Publications by authors named "H L Chang"

Background: Atherosclerotic cardiovascular disease (ASCVD) remains the leading cause of mortality in the western world despite the success of lipid lowering therapies, highlighting the need for novel lipid-independent therapeutic strategies. Genome-wide association studies (GWAS) have identified numerous genes associated with ASCVD that function in the vessel wall, suggesting that vascular cells mediate ASCVD, and that the genes and pathways essential for this vascular cell function may be novel therapeutic targets for the treatment of ASCVD. Furthermore, some of these implicated genes appear to function in the adventitial layer of the vasculature, suggesting these cells are able to potentiate ASCVD.

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In the burgeoning field of spintronics, antiferromagnetic materials (AFMs) are attracting significant attention for their potential to enable ultra-fast, energy-efficient devices. Thin films of AFMs are particularly promising for practical applications due to their compatibility with spin-orbit torque (SOT) mechanisms. However, studying these thin films presents challenges, primarily due to the weak signals they produce and the rapid dynamics driven by SOT, that are too fast for conventional electric transport or microwave techniques to capture.

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In Japan, there are no nationwide guidelines for presymptomatic testing for hereditary neuromuscular diseases. Although each institution has been dealing with this situation by using their own procedures to date, it is necessary to develop a standardized guidelines based on the Japanese medical system, because the development of disease-modifying therapies has progressed, and we are entering an era in which early diagnosis and early treatment are necessary. The guidelines presented here were devised by the Committee on Medical Genetics of the Japan Neurological Society.

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Sargassum fusiforme, widely consumed in Asian countries, has been proven to have various biological activities. However, the impacts and mechanisms of Sargassum fusiforme polysaccharides (SFPs) on intestinal bacterial infection are not yet fully understood. Our findings indicate that SFPs pretreatment ameliorates intestinal inflammation by reducing C.

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Accurately predicting activation energies is crucial for understanding chemical reactions and modeling complex reaction systems. However, the high computational cost of quantum chemistry methods often limits the feasibility of large-scale studies, leading to a scarcity of high-quality activation energy data. In this work, we explore and compare three innovative approaches (transfer learning, delta learning, and feature engineering) to enhance the accuracy of activation energy predictions using graph neural networks, specifically focusing on methods that incorporate low-cost, low-level computational data.

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