dataesr/scientific-paragraphs-categorization huggingface dataset:
As a consequence, about 40% of EXFOR entries include data read (digitized) from graph images  [34] , and the NRDC still routinely performs figure digitization (even for new articles, where all attempts to contact authors are unsuccessful). In order to assure the accuracy in our digitization, the IAEA NDS organized a benchmark of digitization performance in 2005  [35]. All centres were requested to digitize angular differential cross sections published in 2000, and five data centres (CAJaD, CDFE, CNPD, JCPRG, NNDC) submitted their digitization results in a blind intercomparison with the numerical data received from the experimentalist. This was followed with a workshop organized by the NDS to discuss good practice for good digitization, which drafted recommendations for EXFOR compilers  [34].
These benchmark tests show that data digitized from symbols on a linear scale are often equal to the original data when the quality of the graph image is high, while the accuracy cannot be better than 1% when symbols are on a logarithmic scale. Three data centres (CNDC, CNPD, JCPRG) are developing and maintaining their own digitization tools (GD-Graph, InpGraph and GSYS), which are freely available on the internet. The other NRDC data centres make regular use of these tools and contribute to their improvement by providing suggestions to the developers. These three digitization tools are briefly introduced in the following subsections.
GDGraph GDGraph  [36]  has been developed by the CNDC to meet the requirements from evaluators, experimentalists and compilers in China. The first version of GDGraph released in 2000 was written in VC++. The program was re-written in Perl and its second version was released in 2006. Since then, Versions 3. 0, 4. 0, 5. 0 and 5. 1 were released in 2011, 2012, 2013 and 2016, respectively. A friendly interface has been implemented by use of WxWidgets as the graphical user interface toolkit. GDGraph has the following features: • All information for digitization of a graph image can be saved as a "project file", and users can resume or check the digitization work later by loading it. • Clipboards can be used when loading a graph image or generating a numerical data. • The graph image can be rotated by setting any angle.
InpGraph InpGraph has been developed by CNPD as a part of the EXFOR-Editor software package  [37]. Its first "official" release was in 2001, and the version with the modernized interface was released in 2014. It is designed in order that the user can easily achieve a variety of goals with the aid of a comprehensive graphical interface (Figure  7 ). InpGraph implements various mathematical models for extraction of data from a low quality image (e. g. , a graph image where the x-axis and y-axis are not orthogonal to each other). Two types of digitizing errors estimated by InpGraph ("systematic error" and "quantization error") quantify the digitization accuracy so that compilers may input these data into their EXFOR compilations. The systematic error is estimated from the deviation of the digitized values from the true values on ticks on an axis.
GSYS GSYS (Graph Suuchi Yomitori System)  [38]  is a platformindependent Java application tool, which was originally developed by JCPRG for compilation for the Nuclear Reaction Data File (NRDF)  [39]. Various new functions have been added since release of its first version in 2005  [40] , and the current version (Version 2. 4. 7, released in 2014) is used not only for NRDF compilation but also for EXFOR compilation. GSYS offers "automatic axis recognition", "automatic point recognition" and a "feedback function" for better digitization accuracy. Automatic axis recognition: GSYS recognizes the position of the axis and ticks on the graph image when the user drags the mouse pointer to the area including the axis. The accuracy of digitization strongly depends on the accuracy of the axis and tick detection, and one can expect a major improvement in the digitization quality with this function.
Summary The status of NRDC collaboration and our recent effort for improvement of the EXFOR coverage were presented. Our goal is to make all experimental nuclear reaction data accessible, and the NRDC collaborates to increase the completeness and quality of the EXFOR library. Two EX-FOR completeness assessments were discussed, including a comparison between EXFOR and the NSR, and a joint NNDC/IAEA effort to identify missing fission product yield data referenced in well-known compilations. These have revealed many nuclear reaction experimental works that are still missing in EXFOR. Digitization tools developed by NRDC members and routinely used for EXFOR compilation were introduced. These are continually improved, with highlighted features described in this paper, and have been used to provide approximately 40% of the EXFOR entries with numerical data.
