Comparative analysis of the uropathogenic Escherichia coli surface proteome by tandem mass-spectrometry of artificially induced outer membrane vesicles

Uropathogenic Escherichia coli (UPEC) are the major cause of urinary tract infections. For successful colonisation of the urinary tract, UPEC employ multiple surface-exposed or secreted virulence factors, including adhesins and iron uptake systems. Whilst individual UPEC strains and their virulence factors have been the focus of extensive research, there have been no outer membrane (OM) proteomic studies based on large clinical UPEC collections, primarily due to limitations of traditional methods. In this study, a high-throughput method based on tandem mass-spectrometry of EDTA heat-induced outer membrane vesicles (OMVs) was developed for the characterisation of the UPEC surface-associated proteome. The method was applied to compare the OM proteome of fifty-four UPEC isolates, resulting in the identification of 8789 proteins, consisting of 619 unique proteins, which were subsequently interrogated for their subcellular origin, prevalence and homology to characterised virulence factors. Multiple distinct virulence-associated proteins were identified, including two novel putative iron uptake proteins, an uncharacterised type of chaperone-usher fimbriae and various highly prevalent hypothetical proteins. Our results give fundamental insight into the physiology of UPEC and provide a framework for understanding the composition of the UPEC OM proteome.

Comparative analysis of traffic state estimation ─ Cumulative counts-based and trajectory-based methods

The Macroscopic Fundamental Diagram (MFD) relates space-mean density and flow. Since the MFD represents the area-wide network traffic performance, studies on perimeter control strategies and network-wide traffic state estimation utilising the MFD concept have been reported. Most previous works have utilised data from fixed sensors, such as inductive loops, to estimate the MFD, which can cause biased estimation in urban networks due to queue spillovers at intersections. To overcome the limitation, recent literature reports the use of trajectory data obtained from probe vehicles. However, these studies have been conducted using simulated datasets; limited works have discussed the limitations of real datasets and their impact on the variable estimation. This study compares two methods for estimating traffic state variables of signalised arterial sections: a method based on cumulative vehicle counts (CUPRITE), and one based on vehicles’ trajectory from taxi Global Positioning System (GPS) log. The comparisons reveal some characteristics of taxi trajectory data available in Brisbane, Australia. The current trajectory data have limitations in quantity (i.e., the penetration rate), due to which the traffic state variables tend to be underestimated. Nevertheless, the trajectory-based method successfully captures the features of traffic states, which suggests that the trajectories from taxis can be a good estimator for the network-wide traffic states.