Prognostic molecular factors and algorithms in diffuse large B-cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) is the most common of the non-Hodgkin lymphomas. As DLBCL is characterized by heterogeneous clinical and biological features, its prognosis varies. To date, the International Prognostic Index has been the strongest predictor of outcome for DLBCL patients. However, no biological characters of the disease are taken into account. Gene expression profiling studies have identified two major cell-of-origin phenotypes in DLBCL with different prognoses, the favourable germinal centre B-cell-like (GCB) and the unfavourable activated B-cell-like (ABC) phenotypes. However, results of the prognostic impact of the immunohistochemically defined GCB and non-GCB distinction are controversial. Furthermore, since the addition of the CD20 antibody rituximab to chemotherapy has been established as the standard treatment of DLBCL, all molecular markers need to be evaluated in the post-rituximab era. In this study, we aimed to evaluate the predictive value of immunohistochemically defined cell-of-origin classification in DLBCL patients. The GCB and non-GCB phenotypes were defined according to the Hans algorithm (CD10, BCL6 and MUM1/IRF4) among 90 immunochemotherapy- and 104 chemotherapy-treated DLBCL patients. In the chemotherapy group, we observed a significant difference in survival between GCB and non-GCB patients, with a good and a poor prognosis, respectively. However, in the rituximab group, no prognostic value of the GCB phenotype was observed. Likewise, among 29 high-risk de novo DLBCL patients receiving high-dose chemotherapy and autologous stem cell transplantation, the survival of non-GCB patients was improved, but no difference in outcome was seen between GCB and non-GCB subgroups. Since the results suggested that the Hans algorithm was not applicable in immunochemotherapy-treated DLBCL patients, we aimed to further focus on algorithms based on ABC markers. We examined the modified activated B-cell-like algorithm based (MUM1/IRF4 and FOXP1), as well as a previously reported Muris algorithm (BCL2, CD10 and MUM1/IRF4) among 88 DLBCL patients uniformly treated with immunochemotherapy. Both algorithms distinguished the unfavourable ABC-like subgroup with a significantly inferior failure-free survival relative to the GCB-like DLBCL patients. Similarly, the results of the individual predictive molecular markers transcription factor FOXP1 and anti-apoptotic protein BCL2 have been inconsistent and should be assessed in immunochemotherapy-treated DLBCL patients. The markers were evaluated in a cohort of 117 patients treated with rituximab and chemotherapy. FOXP1 expression could not distinguish between patients, with favourable and those with poor outcomes. In contrast, BCL2-negative DLBCL patients had significantly superior survival relative to BCL2-positive patients. Our results indicate that the immunohistochemically defined cell-of-origin classification in DLBCL has a prognostic impact in the immunochemotherapy era, when the identifying algorithms are based on ABC-associated markers. We also propose that BCL2 negativity is predictive of a favourable outcome. Further investigational efforts are, however, warranted to identify the molecular features of DLBCL that could enable individualized cancer therapy in routine patient care.

Studies on diamond-like carbon and novel diamond-like carbon polymer hybrid coatings deposited with filtered pulsed arc discharge method

The main obstacle for the application of high quality diamond-like carbon (DLC) coatings has been the lack of adhesion to the substrate as the coating thickness is increased. The aim of this study was to improve the filtered pulsed arc discharge (FPAD) method. With this method it is possible to achieve high DLC coating thicknesses necessary for practical applications. The energy of the carbon ions was measured with an optoelectronic time-of-flight method. An in situ cathode polishing system used for stabilizing the process yield and the carbon ion energies is presented. Simultaneously the quality of the coatings can be controlled. To optimise the quality of the deposition process a simple, fast and inexpensive method using silicon wafers as test substrates was developed. This method was used for evaluating the suitability of a simplified arc-discharge set-up for the deposition of the adhesion layer of DLC coatings. A whole new group of materials discovered by our research group, the diamond-like carbon polymer hybrid (DLC-p-h) coatings, is also presented. The parent polymers used in these novel coatings were polydimethylsiloxane (PDMS) and polytetrafluoroethylene (PTFE). The energy of the plasma ions was found to increase when the anode-cathode distance and the arc voltage were increased. A constant deposition rate for continuous coating runs was obtained with an in situ cathode polishing system. The novel DLC-p-h coatings were found to be water and oil repellent and harder than any polymers. The lowest sliding angle ever measured from a solid surface, 0.15 ± 0.03°, was measured on a DLC-PDMS-h coating. In the FPAD system carbon ions can be accelerated to high energies (≈ 1 keV) necessary for the optimal adhesion (the substrate is broken in the adhesion and quality test) of ultra thick (up to 200 µm) DLC coatings by increasing the anode-cathode distance and using high voltages (up to 4 kV). An excellent adhesion can also be obtained with the simplified arc-discharge device. To maintain high process yield (5µm/h over a surface area of 150 cm2) and to stabilize the carbon ion energies and the high quality (sp3 fraction up to 85%) of the resulting coating, an in situ cathode polishing system must be used. DLC-PDMS-h coating is the superior candidate coating material for anti-soiling applications where also hardness is required.

