scispace - formally typeset
Search or ask a question
Browse all papers

Journal ArticleDOI
TL;DR: In this article, the catalytic role of reduced graphene oxide (MoS2−x/reduced graphene oxide) was investigated for catalyzing polysulfide reactions to improve the battery performance.
Abstract: Lithium–sulfur batteries are promising next-generation energy storage devices due to their high energy density and low material cost. Efficient conversion of lithium polysulfides to lithium sulfide (during discharge) and to sulfur (during recharge) is a performance-determining factor for lithium–sulfur batteries. Here we show that MoS2−x/reduced graphene oxide (MoS2−x/rGO) can be used to catalyze the polysulfide reactions to improve the battery performance. It was confirmed, through microstructural characterization of the materials, that sulfur deficiencies on the surface participated in the polysulfide reactions and significantly enhanced the polysulfide conversion kinetics. The fast conversion of soluble polysulfides decreased their accumulation in the sulfur cathode and their loss from the cathode by diffusion. Hence in the presence of a small amount of MoS2−x/rGO (4 wt% of the cathode mass), high rate (8C) performance of the sulfur cathode was improved from a capacity of 161.1 mA h g−1 to 826.5 mA h g−1. In addition, MoS2−x/rGO also enhanced the cycle stability of the sulfur cathode from a capacity fade rate of 0.373% per cycle (over 150 cycles) to 0.083% per cycle (over 600 cycles) at a typical 0.5C rate. These results provide direct experimental evidence for the catalytic role of MoS2−x/rGO in promoting the polysulfide conversion kinetics in the sulfur cathode.

747 citations


Journal ArticleDOI
TL;DR: Obeticholic acid administered with ursodiol or as monotherapy for 12 months in patients with primary biliary cholangitis resulted in decreases from baseline in alkaline phosphatase and total bilirubin levels that differed significantly from the changes observed with placebo.
Abstract: Background Primary biliary cholangitis (formerly called primary biliary cirrhosis) can progress to cirrhosis and death despite ursodiol therapy. Alkaline phosphatase and bilirubin levels correlate with the risk of liver transplantation or death. Obeticholic acid, a farnesoid X receptor agonist, has shown potential benefit in patients with this disease. Methods In this 12-month, double-blind, placebo-controlled, phase 3 trial, we randomly assigned 217 patients who had an inadequate response to ursodiol or who found the side effects of ursodiol unacceptable to receive obeticholic acid at a dose of 10 mg (the 10-mg group), obeticholic acid at a dose of 5 mg with adjustment to 10 mg if applicable (the 5-10-mg group), or placebo. The primary end point was an alkaline phosphatase level of less than 1.67 times the upper limit of the normal range, with a reduction of at least 15% from baseline, and a normal total bilirubin level. Results Of 216 patients who underwent randomization and received at least one dose of obeticholic acid or placebo, 93% received ursodiol as background therapy. The primary end point occurred in more patients in the 5-10-mg group (46%) and the 10-mg group (47%) than in the placebo group (10%; P Conclusions Obeticholic acid administered with ursodiol or as monotherapy for 12 months in patients with primary biliary cholangitis resulted in decreases from baseline in alkaline phosphatase and total bilirubin levels that differed significantly from the changes observed with placebo. There were more serious adverse events with obeticholic acid. (Funded by Intercept Pharmaceuticals; POISE ClinicalTrials.gov number, NCT01473524; Current Controlled Trials number, ISRCTN89514817.).

747 citations


Proceedings ArticleDOI
18 Jun 2018
TL;DR: Extensive experimental results show that the proposed convolutional super-resolution network not only can produce favorable results on multiple degradations but also is computationally efficient, providing a highly effective and scalable solution to practical SISR applications.
Abstract: Recent years have witnessed the unprecedented success of deep convolutional neural networks (CNNs) in single image super-resolution (SISR). However, existing CNN-based SISR methods mostly assume that a low-resolution (LR) image is bicubicly downsampled from a high-resolution (HR) image, thus inevitably giving rise to poor performance when the true degradation does not follow this assumption. Moreover, they lack scalability in learning a single model to nonblindly deal with multiple degradations. To address these issues, we propose a general framework with dimensionality stretching strategy that enables a single convolutional super-resolution network to take two key factors of the SISR degradation process, i.e., blur kernel and noise level, as input. Consequently, the super-resolver can handle multiple and even spatially variant degradations, which significantly improves the practicability. Extensive experimental results on synthetic and real LR images show that the proposed convolutional super-resolution network not only can produce favorable results on multiple degradations but also is computationally efficient, providing a highly effective and scalable solution to practical SISR applications.

