Distribution and adhesion-promoting activity of alpha4 and alpha5 chain laminins in human endothelia and carcinomas

Basement membranes are specialized sheets of extracellular matrix found in contact with epithelia, endothelia, and certain isolated cells. They support tissue architecture and regulate cell behaviour. Laminins are among the main constituents of basement membranes. Due to differences between laminin isoforms, laminins confer structural and functional diversity to basement membranes. The first aim of this study was to gain insights into the potential functions of the then least characterized laminins, alpha4 chain laminins, by evaluating their distribution in human tissues. We thus created a monoclonal antibody specific for laminin alpha4 chain. By immunohistochemistry, alpha4 chain laminins were primarily localized to basement membranes of blood vessel endothelia, skeletal, heart, and smooth muscle cells, nerves, and adipocytes. In addition, alpha4 chain laminins were found in the region of certain epithelial basement membranes in the epidermis, salivary gland, pancreas, esophagus, stomach, intestine, and kidney. Because of the consistent presence of alpha4 chain laminins in endothelial basement membranes of blood vessels, we evaluated the potential roles of endothelial laminins in blood vessels, lymphatic vessels, and carcinomas. Human endothelial cells produced alpha4 and alpha5 chain laminins. In quantitative and morphological adhesion assays, human endothelial cells barely adhered to alpha4 chain-containing laminin-411. The weak interaction of endothelial cells with laminin-411 appeared to be mediated by alpha6beta1 integrin. The alpha5 chain-containing laminin-511 promoted endothelial cell adhesion better than laminin-411, but it did not promote the formation of cell-extracellular matrix adhesion complexes. The adhesion of endothelial cells to laminin-511 appeared to be mediated by Lutheran glycoprotein together with beta1 and alphavbeta3 integrins. The results suggest that these laminins may induce a migratory phenotype in endothelial cells. In lymphatic capillaries, endothelial basement membranes showed immunoreactivity for laminin alpha4, beta1, beta2, and gamma1 chains, type IV and XVIII collagens, and nidogen-1. Considering the assumed inability of alpha4 chain laminins to polymerize and to promote basement membrane assembly, the findings may in part explain the incomplete basement membrane formation in these vessels. Lymphatic capillaries of ovarian carcinomas showed immunoreactivity also for laminin alpha5 chain and its receptor Lutheran glycoprotein, emphasizing a difference between normal and ovarian carcinoma lymphatic capillaries. In renal cell carcinomas, immunoreactivity for laminin alpha4 chain was found in stroma and basement membranes of blood vessels. In most tumours, immunoreactivity for laminin alpha4 chain was also observed in the basement membrane region of tumour cell islets. Renal carcinoma cells produced alpha4 chain laminins. Laminin-411 did not promote adhesion of renal carcinoma cells, but inhibited their adhesion to fibronectin. Renal carcinoma cells migrated more on laminin-411 than on fibronectin. The results suggest that alpha4 chain laminins have a counteradhesive function, and may thus have a role in detachment and invasion of renal carcinoma cells.

Automatic Lameness Detection in a Milking Robot : Instrumentation, measurement software, algorithms for data analysis and a neural network model

The aim of this thesis is to develop a fully automatic lameness detection system that operates in a milking robot. The instrumentation, measurement software, algorithms for data analysis and a neural network model for lameness detection were developed. Automatic milking has become a common practice in dairy husbandry, and in the year 2006 about 4000 farms worldwide used over 6000 milking robots. There is a worldwide movement with the objective of fully automating every process from feeding to milking. Increase in automation is a consequence of increasing farm sizes, the demand for more efficient production and the growth of labour costs. As the level of automation increases, the time that the cattle keeper uses for monitoring animals often decreases. This has created a need for systems for automatically monitoring the health of farm animals. The popularity of milking robots also offers a new and unique possibility to monitor animals in a single confined space up to four times daily. Lameness is a crucial welfare issue in the modern dairy industry. Limb disorders cause serious welfare, health and economic problems especially in loose housing of cattle. Lameness causes losses in milk production and leads to early culling of animals. These costs could be reduced with early identification and treatment. At present, only a few methods for automatically detecting lameness have been developed, and the most common methods used for lameness detection and assessment are various visual locomotion scoring systems. The problem with locomotion scoring is that it needs experience to be conducted properly, it is labour intensive as an on-farm method and the results are subjective. A four balance system for measuring the leg load distribution of dairy cows during milking in order to detect lameness was developed and set up in the University of Helsinki Research farm Suitia. The leg weights of 73 cows were successfully recorded during almost 10,000 robotic milkings over a period of 5 months. The cows were locomotion scored weekly, and the lame cows were inspected clinically for hoof lesions. Unsuccessful measurements, caused by cows standing outside the balances, were removed from the data with a special algorithm, and the mean leg loads and the number of kicks during milking was calculated. In order to develop an expert system to automatically detect lameness cases, a model was needed. A probabilistic neural network (PNN) classifier model was chosen for the task. The data was divided in two parts and 5,074 measurements from 37 cows were used to train the model. The operation of the model was evaluated for its ability to detect lameness in the validating dataset, which had 4,868 measurements from 36 cows. The model was able to classify 96% of the measurements correctly as sound or lame cows, and 100% of the lameness cases in the validation data were identified. The number of measurements causing false alarms was 1.1%. The developed model has the potential to be used for on-farm decision support and can be used in a real-time lameness monitoring system.

