Showing papers by "Shanghai Jiao Tong University published in 2017"

The “Golden Age” of Probiotics: A Systematic Review and Meta-Analysis of Randomized and Observational Studies in Preterm Infants

Abstract: Background: Over the last few years, probiotics have been one of the most studied interventions in neonatal medicine. Objectives:

Global, regional, and national burden of neurological disorders, 1990-2016: A systematic analysis for the Global Burden of Disease Study 2016

Abstract: Summary Background Comparable data on the global and country-specific burden of neurological disorders and their trends are crucial for health-care planning and resource allocation. The Global Burden of Diseases, Injuries, and Risk Factors (GBD) Study provides such information but does not routinely aggregate results that are of interest to clinicians specialising in neurological conditions. In this systematic analysis, we quantified the global disease burden due to neurological disorders in 2015 and its relationship with country development level. Methods We estimated global and country-specific prevalence, mortality, disability-adjusted life-years (DALYs), years of life lost (YLLs), and years lived with disability (YLDs) for various neurological disorders that in the GBD classification have been previously spread across multiple disease groupings. The more inclusive grouping of neurological disorders included stroke, meningitis, encephalitis, tetanus, Alzheimer's disease and other dementias, Parkinson's disease, epilepsy, multiple sclerosis, motor neuron disease, migraine, tension-type headache, medication overuse headache, brain and nervous system cancers, and a residual category of other neurological disorders. We also analysed results based on the Socio-demographic Index (SDI), a compound measure of income per capita, education, and fertility, to identify patterns associated with development and how countries fare against expected outcomes relative to their level of development. Findings Neurological disorders ranked as the leading cause group of DALYs in 2015 (250·7 [95% uncertainty interval (UI) 229·1 to 274·7] million, comprising 10·2% of global DALYs) and the second-leading cause group of deaths (9·4 [9·1 to 9·7] million], comprising 16·8% of global deaths). The most prevalent neurological disorders were tension-type headache (1505·9 [UI 1337·3 to 1681·6 million cases]), migraine (958·8 [872·1 to 1055·6] million), medication overuse headache (58·5 [50·8 to 67·4 million]), and Alzheimer's disease and other dementias (46·0 [40·2 to 52·7 million]). Between 1990 and 2015, the number of deaths from neurological disorders increased by 36·7%, and the number of DALYs by 7·4%. These increases occurred despite decreases in age-standardised rates of death and DALYs of 26·1% and 29·7%, respectively; stroke and communicable neurological disorders were responsible for most of these decreases. Communicable neurological disorders were the largest cause of DALYs in countries with low SDI. Stroke rates were highest at middle levels of SDI and lowest at the highest SDI. Most of the changes in DALY rates of neurological disorders with development were driven by changes in YLLs. Interpretation Neurological disorders are an important cause of disability and death worldwide. Globally, the burden of neurological disorders has increased substantially over the past 25 years because of expanding population numbers and ageing, despite substantial decreases in mortality rates from stroke and communicable neurological disorders. The number of patients who will need care by clinicians with expertise in neurological conditions will continue to grow in coming decades. Policy makers and health-care providers should be aware of these trends to provide adequate services. Funding Bill & Melinda Gates Foundation.

Reversed graph embedding resolves complex single-cell trajectories.

Abstract: Single-cell trajectories can unveil how gene regulation governs cell fate decisions. However, learning the structure of complex trajectories with multiple branches remains a challenging computational problem. We present Monocle 2, an algorithm that uses reversed graph embedding to describe multiple fate decisions in a fully unsupervised manner. We applied Monocle 2 to two studies of blood development and found that mutations in the genes encoding key lineage transcription factors divert cells to alternative fates.

