297 resultados para Radial Distribution Functions


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Neurodegenerative disorders are heterogenous in nature and include a range of ataxias with oculomotor apraxia, which are characterised by a wide variety of neurological and ophthalmological features. This family includes recessive and dominant disorders. A subfamily of autosomal recessive cerebellar ataxias are characterised by defects in the cellular response to DNA damage. These include the well characterised disorders Ataxia-Telangiectasia (A-T) and Ataxia-Telangiectasia Like Disorder (A-TLD) as well as the recently identified diseases Spinocerebellar ataxia with axonal neuropathy Type 1 (SCAN1), Ataxia with Oculomotor Apraxia Type 2 (AOA2), as well as the subject of this thesis, Ataxia with Oculomotor Apraxia Type 1 (AOA1). AOA1 is caused by mutations in the APTX gene, which is located at chromosomal locus 9p13. This gene codes for the 342 amino acid protein Aprataxin. Mutations in APTX cause destabilization of Aprataxin, thus AOA1 is a result of Aprataxin deficiency. Aprataxin has three functional domains, an N-terminal Forkhead Associated (FHA) phosphoprotein interaction domain, a central Histidine Triad (HIT) nucleotide hydrolase domain and a C-terminal C2H2 zinc finger. Aprataxins FHA domain has homology to FHA domain of the DNA repair protein 5’ polynucleotide kinase 3’ phosphatase (PNKP). PNKP interacts with a range of DNA repair proteins via its FHA domain and plays a critical role in processing damaged DNA termini. The presence of this domain with a nucleotide hydrolase domain and a DNA binding motif implicated that Aprataxin may be involved in DNA repair and that AOA1 may be caused by a DNA repair deficit. This was substantiated by the interaction of Aprataxin with proteins involved in the repair of both single and double strand DNA breaks (XRay Cross-Complementing 1, XRCC4 and Poly-ADP Ribose Polymerase-1) and the hypersensitivity of AOA1 patient cell lines to single and double strand break inducing agents. At the commencement of this study little was known about the in vitro and in vivo properties of Aprataxin. Initially this study focused on generation of recombinant Aprataxin proteins to facilitate examination of the in vitro properties of Aprataxin. Using recombinant Aprataxin proteins I found that Aprataxin binds to double stranded DNA. Consistent with a role for Aprataxin as a DNA repair enzyme, this binding is not sequence specific. I also report that the HIT domain of Aprataxin hydrolyses adenosine derivatives and interestingly found that this activity is competitively inhibited by DNA. This provided initial evidence that DNA binds to the HIT domain of Aprataxin. The interaction of DNA with the nucleotide hydrolase domain of Aprataxin provided initial evidence that Aprataxin may be a DNA-processing factor. Following these studies, Aprataxin was found to hydrolyse 5’adenylated DNA, which can be generated by unscheduled ligation at DNA breaks with non-standard termini. I found that cell extracts from AOA1 patients do not have DNA-adenylate hydrolase activity indicating that Aprataxin is the only DNA-adenylate hydrolase in mammalian cells. I further characterised this activity by examining the contribution of the zinc finger and FHA domains to DNA-adenylate hydrolysis by the HIT domain. I found that deletion of the zinc finger ablated the activity of the HIT domain against adenylated DNA, indicating that the zinc finger may be required for the formation of a stable enzyme-substrate complex. Deletion of the FHA domain stimulated DNA-adenylate hydrolysis, which indicated that the activity of the HIT domain may be regulated by the FHA domain. Given that the FHA domain is involved in protein-protein interactions I propose that the activity of Aprataxins HIT domain may be regulated by proteins which interact with its FHA domain. We examined this possibility by measuring the DNA-adenylate hydrolase activity of extracts from cells deficient for the Aprataxin-interacting DNA repair proteins XRCC1 and PARP-1. XRCC1 deficiency did not affect Aprataxin activity but I found that Aprataxin is destabilized in the absence of PARP-1, resulting in a deficiency of DNA-adenylate hydrolase activity in PARP-1 knockout cells. This implies a critical role for PARP-1 in the stabilization of Aprataxin. Conversely I found that PARP-1 is destabilized in the absence of Aprataxin. PARP-1 is a central player in a number of DNA repair mechanisms and this implies that not only do AOA1 cells lack Aprataxin, they may also have defects in PARP-1 dependant cellular functions. Based on this I identified a defect in a PARP-1 dependant DNA repair mechanism in AOA1 cells. Additionally, I identified elevated levels of oxidized DNA in AOA1 cells, which is indicative of a defect in Base Excision Repair (BER). I attribute this to the reduced level of the BER protein Apurinic Endonuclease 1 (APE1) I identified in Aprataxin deficient cells. This study has identified and characterised multiple DNA repair defects in AOA1 cells, indicating that Aprataxin deficiency has far-reaching cellular consequences. Consistent with the literature, I show that Aprataxin is a nuclear protein with nucleoplasmic and nucleolar distribution. Previous studies have shown that Aprataxin interacts with the nucleolar rRNA processing factor nucleolin and that AOA1 cells appear to have a mild defect in rRNA synthesis. Given the nucleolar localization of Aprataxin I examined the protein-protein interactions of Aprataxin and found that Aprataxin interacts with a number of rRNA transcription and processing factors. Based on this and the nucleolar localization of Aprataxin I proposed that Aprataxin may have an alternative role in the nucleolus. I therefore examined the transcriptional activity of Aprataxin deficient cells using nucleotide analogue incorporation. I found that AOA1 cells do not display a defect in basal levels of RNA synthesis, however they display defective transcriptional responses to DNA damage. In summary, this thesis demonstrates that Aprataxin is a DNA repair enzyme responsible for the repair of adenylated DNA termini and that it is required for stabilization of at least two other DNA repair proteins. Thus not only do AOA1 cells have no Aprataxin protein or activity, they have additional deficiencies in PolyADP Ribose Polymerase-1 and Apurinic Endonuclease 1 dependant DNA repair mechanisms. I additionally demonstrate DNA-damage inducible transcriptional defects in AOA1 cells, indicating that Aprataxin deficiency confers a broad range of cellular defects and highlighting the complexity of the cellular response to DNA damage and the multiple defects which result from Aprataxin deficiency. My detailed characterization of the cellular consequences of Aprataxin deficiency provides an important contribution to our understanding of interlinking DNA repair processes.

