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Molecular Dynamics (MD) is the simulation of the dynamic behavior of atoms and molecules. As the most popular software for molecular dynamics, GROMACS cannot work on large-scale data because of limit computing resources. In this paper, we propose a CPU and Intel® Xeon Phi Many Integrated Core (MIC) collaborated parallel framework to accelerate GROMACS using the offload mode on a MIC coprocessor, with which the performance of GROMACS is improved significantly, especially with the utility of Tianhe-2 supercomputer. Furthermore, we optimize GROMACS so that it can run on both the CPU and MIC at the same time. In addition, we accelerate multi-node GROMACS so that it can be used in practice. Benchmarking on real data, our accelerated GROMACS performs very well and reduces computation time significantly. Source code: https://github.com/tianhe2/gromacs-mic.

Novel sequence motifs detection is becoming increasingly essential in computational biology. However, the high computational cost greatly constrains the efficiency of most motif discovery algorithms. In this paper, we accelerate MEME algorithm targeted on Intel Many Integrated Core (MIC) Architecture and present a parallel implementation of MEME called MIC-MEME base on hybrid CPU/MIC computing framework. Our method focuses on parallelizing the starting point searching method and improving iteration updating strategy of the algorithm. MIC-MEME has achieved significant speedups of 26.6 for ZOOPS model and 30.2 for OOPS model on average for the overall runtime when benchmarked on the experimental platform with two Xeon Phi 3120 coprocessors. Furthermore, MIC-MEME has been compared with state-of-arts methods and it shows good scalability with respect to dataset size and the number of MICs. Source code: https://github.com/hkwkevin28/MIC-MEME .

Background The predictive value of adiposity indices and the newly developed index for cardiometabolic risk factors and cardiovascular diseases ( CVD s) remains unclear in the Chinese population. This study aimed to compare the predictive value of A Body Shape Index with other 5 conventional obesity‐related anthropometric indices (body mass index, waist circumference, hip circumference, waist‐to‐hip ratio, waist‐to‐height ratio) in Chinese population. Methods and Results A total of 44 048 participants in the study were derived from the baseline data of the PURE ‐China (Prospective Urban and Rural Epidemiology) study in China. All participants’ anthropometric parameters, CVD s, and risk factors (dyslipidemia, abnormal blood pressure, and hyperglycemia) were collected by standard procedures. Multivariable logistic regression models and receiver operator characteristic curve analysis were used to evaluate the predictive values of obesity‐related anthropometric indices to the cardiometabolic risk factors and CVD s. A positive association was observed between each anthropometric index and cardiometabolic risk factors and CVD s in all models ( P AUC s) for predicting dyslipidemia ( AUC s: 0.646, sensitivity: 65%, specificity: 44%), hyperglycemia ( AUC s: 0.595, sensitivity: 60%, specificity: 45%), and CVD s ( AUC s: 0.619, sensitivity: 59%, specificity: 41%). Waist circumference showed the best prediction for abnormal blood pressure ( AUC s: 0.671, sensitivity: 66%, specificity: 40%) compared with other anthropometric indices. However, the new body shape index did not show a better prediction to either cardiometabolic risk factors or CVD s than that of any other traditional obesity‐related indices. Conclusions Waist‐to‐height ratio appeared to be the best indicator for dyslipidemia, hyperglycemia, and CVD s, while waist circumference had a better prediction for abnormal blood pressure.

Digital, Exposure to air pollution has been linked to elevated blood pressure (BP) and hypertension, but most research has focused on short-term (hours, days, or months) exposures at relatively low concentrations. We examined the associations between long-term (3-year average) concentrations of outdoor PM2.5 and household air pollution (HAP) from cooking with solid fuels with BP and hypertension in the Prospective Urban and Rural Epidemiology (PURE) study. Outdoor PM2.5 exposures were estimated at year of enrollment for 137,809 adults aged 35–70 years from 640 urban and rural communities in 21 countries using satellite and ground-based methods. Primary use of solid fuel for cooking was used as an indicator of HAP exposure, with analyses restricted to rural participants (n = 43,313) in 27 study centers in 10 countries. BP was measured following a standardized procedure and associations with air pollution examined with mixed-effect regression models, after adjustment for a comprehensive set of potential confounding factors. Baseline outdoor PM2.5 exposure ranged from 3 to 97 μg/m3 across study communities and was associated with an increased odds ratio (OR) of 1.04 (95% CI: 1.01, 1.07) for hypertension, per 10 μg/m3 increase in concentration. This association demonstrated non-linearity and was strongest for the fourth (PM2.5 > 62 μg/m3) compared to the first (PM2.5 < 14 μg/m3) quartiles (OR = 1.36, 95% CI: 1.10, 1.69). Similar non-linear patterns were observed for systolic BP (β = 2.15 mmHg, 95% CI: −0.59, 4.89) and diastolic BP (β = 1.35, 95% CI: −0.20, 2.89), while there was no overall increase in ORs across the full exposure distribution. Individuals who used solid fuels for cooking had lower BP measures compared to clean fuel users (e.g. 34% of solid fuels users compared to 42% of clean fuel users had hypertension), and even in fully adjusted models had slightly decreased odds of hypertension (OR = 0.93; 95% CI: 0.88, 0.99) and reductions in systolic (−0.51 mmHg; 95% CI: −0.99, −0.03) and diastolic (−0.46 mmHg; 95% CI: −0.75, −0.18) BP. In this large international multi-center study, chronic exposures to outdoor PM2.5 was associated with increased BP and hypertension while there were small inverse associations with HAP., Ciencias Médicas y de la Salud

