The sigma-isotypic decomposition and the sigma-index of reversible-equivariant systems

This work is concerned with dynamical systems in presence of symmetries and reversing symmetries. We describe a construction process of subspaces that are invariant by linear Gamma-reversible-equivariant mappings, where Gamma is the compact Lie group of all the symmetries and reversing symmetries of such systems. These subspaces are the sigma-isotypic components, first introduced by Lamb and Roberts in (1999) [10] and that correspond to the isotypic components for purely equivariant systems. In addition, by representation theory methods derived from the topological structure of the group Gamma, two algebraic formulae are established for the computation of the sigma-index of a closed subgroup of Gamma. The results obtained here are to be applied to general reversible-equivariant systems, but are of particular interest for the more subtle of the two possible cases, namely the non-self-dual case. Some examples are presented. (C) 2011 Elsevier BM. All rights reserved.

Extracytoplasmic function (ECF) sigma factor sigma(F) is involved in Caulobacter crescentus response to heavy metal stress

Background: The alpha-proteobacterium Caulobacter crescentus inhabits low-nutrient environments and can tolerate certain levels of heavy metals in these sites. It has been reported that C. crescentus responds to exposure to various heavy metals by altering the expression of a large number of genes. Results: In this work, we show that the ECF sigma factor sigma(F) is one of the regulatory proteins involved in the control of the transcriptional response to chromium and cadmium. Microarray experiments indicate that sigma(F) controls eight genes during chromium stress, most of which were previously described as induced by heavy metals. Surprisingly, sigma(F) itself is not strongly auto-regulated under metal stress conditions. Interestingly, sigma(F)-dependent genes are not induced in the presence of agents that generate reactive oxygen species. Promoter analyses revealed that a conserved sigma(F)-dependent sequence is located upstream of all genes of the sigma(F) regulon. In addition, we show that the second gene in the sigF operon acts as a negative regulator of sigma(F) function, and the encoded protein has been named NrsF (Negative regulator of sigma F). Substitution of two conserved cysteine residues (C131 and C181) in NrsF affects its ability to maintain the expression of sigma(F)-dependent genes at basal levels. Furthermore, we show that sigma(F) is released into the cytoplasm during chromium stress and in cells carrying point mutations in both conserved cysteines of the protein NrsF. Conclusion: A possible mechanism for induction of the sigma(F)-dependent genes by chromium and cadmium is the inactivation of the putative anti-sigma factor NrsF, leading to the release of sigma(F) to bind RNA polymerase core and drive transcription of its regulon.

Extracytoplasmic function (ECF) sigma factor σF is involved in Caulobacter crescentus response to heavy metal stress

Background The α-proteobacterium Caulobacter crescentus inhabits low-nutrient environments and can tolerate certain levels of heavy metals in these sites. It has been reported that C. crescentus responds to exposure to various heavy metals by altering the expression of a large number of genes. Results In this work, we show that the ECF sigma factor σF is one of the regulatory proteins involved in the control of the transcriptional response to chromium and cadmium. Microarray experiments indicate that σF controls eight genes during chromium stress, most of which were previously described as induced by heavy metals. Surprisingly, σF itself is not strongly auto-regulated under metal stress conditions. Interestingly, σF-dependent genes are not induced in the presence of agents that generate reactive oxygen species. Promoter analyses revealed that a conserved σF-dependent sequence is located upstream of all genes of the σF regulon. In addition, we show that the second gene in the sigF operon acts as a negative regulator of σF function, and the encoded protein has been named NrsF (Negative regulator of sigma F). Substitution of two conserved cysteine residues (C131 and C181) in NrsF affects its ability to maintain the expression of σF-dependent genes at basal levels. Furthermore, we show that σF is released into the cytoplasm during chromium stress and in cells carrying point mutations in both conserved cysteines of the protein NrsF. Conclusion A possible mechanism for induction of the σF-dependent genes by chromium and cadmium is the inactivation of the putative anti-sigma factor NrsF, leading to the release of σF to bind RNA polymerase core and drive transcription of its regulon.

Incorporating IT & AT Convergence into Lean Thinking/Six Sigma via the Smarter Operation Transformation (SOT) Methodology

The growing demands for industrial products are imposing an increasingly intense level of competitiveness on the industrial operations. In the meantime, the convergence of information technology (IT) and automation technology (AT) is showing itself to be a tool of great potential for the modernization and improvement of industrial plants. However, for this technology fully to achieve its potential, several obstacles need to be overcome, including the demonstration of the reasoning behind estimations of benefits, investments and risks used to plan the implementation of corporative technology solutions. This article focuses on the evolutionary development of planning and adopting processes of IT & AT convergence. It proposes the incorporation of IT & AT convergence practices into Lean Thinking/Six Sigma, via the method used for planning the convergence of technological activities, known as the Smarter Operation Transformation (SOT) methodology. This article illustrates the SOT methodology through its application in a Brazilian company in the sector of consumer goods. In this application, it is shown that with IT & AT convergence is possible with low investment, in order to reduce the risk of not achieving the goals of key indicators.