The genetics of behaviour and other adaptive traits in nine-spined sticklebacks (Pungitius pungitius)

One of the main goals in current evolutionary biology research is to identify genes behind adaptive phenotypic variations. The advances in genomic technologies have made it possible to identify genetic loci behind these variations, also concerning non-model species. This thesis investigates the genetics of the behaviour and other adaptive traits of the nine-spined stickleback (Pungitius pungitius) through the application of different genetic approaches. Fennoscandian nine-spined stickleback populations express large phenotypical differences especially in behaviour, life –history traits and morphology. However the underlying genetic bases for these phenotypical differences have not been studied in detail. The results of the project will lay the foundation for further genetics studies and provide valuable information for our understanding of the genetics of the adaptive divergence of the nine-spined stickleback. A candidate gene approach was used to develop microsatellite markers situating close to candidate genes for behaviour in the nine-spined stickleback. Altogether 13 markers were developed and these markers were used in the subsequent studies with the anonymous random markers and physiologically important gene markers which are already currently available for nine-spined sticklebacks. It was shown that heterozygosity correlated with behaviour in one of the marine nine-spined stickleback populations but with contrasting effects: correlations with behaviour were negative when using physiological gene markers and positive with random markers. No correlation was found between behavioural markers and behaviour. From the physiological gene markers, a strong correlation was found between osmoregulation-related gene markers and behaviour. These results indicate that both local (physiological) and general (random) effects are important in the shaping of behaviour and that heterozygosity– behaviour correlations are population dependent. In this thesis a second linkage map for nine-spined sticklebacks was constructed. Compared to the earlier nine-spined stickleback linkage map, genomic rearrangements were observed between autosomal (LG7) and sex-determing (LG12) linkage groups. This newly constructed map was used in QTL mapping studies in order to locate genomic regions associated with pelvic structures, behaviour and body size/growth. One major QTL was found for pelvic structures and Pitx1 gene was related to these traits as was predicted from three-spined stickleback studies, but this was in contrast to earlier nine-spined stickleback study. The QTL studies also revealed that behaviour and body size/growth were genetically more complex by having more QTL than pelvic traits. However, in many cases, pelvic structure, body size/growth and behaviour were linked to similar map locations indicating possible pleiotropic effects of genes locating in these QTL regions. Many of the gene related markers resided in the QTL area. In the future, studying these possible candidate genes in depth might reveal the underlying mechanism behind the measured traits.

Nonlinear dynamics and control of multibody systems

The dynamics of flexible systems, such as robot manipulators , mechanical chains or multibody systems in general, is becoming increasingly important in engineering. This article deals with some nonlinearities that arise in the study of dynamics and control of multibody systems in connection to large rotations. Specifically, a numerical scheme that adresses the conservation of fundamental constants is presented in order to analyse the control-structure interaction problems.

On non-ideal and non-linear portal frame dynamics analysis using bogoliubov averaging method

We apply the Bogoliubov Averaging Method to the study of the vibrations of an elastic foundation, forced by a Non-ideal energy source. The considered model consists of a portal plane frame with quadratic nonlinearities, with internal resonance 1:2, supporting a direct current motor with limited power. The non-ideal excitation is in primary resonance in the order of one-half with the second mode frequency. The results of the averaging method, plotted in time evolution curve and phase diagrams are compared to those obtained by numerically integrating of the original differential equations. The presence of the saturation phenomenon is verified by analytical procedures.

Flexible multibody dynamics and intelligent control of a hydraulically driven hybrid redundant robot machine

