607 research outputs found
New insight into cataract formation -- enhanced stability through mutual attraction
Small-angle neutron scattering experiments and molecular dynamics simulations
combined with an application of concepts from soft matter physics to complex
protein mixtures provide new insight into the stability of eye lens protein
mixtures. Exploring this colloid-protein analogy we demonstrate that weak
attractions between unlike proteins help to maintain lens transparency in an
extremely sensitive and non-monotonic manner. These results not only represent
an important step towards a better understanding of protein condensation
diseases such as cataract formation, but provide general guidelines for tuning
the stability of colloid mixtures, a topic relevant for soft matter physics and
industrial applications.Comment: 4 pages, 4 figures. Accepted for publication on Phys. Rev. Let
Point Source Extraction with MOPEX
MOPEX (MOsaicking and Point source EXtraction) is a package developed at the
Spitzer Science Center for astronomical image processing. We report on the
point source extraction capabilities of MOPEX. Point source extraction is
implemented as a two step process: point source detection and profile fitting.
Non-linear matched filtering of input images can be performed optionally to
increase the signal-to-noise ratio and improve detection of faint point
sources. Point Response Function (PRF) fitting of point sources produces the
final point source list which includes the fluxes and improved positions of the
point sources, along with other parameters characterizing the fit. Passive and
active deblending allows for successful fitting of confused point sources.
Aperture photometry can also be computed for every extracted point source for
an unlimited number of aperture sizes. PRF is estimated directly from the input
images. Implementation of efficient methods of background and noise estimation,
and modified Simplex algorithm contribute to the computational efficiency of
MOPEX. The package is implemented as a loosely connected set of perl scripts,
where each script runs a number of modules written in C/C++. Input parameter
setting is done through namelists, ASCII configuration files. We present
applications of point source extraction to the mosaic images taken at 24 and 70
micron with the Multiband Imaging Photometer (MIPS) as part of the Spitzer
extragalactic First Look Survey and to a Digital Sky Survey image. Completeness
and reliability of point source extraction is computed using simulated data.Comment: 20 pages, 13 Postscript figures, accepted for publication in PAS
Identification of structure in condensed matter with the topological cluster classification
We describe the topological cluster classification (TCC) algorithm. The TCC
detects local structures with bond topologies similar to isolated clusters
which minimise the potential energy for a number of monatomic and binary simple
liquids with particles. We detail a modified Voronoi bond detection
method that optimizes the cluster detection. The method to identify each
cluster is outlined, and a test example of Lennard-Jones liquid and crystal
phases is considered and critically examined.Comment: 28 pages, 28 figure
Gravitational Wilson Loop and Large Scale Curvature
In a quantum theory of gravity the gravitational Wilson loop, defined as a
suitable quantum average of a parallel transport operator around a large
near-planar loop, provides important information about the large-scale
curvature properties of the geometry. Here we shows that such properties can be
systematically computed in the strong coupling limit of lattice regularized
quantum gravity, by performing a local average over rotations, using an assumed
near-uniform measure in group space. We then relate the resulting quantum
averages to an expected semi-classical form valid for macroscopic observers,
which leads to an identification of the gravitational correlation length
appearing in the Wilson loop with an observed large-scale curvature. Our
results suggest that strongly coupled gravity leads to a positively curved (De
Sitter-like) quantum ground state, implying a positive effective cosmological
constant at large distances.Comment: 22 pages, 6 figure
Cell size distribution in a random tessellation of space governed by the Kolmogorov-Johnson-Mehl-Avrami model: Grain size distribution in crystallization
The space subdivision in cells resulting from a process of random nucleation
and growth is a subject of interest in many scientific fields. In this paper,
we deduce the expected value and variance of these distributions while assuming
that the space subdivision process is in accordance with the premises of the
Kolmogorov-Johnson-Mehl-Avrami model. We have not imposed restrictions on the
time dependency of nucleation and growth rates. We have also developed an
approximate analytical cell size probability density function. Finally, we have
applied our approach to the distributions resulting from solid phase
crystallization under isochronal heating conditions
New Monte Carlo method for planar Poisson-Voronoi cells
By a new Monte Carlo algorithm we evaluate the sidedness probability p_n of a
planar Poisson-Voronoi cell in the range 3 \leq n \leq 1600. The algorithm is
developed on the basis of earlier theoretical work; it exploits, in particular,
the known asymptotic behavior of p_n as n\to\infty. Our p_n values all have
between four and six significant digits. Accurate n dependent averages, second
moments, and variances are obtained for the cell area and the cell perimeter.
