<div><div><div><p>Head injuries in collision sports have been linked to long-term neurological disorders. High school collision sport athletes, a population vulnerable to head injuries, are at a greater risk of chronic damage. Various studies have indicated significant deviations in brain function due to the accumulation of repetitive low-level subconcussive impacts to the head without externally observable cognitive symptoms. The aim of this study was to investigate metabolic changes in asymptomatic collision sport athletes across time within their competition season and as a function of mechanical force to their head. For this purpose, Proton Magnetic Resonance Spectroscopy (MRS) was used as a tool to detect altered brain metabolism in high school collision sport athletes (football and soccer) without diagnosed concussion. Also, sensors were attached to each athletes head to collect the count and magnitude of head impacts during their games and practices. Transient neurometabolic alterations along with prolonged recovery were observed in collision sport athletes.</p><div><div><div><p><br></p><p>Multi-site studies are becoming important to increase statistical power, enhance generalizability, and to improve the likelihood of pooling relevant subgroups together activities which are otherwise limited by the availability of patients or funds at a single site. Even with harmonized imaging sequences, site-dependent variability can mask the advantages of these multi-site studies. The aim of this study was to assess multi-site reproducibility in resting-state functional connectivity fingerprints, and to improve identifiability of obtained functional connectomes. We evaluated individual fingerprints in test- retest visit pairs within and across two sites and present a generalized framework based on principal component analysis (PCA) to improve identifiability. The optimally reconstructed functional connectomes using PCA showed a substantial improvement in individual fingerprinting of the subjects within and across the two sites and test-retest visit pairs relative to the original data. Results demonstrate that the data-driven method presented in the study can improve identifiability in resting-state functional connectomes in multi-site studies.</p></div></div></div></div></div></div>

<p>The motivating forestry background for this thesis is the need for fast, non-destructive, and cost-efficient methods to estimate fibre length distributions in standing trees in order to evaluate the effect of silvicultural methods and breeding programs on fibre length. The usage of increment cores is a commonly used non-destructive sampling method in forestry. An increment core is a cylindrical wood sample taken with a special borer, and the methods proposed in this thesis are especially developed for data from increment cores. Nevertheless the methods can be used for data from other sampling frames as well, for example for sticks with the shape of an elongated rectangular box.</p><p>This thesis proposes methods to estimate fibre length distributions based on censored mixture data from wood samples. Due to sampling procedures, wood samples contain cut (censored) and uncut observations. Moreover the samples consist not only of the fibres of interest but of other cells (fines) as well. When the cell lengths are determined by an automatic optical fibre-analyser, there is no practical possibility to distinguish between cut and uncut cells or between fines and fibres. Thus the resulting data come from a censored version of a mixture of the fine and fibre length distributions in the tree. The methods proposed in this thesis can handle this lack of information.</p><p>Two parametric methods are proposed to estimate the fine and fibre length distributions in a tree. The first method is based on grouped data. The probabilities that the length of a cell from the sample falls into different length classes are derived, the censoring caused by the sampling frame taken into account. These probabilities are functions of the unknown parameters, and ML estimates are found from the corresponding multinomial model.</p><p>The second method is a stochastic version of the EM algorithm based on the individual length measurements. The method is developed for the case where the distributions of the true lengths of the cells at least partially appearing in the sample belong to exponential families. The cell length distribution in the sample and the conditional distribution of the true length of a cell at least partially appearing in the sample given the length in the sample are derived. Both these distributions are necessary in order to use the stochastic EM algorithm. Consistency and asymptotic normality of the stochastic EM estimates is proved.</p><p>The methods are applied to real data from increment cores taken from Scots pine trees (Pinus sylvestris L.) in Northern Sweden and further evaluated through simulation studies. Both methods work well for sample sizes commonly obtained in practice.</p>

