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A ground segmentation method based on line fitting of adjacent points was proposed for accurate and real-time segmentation of non-ground information from the LiDAR point cloud. Firstly, the point cloud is divided into several ordered regions depending upon the distribution characteristics of the LiDAR’s concentric circles. Then, the Euclidean distance between adjacent points and the spatial geometric features of ground point clouds is used for adaptive line fitting of ground point clouds. Finally, the ground points are divided by the distance between the adjacent points and the outer points of the line. The experiment was conducted using a real car and the KITTI open-source dataset. The approach presented in this research substantially enhances the accuracy of ground segmentation while ensuring real-time performance.

The redox center of transition metal oxides and hydroxides is generally considered to be the metal site. Interestingly, proton and oxygen in the lattice recently are found to be actively involved in the catalytic reactions, and critically determine the reactivity. Herein, taking glycerol electrooxidation reaction as the model reaction, we reveal systematically the impact of proton and oxygen anion (de)intercalation processes on the elementary steps. Combining density functional theory calculations and advanced spectroscopy techniques, we find that doping Co into Ni-hydroxide promotes the deintercalation of proton and oxygen anion from the catalyst surface. The oxygen vacancies formed in NiCo hydroxide during glycerol electrooxidation reaction increase d -band filling on Co sites, facilitating the charge transfer from catalyst surface to cleaved molecules during the 2 nd C-C bond cleavage. Consequently, NiCo hydroxide exhibits enhanced glycerol electrooxidation activity, with a current density of 100 mA/cm 2 at 1.35 V and a formate selectivity of 94.3%. Developing catalysts for biomass electrooxidation are critical in electric refinery. The reaction mechanism, however, is still ambiguous. Here, the authors reveal how proton and oxygen anion deintercalation in hydroxide determine the elementary reaction steps in a model reaction of glycerol oxidation.

The crucial role of CD4 + and CD8 + T cells in shaping and controlling immune responses during immune disease and cancer development has been well established and used to achieve marked clinical benefits. CD3 + CD4 - CD8 - double-negative (DN) T cells, although constituting a rare subset of peripheral T cells, are gaining interest for their roles in inflammation, immune disease and cancer. Herein, we comprehensively review the origin, distribution and functions of this unique T cell subgroup. First, we focused on characterizing multifunctional DN T cells in various immune responses. DN regulatory T cells have the capacity to prevent graft-versus-host disease and have therapeutic value for autoimmune disease. T helper-like DN T cells protect against or promote inflammation and virus infection depending on the specific settings and promote certain autoimmune disease. Notably, we clarified the role of DN tumor-infiltrating lymphocytes and outlined the potential for malignant proliferation of DN T cells. Finally, we reviewed the recent advances in the applications of DN T cell-based therapy for cancer. In conclusion, a better understanding of the heterogeneity and functions of DN T cells may help to develop DN T cells as a potential therapeutic tool for inflammation, immune disorders and cancer.

The means through which microbes and plants contribute to soil organic carbon (SOC) accumulation remain elusive due to challenges in disentangling the complex components of SOC. Here we use amino sugars and lignin phenols as tracers for microbial necromass and plant lignin components, respectively, and investigate their distribution in the surface soils across Mongolian grasslands in comparison with published data for other grassland soils of the world. While lignin phenols decrease, amino sugars increase with SOC contents in all examined grassland soils, providing continental-scale evidence for the key role of microbial necromass in SOC accumulation. Moreover, in contrast to clay’s control on amino sugar accumulation in fine-textured soils, aridity plays a central role in amino sugar accrual and lignin decomposition in the coarse-textured Mongolian soils. Hence, aridity shifts may have differential impacts on microbial-mediated SOC accumulation in grassland soils of varied textures. It remains unclear how microbes and plants contribute to soil organic carbon (SOC) accrual. Here, using biomarkers, the authors show that microbial necromass and plant-derived lignin components have divergent accumulation mechanisms and that microbial necromass plays a key role in SOC accumulation.

