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Journal ArticleDOI
TL;DR: A large, deep convolutional neural network was trained to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes and employed a recently developed regularization method called "dropout" that proved to be very effective.
Abstract: We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully connected layers we employed a recently developed regularization method called "dropout" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry.

12,532 citations


Journal ArticleDOI
TL;DR: The American Cancer Society estimates the numbers of new cancer cases and deaths that will occur in the United States in the current year and compiles the most recent data on cancer incidence, mortality, and survival.
Abstract: Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths that will occur in the United States in the current year and compiles the most recent data on cancer incidence, mortality, and survival. Incidence data were collected by the Surveillance, Epidemiology, and End Results Program; the National Program of Cancer Registries; and the North American Association of Central Cancer Registries. Mortality data were collected by the National Center for Health Statistics. In 2017, 1,688,780 new cancer cases and 600,920 cancer deaths are projected to occur in the United States. For all sites combined, the cancer incidence rate is 20% higher in men than in women, while the cancer death rate is 40% higher. However, sex disparities vary by cancer type. For example, thyroid cancer incidence rates are 3-fold higher in women than in men (21 vs 7 per 100,000 population), despite equivalent death rates (0.5 per 100,000 population), largely reflecting sex differences in the "epidemic of diagnosis." Over the past decade of available data, the overall cancer incidence rate (2004-2013) was stable in women and declined by approximately 2% annually in men, while the cancer death rate (2005-2014) declined by about 1.5% annually in both men and women. From 1991 to 2014, the overall cancer death rate dropped 25%, translating to approximately 2,143,200 fewer cancer deaths than would have been expected if death rates had remained at their peak. Although the cancer death rate was 15% higher in blacks than in whites in 2014, increasing access to care as a result of the Patient Protection and Affordable Care Act may expedite the narrowing racial gap; from 2010 to 2015, the proportion of blacks who were uninsured halved, from 21% to 11%, as it did for Hispanics (31% to 16%). Gains in coverage for traditionally underserved Americans will facilitate the broader application of existing cancer control knowledge across every segment of the population. CA Cancer J Clin 2017;67:7-30. © 2017 American Cancer Society.

12,284 citations


Journal ArticleDOI
TL;DR: The overall cancer death rate dropped continuously from 1991 to 2016 by a total of 27%, translating into approximately 2,629,200 fewer cancer deaths than would have been expected if death rates had remained at their peak.
Abstract: Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths that will occur in the United States and compiles the most recent data on cancer incidence, mortality, and survival. Incidence data, available through 2015, were collected by the Surveillance, Epidemiology, and End Results Program; the National Program of Cancer Registries; and the North American Association of Central Cancer Registries. Mortality data, available through 2016, were collected by the National Center for Health Statistics. In 2019, 1,762,450 new cancer cases and 606,880 cancer deaths are projected to occur in the United States. Over the past decade of data, the cancer incidence rate (2006-2015) was stable in women and declined by approximately 2% per year in men, whereas the cancer death rate (2007-2016) declined annually by 1.4% and 1.8%, respectively. The overall cancer death rate dropped continuously from 1991 to 2016 by a total of 27%, translating into approximately 2,629,200 fewer cancer deaths than would have been expected if death rates had remained at their peak. Although the racial gap in cancer mortality is slowly narrowing, socioeconomic inequalities are widening, with the most notable gaps for the most preventable cancers. For example, compared with the most affluent counties, mortality rates in the poorest counties were 2-fold higher for cervical cancer and 40% higher for male lung and liver cancers during 2012-2016. Some states are home to both the wealthiest and the poorest counties, suggesting the opportunity for more equitable dissemination of effective cancer prevention, early detection, and treatment strategies. A broader application of existing cancer control knowledge with an emphasis on disadvantaged groups would undoubtedly accelerate progress against cancer.

11,980 citations


Journal ArticleDOI
TL;DR: The combined cancer death rate dropped continuously from 1991 to 2015 by a total of 26%, translating to approximately 2,378,600 fewer cancer deaths than would have been expected if death rates had remained at their peak.
Abstract: Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths that will occur in the United States and compiles the most recent data on cancer incidence, mortality, and survival. Incidence data, available through 2014, were collected by the Surveillance, Epidemiology, and End Results Program; the National Program of Cancer Registries; and the North American Association of Central Cancer Registries. Mortality data, available through 2015, were collected by the National Center for Health Statistics. In 2018, 1,735,350 new cancer cases and 609,640 cancer deaths are projected to occur in the United States. Over the past decade of data, the cancer incidence rate (2005-2014) was stable in women and declined by approximately 2% annually in men, while the cancer death rate (2006-2015) declined by about 1.5% annually in both men and women. The combined cancer death rate dropped continuously from 1991 to 2015 by a total of 26%, translating to approximately 2,378,600 fewer cancer deaths than would have been expected if death rates had remained at their peak. Of the 10 leading causes of death, only cancer declined from 2014 to 2015. In 2015, the cancer death rate was 14% higher in non-Hispanic blacks (NHBs) than non-Hispanic whites (NHWs) overall (death rate ratio [DRR], 1.14; 95% confidence interval [95% CI], 1.13-1.15), but the racial disparity was much larger for individuals aged <65 years (DRR, 1.31; 95% CI, 1.29-1.32) compared with those aged ≥65 years (DRR, 1.07; 95% CI, 1.06-1.09) and varied substantially by state. For example, the cancer death rate was lower in NHBs than NHWs in Massachusetts for all ages and in New York for individuals aged ≥65 years, whereas for those aged <65 years, it was 3 times higher in NHBs in the District of Columbia (DRR, 2.89; 95% CI, 2.16-3.91) and about 50% higher in Wisconsin (DRR, 1.78; 95% CI, 1.56-2.02), Kansas (DRR, 1.51; 95% CI, 1.25-1.81), Louisiana (DRR, 1.49; 95% CI, 1.38-1.60), Illinois (DRR, 1.48; 95% CI, 1.39-1.57), and California (DRR, 1.45; 95% CI, 1.38-1.54). Larger racial inequalities in young and middle-aged adults probably partly reflect less access to high-quality health care. CA Cancer J Clin 2018;68:7-30. © 2018 American Cancer Society.

11,946 citations


Journal ArticleDOI
15 Jan 2015-Bioinformatics
TL;DR: This work presents HTSeq, a Python library to facilitate the rapid development of custom scripts for high-throughput sequencing data analysis, and presents htseq-count, a tool developed with HTSequ that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes.
Abstract: Motivation: A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard workflows, custom scripts are needed. Results: We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data, such as genomic coordinates, sequences, sequencing reads, alignments, gene model information and variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes. Availability and implementation: HTSeq is released as an opensource software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index at https://pypi.python.org/pypi/HTSeq. Contact: sanders@fs.tum.de

11,833 citations


Book ChapterDOI
08 Oct 2016-
TL;DR: The approach, named SSD, discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location, which makes SSD easy to train and straightforward to integrate into systems that require a detection component.
Abstract: We present a method for detecting objects in images using a single deep neural network. Our approach, named SSD, discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location. At prediction time, the network generates scores for the presence of each object category in each default box and produces adjustments to the box to better match the object shape. Additionally, the network combines predictions from multiple feature maps with different resolutions to naturally handle objects of various sizes. SSD is simple relative to methods that require object proposals because it completely eliminates proposal generation and subsequent pixel or feature resampling stages and encapsulates all computation in a single network. This makes SSD easy to train and straightforward to integrate into systems that require a detection component. Experimental results on the PASCAL VOC, COCO, and ILSVRC datasets confirm that SSD has competitive accuracy to methods that utilize an additional object proposal step and is much faster, while providing a unified framework for both training and inference. For \(300 \times 300\) input, SSD achieves 74.3 % mAP on VOC2007 test at 59 FPS on a Nvidia Titan X and for \(512 \times 512\) input, SSD achieves 76.9 % mAP, outperforming a comparable state of the art Faster R-CNN model. Compared to other single stage methods, SSD has much better accuracy even with a smaller input image size. Code is available at https://github.com/weiliu89/caffe/tree/ssd.

