17 resultados para 2-sigma error
Resumo:
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.
Resumo:
Experimental two-phase frictional pressure drop and flow boiling heat transfer results are presented for a horizontal 2.32-mm ID stainless-steel tube using R245fa as working fluid. The frictional pressure drop data was obtained under adiabatic and diabatic conditions. Experiments were performed for mass velocities ranging from 100 to 700 kg m−2 s−1 , heat flux from 0 to 55 kW m−2 , exit saturation temperatures of 31 and 41◦C, and vapor qualities from 0.10 to 0.99. Pressures drop gradients and heat transfer coefficients ranging from 1 to 70 kPa m−1 and from 1 to 7 kW m−2 K−1 were measured. It was found that the heat transfer coefficient is a strong function of the heat flux, mass velocity, and vapor quality. Five frictional pressure drop predictive methods were compared against the experimental database. The Cioncolini et al. (2009) method was found to work the best. Six flow boiling heat transfer predictive methods were also compared against the present database. Liu and Winterton (1991), Zhang et al. (2004), and Saitoh et al. (2007) were ranked as the best methods. They predicted the experimental flow boiling heat transfer data with an average error around 19%.