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The almost complete transcription of the human genome yield in a high number of transcripts, that do not encode proteins. However, the functional elucidation of especially long non cod-ing RNAs is still difficult. Secondary structure analysis is assumed to be a possible method to detect functional relationships of lncRNAs on a large scale, but it is still time consuming and error-prone. GRAPHCLUST, the currently most suitable clustering tool based on RNA secondary structure analysis, lacks mainly in an efficient method for the interpretation of its results. Hence, an independent and interactive RNA clustering interpretation tool was developed to allow visu-alisation and an efficient analysis of RNA clustering results.
A variety of methods have been used to describe natural systems and cellular functions. Most use continuous systems with differential equations. Based upon the neighbourhood relations in graphs and the complex interactions in cellular automata a mathematical model was designed and implemented as an application user interface. This discrete approach called graph automata was utilised to simulate diffusion processes and chemical kinetics. The progression of diffusion in cellular environments was described and resulted in a discrepancy of 20% in comparison to experimental results. Different chemical kinetics were simulated and found to be as accurate as their continuous counterparts. The proposed model appears to be a highly scalable and modular
approach to simulate natural systems.
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This thesis investigated the generation of laser induced periodic surface structures (LIPSS) using femtosecond laser irradiation at a central wavelength of 775 nm.
The metals stainless steel and copper as well as a semiconducting thin film, ITO on glass substrate were investigated. The impact of the processing parameters was studied for single and multiple pulse irradiation to determine the ablation threshold of the materials
and the different types of LIPSS. These observations allowed the optimisation of area structuring with regards to processing speed and LIPSS quality.
The feasibility of the LIPSS generation in dynamic, real time polarisation control was then explored. By using a fast response, liquid-crystal polarisation rotation device, the direction of the linear polarisation of the laser beam could be dynamically controlled and synchronised to the scanning during laser processing. As a result, a range of complex micro- and nano-scale patterns with orthogonal direction of LIPSS were created. The samples were analysed using optical and electron microscopy. The orientation of the LIPSS was determined also from detection of light diffracted by the LIPSS.
Finally, two applications of large area LIPSS patterning were demonstrated, information encoding on metals and periodic structuring of a thin film conducting oxide for solar cells.
This master thesis investigates a new method for the feature extraction of gray scale images, the so called „Non-Euclidean Principal Component Analysis“ 1. Thereby the standard inner product of the Euclidean space is substituted by a semi inner product in the well known learning rule of Oja and Sanger. The new method is compared with the standard principal component analysis (PCA) by extracting features (feature vectors) of different databases with class labels and judged regarding the accuracies of „Border Sensitive Generalized Learning Vector Quantization“ (BSGLVQ), „Feed Forward Neural Networks“ (FFNN) and the „Support Vector Machines“ (SVM).
In this work a novelty detection framework provided by M. Filippone and G. Sanguinetti is considered, which is useful especially when only few training samples are available. It is restricted to Gaussian mixture models and makes use of information theory, applying the Kullback-Leibler divergence. In this work two variations of the framework are presented, applying the symmetric Hellinger divergence and a statistical likelihood approach.
For the first time it was discovered that ultraviolet radiation with a wavelength of 200 to 400 nm (maximum 365 nm) radiated from a distance of 40 cm (intensity: 3500 mW/cm²) to PMMA altered its surface wettability as well as a roughness at the nanoscale that was observed with an atomic force microscope (AFM). The roughness rises and falls again in a short time ( 1-2days ) after 75 min and 180 min irradiation time. However , during the next 10 days roughness became stabilized and there was no influence of UV if PMMA was stored in air or in a Petri dish out of glass.
As widely discussed in literature spatial patterns of amino acids, so-called structural motifs, play an important role in protein function. The functional responsible part of a protein often lies in an evolutionary highly conserved spatial arrangement of only few amino acids, which are held in place tightly by the rest of the structure. In general, these motifs can mediate various functional interactions, such as DNA/RNA targeting and binding, ligand interactions, substrate catalysis, and stabilization of the protein structure.
Hence, characterizing and identifying such conserved structural motifs can contribute to understanding of structurefunction relationships in diverse protein families. Therefore and because of the rapidly increasing number of solved protein structures, it is highly desirable to identify, understand and moreover to search for structural scattered amino acid motifs. The aim of this work was the development and the implementation of a matching algorithm to search for such small structural motifs in large sets of target structures. Furthermore, motif matches were extensively analyzed, statistically assessed and functionally classified. Following a novel approach, hierarchical clustering was combined with functional classification and used to deduce evolutionary structure-function relationships. The proposed methods were combined and implemented to a feature-rich and easy-to-use command line software tool, which is freely available and contributes to the field of structural bioinformatic research.