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

Phase controlled SERS enhancement

Surface-enhanced Raman spectroscopy (SERS) has attracted increasing interest for chemical and biochemical sensing. Several studies have shown that SERS intensities are significantly increased when an optical interference substrate composed of a dielectric spacer and a reflector is used as a supporting substrate. However, the origin of this additional enhancement has not been systematically studied. In this paper, high sensitivity SERS substrates composed of self-assembled core-satellite nanostructures and silica-coated silicon interference layers have been developed. Their SERS enhancement is shown to be a function of the thickness of silica spacer on a more reflective silicon substrate. Finite difference time domain modeling is presented to show that the SERS enhancement is due to a spacer contribution via a sign change of the reflection coefficients at the interfaces. The magnitude of the local-field enhancement is defined by the interference of light reflected from the silica-air and silica-silicon interfaces, which constructively added at the hot spots providing a possibility to maximize intensity in the nanogaps between the self-assembled nanoparticles by changing the thickness of silica layer. The core-satellite assemblies on a 135\u2009nm silica-coated silicon substrate exhibit a SERS activity of approximately 13 times higher than the glass substrate

Psychometric properties of the Chinese version of the health behavior motivation scale: a translation and validation study

ObjectiveThis study’s objectives were to translate the Health Behavior Motivation Scale (HBMS) into Chinese and verify the scale’s validity and reliability among Chinese healthy adults.MethodThe HBMS scales were translated into Chinese based on Brislin’s principles. The Chinese version of HBMS is created through translation, back translation, and cross-cultural adaptation. This investigation implemented the convenience sampling method to conduct a survey on 781 healthy respondents, utilizing the Chinese version of the HBMS and a general demographic questionnaire. We used AMOS (v28.0) and SPSS (v26.0) for statistical analysis. We employed test–retest reliability, split-half reliability, and internal consistency to assess the reliability of the translation questionnaire. Structure validity and content validity were used to assess validity.ResultsThe Chinese version of the Health Behavior Motivation Scale (HBMS) had a Cronbach’s alpha coefficient of 0.885, and the range of Cronbach’s alpha values for each dimension was 0.820–0.885. The scale’s test–retest reliability was 0.824, and its split-half reliability was 0.906. Five public factors with a cumulative variance contribution of 56.527% were retrieved from the exploratory factor analysis. Moreover, the factor loading value for each item exceeded 0.4.In confirmatory factor analysis, the indicators were reported as follows: χ2/df = 1.567, GFI = 0.900, CFI = 0.952, IFI = 0.952, TLI = 0.946, AGFI = 0.881, PGFI = 0.757, PNFI = 0.789, RMSEA = 0.039, and the results of the model fit metrics were within the reference range.ConclusionThe Chinese version of the HBMS exhibits strong discrimination, validity, and reliability. The tool effectively identifies the motivation of healthy people to engage in healthy behaviors. It can be used by healthcare practitioners to assist in the development of follow-up interventions to reduce the prevalence of chronic disease in older people and the incidence of chronic disease in populations of young and middle-aged people

Concentrations and Size Distributions of Airborne Particulate Matter and Bacteria in an Experimental Aviary Laying-Hen Chamber

High levels of airborne particulate matter (PM) and bacteria may exist in animal housing, which can be detrimental to the health of animals and workers. The sizes of these bioaerosols determine their aerial transport behaviors and depositions in the respiratory tracts of animals and humans. However, little is known regarding the size distribution of airborne PM and bacteria in livestock houses, especially in alternative animal housing systems that aim to enhance animal welfare, such as aviary hen-housing systems. The study reported here was therefore conducted to characterize the concentrations and size distributions of airborne bacteria (in count) and PM (both in count and in mass) in a pilot-scale aviary laying-hen chamber. Thirty-four laying hens were kept in the environmentally controlled aviary chamber (L × W × H = 2.2 × 2.3 × 2.4 m) for three months. The hens were given a 16L:8D photoperiod (lights on at 6:00 h and off at 22:00 h) and access to the litter floor from 12:00 h to 22:00 h daily. Airborne bacteria and PM were simultaneously sampled for 15 min at 1.5 m above the litter floor every fourth day at 5:45 h, 9:45 h, 13:45 h, 17:45 h, and 21:45 h. Concentrations of airborne bacteria at six size ranges (0.65 to 1.1 µm, 1.1 to 2.1 µm, 2.1 to 3.3 µm, 3.3 to 4.7 µm, 4.7 to 7.1 µm, and \u3e7.1 µm) and PM concentrations (0.523 to 20.535 µm) were determined. The daily mean (±SD) concentrations of PM count, PM mass, and airborne bacteria were 1.70 (±0.66) × 107 particles m-3, 1.12 (±0.47) mg m-3, and 3.39 (±2.38) × 105 cfu m-3, respectively. Concentrations of airborne PM and bacteria during the litter-access period (12:00 to 22:00 h) were significantly higher than those during the rest of the day when the hens were off the floor (p \u3c 0.05). Median diameter and geometric standard deviation (GSD) for the PM count (0.523 to 20.535 µm) were 2.11 and 2.34 µm, respectively. Median diameter and GSD for the PM mass (0.523 to 20.535 µm) were 7.45 and 4.58 µm, respectively. PM \u3c10 µm accounted for more than 95% of the total PM count, whereas PM \u3e2.5 µm accounted for more than 90% of the total PM mass, in the size range of 0.523 to 20.535 µm. The majority (\u3e95%) of the airborne bacteria were carried by particles \u3e3.3 µm. Airborne bacteria count concentration was positively related to PM mass concentration (p \u3c 0.05) with a slope of 3.84 (±2.70) × 105 cfu mg-1 PM. Results of the study are useful for improving understanding of transport behaviors of aerosols in aviary hen systems, assessing potential respiratory risks to humans and animals, and exploring mitigation techniques

