University of Toronto

Deep learning

ImageNet classification with deep convolutional neural networks

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.

PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation

Scoping reviews, a type of knowledge synthesis, follow a systematic approach to map evidence on a topic and identify main concepts, theories, sources, and knowledge gaps. Although more scoping reviews are being done, their methodological and reporting quality need improvement. This document presents the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) checklist and explanation. The checklist was developed by a 24-member expert panel and 2 research leads following published guidance from the EQUATOR (Enhancing the QUAlity and Transparency Of health Research) Network. The final checklist contains 20 essential reporting items and 2 optional items. The authors provide a rationale and an example of good reporting for each item. The intent of the PRISMA-ScR is to help readers (including researchers, publishers, commissioners, policymakers, health care providers, guideline developers, and patients or consumers) develop a greater understanding of relevant terminology, core concepts, and key items to report for scoping reviews.

Learning representations by back-propagating errors

The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3)

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.

Array programming with NumPy

Abstract Array programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It has an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, materials science, engineering, finance and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves 1 and in the first imaging of a black hole 2 . Here we review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data. NumPy is the foundation upon which the scientific Python ecosystem is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Owing to its central position in the ecosystem, NumPy increasingly acts as an interoperability layer between such array computation libraries and, together with its application programming interface (API), provides a flexible framework to support the next decade of scientific and industrial analysis.

A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010

Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC

Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups

Most current speech recognition systems use hidden Markov models (HMMs) to deal with the temporal variability of speech and Gaussian mixture models (GMMs) to determine how well each state of each HMM fits a frame or a short window of frames of coefficients that represents the acoustic input. An alternative way to evaluate the fit is to use a feed-forward neural network that takes several frames of coefficients as input and produces posterior probabilities over HMM states as output. Deep neural networks (DNNs) that have many hidden layers and are trained using new methods have been shown to outperform GMMs on a variety of speech recognition benchmarks, sometimes by a large margin. This article provides an overview of this progress and represents the shared views of four research groups that have had recent successes in using DNNs for acoustic modeling in speech recognition.

Impulse response analysis in nonlinear multivariate models

A bonded-particle model for rock

The Four-Phase Model of Interest Development

Building on and extending existing research, this article proposes a 4-phase model of interest development. The model describes 4 phases in the development and deepening of learner interest: triggered situational interest, maintained situational interest, emerging (less-developed) individual interest, and well-developed individual interest. Affective as well as cognitive factors are considered. Educational implications of the proposed model are identified.

The Global Burden of Cancer 2013

IMPORTANCE: Cancer is among the leading causes of death worldwide. Current estimates of cancer burden in individual countries and regions are necessary to inform local cancer control strategies. OBJECTIVE: To estimate mortality, incidence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs) for 28 cancers in 188 countries by sex from 1990 to 2013. EVIDENCE REVIEW: The general methodology of the Global Burden of Disease (GBD) 2013 study was used. Cancer registries were the source for cancer incidence data as well as mortality incidence (MI) ratios. Sources for cause of death data include vital registration system data, verbal autopsy studies, and other sources. The MI ratios were used to transform incidence data to mortality estimates and cause of death estimates to incidence estimates. Cancer prevalence was estimated using MI ratios as surrogates for survival data; YLDs were calculated by multiplying prevalence estimates with disability weights, which were derived from population-based surveys; YLLs were computed by multiplying the number of estimated cancer deaths at each age with a reference life expectancy; and DALYs were calculated as the sum of YLDs and YLLs. FINDINGS: In 2013 there were 14.9 million incident cancer cases, 8.2 million deaths, and 196.3 million DALYs. Prostate cancer was the leading cause for cancer incidence (1.4 million) for men and breast cancer for women (1.8 million). Tracheal, bronchus, and lung (TBL) cancer was the leading cause for cancer death in men and women, with 1.6 million deaths. For men, TBL cancer was the leading cause of DALYs (24.9 million). For women, breast cancer was the leading cause of DALYs (13.1 million). Age-standardized incidence rates (ASIRs) per 100 000 and age-standardized death rates (ASDRs) per 100 000 for both sexes in 2013 were higher in developing vs developed countries for stomach cancer (ASIR, 17 vs 14; ASDR, 15 vs 11), liver cancer (ASIR, 15 vs 7; ASDR, 16 vs 7), esophageal cancer (ASIR, 9 vs 4; ASDR, 9 vs 4), cervical cancer (ASIR, 8 vs 5; ASDR, 4 vs 2), lip and oral cavity cancer (ASIR, 7 vs 6; ASDR, 2 vs 2), and nasopharyngeal cancer (ASIR, 1.5 vs 0.4; ASDR, 1.2 vs 0.3). Between 1990 and 2013, ASIRs for all cancers combined (except nonmelanoma skin cancer and Kaposi sarcoma) increased by more than 10% in 113 countries and decreased by more than 10% in 12 of 188 countries. CONCLUSIONS AND RELEVANCE: Cancer poses a major threat to public health worldwide, and incidence rates have increased in most countries since 1990. The trend is a particular threat to developing nations with health systems that are ill-equipped to deal with complex and expensive cancer treatments. The annual update on the Global Burden of Cancer will provide all stakeholders with timely estimates to guide policy efforts in cancer prevention, screening, treatment, and palliation.

