The role of HtrA as a chaperone and protease in bacterial pathogenisis

HtrA (High Temperature Requirement A) is a critical stress response protease and chaperone for many bacteria. HtrA is a multitasking protein which can degrade unfolded proteins, conduct specific proteolysis of some substrates for correct assembly, interact with substrates to ensure correct folding, assembly or localisation, and chaperone unfolded proteins. These functions are critical for the virulence of a number of bacterial pathogens, in some cases not simply due to the broad activities of HtrA in protection against the protein stress conditions which occur during virulence. But also due to the role of HtrA in either specific proteolysis or assembly of key protein substrates which function directly in virulence. Remarkably, these activities are all conducted without any requirement for ATP. The biochemical mechanism of HtrA relies both on the chymotryptic serine protease active site as well as the presence of two PDZ (protein binding) domains. The mechanism is a unique combination of activation by substrate motifs to alter the confirmation of the active site, and assembly into a multimeric complex which has enhanced degradation and may also act as a protective cage for proteins which are not degraded. The role of this protease in the pathogenesis of a number of bacteria and the details of its distinctive biochemical activation and assembly mechanisms are discussed in this chapter.

Distributed data acquisition unit with microsecond-accurate wireless clock synchronisation

This paper describes the implementation of the first portable, embedded data acquisition unit (BabelFuse) that is able to acquire and timestamp generic sensor data and trigger General Purpose I/O (GPIO) events against a microsecond-accurate wirelessly-distributed ‘global’ clock. A significant issue encountered when fusing data received from multiple sensors is the accuracy of the timestamp associated with each piece of data. This is particularly important in applications such as Simultaneous Localisation and Mapping (SLAM) where vehicle velocity forms an important part of the mapping algorithms; on fast-moving vehicles, even millisecond inconsistencies in data timestamping can produce errors which need to be compensated for. The timestamping problem is compounded in a robot swarm environment especially if non-deterministic communication hardware (such as IEEE-802.11-based wireless) and inaccurate clock synchronisation protocols are used. The issue of differing timebases makes correlation of data difficult and prevents the units from reliably performing synchronised operations or manoeuvres. By utilising hardware-assisted timestamping, clock synchronisation protocols based on industry standards and firmware designed to minimise indeterminism, an embedded data acquisition unit capable of microsecond-level clock synchronisation is presented.

Interobserver variability of radiation therapists aligning to fiducial markers for prostate radiation therapy

As the use of fiducial markers (FMs) for the localisation of the prostate during external beam radiation therapy (EBRT) has become part of routine practice, radiation therapists (RTs) have become increasingly responsible for online image interpretation. The aim of this investigation was to quantify the limits of agreement (LoA) between RTs when localising to FMs with orthogonal kilovoltage (kV) imaging. Methods Six patients receiving prostate EBRT utilising FMs were included in this study. Treatment localisation was performed using kV imaging prior to each fraction. Online stereoscopic assessment of FMs, performed by the treating RTs, was compared with the offline assessment by three RTs. Observer agreement was determined by pairwise Bland-Altman analysis. Results Stereoscopic analysis of 225 image pairs was performed online at the time of treatment, and offline by three RT observers. Eighteen pairwise Bland-Altman analyses were completed to assess the level of agreement between observers. Localisation by RTs was found to be within clinically acceptable 95% LoAs. Conclusions Small differences between RTs, in both the online and offline setting, were found to be within clinically acceptable limits. RTs were able to make consistent and reliable judgements when matching FMs on planar kV imaging.

Developing and delivering a software internationalisation subject

This paper describes the content and delivery of a software internationalisation subject (ITN677) that was developed for Master of Information Technology (MIT) students in the Faculty of Information Technology at Queensland University of Technology. This elective subject introduces students to the strategies, technologies, techniques and current development associated with this growing 'software development for the world' specialty area. Students learn what is involved in planning and managing a software internationalisation project as well as designing, building and using a software internationalisation application. Students also learn about how a software internationalisation project must fit into an over-all product localisation and globalisation that may include culturalisation, tailored system architectures, and reliance upon industry standards. In addition, students are exposed to the different software development techniques used by organizations in this arena and the perils and pitfalls of managing software internationalisation projects.

