Objective—
Mitochondria are the important yet most underutilized target for cardio-cerebrovascular function integrity and disorders. The Tom (translocases of outer membrane) complex are the critical determinant of mitochondrial homeostasis for making organs acclimate physiological and pathological insults; however, their roles in the vascular system remain unknown.
Approach and Results—
A combination of studies in the vascular-specific transgenic zebrafish and genetically engineered mice was conducted. Vascular casting and imaging, endothelial angiogenesis, and mitochondrial protein import were performed to dissect potential mechanisms. A loss-of-function genetic screening in zebrafish identified that selective inactivation of the
tomm7
(translocase of outer mitochondrial membrane 7) gene, which encodes a small subunit of the Tom complex, specially impaired cerebrovascular network formation. Ablation of the ortholog
Tomm7
in mice recapitulated cerebrovascular abnormalities. Restoration of the cerebrovascular anomaly by an endothelial-specific transgenesis of
tomm7
further indicated a defect in endothelial function. Mechanistically, Tomm7 deficit in endothelial cells induced an increased import of Rac1 (Ras-related C3 botulinum toxin substrate 1) protein into mitochondria and facilitated the mitochondrial Rac1-coupled redox signaling, which incurred angiogenic impairment that underlies cerebrovascular network malformation.
Conclusions—
Tomm7 drives brain angiogenesis and cerebrovascular network formation through modulating mitochondrial Rac1 signaling within the endothelium.
Human activity recognition is widely used in many fields, such as the monitoring of smart homes, fire detecting and rescuing, hospital patient management, etc. Acoustic waves are an effective method for human activity recognition. In traditional ways, one or a few ultrasonic sensors are used to receive signals, which require many feature quantities of extraction from the received data to improve recognition accuracy. In this study, we propose an approach for human activity recognition based on a two-dimensional acoustic array and convolutional neural networks. A single feature quantity is utilized to characterize the sound of human activities and identify those activities. The results show that the total accuracy of the activities is 97.5% for time-domain data and 100% for frequency-domain data. The influence of the array size on recognition accuracy is discussed, and the accuracy of the proposed approach is compared with traditional recognition approaches such as k-nearest neighbor and support vector machines where it outperformed them.
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