Increased NAD(P)H oxidase-mediated superoxide production in renovascular hypertension: Evidence for an involvement of protein kinase C

Increased NAD(P)H oxidase-mediated superoxide production in renovascular hypertension: Evidence for an involvement of protein kinase C.BackgroundAngiotensin II infusion has been shown to cause hypertension and endothelial dysfunction and to increase superoxide (O-·2) production in vascular tissue, mainly via an activation of nicotinamide adenine dinucleotide (phosphate) [NAD(P)H]-dependent oxidase, the most significant O-·2 source in endothelial and/or smooth muscle cells. With these studies, we sought to determine whether endothelial dysfunction in renovascular hypertension is secondary to an activation of these oxidases.MethodsEndothelial function in aortas from rats with two kidney-one clip (2K-1C) hypertension and age-matched controls was assessed using isometric tension studies in organ chambers. Changes in vascular O-·2 production were measured using lucigenin-enhanced chemiluminescence and electron spin resonance spectroscopy.ResultsIn hypertensive animals, relaxation to endothelium-dependent (acetylcholine) and endothelium-independent nitrovasodilators (nitroglycerin) was impaired. Constriction to a direct activator of protein kinase C (PKC) phorbol ester 12,13 dibutyrate (PDBu) was enhanced, and vascular O-·2 was significantly increased compared with controls. Vascular O-·2 was normalized by the PKC inhibitor calphostin C, by the inhibitor of flavin-dependent oxidases, diphenylene iodonium, and recombinant heparin-binding superoxide dismutase, whereas inhibitors of the xanthine oxidase (oxypurinol), nitric oxide synthase (NG-nitro-L-arginine) and mitochondrial NADH dehydrogenase (rotenone) were ineffective. Studies of vascular homogenates demonstrated that the major source of O-·2 was a NAD(P)H-dependent oxidase. Incubation of intact tissue with PDBu markedly increased O-·2, the increase being significantly stronger in vessels from hypertensive animals as compared with vessels from controls. Endothelial dysfunction was improved by preincubation of vascular tissue with superoxide dismutase and calphostin C.ConclusionsWe therefore conclude that renovascular hypertension in 2K-1C rats is associated with increased vascular O-·2 leading to impaired vasodilator responses to endogenous and exogenous nitrovasodilators. Increased vascular O-·2 is likely secondary to a PKC-mediated activation of a membrane-associated NAD(P)H-dependent oxidase

Improving risk-adjusted performance in high frequency trading using interval type-2 fuzzy logic

In this paper, we investigate the ability of higher order fuzzy systems to handle increased uncertainty, mostly induced by the market microstructure noise inherent in a high frequency trading (HFT) scenario. Whilst many former studies comparing type-1 and type-2 Fuzzy Logic Systems (FLSs) focus on error reduction or market direction accuracy, our interest is predominantly risk-adjusted performance and more in line with both trading practitioners and upcoming regulatory regimes. We propose an innovative approach to design an interval type-2 model which is based on a generalisation of the popular type-1 ANFIS model. The significance of this work stems from the contributions as a result of introducing type-2 fuzzy sets in intelligent trading algorithms, with the objective to improve the risk-adjusted performance with minimal increase in the design and computational complexity. Overall, the proposed ANFIS/T2 model scores significant performance improvements when compared to both standard ANFIS and Buy-and-Hold methods. As a further step, we identify a relationship between the increased trading performance benefits of the proposed type-2 model and higher levels of microstructure noise. The results resolve a desirable need for practitioners, researchers and regulators in the design of expert and intelligent systems for better management of risk in the field of HFT

A Dynamic Fuzzy Money Management Approach for Controlling the Intraday Risk-Adjusted Performance of AI Trading Algorithms

The majority of existing artificial intelligence (AI) studies in computational finance literature are devoted solely to predicting market movements. In this paper we shift the attention to how AI can be applied to control risk-based money management decisions. We propose an innovative fuzzy logic approach which identifies and categorizes technical rules performance across different regions in the trend and volatility space. The model dynamically prioritizes higher performing regions at an intraday level and adapts money management policies with the objective to maximize global risk-adjusted performance. By adopting a hybrid method in conjunction with a popular neural network (NN) trend prediction model, our results show significant performance improvements compared with both standard NN and buy-and-hold approaches