Showing papers by "University of Washington published in 2020"
TL;DR: It is demonstrating that cross-neutralizing antibodies targeting conserved S epitopes can be elicited upon vaccination, and it is shown that SARS-CoV-2 S uses ACE2 to enter cells and that the receptor-binding domains of Sars- coV- 2 S and SARS S bind with similar affinities to human ACE2, correlating with the efficient spread of SATS among humans.
7,219 citations
University of Jyväskylä1, University of California, Los Angeles2, California Polytechnic State University3, Los Alamos National Laboratory4, National Research University – Higher School of Economics5, University of California, Berkeley6, University of Birmingham7, Australian Nuclear Science and Technology Organisation8, University of Washington9, University of Massachusetts Amherst10, University of West Bohemia11, Brigham Young University12, University of Texas at Austin13, Universidade Federal de Minas Gerais14, Google15
TL;DR: SciPy as discussed by the authors is an open-source scientific computing library for the Python programming language, which has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year.
Abstract: SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.
6,244 citations
TL;DR: Global health has steadily improved over the past 30 years as measured by age-standardised DALY rates, and there has been a marked shift towards a greater proportion of burden due to YLDs from non-communicable diseases and injuries.
5,802 citations
TL;DR: This case highlights the importance of close coordination between clinicians and public health authorities at the local, state, and federal levels, as well as the need for rapid dissemination of clinical information related to the care of patients with this emerging infection.
Abstract: An outbreak of novel coronavirus (2019-nCoV) that began in Wuhan, China, has spread rapidly, with cases now confirmed in multiple countries. We report the first case of 2019-nCoV infection confirmed in the United States and describe the identification, diagnosis, clinical course, and management of the case, including the patient's initial mild symptoms at presentation with progression to pneumonia on day 9 of illness. This case highlights the importance of close coordination between clinicians and public health authorities at the local, state, and federal levels, as well as the need for rapid dissemination of clinical information related to the care of patients with this emerging infection.
4,970 citations
TL;DR: A catalogue of predicted loss-of-function variants in 125,748 whole-exome and 15,708 whole-genome sequencing datasets from the Genome Aggregation Database (gnomAD) reveals the spectrum of mutational constraints that affect these human protein-coding genes.
Abstract: Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes that are crucial for the function of an organism will be depleted of such variants in natural populations, whereas non-essential genes will tolerate their accumulation. However, predicted loss-of-function variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes1. Here we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence predicted loss-of-function variants in this cohort after filtering for artefacts caused by sequencing and annotation errors. Using an improved model of human mutation rates, we classify human protein-coding genes along a spectrum that represents tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve the power of gene discovery for both common and rare diseases. A catalogue of predicted loss-of-function variants in 125,748 whole-exome and 15,708 whole-genome sequencing datasets from the Genome Aggregation Database (gnomAD) reveals the spectrum of mutational constraints that affect these human protein-coding genes.
4,913 citations
01 Jul 2020
TL;DR: BART is presented, a denoising autoencoder for pretraining sequence-to-sequence models, which matches the performance of RoBERTa on GLUE and SQuAD, and achieves new state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks.
Abstract: We present BART, a denoising autoencoder for pretraining sequence-to-sequence models. BART is trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text. It uses a standard Tranformer-based neural machine translation architecture which, despite its simplicity, can be seen as generalizing BERT (due to the bidirectional encoder), GPT (with the left-to-right decoder), and other recent pretraining schemes. We evaluate a number of noising approaches, finding the best performance by both randomly shuffling the order of sentences and using a novel in-filling scheme, where spans of text are replaced with a single mask token. BART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. It matches the performance of RoBERTa on GLUE and SQuAD, and achieves new state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains of up to 3.5 ROUGE. BART also provides a 1.1 BLEU increase over a back-translation system for machine translation, with only target language pretraining. We also replicate other pretraining schemes within the BART framework, to understand their effect on end-task performance.
