High-Efficient Parallel CAVLC Encoders on Heterogeneous Multicore Architectures

This article presents two high-efficient parallel realizations of the context-based adaptive variable length coding (CAVLC) based on heterogeneous multicore processors. By optimizing the architecture of the CAVLC encoder, three kinds of dependences are eliminated or weaken, including the context-based data dependence, the memory accessing dependence and the control dependence. The CAVLC pipeline is divided into three stages: two scans, coding, and lag packing, and be implemented on two typical heterogeneous multicore architectures. One is a block-based SIMD parallel CAVLC encoder on multicore stream processor STORM. The other is a component-oriented SIMT parallel encoder on massively parallel architecture GPU. Both of them exploited rich data-level parallelism. Experiments results show that compared with the CPU version, more than 70 times of speedup can be obtained for STORM and over 50 times for GPU. The implementation of encoder on STORM can make a real-time processing for 1080p @30fps and GPU-based version can satisfy the requirements for 720p real-time encoding. The throughput of the presented CAVLC encoders is more than 10 times higher than that of published software encoders on DSP and multicore platforms

Inferring gene regulatory networks from gene expression data by a dynamic Bayesian network-based model

Enabled by recent advances in bioinformatics, the inference of gene regulatory networks (GRNs) from gene expression data has garnered much interest from researchers. This is due to the need of researchers to understand the dynamic behavior and uncover the vast information lay hidden within the networks. In this regard, dynamic Bayesian network (DBN) is extensively used to infer GRNs due to its ability to handle time-series microarray data and modeling feedback loops. However, the efficiency of DBN in inferring GRNs is often hampered by missing values in expression data, and excessive computation time due to the large search space whereby DBN treats all genes as potential regulators for a target gene. In this paper, we proposed a DBN-based model with missing values imputation to improve inference efficiency, and potential regulators detection which aims to lessen computation time by limiting potential regulators based on expression changes. The performance of the proposed model is assessed by using time-series expression data of yeast cell cycle. The experimental results showed reduced computation time and improved efficiency in detecting gene-gene relationships

Genetic polymorphisms and periodontitis in Hong Kong Chinese

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Effects of VEGF on bone cells' metabolism and activity

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The effects of escitalopram on myocardial apoptosis and the expression of Bax and Bcl-2 during myocardial ischemia/reperfusion in a model of rats with depression

BackgroundMajor depressive disorder (MDD) is an independent risk factor for coronary heart disease (CHD), and influences the occurrence and prognosis of cardiovascular events. Although there is evidence that antidepressants may be cardioprotective after acute myocardial infarction (AMI) comorbid with MDD, the operative pathophysiological mechanisms remain unclear. Our aim was therefore to explore the molecular mechanisms of escitalopram on myocardial apoptosis and the expression of Bax and Bcl-2 in a rat model of depression during myocardial ischemia/reperfusion (I/R).MethodsRats were divided randomly into 3 groups (n = 8): D group (depression), DI/R group (depression with myocardial I/R) and escitalopram + DI/R group. The rats in all three groups underwent the same chronic mild stress and separation for 21 days, at the same time, in the escitalopram + DI/R group, rats were administered escitalopram by gavage (10 mg/kg/day). Ligation of the rat¿s left anterior descending branch was done in the myocardial I/R model. Following which behavioral tests were done. The size of the myocardial infarction was detected using 1.5% TTC dye. The Tunel method was used to detect apoptotic myocardial cells, and both the Rt-PCR method and immunohistochemical techniques were used to detect the expression of Bcl¿2 and Bax.ResultsCompared with the D and DI/R groups, rats in Escitalopram + DI/R group showed significantly increased movements and sucrose consumption (P < .01). Compared with the DI/R group, the myocardial infarct size in the escitalopram + DI/R group was significantly decreased (P < .01). Compared with the D group, there were significantly increased apoptotic myocardial cells in the DI/R and escitalopram + DI/R groups (P < .01); however compared with the DI/R group, apoptotic myocardial cell numbers in the escitalopram + DI/R group were significantly decreased (P < .01). Compared with the DI/R group, there was a down-regulated Bax:Bcl-2 ratio in the escitalopram + DI/R group (P < .01).ConclusionsThese results suggest that in patients with AMI comorbid with MDD, there is an increase in pro-apoptotic pathways that is reversed by escitalopram. This suggests that clinically escitalopram may have a direct cardioprotective after acute myocardial infarction

Inferring gene regulatory networks from gene expression data by a dynamic Bayesian network-based model

Enabled by recent advances in bioinformatics, the inference of gene regulatory networks (GRNs) from gene expression data has garnered much interest from researchers. This is due to the need of researchers to understand the dynamic behavior and uncover the vast information lay hidden within the networks. In this regard, dynamic Bayesian network (DBN) is extensively used to infer GRNs due to its ability to handle time-series microarray data and modeling feedback loops. However, the efficiency of DBN in inferring GRNs is often hampered by missing values in expression data, and excessive computation time due to the large search space whereby DBN treats all genes as potential regulators for a target gene. In this paper, we proposed a DBN-based model with missing values imputation to improve inference efficiency, and potential regulators detection which aims to lessen computation time by limiting potential regulators based on expression changes. The performance of the proposed model is assessed by using time-series expression data of yeast cell cycle. The experimental results showed reduced computation time and improved efficiency in detecting gene-gene relationships

Characterization of Malaysian Wild Bananas Based on Anthocyanins

The male buds of 16 Musa species (Musaceae) populations were investigated by HPLC for the occurrence of anthocyanins. The investigation was based on the presence of 6 anthocyanins. The 16 Musa samples could be classified into three distinct species i.e. Musa acuminata, Musa violascens and Musa balbisiana. Musa acuminata could be divided into two subspecies : malaccensis (lowland) and tmncata (highland) according to their constituents and content of major anthocyanins. No variation was observed in the composition of the anthocyanins of Kedah type ssp. siamea and Selangor types ssp. malaccensis. The classification of M. acuminata into two subspecies based on anthocyanin data further supported the current taxonomic grouping of the species