Spatial distribution of actin and tubulin in human sperm nuclear matrix-intermediate filament whole mounts-a new paradigm

Sperm is a highly differentiated cell streamlined for fertilization. The function is thus heavily dependent on the cytoskeletal organization. Conventional methods limit the appreciation and correlation of this intricate cytoskeletal filament network in the context of an entire sperm. Our recent successful localization of nonmuscle myosin IIA on sperm nuclear matrix-intermediate filament (NM-IF) preparations from fertile men by embedment-free electron microscopy (EF-EM), prompted us to investigate the antigenic distribution of two major cytoskeletal proteins-actin and tubulin. The NM-IF preparations were subjected to a cocktail of buffered paraformaldehyde (2%) with a low concentration of glutaraldehyde (0.05%). These proteins were localized by indirect immunogold technique using EF-EM on sperm NM-IF whole mounts. Ultrastructure analysis revealed well preserved centrioles, outer dense fibers, axonemal filaments, and submitochondrial reticulum in the sperm NM-IF. Immunoreactive actin was localized along the length of the sperm whereas beta-tubulin was present in the axoneme alone. The spatial distribution of actin and tubulin in normal human sperm NM-IF reported here together with that of myosin on whole mount offers a powerful technique to understand sperm cytoskeletal supramolecular structure.     

Shape-based searching for product lifecycle applications

Estimates suggest that more than 75% of engineering design activity comprises reuse of previous design knowledge to address a new design problem. Reusing design knowledge has great potential to improve product quality, shorten lead time, and reduce cost. However, PLM systems, which address the issue of reuse by searching for keywords in filenames, part numbers or context attached to CAD models, do not provide a robust tool to search reusable knowledge. This paper presents a brief overview of a novel approach to search for 3D models. The system is built on a client-server-database architecture. The client takes in the query input from the user along with his search preferences and passes it to the server. The server converts the shape input into feature vectors and a unique skeletal graph representation. Details of the algorithms to perform these steps are presented here. Principal advantages of our graph representation are: (i) it preserves geometry and topology of the query model, (ii) it is considerably smaller than the B-Rep graph, and (iii) it is insensitive to minor perturbations in shape, but sensitive enough to capture the major features of a shape. The combined distance of feature vectors and skeletal graphs in the database provide an indirect measure of shape similarity between models. Critical database issues such as search system efficiency, semantic gap reduction and the subjectivity of the similarity definition are addressed. This paper reports our initial results in designing, implementing and running the shape search system.