Coupled non-parametric shape and moment-based inter-shape pose priors for multiple basal ganglia structure segmentation

This paper presents a new active contour-based, statistical method for simultaneous volumetric segmentation of multiple subcortical structures in the brain. In biological tissues, such as the human brain, neighboring structures exhibit co-dependencies which can aid in segmentation, if properly analyzed and modeled. Motivated by this observation, we formulate the segmentation problem as a maximum a posteriori estimation problem, in which we incorporate statistical prior models on the shapes and inter-shape (relative) poses of the structures of interest. This provides a principled mechanism to bring high level information about the shapes and the relationships of anatomical structures into the segmentation problem. For learning the prior densities we use a nonparametric multivariate kernel density estimation framework. We combine these priors with data in a variational framework and develop an active contour-based iterative segmentation algorithm. We test our method on the problem of volumetric segmentation of basal ganglia structures in magnetic resonance (MR) images. We present a set of 2D and 3D experiments as well as a quantitative performance analysis. In addition, we perform a comparison to several existent segmentation methods and demonstrate the improvements provided by our approach in terms of segmentation accuracy

Two RNA-binding motifs in eIF3 direct HCV IRES-dependent translation.

The initiation of protein synthesis plays an essential regulatory role in human biology. At the center of the initiation pathway, the 13-subunit eukaryotic translation initiation factor 3 (eIF3) controls access of other initiation factors and mRNA to the ribosome by unknown mechanisms. Using electron microscopy (EM), bioinformatics and biochemical experiments, we identify two highly conserved RNA-binding motifs in eIF3 that direct translation initiation from the hepatitis C virus internal ribosome entry site (HCV IRES) RNA. Mutations in the RNA-binding motif of subunit eIF3a weaken eIF3 binding to the HCV IRES and the 40S ribosomal subunit, thereby suppressing eIF2-dependent recognition of the start codon. Mutations in the eIF3c RNA-binding motif also reduce 40S ribosomal subunit binding to eIF3, and inhibit eIF5B-dependent steps downstream of start codon recognition. These results provide the first connection between the structure of the central translation initiation factor eIF3 and recognition of the HCV genomic RNA start codon, molecular interactions that likely extend to the human transcriptome

Analysis of Three-Dimensional Protein Images

A fundamental goal of research in molecular biology is to understand protein structure. Protein crystallography is currently the most successful method for determining the three-dimensional (3D) conformation of a protein, yet it remains labor intensive and relies on an expert's ability to derive and evaluate a protein scene model. In this paper, the problem of protein structure determination is formulated as an exercise in scene analysis. A computational methodology is presented in which a 3D image of a protein is segmented into a graph of critical points. Bayesian and certainty factor approaches are described and used to analyze critical point graphs and identify meaningful substructures, such as alpha-helices and beta-sheets. Results of applying the methodologies to protein images at low and medium resolution are reported. The research is related to approaches to representation, segmentation and classification in vision, as well as to top-down approaches to protein structure prediction.Comment: See http://www.jair.org/ for any accompanying file