26,904 research outputs found
-SUP: A clustering algorithm for cryo-electron microscopy images of asymmetric particles
Cryo-electron microscopy (cryo-EM) has recently emerged as a powerful tool
for obtaining three-dimensional (3D) structures of biological macromolecules in
native states. A minimum cryo-EM image data set for deriving a meaningful
reconstruction is comprised of thousands of randomly orientated projections of
identical particles photographed with a small number of electrons. The
computation of 3D structure from 2D projections requires clustering, which aims
to enhance the signal to noise ratio in each view by grouping similarly
oriented images. Nevertheless, the prevailing clustering techniques are often
compromised by three characteristics of cryo-EM data: high noise content, high
dimensionality and large number of clusters. Moreover, since clustering
requires registering images of similar orientation into the same pixel
coordinates by 2D alignment, it is desired that the clustering algorithm can
label misaligned images as outliers. Herein, we introduce a clustering
algorithm -SUP to model the data with a -Gaussian mixture and adopt
the minimum -divergence for estimation, and then use a self-updating
procedure to obtain the numerical solution. We apply -SUP to the
cryo-EM images of two benchmark macromolecules, RNA polymerase II and ribosome.
In the former case, simulated images were chosen to decouple clustering from
alignment to demonstrate -SUP is more robust to misalignment outliers
than the existing clustering methods used in the cryo-EM community. In the
latter case, the clustering of real cryo-EM data by our -SUP method
eliminates noise in many views to reveal true structure features of ribosome at
the projection level.Comment: Published in at http://dx.doi.org/10.1214/13-AOAS680 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
An Imprint of Molecular Cloud Magnetization in the Morphology of the Dust Polarized Emission
We describe a morphological imprint of magnetization found when considering
the relative orientation of the magnetic field direction with respect to the
density structures in simulated turbulent molecular clouds. This imprint was
found using the Histogram of Relative Orientations (HRO): a new technique that
utilizes the gradient to characterize the directionality of density and column
density structures on multiple scales. We present results of the HRO analysis
in three models of molecular clouds in which the initial magnetic field
strength is varied, but an identical initial turbulent velocity field is
introduced, which subsequently decays. The HRO analysis was applied to the
simulated data cubes and mock-observations of the simulations produced by
integrating the data cube along particular lines of sight. In the 3D analysis
we describe the relative orientation of the magnetic field with
respect to the density structures, showing that: 1.The magnetic field shows a
preferential orientation parallel to most of the density structures in the
three simulated cubes. 2.The relative orientation changes from parallel to
perpendicular in regions with density over a critical density in the
highest magnetization case. 3.The change of relative orientation is largest for
the highest magnetization and decreases in lower magnetization cases. This
change in the relative orientation is also present in the projected maps. In
conjunction with simulations HROs can be used to establish a link between the
observed morphology in polarization maps and the physics included in
simulations of molecular clouds.Comment: (16 pages, 11 figures, submitted to ApJ 05MAR2013, accepted
07JUL2013
Unsupervised cryo-EM data clustering through adaptively constrained K-means algorithm
In single-particle cryo-electron microscopy (cryo-EM), K-means clustering
algorithm is widely used in unsupervised 2D classification of projection images
of biological macromolecules. 3D ab initio reconstruction requires accurate
unsupervised classification in order to separate molecular projections of
distinct orientations. Due to background noise in single-particle images and
uncertainty of molecular orientations, traditional K-means clustering algorithm
may classify images into wrong classes and produce classes with a large
variation in membership. Overcoming these limitations requires further
development on clustering algorithms for cryo-EM data analysis. We propose a
novel unsupervised data clustering method building upon the traditional K-means
algorithm. By introducing an adaptive constraint term in the objective
function, our algorithm not only avoids a large variation in class sizes but
also produces more accurate data clustering. Applications of this approach to
both simulated and experimental cryo-EM data demonstrate that our algorithm is
a significantly improved alterative to the traditional K-means algorithm in
single-particle cryo-EM analysis.Comment: 35 pages, 14 figure
GENFIRE: A generalized Fourier iterative reconstruction algorithm for high-resolution 3D imaging
Tomography has made a radical impact on diverse fields ranging from the study
of 3D atomic arrangements in matter to the study of human health in medicine.
Despite its very diverse applications, the core of tomography remains the same,
that is, a mathematical method must be implemented to reconstruct the 3D
structure of an object from a number of 2D projections. In many scientific
applications, however, the number of projections that can be measured is
limited due to geometric constraints, tolerable radiation dose and/or
acquisition speed. Thus it becomes an important problem to obtain the
best-possible reconstruction from a limited number of projections. Here, we
present the mathematical implementation of a tomographic algorithm, termed
GENeralized Fourier Iterative REconstruction (GENFIRE). By iterating between
real and reciprocal space, GENFIRE searches for a global solution that is
concurrently consistent with the measured data and general physical
constraints. The algorithm requires minimal human intervention and also
incorporates angular refinement to reduce the tilt angle error. We demonstrate
that GENFIRE can produce superior results relative to several other popular
tomographic reconstruction techniques by numerical simulations, and by
experimentally by reconstructing the 3D structure of a porous material and a
frozen-hydrated marine cyanobacterium. Equipped with a graphical user
interface, GENFIRE is freely available from our website and is expected to find
broad applications across different disciplines.Comment: 18 pages, 6 figure
Calipso: Physics-based Image and Video Editing through CAD Model Proxies
We present Calipso, an interactive method for editing images and videos in a
physically-coherent manner. Our main idea is to realize physics-based
manipulations by running a full physics simulation on proxy geometries given by
non-rigidly aligned CAD models. Running these simulations allows us to apply
new, unseen forces to move or deform selected objects, change physical
parameters such as mass or elasticity, or even add entire new objects that
interact with the rest of the underlying scene. In Calipso, the user makes
edits directly in 3D; these edits are processed by the simulation and then
transfered to the target 2D content using shape-to-image correspondences in a
photo-realistic rendering process. To align the CAD models, we introduce an
efficient CAD-to-image alignment procedure that jointly minimizes for rigid and
non-rigid alignment while preserving the high-level structure of the input
shape. Moreover, the user can choose to exploit image flow to estimate scene
motion, producing coherent physical behavior with ambient dynamics. We
demonstrate Calipso's physics-based editing on a wide range of examples
producing myriad physical behavior while preserving geometric and visual
consistency.Comment: 11 page
Direct 3D Tomographic Reconstruction and Phase-Retrieval of Far-Field Coherent Diffraction Patterns
We present an alternative numerical reconstruction algorithm for direct
tomographic reconstruction of a sample refractive indices from the measured
intensities of its far-field coherent diffraction patterns. We formulate the
well-known phase-retrieval problem in ptychography in a tomographic framework
which allows for simultaneous reconstruction of the illumination function and
the sample refractive indices in three dimensions. Our iterative reconstruction
algorithm is based on the Levenberg-Marquardt algorithm. We demonstrate the
performance of our proposed method with simulation studies
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