Heat Shock Protein A12B Protects Vascular Endothelial Cells Against Sepsis-Induced Acute Lung Injury in Mice

Abstract: Background: Pulmonary endothelial injury is a critical process in the pathogenesis of acute lung injury (ALI) during sepsis Heat shock protein A12B (HSPA12B) is

Seqgan: sequence generative adversarial nets with policy gradient

Abstract: As a new way of training generative models, Generative Adversarial Net (GAN) that uses a discriminative model to guide the training of the generative model has enjoyed considerable success in generating real-valued data. However, it has limitations when the goal is for generating sequences of discrete tokens. A major reason lies in that the discrete outputs from the generative model make it difficult to pass the gradient update from the discriminative model to the generative model. Also, the discriminative model can only assess a complete sequence, while for a partially generated sequence, it is nontrivial to balance its current score and the future one once the entire sequence has been generated. In this paper, we propose a sequence generation framework, called SeqGAN, to solve the problems. Modeling the data generator as a stochastic policy in reinforcement learning (RL), SeqGAN bypasses the generator differentiation problem by directly performing gradient policy update. The RL reward signal comes from the GAN discriminator judged on a complete sequence, and is passed back to the intermediate state-action steps using Monte Carlo search. Extensive experiments on synthetic data and real-world tasks demonstrate significant improvements over strong baselines.

ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression

Abstract: We propose an efficient and unified framework, namely ThiNet, to simultaneously accelerate and compress CNN models in both training and inference stages. We focus on the filter level pruning, i.e., the whole filter would be discarded if it is less important. Our method does not change the original network structure, thus it can be perfectly supported by any off-the-shelf deep learning libraries. We formally establish filter pruning as an optimization problem, and reveal that we need to prune filters based on statistics information computed from its next layer, not the current layer, which differentiates ThiNet from existing methods. Experimental results demonstrate the effectiveness of this strategy, which has advanced the state-of-the-art. We also show the performance of ThiNet on ILSVRC-12 benchmark. ThiNet achieves 3.31 x FLOPs reduction and 16.63× compression on VGG-16, with only 0.52% top-5 accuracy drop. Similar experiments with ResNet-50 reveal that even for a compact network, ThiNet can also reduce more than half of the parameters and FLOPs, at the cost of roughly 1% top-5 accuracy drop. Moreover, the original VGG-16 model can be further pruned into a very small model with only 5.05MB model size, preserving AlexNet level accuracy but showing much stronger generalization ability.

Traumatic brain injury: integrated approaches to improve prevention, clinical care, and research

Abstract: Additional co-authors: Endre Czeiter, Marek Czosnyka, Ramon Diaz-Arrastia, Jens P Dreier, Ann-Christine Duhaime, Ari Ercole, Thomas A van Essen, Valery L Feigin, Guoyi Gao, Joseph Giacino, Laura E Gonzalez-Lara, Russell L Gruen, Deepak Gupta, Jed A Hartings, Sean Hill, Ji-yao Jiang, Naomi Ketharanathan, Erwin J O Kompanje, Linda Lanyon, Steven Laureys, Fiona Lecky, Harvey Levin, Hester F Lingsma, Marc Maegele, Marek Majdan, Geoffrey Manley, Jill Marsteller, Luciana Mascia, Charles McFadyen, Stefania Mondello, Virginia Newcombe, Aarno Palotie, Paul M Parizel, Wilco Peul, James Piercy, Suzanne Polinder, Louis Puybasset, Todd E Rasmussen, Rolf Rossaint, Peter Smielewski, Jeannette Soderberg, Simon J Stanworth, Murray B Stein, Nicole von Steinbuchel, William Stewart, Ewout W Steyerberg, Nino Stocchetti, Anneliese Synnot, Braden Te Ao, Olli Tenovuo, Alice Theadom, Dick Tibboel, Walter Videtta, Kevin K W Wang, W Huw Williams, Kristine Yaffe for the InTBIR Participants and Investigators

International Consensus on Use of Continuous Glucose Monitoring

Abstract: Measurement of glycated hemoglobin (HbA1c) has been the traditional method for assessing glycemic control. However, it does not reflect intra- and interday glycemic excursions that may lead to acute events (such as hypoglycemia) or postprandial hyperglycemia, which have been linked to both microvascular and macrovascular complications. Continuous glucose monitoring (CGM), either from real-time use (rtCGM) or intermittently viewed (iCGM), addresses many of the limitations inherent in HbA1c testing and self-monitoring of blood glucose. Although both provide the means to move beyond the HbA1c measurement as the sole marker of glycemic control, standardized metrics for analyzing CGM data are lacking. Moreover, clear criteria for matching people with diabetes to the most appropriate glucose monitoring methodologies, as well as standardized advice about how best to use the new information they provide, have yet to be established. In February 2017, the Advanced Technologies & Treatments for Diabetes (ATTD) Congress convened an international panel of physicians, researchers, and individuals with diabetes who are expert in CGM technologies to address these issues. This article summarizes the ATTD consensus recommendations and represents the current understanding of how CGM results can affect outcomes.

