Scheduling techniques to optimise rail operations

In Australia, railway systems play a vital role in transporting the sugarcane crop from farms to mills. The sugarcane transport system is very complex and uses daily schedules, consisting of a set of locomotives runs, to satisfy the requirements of the mill and harvesters. The total cost of sugarcane transport operations is very high; over 35% of the total cost of sugarcane production in Australia is incurred in cane transport. Efficient schedules for sugarcane transport can reduce the cost and limit the negative effects that this system can have on the raw sugar production system. There are several benefits to formulating the train scheduling problem as a blocking parallel-machine job shop scheduling (BPMJSS) problem, namely to prevent two trains passing in one section at the same time; to keep the train activities (operations) in sequence during each run (trip) by applying precedence constraints; to pass the trains on one section in the correct order (priorities of passing trains) by applying disjunctive constraints; and, to ease passing trains by solving rail conflicts by applying blocking constraints and Parallel Machine Scheduling. Therefore, the sugarcane rail operations are formulated as BPMJSS problem. A mixed integer programming and constraint programming approaches are used to describe the BPMJSS problem. The model is solved by the integration of constraint programming, mixed integer programming and search techniques. The optimality performance is tested by Optimization Programming Language (OPL) and CPLEX software on small and large size instances based on specific criteria. A real life problem is used to verify and validate the approach. Constructive heuristics and new metaheuristics including simulated annealing and tabu search are proposed to solve this complex and NP-hard scheduling problem and produce a more efficient scheduling system. Innovative hybrid and hyper metaheuristic techniques are developed and coded using C# language to improve the solutions quality and CPU time. Hybrid techniques depend on integrating heuristic and metaheuristic techniques consecutively, while hyper techniques are the complete integration between different metaheuristic techniques, heuristic techniques, or both.

The roles of the preproghrelin-derived peptides - ghrelin, desacyl ghrelin and obestatin - in prostate cancer

