Thesis (Ph.D.)--University of Melbourne, School of Chemistry, 2007. / Typescript. Includes bibliographical references (leaves 103-109).

Thesis (Ph. D.)--Earth and Atmospheric Sciences, Georgia Institute of Technology, 2008. / Committee Chair: Greg Huey; Committee Member: David Tan; Committee Member: Jennifer Olson; Committee Member: Paul Wine; Committee Member: Rodney Weber.

Title from PDF of title page (University of Missouri--Columbia, viewed on Feb 26, 2010). The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Dissertation advisor: Dr. Ping Yu. Vita. Includes bibliographical references.

Thesis (Ph. D.)--University of Texas at Austin, 2007. / Vita. Includes bibliographical references.

Philosophiae Doctor - PhD / Estrogens play an extraordinary role in the endocrine system regulation through the stimulation and regulation of endocrine pathways. 17β-estradiol is one of the final metabolites in estrogen regulation by hydroxylase enzymes that are well recognized for their metabolic role in hormone fragmentation and dissociation, through hydroxylation reactions that reversibly convert a series of androgens to estrogens (i.e. or one estrogen to the other). However, the 17β-estradiol hormone has been classified as one of the estrogenic endocrine disrupting compounds {i.e. EDC (s)} that show significant adverse effects in the estrogen pathways of male and female animal species. Estrogen receptor alpha (ER-α) is significantly activated by 17β-estradiol, which is a steroid hormone. A biosensor system for the determination of 17β-estradiol was developed based on the highly selective and specific physiological substrate level activation of the ER-α biomolecule by the (17β-estradiol) compound. The chemically-tuned tin selenide quantum dots capped with 3-mercaptopropionic acid were produced at room temperature and employed to capture the ER-α micro-molecule onto the electrode surfaces. These quantum dots possessed average particle size (APS) diameters between 4.6 ± 0.6 nm and an indirect band gap energy (Eg) of 3.14 eV. Surface modification on the quantum dots permitted the formation of efficient amide bonds between the capping molecules of the quantum dots and the estrogen receptor-alpha. The tin selenide quantum dots platform enhanced the surface bio-reactivity of the receptorsensor film. The receptorsensor’s sensitivity towards 17β-estradiol was 5.9 μA/μM associated with a response time (tResponse) of less than 1.2 s. The formal potential, Ep˚ˈ, of the receptorsensor-substrate complex was 149 mV. A detection limit (DL) of 1.9 nM was obtained for the electrochemical biosensing methodology. 17β-estradiol–receptorsensor response kinetics were also evaluated, where a dissociation rate (kd) of 7.6 μM/s, a 50 % inhibition concentration (IC50) value of 3.4 nM and a binding efficiency (Bmax) of 7 nM were obtained. Effective measure of 17β-estradiol concentrations as low as 3.8 nM present in surface waters have been reported to induce feminisation in male aquatic species. The receptorsensor’s dynamic linear range (DLR) nevertheless showed capability of screening a minimum of 0.2 nM to a maximum of 8 nM of the 17β-estradiol concentrations. Furthermore, during the estrogen replacement therapy (ERT), 17β-estradiol concentration levels are monitored at frequent phases, wherein 17β-estradiol concentrations from as low as 0.37 nM are recovered in the serum (i.e. this value was also evaluated to be within the receptorsensor’s-DLR), determining its future capability to be developed for; clinical-diagnosis screening of the 17β-estradiol.

