SummaryControl over the number of mtDNA molecules per cell appears to be tightly regulated, but the mechanisms involved are largely unknown. Reversible alterations in the amount of mtDNA occur in response to stress suggesting that control over the amount of mtDNA is involved in stress-related diseases including major depressive disorder (MDD). Using low-coverage sequence data from 10,442 Chinese women to compute the normalized numbers of reads mapping to the mitochondrial genome as a proxy for the amount of mtDNA, we identified two loci that contribute to mtDNA levels: one within the TFAM gene on chromosome 10 (rs11006126, p value = 8.73 × 10−28, variance explained = 1.90%) and one over the CDK6 gene on chromosome 7 (rs445, p value = 6.03 × 10−16, variance explained = 0.50%). Both loci replicated in an independent cohort. CDK6 is thus a new molecule involved in the control of mtDNA. We identify increased rates of heteroplasmy in women with MDD, and show from an experimental paradigm using mice that the increase is likely due to stress. Furthermore, at least one heteroplasmic variant is significantly associated with changes in the amount of mtDNA (position 513, p value = 3.27 × 10−9, variance explained = 0.48%) suggesting site-specific heteroplasmy as a possible link between stress and increase in amount of mtDNA. These findings indicate the involvement of mitochondrial genome copy number and sequence in an organism’s response to stress.

This paper focuses on the monthly operations of an interprovincial hydropower system (IHS) connected by ultrahigh voltage direct current lines. The IHS consists of the Xiluodu Hydropower Project, which ranks second in China, and local plants in multiple recipient regions. It simultaneously provides electricity for Zhejiang and Guangdong provinces and thus meets their complex operation requirements. This paper develops a multi-objective optimization model of maximizing the minimum of total hydropower generation for each provincial power grid while considering network security constraints, electricity contracts, and plant constraints. The purpose is to enhance the minimum power in dry season by using the differences in hydrology and regulating storage of multiple rivers. The TOPSIS method is utilized to handle this multi-objective optimization, where the complex minimax objective function is transformed into a group of easily solved linear formulations. Nonlinearities of the hydropower system are approximatively described as polynomial formulations. The model was used to solve the problem using mixed integer nonlinear programming that is based on the branch-and-bound technique. The proposed method was applied to the monthly generation scheduling of the IHS. Compared to the conventional method, both the total electricity for Guangdong Power Grid and Zhejiang Power Grid during dry season increased by 6% and 4%, respectively. The minimum monthly power also showed a significant increase of 40% and 31%. It was demonstrated that the hydrological differences between Xiluodu Plant and local hydropower plants in receiving power grids can be fully used to improve monthly hydropower generation.

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Image fusion based on the sparse representation (SR)has become the primary research direction of the transform domain method. However, the SR-based image fusion algorithm has the characteristics of high computational complexity and neglecting the local features of an image, resulting in limited image detail retention and a high registration misalignment sensitivity. In order to overcome these shortcomings and the noise existing in the image of the fusion process, this paper proposes a new signal decomposition model, namely the multi-source image fusion algorithm of the gradient regularization convolution SR (CSR). The main innovation of this work is using the sparse optimization function to perform two-scale decomposition of the source image to obtain high-frequency components and low-frequency components. The sparse coefficient is obtained by the gradient regularization CSR model, and the sparse coefficient is taken as the maximum value to get the optimal high frequency component of the fused image. The best low frequency component is obtained by using the fusion strategy of the extreme or the average value. The final fused image is obtained by adding two optimal components. Experimental results demonstrate that this method greatly improves the ability to maintain image details and reduces image registration sensitivity.