Prostate cancer is the second most common cause of cancer related deaths in Western men. Despite the significant improvements in current treatment techniques, there is no cure for advanced metastatic, castrate-resistant disease. Early detection and prevention of progression to a castrate-resistant state may provide new strategies to improve survival. A number of growth factors have been shown to act in an autocrine/paracrine manner to modulate prostate cancer tumour growth. Our laboratory has previously shown that ghrelin and its receptors (the functional GHS-R1a and the non-functional GHS-R1b) are expressed in prostate cancer specimens and cell lines. We have shown that ghrelin increases cell proliferation in the PC3 and LNCaP prostate cancer cell lines through activation of ERK1/2, suggesting that ghrelin could regulate prostate cancer cell growth and play a role in the progression of the disease. Ghrelin is a 28 amino-acid peptide hormone, identified to be the natural ligand of the growth hormone secretagogue receptor (GHS-R1a). It is well characterised as a growth hormone releasing and as an orexigenic peptide that stimulates appetite and feeding and regulates energy expenditure and bodyweight. In addition to its orexigenic properties, ghrelin has been shown to play a regulatory role in a number of systems, including the reproductive, immune and cardiovascular systems and may play a role in a number of pathological conditions such as chronic heart failure, anorexia, cachexia, obesity, diabetes and cancer. In cancer, ghrelin and its receptor are expressed in a range of tumours and cancer cell lines and ghrelin has been demonstrated to modulate cell proliferation, apoptosis, migration and invasion in some cell types. The ghrelin gene (GHRL) encodes preproghrelin peptide, which is processed to produce three currently known functional peptides - ghrelin, desacyl ghrelin and obestatin. Prohormone convertases (PCs) have been shown to cleave the preproghrelin peptide into two primary products - the 28 amino acid peptide, ghrelin, and the remaining 117 amino acid C-terminal peptide, C-ghrelin. C-ghrelin can then be further processed to produce the 23 amino acid peptide, obestatin. Ghrelin circulates in two different forms - an octanoylated form (known as ghrelin) and a non-octanoylated form, desacyl ghrelin. The unique post-translational addition of octanoic acid to the serine 3 residue of the propeptide chain to form acylated ghrelin is catalysed by ghrelin O-acyltransferase (GOAT). This modification is necessary for binding of ghrelin to its only known functional receptor, the GHS-R1a. As desacyl ghrelin cannot bind and activate the GHS-R1a, it was initially thought to be an inactive peptide, despite the fact that it circulates at much higher levels than ghrelin. Further research has demonstrated that desacyl ghrelin is biologically active and shares some of the actions of ghrelin, as well as having some opposing and distinct roles. Interestingly, both ghrelin and desacyl ghrelin have been shown to modulate apoptosis, cell differentiation and proliferation in some cell types, and to stimulate cell proliferation through activation of ERK1/2 and PI3K/Akt pathways. The third known peptide product of the ghrelin preprohormone, obestatin, was initially thought to oppose the actions of ghrelin in appetite regulation and food intake and to mediate its effects through the G protein-coupled receptor 39 (GPR39). Subsequent research failed to reproduce the initial findings, however, and the possible anorexigenic effects of obestatin, as well as the identity of its receptor, remain unclear. Obestatin plays some important physiological roles, including roles in improving memory, the inhibition of thirst and anxiety, increased secretion of pancreatic juice, and regulation of cell proliferation, survival, apoptosis and differentiation. Preliminary studies have also shown that obestatin stimulates cell proliferation in some cell types through activation of ERK1/2, Akt and PKC pathways. Overall, however, at the commencement of this PhD project, relatively little was known regarding the functions and mechanisms of action of the preproghrelin-derived functional peptides in modulating prostate cancer cell proliferation. The roles of obestatin, and desacyl ghrelin as potential growth factors had not previously been investigated, and the potential expression and regulation of the preproghrelin processing enzymes, GOAT and prohormone convertases was unknown in prostate cancer cell lines. Therefore, the overall objectives of this study were to: 1. investigate the effects of obestatin on cell proliferation and signaling in prostate cancer cell lines 2. compare the effects of desacyl ghrelin and ghrelin on cell proliferation and signaling in prostate cancer cell lines 3. investigate whether prostate cancer cell lines possess the necessary enzymatic machinery to produce ghrelin and desacyl ghrelin and if these peptides can regulate GOAT expression Our laboratory has previously shown that ghrelin stimulates cell proliferation in the PC3 and LNCaP prostate cancer cell line through activation of the ERK1/2 pathway. In this study it has been demonstrated that treatments with either ghrelin, desacyl ghrelin or obestatin over 72 hours significantly increased cell proliferation in the PC3 prostate cancer cell line but had no significant effect in the RWPE-1 transformed normal prostate cell line. Ghrelin (1000nM) stimulated cell proliferation in the PC3 prostate cancer cell line by 31.66 6.68% (p<0.01) with the WST-1 method, and 13.55 5.68% (p<0.05) with the CyQUANT assay. Desacyl ghrelin (1000nM) increased cell proliferation in PC3 cells by 21.73 2.62% (p<0.01) (WST-1), and 15.46 7.05% (p<0.05) (CyQUANT) above untreated control. Obestatin (1000nM) induced a 28.37 7.47% (p<0.01) (WST-1) and 12.14 7.47% (p<0.05) (CyQUANT) significant increase in cell proliferation in the PC3 prostate cancer cell line. Ghrelin and desacyl ghrelin treatments stimulated Akt and ERK phosphorylation across a range of concentrations (p<0.01). Obestatin treatment significantly stimulated Akt, ERK and PKC phosphorylation (p<0.05). Through the use of specific inhibitors, the MAPK inhibitor U0126 and the Akt1/2 kinase inhibitor, it was demonstrated that ghrelin- and obestatin-induced cell proliferation in the PC3 prostate cancer cell line is mediated through activation of ERK1/2 and Akt pathways. Although desacyl ghrelin significantly stimulated Akt and ERK phosphorylation, U0126 failed to prevent desacyl ghrelin-induced cell proliferation suggesting ghrelin and desacyl ghrelin might act through different mechanisms to increase cell proliferation. Ghrelin and desacyl ghrelin have shown a proliferative effect in osteoblasts, pancreatic -cells and cardiomyocytes through activation of ERK1/2 and PI3K/Akt pathways. Here it has been shown that ghrelin and its non-acylated form exert the same function and stimulate cell proliferation in the PC3 prostate cancer cell line through activation of the Akt pathway. Ghrelin-induced proliferation was also mediated through activation of the ERK1/2 pathway, however, desacyl ghrelin seems to stimulate cell proliferation in an ERK1/2-independent manner. As desacyl ghrelin does not bind and activate GHSR1a, the only known functional ghrelin receptor, the finding that both ghrelin and desacyl ghrelin stimulate cell proliferation in the PC3 cell line suggests that these peptides could be acting through the yet unidentified alternative ghrelin receptor in this cell type. Obestatin treatment also stimulated PKC phosphorylation, however, a direct role for this pathway in stimulating cell proliferation could not be proven using available PKC pathway inhibitors, as they caused significant cell death over the extended timeframe of the cell proliferation assays. Obestatin has been shown to stimulate cell proliferation through activation of PKC isoforms in human retinal epithelial cells and in the human gastric cancer cell line KATO-III. We have demonstrated that all of the prostate-derived cell lines examined (PC3, LNCaP, DU145, 22Rv1, RWPE-1 and RWPE-2) expressed GOAT and at least one of the prohormone convertases, which are known to cleave the proghrelin peptide, PC1/3, PC2 and furin, at the mRNA level. These cells, therefore, are likely to possess the necessary machinery to cleave the preproghrelin protein and to produce the mature ghrelin and desacyl ghrelin peptides. In addition to prohormone convertases, the presence of octanoic acid is essential for acylated ghrelin production. In this study octanoic acid supplementation significantly increased cell proliferation in the PC3 prostate cancer cell line by over 20% compared to untreated controls (p<0.01), but surprisingly, not in the DU145, LNCaP or 22Rv1 prostate cancer cell lines or in the RWPE-1 and RWPE-2 prostate-derived cell lines. In addition, we demonstrated that exogenous ghrelin induced a statistically significant two-fold decrease in GOAT mRNA expression in the PC3 cell line (p<0.05), suggesting that ghrelin could pontentially downregulate its own acylation and, therefore, regulate the balance between ghrelin and desacyl ghrelin. This was not observed, however, in the DU145 and LNCaP prostate cancer cell lines. The GOAT-ghrelin system represents a direct link between ingested nutrients and regulation of ghrelin production and the ghrelin/desacyl ghrelin ratio. Regulation of ghrelin acylation is a potentially attractive and desirable tool for the development of better therapies for a number of pathological conditions where ghrelin has been shown to play a key role. The finding that desacyl ghrelin stimulates cell proliferation in the PC3 prostate cancer cell line, and responds to ghrelin in the same way, suggests that this cell line expresses an alternative ghrelin receptor. Although all the cell lines examined expressed both GHS-R1a and GHS-R1b mRNA, it remains uncertain whether these cell lines express the unidentified alternative ghrelin receptor. It is possible that the varied responses seen could be due to the expression of different ghrelin receptors in different cell lines. In addition to GOAT, prohormone convertases and octanoic acid availability may regulate the production of different peptides from the ghrelin preprohormone. The studies presented in this thesis provide significant new information regarding the roles and mechanisms of action of the preproghrelin-derived peptides, ghrelin, desacyl ghrelin and obestatin, in modulating prostate cancer cell line proliferation. A number of key questions remain to be resolved, however, including the identification of the alternative ghrelin/desacyl ghrelin receptor, the identification of the obestatin receptor, a clarification of the signaling mechanisms which mediate cell proliferation in response to obestatin treatment and a better understanding of the regulation at both the gene and post-translational levels of functional peptide generation. Further studies investigating the role of the ghrelin axis using in vivo prostate cancer models may be warranted. Until these issues are determined, the potential for the ghrelin axis, to be recognised as a novel useful target for therapy for cancer or other pathologies will be uncertain.

