<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

The amounts of data collected from each sample of e.g. chemical or biological materials have increased by orders of magnitude since the beginning of the 20th century. Furthermore, the number of ways to collect data from observations is also increasing. Such configurations with several massive data sets increase the demands on the methods used to analyse them. Methods that handle such data are called multiblock methods and they are the topic of this thesis. Data collected from advanced analytical instruments often contain variation from diverse mutually independent sources, which may confound observed patterns and hinder interpretation of latent variable models. For this reason, new methods have been developed that decompose the data matrices, placing variation from different sources of variation into separate parts. Such procedures are no longer merely pre-processing filters, as they initially were, but have become integral elements of model building and interpretation. One strain of such methods, called OPLS, has been particularly successful since it is easy to use, understand and interpret. This thesis describes the development of a new multiblock data analysis method called OnPLS, which extends the OPLS framework to the analysis of multiblock and path models with very general relationships between blocks in both rows and columns. OnPLS utilises OPLS to decompose sets of matrices, dividing each matrix into a globally joint part (a part shared with all the matrices it is connected to), several locally joint parts (parts shared with some, but not all, of the connected matrices) and a unique part that no other matrix shares. The OnPLS method was applied to several synthetic data sets and data sets of “real” measurements. For the synthetic data sets, where the results could be compared to known, true parameters, the method generated global multiblock (and path) models that were more similar to the true underlying structures compared to models without such decompositions. I.e. the globally joint, locally joint and unique models more closely resembled the corresponding true data. When applied to the real data sets, the OnPLS models revealed chemically or biologically relevant information in all kinds of variation, effectively increasing the interpretability since different kinds of variation are distinguished and separately analysed. OnPLS thus improves the quality of the models and facilitates better understanding of the data since it separates and separately analyses different kinds of variation. Each kind of variation is purer and less tainted by other kinds. OnPLS is therefore highly recommended to anyone engaged in multiblock or path model data analysis.

In this thesis, we have used the kernel based orthogonal projection to latent structures (K-OPLS) method to discriminate between Alzheimer's Disease patients (AD) and healthy control subjects (CTL), and to predict conversion from mild cognitive impairment (MCI) to AD. In this regard three cohorts were used to create two different datasets; a small dataset including 63 subjects based on the Alzheimer’s Research Trust (ART) cohort and a large dataset including 1074 subjects combining the AddNeuroMed (ANM) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohorts. In the ART dataset, 34 regional cortical thickness measures and 21 volumetric measures from MRI in addition to 3 metabolite ratios from MRS, altogether 58 variables obtained for 28 AD and 35 CTL subjects. Three different K-OPLS models were created based on MRI and MRS measures and their combination. Combining the MRI and the MRS measures significantly improved the discriminant power resulting in a sensitivity of 96.4% and a specificity of 97.1%. In the combined dataset (ADNI and AddNeuroMed), the Freesurfer pipeline was utilized to extract 34 regional cortical thickness measures and 23 volumetric measures from MRI scans of 295 AD, 335 CTL and 444 MCI subjects. The classification of AD and CTL subjects using the K-OPLS model resulted in a high sensitivity of 85.8% and a specificity of 91.3%. Subsequently, the K-OPLS model was used to prospectively predict conversion from MCI to AD, according to the one year follow up diagnosis. As a result, 78.3% of the MCI converters were classified as AD-like and 57.5% of the MCI non-converters were classified as control-like. Furthermore, an age correction method was proposed to remove the effect of age as a confounding factor. The age correction method successfully removed the age-related changes of the data. Also, the age correction method slightly improved the performance regarding to classification and prediction. This resulted in that 82.1% of the MCI converters were correctly classified. All analyses were performed using 7-fold cross validation. The K-OPLS method shows strong potential for classification of AD and CTL, and for prediction of MCI conversion.