The canonical Wnt pathway is a vitally important signalling pathway that plays an important role in cell proliferation, differentiation and fate decisions in embryonic development and in the maintenance of adult tissues. The twelve Armadillo (ARM) repeat-containing protein beta-catenin acts as the signal transducer in this pathway and is continuously degraded in the cytosol by the beta-catenin destruction complex (BDC). Upon receiving the Wnt signal the BDC is inactivated, allowing beta-catenin to accumulate in the cytosol and be transported to the nucleus where it binds to the TCF/LEF family of transcription factors, inducing the expression of cell cycle promotor genes. In this Thesis I describe investigations into the roles of leucine-rich repeat kinase 2 (LRRK2) and the transcription factor TCF7L2 within this signalling pathway. LRRK2 is a large multi-domain protein with strong links to Parkinson’s disease and suggested to play a role in inactivating the BDC in response to the Wnt signal. A recent paper proposed that the previously uncharacterised regions of LRRK2 contain a series of tandem repeat sub-domains. I began an investigation into these sub-domains but I was unable to produce soluble protein constructs despite the use of a range of common techniques, and so I was forced to conclude this project early. The main body of this thesis focuses on the interaction between the intrinsically disordered TCF7L2 and the repeat protein beta-catenin, a very long interface of approximately 4800 Å2 that spans from the third to the eleventh ARM repeat of beta-catenin and residues 12 to 50 of TCF7L2, as determined by X-ray crystal structures. First, a fluorescence reporter system for the binding interaction was developed and used to determine the kinetic rate constants for the association and dissociation of the wild-type construct using stopped-flow fluorescence spectroscopy and time-dependent fluorescence spectroscopy. It was found that association of TCF7L2 and beta-catenin was rapid (7.3 ± 0.1 x107 M-1s-1) with only a single phase was observed, whereas dissociation was biphasic and slow (5.7 ± 0.4 x10-4 s-1, 15.2 ± 2.8 x10-4 s-1). Using either of these two dissociation rate constants the calculated Kd value obtained is much lower than the values previously reported in the literature (8 ± 1 / 20 ± 2 pM compared with 16 nM). This reporter system was then used to investigate the striking variability between three crystal structures previously obtained for the TCF7L2-beta-catenin complex, in which different regions of TCF7L2 show different elements of secondary structure. Mutational analysis revealed that the interface residues on TCF7L2 identified in these structures make little or no contribution to the overall binding affinity, pointing to a transient nature of these contact in solution and suggesting that the observed differences between the structures are due to differences in crystal packing. Further experiments into the effect of osmolarity on the binding equilibrium and kinetics supported this conclusion and suggest a change in the association/dissociation mechanism as a function of ionic strength. Lastly, further mutational analysis of TCF7L2 revealed two regions that contribute particularly strongly to the binding kinetics, suggesting that TCF7L2-beta-catenin assembly proceeds via a two-site avidity mechanism. Some of the most destabilising variants display two additional dissociation phases, indicating the presence of an alternative dissociation pathway that is inaccessible to the wild-type. In summary, the results presented here provide insights into the kinetics of molecular recognition of a long intrinsically disordered region with an extended repeat protein surface, a process shown to involve multiple routes with multiple steps in each.

O desenvolvimento e aperfeiçoamento de métodos de otimização são pontos de profundo interesse em todas as áreas de pesquisa. Tais técnicas muitas vezes envolvem a aquisição de métodos de controle novos ou melhores, o que está diretamente ligado a duas tarefas importantes: a escolha de formas eficientes de monitoramento do processo e a obtenção de modelos confiáveis para a variável de interesse a partir de dados experimentais. Graças às suas diversas vantagens, os sensores óticos vêm sendo amplamente aplicados na primeira tarefa. Uma vez que é possível a utilização de vários tipos de espectroscopia através deste tipo de sensor, modelos capazes de lidar com dados espectrais estão se tornando cada vez mais atraentes. A segunda tarefa, por sua vez, depende não só de quais preditores são utilizados na construção do modelo, mas também de quantos. Como a qualidade do modelo depende também do número de variáveis selecionadas, é importante desenvolver métodos que identifiquem aqueles que explicam o máximo possível da variabilidade dos dados. O método de otimização Colônia de Formigas (ACO) aparece como uma ferramenta bastante útil na seleção de variáveis, podendo-se encontrar muitas variações desse algoritmo na literatura. O propósito deste trabalho é desenvolver métodos de seleção de variáveis com base no algoritmo ACO, conceitos estatísticos e testes de hipóteses. Para isso, diversos critérios de decisão foram implementados nas etapas do algoritmo referentes à atualização de trilha de feromônios (C1) e à seleção de modelos (C2). A fim de estudar estas modificações, foram realizados dois estudos de caso: o primeiro na área de bioprocessos e o segundo na área de caracterização de alimentos. Ambos os estudos mostraram que, em geral, os modelos com menores erros são obtidos utilizando-se métricas dos componentes do modelo, tal como o tamanho do intervalo de confiança de cada parâmetro e o teste-t de hipóteses. Além disso, a modificação do critério de seleção de modelos parece não interferir significativamente no resultado final do algoritmo. Por último, foi feito um estudo da aplicação dessas versões do ACO no campo de caracterização de combustíveis, mais especificamente diesel, associando-se duas análises espectroscópicas para predição do conteúdo de enxofre. Algumas das versões desenvolvidas mostraram-se superior ao algoritmo ACO utilizado como base para este trabalho, proposto por Ranzan (2014), e todas os versões forneceram melhores resultados na quantificação de enxofre que aqueles obtidos por PCR. Dessa forma, comprova-se a potencialidade de métricas implementadas no algoritmo ACO, associadas à espectroscopia, na seleção de preditores significativos. / The development and improvement of optimization methods are points of deep interest in all areas of research. These techniques are often related to the acquisition of new or better control methods, which are directly attached to two importante tasks: choosing efficient forms of process monitoring and obtaining reliable models for the monitored variable from experimental data. Due to their several advantagens, optical sensors are being widely applied in the first task. Since several types of spectroscopy are possible through this type of sensor, models capable of dealing with spectral data are becoming increasingly attractive. The second task depends not only on which predictors are used in the model, but also on how many. Since the quality of the model depends on the number of selected variables, it is important to develop methods that identify those that explain the greater amount of data variability as possible, without compromising the reliability of the model. The Ant Colony Optimization is an important tool for variable selection, being possible to find a lot of variations of this method in literature. The purpose of this work is to develop a method of variable selection based on the Ant Colony Optimization (ACO) algorithm, statistical concepts and hypothesis testing. For this purpose, several decision criteria for trail update (C1) and model selection (C2) were implemented within the routine. In order to study these modifications, two case study was conducted: one related to bioprocess monitoring and another one envolving the characterization of food products. Both studies showed that, in general, the models with the lowest errors were obtained through the use of model component metrics, such as the length of the confidence interval associated with each parameter and the t hypothesis test. Besides, the modification of the model selection criterion doesn’t seem to affect the algorithm final result. Finally, the aplicattion of these methods in the field of fuels characterization, specifically diesel fuel, was studied, associating two spectroscopical analyses in order to predict the sulfur content. Some of the new developed methods appeared to be better than the ACO algorithm used as basis in this work, proposed by Ranzan (2014), and all methods showed better results than those from the models constructed by PCR. Thus, it is proved the high potencial of using different metrics within ACO algorithm, associated with spectroscopy, in order to select significative predictors.