Bayesian hierarchical models in statistical quality control methods to improve healthcare in hospitals

Quality oriented management systems and methods have become the dominant business and governance paradigm. From this perspective, satisfying customers’ expectations by supplying reliable, good quality products and services is the key factor for an organization and even government. During recent decades, Statistical Quality Control (SQC) methods have been developed as the technical core of quality management and continuous improvement philosophy and now are being applied widely to improve the quality of products and services in industrial and business sectors. Recently SQC tools, in particular quality control charts, have been used in healthcare surveillance. In some cases, these tools have been modified and developed to better suit the health sector characteristics and needs. It seems that some of the work in the healthcare area has evolved independently of the development of industrial statistical process control methods. Therefore analysing and comparing paradigms and the characteristics of quality control charts and techniques across the different sectors presents some opportunities for transferring knowledge and future development in each sectors. Meanwhile considering capabilities of Bayesian approach particularly Bayesian hierarchical models and computational techniques in which all uncertainty are expressed as a structure of probability, facilitates decision making and cost-effectiveness analyses. Therefore, this research investigates the use of quality improvement cycle in a health vii setting using clinical data from a hospital. The need of clinical data for monitoring purposes is investigated in two aspects. A framework and appropriate tools from the industrial context are proposed and applied to evaluate and improve data quality in available datasets and data flow; then a data capturing algorithm using Bayesian decision making methods is developed to determine economical sample size for statistical analyses within the quality improvement cycle. Following ensuring clinical data quality, some characteristics of control charts in the health context including the necessity of monitoring attribute data and correlated quality characteristics are considered. To this end, multivariate control charts from an industrial context are adapted to monitor radiation delivered to patients undergoing diagnostic coronary angiogram and various risk-adjusted control charts are constructed and investigated in monitoring binary outcomes of clinical interventions as well as postintervention survival time. Meanwhile, adoption of a Bayesian approach is proposed as a new framework in estimation of change point following control chart’s signal. This estimate aims to facilitate root causes efforts in quality improvement cycle since it cuts the search for the potential causes of detected changes to a tighter time-frame prior to the signal. This approach enables us to obtain highly informative estimates for change point parameters since probability distribution based results are obtained. Using Bayesian hierarchical models and Markov chain Monte Carlo computational methods, Bayesian estimators of the time and the magnitude of various change scenarios including step change, linear trend and multiple change in a Poisson process are developed and investigated. The benefits of change point investigation is revisited and promoted in monitoring hospital outcomes where the developed Bayesian estimator reports the true time of the shifts, compared to priori known causes, detected by control charts in monitoring rate of excess usage of blood products and major adverse events during and after cardiac surgery in a local hospital. The development of the Bayesian change point estimators are then followed in a healthcare surveillances for processes in which pre-intervention characteristics of patients are viii affecting the outcomes. In this setting, at first, the Bayesian estimator is extended to capture the patient mix, covariates, through risk models underlying risk-adjusted control charts. Variations of the estimator are developed to estimate the true time of step changes and linear trends in odds ratio of intensive care unit outcomes in a local hospital. Secondly, the Bayesian estimator is extended to identify the time of a shift in mean survival time after a clinical intervention which is being monitored by riskadjusted survival time control charts. In this context, the survival time after a clinical intervention is also affected by patient mix and the survival function is constructed using survival prediction model. The simulation study undertaken in each research component and obtained results highly recommend the developed Bayesian estimators as a strong alternative in change point estimation within quality improvement cycle in healthcare surveillances as well as industrial and business contexts. The superiority of the proposed Bayesian framework and estimators are enhanced when probability quantification, flexibility and generalizability of the developed model are also considered. The empirical results and simulations indicate that the Bayesian estimators are a strong alternative in change point estimation within quality improvement cycle in healthcare surveillances. The superiority of the proposed Bayesian framework and estimators are enhanced when probability quantification, flexibility and generalizability of the developed model are also considered. The advantages of the Bayesian approach seen in general context of quality control may also be extended in the industrial and business domains where quality monitoring was initially developed.

Bending and blurring blended learning boundaries [Extended Abstract]

This presentation presents a blended learning model that provides greater opportunity for learning to be self-managed and personalized.

