Evaluating automatic road detection across a large aerial imagery collection

The automated extraction of roads from aerial imagery can be of value for tasks including mapping, surveillance and change detection. Unfortunately, there are no public databases or standard evaluation protocols for evaluating these techniques. Many techniques are further hindered by a reliance on manual initialisation, making large scale application of the techniques impractical. In this paper, we present a public database and evaluation protocol for the evaluation of road extraction algorithms, and propose an improved automatic seed finding technique to initialise road extraction, based on a combination of geometric and colour features.

Using Web Search Logs to Identify Query Classification Terms

Purpose – The work presented in this paper aims to provide an approach to classifying web logs by personal properties of users. Design/methodology/approach – The authors describe an iterative system that begins with a small set of manually labeled terms, which are used to label queries from the log. A set of background knowledge related to these labeled queries is acquired by combining web search results on these queries. This background set is used to obtain many terms that are related to the classification task. The system then ranks each of the related terms, choosing those that most fit the personal properties of the users. These terms are then used to begin the next iteration. Findings – The authors identify the difficulties of classifying web logs, by approaching this problem from a machine learning perspective. By applying the approach developed, the authors are able to show that many queries in a large query log can be classified. Research limitations/implications – Testing results in this type of classification work is difficult, as the true personal properties of web users are unknown. Evaluation of the classification results in terms of the comparison of classified queries to well known age-related sites is a direction that is currently being exploring. Practical implications – This research is background work that can be incorporated in search engines or other web-based applications, to help marketing companies and advertisers. Originality/value – This research enhances the current state of knowledge in short-text classification and query log learning. Classification schemes, Computer networks, Information retrieval, Man-machine systems, User interfaces

A recommendation method for online dating networks based on social relations and demographic information

A new relationship type of social networks - online dating - are gaining popularity. With a large member base, users of a dating network are overloaded with choices about their ideal partners. Recommendation methods can be utilized to overcome this problem. However, traditional recommendation methods do not work effectively for online dating networks where the dataset is sparse and large, and a two-way matching is required. This paper applies social networking concepts to solve the problem of developing a recommendation method for online dating networks. We propose a method by using clustering, SimRank and adapted SimRank algorithms to recommend matching candidates. Empirical results show that the proposed method can achieve nearly double the performance of the traditional collaborative filtering and common neighbor methods of recommendation.

Tissue engineering bone - reconstruction of critical sized segmental bone defects in a large animal model

Currently, well established clinical therapeutic approaches for bone reconstruction are restricted to the transplantation of autografts and allografts, and the implantation of metal devices or ceramic-based implants to assist bone regeneration. Bone grafts possess osteoconductive and osteoinductive properties, their application, however, is associated with disadvantages. These include limited access and availability, donor site morbidity and haemorrhage, increased risk of infection, and insufficient transplant integration. As a result, recent research focuses on the development of complementary therapeutic concepts. The field of tissue engineering has emerged as an important alternative approach to bone regeneration. Tissue engineering unites aspects of cellular biology, biomechanical engineering, biomaterial sciences and trauma and orthopaedic surgery. To obtain approval by regulatory bodies for these novel therapeutic concepts the level of therapeutic benefit must be demonstrated rigorously in well characterized, clinically relevant animal models. Therefore, in this PhD project, a reproducible and clinically relevant, ovine, critically sized, high load bearing, tibial defect model was established and characterized as a prerequisite to assess the regenerative potential of a novel treatment concept in vivo involving a medical grade polycaprolactone and tricalciumphosphate based composite scaffold and recombinant human bone morphogenetic proteins.

