PointOcc: Cylindrical Tri-Perspective View for Point-based 3D Semantic Occupancy Prediction

Semantic segmentation in autonomous driving has been undergoing an evolution from sparse point segmentation to dense voxel segmentation, where the objective is to predict the semantic occupancy of each voxel in the concerned 3D space. The dense nature of the prediction space has rendered existing efficient 2D-projection-based methods (e.g., bird's eye view, range view, etc.) ineffective, as they can only describe a subspace of the 3D scene. To address this, we propose a cylindrical tri-perspective view to represent point clouds effectively and comprehensively and a PointOcc model to process them efficiently. Considering the distance distribution of LiDAR point clouds, we construct the tri-perspective view in the cylindrical coordinate system for more fine-grained modeling of nearer areas. We employ spatial group pooling to maintain structural details during projection and adopt 2D backbones to efficiently process each TPV plane. Finally, we obtain the features of each point by aggregating its projected features on each of the processed TPV planes without the need for any post-processing. Extensive experiments on both 3D occupancy prediction and LiDAR segmentation benchmarks demonstrate that the proposed PointOcc achieves state-of-the-art performance with much faster speed. Specifically, despite only using LiDAR, PointOcc significantly outperforms all other methods, including multi-modal methods, with a large margin on the OpenOccupancy benchmark. Code: https://github.com/wzzheng/PointOcc.Comment: Code is available at https://github.com/wzzheng/PointOc

SelfOcc: Self-Supervised Vision-Based 3D Occupancy Prediction

3D occupancy prediction is an important task for the robustness of vision-centric autonomous driving, which aims to predict whether each point is occupied in the surrounding 3D space. Existing methods usually require 3D occupancy labels to produce meaningful results. However, it is very laborious to annotate the occupancy status of each voxel. In this paper, we propose SelfOcc to explore a self-supervised way to learn 3D occupancy using only video sequences. We first transform the images into the 3D space (e.g., bird's eye view) to obtain 3D representation of the scene. We directly impose constraints on the 3D representations by treating them as signed distance fields. We can then render 2D images of previous and future frames as self-supervision signals to learn the 3D representations. We propose an MVS-embedded strategy to directly optimize the SDF-induced weights with multiple depth proposals. Our SelfOcc outperforms the previous best method SceneRF by 58.7% using a single frame as input on SemanticKITTI and is the first self-supervised work that produces reasonable 3D occupancy for surround cameras on nuScenes. SelfOcc produces high-quality depth and achieves state-of-the-art results on novel depth synthesis, monocular depth estimation, and surround-view depth estimation on the SemanticKITTI, KITTI-2015, and nuScenes, respectively. Code: https://github.com/huang-yh/SelfOcc.Comment: Code is available at: https://github.com/huang-yh/SelfOc

OccWorld: Learning a 3D Occupancy World Model for Autonomous Driving

Understanding how the 3D scene evolves is vital for making decisions in autonomous driving. Most existing methods achieve this by predicting the movements of object boxes, which cannot capture more fine-grained scene information. In this paper, we explore a new framework of learning a world model, OccWorld, in the 3D Occupancy space to simultaneously predict the movement of the ego car and the evolution of the surrounding scenes. We propose to learn a world model based on 3D occupancy rather than 3D bounding boxes and segmentation maps for three reasons: 1) expressiveness. 3D occupancy can describe the more fine-grained 3D structure of the scene; 2) efficiency. 3D occupancy is more economical to obtain (e.g., from sparse LiDAR points). 3) versatility. 3D occupancy can adapt to both vision and LiDAR. To facilitate the modeling of the world evolution, we learn a reconstruction-based scene tokenizer on the 3D occupancy to obtain discrete scene tokens to describe the surrounding scenes. We then adopt a GPT-like spatial-temporal generative transformer to generate subsequent scene and ego tokens to decode the future occupancy and ego trajectory. Extensive experiments on the widely used nuScenes benchmark demonstrate the ability of OccWorld to effectively model the evolution of the driving scenes. OccWorld also produces competitive planning results without using instance and map supervision. Code: https://github.com/wzzheng/OccWorld.Comment: Code is available at: https://github.com/wzzheng/OccWorl

Machine learning-based on cytotoxic T lymphocyte evasion gene develops a novel signature to predict prognosis and immunotherapy responses for kidney renal clear cell carcinoma patients

