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Black carbon (BC) particles accumulated in the Arctic troposphere and deposited on snow have been calculated to have significant effects on radiative forcing of the Arctic regional climate. Applying cluster analysis technique on 10-day backward trajectories, seven distinct transport pathways (or clusters) affecting Alert (82.5° N, 62.5° W), Nunavut in Canada are identified in this work. Transport frequency associated with each pathway is obtained as the fraction of trajectories in that cluster. Based on atmospheric transport frequency and BC surface flux from surrounding regions (i.e. North America, Europe, and former USSR), a linear regression model is constructed to investigate the inter-annual variations of BC observed at Alert in January and April, representative of winter and spring respectively, between 1990 and 2005. Strong correlations are found between BC concentrations predicted with the regression model and measurements at Alert for both seasons (R2 equals 0.77 and 0.81 for winter and spring, respectively). Results imply that atmospheric transport and BC emission are the major contributors to the inter-annual variations in BC concentrations observed at Alert in the cold seasons for the 16-year period. Other factors, such as deposition, could also contribute to the variability in BC concentrations but were not considered in this analysis. Based on the regression model the relative contributions of regional BC emissions affecting Alert are attributed to the Eurasian sector, composed of the European Union and the former USSR, and the North American sector. Considering both seasons, the model suggests that former USSR is the major contributor to the near-surface BC levels at the Canadian high Arctic site with an average contribution of about 67% during the 16-year period, followed by European Union (18%) and North America (15%). In winter, the atmospheric transport of BC aerosols from Eurasia is found to be even more predominant with a multi-year average of 94%. The model estimates smaller contribution from the Eurasian sector in spring (70%) than that in winter. It is also found that the inter-annual variation in Eurasian contributions depends mainly on the reduction of emissions, while the changes in both emission and atmospheric transport contributed to the inter-annual variation of North American contributions.

In Australia, manufacturers can use two government-endorsed approaches to advertise product healthiness: the Health Star Rating (HSR) front-of-pack nutrition labelling system, and health claims. Related, but different, algorithms determine the star rating of a product (the HSR algorithm) and eligibility to display claims (the Nutrient Profiling Scoring Criterion (NPSC) algorithm). The objective of this study was to examine the agreement between the HSR and NPSC algorithms. Food composition information for 41,297 packaged products was extracted from The George Institute's FoodSwitch database. HSR and the NPSC scores were calculated, and the proportion of products in each HSR category that were eligible to display a health claim under the NPSC was examined. The highest agreement between the HSR scoring algorithm and the NPSC threshold to determine eligibility to display a health claim was at the HSR cut-off of 3.5 stars (k = 0.83). Overall, 97.3% (n = 40,167) of products with star ratings of 3.5 or higher were also eligible to display a health claim, and 94.3% (n = 38,939) of products with star ratings less than 3.5 were ineligible to display a health claim. The food group with greatest divergence was "edible oils", with 45% products (n = 342) with HSR >3.5, but 64% (n = 495) eligible to display a claim. Categories with large absolute numbers of products with HSR <3.5, but eligible to display a claim, were "yoghurts and yoghurt drinks" (335 products, 25.4%) and "soft drinks" (299 products, 29.7%). Categories with a large number of products with HSR ≥3.5, but ineligible to display a claim, were "milk" (260 products, 21.2%) and "nuts and seeds" (173 products, 19.7%). We conclude that there is good agreement between the HSR and the NPSC systems overall, but divergence in some food groups is likely to result in confusion for consumers, particularly where foods with low HSRs are eligible to display a health claim. The alignment of the NPSC and HSR scoring algorithms should be improved.

Non-communicable diseases (NCDs) are the leading cause of mortality and morbidity worldwide. Unhealthy diets are one of four main behavioral risk factors contributing to the majority of NCDs. To promote healthy eating and reduce dietary risks, the Australian Commonwealth Government established the Healthy Food Partnership (HFP). In 2018, the HFP consulted on proposed nutrient reformulation targets for 36 food categories to improve the overall quality of the food supply. This study assessed whether the proposed targets were feasible and appropriate. The HFP used a five-step approach to inform the proposed targets. We replicated and extended this approach using a different nutrient composition database (FoodSwitch). Products in FoodSwitch were mapped to the proposed HFP targets. The proportion of products meeting each target was calculated and the FoodSwitch data were compared with HFP data to determine whether the proposed target nutrient levels were appropriate or whether a more stringent target was feasible. Products from the FoodSwitch database (10,599) were mapped against the proposed HFP categories: 8434 products across 30 categories for sodium, 2875 products across seven categories for sugar, and 612 products across five categories for saturated fat. The analyses revealed that 14 of 30 proposed HFP targets for sodium, one of seven targets for sugar, and one of five targets for saturated fat were feasible and appropriate. For the remaining 26 reformulation targets, the results indicate that these target levels could be more stringent and alternative targets are proposed. The draft HFP targets are feasible but the majority are too conservative. If Australia is to meet its commitment to a 30 per cent reduction in the average population salt intake by 2025, these targets could be implemented as interim targets to be reached within two years. However, the opportunity exists to improve the food supply and strengthen the HFP's population health impact by adopting more ambitious and incremental targets. Reformulation programs should be prioritized and closely monitored as part of a coordinated, multi-faceted national food and nutrition strategy.

