2378459
Visualization of Topological Defects in Nematic Liquid Crystals Using Streamtubes, Streamsurfaces and Ellipsoids
Researchers in computational condensed matter physics deal with complex data sets consisting of time varying 3D tensor, vector, and scalar quantities. Particularly, in the research of topological defects in nematic liquid crystals (LC) displaying the results of the computer simulation of molecular dynamics presents a challenge. Combining existing immersive and interactive visualization methods we developed new methods that attempt to provide a clear, efficient, and intuitive way to visualize and explore LC data. In addition, the visualization of the data has presented us with a novel method of obtaining the locations of the topological defects present in a liquid crystal system.
15381284
Color Rapid Prototyping for Diffusion-Tensor MRI Visualization
We describe work toward creating color rapid prototyping (RP) plaster models as visualization tools to support scientific research in diffusion-tensor (DT) MRI analysis. We currently give surgeons and neurologists virtual-reality (VR) applications to visualize different aspects of their brain data, but having physical representations of those virtual models allows them to review the data with a very robust, natural, and fast haptic interface: their own hands. Our initial results are encouraging, and end users are excited about the possibilities of this technique. For example, using these models in conjunction with digital models on the computer screen or VR environment provides a static frame of reference that helps keep users oriented during their analysis tasks.
5242506
A Model for Some Subcortical DTI Planar and Linear Anisotropy
Linear anisotropy, planar anisotropy and isotropy [1] are used as metrics for different kinds of diffusion in diffusion imaging. While linear anisotropy is reported to correlate to coherent neural fiber structures, the cause for planar anisotropy remains ambiguous. We hypothesize that overlapping linear structures and partial-volume averaging generate the planar anisotropy. We identify a subcortical region containing both linear and planar anisotropy in a human volumetric diffusion tensor image (DTI), propose a model of the anatomy and of the imaging process, and calculate simulated diffusion images of the anatomical model that qualitatively agree with the human DTI. Regions of planar anisotropy are common immediately beneath the cortex. Choosing one such region as representative, we model the anatomy with isotropic regions and linearly anisotropic structures. From the possibly overlapping model structures we simulate the diffusion imaging process, generating a series of diffusion weighted images (DWIs) of the anatomical structures. We then fit the DTI from these simulated DWIs and visualize it. The visualization agrees qualitatively with the visualization of a subcortical human DTI.
1893178
An immersive virtual environment for DT-MRI volume visualization applications: a case study
We describe a virtual reality environment for visualizing tensor-valued volumetric datasets acquired with diffusion tensor magnetic resonance imaging (DT-MRI). We have prototyped a virtual environment that displays geometric representations of the volumetric second-order diffusion tensor data and are developing interaction and visualization techniques for two application areas: studying changes in white-matter structures after gamma-knife capsulotomy and pre-operative planning for brain tumor surgery. Our feedback shows that compared to desktop displays, our system helps the user better interpret the large and complex geometric models, and facilitates communication among a group of users.
9519800
Turbulent Decay and Mixing of Accelerated Inhomogeneous Flows Via a Feature Based Analysis
In this paper, we focus on the late time turbulence of an accelerated inhomogeneous flow environment, which is a generalization of the Richtmyer--Meshkov environment. The numerical investigation is based on two-dimensional (2D) compressible Euler simulation, which is initiated by a shock wave hitting a gas layer (curtain). Although our unforced study is based on the intrinsic numerical dissipation, we observe excellent agreement with the previous decay analysis on 2D viscous isotropic homogeneous turbulence at inertial range [J. R. Chasnov, Phys. Fluids, 9 (1997), pp. 171--180]. The baroclinic circulation in our environment plays a major role in the mass transport and mixing. The mass-transport induced density gradient intensification, in turn, enhances the circulation baroclinically and provides an intrinsic forcing at intermediate to high wave number range. With the assistance of a computer graphics based feature extraction and tracking algorithm, we address quantitatively the spatial and temporal diffusivity of the mixing zone. We study and compare both the slow/fast/slow and fast/slow/fast cases to illustrate heuristically the correlation of mass and momentum diffusivity.
