Concurreny based transition refinement for the verification of distributed algorithms

We suggest a new notion of behaviour preserving transition refinement based on partial order semantics. This notion is called transition refinement. We introduced transition refinement for elementary (low-level) Petri Nets earlier. For modelling and verifying complex distributed algorithms, high-level (Algebraic) Petri nets are usually used. In this paper, we define transition refinement for Algebraic Petri Nets. This notion is more powerful than transition refinement for elementary Petri nets because it corresponds to the simultaneous refinement of several transitions in an elementary Petri net. Transition refinement is particularly suitable for refinement steps that increase the degree of distribution of an algorithm, e.g. when synchronous communication is replaced by asynchronous message passing. We study how to prove that a replacement of a transition is a transition refinement.

EFFECTS OF A POTENT PEROXYNITRITE DECOMPOSITION CATALYST IN MURINE MODELS OF ENDOTOXEMIA AND SEPSIS

Excessive free-radical production due to various bacterial components released during bacterial infection has been linked to cell death and tissue injury. Peroxynitrite is a highly reactive oxidant produced by the combination of nitric oxide (NO) and superoxide anion, which has been implicated in cell death and tissue injury in various forms of critical illness. Pharmacological decomposition of peroxynitrite may represent a potential therapeutic approach in diseases associated with the overproduction of NO and superoxide. In the present study, we tested the effect of a potent peroxynitrite decomposition catalyst in murine models of endotoxemia and sepsis. Mice were injected i.p. with LPS 40 mg/kg with or without FP15 [Fe(III) tetrakis-2-(N-triethylene glycol monomethyl ether) pyridyl porphyrin] (0.1, 0.3, 1, 3, or 10 mg/kg per hour). Mice were killed 12 h later, followed by the harvesting of samples from the lung, liver, and gut for malondialdehyde and myeloperoxidase measurements. In other subsets of animals, blood samples were obtained by cardiac puncture at 1.5, 4, and 8 h after LPS administration for cytokine (TNF-alpha, IL-1 beta, and IL-10), nitrite/nitrate, alanine aminotransferase, and blood urea nitrogen measurements. Endotoxemic animals showed an increase in survival from 25% to 80% at the FP15 doses of 0.3 and 1 mg/kg per hour. The same dose of FP15 had no effect on plasma levels of nitrite/nitrate. There was a reduction in liver and lung malondialdehyde in the endotoxemic animals pretreated with FP15, as well as in hepatic myeloperoxidase and biochemical markers of liver and kidney damage (alanine aminotransferase and blood urea nitrogen). In a bacterial model of sepsis induced by cecal ligation and puncture, FP15 treatment (0.3 mg/kg per day) significantly protected against mortality. The current data support the view that peroxynitrite is a critical factor mediating liver, gut, and lung injury in endotoxemia and septic shock: its pharmacological neutralization may be of therapeutic benefit.

A splice variant of the Neurospora crassa hex-1 transcript, which encodes the major protein of the Woronin body, is modulated by extracellular phosphate and pH changes

The Woronin body, a septal pore-associated organelle specific to filamentous ascomycetes, is crucial for preventing cytoplasmic bleeding after hyphal injury. In this study, we show that T1hex-1 transcript and a variant splicing T2hex-1 transcript are up-regulated at alkaline pH. We also show that both hex-1 transcripts are overexpressed in the preg(c), nuc-1(RIP), and pacC(ko) mutant strains of Neurospora crassa grown under conditions of phosphate shortage at alkaline pH, suggesting that hex-1 transcription may be coregulated by these genes. In addition, we present evidence that N. crassa PacC also has metabolic functions at acidic pH. (C) 2008 Federation of European Biochemical Societies. Published by Elsevier B. V. All rights reserved.

Asymmetric page split generalized index search trees for formal concept analysis

Formal Concept Analysis is an unsupervised machine learning technique that has successfully been applied to document organisation by considering documents as objects and keywords as attributes. The basic algorithms of Formal Concept Analysis then allow an intelligent information retrieval system to cluster documents according to keyword views. This paper investigates the scalability of this idea. In particular we present the results of applying spatial data structures to large datasets in formal concept analysis. Our experiments are motivated by the application of the Formal Concept Analysis idea of a virtual filesystem [11,17,15]. In particular the libferris [1] Semantic File System. This paper presents customizations to an RD-Tree Generalized Index Search Tree based index structure to better support the application of Formal Concept Analysis to large data sources.