dataesr/scientific-paragraphs-categorization huggingface dataset:
We analysed ancient genotypes with 1388 Eurasian individuals, 109 Yoruba, and 3 Mbuti individuals from two modern publicly available datasets: the SGDP dataset  30  , the 1000 Genomes dataset  29. Furthermore, we also used a Central Asian specific dataset  53  obtained in our lab using capture including 527 individuals. We merged these modern data using mergeit from EIGENSOFT  49  and we haploidized them by randomly selecting one allele per position. The final merge includes 237,644 SNPs for 5129 individuals. For the analysis requiring more SNPs, we used individuals sequenced by shotgun  48  from only three populations in the Central Asia dataset: 3 Yaghnobis (TJY), 19 Tajiks (TJE) and 24 Turkmens (TUR), and we pseudo-haploidized and combined them with the 1240k panel to obtain a second dataset of 716 743 SNPs and 4648 individuals.
S2 ) indicating no significant limitation in our first PCA. Admixture. We performed ADMIXTURE analysis  47  on 1915 Eurasian present-day individuals and 3109 ancient samples from the first dataset on a subset of 236 665 SNPs pruned for linkage disequilibrium (by using PLINK  77  -indep-pairwise 200 25 0. 4 function). We run ADMIXTURE analyses for clustering with K between 2 and 15, with 20 replicates performed for each K. The best value of K clusters, 10, is the value with lowest crossvalidation error (Supplementary fig. S6 ).
D and F3-statistics. We computed the f3 outgroup statistics using the qp3Pop program with the inbreed option set to YES on our second dataset and D-statistics using the qpDstat program of the ADMIXTOOLS package  51. We used Mbuti as the outgroup for both statistics in the main text, but we obtained similar results using Yoruba population as the outgroup (not shown). qpAdm analysis. We performed rotating qpAdm analysis with ADMIXTOOLS package to model the ancestry of Central Asian modern populations. For Indo-Iranians, we used Mbuti, Han, Natufian, WEHG, Ust-Ishim, MA1, Kostenki14, EEHG as reference populations. Prior to the analysis, we checked if the reference populations could well discriminate between the source populations by computing f4-stat of the form f4(Mbuti, Source1, RefX, RefY), for all the sources and all the combinations of outgroups possible.
Then we plotted the pairwise f4 and calculated a correlation score. We observed that our dataset discriminates well BMAC and Iran_N. We first tested a rotating group with Turkmenistan_IA, XiongNu, and GoldenHordeAsian to assert the best source of East-Asian ancestry in our model. To test different models, we used Iran_N, Europe_EN, BMAC, Turk-menistan_IA, XiongNu, Ukraine_Scythian and Germany_Corded_Ware as rotating source populations. And to model Tajiks, we add Indian_GreatAndaman_100BP to represent a deep ancestry from South Asia. To model Turkmenistan_IA, we used the same reference group with Iran_N and BHG added and we used Germany_CordedWare, Russia_Poltavka, XiongNu, EuropeEN, BMAC, Andronovo, Ukraine_Scythian as the rotating group. We also performed tests with smaller rotating groups: (1) Andronovo, Alakul_Lisakovskiy, BMAC (2) Andronovo, Fedorovo_Shoindykol, BMAC (3) Andronovo, Sintashta, BMAC (4) Andronovo, Karasuk, BMAC, (4) Andronovo, Afanasievo, BMAC.
Dates We used DATES v753  18  to estimate the time of admixture events in Tajiks, Yaghnobis, and Turkmens. To convert the estimated admixture date in generations into years, we assumed 29 years per generation  51. The standard errors of DATES estimates come from the weighted block jackknife with ''binsize: 0. 001, '' ''maxdis: 1, '' ''runmode: 1, '' ''mincount: 1, '' ''lovalfit: 0. 45'' as parameters as in the example file at https:// github. com/ priya moorj ani/ DATES/ blob/ master/ examp le/ par. dates.
Acknowledgements P. G. V. is supported by a PhD grant from  E. N. S de Lyon. We would like to thank  Ludovic Orlando ,  Laure Ségurel  and  Paul Verdu  for their fruitful comments about this work. We would like to thank  Romain Laurent  for his assistance and  Amanda Graham  and  Marcus Kearsey  for proofreading the English. J. B. S addresses his acknowledgments to the Mission archéologique franco-turkmène (MAFTur), the  French Ministry of Foreign Affairs (MEAE) , the  French Archaeological Delegation in Afghanistan (DAFA) , the  Shelby White and Leon Levy Program for Archaeological Publication.
