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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., (© 2024 The Author(s).)

Neural network models of machine learning have shown promising prospects for visual tasks, such as facial emotion recognition (FER). However, the generalization of the model trained from a dataset with a few samples is limited. Unlike the machine, the human brain can effectively realize the required information from a few samples to complete the visual tasks. To learn the generalization ability of the brain, in this article, we propose a novel brain-machine coupled learning method for facial emotion recognition to let the neural network learn the visual knowledge of the machine and cognitive knowledge of the brain simultaneously. The proposed method utilizes visual images and electroencephalogram (EEG) signals to couple training the models in the visual and cognitive domains. Each domain model consists of two types of interactive channels, common and private. Since the EEG signals can reflect brain activity, the cognitive process of the brain is decoded by a model following reverse engineering. Decoding the EEG signals induced by the facial emotion images, the common channel in the visual domain can approach the cognitive process in the cognitive domain. Moreover, the knowledge specific to each domain is found in each private channel using an adversarial strategy. After learning, without the participation of the EEG signals, only the concatenation of both channels in the visual domain is used to classify facial emotion images based on the visual knowledge of the machine and the cognitive knowledge learned from the brain. Experiments demonstrate that the proposed method can produce excellent performance on several public datasets. Further experiments show that the proposed method trained from the EEG signals has good generalization ability on new datasets and can be applied to other network models, illustrating the potential for practical applications.

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 this paper, a simultaneous localization and mapping (SLAM) method that eliminates the influence of moving objects in dynamic environments is proposed. This method utilizes the correlation between map points to separate points that are part of the static scene and points that are part of different moving objects into different groups. A sparse graph is first created using Delaunay triangulation from all map points. In this graph, the vertices represent map points, and each edge represents the correlation between adjacent points. If the relative position between two points remains consistent over time, there is correlation between them, and they are considered to be moving together rigidly. If not, they are considered to have no correlation and to be in separate groups. After the edges between the uncorrelated points are removed during point-correlation optimization, the remaining graph separates the map points of the moving objects from the map points of the static scene. The largest group is assumed to be the group of reliable static map points. Finally, motion estimation is performed using only these points. The proposed method was implemented for RGB-D sensors, evaluated with a public RGB-D benchmark, and tested in several additional challenging environments. The experimental results demonstrate that robust and accurate performance can be achieved by the proposed SLAM method in both slightly and highly dynamic environments. Compared with other state-of-the-art methods, the proposed method can provide competitive accuracy with good real-time performance.

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.

A facile one-pot synthesis has been developed through alkylation/acylation of ortho -tosylaminophenyl-substituted para -quinone methides followed by an intramolecular 1,6-conjugate addition and oxidation sequence. This cascade reaction occurs readily in good yield (up to 95%), providing a divergent synthetic approach to structurally diverse 2,3-disubstituted indoles and 3,4-diaryl-substituted quinolinones., Competing Interests: The authors declare no competing financial interest., (This journal is © The Royal Society of Chemistry.)