The assembly and maintenance of the International Thermonuclear Experimental Reactor (ITER) vacuum vessel (VV) is highly challenging since the tasks performed by the robot involve welding, material handling, and machine cutting from inside the VV. The VV is made of stainless steel, which has poor machinability and tends to work harden very rapidly, and all the machining operations need to be carried out from inside of the ITER VV. A general industrial robot cannot be used due to its poor stiffness in the heavy duty machining process, and this will cause many problems, such as poor surface quality, tool damage, low accuracy. Therefore, one of the most suitable options should be a light weight mobile robot which is able to move around inside of the VV and perform different machining tasks by replacing different cutting tools. Reducing the mass of the robot manipulators offers many advantages: reduced material costs, reduced power consumption, the possibility of using smaller actuators, and a higher payload-to-robot weight ratio. Offsetting these advantages, the lighter weight robot is more flexible, which makes it more difficult to control. To achieve good machining surface quality, the tracking of the end effector must be accurate, and an accurate model for a more flexible robot must be constructed. This thesis studies the dynamics and control of a 10 degree-of-freedom (DOF) redundant hybrid robot (4-DOF serial mechanism and 6-DOF 6-UPS hexapod parallel mechanisms) hydraulically driven with flexible rods under the influence of machining forces. Firstly, the flexibility of the bodies is described using the floating frame of reference method (FFRF). A finite element model (FEM) provided the Craig-Bampton (CB) modes needed for the FFRF. A dynamic model of the system of six closed loop mechanisms was assembled using the constrained Lagrange equations and the Lagrange multiplier method. Subsequently, the reaction forces between the parallel and serial parts were used to study the dynamics of the serial robot. A PID control based on position predictions was implemented independently to control the hydraulic cylinders of the robot. Secondly, in machining, to achieve greater end effector trajectory tracking accuracy for surface quality, a robust control of the actuators for the flexible link has to be deduced. This thesis investigates the intelligent control of a hydraulically driven parallel robot part based on the dynamic model and two schemes of intelligent control for a hydraulically driven parallel mechanism based on the dynamic model: (1) a fuzzy-PID self-tuning controller composed of the conventional PID control and with fuzzy logic, and (2) adaptive neuro-fuzzy inference system-PID (ANFIS-PID) self-tuning of the gains of the PID controller, which are implemented independently to control each hydraulic cylinder of the parallel mechanism based on rod length predictions. The serial component of the hybrid robot can be analyzed using the equilibrium of reaction forces at the universal joint connections of the hexa-element. To achieve precise positional control of the end effector for maximum precision machining, the hydraulic cylinder should be controlled to hold the hexa-element. Thirdly, a finite element approach of multibody systems using the Special Euclidean group SE(3) framework is presented for a parallel mechanism with flexible piston rods under the influence of machining forces. The flexibility of the bodies is described using the nonlinear interpolation method with an exponential map. The equations of motion take the form of a differential algebraic equation on a Lie group, which is solved using a Lie group time integration scheme. The method relies on the local description of motions, so that it provides a singularity-free formulation, and no parameterization of the nodal variables needs to be introduced. The flexible slider constraint is formulated using a Lie group and used for modeling a flexible rod sliding inside a cylinder. The dynamic model of the system of six closed loop mechanisms was assembled using Hamilton’s principle and the Lagrange multiplier method. A linearized hydraulic control system based on rod length predictions was implemented independently to control the hydraulic cylinders. Consequently, the results of the simulations demonstrating the behavior of the robot machine are presented for each case study. In conclusion, this thesis studies the dynamic analysis of a special hybrid (serialparallel) robot for the above-mentioned special task involving the ITER and investigates different control algorithms that can significantly improve machining performance. These analyses and results provide valuable insight into the design and control of the parallel robot with flexible rods.