4,505 citations
University College London1, Camden and Islington NHS Foundation Trust2, Royal Melbourne Hospital3, University of Exeter4, University of Plymouth5, University of Cambridge6, University of Manchester7, Tel Aviv University8, Goa Medical College9, Johns Hopkins University10, University of California, Davis11, Kaiser Permanente12, University College Hospital, Ibadan13, University of Montpellier14, Dalhousie University15, University of Southern California16, Oslo University Hospital17, University of Washington18
TL;DR: Author(s): Livingston, Gill; Huntley, Jonathan; Sommerlad, Andrew ; Sommer Glad, Andrew; Ames, David; Ballard, Clive; Banerjee, Sube; Brayne, Carol; Burns, Alistair; Cohen-Mansfield, Jiska; Cooper, Claudia; Costafreda, Sergi G; Dias, Amit; Fox, Nick; Gitlin, Laura N; Howard, Robert; Kales, Helen C;
3,559 citations
University of Washington1, National Institutes of Health2, Institute for Health Metrics and Evaluation3, Sapienza University of Rome4, Mayo Clinic5, FIU Herbert Wertheim College of Medicine6, Cincinnati Children's Hospital Medical Center7, Boston University8, Essentia Health9, University of Douala10, University of British Columbia11, Medical University of Graz12, Telethon Institute for Child Health Research13, University of Milan14, Cedars-Sinai Medical Center15, Johns Hopkins University16, University of California, San Diego17, University of Michigan18, University of Edinburgh19, University of Texas Southwestern Medical Center20, Queen Mary University of London21, University of Alabama at Birmingham22, Harvard University23, Tufts Medical Center24, All India Institute of Medical Sciences25, Northwestern University26, University of Kentucky27, Casa Sollievo della Sofferenza28, Columbia University29, Icahn School of Medicine at Mount Sinai30, University of Sydney31, University of Cape Town32, Federal University of Rio de Janeiro33, University of Ibadan34, Case Western Reserve University35, Stanford University36, Universidade Federal de Minas Gerais37, The George Institute for Global Health38, Uppsala University39, Dresden University of Technology40, King Fahd Medical City41, Tulane University42, Imperial College London43
TL;DR: CVD burden continues its decades-long rise for almost all countries outside high-income countries, and alarmingly, the age-standardized rate of CVD has begun to rise in some locations where it was previously declining in high- income countries.
3,315 citations
Book•
23 Feb 2020
TL;DR: The ATLAS detector as installed in its experimental cavern at point 1 at CERN is described in this paper, where a brief overview of the expected performance of the detector when the Large Hadron Collider begins operation is also presented.
Abstract: The ATLAS detector as installed in its experimental cavern at point 1 at CERN is described in this paper. A brief overview of the expected performance of the detector when the Large Hadron Collider begins operation is also presented.
3,111 citations
Christopher J L Murray1, Christopher J L Murray2, Christopher J L Murray3, Aleksandr Y. Aravkin2 +2269 more•Institutions (286)
TL;DR: The largest declines in risk exposure from 2010 to 2019 were among a set of risks that are strongly linked to social and economic development, including household air pollution; unsafe water, sanitation, and handwashing; and child growth failure.
3,059 citations
TL;DR: The results suggest that early detection, hand washing, self-isolation, and household quarantine will likely be more effective than travel restrictions at mitigating this pandemic, and sustained 90% travel restrictions to and from mainland China only modestly affect the epidemic trajectory unless combined with a 50% or higher reduction of transmission in the community.
Abstract: Motivated by the rapid spread of coronavirus disease 2019 (COVID-19) in mainland China, we use a global metapopulation disease transmission model to project the impact of travel limitations on the national and international spread of the epidemic. The model is calibrated on the basis of internationally reported cases and shows that, at the start of the travel ban from Wuhan on 23 January 2020, most Chinese cities had already received many infected travelers. The travel quarantine of Wuhan delayed the overall epidemic progression by only 3 to 5 days in mainland China but had a more marked effect on the international scale, where case importations were reduced by nearly 80% until mid-February. Modeling results also indicate that sustained 90% travel restrictions to and from mainland China only modestly affect the epidemic trajectory unless combined with a 50% or higher reduction of transmission in the community.
TL;DR: An explanation method for trees is presented that enables the computation of optimal local explanations for individual predictions, and the authors demonstrate their method on three medical datasets.
