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Coronary heart disease (CHD) is a common noncommunicable disease. This study aimed to explore the association between long-term sulfur dioxide (SO2) exposure and CHD morbidity. We conducted a retrospective cohort study in four northern Chinese cities (Tianjin, Taiyuan, Shenyang and Rizhao). This retrospective cohort study was based on the household registration information of each community in 1998 as the baseline to establish a cohort, and the follow-up of four cities was completed from September to December 2009. A questionnaire was used to collect demographic and sociological information, lifestyle, eating habits and disease history. Air pollutant data in four cities were obtained through the China National Environmental Monitoring Center. The 24-h daily average concentration was used to calculate the annual average concentration, we used annual average concentration as time-varying exposures. A Cox proportional hazard regression model was used to analyze the association between SO2 and the risk of coronary heart disease; the potential effect modifiers were analyzed by stratification. Sensitivity analysis was used to verify the results at the same time. A total of 36,379 participants were analyzed for coronary heart disease in this study, and 1012 developed coronary heart disease; the cumulative incidence rate was 2.78%. After adjusting for age, sex, BMI, lifestyle and other related confounding factors, the risk of coronary heart disease increased by 2.5% for every 10 μg/m3 increase in SO2 concentration. Tea drinkers and people who eat fewer vegetables and people with lower level of education were more sensitive to SO2 exposure.

Numerous cohort studies have reported the association of long-term exposure to particulate matter

In this paper, we extend the classic MaxOut strategy, originally designed for Multiple Layer Preceptors (MLPs), into CO nvolutional M ax O ut (COMO) — a new strategy making deep convolutional neural networks wider with parameter efficiency. Compared to the existing solutions, such as ResNeXt for ResNet or Inception for VGG-alikes, COMO works well on both linear architectures and the ones with skipped connections and residual blocks. More specifically, COMO adopts a novel split-transform-merge paradigm that extends the layers with spatial resolution reduction into multiple parallel splits. For the layer with COMO, each split passes the input feature maps through a 4D convolution operator with independent batch normalization operators for transformation, then merge into the aggregated output of the original sizes through max-pooling . Such a strategy is expected to tackle the potential classification accuracy degradation due to the spatial resolution reduction, by incorporating the multiple splits and max-pooling-based feature selection. Our experiment using a wide range of deep architectures shows that COMO can significantly improve the classification accuracy of ResNet/VGG-alike networks based on a large number of benchmark datasets. COMO further outperforms the existing solutions, e.g., Inceptions, ResNeXts, SE-ResNet, and Xception, that make networks wider, and it dominates in the comparison of accuracy versus parameter sizes.

Several literatures have examined the risk of chronic respiratory diseases in association with short-term ambient PM

Several studies have researched the short-term effect of sulfur dioxide (SO2) exposure on hypertension. However, no evidence has provided the relationship between long-term high pollution exposure of SO2 and morbidity of hypertension in cohort studies in China. This retrospective cohort study aimed to evaluate this association. We used Cox proportional hazards regression models to examine the hazard ratios (HR) for hypertension risks from 1998 to 2009 associated with accumulative exposure of air SO2 among adults in northern China. Annual average concentrations of sulfur dioxide (SO2) were obtained from 15 local environmental monitoring centers. Hypertension was identified according to self-reported diagnostic time and treatment for hypertension with anti-hypertensive medication. Among 37,386 participants, 2619 new cases of hypertension were identified during 426,334 person-years. In the fully adjusted model, HR and 95% confidence interval (CI) of hypertension incidence for each 10 μg/m3 increase in SO2 were 1.176 (1.163 and 1.189). Results from stratified analyses suggested that effects of SO2 on hypertension morbidity were more pronounced in participants