Rational design of serine protease inhibitors

Proteases regulate a spectrum of diverse physiological processes, and dysregulation of proteolytic activity drives a plethora of pathological conditions. Understanding protease function is essential to appreciating many aspects of normal physiology and progression of disease. Consequently, development of potent and specific inhibitors of proteolytic enzymes is vital to provide tools for the dissection of protease function in biological systems and for the treatment of diseases linked to aberrant proteolytic activity. The studies in this thesis describe the rational design of potent inhibitors of three proteases that are implicated in disease development. Additionally, key features of the interaction of proteases and their cognate inhibitors or substrates are analysed and a series of rational inhibitor design principles are expounded and tested. Rational design of protease inhibitors relies on a comprehensive understanding of protease structure and biochemistry. Analysis of known protease cleavage sites in proteins and peptides is a commonly used source of such information. However, model peptide substrate and protein sequences have widely differing levels of backbone constraint and hence can adopt highly divergent structures when binding to a protease’s active site. This may result in identical sequences in peptides and proteins having different conformations and diverse spatial distribution of amino acid functionalities. Regardless of this, protein and peptide cleavage sites are often regarded as being equivalent. One of the key findings in the following studies is a definitive demonstration of the lack of equivalence between these two classes of substrate and invalidation of the common practice of using the sequences of model peptide substrates to predict cleavage of proteins in vivo. Another important feature for protease substrate recognition is subsite cooperativity. This type of cooperativity is commonly referred to as protease or substrate binding subsite cooperativity and is distinct from allosteric cooperativity, where binding of a molecule distant from the protease active site affects the binding affinity of a substrate. Subsite cooperativity may be intramolecular where neighbouring residues in substrates are interacting, affecting the scissile bond’s susceptibility to protease cleavage. Subsite cooperativity can also be intermolecular where a particular residue’s contribution to binding affinity changes depending on the identity of neighbouring amino acids. Although numerous studies have identified subsite cooperativity effects, these findings are frequently ignored in investigations probing subsite selectivity by screening against diverse combinatorial libraries of peptides (positional scanning synthetic combinatorial library; PS-SCL). This strategy for determining cleavage specificity relies on the averaged rates of hydrolysis for an uncharacterised ensemble of peptide sequences, as opposed to the defined rate of hydrolysis of a known specific substrate. Further, since PS-SCL screens probe the preference of the various protease subsites independently, this method is inherently unable to detect subsite cooperativity. However, mean hydrolysis rates from PS-SCL screens are often interpreted as being comparable to those produced by single peptide cleavages. Before this study no large systematic evaluation had been made to determine the level of correlation between protease selectivity as predicted by screening against a library of combinatorial peptides and cleavage of individual peptides. This subject is specifically explored in the studies described here. In order to establish whether PS-SCL screens could accurately determine the substrate preferences of proteases, a systematic comparison of data from PS-SCLs with libraries containing individually synthesised peptides (sparse matrix library; SML) was carried out. These SML libraries were designed to include all possible sequence combinations of the residues that were suggested to be preferred by a protease using the PS-SCL method. SML screening against the three serine proteases kallikrein 4 (KLK4), kallikrein 14 (KLK14) and plasmin revealed highly preferred peptide substrates that could not have been deduced by PS-SCL screening alone. Comparing protease subsite preference profiles from screens of the two types of peptide libraries showed that the most preferred substrates were not detected by PS SCL screening as a consequence of intermolecular cooperativity being negated by the very nature of PS SCL screening. Sequences that are highly favoured as result of intermolecular cooperativity achieve optimal protease subsite occupancy, and thereby interact with very specific determinants of the protease. Identifying these substrate sequences is important since they may be used to produce potent and selective inhibitors of protolytic enzymes. This study found that highly favoured substrate sequences that relied on intermolecular cooperativity allowed for the production of potent inhibitors of KLK4, KLK14 and plasmin. Peptide aldehydes based on preferred plasmin sequences produced high affinity transition state analogue inhibitors for this protease. The most potent of these maintained specificity over plasma kallikrein (known to have a very similar substrate preference to plasmin). Furthermore, the efficiency of this inhibitor in blocking fibrinolysis in vitro was comparable to aprotinin, which previously saw clinical use to reduce perioperative bleeding. One substrate sequence particularly favoured by KLK4 was substituted into the 14 amino acid, circular sunflower trypsin inhibitor (SFTI). This resulted in a highly potent and selective inhibitor (SFTI-FCQR) which attenuated protease activated receptor signalling by KLK4 in vitro. Moreover, SFTI-FCQR and paclitaxel synergistically reduced growth of ovarian cancer cells in vitro, making this inhibitor a lead compound for further therapeutic development. Similar incorporation of a preferred KLK14 amino acid sequence into the SFTI scaffold produced a potent inhibitor for this protease. However, the conformationally constrained SFTI backbone enforced a different intramolecular cooperativity, which masked a KLK14 specific determinant. As a consequence, the level of selectivity achievable was lower than that found for the KLK4 inhibitor. Standard mechanism inhibitors such as SFTI rely on a stable acyl-enzyme intermediate for high affinity binding. This is achieved by a conformationally constrained canonical binding loop that allows for reformation of the scissile peptide bond after cleavage. Amino acid substitutions within the inhibitor to target a particular protease may compromise structural determinants that support the rigidity of the binding loop and thereby prevent the engineered inhibitor reaching its full potential. An in silico analysis was carried out to examine the potential for further improvements to the potency and selectivity of the SFTI-based KLK4 and KLK14 inhibitors. Molecular dynamics simulations suggested that the substitutions within SFTI required to target KLK4 and KLK14 had compromised the intramolecular hydrogen bond network of the inhibitor and caused a concomitant loss of binding loop stability. Furthermore in silico amino acid substitution revealed a consistent correlation between a higher frequency of formation and the number of internal hydrogen bonds of SFTI-variants and lower inhibition constants. These predictions allowed for the production of second generation inhibitors with enhanced binding affinity toward both targets and highlight the importance of considering intramolecular cooperativity effects when engineering proteins or circular peptides to target proteases. The findings from this study show that although PS-SCLs are a useful tool for high throughput screening of approximate protease preference, later refinement by SML screening is needed to reveal optimal subsite occupancy due to cooperativity in substrate recognition. This investigation has also demonstrated the importance of maintaining structural determinants of backbone constraint and conformation when engineering standard mechanism inhibitors for new targets. Combined these results show that backbone conformation and amino acid cooperativity have more prominent roles than previously appreciated in determining substrate/inhibitor specificity and binding affinity. The three key inhibitors designed during this investigation are now being developed as lead compounds for cancer chemotherapy, control of fibrinolysis and cosmeceutical applications. These compounds form the basis of a portfolio of intellectual property which will be further developed in the coming years.