Non-Gaussian Cosmological Perturbations from Hybrid Inflation and Preheating

This thesis consists of four research papers and an introduction providing some background. The structure in the universe is generally considered to originate from quantum fluctuations in the very early universe. The standard lore of cosmology states that the primordial perturbations are almost scale-invariant, adiabatic, and Gaussian. A snapshot of the structure from the time when the universe became transparent can be seen in the cosmic microwave background (CMB). For a long time mainly the power spectrum of the CMB temperature fluctuations has been used to obtain observational constraints, especially on deviations from scale-invariance and pure adiabacity. Non-Gaussian perturbations provide a novel and very promising way to test theoretical predictions. They probe beyond the power spectrum, or two point correlator, since non-Gaussianity involves higher order statistics. The thesis concentrates on the non-Gaussian perturbations arising in several situations involving two scalar fields, namely, hybrid inflation and various forms of preheating. First we go through some basic concepts -- such as the cosmological inflation, reheating and preheating, and the role of scalar fields during inflation -- which are necessary for the understanding of the research papers. We also review the standard linear cosmological perturbation theory. The second order perturbation theory formalism for two scalar fields is developed. We explain what is meant by non-Gaussian perturbations, and discuss some difficulties in parametrisation and observation. In particular, we concentrate on the nonlinearity parameter. The prospects of observing non-Gaussianity are briefly discussed. We apply the formalism and calculate the evolution of the second order curvature perturbation during hybrid inflation. We estimate the amount of non-Gaussianity in the model and find that there is a possibility for an observational effect. The non-Gaussianity arising in preheating is also studied. We find that the level produced by the simplest model of instant preheating is insignificant, whereas standard preheating with parametric resonance as well as tachyonic preheating are prone to easily saturate and even exceed the observational limits. We also mention other approaches to the study of primordial non-Gaussianities, which differ from the perturbation theory method chosen in the thesis work.

Hybrid Modelling of Titan's Interaction with the Magnetosphere of Saturn

In this dissertation we study the interaction between Saturn's moon Titan and the magnetospheric plasma and magnetic field. The method of research is a three-dimensional computer simulation model, that is used to simulate this interaction. The simulation model used is a hybrid model. Hybrid models enable individual tracking or tracing of ions and also take into account the particle motion in the propagation of the electromagnetic fields. The hybrid model has been developed at the Finnish Meteorological Institute. This thesis gives a general description of the effects that the solar wind has on Earth and other planets of our solar system. Planetary satellites can also have similar interactions with the solar wind but also with the plasma flows of planetary magnetospheres. Titan is clearly the largest among the satellites of Saturn and also the only known satellite with a dense atmosphere. It is the atmosphere that makes Titan's plasma interaction with the magnetosphere of Saturn so unique. Nevertheless, comparisons with the plasma interactions of other solar system bodies are valuable. Detecting charged plasma particles requires in situ measurements obtainable through scientific spacecraft. The Cassini mission has been one of the most remarkable international efforts in space science. Since 2004 the measurements and images obtained from instruments onboard the Cassini spacecraft have increased the scientific knowledge of Saturn as well as its satellites and magnetosphere in a way no one was probably able to predict. The current level of science on Titan is practically unthinkable without the Cassini mission. Many of the observations by Cassini instrument teams have influenced this research both the direct measurements of Titan as well as observations of its plasma environment. The theoretical principles of the hybrid modelling approach are presented in connection to the broader context of plasma simulations. The developed hybrid model is described in detail: e.g. the way the equations of the hybrid model are solved is shown explicitly. Several simulation techniques, such as the grid structure and various boundary conditions, are discussed in detail as well. The testing and monitoring of simulation runs is presented as an essential routine when running sophisticated and complex models. Several significant improvements of the model, that are in preparation, are also discussed. A main part of this dissertation are four scientific articles based on the results of the Titan model. The Titan model developed during the course of the Ph.D. research has been shown to be an important tool to understand Titan's plasma interaction. One reason for this is that the structures of the magnetic field around Titan are very much three-dimensional. The simulation results give a general picture of the magnetic fields in the vicinity of Titan. The magnetic fine structure of Titan's wake as seen in the simulations seems connected to Alfvén waves an important wave mode in space plasmas. The particle escape from Titan is also a major part of these studies. Our simulations show a bending or turning of Titan's ionotail that we have shown to be a direct result of the basic principles in plasma physics. Furthermore, the ion flux from the magnetosphere of Saturn into Titan's upper atmosphere has been studied. The modelled ion flux has asymmetries that would likely have a large impact in the heating in different parts of Titan's upper atmosphere.

