dataesr/scientific-paragraphs-categorization huggingface dataset:
Data synthesis and analysis Meta-analyses were performed with the 'meta', 'metafor' and 'dmetar' packages of the statistical software R (V. 3. 6. 2). Prevalence estimates were reported with 95% CI. Prevalence pooling was done with Freeman-Tukey double arcsine transformation using the random-effects metaanalysis model. 26 We adjusted the prevalence in a multivariate meta-analysis to take into account the variance between tools used to identify patients with depressive disorders/symptoms. Egger's test served for detecting the presence of publication bias. 27 A value of p<0. 10 on Egger's test was considered indicative of statistically significant publication bias. Heterogeneity was evaluated by the χ² test on Cochran's Q statistic,  28  which was quantified by I² values. The I² statistic estimates the percentage of total variation across studies due to true between-study differences rather than chance.
RESULTS 2] [33] [34] [35] [36] [37] [38] [39] [40] [41]  Agreement between investigators on selection based on title and abstract was κ=0. 88 and κ=1. 0 for final inclusion. Of the 11 included studies, 8 studies used nonprobabilistic sampling while 3 used probabilistic sampling. All studies prospectively collected and analysed data and used the same method to identify patients with depressive disorders. Sample size was adequate in nine studies and response rate in two studies (online supplemental table 2). Three studies had low risk of bias and eight moderate risk. None of the studies had high risk of bias. The characteristics of included studies are presented in the table 1. All studies were cross-sectional. Patient Health Questionnaire-9 was the most used tool, n=4. The mean age varied between 50.
3 and 59. 6 years. The proportion of men varied between 28% and 54%. Data on depressive disorders/symptoms were collected between 2002 and 2017. Four studies were conducted in South Africa, three studies in Nigeria, one study in Ghana, one study in both Ghana and Nigeria, one study in Ethiopia, and one study in Burkina Faso. None of the studies was conducted in Central Africa and North Africa. Talking about the language of the tool used to assess depressive status, four studies indicated that they used native/local languages back translated in English for reporting. 32 33 35 36 evalence of depressive disorders/symptoms in people with hypertension in Africa In total, 5299 participants with hypertension were included. There was substantial heterogeneity for all analyses, all I²>75% (figure  1 ).
Nuclear data adjustments using integral experiments play since several decades a crucial role in providing reactor designers and fuel cycle analysts with nuclear data with reduced and validated uncertainties in order to allow design optimization while meeting safety and economics requirements. Adjustment methods, choice of integral experiments and covariance data assessment should be carefully defined in order to produce credible and physically acceptable adjustments, applicable to a wide range of systems. The OECD-NEA has established a series of Expert Groups since 2005 to investigate the key scientific issues and to provide recommendations for applications. The present paper discusses the major results of the most recent of these Expert Groups together with an indication of the path forward.
The framework The central role of nuclear data in reactor physics can be summarized as shown in the following scheme: Nuclear Data needs assessment, UQ, and adjustments have been recognized as a key area for research in the nuclear energy domain. The NEA WPEC launched a series of initiatives: The starting point (~2005) has been a data needs assessment for GENERATION-IV, ADS and new nuclear fuel cycles with waste management issues. Consequently a first list of data priorities for GEN-IV and ADS reactors was established within SG26  [2]  and implemented in the HPRL (High Priority Request List) at NEA. The next step did tackle data adjustment and covariance data comparison (Subgroup 33) and a comprehensive compilation of methods and a benchmark exercise were performed to understand if adjustments converge, starting from different x-section data bases and using different covariance data  [3].
Finally, some decommissioning issues will benefit from a better nuclear data knowledge. All these potential needs require: New target uncertainties. The last coordinated international assessment is more than 15 years old and it was performed in a very different environment, mostly associated to the emergence of GENERATION-IV initiative. An update of High Priority Request List (HPRL) at the NEA DataBank to define new priorities for evaluation and experiments. When new target accuracies will have been assessed, a revision of adjustment and bias factor methods could be necessary. The Subgroup 39 recommendation has been to take the target accuracy issue as a key objective of a new WPEC expert group in this domain (SG46 on "Efficient and Effective Use of Integral Experiments for Nuclear Data Validation").