On probability-based inference under data missing by design

Whether a statistician wants to complement a probability model for observed data with a prior distribution and carry out fully probabilistic inference, or base the inference only on the likelihood function, may be a fundamental question in theory, but in practice it may well be of less importance if the likelihood contains much more information than the prior. Maximum likelihood inference can be justified as a Gaussian approximation at the posterior mode, using flat priors. However, in situations where parametric assumptions in standard statistical models would be too rigid, more flexible model formulation, combined with fully probabilistic inference, can be achieved using hierarchical Bayesian parametrization. This work includes five articles, all of which apply probability modeling under various problems involving incomplete observation. Three of the papers apply maximum likelihood estimation and two of them hierarchical Bayesian modeling. Because maximum likelihood may be presented as a special case of Bayesian inference, but not the other way round, in the introductory part of this work we present a framework for probability-based inference using only Bayesian concepts. We also re-derive some results presented in the original articles using the toolbox equipped herein, to show that they are also justifiable under this more general framework. Here the assumption of exchangeability and de Finetti's representation theorem are applied repeatedly for justifying the use of standard parametric probability models with conditionally independent likelihood contributions. It is argued that this same reasoning can be applied also under sampling from a finite population. The main emphasis here is in probability-based inference under incomplete observation due to study design. This is illustrated using a generic two-phase cohort sampling design as an example. The alternative approaches presented for analysis of such a design are full likelihood, which utilizes all observed information, and conditional likelihood, which is restricted to a completely observed set, conditioning on the rule that generated that set. Conditional likelihood inference is also applied for a joint analysis of prevalence and incidence data, a situation subject to both left censoring and left truncation. Other topics covered are model uncertainty and causal inference using posterior predictive distributions. We formulate a non-parametric monotonic regression model for one or more covariates and a Bayesian estimation procedure, and apply the model in the context of optimal sequential treatment regimes, demonstrating that inference based on posterior predictive distributions is feasible also in this case.

Bayesian Inference for Retrospective Population Genetics Models Using Markov Chain Monte Carlo Methods

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.

Composition operators, Aleksandrov measures and value distribution of analytic maps in the unit disc

A composition operator is a linear operator that precomposes any given function with another function, which is held fixed and called the symbol of the composition operator. This dissertation studies such operators and questions related to their theory in the case when the functions to be composed are analytic in the unit disc of the complex plane. Thus the subject of the dissertation lies at the intersection of analytic function theory and operator theory. The work contains three research articles. The first article is concerned with the value distribution of analytic functions. In the literature there are two different conditions which characterize when a composition operator is compact on the Hardy spaces of the unit disc. One condition is in terms of the classical Nevanlinna counting function, defined inside the disc, and the other condition involves a family of certain measures called the Aleksandrov (or Clark) measures and supported on the boundary of the disc. The article explains the connection between these two approaches from a function-theoretic point of view. It is shown that the Aleksandrov measures can be interpreted as kinds of boundary limits of the Nevanlinna counting function as one approaches the boundary from within the disc. The other two articles investigate the compactness properties of the difference of two composition operators, which is beneficial for understanding the structure of the set of all composition operators. The second article considers this question on the Hardy and related spaces of the disc, and employs Aleksandrov measures as its main tool. The results obtained generalize those existing for the case of a single composition operator. However, there are some peculiarities which do not occur in the theory of a single operator. The third article studies the compactness of the difference operator on the Bloch and Lipschitz spaces, improving and extending results given in the previous literature. Moreover, in this connection one obtains a general result which characterizes the compactness and weak compactness of the difference of two weighted composition operators on certain weighted Hardy-type spaces.

