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  • Korpela, Anna Liisa ([s. )
  • Mutshinda Mwanza, Crispin (Helsingin yliopisto, 2010)
    Elucidating the mechanisms responsible for the patterns of species abundance, diversity, and distribution within and across ecological systems is a fundamental research focus in ecology. Species abundance patterns are shaped in a convoluted way by interplays between inter-/intra-specific interactions, environmental forcing, demographic stochasticity, and dispersal. Comprehensive models and suitable inferential and computational tools for teasing out these different factors are quite limited, even though such tools are critically needed to guide the implementation of management and conservation strategies, the efficacy of which rests on a realistic evaluation of the underlying mechanisms. This is even more so in the prevailing context of concerns over climate change progress and its potential impacts on ecosystems. This thesis utilized the flexible hierarchical Bayesian modelling framework in combination with the computer intensive methods known as Markov chain Monte Carlo, to develop methodologies for identifying and evaluating the factors that control the structure and dynamics of ecological communities. These methodologies were used to analyze data from a range of taxa: macro-moths (Lepidoptera), fish, crustaceans, birds, and rodents. Environmental stochasticity emerged as the most important driver of community dynamics, followed by density dependent regulation; the influence of inter-specific interactions on community-level variances was broadly minor. This thesis contributes to the understanding of the mechanisms underlying the structure and dynamics of ecological communities, by showing directly that environmental fluctuations rather than inter-specific competition dominate the dynamics of several systems. This finding emphasizes the need to better understand how species are affected by the environment and acknowledge species differences in their responses to environmental heterogeneity, if we are to effectively model and predict their dynamics (e.g. for management and conservation purposes). The thesis also proposes a model-based approach to integrating the niche and neutral perspectives on community structure and dynamics, making it possible for the relative importance of each category of factors to be evaluated in light of field data.
  • Benner, Christian (2013)
    Background. DNA microarrays measure the expression levels of tens of thousands of genes simultaneously. Some differentially expressed genes may be useful as markers for the diagnosis of diseases. Available statistical tests examine genes individually, which causes challenges due to multiple testing and variance estimation. In this Master’s thesis, Bayesian confirmatory factor analysis (CFA) is proposed as a novel approach for the detection of differential gene expression. Methods. The factor scores represent summary measures that combine the expression levels from biological samples under the same condition. Differential gene expression is assessed by utilizing their distributional assumptions. A mean-field variational Bayesian approximation is employed for computationally fast estimation. Results. Its estimation performance is equal to Gibbs sampling. Point estimation errors of model parameters decrease with increasing number of variables. However, mean centering of the data matrix and standardization of factor scores resulted in an inflation of the false positive rate. Conclusion. Avoiding mean centering and revision of the CFA model is required so that location parameters of factor score distributions can be estimated. The utility of CFA for the detection of differential gene expression needs also to be confirmed by a comparison with different statistical procedures to benchmark its false positive rate and statistical power.
  • Mäntyniemi, Samu (Helsingin yliopisto, 2006)
    In this thesis the use of the Bayesian approach to statistical inference in fisheries stock assessment is studied. The work was conducted in collaboration of the Finnish Game and Fisheries Research Institute by using the problem of monitoring and prediction of the juvenile salmon population in the River Tornionjoki as an example application. The River Tornionjoki is the largest salmon river flowing into the Baltic Sea. This thesis tackles the issues of model formulation and model checking as well as computational problems related to Bayesian modelling in the context of fisheries stock assessment. Each article of the thesis provides a novel method either for extracting information from data obtained via a particular type of sampling system or for integrating the information about the fish stock from multiple sources in terms of a population dynamics model. Mark-recapture and removal sampling schemes and a random catch sampling method are covered for the estimation of the population size. In addition, a method for estimating the stock composition of a salmon catch based on DNA samples is also presented. For most of the articles, Markov chain Monte Carlo (MCMC) simulation has been used as a tool to approximate the posterior distribution. Problems arising from the sampling method are also briefly discussed and potential solutions for these problems are proposed. Special emphasis in the discussion is given to the philosophical foundation of the Bayesian approach in the context of fisheries stock assessment. It is argued that the role of subjective prior knowledge needed in practically all parts of a Bayesian model should be recognized and consequently fully utilised in the process of model formulation.