We compared the healthiness of packaged foods and beverages between selected countries using the Health Star Rating (HSR) nutrient profiling system. Packaged food and beverage data collected 2013–2018 were obtained for Australia, Canada, Chile, China, India, Hong Kong, Mexico, New Zealand, Slovenia, South Africa, the UK, and USA. Each product was assigned to a food or beverage category and mean HSR was calculated overall by category and by country. Median energy density (kJ/100 g), saturated fat (g/100 g), total sugars (g/100 g) and sodium (mg/100 g) contents were calculated. Countries were ranked by mean HSR and median nutrient levels. Mean HSR for all products (n = 394,815) was 2.73 (SD 1.38) out of 5.0 (healthiest profile). The UK, USA, Australia and Canada ranked highest for overall nutrient profile (HSR 2.74–2.83) and India, Hong Kong, China and Chile ranked lowest (HSR 2.27–2.44). Countries with higher overall HSR generally ranked better with respect to nutrient levels. India ranked consistently in the least healthy third for all measures. There is considerable variability in the healthiness of packaged foods and beverages in different countries. The finding that packaged foods and beverages are less healthy in middle-income countries such as China and India suggests that nutrient profiling is an important tool to enable policymakers and industry actors to reformulate products available in the marketplace to reduce the risk of obesity and NCDs among populations.

Objective: Echinococcosis is a major parasitic zoonosis of public health importance in western China. In 2004, the Chinese Ministry of Health estimated that 380,000 people had the disease in the region. The Qinghai-Tibet Plateau is highly co-endemic with both alveolar echinococcosis (AE) and cystic echinococcosis (CE). In the past years, the Chinese government has been increasing the financial support to control the diseases in this region. Therefore, it is very important to identify the significant risk factors of the diseases by reviewing studies done in the region in the past decade to help policymakers design appropriate control strategies. Review: Selection criteria for which literature to review were firstly defined. Medline, CNKI (China National Knowledge Infrastructure), and Google Scholar were systematically searched for literature published between January 2000 and July 2011. Significant risk factors found by single factor and/or multiple factors analysis were listed, counted, and summarized. Literature was examined to check the comparability of the data; age and sex specific prevalence with same data structures were merged and used for further analysis. A variety of assumed social, economical, behavioral, and ecological risk factors were studied on the Plateau. Those most at risk were Tibetan herdsmen, the old and female in particular. By analyzing merged comparable data, it was found that females had a significant higher prevalence, and a positive linearity relationship existed between echinococcosis prevalence and increasing age. In terms of behavioral risk factors, playing with dogs was mostly correlated with CE and/or AE prevalence. In terms of hygiene, employing ground water as the drinking water source was significantly correlated with CE and AE prevalence. For definitive hosts, dog related factors were most frequently identified with prevalence of CE or/and AE; fox was a potential risk factor for AE prevalence only. Overgrazing and deforestation were significant for AE prevalence only. Conclusion: Tibetan herdsmen communities were at the highest risk of echinococcosis prevalence and should be the focus of echinococcosis control. Deworming both owned and stray dogs should be a major measure for controlling echinococcosis; treatment of wild definitive hosts should also be considered for AE endemic areas. Health education activities should be in concert with the local people's education backgrounds and languages in order to be able to improve behaviors. Further researches are needed to clarify the importance of wild hosts for AE/CE prevalence, the extent and range of the impacts of ecologic changes (overgrazing and deforestation) on the AE prevalence, and risk factors in Tibet.

Urbanization is a process that involves simultaneous transitions and transformations across multiple dimensions, including demographic, economic, and physical changes in the landscape. Each of these dimensions presents different indicators and definitions of urbanization. The chapter begins with a brief discussion of the multiple dimensions and definitions of urbanization, including implications for GHG emissions accounting, and then continues with an assessment of historical, current, and future trends across different dimensions of urbanization in the context of GHG emissions (12.2). It then discusses GHG accounting approaches and challenges specific to urban areas and human settlements. In Section 12.3, the chapter assesses the drivers of urban GHG emissions in a systemic fashion, and examines the impacts of drivers on individuals sectors as well as the interaction and interdependence of drivers. In this section, the relative magnitude of each driver's impact on urban GHG emissions is discussed both qualitatively and quantitatively, and provides the context for a more detailed assessment of how urban form and infrastructure affect urban GHG emissions (12.4). Here, the section discusses the individual urban form drivers such as density, connectivity, and land use mix, as well as their interactions with each other. Section 12.4 also examines the links between infrastructure and urban form, as well as their combined and interacting effects on GHG emissions. Section 12.5 identifies spatial planning strategies and policy instruments that can affect multiple drivers, and Section 12.6 examines the institutional, governance, and financial requirements to implement such policies. Of particular importance with regard to mitigation potential at the urban or local scale is a discussion of the geographic and administrative scales for which policies are implemented, overlapping, and / or in conflict. The chapter then identifies the scale and range of mitigation actions currently planned and / or implemented by local governments, and assesses the evidence of successful implementation of the plans, as well as barriers to further implementation (12.7). Next, the chapter discusses major co-benefits and adverse side-effects of mitigation at the local scale, including opportunities for sustainable development (12.8). The chapter concludes with a discussion of the major gaps in knowledge with respect to mitigation of climate change in urban areas (12.9).

