Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal

Pelchem, a commercial subsidiary of Necsa (South African Nuclear Energy Corporation), produces a range of commercial fluorocarbon products while driving research and development initiatives to support the fluorine product portfolio. One such initiative is to develop improved analytical techniques to analyse product composition during development and to quality assure produce. Generally the C–F type products produced by Necsa are in a solution of anhydrous HF, and cannot be directly analyzed with traditional techniques without derivatisation. A technique such as vibrational spectroscopy, that can analyze these products directly without further preparation, will have a distinct advantage. However, spectra of mixtures of similar compounds are complex and not suitable for traditional quantitative regression analysis. Multivariate data analysis (MVA) can be used in such instances to exploit the complex nature of spectra to extract quantitative information on the composition of mixtures. A selection of fluorocarbon alcohols was made to act as representatives for fluorocarbon compounds. Experimental design theory was used to create a calibration range of mixtures of these compounds. Raman and infrared (NIR and ATR–IR) spectroscopy were used to generate spectral data of the mixtures and this data was analyzed with MVA techniques by the construction of regression and prediction models. Selected samples from the mixture range were chosen to test the predictive ability of the models. Analysis and regression models (PCR, PLS2 and PLS1) gave good model fits (R2 values larger than 0.9). Raman spectroscopy was the most efficient technique and gave a high prediction accuracy (at 10% accepted standard deviation), provided the minimum mass of a component exceeded 16% of the total sample. The infrared techniques also performed well in terms of fit and prediction. The NIR spectra were subjected to signal saturation as a result of using long path length sample cells. This was shown to be the main reason for the loss in efficiency of this technique compared to Raman and ATR–IR spectroscopy. It was shown that multivariate data analysis of spectroscopic data of the selected fluorocarbon compounds could be used to quantitatively analyse mixtures with the possibility of further optimization of the method. The study was a representative study indicating that the combination of MVA and spectroscopy can be used successfully in the quantitative analysis of other fluorocarbon compound mixtures. / Thesis (M.Sc. (Chemistry))--North-West University, Potchefstroom Campus, 2012.

Membrane transport proteins are known to influence the absorption, distribution, metabolism, excretion and toxicity (ADMET) of drugs. At the onset of this thesis work, only a few structure-activity models, in general describing P-glycoprotein (Pgp/ABCB1) interactions, were developed using small datasets with little structural diversity. In this thesis, drug-transport protein interactions were explored using large, diverse datasets representing the chemical space of orally administered registered drugs. Focus was set on the ATP-binding cassette (ABC) transport proteins expressed in the canalicular membrane of human hepatocytes. The inhibition of the ABC transport proteins multidrug-resistance associated protein 2 (MRP2/ABCC2) and bile salt export pump (BSEP/ABCB11) was experimentally investigated using membrane vesicles from cells overexpressing the investigated proteins and sandwich cultured human hepatocytes (SCHH). Several previously unknown inhibitors were identified for both of the proteins and predictive in silico models were developed. Furthermore, a clear association between BSEP inhibition and clinically reported drug induced liver injuries (DILI) was identified. For the first time, an in silico model that described combined inhibition of Pgp, MRP2 and breast cancer resistance protein (BCRP/ABCG2) was developed using a large, structurally diverse dataset. Lipophilic weak bases were more often found to be general ABC inhibitors in comparison to other drugs. In early drug discovery, in silico models can be used as predictive filters in the drug candidate selection process and membrane vesicles as a first experimental screening tool to investigate protein interactions. In summary, the present work has led to an increased understanding of molecular properties important in ABC inhibition as well as the potential influence of ABC proteins in adverse drug reactions. A number of previously unknown ABC inhibitors were identified and predictive computational models were developed.