11,792 citations


Journal ArticleDOI
TL;DR: The Molecular Evolutionary Genetics Analysis (Mega) software implements many analytical methods and tools for phylogenomics and phylomedicine and has additionally been upgraded to use multiple computing cores for many molecular evolutionary analyses.
Abstract: The Molecular Evolutionary Genetics Analysis (Mega) software implements many analytical methods and tools for phylogenomics and phylomedicine. Here, we report a transformation of Mega to enable cross-platform use on Microsoft Windows and Linux operating systems. Mega X does not require virtualization or emulation software and provides a uniform user experience across platforms. Mega X has additionally been upgraded to use multiple computing cores for many molecular evolutionary analyses. Mega X is available in two interfaces (graphical and command line) and can be downloaded from www.megasoftware.net free of charge.

11,718 citations


Journal ArticleDOI
TL;DR: This work automates routine small-molecule structure determination starting from single-crystal reflection data, the Laue group and a reasonable guess as to which elements might be present.
Abstract: The new computer program SHELXT employs a novel dual-space algorithm to solve the phase problem for single-crystal reflection data expanded to the space group P1. Missing data are taken into account and the resolution extended if necessary. All space groups in the specified Laue group are tested to find which are consistent with the P1 phases. After applying the resulting origin shifts and space-group symmetry, the solutions are subject to further dual-space recycling followed by a peak search and summation of the electron density around each peak. Elements are assigned to give the best fit to the integrated peak densities and if necessary additional elements are considered. An isotropic refinement is followed for non-centrosymmetric space groups by the calculation of a Flack parameter and, if appropriate, inversion of the structure. The structure is assembled to maximize its connectivity and centred optimally in the unit cell. SHELXT has already solved many thousand structures with a high success rate, and is optimized for multiprocessor computers. It is, however, unsuitable for severely disordered and twinned structures because it is based on the assumption that the structure consists of atoms.

11,698 citations


Book
10 Oct 2016-
Abstract: This paper is concerned with those actions of business firms which have harmful effects on others. The standard example is that of a factory the smoke from which has harmful effects on those occupying neighbouring properties. The economic analysis of such a situation has usually proceeded in terms of a divergence between the private and social product of the factory, in which economists have largely followed the treatment of Pigou in The Economics of Welfare. The conclusions to which this kind of analysis seems to have led most economists is that it would be desirable to make the owner of the factory liable for the damage caused to those injured by the smoke, or alternatively, to place a tax on the factory owner varying with the amount of smoke produced and equivalent in money terms to the damage it would cause, or finally, to exclude the factory from residential districts (and presumably from other areas in which the emission of smoke would have harmful effects on others). It is my contention that the suggested courses of action are inappropriate, in that they lead to results which are not necessarily, or even usually, desirable.

11,439 citations


Journal ArticleDOI
TL;DR: Because of the increased complexity of analysis and interpretation of clinical genetic testing described in this report, the ACMG strongly recommends thatclinical molecular genetic testing should be performed in a Clinical Laboratory Improvement Amendments–approved laboratory, with results interpreted by a board-certified clinical molecular geneticist or molecular genetic pathologist or the equivalent.
Abstract: Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology

11,349 citations


Journal ArticleDOI
01 Jan 2015-Neural Networks
TL;DR: This historical survey compactly summarizes relevant work, much of it from the previous millennium, review deep supervised learning, unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.
Abstract: In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarizes relevant work, much of it from the previous millennium. Shallow and Deep Learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.

11,176 citations


28 Oct 2017-
TL;DR: An automatic differentiation module of PyTorch is described — a library designed to enable rapid research on machine learning models that focuses on differentiation of purely imperative programs, with a focus on extensibility and low overhead.
Abstract: In this article, we describe an automatic differentiation module of PyTorch — a library designed to enable rapid research on machine learning models. It builds upon a few projects, most notably Lua Torch, Chainer, and HIPS Autograd [4], and provides a high performance environment with easy access to automatic differentiation of models executed on different devices (CPU and GPU). To make prototyping easier, PyTorch does not follow the symbolic approach used in many other deep learning frameworks, but focuses on differentiation of purely imperative programs, with a focus on extensibility and low overhead. Note that this preprint is a draft of certain sections from an upcoming paper covering all PyTorch features.

10,996 citations


Proceedings ArticleDOI
02 Nov 2016-
Abstract: TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. Tensor-Flow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general-purpose GPUs, and custom-designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility to the application developer: whereas in previous "parameter server" designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with a focus on training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for machine learning research. In this paper, we describe the TensorFlow dataflow model and demonstrate the compelling performance that TensorFlow achieves for several real-world applications.

10,880 citations


Posted Content
Ross Girshick1
TL;DR: This paper proposes a Fast Region-based Convolutional Network method (Fast R-CNN) for object detection that builds on previous work to efficiently classify object proposals using deep convolutional networks.
Abstract: This paper proposes a Fast Region-based Convolutional Network method (Fast R-CNN) for object detection. Fast R-CNN builds on previous work to efficiently classify object proposals using deep convolutional networks. Compared to previous work, Fast R-CNN employs several innovations to improve training and testing speed while also increasing detection accuracy. Fast R-CNN trains the very deep VGG16 network 9x faster than R-CNN, is 213x faster at test-time, and achieves a higher mAP on PASCAL VOC 2012. Compared to SPPnet, Fast R-CNN trains VGG16 3x faster, tests 10x faster, and is more accurate. Fast R-CNN is implemented in Python and C++ (using Caffe) and is available under the open-source MIT License at this https URL.

10,744 citations


Proceedings ArticleDOI
Ross Girshick1
07 Dec 2015-
Abstract: This paper proposes a Fast Region-based Convolutional Network method (Fast R-CNN) for object detection. Fast R-CNN builds on previous work to efficiently classify object proposals using deep convolutional networks. Compared to previous work, Fast R-CNN employs several innovations to improve training and testing speed while also increasing detection accuracy. Fast R-CNN trains the very deep VGG16 network 9x faster than R-CNN, is 213x faster at test-time, and achieves a higher mAP on PASCAL VOC 2012. Compared to SPPnet, Fast R-CNN trains VGG16 3x faster, tests 10x faster, and is more accurate. Fast R-CNN is implemented in Python and C++ (using Caffe) and is available under the open-source MIT License at https://github.com/rbgirshick/fast-rcnn.

