In patients with LQTS, the frequency of CACNA1C mutations was higher than reported. Even without typical phenotypes of Timothy syndrome, CACNA1C mutations may cause QT prolongation and/or fatal arrhythmia attacks.

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How to develop efficient numerical schemes while preserving the energy stability at the discrete level is a challenging issue for the three component Cahn-Hilliard phase-field model. In this paper, we develop first and second order temporal approximation schemes based on the "Invariant Energy Quadratization" approach, where all nonlinear terms are treated semi-explicitly. Consequently, the resulting numerical schemes lead to a well-posed linear system with the symmetric positive definite operator to be solved at each time step. We rigorously prove that the proposed schemes are unconditionally energy stable. Various 2D and 3D numerical simulations are presented to demonstrate the stability and the accuracy of the schemes.

According to the published literature, we surmise that particulate matter (PM) concentration, individually, may be less important than components in explaining health effects. PM2.5 (aerodynamic diameter < 2.5 μm) had similar cytotoxicity (e.g., cell viability reduction, oxidative damage, inflammatory effects and genetic toxicity) on different types of cells. The studies of cells are readily available for detailed mechanistic investigations, which is more appropriate for learning and comparing the mechanism caused by single or mixed ingredients coating a carbon core. No review exists that holistically examines the evidence from all components-based in vitro studies. We reviewed published studies that focus on the cytotoxicity of normal PM2.5. Those studies suggested that the toxicity of mixed compositions differs greatly from the single ingredients in mixed components and the target cells. The cytotoxic responses caused by PM2.5 components have not shown a consistent association with clear, specific health effects. The results may be beneficial for providing new targets for drugs for the treatment of PM2.5-related diseases.

Anomaly detection from a driver's perspective when driving is important to autonomous vehicles. As a part of Advanced Driver Assistance Systems (ADAS), it can remind the driver about dangers timely. Compared with traditional studied scenes such as the university campus and market surveillance videos, it is difficult to detect abnormal event from a driver's perspective due to camera waggle, abidingly moving background, drastic change of vehicle velocity, etc. To tackle these specific problems, this paper proposes a spatial localization constrained sparse coding approach for anomaly detection in traffic scenes, which firstly measures the abnormality of motion orientation and magnitude respectively and then fuses these two aspects to obtain a robust detection result. The main contributions are threefold: 1) This work describes the motion orientation and magnitude of the object respectively in a new way, which is demonstrated to be better than the traditional motion descriptors. 2) The spatial localization of object is taken into account of the sparse reconstruction framework, which utilizes the scene's structural information and outperforms the conventional sparse coding methods. 3) Results of motion orientation and magnitude are adaptively weighted and fused by a Bayesian model, which makes the proposed method more robust and handle more kinds of abnormal events. The efficiency and effectiveness of the proposed method are validated by testing on nine difficult video sequences captured by ourselves. Observed from the experimental results, the proposed method is more effective and efficient than the popular competitors, and yields a higher performance.

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