[23, 24]  and the IAEA Coordinated Research Project on "Compilation and evaluation of fission yield nuclear data"(1991)(1992)(1993)(1994)(1995)(1996) [25]. Since then, fission product yields have been compiled more regularly, and literature scanning by the NDS (2004-) ensures that EXFOR is mostly complete for fission product yields published in or after 2004. On the other hand, it was not clear what fraction of the data compiled and used by the US and UK evaluators is covered in EXFOR. CCDN (now NEA DB) compared Crouch's 1977 compilation [21]  with EXFOR and CINDA in 1980 [26] , and the NNDC performed conversion of Meek & Rider file [19, 20]  to EXFOR in 1980s and.
Phenotypic Characterization and Measurement of the QT-Interval Clinical data were collected at each of the participating centers. We collected a baseline ECG for each patient, preferably not during β-blocker use. The QT-interval duration was measured as previously described (Figure I in the Data Supplement, and Methods in the Data Supplement). 24 In genotype negative patients, a LQTS diagnosis was additionally curated by 2 clinicians (NL, RT) and in case of uncertainty, 2 senior LQTS experts (AAW, PJS) were consulted. As per international guidelines,  5  we only included genotype negative patients with a LQTS risk score≥3. 5 or with a resting QTc≥500ms in repeated 12-lead ECGs, in the absence of a secondary cause for QT prolongation.
Genome-Wide Array Genotyping, Quality Control, and Imputation We performed genome-wide genotyping for all European cases on the Illumina HumanOmniExpress array and for all Japanese cases on the Illumina Global Screening Array. Genotypic data of 8219 control individuals of European ancestry and 1671 individuals of Japanese ancestry were obtained from different cohorts (Table  II  in the Data Supplement). Quality control,  25  imputation and association analysis were performed separately in the European and Japanese datasets. All genetic variants were mapped to and reported using Genome Reference Consortium Human genome build 37. After quality control (see Methods in the Data Supplement for details), we performed genome-wide imputation using Eagle2 phasing, Minimac3 and the Haplotype reference consortium (HRCr1.
Polygenic Risk Scores For all cases and controls, we calculated a weighted QT polygenic risk score (PRS QT ) comprising 68 SNPs that had been associated with the QT-interval in the general population at genome-wide statistical significance, in a study primarily including Europeans. 29 All 68 SNPs were included in the European dataset analyses whereas only 60/68 SNPs were well-imputed and included in the Japanese dataset analyses (Table  III  in the Data Supplement). PRS QT was calculated by multiplying the alternate allele dosage by the associated effect size (β) in the published QT GWAS for each of the 68 SNPs. Then, the PRS QT was normalized to a mean of 0 and standard deviation of 1. We used logistic regression to test for association of PRS QT with case-control status, correcting for principal components 1 to 10.
SNP ). Before heritability estimation, we conducted additional stringent genetic quality control, as previously suggested (Methods in the Data Supplement). 33 We estimated the SNP-heritability on the liability scale assuming a 0. 04% prevalence with principal components 1-10 as covariates. 1  We assessed the robustness of heritability estimates from GCTA-GREML using the GREML and phenotype-correlation genotype-correlation regression  34  analyses implemented in LDAK. 35 We estimated h 2 SNP in the overall LQTS and genotype positive LQTS dataset in the both European and Japanese ancestries. Because of small sample size we were not able to estimate h 2 SNP in genotype negative patients with LQTS using the approaches implemented in GCTA or LDAK.
Clinical Characteristics of the Case Cohort Demographic and clinical characteristics of the unrelated LQTS cases are presented in Table IV in the Data Supplement separately for the European and Japanese datasets and in Table  1  for the combined cohort. We included a total of 1781 unrelated patients with LQTS of European (n=1344, mean QTc±SD: 484±48ms) and Japanese descent (n=437, QTc: 485±49ms). A total of 1584 cases (89%) were genotype positive, carrying a rare variant in KCNQ1 (LQT1, n=800), KCNH2 (LQT2, n=661), or SCN5A (LQT3, n=123), while in 197 (11%) no disease causing variant was identified (ie, genotype negative) despite extensive genetic testing. The mean QTc interval in genotype negative cases was higher in comparison with genotype positive ones (500±52ms vs 482±47ms, P=2×10 -5 ) and in genotype negative cases a family history of sudden cardiac death at <50 years of age in 1 st and 2 nd degree relatives was less frequent compared with genotype positive ones (12.