Urbanization is a process that involves simultaneous transitions and transformations across multiple dimensions, including demographic, economic, and physical changes in the landscape. Each of these dimensions presents different indicators and definitions of urbanization. The chapter begins with a brief discussion of the multiple dimensions and definitions of urbanization, including implications for GHG emissions accounting, and then continues with an assessment of historical, current, and future trends across different dimensions of urbanization in the context of GHG emissions (12.2). It then discusses GHG accounting approaches and challenges specific to urban areas and human settlements. In Section 12.3, the chapter assesses the drivers of urban GHG emissions in a systemic fashion, and examines the impacts of drivers on individuals sectors as well as the interaction and interdependence of drivers. In this section, the relative magnitude of each driver's impact on urban GHG emissions is discussed both qualitatively and quantitatively, and provides the context for a more detailed assessment of how urban form and infrastructure affect urban GHG emissions (12.4). Here, the section discusses the individual urban form drivers such as density, connectivity, and land use mix, as well as their interactions with each other. Section 12.4 also examines the links between infrastructure and urban form, as well as their combined and interacting effects on GHG emissions. Section 12.5 identifies spatial planning strategies and policy instruments that can affect multiple drivers, and Section 12.6 examines the institutional, governance, and financial requirements to implement such policies. Of particular importance with regard to mitigation potential at the urban or local scale is a discussion of the geographic and administrative scales for which policies are implemented, overlapping, and / or in conflict. The chapter then identifies the scale and range of mitigation actions currently planned and / or implemented by local governments, and assesses the evidence of successful implementation of the plans, as well as barriers to further implementation (12.7). Next, the chapter discusses major co-benefits and adverse side-effects of mitigation at the local scale, including opportunities for sustainable development (12.8). The chapter concludes with a discussion of the major gaps in knowledge with respect to mitigation of climate change in urban areas (12.9).

Glioblastomas (GBM) are aggressive and therapy-resistant brain tumours, which contain a subpopulation of tumour-propagating glioblastoma stem-like cells (GSC) thought to drive progression and recurrence. Diffuse invasion of the brain parenchyma, including along preexisting blood vessels, is a leading cause of therapeutic resistance, but the mechanisms remain unclear. Here, we show that ephrin-B2 mediates GSC perivascular invasion. Intravital imaging, coupled with mechanistic studies in murine GBM models and patient-derived GSC, revealed that endothelial ephrin-B2 compartmentalises non-tumourigenic cells. In contrast, upregulation of the same ephrin-B2 ligand in GSC enabled perivascular migration through homotypic forward signalling. Surprisingly, ephrin-B2 reverse signalling also promoted tumourigenesis cell-autonomously, by mediating anchorage-independent cytokinesis via RhoA. In human GSC-derived orthotopic xenografts, EFNB2 knock-down blocked tumour initiation and treatment of established tumours with ephrin-B2-blocking antibodies suppressed progression. Thus, our results indicate that targeting ephrin-B2 may be an effective strategy for the simultaneous inhibition of invasion and proliferation in GBM. ; Medical Research Council (Cell Interactions and Cancer, MC_AS A652 5PZ10) Regional Government of Madrid (European Social Fund) The Royal Society (RG110360)

Genome sequencing, assembly and annotation were conducted by the Novogene Bioinformatics Institute, Beijing, China; mutual contracts were No. NHT140016 and NVT140016004. This work was supported by funding from the Scientific Project of Shenzhen Urban Administration (201519) and a Major Technical Research Project of the Innovation of Science and Technology Commission of Shenzhen (JSGG20140515164852417). Additional funding was provided in particular by the Scientific Research Program of Sino-Africa Joint Research Center (SAJL201607). We thank X.Q. Wang, G.W. Hu, Z.D. Chen and Y.H. Guo for comments on gnetophyte phylogenetic relationships and ecological issues; H. Wu and X.P. Ning for discussion of related organ development; K.K. Wan and S. Sun for additional help on the analysis of repeats. We also thank X.Y. for support of funding coordination. Y.V.d.P. acknowledges the Multidisciplinary Research Partnership 'Bioinformatics: from nucleotides to networks' Project (no. 01MR0310W) of Ghent University, and funding from the European Union Seventh Framework Programme (FP7/2007-2013) under European Research Council Advanced Grant Agreement 322739-DOUBLEUP.