Pelchem, a commercial subsidiary of Necsa (South African Nuclear Energy Corporation), produces a range of commercial fluorocarbon products while driving research and development initiatives to support the fluorine product portfolio. One such initiative is to develop improved analytical techniques to analyse product composition during development and to quality assure produce. Generally the C–F type products produced by Necsa are in a solution of anhydrous HF, and cannot be directly analyzed with traditional techniques without derivatisation. A technique such as vibrational spectroscopy, that can analyze these products directly without further preparation, will have a distinct advantage. However, spectra of mixtures of similar compounds are complex and not suitable for traditional quantitative regression analysis. Multivariate data analysis (MVA) can be used in such instances to exploit the complex nature of spectra to extract quantitative information on the composition of mixtures. A selection of fluorocarbon alcohols was made to act as representatives for fluorocarbon compounds. Experimental design theory was used to create a calibration range of mixtures of these compounds. Raman and infrared (NIR and ATR–IR) spectroscopy were used to generate spectral data of the mixtures and this data was analyzed with MVA techniques by the construction of regression and prediction models. Selected samples from the mixture range were chosen to test the predictive ability of the models. Analysis and regression models (PCR, PLS2 and PLS1) gave good model fits (R2 values larger than 0.9). Raman spectroscopy was the most efficient technique and gave a high prediction accuracy (at 10% accepted standard deviation), provided the minimum mass of a component exceeded 16% of the total sample. The infrared techniques also performed well in terms of fit and prediction. The NIR spectra were subjected to signal saturation as a result of using long path length sample cells. This was shown to be the main reason for the loss in efficiency of this technique compared to Raman and ATR–IR spectroscopy. It was shown that multivariate data analysis of spectroscopic data of the selected fluorocarbon compounds could be used to quantitatively analyse mixtures with the possibility of further optimization of the method. The study was a representative study indicating that the combination of MVA and spectroscopy can be used successfully in the quantitative analysis of other fluorocarbon compound mixtures. / Thesis (M.Sc. (Chemistry))--North-West University, Potchefstroom Campus, 2012.

A trend in natural science and especially in analytical chemistry is the increasing need for analysis of a large number of complex samples with low analyte concentrations. Biological samples (urine, blood, plasma, cerebral spinal fluid, tissue etc.) are often suitable for analysis with liquid separation mass spectrometry (LS-MS), resulting in two-way data tables (time vs. m/z). Such biological 'fingerprints' taken for all samples in a study correspond to a large amount of data. Detailed characterization requires a high sampling rate in combination with high mass resolution and wide mass range, which presents a challenge in data handling and exploration. This thesis describes methods for managing and exploring large data sets made up of such detailed 'fingerprints' (represented as data matrices). The methods were implemented as scripts and functions in Matlab, a wide-spread environment for matrix manipulations. A single-file structure to hold the imported data facilitated both easy access and fast manipulation. Routines for baseline removal and noise reduction were intended to reduce the amount of data without loosing relevant information. A tool for visualizing and exploring single runs was also included. When comparing two or more 'fingerprints' they usually have to be aligned due to unintended shifts in analyte positions in time and m/z. A PCA-like multivariate method proved to be less sensitive to such shifts, and an ANOVA implementation made it easier to find systematic differences within the data sets. The above strategies and methods were applied to complex samples such as plasma, protein digests, and urine. The field of application included urine profiling (paracetamole intake; beverage effects), peptide mapping (different digestion protocols) and search for potential biomarkers (appendicitis diagnosis) . The influence of the experimental factors was visualized by PCA score plots as well as clustering diagrams (dendrograms).

Directed evolution is generally regarded as a useful approach in protein engineering. By subjecting members of a mutant library to the power of Darwinian evolution, desired protein properties are obtained. Numerous reports have appeared in the literature showing the success of tailoring proteins for various applications by this method. Is it a one-way track that protein practitioners can only learn from nature to enable more efficient protein engineering? A structure-and-mechanism-based approach, supplemented with the use of reduced amino acid alphabets, was proposed as a general means for semi-rational enzyme engineering. Using human GST A2-2*E, the most active human enzyme in the bioactivation of azathioprine, as a parental enzyme to test this approach, a L107G/L108D/F222H triple-point mutant of GST A2-2*E (thereafter designated as GDH) was discovered with 70-fold increased activity, approaching the upper limit of specific activity of the GST scaffold. The approach was further experimentally verified to be more successful than intuitively choosing active-site residues in proximity to the bound substrate for the improvement of enzyme performance. By constructing all intermediates along all putative mutational paths leading from GST A2-2*E to mutant GDH and assaying them with nine alternative substrates, the fitness landscapes were found to be “rugged” in differential fashions in substrate-activity space. The multidimensional fitness landscapes stemming from functional promiscuity can lead to alternative outcomes with enzymes optimized for other features than the selectable markers that were relevant at the origin of the evolutionary process. The results in this thesis suggest that in this manner an evolutionary response to changing environmental conditions can readily be mounted. In summary, the thesis demonstrates the attractive features of the structure-and-mechanism-based semi-rational directed evolution approach for optimizing enzyme performance. Moreover, the results gained from the studies show that laboratory evolution may refine our understanding of evolutionary process in nature.