Biomedical Event Extraction with Machine Learning

Biomedical natural language processing (BioNLP) is a subfield of natural language processing, an area of computational linguistics concerned with developing programs that work with natural language: written texts and speech. Biomedical relation extraction concerns the detection of semantic relations such as protein-protein interactions (PPI) from scientific texts. The aim is to enhance information retrieval by detecting relations between concepts, not just individual concepts as with a keyword search. In recent years, events have been proposed as a more detailed alternative for simple pairwise PPI relations. Events provide a systematic, structural representation for annotating the content of natural language texts. Events are characterized by annotated trigger words, directed and typed arguments and the ability to nest other events. For example, the sentence “Protein A causes protein B to bind protein C” can be annotated with the nested event structure CAUSE(A, BIND(B, C)). Converted to such formal representations, the information of natural language texts can be used by computational applications. Biomedical event annotations were introduced by the BioInfer and GENIA corpora, and event extraction was popularized by the BioNLP'09 Shared Task on Event Extraction. In this thesis we present a method for automated event extraction, implemented as the Turku Event Extraction System (TEES). A unified graph format is defined for representing event annotations and the problem of extracting complex event structures is decomposed into a number of independent classification tasks. These classification tasks are solved using SVM and RLS classifiers, utilizing rich feature representations built from full dependency parsing. Building on earlier work on pairwise relation extraction and using a generalized graph representation, the resulting TEES system is capable of detecting binary relations as well as complex event structures. We show that this event extraction system has good performance, reaching the first place in the BioNLP'09 Shared Task on Event Extraction. Subsequently, TEES has achieved several first ranks in the BioNLP'11 and BioNLP'13 Shared Tasks, as well as shown competitive performance in the binary relation Drug-Drug Interaction Extraction 2011 and 2013 shared tasks. The Turku Event Extraction System is published as a freely available open-source project, documenting the research in detail as well as making the method available for practical applications. In particular, in this thesis we describe the application of the event extraction method to PubMed-scale text mining, showing how the developed approach not only shows good performance, but is generalizable and applicable to large-scale real-world text mining projects. Finally, we discuss related literature, summarize the contributions of the work and present some thoughts on future directions for biomedical event extraction. This thesis includes and builds on six original research publications. The first of these introduces the analysis of dependency parses that leads to development of TEES. The entries in the three BioNLP Shared Tasks, as well as in the DDIExtraction 2011 task are covered in four publications, and the sixth one demonstrates the application of the system to PubMed-scale text mining.

Images of the future of privacy: A privacy dynamics framework and a causal layered

The future of privacy in the information age is a highly debated topic. In particular, new and emerging technologies such as ICTs and cognitive technologies are seen as threats to privacy. This thesis explores images of the future of privacy among non-experts within the time frame from the present until the year 2050. The aims of the study are to conceptualise privacy as a social and dynamic phenomenon, to understand how privacy is conceptualised among citizens and to analyse ideal-typical images of the future of privacy using the causal layered analysis method. The theoretical background of the thesis combines critical futures studies and critical realism, and the empirical material is drawn from three focus group sessions held in spring 2012 as part of the PRACTIS project. From a critical realist perspective, privacy is conceptualised as a social institution which creates and maintains boundaries between normative circles and preserves the social freedom of individuals. Privacy changes when actors with particular interests engage in technology-enabled practices which challenge current privacy norms. The thesis adopts a position of technological realism as opposed to determinism or neutralism. In the empirical part, the focus group participants are divided into four clusters based on differences in privacy conceptions and perceived threats and solutions. The clusters are fundamentalists, pragmatists, individualists and collectivists. Correspondingly, four ideal-typical images of the future are composed: ‘drift to low privacy’, ‘continuity and benign evolution’, ‘privatised privacy and an uncertain future’, and ‘responsible future or moral decline’. The images are analysed using the four layers of causal layered analysis: litany, system, worldview and myth. Each image has its strengths and weaknesses. The individualistic images tend to be fatalistic in character while the collectivistic images are somewhat utopian. In addition, the images have two common weaknesses: lack of recognition of ongoing developments and simplistic conceptions of privacy based on a dichotomy between the individual and society. The thesis argues for a dialectical understanding of futures as present images of the future and as outcomes of real processes and mechanisms. The first steps in promoting desirable futures are the awareness of privacy as a social institution, the awareness of current images of the future, including their assumptions and weaknesses, and an attitude of responsibility where futures are seen as the consequences of present choices.

Signaled two-way avoidance learning using electrical stimulation of the inferior colliculus as negative reinforcement: effects of visual and auditory cues as warning stimuli