Ethical approval and research transparency The AKIKI and the IDEAL-ICU trials received approval for all participating centers from competent French legal authority (Comité de Protection des Personnes d'Ile de France VI, ID RCB 2013-A00765-40, NCT01932190 for AKIKI and Comité de Protection des Personnes Est I ID RCB 2012-A00519-34 for IDEAL-ICU), and consent of patient or relatives was obtained before inclusion (except in emergencies where deferred consent was allowed by the Institutional Review Board). We transparently reported our analysis following the PATH  [15]  and TRI-POD  [16]  statements.
Source of data The study sample included participants from the AKIKI and IDEAL-ICU, two multicenter RCTs conducted in France. The AKIKI trial was conducted at 31 ICUs from September 2013 through January 2016 and recruited 619 patients with severe AKI who required mechanical ventilation, catecholamine infusion, or both (the vast majority with septic shock). The IDEAL-ICU trial recruited in 29 ICUs from July 2012 through October 2016 and included 488 patients with severe AKI and septic shock. Both trials randomly assigned (1:1) patients to either an early or a delayed strategy of RRT initiation. None of these trials showed a significant difference between the two strategies on 60-day mortality. The delayed strategy averted the need for RRT in 49% and 38% of patients in the AKIKI and IDEAL-ICU trials, respectively.
Ninety-five percent confidence intervals (95% CI) were calculated by bootstrapping (1000 iterations). All analyses were performed using the R statistical software version 4. 0. 5 (The R Foundation). More precisely, we used the rms package for model building and internal validation, the survival package for survival analyses, the mgcv package for heterogeneous treatment effects assessment, the boot package for bootstrap, and the mice package for multiple imputation. For transparency and reproducibility, the computer code used in this study is available as an Additional file 1 at the Journal's website.
Variables All of the variables employed in the study were country-level indicators retrieved from three sources: the World Bank (WBank, 2020), the World Economic Forum (WEF, 2020), and  Medina and Schneider (2019). The tax evasion (TaxEvasion) data as measured by the size of the informal (shadow) economy relative to GDP were retrieved from  Medina and Schneider (2019). The Egovernment indicators-namely EParticipation, GovPolStab, GovRespCha, LegalFrame, and GovernVision-were downloaded from the WEF (2020). Moreover, moderators such as education quality (QuaEduSys), math education quality (QuaMath), business management school quality (QuaManSch), and internet connection in schools (InterConnect) were also derived from the WEF (2020). Lastly, the control variables were downloaded from the World Development Indicators published by the World Bank  (WBank, 2020).
71% to 6. 8%, which, although only slightly above 5% on the one end, are significantly less than 10% on the other. According to  Schafer (1999) , a sample with 5% or fewer missing values can be considered inconsequential; while according to  Bennett (2001) , the results of a missing data analysis can be biased if the sample has more than 10% missing values. Even though the number of missing values in the current analysis can be considered inconsequential and therefore did not threaten to bias the results, in the following step of the data screening phase, imputation analysis was performed. To do so, the Markov chain Monte Carlo (MCMC) approach with linear regression as the model type for scale variables was used. Next, potentially significant outliers are examined. For outlier detection, the minimum covariance determinant (MCD) estimator is used.
Moderation analysis The proposed model for the moderation analysis is illustrated in Eq (2), which represents the moderating effect of the proposed moderators between the independent test variable and the dependent variable. Similarly, time fixed-effects panel data regression analysis with a moderator is included in the proposed model. y it ¼ α þ β 1 X 1it þ β 2 M it þ β 3 X 1it � M it ð Þ þ β 4 X 2it þ δ 2 B2 t þ. þ δ T BT t þ 2 it (2) In Equation (  2 ), the moderator (M it ) is represented by QuaEduSys, QuaMath, QuaManSch, and InterConnect; the independent test variable (X 1it ) is represented by Egovernment; the dependent variable (y it ) is represented by TaxEvasion; and the control variables are represented by (X 2it ). The moderation analysis is performed based on Hayes's (2018) methodology, which employs a Stata module  (Jose, 2013).
