dataesr/scientific-paragraphs-categorization huggingface dataset:
We thank the staff of Concordia Station and the Kenn Borek Air flight crew. Additional support was provided by the French ANR Dome A project (ANR-07-BLAN-0125). We would like to thank Mark Wiederspahn for the endless IT support at UTIG and Penelope M. Parr for handling polar data in the Landmark DSD environment. This research contributes to the AntArchitecture action group of SCAR. This is BE-OI publication number 21. This is UTIG contribution 3811. Financial support. This work was supported by NSF (grants nos. ANT-0733025, ARC-0941678 and PLR-1443690), NASA (grant nos. NNX08AN68G, NNX09AR52G and NNX11AD33G (Operation Ice Bridge) to Texas, the Jackson School of Geosciences), the Gale White UTIG Fellowship, the G. Unger Vetlesen Foundation, and NERC (grant no. NE/D003733/1).
Acknowledgements. We would like to thank  Frédéric Parrenin  for his support with the 1D ice flow model and modeling discussions. This publication was generated in the frame of  Beyond EPICA -Oldest Ice (BE-OI). The project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement no. 730258  ( BE-OI CSA ). It has received funding from the  Swiss State Secretariat for Education, Research and Innovation (SERI)  under contract number  16. 0144.
Homoscedasticity and normality were checked using Bartlett and Shapiro-Wilk tests and square and cube roots variable transformations were applied when necessary to meet linear mixed models assumptions  (Quinn and Keough, 2002). Differences between the means of factors levels were assessed by Tukey's HSD. All analysis were carried out using R version 4. 0. 1 (R Core Team, 2020), linear mixed models were fitted using nlme package  (Pinheiro et al. , 2020).
Acknowledgements This research was funded by the  Spanish Ministry of Economy and Competitiveness  (project  AGL2016-76463-P ). A. Y. was funded through a  Spanish Predoctoral Research Grant  ( BES-2017-080326 ). Special thanks are due to the members of the  Ecology and Environmental Group at the Universidad Pública de Navarra  ( David Candel-Pérez ,  Yueh-Hsin Lo ,  Raquel Echeverría ,  Susana García  and  Federico J. Castillo ) who provided assistance in different field and lab tasks. The authors declare not having any conflict of interests.
The few available data on HBV and HCV in SSA are often focused solely on the general population, although the burden of this pandemic is potentially higher among vulnerable populations, who most often lack access to health services  [5]. A study in South Africa indicated a heavy burden of HBV and HCV among key populations (people who inject drugs, sex workers and men who have sex with men) in 2018 with prevalence of hepatitis B surface antigen (HBsAg) of 4% and HCV of 16%  [6]. In Nigeria, the Democratic Republic of Congo (DRC) and Rwanda, prevalence among sex workers was estimated at 17. 1%, 4. 20% and 2. 5% respectively for HBV and 0. 70% and 1. 4% for HCV in the DRC and Rwanda, respectively. Another study estimated the prevalence of anti-HCV among commercial sex workers at 6. 6%  [7] [8] [9] [10].
Data collection After eligibility screening and written informed consent approval, trained study staff (medical students) administered a structured and standardized questionnaire during a face-to-face interview. The questionnaire was adapted from the Family Health International (FHI) 360 validated guide for bio-behavioral surveys  [19]  and collected information on socio-demographic characteristics, risky sexual behaviors, STIs, HIV prevention methods, HIV testing history, access to health care services, and HIV knowledge. Other validated tools were used to assess alcohol and tobacco consumption: the Alcohol Use Disorders Identification Test (AUDIT-C) and a subset of the Tobacco Questions for Surveys  [20, 21]. The AUDIT-C is scored on a scale of 0-12, with scores of 0 reflecting no alcohol use.