67; P=2. 04×10  -17  ) and the Japanese (Figure  3B ; R 2 =0. 52; P=1. 43×10 -10 ) datasets. Overlap between genetic risk for LQTS and genetic determinants of the QT-interval in the general population  29  was further demonstrated by genome-wide bivariate linkage disequilibrium score regression,  36  which detected a significant positive genetic correlation (r g =0. 40, SE=0. 14; P=3. 2×10 -3 ) between these phenotypes. No significant correlation was found for other cardiac electric traits (Figure IX in the Data Supplement).
The association for variant rs1805128 (KCNE1:p. Asp85Asn) is solely driven by the European analysis because it is not well imputed and rare (R 2 <0. 3. minor allele frequency=0. 001) in the Japanese dataset. QT-interval GWAS  30  had less discriminative accuracy in the Japanese case-control dataset compared with the European-derived PRS QT, likely because of the small size of the Japanese QT-interval GWAS (Table VIIIB in the Data Supplement). We next explored whether the genetic architecture of genotype negative patients (ie, those lacking a rare variant after extensive genetic testing of the established LQTS disease genes) differed from that of genotype positive patients. This was done by comparing PRS QT between both groups, uncovering a significantly higher PRS QT in genotype negative patients, pointing to a more prominent role for common variants in disease susceptibility in these patients.
This effect was consistently observed in both the European (P=5. 1×10 -  , Figure  4B ) and the Japanese (P=2. 0×10 -  , Figure  4D ) datasets (Table  3 ). Similar results were obtained in a sensitivity analysis correcting for QT-interval, ensuring that enrichment of QT prolonging alleles in the genotype negative patients was not driven by differences in QT-interval (P=7. 4×10 -  in Europeans; P=2. 6×10 -3 in Japanese, Table  3 ). These associations remained statistically significant when we restricted the analysis to patients with a pathogenic or likely-pathogenic variant according to American College of Medical Genetics and Genomics and Association of Molecular Pathology guidelines (ie, excluding cases with a rare variant of unknown significance; Methods in the Data Supplement and Tables IX and X in the Data Supplement).
Common Variant Heritability of LQTS To evaluate the proportion of variance in LQTS susceptibility explained by common genetic variants (h 2 SNP ) we used GCTA-GREML. 32, 37 Assuming a disease prevalence of 0. 04%, 1 the SNP heritability estimate on the liability scale was h 2 SNP =0. 148 (SE=0. 019 [95% CI, 0. 111-0. 185]; P=5. 0×10  -18  ) in the overall European LQTS dataset. h 2 SNP was similar when the analysis was restricted to genotype positive patients with LQTS. Similar results were also observed in the Japanese dataset and when using the phenotype-correlation genotype-correlation regression  34  and the GREML estimation implemented in LDAK,  35  as well as when we restricted h 2 SNP analyses to only patients with a pathogenic or likely-pathogenic variant (Table XII in the Data Supplement).
Association Analyses of Single SNPs and PRS QT With LQTS Severity To identify genetic modifiers of disease severity we conducted a GWAS for QT-interval within the LQTS cases which did not uncover any genome-wide significant    XIII  in the Data Supplement). Similarly, in a subanalysis restricted to probands (comprising 97% of the total of unrelated LQTS cases) using the multivariable linear regression model, PRS QT was not significantly associated with QTc (data not shown). In exploratory subgroup analyses, PRS QT was independently associated with QTc in KCNH2 rare variant carriers but not in KCNQ1 rare variant carriers (Table XIII in the Data Supplement). This result was not replicated in the Japanese LQTS dataset. PRS QT was not significantly associated with time to LAE in neither Europeans nor Japanese cases (Figure XII in the Data Supplement).
Study Limitations Although in genotype negative patients with LQTS we performed sequencing of the coding region of nonsyndromic LQTS genes, this may have missed copy number variation or disease-causing variants in the noncoding region  44  of established genes as well as mutations in yet unknown disease genes. This may have blunted the differences between genotype negative and genotype positive patients and thus would not affect the study conclusions. Despite being the largest international dataset of unrelated patients with LQTS published to date, the study had limited statistical power to detect lower effect associations at GWAS significance threshold. The prespecified design of meta-analyzing European and Japanese GWAS may also miss disease loci with differences in haplotype structure among European and East Asian chromosomes.