RMPE: Regional Multi-person Pose Estimation

Abstract: Multi-person pose estimation in the wild is challenging. Although state-of-the-art human detectors have demonstrated good performance, small errors in localization and recognition are inevitable. These errors can cause failures for a single-person pose estimator (SPPE), especially for methods that solely depend on human detection results. In this paper, we propose a novel regional multi-person pose estimation (RMPE) framework to facilitate pose estimation in the presence of inaccurate human bounding boxes. Our framework consists of three components: Symmetric Spatial Transformer Network (SSTN), Parametric Pose Non-Maximum-Suppression (NMS), and Pose-Guided Proposals Generator (PGPG). Our method is able to handle inaccurate bounding boxes and redundant detections, allowing it to achieve 76:7 mAP on the MPII (multi person) dataset[3]. Our model and source codes are made publicly available.

Dark Matter Results from 54-Ton-Day Exposure of PandaX-II Experiment

Abstract: We report a new search for weakly interacting massive particles (WIMPs) using the combined low background data sets acquired in 2016 and 2017 from the PandaX-II experiment in China. The latest data set contains a new exposure of 77.1 live days, with the background reduced to a level of 0.8×10^{-3} evt/kg/day, improved by a factor of 2.5 in comparison to the previous run in 2016. No excess events are found above the expected background. With a total exposure of 5.4×10^{4} kg day, the most stringent upper limit on the spin-independent WIMP-nucleon cross section is set for a WIMP with mass larger than 100 GeV/c^{2}, with the lowest 90% C.L. exclusion at 8.6×10^{-47} cm^{2} at 40 GeV/c^{2}.

Diverse Applications of Nanomedicine

Abstract: The design and use of materials in the nanoscale size range for addressing medical and health-related issues continues to receive increasing interest. Research in nanomedicine spans a multitude of areas, including drug delivery, vaccine development, antibacterial, diagnosis and imaging tools, wearable devices, implants, high-throughput screening platforms, etc. using biological, nonbiological, biomimetic, or hybrid materials. Many of these developments are starting to be translated into viable clinical products. Here, we provide an overview of recent developments in nanomedicine and highlight the current challenges and upcoming opportunities for the field and translation to the clinic.

Gut microbiome and serum metabolome alterations in obesity and after weight-loss intervention

Abstract: Emerging evidence has linked the gut microbiome to human obesity. We performed a metagenome-wide association study and serum metabolomics profiling in a cohort of lean and obese, young, Chinese individuals. We identified obesity-associated gut microbial species linked to changes in circulating metabolites. The abundance of Bacteroides thetaiotaomicron, a glutamate-fermenting commensal, was markedly decreased in obese individuals and was inversely correlated with serum glutamate concentration. Consistently, gavage with B. thetaiotaomicron reduced plasma glutamate concentration and alleviated diet-induced body-weight gain and adiposity in mice. Furthermore, weight-loss intervention by bariatric surgery partially reversed obesity-associated microbial and metabolic alterations in obese individuals, including the decreased abundance of B. thetaiotaomicron and the elevated serum glutamate concentration. Our findings identify previously unknown links between intestinal microbiota alterations, circulating amino acids and obesity, suggesting that it may be possible to intervene in obesity by targeting the gut microbiota.

