dataesr/scientific-paragraphs-categorization huggingface dataset:
Third, the prediction of the functional effect of missense variants was performed using the latest version of PolyPhen-2, SIFT, Condel, and Mutation Taster algorithms  [32] [33] [34] [35]. In silico prediction of molecular dynamics. We initially predicted the structure of SLC22A12 using a homology modeling program, SWISS-MODEL (https://swissmodel. expasy. org/). The quality of predicted 3D structures was estimated on the basis of the geometrical analysis of the single model, global model quality estimation (GMQE) score and qualitative model energy analysis (QMEAN)  36. The GenBank accession number used for each amino acid sequence was NP_653186 for SLC22A12. After homology modeling was completed, we selected a suitable SLC2A3 X-ray structure for SLC22A12 (PDB ID: 4ZW9, SLC2A3)  37, 38. For the more stable molecular dynamics simulations, we used I-Tasser generated models  39.
Acknowledgements The bioresources for this study were provided by the  National Biobank of Korea, Centers for Disease Control and Prevention, Republic of Korea. This research was supported by the  Basic Science Research Program  through the  National Research Foundation of Korea (NRF)  funded by the  Ministry of Education  ( NRF-2016R1A6A3A11933380  to S. K. C and  2015R1D1A1A01056685  to H. Y. G) and the  Korea Health Technology R&D Project  through the  Korea Health Industry Development Institute (KHIDI) , funded by the  Ministry of Health & Welfare, Republic of Korea  (Grant Number:  HI17C2372  to S. K. C). We thank the  Research Division, NEXBiO Co. Ltd  for their contribution to our sequencing. We are grateful to  Young Sup Cho MD, PhD  and  Hyekyung Son MD, PhD  for their academic advice during this project.
Probes were filtered for low quality with the pfilter function  15  , and the three samples with a detection P value > 0. 05 in >5% of the sites were removed (Table  S1 ). Additionally, known cross-reactive probes  17  were also excluded from further analysis. The remaining dataset was background-subtracted, and normalized using intra-array beta-mixture quantile normalization  18. Methylation beta values were logarithmically transformed to M values before parametric statistical analyses, as recommended  19. To define differentially methylated positions (DMPs) and differentially methylated regions (DMRs), we modelled the main variables as a categorical variable (i. e. control vs. disease, cell subtype) in a linear regression using the limma package, an empirical Bayesian approach  20, 21. DMPs and DMRs were selected based on a FDR-adjusted p value < 0.
05. DMRs were identified with the DMRcate package using the recommended proximity-based criteria  22. In addition, a DMR was defined by the presence of at least 2 differentially methylated CpG sites with a maximum gap of 1000 bp. Functional pathway enrichment was performed with the Enrichr web application  23. In addition, the package coMET was used for visualisation of regional associations and co-methylation patterns  24. Differentially variable and methylated CpG sites (DVMCs) were identified using the iEVORA algorithm with the recommended parameters of adjusted and non-adjusted p values  25.
To search for interactions between SNPs and methylation levels we adapted the Matrix eQTL package and checked the correlations within 5 Kb upstream and downstream of each of the polymorphisms (FDR-corrected p < 0. 05)  26. Libraries were prepared using the TruSeq Stranded Total RNA kit (Illumina Inc. , cat. no. RS-122-2201), following the TruSeq ® Stranded Total RNA Sample Preparation Guide. Starting from 50 ng of total RNA, Eukaryotic rRNA was depleted and remaining RNA was purified, fragmented and primed for cDNA synthesis. cDNA first strand was synthesized with SuperScript-II Reverse Transcriptase (Life Technologies, cat. no. 18064-014) for 10 min at 25 °C, 15 min at 42 °C, 15 min at 72 °C. Second strand cDNA was synthesized with Illumina reagents at 16 °C for 1 hour. Then, A-tailing and adaptor ligation were performed.
Additionally, both the TAP1-PSMB9 and the HLA-B-pseudogene pairs were regionally coexpressed across the entire dataset (Fig. 5C ), suggesting a common regulatory mechanism. Methylation quantitative trait loci and genotype-methylation interaction analyses in duodenal cell fractions. In order to ascertain whether methylation changes overlap sequence variants potentially explaining the phenotypic effect of some of the previously associated SNPs, methylation results were analyzed together with the Immunochip SNP genotypes  39. We identified 1,423 and 1,457 mQTLs in epithelial and immune cells, respectively (FDR < 0. 05), using previously defined criteria  40  (Table  S6 ). The mQTL-SNPs were more consistent between cell types than the CpGs they regulated (Fig. S6 ). In fact, 46. 5% of the SNPs participating to epithelial-mQTLs also took part in immune-mQTLs, while only 30.
