Measurement of the top quark mass using the matrix element technique in dilepton final states

We present a measurement of the top quark mass in pp¯ collisions at a center-of-mass energy of 1.96 TeV at the Fermilab Tevatron collider. The data were collected by the D0 experiment corresponding to an integrated luminosity of 9.7  fb−1. The matrix element technique is applied to tt¯ events in the final state containing leptons (electrons or muons) with high transverse momenta and at least two jets. The calibration of the jet energy scale determined in the lepton+jets final state of tt¯ decays is applied to jet energies. This correction provides a substantial reduction in systematic uncertainties. We obtain a top quark mass of mt=173.93±1.84  GeV

Studies of X(3872) and ψ(2S) production in p\bar{p}over-bar collisions at 1.96 TeV

We present various properties of the production of the X (3872) and ψ(2S) states based on 10.4fb‾¹ collected by the D0 experiment in Tevatron p\bar{p} collisions at \sqrt{s} = 1.96 TeV. For both states, we measure the nonprompt fraction fNP of the inclusive production rate due to decays of b-flavored hadrons. We find the fNP values systematically below those obtained at the LHC. The fNP fraction for ψ(2S) increases with transverse momentum, whereas for the X(3872) it is constant within large uncertainties, in agreement with the LHC results. The ratio of prompt to nonprompt ψ(2S) production, (1 - fNP)/fNP, decreases only slightly going from the Tevatron to the LHC, but for the X(3872), this ratio decreases by a factor of about 3. We test the soft-pion signature of the X(3872) modeled as a weakly bound charm-meson pair by studying the production of the X(3872) as a function of the kinetic energy of the X(3872) and the pion in the X(3872) π center-of-mass frame. For a subsample consistent with prompt production, the results are incompatible with a strong enhancement in the production of the X(3872) at the small kinetic energy of the X(3872) and the π in the X(3872)π center-of-mass frame expected for the X + soft-pion production mechanism. For events consistent with being due to decays of hadrons, there is no significant evidence for the soft-pion effect, but its presence at the level expected for the binding energy of 0.17 MeV and the momentum scale Λ = M(π) is not ruled out

Properties of Z±c(3900) produced in pp¯ collisions

We study the production of the exotic charged charmoniumlike state Z ± c ( 3900 ) in p ¯ p collisions through the sequential process ψ ( 4260 ) → Z ± c ( 3900 ) π ∓ , Z ± c ( 3900 ) → J / ψ π ± . Using the subsample of candidates originating from semi-inclusive weak decays of b -flavored hadrons, we measure the invariant mass and natural width to be M = 3902.6 + 5.2 − 5.0 ( stat ) + 3.3 − 1.4 ( syst )     MeV and Γ = 3 2 + 28 − 21 ( stat ) + 26 − 7 ( syst )     MeV , respectively. We search for prompt production of the Z ± c ( 3900 ) through the same sequential process. No significant signal is observed, and we set an upper limit of 0.70 at the 95% credibility level on the ratio of prompt production to the production via b -hadron decays. The study is based on 10.4     f b − 1 of p ¯ p collision data collected by the D0 experiment at the Fermilab Tevatron collider

Measurement of angular correlations of jets at root s=1.96 TeV and determination of the strong coupling at high momentum transfers

We present a measurement of the average value of a new observable at hadron colliders that is sensitive to QCD dynamics and to the strong coupling constant, while being only weakly sensitive to parton distribution functions. The observable measures the angular correlations of jets and is defined as the number of neighboring jets above a given transverse momentum threshold which accompany a given jet within a given distance �R in the plane of rapidity and azimuthal angle. The ensemble average over all jets in an inclusive jet sample is measured and the results are presented as a function of transverse momentum of the inclusive jets, in different regions of �R and for different transverse momentum requirements for the neighboring jets. The measurement is based on a data set corresponding to an integrated luminosity of 0.7 fb−1 collected with the D0 detector at the Fermilab Tevatron Collider in p¯p collisions at √ s = 1.96 TeV. The results are well described by a perturbative QCD calculation in next-toleading order in the strong coupling constant, corrected for non-perturbative effects. From these results, we extract the strong coupling and test the QCD predictions for its running over a range of momentum transfers of 50–400 GeV