747 citations


Proceedings ArticleDOI
TL;DR: A new notion of unfairness, disparate mistreatment, is introduced, defined in terms of misclassification rates, which is proposed for decision boundary-based classifiers and can be easily incorporated into their formulation as convex-concave constraints.
Abstract: Automated data-driven decision making systems are increasingly being used to assist, or even replace humans in many settings. These systems function by learning from historical decisions, often taken by humans. In order to maximize the utility of these systems (or, classifiers), their training involves minimizing the errors (or, misclassifications) over the given historical data. However, it is quite possible that the optimally trained classifier makes decisions for people belonging to different social groups with different misclassification rates (e.g., misclassification rates for females are higher than for males), thereby placing these groups at an unfair disadvantage. To account for and avoid such unfairness, in this paper, we introduce a new notion of unfairness, disparate mistreatment, which is defined in terms of misclassification rates. We then propose intuitive measures of disparate mistreatment for decision boundary-based classifiers, which can be easily incorporated into their formulation as convex-concave constraints. Experiments on synthetic as well as real world datasets show that our methodology is effective at avoiding disparate mistreatment, often at a small cost in terms of accuracy.

747 citations


Journal ArticleDOI
TL;DR: The Animals with Attributes 2 (AWA2) dataset as mentioned in this paper is a new dataset for zero-shot learning, which is publicly available both in terms of image features and the images themselves.
Abstract: Due to the importance of zero-shot learning, i.e., classifying images where there is a lack of labeled training data, the number of proposed approaches has recently increased steadily. We argue that it is time to take a step back and to analyze the status quo of the area. The purpose of this paper is three-fold. First, given the fact that there is no agreed upon zero-shot learning benchmark, we first define a new benchmark by unifying both the evaluation protocols and data splits of publicly available datasets used for this task. This is an important contribution as published results are often not comparable and sometimes even flawed due to, e.g., pre-training on zero-shot test classes. Moreover, we propose a new zero-shot learning dataset, the Animals with Attributes 2 (AWA2) dataset which we make publicly available both in terms of image features and the images themselves. Second, we compare and analyze a significant number of the state-of-the-art methods in depth, both in the classic zero-shot setting but also in the more realistic generalized zero-shot setting. Finally, we discuss in detail the limitations of the current status of the area which can be taken as a basis for advancing it.

747 citations


Journal ArticleDOI
TL;DR: A transparent air filter for indoor air protection through windows that uses natural passive ventilation to effectively protect the indoor air quality and has the best PM2.5 removal efficiency in Beijing.
Abstract: Particulate matter pollution is a public health concern in industrialized and urban areas. Here, the authors control the surface chemistry and microstructure of filtration materials to fabricate effective and transparent air filters for the capture of PM2.5 pollutants.

747 citations


Journal ArticleDOI
TL;DR: Mechanistic pathways for individuals with obesity are presented in depth for factors linked with COVID‐19 risk, severity and their potential for diminished therapeutic and prophylactic treatments among these individuals.
Abstract: The linkage of individuals with obesity and COVID-19 is controversial and lacks systematic reviews. After a systematic search of the Chinese and English language literature on COVID-19, 75 studies were used to conduct a series of meta-analyses on the relationship of individuals with obesity-COVID-19 over the full spectrum from risk to mortality. A systematic review of the mechanistic pathways for COVID-19 and individuals with obesity is presented. Pooled analysis show individuals with obesity were more at risk for COVID-19 positive, >46.0% higher (OR = 1.46; 95% CI, 1.30-1.65; p < 0.0001); for hospitalization, 113% higher (OR = 2.13; 95% CI, 1.74-2.60; p < 0.0001); for ICU admission, 74% higher (OR = 1.74; 95% CI, 1.46-2.08); and for mortality, 48% increase in deaths (OR = 1.48; 95% CI, 1.22-1.80; p < 0.001). Mechanistic pathways for individuals with obesity are presented in depth for factors linked with COVID-19 risk, severity and their potential for diminished therapeutic and prophylactic treatments among these individuals. Individuals with obesity are linked with large significant increases in morbidity and mortality from COVID-19. There are many mechanisms that jointly explain this impact. A major concern is that vaccines will be less effective for the individuals with obesity.

747 citations


Journal ArticleDOI
TL;DR: The current state of evidence supports that gamification can have a positive impact in health and wellbeing, particularly for health behaviours, however several studies report mixed or neutral effect.

