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    ABSTRACT: We investigate the relationship between the emergence of chaos synchronization and the information flow in dynamical systems possessing homogeneous or heterogeneous global interactions whose origin can be external (driven systems) or internal (autonomous systems). By employing general models of coupled chaotic maps for such systems, we show that the presence of a homogeneous global field, either external or internal, for all times is not indispensable for achieving complete or generalized synchronization in a system of chaotic elements. Complete synchronization can also appear with heterogeneous global fields; it does not requires the simultaneous sharing of the field by all the elements in a system. We use the normalized mutual information and the information transfer between global and local variables to characterize complete and generalized synchronization. We show that these information measures can characterize both types of synchronized states and also allow us to discern the origin of a global interaction field. A synchronization state emerges when a sufficient amount of information provided by a field is shared by all the elements in the system, on the average over long times. Thus, the maximum value of the top-down information transfer can be used as a predictor of synchronization in a system, as a parameter is varied.
    Physical Review E 10/2013; 88(4-1):042920.
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    ABSTRACT: In this paper, we propose a simple approach for detecting activated voxels in fMRI data by exploiting the inherent sparsity property of the BOLD signal. The proposed approach addresses the solution of the inverse problem induced by the General Linear Model through an l(0)-regularized Least Absolute Deviation (l(0)-LAD) regression method. Under this framework, the activated voxels are detected by a two-stages process: estimation and basis selection. First, an estimate of the coefficients that minimizes the absolute deviation error is found by means of the weighted median operator. Then, a thresholding operator is applied on the estimated value in order to decide whether or not a stimulus is present in the observed BOLD signal. The threshold parameter turns out to be the regularization parameter that controls the model sparseness. The method was proven on real fMRI data leading to similar activated regions than those activated by the Statistical Parametric Mapping (SPM) software.
    Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 08/2011; 2011:7816-9.
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    ABSTRACT: A multi-scale, coarse-grained description of protein conformational dynamics in a solvent is presented. The focus of the paper is on the description of the conformational motions that may accompany enzyme catalysis as the enzyme executes a catalytic cycle, starting with substrate binding and ending with product release and return to the original unbound enzyme. The protein is modeled by a network of beads representing amino acid residues, the solvent is described by multiparticle collision dynamics, and substrate binding and unbinding events are modeled stochastically by conformation-dependent transitions that modify the bonding in the network to correspond to the different binding states of the protein. The solvent dynamics is coupled to that of the protein and hydrodynamic interactions, which are important for the large-scale protein motions, are taken into account. The multi-scale model is used to study the dynamics of the adenylate kinase enzyme in solution. A potential function that describes the different binding and conformational states of the protein and accounts for partial unfolding during the catalytic cycle is constructed as a network built from elastic network and soft potential links. The conformational dynamics of the protein as it undergoes cyclic enzymatic dynamics, as well as its translational diffusion and orientational motion, are investigated using both multiparticle collision dynamics and dynamics that suppresses hydrodynamic coupling. Hydrodynamic interactions are found to have important effects on the large scale conformational motions of the protein and significantly affect the translational diffusion coefficients and orientational correlation times.
    Physical Chemistry Chemical Physics 03/2011; 13(22):10527-37.

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