The aims of the thesis were to examine i) whether the endogenous opioid system (EOS) is lateralized in human brain areas involved in processing of emotions and pain; ii) whether EOS responses to unilateral brain injury depend on side of lesion, and iii) whether in human alcoholics, this system is involved in molecular adaptations in brain areas relevant for cognitive control of addictive behavior and habit formation. The main findings were that (1) opioid peptides but not opioid receptors and classic neurotransmitters are markedly lateralized in the anterior cingulate cortex involved in processing of  positive and negative emotions and affective component of pain. The region-specific lateralization of neuronal networks expressing opioid peptides may underlie in part lateralization of higher functions in the human brain including emotions and pain. (2) Analysis of the effects of traumatic brain injury (TBI) demonstrated predominant alteration of dynorphin levels in the hippocampus ipsilateral to the injury, while injury to the right hemisphere affected dynorphin levels in the striatum and frontal cortex to a greater extent than that to the left hemisphere. Thus, trauma reveals a lateralization in the mechanisms mediating the response of dynorphin expressing neuronal networks in the brain. These networks may differentially mediate effects of left or right brain injury on lateralized brain functions. (3) In human alcoholics, the enkephalin and dynorphin systems were found to be downregulated in the caudate nucleus and / or putamen that may underlie in part changes in goal directed behavior and formation of a compulsive habit in alcoholics. In contrast to downregulation in these areas, PDYN mRNA and dynorphins in dorsolateral prefrontal cortex, k-opioid receptor mRNA in orbitofrontal cortex, and dynorphins in hippocampus were upregulated in alcoholics. Activation of the k-opioid receptor by upregulated dynorphins may underlie in part neurocognitive dysfunctions relevant for addiction and disrupted inhibitory control. We conclude that the EOS exhibits region-specific lateralization in human brain and brain-area specific lateralized response after unilateral TBI in mice; and that the EOS is involved in adaptive processes associated with specific aspects of alcohol dependence.

Le développement régional et l'inspiration puisée dans d'autres disciplines : Les possibilités de l'application des concepts de la biologie évolutionnaire aux sujets sélectionnés du développement régional. - L'application des concepts de la biologie évolutionnaire (BE) à la problématique de la réalité socioéconomique n'est pas un sujet nouveau dans certaines disciplines socioéconomiques, même si cette inspiration pour la problématique de la géographie socioéconomique, ou plutôt du développement régional, est plus récente et en général se fonde sur les applications réalisées notamment en économie. L'objectif principal de cette thèse est d'identifier de nouveaux concepts de la BE et de tenter de les appliquer aux sujets sélectionnés du développement régional dierectement, sans le rôle d'intermédiaire d'autres disciplines. L'attention est dans un premier temps prêtée à la recherche du cadre théorique convenable et d'un aperçu des concepts de la BE déjà appliqués aux sciences sociales. La seconde partie présente certains concepts de la BE pour lesquels les applications potentielles (par l'intermédiaire des analogies et des métaphores) aux thèmes choisis du développement régional ont été identifiées. Les concepts appliqués ont été divisés en quatre ensembles thématiques biologiques plus larges - l'adaptation, la coévolution, la sélection et la spéciation. L'application de la spéciation peut être probablement perçue comme la contribution la plus précieuse de la thèse. Elle s'appuie sur l'hypothèse qu'il est possible d'identifier certains traits analogues entre les concepts socioéconomiques path dependence et lock-in et les concepts de la BE concernant la spéciation et d'identifier et de classifier les mécanismes / structures qui fonctionnent comme des barrières socioéconomiques. Mots-clés : développement régional ; application des concepts de la biologie évolutionnaire ; path dependence