Investigating epidermogenesis in a human skin equivalent model

Skin is the largest, and arguably, the most important organ of the body. It is a complex and multi-dimensional tissue, thus making it essentially impossible to fully model in vitro in conventional 2-dimensional culture systems. In view of this, rodents or pigs are utilised to study wound healing therapeutics or to investigate the biological effects of treatments on skin. However, there are many differences between the wound healing processes in rodents compared to humans (contraction vs. re-epithelialisation) and there are also ethical issues associated with animal testing for scientific research. Therefore, the development of skin equivalent (HSE) models from surgical discard human skin has become an important area of research. The studies in this thesis compare, for the first time, native human skin and the epidermogenesis process in a HSE model. The HSE was reported to be a comparable model for human skin in terms of expression and localisation of key epidermal cell markers. This validated HSE model was utilised to study the potential wound healing therapeutic, hyperbaric oxygen (HBO) therapy. There is a significant body of evidence suggesting that lack of cutaneous oxygen results in and potentiates the chronic, non-healing wound environment. Although the evidence is anecdotal, HBO therapy has displayed positive effects on re-oxygenation of chronic wounds and the clinical outcomes suggest that HBO treatment may be beneficial. Therefore, the HSE was subjected to a daily clinical HBO regime and assessed in terms of keratinocyte migration, proliferation, differentiation and epidermal thickening. HBO treatment was observed to increase epidermal thickness, in particular stratum corneum thickening, but it did not alter the expression or localisation of standard epidermal cell markers. In order to elucidate the mechanistic changes occurring in response to HBO treatment in the HSE model, gene microarrays were performed, followed by qRT-PCR of select genes which were differentially regulated in response to HBO treatment. The biological diversity of the HSEs created from individual skin donors, however, overrode the differences in gene expression between treatment groups. Network analysis of functional changes in the HSE model revealed general trends consistent with normal skin growth and maturation. As a more robust and longer term study of these molecular changes, protein localisation and expression was investigated in sections from the HSEs undergoing epidermogenesis in response to HBO treatment. These proteins were CDCP1, Metallothionein, Kallikrein (KLK) 1 and KLK7 and early growth response 1. While the protein expression within the HSE models exposed to HBO treatment were not consistent in all HSEs derived from all skin donors, this is the first study to detect and compare both KLK1 and CDCP1 protein expression in both a HSE model and native human skin. Furthermore, this is the first study to provide such an in depth analysis of the effect of HBO treatment on a HSE model. The data presented in this thesis, demonstrates high levels of variation between individuals and their response to HBO treatment, consistent with the clinical variation that is currently observed.

Nonparametric semantic segmentation for 3D street scenes

In this paper we propose a method to generate a large scale and accurate dense 3D semantic map of street scenes. A dense 3D semantic model of the environment can significantly improve a number of robotic applications such as autonomous driving, navigation or localisation. Instead of using offline trained classifiers for semantic segmentation, our approach employs a data-driven, nonparametric method to parse scenes which easily scale to a large environment and generalise to different scenes. We use stereo image pairs collected from cameras mounted on a moving car to produce dense depth maps which are combined into a global 3D reconstruction using camera poses from stereo visual odometry. Simultaneously, 2D automatic semantic segmentation using a nonparametric scene parsing method is fused into the 3D model. Furthermore, the resultant 3D semantic model is improved with the consideration of moving objects in the scene. We demonstrate our method on the publicly available KITTI dataset and evaluate the performance against manually generated ground truth.

IS-RT-PCR assay detection of MT-MMP in a human breast cancer cell line

The in situ-reverse transcription-polymerase chain reaction (IS-RT-PCR) is a method that allows the direct localisation of gene expression. The method utilises the dual buffer mediated activity of the enzyme rTth DNA polymerase enabling both reverse transcription and DNA amplification. Labelled nucleoside triphosphates allow the site of expression to be labelled, rather than the PCR primers themselves, giving a more accurate localisation of transcript expression and decreased background than standard in situ hybridisation (ISH) assays. The MDA-MB-231 human breast carcinoma (HBC) cell line was assayed via the IS-RT-PCR technique, using primers encoding MT-MMP (membrane-type matrix metalloproteinase) and human β-actin. Our results clearly indicate baseline expression of MT-MMP in the relatively invasive MDA-MB-231 cell line at a signal intensity similar to the housekeeping gene β-actin, and results following induction with Concanavalin A (Con A) are consistent with our previous results obtained via Northern blotting.

