Cholesteryl ester transfer protein (CETP) mediates the net transfer of cholesteryl esters from atheroprotective high-density lipoproteins to atherogenic low-density lipoproteins or very-low-density lipoproteins. Inhibition of CETP raises high-density lipoprotein cholesterol (good cholesterol) levels and reduces low-density lipoprotein cholesterol (bad cholesterol) levels, making it a promising drug target for the prevention and treatment of coronary heart disease. Although the crystal structure of CETP has been determined, the molecular mechanism mediating cholesteryl esters transfer is still unknown, even the structural features of CETP in a physiological environment remain elusive. We performed molecular dynamics simulations to explore the structural features of CETP in an aqueous solution. Results show that the distal portion flexibility of N-terminal β-barrel domain is considerably greater in solution than in crystal; conversely, the flexibility of helix X is slightly less. During the simulations the distal end of C-terminal β-barrel domain expanded while the hydrophilic surface increasing more than the hydrophobic surface. In addition a new surface pore was generated in this domain. This surface pore and all cavities in CETP are stable. These results suggest that the formation of a continuous tunnel within CETP by connecting cavities is permitted in solution.

Maintaining transient stability is a core requirement for ensuring safe operation of power systems. Hence, quick and accurate assessment of the transient stability of power systems is particularly critical. As the deployment of wide area measurement systems (WAMS) expands, transient stability assessment (TSA) based on machine learning with data of phasors measurement units (PMUs) also develops rapidly. However, unstable samples of the power system are rarely seen in practice which affects greatly the effectiveness of transient instability recognition. To address this problem, we propose a deep imbalanced learning-based TSA framework. First, an improved denoising autoencoder (DAE) is constructed to map the training set to hidden space for dimension reduction. Then, adaptive synthetic sampling (ADASYN) is further used to synthesize unstable samples in hidden space to balance the proportion of different classes. Third, the synthesized data are decoded into the original space to enhance the training set. Finally, an ensemble cost-sensitive classifier based on a stacked denoising autoencoder (SDAE) is designed to extract different feature patterns, and the SDAEs are merged with a fusion layer to classify the status of the power system. The simulation results of two benchmark power systems indicate that the proposed method can effectively improve the recognition accuracy of unstable cases by combining nonlinear data synthesis with ensemble cost-sensitive learning methods. Compared with other imbalanced learning methods, the proposed framework enjoys superiority both in accuracy and G-mean. INDEX TERMS Deep imbalanced learning, transient stability of power system, denoising autoencoder (DAE), ensemble cost-sensitive SDAE, feature patterns, G-mean.

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Osteosarcoma, a leading malignant tumor of bones is diagnosed mostly in adolescents and young adults worldwide. The present study investigated alstonine as anti-osteosarcoma agent in vitro as well as in vivo and evaluated the underlying mechanism. Treatment with alstonine led to a significant (P<0.05) reduction in MG63 and U-2OS cell viability. Alstonine treatment of MG63 and U-2OS cells caused a significant reduction in colony formation compared to the control cells. Viability of osteoblasts was not affected by alstonine treatment in 1.25 to 20 µM concentration range. In alstonine treated MG63 and U-2OS cells apoptotic cells increased significantly (P<0.05) compared to the control cells. Moreover, in MG63 and U-2OS cells treatment with alstonine caused a prominent increase in expression of cleaved caspase-9, caspase-3, and PARP. Treatment of MG63 and U-2OS cells with alstonine caused a prominent increase in AMPKα (Thr172) phosphorylation and elevated the count of mtDNA copies compared to the untreated cells. Alstonine treatment of the cells caused a remarkable increase in expression of PGC-1α and TFAM proteins. Treatment of the mice with alstonine at 5 and 10 mg/kg doses for 30 days caused a significant (P<0.05) reduction in xenograft growth. Expression of PGC-1α and TFAM proteins in tumor tissues of the mice treated with alstonine was significantly (P<0.05) raised compared to the control group. Thus, alstonine inhibits osteosarcoma cell growth and activates apoptosis through AMPK dependent pathway. Therefore, alstonine may be considered for treatment of osteosarcoma as it effectively arrests tumor growth in mice.