In the first part of this work, LC-MS data were used to calculate the in-vitro intrinsic clearances (CLint) for the metabolism of p-methoxyrnethamphetamine (PMMA) and fluoxetine by the CYP2D6 enzyme using a steady-state (SS) approach and a new general enzyme (GE) screening method. For PMMA, the SS experiment resulted in a CLint of 2.7 ± 0.2 µL pmol 2D6-1min-1 and the GE experiment resulted in a CLint of 3.0 ± 0.6 µL pmol 2D6-1min-1. For fluoxetine, the SS experiment resulted in a CLint of 0.33 ± 0.17 µL pmol 2D6-1min-1 and the GE experiment resulted in a CLint of 0.188 ± 0.013 µL pmol 2D6-1min-1. The inhibition of PMMA metabolism by fluoxetine was also demonstrated.In the second part of the work, target factor analysis was used as part of a library search algorithm for the identification of drugs in LC-DAD chromatograms. The ability to resolve highly overlapped peaks using the spectral data afforded by the DAD is what distinguished this method from conventional library searching methods. A validation data set of 70 chromatograms was used to calculate the sensitivity (correct identification of positives) and specificity (correct identification of negatives) of the method, which were 92% and 94% respectively.Finally, the last part of the work shows the development of data analysis methods for four-way data generated by two-dimensional liquid chromatography separations with DAD. Maize seedlings were analyzed, specifically focusing on indole-3-acetic acid (IAA) and related compounds. Window target testing factor analysis was used to identify the spectral groups represented by the standards in the mutant and wild-type chromatograms. Two curve resolution algorithms were applied to resolve overlapped components in the data and to demonstrate the quantitative potential of these methods. A total of 95 peaks were resolved. Of those peaks, 45 were found in both the mutant and wild-type maize, 16 peaks were unique to the mutants, 13 peaks were unique to the wild-types, and the remaining peaks were standards. Several IAA conjugates were quantified in the maize samples at levels of 0.3 - 2 µg/g plant material.

Observações ou outliers estão quase sempre presentes em qualquer conjunto de dados, seja ele grande ou pequeno. Isso pode ocorrer por erro no armazenamento dos dados ou por existirem realmente alguns pontos diferentes dos demais. A presença desses pontos pode causar distorções nos resultados de modelos e estimativas. Por isso, a sua detecção é muito importante e deve ser feita antes do início de uma análise mais profunda dos dados. Após esse diagnóstico, pode-se tomar uma decisão a respeito dos pontos atípicos. Uma possibilidade é corrigi-los caso tenha ocorrido erro na transcrição dos dados. Caso sejam pontos válidos, eles devem ser tratados de forma diferente dos demais, seja com uma ponderação, seja com uma análise especial. Nos casos univariado e bivariado, o outlier pode ser detectado analisando-se o gráfico de dispersão que mostra o comportamento de cada observação do conjunto de dados de interesse. Se houver pontos distantes da massa de dados, eles devem ser considerados atípicos. No caso multivariado, a detecção por meio de gráficos torna-se um pouco mais complexa porque a análise deveria ser feita observando-se duas variáveis por vez, o que tornaria o processo longo e pouco confiável, pois um ponto pode ser atípico com relação a algumas variáveis e não ser com relação a outras, o que faria com que o resultado ficasse mascarado. Neste trabalho, alguns métodos robustos para detecção de outliers em dados multivariados são apresentados. A aplicação de cada um dos métodos é feita para um exemplo. Além disso, os métodos são comparados de acordo com o resultado que cada um apresentar para o exemplo em questão e via simulação. / Unusual observations or outliers are frequent in any data set, if it is large or not. Outliers may occur by typing mistake or by the existence of observations that are really different from the others. The presence of this observations may distort the results of models and estimates. Therefore, their detection is very important and it is recommended to be performed before any detailed analysis, when a decision can be taken about these atypical observations. A possibility is to correct these observations if the problem occurred with the construction of the data set. If the observations are correct, different strategies can be adopted, with some weights or with special analysis. In univariate and bivariate data sets, outliers can be detected analyzing the scatter plot. Observations distant from the cloud formed by the data set are considered unusual. In multivariate data sets, the detection of outliers using graphics is more difficult because we have to analyse a couple of variables each time, which results is a long and less reliable process because we can find an observation that is unusual for one variable and not unusual for the others, masking the results. In this work, some robust methods for detection of multivariate outliers are presented. The application of each one is done for an example. Moreover, the methods are compared by the results of each one in the example and by simulation.

