Microarrays in Molecular Profiling of Ewing Sarcoma Family of Tumors and CDKN2A Aberrations

Background: The Ewing sarcoma family of tumors (ESFT) are rare but highly malignant neoplasms that occur mainly in bone or but also in soft tissue. ESFT affects patients typically in their second decade of life, whereby children and adolescents bear the heaviest incidence burden. Despite recent advances in the clinical management of ESFT patients, their prognosis and survival are still disappointingly poor, especially in cases with metastasis. No targeted therapy for ESFT patients is currently available. Moreover, based merely on current clinical and biological characteristics, accurate classification of ESFT patients often fails at the time of diagnosis. Therefore, there is a constant need for novel molecular biomarkers to be applied in tandem with conventional parameters to further intensify ESFT risk-stratification and treatment selection, and ultimately to develop novel targeted therapies. In this context, a greater understanding of the genetics and immune characteristics of ESFT is needed. Aims: This study sought to open novel insights into gene copy number changes and gene expression in ESFT and, further, to enlighten the role of inflammation in ESFT. For this purpose, microarrays were used to provide gene-level information on a genomewide scale. In addition, this study focused on screening of 9p21.3 deletion sizes and frequencies in ESFT and, in another pediatric cancer, acute lymphocytic leukemia (ALL), in order to define more exact criteria for highrisk patient selection and to provide data for developing a more reliable diagnostic method to detect CDKN2A deletions. Results: In study I, 20 novel ESFT-associated suppressor genes and oncogenes were pinpointed using combined array CGH and expression analysis. In addition, interesting chromosomal rearrangements were identified: (1) Duplication of derivative chromosome der(22)(11;22) was detected in three ESFT patients. This duplication included the EWSR1-FLI1 fusion gene leading to increase in its copy number; (2) Cryptic amplifications on chromosomes 20 and 22 were detected, suggesting a novel translocation between chromosomes 20 and 22, which most probably produces a fusion between EWSR1 and NFATC2. In study II, bioinformatic analysis of ESFT expression profiles showed that inflammatory gene activation is detectable in ESFT patient samples and that the activation is characterized by macrophage gene expression. Most interestingly, ESFT patient samples were shown to express certain inflammatory genes that were prognostically significant. High local expression of C5 and JAK1 at the tumor site was shown to associate with favorable clinical outcome, whereas high local expression of IL8 was shown to be detrimental. Studies III and IV showed that the smallest overlapping region of deletion in 9p21.3 includes CDKN2A in all cases and that the length of this region is 12.2 kb in both Ewing sarcoma and ALL. Furthermore, our results showed that the most widely used commercial CDKN2A FISH probe creates false negative results in the narrowest microdeletion cases (<190 kb). Therefore, more accurate methods should be developed for the detection of deletions in the CDKN2A locus. Conclusions: This study provides novel insights into the genetic changes involved in the biology of ESFT, in the interaction between ESFT cells and immune system, and in the inactivation of CDKN2A. Novel ESFT biomarker genes identified in this study serve as a useful resource for future studies and in developing novel therapeutic strategies to improve the survival of patients with ESFT.

Estimation of local forest attributes, utilizing two-phase sampling and auxiliary data