Consumer Evaluation of Hybrid Innovations (summary section only)

Hybrid innovations, or new products that combine two existing product categories into one, are increasingly popular in today’s marketplace. Despite this proliferation, few studies address them. The purpose of this thesis is to examine consumer evaluation of hybrid innovations by focusing on consumer categorization of such innovations and on factors contributing positively and negatively to their evaluation. This issue is examined by means of three studies. The first study addresses the proportion of consumers categorizing hybrid products as single- versus dual-purpose, what contributes to such a categorization, what differences can be found between the two groups, and if categorization can and should be included in models of innovation adoption. The second study expands on the scope by including motivation as a predictor of consumer evaluation and examines two cognitive and affective factors and their differential impact on innovation evaluation. Finally, the third study examines the product comparisons single- versus dual-purpose categorization induce. These three essays together build up a broader understanding of hybrid innovation evaluation. The thesis uses theories from both psychology and marketing to examine the issues at hand. Conceptual combination and analogical learning theories from psychology are used to comprehend categorization and knowledge transfer. From marketing, consumer behavior and innovation adoption studies are addressed to better understand the link between categorization and product evaluation and the factors contributing to product evaluation. The main results of the current thesis are that (1) most consumers categorize hybrid products as single- and not as dual-purpose products, (2) consumers that categorize them as dual-purpose find them more attractive (3) motivation has a significant effect on consumer evaluation of innovations; cognitive factors promote an emphasis on product net benefits, whereas affective factors induce consumers to consider product meaning in the form of categorization and perceived product complexity, (4) categorization constrains subsequent product evaluation, and (5) categorization can and should be included to models of innovation adoption. Maria Sääksjärvi is associated with CERS, the Center for Relationship Marketing and Service Management at the Swedish School of Economics and Business Administration

The Hybrid Consumer: Exploring the Drivers of a New Consumer Behaviour Type

A dramatically different consumption pattern seems to be emerging among a vast group of consumers. This may mean that conventional consumer stereotypes and segmentation theory are becoming outdated. The so called hybrid consumers seem to increasingly opt for both premium and budget alternatives in various product and service categories while mid-priced alternatives are losing share in their consumption basket. Although this type of polarisation, or dispersion, is already recognized as an important change, hybrid behaviour is still largely under-researched. The present study aims to analyze the possible drivers of hybrid consumption and by identifying typical categories and situations of trading up versus trading down derive tentative characteristics of hybrid consumption for further research on the topic. A tentative pattern of hybrid consumption was identified, which relates trading up to high-involvement, discretional spending and trading down to low-involvement necessities. However, it was also found that hybrid consumption transcends product category boundaries and may thus be less straightforward than previously perhaps assumed. In addition, a purchase pattern continuum was developed, accounting for various degrees of hybrid consumption.

European aspen and hybrid aspen under changing environment : Leaf traits, growth and litter decomposition

Understanding the responses of species and ecosystems to human-induced global environmental change has become a high research priority. The main aim of this thesis was to investigate how certain environmental factors that relate to global change affect European aspen (Populus tremula), a keystone species in boreal forests, and hybrid aspen (P. tremula × P. tremuloides), cultivated in commercial plantations. The main points under consideration were the acclimatization potential of aspen through changes in leaf morphology, as well as effects on growth, leaf litter chemistry and decomposition. The thesis is based on two experiments, in which young aspen (< 1 year) were exposed either to an atmospheric pollutant [elevated ozone (O3)] or variable resource availability [water, nitrogen (N)]; and two field studies, in which mature trees (> 8 years) were growing in environments exposed to multiple environmental stress factors (roadside and urban environments). The field studies included litter decomposition experiments. The results show that young aspen, especially the native European aspen, was sensitive to O3 in terms of visible leaf injuries. Elevated O3 resulted in reduced biomass allocation to roots and accelerated leaf senescence, suggesting negative effects on growth in the long term. Water and N availability modified the frost hardening of young aspen: High N supply, especially when combined with drought, postponed the development of frost hardiness, which in turn may predispose trees to early autumn frosts. This effect was more pronounced in European aspen. The field studies showed that mature aspen acclimatized to roadside and urban environments by producing more xeromorphic leaves. Leaf morphology was also observed to vary in response to interannual climatic variation, which further indicates the ability of aspen for phenotypic plasticity. Intraspecific variation was found in several of the traits measured, although intraspecific differences in response to the abiotic factors examined were generally small throughout the studies. However, some differences between clones were found in sensitivity to O3 and the roadside environment. Aspen leaf litter decomposition was retarded in the roadside environment, but only initially. By contrast, decomposition was found to be faster in the urban than the rural environment throughout the study. The higher quality of urban litter (higher in N, lower in lignin and phenolics), as well as higher temperature, N deposition and humus pH at the urban site were factors likely to promote decay. The phenotypic plasticity combined with intraspecific variation found in the studies imply that aspen has potential for withstanding environmental changes, although some global change factors, such as rising O3 levels, may adversely affect its performance. The results also suggest that the multiple environmental changes taking place in urban areas which correspond closely with the main drivers of global change can modify ecosystem functioning by promoting litter decomposition, mediated partly by alterations in leaf litter quality.