An analytical tool that quantifies cellular morphology changes from three-dimensional fluorescence images

The most common software analysis tools available for measuring fluorescence images are for two-dimensional (2D) data that rely on manual settings for inclusion and exclusion of data points, and computer-aided pattern recognition to support the interpretation and findings of the analysis. It has become increasingly important to be able to measure fluorescence images constructed from three-dimensional (3D) datasets in order to be able to capture the complexity of cellular dynamics and understand the basis of cellular plasticity within biological systems. Sophisticated microscopy instruments have permitted the visualization of 3D fluorescence images through the acquisition of multispectral fluorescence images and powerful analytical software that reconstructs the images from confocal stacks that then provide a 3D representation of the collected 2D images. Advanced design-based stereology methods have progressed from the approximation and assumptions of the original model-based stereology(1) even in complex tissue sections(2). Despite these scientific advances in microscopy, a need remains for an automated analytic method that fully exploits the intrinsic 3D data to allow for the analysis and quantification of the complex changes in cell morphology, protein localization and receptor trafficking. Current techniques available to quantify fluorescence images include Meta-Morph (Molecular Devices, Sunnyvale, CA) and Image J (NIH) which provide manual analysis. Imaris (Andor Technology, Belfast, Northern Ireland) software provides the feature MeasurementPro, which allows the manual creation of measurement points that can be placed in a volume image or drawn on a series of 2D slices to create a 3D object. This method is useful for single-click point measurements to measure a line distance between two objects or to create a polygon that encloses a region of interest, but it is difficult to apply to complex cellular network structures. Filament Tracer (Andor) allows automatic detection of the 3D neuronal filament-like however, this module has been developed to measure defined structures such as neurons, which are comprised of dendrites, axons and spines (tree-like structure). This module has been ingeniously utilized to make morphological measurements to non-neuronal cells(3), however, the output data provide information of an extended cellular network by using a software that depends on a defined cell shape rather than being an amorphous-shaped cellular model. To overcome the issue of analyzing amorphous-shaped cells and making the software more suitable to a biological application, Imaris developed Imaris Cell. This was a scientific project with the Eidgenössische Technische Hochschule, which has been developed to calculate the relationship between cells and organelles. While the software enables the detection of biological constraints, by forcing one nucleus per cell and using cell membranes to segment cells, it cannot be utilized to analyze fluorescence data that are not continuous because ideally it builds cell surface without void spaces. To our knowledge, at present no user-modifiable automated approach that provides morphometric information from 3D fluorescence images has been developed that achieves cellular spatial information of an undefined shape (Figure 1). We have developed an analytical platform using the Imaris core software module and Imaris XT interfaced to MATLAB (Mat Works, Inc.). These tools allow the 3D measurement of cells without a pre-defined shape and with inconsistent fluorescence network components. Furthermore, this method will allow researchers who have extended expertise in biological systems, but not familiarity to computer applications, to perform quantification of morphological changes in cell dynamics.

Measurements of particle emissions from nanotechnology processes, with assessment of measuring techniques and workplace controls

The overall aim of this project was to contribute to existing knowledge regarding methods for measuring characteristics of airborne nanoparticles and controlling occupational exposure to airborne nanoparticles, and to gather data on nanoparticle emission and transport in various workplaces. The scope of this study involved investigating the characteristics and behaviour of particles arising from the operation of six nanotechnology processes, subdivided into nine processes for measurement purposes. It did not include the toxicological evaluation of the aerosol and therefore, no direct conclusion was made regarding the health effects of exposure to these particles. Our research included real-time measurement of sub, and supermicrometre particle number and mass concentration, count median diameter, and alveolar deposited surface area using condensation particle counters, an optical particle counter, DustTrak photometer, scanning mobility particle sizer, and nanoparticle surface area monitor, respectively. Off-line particle analysis included scanning and transmission electron microscopy, energy-dispersive x-ray spectrometry, and thermal optical analysis of elemental carbon. Sources of fibrous and non-fibrous particles were included.

