dataesr/scientific-paragraphs-categorization huggingface dataset:
Age Á sex Á time standardisation To avoid effects of differences and changes in ageÁ sex structures of populations, mortality fractions and rates have been adjusted using the INDEPTH 2013 population standard  (32). A weighting factor was calculated for each site, age group, sex, and year category in relation to the standard for the corresponding age group and sex, and incorporated into the overall dataset. This is referred to in this paper as ageÁsex-time standardisation in the contexts where it is used.
Cause of death assignment The InterVA-4 (version 4. 02) probabilistic model was used for all the cause of death assignments in the overall dataset  (33). InterVA-4 is fully compliant with the WHO 2012 VA standard and generates causes of death categorised by ICD-10 groups  (34). The data reported here were collected before the WHO 2012 VA standard was available, but were transformed into the WHO 2012 and InterVA-4 format to optimise cross-site standardisation in cause of death  attribution. For a small proportion of deaths, VA interviews were not successfully completed; a few others contained inadequate information to arrive at a cause of death. InterVA-4 assigns causes of death (maximum 3) with associated likelihoods; thus, cases for which likely causes did not total to 100% were also assigned a residual indeterminate component.
Overall dataset The overall public-domain dataset  (9)  thus contains between one and four records for each death, with the sum of likelihoods for each individual being unity. Each record includes a specific cause of death, its likelihood, and its ageÁsexÁtime weighting. In this context, all of these data are secondary datasets derived from primary data collected separately by each participating site. In all cases, the primary data collection was covered by site-level ethical approvals relating to ongoing health and demographic surveillance in those specific locations. No individual identity or household location data were included in the secondary data and no specific ethical approvals were required for these pooled analyses.
Because some of the sites reporting the highest rates of NCD mortality were also those with the greatest burden of HIV/AIDS-related mortality (as explored from the same dataset in a separate paper  (35) ), in Fig. 4  we present, separately for the 15Á64 and over-65 year age groups, the correlations between NCD and HIV/AIDS mortality rates, showing points for each site and least absolute deviations regression lines for both age groups. In both age categories, correlations are significant (pB0. 005; R 2 00. 40 and 0. 37, respectively) and suggest that in high HIV settings, around half of the mortality attributed to NCDs may well be associated with HIV. This was also reflected in falling NCD mortality rates among younger adults at most African sites between the 2000Á05 and 2006Á12 periods (Table  1 ), as HIV/AIDSrelated mortality reduced sharply  (35).
Appreciable rates of 'other neoplasms' were recorded from many sites, which may partly reflect a lack of specific information about cancers recalled in VA interviews, or a lack of specific items in some historic VA instruments. Table  3  shows, in the same format as Table  2 , mortality rates for diabetes, cardiovascular disease, and chronic obstructive pulmonary disease. These categories were major contributors to NCD mortality across many sites, although the relative magnitude of these categories of mortality varied between sites. Some sites were using VA instruments designed before NCDs were considered to be major causes of death in the populations concerned. It is likely that the quality of NCD cause assignment achievable by InterVA-4 will increase as primary VA data collected according to the complete WHO 2012 VA specification become more widely available.
Nevertheless, the overall consistency observed across these varied sites in many respects suggests that historical inter-site variations did not have a major influence on the overall pattern of NCD mortality findings. The importance of separating NCD mortality into premature and less-avoidable groupings by age is clearly illustrated in Fig. 3. Due consideration must be given to both NCD mortality rates as well as numbers of NCD deaths in considering how disease burdens may be changing. Here, we have somewhat arbitrarily taken 65 years as the cut-off for considering premature mortality, because that fitted with the WHO 2012 VA age groups used in this dataset. In addition, many of the countries represented here have life expectancies at birth in the range of 60 to 70 years, and so 65 years may represent a reasonable dichotomy in terms of 'prematurity'.
