Smoking during pregnancy and fetal brain development

Background: Although the knowledge of adverse effects of smoking during pregnancy has increased in recent years, more research is needed to gain a better understanding of the effects of smoking during pregnancy. Smoking exposure is the most common preventable factor that causes adverse pregnancy outcomes. Aims and Methods: First, data on smoking habits during pregnancy from the Nordic Medical Birth Registers was used to study the national differences in trends of smoking during pregnancy. Second, the effects of prenatal smoking exposure on fetal brain development, assessed by brain MRI at term age, were studied by using data from the multidisciplinary PIPARI Study consisting of a 6-year cohort of VLBW/VLGA infants (n = 232, of which 18.1% were exposed to prenatal smoking) born in Turku University Hospital, Finland. Third, the effects of prenatal smoking exposure on psychiatric morbidity and use of psychotropic medication were studied in a cohort of children born from 1987–1989 in Finland (n = 175,869, of which 15.3% were exposed). The data used were obtained from population-based longitudinal registers from the National Institute of Health and Welfare, the Statistics Finland, and the Finnish Social Insurance Institution. Results: Smoking rates during pregnancy differed considerably between the countries. Smoking rates were highest among teenagers and women with lower socioeconomic positions. The smoking prevalence was found to be increasing among teenagers in both Finland and Norway. Prenatal smoking exposure was associated with smaller frontal lobe and cerebellar volumes in preterm infants. A clear association was found between prenatal smoking exposure and psychiatric morbidity treated with specialized hospital care and the use of various psychotropic medications. Conclusions: Prenatal smoking exposure had adverse effects on fetal brain development. These effects might explain part of the association found between smoking exposure and psychiatric problems in later life. Our study suggests that prenatal smoking exposure is linked with both mild and severe psychiatric problems. This study emphasizes the importance of efforts to reduce smoking during pregnancy.

Players of Th-cell differentiation and immune dysregulation – focus on GIMAP family genes

The balance of T helper (Th) cell differentiation is the fundamental process that ensures that the immune system functions correctly and effectively. The differentiation is a fine tuned event, the outcome of which is driven by activation of the T-cell in response to recognition of the specific antigen presented. The co-stimulatory signals from the surrounding cytokine milieu help to determine the outcome. An impairment in the differentiation processes may lead to an imbalance in immune responses and lead to immune-mediated pathologies. An over-representation of Th1 type cytokine producing cells leads to tissue-specific inflammation and autoimmunity, and excessive Th2 response is causative for atopy, asthma and allergy. The major factors of Th-cell differentiation and in the related disease mechanisms have been extensively studied, but the fine tuning of these processes by the other factors cannot be discarded. In the work presented in this thesis, the association of T-cell receptor costimulatory molecules CTLA4 and ICOS with autoimmune diabetes were studied. The underlying aspect of the study was to explore the polymorphism in these genes with the different disease rates observed in two geographically close populations. The main focus of this thesis was set on a GTPase of the immunity associated protein (GIMAP) family of small GTPases. GIMAP genes and proteins are differentially regulated during human Th-cell differentiation and have been linked to immune-mediated disorders. GIMAP4 is believed to contribute to the immunological balance via its role in T-cell survival. To elucidate the function of GIMAP4 and GIMAP5 and their role in human immunity, a study combining genetic association in different immunological diseases and complementing functional analyses was conducted. The study revealed interesting connections with the high susceptibility risk genes. In addition, the role of GIMAP4 during Th1-cell differentiation was investigated. A novel function of GIMAP4 in relation to cytokine secretion was discovered. Further assessment of GIMAP4 and GIMAP5 effect for the transcriptomic profile of differentiating Th1-cells revealed new insights for GIMAP4 and GIMAP5 function.

Clinical and genetic markers in disease progression – a study among subjects with obstructive lung diseases

Asthma, COPD, and asthma and COPD overlap syndrome (ACOS) are chronic pulmonary diseases with an obstructive component. In COPD, the obstruction is irreversible and the disease is progressive. The aim of the study was to define and analyze factors that affected disease progression and patients’ well-being, prognosis and mortality in Chronic Airway Disease (CAD) cohort. The main focus was on COPD and ACOS patients. Retrospective data from medical records was combined with genetic and prospective follow-up data. Smoking is the biggest risk factor for COPD and even after the diagnosis of the disease, smoking plays an important role in disease development and patient’s prognosis. Sixty percent of the COPD patients had succeeded in smoking cessation. Patients who had managed to quit smoking had lower mortality rates and less psychiatric diseases and alcohol abuse although they were older and had more cardiovascular diseases than patients who continued smoking. Genetic polymorphism rs1051730 in the nicotinic acethylcholine receptor gene (CHRNA3/5) associated with heavy smoking, cancer prevalence and mortality in two Finnish independent cohorts consisting of COPD patients and male smokers. Challenges in smoking cessation and higher mortality rates may be partly due to individual patient’s genetic composition. Approximately 50% of COPD patients are physically inactive and the proportion was higher among current smokers. Physically active and inactive patients didn’t differ from each other in regard to age, gender or comorbidities. Bronchial obstruction explained inactivity only in severe disease. Subjective sensation of dyspnea, however, had very strong association to inactivity and was also associated to low health related quality of life (HRQoL). ACOS patients had a significantly lower HRQoL than either the patients with asthma or with COPD even though they were younger than COPD patients, had better lung functions and smaller tobacco exposure.