Context. Realistic synthetic observations of theoretical source models are essential for our understanding of real observational data. In using synthetic data, one can verify the extent to which source parameters can be recovered and evaluate how various data corruption effects can be calibrated. These studies are the most important when proposing observations of new sources, in the characterization of the capabilities of new or upgraded instruments, and when verifying model-based theoretical predictions in a direct comparison with observational data. Aims. We present the SYnthetic Measurement creator for long Baseline Arrays (SYMBA), a novel synthetic data generation pipeline for Very Long Baseline Interferometry (VLBI) observations. SYMBA takes into account several realistic atmospheric, instrumental, and calibration effects. Methods. We used SYMBA to create synthetic observations for the Event Horizon Telescope (EHT), a millimetre VLBI array, which has recently captured the first image of a black hole shadow. After testing SYMBA with simple source and corruption models, we study the importance of including all corruption and calibration effects, compared to the addition of thermal noise only. Using synthetic data based on two example general relativistic magnetohydrodynamics (GRMHD) model images of M 87, we performed case studies to assess the image quality that can be obtained with the current and future EHT array for different weather conditions. Results. Our synthetic observations show that the effects of atmospheric and instrumental corruptions on the measured visibilities are significant. Despite these effects, we demonstrate how the overall structure of our GRMHD source models can be recovered robustly with the EHT2017 array after performing calibration steps, which include fringe fitting, a priori amplitude and network calibration, and self-calibration. With the planned addition of new stations to the EHT array in the coming years, images could be reconstructed with higher angular resolution and dynamic range. In our case study, these improvements allowed for a distinction between a thermal and a non-thermal GRMHD model based on salient features in reconstructed images. © 2020 ESO. ; This work is supported by the ERC Synergy Grant "BlackHoleCam: Imaging the Event Horizon of Black Holes" (Grant 610058). I. Natarajan and R. Deane are grateful for the support from the New Scientific Frontiers with Precision Radio Interferometry Fellowship awarded by the South African Radio Astronomy Observatory (SARAO), which is a facility of the National Research Foundation (NRF), an agency of the Department of Science and Technology (DST) of South Africa. The authors of the present paper further thank the following organizations and programmes: the Academy of Finland (projects 274477, 284495, 312496); the Advanced European Network of E-infrastructures for Astronomy with the SKA (AENEAS) project, supported by the European Commission Framework Programme Horizon 2020 Research and Innovation action under grant agreement 731016; the Alexander von Humboldt Stiftung; the Black Hole Initiative at Harvard University, through a grant (60477) from the John Templeton Foundation; the China Scholarship Council; Comision Nacional de Investigacio Cientifica y Tecnologica (CONICYT, Chile, via PIA ACT172033, Fondecyt 1171506, BASAL AFB-170002, ALMAconicyt 31140007); Consejo Nacional de Ciencia y Tecnologia (CONACYT, Mexico, projects 104497, 275201, 279006, 281692); the Delaney Family via the Delaney Family John A. Wheeler Chair at Perimeter Institute; Direccion General de Asuntos del Personal Academico-Universidad Nacional Autonoma de Mexico (DGAPA-UNAM, project IN112417); the Generalitat Valenciana postdoctoral grant APOSTD/2018/177; the Gordon and Betty Moore Foundation (grants GBMF-3561, GBMF-5278); the Istituto Nazionale di Fisica Nucleare (INFN) sezione di Napoli, iniziative specifiche TEONGRAV; the GenT Program (Generalitat Valenciana) under project CIDEGENT/2018/021; the International Max Planck Research School for Astronomy and Astrophysics at the Universities of Bonn and Cologne; the Jansky Fellowship program of the National Radio Astronomy Observatory (NRAO); the Japanese Government (Monbukagakusho: MEXT) Scholarship; the Japan Society for the Promotion of Science (JSPS) Grant-in-Aid for JSPS Research Fellowship (JP17J08829); the Key Research Program of Frontier Sciences, Chinese Academy of Sciences (CAS, grants QYZDJ-SSW-SLH057, QYZDJ-SSW-SYS008); the Leverhulme Trust Early Career Research Fellowship; the Max-Planck-Gesellschaft (MPG); the Max Planck Partner Group of the MPG and the CAS; the MEXT/JSPS KAKENHI (grants 18KK0090, JP18K13594, JP18K03656, JP18H03721, 18K03709, 18H01245, 25120007); the MIT International Science and Technology Initiatives (MISTI) Funds; the Ministry of Science and Technology (MOST) of Taiwan (105-2112-M-001-025-MY3, 106-2112-M-001-011, 106-2119-M-001027, 107-2119-M-001-017, 107-2119-M-001-020, and 107-2119-M-110-005); the National Aeronautics and Space Administration (NASA, Fermi Guest Investigator grant 80NSSC17K0649); NASA through the NASA Hubble Fellowship grant #HST-HF2-51431.001-A awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc. , for NASA, under contract NAS5-26555; the National Institute of Natural Sciences (NINS) of Japan; the National Key Research and Development Program of China (grant 2016YFA0400704, 2016YFA0400702); the National Science Foundation (NSF, grants AST-0096454, AST-0352953, AST-0521233, AST-0705062, AST-0905844, AST-0922984, AST-1126433, AST-1140030, DGE-1144085, AST-1207704, AST-1207730, AST-1207752, MRI-1228509, OPP-1248097, AST-1310896, AST-1312651, AST-1337663, AST-1440254, AST-1555365, AST-1715061, AST-1615796, AST-1716327, OISE-1743747, AST-1816420); the Natural Science Foundation of China (grants 11573051, 11633006, 11650110427, 10625314, 11721303, 11725312, 11933007); the Natural Sciences and Engineering Research Council of Canada (NSERC, including a Discovery Grant and the NSERC Alexander Graham Bell Canada Graduate Scholarships-Doctoral Program); the National Youth Thousand Talents Program of China; the National Research Foundation of Korea (the Global PhD Fellowship Grant: grants NRF-2015H1A2A1033752, 