Acknowledgments The authors thank  Estelle Baron ,  Simon Lecointe ,  Annabelle Rajalu ,  Aurélie Thollet , and  Florence Kyndt  for their help in DNA sample preparation and regulatory processes at the Nantes center, the  Nantes biological resource center for biobanking (CHU Nantes ,  Hôtel Dieu ,  Center de Resources Biologiques, Nantes , F-44093, France [BRIF:  BB-0033-00040 ]), and the  National Referral Center for Inherited Cardiac Arrhythmias of Nantes  and its associated competence centers. We thank  Professor Pier D. Lambiase  and  Professor Patricia B. Munroe  for sharing summary statistics. We thank  Dr Nicky Whiffin  for help with using CardioClassifier. The KORA research platform (KORA, Cooperative Research in the Region of Augsburg) was initiated and financed by the  Helmholtz Zentrum München-German Research Center for Environmental Health , which is funded by the  German Federal Ministry of Education and Research  and by the  State of Bavaria.
Cleveland Jr. Young Investigator Award in Cardiac Channelopathy Research. Drs Bezzina ,  Lahrouchi, Wilde , van  Tintelen , and Tan acknowledge the support from the  Dutch Heart Foundation  (CVON 2018-30  PREDICT2  project to  Drs Bezzina ,  Tan , and Wilde) and the  Netherlands Organization for Scientific Research  ( VICI fellowship ,  016. 150. 610 , to  Dr Bezzina ). Dr Tan  has received funding from the  European Union 's  Horizon 2020 research and innovation program  under acronym  ESCAPE-NET (The European Sudden Cardiac Arrest network toward Prevention, Education, New Effective Treatment ), registered under grant agreement No  733381. Dr Tadros  received support from the  Canadian Heart Rhythm Society  ( George Mines Award ), the  European Society of Cardiology  (research award), and the  Philippa  and  Marvin Carsley Cardiology Chair  and is currently a clinical research scholar of the  Fonds de Recherche du Québec-Santé.
Drs Shaw  and  Crotti  acknowledge the support of the  Leducq Foundation  for cardiovascular research grant  18CVD05 :  Toward Precision Medicine with Human iPSCs for Cardiac Channelopathies. Drs Probst  and  Gourraud  were supported by a grant from  Hopitaux Universitaires du Grand Ouest and Fondation Maladies Rares  ( RC17_0357 ). Dr Breckpot  was supported by the  H2020-MSCA-IF-2014 Program  of the  European Commission  ( RISTRAD-661617 ) and by the  Regional Council of Pays-de-la-Loire  (Etoile montante: REGIOCARD). Dr Schott  was supported by the  Fondation pour la Recherche Médicale  ( DEQ20140329545 ) and by the  National Agency for Research  ( ANR-GENSUD-14-CE10-0001 ). Drs Rydberg  and Diamant are supported by The  Swedish Heart-Lung Foundation. Dr Behr  is supported by the  Higher Education Funding Council for England  and the  British Heart Foundation  and acknowledges support from  Cardiac Risk  in the Young.
Dr Wijeyeratne  had received support through an  Academic Clinical Fellowship  from the  National Institute of Health Research. Drs Wijeyeratne  and  Behr  gratefully acknowledge funding and ongoing support from the  James Lancaster Memorial Fund  sponsored by McColl's  RG Ltd. This work was partly supported by a  Health and Labor Sciences Research Grant  from the  Ministry of Health, Labor, and Welfare of Japan  ( H22-032 ,  H24-033 ,  H26-040 , and  H29-055 ) to  Dr Yoshinaga. Dr Makita  is supported by  AMED  (19kk0305011h0001). Drs Shimizu  and Aung acknowledge support from a  Health Science Research Grant  from the  Ministry of Health, Labor and Welfare of Japan for Clinical Research on Measures for Intractable Diseases  ( H24-033 ,  H26-040 , and  H27-032 ). Dr Arbelo  is supported by  Instituto de Salud Carlos III  ( FIS PI16/01203  and  PI17/01690 ) cofunded by  ERDF/ESF , " Investing in Your Future.
"This work was supported by the  John and Birthe Meyer Foundation , The  Hallas-Møller Emerging Investigator Novo Nordisk  ( NN-F17OC0031204 ). The iPSYCH study was funded by the  Lundbeck Foundation Initiative for Integrative Psychiatric Research. This research has been conducted using the Danish National Biobank resource. The Cardiac Inherited Disease Registry has been supported by  Cure Kids. AW is supported by the  Hugh Green Foundation. Dr Ellinor  is supported by the  Fondation Leducq  ( 14CVD01 ), by grants from the  National Institutes of Health  ( 1RO1HL092577 ,  R01HL128914 ,  K24HL105780 ) and the  American Heart Association  ( 18SFRN34110082 ). SAL is supported by  National Institutes for Health  grant  1R01HL139731  and  American Heart Association   18SFRN34250007. Dr Loeys  is supported by a  European Research Council   consolidator grant.
