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Abstract This study aimed to investigate whether class A1 scavenger receptor (SR-A1) regulated macrophage polarization and gut microbial alteration during intestinal inflammation of colitis. A murine colitis model was established by feeding with dextran sulfate sodium (DSS), and treatment groups were injected intravenously with SR-A1 antibody. Results showed a preventive effect on colitis symptoms and fewer inflammatory cell infiltrates in treatment groups. Down-regulation of inflammatory cytokines and up-regulation of anti-inflammatory cytokine related to macrophages were seen in murine PBMC and LPMC after injected with SR-A1 antibody. The percentage of M2 macrophages was also elevated in treatment groups. In addition, SR-A1 antibody treatment resulted in the decreased apoptosis and increased proliferation of colonic epithelial cells. Other findings indicated that SR-A1 antibody injection could mediate its anti-inflammatory effect via inhibiting TLR4-MyD88-NF-kB signaling pathway and alterating the gut microbiota composition. Our research identified SR-A1 as a potential therapeutic target in inflammatory bowel disease (IBD).

Rock-mass point-cloud registration is a critical yet challenging task in the fields of geology and engineering. Currently, the lack of dedicated datasets for rock-mass point-cloud registration significantly limits the development and application of advanced algorithms in this area. To address this gap, we introduce RockCloud-Align, a large-scale dataset specifically designed for rock-mass point-cloud registration. Created using high-resolution LiDAR scans, this dataset covers a wide range of geological scenarios with varying densities and includes over 14,000 meticulously curated point-cloud pairs. RockCloud-Align provides a comprehensive benchmark for evaluating registration algorithms, along with a robust evaluation protocol to standardize the assessment of these methods. Building upon this dataset, we propose a novel registration method that eliminates the dependence on feature points and random sampling consensus, ensuring high efficiency and precision across diverse scenes and densities. Extensive experiments demonstrate that the proposed method significantly outperforms existing approaches in both accuracy and computational efficiency.

Reconstructing 3D structures from non-overlapping multi-view images is a crucial task in the field of 3D computer vision, since it is difficult to establish feature correspondences and infer depth from overlapping parts of views. Previous methods, whether generating the surface mesh or volume of an object, face challenges in simultaneously ensuring the accuracy of detailed topology and the integrity of the overall structure. In this paper, we introduce a novel coarse-to-fine Transformer-based reconstruction network to generate precise point clouds from multiple input images at sparse and non-overlapping viewpoints. Specifically, we firstly employ a general point cloud generation architecture enhanced by the concept of adaptive centroid constraint for the coarse point cloud corresponding to the object. Subsequently, a Transformer-based refinement module applies deformation to each point. We design an attention-based encoder to encode both image projection features and point cloud geometric features, along with a decoder to calculate deformation residuals. Experiments on ShapeNet demonstrate that our proposed method outperforms other competing methods.

Abstract Background Prostate cancer (PCa) is one of the most common malignant tumors in the male urinary system. In recent years, the morbidity and mortality of PCa have been increasing due to the limited effects of existing treatment strategies. Long non-coding RNA (lncRNA) LINC00893 was reported to inhibit the proliferation and metastasis of papillary thyroid cancer cells, but its role in PCa has not been reported. This study aims to investigate the role and underlying mechanism of LINC00893 in regulating the progression of PCa cells. Methods We first compared LINC00893 expression levels between PCa tissues and normal prostate tissues through TCGA database. The relative LINC00893 expression levels were further validated in 66 pairs of PCa tissues and para-cancerous normal tissues, as well as in PCa cell lines. Gain-of-function experiment was performed by transfecting PCa cell with LINC00893 expression vector, and CCK (Cell count kit)-8, 5-Ethynyl-2′-deoxyuridine (EdU) incorporation, colony information and transwell assays were conducted to assess the functional phenotypes. Dual-luciferase reporter, RNA-binding protein immunoprecipitation (RIP) and RNA pull-down assays were performed to evaluate the molecular interactions. Results LINC00893 was downregulated in PCa tissues and cell lines, and patients with low expression of LINC00893 were associated with a poorer overall survival rate. LINC00893 overexpression hindered the proliferation, epithelial-mesenchymal transition (EMT) as well as the migratory ability of PCa cells, and suppressed the tumorigenesis of PCa cells in nude mice. We further demonstrated that LINC00893 acted as a sponge for miR-3173-5p and inhibited its activity, which in turn regulated the suppressor of cytokine signaling 3 (SOCS3)/Janus Kinase 2 (JAK2)/signal transducer and activator of transcription 3 (STAT3) signaling axis. Conclusions Our study demonstrated that LINC00893 suppresses the progression of PCa cells through targeting miR-3173-5p/SOCS3/JAK2/STAT3 axis. Our data uncovers a novel tumor-suppressor role of LINC00893 in PCa, which may serve as a potential strategy for targeted therapy in PCa. Grapical Abstract

