Neurobiological Correlates of Personality Traits: A Study on Harm Avoidance and Neuroticism

Harm Avoidance and Neuroticism are traits that predispose to mental illnesses. Studying them provides a unique way to study predisposition of mental illnesses. Understanding the biological mechanisms that mediate vulnerability could lead to improvement in treatment and ultimately to pre-emptive psychiatry. These personality traits describe a tendency to feel negative emotions such as fear, shyness and worry. Previous studies suggest these traits are regulated by serotonin and opiate pathways. The aim of this thesis was to test the following hypotheses using personality trait measures and positron emission tomography (PET): 1) Brain serotonin transporter density in vivo is associated with Harm Avoidance and Neuroticism traits. 2) μ-opioid receptor binding is associated with Harm Avoidance. In addition, we developed a methodology for studying neurotransmitter interactions in the brain using the opiate and serotonin pathways. 32 healthy subjects who were consistently in either the highest or lowest quartile of the Harm Avoidance trait were recruited from a population-based cohort. Each subject underwent two PET scans, serotonin transporter binding was measured with [11C] MADAM and μ-opioid receptor binding with [11C]carfentanil. We found that the serotonin transporter is not associated with anxious personality traits. However, Harm Avoidance positively correlated with μ-opioid receptor availability. Particularly the tendency to feel shy and the inability to cope with stress were associated μ-opioid receptor availability. We also demonstrated that serotonin transporter binding correlates with μ-opioid receptor binding, suggesting interplay between the two systems. These findings shed light on the neurobiological correlates of personality and have an impact on etiological considerations of affective disorders.

Mathematics inspired by Darwin. Adaptive dynamics of dispersal and cooperation

In 1859, Charles Darwin published his theory of evolution by natural selection, the process occurring based on fitness benefits and fitness costs at the individual level. Traditionally, evolution has been investigated by biologists, but it has induced mathematical approaches, too. For example, adaptive dynamics has proven to be a very applicable framework to the purpose. Its core concept is the invasion fitness, the sign of which tells whether a mutant phenotype can invade the prevalent phenotype. In this thesis, four real-world applications on evolutionary questions are provided. Inspiration for the first two studies arose from a cold-adapted species, American pika. First, it is studied how the global climate change may affect the evolution of dispersal and viability of pika metapopulations. Based on the results gained here, it is shown that the evolution of dispersal can result in extinction and indeed, evolution of dispersalshould be incorporated into the viability analysis of species living in fragmented habitats. The second study is focused on the evolution of densitydependent dispersal in metapopulations with small habitat patches. It resulted a very surprising unintuitive evolutionary phenomenon, how a non-monotone density-dependent dispersal may evolve. Cooperation is surprisingly common in many levels of life, despite of its obvious vulnerability to selfish cheating. This motivated two applications. First, it is shown that density-dependent cooperative investment can evolve to have a qualitatively different, monotone or non-monotone, form depending on modelling details. The last study investigates the evolution of investing into two public-goods resources. The results suggest one general path by which labour division can arise via evolutionary branching. In addition to applications, two novel methodological derivations of fitness measures in structured metapopulations are given.

Cultural distance and negotiation behavior : Perception of entrepreneurs operating in Turku

This thesis investigates the influence of cultural distance on entrepreneurs’ negotiation behaviour. For this purpose, Turku was chosen as the unit of analysis due to the exponential demographic change experienced during the last two decades that has derived in a more diversified local environment. The research aim set for this study was to identify to what extent entrepreneurs face cultural distance, how cultural distance influences the entrepreneur’s negotiation behaviour and how can it be addressed in order to turn dissimilarities into opportunities. This study presented the relation and apparent dichotomy of cultural distance and global culture, including the component of diversity. The impact of cultural distance in the entrepreneurial mindset and its consequent effect in negotiation behaviour was presented too. Addressing questions about the way individuals perceive, behave and interact allowed the use of interviews for this qualitative research study. In the empirical part of this study it was found that negotiation behaviour differed in terms of how congenial entrepreneurs felt when managing cultural distance, encompassing their performance. It was also acknowledged that after time and effort, some of the personal traits were enhanced while others reduced, allowing for more flexibility and adaptation. Furthermore, depending on the level of trust and shared interests, entrepreneurs determined their attitudinal approach, being adaptive or reactive subject to situational aspects. Additionally, it was found that the acquisition of cultural savvy not necessarily conveyed to more creativity. This experiential learning capability led to the proposition of new ways of behaviour. Likewise, it was proposed that growing cultural intelligence bridge distances, reducing mistrusts and misunderstandings. The capability of building more collaborative relationships allows entrepreneurs to see cultural distance as a cultural perspective instead of as a threat. Therefore it was recommended to focus on proximity rather than distance to better identify and exploit untapped opportunities and better perform when negotiating in whichever cultural conditions.

