Potassium channels and membrane potential in the modulation of intracellular calcium in vascular endothelial cells

K+ Channels and Membrane Potential in Endothelial Cells. The endothelium plays a vital role in the control of vascular functions, including modulation of tone; permeability and barrier properties; platelet adhesion and aggregation; and secretion of paracrine factors. Critical signaling events in many of these functions involve an increase in intracellular free Ca2+ concentration ([Ca2+](i)). This rise in [Ca2+](i) occurs via an interplay between several mechanisms, including release from intracellular stores, entry from the extracellular space through store depletion and second messenger-mediated processes, and the establishment of a favorable electrochemical gradient. The focus of this review centers on the role of potassium channels and membrane potential in the creation of a favorable electrochemical gradient for Ca2+ entry. In addition, evidence is examined for the existence of various classes of potassium channels and the possible influence of regional variation in expression and experimental conditions.

Tracking the states of a nonlinear and nonstationary system in the weight-space of artificial neural networks

We propose a novel interpretation and usage of Neural Network (NN) in modeling physiological signals, which are allowed to be nonlinear and/or nonstationary. The method consists of training a NN for the k-step prediction of a physiological signal, and then examining the connection-weight-space (CWS) of the NN to extract information about the signal generator mechanism. We de. ne a novel feature, Normalized Vector Separation (gamma(ij)), to measure the separation of two arbitrary states i and j in the CWS and use it to track the state changes of the generating system. The performance of the method is examined via synthetic signals and clinical EEG. Synthetic data indicates that gamma(ij) can track the system down to a SNR of 3.5 dB. Clinical data obtained from three patients undergoing carotid endarterectomy of the brain showed that EEG could be modeled (within a root-means-squared-error of 0.01) by the proposed method, and the blood perfusion state of the brain could be monitored via gamma(ij), with small NNs having no more than 21 connection weight altogether.

Improving support vector solutions by selecting a sequence of training subsets

In this paper we demonstrate that it is possible to gradually improve the performance of support vector machine (SVM) classifiers by using a genetic algorithm to select a sequence of training subsets from the available data. Performance improvement is possible because the SVM solution generally lies some distance away from the Bayes optimal in the space of learning parameters. We illustrate performance improvements on a number of benchmark data sets.