Computing the Stochastic Complexity of Simple Probabilistic Graphical Models

Minimum Description Length (MDL) is an information-theoretic principle that can be used for model selection and other statistical inference tasks. There are various ways to use the principle in practice. One theoretically valid way is to use the normalized maximum likelihood (NML) criterion. Due to computational difficulties, this approach has not been used very often. This thesis presents efficient floating-point algorithms that make it possible to compute the NML for multinomial, Naive Bayes and Bayesian forest models. None of the presented algorithms rely on asymptotic analysis and with the first two model classes we also discuss how to compute exact rational number solutions.

A Probabilistic Approach to the Primary Visual Cortex

What can the statistical structure of natural images teach us about the human brain? Even though the visual cortex is one of the most studied parts of the brain, surprisingly little is known about how exactly images are processed to leave us with a coherent percept of the world around us, so we can recognize a friend or drive on a crowded street without any effort. By constructing probabilistic models of natural images, the goal of this thesis is to understand the structure of the stimulus that is the raison d etre for the visual system. Following the hypothesis that the optimal processing has to be matched to the structure of that stimulus, we attempt to derive computational principles, features that the visual system should compute, and properties that cells in the visual system should have. Starting from machine learning techniques such as principal component analysis and independent component analysis we construct a variety of sta- tistical models to discover structure in natural images that can be linked to receptive field properties of neurons in primary visual cortex such as simple and complex cells. We show that by representing images with phase invariant, complex cell-like units, a better statistical description of the vi- sual environment is obtained than with linear simple cell units, and that complex cell pooling can be learned by estimating both layers of a two-layer model of natural images. We investigate how a simplified model of the processing in the retina, where adaptation and contrast normalization take place, is connected to the nat- ural stimulus statistics. Analyzing the effect that retinal gain control has on later cortical processing, we propose a novel method to perform gain control in a data-driven way. Finally we show how models like those pre- sented here can be extended to capture whole visual scenes rather than just small image patches. By using a Markov random field approach we can model images of arbitrary size, while still being able to estimate the model parameters from the data.

Effects of turbidity on feeding and distribution of fish

In aquatic systems, the ability of both the predator and prey to detect each other may be impaired by turbidity. This could lead to significant changes in the trophic interactions in the food web of lakes. Most fish use their vision for predation and the location of prey can be highly influenced by light level and clarity of the water environment. Turbidity is an optical property of water that causes light to be scattered and absorbed by particles and molecules. Turbidity is highly variable in lakes, due to seasonal changes in suspended sediments, algal blooms and wind-driven suspension of sediments especially in shallow waters. There is evidence that human activity has increased erosion leading to increased turbidity in aquatic systems. Turbidity could also play a significant role in distribution of fish. Turbidity could act as a cover for small fish and reduce predation risk. Diel horizontal migration by fish is common in shallow lakes and is considered as consequences of either optimal foraging behaviour for food or as a trade-off between foraging and predator avoidance. In turbid lakes, diel horizontal migration patterns could differ since turbidity can act as a refuge itself and affect the predator-prey interactions. Laboratory experiments were conducted with perch (Perca fluviatilis L.) and white bream (Abramis björkna (L.)) to clarify the effects of turbidity on their feeding. Additionally to clarify the effects of turbidity on predator preying on different types of prey, pikeperch larvae (Sander lucioperca (L.)), Daphnia pulex (Leydig), Sida crystallina (O.F. Müller), and Chaoborus flavicans (Meigen) were used as prey in different experiments. To clarify the role of turbidity in distribution and diel horizontal migration of perch, roach (Rutilus rutilus (L.)) and white bream, field studies were conducted in shallow turbid lakes. A clear and a turbid shallow lake were compared to investigate distribution of perch and roach in these two lakes in a 15-year study period. Feeding efficiency of perch and white bream was not significantly affected with increasing clay turbidity up to 50 NTU. The perch experiments with pikeperch larvae suggested that clay turbidity could act as a refuge especially at turbidity levels higher than 50 NTU. Perch experiments with different prey types suggested that pikeperch larvae probably use turbidity as a refuge better compared to Daphnia. Increase in turbidity probably has stronger affect on perch predating on plant-attached prey. The main findings of the thesis show that turbidity can play a significant role in distribution of fish. Perch and roach could use turbidity as refuge when macrophytes disappear while small perch may also use high turbidity as refuge when macrophytes are present. Floating-leaved macrophytes are probably good refuges for small fish in clay-turbid lakes and provide a certain level of turbidity and not too complex structure for refuge. The results give light to the predator-prey interactions in turbid environments. Turbidity of water should be taken in to account when studying the diel horizontal migrations and distribution of fish in shallow lakes.

