A novel approach to modelling and exploiting uncertainty in stochastic control systems

We consider an inversion-based neurocontroller for solving control problems of uncertain nonlinear systems. Classical approaches do not use uncertainty information in the neural network models. In this paper we show how we can exploit knowledge of this uncertainty to our advantage by developing a novel robust inverse control method. Simulations on a nonlinear uncertain second order system illustrate the approach.

Indwelling catheters and medical implants with FXIIIa inhibitors:a novel approach to the treatment of catheter and medical device-related infections

Central venous catheters (CVCs) are being utilized with increasing frequency in intensive care and general medical wards. In spite of the extensive experience gained in their application, CVCs are related to the long-term risks of catheter sheath formation, infection, and thrombosis (of the catheter or vessel itself) during catheterization. Such CVC-related-complications are associated with increased morbidity, mortality, duration of hospitalization, and medical care cost [1]. The present study incorporates a novel group of Factor XIIIa (FXIIIa, plasma transglutaminase) inhibitors into a lubricious silicone elastomer in order to generate an optimized drug delivery system whereby a secondary sustained drug release profile occurs following an initial burst release for catheters and other medical devices. We propose that the incorporation of FXIIIa inhibitors into catheters, stents, and other medical implant devices would reduce the incidence of catheter sheath formation, thrombotic occlusion, and associated staphylococcal infection. This technique could be used as a local delivery system for extended release with an immediate onset of action for other poorly aqueous soluble compounds. © 2012 Elsevier B.V. All rights reserved.

Greener methodology for the synthesis of alpha-diazocarbonyl compounds and a novel approach to dioxinone derivatives

This thesis outlines a more environmentally benign approach to diazo transfer, and the investigation of the reactivity of -diazocarbonyl compounds when subjected to transition metal and lanthanide catalysis. Extensive studies were carried out to find the optimum conditions for a greener diazo transfer methodology, and this was also applied to a continuous process for the synthesis of -diazo--ketoesters. The first chapter includes a literature review of the synthesis and subsequent reactivity of -diazocarbonyl compounds. An overview of the applications of flow chemistry for the synthesis of hazardous intermediates is also included. The applications of lanthanide catalysts in organic synthesis is also discussed. The second chapter outlines the extensive studies undertaken to determine the optimum conditions for a greener diazo transfer methodology, including base and solvent studies. Use of water as a viable solvent for diazo transfer was successfully investigated. Diazo transfer to a range of -diazo--ketoesters was achieved using 5 mol% triethylamine or DMAP in water with high conversions. Polystyrene-supported benzenesulfonyl azide as an alternative diazo transfer reagent was also explored, as well as investigations into cheaper generation of this safer reagent. This polymer-supported benzenesulfonyl azide was used with 25 mol% of base in water to achieve successful diazo transfer to a range of -diazo--ketoesters. The third chapter describes the application of the new methodology developed in Chapter 2 to a continuous processing approach. Various excellent conditions were identified for both batch and flow reactions. A series of -diazo--ketoesters were synthesised with excellent conversions using 25 mol% triethylamine in 90:10 acetone water using flow chemistry. Successful diazo transfer was also achieved using a polymer-supported benzenesulfonyl azide in water under flow conditions. The fourth chapter discusses the reactivity of -diazo--ketoesters under transition metal and lanthanide catalysis. This chapter describes the synthesis of a range of -ketoesters via transesterification, which were used to synthesise a range of novel -diazo--ketoesters that were used in subsequent decomposition reactions. A novel route to dioxinones via rhodium(II) catalysis is reported. Attempted OH and SH insertion reactions in the presence of various lanthanide(II) catalysts are outlined, leading to some unexpected and interesting rearrangement products. The experimental details, including spectroscopic and analytical data for all compounds prepared, are reported at the end of each chapter.

OC-OT-LIBS: A novel approach to the chemical characterization of single particles

Spectral identification of individual micro- and nano-sized particles by the sequential intervention of optical catapulting, optical trapping and laser-induced breakdown spectroscopy is presented [1]. The three techniques are used for different purposes. Optical catapulting (OC) serves to put the particulate material under inspection in aerosol form [2-4]. Optical trapping (OT) permits the isolation and manipulation of individual particles from the aerosol, which are subsequently analyzed by laser-induced breakdown spectroscopy (LIBS). Once catapulted, the dynamics of particle trapping depends on the laser beam characteristics (power and intensity gradient) and on the particle properties (size, mass and shape). Particles are stably trapped in air at atmospheric pressure and can be conveniently manipulated for a precise positioning for LIBS analysis. The spectra acquired from the individually trapped particles permit a straightforward identification of the inspected material. The current work focuses on the development of a procedure for simultaneously acquiring dual information about the particle under study via LIBS and time-resolved plasma images by taking advantage of the aforementioned features of the OC-OT-LIBS instrument to align the multiple lines in a simple yet highly accurate way. The plasma imaging does not only further reinforce the spectral data, but also allows a better comprehension of the chemical and physical processes involved during laser-particle interaction. Also, a thorough determination of the optimal excitation conditions generating the most information out of each laser event was run along the determination of parameters such as the width of the optical trap, its stability as a function of the laser power and the laser wavelength. The extreme sensibility of the presented OC-OT-LIBS technology allows a detection power of attograms for single/individual particle analysis.

