Association of the SNP rs2623047 in the HSPG modification enzyme SULF1 with an Australian Caucasian Breast Cancer Cohort

Breast cancer is the second most common cancer worldwide and the most common cancer reported in women. This malignant tumour is characterised by a number of specific features including uncontrolled cell proliferation. It ranks fifth in the world as a cause of cancer death in women. Early diagnosis increases 5 year survival rates up to 95%. Heparan sulfate proteoglycans (HSPGs) are complex proteins composed of a core protein to which a number of highly sulfated side chains are synthesised by a highly co-ordinated process resulting in distinct sulfation patterns, which determine specific interations with cell-signaling partners including growth factors, their receptors, ligands and morphogens. The enzymes responsible for chain initiation, elongation and sulfation are critical for creating HS chain variability conferring biological functionality. This study investigated single nucleotide polymorphism in SULF1, the enzyme responsible for the 6-0 desulfation of heparan sulfate side chains. We investigated this SNP in an Australian Caucasian case-control breast cancer population and found a significant association between SULF1 and breast cancer at both the allelic and genotypic level (allele, p=0.016; genotype, p=0.032). Our results suggest the res2623047 SNP in SULF1 may impact breast cancer susceptibility. Specifically, the T allele of rs2623047 in SULF1 is associated with a increased risk of developing breast cancer in our cohort. The identification of markers including SULF1 may improve detection of this disease at its earliest stages improving patient treatment and prognosis.

Eye-safe UV stand-off Raman spectroscopy for explosive detection in the field

This project focused on maximising the detection range of an eye-safe stand-off Raman system for use in detecting explosives. Investigation of the effect on detection range through differing laser parameters in this thesis provided optimal laser settings to achieve the largest possible detection range of explosives, while still remaining under the eye-safe limit.

Preparation and characterization of poly(lactic-co-glycolic acid) microparticles containing DNA molecules encoding a malaria vaccine candidate

Background A novel ultrasonic atomization approach for the formulation of biodegradable poly(lactic-co-glycolic acid) (PLGA) microparticles of a malaria DNA vaccine is presented. A 40 kHz ultrasonic atomization device was used to create the microparticles from a feedstock containing 5 volumes of 0.5% w/v PLGA in acetone and 1 volume of condensed DNA which was fed at a flow rate of 18ml h-1. The plasmid DNA vectors encoding a malaria protein were condensed with a cationic polymer before atomization. Results High levels of gene expression in vitro were observed in COS-7 cells transfected with condensed DNA at a nitrogen to phosphate (N/P) ratio of 10. At this N/P ratio, the condensed DNA exhibited a monodispersed nanoparticle size (Z-average diameter of 60.8 nm) and a highly positive zeta potential of 38.8mV. The microparticle formulations of malaria DNA vaccine were quality assessed and it was shown that themicroparticles displayed high encapsulation efficiencies between 82-96% and a narrow size distribution in the range of 0.8-1.9 μm. In vitro release profile revealed that approximately 82% of the DNA was released within 30 days via a predominantly diffusion controlledmass transfer system. Conclusions This ultrasonic atomization technique showed excellent particle size reproducibility and displayed potential as an industrially viable approach for the formulation of controlled release particles.

Binding properties of peptidic affinity ligands for plasmid DNA capture and detection

Peptides constructed from α-helical subunits of the Lac repressor protein (LacI) were designed then tailored to achieve particular binding kinetics and dissociation constants for plasmid DNA purification and detection. Surface plasmon resonance was employed for quantification and characterization of the binding of double stranded Escherichia coli plasmid DNA (pUC19) via the lac operon (lacO) to "biomimics" of the DNA binding domain of LacI. Equilibrium dissociation constants (K D), association (k a), and dissociation rates (k d) for the interaction between a suite of peptide sequences and pUC19 were determined. K D values measured for the binding of pUC19 to the 47mer, 27mer, 16mer, and 14mer peptides were 8.8 ± 1.3 × 10 -10 M, 7.2 ± 0.6 × 10 -10 M, 4.5 ± 0.5 × 10 -8 M, and 6.2 ± 0.9 × 10 -6 M, respectively. These findings show that affinity peptides, composed of subunits from a naturally occurring operon-repressor interaction, can be designed to achieve binding characteristics suitable for affinity chromatography and biosensor devices.