All samples were stored at -80°C until use. Cell pellets from eight GSC lines (GSC-1, GSC-2, GSC-3, CSG-5, GSC-6, GSC-9, CSG-10, and GSC-11) derived from patients with IDHwt GBM were obtained from Poitiers University Hospital Centre, France, and were previously characterized  [23] [24] [25]. Validation cohorts, obtained from The Cancer Genome Atlas (TCGA) research network, were described in  [26]. For this study, IDHmut (n=415) and IDHwt (n=134) samples with both DNA methylation (HM450K array) and RNA expression (RNA-seq) data were selected. The clinical and molecular data of these patients were retrieved from cBioPortal for Cancer Genomics (https:// www. cbioportal. org/) [27, 28]. Processed RNA-seq and methylation data were obtained from the TCGA web site (https://portal. gdc. cancer. gov/) and analyzed as described below.
Strand-oriented RNA-seq Strand-oriented RNA-seq was performed with total RNA (n=3 brain control, n=8 IDHwt, n=5 IDHmut, and n=2 GSC samples (CSG-1 and CSG-2)) and also with mRNA from the GSC-6 and GSC-11 samples. Sequencing data were analyzed as previously described  [22]. Briefly, RNA-seq data were mapped to the hg19 human genome assembly using Tophat2 (version 2. 1. 0) and a transcript annotation file from GENCODE (Release 19). The read count per gene was obtained with the HTseq-count script. Strandspecific RNA-seq coverage was obtained using the SamTools (v 1. 9), genomeCoverageBed (v2. 27. 1)  and bedGraphToBigWig tools and visualized using the UCSC Genome Browser. Differential expression analyses were based on read counts using the DESeq2 and EdgeR R(3. 6) packages. Genes were considered as differentially expressed between groups when |log 2 (fold change)|>1 with an adjusted pvalue <0.
RT-qPCR analyses Two independent reverse transcription (RT) reactions per sample (250ng RNA/reaction) were performed using SuperScript IV (Invitrogen) and random primers (Invitrogen), according to the manufacturer's recommendations. Real-time quantitative PCR (qPCR) assays were performed using a microfluidic-based approach by the Gentyane facility (INRA Crouël, Clermont-Ferrand, France). Briefly, 5µL of cDNA (diluted to 1/2. 5) was pre-amplified (14 cycles) with the primer pool and the TaqMan PreAmplification Master Mix (Invitrogen). After digestion using an exonuclease (NEB) to remove the remaining primers, qPCR was performed on Fluidigm 96. 96 Dynamic Arrays using the Biomark HD system (Fluidigm Corp. ) according to the manufacturer's instructions. The relative expression level was calculated as follows: E -Ct(Transcript) /geometrical mean (E -Ct(HK genes) ), based on the 2 - ΔCt method (E: efficiency of amplification, Ct: cycle threshold, HK: housekeeping).
The housekeeping genes PPIA, TBP and HPRT1 were used to normalize transcript expression. For each condition, the presented data were obtained from the two independent RT reactions, each analyzed in duplicate using the Fluidigm Real-Time PCR Analysis software. Samples with an unexpected Tm or with a double melting peak were excluded from the analysis. All samples with a Ct >21 for a gene were considered as lacking expression of that gene. Genes were considered as differentially expressed between groups when the p-value was <0. 05 (determined with the Wilcoxon signed-rank test). The primers used are described in Table  S2.
2-3 Copy number variation analyses CNV analyses were performed using 3 control, 36 IDHwt glioma and 12 IDHmut glioma samples and Genome-Wide Human CytoScan HD (Affimetrix), as previously described  [22]. Raw data are accessible at GSE123682 and GSE161275. HOX gene expression was determined using the microfluidic approach described above. Correlations between CNV and expression data (available for 21 IDHwt samples) were analyzed with the Spearman rank correlation test.