747 citations


Journal ArticleDOI
TL;DR: A high and growing prevalence of COPD is suggested, both globally and regionally, and there is a need for governments, policy makers and international organizations to consider strengthening collaborations to address COPD globally.
Abstract: In a follow–up to the 2011 United Nations (UN) high level political declaration on non-communicable diseases (NCDs) [1], the World Health Assembly, in 2012, endorsed a new health goal (the “25 by 25 goal”), which focuses on reduction of premature deaths from COPD and other NCDs by 25% by the year 2025 [2]. Despite this initiative, experts have reported that COPD remains a growing [3], but neglected global epidemic [4]. The World Health Organization (WHO) estimated that there were about 62 million people with moderate to severe COPD in 2002, with the total number of COPD cases predicted to increase to about 200 million in 2010 [5,6]. According to the 2010 Global Burden of Disease (GBD) study, COPD was responsible for about 5% of global disability–adjusted life years – DALYs (76.7 million) – and 5% of total deaths (2.9 million) [7,8]. COPD is currently rated the fourth most common specific cause of death globally and predicted to be the third by 2030, in the absence of interventions that address the risks – especially tobacco smoking, exposures to combustion products of biomass fuels and environmental pollution [9,10]. The burden of COPD has been reported to be high in some high–income countries (HIC), particularly due to high prevalence of smoking in these settings [11]. For example, between years 2000 and 2010, about 4%–10% of adults were diagnosed with non–reversible and progressive airway obstruction (a basic feature of COPD) in population–based surveys across many European countries, with smoking indicated as a major risk [12]. The WHO has estimated that in many HIC up to 73% of COPD deaths are related to tobacco smoking [6]. The European Union (EU) reported that the direct cost from COPD was over 38.6 billion Euros in 2005, representing about 3% of total health care expenditure [13,14]. In the United States (US), over 2.7 million adults were estimated to have COPD in 2011, with about 135 000 deaths reported [15]. In 2010, the US government spent nearly US$ 49.9 billion on COPD, including 29.5 billion spent on direct health care, 8.0 billion on indirect morbidity and 12.4 billion on indirect mortality costs, respectively [15]. Meanwhile, it has been estimated that despite a high prevalence of COPD in some HIC, 90% of COPD deaths still occur in low– and middle–income countries (LMIC)in the future [4] and 40% of these deaths are attributable to smoking [6]. The burden in LMIC has been comparatively high owing to relatively low COPD awareness, challenges with COPD diagnosis and increased exposures to additional risk factors, especially combustion products of biomass fuels [16]. Salvi and colleagues reported that about 3 billion people globally are exposed to smoke from biomass fuel, compared to 1 billion people who smoke tobacco globally [17]. In many developing countries COPD is neglected by governments, physicians, experts and the pharmaceutical industry, although it's been identified as an important public health problem [4]. In the last two decades, the Burden of Obstructive Lung Disease (BOLD) initiative has been collecting country–specific data on the prevalence, risk factors and socioeconomic burden of COPD, using standardized and tested methods for conducting COPD surveys in the general population [18]. This is expected to provide governments of many nations with country–specific evidence on which to develop policy on COPD prevention and management [18]. As noted above, this initiative is yet to take a full effect in many LMIC [19]. In addition, spirometry (the gold standard for COPD diagnosis) is not widely available in many LMIC [16]. Even when it is there, professionals in LMIC are often not being trained properly on how to use spirometers or interpret spirometry results. There is concern that COPD burden has been underestimated, owing to over–reliance on doctor’s diagnosis, with many diagnoses not being based on spirometry and international diagnostic guidelines [20]. The lack of routine COPD data collation and effective health information management system in many LMIC also implies that these settings could have been grossly under–represented in global burden of COPD estimates [11]. Some global and regional estimates of COPD burden have been published [1,21–23]. However, despite the fact that COPD is now prevalent in both HIC and LMIC, experts have raised concerns that reliable estimates of COPD prevalence are still few in many parts of the world. Moreover, many of the estimates are based on varying definitions and diagnostic criteria of COPD [9]. Also, some of the current estimates were reported before the BOLD surveys in several countries, thereby failing to account for the additional spirometry–based epidemiological data from the BOLD surveys. There is a need for a revised and updated estimate of COPD prevalence across world regions. We conducted a systematic review of COPD prevalence based on spirometry data across world regions. Our aim was to provide global and regional prevalence rates of COPD that could facilitate adequate policy response in these regions.