NaHS Impairs the Growth of Human Keratinocyte Progenitors and Stem Cells To assess the impact of NaHS on the growth of keratinocyte progenitors and stem cells, we performed a keratinocyte clonogenic assay (CFU) and measured the effects of NaHS (0. 02 mM or 0. 25 mM) on both the number and size of colonies. While the number of clones remained unchanged under NaHS treatment (Figure  2a ,b), we observed that the clones exhibited smaller sizes (Figure  2a ). Accordingly, the counts of harvested cells obtained 10-14 days post-treatment were nearly 80% lower in cultures treated with 0. 25 mM NaHS as compared to controls (Figure  2c ). Such an effect was not observed when 0. 02 mM NaHS was applied. Similarly, in cultures of human epithelial cell sheets, which are composed of more than 90% CD49f-positive keratinocyte progenitors  [11, 12]  (Data Supplement, Figure  S2  and Table  S1 ), total cell counts were significantly lower in NaHS-treated cultures as compared to controls (Figure  2d ).
NaHS Impairs the Growth of Human Keratinocyte Progenitors and Stem Cells To assess the impact of NaHS on the growth of keratinocyte progenitors and stem cells, we performed a keratinocyte clonogenic assay (CFU) and measured the effects of NaHS (0. 02 mM or 0. 25 mM) on both the number and size of colonies. While the number of clones remained limiting effect was less pronounced under these experimental conditions and reached only 30% in NaHS-treated as compared to control cultures. Again, NaHS applied at concentrations below 0. 25 mM had no impact on the growth of keratinocyte progenitors. Of note, irrespective of the tested NaHS concentrations (from 0. 0025 to 4 mM), we did not observe any impact on keratinocyte cell viability as assessed by trypan blue coloration or MTT (3-(4,5-dimethylthiazol-2-yl)-2,5diphenyltetrazolium bromide) assay (Data Supplement, Figure  S3 ).
We nevertheless combined these results with a manual curation of the literature to identify candidate molecules which would be worth assessing with more sensitive techniques. Among the 10 identified candidate molecules, only superoxide dismutase 2 (SOD2) was previously shown to be regulated by NaHS. SOD2 is an antioxidant enzyme which decreases the level of ROS (reactive oxygen species), thus participating in cellular defense against oxidative stress. On this basis, we thus performed an ELISA measurement of SOD2 on cell extracts derived from NaHS-treated or control epidermal cell sheets and observed that NaHS induced a nearly 100% increase in SOD2 amounts in NaHS-treated epidermal cell sheets (p < 0. 05) (Data Supplement, Figure  S4 ). To further assess the links between NaHS and oxidative stress, we then performed a complementary bioinformatics analysis based on a survey of the comparative Toxocogenomics database (CTD)  [13].
This approach allowed us to retrieve a list of 504 genes (Data Supplement, Table  S3 ), which, in human cells, were previously reported to be modulated by hydrogen-sulfide-containing compounds. Confirming our proteomics data on SOD2, the highest functional enrichment retrieved from this list was obtained with genes annotated with the GO term "response to reactive oxygen species" (Table  1 ). Table  1. Enrichment analysis of genes targeted by hydrogen-sulfide-containing compounds. The whole list of genes targeted by hydrogen-sulfide-containing compounds according to the CTD database  [13]  was submitted to an enrichment analysis. Enrichment in genes annotated with GO (gene ontology) terms of the "biological process" category was assessed using the enrichment analysis platform Enrichr  [14].
GO Term Adjusted We then performed an RNA-seq analysis to assess the genomic impact of 0. 25 mM NaHS on human epidermal cell sheets. While 12. 000 to 14. 000 transcripts were detected in each sample, paired comparisons allowed identifying only 265 protein-coding differentially expressed genes with a fold change >1. 5 and an adjusted p-value < 0. 05 (Data Supplement, Table  S4 ). Among these 265 genes, 122 were up-regulated and 143 were down-regulated by NaHS treatment. While such lists were not enriched in genes signing specific molecular pathways according to the enrichment analysis platform Enrichr, 11 of the identified genes (Table  2 ) belonged to the list of 504 hydrogen sulfide target genes we retrieved from the CTD database. These notably comprised the oxidative stress response genes NQO1 (NAD(P)H Quinone Dehydrogenase 1)  [15]  and CUL3 (Culin 3)  [16] , the chemokine CXCL2 (C-X-C Motif Chemokine Ligand 2)  [17] [18] [19] [20]  and the inflammasome component CASP1 (Caspase-1), which enables the maturation of IL-1β (Interleukin-1β) and IL-18 (Interleukin-18)  [21, 22].
