Task-Aware Sampling Layer for Point-Wise Analysis

Sampling, grouping, and aggregation are three important components in the multi-scale analysis of point clouds. In this paper, we present a novel data-driven sampler learning strategy for point-wise analysis tasks. Unlike the widely used sampling technique, Farthest Point Sampling (FPS), we propose to learn sampling and downstream applications jointly. Our key insight is that uniform sampling methods like FPS are not always optimal for different tasks: sampling more points around boundary areas can make the point-wise classification easier for segmentation. Towards this end, we propose a novel sampler learning strategy that learns sampling point displacement supervised by task-related ground truth information and can be trained jointly with the underlying tasks. We further demonstrate our methods in various point-wise analysis tasks, including semantic part segmentation, point cloud completion, and keypoint detection. Our experiments show that jointly learning of the sampler and task brings better performance than using FPS in various point-based networks.Comment: 14 pages, 13 figures and 14 table

HairStep: Transfer Synthetic to Real Using Strand and Depth Maps for Single-View 3D Hair Modeling

In this work, we tackle the challenging problem of learning-based single-view 3D hair modeling. Due to the great difficulty of collecting paired real image and 3D hair data, using synthetic data to provide prior knowledge for real domain becomes a leading solution. This unfortunately introduces the challenge of domain gap. Due to the inherent difficulty of realistic hair rendering, existing methods typically use orientation maps instead of hair images as input to bridge the gap. We firmly think an intermediate representation is essential, but we argue that orientation map using the dominant filtering-based methods is sensitive to uncertain noise and far from a competent representation. Thus, we first raise this issue up and propose a novel intermediate representation, termed as HairStep, which consists of a strand map and a depth map. It is found that HairStep not only provides sufficient information for accurate 3D hair modeling, but also is feasible to be inferred from real images. Specifically, we collect a dataset of 1,250 portrait images with two types of annotations. A learning framework is further designed to transfer real images to the strand map and depth map. It is noted that, an extra bonus of our new dataset is the first quantitative metric for 3D hair modeling. Our experiments show that HairStep narrows the domain gap between synthetic and real and achieves state-of-the-art performance on single-view 3D hair reconstruction.Comment: CVPR 2023 Highlight, project page: https://paulyzheng.github.io/research/hairstep

Temperature dependence of erythromelalgia mutation L858F in sodium channel Nav1.7

BACKGROUND: The disabling chronic pain syndrome erythromelalgia (also termed erythermalgia) is characterized by attacks of burning pain in the extremities induced by warmth. Pharmacological treatment is often ineffective, but the pain can be alleviated by cooling of the limbs. Inherited erythromelalgia has recently been linked to mutations in the gene SCN9A, which encodes the voltage-gated sodium channel Nav1.7. Nav1.7 is preferentially expressed in most nociceptive DRG neurons and in sympathetic ganglion neurons. It has recently been shown that several disease-causing erythromelalgia mutations alter channel-gating behavior in a manner that increases DRG neuron excitability. RESULTS: Here we tested the effects of temperature on gating properties of wild type Nav1.7 and mutant L858F channels. Whole-cell voltage-clamp measurements on wild type or L858F channels expressed in HEK293 cells revealed that cooling decreases current density, slows deactivation and increases ramp currents for both mutant and wild type channels. However, cooling differentially shifts the midpoint of steady-state activation in a depolarizing direction for L858F but not for wild type channels. CONCLUSION: The cooling-dependent shift of the activation midpoint of L858F to more positive potentials brings the threshold of activation of the mutant channels closer to that of wild type Nav1.7 at lower temperatures, and is likely to contribute to the alleviation of painful symptoms upon cooling in affected limbs in patients with this erythromelalgia mutation

Intra- and interfamily phenotypic diversity in pain syndromes associated with a gain-of-function variant of NaV1.7