Fuzzy methods for analysis of microarrays and networks

Bioinformatics involves analyses of biological data such as DNA sequences, microarrays and protein-protein interaction (PPI) networks. Its two main objectives are the identification of genes or proteins and the prediction of their functions. Biological data often contain uncertain and imprecise information. Fuzzy theory provides useful tools to deal with this type of information, hence has played an important role in analyses of biological data. In this thesis, we aim to develop some new fuzzy techniques and apply them on DNA microarrays and PPI networks. We will focus on three problems: (1) clustering of microarrays; (2) identification of disease-associated genes in microarrays; and (3) identification of protein complexes in PPI networks. The first part of the thesis aims to detect, by the fuzzy C-means (FCM) method, clustering structures in DNA microarrays corrupted by noise. Because of the presence of noise, some clustering structures found in random data may not have any biological significance. In this part, we propose to combine the FCM with the empirical mode decomposition (EMD) for clustering microarray data. The purpose of EMD is to reduce, preferably to remove, the effect of noise, resulting in what is known as denoised data. We call this method the fuzzy C-means method with empirical mode decomposition (FCM-EMD). We applied this method on yeast and serum microarrays, and the silhouette values are used for assessment of the quality of clustering. The results indicate that the clustering structures of denoised data are more reasonable, implying that genes have tighter association with their clusters. Furthermore we found that the estimation of the fuzzy parameter m, which is a difficult step, can be avoided to some extent by analysing denoised microarray data. The second part aims to identify disease-associated genes from DNA microarray data which are generated under different conditions, e.g., patients and normal people. We developed a type-2 fuzzy membership (FM) function for identification of diseaseassociated genes. This approach is applied to diabetes and lung cancer data, and a comparison with the original FM test was carried out. Among the ten best-ranked genes of diabetes identified by the type-2 FM test, seven genes have been confirmed as diabetes-associated genes according to gene description information in Gene Bank and the published literature. An additional gene is further identified. Among the ten best-ranked genes identified in lung cancer data, seven are confirmed that they are associated with lung cancer or its treatment. The type-2 FM-d values are significantly different, which makes the identifications more convincing than the original FM test. The third part of the thesis aims to identify protein complexes in large interaction networks. Identification of protein complexes is crucial to understand the principles of cellular organisation and to predict protein functions. In this part, we proposed a novel method which combines the fuzzy clustering method and interaction probability to identify the overlapping and non-overlapping community structures in PPI networks, then to detect protein complexes in these sub-networks. Our method is based on both the fuzzy relation model and the graph model. We applied the method on several PPI networks and compared with a popular protein complex identification method, the clique percolation method. For the same data, we detected more protein complexes. We also applied our method on two social networks. The results showed our method works well for detecting sub-networks and give a reasonable understanding of these communities.

Managing large collections of business process models - Current techniques and challenges

Nowadays, business process management is an important approach for managing organizations from an operational perspective. As a consequence, it is common to see organizations develop collections of hundreds or even thousands of business process models. Such large collections of process models bring new challenges and provide new opportunities, as the knowledge that they encapsulate requires to be properly managed. Therefore, a variety of techniques for managing large collections of business process models is being developed. The goal of this paper is to provide an overview of the management techniques that currently exist, as well as the open research challenges that they pose.