Our data show that human keratinocytes exposed to NaHS engage an oxidative stress response involving SOD2, NQO1 and CUL3. We also report here that the whole list of currently known targets of sulfide-rich compounds is highly significantly enriched in antioxidant stress response genes. Accordingly, in other models and cell types, NaHS was similarly found to induce an oxidative stress response via the up-regulation of SOD1, SOD2, CAT (Catalase) and/or GPX (glutathione persoxydase 1)  [30] [31] [32]. More specifically, previous reports demonstrated in non-skin cells that the NaHS-induced up-regulation of NQO1 and SOD2 mRNAs  [33, 34]  was mediated by the transcription factor NFEL2 (nuclear factor erythroid 2 like 2, also known as NRF2), a key orchestrator of the genomic oxidative stress response  [35] [36] [37].
Generation of Cultured Human Epidermal Sheets Normal human epidermal keratinocyte were grown on a feeder layer of irradiated human fibroblasts as previously described  [45, 46]  in keratinocyte culture medium (KCM). See the Supplementary Data for further details. When needed, cells were harvested with trypsin-EDTA 0. 05% (Thermo Fisher Scientific, Waltham, MA, USA) and collected for analysis.
Statistical Analysis Quantitative data obtained by ELISA or western blotting were generated from at least 5 independent experiments in which NaHS-stimulated cells were compared to control untreated cells. Statistical significance was calculated using the paired Wilcoxon test. In some experiments, the analyzed epidermal cell sheets were all generated from the same donor, and a Mann and Whitney test was thus used. To identify differentially expressed genes or proteins in RNA-seq and proteomics experiments, respectively, paired Student's t-tests were performed, and adjusted p-values were then calculated using the Benjamini-Hochberg procedure. All statistical analyses were performed using the software GraphPad Prism 4 (GraphPad Software Inc. , La Jolla, CA, USA). Statistically significant differences in figures are indicated by asterisks as follows: * p < 0.
DLBCL patient samples Analysis of the influence of TET2 mutations in DLBCL on expression levels was conducted on the same samples, as described previously. Briefly, a cohort of 128 tumors from patients with pathologic diagnosis of DLBCL were interrogated by targeted sequencing, as described in García-Ramírez et al. (55). The TET2 mutations in patients with DLBCL for this cohort was published and obtained in  (14). Affymetrix U133 plus 2 gene expression microarrays were performed on 84 matched DLBCL tumors in previous work, which were stored in the Gene Expression Omnibus (GEO) database (accession number GSE10846)  (56). Analysis of the influence of TET2 mutations in DLBCL samples on methylation levels was conducted on the methylation array data from Asmar et al. (12) , which is accessible from the GEO database (accession number GSE37362).
Motif enrichment analysis Identification of known TF binding sites overrepresented among DHMRs in Vav-Cre/Tet2 -/-and Vav-Cre/Tet2 +/+ samples was conducted using "findMotifsGenome. pl" from Homer  (59). More specifically, the analysis was conducted for hypo-DHMR regions, identified as described above, overlapping with promoter regions. Moreover, the analysis was also conducted for hypermethylated regions, defined as regions 50 bp upstream or downstream of hyper-DMCs located within promoter regions. Motif enrichment analysis for these hyper-DMCs was conducted with a background of all reference promoters from the gencode (vM3) reference annotation.
Accession numbers Raw sequence data of Vav-Cre RNA-seq, ERRBS, and hMeDIP-seq from the experiments conducted in GC B cells are stored under accession number GSE111700. Sequence data from Vav-Cre RNA-seq and ERRBS NBs are stored under accession numbers GSE132595 and GSE132596. Raw sequence data of Vav-Cre/Tet2 -/-and Vav-Cre/ Tet2 +/+ SHM (somatic hypermutation) analysis are stored under accession number GSE140086.
Acknowledgments: We thank  S. Sampson  from  The Jackson Laboratory  for editing this manuscript. We thank members of  Li Lab and Melnick Lab  for discussion, and we thank the  Jackson Laboratory Computer Sciences team  and the  Research Informatics Technology group , both for technical support. Funding: A. M. is funded by  R35 CA220499 ,  LLS TRP 6572-19 ,  LLS SCOR 7012-16 , the  Follicular Lymphoma Consortium, Samuel Waxman Cancer Research Foundation , and the  Chemotherapy Foundation. S. L. is supported by the  National Institute of General Medical Sciences  of the  NIH  under award number  R35 GM133562 ,  Leukemia Research Foundation   New Investigator Grant ,  The Jackson Laboratory Director's Innovation Fund   19000-17-31 , and  The Jackson Laboratory Cancer Center New Investigator Award.
