25,791 research outputs found
Water exchange at a hydrated platinum electrode is rare and collective
We use molecular dynamics simulations to study the exchange kinetics of water
molecules at a model metal electrode surface -- exchange between water
molecules in the bulk liquid and water molecules bound to the metal. This
process is a rare event, with a mean residence time of a bound water of about
40 ns for the model we consider. With analysis borrowed from the techniques of
rare-event sampling, we show how this exchange or desorption is controlled by
(1) reorganization of the hydrogen bond network within the adlayer of bound
water molecules, and by (2) interfacial density fluctuations of the bulk liquid
adjacent to the adlayer. We define collective coordinates that describe the
desorption mechanism. Spatial and temporal correlations associated with a
single event extend over nanometers and tens of picoseconds.Comment: 10 pages, 9 figure
Demodulation of Spatial Carrier Images: Performance Analysis of Several Algorithms Using a Single Image
http://link.springer.com/article/10.1007%2Fs11340-013-9741-6#Optical full-field techniques have a great importance in modern experimental mechanics. Even if they are reasonably spread among the university laboratories, their diffusion in industrial companies remains very narrow for several reasons, especially a lack of metrological performance assessment. A full-field measurement can be characterized by its resolution, bias, measuring range, and by a specific quantity, the spatial resolution. The present paper proposes an original procedure to estimate in one single step the resolution, bias and spatial resolution for a given operator (decoding algorithms such as image correlation, low-pass filters, derivation tools ...). This procedure is based on the construction of a particular multi-frequential field, and a Bode diagram representation of the results. This analysis is applied to various phase demodulating algorithms suited to estimate in-plane displacements.GDR CNRS 2519 “Mesures de Champs et Identification en Mécanique des Solide
Identification of direct residue contacts in protein-protein interaction by message passing
Understanding the molecular determinants of specificity in protein-protein
interaction is an outstanding challenge of postgenome biology. The availability
of large protein databases generated from sequences of hundreds of bacterial
genomes enables various statistical approaches to this problem. In this context
covariance-based methods have been used to identify correlation between amino
acid positions in interacting proteins. However, these methods have an
important shortcoming, in that they cannot distinguish between directly and
indirectly correlated residues. We developed a method that combines covariance
analysis with global inference analysis, adopted from use in statistical
physics. Applied to a set of >2,500 representatives of the bacterial
two-component signal transduction system, the combination of covariance with
global inference successfully and robustly identified residue pairs that are
proximal in space without resorting to ad hoc tuning parameters, both for
heterointeractions between sensor kinase (SK) and response regulator (RR)
proteins and for homointeractions between RR proteins. The spectacular success
of this approach illustrates the effectiveness of the global inference approach
in identifying direct interaction based on sequence information alone. We
expect this method to be applicable soon to interaction surfaces between
proteins present in only 1 copy per genome as the number of sequenced genomes
continues to expand. Use of this method could significantly increase the
potential targets for therapeutic intervention, shed light on the mechanism of
protein-protein interaction, and establish the foundation for the accurate
prediction of interacting protein partners.Comment: Supplementary information available on
http://www.pnas.org/content/106/1/67.abstrac
Precise algorithms to compute surface correlation functions of two-phase heterogeneous media and their applications
The quantitative characterization of the microstructure of random
heterogeneous media in -dimensional Euclidean space via a
variety of -point correlation functions is of great importance, since the
respective infinite set determines the effective physical properties of the
media. In particular, surface-surface and surface-void
correlation functions (obtainable from radiation scattering experiments)
contain crucial interfacial information that enables one to estimate transport
properties of the media (e.g., the mean survival time and fluid permeability)
and complements the information content of the conventional two-point
correlation function. However, the current technical difficulty involved in
sampling surface correlation functions has been a stumbling block in their
widespread use. We first present a concise derivation of the small-
behaviors of these functions, which are linked to the \textit{mean curvature}
of the system. Then we demonstrate that one can reduce the computational
complexity of the problem by extracting the necessary interfacial information
from a cut of the system with an infinitely long line. Accordingly, we devise
algorithms based on this idea and test them for two-phase media in continuous
and discrete spaces. Specifically for the exact benchmark model of overlapping
spheres, we find excellent agreement between numerical and exact results. We
compute surface correlation functions and corresponding local surface-area
variances for a variety of other model microstructures, including hard spheres
in equilibrium, decorated "stealthy" patterns, as well as snapshots of evolving
pattern formation processes (e.g., spinodal decomposition). It is demonstrated
that the precise determination of surface correlation functions provides a
powerful means to characterize a wide class of complex multiphase
microstructures
Enhancing retinal images by nonlinear registration
Being able to image the human retina in high resolution opens a new era in
many important fields, such as pharmacological research for retinal diseases,
researches in human cognition, nervous system, metabolism and blood stream, to
name a few. In this paper, we propose to share the knowledge acquired in the
fields of optics and imaging in solar astrophysics in order to improve the
retinal imaging at very high spatial resolution in the perspective to perform a
medical diagnosis. The main purpose would be to assist health care
practitioners by enhancing retinal images and detect abnormal features. We
apply a nonlinear registration method using local correlation tracking to
increase the field of view and follow structure evolutions using correlation
techniques borrowed from solar astronomy technique expertise. Another purpose
is to define the tracer of movements after analyzing local correlations to
follow the proper motions of an image from one moment to another, such as
changes in optical flows that would be of high interest in a medical diagnosis.Comment: 21 pages, 7 figures, submitted to Optics Communication
Dynamic Decomposition of Spatiotemporal Neural Signals
Neural signals are characterized by rich temporal and spatiotemporal dynamics
that reflect the organization of cortical networks. Theoretical research has
shown how neural networks can operate at different dynamic ranges that
correspond to specific types of information processing. Here we present a data
analysis framework that uses a linearized model of these dynamic states in
order to decompose the measured neural signal into a series of components that
capture both rhythmic and non-rhythmic neural activity. The method is based on
stochastic differential equations and Gaussian process regression. Through
computer simulations and analysis of magnetoencephalographic data, we
demonstrate the efficacy of the method in identifying meaningful modulations of
oscillatory signals corrupted by structured temporal and spatiotemporal noise.
These results suggest that the method is particularly suitable for the analysis
and interpretation of complex temporal and spatiotemporal neural signals
Deconvolving mutational patterns of poliovirus outbreaks reveals its intrinsic fitness landscape.
Vaccination has essentially eradicated poliovirus. Yet, its mutation rate is higher than that of viruses like HIV, for which no effective vaccine exists. To investigate this, we infer a fitness model for the poliovirus viral protein 1 (vp1), which successfully predicts in vitro fitness measurements. This is achieved by first developing a probabilistic model for the prevalence of vp1 sequences that enables us to isolate and remove data that are subject to strong vaccine-derived biases. The intrinsic fitness constraints derived for vp1, a capsid protein subject to antibody responses, are compared with those of analogous HIV proteins. We find that vp1 evolution is subject to tighter constraints, limiting its ability to evade vaccine-induced immune responses. Our analysis also indicates that circulating poliovirus strains in unimmunized populations serve as a reservoir that can seed outbreaks in spatio-temporally localized sub-optimally immunized populations
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