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The 3-dimensional structure of the nucleocapsid (NC) of bacteriophage φ6 is described utilizing component tree analysis, a topological and geometric image descriptor. The component trees are derived from density maps of cryo-electron microscopy single particle reconstructions. Analysis determines position and occupancy of structure elements responsible for RNA packaging and transcription. Occupancy of the hexameric nucleotide triphosphorylase (P4) and RNA polymerase (P2) are found to be essentially complete in the NC. The P8 protein lattice likely fixes P4 and P2 in place during maturation. We propose that the viral procapsid (PC) is a dynamic structural intermediate where the P4 and P2 can attach and detach until held in place in mature NCs. During packaging, the PC expands to accommodate the RNA, and P2 translates from its original site near the inner 3-fold axis (20 sites) to the inner 5-fold axis (12 sites) with excess P2 positioned inside the central region of the NC.

Abstract. Fuzzy segmentation is a region growing technique that assigns a grade of membership to an object to each element in an image. In this paper we present a method for segmenting video shots by using a fast implementation of the fuzzy segmentation technique. The video shot is treated as a three-dimensional volume with different z slices being occupied by different frames of the video shot. The volume is interactively segmented based on selected seed elements, that will determine the affinity functions based on their intensity and color properties. Experiments with a synthetic video under different noise conditions are performed, as well as examples of two real video shot segmentations are presented, showing the applicability of our method.

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The selection of single particles from an electron microscopic (EM) micrograph is an essential part of the reconstruction process of biological macromolecules. Despite its importance, this process requires a lot of user intervention by either purely manual or semi-automatic processing (with the aid of graphical interfaces). Since reconstructing a 3D model of these macromolecules at nano-scale resolution requires thousands of particles, the particle selection phase is bound to become a serious bottleneck in the reconstruction process. In this article we propose a semi-automatic procedure for selecting particles from a micrograph that aims to reduce the false positive rate. We achieve this by first using a fuzzy-sets-based segmentation method, that requires very little user intervention, to detect the background, and then calculating a cross-correlation measure only on points with low "affinity" to the micrograph's background.

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