On the efficacy of techniques for evaluating multivariate volatility forecasts

The performance of techniques for evaluating multivariate volatility forecasts are not yet as well understood as their univariate counterparts. This paper aims to evaluate the efficacy of a range of traditional statistical-based methods for multivariate forecast evaluation together with methods based on underlying considerations of economic theory. It is found that a statistical-based method based on likelihood theory and an economic loss function based on portfolio variance are the most effective means of identifying optimal forecasts of conditional covariance matrices.

Determining the detection capabilities of existing real time measurement techniques for different engineered nanoparticles

The application of nanotechnology products has increased significantly in recent years. With their broad range of applications, including electronics, food and agriculture, power and energy, scientific instruments, clothing, cosmetics, buildings, biomedical and health, etc (Catanzariti, 2008), nanomaterials are an indispensible part of human life.

Visualisation of complex flows using texture-based techniques

Complex flow datasets are often difficult to represent in detail using traditional vector visualisation techniques such as arrow plots and streamlines. This is particularly true when the flow regime changes in time. Texture-based techniques, which are based on the advection of dense textures, are novel techniques for visualising such flows (i.e., complex dynamics and time-dependent). In this paper, we review two popular texture-based techniques and their application to flow datasets sourced from real research projects. The texture-based techniques investigated were Line Integral Convolution (LIC), and Image-Based Flow Visualisation (IBFV). We evaluated these techniques and in this paper report on their visualisation effectiveness (when compared with traditional techniques), their ease of implementation, and their computational overhead.

Refraction in children : a comparison of two methods of accommodation control

Purpose: The prevalence of refractive errors in children has been extensively researched. Comparisons between studies can, however, be compromised because of differences between accommodation control methods and techniques used for measuring refractive error. The aim of this study was to compare spherical refractive error results obtained at baseline and using two different accommodation control methods – extended optical fogging and cycloplegia, for two measurement techniques – autorefraction and retinoscopy. Methods: Participants comprised twenty-five school children aged between 6 and 13 years (mean age: 9.52 ± 2.06 years). The refractive error of one eye was measured at baseline and again under two different accommodation control conditions: extended optical fogging (+2.00DS for 20 minutes) and cycloplegia (1% cyclopentolate). Autorefraction and retinoscopy were both used to measure most plus spherical power for each condition. Results: A significant interaction was demonstrated between measurement technique and accommodation control method (p = 0.036), with significant differences in spherical power evident between accommodation control methods for each of the measurement techniques (p < 0.005). For retinoscopy, refractive errors were significantly more positive for cycloplegia compared to optical fogging, which were in turn significantly more positive than baseline, while for autorefraction, there were significant differences between cycloplegia and extended optical fogging and between cycloplegia and baseline only. Conclusions: Determination of refractive error under cycloplegia elicits more plus than using extended optical fogging as a method to relax accommodation. These findings support the use of cycloplegic refraction compared with extended optical fogging as a means of controlling accommodation for population based refractive error studies in children.

Sail into one of Europe's greatest cities

A travel article about the university towns of Heidelberg and Tubingen in Germany. "DURING an extended visit to Heidelberg, Germany's oldest university town, Mark Twain was shocked to see students out and about at all hours of the day and night..."--publisher website

Prostate cancer biomarkers and the emerging proteomic techniques used in biomarker research