A presente proposta correlaciona como o investimento direto estrangeiro pode, e deve, promover o desenvolvimento sustentável no país que o acolhe. O investimento direto estrangeiro é capaz de promover uma série de vantagens competitivas quando aplicado de forma coerente como: ganhos de tecnologia, geração de empregos, capacitação de mão de obra e outros benefícios que vão além do econômico. No entanto, há a necessidade de uma mensuração para que a promoção do desenvolvimento gerado se dê de forma sustentável, para que os benefícios obtidos para a nação sejam maiores do que a degradação ambiental, emissão de poluentes e os impactos sociais causados. Utilizando um referencial sobre o investimento direto estrangeiro e desenvolvimento sustentável, o trabalho consiste em verificar se o investimento direto estrangeiro promove o desenvolvimento sustentável. Para isso, uma construção foi feita a partir de dados secundários que pudessem verificar a latência dos constructos de sustentabilidade e assim relacioná-los com o investimento direto estrangeiro com a divisão em setor primário, secundário e terciário. Com essas correlações foi possível verificar como o investimento tem impactado não apenas na economia nacional mas também qual impacto ambiental e social ele trouxe. Posteriormente, uma análise de cluster e discriminante foram feitas com o intuito de agrupar e classificar os impactos do investimento direto estrangeiro utilizando a poupança líquida ajustada, que é um indicador de sustentabilidade promovido pelo Banco Mundial. Essa construção foi possível através das técnicas de análise multivariada de dados que permitiram a relação de variáveis de diferentes categorias e se mostrou adequada para pesquisas de carácter exploratório. As evidências provenientes da discussão desse trabalho contribuem com a recente literatura que busca por estudos que relacionem o investimento direto estrangeiro e o desenvolvimento que eles promovem, melhorando assim a tomada de decisão na captura desses recursos. O trabalho contribui em verificar a possível falta de políticas públicas que integrem as dimensões de desenvolvimento sustentável. Também são apresentadas algumas variáveis que podem auxiliar na busca pelo desenvolvimento sustentável. / The present work aims to highlight the need to correlate how foreign direct investment can, and should, promote sustainable development in the country that hosts it. Foreign direct investment is able to promote a number of competitive advantages when applied consistently such as gains in technology, job creation, training of manpower and other benefits that go beyond the economic level. However, a measurement is necessary so that the promotion of development generated can be satisfactory and thus fulfill the purpose of everyone involved with this financial, environmental and social capital which was invested, so environmental degradation, emissions, incentives offered and social impacts are no greater than the benefits for the nation. By using a reference framework on foreign direct investment and sustainable development, this work aims to formulate hypotheses so this investment can be measured and be considered sustainable. In order to do this, a construction from secondary data to verify the latency of the constructs of sustainability will be made enabling to relate them to foreign direct investment in every sector nationwide. With these correlations will be possible to verify how the investment has impacted not only in the national economy but which environmental and social impact it has brought. Later, a cluster analysis and discriminant will be carried out enabling to group and classify the impacts of foreign direct investment using the adjusted net saving, which is an indicator of sustainability promoted by the World Bank. This construction will be possible through techniques of multivariate data analysis, allowing the relationship of variables of different categories, which is adequate for an exploratory research. The evidences arising from the discussion of this study contribute to the recent literature that searches for studies that relate foreign direct investment and development they promote, thereby improving decision making in the capture of these resources. The study aims to verify the possible lack of public policies that promote sustainable development dimensions. Some variables are also presented which may also contribute to the search for sustainable development.

