Transgenic system for conditional induction and rescue of chronic myocardial hibernation provides insights into genomic programs of hibernation

A key energy-saving adaptation to chronic hypoxia that enables cardiomyocytes to withstand severe ischemic insults is hibernation, i.e., a reversible arrest of contractile function. Whereas hibernating cardiomyocytes represent the critical reserve of dysfunctional cells that can be potentially rescued, a lack of a suitable animal model has hampered insights on this medically important condition. We developed a transgenic mouse system for conditional induction of long-term hibernation and a system to rescue hibernating cardiomyocytes at will. Via myocardium-specific induction (and, in turn, deinduction) of a VEGF-sequestering soluble receptor, we show that VEGF is indispensable for adjusting the coronary vasculature to match increased oxygen consumption and exploit this finding to generate a hypoperfused heart. Importantly, ensuing ischemia is tunable to a level at which large cohorts of cardiomyocytes are driven to enter a hibernation mode, without cardiac cell death. Relieving the VEGF blockade even months later resulted in rapid revascularization and full recovery of contractile function. Furthermore, we show that left ventricular remodeling associated with hibernation is also fully reversible. The unique opportunity to uncouple hibernation from other ischemic heart phenotypes (e.g., infarction) was used to determine the genetic program of hibernation; uncovering hypoxia-inducible factor target genes associated with metabolic adjustments and induced expression of several cardioprotective genes. Autophagy, specifically self-digestion of mitochondria, was identified as a key prosurvival mechanism in hibernating cardiomyocytes. This system may lend itself for examining the potential utility of treatments to rescue dysfunctional cardiomyocytes and reverse maladaptive remodeling.

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.