The inferior colliculus is a primary relay for the processing of auditory information in the brainstem. The inferior colliculus is also part of the so-called brain aversion system as animals learn to switch off the electrical stimulation of this structure. The purpose of the present study was to determine whether associative learning occurs between aversion induced by electrical stimulation of the inferior colliculus and visual and auditory warning stimuli. Rats implanted with electrodes into the central nucleus of the inferior colliculus were placed inside an open-field and thresholds for the escape response to electrical stimulation of the inferior colliculus were determined. The rats were then placed inside a shuttle-box and submitted to a two-way avoidance paradigm. Electrical stimulation of the inferior colliculus at the escape threshold (98.12 ± 6.15 (A, peak-to-peak) was used as negative reinforcement and light or tone as the warning stimulus. Each session consisted of 50 trials and was divided into two segments of 25 trials in order to determine the learning rate of the animals during the sessions. The rats learned to avoid the inferior colliculus stimulation when light was used as the warning stimulus (13.25 ± 0.60 s and 8.63 ± 0.93 s for latencies and 12.5 ± 2.04 and 19.62 ± 1.65 for frequencies in the first and second halves of the sessions, respectively, P<0.01 in both cases). No significant changes in latencies (14.75 ± 1.63 and 12.75 ± 1.44 s) or frequencies of responses (8.75 ± 1.20 and 11.25 ± 1.13) were seen when tone was used as the warning stimulus (P>0.05 in both cases). Taken together, the present results suggest that rats learn to avoid the inferior colliculus stimulation when light is used as the warning stimulus. However, this learning process does not occur when the neutral stimulus used is an acoustic one. Electrical stimulation of the inferior colliculus may disturb the signal transmission of the stimulus to be conditioned from the inferior colliculus to higher brain structures such as amygdala

The brain decade in debate: I. Neurobiology of learning and memory

This article is a transcription of an electronic symposium in which some active researchers were invited by the Brazilian Society for Neuroscience and Behavior (SBNeC) to discuss the last decade's advances in neurobiology of learning and memory. The way different parts of the brain are recruited during the storage of different kinds of memory (e.g., short-term vs long-term memory, declarative vs procedural memory) and even the property of these divisions were discussed. It was pointed out that the brain does not really store memories, but stores traces of information that are later used to create memories, not always expressing a completely veridical picture of the past experienced reality. To perform this process different parts of the brain act as important nodes of the neural network that encode, store and retrieve the information that will be used to create memories. Some of the brain regions are recognizably active during the activation of short-term working memory (e.g., prefrontal cortex), or the storage of information retrieved as long-term explicit memories (e.g., hippocampus and related cortical areas) or the modulation of the storage of memories related to emotional events (e.g., amygdala). This does not mean that there is a separate neural structure completely supporting the storage of each kind of memory but means that these memories critically depend on the functioning of these neural structures. The current view is that there is no sense in talking about hippocampus-based or amygdala-based memory since this implies that there is a one-to-one correspondence. The present question to be solved is how systems interact in memory. The pertinence of attributing a critical role to cellular processes like synaptic tagging and protein kinase A activation to explain the memory storage processes at the cellular level was also discussed.

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.

JNK regulates dendrite and spine architecture in the central nervous system influencing spatial learning and motor tasks