746 citations


Journal ArticleDOI
TL;DR: The SARS-CoV-2 spike glycoprotein, which binds to ACE2, is a potential target for developing specific drugs, antibodies, and vaccines and Restoring the balance between the RAS and ACE2/angiotensin-(1–7)/MAS may help attenuate organ injuries.
Abstract: An outbreak of pneumonia caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that started in Wuhan, China, at the end of 2019 has become a global pandemic. Both SARS-CoV-2 and SARS-CoV enter host cells via the angiotensin-converting enzyme 2 (ACE2) receptor, which is expressed in various human organs. We have reviewed previously published studies on SARS and recent studies on SARS-CoV-2 infection, named coronavirus disease 2019 (COVID-19) by the World Health Organization (WHO), confirming that many other organs besides the lungs are vulnerable to the virus. ACE2 catalyzes angiotensin II conversion to angiotensin-(1–7), and the ACE2/angiotensin-(1–7)/MAS axis counteracts the negative effects of the renin-angiotensin system (RAS), which plays important roles in maintaining the physiological and pathophysiological balance of the body. In addition to the direct viral effects and inflammatory and immune factors associated with COVID-19 pathogenesis, ACE2 downregulation and the imbalance between the RAS and ACE2/angiotensin-(1–7)/MAS after infection may also contribute to multiple organ injury in COVID-19. The SARS-CoV-2 spike glycoprotein, which binds to ACE2, is a potential target for developing specific drugs, antibodies, and vaccines. Restoring the balance between the RAS and ACE2/angiotensin-(1–7)/MAS may help attenuate organ injuries. SARS-CoV-2 enters lung cells via the ACE2 receptor. The cell-free and macrophage-phagocytosed virus can spread to other organs and infect ACE2-expressing cells at local sites, causing multi-organ injury.

746 citations


Journal ArticleDOI
16 Mar 2015
TL;DR: A review will highlight the involvement of AGEs in the development and progression of T2DM and their role in diabetic complications and the role of oxidative stress in hyperglycemia-induced tissue injury.
Abstract: Type 2 diabetes mellitus (T2DM) is a very complex and multifactorial metabolic disease characterized by insulin resistance and β cell failure leading to elevated blood glucose levels Hyperglycemia is suggested to be the main cause of diabetic complications, which not only decrease life quality and expectancy, but are also becoming a problem regarding the financial burden for health care systems Therefore, and to counteract the continually increasing prevalence of diabetes, understanding the pathogenesis, the main risk factors, and the underlying molecular mechanisms may establish a basis for prevention and therapy In this regard, research was performed revealing further evidence that oxidative stress has an important role in hyperglycemia-induced tissue injury as well as in early events relevant for the development of T2DM The formation of advanced glycation end products (AGEs), a group of modified proteins and/or lipids with damaging potential, is one contributing factor On the one hand it has been reported that AGEs increase reactive oxygen species formation and impair antioxidant systems, on the other hand the formation of some AGEs is induced per se under oxidative conditions Thus, AGEs contribute at least partly to chronic stress conditions in diabetes As AGEs are not only formed endogenously, but also derive from exogenous sources, ie, food, they have been assumed as risk factors for T2DM However, the role of AGEs in the pathogenesis of T2DM and diabetic complications—if they are causal or simply an effect—is only partly understood This review will highlight the involvement of AGEs in the development and progression of T2DM and their role in diabetic complications

Journal ArticleDOI
07 Mar 2018-eLife
TL;DR: New open-source software called cisTEM (computational imaging system for transmission electron microscopy) for the processing of data for high-resolution electron cryo-microscopy and single-particle averaging is developed, optimized to enable processing of typical datasets on a high-end, CPU-based workstation in half a day or less, comparable to GPU-accelerated processing.
Abstract: We have developed new open-source software called cisTEM (computational imaging system for transmission electron microscopy) for the processing of data for high-resolution electron cryo-microscopy and single-particle averaging. cisTEM features a graphical user interface that is used to submit jobs, monitor their progress, and display results. It implements a full processing pipeline including movie processing, image defocus determination, automatic particle picking, 2D classification, ab-initio 3D map generation from random parameters, 3D classification, and high-resolution refinement and reconstruction. Some of these steps implement newly-developed algorithms; others were adapted from previously published algorithms. The software is optimized to enable processing of typical datasets (2000 micrographs, 200 k - 300 k particles) on a high-end, CPU-based workstation in half a day or less, comparable to GPU-accelerated processing. Jobs can also be scheduled on large computer clusters using flexible run profiles that can be adapted for most computing environments. cisTEM is available for download from cistem.org.

Journal ArticleDOI
TL;DR: Efficacy of the BNT162b2 mRNA Vaccine in Qatar As of March 31, 2021, more than 265,000 people in Qatar had received both doses of the vaccine as mentioned in this paper.
Abstract: Efficacy of the BNT162b2 mRNA Vaccine in Qatar As of March 31, 2021, more than 265,000 people in Qatar had received both doses of the BNT162b2 vaccine. Viral sequencing indicated that 50.0% of infe...