Baseline analysis The results of the baseline analysis used to test the first proposed hypothesis (H1) are provided in Table  5. The time fixed-effects regression analysis for the panel data is used as it was the most appropriate analytical tool, as previously explained in detail. As described in the factorial analysis, the independent test variable included in the analysis had four alternative composite scores of Egovernment obtained from the results of EFA: Egovernment_sum, Egovernment_ave, Egovernment_weighted, and Egovernment_fact. The results show that Egovernment based on sum, average, weighted, and factor scores has a significant negative relationship with TaxExasion (p < 0. 01). Hence, the negative association between e-government proxies and tax evasion is robustly confirmed by various methodologies, thereby leading to the acceptance of H1.
Introduction: Child Access Prevention Negligent Storage (CAP-NS) laws seek to reduce pediatric firearm injury by imposing sanctions on gun owners if children gain access to unlocked guns. Whether these laws affect the storage behavior they aim to encourage is not known because historical panel data on firearm storage do not exist. As a result, assessing how much, if at all, firearm storage changed because of CAP-NS laws requires an indirect approach. Methods: Data for this study came from a web-based survey conducted by the research firm Ipsos from July 30, 2019 to August 11, 2019. Respondents were adult gun owners drawn from an online sampling frame comprising approximately 55,000 U. S. adults recruited using address-based sampling methods to be representative of the U. S. population. The primary outcome was the proportion of gun owners in CAP-NS versus non-CAP-NS states who had ≥1 unlocked firearm.
Study Sample Data for this study came from a web-based survey conducted by the research firm Ipsos from July 30, 2019 to August 11, 2019. Respondents were drawn from Ipsos's KnowledgePanel, an online sampling frame comprising approximately 55,000 U. S. adults recruited using address-based sampling methods. Samples are representative of the U. S. population. The target population was non-institutionalized adults (aged ≥18 years) residing in the U. S. in gun-owning households. Whether panel members live in a home with firearms is a baseline characteristic collected by Ipsos; invitations to participate were restricted to those who had previously reported living in a home with firearms. Household and personal firearm ownership were confirmed for respondents who accepted the invitation to participate.
8  Moreover, the American College of Medical Genetics and Genomics (ACMG) list LDLR, APOB, and PCSK9 among the 59 'medically actionable' genes,  9  which has, in part, led to frequent inclusion of these genes on commercially available clinical panels,  10  as well as direct-to-consumer tests. With the increasingly widespread implementation of genetic testing for FH, it is becoming ever more essential to establish a consensus, standardized method for the clinical classification of identified variants. In a 2018 study of >6,500 FH-associated variants submitted to the ClinVar database, there were at least 12 different variant classification criteria being used among 30 submitters from 14 different countries. 11 This heterogeneity leads to discordance in variant classification. For instance, 379 unique FH variants had conflicting classifications in ClinVar (i.
Validation and pilot testing Following guideline approval from the SVI committee, a formalized pilot study of 54 LDLR variants was performed in the ClinGen Variant Curation Interface (VCI; https://curation. clinicalgenome. org/). The VCI is a publicly available, comprehensive resource that systematically facilitates individual and group-level curation activities in accordance with the ACMG/AMP guidelines. Pilot study curations in the VCI were performed independently by two biocurators, followed by a review from two VCEP leadership members. Publicly available data used for curation were supplemented with internal case-level data from VCEP member laboratories. When applicable, internal laboratory data used in the classifications were uploaded and saved into the VCI. Following independent curation of the 54 LDLR pilot-variants, biocurators extracted the data from the VCI and sent it to the reviewers.
e. , higher total allele number). When evaluating whole-exon deletions and duplications, which are a relatively common pathogenic variant type in LDLR (~10% of affected individuals),  16  the gnomAD Structural Variant (SV) dataset should be queried. It is important to keep in mind that both case and control gnomAD cohorts are expected to contain many individuals with FH, given FH is relatively common in the general population (1 in 250 individuals; or an estimated ~34 million affected worldwide), and >90% of individuals are thought to be undiagnosed. 2, 3 Further, there are multiple cardiac case cohorts included in gnomAD, such as those from the Framingham Heart Study, Jackson Heart Study, Multi-Ethnic Study of Atherosclerosis, and Myocardial Infarction Genetics Consortium studies.
