Calcium-binding proteins as markers and functional determinants of neurons in pain networks

The thesis focuses on the anatomical and cellular distribution of three EF-hand calcium binding proteins, secretagogin, neuronal calcium-binding protein 1 (NECAB1) and NECAB2 in dorsal root ganglia (DRGs) and spinal cord of three species, mouse, rat and human, and their possible roles in pathophysiological pain. In Paper I and Paper IV, we report that the expression of secretagogin is limited to a small subpopulation of peptidergic neurons in mouse and human DRGs expressing calcitonin generelated peptide (CGRP). Secretagogin is present both in the cell bodies in the DRGs and in the central branches in lamina I of the dorsal horn and in peripheral branches together with CGRP; it thus centrifugally transported. The loss of secretagogin (a knockout mouse) does not affect the development of pain hypersensitivity after nerve injury or experimentally induced inflammation. In Paper II, we demonstrate a wide expression of NECAB1/2 in many cell bodies in mouse DRGs and in cell bodies/nerve terminals with a wide distribution in different laminae in the spinal cord. NECAB2 is expressed in excitatory neurons in the spinal cord, showing a punctate staining and often co-localization with vesicular glutamate transporter 2 (VGLUT2) and synaptophysin. NECAB2 in DRGs is distinctly down regulated, at both mRNA and protein levels, by peripheral nerve injury. In Paper III, we show a conserved excitatory property and laminar distribution of NECAB2 in mouse, rat and human spinal cord, while NECAB1 exhibits species diversity with regards to neurochemical properties in mouse and rat spinal cord. NECAB1 is present in oligodendrocytes surrounding axons in the white matter of the human spinal cord. We also reveal a differential expression of NECAB2, calbindin-D28k and calretinin in ependymal cells surrounding/within (human) the spinal central canal when comparing rodents and human. In Paper IV, we characterize a NECAB2 population in mouse DRGs using a new NECAB2 antibody validated with help of a Necab2 knockout mouse. These NECAB2 neurons cover previously defined the C-low threshold mechanoreceptors (LTMRs) and Aδ D-hair LTMRs. Genetically induced loss of NECAB2 attenuates inflammatory but not neuropathic pain. This may, tentatively, be mediated by modulation of brain-derived neurotrophic factor (BDNF) expressed in DRGs, and through the interaction with its receptor tyrosine receptor kinase B (TrkB) in the spinal cord to modulate spinal glutamatergic neurotransmission

GenPose: Generative Category-level Object Pose Estimation via Diffusion Models

Object pose estimation plays a vital role in embodied AI and computer vision, enabling intelligent agents to comprehend and interact with their surroundings. Despite the practicality of category-level pose estimation, current approaches encounter challenges with partially observed point clouds, known as the multihypothesis issue. In this study, we propose a novel solution by reframing categorylevel object pose estimation as conditional generative modeling, departing from traditional point-to-point regression. Leveraging score-based diffusion models, we estimate object poses by sampling candidates from the diffusion model and aggregating them through a two-step process: filtering out outliers via likelihood estimation and subsequently mean-pooling the remaining candidates. To avoid the costly integration process when estimating the likelihood, we introduce an alternative method that trains an energy-based model from the original score-based model, enabling end-to-end likelihood estimation. Our approach achieves state-of-the-art performance on the REAL275 dataset, surpassing 50% and 60% on strict 5d2cm and 5d5cm metrics, respectively. Furthermore, our method demonstrates strong generalizability to novel categories sharing similar symmetric properties without fine-tuning and can readily adapt to object pose tracking tasks, yielding comparable results to the current state-of-the-art baselines

GraspGF: Learning Score-based Grasping Primitive for Human-assisting Dexterous Grasping

The use of anthropomorphic robotic hands for assisting individuals in situations where human hands may be unavailable or unsuitable has gained significant importance. In this paper, we propose a novel task called human-assisting dexterous grasping that aims to train a policy for controlling a robotic hand's fingers to assist users in grasping objects. Unlike conventional dexterous grasping, this task presents a more complex challenge as the policy needs to adapt to diverse user intentions, in addition to the object's geometry. We address this challenge by proposing an approach consisting of two sub-modules: a hand-object-conditional grasping primitive called Grasping Gradient Field~(GraspGF), and a history-conditional residual policy. GraspGF learns `how' to grasp by estimating the gradient from a success grasping example set, while the residual policy determines `when' and at what speed the grasping action should be executed based on the trajectory history. Experimental results demonstrate the superiority of our proposed method compared to baselines, highlighting the user-awareness and practicality in real-world applications. The codes and demonstrations can be viewed at "https://sites.google.com/view/graspgf"

Score-PA: Score-based 3D Part Assembly

Autonomous 3D part assembly is a challenging task in the areas of robotics and 3D computer vision. This task aims to assemble individual components into a complete shape without relying on predefined instructions. In this paper, we formulate this task from a novel generative perspective, introducing the Score-based 3D Part Assembly framework (Score-PA) for 3D part assembly. Knowing that score-based methods are typically time-consuming during the inference stage. To address this issue, we introduce a novel algorithm called the Fast Predictor-Corrector Sampler (FPC) that accelerates the sampling process within the framework. We employ various metrics to assess assembly quality and diversity, and our evaluation results demonstrate that our algorithm outperforms existing state-of-the-art approaches. We release our code at https://github.com/J-F-Cheng/Score-PA_Score-based-3D-Part-Assembly.Comment: BMVC 202