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Objective To investigate the mechanisms of mitochondrial dysfunction-induced vitiligo based on bioinformatics. Methods The vitiligo target genes were obtained from CTD/Gene Cards/OMIM/NCBI Gene databases, while mitochondrial dysfunction target genes were downloaded from the MITOMAP database, followed by intersection analysis to identify the common genes. The latter were subjected to GO and KEGG enrichment analysis. The core genes were screened by analysis of protein-protein interaction. The NetworkAnalyst and Toppgene databases were used to predict the core gene-related transcription factors, miRNAs and the treatments. Results There were 90 common genes in vitiligo and mitochondrial dysfunction, and 13 core genes were screened by analysis of protein-protein interaction. The biological processes of the common genes were mainly enriched in mitochondrial respiratory chain complex assembly, while the cellular components were mainly enriched in mitochondria. The molecular functions were mainly enriched in the activities of redox-driven transmembrane transporter and oxidoreductase, and KEGG was mainly enriched in respiratory electron transport. The transcription factors with high degree of interactions with the core genes included ATF1, KDM5A, KDM5B and PHF8, and the miRNAs with high interaction with core genes included hsa-mir-1-3p and hsa-mir-16-5p. The predicted effective drugs were lipoic acid and ubiquinols. Conclusions Mitochondrial dysfunction may contribute to pathogenesis of vitiligo by affecting biological processes, such as complex assembly and electron transport in the mitochondrial respiratory chain. Transcription factors, such as ATF1 and KDM5, and miRNAs, including miR-1 and miR-493, may play an important pathogenic role in vitiligo by interaction with key genes, such as NDUFA9, SURF1, NDUFS1, NDUFS4 and LRPPRC.

The RSS-based multi-target localization has the natural property of the sparsity in wireless sensor networks.A multi-target localization algorithm based on adaptive grid in wireless sensor networks was proposed,which divided the multi-target localization problem into two phases:large-scale grid-based localization and adaptive grid-based localization.In the large-scale grid-based localization phase,the optimal number of measurements was determined due to the sequential compressed sensing theory,and then the locations of the initial candidate grids were reconstructed by applying lp (0＆lt; p＆lt;1) optimization.In the adaptive grid-based localization phase,the initial candidate grids were adaptively partitioned according to the compressed sensing theory,and then the locations of the targets were precisely estimated by applying lpoptimization once again.Compared with the traditional multi-target localization algorithm based on compressed sensing,the simulation results show that the proposed algorithm has higher localization accuracy and lower localization delay without foreknowing the number of targets.Therefore,it is more appropriate for the multi-target localization problem in the large-scale wireless sensor networks.