With the increasing availability of high-throughput systems for parallel monitoring of multiple variables, e.g. levels of large numbers of transcripts in functional genomics experiments, massive amounts of data are being collected even from single experiments. Extracting useful information from such systems is a non-trivial task that requires powerful computational methods to identify common trends and to help detect the underlying biological patterns. This thesis deals with the general computational problems of classifying and integrating high-dimensional empirical data using a latent variable based modeling approach. The underlying principle of this approach is that a complex system can be characterized by a few independent components that characterize the systematic properties of the system. Such a strategy is well suited for handling noisy, multivariate data sets with strong multicollinearity structures, such as those typically encountered in many biological and chemical applications. The main foci of the studies this thesis is based upon are applications and extensions of the orthogonal projections to latent structures (OPLS) method in life science contexts. OPLS is a latent variable based regression method that separately describes systematic sources of variation that are related and unrelated to the modeling aim (for instance, classifying two different categories of samples). This separation of sources of variation can be used to pre-process data, but also has distinct advantages for model interpretation, as exemplified throughout the work. For classification cases, a probabilistic framework for OPLS has been developed that allows the incorporation of both variance and covariance into classification decisions. This can be seen as a unification of two historical classification paradigms based on either variance or covariance. In addition, a non-linear reformulation of the OPLS algorithm is outlined, which is useful for particularly complex regression or classification tasks. The general trend in functional genomics studies in the post-genomics era is to perform increasingly comprehensive characterizations of organisms in order to study the associations between their molecular and cellular components in greater detail. Frequently, abundances of all transcripts, proteins and metabolites are measured simultaneously in an organism at a current state or over time. In this work, a generalization of OPLS is described for the analysis of multiple data sets. It is shown that this method can be used to integrate data in functional genomics experiments by separating the systematic variation that is common to all data sets considered from sources of variation that are specific to each data set. / Funktionsgenomik är ett forskningsområde med det slutgiltiga målet att karakterisera alla gener i ett genom hos en organism. Detta inkluderar studier av hur DNA transkriberas till mRNA, hur det sedan translateras till proteiner och hur dessa proteiner interagerar och påverkar organismens biokemiska processer. Den traditionella ansatsen har varit att studera funktionen, regleringen och translateringen av en gen i taget. Ny teknik inom fältet har dock möjliggjort studier av hur tusentals transkript, proteiner och små molekyler uppträder gemensamt i en organism vid ett givet tillfälle eller över tid. Konkret innebär detta även att stora mängder data genereras även från små, isolerade experiment. Att hitta globala trender och att utvinna användbar information från liknande data-mängder är ett icke-trivialt beräkningsmässigt problem som kräver avancerade och tolkningsbara matematiska modeller. Denna avhandling beskriver utvecklingen och tillämpningen av olika beräkningsmässiga metoder för att klassificera och integrera stora mängder empiriskt (uppmätt) data. Gemensamt för alla metoder är att de baseras på latenta variabler: variabler som inte uppmätts direkt utan som beräknats från andra, observerade variabler. Detta koncept är väl anpassat till studier av komplexa system som kan beskrivas av ett fåtal, oberoende faktorer som karakteriserar de huvudsakliga egenskaperna hos systemet, vilket är kännetecknande för många kemiska och biologiska system. Metoderna som beskrivs i avhandlingen är generella men i huvudsak utvecklade för och tillämpade på data från biologiska experiment. I avhandlingen demonstreras hur dessa metoder kan användas för att hitta komplexa samband mellan uppmätt data och andra faktorer av intresse, utan att förlora de egenskaper hos metoden som är kritiska för att tolka resultaten. Metoderna tillämpas för att hitta gemensamma och unika egenskaper hos regleringen av transkript och hur dessa påverkas av och påverkar små molekyler i trädet poppel. Utöver detta beskrivs ett större experiment i poppel där relationen mellan nivåer av transkript, proteiner och små molekyler undersöks med de utvecklade metoderna.