This thesis investigates the distinction, or distinctions, between response-dependent and response-independent concepts or subject matters. I present and discuss the three most influential versions of the distinction: Crispin Wright’s, Mark Johnston’s, and Philip Pettit’s. I argue that the versions do not compete for a single job, but that they can supplement each other, and that a system of different distinctions is more useful than a single distinction. I distinguish two main paradigms of response-dependence: response-dependence of subject matter (Johnston and Wright), and response-dependence of concepts only (Pettit). I develop Pettit’s ‘ethocentric’ story of concept acquisition into an account of concept evolution that suggests answers to a range of hard questions about language, reality, and the relation between them. I argue that while response-dependence theses of subject matter can be motivated in very different ways, the resulting theses are less different than they might seem. I suggest that the traditional ways of distinguishing response-dependent subject matters from response-independent ones – in terms of a priori biconditionals connecting facts of the disputed class with responses in subjects in favourable conditions, and fulfilling some further conditions such as non-triviality and sometimes necessity – may not be the best approach. I also discuss two general problems for response-dependence theses: the problem of ‘finkish’ counterexamples, and the problem of specifying the ‘favourable conditions’ a priori, yet in a non-trivial way. The discussion of response-dependence is informed by a framework based on the idea that some realism disputes can be viewed as location disputes: disputes over the correct location of the disputed properties among several levels of candidate properties. The approach taken in this work is a charitable one: I try to make sense of response-dependence. The conclusion is the correspondingly optimistic one that the idea(s) of response-dependence makes sense.

<p>Many modern applications fall into the category of "large-scale" statistical problems, in which both the number of observations n and the number of features or parameters p may be large. Many existing methods focus on point estimation, despite the continued relevance of uncertainty quantification in the sciences, where the number of parameters to estimate often exceeds the sample size, despite huge increases in the value of n typically seen in many fields. Thus, the tendency in some areas of industry to dispense with traditional statistical analysis on the basis that "n=all" is of little relevance outside of certain narrow applications. The main result of the Big Data revolution in most fields has instead been to make computation much harder without reducing the importance of uncertainty quantification. Bayesian methods excel at uncertainty quantification, but often scale poorly relative to alternatives. This conflict between the statistical advantages of Bayesian procedures and their substantial computational disadvantages is perhaps the greatest challenge facing modern Bayesian statistics, and is the primary motivation for the work presented here. </p><p>Two general strategies for scaling Bayesian inference are considered. The first is the development of methods that lend themselves to faster computation, and the second is design and characterization of computational algorithms that scale better in n or p. In the first instance, the focus is on joint inference outside of the standard problem of multivariate continuous data that has been a major focus of previous theoretical work in this area. In the second area, we pursue strategies for improving the speed of Markov chain Monte Carlo algorithms, and characterizing their performance in large-scale settings. Throughout, the focus is on rigorous theoretical evaluation combined with empirical demonstrations of performance and concordance with the theory.</p><p>One topic we consider is modeling the joint distribution of multivariate categorical data, often summarized in a contingency table. Contingency table analysis routinely relies on log-linear models, with latent structure analysis providing a common alternative. Latent structure models lead to a reduced rank tensor factorization of the probability mass function for multivariate categorical data, while log-linear models achieve dimensionality reduction through sparsity. Little is known about the relationship between these notions of dimensionality reduction in the two paradigms. In Chapter 2, we derive several results relating the support of a log-linear model to nonnegative ranks of the associated probability tensor. Motivated by these findings, we propose a new collapsed Tucker class of tensor decompositions, which bridge existing PARAFAC and Tucker decompositions, providing a more flexible framework for parsimoniously characterizing multivariate categorical data. Taking a Bayesian approach to inference, we illustrate empirical advantages of the new decompositions.</p><p>Latent class models for the joint distribution of multivariate categorical, such as the PARAFAC decomposition, data play an important role in the analysis of population structure. In this context, the number of latent classes is interpreted as the number of genetically distinct subpopulations of an organism, an important factor in the analysis of evolutionary processes and conservation status. Existing methods focus on point estimates of the number of subpopulations, and lack robust uncertainty quantification. Moreover, whether the number of latent classes in these models is even an identified parameter is an open question. In Chapter 3, we show that when the model is properly specified, the correct number of subpopulations can be recovered almost surely. We then propose an alternative method for estimating the number of latent subpopulations that provides good quantification of uncertainty, and provide a simple procedure for verifying that the proposed method is consistent for the number of subpopulations. The performance of the model in estimating the number of subpopulations and other common population structure inference problems is assessed in simulations and a real data application.</p><p>In contingency table analysis, sparse data is frequently encountered for even modest numbers of variables, resulting in non-existence of maximum likelihood estimates. A common solution is to obtain regularized estimates of the parameters of a log-linear model. Bayesian methods provide a coherent approach to regularization, but are often computationally intensive. Conjugate priors ease computational demands, but the conjugate Diaconis--Ylvisaker priors for the parameters of log-linear models do not give rise to closed form credible regions, complicating posterior inference. In Chapter 4 we derive the optimal Gaussian approximation to the posterior for log-linear models with Diaconis--Ylvisaker priors, and provide convergence rate and finite-sample bounds for the Kullback-Leibler divergence between the exact posterior and the optimal Gaussian approximation. We demonstrate empirically in simulations and a real data application that the approximation is highly accurate, even in relatively small samples. The proposed approximation provides a computationally scalable and principled approach to regularized estimation and approximate Bayesian inference for log-linear models. </p><p>Another challenging and somewhat non-standard joint modeling problem is inference on tail dependence in stochastic processes. In applications where extreme dependence is of interest, data are almost always time-indexed. Existing methods for inference and modeling in this setting often cluster extreme events or choose window sizes with the goal of preserving temporal information. In Chapter 5, we propose an alternative paradigm for inference on tail dependence in stochastic processes with arbitrary temporal dependence structure in the extremes, based on the idea that the information on strength of tail dependence and the temporal structure in this dependence are both encoded in waiting times between exceedances of high thresholds. We construct a class of time-indexed stochastic processes with tail dependence obtained by endowing the support points in de Haan's spectral representation of max-stable processes with velocities and lifetimes. We extend Smith's model to these max-stable velocity processes and obtain the distribution of waiting times between extreme events at multiple locations. Motivated by this result, a new definition of tail dependence is proposed that is a function of the distribution of waiting times between threshold exceedances, and an inferential framework is constructed for estimating the strength of extremal dependence and quantifying uncertainty in this paradigm. The method is applied to climatological, financial, and electrophysiology data. </p><p>The remainder of this thesis focuses on posterior computation by Markov chain Monte Carlo. The Markov Chain Monte Carlo method is the dominant paradigm for posterior computation in Bayesian analysis. It has long been common to control computation time by making approximations to the Markov transition kernel. Comparatively little attention has been paid to convergence and estimation error in these approximating Markov Chains. In Chapter 6, we propose a framework for assessing when to use approximations in MCMC algorithms, and how much error in the transition kernel should be tolerated to obtain optimal estimation performance with respect to a specified loss function and computational budget. The results require only ergodicity of the exact kernel and control of the kernel approximation accuracy. The theoretical framework is applied to approximations based on random subsets of data, low-rank approximations of Gaussian processes, and a novel approximating Markov chain for discrete mixture models.</p><p>Data augmentation Gibbs samplers are arguably the most popular class of algorithm for approximately sampling from the posterior distribution for the parameters of generalized linear models. The truncated Normal and Polya-Gamma data augmentation samplers are standard examples for probit and logit links, respectively. Motivated by an important problem in quantitative advertising, in Chapter 7 we consider the application of these algorithms to modeling rare events. We show that when the sample size is large but the observed number of successes is small, these data augmentation samplers mix very slowly, with a spectral gap that converges to zero at a rate at least proportional to the reciprocal of the square root of the sample size up to a log factor. In simulation studies, moderate sample sizes result in high autocorrelations and small effective sample sizes. Similar empirical results are observed for related data augmentation samplers for multinomial logit and probit models. When applied to a real quantitative advertising dataset, the data augmentation samplers mix very poorly. Conversely, Hamiltonian Monte Carlo and a type of independence chain Metropolis algorithm show good mixing on the same dataset.</p> / Dissertation