Segregation data (PP1, BS4) We have determined three strength levels for application of PP1 depending on the number of families/individuals studied. PP1_Strong is applicable when there is co-segregation of the variant with affected status in ≥6 informative meioses; PP1_Moderate when in 4-5 informative meioses; and PP1_Supporting when in 2-3 informative meioses. Index cases should not be counted as positive cases for co-segregation results. When the same variant is identified in more than one family, data can be added to reach stronger evidence levels. Supplementary Figure  2  shows a typical example of co-segregation in a pedigree, with an explanation on informative meiosis for a FH-associated variant. Note that when an index case presents with a heterozygous FH phenotype and the hypercholesterolemia is associated with one branch of the family, individuals from the other branch should not be considered for cosegregation analysis.
Use of a single meta-predictor such as REVEL will eliminate discrepancies in which programs are used by curators, which transcripts are selected, how many tools should be concordant and what specificities to account for when manually performing in silico analysis. To determine LDLR-specific score thresholds for PP3/BP4 we evaluated REVEL scores for LDLR missense variants with 1) Pathogenic/Likely pathogenic or Benign/Likely Benign classifications in ClinVar, 2) damaging or neutral results according to LDLR-specific PS3/BS3 functional study evidence, and 3) concordant in silico results for Poly-Phen, SIFT, PROVEAN and MutationTaster (Supplementary Figure  3 ). Using the PP3 threshold of ≥0. 75 (as defined in Ioannidis et al. , 2016)  38  , the vast majority of variants with an association of 'pathogenic' using any comparison (as above) have REVEL scores above this threshold.
Pilot study The pilot study of the final specifications was done on 54 LDLR variants in the ClinGen VCI. Variants, listed in Supplementary Table  5 , were chosen to reflect LDLR variant variability and included one multi-exon deletion, 40 missense, seven intronic/splicing, four nonsense and two synonymous variants. A total of nine institutions, which included six clinical and three research laboratories, provided internal case-level data (via standardized template), supplementing classifications for 42 of 54 variants. For the remaining variants, and whenever necessary, data from published literature was used to count number of cases, and to evaluate co-segregation and functional evidence. Preliminary results had complete agreement (in both classification and individual criteria used) for 16 variants, agreement in classifications but not in each criterion used for 27 variants, and discrepancies in both classification and criteria used for the remaining 11 variants.
LIMITATIONS There are multiple criteria specifications that require diagnostic information and case-level data, which if not readily available, may limit the classification of variants. For instance, PS4 and PP4 criteria require that cases considered are clinically diagnosed with FH based on validated clinical criteria. PP1-level, PS2/PM6, PM3, BS2, BS4, and BP2 criteria require that further case-level data are available, including LDL-C measurements, genetic results, family history, and medication status. However, although this information may be difficult to ascertain in some settings, it is necessary to apply these criteria correctly. Whenever possible, we encourage curators to actively seek this information if it is not initially available. As the FH VCEP works toward classifying all ~2,300 LDLR variants currently in the ClinVar database using the guidelines presented here, we are hopeful we can overcome some of these limitations through internal data sharing efforts.
ACKNOWLEDGEMENTS ClinGen is primarily funded by the  National Human Genome Research Institute (NHGRI) , through the following three grants:  U41HG006834 ,  U41HG009649 ,  U41HG009650. ClinGen also receives support for content curation from the  Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) , through the following three grants:  U24HD093483 ,  U24HD093486 ,  U24HD093487. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. JRC acknowledges her PhD fellowship funded by the  Science and Technology Foundation  ( SFRH/BD/108503/2015 ). LT and TF are supported by the  Ministry of Health of the Czech Republic , grant  NU20-02-00261. SEH is an  Emeritus British Heart Foundation  Professor and is funded by  PG08/008 , and by the  National Institute for Health Research University College London Hospitals Biomedical Research Centre.
Obesity perturbs phosphocreatine metabolism in human WAT. To identify polar metabolites altered by excess fat mass, we performed an untargeted metabolomic analysis of subcutaneous abdominal WAT obtained from adult women with (n = 13) or without (n = 13) obesity (cohort 1, described in ref. 19  and Supplementary Table  1 , Trial registration no. NCT01727245). Among all detected metabolites (n = 310), phosphocreatine displayed the highest fold-enrichment in the obese versus the non-obese state (Fig. 1a ). Phosphocreatine and ADP are generated from creatine and ATP via specific creatine kinases (CK-MTs, CK-B and CK-M, forward reaction) where CK-B/CK-M can also catalyse the dephosphorylation back to creatine and ATP (reverse reaction)  16. Cellular creatine can either be taken up directly via the plasma membrane transporter SLC6A8, or be synthesized de novo from the precursor guanidinoacetate, which in turn can be taken up by SLC6A6 or synthesized.
