Mechanism of Apoptosis Induced by Scopoletin in Breast Cancer Cells

[Objectives] This study was conducted to investigate the effect of scopoletin on the growth of breast cancer cell lines.[Methods] Cell culture,light microscopy and MTT assay for measuring cell viability were used to compare the inhibition of different concentrations of scopoletin to breast cancer cells,so as to provide an experimental basis for finding new therapeutic drugs for breast cancer cells.[Results] It was shown that a high concentration of scopoletin can inhibit the growth of breast cancer cells and promote apoptosis.[Conclusions] Scopoletin can be used as a potential drug for the treatment of breast cancer.

Channel Capacity Optimization Based on Riemannian Trust Region Algorithm in IRS-Aided Communication System

Intelligent Reflecting Surface (IRS) can offer unprecedented channel capacity gains since it can reconfigure the signal propagation environment. We decide to maximize the channel capacity by jointly optimizing the transmit-power-constrained precoding matrix at the base station and the unit-modulus-constrained phase shift vector at the IRS in IRS-assisted multi-user downlink communication. We first convert the resulting non-convex problem into an equivalent problem, then use the alternate optimization algorithm. While fixing the phase shift vector, we can obtain the optimal precoding matrix directly by adopting standard optimization packages. While fixing the precoding matrix, we propose the Riemannian Trust-Region (RTR) algorithm to solve this optimization problem. And the key of the RTR algorithm is the solution of the trust-region sub-problem. We first adopt the accurate solution based on Newton's (ASNT) method to solve this sub-problem, which can obtain the global solution but cannot guarantee that the solution is optimal since the initial iteration point is difficult to choose. Then, we propose the Improved-Polyline (IPL) method, which can avoid the difficulty of the ASNT method and improve convergence speed and calculation efficiency. The numerical results show that the RTR algorithm has more significant performance gains and faster convergence speed compared with the existing approaches.

Study on the Inhibitory Effect of Scopoletin on Proliferation and Metastasis of Breast Cancer Cells

[Objectives]This study was conducted to investigate the effect of scopoletin on the proliferation of breast cancer cell lines.[Methods]Cell viability,gene expression and cell cycle were measured by cell culture,RT-PCR and flow cytometry.The effects of different concentrations of scopoletin on proliferation and cell cycle of breast cancer cells were compared.[Results]After a certain period of time,the breast cancer cells showed different degrees of apoptosis,and the growth cycle was arrested in the G1 phase,which promoted the down-regulation of three genes in breast cancer metastasis.[Conclusions]The study proves that scopoletin inhibits the proliferation and metastasis of breast cancer cells in a concentration-and time-dependent manner.

Application of Ramp to Improve the Efficiency of RAPD in Longan

[ Objective] This study aimed to explore a modified method for improving the efficiency of RAPD technology. [ Method] Longans collected from Si- chuan, Fujian, Guangxi, Guangdong and Hainan were used as experimental materials. Total genomic DNA was extracted from langan leaves with modified CTAB method. 18 random primers were selected for RAPD amplification with ramp rates of 0.3 and 3.0℃/s, respectively. Effects of different ramp rates on RAPD am- plification were compared and the relationship among longan samples was analyzed. [ Result] The resolution and yield could be improved significantly by declining the ramp rate from 3.0 to 0.3℃/s. Cluster analysis showed that similarity coefficient of the five varieties of longan ranged from 0.69 to 0.76. [ Conclusion] This study had confirmed that reducing ramp rate could significantly increase the resolution and yield of RAPD technology, which had provided a new method to improve the efficiency of RAPD.

Automatic Real-Time Detection of Infant Drowning Using YOLOv5 and Faster R-CNN Models Based on Video Surveillance

Infant drowning has occurred frequently in swimming pools recent years,which motivates the research on automatic real-time detection of the accident.Unlike youths or adults,swimming infants are small in terms of size and motion range,and unable to send out distress signals in emergencies,which exerts negative effects on the detection of drowning.Aiming at this problem,a new step is initialized towards detecting infant drowning automatically and efficiently based on video surveillance.Diverse live-scene videos of infant swimming and drowning are collected from a variety of natatoriums and labeled as datasets.A part of the datasets is downscaled or enlarged to enhance generalization ability of the model.On this basis,advantages of Faster R-CNN and a series of YOLOv5 models are specifically explored to enable fast and accurate detection of infant drowning in real-world.Supervised learning experiments are carried out,model test results show that mean Average Precision(mAP)of either Faster R-CNN or YOLOv5s of the series of YOLOv5 can be over 89%;the former can process merely 6 frames of videos per second with the precision of only 62.04%,while the latter can reach an average speed of 75 frames/s with the precision of about 86.6%.The YOLOv5s eventually stands out as an optimal model for detecting infant drowning in view of comprehensive performance,which is of great application value to reduce the accidents in swimming pools.