Array-based DNA methylation analysis DNA from 78 samples (n=55 IDHwt glioma, n=15 IDHmut glioma, n=8 control samples) was analyzed using Infinium Human Methylation 450k (Illumina), and DNA form GSC samples (n=2) was analyzed using Infinium Human Methylation EPIC (Illumina). DNA bisulfite conversion and array hybridization were performed by IntegraGen, SA (Evry, France) using the Illumina Infinium HD methylation protocol. Analyses were performed as previously described  [29]. Raw data are accessible at GSE123678 (HM450K data) and GSE161175 (EPIC data). DNA methylation and expression data were correlated using data from 43 IDHwt samples of our cohort and from 134 IDHwt samples of the TCGA cohort. The DNA methylation level of CpG sites was correlated with the gene expression level in the HOX clusters using the Spearman's rank correlation coefficient.
ChIP-seq data-mining associated with chromatin analyses ChIP-seq data for the H3K4me3, H3K27me3 and histone H3 methylated at lysine 36 (H3K36me3) profiles were obtained from the NIH Roadmap Epigenomics project (http://www. roadmapepigenomics. org/) (neural progenitor cells, NPCs, and brain samples) or from the GEO database (glioblastoma and GSC samples), as follows: NPC samples (H3K4me3: GSM818043, GSM772736. H3K27me3: GSM956010, GSM772801. H3K36me3: GSM772795, GSM1013141), brain samples (H3K4me3: GSM772996, GSM669992. H3K27me3: GSM772772, GSM772993. H3K36me3: GSM670011, GSM772982), GBM samples (H3K4me3: GSM1121865, GSM1121875, GSM1121888. H3K27me3: GSM1121862, GSM1121872, GSM1121885. H3K36me3: GSM1121863, GSM1121873, GSM1121886), and GSC samples (H3K4me3: GSM1121860, GSM1121870, GSM1121882.
2-6 Functional annotations Regulatory features and cis-regulatory modules were predicted using i-cis Target  [31]. This tool allows analyzing the regulatory regions of gene lists to detect enrichment of transcription factor binding sites (TFBSs; i. e. consensus DNA sequences to which a transcription factor binds and represented as position weight matrices [PWM]). Only motifs with a Normalized Enrichment Score (NES) above a specified threshold (here 3. 0) are considered enriched. More details are provided at the i-cis Target website: (https://gbiomed. kuleuven. be/apps/lcb/i-cisTarget/) (v5. 0). PWM data (n= 20 003) for the promoter (defined as +/-1kbp of RefSeq TSS) of overexpressed and not affected HOX genes were analyzed.
These areas overlapped mostly with unmethylated CGIs among which many contained also a HOX gene transcription start site (TSS) (Fig. 4B , Fig. S3 ). DNA methylation and H3K4me3 are documented to be antagonist in the genome. To evaluate whether this permissive histone mark could protect TSS from DNA hypermethylation, we assessed its distribution along the four HOX clusters by using publicly available ChIP-seq data. H3K4me3 specifically marked the areas of negative correlation between DNA methylation and gene expression in IDHwt glioma samples, while it was depleted in control samples (Fig. 4B ). Accordingly, TSS could be divided in two groups (H3K4me3enriched and -depleted) (Fig. 4C ). Analysis of the HM450K data showed that H3K4me3-enriched TSS were mainly unmethylated, with DNA methylation mostly at their borders (Fig.
1. 3) (options: normalizeUsing RPKM, extendReads 200, ignoreDuplicates, binSize 20) and visualized with UCSC Genome Browser. Peaks were detected with MACS (v 1. 4. 2), using Input as control (options: nomodel shiftsize 73, for H3K27ac and H3K4me3 p value= 1e -5 , for H3K27me3 p value= 1e -3 ). K-means clustering of H3K4me3 TSS enrichment was performed with the computeMatrix and plotHeatmap tools from the deeptools (3. 1. 3) suite and the GSM1121865 coverage data. Raw data are accessible at GSE161436.