Visual Measurement and Modelling of Tool Wear in Rough Turning

Huolimatta korkeasta automaatioasteesta sorvausteollisuudessa, muutama keskeinen ongelma estää sorvauksen täydellisen automatisoinnin. Yksi näistä ongelmista on työkalun kuluminen. Tämä työ keskittyy toteuttamaan automaattisen järjestelmän kulumisen, erityisesti viistekulumisen, mittaukseen konenäön avulla. Kulumisen mittausjärjestelmä poistaa manuaalisen mittauksen tarpeen ja minimoi ajan, joka käytetään työkalun kulumisen mittaukseen. Mittauksen lisäksi tutkitaan kulumisen mallinnusta sekä ennustamista. Automaattinen mittausjärjestelmä sijoitettiin sorvin sisälle ja järjestelmä integroitiin onnistuneesti ulkopuolisten järjestelmien kanssa. Tehdyt kokeet osoittivat, että mittausjärjestelmä kykenee mittaamaan työkalun kulumisen järjestelmän oikeassa ympäristössä. Mittausjärjestelmä pystyy myös kestämään häiriöitä, jotka ovat konenäköjärjestelmille yleisiä. Työkalun kulumista mallinnusta tutkittiin useilla eri menetelmillä. Näihin kuuluivat muiden muassa neuroverkot ja tukivektoriregressio. Kokeet osoittivat, että tutkitut mallit pystyivät ennustamaan työkalun kulumisasteen käytetyn ajan perusteella. Parhaan tuloksen antoivat neuroverkot Bayesiläisellä regularisoinnilla.

A Diagnostic view of the Genetics of CASADIL

Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL, OMIM #125310) is an inherited vascular disease. The main symptoms include migraineous headache, recurrent strokes and progressive cognitive impairment. CADASIL is caused by mutations in the NOTCH3 gene which result in degeneration of vascular smooth muscle cells, arteriolar stenosis and impaired cerebral blood flow. The aims of this study were assessment of the genetic background of Finnish and Swedish CADASIL patients, analysis of genetic and environmental factors that may influence the phenotype, and identification of the optimal diagnostic strategy. The majority of Finnish CADASIL patients carry the p.Arg133Cys mutation. Haplotype analysis of 18 families revealed a region of linkage disequilibrium around the NOTCH3 locus, which is evidence for a founder effect and a common ancestral mutation. Despite the same mutational background, the clinical course of CADASIL is highly variable between and even within families. The association of several genetic factors with the phenotypic variation was investigated in 120 CADASIL patients. Apolipoprotein E allele 4 was associated with earlier occurrence of strokes, especially in younger patients. Study of a pair of monozygotic twins with CADASIL revealed environmental factors which may influence the phenotype, i.e. smoking, statin medication and physical activity. Knowledge of these factors is useful, since life-style choices may influence the disease progression. The clinical CADASIL diagnosis can be confirmed by detection of either the NOTCH3 mutation or granular osmiophilic material by electron microscopy in skin biopsy, although the sensitivity estimates have been contradictory. Comparison of these two methods in a group of 131 diagnostic cases from Finland, Sweden and France demonstrated that both methods are highly sensitive and reliable.

Demographic Modelling of Human Population Growth

This work presents models and methods that have been used in producing forecasts of population growth. The work is intended to emphasize the reliability bounds of the model forecasts. Leslie model and various versions of logistic population models are presented. References to literature and several studies are given. A lot of relevant methodology has been developed in biological sciences. The Leslie modelling approach involves the use of current trends in mortality,fertility, migration and emigration. The model treats population divided in age groups and the model is given as a recursive system. Other group of models is based on straightforward extrapolation of census data. Trajectories of simple exponential growth function and logistic models are used to produce the forecast. The work presents the basics of Leslie type modelling and the logistic models, including multi- parameter logistic functions. The latter model is also analysed from model reliability point of view. Bayesian approach and MCMC method are used to create error bounds of the model predictions.

