Functional Genomic Approaches for Deciphering Plant-Virus Interactions

Plant-virus interactions are very complex in nature and lead to disease and symptom formation by causing various physiological, metabolic and developmental changes in the host plants. These interactions are mainly the outcomes of viral hijacking of host components to complete their infection cycles and of host defensive responses to restrict the viral infections. Viral genomes contain only a small number of genes often encoding for multifunctional proteins, and all are essential in establishing a viral infection. Thus, it is important to understand the specific roles of individual viral genes and their contribution to the viral life cycles. Among the most important viral proteins are the suppressors of RNA silencing (VSRs). These proteins function to suppress host defenses mediated by RNA silencing and can also serve in other functions, e.g. in viral movement, transactivation of host genes, virus replication and protein processing. Thus these proteins are likely to have a significant impact on host physiology and metabolism. In the present study, I have examined the plant-virus interactions and the effects of three different VSRs on host physiology and gene expression levels by microarray analysis of transgenic plants that express these VSR genes. I also studied the gene expression changes related to the expression of the whole genome of Tobacco mosaic virus (TMV) in transgenic tobacco plants. Expression of the VSR genes in the transgenic tobacco plants causes significant changes in the gene expression profiles. HC-Pro gene derived from the Potyvirus Y (PVY) causes alteration of 748 and 332 transcripts, AC2 gene derived from the African cassava mosaic virus (ACMV) causes alteration of 1118 and 251transcripts, and P25 gene derived from the Potyvirus X (PVX) causes alterations of 1355 and 64 transcripts in leaves and flowers, respectively. All three VSRs cause similar up-regulation in defense, hormonally regulated and different stress-related genes and down-regulation in the photosynthesis and starch metabolism related genes. They also induce alterations that are specific to each viral VSR. The phenotype and transcriptome alterations of the HC-Pro expressing transgenic plants are similar to those observed in some Potyvirus-infected plants. The plants show increased protein degradation, which may be due to the HC-Pro cysteine endopeptidase and thioredoxin activities. The AC2-expressing transgenic plants show a similar phenotype and gene expression pattern as HC-Pro-expressing plants, but also alter pathways related to jasmonic acid, ethylene and retrograde signaling. In the P25 expressing transgenic plants, high numbers of genes (total of 1355) were up-regulated in the leaves, compared to a very low number of down-regulated genes (total of 5). Despite of strong induction of the transcripts, only mild growth reduction and no other distinct phenotype was observed in these plants. As an example of whole virus interactions with its host, I also studied gene expression changes caused by Tobacco mosaic virus (TMV) in tobacco host in three different conditions, i.e. in transgenic plants that are first resistant to the virus, and then become susceptible to it and in wild type plants naturally infected with this virus. The microarray analysis revealed up and down-regulation of 1362 and 1422 transcripts in the TMV resistant young transgenic plants, and up and down-regulation of a total of 1150 and 1200 transcripts, respectively, in the older plants, after the resistance break. Natural TMV infections in wild type plants caused up-regulation of 550 transcripts and down-regulation of 480 transcripts. 124 up-regulated and 29 down-regulated transcripts were commonly altered between young and old TMV transgenic plants, and only 6 up-regulated and none of the down-regulated transcripts were commonly altered in all three plants. During the resistant stage, the strong down-regulation in translation-related transcripts (total of 750 genes) was observed. Additionally, transcripts related to the hormones, protein degradation and defense pathways, cell division and stress were distinctly altered. All these alterations may contribute to the TMV resistance in the young transgenic plants, and the resistance may also be related to RNA silencing, despite of the low viral abundance and lack of viral siRNAs or TMV methylation activity in the plants.

