Multimodal polymer nanoparticles with combined 19F magnetic resonance and optical detection for tunable, targeted, multimodal imagingin vivo

Understanding the complex nature of diseased tissue in vivo requires development of more advanced nanomedicines, where synthesis of multifunctional polymers combines imaging multimodality with a biocompatible, tunable, and functional nanomaterial carrier. Here we describe the development of polymeric nanoparticles for multimodal imaging of disease states in vivo. The nanoparticle design utilizes the abundant functionality and tunable physicochemical properties of synthetically robust polymeric systems to facilitate targeted imaging of tumors in mice. For the first time, high-resolution 19F/1H magnetic resonance imaging is combined with sensitive and versatile fluorescence imaging in a polymeric material for in vivo detection of tumors. We highlight how control over the chemistry during synthesis allows manipulation of nanoparticle size and function and can lead to very high targeting efficiency to B16 melanoma cells, both in vitro and in vivo. Importantly, the combination of imaging modalities within a polymeric nanoparticle provides information on the tumor mass across various size scales in vivo, from millimeters down to tens of micrometers.

Community uses of co-creative media digital storytelling and co-creative Media: The role of community arts and media in propagating and coordinating population-wide creative practice

How is creative expression and communication extended among whole populations? What is the social and cultural value of this activity? What roles do formal agencies, community-based organisations and content producer networks play? Specifically, how do participatory media and arts projects and networks contribute to building this capacity in the contemporary communications environment? The latest issue of CSJ article in a special issue on “Broadening Digital Storytelling Horizons” edited by Burcu Simsek.

Raman spectroscopy techniques for detection of energetic materials at high and low concentration

This thesis presents the development of a rapid, sensitive and reproducible spectroscopic method for the detection of TNT in forensic and environmental applications. Simple nano sensors prepared by cost effective methods were utilized as sensitive platforms for the detection of TNT by surface enhanced Raman spectroscopy. The optimization of the substrate and the careful selection of a suitable recognition molecule contributed to the significant improvements of sensitive and selective targeting over current detection methods. The work presented in this thesis paves the way for effective detection and monitoring of explosives residues in law enforcement and environmental health applications.

Automating marine mammal detection in aerial images captured during wildlife surveys: A deep learning approach

Aerial surveys conducted using manned or unmanned aircraft with customized camera payloads can generate a large number of images. Manual review of these images to extract data is prohibitive in terms of time and financial resources, thus providing strong incentive to automate this process using computer vision systems. There are potential applications for these automated systems in areas such as surveillance and monitoring, precision agriculture, law enforcement, asset inspection, and wildlife assessment. In this paper, we present an efficient machine learning system for automating the detection of marine species in aerial imagery. The effectiveness of our approach can be credited to the combination of a well-suited region proposal method and the use of Deep Convolutional Neural Networks (DCNNs). In comparison to previous algorithms designed for the same purpose, we have been able to dramatically improve recall to more than 80% and improve precision to 27% by using DCNNs as the core approach.

Circulating tumor cell detection in high-risk non-metastatic prostate cancer

Purpose The detection of circulating tumor cells (CTCs) provides important prognostic information in men with metastatic prostate cancer. We aim to determine the rate of detection of CTCs in patients with high-risk non-metastatic prostate cancer using the CellSearch® method. Method Samples of peripheral blood (7.5 mL) were drawn from 36 men with newly diagnosed high-risk non-metastatic prostate cancer, prior to any initiation of therapy and analyzed for CTCs using the CellSearch® method. Results The median age was 70 years, median PSA was 14.1, and the median Gleason score was 9. The median 5-year risk of progression of disease using a validated nomogram was 39 %. Five out of 36 patients (14 %, 95 % CI 5–30 %) had CTCs detected in their circulation. Four patients had only 1 CTC per 7.5 mL of blood detected. One patient had 3 CTCs per 7.5 mL of blood detected, which included a circulating tumor microemboli. Both on univariate analysis and multivariate analysis, there were no correlations found between CTC positivity and the classic prognostic factors including PSA, Gleason score, T-stage and age. Conclusion This study demonstrates that patients with high-risk, non-metastatic prostate cancer present infrequently with small number of CTCs in peripheral blood. This finding is consistent with the limited literature available in this setting. Other CTC isolation and detection technologies with improved sensitivity and specificity may enable detection of CTCs with mesenchymal phenotypes, although none as yet have been validated for clinical use. Newer assays are emerging for detection of new putative biomarkers for prostate cancer. Correlation of disease control outcomes with CTC detection will be important.

