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The climate system can be regarded as a dynamic nonlinear system. Thus, traditional linear statistical methods fail to model the nonlinearities of such a system. These nonlinearities render it necessary to find alternative statistical techniques. Since artificial neural network models (NNM) represent such a nonlinear statistical method their use in analyzing the climate system has been studied for a couple of years now. Most authors use the standard Backpropagation Network (BPN) for their investigations, although this specific model architecture carries a certain risk of over-/underfitting. Here we use the so called Cauchy Machine (CM) with an implemented Fast Simulated Annealing schedule (FSA) (Szu, 1986) for the purpose of attributing and detecting anthropogenic climate change instead. Under certain conditions the CM-FSA guarantees to find the global minimum of a yet undefined cost function (Geman and Geman, 1986). In addition to potential anthropogenic influences on climate (greenhouse gases (GHG), sulphur dioxide (SO2)) natural influences on near surface air temperature (variations of solar activity, explosive volcanism and the El Nino = Southern Oscillation phenomenon) serve as model inputs. The simulations are carried out on different spatial scales: global and area weighted averages. In addition, a multiple linear regression analysis serves as a linear reference. It is shown that the adaptive nonlinear CM-FSA algorithm captures the dynamics of the climate system to a great extent. However, free parameters of this specific network architecture have to be optimized subjectively. The quality of the simulations obtained by the CM-FSA algorithm exceeds the results of a multiple linear regression model; the simulation quality on the global scale amounts up to 81% explained variance. Furthermore the combined anthropogenic effect corresponds to the observed increase in temperature Jones et al. (1994), updated by Jones (1999a), for the examined period 1856–1998 on all investigated scales. In accordance to recent findings of physical climate models, the CM-FSA succeeds with the detection of anthropogenic induced climate change on a high significance level. Thus, the CMFSA algorithm can be regarded as a suitable nonlinear statistical tool for modeling and diagnosing the climate system.
Abstract Geant4 is a toolkit for simulating the passage of particles through matter. It includes a complete range of functionality including tracking, geometry, physics models and hits. The physics processes offered cover a comprehensive range, including electromagnetic, hadronic and optical processes, a large set of long-lived particles, materials and elements, over a wide energy range starting, in some cases, from 250 eV and extending in others to the TeV energy range. It has been designed and constructed to expose the physics models utilised, to handle complex geometries, and to enable its easy adaptation for optimal use in different sets of applications. The toolkit is the result of a worldwide collaboration of physicists and software engineers. It has been created exploiting software engineering and object-oriented technology and implemented in the C++ programming language. It has been used in applications in particle physics, nuclear physics, accelerator design, space engineering and medical physics. PACS: 07.05.Tp; 13; 23
Endothelium-dependent vasodilation is thought to be mediated primarily by the NO/cGMP signaling pathway whereas cAMP-elevating vasodilators are considered to act independent of the endothelial cell layer. However, recent functional data suggest that cAMP-elevating vasodilators such as β-receptor agonists, adenosine or forskolin may also be endothelium-dependent. Here we used functional and biochemical assays to analyze endothelium-dependent, cGMP- and cAMP-mediated signaling in rat aorta. Acetylcholine and sodium nitroprusside (SNP) induced a concentration-dependent relaxation of phenylephrine-precontracted aorta. This response was reflected by the phosphorylation of the vasodilator-stimulated phosphoprotein (VASP), a validated substrate of cGMP- and cAMP-dependent protein kinases (cGK, cAK), on Ser157 and Ser239. As expected, the effects of acetylcholine were endothelium-dependent. However, relaxation induced by the β-receptor agonist isoproterenol was also almost completely impaired after endothelial denudation. At the biochemical level, acetylcholine- and isoproterenol-evoked cGK and cAK activation, respectively, as measured by VASP Ser239 and Ser157 phosphorylation, was strongly diminished. Furthermore, the effects of isoproterenol were repressed by eNOS inhibition when endothelium was present. We also observed that the relaxing and biochemical effects of forskolin were at least partially endothelium-dependent. We conclude that cAMP-elevating vasodilators, i.e. isoproterenol and to a lesser extent also forskolin, induce vasodilation and concomitant cyclic nucleotide protein kinase activation in the vessel wall in an endothelium-dependent way.