Adaptive control of Hydraulic Drive

Adaptive control systems are one of the most significant research directions of modern control theory. It is well known that every mechanical appliance’s behavior noticeably depends on environmental changes, functioning-mode parameter changes and changes in technical characteristics of internal functional devices. An adaptive controller involved in control process allows reducing an influence of such changes. In spite of this such type of control methods is applied seldom due to specifics of a controller designing. The work presented in this paper shows the design process of the adaptive controller built by Lyapunov’s function method for the Hydraulic Drive. The calculation needed and the modeling were conducting with MATLAB® software including Simulink® and Symbolic Math Toolbox™ etc. In the work there was applied the Jacobi matrix linearization of the object’s mathematical model and derivation of the suitable reference models based on Newton’s characteristic polynomial. The intelligent adaptive to nonlinearities algorithm for solving Lyapunov’s equation was developed. Developed algorithm works properly but considered plant is not met requirement of functioning with. The results showed confirmation that adaptive systems application significantly increases possibilities in use devices and might be used for correction a system’s behavior dynamics.

Adaptive Markov chain Monte Carlo and Bayesian filtering for state space models

This thesis is concerned with the state and parameter estimation in state space models. The estimation of states and parameters is an important task when mathematical modeling is applied to many different application areas such as the global positioning systems, target tracking, navigation, brain imaging, spread of infectious diseases, biological processes, telecommunications, audio signal processing, stochastic optimal control, machine learning, and physical systems. In Bayesian settings, the estimation of states or parameters amounts to computation of the posterior probability density function. Except for a very restricted number of models, it is impossible to compute this density function in a closed form. Hence, we need approximation methods. A state estimation problem involves estimating the states (latent variables) that are not directly observed in the output of the system. In this thesis, we use the Kalman filter, extended Kalman filter, Gauss–Hermite filters, and particle filters to estimate the states based on available measurements. Among these filters, particle filters are numerical methods for approximating the filtering distributions of non-linear non-Gaussian state space models via Monte Carlo. The performance of a particle filter heavily depends on the chosen importance distribution. For instance, inappropriate choice of the importance distribution can lead to the failure of convergence of the particle filter algorithm. In this thesis, we analyze the theoretical Lᵖ particle filter convergence with general importance distributions, where p ≥2 is an integer. A parameter estimation problem is considered with inferring the model parameters from measurements. For high-dimensional complex models, estimation of parameters can be done by Markov chain Monte Carlo (MCMC) methods. In its operation, the MCMC method requires the unnormalized posterior distribution of the parameters and a proposal distribution. In this thesis, we show how the posterior density function of the parameters of a state space model can be computed by filtering based methods, where the states are integrated out. This type of computation is then applied to estimate parameters of stochastic differential equations. Furthermore, we compute the partial derivatives of the log-posterior density function and use the hybrid Monte Carlo and scaled conjugate gradient methods to infer the parameters of stochastic differential equations. The computational efficiency of MCMC methods is highly depend on the chosen proposal distribution. A commonly used proposal distribution is Gaussian. In this kind of proposal, the covariance matrix must be well tuned. To tune it, adaptive MCMC methods can be used. In this thesis, we propose a new way of updating the covariance matrix using the variational Bayesian adaptive Kalman filter algorithm.