The thermal formation of polychlorinated dibenzo-p-dioxins (PCDDs), dibenzofurans (PCDFs), biphenyls (PCBs), and naphthalenes (PCNs) is a major problem in waste incineration. Ideally, rather than relying on air cleaning systems and treatment techniques, their formation should be minimized or, if possible eliminated. The work presented in this thesis was conducted to obtain a deeper understanding of the thermal formation of PCDDs, PCDFs, PCBs, and PCNs during incineration using a 5 kW laboratory scale incinerator and two artificial wastes that were designed to reflect regional differences in waste composition. The first part of the thesis focuses on the validation of a recently-developed flue-gas sampling probe with enhanced cooling capabilities. Artifact formation of PCDDs and PCDFs can occur during the sampling of hot flue gases if the cooling is insufficient. The new probe was successfully used to collect samples at 700 °C without biasing the measured POP levels. The thermal formation of PCDDs, PCDFs, PCBs, and PCNs in the post-combustion zone of the incinerator was then studied by collecting flue gas samples at 400 °C, 300 °C, and 200 °C during the incineration of the two artificial wastes. Highly chlorinated POPs were formed in larger quantities when burning the waste with the higher content of metals and chlorine, which suggests that high metal levels in the waste favor the chlorination of less chlorinated POPs or otherwise facilitate the formation of highly chlorinated polyaromatics, possibly via the condensation of highly chlorinated phenols. The concentrations of these pollutants and the abundance of highly chlorinated homologues increased as the flue gas cooled. Fly ash particles play an important role in thermal POP formation by providing essential elements (carbon, chlorine, etc.) and catalytic sites. The chemical and mineralogical properties of fly ash samples were studied by X-ray diffraction (XRD), Fourier Transform Infrared (FTIR), Scanning Electron Microscopy/Energy Dispersive X-ray (SEM/EDX), and X-ray photoelectron spectroscopy (XPS) to determine their impact on thermal POP formation. Orthogonal Partial Least Squares (OPLS) modeling was used to identify correlations between the observed POP distributions and the physicochemical data. This investigation provided new insights into the impact of fly ash chemistry on thermal POP formation. In addition, the POP isomer distribution patterns generated during waste combustion were examined. These patterns are used to “fingerprint” mechanisms of POP formation. It was found that wastes containing large quantities of metals and chlorine favored the formation of highly chlorinated homologues including the very toxic 2,3,7,8-congeners. The data suggest that reducing fly ash emissions might increase the SO2 content of the flue gas and thereby suppress the Deacon process and the formation of harmful highly chlorinated aromatic species.

Chemical compounds in indoor air can adversely affect our comfort and health. However, in most cases there is only a limited amount of information available that can be used to assess their health risk. Instead the precautionary principle is often applied, i.e. efforts are made to ensure that the concentrations of pollutants are kept at a minimum when constructing new buildings or conducting renovations by using low-emitting building materials. Today, when investigating buildings in order to solve indoor air quality problems, volatile organic compounds (VOCs) are sampled in the air within rooms. The chemical composition of indoor air is complex and there are many sources for the chemicals present. The potential for emissions from sources in hidden spaces such as wall cavities is poorly understood and little information exists on the toxic potential of chemical releases resulting from moisture-related degradation of building materials. Most of the non-reactive VOCs that have been detected in indoor air in field studies and from building products are not believed to cause health problems. However, reactive compounds and chemical reaction products have the potential to negatively influence our comfort and health even at low concentrations. Even though the impact of chemical compounds on health is unclear in many cases, they can be used to identify technical problems in buildings. When a building is investigated, the air inside building structures could be sampled. This method would eliminate emissions from sources other than the construction materials and the samples would contain higher levels of individual compounds. The aims of this work was to identify emissions profiles for different types of building structures, to see if the emission profiles for moisture damaged and undamaged structures differed, and to determine whether any of the emissions profiles for specific structures also could be found in indoor air. Technical investigations and VOC sampling were performed in 21 different buildings with and without previous moisture damage. Seven of the buildings were investigated in the years 2005-2006 (study 1) and fourteen in the years 2009-2010 (study 2). In study 1, sixty samples were analyzed by PCA at the chemical group level (18 chemical groups, i.e. aldehydes, ketones etc). 