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Light Detection and Ranging (LiDAR) systems are novel sensors that provide robust distance and reflection strength by active pulsed laser beams. They have significant advantages over visual cameras by providing active depth and intensity measurements that are robust to ambient illumination. However, the systemsstill pay limited attention to intensity measurements since the output intensity maps of LiDAR sensors are different from conventional cameras and are too sparse. In this work, we propose exploiting the information from both intensity and depth measurements simultaneously to complete the LiDAR intensity maps. With the completed intensity maps, mature computer vision techniques can work well on the LiDAR data without any specific adjustment. We propose an end-to-end convolutional neural network named LiDAR-Net to jointly complete the sparse intensity and depth measurements by exploiting their correlations. For network training, an intensity fusion method is proposed to generate the ground truth. Experiment results indicate that intensity–depth fusion can benefit the task and improve performance. We further apply an off-the-shelf object (lane) segmentation algorithm to the completed intensity maps, which delivers consistent robust to ambient illumination performance. We believe that the intensity completion method allows LiDAR sensors to cope with a broader range of practice applications.

In recent advancements in novel view synthesis and neural rendering, neural radiance field (NeRF) has emerged as a powerful technique for synthesizing high-quality novel views of complex 3D scenes. However, the computational and storage demands of NeRF limit its applicability. In this paper, we present a novel approach to NeRF by combining low-rank decomposition and multi-hash encoding through a novel integration process to enhance efficiency and scalability. Our method reduces the model complexity and accelerates the training processes while maintaining high rendering quality. We demonstrate the effectiveness of our approach through extensive experiments on various datasets, showing significant improvements in performance and memory usage compared to traditional NeRF implementations. These results suggest that our approach can make NeRF more practical for real-world applications, such as virtual reality, gaming, and 3D reconstruction.

Background: Microtubule-targeted drugs are the most effective drugs for adult patients with certain solid tumors. Taccalonolide AJ (AJ) can stabilize tubulin polymerization by covalently binding to β-tubulin, which enables it to play a role in the treatment of tumors. However, its clinical applications are largely limited by low water solubility, chemical instability in water, and a narrow therapeutic window. Clear-cell renal-cell carcinoma (cc RCC) accounts for approximately 70% of RCC cases and is prone to resistance to particularly targeted therapy drugs. Methods: we prepared a water-soluble cyclodextrin-based carrier to serve as an effective treatment for cc RCC. Results: Compared with AJ, taccalonolide AJ-hydroxypropyl-β-cyclodextrin (AJ-HP-β-CD) exhibited superior selectivity and activity toward the cc RCC cell line 786-O vs. normal kidney cells by inducing apoptosis and cell cycle arrest and inhibiting migration and invasion of tumor cells in vitro. According to acute toxicity testing, the maximum tolerated dose (MTD) of AJ-HP-β-CD was 10.71 mg/kg, which was 20 times greater than that of AJ. Assessment of weight changes showed that mouse body weight recovered over 7–8 days, and the toxicity could be greatly reduced by adjusting the injections from once every three days to once per week. In addition, we inoculated 786-O cells to generate xenografted mice to evaluate the anti-tumor activity of AJ-HP-β-CD in vivo and found that AJ-HP-β-CD had a better tumor inhibitory effect than that of docetaxel and sunitinib in terms of tumor growth and endpoint tumor weight. These results indicated that cyclodextrin inclusion greatly increased the anti-tumor therapeutic window of AJ. Conclusions: the AJ-HP-β-CD complex developed in this study may prove to be a novel tubulin stabilizer for the treatment of cc RCC. In addition, this drug delivery system may broaden the horizon in the translational study of other chemotherapeutic drugs.

Cancer genomics has led to the discovery of numerous oncogenes and tumor suppressor genes that play critical roles in cancer development and progression. Oncogenes promote cell growth and proliferation, whereas tumor suppressor genes inhibit cell growth and division. The dysregulation of these genes can lead to the development of cancer. Recent studies have focused on non-coding RNAs (ncRNAs), including circular RNA (circRNA), long non-coding RNA (lncRNA), and microRNA (miRNA), as therapeutic targets for cancer. In this article, we discuss the oncogenes and tumor suppressor genes of ncRNAs associated with different types of cancer and their potential as therapeutic targets. Here, we highlight the mechanisms of action of these genes and their clinical applications in cancer treatment. Understanding the molecular mechanisms underlying cancer development and identifying specific therapeutic targets are essential steps towards the development of effective cancer treatments. [Display omitted] •The classification, synthesis, and function of non-coding RNA are introduced.•The oncogenes and tumor suppressor genes of three types of non-coding RNA (circRNA, lncRNA, and miRNA) have been summarized.•The review covers the clinical application of non-coding RNA as a therapeutic target for cancer treatment.