Abstract: Tree-based machine learning models such as random forests, decision trees and gradient boosted trees are popular nonlinear predictive models, yet comparatively little attention has been paid to explaining their predictions. Here we improve the interpretability of tree-based models through three main contributions. (1) A polynomial time algorithm to compute optimal explanations based on game theory. (2) A new type of explanation that directly measures local feature interaction effects. (3) A new set of tools for understanding global model structure based on combining many local explanations of each prediction. We apply these tools to three medical machine learning problems and show how combining many high-quality local explanations allows us to represent global structure while retaining local faithfulness to the original model. These tools enable us to (1) identify high-magnitude but low-frequency nonlinear mortality risk factors in the US population, (2) highlight distinct population subgroups with shared risk characteristics, (3) identify nonlinear interaction effects among risk factors for chronic kidney disease and (4) monitor a machine learning model deployed in a hospital by identifying which features are degrading the model’s performance over time. Given the popularity of tree-based machine learning models, these improvements to their interpretability have implications across a broad set of domains. Tree-based machine learning models are widely used in domains such as healthcare, finance and public services. The authors present an explanation method for trees that enables the computation of optimal local explanations for individual predictions, and demonstrate their method on three medical datasets.
University of Pittsburgh1, Institute for Health Metrics and Evaluation2, University of Washington3, University of British Columbia4, The George Institute for Global Health5, Federal University of São Paulo6, Charité7, University of London8, Seattle Children's9, University of São Paulo10, University of Melbourne11
TL;DR: Despite declining age-standardised incidence and mortality, sepsis remains a major cause of health loss worldwide and has an especially high health-related burden in sub-Saharan Africa.
TL;DR: The PICRUSt2 algorithm includes steps that optimize genome prediction, including placing sequences into a reference phylogeny rather than relying on predictions limited to reference OTUs, and basing predictions on a larger database of reference genomes and gene families, and enabling predictions of complex phenotypes and integration of custom databases.
Abstract: To the Editor — One limitation of microbial community marker-gene sequencing is that it does not provide information about the functional composition of sampled communities. PICRUSt1 was developed in 2013 to predict the functional potential of a bacterial community on the basis of marker gene sequencing profiles, and now we present PICRUSt2 (https://github. com/picrust/picrust2), which improves on the original method. Specifically, PICRUSt2 contains an updated and larger database of gene families and reference genomes, provides interoperability with any operational taxonomic unit (OTU)-picking or denoising algorithm, and enables phenotype predictions. Benchmarking shows that PICRUSt2 is more accurate than PICRUSt and other competing methods overall. PICRUSt2 also allows the addition of custom reference databases. We highlight these improvements and also important caveats regarding the use of predicted metagenomes. The most common method for profiling bacterial communities is to sequence the conserved 16S rRNA gene. Functional profiles cannot be directly identified using 16S rRNA gene sequence data owing to strain variation, so several methods have been developed to predict microbial community functions from taxonomic profiles (amplicon sequences) alone1–5. Shotgun metagenomics sequencing (MGS), which sequences entire genomes rather than marker genes, can also be used to characterize the functions of a community, but does not work well if there is host contamination — for example, in a biopsy — or if there is very little community biomass. PICRUSt (hereafter “PICRUSt1”) was developed for prediction of functions from 16S marker sequences, and it is widely used but has some limitations. Standard PICRUSt1 workflows require input sequences to be OTUs generated from closed-reference OTU-picking against a compatible version of the Greengenes database6. Due to this restriction to reference OTUs, the default PICRUSt1 workflow is incompatible with sequence denoising methods, which produce amplicon sequence variants (ASVs) rather than OTUs. ASVs have finer resolution, allowing closely related organisms to be more readily distinguished. Furthermore, the bacterial reference databases used by PICRUSt1 have not been updated since 2013 and lack thousands of recently added gene families. We expected that optimizing genome prediction would improve accuracy of functional predictions. Therefore, the PICRUSt2 algorithm (Fig. 1a) includes steps that optimize genome prediction, including placing sequences into a reference phylogeny rather than relying on predictions limited to reference OTUs (Fig. 1b); basing predictions on a larger database of reference genomes and gene families (Fig. 1c); more stringently predicting pathway abundance (Supplementary Fig. 1); and enabling predictions of complex phenotypes and integration of custom databases. PICRUSt2 integrates existing open-source tools to predict genomes of environmentally sampled 16S rRNA gene sequences. ASVs are placed into a reference tree, which is used as the basis of functional predictions. 0 5,000 10,000 15,000 20,000
TL;DR: This case series describes the clinical presentation, characteristics, and outcomes of patients with coronavirus disease 2019 (COVID-19) admitted to the intensive care unit at a public hospital in Washington State in February 2020, including initial reports of cardiomyopathy in one-third of the patients.