2A ), which reproduces values previously found in the cornea and lens of the eye  [4] , and from this the longitudinal elastic modulus M 0 was derived. As already noted  [2] , the longitudinal modulus derived from Brillouin scattering includes the contribution of the adiabatic bulk modulus that is of the order of GPa even in water. A plot of M 0 vs. solute volume fraction x enables the Voigt model (rule of mixing)  [5, 6]  to be applied to these data (Fig. 2B ) according to the expression:  Value of the Data These data relate to gelatin hydrogels derived from denatured collagen that are biologically relevant homogeneous materials, useful to extract and understand the information contained within Brillouin spectra. They can benefit the whole BioBrillouin community, providing a benchmark for testing and validation of instruments.
They can also benefit life scientists, biologists and clinicians who are interested in novel biophotonic techniques. In addition, these data can be used to draw comparisons between similar lab-built spectrometers, to gain further insights and to promote the development of new concepts for faster high-contrast, high-resolution Brillouin spectroscopy. The use of transparent homogeneous materials that are reasonably stable at ambient conditions adds additional value to these data for system benchmarking and standardisation. M 0 ¼ M s x þ M w ð1 À xÞ (1) where M s and M w are the longitudinal elastic moduli of the solute (gelatin) and water, respectively. All the data presented in this article are available for download and reuse from the Open Research Exeter (ORE) repository, University of Exeter UK.
Acknowledgments This work was supported by  EPSRC  and  CRUK  through the grants  EP/M028739/1  and  NS/A000063/1  (to F. P. ) and a  Leverhulme Trust Visiting Professorship  (to S. B. ). F. P. gratefully acknowledges support through the  COST Action BioBrillouin  ( CA16124 ) for helpful discussions on the topic of this article.
87)-indicative of genuine expression-modulating effects that are likely to be physiologically relevant  (Fig 2D). To determine whether these variants could have been prioritised by other means, we compared the MPRA results with in silico methods designed to identify functional variants-DeepSEA  (Zhou & Troyanskaya, 2015)  and RegulomeDB  (Dong & Boyle, 2019; Dataset EV1). Considering these approaches together, the lead MPRA SNP was predicted to be the most functionally significant variant at 3/14 loci (2 by DeepSEA, 1 by Regu-lomeDB). DeepSEA also predicted that 3 more lead SNPs would be functionally significant, but prioritised other candidate SNPs at these loci (most of which had no expression-modulating effect in CD4 T cells). At the remaining eight loci, the lead MPRA SNP was not predicted to have an expression-modulating effect-consistent with these methods being better at predicting negative effects than positive effects  (Dong & Boyle, 2019)  and highlighting the value of studying disease-associated loci in relevant primary cells.
Adapted MPRA in CD4 T cells provides insights into the biological effects of genetic associations After establishing that MPRA could be performed in primary CD4 T cells, we next examined the results at disease-associated loci (Appendix Figs  S2  and S3, Datasets EV2  and EV3 ). To assess whether adapted MPRA would identify known causal variants, we used an inflammatory bowel disease (IBD)-associated locus that has been fine-mapped to a single variant, rs1736137  (Huang et al, 2017). In both resting and stimulated T cells, this SNP had highly significant expression-modulating activity, with the IBD-risk allele consistently increasing transcription  (Fig 3A). As a further proof of principle, we next examined an ankylosing spondylitis-associated locus, where Bayesian fine-mapping using corrected coverage estimates resolves the association to three SNPs in the 99% credible set (rs6759298, rs4672505 and rs13001372).
Dotted horizontal line represents significance threshold (corrected for multiple testing). D GWAS results at a type 1 diabetes-associated locus on 14q32 (Onengut-Gumuscu et al, 2015; left panel). MPRA for the candidate SNPs in stimulated T cells (right panel). The construct with the largest and most significant effect contains the risk alleles for 2 SNPs (rs1988588 and rs3902659), each of which has a smaller effect when tested individually (position indicated by vertical dotted line). Dotted horizontal line represents significance threshold (corrected for multiple testing). C D A B • • • • • • • • • • • • • • • • • • • • • •• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •• ••• • • • • • • • • • • •• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •• • • • • • • • • • • • • • • • • • • •• •• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •• • • • • • • • • • • • •• • • • • • • Data information: Box and whisker plots represent median and interquartile range (box) and min to max (whiskers).