" More and more researchers are focusing on the management, querying and pattern mining of streaming data. The visualization of streaming data, however, is still a very new topic. Streaming data is very similar to time-series data since each datapoint has a time dimension. Although the latter has been well studied in the area of information visualization, a key characteristic of streaming data, unbounded and large-scale input, is rarely investigated. Moreover, most techniques for visualizing time-series data focus on univariate data and seldom convey multidimensional relationships, which is an important requirement in many application areas. Therefore, it is necessary to develop appropriate techniques for streaming data instead of directly applying time-series visualization techniques to it. As one of the main contributions of this dissertation, I introduce a user-driven approach for the visual analytics of multivariate data streams based on effective visualizations via a combination of windowing and sampling strategies. To help users identify and track how data patterns change over time, not only the current sliding window content but also abstractions of past data in which users are interested are displayed. Sampling is applied within each single time window to help reduce visual clutter as well as preserve data patterns. Sampling ratios scheduled for different windows reflect the degree of user interest in the content. A degree of interest (DOI) function is used to represent a user's interest in different windows of the data. Users can apply two types of pre-defined DOI functions, namely RC (recent change) and PP (periodic phenomena) functions. The developed tool also allows users to interactively adjust DOI functions, in a manner similar to transfer functions in volume visualization, to enable a trial-and-error exploration process. In order to visually convey the change of multidimensional correlations, four layout strategies were designed. User studies showed that three of these are effective techniques for conveying data pattern changes compared to traditional time-series data visualization techniques. Based on this evaluation, a guide for the selection of appropriate layout strategies was derived, considering the characteristics of the targeted datasets and data analysis tasks. Case studies were used to show the effectiveness of DOI functions and the various visualization techniques. A second contribution of this dissertation is a data-driven framework to merge and thus condense time windows having small or no changes and distort the time axis. Only significant changes are shown to users. Pattern vectors are introduced as a compact format for representing the discovered data model. Three views, juxtaposed views, pattern vector views, and pattern change views, were developed for conveying data pattern changes. The first shows more details of the data but needs more canvas space; the last two need much less canvas space via conveying only the pattern parameters, but lose many data details. The experiments showed that the proposed merge algorithms preserves more change information than an intuitive pattern-blind averaging. A user study was also conducted to confirm that the proposed techniques can help users find pattern changes more quickly than via a non-distorted time axis. A third contribution of this dissertation is the history views with related interaction techniques were developed to work under two modes: non-merge and merge. In the former mode, the framework can use natural hierarchical time units or one defined by domain experts to represent timelines. This can help users navigate across long time periods. Grid or virtual calendar views were designed to provide a compact overview for the history data. In addition, MDS pattern starfields, distance maps, and pattern brushes were developed to enable users to quickly investigate the degree of pattern similarity among different time periods. For the merge mode, merge algorithms were applied to selected time windows to generate a merge-based hierarchy. The contiguous time windows having similar patterns are merged first. Users can choose different levels of merging with the tradeoff between more details in the data and less visual clutter in the visualizations. The usability evaluation demonstrated that most participants could understand the concepts of the history views correctly and finished assigned tasks with a high accuracy and relatively fast response time. "