7% of the CpGs in epithelial-mQTLs were also present in the immune (Monte Carlo-corrected χ2 = 75. 036, p < 9. 99 × 1e-5). Additionally, there was an excess of promoter and TSS proximal regions in both epithelial and immune mQTL-SNPs compared to a random list of Immunochip SNPs (T-test, p value < 0. 001). The mQTL-SNPs identified in each particular cell type were enriched in enhancer elements that have been described in cell lines resembling each of the two cellular compartments, according to the Dragon ENhancers database (DENdb)  41. Particularly, 21. 7% of the SNPs participating to the epithelia-specific mQTLs were located in epithelia-specific enhancers, compared to the 13. 3% that mapped to non-specific enhancers; and 32. 1% of the SNPs participating to the immune fraction-specific mQTLs were located in immune-enhancers, compared to the 12.
On the other hand, expression analysis of the genes located in close proximity to the cell type-specific DMPs showed that in general, methylation alterations in the epithelial cell compartment have a greater functional impact compared to those in the the immune fraction. Interestingly, many of the HLA genes affected by these methylation changes in the celiac epithelium were not only expressed in our dataset but also differentially expressed depending on the disease status. Nevertheless, it is important to note that even if expression alterations in genes mapping close to the immune-specific DMPs were limited, they affected interesting genes such as IL15RA and ZFAT, that appeared to be overexpressed. The former is a well-known mediator of IL15 activity that is associated to CD  42  and that can promote IEL survival in the disease, leading to the emergence of T cell clonal expansion  43.
Acknowledgements The authors thank the technical and human support provided by  SGIker of the UPV/EHU  and the  Genomics Platform at the CIC bioGUNE. We also thank  Dr Florence Le Calvez-Kelm  and  Mr Geoffroy Durand  from the  Genetic Cancer Susceptibility group  ( IARC, Lyon ) for their help with the HM450 beadchip assays. The work was funded by  ISCIII  Research Project Grants  PI13/01201  and  PI16/00258 , cofunded by the  European Union ERDF/ ESF  "A way to make Europe" co-financed by the  Spanish Ministry of Economy and Competitiveness  -http:// www. mineco. gob. es/-and by the  European Union ERDF/ESF  "A way to make Europe" and project  2011/111034  from the  Basque Department of Health  -http://www. euskadi. eus/gobierno-vasco/departamento-salud/inicio/-toJ. R. B. N. F. J. was supported by an  IARC Postodctoral Fellowship (FP7 Marie Curie Actions-People-COFUND ) and a  Postdoctoral Fellowship  from the  Basque Department of Education  -http://training.
Availability of Data Methylation and SNP genotype results are accessible in NCBI's Gene Expression Omnibus (GEO) repository through accession no. GSE84745 (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE84745), and RNAseq data have been deposited in NCBI's Sequence Read Archive (SRA) with accession no. SRP077708 (http:// www. ncbi. nlm. nih. gov/sra/SRP077708).
Achievement of this goal requires a comprehensive understanding of the durability of first-line ART regimens and patterns of switching to second-line ART across geographical regions and different country-income settings to ensure future treatment needs are met. 5 7] [8]  Comparatively less data are available on the durability of first-line ART and the use of second-line treatment in children. The PENPACT trial,  9  which comprised patients from predominately high-income countries, reported that 71% (188 of 266) of children remained on their first-line regimen 5 years after starting ART, compared with 95% or more of children in the CHER  8  and ARROW  7  trials that comprised children from Africa. Observational cohorts have reported wide variations in the probability of switching to second-line ART after treatment failure, with the definition of Individual patient-level data were sent to the University of Cape Town (Cape Town, South Africa) for data cleaning and data management using a standardised protocol based on the HIV Cohorts Data Exchange Protocol.
