Influence of atenolol and nifedipine on nitric oxide deficient cardiomyocyte hypertrophy and expression of the cardio-endocrine peptide intermedin and its receptor components.

BACKGROUND/AIMS: Chronic inhibition of nitric oxide (NO) synthesis is associated with hypertension, myocardial ischemia, oxidative stress and hypertrophy; expression of adrenomedullin (AM) and intermedin (IMD) and their receptor activity modifying proteins (RAMPs 1-3) is augmented in cardiomyocytes, indicating that the myocardial AM/ IMD system may be activated in response to pressure loading and ischemic insult. The aim was to examine effects on (i) parameters of cardiomyocyte hypertrophy and on (ii) expression of AM and IMD and their receptor components in NO-deficient cardiomyocytes of an intervention chosen specifically for ability to alleviate pressure loading and ischemic injury concurrently. METHODS: The NO synthesis inhibitor, N(G)-nitro-L-arginine methyl ester (L-NAME, 35 mg.kg(-1).day(-1)) was given to rats for 8 weeks, with/ without concurrent administration of beta-adrenoceptor antagonist, atenolol (25 mg.kg(-1).day(-1)) / calcium channel blocker, nifedipine (20mg.kg(-1).day(-1)). RESULTS: In L-NAME treated rats, atenolol / nifedipine abolished increases in systolic blood pressure and plasma AM and IMD levels and in left ventricular cardiomyocytes: (i) normalized increased cell width and mRNA expression of hypertrophic (sk-alpha-actin) and cardio-endocrine (ANP, BNP, ET) genes; (ii) normalized augmented membrane protein oxidation; (iii) normalized mRNA expression of AM, IMD, RAMP1, RAMP2 and RAMP3. CONCLUSIONS: normalization of blood pressure and membrane oxidant status together with prevention of hypertrophy and normalization of the augmented expression of AM, IMD and their receptor components in NO-deficient cardiomyocytes by atenolol / nifedipine supports involvement of both pressure loading and ischemic insult in stimulating cardiomyocyte hypertrophy and induction of these counter-regulatory peptides and their receptor components. Attenuation of augmented expression of IMD in this model cannot however be explained simply by prevention of cardiomyocyte hypertrophy.

Expression of the counter-regulatory peptide intermedin is augmented in the presence of oxidative stress in hypertrophied cardiomyocytes.

Background: Intermedin (IMD), a novel cardiac peptide related to adrenomedullin (AM), protects against myocardial ischemia-reperfusion injury and attenuates ventricular remodelling. IMD’s actions are mediated by a calcitonin receptor-like receptor in association with receptor activity modifying proteins (RAMPs 1-3). Aim/method: using the spontaneously hypertensive rat (SHR) and normotensive Wistar Kyoto (WKY) rat at 20 weeks of age, to examine (i) the presence of myocardial oxidative stress and concentric hypertrophy; (ii) expression of IMD, AM and receptor components. Results: In left and right ventricular cardiomyocytes from SHR vs. WKY cell width (26% left, 15% right) and mRNA expression of hypertrophic markers ANP (2.7 fold left, 2.7 fold right) and BNP (2.2 fold left, 2.0 fold right) were enhanced. In left ventricular cardiomyocytes only (i) oxidative stress was indicated by increased membrane protein carbonyl content (71%) and augmented production of O2- anion (64%); (ii) IMD (6.8 fold), RAMP1 (2.5 fold) and RAMP3 (2.0 fold) mRNA was increased while AM and RAMP2 mRNA was not altered; (iii) abundance of RAMP1 (by 48%), RAMP2 (by 41%) and RAMP3 (by 90%) monomers in cell membranes was decreased. Conclusion: robust augmentation of IMD expression in hypertrophied left ventricular cardiomyocytes indicates a prominent role for this counter-regulatory peptide in the adaptation of the SHR myocardium to the stresses imposed by chronic hypertension. The local concentration and action of IMD may be further enhanced by down-regulation of NEP within the left ventricle.

Current methods and advances in forecasting of wind power generation

Wind power generation differs from conventional thermal generation due to the stochastic nature of wind. Thus wind power forecasting plays a key role in dealing with the challenges of balancing supply and demand in any electricity system, given the uncertainty associated with the wind farm power output. Accurate wind power forecasting reduces the need for additional balancing energy and reserve power to integrate wind power. Wind power forecasting tools enable better dispatch, scheduling and unit commitment of thermal generators, hydro plant and energy storage plant and more competitive market trading as wind power ramps up and down on the grid. This paper presents an in-depth review of the current methods and advances in wind power forecasting and prediction. Firstly, numerical wind prediction methods from global to local scales, ensemble forecasting, upscaling and downscaling processes are discussed. Next the statistical and machine learning approach methods are detailed. Then the techniques used for benchmarking and uncertainty analysis of forecasts are overviewed, and the performance of various approaches over different forecast time horizons is examined. Finally, current research activities, challenges and potential future developments are appraised. (C) 2011 Elsevier Ltd. All rights reserved.

Special condition wind power forecasting based on Gaussian process and similar historical data

Due to the variability of wind power, it is imperative to accurately and timely forecast the wind generation to enhance the flexibility and reliability of the operation and control of real-time power. Special events such as ramps, spikes are hard to predict with traditional methods using solely recently measured data. In this paper, a new Gaussian Process model with hybrid training data taken from both the local time and historic dataset is proposed and applied to make short-term predictions from 10 minutes to one hour ahead. A key idea is that the similar pattern data in history are properly selected and embedded in Gaussian Process model to make predictions. The results of the proposed algorithms are compared to those of standard Gaussian Process model and the persistence model. It is shown that the proposed method not only reduces magnitude error but also phase error.