Today’s market offers an ever-increasing range of cellulose pulps (derivative pulps) made fromvarious wood types through different delignification processes. Each pulp segment has its uniquecharacteristics, which makes it difficult for the producer of cellulose derivatives to choose the mostsuitable pulp for optimum processability and product quality. The objective of this study was toimprove knowledge of cellulose pulps and to describe how different pulp properties affectprocessability and quality in the production of viscose dope and cellulose ethers.Ten pulp samples were investigated, originating from both sulfite and sulfate processes, with highand low viscosities and with softwood and hardwood as raw material. The pulps were analyzed fortheir properties and then processed to viscose dope and a cellulose ether in two separate pilotfacilities. The intermediates in the viscose process as well as the quality of the viscose dope andcellulose ether were analyzed and the results correlated to pulp properties.Multivariate regression methods were applied to investigate the dominating physical and chemicalproperties of each pulp and pulp segment, and to study the use of spectroscopic analyses inpredicting pulp origin, concentration and composition of hemicelluloses as well as the content ofreducing end groups in cellulose. For the production of viscose dope, the models presented showedthe most important pulp properties for good cellulose reactivity and viscose filterability. In addition,the properties affecting gel formation, flocculation, degree of substitution and clarity in theproduction of cellulose ether were highlighted. The study also emphasized the need to supplementthe use of conventional analyses on pulps and viscose intermediates with other analytical methods,such as molecular weight distribution and carbohydrate analysis, to better predict the quality ofboth viscose dope and viscose fiber.The results of the present study could be useful to predict the origin and properties of new pulps, toreplace or supplement otherwise expensive pulp analyses, and to assess the impact of pulpproperties on the production of cellulose derivatives without extensive pilot-scale trials.

The human being and other animals contain immensely complex biochemical processes that govern their function on a cellular level. It is estimated that several thousand small molecules (metabolites) are produced by various biochemical pathways in humans. Pathological processes can introduce perturbations in these biochemical pathways which can lead to changes in the amounts of some metabolites.Developments in analytical chemistry have made it possible measure a large number metabolites in a single blood sample, which gives a metabolic profile. In this thesis I have worked on establishing and understanding metabolic profiles from patients with rheumatoid arthritis (RA) and from animal models of the autoimmune diseases diabetes mellitus type 1 (T1D) and RA.Using multivariate statistical methods it is possible to identify differences between metabolic profiles of different groups. As an example we identified differences between patients with RA and healthy volunteers. This can be used to elucidate the biochemical processes that are active in a given pathological condition.Metabolite concentrations are affected by a many other things than the presence or absence of a disease. Both genomic and environmental factors are known to influence metabolic profiles. A main focus of my work has therefore been on finding strategies for ensuring that the results obtained when comparing metabolic profiles were valid and relevant. This strategy has included repetition of experiments and repeated measurement of individuals’ metabolic profiles in order to understand the sources of variation.Finding the most stable and reproducible metabolic effects has allowed us to better understand the biochemical processes seen in the metabolic profiles. This makes it possible to relate the metabolic profile differences to pathological processes and to genes and proteins involved in these.The hope is that metabolic profiling in the future can be an important tool for finding biomarkers useful for disease diagnosis, for identifying new targets for drug design and for mapping functional changes of genomic mutations. This has the potential to revolutionize our understanding of disease pathology and thus improving health care.

Evolution of enzymes with novel functional properties has gained much attention in recent years. Naturally evolved enzymes are adapted to work in living cells under physiological conditions, circumstances that are not always available for industrial processes calling for novel and better catalysts. Furthermore, altering enzyme function also affords insight into how enzymes work and how natural evolution operates. Previous investigations have explored catalytic properties in the directed evolution of mutant libraries with high sequence variation. Before this study was initiated, functional analysis of mutant libraries was, to a large extent, restricted to uni- or bivariate methods. Consequently, there was a need to apply multivariate data analysis (MVA) techniques in this context. Directed evolution was approached by DNA shuffling of glutathione transferases (GSTs) in this thesis. GSTs are multifarious enzymes that have detoxication of both exo- and endogenous compounds as their primary function. They catalyze the nucleophilic attack by the tripeptide glutathione on many different electrophilic substrates. Several multivariate analysis tools, e.g. principal component (PC), hierarchical cluster, and K-means cluster analyses, were applied to large mutant libraries assayed with a battery of GST substrates. By this approach, evolvable units (quasi-species) fit for further evolution were identified. It was clear that different substrates undergoing different kinds of chemical transformation can group together in a multi-dimensional substrate-activity space, thus being responsible for a certain quasi-species cluster. Furthermore, the importance of the chemical environment, or substrate matrix, in enzyme evolution was recognized. Diverging substrate selectivity profiles among homologous enzymes acting on substrates performing the same kind of chemistry were identified by MVA. Important structure-function activity relationships with the prodrug azathioprine were elucidated by segment analysis of a shuffled GST mutant library. Together, these results illustrate important methods applied to molecular enzyme evolution.