Distributed algorithms for edge dominating sets

An edge dominating set for a graph G is a set D of edges such that each edge of G is in D or adjacent to at least one edge in D. This work studies deterministic distributed approximation algorithms for finding minimum-size edge dominating sets. The focus is on anonymous port-numbered networks: there are no unique identifiers, but a node of degree d can refer to its neighbours by integers 1, 2, ..., d. The present work shows that in the port-numbering model, edge dominating sets can be approximated as follows: in d-regular graphs, to within 4 − 6/(d + 1) for an odd d and to within 4 − 2/d for an even d; and in graphs with maximum degree Δ, to within 4 − 2/(Δ − 1) for an odd Δ and to within 4 − 2/Δ for an even Δ. These approximation ratios are tight for all values of d and Δ: there are matching lower bounds.

Fast distributed approximation algorithms for vertex cover and set cover in anonymous networks

We present a distributed algorithm that finds a maximal edge packing in O(Δ + log* W) synchronous communication rounds in a weighted graph, independent of the number of nodes in the network; here Δ is the maximum degree of the graph and W is the maximum weight. As a direct application, we have a distributed 2-approximation algorithm for minimum-weight vertex cover, with the same running time. We also show how to find an f-approximation of minimum-weight set cover in O(f2k2 + fk log* W) rounds; here k is the maximum size of a subset in the set cover instance, f is the maximum frequency of an element, and W is the maximum weight of a subset. The algorithms are deterministic, and they can be applied in anonymous networks.

Analysing local algorithms in location-aware quasi-unit-disk graphs

A local algorithm with local horizon r is a distributed algorithm that runs in r synchronous communication rounds; here r is a constant that does not depend on the size of the network. As a consequence, the output of a node in a local algorithm only depends on the input within r hops from the node. We give tight bounds on the local horizon for a class of local algorithms for combinatorial problems on unit-disk graphs (UDGs). Most of our bounds are due to a refined analysis of existing approaches, while others are obtained by suggesting new algorithms. The algorithms we consider are based on network decompositions guided by a rectangular tiling of the plane. The algorithms are applied to matching, independent set, graph colouring, vertex cover, and dominating set. We also study local algorithms on quasi-UDGs, which are a popular generalisation of UDGs, aimed at more realistic modelling of communication between the network nodes. Analysing the local algorithms on quasi-UDGs allows one to assume that the nodes know their coordinates only approximately, up to an additive error. Despite the localisation error, the quality of the solution to problems on quasi-UDGs remains the same as for the case of UDGs with perfect location awareness. We analyse the increase in the local horizon that comes along with moving from UDGs to quasi-UDGs.

Algorithms for Exact Structure Discovery in Bayesian Networks

Bayesian networks are compact, flexible, and interpretable representations of a joint distribution. When the network structure is unknown but there are observational data at hand, one can try to learn the network structure. This is called structure discovery. This thesis contributes to two areas of structure discovery in Bayesian networks: space--time tradeoffs and learning ancestor relations. The fastest exact algorithms for structure discovery in Bayesian networks are based on dynamic programming and use excessive amounts of space. Motivated by the space usage, several schemes for trading space against time are presented. These schemes are presented in a general setting for a class of computational problems called permutation problems; structure discovery in Bayesian networks is seen as a challenging variant of the permutation problems. The main contribution in the area of the space--time tradeoffs is the partial order approach, in which the standard dynamic programming algorithm is extended to run over partial orders. In particular, a certain family of partial orders called parallel bucket orders is considered. A partial order scheme that provably yields an optimal space--time tradeoff within parallel bucket orders is presented. Also practical issues concerning parallel bucket orders are discussed. Learning ancestor relations, that is, directed paths between nodes, is motivated by the need for robust summaries of the network structures when there are unobserved nodes at work. Ancestor relations are nonmodular features and hence learning them is more difficult than modular features. A dynamic programming algorithm is presented for computing posterior probabilities of ancestor relations exactly. Empirical tests suggest that ancestor relations can be learned from observational data almost as accurately as arcs even in the presence of unobserved nodes.