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https://www.selleckchem.com/products/dcz0415.html Minimizing human intervention in engines, such as traffic lights, through automatic applications and sensors has been the focus of many studies. Thus, Deep Learning (DL) algorithms have been studied for traffic signs and vehicle identification in an urban traffic context. However, there is a lack of priority vehicle classification algorithms with high accuracy, fast processing, and a lightweight solution. For filling those gaps, a vehicle detection system is proposed, which is integrated with an intelligent traffic light. Thus, this work proposes (1) a novel vehicle detection model named Priority Vehicle Image Detection Network (PVIDNet), based on YOLOV3, (2) a lightweight design strategy for the PVIDNet model using an activation function to decrease the execution time of the proposed model, (3) a traffic control algorithm based on the Brazilian Traffic Code, and (4) a database containing Brazilian vehicle images. The effectiveness of the proposed solutions were evaluated using the Simulation of Urban MObility (SUMO) tool. Results show that PVIDNet reached an accuracy higher than 0.95, and the waiting time of priority vehicles was reduced by up to 50%, demonstrating the effectiveness of the proposed solution.Diabetes is a major risk factor for atherosclerosis and ischemic vascular diseases. Recently, regenerative medicine is expected to be a novel therapy for ischemic diseases. Our previous studies have reported that transplantation of stem cells promoted therapeutic angiogenesis for diabetic neuropathy and ischemic vascular disease in a paracrine manner, but the precise mechanism is unclear. Therefore, we examined whether secreted factors from stem cells had direct beneficial effects on endothelial cells to promote angiogenesis. The soluble factors were collected as conditioned medium (CM) 48 h after culturing stem cells from human exfoliated deciduous teeth (SHED) in serum-free DMEM. SHED-CM significantly increased

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