Proceedings ArticleDOI
01 Jul 2017
TL;DR: A new DLP-CNN (Deep Locality-Preserving CNN) method, which aims to enhance the discriminative power of deep features by preserving the locality closeness while maximizing the inter-class scatters, is proposed.
Abstract: Past research on facial expressions have used relatively limited datasets, which makes it unclear whether current methods can be employed in real world. In this paper, we present a novel database, RAF-DB, which contains about 30000 facial images from thousands of individuals. Each image has been individually labeled about 40 times, then EM algorithm was used to filter out unreliable labels. Crowdsourcing reveals that real-world faces often express compound emotions, or even mixture ones. For all we know, RAF-DB is the first database that contains compound expressions in the wild. Our cross-database study shows that the action units of basic emotions in RAF-DB are much more diverse than, or even deviate from, those of lab-controlled ones. To address this problem, we propose a new DLP-CNN (Deep Locality-Preserving CNN) method, which aims to enhance the discriminative power of deep features by preserving the locality closeness while maximizing the inter-class scatters. The benchmark experiments on the 7-class basic expressions and 11-class compound expressions, as well as the additional experiments on SFEW and CK+ databases, show that the proposed DLP-CNN outperforms the state-of-the-art handcrafted features and deep learning based methods for the expression recognition in the wild.

Journal ArticleDOI
TL;DR: This scholarly review article provides clinicians with a reference tool regarding this robust measure and recommends on testing procedures for assessing WS, including optimal distance, inclusion of acceleration and deceleration phases, instructions, and instrumentation.
Abstract: Walking speed (WS) is a valid, reliable, and sensitive measure appropriate for assessing and monitoring functional status and overall health in a wide range of populations. These capabilities have led to its designation as the "sixth vital sign". By synthesizing the available evidence on WS, this scholarly review article provides clinicians with a reference tool regarding this robust measure. Recommendations on testing procedures for assessing WS, including optimal distance, inclusion of acceleration and deceleration phases, instructions, and instrumentation are given. After assessing an individual's WS, clinicians need to know what this value represents. Therefore, WS cut-off values and the corresponding predicted outcomes, as well as minimal detectable change values for specific populations and settings are provided.

Journal ArticleDOI
TL;DR: Analysis of autopsy tissue from patients exposed to nusinersen showed drug uptake into motor neurons throughout the spinal cord and neurons and other cell types in the brainstem and other brain regions, exposure at therapeutic concentrations, and increased SMN2 mRNA exon 7 inclusion and SMN protein concentrations in the spinal Cord.

Journal ArticleDOI
TL;DR: The high costs associated with cancer care have created a difficult situation for patients and the oncologists who care for them and will require greater understanding of all the risks and benefits of various treatment options as well as the consequences of specific choices.
Abstract: Health care costs in the United States present a major challenge to the national economic well being. The Centers for Medicare and Medicaid Services (CMS) has projected that US health care spending will reach $4.3 trillion and account for 19.3% of the national gross domestic product by 2019.1 This growth in spending—both in absolute terms and as a proportion of our gross domestic product—has not been accompanied by commensurate improvements in health outcomes, despite expenditures far exceeding those of other countries.2–4 One of the fastest growing components of US health care costs is cancer care, the cost of which is now estimated to increase from $125 billion in 2010 to $158 billion in 2020.1 Although cancer care represents a small fraction of overall health care costs, its contribution to health care cost escalation is increasing faster than those of most other areas because of several factors: the increasing prevalence of cancer due to the overall aging of the population and better control of some causes of competing mortality; the introduction of costly new drugs and techniques in radiation therapy and surgery; and the adoption of more expensive diagnostic tests. In some cases, the adoption of newer, more expensive diagnostic and therapeutic interventions may not be well supported by medical evidence, thereby raising costs without improving outcomes.5 Coupled with, or even driving, some of these rising costs are sometimes unrealistic patient and family expectations that lead clinicians to offer or recommend some of these services, despite the lack of supporting evidence of utility or benefit.6 Historically, most individuals in the United States were shielded from the acute economic impact of expensive care because they had health insurance. However, current trends suggest that patients will find themselves increasingly responsible for a greater proportion of the cost of their health care. Cost shifting or sharing can occur through the increased use of high-deductible policies and larger copayments. These increased costs are already commonplace and may not be affordable for many families. Indeed, health care expenditures are cited as a major cause of personal bankruptcy,7 and the term financial toxicity has entered the vernacular as a means of describing the financial distress that now often accompanies cancer treatment.8 Like other toxicities of cancer treatment, financial toxicity resulting from out-of-pocket treatment expenses can reduce quality of life and impede delivery of high-quality care.9,10 Patients experiencing high out-of-pocket costs have reported reducing their spending on food and clothing, reducing the frequency with which they take prescribed medications, avoiding recommended procedures, and skipping physician appointments to save money.10,11 These unintended consequences risk an increase in health disparities, which runs counter to some of the key goals of health care reform. In many communities, the high costs associated with cancer care have created a difficult situation for patients and the oncologists who care for them. Addressing this situation will require greater understanding of all the risks and benefits of various treatment options as well as the consequences of specific choices. In this regard, studies have shown that patients specifically want financial information about treatment alternatives along with information about medical effectiveness and treatment toxicity. However, they often do not receive it. Closing this knowledge gap will require educated providers who are able to sensitively initiate a dialogue about the cost of care with their patients when appropriate.12,13 Patients with cancer are often surprised by and unprepared for the high out-of-pocket costs of treatments. They also overestimate the benefits of treatments that sometimes extend life by only weeks or months or not at all. Oncologists are generally aware of this conundrum but uncertain about whether and how the cost of care should affect their recommendations.14 Although raising awareness of costs and providing tools to assess value may help to manage costs while maintaining high-quality care, some oncologists see this as being in conflict with their duty to individual patients.15 Recent American Society of Clinical Oncology Efforts Motivated by our responsibility to help oncologists deliver the highest-quality care to patients everywhere, the American Society of Clinical Oncology (ASCO) formed the Task Force on the Cost of Cancer Care in 2007. Its mission includes educating oncologists about the importance of discussing costs associated with recommended treatments, empowering patients to ask questions pertaining to the anticipated costs of their treatment options, identifying the drivers of the rising costs of cancer care, and ultimately developing policy positions that will help Americans move toward more equal access to the highest-quality care at the lowest cost.16 In 2012, through the work of the Task Force, ASCO responded to the Choosing Wisely Campaign of the American Board of Internal Medicine Foundation and identified specific instances of overuse in the delivery of cancer care. ASCO used a deliberative consensus process to identify five common clinical practices that are not supported by high-level evidence. A second list of five was developed using the same process and submitted to the Choosing Wisely Campaign in 2013. ASCO amplified the evidence basis for both top-five lists in two publications17,18 and is now developing measures to evaluate the use of these practices as part of its Quality Oncology Practice Initiative. These exercises have provided opportunities to develop a rigorous but flexible approach to assessing efficacy across diagnostic and treatment domains.