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Bag sampling techniques can be used to temporarily store an aerosol and therefore provide sufficient time to utilize sensitive but slow instrumental techniques for recording detailed particle size distributions. Laboratory based assessment of the method were conducted to examine size dependant deposition loss coefficients for aerosols held in VelostatTM bags conforming to a horizontal cylindrical geometry. Deposition losses of NaCl particles in the range of 10 nm to 160 nm were analysed in relation to the bag size, storage time, and sampling flow rate. Results of this study suggest that the bag sampling method is most useful for moderately short sampling periods of about 5 minutes.

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Matrix function approximation is a current focus of worldwide interest and finds application in a variety of areas of applied mathematics and statistics. In this thesis we focus on the approximation of A^(-α/2)b, where A ∈ ℝ^(n×n) is a large, sparse symmetric positive definite matrix and b ∈ ℝ^n is a vector. In particular, we will focus on matrix function techniques for sampling from Gaussian Markov random fields in applied statistics and the solution of fractional-in-space partial differential equations. Gaussian Markov random fields (GMRFs) are multivariate normal random variables characterised by a sparse precision (inverse covariance) matrix. GMRFs are popular models in computational spatial statistics as the sparse structure can be exploited, typically through the use of the sparse Cholesky decomposition, to construct fast sampling methods. It is well known, however, that for sufficiently large problems, iterative methods for solving linear systems outperform direct methods. Fractional-in-space partial differential equations arise in models of processes undergoing anomalous diffusion. Unfortunately, as the fractional Laplacian is a non-local operator, numerical methods based on the direct discretisation of these equations typically requires the solution of dense linear systems, which is impractical for fine discretisations. In this thesis, novel applications of Krylov subspace approximations to matrix functions for both of these problems are investigated. Matrix functions arise when sampling from a GMRF by noting that the Cholesky decomposition A = LL^T is, essentially, a `square root' of the precision matrix A. Therefore, we can replace the usual sampling method, which forms x = L^(-T)z, with x = A^(-1/2)z, where z is a vector of independent and identically distributed standard normal random variables. Similarly, the matrix transfer technique can be used to build solutions to the fractional Poisson equation of the form ϕn = A^(-α/2)b, where A is the finite difference approximation to the Laplacian. Hence both applications require the approximation of f(A)b, where f(t) = t^(-α/2) and A is sparse. In this thesis we will compare the Lanczos approximation, the shift-and-invert Lanczos approximation, the extended Krylov subspace method, rational approximations and the restarted Lanczos approximation for approximating matrix functions of this form. A number of new and novel results are presented in this thesis. Firstly, we prove the convergence of the matrix transfer technique for the solution of the fractional Poisson equation and we give conditions by which the finite difference discretisation can be replaced by other methods for discretising the Laplacian. We then investigate a number of methods for approximating matrix functions of the form A^(-α/2)b and investigate stopping criteria for these methods. In particular, we derive a new method for restarting the Lanczos approximation to f(A)b. We then apply these techniques to the problem of sampling from a GMRF and construct a full suite of methods for sampling conditioned on linear constraints and approximating the likelihood. Finally, we consider the problem of sampling from a generalised Matern random field, which combines our techniques for solving fractional-in-space partial differential equations with our method for sampling from GMRFs.