332775
Chemometric Analysis of Ligand Receptor Complementarity: Identifying Complementary Ligands Based on Receptor Information (CoLiBRI)
We have developed a novel structure-based chemoinformatics approach to search for Complimentary Ligands Based on Receptor Information (CoLiBRI). CoLiBRI is based on the representation of both receptor binding sites and their respective ligands in a space of universal chemical descriptors. The binding site atoms involved in the interaction with ligands are identified by the means of a computational geometry technique known as Delaunay tessellation as applied to X-ray characterized ligand-receptor complexes. TAE/RECON multiple chemical descriptors are calculated independently for each ligand as well as for its active site atoms. The representation of both ligands and active sites using chemical descriptors allows the application of well-known chemometric techniques in order to correlate chemical similarities between active sites and their respective ligands. We have established a protocol to map patterns of nearest neighbor active site vectors in a multidimensional TAE/RECON space onto those of their complementary ligands and vice versa. This protocol affords the prediction of a virtual complementary ligand vector in the ligand chemical space from the position of a known active site vector. This prediction is followed by chemical similarity calculations between this virtual ligand vector and those calculated for molecules in a chemical database to identify real compounds most similar to the virtual ligand. Consequently, the knowledge of the receptor active site structure affords straightforward and efficient identification of its complementary ligands in large databases of chemical compounds using rapid chemical similarity searches. Conversely, starting from the ligand chemical structure, one may identify possible complementary receptor cavities as well. We have applied the CoLiBRI approach to a data set of 800 X-ray characterized ligand-receptor complexes in the PDBbind database. Using a k nearest neighbor (kNN) pattern recognition approach and variable selection, we have shown that knowledge of the active site structure affords identification of its complimentary ligand among the top 1% of a large chemical database in over 90% of all test active sites when a binding site of the same protein family was present in the training set. In the case where test receptors are highly dissimilar and not present among the receptor families in the training set, the prediction accuracy is decreased; however, CoLiBRI was still able to quickly eliminate 75% of the chemical database as improbable ligands. CoLiBRI affords rapid prefiltering of a large chemical database to eliminate compounds that have little chance of binding to a receptor active site.
40273501
Pattern Discovery in Probabilistic Databases
Modeling probabilistic data is one of important issues in databases due to the fact that data is often uncertainty in real-world applications. So, it is necessary to identify potentially useful patterns in probabilistic databases. Because probabilistic data in 1NF relations is redundant, previous mining techniques don't work well on probabilistic databases. For this reason, this paper proposes a new model for mining probabilistic databases. A partition is thus developed for preprocessing probabilistic data in a probabilistic databases. We evaluated the proposed technique, and the experimental results demonstrate that our approach is effective and efficient.
1826127
On a Family-Based Haplotype Pattern Mining Method for Linkage Disequilibrium Mapping
Linkage disequilibrium mapping is an important tool in disease gene mapping. Recently, Toivonen et al. [1] introduced a haplotype mining (HPM) method that is applicable to data consisting of unrelated high-risk and normal haplotypes. The HPM method orders haplotypes by their strength of association with trait values, and uses all haplotypes exceeding a given threshold of strength of association to predict the gene location. In this study, we extend the HPM method to pedigree data by measuring the strength of association between a haplotype and quantitative traits of interest using the Quantitative Pedigree Disequilibrium Test proposed by Zhang et al. [2]. This family-based HPM (F-HPM) method can incorporate haplotype information across a set of markers and allow both missing marker data and ambiguous haplotype information. We use a simulation procedure to evaluate the statistical significance of the patterns identified from the F-HPM method. When the F-HPM method is applied to analyze the sequence data from the seven candidate genes in the simulated data sets in the 12th Genetic Analysis Workshop, the association between genes and traits can be detected with high power, and the estimated locations of the trait loci are close to the true sites.
8339552
A sequence-based filtering method for ncRNA identification and its application to searching for riboswitch elements
MOTIVATION
Recent studies have uncovered an "RNA world", in which non coding RNA (ncRNA) sequences play a central role in the regulation of gene expression. Computational studies on ncRNA have been directed toward developing detection methods for ncRNAs. State-of-the-art methods for the problem, like covariance models, suffer from high computational cost, underscoring the need for efficient filtering approaches that can identify promising sequence segments and speedup the detection process.


RESULTS
In this paper we make several contributions toward this goal. First, we formalize the concept of a filter and provide figures of merit that allow comparison between filters. Second, we design efficient sequence based filters that dominate the current state-of-the-art HMM filters. Third, we provide a new formulation of the covariance model that allows speeding up RNA alignment. We demonstrate the power of our approach on both synthetic data and real bacterial genomes. We then apply our algorithm to the detection of novel riboswitch elements from the whole bacterial and archaeal genomes. Our results point to a number of novel riboswitch candidates, and include genomes that were not previously known to contain riboswitches.


AVAILABILITY
The program is available upon request from the authors.