Abstract This study aimed to investigate whether serum indicators related to iron stores in the body are associated with clinical and endoscopic disease severity. Eighty-four patients with Crohn’s disease (CD) and twenty-four healthy volunteers were included. The indicators related to iron stores were detected within one week after endoscopic and CT enterography examinations. Patients were divided into three groups according to the CDAI(Crohn's disease activity index)scores. Serum iron levels were decreased in all groups (p 0.05). Underweight, decreased serum iron and TIBC were independent risk factors for moderate clinical disease. Combined detection of decreased serum iron and TIBC was helpful in differentiating severe patients. The sensitivity and specificity were 32.7% and 100%, respectively (AUC = 0.812, p

Background: Grading of endoscopic lesions is important for determining the severity of ulcerative colitis and developing treatment strategies, but the commonly used methods are not sufficient. Objectives: This study aimed to investigate whether new endoscopic scoring systems incorporating lesions and disease extent are associated with clinical disease severity and maintainable remission. Design: This was a retrospective study. In all, 110 patients with ulcerative colitis were included and 87 completed 12-month follow-up. Methods: Colonoscopy was performed within 1 week before blood samples were taken. Degree of ulcerative colitis burden of luminal inflammation (DUBLIN) scores were calculated as the product of Mayo endoscopic score (MES) by disease extent and ulcerative colitis endoscopic index of severity was used to replace MES when calculating modified DUBLIN scores. Results: DUBLIN and modified DUBLIN scores were increased in the moderate and severe groups significantly ( p 7) were associated with an increased risk of treatment failure (hazard ratio = 4.96, 95% confidence interval: 1.17–21.00, p = 0.03), but there were no association between DUBLIN scores and long-term remission ( p > 0.05). Conclusion: Increased DUBLIN and modified DUBLIN scores were conducive to screening serious disease, but only modified DUBLIN scores had the potential to assist in making an upgraded therapeutic schedule.

The surface mesh reconstruction from point clouds has been a fundamental research topic in Computer Vision and Computer Graphics. Recently, the Neural Implicit Representation (NIR)-based reconstruction has revolutionized this research topic. This work summarizes and analyzes representative works on NIR-based reconstruction and highlights several important insights. However, one major problem with existing works is that they struggle to handle high-resolution meshes. To address this, this paper introduces HRE-NDC, a novel High-Resolution and Efficient Neural Dual Contouring approach for mesh reconstruction from point clouds, which takes the previous state-of-the-art as a baseline and adopts a coarse-to-fine network structure design, along with feature-preserving downsampling and other improvements. HRE-NDC significantly reduces training time and memory usage while achieving better surface reconstruction results. Experimental results demonstrate the superiority of our method in both visualization and quantitative results, and it shows excellent generalization performance on various data including large indoor scenes, real-scanned urban buildings, clothing, and human bodies.

Impact craters refer to the most salient features on the moon surface. They are of huge significance for analyzing the moon topography, selecting the lunar landing site and other lunar exploration missions, etc. However, existing methods of impact crater detection have been largely implemented on the optical image data, thereby causing them to be sensitive to the sunlight. Thus, these methods can easily achieve unsatisfactory detection results. In this study, an original two-stage small crater detection method is proposed, which is sufficiently effective in addressing the sunlight effects. At the first stage of the proposed method, a semantic segmentation is conducted to detect small impact craters by fully exploiting the elevation information in the digital elevation map (DEM) data. Subsequently, at the second stage, the detection accuracy is improved under the special post-processing. As opposed to other methods based on DEM images, the proposed method, respectively, increases the new crusher percentage, recall and crusher level F1 by 4.89%, 5.42% and 0.67%.

Point cloud registration is one of the basic research hotspots in the field of 3D reconstruction. Although many previous studies have made great progress, the registration of rock point clouds remains an ongoing challenge, due to the complex surface, arbitrary shape, and high resolution of rock masses. To overcome these challenges, a novel registration method for rock point clouds, based on local invariants, is proposed in this paper. First, to handle the massive point clouds, a point of interest filtering method based on a sum vector is adopted to reduce the number of points. Second, the remaining points of interest are divided into several cluster point sets and the centroid of each cluster is calculated. Then, we determine the correspondence between the original point cloud and the target point cloud by proving the inherent similarity (using the trace of the covariance matrix) of the remaining point sets. Finally, the rotation matrix and translation vector are calculated, according to the corresponding centroids, and a correction method is used to adjust the positions of the centroids. To illustrate the superiority of our method, in terms of accuracy and efficiency, we conducted experiments on multiple datasets. The experimental results show that the method has higher accuracy (about ten times) and efficiency than similar existing methods.