Statistical analysis We explored cohort-level and patient-level potential predictors for switching. We generated cohort-level factors, which were geographical region, treatment monitoring strategy, and country-income group. The geographical regions with eligible data were categorised as Europe, the USA, Asia, Latin America (ie, the Caribbean, and Central and South America), southern Africa, and the rest of sub-Saharan Africa. Southern Africa was defined as Botswana and South Africa and was considered separate from the rest of sub-Saharan Africa because lopinavir-based regimens were introduced in 2010 as first-line ART for children younger than 3 years in Botswana and South Africa and are not part of the standardised firstline regimen in the rest of sub-Saharan Africa, and Botswana and South Africa also had early roll-out of routine viral load monitoring in 2007-08,  14, 15  unlike the rest of sub-Saharan Africa.
Country-income groups were assigned by use of the World Bank classification  17  (high-income and upper-middle-income, lower-middleincome, or low-income economies) at the median year of ART initiation in the cohort. For the HIV Cohorts Data Exchange Protocol website see http://www. hicdep. org We investigated the following patient-level independent variables measured at ART initiation: sex, age (<3, 3-5, 6-9, and ≥10 years), known previous AIDS diagnosis (WHO stage 3-4 or US Centres for Disease Control and Prevention stage C; yes or no), initial ART regimen (protease inhibitor or NNRTI based), and calendar year (≤2004, 2005-2007, 2008-2010, and ≥2011). We also collected data on bodyweight and CD4% or CD4 count at initiation of ART where available. We calculated the weight-for-age Z scores relative to the 1990 British Growth Reference values in Stata 18 (≤-2, >-2 to 0, and >0) and immunodeficiency for age with the WHO standard definition  19  based on CD4% or CD4 cell count.
We did two sensitivity analyses. First, to assess the potential association between low weight-for-age Z score, immunosuppression at initiation of ART, and likelihood of switching. We repeated the regression models using patient-level data from a subset of cohorts in which more than 60% of children had bodyweight and CD4 measurements at ART initiation, and did the multivariable analysis with and without weight-for-age Z score and immunodeficiency for age. In the second sensitivity analysis, we repeated all analyses redefining switching to secondline ART by removing the requirement for a simultaneous change of one or more NRTI when changing across drug class (NNRTI to protease inhibitor or vice versa) or within the protease inhibitor drug class. We did all analyses using Stata version 14. 2.
Results At the time of data cutoff, Sept 16, 2015, the CIPHER dataset comprised patient-level data on 182 747 children living with HIV, of whom 93 351 (51%) met our inclusion criteria (appendix p 20). 12 networks covering 52 countries in the CIPHER database had eligible children, and most children were in the rest of sub-Saharan Africa (71%), with 19•1% in southern Africa, 6•6% in Asia, and the remaining 3•5% in Europe, Latin America, and the USA (figure  1 ). The calendar year of ART initiation ranged from 1993 to 2015, with over 70% of children initiating ART in 2008 or later (table  1 ). Half of patients were male, and the median age at ART initiation was 3•9 years (IQR 1•6-6•9), with two-thirds aged 5 years or younger. The median age at ART initiation was similar across all regions, except for in the USA where the median age was younger than 1 year.
Evidence before this study We searched PubMed for studies published in English from database inception to Nov 16, 2017, that assessed the probability of switching to second-line antiretroviral therapy (ART) in children with HIV across geographical regions and under different monitoring strategies using the search terms "child", "children", OR "adolescent"; "HIV", "antiretroviral therapy", "switch", AND "second-line". We identified several clinical trials and several cohort studies that reported on the probability of switching that used various different definitions of switching. Few studies estimated the incidence of switching to second-line ART in children across several countries with different strategies of treatment monitoring. To our knowledge, no published global-level analysis of switching to second-line ART among children exists that uses a uniform definition of switching.
Acknowledgments We thank all participating networks, clinics, clinic personnel, and patients who contributed data and made the  Collaborative Initiative for Paediatric HIV Education and Research (CIPHER)  Global Cohort Collaboration Time on First Line Project possible; a full list is in the appendix (pp  1-13 ). We also thank  Martina Penazzato  and  Rohan Hazra  for their guidance and support as part of the  CIPHER Executive Committee , and  Samantha Hodgetts  for her administrative support. The study was sponsored by the  International AIDS Society-CIPHER. CIPHER is made possible by support from founding sponsor  ViiV Healthcare and Janssen. Funding details of the individual cohort networks are listed in the appendix (pp 2-3). Funding  International AIDS Society-CIPHER, UK Medical Research Council.