Abstract: This case series describes the clinical presentation, characteristics, and outcomes of patients with coronavirus disease 2019 (COVID-19) admitted to the intensive care unit at a public hospital in Washington State in February 2020, including initial reports of cardiomyopathy in one-third of the patients.
University of KwaZulu-Natal1, University of Massachusetts Medical School2, Ragon Institute of MGH, MIT and Harvard3, Harvard University4, Broad Institute5, Massachusetts Institute of Technology6, Boston Children's Hospital7, Aix-Marseille University8, Centre national de la recherche scientifique9, Vanderbilt University Medical Center10, Brigham and Women's Hospital11, University of California, Berkeley12, University of Washington13, Fred Hutchinson Cancer Research Center14, Seattle Children's15, University of Pittsburgh16, University of Sheffield17, United States Department of Veterans Affairs18, University College London19, Scripps Research Institute20
TL;DR: The data suggest that SARS-CoV-2 could exploit species-specific interferon-driven upregulation of ACE2, a tissue-protective mediator during lung injury, to enhance infection.
Emory University1, United States Public Health Service2, Rutgers University3, Harvard University4, Central Michigan University5, Westchester Medical Center6, Icahn School of Medicine at Mount Sinai7, New York University8, Saint Barnabas Medical Center9, University of Pennsylvania10, SUNY Downstate Medical Center11, Yale University12, University of Colorado Denver13, Boston Children's Hospital14, Case Western Reserve University15, Louisiana State University16, University of Washington17, Johns Hopkins University18, University of Texas Health Science Center at Houston19, University of Mississippi20, Tufts University21, Vanderbilt University22
TL;DR: Multisystem inflammatory syndrome in children associated with SARS-CoV-2 led to serious and life-threatening illness in previously healthy children and adolescents.
Abstract: Background Understanding the epidemiology and clinical course of multisystem inflammatory syndrome in children (MIS-C) and its temporal association with coronavirus disease 2019 (Covid-19)...
École Normale Supérieure1, University of Exeter2, Norwich Research Park3, Wageningen University and Research Centre4, University of Groningen5, Max Planck Society6, Ludwig Maximilian University of Munich7, Commonwealth Scientific and Industrial Research Organisation8, Université Paris-Saclay9, Stanford University10, National Oceanic and Atmospheric Administration11, National Institute for Space Research12, Bermuda Institute of Ocean Sciences13, University of Southampton14, PSL Research University15, Japan Agency for Marine-Earth Science and Technology16, National Institute for Environmental Studies17, University of Maryland, College Park18, University of Leeds19, International Institute of Minnesota20, Flanders Marine Institute21, ETH Zurich22, University of East Anglia23, German Aerospace Center24, Woods Hole Research Center25, University of Illinois at Urbana–Champaign26, University of Toulouse27, Japan Meteorological Agency28, Plymouth Marine Laboratory29, University of Paris30, Hobart Corporation31, Oeschger Centre for Climate Change Research32, Tsinghua University33, National Center for Atmospheric Research34, Appalachian State University35, University of Colorado Boulder36, University of Washington37, Atlantic Oceanographic and Meteorological Laboratory38, Princeton University39, Met Office40, Leibniz Institute of Marine Sciences41, Auburn University42, University of Tasmania43, VU University Amsterdam44, Oak Ridge National Laboratory45, Sun Yat-sen University46, Nanjing University47
TL;DR: In this paper, the authors describe and synthesize data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties, including emissions from land use and land-use change data and bookkeeping models.