In today's competitive business and industrial environment, it is becoming more crucial than ever to assess precisely process losses due to non-compliance to customer specifications. To assess these losses, industry is extensively using Process Capability Indices for performance evaluation of their processes. Determination of the performance capability of a stable process using the standard process capability indices such as and requires that the underlying quality characteristics data follow a normal distribution. However it is an undisputed fact that real processes very often produce non-normal quality characteristics data and also these quality characteristics are very often correlated with each other. For such non-normal and correlated multivariate quality characteristics, application of standard capability measures using conventional methods can lead to erroneous results. The research undertaken in this PhD thesis presents several capability assessment methods to estimate more precisely and accurately process performances based on univariate as well as multivariate quality characteristics. The proposed capability assessment methods also take into account the correlation, variance and covariance as well as non-normality issues of the quality characteristics data. A comprehensive review of the existing univariate and multivariate PCI estimations have been provided. We have proposed fitting Burr XII distributions to continuous positively skewed data. The proportion of nonconformance (PNC) for process measurements is then obtained by using Burr XII distribution, rather than through the traditional practice of fitting different distributions to real data. Maximum likelihood method is deployed to improve the accuracy of PCI based on Burr XII distribution. Different numerical methods such as Evolutionary and Simulated Annealing algorithms are deployed to estimate parameters of the fitted Burr XII distribution. We have also introduced new transformation method called Best Root Transformation approach to transform non-normal data to normal data and then apply the traditional PCI method to estimate the proportion of non-conforming data. Another approach which has been introduced in this thesis is to deploy Burr XII cumulative density function for PCI estimation using Cumulative Density Function technique. The proposed approach is in contrast to the approach adopted in the research literature i.e. use of best-fitting density function from known distributions to non-normal data for PCI estimation. The proposed CDF technique has also been extended to estimate process capability for bivariate non-normal quality characteristics data. A new multivariate capability index based on the Generalized Covariance Distance (GCD) is proposed. This novel approach reduces the dimension of multivariate data by transforming correlated variables into univariate ones through a metric function. This approach evaluates process capability for correlated non-normal multivariate quality characteristics. Unlike the Geometric Distance approach, GCD approach takes into account the scaling effect of the variance-covariance matrix and produces a Covariance Distance variable that is based on the Mahanalobis distance. Another novelty introduced in this research is to approximate the distribution of these distances by a Burr XII distribution and then estimate its parameters using numerical search algorithm. It is demonstrates that the proportion of nonconformance (PNC) using proposed method is very close to the actual PNC value.