The gut microbiome in atherosclerotic cardiovascular disease

Abstract: The gut microbiota has been linked to cardiovascular diseases. However, the composition and functional capacity of the gut microbiome in relation to cardiovascular diseases have not been systematically examined. Here, we perform a metagenome-wide association study on stools from 218 individuals with atherosclerotic cardiovascular disease (ACVD) and 187 healthy controls. The ACVD gut microbiome deviates from the healthy status by increased abundance of Enterobacteriaceae and Streptococcus spp. and, functionally, in the potential for metabolism or transport of several molecules important for cardiovascular health. Although drug treatment represents a confounding factor, ACVD status, and not current drug use, is the major distinguishing feature in this cohort. We identify common themes by comparison with gut microbiome data associated with other cardiometabolic diseases (obesity and type 2 diabetes), with liver cirrhosis, and rheumatoid arthritis. Our data represent a comprehensive resource for further investigations on the role of the gut microbiome in promoting or preventing ACVD as well as other related diseases. The gut microbiota may play a role in cardiovascular diseases. Here, the authors perform a metagenome-wide association study on stools from individuals with atherosclerotic cardiovascular disease and healthy controls, identifying microbial strains and functions associated with the disease.

RACE: Large-scale ReAding Comprehension Dataset From Examinations

Abstract: We present RACE, a new dataset for benchmark evaluation of methods in the reading comprehension task. Collected from the English exams for middle and high school Chinese students in the age range between 12 to 18, RACE consists of near 28,000 passages and near 100,000 questions generated by human experts (English instructors), and covers a variety of topics which are carefully designed for evaluating the students’ ability in understanding and reasoning. In particular, the proportion of questions that requires reasoning is much larger in RACE than that in other benchmark datasets for reading comprehension, and there is a significant gap between the performance of the state-of-the-art models (43%) and the ceiling human performance (95%). We hope this new dataset can serve as a valuable resource for research and evaluation in machine comprehension. The dataset is freely available at http://www.cs.cmu.edu/~glai1/data/race/and the code is available at https://github.com/qizhex/RACE_AR_baselines.

Postsynthetically Modified Covalent Organic Frameworks for Efficient and Effective Mercury Removal.

Abstract: A key challenge in environmental remediation is the design of adsorbents bearing an abundance of accessible chelating sites with high affinity, to achieve both rapid uptake and high capacity for the contaminants. Herein, we demonstrate how two-dimensional covalent organic frameworks (COFs) with well-defined mesopore structures display the right combination of properties to serve as a scaffold for decorating coordination sites to create ideal adsorbents. The proof-of-concept design is illustrated by modifying sulfur derivatives on a newly designed vinyl-functionalized mesoporous COF (COF-V) via thiol–ene “click” reaction. Representatively, the material (COF-S-SH) synthesized by treating COF-V with 1,2-ethanedithiol exhibits high efficiency in removing mercury from aqueous solutions and the air, affording Hg2+ and Hg0 capacities of 1350 and 863 mg g–1, respectively, surpassing all those of thiol and thioether functionalized materials reported thus far. More significantly, COF-S-SH demonstrates an ultrahigh ...

Osteoarthritis: toward a comprehensive understanding of pathological mechanism.

Abstract: Osteoarthritis (OA) is the most common degenerative joint disease and a major cause of pain and disability in adult individuals. The etiology of OA includes joint injury, obesity, aging, and heredity. However, the detailed molecular mechanisms of OA initiation and progression remain poorly understood and, currently, there are no interventions available to restore degraded cartilage or decelerate disease progression. The diathrodial joint is a complicated organ and its function is to bear weight, perform physical activity and exhibit a joint-specific range of motion during movement. During OA development, the entire joint organ is affected, including articular cartilage, subchondral bone, synovial tissue and meniscus. A full understanding of the pathological mechanism of OA development relies on the discovery of the interplaying mechanisms among different OA symptoms, including articular cartilage degradation, osteophyte formation, subchondral sclerosis and synovial hyperplasia, and the signaling pathway(s) controlling these pathological processes.