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While there is substantial research on attitudinal and behavioral loyalty, the deconstruction of attitudinal loyalty into its two key components – emotional and cognitive loyalty – has been largely ignored. Despite the existence of managerial strategies aimed at increasing each of these two components, there is little academic research to support these managerial efforts. This paper seeks to advance the understanding of emotional and cognitive brand loyalty by examining the psychological function that these dimensions of brand loyalty perform for the consumer. We employ Katz’s (1960) four functions of attitudes (utilitarian, knowledge, value-expression, ego-defence) to investigate this question. Surveys using a convenience sample were completed by 268 consumers in two metropolitan cities on a variety of goods, services and durable products. The relationship between the functions and dimensions of loyalty were examined using MANOVA. The results show that both the utilitarian and knowledge functions of loyalty are significantly positively related to cognitive loyalty while the ego-defensive function of loyalty is significantly positively related to emotional loyalty. The results for the value-expressive function of loyalty were nonsignificant.

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1. Species' distribution modelling relies on adequate data sets to build reliable statistical models with high predictive ability. However, the money spent collecting empirical data might be better spent on management. A less expensive source of species' distribution information is expert opinion. This study evaluates expert knowledge and its source. In particular, we determine whether models built on expert knowledge apply over multiple regions or only within the region where the knowledge was derived. 2. The case study focuses on the distribution of the brush-tailed rock-wallaby Petrogale penicillata in eastern Australia. We brought together from two biogeographically different regions substantial and well-designed field data and knowledge from nine experts. We used a novel elicitation tool within a geographical information system to systematically collect expert opinions. The tool utilized an indirect approach to elicitation, asking experts simpler questions about observable rather than abstract quantities, with measures in place to identify uncertainty and offer feedback. Bayesian analysis was used to combine field data and expert knowledge in each region to determine: (i) how expert opinion affected models based on field data and (ii) how similar expert-informed models were within regions and across regions. 3. The elicitation tool effectively captured the experts' opinions and their uncertainties. Experts were comfortable with the map-based elicitation approach used, especially with graphical feedback. Experts tended to predict lower values of species occurrence compared with field data. 4. Across experts, consensus on effect sizes occurred for several habitat variables. Expert opinion generally influenced predictions from field data. However, south-east Queensland and north-east New South Wales experts had different opinions on the influence of elevation and geology, with these differences attributable to geological differences between these regions. 5. Synthesis and applications. When formulated as priors in Bayesian analysis, expert opinion is useful for modifying or strengthening patterns exhibited by empirical data sets that are limited in size or scope. Nevertheless, the ability of an expert to extrapolate beyond their region of knowledge may be poor. Hence there is significant merit in obtaining information from local experts when compiling species' distribution models across several regions.

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Vitamin D is unique among the vitamins in that humans can synthesize it via the action of UV radiation upon the skin. This combined with its ability to act on specific target tissues via Vitamin D Receptor’s (VDR) make its classification as a steroid hormone more appropriate. While Vitamin D deficiency is a recognized problem in some northern latitude countries, recent studies have shown even in sunny countries such as Australia, vitamin D deficiency may be more prevalent than first thought. Vitamin D is most well known for its role in bone health, however, the discovery of VDR’s on a wide variety of tissue types has also opened up roles for vitamin D far beyond traditional bone health. These include possible associations with autoimmune diseases such as multiple sclerosis and inflammatory bowel diseases, cancer, cardiovascular diseases and muscle strength. Firstly, this paper presents an overview of the two sources of vitamin D: exposure to ultraviolet-B radiation and food sources of vitamin D, with particular focus on both Australian and international studies on dietary vitamin D intake and national fortification strategies. Secondly, the paper reviews recent epidemiological and experimental evidence linking vitamin D and its role in health and disease for the major conditions linked to suboptimal vitamin D, while identifying significant gaps in the research and possible future directions for research.