A dispersão de óxidos sulfurados no meio ambiente, proveniente da queima de combustíveis fosseis, contribui significativamente para a poluição atmosférica. A presença de compostos sulfurados em combustíveis também apresenta desvantagens práticas na operação das unidades, corroendo tubulações e unidades de armazenamento, além de causar danos aos motores. De acordo com a Resolução nº 50, de 23 de dezembro de 2013 da ANP, todo o diesel metropolitano vendido no Brasil deve conter no máximo 10 ppm de enxofre, forçando uma adaptação dos processos produtivos, que, por sua vez, forçam as industrias a investirem em sistemas de controle e consequentemente metodologias de análise on-line de correntes de processo. As análises atuais certificadoras de enxofre em diesel requerem preparação amostral, equipamentos de alto custo e possuem alto tempo morto associado. Surge a necessidade de estudo de uma metodologia capaz de ser aplicada em analisadores de linha para predição de enxofre em tempo real. Com este intuito, este trabalho visa estudar a viabilidade do uso de espectroscopia por fluorescência 2D para predição de enxofre em óleo diesel. A espectroscopia por fluorescência é uma técnica rápida, que dispensa preparação de amostra e possui alta sensibilidade para compostos naturalmente fluorescentes. Um estudo envolvendo quatro componentes sulfurados presentes em diesel foi realizado por meio de metodologias não supervisionadas - PCA, e supervisionadas - PSCM. Não foi possível segmentar as quatro soluções-padrão por meio de gráfico de escores da PCA, mas com PSCM foi factível o ajuste de modelos multilineares para predição de enxofre nas soluções-padrão, com coeficientes de determinação superiores a 0,97. Ainda, é possível definir a região de fluorescência mais significativa para cada um dos padrões, constatando que todos possuem regiões de emissão de fluorescência similares. Com relação à amostras de diesel, foram analisados dois grupos distintos de óleo diesel, (i) diesel HDT com média de 100 ppm de enxofre, e (ii) diesel S10 com média de 6,5 ppm de enxofre. Avaliando os resultados da PCA, foi possível segmentar os dois conjuntos de diesel utilizando os dados de escores. Por PSCM, foi possível ajustar modelos baseados em pares de fluorescência capazes de predizer satisfatoriamente concentrações de enxofre em amostras de diesel S10. Os modelos ajustados para diesel HDT apresentaram resultados menos significativos. Assim, a viabilidade do uso de espectroscopia por fluorescência 2D para a caracterização de enxofre em correntes de diesel foi confirmada, viabilizando a construção de sensores de processos baseados nesta técnica analítica. / The dispersion of sulfur oxides in the environment from the burning of fossil fuels contributes significantly to air pollution. The presence of sulfur compounds in fuel also presents disadvantages in operation units, corroding pipes and storage units and may even damage the engine. According to ANP Resolution No. 50, dated December 23, 2013 the entire metropolitan diesel sold in Brazil must contain no more than 10 ppm sulfur, forcing an adaptation of production processes. The current analysis to certify sulfur content in diesel requires sample preparation, expensive equipment and have high dead time associated. Therefore, it is important the study of a method capable of being applied to online analyzers for predicting sulfur in real time. This work aims to study the feasibility of using 2D fluorescence spectroscopy for prediction of sulfur in diesel fuel. Fluorescence spectroscopy is a rapid technique that, usually, does not require sample preparation and has high sensitivity to naturally fluorescent compounds. Initially, a study involving four sulfur containing compounds present in diesel was carried out by means of unsupervised methods - PCA and supervised - PSCM. It was not possible to segregate the four standard solutions by score plot from the PCA, but PSCM was able to fit general multilinear models for the prediction of sulfur in standard solutions with coefficients of determination greater than 0.97. It was possible to define the region of most significant fluorescence for each standard, noting that all standards present similar fluorescence emission regions. After, two groups of different diesel, diesel HDT averaging 100 ppm sulfur and diesel S10 averaged 6.5 ppm sulfur were analyzed. Evaluating the results of PCA was possible to segregate both sets of diesel data using score plot. With PSCM was possible to find models based on fluorescence able to satisfactorily predict concentrations of sulfur in diesel samples S10. The models adjusted for diesel HDT showed less significant results. Thus, the feasibility of prediction of sulfur in diesel using 2D fluorescence spectroscopy was confirmed.

