Activating the cyclic guanosine monophosphate-adenosine monophosphate synthase/stimulator of interferon genes(cGAS/STING)signaling has emerged as a promising anti-tumor strategy due to the important role of the pathwa...Activating the cyclic guanosine monophosphate-adenosine monophosphate synthase/stimulator of interferon genes(cGAS/STING)signaling has emerged as a promising anti-tumor strategy due to the important role of the pathway in innate and adaptive immunity,yet the selective delivery of STING agonists to tumors following systemic administration remains challenging.Herein,we develop a nano-STING agonist-decorated microrobot platform to achieve the enhanced anti-tumor effect.Fe ions and the STING agonist 2’3’-cyclic guanosine monophosphate-adenosine monophosphate(cGAMP)are co-encapsulated in the mitochondria-targeting nanoparticles(mTNPs),which can trigger the release of mitochondrial DNA(mtDNA)by Fenton reactioninduced mitochondria oxidative damage.The exogenous cGAMP and the endogenous mtDNA can work synergistically to induce potent cGAS/STING signaling activation.Furthermore,we decorate mTNPs onto Salmonella typhimurium VNP20009(VNP)bacteria to facilitate tumor accumulation and deep penetration.We demonstrate that the systemic administration of this microrobot activates both innate and adaptive immunity,improving the immunotherapeutic efficacy of the STING agonists.展开更多
A fundamental task in robotics is to plan collision-free motions among a set of obstacles.Recently,learning-based motion-planning methods have shown significant advantages in solving different planning problems in hig...A fundamental task in robotics is to plan collision-free motions among a set of obstacles.Recently,learning-based motion-planning methods have shown significant advantages in solving different planning problems in high-dimensional spaces and complex environments.This article serves as a survey of various different learning-based methods that have been applied to robot motion-planning problems,including supervised,unsupervised learning,and reinforcement learning.These learning-based methods either rely on a human-crafted reward function for specific tasks or learn from successful planning experiences.The classical definition and learning-related definition of motion-planning problem are provided in this article.Different learning-based motion-planning algorithms are introduced,and the combination of classical motion-planning and learning techniques is discussed in detail.展开更多
基金This work was supported by the start-up package from the University of Wisconsin-Madison(to Q.Y.H.).
文摘Activating the cyclic guanosine monophosphate-adenosine monophosphate synthase/stimulator of interferon genes(cGAS/STING)signaling has emerged as a promising anti-tumor strategy due to the important role of the pathway in innate and adaptive immunity,yet the selective delivery of STING agonists to tumors following systemic administration remains challenging.Herein,we develop a nano-STING agonist-decorated microrobot platform to achieve the enhanced anti-tumor effect.Fe ions and the STING agonist 2’3’-cyclic guanosine monophosphate-adenosine monophosphate(cGAMP)are co-encapsulated in the mitochondria-targeting nanoparticles(mTNPs),which can trigger the release of mitochondrial DNA(mtDNA)by Fenton reactioninduced mitochondria oxidative damage.The exogenous cGAMP and the endogenous mtDNA can work synergistically to induce potent cGAS/STING signaling activation.Furthermore,we decorate mTNPs onto Salmonella typhimurium VNP20009(VNP)bacteria to facilitate tumor accumulation and deep penetration.We demonstrate that the systemic administration of this microrobot activates both innate and adaptive immunity,improving the immunotherapeutic efficacy of the STING agonists.
基金National Key R&D program of China,Grant/Award Number:2019YFB1312400Hong Kong RGC CRF grant,Grant/Award Number:#C4063-18GF+3 种基金Hong Kong RGC TRS grant,Grant/Award Number:#T42-409/18-RHong Kong RGC GRF grant,Grant/Award Number:#14200618Shenzhen Science and Technology Innovation projects:JCYJ20170413161503220This research was funded by National Key R&D program of China with Grant No.2019YFB1312400,Hong Kong RGC GRF grant No.14200618,Hong Kong RGC TRS grant No.T42-409/18-R and Hong Kong RGC CRF grant No.C4063-18GF.
文摘A fundamental task in robotics is to plan collision-free motions among a set of obstacles.Recently,learning-based motion-planning methods have shown significant advantages in solving different planning problems in high-dimensional spaces and complex environments.This article serves as a survey of various different learning-based methods that have been applied to robot motion-planning problems,including supervised,unsupervised learning,and reinforcement learning.These learning-based methods either rely on a human-crafted reward function for specific tasks or learn from successful planning experiences.The classical definition and learning-related definition of motion-planning problem are provided in this article.Different learning-based motion-planning algorithms are introduced,and the combination of classical motion-planning and learning techniques is discussed in detail.