JNK1 is a MAP-kinase that has proven a significant player in the central nervous system. It regulates brain development and the maintenance of dendrites and axons. Several novel phosphorylation targets of JNK1 were identified in a screen performed in the Coffey lab. These proteins were mainly involved in the regulation of neuronal cytoskeleton, influencing the dynamics and stability of microtubules and actin. These structural proteins form the dynamic backbone for the elaborate architecture of the dendritic tree of a neuron. The initiation and branching of the dendrites requires a dynamic interplay between the cytoskeletal building blocks. Both microtubules and actin are decorated by associated proteins which regulate their dynamics. The dendrite-specific, high molecular weight microtubule associated protein 2 (MAP2) is an abundant protein in the brain, the binding of which stabilizes microtubules and influences their bundling. Its expression in non-neuronal cells induces the formation of neurite-like processes from the cell body, and its function is highly regulated by phosphorylation. JNK1 was shown to phosphorylate the proline-rich domain of MAP2 in vivo in a previous study performed in the group. Here we verify three threonine residues (T1619, T1622 and T1625) as JNK1 targets, the phosphorylation of which increases the binding of MAP2 to microtubules. This binding stabilizes the microtubules and increases process formation in non-neuronal cells. Phosphorylation-site mutants were engineered in the lab. The non-phosphorylatable mutant of MAP2 (MAP2- T1619A, T1622A, T1625A) in these residues fails to bind microtubules, while the pseudo-phosphorylated form, MAP2- T1619D, T1622D, Thr1625D, efficiently binds and induces process formation even without the presence of active JNK1. Ectopic expression of the MAP2- T1619D, T1622D, Thr1625D in vivo in mouse brain led to a striking increase in the branching of cortical layer 2/3 (L2/3) pyramidal neurons, compared to MAP2-WT. The dendritic complexity defines the receptive field of a neuron and dictates the output to the postsynaptic cells. Previous studies in the group indicated altered dendrite architecture of the pyramidal neurons in the Jnk1-/- mouse motor cortex. Here, we used Lucifer Yellow loading and Sholl analysis of neurons in order to study the dendritic branching in more detail. We report a striking, opposing effect in the absence of Jnk1 in the cortical layers 2/3 and 5 of the primary motor cortex. The basal dendrites of pyramidal neurons close to the pial surface at L2/3 show a reduced complexity. In contrast, the L5 neurons, which receive massive input from the L2/3 neurons, show greatly increased branching. Another novel substrate identified for JNK1 was MARCKSL1, a protein that regulates actin dynamics. It is highly expressed in neurons, but also in various cancer tissues. Three phosphorylation target residues for JNK1 were identified, and it was demonstrated that their phosphorylation reduces actin turnover and retards migration of these cells. Actin is the main cytoskeletal component in dendritic spines, the site of most excitatory synapses in pyramidal neurons. The density and gross morphology of the Lucifer Yellow filled dendrites were characterized and we show reduced density and altered morphology of spines in the motor cortex and in the hippocampal area CA3. The dynamic dendritic spines are widely considered to function as the cellular correlate during learning. We used a Morris water maze to test spatial memory. Here, the wild-type mice outperformed the knock-out mice during the acquisition phase of the experiment indicating impaired special memory. The L5 pyramidal neurons of the motor cortex project to the spinal cord and regulate the movement of distinct muscle groups. Thus the altered dendrite morphology in the motor cortex was expected to have an effect on the input-output balance in the signaling from the cortex to the lower motor circuits. A battery of behavioral tests were conducted for the wild-type and Jnk1-/- mice, and the knock-outs performed poorly compared to wild-type mice in tests assessing balance and fine motor movements. This study expands our knowledge of JNK1 as an important regulator of the dendritic fields of neurons and their manifestations in behavior.

Novel word learning ability in chronic post-stroke aphasia : variability and modality effects

Novel word learning has been rarely studied in people with aphasia (PWA), although it can provide a relatively pure measure of their learning potential, and thereby contribute to the development of effective aphasia treatment methods. The main aim of the present thesis was to explore the capacity of PWA for associative learning of word–referent pairings and cognitive-linguistic factors related to it. More specifically, the thesis examined learning and long-term maintenance of the learned pairings, the role of lexical-semantic abilities in learning as well as acquisition of phonological versus semantic information in associative novel word learning. Furthermore, the effect of modality on associative novel word learning and the neural underpinnings of successful learning were explored. The learning experiments utilized the Ancient Farming Equipment (AFE) paradigm that employs drawings of unfamiliar referents and their unfamiliar names. Case studies of Finnishand English-speaking people with chronic aphasia (n = 6) were conducted in the investigation. The learning results of PWA were compared to those of healthy control participants, and active production of the novel words and their semantic definitions was used as learning outcome measures. PWA learned novel word–novel referent pairings, but the variation between individuals was very wide, from more modest outcomes (Studies I–II) up to levels on a par with healthy individuals (Studies III–IV). In incidental learning of semantic definitions, none of the PWA reached the performance level of the healthy control participants. Some PWA maintained part of the learning outcomes up to months post-training, and one individual showed full maintenance of the novel words at six months post-training (Study IV). Intact lexical-semantic processing skills promoted learning in PWA (Studies I–II) but poor phonological short-term memory capacities did not rule out novel word learning. In two PWA with successful learning and long-term maintenance of novel word–novel referent pairings, learning relied on orthographic input while auditory input led to significantly inferior learning outcomes (Studies III–IV). In one of these individuals, this previously undetected modalityspecific learning ability was successfully translated into training with familiar but inaccessible everyday words (Study IV). Functional magnetic resonance imaging revealed that this individual had a disconnected dorsal speech processing pathway in the left hemisphere, but a right-hemispheric neural network mediated successful novel word learning via reading. Finally, the results of Study III suggested that the cognitive-linguistic profile may not always predict the optimal learning channel for an individual with aphasia. Small-scale learning probes seem therefore useful in revealing functional learning channels in post-stroke aphasia.