Evidence is still limited for the role of long-term PM2.5 exposure in cerebrovascular diseases among residents in high pollution regions. The study is aimed to investigate the long-term effects of PM2.5 exposure on stroke mortality, and further explore the effect modification of temperature variation on the PM2.5-mortality association in northern China. Based on a cohort data with an average follow-up of 9.8 years among 38,435 urban adults, high-resolution estimates of PM2.5 derived from a satellite-based model were assigned to each participant. A Cox regression model with time-varying exposures and strata of geographic regions was employed to assess the risks of stroke mortality associated with PM2.5, after adjusting for individual risk factors. The cross-product term of PM2.5 exposure and annual temperature range was further added into the regression model to test whether the long-term temperature variation would modify the association of PM2.5 with stroke mortality. Among the study participants, the annual mean level of PM2.5 concentration was 66.3 μg/m3 ranging from 39.0 μg/m3 to 100.6 μg/m3. For each 10 μg/m3 increment in PM2.5, the hazard ratio (HR) was 1.31 (95% CI: 1.04–1.65) for stroke mortality after multivariable adjustment. In addition, the HRs of PM2.5 decreased gradually as the increase of annual temperature range with the HRs of 1.95 (95% CI: 1.36–2.81), 1.53 (95% CI: 1.06–2.22), and 1.11 (95% CI: 0.75–1.63) in the low, middle, and high group of annual temperature range, respectively. The findings provided further evidence of long-term PM2.5 exposure on stroke mortality in high-exposure settings such as northern China, and also highlighted the view that assessing the adverse health effects of air pollution might not ignore the role of temperature variations in the context of climate change.

Federated machine learning systems have been widely used to facilitate the joint data analytics across the distributed datasets owned by the different parties that do not trust each others. In this paper, we proposed a novel Gradient Boosting Machines (GBM) framework SecureGBM built-up with a multi-party computation model based on semi-homomorphic encryption, where every involved party can jointly obtain a shared Gradient Boosting machines model while protecting their own data from the potential privacy leakage and inferential identification. More specific, our work focused on a specific "dual--party" secure learning scenario based on two parties -- both party own an unique view (i.e., attributes or features) to the sample group of samples while only one party owns the labels. In such scenario, feature and label data are not allowed to share with others. To achieve the above goal, we firstly extent -- LightGBM -- a well known implementation of tree-based GBM through covering its key operations for training and inference with SEAL homomorphic encryption schemes. However, the performance of such re-implementation is significantly bottle-necked by the explosive inflation of the communication payloads, based on ciphertexts subject to the increasing length of plaintexts. In this way, we then proposed to use stochastic approximation techniques to reduced the communication payloads while accelerating the overall training procedure in a statistical manner. Our experiments using the real-world data showed that SecureGBM can well secure the communication and computation of LightGBM training and inference procedures for the both parties while only losing less than 3% AUC, using the same number of iterations for gradient boosting, on a wide range of benchmark datasets., The first two authors contributed equally to the manuscript. The paper has been accepted for publication in IEEE BigData 2019

Background Ambient air pollution has recently been related to type 2 diabetes mellitus (T2DM), a disease that has caused an economic and health burden worldwide. Evidence of an association between air pollution and T2DM was reported in the United States and Europe. However, few studies have focused on the association with high levels of air pollutants in a developing country. Objectives We conducted a 12-year cohort study to assess the incidence and mortality of T2DM associated with long-term exposure to PM10, SO2, and NO2. Methods A retrospective cohort with participants from four cities in northern China was conducted to assess mortality and incidence of T2DM from 1998 to 2009. Incidence of T2DM was self-reported, and incident intake of an antidiabetic drug or injection of insulin simultaneously and mortality of T2DM was obtained from a family member and double checked against death certificates provided from the local center for disease control and prevention. Individual pollution exposures were the mean concentrations of pollutants estimated from the local environmental monitoring centers over the survival years. Hazard ratios (HRs) were estimated using Cox regression models after adjusting for potential confounding factors. Results A total of 39 054 participants were recruited into the mortality cohort, among which 59 subjects died from T2DM; 38 529 participants were analyzed in the incidence cohort, and 1213 developed new cases of T2DM. For each 10 μg/m3 increase in PM10, SO2, and NO2, the adjusted HRs and 95% confidence interval (CI) for diabetic incidence were 1.831 (1.778, 1.886), 1.287 (1.256, 1.318), and 1.472 (1.419, 1.528), respectively. Similar results can be observed in the analysis of diabetic mortality with HRs (95% CI) up to 2.260 (1.732, 2.950), 1.130 (1.042, 1.225), and 1.525 (1.280, 1.816), respectively. Conclusions Our results suggested that long-term exposure to high levels of PM10, SO2, and NO2 increase risk of incident and mortality of T2DM in China.

