The impact of PKR activation: from neurodegeneration to cancer.

An inverse association between cancer and neurodegeneration is plausible because these biological processes share several genes and signaling pathways. Whereas uncontrolled cell proliferation and decreased apoptotic cell death governs cancer, excessive apoptosis contributes to neurodegeneration. Protein kinase R (PKR), an interferon-inducible double-stranded RNA protein kinase, is involved in both diseases. PKR activation blocks global protein synthesis through eIF2α phosphorylation, leading to cell death in response to a variety of cellular stresses. However, PKR also has the dual role of activating the nuclear factor κ-B pathway, promoting cell proliferation. Whereas PKR is recognized for its negative effects on neurodegenerative diseases, in part, inducing high level of apoptosis, the role of PKR activation in cancer remains controversial. In general, PKR is considered to have a tumor suppressor function, and some clinical data show a correlation between suppressed or inactivated PKR and a poor prognosis for several cancers. However, other studies show high PKR expression and activation levels in various cancers, suggesting that PKR might contribute to neoplastic progression. Understanding the cellular factors and signals involved in the regulation of PKR in these age-related diseases is relevant and may have important clinical implications. The present review highlights the current knowledge on the role of PKR in neurodegeneration and cancer, with special emphasis on its regulation and clinical implications.

Hybrid AC/DC microgrid test system simulation: grid-connected mode.

In this paper, a Microgrid (MG) test model based on the 14-busbar IEEE distribution system is proposed. This model can constitute an important research tool for the analysis of electrical grids in its transition to Smart Grids (SG). The benchmark is used as a base case for power flow analysis and quality variables related with SG and holds distributed resources. The proposed MG consists of DC and AC buses with different types of loads and distributed generation at two voltage levels. A complete model of this MG has been simulated using the MATLAB/Simulink environmental simulation platform. The proposed electrical system will provide a base case for other studies such as: reactive power compensation, stability and inertia analysis, reliability, demand response studies, hierarchical control, fault tolerant control, optimization and energy storage strategies.

Radial basis function for fast voltage stability assessment using Phasor Measurement Units.

A simple method, based on Machine Learning Radial Basis Functions, RBF, is developed for estimating voltage stability margins in power systems. A reduced set of magnitude and angles of bus voltage phasors is used as input. Observability optimization technique for locating Phasor Measurement Units, PMUs, is applied. A RBF is designed and used for fast calculation of voltage stability margins for online applications with PMUs. The method allows estimating active local and global power margins in normal operation and under contingencies. Optimized placement of PMUs leads to a minimum number of these devices to estimate the margins, but is shown that it is not a matter of PMUs quantity but of PMUs location for decreasing training time or having success in estimation convergence. Compared with previous work, the most significant enhancement is that our RBF learns from PMU data. To test the proposed method, validations in the IEEE 14-bus system and in a real electrical network are done.