Abstract: Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate – the “global carbon budget” – is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) and terrestrial CO2 sink (SLAND) are estimated with global process models constrained by observations. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the last decade available (2010–2019), EFOS was 9.6 ± 0.5 GtC yr−1 excluding the cement carbonation sink (9.4 ± 0.5 GtC yr−1 when the cement carbonation sink is included), and ELUC was 1.6 ± 0.7 GtC yr−1. For the same decade, GATM was 5.1 ± 0.02 GtC yr−1 (2.4 ± 0.01 ppm yr−1), SOCEAN 2.5 ± 0.6 GtC yr−1, and SLAND 3.4 ± 0.9 GtC yr−1, with a budget imbalance BIM of −0.1 GtC yr−1 indicating a near balance between estimated sources and sinks over the last decade. For the year 2019 alone, the growth in EFOS was only about 0.1 % with fossil emissions increasing to 9.9 ± 0.5 GtC yr−1 excluding the cement carbonation sink (9.7 ± 0.5 GtC yr−1 when cement carbonation sink is included), and ELUC was 1.8 ± 0.7 GtC yr−1, for total anthropogenic CO2 emissions of 11.5 ± 0.9 GtC yr−1 (42.2 ± 3.3 GtCO2). Also for 2019, GATM was 5.4 ± 0.2 GtC yr−1 (2.5 ± 0.1 ppm yr−1), SOCEAN was 2.6 ± 0.6 GtC yr−1, and SLAND was 3.1 ± 1.2 GtC yr−1, with a BIM of 0.3 GtC. The global atmospheric CO2 concentration reached 409.85 ± 0.1 ppm averaged over 2019. Preliminary data for 2020, accounting for the COVID-19-induced changes in emissions, suggest a decrease in EFOS relative to 2019 of about −7 % (median estimate) based on individual estimates from four studies of −6 %, −7 %, −7 % (−3 % to −11 %), and −13 %. Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2019, but discrepancies of up to 1 GtC yr−1 persist for the representation of semi-decadal variability in CO2 fluxes. Comparison of estimates from diverse approaches and observations shows (1) no consensus in the mean and trend in land-use change emissions over the last decade, (2) a persistent low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) an apparent discrepancy between the different methods for the ocean sink outside the tropics, particularly in the Southern Ocean. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this data set (Friedlingstein et al., 2019; Le Quere et al., 2018b, a, 2016, 2015b, a, 2014, 2013). The data presented in this work are available at https://doi.org/10.18160/gcp-2020 (Friedlingstein et al., 2020).
McMaster University1, Copenhagen University Hospital2, King Saud bin Abdulaziz University for Health Sciences3, Albert Einstein College of Medicine4, University of Toronto5, Rhode Island Hospital6, Brown University7, Utrecht University8, Oklahoma State University Center for Health Sciences9, NewYork–Presbyterian Hospital10, Peking Union Medical College Hospital11, Sunnybrook Health Sciences Centre12, Humanitas University13, University of Ulsan14, National Institutes of Health15, Imperial College London16, United Arab Emirates University17, Population Health Research Institute18, St George’s University Hospitals NHS Foundation Trust19, Emory University Hospital20, University at Buffalo21, Baylor College of Medicine22, University of Milano-Bicocca23, King Abdulaziz Medical City24, King Saud Medical City25, The George Institute for Global Health26, Royal North Shore Hospital27, University of Virginia28, University of Washington29
TL;DR: The Surviving Sepsis Campaign CO VID-19 panel issued several recommendations to help support healthcare workers caring for critically ill ICU patients with COVID-19, and will provide new recommendations in further releases of these guidelines.
Abstract: The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of a rapidly spreading illness, Coronavirus Disease 2019 (COVID-19), affecting thousands of people around the world. Urgent guidance for clinicians caring for the sickest of these patients is needed.
We formed a panel of 36 experts from 12 countries. All panel members completed the World Health Organization conflict of interest disclosure form. The panel proposed 53 questions that are relevant to the management of COVID-19 in the ICU. We searched the literature for direct and indirect evidence on the management of COVID-19 in critically ill patients in the ICU. We identified relevant and recent systematic reviews on most questions relating to supportive care. We assessed the certainty in the evidence using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach, then generated recommendations based on the balance between benefit and harm, resource and cost implications, equity, and feasibility. Recommendations were either strong or weak, or in the form of best practice recommendations.
The Surviving Sepsis Campaign COVID-19 panel issued 54 statements, of which 4 are best practice statements, 9 are strong recommendations, and 35 are weak recommendations. No recommendation was provided for 6 questions. The topics were: (1) infection control, (2) laboratory diagnosis and specimens, (3) hemodynamic support, (4) ventilatory support, and (5) COVID-19 therapy.
The Surviving Sepsis Campaign COVID-19 panel issued several recommendations to help support healthcare workers caring for critically ill ICU patients with COVID-19. When available, we will provide new recommendations in further releases of these guidelines.
TL;DR: The flagship paper of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium describes the generation of the integrative analyses of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types, the structures for international data sharing and standardized analyses, and the main scientific findings from across the consortium studies.