Journal ArticleDOI
TL;DR: A deep learning model is developed that combines a linear model that is fitted using l 1 regularization and a sequence of tanh layers to predict traffic flows and identifies spatio-temporal relations among predictors and other layers model nonlinear relations.
Abstract: We develop a deep learning model to predict traffic flows. The main contribution is development of an architecture that combines a linear model that is fitted using l 1 regularization and a sequence of tanh layers. The challenge of predicting traffic flows are the sharp nonlinearities due to transitions between free flow, breakdown, recovery and congestion. We show that deep learning architectures can capture these nonlinear spatio-temporal effects. The first layer identifies spatio-temporal relations among predictors and other layers model nonlinear relations. We illustrate our methodology on road sensor data from Interstate I-55 and predict traffic flows during two special events; a Chicago Bears football game and an extreme snowstorm event. Both cases have sharp traffic flow regime changes, occurring very suddenly, and we show how deep learning provides precise short term traffic flow predictions.

Journal ArticleDOI
TL;DR: A global health emergency has been declared by the World Health Organization as the 2019-nCoV outbreak spreads across the world, with confirmed patients in Canada as discussed by the authors, and patients infected with 2019-CoV are at risk for developing respiratory failure and requiring admission to critical care units.
Abstract: A global health emergency has been declared by the World Health Organization as the 2019-nCoV outbreak spreads across the world, with confirmed patients in Canada. Patients infected with 2019-nCoV are at risk for developing respiratory failure and requiring admission to critical care units. While providing optimal treatment for these patients, careful execution of infection control measures is necessary to prevent nosocomial transmission to other patients and to healthcare workers providing care. Although the exact mechanisms of transmission are currently unclear, human-to-human transmission can occur, and the risk of airborne spread during aerosol-generating medical procedures remains a concern in specific circumstances. This paper summarizes important considerations regarding patient screening, environmental controls, personal protective equipment, resuscitation measures (including intubation), and critical care unit operations planning as we prepare for the possibility of new imported cases or local outbreaks of 2019-nCoV. Although understanding of the 2019-nCoV virus is evolving, lessons learned from prior infectious disease challenges such as Severe Acute Respiratory Syndrome will hopefully improve our state of readiness regardless of the number of cases we eventually manage in Canada.

