Introduction EP300 is considered to be a cancer suppressor gene that plays a role in tumor development, but some studies have reported that it is not an oral squamous cell carcinoma suppressor gene, because there was neither epigenetic inactivation of the gene nor a mutation resulting in functional impairment. However, there is no relevant study on whether EP300 is the exact carcinogenic effect and its mechanisms of carcinogenic effects in oral squamous cell carcinoma. Methods Western blot analysis and quantitative real time polymerase chain reaction experiments verified the protein and mRNA expression of EP300 in oral squamous cell carcinoma; The effects of EP300 knockout on glucose consumption and lactic acid production were detected by glycolysis experiments; The relationship between pathway‐related proteins and EP300 was verified by bioinformatics analysis and co‐immunoprecipitation experiment. Results Our experimental results confirm that the protein and mRNA of EP300 are highly expressed in oral squamous cell carcinoma, and after knocking out the EP300, the glycolysis ability, invasion, migration, and other biological functions of oral squamous cell carcinoma, are inhibited at the same time. Pathway‐related experiments have confirmed that EP300 plays a role in promoting cancer through the transforming growth factor‐beta receptor II (TGF‐βRII)/EP300/Smad4 cascade pathway. Conclusion EP300 plays a carcinogenic role in OSCC showed that the TGF‐βRII/EP300/Smad4 cascade pathway is involved in oral squamous cell carcinoma.

A join order directly affects database query performance and computational overhead. Deep reinforcement learning (DRL) can explore efficient query plans while not exhausting the search space. However, the deep Q network (DQN) suffers from the overestimation of action values in query optimization, which can lead to limited query performance. In addition, ε-greedy exploration is not efficient enough and does not enable deep exploration. Accordingly, in this paper, we propose a dynamic double DQN (DDQN) order selection method(DDOS) for join order optimization. First, the method models the join query as a Markov decision process (MDP), then solves the DRL model by integrating the network model DQN and DDQN weighting into the DRL model’s estimation error problem in query joining, and finally improves the quality of developing query plans. And actions are selected using a dynamic progressive search strategy to improve the randomness and depth of exploration and accumulate a high information gain of exploration. The performance of the proposed method is compared with those of dynamic programming, heuristic algorithms, and DRL optimization methods based on the query set Join Order Benchmark (JOB). The experimental results show that the proposed method can effectively improve the query performance with a favorable generalization ability and robustness, and outperforms other baselines in multi-join query applications.

Framing effect is an important judgemental bias observed in human behavior. However, despite numerous theoretical and empirical progressions on framing effect in economics, there are still shortcomings to rationalize the discussion. This paper tries to provide a comprehensive understanding of current research on framing effect by discussing/ its definition, its relationship with preferences, and its current theoretical stage. More specifically, its application in people's retirement social benefit choosing behavior is extensively discussed. Based on the current literature about framing, this paper also points out the potential future directions of theoretical and empirical research.