10,703 citations


Journal ArticleDOI
TL;DR: Many of the estimated cancer cases and deaths can be prevented through reducing the prevalence of risk factors, while increasing the effectiveness of clinical care delivery, particularly for those living in rural areas and in disadvantaged populations.
Abstract: With increasing incidence and mortality, cancer is the leading cause of death in China and is a major public health problem. Because of China's massive population (1.37 billion), previous national incidence and mortality estimates have been limited to small samples of the population using data from the 1990s or based on a specific year. With high-quality data from an additional number of population-based registries now available through the National Central Cancer Registry of China, the authors analyzed data from 72 local, population-based cancer registries (2009-2011), representing 6.5% of the population, to estimate the number of new cases and cancer deaths for 2015. Data from 22 registries were used for trend analyses (2000-2011). The results indicated that an estimated 4292,000 new cancer cases and 2814,000 cancer deaths would occur in China in 2015, with lung cancer being the most common incident cancer and the leading cause of cancer death. Stomach, esophageal, and liver cancers were also commonly diagnosed and were identified as leading causes of cancer death. Residents of rural areas had significantly higher age-standardized (Segi population) incidence and mortality rates for all cancers combined than urban residents (213.6 per 100,000 vs 191.5 per 100,000 for incidence; 149.0 per 100,000 vs 109.5 per 100,000 for mortality, respectively). For all cancers combined, the incidence rates were stable during 2000 through 2011 for males (+0.2% per year; P = .1), whereas they increased significantly (+2.2% per year; P < .05) among females. In contrast, the mortality rates since 2006 have decreased significantly for both males (-1.4% per year; P < .05) and females (-1.1% per year; P < .05). Many of the estimated cancer cases and deaths can be prevented through reducing the prevalence of risk factors, while increasing the effectiveness of clinical care delivery, particularly for those living in rural areas and in disadvantaged populations.

10,557 citations


Journal ArticleDOI
28 Jan 2016-Nature
TL;DR: Using this search algorithm, the program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0.5, the first time that a computer program has defeated a human professional player in the full-sized game of Go.
Abstract: The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and moves. Here we introduce a new approach to computer Go that uses ‘value networks’ to evaluate board positions and ‘policy networks’ to select moves. These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play. Without any lookahead search, the neural networks play Go at the level of stateof-the-art Monte Carlo tree search programs that simulate thousands of random games of self-play. We also introduce a new search algorithm that combines Monte Carlo simulation with value and policy networks. Using this search algorithm, our program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0. This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away.

10,555 citations


Journal ArticleDOI
07 Apr 2020-JAMA
TL;DR: Hospitalised COVID-19 patients are frequently elderly subjects with co-morbidities receiving polypharmacy, all of which are known risk factors for d
Abstract: Background: Hospitalised COVID-19 patients are frequently elderly subjects with co-morbidities receiving polypharmacy, all of which are known risk factors for d

10,464 citations


Proceedings ArticleDOI
13 Aug 2016-
Abstract: Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges. We propose a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning. More importantly, we provide insights on cache access patterns, data compression and sharding to build a scalable tree boosting system. By combining these insights, XGBoost scales beyond billions of examples using far fewer resources than existing systems.

10,428 citations


Journal ArticleDOI
TL;DR: The overall cancer death rate decreased from 215.1 (per 100,000 population) in 1991 to 168.7 in 2011, a total relative decline of 22%.
Abstract: Each year the American Cancer Society estimates the numbers of new cancer cases and deaths that will occur in the United States in the current year and compiles the most recent data on cancer incidence, mortality, and survival. Incidence data were collected by the National Cancer Institute (Surveillance, Epidemiology, and End Results [SEER] Program), the Centers for Disease Control and Prevention (National Program of Cancer Registries), and the North American Association of Central Cancer Registries. Mortality data were collected by the National Center for Health Statistics. A total of 1,658,370 new cancer cases and 589,430 cancer deaths are projected to occur in the United States in 2015. During the most recent 5 years for which there are data (2007-2011), delay-adjusted cancer incidence rates (13 oldest SEER registries) declined by 1.8% per year in men and were stable in women, while cancer death rates nationwide decreased by 1.8% per year in men and by 1.4% per year in women. The overall cancer death rate decreased from 215.1 (per 100,000 population) in 1991 to 168.7 in 2011, a total relative decline of 22%. However, the magnitude of the decline varied by state, and was generally lowest in the South (∼15%) and highest in the Northeast (≥20%). For example, there were declines of 25% to 30% in Maryland, New Jersey, Massachusetts, New York, and Delaware, which collectively averted 29,000 cancer deaths in 2011 as a result of this progress. Further gains can be accelerated by applying existing cancer control knowledge across all segments of the population.

10,414 citations


Journal ArticleDOI
01 Jan 2015-Systematic Reviews
TL;DR: A reporting guideline is described, the Preferred Reporting Items for Systematic reviews and Meta-Analyses for Protocols 2015 (PRISMA-P 2015), which consists of a 17-item checklist intended to facilitate the preparation and reporting of a robust protocol for the systematic review.
Abstract: Systematic reviews should build on a protocol that describes the rationale, hypothesis, and planned methods of the review; few reviews report whether a protocol exists. Detailed, well-described protocols can facilitate the understanding and appraisal of the review methods, as well as the detection of modifications to methods and selective reporting in completed reviews. We describe the development of a reporting guideline, the Preferred Reporting Items for Systematic reviews and Meta-Analyses for Protocols 2015 (PRISMA-P 2015). PRISMA-P consists of a 17-item checklist intended to facilitate the preparation and reporting of a robust protocol for the systematic review. Funders and those commissioning reviews might consider mandating the use of the checklist to facilitate the submission of relevant protocol information in funding applications. Similarly, peer reviewers and editors can use the guidance to gauge the completeness and transparency of a systematic review protocol submitted for publication in a journal or other medium.

10,370 citations


Book ChapterDOI
Abstract: We present a method for detecting objects in images using a single deep neural network. Our approach, named SSD, discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location. At prediction time, the network generates scores for the presence of each object category in each default box and produces adjustments to the box to better match the object shape. Additionally, the network combines predictions from multiple feature maps with different resolutions to naturally handle objects of various sizes. Our SSD model is simple relative to methods that require object proposals because it completely eliminates proposal generation and subsequent pixel or feature resampling stage and encapsulates all computation in a single network. This makes SSD easy to train and straightforward to integrate into systems that require a detection component. Experimental results on the PASCAL VOC, MS COCO, and ILSVRC datasets confirm that SSD has comparable accuracy to methods that utilize an additional object proposal step and is much faster, while providing a unified framework for both training and inference. Compared to other single stage methods, SSD has much better accuracy, even with a smaller input image size. For $300\times 300$ input, SSD achieves 72.1% mAP on VOC2007 test at 58 FPS on a Nvidia Titan X and for $500\times 500$ input, SSD achieves 75.1% mAP, outperforming a comparable state of the art Faster R-CNN model. Code is available at this https URL .