Impact of reliability of supply on long-term development approaches to electricity distribution networks

The Finnish electricity distribution sector, rural areas in particular, is facing major challenges because of the economic regulation, tightening supply security requirements and the ageing network asset. Therefore, the target in the distribution network planning and asset management is to develop and renovate the networks to meet these challenges in compliance with the regulations in an economically feasible way. Concerning supply security, the new Finnish Electricity Market Act limits the maximum duration of electricity supply interruptions to six hours in urban areas and 36 hours in rural areas. This has a significant impact on distribution network planning, especially in rural areas where the distribution networks typically require extensive modifications and renovations to meet the supply security requirements. This doctoral thesis introduces a methodology to analyse electricity distribution system development. The methodology is based on and combines elements of reliability analysis, asset management and economic regulation. The analysis results can be applied, for instance, to evaluate the development of distribution reliability and to consider actions to meet the tightening regulatory requirements. Thus, the methodology produces information for strategic decision-making so that DSOs can respond to challenges arising in the electricity distribution sector. The key contributions of the thesis are a network renovation concept for rural areas, an analysis to assess supply security, and an evaluation of the effects of economic regulation on the strategic network planning. In addition, the thesis demonstrates how the reliability aspect affects the placement of automation devices and how the reserve power can be arranged in a rural area network.

Open Preservation: Community Approaches to Ensuring Long-Term Access to Digital Collections

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

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.

Social Solidarity and Fear of Crime - Community and Individual Approaches in Responses to Mass Violence after School Shootings

The aim of this study was to examine community and individual approaches in responses to mass violence after the school shooting incidents in Jokela (November 2007) and Kauhajoki (September 2008), Finland. In considering the community approach, responses to any shocking criminal event may have integrative, as well as disintegrative effects, within the neighborhood. The integration perspective argues that a heinous criminal event within one’s community is a matter of offence to collectively held feelings and beliefs, and increases perceived solidarity; whereas the disintegration perspective suggests that a criminal event weakens the social fabric of community life by increasing fear of crime and mistrust among locals. In considering the individual approach, socio-demographic factors, such as one’s gender, are typically significant indicators, which explain variation in fear of crime. Beyond this, people are not equally exposed to violent crime and therefore prior victimization and event related experiences may further explain why people differ in their sensitivity to risk from mass violence. Finally, factors related to subjective mental health, such as depressed mood, are also likely to moderate individual differences in responses to mass violence. This study is based on the correlational design of four independent cross-sectional postal surveys. The sampling frames (N=700) for the surveys were the Finnish speaking adult population aged 18–74-years. The first mail survey in Jokela (n=330) was conducted between May and June 2008, approximately six months from the shooting incident at the local high-school. The second Jokela survey (n=278) was conducted in May–June of 2009, 18 months removed from the incident. The first survey in Kauhajoki (n=319) was collected six months after the incident at the local University of Applied Sciences, March– April 2009, and the second (n=339) in March–April 2010, approximately 18 months after the event. Linear and ordinal regression and path analysis are used as methods of analyses. The school shootings in Jokela and Kauhajoki were extremely disturbing events, which deeply affected the communities involved. However, based on the results collected, community responses to mass violence between the two localities were different. An increase in social solidarity appears to apply in the case of the Jokela community, but not in the case of the Kauhajoki community. Thus a criminal event does not necessarily impact the wider community. Every empirical finding is most likely related to different contextual and event-specific factors. Beyond this, community responses to mass violence in Jokela also indicated that the incident was related to a more general sense of insecurity and was also associating with perceived community deterioration and further suggests that responses to mass violence may have both integrating and disintegrating effects. Moreover, community responses to mass violence should also be examined in relation to broader social anxieties and as a proxy for generalized insecurity. Community response is an emotive process and incident related feelings are perhaps projected onto other identifiable concerns. However, this may open the door for social errors and, despite integrative effects, this may also have negative consequences within the neighborhood. The individual approach suggests that women are more fearful than men when a threat refers to violent crime. Young women (aged 18–34) were the most worried age and gender group as concerns perception of threat from mass violence at schools compared to young men (aged 18–34), who were also the least worried age and gender group when compared to older men. It was also found that concerns about mass violence were stronger among respondents with the lowest level of monthly household income compared to financially better-off respondents. Perhaps more importantly, responses to mass violence were affected by the emotional proximity to the event; and worry about the recurrence of school shootings was stronger among respondents who either were a parent of a school-aged child, or knew a victim. Finally, results indicate that psychological wellbeing is an important individual level factor. Respondents who expressed depressed mood consistently expressed their concerns about mass violence and community deterioration. Systematic assessments of the impact of school shooting events on communities are therefore needed. This requires the consolidation of community and individual approaches. Comparative study designs would further benefit from international collaboration across disciplines. Extreme school violence has also become a national concern and deeper understanding of crime related anxieties in contemporary Finland also requires community-based surveys.