This paper presents a visual simultaneous localization and mapping (SLAM) system designed for highly dynamic environments, capable of eliminating dynamic objects using only visual information. The proposed system integrates learning‐based and geometry‐based methods to address the challenges posed by moving objects. The learning‐based approach leverages image segmentation to remove previously trained objects, whereas the geometry‐based approach utilises point correlation to eliminate unseen objects. By complementing each other, these methods enhance the robustness of the SLAM system in dynamic scenarios. Experimental results demonstrate that the proposed method effectively removes dynamic objects. Comparative studies with state‐of‐the‐art algorithms further show that the proposed method achieves superior accuracy and robustness.

Simultaneous localization and mapping methods are fundamental to many robotic applications. In dynamic environments, SLAM methods focus on eliminating the influence of moving objects to construct a static map since they assume a static world. To improve localization robustness in dynamic environments, an RGB‐D SLAM method is proposed to build a complete 3D map containing both static and dynamic maps, the latter of which consists of the trajectories and points of the moving objects. Without any prior knowledge of the moving targets, the proposed method uses only the correlation between map points to discriminate between the static scene and the moving objects. After the static points are determined, camera motion estimation is performed only on reliable static map points to eliminate the influence of moving objects. The motion of the moving objects will then be estimated with the obtained camera motion by tracking their dynamic points in subsequent frames. Moreover, multiple groups of dynamic points that belong to the same moving object are fused by a volume overlap checking step. Experimental results are presented to demonstrate the feasibility and performance of the proposed method.

In recent years, significant advancements have been made in enabling intelligent unmanned agents to achieve autonomous navigation and positioning within large‐scale indoor or underground environments. Central to these achievements is simultaneous localization and mapping (SLAM) technology. Concurrently, the rapid evolution of LiDAR technologies has revolutionised SLAM, enhancing localisation and mapping capabilities in extreme environments characterised by high dynamics, sparse features or GPS‐denied environment. Although much research has concentrated on camera‐based SLAM or GPS‐fused SLAM, this paper provides a comprehensive review of the development of LiDAR‐based multi‐sensor fusion SLAM with a particular emphasis on GPS‐denied environments and filter‐based sensor fusion techniques. The paper is structured as follows: The first section introduces the relevant hardware and datasets. The second section delves into the localisation methodologies employed. The third section discusses the mapping processes involved. The fourth section addresses open problems and suggests future research directions. Overall, this review aims to offer a thorough analysis of the development trends in SLAM with a focus on LiDAR‐based methods, covering both hardware and software aspects, providing readers with a clear reference on workflow for engineering deliverable technologies that can be adapted to various application scenarios.

In this study, a localisation system without cumulative errors is proposed. First, depth odometry is achieved only by using the depth information from the depth camera. Then the point cloud cross‐source map registration is realised by 3D particle filtering to obtain the pose of the point cloud relative to the map. Furthermore, we fuse the odometry results with the point cloud to map registration results, so the system can operate effectively even if the map is incomplete. The effectiveness of the system for long‐term localisation, localisation in the incomplete map, and localisation in low light through multiple experiments on the self‐recorded dataset is demonstrated. Compared with other methods, the results are better than theirs and achieve high indoor localisation accuracy.

In order to quantitatively study the seismic absorption effect of the cushion on a superstructure, a numerical simulation and parametric study are carried out on the overall FEA model of a rigid-pile composite foundation in ABAQUS. A simulation of a shaking table test on a rigid mass block is first completed with ABAQUS and EERA, and the effectiveness of the Drucker-Prager constitutive model and the finite-infinite element coupling method is proved. Dynamic time-history analysis of the overall model under frequent and rare earthquakes is carried out using seismic waves from the El Centro, Kobe, and Bonds earthquakes. The different responses of rigid-pile composite foundations and pile-raft foundations are discussed. Furthermore, the influence of thickness and modulus of cushion, and ground acceleration on the seismic absorption effect of the cushion are analyzed. The results show that： 1） the seismic absorption effect of a cushion is good under rare earthquakes, with an absorption ratio of about 0.85; and 2） the seismic absorption effect is strongly affected by cushion thickness and ground acceleration.

Neural Radiance Fields (NeRF) have garnered considerable attention as a paradigm for novel view synthesis by learning scene representations from discrete observations. Nevertheless, NeRF exhibit pronounced performance degradation when confronted with sparse view inputs, consequently curtailing its further applicability. In this work, we introduce Hierarchical Geometric, Semantic, and Photometric Guided NeRF (HG3-NeRF), a novel methodology that can address the aforementioned limitation and enhance consistency of geometry, semantic content, and appearance across different views. We propose Hierarchical Geometric Guidance (HGG) to incorporate the attachment of Structure from Motion (SfM), namely sparse depth prior, into the scene representations. Different from direct depth supervision, HGG samples volume points from local-to-global geometric regions, mitigating the misalignment caused by inherent bias in the depth prior. Furthermore, we draw inspiration from notable variations in semantic consistency observed across images of different resolutions and propose Hierarchical Semantic Guidance (HSG) to learn the coarse-to-fine semantic content, which corresponds to the coarse-to-fine scene representations. Experimental results demonstrate that HG3-NeRF can outperform other state-of-the-art methods on different standard benchmarks and achieve high-fidelity synthesis results for sparse view inputs.