The PPP–RTK method, which combines the concepts of Precise of Point Positioning (PPP) and Real-Time Kinematic (RTK), is proposed to provide a centimeter-accuracy positioning service for an unlimited number of users. Recently, the PPP–RTK technique is becoming a promising tool for emerging applications such as autonomous vehicles and unmanned logistics as it has several advantages including high precision, full flexibility, and good privacy. This paper gives a detailed review of PPP–RTK focusing on its implementation methods, recent achievements as well as challenges and opportunities. Firstly, the fundamental approach to implement PPP–RTK is described and an overview of the research on key techniques, such as Uncalibrated Phase Delay (UPD) estimation, precise atmospheric correction retrieval and modeling, and fast PPP ambiguity resolution, is given. Then, the recent efforts and progress are addressed, such as improving the performance of PPP–RTK by combining multi-GNSS and multi-frequency observations, single-frequency PPP–RTK for low-cost devices, and PPP–RTK for vehicle navigation. Also, the system construction and applications based on the PPP–RTK method are summarized. Moreover, the main issues that impact PPP–RTK performance are highlighted, including signal occlusion in complex urban areas and atmosphere modeling in extreme weather events. The new opportunities brought by the rapid development of low-cost markets, multiple sensors, and new-generation Low Earth Orbit (LEO) navigation constellation are also discussed. Finally, the paper concludes with some comments and the prospects for future research.

Increasing Arctic rainfall significantly impacts snow and ice processes, land runoff, and the ecological environment. However, the extent to which the rainfall increase is regionally dependent and how it responds to the large retreat of sea ice remains inadequately understood. This study quantifies the Arctic land rainfall increases attributable to sea ice loss under 2°C global warming using multi‐ensemble experiments combining all forcing with sea ice loss forcing. The findings indicate that sea ice retreat is responsible for 16% of the increase in summer Arctic land rainfall, with significant increases covering 46% of the region responses to 2°C warming. The most pronounced responses were observed along the Arctic coasts of Siberia and North America. Local warming caused by sea ice retreat contributes 68% of the rainfall increase, while the remainder results from the increase in total precipitation.

Xiangdong Fu and colleagues show that variation in DEP1 , which encodes a G protein subunit known to influence rice panicle architecture, underlies a major quantitative trait locus for nitrogen-use efficiency. These findings suggest that modulating heterotrimeric G protein activity could contribute to environmentally sustainable increases in rice grain yield. The drive toward more sustainable agriculture has raised the profile of crop plant nutrient-use efficiency. Here we show that a major rice nitrogen-use efficiency quantitative trait locus ( qNGR9 ) is synonymous with the previously identified gene DEP1 ( DENSE AND ERECT PANICLES 1 ). The different DEP1 alleles confer different nitrogen responses, and genetic diversity analysis suggests that DEP1 has been subjected to artificial selection during Oryza sativa spp. japonica rice domestication. The plants carrying the dominant dep1-1 allele exhibit nitrogen-insensitive vegetative growth coupled with increased nitrogen uptake and assimilation, resulting in improved harvest index and grain yield at moderate levels of nitrogen fertilization. The DEP1 protein interacts in vivo with both the Gα (RGA1) and Gβ (RGB1) subunits, and reduced RGA1 or enhanced RGB1 activity inhibits nitrogen responses. We conclude that the plant G protein complex regulates nitrogen signaling and modulation of heterotrimeric G protein activity provides a strategy for environmentally sustainable increases in rice grain yield.

The objective of this study was to employ machine learning to identify shared differentially expressed genes (DEGs) in prostate cancer (PCa) initiation and castration resistance, aiming to establish a robust prognostic model and enhance understanding of patient prognosis for personalized treatment strategies. mRNA transcriptome data associated with Castration-Resistant Prostate Cancer (CRPC) were obtained from the GEO database. Differential expression analysis was conducted using the limma R package to compare normal prostate samples with PCa samples, and PCa samples with CRPC samples. Next, we applied LASSO regression, univariate, and multivariate COX regression analyses to pinpoint genes linked to prognosis and build prognostic models. Validation was performed using the TCGA_PRAD dataset to confirm expression differences of hub genes and explore their correlation with clinical variables and prognostic significance. We successfully established a prostate cancer risk prognostic model containing seven genes (KIF4A, UBE2C, FAM72D, CCDC78, HOXD9, LIX1 and SLC5A8) and verified its accuracy on an independent data set. The results of calibration curve and decision curve show that the model has potential clinical application value. The nomogram can accurately predict the prognosis of patients. Additionally, elevated expression of KIF4A, UBE2C, and FAM72D, or reduced expression of LIX1, correlated with advanced pathological T and N stages, clinical T stage, prostate-specific antigen (PSA) level, age at diagnosis, Gleason score, and shorter progression-free interval (PFI) ( P  < 0.05). By integrating bioinformatics analysis and clinical data, we not only established a reliable prognostic model for prostate cancer but also identified key genes pivotal in disease progression and treatment resistance. These findings provide novel insights and methodologies for assessing prognosis and tailoring treatment strategies for prostate cancer patients.