Acknowledgements We thank the  Clermont-Ferrand hospital neurosurgery department  ( Prof. J. J. Lemaire ) for help in completing this study and the  "Gentyane" facility  (http://gentyane. clermont. inra. fr/)for technical help with the Fluidigm 96. 96 Dynamic Arrays. We also thank all members of P. A. 's team for critical reading of the manuscript.
Funding sources This study was funded by the  Plan Cancer-INSERM  ( CS14085 ) (to L. K. -T. , P. V. , and P. A. ), the  Cancéropole CLARA (Oncostarter"Gliohoxas")  (to P. A. ), the  Fonds de dotation Patrick Brou de Lauriére  (to P. A. ),  Association pour la Recherche Contre le Cancer (ARC ; project  PJA32020060002253 ; to P. A), the  Ligue Contre le Cancer  from the  Ardéche, Cantal and Puy de Dôme committee  (to F. C. and P. A. ). E. L. B. had a fellowship from  La Ligue Nationale Contre le Cancer. This research is supported by the  French government IDEX-ISITE  initiative  16-IDEX 0001  ( CAP 20-25 ).
Data accessibility Data on the two GSC lines generated for this study were submitted to NCBI Gene Expression Omnibus (GEO; https://www. ncbi. nlm. nih. gov/geo/) under the following accession numbers: GSE161175 for the Epic DNA methylation data, GSE161438 (Ribo-zero) and GSE161437 (mRNA) for the oriented RNAseq data and GSE161436 for Chip-seq data. CNV data generated using glioma samples for this study were submitted under the GSE161275 accession number.
0, collected during routine clinic visits and collated via case report forms. Results: In total, 956 patients with CTEPH were included in the analysis. The most common AEs in these patients were peripheral edema/edema (11. 7%), dizziness (7. 5%), right ventricular (RV)/cardiac failure (7. 7%), and pneumonia (5. 0%). The most common SAEs were RV/cardiac failure (7. 4%), pneumonia (4. 1%), dyspnea (3. 6%), and syncope (2. 5%). Exposure-adjusted rates of hemoptysis/pulmonary hemorrhage and hypotension were low and comparable to those in the long-term extension study of riociguat (Chronic Thromboembolic Pulmonary Hypertension Soluble Guanylate Cyclase-Stimulator Trial [CHEST-2]). Conclusion: Data from EXPERT show that in patients with CTEPH, the safety of riociguat in routine practice was consistent with the known safety profile of the drug, and no new safety concerns were identified.
Study design EXPERT (NCT02092818) was an international, multicenter, prospective, uncontrolled, non-interventional cohort study in patients treated with riociguat in 28 countries. The design is described in detail elsewhere  [18]. EXPERT was linked with the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA [https://com pera. org/]), one of the largest global academic PH registries. This was consistent with guidance from regulatory authorities to use existing registries. EXPERT was conducted in accordance with good pharmacovigilance practices and was not requested, but was accepted, by the European Medicines Agency for the collection of additional long-term post-approval data on riociguat. Patients were followed for 1-4 years from enrollment (including posttreatment safety follow-up) during a recruitment period of 3 years or until 30 days after stopping riociguat treatment.
Statistical methods and populations analyzed EXPERT was an observational study. All variables and outcomes, including comparisons between predefined groups (such as newly treated versus pretreated patients and prevalent versus incident patients) were analyzed descriptively. Statistical analyses of these comparisons were not performed because they would be of limited value without adjustment for differences between the groups. All analyses were performed with SAS 9. 3. Categorical variables were analyzed using frequency tables and continuous variables by summary statistics (mean ± standard deviation [SD], median, and minimum-maximum range). The evaluable population consisted of all enrolled patients who did not withdraw consent and had received at least one dose of riociguat with dosing data available.