Study Design GEMINI-1 (NCT02831673) and GEMINI-2 (NCT02831764) are ongoing, identically designed, phase III, randomized, double-blind, noninferiority studies conducted at 187 centers in 21 countries. This report describes results through the final visit of the double-blind randomized phase at week 96. Protocols for GEMINI-1 and GEMINI-2 are available at https://www. viiv-clinicalstudyregister. com/ study/204861#ps and https://www. viiv-clinicalstudyregister. com/study/205543#ps, respectively. Methods, including information regarding ethical compliance, have previously been described. 13.
In GEMINI- ). In the week 96 pooled analysis, proportions of participants in the HIV-1 RNA $50 copies/mL Snapshot category were 3. 1% in the dolutegravir + lamivudine group and 2. 0% in the dolutegravir + tenofovir disoproxil fumarate/emtricitabine group and were largely unchanged from week 48 to week 96 in both groups; 10. 9% and 8. 5%, respectively, had no virologic data (Fig. 1B ). Most Snapshot failures that occurred after week 48 in both groups were discontinuations for nonvirologic or non-treatment-related reasons, including withdrawal of consent, lost to follow-up, protocol deviation, and physician decision (see Table, Supplemental Digital Content 3, http:// links. lww. com/QAI/B418); this was most notable in the dolutegravir + lamivudine group in GEMINI-1. In the prespecified TRDF Kaplan-Meier analysis at week 96, 96.
ACKNOWLEDGMENTS The authors thank the study participants; their families and caregivers; investigators (see list of investigators in Supplemental Digital Content 11, http://links. lww. com /QAI/B418 ) and site staff who participated in the study; and the  ViiV Healthcare ,  GlaxoSmithKline, Pharmaceutical Product Development, and Parexel  study team members. Editorial assistance was provided under the direction of the authors by  Jonathan Morgan, PhD , CMPP, and  Jennifer Rossi , MA, ELS,  MedThink SciCom , and was funded by  ViiV Healthcare.
This suggests that at least half of mode 3 services trade may be completely stalled due to the pandemic, with significant disruptions for the other half too. Thus, at least half of global services trade -nearly US$7 trillion -easily stands compromised by COVID-19. However, this is still likely to underestimate the impact because several mode 1 services are complementary inputs into manufacturing and other services. These activities have been drastically affected, if not completely stalled, by the lockdowns. Moreover, data security, client confidentiality, access to information and communications technology (ICT) and related issues are likely to render even remotely deliverable mode 1 services activities infeasible. Against this background, this International Trade Working Paper examines the effect of the pandemic on the services trade of Commonwealth countries using both descriptive statistics and qualitative analysis.
The Commonwealth's five largest services traders in 2018 were the UK, Singapore, India, Canada and Australia (Figure  3a ). The top three countries exported services exceeding US$150 billion. The UK and Singapore also imported services above $150 billion each. For the remaining Commonwealth countries, services trade was well below $50 billion each in 2018, although Malaysia stands out with services trade of $42. 5 billion. However, for the majority of Commonwealth countries (over three quarters of the membership), services trade was below $10 billion (Figure  3b ). At the sector level, services exports are dominated by travel (mostly personal), transport and distribution services across Commonwealth member countries (Figure  4 ). However, there Unfortunately, more complete data on bilateral services trade to discern the intra-Commonwealth share within Commonwealth services trade are only available until 2012 in the OECD-WTO BaTiS database.
Across Commonwealth member countries and sectors, services are used the most intensively in the domestic economy in Pakistan and the least in Lesotho, Malaysia and Singapore (Table  3 ). For the Commonwealth as a whole, global value chain (GVC) participation (the sum of backward and forward participation) is particularly large for wholesale trade; maintenance and repair; and post and telecommunications services, followed by financial intermediation and transport services (Table  4 ). Table  3  reports domestic value added as a share of gross exports across Commonwealth member countries and services sectors calculated from the EORA MRIO database. Commonwealth countries with domestic valueadded shares that are significantly higher than the average and the total for the Commonwealth across the different sectors are highlighted in red.