Learning Perception-Aware Agile Flight in Cluttered Environments

Recently, neural control policies have outperformed existing model-based planning-and-control methods for autonomously navigating quadrotors through cluttered environments in minimum time. However, they are not perception aware, a crucial requirement in vision-based navigation due to the camera's limited field of view and the underactuated nature of a quadrotor. We propose a learning-based system that achieves perception-aware, agile flight in cluttered environments. Our method combines imitation learning with reinforcement learning (RL) by leveraging a privileged learning-by-cheating framework. Using RL, we first train a perception-aware teacher policy with full-state information to fly in minimum time through cluttered environments. Then, we use imitation learning to distill its knowledge into a vision-based student policy that only perceives the environment via a camera. Our approach tightly couples perception and control, showing a significant advantage in computation speed (10×faster) and success rate. We demonstrate the closed-loop control performance using hardware-in-the-loop simulation

Advancing EEG/MEG Source Imaging with Geometric-Informed Basis Functions

Electroencephalography (EEG) and Magnetoencephalography (MEG) are pivotal in understanding brain activity but are limited by their poor spatial resolution. EEG/MEG source imaging (ESI) infers the high-resolution electric field distribution in the brain based on the low-resolution scalp EEG/MEG observations. However, the ESI problem is ill-posed, and how to bring neuroscience priors into ESI method is the key. Here, we present a novel method which utilizes the Brain Geometric-informed Basis Functions (GBFs) as priors to enhance EEG/MEG source imaging. Through comprehensive experiments on both synthetic data and real task EEG data, we demonstrate the superiority of GBFs over traditional spatial basis functions (e.g., Harmonic and MSP), as well as existing ESI methods (e.g., dSPM, MNE, sLORETA, eLORETA). GBFs provide robust ESI results under different noise levels, and result in biologically interpretable EEG sources. We believe the high-resolution EEG source imaging from GBFs will greatly advance neuroscience research

Adipose-derived mesenchymal stem cells attenuate ischemic brain injuries in rats by modulating miR-21-3p/MAT2B signaling transduction

Aim To explore the mechanism underlying the protective effect of adipose-derived mesenchymal stem cells (ADMSCs) against ischemic stroke by focusing on miR-21-3p/ MAT2B axis. Methods Ischemic brain injury was induced in 126 rats by middle cerebral artery occlusion (MCAO). The effect of ADMSC administration on blood-brain barrier (BBB) condition, apoptosis, inflammation, and the activity of miR-21- 3p/MAT2B axis was assessed. The role of miR-21-3p inhibition in the function of ADMSCs was further validated in in vitro neural cells. Results ADMSCs administration improved BBB condition, inhibited apoptosis, and suppressed inflammation. It also reduced the abnormally high level of miR-21-3p in MCAO rats. Dual luciferase assays showed that miR-21-3p directly inhibited the MAT2B expression in neural cells, and miR-21 -3p inhibition by inhibitor or ADMSC-derived exosomes in neurons attenuated hypoxia/reoxygenation-induced impairments similarly to that of ADMSCs in vivo. Conclusion This study confirmed the protective effect of ADMSCs against ischemic brain injury exerted by suppressing miR-21-3p level and up-regulating MAT2B level