Acknowledgements We are grateful to all the residents of INDEPTH HDSS sites who have contributed personal information to this mortality dataset, to the field staff who undertook so many verbal autopsy interviews, and the data management staff who handled the data at every participating site. INDEPTH thanks all the site scientists who participated in bringing this work together, and who variously participated in analysis workshops in Ghana,  Belgium, Thailand , and the  United Kingdom. The INDEPTH Network  is grateful for core funding from  Sida , the  Wellcome Trust , and the  William & Flora Hewlett Foundation. The Umea ˚Centre for Global Health Research is core funded by  Forte , the  Swedish Research Council for Health, Working Life and Welfare  (grant  2006-1512 ). PB's residency at the  University of the Witwatersrand Rural Knowledge Hub  to analyse and draft these results was supported by the  European Community Marie Curie Actions IPHTRE  project (no.
295168 ). icddr,b is thankful to the  Governments of Australia, Bangladesh ,  Canada, Sweden  and the  UK  for providing core/unrestricted support. The Ouagadougou site thanks the  Wellcome Trust  for its financial support to the  Ouagadougou HDSS  (grant number  WT081993MA ). The Kilite Awlaelo HDSS is supported by the  US Centers for Disease Control and Prevention (CDC)  and the  Ethiopian Public Health Association (EPHA) , in accordance with the  EPHA-CDC  Cooperative Agreement No. 5U22/PS022179_10  and  Mekelle University , though these findings do not necessarily represent the funders' official views. The Farafenni site is supported by the  UK Medical Research Council. The Vadu site is thankful for the core continued funding support from  KEM Hospital  Research Centre since 2004. The Kilifi site is supported through core support to the  KEMRI-Wellcome Trust Major Overseas Programme  from the  Wellcome Trust.
The Atmospheric Monitoring system The Atmospheric Monitoring (AM) system of JEM-EUSO will include: which will be used for the detection of aerosol layers and clouds (especially optically thin) and the measurement of cloud altitude and optical depth; 3. global atmospheric models generated from the analysis of all available meteorological data by forecasting services such as the National Centers for Environmental Predictions (NCEP)  [3] , the Global Modeling and Assimilation Office (GMAO) [4] and the European Centre for Medium-Range Weather Forecasts (ECMWF) [5]; 4. the slow mode data of JEM-EUSO, the monitoring of the pixel signal rate every 3. 5 sec for the observation of transient luminous events (TLEs), which will give additional information on cloud distribution and the intensity of the night sky airglow.
The LIDAR will be used to measure the clouds altitude and optical depth as well as the optical depth vertical profile of the atmosphere along these directions with a range accuracy of 375 m in nadir. The IR camera and the LIDAR have been designed to work in a complementary way. On one hand the IR camera will give information on the cloud cover over the entire FoV, which is not accessible with the LIDAR, since this device can measure the optical properties of the atmosphere only in a given direction. On the other hand, the LIDAR will measure the optical properties of optically thin clouds which are difficult to identify with the IR camera. The analysis of the atmospheric data will be complemented by the use of the atmospheric global models and the slow mode data of JEM-EUSO. Statistical studies show that ∼ 70% of the EAS triggered events will develop in cloudy conditions  [7, 8] , meaning that only ∼ 30% of the triggered events will be tagged as "golden" and used for physics analysis if no correction is applied to the cloud-affected events.
Conclusions The JEM-EUSO telescope will monitor the earth's atmosphere from space looking for EAS generated by the passage of UHECRs hitting the Earth. The detector footprint will experience all possible weather conditions during the overall data taking period. The Atmospheric Monitoring system designed for the JEM-EUSO experiment will continuously monitor the telescope FoV in order to get the cloud cover and cloud top height as well as the measurement of the optical depth profile of the atmosphere around the EAS location. The information coming from the IR camera and the LIDAR will be complemented by the use of Global at- mospheric models and the JEM-EUSO slow data. The atmospheric data will be used to correct the profiles of cloudaffected EAS events for the effect of clouds and aerosol layers, so that most of them could be retained for the UHECR data analysis.
Mechanisms Leading to Overexpression of GPER in Human Seminomas Such a GPER overexpression has been already linked to the development of advanced breast cancer  [21] , high-grade endometrial tumors  [19] , and poor prognosis for ovarian cancer  [20]. Thus, it is critical to determine the precise mechanisms leading to such a GPER overexpression in seminomas and to assess its role in testicular carcinogenesis considering the ability for xeno-estrogens to promote seminoma cell proliferation  [26, 34]. One explanation for this overexpression could be single nucleotide polymorphisms (SNPs), which are the most frequent genetic variations in genomic sequences and are widely associated with human diseases  [35]  and TGCTs  [36]. Among the 40 SNPs identified in the GPER gene (GenBank accession no. NM_001039966), only four were reported to be associated with human neoplasms, according to the internet database  [37]  The first of these four SNPs has been reported in gastric cancer  [38]  and creates an alternative splice site that produces a frameshift protein with no cellular relevance.