Opportunities and obstacles for deep learning in biology and medicine

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems-patient classification, fundamental biological processes and treatment of patients-and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine.

Random-matrix physics: spectrum and strength fluctuations

It now appears that the general nature of the deviations from uniformity in the spectrum of a complicated nucleus is essentially the same in all regions of the spectrum and over the entire Periodic Table. This behavior, moreover, is describable in terms of standard Hamiltonian ensembles which could be generated on the basis of simple information-theory concepts, and which give also a good account of fluctuation phenomena of other kinds and, apparently, in other many-body systems besides nuclei. The main departures from simple behavior are ascribable to the moderation of the level repulsion by effects due to symmetries and collectivities, for the description of which more complicated ensembles are called for. One purpose of this review is to give a self-contained account of the theory, using methods---sometimes approximate---which are consonant with the usual theory of stochastic processes. Another purpose is to give a proper foundation for the use of ensemble theory, to make clear the origin of the simplicities in the observable fluctuations, and to derive other general fluctuation results. In comparing theory and experiment, the authors give an analysis of much of the nuclear-energy-level data, as well as an extended discussion of observable effects in nuclear transitions and reactions and in the low-temperature thermodynamics of aggregates of small metallic particles.

Pollution and health: a progress update

The Lancet Commission on pollution and health reported that pollution was responsible for 9 million premature deaths in 2015, making it the world's largest environmental risk factor for disease and premature death. We have now updated this estimate using data from the Global Burden of Diseases, Injuriaes, and Risk Factors Study 2019. We find that pollution remains responsible for approximately 9 million deaths per year, corresponding to one in six deaths worldwide. Reductions have occurred in the number of deaths attributable to the types of pollution associated with extreme poverty. However, these reductions in deaths from household air pollution and water pollution are offset by increased deaths attributable to ambient air pollution and toxic chemical pollution (ie, lead). Deaths from these modern pollution risk factors, which are the unintended consequence of industrialisation and urbanisation, have risen by 7% since 2015 and by over 66% since 2000. Despite ongoing efforts by UN agencies, committed groups, committed individuals, and some national governments (mostly in high-income countries), little real progress against pollution can be identified overall, particularly in the low-income and middle-income countries, where pollution is most severe. Urgent attention is needed to control pollution and prevent pollution-related disease, with an emphasis on air pollution and lead poisoning, and a stronger focus on hazardous chemical pollution. Pollution, climate change, and biodiversity loss are closely linked. Successful control of these conjoined threats requires a globally supported, formal science-policy interface to inform intervention, influence research, and guide funding. Pollution has typically been viewed as a local issue to be addressed through subnational and national regulation or, occasionally, using regional policy in higher-income countries. Now, however, it is increasingly clear that pollution is a planetary threat, and that its drivers, its dispersion, and its effects on health transcend local boundaries and demand a global response. Global action on all major modern pollutants is needed. Global efforts can synergise with other global environmental policy programmes, especially as a large-scale, rapid transition away from all fossil fuels to clean, renewable energy is an effective strategy for preventing pollution while also slowing down climate change, and thus achieves a double benefit for planetary health.

Grand challenges in global mental health

Global, regional, and national levels and causes of maternal mortality during 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013

The naked truth: a comprehensive clarification and classification of current ‘myths’ in naked mole‐rat biology

The naked mole-rat (Heterocephalus glaber) has fascinated zoologists for at least half a century. It has also generated considerable biomedical interest not only because of its extraordinary longevity, but also because of unusual protective features (e.g. its tolerance of variable oxygen availability), which may be pertinent to several human disease states, including ischemia/reperfusion injury and neurodegeneration. A recent article entitled 'Surprisingly long survival of premature conclusions about naked mole-rat biology' described 28 'myths' which, those authors claimed, are a 'perpetuation of beautiful, but falsified, hypotheses' and impede our understanding of this enigmatic mammal. Here, we re-examine each of these 'myths' based on evidence published in the scientific literature. Following Braude et al., we argue that these 'myths' fall into four main categories: (i) 'myths' that would be better described as oversimplifications, some of which persist solely in the popular press; (ii) 'myths' that are based on incomplete understanding, where more evidence is clearly needed; (iii) 'myths' where the accumulation of evidence over the years has led to a revision in interpretation, but where there is no significant disagreement among scientists currently working in the field; (iv) 'myths' where there is a genuine difference in opinion among active researchers, based on alternative interpretations of the available evidence. The term 'myth' is particularly inappropriate when applied to competing, evidence-based hypotheses, which form part of the normal evolution of scientific knowledge. Here, we provide a comprehensive critical review of naked mole-rat biology and attempt to clarify some of these misconceptions.