  • Pirinen, Matti (Helsingin yliopisto, 2009)
    Genetics, the science of heredity and variation in living organisms, has a central role in medicine, in breeding crops and livestock, and in studying fundamental topics of biological sciences such as evolution and cell functioning. Currently the field of genetics is under a rapid development because of the recent advances in technologies by which molecular data can be obtained from living organisms. In order that most information from such data can be extracted, the analyses need to be carried out using statistical models that are tailored to take account of the particular genetic processes. In this thesis we formulate and analyze Bayesian models for genetic marker data of contemporary individuals. The major focus is on the modeling of the unobserved recent ancestry of the sampled individuals (say, for tens of generations or so), which is carried out by using explicit probabilistic reconstructions of the pedigree structures accompanied by the gene flows at the marker loci. For such a recent history, the recombination process is the major genetic force that shapes the genomes of the individuals, and it is included in the model by assuming that the recombination fractions between the adjacent markers are known. The posterior distribution of the unobserved history of the individuals is studied conditionally on the observed marker data by using a Markov chain Monte Carlo algorithm (MCMC). The example analyses consider estimation of the population structure, relatedness structure (both at the level of whole genomes as well as at each marker separately), and haplotype configurations. For situations where the pedigree structure is partially known, an algorithm to create an initial state for the MCMC algorithm is given. Furthermore, the thesis includes an extension of the model for the recent genetic history to situations where also a quantitative phenotype has been measured from the contemporary individuals. In that case the goal is to identify positions on the genome that affect the observed phenotypic values. This task is carried out within the Bayesian framework, where the number and the relative effects of the quantitative trait loci are treated as random variables whose posterior distribution is studied conditionally on the observed genetic and phenotypic data. In addition, the thesis contains an extension of a widely-used haplotyping method, the PHASE algorithm, to settings where genetic material from several individuals has been pooled together, and the allele frequencies of each pool are determined in a single genotyping.
  • Cheng, Lu (Helsingin yliopisto, 2013)
    Vast amounts of molecular data are being generated every day. However, how to properly harness the data remains often a challenge for many biologists. Firstly, due to the typical large dimension of the molecular data, analyses can either require exhaustive amounts of computer memory or be very time-consuming, or both. Secondly, biological problems often have their own special features, which put demand on specially designed software to obtain meaningful results from statistical analyses without imposing too much requirements on the available computing resources. Finally, the general complexity of many biological research questions necessitates joint use of many different methods, which requires a considerable expertise in properly understanding the possibilities and limitations of the analysis tools. In the first part of this thesis, we discuss three general Bayesian classification/clustering frameworks, which in the considered applications are targeted towards clustering of DNA sequence data, in particular in the context of bacterial population genomics and evolutionary epidemiology. Based on more generic Bayesian concepts, we have developed several statistical tools for analyzing DNA sequence data in bacterial metagenomics and population genomics. In the second part of this thesis, we focus on discussing how to reconstruct bacterial evolutionary history from a combination of whole genome sequences and a number of core genes for which a large set of samples are available. A major problem is that for many bacterial species horizontal gene transfer of DNA, which is often termed as recombination, is relatively frequent and the recombined fragments within genome sequences have a tendency to severely distort the phylogenetic inferences. To obtain computationally viable solutions in practice for a majority of currently emerging genome data sets, it is necessary to divide the problem into parts and use different approaches in combination to perform the whole analysis. We demonstrate this strategy by application to two challenging data sets in the context of evolutionary epidemiology and show that biologically significant conclusions can be drawn by shedding light into the complex patterns of relatedness among strains of bacteria. Both studied organisms (\textit{Escherichia coli} and \textit{Campylobacter jejuni}) are major pathogens of humans and understanding the mechanisms behind the evolution of their populations is of vital importance for human health.