The numerical large n behavior of these quantities is analyzed in terms of
asymptotic power series in 1/n. Snapshots are shown of typical occurrences of
extremely rare events implicating cells of up to n=1600 sides embedded in an
ordinary Poisson-Voronoi diagram. We reveal and discuss the characteristic
features of such many-sided cells and their immediate environment. Their
relevance for observable properties is stressed.Comment: 35 pages including 10 figures and 4 table
Asymptotic statistics of the n-sided planar Poisson-Voronoi cell. I. Exact results
We achieve a detailed understanding of the -sided planar Poisson-Voronoi
cell in the limit of large . Let be the probability for a cell to
have sides. We construct the asymptotic expansion of up to
terms that vanish as . We obtain the statistics of the lengths of
the perimeter segments and of the angles between adjoining segments: to leading
order as , and after appropriate scaling, these become independent
random variables whose laws we determine; and to next order in they have
nontrivial long range correlations whose expressions we provide. The -sided
cell tends towards a circle of radius (n/4\pi\lambda)^{\half}, where
is the cell density; hence Lewis' law for the average area of
the -sided cell behaves as with . For
the cell perimeter, expressed as a function of the polar
angle , satisfies , where is known Gaussian
noise; we deduce from it the probability law for the perimeter's long
wavelength deviations from circularity. Many other quantities related to the
asymptotic cell shape become accessible to calculation.Comment: 54 pages, 3 figure
Image informatics strategies for deciphering neuronal network connectivity
Brain function relies on an intricate network of highly dynamic neuronal connections that rewires dramatically under the impulse of various external cues and pathological conditions. Among the neuronal structures that show morphologi- cal plasticity are neurites, synapses, dendritic spines and even nuclei. This structural remodelling is directly connected with functional changes such as intercellular com- munication and the associated calcium-bursting behaviour. In vitro cultured neu- ronal networks are valuable models for studying these morpho-functional changes. Owing to the automation and standardisation of both image acquisition and image analysis, it has become possible to extract statistically relevant readout from such networks. Here, we focus on the current state-of-the-art in image informatics that enables quantitative microscopic interrogation of neuronal networks. We describe the major correlates of neuronal connectivity and present workflows for analysing them. Finally, we provide an outlook on the challenges that remain to be addressed, and discuss how imaging algorithms can be extended beyond in vitro imaging studies
Edge detection in microscopy images using curvelets
BACKGROUND: Despite significant progress in imaging technologies, the efficient detection of edges and elongated features in images of intracellular and multicellular structures acquired using light or electron microscopy is a challenging and time consuming task in many laboratories. RESULTS: We present a novel method, based on the discrete curvelet transform, to extract a directional field from the image that indicates the location and direction of the edges. This directional field is then processed using the non-maximal suppression and thresholding steps of the Canny algorithm to trace along the edges and mark them. Optionally, the edges may then be extended along the directions given by the curvelets to provide a more connected edge map. We compare our scheme to the Canny edge detector and an edge detector based on Gabor filters, and show that our scheme performs better in detecting larger, elongated structures possibly composed of several step or ridge edges. CONCLUSION: The proposed curvelet based edge detection is a novel and competitive approach for imaging problems. We expect that the methodology and the accompanying software will facilitate and improve edge detection in images available using light or electron microscopy
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