Oxidized LDL and physical fitness in healthy young men: associations with body composition, smoking, metabolic syndrome and androgen status

Background: In the past, oxidized low density lipoprotein (ox-LDL) has been associated with an unbeneficial lipid profile. This atherogenic lipid profile increases the risk of atherosclerotic cardiovascular diseases. Physical fitness has substantial effect on serum lipoprotein concentration as well as body composition and humoral responses, however interrelationships between ox-LDL and physical fitness have not been widely examined in a nationally representative sample. Aims: This thesis evaluates how cardiorespiratory and muscular fitness associate with ox-LDL lipids and how the other known risk factors of atherosclerosis might alter these associations. Subjects and Methods: The study cohort consisted of 846 healthy young males (mean age 25.1, SD 4.6) who were gathered by voluntary nationwide recruitment. Each participant conducted a series of physical fitness tests (cardiorespiratory and muscular fitness) and answered a detailed questionnaire that included lifestyle habits (i.e. smoking and leisuretime physical activity). Venous blood samples including ox-LDL and serum lipids were also collected. Results: Higher levels of ox-LDL were found in overweight and obese men, however, high cardiorespiratory fitness seemed to protect the overweight from high levels of ox-LDL. Young men who smoked and had poor cardiorespiratory or muscular fitness possessed a higher concentration of ox-LDL lipids when compared to comparable levels of cardiorespiratory or muscular fitness non-smoking young men. Metabolic syndrome was associated with increased levels of ox-LDL and high levels of ox-LDL combined with poor cardiorespiratory and abdominal muscle fitness seems to predict metabolic syndrome in young men. Also, participants with poor cardiorespiratory fitness and low levels of testosterone had higher levels of ox-LDL when compared to participants with high cardiorespiratory fitness / low testosterone as well as those with poor cardiorespiratory fitness / high testosterone. Conclusions: Good cardiorespiratory and muscular fitness protects young men from increased levels of ox-LDL lipids. This association was discovered in young men who were categorized as being overweight, smokers, metabolic syndrome or with low levels of testosterone. Being fit seems to prevent higher levels of ox-LDL, even in young healthy

Modelling of potentiometric ion sensors

Potentiometric ion sensors are a very important subgroup of electrochemical sensors, very attractive for practical applications due to their small size, portability, low-energy consumption, relatively low cost and not changing the sample composition. They are investigated by the researchers from many fields of science. The continuous development of this field creates the necessity for a detailed description of sensor response and the electrochemical processes important in the practical applications of ion sensors. The aim of this thesis is to present the existing models available for the description of potentiometric ion sensors as well as their applicability and limitations. This includes the description of the diffusion potential occurring at the reference electrodes. The wide range of existing models, from most idealised phase boundary models to most general models, including migration, is discussed. This work concentrates on the advanced modelling of ion sensors, namely the Nernst-Planck-Poisson (NPP) model, which is the most general of the presented models, therefore the most widely applicable. It allows the modelling of the transport processes occurring in ion sensors and generating the potentiometric response. Details of the solution of the NPP model (including the numerical methods used) are shown. The comparisons between NPP and the more idealized models are presented. The applicability of the model to describe the formation of diffusion potential in reference electrode, the lower detection limit of both ion-exchanger and neutral carrier electrodes and the effect of the complexation in the membrane are discussed. The model was applied for the description of both types of electrodes, i.e. with the inner filling solution and solidcontact electrodes. The NPP model allows the electrochemical methods other than potentiometry to be described. Application of this model in Electrochemical Impedance Spectroscopy is discussed and a possible use in chrono-potentiometry is indicated. By combining the NPP model with evolutionary algorithms, namely Hierarchical Genetic Strategy (HGS), a novel method allowing the facilitation of the design of ion sensors was created. It is described in detail in this thesis and its possible applications in the field of ion sensors are indicated. Finally, some interesting effects occurring in the ion sensors (i.e. overshot response and influence of anionic sites) as well as the possible applications of NPP in biochemistry are described.

Multidata inverse problems and Bayesian solution methods in astronomy

Statistical analyses of measurements that can be described by statistical models are of essence in astronomy and in scientific inquiry in general. The sensitivity of such analyses, modelling approaches, and the consequent predictions, is sometimes highly dependent on the exact techniques applied, and improvements therein can result in significantly better understanding of the observed system of interest. Particularly, optimising the sensitivity of statistical techniques in detecting the faint signatures of low-mass planets orbiting the nearby stars is, together with improvements in instrumentation, essential in estimating the properties of the population of such planets, and in the race to detect Earth-analogs, i.e. planets that could support liquid water and, perhaps, life on their surfaces. We review the developments in Bayesian statistical techniques applicable to detections planets orbiting nearby stars and astronomical data analysis problems in general. We also discuss these techniques and demonstrate their usefulness by using various examples and detailed descriptions of the respective mathematics involved. We demonstrate the practical aspects of Bayesian statistical techniques by describing several algorithms and numerical techniques, as well as theoretical constructions, in the estimation of model parameters and in hypothesis testing. We also apply these algorithms to Doppler measurements of nearby stars to show how they can be used in practice to obtain as much information from the noisy data as possible. Bayesian statistical techniques are powerful tools in analysing and interpreting noisy data and should be preferred in practice whenever computational limitations are not too restrictive.

