In this article we summarise present knowledge on the role of pro-inflammatory cytokines on chronic inflammation leading to organismal aging, a phenomenon we proposed to call "inflamm-aging". In particular, we review genetic data regarding polymorphisms of genes encoding for cytokines and proteins involved in natural immunity (such as Toll-like Receptors and Heat Shock Proteins) obtained from large population studies including young, old and very old people in good health status or affected by age-related diseases such as Alzheimer's Disease and Type II Diabetes. On the whole, despite some controversial results, the available data are in favour of the hypothesis that pro-inflammatory cytokines play an important role in aging and longevity. Further, we present a possible hypothesis to reconcile energetic dysfunction, including mitochondria, and inflamm-aging. New perspectives for future studies, including phylogenetic studies in animal models and in silico studies on mathematical and bioinformatic models inspired by the systems biology approach, are also proposed. © 2006 Bentham Science Publishers Ltd.

We propose a network model in which the communication between its elements (cells, neurons and lymphocytes) can be established in various ways. The system evolution is driven by a set of equations that encodes various degrees of competition between elements. Each element has an "internal plasticity threshold" that, by setting the number of inputs and outputs, determines different network global topologies.

Given a graph G with a label (color) assigned to each edge (not necessarily properly) we look for an hamiltonian cycle of G with the minimum number of different colors. The problem has several applications in telecommunication networks, electric networks, multimodal transportation networks, among others, where one aims to ensure connectivity or other properties by means of limited number of different connections. We analyze the complexity of the problem on special graph classes and propose, for the general case, heuristic resolution algorithms. Performances of the algorithms are experimentally evaluated on a set of instances and compared with the exact solution value provided by a solver. © 2006 Elsevier B.V. All rights reserved.

An approach based on a lattice version of the Boltzmann kinetic equation for describing multiphase flows in nano- and microcorrugated devices is proposed. We specialize it to describe the wetting-dewetting transition of fluids in the presence of nanoscopic grooves etched on the boundaries. This approach permits us to retain the essential supramolecular details of fluid-solid interactions without surrendering--actually boosting--the computational efficiency of continuum methods. The method is used to analyze the importance of conspiring effects between hydrophobicity and roughness on the global mass flow rate of the microchannel. In particular we show that smart surfaces can be tailored to yield very different mass throughput by changing the bulk pressure. The mesoscopic method is also validated quantitatively against the molecular dynamics results of [ Cottin-Bizonne et al. Nat. Mater. 2 237 (2003)].

The classical smoothing data problem is analyzed in a Sobolev space under the assumption of white noise. A Fourier series method based on regularization endowed with Generalized Cross Validation is considered to approximate the unknown function. This approximation is globally optimal, i.e., the Mean Integrated Squared Error reaches the optimal rate in the minimax sense. In this paper the pointwise convergence property is studied. Specifically it is proved that the smoothed solution is locally convergent but not locally optimal. Examples of functions for which the approximation is subefficient are given. It is shown that optimality and superefficiency are possible when restricting to more regular subspaces of the Sobolev space.

This work focuses on the numerical analysis of one-dimensional nonlinear diffusion equations involving a convolution product. First, homogeneous friction equations are considered. Algorithms follow recent ideas on mass transportation methods and lead to simple schemes which can be proved to be stable, to decrease entropy, and to converge toward the unique solution of the continuous problem. In particular, for the first time, homogeneous cooling states are displayed numerically. Further, we present results on the more delicate fourth-order thin-film equation for which a nonnegativity-preserving scheme is derived. The dead core phenomenon is presented for the Hele–Shaw cell.

The motion of massless spinning test particles is investigated using the Newman-Penrose formalism within the Mathisson-Papapetrou model extended to massless particles by Mashhoon and supplemented by the Pirani condition. When the \lq\lq multipole reduction world line" lies along a principal null direction of an algebraically special vacuum spacetime, the equations of motion can be explicitly integrated. Examples are given for some familiar spacetimes of this type in the interest of shedding some light on the consequences of this model.

Blood flowing in a vessel is modelled using one-dimensional equations derived from the Navier-Stokes theory on the base of long pressure wavelength.The vessel wall is modelled as an initially highly prestressed elastic membrane, which slightly deforms under the blood pressure pulses. On the stressed configuration, the vessel wall undergoes, even in larger arteries, small deformation and its motion is linearized around such initial prestressed state. The mechanical fluid-wall interaction is expressed by a set of four partial differential equations. To account for a global circulation features, the distributed model is coupled with a six compartments lumped parameter model which provide the proper boundary conditions by reproducing the correct waveforms entering into the vessel and avoid unphysical reflections. The solution has been computed numerically: the space derivatives are discretized by a finite difference method on a staggered grid and a Runge-Kutta scheme is used to advance the solution in time. Numerical experiments show the role of the initial stresses in the flow dynamics and the wall deformation.

We present the numerical results of simulations of complex fluids under shear flow. We employ a mixed approach which combines the lattice Boltzmann method for solving the Navier-Stokes equation and a finite difference scheme for the convection-diffusion equation. The evolution in time of shear banding phenomenon is studied. This is allowed by the presented numerical model which takes into account the evolution of local structures and their effect on fluid flow.

A contrast enhanced dynamic Magnetic Resonance clinical exam produces a set of images displaying over time the concentration of a contrast agent in the blood stream of an organ. The portion of tissue represented by each pixel can be classified as normal, benign or malignant tumoral, according to the qualitative behavior of the contrast agent uptake associated to it. These responses can be considered as the noisy output of a pharmacokinetic distributed model whose parameters have an intrinsic diagnostic importance. Fundamental MR imaging characteristics force a compromise between the noise level and the spatial and temporal resolution of the dynamic sequence. This makes the identification of the pharmacokinetic parameters and the classification problem difficult especially if short computation time is required by physicians. In this paper, a fast method is proposed to solve simultaneously the parameter identification and the classification problems. The complexity of the algorithm is $O(N\cdot n_p)$ flops where $N$ is the number of pixels and $n_p$ is the number of pharmacokinetic parameters per pixel. A family of functions for the parameters and the classification labels is defined. Each function is the weighted sum, with unknown weights, of a coherence-to-data term, several terms which enforce a roughness penalty on the model parameters, a term measuring the distance between the parameters in each pixel and the expected parameters for each class and a term which enforces a roughness penalty on the classification labels. A constrained optimization problem is solved to choose a member of the family, i.e. to estimate the unknown weights, and to minimize it in order to jointly estimate the parameters and the classification labels. A tuning procedure have been also devised, which makes the algorithm fully automated. The performances of the method are illustrated on real data sets.

This paper deals with the numerical solution of optimal control problems for ODEs. The approach is based on the coupling between quadrature rules and continuous Runge-Kutta solvers and it lies in the framework of direct optimization methods and recursive discretization techniques. The analysis of discrete solution accuracy has been carried out and coupling criteria are established in order to have global methods featured by a given accuracy order. Consequently numerical schemes are built up to high orders. The effectiveness of the proposed schemes has been validated on several test problems arising in the field of economic applications. Results have been compared with the ones by classical Runge-Kutta methods, in terms of single function evaluations and average cpu time of the optimization process. The search for optimal solutions has been performed by standard algorithms in Matlab environment.