Flow cytometry CD4 T-cell purity, composition and transfection efficiency were assessed by flow cytometry (BD LSR II). Purity and composition panel (stained in 250 ll): CD4 APC (#357408), CCR4 BV421 (#359414), CCR6 AF700 (#353434), CD3 FITC (#300306), CD62L PerCP/Cy5. 5 (#304824), CXCR3 PE/Dazzle 594 (#353736) and CD45RA PE/Cy7 (#304126)-all used at 1:100 dilution (all BioLegend), Zombie Aqua Fixable Viability Kit (BioLegend) and Fc receptor blocking reagent (Miltenyi). Transfection efficiency panel: EGFP, CD4 APC (as above), Zombie Aqua Fixable Viability Kit, Fc receptor blocking reagent. Data were gated using FlowJo v10 (BD).
Tiling analysis To measure enhancer activity, we used the sharpr2 package  (Wang et al, 2018)  in R. This was used because (i) the tiled regions were of different sizes, (ii) the offset between constructs (50 bp) was not a factor of the length of genomic sequence (114 bp), and (iii) we could include reference allele SNP constructs to improve genomic coverage. After removing alternate allele constructs, the median counts for the remaining constructs in RNA and DNA were analysed. The regulatory scores for each region were based on standardised log(RNA/PLASMID), and a regional FWER cut-off (0. 05) was used to identify high-resolution driver elements indicative of enhancer activity.
SNP analysis For the SNP analysis, we used QuASAR-MPRA  (Kalita et al, 2018)  in the QuASAR package in R. After removing enhancer constructs, 964/970 SNP constructs were available for analysis. As this can only analyse one sample at a time, a standard fixed-effect meta-analysis was used to combine the results for biological replicates. To do this, we used the logit transformation of the proportion of reference reads in RNA (b l ) and the standard error of this estimate (r b l ). We then calculated logit transformation of the proportion of reference reads in DNA (b 0 ). So, for k samples: b Ã l:adj ¼ 1 w Ã l X k i ð bi;l À b 0;l Þw i;l where w i;l ¼ 1 r 2 bi;l and w Ã l ¼ P k i w i;l. We then calculated a metaanalysis Z score and P value: Z ¼ b Ã l:adj rÃ l where rÃ l ¼ 1 ffiffiffiffi w Ã l p.
Five biological replicates were performed. Fine-mapping ankylosing spondylitis (AS) association on 2p15 AS summary statistics (International Genetics of Ankylosing Spondylitis C et al, 2013) were obtained from the GWAS catalog, and SNPs in chr2:62518445. 62618445 (hg19) were extracted. Approximate Bayes factors summarising the association at each SNP, and thus the posterior probabilities for each SNP being causal, were calculated  (Wakefield, 2009) -assuming a single causal variant model. The 99% credible set contained four SNPs. Recent work has shown that this method can be biased  (Hutchinson et al, 2020)  and so we used the corrcoverage R package to correct any bias, identifying a 99% credible set containing three SNPs: rs6759298, rs4672505 and rs13001372 (corrected coverage 99. 2%).
In silico transcription factor binding analysis Transcription factor binding motifs enriched at constituent elements within the 6q23 super-enhancer were identified using TRAP (multiple sequences; Thomas-Chollier et al, 2011), with all human ª 2020 The Authors EMBO Molecular Medicine 12: e12112 | 2020 promoters as the reference and an FDR correction for multiple testing  (Benjamini & Hochberg, 1995). Motifs were obtained from the JASPAR CORE vertebrate database. Pathway analysis of enriched transcription factors within KEGG pathways was performed using g: Profiler (https://biit. cs. ut. ee/gprofiler/gost) with an FDR correction  (Benjamini & Hochberg, 1995).
Genotyping was performed using the Illumina Huma-nOmniExpress 12v1. 0 BeadChip, and data were processed as previously described  (Peters et al, 2016). qPCR for genes at the 6q23 locus was performed in triplicate on a Roche LightCycler 480, using exon-spanning primers, with beta-actin as a reference (QuantiFast SYBR Green PCR Kit; Qiagen).