Riociguat safety 3. 3. 1. Total CTEPH population In the total population with CTEPH, the median (range) duration of observation and riociguat treatment was 504. 0 (0. 0-1367. 0) days and 493. 5 (0. 0-1367. 0) days, respectively. In total, 615 patients (64. 3%) experienced AEs and 365 (38. 2%) experienced SAEs. These events were considered drug-related by the investigator in 148 (15. 5%) and 34 (3. 6%) patients, respectively. The most common AEs and SAEs are shown in Table  2. Discontinuation due to AEs and SAEs occurred in 55 patients (5. 8%) and 38 patients (4. 0%), respectively. The most common AEs or SAEs leading to discontinuation are shown in Supplementary Table  2. Safety data for patients according to use of riociguat as monotherapy or in combination with other PAH-approved drugs are shown in Supplementary Table  3.
Safety findings EXPERT provided data on the safety and tolerability of riociguat in more than 950 patients with CTEPH in real-world clinical practice. The types of AEs observed were consistent with those reported in Chronic Thromboembolic Pulmonary Hypertension Soluble Guanylate Cyclase-Stimulator Trial-1 (CHEST-1)  [15] , CHEST-2  [16, 19] , and the CTEPH Early Access Study  [20] , with no new safety signals identified. The most common events (e. g. , peripheral edema/edema, dizziness, and dyspnea) were consistent with symptoms of the underlying disease or with vasodilatation by riociguat. Although the numbers of patients undergoing BPA or PEA were small, there was no evidence of an increased risk of drug-related events shortly after these procedures. The exposure-adjusted rate of hypotension (2.
7 events per 100 patient-years) was lower than that reported in CHEST-2 (4. 0 events per patient-years)  [19]. The rates of hemoptysis/pulmonary hemorrhage were similar (2. 2 and 2. 8 events per 100 patient-years in EXPERT and CHEST-2  [19] , respectively). Most patients were receiving anticoagulants as required by CTEPH treatment guidelines: these may have contributed to the bleeding AEs. A separate publication comparing hemorrhagic and thrombotic/embolic events in patients receiving VKAs or NOACs at baseline is in preparation. In patients receiving riociguat as part of a combination regimen, the incidences of AEs and SAEs were higher than in patients receiving monotherapy. These data should be interpreted with caution as they are descriptive, they are not adjusted for differences between the combination and monotherapy groups, they refer to treatment at baseline, and the numbers of patients receiving prostanoids were small.
Comparison with other studies Compared with the 247 non-operable patients described in the international prospective CTEPH registry  [9, 24] , CTEPH patients in EXPERT had less severe functional impairment, with 53. 1% categorized as WHO FC III/IV at baseline versus 83. 0% and a slightly higher mean 6MWD at baseline (365 vs. 315 m). The international registry enrolled only incident patients, whereas 74. 6% of patients with CTEPH in EXPERT had prevalent disease. In a prospective study of 392 patients newly diagnosed with CTEPH in Germany, riociguat was the initial treatment in 81. 1% of patients who received medical therapy, illustrating the importance of obtaining safety data for riociguat  [25]. The patients in this study had generally similar characteristics to those in the EXPERT population.
They may also detect previously unsuspected safety signals. No such signals were identified in EXPERT. Limitations inherent in registries including confounding, lack of randomization, missing values, and the hazards of generalizing data from the registry population to other populations  [32] , also apply to EXPERT. In addition, EXPERT was designed to collect safety information on riociguat; it was not designed to provide data on the long-term efficacy of this drug. Strengths of EXPERT include the relatively long observation time (median: 504 days), the large number (956) of patients with CTEPH, the large proportion of patients completing the study. The planned enrollment for the entire study was 900 patients to allow for detection of ≥ 3 "uncommon" AEs with an incidence ≥ 0. 5%. Given that 956 patients with CTEPH were enrolled, there were enough patients in the CTEPH cohort alone to achieve this power level.