10,351 citations


Journal ArticleDOI
Peter A. R. Ade1, Nabila Aghanim2, Monique Arnaud3, M. Ashdown4, J. Aumont2, Carlo Baccigalupi5, A. J. Banday6, A. J. Banday7, R. B. Barreiro8, James G. Bartlett9, James G. Bartlett3, N. Bartolo10, N. Bartolo11, E. Battaner12, Richard A. Battye13, K. Benabed14, Alain Benoit15, A. Benoit-Lévy14, A. Benoit-Lévy16, J.-P. Bernard6, J.-P. Bernard7, Marco Bersanelli17, Marco Bersanelli18, P. Bielewicz5, P. Bielewicz7, J. J. Bock9, Anna Bonaldi13, Laura Bonavera8, J. R. Bond19, Julian Borrill20, Julian Borrill21, François R. Bouchet14, Francois Boulanger2, M. Bucher3, Carlo Burigana17, Carlo Burigana22, R. C. Butler17, Erminia Calabrese23, Jean-François Cardoso3, Jean-François Cardoso24, Jean-François Cardoso14, A. Catalano25, A. Catalano26, Anthony Challinor4, A. Chamballu27, A. Chamballu3, A. Chamballu2, Ranga-Ram Chary9, H. C. Chiang28, H. C. Chiang29, Jens Chluba30, P. R. Christensen31, Sarah E. Church32, David L. Clements33, S. Colombi14, L. P. L. Colombo34, L. P. L. Colombo9, C. Combet26, A. Coulais25, B. P. Crill9, A. Curto4, A. Curto8, F. Cuttaia17, Luigi Danese5, R. D. Davies13, R. J. Davis13, P. de Bernardis35, A. de Rosa17, G. de Zotti5, G. de Zotti17, Jacques Delabrouille3, F.-X. Désert26, E. Di Valentino14, Clive Dickinson13, Jose M. Diego8, Klaus Dolag36, Klaus Dolag37, H. Dole38, H. Dole2, S. Donzelli17, Olivier Doré9, Marian Douspis2, A. Ducout33, A. Ducout14, Jo Dunkley23, X. Dupac39, George Efstathiou4, F. Elsner16, F. Elsner14, Torsten A. Enßlin36, H. K. Eriksen40, Marzieh Farhang41, Marzieh Farhang19, James R. Fergusson4, Fabio Finelli17, Olivier Forni7, Olivier Forni6, M. Frailis17, A. A. Fraisse28, E. Franceschi17, A. Frejsel31, S. Galeotta17, S. Galli42, K. Ganga3, C. Gauthier3, C. Gauthier43, M. Gerbino44, M. Gerbino45, M. Gerbino35, Tuhin Ghosh2, M. Giard6, M. Giard7, Y. Giraud-Héraud3, Elena Giusarma35, E. Gjerløw40, J. González-Nuevo46, J. González-Nuevo8, Krzysztof M. Gorski47, Krzysztof M. Gorski9, Serge Gratton4, A. Gregorio17, A. Gregorio48, Alessandro Gruppuso17, Jon E. Gudmundsson44, Jon E. Gudmundsson45, Jon E. Gudmundsson28, Jan Hamann49, Jan Hamann50, F. K. Hansen40, Duncan Hanson9, Duncan Hanson19, Duncan Hanson51, D. L. Harrison4, George Helou9, Sophie Henrot-Versille52, C. Hernández-Monteagudo36, D. Herranz8, S. R. Hildebrandt9, E. Hivon14, Michael P. Hobson4, W. A. Holmes9, Allan Hornstrup53, W. Hovest36, Zhiqi Huang19, Kevin M. Huffenberger54, G. Hurier2, Andrew H. Jaffe33, T. R. Jaffe7, T. R. Jaffe6, W. C. Jones28, Mika Juvela55, E. Keihänen55, Reijo Keskitalo21, Theodore Kisner21, R. Kneissl56, R. Kneissl57, J. Knoche36, Lloyd Knox58, Martin Kunz59, Martin Kunz2, Martin Kunz60, Hannu Kurki-Suonio55, Guilaine Lagache61, Guilaine Lagache2, Anne Lähteenmäki62, Anne Lähteenmäki63, J.-M. Lamarre25, Anthony Lasenby4, Massimiliano Lattanzi22, Charles R. Lawrence9, J. P. Leahy13, R. Leonardi, Julien Lesgourgues64, Julien Lesgourgues50, François Levrier25, Antony Lewis65, Michele Liguori10, Michele Liguori11, P. B. Lilje40, M. Linden-Vørnle53, M. López-Caniego8, M. López-Caniego39, Philip Lubin66, J. F. Macías-Pérez26, G. Maggio17, Davide Maino18, Davide Maino17, N. Mandolesi17, N. Mandolesi22, A. Mangilli2, A. Mangilli52, A. Marchini, Michele Maris17, Peter G. Martin19, M. Martinelli67, E. Martínez-González8, Silvia Masi35, Sabino Matarrese11, Sabino Matarrese10, P. McGehee9, Peter Meinhold66, Alessandro Melchiorri35, Jean-Baptiste Melin27, L. Mendes39, A. Mennella17, A. Mennella18, M. Migliaccio4, Marius Millea58, Subhasish Mitra68, Subhasish Mitra9, M.-A. Miville-Deschênes19, M.-A. Miville-Deschênes2, A. Moneti14, L. Montier7, L. Montier6, Gianluca Morgante17, Daniel J. Mortlock33, Adam Moss69, Dipak Munshi1, J. A. Murphy70, Pavel Naselsky31, Federico Nati28, Paolo Natoli71, Paolo Natoli22, Calvin B. Netterfield19, Hans Ulrik Nørgaard-Nielsen53, F. Noviello13, Dmitry Novikov72, I. D. Novikov72, I. D. Novikov31, C. A. Oxborrow53, F. Paci5, L. Pagano35, F. Pajot2, Roberta Paladini9, Daniela Paoletti17, Bruce Partridge73, F. Pasian17, G. Patanchon3, T. J. Pearson9, O. Perdereau52, L. Perotto26, Francesca Perrotta5, Valeria Pettorino67, F. Piacentini35, M. Piat3, E. Pierpaoli34, Davide Pietrobon9, Stéphane Plaszczynski52, Etienne Pointecouteau6, Etienne Pointecouteau7, G. Polenta17, G. Polenta71, L. Popa, G. W. Pratt3, G. Prézeau9, Simon Prunet14, J.-L. Puget2, Jörg P. Rachen36, Jörg P. Rachen74, William T. Reach75, Rafael Rebolo8, Rafael Rebolo76, M. Reinecke36, Mathieu Remazeilles13, Mathieu Remazeilles2, Mathieu Remazeilles3, C. Renault26, A. Renzi77, I. Ristorcelli6, I. Ristorcelli7, Graca Rocha9, C. Rosset3, M. Rossetti18, M. Rossetti17, G. Roudier3, G. Roudier9, G. Roudier25, B. Rouillé d'Orfeuil52, Michael Rowan-Robinson33, Jose Alberto Rubino-Martin76, Jose Alberto Rubino-Martin8, Ben Rusholme9, Najla Said35, Valentina Salvatelli61, Valentina Salvatelli35, Laura Salvati35, M. Sandri17, D. Santos26, M. Savelainen55, Giorgio Savini16, Douglas Scott78, Michael Seiffert9, Paolo Serra2, E. P. S. Shellard4, Locke D. Spencer1, M. Spinelli52, V. Stolyarov79, V. Stolyarov4, V. Stolyarov72, R. Stompor3, R. Sudiwala1, R. A. Sunyaev72, R. A. Sunyaev36, D. Sutton4, A.-S. Suur-Uski55, J.-F. Sygnet14, J. A. Tauber39, Luca Terenzi80, Luca Terenzi17, L. Toffolatti46, L. Toffolatti8, L. Toffolatti17, M. Tomasi17, M. Tomasi18, M. Tristram52, Tiziana Trombetti22, Tiziana Trombetti17, M. Tucci60, J. Tuovinen81, Marc Türler60, G. Umana17, Luca Valenziano17, Jussi-Pekka Väliviita55, F. Van Tent52, P. Vielva8, Fabrizio Villa17, L. A. Wade9, Benjamin D. Wandelt82, Benjamin D. Wandelt14, Ingunn Kathrine Wehus9, Ingunn Kathrine Wehus40, Martin White20, Simon D. M. White36, Althea Wilkinson13, D. Yvon27, Andrea Zacchei17, Andrea Zonca66 
Cardiff University1, Université Paris-Saclay2, Paris Diderot University3, University of Cambridge4, International School for Advanced Studies5, University of Toulouse6, Hoffmann-La Roche7, Spanish National Research Council8, California Institute of Technology9, University of Padua10, Istituto Nazionale di Fisica Nucleare11, University of Granada12, University of Manchester13, Institut d'Astrophysique de Paris14, Joseph Fourier University15, University College London16, INAF17, University of Milan18, University of Toronto19, University of California, Berkeley20, Lawrence Berkeley National Laboratory21, University of Ferrara22, University of Oxford23, Télécom ParisTech24, Centre national de la recherche scientifique25, University of Grenoble26, DSM27, Princeton University28, University of KwaZulu-Natal29, Johns Hopkins University30, Niels Bohr Institute31, Stanford University32, Imperial College London33, University of Southern California34, Sapienza University of Rome35, Max Planck Society36, Ludwig Maximilian University of Munich37, Institut Universitaire de France38, European Space Agency39, University of Oslo40, Shahid Beheshti University41, University of Chicago42, National Taiwan University43, Nordic Institute for Theoretical Physics44, Stockholm University45, University of Oviedo46, University of Warsaw47, University of Trieste48, University of Sydney49, CERN50, McGill University51, University of Paris-Sud52, Technical University of Denmark53, Florida State University54, University of Helsinki55, Atacama Large Millimeter Submillimeter Array56, European Southern Observatory57, University of California, Davis58, African Institute for Mathematical Sciences59, University of Geneva60, Aix-Marseille University61, Aalto University62, Helsinki Institute of Physics63, RWTH Aachen University64, University of Sussex65, University of California, Santa Barbara66, Heidelberg University67, Savitribai Phule Pune University68, University of Nottingham69, National University of Ireland70, Agenzia Spaziale Italiana71, Russian Academy of Sciences72, Haverford College73, Radboud University Nijmegen74, Universities Space Research Association75, University of La Laguna76, University of Rome Tor Vergata77, University of British Columbia78, Kazan Federal University79, Università degli Studi eCampus80, Trinity College, Dublin81, University of Illinois at Urbana–Champaign82
Abstract: This paper presents cosmological results based on full-mission Planck observations of temperature and polarization anisotropies of the cosmic microwave background (CMB) radiation. Our results are in very good agreement with the 2013 analysis of the Planck nominal-mission temperature data, but with increased precision. The temperature and polarization power spectra are consistent with the standard spatially-flat 6-parameter ΛCDM cosmology with a power-law spectrum of adiabatic scalar perturbations (denoted “base ΛCDM” in this paper). From the Planck temperature data combined with Planck lensing, for this cosmology we find a Hubble constant, H0 = (67.8 ± 0.9) km s-1Mpc-1, a matter density parameter Ωm = 0.308 ± 0.012, and a tilted scalar spectral index with ns = 0.968 ± 0.006, consistent with the 2013 analysis. Note that in this abstract we quote 68% confidence limits on measured parameters and 95% upper limits on other parameters. We present the first results of polarization measurements with the Low Frequency Instrument at large angular scales. Combined with the Planck temperature and lensing data, these measurements give a reionization optical depth of τ = 0.066 ± 0.016, corresponding to a reionization redshift of . These results are consistent with those from WMAP polarization measurements cleaned for dust emission using 353-GHz polarization maps from the High Frequency Instrument. We find no evidence for any departure from base ΛCDM in the neutrino sector of the theory; for example, combining Planck observations with other astrophysical data we find Neff = 3.15 ± 0.23 for the effective number of relativistic degrees of freedom, consistent with the value Neff = 3.046 of the Standard Model of particle physics. The sum of neutrino masses is constrained to ∑ mν < 0.23 eV. The spatial curvature of our Universe is found to be very close to zero, with | ΩK | < 0.005. Adding a tensor component as a single-parameter extension to base ΛCDM we find an upper limit on the tensor-to-scalar ratio of r0.002< 0.11, consistent with the Planck 2013 results and consistent with the B-mode polarization constraints from a joint analysis of BICEP2, Keck Array, and Planck (BKP) data. Adding the BKP B-mode data to our analysis leads to a tighter constraint of r0.002 < 0.09 and disfavours inflationarymodels with a V(φ) ∝ φ2 potential. The addition of Planck polarization data leads to strong constraints on deviations from a purely adiabatic spectrum of fluctuations. We find no evidence for any contribution from isocurvature perturbations or from cosmic defects. Combining Planck data with other astrophysical data, including Type Ia supernovae, the equation of state of dark energy is constrained to w = −1.006 ± 0.045, consistent with the expected value for a cosmological constant. The standard big bang nucleosynthesis predictions for the helium and deuterium abundances for the best-fit Planck base ΛCDM cosmology are in excellent agreement with observations. We also constraints on annihilating dark matter and on possible deviations from the standard recombination history. In neither case do we find no evidence for new physics. The Planck results for base ΛCDM are in good agreement with baryon acoustic oscillation data and with the JLA sample of Type Ia supernovae. However, as in the 2013 analysis, the amplitude of the fluctuation spectrum is found to be higher than inferred from some analyses of rich cluster counts and weak gravitational lensing. We show that these tensions cannot easily be resolved with simple modifications of the base ΛCDM cosmology. Apart from these tensions, the base ΛCDM cosmology provides an excellent description of the Planck CMB observations and many other astrophysical data sets.