  • Kärkkäinen, Hanni Pauliina (Helsingin yliopisto, 2013)
    Genome-wide marker data is used in animal and plant breeding in computing genomic breeding values, and in human genetics in identifying disease susceptibility genes, predicting unobserved phenotypes and assessing disease risks. While the tremendous number of markers available for easy and cost-effective genotyping is an invaluable asset in genetic research and in animal and plant breeding, the ever increasing data sets are placing heavy demands on the statistical analysis methodology. The statistical methods proposed for genomic selection are based on either traditional best linear unbiased prediction (BLUP) or on different Bayesian multilocus association models. The marker based genomic best linear unbiased prediction (G-BLUP) employs the marker information in estimating genomic relationships between the individuals, and then utilizes the marker-estimated genomic relationship matrix in a mixed model context. A multilocus association model, on the other hand, uses the marker information directly by assigning different effects to the marker alleles, and quantifies the genomic breeding value of an individual as the sum of the marker effects. The advantage of a multilocus association over the G-BLUP is that the former allows the estimated effect size to vary over the set of markers, while the latter assumes a constant impact throughout the genome. In human genetics the most prevalent approach is a single SNP association model. These models consider only one marker at the time, ignoring the possible effects of the other major loci. This is less than ideal in genome-wide study for a complex trait, as such traits are assumed to be affected by a multitude of genes. The objective of this work is to obtain further understanding of the behavior of the different Bayesian multilocus association models and of the instances in which different methods work best, to seek connections between the different Bayesian models, and to develop a Bayesian multilocus association model framework, along with an efficient parameter estimation machinery, that can be utilized in phenotype prediction, genomic breeding value estimation and quantitative trait locus (QTL) location and effect estimation from a variety of genome-wide data.
  • Lehikoinen, Annukka (Helsingin yliopisto, 2014)
    Environmental risk assessment (ERA) is a process of estimating the probability and consequences of an adverse event due to pressures or changes in environmental conditions resulting from human activities. Its purpose is to search the optimal courses of action under uncertainty when striving for the sustainable use of environment through minimizing the potential losses. As environmental issues are typically multidisciplinary, addressing large amount of eco-societal inter-linkages, an optimal tool for the ERA should enable the efficient integration and meta-analysis of multidisciplinary knowledge. By describing the causalities and studying the interactions among its components, this kind of integrative analysis provides us better understanding about the environmental system in focus. In addition, the functional ERA application should allow exploring, explaining and forecasting the responses of an environmental system to changes in natural and human induced stressors, serving as a decision support model that enables the search of optimal management strategy, also in the presence of imperfect knowledge. Bayesian Network (BN) is a graphical model that enables the integration of both quantitative and qualitative data and knowledge to a causal chain of inference. It is a powerful tool for synthesising knowledge, logic and rules, providing aid for thinking about complex systems that are too demanding to be analysed by human brains alone. In a BN, all the knowledge is handled in the form of probability distributions, thus the result represents the prevailing state of knowledge. The method facilitates analysing the location and amount of uncertainty explicitly, as well as enables studying its significance when it comes to the decision making. The main contribution of this thesis is to share experiences and ideas about the development and use of the ERA applications executed by using the BN as method. The perspective of the work is dichotomic. The objective in the separate studies presented in the articles have been on one hand to develop tools for integrating available knowledge and materials to enable the quantitative assessment of the environmental risks. On the other hand, the ultimate aim has been to learn more about the environmental risks and their potential management in the case study area of the Gulf of Finland. In this thesis, both of these perspectives are considered. Eutrophication and oil transportations at the Gulf of Finland are used as the case issues. The thesis concludes that Bayesian networks have plenty of properties that are useful for ERA and the method can be used for solving problems typical for that field analytically. By planting the developed graphical BNs in the commonly used Drivers-Pressures-States-Impacts-Responses -problem structuring framework, it is also demonstrated that combining these two approaches can be helpful in conceptual modeling, enabling the better framing of the research problem at hand and thinking about it systematically. The greatest challenges concerning the BN-ERA modeling are found to be related to the computational limitations of the current BN software, when it comes to the joint use of the discretised and continuous variables, as well as the restricted capacity to include the spatial resolution to the models. Producing the prior probability distributions by using deterministic models is also noted to be relatively tedious and time-consuming. The issues of end use of the applications, problems related to the scientific publishing of them, as well as the advantages and challenges of working in the multidisciplinary research teams are discussed.