2015-R1D1A1A01056807, the Korea Research Fellowship Program: NRF-2015H1D3A1066561); the Netherlands Organization for Scientific Research (NWO) VICI award (grant 639.043.513) and Spinoza Prize SPI 78-409; the New Scientific Frontiers with Precision Radio Interferometry Fellowship awarded by the South African Radio Astronomy Observatory (SARAO), which is a facility of the National Research Foundation (NRF), an agency of the Department of Science and Technology (DST) of South Africa; the Onsala Space Observatory (OSO) national infrastructure, for the provisioning of its facilities/observational support (OSO receives funding through the Swedish Research Council under grant 2017-00648) the Perimeter Institute for Theoretical Physics (research at Perimeter Institute is supported by the Government of Canada through the Department of Innovation, Science and Economic Development and by the Province of Ontario through the Ministry of Research, Innovation and Science); the Princeton/Flatiron Postdoctoral Prize Fellowship; the Russian Science Foundation (grant 17-12-01029); the Spanish Ministerio de Economia y Competitividad (grants AYA2015-63939-C21-P, AYA2016-80889-P); the State Agency for Research of the Spanish MCIU through the "Center of Excellence Severo Ochoa" award for the Instituto de Astrofisica de Andalucia (SEV-2017-0709); the Toray Science Foundation; the US Department of Energy (USDOE) through the Los Alamos National Laboratory (operated by Triad National Security, LLC, for the National Nuclear Security Administration of the USDOE (Contract 89233218CNA000001)); the Italian Ministero dell'Istruzione Universita e Ricerca through the grant Progetti Premiali 2012-iALMA (CUP C52I13000140001); the European Union's Horizon 2020 research and innovation programme under grant agreement No 730562 RadioNet; ALMA North America Development Fund; the Academia Sinica; Chandra TM6-17006X. This work used the Extreme Science and Engineering Discovery Environment (XSEDE), supported by NSF grant ACI-1548562, and CyVerse, supported by NSF grants DBI-0735191, DBI-1265383, and DBI1743442. XSEDE Stampede2 resource at TACC was allocated through TGAST170024 and TG-AST080026N. XSEDE JetStream resource at PTI and TACC was allocated through AST170028. The simulations were performed in part on the SuperMUC cluster at the LRZ in Garching, on the LOEWE cluster in CSC in Frankfurt, and on the HazelHen cluster at the HLRS in Stuttgart. This research was enabled in part by support provided by Compute Ontario (http://computeontario.ca), Calcul Quebec (http://www. calculquebec.ca) and Compute Canada (http://www.computecanada.ca).We thank the sta ff at the participating observatories, correlation centers, and institutions for their enthusiastic support. This paper makes use of the following ALMA data: ADS/JAO.ALMA#2017.1.00841.V. ALMA is a partnership of the European Southern Observatory (ESO; Europe, representing its member states), NSF, and National Institutes of Natural Sciences of Japan, together with National Research Council (Canada), Ministry of Science and Technology (MOST; Taiwan), Academia Sinica Institute of Astronomy and Astrophysics (ASIAA; Taiwan), and Korea Astronomy and Space Science Institute (KASI; Republic of Korea), in cooperation with the Republic of Chile. The Joint ALMA Observatory is operated by ESO, Associated Universities, Inc. (AUI)/NRAO, and the National Astronomical Observatory of Japan (NAOJ). The NRAO is a facility of the NSF operated under cooperative agreement by AUI. APEX is a collaboration between the Max-Planck-Institut fur Radioastronomie (Germany), ESO, and the Onsala Space Observatory (Sweden). The SMA is a joint project between the SAO and ASIAA and is funded by the Smithsonian Institution and the Academia Sinica. The JCMT is operated by the East Asian Observatory on behalf of the NAOJ, ASIAA, and KASI, as well as the Ministry of Finance of China, Chinese Academy of Sciences, and the National Key R&D Program (No. 2017YFA0402700) of China. Additional funding support for the JCMT is provided by the Science and Technologies Facility Council (UK) and participating universities in the UK and Canada. The LMT is a project operated by the Instituto Nacional de Astrofisica, Optica, y Electronica (Mexico) and the University of Massachusetts at Amherst (USA). The IRAM 30m telescope on Pico Veleta, Spain is operated by IRAM and supported by CNRS (Centre National de la Recherche Scientifique, France), MPG (Max-Planck-Gesellschaft, Germany) and IGN (Instituto Geografico Nacional, Spain). The SMT is operated by the Arizona Radio Observatory, a part of the Steward Observatory of the University of Arizona, with financial support of operations from the State of Arizona and financial support for instrumentation development from the NSF. The SPT is supported by the National Science Foundation through grant PLR-1248097. Partial support is also provided by the NSF Physics Frontier Center grant PHY-1125897 to the Kavli Institute of Cosmological Physics at the University of Chicago, the Kavli Foundation and the Gordon and Betty Moore Foundation grant GBMF 947. The SPT hydrogen maser was provided on loan from the GLT, courtesy of ASIAA. The EHTC has received generous donations of FPGA chips from Xilinx Inc., under the Xilinx University Program. The EHTC has benefited from technology shared under open-source license by the Collaboration for Astronomy Signal Processing and Electronics Research (CASPER). The EHT project is grateful to T4Science and Microsemi for their assistance with Hydrogen Masers. This research has made use of NASA's Astrophysics Data System. We gratefully acknowledge the support provided by the extended staff of the ALMA, both from the inception of the ALMA Phasing Project through the observational campaigns of 2017 and 2018. We would like to thank A. Deller and W. Brisken for EHT-specific support with the use of DiFX. We acknowledge the significance that Maunakea, where the SMA and JCMT EHT stations are located, has for the indigenous Hawaiian people. The software presented in this work makes use of the Numpy (van derWalt et al. 2011), Scipy (Jones et al. 2001), Astropy (Astropy Collaboration 2013, 2018) libraries and the KERN software bundle (Molenaar & Smirnov 2018).

