, Genome Wild Association Study -Epigenetics Objective measures of sleep: 1) 1-week wrist actigraphy recording EudraCT: 2016-002441-36; IDRCB: 2016-A01767-44).
Study progress From May 2016 to September 2018, 45 individuals responded to flyers, conferences at meditation centers, inperson request, and e-mail request. Among those, 21 participants were interested in participating and screened (V0 visit) and 20 were included (70% men and 30% women). The reason for noninclusion was abnormal performance in the diagnostic battery. Recruitment will be achieved in late 2019. Electronic data entry and processing is currently ongoing.
Acknowledgments The authors would like to thank all the contributors listed in the Medit-Ageing Research Group, as well as  Dr Mathieu Ricard ,  Clara Benson ,  El ea Perraud ,  Gwendoline Le Du ,  Val erie Lefranc ,  Aur elia Cognet ,  Clarisse Gaubert ,  Sylvie Brucato ,  Dr Laurence Michel ,  Marine Faure ,  Rhonda Smith ,.
Overview of Participating Studies Men and women between the ages of 18 and 80 years from five self-reported ancestry groups are represented in this study: European (EUR), African (AFR), Asian (ASN), Hispanic (HIS), and Brazilian admixed (BRA). These participating studies are described in the Supplemental Note. Each study obtained informed consent from participants and approval from the appropriate institutional review boards. Although the participating studies are based on different study designs and populations, all of them have data on BP, smoking, and genotypes across the genome (data imputed using the 1000 Genomes reference panel in most cohorts). In total, this study involves two stages comprising 610,091 individuals. A total of 48 cohorts participated in stage 1 and performed genome-wide interaction analyses (Table  S1 ).
We considered two dichotomized smoking variables, ''current smoking'' status (CurSmk) and ''ever smoking'' status (EverSmk), as they were the most widely available information (Table  1 ). Current smoking status was coded as 1 if the subject smoked regularly in past year (and as 0 for non-current smokers, which includes both never and former smokers). Ever smoking status was coded as 1 if the subject smoked at least 100 cigarettes during his/her lifetime (and as 0 for the never-smokers). Smoking status was assessed at the time of the BP measurements. When subjects had multiple smoking measures that were inconsistent, they were excluded from analysis. Subjects with missing data for BP, the smoking variable, or any covariates were excluded from analysis.
Genotype Data Genotyping was performed using Illumina or Affymetrix genotyping arrays. Each study performed imputation to impute genotypes for SNPs, short insertions and deletions (indels), and larger deletions that were not genotyped directly but are available from the 1000 Genomes Project. 18 Information on genotype and imputation for each study is presented in Tables  S6  and S7. For imputation, most studies used the 1000 Genomes Project Phase I Integrated Release Version 3 Haplotypes (2010-11 data freeze, 2012-03-14 haplotypes), which contain haplotypes of 1,092 individuals of all ethnic backgrounds.
Robust estimates of covariance matrices and SEs were used to safeguard against both mis-specification of the mean model and violation of the assumption of constant BP variance across smoking groups (heteroscedasticity). 20, 21  Association analysis was performed using various software (Tables  S6  and S7 ). To obtain robust estimates of covariance matrices and robust SEs, studies of unrelated subjects used either the R package sandwich  22  or ProbABEL. 23 To account for relatedness in families, family studies used either the generalized estimating equations (GEE) approach, treating each family as a cluster, or the linear mixed effect model approach with a random polygenic component (for which the covariance matrix depends on the kinship matrix).
Quality Control Study investigators participating in this study have ample experience in main-effect-based GWASs for multiple phenotypes and are very familiar with validated approaches for quality control (QC) of phenotype, genotype, and imputed data. For example, cohortlevel analyses used PCs as covariates to deal with population structure; family studies used suitable software packages to deal with relatedness (Table  S6 ). Overlap among some of the participating cohorts is a potential possibility. However, when there was known overlap of samples across cohorts, one of the cohorts used a nonoverlapping sub-sample for their analysis. We performed extensive QC using the R package EasyQC 24 for all cohort-specific GWAS results. In stage 1, each cohort provided 12 GWAS result files (2 BPs 3 2 smoking exposures 3 3 analyses, 1 for model 1 and 2 for model 2) for each ancestry group.