A comprehensive review of Pt electrocatalysts for the oxygen reduction reaction: Nanostructure, activity, mechanism and carbon support in PEM fuel cells

Abstract: The sluggish rate of the oxygen reduction reaction (ORR) in proton exchange membrane (PEM) fuel cells has been a major challenge. Significantly increasing efforts have been made worldwide towards a highly active ORR catalyst with high durability. Among all the catalysts exploited, Pt electrocatalysts are still the best in terms of a comprehensive evaluation. The investigation of Pt-based ORR catalysts is necessary for a practical ORR catalyst with low Pt content. This paper reviews recent progress in the studies of the mechanism, nanostructure, size effect and carbon supports of Pt electrocatalysts for the ORR. The importance of the size and structure control of Pt ORR catalysts, related with carbon support materials, is indicated. The potential methods for such control are discussed. The progress in theoretical studies and in situ catalyst characterization are also discussed. Finally, challenges and future developments are addressed.

A solvent- and vacuum-free route to large-area perovskite films for efficient solar modules

Abstract: A new deposition method for solar-panel polycrystalline perovskite thin films enables the production of large-area uniform films and avoids the need for common solvents or vacuum. Hybrid inorganic–organic perovskites are the most likely materials to replace silicon as absorber layers for solar cells, but issues with stability and scaling-up thin films mean that large-scale production is not yet possible. Liyuan Han and colleagues have developed a new deposition method for polycrystalline thin films that does not rely on spin- or drip-coating precursors in solvent or on vacuum deposition, and is therefore more amenable to larger-area films than are previous techniques. The procedure uses gaseous precursors and application of pressure, and has enabled a device with an area of 36 square centimetres to be certified at 12.1 per cent power conversion efficiency. Recent advances in the use of organic–inorganic hybrid perovskites for optoelectronics have been rapid, with reported power conversion efficiencies of up to 22 per cent for perovskite solar cells1,2,3,4,5,6,7,8,9. Improvements in stability have also enabled testing over a timescale of thousands of hours10,11,12,13,14. However, large-scale deployment of such cells will also require the ability to produce large-area, uniformly high-quality perovskite films. A key challenge is to overcome the substantial reduction in power conversion efficiency when a small device is scaled up: a reduction from over 20 per cent to about 10 per cent is found15,16,17,18,19,20,21 when a common aperture area of about 0.1 square centimetres is increased to more than 25 square centimetres. Here we report a new deposition route for methyl ammonium lead halide perovskite films that does not rely on use of a common solvent1,2,4,5,6,7,8,9,10,11,12,13,14,15 or vacuum3: rather, it relies on the rapid conversion of amine complex precursors to perovskite films, followed by a pressure application step. The deposited perovskite films were free of pin-holes and highly uniform. Importantly, the new deposition approach can be performed in air at low temperatures, facilitating fabrication of large-area perovskite devices. We reached a certified power conversion efficiency of 12.1 per cent with an aperture area of 36.1 square centimetres for a mesoporous TiO2-based perovskite solar module architecture.

Cellulose Nanofiber Supported 3D Interconnected BN Nanosheets for Epoxy Nanocomposites with Ultrahigh Thermal Management Capability

Abstract: Thermally conductive but electrically insulating polymer composites are highly desirable for thermal management applications because of their wide range of utilization, ease of processing, and low cost. However, the traditional approaches to thermally conductive polymer composites usually suffer from the low thermal conductivity enhancement and/or the deterioration of electrical insulating property. In this study, using cellulose nanofiber-supported 3D interconnected boron nitride nanosheet (3D–C–BNNS) aerogels, a novel method for highly thermally conductive but electrically insulating epoxy nanocomposites is reported. The nanocomposites exhibit thermal conductivity enhancement of about 1400% at a low BNNS loading of 9.6 vol%. In addition, the epoxy nanocomposites are still highly insulating, having a volume electrical resistivity of 1015 Ω cm. The strong potential application for thermal management has been demonstrated by the surface temperature variations of the nanocomposites with time during heating and cooling.