  • Sillanpää, Mikko (Helsingin yliopisto, 2000)
  • Li, Zitong (Helsingin yliopisto, 2014)
    Quantitative trait loci (QTL) /association mapping aims to identify the genomic loci associated with the complex traits. From a statistical perspective, multiple linear regression is often used to model, estimate and test the effects of molecular markers on a trait. With genotype data derived from contemporary genomics techniques, however, the number of markers typically exceed the number of individuals, and it is therefore necessary to perform some sort of variable selection or parameter regularization to provide reliable estimates of model parameters. In addition, many quantitative traits are changing during their development process of life. Accordingly, a longitudinal study that jointly maps the repeated measurements of the phenotype over time may increase the statistical power to identify QTLs, compared with the single trait analysis. In this thesis, a series of Bayesian variable selection/regularization linear methods were developed and applied for analyzing quantitative traits measured at either single or multiple time points. The first work provided an overview of the principal frequentist regularization methods for analyzing single traits. The second work also focused on single trait analysis, where a variational Bayesian (VB) algorithm was derived for estimating parameters in several Bayesian regularization methods. The VB methods can be quickly implemented on large data sets in contrast to the classical Markov Chain Monte Carlo methods. In the third work, the Bayesian regularization method was extended to a non-parametric varying coefficient model to analyze longitudinal traits. Particularly, an efficient VB stepwise algorithm was used for variable selection, so that the method can be quickly implemented even on data sets with a large number of time points and/or a large number of markers. The fourth work is an application of variable selection methods on forest genetics data collected from Northern Sweden. From several conifer wood properties traits with multiple time points, four QTLs located at genes were identified, which are promising targets for future research in wood molecular biology and breeding.
  • Tang, Jing (Helsingin yliopisto, 2009)
    Bacteria play an important role in many ecological systems. The molecular characterization of bacteria using either cultivation-dependent or cultivation-independent methods reveals the large scale of bacterial diversity in natural communities, and the vastness of subpopulations within a species or genus. Understanding how bacterial diversity varies across different environments and also within populations should provide insights into many important questions of bacterial evolution and population dynamics. This thesis presents novel statistical methods for analyzing bacterial diversity using widely employed molecular fingerprinting techniques. The first objective of this thesis was to develop Bayesian clustering models to identify bacterial population structures. Bacterial isolates were identified using multilous sequence typing (MLST), and Bayesian clustering models were used to explore the evolutionary relationships among isolates. Our method involves the inference of genetic population structures via an unsupervised clustering framework where the dependence between loci is represented using graphical models. The population dynamics that generate such a population stratification were investigated using a stochastic model, in which homologous recombination between subpopulations can be quantified within a gene flow network. The second part of the thesis focuses on cluster analysis of community compositional data produced by two different cultivation-independent analyses: terminal restriction fragment length polymorphism (T-RFLP) analysis, and fatty acid methyl ester (FAME) analysis. The cluster analysis aims to group bacterial communities that are similar in composition, which is an important step for understanding the overall influences of environmental and ecological perturbations on bacterial diversity. A common feature of T-RFLP and FAME data is zero-inflation, which indicates that the observation of a zero value is much more frequent than would be expected, for example, from a Poisson distribution in the discrete case, or a Gaussian distribution in the continuous case. We provided two strategies for modeling zero-inflation in the clustering framework, which were validated by both synthetic and empirical complex data sets. We show in the thesis that our model that takes into account dependencies between loci in MLST data can produce better clustering results than those methods which assume independent loci. Furthermore, computer algorithms that are efficient in analyzing large scale data were adopted for meeting the increasing computational need. Our method that detects homologous recombination in subpopulations may provide a theoretical criterion for defining bacterial species. The clustering of bacterial community data include T-RFLP and FAME provides an initial effort for discovering the evolutionary dynamics that structure and maintain bacterial diversity in the natural environment.
  • Jääskinen, Väinö (Helsingin yliopisto, 2015)
    In various fields of knowledge we can observe that the availability of potentially useful data is increasing fast. A prime example is the DNA sequence data. This increase is both an opportunity and a challenge as new methods are needed to benefit from the big data sets. This has sparked a fruitful line of research in statistics and computer science that can be called machine learning. In this thesis, we develop machine learning methods based on the Bayesian approach to statistics. We address a fairly general problem called clustering, i.e. dividing a set of objects to non-overlapping group based on their similarity, and apply it to models with Markovian dependence structures. We consider sequence data in a finite alphabet and present a model class called the Sparse Markov chain (SMC). It is a special case of a Markov chain (MC) model and offers a parsimonious description of the data generating mechanism. A Variable length Markov chain (VLMC) is a popular sparse model presented earlier in the literature and it has a representation as an SMC model. We develop Bayesian clustering methodology for learning the SMC and other Markovian models. Another problem that we study in this thesis is causal inference. We present a model and an algorithm for learning causal mechanisms from data. The model can be considered as a stochastic extension of the sufficient-component cause model that is popular in epidemiology. In our model there are several causal mechanisms each with its own parameters. A mixture distribution gives a probability that an outcome variable is associated with a mechanism. Applications that are considered in this thesis come mainly from computational biology. We cluster states of Markovian models estimated from DNA sequences. This gives an efficient description of the sequence data when comparing to methods reported in the literature. We also cluster DNA sequences with Markov chains, which results in a method that can be used for example in the estimation of bacterial community composition in a sample from which DNA is extracted. The causal model and the related learning algorithm are able to estimate mechanisms from fairly challenging data. We have developed the learning algorithms with big data sets in mind. Still, there is a need to develop them further to handle ever larger data sets.
