2,767 research outputs found
The mechanical response of cellular materials with spinodal topologies
The mechanical response of cellular materials with spinodal topologies is
numerically and experimentally investigated. Spinodal microstructures are
generated by the numerical solution of the Cahn-Hilliard equation. Two
different topologies are investigated: "solid models," where one of the two
phases is modeled as a solid material and the remaining volume is void space;
and "shell models," where the interface between the two phases is assumed to be
a solid shell, with the rest of the volume modeled as void space. In both
cases, a wide range of relative densities and spinodal characteristic feature
sizes are investigated. The topology and morphology of all the numerically
generated models are carefully characterized to extract key geometrical
features and ensure that the distribution of curvatures and the aging law are
consistent with the physics of spinodal decomposition. Finite element meshes
are generated for each model, and the uniaxial compressive stiffness and
strength are extracted. We show that while solid spinodal models in the density
range of 30-70% are relatively inefficient (i.e., their strength and stiffness
exhibit a high-power scaling with relative density), shell spinodal models in
the density range of 0.01-1% are exceptionally stiff and strong. Spinodal shell
materials are also shown to be remarkably imperfection insensitive. These
findings are verified experimentally by in-situ uniaxial compression of
polymeric samples printed at the microscale by Direct Laser Writing (DLW). At
low relative densities, the strength and stiffness of shell spinodal models
outperform those of most lattice materials and approach theoretical bounds for
isotropic cellular materials. Most importantly, these materials can be produced
by self-assembly techniques over a range of length scales, providing unique
scalability
3D mesh processing using GAMer 2 to enable reaction-diffusion simulations in realistic cellular geometries
Recent advances in electron microscopy have enabled the imaging of single
cells in 3D at nanometer length scale resolutions. An uncharted frontier for in
silico biology is the ability to simulate cellular processes using these
observed geometries. Enabling such simulations requires watertight meshing of
electron micrograph images into 3D volume meshes, which can then form the basis
of computer simulations of such processes using numerical techniques such as
the Finite Element Method. In this paper, we describe the use of our recently
rewritten mesh processing software, GAMer 2, to bridge the gap between poorly
conditioned meshes generated from segmented micrographs and boundary marked
tetrahedral meshes which are compatible with simulation. We demonstrate the
application of a workflow using GAMer 2 to a series of electron micrographs of
neuronal dendrite morphology explored at three different length scales and show
that the resulting meshes are suitable for finite element simulations. This
work is an important step towards making physical simulations of biological
processes in realistic geometries routine. Innovations in algorithms to
reconstruct and simulate cellular length scale phenomena based on emerging
structural data will enable realistic physical models and advance discovery at
the interface of geometry and cellular processes. We posit that a new frontier
at the intersection of computational technologies and single cell biology is
now open.Comment: 39 pages, 14 figures. High resolution figures and supplemental movies
available upon reques
Polar Chemoreceptor Clustering by Coupled Trimers of Dimers
Receptors of bacterial chemotaxis form clusters at the cell poles, where
clusters act as "antennas" to amplify small changes in ligand concentration.
Interestingly, chemoreceptors cluster at multiple length scales. At the
smallest scale, receptors form dimers, which assemble into stable timers of
dimers. At a large scale, trimers form large polar clusters composed of
thousands of receptors. Although much is known about the signaling properties
emerging from receptor clusters, it is unknown how receptors localize at the
cell poles and what the cluster-size determining factors are. Here, we present
a model of polar receptor clustering based on coupled trimers of dimers, where
cluster size is determined as a minimum of the cluster-membrane free energy.
This energy has contributions from the cluster-membrane elastic energy,
penalizing large clusters due to their high intrinsic curvature, and
receptor-receptor coupling favoring large clusters. We find that the reduced
cluster-membrane curvature mismatch at the curved cell poles leads to large and
robust polar clusters in line with experimental observation, while lateral
clusters are efficiently suppressed.Comment: 11 pages, 6 figures, and 1 tabl
Towards multi-scale feature detection repeatable over intensity and depth images.
Object recognition based on local features computed at multiple locations is robust to occlusions, strong viewpoint changes and object deformations. These features should be repeatable, precise and distinctive. We present an operator for repeatable feature detection on depth images (relative to 3D models) as well as 2D intensity images. The proposed detector is based on estimating the curviness saliency at multiple scales in each kind of image. We also propose quality measures that evaluate the repeatability of the features between depth and intensity images. The experiments show that the proposed detector outperforms both the most powerful, classical point detectors (e.g., SIFT) and edge detection techniques
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