This thesis examines the feasibility of a forest inventory method based on two-phase sampling in estimating forest attributes at the stand or substand levels for forest management purposes. The method is based on multi-source forest inventory combining auxiliary data consisting of remote sensing imagery or other geographic information and field measurements. Auxiliary data are utilized as first-phase data for covering all inventory units. Various methods were examined for improving the accuracy of the forest estimates. Pre-processing of auxiliary data in the form of correcting the spectral properties of aerial imagery was examined (I), as was the selection of aerial image features for estimating forest attributes (II). Various spatial units were compared for extracting image features in a remote sensing aided forest inventory utilizing very high resolution imagery (III). A number of data sources were combined and different weighting procedures were tested in estimating forest attributes (IV, V). Correction of the spectral properties of aerial images proved to be a straightforward and advantageous method for improving the correlation between the image features and the measured forest attributes. Testing different image features that can be extracted from aerial photographs (and other very high resolution images) showed that the images contain a wealth of relevant information that can be extracted only by utilizing the spatial organization of the image pixel values. Furthermore, careful selection of image features for the inventory task generally gives better results than inputting all extractable features to the estimation procedure. When the spatial units for extracting very high resolution image features were examined, an approach based on image segmentation generally showed advantages compared with a traditional sample plot-based approach. Combining several data sources resulted in more accurate estimates than any of the individual data sources alone. The best combined estimate can be derived by weighting the estimates produced by the individual data sources by the inverse values of their mean square errors. Despite the fact that the plot-level estimation accuracy in two-phase sampling inventory can be improved in many ways, the accuracy of forest estimates based mainly on single-view satellite and aerial imagery is a relatively poor basis for making stand-level management decisions.

Plant species diversity of buffer zones in agricultural landscapes: in search of determinants from the local to regional scale

Buffer zones are vegetated strip-edges of agricultural fields along watercourses. As linear habitats in agricultural ecosystems, buffer strips dominate and play a leading ecological role in many areas. This thesis focuses on the plant species diversity of the buffer zones in a Finnish agricultural landscape. The main objective of the present study is to identify the determinants of floral species diversity in arable buffer zones from local to regional levels. This study was conducted in a watershed area of a farmland landscape of southern Finland. The study area, Lepsämänjoki, is situated in the Nurmijärvi commune 30 km to the north of Helsinki, Finland. The biotope mosaics were mapped in GIS. A total of 59 buffer zones were surveyed, of which 29 buffer strips surveyed were also sampled by plot. Firstly, two diversity components (species richness and evenness) were investigated to determine whether the relationship between the two is equal and predictable. I found no correlation between species richness and evenness. The relationship between richness and evenness is unpredictable in a small-scale human-shaped ecosystem. Ordination and correlation analyses show that richness and evenness may result from different ecological processes, and thus should be considered separately. Species richness correlated negatively with phosphorus content, and species evenness correlated negatively with the ratio of organic carbon to total nitrogen in soil. The lack of a consistent pattern in the relationship between these two components may be due to site-specific variation in resource utilization by plant species. Within-habitat configuration (width, length, and area) were investigated to determine which is more effective for predicting species richness. More species per unit area increment could be obtained from widening the buffer strip than from lengthening it. The width of the strips is an effective determinant of plant species richness. The increase in species diversity with an increase in the width of buffer strips may be due to cross-sectional habitat gradients within the linear patches. This result can serve as a reference for policy makers, and has application value in agricultural management. In the framework of metacommunity theory, I found that both mass effect(connectivity) and species sorting (resource heterogeneity) were likely to explain species composition and diversity on a local and regional scale. The local and regional processes were interactively dominated by the degree to which dispersal perturbs local communities. In the lowly and intermediately connected regions, species sorting was of primary importance to explain species diversity, while the mass effect surpassed species sorting in the highly connected region. Increasing connectivity in communities containing high habitat heterogeneity can lead to the homogenization of local communities, and consequently, to lower regional diversity, while local species richness was unrelated to the habitat connectivity. Of all species found, Anthriscus sylvestris, Phalaris arundinacea, and Phleum pretense significantly responded to connectivity, and showed high abundance in the highly connected region. We suggest that these species may play a role in switching the force from local resources to regional connectivity shaping the community structure. On the landscape context level, the different responses of local species richness and evenness to landscape context were investigated. Seven landscape structural parameters served to indicate landscape context on five scales. On all scales but the smallest scales, the Shannon-Wiener diversity of land covers (H') correlated positively with the local richness. The factor (H') showed the highest correlation coefficients in species richness on the second largest scale. The edge density of arable field was the only predictor that correlated with species evenness on all scales, which showed the highest predictive power on the second smallest scale. The different predictive power of the factors on different scales showed a scaledependent relationship between the landscape context and local plant species diversity, and indicated that different ecological processes determine species richness and evenness. The local richness of species depends on a regional process on large scales, which may relate to the regional species pool, while species evenness depends on a fine- or coarse-grained farming system, which may relate to the patch quality of the habitats of field edges near the buffer strips. My results suggested some guidelines of species diversity conservation in the agricultural ecosystem. To maintain a high level of species diversity in the strips, a high level of phosphorus in strip soil should be avoided. Widening the strips is the most effective mean to improve species richness. Habitat connectivity is not always favorable to species diversity because increasing connectivity in communities containing high habitat heterogeneity can lead to the homogenization of local communities (beta diversity) and, consequently, to lower regional diversity. Overall, a synthesis of local and regional factors emerged as the model that best explain variations in plant species diversity. The studies also suggest that the effects of determinants on species diversity have a complex relationship with scale.