Phospholipids of lipid-containing bacteriophages and their transbilayer distribution

In this study we used electro-spray ionization mass-spectrometry to determine phospholipid class and molecular species compositions in bacteriophages PM2, PRD1, Bam35 and phi6 as well as their hosts. To obtain compositional data of the individual leaflets, phospholipid transbilayer distribution in the viral membranes was studied. We found that 1) the membranes of all studied bacteriophage are enriched in PG as compared to the host membranes, 2) molecular species compositions in the phage and host membranes are similar, and 3) phospholipids in the viral membranes are distributed asymmetrically with phosphatidylglycerol enriched in the outer leaflet and phosphatidylethanolamine in the inner one (except Bam35). Alternative models for selective incorporation of phospholipids to phages and for the origins of the asymmetric phospholipid transbilayer distribution are discussed. Notably, the present data are also useful when constructing high resolution structural models of bacteriophages, since diffraction methods cannot provide a detailed structure of the membrane due to high motility of the lipids and lack of symmetric organization of membrane proteins.

Distribution of non-biting midges (Diptera, Chironomidae) in subarctic lakes of Finnish Lapland applications in lake classification and palaeolimnology

Climate change contributes directly or indirectly to changes in species distributions, and there is very high confidence that recent climate warming is already affecting ecosystems. The Arctic has already experienced the greatest regional warming in recent decades, and the trend is continuing. However, studies on the northern ecosystems are scarce compared to more southerly regions. Better understanding of the past and present environmental change is needed to be able to forecast the future. Multivariate methods were used to explore the distributional patterns of chironomids in 50 shallow (≤ 10m) lakes in relation to 24 variables determined in northern Fennoscandia at the ecotonal area from the boreal forest in the south to the orohemiarctic zone in the north. Highest taxon richness was noted at middle elevations around 400 m a.s.l. Significantly lower values were observed from cold lakes situated in the tundra zone. Lake water alkalinity had the strongest positive correlation with the taxon richness. Many taxa had preference for lakes either on tundra area or forested area. The variation in the chironomid abundance data was best correlated with sediment organic content (LOI), lake water total organic carbon content, pH and air temperature, with LOI being the strongest variable. Three major lake groups were separated on the basis of their chironomid assemblages: (i) small and shallow organic-rich lakes, (ii) large and base-rich lakes, and (iii) cold and clear oligotrophic tundra lakes. Environmental variables best discriminating the lake groups were LOI, taxon richness, and Mg. When repeated, this kind of an approach could be useful and efficient in monitoring the effects of global change on species ranges. Many species of fast spreading insects, including chironomids, show a remarkable ability to track environmental changes. Based on this ability, past environmental conditions have been reconstructed using their chitinous remains in the lake sediment profiles. In order to study the Holocene environmental history of subarctic aquatic systems, and quantitatively reconstruct the past temperatures at or near the treeline, long sediment cores covering the last 10000 years (the Holocene) were collected from three lakes. Lower temperature values than expected based on the presence of pine in the catchment during the mid-Holocene were reconstructed from a lake with great water volume and depth. The lake provided thermal refuge for profundal, cold adapted taxa during the warm period. In a shallow lake, the decrease in the reconstructed temperatures during the late Holocene may reflect the indirect response of the midges to climate change through, e.g., pH change. The results from three lakes indicated that the response of chironomids to climate have been more or less indirect. However, concurrent shifts in assemblages of chironomids and vegetation in two lakes during the Holocene time period indicated that the midges together with the terrestrial vegetation had responded to the same ultimate cause, which most likely was the Holocene climate change. This was also supported by the similarity in the long-term trends in faunal succession for the chironomid assemblages in several lakes in the area. In northern Finnish Lapland the distribution of chironomids were significantly correlated with physical and limnological factors that are most likely to change as a result of future climate change. The indirect and individualistic response of aquatic systems, as reconstructed using the chironomid assemblages, to the climate change in the past suggests that in the future, the lake ecosystems in the north do not respond in one predictable way to the global climate change. Lakes in the north may respond to global climate change in various ways that are dependent on the initial characters of the catchment area and the lake.