A Bayesian Approach for Forecasting Heat Load in a District Heating System

The growing population in cities increases the energy demand and affects the environment by increasing carbon emissions. Information and communications technology solutions which enable energy optimization are needed to address this growing energy demand in cities and to reduce carbon emissions. District heating systems optimize the energy production by reusing waste energy with combined heat and power plants. Forecasting the heat load demand in residential buildings assists in optimizing energy production and consumption in a district heating system. However, the presence of a large number of factors such as weather forecast, district heating operational parameters and user behavioural parameters, make heat load forecasting a challenging task. This thesis proposes a probabilistic machine learning model using a Naive Bayes classifier, to forecast the hourly heat load demand for three residential buildings in the city of Skellefteå, Sweden over a period of winter and spring seasons. The district heating data collected from the sensors equipped at the residential buildings in Skellefteå, is utilized to build the Bayesian network to forecast the heat load demand for horizons of 1, 2, 3, 6 and 24 hours. The proposed model is validated by using four cases to study the influence of various parameters on the heat load forecast by carrying out trace driven analysis in Weka and GeNIe. Results show that current heat load consumption and outdoor temperature forecast are the two parameters with most influence on the heat load forecast. The proposed model achieves average accuracies of 81.23 % and 76.74 % for a forecast horizon of 1 hour in the three buildings for winter and spring seasons respectively. The model also achieves an average accuracy of 77.97 % for three buildings across both seasons for the forecast horizon of 1 hour by utilizing only 10 % of the training data. The results indicate that even a simple model like Naive Bayes classifier can forecast the heat load demand by utilizing less training data.

Bayesian analysis of SEIR epidemic models

This thesis concerns the analysis of epidemic models. We adopt the Bayesian paradigm and develop suitable Markov Chain Monte Carlo (MCMC) algorithms. This is done by considering an Ebola outbreak in the Democratic Republic of Congo, former Zaïre, 1995 as a case of SEIR epidemic models. We model the Ebola epidemic deterministically using ODEs and stochastically through SDEs to take into account a possible bias in each compartment. Since the model has unknown parameters, we use different methods to estimate them such as least squares, maximum likelihood and MCMC. The motivation behind choosing MCMC over other existing methods in this thesis is that it has the ability to tackle complicated nonlinear problems with large number of parameters. First, in a deterministic Ebola model, we compute the likelihood function by sum of square of residuals method and estimate parameters using the LSQ and MCMC methods. We sample parameters and then use them to calculate the basic reproduction number and to study the disease-free equilibrium. From the sampled chain from the posterior, we test the convergence diagnostic and confirm the viability of the model. The results show that the Ebola model fits the observed onset data with high precision, and all the unknown model parameters are well identified. Second, we convert the ODE model into a SDE Ebola model. We compute the likelihood function using extended Kalman filter (EKF) and estimate parameters again. The motivation of using the SDE formulation here is to consider the impact of modelling errors. Moreover, the EKF approach allows us to formulate a filtered likelihood for the parameters of such a stochastic model. We use the MCMC procedure to attain the posterior distributions of the parameters of the SDE Ebola model drift and diffusion parts. In this thesis, we analyse two cases: (1) the model error covariance matrix of the dynamic noise is close to zero , i.e. only small stochasticity added into the model. The results are then similar to the ones got from deterministic Ebola model, even if methods of computing the likelihood function are different (2) the model error covariance matrix is different from zero, i.e. a considerable stochasticity is introduced into the Ebola model. This accounts for the situation where we would know that the model is not exact. As a results, we obtain parameter posteriors with larger variances. Consequently, the model predictions then show larger uncertainties, in accordance with the assumption of an incomplete model.

