ACUTE EFFECT OF VIBRATORY STIMULATION ON ELBOW JOINT FLEXOR PERFORMANCE

A novel design of vibratory stimulation training system was developed in this study. Each participant took a pre-test, before receiving treatment with 20 seconds of vibratory stimulation (VS) at a specific frequency and amplitude. The participants then took a post-test. Percentage improvement was then calculated by comparing the pre- and post-test values for each index. The experimental data were analyzed through a two-way repeated-measures ANOVA analysis, with the independent variables being vibratory frequency and amplitude and the dependent variables being EMG root mean square, maximal force, rate of force development, and average force. The optimal vibratory stimulation pattern was found from this study that being a 60% maximal force loading combined with VS at 2.5 Hz and 1 N amplitude sustained over 20 s

Positive outcome expectancy mediates the relationship between social influence and Internet addiction among senior high-school students

Background and aims Based on the foundations of Bandura’s social cognitive theory and theory of triadic influence (TTI) theoretical framework, this study was designed to examine the mediating role of positive outcome expectancy of Internet use in the relationship between social influence and Internet addiction (IA) in a large representative sample of senior high-school students in Taiwan. Methods Using a cross-sectional design, 1,922 participants were recruited from senior high schools throughout Taiwan using both stratified and cluster sampling, and a comprehensive survey was administered. Results Structural equation modeling and bootstrap analyses results showed that IA severity was significantly and positively predicted by social influence, and fully mediated through positive outcome expectancy of Internet use. Discussion and conclusions The results not only support Bandura’s social cognitive theory and TTI framework, but can also serve as a reference to help educational agencies and mental health organizations design programs and create policies that will help in the prevention of IA among adolescents

Distributed Training Large-Scale Deep Architectures

Scale of data and scale of computation infrastructures together enable the current deep learning renaissance. However, training large-scale deep architectures demands both algorithmic improvement and careful system configuration. In this paper, we focus on employing the system approach to speed up large-scale training. Via lessons learned from our routine benchmarking effort, we first identify bottlenecks and overheads that hinter data parallelism. We then devise guidelines that help practitioners to configure an effective system and fine-tune parameters to achieve desired speedup. Specifically, we develop a procedure for setting minibatch size and choosing computation algorithms. We also derive lemmas for determining the quantity of key components such as the number of GPUs and parameter servers. Experiments and examples show that these guidelines help effectively speed up large-scale deep learning training

Analyzing Tropical Waves Using the Parallel Ensemble Empirical Model Decomposition Method: Preliminary Results from Hurricane Sandy

In this study, we discuss the performance of the parallel ensemble empirical mode decomposition (EMD) in the analysis of tropical waves that are associated with tropical cyclone (TC) formation. To efficiently analyze high-resolution, global, multiple-dimensional data sets, we first implement multilevel parallelism into the ensemble EMD (EEMD) and obtain a parallel speedup of 720 using 200 eight-core processors. We then apply the parallel EEMD (PEEMD) to extract the intrinsic mode functions (IMFs) from preselected data sets that represent (1) idealized tropical waves and (2) large-scale environmental flows associated with Hurricane Sandy (2012). Results indicate that the PEEMD is efficient and effective in revealing the major wave characteristics of the data, such as wavelengths and periods, by sifting out the dominant (wave) components. This approach has a potential for hurricane climate study by examining the statistical relationship between tropical waves and TC formation

Iloprost improves running performance at 5,000 m in Han but not in Tibetans

Background: Tibetans experience lose less aerobic exercise capacity in hypoxia compared to lowland Han. We tested if inhalation of iloprost (to counter hypoxic pulmonary vasoconstriction) and furosemide (to decrease afferent vagal traffic from pulmonary receptors) improve performance in hypoxia in Han compared to Tibetans. Methods: 8 Tibetans and 8 Han, living at 2,260 m, did incremental uphill treadmill running to exhaustion at ambient pressure on day 1, followed by three runs at 5,000 m (hypobaric chamber) after inhalation of iloprost (ILO), furosemide (FUR) or placebo (PLA), on different days in a counter-balanced order. Results: In Han the performance decrement from 2,260 m to 5,000 m was greater than in Tibetans (p<0.05). In Han iloprost improved performance at 5,000 m compared to placebo (p<0.05 vs. PLA); furosemide had no effects. In Tibetans there were no treatment effects. Peripheral O2saturations at peak exercise at 5,000 m, were higher by ～8 % in the Tibetans (p<0.05 vs. Han). Maximum heart rate was lowered by 13±6 bpm in Han at 5,000 m regardless of treatment compared to 2,260 m (p<0.05). Tibetans reached similar maximum heart rates ∼200 bpmat 5,000 m and 2,260 m, independent of treatment. Conclusions: The blunting of the exercise impairment in severe hypoxia in Han during maximal exercise after inhalation of iloprost suggests that hypoxic pulmonary vasoconstriction and right ventricular function are potential performance limiting factors in Han in hypoxia