Journal ArticleDOI
TL;DR: The Multi-Source Weighted-Ensemble Precipitation (MSWEP) dataset as discussed by the authors is a global precipitation dataset for the period 1979-2015 with a 3-hourly temporal and 0.25° spatial resolution designed for hydrological modeling.
Abstract: . Current global precipitation (P) datasets do not take full advantage of the complementary nature of satellite and reanalysis data. Here, we present Multi-Source Weighted-Ensemble Precipitation (MSWEP) version 1.1, a global P dataset for the period 1979–2015 with a 3-hourly temporal and 0.25° spatial resolution, specifically designed for hydrological modeling. The design philosophy of MSWEP was to optimally merge the highest quality P data sources available as a function of timescale and location. The long-term mean of MSWEP was based on the CHPclim dataset but replaced with more accurate regional datasets where available. A correction for gauge under-catch and orographic effects was introduced by inferring catchment-average P from streamflow (Q) observations at 13 762 stations across the globe. The temporal variability of MSWEP was determined by weighted averaging of P anomalies from seven datasets; two based solely on interpolation of gauge observations (CPC Unified and GPCC), three on satellite remote sensing (CMORPH, GSMaP-MVK, and TMPA 3B42RT), and two on atmospheric model reanalysis (ERA-Interim and JRA-55). For each grid cell, the weight assigned to the gauge-based estimates was calculated from the gauge network density, while the weights assigned to the satellite- and reanalysis-based estimates were calculated from their comparative performance at the surrounding gauges. The quality of MSWEP was compared against four state-of-the-art gauge-adjusted P datasets (WFDEI-CRU, GPCP-1DD, TMPA 3B42, and CPC Unified) using independent P data from 125 FLUXNET tower stations around the globe. MSWEP obtained the highest daily correlation coefficient (R) among the five P datasets for 60.0 % of the stations and a median R of 0.67 vs. 0.44–0.59 for the other datasets. We further evaluated the performance of MSWEP using hydrological modeling for 9011 catchments ( http://www.gloh2o.org .

Book ChapterDOI
08 Sep 2018
TL;DR: This work presents an end-to-end deep learning architecture for depth map inference from multi-view images that flexibly adapts arbitrary N-view inputs using a variance-based cost metric that maps multiple features into one cost feature.
Abstract: We present an end-to-end deep learning architecture for depth map inference from multi-view images. In the network, we first extract deep visual image features, and then build the 3D cost volume upon the reference camera frustum via the differentiable homography warping. Next, we apply 3D convolutions to regularize and regress the initial depth map, which is then refined with the reference image to generate the final output. Our framework flexibly adapts arbitrary N-view inputs using a variance-based cost metric that maps multiple features into one cost feature. The proposed MVSNet is demonstrated on the large-scale indoor DTU dataset. With simple post-processing, our method not only significantly outperforms previous state-of-the-arts, but also is several times faster in runtime. We also evaluate MVSNet on the complex outdoor Tanks and Temples dataset, where our method ranks first before April 18, 2018 without any fine-tuning, showing the strong generalization ability of MVSNet.

Journal ArticleDOI
08 Jun 2016-Pain
TL;DR: A revised grading system of possible, probable, and definite neuropathic pain from 2008 is presented with an adjusted order, better reflecting clinical practice, improvements in the specifications, and a word of caution that even the “definite” level of neuropathicPain does not always indicate causality.
Abstract: The redefinition of neuropathic pain as "pain arising as a direct consequence of a lesion or disease affecting the somatosensory system," which was suggested by the International Association for the Study of Pain (IASP) Special Interest Group on Neuropathic Pain (NeuPSIG) in 2008, has been widely accepted. In contrast, the proposed grading system of possible, probable, and definite neuropathic pain from 2008 has been used to a lesser extent. Here, we report a citation analysis of the original NeuPSIG grading paper of 2008, followed by an analysis of its use by an expert panel and recommendations for an improved grading system. As of February, 2015, 608 eligible articles in Scopus cited the paper, 414 of which cited the neuropathic pain definition. Of 220 clinical studies citing the paper, 56 had used the grading system. The percentage using the grading system increased from 5% in 2009 to 30% in 2014. Obstacles to a wider use of the grading system were identified, including (1) questions about the relative significance of confirmatory tests, (2) the role of screening tools, and (3) uncertainties about what is considered a neuroanatomically plausible pain distribution. Here, we present a revised grading system with an adjusted order, better reflecting clinical practice, improvements in the specifications, and a word of caution that even the "definite" level of neuropathic pain does not always indicate causality. In addition, we add a table illustrating the area of pain and sensory abnormalities in common neuropathic pain conditions and propose areas for further research.

Journal ArticleDOI
TL;DR: In stage II to III TNBC, addition of either carboplatin or bevacizumab to NACT increased pCR rates, but whether this will improve relapse-free or overall survival is unknown.
Abstract: Purpose One third of patients with triple-negative breast cancer (TNBC) achieve pathologic complete response (pCR) with standard neoadjuvant chemotherapy (NACT). CALGB 40603 (Alliance), a 2 × 2 factorial, open-label, randomized phase II trial, evaluated the impact of adding carboplatin and/or bevacizumab. Patients and Methods Patients (N = 443) with stage II to III TNBC received paclitaxel 80 mg/m2 once per week (wP) for 12 weeks, followed by doxorubicin plus cyclophosphamide once every 2 weeks (ddAC) for four cycles, and were randomly assigned to concurrent carboplatin (area under curve 6) once every 3 weeks for four cycles and/or bevacizumab 10 mg/kg once every 2 weeks for nine cycles. Effects of adding these agents on pCR breast (ypT0/is), pCR breast/axilla (ypT0/isN0), treatment delivery, and toxicities were analyzed. Results Patients assigned to either carboplatin or bevacizumab were less likely to complete wP and ddAC without skipped doses, dose modification, or early discontinuation resulting from ...