<p>Abstract</p> <p>Background</p> <p>Sodium channel Na_V1.7 is preferentially expressed within dorsal root ganglia (DRG), trigeminal ganglia and sympathetic ganglion neurons and their fine-diamter axons, where it acts as a threshold channel, amplifying stimuli such as generator potentials in nociceptors. Gain-of-function mutations and variants (single amino acid substitutions) of Na_V1.7 have been linked to three pain syndromes: Inherited Erythromelalgia (IEM), Paroxysmal Extreme Pain Disorder (PEPD), and Small Fiber Neuropathy (SFN). IEM is characterized clinically by burning pain and redness that is usually focused on the distal extremities, precipitated by mild warmth and relieved by cooling, and is caused by mutations that hyperpolarize activation, slow deactivation, and enhance the channel ramp response. PEPD is characterized by perirectal, periocular or perimandibular pain, often triggered by defecation or lower body stimulation, and is caused by mutations that severely impair fast-inactivation. SFN presents a clinical picture dominated by neuropathic pain and autonomic symptoms; gain-of-function variants have been reported to be present in approximately 30% of patients with biopsy-confirmed idiopathic SFN, and functional testing has shown altered fast-inactivation, slow-inactivation or resurgent current. In this paper we describe three patients who house the Na_V1.7/I228M variant.</p> <p>Methods</p> <p>We have used clinical assessment of patients, quantitative sensory testing and skin biopsy to study these patients, including two siblings in one family, in whom genomic screening demonstrated the I228M Na_V1.7 variant. Electrophysiology (voltage-clamp and current-clamp) was used to test functional effects of the variant channel.</p> <p>Results</p> <p>We report three different clinical presentations of the I228M Na_V1.7 variant: presentation with severe facial pain, presentation with distal (feet, hands) pain, and presentation with scalp discomfort in three patients housing this Na_V1.7 variant, two of which are from a single family. We also demonstrate that the Na_V1.7/I228M variant impairs slow-inactivation, and produces hyperexcitability in both trigeminal ganglion and DRG neurons.</p> <p>Conclusion</p> <p>Our results demonstrate intra- and interfamily phenotypic diversity in pain syndromes produced by a gain-of-function variant of Na_V1.7.</p

Volumes of hippocampal subfields suggest a continuum between schizophrenia, major depressive disorder and bipolar disorder

ObjectiveThere is considerable debate as to whether the continuum of major psychiatric disorders exists and to what extent the boundaries extend. Converging evidence suggests that alterations in hippocampal volume are a common sign in psychiatric disorders; however, there is still no consensus on the nature and extent of hippocampal atrophy in schizophrenia (SZ), major depressive disorder (MDD) and bipolar disorder (BD). The aim of this study was to verify the continuum of SZ – BD – MDD at the level of hippocampal subfield volume and to compare the volume differences in hippocampal subfields in the continuum.MethodsA total of 412 participants (204 SZ, 98 MDD, and 110 BD) underwent 3 T MRI scans, structured clinical interviews, and clinical scales. We segmented the hippocampal subfields with FreeSurfer 7.1.1 and compared subfields volumes across the three diagnostic groups by controlling for age, gender, education, and intracranial volumes.ResultsThe results showed a gradual increase in hippocampal subfield volumes from SZ to MDD to BD. Significant volume differences in the total hippocampus and 13 of 26 hippocampal subfields, including CA1, CA3, CA4, GC-ML-DG, molecular layer and the whole hippocampus, bilaterally, and parasubiculum in the right hemisphere, were observed among diagnostic groups. Medication treatment had the most effect on subfields of MDD compared to SZ and BD. Subfield volumes were negatively correlated with illness duration of MDD. Positive correlations were found between subfield volumes and drug dose in SZ and MDD. There was no significant difference in laterality between diagnostic groups.ConclusionThe pattern of hippocampal volume reduction in SZ, MDD and BD suggests that there may be a continuum of the three disorders at the hippocampal level. The hippocampus represents a phenotype that is distinct from traditional diagnostic strategies. Combined with illness duration and drug intervention, it may better reflect shared pathophysiology and mechanisms across psychiatric disorders

Visual Navigation and Obstacle Avoidance Control for Agricultural Robots via LiDAR and Camera

Obstacle avoidance control and navigation in unstructured agricultural environments are key to the safe operation of autonomous robots, especially for agricultural machinery, where cost and stability should be taken into account. In this paper, we designed a navigation and obstacle avoidance system for agricultural robots based on LiDAR and a vision camera. The improved clustering algorithm is used to quickly and accurately analyze the obstacle information collected by LiDAR in real time. Also, the convex hull algorithm is combined with the rotating calipers algorithm to obtain the maximum diameter of the convex polygon of the clustered data. Obstacle avoidance paths and course control methods are developed based on the danger zones of obstacles. Moreover, by performing color space analysis and feature analysis on the complex orchard environment images, the optimal H-component of HSV color space is selected to obtain the ideal vision-guided trajectory images based on mean filtering and corrosion treatment. Finally, the proposed algorithm is integrated into the Three-Wheeled Mobile Differential Robot (TWMDR) platform to carry out obstacle avoidance experiments, and the results show the effectiveness and robustness of the proposed algorithm. The research conclusion can achieve satisfactory results in precise obstacle avoidance and intelligent navigation control of agricultural robots