Glioblastoma multiforme (GBM) has the highest mortality rate among patients with brain tumors, and radiotherapy forms an important part of its treatment. Thus, there is an urgent requirement to elucidate the mechanisms conferring GBM progression and radioresistance. In the present study, it was identified that antisense transcript of hypoxia-inducible factor-1α (AHIF) was significantly upregulated in GBM cancerous tissues, as well as in radioresistant GBM cells. The expression of AHIF was also upregulated in response to radiation. Knockdown of AHIF in GBM cells decreased viability and invasive capacities, and increased the proportion of apoptotic cells. By contrast, overexpression of AHIF in GBM cells increased viability and invasive capacities, and decreased the proportion of apoptotic cells. Furthermore, exosomes derived from AHIF-knockdown GBM cells inhibited viability, invasion and radioresistance, whereas exosomes derived from AHIF-overexpressing GBM cells promoted viability, invasion and radioresistance. Further biochemical analysis identified that AHIF regulates factors associated with migration and angiogenesis in exosomes. To the best of our knowledge, the present study is the first to establish that AHIF promotes glioblastoma progression and radioresistance via exosomes, which suggests that AHIF is a potential therapeutic target for GBM.

Autonomous Racing awards agents that react to opponents' behaviors with agile maneuvers towards progressing along the track while penalizing both over-aggressive and overconservative agents. Understanding the intent of other agents is crucial to deploying autonomous systems in adversarial multiagent environments. Current approaches either oversimplify the discretization of the action space of agents or fail to recognize the long-term effect of actions and become myopic. Our work focuses on addressing these two challenges. First, we propose a novel dimension reduction method that encapsulates diverse agent behaviors while conserving the continuity of agent actions. Second, we formulate the two-agent racing game as a regret minimization problem and provide a solution for tractable counterfactual regret minimization with a regret prediction model. Finally, we validate our findings experimentally on scaled autonomous vehicles. We demonstrate that using the proposed game-theoretic planner using agent characterization with the objective space significantly improves the win rate against different opponents, and the improvement is transferable to unseen opponents in an unseen environment.

Warship defense is very important, especially close-in air-defense. Many researchers study methods of defending anti-ship missile. In those methods, the firing method of future airspace window is a promising method. Although it has many problems need to solve. In this paper using the "soft" and "hard" way, two methods of future airspace window are studied and analyzed deeply, one is the firing data distribution method and another is preplacing gun barrel angle method.Study conclusion shows our method is s a kind of effective way to improve the high-speed and complicated maneuvering anti-ship missile attacking.