Journal ArticleDOI
TL;DR: The applications of clustering in some fields like image segmentation, object and character recognition and data mining are highlighted and the approaches used in these methods are discussed with their respective states of art and applicability.

Proceedings ArticleDOI
15 Jun 2019
TL;DR: CBDNet as discussed by the authors proposes to train a convolutional blind denoising network with more realistic noise model and real-world clean image pairs to improve the generalization ability of deep CNN denoisers.
Abstract: While deep convolutional neural networks (CNNs) have achieved impressive success in image denoising with additive white Gaussian noise (AWGN), their performance remains limited on real-world noisy photographs. The main reason is that their learned models are easy to overfit on the simplified AWGN model which deviates severely from the complicated real-world noise model. In order to improve the generalization ability of deep CNN denoisers, we suggest training a convolutional blind denoising network (CBDNet) with more realistic noise model and real-world noisy-clean image pairs. On the one hand, both signal-dependent noise and in-camera signal processing pipeline is considered to synthesize realistic noisy images. On the other hand, real-world noisy photographs and their nearly noise-free counterparts are also included to train our CBDNet. To further provide an interactive strategy to rectify denoising result conveniently, a noise estimation subnetwork with asymmetric learning to suppress under-estimation of noise level is embedded into CBDNet. Extensive experimental results on three datasets of real-world noisy pho- tographs clearly demonstrate the superior performance of CBDNet over state-of-the-arts in terms of quantitative met- rics and visual quality. The code has been made available at https://github.com/GuoShi28/CBDNet.

Journal ArticleDOI
30 Jul 2015-Nature
TL;DR: Using low-coverage whole-genome sequencing of 5,303 Chinese women with recurrent MDD selected to reduce phenotypic heterogeneity, and 5,337 controls screened to exclude MDD, two loci contributing to risk of MDD on chromosome 10 are identified: one near the SIRT1 gene and the other in an intron of the LHPP gene.
Abstract: Genomic analysis of 5,303 Chinese women with recurrent major depressive disorder (MDD) enables the identification and replication of two genome-wide significant loci contributing to risk of MDD on chromosome 10: one near the SIRT1 gene; the other in an intron of the LHPP gene.

Journal ArticleDOI
TL;DR: In this article, the authors present an overview of available machine learning techniques and structuring this rather complicated area, and a special focus is laid on the potential benefit and examples of successful applications in a manufacturing environment.
Abstract: The nature of manufacturing systems faces ever more complex, dynamic and at times even chaotic behaviors. In order to being able to satisfy the demand for high-quality products in an efficient manner, it is essential to utilize all means available. One area, which saw fast pace developments in terms of not only promising results but also usability, is machine learning. Promising an answer to many of the old and new challenges of manufacturing, machine learning is widely discussed by researchers and practitioners alike. However, the field is very broad and even confusing which presents a challenge and a barrier hindering wide application. Here, this paper contributes in presenting an overview of available machine learning techniques and structuring this rather complicated area. A special focus is laid on the potential benefit, and examples of successful applications in a manufacturing environment.

Journal ArticleDOI
01 Jun 2021-JAMA
TL;DR: In this article, the antibody response following the second dose of SARS-CoV2 mRNA vaccine in recipients of solid organ transplants was measured using a follow-up study.
Abstract: This follow-up study measures the antibody response following the second dose of SARS-CoV-2 mRNA vaccine in recipients of solid organ transplants.

Journal ArticleDOI
04 Sep 2015-Science
TL;DR: Yeast is engineered to produce the opioid compounds thebaine and hydrocodone, starting from sugar, a proof of principle, and major hurdles remain before optimization and scale-up could be achieved.
Abstract: Opioids are the primary drugs used in Western medicine for pain management and palliative care. Farming of opium poppies remains the sole source of these essential medicines, despite diverse market demands and uncertainty in crop yields due to weather, climate change, and pests. We engineered yeast to produce the selected opioid compounds thebaine and hydrocodone starting from sugar. All work was conducted in a laboratory that is permitted and secured for work with controlled substances. We combined enzyme discovery, enzyme engineering, and pathway and strain optimization to realize full opiate biosynthesis in yeast. The resulting opioid biosynthesis strains required the expression of 21 (thebaine) and 23 (hydrocodone) enzyme activities from plants, mammals, bacteria, and yeast itself. This is a proof of principle, and major hurdles remain before optimization and scale-up could be achieved. Open discussions of options for governing this technology are also needed in order to responsibly realize alternative supplies for these medically relevant compounds.