A novel approach to Non-Orthogonal Random Access with application to NB-IoT NTN systems

In the Massive IoT vision, millions of devices need to be connected to the Internet through a wireless access technology. However, current IoT-focused standards are not fully prepared for this future. In this thesis, a novel approach to Non-Orthogonal techniques for Random Access, which is the main bottleneck in high density systems, is proposed. First, the most popular wireless access standards are presented, with a focus on Narrowband-IoT. Then, the Random Access procedure as implemented in NB-IoT is analyzed. The Non-Orthogonal Random Access technique is presented next, along with two potential algorithms for the detection of non-orthogonal preambles. Finally, the performance of the proposed solutions are obtained through numerical simulations.

A Bayesian approach to disease gene location using allelic association

A Bayesian approach to analysing data from family-based association studies is developed. This permits direct assessment of the range of possible values of model parameters, such as the recombination frequency and allelic associations, in the light of the data. In addition, sophisticated comparisons of different models may be handled easily, even when such models are not nested. The methodology is developed in such a way as to allow separate inferences to be made about linkage and association by including theta, the recombination fraction between the marker and disease susceptibility locus under study, explicitly in the model. The method is illustrated by application to a previously published data set. The data analysis raises some interesting issues, notably with regard to the weight of evidence necessary to convince us of linkage between a candidate locus and disease.

Gene-based approach to human gene-phenotype correlations

Elucidating the genetic basis of human phenotypes is a major goal of contemporary geneticists. Logically, two fundamental and contrasting approaches are available, one that begins with a phenotype and concludes with the identification of a responsible gene or genes; the other that begins with a gene and works toward identifying one or more phenotypes resulting from allelic variation of it. This paper provides a conceptual overview of phenotype-based vs. gene-based procedures with emphasis on gene-based methods. A key feature of a gene-based approach is that laboratory effort first is devoted to developing an assay for mutations in the gene under regard; the assay then is applied to the evaluation of large numbers of unrelated individuals with a variety of phenotypes that are deemed potentially resulting from alleles at the gene. No effort is directed toward chromosomally mapping the loci responsible for the phenotypes scanned. Example is made of my laboratory’s successful use of a gene-based approach to identify genes causing hereditary diseases of the retina such as retinitis pigmentosa. Reductions in the cost and improvements in the speed of scanning individuals for DNA sequence anomalies may make a gene-based approach an efficient alternative to phenotype-based approaches to correlating genes with phenotypes.

An integrative approach to face and expression recognition from 3D scans

Biometrics is afield of study which pursues the association of a person's identity with his/her physiological or behavioral characteristics.^ As one aspect of biometrics, face recognition has attracted special attention because it is a natural and noninvasive means to identify individuals. Most of the previous studies in face recognition are based on two-dimensional (2D) intensity images. Face recognition based on 2D intensity images, however, is sensitive to environment illumination and subject orientation changes, affecting the recognition results. With the development of three-dimensional (3D) scanners, 3D face recognition is being explored as an alternative to the traditional 2D methods for face recognition.^ This dissertation proposes a method in which the expression and the identity of a face are determined in an integrated fashion from 3D scans. In this framework, there is a front end expression recognition module which sorts the incoming 3D face according to the expression detected in the 3D scans. Then, scans with neutral expressions are processed by a corresponding 3D neutral face recognition module. Alternatively, if a scan displays a non-neutral expression, e.g., a smiling expression, it will be routed to an appropriate specialized recognition module for smiling face recognition.^ The expression recognition method proposed in this dissertation is innovative in that it uses information from 3D scans to perform the classification task. A smiling face recognition module was developed, based on the statistical modeling of the variance between faces with neutral expression and faces with a smiling expression.^ The proposed expression and face recognition framework was tested with a database containing 120 3D scans from 30 subjects (Half are neutral faces and half are smiling faces). It is shown that the proposed framework achieves a recognition rate 10% higher than attempting the identification with only the neutral face recognition module.^

A study of computational methods to analyze gene expression data

The recent advent of new technologies has led to huge amounts of genomic data. With these data come new opportunities to understand biological cellular processes underlying hidden regulation mechanisms and to identify disease related biomarkers for informative diagnostics. However, extracting biological insights from the immense amounts of genomic data is a challenging task. Therefore, effective and efficient computational techniques are needed to analyze and interpret genomic data. In this thesis, novel computational methods are proposed to address such challenges: a Bayesian mixture model, an extended Bayesian mixture model, and an Eigen-brain approach. The Bayesian mixture framework involves integration of the Bayesian network and the Gaussian mixture model. Based on the proposed framework and its conjunction with K-means clustering and principal component analysis (PCA), biological insights are derived such as context specific/dependent relationships and nested structures within microarray where biological replicates are encapsulated. The Bayesian mixture framework is then extended to explore posterior distributions of network space by incorporating a Markov chain Monte Carlo (MCMC) model. The extended Bayesian mixture model summarizes the sampled network structures by extracting biologically meaningful features. Finally, an Eigen-brain approach is proposed to analyze in situ hybridization data for the identification of the cell-type specific genes, which can be useful for informative blood diagnostics. Computational results with region-based clustering reveals the critical evidence for the consistency with brain anatomical structure.

Using biplots to interpret gene expression patterns in plants

Sum: Plant biologists in fields of ecology, evolution, genetics and breeding frequently use multivariate methods. This paper illustrates Principal Component Analysis (PCA) and Gabriel's biplot as applied to microarray expression data from plant pathology experiments. Availability: An example program in the publicly distributed statistical language R is available from the web site (www.tpp.uq.edu.au) and by e-mail from the contact. Contact: scott.chapman@csiro.au.