Dans cette thèse on s’intéresse au problème des décompositions canoniques polyadiques de tenseurs d’ordre $N$ potentiellement grands et sous différentes contraintes (non-négativité, aspect creux lié à une possible surestimation du rang du tenseur). Pour traiter ce problème, nous proposons trois nouvelles approches itératives différentes: deux approches déterministes dont une approche proximale, et une approche stochastique. La première approche étend les travaux de thèse de J-P. Royer au cas de tenseurs de dimension $N$. Dans l’approche stochastique, nous considérons pour la première fois dans le domaine des décompositions tensorielles, des algorithmes génétiques (mimétiques) dont principe général repose sur l’évolution d’une population de candidats. Dans le dernier type d’approche, nous avons considéré un algorithme proximal pré-conditionné (le Block-Coordinate Variable Metric Forward-Backward), algorithme fonctionnant par blocs de données avec une matrice de pré-conditionnement liée à chaque bloc et fondé sur deux étapes successives principales : une étape de gradient et une étape proximale. Finalement, les différentes méthodes suggérées sont comparées entre elles et avec d’autres algorithmes classiques de la littérature sur des données synthétiques (à la fois aléatoires ou proches des données observées en spectroscopie de fluorescence) et sur des données expérimentales réelles correspondant à une campagne de surveillance des eaux d’une rivière et visant à la détection d’apparition de polluants. / In this PhD thesis, we consider the problem of the Canonical Polyadic Decomposition (CPD) of potentially large $N$-th order tensors under different constraints (non-negativity, sparsity due to a possible overestimation of the tensor rank, etc.). To tackle such a problem, we propose three new iterative methods: a standard gradient based deterministic approach, a stochastic approach (memetic) and finally a proximal approach (Block-Coordinate Variable Metric Forward-Backward). The first approach extends J-P. Royer's works to the case of non-negative N-th order tensors. In the stochastic approach, genetic (memetic) methods are considered for the first time to solve the CPD problem. Their general principle is based on the evolution of a family of candidates. In the third type of approaches, a proximal algorithm namely the Block-Coordinate Variable Metric Forward-Backward is presented. The algorithm relies on two main steps: a gradient step and a proximal step. The blocks of coordinates naturally correspond to latent matrices. We propose a majorant function as well as a preconditioner with regard to each block. All methods are compared with other popular algorithms of the literature on synthetic (fluorescence spectroscopy like or random) data and on real experimental data corresponding to a water monitoring campaign aiming at detecting the appearance of pollutants.

Made available in DSpace on 2014-10-09T12:53:39Z (GMT). No. of bitstreams: 0 / Made available in DSpace on 2014-10-09T13:58:43Z (GMT). No. of bitstreams: 0 / Dissertação (Mestrado) / IPEN/D / Instituto de Pesquisas Energéticas e Nucleares - IPEN-CNEN/SP