In addition, we show the usefulness of the theoretical findings thought some simulation experiments. Finally, a real data set have been analyzed for illustrative purposes of our results.Disease-related gene prioritization is one of the most well-established pharmaceutical techniques used to identify genes that are important to a biological process relevant to a disease. In identifying these essential genes, the network diffusion (ND) approach is a widely used technique applied in gene prioritization. However, there is still a large number of candidate genes that need to be evaluated experimentally. Therefore, it would be of great value to develop a new strategy to improve the precision of the prioritization. Given the efficiency and simplicity of centrality measures in capturing a gene that might be important to the network structure, herein, we propose a technique that extends the scope of ND through a centrality measure to identify new disease-related genes. Five common centrality measures with different aspects were examined for integration in the traditional ND model. A total of 40 diseases were used to test our developed approach and to find new genes that might be related to a disease. Results indicated that the best measure to combine with the diffusion is closeness centrality. The novel candidate genes identified by the model for all 40 diseases were provided along with supporting evidence. In conclusion, the integration of network centrality in ND is a simple but effective technique to discover more precise disease-related genes, which is extremely useful for biomedical science.Among the other cancer types, the brain tumor is one the leading cause of cancer across globe. If the tumor is properly identified at an earlier stage, then the chances of the survival can be increased. To categorize the brain tumor there are several factors including texture, type and location of brain tumor. We proposed a novel reconstruction independent component analysis (RICA) feature extraction method to detect multi-class brain tumor types (pituitary, meningioma, and glioma). We then employed the robust machine learning techniques as support vector machine (SVM) with quadratic and linear kernels and linear discriminant analysis (LDA). For training and testing of the data validation, a 10-fold cross validation was employed. For the multi-class classification, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy and AUC were, respectively, 97.78%, 100%, 100%, 99.07, 99.34% and 0.9892 to detect pituitary using SVM Cubic followed by meningioma with accuracy (96.96%0, AUC (0.9348) and glioma with accuracy (95.88%), AUC (0.9635). The findings indicates that RICA feature based proposed methodology has more potential to detect the multiclass brain tumor types for improving diagnostic efficiency and can further improve the prediction accuracy to achieve the clinical outcomes.Active fluids consume fuel at the microscopic scale, converting this energy into forces that can drive macroscopic motions over scales far larger than their microscopic constituents. In some cases, the mechanisms that give rise to this phenomenon have been well characterized, and can explain experimentally observed behaviors in both bulk fluids and those confined in simple stationary geometries. More recently, active fluids have been encapsulated in viscous drops or elastic shells so as to interact with an outer environment or a deformable boundary. Such systems are not as well understood.  https://www.selleckchem.com/products/oxidopamine-hydrobromide.html  In this work, we examine the behavior of droplets of an active nematic fluid. We study their linear stability about the isotropic equilibrium over a wide range of parameters, identifying regions in which different modes of instability dominate. Simulations of their full dynamics are used to identify their nonlinear behavior within each region. When a single mode dominates, the droplets behave simply as rotors, swimmers, or extensors. When parameters are tuned so that multiple modes have nearly the same growth rate, a pantheon of modes appears, including zigzaggers, washing machines, wanderers, and pulsators.In this paper, we study the initial boundary value problem for a class of fractional p-Laplacian Kirchhoff type diffusion equations with logarithmic nonlinearity. Under suitable assumptions, we obtain the extinction property and accurate decay estimates of solutions by virtue of the logarithmic Sobolev inequality. Moreover, we discuss the blow-up property and global boundedness of solutions.In this paper, a prey-predator model with modified Leslie-Gower and simplified Holling-type Ⅳ functional responses is proposed to study the dynamic behaviors. For the deterministic system, we analyze the permanence of the system and the stability of the positive equilibrium point. For the stochastic system, we not only prove the existence and uniqueness of global positive solution, but also discuss the persistence in mean and extinction of the populations. In addition, we find that stochastic system has an ergodic stationary distribution under some parameter constraints. Finally, our theoretical results are verified by numerical simulations.This paper is an extended and supplemented version of the paper "Recommendation Rules Mining for Reducing the Spread of COVID-19 Cases", presented by the authors at the 3rd International Conference on Informatics & Data-Driven Medicine in November 2020. The paper examines the impact of government restrictive measures on the spread and effects of COVID-19. The work is devoted to the improvement of recommendation rules based on novel ensemble of machine learning methods such as regression tree and clustering. The dynamics of migration between countries in clusters, and their relationship with the number of confirmed cases and the percentage of deaths caused by COVID-19, were studied on the example of Poland, Italy and Germany. It is shown that there is a clear relationship between the cluster number and the number of new cases of diseases and death. It has also been shown that different countries' policies to prevent the disease, in particular the timing of restrictive measures, correlate with the dynamics of the spread of COVID-19 and the consequences of the disease.