Learning about brain physiology and complexity from the study of the epilepsies

The brain is a complex system, which produces emergent properties such as those associated with activity-dependent plasticity in processes of learning and memory. Therefore, understanding the integrated structures and functions of the brain is well beyond the scope of either superficial or extremely reductionistic approaches. Although a combination of zoom-in and zoom-out strategies is desirable when the brain is studied, constructing the appropriate interfaces to connect all levels of analysis is one of the most difficult challenges of contemporary neuroscience. Is it possible to build appropriate models of brain function and dysfunctions with computational tools? Among the best-known brain dysfunctions, epilepsies are neurological syndromes that reach a variety of networks, from widespread anatomical brain circuits to local molecular environments. One logical question would be: are those complex brain networks always producing maladaptive emergent properties compatible with epileptogenic substrates? The present review will deal with this question and will try to answer it by illustrating several points from the literature and from our laboratory data, with examples at the behavioral, electrophysiological, cellular and molecular levels. We conclude that, because the brain is a complex system compatible with the production of emergent properties, including plasticity, its functions should be approached using an integrated view. Concepts such as brain networks, graphics theory, neuroinformatics, and e-neuroscience are discussed as new transdisciplinary approaches dealing with the continuous growth of information about brain physiology and its dysfunctions. The epilepsies are discussed as neurobiological models of complex systems displaying maladaptive plasticity.

Effects of isoflavone on the learning and memory of women in menopause: a double-blind placebo-controlled study

Hormone decline is common to all women during aging and, associated with other factors, leads to cognitive impairment. Its replacement enhances cognitive performance, but not all women present a clinical and family or personal history that justifies its use, mainly women with a history of cancer. The aim of this study was to determine whether a daily oral dose of 80 mg of isoflavone extract for 4 months can produce benefits in women with low hormone levels, contributing to improvement in cognitive aspects. The sample comprised 50- to 65-year-old women whose menstruation had ceased at least 1 year before and who had not undergone hormone replacement. The volunteers were allocated to two groups of 19 individuals each, i.e., isoflavone and placebo. There was a weak correlation between menopause duration and low performance in the capacity to manipulate information (central executive). We observed an increase in the capacity to integrate information in the group treated with isoflavone, but no improvement in the capacity to form new memories. We did not observe differences between groups in terms of signs and symptoms suggestive of depression according to the Geriatric Depression Scale. Our results point to a possible beneficial effect of isoflavone on some abilities of the central executive. These effects could also contribute to minimizing the impact of memory impairment. Further research based on controlled clinical trials is necessary to reach consistent conclusions.

Structural dynamics in the global pulp and paper industry

The pulp and paper industry is currently facing broad structural changes due to global shifts in demand and supply. These changes have significant impacts on national economies worldwide. Planted forests (especially eucalyptus) and recovered paper have quickly increased their importance as raw material for paper and paperboard production. Although advances in information and communication technologies could reduce the demand for communication papers, and the growth of paper consumption has indeed flattened in developed economies, particularly in North America and Western Europe, the consumption is increasing on a global scale. Moreover, the focal point of production and consumption is moving from the Western world to the rapidly growing markets of Southeast Asia. This study analyzes how the so-called megatrends (globalization, technological development, and increasing environmental awareness) affect the pulp and paper industry’s external environment, and seeks reliable ways to incorporate the impact of the megatrends on the models concerning the demand, trade, and use of paper and pulp. The study expands current research in several directions and points of view, for example, by applying and incorporating several quantitative methods and different models. As a result, the thesis makes a significant contribution to better understand and measure the impacts of structural changes on the pulp and paper industry. It also provides some managerial and policy implications.

