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Results for “multiscale clustering”

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PubMed2006

Multiscale modeling of nucleosome dynamics.

Nucleosomes form the fundamental building blocks of chromatin. Subtle modifications of the constituent histone tails mediate chromatin stability and regulate gene expression. For this reason, it is important to understand structural dynamics of nucleosomes at atomic levels. We report a novel multiscale model of the fundamental chromatin unit, a nucleosome, using a simplified model for rapid discrete molecular dynamics simulations and an all-atom model for detailed structural investigation. Using a simplified structural model, we perform equilibrium simulations of a single nucleosome at various temperatures. We further reconstruct all-atom nucleosome structures from simulation trajectories. We find that histone tails bind to nucleosomal DNA via strong salt-bridge interactions over a wide range of temperatures, suggesting a mechanism of chromatin structural organization whereby histone tails regulate inter- and intranucleosomal assemblies via binding with nucleosomal DNA. We identify specific regions of the histone core H2A/H2B-H4/H3-H3/H4-H2B/H2A, termed "cold sites", which retain a significant fraction of contacts with adjoining residues throughout the simulation, indicating their functional role in nucleosome organization. Cold sites are clustered around H3-H3, H2A-H4 and H4-H2A interhistone interfaces, indicating the necessity of these contacts for nucleosome stability. Essential dynamics analysis of simulation trajectories shows that bending across the H3-H3 is a prominent mode of intranucleosomal dynamics. We postulate that effects of salts on mononucleosomes can be modeled in discrete molecular dynamics by modulating histone-DNA interaction potentials. Local fluctuations in nucleosomal DNA vary significantly along the DNA sequence, suggesting that only a fraction of histone-DNA contacts make strong interactions dominating mononucleosomal dynamics. Our findings suggest that histone tails have a direct functional role in stabilizing higher-order chromatin structure, mediated by salt-bridge interactions with adjacent DNA.

Chromatin↗

PubMed2004

Determining correspondence in 3-D MR brain images using attribute vectors as morphological signatures of voxels.

Finding point correspondence in anatomical images is a key step in shape analysis and deformable registration. This paper proposes an automatic correspondence detection algorithm for intramodality MR brain images of different subjects using wavelet-based attribute vectors (WAVs) defined on every image voxel. The attribute vector (AV) is extracted from the wavelet subimages and reflects the image structure in a large neighborhood around the respective voxel in a multiscale fashion. It plays the role of a morphological signature for each voxel, and our goal is, therefore, to make it distinctive of the respective voxel. Correspondence is then determined from similarities of AVs. By incorporating the prior knowledge of the spatial relationship among voxels, the ability of the proposed algorithm to find anatomical correspondence is further improved. Experiments with MR images of human brains show that the algorithm performs similarly to experts, even for complex cortical structures.

Aged↗

PubMed2005

Reconstruction of the src-SH3 protein domain transition state ensemble using multiscale molecular dynamics simulations.

We use an integrated computational approach to reconstruct accurately the transition state ensemble (TSE) for folding of the src-SH3 protein domain. We first identify putative TSE conformations from free energy surfaces generated by importance sampling molecular dynamics for a fully atomic, solvated model of the src-SH3 protein domain. These putative TSE conformations are then subjected to a folding analysis using a coarse-grained representation of the protein and rapid discrete molecular dynamics simulations. Those conformations that fold to the native conformation with a probability (P(fold)) of approximately 0.5, constitute the true transition state. Approximately 20% of the putative TSE structures were found to have a P(fold) near 0.5, indicating that, although correct TSE conformations are populated at the free energy barrier, there is a critical need to refine this ensemble. Our simulations indicate that the true TSE conformations are compact, with a well-defined central beta sheet, in good agreement with previous experimental and theoretical studies. A structured central beta sheet was found to be present in a number of pre-TSE conformations, however, indicating that this element, although required in the transition state, does not define it uniquely. An additional tight cluster of contacts between highly conserved residues belonging to the diverging turn and second beta-sheet of the protein emerged as being critical elements of the folding nucleus. A number of commonly used order parameters to identify the transition state for folding were investigated, with the number of native Cbeta contacts displaying the most satisfactory correlation with P(fold) values.

Models, Molecular↗

PubMed2004

MMTSB Tool Set: enhanced sampling and multiscale modeling methods for applications in structural biology.