  • Blomstedt, Paul (2007)
    Tilastollisessa luokittelussa kiinnostuksen kohteena oleva havaintoyksikkö sijoitetaan tätä kuvaavien havaittujen ominaisuuksien perusteella johonkin luokkaan. Esim. sähköpostiohjelmien roskapostisuodattimet hyödyntävät luokittelumenetelmiä luokitellessaan viestit näiden sisällön perusteella joko roskapostiksi tai ”oikeaksi” sähköpostiviestiksi. Tässä työssä taas tarkastellaan lääketieteellistä sovellusta, jossa potilaan terveydentilaa koskevien tietojen perusteella pyritään päättelemään onko potilaalla jokin määrätty sairaus vai ei. Luokitelussa käytettävä luokittelumalli estimoidaan luokiteltavan havaintoyksikön kanssa samasta perusjoukosta olevasta, valmiiksi luokitellusta aineistosta, jota kutsutaan opetusaineistoksi. Luokittelumalleja voidaan muodostaa monin eri tavoin. Tässä työssä käsiteltävät mallit perustuvat havaintoyksikön ominaisuuksille ehdollistetun, luokkamuuttujan ehdollisen jakauman mallintamiseen. Luokittelija sijoittaa tällöin havaintoyksikön luokkaan, jonka ehdollinen todennäköisyys on suurin. Ehdollisiin todennäköisyyksiin perustuvat luokittelijat voidaan muodostaa joko diskriminatiivisesti tai generatiivisesti. Edellisessä estimoidaan suoraan luokkamuuttujan ehdollista jakaumaa vastaava malli kun taas jälkimmäisessä estimoidaan ensin havaintoyksikön ominaisuuksia kuvaavien muuttujien sekä luokkamuuttujan yhteisjakaumaa vastaava malli, josta etsitty ehdollinen jakauma saadaan käyttämällä Bayesin kaavaa. Tutkimuksessa tarkastellaan binääriseen luokitteluun soveltuvaa, diskriminatiivisesti muodostettavaa logistista regressiota sekä naiivia Bayes-luokittelijaa, joka tiettyjen oletusten vallitessa on tämän generatiivinen vastine. Modernissa tilastotieteessä on viime vuosina huomattavasti lisääntynyt ns. bayesläisten menetelmien käyttö. Ominaista näille menetelmille on kaiken tilastollisen epävarmuuden ilmaiseminen todennäköisyysjakaumien avulla. Tässä työssä tutkitaan kokeellisesti bayesläisen lähestymistavan vaikutusta naiivin Bayes-luokittelijan ja logistisen regressiomallin luokitustarkkuuteen. Tämän lisäksi tarkastellan diskriminatiivisten ja generatiivisten luokittelumallien välisiä eroja ja arvioidaan opetusaineiston koon vaikutusta näiden luokituskykyyn. Luokittelumallien vertailussa käytetään Tampereen yliopistollisesta sairaalasta peräisin olevaa aineistoa, joka koostuu sepelvaltimovarjoainekuvattujen potilaiden terveydentilaa koskevista tiedoista. Luokitustarkkuudeltaan generatiivinen luokittelija oli diskriminatiivista luokittelijaa parempi, joskin erot pienenivät mitä suuremmaksi opetusaineiston kokoa kasvatettiin. Tämä on sopusoinnussa kirjallisuudessa esitetyn tuloksen kanssa, jonka mukaan generatiiviset luokittelijat ovat diskriminatiivisia luokittelijoita tarkempia juuri pienillä opetusaineistoilla kun taas jälkimmäiset ovat tarkempia suurilla opetusaineistoilla. Bayesläisen lähestymistavan soveltaminen paransi jossain määrin kummankin mallin luokituskykyä etenkin pienimmillä opetusaineistoilla.