Distributed low-power image processing in wireless sensor networks for intelligent video surveillance applications

Distributed Wireless Smart Camera (DWSC) network is a special type of Wireless Sensor Network (WSN) that processes captured images in a distributed manner. While image processing on DWSCs sees a great potential for growth, with its applications possessing a vast practical application domain such as security surveillance and health care, it suffers from tremendous constraints. In addition to the limitations of conventional WSNs, image processing on DWSCs requires more computational power, bandwidth and energy that presents significant challenges for large scale deployments. This dissertation has developed a number of algorithms that are highly scalable, portable, energy efficient and performance efficient, with considerations of practical constraints imposed by the hardware and the nature of WSN. More specifically, these algorithms tackle the problems of multi-object tracking and localisation in distributed wireless smart camera net- works and optimal camera configuration determination. Addressing the first problem of multi-object tracking and localisation requires solving a large array of sub-problems. The sub-problems that are discussed in this dissertation are calibration of internal parameters, multi-camera calibration for localisation and object handover for tracking. These topics have been covered extensively in computer vision literatures, however new algorithms must be invented to accommodate the various constraints introduced and required by the DWSC platform. A technique has been developed for the automatic calibration of low-cost cameras which are assumed to be restricted in their freedom of movement to either pan or tilt movements. Camera internal parameters, including focal length, principal point, lens distortion parameter and the angle and axis of rotation, can be recovered from a minimum set of two images of the camera, provided that the axis of rotation between the two images goes through the camera's optical centre and is parallel to either the vertical (panning) or horizontal (tilting) axis of the image. For object localisation, a novel approach has been developed for the calibration of a network of non-overlapping DWSCs in terms of their ground plane homographies, which can then be used for localising objects. In the proposed approach, a robot travels through the camera network while updating its position in a global coordinate frame, which it broadcasts to the cameras. The cameras use this, along with the image plane location of the robot, to compute a mapping from their image planes to the global coordinate frame. This is combined with an occupancy map generated by the robot during the mapping process to localised objects moving within the network. In addition, to deal with the problem of object handover between DWSCs of non-overlapping fields of view, a highly-scalable, distributed protocol has been designed. Cameras that follow the proposed protocol transmit object descriptions to a selected set of neighbours that are determined using a predictive forwarding strategy. The received descriptions are then matched at the subsequent camera on the object's path using a probability maximisation process with locally generated descriptions. The second problem of camera placement emerges naturally when these pervasive devices are put into real use. The locations, orientations, lens types etc. of the cameras must be chosen in a way that the utility of the network is maximised (e.g. maximum coverage) while user requirements are met. To deal with this, a statistical formulation of the problem of determining optimal camera configurations has been introduced and a Trans-Dimensional Simulated Annealing (TDSA) algorithm has been proposed to effectively solve the problem.

Nonrigid face alignment for unknown subject in video

Non-rigid face alignment is a very important task in a large range of applications but the existing tracking based non-rigid face alignment methods are either inaccurate or requiring person-specific model. This dissertation has developed simultaneous alignment algorithms that overcome these constraints and provide alignment with high accuracy, efficiency, robustness to varying image condition, and requirement of only generic model.

Towards persistent visual navigation using SMART

In this paper, we present SMART (Sequence Matching Across Route Traversals): a vision- based place recognition system that uses whole image matching techniques and odometry information to improve the precision-recall performance, latency and general applicability of the SeqSLAM algorithm. We evaluate the system’s performance on challenging day and night journeys over several kilometres at widely varying vehicle velocities from 0 to 60 km/h, compare performance to the current state-of- the-art SeqSLAM algorithm, and provide parameter studies that evaluate the effectiveness of each system component. Using 30-metre sequences, SMART achieves place recognition performance of 81% recall at 100% precision, outperforming SeqSLAM, and is robust to significant degradations in odometry.

Improved sub-cellular resolution via simultaneous analysis of organelle proteomics data across varied experimental conditions

Spatial organisation of proteins according to their function plays an important role in the specificity of their molecular interactions. Emerging proteomics methods seek to assign proteins to sub-cellular locations by partial separation of organelles and computational analysis of protein abundance distributions among partially separated fractions. Such methods permit simultaneous analysis of unpurified organelles and promise proteome-wide localisation in scenarios wherein perturbation may prompt dynamic re-distribution. Resolving organelles that display similar behavior during a protocol designed to provide partial enrichment represents a possible shortcoming. We employ the Localisation of Organelle Proteins by Isotope Tagging (LOPIT) organelle proteomics platform to demonstrate that combining information from distinct separations of the same material can improve organelle resolution and assignment of proteins to sub-cellular locations. Two previously published experiments, whose distinct gradients are alone unable to fully resolve six known protein-organelle groupings, are subjected to a rigorous analysis to assess protein-organelle association via a contemporary pattern recognition algorithm. Upon straightforward combination of single-gradient data, we observe significant improvement in protein-organelle association via both a non-linear support vector machine algorithm and partial least-squares discriminant analysis. The outcome yields suggestions for further improvements to present organelle proteomics platforms, and a robust analytical methodology via which to associate proteins with sub-cellular organelles.