Spatial variation of climate and the impact of disturbances on local climate and forest recovery in northern Finland

The structure and function of northern ecosystems are strongly influenced by climate change and variability and by human-induced disturbances. The projected global change is likely to have a pronounced effect on the distribution and productivity of different species, generating large changes in the equilibrium at the tree-line. In turn, movement of the tree-line and the redistribution of species produce feedback to both the local and the regional climate. This research was initiated with the objective of examining the influence of natural conditions on the small-scale spatial variation of climate in Finnish Lapland, and to study the interaction and feedback mechanisms in the climate-disturbances-vegetation system near the climatological border of boreal forest. The high (1 km) resolution spatial variation of climate parameters over northern Finland was determined by applying the Kriging interpolation method that takes into account the effect of external forcing variables, i.e., geographical coordinates, elevation, sea and lake coverage. Of all the natural factors shaping the climate, the geographical position, local topography and altitude proved to be the determining ones. Spatial analyses of temperature- and precipitation-derived parameters based on a 30-year dataset (1971-2000) provide a detailed description of the local climate. Maps of the mean, maximum and minimum temperatures, the frost-free period and the growing season indicate that the most favourable thermal conditions exist in the south-western part of Lapland, around large water bodies and in the Kemijoki basin, while the coldest regions are in highland and fell Lapland. The distribution of precipitation is predominantly longitudinally dependent but with the definite influence of local features. The impact of human-induced disturbances, i.e., forest fires, on local climate and its implication for forest recovery near the northern timberline was evaluated in the Tuntsa area of eastern Lapland, damaged by a widespread forest fire in 1960 and suffering repeatedly-failed vegetation recovery since that. Direct measurements of the local climate and simulated heat and water fluxes indicated the development of a more severe climate and physical conditions on the fire-disturbed site. Removal of the original, predominantly Norway spruce and downy birch vegetation and its substitution by tundra vegetation has generated increased wind velocity and reduced snow accumulation, associated with a large variation in soil temperature and moisture and deep soil frost. The changed structural parameters of the canopy have determined changes in energy fluxes by reducing the latter over the tundra vegetation. The altered surface and soil conditions, as well as the evolved severe local climate, have negatively affected seedling growth and survival, leading to more unfavourable conditions for the reproduction of boreal vegetation and thereby causing deviations in the regional position of the timberline. However it should be noted that other factors, such as an inadequate seed source or seedbed, the poor quality of the soil and the intensive logging of damaged trees could also exacerbate the poor tree regeneration. In spite of the failed forest recovery at Tunsta, the position and composition of the timberline and tree-line in Finnish Lapland may also benefit from present and future changes in climate. The already-observed and the projected increase in temperature, the prolonged growing season, as well as changes in the precipitation regime foster tree growth and new regeneration, resulting in an advance of the timberline and tree-line northward and upward. This shift in the distribution of vegetation might be decelerated or even halted by local topoclimatic conditions and by the expected increase in the frequency of disturbances.

