Nitric oxide regulates the heart by spatial confinement of nitric oxide synthase isoforms

Subcellular localization of nitric oxide (NO) synthases with effector molecules is an important regulatory mechanism for NO signalling. In the heart, NO inhibits L-type Ca2+ channels but stimulates sarcoplasmic reticulum (SR) Ca2+ release, leading to variable effects on myocardial contractility. Here we show that spatial confinement of specific NO synthase isoforms regulates this process. Endothelial NO synthase (NOS3) localizes to caveolae, where compartmentalization with beta-adrenergic receptors and L-type Ca2+ channels allows NO to inhibit beta-adrenergic-induced inotropy. Neuronal NO synthase (NOS1), however, is targeted to cardiac SR. NO stimulation of SR Ca2+ release via the ryanodine receptor (RyR) in vitro, suggests that NOS1 has an opposite, facilitative effect on contractility. We demonstrate that NOS1-deficient mice have suppressed inotropic response, whereas NOS3-deficient mice have enhanced contractility, owing to corresponding changes in SR Ca2+ release. Both NOS1-/- and NOS3-/- mice develop age-related hypertrophy, although only NOS3-/- mice are hypertensive. NOS1/3-/- double knockout mice have suppressed beta-adrenergic responses and an additive phenotype of marked ventricular remodelling. Thus, NOS1 and NOS3 mediate independent, and in some cases opposite, effects on cardiac structure and function.     

Fault tolerant control based on stochastic distribution via RBF neural networks

A new fault tolerant control (FTC) via a controller reconfiguration approach for general stochastic nonlinear systems is studied. Different from the formulation of classical FTC methods, it is supposed that the measured information for the FTC is the probability density functions (PDFs) of the system output rather than its measured value. A radial basis functions (RBFs) neural network technique is proposed so that the output PDFs can be formulated in terms of the dynamic weighings of the RBFs neural network. As a result, the nonlinear FTC problem subject to dynamic relation between the input and the output PDFs can be transformed into a nonlinear FTC problem subject to dynamic relation between the control input and the weights of the RBFs neural network approximation to the output PDFs. The FTC design consists of two steps. The first step is fault detection and diagnosis (FDD), which can produce an alarm when there is a fault in the system and also locate which component has a fault. The second step is to adapt the controller to the faulty case so that the system is able to achieve its target. A linear matrix inequality (LMI) based feasible FTC method is applied such that the fault can be detected and diagnosed. An illustrated example is included to demonstrate the efficiency of the proposed algorithm, and satisfactory results have been obtained.