The objectives of the thesis - which comprises six parts – can be summarized in i) implementing linear and nonlinear/nonparametric approaches toward detecting, measuring and analyzing the nature and directionality of causal relationships in financial markets, ii) elaborating on modern topics in financial investment analysis, iii) probing into the role of commodity futures in constructing optimal portfolios as well as iv) investigating growth dynamics via aggregated and disaggregated indices. The first paper named “Analyzing causal interactions between sectoral equity returns and commodity futures returns in the aftermath of the global financial crisis: The case of the US and EU equity returns”, aims to explore and compare the dependence and co-movement structure between commodity and various asset classes’ returns including the USA and EU stock markets via the use of linear and non-linear causality testing in a comparative context with the additional adjustment for cointegration and conditional heteroscedasticity. The findings provide important implications for optimal asset allocation and portfolio diversification with respect to various market conditions, namely both in “good” and “bad” (crisis) times. The second paper is entitled “On the time scale behaviour of Equity-Commodity links: Implications for Portfolio Management”, and has been published in the Journal of International Financial Markets, Institutions and Money (2016). The study is co-authored with Professors S. Bekiros, D.K. Nguyen, and B. Sjö. It develops a holistic framework for the investigation of the multi-horizon and intra-frequency causal directionalities of various asset classes, by means of multi-resolution analysis. The results verify the assumption that financial markets exhibit time-varying co-movement patterns, which are fundamentally important in a) generating profitable trading strategies according to different investor horizon expectations and b) decoding the financialization mechanism across various asset classes. The third paper entitled “Business Cycle (de) Synchronization in the aftermath of the Global Financial Crisis: Implications for the Euro Area”, was published at Studies in Nonlinear Dynamics and Econometrics (2015) and is co-authored with S. Bekiros, D.K Nguyen and B. Sjö. In this work, the scale-dependent time-varying (de)synchronization effects between the Eurozone and the broad Euro area business cycles are revealed, before and after the global financial crisis. The results, which point towards an increased observed comovement during the crisis period for the Euro area, could be catalytic for the introduction of a more efficient monetary policy by EU institutions and in particular by the European Central Bank. In the fourth paper, “Do financial stress and policy uncertainty have an impact on the energy and metals markets? A quantile regression approach”, which was published in the International Review of Economics and Finance (2016) and co-authored with J.C. Reboredo, the financial and policy uncertainty is investigated in relation to the price dynamics of energy and metal commodity futures’ markets. This work lead to the analysis of the asymmetric interrelationships with respect to changes in the perceptions of various risk measures, covering various periods, i.e., “normal” vs. “turbulent” such as upward or downward market episodes. The fifth paper, co-authored with P. Andreasson, S. Bekiros and D.K. Nguyen, is entitled “The impact of speculation and economic uncertainty on commodity markets”, and is published in the International Review of Financial Analysis (2016). This paper attempts a novel methodological approach to measuring speculation in commodity markets, in particular whether market speculation drives agricultural commodity prices or viceversa. The assessment of the empirical analysis demonstrates that agricultural prices are not affected by speculation. Finally, the sixth paper “Energy and Output Dynamics in Bangladesh”, co-authored with B.P. Paul, was published in Energy Economics (2011) and explores the relationship between energy utilization and economic growth in Bangladesh. Specifically, it deals with the important issue of whether energy consumption can be reduced without affecting economic growth while at the same time implicitly may lead to poverty reduction. The findings substantiate the fact that a) energy usage has become more efficient in recent times, as well as indicate that b) fluctuations in energy consumption did not have a significant impact on economic output.