<p>This thesis concerns the identifiability issue; which, if any, parameters can be deduced from the input and output behavior of a model? The two types of identifiability concepts, a priori and practical, will be addressed and explained. Two methods for identifiability testing are evaluated and the result shows that the two methods work well if they are combined. The first method is for a priori identifiability analysis and it can determine the a priori identifiability of a system in polynomial time. The result from the method is probabilistic with a high probability of correct answer. The other method takes the simulation approach to determine whether the model is practically identifiable. Non-identifiable parameters manifest themselves as a functional relationship between the parameters and the method uses transformations of the parameter estimates to conclude if the parameters are linked. The two methods are verified on models with known identifiability properties and then tested on some examples from systems biology. Although the output from one of the methods is cumbersome to interpret, the results show that the number of parameters that can be determined in practice (practical identifiability) are far fewer than the ones that can be determined in theory (a priori identifiability). The reason for this is the lack of quality, noise and lack of excitation, of the measurements.</p> / <p>Fokus i denna rapport är på identifierbarhetsproblemet. Vilka parametrar kan unikt bestämmas från en modell? Det existerar två typer av identifierbarhetsbegrepp, a priori och praktisk identifierbarhet, som kommer att förklaras. Två metoder för identifierbarhetstestning är utvärderade och resultaten visar på att de två metoderna fungerar bra om de kombineras med varandra. Den första metoden är för a priori identifierbarhetsanalys och den kan avgöra identifierbarheten för ett system i polynomiell tid. Resultaten från metoden är slumpmässigt med hög sannolikhet för ett korrekt svar. Den andra metoden använder sig av simuleringar för att avgöra om modellen är praktiskt identifierbar. Icke-identifierbara parametrar yttrar sig som funktionella kopplingar mellan parametrar och metoden använder sig av transformationer av parameterskattningarna för att avgöra om parametrarna är kopplade. De två metoderna är verifierade på modeller där identifierbarheten är känd och är därefter testade på några exempel från systembiologi. Trots att resultaten från den ena metoden är besvärliga att tolka visar resultaten på att antalet parametrar som går att bestämma i verkligheten (praktiskt identifierbara) är betydligt färre än de parametrar som kan bestämmas i teorin (a priori identifierbara). Anledningen beror på brist på kvalitet, både brus och brist på excitation, i mätningarna.</p>

The motivating forestry background for this thesis is the need for fast, non-destructive, and cost-efficient methods to estimate fibre length distributions in standing trees in order to evaluate the effect of silvicultural methods and breeding programs on fibre length. The usage of increment cores is a commonly used non-destructive sampling method in forestry. An increment core is a cylindrical wood sample taken with a special borer, and the methods proposed in this thesis are especially developed for data from increment cores. Nevertheless the methods can be used for data from other sampling frames as well, for example for sticks with the shape of an elongated rectangular box. This thesis proposes methods to estimate fibre length distributions based on censored mixture data from wood samples. Due to sampling procedures, wood samples contain cut (censored) and uncut observations. Moreover the samples consist not only of the fibres of interest but of other cells (fines) as well. When the cell lengths are determined by an automatic optical fibre-analyser, there is no practical possibility to distinguish between cut and uncut cells or between fines and fibres. Thus the resulting data come from a censored version of a mixture of the fine and fibre length distributions in the tree. The methods proposed in this thesis can handle this lack of information. Two parametric methods are proposed to estimate the fine and fibre length distributions in a tree. The first method is based on grouped data. The probabilities that the length of a cell from the sample falls into different length classes are derived, the censoring caused by the sampling frame taken into account. These probabilities are functions of the unknown parameters, and ML estimates are found from the corresponding multinomial model. The second method is a stochastic version of the EM algorithm based on the individual length measurements. The method is developed for the case where the distributions of the true lengths of the cells at least partially appearing in the sample belong to exponential families. The cell length distribution in the sample and the conditional distribution of the true length of a cell at least partially appearing in the sample given the length in the sample are derived. Both these distributions are necessary in order to use the stochastic EM algorithm. Consistency and asymptotic normality of the stochastic EM estimates is proved. The methods are applied to real data from increment cores taken from Scots pine trees (Pinus sylvestris L.) in Northern Sweden and further evaluated through simulation studies. Both methods work well for sample sizes commonly obtained in practice.

Knowledge Space Theory (KST) links in several ways to Formal Concept Analysis (FCA). Recently, the probabilistic and statistical aspects of KST have been further developed by several authors. We review part of the recent results, and describe some of the open problems. The question of whether the outcomes can be useful in FCA remains to be investigated.

