A hyperbolic reaction–diffusion model for the hantavirus infection

A hyperbolic reaction–diffusion model for the hantavirus infection, generalizing the parabolic set of equations recently derived by Abramson and Kenkre, is proposed within the context of Extended Thermodynamics. The model, as in the parabolic case, captures some of the realistic features of the dynamics of hantavirus in mice population, while it avoids the unphysical features concerning the instantaneous diffusive effects typical of parabolic equations. Traveling wave solutions, related to the spread of the infection in the landscape, are investigated. Both analytical and numerical results obtained herein are discussed and validated from the behavior of the biological system. Copyright © 2007 John Wiley & Sons, Ltd.     

Monte Carlo simulation of salt effect on water structure through 3D mechanistic potentials

Highly concentrated electrolyte solutions were studied through a Monte Carlo-based simulator, developed to consider the water molecules not a homogeneous dielectric as usual, but as dipoles that can move and rotate within a 3D lattice. This approach allowed fast calculations of detailed interactions between the particles, which were described from mechanistic potentials including dipole–dipole, ion–dipole, ion–ion, and hydrogen bonding (HB) interactions. A good agreement was found between experimental data and simulated results. The study also provides new insights about the balance of the different interactions in systems with or without electrolytes, and the effects of the electrolytes addition on the original water structure. The proposed model was also compared with previous explicit models.     

On-chip photoactivation of heterologously expressed rhodopsin allows kinetic analysis of G-protein signaling by surface plasmon resonance spectroscopy

Surface plasmon resonance spectroscopy allows the study of protein interaction dynamics in real-time. Application of this technique to G-protein coupled receptors, the largest family of receptors involved in signal transduction, has been complicated by their low level of expression and the critical dependence of their native conformation on the hydrophobic transmembrane lipid environment. Here, we investigate and compare three different strategies to immobilize rhodopsin, a prototypical G-protein coupled receptor on a sensor chip surface using antibodies and a lectin for receptor capturing. By further probing of different experimental conditions (pH, detergent type) we identified the optimal factors to maintain rhodopsin in a functional conformation and extended this approach to recombinant rhodopsin that was heterologously expressed in COS cells. Functional operation of rhodopsin on the sensor chip surface was proven by its activation and subsequent light-stimulated G-protein coupling. The influence of these experimental parameters on the association and dissociation kinetics of G-protein receptor coupling was determined. Thereby, we found that the kinetics of G_t interaction were not changed by the strategy of immobilization or the type of detergent. Regeneration of opsin directly on a chip allowed recycling of the immobilized native and recombinant receptor. Thus, the approach provides an experimental framework for choosing the most suitable conditions for the solubilization, immobilization, and for functional tests of rhodopsin on a biosensor surface.     

Single crystal EPR study of the dinuclear Cu(II) complex [Cu(tda)(phen)](2)·H(2)tda (tda = thiodiacetate, phen = phenanthroline): influence of weak interdimeric magnetic interactions

We report powder and single crystal EPR measurements of [Cu(tda)(phen)](2)·H(2)tda (tda = thiodiacetate, phen = phenanthroline) at 9.7 GHz. This compound consists of centrosymmetric copper(II) ion dimers, weakly ferromagnetically exchange-coupled (J = +3.2 cm(-1)), in which the dimeric units are linked by hydrophobic chemical paths involving the phen molecules. EPR revealed that the triplet spectra are collapsed by interdimeric exchange interactions mediated by that chemical path. Analysis and simulation of the single crystal EPR spectra were performed using Anderson's exchange narrowing model, together with statistical arguments. This approach allowed us to interpret the spectra modulated by the interdimeric interactions in situations of weak, intermediate, and strong exchange. We evaluated an interdimeric exchange constant J' = 0.0070(3) cm(-1), indicating that hydrophobic paths can transmit weak exchange interactions between centers at relatively long distances of the order of ～10 ?.     

Modified Stöber synthesis of highly luminescent dye-doped silica nanoparticles

Dye-doped silica nanoparticles (NPs) have been demonstrated to be sensitive labeling markers for biosensing and bioimaging. Their flexible conjugation, excellent photostability and ultrasensitivity make them a powerful tool in biological analysis. Although there have been many reports on the basic research and application of these NPs, they are far from reaching their full potential. Silica NPs can be obtained through two principal approaches: the Stöber synthesis and the microemulsion method. The Stöber synthesis has the advantage of being easily scaled up for commercial applications and the possibility to effortlessly transfer the NPs into aqueous solutions (typical of bioanalysis). However, further investigation on the impact of the synthesis parameters on the particles size and on the doping process are needed in order to obtain highly luminescent particles. In this study a modified Stöber synthesis is proposed and a systematic study of the different reagents is reported, which provides a better picture on the influence of ethanol, ammonia, water, silica precursors, and dye concentration on the final morpho-optical properties. As a result, samples of luminescent silica NPs from 10 to 300 nm have been synthesized and optimized to be highly promising labels for biological applications.     