We obtained clinical data from 56 genotyped patients with seminomas. The allele and genotype frequencies of each SNP in these patients were not significantly different from those observed in the entire neoplastic population, and from those observed in the reference population (Table  2 ). The mean age at diagnosis was 38. 6 ± 9. 5 years. The mean tumor size was 46. 2 ± 20. 4 mm, with tumor spread beyond testis according to TNM (Tumor, Nodes, Metastasis) classification (≥pT2) in 20 of 56 cases (35. 7%). We did not find a correlation between a specific genotype and tumor characteristics (age at diagnosis, tumor size, or tumor spread) for these patients.
Cell Proliferation Assay JKT-1 cells were seeded in six-well plates (0. 6 × 10 6 cells/well). After 48 h, the JKT-1 cells were washed and oestrogen starved overnight in phenol red-free DMEM supplemented with 1% charcoal-stripped FBS. We then added E2 (Sigma-Aldrich ® , Saint Louis, MO, USA), freshly prepared E2-BSA (Sigma-Aldrich ® , Saint Louis, MO, USA) devoid of free E2, which is removed by filtration, ICI-182,780 (fulvestrant; Falsodex ® , AstraZeneca, Wilmington, DE, USA), G1 (Calbiochem ® , Merck KGaA, Darmstadt, Germany), G15 (kindly supplied by Eric R. Prossnitz)  [42] , or ethanol (as a vehicle control) at 10 -9 M concentration  [15, 43] , and incubated them for 24 h. We harvested the cells using trypsin and counted them using the Vi-CELL software (Beckman Coulter, Fullerton, CA, USA).
4) containing protease inhibitors. Protein concentrations of the cell and tissue lysates were determined by the Bradford method. Equal amounts of the whole protein extract were resolved on a 12% SDS-polyacrylamide gel. The proteins were transferred to a polyvinylidene difluoride membrane (Immobilon P; Millipore™, Billerica, MA, USA), probed with anti-GPER Ab (Santa Cruz Biotechnology ® , Santa Cruz, CA, USA), and detected using HRP-linked secondary Ab and the ECL System (GE Healthcare ® , Chalfont St. Giles, UK). After the blots were stripped, we verified equal loading of the protein by reprobing the same blots with anti-actin Ab (Cell Signaling Technology ® , Danvers, MA, USA). All experiments were performed in triplicate and the blots shown are representative. All data were analyzed using the StatView ® 5 software (SAS Institute Inc.
Genomic DNA extraction was performed using 10-µm -thick serial sections cut from each specimen block, as previously reported  [40]. The DNA-containing lysate was subjected to tetra-primer ARMS-PCR to achieve allele-specific amplification  [35]  by using primers listed in Figure  5  (synthesised by Eurogentec™, Liè ge, Belgium) and QIAGEN ® Multiplex PCR Plus Kit (Qiagen Inc. , Valencia, CA, USA). Polymerase Chain Reaction (PCR) mixture contained 0. 2 µM final concentration of each primer and 100 ng genomic DNA and was prepared according to the manufacturer's instructions. PCR was performed using Thermocycler Q-Cycler II (Quanta Research™, Taoyuan County, Taiwan) by using the following reaction conditions: initial activation at 95 °C for 5 min;  35   SNP frequencies were compared with genotype frequencies determined in the HapMap project and reported on the internet database  [37]  as follows: for SNP rs3808350, genotype frequencies are 0.