10,334 citations


Book
28 Apr 2021-
Abstract: Preface.1. Introduction.1.1 Panel Data: Some Examples.1.2 Why Should We Use Panel Data? Their Benefits and Limitations.Note.2. The One-way Error Component Regression Model.2.1 Introduction.2.2 The Fixed Effects Model.2.3 The Random Effects Model.2.4 Maximum Likelihood Estimation.2.5 Prediction.2.6 Examples.2.7 Selected Applications.2.8 Computational Note.Notes.Problems.3. The Two-way Error Component Regression Model.3.1 Introduction.3.2 The Fixed Effects Model.3.3 The Random Effects Model.3.4 Maximum Likelihood Estimation.3.5 Prediction.3.6 Examples.3.7 Selected Applications.Notes.Problems.4. Test of Hypotheses with Panel Data.4.1 Tests for Poolability of the Data.4.2 Tests for Individual and Time Effects.4.3 Hausman's Specification Test.4.4 Further Reading.Notes.Problems.5. Heteroskedasticity and Serial Correlation in the Error Component Model.5.1 Heteroskedasticity.5.2 Serial Correlation.Notes.Problems.6. Seemingly Unrelated Regressions with Error Components.6.1 The One-way Model.6.2 The Two-way Model.6.3 Applications and Extensions.Problems.7. Simultaneous Equations with Error Components.7.1 Single Equation Estimation.7.2 Empirical Example: Crime in North Carolina.7.3 System Estimation.7.4 The Hausman and Taylor Estimator.7.5 Empirical Example: Earnings Equation Using PSID Data.7.6 Extensions.Notes.Problems.8. Dynamic Panel Data Models.8.1 Introduction.8.2 The Arellano and Bond Estimator.8.3 The Arellano and Bover Estimator.8.4 The Ahn and Schmidt Moment Conditions.8.5 The Blundell and Bond System GMM Estimator.8.6 The Keane and Runkle Estimator.8.7 Further Developments.8.8 Empirical Example: Dynamic Demand for Cigarettes.8.9 Further Reading.Notes.Problems.9. Unbalanced Panel Data Models.9.1 Introduction.9.2 The Unbalanced One-way Error Component Model.9.3 Empirical Example: Hedonic Housing.9.4 The Unbalanced Two-way Error Component Model.9.5 Testing for Individual and Time Effects Using Unbalanced Panel Data.9.6 The Unbalanced Nested Error Component Model.Notes.Problems.10. Special Topics.10.1 Measurement Error and Panel Data.10.2 Rotating Panels.10.3 Pseudo-panels.10.4 Alternative Methods of Pooling Time Series of Cross-section Data.10.5 Spatial Panels.10.6 Short-run vs Long-run Estimates in Pooled Models.10.7 Heterogeneous Panels.Notes.Problems.11. Limited Dependent Variables and Panel Data.11.1 Fixed and Random Logit and Probit Models.11.2 Simulation Estimation of Limited Dependent Variable Models with Panel Data.11.3 Dynamic Panel Data Limited Dependent Variable Models.11.4 Selection Bias in Panel Data.11.5 Censored and Truncated Panel Data Models.11.6 Empirical Applications.11.7 Empirical Example: Nurses' Labor Supply.11.8 Further Reading.Notes.Problems.12. Nonstationary Panels.12.1 Introduction.12.2 Panel Unit Roots Tests Assuming Cross-sectional Independence.12.3 Panel Unit Roots Tests Allowing for Cross-sectional Dependence.12.4 Spurious Regression in Panel Data.12.5 Panel Cointegration Tests.12.6 Estimation and Inference in Panel Cointegration Models.12.7 Empirical Example: Purchasing Power Parity.12.8 Further Reading.Notes.Problems.References.Index.

