In vivo regulation of the Vi antigen in Salmonella and induction of immune responses with an in vivo-inducible promoter.

Salmonella enterica serovar Typhi, the agent of typhoid fever in humans, expresses the surface Vi polysaccharide antigen that contributes to virulence. However, Vi expression can also be detrimental to some key steps of S. Typhi infectivity, for example, invasion, and Vi is the target of protective immune responses. We used a strain of S. Typhimurium carrying the whole Salmonella pathogenicity island 7 (SPI-7) to monitor in vivo Vi expression within phagocytic cells of mice at different times after systemic infection. We also tested whether it is possible to modulate Vi expression via the use of in vivo-inducible promoters and whether this would trigger anti-Vi antibodies through the use of Vi-expressing live bacteria. Our results show that Vi expression in the liver and spleen is downregulated with the progression of infection and that the Vi-negative population of bacteria becomes prevalent by day 4 postinfection. Furthermore, we showed that replacing the natural tviA promoter with the promoter of the SPI-2 gene ssaG resulted in sustained Vi expression in the tissues. Intravenous or oral infection of mice with a strain of S. Typhimurium expressing Vi under the control of the ssaG promoter triggered detectable levels of all IgG subclasses specific for Vi. Our work highlights that Vi is downregulated in vivo and provides proof of principle that it is possible to generate a live attenuated vaccine that induces Vi-specific antibodies after single oral administration.

Statistical gear health analysis which is robust to fluctuating loads and operating speeds

Condition-based maintenance is concerned with the collection and interpretation of data to support maintenance decisions. The non-intrusive nature of vibration data enables the monitoring of enclosed systems such as gearboxes. It remains a significant challenge to analyze vibration data that are generated under fluctuating operating conditions. This is especially true for situations where relatively little prior knowledge regarding the specific gearbox is available. It is therefore investigated how an adaptive time series model, which is based on Bayesian model selection, may be used to remove the non-fault related components in the structural response of a gear assembly to obtain a residual signal which is robust to fluctuating operating conditions. A statistical framework is subsequently proposed which may be used to interpret the structure of the residual signal in order to facilitate an intuitive understanding of the condition of the gear system. The proposed methodology is investigated on both simulated and experimental data from a single stage gearbox. © 2011 Elsevier Ltd. All rights reserved.