Combination of D0 measurements of the top quark mass

We present a combination of measurements of the top quark mass by the D0 experiment in the lepton+jets and dilepton channels. We use all the data collected in Run I (1992–1996) at √s=1.8  TeV and Run II (2001–2011) at √s=1.96  TeV of the Tevatron p¯p collider, corresponding to integrated luminosities of 0.1  fb−1 and 9.7  fb−1, respectively. The combined result is: mt=174.95±0.40(stat)±0.64(syst)  GeV=174.95±0.75  GeV

Primary Prevention of Cardiovascular Disease

Cardiovascular disease (CVD) is the leading cause of death worldwide. This article focuses on current guidelines for the primary prevention of CVD and addresses management of key risk factors. Dietary modification, weight loss, exercise, and tobacco use cessation are specific areas where focused efforts can successfully reduce CVD risk on both an individual and a societal level. Specific areas requiring management include dyslipidemia, hypertension, physical activity, diabetes, aspirin use, and alcohol intake. These preventive efforts have major public health implications. As the global population continues to grow, health care expenditures will also rise, with the potential to eventually overwhelm the health care system. Therefore it is imperative to apply our collective efforts on CVD prevention to improve the cardiovascular health of individuals, communities, and nations

GWAS and meta-analysis identifies 49 genetic variants underlying critical COVID-19

