Myeloid cells are pivotal in the inflammatory and remodeling phases of fracture repair.Here,we investigate the effect of periostin expressed by myeloid cells on bone regeneration in a monocortical tibial defect(MTD)mo...Myeloid cells are pivotal in the inflammatory and remodeling phases of fracture repair.Here,we investigate the effect of periostin expressed by myeloid cells on bone regeneration in a monocortical tibial defect(MTD)model.In this study,we show that periostin is expressed by periosteal myeloid cells,primarily the M2 macrophages during bone regeneration.Knockout of periostin in myeloid cells reduces cortical bone thickness,disrupts trabecular bone connectivity,impairs repair impairment,and hinders M2 macrophage polarization.Mechanical stimulation is a regulator of periostin in macrophages.By activating transforming growth factor-β(TGF-β),it increases periostin expression in macrophages and induces M2 polarization.This mechanosensitive effect also reverses the delayed bone repair induced by periostin deficiency in myeloid cells by strengthening the angiogenesis-osteogenesis coupling.In addition,transplantation of mechanically conditioned macrophages into the periosteum over a bone defect results in substantially enhanced repair,confirming the critical role of macrophage-secreted periostin in bone repair.In summary,our findings suggest that mechanical stimulation regulates periostin expression and promotes M2 macrophage polarization,highlighting the potential of mechanically conditioned macrophages as a therapeutic strategy for enhancing bone repair.展开更多
This paper proposes a novel reinforcement-learning-based intelligent fault-tolerant assistance control framework for Air-breathing Hypersonic Vehicles(AHVs).Considering that Reinforcement Learning(RL)has the advantage...This paper proposes a novel reinforcement-learning-based intelligent fault-tolerant assistance control framework for Air-breathing Hypersonic Vehicles(AHVs).Considering that Reinforcement Learning(RL)has the advantage of exploring approximate optimal strategies,an RL-based assistance controller parallel to the fundamental controller is introduced to generate the assistance control signal.Specifically,the Incremental model-based Dual Heuristic Programming(IDHP)method is adopted to design the RL-based assistance control law.In order to extend the IDHP method to the assistance control scenario,a novel linear time-varying incremental model of the closed-loop augmented system is constructed and identified in real time,which consists of the AHV plant,the fundamental controller,and the command generator.The RL agent continuously updates its neural-network weights according to the real-time identification information,and adjusts its control policy,i.e.,the assistance control signal,after detecting sudden model changes.Simulation results have validated the effectiveness of the proposed intelligent fault-tolerant control scheme under various types of elevator faults and aerodynamic/configuration parameter uncertainties.The fault-tolerant ability of the whole control system with the proposed RL-based assistance controller is validated in both inner-loop attitude and outer-loop altitude tracking tasks.展开更多
基金supported by grants from Shenzhen Science and Technology Innovation Commission (KQTD20200820113012029,JCYJ20190809114209434)。
文摘Myeloid cells are pivotal in the inflammatory and remodeling phases of fracture repair.Here,we investigate the effect of periostin expressed by myeloid cells on bone regeneration in a monocortical tibial defect(MTD)model.In this study,we show that periostin is expressed by periosteal myeloid cells,primarily the M2 macrophages during bone regeneration.Knockout of periostin in myeloid cells reduces cortical bone thickness,disrupts trabecular bone connectivity,impairs repair impairment,and hinders M2 macrophage polarization.Mechanical stimulation is a regulator of periostin in macrophages.By activating transforming growth factor-β(TGF-β),it increases periostin expression in macrophages and induces M2 polarization.This mechanosensitive effect also reverses the delayed bone repair induced by periostin deficiency in myeloid cells by strengthening the angiogenesis-osteogenesis coupling.In addition,transplantation of mechanically conditioned macrophages into the periosteum over a bone defect results in substantially enhanced repair,confirming the critical role of macrophage-secreted periostin in bone repair.In summary,our findings suggest that mechanical stimulation regulates periostin expression and promotes M2 macrophage polarization,highlighting the potential of mechanically conditioned macrophages as a therapeutic strategy for enhancing bone repair.
基金co-supported by the Aeronautical Science Foundation of China(Nos.20220048051001,20230013051002)the“1912 Project”of China。
文摘This paper proposes a novel reinforcement-learning-based intelligent fault-tolerant assistance control framework for Air-breathing Hypersonic Vehicles(AHVs).Considering that Reinforcement Learning(RL)has the advantage of exploring approximate optimal strategies,an RL-based assistance controller parallel to the fundamental controller is introduced to generate the assistance control signal.Specifically,the Incremental model-based Dual Heuristic Programming(IDHP)method is adopted to design the RL-based assistance control law.In order to extend the IDHP method to the assistance control scenario,a novel linear time-varying incremental model of the closed-loop augmented system is constructed and identified in real time,which consists of the AHV plant,the fundamental controller,and the command generator.The RL agent continuously updates its neural-network weights according to the real-time identification information,and adjusts its control policy,i.e.,the assistance control signal,after detecting sudden model changes.Simulation results have validated the effectiveness of the proposed intelligent fault-tolerant control scheme under various types of elevator faults and aerodynamic/configuration parameter uncertainties.The fault-tolerant ability of the whole control system with the proposed RL-based assistance controller is validated in both inner-loop attitude and outer-loop altitude tracking tasks.
