In einem klassischen Kontext bedeutet Synchronisierung die Anpassung der Rhythmen von selbst-erregten periodischen Oszillatoren aufgrund ihrer schwachen Wechselwirkung. <br /> Der Begriff der Synchronisierung geht auf den berühmten niederläandischen Wissenschaftler Christiaan Huygens im 17. Jahrhundert zurück, der über seine Beobachtungen mit Pendeluhren berichtete. Wenn zwei solche Uhren auf der selben Unterlage plaziert wurden, schwangen ihre Pendel in perfekter Übereinstimmung. <br /> Mathematisch bedeutet das, daß infolge der Kopplung, die Uhren mit gleichen Frequenzen und engverwandten Phasen zu oszillieren begannen. <br /> Als wahrscheinlich ältester beobachteter nichtlinearer Effekt wurde die Synchronisierung erst nach den Arbeiten von E. V. Appleton und B. Van der Pol gegen 1920 verstanden, die die Synchronisierung in Triodengeneratoren systematisch untersucht haben. Seitdem wurde die Theorie gut entwickelt, und hat viele Anwendungen gefunden. <br /> <br /> Heutzutage weiss man, dass bestimmte, sogar ziemlich einfache, Systeme, ein chaotisches Verhalten ausüben können. Dies bedeutet, dass ihre Rhythmen unregelmäßig sind und nicht durch nur eine einzige Frequenz charakterisiert werden können. <br /> Wie in der Habilitationsarbeit gezeigt wurde, kann man jedoch den Begriff der Phase und damit auch der Synchronisierung auf chaotische Systeme ausweiten. Wegen ihrer sehr schwachen Wechselwirkung treten Beziehungen zwischen den Phasen und den gemittelten Frequenzen auf und führen damit zur Übereinstimmung der immer noch unregelmäßigen Rhythmen. Dieser Effekt, sogenannter Phasensynchronisierung, konnte später in Laborexperimenten anderer wissenschaftlicher Gruppen bestätigt werden. <br /> <br /> Das Verständnis der Synchronisierung unregelmäßiger Oszillatoren erlaubte es uns, wichtige Probleme der Datenanalyse zu untersuchen. <br /> Ein Hauptbeispiel ist das Problem der Identifikation schwacher Wechselwirkungen zwischen Systemen, die nur eine passive Messung erlauben. Diese Situation trifft häufig in lebenden Systemen auf, wo Synchronisierungsphänomene auf jedem Niveau erscheinen - auf der Ebene von Zellen bis hin zu makroskopischen physiologischen Systemen; in normalen Zuständen und auch in Zuständen ernster Pathologie. <br /> Mit unseren Methoden konnten wir eine Anpassung in den Rhythmen von Herz-Kreislauf und Atmungssystem in Menschen feststellen, wobei der Grad ihrer Interaktion mit der Reifung zunimmt. Weiterhin haben wir unsere Algorithmen benutzt, um die Gehirnaktivität von an Parkinson Erkrankten zu analysieren. Die Ergebnisse dieser Kollaboration mit Neurowissenschaftlern zeigen, dass sich verschiedene Gehirnbereiche genau vor Beginn des pathologischen Zitterns synchronisieren. Außerdem gelang es uns, die für das Zittern verantwortliche Gehirnregion zu lokalisieren. / In a classical context, synchronization means adjustment of rhythms of self-sustained periodic oscillators due to their weak interaction. The history of synchronization goes back to the 17th century when the famous Dutch scientist Christiaan Huygens reported on his observation of synchronization of pendulum clocks: when two such clocks were put on a common support, their pendula moved in a perfect agreement. In rigorous terms, it means that due to coupling the clocks started to oscillate with identical frequencies and tightly related phases. Being, probably, the oldest scientifically studied nonlinear effect, synchronization was understood only in 1920-ies when E. V. Appleton and B. Van der Pol systematically - theoretically and experimentally - studied synchronization of triode generators. Since that the theory was well developed and found many applications. <br /> Nowadays it is well-known that certain systems, even rather simple ones, can exhibit chaotic behaviour. It means that their rhythms are irregular, and cannot be characterized only by one frequency. However, as is shown in the Habilitation work, one can extend the notion of phase for systems of this class as well and observe their synchronization, i.e., agreement of their (still irregular!) rhythms: due to very weak interaction there appear relations between the phases and average frequencies. This effect, called phase synchronization, was later confirmed in laboratory experiments of other scientific groups. <br /> Understanding of synchronization of irregular oscillators allowed us to address important problem of data analysis: how to reveal weak interaction between the systems if we cannot influence them, but can only passively observe, measuring some signals. This situation is very often encountered in biology, where synchronization phenomena appear on every level - from cells to macroscopic physiological systems; in normal states as well as in severe pathologies. With our methods we found that cardiovascular and respiratory systems in humans can adjust their rhythms; the strength of their interaction increases with maturation. Next, we used our algorithms to analyse brain activity of Parkinsonian patients. The results of this collaborative work with neuroscientists show that different brain areas synchronize just before the onset of pathological tremor. Morevoever, we succeeded in localization of brain areas responsible for tremor generation.

