Schwefelverbindungen

Ohne Zusammenfassung     

Untersuchung von Honig

Ohne Zusammenfassung     

Neuroprotection from NMDA excitotoxic lesion by Cu/Zn superoxide dismutase gene delivery to the postnatal rat brain by a modular protein vector

Background  

Superoxide mediated oxidative stress is a key neuropathologic mechanism in acute central nervous system injuries. We have analyzed the neuroprotective efficacy of the transient overexpression of antioxidant enzyme Cu/Zn Superoxide dismutase (SOD) after excitotoxic injury to the immature rat brain by using a recently constructed modular protein vector for non-viral gene delivery termed NLSCt. For this purpose, animals were injected with the NLSCt vector carrying the Cu/Zn SOD or the control GFP transgenes 2 hours after intracortical N-methyl-D-aspartate (NMDA) administration, and daily functional evaluation was performed. Moreover, 3 days after, lesion volume, neuronal degeneration and nitrotyrosine immunoreactivity were evaluated.     

A wavelet-based methodology for monitoring thermoelastic structures

A wavelet-based methodology for detecting critical events in displacement and temperature histories, obtained by monitoring in-situ thermoelastic structures, is proposed. It is based on the wavelet analysis of thermoelastic potentials furnishing also the possibility to detect separately cases in which sudden jumps occur in the displacement history from those in which they occur on temperature history.     

On the optimality equation for average cost Markov control processes with Feller transition probabilities

We consider Markov control processes with Borel state space and Feller transition probabilities, satisfying some generalized geometric ergodicity conditions. We provide a new theorem on the existence of a solution to the average cost optimality equation.     

Asymptotic Information-Theoretic Detection of Dynamical Organization in Complex Systems

The identification of emergent structures in complex dynamical systems is a formidable challenge. We propose a computationally efficient methodology to address such a challenge, based on modeling the state of the system as a set of random variables. Specifically, we present a sieving algorithm to navigate the huge space of all subsets of variables and compare them in terms of a simple index that can be computed without resorting to simulations. We obtain such a simple index by studying the asymptotic distribution of an information-theoretic measure of coordination among variables, when there is no coordination at all, which allows us to fairly compare subsets of variables having different cardinalities. We show that increasing the number of observations allows the identification of larger and larger subsets. As an example of relevant application, we make use of a paradigmatic case regarding the identification of groups in autocatalytic sets of reactions, a chemical situation related to the origin of life problem.     

Disentangling the Information in Species Interaction Networks

Shannon’s entropy measure is a popular means for quantifying ecological diversity. We explore how one can use information-theoretic measures (that are often called indices in ecology) on joint ensembles to study the diversity of species interaction networks. We leverage the little-known balance equation to decompose the network information into three components describing the species abundance, specificity, and redundancy. This balance reveals that there exists a fundamental trade-off between these components. The decomposition can be straightforwardly extended to analyse networks through time as well as space, leading to the corresponding notions for alpha, beta, and gamma diversity. Our work aims to provide an accessible introduction for ecologists. To this end, we illustrate the interpretation of the components on numerous real networks. The corresponding code is made available to the community in the specialised Julia package EcologicalNetworks.jl.