Publisher's version (útgefin grein). ; Background: Genome-wide association studies conducted on QRS duration, an electrocardiographic measurement associated with heart failure and sudden cardiac death, have led to novel biological insights into cardiac function. However, the variants identified fall predominantly in non-coding regions and their underlying mechanisms remain unclear. Results: Here, we identify putative functional coding variation associated with changes in the QRS interval duration by combining Illumina HumanExome BeadChip genotype data from 77,898 participants of European ancestry and 7695 of African descent in our discovery cohort, followed by replication in 111,874 individuals of European ancestry from the UK Biobank and deCODE cohorts. We identify ten novel loci, seven within coding regions, including ADAMTS6, significantly associated with QRS duration in gene-based analyses. ADAMTS6 encodes a secreted metalloprotease of currently unknown function. In vitro validation analysis shows that the QRS-associated variants lead to impaired ADAMTS6 secretion and loss-of function analysis in mice demonstrates a previously unappreciated role for ADAMTS6 in connexin 43 gap junction expression, which is essential for myocardial conduction. Conclusions: Our approach identifies novel coding and non-coding variants underlying ventricular depolarization and provides a possible mechanism for the ADAMTS6-associated conduction changes. ; Funding This work was funded by a grant to YJ from the British Heart Foundation (PG/12/38/29615). AGES: This study has been funded by NIH contracts N01-AG-1-2100 and 271201200022C, the NIA Intramural Research Program, Hjartavernd (the Icelandic Heart Association), and the Althingi (the Icelandic Parliament). The study is approved by the Icelandic National Bioethics Committee, VSN: 00–063. The researchers are indebted to the participants for their willingness to participate in the study. ARIC: The Atherosclerosis Risk in Communities Study is carried out as a collaborative study supported by National Heart, Lung, and Blood Institute contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HHSN268201100012C), R01HL087641, R01HL59367, and R01HL086694; National Human Genome Research Institute contract U01HG004402; and National Institutes of Health contract HHSN268200625226C. The authors thank the staff and participants of the ARIC study for their important contributions. Infrastructure was partly supported by Grant Number UL1RR025005, a component of the National Institutes of Health and NIH Roadmap for Medical Research. Funding support for "Building on GWAS for NHLBI-diseases: the U.S. CHARGE consortium" was provided by the NIH through the American Recovery and Reinvestment Act of 2009 (ARRA) (5RC2HL102419). BRIGHT: The Exome Chip genotyping was funded by Wellcome Trust Strategic Awards (083948 and 085475). This work was also supported by the Medical Research Council of Great Britain (Grant no. G9521010D); and by the British Heart Foundation (Grant no. PG/02/128). AFD was supported by the British Heart Foundation (Grant nos. RG/07/005/23633 and SP/08/005/25115); and by the European Union Ingenious HyperCare Consortium: Integrated Genomics, Clinical Research, and Care in Hypertension (grant no. LSHM-C7–2006-037093). The BRIGHT study is extremely grateful to all the patients who participated in the study and the BRIGHT nursing team. We would also like to thank the Barts Genome Centre staff for their assistance with this project. CHS: This Cardiovascular Health Study (CHS) research was supported by NHLBI contracts HHSN268201800001C, HHSN268201200036C, HHSN268200800007C, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086; and NHLBI grants R01HL068986, U01HL080295, R01HL087652, R01HL105756, R01HL103612, R01HL120393, and U01HL130114 with additional contribution from the National Institute of Neurological Disorders and Stroke (NINDS). Additional support was provided through R01AG023629 from the National Institute on Aging (NIA). A full list of principal CHS investigators and institutions can be found at CHS-NHLBI.org. The provision of genotyping data was supported in part by the National Center for Advancing Translational Sciences, CTSI grant UL1TR001881, and the National Institute of Diabetes and Digestive and Kidney Disease Diabetes Research Center (DRC) grant DK063491 to the Southern California Diabetes Endocrinology Research Center. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. ERF: The ERF study as a part of EUROSPAN (European Special Populations Research Network) was supported by European Commission FP6 STRP grant number 018947 (LSHG-CT-2006-01947) and also received funding from the European Community's Seventh Framework Programme (FP7/2007–2013)/grant agreement HEALTH-F4–2007-201413 by the European Commission under the programme "Quality of Life and Management of the Living Resources" of 5th Framework Programme (no. QLG2-CT-2002-01254). The ERF study was further supported by ENGAGE consortium and CMSB. High-throughput analysis of the ERF data was supported by joint grant from Netherlands Organization for Scientific Research and the Russian Foundation for Basic Research (NWO-RFBR 047.017.043). We are grateful to all study participants and their relatives, general practitioners, and neurologists for their contributions to the ERF study and to P Veraart for