41 % of all identified chemical compounds belonged to the hydrocarbon chemical group. The second largest chemical groups, each of which accounted for 5-10 % of all identified compounds, were alcohols, aldehydes, ketones, polyaromatic hydrocarbons (PAHs) and terpenes. The results indicated that one of the main factors that determined the emissions profile of a building structure was the materials used in its construction. Notably, concrete and wooden structures were found to have different emissions profiles. The sum of VOC (TVOC) concentrations for all 241 samples from both study 1 and study 2 was used to compare total emissions between different building elements (ground and higher floors, external walls and roof spaces). Most building elements exhibited relatively low emissions compared to concrete ground floors, which generally had higher TVOC emissions. Emissions from both polystyrene insulation and PVC flooring could be identified in concrete ground floors and were the main cause for the higher emissions found in these structures. Profiles for wood preservatives such as creosote and pentachlorophenol were also identified in external walls. The emission profiles found in the structures could not be identified in the indoor air in the adjacent rooms, although individual compounds were sometimes detected at low concentrations. Our results showed that the main factors influencing emissions in building structures were the construction materials and the nature of the building element in question. Because of difficulties with finding active water damage at the times of sampling and because of sampling inside closed building structures with old dried-out moisture damages, the field method used in this work was unsuitable for identifying differences in emission profiles between moisture damaged and undamaged structures. It will thus be necessary to investigate this difference in a laboratory where the precise composition of all tested structures is known, a range of RH values can be tested and the accumulation of emissions can be followed. / Kompetenscentrum Byggnad - Luftkvalitet - Hälsa 2 (KLUCK 2)

Background: The aim of this thesis was to study transcription patterns and extracellular signalling of the hypertrophic heart to better understand the mechanisms initiating, controlling and maintaining cardiac hypertrophy. Cardiac hypertrophy is a risk factor for cardiovascular morbidity and mortality. Hypertrophy of the myocardium is a state, independent of underlying disease, where the myocardium strives to compensate for an increased workload. This remodelling of the heart includes physiological changes induced by a changed gene expression, alteration of the extracellular matrix and diverse cell-to-cell signalling. Shedding microvesicles and exosomes are membrane released vesicles derived from the plasma membrane, which can mediate messages between cells and induce various cell-related processes in target cells. Methods and materials: Two different microarray studies on different materials were performed. In the first study, cardiac myectomies from 8 patients with hypertrophic obstructive cardiomyopathy (HOCM) and 5 controls without cardiac disease were used. In the second study, myocardial tissue from 6 aorta ligated and 6 sham operated (controls) rats at three different time points (1, 6 and 42 days post-surgically) were analysed. To reveal differences in gene expression the materials were analyzed with Illumina whole genome microarray and multivariate data analysis (PCA and OPLS-DA). Cultured cardiomyocytes (HL-1) were incubated with and without growth factors (TGF-β2 or PDGF BB). Microvesicles and exosomes were collected and isolated after differential centrifugations and ultracentrifugations of the cell culture medium. The microvesicles and exosomes were characterized with dynamic light scattering (DLS), flow cytometry, western blot, electron microscopy and Illumina whole genome microarray. Results: The two different microarray studies revealed differentially expressed gene transcripts and groups of transcripts. When comparing HOCM patients to controls significant down-regulation of the MYH6 gene transcript and two immediate early genes (IEGs, EGR1 and FOS), as well as significant up-regulation of the ACE2, JAK2 and HDAC5 gene transcripts were found. In the rat model, 5 gene groups showed interesting clustering after multivariate data analysis (OPLS-DA) associated with the hypertrophic development: “Atherosclerosis”, “ECM and adhesion molecules”, “Fatty acid metabolism”, “Glucose metabolism” and “Mitochondria”. The shedding microvesicles were rounded vesicles, 40-300 nm in size and surrounded by a bilayered membrane. Chromosomal DNA sequences were identified in the microvesicles. The microvesicles could be taken up by fibroblasts resulting in an altered gene expression in the fibroblasts. The exosomes from cultured cardiomyocytes (incubated with TGF-β2 or PDGF BB) had an average diameter of 50-80 nm, similar to the unstimulated control exosomes. A large, for all cardiomyocyte derived exosomes, common pool of mRNA seems stable