We propose a stochastic algorithm for solving NP-hard combinatorial optimization problems and study its convergence

In this article an accurate and efficient technique for tissue typing is presented. The proposed technique is based on Canonical Correlation Analysis, a statistical method able to simultaneously exploit the spectral and spatial information characterizing the Magnetic Resonance Spectroscopic Imaging (MRSI) data. Recently, Canonical Correlation Analysis has been successfully applied to other types of biomedical data, such as functional MRI data. Here, Canonical Correlation Analysis is adapted for MRSI data processing in order to retrieve in an accurate and efficient way the possible tissue types that characterize the organ under investigation. The potential and limitations of the new technique have been investigated by using simulated as well as in vivo prostate MRSI data, and extensive studies demonstrate a high accuracy, robustness, and efficiency. Moreover, the performance of Canonical Correlation Analysis has been compared to that of ordinary correlation analysis. The test results show that Canonical Correlation Analysis performs best in terms of accuracy and robustness

There is a growing need for statistical methods that generate an ensemble of plausible realizations of a hierarchical process from a single run or experiment. The main challenge is how to construct such an ensemble in a manner that preserves the internal dynamics (e.g. intermittency) and temporal persistency of the hierarchical process. A popular hierarchical process often used as a case study in such problems is atmospheric turbulent flow. Analogies to turbulence are often called upon when information flow from large to small scales, non Gaussian statistics, and intermittency are inherent attributes of the process under consideration. These attributes are key defining syndromes of the turbulent cascade thereby making turbulence time series ideal for testing such ensemble generation schemes. In this study, we propose a wavelet based resampling scheme (WB) and compare it to the traditional Fourier based phase randomization bootstrap (FB) approach within the context of the turbulence energy cascade. The comparison between the two resampling methods and observed ensemble statistics constructed by clustering similar meteorological conditions demonstrate that the WB reproduces several features related to intermittency of the ensemble series when compared to $FB$. In particular, the WB exhibited an increase in wavelet energy activity and an increase in the wavelet flatness factor with increasing frequency consistent with the cluster of ensemble statistics. On the other hand, the $FB$ yielded no increase in such energy activity with scale and resulted in near Gaussian wavelet coefficients at all frequencies within the inertial subrange. The extension of WB to the multivariate case is also demonstrated via the conservation of co-spectra between longitudinal and vertical velocity time series. Because the resampling strategy proposed here is conducted in the wavelet domain, gap-infected and uneven sampled time series can be readily accommodated within the WB. Finally, recommendations about the filter and block sizes are discussed.

The statistical properties of fluid particles transported by a three-dimensional fully developed turbulent flow are investigated by means of high resolution direct numerical simulations. Single trajectory statistics are investigated in a time range spanning more than three decades, from less than a tenth of the Kolmogorov time scale, taueta, up to one large-eddy turnover time. Our analysis reveals the existence of relatively rare trapping events in vortex filaments which give rise to enhanced intermittency on Lagrangian observables up to 10taueta. Lagrangian velocity structure function attain scaling properties in agreement with the multifractal prediction only for time lags larger than those affected by trapping.

We introduce a mesoscopic three-dimensional lattice Boltzmann model which attempts to mimic the physical features associated with cage effects in dynamically heterogeneous fluids. To this purpose, we extend the standard lattice Boltzmann dynamics with self-consistent constraints based on the nonlocal density of the surrounding fluid. The resulting dynamics exhibits typical features of dynamic heterogeneous fluids, such as non-Gaussian density distributions and long-time relaxation. Because of its intrinsically parallel dynamics, and absence of statistical noise, the method is expected to compute significantly faster than molecular dynamics, Monte Carlo, and lattice glass models.

We report recent results from a high-resolution numerical study of fluid particles transported by a fully developed turbulent flow. Single-particle trajectories were followed for a time range spanning more than three decades, from less than a tenth of the Kolmogorov timescale up to one large-eddy turnover time. We present some results concerning acceleration statistics and the statistics of trapping by vortex filaments.

Estimating the solvent content in protein crystals is one of the first steps in a macromolecular structure determination. We apply a new statistical technique, the independent component analysis (ICA), to determine the volume fraction of the asymmetric unit of proteins occupied by the solvent. The results for several crystal forms are in good agreement with available ones and allow to validate the method. Its main advantage with respect to existing techniques is that it requires only the knowledge of crystallographic data of structure factors and no a priori information about protein.

We present a detailed investigation of the particle pair separation process in homogeneous isotropic turbulence. We use data from direct numerical simulations up to Taylor's Reynolds number 280 following the evolution of about two million passive tracers advected by the flow over a time span of about three decades. We present data for both the separation distance and the relative velocity statistics. Statistics are measured along the particle pair trajectories both as a function of time and as a function of their separation, i.e. at fixed scales. We compare and contrast both sets of statistics in order to gain an insight into the mechanisms governing the separation process. We find very high levels of intermittency in the early stages, that is, for travel times up to order ten Kolmogorov time scales. The fixed scale statistics allow us to quantify anomalous corrections to Richardson diffusion in the inertial range of scales for those pairs that separate rapidly. It also allows a quantitative analysis of intermittency corrections for the relative velocity statistics.

Grazie alle simulazioni con i supercalcolatori, si cominciano a scoprire i meccanismi microscopici che governano il moto collettivo dei fluidi

Agent-based models, an emerging paradigm of simulation of complex systems, appear very suitable to parallel processing. However, during the parallelization of a simulator of financial markets, we found that some features of these codes highlight non-trivial issues of the present hardware/software platforms for parallel processing. Here we present the results of a series of tests, on different platforms, of simplified codes that reproduce such problems and can be used as a starting point in the search of a possible solution.