Differential Regulation of Clusterin Isoforms by the Androgen Receptor

Clusterin (CLU) was initially reported as an androgen-repressed gene which is now shown to be an androgen-regulated ATP-independent cytoprotective molecular chaperone. CLU binds to a wide variety of client proteins to potently inhibit stress-induced protein aggregation and chaperone or stabilise conformations of proteins at times of cell stress. CLU is an enigmatic protein, being ascribed both pro- and anti-apoptotic roles. Recent evidence has shown that both secreted (sCLU) and nuclear (nCLU) isoforms can be produced, and that protein function is dependent on the sub-cellular localisation. We and others have shown that sCLU is cytoprotective, while nCLU is pro-apoptotic. It now seems likely that the apparently dichotomous functions of CLU result from the expression of different but related CLU isoforms and splice variants, and that cell survival depends in part on the relative expression of pro- versus anti-apoptotic CLU proteins. In cancer cells, increased sCLU expression is associated with increased resistance to apoptotic triggers and treatment resistance. CLU is a stress-induced protein upregulated after apoptotic triggers like androgen ablation and chemotherapy. Treatment strategies targeting stress-associated increases in sCLU expression enhance treatment-induced apoptosis and delay the emergence of androgen independence. Differential regulation of CLU isoforms and splice variants by androgens may be a pathway whereby cancer cells develop treatment resistance and evade apoptosis.

Video analytics for the detection of near-miss incidents on approach to railway level crossings

Recent modelling of socio-economic costs by the Australian railway industry in 2010 has estimated the cost of level crossing accidents to exceed AU$116 million annually. To better understand causal factors that contribute to these accidents, the Cooperative Research Centre for Rail Innovation is running a project entitled Baseline Level Crossing Video. The project aims to improve the recording of level crossing safety data by developing an intelligent system capable of detecting near-miss incidents and capturing quantitative data around these incidents. To detect near-miss events at railway level crossings a video analytics module is being developed to analyse video footage obtained from forward-facing cameras installed on trains. This paper presents a vision base approach for the detection of these near-miss events. The video analytics module is comprised of object detectors and a rail detection algorithm, allowing the distance between a detected object and the rail to be determined. An existing publicly available Histograms of Oriented Gradients (HOG) based object detector algorithm is used to detect various types of vehicles in each video frame. As vehicles are usually seen from a sideway view from the cabin’s perspective, the results of the vehicle detector are verified using an algorithm that can detect the wheels of each detected vehicle. Rail detection is facilitated using a projective transformation of the video, such that the forward-facing view becomes a bird’s eye view. Line Segment Detector is employed as the feature extractor and a sliding window approach is developed to track a pair of rails. Localisation of the vehicles is done by projecting the results of the vehicle and rail detectors on the ground plane allowing the distance between the vehicle and rail to be calculated. The resultant vehicle positions and distance are logged to a database for further analysis. We present preliminary results regarding the performance of a prototype video analytics module on a data set of videos containing more than 30 different railway level crossings. The video data is captured from a journey of a train that has passed through these level crossings.

Resilient Navigation through Probabilistic Modality Reconfiguration

This paper proposes an approach to achieve resilient navigation for indoor mobile robots. Resilient navigation seeks to mitigate the impact of control, localisation, or map errors on the safety of the platform while enforcing the robot’s ability to achieve its goal. We show that resilience to unpredictable errors can be achieved by combining the benefits of independent and complementary algorithmic approaches to navigation, or modalities, each tuned to a particular type of environment or situation. In this paper, the modalities comprise a path planning method and a reactive motion strategy. While the robot navigates, a Hidden Markov Model continually estimates the most appropriate modality based on two types of information: context (information known a priori) and monitoring (evaluating unpredictable aspects of the current situation). The robot then uses the recommended modality, switching between one and another dynamically. Experimental validation with a SegwayRMP- based platform in an office environment shows that our approach enables failure mitigation while maintaining the safety of the platform. The robot is shown to reach its goal in the presence of: 1) unpredicted control errors, 2) unexpected map errors and 3) a large injected localisation fault.

Handheld 3D thermography using range sensing and computer vision

This thesis developed a method for real-time and handheld 3D temperature mapping using a combination of off-the-shelf devices and efficient computer algorithms. It contributes a new sensing and data processing framework to the science of 3D thermography, unlocking its potential for application areas such as building energy auditing and industrial monitoring. New techniques for the precise calibration of multi-sensor configurations were developed, along with several algorithms that ensure both accurate and comprehensive surface temperature estimates can be made for rich 3D models as they are generated by a non-expert user.