The central nucleus of amygdala (CeA) plays key roles in a diverse set of adaptive behaviors, including defensive and appetitive response. Early studies of CeA emphasized its function in aversive Pavlovian learning, it was described as a relay connecting basolateral complex of the amygdala to the brainstem effectors. With methodological advances, we demonstrated the somatostatin (SST) expressing neurons in the CeA was actively encoding fear memory through fiber photometry in mice undergoing fear conditioning. Closer observation activities of individual SST+ CeA neurons with endoscopy imaging revealed their roles encoding both aversive and appetitive salient events. In parallel study of PKC-δ -expressing neurons, another major population of the CeA, reveals they are essential for the synaptic plasticity underlying learning in the lateral amygdala, which challenges traditional model of fear learning.

Molecular dynamics (MD) simulations are statistical mechanics-based tools for predicting microscopic and macroscopic properties/dynamics based on the details of molecule-molecule interactions. They have been widely applied in biomedical and engineering fields. The application of MD on human b defensins (hBDs) type 2 (hBD-2) and type 3 (hBD-3) is covered in this talk. hBDs belong to the human innate immune system and have microbicidal activities against bacteria, fungi, yeast, viruses, in addition to having chemotactic activities. In order to understand its functional mechanism in molecular detail, the binding structure of hBD-3 on model bacterial membrane was predicted using microsecond-long MD simulations and validated by solid-state NMR method. It was found that hBD-3 binds with bacterial membrane on the two loop regions. Free energy perturbation (FEP) calculation further clarified the contribution of each residue to the membrane binding, which showed that the tail region of hBD-3 drives the process. The results emphasized the importance of electrostatic interaction in hBD-3’s binding with bacterial membrane. Besides that, it was found that hBD-2 can bind on the receptor binding domain (RBD) of SARS-CoV-2 (Cov-2) at the same site as the receptor angiotensin-converting enzyme 2 (ACE2), thus blocking CoV-2 virus from attacking ACE2 expressing cells. The result supplies insight on the role human innate immune system plays in combating CoV-2 infection.

Nature systems having a body with many degrees of freedom are often considered the ultimate model for machines. To confer the performance advantage of animal systems on robotic machines, we are conducting studies on a thoroughunderstanding of the biological systems at the biomechanical level and the developments of biomimetic intelligent machines and biologically inspired robots. Unfortunately, many of these robots are crucial for investigation but have yet to be widely applied to real applications. We, however, can use the insight from the study of biologically inspired robots to develop robotic machines for possible applications.
In this talk, I would like to introduce some of our studies on biologically inspired robots first and then provide some designs of environment-adaptive robots for real applications.

1. 深圳市地面公交财政补贴政策研究2. 公共交通导向下的建成环境资源配置策略3. 交通运行监测中心与调度平台的北京实践4. 交通拥堵防治决策系统与技术的广西实践

Over the past decade, programmable Cas9 nucleases have revolutionized basic research and clinical applications. However, compared to Cas9-based DNA targeting tools, there is a lack of simple and effective tools for studying and manipulating RNA. The Type VI CRISPR-Cas13 system is currently the only known efficient programmable system exclusively targeting RNA. My research is focused on the type VI-D effector protein Cas13d, which is one of the smallest effectors in the Cas13 family, making it more advantageous for packaging into adeno-associated viruses. We utilized cryo-electron microscopy (cryo-EM), biochemical approaches, and hydrogen-deuterium exchange mass spectrometry to explore the conformational landscape of Cas13d during RNA processing, and utilized the structural insights to guide Cas13d protein engineering. Additionally, we established in vitro and cellular assays of using Cas13d to achieve mRNA knockdown, pre-mRNA splicing regulation, and RNA detection, providing proof-of-concept for future RNA-centric applications. Overall, our findings have the potential to significantly advance the field of transcriptome engineering and lead to more effective gene therapies.

The past decades have witnessed spurred development of robots for various applications ranging from industry and service. Smart robots are demanding devices to realize automated manipulation of objects in multi-scale. Flexible robot manipulators based on compliant mechanisms provide precise and dexterous manipulation in macro/micro scales.

With the development of the autonomous driving technologies, the traditional vehicular test and evaluation methods cannot meet the testing requirements of Intelligent Connected Vehicles (ICVs). Scenario-based crash risk testing method has become one essential approach to the safety evaluation of ICVs. The construction of testing scenarios and the development of scientific testing methods as well as evaluation framework are the key scientific issues that need to be addressed. This presentation introduces in-depth crash data collection process and collision mechanism decoupling methods, and proposes a scenario-targeted testing method for ICVs. Then, a scenario derivation method is proposed to enlarge from a limited number of real crashes to a large number of high-risk scenarios. Finally, a hierarchical crash risk evaluation framework for ICVs is presented.

Construction activities frequently generate excessive vibrations that adversely affect nearby structures, facilities, and human beings. Vibration monitoring is commonly adopted to assess the impact of construction. Traditional monitoring solutions are associated with various limitations, such as high costs, specialized operation, difficult maintenance, and limited functions. Moreover, huge amounts of monitoring data cannot be efficiently used in other studies or projects due to missed information labels. This talk will present the recent efforts of my research group to address these problems by leveraging emerging IoT and deep learning approaches. IoT sensing solutions and deep learning-based semi-supervised classification of construction activities will be introduced.

Smart transportation hinges on collaborative machine learning among multiple distributed vehicles and the infrastructure to acquire real-time traffic patterns and road alerts for navigation with safety and efficiency. Collaborative learning is often carried out in a federated manner, where a number of distributed nodes jointly carry out a common learning task without sharing their private local raw data and often in the absence of centralized task coordination. These distributed nodes may adopt cognitive radio (CR) technology to dynamically share wireless spectrum, in order to improve the spectrum utilization efficiency. While federated learning is widely adopted in many distributed learning systems, it is not well suited for multi-task learning in heterogeneous network environments, such as for the wideband spectrum sensing problem in wireless CR networks. Given the limited sensing and communication capability of practical CR devices, individual CRs can only gain partial observations of the wide spectrum band, rendering the federated averaging algorithm inapplicable. Meanwhile, the limitations on sensing time and communication bandwidth make it ineffective for wireless CRs to collaboratively train a large-size deep neural network (DNN) model. To overcome these challenges in practical distributed systems, this talk presents our recent progress on cooperative wideband spectrum sensing via collaborative learning among heterogeneous CRs. A multi-task DNN architecture is introduced to detect wideband spectrum occupancy under partial observations, which decouples a dense DNN into band-specific sub-networks to enable heterogeneous feature extraction among collaborative CRs. A Spectrum Transformer architecture with multi-head self-attention mechanisms is also developed to efficiently capture the spectrum correlation from small data within a short sensing time. Simulation results illustrate the effectiveness of the developed techniques in coping with some practical issues in communication-efficient distributed learning for heterogenous networks.

Additive Manufacturing (AM) has been widely recognized as a disruptive manufacturing technology for various applications thanks to its capability of fabricating 3D objects with unprecedented geometric complexity and multiple functionalities. Also, AM enables revolutionary designs by using complex 3D shapes and heterogeneous materials. The structures with various material compositions and geometric feature sizes spanning from 10 μm up to 200 mm have a wide variety of interesting properties. However, the fabrication of such multi-scale and multi-material structures requires the integration of micro-, meso- and macro-scale manufacturing processes. How to develop AM processes to effectively and efficiently fabricate such structures with various engineering materials is still an open question.