Knowledge Reasoning Method Based on Deep Transfer Reinforcement Learning:DTRLpath

In recent years,with the continuous development of deep learning and knowledge graph reasoning methods,more and more researchers have shown great interest in improving knowledge graph reasoning methods by inferring missing facts through reasoning.By searching paths on the knowledge graph and making fact and link predictions based on these paths,deep learning-based Reinforcement Learning(RL)agents can demonstrate good performance and interpretability.Therefore,deep reinforcement learning-based knowledge reasoning methods have rapidly emerged in recent years and have become a hot research topic.However,even in a small and fixed knowledge graph reasoning action space,there are still a large number of invalid actions.It often leads to the interruption of RL agents’wandering due to the selection of invalid actions,resulting in a significant decrease in the success rate of path mining.In order to improve the success rate of RL agents in the early stages of path search,this article proposes a knowledge reasoning method based on Deep Transfer Reinforcement Learning path(DTRLpath).Before supervised pre-training and retraining,a pre-task of searching for effective actions in a single step is added.The RL agent is first trained in the pre-task to improve its ability to search for effective actions.Then,the trained agent is transferred to the target reasoning task for path search training,which improves its success rate in searching for target task paths.Finally,based on the comparative experimental results on the FB15K-237 and NELL-995 datasets,it can be concluded that the proposed method significantly improves the success rate of path search and outperforms similar methods in most reasoning tasks.

Intracellular carbon monoxide release unveils selective antibacterial effects

Carbon monoxide(CO)is recognized as a diffusible and biologically membrane-permeable gasotransmitter.However,the question of whether extracellular and intracellular CO delivery would yield similar or distinct biological functions remains unresolved.In this study,utilizing nonmetallic CO-releasing micelles as a platform for localized CO delivery,we present evidence suggesting that selective antibacterial effects against Staphylococcus aureus(S.aureus)are exclusively evident upon intracellular CO release,even in cases of extracellular release with higher CO concentrations showing no comparable effect.To substantiate this assertion,we systematically design micellar nanoparticles with varying sizes,monomer sequences,and shell compositions.Among these variants,only the micelles taken up by S.aureus and capable of intracellular CO release exhibit efficient bacteria-killing properties.We further demonstrate that the selective bactericidal effect is closely linked to the production of hydroxyl radicals after intracellular CO release.Additionally,intracellular CO release proves to be an efficient treatment for S.aureus-induced skin abscesses without the need for additional antibiotics,showcasing synergistic antibacterial and anti-inflammatory effects.These findings underscore the pivotal role of the spatial location of CO release,significantly enhancing our understanding of the pathophysiological functions of gasotransmitters.