Learning alters theta amplitude, theta-gamma coupling and neuronal synchronization in inferotemporal cortex

Background

How oscillatory brain rhythms alone, or in combination, influence cortical information processing to support learning has yet to be fully established. Local field potential and multi-unit neuronal activity recordings were made from 64-electrode arrays in the inferotemporal cortex of conscious sheep during and after visual discrimination learning of face or object pairs. A neural network model has been developed to simulate and aid functional interpretation of learning-evoked changes.

Results

Following learning the amplitude of theta (4-8 Hz), but not gamma (30-70 Hz) oscillations was increased, as was the ratio of theta to gamma. Over 75% of electrodes showed significant coupling between theta phase and gamma amplitude (theta-nested gamma). The strength of this coupling was also increased following learning and this was not simply a consequence of increased theta amplitude. Actual discrimination performance was significantly correlated with theta and theta-gamma coupling changes. Neuronal activity was phase-locked with theta but learning had no effect on firing rates or the magnitude or latencies of visual evoked potentials during stimuli. The neural network model developed showed that a combination of fast and slow inhibitory interneurons could generate theta-nested gamma. By increasing N-methyl-D-aspartate receptor sensitivity in the model similar changes were produced as in inferotemporal cortex after learning. The model showed that these changes could potentiate the firing of downstream neurons by a temporal desynchronization of excitatory neuron output without increasing the firing frequencies of the latter. This desynchronization effect was confirmed in IT neuronal activity following learning and its magnitude was correlated with discrimination performance.

Conclusions

Face discrimination learning produces significant increases in both theta amplitude and the strength of theta-gamma coupling in the inferotemporal cortex which are correlated with behavioral performance. A network model which can reproduce these changes suggests that a key function of such learning-evoked alterations in theta and theta-nested gamma activity may be increased temporal desynchronization in neuronal firing leading to optimal timing of inputs to downstream neural networks potentiating their responses. In this way learning can produce potentiation in neural networks simply through altering the temporal pattern of their inputs.     

Subshifts with slow complexity and simple groups with the Liouville property

We study random walk on topological full groups of subshifts, and show the existence of infinite, finitely generated, simple groups with the Liouville property. Results by Matui and Juschenko-Monod have shown that the derived subgroups of topological full groups of minimal subshifts provide the first examples of finitely generated, simple amenable groups. We show that if the (not necessarily minimal) subshift has a complexity function that grows slowly enough (e.g. linearly), then every symmetric and finitely supported probability measure on the topological full group has trivial Poisson–Furstenberg boundary. We also get explicit upper bounds for the growth of Følner sets.     

Model-based clustering of probability density functions

Complex data such as those where each statistical unit under study is described not by a single observation (or vector variable), but by a unit-specific sample of several or even many observations, are becoming more and more popular. Reducing these sample data by summary statistics, like the average or the median, implies that most inherent information (about variability, skewness or multi-modality) gets lost. Full information is preserved only if each unit is described by a whole distribution. This new kind of data, a.k.a. “distribution-valued data”, require the development of adequate statistical methods. This paper presents a method to group a set of probability density functions (pdfs) into homogeneous clusters, provided that the pdfs have to be estimated nonparametrically from the unit-specific data. Since elements belonging to the same cluster are naturally thought of as samples from the same probability model, the idea is to tackle the clustering problem by defining and estimating a proper mixture model on the space of pdfs. The issue of model building is challenging here because of the infinite-dimensionality and the non-Euclidean geometry of the domain space. By adopting a wavelet-based representation for the elements in the space, the task is accomplished by using mixture models for hyper-spherical data. The proposed solution is illustrated through a simulation experiment and on two real data sets.     

A fast solver for linear systems with displacement structure

We describe a fast solver for linear systems with reconstructible Cauchy-like structure, which requires O(rn ²) floating point operations and O(rn) memory locations, where n is the size of the matrix and r its displacement rank. The solver is based on the application of the generalized Schur algorithm to a suitable augmented matrix, under some assumptions on the knots of the Cauchy-like matrix. It includes various pivoting strategies, already discussed in the literature, and a new algorithm, which only requires reconstructibility. We have developed a software package, written in Matlab and C-MEX, which provides a robust implementation of the above method. Our package also includes solvers for Toeplitz(+Hankel)-like and Vandermonde-like linear systems, as these structures can be reduced to Cauchy-like by fast and stable transforms. Numerical experiments demonstrate the effectiveness of the software.