and a smaller pool varied in mRNA content according to treatment of the cardiomyocyte. Of the common mRNA about 14% were ribosomal, 14% were of unknown locus and 5% connected to the function of the mitochondria. Conclusions: The microarray studies showed that transcriptional regulation at a stable stage of the hypertrophic development is a balance of pro and anti hypertrophic mechanisms and that diverse gene groups are differently regulated at different time points in the hypertrophic progression. OPLS-DA is a very useful and powerful tool when analyzing gene expression data, especially in finding clusters of gene groups not seen with traditional statistics. The extracellular vesicle studies suggests that microvesicles and exosomes released from cardiomyocytes contain DNA and can be involved in events in target cells by facilitating an array of processes including gene expression changes. Different treatment of the cardiomyocyte influence the content of the exosome produced, indicating that the signal function of the exosome might vary according to the state of the cardiomyocyte. / Bakgrund: Syftet med den här avhandlingen var att studera transkriptions-mönster och extracellulär signalering vid hjärthypertrofi för att bättre förstå de mekanismer som startar, styr och underhåller tillväxten. Hjärthypertrofi, onormal tillväxt av hjärtmuskeln, är en riskfaktor för andra hjärt-kärlsjukdomar och dödlighet. Hypertrofi av hjärtmuskeln är ett tillstånd, oberoende av bakomliggande sjukdom, där hjärtmuskeln strävar efter att kompensera för ökad arbetsbelastning. Denna omställning av hjärtat innefattar fysiologiska förändringar orsakade av ett förändrat genuttryck, modifiering av miljön utanför cellen och ändrad cell-till-cell signalering. Mikrovesiklar och exosomer är små membranomslutna bubblor som frisätts från cellmembranet, ut i cellens omgivning. De kan förmedla budskap mellan celler och påverka olika processer i målceller. Metoder och material: Avhandlingen innefattar två olika microarraystudier på olika material. I den första studien användes hjärtbiopsier från 8 patienter med hypertrofisk obstruktiv kardiomyopati (HOCM) och 5 kontroller utan hjärtsjukdom. I det andra projektet användes hjärtvävnad från 6 aortaligerade och 6 skenopererade (kontroller) råttor vid tre olika tidpunkter (1, 6 och 42 dagar efter kirurgiskt ingrepp). För att påvisa skillnader i genuttryck analyserades proverna med Illumina helgenom microarray och multivariat dataanalys. Avhandlingens andra del innehåller två studier om mikrovesiklar och exosomer. Odlade hjärtmuskelceller (HL-1) stimulerades med tillväxt-faktorer (TGF-β2 eller PDGF BB) och ostimulerade celler användes som kontroll. Mikrovesiklar och exosomer renades fram med centrifugeringar och ultracentrifugering av cellodlingsmediet för att sedan karakteriseras med olika metoder för att studera storlek, ytmarkörer och innehåll. Illumina helgenom microarray användes för att studera microvesiklarnas och exosomernas mRNA innehåll. Resultat: I de två olika microarraystudierna hittades gentranskript och grupper av gentranskript som skiljde sig mellan kontroller och den hypertrofa hjärtvävnaden. När HOCM patientproverna jämfördes med kontroller hittades nedreglering av MYH6, EGR1 och FOS samt uppreglering av ACE2, JAK2 och HDAC5. Efter multivariat dataanalys av materialet från råtta, hittades 5 grupper av gentranskript med intressanta mönster som kunde kopplas till den hypertrofiska utvecklingen av hjärtmuskeln: "Ateroskleros", "ECM och adhesionsmolekyler", "Fettsyrametabolism", "Glukosmetabolis-men" och "Mitokondrien". Mikrovesiklarna hade en diameter på 40-300 nm och innehöll kromosomala DNA-sekvenser. När mikrovesiklarna överfördes till en annan celltyp (fibroblaster) resulterade det i ett förändrat genuttryck i fibroblasterna. Exosomer från hjärtmuskelcellerna som odlats med eller utan tillväxtfaktor hade en diameter på 50-80 nm. En stor pool av olika gentranskript var gemensam för alla exosomer oavsett stimulering eller ej. En mindre pool av gentranskript varierade i innehåll mellan de stimulerade och ostimulerade hjärtmuskelcellerna. I den gemensamma gentranskript poolen var ca 14 % ribosomala, ca 14 % var okända och ca 5 % var associerade till mitokondrien och dess funktion. Slutsats: Microarraystudierna visade att transkriptionsreglering i ett stabilt skede av hypertrofiutvecklingen är en balans mellan pro- och anti-hypertrofiska mekanismer och att olika gengrupper var olika reglerade vid olika tidpunkter i hjärtmuskeltillväxten. OPLS-DA är ett mycket användbart och kraftfullt verktyg när man analyserar genexpressionsdata, särskilt för att hitta grupper av gen-transkript som är svåra att upptäcka med traditionell statistik. Microvesikel- och exosomstudierna visade att mikrovesiklar och exosomer som frisätts från hjärtmuskelceller innehåller både DNA och RNA och kan vara inblandade i händelserna i målceller genom att underlätta en rad processer, inklusive ändringar av genuttryck. Olika stimulering av hjärtmuskelcellen kan påverka innehållet i exosomernas som produceras, vilket indikerar att exosomernas signalfunktion kan variera beroende på hjärtmuskelcellens tillstånd.