Pronounced central uplift identified in the Bosumtwi impact structure, Ghana, using multichannel seismic reflection data

Research Article| October 01, 2002 Pronounced central uplift identified in the Bosumtwi impact structure, Ghana, using multichannel seismic reflection data Christopher A. Scholz; Christopher A. Scholz 1204 Heroy Geology Laboratory, Department of Earth Sciences, Syracuse University, Syracuse, New York 13244, USA Search for other works by this author on: GSW Google Scholar Tobias Karp; Tobias Karp 2Institut für Geowissenschaften, Abteilung Geophysik Otto-Hahn-Platz 1, 24118 Kiel, Germany Search for other works by this author on: GSW Google Scholar Keely M. Brooks; Keely M. Brooks 3204 Heroy Geology Laboratory, Department of Earth Sciences, Syracuse University, Syracuse, New York 13244, USA Search for other works by this author on: GSW Google Scholar Bernd Milkereit; Bernd Milkereit 4Department of Physics, University of Toronto, 60 St. George Street, Toronto, Ontario M5S 1A7, Canada Search for other works by this author on: GSW Google Scholar Philip Y.O. Amoako; Philip Y.O. Amoako 5Ghana Geological Survey, P.O. Box M80, Accra, Ghana Search for other works by this author on: GSW Google Scholar Justice A. Arko Justice A. Arko 6Institute of Mining and Mineral Engineering, University of Science and Technology, Kumasi, Ghana Search for other works by this author on: GSW Google Scholar Author and Article Information Christopher A. Scholz 1204 Heroy Geology Laboratory, Department of Earth Sciences, Syracuse University, Syracuse, New York 13244, USA Tobias Karp 2Institut für Geowissenschaften, Abteilung Geophysik Otto-Hahn-Platz 1, 24118 Kiel, Germany Keely M. Brooks 3204 Heroy Geology Laboratory, Department of Earth Sciences, Syracuse University, Syracuse, New York 13244, USA Bernd Milkereit 4Department of Physics, University of Toronto, 60 St. George Street, Toronto, Ontario M5S 1A7, Canada Philip Y.O. Amoako 5Ghana Geological Survey, P.O. Box M80, Accra, Ghana Justice A. Arko 6Institute of Mining and Mineral Engineering, University of Science and Technology, Kumasi, Ghana Publisher: Geological Society of America Received: 22 Jan 2002 Revision Received: 13 Jun 2002 Accepted: 18 Jun 2002 First Online: 02 Jun 2017 Online ISSN: 1943-2682 Print ISSN: 0091-7613 Geological Society of America Geology (2002) 30 (10): 939–942. https://doi.org/10.1130/0091-7613(2002)0302.0.CO;2 Article history Received: 22 Jan 2002 Revision Received: 13 Jun 2002 Accepted: 18 Jun 2002 First Online: 02 Jun 2017 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Christopher A. Scholz, Tobias Karp, Keely M. Brooks, Bernd Milkereit, Philip Y.O. Amoako, Justice A. Arko; Pronounced central uplift identified in the Bosumtwi impact structure, Ghana, using multichannel seismic reflection data. Geology 2002;; 30 (10): 939–942. doi: https://doi.org/10.1130/0091-7613(2002)0302.0.CO;2 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietyGeology Search Advanced Search Abstract The Lake Bosumtwi impact structure is the youngest and best-preserved complex terrestrial impact crater and serves as an important reference site for the study of cratering processes. Because the impacting body struck continental crystalline target rocks and not a submerged sedimentary platform, no significant backwash processes have modified the crater morphology. Not only may Bosumtwi contain the best-preserved central uplift structure on Earth, but it is the most accessible relatively large, young crater in the solar system generated in a large gravity field. There is a well-established link between the Lake Bosumtwi impact structure and the Ivory Coast tektite field, and the lacustrine sediments within the crater contain a unique 1 m.y. record of paleoclimate in the continental tropics south of the Sahel. Eight profiles of marine-type multichannel seismic reflection (MCS) data were acquired from the 8-km-diameter, ∼75-m-deep lake that fills much of the crater. These were augmented by wide-angle seismic data acquired with ocean-bottom hydrophones. MCS data reveal a well-defined central uplift near the northwest-central part of the lake and a maximum postimpact lacustrine sediment thickness of ∼310 m. The central uplift structure has a diameter of 1.9 km and a maximum height of 130 m above the annular moat inside the crater. An intermediate velocity layer (3200 m/s) beneath the lacustrine sediment is interpreted as fallback breccia or a breccia-melt horizon. The measured apparent depth of the crater (da) is 500 m, implying a slightly higher aspect ratio for the structure than predicted from published empirical relationships. The Bosumtwi structure is a small complex crater that deviates slightly from trends predicted from classical scaling laws, perhaps because of the effects of a large gravity field. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.