Abstract: Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale1,2,3. Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4–5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter4; identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation5,6; analyses timings and patterns of tumour evolution7; describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity8,9; and evaluates a range of more-specialized features of cancer genomes8,10,11,12,13,14,15,16,17,18.
TL;DR: Several monoclonal antibodies that target the S glycoprotein of SARS-CoV-2, which was identified from memory B cells of an individual who was infected with severe acute respiratory syndrome coronavirus (SARS- coV) in 2003, and one antibody (named S309) potently neutralization, which may limit the emergence of neutralization-escape mutants.
Abstract: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a newly emerged coronavirus that is responsible for the current pandemic of coronavirus disease 2019 (COVID-19), which has resulted in more than 3.7 million infections and 260,000 deaths as of 6 May 20201,2. Vaccine and therapeutic discovery efforts are paramount to curb the pandemic spread of this zoonotic virus. The SARS-CoV-2 spike (S) glycoprotein promotes entry into host cells and is the main target of neutralizing antibodies. Here we describe several monoclonal antibodies that target the S glycoprotein of SARS-CoV-2, which we identified from memory B cells of an individual who was infected with severe acute respiratory syndrome coronavirus (SARS-CoV) in 2003. One antibody (named S309) potently neutralizes SARS-CoV-2 and SARS-CoV pseudoviruses as well as authentic SARS-CoV-2, by engaging the receptor-binding domain of the S glycoprotein. Using cryo-electron microscopy and binding assays, we show that S309 recognizes an epitope containing a glycan that is conserved within the Sarbecovirus subgenus, without competing with receptor attachment. Antibody cocktails that include S309 in combination with other antibodies that we identified further enhanced SARS-CoV-2 neutralization, and may limit the emergence of neutralization-escape mutants. These results pave the way for using S309 and antibody cocktails containing S309 for prophylaxis in individuals at a high risk of exposure or as a post-exposure therapy to limit or treat severe disease.
23 Apr 2020
TL;DR: It is consistently found that multi-phase adaptive pretraining offers large gains in task performance, and it is shown that adapting to a task corpus augmented using simple data selection strategies is an effective alternative, especially when resources for domain-adaptive pretraining might be unavailable.
Abstract: Language models pretrained on text from a wide variety of sources form the foundation of today’s NLP. In light of the success of these broad-coverage models, we investigate whether it is still helpful to tailor a pretrained model to the domain of a target task. We present a study across four domains (biomedical and computer science publications, news, and reviews) and eight classification tasks, showing that a second phase of pretraining in-domain (domain-adaptive pretraining) leads to performance gains, under both high- and low-resource settings. Moreover, adapting to the task’s unlabeled data (task-adaptive pretraining) improves performance even after domain-adaptive pretraining. Finally, we show that adapting to a task corpus augmented using simple data selection strategies is an effective alternative, especially when resources for domain-adaptive pretraining might be unavailable. Overall, we consistently find that multi-phase adaptive pretraining offers large gains in task performance.
TL;DR: It is found that a substantial number of mutations to the RBD are well tolerated or even enhance ACE2 binding, including at ACE2 interface residues that vary across SARS-related coronaviruses.
Johns Hopkins University School of Medicine1, Tufts University2, University of New South Wales3, Aarhus University Hospital4, Aarhus University5, Heidelberg University6, University of Melbourne7, Duke University8, McGill University9, University of Iowa10, Southern University of Science and Technology11, University of Toronto12, University of Washington13, Dalhousie University14, Aichi Medical University15, Queen's University16
TL;DR: This review provides a synopsis of the critical concepts, the analysis of comments from the IASP membership and public, and the committee's final recommendations for revisions to the definition and notes, which were discussed over a 2-year period.
Abstract: The current International Association for the Study of Pain (IASP) definition of pain as "An unpleasant sensory and emotional experience associated with actual or potential tissue damage, or described in terms of such damage" was recommended by the Subcommittee on Taxonomy and adopted by the IASP Council in 1979. This definition has become accepted widely by health care professionals and researchers in the pain field and adopted by several professional, governmental, and nongovernmental organizations, including the World Health Organization. In recent years, some in the field have reasoned that advances in our understanding of pain warrant a reevaluation of the definition and have proposed modifications. Therefore, in 2018, the IASP formed a 14-member, multinational Presidential Task Force comprising individuals with broad expertise in clinical and basic science related to pain, to evaluate the current definition and accompanying note and recommend whether they should be retained or changed. This review provides a synopsis of the critical concepts, the analysis of comments from the IASP membership and public, and the committee's final recommendations for revisions to the definition and notes, which were discussed over a 2-year period. The task force ultimately recommended that the definition of pain be revised to "An unpleasant sensory and emotional experience associated with, or resembling that associated with, actual or potential tissue damage," and that the accompanying notes be updated to a bulleted list that included the etymology. The revised definition and notes were unanimously accepted by the IASP Council early this year.