The desire to use the tools and concepts of chemometrics when studying problems in the life sciences, especially biology and medicine, has prompted chemometricians to shift their focus away from their field‘s traditional emphasis on model predictivity and towards the more contemporary objective of optimizing information exchange via model interpretation. The complex data structures that are captured by modern advanced analytical instruments open up new possibilities for extracting information from complex data sets. This in turn imposes higher demands on the quality of data and the modeling techniques used. The introduction of the concept of orthogonal variation in the late 1990‘s led to a shift of focus within chemometrics; the information gained from analysis of orthogonal structures complements that obtained from the predictive structures that were the discipline‘s previous focus. OPLS, which was introduced in the beginning of 2000‘s, refined this view by formalizing the model structure and the separation of orthogonal variations. Orthogonal variation stems from experimental/analytical issues such as time trends, process drift, storage, sample handling, and instrumental differences, or from inherent properties of the sample such as age, gender, genetics, and environmental influence. The usefulness and versatility of OPLS has been demonstrated in over 500 citations, mainly in the fields of metabolomics and transcriptomics but also in NIR, UV and FTIR spectroscopy. In all cases, the predictive precision of OPLS is identical to that of PLS, but OPLS is superior when it comes to the interpretation of both predictive and orthogonal variation. Thus, OPLS models the same data structures but provides increased scope for interpretation, making it more suitable for contemporary applications in the life sciences. This thesis discusses four different research projects, including analyses of NIR, FTIR and NMR spectroscopic data. The discussion includes comparisons of OPLS and PLS models of complex datasets in which experimental variation conceals and confounds relevant information. The PLS and OPLS methods are discussed in detail. In addition, the thesis describes new OPLS-based methods developed to accommodate hyperspectral images for supervised modeling. Proper handling of orthogonal structures revealed the weaknesses in the analytical chains examined. In all of the studies described, the orthogonal structures were used to validate the quality of the generated models as well as gaining new knowledge. These aspects are crucial in order to enhance the information exchange from both past and future studies.

The aim of the study was to analyse where different organic molecules situated themselves in relation to the water surface of a water microdroplet and use the resulting data to compare three different forcefields in the simulation package GROMACS. The forcefields used were: General AMBER forcefield (GAFF), Optimized potentials for liquid simulations - all atoms (OPLS-AA), and CHARMM general force field (CGenFF). A library of 146 molecules were simulated using molecular dynamics. Out of the 146 molecules only 65 resulted in useful data for the comparison of the forcefields. The molecules were placed in the centre of a water microdroplet and their movements were simulated for a duration of 1 ns. The trajectories and positions of the molecules were stored and from each simulation a density profile was generated, showing where the molecules situated themselves. The distance from the peak of the density profile to the water surface was calculated and compared between the different forcefields. To analyse the data further some of the molecules were divided into subsets based on their functional groups to see if any trends were visible. Although inconclusive, the data suggested that different forcefields were more or less agreeable depending on the functional group of the molecules, for example OPLS-AA differed from CGenFF and GAFF in the case of alcohols.