Vestibulospinal circuit in the larval zebrafish

The vestibular system sense gravity and self-motion to help animals maintain body balance. Although vestibular signals inform the brain of the directions and speed of our body movements, it still remains unclear how these sensory information are processed and organized in the central nervous system. My thesis aims to illustrate neural computation underlying central vestibular tuning and the topographic organization of the vestibular circuits. First I established a novel approach to perform whole-cell recording of synaptic inputs in vivo during multi-axis movements in the central vestibular neurons. This technical advance allowed me to simultaneously measure presynaptic and postsynaptic tuning, along with the presynaptic convergence pattern and synaptic strengths, all at the larval zebrafish vestibulospinal nucleus. I showed that convergence of inputs with dissimilar sensory responses can create complex postsynaptic tuning, whereas convergence of inputs with similar responses mediates simpler postsynaptic tuning. This direct demonstration of how simple and complex vestibular tuning are computed centrally, resolved a major gap in the vestibular field between theoretical prediction and experimental evidence. Next, I used serial-section electron microscopy to reconstruct a high- resolution ultrastructure of the entire vestibular peripheral circuit. I mapped the connectivity of all 91 vestibular hair cells and 105 afferents in one utricle and traced afferent projections to the vestibular brainstem. This work reveals the first known topographic map organized by both sensory tuning and developmental age in the vestibular ganglion. It also shows that the early born and late born peripheral pathways coincide with two vestibular streams encoding the phasic and tonic signals, respectively. Together my study suggests that vestibular circuits from the peripheral sensors to the central neurons are potentially organized by development and movement speed

Students' Intention of Visiting Urban Green Spaces after the COVID-19 Lockdown in China.

This study addresses students' perceptions of using urban green spaces (UGSs) after the easing of COVID-19 lockdown in China. We questioned whether they are still mindful of the risks from the outdoor gathering, or conversely, starting to learn the restoration benefits from the green spaces. Online self-reported surveys were distributed to the Chinese students aging from 14 to 30 who study in Hunan and Jiangsu Provinces, China. We finally obtained 608 complete and valid questionnaire forms from all participants. Their intentions of visiting UGSs were investigated based on the extended theory of planned behavior model. Structural equation modeling was employed to test the hypothesized psychological model. The results have shown good estimation performance on risk perception and perceived knowledge to explain the variances in their attitudes, social norms, and perceived behavior control. Among these three endogenous variables, the perceived behavior control owns the greatest and positive influence on the behavioral intention, inferring that controllability is crucial for students to make decisions of visiting green spaces in a post-pandemic context

Fault Tolerant Free Gait and Footstep Planning for Hexapod Robot Based on Monte-Carlo Tree

Legged robots can pass through complex field environments by selecting gaits and discrete footholds carefully. Traditional methods plan gait and foothold separately and treat them as the single-step optimal process. However, such processing causes its poor passability in a sparse foothold environment. This paper novelly proposes a coordinative planning method for hexapod robots that regards the planning of gait and foothold as a sequence optimization problem with the consideration of dealing with the harshness of the environment as leg fault. The Monte Carlo tree search algorithm(MCTS) is used to optimize the entire sequence. Two methods, FastMCTS, and SlidingMCTS are proposed to solve some defeats of the standard MCTS applicating in the field of legged robot planning. The proposed planning algorithm combines the fault-tolerant gait method to improve the passability of the algorithm. Finally, compared with other planning methods, experiments on terrains with different densities of footholds and artificially-designed challenging terrain are carried out to verify our methods. All results show that the proposed method dramatically improves the hexapod robot's ability to pass through sparse footholds environment

Label-free Node Classification on Graphs with Large Language Models (LLMS)

In recent years, there have been remarkable advancements in node classification achieved by Graph Neural Networks (GNNs). However, they necessitate abundant high-quality labels to ensure promising performance. In contrast, Large Language Models (LLMs) exhibit impressive zero-shot proficiency on text-attributed graphs. Yet, they face challenges in efficiently processing structural data and suffer from high inference costs. In light of these observations, this work introduces a label-free node classification on graphs with LLMs pipeline, LLM-GNN. It amalgamates the strengths of both GNNs and LLMs while mitigating their limitations. Specifically, LLMs are leveraged to annotate a small portion of nodes and then GNNs are trained on LLMs' annotations to make predictions for the remaining large portion of nodes. The implementation of LLM-GNN faces a unique challenge: how can we actively select nodes for LLMs to annotate and consequently enhance the GNN training? How can we leverage LLMs to obtain annotations of high quality, representativeness, and diversity, thereby enhancing GNN performance with less cost? To tackle this challenge, we develop an annotation quality heuristic and leverage the confidence scores derived from LLMs to advanced node selection. Comprehensive experimental results validate the effectiveness of LLM-GNN. In particular, LLM-GNN can achieve an accuracy of 74.9% on a vast-scale dataset \products with a cost less than 1 dollar.Comment: The code will be available soon via https://github.com/CurryTang/LLMGN

Organization of the gravity-sensing system in zebrafish

Motor circuits develop in sequence from those governing fast movements to those governing slow. Here we examine whether upstream sensory circuits are organized by similar principles. Using serial-section electron microscopy in larval zebrafish, we generated a complete map of the gravity-sensing (utricular) system spanning from the inner ear to the brainstem. We find that both sensory tuning and developmental sequence are organizing principles of vestibular topography. Patterned rostrocaudal innervation from hair cells to afferents creates an anatomically inferred directional tuning map in the utricular ganglion, forming segregated pathways for rostral and caudal tilt. Furthermore, the mediolateral axis of the ganglion is linked to both developmental sequence and neuronal temporal dynamics. Early-born pathways carrying phasic information preferentially excite fast escape circuits, whereas later-born pathways carrying tonic signals excite slower postural and oculomotor circuits. These results demonstrate that vestibular circuits are organized by tuning direction and dynamics, aligning them with downstream motor circuits and behaviors