An increasing number of power plants in Scandinavia are beginning to use biofuel instead of coal or oil. The material in the new fuel is a mixture of woodchips, mostly Pine, Spruce and Salix, bark, GROT (tops and branches from felling waste) and sawdust from sawmills. It is heterogeneous, having a moisture content varying from 15% up to 65%. The moisture content affects the combustion of the fuel and therefore its commercial value. The industry is now interested in obtaining a method for measuring the moisture content of biofuel, quickly and reliably; preferably on delivery at the power plant. The measuring technique presented in this thesis is the first reported in the literature capable of measuring the moisture content of a large sample of such an heterogeneous material as biofuel. The equipment is today calibrated for a sample volume of 0.1 m3. A radio frequent signal is supplied from an antenna and penetrates the biofuel. Its reflection is modeled using partial least squares. As part of the work presented in this thesis, a new type of measuring rig and an analysis method for measurement of the moisture content of large samples of heterogeneous material have been developed. A statistical model for moisture content measurements of five different biofuel materials using radio waves has been built, having a root mean square error of prediction of 2.7. The interactions between biofuels and radio frequent signals have been demonstrated, indicating a variation of the reflection with varying types of biofuel material and variation in the reflection and delay of the signal with varying moisture content.

Hydrophilic interaction chromatography is well known as a powerful technique separation of polar and ionizable compound nowadays. However the retention mechanism of the technique is still under debate. Understanding retention mechanism would facilitate the method development using the technique and its future improvement. This was inspiring and became the goal of this thesis. This work involves the characterization of the water enriched layer regarding to water and buffer salt accumulation. Twelve HILIC stationary phase with a diverse surface chemistry regarding to function groups and modification type were studied. Effect of water and salt on regarding to the retention mechanism was investigated by correlating the adsorption data to the retention of selected solutes This also involved the characterization of interactions involve in the separation of 21 HILIC columns. Interactions was probe by retention ratio of pair solutes which are characteristic for each specific interaction. The data was evaluate using principle component analysis – a multivariable data analysis method. The model was comprehensive and its outcomes were confirmed by the studies on adsorptions of water and salts.

Prescriptions for effective display of quantitative information involving more than two variables are not available. To explore the effectiveness of retinal variables in facilitating the interpretation of information and decision making when used in conjunction, a study with 135 participants was conducted. The study involved the use of color shape, color value, and value shape as retinal variables in interactive displays that required participants to answer nine questions in three levels of complexity (identification of data points, analyses of local comparisons and global trends). Time-on-task scores and performance scores were measured. In addition, a View Clamp eye tracker system was used and 12 out of the 135 participants completed the task of answering questions while their eye movements were recorded. Repeated measures analysis followed by multiple comparisons of means showed that participants in the color and shape group performed significantly better and faster than color/value and shape/value groups only for questions that involved studying global trends and decision making (level 3). The shape and value group was significantly faster than color and shape group in answering level 1. Color and value as retinal variables produced results that indicated that the two variables when used in conjunction could be suitable for display of data that involved comparison. This needs to be explored further. Eye movements provided further evidence to the feature integration theory (Treisman, 1982) and showed feature search occurred right away as participants entered the display. 78% of those who reported mental strategies indicated that they identified the features used in the display first.