<p>Lung cancer is the cancer form that has the highest mortality worldwide and inaddition the survival of lung cancer is very low. Only 15% of the patients are alivefive years from set diagnosis. More research is needed to understand the biologyof lung cancer and thus make it possible to discover the disease at an early stage.Early diagnosis leads to an increased chance of survival. In this thesis 179 lungcancer- and 116 control samples of blood serum were analyzed for identificationof metabolomic biomarkers. The control samples were derived from patients withbenign lung diseases.Data was gained from GC/TOF-MS analysis and analyzed with the help ofthe multivariate analysis methods PCA and OPLS/OPLS-DA. In this thesis it isinvestigated how to pre-treat and analyze the data in the best way in order todiscover biomarkers. One part of the aim was to give directions for how to selectsamples from a biobank for further biological validation of suspected biomarkers.Models for different stages of lung cancer versus control samples were computedand validated. The most influencing metabolites in the models were selected andconfoundings with other clinical characteristics like gender and hemoglobin levelswere studied. 13 lung cancer biomakers were identified and validated by raw dataand new OPLS models based solely upon the biomarkers.In summary the identified biomarkers are able to separate fairly good betweencontrol samples and late lung cancer, but are poor for separation of early lungcancer from control samples. The recommendation is to select controls and latelung cancer samples from the biobank for further confirmation of the biomarkers.NyckelordLung cancer is the cancer form that has the highest mortality worldwide and inaddition the survival of lung cancer is very low. Only 15% of the patients are alivefive years from set diagnosis. More research is needed to understand the biologyof lung cancer and thus make it possible to discover the disease at an early stage.Early diagnosis leads to an increased chance of survival. In this thesis 179 lungcancer- and 116 control samples of blood serum were analyzed for identificationof metabolomic biomarkers. The control samples were derived from patients withbenign lung diseases.Data was gained from GC/TOF-MS analysis and analyzed with the help ofthe multivariate analysis methods PCA and OPLS/OPLS-DA. In this thesis it isinvestigated how to pre-treat and analyze the data in the best way in order todiscover biomarkers. One part of the aim was to give directions for how to selectsamples from a biobank for further biological validation of suspected biomarkers.Models for different stages of lung cancer versus control samples were computedand validated. The most influencing metabolites in the models were selected andconfoundings with other clinical characteristics like gender and hemoglobin levelswere studied. 13 lung cancer biomakers were identified and validated by raw dataand new OPLS models based solely upon the biomarkers.In summary the identified biomarkers are able to separate fairly good betweencontrol samples and late lung cancer, but are poor for separation of early lungcancer from control samples. The recommendation is to select controls and latelung cancer samples from the biobank for further confirmation of the biomarkers.Nyckelord</p>

Lung cancer is the cancer form that has the highest mortality worldwide and inaddition the survival of lung cancer is very low. Only 15% of the patients are alivefive years from set diagnosis. More research is needed to understand the biologyof lung cancer and thus make it possible to discover the disease at an early stage.Early diagnosis leads to an increased chance of survival. In this thesis 179 lungcancer- and 116 control samples of blood serum were analyzed for identificationof metabolomic biomarkers. The control samples were derived from patients withbenign lung diseases.Data was gained from GC/TOF-MS analysis and analyzed with the help ofthe multivariate analysis methods PCA and OPLS/OPLS-DA. In this thesis it isinvestigated how to pre-treat and analyze the data in the best way in order todiscover biomarkers. One part of the aim was to give directions for how to selectsamples from a biobank for further biological validation of suspected biomarkers.Models for different stages of lung cancer versus control samples were computedand validated. The most influencing metabolites in the models were selected andconfoundings with other clinical characteristics like gender and hemoglobin levelswere studied. 13 lung cancer biomakers were identified and validated by raw dataand new OPLS models based solely upon the biomarkers.In summary the identified biomarkers are able to separate fairly good betweencontrol samples and late lung cancer, but are poor for separation of early lungcancer from control samples. The recommendation is to select controls and latelung cancer samples from the biobank for further confirmation of the biomarkers.NyckelordLung cancer is the cancer form that has the highest mortality worldwide and inaddition the survival of lung cancer is very low. Only 15% of the patients are alivefive years from set diagnosis. More research is needed to understand the biologyof lung cancer and thus make it possible to discover the disease at an early stage.Early diagnosis leads to an increased chance of survival. In this thesis 179 lungcancer- and 116 control samples of blood serum were analyzed for identificationof metabolomic biomarkers. The control samples were derived from patients withbenign lung diseases.Data was gained from GC/TOF-MS analysis and analyzed with the help ofthe multivariate analysis methods PCA and OPLS/OPLS-DA. In this thesis it isinvestigated how to pre-treat and analyze the data in the best way in order todiscover biomarkers. One part of the aim was to give directions for how to selectsamples from a biobank for further biological validation of suspected biomarkers.Models for different stages of lung cancer versus control samples were computedand validated. The most influencing metabolites in the models were selected andconfoundings with other clinical characteristics like gender and hemoglobin levelswere studied. 13 lung cancer biomakers were identified and validated by raw dataand new OPLS models based solely upon the biomarkers.In summary the identified biomarkers are able to separate fairly good betweencontrol samples and late lung cancer, but are poor for separation of early lungcancer from control samples. The recommendation is to select controls and latelung cancer samples from the biobank for further confirmation of the biomarkers.Nyckelord

No description available.

<p>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. </p><p>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. </p><p>Several multivariate analysis tools, <i>e.g.</i> 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.</p>

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.

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.