Expression of TRAP1 in gastric cancer tissue and its correlation with malignant biology

Objective:To study the expression of tumor necrosis factor receptor-associated protein 1(TRAP1)in gastric cancer tissue and its correlation with malignant biology.Methods:Gastric cancer tissue and adjacent normal tissue were collected,and mRNA content and protein content of TRAP1 were detected;gastric cancer cell lines SGC7901,BGC823,AGS and MGC803 were cultured,and mRNA contents and protein contents of TRAP1,CyclinB1,CyclinD1,CyclinE,MMP-2 and VEGF were detected.Results:mRNA and protein expression levels of TRAP1 in gastric cancer tissue were significantly higher than those in adjacent normal tissue,and mRNA and protein expression levels of TRAP1 in gastric cancer tissue with muscularis and serosa infiltration,lymph node metastasis,distant organ metastasis and TNM Ⅲ/Ⅳ stage were significantly higher than those in gastric cancer tissue with mucosa and submucosa infiltration,non-lymph node metastasis,non-distant organ metastasis and TNM Ⅰ/Ⅱ stage.mRNA and protein expression levels of TRAP1,CyclinB1,CyclinD1,CyclinE,MMP-2 and VEGF in MGC803 were the highest,and mRNA and protein expression levels of TRAP1,CyclinB1,CyclinD1,CyclinE,MMP-2 and VEGF in SGC7901 were the lowest.mRNA and protein expression levels of TRAP1 in gastric cancer cell lines were positively correlated with mRNA and protein expression of CyclinB1,CyclinD1.CyclinE,MMP-2 and VEGF.Conclusions:The expression of TRAP1 significantly increases in gastric cancer tissue;TRAP1 may regulate the malignant biology of cells by increasing the expression of CyclinB1,CyclinD1,CyclinE,MMP-2 and VEGF,thereby resulting in the occurrence and development of gastric cancer.

Proximal policy optimization with an integral compensator for quadrotor control

We use the advanced proximal policy optimization(PPO)reinforcement learning algorithm to optimize the stochastic control strategy to achieve speed control of the"model-free"quadrotor.The model is controlled by four learned neural networks,which directly map the system states to control commands in an end-to-end style.By introducing an integral compensator into the actor-critic framework,the speed tracking accuracy and robustness have been greatly enhanced.In addition,a two-phase learning scheme which includes both offline-and online-learning is developed for practical use.A model with strong generalization ability is learned in the offline phase.Then,the flight policy of the model is continuously optimized in the online learning phase.Finally,the performances of our proposed algorithm are compared with those of the traditional PID algorithm.