Detection of anuran calling activity in long field recordings for bio-acoustic monitoring

This paper presents a system to analyze long field recordings with low signal-to-noise ratio (SNR) for bio-acoustic monitoring. A method based on spectral peak track, Shannon entropy, harmonic structure and oscillation structure is proposed to automatically detect anuran (frog) calling activity. Gaussian mixture model (GMM) is introduced for modelling those features. Four anuran species widespread in Queensland, Australia, are selected to evaluate the proposed system. A visualization method based on extracted indices is employed for detection of anuran calling activity which achieves high accuracy.

Early detection of melanoma metastases using microRNAs as novel biomarkers

Early detection of melanoma skin cancer, prior to metastatic spread, is critical to improve survival outcomes in patients. This study identified a melanoma-related panel of blood markers that can detect the presence of melanoma with high sensitivity and accuracy which is superior to currently used markers for melanoma progression, recurrence, and survival. Overall, the findings discussed in this thesis may lead to more precise measurement of disease progression allowing for better treatments and an increase in overall survival.

Chronicling the patient journey: Co-creating value with digital health ecosystems

With the introduction of the PCEHR (Personally Controlled Electronic Health Record), the Australian public is being asked to accept greater responsibility for the management of their health information. However, the implementation of the PCEHR has occasioned poor adoption rates underscored by criticism from stakeholders with concerns about transparency, accountability, privacy, confidentiality, governance, and limited capabilities. This study adopts an ethnographic lens to observe how information is created and used during the patient journey and the social factors impacting on the adoption of the PCEHR at the micro-level in order to develop a conceptual model that will encourage the sharing of patient information within the cycle of care. Objective: This study aims to firstly, establish a basic understanding of healthcare professional attitudes toward a national platform for sharing patient summary information in the form of a PCEHR. Secondly, the studies aims to map the flow of patient related information as it traverses a patient’s personal cycle of care. Thus, an ethnographic approach was used to bring a “real world” lens to information flow in a series of case studies in the Australian healthcare system to discover themes and issues that are important from the patient’s perspective. Design: Qualitative study utilising ethnographic case studies. Setting: Case studies were conducted at primary and allied healthcare professionals located in Brisbane Queensland between October 2013 and July 2014. Results: In the first dimension, it was identified that healthcare professionals’ concerns about trust and medico-legal issues related to patient control and information quality, and the lack of clinical value available with the PCEHR emerged as significant barriers to use. The second dimension of the study which attempted to map patient information flow identified information quality issues, clinical workflow inefficiencies and interoperability misconceptions resulting in duplication of effort, unnecessary manual processes, data quality and integrity issues and an over reliance on the understanding and communication skills of the patient. Conclusion: Opportunities for process efficiencies, improved data quality and increased patient safety emerge with the adoption of an appropriate information sharing platform. More importantly, large scale eHealth initiatives must be aligned with the value proposition of individual stakeholders in order to achieve widespread adoption. Leveraging an Australian national eHealth infrastructure and the PCEHR we offer a practical example of a service driven digital ecosystem suitable for co-creating value in healthcare.

Improving spoken term detection using complementary information

This research has made contributions to the area of spoken term detection (STD), defined as the process of finding all occurrences of a specified search term in a large collection of speech segments. The use of visual information in the form of lip movements of the speaker in addition to audio and the use of topic of the speech segments, and the expected frequency of words in the target speech domain, are proposed. By using these complementary information, improvement in the performance of STD has been achieved which enables efficient search of key words in large collection of multimedia documents.