TL;DR: The outcomes of a cohort of patients with cancer and COVID-19 are characterised and potential prognostic factors for mortality and severe illness are identified and race and ethnicity, obesity status, cancer type, type of anticancer therapy, and recent surgery were not associated with mortality.
TL;DR: In 2019, the LIGO Livingston detector observed a compact binary coalescence with signal-to-noise ratio 12.9 and the Virgo detector was also taking data that did not contribute to detection due to a low SINR but were used for subsequent parameter estimation as discussed by the authors.
Abstract: On 2019 April 25, the LIGO Livingston detector observed a compact binary coalescence with signal-to-noise ratio 12.9. The Virgo detector was also taking data that did not contribute to detection due to a low signal-to-noise ratio, but were used for subsequent parameter estimation. The 90% credible intervals for the component masses range from to if we restrict the dimensionless component spin magnitudes to be smaller than 0.05). These mass parameters are consistent with the individual binary components being neutron stars. However, both the source-frame chirp mass and the total mass of this system are significantly larger than those of any other known binary neutron star (BNS) system. The possibility that one or both binary components of the system are black holes cannot be ruled out from gravitational-wave data. We discuss possible origins of the system based on its inconsistency with the known Galactic BNS population. Under the assumption that the signal was produced by a BNS coalescence, the local rate of neutron star mergers is updated to 250-2810.
Technical University of Denmark1, University of Paris2, University of Washington3, Free University of Berlin4, Paris Descartes University5, Novo Nordisk Foundation6, Robert Koch Institute7, Federal Institute for Risk Assessment8, University of Antwerp9, University of Copenhagen10, Hvidovre Hospital11, Animal and Plant Health Agency12, ANSES13
TL;DR: WGS-based AST using ResFinder 4.0 provides in silico antibiograms as reliable as those obtained by phenotypic AST at least for the bacterial species/antimicrobial agents of major public health relevance considered.
Abstract: WGS-based antimicrobial susceptibility testing (AST) is as reliable as phenotypic AST for several antimicrobial/bacterial species combinations. However, routine use of WGS-based AST is hindered by the need for bioinformatics skills and knowledge of antimicrobial resistance (AMR) determinants to operate the vast majority of tools developed to date. By leveraging on ResFinder and PointFinder, two freely accessible tools that can also assist users without bioinformatics skills, we aimed at increasing their speed and providing an easily interpretable antibiogram as output.
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TL;DR: This work shows that retrieval can be practically implemented using dense representations alone, where embeddings are learned from a small number of questions and passages by a simple dual-encoder framework.
Abstract: Open-domain question answering relies on efficient passage retrieval to select candidate contexts, where traditional sparse vector space models, such as TF-IDF or BM25, are the de facto method. In this work, we show that retrieval can be practically implemented using dense representations alone, where embeddings are learned from a small number of questions and passages by a simple dual-encoder framework. When evaluated on a wide range of open-domain QA datasets, our dense retriever outperforms a strong Lucene-BM25 system largely by 9%-19% absolute in terms of top-20 passage retrieval accuracy, and helps our end-to-end QA system establish new state-of-the-art on multiple open-domain QA benchmarks.
TL;DR: In this paper, the authors proposed that long-term care facilities are high-risk settings for severe outcomes from outbreaks of Covid-19, owing to both the advanced age and frequent chronic underlying health conditions.
Abstract: Background Long-term care facilities are high-risk settings for severe outcomes from outbreaks of Covid-19, owing to both the advanced age and frequent chronic underlying health conditions...
TL;DR: Covid-19 (the illness caused by SARS-CoV-2) has a range of clinical manifestations, including cough, fever, malaise, myalgias, gastrointestinal symptom...
Abstract: Key Clinical Points Mild or Moderate Covid-19 Covid-19 (the illness caused by SARS-CoV-2) has a range of clinical manifestations, including cough, fever, malaise, myalgias, gastrointestinal symptom...