Genome-wide Significant Variants If a variant reached genome-wide significance (p < 5 3 10 À8 ) through any of these 48 combined association meta-analyses (which are not independent), then the variant was considered as genome-wide significant. To identify a set of independent (index) variants through ancestry-specific and trans-ancestry analysis, we performed the linkage disequilibrium (LD)-based clumping procedure using PLINK  32  and EasyStrata. 30 A locus is defined through LD-based clumping that uses both physical distance (51 Mb) and LD threshold of r 2 > 0. 1. Since valid methods do not exist for conditional analysis involving interactions across multi-ancestry studies, we relied on a relatively more stringent LD threshold (r 2 > 0. 1) for identifying ''independent'' loci. As LD reference, ancestry-specific 1000 Genomes Project data were used for ancestry-specific results and the entire cosmopolitan dataset was used for trans-ancestry results.
Functional Inference Variant Effect Predictor (VEP) from Ensembl was used to obtain the gene name for each locus. For the variants whose gene names were not identified by VEP, NCBI SNP database was used to obtain the closest gene. We applied several computational strategies to infer biological functions associated with our newly identified loci. We used HaploReg, RegulomeDB, and GTEx 34 to obtain annotations of the noncoding genome, chromatin state, and protein binding annotation from the Roadmap Epigenomics and ENCODE projects, sequence conservation across mammals, and the effect of SNPs on expression from eQTL studies. To further assess putative functionality for the new loci, we searched for cis associations between new variants and gene transcripts using previously published eQTL analyses, which includes the GTEx.
34 urther eQTL evidence was queried using the eQTL database of Joehanes et al. 35  for transcripts associated in both cis and trans in more than 5,000 individuals from the Framingham Heart Study, with genome-wide false discovery rate (FDR) < 0. 05. Two geneset enrichment analysis (GSEA) queries were then performed on December 23, 2016 to determine the enrichment of biological processes and disease pathways of the resulting transcripts. Prior to the queries, duplicated gene names and genes with provisional names (such as LOCXXX) were removed. Then, for each transcript probe associated with more than one gene name, only the first gene name was taken. This process yielded 127 gene names for the GSEA query. For querying biological processes, option C5:BP was selected on the GSEA website. For querying disease pathway, option C2:CP was selected.
5 with a functional coding variant within the gene or cis-acting regulatory variant), and the major histocompatibility complex region on chromosome 6 (base position 25,000,000-35,000,000) was excluded. DEPICT prioritized genes at the associated loci based on their functional similarity. Functional similarity of genes across associated loci was quantified by computing a gene score that was adjusted for bias through confounders such as gene length. Experiment-wide FDR for the gene prioritization was obtained by repeating the scoring step 50 times based on lead variants from 500 pre-compiled null GWASs. For the gene-set enrichment analyses, DEPICT utilized a total of 14,461 pre-compiled reconstituted gene sets comprising 737 Reactome database pathways, 2,473 phenotypic gene sets (derived from the Mouse Genetics Initiative), 184 Kyoto Encyclopedia of Genes and Genomes (KEGG) database pathways, 5,083 Gene Ontology database terms, and 5,984 protein molecular pathways (derived from protein-protein interactions).
e. , combining all ancestry groups through meta-analysis). a Each locus was determined through LD-based clumping, using 5 1 Mb around index variants, followed by LD threshold of r 2 > 0. 1; ancestry-specific LDs from 1000 Genomes Project were used when clumping within each ancestry and the entire cosmopolitan data were used for trans-ancestry clumping. b Gene names were obtained using variant effect predictor (VEP) from Ensembl. Genes with intragenic index variants are indicated with an asterisk (*). The most significant p value (between 1 df interaction test and 2 df joint test) are indicated with an asterisk (*). f These loci indicate ''completely novel'' loci, at least 1 Mb away from any of known BP loci. published BP variants, and we term them ''completely novel. '' Three of them (near PRAG1, MIR124-1, and FTO) show compelling biological relevance (see below) and eQTL evidence (Figure  2 ).