We describe the Multiscale Modeling Tools for Structural Biology (MMTSB) Tool Set (https://mmtsb.scripps.edu/software/mmtsbToolSet.html), which is a novel set of utilities and programming libraries that provide new enhanced sampling and multiscale modeling techniques for the simulation of proteins and nucleic acids. The tool set interfaces with the existing molecular modeling packages CHARMM and Amber for classical all-atom simulations, and with MONSSTER for lattice-based low-resolution conformational sampling. In addition, it adds new functionality for the integration and translation between both levels of detail. The replica exchange method is implemented to allow enhanced sampling of both the all-atom and low-resolution models. The tool set aims at applications in structural biology that involve protein or nucleic acid structure prediction, refinement, and/or extended conformational sampling. With structure prediction applications in mind, the tool set also implements a facility that allows the control and application of modeling tasks on a large set of conformations in what we have termed ensemble computing. Ensemble computing encompasses loosely coupled, parallel computation on high-end parallel computers, clustered computational grids and desktop grid environments. This paper describes the design and implementation of the MMTSB Tool Set and illustrates its utility with three typical examples--scoring of a set of predicted protein conformations in order to identify the most native-like structures, ab initio folding of peptides in implicit solvent with the replica exchange method, and the prediction of a missing fragment in a larger protein structure.

Computer Simulation↗

PubMed2005

Dynamic Trees for unsupervised segmentation and matching of image regions.

We present a probabilistic framework--namely, multiscale generative models known as Dynamic Trees (DT)--for unsupervised image segmentation and subsequent matching of segmented regions in a given set of images. Beyond these novel applications of DTs, we propose important additions for this modeling paradigm. First, we introduce a novel DT architecture, where multilayered observable data are incorporated at all scales of the model. Second, we derive a novel probabilistic inference algorithm for DTs--Structured Variational Approximation (SVA)--which explicitly accounts for the statistical dependence of node positions and model structure in the approximate posterior distribution, thereby relaxing poorly justified independence assumptions in previous work. Finally, we propose a similarity measure for matching dynamic-tree models, representing segmented image regions, across images. Our results for several data sets show that DTs are capable of capturing important component-subcomponent relationships among objects and their parts, and that DTs perform well in segmenting images into plausible pixel clusters. We demonstrate the significantly improved properties of the SVA algorithm--both in terms of substantially faster convergence rates and larger approximate posteriors for the inferred models--when compared with competing inference algorithms. Furthermore, results on unsupervised object recognition demonstrate the viability of the proposed similarity measure for matching dynamic-structure statistical models.

Algorithms↗

PubMed2005

Affine invariant pattern recognition using Multiscale Autoconvolution.

This paper presents a new affine invariant image transform called Multiscale Autoconvolution (MSA). The proposed transform is based on a probabilistic interpretation of the image function. The method is directly applicable to isolated objects and does not require extraction of boundaries or interest points, and the computational load is significantly reduced using the Fast Fourier Transform. The transform values can be used as descriptors for affine invariant pattern classification and, in this article, we illustrate their performance in various object classification tasks. As shown by a comparison with other affine invariant techniques, the new method appears to be suitable for problems where image distortions can be approximated with affine transformations.

Algorithms↗

PubMed1996

Analysis of dynamic spectra in ferret primary auditory cortex. II. Prediction of unit responses to arbitrary dynamic spectra.

1. Responses of single units and multiunit clusters were recorded in the ferret primary auditory cortex (AI) with the use of broadband complex dynamic spectra. Previous work has demonstrated that simpler spectra consisting of single moving ripples (i.e., sinusoidally modulated spectral profiles that travel at a constant velocity along the logarithmic frequency axis) could be used effectively to characterize the response fields and transfer functions of AI cells. 2. A complex dynamic spectral profile can be thought of as being the sum of moving ripple spectra. Such a decomposition can be computed from a two-dimensional spectrotemporal Fourier transform of the dynamic spectral profile with moving ripples as the basis function. 3. Therefore, if AI units were essentially linear, satisfying the superposition principle, then their responses to arbitrary dynamic spectra could be predicted from the responses to single moving ripples, i.e., from the units' response fields and transfer functions (spectral and temporal impulse response functions, respectively). 4. This conjecture was tested and confirmed with data from 293 combinations of moving ripples, involving complex spectra composed of up to 15 moving ripples of different ripple frequencies and velocities. For each case, response predictions based on the unit transfer functions were compared with measured responses. The correlation between predicted and measured responses was found to be consistently high (84% with rho > 0.6). 5. The distribution of response parameters suggests that AI cells may encode the profile of a dynamic spectrum by performing a multiscale spectrotemporal decomposition of the dynamic spectral profile in a largely linear manner.