Bakgrund: Ortorexia nervosa är ett fenomen som beskrivs olika runt om i världen och tidigare forskning är bristfällig. Orsakerna till fenomenet är oklara men det kan ses som en samsjuklighet mellan Anorexia nervosa, tvångssyndrom och överträningssyndrom. Syfte: Syftet med denna studie var att beskriva personers erfarenheter av att drabbas av Ortorexia nervosa. Metod: Data samlades in genom sex bloggar och analyserades med en kvalitativ innehållsanalys med induktiv ansats. Resultat: Ur analysen av datamaterialet framträdde tre huvudkategorier; När hälsa blir ohälsa, vara i ett missbruk och vägen mot frihet. Huvudkategorierna innefattar tio underkategorier. Konklusion: Denna studie har bidragit med mer kunskap om personers erfarenheter av Ortorexia nervosa. Sjuksköterskan kan med fördel använda denna kunskap för att ge en personcentrerad vård. En ytterligare slutsats är att mer kunskap om fenomenet krävs vilket kan bidra till tidigare identifiering och effektivare behandling. / Background: Orthorexia nervosa is a phenomenon that is described differently around the world. The reasons for this phenomenon is unclear but it may be seen as a comorbidity between anorexia nervosa, obsessive compulsive disorder and overtraining syndrome. Aim: The aim of this study was to describe people's experiences of suffering from Ortorexia nervosa. Method: Data were collected through six blogs and analyzed by a qualitative content analysis with inductive approach. Results: From the analysis of the data emerged three main categories; When health becomes unhealthy, being in an addiction and the way to freedom. The main categories include ten subcategories. Conclusion: This study has contributed more knowledge about people's experiences of orthorexia nervosa. Nurses can benefit from using this knowledge to provide person-centered care. A further conclusion is that more knowledge about the phenomenon is required, which can contribute to earlier detection and more effective treatment.

