Skeletal muscle requires adequate membrane trafficking and remodeling to maintain its normal structure and functions. Consequently, many human myopathies are caused by mutations in membrane trafficking machinery. The large GTPase dynamin-2 (Dyn2) is best known for catalyzing membrane fission during clathrin-mediated endocytosis (CME), which is critical for cell signaling and survival. Despite its ubiquitous expression, mutations of Dyn2 are associated with two tissue-specific congenital disorders: centronuclear myopathy (CNM) and Charcot-Marie-Tooth (CMT) neuropathy. Several disease models for CNM-Dyn2 have been established to study its pathogenic mechanism; yet the cellular and biochemical effects of these mutations are still not fully understood. Here we comprehensively compared the biochemical activities of disease-associated Dyn2 mutations and found that CNM-Dyn2 mutants are hypermorphic with enhanced membrane fission activity, whereas CMT-Dyn2 is hypomorphic. More importantly, we found that the expression of CNM-Dyn2 mutants does not impair CME in myoblast, but leads to T-tubule fragmentation in both C2C12-derived myotubes and Drosophila body wall muscle. Our results demonstrate that CNM-Dyn2 mutants are gain-of-function mutations, and their primary effect in muscle is T-tubule disorganization, which explains the susceptibility of muscle to Dyn2 hyperactivity.

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This paper presents an efficient approach for analyzing harmonic currents generated by a six-pulse ac/dc converter in steady state. The approach is carried out in time-domain and the interactions between the system and the converter are considered in the study. In calculating harmonic currents generated by the converter, the Poincaré map based approach is applied to increase the computational efficiency and solution accuracy. The computed ac-side harmonic components of the converter current are then extracted via FFT. Solutions obtained by the proposed method are compared with those obtained by using a brute force time-domain simulation tool, Simulink of Matlab. It is shown that the harmonic currents determined by the proposed approach well agree with those obtained by the simulation tool, but the solution time is significantly reduced. In addition, the proposed method also can be applied to analyze harmonic currents produced by other types of power-electronic devices operating periodically.

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Frequency is an important parameter for the power quality analysis. When two or more adjacent spectral lines are too close, many spectrum estimation algorithms fail to distinguish these frequency components. A modified high-resolution Singular Value Decomposition (SVD) method for power quality signal analysis by using down-sampling technique is proposed in this paper. With adopting down-sampling technique, a scaling factor is introduced to separate the spectral lines from each other, and then the correct spectra can be estimated. The performance of the proposed method is validated by testing the actual measured signal. Results are compared with those obtained from several FFT-based and SVD methods, and the commercialized power quality meter. It shows that the proposed method can precisely detect the frequency components of the measured power signal with a high resolution.