A review of commuter exposure to ultrafine particles and its health effects

Ultrafine particles (UFPs, <100 nm) are produced in large quantities by vehicular combustion and are implicated in causing several adverse human health effects. Recent work has suggested that a large proportion of daily UFP exposure may occur during commuting. However, the determinants, variability and transport mode-dependence of such exposure are not well-understood. The aim of this review was to address these knowledge gaps by distilling the results of ‘in-transit’ UFP exposure studies performed to-date, including studies of health effects. We identified 47 exposure studies performed across 6 transport modes: automobile, bicycle, bus, ferry, rail and walking. These encompassed approximately 3000 individual trips where UFP concentrations were measured. After weighting mean UFP concentrations by the number of trips in which they were collected, we found overall mean UFP concentrations of 3.4, 4.2, 4.5, 4.7, 4.9 and 5.7 × 10^4 particles cm^-3 for the bicycle, bus, automobile, rail, walking and ferry modes, respectively. The mean concentration inside automobiles travelling through tunnels was 3.0 × 10^5 particles cm^-3. While the mean concentrations were indicative of general trends, we found that the determinants of exposure (meteorology, traffic parameters, route, fuel type, exhaust treatment technologies, cabin ventilation, filtration, deposition, UFP penetration) exhibited marked variability and mode-dependence, such that it is not necessarily appropriate to rank modes in order of exposure without detailed consideration of these factors. Ten in-transit health effects studies have been conducted and their results indicate that UFP exposure during commuting can elicit acute effects in both healthy and health-compromised individuals. We suggest that future work should focus on further defining the contribution of in-transit UFP exposure to total UFP exposure, exploring its specific health effects and investigating exposures in the developing world. Keywords: air pollution; transport modes; acute health effects; travel; public transport

Influence of rainfall and catchment characteristics on urban stormwater quality

This thesis presents the outcomes of a comprehensive research study undertaken to investigate the influence of rainfall and catchment characteristics on urban stormwater quality. The knowledge created is expected to contribute to a greater understanding of urban stormwater quality and thereby enhance the design of stormwater quality treatment systems. The research study was undertaken based on selected urban catchments in Gold Coast, Australia. The research methodology included field investigations, laboratory testing, computer modelling and data analysis. Both univariate and multivariate data analysis techniques were used to investigate the influence of rainfall and catchment characteristics on urban stormwater quality. The rainfall characteristics investigated included average rainfall intensity and rainfall duration whilst catchment characteristics included land use, impervious area percentage, urban form and pervious area location. The catchment scale data for the analysis was obtained from four residential catchments, including rainfall-runoff records, drainage network data, stormwater quality data and land use and land cover data. Pollutants build-up samples were collected from twelve road surfaces in residential, commercial and industrial land use areas. The relationships between rainfall characteristics, catchment characteristics and urban stormwater quality were investigated based on residential catchments and then extended to other land uses. Based on the influence rainfall characteristics exert on urban stormwater quality, rainfall events can be classified into three different types, namely, high average intensity-short duration (Type 1), high average intensity-long duration (Type 2) and low average intensity-long duration (Type 3). This provides an innovative approach to conventional modelling which does not commonly relate stormwater quality to rainfall characteristics. Additionally, it was found that the threshold intensity for pollutant wash-off from urban catchments is much less than for rural catchments. High average intensity-short duration rainfall events are cumulatively responsible for the generation of a major fraction of the annual pollutants load compared to the other rainfall event types. Additionally, rainfall events less than 1 year ARI such as 6- month ARI should be considered for treatment design as they generate a significant fraction of the annual runoff volume and by implication a significant fraction of the pollutants load. This implies that stormwater treatment designs based on larger rainfall events would not be feasible in the context of cost-effectiveness, efficiency in treatment performance and possible savings in land area needed. This also suggests that the simulation of long-term continuous rainfall events for stormwater treatment design may not be needed and that event based simulations would be adequate. The investigations into the relationship between catchment characteristics and urban stormwater quality found that other than conventional catchment characteristics such as land use and impervious area percentage, other catchment characteristics such as urban form and pervious area location also play important roles in influencing urban stormwater quality. These outcomes point to the fact that the conventional modelling approach in the design of stormwater quality treatment systems which is commonly based on land use and impervious area percentage would be inadequate. It was also noted that the small uniformly urbanised areas within a larger mixed catchment produce relatively lower variations in stormwater quality and as expected lower runoff volume with the opposite being the case for large mixed use urbanised catchments. Therefore, a decentralised approach to water quality treatment would be more effective rather than an "end-of-pipe" approach. The investigation of pollutants build-up on different land uses showed that pollutant build-up characteristics vary even within the same land use. Therefore, the conventional approach in stormwater quality modelling, which is based solely on land use, may prove to be inappropriate. Industrial land use has relatively higher variability in maximum pollutant build-up, build-up rate and particle size distribution than the other two land uses. However, commercial and residential land uses had relatively higher variations of nutrients and organic carbon build-up. Additionally, it was found that particle size distribution had a relatively higher variability for all three land uses compared to the other build-up parameters. The high variability in particle size distribution for all land uses illustrate the dissimilarities associated with the fine and coarse particle size fractions even within the same land use and hence the variations in stormwater quality in relation to pollutants adsorbing to different sizes of particles.