The purpose of this study is to identify what detail design tools are more productive for the different types of professional software developers. By establishing a match between the detail design tool and the cognitive style of the professional programmer, the end product (Information Systems) should be of a higher quality. Two laboratory experiments were conducted. The first experiment was with professional Software Developers; the second one was with students. The dependant variables considered in this study were the number of semantic errors and the time required to complete a design task for conditional logic. The independent variables were the cognitive style of the subject, the complexity of the task, and the detail design tools. Decision trees, flowcharts and pseudocode were used as detailed design tools. Field dependence was the only dimension of cognitive style that was tested.

The kinetics of the pressure dependent O + SO2 + Ar reaction have been investigated using laser photolysis resonance fluorescence at temperatures of 289 K, 399 K, 581 K, 699 K, 842 K and 1040 K and at pressures from 30-665 torr. Falloff was observed for the first time in the pressure dependence. Application of Lindemann theory yielded an Arrhenius expression of k(T) = 3.3 x 10-32exp(-992/T) cm6 molecule-1 s-1 for the low pressure limit and k(T) = 8.47 x 10-14exp(-468/T) cm3 molecule-1 s-1 for the high pressure limit at temperatures between 289 and 842 K. The reaction is unusual as it possesses a positive activation energy at low temperature, yet at higher temperatures the activation energy is negative, illustrating a reaction barrier.

Family, twin and adoption studies consistently suggest that genetic factors strongly influence the risk for alcohol dependence (AD). Although the literature supports the role of genetics in AD, identification of specific genes contributing to the etiology of AD has proven difficult. These difficulties are due in part to the complex set of risk factors contributing to the development of AD. These risk factors include comorbidities with other clinical diagnoses and behavioral phenotypes (e.g., major depression), physiological differences that contribute to the differences between people in their level of response to ethanol (e.g., initial sensitivity) and finally the large number of biological pathways targeted by and involved in the processing of ethanol. These complexities have probably contributed to the limited success of linkage and candidate gene association studies in finding genes underlying AD. The powerful and unbiased genome-wide association study (GWAS) offers promise in the study of complex diseases. However, due to the complexities of known risk factors, GWAS data has yet to provide consistent, replicable results. In light of these difficulties, this dissertation has five specific aims which attempt to investigate genetic risk loci for AD and related phenotypes through improved methods for candidate gene selection, analysis of a pooled genome-wide association study, genome-wide analyses of initial sensitivity and maximum alcohol consumption in a twenty-four hour period and finally, creation of a multivariate AD/internalizing phenotype.

Hepatitis C is the most common chronic blood-borne infection in the United States. Research has focused on contributing factors to the development and progression of liver disease, but few studies have considered nicotine use as a potential prognostic factor with CHC. Research has commonly found that CHC patients report with a diminished quality of life. Several factors have been proposed to account for a decrease in QOL; however, the mechanisms underlying the impairment in QOL have not yet been elicited. 76 CHC patients completed self-report measures on a variety of psychosocial variables and biochemical data for determining the patient's liver disease severity was obtained. The findings revealed strong support for the deleterious effects of smoking cigarettes on liver disease symptomatology and its progression. Smokers endorsed experiencing significantly more severe symptoms of fatigue, poor appetite, and headaches. The CHC smokers tended to present with higher scores on the Aspartate Arninotransferase to platelet ratio index (APRI). The smokers' mean score is above the cut-off value of 1.50 that indicates a .88 predictive value for the presence of hepatic fibrosis. The level of cigarette consumption could also be a factor in the progression of liver disease. Individuals smoking more than one pack per day tended to report more severe symptoms of fatigue and a poorer appetite. Heavy smokers presented with an APRI mean score above the cut-off value of 2.00 that indicates a .93 negative predictive value for the presence of cirrhosis below the cut-off value.General active coping moderated the relationship between liver disease severity and QOL. The results revealed that patients using more avoidant coping reported lower levels of QOL on the physical and mental component of the SF-36. Tobacco use moderated the relationship between liver disease severity and QOL. Interestingly, smokers reported a higher level of QOL compared to nonsmokers when experiencing more severe liver disease. CHC patients with higher levels of psychological distress reported lower QOL on both physical and mental functioning. Individuals smoking marijuana also tended to report lower levels of QOL on mental functioning. Information garnered from this study is aimed to help slow the progression of advanced liver disease in CHC patients in addition to improving their QOL.