Due to the ever-escalating power consumption, a significant proportion of the future many-core chips is mandatory to be switched off to meet the power budgets. This trend has brought up a paradigm shift from conventional low-power to power budgeting designs, where performance optimization needs to be performed under a tight power budget constraint. There are two key issues to be considered when moving this new design paradigm forward. Firstly, with per-core frequency scaling, the number of frequency combinations of the cores grows exponentially. As more cores are integrated onto a chip, it becomes more challenging to achieve the optimal performance over a given power budget. Secondly, the power budgets of many-core system might undergo a rapid fluctuation. Consequently, the power budgeting scheme needs to be prompt to make appropriate changes to track such power budget variation. This paper is aiming at resolving the problem of optimizing overall performance over a power budget using frequency scaling technique. To solve the problem efficiently at runtime, we propose a parallel dynamic programming network, in which the Pareto-optimal solutions can be obtained using linear time complexity. Experimental results have confirmed that the proposed approach can reduce the execution time by 45% when compared to other existing methods. The runtime overhead and hardware cost of the proposed approach are reasonably small, such as the average area and power consumption are less than 1% of the whole network-on-chip. This paper demonstrates an effective formulation for delivering Pareto-optimal solutions for power budgeting in future many-core systems.

Power budgeting is an essential aspect of networks-on-chip (NoC) to meet the power constraint for on-chip communications while assuring the best possible overall system performance. For simplicity and ease of implementation, existing NoC power budgeting schemes treat all the individual routers uniformly when allocating power to them. However, such homogeneous power budgeting schemes ignore the fact that the workloads of different NoC routers may vary significantly, and thus may provide excess power to routers with low workloads, whereas insufficient power to those with high workloads. In this paper, we formulate the NoC power budgeting problem in order to optimize the network performance over a power budget through per-router frequency scaling. We take into account of heterogeneous workloads across different routers as imposed by variations in traffic. Correspondingly, we propose a fine-grained solution using an agile algorithm with low time complexity. Frequency of each router is set individually according to its contribution to the average network latency while meeting the power budget. Experimental results have confirmed that with fairly low runtime and hardware overhead, the proposed scheme can help save up to $50$ percent application execution time when compared with the latest proposed methods.

The health effects of metals and benzene exposure have been extensively investigated; however, information on the impact of chronic environmental metal and benzene exposure on human urinary metabolome is limited. In this study, a total of 566 participants, including 352 elderly and 214 children, were split into the “exposed” and “control” groups. The urine samples of all the participants were collected and stored at − 80 °C until analysis. The urinary levels of 17 metals and S-phenylmercapturic acid (S-PMA) were determined by the ICP–MS and LC–MS/MS methods to comprehensively assess the personal metal and benzene exposure levels, respectively. Then, the individual levels of metal and benzene exposure were correlated to the metabolic consequences of ambient pollutant exposure, which were previously observed in our metabolomics study. As a result, multiple metals, including Cd, Co, Cr, Cu, Fe, Hg, Li, Mo, Ni, Pb, Se, and Zn, exhibited a significant linear dose–dependent association with one or more urinary metabolites, including two amino acids (pyroglutamic acid and 3-methylhistidine), three organic acids (azelaic acid, decenedioic acid, and hydroxytetradecanedioic acid), ten medium-chainacylcarnitines (heptenedioylcarnitine, octenedioylcarnitine, nonenedioylcarnitine, decenedioylglucuronide, 3-hydroxydecanoylcarnitine, dodecanedioylcarnitine, nonanoylcarnitine, decadienylcarnitine, hydroxydodecenoylcarnitine, dodecadienylcarnitine, and dodecenoylcarnitine), and one glucuronide conjugate (decenedioylglucuronide). These observations indicate that the increased environmental metal exposure has caused various oxidative stress-related effects, including the depletion of antioxidants, accelerated muscle proteolysis, elevated activity of UGTs, increased lipid peroxidation, and the disorder of mitochondrial lipid metabolism among exposed children and the elderly. The current study provides new insights into the biological effects induced by metal exposure in the environment.