Prognostic performance of EPOCE to predict ROSC absence in patients with ventricular fibrillation without cardiac motion will be estimated. A multifactorial composite prognostic score associated with the absence of ROSC will be constructed with a logistic regression model. Parameters that will be taken into account will be myocardial and/or electrical activity, capnography, no/ low flow duration, and clinical profile including sex and age. Measure of mean duration of cardiac massage interruption during EPOCE will be estimated. P values less than 0. 05 will be considered statistically significant. All analyses will be performed using SAS V. 9. 4.
EthICS And dISSEMInAtIon In accordance with the recommendations of Comité de Protection des Personnes 'Ile de France II' France, patient and/or legal authority consent will be requested only for survivors. Furthermore, with regard to the inclusion criteria, it will be impossible to seek for relatives' consent. With regard to the very low survival rate of patients with OHCA (5%), anonymised database and family-induced traumatism, we have asked for a derogation to surrogate information for deceased patients. Consent will be requested for surviving patients. We intend to publish ACE results in a major journal of emergency medicine, and raw data will be available on reasonable request.
Acknowledgements This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (Grant agreement no. 639109 ). M. I. acknowledges  JSPS KAKENHI  Grant (number  JP17H01066 ). The authors thank  Jun Matsuzawa  for technical assistance in UV NIL. The  UV NIL  was conducted at Nanofabrication platform and  Namiki Foundry in NIMS.
To assess whether the mean percentage of stained cells differs between any two subtypes, we performed Student t tests. The TCGA breast invasive carcinoma (TCGA-BRCA) cohort is publicly available. 19 The RNA-SeqV2 Level 3 data (Jan 2015) were downloaded from the TCGA Research Network (http://cancergenome. nih. gov/) and integrated into a platform in knowledge data integration (KDI) at Institut Curie (https:// bioinfo-portal. curie. fr). Subtype classification was based on immunohistochemical status for the estrogen receptor (ER), progesterone receptor (PR) and HER2, as follows. TNBC: ER-, PR-and HER2-negative (n = 157); HER2+/ ER-: ER-and PR-negative, HER2-positive (n = 41); luminal B: ER-and/or PR-positive, HER2-positive (n = 153); luminal A: ER-and/or PR-positive, HER2-negative (n = 663).
| Cellular assays ] [22]  Briefly, cells were incubated with DMSO or a PRMT5 inhibitor (EPZ015666, EPZ015938), or transfected with 40 nmol/L siRNA (Qiagen) using INTERFERin (Polyplus Transfection) (BT-20, Hs578T, MCF-10A, MDA-MB-453, MDA-MB-468) or Lipofectamine RNAiMAX (Life Technologies) (HCC38, HCC70). Cell proliferation determined by MTT (Sigma). Apoptotic activity was determined by the Caspase-Glo 3/7 luminescent assay (Promega) or by Western blot analysis. Caspase activity using the luminescent assay was normalized to cell viability, measured by a concomitant MTT assay. Cell-cycle analysis was carried out with LSRII (Becton Dickinson) using BD FACSDIVA SoftwareTM (BD Bioscience) to determine cellular DNA content, and analyzed using FlowJo and Modfit LT softwares. For the colony formation assay, cells were treated with drugs or siRNA, and incubated for 5 (MCF10A), 9 (MDA-MB-468) or 14 days (BT20, HCC38, HCC70, MDA-MB-453).
Cell viability was monitored using the CellTiter-Glo (Promega) assay after 96 hours using a CLARIOStar (BMG Labtech). The experiment was carried out in duplicates. Positive hits for each compound were identified as follows: data were first transformed with log functions; B-score normalization was then applied to each replicate separately, and includes corrections for plates, rows, and columns. Median and median absolute deviation (MAD) were computed and used to calculate Robust Zscores (RZ-scores) for each sample, according to the formula: score = (value -median)/(1. 4826 × Median MAD). RZscores were calculated for the comparison of each compound against the DMSO-treated cells. A compound was identified as a 'hit', if the RZ-score was <-2 in the two replicates. The correspondence between RZ-score and cell proliferation is given by the following formula: A ΔRZscore = RZscore (Prestwick + EPZ015666) -RZscore (Prestwick) was calculated for each 'hit' to quantify the effect of the Prestwick + EPZ015666 drug combination.