BackgroundImmunotherapy resistance has become a difficult point in treating kidney renal clear cell carcinoma (KIRC) patients, mainly because of immune evasion. Currently, there is no effective signature to predict immunotherapy. Therefore, we use machine learning algorithms to construct a signature based on cytotoxic T lymphocyte evasion genes (CTLEGs) to predict the immunotherapy responses of patients, so as to screen patients effective for immunotherapy.MethodsIn public data sets and our in-house cohort, we used 10 machine learning algorithms to screen the optimal model with 89 combinations under the cross-validation framework, and 101 published signatures were collected. The relationship between the CTLEG signature (CTLEGS) and clinical variables was analyzed. We analyzed the role of CTLES in other types of cancer by pan-cancer analysis. The immune cell infiltration and biological characteristics were evaluated. Moreover, the response to immunotherapy and drug sensitivity of different risk groups were investigated. The key gene closely related to the signature was identified by WGCNA. We also conducted cell functional experiments and clinical tissue validation of key gene.ResultsIn public data sets and our in-house cohort, the CTLEGS shows good prediction performance. The CTLEGS can be regard as an independent risk factor for KIRC. Compared with 101 published models, our signature shows considerable superiority. The high-risk group has abundant infiltration of immunosuppressive cells and high expression of T cell depletion markers, which are characterized by immunosuppressive phenotype, minimal benefit from immunotherapy, and resistance to sunitinib and sorafenib. The CTLEGS was also strongly correlated with immunity in pan-cancer. Immunohistochemistry verified that T cell depletion marker LAG3 is highly expressed in high-risk groups in the clinical in-house cohort. The key CTLEG STAT2 can promote the proliferation, migration and invasion of KIRC cell.ConclusionsCTLEGS can accurately predict the prognosis of patients and their response to immunotherapy. It can provide guidance for the precise treatment of KIRC and help clinicians identify patients who may benefit from immunotherapy

Mineralogical study of surface sediments in the western Arctic Ocean and their implications for material sources

Mineralogical analysis was performed on bulk sediments of 79 surface samples using X-ray diffraction. The analytical results, combined with data on ocean currents and the regional geological background, were used to investigate the mineral sources. Mineral assemblages in sediments and their distribution in the study area indicate that the material sources are complex. (1) Feldspar is abundant in the sediments of the middle Chukchi Sea near the Bering Strait, originating from sediments in the Anadyr River carried by the Anadyr Current. Sediments deposited on the western side of the Chukchi Sea are rich in feldspar. Compared with other areas, sediments in this region are rich in hornblende transported from volcanic and sedimentary rocks in Siberia by the Anadyr Stream and the Siberian Coastal Current. Sediments in the eastern Chukchi Sea are rich in quartz sourced from sediments of the Yukon and Kuskokwim rivers carried by the Alaska Coastal Current. Sediments in the northern Chukchi Sea are rich in quartz and carbonates from the Mackenzie River sediments. (2) Sediments of the southern and central Canada Basin contain little calcite and dolomite, mainly due to the small impact of the Beaufort Gyre carrying carbonates from the Canadian Arctic Islands. Compared with other areas, the mica content in the region is high, implying that the Laptev Sea is the main sediment source for the southern and central Canada Basin. In the other deep sea areas, calcite and dolomite levels are high caused by the input of large amounts of sediment carried by the Beaufort Gyre from the Canadian Arctic Islands (Banks and Victoria). The Siberian Laptev Sea also provides small amounts of sediment for this region. Furthermore, the Atlantic mid-water contributes some fi ne-grained material to the entire deep western Arctic Ocean

Croconaine-based nanoparticles enable efficient optoacoustic imaging of murine brain tumors

Contrast enhancement in optoacoustic (photoacoustic) imaging can be achieved with agents that exhibit high absorption cross-sections, high photostability, low quantum yield, low toxicity, and preferential bio-distribution and clearance profiles. Based on advantageous photophysical properties of croconaine dyes, we explored croconaine-based nanoparticles (CR780RGD-NPs) as highly efficient contrast agents for targeted optoacoustic imaging of challenging preclinical tumor targets. Initial characterization of the CR780 dye was followed by modifications using polyethylene glycol and the cancer-targeting c(RGDyC) peptide, resulting in self-assembled ultrasmall particles with long circulation time and active tumor targeting. Preferential bio-distribution was demonstrated in orthotopic mouse brain tumor models by multispectral optoacoustic tomography (MSOT) imaging and histological analysis. Our findings showcase particle accumulation in brain tumors with sustainable strong optoacoustic signals and minimal toxic side effects. This work points to CR780RGD-NPs as a promising optoacoustic contrast agent for potential use in the diagnosis and image-guided resection of brain tumors