366 (AA), 0. 491 (AG), and 0. 143 (GG), and allele frequencies are 0. 612 (A) and 0. 388 (G); for SNP rs3808351, genotype frequencies are 0. 054 (AA), 0. 514 (AG), and 0. 432 (GG), and allele frequencies are 0. 311 (A) and 0. 689 (G); and for SNP rs11544331, genotype frequencies are 0. 650 (CC), 0. 302 (CT), and 0. 048 (TT), and allele frequencies are 0. 801 (C) and 0. 199 (T). Deviation from the Hardy-Weinberg equilibrium between normal and neoplastic populations was estimated using chi-square test. Statistical tests for association (with 95% confidence interval) and significance were performed using StatView ® 5 software (SAS Institute Inc. , Cary, NC, USA). Odds ratio (OR) was calculated using the more frequent homozygous genotypes as reference group. A p value < 0. 05 was considered statistically significant.
Acknowledgments We gratefully acknowledge  Anne Doye  (INSERM,  UMR U1065/UNS ,  C3M , Team 6) for her help in subcellular fractionation. This work was supported by the  Institut National de la Santé et de la Recherche (INSERM)  and by the  Fondation pour la Recherche Mé dicale (FRM)  (to Nicolas Chevalier). Nicolas Chevalier  and  Rachel Paul-Bellon  performed the research and analyzed the data;  Philippe Camparo ,  Jean-Franç ois Michiels  and  Daniel Chevallier  contributed the tumor samples;  Nicolas Chevalier  and  Patrick Fé  nichel designed the study and wrote the paper.
Determination of the cut-off value for SF positivity Receiver operating characteristic (ROC) curves, generated using XL-Stat software, were used to determine the cut-off value for plasma SF that minimizes the number of false positives and false negatives. Minimizing the false positives and false negatives is equivalent to maximizing sensitivity and specificity. ROC curves were performed using plasma SF (or D-dimer) concentrations in all patients tested for suspicion of thromboembolic disease. Knowledge of the presence or absence of thromboembolic disease, evaluated by imaging, was used to create a ROC curve for the SF (or D-dimer) assay, the levels of SF (or D-dimer) being classified according to the presence or absence of thromboembolic disease. The ROC curve was obtained [Y-axis = the true positive rate (Sensitivity) and X-axis = the false positive rate (1-Specificity)], and the area under the curve (AUC) was evaluated  [19].
Statistical analyses SF and D-dimer levels are reported as means 6 1 standard deviation (SD) and as median (25th and 75th percentiles). The non-parametric Wilcoxon signed-rank test was used to compare the SF or D-dimer concentrations of patients with or without PE and/or DVT. Sensitivity, specificity, negative-predictive values (NPV) and positive-predictive values (PPV) and their 95% confidence intervals (CI) for VTE diagnosis were calculated using SAS v9. 2 software (SAS Institute Inc. , Cary, NC). Tests were two-sided, and a p value ,0. 05 was considered to be statistically significant. To compare the performance characteristics of SF with that of D-dimer for diagnosis of venous thromboembolism  [19] , the area under the ROC curve (AUC), an important measure of the accuracy of the clinical test, was determined.
Acknowledgements We are grateful to  Damir Becirevic  for many useful and enlightening discussions. A. V. thanks  Tommaso Spadaro  for his insight on the experimental perspectives of the NA62 experiment. This work has been partly done under the  ANR  project  CPV-LFV-LHC NT09-508531. The work of C. W. is suppported by the  Spanish MINECO  under grant  FPA-2012-31880. The authors acknowledge partial support from the  European Union   FP7 ITN INVISIBLES (Marie Curie Actions ,  PITN-GA-2011-289442 ).
Methods: We used ''e'' case vignette methodology for this non interventional study. A national panel of randomly selected GPs was included. We used eight ''e'' case-vignettes relating to chronic pain, differing in terms of the type of pain (neuropathic/non neuropathic), etiology (cancer, postoperative pain, low back pain with or without radicular pain, diabetes) and symptoms. GPs received two randomly selected consecutive ''e'' case vignettes (with/without neuropathic pain). We analyzed their ability to recognize neuropathic pain and to prescribe appropriate first-line treatment. Results: From the 1265 GPs in the database, we recruited 443 (35. 0%), 334 of whom logged onto the web site (26. 4%) and 319 (25. 2%) of whom completed the survey. Among these GPs, 170 (53. 3%) were aware of the guidelines, 136 (42.