Acoustic Stimulation↗

PubMed2006

The multiscale hermite transform for local orientation analysis.

The efficient representation of local differential structure at various resolutions has been a matter of great interest for adaptive image processing and computer vision tasks. In this paper, we derive a multiscale model to represent natural images based on the scale-space representation: a model that has an inspiration in the human visual system. We first derive the one-dimensional case and then extend the results to two and three dimensions. The operators obtained for analysis and synthesis stages are derivatives of the Gaussian smoothing kernel, so that, for the two-dimensional case, we can represent them either in a rotated coordinate system or in terms of directional derivatives. The method to perform the rotation is efficient because it is implemented by means of the application of the so-called generalized binomial filters. Such a family of discrete sequences fulfills a number of properties that allows estimating the local orientation for several image structures. We also define the discrete counterpart in which the coordinate normalization of the continuous case is approximated as a subsampling of the discrete domain.

Algorithms↗

PubMed2004

First order augmentation to tensor voting for boundary inference and multiscale analysis in 3D.

Most computer vision applications require the reliable detection of boundaries. In the presence of outliers, missing data, orientation discontinuities, and occlusion, this problem is particularly challenging. We propose to address it by complementing the tensor voting framework, which was limited to second order properties, with first order representation and voting. First order voting fields and a mechanism to vote for 3D surface and volume boundaries and curve endpoints in 3D are defined. Boundary inference is also useful for a second difficult problem in grouping, namely, automatic scale selection. We propose an algorithm that automatically infers the smallest scale that can preserve the finest details. Our algorithm then proceeds with progressively larger scales to ensure continuity where it has not been achieved. Therefore, the proposed approach does not oversmooth features or delay the handling of boundaries and discontinuities until model misfit occurs. The interaction of smooth features, boundaries, and outliers is accommodated by the unified representation, making possible the perceptual organization of data in curves, surfaces, volumes, and their boundaries simultaneously. We present results on a variety of data sets to show the efficacy of the improved formalism.

Algorithms↗

PubMed2006

Multiscale classification using nearest neighbor density estimates.

Density estimates based on k-nearest neighbors have useful applications in nonparametric discriminant analysis. In classification problems, optimal values of k are usually estimated by minimizing the cross-validated misclassification rates. However, these cross-validation techniques allow only one value of k for each population density estimate, while in a classification problem, the optimum value of k for a class may also depend on its competing population densities. Further, it is computationally difficult to minimize the cross-validated error rate when there are several competing populations. Moreover, in addition to depending on the entire training data set, a good choice of k should also depend on the specific observation to be classified. Therefore, instead of using a single value of k for each population density estimate, it is more useful in practice to consider the results for multiple values of k to arrive at the final decision. This paper presents one such approach along with a graphical device, which gives more information about classification results for various choices of k and the related statistical uncertainties present there. The utility of this proposed methodology has been illustrated using some benchmark data sets.

Algorithms↗

PubMed2026

Multi-level aggregation analysis of microbiome composition and host gene expression reveals associations with systemic and local immunity.

The human gut microbiome plays a critical role in immune regulation, yet the molecular links between microbiome composition and host gene expression remain incompletely understood. We analyzed associations between host gene expression and microbiome composition in a cohort of 315 healthy individuals, integrating microarray-based gene expression data from three intestinal sites (ileum, transverse colon, and rectum) and six immune cell types with microbiome sequencing data. Using a hierarchical feature aggregation strategy combining principal component analysis, clustering, and covariate correction, we discovered significant associations primarily related to immunity. While microbial profiles were similar across the three intestinal sites, the transverse colon yielded the most "microbiome-host gene expression" associations. Among the immune cell types, CD8+ cells showed the highest number of associations. The first principal component of microbiome composition, reflecting a gradient from commensals (e.g., Ruminococcaceae and Christensenellaceae) to proinflammatory taxa ([Ruminococcus] gnavus and Lachnoclostridium), correlated with the expression of TNF-α-linked genes (HMOX1, CPI17, HSD3B2, and SLC5A1). Among individual genera, Catenibacterium abundance was associated with gene expression in both intestinal and immune cells, including negative associations with MRPS21 (related to mitochondrial function) in the transverse colon and with CD8+ gene programs related to T cell differentiation. These findings align with emerging evidence implicating mitochondrial dysfunction in intestinal inflammation. Our results identify multi-level associations between the gut microbiome and host gene expression, suggesting potential mechanisms by which microbiota shape local and systemic immunity and vice versa. The implicated genes and taxa represent candidates for experimental validation to improve understanding of host-microbiome homeostasis and its disruption in disease.IMPORTANCEThe gut microbiome and immune system are engaged in a complex interplay throughout human life. While most associative studies focus on case-control comparisons-typically examining patients with conditions such as inflammatory bowel disease or metabolic diseases-less is known about the molecular links between the microbiome and immune system in healthy individuals. In this study of a large cohort of healthy individuals, we addressed this gap by applying multiscale modeling to tackle the high dimensionality of host-microbiome data. We identified multi-level associations between microbiome composition and host gene expression in both intestinal tissues and immune cells. These findings offer a valuable reference for understanding baseline host-microbiome communication and highlight molecular candidates-such as TNF-α-related genes and mitochondrial pathways-for future experimental validation.