El control i supervisió de processos es basa generalment en la utilització de models. Models que han de ser tan acurats com sigui possible. Processos complexes com les xarxes de distribució d'aigua no escapen d'aquesta situació. Una bona gestió d'aquest element tan necessari i cada cop més escàs en les condicions adequades és una necessitat vital.Aquesta tesi ha estat realitzada amb la col·laboració de dos grups de recerca, un més orientat a l'aplicació -xarxes d'aigua- i l'altre ala metodologia -control i supervisió -. L'experiència d'ambdós grups va generar la necessitat de la calibració de models de xarxes per tal de poder realitzar bones simulacions, optimitzacions, supervisió, detecció de fuites, etc. L'objectiu principal d'aquesta tesi és desenvolupar una metodologia per aquesta calibració.L'originalitat d'aquest treball rau tan en l'abast del problema com les tècniques emprades. Una xarxa inclou elements diversos (nodes, dipòsits, canonades, vàlvules i bombes) i la calibració requereix l'estudi detallat de cada element així com l'aplicació a sistemes immensos. S'ha parat especial atenció a als tres passos principals de la calibració: estudi d'identificabilitat, macrocalibració i microcalibració. Cada un d'aquests passos requereix tècniques específiques. En aquesta tesi s'ha aplicat tècniques poc o gens conegudes en l'àmbit de les xarxes d'aigua.L'estudi d'identificabilitat s'ha desenvolupat per diferents escenaris, des del cas simple i il·lustratiu fins a xarxes reals i grans. Els experiments més simples es van fer amb xarxes lineals i estàtiques. En general les xarxes són no lineals i l'ús de més d'un instant en el temps en les mesures millora les condicions d'dentificabilitat. La metodologia proposada permet la determinació de la identificabilitat per xarxes en general (no lineals). L'eina obtinguda ajuda en el disseny dels problemes d'identificació fent servir informació topològica de la xarxa.Quan el model es genera, s'introdueixen grans errors. Aquests errors són detectats en un primer esforç de calibració, macrocalibració. Aquest procés es realitza manualment i l'objectiu d'aquesta tesi és donar suport a aquesta feina. La metodologia emprada pels experts s'ha analitzat. S'han fet servir algorismes específics per cada tipus d'error. Per tal de detectar errors en un conjunt gran d'elements s'han fet servir algorismes de classificació. Aquests algorismes permeten la generació de coneixement a partir d'experiments simulats i l'optimització de funcions de versemblança.La sintonia de paràmetres, es tractada com un problema d'optimització. La no convexitat del problema es determina en una caracterització acurada del problema. Aquesta no convexitat mostra els problemes que els optimitzadors locals tindran per resoldre'l. Les possibilitats d'alguns optimitzadors globals s'han explorat. El cost computacional dels optimitzadors globals, especialment quan les xarxes creixen, representa una gran limitació. El filtre de Kalman estès s'ha fet servir amb resultats prometedors.Els resultats d'aquesta tesi s'han presentat en tres congressos [Per-01a], [Per-01b], [Per-03] i s'està redactant un article per a revista abans de final d'any.Process control and supervision is based mainly in the use of models, which have to be as accurate as possible. Complex processes, like water distribution networks, fall into such a situation too. Water is a necessary element and its shortage in good conditions is a major problem. Therefore, a good management of water distribution is vital. / This thesis has been carried out with the collaboration of two research groups. One is more oriented to the application -water networks- and the other one is more oriented to the technology -control and supervision-. Experience of both groups has generated the necessity of calibration of water network models in order to be able to do good simulations, optimisations, supervisions, leak detections, etc. For this reason, the main objective of this thesis is to develop a closed methodology for the calibration process of water distribution network models.The originality of this work comes both from the magnitude of the problem and the techniques used. On the one hand, a water network includes different elements (nodes, reservoirs, pipes, valves, and pumps), and its calibration requires the study of those elements in detail. On the other hand it has to be applied to huge systems. Special attention has been paid to the three main parts of calibration: identifiability study, macrocalibration and microcalibration. Each of those steps needs specific techniques. Some of the techniques used in this thesis are little known or unknown at all in the water industry.The identifiability study has been developed for different case studies, ranging from simple, illustrating case to real huge networks. The simplest experiments were performed with linear and static networks. In general, networks are non-linear and the use of more than one time-step in the measurements provides better identifiability conditions. The methodology proposed allows the determination of the extended-period identifiability for general networks (non-linear). The obtained tool helps in the design of identification problems using topological information of the network.When the model is generated, large errors are introduced. These errors are detected in a first calibration effort, macrocalibration. This process is done manually and the objective in this thesis is to give support to such work. The methodology followed by the experts has been analysed. Specific algorithms have been used in this thesis for each kind of error. In order to detect errors in huge amount of elements classification algorithms have been used. Those algorithms allow the generation of knowledge from simulation experiments and optimisation of likelihood functions.The parameter tuning, microcalibration, is treated as an optimisation problem. The non-convexity of the problem is detected by a detailed characterisation. This non-convexity shows to be a problem for the local optimisers. The capabilities of some global optimisation algorithms have been explored. The computing cost for the global optimisers, especially when huge networks are identified, represents a major limitation. The Extended Kalman Filter has been used with promising results.Results of this thesis have been presented in three Conferences [Per-01a], [Per-01b], [Per-03] and a paper is will be finished before the end of the year.

