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

Effects of VEGF on bone cells' metabolism and activity

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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

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

Determination of Intrinsic Ferroelectric Polarization in Orthorhombic Manganites with E-type Spin Order

By directly measuring electrical hysteresis loops using the Positive-Up Negative-Down (PUND) method, we accurately determined the remanent ferroelectric polarization Pr of orthorhombic RMnO3 (R = Ho, Tm, Yb, and Lu) compounds below their E-type spin ordering temperatures. We found that LuMnO3 has the largest Pr of 0.17 uC/cm^2 at 6 K in the series, indicating that its single-crystal form can produce a Pr of at least 0.6 \muuC/cm^2 at 0 K. Furthermore, at a fixed temperature, Pr decreases systematically with increasing rare earth ion radius from R = Lu to Ho, exhibiting a strong correlation with the variations in the in-plane Mn-O-Mn bond angle and Mn-O distances. Our experimental results suggest that the contribution of the Mn t2g orbitals dominates the ferroelectric polarization.Comment: 16 pages, 4 figure

Numerical Simulation of Electroosmotic Flow with Step Change in Zeta Potential

Electroosmotic flow is a convenient mechanism for transporting polar fluid in a microfluidic device. The flow is generated through the application of an external electric field that acts on the free charges that exists in a thin Debye layer at the channel walls. The charge on the wall is due to the chemistry of the solid-fluid interface, and it can vary along the channel, e.g. due to modification of the wall. This investigation focuses on the simulation of the electroosmotic flow (EOF) profile in a cylindrical microchannel with step change in zeta potential. The modified Navier-Stoke equation governing the velocity field and a non-linear two-dimensional Poisson-Boltzmann equation governing the electrical double-layer (EDL) field distribution are solved numerically using finite control-volume method. Continuities of flow rate and electric current are enforced resulting in a non-uniform electrical field and pressure gradient distribution along the channel. The resulting parabolic velocity distribution at the junction of the step change in zeta potential, which is more typical of a pressure-driven velocity flow profile, is obtained.Singapore-MIT Alliance (SMA

Physical-Biological Oceanographic Coupling Influencing Phytoplankton and Primary Production in the South China Sea

Two cruises were carried out in the summer and winter of 1998 to study coupled physical-chemical-biological processes in the South China Sea and their effects on phytoplankton stock and production. The results clearly show that the seasonal distributions of phytoplankton were closely related to the coupled processes driven by the East Asian Monsoon. Summer southwesterly monsoon induced upwelling along the China and Vietnam coasts. Several mesoscale cyclonic cold eddies and anticyclonic warm pools were identified in both seasons. In the summer, the upwelling and cold eddies, both associated with rich nutrients, low dissolved oxygen ( DO), high chlorophyll a (Chl a) and primary production ( PP), were found in the areas off the coast of central Vietnam, southeast of Hainan Island and north of the Sunda shelf, whereas in the winter they form a cold trough over the deep basin aligning from southwest to northeast. The warm pools with poor nutrients, high DO, low Chl a, and PP were found in the areas southeast of Vietnam, east of Hainan, and west of Luzon during the summer, and a northwestward warm jet from the Sulu Sea with properties similar to the warm pools was encountered during the winter. The phytoplankton stock and primary production were lower in summer due to nutrient depletion near the surface, particularly PO4. This phosphorus depletion resulted in phytoplankton species succession from diatoms to dinoflagellates and cyanophytes. A strong subsurface Chl a maximum, dominated by photosynthetic picoplankton, was found to contribute significantly to phytoplankton stocks and production

Evaluation of Glucosidase Inhibitory and Cytotoxic Potential of Five Selected Edible and Medicinal Ferns

Purpose: To evaluate the glucosidase inhibitory and cytotoxic activities of five selected edible and medicinal ferns, namely, Blechnum orientale, Davallia denticulata, Diplazium esculentum, Nephrolepis biserrata, and Pteris vittata.Methods: The aqueous extracts of the five ferns were prepared by water extraction at 90 ºC for 1 h. Antiglucosidase assay was used to determine the effect of each extract on yeast alpha-glucosidase activity in vitro. Cytotoxicity was evaluated using methylthiazol tetrazolium assay on chronic myelogenous leukaemia cell line (K562). The phenolic, hydroxycinnamic acid, flavonoid and proanthocyanidin contents of the extracts were also determined.Results: The α-glucosidase inhibitory activity of D. esculentum (half maximal effective concentration, EC50 = 6.85 μg/ml) was considerably stronger than that of myricetin (EC50 = 53.21 μg/ml). B. orientale, D. esculentum, N. biserrata, and P. vittata were cytotoxic to K562 cells. P. vittata had the strongest cytotoxicity, although it was less potent than 5-fluorouracil. D. denticulata had the highest phenolic, hydroxycinnamic acid and flavonoid contents of all the extracts while B. orientale had the highest proanthocyanidin content.Conclusion: Among the five ferns evaluated, D. esculentum is a potential source of an antidiabetic agent and is recommended for further investigation in this regard. All the fern extracts, except D. denticulata, exhibited dose-dependent cytotoxicity against K562 cells.Keywords: Medicinal fern, α-Glucosidase inhibition, Cytotoxicity, Blechnum orientale, Davallia denticulata, Diplazium esculentum, Nephrolepis biserrata, Pteris vittat

Anti-oxidative, metal chelating and radical scavenging effects of protein hydrolysates from blue-spotted stingray

Purpose: To evaluate protein hydrolysates and membrane ultrafiltration fractions of blue-spotted stingray for metal chelating and radical scavenging activities, as well as protection against oxidative protein damage.Methods: Stingray protein isolates were hydrolysed with alcalase, papain and trypsin for 3 h. Alcalase hydrolysate was fractionated by membrane ultrafiltration to yield < 3, 3 - 10 and > 10 kDa fractions. Peptide contents, iron and copper chelating activity, 2, 2'-azino-bis(3- ethylbenzothiazoline-6-sulphonic acid) (ABTS) and hydroxyl radical scavenging activities, and protection against oxidative protein damage were evaluated.Results: Three-hour alcalase hydrolysate (3AH) had the highest peptide content and the lowest half maximal effective concentration (EC50) for ABTS radical scavenging (793.9 μg/mL), hydroxyl radical scavenging (6.93 mg/mL), iron chelating (116.4 μg/mL) and copper chelating  activity (2136.9 μg/mL) among the hydrolysates. Among the fractions of 3AH, < 3 kDa fraction had the best iron chelating activity, 3 - 10 kDa fraction exhibited the highest ABTS radical scavenging activity, while > 10 kDa fraction showed the best copper chelating activity. The < 3 kDa and 3 - 10 kDa fractions had similar levels of hydroxyl radical scavenging activity to reduced glutathione. The protective effects of 3AH and < 3 kDa fraction against oxidative protein damage were comparable to that of reduced glutathione.Conclusion: Alcalase is the best protease for producing hydrolysates with metal chelating and antioxidant activities from stingray proteins. Alcalase hydrolysate, specifically its < 3 kDa fraction, has potential for future applications in antioxidant therapy and health food formulation