10,280 citations


Journal ArticleDOI
TL;DR: There is evidence that human-to-human transmission has occurred among close contacts since the middle of December 2019 and considerable efforts to reduce transmission will be required to control outbreaks if similar dynamics apply elsewhere.
Abstract: Background The initial cases of novel coronavirus (2019-nCoV)–infected pneumonia (NCIP) occurred in Wuhan, Hubei Province, China, in December 2019 and January 2020. We analyzed data on the...

10,234 citations


Posted Content
TL;DR: This work is exploring ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization.
Abstract: Convolutional networks are at the core of most state-of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become mainstream, yielding substantial gains in various benchmarks. Although increased model size and computational cost tend to translate to immediate quality gains for most tasks (as long as enough labeled data is provided for training), computational efficiency and low parameter count are still enabling factors for various use cases such as mobile vision and big-data scenarios. Here we explore ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization. We benchmark our methods on the ILSVRC 2012 classification challenge validation set demonstrate substantial gains over the state of the art: 21.2% top-1 and 5.6% top-5 error for single frame evaluation using a network with a computational cost of 5 billion multiply-adds per inference and with using less than 25 million parameters. With an ensemble of 4 models and multi-crop evaluation, we report 3.5% top-5 error on the validation set (3.6% error on the test set) and 17.3% top-1 error on the validation set.

10,056 citations


Posted Content
TL;DR: This work introduces two simple global hyper-parameters that efficiently trade off between latency and accuracy and demonstrates the effectiveness of MobileNets across a wide range of applications and use cases including object detection, finegrain classification, face attributes and large scale geo-localization.
Abstract: We present a class of efficient models called MobileNets for mobile and embedded vision applications. MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight deep neural networks. We introduce two simple global hyper-parameters that efficiently trade off between latency and accuracy. These hyper-parameters allow the model builder to choose the right sized model for their application based on the constraints of the problem. We present extensive experiments on resource and accuracy tradeoffs and show strong performance compared to other popular models on ImageNet classification. We then demonstrate the effectiveness of MobileNets across a wide range of applications and use cases including object detection, finegrain classification, face attributes and large scale geo-localization.

9,967 citations


Proceedings Article
01 Jan 2019-
TL;DR: This paper details the principles that drove the implementation of PyTorch and how they are reflected in its architecture, and explains how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance.
Abstract: Deep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible: it was designed from first principles to support an imperative and Pythonic programming style that supports code as a model, makes debugging easy and is consistent with other popular scientific computing libraries, while remaining efficient and supporting hardware accelerators such as GPUs. In this paper, we detail the principles that drove the implementation of PyTorch and how they are reflected in its architecture. We emphasize that every aspect of PyTorch is a regular Python program under the full control of its user. We also explain how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance. We demonstrate the efficiency of individual subsystems, as well as the overall speed of PyTorch on several commonly used benchmarks.

9,926 citations


Journal ArticleDOI
23 Feb 2016-JAMA
TL;DR: The task force concluded the term severe sepsis was redundant and updated definitions and clinical criteria should replace previous definitions, offer greater consistency for epidemiologic studies and clinical trials, and facilitate earlier recognition and more timely management of patients with sepsi or at risk of developing sepsic shock.
Abstract: Importance Definitions of sepsis and septic shock were last revised in 2001. Considerable advances have since been made into the pathobiology (changes in organ function, morphology, cell biology, biochemistry, immunology, and circulation), management, and epidemiology of sepsis, suggesting the need for reexamination. Objective To evaluate and, as needed, update definitions for sepsis and septic shock. Process A task force (n = 19) with expertise in sepsis pathobiology, clinical trials, and epidemiology was convened by the Society of Critical Care Medicine and the European Society of Intensive Care Medicine. Definitions and clinical criteria were generated through meetings, Delphi processes, analysis of electronic health record databases, and voting, followed by circulation to international professional societies, requesting peer review and endorsement (by 31 societies listed in the Acknowledgment). Key Findings From Evidence Synthesis Limitations of previous definitions included an excessive focus on inflammation, the misleading model that sepsis follows a continuum through severe sepsis to shock, and inadequate specificity and sensitivity of the systemic inflammatory response syndrome (SIRS) criteria. Multiple definitions and terminologies are currently in use for sepsis, septic shock, and organ dysfunction, leading to discrepancies in reported incidence and observed mortality. The task force concluded the term severe sepsis was redundant. Recommendations Sepsis should be defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. For clinical operationalization, organ dysfunction can be represented by an increase in the Sequential [Sepsis-related] Organ Failure Assessment (SOFA) score of 2 points or more, which is associated with an in-hospital mortality greater than 10%. Septic shock should be defined as a subset of sepsis in which particularly profound circulatory, cellular, and metabolic abnormalities are associated with a greater risk of mortality than with sepsis alone. Patients with septic shock can be clinically identified by a vasopressor requirement to maintain a mean arterial pressure of 65 mm Hg or greater and serum lactate level greater than 2 mmol/L (>18 mg/dL) in the absence of hypovolemia. This combination is associated with hospital mortality rates greater than 40%. In out-of-hospital, emergency department, or general hospital ward settings, adult patients with suspected infection can be rapidly identified as being more likely to have poor outcomes typical of sepsis if they have at least 2 of the following clinical criteria that together constitute a new bedside clinical score termed quickSOFA (qSOFA): respiratory rate of 22/min or greater, altered mentation, or systolic blood pressure of 100 mm Hg or less. Conclusions and Relevance These updated definitions and clinical criteria should replace previous definitions, offer greater consistency for epidemiologic studies and clinical trials, and facilitate earlier recognition and more timely management of patients with sepsis or at risk of developing sepsis.