  • Koivula, Mari (1998)
    Tutkimuksessa määritettiiin sytologiset viitearvot keuhkohuuhtelunäytteille. 11 koirasta otettiin keuhkohuuhtelu (BAL) 5-7 viikon väliajoin seitsemän kertaa. Jokaiselle koiralle tehtiin yleistutkimus ennen rauhoitusta. Koirat rauhoitettiin medetomidiinilla ja riittävä anestesia saatiin propofolilla. Huuhtelunesteenä käytettiin fysiologista natriumkloridia 1 ml/kg jokaiseen huuhteluerään. Huuhtelu suoritettiin kummallekin pallealohkolle kahdesti peräkkäin fiberoskoopin näytteenottokanavan kautta. Välittömästi näytteenoton jälkeen jäähauteessa lasipurkissa olevat näytteet vietiin laboratorioon käsiteltäviksi. Näytteen kokonaistilavuus ja solupitoisuus (elävät ja kuolleet solut) määritettiin.Yhdenkertaisen sideharson läpi suodatetusta ja kahdesti sentrifugoidusta ja pestystä näytteestä sytosentrifugoitiin 40 000 elävää solua objektilasille. Saadut lasit värjättiin May-Grünwald-Giemsalla. Mikroskopoimalla eriteltiin 300 solua ja laskettiin eri solujen prosenttiosuudet. Sytologiset viitearvot määritettiin kunkin koiran keskiarvojen keskiarvosta 95 %:n luottamusvälillä.Saantoprosentti oli 57-63 % ja saaliin kokonaissolut 0,086-0,153 x 106 solua/ml. BAL-näytteidenerittelylaskennassa oli makrofageja 68,5-76 %, lymfosyyttejä 13,8-18,9 %, neutrofiilejä 3,9-5,8 %, eosinofiilejä 1,0-6,4 %, plasmasoluja 0,3-1,0 %, basofiilejä 1,0-1,9 % ja epiteelisoluja 0,4-1,2 %. Kirjallisuuskatsauksessa on käsitelty koiran akuuttien alempien hengitysteiden sairauksien etiologiaa,oireita, diagnoosia ja hoitoa. Kennelyskä ja keuhkokuume ovat tärkeimmät infektiiviset sairaudet.Parasiiteista vaeltavia suolikaistoukkia esiintyy Suomessa, mutta varsinaiset keuhkomadot ovat vielä harvinaisia. Keuhkoödeemi voidaan jaotella kardiogeeniseen ja ei-kardiogeeniseen. Yleisin keuhkoödeemin aiheuttaja on kardiogeeninen sairaus. Muita koiran keuhkosairauksia ovat keuhkojen eosinofiili-infiltraatio syndrooma, keuhkoruhje, keuhkojen tromboembolia, keuhkoverenvuoto ja akuuttihengitysvaikeusoireyhtymä.
  • Sómby, Seija (2003)
  • Hämäläinen, Riku (Tatanka Press, 2011)
    The study is the outcome of two research projects on the North American Indian traditions: the role of the shields within the Plains Indians traditional culture and religion, and the bear ceremonialism of the Native North America, especially the significance of the bear among the Plains Indians. This article-based dissertation includes seven separately published scholar papers, forming Chapters 6 12. The introduction formulates the objectives and frame of reference of the study and the conclusions pulls together its results. The study reconsiders the role of the Plains Indian shields with bear motifs. Such shields are found in rock art, in the Plains Indian s paintings and drawings, and in various collections, the main source material being the shields in European and North American museums. The aim is not only to study shields with bear power motifs and the meanings of the bear, but also to discuss appropriate methods for studying these subjects. There are three major aims of the study: to consider methodical questions in studying Plains Indian shields, to examine the complexity of the Plains Indian shields with the bear power motifs, and to offer new interpretations for the basic meanings of the bear among the Plains Indians and the interrelationship between individualism and collectivism in the Plains Indians visionary art that show bear power motifs on the shields. The study constructs a view on the bear shields taking account of all sources of information available and analysing the shields both as physical artefacts and religious objects from different perspectives, studying them as a part of the ensemble of Plains culture and religious traditions. The bear motifs represented the superhuman power that medicine men and warriors could exploit through visions. For the Plains Indians, the bear was a wise animal from which medicine men could get power for healing but also a dangerous animal from which warriors could get power for warfare. The shields with bear motifs represented the bear powers of the owners of the shields. The bear shield was made to represent the vision, and the principal interpretation of the symbolism was based on the individual experience of spiritual world and its powers. The study argues that the bear shield as personal medicine object is based on wider tribal traditions, and the basic meaning is derived from the collective tradition. This means that the bear seen in vision represented particular affairs and it was represented on the shield surface using conventional ways of traditional artistry. In consequence of this, the bear shields reflect not only the individual experiences of bear power but whole field of tribal traditions that legitimated the experiences and offered acceptable interpretations and conventional modes for the bear symbols.