Prostate cancer (CaP) is the second leading cause of cancer-related deaths in North American males and the most common newly diagnosed cancer in men world wide. Biomarkers are widely used for both early detection and prognostic tests for cancer. The current, commonly used biomarker for CaP is serum prostate specific antigen (PSA). However, the specificity of this biomarker is low as its serum level is not only increased in CaP but also in various other diseases, with age and even body mass index. Human body fluids provide an excellent resource for the discovery of biomarkers, with the advantage over tissue/biopsy samples of their ease of access, due to the less invasive nature of collection. However, their analysis presents challenges in terms of variability and validation. Blood and urine are two human body fluids commonly used for CaP research, but their proteomic analyses are limited both by the large dynamic range of protein abundance making detection of low abundance proteins difficult and in the case of urine, by the high salt concentration. To overcome these challenges, different techniques for removal of high abundance proteins and enrichment of low abundance proteins are used. Their applications and limitations are discussed in this review. A number of innovative proteomic techniques have improved detection of biomarkers. They include two dimensional differential gel electrophoresis (2D-DIGE), quantitative mass spectrometry (MS) and functional proteomic studies, i.e., investigating the association of post translational modifications (PTMs) such as phosphorylation, glycosylation and protein degradation. The recent development of quantitative MS techniques such as stable isotope labeling with amino acids in cell culture (SILAC), isobaric tags for relative and absolute quantitation (iTRAQ) and multiple reaction monitoring (MRM) have allowed proteomic researchers to quantitatively compare data from different samples. 2D-DIGE has greatly improved the statistical power of classical 2D gel analysis by introducing an internal control. This chapter aims to review novel CaP biomarkers as well as to discuss current trends in biomarker research from two angles: the source of biomarkers (particularly human body fluids such as blood and urine), and emerging proteomic approaches for biomarker research.

Visualisation of complex flows using texture-based techniques

Detailed representations of complex flow datasets are often difficult to generate using traditional vector visualisation techniques such as arrow plots and streamlines. This is particularly true when the flow regime changes in time. Texture-based techniques, which are based on the advection of dense textures, are novel techniques for visualising such flows. We review two popular texture based techniques and their application to flow datasets sourced from active research projects. The techniques investigated were Line integral convolution (LIC) [1], and Image based flow visualisation (IBFV) [18]. We evaluated these and report on their effectiveness from a visualisation perspective. We also report on their ease of implementation and computational overheads.

Prediction of transcriptional regulatory interactions in bacteria : a comparative genomics approach