L’Évaluation d’Impact sur la Santé (EIS) est un instrument de support à la décision, pour juger une politique quant aux effets potentiels sur la santé et leur distribution (équité). C’est encore souvent une approche qualitative.L’objectif principal est de montrer l’utilité de méthodologies statistiques quantitatives multivariées pour enrichir la pratique d’EIS, améliorant la compréhension des résultats par des professionnels non-statisticiens.Les futures réformes des systèmes de santé déplacent le centre d’évaluation des services de santé des fournisseurs aux citoyens (besoins, préférences, équité d’accès aux gains de santé), exploitant big data associant information de soins aux données sociales, économiques et de déterminants de santé. Des méthodologies statistiques et d’évaluation innovantes sont nécessaires à cette transformation.Les méthodes de data mining et data science, souvent complexes, peuvent gérer des résultats graphiques compréhensibles pour amplifier l’usage d’EIS, qui deviendrait ainsi un outil précieux d’évaluation de politiques publiques pour amener les citoyens au centre de la prise de décision. / Health Impact Assessment (HIA) is a decision-making support tool to judge a policy as to its potential effects and its distribution on a population’s health (equity). It’s still very often a qualitative approach.The main aim here is to show the usefulness of applying quantified multivariate statistical methodologies to enrich HIA practice, while making the decision-making process easier, by issuing understandable outputs even for non-statisticians.The future of healthcare reforms shifts the center of evaluation of health systems from providers to people’s individual needs and preferences, reducing health inequities in access and health outcomes, using big data linking information from providers to social and economic health determinants. Innovative statistical and assessment methodologies are needed to make this transformation.Data mining and data science methods, however complex, may lead to graphical outputs simple to understand by decision makers. HIA is thus a valuable tool to assure public policies are indeed evaluated while considering health determinants and equity and bringing citizens to the center of the decision-making process. / A Avaliação de Impacte na Saúde (AIS) é um instrumento de suporte à decisão para julgar política quanto aos seus efeitos potenciais e à sua distribuição na saúde de uma população (equidade). É geralmente ainda uma abordagem qualitativa.O principal objetivo é mostrar a utilidade das metodologias estatísticas quantitativas e multivariadas para enriquecer a prática de AIS, melhorando a compreensão dos resultados por profissionais não-estatísticos.As futuras reformas dos sistemas de saúde deslocam o centro da avaliação dos serviços de saúde dos prestadores para as necessidades e preferências dos cidadãos, reduzindo iniquidades no acesso à saúde e ganhos em saúde, usando big data que associam informação de prestadores a dados sociais e económicos de determinantes de saúde. São necessárias metodologias estatísticas e de avaliação inovadoras para esta transformação.Métodos de data mining e data science, mesmo complexos, podem gerar resultados gráficos compreensíveis para os decisores. A AIS é assim uma ferramenta valiosa para avaliar políticas públicas considerando determinantes de saúde, equidade e trazendo os cidadãos para o centro da tomada de decisão.