Non-Markovian dynamics in quantum optical dephasing channels

Quantum computation and quantum communication are two of the most promising future applications of quantum mechanics. Since the information carriers used in both of them are essentially open quantum systems it is necessary to understand both quantum information theory and the theory of open quantum systems in order to investigate realistic implementations of such quantum technologies. In this thesis we consider the theory of open quantum systems from a quantum information theory perspective. The thesis is divided into two parts: review of the literature and original research. In the review of literature we present some important definitions and known results of open quantum systems and quantum information theory. We present the definitions of trace distance, two channel capacities and superdense coding capacity and give a reasoning why they can be used to represent the transmission efficiency of a communication channel. We also show derivations of some properties useful to link completely positive and trace preserving maps to trace distance and channel capacities. With the help of these properties we construct three measures of non-Markovianity and explain why they detect non-Markovianity. In the original research part of the thesis we study the non-Markovian dynamics in an experimentally realized quantum optical set-up. For general one-qubit dephasing channels we calculate the explicit forms of the two channel capacities and the superdense coding capacity. For the general two-qubit dephasing channel with uncorrelated local noises we calculate the explicit forms of the quantum capacity and the mutual information of a four-letter encoding. By using the dynamics in the experimental implementation as a set of specific dephasing channels we also calculate and compare the measures in one- and two-qubit dephasing channels and study the options of manipulating the environment to achieve revivals and higher transmission rates in superdense coding protocol with dephasing noise. Kvanttilaskenta ja kvanttikommunikaatio ovat kaksi puhutuimmista tulevaisuuden kvanttimekaniikan käytännön sovelluksista. Koska molemmissa näistä informaatio koodataan systeemeihin, jotka ovat oleellisesti avoimia kvanttisysteemejä, sekä kvantti-informaatioteorian, että avointen kvanttisysteemien tuntemus on välttämätöntä. Tässä tutkielmassa käsittelemme avointen kvanttisysteemien teoriaa kvantti-informaatioteorian näkökulmasta. Tutkielma on jaettu kahteen osioon: kirjallisuuskatsaukseen ja omaan tutkimukseen. Kirjallisuuskatsauksessa esitämme joitakin avointen kvanttisysteemien ja kvantti-informaatioteorian tärkeitä määritelmiä ja tunnettuja tuloksia. Esitämme jälkietäisyyden, kahden kanavakapasiteetin ja superdense coding -kapasiteetin määritelmät ja esitämme perustelun sille, miksi niitä voidaan käyttää kuvaamaan kommunikointikanavan lähetystehokkuutta. Näytämme myös todistukset kahdelle ominaisuudelle, jotka liittävät täyspositiiviset ja jäljensäilyttävät kuvaukset jälkietäisyyteen ja kanavakapasiteetteihin. Näiden ominaisuuksien avulla konstruoimme kolme epä-Markovisuusmittaa ja perustelemme, miksi ne havaitsevat dynamiikan epä-Markovisuutta. Oman tutkimuksen osiossa tutkimme epä-Markovista dynamiikkaa kokeellisesti toteutetussa kvanttioptisessa mittausjärjestelyssä. Yleisen yhden qubitin dephasing-kanavan tapauksessa laskemme molempien kanavakapasiteettien ja superdense coding -kapasiteetin eksplisiittiset muodot. Yleisen kahden qubitin korreloimattomien ympäristöjen dephasing-kanavan tapauksessa laskemme yhteisen informaation lausekkeen nelikirjaimisessa koodauksessa ja kvanttikanavakapasiteetin. Käyttämällä kokeellisen mittajärjestelyn dynamiikkoja esimerkki dephasing-kanavina me myös laskemme konstruoitujen epä-Markovisuusmittojen arvot ja vertailemme niitä yksi- ja kaksi-qubitti-dephasing-kanavissa. Lisäksi käyttäen kokeellisia esimerkkikanavia tutkimme, kuinka ympäristöä manipuloimalla superdense coding –skeemassa voidaan saada yhteinen informaatio ajoittain kasvamaan tai saavuttaa kaikenkaikkiaan korkeampi lähetystehokkuus.