Individuals on the Move : Body Condition Dependent Dispersal and Quasi-Local Competition in Metapopulations

Individual movement is very versatile and inevitable in ecology. In this thesis, I investigate two kinds of movement body condition dependent dispersal and small-range foraging movements resulting in quasi-local competition and their causes and consequences on the individual, population and metapopulation level. Body condition dependent dispersal is a widely evident but barely understood phenomenon. In nature, diverse relationships between body condition and dispersal are observed. I develop the first models that study the evolution of dispersal strategies that depend on individual body condition. In a patchy environment where patches differ in environmental conditions, individuals born in rich (e.g. nutritious) patches are on average stronger than their conspecifics that are born in poorer patches. Body condition (strength) determines competitive ability such that stronger individuals win competition with higher probability than weak individuals. Individuals compete for patches such that kin competition selects for dispersal. I determine the evolutionarily stable strategy (ESS) for different ecological scenarios. My models offer explanations for both dispersal of strong individuals and dispersal of weak individuals. Moreover, I find that within-family dispersal behaviour is not always reflected on the population level. This supports the fact that no consistent pattern is detected in data on body condition dependent dispersal. It also encourages the refining of empirical investigations. Quasi-local competition defines interactions between adjacent populations where one population negatively affects the growth of the other population. I model a metapopulation in a homogeneous environment where adults of different subpopulations compete for resources by spending part of their foraging time in the neighbouring patches, while their juveniles only feed on the resource in their natal patch. I show that spatial patterns (different population densities in the patches) are stable only if one age class depletes the resource very much but mainly the other age group depends on it.

Inverse Problem for Fractional Brownian Motion with Discrete Data

The problem of recovering information from measurement data has already been studied for a long time. In the beginning, the methods were mostly empirical, but already towards the end of the sixties Backus and Gilbert started the development of mathematical methods for the interpretation of geophysical data. The problem of recovering information about a physical phenomenon from measurement data is an inverse problem. Throughout this work, the statistical inversion method is used to obtain a solution. Assuming that the measurement vector is a realization of fractional Brownian motion, the goal is to retrieve the amplitude and the Hurst parameter. We prove that under some conditions, the solution of the discretized problem coincides with the solution of the corresponding continuous problem as the number of observations tends to infinity. The measurement data is usually noisy, and we assume the data to be the sum of two vectors: the trend and the noise. Both vectors are supposed to be realizations of fractional Brownian motions, and the goal is to retrieve their parameters using the statistical inversion method. We prove a partial uniqueness of the solution. Moreover, with the support of numerical simulations, we show that in certain cases the solution is reliable and the reconstruction of the trend vector is quite accurate.

Jumping numbers of a simple complete ideal in a two-dimensional regular local ring

The multiplier ideals of an ideal in a regular local ring form a family of ideals parametrized by non-negative rational numbers. As the rational number increases the corresponding multiplier ideal remains unchanged until at some point it gets strictly smaller. A rational number where this kind of diminishing occurs is called a jumping number of the ideal. In this manuscript we shall give an explicit formula for the jumping numbers of a simple complete ideal in a two dimensional regular local ring. In particular, we obtain a formula for the jumping numbers of an analytically irreducible plane curve. We then show that the jumping numbers determine the equisingularity class of the curve.

Computational Techniques for Haplotype Inference and for Local Alignment Significance

