Aminoglycoside antibiotics: modification and novel applications in biomedicine

As people's standard of living increases, the demand for treatment of diseases is increasing day by day. At a time of rapid medical development, in-depth research into antibiotics to make them better at killing bacteria and reducing their side effects on the human body is an important topic. This thesis conducts research on the following aspects: the use of aminoglycoside antibiotics as targeted drugs for the diagnosis of bacterial infections; the use of aminoglycoside antibiotics as carriers for drug release; and the use of aminoglycoside antibiotics as scaffold for the design of novel drugs against Alzheimer's disease. We have made considerable progress in the above studies, and contributed certainly to the design of novel antibiotic-based drugs

A multi-protein receptor-ligand complex underlies combinatorial dendrite guidance choices in C. elegans.

Ligand receptor interactions instruct axon guidance during development. How dendrites are guided to specific targets is less understood. The C. elegans PVD sensory neuron innervates muscle-skin interface with its elaborate dendritic branches. Here, we found that LECT-2, the ortholog of leukocyte cell-derived chemotaxin-2 (LECT2), is secreted from the muscles and required for muscle innervation by PVD. Mosaic analyses showed that LECT-2 acted locally to guide the growth of terminal branches. Ectopic expression of LECT-2 from seam cells is sufficient to redirect the PVD dendrites onto seam cells. LECT-2 functions in a multi-protein receptor-ligand complex that also contains two transmembrane ligands on the skin, SAX-7/L1CAM and MNR-1, and the neuronal transmembrane receptor DMA-1. LECT-2 greatly enhances the binding between SAX-7, MNR-1 and DMA-1. The activation of DMA-1 strictly requires all three ligands, which establishes a combinatorial code to precisely target and pattern dendritic arbors

Essays on Factor Reallocation and General Equilibrium Analysis

My dissertation studies the sectoral and regional reallocation of capital and labor for an economy in transition using general equilibrium model framework. The first chapter, ``Capital in Transition: Housing and Sectoral Reallocation in the Long Run'', studies the sectoral allocation of capital between housing and non-residential sectors using a two-sector general equilibrium model in a neoclassical growth environment. Calibrated to both the United States and China, the model can account for both the positive correlation between the share of housing capital and the consumption-output ratio in the United States and the negative correlation between the share of housing capital and the consumption-output ratio in China. The calibration to the Chinese economy implies that the rapid increase in the share of housing capital and the simultaneous decrease in the consumption-output ratio observed in China can be rationalized by a combination of three factors: a high elasticity of substitution between the two sectors, a high capital intensity of production of the housing sector, and a low initial share of housing capital before the Chinese housing market reform. This paper provides a tractable framework to understand the sectoral allocation of capital between housing and non-residential sectors across countries.The second chapter, ``Human Capital Spillover and Housing price'', provides a model framework to study the relationship between the external effect of human capital and housing price growth in the SEZ economy in China. In a two-region model, high wage in the SEZ region reflects high level of human capital, and these jobs are not available to low human capital migrants from the non-SEZ economy. The migrants come to the SEZ economy for two reasons: on the one hand, the SEZ economy is a better place to accumulate human capital and earn a higher wage in the future; on the other hand, the SEZ economy has a better amenities for living. In the baseline model with migration, the share of population that choose to migrate to the SEZ economy is determined by the utility equalization between living in either economy. In the baseline model, the migration occurs all at once at the first period. Further, I extend the baseline model by incorporating the spillover effect of human capital: time invested in human capital accumulation has a higher return in high human capital environment. In this case, the migration to the SEZ economy becomes increasingly attractive as the gap between the human capital leaders and followers increase. By comparing the extended model with the baseline, I capture the significant positive impact of human capital spillover on the increase of housing prices.The third chapter, `'Dynamic Arrow-Debreu Economy for General Equilibrium Analysis'', coauthored with Cheng-Zhong Qin, develops a dynamic Arrow-Debreu abstract economy to more closely capture the timing of moves of Walrasian general equilibrium model. Instead of inducing a pseudo game, the extensive form of the dynamic Arrow-Debreu abstract economy is well defined. As such, various game-theoretic solutions with and without symmetric information can be applied. We show that the set of subgame-perfectequilibrium allocations coincides with the set of Walrasian equilibrium allocations when information is symmetric.The set of perfect Bayesian equilibrium allocations coincides with the set ofrational expectations equilibrium allocations when information is asymmetric. These results are usefulfor analyzing and refining Walrasian and rational expectations equilibrium allocations