Guanidinoacetate is converted to creatine through guanidinoacetate N-methyltransferase (GAMT). To determine if the alterations in phosphocreatine metabolism were reflected at the gene expression level, we analysed transcriptomic data of subcutaneous abdominal WAT from women with (n = 30) or without (n = 26) obesity (cohort 2, described in ref. 21 and Supplementary Table  1 ). We found that CKB displayed markedly lower expression in the obese state (Fig. 1b ). As displayed in Extended Data Fig. 1a-b , this was confirmed in two independent datasets, where CKB levels were lower in 15 obese versus 15 never-obese women (cohort 3, described in ref. 22  and Supplementary Table  1 , Trial registration no. NCT01785134) and 18 obese versus 17 non-obese men (cohort 4, Supplementary Table  1 , Trial registration no.
NCT01727245). In cohort 3, CKB expression levels were completely normalized in the obese subjects following weight loss induced by bariatric surgery (Extended Data Fig. 1a ). Western blot and immunofluorescence analyses confirmed that CK-B levels were lower in WAT of obese versus non-obese subjects (Fig. 1c, d ), which was mirrored by attenuated CK activity in lysates obtained from both intact WAT and isolated mature fat cells of non-obese and obese women (Fig. 1e ). Altogether, our systematic analyses of metabolomic and transcriptomic data, as well as protein levels and creatine kinase activity measures in human WAT, demonstrate that phosphocreatine abundance is increased and that CK-B expression/activity is decreased in obesity (Fig. 1f ).
2j ) and ELISA (Fig. 2k ) and thus confirmed that CKB depletion upregulated both CCL2 gene expression and CCL2 protein secretion. The data above were obtained in cells from a male donor. However, similar effects were observed in differentitated adipocytes derived from a female donor (Extended Data Fig. 2h, i ). The clinical relevance of these findings was further supported by multiple regression analyses in cohort 2 where CCL2 mRNA levels (Fig. 2l ), CCL2 secretion (Fig. 2m ) and high sensitivity CRP levels (a measure of inflammation at the whole-body level, standardized β = -0. 40, P = 0. 0088, graph not shown) were negatively associated with CKB expression after correction for body mass index (BMI). CKB depletion regulates CCL2 through mitochondrial activity. Creatine metabolism regulates the bioenergetic state of cells  29  and the latter is linked to inflammation and immune cell activation  [30] [31] [32] [33].
***P = 0. 0002. e, Creatine kinase activity measured in lysates of human adipocytes (three to four replicates per condition, repeated three times) transfected with siC or siCKB. ***P = 0. 0003. f, Metabolite levels in human adipocytes (ten replicates per condition, repeated twice) transfected with siC or siCKB. **P = 0. 0093 for phosphocreatine and 0. 002 for phosphocreatine/creatine ratio. g, Western blot showing the protein levels in mitochondrial and cytoplasmic fractions of human adipocytes transfected with siC, siCKB or siCKMT2 (repeated three times). Creatine kinase activity was determined in paired lysates as indicated in the right panel from one experiment. h, Principal component analysis based on microarray data from human adipocytes (three replicates per condition) transfected with siC or siCKB.
ellipses indicate 95% confidence intervals. i, log 2 (fold-change) of genes regulated by siCKB in human adipocytes (upper half, from microarray data presented in h). For each gene, the association (Spearman's rank correlation coefficient ρ value) with CKB expression in WAT transcriptomic data from cohort 2 is shown (lower half). Leading edge genes in the GSeA 'HALLMARK_INFLAMMATORy _ReSPONSe' pathway identified in extended Data Fig. 2g  are shown. j, CCL2 mRNA expression in human adipocytes (six replicates per condition, repeated three times) transfected with siC or siCKB. ***P = 0. 0002. k, CCL2 secretion measured by eLISA from human adipocytes (three replicates per condition, repeated three times) transfected with siC or siCKB. **P = 0. 0059. l, Correlation between CCL2 and CKB mRNA levels in human WAT from cohort 2.