Data availability: Downloadable summary data are available through the GenOMICC data site (https://genomicc.org/data). Summary statistics are available, but without the 23andMe summary statistics, except for the 10,000 most significant hits, for which full summary statistics are available. The full GWAS summary statistics for the 23andMe discovery dataset will be made available through 23andMe to qualified researchers under an agreement with 23andMe that protects the privacy of the 23andMe participants. For further information and to apply for access to the data, see the 23andMe website (https://research.23andMe.com/dataset-access/). All individual-level genotype and whole-genome sequencing data (for both academic and commercial uses) can be accessed through the UKRI/HDR UK Outbreak Data Analysis Platform (https://odap.ac.uk). A restricted dataset for a subset of GenOMICC participants is also available through the Genomics England data service. Monocyte RNA-seq data are available under the title ‘Monocyte gene expression data’ within the Oxford University Research Archives (https://doi.org/10.5287/ora-ko7q2nq66). Sequencing data will be made freely available to organizations and researchers to conduct research in accordance with the UK Policy Framework for Health and Social Care Research through a data access agreement. Sequencing data have been deposited at the European Genome–Phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGAS00001007111.Extended data figures and tables are available online at https://www.nature.com/articles/s41586-023-06034-3#Sec21 .Supplementary information is available online at https://www.nature.com/articles/s41586-023-06034-3#Sec22 .Code availability: Code to calculate the imputation of P values on the basis of SNPs in linkage disequilibrium is available at GitHub (https://github.com/baillielab/GenOMICC_GWAS).Acknowledgements: We thank the members of the Banco Nacional de ADN and the GRA@CE cohort group; and the research participants and employees of 23andMe for making this work possible. A full list of contributors who have provided data that were collated in the HGI project, including previous iterations, is available online (https://www.covid19hg.org/acknowledgements).Change history: 11 July 2023: A Correction to this paper has been published at: https://doi.org/10.1038/s41586-023-06383-z. -- In the version of this article initially published, the name of Ana Margarita Baldión-Elorza, of the SCOURGE Consortium, appeared incorrectly (as Ana María Baldion) and has now been amended in the HTML and PDF versions of the article.Copyright © The Author(s) 2023, Critical illness in COVID-19 is an extreme and clinically homogeneous disease phenotype that we have previously shown1 to be highly efficient for discovery of genetic associations2. Despite the advanced stage of illness at presentation, we have shown that host genetics in patients who are critically ill with COVID-19 can identify immunomodulatory therapies with strong beneficial effects in this group3. Here we analyse 24,202 cases of COVID-19 with critical illness comprising a combination of microarray genotype and whole-genome sequencing data from cases of critical illness in the international GenOMICC (11,440 cases) study, combined with other studies recruiting hospitalized patients with a strong focus on severe and critical disease: ISARIC4C (676 cases) and the SCOURGE consortium (5,934 cases). To put these results in the context of existing work, we conduct a meta-analysis of the new GenOMICC genome-wide association study (GWAS) results with previously published data. We find 49 genome-wide significant associations, of which 16 have not been reported previously. To investigate the therapeutic implications of these findings, we infer the structural consequences of protein-coding variants, and combine our GWAS results with gene expression data using a monocyte transcriptome-wide association study (TWAS) model, as well as gene and protein expression using Mendelian randomization. We identify potentially druggable targets in multiple systems, including inflammatory signalling (JAK1), monocyte–macrophage activation and endothelial permeability (PDE4A), immunometabolism (SLC2A5 and AK5), and host factors required for viral entry and replication (TMPRSS2 and RAB2A).GenOMICC was funded by Sepsis Research (the Fiona Elizabeth Agnew Trust), the Intensive Care Society, a Wellcome Trust Senior Research Fellowship (to J.K.B., 223164/Z/21/Z), the Department of Health and Social Care (DHSC), Illumina, LifeArc, the Medical Research Council, UKRI, a BBSRC Institute Program Support Grant to the Roslin Institute (BBS/E/D/20002172, BBS/E/D/10002070 and BBS/E/D/30002275) and UKRI grants MC_PC_20004, MC_PC_19025, MC_PC_1905 and MRNO2995X/1. A.D.B. acknowledges funding from the Wellcome PhD training fellowship for clinicians (204979/Z/16/Z), the Edinburgh Clinical Academic Track (ECAT) programme. This research is supported in part by the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (grant MC_PC_20029). Laboratory work was funded by a Wellcome Intermediate Clinical Fellowship to B.F. (201488/Z/16/Z). We acknowledge the staff at NHS Digital, Public Health England and the Intensive Care National Audit and Research Centre who provided clinical data on the participants; and the National Institute for Healthcare Research Clinical Research Network (NIHR CRN) and the Chief Scientist’s Office (Scotland), who facilitate recruitment into research studies in NHS hospitals, and to the global ISARIC and InFACT consortia. GenOMICC genotype controls were obtained using UK Biobank Resource under project 788 funded by Roslin Institute Strategic Programme Grants from the BBSRC (BBS/E/D/10002070 and BBS/E/D/30002275) and Health Data Research UK (HDR-9004 and HDR-9003). UK Biobank data were used in the GSMR analyses presented here under project 66982. The UK Biobank was established by the Wellcome Trust medical charity, Medical Research Council, Department of Health, Scottish Government and the Northwest Regional Development Agency. It has also had funding from the Welsh Assembly Government, British Heart Foundation and Diabetes UK. The work of L.K. was supported by an RCUK Innovation Fellowship from the National Productivity Investment Fund (MR/R026408/1). J.Y. is supported by the Westlake Education Foundation. SCOURGE is funded by the Instituto de Salud Carlos III (COV20_00622 to A.C., PI20/00876 to C.F.), European Union (ERDF) ‘A way of making Europe’, Fundación Amancio Ortega, Banco de Santander (to A.C.), Cabildo Insular de Tenerife (CGIEU0000219140 ‘Apuestas científicas del ITER para colaborar en la lucha contra la COVID-19’ to C.F.) and Fundación Canaria Instituto de Investigación Sanitaria de Canarias (PIFIISC20/57 to C.F.). We also acknowledge the contribution of the Centro National de Genotipado (CEGEN) and Centro de Supercomputación de Galicia (CESGA) for funding this project by providing supercomputing infrastructures. A.D.L. is a recipient of fellowships from the National Council for Scientific and Technological Development (CNPq)-Brazil (309173/2019-1 and 201527/2020-0)