Power and Performance Management Strategies on NoC-Based Multicore Platforms, Using DVFS, Adaptive Algorithm and Task Parallelism

This work presents synopsis of efficient strategies used in power managements for achieving the most economical power and energy consumption in multicore systems, FPGA and NoC Platforms. In this work, a practical approach was taken, in an effort to validate the significance of the proposed Adaptive Power Management Algorithm (APMA), proposed for system developed, for this thesis project. This system comprise arithmetic and logic unit, up and down counters, adder, state machine and multiplexer. The essence of carrying this project firstly, is to develop a system that will be used for this power management project. Secondly, to perform area and power synopsis of the system on these various scalable technology platforms, UMC 90nm nanotechnology 1.2v, UMC 90nm nanotechnology 1.32v and UMC 0.18 μmNanotechnology 1.80v, in order to examine the difference in area and power consumption of the system on the platforms. Thirdly, to explore various strategies that can be used to reducing system’s power consumption and to propose an adaptive power management algorithm that can be used to reduce the power consumption of the system. The strategies introduced in this work comprise Dynamic Voltage Frequency Scaling (DVFS) and task parallelism. After the system development, it was run on FPGA board, basically NoC Platforms and on these various technology platforms UMC 90nm nanotechnology1.2v, UMC 90nm nanotechnology 1.32v and UMC180 nm nanotechnology 1.80v, the system synthesis was successfully accomplished, the simulated result analysis shows that the system meets all functional requirements, the power consumption and the area utilization were recorded and analyzed in chapter 7 of this work. This work extensively reviewed various strategies for managing power consumption which were quantitative research works by many researchers and companies, it's a mixture of study analysis and experimented lab works, it condensed and presents the whole basic concepts of power management strategy from quality technical papers.

Real-time adaptive control of ultra-fast laser scribing process with spectrometer online monitoring

There exist several researches and applications about laser welding monitoring and parameter control but not a single one have been created for controlling of laser scribing processes. Laser scribing is considered to be very fast and accurate process and thus it would be necessary to develop accurate turning and monitoring system for such a process. This research focuses on finding out whether it would be possible to develop real-time adaptive control for ultra-fast laser scribing processes utilizing spectrometer online monitoring. The thesis accurately presents how control code for laser parameter tuning is developed using National Instrument's LabVIEW and how spectrometer is being utilized in online monitoring. Results are based on behavior of the control code and accuracy of the spectrometer monitoring when scribing different steel materials. Finally control code success is being evaluated and possible development ideas for future are presented.

Macroalgal Defenses Against Herbivory: Causes and Consequences of Intraspecific Variation

In marine benthic communities, herbivores consume a considerable proportion of primary producer biomass and, thus, generate selection for the evolution of resistance traits. According to the theory of plant defenses, resistance traits are costly to produce and, consequently, inducible resistance traits are adaptive in conditions of variable herbivory, while in conditions of constant/strong herbivory constitutive resistance traits are selected for. The evolution of resistance plasticity may be constrained by the costs of resistance or lack of genetic variation in resistance. Furthermore, resource allocation to induced resistance may be affected by higher trophic levels preying on herbivores. I studied the resistance to herbivory of a foundation species, the brown alga Fucus vesiculosus. By using factorial field experiments, I explored the effects of herbivores and fish predators on growth and resistance of the alga in two seasons. I explored genetic variation in and allocation costs of resistance traits as well as their chemical basis and their effects on herbivore performance. Using a field experiment I tested if induced resistance spreads via water-borne cues from one individual to another in relevant ecological conditions. I found that in the northern Baltic Sea F. vesiculosus communities, strength of three trophic interactions strongly vary among seasons. The highly synchronized summer reproduction of herbivores promoted their escape from the top-down control of fish predators in autumn. This resulted into large grazing losses in algal stands. In spring, herbivore densities were low and regulated by fish, which, thus,enhanced algal growth. The resistance of algae to herbivory increased with an increase in constitutive phlorotannin content. Furthermore, individuals adopted induced resistance when grazed and when exposed to water-borne cues originating from grazing of conspecific algae both in the laboratory and in field conditions. Induced resistance was adopted to a lesser extent in the presence of fish predators. The results in this thesis indicate that inducible resistance in F. vesiculosus is an adaptation to varying herbivory in the northern Baltic Sea. The costs of resistance and strong seasonality of herbivory have likely contributed to the evolution of this defense strategy. My findings also show that fish predators have positive cascading effects on F. vesiculosus which arise via reduced herbivory but possibly also through reduced resource allocation to resistance. I further found evidence that the spread of resistance via water-borne cues also occurs in ecologically realistic conditions in natural marine sublittoral. Thus, water-borne induction may enable macroalgae to cope with the strong grazing pressure characteristic of marine benthic communities. The results presented here show that seasonality can have pronounced effects on the biotic interactions in marine benthic communities and thereafter influence the evolution of resistance traits in primary producers.