This thesis is concerned with drawing out high-level insight from otherwise complex mathematical models of physical processes. This is achieved through detailed analysis of model behaviour as constituent parameters are varied. A particular focus is the well-posedness of parameter estimation from noisy data, and its relationship to the parametric sensitivity properties of the model. Other topics investigated include the verification of model performance properties over large ranges of parameters, and the simplification of models based upon their response to parameter perturbation. Several methodologies are proposed, which account for various model classes. However, shared features of the models considered include nonlinearity, parameters with considerable scope for variability, and experimental data corrupted by significant measurement uncertainty. We begin by considering models described by systems of nonlinear ordinary differen- tial equations with parameter dependence. Model output, in this case, can only be obtained by numerical integration of the relevant equations. Therefore, assessment of model behaviour over tracts of parameter space is usually carried out by repeated model simulation over a grid of parameter values. We instead reformulate this as- sessment as an algebraic problem, using polynomial programming techniques. The result is an algorithm that produces parameter-dependent algebraic functions that are guaranteed to bound user-defined aspects of model behaviour over parameter space. We then consider more general classes of parameter-dependent model. A theoretical framework is constructed through which we can explore the duality between model sensitivity to non-local parameter perturbations, and the well-posedness of parameter estimation from significantly noisy data. This results in an algorithm that can uncover functional relations on parameter space over which model output is insensitive and parameters cannot be estimated. The methodology used derives from techniques of nonlinear optimal control. We use this algorithm to simplify benchmark models from the systems biology literature. Specifically, we uncover features such as fast-timescale subsystems and redundant model interactions, together with the sets of parameter values over which the features are valid. We finally consider parameter estimation in models that are acknowledged to im- perfectly describe the modelled process. We show that this invalidates standard statistical theory associated with uncertainty quantification of parameter estimates. Alternative theory that accounts for this situation is then developed, resulting in a computationally tractable approximation of the covariance of a parameter estimate with respect to noise-induced fluctuation of experimental data.

A complete chain of analysis and synthesis system identification tools for detectability assessment and adaptive identification of parameters with physical interpretation that can be found commonly in control-oriented powertrain models is presented. This research is motivated from the fact that future powertrain control and monitoring systems will depend increasingly on physically oriented system models to reduce the complexity of existing control strategies and open the road to new environmentally friendly technologies. At the outset of this study a physics-based control-oriented dynamic model of a complete transient engine testing facility, consisting of a single cylinder engine, an alternating current dynamometer and a coupling shaft unit, is developed to investigate the functional relationships of the inputs, outputs and parameters of the system. Having understood these, algorithms for identifiability analysis and adaptive identification of parameters with physical interpretation are proposed. The efficacy of the recommended algorithms is illustrated with three novel practical applications. These are, the development of an on-line health monitoring system for engine dynamometer coupling shafts based on recursive estimation of shaft’s physical parameters, the sensitivity analysis and adaptive identification of engine friction parameters, and the non-linear recursive parameter estimation with parameter estimability analysis of physical and semi-physical cyclic engine torque model parameters. The findings of this research suggest that the combination of physics-based control oriented models with adaptive identification algorithms can lead to the development of component-based diagnosis and control strategies. Ultimately, this work contributes in the area of on-line fault diagnosis, fault tolerant and adaptive control for vehicular systems.