Molecular docking is an important method in computational drug discovery. In large-scale virtual screening, millions of small drug-like molecules (chemical compounds) are compared against a designated target protein (receptor). Depending on the utilized docking algorithm for screening, this can take several weeks on conventional HPC systems. However, for certain applications including large-scale screening tasks for newly emerging infectious diseases such high runtimes can be highly prohibitive. In this paper, we investigate how the massively parallel neo-heterogeneous architecture of Tianhe-2 Supercomputer consisting of thousands of nodes comprising CPUs and MIC coprocessors that can efficiently be used for virtual screening tasks. Our proposed approach is based on a coordinated parallel framework called mD3DOCKxb in which CPUs collaborate with MICs to achieve high hardware utilization. mD3DOCKxb comprises a novel efficient communication engine for dynamic task scheduling and load balancing between nodes in order to reduce communication and I/O latency. This results in a highly scalable implementation with parallel efficiency of over 84% (strong scaling) when executing on 8,000 Tianhe-2 nodes comprising 192,000 CPU cores and 1,368,000 MIC cores.

The mechanical properties and precipitation scenarios of Al–Cu–Mg alloys with different Cu/Mg atomic ratios have been investigated. We show that the designed alloy with an intermediate Cu/Mg ratio has better mechanical properties due to the coexistence of S and θ′ series precipitates. Compared to the commercial alloys with higher or lower Cu/Mg ratios, the evolution of θ′ precipitates in the designed alloy is slowed by the S phase, resulting in a longer duration of higher hardness with three peaks.

Background Most cardiovascular diseases occur in low‐ and middle‐income regions of the world, but the socioeconomic distribution within China remains unclear. Our study aims to investigate whether the prevalence of cardiovascular diseases differs among high‐, middle‐, and low‐income regions of China and to explore the reasons for the disparities. Methods and Results We enrolled 46 285 individuals from 115 urban and rural communities in 12 provinces across China between 2005 and 2009. We recorded their medical histories of cardiovascular diseases and calculated the INTERHEART Risk Score for the assessment of cardiovascular risk‐factor burden, with higher scores indicating greater burden. The mean INTERHEART Risk Score was higher in high‐ and middle‐income regions than in low‐income regions (9.47, 9.48, and 8.58, respectively, P P trend =0.0064). In high‐ and middle‐income regions, urban communities have higher INTERHEART Risk Score and higher prevalent rate than rural communities. In low‐income regions, however, the prevalence of total cardiovascular disease was similar between urban and rural areas despite the significantly higher INTERHEART Risk Score for urban settings. Conclusions We detected an inverse trend between risk‐factor burden and cardiovascular disease prevalence in urban and rural communities in high‐, middle‐, and low‐income regions of China. Such asymmetry may be attributed to the interregional differences in residents’ awareness, quality of healthcare, and availability and affordability of medical services.

Identification and treatment of hypertension in China remain suboptimal despite high prevalence of hypertension and increasing incidence of stroke and myocardial infarction.This study reported blood pressure levels, prevalence, awareness, treatment, and control rates of hypertension, in addition to drug treatments in China.This is a country-specific analysis of 45 108 individuals, average age 51.4 (standard deviation 9.6) (35-70) years, enrolled between 2005 and 2009, from 70 rural and 45 urban communities in 12 provinces.Among 18 915 (41.9% overall population) hypertensive participants, 7866 (41.6%) were aware, 6503 (34.4%) treated but only 1545 (8.2%) controlled. Prevalence of hypertension was higher, but awareness, treatment, and control were lower in rural than urban residents. Prevalence of hypertension was highest in eastern (44.3%), intermediate in central (39.3%), and lowest in western regions (37.0%). Awareness was higher in central (44.3%) and eastern (42.4%) but lower in western regions (37.0%). Similar patterns were observed in treatment rates, 37.7% central, 35.2% eastern, and 26.7% in western regions with control rates of 8.3% in eastern, 7.6% central, and 5.3% west regions. Of 4744 participants receiving documented treatments, 37.5% received traditional combination drugs alone, 55.4% western drugs alone and 7.1% combination of traditional combination drug in addition to western drugs.In China, hypertension is common, and while recent studies suggest some improvements, more than half of affected individuals were unaware that they had hypertension. Rates of control remain low. National programs effective in preventing and controlling hypertension in China are urgently needed.

More studies have been conducted using gene expression similarity to identify functional connections among genes, diseases and drugs. Gene Set Enrichment Analysis (GSEA) is a powerful analytical method for interpreting gene expression data. However, due to its enormous computational overhead in the estimation of significance level step and multiple hypothesis testing step, the computation scalability and efficiency are poor on large-scale datasets. We proposed paraGSEA for efficient large-scale transcriptome data analysis. By optimization, the overall time complexity of paraGSEA is reduced from O(mn) to O(m+n), where m is the length of the gene sets and n is the length of the gene expression profiles, which contributes more than 100-fold increase in performance compared with other popular GSEA implementations such as GSEA-P, SAM-GS and GSEA2. By further parallelization, a near-linear speed-up is gained on both workstations and clusters in an efficient manner with high scalability and performance on large-scale datasets. The analysis time of whole LINCS phase I dataset (GSE92742) was reduced to nearly half hour on a 1000 node cluster on Tianhe-2, or within 120 hours on a 96-core workstation. The source code of paraGSEA is licensed under the GPLv3 and available at http://github.com/ysycloud/paraGSEA.