Macro- and Microevolution of Languages: Exploring Linguistic Divergence with Approaches from Evolutionary Biology

There are more than 7000 languages in the world, and many of these have emerged through linguistic divergence. While questions related to the drivers of linguistic diversity have been studied before, including studies with quantitative methods, there is no consensus as to which factors drive linguistic divergence, and how. In the thesis, I have studied linguistic divergence with a multidisciplinary approach, applying the framework and quantitative methods of evolutionary biology to language data. With quantitative methods, large datasets may be analyzed objectively, while approaches from evolutionary biology make it possible to revisit old questions (related to, for example, the shape of the phylogeny) with new methods, and adopt novel perspectives to pose novel questions. My chief focus was on the effects exerted on the speakers of a language by environmental and cultural factors. My approach was thus an ecological one, in the sense that I was interested in how the local environment affects humans and whether this human-environment connection plays a possible role in the divergence process. I studied this question in relation to the Uralic language family and to the dialects of Finnish, thus covering two different levels of divergence. However, as the Uralic languages have not previously been studied using quantitative phylogenetic methods, nor have population genetic methods been previously applied to any dialect data, I first evaluated the applicability of these biological methods to language data. I found the biological methodology to be applicable to language data, as my results were rather similar to traditional views as to both the shape of the Uralic phylogeny and the division of Finnish dialects. I also found environmental conditions, or changes in them, to be plausible inducers of linguistic divergence: whether in the first steps in the divergence process, i.e. dialect divergence, or on a large scale with the entire language family. My findings concerning Finnish dialects led me to conclude that the functional connection between linguistic divergence and environmental conditions may arise through human cultural adaptation to varying environmental conditions. This is also one possible explanation on the scale of the Uralic language family as a whole. The results of the thesis bring insights on several different issues in both a local and a global context. First, they shed light on the emergence of the Finnish dialects. If the approach used in the thesis is applied to the dialects of other languages, broader generalizations may be drawn as to the inducers of linguistic divergence. This again brings us closer to understanding the global patterns of linguistic diversity. Secondly, the quantitative phylogeny of the Uralic languages, with estimated times of language divergences, yields another hypothesis as to the shape and age of the language family tree. In addition, the Uralic languages can now be added to the growing list of language families studied with quantitative methods. This will allow broader inferences as to global patterns of language evolution, and more language families can be included in constructing the tree of the world’s languages. Studying history through language, however, is only one way to illuminate the human past. Therefore, thirdly, the findings of the thesis, when combined with studies of other language families, and those for example in genetics and archaeology, bring us again closer to an understanding of human history.

Energy Efficiency in Electrical Drive Systems: Technical potentials, barriers and policy approaches for small and medium industries in Ghana

Today industries and commerce in Ghana are facing enormous energy challenge. The pressure is on for industries to reduce energy consumption, lower carbon emissions and provide se-cured power supply. Industrial electric motor energy efficiency improvement is one of the most important tools to reduce global warming threat and reduce electricity bills. In order to develop a strategic industrial energy efficiency policy, it is therefore necessary to study the barriers that inhibit the implementation of cost – effective energy efficiency measures and the driving forces that promote the implementation. The aim of this thesis is to analyse the energy consumption pattern of electric motors, study factors that promote or inhibit energy efficiency improvements in EMDS and provide cost – effective solutions that improve energy efficiency to bridge the existing energy efficiency gap in the surveyed industries. The results from this thesis has revealed that, the existence of low energy efficiency in motor-driven systems in the surveyed industries were due to poor maintenance practices, absence of standards, power quality issues, lack of access to capital and limited awareness to the im-portance of energy efficiency improvements in EMDS. However, based on the results pre-sented in this thesis, a policy approach towards industrial SMEs should primarily include dis-counted or free energy audit in providing the industries with the necessary information on potential energy efficiency measures, practice best motor management programmes and estab-lish a minimum energy performance standard (MEPS) for motors imported into the country. The thesis has also shown that education and capacity development programmes, financial incentives and system optimization are effective means to promote energy efficiency in elec-tric motor – driven systems in industrial SMEs in Ghana