Ethics statement This study was conducted in accordance with French regulatory requirements. The protocol and all administrative documents, including the financial agreement, with investigators paid for their participation, were approved by the National Medical Council (Conseil National de l'Ordre des Medecins; CNOM). The database was declared to the National Data Protection Authority (Commission Nationale de l'Informatique et des Libertes; CNIL). Submission to an ethics committee was not required under French law.
Selection of participants We calculated that a sample of 300 GPs would be required to estimate any percentage with a maximum 95% CI of 66. 5%, taking into account a design effect related to the non-independence of observations made by the same physician. The rate of participation in e-CRF studies is generally about 30%, with 75% of participants being highly active. We therefore decided to select a representative random sample of 1,332 GPs for this study. To this end, we sent a questionnaire, by mail, to the 84,832 GPs practicing in mainland France (listed in the CEGEDIM database), on January 2 nd , 2012, asking them whether they would be interested in participating in a non-interventional study. We selected a random sample of 1,332 physicians from the 4,299 GPs who expressed interest in participating in this study between January and July 2012.
Contact information was incorrect for 67 GPs, so 1,265 GPs were finally included in our database. In October 2012, we sent these GPs a proposal for participation in this survey. Any physician who did not respond to this mailing was contacted by telephone two weeks later, and attempts at contact were continued until the planned number of participants was reached. In total, 465 GPs agreed to take part in this survey, of whom 443 signed a contract for participation and were issued with a center number. The flow chart for GP selection is summarized in Figure  1.
Construction of case-vignettes Case vignettes were developed by a multidisciplinary panel of experts (authors of this paper) in the field of neuropathic pain, practicing as GPs, clinicians or nurses in neurology or anesthe-siology. The description of clinical cases was based on epidemiological and descriptive data characterizing neuropathic pain  [21]. Each clinical case was designed to be realistic and concrete, matching as closely as possible the cases observed in clinical practice. We focused on four etiologies of peripheral neuropathic pain frequently encountered in general medical practice: painful diabetic polyneuropathy, cancer chemotherapy-induced peripheral neuropathy  [22] , typical radicular pain  [23]  and post-operative neuropathic pain  [24] [25] [26]. In assessments of performance for the identification of neuropathic pain, we considered two types of chronic pain for each etiology: nociceptive chronic pain and peripheral neuropathic pain.
Based on these conditions, we constructed eight case-vignettes, each with a different scenario. The eight case-vignettes are given in File S1. The scenario needed to be simple, brief (length ,15 lines-200 words), with no potential pitfalls. Each case-vignette was constructed in the same way: age, sex, patient's history, chronic pain history, reason for consultation, clinical symptoms, data from clinical examination, with or without results from additional investigations. Scenarios differed in terms of both symptoms (e. g. burning pain vs aching pain) and elements of clinical examination (e. g. allodynia vs pain triggered by joint mobilization). The symptoms and clinical examination findings leading to the diagnosis of neuropathic pain were chosen from among the most discriminative, although none of them is specific  [7, 11, 27]  and were consistent with those used in validated screening tools, such as the DN4 (Douleur neuropathique en 4 questions)  [7].
Procedure Eight case-vignettes were constructed, corresponding to four neuropathic cases and four non-neuropathic cases (Table  1 ). By combining these vignettes two-by-two (one neuropathic case and one non-neuropathic case), we obtained 12 possible combinations, which we tested on 20 GPs, to evaluate their comprehensibility. This testing step identified two combinations as too similar to each other (diabetic polyneuropathy and cancer chemotherapy-induced polyneuropathy), and these combinations were therefore removed. Each GP had to provide a response for a set of two case-vignettes randomly selected from the 10 remaining combinations. GPs were blind to the mode of case distribution. The case-vignettes were stored in a database on a dedicated server for this survey (a web-based application).
The results obtained were compared with the expected responses. Prescriptions of analgesic treatments were described separately for each of the eight case-vignettes and according to: the type of pain (neuropathic or non-neuropathic), knowledge of the recommendations (YES/NO) and their implementation (YES/NO). We aimed to evaluate compliance with SFETD guidelines. Thus, for each question on the case-vignette questionnaire, we analyzed the number of correct answers and the number of answers containing at least one element of the correct answer, without any element wrongly ticked. Analyses were performed with SAS version 9. 3 (SAS Institute, Cary, NC).