Robotic object manipulation via hierarchical and affordance learning

With the rise of computation power and machine learning techniques, a shift of research interest is happening to roboticists. Against this background, this thesis seeks to develop or enhance learning-based grasping and manipulation systems. This thesis first proposes a method, named A2, to improve the sample efficiency of end-to-end deep reinforcement learning algorithms for long horizon, multi-step and sparse reward manipulation. The named A2 comes from the fact that it uses Abstract demonstrations to guide the learning process and Adaptively adjusts exploration according to online performances. Experiments in a series of multi-step grid world tasks and manipulation tasks demonstrate significant performance gains over baselines. Then, this thesis develops a hierarchical reinforcement learning approach towards solving the long-horizon manipulation tasks. Specifically, the proposed universal option framework integrates the knowledge-sharing advantage of goal-conditioned reinforcement learning into hierarchical reinforcement learning. An analysis of the parallel training non-stationarity problem is also conducted, and the A2 method is employed to address the issue. Experiments in a series of continuous multi-step, multi-outcome block stacking tasks demonstrate significant performance gains as well as reductions of memory and repeated computation over baselines. Finally, this thesis studies the interplay between grasp generation and manipulation motion generation, arguing that selecting a good grasp before manipulation is essential for contact-rich manipulation tasks. A theory of general affordances based on the reinforcement learning paradigm is developed and used to represent the relationship between grasp generation and manipulation performances. This leads to the general affordance-aware manipulation framework, which selects task-agnostic grasps for downstream manipulation based on the predicted manipulation performances. Experiments on a series of contact-rich hook separation tasks prove the effectiveness of the proposed framework and showcase significant performance gains by filtering away unsatisfactory grasps

Recent Advances of Deep Robotic Affordance Learning: A Reinforcement Learning Perspective

As a popular concept proposed in the field of psychology, affordance has been regarded as one of the important abilities that enable humans to understand and interact with the environment. Briefly, it captures the possibilities and effects of the actions of an agent applied to a specific object or, more generally, a part of the environment. This paper provides a short review of the recent developments of deep robotic affordance learning (DRAL), which aims to develop data-driven methods that use the concept of affordance to aid in robotic tasks. We first classify these papers from a reinforcement learning (RL) perspective, and draw connections between RL and affordances. The technical details of each category are discussed and their limitations identified. We further summarise them and identify future challenges from the aspects of observations, actions, affordance representation, data-collection and real-world deployment. A final remark is given at the end to propose a promising future direction of the RL-based affordance definition to include the predictions of arbitrary action consequences.Comment: This paper is under revie

Abstract Demonstrations and Adaptive Exploration for Efficient and Stable Multi-step Sparse Reward Reinforcement Learning

Although Deep Reinforcement Learning (DRL) has been popular in many disciplines including robotics, state-of-the-art DRL algorithms still struggle to learn long-horizon, multi-step and sparse reward tasks, such as stacking several blocks given only a task-completion reward signal. To improve learning efficiency for such tasks, this paper proposes a DRL exploration technique, termed A^2, which integrates two components inspired by human experiences: Abstract demonstrations and Adaptive exploration. A^2 starts by decomposing a complex task into subtasks, and then provides the correct orders of subtasks to learn. During training, the agent explores the environment adaptively, acting more deterministically for well-mastered subtasks and more stochastically for ill-learnt subtasks. Ablation and comparative experiments are conducted on several grid-world tasks and three robotic manipulation tasks. We demonstrate that A^2 can aid popular DRL algorithms (DQN, DDPG, and SAC) to learn more efficiently and stably in these environments.Comment: Accepted by The 27th IEEE International Conference on Automation and Computing (ICAC2022

Assessing Callous-Unemotional Traits in Chinese Detained Boys: Factor Structure and Construct Validity of the Inventory of Callous-Unemotional Traits

The Inventory of Callous-Unemotional Traits (ICU) was designed to evaluate multiple facets of Callous-Unemotional (CU) traits in youths. However, no study has examined the factor structure and psychometrical properties of the ICU in Chinese detained juveniles. The current study assesses the factor structure, internal consistency and convergent validity of the ICU in 613 Chinese detained boys. Confirmatory factor analysis results indicated that the original three-factor model with 24 items showed an unacceptable fit to the data, however, the 11-item shortened version of the ICU (ICU-11) with callousness and uncaring dimensions showed the best fit. Moreover, the ICU-11 total score and factor scores had good and acceptable internal consistencies. The convergent and criterion validity of the ICU-11 was demonstrated by comparable and significant associations in the expected direction with relevant external criteria (e.g., psychopathy, aggression, and empathy). In conclusion, present findings indicated that the ICU-11 is a reliable and efficient instrument to replace the original ICU when assessing CU traits in the Chinese male detained juvenile sample.This work was supported by the National Natural Science Foundation of China (Grant Nos. 31800945 and 31400904) and Guangzhou University’s 2017 training program for young topnotch personnels (BJ201715)