Research on the Enhancement Mechanism of Dihydromyricetin on the Inhibitory Role of Cisplatin Towards Breast Cancer Cell Activity

Objective: The purpose of our study was to evaluate Enhancement Mechanism of Dihydromyricetin (DMY) on the Inhibitory Role of Cisplatin Towards Breast Cancer Cell Activity. Materials and Methods: The MCF-7 were divided into NC, DMY, Cis and DMY+Cis groups. Using relative methods (MTT, TUNEL, Transwell, flow cytometry and wound healing) to evaluate MCF-7 cell biological activities including cell viability, apoptosis, invasion cell number and wound healing rate. The relative proteins expressions including FOXO-1, Noxa, Bim, Cyto C, Caspase-3, Caspase-9 and Apaf-1 were evaluated by WB assay. Results: MCF-7 cell viability, invasion cell number and wound healing rates were significantly depressed and apoptosis rate were significantly increased in DMY, Cis and DMY+Cis groups (P < 0.01, respectively). Compared with Cis group, cell viability, invasion cell number and wound healing rates were significantly depressed and apoptosis rate were significantly increased in DMY+Cis group (P < 0.05, respectively). Conclusion: Dihydromyricetin can effectively enhance the inhibitory effect of cisplatin on breast cancer cells.

Traffic Lights Detection and Recognition Method Based on the Improved YOLOv4 Algorithm

For facing of the problems caused by the YOLOv4 algorithm’s insensitivity to small objects and low detection precision in traffic light detection and recognition, the Improved YOLOv4 algorithm is investigated in the paper using the shallow feature enhancement mechanism and the bounding box uncertainty prediction mechanism. The shallow feature enhancement mechanism is used to extract features from the network and improve the network’s ability to locate small objects and color resolution by merging two shallow features at different stages with the high-level semantic features obtained after two rounds of upsampling. Uncertainty is introduced in the bounding box prediction mechanism to improve the reliability of the prediction of the bounding box by modeling the output coordinates of the prediction bounding box and adding the Gaussian model to calculate the uncertainty of the coordinate information. The LISA traffic light data set is used to perform detection and recognition experiments separately. The Improved YOLOv4 algorithm is shown to have a high effectiveness in enhancing the detection and recognition precision of traffic lights. In the detection experiment, the area under the PR curve value of the Improved YOLOv4 algorithm is found to be 97.58%, which represents an increase of 7.09% in comparison to the 90.49% score gained in the Vision for Intelligent Vehicles and Applications Challenge Competition. In the recognition experiment, the mean average precision of the Improved YOLOv4 algorithm is 82.15%, which is 2.86% higher than that of the original YOLOv4 algorithm. The Improved YOLOv4 algorithm shows remarkable advantages as a robust and practical method for use in the real-time detection and recognition of traffic signal lights.