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.

This dissertation investigated the optimal strategy for the model specification search in the latent growth modeling. Although developing an initial model based on the theory from prior research is favored, sometimes researchers may need to specify the starting model in the absence of theory. In this simulation study, the effectiveness of the start models in searching for the true population model was examined. The four possible start models adopted in this study were: the simplest mean and covariance structure model, the simplest mean and the most complex covariance structure model, the most complex mean and the simplest covariance structure model, and the most complex mean and covariance structure model. Six model selection criteria were used to determine the recovery of the true model: Likelihood ratio test (LRT), DeltaCFI, DeltaRMSEA, DeltaSRMR, DeltaAIC, and DeltaBIC. The results showed that specifying the most complex covariance structure (UN) with the most complex mean structure recovered the true mean trajectory most successfully with the average hit rate above 90% using the DeltaCFI, DeltaBIC, DeltaAIC, and DeltaSRMR. In searching for the true covariance structure, LRT, DeltaCFI, DeltaAIC, and DeltaBIC performed successfully regardless of the searching method with different start models.

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.

The present study investigated the cognitive styles of Indian, Metis, Inuit and non-native adults and adolescents of northern Canada and Alaska. The study identified three relational and two analytical cognitive styles. The styles differed significantly from each other in relation to cultural background, language facility, level of post-secondary education, sex and age of the respondents. Cultural background was found to be the most significant discriminator of those under investigation.<p> Procedure of the study involved the collection of verbalized responses to five open-ended questions concerning education from one hundred northern residents. A total of 528 minutes 32 seconds of taperecorded responses was available from twenty treaty and status Indians, twenty Metis, twenty Inuit and forty non-natives. Subjects included parents, university students, high school students, teacher trainees, teachers, education administrators, native politicians and general community members. The data were submitted to content analysis procedures with items coded according to the Data Analysis of Cognitive Style (DACS) Scale which had been adapted for use in the present study from the work of E. S. Schneidman (1966). Scale item frequencies for each respondent were tabulated and submitted for statistical analyses to the SPSS program discriminant analysis. This analysis identified significantly different functions which translated into patterns of thinking or cognitive styles. In addition this analysis identified the relative importance of functions as discriminators among groups and computed predictability scores which showed the percentage of respondents who were correctly classified according to cognitive styles. and demographic variables.<p>Findings of this study must be considered in relation to the following limitations: the size and nature of the stratified random sample; the reliability of the coders; the use of the unvalidated DACS scale; the ability of the analytical procedures to correctly discriminate among the study groups.<p> The study found that the groups which tended to think in relational styles were: Natives (Indian, Metis, Inuit), people with no university education or with less than one year at university; bilinguals (English and a native language); males; people under twenty years and over forty years of age. The terms Conflict-relational, Moral-relational and Inexactrelational were used to more precisely identify differing cognitive behaviors within the overall relational category. The groups which were found to exhibit analytical cognitive style behaviors included: the nonnative group; those respondents with two to four years of university education; and respondents between thirty and forty years of age. Subcategories within analytical styles were Conflict-analytical and Inexactanalytical.<p>When the Indian, Metis and Inuit respondents were combined into a "native" cultural group they strongly identified with the Moral-relational cognitive style (people-oriented, subjective, holistic, concerned with morals and ethics). The non-native group showed a strong negative relationship to this style. However, when each cultural group was analyzed separately, it was found that the Indian and Inuit subjects were somewhat more analytical (objective, linear, field-independent) than the Metis but less so than the non-natives. On the analysis of four groups, the nonnatives were found to relate to both relational and analytical styles of thinking, indicating a wide range of differences within the group.<p>It was concluded that significant differences existed in the cognitive styles preferred by respondents of different cultural, language, education, sex and age groups in this study. Cultural background was found to be the strongest discriminator in relation to cognitive style differences. It was further concluded that according to extrapolation of findings to the theoretical model it may be possible and desirable to modify curricula content and teaching techniques to achieve a closer match between teaching styles and cognitive and learning styles of. students of indigenous cultural backgrounds.

