Mechanism of regulation of Raf-1 by Ca2+/Calmodulin-dependent kinase II

The calcium-calmodulin dependent kinase II (CaMKII) is an ubiquitous serine/threonine protein kinase involved in multiple signalings and biological functions. It has been demonstrated that in epithelial and mesenchimal cells CaMKII participates with Ras to Raf-1 activation and that it is necessary for ERK activation by diverse factors. Raf-1 activation is complex. Maximal Raf-1 activation is reached by phosphorylation at Y341 by Src and at S338. Although early data proposed the involvement of p21-activated kinase 3 (Pak3), the kinase phosphorylating S338 is not definitively identified. Aim of my thesis is to go more insight into the molecular mechanisms of CaMKII/Raf-1 interaction and to verify the hypothesis that CaMKII phosphorylates Raf-1 at Ser338. To this purpose, I investigated the role of CaMKII in Raf-1 and ERK activation by oncogenic Ras and other factors, in COS-7 and NIH3T3 cells. Serum, SrcY527 and RasV12 activated CaMKII. CaMKII was necessary for Raf-1 and ERK activation by all these factors. CaMKII was necessary to the phosphorylation of S338 Raf-1 by serum, fibronectin or oncogenic Ras. Conversely, the inhibition of phosphatidylinositol 3-kinase, which in turn activates Pak3, was ineffective. The direct kinase activity of CaMKII on the serine 338 residue, was demonstrated in vitro by interaction of purified kinases. These results demonstrate that CaMKII phosphorylates Raf-1 at S338 and partecipates to ERK activation upon different physiologic and pathologic stimuli in the MAPK cascade. This kinase, might have a role in cancers harbouring oncogenic Ras and could represent a new therapeutic target for pharmacological intervention in these tumors

Neural Networks as tools for increasing the forecast and control of complex economic systems. Economics & Complexity - 1999\Vol2 N2 Spec. NEU 99-a

The idea that NN can be usefully used for a better understanding of economic complex mechanisms is present in the literature. Our interest is to show that this is correct if we use the larger possible amounts of information that data conveys. At this end we will start with the consideration expressed by Mandelbrot that a traditional model could explain the economic behaviour 95% of time, but that in terms of amount the remaining 5% means quite the complete set of phenomena that we want to understand. We need complex models for dealing with this part. For their characteristic of being general approximators NNs seem one of most interesting instrument. This is true both for macroeconomic and for financial data.Often, the economic system is so complex that, to grasp the meaning of the information conveyed by the data, even a general approximator like NN is not enough. Larger information could be obtained using 2 or more instruments in cascade or in parallel. We will concentrate on this topic. We will try to illustrate how the combination of tools is possible. Applications will refer to Italian macroeconomic and financial data.Neural Network, Public Finance, Control of Economics, Macroeconomics

Control of Complex Economy through Fiscal Variables. Economics & Complexity - Spring - 1998 - Vol2 N1

The aim of this work is that of exemplifying some applications of the modern theory of the complexity to the economic sector; we will highlight some of the possibilities of control of chaotic systems and some of that possibilities which are opened by the study of such systems. Remembering how a simple traditional macroeconomic model can give place to deterministic chaotic phenomena we will highlight: a) how it is possible to control such a system using opportune values of the fiscal variables; b) how it is possible to foresee the trend of the objective variable through a neural network, and, therefore, subsequently to control it on the basis of the value instruments chosen by the neural network. This will be done either in the presence of casual noises or in the case of a completely deterministic model; c) finally a different and more recent method of controlling chaotic systems will be indicated.Public Finance, Complexity, Control of Economics, Macroeconomics

Observational constraints on the unified dark matter and dark energy model based on the quark bag model

In this work we investigate if a small fraction of quarks and gluons, which escaped hadronization and survived as a uniformly spread perfect fluid, can play the role of both dark matter and dark energy. This fluid, as developed in \citep{Brilenkov}, is characterized by two main parameters: $\beta$, related to the amount of quarks and gluons which act as dark matter; and $\gamma$, acting as the cosmological constant. We explore the feasibility of this model at cosmological scales using data from type Ia Supernovae (SNeIa), Long Gamma-Ray Bursts (LGRB) and direct observational Hubble data. We find that: (i) in general, $\beta$ cannot be constrained by SNeIa data nor by LGRB or H(z) data; (ii) $\gamma$ can be constrained quite well by all three data sets, contributing with $\approx78\%$ to the energy-matter content; (iii) when a strong prior on (only) baryonic matter is assumed, the two parameters of the model are constrained successfully.Comment: 10 pages, 6 figures, 3 table

Revisiting a model-independent dark energy reconstruction method

Model independent reconstructions of dark energy have received some attention. The approach that addresses the reconstruction of the dimensionless coordinate distance and its two first derivatives using a polynomial fit in different redshift windows is well developed \cite{DalyDjorgovski1,DalyDjorgovski2,DalyDjorgovski3}. In this work we offer new insights into the problem by focusing on two types of observational probes: SNeIa and GRBs. Our results allow to highlight some of the intrinsic weaknesses of the method. One of the directions we follow is to consider updated observational samples. Our results indicate than conclusions on the main dark energy features as drawn from this method are intimately related to the features of the samples themselves (which are not quite ideal). This is particularly true of GRBs, which manifest themselves as poor performers in this context. In contrast to original works, we conclude they cannot be used for cosmological purposes, and the state of the art does not allow to regard them on the same quality basis as SNeIa. The next direction we contribute to is the question of how the adjusting of some parameters (window width, overlap, selection criteria) affect the results. We find again there is a considerable sensitivity to these features. Then, we try to establish what is the current redshift range for which one can make solid predictions on dark energy evolution. Finally, we strengthen the former view that this model is modest in the sense it provides only a picture of the global trend. But, on the other hand, we believe it offers an interesting complement to other approaches given that it works on minimal assumptions.Comment: revtex4-1, 17 page