RPS23RG1 modulates tau phosphorylation and axon outgrowth through regulating p35 proteasomal degradation

Tau蛋白病（Tauopathies）是由过度磷酸化的tau蛋白聚集形成神经纤维缠结为特征的一类神经退行性疾病，包括阿尔茨海默病（Alzheimer’s disease, AD）、进行性核上性麻痹（Progressive superanuclear palsy, PSP）、额颞叶痴呆（Frontotemporal dementia, FTD）等。随着全球社会结构的老龄化，tau蛋白病患者比率迅速增加，给个人和社会带来巨大的经济及精神负担。厦门大学神经科学研究所张云武教授团队最新发现RPS23RG1（RR1）的胞内羧基端区域能够与Cdk5激酶的激活蛋白p35的氨基端相互作用，介导p35的膜定位并影响其泛素化降解，从而调控在tau蛋白异常磷酸化过程中发挥重要作用的Cdk5激酶的活性。团队研究表明RPS23RG1通过其胞内羧基端与p35相互作用，介导p35膜结合和降解，从而抑制Cdk5活性，平衡tau磷酸化水平，促进轴突生长。此外，RPS23RG1的跨膜区与腺苷酸环化酶AC相互作用，抑制GSK3-β活性，同样控制tau过度磷酸化。提示RPS23RG1是改善tau过度磷酸化水平及治疗tau蛋白病的潜在靶点。 厦门大学医学院神经科学研究所博士后赵东栋为该研究第一作者，张云武教授为通讯作者。【Abstract】Tauopathies are a group of neurodegenerative diseases characterized by hyperphosphorylation of the microtubule-binding protein, tau, and typically feature axon impairment and synaptic dysfunction. Cyclin-dependent kinase5 (Cdk5) is a major tau kinase and its activity requires p35 or p25 regulatory subunits. P35 is subjected to rapid proteasomal degradation in its membrane-bound form and is cleaved by calpain under stress to a stable p25 form, leading to aberrant Cdk5 activation and tau hyperphosphorylation. The type Ib transmembrane protein RPS23RG1 has been implicated in Alzheimer’s disease (AD). However, physiological and pathological roles for RPS23RG1 in AD and other tauopathies are largely unclear. Herein, we observed retarded axon outgrowth, elevated p35 and p25 protein levels, and increased tau phosphorylation at major Cdk5 phosphorylation sites in Rps23rg1 knockout (KO) mice. Both downregulation of p35 and the Cdk5 inhibitor roscovitine attenuated tau hyperphosphorylation and axon outgrowth impairment in Rps23rg1 KO neurons. Interestingly, interactions between the RPS23RG1 carboxyl-terminus and p35 amino-terminus promoted p35 membrane distribution and proteasomal degradation. Moreover, P301L tau transgenic (Tg) mice showed increased tau hyperphosphorylation with reduced RPS23RG1 levels and impaired axon outgrowth. Overexpression of RPS23RG1 markedly attenuated tau hyperphosphorylation and axon outgrowth defects in P301L tau Tg neurons. Our results demonstrate the involvement of RPS23RG1 in tauopathy disorders, and implicate a role for RPS23RG1 in inhibiting tau hyperphosphorylation through homeostatic p35 degradation and suppression of Cdk5 activation. Reduced RPS23RG1 levels in tauopathy trigger aberrant Cdk5-p35 activation, consequent tau hyperphosphorylation, and axon outgrowth impairment, suggesting that RPS23RG1 may be a potential therapeutic target in tauopathy disorders.This work was supported by grants from National Key Research and Development Program of China (2016YFC1305903 and 2018YFC2000400 to Y-wZ), National Natural Science Foundation of China (81771377, U1705285, 91332112, and 81225008 to Y-wZ), Fundamental Research Funds for the Central Universities (20720180049 to Y-wZ), the Fujian Provincial Health Commission-Education Department Joint Tackling Plan (WKJ2016-2-18 to F-rL), and Postdoctoral Science Foundation of China (2020M671948 to DZ)