F9 fimbriae of uropathogenic Escherichia coli are expressed at low temperature and recognise Galβ1-3GlcNAc-containing glycans

Uropathogenic Escherichia coli (UPEC) is the leading causative agent of urinary tract infections (UTI) in the developed world. Among the major virulence factors of UPEC, surface expressed adhesins mediate attachment and tissue tropism. UPEC strains typically possess a range of adhesins, with type 1 fimbriae and P fimbriae of the chaperone-usher class the best characterised. We previously identified and characterised F9 as a new chaperone-usher fimbrial type that mediates biofilm formation. However, the regulation and specific role of F9 fimbriae remained to be determined in the context of wild-type clinical UPEC strains. In this study we have assessed the distribution and genetic context of the f9 operon among diverse E. coli lineages and pathotypes and demonstrated that f9 genes are significantly more conserved in a UPEC strain collection in comparison to the well-defined E. coli reference (ECOR) collection. In the prototypic UPEC strain CFT073, the global regulator protein H-NS was identified as a transcriptional repressor of f9 gene expression at 37°C through its ability to bind directly to the f9 promoter region. F9 fimbriae expression was demonstrated at 20°C, representing the first evidence of functional F9 fimbriae expression by wild-type E. coli. Finally, glycan array analysis demonstrated that F9 fimbriae recognise and bind to terminal Galβ1-3GlcNAc structures.

Human identification from video using advanced gait recognition techniques

The solutions proposed in this thesis contribute to improve gait recognition performance in practical scenarios that further enable the adoption of gait recognition into real world security and forensic applications that require identifying humans at a distance. Pioneering work has been conducted on frontal gait recognition using depth images to allow gait to be integrated with biometric walkthrough portals. The effects of gait challenging conditions including clothing, carrying goods, and viewpoint have been explored. Enhanced approaches are proposed on segmentation, feature extraction, feature optimisation and classification elements, and state-of-the-art recognition performance has been achieved. A frontal depth gait database has been developed and made available to the research community for further investigation. Solutions are explored in 2D and 3D domains using multiple images sources, and both domain-specific and independent modality gait features are proposed.

Temperature and strain-rate dependent fracture strength of graphynes

Graphyne is an allotrope of graphene. The mechanical properties of graphynes (α-, β-, γ- and 6,6,12-graphynes) under uniaxial tension deformation at different temperatures and strain rates are studied using molecular dynamics simulations. It is found that graphynes are more sensitive to temperature changes than graphene in terms of fracture strength and Young's modulus. The temperature sensitivity of the different graphynes is proportionally related to the percentage of acetylenic linkages in their structures, with the α-graphyne (having 100% of acetylenic linkages) being most sensitive to temperature. For the same graphyne, temperature exerts a more pronounced effect on the Young's modulus than fracture strength, which is different from that of graphene. The mechanical properties of graphynes are also sensitive to strain rate, in particular at higher temperatures.

The impact of Cool Roof applications on energy performance : results from Australian sub-tropical and tropical field studies

Cool roof coatings are identified by their solar reflectance index. They have been reported to have multiple benefits, the extent of which are strongly dependent on the peculiarities of the local climate, building stock and electricity network. This paper presents measured and simulated data from residential, educational and commercial buildings involved in recent field trials in Australia. The purpose of the field trials was to evaluate the impact of such coatings on electricity demand and load and to assess their potential application to improve comfort whilst avoiding the need for air conditioners. Measured reductions in temperature, power (kW) and energy (kWh) were used to develop a predictive model that correlates ambient temperature distribution profiles, building demand reduction profiles and electricity network peak demand times. Combined with simulated data, the study indicates the types of buildings that could be targeted in Demand Management programs for the mutual benefit of electricity networks and building occupants.

Anomaly detection in online social networks : using data-mining techniques and fuzzy logic

This research is a step forward in improving the accuracy of detecting anomaly in a data graph representing connectivity between people in an online social network. The proposed hybrid methods are based on fuzzy machine learning techniques utilising different types of structural input features. The methods are presented within a multi-layered framework which provides the full requirements needed for finding anomalies in data graphs generated from online social networks, including data modelling and analysis, labelling, and evaluation.

Prediction of activity type in preschool children using machine learning techniques

Objectives Recent research has shown that machine learning techniques can accurately predict activity classes from accelerometer data in adolescents and adults. The purpose of this study is to develop and test machine learning models for predicting activity type in preschool-aged children. Design Participants completed 12 standardised activity trials (TV, reading, tablet game, quiet play, art, treasure hunt, cleaning up, active game, obstacle course, bicycle riding) over two laboratory visits. Methods Eleven children aged 3–6 years (mean age = 4.8 ± 0.87; 55% girls) completed the activity trials while wearing an ActiGraph GT3X+ accelerometer on the right hip. Activities were categorised into five activity classes: sedentary activities, light activities, moderate to vigorous activities, walking, and running. A standard feed-forward Artificial Neural Network and a Deep Learning Ensemble Network were trained on features in the accelerometer data used in previous investigations (10th, 25th, 50th, 75th and 90th percentiles and the lag-one autocorrelation). Results Overall recognition accuracy for the standard feed forward Artificial Neural Network was 69.7%. Recognition accuracy for sedentary activities, light activities and games, moderate-to-vigorous activities, walking, and running was 82%, 79%, 64%, 36% and 46%, respectively. In comparison, overall recognition accuracy for the Deep Learning Ensemble Network was 82.6%. For sedentary activities, light activities and games, moderate-to-vigorous activities, walking, and running recognition accuracy was 84%, 91%, 79%, 73% and 73%, respectively. Conclusions Ensemble machine learning approaches such as Deep Learning Ensemble Network can accurately predict activity type from accelerometer data in preschool children.