  • Johansson, Tino (Helsingin yliopisto, 2008)
    Human-wildlife conflicts are today an integral part of the rural development discourse. In this research, the main focus is on the spatial explanation which is not a very common approach in the reviewed literature. My research hypothesis is based on the assumption that human-wildlife conflicts occur when a wild animal crosses a perceived borderline between the nature and culture and enters into the realms of the other. The borderline between nature and culture marks a perceived division of spatial content in our senses of place. The animal subject that crosses this border becomes a subject out of place meaning that the animal is then spatially located in a space where it should not be or where it does not belong according to tradition, custom, rules, law, public opinion, prevailing discourse or some other criteria set by human beings. An appearance of a wild animal in a domesticated space brings an uncontrolled subject into that space where humans have previously commanded total control of all other natural elements. A wild animal out of place may also threaten the biosecurity of the place in question. I carried out a case study in the Liwale district in south-eastern Tanzania to test my hypothesis during June and July 2002. I also collected documents and carried out interviews in Dar es Salaam in 2003. I studied the human-wildlife conflicts in six rural villages, where a total of 183 persons participated in the village meetings. My research methods included semi-structured interviews, participatory mapping, questionnaire survey and Q- methodology. The rural communities in the Liwale district have a long-history of co-existing with wildlife and they still have traditional knowledge of wildlife management and hunting. Wildlife conservation through the establishment of game reserves during the colonial era has escalated human-wildlife conflicts in the Liwale district. This study shows that the villagers perceive some wild animals differently in their images of the African countryside than the district and regional level civil servants do. From the small scale subsistence farmers point of views, wild animals continue to challenge the separation of the wild (the forests) and the domestics spaces (the cultivated fields) by moving across the perceived borders in search of food and shelter. As a result, the farmers may loose their crops, livestock or even their own lives in the confrontations of wild animals. Human-wildlife conflicts in the Liwale district are manifold and cannot be explained simply on the basis of attitudes or perceived images of landscapes. However, the spatial explanation of these conflicts provides us some more understanding of why human-wildlife conflicts are so widely found across the world.
  • Kitanov, Severin (Severin Valentinov Kitanov, 2006)
    This dissertation examines the concept of beatific enjoyment (fruitio beatifica) in scholastic theology and philosophy in the thirteenth and early fourteenth century. The aim of the study is to explain what is enjoyment and to show why scholastic thinkers were interested in discussing it. The dissertation consists of five chapters. The first chapter deals with Aurelius Augustine's distinction between enjoyment and use and the place of enjoyment in the framework of Augustine's view of the passions and the human will. The first chapter also focuses upon the importance of Peter Lombard's Sentences for the transmission of Augustine's treatment of enjoyment in scholastic thought as well as upon Lombard's understanding of enjoyment. The second chapter treats thirteenth-century conceptions of the object and psychology of enjoyment. Material for this chapter is provided by the writings - mostly Sentences commentaries - of Alexander of Hales, Albert the Great, Bonaventure, Thomas Aquinas, Peter of Tarentaise, Robert Kilwardby, William de la Mare, Giles of Rome, and Richard of Middleton. The third chapter inspects early fourteenth-century views of the object and psychology of enjoyment. The fourth chapter focuses upon discussions of the enjoyment of the Holy Trinity. The fifth chapter discusses the contingency of beatific enjoyment. The main writers studied in the third, fourth and fifth chapters are John Duns Scotus, Peter Aureoli, Durandus of Saint Pourçain, William of Ockham, Walter Chatton, Robert Holcot, and Adam Wodeham. Historians of medieval intellectual history have emphasized the significance of the concept of beatific enjoyment for understanding the character and aims of scholastic theology and philosophy. The concept of beatific enjoyment was developed by Augustine on the basis of the insight that only God can satisfy our heart's desire. The possibility of satisfying this desire requires a right ordering of the human mind and a detachment of the will from the relative goals of earthly existence. Augustine placed this insight at the very foundation of the notion of Christian learning and education in his treatise On Christian Doctrine. Following Augustine, the twelfth-century scholastic theologian Peter Lombard made the concept of enjoyment