Basement membrane and provisional matrix components (collagen type IV, laminins and von Willebrand factor) in rheumatoid arthritis and Sjögren s syndrome

The aim of this study was twofold- Firstly, to determine the composition of the type IV collagen which are the major components of the basement membrane (BM), in the synovial lining of the rheumatoid arthritis (RA) patient and in the BM in the labial salivary gland of the Sjögrens syndrome (SS) patient. Secondly, this thesis aimed to investigate the role of the BM component laminin α4 and laminin α5 in the migration of neutrophils from the blood vessels thorough the synovial lining layer into synovial fluid and the presence of vWF in the microvasculature of labial salivary gland in SS. Our studies showed that certain α chains type IV collagen are low in RA compared to control synovial linings, while laminin α5 exhibited a pattern of low expression regions at the synovial lining interface towards the joint cavity and fluid. Also, high numbers of macrophage-like lining cells containing MMP-9 were found in the lining. MMP-9 was also found in the synovial fluid. Collagen α1/2 (IV) mRNA was found to be present in high amount compared to the other α(IV) chains and also showed intense labelling in immunohistochemical staining in normal and SS patients. In healthy glands α5(IV) and α6(IV) chains were found to be continuous around ducts but discontinuous around acini. The α5(IV) and α6(IV) mRNAs were present in LSG explants and HSG cell line, while in SS these chains seemed to be absent or appear only in patches around the ductal BM and tended to be absent around acini in immunohistochemical staining, indicating that their synthesis and/or degradation seemed to be locally regulated around acinar cells. The provisional matrix component vWF serves as a marker of vascular damage. Microvasculature in SS showed signs of focal damage which in turn might impair arteriolar feeding, capillary transudation and venular drainage of blood. However, capillary density was not decreased but rather increased, perhaps as a result of angiogenesis compensatory to microvascular damage. Microvascular involvement of LSG may contribute to the pathogenesis of this syndrome. This twofold approach allows us to understand the intricate relation between the ECM components and the immunopathological changes that occur during the pathogenesis of these inflammatory rheumatic disease processes. Also notably this study highlights the importance of maintaining a healthy ECM to prevent the progression or possibly allow reversal of the disease to a considerable level. Furthermore, it can be speculated that a healthy BM could quarantine the inflamed region or in case of cancer cells barricade the movement of malignant cells thereby preventing further spread to the surrounding areas. This understanding can be further applied to design appropriate drugs which act specifically to maintain a proper BM/BM like intercellular matrix composition.

Rolling tachyons, random matrices, and Coulomb gas thermodynamics

Time-dependent backgrounds in string theory provide a natural testing ground for physics concerning dynamical phenomena which cannot be reliably addressed in usual quantum field theories and cosmology. A good, tractable example to study is the rolling tachyon background, which describes the decay of an unstable brane in bosonic and supersymmetric Type II string theories. In this thesis I use boundary conformal field theory along with random matrix theory and Coulomb gas thermodynamics techniques to study open and closed string scattering amplitudes off the decaying brane. The calculation of the simplest example, the tree-level amplitude of n open strings, would give us the emission rate of the open strings. However, even this has been unknown. I will organize the open string scattering computations in a more coherent manner and will argue how to make further progress.

Practical Proposals for Specifying k-Nearest Neighbours Weights Matrices

In this article we introduce and evaluate testing procedures for specifying the number k of nearest neighbours in the weights matrix of spatial econometric models. The spatial J-test is used for specification search. Two testing procedures are suggested: an increasing neighbours testing procedure and a decreasing neighbours testing procedure. Simulations show that the increasing neighbours testing procedures can be used in large samples to determine k. The decreasing neighbours testing procedure is found to have low power, and is not recommended for use in practice. An empirical example involving house price data is provided to show how to use the testing procedures with real data.

Patterns in permuted binary matrices

Reorganizing a dataset so that its hidden structure can be observed is useful in any data analysis task. For example, detecting a regularity in a dataset helps us to interpret the data, compress the data, and explain the processes behind the data. We study datasets that come in the form of binary matrices (tables with 0s and 1s). Our goal is to develop automatic methods that bring out certain patterns by permuting the rows and columns. We concentrate on the following patterns in binary matrices: consecutive-ones (C1P), simultaneous consecutive-ones (SC1P), nestedness, k-nestedness, and bandedness. These patterns reflect specific types of interplay and variation between the rows and columns, such as continuity and hierarchies. Furthermore, their combinatorial properties are interlinked, which helps us to develop the theory of binary matrices and efficient algorithms. Indeed, we can detect all these patterns in a binary matrix efficiently, that is, in polynomial time in the size of the matrix. Since real-world datasets often contain noise and errors, we rarely witness perfect patterns. Therefore we also need to assess how far an input matrix is from a pattern: we count the number of flips (from 0s to 1s or vice versa) needed to bring out the perfect pattern in the matrix. Unfortunately, for most patterns it is an NP-complete problem to find the minimum distance to a matrix that has the perfect pattern, which means that the existence of a polynomial-time algorithm is unlikely. To find patterns in datasets with noise, we need methods that are noise-tolerant and work in practical time with large datasets. The theory of binary matrices gives rise to robust heuristics that have good performance with synthetic data and discover easily interpretable structures in real-world datasets: dialectical variation in the spoken Finnish language, division of European locations by the hierarchies found in mammal occurrences, and co-occuring groups in network data. In addition to determining the distance from a dataset to a pattern, we need to determine whether the pattern is significant or a mere occurrence of a random chance. To this end, we use significance testing: we deem a dataset significant if it appears exceptional when compared to datasets generated from a certain null hypothesis. After detecting a significant pattern in a dataset, it is up to domain experts to interpret the results in the terms of the application.