Salivary antibodies directed against outer membrane proteins of Moraxella catarrhalis in healthy adults

Moraxella catarrhalis is a major mucosal pathogen of the human respiratory tract, but the mucosal immune response directed against surface components of this organism has not been characterized in detail. The aim of this study was to investigate the salivary immunoglobulin A (IgA) response toward outer membrane proteins (OMP) of M. catarrhalis in healthy adults, the group of individuals least likely to be colonized and thus most likely to display mucosal immunity. Unstimulated saliva samples collected from 14 healthy adult volunteers were subjected to IgA immunoblot analysis with OMP preparations of M. catarrhalis strain O35E. Immunoblot analysis revealed a consistent pattern of IgA reactivity, with the appearance of five major bands located at >250, 200, 120, 80, and 60 kDa. Eleven (79%) of 14 saliva samples elicited reactivity to all five bands. Immunoblot analysis with a set of isogenic knockout mutants lacking the expression of individual OMP was used to determine the identities of OMP giving rise to IgA bands. Human saliva was shown consistently to exhibit IgA-binding activity for oligomeric UspA2 (>250 kDa), hemagglutinin (200 kDa), monomeric UspA1 (120 kDa), transferrin-binding protein B (TbpB), monomeric UspA2, CopB, and presumably OMP CD. TbpB, oligomeric UspA2, and CopB formed a cluster of bands at about 80 kDa. These data indicate that the human salivary IgA response is directed consistently against a small number of major OMP, some of which are presently considered vaccine candidates. The functional properties of these mucosal antibodies remain to be elucidated.

Applied Graph Theory in Computer Vision and Pattern Recognition

This book will serve as a foundation for a variety of useful applications of graph theory to computer vision, pattern recognition, and related areas. It covers a representative set of novel graph-theoretic methods for complex computer vision and pattern recognition tasks. The first part of the book presents the application of graph theory to low-level processing of digital images such as a new method for partitioning a given image into a hierarchy of homogeneous areas using graph pyramids, or a study of the relationship between graph theory and digital topology. Part II presents graph-theoretic learning algorithms for high-level computer vision and pattern recognition applications, including a survey of graph based methodologies for pattern recognition and computer vision, a presentation of a series of computationally efficient algorithms for testing graph isomorphism and related graph matching tasks in pattern recognition and a new graph distance measure to be used for solving graph matching problems. Finally, Part III provides detailed descriptions of several applications of graph-based methods to real-world pattern recognition tasks. It includes a critical review of the main graph-based and structural methods for fingerprint classification, a new method to visualize time series of graphs, and potential applications in computer network monitoring and abnormal event detection.