9,896 citations


Journal ArticleDOI
Adam Auton1, Gonçalo R. Abecasis2, David Altshuler3, Richard Durbin4, David R. Bentley5, Aravinda Chakravarti6, Andrew G. Clark7, Peter Donnelly8, Evan E. Eichler9, Paul Flicek, Stacey Gabriel10, Richard A. Gibbs11, Eric D. Green12, Matthew E. Hurles4, Bartha Maria Knoppers13, Jan O. Korbel, Eric S. Lander10, Charles Lee14, Hans Lehrach15, Elaine R. Mardis16, Gabor T. Marth17, Gil McVean8, Deborah A. Nickerson9, Jeanette Schmidt18, Stephen T. Sherry12, Jun Wang, Richard K. Wilson16, Eric Boerwinkle11, Harsha Doddapaneni11, Yi Han11, Viktoriya Korchina11, Christie Kovar11, Sandra L. Lee11, Donna M. Muzny11, Jeffrey G. Reid11, Yiming Zhu11, Yuqi Chang19, Qiang Feng20, Qiang Feng19, Xiaodong Fang20, Xiaodong Fang19, Xiaosen Guo19, Xiaosen Guo20, Min Jian19, Min Jian20, Hui Jiang20, Hui Jiang19, Xin Jin19, Tianming Lan19, Guoqing Li19, Jingxiang Li19, Yingrui Li19, Shengmao Liu19, Xiao Liu19, Xiao Liu20, Yao Lu19, Xuedi Ma19, Meifang Tang19, Bo Wang19, Guangbiao Wang19, Honglong Wu19, Renhua Wu19, Xun Xu19, Ye Yin19, Dandan Zhang19, Wenwei Zhang19, Jiao Zhao19, Meiru Zhao19, Xiaole Zheng19, Namrata Gupta10, Neda Gharani21, Lorraine Toji21, Norman P. Gerry21, Alissa M. Resch21, Jonathan Barker, Laura Clarke, Laurent Gil, Sarah E. Hunt, Gavin Kelman, Eugene Kulesha, Rasko Leinonen, William M. McLaren, Rajesh Radhakrishnan, Asier Roa, Dmitriy Smirnov, Richard Smith, Ian Streeter, Anja Thormann, Iliana Toneva, Brendan Vaughan, Xiangqun Zheng-Bradley, Russell J. Grocock5, Sean Humphray5, Terena James5, Zoya Kingsbury5, Ralf Sudbrak22, M. Albrecht, Vyacheslav Amstislavskiy15, Tatiana A. Borodina, Matthias Lienhard15, Florian Mertes15, Marc Sultan15, Bernd Timmermann15, Marie-Laure Yaspo15, Lucinda Fulton16, Victor Ananiev12, Zinaida Belaia12, Dimitriy Beloslyudtsev12, Nathan Bouk12, Chao Chen12, Deanna M. Church, Robert M. Cohen12, Charles Cook12, John Garner12, Timothy Hefferon12, Mikhail Kimelman12, Chunlei Liu12, John Lopez12, Peter Meric12, Chris O’Sullivan12, Yuri Ostapchuk12, Lon Phan12, Sergiy Ponomarov12, Valerie A. Schneider12, Eugene Shekhtman12, Karl Sirotkin12, Douglas J. Slotta12, Hua Zhang12, Senduran Balasubramaniam4, John Burton4, Petr Danecek4, Thomas M. Keane4, Anja Kolb-Kokocinski4, Shane A. McCarthy4, James Stalker4, Michael A. Quail4, Christopher Davies18, Jeremy Gollub18, Teresa Webster18, Brant Wong18, Yiping Zhan18, Christopher L. Campbell1, Yu Kong1, Anthony Marcketta1, Fuli Yu11, Lilian Antunes11, Matthew N. Bainbridge11, Aniko Sabo11, Zhuoyi Huang11, Lachlan J. M. Coin19, Lin Fang19, Lin Fang20, Qibin Li19, Zhenyu Li19, Haoxiang Lin19, Binghang Liu19, Ruibang Luo19, Haojing Shao23, Haojing Shao19, Yinlong Xie19, Chen Ye19, Chang Yu19, Fan Zhang19, Hancheng Zheng19, Zhu Hongmei19, Can Alkan24, Elif Dal24, Fatma Kahveci24, Erik Garrison4, Deniz Kural, Wan-Ping Lee, Wen Fung Leong25, Michael Strömberg5, Alistair Ward17, Jiantao Wu5, Mengyao Zhang26, Mark J. Daly10, Mark A. DePristo, Robert E. Handsaker10, Robert E. Handsaker26, Eric Banks10, Gaurav Bhatia10, Guillermo del Angel10, Giulio Genovese10, Heng Li10, Seva Kashin26, Seva Kashin10, Steven A. McCarroll10, Steven A. McCarroll26, James Nemesh10, Ryan Poplin10, Seungtai Yoon27, Jayon Lihm27, Vladimir Makarov28, Srikanth Gottipati7, Alon Keinan7, Juan L. Rodriguez-Flores7, Tobias Rausch, Markus Hsi-Yang Fritz, Adrian M. Stütz, Kathryn Beal, Avik Datta, Javier Herrero29, Graham R. S. Ritchie, Daniel R. Zerbino, Pardis C. Sabeti26, Pardis C. Sabeti10, Ilya Shlyakhter26, Ilya Shlyakhter10, Stephen F. Schaffner10, Stephen F. Schaffner26, Joseph J. Vitti26, Joseph J. Vitti10, David Neil Cooper30, Edward V. Ball30, Peter D. Stenson30, Bret Barnes5, Markus J. Bauer5, R. Keira Cheetham5, Anthony J. Cox5, Michael A. Eberle5, Scott Kahn5, Lisa Murray5, John F. Peden5, Richard Shaw5, Eimear E. Kenny28, Mark A. Batzer31, Miriam K. Konkel31, Jerilyn A. Walker31, Daniel G. MacArthur26, Monkol Lek26, Ralf Herwig15, Li Ding16, Daniel C. Koboldt16, David E. Larson16, Kai Ye16, Simon Gravel13, Anand Swaroop12, Emily Y. Chew12, Tuuli Lappalainen32, Yaniv Erlich32, Melissa Gymrek26, Melissa Gymrek10, Thomas Willems33, Jared T. Simpson34, Mark D. Shriver35, Jeffrey A. Rosenfeld36, Carlos Bustamante37, Stephen B. Montgomery37, Francisco M. De La Vega37, Jake K. Byrnes, Andrew Carroll, Marianne K. DeGorter37, Phil Lacroute37, Brian K. Maples37, Alicia R. Martin37, Andrés Moreno-Estrada38, Andrés Moreno-Estrada37, Suyash Shringarpure37, Fouad Zakharia37, Eran Halperin39, Eran Halperin40, Yael Baran40, Eliza Cerveira, Jaeho Hwang, Ankit Malhotra, Dariusz Plewczynski, Kamen Radew, Mallory Romanovitch, Chengsheng Zhang, Fiona Hyland18, David Craig41, Alexis Christoforides41, Nils Homer42, Tyler Izatt41, Ahmet Kurdoglu41, Shripad Sinari41, Kevin Squire43, Chunlin Xiao12, Jonathan Sebat44, Danny Antaki44, Madhusudan Gujral44, Amina Noor44, Kenny Ye1, Esteban G. Burchard45, Ryan D. Hernandez45, Christopher R. Gignoux45, David Haussler46, David Haussler47, Sol Katzman46, W. James Kent46, Bryan Howie48, Andres Ruiz-Linares29, Emmanouil T. Dermitzakis49, Emmanouil T. Dermitzakis50, Scott E. Devine51, Hyun Min Kang2, Jeffrey M. Kidd2, Thomas W. Blackwell2, Sean Caron2, Wei Chen52, S. Emery2, Lars G. Fritsche2, Christian Fuchsberger2, Goo Jun2, Goo Jun53, Bingshan Li54, Robert H. Lyons2, Chris Scheller2, Carlo Sidore55, Carlo Sidore56, Carlo Sidore2, Shiya Song2, Elzbieta Sliwerska2, Daniel Taliun2, Adrian Tan2, Ryan P. Welch2, Mary Kate Wing2, Xiaowei Zhan57, Philip Awadalla58, Philip Awadalla34, Alan Hodgkinson58, Yun Li59, Xinghua Shi60, Andrew Quitadamo60, Gerton Lunter8, Jonathan Marchini8, Simon Myers8, Claire Churchhouse8, Olivier Delaneau8, Olivier Delaneau50, Anjali Gupta-Hinch8, Warren W. Kretzschmar8, Zamin Iqbal8, Iain Mathieson8, Androniki Menelaou61, Androniki Menelaou8, Andy Rimmer50, Dionysia Kiara Xifara8, Taras K. Oleksyk62, Yunxin Fu53, Xiaoming Liu53, Momiao Xiong53, Lynn B. Jorde17, David J. Witherspoon17, Jinchuan Xing36, Brian