The Study on Antecedents of Consumer Buying Impulsiveness in an Online Context

The global recession caused by the financial tsunami has seriously impacted numerous industries. Although the market scale of global e-commerce market has declined, global online shopping continues to grow. Many previous researches focused on the effect of website design characteristics on online impulsive buying behavior, and few have explored such behavior from consumer individual internal factor perspectives. This paper aims to explore and integrate individual internal factors influencing consumer online buying impulsiveness, and further to recognize the relationships among these factors. The results showed as follows: (1) hedonic consumption needs, impulsive buying tendency, positive affect and normative evaluations positively influence buying impulsiveness, respectively; (2) hedonic consumption needs positively influence positive affect; (3) impulsive buying tendency positively influences normative evaluations; (4) normative evaluations positively influence positive affect

Differential Brain and Muscle Tissue Oxygenation Responses to Exercise in Tibetans Compared to Han Chinese

The Tibetans’ better aerobic exercise capacity at altitude remains ill-understood. We tested the hypothesis that Tibetans display better muscle and brain tissue oxygenation during exercise in hypoxia. Using near-infrared spectrometry (NIRS) to provide indices of tissue oxygenation, we measured oxy- and deoxy-hemoglobin ([O2Hb] and [HHb], respectively) responses of the vastus lateralis muscle and the right prefrontal cortex in ten Han Chinese and ten Tibetans during incremental cycling to exhaustion in a pressure-regulated chamber at simulated sea-level (air at 1 atm: normobaric normoxia) and 5,000 m (air at 0.5 atm: hypobaric hypoxia). Hypoxia reduced aerobic capacity by ∼22% in both groups (d = 0.8, p &lt; 0.001 vs. normoxia), while Tibetans consistently outperformed their Han Chinese counterpart by ∼32% in normoxia and hypoxia (d = 1.0, p = 0.008). We found cerebral [O2Hb] was higher in Tibetans at normoxic maximal effort compared Han (p = 0.001), while muscle [O2Hb] was not different (p = 0.240). Hypoxic exercise lowered muscle [O2Hb] in Tibetans by a greater extent than in Han (interaction effect: p &lt; 0.001 vs. normoxic exercise). Muscle [O2Hb] was lower in Tibetans when compared to Han during hypoxic exercise (d = 0.9, p = 0.003), but not during normoxic exercise (d = 0.4, p = 0.240). Muscle [HHb] was not different between the two groups during normoxic and hypoxic exercise (p = 0.778). Compared to Han, our findings revealed a higher brain tissue oxygenation in Tibetans during maximal exercise in normoxia, but lower muscle tissue oxygenation during exercise in hypoxia. This would suggest that the Tibetans privileged oxygenation of the brain at the expense of that of the muscle

MethylC-analyzer: A comprehensive downstream pipeline for the analysis of genome-wide DNA methylation

DNA methylation is a crucial epigenetic modification involved in multiple biological processes and diseases. Current approaches for measuring genome-wide DNA methylation via bisulfite sequencing (BS-seq) include whole-genome bisulfite sequencing (WGBS), reduced representation bisulfite sequencing (RRBS), and enzymatic methyl-seq (EM-seq). The computational analysis tools available for BS-seq data include customized aligners for mapping bisulfite-converted reads and computational pipelines for downstream data analysis. Current post-alignment methylation tools are specialized for the interpretation of CG methylation, which is known to dominate mammalian genomes, however, non-CG methylation (CHG and CHH, where H refers to A, C, or T) is commonly observed in plants and fungi and is closely associated with gene regulation, transposon silencing, and plant development. Thus, we have developed a MethylC-analyzer to analyze and visualize post-alignment WGBS, RRBS, and EM-seq data focusing on CG. The tool is able to also analyze non-CG sites to enhance deciphering genomes of plants and fungi. By processing aligned data and gene location files, MethylC-analyzer generates a genome-wide view of methylation levels and methylation in user-specified genomic regions. The meta-plot, for example, allows the investigation of DNA methylation within specific genomic elements. Moreover, our tool identifies differentially methylated regions (DMRs) and investigates the enrichment of genomic features associated with variable methylation. MethylC-analyzer functionality is not limited to specific genomes, and we demonstrated its performance on both plant and human BS-seq data. MethylC-analyzer is a Python- and R-based program designed to perform comprehensive downstream analyses of methylation data, providing an intuitive analysis platform for scientists unfamiliar with DNA methylation analysis. It is available as either a standalone version for command-line uses or a graphical user interface (GUI) and is publicly accessible at https://github.com/RitataLU/MethylC-analyzer