Exponential growth of genomic data in the last two decades has made manual analyses impractical for all but trial studies. As genomic analyses have become more sophisticated, and move toward comparisons across large datasets, computational approaches have become essential. One of the most important biological questions is to understand the mechanisms underlying gene regulation. Genetic regulation is commonly investigated and modelled through the use of transcriptional regulatory network (TRN) structures. These model the regulatory interactions between two key components: transcription factors (TFs) and the target genes (TGs) they regulate. Transcriptional regulatory networks have proven to be invaluable scientific tools in Bioinformatics. When used in conjunction with comparative genomics, they have provided substantial insights into the evolution of regulatory interactions. Current approaches to regulatory network inference, however, omit two additional key entities: promoters and transcription factor binding sites (TFBSs). In this study, we attempted to explore the relationships among these regulatory components in bacteria. Our primary goal was to identify relationships that can assist in reducing the high false positive rates associated with transcription factor binding site predictions and thereupon enhance the reliability of the inferred transcription regulatory networks. In our preliminary exploration of relationships between the key regulatory components in Escherichia coli transcription, we discovered a number of potentially useful features. The combination of location score and sequence dissimilarity scores increased de novo binding site prediction accuracy by 13.6%. Another important observation made was with regards to the relationship between transcription factors grouped by their regulatory role and corresponding promoter strength. Our study of E.coli ��70 promoters, found support at the 0.1 significance level for our hypothesis | that weak promoters are preferentially associated with activator binding sites to enhance gene expression, whilst strong promoters have more repressor binding sites to repress or inhibit gene transcription. Although the observations were specific to �70, they nevertheless strongly encourage additional investigations when more experimentally confirmed data are available. In our preliminary exploration of relationships between the key regulatory components in E.coli transcription, we discovered a number of potentially useful features { some of which proved successful in reducing the number of false positives when applied to re-evaluate binding site predictions. Of chief interest was the relationship observed between promoter strength and TFs with respect to their regulatory role. Based on the common assumption, where promoter homology positively correlates with transcription rate, we hypothesised that weak promoters would have more transcription factors that enhance gene expression, whilst strong promoters would have more repressor binding sites. The t-tests assessed for E.coli �70 promoters returned a p-value of 0.072, which at 0.1 significance level suggested support for our (alternative) hypothesis; albeit this trend may only be present for promoters where corresponding TFBSs are either all repressors or all activators. Nevertheless, such suggestive results strongly encourage additional investigations when more experimentally confirmed data will become available. Much of the remainder of the thesis concerns a machine learning study of binding site prediction, using the SVM and kernel methods, principally the spectrum kernel. Spectrum kernels have been successfully applied in previous studies of protein classification [91, 92], as well as the related problem of promoter predictions [59], and we have here successfully applied the technique to refining TFBS predictions. The advantages provided by the SVM classifier were best seen in `moderately'-conserved transcription factor binding sites as represented by our E.coli CRP case study. Inclusion of additional position feature attributes further increased accuracy by 9.1% but more notable was the considerable decrease in false positive rate from 0.8 to 0.5 while retaining 0.9 sensitivity. Improved prediction of transcription factor binding sites is in turn extremely valuable in improving inference of regulatory relationships, a problem notoriously prone to false positive predictions. Here, the number of false regulatory interactions inferred using the conventional two-component model was substantially reduced when we integrated de novo transcription factor binding site predictions as an additional criterion for acceptance in a case study of inference in the Fur regulon. This initial work was extended to a comparative study of the iron regulatory system across 20 Yersinia strains. This work revealed interesting, strain-specific difierences, especially between pathogenic and non-pathogenic strains. Such difierences were made clear through interactive visualisations using the TRNDifi software developed as part of this work, and would have remained undetected using conventional methods. This approach led to the nomination of the Yfe iron-uptake system as a candidate for further wet-lab experimentation due to its potential active functionality in non-pathogens and its known participation in full virulence of the bubonic plague strain. Building on this work, we introduced novel structures we have labelled as `regulatory trees', inspired by the phylogenetic tree concept. Instead of using gene or protein sequence similarity, the regulatory trees were constructed based on the number of similar regulatory interactions. While the common phylogentic trees convey information regarding changes in gene repertoire, which we might regard being analogous to `hardware', the regulatory tree informs us of the changes in regulatory circuitry, in some respects analogous to `software'. In this context, we explored the `pan-regulatory network' for the Fur system, the entire set of regulatory interactions found for the Fur transcription factor across a group of genomes. In the pan-regulatory network, emphasis is placed on how the regulatory network for each target genome is inferred from multiple sources instead of a single source, as is the common approach. The benefit of using multiple reference networks, is a more comprehensive survey of the relationships, and increased confidence in the regulatory interactions predicted. In the present study, we distinguish between relationships found across the full set of genomes as the `core-regulatory-set', and interactions found only in a subset of genomes explored as the `sub-regulatory-set'. We found nine Fur target gene clusters present across the four genomes studied, this core set potentially identifying basic regulatory processes essential for survival. Species level difierences are seen at the sub-regulatory-set level; for example the known virulence factors, YbtA and PchR were found in Y.pestis and P.aerguinosa respectively, but were not present in both E.coli and B.subtilis. Such factors and the iron-uptake systems they regulate, are ideal candidates for wet-lab investigation to determine whether or not they are pathogenic specific. In this study, we employed a broad range of approaches to address our goals and assessed these methods using the Fur regulon as our initial case study. We identified a set of promising feature attributes; demonstrated their success in increasing transcription factor binding site prediction specificity while retaining sensitivity, and showed the importance of binding site predictions in enhancing the reliability of regulatory interaction inferences. Most importantly, these outcomes led to the introduction of a range of visualisations and techniques, which are applicable across the entire bacterial spectrum and can be utilised in studies beyond the understanding of transcriptional regulatory networks.

Non-parametric techniques for fast and robust stereo matching

The mining environment presents a challenging prospect for stereo vision. Our objective is to produce a stereo vision sensor suited to close-range scenes consisting mostly of rocks. This sensor should produce a dense depth map within real-time constraints. Speed and robustness are of foremost importance for this application. This paper compares a number of stereo matching algorithms in terms of robustness and suitability to fast implementation. These include traditional area-based algorithms, and algorithms based on non-parametric transforms, notably the rank and census transforms. Our experimental results show that the rank and census transforms are robust with respect to radiometric distortion and introduce less computational complexity than conventional area-based matching techniques.