Low permeability is the main constraint on the high-efficiency coalbed methane recovery in deep coal seams. Hydraulic slotting has been proved to be a favorable method to stimulate low-permeability coal seams. In this paper, the coal samples with various slot inclination angles and borehole-slot ratios were used to investigate the weakening effect of slot inclination angle and borehole-slot ratio on the mechanical property of the pre-cracked coal. Besides, the crack patterns of the slotted coal specimens were identified to reveal the slot weakening mechanism. It is revealed that the variations in compression strength, elastic modulus and Poisson’s ratio with the slot inclination angle generally conform to a Boltzmann function, logistic function and quadratic function, respectively. With the increase in the borehole-slot ratio, the curve clusters of compression strength and elastic modulus show the horizontal “V” with left opening, and the curve clusters of Poisson’s ratio show the trend of rapid increase after slow increase. Compared with elastic modulus, the slot weakening degrees of compression strength and Poisson’s ratio are more significant. Moreover, the tensile and shear cracks mainly appear in the coal samples with small and large slot inclination angles, respectively, which verify the fact that the slot weakening effect on mechanical property of the slotted coal samples with small slot inclination angles is more significant. The research achievements are attributed to the improvement in the efficiency of the hydraulic slotting-based enhanced coalbed methane recovery.

This study aims to identify clinically relevant diagnostic biomarkers in chronic obstructive pulmonary disease (COPD) while exploring how immune cell infiltration contributes towards COPD pathogenesis. The GEO database provided two human COPD gene expression datasets (GSE38974 and GSE76925; n=134) along with the relevant controls (n=49) for differentially expressed gene (DEG) analyses. Candidate biomarkers were identified using the support vector machine recursive feature elimination (SVM-RFE) analysis and the LASSO regression model. The discriminatory ability was determined using the area under the receiver operating characteristic curve (AUC) values. These candidate biomarkers were characterized in the GSE106986 dataset (14 COPD patients and 5 controls) in terms of their respective diagnostic values and expression levels. The CIBERSORT program was used to estimate patterns of tissue infiltration of 22 types of immune cells. Furthermore, the and model of COPD was established using cigarette smoke extract (CSE) to validated the bioinformatics results. 80 genes were identified DEG analysis that were primarily involved in cellular amino acid and metabolic processes, regulation of telomerase activity and phagocytosis, antigen processing and MHC class I-mediated peptide antigen presentation, and other biological processes. LASSO and SVM-RFE were used to further characterize the candidate diagnostic markers for COPD, SLC27A3, and STAU1. SLC27A3 and STAU1 were found to be diagnostic markers of COPD in the metadata cohort (AUC=0.734, AUC=0.745). Their relevance in COPD were validated in the GSE106986 dataset (AUC=0.900 AUC=0.971). Subsequent analysis of immune cell infiltration discovered an association between SLC27A3 and STAU1 with resting NK cells, plasma cells, eosinophils, activated mast cells, memory B cells, CD8+, CD4+, and helper follicular T-cells. The expressions of SLC27A3 and STAU1 were upregulated in COPD models both and . Immune infiltration activation was observed in COPD models, accompanied by the enhanced expression of SLC27A3 and STAU1. Whereas, the knockdown of SLC27A3 or STAU1 attenuated the effect of CSE on BEAS-2B cells. STUA1 and SLC27A3 are valuable diagnostic biomarkers of COPD. COPD pathogenesis is heavily influenced by patterns of immune cell infiltration. This study provides a molecular biology insight into COPD occurrence and in exploring new therapeutic means useful in COPD.