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This paper presents a reliability-based reconfiguration methodology for power distribution systems. Probabilistic reliability models of the system components are considered and Monte Carlo method is used while evaluating the reliability of the distribution system. The reconfiguration is aimed at maximizing the reliability of the power supplied to the customers. A binary particle swarm optimization (BPSO) algorithm is used as a tool to determine the optimal configuration of the sectionalizing and tie switches in the system. The proposed methodology is applied on a modified IEEE 13-bus distribution system.

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High-speed videokeratoscopy is an emerging technique that enables study of the corneal surface and tear-film dynamics. Unlike its static predecessor, this new technique results in a very large amount of digital data for which storage needs become significant. We aimed to design a compression technique that would use mathematical functions to parsimoniously fit corneal surface data with a minimum number of coefficients. Since the Zernike polynomial functions that have been traditionally used for modeling corneal surfaces may not necessarily correctly represent given corneal surface data in terms of its optical performance, we introduced the concept of Zernike polynomial-based rational functions. Modeling optimality criteria were employed in terms of both the rms surface error as well as the point spread function cross-correlation. The parameters of approximations were estimated using a nonlinear least-squares procedure based on the Levenberg-Marquardt algorithm. A large number of retrospective videokeratoscopic measurements were used to evaluate the performance of the proposed rational-function-based modeling approach. The results indicate that the rational functions almost always outperform the traditional Zernike polynomial approximations with the same number of coefficients.

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In this paper, we present a finite sample analysis of the sample minimum-variance frontier under the assumption that the returns are independent and multivariate normally distributed. We show that the sample minimum-variance frontier is a highly biased estimator of the population frontier, and we propose an improved estimator of the population frontier. In addition, we provide the exact distribution of the out-of-sample mean and variance of sample minimum-variance portfolios. This allows us to understand the impact of estimation error on the performance of in-sample optimal portfolios. Key Words: minimum-variance frontier; efficiency set constants; finite sample distribution

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A comprehensive voltage imbalance sensitivity analysis and stochastic evaluation based on the rating and location of single-phase grid-connected rooftop photovoltaic cells (PVs) in a residential low voltage distribution network are presented. The voltage imbalance at different locations along a feeder is investigated. In addition, the sensitivity analysis is performed for voltage imbalance in one feeder when PVs are installed in other feeders of the network. A stochastic evaluation based on Monte Carlo method is carried out to investigate the risk index of the non-standard voltage imbalance in the network in the presence of PVs. The network voltage imbalance characteristic based on different criteria of PV rating and location and network conditions is generalized. Improvement methods are proposed for voltage imbalance reduction and their efficacy is verified by comparing their risk index using Monte Carlo simulations.

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In this paper, the placement and sizing of Distributed Generators (DG) in distribution networks are determined optimally. The objective is to minimize the loss and to improve the reliability. The constraints are the bus voltage, feeder current and the reactive power flowing back to the source side. The placement and size of DGs are optimized using a combination of Discrete Particle Swarm Optimization (DPSO) and Genetic Algorithm (GA). This increases the diversity of the optimizing variables in DPSO not to be stuck in the local minima. To evaluate the proposed algorithm, the semi-urban 37-bus distribution system connected at bus 2 of the Roy Billinton Test System (RBTS), which is located at the secondary side of a 33/11 kV distribution substation, is used. The results finally illustrate the efficiency of the proposed method.

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Network induced delay in networked control systems (NCS) is inherently non-uniformly distributed and behaves with multifractal nature. However, such network characteristics have not been well considered in NCS analysis and synthesis. Making use of the information of the statistical distribution of NCS network induced delay, a delay distribution based stochastic model is adopted to link Quality-of-Control and network Quality-of-Service for NCS with uncertainties. From this model together with a tighter bounding technology for cross terms, H∞ NCS analysis is carried out with significantly improved stability results. Furthermore, a memoryless H∞ controller is designed to stabilize the NCS and to achieve the prescribed disturbance attenuation level. Numerical examples are given to demonstrate the effectiveness of the proposed method.