Increased cerebral output of free radicals during hypoxia: implications for acute mountain sickness?

Bailey DM, Taudorf S, Berg RMG, Lundby C, McEneny J, Young IS, Evans KA, James PE, Shore A, Hullin DA, McCord JM, Pedersen BK, Moller K. Increased cerebral output of free radicals during hypoxia: implications for acute mountain sickness? Am J Physiol Regul Integr Comp Physiol 297: R1283-R1292, 2009. First published September 2, 2009; doi: 10.1152/ajpregu.00366.2009.-This study examined whether hypoxia causes free radical-mediated disruption of the blood-brain barrier (BBB) and impaired cerebral oxidative metabolism and whether this has any bearing on neurological symptoms ascribed to acute mountain sickness (AMS). Ten men provided internal jugular vein and radial artery blood samples during normoxia and 9-h passive exposure to hypoxia (12.9% O-2). Cerebral blood flow was determined by the Kety-Schmidt technique with net exchange calculated by the Fick principle. AMS and headache were determined with clinically validated questionnaires. Electron paramagnetic resonance spectroscopy and ozone-based chemiluminescence were employed for direct detection of spin-trapped free radicals and nitric oxide metabolites. Neuron-specific enolase (NSE), S100 beta, and 3-nitrotyrosine (3-NT) were determined by ELISA. Hypoxia increased the arterio-jugular venous concentration difference (a-v(D)) and net cerebral output of lipid-derived alkoxyl-alkyl free radicals and lipid hydroperoxides (P

A fast multi-output RBF neural network construction method

This paper investigates the center selection of multi-output radial basis function (RBF) networks, and a multi-output fast recursive algorithm (MFRA) is proposed. This method can not only reveal the significance of each candidate center based on the reduction in the trace of the error covariance matrix, but also can estimate the network weights simultaneously using a back substitution approach. The main contribution is that the center selection procedure and the weight estimation are performed within a well-defined regression context, leading to a significantly reduced computational complexity. The efficiency of the algorithm is confirmed by a computational complexity analysis, and simulation results demonstrate its effectiveness. (C) 2010 Elsevier B.V. All rights reserved.

Output Additionality of Public Support for Innovation: Evidence for Irish Manufacturing Plants

Public support for private R&D and innovation is part of most national and regional innovation support regimes. In this article, we estimate the effect of public innovation support on innovation outputs in Ireland and Northern Ireland. Three dimensions of output additionality are considered: extensive additionality, in which public support encourages a larger proportion of the population of firms to innovate; improved product additionality, in which there is an increase in the average importance of incremental innovation; new product additionality, in which there is an increase in the average importance of more radical innovation. Using an instrumental variable approach, our results are generally positive, with public support for innovation having positive, and generally significant, extensive, improved and new product additionality effects. These results hold both for all plants and indigenously owned plants, a specific target of policy in both jurisdictions. The suggestion is that grant aid to firms can be effective in both encouraging firms to initiate new innovation and improve the quality and sophistication of their innovation activity. Our results also emphasize the importance for innovation of in-house R&D, supply-chain linkages, skill levels and capital investment, all of which may be the focus of complementary policy initiatives.

Potential output in a rapidly developing economy: the case of China and a comparison with the United States and the European Union

The authors use a growth accounting framework to examine growth of the rapidly developing Chinese economy. Their findings support the view that, although feasible in the intermediate term, China's recent pattern of extensive growth is not sustainable in the long run. The authors believe that China will be able to sustain a growth rate of 8 to 9 percent for an extended period if it moves from extensive to intensive growth. They next compare potential growth in China with historical developments in the United States and the European Union. They discuss the differences in production structure and level of development across the three economies that may explain the countries' varied intermediate-term growth prospects. Finally, the authors provide an analysis of "green" gross domestic product and the role of natural resources in China's growth. © 2009, The Federal Reserve Bank of St. Louis.

Neural network forecasts of input-output technology

A significant part of the literature on input-output (IO) analysis is dedicated to the development and application of methodologies forecasting and updating technology coefficients and multipliers. Prominent among such techniques is the RAS method, while more information demanding econometric methods, as well as other less promising ones, have been proposed. However, there has been little interest expressed in the use of more modern and often more innovative methods, such as neural networks in IO analysis in general. This study constructs, proposes and applies a Backpropagation Neural Network (BPN) with the purpose of forecasting IO technology coefficients and subsequently multipliers. The RAS method is also applied on the same set of UK IO tables, and the discussion of results of both methods is accompanied by a comparative analysis. The results show that the BPN offers a valid alternative way of IO technology forecasting and many forecasts were more accurate using this method. Overall, however, the RAS method outperformed the BPN but the difference is rather small to be systematic and there are further ways to improve the performance of the BPN.

Transport, atom blockade and output coupling in a Tonks-Girardeau gas

Recent landmark experiments have demonstrated how quantum mechanical impurities can be created within strongly correlated quantum gases and used to probe the coherence properties of these systems. Here we present a theoretical model to simulate such an output coupler for a Tonks- Girardeau gas that shows excellent agreement with the experimental results for atom transport and output coupling. The solid theoretical basis our model provides allows us to explore non-equilibrium transport phenomena in ultra-cold quantum gases and leads us to predict a regime of atom blockade, where the impurity component becomes localised in the parent cloud despite the presence of gravity. We show that this provides a stable mixed-species quantum gas in the strongly correlated limit.

Constitutive activation of the Raf-MAPK pathway causes negative feedback inhibition of Ras-PI3K-AKT and cellular arrest through the EphA(2) receptor

The Raf-mitogen-activated protein kinase (MAPK) and phosphatidylinositide 3-kinase (PI3K)-AKT pathways are two downstream effectors of the small GTPase Ras. Although both pathways are positively regulated by Ras, the Raf-MAPK and PI3K-AKT pathways have been shown to control opposing functions within the cell, suggesting a need for cross-talk regulation. The PI3K -AKT pathway can inhibit the Raf-MAPK pathway directly during processes such as muscle differentiation. Here we describe the ability of the Raf-MAPK pathway to negatively regulate the PI3K-AKT pathway during cellular arrest. Constitutive activation of Raf or methyl ethyl ketone 1 (MEK1) leads to inhibition of AKT and cellular arrest. Furthermore, we show that activation of Raf-MEK1 signaling causes negative feedback inhibition of Ras through the ephrin receptor EphA(2). EphA(2)-mediated negative feedback inhibition is required for Raf-induced AKT inhibition and cell cycle arrest, therefore establishing the inhibition of the Ras-PI3K-AKT pathway as a necessary event for the Raf-MEK1-regulated cellular arrest.