The bacterial cytoskeleton modulates motility, type 3 secretion, and colonization in Salmonella.

Although there have been great advances in our understanding of the bacterial cytoskeleton, major gaps remain in our knowledge of its importance to virulence. In this study we have explored the contribution of the bacterial cytoskeleton to the ability of Salmonella to express and assemble virulence factors and cause disease. The bacterial actin-like protein MreB polymerises into helical filaments and interacts with other cytoskeletal elements including MreC to control cell-shape. As mreB appears to be an essential gene, we have constructed a viable ΔmreC depletion mutant in Salmonella. Using a broad range of independent biochemical, fluorescence and phenotypic screens we provide evidence that the Salmonella pathogenicity island-1 type three secretion system (SPI1-T3SS) and flagella systems are down-regulated in the absence of MreC. In contrast the SPI-2 T3SS appears to remain functional. The phenotypes have been further validated using a chemical genetic approach to disrupt the functionality of MreB. Although the fitness of ΔmreC is reduced in vivo, we observed that this defect does not completely abrogate the ability of Salmonella to cause disease systemically. By forcing on expression of flagella and SPI-1 T3SS in trans with the master regulators FlhDC and HilA, it is clear that the cytoskeleton is dispensable for the assembly of these structures but essential for their expression. As two-component systems are involved in sensing and adapting to environmental and cell surface signals, we have constructed and screened a panel of such mutants and identified the sensor kinase RcsC as a key phenotypic regulator in ΔmreC. Further genetic analysis revealed the importance of the Rcs two-component system in modulating the expression of these virulence factors. Collectively, these results suggest that expression of virulence genes might be directly coordinated with cytoskeletal integrity, and this regulation is mediated by the two-component system sensor kinase RcsC.

A Bayesian Approach to the Restoration of Degraded Audio Signals

In this paper we derive the a posteriori probability for the location of bursts of noise additively superimposed on a Gaussian AR process. The theory is developed to give a sequentially based restoration algorithm suitable for real-time applications. The algorithm is particularly appropriate for digital audio restoration, where clicks and scratches may be modelled as additive bursts of noise. Experiments are carried out on both real audio data and synthetic AR processes and Significant improvements are demonstrated over existing restoration techniques. © 1995 IEEE

Robust noise reduction for speech and audio signals

Statistical model-based methods are presented for the reconstruction of autocorrelated signals in impulsive plus continuous noise environments. Signals are modelled as autoregressive and noise sources as discrete and continuous mixtures of Gaussians, allowing for robustness in highly impulsive and non-Gaussian environments. Markov Chain Monte Carlo methods are used for reconstruction of the corrupted waveforms within a Bayesian probabilistic framework and results are presented for contaminated voice and audio signals.

Robust modelling of noisy ARMA signals

In this paper methods are developed for enhancement and analysis of autoregressive moving average (ARMA) signals observed in additive noise which can be represented as mixtures of heavy-tailed non-Gaussian sources and a Gaussian background component. Such models find application in systems such as atmospheric communications channels or early sound recordings which are prone to intermittent impulse noise. Markov Chain Monte Carlo (MCMC) simulation techniques are applied to the joint problem of signal extraction, model parameter estimation and detection of impulses within a fully Bayesian framework. The algorithms require only simple linear iterations for all of the unknowns, including the MA parameters, which is in contrast with existing MCMC methods for analysis of noise-free ARMA models. The methods are illustrated using synthetic data and noise-degraded sound recordings.