Airborne Particulate Matter and Culturable Bacteria Reduction from Spraying Slightly Acidic Electrolyzed Water in an Experimental Aviary Laying-Hen Housing Chamber

Compared to conventional cage laying-hen houses, aviary hen houses generally have much higher concentrations of airborne dust and bacteria due to generation of bioaerosols by the hens’ access to and activities on the litter floor. Hence, reducing these airborne agents is important to safeguard the health of the animals and workers in such housing systems. Spraying slightly acidic electrolyzed water (SAEW) is a novel approach to reducing airborne culturable bacteria (CB) and particulate matter (PM) levels in hen houses. The objective of this study was to evaluate the efficacy of reducing airborne CB and PM in an experimental aviary chamber by periodic spraying of SAEW (Trt), as compared to no spraying (Ctrlns) or spraying of tap water (Ctrlw). The hens were provided 16 h light and 8 h dark (lights on at 6:00 h and off at 22:00 h) and were given access to the litter floor from 12:00 h to 22:00 h. The Trt regimen sprayed SAEW at 14:00 h for 15 min at a dosage of 80 mL m-2; the Ctrlns regimen had no spraying; and the Ctrlw regimen sprayed tap water following the same procedure as with Trt. Concentrations of airborne CB and PM in six aerodynamic size ranges (0.65-1.1, 1.1-2.1, 2.1-3.3, 3.3-4.7, 4.7-7.1, and \u3e7.1 μm) were measured at 1.5 m above the floor in the center of the room during the periods of 13:45-14:00 h and 14:45-15:00 h. Compared to Ctrlns, spraying SAEW significantly reduced airborne CB (\u3e2.1 μm) by up to 49% ±10% (p \u3c 0.05), while Ctrlw did not show a reduction effect. No significant difference was found between Trt and Ctrlw in reducing airborne PM, although both reduced or tended to suppress PM \u3e7.1 μm in size. The results show that spraying SAEW can inactivate airborne CB attached to PM. Thus, this is a promising technique for alleviating the adverse health impacts of bioaerosols in aviary laying-hen housing systems

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

Deglacial biogenic opal peaks revealing enhanced Southern Ocean upwelling during the last 513 ka

Strength of Southern Ocean upwelling controls the exchange of carbon dioxide (CO2) between deep ocean reservoirs and atmosphere, as well as the communication of dissolved silicon with the euphotic zone of the Southern Ocean. The silicon supply could limit diatom opal productivity in the high-latitudes of Southern Ocean and the subsequent burial of biogenic opal in underlying sediments. Here we report a record of biogenic opal export off the Prydz Bay south of the polar front of the Southern Ocean, indicating strengthened upwelling during the past five glacial terminations. In all five terminations (Isingle bondV), opal peaks occur in line with Northern Hemisphere summer insolation intensity as well as the existing IRDs, indicating that freshwater injection associated with retreat of the Northern Hemisphere ice sheets could be the cause of enhanced upwelling in the Southern Ocean during terminations. This could in turn promote CO2 outgassing, finally accelerating the completion of the terminations. In addition, the enhanced upwelling could export the Si-rich deep water to low latitudes via Antarctic Intermediate Water (AAIW) and Subantarctic Mode Water (SAMW), potentially leading to deglacial opal peaks in subtropical North Atlantic