We consider the phase ordering process of a system quenched into a lamellar phase in the presence of a shear flow. By studying the continuum model based on the Brazowskii free energy in a self-consistent approximation, we analyse the effects of a weak anisotropy in the quartic coupling constant, finding that it radically changes the evolution of the system.

The kinetics of domain growth of fluid mixtures quenched from a disordered to a lamellar phase has been studied in three dimensions. We use a numerical approach based on the lattice Boltzmann method (LBM). A novel implementation for LBM which "fuses" the collision and streaming steps is used in order to reduce memory and bandwidth requirements. We find that extended defects between stacks of lamellae with different orientation dominate the late time dynamics.

We review the problem of network mobility and internetworking between heterogeneous data networks and present an approach to the integration of WLAN and cellular networks based on loose coupling and the use of emerging mobility protocols. The handoff performance of such approach is studied, at network and transport level, in a realistic scenario along with the impact on global performance of transport protocols. Finally, a method for eliminating any packet loss at the network layer during the handoff is presented and evaluated.

Vaccination protocols designed to elicit anti-cancer immune responses have, many times, failed in producing tumor eradication and in prolonging patient survival. Usually in cancer vaccination, epitopes from one organism are included in the genome or linked with some protein of another in the hope that the immunogenic properties of the latter will boost an immune response to the former. However, recent results have demonstrated that injections of two different vectors encoding the same recombinant antigen generate high levels of speci c immunity. Systematic comparison of the ef cacy of different vaccination protocols has been hampered by technical limitations, and clear evidence that the use of multiple vectors has advantages over single carrier injections is lacking. We used a computational model to investigate the dynamics of the immune response to different anti-cancer vaccines based on randomly generated antigen/carrier compounds. The computer model was adapted for simulations to this new area in immunology research and carefully validated to the purpose. As a matter of fact, it reproduces a relevant number of experimental observations. The model shows that when priming and boosting with the same construct, competition rather than cooperation develops amongst T cell clones of different speci cities. Moreover, from the simulations, it appears that the sequential use of multiple carriers may generate more robust anti-tumor immune responses and may lead to effective tumor eradication in a higher percentage of cases. Our results provide a rational background for the design of novel strategies for the achievement of immune control of cancer. r 2005 Elsevier Ltd. All rights reserved.

We present an in silico model that simulates the immune system responses to tumor cells in naive and vaccinated mice. We have demonstrated the ability of this model to accurately reproduce the experimental results. Motivation: In vivo experiments on HER-2/neu mice have shown the effectiveness of Triplex vaccine in protection of mice from mammary carcinoma. Full protection was conferred using Chronic (prophylactic) vaccination protocol while therapeutic vaccination was less ef cient. Our in silico model was able to closely reproduce the effects of various vaccination protocols. This model is the rst step towards the development of in silico experiments searching for optimal vaccination protocols. Results: In silico experiments carried out on two large statistical samples of virtual mice showed very good agreements with in vivo experiments for all experimental vaccination protocols. They also show, as supported by in vivo experiments, that the humoral response is fundamental in controlling the tumor growth and therefore suggest the selection and timing of experiments for measuring the activity of T cells.

Mathematical and computational models are designed to improve our understanding of biological phenomena, to confirm/reject hypotheses, and to find points of intervention by altering the behavior of the studied systems. Here we describe the role of mathematical/computational models of the immune system. In particular, we analyze some examples of how mathematical modeling can contribute to finding optimal vaccination strategies. Indeed, computational modeling offers an intriguing opportunity from the theoretical point of view, and it will be of interest for clinically oriented investigators who wish to find optimal therapeutic strategies and for pharmaceutical industries that want to produce effective and successful drugs.

Cellular signaling (or signal transduction) is the process by which extracellular signals are converted into cellular responses. Mathematical models of signaling pathways may help understanding their general regulatory principles, as well as the roles of the different components often involved in more than one pathway. The aim of the present work is to describe a discrete model in time and space to study the dynamics of the population of molecules involved in a specific pathway. To this purpose, we use a multi-compartment stochastic cellular automaton to simulate one of the best understood cell signaling pathways: the GPCR-pathway. We then show the effect on the signal-carrying ef ciency of condensing a molecular species, which is pivotal to the reaction pathway, in a closed region of the cytosol. We nd that localization increases the robustness of the translocation mechanism only above a density threshold. For homogeneous settings, by increasing the concentration above a critical value, the translocation is obstructed by mere physical occupation constraints of the signal-carrying molecule. This conclusion is in line with previous findings, according to which in highly dense regions of the cytosol, convection by autocatalytic activation of adjacent molecules might be more ef cient than diffusion as a main signal propagation mechanism.

We are interested in developing a simple model to investigate blood-vessel interactions in a finite arterial segment of the cardiovascular tree. For this purpose, we developed a continuum model for a vascular segment, and we coupled it with a discrete model for the remaining systemic circulation. In working out the modeling, we addressed some main issues, such as the nonlinearity of blood flow, the compliance of the vessel and the prestress state of the artery walls, that is always present aside from the filling of blood. Moreover, we set a discrete model capable of providing appropriate boundary conditions to the continuum model, by reproducing the proper waveforms entering the vessel and avoiding spurious reflections.