her help in genealogy, J Vergeer for the supervision of the laboratory work, and P Snijders for his help in data collection. FHS: The Framingham Heart Study (FHS) research reported in this article was supported by a grant from the National Heart, Lung, and Blood Institute (NHLBI), HL120393. Generation Scotland: Generation Scotland received core support from the Chief Scientist Office of the Scottish Government Health Directorates (CZD/16/6) and the Scottish Funding Council (HR03006). Genotyping of the Generation Scotland and Scottish Family Health Study samples was carried out by the Genetics Core Laboratory at the Clinical Research Facility, Edinburgh, Scotland and was funded by the UK's Medical Research Council. GOCHA: The Genetics of Cerebral Hemorrhage with Anticoagulation was carried out as a collaborative study supported by grants R01NS073344, R01NS059727, and 5K23NS059774 from the NIH–National Institute of Neurological Disorders and Stroke (NIH-NINDS). GRAPHIC: The GRAPHIC Study was funded by the British Heart Foundation (BHF/RG/2000004). NJS and CPN are supported by the British Heart Foundation and is a NIHR Senior Investigator. This work falls under the portfolio of research supported by the NIHR Leicester Cardiovascular Biomedical Research. INGI-FVG: This study has been funded by Regione FVG (L.26.2008). INTER99: The Inter99 was initiated by Torben Jørgensen (PI), Knut Borch-Johnsen (co-PI), Hans Ibsen and Troels F. Thomsen. The steering committee comprises the former two and Charlotta Pisinger. The study was financially supported by research grants from the Danish Research Council, the Danish Centre for Health Technology Assessment, Novo Nordisk Inc., Research Foundation of Copenhagen County, Ministry of Internal Affairs and Health, the Danish Heart Foundation, the Danish Pharmaceutical Association, the Augustinus Foundation, the Ib Henriksen Foundation, the Becket Foundation, and the Danish Diabetes Association. The Novo Nordisk Foundation Center for Basic Metabolic Research is an independent Research Center at the University of Copenhagen partially funded by an unrestricted donation from the Novo Nordisk Foundation (www.metabol.ku.dk). JHS: We thank the Jackson Heart Study (JHS) participants and staff for their contributions to this work. The JHS is supported by contracts HHSN268201300046C, HHSN268201300047C, HHSN268201300048C, HHSN268201300049C, HHSN268201300050C from the National Heart, Lung, and Blood Institute and the National Institute on Minority Health and Health Disparities. Dr. Wilson is supported by U54GM115428 from the National Institute of General Medical Sciences. KORA: The KORA study 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 (BMBF) and by the State of Bavaria. Furthermore, KORA research was supported within the Munich Center of Health Sciences (MC-Health), Ludwig-Maximilians-Universität, as part of LMUinnovativ. Korcula: This work was funded by the Medical Research Council UK, The Croatian Ministry of Science, Education and Sports (grant 216–1080315-0302), the Croatian Science Foundation (grant 8875), the Centre of Excellence in Personalized health care, and the Centre of Competencies for Integrative Treatment, Prevention and Rehabilitation using TMS. LifeLines: The LifeLines Cohort Study and generation and management of GWAS genotype data for the LifeLines Cohort Study are supported by The Netherlands Organization of Scientific Research NWO (grant 175.010.2007.006), the Economic Structure Enhancing Fund (FES) of the Dutch government, the Ministry of Economic Affairs, the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the Northern Netherlands Collaboration of Provinces (SNN), the Province of Groningen, University Medical Center Groningen, the University of Groningen, Dutch Kidney Foundation, and Dutch Diabetes Research Foundation. Niek Verweij is supported by NWO-VENI (016.186.125) and Marie Sklodowska-Curie GF (call: H2020-MSCA-IF-2014, Project ID: 661395). UHP: Folkert W. Asselbergs is supported by UCL Hospitals NIHR Biomedical Research Centre. Ilonca Vaartjes is supported by a Dutch Heart Foundation grant DHF project "Facts and Figures." MGH-CAMP: Dr. Patrick Ellinor is funded by NIH grants (2R01HL092577, 1R01HL128914, R01HL104156, and K24HL105780) and American Heart Association Established Investigator Award 13EIA14220013 (Ellinor). Dr. Steve Lubitz is funded by NIH grants K23HL114724 and a Doris Duke Charitable Foundation Clinical Scientist Development Award 2014105. NEO: The authors of the NEO study thank all individuals who participated in the Netherlands Epidemiology in Obesity study, all participating general practitioners for inviting eligible participants, and all research nurses for collection of the data. We thank the NEO study group, Pat van Beelen, Petra Noordijk, and Ingeborg de Jonge for the coordination, lab, and data management of the NEO study. We also thank Arie Maan for the analyses of the electrocardiograms. The genotyping in the NEO study was supported by the Centre National de Génotypage (Paris, France), headed by Jean-Francois Deleuze. The NEO study is supported by the participating Departments, the Division and the Board of Directors of the Leiden University Medical Center, and by the Leiden University, Research Profile Area Vascular and Regenerative Medicine. Dennis Mook-Kanamori is supported by Dutch Science Organization (ZonMW-VENI Grant 916.14.023). RS-I: The generation and management of the Illumina Exome Chip v1.0 array data for the Rotterdam Study (RS-I) was executed by the Human Genotyping Facility of the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands. The Exome chip array dataset was funded by the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, from the Netherlands Genomics Initiative (NGI)/Netherlands Organization for Scientific Research (NWO)-sponsored Netherlands Consortium for Healthy Aging (NCHA; project nr. 050–060-810); the Netherlands Organization for Scientific Research (NWO; project number 184021007); and by the Rainbow Project (RP10; Netherlands Exome Chip Project) of the Biobanking and Biomolecular Research Infrastructure Netherlands (BBMRI-NL; www.bbmri.nl). We thank Ms. Mila Jhamai, Ms. Sarah Higgins, and Mr. Marijn Verkerk for their help in creating the exome chip database, and Carolina Medina-Gomez, MSc, Lennard Karsten, MSc, and Linda Broer PhD for QC and variant calling. Variants were called using the best practice protocol developed by Grove et al. as part of the CHARGE consortium exome chip central calling effort. The Rotterdam Study is funded by Erasmus Medical Center and Erasmus University, Rotterdam, Netherlands Organization for the Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam. The authors are grateful to the study participants, the staff from the Rotterdam Study, and the participating general practitioners and pharmacists. The work of Bruno H. Stricker is supported by grants from the Netherlands Organization for Health Research and Development (ZonMw) (Priority Medicines Elderly 113102005 to ME and DoelmatigheidsOnderzoek 80–82500–98-10208 to BHS). The work of Mark Eijgelsheim is supported by grants from the Netherlands Organization for Health Research and Development (ZonMw) (Priority Medicines Elderly 113102005 to ME and DoelmatigheidsOnderzoek 80–82500–98-10208 to BHS). SHIP: SHIP is supported by the BMBF (grants 01ZZ9603, 01ZZ0103, and 01ZZ0403) and the German Research Foundation (Deutsche Forschungsgemeinschaft [DFG]; grant GR 1912/5–1). SHIP and SHIP-TREND are part of the Community Medicine Research net (CMR) of the Ernst-Moritz-Arndt University Greifswald (EMAU) which is funded by the BMBF as well as the Ministry for Education, Science and Culture and the Ministry of Labor, Equal Opportunities, and Social Affairs of the Federal State of Mecklenburg-West Pomerania. The CMR encompasses several research projects that share data from SHIP. The EMAU is a member of the Center of Knowledge Interchange (CKI) program of the Siemens AG. SNP typing of SHIP and SHIP-TREND using the Illumina Infinium HumanExome BeadChip (version v1.0) was supported by the BMBF (grant 03Z1CN22). We thank all SHIP and SHIP-TREND participants and staff members as well as the genotyping staff involved in the generation of the SNP data. TWINSUK: TwinsUK is funded by the Wellcome Trust, Medical Research Council, European Union, the National Institute for Health Research (NIHR)-funded BioResource, Clinical Research Facility and Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust in partnership with King's College London. UKBB: This research has been conducted using the UK Biobank Resource (application 8256 - Understanding genetic influences in the response of the cardiac electrical system to exercise) and is supported by Medical Research Council grant MR/N025083/1. We also wish to acknowledge the support of the NIHR Cardiovascular Biomedical Research Unit at Barts and Queen Mary University of London, UK. PD Lambiase acknowledges support from the UCLH Biomedicine NIHR. MO is supported by an IEF 2013 Marie Curie fellowship. JR acknowledges support from the People Programme (Marie Curie Actions) of the European Union's Seventh Framework Programme (FP7/2007–2013) under REA grant agreement no. 608765. YFS: The Young Finns Study has been financially supported by the Academy of Finland: grants 286284, 134309 (Eye), 126925, 121584, 124282, 129378 (Salve), 117787 (Gendi), and 41071 (Skidi); the Social Insurance Institution of Finland; Competitive State Research Financing of the Expert Responsibility area of Kuopio, Tampere and Turku University Hospitals (grant X51001); Juho Vainio Foundation; Paavo Nurmi Foundation; Finnish Foundation for Cardiovascular Research; Finnish Cultural Foundation; Tampere Tuberculosis Foundation; Emil Aaltonen Foundation; Yrjö Jahnsson Foundation; Signe and Ane Gyllenberg Foundation; and Diabetes Research Foundation of Finnish Diabetes Association. The expert technical assistance in the statistical analyses by Irina Lisinen is gratefully acknowledged. Cell culture and biochemistry: Funding was provided by the National Institutes of Health (Program of Excellence in Glycoscience award HL107147 to SSA and F32AR063548 to TJM) and the David and Lindsay Morgenthaler Postdoctoral Fellowship (to TJM) and by the Allen Distinguished Investigator Program, through support made by The Paul G. Allen Frontiers Group and the American Heart Association (to SSA). Mutant mouse model: Adamts6 mutant mice were generated and further propagated and analyzed by funding provided by NIH grants HL098180 and HL132024 (to CWL) and by the Allen Distinguished Investigator Program, through support made by The Paul G. Allen Frontiers Group and the American Heart Association (to SSA). ; Peer Reviewed