| PRMT5 is differentially localized in TNBC compared to other breast cancer subtypes and to normal mammary tissues We examined the expression of PRMT5 in a previously generated cohort of 150 breast cancer biopsy specimens and normal breast tissues from the Institut Curie Hospital (Curie cohort). 17 We find that TNBC express similar levels of PRMT5 mRNA compared to luminal breast cancers and healthy breast tissues, and higher levels of PRMT5 mRNA compared to HER2 + breast cancers (Figure  1A , left panel). To confirm these observations, we analyzed publically available data from the TCGA breast invasive carcinoma cohort. 19 We find that PRMT5 is overexpressed in breast cancers-encompassing all subtypes-compared to normal breast tissues (data not shown), as previously reported. 25 In contrast with the Curie cohort, the TCGA data shows no difference in PRMT5 mRNA expression between TNBC and HER2 + breast cancers (Figure  1A , right panel).
| High PRMT5 expression is associated with poor prognosis in TNBC Our analysis of PRMT5 mRNA expression in TNBC shows up to eightfold variability between samples (Figure  1A ), paralleling TNBC heterogeneity. 2  To determine the clinical significance of PRMT5 in TNBC, we analyzed PRMT5 expression and survival outcomes in TNBC using data from the Kaplan-Meier plotter online database  26  (www. kmplot. com) (Figure  2 ). Kaplan-Meier analyses indicate an association between high PRMT5 expression and lower probabilities of distant metastasis-free survival (DMFS, P = 0. 085) and overall survival (OS, P = 0. 012) (Figure  2 ), outlining the potential therapeutic value of PRMT5 targeting in a subset of TNBC.
This study is a prospective randomised open blinded evaluation (PROBE) design, feasibility, RCT, which aimed to recruit 90 participants with FEP. As a PROBE study, persons (research assistants) that were unaware of the randomisation allocation made decisions about the scoring of outcome measures. We did not have a blinded endpoint committee, however, our independent Data Monitoring and Ethics Committee (iDMC) provided oversight of unblinding. The randomisation ratio was 1:1:1, stratified by centre and family contact (as participants without regular family contact only received individual CBT and not FI, if randomised to the PI-only and combined groups). Randomisation at the individual level was independent and concealed, using random permuted blocks. Research assistants (RAs) performed the randomisation procedure via a study-specific website developed by the Centre for Healthcare Randomised Trials (CHaRT), the UK Clinical Research Collaboration (UKCRC) registered Clinical Trials Unit (CTU #7) supporting the study.
RAs and therapists received rigorous in-house training on the importance of maintaining the blind and methods to achieve this, and were required to read and sign our standard operating procedure (SOP) for retaining and managing blinding. Methods outlined in the SOP included separate offices for the therapists and RAs, protocols for answering telephones, including reminders for clinicians, participants and family members about the blind, protocols for message taking and secretarial support, separate diaries, pigeon holes and databases, using passwords and encryption of randomisation information. Any accidental unblinding was recorded. The Chief Investigator (CI) reviewed all unblinding to determine any patterns. We recruited participants from Early Intervention in Psychosis (EIP) teams and Child and Adolescent Mental Health Services (CAMHS) in UK National Health Service (NHS) Trusts across seven sites; Greater Manchester, Lancashire, Oxfordshire and Buckinghamshire, Sussex, Northumberland, Tyne and Wear, Norfolk & Suffolk, and Birmingham.
The decision about the type and dose of AP was made by the psychiatrist as per their usual practice, i. e. we did not ask them to choose from a pre-specified list of antipsychotic medications. Psychiatrists were free to change dose and type of AP in response to efficacy and adverse events. A member of the research team who was not blind to allocation screened participants' medical records to collect details about AP prescription. Additionally, self-report data were provided by participants either via our web-based platform or if preferred, in paper format that was returned to a research team member who was not blind to allocation. Psychiatrists on the study were available for consultation if participants' CAMHS or EIP psychiatrists wished to discuss AP prescription. We did not restrict the treatment options offered by the care team as it would have been unethical to do so.
Additionally, participants were eligible to receive mental health medications other than APs, and psychological therapies other than and including CBTp or FI, throughout the course of the trial. A member of the research team who was not blind to allocation screened participants' medical records for information on concomitant therapies received. In addition, self-report data on concomitant therapies were provided by participants using the method described above. Data from participants' medical records and self-report data provided important feasibility information about participants' adherence to their randomised treatment allocation.