N6‐Methyladenosine (m ⁶ A) modification, the most prevalent modification of eukaryotic messenger RNA (mRNA), is involved in the progression of various tumours. However, the specific role of m ⁶ A in bladder cancer (BCa) is still poorly understood. In this study, we demonstrated the tumour‐promoting function and specific regulatory mechanism of m ⁶ A axis, consisting of the core ‘writer’ protein METTL3 and the major reader protein YTHDF2. Depletion of METTL3 impaired cancer proliferation and cancer metastasis in vitro and in vivo. Through transcriptome sequencing, m ⁶ A methylated RNA immunoprecipitation (MeRIP) and RIP, we determined that the METTL3/YTHDF2 m ⁶ A axis directly degraded the mRNAs of the tumour suppressors SETD7 and KLF4, contributing to the progression of BCa. In addition, overexpression of SETD7 and KLF4 revealed a phenotype consistent with that induced by depletion of the m ⁶ A axis. Thus, our findings on the METTL3/YTHDF2/SETD7/KLF4 m ⁶ A axis provide the insight into the underlying mechanism of carcinogenesis and highlight potential therapeutic targets for BCa.

Low light images captured in a non-uniform illumination environment usually are degraded with the scene depth and the corresponding environment lights. This degradation results in severe object information loss in the degraded image modality, which makes the salient object detection more challenging due to low contrast property and artificial light influence. However, existing salient object detection models are developed based on the assumption that the images are captured under a sufficient brightness environment, which is impractical in real-world scenarios. In this work, we propose an image enhancement approach to facilitate the salient object detection in low light images. The proposed model directly embeds the physical lighting model into the deep neural network to describe the degradation of low light images, in which the environment light is treated as a point-wise variate and changes with local content. Moreover, a Non-Local-Block Layer is utilized to capture the difference of local content of an object against its local neighborhood favoring regions. To quantitative evaluation, we construct a low light Images dataset with pixel-level human-labeled ground-truth annotations and report promising results on four public datasets and our benchmark dataset.

With the wide applications of vision based intelligent systems, image and video analysis technologies have attracted the attention of researchers in the computer vision field. In image and video analysis, human activity recognition is an important research direction. By interpreting and understanding human activity, we can recognize and predict the occurrence of crimes and help the police or other agencies react immediately. In the past, a large number of papers have been published on human activity recognition in video and image sequences. In this paper, we provide a comprehensive survey of the recent development of the techniques, including methods, systems, and quantitative evaluation towards the performance of human activity recognition.

Proteins are not isolated biological molecules, which have the specific three-dimensional structures and interact with other proteins to perform functions. A small number of residues (hot spots) in protein-protein interactions (PPIs) play the vital role in bioinformatics to influence and control of biological processes. This paper uses the boosting algorithm and gradient boosting algorithm based on two feature selection strategies to classify hot spots with three common datasets and two hub protein datasets. Firstly, the correlation-based feature selection is used to remove the highly related features for improving accuracy of prediction. Then, the recursive feature elimination based on support vector machine (SVM-RFE) is adopted to select the optimal feature subset to improve the training performance. Finally, boosting and gradient boosting (G-boosting) methods are invoked to generate classification results. Gradient boosting is capable of obtaining an excellent model by reducing the loss function in the gradient direction to avoid overfitting. Five datasets from different protein databases are used to verify our models in the experiments. Experimental results show that our proposed classification models have the competitive performance compared with existing classification methods.

Typically associated with solid tumors, hypoxia contributes to tumor angiogenesis and lymphangiogenesis through various molecular mechanisms. Accumulating studies indicate that hypoxia-inducible factor is the key transcription factor coordinating endothelial cells to respond to hypoxia in urological cancers, mainly renal cell carcinoma, prostate cancer, and bladder cancer. Moreover, it has been suggested that tumor hypoxia in tumor microenvironment simultaneously recruits stromal cells to suppress immune activities. This review summarizes the mechanisms by which HIF regulates tumorigenesis and elaborates on the associations between HIF and angiogenesis, lymphangiogenesis, and tumor microenvironment in urological cancers.