China’s Chang’e lunar exploration project obtains digital orthophoto image (DOM) and digital elevation model (DEM) data covering the whole Moon, which are critical to lunar research. The DOM data have three resolutions (i.e., 7, 20 and 50 m), while the DEM has two resolutions (i.e., 20 and 50 m). Analysis and research on these image data effectively help humans to understand the Moon. In addition, impact craters are considered the most basic feature of the Moon’s surface. Statistics regarding the size and distribution of impact craters are essential for lunar geology. In existing works, however, the lunar surface has been reconstructed less accurately, and there is insufficient semantic information regarding the craters. In order to build a three-dimensional (3D) model of the Moon with crater information using Chang‘e data in the Chang‘e reference frame, we propose a four-step framework. First, software is implemented to annotate the lunar impact craters from Chang’e data by complying with our existing study on an auxiliary annotation method and open-source software LabelMe. Second, auxiliary annotation software is adopted to annotate six segments in the Chang’e data for an overall 25,250 impact crater targets. The existing but inaccurate craters are combined with our labeled data to generate a larger dataset of craters. This data set is analyzed and compared with the common detection data. Third, deep learning detection methods are employed to detect impact craters. To address the problem attributed to the resolution of Chang’e data being too high, a quadtree decomposition is conducted. Lastly, a geographic information system is used to map the DEM data to 3D space and annotate the semantic information of the impact craters. In brief, a 3D model of the Moon with crater information is implemented based on Chang’e data in the Chang‘e reference frame, which is of high significance.

In the fields of 3D modeling, analysis of discontinuities and engineering calculation, surface extraction is of great importance. The rapid development of photogrammetry and Light Detection and Ranging (LiDAR) technology facilitates the study of surface extraction. Automatic extraction of rock surfaces from 3D rock-mass point clouds also becomes the basis of 3D modeling and engineering calculation of rock mass. This paper presents an automated and effective method for extracting rock surfaces from unorganized rock-mass point clouds. This method consists of three stages: (i) clustering based on voxels; (ii) estimating major orientations based on Gaussian Kernel and (iii) rock surface extraction. Firstly, the two-level spatial grid is used for fast voxelization and segmenting the point cloud into three types of voxels, including coplanar, non-coplanar and sparse voxels. Secondly, the coplanar voxels, rather than the scattered points, are employed to estimate major orientations by using a bivariate Gaussian Kernel. Finally, the seed voxels are selected on the basis of major orientations and the region growing method based on voxels is applied to extract rock surfaces, resulting in sets of surface clusters. The sub-surfaces of each cluster are coplanar or parallel. In this paper, artificial icosahedron point cloud and natural rock-mass point clouds are used for testing the proposed method, respectively. The experimental results show that, the proposed method can effectively and accurately extract rock surfaces in unorganized rock-mass point clouds.

Crops are prone to lodging with the decline of stem moisture and the intervention of other factors in the mature harvest period, such as garlic, which is difficult to harvest mechanically. To solve this problem, the plant-correcting reel for harvesting lodging garlic plants, bumped and deformed with plants many times to pull and lift them into a conveyor, is proposed in this study. We analyzed the motion trajectory equation and key influencing factors of the reel and defined the lifting and plant-correcting stages as three processes of contact, stirring and release. For example, the contact deformation model and system energy equations were established in the contact process. Besides, in the stirring process, the garlic plantcorrecting conditions were established through the dynamic simulation test analysis of garlic seedling trajectories and the deflection model of garlic stem was constructed. Furthermore, in the release process, the expressions of rubber bars rotation and garlic plant offset bending curvature were constructed and the optimal number and distribution form of bars were determined. Meanwhile, the mechanism and key operating parameters of the auxiliary lifting mechanism of the divider were established. Through the single-factor test, the influence of reel speed, forward speed and reel height on the success feeding rate was analyzed under different bars distribution forms; Through multi-factor experiments, the interaction contour map of various factors was constructed. When reel speed, forward speed and reel height were 3 rad/s, 3.5 m/s, and 540 mm, the feeding success rate was 98.73%. The optimization factors were tested and verified, which met the operational requirements of a high feeding success rate and low loss rate of garlic harvest. This study combines laboratory virtual as well as field experiments and analyzes of trajectory of bars, contact deformation and deflection model of garlic plant, and reel rotation and garlic plant offset bending curvature to solve the problem of garlic lodging mechanized harvest and yield reduction. [ABSTRACT FROM AUTHOR]