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Natural gas hydrate (NGH) is regarded worldwide as the most promising clean energy. Research on the exploration and exploitation of NGH has come to a stage of breakthrough in China. However, the accumulation mechanism and occurrence regularity of NGH from muddy silt in the South China Sea remain unclear, and the hydrate decomposition and phase transformation mechanisms during exploitation as well as safe and efficient technologies require further study. This study reviewed the research and application status of NGH exploitation technologies including depressurization and solid fluidization. Moreover, corresponding suggestions were proposed considering the existing problems regarding current NGH exploitation technologies. Specifically, basic research and major engineering should be promoted for multi-gas exploitation of NGH and deep oil and gas; technology innovation and integration should be strengthened to promote synergy between theory and practice; a numerical simulation big science platform should be established for pilot scale tests and multi-field coupling; and relevant standards need to be established and improved. This can improve the research level and technical maturity of NGH exploitation in China, facilitate its large-scale commercial development, and ensure energy security of China.

Objective To explore the potential prognostic role and value of autophagy-related lncRNA risk prediction signature in patients with bladder cancer. Methods The transcriptome data and corresponding clinical data of patients with bladder cancer were obtained from the Cancer Genome Atlas (TCGA) database. Four hundreds and two bladder cancer samples were randomly divided into training set and verification set according to the ratio of 3 ∶1 by R software. Autophagy-related lncRNA, was screened by Pearson correlation analysis, and autophagy-related lncRNA prognostic signature was constructed by univariate and multivariate Cox regression analyses. The accuracy of the signature was evaluated by drawing ROC curve. The sample risk score was further calculated, and the relationship of the sample risk score with clinical characteristics and survival prognosis was analyzed. In addition, the ability of the signature to predict the prognosis of patients with bladder cancer was evaluated by constructing a nomogram. Finally, the possible enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis in different risk groups were analyzed by gene set enrichment analysis (GSEA). Finally, the expression of high-risk lncRNA was verified in the signature by RT-qPCR. Results An 8-autophagy-related lncRNA signature was successfully constructed. In the training set, validation set and whole set, the AUC values of the ROC curve of the prognostic signature were all greater than 0.75. The risk score of the signature was related to age, American Joint Committee on Cancer (AJCC) stage and T stage of the patients (P < 0.05), but not to their gender. The nomogram established by integrating clinical variables and risk scores confirmed that the signature had a good ability to predict prognosis. GSEA showed that some tumor and autophagy-related pathways were enriched in the high-risk group, while immune-related pathways were enriched in the low-risk group. lncRNA AC005229.4 was highly expressed in bladder cancer cell lines. Conclusion An 8-autophagy-related lncRNA signature is effective in the prognostic prediction of patients with bladder can

Objective To explore the changes of malignant proliferation of bladder cancer cells under starvation microenvironment and its possible mechanism. Methods Bladder cancer T24 cells were divided into 2 groups: the normal group was cultured normally, while the starvation group was treated with serum-free HBSS for 6 h to simulate starvation microenvironment for cancer cells. The changes of autophagy and proliferation ability of cells were observed after treating with autophagy inhibitor CQ or transfection of siRNA of Yes-associated protein 1 (YAP1) gene, respectively. Finally, a risk prediction model was established based on the expression of YAP1 and autophagy-related gene LC3B, and the correlation between their levels and clinical prognosis was subsequently analyzed. Results CCK-8 and clone formation assays showed that the proliferation ability of bladder cancer cells was significantly increased after starvation induction (P < 0.01), which was caused by promoting cell cycle from G1 phase to S phase, while there was no significant difference in apoptosis between the 2 groups. The degradation of autophagy-related marker P62 protein and LC3-Ⅱ/Ⅰ ratio were detected by Western blotting and immunofluorescence assay, and the Results indicated that starvation induced up-regulation of autophagy in bladder cancer cells (P < 0.05). However, the proliferation of cancer cells was no longer affected by starvation after the autophagy inhibitor CQ blocked the autophagy tide. Overexpression and declined phosphorylation of YAP1 protein were observed after starvation induction (P < 0.01), and this result was not affected by inhibition of autophagy. In addition, the autophagy level and the proliferation of cells were significantly inhibited by siRNA interference in YAP1 expression (P < 0.05). Bioinformatics analysis showed that the level of YAP1 was higher in the cancer tissues of patients with higher malignant grade of bladder cancer, and the patients with higher expression of YAP1 and LC3B genes had worse prognosis. Conclusion The overexpression of YAP1 protein induced by starvation promotes the malignant proliferation of bladder cancer cells by enhancing the level of autophagy, and YAP1 and autophagy play a synergistic role in the development of bladder cancer.

The multi-armed bandit (MAB) problem is a typical problem of exploration and exploitation. As a classical MAB problem, the stochastic multi-armed bandit (SMAB) is the basis of reinforcement learning recommendation. However, most existing SMAB and MAB algorithms have two limitations: (1) they do not make full use of feedback from the environment or agent, such as the number of arms and rewards contained in user feedback; (2) they overlook the utilization of different action selections, which can affect the exploration and exploitation of the algorithm. These limitations motivate us to propose a novel dynamic grouping within the minimax optimal strategy in the stochastic case (DG-MOSS) algorithm for reinforcement learning recommendation for small and medium-sized data scenarios. DG-MOSS does not require additional contextual data and can be used for recommendation of various types of data. Specifically, we designed a new exploration calculation method based on dynamic grouping which uses the feedback information automatically in the selection process and adopts different action selections. During the thorough training of the algorithm, we designed an adaptive episode length to effectively improve the training efficiency. We also analyzed and proved the upper bound of DG-MOSS's regret. Our experimental results for different scales, densities, and field datasets show that DG-MOSS can yield greater rewards than nine baselines with sufficiently trained recommendation and demonstrate that it has better robustness. [ABSTRACT FROM AUTHOR]