Using turning technologies to engage tutors and facilitate effective marking and moderation in large classes

Audience Response Systems (ARS) have been successfully used by academics to facilitate student learning and engagement, particularly in large lecture settings. However, in large core subjects a key challenge is not only to engage students, but also to engage large and diverse teaching teams in order to ensure a consistent approach to grading assessments. This paper provides an insight into the ways in which ARS can be used to encourage participation by tutors in marking and moderation meetings. It concludes that ARS can improve the consistency of grading and the quality of feedback provided to students.

Effect of topic domain and task type on Web image searching

Many user studies in Web information searching have found the significant effect of task types on search strategies. However, little attention was given to Web image searching strategies, especially the query reformulation activity despite that this is a crucial part in Web image searching. In this study, we investigated the effects of topic domains and task types on user’s image searching behavior and query reformulation strategies. Some significant differences in user’s tasks specificity and initial concepts were identified among the task domains. Task types are also found to influence participant’s result reviewing behavior and query reformulation strategies.

When the shoe doesn't quite fit: Regulating large commercial airports with expanding roles

Airports, whether publicly or privately owned or operated fill both public and private roles. They need to act as public infrastructure providers and as businesses which cover their operating costs. That leads to special governance concerns with respect to consumers and competitors which are only beginning to be addressed. These challenges are highlighted both by shifts in ownership status and by the expansion of roles performed by airports as passenger and cargo volumes continue to increase and as nearby urban areas expand outward towards airports. We survey five ways in which the regulatory shoe doesn‟t quite fit the needs. Our findings suggest that, while ad hoc measures limit political tension, new governance measures are needed.

The impact of tenure type on the desire for retirement village living

In 2005 17.3% of Australians were aged 60 years and older (Australian Bureau of Statistics). A consequence of this aging population is the increased use of self-contained independent living units (SCILU) in Retirement Villages by older Australians. The retirement village sector has thus become a significant sector within the residential property market. In seeking to determine the impact of tenure type on the desirability of RV living this paper first profiles a typical SCILU in Australia, before explaining and examining the various tenure types offered by the market. This paper concludes that the multiplicity of offerings of the SCILU product with respect to tenure type, when combined with deferred management fees and participation in capital gains/losses, may be contributing to a lack of clarity in what the SCILU product entails and the security of investment it offers. This perception is supported by litigated disputes and may be damaging the reputation, ongoing viability and desirability of SCILUs.

Age-related loss of brain volume and T2 relaxation time in youth with type 1 diabetes

OBJECTIVE: Childhood-onset type 1 diabetes is associated with neurocognitive deficits, but there is limited evidence to date regarding associated neuroanatomical brain changes and their relationship to illness variables such as age at disease onset. This report examines age-related changes in volume and T2 relaxation time (a fundamental parameter of magnetic resonance imaging that reflects tissue health) across the whole brain. RESEARCH DESIGN AND METHODS: Type 1 diabetes, N = 79 (mean age 20.32 ± 4.24 years), and healthy control participants, N = 50 (mean age 20.53 ± 3.60 years). There were no substantial group differences on socioeconomic status, sex ratio, or intelligence quotient. RESULTS: Regression analyses revealed a negative correlation between age and brain changes, with decreasing gray matter volume and T2 relaxation time with age in multiple brain regions in the type 1 diabetes group. In comparison, the age-related decline in the control group was small. Examination of the interaction of group and age confirmed a group difference (type 1 diabetes vs. control) in the relationship between age and brain volume/T2 relaxation time. CONCLUSIONS: We demonstrated an interaction between age and group in predicting brain volumes and T2 relaxation time such that there was a decline in these outcomes in type 1 diabetic participants that was much less evident in control subjects. Findings suggest the neurodevelopmental pathways of youth with type 1 diabetes have diverged from those of their healthy peers by late adolescence and early adulthood but the explanation for this phenomenon remains to be clarified.