Scalable Feature Selection Applications for Genome-Wide Association Studies of Complex Diseases

Personalized medicine will revolutionize our capabilities to combat disease. Working toward this goal, a fundamental task is the deciphering of geneticvariants that are predictive of complex diseases. Modern studies, in the formof genome-wide association studies (GWAS) have afforded researchers with the opportunity to reveal new genotype-phenotype relationships through the extensive scanning of genetic variants. These studies typically contain over half a million genetic features for thousands of individuals. Examining this with methods other than univariate statistics is a challenging task requiring advanced algorithms that are scalable to the genome-wide level. In the future, next-generation sequencing studies (NGS) will contain an even larger number of common and rare variants. Machine learning-based feature selection algorithms have been shown to have the ability to effectively create predictive models for various genotype-phenotype relationships. This work explores the problem of selecting genetic variant subsets that are the most predictive of complex disease phenotypes through various feature selection methodologies, including filter, wrapper and embedded algorithms. The examined machine learning algorithms were demonstrated to not only be effective at predicting the disease phenotypes, but also doing so efficiently through the use of computational shortcuts. While much of the work was able to be run on high-end desktops, some work was further extended so that it could be implemented on parallel computers helping to assure that they will also scale to the NGS data sets. Further, these studies analyzed the relationships between various feature selection methods and demonstrated the need for careful testing when selecting an algorithm. It was shown that there is no universally optimal algorithm for variant selection in GWAS, but rather methodologies need to be selected based on the desired outcome, such as the number of features to be included in the prediction model. It was also demonstrated that without proper model validation, for example using nested cross-validation, the models can result in overly-optimistic prediction accuracies and decreased generalization ability. It is through the implementation and application of machine learning methods that one can extract predictive genotype–phenotype relationships and biological insights from genetic data sets.

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.

Sedentary behaviour and health with special reference to obesity and fatty liver in early midlife. The Cardiovascular risk in Young Finns Study

Background: Physical inactivity and positive energy balance pose a risk to health. They increase the risk of obesity and associated non-communicable diseases. Recently, also sedentary behaviour has been associated with obesity and non-communicable diseases. Nevertheless, it has been unclear which type of sedentary behaviour is the most harmful. It is also unknown whether the relationship of sedentary behaviour with obesity is truly independent of other factors, for example physical activity and diet. Longitudinal data are limited, and the direction of causality and the mechanism of action are still unknown. Aims: The aim of this study was 1) to identify the type of sedentary behaviour having the strongest association with obesity, 2) to explore the causal relationship of sedentary behaviour and weight increase, and 3) to additionally, investigate the relationship of sedentary behaviour with fatty liver. These were studied in cross-sectional and/or longitudinal settings using data from the Cardiovascular Risk in Young Finns Study. Special emphasis was put on the evaluation of a wide range of other lifestyle factors and risks for obesity and fatty liver. Subjects: 2,060 subjects (aged 33-50 years in 2011, of which 55 % were female) from the Cardiovascular Risk in Young Finns Study participating in follow-ups in 2001, 2007, and 2011. Measures: Self-reported time spent in various types of sedentary behaviour (I), or TV viewing time (I-III). Measured body weight, height and waist circumference (I-III), and genetic variants for high BMI (I). Fasting plasma concentrations of gamma-glutamyltransferase enzyme and triglyceride, calculated Fatty Liver Index (based on gamma-glutamyltransferase and triglyceride concentration, BMI and waist circumference), and the amount of intrahepatic fat measured with ultrasound (III). Self-reported leisure-time physical activity and active commuting, occupational physical activity, energy intake, diet, alcohol consumption, smoking, socioeconomic status, and sleep duration as possible confounders were considered (I-III). Results: TV viewing is the sedentary behaviour type that has the strongest association with obesity. Sedentary behaviour (TV viewing) precedes weight increase, and not the other way around. Sedentary behaviour (TV viewing) is associated with increased risk of fatty liver. Conclusions: Sedentary behaviour (especially high TV viewing time) is associated with increased risks of obesity and fatty liver. Intervention studies are needed to assess whether reduction of TV time would prevent obesity and fatty liver.