Genetic data analysis has been capturing a lot of attentions for understanding the mechanism of the development and progressing of diseases like cancers, and is crucial in discovering genetic markers and treatment targets in medical research. This dissertation focuses on several important issues in genetic data analysis, graphical network modeling, feature selection, and covariance estimation. First, we develop a gene network modeling method for discrete gene expression data, produced by technologies such as serial analysis of gene expression and RNA sequencing experiment, which generate counts of mRNA transcripts in cell samples. We propose a generalized linear model to fit the discrete gene expression data and assume that the log ratios of the mean expression levels follow a Gaussian distribution. We derive the gene network structures by selecting covariance matrices of the Gaussian distribution with a hyper-inverse Wishart prior. We incorporate prior network models based on Gene Ontology information, which avails existing biological information on the genes of interest. Next, we consider a variable selection problem, where the variables have natural grouping structures, with application to analysis of chromosomal copy number data. The chromosomal copy number data are produced by molecular inversion probes experiments which measure probe-specific copy number changes. We propose a novel Bayesian variable selection method, the hierarchical structured variable se- lection (HSVS) method, which accounts for the natural gene and probe-within-gene architecture to identify important genes and probes associated with clinically relevant outcomes. We propose the HSVS model for grouped variable selection, where simultaneous selection of both groups and within-group variables is of interest. The HSVS model utilizes a discrete mixture prior distribution for group selection and group-specific Bayesian lasso hierarchies for variable selection within groups. We further provide methods for accounting for serial correlations within groups that incorporate Bayesian fused lasso methods for within-group selection. Finally, we propose a Bayesian method of estimating high-dimensional covariance matrices that can be decomposed into a low rank and sparse component. This covariance structure has a wide range of applications including factor analytical model and random effects model. We model the covariance matrices with the decomposition structure by representing the covariance model in the form of a factor analytic model where the number of latent factors is unknown. We introduce binary indicators for estimating the rank of the low rank component combined with a Bayesian graphical lasso method for estimating the sparse component. We further extend our method to a graphical factor analytic model where the graphical model of the residuals is of interest. We achieve sparse estimation of the inverse covariance of the residuals in the graphical factor model by employing a hyper-inverse Wishart prior method for a decomposable graph and a Bayesian graphical lasso method for an unrestricted graph.

This thesis extends the application of the localized realized volatility model created by Ying Chen, Wolfgang Karl Härdle, and Uta Pigorsch to other futures markets, particularly the CAC 40 and the NI 225. The research attempted to replicate results though ultimately, those results were invalidated by procedural difficulties.