Chemical modification was used to quantitatively determine the flexibility of nearly the entire rRNA component of the yeast ribosome through 8 discrete stages of translational elongation, revealing novel observations at the gross and fine-scales. These include (i) the bulk transfer of energy through the intersubunit bridges from the large to the small subunit after peptidyltransfer, (ii) differences in the interaction of the sarcin ricin loop with the two elongation factors and (iii) networked information exchange pathways that may functionally facilitate intra- and intersubunit coordination, including the 5.8S rRNA. These analyses reveal hot spots of fluctuations that set the stage for large-scale conformational changes essential for translocation and enable the first molecular dynamics simulation of an 80S complex. Comprehensive datasets of rRNA base flexibilities provide a unique resource to the structural biology community that can be computationally mined to complement ongoing research toward the goal of understanding the dynamic ribosome.
Diffusion kurtosis imaging (DKI) is gaining rapid adoption in the medical imaging community due to its ability to measure the non-Gaussian property of water diffusion in biological tissues. Compared to traditional diffusion tensor imaging (DTI), DKI can provide additional details about the underlying microstructural characteristics of the neural tissues. It has shown promising results in studies on changes in gray matter and mild traumatic brain injury where DTI is often found to be inadequate. The DKI dataset, which has high-fidelity spatio-angular fields, is difficult to visualize. Glyph-based visualization techniques are commonly used for visualization of DTI datasets; however, due to the rapid changes in orientation, lighting, and occlusion, visually analyzing the much more higher fidelity DKI data is a challenge. In this paper, we provide a systematic way to manage, analyze, and visualize high-fidelity spatio-angular fields from DKI datasets, by using spherical harmonics lighting functions to facilitate insights into the brain microstructure.
In this paper, we introduce a graphic processing unit (GPU)-based framework for simulating particle trajectories under both static and dynamic force fields. By exploiting the highly parallel nature of the problem and making efficient use of the available hardware, our simulator exhibits a significant speedup over its CPU-based analog. We apply our framework to a specific experimental simulation: the computation of trapping probabilities associated with micron-sized silica beads in optical trapping workbenches. When evaluating large numbers of trajectories (4096), we see approximately a 356 times speedup of the GPU-based simulator over its CPU-based counterpart.
Contemporary molecular dynamics simulations result in a glut of simulation data, making analysis and discovery a difficult and burdensome task. We present MDMap, a system designed to summarize long-running molecular dynamics (MD) simulations. We represent a molecular dynamics simulation as a state transition graph over a set of intermediate (stable and semi-stable) states. The transitions amongst the states together with their frequencies represent the flow of a biomolecule through the trajectory space. MDMap automatically determines potential intermediate conformations and the transitions amongst them by analyzing the conformational space explored by the MD simulation. MDMap is an automated system to visualize MD simulations as state-transition diagrams, and can replace the current tedious manual layouts of biomolecular folding landscapes with an automated tool. The layout of the representative states and the corresponding transitions among them is presented to the user as a visual synopsis of the long-running MD simulation. We compare and contrast multiple presentations of the state transition diagrams, such as conformational embedding, and spectral, hierarchical, and force-directed graph layouts. We believe this system could provide a road-map for the visualization of other stochastic time-varying simulations in a variety of different domains.
surveillance video streams calibration of camera world matrices static 3D models and satellite image Video Fields Mapping automatic segmentation and view-dependent rendering dynamic virtual environment Figure 1: Video Fields system fuses multiple videos, camera-world matrices from a calibration interface, static 3D models, as well as satellite imagery into a novel dynamic virtual environment. Video Fields integrates automatic segmentation of moving entities during the rendering pass and achieves view-dependent rendering in two ways: early pruning and deferred pruning. Video Fields takes advantage of the WebGL and WebVR technology to achieve cross-platform compatibility across smart phones, tablets, desktops, high-resolution tiled curved displays, as well as virtual reality head-mounted displays. See the supplementary video at http:// augmentarium.umd.edu and http:// video-fields.com.
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