Submitted by Alzir Antônio Mahl (alzir_mahl@hotmail.com) on 2017-05-10T11:54:36Z No. of bitstreams: 1 ALZIR ANTONIO MAHL_Impressão.pdf: 14048608 bytes, checksum: 9be7df17ffa2c77f8b1b9025cc96badd (MD5) / Approved for entry into archive by Maria Auxiliadora da Silva Lopes (silopes@ufba.br) on 2017-05-10T14:52:49Z (GMT) No. of bitstreams: 1 ALZIR ANTONIO MAHL_Impressão.pdf: 14048608 bytes, checksum: 9be7df17ffa2c77f8b1b9025cc96badd (MD5) / Made available in DSpace on 2017-05-10T14:52:49Z (GMT). No. of bitstreams: 1 ALZIR ANTONIO MAHL_Impressão.pdf: 14048608 bytes, checksum: 9be7df17ffa2c77f8b1b9025cc96badd (MD5) / A pesquisa buscou avaliar a evolução dos sistemas regionais de inovação no Brasil no período 2000 a 2011. Foram analisados os sistemas de inovação de 13 (treze) estados selecionados das cinco macrorregiões do Brasil, considerando para tanto: se as empresas empregaram conhecimento tecnológico nas atividades de inovação; se a produção e difusão do conhecimento tecnológico são elementos que melhoram o desempenho de um sistema regional de inovação; se é possível caracterizar estes sistemas regionais de inovação a partir das informações sobre as inovações das empresas e; se a maturidade dos SRIs pode ser avaliada por meio de variáveis relacionadas com as atividades de inovação. Para tanto, utilizaram-se as informações relatadas nas atividades de inovação pelas empresas na PINTEC destes estados como variável proxy para representar os sistemas regionais. Realizou-se uma revisão bibliográfica sobre conceitos relacionados ao trabalho, como conhecimento tecnológico, sistema de inovação e sistema regional de inovação, bem como discutiu-se o tema das políticas multinível ou mix de políticas, que podem ser, por exemplo, a combinação das políticas industrial e de inovação. Ademais, utilizaram-se os estados como unidades de análise dos SRIs, pelo fato destes possuírem os ingredientes necessários para caracterização dos sistemas regionais de inovação. A metodologia da pesquisa foi baseada na análise multivariada de dados, na qual os dados capturados das empresas participantes da pesquisa PINTEC dos anos de 2000, 2003, 2005, 2008 e 2011, foram agrupadas em variáveis onde aplicou-se a técnica da análise fatorial. Esta técnica permitiu a redução das 46 variáveis iniciais para uma matriz 13x67 (treze variáveis e sessenta e sete observações), o que permitiu a análise dos 13 SRIs a partir da obtenção de três fatores após a análise fatorial, denominados de Produção de Conhecimentos, Impactos e Obstáculos. No período da pesquisa, verificou-se a evolução dos SRIs em geral, pelo aumento da produção de conhecimentos e dos impactos das inovações, bem como da diminuição dos obstáculos às atividades de inovação das empresas. Como resultado, foi realizada uma análise das correlações entre os três fatores e indicadores de desenvolvimento socioeconômico (PIB per capita, Índice de Gini e Produtividade do Trabalho na Indústria) para os trezes SRIs. A definição de um indicador de correlação permitiu classificar os estados quanto à correlação entre as atividades de inovação e o desenvolvimento socioeconômico, resultando na formação de quatro grupos de estados, a saber: estados com correlação mais forte, moderada, média e fraca. / ABSTRACT The research aimed to evaluate the development of regional innovation systems in Brazil from 2000 to 2011. The innovation systems of thirteen selected states from the five macro regions of Brazil were analyzed considering: if companies used technological knowledge in innovation activities; if the production and dissemination of technological knowledge improve the performance of a regional innovation system; if it is possible to characterize these regional innovation systems from the information on the companies’ innovations and; if the maturity of SRIs can be evaluated by means of variables related to innovation activities. The information reported during innovation activities by the PINTEC companies in these states were used as a proxy variable to represent the regional systems. A literature review was conducted on concepts related to work, such as technological knowledge, innovation system and regional innovation system as well as the issue of multilevel policies or policy mix, which can be, for example, the combination of industrial and innovation policies. Moreover, the states were used as units of analysis of SRIs, because they have the necessary ingredients to characterize the regional innovation systems. The research methodology was based on multivariate data analysis, in which the captured data of the participating companies on PINTEC research from the years 2000, 2003, 2005, 2008 and 2011 were grouped into variables, where the technique of factor analysis was applied. This technique allowed the reduction of the 46 initial variables for a 13x67 matrix (thirteen variables and sixty-seven observations), which allowed the analysis of 13 SRIs from the achievement of three factors after the factor analysis, named Knowledge Production, Impacts and Obstacles. During the survey, it was possible to notice the evolution of SRIs in general, by the increase of knowledge production and the impact of innovation, as well as the reduction of barriers to the companies innovation activities. As a result, an analysis of correlations between the three factors and socio-economic development indicators (GDP per capita, Gini Index and Labor Productivity in Industry) was held for the thirteen SRIs. The definition of a correlation indicator allowed to classify the states about the correlation between innovation activity and socioeconomic development, resulting in the formation of four groups of states: states with stronger, moderate, medium and weak correlation.

The FT-Raman techniques were developed to quantify reactions that follow on mixing aqueous solutions of bis-(1,3-diaminopropane)diaquacobalt( III) ion ([Co(tn)2(0H)(H20)]2+) and p-nitrophenylphosphate (PNPP). For the development and validation of the kinetic modelling technique, the well-studied inversion of sucrose was utilized. Rate constants and concentrations could be estimated using calibration solutions and modelling methods. It was found that the results obtained are comparable to literature values. Hence this technique could be further used for the [Co(tn)2(0H)(H20)]2+ assisted hydrolysis of PNPP. It was found that rate constants where the pH is maintained at 7.30 give results which differ from those where the pH is started at 7.30 and allowed to change during the reaction. The average rate constant for 2:1 ([Co(tn)2(0H)(H20)]2+:PNPP reactions was found to be approximately 3 x 104 times the unassisted PNPP hydrolysis rate. / Chemistry / M. Sc. (Chemistry)