Acknowledgments and funding The EXPERT registry was funded by  Bayer AG (Berlin, Germany)  and  Merck Sharp & Dohme Corp. , a subsidiary of  Merck & Co. , Inc. , Kenilworth, NJ,  USA. The authors acknowledge the database administration by  Torsten Tille ,  Dresden , and the project administration of  Mrs Romy Hoppenz  and  Mrs Linda Kottke  at  GWT-TUD GmbH, Dresden. Medical writing services provided by  Richard Murphy PhD of Adelphi Communications Ltd , Macclesfield, UK were funded by  Bayer AG  in accordance with Good Publication Practice (GPP3) guidelines.
Applying the 10-step RAX framework in a NGRA for caffeine This NGRA approach follows the recommendations of the SEURAT-1 project  (Berggren et al. , 2017) , resulting in the tiered 10-step framework  (Alexander-White et al. , 2021)  that is applied in this case study for caffeine, as shown in Fig. 1. Starting with the problem formulation we go step-by-step through the framework. For all NGRA it is important to begin with a clear problem formulation. In this case, human safety of the target chemical caffeine has to be assured despite the (hypothetical) lack of animal systemic toxicity data. It has to be decided what exposure represents an acceptable 'safe' concentration, without the option for new animal testing as products containing caffeine are marketed in Europe. To begin with, data are collected on the use and exposure to caffeine to estimate an external dose metric.
D. Bury et al. The target chemical caffeine (1,3,7-trimethylpurine-2,6-dione) appears as odorless white powder or white glistening needles with bitter taste. It consists of a xanthine scaffold with three N-methyl groups as shown in Fig. 2. For the purpose of this case study, it was assumed that caffeine was a pure chemical and did not contain any impurities. Step 3: Collate supporting data for caffeine and define data gap(s) All attempts were made to collate data for caffeine on physicochemical properties (see Table  2 ), absorption, distribution, metabolism and excretion (ADME), and toxicity endpoints. A literature and database search was performed using the major authoritative worldwide sources of information such as ChemSpider, PubMed, ECHA (REACH), NTP, OECD, CIR etc. (Alexander-White et al.
Identify analogues An initial screening of structurally similar analogues for caffeine, performed with the ChemTunes-ToxGPS software (https://www. mn-am. com/), resulted in the identification of 70 potential analogues that had a chemical structural similarity of above 70% based on the Tanimoto score, a similarity measure for comparing chemical structures. Structures are usually considered similar if the Tanimoto score is above 85%. Three chemicals had a score of greater than 90% similarity, i. e. theophylline, theobromine and paraxanthine (Table  2 ). But far more essential for the identification of analogues are other considerations such as presence of reactive groups, metabolism and physical chemical properties. As mentioned in Step 2, caffeine consists of a xanthine scaffold with three N-methyl groups.
Collate existing data A determinant of the outlined RAX approach was the availability of relevant data on physicochemical properties, biological activity and toxicity for the target chemical caffeine and the three highest scoring analogues (Tables  2 3 4 5 ). A literature and database search (OECD QSAR toolbox v4. 3, scientific literature, COSMOS database) revealed predictive and in vivo systemic toxicity data in experimental animals for paraxanthine, theobromine and theophylline which supported the choice of the three chemical structure analogues.
Hypothesis for RAX On the basis of the information collated so far, the hypothesis is that animal systemic toxicity data can be used from a structurally similar analogue of caffeine based upon a common metabolic pathway  (Gracia-Lor et al. , 2017; Schultz et al. , 2015). Among the four possible RAX approaches, this scenario can be classified as using 'Chemical similarity involving metabolism (resulting in exposure to the same/similar substance(s))' (see  Alexander-White et al. , 2021). The use of a common metabolite approach is a cornerstone approach to RAX as per the ECHA Read-Across Assessment Framework (RAAF) (ECHA, 2017). In addition, this approach is most effective when the metabolites are major primary metabolites, as is the case here for caffeine. The hypothesis here is that all four chemicals, caffeine and the primary metabolites, have a common MOA.