This thesis which consists of an introduction and four peer-reviewed original publications studies the problems of haplotype inference (haplotyping) and local alignment significance. The problems studied here belong to the broad area of bioinformatics and computational biology. The presented solutions are computationally fast and accurate, which makes them practical in high-throughput sequence data analysis. Haplotype inference is a computational problem where the goal is to estimate haplotypes from a sample of genotypes as accurately as possible. This problem is important as the direct measurement of haplotypes is difficult, whereas the genotypes are easier to quantify. Haplotypes are the key-players when studying for example the genetic causes of diseases. In this thesis, three methods are presented for the haplotype inference problem referred to as HaploParser, HIT, and BACH. HaploParser is based on a combinatorial mosaic model and hierarchical parsing that together mimic recombinations and point-mutations in a biologically plausible way. In this mosaic model, the current population is assumed to be evolved from a small founder population. Thus, the haplotypes of the current population are recombinations of the (implicit) founder haplotypes with some point--mutations. HIT (Haplotype Inference Technique) uses a hidden Markov model for haplotypes and efficient algorithms are presented to learn this model from genotype data. The model structure of HIT is analogous to the mosaic model of HaploParser with founder haplotypes. Therefore, it can be seen as a probabilistic model of recombinations and point-mutations. BACH (Bayesian Context-based Haplotyping) utilizes a context tree weighting algorithm to efficiently sum over all variable-length Markov chains to evaluate the posterior probability of a haplotype configuration. Algorithms are presented that find haplotype configurations with high posterior probability. BACH is the most accurate method presented in this thesis and has comparable performance to the best available software for haplotype inference. Local alignment significance is a computational problem where one is interested in whether the local similarities in two sequences are due to the fact that the sequences are related or just by chance. Similarity of sequences is measured by their best local alignment score and from that, a p-value is computed. This p-value is the probability of picking two sequences from the null model that have as good or better best local alignment score. Local alignment significance is used routinely for example in homology searches. In this thesis, a general framework is sketched that allows one to compute a tight upper bound for the p-value of a local pairwise alignment score. Unlike the previous methods, the presented framework is not affeced by so-called edge-effects and can handle gaps (deletions and insertions) without troublesome sampling and curve fitting.

Optimisation Problems in Wireless Sensor Networks : Local Algorithms and Local Graphs

This thesis studies optimisation problems related to modern large-scale distributed systems, such as wireless sensor networks and wireless ad-hoc networks. The concrete tasks that we use as motivating examples are the following: (i) maximising the lifetime of a battery-powered wireless sensor network, (ii) maximising the capacity of a wireless communication network, and (iii) minimising the number of sensors in a surveillance application. A sensor node consumes energy both when it is transmitting or forwarding data, and when it is performing measurements. Hence task (i), lifetime maximisation, can be approached from two different perspectives. First, we can seek for optimal data flows that make the most out of the energy resources available in the network; such optimisation problems are examples of so-called max-min linear programs. Second, we can conserve energy by putting redundant sensors into sleep mode; we arrive at the sleep scheduling problem, in which the objective is to find an optimal schedule that determines when each sensor node is asleep and when it is awake. In a wireless network simultaneous radio transmissions may interfere with each other. Task (ii), capacity maximisation, therefore gives rise to another scheduling problem, the activity scheduling problem, in which the objective is to find a minimum-length conflict-free schedule that satisfies the data transmission requirements of all wireless communication links. Task (iii), minimising the number of sensors, is related to the classical graph problem of finding a minimum dominating set. However, if we are not only interested in detecting an intruder but also locating the intruder, it is not sufficient to solve the dominating set problem; formulations such as minimum-size identifying codes and locating dominating codes are more appropriate. This thesis presents approximation algorithms for each of these optimisation problems, i.e., for max-min linear programs, sleep scheduling, activity scheduling, identifying codes, and locating dominating codes. Two complementary approaches are taken. The main focus is on local algorithms, which are constant-time distributed algorithms. The contributions include local approximation algorithms for max-min linear programs, sleep scheduling, and activity scheduling. In the case of max-min linear programs, tight upper and lower bounds are proved for the best possible approximation ratio that can be achieved by any local algorithm. The second approach is the study of centralised polynomial-time algorithms in local graphs these are geometric graphs whose structure exhibits spatial locality. Among other contributions, it is shown that while identifying codes and locating dominating codes are hard to approximate in general graphs, they admit a polynomial-time approximation scheme in local graphs.