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https://www.selleckchem.com/products/bozitinib.html To improve the neural network detection accuracy of the electric power bushings in infrared images, a modified algorithm based on the You Only Look Once version 2 (YOLOv2) network is proposed to achieve better recognition results. Specifically, YOLOv2 corresponds to a convolutional neural network (CNN), although its rotation invariance is poor, and some bounding boxes (BBs) exhibit certain deviations. To solve this problem, the standard Hough transform and image rotation are utilized to determine the optimal recognition angle for target detection, such that an optimal recognition effect of YOLOv2 on inclined objects (for example, bushing) is achieved. With respect to the problem that the BB is biased, the shape feature of the bushing is extracted by the Gap statistic algorithm, based on K-means clustering; thereafter, the sliding window (SW) is utilized to determine the optimal recognition area. Experimental verification indicates that the proposed rotating image method can improve the recognition effect, and the SW can further modify the BB. The accuracy of target detection increases to 97.33%, and the recall increases to 95%.Aberrant angiogenesis is a pathological feature of a number of diseases and arises from the uncoordinated expression of angiogenic factors as response to different cellular stresses. Age-related macular degeneration (AMD), a leading cause of vision loss, can result from pathological angiogenesis. As a mutation in the mitochondrial ferritin (FTMT) gene has been associated with AMD, its possible role in modulating angiogenic factors and angiogenesis was investigated. FTMT is an iron-sequestering protein primarily expressed in metabolically active cells and tissues with high oxygen demand, including retina. In this study, we utilized the human retinal pigment epithelial cell line ARPE-19, both as undifferentiated and differentiated cells. The effects of proinflammatory cytokines, FTMT knockdown,

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