Les bactéries ajustent constamment leur composition moléculaire pour répondre à deschangements environnementaux. Nous nous intéressons aux systèmes de régulation métabolique et génique permettant une telle adaptation, notamment dans le contexte de la diauxie chez Escherichia coli lors de la transition de croissance sur une source de carbone riche, le glucose, à une source plus pauvre, l’acétate. Afin de modéliser de tels réseaux métaboliques, nous utilisons un formalisme cinétique approché appelé linlog et abordons les problèmes ren- contrés lors de l’estimation de paramètres. Ainsi, nous proposons une méthode d’estimationde paramètres à partir de jeux de données incomplets basée sur l’algorithme EM (“Expec- tation Maximization”) et l’appliquons au modèle linlog du métabolisme central du carbone. Nous proposons également une méthode d’analyse d’identifiabilité et de réduction de modèles non identifiables que nous appliquons ensuite sur des jeux de données simulés ou obtenus expérimentalement. Par ailleurs, nous mesurons des profils temporels d’expression de gènes impliqués dans le contrôle de la diauxie et mettons en évidence, à l’aide de modèles cinétiques développés dans ces travaux, l’importance de la contribution de l’état physiologique de la cellule dans la régulation génique. En se confrontant aux défis méthodologiques rencontrés lors du développement de modèles de réseaux métabolique et génique, cette thèse contribue aux efforts futurs portant sur l’intégration de ces deux types de réseaux dans des modèles quantitatifs. / Bacteria manage to constantly adapt their molecular composition to respond to environmentalchanges. We focus on systems of both metabolic and gene regulation that enablesuch type of adaptation, notably in the context of diauxic growth of Escherichia coli, when itshifts from glucose to acetate as a carbon source. To model a metabolic network, we use anapproximate kinetic formalism called linlog and address methodological issues encounteredwhen performing parameter estimation. We propose a maximum-likelihood method basedon Expectation Maximization for parameter estimation from incomplete datasets. We then apply it to the linlog model of central carbon metabolism. We also propose a method foridentifiability analysis and reduction of nonidentifiable models that we then apply to bothsimulated and experimental datasets. Moreover, we monitored gene expression patterns for agene network involved in the control of diauxie and highlight, by means of kinetic models developedin this study, the role of the global physiological state of the cell in regulation of geneexpression. By addressing methodological challenges encountered with models of metabolicand gene networks, this thesis contributes to future efforts integrating both types of networksinto quantitative models

This thesis concerns the identifiability issue; which, if any, parameters can be deduced from the input and output behavior of a model? The two types of identifiability concepts, a priori and practical, will be addressed and explained. Two methods for identifiability testing are evaluated and the result shows that the two methods work well if they are combined. The first method is for a priori identifiability analysis and it can determine the a priori identifiability of a system in polynomial time. The result from the method is probabilistic with a high probability of correct answer. The other method takes the simulation approach to determine whether the model is practically identifiable. Non-identifiable parameters manifest themselves as a functional relationship between the parameters and the method uses transformations of the parameter estimates to conclude if the parameters are linked. The two methods are verified on models with known identifiability properties and then tested on some examples from systems biology. Although the output from one of the methods is cumbersome to interpret, the results show that the number of parameters that can be determined in practice (practical identifiability) are far fewer than the ones that can be determined in theory (a priori identifiability). The reason for this is the lack of quality, noise and lack of excitation, of the measurements. / Fokus i denna rapport är på identifierbarhetsproblemet. Vilka parametrar kan unikt bestämmas från en modell? Det existerar två typer av identifierbarhetsbegrepp, a priori och praktisk identifierbarhet, som kommer att förklaras. Två metoder för identifierbarhetstestning är utvärderade och resultaten visar på att de två metoderna fungerar bra om de kombineras med varandra. Den första metoden är för a priori identifierbarhetsanalys och den kan avgöra identifierbarheten för ett system i polynomiell tid. Resultaten från metoden är slumpmässigt med hög sannolikhet för ett korrekt svar. Den andra metoden använder sig av simuleringar för att avgöra om modellen är praktiskt identifierbar. Icke-identifierbara parametrar yttrar sig som funktionella kopplingar mellan parametrar och metoden använder sig av transformationer av parameterskattningarna för att avgöra om parametrarna är kopplade. De två metoderna är verifierade på modeller där identifierbarheten är känd och är därefter testade på några exempel från systembiologi. Trots att resultaten från den ena metoden är besvärliga att tolka visar resultaten på att antalet parametrar som går att bestämma i verkligheten (praktiskt identifierbara) är betydligt färre än de parametrar som kan bestämmas i teorin (a priori identifierbara). Anledningen beror på brist på kvalitet, både brus och brist på excitation, i mätningarna.