A Bayesian Framework for the Assessment of Vision-based Weed and Fruit Detection and Classification Algorithms

This paper proposes new metrics and a performance-assessment framework for vision-based weed and fruit detection and classification algorithms. In order to compare algorithms, and make a decision on which one to use fora particular application, it is necessary to take into account that the performance obtained in a series of tests is subject to uncertainty. Such characterisation of uncertainty seems not to be captured by the performance metrics currently reported in the literature. Therefore, we pose the problem as a general problem of scientific inference, which arises out of incomplete information, and propose as a metric of performance the(posterior) predictive probabilities that the algorithms will provide a correct outcome for target and background detection. We detail the framework through which these predicted probabilities can be obtained, which is Bayesian in nature. As an illustration example, we apply the framework to the assessment of performance of four algorithms that could potentially be used in the detection of capsicums (peppers).

On Visual Detection of Highly-occluded Objects for Harvesting Automation in Horticulture

Developing accurate and reliable crop detection algorithms is an important step for harvesting automation in horticulture. This paper presents a novel approach to visual detection of highly-occluded fruits. We use a conditional random field (CRF) on multi-spectral image data (colour and Near-Infrared Reflectance, NIR) to model two classes: crop and background. To describe these two classes, we explore a range of visual-texture features including local binary pattern, histogram of oriented gradients, and learn auto-encoder features. The pro-posed methods are evaluated using hand-labelled images from a dataset captured on a commercial capsicum farm. Experimental results are presented, and performance is evaluated in terms of the Area Under the Curve (AUC) of the precision-recall curves.Our current results achieve a maximum performance of 0.81AUC when combining all of the texture features in conjunction with colour information.

Nonparametric rank regression for analyzing water quality concentration data with multiple detection limits

Environmental data usually include measurements, such as water quality data, which fall below detection limits, because of limitations of the instruments or of certain analytical methods used. The fact that some responses are not detected needs to be properly taken into account in statistical analysis of such data. However, it is well-known that it is challenging to analyze a data set with detection limits, and we often have to rely on the traditional parametric methods or simple imputation methods. Distributional assumptions can lead to biased inference and justification of distributions is often not possible when the data are correlated and there is a large proportion of data below detection limits. The extent of bias is usually unknown. To draw valid conclusions and hence provide useful advice for environmental management authorities, it is essential to develop and apply an appropriate statistical methodology. This paper proposes rank-based procedures for analyzing non-normally distributed data collected at different sites over a period of time in the presence of multiple detection limits. To take account of temporal correlations within each site, we propose an optimal linear combination of estimating functions and apply the induced smoothing method to reduce the computational burden. Finally, we apply the proposed method to the water quality data collected at Susquehanna River Basin in United States of America, which dearly demonstrates the advantages of the rank regression models.

Statistical power calculation and sample size determination for environmental studies with data below detection limits

Power calculation and sample size determination are critical in designing environmental monitoring programs. The traditional approach based on comparing the mean values may become statistically inappropriate and even invalid when substantial proportions of the response values are below the detection limits or censored because strong distributional assumptions have to be made on the censored observations when implementing the traditional procedures. In this paper, we propose a quantile methodology that is robust to outliers and can also handle data with a substantial proportion of below-detection-limit observations without the need of imputing the censored values. As a demonstration, we applied the methods to a nutrient monitoring project, which is a part of the Perth Long-Term Ocean Outlet Monitoring Program. In this example, the sample size required by our quantile methodology is, in fact, smaller than that by the traditional t-test, illustrating the merit of our method.

Co-designing social marketing programs

Most alcohol education programs are designed by experts, with the target audience largely excluded from this process. Theoretically, application of co-creation which comprises co-design and co- production offers an opportunity to better orient programs to meet audience needs and wants and thereby enhance program outcomes. To date, research focus has centred on value co-creation with content co-design receiving limited research attention. The current study seeks to understand how young people would design an intervention and continues by contrasting an audience designed program with the earlier implemented expert designed program.