Undertreatment of people with major depressive disorder in 21 countries

Abstract: Background Major depressive disorder (MDD) is a leading cause of disability worldwide. Aims To examine the: (a) 12-month prevalence of DSM-IV MDD; (b) proportion aware that they have a problem needing treatment and who want care; (c) proportion of the latter receiving treatment; and (d) proportion of such treatment meeting minimal standards. Method Representative community household surveys from 21 countries as part of the World Health Organization World Mental Health Surveys. Results Of 51 547 respondents, 4.6% met 12-month criteria for DSM-IV MDD and of these 56.7% reported needing treatment. Among those who recognised their need for treatment, most (71.1%) made at least one visit to a service provider. Among those who received treatment, only 41.0% received treatment that met minimal standards. This resulted in only 16.5% of all individuals with 12-month MDD receiving minimally adequate treatment. Conclusions Only a minority of participants with MDD received minimally adequate treatment: 1 in 5 people in high-income and 1 in 27 in low-/lower-middle-income countries. Scaling up care for MDD requires fundamental transformations in community education and outreach, supply of treatment and quality of services.

Medical Image Synthesis with Context-Aware Generative Adversarial Networks

Abstract: Computed tomography (CT) is critical for various clinical applications, e.g., radiation treatment planning and also PET attenuation correction in MRI/PET scanner. However, CT exposes radiation during acquisition, which may cause side effects to patients. Compared to CT, magnetic resonance imaging (MRI) is much safer and does not involve radiations. Therefore, recently researchers are greatly motivated to estimate CT image from its corresponding MR image of the same subject for the case of radiation planning. In this paper, we propose a data-driven approach to address this challenging problem. Specifically, we train a fully convolutional network (FCN) to generate CT given the MR image. To better model the nonlinear mapping from MRI to CT and produce more realistic images, we propose to use the adversarial training strategy to train the FCN. Moreover, we propose an image-gradient-difference based loss function to alleviate the blurriness of the generated CT. We further apply Auto-Context Model (ACM) to implement a context-aware generative adversarial network. Experimental results show that our method is accurate and robust for predicting CT images from MR images, and also outperforms three state-of-the-art methods under comparison.

Collaborative filtering and deep learning based recommendation system for cold start items

Abstract: Recommender system is a specific type of intelligent systems, which exploits historical user ratings on items and/or auxiliary information to make recommendations on items to the users. It plays a critical role in a wide range of online shopping, e-commercial services and social networking applications. Collaborative filtering (CF) is the most popular approaches used for recommender systems, but it suffers from complete cold start (CCS) problem where no rating record are available and incomplete cold start (ICS) problem where only a small number of rating records are available for some new items or users in the system. In this paper, we propose two recommendation models to solve the CCS and ICS problems for new items, which are based on a framework of tightly coupled CF approach and deep learning neural network. A specific deep neural network SADE is used to extract the content features of the items. The state of the art CF model, timeSVD++, which models and utilizes temporal dynamics of user preferences and item features, is modified to take the content features into prediction of ratings for cold start items. Extensive experiments on a large Netflix rating dataset of movies are performed, which show that our proposed recommendation models largely outperform the baseline models for rating prediction of cold start items. The two proposed recommendation models are also evaluated and compared on ICS items, and a flexible scheme of model retraining and switching is proposed to deal with the transition of items from cold start to non-cold start status. The experiment results on Netflix movie recommendation show the tight coupling of CF approach and deep learning neural network is feasible and very effective for cold start item recommendation. The design is general and can be applied to many other recommender systems for online shopping and social networking applications. The solution of cold start item problem can largely improve user experience and trust of recommender systems, and effectively promote cold start items.

An Overview of Sustainable Green 5G Networks

Abstract: The stringent requirements of a 1000x increase in data traffic and 1 ms round-trip latency have made limiting the potentially tremendous ensuing energy consumption one of the most challenging problems for the design of the upcoming 5G networks. To enable sustainable 5G networks, new technologies have been proposed to improve the system energy efficiency, and alternative energy sources are introduced to reduce our dependence on traditional fossil fuels. In particular, various 5G techniques target the reduction of the energy consumption without sacrificing the quality of service. Meanwhile, energy harvesting technologies, which enable communication transceivers to harvest energy from various renewable resources and ambient radio frequency signals for communication, have drawn significant interest from both academia and industry. In this article, we provide an overview of the latest research on both green 5G techniques and energy harvesting for communication. In addition, some technical challenges and potential research topics for realizing sustainable green 5G networks are also identified.