Nonlinear Associations Between Working Hours and Overwork-Related Cerebrovascular and Cardiovascular Diseases (CCVD)

Long working hours are recognized as a risk factor for cerebrovascular and cardiovascular diseases (CCVD). We investigated the relationship between working hours and different CCVD severity outcomes—death, disability, and illness—across industries in Taiwan from 2006 to 2016. We applied a generalized additive mixed model to estimate the association between working hours and the rate of each severity outcome, adjusted for salary, unemployment rate, time, and a random intercept. Industry-average working hours were significantly associated with each outcome level of overwork-related CCVD, especially when monthly working hours increased from 169 (relative risk [RR] = 1.46, 95% confidence interval [CI] 1.002–2.12) to 187 (RR = 5.73, 95% CI 3.61–9.08). Although RR trends declined after monthly working hours exceeded 187, excess risks remained statistically significant. Each 1-hour increase in working hours had a stronger effect on the RR increase in death and disability than on illness. Variations in CCVD risks existed across industries, with the highest risk in transportation and information. Reducing working hours is essential to preventing overwork-related CCVD, especially the more severe outcomes. We recommend further research to address possible underreporting of less severe cases, and to explore actions to narrow the gaps in risk across industries

Diffusion Dynamics, Moments, and Distribution of First Passage Time on the Protein-Folding Energy Landscape, with Applications to Single Molecules

We study the dynamics of protein folding via statistical energy-landscape theory. In particular, we concentrate on the local-connectivity case with the folding progress described by the fraction of native conformations. We obtain information for the first passage-time (FPT) distribution and its moments. The results show a dynamic transition temperature below which the FPT distribution develops a power-law tail, a signature of the intermittency phenomena of the folding dynamics. We also discuss the possible application of the results to single-molecule dynamics experiments

On the disappearance of Tuesday effect in Australia

Day of the week (DOW) effect has been well known in many markets. The United States, the United Kingdom, Canada, and Switzerland all have been found to exhibit significant average negative Monday returns [Agrawal and Tandon, 1998]. Other developing markets in Indonesia, Malaysia and Thailand are also found to have the same seasonality [Choudhry, 2000]. Australia however displays its DOW effect on Tuesdays rather than on Mondays (Jaffe and Westerfield [1985], Easton and Faff [1994]). Jaffe and Westerfield [1985] suggest that there might be a linkage between the U.S. Monday seasonal and the Asia-Pacific DOW effect as they are one day out of phase due to different time zone. Since then, a few studies have examined the relationship of daily returns among the markets. But to our knowledge, no study has directly investigated the relationship between U.S. Monday and Australia Tuesday effect. We therefore re-examine the anomaly and document that the DOW effect in Australia is Granger caused by the weekend effect in U.S. and not the other way conditional on the weekend effects in the U.K. and Japanese markets. We also find that in the post 1987 period, where the U.S. Monday returns are positively significant, Australia Tuesday returns also reverses its effect. This latter finding provides further evidence that the anomaly in Australia is induced by the weekend effect in the U.S

Computation-Performance Optimization of Convolutional Neural Networks with Redundant Kernel Removal

Deep Convolutional Neural Networks (CNNs) are widely employed in modern computer vision algorithms, where the input image is convolved iteratively by many kernels to extract the knowledge behind it. However, with the depth of convolutional layers getting deeper and deeper in recent years, the enormous computational complexity makes it difficult to be deployed on embedded systems with limited hardware resources. In this paper, we propose two computation-performance optimization methods to reduce the redundant convolution kernels of a CNN with performance and architecture constraints, and apply it to a network for super resolution (SR). Using PSNR drop compared to the original network as the performance criterion, our method can get the optimal PSNR under a certain computation budget constraint. On the other hand, our method is also capable of minimizing the computation required under a given PSNR drop.Comment: This paper was accepted by 2018 The International Symposium on Circuits and Systems (ISCAS

5-ALA mediated photodynamic therapy induces autophagic cell death via AMP-activated protein kinase

Photodynamic therapy (PDT) has been developed as an anticancer treatment, which is based on the tumor-specific accumulation of a photosensitizer that induces cell death after irradiation of light with a specific wavelength. Depending on the subcellular localization of the photosensitizer, PDT could trigger various signal transduction cascades and induce cell death such as apoptosis, autophagy, and necrosis. In this study, we report that both AMP-activated protein kinase (AMPK) and mitogen-activated protein kinase (MAPK) signaling cascades are activated following 5-aminolevulinic acid (ALA)-mediated PDT in both PC12 and CL1-0 cells. Although the activities of caspase-9 and -3 are elevated, the caspase inhibitor zVAD-fmk did not protect cells against ALA-PDT-induced cell death. Instead, autophagic cell death was found in PC12 and CL1-0 cells treated with ALA-PDT. Most importantly, we report here for the first time that it is the activation of AMPK, but not MAPKs that plays a crucial role in mediating autophagic cell death induced by ALA-PDT. This novel observation indicates that the AMPK pathway play an important role in ALA-PDT-induced autophagy

Particle bonding mechanism in CGDS-a three-dimensional approach

Abstract: Cold gas dynamics spray (CGDS) is a surface coating process using highly accelerated particles to form the surface coating by high speed impact of the particles. In the CGDS process, metal particles of generally 1-50 μm diameter is carried by a gas stream in high pressure (typically 20-30 atm) through a DE Laval type nozzle to achieve supersonic flying so as to impact on the substrate. Typically, the impact velocity ranges between 300 and 1200 m/s in the CGDS process. When the particle gains its critical velocity, the minimum in-flight speed at which it can deposit, adiabatic shear instabilities will occur. Herein, to ascertain the critical velocities of different particle sizes on the bonding efficiency in CGDS process, three-dimensional numerical simulations of single particle deposition process were performed. In the CGDS process, one of the most important parameters which determine the bonding strength with the substrate is particle impact temperature. Bonding will occur when the particle’s impacting velocity surpass the critical velocity, at which the interface can achieve 60 % of melting temperature of particle material (Ref 1). Therefore, critical velocity should be a main parameter on the coating quality. The particle critical velocity is determined not only by its size, but also by its material properties. This study numerically investigate the critical velocity for the particle deposition process in CGDS. In the present numerical analysis, copper (Cu) was chosen as particle material and aluminum (Al) as substrate material for this study. The impacting velocities were selected between 300 m/s and 800 m/s increasing in steps of 100 m/s. The simulation result reveals temporal and spatial interfacial temperature distribution and deformation between particle(s) and substrate. Finally, comparison is carried out between the computed results and experimental data