Rapid production of a plasmid DNA encoding a malaria vaccine candidate via amino-functionalized poly(GMA-co-EDMA) monolith

Malaria is a global health problem; an effective vaccine is urgently needed. Due to the relative poverty and lack of infrastructure in malaria endemic areas, DNA-based vaccines that are stable at ambient temperatures and easy to formulate have great potential. While attention has been focused mainly on antigen selection, vector design and efficacy assessment, the development of a rapid and commercially viable process to manufacture DNA is generally overlooked. We report here a continuous purification technique employing an optimized stationary adsorbent to allow high-vaccine recovery, low-processing time, and, hence, high-productivity. A 40.0 mL monolithic stationary phase was synthesized and functionalized with amino groups from 2-Chloro-N,N- diethylethylamine hydrochloride for anion-exchange isolation of a plasmid DNA (pDNA) that encodes a malaria vaccine candidate, VR1020-PyMSP4/5. Physical characterization of the monolithic polymer showed a macroporous material with a modal pore diameter of 750 nm. The final vaccine product isolated after 3 min elution was homogeneous supercoiled plasmid with gDNA, RNA and protein levels in keeping with clinical regulatory standards. Toxicological studies of the pVR1020-PyMSP4/5 showed a minimum endotoxin level of 0.28 EU/m.g pDNA. This cost-effective technique is cGMP compatible and highly scalable for the production of DNA-based vaccines in commercial quantities, when such vaccines prove to be effective against malaria. © 2008 American Institute of Chemical Engineers.

Performance of R-N(R′)-R″ functionalised poly(glycidyl methacrylate-co-ethylene glycol dimethacrylate) monolithic sorbent for plasmid DNA adsorption

High-throughput plasmid DNA (pDNA) manufacture is obstructed predominantly by the performance of conventional stationary phases. For this reason, the search for new materials for fast chromatographic separation of pDNA is ongoing. A poly(glycidyl methacrylate-co-ethylene glycol dimethacrylate) (GMA-EGDMA) monolithic material was synthesised via a thermal-free radical reaction, functionalised with different amino groups from urea, 2-chloro-N,N-diethylethylamine hydrochloride (DEAE-Cl) and ammonia in order to investigate their plasmid adsorption capacities. Physical characterisation of the monolithic polymer showed a macroporous polymer having a unimodal pore size distribution pivoted at 600 nm. Chromatographic characterisation of the functionalised polymers using pUC19 plasmid isolated from E. coli DH5α-pUC19 showed a maximum plasmid adsorption capacity of 18.73 mg pDNA/mL with a dissociation constant (KD) of 0.11 mg/mL for GMA-EGDMA/DEAE-Cl polymer. Studies on ligand leaching and degradation demonstrated the stability of GMA-EGDMA/DEAE-Cl after the functionalised polymers were contacted with 1.0 M NaOH, which is a model reagent for most 'cleaning in place' (CIP) systems. However, it is the economic advantage of an adsorbent material that makes it so attractive for commercial purification purposes. Economic evaluation of the performance of the functionalised polymers on the grounds of polymer cost (PC)/mg pDNA retained endorsed the suitability of GMA-EGDMA/DEAE-Cl polymer.

The suitability of DEAE-Cl active groups on customized poly(GMA-co-EDMA) continuous stationary phase for fast enzyme-free isolation of plasmid DNA

The creation of a commercially viable and a large-scale purification process for plasmid DNA (pDNA) production requires a whole-systems continuous or semi-continuous purification strategy employing optimised stationary adsorption phase(s) without the use of expensive and toxic chemicals, avian/bovine-derived enzymes and several built-in unit processes, thus affecting overall plasmid recovery, processing time and economics. Continuous stationary phases are known to offer fast separation due to their large pore diameter making large molecule pDNA easily accessible with limited mass transfer resistance even at high flow rates. A monolithic stationary sorbent was synthesised via free radical liquid porogenic polymerisation of ethylene glycol dimethacrylate (EDMA) and glycidyl methacrylate (GMA) with surface and pore characteristics tailored specifically for plasmid binding, retention and elution. The polymer was functionalised with an amine active group for anion-exchange purification of pDNA from cleared lysate obtained from E. coli DH5α-pUC19 pellets in RNase/protease-free process. Characterization of the resin showed a unique porous material with 70% of the pores sizes above 300 nm. The final product isolated from anion-exchange purification in only 5 min was pure and homogenous supercoiled pDNA with no gDNA, RNA and protein contamination as confirmed with DNA electrophoresis, restriction analysis and SDS page. The resin showed a maximum binding capacity of 15.2 mg/mL and this capacity persisted after several applications of the resin. This technique is cGMP compatible and commercially viable for rapid isolation of pDNA.

Corner detection in images under different noise levels

Corner detection has shown its great importance in many computer vision tasks. However, in real-world applications, noise in the image strongly affects the performance of corner detectors. Few corner detectors have been designed to be robust to heavy noise by now, partly because the noise could be reduced by a denoising procedure. In this paper, we present a corner detector that could find discriminative corners in images contaminated by noise of different levels, without any denoising procedure. Candidate corners (i.e., features) are firstly detected by a modified SUSAN approach, and then false corners in noise are rejected based on their local characteristics. Features in flat regions are removed based on their intensity centroid, and features on edge structures are removed using the Harris response. The detector is self-adaptive to noise since the image signal-to-noise ratio (SNR) is automatically estimated to choose an appropriate threshold for refining features. Experimental results show that our detector has better performance at locating discriminative corners in images with strong noise than other widely used corner or keypoint detectors.