Humans↗

PubMed1998

Self-care and deviance in elementary school-age children.

Fourth-, fifth- and sixth-grade students were surveyed to investigate whether self-care was related to self-reports of behavioral or attitudinal deviance, liking for school, or both. The Child Self-Care Measure (CSCM), a multiscale self-report instrument, measured self-care as a developmental task with four major dimensions: temporal, physical, structural, and psychological. Self-care in general was not linked to deviance. However, increases in psychological self-care were strongly correlated with reductions in children's liking for school. Additionally, children in self-care who cared for younger siblings for more than a year reported more deviant behaviors than those without responsibility for younger siblings; children in the care of older siblings less than 16 years old for more than 4 years reported more tolerance for deviance than peers in self-care without older sibling caregivers. Findings support earlier speculations that children in self-care may not be developmentally ready to take responsibility for elementary school-aged siblings. Results also indicated that although girls in self-care manifest problems earlier than boys, long term self-care may be more problematic for boys than girls.

Analysis of Variance↗

PubMed2006

Automatic object extraction over multiscale edge field for multimedia retrieval.

In this work, we focus on automatic extraction of object boundaries from Canny edge field for the purpose of content-based indexing and retrieval over image and video databases. A multiscale approach is adopted where each successive scale provides further simplification of the image by removing more details, such as texture and noise, while keeping major edges. At each stage of the simplification, edges are extracted from the image and gathered in a scale-map, over which a perceptual subsegment analysis is performed in order to extract true object boundaries. The analysis is mainly motivated by Gestalt laws and our experimental results suggest a promising performance for main objects extraction, even for images with crowded textural edges and objects with color, texture, and illumination variations. Finally, integrating the whole process as feature extraction module into MUVIS framework allows us to test the mutual performance of the proposed object extraction method and subsequent shape description in the context of multimedia indexing and retrieval. A promising retrieval performance is achieved, and especially in some particular examples, the experimental results show that the proposed method presents such a retrieval performance that cannot be achieved by using other features such as color or texture.

Algorithms↗

PubMed2005

Robust anisotropic Gaussian fitting for volumetric characterization of pulmonary nodules in multislice CT.

This paper proposes a robust statistical estimation and verification framework for characterizing the ellipsoidal (anisotropic) geometrical structure of pulmonary nodules in the Multislice X-ray computed tomography (CT) images. Given a marker indicating a rough location of a target, the proposed solution estimates the target's center location, ellipsoidal boundary approximation, volume, maximum/average diameters, and isotropy by robustly and efficiently fitting an anisotropic Gaussian intensity model. We propose a novel multiscale joint segmentation and model fitting solution which extends the robust mean shift-based analysis to the linear scale-space theory. The design is motivated for enhancing the robustness against margin-truncation induced by neighboring structures, data with large deviations from the chosen model, and marker location variability. A chi-square-based statistical verification and analytical volumetric measurement solutions are also proposed to complement this estimation framework. Experiments with synthetic one-dimensional and two-dimensional data clearly demonstrate the advantage of our solution in comparison with the gamma-normalized Laplacian approach (Linderberg, 1998) and the standard sample estimation approach (Matei, 2001). A quasi-real-time three-dimensional nodule characterization system is developed using this framework and validated with two clinical data sets of thin-section chest CT images. Our experiments with 1310 nodules resulted in (1) robustness against intraoperator and interoperator variability due to varying marker locations, (2) 81% correct estimation rate, (3) 3% false acceptance and 5% false rejection rates, and (4) correct characterization of clinically significant nonsolid ground-glass opacity nodules. This system processes each 33-voxel volume-of-interest by an average of 2 s with a 2.4-GHz Intel CPU. Our solution is generic and can be applied for the analysis of blob-like structures in various other applications.

Algorithms↗