L. Browning9, Sharon R. Browning9, Fereydoun Hormozdiari9, Peter H. Sudmant9, Ekta Khurana7, Chris Tyler-Smith4, Cornelis A. Albers63, Qasim Ayub4, Yuan Chen4, Vincenza Colonna4, Vincenza Colonna55, Luke Jostins8, Klaudia Walter4, Yali Xue4, Mark Gerstein64, Alexej Abyzov65, Suganthi Balasubramanian64, Jieming Chen64, Declan Clarke64, Yao Fu64, Arif Harmanci64, Mike Jin64, Dong-Hoon Lee64, Jeremy Liu64, Xinmeng Jasmine Mu10, Xinmeng Jasmine Mu64, Jing Zhang64, Yan Zhang64, Christopher Hartl10, Khalid Shakir10, Jeremiah D. Degenhardt7, Sascha Meiers, Benjamin Raeder, Francesco Paolo Casale, Oliver Stegle, Eric-Wubbo Lameijer66, Ira M. Hall16, Vineet Bafna44, Jacob J. Michaelson44, Eugene J. Gardner51, Ryan E. Mills2, Gargi Dayama2, Ken Chen67, Xian Fan67, Zechen Chong67, Tenghui Chen67, Mark Chaisson9, John Huddleston9, Maika Malig9, Bradley J. Nelson9, Nicholas F. Parrish54, Ben Blackburne4, Sarah J. Lindsay4, Zemin Ning4, Yujun Zhang4, Hugo Y. K. Lam, Cristina Sisu64, Danny Challis11, Uday S. Evani11, James T. Lu11, Uma Nagaswamy11, Jin Yu11, Wangshen Li19, Lukas Habegger64, Haiyuan Yu7, Fiona Cunningham, Ian Dunham, Kasper Lage26, Kasper Lage10, Jakob Berg Jespersen26, Jakob Berg Jespersen10, Jakob Berg Jespersen68, Heiko Horn26, Heiko Horn10, Donghoon Kim64, Rob DeSalle69, Apurva Narechania69, Melissa A. Wilson Sayres70, Fernando L. Mendez37, G. David Poznik37, Peter A. Underhill37, David Mittelman71, Ruby Banerjee4, Maria Cerezo4, Thomas W. Fitzgerald4, Sandra Louzada4, Andrea Massaia4, Fengtang Yang4, Divya Kalra11, Walker Hale11, Xu Dan19, Kathleen C. Barnes6, Christine Beiswanger21, Hongyu Cai19, Hongzhi Cao19, Hongzhi Cao20, Brenna M. Henn72, Danielle Jones7, Jane Kaye8, Alastair Kent73, Angeliki Kerasidou8, Rasika A. Mathias6, Pilar N. Ossorio74, Michael Parker8, Charles N. Rotimi12, Charmaine D.M. Royal75, Karla Sandoval37, Yeyang Su19, Zhongming Tian19, Sarah A. Tishkoff76, Marc Via77, Yuhong Wang19, Huanming Yang19, Ling Yang19, Jiayong Zhu19, Walter F. Bodmer8, Gabriel Bedoya78, Zhiming Cai19, Yang Gao79, Jiayou Chu80, Leena Peltonen, Andrés C. García-Montero81, Alberto Orfao81, Julie Dutil82, Juan Carlos Martínez-Cruzado62, R. Mathias6, Anselm Hennis83, Harold Watson83, Colin A. McKenzie83, Firdausi Qadri84, Regina C. LaRocque84, Xiaoyan Deng, Danny Asogun, Onikepe A. Folarin, Christian T. Happi26, Omonwunmi Omoniwa26, Matt Stremlau26, Matt Stremlau10, Ridhi Tariyal26, Ridhi Tariyal10, M Jallow8, M Jallow85, Fatoumatta Sisay Joof8, Fatoumatta Sisay Joof85, Tumani Corrah85, Tumani Corrah8, Kirk A. Rockett8, Kirk A. Rockett85, Dominic P. Kwiatkowski8, Dominic P. Kwiatkowski85, Jaspal S. Kooner86, Tran Tinh Hien8, Sarah J. Dunstan87, Sarah J. Dunstan8, Nguyen ThuyHang8, Richard Fonnie, Robert F. Garry88, Lansana Kanneh, Lina M. Moses88, John S. Schieffelin88, Donald S. Grant88, Carla Gallo89, Giovanni Poletti89, Danish Saleheen76, Asif Rasheed, Lisa D. Brooks12, Adam Felsenfeld12, Jean E. McEwen12, Yekaterina Vaydylevich12, Audrey Duncanson90, Michael Dunn90, Jeffery A. Schloss12 
Yeshiva University1, University of Michigan2, Vertex Pharmaceuticals3, Wellcome Trust Sanger Institute4, Illumina5, Johns Hopkins University6, Cornell University7, University of Oxford8, University of Washington9, Broad Institute10, Baylor College of Medicine11, National Institutes of Health12, McGill University13, Ewha Womans University14, Max Planck Society15, Washington University in St. Louis16, University of Utah17, Thermo Fisher Scientific18, Beijing Institute of Genomics19, University of Copenhagen20, Coriell Institute For Medical Research21, Maastricht University22, University of Queensland23, Bilkent University24, Kansas State University25, Harvard University26, Cold Spring Harbor Laboratory27, Icahn School of Medicine at Mount Sinai28, University College London29, Cardiff University30, Louisiana State University31, Columbia University32, Massachusetts Institute of Technology33, Ontario Institute for Cancer Research34, Pennsylvania State University35, Rutgers University36, Stanford University37, CINVESTAV38, University of California, Berkeley39, Tel Aviv University40, Translational Genomics Research Institute41, Life Technologies42, University of California, Los Angeles43, University of California, San Diego44, University of California, San Francisco45, University of California, Santa Cruz46, Howard Hughes Medical Institute47, University of Chicago48, Swiss Institute of Bioinformatics49, University of Geneva50, University of Maryland, Baltimore51, University of Pittsburgh52, University of Texas Health Science Center at Houston53, Vanderbilt University54, National Research Council55, University of Sassari56, University of Texas Southwestern Medical Center57, Université de Montréal58, University of North Carolina at Chapel Hill59, University of North Carolina at Charlotte60, Utrecht University61, University of Puerto Rico at Mayagüez62, Radboud University Nijmegen63, Yale University64, Mayo Clinic65, Leiden University66, University of Texas MD Anderson Cancer Center67, Technical University of Denmark68, American Museum of Natural History69, Arizona State University70, Virginia Tech71, Stony Brook University72, Genetic Alliance73, University of Wisconsin-Madison74, Duke University75, University of Pennsylvania76, University of Barcelona77, University of Antioquia78, Peking University79, Peking Union Medical College80, University of Salamanca81, Ponce Health Sciences University82, University of the West Indies83, International Centre for Diarrhoeal Disease Research, Bangladesh84, Medical Research Council85, Hammersmith Hospital86, University of Melbourne87, Tulane University88, Cayetano Heredia University89, Wellcome Trust90
01 Oct 2015-Nature
TL;DR: The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations, and has reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-generation sequencing, deep exome sequencing, and dense microarray genotyping.
Abstract: The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.

9,821 citations