An outbreak of fatal hemorrhagic pneumonia caused by Streptococcus equi subsp. zooepidemicus in shelter dogs

An outbreak of fatal hemorrhagic pneumonia with 70~90% morbidity and 50% mortality occurred in an animal shelter in Yangju, Gyeonggi Province, Korea. Clinically, the affected dogs showed severe respiratory distress within 48 h after arriving in the shelter. The dead were found mainly with nasal bleeding and hematemesis. At necropsy, hemothorax and hemorrhagic pneumonia along with severe pulmonary consolidation was observed, though histopathological analysis showed mainly hemorrhagic bronchopneumonia. Lymphoid depletion was inconsistently seen in the spleen, tonsil and bronchial lymph node. Gram-positive colonies were shown in blood vessels or parenchyma of cerebrum, lung, liver, spleen, and kidney. Also, Streptococcus (S.) equi subsp. zooepidemicus was isolated from the various organs in which the bacterium was microscopically and histologically detected. In addition, approximately 0.9 Kb specific amplicon, antiphagocytic factor H binding protein, was amplified in the bacterial isolates. In this study, we reported an outbreak of canine hemorrhagic bronchopneumonia caused by S. equi subsp. zooepidemicus in an animal shelter in Yangju, Korea

De Novo Structural Pattern Mining in Cellular Electron Cryotomograms

Electron cryotomography enables 3D visualization of cells in a near-native state at molecular resolution. The produced cellular tomograms contain detailed information about a plethora of macromolecular complexes, their structures, abundances, and specific spatial locations in the cell. However, extracting this information in a systematic way is very challenging, and current methods usually rely on individual templates of known structures. Here, we propose a framework called “Multi-Pattern Pursuit” for de novo discovery of different complexes from highly heterogeneous sets of particles extracted from entire cellular tomograms without using information of known structures. These initially detected structures can then serve as input for more targeted refinement efforts. Our tests on simulated and experimental tomograms show that our automated method is a promising tool for supporting large-scale template-free visual proteomics analysis

Deformed Harry Dym and Hunter-Zheng Equations

We study the deformed Harry Dym and Hunter-Zheng equations with two arbitrary deformation parameters. These reduce to various other known models in appropriate limits. We show that both these systems are bi-Hamiltonian with the same Hamiltonian structures. They are integrable and belong to the same hierarchy corresponding to positive and negative flows. We present the Lax pair description for both the systems and construct the conserved charges of negative order from the Lax operator. For the deformed Harry Dym equation, we construct the non-standard Lax representation for two special classes of values of the deformation parameters. In general, we argue that a non-standard description will involve a pseudo-differential operator of infinite order.Comment: Latex file, 15 page

On a Camassa-Holm type equation with two dependent variables

We consider a generalization of the Camassa Holm (CH) equation with two dependent variables, called CH2, introduced by Liu and Zhang. We briefly provide an alternative derivation of it based on the theory of Hamiltonian structures on (the dual of) a Lie Algebra. The Lie Algebra here involved is the same algebra underlying the NLS hierarchy. We study the structural properties of the CH2 hierarchy within the bihamiltonian theory of integrable PDEs, and provide its Lax representation. Then we explicitly discuss how to construct classes of solutions, both of peakon and of algebro-geometrical type. We finally sketch the construction of a class of singular solutions, defined by setting to zero one of the two dependent variables.Comment: 22 pages, 2 figures. A few typos correcte

De Novo Structural Pattern Mining in Cellular Electron Cryotomograms

Electron cryotomography enables 3D visualization of cells in a near-native state at molecular resolution. The produced cellular tomograms contain detailed information about a plethora of macromolecular complexes, their structures, abundances, and specific spatial locations in the cell. However, extracting this information in a systematic way is very challenging, and current methods usually rely on individual templates of known structures. Here, we propose a framework called “Multi-Pattern Pursuit” for de novo discovery of different complexes from highly heterogeneous sets of particles extracted from entire cellular tomograms without using information of known structures. These initially detected structures can then serve as input for more targeted refinement efforts. Our tests on simulated and experimental tomograms show that our automated method is a promising tool for supporting large-scale template-free visual proteomics analysis

A Characterization of Scale Invariant Responses in Enzymatic Networks

An ubiquitous property of biological sensory systems is adaptation: a step increase in stimulus triggers an initial change in a biochemical or physiological response, followed by a more gradual relaxation toward a basal, pre-stimulus level. Adaptation helps maintain essential variables within acceptable bounds and allows organisms to readjust themselves to an optimum and non-saturating sensitivity range when faced with a prolonged change in their environment. Recently, it was shown theoretically and experimentally that many adapting systems, both at the organism and single-cell level, enjoy a remarkable additional feature: scale invariance, meaning that the initial, transient behavior remains (approximately) the same even when the background signal level is scaled. In this work, we set out to investigate under what conditions a broadly used model of biochemical enzymatic networks will exhibit scale-invariant behavior. An exhaustive computational study led us to discover a new property of surprising simplicity and generality, uniform linearizations with fast output (ULFO), whose validity we show is both necessary and sufficient for scale invariance of enzymatic networks. Based on this study, we go on to develop a mathematical explanation of how ULFO results in scale invariance. Our work provides a surprisingly consistent, simple, and general framework for understanding this phenomenon, and results in concrete experimental predictions