IMB-6G is a novel N-substituted sophoridine acid that has been reported to have anticancer effects. The purpose of the present study was to investigate the effect and underlying mechanism of IMB-6G on human nasopharyngeal carcinoma (NPC) cells. The NPC cell line C666-1 was used in the present study and treated with different concentrations of IMB-6G (0, 1, 2 and 5 µM) for 24 h. Subsequently, cell viability was determined using the Cell Counting kit-8 assay and cell apoptosis was analyzed by performing flow cytometry. The expression levels of genes and proteins in the current study were determined using reverse transcription-quantitative polymerase chain reaction and western blot analysis, respectively. Results indicated that IMB-6G dose-dependently inhibited C666-1 cell viability and induced apoptosis. It was also revealed that IMB-6G induced apoptosis via inducing endoplasmic reticulum (ER) stress activation. Notably, IMB-6G administration enhanced the expression levels of Binding immunoglobulin protein and CCAAT-enhancer-binding protein homologous protein in C666-1 cells. Further analysis suggested that IMB-6G treatment activated inositol-requiring enzyme 1α (IRE1α) and PKR-like ER kinase (PERK) signaling pathways in C666-1 cells. In addition, silencing of IRE1α and PERK significantly reversed IMB-6G-induced cell growth inhibition and apoptosis. In conclusion, the present findings indicated that IMB-6G induced ER stress-mediated apoptosis through activating IRE1α and PERK signaling pathways. The present study suggests that IMB-6G may be a promising agent for NPC treatment.

In this paper, two efficient two-grid algorithms for the convection-diffusion problem with a modified characteristic finite element method are studied. We present an optimal error estimate in L p -norm for the characteristic finite element method unconditionally, while all previous works require certain time-step restrictions. To linearize the characteristic method equations, two-grid algorithms based on the Newton iteration approach and the correction method are applied. The error estimate and the convergence result of the two-grid method are derived in detail. It is shown that the coarse space can be extremely coarse and achieve asymptotically optimal approximations as long as the mesh sizes H = O h 1 / 3 in the first algorithm and H = O h 1 / 4 in the second algorithm, respectively. Finally, two numerical examples are presented to demonstrate the theoretical analysis.

Image deraining has become a hot topic in the field of computer vision. It is the process of removing rain streaks from an image to reconstruct a high-quality background. This study aims at improving the performance of image rain streak removal and reducing the disruptive effects caused by rain. To better fit the rain removal task, an innovative image deraining method is proposed, where a kernel prediction network with Unet++ is designed and used to filter rainy images, and rainy-day images are used to estimate the pixel-level kernel for rain removal. To minimize the gap between synthetic and real data and improve the performance in real rainy image handling, a loss function and an effective data optimization method are suggested. In contrast with other methods, the loss function consists of Structural Similarity Index loss, edge loss, and L1 loss, and it is adopted to improve performance. The proposed algorithm can improve the Peak Signal-to-Noise ratio by 1.3% when compared to conventional approaches. Experimental results indicate that the proposed method can achieve a better efficiency and preserve more image structure than several classical methods.

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