Co-optimisation of indoor environmental quality and energy consumption within urban office buildings

This study aimed to develop a multi-component model that can be used to maximise indoor environmental quality inside mechanically ventilated office buildings, while minimising energy usage. The integrated model, which was developed and validated from fieldwork data, was employed to assess the potential improvement of indoor air quality and energy saving under different ventilation conditions in typical air-conditioned office buildings in the subtropical city of Brisbane, Australia. When operating the ventilation system under predicted optimal conditions of indoor environmental quality and energy conservation and using outdoor air filtration, average indoor particle number (PN) concentration decreased by as much as 77%, while indoor CO2 concentration and energy consumption were not significantly different compared to the normal summer time operating conditions. Benefits of operating the system with this algorithm were most pronounced during the Brisbane’s mild winter. In terms of indoor air quality, average indoor PN and CO2 concentrations decreased by 48% and 24%, respectively, while potential energy savings due to free cooling went as high as 108% of the normal winter time operating conditions. The application of such a model to the operation of ventilation systems can help to significantly improve indoor air quality and energy conservation in air-conditioned office buildings.

Game On: Know Alcohol - Co-creating a tailored alcohol social marketing program

Research demonstrates that education programs are more effective when their target audiences and other key stakeholder groups are involved in the design. The majority of programs continue to be researcher and expert driven. This study extends upon previous research by employing a co-creation research design to offer a consumer driven alternative to education program design. Two co-creation groups involving twenty 14-16 year old Year 10 students who had previously participated in the Game On:Know Alcohol (GOKA) program, which aims to moderate alcohol drinking attitudes and behaviour, were conducted. Analysis revealed that a co-created GOKA program will differ substantially from the researcher and expert driven program that is currently being field tested. Students prefer interactive activities and activities that engage and challenge. Co-creation offers the potential to contest researcher and expert views and may offer to assist in the generation of new insights for the development of education programs.

On the use of optical flow for scene change detection and description

We propose the use of optical flow information as a method for detecting and describing changes in the environment, from the perspective of a mobile camera. We analyze the characteristics of the optical flow signal and demonstrate how robust flow vectors can be generated and used for the detection of depth discontinuities and appearance changes at key locations. To successfully achieve this task, a full discussion on camera positioning, distortion compensation, noise filtering, and parameter estimation is presented. We then extract statistical attributes from the flow signal to describe the location of the scene changes. We also employ clustering and dominant shape of vectors to increase the descriptiveness. Once a database of nodes (where a node is a detected scene change) and their corresponding flow features is created, matching can be performed whenever nodes are encountered, such that topological localization can be achieved. We retrieve the most likely node according to the Mahalanobis and Chi-square distances between the current frame and the database. The results illustrate the applicability of the technique for detecting and describing scene changes in diverse lighting conditions, considering indoor and outdoor environments and different robot platforms.

The Business of Unfinished Business : Reflections on Co-construction of Meanings in Research Encounters

My concern in this commentary is the discrepancy between cultural psychologists' theoretical claims that meanings are co-constructed by, with and for individuals in ongoing social interaction, and their research practices where researcher's and research participant's meaning-making processes are separated in time into sequential turns. I argue for the need to live up to these theoretical assumptions, by making both the initial research encounter and the researcher's later interpretation process more co-constructive. I suggest making the initial research encounter more co-constructive by paying attention to these moments when the negotiated flow of interaction between researcher and research participant breaks down, for it allows the research participant's meaning-making to be traced and makes the researcher's efforts towards meaning more explicit. I propose to make the later interpretation process more co-constructive by adopting a more open-ended and dialogical way of writing that is specifically addressed to research participants and invites them to actively engage with researcher's meaning-making.

Automatic UAV forced landing site detection using machine learning

The commercialization of aerial image processing is highly dependent on the platforms such as UAVs (Unmanned Aerial Vehicles). However, the lack of an automated UAV forced landing site detection system has been identified as one of the main impediments to allow UAV flight over populated areas in civilian airspace. This article proposes a UAV forced landing site detection system that is based on machine learning approaches including the Gaussian Mixture Model and the Support Vector Machine. A range of learning parameters are analysed including the number of Guassian mixtures, support vector kernels including linear, radial basis function Kernel (RBF) and polynormial kernel (poly), and the order of RBF kernel and polynormial kernel. Moreover, a modified footprint operator is employed during feature extraction to better describe the geometric characteristics of the local area surrounding a pixel. The performance of the presented system is compared to a baseline UAV forced landing site detection system which uses edge features and an Artificial Neural Network (ANN) region type classifier. Experiments conducted on aerial image datasets captured over typical urban environments reveal improved landing site detection can be achieved with an SVM classifier with an RBF kernel using a combination of colour and texture features. Compared to the baseline system, the proposed system provides significant improvement in term of the chance to detect a safe landing area, and the performance is more stable than the baseline in the presence of changes to the UAV altitude.