Clostridium difficile multilocus sequence type 37 (ST37), which mainly corresponds to ribotype 017, has been a dominant genotype circulating in China. In this study, we report the use of matrix-assisted laser desorption ionization–time of flight mass spectrometry (MALDI-TOF MS) to analyze and characterize 204 C. difficile clinical isolates, including 49 ST37 and 155 non-ST37 isolates collected in China and other countries. The distributions of two major protein peaks ( m/z 3,242 and 3,286) were significantly different between ST37 and non-ST37 prototype strains and clinical isolates. This difference was reproducible when analysis was performed on different colonies in different runs. This finding was repeated and confirmed by both bioMérieux Vitek MS and Bruker Microflex LT systems on isolates recovered from a variety of geographic regions worldwide. The combination of the two peaks was present in 47 of 49 ST37 isolates, resulting in a sensitivity of 95.9%. In contrast, the peak combination was absent in 153 of 155 non-ST37 isolates, resulting in a specificity of 98.7%. Our results suggest that MALDI-TOF MS is a rapid and reliable tool to identify C. difficile genotype ST37. Work is in progress to characterize the two molecules having peaks at m/z 3,242 and 3,286, which appear to be specific to C. difficile genotype ST37.

Clostridium difficile multilocus sequence type 37 (ST37), which mainly corresponds to ribotype 017, has been a dominant genotype circulating in China. In this study, we report the use of matrix-assisted laser desorption ionization–time of flight mass spectrometry (MALDI-TOF MS) to analyze and characterize 204 C. difficile clinical isolates, including 49 ST37 and 155 non-ST37 isolates collected in China and other countries. The distributions of two major protein peaks (m/z 3,242 and 3,286) were significantly different between ST37 and non-ST37 prototype strains and clinical isolates. This difference was reproducible when analysis was performed on different colonies in different runs. This finding was repeated and confirmed by both bioMérieux Vitek MS and Bruker Microflex LT systems on isolates recovered from a variety of geographic regions worldwide. The combination of the two peaks was present in 47 of 49 ST37 isolates, resulting in a sensitivity of 95.9%. In contrast, the peak combination was absent in 153 of 155 non-ST37 isolates, resulting in a specificity of 98.7%. Our results suggest that MALDI-TOF MS is a rapid and reliable tool to identify C. difficile genotype ST37. Work is in progress to characterize the two molecules having peaks at m/z 3,242 and 3,286, which appear to be specific to C. difficile genotype ST37.

The toxicities of polycyclic aromatic hydrocarbons (PAHs) have been extensively explored due to their carcinogenic and mutagenic potency; however, little is known about the metabolic responses to chronic environmental PAH exposure among the general population. In the present study, 566 healthy volunteers were dichotomized into exposed and control groups to investigate PAH-induced perturbations in the metabolic profiles. Nine urine PAH metabolites were measured by a sensitive LC-MS/MS method to comprehensively evaluate the PAH exposure level of each individual, and the metabolic profiles were characterized via a LC-MS-based metabolomic approach. PAH exposure was correlated to its metabolic outcomes by linear and logistic regression analyses. Metabolites related to amino acid, purine, lipid, and glucuronic acid metabolism were significantly changed in the exposed group. 1-Hydroxyphenanthrene and dodecadienylcarnitine have potential as sensitive and reliable biomarkers for PAH exposure and its metabolic outcomes, respectively, in the general population. These findings generally support the hypothesis that environmental PAH exposure causes oxidative stress-related effects in humans. The current study provides new insight into the early molecular events induced by PAH exposure in the actual environment.

Objective The aim of this study was to establish the concentration–response relationships between the levels of SO2 and PM10 and the daily numbers of pediatric outpatient visits and to estimate ben...

Objective: To explore and descript the association of air pollution with outpatient visits in a hospital Taiyuan during 2013 haze episode. Methods: Data of outpatient visits were collected in a com...

OBJECTIVE: To determine the shape of concentration-response function for air pollution related children’s hospital admissions across a high and wide range of air pollution in Yangquan, China (PM10:...