the first topic in his plan of systematic theology. The official inclusion of Lombard's Sentences in the curriculum of theological studies in the early universities stimulated vigorous discussions of enjoyment. Enjoyment was understood as a volition and was analyzed in relation to cognition and other psychic features such as rest and pleasure. This study shows that early fourteenth-century authors deepened the analysis of enjoyment by concentrating upon the relationship between enjoyment and mental pleasure, the relationship between cognition and volition, and the relationship between the will and the beatific object (i.e., the Holy Trinity). The study also demonstrates the way in which the idea of enjoyment was affected by changes in the method of theological analysis - the application of Aristotelian logic in a Trinitarian context and the shift from virtue ethics to normative ethics.
  • Stubb, Jenni Katarina (Helsingin yliopisto, 2012)
    This dissertation focused on exploring doctoral students conceptions of the scholarly community and research, and further, on analysing the relation between these conceptions and well-being as well as study persistence in the doctoral process. The first two studies concentrated on analysing students conceptions of the scholarly community and their meaning with respect to one s own thesis process. The last two studies focused on students conceptions of the personal meaning of the thesis work and conceptions of research in the context of their own doctoral journeys. The data were collected by surveys and interviews. Altogether 669 students from different disciplines participated in the surveys and 32 in the interviews. The analysis was conducted by combining both qualitative and quantitative methods. Study I examined how doctoral students perceived their own role in the scholarly community and their experiences of their learning environment. The relation between their experienced role in the community and well-being as well as study persistence was also analysed. The results indicated that the experienced role in the community varied from a sense of belonging to feeling like an outsider and perceiving one s own role as incoherent or contradictory. Students who experienced a sense of being part of the academic community also reported experiencing their learning environment in a more positive way. These students also reported less stress, anxiety, and exhaustion and greater interest in their own doctoral projects. Moreover, students who felt themselves to be part of the community had considered interrupting their studies less often than others. Their conceptions were also related to the faculties in question. Study II took a more in-depth look at students experienced socio-psychological well-being by focusing on how they saw the scholarly community in terms of one s own doctoral process. The results suggested that experiences of the community varied from perceiving it as empowering to experiencing it as a burden. Seeing one s own scholarly community as empowering was related to lower levels of reported stress, anxiety, and exhaustion and higher levels of interest in one s own doctoral project. The students who experienced empowerment had also considered interrupting their studies less often. Study III explored the thesis work s personal meaning for the doctoral students and its relation to the experienced well-being as well as study persistence. The relations between the personal meaning of the thesis work and discipline were also looked at. The results suggested that personal meaning varied between emphasizing the process, the product, or both. Highlighting the meaning of the process was related to lower levels of reported stress, anxiety, and exhaustion, but to higher levels of interest. Students who emphasized the meaning of the process had considered interrupting their doctoral studies less often than other students. Differences were also apparent between faculties: students in medicine emphasized the meaning of the end-product more often than students in other faculties. Study IV analysed doctoral students conceptions of research and whether these were discipline-related. The results indicated that research was most often seen as a personal journey. Rather typical was also seeing it as a job to do , as answering certain demands. Research was also seen as a means to qualify oneself or as making a difference by contributing to the discipline or to society. The students conceptions were found to be related to the discipline: students in medicine most often described research as a job to do while those in natural science and behavioural sciences emphasized research as a personal journey. The results suggested that doctoral students experienced the meaning of research and the academic community very differently. Their conceptions were related to well-being and study persistence during the Ph.D. process, and were found to be discipline-related. The results encourage viewing the doctoral process not only as a cognitive effort but also as a process that is mediated by experiencing a sense of belonging and meaningfulness.