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Spectral similarity measures can be regarded as potential metrics for kernel functions, and can be used to generate spectral-similarity-based kernels. However, spectral-similarity-based kernels have not received significant attention from researchers. In this paper, we propose two novel spectral-similarity-based kernels based on spectral angle mapper (SAM) and spectral information divergence (SID) combined with the radial basis function (RBF) kernel: Power spectral angle mapper RBF (Power-SAM-RBF) and normalized spectral information divergence-based RBF (Normalized-SID-RBF) kernels. First, we prove these spectral-similarity-based kernels to be Mercer’s kernels. Second, we analyze their efficiency in terms of local and global kernels. Finally, we consider three hyperspectral datasets to analyze the effectiveness of the proposed spectral-similarity-based kernels. Experimental results demonstrate that the Power-SAM-RBF and SAM-RBF kernels can obtain an impressive performance, particularly the Power-SAM-RBF kernel. For example, when the ratio of the training set is 20 % , the kappa coefficient of Power-SAM-RBF kernel (0.8561) is 1.61 % , 1.32 % , and 1.23 % higher than that of the RBF kernel on the Indian Pines, University of Pavia, and Salinas Valley datasets, respectively. We present three conclusions. First, the superiority of the Power-SAM-RBF kernel compared to other kernels is evident. Second, the Power-SAM-RBF kernel can provide an outstanding performance when the similarity between spectral signatures in the same hyperspectral dataset is either extremely high or extremely low. Third, the Power-SAM-RBF kernel provides even greater benefits compared to other commonly used kernels when the sizes of the training sets increase. In future work, multiple kernels combining with the spectral-similarity-based kernel are expected to be provide better hyperspectral classification.

The level of cholesterol in the body is closely related to human health. Although cholesterol has many irreplaceable physiological functions, high cholesterol is one of the causes of many cardiovascular and cerebrovascular diseases. With more extensive and in-depth research on probiotics, many strains have been proved to have a good cholesterol-lowering effect, and their cholesterol-lowering effect has aroused more and more attention. Thus, the development and application of cholesterol-lowering probiotics have a bright future. In this paper, the mechanism of cholesterol synthesis and metabolism in the human body is briefly introduced in terms of its intake, synthesis, conversion and transport. The in vitro cholesterol-lowering mechanism of probiotics by absorbing and binding cholesterol as well as producing cholesterol-lowering metabolites, and the in vivo cholesterol-lowering mechanism of probiotics by inhibiting the intake and synthesis of cholesterol, promoting its transformation and regulating its transport are reviewed. Finally, the evaluation strategies for cholesterol-lowering probiotics are summarized including in vitro tests, animal experiments and clinical studies. This review is expected to provide a reference for the development of probiotic strains with a potent cholesterol-lowering effect.

Metabolism-associated genes (MAGs) are important regulators of tumor progression and can affect a variety of physiological processes. In this study, we focused on the relationship between MAGs and Ovarian cancer (OC) prognosis. Method: Metabolism-related genes were extracted from the Cancer Genome Atlas (TCGA) database. Through univariate COX and lasso regression models, a dynamic risk model based on MAGs was established. Compared with other clinical factors, demonstrated the ability of the model to predict the prognosis of patients with OC. The clinical samples were used to verify the expression of these MAGs. Results: A metabolism-associated gene signature was constructed by LASSO Cox regression analysis in OC, which was composed of 3-MAGs (PTGIS, AOC3, and IDO1). The signature was used to classify the OC patients into high-risk and low-risk groups. The overall survival of the low-risk group was significantly better than that of the high-risk group. The analysis of the therapeutic effect of bevacizumab showed that bevacizumab was not conducive to improving the prognosis of the low-risk group. Conclusions: We constructed a prognostic model of MAGs in OC, which can be used to predict the prognosis of OC patients and may have a good guiding significance in the individualized treatment of patients. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the role of miR-455-3p in gliomas. Method: Quantitative real-time polymerase chain reaction was used to measure miR-455-3p and paired box 6 (PAX6) levels in glioma cell lines. Western blot analysis was used to determine the expression of cell cycle regulators. In addition to over-expression, silencing of miR-455-3p or PAX6 was performed to study the functions of miR-455-3p in gliomas. Results: The levels of miR-455-3p were significantly up-regulated in glioma cell lines (p < 0.05), while miR-455-3p over-expression increased glioma cell proliferation and interfered with the progress of the cell cycle (p < 0.01). Furthermore, endogenous miR-455-3p silencing prevented glioma cell proliferation by regulating cell cycle progression (p < 0.05).The results also showed that PAX6 controlled the cell cycle while PAX6 silencing selectively regulated p21 expression (p < 0.01). Furthermore, miR-455-3p and PAX6 influenced p53 expression. Re-introduction of PAX6 expressing vector into glioma cells rescued the pro-tumoral effect of miR-455-3p overexpression. Conclusion: These findings demonstrate the role of miR-455-3p as a tumour oncogene in gliomas via regulation of the cell cycle, indicating that miR-455-3p might act as a new treatment strategy for glioma cell tumours and a predictor of survival in glioma patients. [ABSTRACT FROM AUTHOR]