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Many lines of evidence have indicated the therapeutic potential of rescuing mitochondrial integrity by targeting specific mitochondrial quality control pathways in neurodegenerative diseases, such as Parkinson’s disease, Huntington’s disease, and Alzheimer’s disease. In addition to ATP synthesis, mitochondria are critical regulators of ROS production, lipid metabolism, calcium buffering, and cell death. The mitochondrial unfolded protein response, mitochondrial dynamics, and mitophagy are the three main quality control mechanisms responsible for maintaining mitochondrial proteostasis and bioenergetics. The proper functioning of these complex processes is necessary to surveil and restore mitochondrial homeostasis and the healthy pool of mitochondria in cells. Mitochondrial dysfunction occurs early and causally in disease pathogenesis. A significant accumulation of mitochondrial damage resulting from compromised quality control pathways leads to the development of neuropathology. Moreover, genetic or pharmaceutical manipulation targeting the mitochondrial quality control mechanisms can sufficiently rescue mitochondrial integrity and ameliorate disease progression. Thus, therapies that can improve mitochondrial quality control have great promise for the treatment of neurodegenerative diseases. In this review, we summarize recent progress in the field that underscores the essential role of impaired mitochondrial quality control pathways in the pathogenesis of neurodegenerative diseases. We also discuss the translational approaches targeting mitochondrial function, with a focus on the restoration of mitochondrial integrity, including mitochondrial dynamics, mitophagy, and mitochondrial proteostasis.

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In recent years, pattern analysis plays an important role in data mining and recognition, and many variants have been proposed to handle complicated scenarios. In the literature, it has been quite familiar with high dimensionality of data samples, but either such characteristics or large data sets have become usual sense in real-world applications. In this work, an improved maximum margin criterion (MMC) method is introduced firstly. With the new definition of MMC, several variants of MMC, including random MMC, layered MMC, 2D 2 MMC, are designed to make adaptive learning applicable. Particularly, the MMC network is developed to learn deep features of images in light of simple deep networks. Experimental results on a diversity of data sets demonstrate the discriminant ability of proposed MMC methods are compenent to be adopted in complicated application scenarios.

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